2022-05-03 11:40:27,486 INFO [train.py:775] (1/8) Training started
2022-05-03 11:40:27,486 INFO [train.py:785] (1/8) Device: cuda:1
2022-05-03 11:40:27,489 INFO [train.py:794] (1/8) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'encoder_dim': 512, 'nhead': 8, 'dim_feedforward': 2048, 'num_encoder_layers': 12, 'decoder_dim': 512, 'joiner_dim': 512, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.14', 'k2-build-type': 'Debug', 'k2-with-cuda': True, 'k2-git-sha1': '1b29f0a946f50186aaa82df46a59f492ade9692b', 'k2-git-date': 'Tue Apr 12 20:46:49 2022', 'lhotse-version': '1.1.0', 'torch-version': '1.10.1+cu111', 'torch-cuda-available': True, 'torch-cuda-version': '11.1', 'python-version': '3.8', 'icefall-git-branch': 'spgi', 'icefall-git-sha1': 'e2e5c77-dirty', 'icefall-git-date': 'Mon May 2 14:38:25 2022', 'icefall-path': '/exp/draj/mini_scale_2022/icefall', 'k2-path': '/exp/draj/mini_scale_2022/k2/k2/python/k2/__init__.py', 'lhotse-path': '/exp/draj/mini_scale_2022/lhotse/lhotse/__init__.py', 'hostname': 'r8n04', 'IP address': '10.1.8.4'}, 'world_size': 8, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 20, 'start_epoch': 0, 'start_batch': 0, 'exp_dir': PosixPath('pruned_transducer_stateless2/exp/v2'), 'bpe_model': 'data/lang_bpe_500/bpe.model', 'initial_lr': 0.003, 'lr_batches': 5000, 'lr_epochs': 4, 'context_size': 2, 'prune_range': 5, 'lm_scale': 0.25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'save_every_n': 8000, 'keep_last_k': 10, 'use_fp16': True, 'manifest_dir': PosixPath('data/manifests'), 'enable_musan': True, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'max_duration': 200, 'num_buckets': 30, 'on_the_fly_feats': False, 'shuffle': True, 'num_workers': 8, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'blank_id': 0, 'vocab_size': 500}
2022-05-03 11:40:27,489 INFO [train.py:796] (1/8) About to create model
2022-05-03 11:40:27,835 INFO [train.py:800] (1/8) Number of model parameters: 78648040
2022-05-03 11:40:33,479 INFO [train.py:806] (1/8) Using DDP
2022-05-03 11:40:34,135 INFO [asr_datamodule.py:321] (1/8) About to get SPGISpeech train cuts
2022-05-03 11:40:34,139 INFO [asr_datamodule.py:179] (1/8) About to get Musan cuts
2022-05-03 11:40:35,944 INFO [asr_datamodule.py:184] (1/8) Enable MUSAN
2022-05-03 11:40:35,944 INFO [asr_datamodule.py:207] (1/8) Enable SpecAugment
2022-05-03 11:40:35,945 INFO [asr_datamodule.py:208] (1/8) Time warp factor: 80
2022-05-03 11:40:35,945 INFO [asr_datamodule.py:221] (1/8) About to create train dataset
2022-05-03 11:40:35,945 INFO [asr_datamodule.py:234] (1/8) Using DynamicBucketingSampler.
2022-05-03 11:40:36,354 INFO [asr_datamodule.py:242] (1/8) About to create train dataloader
2022-05-03 11:40:36,355 INFO [asr_datamodule.py:326] (1/8) About to get SPGISpeech dev cuts
2022-05-03 11:40:36,356 INFO [asr_datamodule.py:274] (1/8) About to create dev dataset
2022-05-03 11:40:36,513 INFO [asr_datamodule.py:289] (1/8) About to create dev dataloader
2022-05-03 11:41:08,013 INFO [train.py:715] (1/8) Epoch 0, batch 0, loss[loss=3.355, simple_loss=6.709, pruned_loss=5.755, over 4795.00 frames.], tot_loss[loss=3.355, simple_loss=6.709, pruned_loss=5.755, over 4795.00 frames.], batch size: 21, lr: 3.00e-03
2022-05-03 11:41:08,404 INFO [distributed.py:874] (1/8) Reducer buckets have been rebuilt in this iteration.
2022-05-03 11:41:46,313 INFO [train.py:715] (1/8) Epoch 0, batch 50, loss[loss=0.4704, simple_loss=0.9408, pruned_loss=6.778, over 4962.00 frames.], tot_loss[loss=1.327, simple_loss=2.655, pruned_loss=6.464, over 219183.80 frames.], batch size: 35, lr: 3.00e-03
2022-05-03 11:42:25,575 INFO [train.py:715] (1/8) Epoch 0, batch 100, loss[loss=0.3649, simple_loss=0.7298, pruned_loss=6.642, over 4941.00 frames.], tot_loss[loss=0.82, simple_loss=1.64, pruned_loss=6.58, over 387272.05 frames.], batch size: 21, lr: 3.00e-03
2022-05-03 11:43:04,752 INFO [train.py:715] (1/8) Epoch 0, batch 150, loss[loss=0.3498, simple_loss=0.6996, pruned_loss=6.604, over 4784.00 frames.], tot_loss[loss=0.6323, simple_loss=1.265, pruned_loss=6.591, over 517499.08 frames.], batch size: 18, lr: 3.00e-03
2022-05-03 11:43:43,120 INFO [train.py:715] (1/8) Epoch 0, batch 200, loss[loss=0.3154, simple_loss=0.6308, pruned_loss=6.607, over 4920.00 frames.], tot_loss[loss=0.5345, simple_loss=1.069, pruned_loss=6.584, over 618096.25 frames.], batch size: 17, lr: 3.00e-03
2022-05-03 11:44:22,062 INFO [train.py:715] (1/8) Epoch 0, batch 250, loss[loss=0.3368, simple_loss=0.6736, pruned_loss=6.619, over 4941.00 frames.], tot_loss[loss=0.4742, simple_loss=0.9483, pruned_loss=6.593, over 697355.99 frames.], batch size: 21, lr: 3.00e-03
2022-05-03 11:45:01,533 INFO [train.py:715] (1/8) Epoch 0, batch 300, loss[loss=0.3365, simple_loss=0.673, pruned_loss=6.713, over 4984.00 frames.], tot_loss[loss=0.4343, simple_loss=0.8685, pruned_loss=6.606, over 758583.64 frames.], batch size: 26, lr: 3.00e-03
2022-05-03 11:45:41,182 INFO [train.py:715] (1/8) Epoch 0, batch 350, loss[loss=0.3511, simple_loss=0.7022, pruned_loss=6.69, over 4900.00 frames.], tot_loss[loss=0.4064, simple_loss=0.8127, pruned_loss=6.624, over 805867.20 frames.], batch size: 22, lr: 3.00e-03
2022-05-03 11:46:19,549 INFO [train.py:715] (1/8) Epoch 0, batch 400, loss[loss=0.3056, simple_loss=0.6113, pruned_loss=6.517, over 4788.00 frames.], tot_loss[loss=0.3857, simple_loss=0.7714, pruned_loss=6.638, over 842130.49 frames.], batch size: 14, lr: 3.00e-03
2022-05-03 11:46:58,903 INFO [train.py:715] (1/8) Epoch 0, batch 450, loss[loss=0.3183, simple_loss=0.6366, pruned_loss=6.645, over 4985.00 frames.], tot_loss[loss=0.3693, simple_loss=0.7386, pruned_loss=6.644, over 870579.08 frames.], batch size: 28, lr: 2.99e-03
2022-05-03 11:47:38,000 INFO [train.py:715] (1/8) Epoch 0, batch 500, loss[loss=0.3095, simple_loss=0.6191, pruned_loss=6.582, over 4819.00 frames.], tot_loss[loss=0.3558, simple_loss=0.7116, pruned_loss=6.643, over 892723.79 frames.], batch size: 25, lr: 2.99e-03
2022-05-03 11:48:17,107 INFO [train.py:715] (1/8) Epoch 0, batch 550, loss[loss=0.3211, simple_loss=0.6421, pruned_loss=6.745, over 4840.00 frames.], tot_loss[loss=0.3457, simple_loss=0.6914, pruned_loss=6.649, over 910787.33 frames.], batch size: 30, lr: 2.99e-03
2022-05-03 11:48:55,922 INFO [train.py:715] (1/8) Epoch 0, batch 600, loss[loss=0.2757, simple_loss=0.5515, pruned_loss=6.681, over 4970.00 frames.], tot_loss[loss=0.3349, simple_loss=0.6698, pruned_loss=6.659, over 924103.35 frames.], batch size: 15, lr: 2.99e-03
2022-05-03 11:49:35,145 INFO [train.py:715] (1/8) Epoch 0, batch 650, loss[loss=0.2695, simple_loss=0.539, pruned_loss=6.721, over 4867.00 frames.], tot_loss[loss=0.3241, simple_loss=0.6483, pruned_loss=6.68, over 935141.16 frames.], batch size: 32, lr: 2.99e-03
2022-05-03 11:50:14,493 INFO [train.py:715] (1/8) Epoch 0, batch 700, loss[loss=0.245, simple_loss=0.4899, pruned_loss=6.737, over 4988.00 frames.], tot_loss[loss=0.3113, simple_loss=0.6227, pruned_loss=6.694, over 943451.15 frames.], batch size: 31, lr: 2.99e-03
2022-05-03 11:50:52,997 INFO [train.py:715] (1/8) Epoch 0, batch 750, loss[loss=0.2715, simple_loss=0.543, pruned_loss=6.887, over 4796.00 frames.], tot_loss[loss=0.2999, simple_loss=0.5997, pruned_loss=6.711, over 950074.88 frames.], batch size: 21, lr: 2.98e-03
2022-05-03 11:51:32,777 INFO [train.py:715] (1/8) Epoch 0, batch 800, loss[loss=0.246, simple_loss=0.4921, pruned_loss=6.671, over 4977.00 frames.], tot_loss[loss=0.2877, simple_loss=0.5753, pruned_loss=6.711, over 955268.04 frames.], batch size: 24, lr: 2.98e-03
2022-05-03 11:52:12,741 INFO [train.py:715] (1/8) Epoch 0, batch 850, loss[loss=0.2349, simple_loss=0.4697, pruned_loss=6.795, over 4956.00 frames.], tot_loss[loss=0.2769, simple_loss=0.5538, pruned_loss=6.714, over 958501.07 frames.], batch size: 35, lr: 2.98e-03
2022-05-03 11:52:51,636 INFO [train.py:715] (1/8) Epoch 0, batch 900, loss[loss=0.2437, simple_loss=0.4875, pruned_loss=6.732, over 4985.00 frames.], tot_loss[loss=0.2661, simple_loss=0.5322, pruned_loss=6.707, over 961669.91 frames.], batch size: 14, lr: 2.98e-03
2022-05-03 11:53:30,227 INFO [train.py:715] (1/8) Epoch 0, batch 950, loss[loss=0.2077, simple_loss=0.4155, pruned_loss=6.495, over 4779.00 frames.], tot_loss[loss=0.2564, simple_loss=0.5129, pruned_loss=6.704, over 964408.02 frames.], batch size: 12, lr: 2.97e-03
2022-05-03 11:54:09,539 INFO [train.py:715] (1/8) Epoch 0, batch 1000, loss[loss=0.2095, simple_loss=0.4189, pruned_loss=6.653, over 4789.00 frames.], tot_loss[loss=0.2492, simple_loss=0.4985, pruned_loss=6.707, over 966308.55 frames.], batch size: 14, lr: 2.97e-03
2022-05-03 11:54:48,893 INFO [train.py:715] (1/8) Epoch 0, batch 1050, loss[loss=0.221, simple_loss=0.4419, pruned_loss=6.748, over 4960.00 frames.], tot_loss[loss=0.2419, simple_loss=0.4839, pruned_loss=6.706, over 967673.13 frames.], batch size: 39, lr: 2.97e-03
2022-05-03 11:55:27,472 INFO [train.py:715] (1/8) Epoch 0, batch 1100, loss[loss=0.2003, simple_loss=0.4006, pruned_loss=6.701, over 4847.00 frames.], tot_loss[loss=0.2353, simple_loss=0.4706, pruned_loss=6.706, over 969172.78 frames.], batch size: 13, lr: 2.96e-03
2022-05-03 11:56:07,473 INFO [train.py:715] (1/8) Epoch 0, batch 1150, loss[loss=0.2351, simple_loss=0.4702, pruned_loss=6.877, over 4958.00 frames.], tot_loss[loss=0.2293, simple_loss=0.4586, pruned_loss=6.707, over 970005.61 frames.], batch size: 24, lr: 2.96e-03
2022-05-03 11:56:47,813 INFO [train.py:715] (1/8) Epoch 0, batch 1200, loss[loss=0.193, simple_loss=0.3861, pruned_loss=6.676, over 4974.00 frames.], tot_loss[loss=0.2244, simple_loss=0.4487, pruned_loss=6.708, over 970063.01 frames.], batch size: 24, lr: 2.96e-03
2022-05-03 11:57:28,444 INFO [train.py:715] (1/8) Epoch 0, batch 1250, loss[loss=0.1815, simple_loss=0.3631, pruned_loss=6.767, over 4923.00 frames.], tot_loss[loss=0.22, simple_loss=0.4399, pruned_loss=6.706, over 970209.77 frames.], batch size: 18, lr: 2.95e-03
2022-05-03 11:58:07,337 INFO [train.py:715] (1/8) Epoch 0, batch 1300, loss[loss=0.188, simple_loss=0.3759, pruned_loss=6.728, over 4779.00 frames.], tot_loss[loss=0.2157, simple_loss=0.4314, pruned_loss=6.709, over 970524.19 frames.], batch size: 14, lr: 2.95e-03
2022-05-03 11:58:47,748 INFO [train.py:715] (1/8) Epoch 0, batch 1350, loss[loss=0.2389, simple_loss=0.4778, pruned_loss=6.784, over 4878.00 frames.], tot_loss[loss=0.2113, simple_loss=0.4227, pruned_loss=6.705, over 970257.34 frames.], batch size: 16, lr: 2.95e-03
2022-05-03 11:59:28,711 INFO [train.py:715] (1/8) Epoch 0, batch 1400, loss[loss=0.1902, simple_loss=0.3803, pruned_loss=6.608, over 4814.00 frames.], tot_loss[loss=0.2086, simple_loss=0.4173, pruned_loss=6.705, over 970982.88 frames.], batch size: 27, lr: 2.94e-03
2022-05-03 12:00:09,327 INFO [train.py:715] (1/8) Epoch 0, batch 1450, loss[loss=0.1799, simple_loss=0.3599, pruned_loss=6.755, over 4988.00 frames.], tot_loss[loss=0.2057, simple_loss=0.4115, pruned_loss=6.702, over 970727.62 frames.], batch size: 14, lr: 2.94e-03
2022-05-03 12:00:48,850 INFO [train.py:715] (1/8) Epoch 0, batch 1500, loss[loss=0.1853, simple_loss=0.3707, pruned_loss=6.797, over 4946.00 frames.], tot_loss[loss=0.203, simple_loss=0.4059, pruned_loss=6.7, over 971459.25 frames.], batch size: 21, lr: 2.94e-03
2022-05-03 12:01:29,919 INFO [train.py:715] (1/8) Epoch 0, batch 1550, loss[loss=0.1989, simple_loss=0.3979, pruned_loss=6.686, over 4776.00 frames.], tot_loss[loss=0.2006, simple_loss=0.4012, pruned_loss=6.7, over 971417.21 frames.], batch size: 18, lr: 2.93e-03
2022-05-03 12:02:11,269 INFO [train.py:715] (1/8) Epoch 0, batch 1600, loss[loss=0.1948, simple_loss=0.3896, pruned_loss=6.69, over 4910.00 frames.], tot_loss[loss=0.1993, simple_loss=0.3986, pruned_loss=6.697, over 972189.72 frames.], batch size: 18, lr: 2.93e-03
2022-05-03 12:02:51,040 INFO [train.py:715] (1/8) Epoch 0, batch 1650, loss[loss=0.1941, simple_loss=0.3882, pruned_loss=6.696, over 4971.00 frames.], tot_loss[loss=0.1967, simple_loss=0.3934, pruned_loss=6.695, over 972132.50 frames.], batch size: 15, lr: 2.92e-03
2022-05-03 12:03:32,812 INFO [train.py:715] (1/8) Epoch 0, batch 1700, loss[loss=0.1853, simple_loss=0.3705, pruned_loss=6.53, over 4892.00 frames.], tot_loss[loss=0.1944, simple_loss=0.3888, pruned_loss=6.689, over 972714.38 frames.], batch size: 17, lr: 2.92e-03
2022-05-03 12:04:14,554 INFO [train.py:715] (1/8) Epoch 0, batch 1750, loss[loss=0.1612, simple_loss=0.3225, pruned_loss=6.577, over 4911.00 frames.], tot_loss[loss=0.1926, simple_loss=0.3851, pruned_loss=6.688, over 972932.29 frames.], batch size: 23, lr: 2.91e-03
2022-05-03 12:04:56,006 INFO [train.py:715] (1/8) Epoch 0, batch 1800, loss[loss=0.1926, simple_loss=0.3851, pruned_loss=6.653, over 4761.00 frames.], tot_loss[loss=0.1913, simple_loss=0.3826, pruned_loss=6.681, over 972699.19 frames.], batch size: 17, lr: 2.91e-03
2022-05-03 12:05:36,580 INFO [train.py:715] (1/8) Epoch 0, batch 1850, loss[loss=0.1983, simple_loss=0.3966, pruned_loss=6.822, over 4885.00 frames.], tot_loss[loss=0.1903, simple_loss=0.3805, pruned_loss=6.677, over 972718.06 frames.], batch size: 22, lr: 2.91e-03
2022-05-03 12:06:18,616 INFO [train.py:715] (1/8) Epoch 0, batch 1900, loss[loss=0.1617, simple_loss=0.3235, pruned_loss=6.483, over 4804.00 frames.], tot_loss[loss=0.1889, simple_loss=0.3778, pruned_loss=6.677, over 972485.40 frames.], batch size: 12, lr: 2.90e-03
2022-05-03 12:07:00,150 INFO [train.py:715] (1/8) Epoch 0, batch 1950, loss[loss=0.1597, simple_loss=0.3193, pruned_loss=6.554, over 4748.00 frames.], tot_loss[loss=0.1868, simple_loss=0.3736, pruned_loss=6.674, over 972261.26 frames.], batch size: 12, lr: 2.90e-03
2022-05-03 12:07:38,876 INFO [train.py:715] (1/8) Epoch 0, batch 2000, loss[loss=0.2164, simple_loss=0.4327, pruned_loss=6.72, over 4844.00 frames.], tot_loss[loss=0.1851, simple_loss=0.3701, pruned_loss=6.67, over 972660.67 frames.], batch size: 26, lr: 2.89e-03
2022-05-03 12:08:19,993 INFO [train.py:715] (1/8) Epoch 0, batch 2050, loss[loss=0.1758, simple_loss=0.3516, pruned_loss=6.791, over 4982.00 frames.], tot_loss[loss=0.184, simple_loss=0.3679, pruned_loss=6.664, over 972438.45 frames.], batch size: 14, lr: 2.89e-03
2022-05-03 12:09:00,595 INFO [train.py:715] (1/8) Epoch 0, batch 2100, loss[loss=0.1528, simple_loss=0.3056, pruned_loss=6.616, over 4771.00 frames.], tot_loss[loss=0.1835, simple_loss=0.3671, pruned_loss=6.665, over 972481.35 frames.], batch size: 17, lr: 2.88e-03
2022-05-03 12:09:41,212 INFO [train.py:715] (1/8) Epoch 0, batch 2150, loss[loss=0.1746, simple_loss=0.3492, pruned_loss=6.542, over 4833.00 frames.], tot_loss[loss=0.1821, simple_loss=0.3642, pruned_loss=6.664, over 972606.28 frames.], batch size: 15, lr: 2.88e-03
2022-05-03 12:10:20,507 INFO [train.py:715] (1/8) Epoch 0, batch 2200, loss[loss=0.1479, simple_loss=0.2957, pruned_loss=6.62, over 4821.00 frames.], tot_loss[loss=0.1809, simple_loss=0.3619, pruned_loss=6.666, over 972080.77 frames.], batch size: 13, lr: 2.87e-03
2022-05-03 12:11:01,494 INFO [train.py:715] (1/8) Epoch 0, batch 2250, loss[loss=0.1758, simple_loss=0.3516, pruned_loss=6.647, over 4962.00 frames.], tot_loss[loss=0.1806, simple_loss=0.3613, pruned_loss=6.666, over 972472.55 frames.], batch size: 14, lr: 2.86e-03
2022-05-03 12:11:42,779 INFO [train.py:715] (1/8) Epoch 0, batch 2300, loss[loss=0.1897, simple_loss=0.3793, pruned_loss=6.691, over 4988.00 frames.], tot_loss[loss=0.1794, simple_loss=0.3587, pruned_loss=6.665, over 972324.90 frames.], batch size: 28, lr: 2.86e-03
2022-05-03 12:12:22,381 INFO [train.py:715] (1/8) Epoch 0, batch 2350, loss[loss=0.1814, simple_loss=0.3628, pruned_loss=6.768, over 4858.00 frames.], tot_loss[loss=0.1783, simple_loss=0.3566, pruned_loss=6.664, over 972125.68 frames.], batch size: 20, lr: 2.85e-03
2022-05-03 12:13:03,132 INFO [train.py:715] (1/8) Epoch 0, batch 2400, loss[loss=0.1873, simple_loss=0.3745, pruned_loss=6.719, over 4971.00 frames.], tot_loss[loss=0.1773, simple_loss=0.3546, pruned_loss=6.667, over 971288.09 frames.], batch size: 35, lr: 2.85e-03
2022-05-03 12:13:43,818 INFO [train.py:715] (1/8) Epoch 0, batch 2450, loss[loss=0.1714, simple_loss=0.3428, pruned_loss=6.728, over 4906.00 frames.], tot_loss[loss=0.177, simple_loss=0.354, pruned_loss=6.668, over 971673.15 frames.], batch size: 19, lr: 2.84e-03
2022-05-03 12:14:24,680 INFO [train.py:715] (1/8) Epoch 0, batch 2500, loss[loss=0.1703, simple_loss=0.3405, pruned_loss=6.596, over 4778.00 frames.], tot_loss[loss=0.1754, simple_loss=0.3507, pruned_loss=6.665, over 971608.55 frames.], batch size: 17, lr: 2.84e-03
2022-05-03 12:15:03,913 INFO [train.py:715] (1/8) Epoch 0, batch 2550, loss[loss=0.1959, simple_loss=0.3918, pruned_loss=6.694, over 4698.00 frames.], tot_loss[loss=0.1746, simple_loss=0.3492, pruned_loss=6.663, over 971272.54 frames.], batch size: 15, lr: 2.83e-03
2022-05-03 12:15:44,625 INFO [train.py:715] (1/8) Epoch 0, batch 2600, loss[loss=0.2045, simple_loss=0.4091, pruned_loss=6.759, over 4950.00 frames.], tot_loss[loss=0.1738, simple_loss=0.3475, pruned_loss=6.659, over 973401.15 frames.], batch size: 39, lr: 2.83e-03
2022-05-03 12:16:25,708 INFO [train.py:715] (1/8) Epoch 0, batch 2650, loss[loss=0.1612, simple_loss=0.3223, pruned_loss=6.686, over 4893.00 frames.], tot_loss[loss=0.1726, simple_loss=0.3452, pruned_loss=6.654, over 973750.71 frames.], batch size: 17, lr: 2.82e-03
2022-05-03 12:17:08,081 INFO [train.py:715] (1/8) Epoch 0, batch 2700, loss[loss=0.1771, simple_loss=0.3543, pruned_loss=6.84, over 4813.00 frames.], tot_loss[loss=0.1727, simple_loss=0.3455, pruned_loss=6.648, over 972890.90 frames.], batch size: 25, lr: 2.81e-03
2022-05-03 12:17:48,873 INFO [train.py:715] (1/8) Epoch 0, batch 2750, loss[loss=0.1476, simple_loss=0.2952, pruned_loss=6.605, over 4862.00 frames.], tot_loss[loss=0.173, simple_loss=0.3459, pruned_loss=6.648, over 973639.38 frames.], batch size: 20, lr: 2.81e-03
2022-05-03 12:18:29,711 INFO [train.py:715] (1/8) Epoch 0, batch 2800, loss[loss=0.1762, simple_loss=0.3523, pruned_loss=6.606, over 4860.00 frames.], tot_loss[loss=0.172, simple_loss=0.344, pruned_loss=6.644, over 973121.99 frames.], batch size: 20, lr: 2.80e-03
2022-05-03 12:19:10,264 INFO [train.py:715] (1/8) Epoch 0, batch 2850, loss[loss=0.197, simple_loss=0.3941, pruned_loss=6.706, over 4784.00 frames.], tot_loss[loss=0.1716, simple_loss=0.3433, pruned_loss=6.645, over 972928.32 frames.], batch size: 14, lr: 2.80e-03
2022-05-03 12:19:49,119 INFO [train.py:715] (1/8) Epoch 0, batch 2900, loss[loss=0.2068, simple_loss=0.4137, pruned_loss=6.851, over 4691.00 frames.], tot_loss[loss=0.1715, simple_loss=0.3429, pruned_loss=6.64, over 971590.76 frames.], batch size: 15, lr: 2.79e-03
2022-05-03 12:20:29,371 INFO [train.py:715] (1/8) Epoch 0, batch 2950, loss[loss=0.1793, simple_loss=0.3587, pruned_loss=6.804, over 4936.00 frames.], tot_loss[loss=0.1711, simple_loss=0.3423, pruned_loss=6.644, over 971649.75 frames.], batch size: 23, lr: 2.78e-03
2022-05-03 12:21:11,357 INFO [train.py:715] (1/8) Epoch 0, batch 3000, loss[loss=0.8308, simple_loss=0.3316, pruned_loss=6.65, over 4805.00 frames.], tot_loss[loss=0.2078, simple_loss=0.3427, pruned_loss=6.647, over 972180.71 frames.], batch size: 25, lr: 2.78e-03
2022-05-03 12:21:11,358 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 12:21:21,130 INFO [train.py:742] (1/8) Epoch 0, validation: loss=2.223, simple_loss=0.2788, pruned_loss=2.083, over 914524.00 frames.
2022-05-03 12:22:02,156 INFO [train.py:715] (1/8) Epoch 0, batch 3050, loss[loss=0.2291, simple_loss=0.3285, pruned_loss=0.6488, over 4822.00 frames.], tot_loss[loss=0.2224, simple_loss=0.3425, pruned_loss=5.403, over 971988.32 frames.], batch size: 25, lr: 2.77e-03
2022-05-03 12:22:41,563 INFO [train.py:715] (1/8) Epoch 0, batch 3100, loss[loss=0.201, simple_loss=0.3305, pruned_loss=0.3578, over 4910.00 frames.], tot_loss[loss=0.2208, simple_loss=0.3405, pruned_loss=4.31, over 972592.73 frames.], batch size: 17, lr: 2.77e-03
2022-05-03 12:23:22,422 INFO [train.py:715] (1/8) Epoch 0, batch 3150, loss[loss=0.1571, simple_loss=0.2749, pruned_loss=0.1972, over 4868.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3393, pruned_loss=3.42, over 973411.97 frames.], batch size: 22, lr: 2.76e-03
2022-05-03 12:24:03,660 INFO [train.py:715] (1/8) Epoch 0, batch 3200, loss[loss=0.2012, simple_loss=0.3518, pruned_loss=0.253, over 4871.00 frames.], tot_loss[loss=0.211, simple_loss=0.339, pruned_loss=2.719, over 972898.29 frames.], batch size: 20, lr: 2.75e-03
2022-05-03 12:24:44,875 INFO [train.py:715] (1/8) Epoch 0, batch 3250, loss[loss=0.2021, simple_loss=0.3556, pruned_loss=0.2435, over 4951.00 frames.], tot_loss[loss=0.2062, simple_loss=0.3376, pruned_loss=2.167, over 972721.15 frames.], batch size: 24, lr: 2.75e-03
2022-05-03 12:25:24,112 INFO [train.py:715] (1/8) Epoch 0, batch 3300, loss[loss=0.1888, simple_loss=0.3323, pruned_loss=0.2267, over 4851.00 frames.], tot_loss[loss=0.2031, simple_loss=0.3382, pruned_loss=1.736, over 972533.17 frames.], batch size: 13, lr: 2.74e-03
2022-05-03 12:26:05,353 INFO [train.py:715] (1/8) Epoch 0, batch 3350, loss[loss=0.1634, simple_loss=0.2882, pruned_loss=0.1927, over 4762.00 frames.], tot_loss[loss=0.1994, simple_loss=0.3371, pruned_loss=1.395, over 972796.57 frames.], batch size: 12, lr: 2.73e-03
2022-05-03 12:26:46,184 INFO [train.py:715] (1/8) Epoch 0, batch 3400, loss[loss=0.1865, simple_loss=0.3323, pruned_loss=0.204, over 4943.00 frames.], tot_loss[loss=0.1976, simple_loss=0.3379, pruned_loss=1.133, over 971909.75 frames.], batch size: 15, lr: 2.73e-03
2022-05-03 12:27:25,310 INFO [train.py:715] (1/8) Epoch 0, batch 3450, loss[loss=0.2026, simple_loss=0.3624, pruned_loss=0.2143, over 4803.00 frames.], tot_loss[loss=0.1957, simple_loss=0.3379, pruned_loss=0.9254, over 972584.54 frames.], batch size: 21, lr: 2.72e-03
2022-05-03 12:28:06,924 INFO [train.py:715] (1/8) Epoch 0, batch 3500, loss[loss=0.1919, simple_loss=0.3408, pruned_loss=0.2146, over 4907.00 frames.], tot_loss[loss=0.1929, simple_loss=0.336, pruned_loss=0.7609, over 973114.63 frames.], batch size: 17, lr: 2.72e-03
2022-05-03 12:28:48,554 INFO [train.py:715] (1/8) Epoch 0, batch 3550, loss[loss=0.183, simple_loss=0.3258, pruned_loss=0.2015, over 4745.00 frames.], tot_loss[loss=0.1905, simple_loss=0.3342, pruned_loss=0.6322, over 973046.71 frames.], batch size: 16, lr: 2.71e-03
2022-05-03 12:29:29,801 INFO [train.py:715] (1/8) Epoch 0, batch 3600, loss[loss=0.1687, simple_loss=0.3071, pruned_loss=0.1518, over 4935.00 frames.], tot_loss[loss=0.1883, simple_loss=0.3323, pruned_loss=0.5312, over 973376.08 frames.], batch size: 29, lr: 2.70e-03
2022-05-03 12:30:09,002 INFO [train.py:715] (1/8) Epoch 0, batch 3650, loss[loss=0.161, simple_loss=0.2925, pruned_loss=0.1475, over 4850.00 frames.], tot_loss[loss=0.1854, simple_loss=0.3291, pruned_loss=0.45, over 973742.97 frames.], batch size: 15, lr: 2.70e-03
2022-05-03 12:30:50,510 INFO [train.py:715] (1/8) Epoch 0, batch 3700, loss[loss=0.1739, simple_loss=0.3166, pruned_loss=0.1563, over 4884.00 frames.], tot_loss[loss=0.1843, simple_loss=0.3284, pruned_loss=0.3893, over 973284.55 frames.], batch size: 16, lr: 2.69e-03
2022-05-03 12:31:32,102 INFO [train.py:715] (1/8) Epoch 0, batch 3750, loss[loss=0.1766, simple_loss=0.32, pruned_loss=0.1661, over 4812.00 frames.], tot_loss[loss=0.1831, simple_loss=0.3272, pruned_loss=0.3409, over 973583.29 frames.], batch size: 24, lr: 2.68e-03
2022-05-03 12:32:11,310 INFO [train.py:715] (1/8) Epoch 0, batch 3800, loss[loss=0.1682, simple_loss=0.3055, pruned_loss=0.1543, over 4977.00 frames.], tot_loss[loss=0.1833, simple_loss=0.3283, pruned_loss=0.305, over 972786.03 frames.], batch size: 33, lr: 2.68e-03
2022-05-03 12:33:05,629 INFO [train.py:715] (1/8) Epoch 0, batch 3850, loss[loss=0.1911, simple_loss=0.341, pruned_loss=0.2055, over 4701.00 frames.], tot_loss[loss=0.1823, simple_loss=0.3274, pruned_loss=0.2746, over 972607.37 frames.], batch size: 15, lr: 2.67e-03
2022-05-03 12:33:46,701 INFO [train.py:715] (1/8) Epoch 0, batch 3900, loss[loss=0.1864, simple_loss=0.3387, pruned_loss=0.1704, over 4858.00 frames.], tot_loss[loss=0.1813, simple_loss=0.3264, pruned_loss=0.2506, over 971700.01 frames.], batch size: 30, lr: 2.66e-03
2022-05-03 12:34:26,858 INFO [train.py:715] (1/8) Epoch 0, batch 3950, loss[loss=0.1776, simple_loss=0.3227, pruned_loss=0.1627, over 4782.00 frames.], tot_loss[loss=0.1808, simple_loss=0.3258, pruned_loss=0.232, over 971820.41 frames.], batch size: 14, lr: 2.66e-03
2022-05-03 12:35:06,666 INFO [train.py:715] (1/8) Epoch 0, batch 4000, loss[loss=0.1736, simple_loss=0.313, pruned_loss=0.171, over 4881.00 frames.], tot_loss[loss=0.1802, simple_loss=0.3252, pruned_loss=0.2175, over 971533.88 frames.], batch size: 16, lr: 2.65e-03
2022-05-03 12:35:47,594 INFO [train.py:715] (1/8) Epoch 0, batch 4050, loss[loss=0.1751, simple_loss=0.318, pruned_loss=0.161, over 4806.00 frames.], tot_loss[loss=0.1803, simple_loss=0.3258, pruned_loss=0.2068, over 972627.56 frames.], batch size: 14, lr: 2.64e-03
2022-05-03 12:36:28,809 INFO [train.py:715] (1/8) Epoch 0, batch 4100, loss[loss=0.1728, simple_loss=0.3145, pruned_loss=0.1558, over 4874.00 frames.], tot_loss[loss=0.1798, simple_loss=0.3252, pruned_loss=0.1972, over 972310.02 frames.], batch size: 38, lr: 2.64e-03
2022-05-03 12:37:07,957 INFO [train.py:715] (1/8) Epoch 0, batch 4150, loss[loss=0.1845, simple_loss=0.3342, pruned_loss=0.1744, over 4818.00 frames.], tot_loss[loss=0.1796, simple_loss=0.3251, pruned_loss=0.1904, over 972361.57 frames.], batch size: 25, lr: 2.63e-03
2022-05-03 12:37:49,190 INFO [train.py:715] (1/8) Epoch 0, batch 4200, loss[loss=0.2068, simple_loss=0.3723, pruned_loss=0.2063, over 4869.00 frames.], tot_loss[loss=0.1791, simple_loss=0.3245, pruned_loss=0.1842, over 972672.37 frames.], batch size: 38, lr: 2.63e-03
2022-05-03 12:38:30,919 INFO [train.py:715] (1/8) Epoch 0, batch 4250, loss[loss=0.1847, simple_loss=0.3351, pruned_loss=0.1719, over 4779.00 frames.], tot_loss[loss=0.178, simple_loss=0.3229, pruned_loss=0.1773, over 971862.01 frames.], batch size: 17, lr: 2.62e-03
2022-05-03 12:39:11,495 INFO [train.py:715] (1/8) Epoch 0, batch 4300, loss[loss=0.178, simple_loss=0.3269, pruned_loss=0.1453, over 4742.00 frames.], tot_loss[loss=0.1767, simple_loss=0.3211, pruned_loss=0.1712, over 971686.62 frames.], batch size: 16, lr: 2.61e-03
2022-05-03 12:39:51,578 INFO [train.py:715] (1/8) Epoch 0, batch 4350, loss[loss=0.1988, simple_loss=0.3566, pruned_loss=0.2053, over 4775.00 frames.], tot_loss[loss=0.1774, simple_loss=0.3222, pruned_loss=0.1702, over 972135.02 frames.], batch size: 14, lr: 2.61e-03
2022-05-03 12:40:33,092 INFO [train.py:715] (1/8) Epoch 0, batch 4400, loss[loss=0.1523, simple_loss=0.2809, pruned_loss=0.119, over 4818.00 frames.], tot_loss[loss=0.1759, simple_loss=0.3198, pruned_loss=0.1656, over 972163.77 frames.], batch size: 13, lr: 2.60e-03
2022-05-03 12:41:14,314 INFO [train.py:715] (1/8) Epoch 0, batch 4450, loss[loss=0.1867, simple_loss=0.3395, pruned_loss=0.1694, over 4939.00 frames.], tot_loss[loss=0.1754, simple_loss=0.3192, pruned_loss=0.1626, over 971674.06 frames.], batch size: 21, lr: 2.59e-03
2022-05-03 12:41:53,442 INFO [train.py:715] (1/8) Epoch 0, batch 4500, loss[loss=0.1919, simple_loss=0.349, pruned_loss=0.1743, over 4751.00 frames.], tot_loss[loss=0.1746, simple_loss=0.318, pruned_loss=0.1593, over 971687.64 frames.], batch size: 19, lr: 2.59e-03
2022-05-03 12:42:34,819 INFO [train.py:715] (1/8) Epoch 0, batch 4550, loss[loss=0.2039, simple_loss=0.3643, pruned_loss=0.2175, over 4964.00 frames.], tot_loss[loss=0.1742, simple_loss=0.3174, pruned_loss=0.1578, over 971451.72 frames.], batch size: 15, lr: 2.58e-03
2022-05-03 12:43:16,368 INFO [train.py:715] (1/8) Epoch 0, batch 4600, loss[loss=0.1392, simple_loss=0.2559, pruned_loss=0.1124, over 4833.00 frames.], tot_loss[loss=0.1743, simple_loss=0.3177, pruned_loss=0.1567, over 972357.77 frames.], batch size: 12, lr: 2.57e-03
2022-05-03 12:43:56,535 INFO [train.py:715] (1/8) Epoch 0, batch 4650, loss[loss=0.1576, simple_loss=0.287, pruned_loss=0.1407, over 4915.00 frames.], tot_loss[loss=0.1731, simple_loss=0.3159, pruned_loss=0.1533, over 971931.30 frames.], batch size: 17, lr: 2.57e-03
2022-05-03 12:44:36,474 INFO [train.py:715] (1/8) Epoch 0, batch 4700, loss[loss=0.1837, simple_loss=0.3365, pruned_loss=0.1548, over 4768.00 frames.], tot_loss[loss=0.1737, simple_loss=0.3169, pruned_loss=0.1535, over 972291.21 frames.], batch size: 16, lr: 2.56e-03
2022-05-03 12:45:17,608 INFO [train.py:715] (1/8) Epoch 0, batch 4750, loss[loss=0.1739, simple_loss=0.3207, pruned_loss=0.1356, over 4810.00 frames.], tot_loss[loss=0.1734, simple_loss=0.3166, pruned_loss=0.1522, over 972080.57 frames.], batch size: 26, lr: 2.55e-03
2022-05-03 12:45:58,871 INFO [train.py:715] (1/8) Epoch 0, batch 4800, loss[loss=0.1719, simple_loss=0.3135, pruned_loss=0.1515, over 4809.00 frames.], tot_loss[loss=0.1732, simple_loss=0.3162, pruned_loss=0.1519, over 973091.70 frames.], batch size: 25, lr: 2.55e-03
2022-05-03 12:46:38,835 INFO [train.py:715] (1/8) Epoch 0, batch 4850, loss[loss=0.1839, simple_loss=0.3328, pruned_loss=0.1747, over 4918.00 frames.], tot_loss[loss=0.1728, simple_loss=0.3157, pruned_loss=0.1506, over 973242.72 frames.], batch size: 19, lr: 2.54e-03
2022-05-03 12:47:19,641 INFO [train.py:715] (1/8) Epoch 0, batch 4900, loss[loss=0.1775, simple_loss=0.3237, pruned_loss=0.1559, over 4816.00 frames.], tot_loss[loss=0.1725, simple_loss=0.3151, pruned_loss=0.1502, over 971860.21 frames.], batch size: 26, lr: 2.54e-03
2022-05-03 12:48:01,145 INFO [train.py:715] (1/8) Epoch 0, batch 4950, loss[loss=0.1462, simple_loss=0.2724, pruned_loss=0.1004, over 4814.00 frames.], tot_loss[loss=0.1729, simple_loss=0.3158, pruned_loss=0.1503, over 972187.02 frames.], batch size: 25, lr: 2.53e-03
2022-05-03 12:48:41,422 INFO [train.py:715] (1/8) Epoch 0, batch 5000, loss[loss=0.1578, simple_loss=0.291, pruned_loss=0.1232, over 4973.00 frames.], tot_loss[loss=0.1722, simple_loss=0.3147, pruned_loss=0.1487, over 972526.08 frames.], batch size: 14, lr: 2.52e-03
2022-05-03 12:49:22,152 INFO [train.py:715] (1/8) Epoch 0, batch 5050, loss[loss=0.181, simple_loss=0.3319, pruned_loss=0.1498, over 4979.00 frames.], tot_loss[loss=0.1717, simple_loss=0.3139, pruned_loss=0.1476, over 972517.21 frames.], batch size: 28, lr: 2.52e-03
2022-05-03 12:50:05,001 INFO [train.py:715] (1/8) Epoch 0, batch 5100, loss[loss=0.1704, simple_loss=0.3144, pruned_loss=0.1321, over 4934.00 frames.], tot_loss[loss=0.1711, simple_loss=0.3128, pruned_loss=0.1466, over 973495.09 frames.], batch size: 23, lr: 2.51e-03
2022-05-03 12:50:48,211 INFO [train.py:715] (1/8) Epoch 0, batch 5150, loss[loss=0.1956, simple_loss=0.3567, pruned_loss=0.1724, over 4974.00 frames.], tot_loss[loss=0.171, simple_loss=0.3129, pruned_loss=0.146, over 973852.63 frames.], batch size: 14, lr: 2.50e-03
2022-05-03 12:51:28,079 INFO [train.py:715] (1/8) Epoch 0, batch 5200, loss[loss=0.1924, simple_loss=0.3499, pruned_loss=0.1749, over 4855.00 frames.], tot_loss[loss=0.171, simple_loss=0.3128, pruned_loss=0.1457, over 973855.85 frames.], batch size: 30, lr: 2.50e-03
2022-05-03 12:52:08,698 INFO [train.py:715] (1/8) Epoch 0, batch 5250, loss[loss=0.174, simple_loss=0.3176, pruned_loss=0.1521, over 4959.00 frames.], tot_loss[loss=0.1705, simple_loss=0.3121, pruned_loss=0.1448, over 973876.99 frames.], batch size: 14, lr: 2.49e-03
2022-05-03 12:52:49,812 INFO [train.py:715] (1/8) Epoch 0, batch 5300, loss[loss=0.1705, simple_loss=0.3148, pruned_loss=0.1312, over 4956.00 frames.], tot_loss[loss=0.1694, simple_loss=0.3104, pruned_loss=0.1426, over 973634.50 frames.], batch size: 35, lr: 2.49e-03
2022-05-03 12:53:30,338 INFO [train.py:715] (1/8) Epoch 0, batch 5350, loss[loss=0.1876, simple_loss=0.3411, pruned_loss=0.1709, over 4923.00 frames.], tot_loss[loss=0.1693, simple_loss=0.3102, pruned_loss=0.1419, over 973872.56 frames.], batch size: 17, lr: 2.48e-03
2022-05-03 12:54:10,019 INFO [train.py:715] (1/8) Epoch 0, batch 5400, loss[loss=0.1675, simple_loss=0.3035, pruned_loss=0.1575, over 4821.00 frames.], tot_loss[loss=0.1697, simple_loss=0.311, pruned_loss=0.1419, over 973049.85 frames.], batch size: 25, lr: 2.47e-03
2022-05-03 12:54:50,452 INFO [train.py:715] (1/8) Epoch 0, batch 5450, loss[loss=0.153, simple_loss=0.2838, pruned_loss=0.1112, over 4911.00 frames.], tot_loss[loss=0.1694, simple_loss=0.3106, pruned_loss=0.1408, over 973409.29 frames.], batch size: 19, lr: 2.47e-03
2022-05-03 12:55:31,409 INFO [train.py:715] (1/8) Epoch 0, batch 5500, loss[loss=0.1417, simple_loss=0.2648, pruned_loss=0.09272, over 4779.00 frames.], tot_loss[loss=0.1693, simple_loss=0.3105, pruned_loss=0.1402, over 972049.56 frames.], batch size: 17, lr: 2.46e-03
2022-05-03 12:56:11,127 INFO [train.py:715] (1/8) Epoch 0, batch 5550, loss[loss=0.1798, simple_loss=0.3279, pruned_loss=0.1581, over 4799.00 frames.], tot_loss[loss=0.169, simple_loss=0.31, pruned_loss=0.1399, over 972568.90 frames.], batch size: 25, lr: 2.45e-03
2022-05-03 12:56:51,161 INFO [train.py:715] (1/8) Epoch 0, batch 5600, loss[loss=0.1702, simple_loss=0.3127, pruned_loss=0.1388, over 4899.00 frames.], tot_loss[loss=0.1688, simple_loss=0.3097, pruned_loss=0.1394, over 971931.71 frames.], batch size: 17, lr: 2.45e-03
2022-05-03 12:57:32,359 INFO [train.py:715] (1/8) Epoch 0, batch 5650, loss[loss=0.1652, simple_loss=0.3041, pruned_loss=0.1312, over 4863.00 frames.], tot_loss[loss=0.1686, simple_loss=0.3094, pruned_loss=0.139, over 972135.00 frames.], batch size: 16, lr: 2.44e-03
2022-05-03 12:58:12,927 INFO [train.py:715] (1/8) Epoch 0, batch 5700, loss[loss=0.1748, simple_loss=0.3168, pruned_loss=0.1639, over 4982.00 frames.], tot_loss[loss=0.1675, simple_loss=0.3077, pruned_loss=0.1372, over 972982.31 frames.], batch size: 35, lr: 2.44e-03
2022-05-03 12:58:52,125 INFO [train.py:715] (1/8) Epoch 0, batch 5750, loss[loss=0.1883, simple_loss=0.344, pruned_loss=0.1633, over 4953.00 frames.], tot_loss[loss=0.168, simple_loss=0.3084, pruned_loss=0.1385, over 973172.20 frames.], batch size: 21, lr: 2.43e-03
2022-05-03 12:59:33,130 INFO [train.py:715] (1/8) Epoch 0, batch 5800, loss[loss=0.1586, simple_loss=0.2918, pruned_loss=0.1274, over 4985.00 frames.], tot_loss[loss=0.1673, simple_loss=0.3072, pruned_loss=0.1371, over 973125.03 frames.], batch size: 24, lr: 2.42e-03
2022-05-03 13:00:14,316 INFO [train.py:715] (1/8) Epoch 0, batch 5850, loss[loss=0.1882, simple_loss=0.3403, pruned_loss=0.1807, over 4857.00 frames.], tot_loss[loss=0.1678, simple_loss=0.308, pruned_loss=0.1373, over 973280.70 frames.], batch size: 30, lr: 2.42e-03
2022-05-03 13:00:54,234 INFO [train.py:715] (1/8) Epoch 0, batch 5900, loss[loss=0.167, simple_loss=0.3097, pruned_loss=0.1213, over 4748.00 frames.], tot_loss[loss=0.1679, simple_loss=0.3083, pruned_loss=0.1373, over 973153.50 frames.], batch size: 19, lr: 2.41e-03
2022-05-03 13:01:33,983 INFO [train.py:715] (1/8) Epoch 0, batch 5950, loss[loss=0.1623, simple_loss=0.2983, pruned_loss=0.1309, over 4949.00 frames.], tot_loss[loss=0.168, simple_loss=0.3086, pruned_loss=0.1372, over 972645.21 frames.], batch size: 21, lr: 2.41e-03
2022-05-03 13:02:14,778 INFO [train.py:715] (1/8) Epoch 0, batch 6000, loss[loss=0.312, simple_loss=0.3192, pruned_loss=0.1524, over 4745.00 frames.], tot_loss[loss=0.1685, simple_loss=0.3076, pruned_loss=0.1359, over 972666.74 frames.], batch size: 16, lr: 2.40e-03
2022-05-03 13:02:14,779 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 13:02:25,810 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1779, simple_loss=0.2457, pruned_loss=0.05502, over 914524.00 frames.
2022-05-03 13:03:07,310 INFO [train.py:715] (1/8) Epoch 0, batch 6050, loss[loss=0.3149, simple_loss=0.3103, pruned_loss=0.1598, over 4783.00 frames.], tot_loss[loss=0.1998, simple_loss=0.3096, pruned_loss=0.1393, over 972332.91 frames.], batch size: 17, lr: 2.39e-03
2022-05-03 13:03:47,840 INFO [train.py:715] (1/8) Epoch 0, batch 6100, loss[loss=0.2924, simple_loss=0.3158, pruned_loss=0.1345, over 4784.00 frames.], tot_loss[loss=0.2214, simple_loss=0.3108, pruned_loss=0.1394, over 972759.91 frames.], batch size: 18, lr: 2.39e-03
2022-05-03 13:04:27,377 INFO [train.py:715] (1/8) Epoch 0, batch 6150, loss[loss=0.309, simple_loss=0.3202, pruned_loss=0.1489, over 4781.00 frames.], tot_loss[loss=0.2369, simple_loss=0.3107, pruned_loss=0.1387, over 972890.18 frames.], batch size: 17, lr: 2.38e-03
2022-05-03 13:05:08,109 INFO [train.py:715] (1/8) Epoch 0, batch 6200, loss[loss=0.255, simple_loss=0.2848, pruned_loss=0.1126, over 4896.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3101, pruned_loss=0.1369, over 973497.34 frames.], batch size: 19, lr: 2.38e-03
2022-05-03 13:05:48,910 INFO [train.py:715] (1/8) Epoch 0, batch 6250, loss[loss=0.2799, simple_loss=0.2972, pruned_loss=0.1313, over 4912.00 frames.], tot_loss[loss=0.2581, simple_loss=0.311, pruned_loss=0.1372, over 974544.67 frames.], batch size: 18, lr: 2.37e-03
2022-05-03 13:06:29,112 INFO [train.py:715] (1/8) Epoch 0, batch 6300, loss[loss=0.2574, simple_loss=0.2965, pruned_loss=0.1091, over 4934.00 frames.], tot_loss[loss=0.2641, simple_loss=0.3106, pruned_loss=0.1358, over 973058.28 frames.], batch size: 23, lr: 2.37e-03
2022-05-03 13:07:09,797 INFO [train.py:715] (1/8) Epoch 0, batch 6350, loss[loss=0.3195, simple_loss=0.3423, pruned_loss=0.1484, over 4886.00 frames.], tot_loss[loss=0.2679, simple_loss=0.3093, pruned_loss=0.1342, over 971947.17 frames.], batch size: 22, lr: 2.36e-03
2022-05-03 13:07:50,719 INFO [train.py:715] (1/8) Epoch 0, batch 6400, loss[loss=0.2703, simple_loss=0.2972, pruned_loss=0.1217, over 4879.00 frames.], tot_loss[loss=0.2714, simple_loss=0.3091, pruned_loss=0.1331, over 972609.52 frames.], batch size: 22, lr: 2.35e-03
2022-05-03 13:08:30,730 INFO [train.py:715] (1/8) Epoch 0, batch 6450, loss[loss=0.2784, simple_loss=0.2905, pruned_loss=0.1331, over 4778.00 frames.], tot_loss[loss=0.2736, simple_loss=0.3088, pruned_loss=0.1319, over 972006.24 frames.], batch size: 17, lr: 2.35e-03
2022-05-03 13:09:10,065 INFO [train.py:715] (1/8) Epoch 0, batch 6500, loss[loss=0.2922, simple_loss=0.3148, pruned_loss=0.1348, over 4851.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3089, pruned_loss=0.1312, over 973023.05 frames.], batch size: 32, lr: 2.34e-03
2022-05-03 13:09:50,937 INFO [train.py:715] (1/8) Epoch 0, batch 6550, loss[loss=0.3086, simple_loss=0.3175, pruned_loss=0.1499, over 4974.00 frames.], tot_loss[loss=0.2779, simple_loss=0.3094, pruned_loss=0.1309, over 972915.18 frames.], batch size: 35, lr: 2.34e-03
2022-05-03 13:10:31,735 INFO [train.py:715] (1/8) Epoch 0, batch 6600, loss[loss=0.3267, simple_loss=0.3411, pruned_loss=0.1562, over 4797.00 frames.], tot_loss[loss=0.2792, simple_loss=0.3099, pruned_loss=0.1302, over 972786.88 frames.], batch size: 21, lr: 2.33e-03
2022-05-03 13:11:11,224 INFO [train.py:715] (1/8) Epoch 0, batch 6650, loss[loss=0.2697, simple_loss=0.2942, pruned_loss=0.1225, over 4941.00 frames.], tot_loss[loss=0.2787, simple_loss=0.309, pruned_loss=0.1289, over 972244.18 frames.], batch size: 21, lr: 2.33e-03
2022-05-03 13:11:51,651 INFO [train.py:715] (1/8) Epoch 0, batch 6700, loss[loss=0.1967, simple_loss=0.2432, pruned_loss=0.07506, over 4823.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3082, pruned_loss=0.1278, over 973084.17 frames.], batch size: 27, lr: 2.32e-03
2022-05-03 13:12:32,418 INFO [train.py:715] (1/8) Epoch 0, batch 6750, loss[loss=0.2699, simple_loss=0.3057, pruned_loss=0.1171, over 4889.00 frames.], tot_loss[loss=0.2797, simple_loss=0.3086, pruned_loss=0.1282, over 972350.56 frames.], batch size: 16, lr: 2.31e-03
2022-05-03 13:13:12,499 INFO [train.py:715] (1/8) Epoch 0, batch 6800, loss[loss=0.2212, simple_loss=0.2668, pruned_loss=0.0878, over 4878.00 frames.], tot_loss[loss=0.2786, simple_loss=0.3078, pruned_loss=0.127, over 973142.13 frames.], batch size: 16, lr: 2.31e-03
2022-05-03 13:13:52,215 INFO [train.py:715] (1/8) Epoch 0, batch 6850, loss[loss=0.2524, simple_loss=0.2986, pruned_loss=0.1031, over 4869.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3079, pruned_loss=0.126, over 973581.94 frames.], batch size: 22, lr: 2.30e-03
2022-05-03 13:14:32,492 INFO [train.py:715] (1/8) Epoch 0, batch 6900, loss[loss=0.2753, simple_loss=0.3, pruned_loss=0.1253, over 4706.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3073, pruned_loss=0.125, over 972831.24 frames.], batch size: 15, lr: 2.30e-03
2022-05-03 13:15:12,914 INFO [train.py:715] (1/8) Epoch 0, batch 6950, loss[loss=0.2565, simple_loss=0.2845, pruned_loss=0.1143, over 4792.00 frames.], tot_loss[loss=0.277, simple_loss=0.307, pruned_loss=0.1246, over 972566.85 frames.], batch size: 12, lr: 2.29e-03
2022-05-03 13:15:53,033 INFO [train.py:715] (1/8) Epoch 0, batch 7000, loss[loss=0.2461, simple_loss=0.2978, pruned_loss=0.09724, over 4900.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3068, pruned_loss=0.1238, over 972067.96 frames.], batch size: 17, lr: 2.29e-03
2022-05-03 13:16:33,738 INFO [train.py:715] (1/8) Epoch 0, batch 7050, loss[loss=0.2907, simple_loss=0.3188, pruned_loss=0.1313, over 4792.00 frames.], tot_loss[loss=0.276, simple_loss=0.3065, pruned_loss=0.1234, over 971681.07 frames.], batch size: 18, lr: 2.28e-03
2022-05-03 13:17:14,923 INFO [train.py:715] (1/8) Epoch 0, batch 7100, loss[loss=0.2159, simple_loss=0.2545, pruned_loss=0.08867, over 4818.00 frames.], tot_loss[loss=0.2762, simple_loss=0.3065, pruned_loss=0.1235, over 971813.86 frames.], batch size: 13, lr: 2.28e-03
2022-05-03 13:17:55,868 INFO [train.py:715] (1/8) Epoch 0, batch 7150, loss[loss=0.2416, simple_loss=0.2834, pruned_loss=0.09995, over 4800.00 frames.], tot_loss[loss=0.2745, simple_loss=0.3054, pruned_loss=0.1222, over 971655.81 frames.], batch size: 21, lr: 2.27e-03
2022-05-03 13:18:35,507 INFO [train.py:715] (1/8) Epoch 0, batch 7200, loss[loss=0.2085, simple_loss=0.2542, pruned_loss=0.08136, over 4756.00 frames.], tot_loss[loss=0.2735, simple_loss=0.3053, pruned_loss=0.1211, over 970784.14 frames.], batch size: 12, lr: 2.27e-03
2022-05-03 13:19:16,087 INFO [train.py:715] (1/8) Epoch 0, batch 7250, loss[loss=0.2582, simple_loss=0.3039, pruned_loss=0.1062, over 4932.00 frames.], tot_loss[loss=0.2722, simple_loss=0.3044, pruned_loss=0.1202, over 970676.60 frames.], batch size: 23, lr: 2.26e-03
2022-05-03 13:19:55,971 INFO [train.py:715] (1/8) Epoch 0, batch 7300, loss[loss=0.1975, simple_loss=0.2586, pruned_loss=0.06815, over 4756.00 frames.], tot_loss[loss=0.2725, simple_loss=0.3052, pruned_loss=0.12, over 971010.98 frames.], batch size: 19, lr: 2.26e-03
2022-05-03 13:20:36,060 INFO [train.py:715] (1/8) Epoch 0, batch 7350, loss[loss=0.292, simple_loss=0.3169, pruned_loss=0.1336, over 4786.00 frames.], tot_loss[loss=0.2728, simple_loss=0.3053, pruned_loss=0.1203, over 971033.58 frames.], batch size: 17, lr: 2.25e-03
2022-05-03 13:21:16,437 INFO [train.py:715] (1/8) Epoch 0, batch 7400, loss[loss=0.2301, simple_loss=0.2826, pruned_loss=0.08876, over 4796.00 frames.], tot_loss[loss=0.2718, simple_loss=0.3047, pruned_loss=0.1196, over 971481.17 frames.], batch size: 24, lr: 2.24e-03
2022-05-03 13:21:57,042 INFO [train.py:715] (1/8) Epoch 0, batch 7450, loss[loss=0.2324, simple_loss=0.2789, pruned_loss=0.09297, over 4992.00 frames.], tot_loss[loss=0.2721, simple_loss=0.3051, pruned_loss=0.1196, over 971855.62 frames.], batch size: 26, lr: 2.24e-03
2022-05-03 13:22:36,845 INFO [train.py:715] (1/8) Epoch 0, batch 7500, loss[loss=0.3046, simple_loss=0.337, pruned_loss=0.1361, over 4976.00 frames.], tot_loss[loss=0.2727, simple_loss=0.3057, pruned_loss=0.12, over 971973.28 frames.], batch size: 35, lr: 2.23e-03
2022-05-03 13:23:16,573 INFO [train.py:715] (1/8) Epoch 0, batch 7550, loss[loss=0.2951, simple_loss=0.3039, pruned_loss=0.1431, over 4711.00 frames.], tot_loss[loss=0.2729, simple_loss=0.3055, pruned_loss=0.1202, over 972124.30 frames.], batch size: 12, lr: 2.23e-03
2022-05-03 13:23:57,045 INFO [train.py:715] (1/8) Epoch 0, batch 7600, loss[loss=0.2575, simple_loss=0.2913, pruned_loss=0.1118, over 4758.00 frames.], tot_loss[loss=0.2727, simple_loss=0.3056, pruned_loss=0.1199, over 970838.41 frames.], batch size: 19, lr: 2.22e-03
2022-05-03 13:24:37,508 INFO [train.py:715] (1/8) Epoch 0, batch 7650, loss[loss=0.2577, simple_loss=0.2984, pruned_loss=0.1086, over 4946.00 frames.], tot_loss[loss=0.271, simple_loss=0.3046, pruned_loss=0.1187, over 970819.66 frames.], batch size: 21, lr: 2.22e-03
2022-05-03 13:25:16,995 INFO [train.py:715] (1/8) Epoch 0, batch 7700, loss[loss=0.2935, simple_loss=0.3394, pruned_loss=0.1238, over 4978.00 frames.], tot_loss[loss=0.2718, simple_loss=0.3051, pruned_loss=0.1193, over 970937.09 frames.], batch size: 15, lr: 2.21e-03
2022-05-03 13:25:57,323 INFO [train.py:715] (1/8) Epoch 0, batch 7750, loss[loss=0.2447, simple_loss=0.2866, pruned_loss=0.1014, over 4858.00 frames.], tot_loss[loss=0.2686, simple_loss=0.3029, pruned_loss=0.1171, over 971463.70 frames.], batch size: 15, lr: 2.21e-03
2022-05-03 13:26:38,374 INFO [train.py:715] (1/8) Epoch 0, batch 7800, loss[loss=0.2281, simple_loss=0.2784, pruned_loss=0.08887, over 4850.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3016, pruned_loss=0.1161, over 971866.50 frames.], batch size: 30, lr: 2.20e-03
2022-05-03 13:27:18,718 INFO [train.py:715] (1/8) Epoch 0, batch 7850, loss[loss=0.286, simple_loss=0.3117, pruned_loss=0.1302, over 4898.00 frames.], tot_loss[loss=0.269, simple_loss=0.303, pruned_loss=0.1175, over 972043.09 frames.], batch size: 19, lr: 2.20e-03
2022-05-03 13:27:58,880 INFO [train.py:715] (1/8) Epoch 0, batch 7900, loss[loss=0.3013, simple_loss=0.3292, pruned_loss=0.1367, over 4888.00 frames.], tot_loss[loss=0.2685, simple_loss=0.3027, pruned_loss=0.1171, over 971669.29 frames.], batch size: 17, lr: 2.19e-03
2022-05-03 13:28:39,523 INFO [train.py:715] (1/8) Epoch 0, batch 7950, loss[loss=0.2715, simple_loss=0.3096, pruned_loss=0.1167, over 4791.00 frames.], tot_loss[loss=0.2683, simple_loss=0.3029, pruned_loss=0.1169, over 972530.37 frames.], batch size: 18, lr: 2.19e-03
2022-05-03 13:29:22,243 INFO [train.py:715] (1/8) Epoch 0, batch 8000, loss[loss=0.2284, simple_loss=0.2605, pruned_loss=0.09817, over 4920.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3025, pruned_loss=0.1164, over 972220.56 frames.], batch size: 21, lr: 2.18e-03
2022-05-03 13:30:02,106 INFO [train.py:715] (1/8) Epoch 0, batch 8050, loss[loss=0.3574, simple_loss=0.387, pruned_loss=0.1639, over 4810.00 frames.], tot_loss[loss=0.268, simple_loss=0.3034, pruned_loss=0.1163, over 972012.12 frames.], batch size: 21, lr: 2.18e-03
2022-05-03 13:30:41,972 INFO [train.py:715] (1/8) Epoch 0, batch 8100, loss[loss=0.2634, simple_loss=0.29, pruned_loss=0.1184, over 4746.00 frames.], tot_loss[loss=0.2675, simple_loss=0.3029, pruned_loss=0.116, over 971602.19 frames.], batch size: 16, lr: 2.17e-03
2022-05-03 13:31:22,997 INFO [train.py:715] (1/8) Epoch 0, batch 8150, loss[loss=0.2672, simple_loss=0.2996, pruned_loss=0.1174, over 4868.00 frames.], tot_loss[loss=0.2703, simple_loss=0.305, pruned_loss=0.1178, over 972244.82 frames.], batch size: 20, lr: 2.17e-03
2022-05-03 13:32:02,627 INFO [train.py:715] (1/8) Epoch 0, batch 8200, loss[loss=0.2393, simple_loss=0.2844, pruned_loss=0.09708, over 4882.00 frames.], tot_loss[loss=0.2698, simple_loss=0.3046, pruned_loss=0.1175, over 972248.21 frames.], batch size: 22, lr: 2.16e-03
2022-05-03 13:32:42,140 INFO [train.py:715] (1/8) Epoch 0, batch 8250, loss[loss=0.2634, simple_loss=0.3048, pruned_loss=0.111, over 4869.00 frames.], tot_loss[loss=0.269, simple_loss=0.3043, pruned_loss=0.1168, over 972943.96 frames.], batch size: 20, lr: 2.16e-03
2022-05-03 13:33:22,997 INFO [train.py:715] (1/8) Epoch 0, batch 8300, loss[loss=0.2427, simple_loss=0.2845, pruned_loss=0.1004, over 4979.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3034, pruned_loss=0.1152, over 973159.90 frames.], batch size: 35, lr: 2.15e-03
2022-05-03 13:34:03,436 INFO [train.py:715] (1/8) Epoch 0, batch 8350, loss[loss=0.2461, simple_loss=0.282, pruned_loss=0.1051, over 4760.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3035, pruned_loss=0.1151, over 972431.74 frames.], batch size: 19, lr: 2.15e-03
2022-05-03 13:34:43,099 INFO [train.py:715] (1/8) Epoch 0, batch 8400, loss[loss=0.261, simple_loss=0.2946, pruned_loss=0.1137, over 4823.00 frames.], tot_loss[loss=0.2666, simple_loss=0.3031, pruned_loss=0.115, over 972344.27 frames.], batch size: 26, lr: 2.15e-03
2022-05-03 13:35:23,382 INFO [train.py:715] (1/8) Epoch 0, batch 8450, loss[loss=0.2471, simple_loss=0.278, pruned_loss=0.1081, over 4857.00 frames.], tot_loss[loss=0.2658, simple_loss=0.3025, pruned_loss=0.1146, over 972139.17 frames.], batch size: 22, lr: 2.14e-03
2022-05-03 13:36:04,640 INFO [train.py:715] (1/8) Epoch 0, batch 8500, loss[loss=0.2306, simple_loss=0.2778, pruned_loss=0.09174, over 4979.00 frames.], tot_loss[loss=0.2646, simple_loss=0.3014, pruned_loss=0.1139, over 972942.13 frames.], batch size: 24, lr: 2.14e-03
2022-05-03 13:36:45,705 INFO [train.py:715] (1/8) Epoch 0, batch 8550, loss[loss=0.2833, simple_loss=0.3128, pruned_loss=0.1269, over 4846.00 frames.], tot_loss[loss=0.2651, simple_loss=0.3019, pruned_loss=0.1142, over 973705.34 frames.], batch size: 20, lr: 2.13e-03
2022-05-03 13:37:25,352 INFO [train.py:715] (1/8) Epoch 0, batch 8600, loss[loss=0.2603, simple_loss=0.3034, pruned_loss=0.1086, over 4898.00 frames.], tot_loss[loss=0.2637, simple_loss=0.3012, pruned_loss=0.1131, over 973519.34 frames.], batch size: 39, lr: 2.13e-03
2022-05-03 13:38:06,737 INFO [train.py:715] (1/8) Epoch 0, batch 8650, loss[loss=0.2337, simple_loss=0.2819, pruned_loss=0.09271, over 4948.00 frames.], tot_loss[loss=0.2623, simple_loss=0.3004, pruned_loss=0.1121, over 973572.36 frames.], batch size: 18, lr: 2.12e-03
2022-05-03 13:38:47,678 INFO [train.py:715] (1/8) Epoch 0, batch 8700, loss[loss=0.2621, simple_loss=0.3167, pruned_loss=0.1038, over 4794.00 frames.], tot_loss[loss=0.2642, simple_loss=0.3013, pruned_loss=0.1135, over 974124.78 frames.], batch size: 24, lr: 2.12e-03
2022-05-03 13:39:27,759 INFO [train.py:715] (1/8) Epoch 0, batch 8750, loss[loss=0.2491, simple_loss=0.289, pruned_loss=0.1045, over 4958.00 frames.], tot_loss[loss=0.2627, simple_loss=0.3004, pruned_loss=0.1126, over 973873.25 frames.], batch size: 15, lr: 2.11e-03
2022-05-03 13:40:08,239 INFO [train.py:715] (1/8) Epoch 0, batch 8800, loss[loss=0.2541, simple_loss=0.2925, pruned_loss=0.1078, over 4968.00 frames.], tot_loss[loss=0.262, simple_loss=0.3004, pruned_loss=0.1118, over 973899.39 frames.], batch size: 24, lr: 2.11e-03
2022-05-03 13:40:48,809 INFO [train.py:715] (1/8) Epoch 0, batch 8850, loss[loss=0.2697, simple_loss=0.2975, pruned_loss=0.1209, over 4667.00 frames.], tot_loss[loss=0.2612, simple_loss=0.2997, pruned_loss=0.1113, over 974065.83 frames.], batch size: 14, lr: 2.10e-03
2022-05-03 13:41:29,536 INFO [train.py:715] (1/8) Epoch 0, batch 8900, loss[loss=0.266, simple_loss=0.2925, pruned_loss=0.1197, over 4776.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3004, pruned_loss=0.1122, over 973732.70 frames.], batch size: 17, lr: 2.10e-03
2022-05-03 13:42:09,373 INFO [train.py:715] (1/8) Epoch 0, batch 8950, loss[loss=0.2474, simple_loss=0.2838, pruned_loss=0.1055, over 4922.00 frames.], tot_loss[loss=0.2627, simple_loss=0.3006, pruned_loss=0.1124, over 973347.06 frames.], batch size: 18, lr: 2.10e-03
2022-05-03 13:42:49,917 INFO [train.py:715] (1/8) Epoch 0, batch 9000, loss[loss=0.2293, simple_loss=0.2774, pruned_loss=0.09065, over 4774.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3004, pruned_loss=0.1122, over 973591.37 frames.], batch size: 14, lr: 2.09e-03
2022-05-03 13:42:49,917 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 13:43:03,385 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1592, simple_loss=0.2426, pruned_loss=0.03794, over 914524.00 frames.
2022-05-03 13:43:44,296 INFO [train.py:715] (1/8) Epoch 0, batch 9050, loss[loss=0.2197, simple_loss=0.2691, pruned_loss=0.08517, over 4772.00 frames.], tot_loss[loss=0.2585, simple_loss=0.2977, pruned_loss=0.1097, over 973405.52 frames.], batch size: 17, lr: 2.09e-03
2022-05-03 13:44:24,660 INFO [train.py:715] (1/8) Epoch 0, batch 9100, loss[loss=0.2227, simple_loss=0.2674, pruned_loss=0.08902, over 4753.00 frames.], tot_loss[loss=0.2579, simple_loss=0.2974, pruned_loss=0.1092, over 972881.36 frames.], batch size: 16, lr: 2.08e-03
2022-05-03 13:45:04,786 INFO [train.py:715] (1/8) Epoch 0, batch 9150, loss[loss=0.2335, simple_loss=0.2709, pruned_loss=0.09807, over 4759.00 frames.], tot_loss[loss=0.2575, simple_loss=0.2971, pruned_loss=0.1089, over 973373.76 frames.], batch size: 16, lr: 2.08e-03
2022-05-03 13:45:44,984 INFO [train.py:715] (1/8) Epoch 0, batch 9200, loss[loss=0.2299, simple_loss=0.2742, pruned_loss=0.09282, over 4819.00 frames.], tot_loss[loss=0.2573, simple_loss=0.2968, pruned_loss=0.1089, over 972638.67 frames.], batch size: 13, lr: 2.07e-03
2022-05-03 13:46:26,072 INFO [train.py:715] (1/8) Epoch 0, batch 9250, loss[loss=0.2602, simple_loss=0.3046, pruned_loss=0.1079, over 4847.00 frames.], tot_loss[loss=0.2588, simple_loss=0.2984, pruned_loss=0.1096, over 972406.35 frames.], batch size: 13, lr: 2.07e-03
2022-05-03 13:47:06,385 INFO [train.py:715] (1/8) Epoch 0, batch 9300, loss[loss=0.2717, simple_loss=0.3139, pruned_loss=0.1148, over 4734.00 frames.], tot_loss[loss=0.2586, simple_loss=0.2988, pruned_loss=0.1092, over 971856.64 frames.], batch size: 16, lr: 2.06e-03
2022-05-03 13:47:45,668 INFO [train.py:715] (1/8) Epoch 0, batch 9350, loss[loss=0.2619, simple_loss=0.3005, pruned_loss=0.1117, over 4769.00 frames.], tot_loss[loss=0.2568, simple_loss=0.2969, pruned_loss=0.1083, over 972295.18 frames.], batch size: 18, lr: 2.06e-03
2022-05-03 13:48:27,107 INFO [train.py:715] (1/8) Epoch 0, batch 9400, loss[loss=0.2371, simple_loss=0.2859, pruned_loss=0.09411, over 4942.00 frames.], tot_loss[loss=0.2561, simple_loss=0.2961, pruned_loss=0.1081, over 973056.80 frames.], batch size: 35, lr: 2.06e-03
2022-05-03 13:49:07,596 INFO [train.py:715] (1/8) Epoch 0, batch 9450, loss[loss=0.2595, simple_loss=0.3, pruned_loss=0.1095, over 4872.00 frames.], tot_loss[loss=0.2557, simple_loss=0.296, pruned_loss=0.1077, over 972592.52 frames.], batch size: 22, lr: 2.05e-03
2022-05-03 13:49:47,922 INFO [train.py:715] (1/8) Epoch 0, batch 9500, loss[loss=0.2606, simple_loss=0.2996, pruned_loss=0.1108, over 4949.00 frames.], tot_loss[loss=0.2572, simple_loss=0.2971, pruned_loss=0.1086, over 971662.34 frames.], batch size: 24, lr: 2.05e-03
2022-05-03 13:50:28,016 INFO [train.py:715] (1/8) Epoch 0, batch 9550, loss[loss=0.2296, simple_loss=0.2864, pruned_loss=0.08636, over 4924.00 frames.], tot_loss[loss=0.2565, simple_loss=0.2969, pruned_loss=0.1081, over 972446.73 frames.], batch size: 23, lr: 2.04e-03
2022-05-03 13:51:08,458 INFO [train.py:715] (1/8) Epoch 0, batch 9600, loss[loss=0.4016, simple_loss=0.3924, pruned_loss=0.2054, over 4691.00 frames.], tot_loss[loss=0.2561, simple_loss=0.2966, pruned_loss=0.1078, over 972208.48 frames.], batch size: 15, lr: 2.04e-03
2022-05-03 13:51:48,910 INFO [train.py:715] (1/8) Epoch 0, batch 9650, loss[loss=0.2098, simple_loss=0.2699, pruned_loss=0.07482, over 4820.00 frames.], tot_loss[loss=0.2554, simple_loss=0.2957, pruned_loss=0.1075, over 972116.80 frames.], batch size: 26, lr: 2.03e-03
2022-05-03 13:52:27,667 INFO [train.py:715] (1/8) Epoch 0, batch 9700, loss[loss=0.1915, simple_loss=0.2471, pruned_loss=0.0679, over 4765.00 frames.], tot_loss[loss=0.2545, simple_loss=0.2949, pruned_loss=0.1071, over 972430.92 frames.], batch size: 12, lr: 2.03e-03
2022-05-03 13:53:08,243 INFO [train.py:715] (1/8) Epoch 0, batch 9750, loss[loss=0.2877, simple_loss=0.3203, pruned_loss=0.1276, over 4956.00 frames.], tot_loss[loss=0.2552, simple_loss=0.2958, pruned_loss=0.1073, over 973270.28 frames.], batch size: 24, lr: 2.03e-03
2022-05-03 13:53:47,973 INFO [train.py:715] (1/8) Epoch 0, batch 9800, loss[loss=0.246, simple_loss=0.2962, pruned_loss=0.09794, over 4837.00 frames.], tot_loss[loss=0.2556, simple_loss=0.2966, pruned_loss=0.1073, over 972898.29 frames.], batch size: 15, lr: 2.02e-03
2022-05-03 13:54:27,872 INFO [train.py:715] (1/8) Epoch 0, batch 9850, loss[loss=0.2138, simple_loss=0.2618, pruned_loss=0.08292, over 4824.00 frames.], tot_loss[loss=0.2562, simple_loss=0.2977, pruned_loss=0.1073, over 973475.49 frames.], batch size: 13, lr: 2.02e-03
2022-05-03 13:55:07,638 INFO [train.py:715] (1/8) Epoch 0, batch 9900, loss[loss=0.3428, simple_loss=0.3644, pruned_loss=0.1606, over 4974.00 frames.], tot_loss[loss=0.2573, simple_loss=0.2988, pruned_loss=0.1079, over 973641.38 frames.], batch size: 31, lr: 2.01e-03
2022-05-03 13:55:47,707 INFO [train.py:715] (1/8) Epoch 0, batch 9950, loss[loss=0.238, simple_loss=0.2859, pruned_loss=0.09504, over 4975.00 frames.], tot_loss[loss=0.2563, simple_loss=0.2975, pruned_loss=0.1076, over 973800.99 frames.], batch size: 39, lr: 2.01e-03
2022-05-03 13:56:27,930 INFO [train.py:715] (1/8) Epoch 0, batch 10000, loss[loss=0.2906, simple_loss=0.3147, pruned_loss=0.1332, over 4705.00 frames.], tot_loss[loss=0.2546, simple_loss=0.2961, pruned_loss=0.1066, over 972246.16 frames.], batch size: 15, lr: 2.01e-03
2022-05-03 13:57:07,310 INFO [train.py:715] (1/8) Epoch 0, batch 10050, loss[loss=0.2396, simple_loss=0.2826, pruned_loss=0.09829, over 4963.00 frames.], tot_loss[loss=0.255, simple_loss=0.2966, pruned_loss=0.1067, over 972707.01 frames.], batch size: 35, lr: 2.00e-03
2022-05-03 13:57:47,858 INFO [train.py:715] (1/8) Epoch 0, batch 10100, loss[loss=0.1991, simple_loss=0.2538, pruned_loss=0.07219, over 4824.00 frames.], tot_loss[loss=0.2531, simple_loss=0.2956, pruned_loss=0.1053, over 972947.74 frames.], batch size: 15, lr: 2.00e-03
2022-05-03 13:58:27,709 INFO [train.py:715] (1/8) Epoch 0, batch 10150, loss[loss=0.2766, simple_loss=0.3184, pruned_loss=0.1174, over 4974.00 frames.], tot_loss[loss=0.2536, simple_loss=0.2959, pruned_loss=0.1056, over 973587.08 frames.], batch size: 25, lr: 1.99e-03
2022-05-03 13:59:07,281 INFO [train.py:715] (1/8) Epoch 0, batch 10200, loss[loss=0.2662, simple_loss=0.3091, pruned_loss=0.1116, over 4888.00 frames.], tot_loss[loss=0.2529, simple_loss=0.2953, pruned_loss=0.1053, over 972714.75 frames.], batch size: 39, lr: 1.99e-03
2022-05-03 13:59:47,208 INFO [train.py:715] (1/8) Epoch 0, batch 10250, loss[loss=0.224, simple_loss=0.2687, pruned_loss=0.08971, over 4975.00 frames.], tot_loss[loss=0.2516, simple_loss=0.2947, pruned_loss=0.1043, over 973043.91 frames.], batch size: 35, lr: 1.99e-03
2022-05-03 14:00:28,082 INFO [train.py:715] (1/8) Epoch 0, batch 10300, loss[loss=0.2357, simple_loss=0.2771, pruned_loss=0.09717, over 4846.00 frames.], tot_loss[loss=0.2514, simple_loss=0.2943, pruned_loss=0.1043, over 972814.75 frames.], batch size: 32, lr: 1.98e-03
2022-05-03 14:01:08,330 INFO [train.py:715] (1/8) Epoch 0, batch 10350, loss[loss=0.3185, simple_loss=0.3523, pruned_loss=0.1424, over 4794.00 frames.], tot_loss[loss=0.2519, simple_loss=0.2945, pruned_loss=0.1047, over 972539.99 frames.], batch size: 17, lr: 1.98e-03
2022-05-03 14:01:47,787 INFO [train.py:715] (1/8) Epoch 0, batch 10400, loss[loss=0.2716, simple_loss=0.3187, pruned_loss=0.1122, over 4830.00 frames.], tot_loss[loss=0.2518, simple_loss=0.2946, pruned_loss=0.1045, over 972966.55 frames.], batch size: 15, lr: 1.97e-03
2022-05-03 14:02:28,429 INFO [train.py:715] (1/8) Epoch 0, batch 10450, loss[loss=0.3146, simple_loss=0.3378, pruned_loss=0.1457, over 4776.00 frames.], tot_loss[loss=0.2516, simple_loss=0.2946, pruned_loss=0.1043, over 972382.22 frames.], batch size: 14, lr: 1.97e-03
2022-05-03 14:03:09,167 INFO [train.py:715] (1/8) Epoch 0, batch 10500, loss[loss=0.2354, simple_loss=0.2796, pruned_loss=0.09557, over 4966.00 frames.], tot_loss[loss=0.2518, simple_loss=0.2946, pruned_loss=0.1045, over 973451.28 frames.], batch size: 28, lr: 1.97e-03
2022-05-03 14:03:48,862 INFO [train.py:715] (1/8) Epoch 0, batch 10550, loss[loss=0.2151, simple_loss=0.2763, pruned_loss=0.07694, over 4776.00 frames.], tot_loss[loss=0.2523, simple_loss=0.2951, pruned_loss=0.1047, over 973054.30 frames.], batch size: 18, lr: 1.96e-03
2022-05-03 14:04:28,876 INFO [train.py:715] (1/8) Epoch 0, batch 10600, loss[loss=0.3356, simple_loss=0.358, pruned_loss=0.1567, over 4961.00 frames.], tot_loss[loss=0.2524, simple_loss=0.2956, pruned_loss=0.1046, over 972624.63 frames.], batch size: 15, lr: 1.96e-03
2022-05-03 14:05:09,747 INFO [train.py:715] (1/8) Epoch 0, batch 10650, loss[loss=0.3056, simple_loss=0.3271, pruned_loss=0.1421, over 4975.00 frames.], tot_loss[loss=0.2521, simple_loss=0.2951, pruned_loss=0.1046, over 971750.65 frames.], batch size: 15, lr: 1.96e-03
2022-05-03 14:05:49,653 INFO [train.py:715] (1/8) Epoch 0, batch 10700, loss[loss=0.2414, simple_loss=0.2784, pruned_loss=0.1022, over 4813.00 frames.], tot_loss[loss=0.2507, simple_loss=0.2944, pruned_loss=0.1035, over 971915.91 frames.], batch size: 25, lr: 1.95e-03
2022-05-03 14:06:29,544 INFO [train.py:715] (1/8) Epoch 0, batch 10750, loss[loss=0.2736, simple_loss=0.3094, pruned_loss=0.1189, over 4788.00 frames.], tot_loss[loss=0.2497, simple_loss=0.2935, pruned_loss=0.103, over 971461.59 frames.], batch size: 18, lr: 1.95e-03
2022-05-03 14:07:09,720 INFO [train.py:715] (1/8) Epoch 0, batch 10800, loss[loss=0.2048, simple_loss=0.2637, pruned_loss=0.07296, over 4925.00 frames.], tot_loss[loss=0.2492, simple_loss=0.2929, pruned_loss=0.1027, over 971770.44 frames.], batch size: 23, lr: 1.94e-03
2022-05-03 14:07:50,562 INFO [train.py:715] (1/8) Epoch 0, batch 10850, loss[loss=0.2439, simple_loss=0.2863, pruned_loss=0.1008, over 4778.00 frames.], tot_loss[loss=0.2478, simple_loss=0.2918, pruned_loss=0.1019, over 971306.05 frames.], batch size: 17, lr: 1.94e-03
2022-05-03 14:08:30,100 INFO [train.py:715] (1/8) Epoch 0, batch 10900, loss[loss=0.2036, simple_loss=0.248, pruned_loss=0.07964, over 4856.00 frames.], tot_loss[loss=0.246, simple_loss=0.2903, pruned_loss=0.1009, over 972839.14 frames.], batch size: 32, lr: 1.94e-03
2022-05-03 14:09:10,036 INFO [train.py:715] (1/8) Epoch 0, batch 10950, loss[loss=0.3056, simple_loss=0.3418, pruned_loss=0.1347, over 4881.00 frames.], tot_loss[loss=0.2457, simple_loss=0.2904, pruned_loss=0.1005, over 972182.22 frames.], batch size: 16, lr: 1.93e-03
2022-05-03 14:09:50,818 INFO [train.py:715] (1/8) Epoch 0, batch 11000, loss[loss=0.2385, simple_loss=0.3076, pruned_loss=0.08473, over 4908.00 frames.], tot_loss[loss=0.2466, simple_loss=0.2914, pruned_loss=0.1008, over 972709.75 frames.], batch size: 29, lr: 1.93e-03
2022-05-03 14:10:31,098 INFO [train.py:715] (1/8) Epoch 0, batch 11050, loss[loss=0.2318, simple_loss=0.2713, pruned_loss=0.09611, over 4964.00 frames.], tot_loss[loss=0.2456, simple_loss=0.2906, pruned_loss=0.1003, over 972643.89 frames.], batch size: 15, lr: 1.93e-03
2022-05-03 14:11:11,142 INFO [train.py:715] (1/8) Epoch 0, batch 11100, loss[loss=0.2076, simple_loss=0.2684, pruned_loss=0.07341, over 4892.00 frames.], tot_loss[loss=0.2467, simple_loss=0.2918, pruned_loss=0.1008, over 972900.57 frames.], batch size: 22, lr: 1.92e-03
2022-05-03 14:11:51,019 INFO [train.py:715] (1/8) Epoch 0, batch 11150, loss[loss=0.3647, simple_loss=0.3591, pruned_loss=0.1851, over 4976.00 frames.], tot_loss[loss=0.2479, simple_loss=0.292, pruned_loss=0.1019, over 972685.58 frames.], batch size: 31, lr: 1.92e-03
2022-05-03 14:12:31,463 INFO [train.py:715] (1/8) Epoch 0, batch 11200, loss[loss=0.2846, simple_loss=0.3283, pruned_loss=0.1204, over 4909.00 frames.], tot_loss[loss=0.2475, simple_loss=0.2919, pruned_loss=0.1016, over 972020.69 frames.], batch size: 38, lr: 1.92e-03
2022-05-03 14:13:10,950 INFO [train.py:715] (1/8) Epoch 0, batch 11250, loss[loss=0.2298, simple_loss=0.2828, pruned_loss=0.0884, over 4843.00 frames.], tot_loss[loss=0.2463, simple_loss=0.2909, pruned_loss=0.1009, over 971975.51 frames.], batch size: 30, lr: 1.91e-03
2022-05-03 14:13:51,038 INFO [train.py:715] (1/8) Epoch 0, batch 11300, loss[loss=0.2496, simple_loss=0.3049, pruned_loss=0.0972, over 4956.00 frames.], tot_loss[loss=0.2464, simple_loss=0.291, pruned_loss=0.1009, over 972799.48 frames.], batch size: 21, lr: 1.91e-03
2022-05-03 14:14:31,682 INFO [train.py:715] (1/8) Epoch 0, batch 11350, loss[loss=0.2802, simple_loss=0.3032, pruned_loss=0.1285, over 4911.00 frames.], tot_loss[loss=0.2463, simple_loss=0.2911, pruned_loss=0.1008, over 972733.82 frames.], batch size: 18, lr: 1.90e-03
2022-05-03 14:15:12,112 INFO [train.py:715] (1/8) Epoch 0, batch 11400, loss[loss=0.2774, simple_loss=0.3172, pruned_loss=0.1188, over 4805.00 frames.], tot_loss[loss=0.2459, simple_loss=0.291, pruned_loss=0.1004, over 972820.63 frames.], batch size: 25, lr: 1.90e-03
2022-05-03 14:15:51,355 INFO [train.py:715] (1/8) Epoch 0, batch 11450, loss[loss=0.2751, simple_loss=0.3027, pruned_loss=0.1237, over 4755.00 frames.], tot_loss[loss=0.2459, simple_loss=0.2906, pruned_loss=0.1005, over 971767.09 frames.], batch size: 16, lr: 1.90e-03
2022-05-03 14:16:32,014 INFO [train.py:715] (1/8) Epoch 0, batch 11500, loss[loss=0.2383, simple_loss=0.2847, pruned_loss=0.09594, over 4806.00 frames.], tot_loss[loss=0.2463, simple_loss=0.2907, pruned_loss=0.1009, over 971437.91 frames.], batch size: 26, lr: 1.89e-03
2022-05-03 14:17:12,405 INFO [train.py:715] (1/8) Epoch 0, batch 11550, loss[loss=0.2043, simple_loss=0.2663, pruned_loss=0.07111, over 4791.00 frames.], tot_loss[loss=0.2465, simple_loss=0.291, pruned_loss=0.101, over 972148.09 frames.], batch size: 18, lr: 1.89e-03
2022-05-03 14:17:52,480 INFO [train.py:715] (1/8) Epoch 0, batch 11600, loss[loss=0.2177, simple_loss=0.2682, pruned_loss=0.08355, over 4960.00 frames.], tot_loss[loss=0.2474, simple_loss=0.2917, pruned_loss=0.1016, over 971377.74 frames.], batch size: 35, lr: 1.89e-03
2022-05-03 14:18:32,574 INFO [train.py:715] (1/8) Epoch 0, batch 11650, loss[loss=0.195, simple_loss=0.248, pruned_loss=0.07098, over 4848.00 frames.], tot_loss[loss=0.2445, simple_loss=0.2898, pruned_loss=0.09954, over 971330.20 frames.], batch size: 34, lr: 1.88e-03
2022-05-03 14:19:13,489 INFO [train.py:715] (1/8) Epoch 0, batch 11700, loss[loss=0.3036, simple_loss=0.3346, pruned_loss=0.1363, over 4794.00 frames.], tot_loss[loss=0.244, simple_loss=0.2893, pruned_loss=0.09934, over 970182.52 frames.], batch size: 18, lr: 1.88e-03
2022-05-03 14:19:53,840 INFO [train.py:715] (1/8) Epoch 0, batch 11750, loss[loss=0.2682, simple_loss=0.2967, pruned_loss=0.1199, over 4901.00 frames.], tot_loss[loss=0.2432, simple_loss=0.2886, pruned_loss=0.09893, over 970399.51 frames.], batch size: 17, lr: 1.88e-03
2022-05-03 14:20:34,216 INFO [train.py:715] (1/8) Epoch 0, batch 11800, loss[loss=0.2264, simple_loss=0.2695, pruned_loss=0.09169, over 4890.00 frames.], tot_loss[loss=0.2436, simple_loss=0.2883, pruned_loss=0.09949, over 971582.27 frames.], batch size: 32, lr: 1.87e-03
2022-05-03 14:21:14,573 INFO [train.py:715] (1/8) Epoch 0, batch 11850, loss[loss=0.2193, simple_loss=0.2677, pruned_loss=0.08541, over 4819.00 frames.], tot_loss[loss=0.2439, simple_loss=0.2886, pruned_loss=0.09959, over 971690.61 frames.], batch size: 25, lr: 1.87e-03
2022-05-03 14:21:55,671 INFO [train.py:715] (1/8) Epoch 0, batch 11900, loss[loss=0.2722, simple_loss=0.3013, pruned_loss=0.1216, over 4892.00 frames.], tot_loss[loss=0.2442, simple_loss=0.2889, pruned_loss=0.09976, over 971725.85 frames.], batch size: 16, lr: 1.87e-03
2022-05-03 14:22:35,858 INFO [train.py:715] (1/8) Epoch 0, batch 11950, loss[loss=0.1831, simple_loss=0.2477, pruned_loss=0.05924, over 4986.00 frames.], tot_loss[loss=0.2427, simple_loss=0.2881, pruned_loss=0.09864, over 972028.02 frames.], batch size: 15, lr: 1.86e-03
2022-05-03 14:23:15,975 INFO [train.py:715] (1/8) Epoch 0, batch 12000, loss[loss=0.2388, simple_loss=0.2812, pruned_loss=0.09815, over 4750.00 frames.], tot_loss[loss=0.2433, simple_loss=0.2888, pruned_loss=0.09888, over 972532.07 frames.], batch size: 19, lr: 1.86e-03
2022-05-03 14:23:15,976 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 14:23:31,274 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1516, simple_loss=0.2368, pruned_loss=0.03315, over 914524.00 frames.
2022-05-03 14:24:11,266 INFO [train.py:715] (1/8) Epoch 0, batch 12050, loss[loss=0.2445, simple_loss=0.3, pruned_loss=0.09446, over 4754.00 frames.], tot_loss[loss=0.2434, simple_loss=0.2891, pruned_loss=0.09885, over 972434.18 frames.], batch size: 16, lr: 1.86e-03
2022-05-03 14:24:51,292 INFO [train.py:715] (1/8) Epoch 0, batch 12100, loss[loss=0.2542, simple_loss=0.2938, pruned_loss=0.1073, over 4649.00 frames.], tot_loss[loss=0.2439, simple_loss=0.2899, pruned_loss=0.09895, over 971515.87 frames.], batch size: 13, lr: 1.85e-03
2022-05-03 14:25:31,588 INFO [train.py:715] (1/8) Epoch 0, batch 12150, loss[loss=0.2292, simple_loss=0.288, pruned_loss=0.08518, over 4964.00 frames.], tot_loss[loss=0.2449, simple_loss=0.2906, pruned_loss=0.09965, over 971941.71 frames.], batch size: 24, lr: 1.85e-03
2022-05-03 14:26:11,160 INFO [train.py:715] (1/8) Epoch 0, batch 12200, loss[loss=0.1716, simple_loss=0.2349, pruned_loss=0.05412, over 4824.00 frames.], tot_loss[loss=0.245, simple_loss=0.2906, pruned_loss=0.09975, over 972030.46 frames.], batch size: 26, lr: 1.85e-03
2022-05-03 14:26:51,061 INFO [train.py:715] (1/8) Epoch 0, batch 12250, loss[loss=0.2199, simple_loss=0.2729, pruned_loss=0.08347, over 4798.00 frames.], tot_loss[loss=0.2457, simple_loss=0.2909, pruned_loss=0.1002, over 971682.27 frames.], batch size: 17, lr: 1.84e-03
2022-05-03 14:27:31,548 INFO [train.py:715] (1/8) Epoch 0, batch 12300, loss[loss=0.2791, simple_loss=0.3206, pruned_loss=0.1188, over 4984.00 frames.], tot_loss[loss=0.2445, simple_loss=0.2902, pruned_loss=0.09941, over 971496.04 frames.], batch size: 33, lr: 1.84e-03
2022-05-03 14:28:10,857 INFO [train.py:715] (1/8) Epoch 0, batch 12350, loss[loss=0.1681, simple_loss=0.2248, pruned_loss=0.05569, over 4774.00 frames.], tot_loss[loss=0.2435, simple_loss=0.2891, pruned_loss=0.09893, over 971603.70 frames.], batch size: 12, lr: 1.84e-03
2022-05-03 14:28:50,836 INFO [train.py:715] (1/8) Epoch 0, batch 12400, loss[loss=0.2346, simple_loss=0.2897, pruned_loss=0.0898, over 4879.00 frames.], tot_loss[loss=0.2421, simple_loss=0.2882, pruned_loss=0.09797, over 971848.33 frames.], batch size: 22, lr: 1.83e-03
2022-05-03 14:29:31,166 INFO [train.py:715] (1/8) Epoch 0, batch 12450, loss[loss=0.1845, simple_loss=0.2515, pruned_loss=0.05877, over 4946.00 frames.], tot_loss[loss=0.2412, simple_loss=0.2877, pruned_loss=0.09736, over 971811.67 frames.], batch size: 23, lr: 1.83e-03
2022-05-03 14:30:11,381 INFO [train.py:715] (1/8) Epoch 0, batch 12500, loss[loss=0.2617, simple_loss=0.2951, pruned_loss=0.1142, over 4696.00 frames.], tot_loss[loss=0.2422, simple_loss=0.2882, pruned_loss=0.0981, over 971427.99 frames.], batch size: 15, lr: 1.83e-03
2022-05-03 14:30:50,305 INFO [train.py:715] (1/8) Epoch 0, batch 12550, loss[loss=0.2845, simple_loss=0.3164, pruned_loss=0.1263, over 4892.00 frames.], tot_loss[loss=0.2437, simple_loss=0.2889, pruned_loss=0.09927, over 971313.28 frames.], batch size: 22, lr: 1.83e-03
2022-05-03 14:31:30,330 INFO [train.py:715] (1/8) Epoch 0, batch 12600, loss[loss=0.238, simple_loss=0.2941, pruned_loss=0.09095, over 4960.00 frames.], tot_loss[loss=0.2428, simple_loss=0.2885, pruned_loss=0.09858, over 971125.27 frames.], batch size: 15, lr: 1.82e-03
2022-05-03 14:32:11,362 INFO [train.py:715] (1/8) Epoch 0, batch 12650, loss[loss=0.2607, simple_loss=0.3167, pruned_loss=0.1023, over 4900.00 frames.], tot_loss[loss=0.2422, simple_loss=0.2886, pruned_loss=0.09792, over 971197.18 frames.], batch size: 19, lr: 1.82e-03
2022-05-03 14:32:51,085 INFO [train.py:715] (1/8) Epoch 0, batch 12700, loss[loss=0.2233, simple_loss=0.2726, pruned_loss=0.08703, over 4767.00 frames.], tot_loss[loss=0.2419, simple_loss=0.2884, pruned_loss=0.09772, over 971529.47 frames.], batch size: 16, lr: 1.82e-03
2022-05-03 14:33:30,729 INFO [train.py:715] (1/8) Epoch 0, batch 12750, loss[loss=0.2057, simple_loss=0.2587, pruned_loss=0.07632, over 4947.00 frames.], tot_loss[loss=0.2401, simple_loss=0.287, pruned_loss=0.09661, over 971965.92 frames.], batch size: 21, lr: 1.81e-03
2022-05-03 14:34:11,170 INFO [train.py:715] (1/8) Epoch 0, batch 12800, loss[loss=0.2342, simple_loss=0.281, pruned_loss=0.09371, over 4811.00 frames.], tot_loss[loss=0.2411, simple_loss=0.2877, pruned_loss=0.09722, over 971724.07 frames.], batch size: 27, lr: 1.81e-03
2022-05-03 14:34:51,660 INFO [train.py:715] (1/8) Epoch 0, batch 12850, loss[loss=0.1865, simple_loss=0.2511, pruned_loss=0.06093, over 4976.00 frames.], tot_loss[loss=0.2404, simple_loss=0.2871, pruned_loss=0.09684, over 971642.37 frames.], batch size: 28, lr: 1.81e-03
2022-05-03 14:35:31,481 INFO [train.py:715] (1/8) Epoch 0, batch 12900, loss[loss=0.3, simple_loss=0.3372, pruned_loss=0.1314, over 4779.00 frames.], tot_loss[loss=0.2385, simple_loss=0.2855, pruned_loss=0.09573, over 970951.15 frames.], batch size: 14, lr: 1.80e-03
2022-05-03 14:36:11,740 INFO [train.py:715] (1/8) Epoch 0, batch 12950, loss[loss=0.2521, simple_loss=0.3074, pruned_loss=0.09837, over 4866.00 frames.], tot_loss[loss=0.2386, simple_loss=0.2857, pruned_loss=0.09576, over 971856.96 frames.], batch size: 32, lr: 1.80e-03
2022-05-03 14:36:52,261 INFO [train.py:715] (1/8) Epoch 0, batch 13000, loss[loss=0.2372, simple_loss=0.2978, pruned_loss=0.0883, over 4821.00 frames.], tot_loss[loss=0.2404, simple_loss=0.2872, pruned_loss=0.09682, over 972143.10 frames.], batch size: 26, lr: 1.80e-03
2022-05-03 14:37:32,734 INFO [train.py:715] (1/8) Epoch 0, batch 13050, loss[loss=0.2396, simple_loss=0.2777, pruned_loss=0.1007, over 4855.00 frames.], tot_loss[loss=0.2408, simple_loss=0.2876, pruned_loss=0.097, over 971618.23 frames.], batch size: 20, lr: 1.79e-03
2022-05-03 14:38:12,067 INFO [train.py:715] (1/8) Epoch 0, batch 13100, loss[loss=0.2147, simple_loss=0.2711, pruned_loss=0.07911, over 4755.00 frames.], tot_loss[loss=0.2416, simple_loss=0.2877, pruned_loss=0.09774, over 971812.52 frames.], batch size: 19, lr: 1.79e-03
2022-05-03 14:38:52,505 INFO [train.py:715] (1/8) Epoch 0, batch 13150, loss[loss=0.2823, simple_loss=0.3192, pruned_loss=0.1227, over 4846.00 frames.], tot_loss[loss=0.2411, simple_loss=0.2876, pruned_loss=0.09728, over 971022.59 frames.], batch size: 20, lr: 1.79e-03
2022-05-03 14:39:32,991 INFO [train.py:715] (1/8) Epoch 0, batch 13200, loss[loss=0.2416, simple_loss=0.3006, pruned_loss=0.09128, over 4803.00 frames.], tot_loss[loss=0.2422, simple_loss=0.2887, pruned_loss=0.09788, over 971370.34 frames.], batch size: 21, lr: 1.79e-03
2022-05-03 14:40:12,563 INFO [train.py:715] (1/8) Epoch 0, batch 13250, loss[loss=0.2949, simple_loss=0.3101, pruned_loss=0.1398, over 4936.00 frames.], tot_loss[loss=0.2417, simple_loss=0.2883, pruned_loss=0.09759, over 971531.02 frames.], batch size: 35, lr: 1.78e-03
2022-05-03 14:40:52,440 INFO [train.py:715] (1/8) Epoch 0, batch 13300, loss[loss=0.2171, simple_loss=0.2669, pruned_loss=0.08366, over 4785.00 frames.], tot_loss[loss=0.2417, simple_loss=0.288, pruned_loss=0.09764, over 972165.83 frames.], batch size: 18, lr: 1.78e-03
2022-05-03 14:41:32,814 INFO [train.py:715] (1/8) Epoch 0, batch 13350, loss[loss=0.2182, simple_loss=0.2749, pruned_loss=0.08074, over 4957.00 frames.], tot_loss[loss=0.2416, simple_loss=0.2881, pruned_loss=0.09755, over 972278.52 frames.], batch size: 35, lr: 1.78e-03
2022-05-03 14:42:13,148 INFO [train.py:715] (1/8) Epoch 0, batch 13400, loss[loss=0.2561, simple_loss=0.3177, pruned_loss=0.09723, over 4848.00 frames.], tot_loss[loss=0.2405, simple_loss=0.2874, pruned_loss=0.09676, over 972930.04 frames.], batch size: 20, lr: 1.77e-03
2022-05-03 14:42:52,940 INFO [train.py:715] (1/8) Epoch 0, batch 13450, loss[loss=0.194, simple_loss=0.2442, pruned_loss=0.0719, over 4743.00 frames.], tot_loss[loss=0.2379, simple_loss=0.2857, pruned_loss=0.095, over 972575.95 frames.], batch size: 12, lr: 1.77e-03
2022-05-03 14:43:33,175 INFO [train.py:715] (1/8) Epoch 0, batch 13500, loss[loss=0.2117, simple_loss=0.2668, pruned_loss=0.07832, over 4984.00 frames.], tot_loss[loss=0.2393, simple_loss=0.2862, pruned_loss=0.09613, over 973305.79 frames.], batch size: 28, lr: 1.77e-03
2022-05-03 14:44:13,350 INFO [train.py:715] (1/8) Epoch 0, batch 13550, loss[loss=0.2642, simple_loss=0.3132, pruned_loss=0.1076, over 4931.00 frames.], tot_loss[loss=0.2395, simple_loss=0.2862, pruned_loss=0.09641, over 973173.61 frames.], batch size: 29, lr: 1.77e-03
2022-05-03 14:44:52,792 INFO [train.py:715] (1/8) Epoch 0, batch 13600, loss[loss=0.2219, simple_loss=0.288, pruned_loss=0.07789, over 4818.00 frames.], tot_loss[loss=0.2371, simple_loss=0.2848, pruned_loss=0.09468, over 972601.10 frames.], batch size: 27, lr: 1.76e-03
2022-05-03 14:45:32,767 INFO [train.py:715] (1/8) Epoch 0, batch 13650, loss[loss=0.2327, simple_loss=0.2774, pruned_loss=0.09396, over 4886.00 frames.], tot_loss[loss=0.2349, simple_loss=0.2832, pruned_loss=0.09325, over 972387.58 frames.], batch size: 19, lr: 1.76e-03
2022-05-03 14:46:12,696 INFO [train.py:715] (1/8) Epoch 0, batch 13700, loss[loss=0.261, simple_loss=0.3087, pruned_loss=0.1066, over 4921.00 frames.], tot_loss[loss=0.2346, simple_loss=0.283, pruned_loss=0.09311, over 972537.23 frames.], batch size: 21, lr: 1.76e-03
2022-05-03 14:46:52,708 INFO [train.py:715] (1/8) Epoch 0, batch 13750, loss[loss=0.2248, simple_loss=0.2801, pruned_loss=0.08475, over 4754.00 frames.], tot_loss[loss=0.2354, simple_loss=0.2834, pruned_loss=0.09371, over 972394.05 frames.], batch size: 19, lr: 1.75e-03
2022-05-03 14:47:32,540 INFO [train.py:715] (1/8) Epoch 0, batch 13800, loss[loss=0.1932, simple_loss=0.2488, pruned_loss=0.06879, over 4779.00 frames.], tot_loss[loss=0.2369, simple_loss=0.2847, pruned_loss=0.0945, over 971855.42 frames.], batch size: 14, lr: 1.75e-03
2022-05-03 14:48:12,872 INFO [train.py:715] (1/8) Epoch 0, batch 13850, loss[loss=0.2552, simple_loss=0.2956, pruned_loss=0.1074, over 4826.00 frames.], tot_loss[loss=0.2371, simple_loss=0.2847, pruned_loss=0.09474, over 972365.52 frames.], batch size: 26, lr: 1.75e-03
2022-05-03 14:48:53,731 INFO [train.py:715] (1/8) Epoch 0, batch 13900, loss[loss=0.1718, simple_loss=0.2461, pruned_loss=0.04875, over 4886.00 frames.], tot_loss[loss=0.2358, simple_loss=0.2835, pruned_loss=0.09403, over 973053.07 frames.], batch size: 19, lr: 1.75e-03
2022-05-03 14:49:33,777 INFO [train.py:715] (1/8) Epoch 0, batch 13950, loss[loss=0.247, simple_loss=0.2974, pruned_loss=0.09825, over 4932.00 frames.], tot_loss[loss=0.2369, simple_loss=0.2845, pruned_loss=0.0946, over 973167.84 frames.], batch size: 23, lr: 1.74e-03
2022-05-03 14:50:14,376 INFO [train.py:715] (1/8) Epoch 0, batch 14000, loss[loss=0.2732, simple_loss=0.3081, pruned_loss=0.1192, over 4947.00 frames.], tot_loss[loss=0.2366, simple_loss=0.2845, pruned_loss=0.0944, over 972362.59 frames.], batch size: 21, lr: 1.74e-03
2022-05-03 14:50:55,232 INFO [train.py:715] (1/8) Epoch 0, batch 14050, loss[loss=0.2042, simple_loss=0.2555, pruned_loss=0.07641, over 4964.00 frames.], tot_loss[loss=0.237, simple_loss=0.2846, pruned_loss=0.09469, over 972167.92 frames.], batch size: 15, lr: 1.74e-03
2022-05-03 14:51:35,695 INFO [train.py:715] (1/8) Epoch 0, batch 14100, loss[loss=0.2677, simple_loss=0.3133, pruned_loss=0.111, over 4866.00 frames.], tot_loss[loss=0.2389, simple_loss=0.2865, pruned_loss=0.09563, over 972589.12 frames.], batch size: 22, lr: 1.73e-03
2022-05-03 14:52:16,207 INFO [train.py:715] (1/8) Epoch 0, batch 14150, loss[loss=0.2065, simple_loss=0.2612, pruned_loss=0.07586, over 4940.00 frames.], tot_loss[loss=0.2383, simple_loss=0.2859, pruned_loss=0.09534, over 972436.39 frames.], batch size: 29, lr: 1.73e-03
2022-05-03 14:52:56,855 INFO [train.py:715] (1/8) Epoch 0, batch 14200, loss[loss=0.2239, simple_loss=0.2743, pruned_loss=0.0867, over 4787.00 frames.], tot_loss[loss=0.2379, simple_loss=0.2854, pruned_loss=0.09519, over 972351.52 frames.], batch size: 14, lr: 1.73e-03
2022-05-03 14:53:37,709 INFO [train.py:715] (1/8) Epoch 0, batch 14250, loss[loss=0.2652, simple_loss=0.3287, pruned_loss=0.1008, over 4780.00 frames.], tot_loss[loss=0.2381, simple_loss=0.2859, pruned_loss=0.09514, over 973256.00 frames.], batch size: 17, lr: 1.73e-03
2022-05-03 14:54:18,413 INFO [train.py:715] (1/8) Epoch 0, batch 14300, loss[loss=0.326, simple_loss=0.3362, pruned_loss=0.1579, over 4985.00 frames.], tot_loss[loss=0.2379, simple_loss=0.2861, pruned_loss=0.09489, over 972865.00 frames.], batch size: 20, lr: 1.72e-03
2022-05-03 14:54:59,479 INFO [train.py:715] (1/8) Epoch 0, batch 14350, loss[loss=0.1974, simple_loss=0.2516, pruned_loss=0.0716, over 4842.00 frames.], tot_loss[loss=0.237, simple_loss=0.2856, pruned_loss=0.0942, over 972394.57 frames.], batch size: 32, lr: 1.72e-03
2022-05-03 14:55:40,712 INFO [train.py:715] (1/8) Epoch 0, batch 14400, loss[loss=0.2285, simple_loss=0.2811, pruned_loss=0.08794, over 4916.00 frames.], tot_loss[loss=0.2354, simple_loss=0.2846, pruned_loss=0.09316, over 972731.85 frames.], batch size: 23, lr: 1.72e-03
2022-05-03 14:56:21,182 INFO [train.py:715] (1/8) Epoch 0, batch 14450, loss[loss=0.2869, simple_loss=0.3201, pruned_loss=0.1269, over 4830.00 frames.], tot_loss[loss=0.2345, simple_loss=0.2838, pruned_loss=0.0926, over 972487.25 frames.], batch size: 30, lr: 1.72e-03
2022-05-03 14:57:01,536 INFO [train.py:715] (1/8) Epoch 0, batch 14500, loss[loss=0.254, simple_loss=0.2948, pruned_loss=0.1066, over 4909.00 frames.], tot_loss[loss=0.2344, simple_loss=0.2838, pruned_loss=0.09243, over 972847.57 frames.], batch size: 17, lr: 1.71e-03
2022-05-03 14:57:42,205 INFO [train.py:715] (1/8) Epoch 0, batch 14550, loss[loss=0.2833, simple_loss=0.3064, pruned_loss=0.1301, over 4951.00 frames.], tot_loss[loss=0.2336, simple_loss=0.2836, pruned_loss=0.09185, over 972658.21 frames.], batch size: 35, lr: 1.71e-03
2022-05-03 14:58:22,168 INFO [train.py:715] (1/8) Epoch 0, batch 14600, loss[loss=0.2394, simple_loss=0.2844, pruned_loss=0.09723, over 4758.00 frames.], tot_loss[loss=0.2337, simple_loss=0.2837, pruned_loss=0.09189, over 972441.93 frames.], batch size: 19, lr: 1.71e-03
2022-05-03 14:59:01,454 INFO [train.py:715] (1/8) Epoch 0, batch 14650, loss[loss=0.162, simple_loss=0.2262, pruned_loss=0.0489, over 4782.00 frames.], tot_loss[loss=0.2329, simple_loss=0.2829, pruned_loss=0.09146, over 972150.31 frames.], batch size: 14, lr: 1.70e-03
2022-05-03 14:59:41,806 INFO [train.py:715] (1/8) Epoch 0, batch 14700, loss[loss=0.2076, simple_loss=0.2788, pruned_loss=0.0682, over 4923.00 frames.], tot_loss[loss=0.232, simple_loss=0.2822, pruned_loss=0.09088, over 971888.90 frames.], batch size: 23, lr: 1.70e-03
2022-05-03 15:00:22,079 INFO [train.py:715] (1/8) Epoch 0, batch 14750, loss[loss=0.2196, simple_loss=0.2776, pruned_loss=0.08081, over 4980.00 frames.], tot_loss[loss=0.2324, simple_loss=0.2822, pruned_loss=0.09123, over 972007.89 frames.], batch size: 15, lr: 1.70e-03
2022-05-03 15:01:02,116 INFO [train.py:715] (1/8) Epoch 0, batch 14800, loss[loss=0.2089, simple_loss=0.2597, pruned_loss=0.07904, over 4833.00 frames.], tot_loss[loss=0.2347, simple_loss=0.2834, pruned_loss=0.09296, over 972710.44 frames.], batch size: 30, lr: 1.70e-03
2022-05-03 15:01:41,994 INFO [train.py:715] (1/8) Epoch 0, batch 14850, loss[loss=0.2277, simple_loss=0.2814, pruned_loss=0.08703, over 4927.00 frames.], tot_loss[loss=0.2346, simple_loss=0.2832, pruned_loss=0.09302, over 973512.63 frames.], batch size: 23, lr: 1.69e-03
2022-05-03 15:02:22,718 INFO [train.py:715] (1/8) Epoch 0, batch 14900, loss[loss=0.2315, simple_loss=0.2802, pruned_loss=0.09139, over 4779.00 frames.], tot_loss[loss=0.2335, simple_loss=0.2824, pruned_loss=0.09229, over 972580.58 frames.], batch size: 17, lr: 1.69e-03
2022-05-03 15:03:02,603 INFO [train.py:715] (1/8) Epoch 0, batch 14950, loss[loss=0.2563, simple_loss=0.3034, pruned_loss=0.1046, over 4831.00 frames.], tot_loss[loss=0.233, simple_loss=0.2823, pruned_loss=0.09182, over 972341.70 frames.], batch size: 15, lr: 1.69e-03
2022-05-03 15:03:42,035 INFO [train.py:715] (1/8) Epoch 0, batch 15000, loss[loss=0.2201, simple_loss=0.2709, pruned_loss=0.08469, over 4797.00 frames.], tot_loss[loss=0.2326, simple_loss=0.2822, pruned_loss=0.09149, over 972164.77 frames.], batch size: 14, lr: 1.69e-03
2022-05-03 15:03:42,036 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 15:03:53,633 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1454, simple_loss=0.2314, pruned_loss=0.02968, over 914524.00 frames.
2022-05-03 15:04:32,997 INFO [train.py:715] (1/8) Epoch 0, batch 15050, loss[loss=0.2146, simple_loss=0.2703, pruned_loss=0.07945, over 4855.00 frames.], tot_loss[loss=0.2311, simple_loss=0.2809, pruned_loss=0.09066, over 971209.68 frames.], batch size: 20, lr: 1.68e-03
2022-05-03 15:05:13,559 INFO [train.py:715] (1/8) Epoch 0, batch 15100, loss[loss=0.2063, simple_loss=0.2594, pruned_loss=0.07661, over 4931.00 frames.], tot_loss[loss=0.2321, simple_loss=0.2815, pruned_loss=0.0914, over 972203.71 frames.], batch size: 29, lr: 1.68e-03
2022-05-03 15:05:53,895 INFO [train.py:715] (1/8) Epoch 0, batch 15150, loss[loss=0.2023, simple_loss=0.2602, pruned_loss=0.07216, over 4931.00 frames.], tot_loss[loss=0.231, simple_loss=0.2809, pruned_loss=0.09057, over 972744.25 frames.], batch size: 18, lr: 1.68e-03
2022-05-03 15:06:33,820 INFO [train.py:715] (1/8) Epoch 0, batch 15200, loss[loss=0.2136, simple_loss=0.2645, pruned_loss=0.08131, over 4866.00 frames.], tot_loss[loss=0.2312, simple_loss=0.2812, pruned_loss=0.09058, over 972583.98 frames.], batch size: 30, lr: 1.68e-03
2022-05-03 15:07:13,391 INFO [train.py:715] (1/8) Epoch 0, batch 15250, loss[loss=0.2446, simple_loss=0.2875, pruned_loss=0.1008, over 4820.00 frames.], tot_loss[loss=0.231, simple_loss=0.2808, pruned_loss=0.09056, over 971988.16 frames.], batch size: 26, lr: 1.67e-03
2022-05-03 15:07:53,253 INFO [train.py:715] (1/8) Epoch 0, batch 15300, loss[loss=0.2748, simple_loss=0.3107, pruned_loss=0.1195, over 4916.00 frames.], tot_loss[loss=0.2314, simple_loss=0.2808, pruned_loss=0.091, over 972011.54 frames.], batch size: 18, lr: 1.67e-03
2022-05-03 15:08:33,605 INFO [train.py:715] (1/8) Epoch 0, batch 15350, loss[loss=0.1901, simple_loss=0.2489, pruned_loss=0.0657, over 4916.00 frames.], tot_loss[loss=0.232, simple_loss=0.2813, pruned_loss=0.09134, over 972623.96 frames.], batch size: 29, lr: 1.67e-03
2022-05-03 15:09:13,457 INFO [train.py:715] (1/8) Epoch 0, batch 15400, loss[loss=0.1783, simple_loss=0.2303, pruned_loss=0.06317, over 4775.00 frames.], tot_loss[loss=0.2323, simple_loss=0.2817, pruned_loss=0.09149, over 972112.15 frames.], batch size: 18, lr: 1.67e-03
2022-05-03 15:09:53,906 INFO [train.py:715] (1/8) Epoch 0, batch 15450, loss[loss=0.2781, simple_loss=0.2961, pruned_loss=0.1301, over 4704.00 frames.], tot_loss[loss=0.2321, simple_loss=0.2817, pruned_loss=0.09123, over 972424.93 frames.], batch size: 15, lr: 1.66e-03
2022-05-03 15:10:33,370 INFO [train.py:715] (1/8) Epoch 0, batch 15500, loss[loss=0.2464, simple_loss=0.2892, pruned_loss=0.1018, over 4876.00 frames.], tot_loss[loss=0.232, simple_loss=0.2821, pruned_loss=0.09101, over 972391.21 frames.], batch size: 16, lr: 1.66e-03
2022-05-03 15:11:12,568 INFO [train.py:715] (1/8) Epoch 0, batch 15550, loss[loss=0.2157, simple_loss=0.2693, pruned_loss=0.08107, over 4827.00 frames.], tot_loss[loss=0.2309, simple_loss=0.2812, pruned_loss=0.09028, over 972779.87 frames.], batch size: 13, lr: 1.66e-03
2022-05-03 15:11:52,063 INFO [train.py:715] (1/8) Epoch 0, batch 15600, loss[loss=0.2532, simple_loss=0.2919, pruned_loss=0.1073, over 4747.00 frames.], tot_loss[loss=0.2298, simple_loss=0.2803, pruned_loss=0.08966, over 972568.18 frames.], batch size: 16, lr: 1.66e-03
2022-05-03 15:12:31,511 INFO [train.py:715] (1/8) Epoch 0, batch 15650, loss[loss=0.2579, simple_loss=0.3034, pruned_loss=0.1062, over 4813.00 frames.], tot_loss[loss=0.2296, simple_loss=0.2798, pruned_loss=0.08967, over 972360.51 frames.], batch size: 26, lr: 1.65e-03
2022-05-03 15:13:11,295 INFO [train.py:715] (1/8) Epoch 0, batch 15700, loss[loss=0.3184, simple_loss=0.3364, pruned_loss=0.1502, over 4786.00 frames.], tot_loss[loss=0.2296, simple_loss=0.2794, pruned_loss=0.0899, over 973233.43 frames.], batch size: 17, lr: 1.65e-03
2022-05-03 15:13:50,900 INFO [train.py:715] (1/8) Epoch 0, batch 15750, loss[loss=0.2348, simple_loss=0.2889, pruned_loss=0.09036, over 4912.00 frames.], tot_loss[loss=0.2312, simple_loss=0.28, pruned_loss=0.09124, over 973737.93 frames.], batch size: 39, lr: 1.65e-03
2022-05-03 15:14:30,841 INFO [train.py:715] (1/8) Epoch 0, batch 15800, loss[loss=0.31, simple_loss=0.3461, pruned_loss=0.1369, over 4752.00 frames.], tot_loss[loss=0.2308, simple_loss=0.2802, pruned_loss=0.09066, over 973742.17 frames.], batch size: 19, lr: 1.65e-03
2022-05-03 15:15:10,662 INFO [train.py:715] (1/8) Epoch 0, batch 15850, loss[loss=0.2085, simple_loss=0.2621, pruned_loss=0.07744, over 4794.00 frames.], tot_loss[loss=0.2301, simple_loss=0.2801, pruned_loss=0.09002, over 973557.97 frames.], batch size: 13, lr: 1.65e-03
2022-05-03 15:15:50,239 INFO [train.py:715] (1/8) Epoch 0, batch 15900, loss[loss=0.2098, simple_loss=0.2587, pruned_loss=0.0804, over 4839.00 frames.], tot_loss[loss=0.2291, simple_loss=0.2794, pruned_loss=0.08943, over 973060.91 frames.], batch size: 30, lr: 1.64e-03
2022-05-03 15:16:30,470 INFO [train.py:715] (1/8) Epoch 0, batch 15950, loss[loss=0.1915, simple_loss=0.2513, pruned_loss=0.0659, over 4767.00 frames.], tot_loss[loss=0.229, simple_loss=0.2795, pruned_loss=0.08931, over 973143.89 frames.], batch size: 19, lr: 1.64e-03
2022-05-03 15:17:12,822 INFO [train.py:715] (1/8) Epoch 0, batch 16000, loss[loss=0.2202, simple_loss=0.2752, pruned_loss=0.0826, over 4805.00 frames.], tot_loss[loss=0.2286, simple_loss=0.2792, pruned_loss=0.08896, over 972761.71 frames.], batch size: 21, lr: 1.64e-03
2022-05-03 15:17:52,701 INFO [train.py:715] (1/8) Epoch 0, batch 16050, loss[loss=0.194, simple_loss=0.2504, pruned_loss=0.06877, over 4948.00 frames.], tot_loss[loss=0.2278, simple_loss=0.2791, pruned_loss=0.08823, over 971818.06 frames.], batch size: 21, lr: 1.64e-03
2022-05-03 15:18:33,255 INFO [train.py:715] (1/8) Epoch 0, batch 16100, loss[loss=0.2275, simple_loss=0.2749, pruned_loss=0.09003, over 4939.00 frames.], tot_loss[loss=0.2278, simple_loss=0.2789, pruned_loss=0.08832, over 972142.63 frames.], batch size: 21, lr: 1.63e-03
2022-05-03 15:19:13,428 INFO [train.py:715] (1/8) Epoch 0, batch 16150, loss[loss=0.2208, simple_loss=0.2888, pruned_loss=0.07642, over 4926.00 frames.], tot_loss[loss=0.228, simple_loss=0.2795, pruned_loss=0.08831, over 972919.62 frames.], batch size: 23, lr: 1.63e-03
2022-05-03 15:19:52,896 INFO [train.py:715] (1/8) Epoch 0, batch 16200, loss[loss=0.1603, simple_loss=0.237, pruned_loss=0.04176, over 4935.00 frames.], tot_loss[loss=0.2291, simple_loss=0.2803, pruned_loss=0.08897, over 973394.09 frames.], batch size: 23, lr: 1.63e-03
2022-05-03 15:20:32,318 INFO [train.py:715] (1/8) Epoch 0, batch 16250, loss[loss=0.2329, simple_loss=0.2745, pruned_loss=0.09564, over 4976.00 frames.], tot_loss[loss=0.2299, simple_loss=0.2809, pruned_loss=0.08951, over 972946.14 frames.], batch size: 35, lr: 1.63e-03
2022-05-03 15:21:12,241 INFO [train.py:715] (1/8) Epoch 0, batch 16300, loss[loss=0.2203, simple_loss=0.275, pruned_loss=0.08274, over 4949.00 frames.], tot_loss[loss=0.2291, simple_loss=0.2805, pruned_loss=0.08892, over 972015.54 frames.], batch size: 23, lr: 1.62e-03
2022-05-03 15:21:51,671 INFO [train.py:715] (1/8) Epoch 0, batch 16350, loss[loss=0.2447, simple_loss=0.2998, pruned_loss=0.09477, over 4932.00 frames.], tot_loss[loss=0.2294, simple_loss=0.2809, pruned_loss=0.08898, over 972415.06 frames.], batch size: 35, lr: 1.62e-03
2022-05-03 15:22:31,100 INFO [train.py:715] (1/8) Epoch 0, batch 16400, loss[loss=0.2432, simple_loss=0.2944, pruned_loss=0.09596, over 4957.00 frames.], tot_loss[loss=0.2313, simple_loss=0.2825, pruned_loss=0.09007, over 972685.39 frames.], batch size: 21, lr: 1.62e-03
2022-05-03 15:23:11,046 INFO [train.py:715] (1/8) Epoch 0, batch 16450, loss[loss=0.1951, simple_loss=0.257, pruned_loss=0.06664, over 4802.00 frames.], tot_loss[loss=0.2293, simple_loss=0.2807, pruned_loss=0.08896, over 972923.68 frames.], batch size: 12, lr: 1.62e-03
2022-05-03 15:23:51,582 INFO [train.py:715] (1/8) Epoch 0, batch 16500, loss[loss=0.1717, simple_loss=0.2357, pruned_loss=0.05386, over 4939.00 frames.], tot_loss[loss=0.2277, simple_loss=0.2798, pruned_loss=0.08781, over 972484.32 frames.], batch size: 29, lr: 1.62e-03
2022-05-03 15:24:31,538 INFO [train.py:715] (1/8) Epoch 0, batch 16550, loss[loss=0.2496, simple_loss=0.3079, pruned_loss=0.09567, over 4832.00 frames.], tot_loss[loss=0.2257, simple_loss=0.2781, pruned_loss=0.08661, over 972584.05 frames.], batch size: 30, lr: 1.61e-03
2022-05-03 15:25:11,226 INFO [train.py:715] (1/8) Epoch 0, batch 16600, loss[loss=0.2252, simple_loss=0.2835, pruned_loss=0.08351, over 4877.00 frames.], tot_loss[loss=0.2262, simple_loss=0.2785, pruned_loss=0.08698, over 973097.06 frames.], batch size: 16, lr: 1.61e-03
2022-05-03 15:25:50,678 INFO [train.py:715] (1/8) Epoch 0, batch 16650, loss[loss=0.1922, simple_loss=0.2449, pruned_loss=0.0697, over 4959.00 frames.], tot_loss[loss=0.2245, simple_loss=0.2769, pruned_loss=0.08606, over 972171.55 frames.], batch size: 14, lr: 1.61e-03
2022-05-03 15:26:30,538 INFO [train.py:715] (1/8) Epoch 0, batch 16700, loss[loss=0.2226, simple_loss=0.2697, pruned_loss=0.08777, over 4774.00 frames.], tot_loss[loss=0.2252, simple_loss=0.2776, pruned_loss=0.0864, over 971525.67 frames.], batch size: 18, lr: 1.61e-03
2022-05-03 15:27:09,631 INFO [train.py:715] (1/8) Epoch 0, batch 16750, loss[loss=0.2509, simple_loss=0.285, pruned_loss=0.1084, over 4837.00 frames.], tot_loss[loss=0.2262, simple_loss=0.2782, pruned_loss=0.08711, over 971062.31 frames.], batch size: 15, lr: 1.60e-03
2022-05-03 15:27:48,775 INFO [train.py:715] (1/8) Epoch 0, batch 16800, loss[loss=0.278, simple_loss=0.3083, pruned_loss=0.1239, over 4978.00 frames.], tot_loss[loss=0.2257, simple_loss=0.2777, pruned_loss=0.08684, over 972701.57 frames.], batch size: 31, lr: 1.60e-03
2022-05-03 15:28:28,413 INFO [train.py:715] (1/8) Epoch 0, batch 16850, loss[loss=0.252, simple_loss=0.301, pruned_loss=0.1015, over 4852.00 frames.], tot_loss[loss=0.2251, simple_loss=0.2771, pruned_loss=0.08654, over 971955.39 frames.], batch size: 20, lr: 1.60e-03
2022-05-03 15:29:08,019 INFO [train.py:715] (1/8) Epoch 0, batch 16900, loss[loss=0.2059, simple_loss=0.2651, pruned_loss=0.07335, over 4780.00 frames.], tot_loss[loss=0.2245, simple_loss=0.2764, pruned_loss=0.08636, over 972608.86 frames.], batch size: 18, lr: 1.60e-03
2022-05-03 15:29:47,263 INFO [train.py:715] (1/8) Epoch 0, batch 16950, loss[loss=0.206, simple_loss=0.2632, pruned_loss=0.07443, over 4804.00 frames.], tot_loss[loss=0.2244, simple_loss=0.2767, pruned_loss=0.08604, over 972784.72 frames.], batch size: 21, lr: 1.60e-03
2022-05-03 15:30:27,230 INFO [train.py:715] (1/8) Epoch 0, batch 17000, loss[loss=0.2203, simple_loss=0.2792, pruned_loss=0.08067, over 4945.00 frames.], tot_loss[loss=0.2237, simple_loss=0.2766, pruned_loss=0.08543, over 973079.53 frames.], batch size: 39, lr: 1.59e-03
2022-05-03 15:31:07,730 INFO [train.py:715] (1/8) Epoch 0, batch 17050, loss[loss=0.1999, simple_loss=0.2544, pruned_loss=0.07267, over 4994.00 frames.], tot_loss[loss=0.2228, simple_loss=0.2756, pruned_loss=0.08499, over 972198.06 frames.], batch size: 15, lr: 1.59e-03
2022-05-03 15:31:47,483 INFO [train.py:715] (1/8) Epoch 0, batch 17100, loss[loss=0.2413, simple_loss=0.2861, pruned_loss=0.09829, over 4954.00 frames.], tot_loss[loss=0.2237, simple_loss=0.2763, pruned_loss=0.08554, over 972475.70 frames.], batch size: 35, lr: 1.59e-03
2022-05-03 15:32:26,647 INFO [train.py:715] (1/8) Epoch 0, batch 17150, loss[loss=0.2185, simple_loss=0.2789, pruned_loss=0.07909, over 4798.00 frames.], tot_loss[loss=0.2241, simple_loss=0.2766, pruned_loss=0.08586, over 972599.52 frames.], batch size: 21, lr: 1.59e-03
2022-05-03 15:33:06,905 INFO [train.py:715] (1/8) Epoch 0, batch 17200, loss[loss=0.3016, simple_loss=0.3295, pruned_loss=0.1369, over 4913.00 frames.], tot_loss[loss=0.2247, simple_loss=0.2768, pruned_loss=0.08632, over 972654.63 frames.], batch size: 17, lr: 1.58e-03
2022-05-03 15:33:46,678 INFO [train.py:715] (1/8) Epoch 0, batch 17250, loss[loss=0.2768, simple_loss=0.3072, pruned_loss=0.1233, over 4775.00 frames.], tot_loss[loss=0.225, simple_loss=0.2769, pruned_loss=0.08656, over 972589.55 frames.], batch size: 14, lr: 1.58e-03
2022-05-03 15:34:26,235 INFO [train.py:715] (1/8) Epoch 0, batch 17300, loss[loss=0.1972, simple_loss=0.2568, pruned_loss=0.06885, over 4939.00 frames.], tot_loss[loss=0.2256, simple_loss=0.2775, pruned_loss=0.0868, over 972781.75 frames.], batch size: 23, lr: 1.58e-03
2022-05-03 15:35:06,290 INFO [train.py:715] (1/8) Epoch 0, batch 17350, loss[loss=0.2273, simple_loss=0.281, pruned_loss=0.08676, over 4919.00 frames.], tot_loss[loss=0.2252, simple_loss=0.2778, pruned_loss=0.08631, over 972085.95 frames.], batch size: 39, lr: 1.58e-03
2022-05-03 15:35:46,524 INFO [train.py:715] (1/8) Epoch 0, batch 17400, loss[loss=0.264, simple_loss=0.3049, pruned_loss=0.1115, over 4896.00 frames.], tot_loss[loss=0.2253, simple_loss=0.2783, pruned_loss=0.08612, over 971804.94 frames.], batch size: 19, lr: 1.58e-03
2022-05-03 15:36:26,419 INFO [train.py:715] (1/8) Epoch 0, batch 17450, loss[loss=0.1842, simple_loss=0.2399, pruned_loss=0.06425, over 4842.00 frames.], tot_loss[loss=0.2236, simple_loss=0.2768, pruned_loss=0.0852, over 971566.27 frames.], batch size: 13, lr: 1.57e-03
2022-05-03 15:37:07,031 INFO [train.py:715] (1/8) Epoch 0, batch 17500, loss[loss=0.2364, simple_loss=0.297, pruned_loss=0.08789, over 4908.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2759, pruned_loss=0.08432, over 971264.37 frames.], batch size: 18, lr: 1.57e-03
2022-05-03 15:37:47,457 INFO [train.py:715] (1/8) Epoch 0, batch 17550, loss[loss=0.2173, simple_loss=0.2612, pruned_loss=0.08672, over 4958.00 frames.], tot_loss[loss=0.2238, simple_loss=0.2771, pruned_loss=0.08526, over 972120.72 frames.], batch size: 14, lr: 1.57e-03
2022-05-03 15:38:27,018 INFO [train.py:715] (1/8) Epoch 0, batch 17600, loss[loss=0.2554, simple_loss=0.2675, pruned_loss=0.1216, over 4766.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2761, pruned_loss=0.08509, over 971668.03 frames.], batch size: 12, lr: 1.57e-03
2022-05-03 15:39:06,943 INFO [train.py:715] (1/8) Epoch 0, batch 17650, loss[loss=0.2439, simple_loss=0.2972, pruned_loss=0.09524, over 4962.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2761, pruned_loss=0.08512, over 971758.96 frames.], batch size: 40, lr: 1.57e-03
2022-05-03 15:39:47,475 INFO [train.py:715] (1/8) Epoch 0, batch 17700, loss[loss=0.2298, simple_loss=0.2803, pruned_loss=0.08965, over 4977.00 frames.], tot_loss[loss=0.2215, simple_loss=0.275, pruned_loss=0.08403, over 971982.37 frames.], batch size: 35, lr: 1.56e-03
2022-05-03 15:40:27,378 INFO [train.py:715] (1/8) Epoch 0, batch 17750, loss[loss=0.2184, simple_loss=0.2675, pruned_loss=0.08461, over 4872.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2748, pruned_loss=0.0839, over 971962.27 frames.], batch size: 22, lr: 1.56e-03
2022-05-03 15:41:07,055 INFO [train.py:715] (1/8) Epoch 0, batch 17800, loss[loss=0.212, simple_loss=0.266, pruned_loss=0.079, over 4984.00 frames.], tot_loss[loss=0.2225, simple_loss=0.2757, pruned_loss=0.08462, over 971896.10 frames.], batch size: 31, lr: 1.56e-03
2022-05-03 15:41:47,858 INFO [train.py:715] (1/8) Epoch 0, batch 17850, loss[loss=0.2625, simple_loss=0.3158, pruned_loss=0.1045, over 4922.00 frames.], tot_loss[loss=0.2249, simple_loss=0.2777, pruned_loss=0.08603, over 971873.69 frames.], batch size: 18, lr: 1.56e-03
2022-05-03 15:42:28,480 INFO [train.py:715] (1/8) Epoch 0, batch 17900, loss[loss=0.2475, simple_loss=0.2932, pruned_loss=0.1009, over 4811.00 frames.], tot_loss[loss=0.225, simple_loss=0.2773, pruned_loss=0.08631, over 971940.56 frames.], batch size: 13, lr: 1.56e-03
2022-05-03 15:43:07,984 INFO [train.py:715] (1/8) Epoch 0, batch 17950, loss[loss=0.1808, simple_loss=0.2334, pruned_loss=0.06411, over 4798.00 frames.], tot_loss[loss=0.2258, simple_loss=0.2781, pruned_loss=0.08677, over 972971.20 frames.], batch size: 18, lr: 1.55e-03
2022-05-03 15:43:48,222 INFO [train.py:715] (1/8) Epoch 0, batch 18000, loss[loss=0.2081, simple_loss=0.2594, pruned_loss=0.07839, over 4751.00 frames.], tot_loss[loss=0.2257, simple_loss=0.2779, pruned_loss=0.08678, over 972723.37 frames.], batch size: 19, lr: 1.55e-03
2022-05-03 15:43:48,223 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 15:43:57,827 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.141, simple_loss=0.228, pruned_loss=0.02706, over 914524.00 frames.
2022-05-03 15:44:38,089 INFO [train.py:715] (1/8) Epoch 0, batch 18050, loss[loss=0.2377, simple_loss=0.2911, pruned_loss=0.09214, over 4782.00 frames.], tot_loss[loss=0.2263, simple_loss=0.2785, pruned_loss=0.08707, over 972251.52 frames.], batch size: 17, lr: 1.55e-03
2022-05-03 15:45:18,346 INFO [train.py:715] (1/8) Epoch 0, batch 18100, loss[loss=0.2034, simple_loss=0.2579, pruned_loss=0.07447, over 4786.00 frames.], tot_loss[loss=0.2263, simple_loss=0.2786, pruned_loss=0.08696, over 972799.41 frames.], batch size: 18, lr: 1.55e-03
2022-05-03 15:45:58,155 INFO [train.py:715] (1/8) Epoch 0, batch 18150, loss[loss=0.2774, simple_loss=0.3367, pruned_loss=0.1091, over 4788.00 frames.], tot_loss[loss=0.2253, simple_loss=0.2775, pruned_loss=0.08654, over 972693.34 frames.], batch size: 18, lr: 1.55e-03
2022-05-03 15:46:37,569 INFO [train.py:715] (1/8) Epoch 0, batch 18200, loss[loss=0.2567, simple_loss=0.3004, pruned_loss=0.1066, over 4891.00 frames.], tot_loss[loss=0.2259, simple_loss=0.2779, pruned_loss=0.08694, over 972863.94 frames.], batch size: 19, lr: 1.54e-03
2022-05-03 15:47:17,742 INFO [train.py:715] (1/8) Epoch 0, batch 18250, loss[loss=0.2743, simple_loss=0.3187, pruned_loss=0.1149, over 4855.00 frames.], tot_loss[loss=0.2261, simple_loss=0.2782, pruned_loss=0.08701, over 972153.38 frames.], batch size: 30, lr: 1.54e-03
2022-05-03 15:47:59,024 INFO [train.py:715] (1/8) Epoch 0, batch 18300, loss[loss=0.189, simple_loss=0.2504, pruned_loss=0.06386, over 4698.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2758, pruned_loss=0.08521, over 971454.53 frames.], batch size: 15, lr: 1.54e-03
2022-05-03 15:48:38,799 INFO [train.py:715] (1/8) Epoch 0, batch 18350, loss[loss=0.2019, simple_loss=0.2611, pruned_loss=0.07137, over 4781.00 frames.], tot_loss[loss=0.2234, simple_loss=0.2762, pruned_loss=0.08535, over 971191.52 frames.], batch size: 14, lr: 1.54e-03
2022-05-03 15:49:19,075 INFO [train.py:715] (1/8) Epoch 0, batch 18400, loss[loss=0.2125, simple_loss=0.2681, pruned_loss=0.07848, over 4873.00 frames.], tot_loss[loss=0.2245, simple_loss=0.277, pruned_loss=0.08597, over 971913.94 frames.], batch size: 16, lr: 1.54e-03
2022-05-03 15:49:59,572 INFO [train.py:715] (1/8) Epoch 0, batch 18450, loss[loss=0.2417, simple_loss=0.2829, pruned_loss=0.1002, over 4841.00 frames.], tot_loss[loss=0.2242, simple_loss=0.2773, pruned_loss=0.08559, over 972387.54 frames.], batch size: 20, lr: 1.53e-03
2022-05-03 15:50:39,244 INFO [train.py:715] (1/8) Epoch 0, batch 18500, loss[loss=0.2209, simple_loss=0.2872, pruned_loss=0.07725, over 4932.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2763, pruned_loss=0.08505, over 972446.24 frames.], batch size: 21, lr: 1.53e-03
2022-05-03 15:51:19,774 INFO [train.py:715] (1/8) Epoch 0, batch 18550, loss[loss=0.2033, simple_loss=0.2519, pruned_loss=0.07741, over 4759.00 frames.], tot_loss[loss=0.224, simple_loss=0.2766, pruned_loss=0.08574, over 971861.52 frames.], batch size: 19, lr: 1.53e-03
2022-05-03 15:52:00,083 INFO [train.py:715] (1/8) Epoch 0, batch 18600, loss[loss=0.2145, simple_loss=0.2701, pruned_loss=0.0795, over 4925.00 frames.], tot_loss[loss=0.2242, simple_loss=0.2764, pruned_loss=0.08593, over 972342.53 frames.], batch size: 29, lr: 1.53e-03
2022-05-03 15:52:40,194 INFO [train.py:715] (1/8) Epoch 0, batch 18650, loss[loss=0.2148, simple_loss=0.2631, pruned_loss=0.08331, over 4891.00 frames.], tot_loss[loss=0.2227, simple_loss=0.2754, pruned_loss=0.08505, over 972610.21 frames.], batch size: 17, lr: 1.53e-03
2022-05-03 15:53:19,596 INFO [train.py:715] (1/8) Epoch 0, batch 18700, loss[loss=0.246, simple_loss=0.2963, pruned_loss=0.09782, over 4780.00 frames.], tot_loss[loss=0.222, simple_loss=0.2752, pruned_loss=0.08445, over 972118.23 frames.], batch size: 17, lr: 1.52e-03
2022-05-03 15:53:59,904 INFO [train.py:715] (1/8) Epoch 0, batch 18750, loss[loss=0.1836, simple_loss=0.2464, pruned_loss=0.06038, over 4686.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2748, pruned_loss=0.08468, over 972564.74 frames.], batch size: 15, lr: 1.52e-03
2022-05-03 15:54:41,174 INFO [train.py:715] (1/8) Epoch 0, batch 18800, loss[loss=0.2259, simple_loss=0.272, pruned_loss=0.08991, over 4958.00 frames.], tot_loss[loss=0.2225, simple_loss=0.275, pruned_loss=0.08499, over 972437.93 frames.], batch size: 15, lr: 1.52e-03
2022-05-03 15:55:20,400 INFO [train.py:715] (1/8) Epoch 0, batch 18850, loss[loss=0.2356, simple_loss=0.2823, pruned_loss=0.09443, over 4767.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2745, pruned_loss=0.0848, over 972665.75 frames.], batch size: 19, lr: 1.52e-03
2022-05-03 15:56:01,301 INFO [train.py:715] (1/8) Epoch 0, batch 18900, loss[loss=0.2148, simple_loss=0.2675, pruned_loss=0.08102, over 4796.00 frames.], tot_loss[loss=0.2226, simple_loss=0.2751, pruned_loss=0.08507, over 972813.98 frames.], batch size: 14, lr: 1.52e-03
2022-05-03 15:56:41,739 INFO [train.py:715] (1/8) Epoch 0, batch 18950, loss[loss=0.2227, simple_loss=0.2786, pruned_loss=0.08343, over 4852.00 frames.], tot_loss[loss=0.2222, simple_loss=0.2752, pruned_loss=0.08465, over 972781.83 frames.], batch size: 32, lr: 1.52e-03
2022-05-03 15:57:21,400 INFO [train.py:715] (1/8) Epoch 0, batch 19000, loss[loss=0.2132, simple_loss=0.2583, pruned_loss=0.08406, over 4785.00 frames.], tot_loss[loss=0.2229, simple_loss=0.2759, pruned_loss=0.0849, over 970701.21 frames.], batch size: 17, lr: 1.51e-03
2022-05-03 15:58:01,846 INFO [train.py:715] (1/8) Epoch 0, batch 19050, loss[loss=0.2158, simple_loss=0.2765, pruned_loss=0.07756, over 4821.00 frames.], tot_loss[loss=0.2235, simple_loss=0.2766, pruned_loss=0.08525, over 971501.89 frames.], batch size: 13, lr: 1.51e-03
2022-05-03 15:58:42,180 INFO [train.py:715] (1/8) Epoch 0, batch 19100, loss[loss=0.2004, simple_loss=0.2648, pruned_loss=0.06801, over 4914.00 frames.], tot_loss[loss=0.2225, simple_loss=0.2757, pruned_loss=0.08461, over 972319.09 frames.], batch size: 19, lr: 1.51e-03
2022-05-03 15:59:22,500 INFO [train.py:715] (1/8) Epoch 0, batch 19150, loss[loss=0.1905, simple_loss=0.237, pruned_loss=0.07202, over 4652.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2753, pruned_loss=0.08413, over 972520.08 frames.], batch size: 13, lr: 1.51e-03
2022-05-03 16:00:01,716 INFO [train.py:715] (1/8) Epoch 0, batch 19200, loss[loss=0.1793, simple_loss=0.2405, pruned_loss=0.05901, over 4858.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2744, pruned_loss=0.08345, over 972953.57 frames.], batch size: 20, lr: 1.51e-03
2022-05-03 16:00:42,577 INFO [train.py:715] (1/8) Epoch 0, batch 19250, loss[loss=0.2058, simple_loss=0.2712, pruned_loss=0.07018, over 4934.00 frames.], tot_loss[loss=0.222, simple_loss=0.2749, pruned_loss=0.08449, over 973193.04 frames.], batch size: 21, lr: 1.50e-03
2022-05-03 16:01:23,357 INFO [train.py:715] (1/8) Epoch 0, batch 19300, loss[loss=0.1952, simple_loss=0.2498, pruned_loss=0.07029, over 4816.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2746, pruned_loss=0.08412, over 972961.58 frames.], batch size: 25, lr: 1.50e-03
2022-05-03 16:02:03,052 INFO [train.py:715] (1/8) Epoch 0, batch 19350, loss[loss=0.1932, simple_loss=0.2444, pruned_loss=0.07095, over 4662.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2746, pruned_loss=0.08423, over 973102.21 frames.], batch size: 13, lr: 1.50e-03
2022-05-03 16:02:43,218 INFO [train.py:715] (1/8) Epoch 0, batch 19400, loss[loss=0.198, simple_loss=0.2568, pruned_loss=0.06964, over 4987.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2732, pruned_loss=0.08266, over 973082.47 frames.], batch size: 28, lr: 1.50e-03
2022-05-03 16:03:24,057 INFO [train.py:715] (1/8) Epoch 0, batch 19450, loss[loss=0.2378, simple_loss=0.288, pruned_loss=0.0938, over 4865.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2729, pruned_loss=0.08283, over 972516.57 frames.], batch size: 30, lr: 1.50e-03
2022-05-03 16:04:03,573 INFO [train.py:715] (1/8) Epoch 0, batch 19500, loss[loss=0.201, simple_loss=0.2536, pruned_loss=0.07424, over 4923.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2737, pruned_loss=0.08323, over 972600.59 frames.], batch size: 23, lr: 1.50e-03
2022-05-03 16:04:42,925 INFO [train.py:715] (1/8) Epoch 0, batch 19550, loss[loss=0.2711, simple_loss=0.3189, pruned_loss=0.1117, over 4909.00 frames.], tot_loss[loss=0.222, simple_loss=0.2748, pruned_loss=0.08461, over 972030.14 frames.], batch size: 17, lr: 1.49e-03
2022-05-03 16:05:23,276 INFO [train.py:715] (1/8) Epoch 0, batch 19600, loss[loss=0.2436, simple_loss=0.2922, pruned_loss=0.09748, over 4702.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2746, pruned_loss=0.08428, over 972046.64 frames.], batch size: 15, lr: 1.49e-03
2022-05-03 16:06:03,058 INFO [train.py:715] (1/8) Epoch 0, batch 19650, loss[loss=0.3129, simple_loss=0.3488, pruned_loss=0.1384, over 4854.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2744, pruned_loss=0.08373, over 971899.36 frames.], batch size: 20, lr: 1.49e-03
2022-05-03 16:06:42,546 INFO [train.py:715] (1/8) Epoch 0, batch 19700, loss[loss=0.2084, simple_loss=0.2598, pruned_loss=0.07852, over 4872.00 frames.], tot_loss[loss=0.2228, simple_loss=0.276, pruned_loss=0.08483, over 971748.74 frames.], batch size: 16, lr: 1.49e-03
2022-05-03 16:07:22,615 INFO [train.py:715] (1/8) Epoch 0, batch 19750, loss[loss=0.2841, simple_loss=0.3281, pruned_loss=0.12, over 4928.00 frames.], tot_loss[loss=0.2234, simple_loss=0.2766, pruned_loss=0.08517, over 971583.27 frames.], batch size: 23, lr: 1.49e-03
2022-05-03 16:08:02,293 INFO [train.py:715] (1/8) Epoch 0, batch 19800, loss[loss=0.2084, simple_loss=0.2589, pruned_loss=0.07899, over 4943.00 frames.], tot_loss[loss=0.225, simple_loss=0.2779, pruned_loss=0.08603, over 972309.77 frames.], batch size: 35, lr: 1.48e-03
2022-05-03 16:08:42,107 INFO [train.py:715] (1/8) Epoch 0, batch 19850, loss[loss=0.2033, simple_loss=0.253, pruned_loss=0.07678, over 4838.00 frames.], tot_loss[loss=0.2243, simple_loss=0.2773, pruned_loss=0.08559, over 971869.50 frames.], batch size: 15, lr: 1.48e-03
2022-05-03 16:09:21,341 INFO [train.py:715] (1/8) Epoch 0, batch 19900, loss[loss=0.2085, simple_loss=0.2792, pruned_loss=0.06884, over 4926.00 frames.], tot_loss[loss=0.223, simple_loss=0.2766, pruned_loss=0.08469, over 972551.87 frames.], batch size: 29, lr: 1.48e-03
2022-05-03 16:10:02,118 INFO [train.py:715] (1/8) Epoch 0, batch 19950, loss[loss=0.1769, simple_loss=0.2316, pruned_loss=0.06106, over 4963.00 frames.], tot_loss[loss=0.2235, simple_loss=0.277, pruned_loss=0.08498, over 972383.78 frames.], batch size: 15, lr: 1.48e-03
2022-05-03 16:10:42,165 INFO [train.py:715] (1/8) Epoch 0, batch 20000, loss[loss=0.1771, simple_loss=0.2455, pruned_loss=0.05436, over 4794.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2761, pruned_loss=0.08406, over 972604.62 frames.], batch size: 14, lr: 1.48e-03
2022-05-03 16:11:21,520 INFO [train.py:715] (1/8) Epoch 0, batch 20050, loss[loss=0.2233, simple_loss=0.2699, pruned_loss=0.08836, over 4783.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2748, pruned_loss=0.08307, over 972817.20 frames.], batch size: 14, lr: 1.48e-03
2022-05-03 16:12:01,700 INFO [train.py:715] (1/8) Epoch 0, batch 20100, loss[loss=0.2076, simple_loss=0.2694, pruned_loss=0.07289, over 4781.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2738, pruned_loss=0.08184, over 973046.30 frames.], batch size: 18, lr: 1.47e-03
2022-05-03 16:12:41,691 INFO [train.py:715] (1/8) Epoch 0, batch 20150, loss[loss=0.2062, simple_loss=0.2661, pruned_loss=0.07317, over 4770.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2735, pruned_loss=0.0814, over 972659.26 frames.], batch size: 17, lr: 1.47e-03
2022-05-03 16:13:21,724 INFO [train.py:715] (1/8) Epoch 0, batch 20200, loss[loss=0.1988, simple_loss=0.2512, pruned_loss=0.07316, over 4983.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2717, pruned_loss=0.08075, over 972915.08 frames.], batch size: 25, lr: 1.47e-03
2022-05-03 16:14:01,254 INFO [train.py:715] (1/8) Epoch 0, batch 20250, loss[loss=0.1834, simple_loss=0.2499, pruned_loss=0.05847, over 4825.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2724, pruned_loss=0.0814, over 973075.75 frames.], batch size: 13, lr: 1.47e-03
2022-05-03 16:14:42,003 INFO [train.py:715] (1/8) Epoch 0, batch 20300, loss[loss=0.1939, simple_loss=0.2545, pruned_loss=0.06665, over 4901.00 frames.], tot_loss[loss=0.2204, simple_loss=0.2746, pruned_loss=0.08306, over 972349.73 frames.], batch size: 17, lr: 1.47e-03
2022-05-03 16:15:21,890 INFO [train.py:715] (1/8) Epoch 0, batch 20350, loss[loss=0.1711, simple_loss=0.2361, pruned_loss=0.05304, over 4806.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2734, pruned_loss=0.08215, over 971961.49 frames.], batch size: 13, lr: 1.47e-03
2022-05-03 16:16:00,952 INFO [train.py:715] (1/8) Epoch 0, batch 20400, loss[loss=0.2646, simple_loss=0.3151, pruned_loss=0.1071, over 4844.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2735, pruned_loss=0.08239, over 971781.06 frames.], batch size: 15, lr: 1.46e-03
2022-05-03 16:16:40,898 INFO [train.py:715] (1/8) Epoch 0, batch 20450, loss[loss=0.1973, simple_loss=0.248, pruned_loss=0.07326, over 4752.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2736, pruned_loss=0.08253, over 972005.96 frames.], batch size: 16, lr: 1.46e-03
2022-05-03 16:17:20,436 INFO [train.py:715] (1/8) Epoch 0, batch 20500, loss[loss=0.2134, simple_loss=0.2763, pruned_loss=0.0753, over 4821.00 frames.], tot_loss[loss=0.218, simple_loss=0.2723, pruned_loss=0.08183, over 972189.78 frames.], batch size: 13, lr: 1.46e-03
2022-05-03 16:18:00,500 INFO [train.py:715] (1/8) Epoch 0, batch 20550, loss[loss=0.2287, simple_loss=0.285, pruned_loss=0.08626, over 4791.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2728, pruned_loss=0.08197, over 972024.18 frames.], batch size: 18, lr: 1.46e-03
2022-05-03 16:18:39,955 INFO [train.py:715] (1/8) Epoch 0, batch 20600, loss[loss=0.1758, simple_loss=0.2388, pruned_loss=0.05641, over 4919.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2709, pruned_loss=0.08036, over 971638.36 frames.], batch size: 23, lr: 1.46e-03
2022-05-03 16:19:19,649 INFO [train.py:715] (1/8) Epoch 0, batch 20650, loss[loss=0.1905, simple_loss=0.2474, pruned_loss=0.0668, over 4967.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2706, pruned_loss=0.08023, over 971957.75 frames.], batch size: 15, lr: 1.46e-03
2022-05-03 16:20:00,379 INFO [train.py:715] (1/8) Epoch 0, batch 20700, loss[loss=0.2259, simple_loss=0.2724, pruned_loss=0.08965, over 4735.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2706, pruned_loss=0.07977, over 972434.85 frames.], batch size: 12, lr: 1.45e-03
2022-05-03 16:20:39,701 INFO [train.py:715] (1/8) Epoch 0, batch 20750, loss[loss=0.1989, simple_loss=0.2638, pruned_loss=0.06702, over 4942.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2709, pruned_loss=0.08027, over 973088.52 frames.], batch size: 21, lr: 1.45e-03
2022-05-03 16:21:19,880 INFO [train.py:715] (1/8) Epoch 0, batch 20800, loss[loss=0.2406, simple_loss=0.2879, pruned_loss=0.09661, over 4932.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2722, pruned_loss=0.08124, over 973396.37 frames.], batch size: 23, lr: 1.45e-03
2022-05-03 16:21:59,638 INFO [train.py:715] (1/8) Epoch 0, batch 20850, loss[loss=0.1867, simple_loss=0.2355, pruned_loss=0.06898, over 4790.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2721, pruned_loss=0.08111, over 972733.45 frames.], batch size: 14, lr: 1.45e-03
2022-05-03 16:22:39,125 INFO [train.py:715] (1/8) Epoch 0, batch 20900, loss[loss=0.2021, simple_loss=0.2556, pruned_loss=0.07428, over 4739.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2721, pruned_loss=0.08117, over 971799.62 frames.], batch size: 16, lr: 1.45e-03
2022-05-03 16:23:19,648 INFO [train.py:715] (1/8) Epoch 0, batch 20950, loss[loss=0.1867, simple_loss=0.2455, pruned_loss=0.06396, over 4902.00 frames.], tot_loss[loss=0.217, simple_loss=0.2714, pruned_loss=0.08134, over 971884.62 frames.], batch size: 19, lr: 1.45e-03
2022-05-03 16:24:00,683 INFO [train.py:715] (1/8) Epoch 0, batch 21000, loss[loss=0.2685, simple_loss=0.3104, pruned_loss=0.1133, over 4943.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2707, pruned_loss=0.08071, over 972256.12 frames.], batch size: 39, lr: 1.44e-03
2022-05-03 16:24:00,684 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 16:24:16,220 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1386, simple_loss=0.2255, pruned_loss=0.02581, over 914524.00 frames.
2022-05-03 16:24:57,012 INFO [train.py:715] (1/8) Epoch 0, batch 21050, loss[loss=0.2463, simple_loss=0.2932, pruned_loss=0.09973, over 4837.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2721, pruned_loss=0.08154, over 972750.36 frames.], batch size: 32, lr: 1.44e-03
2022-05-03 16:25:36,594 INFO [train.py:715] (1/8) Epoch 0, batch 21100, loss[loss=0.1567, simple_loss=0.2234, pruned_loss=0.04501, over 4990.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2715, pruned_loss=0.08106, over 972968.44 frames.], batch size: 16, lr: 1.44e-03
2022-05-03 16:26:16,949 INFO [train.py:715] (1/8) Epoch 0, batch 21150, loss[loss=0.1933, simple_loss=0.2535, pruned_loss=0.06659, over 4898.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2711, pruned_loss=0.08087, over 973089.01 frames.], batch size: 17, lr: 1.44e-03
2022-05-03 16:26:56,813 INFO [train.py:715] (1/8) Epoch 0, batch 21200, loss[loss=0.1912, simple_loss=0.2652, pruned_loss=0.05866, over 4965.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2715, pruned_loss=0.08097, over 972587.55 frames.], batch size: 24, lr: 1.44e-03
2022-05-03 16:27:37,352 INFO [train.py:715] (1/8) Epoch 0, batch 21250, loss[loss=0.2264, simple_loss=0.2624, pruned_loss=0.09524, over 4777.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2717, pruned_loss=0.08123, over 972919.48 frames.], batch size: 12, lr: 1.44e-03
2022-05-03 16:28:17,124 INFO [train.py:715] (1/8) Epoch 0, batch 21300, loss[loss=0.2274, simple_loss=0.2863, pruned_loss=0.08426, over 4784.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2708, pruned_loss=0.08068, over 972338.89 frames.], batch size: 18, lr: 1.43e-03
2022-05-03 16:28:57,544 INFO [train.py:715] (1/8) Epoch 0, batch 21350, loss[loss=0.2076, simple_loss=0.2708, pruned_loss=0.07218, over 4909.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2703, pruned_loss=0.08092, over 973131.91 frames.], batch size: 18, lr: 1.43e-03
2022-05-03 16:29:38,280 INFO [train.py:715] (1/8) Epoch 0, batch 21400, loss[loss=0.2458, simple_loss=0.2899, pruned_loss=0.1008, over 4917.00 frames.], tot_loss[loss=0.216, simple_loss=0.27, pruned_loss=0.08099, over 973590.76 frames.], batch size: 18, lr: 1.43e-03
2022-05-03 16:30:17,948 INFO [train.py:715] (1/8) Epoch 0, batch 21450, loss[loss=0.2031, simple_loss=0.2624, pruned_loss=0.07186, over 4961.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2706, pruned_loss=0.08104, over 972935.63 frames.], batch size: 24, lr: 1.43e-03
2022-05-03 16:30:57,792 INFO [train.py:715] (1/8) Epoch 0, batch 21500, loss[loss=0.2357, simple_loss=0.2799, pruned_loss=0.09579, over 4914.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2712, pruned_loss=0.08111, over 972449.69 frames.], batch size: 18, lr: 1.43e-03
2022-05-03 16:31:38,008 INFO [train.py:715] (1/8) Epoch 0, batch 21550, loss[loss=0.2042, simple_loss=0.2639, pruned_loss=0.07227, over 4964.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2714, pruned_loss=0.08138, over 971465.37 frames.], batch size: 24, lr: 1.43e-03
2022-05-03 16:32:18,472 INFO [train.py:715] (1/8) Epoch 0, batch 21600, loss[loss=0.2746, simple_loss=0.3223, pruned_loss=0.1134, over 4944.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2717, pruned_loss=0.08143, over 972319.22 frames.], batch size: 21, lr: 1.42e-03
2022-05-03 16:32:58,235 INFO [train.py:715] (1/8) Epoch 0, batch 21650, loss[loss=0.1571, simple_loss=0.2269, pruned_loss=0.04364, over 4836.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2713, pruned_loss=0.08028, over 971612.97 frames.], batch size: 12, lr: 1.42e-03
2022-05-03 16:33:39,050 INFO [train.py:715] (1/8) Epoch 0, batch 21700, loss[loss=0.2065, simple_loss=0.2769, pruned_loss=0.06805, over 4849.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2711, pruned_loss=0.07973, over 971450.57 frames.], batch size: 15, lr: 1.42e-03
2022-05-03 16:34:19,209 INFO [train.py:715] (1/8) Epoch 0, batch 21750, loss[loss=0.1915, simple_loss=0.2549, pruned_loss=0.0641, over 4860.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2703, pruned_loss=0.0796, over 972670.52 frames.], batch size: 15, lr: 1.42e-03
2022-05-03 16:34:58,789 INFO [train.py:715] (1/8) Epoch 0, batch 21800, loss[loss=0.2194, simple_loss=0.2756, pruned_loss=0.08158, over 4856.00 frames.], tot_loss[loss=0.216, simple_loss=0.2712, pruned_loss=0.08036, over 972429.64 frames.], batch size: 20, lr: 1.42e-03
2022-05-03 16:35:38,618 INFO [train.py:715] (1/8) Epoch 0, batch 21850, loss[loss=0.2559, simple_loss=0.325, pruned_loss=0.09335, over 4898.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2716, pruned_loss=0.08062, over 972944.58 frames.], batch size: 17, lr: 1.42e-03
2022-05-03 16:36:19,089 INFO [train.py:715] (1/8) Epoch 0, batch 21900, loss[loss=0.1998, simple_loss=0.2577, pruned_loss=0.07091, over 4864.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2713, pruned_loss=0.08072, over 972917.88 frames.], batch size: 20, lr: 1.42e-03
2022-05-03 16:36:59,001 INFO [train.py:715] (1/8) Epoch 0, batch 21950, loss[loss=0.2213, simple_loss=0.2676, pruned_loss=0.08749, over 4781.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2712, pruned_loss=0.08035, over 972118.85 frames.], batch size: 14, lr: 1.41e-03
2022-05-03 16:37:38,286 INFO [train.py:715] (1/8) Epoch 0, batch 22000, loss[loss=0.1865, simple_loss=0.2482, pruned_loss=0.06238, over 4908.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2707, pruned_loss=0.08018, over 972016.99 frames.], batch size: 19, lr: 1.41e-03
2022-05-03 16:38:18,441 INFO [train.py:715] (1/8) Epoch 0, batch 22050, loss[loss=0.1828, simple_loss=0.2492, pruned_loss=0.05821, over 4875.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2712, pruned_loss=0.08076, over 971441.73 frames.], batch size: 20, lr: 1.41e-03
2022-05-03 16:38:58,602 INFO [train.py:715] (1/8) Epoch 0, batch 22100, loss[loss=0.1892, simple_loss=0.2372, pruned_loss=0.07059, over 4845.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2716, pruned_loss=0.08108, over 972917.47 frames.], batch size: 13, lr: 1.41e-03
2022-05-03 16:39:38,119 INFO [train.py:715] (1/8) Epoch 0, batch 22150, loss[loss=0.2037, simple_loss=0.2596, pruned_loss=0.07388, over 4771.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2715, pruned_loss=0.08073, over 972379.11 frames.], batch size: 17, lr: 1.41e-03
2022-05-03 16:40:17,924 INFO [train.py:715] (1/8) Epoch 0, batch 22200, loss[loss=0.2094, simple_loss=0.2707, pruned_loss=0.07404, over 4980.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2711, pruned_loss=0.08038, over 973312.44 frames.], batch size: 35, lr: 1.41e-03
2022-05-03 16:40:58,307 INFO [train.py:715] (1/8) Epoch 0, batch 22250, loss[loss=0.1855, simple_loss=0.2514, pruned_loss=0.05976, over 4848.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2711, pruned_loss=0.07998, over 973286.84 frames.], batch size: 13, lr: 1.40e-03
2022-05-03 16:41:38,376 INFO [train.py:715] (1/8) Epoch 0, batch 22300, loss[loss=0.2395, simple_loss=0.2825, pruned_loss=0.09827, over 4858.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2707, pruned_loss=0.07954, over 972902.50 frames.], batch size: 32, lr: 1.40e-03
2022-05-03 16:42:18,076 INFO [train.py:715] (1/8) Epoch 0, batch 22350, loss[loss=0.2172, simple_loss=0.2802, pruned_loss=0.07715, over 4919.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2702, pruned_loss=0.07914, over 972565.63 frames.], batch size: 29, lr: 1.40e-03
2022-05-03 16:42:58,251 INFO [train.py:715] (1/8) Epoch 0, batch 22400, loss[loss=0.2196, simple_loss=0.2745, pruned_loss=0.08236, over 4754.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2702, pruned_loss=0.07916, over 972197.87 frames.], batch size: 16, lr: 1.40e-03
2022-05-03 16:43:38,082 INFO [train.py:715] (1/8) Epoch 0, batch 22450, loss[loss=0.1848, simple_loss=0.2516, pruned_loss=0.059, over 4974.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2702, pruned_loss=0.07945, over 972509.52 frames.], batch size: 24, lr: 1.40e-03
2022-05-03 16:44:17,445 INFO [train.py:715] (1/8) Epoch 0, batch 22500, loss[loss=0.1913, simple_loss=0.2593, pruned_loss=0.06165, over 4835.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2707, pruned_loss=0.07932, over 971388.62 frames.], batch size: 15, lr: 1.40e-03
2022-05-03 16:44:57,225 INFO [train.py:715] (1/8) Epoch 0, batch 22550, loss[loss=0.2486, simple_loss=0.2991, pruned_loss=0.09909, over 4903.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2704, pruned_loss=0.07922, over 970962.69 frames.], batch size: 39, lr: 1.40e-03
2022-05-03 16:45:37,437 INFO [train.py:715] (1/8) Epoch 0, batch 22600, loss[loss=0.279, simple_loss=0.3086, pruned_loss=0.1247, over 4985.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2699, pruned_loss=0.07928, over 970758.68 frames.], batch size: 14, lr: 1.39e-03
2022-05-03 16:46:18,081 INFO [train.py:715] (1/8) Epoch 0, batch 22650, loss[loss=0.1921, simple_loss=0.2598, pruned_loss=0.06224, over 4930.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2704, pruned_loss=0.07923, over 970943.11 frames.], batch size: 23, lr: 1.39e-03
2022-05-03 16:46:57,299 INFO [train.py:715] (1/8) Epoch 0, batch 22700, loss[loss=0.2393, simple_loss=0.2865, pruned_loss=0.09599, over 4888.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2709, pruned_loss=0.07939, over 971608.39 frames.], batch size: 22, lr: 1.39e-03
2022-05-03 16:47:37,374 INFO [train.py:715] (1/8) Epoch 0, batch 22750, loss[loss=0.255, simple_loss=0.3046, pruned_loss=0.1026, over 4704.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2702, pruned_loss=0.07959, over 971079.24 frames.], batch size: 15, lr: 1.39e-03
2022-05-03 16:48:17,856 INFO [train.py:715] (1/8) Epoch 0, batch 22800, loss[loss=0.2051, simple_loss=0.2685, pruned_loss=0.07082, over 4856.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2696, pruned_loss=0.07927, over 971262.78 frames.], batch size: 20, lr: 1.39e-03
2022-05-03 16:48:57,454 INFO [train.py:715] (1/8) Epoch 0, batch 22850, loss[loss=0.1848, simple_loss=0.2425, pruned_loss=0.06349, over 4840.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2692, pruned_loss=0.07958, over 972025.13 frames.], batch size: 15, lr: 1.39e-03
2022-05-03 16:49:37,559 INFO [train.py:715] (1/8) Epoch 0, batch 22900, loss[loss=0.1803, simple_loss=0.2441, pruned_loss=0.05831, over 4739.00 frames.], tot_loss[loss=0.2145, simple_loss=0.27, pruned_loss=0.07948, over 972245.76 frames.], batch size: 16, lr: 1.39e-03
2022-05-03 16:50:17,829 INFO [train.py:715] (1/8) Epoch 0, batch 22950, loss[loss=0.2724, simple_loss=0.3052, pruned_loss=0.1198, over 4978.00 frames.], tot_loss[loss=0.214, simple_loss=0.2691, pruned_loss=0.07947, over 972861.31 frames.], batch size: 14, lr: 1.38e-03
2022-05-03 16:50:58,459 INFO [train.py:715] (1/8) Epoch 0, batch 23000, loss[loss=0.209, simple_loss=0.2665, pruned_loss=0.07571, over 4796.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2686, pruned_loss=0.07914, over 972600.87 frames.], batch size: 24, lr: 1.38e-03
2022-05-03 16:51:37,484 INFO [train.py:715] (1/8) Epoch 0, batch 23050, loss[loss=0.1944, simple_loss=0.2521, pruned_loss=0.06833, over 4761.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2688, pruned_loss=0.07902, over 972620.77 frames.], batch size: 19, lr: 1.38e-03
2022-05-03 16:52:18,415 INFO [train.py:715] (1/8) Epoch 0, batch 23100, loss[loss=0.2172, simple_loss=0.272, pruned_loss=0.08118, over 4746.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2681, pruned_loss=0.07859, over 972576.19 frames.], batch size: 19, lr: 1.38e-03
2022-05-03 16:52:59,442 INFO [train.py:715] (1/8) Epoch 0, batch 23150, loss[loss=0.1881, simple_loss=0.2506, pruned_loss=0.06284, over 4763.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2682, pruned_loss=0.07873, over 973303.47 frames.], batch size: 19, lr: 1.38e-03
2022-05-03 16:53:39,182 INFO [train.py:715] (1/8) Epoch 0, batch 23200, loss[loss=0.1959, simple_loss=0.2615, pruned_loss=0.0652, over 4959.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2685, pruned_loss=0.0785, over 972841.92 frames.], batch size: 24, lr: 1.38e-03
2022-05-03 16:54:19,751 INFO [train.py:715] (1/8) Epoch 0, batch 23250, loss[loss=0.1835, simple_loss=0.241, pruned_loss=0.06301, over 4794.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2677, pruned_loss=0.07795, over 972676.90 frames.], batch size: 21, lr: 1.38e-03
2022-05-03 16:55:00,170 INFO [train.py:715] (1/8) Epoch 0, batch 23300, loss[loss=0.2192, simple_loss=0.2732, pruned_loss=0.08261, over 4844.00 frames.], tot_loss[loss=0.212, simple_loss=0.2677, pruned_loss=0.07814, over 973004.46 frames.], batch size: 30, lr: 1.37e-03
2022-05-03 16:55:40,656 INFO [train.py:715] (1/8) Epoch 0, batch 23350, loss[loss=0.1884, simple_loss=0.2558, pruned_loss=0.06053, over 4917.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2661, pruned_loss=0.07676, over 972779.77 frames.], batch size: 23, lr: 1.37e-03
2022-05-03 16:56:21,254 INFO [train.py:715] (1/8) Epoch 0, batch 23400, loss[loss=0.1868, simple_loss=0.2476, pruned_loss=0.06306, over 4858.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2659, pruned_loss=0.07688, over 972701.56 frames.], batch size: 20, lr: 1.37e-03
2022-05-03 16:57:02,262 INFO [train.py:715] (1/8) Epoch 0, batch 23450, loss[loss=0.1788, simple_loss=0.2367, pruned_loss=0.06047, over 4975.00 frames.], tot_loss[loss=0.211, simple_loss=0.2671, pruned_loss=0.07743, over 972579.06 frames.], batch size: 14, lr: 1.37e-03
2022-05-03 16:57:43,369 INFO [train.py:715] (1/8) Epoch 0, batch 23500, loss[loss=0.1859, simple_loss=0.2451, pruned_loss=0.0633, over 4807.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2681, pruned_loss=0.07845, over 971931.03 frames.], batch size: 21, lr: 1.37e-03
2022-05-03 16:58:23,221 INFO [train.py:715] (1/8) Epoch 0, batch 23550, loss[loss=0.1892, simple_loss=0.2532, pruned_loss=0.06258, over 4905.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2681, pruned_loss=0.07822, over 972346.06 frames.], batch size: 19, lr: 1.37e-03
2022-05-03 16:59:04,086 INFO [train.py:715] (1/8) Epoch 0, batch 23600, loss[loss=0.2109, simple_loss=0.2754, pruned_loss=0.07317, over 4873.00 frames.], tot_loss[loss=0.2134, simple_loss=0.269, pruned_loss=0.07886, over 973028.59 frames.], batch size: 16, lr: 1.37e-03
2022-05-03 16:59:44,346 INFO [train.py:715] (1/8) Epoch 0, batch 23650, loss[loss=0.1824, simple_loss=0.2337, pruned_loss=0.06561, over 4648.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2683, pruned_loss=0.07855, over 972878.61 frames.], batch size: 13, lr: 1.36e-03
2022-05-03 17:00:24,464 INFO [train.py:715] (1/8) Epoch 0, batch 23700, loss[loss=0.2124, simple_loss=0.2664, pruned_loss=0.07917, over 4959.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2672, pruned_loss=0.07789, over 972671.57 frames.], batch size: 24, lr: 1.36e-03
2022-05-03 17:01:03,662 INFO [train.py:715] (1/8) Epoch 0, batch 23750, loss[loss=0.1806, simple_loss=0.2456, pruned_loss=0.05777, over 4841.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2666, pruned_loss=0.07763, over 973305.34 frames.], batch size: 34, lr: 1.36e-03
2022-05-03 17:01:43,660 INFO [train.py:715] (1/8) Epoch 0, batch 23800, loss[loss=0.1622, simple_loss=0.2277, pruned_loss=0.04832, over 4799.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2669, pruned_loss=0.07806, over 971644.55 frames.], batch size: 12, lr: 1.36e-03
2022-05-03 17:02:24,145 INFO [train.py:715] (1/8) Epoch 0, batch 23850, loss[loss=0.2288, simple_loss=0.2826, pruned_loss=0.08752, over 4973.00 frames.], tot_loss[loss=0.2117, simple_loss=0.267, pruned_loss=0.07825, over 971131.92 frames.], batch size: 25, lr: 1.36e-03
2022-05-03 17:03:03,307 INFO [train.py:715] (1/8) Epoch 0, batch 23900, loss[loss=0.1991, simple_loss=0.255, pruned_loss=0.0716, over 4791.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2664, pruned_loss=0.07739, over 970909.96 frames.], batch size: 12, lr: 1.36e-03
2022-05-03 17:03:43,449 INFO [train.py:715] (1/8) Epoch 0, batch 23950, loss[loss=0.2032, simple_loss=0.2604, pruned_loss=0.07304, over 4919.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2646, pruned_loss=0.07588, over 970906.01 frames.], batch size: 18, lr: 1.36e-03
2022-05-03 17:04:26,565 INFO [train.py:715] (1/8) Epoch 0, batch 24000, loss[loss=0.2021, simple_loss=0.2582, pruned_loss=0.07296, over 4778.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2653, pruned_loss=0.07693, over 970302.59 frames.], batch size: 17, lr: 1.35e-03
2022-05-03 17:04:26,566 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 17:04:40,851 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1357, simple_loss=0.2226, pruned_loss=0.02435, over 914524.00 frames.
2022-05-03 17:05:21,170 INFO [train.py:715] (1/8) Epoch 0, batch 24050, loss[loss=0.2084, simple_loss=0.2536, pruned_loss=0.08159, over 4784.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2657, pruned_loss=0.07728, over 970727.36 frames.], batch size: 18, lr: 1.35e-03
2022-05-03 17:06:00,595 INFO [train.py:715] (1/8) Epoch 0, batch 24100, loss[loss=0.2488, simple_loss=0.3016, pruned_loss=0.09795, over 4893.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2675, pruned_loss=0.07839, over 970665.26 frames.], batch size: 17, lr: 1.35e-03
2022-05-03 17:06:40,575 INFO [train.py:715] (1/8) Epoch 0, batch 24150, loss[loss=0.1901, simple_loss=0.2464, pruned_loss=0.06689, over 4864.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2675, pruned_loss=0.07764, over 970536.39 frames.], batch size: 30, lr: 1.35e-03
2022-05-03 17:07:20,607 INFO [train.py:715] (1/8) Epoch 0, batch 24200, loss[loss=0.2097, simple_loss=0.2562, pruned_loss=0.08162, over 4913.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2676, pruned_loss=0.07797, over 971345.41 frames.], batch size: 17, lr: 1.35e-03
2022-05-03 17:08:01,227 INFO [train.py:715] (1/8) Epoch 0, batch 24250, loss[loss=0.1896, simple_loss=0.2464, pruned_loss=0.06644, over 4835.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2675, pruned_loss=0.07775, over 971309.11 frames.], batch size: 30, lr: 1.35e-03
2022-05-03 17:08:40,827 INFO [train.py:715] (1/8) Epoch 0, batch 24300, loss[loss=0.1945, simple_loss=0.259, pruned_loss=0.06504, over 4814.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2679, pruned_loss=0.07833, over 971443.86 frames.], batch size: 26, lr: 1.35e-03
2022-05-03 17:09:21,010 INFO [train.py:715] (1/8) Epoch 0, batch 24350, loss[loss=0.204, simple_loss=0.2614, pruned_loss=0.07331, over 4769.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2678, pruned_loss=0.07822, over 971050.90 frames.], batch size: 17, lr: 1.35e-03
2022-05-03 17:10:01,414 INFO [train.py:715] (1/8) Epoch 0, batch 24400, loss[loss=0.2097, simple_loss=0.2621, pruned_loss=0.07865, over 4835.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2666, pruned_loss=0.07735, over 971738.00 frames.], batch size: 26, lr: 1.34e-03
2022-05-03 17:10:40,937 INFO [train.py:715] (1/8) Epoch 0, batch 24450, loss[loss=0.2333, simple_loss=0.2953, pruned_loss=0.08571, over 4770.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2668, pruned_loss=0.07775, over 970734.27 frames.], batch size: 19, lr: 1.34e-03
2022-05-03 17:11:21,044 INFO [train.py:715] (1/8) Epoch 0, batch 24500, loss[loss=0.2082, simple_loss=0.2616, pruned_loss=0.07734, over 4873.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2664, pruned_loss=0.07745, over 971263.46 frames.], batch size: 22, lr: 1.34e-03
2022-05-03 17:12:01,317 INFO [train.py:715] (1/8) Epoch 0, batch 24550, loss[loss=0.2062, simple_loss=0.2575, pruned_loss=0.07743, over 4935.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2663, pruned_loss=0.07723, over 971528.98 frames.], batch size: 39, lr: 1.34e-03
2022-05-03 17:12:41,510 INFO [train.py:715] (1/8) Epoch 0, batch 24600, loss[loss=0.2461, simple_loss=0.3017, pruned_loss=0.09519, over 4985.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2673, pruned_loss=0.07778, over 972548.53 frames.], batch size: 39, lr: 1.34e-03
2022-05-03 17:13:20,991 INFO [train.py:715] (1/8) Epoch 0, batch 24650, loss[loss=0.237, simple_loss=0.2929, pruned_loss=0.09057, over 4926.00 frames.], tot_loss[loss=0.211, simple_loss=0.2668, pruned_loss=0.07756, over 972695.81 frames.], batch size: 23, lr: 1.34e-03
2022-05-03 17:14:01,413 INFO [train.py:715] (1/8) Epoch 0, batch 24700, loss[loss=0.1994, simple_loss=0.2553, pruned_loss=0.07176, over 4817.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2668, pruned_loss=0.07788, over 972862.13 frames.], batch size: 13, lr: 1.34e-03
2022-05-03 17:14:42,118 INFO [train.py:715] (1/8) Epoch 0, batch 24750, loss[loss=0.2509, simple_loss=0.2964, pruned_loss=0.1027, over 4839.00 frames.], tot_loss[loss=0.212, simple_loss=0.2677, pruned_loss=0.0782, over 972237.00 frames.], batch size: 32, lr: 1.33e-03
2022-05-03 17:15:21,175 INFO [train.py:715] (1/8) Epoch 0, batch 24800, loss[loss=0.1523, simple_loss=0.2302, pruned_loss=0.03718, over 4822.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2678, pruned_loss=0.07795, over 972186.30 frames.], batch size: 26, lr: 1.33e-03
2022-05-03 17:16:01,311 INFO [train.py:715] (1/8) Epoch 0, batch 24850, loss[loss=0.1927, simple_loss=0.2656, pruned_loss=0.05987, over 4962.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2682, pruned_loss=0.07804, over 972037.75 frames.], batch size: 39, lr: 1.33e-03
2022-05-03 17:16:41,583 INFO [train.py:715] (1/8) Epoch 0, batch 24900, loss[loss=0.1896, simple_loss=0.2482, pruned_loss=0.06551, over 4695.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2681, pruned_loss=0.07791, over 970992.22 frames.], batch size: 15, lr: 1.33e-03
2022-05-03 17:17:21,628 INFO [train.py:715] (1/8) Epoch 0, batch 24950, loss[loss=0.2431, simple_loss=0.2888, pruned_loss=0.09867, over 4861.00 frames.], tot_loss[loss=0.2119, simple_loss=0.268, pruned_loss=0.07792, over 970762.89 frames.], batch size: 32, lr: 1.33e-03
2022-05-03 17:18:01,146 INFO [train.py:715] (1/8) Epoch 0, batch 25000, loss[loss=0.1922, simple_loss=0.2601, pruned_loss=0.06212, over 4779.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2668, pruned_loss=0.07685, over 970916.33 frames.], batch size: 17, lr: 1.33e-03
2022-05-03 17:18:41,401 INFO [train.py:715] (1/8) Epoch 0, batch 25050, loss[loss=0.1508, simple_loss=0.2189, pruned_loss=0.04134, over 4868.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2665, pruned_loss=0.07665, over 972206.38 frames.], batch size: 12, lr: 1.33e-03
2022-05-03 17:19:21,100 INFO [train.py:715] (1/8) Epoch 0, batch 25100, loss[loss=0.2266, simple_loss=0.2877, pruned_loss=0.08272, over 4848.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2669, pruned_loss=0.07662, over 972007.47 frames.], batch size: 30, lr: 1.33e-03
2022-05-03 17:20:00,596 INFO [train.py:715] (1/8) Epoch 0, batch 25150, loss[loss=0.2252, simple_loss=0.2692, pruned_loss=0.09055, over 4808.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2651, pruned_loss=0.0757, over 971507.36 frames.], batch size: 13, lr: 1.32e-03
2022-05-03 17:20:41,131 INFO [train.py:715] (1/8) Epoch 0, batch 25200, loss[loss=0.2038, simple_loss=0.2652, pruned_loss=0.0712, over 4977.00 frames.], tot_loss[loss=0.2078, simple_loss=0.265, pruned_loss=0.07533, over 970328.48 frames.], batch size: 24, lr: 1.32e-03
2022-05-03 17:21:21,698 INFO [train.py:715] (1/8) Epoch 0, batch 25250, loss[loss=0.2098, simple_loss=0.2602, pruned_loss=0.07965, over 4797.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2653, pruned_loss=0.07548, over 970826.58 frames.], batch size: 25, lr: 1.32e-03
2022-05-03 17:22:02,260 INFO [train.py:715] (1/8) Epoch 0, batch 25300, loss[loss=0.2176, simple_loss=0.2751, pruned_loss=0.08009, over 4963.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2652, pruned_loss=0.0752, over 971814.23 frames.], batch size: 21, lr: 1.32e-03
2022-05-03 17:22:42,093 INFO [train.py:715] (1/8) Epoch 0, batch 25350, loss[loss=0.157, simple_loss=0.2159, pruned_loss=0.04906, over 4828.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2654, pruned_loss=0.07566, over 971621.29 frames.], batch size: 13, lr: 1.32e-03
2022-05-03 17:23:22,548 INFO [train.py:715] (1/8) Epoch 0, batch 25400, loss[loss=0.2511, simple_loss=0.2966, pruned_loss=0.1028, over 4928.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2655, pruned_loss=0.07569, over 972316.15 frames.], batch size: 18, lr: 1.32e-03
2022-05-03 17:24:02,721 INFO [train.py:715] (1/8) Epoch 0, batch 25450, loss[loss=0.2185, simple_loss=0.2654, pruned_loss=0.0858, over 4763.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2656, pruned_loss=0.07606, over 972412.35 frames.], batch size: 12, lr: 1.32e-03
2022-05-03 17:24:41,707 INFO [train.py:715] (1/8) Epoch 0, batch 25500, loss[loss=0.2087, simple_loss=0.2608, pruned_loss=0.07831, over 4835.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2667, pruned_loss=0.07718, over 971109.41 frames.], batch size: 30, lr: 1.32e-03
2022-05-03 17:25:22,421 INFO [train.py:715] (1/8) Epoch 0, batch 25550, loss[loss=0.2489, simple_loss=0.3038, pruned_loss=0.09704, over 4961.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2669, pruned_loss=0.0769, over 971443.05 frames.], batch size: 24, lr: 1.31e-03
2022-05-03 17:26:02,026 INFO [train.py:715] (1/8) Epoch 0, batch 25600, loss[loss=0.1822, simple_loss=0.2483, pruned_loss=0.05801, over 4981.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2661, pruned_loss=0.07646, over 972037.20 frames.], batch size: 31, lr: 1.31e-03
2022-05-03 17:26:41,731 INFO [train.py:715] (1/8) Epoch 0, batch 25650, loss[loss=0.2388, simple_loss=0.2928, pruned_loss=0.09246, over 4879.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2673, pruned_loss=0.07742, over 971954.71 frames.], batch size: 39, lr: 1.31e-03
2022-05-03 17:27:21,451 INFO [train.py:715] (1/8) Epoch 0, batch 25700, loss[loss=0.2148, simple_loss=0.2737, pruned_loss=0.07798, over 4786.00 frames.], tot_loss[loss=0.2097, simple_loss=0.266, pruned_loss=0.07664, over 971558.90 frames.], batch size: 12, lr: 1.31e-03
2022-05-03 17:28:01,727 INFO [train.py:715] (1/8) Epoch 0, batch 25750, loss[loss=0.1887, simple_loss=0.2382, pruned_loss=0.06958, over 4729.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2662, pruned_loss=0.07671, over 971333.00 frames.], batch size: 16, lr: 1.31e-03
2022-05-03 17:28:41,511 INFO [train.py:715] (1/8) Epoch 0, batch 25800, loss[loss=0.2177, simple_loss=0.2719, pruned_loss=0.08175, over 4932.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2666, pruned_loss=0.07639, over 971647.78 frames.], batch size: 29, lr: 1.31e-03
2022-05-03 17:29:20,754 INFO [train.py:715] (1/8) Epoch 0, batch 25850, loss[loss=0.2352, simple_loss=0.2736, pruned_loss=0.09838, over 4763.00 frames.], tot_loss[loss=0.2092, simple_loss=0.266, pruned_loss=0.07622, over 972095.33 frames.], batch size: 18, lr: 1.31e-03
2022-05-03 17:30:01,471 INFO [train.py:715] (1/8) Epoch 0, batch 25900, loss[loss=0.1614, simple_loss=0.2344, pruned_loss=0.04417, over 4811.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2652, pruned_loss=0.07617, over 972590.29 frames.], batch size: 25, lr: 1.31e-03
2022-05-03 17:30:41,215 INFO [train.py:715] (1/8) Epoch 0, batch 25950, loss[loss=0.1969, simple_loss=0.2517, pruned_loss=0.07106, over 4691.00 frames.], tot_loss[loss=0.2093, simple_loss=0.266, pruned_loss=0.07628, over 972225.47 frames.], batch size: 15, lr: 1.30e-03
2022-05-03 17:31:21,225 INFO [train.py:715] (1/8) Epoch 0, batch 26000, loss[loss=0.2251, simple_loss=0.28, pruned_loss=0.08509, over 4771.00 frames.], tot_loss[loss=0.21, simple_loss=0.2664, pruned_loss=0.07682, over 971606.47 frames.], batch size: 18, lr: 1.30e-03
2022-05-03 17:32:01,170 INFO [train.py:715] (1/8) Epoch 0, batch 26050, loss[loss=0.2327, simple_loss=0.2749, pruned_loss=0.09522, over 4968.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2671, pruned_loss=0.0769, over 972389.80 frames.], batch size: 28, lr: 1.30e-03
2022-05-03 17:32:41,633 INFO [train.py:715] (1/8) Epoch 0, batch 26100, loss[loss=0.2118, simple_loss=0.2748, pruned_loss=0.07442, over 4891.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2667, pruned_loss=0.07635, over 972469.85 frames.], batch size: 19, lr: 1.30e-03
2022-05-03 17:33:21,950 INFO [train.py:715] (1/8) Epoch 0, batch 26150, loss[loss=0.1732, simple_loss=0.2247, pruned_loss=0.06085, over 4863.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2665, pruned_loss=0.07664, over 972420.21 frames.], batch size: 32, lr: 1.30e-03
2022-05-03 17:34:00,856 INFO [train.py:715] (1/8) Epoch 0, batch 26200, loss[loss=0.2577, simple_loss=0.3099, pruned_loss=0.1027, over 4953.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2663, pruned_loss=0.07663, over 971753.71 frames.], batch size: 24, lr: 1.30e-03
2022-05-03 17:34:41,485 INFO [train.py:715] (1/8) Epoch 0, batch 26250, loss[loss=0.1729, simple_loss=0.2486, pruned_loss=0.04855, over 4778.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2654, pruned_loss=0.07578, over 972474.20 frames.], batch size: 17, lr: 1.30e-03
2022-05-03 17:35:21,436 INFO [train.py:715] (1/8) Epoch 0, batch 26300, loss[loss=0.1856, simple_loss=0.2493, pruned_loss=0.06091, over 4984.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2666, pruned_loss=0.07633, over 972996.33 frames.], batch size: 25, lr: 1.30e-03
2022-05-03 17:36:01,278 INFO [train.py:715] (1/8) Epoch 0, batch 26350, loss[loss=0.2077, simple_loss=0.2657, pruned_loss=0.07484, over 4937.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2663, pruned_loss=0.07621, over 972642.90 frames.], batch size: 39, lr: 1.30e-03
2022-05-03 17:36:41,214 INFO [train.py:715] (1/8) Epoch 0, batch 26400, loss[loss=0.197, simple_loss=0.259, pruned_loss=0.06754, over 4807.00 frames.], tot_loss[loss=0.209, simple_loss=0.266, pruned_loss=0.07598, over 972358.20 frames.], batch size: 25, lr: 1.29e-03
2022-05-03 17:37:21,340 INFO [train.py:715] (1/8) Epoch 0, batch 26450, loss[loss=0.2472, simple_loss=0.2888, pruned_loss=0.1028, over 4851.00 frames.], tot_loss[loss=0.2097, simple_loss=0.266, pruned_loss=0.07672, over 972006.35 frames.], batch size: 15, lr: 1.29e-03
2022-05-03 17:38:02,050 INFO [train.py:715] (1/8) Epoch 0, batch 26500, loss[loss=0.1888, simple_loss=0.2616, pruned_loss=0.05801, over 4860.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2651, pruned_loss=0.07598, over 971528.38 frames.], batch size: 38, lr: 1.29e-03
2022-05-03 17:38:41,409 INFO [train.py:715] (1/8) Epoch 0, batch 26550, loss[loss=0.2316, simple_loss=0.2783, pruned_loss=0.09248, over 4989.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2649, pruned_loss=0.07533, over 971666.84 frames.], batch size: 16, lr: 1.29e-03
2022-05-03 17:39:21,082 INFO [train.py:715] (1/8) Epoch 0, batch 26600, loss[loss=0.1894, simple_loss=0.2426, pruned_loss=0.06812, over 4830.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2649, pruned_loss=0.07505, over 972280.95 frames.], batch size: 15, lr: 1.29e-03
2022-05-03 17:40:01,329 INFO [train.py:715] (1/8) Epoch 0, batch 26650, loss[loss=0.1547, simple_loss=0.2238, pruned_loss=0.04278, over 4902.00 frames.], tot_loss[loss=0.2065, simple_loss=0.264, pruned_loss=0.07449, over 971741.70 frames.], batch size: 17, lr: 1.29e-03
2022-05-03 17:40:40,794 INFO [train.py:715] (1/8) Epoch 0, batch 26700, loss[loss=0.2544, simple_loss=0.3126, pruned_loss=0.09814, over 4838.00 frames.], tot_loss[loss=0.208, simple_loss=0.2652, pruned_loss=0.07542, over 971787.48 frames.], batch size: 15, lr: 1.29e-03
2022-05-03 17:41:20,819 INFO [train.py:715] (1/8) Epoch 0, batch 26750, loss[loss=0.2158, simple_loss=0.2632, pruned_loss=0.08424, over 4964.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2661, pruned_loss=0.07609, over 972560.29 frames.], batch size: 24, lr: 1.29e-03
2022-05-03 17:42:01,244 INFO [train.py:715] (1/8) Epoch 0, batch 26800, loss[loss=0.2147, simple_loss=0.2665, pruned_loss=0.08142, over 4885.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2655, pruned_loss=0.0758, over 972161.18 frames.], batch size: 22, lr: 1.28e-03
2022-05-03 17:42:41,667 INFO [train.py:715] (1/8) Epoch 0, batch 26850, loss[loss=0.2553, simple_loss=0.3029, pruned_loss=0.1039, over 4929.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2649, pruned_loss=0.07491, over 972445.18 frames.], batch size: 39, lr: 1.28e-03
2022-05-03 17:43:21,531 INFO [train.py:715] (1/8) Epoch 0, batch 26900, loss[loss=0.1649, simple_loss=0.2138, pruned_loss=0.05801, over 4965.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2644, pruned_loss=0.07439, over 972395.46 frames.], batch size: 14, lr: 1.28e-03
2022-05-03 17:44:02,266 INFO [train.py:715] (1/8) Epoch 0, batch 26950, loss[loss=0.2655, simple_loss=0.3065, pruned_loss=0.1123, over 4747.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2654, pruned_loss=0.0752, over 972283.02 frames.], batch size: 12, lr: 1.28e-03
2022-05-03 17:44:42,416 INFO [train.py:715] (1/8) Epoch 0, batch 27000, loss[loss=0.2026, simple_loss=0.2554, pruned_loss=0.07495, over 4799.00 frames.], tot_loss[loss=0.209, simple_loss=0.266, pruned_loss=0.07597, over 971526.91 frames.], batch size: 13, lr: 1.28e-03
2022-05-03 17:44:42,417 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 17:44:51,202 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1338, simple_loss=0.2208, pruned_loss=0.02337, over 914524.00 frames.
2022-05-03 17:45:31,270 INFO [train.py:715] (1/8) Epoch 0, batch 27050, loss[loss=0.2004, simple_loss=0.2554, pruned_loss=0.07268, over 4873.00 frames.], tot_loss[loss=0.209, simple_loss=0.2658, pruned_loss=0.07613, over 970878.03 frames.], batch size: 16, lr: 1.28e-03
2022-05-03 17:46:10,744 INFO [train.py:715] (1/8) Epoch 0, batch 27100, loss[loss=0.2971, simple_loss=0.3188, pruned_loss=0.1377, over 4872.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2663, pruned_loss=0.07611, over 971241.32 frames.], batch size: 38, lr: 1.28e-03
2022-05-03 17:46:51,332 INFO [train.py:715] (1/8) Epoch 0, batch 27150, loss[loss=0.1951, simple_loss=0.2708, pruned_loss=0.05965, over 4897.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2654, pruned_loss=0.07562, over 971898.75 frames.], batch size: 19, lr: 1.28e-03
2022-05-03 17:47:31,711 INFO [train.py:715] (1/8) Epoch 0, batch 27200, loss[loss=0.2052, simple_loss=0.2662, pruned_loss=0.0721, over 4824.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2652, pruned_loss=0.07577, over 972038.88 frames.], batch size: 27, lr: 1.28e-03
2022-05-03 17:48:11,812 INFO [train.py:715] (1/8) Epoch 0, batch 27250, loss[loss=0.2098, simple_loss=0.2786, pruned_loss=0.07052, over 4703.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2654, pruned_loss=0.07514, over 972545.17 frames.], batch size: 15, lr: 1.27e-03
2022-05-03 17:48:51,957 INFO [train.py:715] (1/8) Epoch 0, batch 27300, loss[loss=0.1755, simple_loss=0.2371, pruned_loss=0.05691, over 4777.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2658, pruned_loss=0.07562, over 972616.38 frames.], batch size: 18, lr: 1.27e-03
2022-05-03 17:49:31,864 INFO [train.py:715] (1/8) Epoch 0, batch 27350, loss[loss=0.2266, simple_loss=0.2977, pruned_loss=0.07772, over 4945.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2651, pruned_loss=0.07496, over 971952.82 frames.], batch size: 21, lr: 1.27e-03
2022-05-03 17:50:11,824 INFO [train.py:715] (1/8) Epoch 0, batch 27400, loss[loss=0.1835, simple_loss=0.2447, pruned_loss=0.06118, over 4905.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2637, pruned_loss=0.07392, over 972507.22 frames.], batch size: 19, lr: 1.27e-03
2022-05-03 17:50:51,091 INFO [train.py:715] (1/8) Epoch 0, batch 27450, loss[loss=0.2263, simple_loss=0.2858, pruned_loss=0.08341, over 4829.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2644, pruned_loss=0.07438, over 973020.74 frames.], batch size: 13, lr: 1.27e-03
2022-05-03 17:51:31,237 INFO [train.py:715] (1/8) Epoch 0, batch 27500, loss[loss=0.2317, simple_loss=0.2837, pruned_loss=0.08983, over 4818.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2644, pruned_loss=0.07438, over 973080.77 frames.], batch size: 26, lr: 1.27e-03
2022-05-03 17:52:11,050 INFO [train.py:715] (1/8) Epoch 0, batch 27550, loss[loss=0.2215, simple_loss=0.2828, pruned_loss=0.08008, over 4921.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2653, pruned_loss=0.07473, over 973319.59 frames.], batch size: 29, lr: 1.27e-03
2022-05-03 17:52:50,534 INFO [train.py:715] (1/8) Epoch 0, batch 27600, loss[loss=0.1876, simple_loss=0.264, pruned_loss=0.05557, over 4899.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2656, pruned_loss=0.07486, over 972254.35 frames.], batch size: 19, lr: 1.27e-03
2022-05-03 17:53:29,967 INFO [train.py:715] (1/8) Epoch 0, batch 27650, loss[loss=0.1972, simple_loss=0.2596, pruned_loss=0.06735, over 4781.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2664, pruned_loss=0.07542, over 971968.82 frames.], batch size: 17, lr: 1.27e-03
2022-05-03 17:54:09,972 INFO [train.py:715] (1/8) Epoch 0, batch 27700, loss[loss=0.2108, simple_loss=0.2657, pruned_loss=0.07801, over 4773.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2649, pruned_loss=0.07493, over 972393.96 frames.], batch size: 17, lr: 1.26e-03
2022-05-03 17:54:50,340 INFO [train.py:715] (1/8) Epoch 0, batch 27750, loss[loss=0.1816, simple_loss=0.2497, pruned_loss=0.05676, over 4804.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2648, pruned_loss=0.07475, over 972320.33 frames.], batch size: 13, lr: 1.26e-03
2022-05-03 17:55:30,111 INFO [train.py:715] (1/8) Epoch 0, batch 27800, loss[loss=0.2099, simple_loss=0.2631, pruned_loss=0.07833, over 4969.00 frames.], tot_loss[loss=0.2052, simple_loss=0.263, pruned_loss=0.07364, over 971616.28 frames.], batch size: 15, lr: 1.26e-03
2022-05-03 17:56:10,357 INFO [train.py:715] (1/8) Epoch 0, batch 27850, loss[loss=0.2431, simple_loss=0.296, pruned_loss=0.09509, over 4895.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2641, pruned_loss=0.07472, over 970880.86 frames.], batch size: 22, lr: 1.26e-03
2022-05-03 17:56:49,941 INFO [train.py:715] (1/8) Epoch 0, batch 27900, loss[loss=0.1947, simple_loss=0.268, pruned_loss=0.06068, over 4942.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2646, pruned_loss=0.07507, over 971271.27 frames.], batch size: 29, lr: 1.26e-03
2022-05-03 17:57:29,404 INFO [train.py:715] (1/8) Epoch 0, batch 27950, loss[loss=0.2421, simple_loss=0.3009, pruned_loss=0.09167, over 4894.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2652, pruned_loss=0.07564, over 973025.30 frames.], batch size: 19, lr: 1.26e-03
2022-05-03 17:58:09,431 INFO [train.py:715] (1/8) Epoch 0, batch 28000, loss[loss=0.1967, simple_loss=0.2574, pruned_loss=0.06806, over 4719.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2637, pruned_loss=0.07448, over 973067.27 frames.], batch size: 15, lr: 1.26e-03
2022-05-03 17:58:49,657 INFO [train.py:715] (1/8) Epoch 0, batch 28050, loss[loss=0.2015, simple_loss=0.2599, pruned_loss=0.07156, over 4983.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2638, pruned_loss=0.07472, over 973462.76 frames.], batch size: 33, lr: 1.26e-03
2022-05-03 17:59:29,707 INFO [train.py:715] (1/8) Epoch 0, batch 28100, loss[loss=0.1798, simple_loss=0.2509, pruned_loss=0.05439, over 4698.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2631, pruned_loss=0.07387, over 972082.22 frames.], batch size: 15, lr: 1.26e-03
2022-05-03 18:00:08,962 INFO [train.py:715] (1/8) Epoch 0, batch 28150, loss[loss=0.2178, simple_loss=0.2758, pruned_loss=0.07992, over 4870.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2648, pruned_loss=0.07499, over 972340.29 frames.], batch size: 16, lr: 1.25e-03
2022-05-03 18:00:49,198 INFO [train.py:715] (1/8) Epoch 0, batch 28200, loss[loss=0.1689, simple_loss=0.2345, pruned_loss=0.05159, over 4786.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2645, pruned_loss=0.07507, over 972330.37 frames.], batch size: 17, lr: 1.25e-03
2022-05-03 18:01:28,908 INFO [train.py:715] (1/8) Epoch 0, batch 28250, loss[loss=0.2459, simple_loss=0.2825, pruned_loss=0.1046, over 4778.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2647, pruned_loss=0.07522, over 973141.44 frames.], batch size: 14, lr: 1.25e-03
2022-05-03 18:02:07,671 INFO [train.py:715] (1/8) Epoch 0, batch 28300, loss[loss=0.1992, simple_loss=0.2539, pruned_loss=0.07223, over 4700.00 frames.], tot_loss[loss=0.208, simple_loss=0.2653, pruned_loss=0.0754, over 972447.33 frames.], batch size: 15, lr: 1.25e-03
2022-05-03 18:02:48,209 INFO [train.py:715] (1/8) Epoch 0, batch 28350, loss[loss=0.2377, simple_loss=0.2866, pruned_loss=0.09439, over 4951.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2645, pruned_loss=0.07521, over 971721.16 frames.], batch size: 35, lr: 1.25e-03
2022-05-03 18:03:27,709 INFO [train.py:715] (1/8) Epoch 0, batch 28400, loss[loss=0.2184, simple_loss=0.2728, pruned_loss=0.08197, over 4809.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2655, pruned_loss=0.07607, over 971267.49 frames.], batch size: 21, lr: 1.25e-03
2022-05-03 18:04:07,959 INFO [train.py:715] (1/8) Epoch 0, batch 28450, loss[loss=0.1945, simple_loss=0.2534, pruned_loss=0.06779, over 4814.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2647, pruned_loss=0.07502, over 972028.14 frames.], batch size: 21, lr: 1.25e-03
2022-05-03 18:04:47,631 INFO [train.py:715] (1/8) Epoch 0, batch 28500, loss[loss=0.1838, simple_loss=0.2455, pruned_loss=0.06106, over 4684.00 frames.], tot_loss[loss=0.2072, simple_loss=0.265, pruned_loss=0.07464, over 972376.31 frames.], batch size: 15, lr: 1.25e-03
2022-05-03 18:05:28,103 INFO [train.py:715] (1/8) Epoch 0, batch 28550, loss[loss=0.1928, simple_loss=0.2554, pruned_loss=0.06514, over 4918.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2642, pruned_loss=0.07417, over 972648.18 frames.], batch size: 23, lr: 1.25e-03
2022-05-03 18:06:07,734 INFO [train.py:715] (1/8) Epoch 0, batch 28600, loss[loss=0.2046, simple_loss=0.2663, pruned_loss=0.07141, over 4818.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2644, pruned_loss=0.07411, over 972436.14 frames.], batch size: 27, lr: 1.24e-03
2022-05-03 18:06:46,958 INFO [train.py:715] (1/8) Epoch 0, batch 28650, loss[loss=0.2155, simple_loss=0.2747, pruned_loss=0.07811, over 4921.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2629, pruned_loss=0.0734, over 973296.75 frames.], batch size: 29, lr: 1.24e-03
2022-05-03 18:07:26,842 INFO [train.py:715] (1/8) Epoch 0, batch 28700, loss[loss=0.1372, simple_loss=0.2032, pruned_loss=0.03555, over 4797.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2628, pruned_loss=0.07381, over 972728.78 frames.], batch size: 25, lr: 1.24e-03
2022-05-03 18:08:06,485 INFO [train.py:715] (1/8) Epoch 0, batch 28750, loss[loss=0.207, simple_loss=0.2678, pruned_loss=0.07308, over 4750.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2631, pruned_loss=0.07431, over 972535.47 frames.], batch size: 16, lr: 1.24e-03
2022-05-03 18:08:46,804 INFO [train.py:715] (1/8) Epoch 0, batch 28800, loss[loss=0.1938, simple_loss=0.2502, pruned_loss=0.06873, over 4834.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2625, pruned_loss=0.07363, over 971587.06 frames.], batch size: 13, lr: 1.24e-03
2022-05-03 18:09:25,924 INFO [train.py:715] (1/8) Epoch 0, batch 28850, loss[loss=0.1639, simple_loss=0.226, pruned_loss=0.05092, over 4872.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2628, pruned_loss=0.07332, over 971703.86 frames.], batch size: 16, lr: 1.24e-03
2022-05-03 18:10:05,951 INFO [train.py:715] (1/8) Epoch 0, batch 28900, loss[loss=0.2177, simple_loss=0.2755, pruned_loss=0.07992, over 4699.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2635, pruned_loss=0.07353, over 971990.26 frames.], batch size: 15, lr: 1.24e-03
2022-05-03 18:10:45,833 INFO [train.py:715] (1/8) Epoch 0, batch 28950, loss[loss=0.2039, simple_loss=0.2653, pruned_loss=0.07132, over 4828.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2634, pruned_loss=0.07342, over 972778.80 frames.], batch size: 26, lr: 1.24e-03
2022-05-03 18:11:24,707 INFO [train.py:715] (1/8) Epoch 0, batch 29000, loss[loss=0.2469, simple_loss=0.306, pruned_loss=0.09386, over 4872.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2629, pruned_loss=0.0738, over 971333.36 frames.], batch size: 16, lr: 1.24e-03
2022-05-03 18:12:05,306 INFO [train.py:715] (1/8) Epoch 0, batch 29050, loss[loss=0.1982, simple_loss=0.2585, pruned_loss=0.06896, over 4906.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2632, pruned_loss=0.07383, over 971312.09 frames.], batch size: 19, lr: 1.24e-03
2022-05-03 18:12:45,440 INFO [train.py:715] (1/8) Epoch 0, batch 29100, loss[loss=0.1949, simple_loss=0.262, pruned_loss=0.06394, over 4979.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2631, pruned_loss=0.07351, over 972268.45 frames.], batch size: 28, lr: 1.23e-03
2022-05-03 18:13:25,065 INFO [train.py:715] (1/8) Epoch 0, batch 29150, loss[loss=0.253, simple_loss=0.3034, pruned_loss=0.1013, over 4802.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2626, pruned_loss=0.07318, over 971591.78 frames.], batch size: 14, lr: 1.23e-03
2022-05-03 18:14:04,262 INFO [train.py:715] (1/8) Epoch 0, batch 29200, loss[loss=0.1822, simple_loss=0.2404, pruned_loss=0.06204, over 4829.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2626, pruned_loss=0.07282, over 972240.43 frames.], batch size: 26, lr: 1.23e-03
2022-05-03 18:14:44,210 INFO [train.py:715] (1/8) Epoch 0, batch 29250, loss[loss=0.2158, simple_loss=0.269, pruned_loss=0.08136, over 4923.00 frames.], tot_loss[loss=0.203, simple_loss=0.2614, pruned_loss=0.07232, over 971306.58 frames.], batch size: 17, lr: 1.23e-03
2022-05-03 18:15:24,224 INFO [train.py:715] (1/8) Epoch 0, batch 29300, loss[loss=0.2166, simple_loss=0.2796, pruned_loss=0.07676, over 4921.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2617, pruned_loss=0.07277, over 971269.54 frames.], batch size: 29, lr: 1.23e-03
2022-05-03 18:16:04,639 INFO [train.py:715] (1/8) Epoch 0, batch 29350, loss[loss=0.1652, simple_loss=0.2307, pruned_loss=0.04988, over 4843.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2613, pruned_loss=0.07277, over 972456.23 frames.], batch size: 20, lr: 1.23e-03
2022-05-03 18:16:44,085 INFO [train.py:715] (1/8) Epoch 0, batch 29400, loss[loss=0.1507, simple_loss=0.218, pruned_loss=0.04171, over 4966.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2622, pruned_loss=0.07357, over 973166.23 frames.], batch size: 14, lr: 1.23e-03
2022-05-03 18:17:23,554 INFO [train.py:715] (1/8) Epoch 0, batch 29450, loss[loss=0.161, simple_loss=0.2283, pruned_loss=0.04686, over 4978.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2626, pruned_loss=0.07386, over 973198.28 frames.], batch size: 14, lr: 1.23e-03
2022-05-03 18:18:03,747 INFO [train.py:715] (1/8) Epoch 0, batch 29500, loss[loss=0.2172, simple_loss=0.2873, pruned_loss=0.07355, over 4809.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2616, pruned_loss=0.07313, over 972901.20 frames.], batch size: 25, lr: 1.23e-03
2022-05-03 18:18:42,858 INFO [train.py:715] (1/8) Epoch 0, batch 29550, loss[loss=0.2118, simple_loss=0.2703, pruned_loss=0.07669, over 4851.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2627, pruned_loss=0.07377, over 972544.31 frames.], batch size: 38, lr: 1.23e-03
2022-05-03 18:19:23,017 INFO [train.py:715] (1/8) Epoch 0, batch 29600, loss[loss=0.2195, simple_loss=0.2767, pruned_loss=0.08118, over 4815.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2632, pruned_loss=0.07446, over 973384.34 frames.], batch size: 27, lr: 1.22e-03
2022-05-03 18:20:02,965 INFO [train.py:715] (1/8) Epoch 0, batch 29650, loss[loss=0.1871, simple_loss=0.2576, pruned_loss=0.05829, over 4762.00 frames.], tot_loss[loss=0.2067, simple_loss=0.264, pruned_loss=0.07467, over 973290.17 frames.], batch size: 14, lr: 1.22e-03
2022-05-03 18:20:42,825 INFO [train.py:715] (1/8) Epoch 0, batch 29700, loss[loss=0.1898, simple_loss=0.2515, pruned_loss=0.06408, over 4805.00 frames.], tot_loss[loss=0.2052, simple_loss=0.263, pruned_loss=0.07373, over 973221.31 frames.], batch size: 24, lr: 1.22e-03
2022-05-03 18:21:23,324 INFO [train.py:715] (1/8) Epoch 0, batch 29750, loss[loss=0.1957, simple_loss=0.2596, pruned_loss=0.06593, over 4947.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2625, pruned_loss=0.07352, over 972826.98 frames.], batch size: 21, lr: 1.22e-03
2022-05-03 18:22:03,150 INFO [train.py:715] (1/8) Epoch 0, batch 29800, loss[loss=0.2046, simple_loss=0.2529, pruned_loss=0.07812, over 4916.00 frames.], tot_loss[loss=0.206, simple_loss=0.2639, pruned_loss=0.07402, over 972643.98 frames.], batch size: 17, lr: 1.22e-03
2022-05-03 18:22:44,054 INFO [train.py:715] (1/8) Epoch 0, batch 29850, loss[loss=0.1795, simple_loss=0.2502, pruned_loss=0.05442, over 4967.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2625, pruned_loss=0.073, over 973494.40 frames.], batch size: 28, lr: 1.22e-03
2022-05-03 18:23:23,987 INFO [train.py:715] (1/8) Epoch 0, batch 29900, loss[loss=0.195, simple_loss=0.2477, pruned_loss=0.07114, over 4844.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2617, pruned_loss=0.07243, over 972801.30 frames.], batch size: 13, lr: 1.22e-03
2022-05-03 18:24:03,882 INFO [train.py:715] (1/8) Epoch 0, batch 29950, loss[loss=0.2039, simple_loss=0.275, pruned_loss=0.06639, over 4898.00 frames.], tot_loss[loss=0.2033, simple_loss=0.262, pruned_loss=0.0723, over 972959.48 frames.], batch size: 19, lr: 1.22e-03
2022-05-03 18:24:43,767 INFO [train.py:715] (1/8) Epoch 0, batch 30000, loss[loss=0.2288, simple_loss=0.2779, pruned_loss=0.08979, over 4770.00 frames.], tot_loss[loss=0.2026, simple_loss=0.262, pruned_loss=0.07157, over 972951.39 frames.], batch size: 17, lr: 1.22e-03
2022-05-03 18:24:43,768 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 18:25:00,382 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1316, simple_loss=0.2189, pruned_loss=0.02213, over 914524.00 frames.
2022-05-03 18:25:40,682 INFO [train.py:715] (1/8) Epoch 0, batch 30050, loss[loss=0.1653, simple_loss=0.2378, pruned_loss=0.04641, over 4921.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2622, pruned_loss=0.07165, over 972618.04 frames.], batch size: 29, lr: 1.22e-03
2022-05-03 18:26:21,235 INFO [train.py:715] (1/8) Epoch 0, batch 30100, loss[loss=0.2116, simple_loss=0.2617, pruned_loss=0.08077, over 4917.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2621, pruned_loss=0.07182, over 972918.50 frames.], batch size: 19, lr: 1.21e-03
2022-05-03 18:27:01,914 INFO [train.py:715] (1/8) Epoch 0, batch 30150, loss[loss=0.1844, simple_loss=0.251, pruned_loss=0.05889, over 4882.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2609, pruned_loss=0.07097, over 971508.66 frames.], batch size: 20, lr: 1.21e-03
2022-05-03 18:27:42,051 INFO [train.py:715] (1/8) Epoch 0, batch 30200, loss[loss=0.2448, simple_loss=0.2869, pruned_loss=0.1013, over 4745.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2612, pruned_loss=0.07172, over 972499.23 frames.], batch size: 16, lr: 1.21e-03
2022-05-03 18:28:22,539 INFO [train.py:715] (1/8) Epoch 0, batch 30250, loss[loss=0.2409, simple_loss=0.2893, pruned_loss=0.09625, over 4896.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2622, pruned_loss=0.07214, over 973062.39 frames.], batch size: 39, lr: 1.21e-03
2022-05-03 18:29:02,642 INFO [train.py:715] (1/8) Epoch 0, batch 30300, loss[loss=0.1789, simple_loss=0.2431, pruned_loss=0.05734, over 4841.00 frames.], tot_loss[loss=0.204, simple_loss=0.2623, pruned_loss=0.0729, over 972658.13 frames.], batch size: 15, lr: 1.21e-03
2022-05-03 18:29:43,073 INFO [train.py:715] (1/8) Epoch 0, batch 30350, loss[loss=0.1921, simple_loss=0.261, pruned_loss=0.06165, over 4832.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2608, pruned_loss=0.07201, over 973249.93 frames.], batch size: 26, lr: 1.21e-03
2022-05-03 18:30:23,199 INFO [train.py:715] (1/8) Epoch 0, batch 30400, loss[loss=0.218, simple_loss=0.2737, pruned_loss=0.0811, over 4980.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2616, pruned_loss=0.07215, over 972946.73 frames.], batch size: 35, lr: 1.21e-03
2022-05-03 18:31:02,968 INFO [train.py:715] (1/8) Epoch 0, batch 30450, loss[loss=0.1838, simple_loss=0.2463, pruned_loss=0.06064, over 4968.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2626, pruned_loss=0.07207, over 973064.27 frames.], batch size: 24, lr: 1.21e-03
2022-05-03 18:31:42,718 INFO [train.py:715] (1/8) Epoch 0, batch 30500, loss[loss=0.1778, simple_loss=0.2308, pruned_loss=0.06243, over 4784.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2609, pruned_loss=0.07087, over 972776.09 frames.], batch size: 14, lr: 1.21e-03
2022-05-03 18:32:22,641 INFO [train.py:715] (1/8) Epoch 0, batch 30550, loss[loss=0.2173, simple_loss=0.276, pruned_loss=0.07934, over 4778.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2616, pruned_loss=0.0713, over 972891.52 frames.], batch size: 14, lr: 1.21e-03
2022-05-03 18:33:01,759 INFO [train.py:715] (1/8) Epoch 0, batch 30600, loss[loss=0.2244, simple_loss=0.277, pruned_loss=0.08597, over 4769.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2615, pruned_loss=0.07105, over 973179.91 frames.], batch size: 17, lr: 1.20e-03
2022-05-03 18:33:41,703 INFO [train.py:715] (1/8) Epoch 0, batch 30650, loss[loss=0.242, simple_loss=0.2964, pruned_loss=0.09382, over 4929.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2609, pruned_loss=0.07072, over 972102.66 frames.], batch size: 23, lr: 1.20e-03
2022-05-03 18:34:21,516 INFO [train.py:715] (1/8) Epoch 0, batch 30700, loss[loss=0.2024, simple_loss=0.2611, pruned_loss=0.07184, over 4981.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2619, pruned_loss=0.07176, over 972411.88 frames.], batch size: 15, lr: 1.20e-03
2022-05-03 18:35:01,619 INFO [train.py:715] (1/8) Epoch 0, batch 30750, loss[loss=0.1749, simple_loss=0.2355, pruned_loss=0.05714, over 4761.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2617, pruned_loss=0.07187, over 972306.52 frames.], batch size: 19, lr: 1.20e-03
2022-05-03 18:35:40,965 INFO [train.py:715] (1/8) Epoch 0, batch 30800, loss[loss=0.2131, simple_loss=0.2645, pruned_loss=0.08088, over 4959.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2607, pruned_loss=0.07092, over 972210.09 frames.], batch size: 24, lr: 1.20e-03
2022-05-03 18:36:21,306 INFO [train.py:715] (1/8) Epoch 0, batch 30850, loss[loss=0.2071, simple_loss=0.2619, pruned_loss=0.07616, over 4834.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2599, pruned_loss=0.07041, over 972201.06 frames.], batch size: 15, lr: 1.20e-03
2022-05-03 18:37:01,148 INFO [train.py:715] (1/8) Epoch 0, batch 30900, loss[loss=0.1835, simple_loss=0.2365, pruned_loss=0.0653, over 4820.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2602, pruned_loss=0.0706, over 972042.08 frames.], batch size: 12, lr: 1.20e-03
2022-05-03 18:37:40,862 INFO [train.py:715] (1/8) Epoch 0, batch 30950, loss[loss=0.1888, simple_loss=0.2475, pruned_loss=0.06502, over 4972.00 frames.], tot_loss[loss=0.202, simple_loss=0.2611, pruned_loss=0.07141, over 972502.18 frames.], batch size: 24, lr: 1.20e-03
2022-05-03 18:38:20,953 INFO [train.py:715] (1/8) Epoch 0, batch 31000, loss[loss=0.1485, simple_loss=0.2299, pruned_loss=0.03353, over 4939.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2619, pruned_loss=0.07177, over 972792.23 frames.], batch size: 23, lr: 1.20e-03
2022-05-03 18:39:00,966 INFO [train.py:715] (1/8) Epoch 0, batch 31050, loss[loss=0.1662, simple_loss=0.2248, pruned_loss=0.05383, over 4833.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2615, pruned_loss=0.07188, over 972902.51 frames.], batch size: 13, lr: 1.20e-03
2022-05-03 18:39:40,373 INFO [train.py:715] (1/8) Epoch 0, batch 31100, loss[loss=0.2159, simple_loss=0.2702, pruned_loss=0.08073, over 4815.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2613, pruned_loss=0.07177, over 972476.80 frames.], batch size: 27, lr: 1.20e-03
2022-05-03 18:40:19,540 INFO [train.py:715] (1/8) Epoch 0, batch 31150, loss[loss=0.1974, simple_loss=0.259, pruned_loss=0.06796, over 4814.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2608, pruned_loss=0.07205, over 971696.63 frames.], batch size: 25, lr: 1.19e-03
2022-05-03 18:40:59,614 INFO [train.py:715] (1/8) Epoch 0, batch 31200, loss[loss=0.1854, simple_loss=0.2458, pruned_loss=0.06246, over 4778.00 frames.], tot_loss[loss=0.203, simple_loss=0.2615, pruned_loss=0.07227, over 971831.85 frames.], batch size: 17, lr: 1.19e-03
2022-05-03 18:41:39,405 INFO [train.py:715] (1/8) Epoch 0, batch 31250, loss[loss=0.1705, simple_loss=0.2352, pruned_loss=0.05292, over 4977.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2619, pruned_loss=0.07251, over 971782.15 frames.], batch size: 15, lr: 1.19e-03
2022-05-03 18:42:18,883 INFO [train.py:715] (1/8) Epoch 0, batch 31300, loss[loss=0.1895, simple_loss=0.2457, pruned_loss=0.06665, over 4978.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2606, pruned_loss=0.07131, over 971768.11 frames.], batch size: 35, lr: 1.19e-03
2022-05-03 18:42:59,217 INFO [train.py:715] (1/8) Epoch 0, batch 31350, loss[loss=0.2384, simple_loss=0.2871, pruned_loss=0.0948, over 4852.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2607, pruned_loss=0.07142, over 972185.37 frames.], batch size: 32, lr: 1.19e-03
2022-05-03 18:43:38,895 INFO [train.py:715] (1/8) Epoch 0, batch 31400, loss[loss=0.1924, simple_loss=0.2532, pruned_loss=0.06578, over 4953.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2601, pruned_loss=0.07079, over 973284.27 frames.], batch size: 24, lr: 1.19e-03
2022-05-03 18:44:18,171 INFO [train.py:715] (1/8) Epoch 0, batch 31450, loss[loss=0.2082, simple_loss=0.2761, pruned_loss=0.0702, over 4890.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2598, pruned_loss=0.07085, over 972934.24 frames.], batch size: 17, lr: 1.19e-03
2022-05-03 18:44:57,274 INFO [train.py:715] (1/8) Epoch 0, batch 31500, loss[loss=0.212, simple_loss=0.2848, pruned_loss=0.06957, over 4835.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2591, pruned_loss=0.0704, over 972233.03 frames.], batch size: 15, lr: 1.19e-03
2022-05-03 18:45:37,323 INFO [train.py:715] (1/8) Epoch 0, batch 31550, loss[loss=0.2131, simple_loss=0.2648, pruned_loss=0.08065, over 4809.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2595, pruned_loss=0.07074, over 971966.02 frames.], batch size: 21, lr: 1.19e-03
2022-05-03 18:46:17,100 INFO [train.py:715] (1/8) Epoch 0, batch 31600, loss[loss=0.2021, simple_loss=0.2738, pruned_loss=0.06525, over 4927.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2602, pruned_loss=0.07103, over 972067.77 frames.], batch size: 29, lr: 1.19e-03
2022-05-03 18:46:56,334 INFO [train.py:715] (1/8) Epoch 0, batch 31650, loss[loss=0.1566, simple_loss=0.2248, pruned_loss=0.0442, over 4971.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2606, pruned_loss=0.07109, over 972484.75 frames.], batch size: 14, lr: 1.19e-03
2022-05-03 18:47:36,246 INFO [train.py:715] (1/8) Epoch 0, batch 31700, loss[loss=0.1729, simple_loss=0.2372, pruned_loss=0.0543, over 4835.00 frames.], tot_loss[loss=0.2008, simple_loss=0.26, pruned_loss=0.0708, over 972769.94 frames.], batch size: 13, lr: 1.18e-03
2022-05-03 18:48:16,471 INFO [train.py:715] (1/8) Epoch 0, batch 31750, loss[loss=0.19, simple_loss=0.2572, pruned_loss=0.06142, over 4804.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2613, pruned_loss=0.07144, over 973372.19 frames.], batch size: 21, lr: 1.18e-03
2022-05-03 18:48:56,202 INFO [train.py:715] (1/8) Epoch 0, batch 31800, loss[loss=0.1992, simple_loss=0.2624, pruned_loss=0.06798, over 4963.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2619, pruned_loss=0.07146, over 973325.85 frames.], batch size: 24, lr: 1.18e-03
2022-05-03 18:49:35,464 INFO [train.py:715] (1/8) Epoch 0, batch 31850, loss[loss=0.1778, simple_loss=0.2506, pruned_loss=0.05256, over 4838.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2618, pruned_loss=0.07126, over 972739.67 frames.], batch size: 26, lr: 1.18e-03
2022-05-03 18:50:15,965 INFO [train.py:715] (1/8) Epoch 0, batch 31900, loss[loss=0.206, simple_loss=0.2719, pruned_loss=0.07002, over 4897.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2612, pruned_loss=0.0707, over 972639.47 frames.], batch size: 19, lr: 1.18e-03
2022-05-03 18:50:55,672 INFO [train.py:715] (1/8) Epoch 0, batch 31950, loss[loss=0.1751, simple_loss=0.2363, pruned_loss=0.05694, over 4924.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2607, pruned_loss=0.07085, over 973101.99 frames.], batch size: 18, lr: 1.18e-03
2022-05-03 18:51:37,234 INFO [train.py:715] (1/8) Epoch 0, batch 32000, loss[loss=0.2123, simple_loss=0.2696, pruned_loss=0.07749, over 4770.00 frames.], tot_loss[loss=0.2021, simple_loss=0.261, pruned_loss=0.07163, over 972768.66 frames.], batch size: 14, lr: 1.18e-03
2022-05-03 18:52:17,385 INFO [train.py:715] (1/8) Epoch 0, batch 32050, loss[loss=0.2002, simple_loss=0.2482, pruned_loss=0.07609, over 4973.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2607, pruned_loss=0.07121, over 972569.83 frames.], batch size: 35, lr: 1.18e-03
2022-05-03 18:52:57,282 INFO [train.py:715] (1/8) Epoch 0, batch 32100, loss[loss=0.1951, simple_loss=0.2466, pruned_loss=0.07173, over 4945.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2612, pruned_loss=0.07166, over 972949.76 frames.], batch size: 39, lr: 1.18e-03
2022-05-03 18:53:36,626 INFO [train.py:715] (1/8) Epoch 0, batch 32150, loss[loss=0.1911, simple_loss=0.2528, pruned_loss=0.06466, over 4953.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2609, pruned_loss=0.07143, over 972667.52 frames.], batch size: 39, lr: 1.18e-03
2022-05-03 18:54:15,807 INFO [train.py:715] (1/8) Epoch 0, batch 32200, loss[loss=0.1608, simple_loss=0.2323, pruned_loss=0.04466, over 4958.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2604, pruned_loss=0.07065, over 972963.60 frames.], batch size: 24, lr: 1.18e-03
2022-05-03 18:54:55,962 INFO [train.py:715] (1/8) Epoch 0, batch 32250, loss[loss=0.1877, simple_loss=0.2566, pruned_loss=0.05935, over 4908.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2608, pruned_loss=0.07106, over 972732.33 frames.], batch size: 17, lr: 1.17e-03
2022-05-03 18:55:35,807 INFO [train.py:715] (1/8) Epoch 0, batch 32300, loss[loss=0.1489, simple_loss=0.2233, pruned_loss=0.03727, over 4890.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2609, pruned_loss=0.07104, over 972770.56 frames.], batch size: 19, lr: 1.17e-03
2022-05-03 18:56:15,320 INFO [train.py:715] (1/8) Epoch 0, batch 32350, loss[loss=0.2374, simple_loss=0.2862, pruned_loss=0.09429, over 4989.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2611, pruned_loss=0.07077, over 972597.37 frames.], batch size: 25, lr: 1.17e-03
2022-05-03 18:56:55,313 INFO [train.py:715] (1/8) Epoch 0, batch 32400, loss[loss=0.229, simple_loss=0.2831, pruned_loss=0.08747, over 4948.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2598, pruned_loss=0.06983, over 971547.05 frames.], batch size: 39, lr: 1.17e-03
2022-05-03 18:57:35,386 INFO [train.py:715] (1/8) Epoch 0, batch 32450, loss[loss=0.2034, simple_loss=0.2655, pruned_loss=0.0706, over 4829.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2597, pruned_loss=0.07011, over 970821.35 frames.], batch size: 26, lr: 1.17e-03
2022-05-03 18:58:15,183 INFO [train.py:715] (1/8) Epoch 0, batch 32500, loss[loss=0.1544, simple_loss=0.2245, pruned_loss=0.04211, over 4884.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2594, pruned_loss=0.07049, over 971404.65 frames.], batch size: 16, lr: 1.17e-03
2022-05-03 18:58:54,506 INFO [train.py:715] (1/8) Epoch 0, batch 32550, loss[loss=0.2298, simple_loss=0.2717, pruned_loss=0.09394, over 4950.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2605, pruned_loss=0.07122, over 971413.65 frames.], batch size: 39, lr: 1.17e-03
2022-05-03 18:59:34,019 INFO [train.py:715] (1/8) Epoch 0, batch 32600, loss[loss=0.249, simple_loss=0.3033, pruned_loss=0.09734, over 4777.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2616, pruned_loss=0.07156, over 971449.65 frames.], batch size: 18, lr: 1.17e-03
2022-05-03 19:00:13,281 INFO [train.py:715] (1/8) Epoch 0, batch 32650, loss[loss=0.1966, simple_loss=0.2673, pruned_loss=0.06292, over 4846.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2603, pruned_loss=0.07077, over 970858.89 frames.], batch size: 15, lr: 1.17e-03
2022-05-03 19:00:52,615 INFO [train.py:715] (1/8) Epoch 0, batch 32700, loss[loss=0.135, simple_loss=0.2052, pruned_loss=0.03236, over 4816.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2599, pruned_loss=0.07026, over 971605.28 frames.], batch size: 12, lr: 1.17e-03
2022-05-03 19:01:32,097 INFO [train.py:715] (1/8) Epoch 0, batch 32750, loss[loss=0.1619, simple_loss=0.2294, pruned_loss=0.04726, over 4848.00 frames.], tot_loss[loss=0.2002, simple_loss=0.26, pruned_loss=0.07026, over 972710.75 frames.], batch size: 13, lr: 1.17e-03
2022-05-03 19:02:12,127 INFO [train.py:715] (1/8) Epoch 0, batch 32800, loss[loss=0.2237, simple_loss=0.2837, pruned_loss=0.08187, over 4841.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2593, pruned_loss=0.06963, over 973058.88 frames.], batch size: 20, lr: 1.16e-03
2022-05-03 19:02:51,636 INFO [train.py:715] (1/8) Epoch 0, batch 32850, loss[loss=0.191, simple_loss=0.2606, pruned_loss=0.06071, over 4809.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2595, pruned_loss=0.07031, over 972946.47 frames.], batch size: 26, lr: 1.16e-03
2022-05-03 19:03:31,117 INFO [train.py:715] (1/8) Epoch 0, batch 32900, loss[loss=0.1826, simple_loss=0.2486, pruned_loss=0.05834, over 4752.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2586, pruned_loss=0.06977, over 972613.13 frames.], batch size: 19, lr: 1.16e-03
2022-05-03 19:04:11,180 INFO [train.py:715] (1/8) Epoch 0, batch 32950, loss[loss=0.1711, simple_loss=0.2306, pruned_loss=0.05579, over 4814.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2583, pruned_loss=0.06969, over 972926.47 frames.], batch size: 13, lr: 1.16e-03
2022-05-03 19:04:50,682 INFO [train.py:715] (1/8) Epoch 0, batch 33000, loss[loss=0.2104, simple_loss=0.2645, pruned_loss=0.07815, over 4812.00 frames.], tot_loss[loss=0.199, simple_loss=0.2583, pruned_loss=0.06987, over 973575.40 frames.], batch size: 15, lr: 1.16e-03
2022-05-03 19:04:50,682 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 19:05:00,797 INFO [train.py:742] (1/8) Epoch 0, validation: loss=0.1303, simple_loss=0.2174, pruned_loss=0.02158, over 914524.00 frames.
2022-05-03 19:05:40,739 INFO [train.py:715] (1/8) Epoch 0, batch 33050, loss[loss=0.1761, simple_loss=0.2421, pruned_loss=0.05506, over 4878.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2588, pruned_loss=0.06998, over 973207.02 frames.], batch size: 22, lr: 1.16e-03
2022-05-03 19:06:20,345 INFO [train.py:715] (1/8) Epoch 0, batch 33100, loss[loss=0.1772, simple_loss=0.2541, pruned_loss=0.05019, over 4762.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2592, pruned_loss=0.07023, over 973396.40 frames.], batch size: 18, lr: 1.16e-03
2022-05-03 19:07:01,014 INFO [train.py:715] (1/8) Epoch 0, batch 33150, loss[loss=0.2132, simple_loss=0.2686, pruned_loss=0.07892, over 4984.00 frames.], tot_loss[loss=0.2007, simple_loss=0.26, pruned_loss=0.07068, over 972907.62 frames.], batch size: 28, lr: 1.16e-03
2022-05-03 19:07:41,360 INFO [train.py:715] (1/8) Epoch 0, batch 33200, loss[loss=0.2045, simple_loss=0.2582, pruned_loss=0.07538, over 4687.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2596, pruned_loss=0.07047, over 972476.20 frames.], batch size: 15, lr: 1.16e-03
2022-05-03 19:08:21,595 INFO [train.py:715] (1/8) Epoch 0, batch 33250, loss[loss=0.2308, simple_loss=0.2829, pruned_loss=0.08934, over 4836.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2596, pruned_loss=0.0703, over 972325.54 frames.], batch size: 30, lr: 1.16e-03
2022-05-03 19:09:01,802 INFO [train.py:715] (1/8) Epoch 0, batch 33300, loss[loss=0.1853, simple_loss=0.2462, pruned_loss=0.0622, over 4831.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2593, pruned_loss=0.06984, over 972987.01 frames.], batch size: 15, lr: 1.16e-03
2022-05-03 19:09:42,525 INFO [train.py:715] (1/8) Epoch 0, batch 33350, loss[loss=0.2442, simple_loss=0.2856, pruned_loss=0.1014, over 4942.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2591, pruned_loss=0.0699, over 972938.46 frames.], batch size: 21, lr: 1.16e-03
2022-05-03 19:10:22,671 INFO [train.py:715] (1/8) Epoch 0, batch 33400, loss[loss=0.1907, simple_loss=0.2554, pruned_loss=0.063, over 4789.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2585, pruned_loss=0.06932, over 973096.51 frames.], batch size: 17, lr: 1.15e-03
2022-05-03 19:11:02,699 INFO [train.py:715] (1/8) Epoch 0, batch 33450, loss[loss=0.1546, simple_loss=0.2231, pruned_loss=0.04304, over 4982.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2585, pruned_loss=0.06942, over 972841.29 frames.], batch size: 28, lr: 1.15e-03
2022-05-03 19:11:43,358 INFO [train.py:715] (1/8) Epoch 0, batch 33500, loss[loss=0.2001, simple_loss=0.2586, pruned_loss=0.07074, over 4979.00 frames.], tot_loss[loss=0.2, simple_loss=0.2598, pruned_loss=0.07011, over 973128.82 frames.], batch size: 25, lr: 1.15e-03
2022-05-03 19:12:23,712 INFO [train.py:715] (1/8) Epoch 0, batch 33550, loss[loss=0.2017, simple_loss=0.2623, pruned_loss=0.07054, over 4928.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2588, pruned_loss=0.06924, over 973026.14 frames.], batch size: 23, lr: 1.15e-03
2022-05-03 19:13:02,895 INFO [train.py:715] (1/8) Epoch 0, batch 33600, loss[loss=0.1964, simple_loss=0.2506, pruned_loss=0.07115, over 4828.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2588, pruned_loss=0.06881, over 972828.17 frames.], batch size: 24, lr: 1.15e-03
2022-05-03 19:13:43,469 INFO [train.py:715] (1/8) Epoch 0, batch 33650, loss[loss=0.1741, simple_loss=0.2274, pruned_loss=0.06039, over 4783.00 frames.], tot_loss[loss=0.198, simple_loss=0.2583, pruned_loss=0.06878, over 972503.24 frames.], batch size: 14, lr: 1.15e-03
2022-05-03 19:14:23,805 INFO [train.py:715] (1/8) Epoch 0, batch 33700, loss[loss=0.2325, simple_loss=0.3001, pruned_loss=0.08241, over 4802.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2579, pruned_loss=0.06821, over 972155.67 frames.], batch size: 24, lr: 1.15e-03
2022-05-03 19:15:03,029 INFO [train.py:715] (1/8) Epoch 0, batch 33750, loss[loss=0.165, simple_loss=0.2334, pruned_loss=0.0483, over 4967.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2585, pruned_loss=0.06857, over 972132.69 frames.], batch size: 25, lr: 1.15e-03
2022-05-03 19:15:42,516 INFO [train.py:715] (1/8) Epoch 0, batch 33800, loss[loss=0.1652, simple_loss=0.2331, pruned_loss=0.04863, over 4838.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2586, pruned_loss=0.06861, over 971685.67 frames.], batch size: 15, lr: 1.15e-03
2022-05-03 19:16:22,769 INFO [train.py:715] (1/8) Epoch 0, batch 33850, loss[loss=0.1716, simple_loss=0.2443, pruned_loss=0.04943, over 4984.00 frames.], tot_loss[loss=0.198, simple_loss=0.2583, pruned_loss=0.0689, over 972968.53 frames.], batch size: 28, lr: 1.15e-03
2022-05-03 19:17:02,058 INFO [train.py:715] (1/8) Epoch 0, batch 33900, loss[loss=0.2034, simple_loss=0.2668, pruned_loss=0.07002, over 4967.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2587, pruned_loss=0.06889, over 973066.36 frames.], batch size: 24, lr: 1.15e-03
2022-05-03 19:17:41,115 INFO [train.py:715] (1/8) Epoch 0, batch 33950, loss[loss=0.2116, simple_loss=0.26, pruned_loss=0.08159, over 4779.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2595, pruned_loss=0.07001, over 972815.63 frames.], batch size: 14, lr: 1.15e-03
2022-05-03 19:18:21,086 INFO [train.py:715] (1/8) Epoch 0, batch 34000, loss[loss=0.2588, simple_loss=0.302, pruned_loss=0.1078, over 4984.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2583, pruned_loss=0.06915, over 973013.59 frames.], batch size: 31, lr: 1.14e-03
2022-05-03 19:19:00,963 INFO [train.py:715] (1/8) Epoch 0, batch 34050, loss[loss=0.2092, simple_loss=0.2763, pruned_loss=0.071, over 4920.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2581, pruned_loss=0.06856, over 973100.91 frames.], batch size: 18, lr: 1.14e-03
2022-05-03 19:19:40,629 INFO [train.py:715] (1/8) Epoch 0, batch 34100, loss[loss=0.1995, simple_loss=0.2673, pruned_loss=0.06591, over 4807.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2586, pruned_loss=0.0692, over 972171.27 frames.], batch size: 24, lr: 1.14e-03
2022-05-03 19:20:19,826 INFO [train.py:715] (1/8) Epoch 0, batch 34150, loss[loss=0.1581, simple_loss=0.2315, pruned_loss=0.04236, over 4946.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2599, pruned_loss=0.07021, over 972110.72 frames.], batch size: 29, lr: 1.14e-03
2022-05-03 19:20:59,749 INFO [train.py:715] (1/8) Epoch 0, batch 34200, loss[loss=0.2385, simple_loss=0.2884, pruned_loss=0.09424, over 4825.00 frames.], tot_loss[loss=0.2002, simple_loss=0.26, pruned_loss=0.07021, over 971893.96 frames.], batch size: 15, lr: 1.14e-03
2022-05-03 19:21:39,295 INFO [train.py:715] (1/8) Epoch 0, batch 34250, loss[loss=0.1802, simple_loss=0.2317, pruned_loss=0.06434, over 4760.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2598, pruned_loss=0.07031, over 971527.48 frames.], batch size: 12, lr: 1.14e-03
2022-05-03 19:22:18,600 INFO [train.py:715] (1/8) Epoch 0, batch 34300, loss[loss=0.1731, simple_loss=0.2392, pruned_loss=0.05349, over 4893.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2598, pruned_loss=0.07058, over 972041.45 frames.], batch size: 19, lr: 1.14e-03
2022-05-03 19:22:58,854 INFO [train.py:715] (1/8) Epoch 0, batch 34350, loss[loss=0.2188, simple_loss=0.2706, pruned_loss=0.08349, over 4968.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2591, pruned_loss=0.07002, over 971730.18 frames.], batch size: 24, lr: 1.14e-03
2022-05-03 19:23:39,058 INFO [train.py:715] (1/8) Epoch 0, batch 34400, loss[loss=0.2547, simple_loss=0.2936, pruned_loss=0.1079, over 4969.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2596, pruned_loss=0.07054, over 972215.96 frames.], batch size: 35, lr: 1.14e-03
2022-05-03 19:24:18,629 INFO [train.py:715] (1/8) Epoch 0, batch 34450, loss[loss=0.1768, simple_loss=0.2352, pruned_loss=0.05921, over 4912.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2607, pruned_loss=0.07097, over 972523.39 frames.], batch size: 23, lr: 1.14e-03
2022-05-03 19:24:57,901 INFO [train.py:715] (1/8) Epoch 0, batch 34500, loss[loss=0.182, simple_loss=0.241, pruned_loss=0.06155, over 4850.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2582, pruned_loss=0.06913, over 972872.06 frames.], batch size: 26, lr: 1.14e-03
2022-05-03 19:25:38,247 INFO [train.py:715] (1/8) Epoch 0, batch 34550, loss[loss=0.1805, simple_loss=0.251, pruned_loss=0.05498, over 4933.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2587, pruned_loss=0.06917, over 972526.56 frames.], batch size: 29, lr: 1.14e-03
2022-05-03 19:26:17,980 INFO [train.py:715] (1/8) Epoch 0, batch 34600, loss[loss=0.1887, simple_loss=0.2606, pruned_loss=0.05841, over 4778.00 frames.], tot_loss[loss=0.1992, simple_loss=0.259, pruned_loss=0.06974, over 972701.57 frames.], batch size: 18, lr: 1.13e-03
2022-05-03 19:26:57,210 INFO [train.py:715] (1/8) Epoch 0, batch 34650, loss[loss=0.2199, simple_loss=0.2762, pruned_loss=0.08182, over 4954.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2603, pruned_loss=0.07065, over 971909.95 frames.], batch size: 39, lr: 1.13e-03
2022-05-03 19:27:37,738 INFO [train.py:715] (1/8) Epoch 0, batch 34700, loss[loss=0.1889, simple_loss=0.2493, pruned_loss=0.06429, over 4840.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2603, pruned_loss=0.0712, over 971765.43 frames.], batch size: 30, lr: 1.13e-03
2022-05-03 19:28:15,917 INFO [train.py:715] (1/8) Epoch 0, batch 34750, loss[loss=0.1798, simple_loss=0.2438, pruned_loss=0.05797, over 4757.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2595, pruned_loss=0.07033, over 971393.55 frames.], batch size: 16, lr: 1.13e-03
2022-05-03 19:28:53,213 INFO [train.py:715] (1/8) Epoch 0, batch 34800, loss[loss=0.1709, simple_loss=0.243, pruned_loss=0.0494, over 4787.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2585, pruned_loss=0.06989, over 971404.70 frames.], batch size: 12, lr: 1.13e-03
2022-05-03 19:29:42,569 INFO [train.py:715] (1/8) Epoch 1, batch 0, loss[loss=0.2012, simple_loss=0.2579, pruned_loss=0.0723, over 4934.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2579, pruned_loss=0.0723, over 4934.00 frames.], batch size: 18, lr: 1.11e-03
2022-05-03 19:30:21,873 INFO [train.py:715] (1/8) Epoch 1, batch 50, loss[loss=0.223, simple_loss=0.2788, pruned_loss=0.08363, over 4833.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2588, pruned_loss=0.06929, over 219186.94 frames.], batch size: 15, lr: 1.11e-03
2022-05-03 19:31:01,842 INFO [train.py:715] (1/8) Epoch 1, batch 100, loss[loss=0.2051, simple_loss=0.2671, pruned_loss=0.07157, over 4905.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2544, pruned_loss=0.06659, over 386442.50 frames.], batch size: 17, lr: 1.11e-03
2022-05-03 19:31:41,278 INFO [train.py:715] (1/8) Epoch 1, batch 150, loss[loss=0.1858, simple_loss=0.2555, pruned_loss=0.05805, over 4767.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2546, pruned_loss=0.06715, over 515846.67 frames.], batch size: 17, lr: 1.11e-03
2022-05-03 19:32:20,517 INFO [train.py:715] (1/8) Epoch 1, batch 200, loss[loss=0.175, simple_loss=0.2399, pruned_loss=0.05507, over 4858.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2535, pruned_loss=0.06628, over 617711.93 frames.], batch size: 13, lr: 1.11e-03
2022-05-03 19:33:00,050 INFO [train.py:715] (1/8) Epoch 1, batch 250, loss[loss=0.1765, simple_loss=0.2378, pruned_loss=0.05761, over 4825.00 frames.], tot_loss[loss=0.196, simple_loss=0.2566, pruned_loss=0.06772, over 697148.78 frames.], batch size: 12, lr: 1.11e-03
2022-05-03 19:33:40,739 INFO [train.py:715] (1/8) Epoch 1, batch 300, loss[loss=0.1955, simple_loss=0.2621, pruned_loss=0.06441, over 4971.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2566, pruned_loss=0.06805, over 758094.18 frames.], batch size: 24, lr: 1.11e-03
2022-05-03 19:34:21,106 INFO [train.py:715] (1/8) Epoch 1, batch 350, loss[loss=0.136, simple_loss=0.2023, pruned_loss=0.03485, over 4806.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2561, pruned_loss=0.06733, over 805101.75 frames.], batch size: 12, lr: 1.11e-03
2022-05-03 19:35:01,377 INFO [train.py:715] (1/8) Epoch 1, batch 400, loss[loss=0.2007, simple_loss=0.265, pruned_loss=0.06822, over 4695.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2568, pruned_loss=0.06736, over 842699.59 frames.], batch size: 15, lr: 1.11e-03
2022-05-03 19:35:42,058 INFO [train.py:715] (1/8) Epoch 1, batch 450, loss[loss=0.2, simple_loss=0.2606, pruned_loss=0.06969, over 4789.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2559, pruned_loss=0.06685, over 871616.13 frames.], batch size: 12, lr: 1.11e-03
2022-05-03 19:36:22,763 INFO [train.py:715] (1/8) Epoch 1, batch 500, loss[loss=0.1734, simple_loss=0.2299, pruned_loss=0.05846, over 4872.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2568, pruned_loss=0.06788, over 893095.95 frames.], batch size: 32, lr: 1.11e-03
2022-05-03 19:37:03,285 INFO [train.py:715] (1/8) Epoch 1, batch 550, loss[loss=0.1888, simple_loss=0.2383, pruned_loss=0.06962, over 4749.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2581, pruned_loss=0.06876, over 909748.08 frames.], batch size: 16, lr: 1.11e-03
2022-05-03 19:37:43,266 INFO [train.py:715] (1/8) Epoch 1, batch 600, loss[loss=0.1907, simple_loss=0.2503, pruned_loss=0.06557, over 4695.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2586, pruned_loss=0.06943, over 923167.31 frames.], batch size: 15, lr: 1.10e-03
2022-05-03 19:38:23,972 INFO [train.py:715] (1/8) Epoch 1, batch 650, loss[loss=0.1791, simple_loss=0.244, pruned_loss=0.05706, over 4857.00 frames.], tot_loss[loss=0.1983, simple_loss=0.258, pruned_loss=0.06926, over 934600.00 frames.], batch size: 32, lr: 1.10e-03
2022-05-03 19:39:04,139 INFO [train.py:715] (1/8) Epoch 1, batch 700, loss[loss=0.1919, simple_loss=0.2427, pruned_loss=0.07052, over 4772.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2572, pruned_loss=0.06834, over 942281.64 frames.], batch size: 14, lr: 1.10e-03
2022-05-03 19:39:44,116 INFO [train.py:715] (1/8) Epoch 1, batch 750, loss[loss=0.1967, simple_loss=0.2627, pruned_loss=0.06538, over 4863.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2564, pruned_loss=0.06724, over 949715.99 frames.], batch size: 20, lr: 1.10e-03
2022-05-03 19:40:24,214 INFO [train.py:715] (1/8) Epoch 1, batch 800, loss[loss=0.1944, simple_loss=0.2527, pruned_loss=0.0681, over 4913.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2562, pruned_loss=0.06735, over 954557.19 frames.], batch size: 18, lr: 1.10e-03
2022-05-03 19:41:04,458 INFO [train.py:715] (1/8) Epoch 1, batch 850, loss[loss=0.166, simple_loss=0.2271, pruned_loss=0.05246, over 4985.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2556, pruned_loss=0.06739, over 958667.56 frames.], batch size: 14, lr: 1.10e-03
2022-05-03 19:41:43,686 INFO [train.py:715] (1/8) Epoch 1, batch 900, loss[loss=0.2002, simple_loss=0.2514, pruned_loss=0.07449, over 4855.00 frames.], tot_loss[loss=0.1959, simple_loss=0.256, pruned_loss=0.0679, over 961666.13 frames.], batch size: 32, lr: 1.10e-03
2022-05-03 19:42:22,970 INFO [train.py:715] (1/8) Epoch 1, batch 950, loss[loss=0.1757, simple_loss=0.2341, pruned_loss=0.05867, over 4841.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2577, pruned_loss=0.06892, over 964314.85 frames.], batch size: 15, lr: 1.10e-03
2022-05-03 19:43:02,560 INFO [train.py:715] (1/8) Epoch 1, batch 1000, loss[loss=0.168, simple_loss=0.2334, pruned_loss=0.05133, over 4844.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2576, pruned_loss=0.06882, over 966681.36 frames.], batch size: 15, lr: 1.10e-03
2022-05-03 19:43:41,900 INFO [train.py:715] (1/8) Epoch 1, batch 1050, loss[loss=0.1565, simple_loss=0.2187, pruned_loss=0.04719, over 4821.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2571, pruned_loss=0.06894, over 967626.76 frames.], batch size: 26, lr: 1.10e-03
2022-05-03 19:44:20,963 INFO [train.py:715] (1/8) Epoch 1, batch 1100, loss[loss=0.2157, simple_loss=0.275, pruned_loss=0.07825, over 4882.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2572, pruned_loss=0.06847, over 969447.74 frames.], batch size: 16, lr: 1.10e-03
2022-05-03 19:45:00,273 INFO [train.py:715] (1/8) Epoch 1, batch 1150, loss[loss=0.2028, simple_loss=0.2661, pruned_loss=0.06977, over 4962.00 frames.], tot_loss[loss=0.1955, simple_loss=0.256, pruned_loss=0.06747, over 970469.55 frames.], batch size: 15, lr: 1.10e-03
2022-05-03 19:45:40,269 INFO [train.py:715] (1/8) Epoch 1, batch 1200, loss[loss=0.2464, simple_loss=0.298, pruned_loss=0.09737, over 4853.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2555, pruned_loss=0.06741, over 971008.68 frames.], batch size: 32, lr: 1.10e-03
2022-05-03 19:46:19,420 INFO [train.py:715] (1/8) Epoch 1, batch 1250, loss[loss=0.1929, simple_loss=0.2494, pruned_loss=0.06822, over 4836.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2557, pruned_loss=0.06753, over 971980.15 frames.], batch size: 32, lr: 1.10e-03
2022-05-03 19:46:58,954 INFO [train.py:715] (1/8) Epoch 1, batch 1300, loss[loss=0.1898, simple_loss=0.2503, pruned_loss=0.06459, over 4898.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2561, pruned_loss=0.06726, over 972128.36 frames.], batch size: 22, lr: 1.09e-03
2022-05-03 19:47:39,266 INFO [train.py:715] (1/8) Epoch 1, batch 1350, loss[loss=0.2234, simple_loss=0.274, pruned_loss=0.08641, over 4943.00 frames.], tot_loss[loss=0.1953, simple_loss=0.256, pruned_loss=0.06736, over 972189.14 frames.], batch size: 35, lr: 1.09e-03
2022-05-03 19:48:18,891 INFO [train.py:715] (1/8) Epoch 1, batch 1400, loss[loss=0.2169, simple_loss=0.2777, pruned_loss=0.07803, over 4804.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2564, pruned_loss=0.06764, over 971428.56 frames.], batch size: 21, lr: 1.09e-03
2022-05-03 19:48:58,737 INFO [train.py:715] (1/8) Epoch 1, batch 1450, loss[loss=0.2223, simple_loss=0.28, pruned_loss=0.08234, over 4973.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2572, pruned_loss=0.0681, over 971861.73 frames.], batch size: 35, lr: 1.09e-03
2022-05-03 19:49:38,350 INFO [train.py:715] (1/8) Epoch 1, batch 1500, loss[loss=0.1969, simple_loss=0.2478, pruned_loss=0.07296, over 4979.00 frames.], tot_loss[loss=0.197, simple_loss=0.2573, pruned_loss=0.06834, over 973201.58 frames.], batch size: 35, lr: 1.09e-03
2022-05-03 19:50:17,872 INFO [train.py:715] (1/8) Epoch 1, batch 1550, loss[loss=0.1546, simple_loss=0.2175, pruned_loss=0.04582, over 4852.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2579, pruned_loss=0.06893, over 973036.04 frames.], batch size: 13, lr: 1.09e-03
2022-05-03 19:50:57,102 INFO [train.py:715] (1/8) Epoch 1, batch 1600, loss[loss=0.2104, simple_loss=0.2733, pruned_loss=0.0737, over 4795.00 frames.], tot_loss[loss=0.197, simple_loss=0.2575, pruned_loss=0.06825, over 974097.76 frames.], batch size: 18, lr: 1.09e-03
2022-05-03 19:51:36,399 INFO [train.py:715] (1/8) Epoch 1, batch 1650, loss[loss=0.1882, simple_loss=0.2531, pruned_loss=0.06168, over 4792.00 frames.], tot_loss[loss=0.1966, simple_loss=0.257, pruned_loss=0.06812, over 973171.29 frames.], batch size: 17, lr: 1.09e-03
2022-05-03 19:52:16,980 INFO [train.py:715] (1/8) Epoch 1, batch 1700, loss[loss=0.2303, simple_loss=0.2626, pruned_loss=0.09897, over 4845.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2575, pruned_loss=0.06854, over 974241.30 frames.], batch size: 12, lr: 1.09e-03
2022-05-03 19:52:56,157 INFO [train.py:715] (1/8) Epoch 1, batch 1750, loss[loss=0.1813, simple_loss=0.2373, pruned_loss=0.06265, over 4965.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2566, pruned_loss=0.06809, over 973708.42 frames.], batch size: 35, lr: 1.09e-03
2022-05-03 19:53:35,892 INFO [train.py:715] (1/8) Epoch 1, batch 1800, loss[loss=0.1909, simple_loss=0.2485, pruned_loss=0.06668, over 4757.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2563, pruned_loss=0.06748, over 973774.57 frames.], batch size: 12, lr: 1.09e-03
2022-05-03 19:54:15,256 INFO [train.py:715] (1/8) Epoch 1, batch 1850, loss[loss=0.2112, simple_loss=0.2596, pruned_loss=0.08137, over 4895.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2564, pruned_loss=0.06795, over 972741.25 frames.], batch size: 38, lr: 1.09e-03
2022-05-03 19:54:54,775 INFO [train.py:715] (1/8) Epoch 1, batch 1900, loss[loss=0.1765, simple_loss=0.2401, pruned_loss=0.05642, over 4915.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2556, pruned_loss=0.06775, over 972451.59 frames.], batch size: 29, lr: 1.09e-03
2022-05-03 19:55:34,084 INFO [train.py:715] (1/8) Epoch 1, batch 1950, loss[loss=0.205, simple_loss=0.2667, pruned_loss=0.07159, over 4810.00 frames.], tot_loss[loss=0.196, simple_loss=0.2562, pruned_loss=0.06788, over 971562.51 frames.], batch size: 25, lr: 1.08e-03
2022-05-03 19:56:14,072 INFO [train.py:715] (1/8) Epoch 1, batch 2000, loss[loss=0.1782, simple_loss=0.2489, pruned_loss=0.05372, over 4745.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2567, pruned_loss=0.0683, over 972077.85 frames.], batch size: 19, lr: 1.08e-03
2022-05-03 19:56:53,565 INFO [train.py:715] (1/8) Epoch 1, batch 2050, loss[loss=0.1726, simple_loss=0.2324, pruned_loss=0.0564, over 4786.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2563, pruned_loss=0.06788, over 972965.74 frames.], batch size: 17, lr: 1.08e-03
2022-05-03 19:57:33,038 INFO [train.py:715] (1/8) Epoch 1, batch 2100, loss[loss=0.2168, simple_loss=0.2741, pruned_loss=0.07969, over 4946.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2563, pruned_loss=0.06779, over 972187.11 frames.], batch size: 21, lr: 1.08e-03
2022-05-03 19:58:12,718 INFO [train.py:715] (1/8) Epoch 1, batch 2150, loss[loss=0.1804, simple_loss=0.2457, pruned_loss=0.0575, over 4843.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2568, pruned_loss=0.06814, over 972110.78 frames.], batch size: 15, lr: 1.08e-03
2022-05-03 19:58:52,400 INFO [train.py:715] (1/8) Epoch 1, batch 2200, loss[loss=0.1765, simple_loss=0.2341, pruned_loss=0.05947, over 4812.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2565, pruned_loss=0.06797, over 973185.56 frames.], batch size: 21, lr: 1.08e-03
2022-05-03 19:59:32,129 INFO [train.py:715] (1/8) Epoch 1, batch 2250, loss[loss=0.1877, simple_loss=0.2497, pruned_loss=0.06289, over 4969.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2577, pruned_loss=0.0689, over 973203.54 frames.], batch size: 24, lr: 1.08e-03
2022-05-03 20:00:11,171 INFO [train.py:715] (1/8) Epoch 1, batch 2300, loss[loss=0.2099, simple_loss=0.2752, pruned_loss=0.07232, over 4883.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2576, pruned_loss=0.06859, over 973265.88 frames.], batch size: 19, lr: 1.08e-03
2022-05-03 20:00:51,303 INFO [train.py:715] (1/8) Epoch 1, batch 2350, loss[loss=0.1767, simple_loss=0.2498, pruned_loss=0.05178, over 4877.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2568, pruned_loss=0.0681, over 972058.35 frames.], batch size: 16, lr: 1.08e-03
2022-05-03 20:01:30,584 INFO [train.py:715] (1/8) Epoch 1, batch 2400, loss[loss=0.1759, simple_loss=0.2408, pruned_loss=0.05548, over 4912.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2576, pruned_loss=0.0687, over 972378.42 frames.], batch size: 17, lr: 1.08e-03
2022-05-03 20:02:09,729 INFO [train.py:715] (1/8) Epoch 1, batch 2450, loss[loss=0.1997, simple_loss=0.2472, pruned_loss=0.0761, over 4862.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2573, pruned_loss=0.06797, over 972590.57 frames.], batch size: 32, lr: 1.08e-03
2022-05-03 20:02:48,977 INFO [train.py:715] (1/8) Epoch 1, batch 2500, loss[loss=0.1518, simple_loss=0.2105, pruned_loss=0.04654, over 4730.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2559, pruned_loss=0.06732, over 972597.68 frames.], batch size: 12, lr: 1.08e-03
2022-05-03 20:03:28,531 INFO [train.py:715] (1/8) Epoch 1, batch 2550, loss[loss=0.1861, simple_loss=0.2445, pruned_loss=0.06383, over 4945.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2568, pruned_loss=0.06793, over 972858.13 frames.], batch size: 21, lr: 1.08e-03
2022-05-03 20:04:08,262 INFO [train.py:715] (1/8) Epoch 1, batch 2600, loss[loss=0.262, simple_loss=0.2944, pruned_loss=0.1148, over 4699.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2578, pruned_loss=0.06845, over 972661.96 frames.], batch size: 15, lr: 1.08e-03
2022-05-03 20:04:47,472 INFO [train.py:715] (1/8) Epoch 1, batch 2650, loss[loss=0.212, simple_loss=0.2709, pruned_loss=0.07656, over 4969.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2574, pruned_loss=0.06809, over 972488.45 frames.], batch size: 24, lr: 1.07e-03
2022-05-03 20:05:27,540 INFO [train.py:715] (1/8) Epoch 1, batch 2700, loss[loss=0.1973, simple_loss=0.2451, pruned_loss=0.0748, over 4772.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2568, pruned_loss=0.06811, over 973586.65 frames.], batch size: 12, lr: 1.07e-03
2022-05-03 20:06:06,952 INFO [train.py:715] (1/8) Epoch 1, batch 2750, loss[loss=0.187, simple_loss=0.2419, pruned_loss=0.06601, over 4918.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2561, pruned_loss=0.06751, over 973525.40 frames.], batch size: 18, lr: 1.07e-03
2022-05-03 20:06:45,688 INFO [train.py:715] (1/8) Epoch 1, batch 2800, loss[loss=0.1744, simple_loss=0.235, pruned_loss=0.05693, over 4932.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2555, pruned_loss=0.06708, over 973129.10 frames.], batch size: 29, lr: 1.07e-03
2022-05-03 20:07:25,351 INFO [train.py:715] (1/8) Epoch 1, batch 2850, loss[loss=0.1821, simple_loss=0.2561, pruned_loss=0.05405, over 4974.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2557, pruned_loss=0.06731, over 973203.82 frames.], batch size: 15, lr: 1.07e-03
2022-05-03 20:08:05,006 INFO [train.py:715] (1/8) Epoch 1, batch 2900, loss[loss=0.2199, simple_loss=0.2637, pruned_loss=0.08807, over 4848.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2564, pruned_loss=0.06739, over 972552.22 frames.], batch size: 20, lr: 1.07e-03
2022-05-03 20:08:44,123 INFO [train.py:715] (1/8) Epoch 1, batch 2950, loss[loss=0.2329, simple_loss=0.2869, pruned_loss=0.08947, over 4960.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2568, pruned_loss=0.06779, over 972769.21 frames.], batch size: 24, lr: 1.07e-03
2022-05-03 20:09:22,832 INFO [train.py:715] (1/8) Epoch 1, batch 3000, loss[loss=0.2067, simple_loss=0.2756, pruned_loss=0.06884, over 4913.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2576, pruned_loss=0.06808, over 972764.39 frames.], batch size: 18, lr: 1.07e-03
2022-05-03 20:09:22,833 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 20:09:34,564 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1276, simple_loss=0.2149, pruned_loss=0.0201, over 914524.00 frames.
2022-05-03 20:10:13,439 INFO [train.py:715] (1/8) Epoch 1, batch 3050, loss[loss=0.2162, simple_loss=0.2877, pruned_loss=0.07232, over 4837.00 frames.], tot_loss[loss=0.196, simple_loss=0.2568, pruned_loss=0.06756, over 971912.00 frames.], batch size: 15, lr: 1.07e-03
2022-05-03 20:10:53,449 INFO [train.py:715] (1/8) Epoch 1, batch 3100, loss[loss=0.1815, simple_loss=0.2448, pruned_loss=0.05914, over 4814.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2569, pruned_loss=0.06716, over 972225.37 frames.], batch size: 21, lr: 1.07e-03
2022-05-03 20:11:32,597 INFO [train.py:715] (1/8) Epoch 1, batch 3150, loss[loss=0.249, simple_loss=0.3147, pruned_loss=0.09167, over 4979.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2574, pruned_loss=0.06787, over 973111.24 frames.], batch size: 15, lr: 1.07e-03
2022-05-03 20:12:11,817 INFO [train.py:715] (1/8) Epoch 1, batch 3200, loss[loss=0.2268, simple_loss=0.2923, pruned_loss=0.08061, over 4858.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2567, pruned_loss=0.06751, over 973229.73 frames.], batch size: 39, lr: 1.07e-03
2022-05-03 20:12:51,454 INFO [train.py:715] (1/8) Epoch 1, batch 3250, loss[loss=0.1534, simple_loss=0.219, pruned_loss=0.04389, over 4708.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2571, pruned_loss=0.068, over 973164.50 frames.], batch size: 15, lr: 1.07e-03
2022-05-03 20:13:31,211 INFO [train.py:715] (1/8) Epoch 1, batch 3300, loss[loss=0.1619, simple_loss=0.2387, pruned_loss=0.04253, over 4779.00 frames.], tot_loss[loss=0.196, simple_loss=0.2569, pruned_loss=0.06759, over 971995.79 frames.], batch size: 14, lr: 1.07e-03
2022-05-03 20:14:10,763 INFO [train.py:715] (1/8) Epoch 1, batch 3350, loss[loss=0.1868, simple_loss=0.2522, pruned_loss=0.06067, over 4811.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2569, pruned_loss=0.06747, over 972547.97 frames.], batch size: 27, lr: 1.07e-03
2022-05-03 20:14:50,046 INFO [train.py:715] (1/8) Epoch 1, batch 3400, loss[loss=0.2141, simple_loss=0.2684, pruned_loss=0.07989, over 4831.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2563, pruned_loss=0.06706, over 972095.94 frames.], batch size: 13, lr: 1.06e-03
2022-05-03 20:15:30,663 INFO [train.py:715] (1/8) Epoch 1, batch 3450, loss[loss=0.1887, simple_loss=0.2472, pruned_loss=0.06512, over 4899.00 frames.], tot_loss[loss=0.194, simple_loss=0.2554, pruned_loss=0.06632, over 972013.03 frames.], batch size: 32, lr: 1.06e-03
2022-05-03 20:16:09,587 INFO [train.py:715] (1/8) Epoch 1, batch 3500, loss[loss=0.1814, simple_loss=0.2398, pruned_loss=0.0615, over 4844.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2549, pruned_loss=0.06635, over 971128.74 frames.], batch size: 30, lr: 1.06e-03
2022-05-03 20:16:48,611 INFO [train.py:715] (1/8) Epoch 1, batch 3550, loss[loss=0.216, simple_loss=0.2712, pruned_loss=0.08035, over 4989.00 frames.], tot_loss[loss=0.193, simple_loss=0.2544, pruned_loss=0.06581, over 970993.87 frames.], batch size: 25, lr: 1.06e-03
2022-05-03 20:17:28,372 INFO [train.py:715] (1/8) Epoch 1, batch 3600, loss[loss=0.1821, simple_loss=0.2388, pruned_loss=0.06273, over 4959.00 frames.], tot_loss[loss=0.193, simple_loss=0.2539, pruned_loss=0.06606, over 970332.04 frames.], batch size: 35, lr: 1.06e-03
2022-05-03 20:18:08,019 INFO [train.py:715] (1/8) Epoch 1, batch 3650, loss[loss=0.2184, simple_loss=0.2717, pruned_loss=0.08248, over 4981.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2541, pruned_loss=0.06633, over 969844.08 frames.], batch size: 39, lr: 1.06e-03
2022-05-03 20:18:46,979 INFO [train.py:715] (1/8) Epoch 1, batch 3700, loss[loss=0.1651, simple_loss=0.2241, pruned_loss=0.05305, over 4841.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2531, pruned_loss=0.06594, over 970758.31 frames.], batch size: 13, lr: 1.06e-03
2022-05-03 20:19:25,659 INFO [train.py:715] (1/8) Epoch 1, batch 3750, loss[loss=0.2913, simple_loss=0.3325, pruned_loss=0.1251, over 4918.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2544, pruned_loss=0.06633, over 971754.30 frames.], batch size: 23, lr: 1.06e-03
2022-05-03 20:20:05,931 INFO [train.py:715] (1/8) Epoch 1, batch 3800, loss[loss=0.2014, simple_loss=0.2689, pruned_loss=0.06699, over 4950.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2552, pruned_loss=0.06646, over 973043.51 frames.], batch size: 23, lr: 1.06e-03
2022-05-03 20:20:44,901 INFO [train.py:715] (1/8) Epoch 1, batch 3850, loss[loss=0.1923, simple_loss=0.2605, pruned_loss=0.06206, over 4980.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2544, pruned_loss=0.06645, over 973944.17 frames.], batch size: 25, lr: 1.06e-03
2022-05-03 20:21:23,754 INFO [train.py:715] (1/8) Epoch 1, batch 3900, loss[loss=0.2302, simple_loss=0.2876, pruned_loss=0.08642, over 4663.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2544, pruned_loss=0.06618, over 972805.62 frames.], batch size: 14, lr: 1.06e-03
2022-05-03 20:22:03,278 INFO [train.py:715] (1/8) Epoch 1, batch 3950, loss[loss=0.1859, simple_loss=0.25, pruned_loss=0.06088, over 4947.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2537, pruned_loss=0.0662, over 973806.32 frames.], batch size: 21, lr: 1.06e-03
2022-05-03 20:22:42,793 INFO [train.py:715] (1/8) Epoch 1, batch 4000, loss[loss=0.1749, simple_loss=0.2393, pruned_loss=0.05526, over 4765.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2536, pruned_loss=0.06624, over 973040.82 frames.], batch size: 19, lr: 1.06e-03
2022-05-03 20:23:21,454 INFO [train.py:715] (1/8) Epoch 1, batch 4050, loss[loss=0.3244, simple_loss=0.3723, pruned_loss=0.1382, over 4781.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2553, pruned_loss=0.06696, over 972794.20 frames.], batch size: 14, lr: 1.06e-03
2022-05-03 20:24:00,887 INFO [train.py:715] (1/8) Epoch 1, batch 4100, loss[loss=0.1885, simple_loss=0.2508, pruned_loss=0.06306, over 4842.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2549, pruned_loss=0.06635, over 972139.55 frames.], batch size: 34, lr: 1.05e-03
2022-05-03 20:24:40,536 INFO [train.py:715] (1/8) Epoch 1, batch 4150, loss[loss=0.1666, simple_loss=0.235, pruned_loss=0.04911, over 4752.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2551, pruned_loss=0.0664, over 972238.43 frames.], batch size: 19, lr: 1.05e-03
2022-05-03 20:25:19,583 INFO [train.py:715] (1/8) Epoch 1, batch 4200, loss[loss=0.1975, simple_loss=0.264, pruned_loss=0.06557, over 4851.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2556, pruned_loss=0.06685, over 973351.95 frames.], batch size: 20, lr: 1.05e-03
2022-05-03 20:25:58,623 INFO [train.py:715] (1/8) Epoch 1, batch 4250, loss[loss=0.1921, simple_loss=0.2664, pruned_loss=0.05894, over 4916.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2557, pruned_loss=0.06641, over 973038.44 frames.], batch size: 18, lr: 1.05e-03
2022-05-03 20:26:38,139 INFO [train.py:715] (1/8) Epoch 1, batch 4300, loss[loss=0.1989, simple_loss=0.2648, pruned_loss=0.06653, over 4896.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2545, pruned_loss=0.0653, over 971480.54 frames.], batch size: 39, lr: 1.05e-03
2022-05-03 20:27:17,799 INFO [train.py:715] (1/8) Epoch 1, batch 4350, loss[loss=0.1517, simple_loss=0.22, pruned_loss=0.04169, over 4825.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2546, pruned_loss=0.0659, over 972068.07 frames.], batch size: 27, lr: 1.05e-03
2022-05-03 20:27:56,251 INFO [train.py:715] (1/8) Epoch 1, batch 4400, loss[loss=0.2366, simple_loss=0.2878, pruned_loss=0.09269, over 4853.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2543, pruned_loss=0.06603, over 972723.70 frames.], batch size: 30, lr: 1.05e-03
2022-05-03 20:28:35,843 INFO [train.py:715] (1/8) Epoch 1, batch 4450, loss[loss=0.1935, simple_loss=0.2511, pruned_loss=0.06797, over 4925.00 frames.], tot_loss[loss=0.193, simple_loss=0.2541, pruned_loss=0.06591, over 973260.22 frames.], batch size: 18, lr: 1.05e-03
2022-05-03 20:29:15,593 INFO [train.py:715] (1/8) Epoch 1, batch 4500, loss[loss=0.2111, simple_loss=0.253, pruned_loss=0.08457, over 4750.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2537, pruned_loss=0.06633, over 973285.13 frames.], batch size: 16, lr: 1.05e-03
2022-05-03 20:29:54,816 INFO [train.py:715] (1/8) Epoch 1, batch 4550, loss[loss=0.1485, simple_loss=0.2241, pruned_loss=0.0365, over 4811.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2528, pruned_loss=0.06545, over 972336.86 frames.], batch size: 12, lr: 1.05e-03
2022-05-03 20:30:33,519 INFO [train.py:715] (1/8) Epoch 1, batch 4600, loss[loss=0.2101, simple_loss=0.2647, pruned_loss=0.07774, over 4747.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2548, pruned_loss=0.06688, over 972691.40 frames.], batch size: 16, lr: 1.05e-03
2022-05-03 20:31:13,058 INFO [train.py:715] (1/8) Epoch 1, batch 4650, loss[loss=0.1917, simple_loss=0.2577, pruned_loss=0.0628, over 4867.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2561, pruned_loss=0.0675, over 972092.78 frames.], batch size: 16, lr: 1.05e-03
2022-05-03 20:31:52,499 INFO [train.py:715] (1/8) Epoch 1, batch 4700, loss[loss=0.1702, simple_loss=0.2447, pruned_loss=0.04784, over 4948.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2561, pruned_loss=0.06712, over 972786.52 frames.], batch size: 35, lr: 1.05e-03
2022-05-03 20:32:31,317 INFO [train.py:715] (1/8) Epoch 1, batch 4750, loss[loss=0.1606, simple_loss=0.2326, pruned_loss=0.04435, over 4788.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2554, pruned_loss=0.06686, over 973161.18 frames.], batch size: 14, lr: 1.05e-03
2022-05-03 20:33:11,338 INFO [train.py:715] (1/8) Epoch 1, batch 4800, loss[loss=0.1806, simple_loss=0.2419, pruned_loss=0.05961, over 4977.00 frames.], tot_loss[loss=0.194, simple_loss=0.2554, pruned_loss=0.06628, over 974028.35 frames.], batch size: 24, lr: 1.05e-03
2022-05-03 20:33:51,178 INFO [train.py:715] (1/8) Epoch 1, batch 4850, loss[loss=0.1897, simple_loss=0.2507, pruned_loss=0.06436, over 4939.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2546, pruned_loss=0.06589, over 974215.17 frames.], batch size: 35, lr: 1.05e-03
2022-05-03 20:34:30,466 INFO [train.py:715] (1/8) Epoch 1, batch 4900, loss[loss=0.2111, simple_loss=0.2648, pruned_loss=0.07865, over 4848.00 frames.], tot_loss[loss=0.193, simple_loss=0.2541, pruned_loss=0.06591, over 974593.86 frames.], batch size: 20, lr: 1.04e-03
2022-05-03 20:35:09,821 INFO [train.py:715] (1/8) Epoch 1, batch 4950, loss[loss=0.2436, simple_loss=0.2798, pruned_loss=0.1037, over 4820.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2544, pruned_loss=0.06611, over 974710.47 frames.], batch size: 27, lr: 1.04e-03
2022-05-03 20:35:50,162 INFO [train.py:715] (1/8) Epoch 1, batch 5000, loss[loss=0.2006, simple_loss=0.2678, pruned_loss=0.06666, over 4813.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2537, pruned_loss=0.06553, over 974413.87 frames.], batch size: 25, lr: 1.04e-03
2022-05-03 20:36:29,717 INFO [train.py:715] (1/8) Epoch 1, batch 5050, loss[loss=0.1873, simple_loss=0.2516, pruned_loss=0.06149, over 4743.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2542, pruned_loss=0.06564, over 973675.02 frames.], batch size: 16, lr: 1.04e-03
2022-05-03 20:37:08,715 INFO [train.py:715] (1/8) Epoch 1, batch 5100, loss[loss=0.1737, simple_loss=0.237, pruned_loss=0.05516, over 4835.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2553, pruned_loss=0.06627, over 974431.93 frames.], batch size: 13, lr: 1.04e-03
2022-05-03 20:37:48,744 INFO [train.py:715] (1/8) Epoch 1, batch 5150, loss[loss=0.1853, simple_loss=0.2568, pruned_loss=0.05694, over 4879.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2551, pruned_loss=0.06584, over 973882.78 frames.], batch size: 22, lr: 1.04e-03
2022-05-03 20:38:30,128 INFO [train.py:715] (1/8) Epoch 1, batch 5200, loss[loss=0.1941, simple_loss=0.2587, pruned_loss=0.06469, over 4887.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2548, pruned_loss=0.06551, over 973241.12 frames.], batch size: 19, lr: 1.04e-03
2022-05-03 20:39:09,104 INFO [train.py:715] (1/8) Epoch 1, batch 5250, loss[loss=0.1357, simple_loss=0.2046, pruned_loss=0.03343, over 4646.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2552, pruned_loss=0.06559, over 973518.17 frames.], batch size: 13, lr: 1.04e-03
2022-05-03 20:39:48,463 INFO [train.py:715] (1/8) Epoch 1, batch 5300, loss[loss=0.1506, simple_loss=0.2171, pruned_loss=0.04209, over 4959.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2536, pruned_loss=0.06488, over 973099.41 frames.], batch size: 21, lr: 1.04e-03
2022-05-03 20:40:28,102 INFO [train.py:715] (1/8) Epoch 1, batch 5350, loss[loss=0.2197, simple_loss=0.2786, pruned_loss=0.08035, over 4897.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2533, pruned_loss=0.06484, over 973842.25 frames.], batch size: 17, lr: 1.04e-03
2022-05-03 20:41:07,643 INFO [train.py:715] (1/8) Epoch 1, batch 5400, loss[loss=0.2201, simple_loss=0.2652, pruned_loss=0.08746, over 4842.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2526, pruned_loss=0.06478, over 972653.32 frames.], batch size: 32, lr: 1.04e-03
2022-05-03 20:41:46,693 INFO [train.py:715] (1/8) Epoch 1, batch 5450, loss[loss=0.2244, simple_loss=0.2753, pruned_loss=0.08671, over 4920.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2538, pruned_loss=0.06556, over 972597.54 frames.], batch size: 18, lr: 1.04e-03
2022-05-03 20:42:26,574 INFO [train.py:715] (1/8) Epoch 1, batch 5500, loss[loss=0.2088, simple_loss=0.2643, pruned_loss=0.07667, over 4889.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2548, pruned_loss=0.0663, over 973112.05 frames.], batch size: 19, lr: 1.04e-03
2022-05-03 20:43:06,474 INFO [train.py:715] (1/8) Epoch 1, batch 5550, loss[loss=0.1747, simple_loss=0.2366, pruned_loss=0.05638, over 4817.00 frames.], tot_loss[loss=0.194, simple_loss=0.2546, pruned_loss=0.06665, over 972773.21 frames.], batch size: 26, lr: 1.04e-03
2022-05-03 20:43:45,490 INFO [train.py:715] (1/8) Epoch 1, batch 5600, loss[loss=0.16, simple_loss=0.2272, pruned_loss=0.04638, over 4826.00 frames.], tot_loss[loss=0.194, simple_loss=0.2544, pruned_loss=0.06681, over 972766.56 frames.], batch size: 30, lr: 1.04e-03
2022-05-03 20:44:24,783 INFO [train.py:715] (1/8) Epoch 1, batch 5650, loss[loss=0.1937, simple_loss=0.2619, pruned_loss=0.06279, over 4915.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2532, pruned_loss=0.06619, over 972215.04 frames.], batch size: 23, lr: 1.03e-03
2022-05-03 20:45:04,548 INFO [train.py:715] (1/8) Epoch 1, batch 5700, loss[loss=0.2294, simple_loss=0.2832, pruned_loss=0.0878, over 4810.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2535, pruned_loss=0.06565, over 972115.20 frames.], batch size: 15, lr: 1.03e-03
2022-05-03 20:45:44,078 INFO [train.py:715] (1/8) Epoch 1, batch 5750, loss[loss=0.1789, simple_loss=0.2377, pruned_loss=0.06003, over 4969.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2532, pruned_loss=0.06532, over 972135.83 frames.], batch size: 35, lr: 1.03e-03
2022-05-03 20:46:23,089 INFO [train.py:715] (1/8) Epoch 1, batch 5800, loss[loss=0.1715, simple_loss=0.2293, pruned_loss=0.05681, over 4842.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2544, pruned_loss=0.06612, over 971964.16 frames.], batch size: 15, lr: 1.03e-03
2022-05-03 20:47:03,036 INFO [train.py:715] (1/8) Epoch 1, batch 5850, loss[loss=0.1749, simple_loss=0.2372, pruned_loss=0.0563, over 4957.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2544, pruned_loss=0.06645, over 971609.46 frames.], batch size: 21, lr: 1.03e-03
2022-05-03 20:47:42,850 INFO [train.py:715] (1/8) Epoch 1, batch 5900, loss[loss=0.2126, simple_loss=0.2626, pruned_loss=0.08131, over 4854.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2546, pruned_loss=0.06674, over 971806.64 frames.], batch size: 32, lr: 1.03e-03
2022-05-03 20:48:21,954 INFO [train.py:715] (1/8) Epoch 1, batch 5950, loss[loss=0.1555, simple_loss=0.2313, pruned_loss=0.0399, over 4785.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2541, pruned_loss=0.06655, over 973146.89 frames.], batch size: 23, lr: 1.03e-03
2022-05-03 20:49:01,781 INFO [train.py:715] (1/8) Epoch 1, batch 6000, loss[loss=0.1873, simple_loss=0.2518, pruned_loss=0.06146, over 4700.00 frames.], tot_loss[loss=0.1924, simple_loss=0.253, pruned_loss=0.06585, over 971585.17 frames.], batch size: 15, lr: 1.03e-03
2022-05-03 20:49:01,782 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 20:49:14,259 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1267, simple_loss=0.2135, pruned_loss=0.01993, over 914524.00 frames.
2022-05-03 20:49:53,678 INFO [train.py:715] (1/8) Epoch 1, batch 6050, loss[loss=0.1782, simple_loss=0.2354, pruned_loss=0.06057, over 4786.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2531, pruned_loss=0.06576, over 971710.56 frames.], batch size: 14, lr: 1.03e-03
2022-05-03 20:50:33,748 INFO [train.py:715] (1/8) Epoch 1, batch 6100, loss[loss=0.1848, simple_loss=0.2424, pruned_loss=0.06354, over 4749.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2536, pruned_loss=0.06601, over 971744.32 frames.], batch size: 12, lr: 1.03e-03
2022-05-03 20:51:13,274 INFO [train.py:715] (1/8) Epoch 1, batch 6150, loss[loss=0.1918, simple_loss=0.2481, pruned_loss=0.06772, over 4853.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2537, pruned_loss=0.06564, over 972228.51 frames.], batch size: 20, lr: 1.03e-03
2022-05-03 20:51:51,970 INFO [train.py:715] (1/8) Epoch 1, batch 6200, loss[loss=0.2197, simple_loss=0.2758, pruned_loss=0.08174, over 4909.00 frames.], tot_loss[loss=0.1926, simple_loss=0.254, pruned_loss=0.0656, over 972044.94 frames.], batch size: 23, lr: 1.03e-03
2022-05-03 20:52:32,162 INFO [train.py:715] (1/8) Epoch 1, batch 6250, loss[loss=0.1963, simple_loss=0.2433, pruned_loss=0.07462, over 4848.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2538, pruned_loss=0.06545, over 972265.48 frames.], batch size: 15, lr: 1.03e-03
2022-05-03 20:53:11,871 INFO [train.py:715] (1/8) Epoch 1, batch 6300, loss[loss=0.1749, simple_loss=0.2361, pruned_loss=0.05683, over 4857.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2543, pruned_loss=0.06621, over 972884.97 frames.], batch size: 30, lr: 1.03e-03
2022-05-03 20:53:51,074 INFO [train.py:715] (1/8) Epoch 1, batch 6350, loss[loss=0.1867, simple_loss=0.2504, pruned_loss=0.06154, over 4959.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2539, pruned_loss=0.0655, over 972739.45 frames.], batch size: 24, lr: 1.03e-03
2022-05-03 20:54:30,384 INFO [train.py:715] (1/8) Epoch 1, batch 6400, loss[loss=0.1825, simple_loss=0.2337, pruned_loss=0.06562, over 4855.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2539, pruned_loss=0.06536, over 972827.82 frames.], batch size: 30, lr: 1.03e-03
2022-05-03 20:55:09,940 INFO [train.py:715] (1/8) Epoch 1, batch 6450, loss[loss=0.1681, simple_loss=0.2408, pruned_loss=0.04775, over 4829.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2543, pruned_loss=0.06546, over 971658.99 frames.], batch size: 27, lr: 1.02e-03
2022-05-03 20:55:49,576 INFO [train.py:715] (1/8) Epoch 1, batch 6500, loss[loss=0.2026, simple_loss=0.263, pruned_loss=0.07108, over 4951.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2543, pruned_loss=0.06557, over 972122.75 frames.], batch size: 21, lr: 1.02e-03
2022-05-03 20:56:28,198 INFO [train.py:715] (1/8) Epoch 1, batch 6550, loss[loss=0.2137, simple_loss=0.261, pruned_loss=0.08324, over 4773.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2543, pruned_loss=0.06516, over 972306.68 frames.], batch size: 14, lr: 1.02e-03
2022-05-03 20:57:08,075 INFO [train.py:715] (1/8) Epoch 1, batch 6600, loss[loss=0.1768, simple_loss=0.236, pruned_loss=0.0588, over 4900.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2538, pruned_loss=0.06532, over 971728.19 frames.], batch size: 19, lr: 1.02e-03
2022-05-03 20:57:48,548 INFO [train.py:715] (1/8) Epoch 1, batch 6650, loss[loss=0.2468, simple_loss=0.2978, pruned_loss=0.09793, over 4823.00 frames.], tot_loss[loss=0.1922, simple_loss=0.254, pruned_loss=0.06522, over 970824.67 frames.], batch size: 27, lr: 1.02e-03
2022-05-03 20:58:28,001 INFO [train.py:715] (1/8) Epoch 1, batch 6700, loss[loss=0.2088, simple_loss=0.2675, pruned_loss=0.07505, over 4948.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2544, pruned_loss=0.06524, over 970878.76 frames.], batch size: 23, lr: 1.02e-03
2022-05-03 20:59:07,322 INFO [train.py:715] (1/8) Epoch 1, batch 6750, loss[loss=0.2269, simple_loss=0.2842, pruned_loss=0.08483, over 4810.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2546, pruned_loss=0.06563, over 971367.84 frames.], batch size: 21, lr: 1.02e-03
2022-05-03 20:59:47,252 INFO [train.py:715] (1/8) Epoch 1, batch 6800, loss[loss=0.2015, simple_loss=0.2689, pruned_loss=0.06701, over 4868.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2548, pruned_loss=0.06553, over 971129.35 frames.], batch size: 20, lr: 1.02e-03
2022-05-03 21:00:26,799 INFO [train.py:715] (1/8) Epoch 1, batch 6850, loss[loss=0.1716, simple_loss=0.2354, pruned_loss=0.05388, over 4949.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2547, pruned_loss=0.06521, over 972099.76 frames.], batch size: 21, lr: 1.02e-03
2022-05-03 21:01:05,422 INFO [train.py:715] (1/8) Epoch 1, batch 6900, loss[loss=0.1847, simple_loss=0.2444, pruned_loss=0.06255, over 4845.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2551, pruned_loss=0.06562, over 972736.20 frames.], batch size: 15, lr: 1.02e-03
2022-05-03 21:01:44,715 INFO [train.py:715] (1/8) Epoch 1, batch 6950, loss[loss=0.1898, simple_loss=0.2518, pruned_loss=0.06387, over 4879.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2542, pruned_loss=0.06497, over 972360.58 frames.], batch size: 16, lr: 1.02e-03
2022-05-03 21:02:24,794 INFO [train.py:715] (1/8) Epoch 1, batch 7000, loss[loss=0.2066, simple_loss=0.2645, pruned_loss=0.07439, over 4946.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2535, pruned_loss=0.0648, over 971729.04 frames.], batch size: 29, lr: 1.02e-03
2022-05-03 21:03:03,641 INFO [train.py:715] (1/8) Epoch 1, batch 7050, loss[loss=0.1988, simple_loss=0.2538, pruned_loss=0.07188, over 4820.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2537, pruned_loss=0.0647, over 971951.67 frames.], batch size: 15, lr: 1.02e-03
2022-05-03 21:03:42,607 INFO [train.py:715] (1/8) Epoch 1, batch 7100, loss[loss=0.158, simple_loss=0.2206, pruned_loss=0.04773, over 4832.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2546, pruned_loss=0.06545, over 971930.65 frames.], batch size: 12, lr: 1.02e-03
2022-05-03 21:04:22,596 INFO [train.py:715] (1/8) Epoch 1, batch 7150, loss[loss=0.1818, simple_loss=0.2445, pruned_loss=0.05959, over 4756.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2553, pruned_loss=0.06576, over 972664.79 frames.], batch size: 19, lr: 1.02e-03
2022-05-03 21:05:02,514 INFO [train.py:715] (1/8) Epoch 1, batch 7200, loss[loss=0.2209, simple_loss=0.2949, pruned_loss=0.0735, over 4974.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2545, pruned_loss=0.06529, over 972849.37 frames.], batch size: 24, lr: 1.02e-03
2022-05-03 21:05:41,153 INFO [train.py:715] (1/8) Epoch 1, batch 7250, loss[loss=0.1903, simple_loss=0.2517, pruned_loss=0.06449, over 4982.00 frames.], tot_loss[loss=0.194, simple_loss=0.2559, pruned_loss=0.06605, over 974128.05 frames.], batch size: 25, lr: 1.02e-03
2022-05-03 21:06:21,087 INFO [train.py:715] (1/8) Epoch 1, batch 7300, loss[loss=0.2126, simple_loss=0.269, pruned_loss=0.07811, over 4860.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2558, pruned_loss=0.06601, over 973049.80 frames.], batch size: 22, lr: 1.01e-03
2022-05-03 21:07:00,827 INFO [train.py:715] (1/8) Epoch 1, batch 7350, loss[loss=0.1878, simple_loss=0.2467, pruned_loss=0.06448, over 4941.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2555, pruned_loss=0.06596, over 972853.53 frames.], batch size: 21, lr: 1.01e-03
2022-05-03 21:07:39,614 INFO [train.py:715] (1/8) Epoch 1, batch 7400, loss[loss=0.178, simple_loss=0.2536, pruned_loss=0.05115, over 4745.00 frames.], tot_loss[loss=0.193, simple_loss=0.2548, pruned_loss=0.06558, over 973154.81 frames.], batch size: 16, lr: 1.01e-03
2022-05-03 21:08:18,526 INFO [train.py:715] (1/8) Epoch 1, batch 7450, loss[loss=0.2046, simple_loss=0.2726, pruned_loss=0.06832, over 4787.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2555, pruned_loss=0.06613, over 972877.82 frames.], batch size: 18, lr: 1.01e-03
2022-05-03 21:08:58,343 INFO [train.py:715] (1/8) Epoch 1, batch 7500, loss[loss=0.1742, simple_loss=0.2482, pruned_loss=0.05014, over 4833.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2561, pruned_loss=0.06681, over 972439.86 frames.], batch size: 26, lr: 1.01e-03
2022-05-03 21:09:38,018 INFO [train.py:715] (1/8) Epoch 1, batch 7550, loss[loss=0.2054, simple_loss=0.2754, pruned_loss=0.06767, over 4797.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2552, pruned_loss=0.06616, over 971660.39 frames.], batch size: 21, lr: 1.01e-03
2022-05-03 21:10:16,231 INFO [train.py:715] (1/8) Epoch 1, batch 7600, loss[loss=0.1586, simple_loss=0.2367, pruned_loss=0.04029, over 4807.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2554, pruned_loss=0.06622, over 971464.76 frames.], batch size: 15, lr: 1.01e-03
2022-05-03 21:10:55,969 INFO [train.py:715] (1/8) Epoch 1, batch 7650, loss[loss=0.1978, simple_loss=0.2536, pruned_loss=0.07098, over 4915.00 frames.], tot_loss[loss=0.195, simple_loss=0.2563, pruned_loss=0.06686, over 971795.63 frames.], batch size: 23, lr: 1.01e-03
2022-05-03 21:11:35,784 INFO [train.py:715] (1/8) Epoch 1, batch 7700, loss[loss=0.1677, simple_loss=0.2351, pruned_loss=0.05013, over 4892.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2547, pruned_loss=0.06597, over 971465.13 frames.], batch size: 19, lr: 1.01e-03
2022-05-03 21:12:14,132 INFO [train.py:715] (1/8) Epoch 1, batch 7750, loss[loss=0.197, simple_loss=0.2553, pruned_loss=0.06935, over 4772.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2533, pruned_loss=0.06471, over 971985.83 frames.], batch size: 17, lr: 1.01e-03
2022-05-03 21:12:53,244 INFO [train.py:715] (1/8) Epoch 1, batch 7800, loss[loss=0.1704, simple_loss=0.2315, pruned_loss=0.05467, over 4698.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2544, pruned_loss=0.06558, over 971716.07 frames.], batch size: 15, lr: 1.01e-03
2022-05-03 21:13:33,311 INFO [train.py:715] (1/8) Epoch 1, batch 7850, loss[loss=0.262, simple_loss=0.301, pruned_loss=0.1115, over 4975.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2542, pruned_loss=0.06571, over 971860.55 frames.], batch size: 14, lr: 1.01e-03
2022-05-03 21:14:12,710 INFO [train.py:715] (1/8) Epoch 1, batch 7900, loss[loss=0.1623, simple_loss=0.2328, pruned_loss=0.04588, over 4780.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2546, pruned_loss=0.06553, over 971721.64 frames.], batch size: 14, lr: 1.01e-03
2022-05-03 21:14:51,151 INFO [train.py:715] (1/8) Epoch 1, batch 7950, loss[loss=0.1695, simple_loss=0.2363, pruned_loss=0.05136, over 4900.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2536, pruned_loss=0.06486, over 971911.16 frames.], batch size: 17, lr: 1.01e-03
2022-05-03 21:15:31,255 INFO [train.py:715] (1/8) Epoch 1, batch 8000, loss[loss=0.1579, simple_loss=0.2202, pruned_loss=0.04786, over 4961.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2541, pruned_loss=0.06576, over 972131.94 frames.], batch size: 14, lr: 1.01e-03
2022-05-03 21:16:11,048 INFO [train.py:715] (1/8) Epoch 1, batch 8050, loss[loss=0.185, simple_loss=0.2416, pruned_loss=0.06421, over 4938.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2536, pruned_loss=0.0654, over 972759.31 frames.], batch size: 35, lr: 1.01e-03
2022-05-03 21:16:50,418 INFO [train.py:715] (1/8) Epoch 1, batch 8100, loss[loss=0.1881, simple_loss=0.248, pruned_loss=0.06407, over 4925.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2536, pruned_loss=0.06559, over 972408.34 frames.], batch size: 21, lr: 1.01e-03
2022-05-03 21:17:28,623 INFO [train.py:715] (1/8) Epoch 1, batch 8150, loss[loss=0.2245, simple_loss=0.2776, pruned_loss=0.08574, over 4877.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2538, pruned_loss=0.06537, over 971649.33 frames.], batch size: 39, lr: 1.00e-03
2022-05-03 21:18:08,541 INFO [train.py:715] (1/8) Epoch 1, batch 8200, loss[loss=0.1675, simple_loss=0.2347, pruned_loss=0.05022, over 4688.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2533, pruned_loss=0.06509, over 971028.42 frames.], batch size: 15, lr: 1.00e-03
2022-05-03 21:18:48,019 INFO [train.py:715] (1/8) Epoch 1, batch 8250, loss[loss=0.1975, simple_loss=0.2591, pruned_loss=0.06791, over 4780.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2539, pruned_loss=0.06513, over 972157.74 frames.], batch size: 14, lr: 1.00e-03
2022-05-03 21:19:26,200 INFO [train.py:715] (1/8) Epoch 1, batch 8300, loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.03808, over 4819.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2534, pruned_loss=0.06516, over 972241.93 frames.], batch size: 15, lr: 1.00e-03
2022-05-03 21:20:06,142 INFO [train.py:715] (1/8) Epoch 1, batch 8350, loss[loss=0.1999, simple_loss=0.2413, pruned_loss=0.07928, over 4728.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2527, pruned_loss=0.0647, over 971789.22 frames.], batch size: 12, lr: 1.00e-03
2022-05-03 21:20:45,727 INFO [train.py:715] (1/8) Epoch 1, batch 8400, loss[loss=0.1884, simple_loss=0.2501, pruned_loss=0.06334, over 4748.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2538, pruned_loss=0.06551, over 971742.52 frames.], batch size: 19, lr: 1.00e-03
2022-05-03 21:21:25,101 INFO [train.py:715] (1/8) Epoch 1, batch 8450, loss[loss=0.1701, simple_loss=0.2423, pruned_loss=0.04898, over 4824.00 frames.], tot_loss[loss=0.1917, simple_loss=0.253, pruned_loss=0.06517, over 971785.46 frames.], batch size: 13, lr: 1.00e-03
2022-05-03 21:22:03,495 INFO [train.py:715] (1/8) Epoch 1, batch 8500, loss[loss=0.1841, simple_loss=0.2459, pruned_loss=0.06117, over 4846.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2528, pruned_loss=0.06503, over 971798.13 frames.], batch size: 15, lr: 1.00e-03
2022-05-03 21:22:43,389 INFO [train.py:715] (1/8) Epoch 1, batch 8550, loss[loss=0.1755, simple_loss=0.2429, pruned_loss=0.054, over 4983.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2528, pruned_loss=0.06475, over 972091.90 frames.], batch size: 14, lr: 1.00e-03
2022-05-03 21:23:22,901 INFO [train.py:715] (1/8) Epoch 1, batch 8600, loss[loss=0.2219, simple_loss=0.2783, pruned_loss=0.08271, over 4831.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2506, pruned_loss=0.06361, over 972885.55 frames.], batch size: 15, lr: 1.00e-03
2022-05-03 21:24:00,902 INFO [train.py:715] (1/8) Epoch 1, batch 8650, loss[loss=0.1638, simple_loss=0.2327, pruned_loss=0.04748, over 4805.00 frames.], tot_loss[loss=0.1896, simple_loss=0.251, pruned_loss=0.06408, over 971998.35 frames.], batch size: 21, lr: 9.99e-04
2022-05-03 21:24:41,122 INFO [train.py:715] (1/8) Epoch 1, batch 8700, loss[loss=0.2146, simple_loss=0.2745, pruned_loss=0.07741, over 4907.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2507, pruned_loss=0.06412, over 971311.05 frames.], batch size: 19, lr: 9.98e-04
2022-05-03 21:25:21,111 INFO [train.py:715] (1/8) Epoch 1, batch 8750, loss[loss=0.1808, simple_loss=0.2407, pruned_loss=0.0604, over 4987.00 frames.], tot_loss[loss=0.191, simple_loss=0.2521, pruned_loss=0.06494, over 972126.74 frames.], batch size: 20, lr: 9.98e-04
2022-05-03 21:26:00,207 INFO [train.py:715] (1/8) Epoch 1, batch 8800, loss[loss=0.1848, simple_loss=0.2584, pruned_loss=0.0556, over 4773.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2517, pruned_loss=0.06443, over 971810.24 frames.], batch size: 14, lr: 9.97e-04
2022-05-03 21:26:39,528 INFO [train.py:715] (1/8) Epoch 1, batch 8850, loss[loss=0.18, simple_loss=0.2466, pruned_loss=0.05667, over 4853.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2533, pruned_loss=0.0654, over 972536.57 frames.], batch size: 20, lr: 9.97e-04
2022-05-03 21:27:19,649 INFO [train.py:715] (1/8) Epoch 1, batch 8900, loss[loss=0.1656, simple_loss=0.2317, pruned_loss=0.04972, over 4927.00 frames.], tot_loss[loss=0.193, simple_loss=0.2542, pruned_loss=0.06589, over 972519.14 frames.], batch size: 23, lr: 9.96e-04
2022-05-03 21:27:59,346 INFO [train.py:715] (1/8) Epoch 1, batch 8950, loss[loss=0.2103, simple_loss=0.274, pruned_loss=0.07328, over 4979.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2534, pruned_loss=0.06541, over 972231.88 frames.], batch size: 35, lr: 9.96e-04
2022-05-03 21:28:37,780 INFO [train.py:715] (1/8) Epoch 1, batch 9000, loss[loss=0.1832, simple_loss=0.241, pruned_loss=0.06272, over 4982.00 frames.], tot_loss[loss=0.1926, simple_loss=0.254, pruned_loss=0.06559, over 972166.23 frames.], batch size: 35, lr: 9.95e-04
2022-05-03 21:28:37,781 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 21:28:47,502 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1253, simple_loss=0.2125, pruned_loss=0.01906, over 914524.00 frames.
2022-05-03 21:29:25,993 INFO [train.py:715] (1/8) Epoch 1, batch 9050, loss[loss=0.1924, simple_loss=0.2603, pruned_loss=0.06228, over 4848.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2536, pruned_loss=0.0651, over 972594.84 frames.], batch size: 30, lr: 9.94e-04
2022-05-03 21:30:06,206 INFO [train.py:715] (1/8) Epoch 1, batch 9100, loss[loss=0.1823, simple_loss=0.2468, pruned_loss=0.05888, over 4928.00 frames.], tot_loss[loss=0.1926, simple_loss=0.254, pruned_loss=0.06559, over 972628.86 frames.], batch size: 23, lr: 9.94e-04
2022-05-03 21:30:45,843 INFO [train.py:715] (1/8) Epoch 1, batch 9150, loss[loss=0.2022, simple_loss=0.2622, pruned_loss=0.07113, over 4785.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2526, pruned_loss=0.06478, over 971704.51 frames.], batch size: 17, lr: 9.93e-04
2022-05-03 21:31:24,120 INFO [train.py:715] (1/8) Epoch 1, batch 9200, loss[loss=0.2037, simple_loss=0.2622, pruned_loss=0.07258, over 4794.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2532, pruned_loss=0.06493, over 971805.63 frames.], batch size: 24, lr: 9.93e-04
2022-05-03 21:32:03,941 INFO [train.py:715] (1/8) Epoch 1, batch 9250, loss[loss=0.2098, simple_loss=0.2687, pruned_loss=0.07551, over 4829.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2531, pruned_loss=0.06508, over 973559.31 frames.], batch size: 27, lr: 9.92e-04
2022-05-03 21:32:43,817 INFO [train.py:715] (1/8) Epoch 1, batch 9300, loss[loss=0.1479, simple_loss=0.1998, pruned_loss=0.04802, over 4916.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2524, pruned_loss=0.06447, over 973022.07 frames.], batch size: 18, lr: 9.92e-04
2022-05-03 21:33:22,872 INFO [train.py:715] (1/8) Epoch 1, batch 9350, loss[loss=0.1434, simple_loss=0.217, pruned_loss=0.03494, over 4785.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2526, pruned_loss=0.06461, over 973034.36 frames.], batch size: 17, lr: 9.91e-04
2022-05-03 21:34:02,355 INFO [train.py:715] (1/8) Epoch 1, batch 9400, loss[loss=0.1833, simple_loss=0.2309, pruned_loss=0.0678, over 4921.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2521, pruned_loss=0.06457, over 972647.04 frames.], batch size: 17, lr: 9.91e-04
2022-05-03 21:34:42,532 INFO [train.py:715] (1/8) Epoch 1, batch 9450, loss[loss=0.1585, simple_loss=0.2289, pruned_loss=0.044, over 4979.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2518, pruned_loss=0.0644, over 973440.91 frames.], batch size: 28, lr: 9.90e-04
2022-05-03 21:35:22,122 INFO [train.py:715] (1/8) Epoch 1, batch 9500, loss[loss=0.2094, simple_loss=0.2662, pruned_loss=0.07635, over 4952.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2513, pruned_loss=0.064, over 973375.08 frames.], batch size: 15, lr: 9.89e-04
2022-05-03 21:36:00,396 INFO [train.py:715] (1/8) Epoch 1, batch 9550, loss[loss=0.1529, simple_loss=0.221, pruned_loss=0.04242, over 4925.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2512, pruned_loss=0.06395, over 972706.48 frames.], batch size: 29, lr: 9.89e-04
2022-05-03 21:36:40,619 INFO [train.py:715] (1/8) Epoch 1, batch 9600, loss[loss=0.1985, simple_loss=0.247, pruned_loss=0.07498, over 4809.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2514, pruned_loss=0.06419, over 972885.13 frames.], batch size: 21, lr: 9.88e-04
2022-05-03 21:37:20,356 INFO [train.py:715] (1/8) Epoch 1, batch 9650, loss[loss=0.1882, simple_loss=0.2544, pruned_loss=0.06093, over 4900.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2523, pruned_loss=0.06477, over 972493.95 frames.], batch size: 17, lr: 9.88e-04
2022-05-03 21:37:58,744 INFO [train.py:715] (1/8) Epoch 1, batch 9700, loss[loss=0.248, simple_loss=0.2811, pruned_loss=0.1074, over 4871.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2521, pruned_loss=0.0647, over 973031.32 frames.], batch size: 32, lr: 9.87e-04
2022-05-03 21:38:38,641 INFO [train.py:715] (1/8) Epoch 1, batch 9750, loss[loss=0.1898, simple_loss=0.2585, pruned_loss=0.06056, over 4752.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2528, pruned_loss=0.06546, over 973687.66 frames.], batch size: 19, lr: 9.87e-04
2022-05-03 21:39:19,058 INFO [train.py:715] (1/8) Epoch 1, batch 9800, loss[loss=0.2553, simple_loss=0.3137, pruned_loss=0.0984, over 4973.00 frames.], tot_loss[loss=0.191, simple_loss=0.2526, pruned_loss=0.06466, over 973849.96 frames.], batch size: 15, lr: 9.86e-04
2022-05-03 21:39:58,296 INFO [train.py:715] (1/8) Epoch 1, batch 9850, loss[loss=0.1953, simple_loss=0.2621, pruned_loss=0.06427, over 4752.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2533, pruned_loss=0.06441, over 973558.10 frames.], batch size: 16, lr: 9.86e-04
2022-05-03 21:40:37,080 INFO [train.py:715] (1/8) Epoch 1, batch 9900, loss[loss=0.179, simple_loss=0.249, pruned_loss=0.05451, over 4914.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2526, pruned_loss=0.06394, over 973524.81 frames.], batch size: 39, lr: 9.85e-04
2022-05-03 21:41:17,360 INFO [train.py:715] (1/8) Epoch 1, batch 9950, loss[loss=0.1648, simple_loss=0.2342, pruned_loss=0.04769, over 4979.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2525, pruned_loss=0.0636, over 973488.71 frames.], batch size: 25, lr: 9.85e-04
2022-05-03 21:41:57,263 INFO [train.py:715] (1/8) Epoch 1, batch 10000, loss[loss=0.1775, simple_loss=0.2493, pruned_loss=0.05289, over 4867.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2532, pruned_loss=0.06375, over 974069.46 frames.], batch size: 22, lr: 9.84e-04
2022-05-03 21:42:36,322 INFO [train.py:715] (1/8) Epoch 1, batch 10050, loss[loss=0.2051, simple_loss=0.2573, pruned_loss=0.07646, over 4830.00 frames.], tot_loss[loss=0.19, simple_loss=0.2527, pruned_loss=0.06359, over 973319.44 frames.], batch size: 13, lr: 9.83e-04
2022-05-03 21:43:15,948 INFO [train.py:715] (1/8) Epoch 1, batch 10100, loss[loss=0.162, simple_loss=0.2374, pruned_loss=0.0433, over 4804.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2524, pruned_loss=0.06353, over 972787.34 frames.], batch size: 25, lr: 9.83e-04
2022-05-03 21:43:55,969 INFO [train.py:715] (1/8) Epoch 1, batch 10150, loss[loss=0.1845, simple_loss=0.2549, pruned_loss=0.0571, over 4981.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2528, pruned_loss=0.06412, over 973589.37 frames.], batch size: 28, lr: 9.82e-04
2022-05-03 21:44:35,075 INFO [train.py:715] (1/8) Epoch 1, batch 10200, loss[loss=0.1805, simple_loss=0.2381, pruned_loss=0.06152, over 4826.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2527, pruned_loss=0.06409, over 972487.23 frames.], batch size: 12, lr: 9.82e-04
2022-05-03 21:45:14,036 INFO [train.py:715] (1/8) Epoch 1, batch 10250, loss[loss=0.1805, simple_loss=0.2458, pruned_loss=0.05756, over 4978.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2529, pruned_loss=0.06386, over 973329.61 frames.], batch size: 35, lr: 9.81e-04
2022-05-03 21:45:54,203 INFO [train.py:715] (1/8) Epoch 1, batch 10300, loss[loss=0.2011, simple_loss=0.2652, pruned_loss=0.06852, over 4906.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2535, pruned_loss=0.06437, over 973176.03 frames.], batch size: 18, lr: 9.81e-04
2022-05-03 21:46:34,447 INFO [train.py:715] (1/8) Epoch 1, batch 10350, loss[loss=0.1627, simple_loss=0.2369, pruned_loss=0.04427, over 4876.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2539, pruned_loss=0.06428, over 973445.83 frames.], batch size: 22, lr: 9.80e-04
2022-05-03 21:47:13,903 INFO [train.py:715] (1/8) Epoch 1, batch 10400, loss[loss=0.1638, simple_loss=0.2354, pruned_loss=0.04613, over 4940.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2528, pruned_loss=0.06372, over 973379.79 frames.], batch size: 29, lr: 9.80e-04
2022-05-03 21:47:53,939 INFO [train.py:715] (1/8) Epoch 1, batch 10450, loss[loss=0.1897, simple_loss=0.2599, pruned_loss=0.05978, over 4863.00 frames.], tot_loss[loss=0.1904, simple_loss=0.253, pruned_loss=0.06391, over 972983.97 frames.], batch size: 20, lr: 9.79e-04
2022-05-03 21:48:34,474 INFO [train.py:715] (1/8) Epoch 1, batch 10500, loss[loss=0.1503, simple_loss=0.2077, pruned_loss=0.04645, over 4783.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2513, pruned_loss=0.06304, over 972828.72 frames.], batch size: 17, lr: 9.79e-04
2022-05-03 21:49:13,759 INFO [train.py:715] (1/8) Epoch 1, batch 10550, loss[loss=0.2123, simple_loss=0.2623, pruned_loss=0.08114, over 4845.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2505, pruned_loss=0.06236, over 973827.94 frames.], batch size: 32, lr: 9.78e-04
2022-05-03 21:49:52,636 INFO [train.py:715] (1/8) Epoch 1, batch 10600, loss[loss=0.2113, simple_loss=0.2712, pruned_loss=0.07573, over 4862.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2509, pruned_loss=0.06234, over 973819.46 frames.], batch size: 39, lr: 9.78e-04
2022-05-03 21:50:33,175 INFO [train.py:715] (1/8) Epoch 1, batch 10650, loss[loss=0.2014, simple_loss=0.2656, pruned_loss=0.0686, over 4826.00 frames.], tot_loss[loss=0.1871, simple_loss=0.25, pruned_loss=0.06212, over 973230.18 frames.], batch size: 25, lr: 9.77e-04
2022-05-03 21:51:13,724 INFO [train.py:715] (1/8) Epoch 1, batch 10700, loss[loss=0.2224, simple_loss=0.2869, pruned_loss=0.07898, over 4758.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2514, pruned_loss=0.06272, over 973148.93 frames.], batch size: 19, lr: 9.76e-04
2022-05-03 21:51:52,990 INFO [train.py:715] (1/8) Epoch 1, batch 10750, loss[loss=0.1841, simple_loss=0.2365, pruned_loss=0.06584, over 4911.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2528, pruned_loss=0.06396, over 972907.23 frames.], batch size: 17, lr: 9.76e-04
2022-05-03 21:52:32,271 INFO [train.py:715] (1/8) Epoch 1, batch 10800, loss[loss=0.1772, simple_loss=0.2439, pruned_loss=0.05518, over 4787.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2524, pruned_loss=0.06362, over 972474.20 frames.], batch size: 14, lr: 9.75e-04
2022-05-03 21:53:12,728 INFO [train.py:715] (1/8) Epoch 1, batch 10850, loss[loss=0.1565, simple_loss=0.2152, pruned_loss=0.04893, over 4839.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2523, pruned_loss=0.06344, over 972181.43 frames.], batch size: 30, lr: 9.75e-04
2022-05-03 21:53:52,217 INFO [train.py:715] (1/8) Epoch 1, batch 10900, loss[loss=0.1752, simple_loss=0.2385, pruned_loss=0.0559, over 4958.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2529, pruned_loss=0.06399, over 972940.37 frames.], batch size: 24, lr: 9.74e-04
2022-05-03 21:54:30,705 INFO [train.py:715] (1/8) Epoch 1, batch 10950, loss[loss=0.1625, simple_loss=0.2285, pruned_loss=0.04821, over 4773.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2531, pruned_loss=0.06441, over 973137.72 frames.], batch size: 19, lr: 9.74e-04
2022-05-03 21:55:10,752 INFO [train.py:715] (1/8) Epoch 1, batch 11000, loss[loss=0.169, simple_loss=0.2313, pruned_loss=0.05331, over 4948.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2526, pruned_loss=0.06376, over 972651.05 frames.], batch size: 21, lr: 9.73e-04
2022-05-03 21:55:50,512 INFO [train.py:715] (1/8) Epoch 1, batch 11050, loss[loss=0.1699, simple_loss=0.234, pruned_loss=0.05289, over 4926.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2528, pruned_loss=0.06398, over 972796.20 frames.], batch size: 21, lr: 9.73e-04
2022-05-03 21:56:29,266 INFO [train.py:715] (1/8) Epoch 1, batch 11100, loss[loss=0.2086, simple_loss=0.2714, pruned_loss=0.0729, over 4899.00 frames.], tot_loss[loss=0.19, simple_loss=0.2527, pruned_loss=0.06371, over 972370.32 frames.], batch size: 17, lr: 9.72e-04
2022-05-03 21:57:08,671 INFO [train.py:715] (1/8) Epoch 1, batch 11150, loss[loss=0.1852, simple_loss=0.2473, pruned_loss=0.06152, over 4899.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2523, pruned_loss=0.06325, over 973273.00 frames.], batch size: 22, lr: 9.72e-04
2022-05-03 21:57:48,800 INFO [train.py:715] (1/8) Epoch 1, batch 11200, loss[loss=0.2085, simple_loss=0.2618, pruned_loss=0.07761, over 4850.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2521, pruned_loss=0.06321, over 972777.44 frames.], batch size: 30, lr: 9.71e-04
2022-05-03 21:58:28,393 INFO [train.py:715] (1/8) Epoch 1, batch 11250, loss[loss=0.1829, simple_loss=0.2479, pruned_loss=0.059, over 4939.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2524, pruned_loss=0.06408, over 971809.79 frames.], batch size: 21, lr: 9.71e-04
2022-05-03 21:59:06,583 INFO [train.py:715] (1/8) Epoch 1, batch 11300, loss[loss=0.1762, simple_loss=0.2435, pruned_loss=0.05443, over 4907.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2512, pruned_loss=0.06334, over 972328.87 frames.], batch size: 19, lr: 9.70e-04
2022-05-03 21:59:46,981 INFO [train.py:715] (1/8) Epoch 1, batch 11350, loss[loss=0.1872, simple_loss=0.2572, pruned_loss=0.05861, over 4770.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2524, pruned_loss=0.06401, over 972085.76 frames.], batch size: 19, lr: 9.70e-04
2022-05-03 22:00:26,696 INFO [train.py:715] (1/8) Epoch 1, batch 11400, loss[loss=0.1793, simple_loss=0.2544, pruned_loss=0.05216, over 4810.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2531, pruned_loss=0.06411, over 971936.24 frames.], batch size: 26, lr: 9.69e-04
2022-05-03 22:01:04,853 INFO [train.py:715] (1/8) Epoch 1, batch 11450, loss[loss=0.1972, simple_loss=0.2518, pruned_loss=0.07135, over 4851.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2522, pruned_loss=0.06336, over 972091.43 frames.], batch size: 30, lr: 9.69e-04
2022-05-03 22:01:44,067 INFO [train.py:715] (1/8) Epoch 1, batch 11500, loss[loss=0.2327, simple_loss=0.2771, pruned_loss=0.09417, over 4835.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2518, pruned_loss=0.06361, over 972786.14 frames.], batch size: 15, lr: 9.68e-04
2022-05-03 22:02:23,953 INFO [train.py:715] (1/8) Epoch 1, batch 11550, loss[loss=0.1514, simple_loss=0.2079, pruned_loss=0.04748, over 4830.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2519, pruned_loss=0.06366, over 973257.07 frames.], batch size: 12, lr: 9.68e-04
2022-05-03 22:03:03,167 INFO [train.py:715] (1/8) Epoch 1, batch 11600, loss[loss=0.1544, simple_loss=0.211, pruned_loss=0.04888, over 4751.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2528, pruned_loss=0.06397, over 973866.59 frames.], batch size: 16, lr: 9.67e-04
2022-05-03 22:03:41,493 INFO [train.py:715] (1/8) Epoch 1, batch 11650, loss[loss=0.1557, simple_loss=0.2285, pruned_loss=0.04148, over 4923.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2523, pruned_loss=0.06375, over 972729.42 frames.], batch size: 18, lr: 9.67e-04
2022-05-03 22:04:21,432 INFO [train.py:715] (1/8) Epoch 1, batch 11700, loss[loss=0.2334, simple_loss=0.2803, pruned_loss=0.09322, over 4749.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2515, pruned_loss=0.06363, over 972687.47 frames.], batch size: 16, lr: 9.66e-04
2022-05-03 22:05:01,248 INFO [train.py:715] (1/8) Epoch 1, batch 11750, loss[loss=0.1719, simple_loss=0.2487, pruned_loss=0.04753, over 4764.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2524, pruned_loss=0.06394, over 972204.95 frames.], batch size: 18, lr: 9.66e-04
2022-05-03 22:05:40,547 INFO [train.py:715] (1/8) Epoch 1, batch 11800, loss[loss=0.156, simple_loss=0.2166, pruned_loss=0.04772, over 4826.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2518, pruned_loss=0.06361, over 972714.19 frames.], batch size: 15, lr: 9.65e-04
2022-05-03 22:06:19,254 INFO [train.py:715] (1/8) Epoch 1, batch 11850, loss[loss=0.1411, simple_loss=0.2139, pruned_loss=0.03412, over 4940.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2506, pruned_loss=0.06287, over 972900.99 frames.], batch size: 21, lr: 9.65e-04
2022-05-03 22:06:59,287 INFO [train.py:715] (1/8) Epoch 1, batch 11900, loss[loss=0.1687, simple_loss=0.2396, pruned_loss=0.0489, over 4830.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2504, pruned_loss=0.0629, over 973231.46 frames.], batch size: 30, lr: 9.64e-04
2022-05-03 22:07:38,635 INFO [train.py:715] (1/8) Epoch 1, batch 11950, loss[loss=0.1887, simple_loss=0.2513, pruned_loss=0.06302, over 4801.00 frames.], tot_loss[loss=0.1878, simple_loss=0.25, pruned_loss=0.06281, over 972769.75 frames.], batch size: 21, lr: 9.63e-04
2022-05-03 22:08:17,117 INFO [train.py:715] (1/8) Epoch 1, batch 12000, loss[loss=0.2268, simple_loss=0.2821, pruned_loss=0.08578, over 4953.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2495, pruned_loss=0.06292, over 972718.39 frames.], batch size: 24, lr: 9.63e-04
2022-05-03 22:08:17,118 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 22:08:27,632 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1244, simple_loss=0.2116, pruned_loss=0.01858, over 914524.00 frames.
2022-05-03 22:09:06,364 INFO [train.py:715] (1/8) Epoch 1, batch 12050, loss[loss=0.1971, simple_loss=0.262, pruned_loss=0.06616, over 4776.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2501, pruned_loss=0.06331, over 973166.40 frames.], batch size: 18, lr: 9.62e-04
2022-05-03 22:09:46,985 INFO [train.py:715] (1/8) Epoch 1, batch 12100, loss[loss=0.191, simple_loss=0.255, pruned_loss=0.06344, over 4850.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2497, pruned_loss=0.06288, over 972445.20 frames.], batch size: 20, lr: 9.62e-04
2022-05-03 22:10:27,670 INFO [train.py:715] (1/8) Epoch 1, batch 12150, loss[loss=0.1829, simple_loss=0.2439, pruned_loss=0.06093, over 4908.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2499, pruned_loss=0.0627, over 972104.46 frames.], batch size: 17, lr: 9.61e-04
2022-05-03 22:11:06,639 INFO [train.py:715] (1/8) Epoch 1, batch 12200, loss[loss=0.2237, simple_loss=0.2806, pruned_loss=0.0834, over 4976.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2501, pruned_loss=0.063, over 972472.68 frames.], batch size: 39, lr: 9.61e-04
2022-05-03 22:11:46,543 INFO [train.py:715] (1/8) Epoch 1, batch 12250, loss[loss=0.232, simple_loss=0.2899, pruned_loss=0.08707, over 4825.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2508, pruned_loss=0.06252, over 972659.61 frames.], batch size: 15, lr: 9.60e-04
2022-05-03 22:12:27,152 INFO [train.py:715] (1/8) Epoch 1, batch 12300, loss[loss=0.1594, simple_loss=0.2381, pruned_loss=0.04035, over 4981.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2513, pruned_loss=0.06327, over 973115.30 frames.], batch size: 28, lr: 9.60e-04
2022-05-03 22:13:06,772 INFO [train.py:715] (1/8) Epoch 1, batch 12350, loss[loss=0.216, simple_loss=0.2741, pruned_loss=0.07895, over 4965.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2512, pruned_loss=0.06291, over 972755.14 frames.], batch size: 14, lr: 9.59e-04
2022-05-03 22:13:45,541 INFO [train.py:715] (1/8) Epoch 1, batch 12400, loss[loss=0.2329, simple_loss=0.301, pruned_loss=0.08243, over 4771.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2511, pruned_loss=0.06282, over 972128.65 frames.], batch size: 17, lr: 9.59e-04
2022-05-03 22:14:25,685 INFO [train.py:715] (1/8) Epoch 1, batch 12450, loss[loss=0.1984, simple_loss=0.2571, pruned_loss=0.06987, over 4985.00 frames.], tot_loss[loss=0.1881, simple_loss=0.251, pruned_loss=0.06262, over 972869.89 frames.], batch size: 31, lr: 9.58e-04
2022-05-03 22:15:05,668 INFO [train.py:715] (1/8) Epoch 1, batch 12500, loss[loss=0.1784, simple_loss=0.2458, pruned_loss=0.05548, over 4801.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2514, pruned_loss=0.06305, over 972657.20 frames.], batch size: 17, lr: 9.58e-04
2022-05-03 22:15:44,872 INFO [train.py:715] (1/8) Epoch 1, batch 12550, loss[loss=0.2017, simple_loss=0.2677, pruned_loss=0.06779, over 4952.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2517, pruned_loss=0.06357, over 972488.74 frames.], batch size: 15, lr: 9.57e-04
2022-05-03 22:16:24,271 INFO [train.py:715] (1/8) Epoch 1, batch 12600, loss[loss=0.1563, simple_loss=0.2362, pruned_loss=0.0382, over 4797.00 frames.], tot_loss[loss=0.1895, simple_loss=0.252, pruned_loss=0.06346, over 973320.20 frames.], batch size: 24, lr: 9.57e-04
2022-05-03 22:17:04,545 INFO [train.py:715] (1/8) Epoch 1, batch 12650, loss[loss=0.2359, simple_loss=0.2817, pruned_loss=0.09508, over 4808.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2513, pruned_loss=0.06307, over 973480.34 frames.], batch size: 21, lr: 9.56e-04
2022-05-03 22:17:43,553 INFO [train.py:715] (1/8) Epoch 1, batch 12700, loss[loss=0.1796, simple_loss=0.2477, pruned_loss=0.05573, over 4984.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2507, pruned_loss=0.06253, over 972968.34 frames.], batch size: 14, lr: 9.56e-04
2022-05-03 22:18:22,945 INFO [train.py:715] (1/8) Epoch 1, batch 12750, loss[loss=0.1856, simple_loss=0.2549, pruned_loss=0.05812, over 4973.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2505, pruned_loss=0.06242, over 972735.56 frames.], batch size: 14, lr: 9.55e-04
2022-05-03 22:19:03,051 INFO [train.py:715] (1/8) Epoch 1, batch 12800, loss[loss=0.2163, simple_loss=0.2697, pruned_loss=0.08149, over 4940.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2514, pruned_loss=0.06322, over 972947.20 frames.], batch size: 35, lr: 9.55e-04
2022-05-03 22:19:42,867 INFO [train.py:715] (1/8) Epoch 1, batch 12850, loss[loss=0.1541, simple_loss=0.2184, pruned_loss=0.04492, over 4936.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2508, pruned_loss=0.06328, over 972587.17 frames.], batch size: 23, lr: 9.54e-04
2022-05-03 22:20:21,820 INFO [train.py:715] (1/8) Epoch 1, batch 12900, loss[loss=0.1776, simple_loss=0.2369, pruned_loss=0.05912, over 4807.00 frames.], tot_loss[loss=0.189, simple_loss=0.251, pruned_loss=0.06344, over 971441.49 frames.], batch size: 25, lr: 9.54e-04
2022-05-03 22:21:01,112 INFO [train.py:715] (1/8) Epoch 1, batch 12950, loss[loss=0.163, simple_loss=0.2276, pruned_loss=0.04922, over 4995.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2504, pruned_loss=0.06285, over 971668.48 frames.], batch size: 14, lr: 9.53e-04
2022-05-03 22:21:41,526 INFO [train.py:715] (1/8) Epoch 1, batch 13000, loss[loss=0.2127, simple_loss=0.2637, pruned_loss=0.08082, over 4845.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2501, pruned_loss=0.06283, over 971902.36 frames.], batch size: 30, lr: 9.53e-04
2022-05-03 22:22:21,096 INFO [train.py:715] (1/8) Epoch 1, batch 13050, loss[loss=0.2024, simple_loss=0.2695, pruned_loss=0.06767, over 4817.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2509, pruned_loss=0.06321, over 972076.46 frames.], batch size: 25, lr: 9.52e-04
2022-05-03 22:23:01,177 INFO [train.py:715] (1/8) Epoch 1, batch 13100, loss[loss=0.1735, simple_loss=0.2453, pruned_loss=0.05088, over 4813.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2523, pruned_loss=0.06392, over 972440.30 frames.], batch size: 15, lr: 9.52e-04
2022-05-03 22:23:41,361 INFO [train.py:715] (1/8) Epoch 1, batch 13150, loss[loss=0.2284, simple_loss=0.2742, pruned_loss=0.09131, over 4923.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2515, pruned_loss=0.06366, over 972676.13 frames.], batch size: 18, lr: 9.51e-04
2022-05-03 22:24:23,879 INFO [train.py:715] (1/8) Epoch 1, batch 13200, loss[loss=0.1898, simple_loss=0.2606, pruned_loss=0.05946, over 4929.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2523, pruned_loss=0.06356, over 972431.03 frames.], batch size: 39, lr: 9.51e-04
2022-05-03 22:25:03,003 INFO [train.py:715] (1/8) Epoch 1, batch 13250, loss[loss=0.1661, simple_loss=0.2337, pruned_loss=0.04924, over 4957.00 frames.], tot_loss[loss=0.1894, simple_loss=0.252, pruned_loss=0.06343, over 972128.49 frames.], batch size: 35, lr: 9.51e-04
2022-05-03 22:25:41,751 INFO [train.py:715] (1/8) Epoch 1, batch 13300, loss[loss=0.2205, simple_loss=0.273, pruned_loss=0.08405, over 4810.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2515, pruned_loss=0.06348, over 972145.34 frames.], batch size: 21, lr: 9.50e-04
2022-05-03 22:26:21,980 INFO [train.py:715] (1/8) Epoch 1, batch 13350, loss[loss=0.1802, simple_loss=0.2514, pruned_loss=0.05452, over 4777.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2517, pruned_loss=0.06387, over 971863.85 frames.], batch size: 18, lr: 9.50e-04
2022-05-03 22:27:01,381 INFO [train.py:715] (1/8) Epoch 1, batch 13400, loss[loss=0.2084, simple_loss=0.2661, pruned_loss=0.07537, over 4924.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2522, pruned_loss=0.06432, over 972306.89 frames.], batch size: 29, lr: 9.49e-04
2022-05-03 22:27:41,356 INFO [train.py:715] (1/8) Epoch 1, batch 13450, loss[loss=0.2071, simple_loss=0.2606, pruned_loss=0.0768, over 4900.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2517, pruned_loss=0.06365, over 972644.13 frames.], batch size: 19, lr: 9.49e-04
2022-05-03 22:28:21,068 INFO [train.py:715] (1/8) Epoch 1, batch 13500, loss[loss=0.1909, simple_loss=0.2584, pruned_loss=0.06169, over 4770.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2522, pruned_loss=0.06348, over 972166.08 frames.], batch size: 18, lr: 9.48e-04
2022-05-03 22:29:01,037 INFO [train.py:715] (1/8) Epoch 1, batch 13550, loss[loss=0.1826, simple_loss=0.2485, pruned_loss=0.05835, over 4854.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2523, pruned_loss=0.06329, over 972708.81 frames.], batch size: 30, lr: 9.48e-04
2022-05-03 22:29:39,300 INFO [train.py:715] (1/8) Epoch 1, batch 13600, loss[loss=0.215, simple_loss=0.2787, pruned_loss=0.07562, over 4850.00 frames.], tot_loss[loss=0.1895, simple_loss=0.252, pruned_loss=0.06346, over 971646.48 frames.], batch size: 20, lr: 9.47e-04
2022-05-03 22:30:18,508 INFO [train.py:715] (1/8) Epoch 1, batch 13650, loss[loss=0.1613, simple_loss=0.2214, pruned_loss=0.05065, over 4913.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2504, pruned_loss=0.06237, over 971715.35 frames.], batch size: 19, lr: 9.47e-04
2022-05-03 22:30:58,735 INFO [train.py:715] (1/8) Epoch 1, batch 13700, loss[loss=0.2311, simple_loss=0.2929, pruned_loss=0.08472, over 4928.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2501, pruned_loss=0.06228, over 971984.71 frames.], batch size: 21, lr: 9.46e-04
2022-05-03 22:31:38,130 INFO [train.py:715] (1/8) Epoch 1, batch 13750, loss[loss=0.1726, simple_loss=0.2389, pruned_loss=0.05313, over 4803.00 frames.], tot_loss[loss=0.187, simple_loss=0.2498, pruned_loss=0.06216, over 972139.57 frames.], batch size: 14, lr: 9.46e-04
2022-05-03 22:32:17,281 INFO [train.py:715] (1/8) Epoch 1, batch 13800, loss[loss=0.1974, simple_loss=0.2478, pruned_loss=0.07348, over 4980.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2499, pruned_loss=0.06259, over 972231.91 frames.], batch size: 31, lr: 9.45e-04
2022-05-03 22:32:56,966 INFO [train.py:715] (1/8) Epoch 1, batch 13850, loss[loss=0.1762, simple_loss=0.2395, pruned_loss=0.05648, over 4961.00 frames.], tot_loss[loss=0.187, simple_loss=0.2498, pruned_loss=0.06213, over 972166.41 frames.], batch size: 35, lr: 9.45e-04
2022-05-03 22:33:36,811 INFO [train.py:715] (1/8) Epoch 1, batch 13900, loss[loss=0.2281, simple_loss=0.2873, pruned_loss=0.08448, over 4943.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2482, pruned_loss=0.0608, over 972138.94 frames.], batch size: 21, lr: 9.44e-04
2022-05-03 22:34:15,306 INFO [train.py:715] (1/8) Epoch 1, batch 13950, loss[loss=0.17, simple_loss=0.2257, pruned_loss=0.05713, over 4884.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2486, pruned_loss=0.06145, over 971809.04 frames.], batch size: 32, lr: 9.44e-04
2022-05-03 22:34:54,565 INFO [train.py:715] (1/8) Epoch 1, batch 14000, loss[loss=0.2213, simple_loss=0.2889, pruned_loss=0.07689, over 4867.00 frames.], tot_loss[loss=0.1873, simple_loss=0.25, pruned_loss=0.06224, over 972013.27 frames.], batch size: 16, lr: 9.43e-04
2022-05-03 22:35:34,712 INFO [train.py:715] (1/8) Epoch 1, batch 14050, loss[loss=0.1823, simple_loss=0.2497, pruned_loss=0.05738, over 4889.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2511, pruned_loss=0.06279, over 972235.45 frames.], batch size: 16, lr: 9.43e-04
2022-05-03 22:36:13,514 INFO [train.py:715] (1/8) Epoch 1, batch 14100, loss[loss=0.1927, simple_loss=0.2561, pruned_loss=0.06465, over 4979.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2508, pruned_loss=0.06237, over 973229.54 frames.], batch size: 24, lr: 9.42e-04
2022-05-03 22:36:52,744 INFO [train.py:715] (1/8) Epoch 1, batch 14150, loss[loss=0.1966, simple_loss=0.2572, pruned_loss=0.06801, over 4759.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2514, pruned_loss=0.06345, over 972904.11 frames.], batch size: 16, lr: 9.42e-04
2022-05-03 22:37:31,981 INFO [train.py:715] (1/8) Epoch 1, batch 14200, loss[loss=0.1888, simple_loss=0.2546, pruned_loss=0.06146, over 4834.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2512, pruned_loss=0.06293, over 972793.66 frames.], batch size: 30, lr: 9.41e-04
2022-05-03 22:38:12,094 INFO [train.py:715] (1/8) Epoch 1, batch 14250, loss[loss=0.1827, simple_loss=0.2524, pruned_loss=0.05646, over 4924.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2513, pruned_loss=0.06311, over 972979.53 frames.], batch size: 18, lr: 9.41e-04
2022-05-03 22:38:50,570 INFO [train.py:715] (1/8) Epoch 1, batch 14300, loss[loss=0.1739, simple_loss=0.2378, pruned_loss=0.05498, over 4901.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2514, pruned_loss=0.06311, over 972956.03 frames.], batch size: 19, lr: 9.40e-04
2022-05-03 22:39:29,557 INFO [train.py:715] (1/8) Epoch 1, batch 14350, loss[loss=0.1512, simple_loss=0.2161, pruned_loss=0.04321, over 4684.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2511, pruned_loss=0.06293, over 971791.95 frames.], batch size: 15, lr: 9.40e-04
2022-05-03 22:40:09,903 INFO [train.py:715] (1/8) Epoch 1, batch 14400, loss[loss=0.1966, simple_loss=0.2508, pruned_loss=0.07127, over 4855.00 frames.], tot_loss[loss=0.188, simple_loss=0.2509, pruned_loss=0.06258, over 971833.90 frames.], batch size: 32, lr: 9.39e-04
2022-05-03 22:40:48,727 INFO [train.py:715] (1/8) Epoch 1, batch 14450, loss[loss=0.2094, simple_loss=0.2574, pruned_loss=0.0807, over 4967.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2499, pruned_loss=0.06163, over 971507.84 frames.], batch size: 15, lr: 9.39e-04
2022-05-03 22:41:28,251 INFO [train.py:715] (1/8) Epoch 1, batch 14500, loss[loss=0.1928, simple_loss=0.2499, pruned_loss=0.06792, over 4918.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2494, pruned_loss=0.06117, over 970775.47 frames.], batch size: 17, lr: 9.39e-04
2022-05-03 22:42:08,354 INFO [train.py:715] (1/8) Epoch 1, batch 14550, loss[loss=0.2271, simple_loss=0.276, pruned_loss=0.08912, over 4920.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2501, pruned_loss=0.06189, over 971420.56 frames.], batch size: 18, lr: 9.38e-04
2022-05-03 22:42:47,867 INFO [train.py:715] (1/8) Epoch 1, batch 14600, loss[loss=0.1708, simple_loss=0.2419, pruned_loss=0.04991, over 4891.00 frames.], tot_loss[loss=0.186, simple_loss=0.2491, pruned_loss=0.06147, over 972395.09 frames.], batch size: 16, lr: 9.38e-04
2022-05-03 22:43:26,826 INFO [train.py:715] (1/8) Epoch 1, batch 14650, loss[loss=0.162, simple_loss=0.2223, pruned_loss=0.05083, over 4978.00 frames.], tot_loss[loss=0.1859, simple_loss=0.249, pruned_loss=0.0614, over 972798.53 frames.], batch size: 35, lr: 9.37e-04
2022-05-03 22:44:05,663 INFO [train.py:715] (1/8) Epoch 1, batch 14700, loss[loss=0.1816, simple_loss=0.2658, pruned_loss=0.04876, over 4914.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2494, pruned_loss=0.06147, over 972978.87 frames.], batch size: 18, lr: 9.37e-04
2022-05-03 22:44:45,792 INFO [train.py:715] (1/8) Epoch 1, batch 14750, loss[loss=0.1764, simple_loss=0.2491, pruned_loss=0.05188, over 4697.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2492, pruned_loss=0.06153, over 971955.70 frames.], batch size: 15, lr: 9.36e-04
2022-05-03 22:45:24,937 INFO [train.py:715] (1/8) Epoch 1, batch 14800, loss[loss=0.1572, simple_loss=0.2125, pruned_loss=0.05093, over 4850.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2491, pruned_loss=0.0614, over 972819.91 frames.], batch size: 13, lr: 9.36e-04
2022-05-03 22:46:04,489 INFO [train.py:715] (1/8) Epoch 1, batch 14850, loss[loss=0.2301, simple_loss=0.2806, pruned_loss=0.0898, over 4930.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2484, pruned_loss=0.06089, over 972705.37 frames.], batch size: 23, lr: 9.35e-04
2022-05-03 22:46:43,812 INFO [train.py:715] (1/8) Epoch 1, batch 14900, loss[loss=0.1478, simple_loss=0.2032, pruned_loss=0.04614, over 4755.00 frames.], tot_loss[loss=0.1843, simple_loss=0.248, pruned_loss=0.06031, over 972720.02 frames.], batch size: 12, lr: 9.35e-04
2022-05-03 22:47:22,419 INFO [train.py:715] (1/8) Epoch 1, batch 14950, loss[loss=0.1976, simple_loss=0.2504, pruned_loss=0.07237, over 4928.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2494, pruned_loss=0.06144, over 973989.26 frames.], batch size: 35, lr: 9.34e-04
2022-05-03 22:48:02,034 INFO [train.py:715] (1/8) Epoch 1, batch 15000, loss[loss=0.1957, simple_loss=0.2574, pruned_loss=0.06701, over 4876.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2507, pruned_loss=0.06235, over 973872.97 frames.], batch size: 22, lr: 9.34e-04
2022-05-03 22:48:02,035 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 22:48:17,510 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1242, simple_loss=0.2115, pruned_loss=0.01842, over 914524.00 frames.
2022-05-03 22:48:57,644 INFO [train.py:715] (1/8) Epoch 1, batch 15050, loss[loss=0.1683, simple_loss=0.2331, pruned_loss=0.05168, over 4910.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2514, pruned_loss=0.06273, over 973649.38 frames.], batch size: 18, lr: 9.33e-04
2022-05-03 22:49:37,558 INFO [train.py:715] (1/8) Epoch 1, batch 15100, loss[loss=0.1771, simple_loss=0.2376, pruned_loss=0.05825, over 4869.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2509, pruned_loss=0.06229, over 972815.28 frames.], batch size: 20, lr: 9.33e-04
2022-05-03 22:50:18,096 INFO [train.py:715] (1/8) Epoch 1, batch 15150, loss[loss=0.1728, simple_loss=0.2318, pruned_loss=0.05688, over 4910.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2506, pruned_loss=0.06253, over 972503.23 frames.], batch size: 17, lr: 9.32e-04
2022-05-03 22:50:57,475 INFO [train.py:715] (1/8) Epoch 1, batch 15200, loss[loss=0.1528, simple_loss=0.2173, pruned_loss=0.04414, over 4786.00 frames.], tot_loss[loss=0.187, simple_loss=0.2499, pruned_loss=0.06207, over 973518.68 frames.], batch size: 21, lr: 9.32e-04
2022-05-03 22:51:37,954 INFO [train.py:715] (1/8) Epoch 1, batch 15250, loss[loss=0.1971, simple_loss=0.2514, pruned_loss=0.07147, over 4765.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2508, pruned_loss=0.06256, over 973999.89 frames.], batch size: 14, lr: 9.32e-04
2022-05-03 22:52:17,871 INFO [train.py:715] (1/8) Epoch 1, batch 15300, loss[loss=0.1785, simple_loss=0.2473, pruned_loss=0.05485, over 4883.00 frames.], tot_loss[loss=0.187, simple_loss=0.2501, pruned_loss=0.06191, over 973390.94 frames.], batch size: 22, lr: 9.31e-04
2022-05-03 22:52:57,760 INFO [train.py:715] (1/8) Epoch 1, batch 15350, loss[loss=0.1966, simple_loss=0.2534, pruned_loss=0.06987, over 4885.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2513, pruned_loss=0.06275, over 972932.29 frames.], batch size: 19, lr: 9.31e-04
2022-05-03 22:53:37,898 INFO [train.py:715] (1/8) Epoch 1, batch 15400, loss[loss=0.1866, simple_loss=0.2533, pruned_loss=0.05995, over 4742.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2502, pruned_loss=0.06165, over 971915.75 frames.], batch size: 16, lr: 9.30e-04
2022-05-03 22:54:18,169 INFO [train.py:715] (1/8) Epoch 1, batch 15450, loss[loss=0.2037, simple_loss=0.2641, pruned_loss=0.07167, over 4820.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2485, pruned_loss=0.06094, over 971736.92 frames.], batch size: 14, lr: 9.30e-04
2022-05-03 22:54:58,642 INFO [train.py:715] (1/8) Epoch 1, batch 15500, loss[loss=0.1889, simple_loss=0.2595, pruned_loss=0.05918, over 4778.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2483, pruned_loss=0.06095, over 972078.88 frames.], batch size: 18, lr: 9.29e-04
2022-05-03 22:55:37,736 INFO [train.py:715] (1/8) Epoch 1, batch 15550, loss[loss=0.1901, simple_loss=0.251, pruned_loss=0.06456, over 4788.00 frames.], tot_loss[loss=0.185, simple_loss=0.2482, pruned_loss=0.06092, over 972350.24 frames.], batch size: 18, lr: 9.29e-04
2022-05-03 22:56:18,059 INFO [train.py:715] (1/8) Epoch 1, batch 15600, loss[loss=0.2054, simple_loss=0.2794, pruned_loss=0.06563, over 4950.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2484, pruned_loss=0.06098, over 972414.08 frames.], batch size: 21, lr: 9.28e-04
2022-05-03 22:56:58,353 INFO [train.py:715] (1/8) Epoch 1, batch 15650, loss[loss=0.1635, simple_loss=0.2381, pruned_loss=0.04443, over 4748.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2472, pruned_loss=0.06011, over 973213.74 frames.], batch size: 16, lr: 9.28e-04
2022-05-03 22:57:38,269 INFO [train.py:715] (1/8) Epoch 1, batch 15700, loss[loss=0.1893, simple_loss=0.2418, pruned_loss=0.06845, over 4867.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2479, pruned_loss=0.06089, over 972831.05 frames.], batch size: 32, lr: 9.27e-04
2022-05-03 22:58:17,913 INFO [train.py:715] (1/8) Epoch 1, batch 15750, loss[loss=0.1596, simple_loss=0.2268, pruned_loss=0.04626, over 4960.00 frames.], tot_loss[loss=0.185, simple_loss=0.248, pruned_loss=0.06096, over 973815.46 frames.], batch size: 28, lr: 9.27e-04
2022-05-03 22:58:58,192 INFO [train.py:715] (1/8) Epoch 1, batch 15800, loss[loss=0.1586, simple_loss=0.2226, pruned_loss=0.04732, over 4802.00 frames.], tot_loss[loss=0.1858, simple_loss=0.249, pruned_loss=0.06126, over 973091.59 frames.], batch size: 26, lr: 9.27e-04
2022-05-03 22:59:38,877 INFO [train.py:715] (1/8) Epoch 1, batch 15850, loss[loss=0.2172, simple_loss=0.2625, pruned_loss=0.08596, over 4849.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2489, pruned_loss=0.06108, over 972891.30 frames.], batch size: 30, lr: 9.26e-04
2022-05-03 23:00:18,433 INFO [train.py:715] (1/8) Epoch 1, batch 15900, loss[loss=0.2023, simple_loss=0.2634, pruned_loss=0.07058, over 4934.00 frames.], tot_loss[loss=0.1858, simple_loss=0.249, pruned_loss=0.06124, over 972609.86 frames.], batch size: 29, lr: 9.26e-04
2022-05-03 23:00:58,071 INFO [train.py:715] (1/8) Epoch 1, batch 15950, loss[loss=0.181, simple_loss=0.2492, pruned_loss=0.05639, over 4912.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2484, pruned_loss=0.06114, over 972375.41 frames.], batch size: 17, lr: 9.25e-04
2022-05-03 23:01:37,502 INFO [train.py:715] (1/8) Epoch 1, batch 16000, loss[loss=0.1727, simple_loss=0.2412, pruned_loss=0.0521, over 4948.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2489, pruned_loss=0.0614, over 972372.92 frames.], batch size: 21, lr: 9.25e-04
2022-05-03 23:02:16,260 INFO [train.py:715] (1/8) Epoch 1, batch 16050, loss[loss=0.1745, simple_loss=0.2333, pruned_loss=0.05786, over 4916.00 frames.], tot_loss[loss=0.1864, simple_loss=0.249, pruned_loss=0.06194, over 973029.05 frames.], batch size: 17, lr: 9.24e-04
2022-05-03 23:02:55,583 INFO [train.py:715] (1/8) Epoch 1, batch 16100, loss[loss=0.2313, simple_loss=0.2811, pruned_loss=0.09079, over 4899.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2483, pruned_loss=0.06128, over 973593.21 frames.], batch size: 19, lr: 9.24e-04
2022-05-03 23:03:35,233 INFO [train.py:715] (1/8) Epoch 1, batch 16150, loss[loss=0.18, simple_loss=0.2378, pruned_loss=0.06115, over 4946.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2495, pruned_loss=0.06187, over 972773.12 frames.], batch size: 35, lr: 9.23e-04
2022-05-03 23:04:15,418 INFO [train.py:715] (1/8) Epoch 1, batch 16200, loss[loss=0.1735, simple_loss=0.2453, pruned_loss=0.05085, over 4959.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2481, pruned_loss=0.06102, over 972645.55 frames.], batch size: 35, lr: 9.23e-04
2022-05-03 23:04:53,727 INFO [train.py:715] (1/8) Epoch 1, batch 16250, loss[loss=0.1728, simple_loss=0.2443, pruned_loss=0.05069, over 4879.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2486, pruned_loss=0.06104, over 972123.12 frames.], batch size: 16, lr: 9.22e-04
2022-05-03 23:05:33,193 INFO [train.py:715] (1/8) Epoch 1, batch 16300, loss[loss=0.1836, simple_loss=0.2416, pruned_loss=0.06278, over 4892.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2495, pruned_loss=0.06061, over 971477.28 frames.], batch size: 19, lr: 9.22e-04
2022-05-03 23:06:12,739 INFO [train.py:715] (1/8) Epoch 1, batch 16350, loss[loss=0.1575, simple_loss=0.2209, pruned_loss=0.04708, over 4886.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2494, pruned_loss=0.06107, over 972133.84 frames.], batch size: 32, lr: 9.22e-04
2022-05-03 23:06:51,399 INFO [train.py:715] (1/8) Epoch 1, batch 16400, loss[loss=0.1958, simple_loss=0.2544, pruned_loss=0.06858, over 4919.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2501, pruned_loss=0.06154, over 972294.65 frames.], batch size: 23, lr: 9.21e-04
2022-05-03 23:07:30,897 INFO [train.py:715] (1/8) Epoch 1, batch 16450, loss[loss=0.1616, simple_loss=0.2306, pruned_loss=0.04629, over 4976.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2499, pruned_loss=0.06139, over 972146.55 frames.], batch size: 39, lr: 9.21e-04
2022-05-03 23:08:10,542 INFO [train.py:715] (1/8) Epoch 1, batch 16500, loss[loss=0.195, simple_loss=0.2529, pruned_loss=0.06858, over 4978.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2502, pruned_loss=0.06176, over 971868.22 frames.], batch size: 15, lr: 9.20e-04
2022-05-03 23:08:50,451 INFO [train.py:715] (1/8) Epoch 1, batch 16550, loss[loss=0.1577, simple_loss=0.2358, pruned_loss=0.03977, over 4943.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2505, pruned_loss=0.06161, over 972243.24 frames.], batch size: 21, lr: 9.20e-04
2022-05-03 23:09:28,846 INFO [train.py:715] (1/8) Epoch 1, batch 16600, loss[loss=0.1874, simple_loss=0.2603, pruned_loss=0.05728, over 4833.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2507, pruned_loss=0.06172, over 972318.95 frames.], batch size: 15, lr: 9.19e-04
2022-05-03 23:10:09,003 INFO [train.py:715] (1/8) Epoch 1, batch 16650, loss[loss=0.1737, simple_loss=0.2491, pruned_loss=0.04919, over 4801.00 frames.], tot_loss[loss=0.186, simple_loss=0.2497, pruned_loss=0.06115, over 972638.99 frames.], batch size: 12, lr: 9.19e-04
2022-05-03 23:10:48,682 INFO [train.py:715] (1/8) Epoch 1, batch 16700, loss[loss=0.2117, simple_loss=0.2616, pruned_loss=0.08095, over 4968.00 frames.], tot_loss[loss=0.1875, simple_loss=0.251, pruned_loss=0.06201, over 972837.83 frames.], batch size: 35, lr: 9.18e-04
2022-05-03 23:11:28,441 INFO [train.py:715] (1/8) Epoch 1, batch 16750, loss[loss=0.1932, simple_loss=0.2499, pruned_loss=0.06823, over 4833.00 frames.], tot_loss[loss=0.1867, simple_loss=0.25, pruned_loss=0.0617, over 972226.54 frames.], batch size: 30, lr: 9.18e-04
2022-05-03 23:12:08,275 INFO [train.py:715] (1/8) Epoch 1, batch 16800, loss[loss=0.1905, simple_loss=0.2554, pruned_loss=0.06277, over 4760.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2499, pruned_loss=0.06148, over 972976.96 frames.], batch size: 19, lr: 9.18e-04
2022-05-03 23:12:47,924 INFO [train.py:715] (1/8) Epoch 1, batch 16850, loss[loss=0.1878, simple_loss=0.2495, pruned_loss=0.06306, over 4786.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2491, pruned_loss=0.06103, over 973515.14 frames.], batch size: 14, lr: 9.17e-04
2022-05-03 23:13:27,906 INFO [train.py:715] (1/8) Epoch 1, batch 16900, loss[loss=0.2057, simple_loss=0.2573, pruned_loss=0.07703, over 4748.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2508, pruned_loss=0.06225, over 973965.37 frames.], batch size: 19, lr: 9.17e-04
2022-05-03 23:14:06,929 INFO [train.py:715] (1/8) Epoch 1, batch 16950, loss[loss=0.1605, simple_loss=0.2317, pruned_loss=0.0447, over 4909.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2507, pruned_loss=0.06243, over 973739.01 frames.], batch size: 23, lr: 9.16e-04
2022-05-03 23:14:46,345 INFO [train.py:715] (1/8) Epoch 1, batch 17000, loss[loss=0.1866, simple_loss=0.2379, pruned_loss=0.06767, over 4897.00 frames.], tot_loss[loss=0.187, simple_loss=0.2498, pruned_loss=0.06217, over 972980.66 frames.], batch size: 19, lr: 9.16e-04
2022-05-03 23:15:26,352 INFO [train.py:715] (1/8) Epoch 1, batch 17050, loss[loss=0.2198, simple_loss=0.2776, pruned_loss=0.08095, over 4948.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2507, pruned_loss=0.06238, over 972715.40 frames.], batch size: 14, lr: 9.15e-04
2022-05-03 23:16:05,138 INFO [train.py:715] (1/8) Epoch 1, batch 17100, loss[loss=0.2021, simple_loss=0.265, pruned_loss=0.0696, over 4761.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2509, pruned_loss=0.06229, over 973310.55 frames.], batch size: 19, lr: 9.15e-04
2022-05-03 23:16:44,846 INFO [train.py:715] (1/8) Epoch 1, batch 17150, loss[loss=0.1691, simple_loss=0.2416, pruned_loss=0.0483, over 4822.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2491, pruned_loss=0.0611, over 973303.28 frames.], batch size: 25, lr: 9.15e-04
2022-05-03 23:17:25,476 INFO [train.py:715] (1/8) Epoch 1, batch 17200, loss[loss=0.1903, simple_loss=0.2524, pruned_loss=0.06411, over 4983.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2491, pruned_loss=0.06129, over 973146.14 frames.], batch size: 28, lr: 9.14e-04
2022-05-03 23:18:05,277 INFO [train.py:715] (1/8) Epoch 1, batch 17250, loss[loss=0.2049, simple_loss=0.2571, pruned_loss=0.07641, over 4853.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2489, pruned_loss=0.06107, over 973125.78 frames.], batch size: 13, lr: 9.14e-04
2022-05-03 23:18:43,788 INFO [train.py:715] (1/8) Epoch 1, batch 17300, loss[loss=0.1841, simple_loss=0.2425, pruned_loss=0.06286, over 4912.00 frames.], tot_loss[loss=0.186, simple_loss=0.2488, pruned_loss=0.06159, over 972607.64 frames.], batch size: 39, lr: 9.13e-04
2022-05-03 23:19:23,807 INFO [train.py:715] (1/8) Epoch 1, batch 17350, loss[loss=0.2023, simple_loss=0.257, pruned_loss=0.07382, over 4921.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2487, pruned_loss=0.06136, over 973005.33 frames.], batch size: 18, lr: 9.13e-04
2022-05-03 23:20:03,642 INFO [train.py:715] (1/8) Epoch 1, batch 17400, loss[loss=0.186, simple_loss=0.2424, pruned_loss=0.06479, over 4975.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2487, pruned_loss=0.06129, over 972145.06 frames.], batch size: 15, lr: 9.12e-04
2022-05-03 23:20:42,893 INFO [train.py:715] (1/8) Epoch 1, batch 17450, loss[loss=0.2065, simple_loss=0.2468, pruned_loss=0.08311, over 4914.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2483, pruned_loss=0.06109, over 972015.51 frames.], batch size: 17, lr: 9.12e-04
2022-05-03 23:21:23,296 INFO [train.py:715] (1/8) Epoch 1, batch 17500, loss[loss=0.1877, simple_loss=0.253, pruned_loss=0.06117, over 4928.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2475, pruned_loss=0.06077, over 971986.97 frames.], batch size: 29, lr: 9.11e-04
2022-05-03 23:22:03,723 INFO [train.py:715] (1/8) Epoch 1, batch 17550, loss[loss=0.2487, simple_loss=0.2974, pruned_loss=0.1, over 4968.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2487, pruned_loss=0.06113, over 972414.63 frames.], batch size: 35, lr: 9.11e-04
2022-05-03 23:22:44,345 INFO [train.py:715] (1/8) Epoch 1, batch 17600, loss[loss=0.1796, simple_loss=0.2545, pruned_loss=0.05242, over 4983.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2487, pruned_loss=0.06131, over 973161.60 frames.], batch size: 25, lr: 9.11e-04
2022-05-03 23:23:24,042 INFO [train.py:715] (1/8) Epoch 1, batch 17650, loss[loss=0.156, simple_loss=0.228, pruned_loss=0.04202, over 4922.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2479, pruned_loss=0.06114, over 972952.00 frames.], batch size: 29, lr: 9.10e-04
2022-05-03 23:24:04,738 INFO [train.py:715] (1/8) Epoch 1, batch 17700, loss[loss=0.2172, simple_loss=0.2663, pruned_loss=0.08409, over 4739.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2472, pruned_loss=0.06098, over 972997.72 frames.], batch size: 16, lr: 9.10e-04
2022-05-03 23:24:44,983 INFO [train.py:715] (1/8) Epoch 1, batch 17750, loss[loss=0.1692, simple_loss=0.2314, pruned_loss=0.05349, over 4800.00 frames.], tot_loss[loss=0.1851, simple_loss=0.248, pruned_loss=0.06116, over 972586.81 frames.], batch size: 12, lr: 9.09e-04
2022-05-03 23:25:24,521 INFO [train.py:715] (1/8) Epoch 1, batch 17800, loss[loss=0.1902, simple_loss=0.2516, pruned_loss=0.06442, over 4791.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2488, pruned_loss=0.06168, over 972439.46 frames.], batch size: 17, lr: 9.09e-04
2022-05-03 23:26:04,927 INFO [train.py:715] (1/8) Epoch 1, batch 17850, loss[loss=0.1958, simple_loss=0.2628, pruned_loss=0.06433, over 4871.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2494, pruned_loss=0.06184, over 972799.83 frames.], batch size: 20, lr: 9.08e-04
2022-05-03 23:26:44,323 INFO [train.py:715] (1/8) Epoch 1, batch 17900, loss[loss=0.1961, simple_loss=0.2388, pruned_loss=0.07672, over 4888.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2486, pruned_loss=0.06114, over 972923.35 frames.], batch size: 32, lr: 9.08e-04
2022-05-03 23:27:23,557 INFO [train.py:715] (1/8) Epoch 1, batch 17950, loss[loss=0.1977, simple_loss=0.2715, pruned_loss=0.06194, over 4929.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2483, pruned_loss=0.06094, over 972292.70 frames.], batch size: 18, lr: 9.08e-04
2022-05-03 23:28:02,858 INFO [train.py:715] (1/8) Epoch 1, batch 18000, loss[loss=0.1791, simple_loss=0.2443, pruned_loss=0.05697, over 4866.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2482, pruned_loss=0.06065, over 971780.33 frames.], batch size: 20, lr: 9.07e-04
2022-05-03 23:28:02,858 INFO [train.py:733] (1/8) Computing validation loss
2022-05-03 23:28:17,471 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.123, simple_loss=0.21, pruned_loss=0.01804, over 914524.00 frames.
2022-05-03 23:28:56,681 INFO [train.py:715] (1/8) Epoch 1, batch 18050, loss[loss=0.1941, simple_loss=0.2593, pruned_loss=0.06448, over 4934.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2486, pruned_loss=0.06063, over 971530.59 frames.], batch size: 39, lr: 9.07e-04
2022-05-03 23:29:37,117 INFO [train.py:715] (1/8) Epoch 1, batch 18100, loss[loss=0.1888, simple_loss=0.258, pruned_loss=0.05975, over 4795.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2484, pruned_loss=0.06061, over 970879.64 frames.], batch size: 14, lr: 9.06e-04
2022-05-03 23:30:16,931 INFO [train.py:715] (1/8) Epoch 1, batch 18150, loss[loss=0.1825, simple_loss=0.2526, pruned_loss=0.0562, over 4927.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2483, pruned_loss=0.06039, over 971157.34 frames.], batch size: 29, lr: 9.06e-04
2022-05-03 23:30:55,300 INFO [train.py:715] (1/8) Epoch 1, batch 18200, loss[loss=0.1294, simple_loss=0.1991, pruned_loss=0.02981, over 4756.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2481, pruned_loss=0.0604, over 971326.13 frames.], batch size: 12, lr: 9.05e-04
2022-05-03 23:31:34,988 INFO [train.py:715] (1/8) Epoch 1, batch 18250, loss[loss=0.1972, simple_loss=0.2532, pruned_loss=0.07061, over 4825.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2482, pruned_loss=0.06083, over 970881.40 frames.], batch size: 13, lr: 9.05e-04
2022-05-03 23:32:14,613 INFO [train.py:715] (1/8) Epoch 1, batch 18300, loss[loss=0.2136, simple_loss=0.2673, pruned_loss=0.07996, over 4836.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2494, pruned_loss=0.0616, over 971264.32 frames.], batch size: 13, lr: 9.05e-04
2022-05-03 23:32:53,394 INFO [train.py:715] (1/8) Epoch 1, batch 18350, loss[loss=0.1711, simple_loss=0.2297, pruned_loss=0.05623, over 4806.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2491, pruned_loss=0.06193, over 971424.12 frames.], batch size: 12, lr: 9.04e-04
2022-05-03 23:33:33,133 INFO [train.py:715] (1/8) Epoch 1, batch 18400, loss[loss=0.1782, simple_loss=0.2513, pruned_loss=0.05257, over 4762.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2482, pruned_loss=0.06162, over 971393.59 frames.], batch size: 19, lr: 9.04e-04
2022-05-03 23:34:13,408 INFO [train.py:715] (1/8) Epoch 1, batch 18450, loss[loss=0.2181, simple_loss=0.2784, pruned_loss=0.07894, over 4942.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2477, pruned_loss=0.0613, over 970586.56 frames.], batch size: 23, lr: 9.03e-04
2022-05-03 23:34:52,235 INFO [train.py:715] (1/8) Epoch 1, batch 18500, loss[loss=0.1719, simple_loss=0.2341, pruned_loss=0.05479, over 4742.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2478, pruned_loss=0.0608, over 970265.49 frames.], batch size: 16, lr: 9.03e-04
2022-05-03 23:35:31,272 INFO [train.py:715] (1/8) Epoch 1, batch 18550, loss[loss=0.221, simple_loss=0.2838, pruned_loss=0.07909, over 4800.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2481, pruned_loss=0.06066, over 971198.72 frames.], batch size: 21, lr: 9.03e-04
2022-05-03 23:36:11,451 INFO [train.py:715] (1/8) Epoch 1, batch 18600, loss[loss=0.2174, simple_loss=0.2656, pruned_loss=0.08457, over 4705.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2467, pruned_loss=0.05981, over 971488.77 frames.], batch size: 15, lr: 9.02e-04
2022-05-03 23:36:50,768 INFO [train.py:715] (1/8) Epoch 1, batch 18650, loss[loss=0.1549, simple_loss=0.229, pruned_loss=0.04041, over 4864.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2466, pruned_loss=0.05935, over 971813.62 frames.], batch size: 20, lr: 9.02e-04
2022-05-03 23:37:29,511 INFO [train.py:715] (1/8) Epoch 1, batch 18700, loss[loss=0.191, simple_loss=0.2407, pruned_loss=0.07063, over 4836.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2465, pruned_loss=0.05952, over 972261.10 frames.], batch size: 30, lr: 9.01e-04
2022-05-03 23:38:08,760 INFO [train.py:715] (1/8) Epoch 1, batch 18750, loss[loss=0.1658, simple_loss=0.2356, pruned_loss=0.04802, over 4649.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2467, pruned_loss=0.05947, over 972165.34 frames.], batch size: 13, lr: 9.01e-04
2022-05-03 23:38:48,686 INFO [train.py:715] (1/8) Epoch 1, batch 18800, loss[loss=0.1737, simple_loss=0.2407, pruned_loss=0.05334, over 4894.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2469, pruned_loss=0.06007, over 971876.87 frames.], batch size: 32, lr: 9.00e-04
2022-05-03 23:39:27,388 INFO [train.py:715] (1/8) Epoch 1, batch 18850, loss[loss=0.1841, simple_loss=0.2482, pruned_loss=0.05998, over 4860.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2473, pruned_loss=0.06018, over 972016.38 frames.], batch size: 30, lr: 9.00e-04
2022-05-03 23:40:06,872 INFO [train.py:715] (1/8) Epoch 1, batch 18900, loss[loss=0.1578, simple_loss=0.2296, pruned_loss=0.04301, over 4921.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2485, pruned_loss=0.06084, over 971620.17 frames.], batch size: 18, lr: 9.00e-04
2022-05-03 23:40:46,607 INFO [train.py:715] (1/8) Epoch 1, batch 18950, loss[loss=0.1666, simple_loss=0.237, pruned_loss=0.04812, over 4839.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2474, pruned_loss=0.06073, over 972569.87 frames.], batch size: 15, lr: 8.99e-04
2022-05-03 23:41:25,994 INFO [train.py:715] (1/8) Epoch 1, batch 19000, loss[loss=0.1581, simple_loss=0.223, pruned_loss=0.0466, over 4637.00 frames.], tot_loss[loss=0.1837, simple_loss=0.247, pruned_loss=0.06017, over 972225.63 frames.], batch size: 13, lr: 8.99e-04
2022-05-03 23:42:05,676 INFO [train.py:715] (1/8) Epoch 1, batch 19050, loss[loss=0.1823, simple_loss=0.2531, pruned_loss=0.05575, over 4697.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2471, pruned_loss=0.06069, over 971813.63 frames.], batch size: 15, lr: 8.98e-04
2022-05-03 23:42:44,846 INFO [train.py:715] (1/8) Epoch 1, batch 19100, loss[loss=0.1664, simple_loss=0.2261, pruned_loss=0.05336, over 4908.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2467, pruned_loss=0.06048, over 971005.04 frames.], batch size: 18, lr: 8.98e-04
2022-05-03 23:43:24,773 INFO [train.py:715] (1/8) Epoch 1, batch 19150, loss[loss=0.1815, simple_loss=0.2481, pruned_loss=0.05746, over 4767.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2473, pruned_loss=0.06113, over 971296.39 frames.], batch size: 18, lr: 8.98e-04
2022-05-03 23:44:03,410 INFO [train.py:715] (1/8) Epoch 1, batch 19200, loss[loss=0.1619, simple_loss=0.2246, pruned_loss=0.04955, over 4812.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2478, pruned_loss=0.06162, over 971611.93 frames.], batch size: 25, lr: 8.97e-04
2022-05-03 23:44:42,698 INFO [train.py:715] (1/8) Epoch 1, batch 19250, loss[loss=0.1838, simple_loss=0.2565, pruned_loss=0.05553, over 4763.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2467, pruned_loss=0.06087, over 972008.60 frames.], batch size: 19, lr: 8.97e-04
2022-05-03 23:45:23,322 INFO [train.py:715] (1/8) Epoch 1, batch 19300, loss[loss=0.222, simple_loss=0.2774, pruned_loss=0.08328, over 4677.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2461, pruned_loss=0.06033, over 972043.29 frames.], batch size: 15, lr: 8.96e-04
2022-05-03 23:46:02,785 INFO [train.py:715] (1/8) Epoch 1, batch 19350, loss[loss=0.192, simple_loss=0.253, pruned_loss=0.06552, over 4743.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2462, pruned_loss=0.06018, over 971727.12 frames.], batch size: 19, lr: 8.96e-04
2022-05-03 23:46:41,169 INFO [train.py:715] (1/8) Epoch 1, batch 19400, loss[loss=0.1475, simple_loss=0.2139, pruned_loss=0.04048, over 4946.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2471, pruned_loss=0.06027, over 972213.54 frames.], batch size: 21, lr: 8.95e-04
2022-05-03 23:47:20,595 INFO [train.py:715] (1/8) Epoch 1, batch 19450, loss[loss=0.1622, simple_loss=0.2256, pruned_loss=0.04941, over 4765.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2471, pruned_loss=0.06067, over 972090.27 frames.], batch size: 19, lr: 8.95e-04
2022-05-03 23:48:00,497 INFO [train.py:715] (1/8) Epoch 1, batch 19500, loss[loss=0.1644, simple_loss=0.2345, pruned_loss=0.04709, over 4794.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2463, pruned_loss=0.06008, over 972814.24 frames.], batch size: 24, lr: 8.95e-04
2022-05-03 23:48:39,200 INFO [train.py:715] (1/8) Epoch 1, batch 19550, loss[loss=0.1707, simple_loss=0.2412, pruned_loss=0.05014, over 4963.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2469, pruned_loss=0.06009, over 972350.48 frames.], batch size: 29, lr: 8.94e-04
2022-05-03 23:49:18,325 INFO [train.py:715] (1/8) Epoch 1, batch 19600, loss[loss=0.1624, simple_loss=0.2257, pruned_loss=0.04956, over 4860.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2466, pruned_loss=0.05987, over 972245.93 frames.], batch size: 32, lr: 8.94e-04
2022-05-03 23:49:58,540 INFO [train.py:715] (1/8) Epoch 1, batch 19650, loss[loss=0.1595, simple_loss=0.2266, pruned_loss=0.04622, over 4767.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2478, pruned_loss=0.06042, over 972300.86 frames.], batch size: 18, lr: 8.93e-04
2022-05-03 23:50:37,445 INFO [train.py:715] (1/8) Epoch 1, batch 19700, loss[loss=0.1448, simple_loss=0.2035, pruned_loss=0.04308, over 4648.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2474, pruned_loss=0.06008, over 972728.17 frames.], batch size: 13, lr: 8.93e-04
2022-05-03 23:51:16,596 INFO [train.py:715] (1/8) Epoch 1, batch 19750, loss[loss=0.1857, simple_loss=0.2492, pruned_loss=0.0611, over 4952.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2479, pruned_loss=0.06017, over 973277.69 frames.], batch size: 35, lr: 8.93e-04
2022-05-03 23:51:56,237 INFO [train.py:715] (1/8) Epoch 1, batch 19800, loss[loss=0.2022, simple_loss=0.2607, pruned_loss=0.07182, over 4764.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2485, pruned_loss=0.06034, over 972994.11 frames.], batch size: 19, lr: 8.92e-04
2022-05-03 23:52:36,505 INFO [train.py:715] (1/8) Epoch 1, batch 19850, loss[loss=0.2413, simple_loss=0.2992, pruned_loss=0.09173, over 4924.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2493, pruned_loss=0.06099, over 972355.21 frames.], batch size: 18, lr: 8.92e-04
2022-05-03 23:53:15,890 INFO [train.py:715] (1/8) Epoch 1, batch 19900, loss[loss=0.1761, simple_loss=0.2387, pruned_loss=0.05677, over 4947.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2491, pruned_loss=0.06116, over 972676.62 frames.], batch size: 29, lr: 8.91e-04
2022-05-03 23:53:54,987 INFO [train.py:715] (1/8) Epoch 1, batch 19950, loss[loss=0.1932, simple_loss=0.2489, pruned_loss=0.06876, over 4901.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2487, pruned_loss=0.06085, over 972765.96 frames.], batch size: 19, lr: 8.91e-04
2022-05-03 23:54:35,249 INFO [train.py:715] (1/8) Epoch 1, batch 20000, loss[loss=0.1883, simple_loss=0.2517, pruned_loss=0.06249, over 4867.00 frames.], tot_loss[loss=0.185, simple_loss=0.2481, pruned_loss=0.06091, over 972222.85 frames.], batch size: 13, lr: 8.91e-04
2022-05-03 23:55:14,860 INFO [train.py:715] (1/8) Epoch 1, batch 20050, loss[loss=0.2107, simple_loss=0.2728, pruned_loss=0.0743, over 4902.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2488, pruned_loss=0.06088, over 971922.15 frames.], batch size: 19, lr: 8.90e-04
2022-05-03 23:55:54,263 INFO [train.py:715] (1/8) Epoch 1, batch 20100, loss[loss=0.1842, simple_loss=0.2472, pruned_loss=0.06063, over 4840.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2484, pruned_loss=0.06066, over 971876.16 frames.], batch size: 32, lr: 8.90e-04
2022-05-03 23:56:34,284 INFO [train.py:715] (1/8) Epoch 1, batch 20150, loss[loss=0.1849, simple_loss=0.2461, pruned_loss=0.06188, over 4843.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2489, pruned_loss=0.06063, over 972188.24 frames.], batch size: 12, lr: 8.89e-04
2022-05-03 23:57:15,157 INFO [train.py:715] (1/8) Epoch 1, batch 20200, loss[loss=0.2245, simple_loss=0.2895, pruned_loss=0.07969, over 4962.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2494, pruned_loss=0.06123, over 971529.96 frames.], batch size: 24, lr: 8.89e-04
2022-05-03 23:57:53,971 INFO [train.py:715] (1/8) Epoch 1, batch 20250, loss[loss=0.1588, simple_loss=0.2302, pruned_loss=0.0437, over 4824.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2487, pruned_loss=0.06077, over 971750.93 frames.], batch size: 26, lr: 8.89e-04
2022-05-03 23:58:33,270 INFO [train.py:715] (1/8) Epoch 1, batch 20300, loss[loss=0.1588, simple_loss=0.2239, pruned_loss=0.04682, over 4781.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2488, pruned_loss=0.06136, over 971714.09 frames.], batch size: 17, lr: 8.88e-04
2022-05-03 23:59:13,199 INFO [train.py:715] (1/8) Epoch 1, batch 20350, loss[loss=0.1975, simple_loss=0.2574, pruned_loss=0.06877, over 4805.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2487, pruned_loss=0.06181, over 971933.43 frames.], batch size: 24, lr: 8.88e-04
2022-05-03 23:59:51,738 INFO [train.py:715] (1/8) Epoch 1, batch 20400, loss[loss=0.1704, simple_loss=0.2424, pruned_loss=0.04922, over 4931.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2482, pruned_loss=0.06103, over 972347.73 frames.], batch size: 29, lr: 8.87e-04
2022-05-04 00:00:31,297 INFO [train.py:715] (1/8) Epoch 1, batch 20450, loss[loss=0.1826, simple_loss=0.2436, pruned_loss=0.06086, over 4970.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2484, pruned_loss=0.06058, over 972223.67 frames.], batch size: 15, lr: 8.87e-04
2022-05-04 00:01:10,342 INFO [train.py:715] (1/8) Epoch 1, batch 20500, loss[loss=0.2153, simple_loss=0.2756, pruned_loss=0.07745, over 4878.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2487, pruned_loss=0.06089, over 972560.14 frames.], batch size: 16, lr: 8.87e-04
2022-05-04 00:01:50,040 INFO [train.py:715] (1/8) Epoch 1, batch 20550, loss[loss=0.2043, simple_loss=0.2697, pruned_loss=0.06947, over 4791.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2487, pruned_loss=0.06116, over 973121.64 frames.], batch size: 14, lr: 8.86e-04
2022-05-04 00:02:28,906 INFO [train.py:715] (1/8) Epoch 1, batch 20600, loss[loss=0.1872, simple_loss=0.2593, pruned_loss=0.05756, over 4987.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2471, pruned_loss=0.05959, over 973050.11 frames.], batch size: 25, lr: 8.86e-04
2022-05-04 00:03:08,452 INFO [train.py:715] (1/8) Epoch 1, batch 20650, loss[loss=0.1603, simple_loss=0.2343, pruned_loss=0.04316, over 4990.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2466, pruned_loss=0.05914, over 973069.18 frames.], batch size: 25, lr: 8.85e-04
2022-05-04 00:03:48,936 INFO [train.py:715] (1/8) Epoch 1, batch 20700, loss[loss=0.1839, simple_loss=0.2432, pruned_loss=0.06225, over 4756.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2453, pruned_loss=0.05888, over 973200.71 frames.], batch size: 19, lr: 8.85e-04
2022-05-04 00:04:28,576 INFO [train.py:715] (1/8) Epoch 1, batch 20750, loss[loss=0.1676, simple_loss=0.2402, pruned_loss=0.0475, over 4915.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2464, pruned_loss=0.05938, over 972826.98 frames.], batch size: 18, lr: 8.85e-04
2022-05-04 00:05:07,878 INFO [train.py:715] (1/8) Epoch 1, batch 20800, loss[loss=0.1962, simple_loss=0.2626, pruned_loss=0.0649, over 4809.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2466, pruned_loss=0.05935, over 971734.61 frames.], batch size: 25, lr: 8.84e-04
2022-05-04 00:05:47,730 INFO [train.py:715] (1/8) Epoch 1, batch 20850, loss[loss=0.14, simple_loss=0.2121, pruned_loss=0.03391, over 4828.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2459, pruned_loss=0.05867, over 971329.39 frames.], batch size: 26, lr: 8.84e-04
2022-05-04 00:06:27,486 INFO [train.py:715] (1/8) Epoch 1, batch 20900, loss[loss=0.1658, simple_loss=0.2364, pruned_loss=0.04759, over 4977.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2467, pruned_loss=0.05914, over 972292.21 frames.], batch size: 24, lr: 8.83e-04
2022-05-04 00:07:06,275 INFO [train.py:715] (1/8) Epoch 1, batch 20950, loss[loss=0.177, simple_loss=0.2384, pruned_loss=0.05784, over 4961.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2455, pruned_loss=0.05844, over 972813.78 frames.], batch size: 14, lr: 8.83e-04
2022-05-04 00:07:45,656 INFO [train.py:715] (1/8) Epoch 1, batch 21000, loss[loss=0.2042, simple_loss=0.2678, pruned_loss=0.07027, over 4754.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2447, pruned_loss=0.05808, over 973369.19 frames.], batch size: 19, lr: 8.83e-04
2022-05-04 00:07:45,656 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 00:08:00,762 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1226, simple_loss=0.2094, pruned_loss=0.01784, over 914524.00 frames.
2022-05-04 00:08:40,107 INFO [train.py:715] (1/8) Epoch 1, batch 21050, loss[loss=0.2068, simple_loss=0.2762, pruned_loss=0.06869, over 4928.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2453, pruned_loss=0.05856, over 973808.06 frames.], batch size: 23, lr: 8.82e-04
2022-05-04 00:09:19,948 INFO [train.py:715] (1/8) Epoch 1, batch 21100, loss[loss=0.1664, simple_loss=0.2349, pruned_loss=0.04893, over 4985.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2452, pruned_loss=0.05881, over 973277.56 frames.], batch size: 24, lr: 8.82e-04
2022-05-04 00:09:58,321 INFO [train.py:715] (1/8) Epoch 1, batch 21150, loss[loss=0.1776, simple_loss=0.2489, pruned_loss=0.05318, over 4957.00 frames.], tot_loss[loss=0.1799, simple_loss=0.244, pruned_loss=0.05784, over 973788.01 frames.], batch size: 21, lr: 8.81e-04
2022-05-04 00:10:40,727 INFO [train.py:715] (1/8) Epoch 1, batch 21200, loss[loss=0.1696, simple_loss=0.2315, pruned_loss=0.0539, over 4806.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2453, pruned_loss=0.05856, over 974682.21 frames.], batch size: 25, lr: 8.81e-04
2022-05-04 00:11:20,088 INFO [train.py:715] (1/8) Epoch 1, batch 21250, loss[loss=0.1723, simple_loss=0.2324, pruned_loss=0.05609, over 4931.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2458, pruned_loss=0.0593, over 975248.63 frames.], batch size: 29, lr: 8.81e-04
2022-05-04 00:11:59,260 INFO [train.py:715] (1/8) Epoch 1, batch 21300, loss[loss=0.2139, simple_loss=0.2668, pruned_loss=0.08051, over 4893.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2457, pruned_loss=0.05908, over 975436.88 frames.], batch size: 16, lr: 8.80e-04
2022-05-04 00:12:38,145 INFO [train.py:715] (1/8) Epoch 1, batch 21350, loss[loss=0.2088, simple_loss=0.2779, pruned_loss=0.06987, over 4757.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2454, pruned_loss=0.05897, over 974953.98 frames.], batch size: 19, lr: 8.80e-04
2022-05-04 00:13:17,801 INFO [train.py:715] (1/8) Epoch 1, batch 21400, loss[loss=0.2034, simple_loss=0.268, pruned_loss=0.06939, over 4967.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2465, pruned_loss=0.05964, over 974738.35 frames.], batch size: 24, lr: 8.80e-04
2022-05-04 00:13:57,966 INFO [train.py:715] (1/8) Epoch 1, batch 21450, loss[loss=0.187, simple_loss=0.2459, pruned_loss=0.06407, over 4992.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2464, pruned_loss=0.05962, over 974628.65 frames.], batch size: 16, lr: 8.79e-04
2022-05-04 00:14:36,212 INFO [train.py:715] (1/8) Epoch 1, batch 21500, loss[loss=0.189, simple_loss=0.2406, pruned_loss=0.0687, over 4847.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2469, pruned_loss=0.05948, over 974116.80 frames.], batch size: 13, lr: 8.79e-04
2022-05-04 00:15:15,308 INFO [train.py:715] (1/8) Epoch 1, batch 21550, loss[loss=0.1559, simple_loss=0.2274, pruned_loss=0.04221, over 4690.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2478, pruned_loss=0.05984, over 974472.67 frames.], batch size: 15, lr: 8.78e-04
2022-05-04 00:15:54,607 INFO [train.py:715] (1/8) Epoch 1, batch 21600, loss[loss=0.1974, simple_loss=0.2544, pruned_loss=0.07021, over 4860.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2467, pruned_loss=0.0592, over 973235.61 frames.], batch size: 20, lr: 8.78e-04
2022-05-04 00:16:33,916 INFO [train.py:715] (1/8) Epoch 1, batch 21650, loss[loss=0.1508, simple_loss=0.2251, pruned_loss=0.03826, over 4759.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2479, pruned_loss=0.05987, over 973353.51 frames.], batch size: 19, lr: 8.78e-04
2022-05-04 00:17:12,479 INFO [train.py:715] (1/8) Epoch 1, batch 21700, loss[loss=0.1769, simple_loss=0.2467, pruned_loss=0.05355, over 4973.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2477, pruned_loss=0.05988, over 972650.07 frames.], batch size: 24, lr: 8.77e-04
2022-05-04 00:17:52,129 INFO [train.py:715] (1/8) Epoch 1, batch 21750, loss[loss=0.1733, simple_loss=0.2287, pruned_loss=0.05901, over 4868.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2475, pruned_loss=0.05985, over 972809.52 frames.], batch size: 16, lr: 8.77e-04
2022-05-04 00:18:31,688 INFO [train.py:715] (1/8) Epoch 1, batch 21800, loss[loss=0.1966, simple_loss=0.2559, pruned_loss=0.06865, over 4740.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2474, pruned_loss=0.0597, over 972758.44 frames.], batch size: 16, lr: 8.76e-04
2022-05-04 00:19:10,438 INFO [train.py:715] (1/8) Epoch 1, batch 21850, loss[loss=0.1526, simple_loss=0.2235, pruned_loss=0.0408, over 4988.00 frames.], tot_loss[loss=0.1822, simple_loss=0.246, pruned_loss=0.05921, over 972336.46 frames.], batch size: 28, lr: 8.76e-04
2022-05-04 00:19:50,596 INFO [train.py:715] (1/8) Epoch 1, batch 21900, loss[loss=0.2098, simple_loss=0.2711, pruned_loss=0.07427, over 4958.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2455, pruned_loss=0.05893, over 973295.60 frames.], batch size: 24, lr: 8.76e-04
2022-05-04 00:20:30,151 INFO [train.py:715] (1/8) Epoch 1, batch 21950, loss[loss=0.2055, simple_loss=0.2648, pruned_loss=0.07304, over 4891.00 frames.], tot_loss[loss=0.182, simple_loss=0.2453, pruned_loss=0.05936, over 972769.19 frames.], batch size: 22, lr: 8.75e-04
2022-05-04 00:21:09,934 INFO [train.py:715] (1/8) Epoch 1, batch 22000, loss[loss=0.1926, simple_loss=0.2546, pruned_loss=0.06526, over 4878.00 frames.], tot_loss[loss=0.181, simple_loss=0.2446, pruned_loss=0.05865, over 972689.64 frames.], batch size: 16, lr: 8.75e-04
2022-05-04 00:21:48,904 INFO [train.py:715] (1/8) Epoch 1, batch 22050, loss[loss=0.1526, simple_loss=0.2188, pruned_loss=0.04325, over 4990.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2457, pruned_loss=0.05923, over 973135.83 frames.], batch size: 27, lr: 8.75e-04
2022-05-04 00:22:28,892 INFO [train.py:715] (1/8) Epoch 1, batch 22100, loss[loss=0.1995, simple_loss=0.2581, pruned_loss=0.07049, over 4801.00 frames.], tot_loss[loss=0.1821, simple_loss=0.246, pruned_loss=0.05908, over 973324.16 frames.], batch size: 21, lr: 8.74e-04
2022-05-04 00:23:08,222 INFO [train.py:715] (1/8) Epoch 1, batch 22150, loss[loss=0.174, simple_loss=0.249, pruned_loss=0.04954, over 4788.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2465, pruned_loss=0.05964, over 973284.38 frames.], batch size: 18, lr: 8.74e-04
2022-05-04 00:23:46,647 INFO [train.py:715] (1/8) Epoch 1, batch 22200, loss[loss=0.1984, simple_loss=0.2523, pruned_loss=0.07224, over 4819.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2467, pruned_loss=0.05981, over 973340.29 frames.], batch size: 26, lr: 8.73e-04
2022-05-04 00:24:25,881 INFO [train.py:715] (1/8) Epoch 1, batch 22250, loss[loss=0.1772, simple_loss=0.2486, pruned_loss=0.05288, over 4847.00 frames.], tot_loss[loss=0.183, simple_loss=0.2465, pruned_loss=0.0598, over 973203.16 frames.], batch size: 30, lr: 8.73e-04
2022-05-04 00:25:05,561 INFO [train.py:715] (1/8) Epoch 1, batch 22300, loss[loss=0.2107, simple_loss=0.2743, pruned_loss=0.07359, over 4908.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2467, pruned_loss=0.06013, over 973324.42 frames.], batch size: 19, lr: 8.73e-04
2022-05-04 00:25:45,326 INFO [train.py:715] (1/8) Epoch 1, batch 22350, loss[loss=0.1902, simple_loss=0.2547, pruned_loss=0.06292, over 4979.00 frames.], tot_loss[loss=0.183, simple_loss=0.247, pruned_loss=0.05954, over 973533.76 frames.], batch size: 24, lr: 8.72e-04
2022-05-04 00:26:24,288 INFO [train.py:715] (1/8) Epoch 1, batch 22400, loss[loss=0.2177, simple_loss=0.2741, pruned_loss=0.08063, over 4896.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2456, pruned_loss=0.05853, over 973073.82 frames.], batch size: 17, lr: 8.72e-04
2022-05-04 00:27:04,012 INFO [train.py:715] (1/8) Epoch 1, batch 22450, loss[loss=0.2154, simple_loss=0.2726, pruned_loss=0.07909, over 4814.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2454, pruned_loss=0.05823, over 973273.94 frames.], batch size: 26, lr: 8.72e-04
2022-05-04 00:27:43,649 INFO [train.py:715] (1/8) Epoch 1, batch 22500, loss[loss=0.1603, simple_loss=0.2238, pruned_loss=0.04846, over 4655.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2466, pruned_loss=0.05949, over 973273.24 frames.], batch size: 13, lr: 8.71e-04
2022-05-04 00:28:22,146 INFO [train.py:715] (1/8) Epoch 1, batch 22550, loss[loss=0.1862, simple_loss=0.24, pruned_loss=0.06622, over 4808.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2458, pruned_loss=0.05902, over 973957.22 frames.], batch size: 21, lr: 8.71e-04
2022-05-04 00:29:02,211 INFO [train.py:715] (1/8) Epoch 1, batch 22600, loss[loss=0.1809, simple_loss=0.2513, pruned_loss=0.05531, over 4981.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2457, pruned_loss=0.05834, over 974149.17 frames.], batch size: 35, lr: 8.70e-04
2022-05-04 00:29:42,690 INFO [train.py:715] (1/8) Epoch 1, batch 22650, loss[loss=0.1862, simple_loss=0.2523, pruned_loss=0.06003, over 4775.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2455, pruned_loss=0.05844, over 974012.26 frames.], batch size: 17, lr: 8.70e-04
2022-05-04 00:30:22,584 INFO [train.py:715] (1/8) Epoch 1, batch 22700, loss[loss=0.2613, simple_loss=0.3188, pruned_loss=0.1019, over 4786.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2481, pruned_loss=0.06004, over 973276.41 frames.], batch size: 17, lr: 8.70e-04
2022-05-04 00:31:00,978 INFO [train.py:715] (1/8) Epoch 1, batch 22750, loss[loss=0.1943, simple_loss=0.2569, pruned_loss=0.06585, over 4858.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2489, pruned_loss=0.06031, over 972964.97 frames.], batch size: 32, lr: 8.69e-04
2022-05-04 00:31:41,159 INFO [train.py:715] (1/8) Epoch 1, batch 22800, loss[loss=0.1864, simple_loss=0.24, pruned_loss=0.0664, over 4938.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2494, pruned_loss=0.06066, over 972645.97 frames.], batch size: 29, lr: 8.69e-04
2022-05-04 00:32:20,888 INFO [train.py:715] (1/8) Epoch 1, batch 22850, loss[loss=0.2061, simple_loss=0.251, pruned_loss=0.08064, over 4941.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2481, pruned_loss=0.0599, over 972070.73 frames.], batch size: 23, lr: 8.68e-04
2022-05-04 00:32:59,719 INFO [train.py:715] (1/8) Epoch 1, batch 22900, loss[loss=0.1725, simple_loss=0.2338, pruned_loss=0.0556, over 4965.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2474, pruned_loss=0.05963, over 971492.68 frames.], batch size: 14, lr: 8.68e-04
2022-05-04 00:33:39,274 INFO [train.py:715] (1/8) Epoch 1, batch 22950, loss[loss=0.1712, simple_loss=0.2458, pruned_loss=0.04833, over 4906.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2462, pruned_loss=0.05878, over 972484.33 frames.], batch size: 19, lr: 8.68e-04
2022-05-04 00:34:19,072 INFO [train.py:715] (1/8) Epoch 1, batch 23000, loss[loss=0.1439, simple_loss=0.2038, pruned_loss=0.04201, over 4823.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2462, pruned_loss=0.05906, over 971978.01 frames.], batch size: 12, lr: 8.67e-04
2022-05-04 00:34:57,982 INFO [train.py:715] (1/8) Epoch 1, batch 23050, loss[loss=0.2078, simple_loss=0.2868, pruned_loss=0.06439, over 4899.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2472, pruned_loss=0.05963, over 971985.70 frames.], batch size: 38, lr: 8.67e-04
2022-05-04 00:35:37,119 INFO [train.py:715] (1/8) Epoch 1, batch 23100, loss[loss=0.1728, simple_loss=0.236, pruned_loss=0.05486, over 4773.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2458, pruned_loss=0.0589, over 971770.76 frames.], batch size: 17, lr: 8.67e-04
2022-05-04 00:36:16,856 INFO [train.py:715] (1/8) Epoch 1, batch 23150, loss[loss=0.1607, simple_loss=0.2265, pruned_loss=0.04749, over 4856.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2467, pruned_loss=0.05958, over 972904.29 frames.], batch size: 34, lr: 8.66e-04
2022-05-04 00:36:56,383 INFO [train.py:715] (1/8) Epoch 1, batch 23200, loss[loss=0.1307, simple_loss=0.1985, pruned_loss=0.03142, over 4848.00 frames.], tot_loss[loss=0.182, simple_loss=0.246, pruned_loss=0.05898, over 973851.54 frames.], batch size: 12, lr: 8.66e-04
2022-05-04 00:37:34,613 INFO [train.py:715] (1/8) Epoch 1, batch 23250, loss[loss=0.1792, simple_loss=0.2416, pruned_loss=0.05838, over 4775.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2464, pruned_loss=0.05921, over 972684.48 frames.], batch size: 18, lr: 8.66e-04
2022-05-04 00:38:14,196 INFO [train.py:715] (1/8) Epoch 1, batch 23300, loss[loss=0.1852, simple_loss=0.2502, pruned_loss=0.06008, over 4855.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2464, pruned_loss=0.05896, over 972839.23 frames.], batch size: 30, lr: 8.65e-04
2022-05-04 00:38:53,768 INFO [train.py:715] (1/8) Epoch 1, batch 23350, loss[loss=0.1724, simple_loss=0.2323, pruned_loss=0.05627, over 4789.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2456, pruned_loss=0.05861, over 973007.30 frames.], batch size: 21, lr: 8.65e-04
2022-05-04 00:39:32,069 INFO [train.py:715] (1/8) Epoch 1, batch 23400, loss[loss=0.1797, simple_loss=0.243, pruned_loss=0.05825, over 4851.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2471, pruned_loss=0.05968, over 973224.99 frames.], batch size: 34, lr: 8.64e-04
2022-05-04 00:40:11,306 INFO [train.py:715] (1/8) Epoch 1, batch 23450, loss[loss=0.1437, simple_loss=0.2058, pruned_loss=0.04083, over 4798.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2458, pruned_loss=0.05931, over 973071.37 frames.], batch size: 12, lr: 8.64e-04
2022-05-04 00:40:50,693 INFO [train.py:715] (1/8) Epoch 1, batch 23500, loss[loss=0.2402, simple_loss=0.2864, pruned_loss=0.09699, over 4754.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2459, pruned_loss=0.05922, over 972333.40 frames.], batch size: 14, lr: 8.64e-04
2022-05-04 00:41:29,527 INFO [train.py:715] (1/8) Epoch 1, batch 23550, loss[loss=0.2, simple_loss=0.2524, pruned_loss=0.07382, over 4964.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2462, pruned_loss=0.05952, over 971306.87 frames.], batch size: 35, lr: 8.63e-04
2022-05-04 00:42:07,726 INFO [train.py:715] (1/8) Epoch 1, batch 23600, loss[loss=0.2012, simple_loss=0.2604, pruned_loss=0.07101, over 4941.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2458, pruned_loss=0.0592, over 971405.68 frames.], batch size: 35, lr: 8.63e-04
2022-05-04 00:42:47,235 INFO [train.py:715] (1/8) Epoch 1, batch 23650, loss[loss=0.1573, simple_loss=0.2356, pruned_loss=0.03951, over 4889.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2448, pruned_loss=0.05883, over 971801.06 frames.], batch size: 22, lr: 8.63e-04
2022-05-04 00:43:26,750 INFO [train.py:715] (1/8) Epoch 1, batch 23700, loss[loss=0.1836, simple_loss=0.2498, pruned_loss=0.05873, over 4819.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2446, pruned_loss=0.05859, over 971553.63 frames.], batch size: 27, lr: 8.62e-04
2022-05-04 00:44:05,090 INFO [train.py:715] (1/8) Epoch 1, batch 23750, loss[loss=0.1801, simple_loss=0.2372, pruned_loss=0.06157, over 4920.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2452, pruned_loss=0.0589, over 972075.55 frames.], batch size: 18, lr: 8.62e-04
2022-05-04 00:44:44,143 INFO [train.py:715] (1/8) Epoch 1, batch 23800, loss[loss=0.1513, simple_loss=0.2294, pruned_loss=0.03662, over 4803.00 frames.], tot_loss[loss=0.182, simple_loss=0.2457, pruned_loss=0.05909, over 973330.49 frames.], batch size: 21, lr: 8.61e-04
2022-05-04 00:45:24,225 INFO [train.py:715] (1/8) Epoch 1, batch 23850, loss[loss=0.1667, simple_loss=0.2384, pruned_loss=0.04748, over 4878.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2471, pruned_loss=0.06003, over 973162.58 frames.], batch size: 22, lr: 8.61e-04
2022-05-04 00:46:03,789 INFO [train.py:715] (1/8) Epoch 1, batch 23900, loss[loss=0.1903, simple_loss=0.2367, pruned_loss=0.0719, over 4875.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2468, pruned_loss=0.05996, over 972256.12 frames.], batch size: 32, lr: 8.61e-04
2022-05-04 00:46:42,588 INFO [train.py:715] (1/8) Epoch 1, batch 23950, loss[loss=0.2038, simple_loss=0.2513, pruned_loss=0.0782, over 4800.00 frames.], tot_loss[loss=0.1835, simple_loss=0.247, pruned_loss=0.06, over 971767.66 frames.], batch size: 17, lr: 8.60e-04
2022-05-04 00:47:22,330 INFO [train.py:715] (1/8) Epoch 1, batch 24000, loss[loss=0.2161, simple_loss=0.2756, pruned_loss=0.07833, over 4928.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2463, pruned_loss=0.05922, over 971892.55 frames.], batch size: 18, lr: 8.60e-04
2022-05-04 00:47:22,330 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 00:47:34,530 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1217, simple_loss=0.2087, pruned_loss=0.01736, over 914524.00 frames.
2022-05-04 00:48:14,355 INFO [train.py:715] (1/8) Epoch 1, batch 24050, loss[loss=0.1914, simple_loss=0.2561, pruned_loss=0.06339, over 4974.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2463, pruned_loss=0.05938, over 972001.68 frames.], batch size: 31, lr: 8.60e-04
2022-05-04 00:48:53,680 INFO [train.py:715] (1/8) Epoch 1, batch 24100, loss[loss=0.1802, simple_loss=0.2408, pruned_loss=0.05974, over 4982.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2468, pruned_loss=0.05948, over 971897.73 frames.], batch size: 25, lr: 8.59e-04
2022-05-04 00:49:32,278 INFO [train.py:715] (1/8) Epoch 1, batch 24150, loss[loss=0.1643, simple_loss=0.2314, pruned_loss=0.04858, over 4880.00 frames.], tot_loss[loss=0.1819, simple_loss=0.246, pruned_loss=0.05889, over 972174.78 frames.], batch size: 16, lr: 8.59e-04
2022-05-04 00:50:11,571 INFO [train.py:715] (1/8) Epoch 1, batch 24200, loss[loss=0.1838, simple_loss=0.2516, pruned_loss=0.05795, over 4903.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2464, pruned_loss=0.05906, over 971691.07 frames.], batch size: 23, lr: 8.59e-04
2022-05-04 00:50:52,248 INFO [train.py:715] (1/8) Epoch 1, batch 24250, loss[loss=0.1777, simple_loss=0.2387, pruned_loss=0.05836, over 4949.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2458, pruned_loss=0.05789, over 972792.26 frames.], batch size: 21, lr: 8.58e-04
2022-05-04 00:51:31,678 INFO [train.py:715] (1/8) Epoch 1, batch 24300, loss[loss=0.1683, simple_loss=0.2305, pruned_loss=0.05298, over 4916.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2461, pruned_loss=0.05818, over 973247.94 frames.], batch size: 17, lr: 8.58e-04
2022-05-04 00:52:11,121 INFO [train.py:715] (1/8) Epoch 1, batch 24350, loss[loss=0.1779, simple_loss=0.246, pruned_loss=0.05487, over 4933.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2459, pruned_loss=0.05849, over 972626.94 frames.], batch size: 23, lr: 8.57e-04
2022-05-04 00:52:51,499 INFO [train.py:715] (1/8) Epoch 1, batch 24400, loss[loss=0.1769, simple_loss=0.2518, pruned_loss=0.05105, over 4748.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2465, pruned_loss=0.05842, over 973011.79 frames.], batch size: 19, lr: 8.57e-04
2022-05-04 00:53:30,579 INFO [train.py:715] (1/8) Epoch 1, batch 24450, loss[loss=0.1919, simple_loss=0.2568, pruned_loss=0.06343, over 4898.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2467, pruned_loss=0.05899, over 972934.20 frames.], batch size: 17, lr: 8.57e-04
2022-05-04 00:54:09,301 INFO [train.py:715] (1/8) Epoch 1, batch 24500, loss[loss=0.1945, simple_loss=0.2613, pruned_loss=0.06389, over 4917.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2482, pruned_loss=0.05968, over 972308.56 frames.], batch size: 18, lr: 8.56e-04
2022-05-04 00:54:48,963 INFO [train.py:715] (1/8) Epoch 1, batch 24550, loss[loss=0.2239, simple_loss=0.276, pruned_loss=0.0859, over 4869.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2496, pruned_loss=0.06063, over 971972.80 frames.], batch size: 20, lr: 8.56e-04
2022-05-04 00:55:29,261 INFO [train.py:715] (1/8) Epoch 1, batch 24600, loss[loss=0.1679, simple_loss=0.2415, pruned_loss=0.04719, over 4703.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2485, pruned_loss=0.06027, over 972581.31 frames.], batch size: 15, lr: 8.56e-04
2022-05-04 00:56:08,133 INFO [train.py:715] (1/8) Epoch 1, batch 24650, loss[loss=0.1996, simple_loss=0.2603, pruned_loss=0.06946, over 4768.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2485, pruned_loss=0.06016, over 972553.81 frames.], batch size: 19, lr: 8.55e-04
2022-05-04 00:56:47,161 INFO [train.py:715] (1/8) Epoch 1, batch 24700, loss[loss=0.1662, simple_loss=0.2255, pruned_loss=0.05346, over 4831.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2472, pruned_loss=0.05952, over 972879.86 frames.], batch size: 15, lr: 8.55e-04
2022-05-04 00:57:27,341 INFO [train.py:715] (1/8) Epoch 1, batch 24750, loss[loss=0.1422, simple_loss=0.2126, pruned_loss=0.03588, over 4841.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2472, pruned_loss=0.05925, over 972077.49 frames.], batch size: 26, lr: 8.55e-04
2022-05-04 00:58:06,479 INFO [train.py:715] (1/8) Epoch 1, batch 24800, loss[loss=0.1932, simple_loss=0.2544, pruned_loss=0.06605, over 4789.00 frames.], tot_loss[loss=0.1827, simple_loss=0.247, pruned_loss=0.05918, over 972361.34 frames.], batch size: 17, lr: 8.54e-04
2022-05-04 00:58:45,103 INFO [train.py:715] (1/8) Epoch 1, batch 24850, loss[loss=0.1769, simple_loss=0.2364, pruned_loss=0.05868, over 4892.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2471, pruned_loss=0.05928, over 972518.29 frames.], batch size: 17, lr: 8.54e-04
2022-05-04 00:59:25,589 INFO [train.py:715] (1/8) Epoch 1, batch 24900, loss[loss=0.1791, simple_loss=0.2383, pruned_loss=0.05993, over 4695.00 frames.], tot_loss[loss=0.183, simple_loss=0.2473, pruned_loss=0.05932, over 971965.74 frames.], batch size: 15, lr: 8.54e-04
2022-05-04 01:00:05,520 INFO [train.py:715] (1/8) Epoch 1, batch 24950, loss[loss=0.1926, simple_loss=0.2715, pruned_loss=0.05681, over 4780.00 frames.], tot_loss[loss=0.1827, simple_loss=0.247, pruned_loss=0.05919, over 972006.20 frames.], batch size: 18, lr: 8.53e-04
2022-05-04 01:00:44,291 INFO [train.py:715] (1/8) Epoch 1, batch 25000, loss[loss=0.1472, simple_loss=0.2143, pruned_loss=0.0401, over 4792.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2459, pruned_loss=0.0588, over 971576.75 frames.], batch size: 24, lr: 8.53e-04
2022-05-04 01:01:22,935 INFO [train.py:715] (1/8) Epoch 1, batch 25050, loss[loss=0.1789, simple_loss=0.2487, pruned_loss=0.05453, over 4822.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2455, pruned_loss=0.05855, over 972610.59 frames.], batch size: 26, lr: 8.53e-04
2022-05-04 01:02:02,852 INFO [train.py:715] (1/8) Epoch 1, batch 25100, loss[loss=0.1874, simple_loss=0.2463, pruned_loss=0.0643, over 4806.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2455, pruned_loss=0.05893, over 973028.15 frames.], batch size: 25, lr: 8.52e-04
2022-05-04 01:02:42,033 INFO [train.py:715] (1/8) Epoch 1, batch 25150, loss[loss=0.2173, simple_loss=0.2771, pruned_loss=0.07869, over 4946.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2464, pruned_loss=0.05927, over 972583.79 frames.], batch size: 24, lr: 8.52e-04
2022-05-04 01:03:20,874 INFO [train.py:715] (1/8) Epoch 1, batch 25200, loss[loss=0.157, simple_loss=0.2268, pruned_loss=0.04364, over 4917.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2462, pruned_loss=0.05935, over 972110.72 frames.], batch size: 39, lr: 8.51e-04
2022-05-04 01:04:00,098 INFO [train.py:715] (1/8) Epoch 1, batch 25250, loss[loss=0.1718, simple_loss=0.2405, pruned_loss=0.05155, over 4814.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2466, pruned_loss=0.05959, over 971550.67 frames.], batch size: 13, lr: 8.51e-04
2022-05-04 01:04:40,220 INFO [train.py:715] (1/8) Epoch 1, batch 25300, loss[loss=0.157, simple_loss=0.2244, pruned_loss=0.04483, over 4820.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2456, pruned_loss=0.0593, over 972220.88 frames.], batch size: 25, lr: 8.51e-04
2022-05-04 01:05:18,875 INFO [train.py:715] (1/8) Epoch 1, batch 25350, loss[loss=0.1526, simple_loss=0.2236, pruned_loss=0.04078, over 4763.00 frames.], tot_loss[loss=0.182, simple_loss=0.2454, pruned_loss=0.05925, over 972475.75 frames.], batch size: 19, lr: 8.50e-04
2022-05-04 01:05:58,212 INFO [train.py:715] (1/8) Epoch 1, batch 25400, loss[loss=0.1681, simple_loss=0.2369, pruned_loss=0.0496, over 4775.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2447, pruned_loss=0.0587, over 973091.05 frames.], batch size: 17, lr: 8.50e-04
2022-05-04 01:06:38,478 INFO [train.py:715] (1/8) Epoch 1, batch 25450, loss[loss=0.1996, simple_loss=0.2492, pruned_loss=0.07506, over 4899.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2449, pruned_loss=0.05897, over 972542.80 frames.], batch size: 19, lr: 8.50e-04
2022-05-04 01:07:18,420 INFO [train.py:715] (1/8) Epoch 1, batch 25500, loss[loss=0.2003, simple_loss=0.2503, pruned_loss=0.07511, over 4835.00 frames.], tot_loss[loss=0.181, simple_loss=0.2446, pruned_loss=0.05868, over 972163.10 frames.], batch size: 30, lr: 8.49e-04
2022-05-04 01:07:56,848 INFO [train.py:715] (1/8) Epoch 1, batch 25550, loss[loss=0.1887, simple_loss=0.2559, pruned_loss=0.06071, over 4780.00 frames.], tot_loss[loss=0.1813, simple_loss=0.245, pruned_loss=0.05878, over 972420.58 frames.], batch size: 18, lr: 8.49e-04
2022-05-04 01:08:36,979 INFO [train.py:715] (1/8) Epoch 1, batch 25600, loss[loss=0.1934, simple_loss=0.2435, pruned_loss=0.0716, over 4904.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2459, pruned_loss=0.05924, over 972460.84 frames.], batch size: 23, lr: 8.49e-04
2022-05-04 01:09:17,497 INFO [train.py:715] (1/8) Epoch 1, batch 25650, loss[loss=0.1619, simple_loss=0.2321, pruned_loss=0.04589, over 4925.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2452, pruned_loss=0.05863, over 973587.31 frames.], batch size: 23, lr: 8.48e-04
2022-05-04 01:09:56,988 INFO [train.py:715] (1/8) Epoch 1, batch 25700, loss[loss=0.1662, simple_loss=0.2314, pruned_loss=0.05053, over 4909.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2448, pruned_loss=0.05866, over 973556.02 frames.], batch size: 19, lr: 8.48e-04
2022-05-04 01:10:36,900 INFO [train.py:715] (1/8) Epoch 1, batch 25750, loss[loss=0.1629, simple_loss=0.2239, pruned_loss=0.05089, over 4835.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2445, pruned_loss=0.05825, over 973276.78 frames.], batch size: 15, lr: 8.48e-04
2022-05-04 01:11:17,395 INFO [train.py:715] (1/8) Epoch 1, batch 25800, loss[loss=0.1787, simple_loss=0.2422, pruned_loss=0.0576, over 4797.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2451, pruned_loss=0.05874, over 971939.99 frames.], batch size: 24, lr: 8.47e-04
2022-05-04 01:11:56,813 INFO [train.py:715] (1/8) Epoch 1, batch 25850, loss[loss=0.1495, simple_loss=0.2179, pruned_loss=0.04056, over 4953.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2436, pruned_loss=0.05778, over 972742.44 frames.], batch size: 14, lr: 8.47e-04
2022-05-04 01:12:35,647 INFO [train.py:715] (1/8) Epoch 1, batch 25900, loss[loss=0.1795, simple_loss=0.2453, pruned_loss=0.05684, over 4948.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2425, pruned_loss=0.05695, over 971943.65 frames.], batch size: 29, lr: 8.47e-04
2022-05-04 01:13:15,319 INFO [train.py:715] (1/8) Epoch 1, batch 25950, loss[loss=0.167, simple_loss=0.239, pruned_loss=0.04752, over 4796.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2426, pruned_loss=0.05694, over 972360.80 frames.], batch size: 25, lr: 8.46e-04
2022-05-04 01:13:55,204 INFO [train.py:715] (1/8) Epoch 1, batch 26000, loss[loss=0.1976, simple_loss=0.2572, pruned_loss=0.06897, over 4977.00 frames.], tot_loss[loss=0.1791, simple_loss=0.243, pruned_loss=0.05762, over 972729.19 frames.], batch size: 24, lr: 8.46e-04
2022-05-04 01:14:34,088 INFO [train.py:715] (1/8) Epoch 1, batch 26050, loss[loss=0.1721, simple_loss=0.2306, pruned_loss=0.05685, over 4820.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2438, pruned_loss=0.05831, over 971561.98 frames.], batch size: 15, lr: 8.46e-04
2022-05-04 01:15:13,489 INFO [train.py:715] (1/8) Epoch 1, batch 26100, loss[loss=0.1695, simple_loss=0.2301, pruned_loss=0.05442, over 4960.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2439, pruned_loss=0.05812, over 971738.15 frames.], batch size: 14, lr: 8.45e-04
2022-05-04 01:15:53,621 INFO [train.py:715] (1/8) Epoch 1, batch 26150, loss[loss=0.1468, simple_loss=0.2124, pruned_loss=0.04065, over 4874.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2439, pruned_loss=0.05773, over 972054.26 frames.], batch size: 16, lr: 8.45e-04
2022-05-04 01:16:32,570 INFO [train.py:715] (1/8) Epoch 1, batch 26200, loss[loss=0.2065, simple_loss=0.2603, pruned_loss=0.07629, over 4853.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2449, pruned_loss=0.05822, over 972860.83 frames.], batch size: 30, lr: 8.44e-04
2022-05-04 01:17:11,434 INFO [train.py:715] (1/8) Epoch 1, batch 26250, loss[loss=0.2069, simple_loss=0.2703, pruned_loss=0.07175, over 4938.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2447, pruned_loss=0.05819, over 971715.05 frames.], batch size: 21, lr: 8.44e-04
2022-05-04 01:17:51,338 INFO [train.py:715] (1/8) Epoch 1, batch 26300, loss[loss=0.1894, simple_loss=0.2547, pruned_loss=0.06201, over 4968.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2445, pruned_loss=0.05819, over 972366.64 frames.], batch size: 25, lr: 8.44e-04
2022-05-04 01:18:31,200 INFO [train.py:715] (1/8) Epoch 1, batch 26350, loss[loss=0.1777, simple_loss=0.2444, pruned_loss=0.05551, over 4871.00 frames.], tot_loss[loss=0.1796, simple_loss=0.244, pruned_loss=0.05766, over 972273.29 frames.], batch size: 20, lr: 8.43e-04
2022-05-04 01:19:09,966 INFO [train.py:715] (1/8) Epoch 1, batch 26400, loss[loss=0.1476, simple_loss=0.2165, pruned_loss=0.03934, over 4969.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2445, pruned_loss=0.05803, over 970975.52 frames.], batch size: 25, lr: 8.43e-04
2022-05-04 01:19:49,169 INFO [train.py:715] (1/8) Epoch 1, batch 26450, loss[loss=0.1858, simple_loss=0.241, pruned_loss=0.06524, over 4808.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2438, pruned_loss=0.05793, over 971426.18 frames.], batch size: 26, lr: 8.43e-04
2022-05-04 01:20:28,904 INFO [train.py:715] (1/8) Epoch 1, batch 26500, loss[loss=0.1718, simple_loss=0.2444, pruned_loss=0.04961, over 4824.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2442, pruned_loss=0.05784, over 971756.84 frames.], batch size: 26, lr: 8.42e-04
2022-05-04 01:21:08,258 INFO [train.py:715] (1/8) Epoch 1, batch 26550, loss[loss=0.1861, simple_loss=0.2414, pruned_loss=0.06537, over 4968.00 frames.], tot_loss[loss=0.1795, simple_loss=0.244, pruned_loss=0.05748, over 971681.79 frames.], batch size: 35, lr: 8.42e-04
2022-05-04 01:21:47,616 INFO [train.py:715] (1/8) Epoch 1, batch 26600, loss[loss=0.162, simple_loss=0.2345, pruned_loss=0.04473, over 4904.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2436, pruned_loss=0.0571, over 971508.64 frames.], batch size: 17, lr: 8.42e-04
2022-05-04 01:22:27,656 INFO [train.py:715] (1/8) Epoch 1, batch 26650, loss[loss=0.179, simple_loss=0.2514, pruned_loss=0.05324, over 4931.00 frames.], tot_loss[loss=0.179, simple_loss=0.2438, pruned_loss=0.05714, over 970879.32 frames.], batch size: 21, lr: 8.41e-04
2022-05-04 01:23:07,611 INFO [train.py:715] (1/8) Epoch 1, batch 26700, loss[loss=0.223, simple_loss=0.273, pruned_loss=0.08649, over 4692.00 frames.], tot_loss[loss=0.1795, simple_loss=0.244, pruned_loss=0.05751, over 970161.66 frames.], batch size: 15, lr: 8.41e-04
2022-05-04 01:23:46,583 INFO [train.py:715] (1/8) Epoch 1, batch 26750, loss[loss=0.1778, simple_loss=0.2486, pruned_loss=0.05348, over 4778.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2453, pruned_loss=0.05827, over 970897.02 frames.], batch size: 17, lr: 8.41e-04
2022-05-04 01:24:26,597 INFO [train.py:715] (1/8) Epoch 1, batch 26800, loss[loss=0.1745, simple_loss=0.2376, pruned_loss=0.0557, over 4873.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2457, pruned_loss=0.05861, over 971555.97 frames.], batch size: 16, lr: 8.40e-04
2022-05-04 01:25:06,138 INFO [train.py:715] (1/8) Epoch 1, batch 26850, loss[loss=0.1695, simple_loss=0.237, pruned_loss=0.05101, over 4888.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2449, pruned_loss=0.05834, over 971566.12 frames.], batch size: 16, lr: 8.40e-04
2022-05-04 01:25:45,417 INFO [train.py:715] (1/8) Epoch 1, batch 26900, loss[loss=0.1981, simple_loss=0.2585, pruned_loss=0.06886, over 4862.00 frames.], tot_loss[loss=0.1811, simple_loss=0.245, pruned_loss=0.05861, over 971557.17 frames.], batch size: 20, lr: 8.40e-04
2022-05-04 01:26:24,109 INFO [train.py:715] (1/8) Epoch 1, batch 26950, loss[loss=0.1645, simple_loss=0.2321, pruned_loss=0.04846, over 4869.00 frames.], tot_loss[loss=0.1819, simple_loss=0.246, pruned_loss=0.05892, over 971081.24 frames.], batch size: 16, lr: 8.39e-04
2022-05-04 01:27:04,119 INFO [train.py:715] (1/8) Epoch 1, batch 27000, loss[loss=0.1681, simple_loss=0.2373, pruned_loss=0.04939, over 4913.00 frames.], tot_loss[loss=0.1822, simple_loss=0.246, pruned_loss=0.05916, over 972666.50 frames.], batch size: 19, lr: 8.39e-04
2022-05-04 01:27:04,120 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 01:27:12,720 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1212, simple_loss=0.2081, pruned_loss=0.01718, over 914524.00 frames.
2022-05-04 01:27:53,055 INFO [train.py:715] (1/8) Epoch 1, batch 27050, loss[loss=0.173, simple_loss=0.2286, pruned_loss=0.05866, over 4972.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2467, pruned_loss=0.05976, over 971649.88 frames.], batch size: 33, lr: 8.39e-04
2022-05-04 01:28:33,372 INFO [train.py:715] (1/8) Epoch 1, batch 27100, loss[loss=0.182, simple_loss=0.262, pruned_loss=0.05105, over 4930.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2457, pruned_loss=0.05891, over 971859.31 frames.], batch size: 21, lr: 8.38e-04
2022-05-04 01:29:11,777 INFO [train.py:715] (1/8) Epoch 1, batch 27150, loss[loss=0.2042, simple_loss=0.2708, pruned_loss=0.06881, over 4741.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2459, pruned_loss=0.05869, over 972358.08 frames.], batch size: 16, lr: 8.38e-04
2022-05-04 01:29:51,720 INFO [train.py:715] (1/8) Epoch 1, batch 27200, loss[loss=0.1788, simple_loss=0.2353, pruned_loss=0.06114, over 4882.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2459, pruned_loss=0.05897, over 972322.27 frames.], batch size: 32, lr: 8.38e-04
2022-05-04 01:30:32,011 INFO [train.py:715] (1/8) Epoch 1, batch 27250, loss[loss=0.1889, simple_loss=0.2607, pruned_loss=0.0585, over 4910.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2466, pruned_loss=0.05898, over 973086.53 frames.], batch size: 19, lr: 8.37e-04
2022-05-04 01:31:11,129 INFO [train.py:715] (1/8) Epoch 1, batch 27300, loss[loss=0.173, simple_loss=0.2371, pruned_loss=0.05448, over 4849.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2464, pruned_loss=0.0589, over 973371.87 frames.], batch size: 30, lr: 8.37e-04
2022-05-04 01:31:49,672 INFO [train.py:715] (1/8) Epoch 1, batch 27350, loss[loss=0.2078, simple_loss=0.2643, pruned_loss=0.07562, over 4688.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2462, pruned_loss=0.05856, over 973150.13 frames.], batch size: 15, lr: 8.37e-04
2022-05-04 01:32:29,597 INFO [train.py:715] (1/8) Epoch 1, batch 27400, loss[loss=0.1964, simple_loss=0.2551, pruned_loss=0.06885, over 4956.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2456, pruned_loss=0.05862, over 972719.14 frames.], batch size: 15, lr: 8.36e-04
2022-05-04 01:33:09,595 INFO [train.py:715] (1/8) Epoch 1, batch 27450, loss[loss=0.2022, simple_loss=0.2757, pruned_loss=0.06441, over 4957.00 frames.], tot_loss[loss=0.1819, simple_loss=0.246, pruned_loss=0.05891, over 972703.46 frames.], batch size: 24, lr: 8.36e-04
2022-05-04 01:33:48,103 INFO [train.py:715] (1/8) Epoch 1, batch 27500, loss[loss=0.16, simple_loss=0.2346, pruned_loss=0.04271, over 4817.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2452, pruned_loss=0.05825, over 972430.79 frames.], batch size: 25, lr: 8.36e-04
2022-05-04 01:34:27,759 INFO [train.py:715] (1/8) Epoch 1, batch 27550, loss[loss=0.151, simple_loss=0.2233, pruned_loss=0.03937, over 4850.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2448, pruned_loss=0.05797, over 973102.07 frames.], batch size: 32, lr: 8.35e-04
2022-05-04 01:35:07,987 INFO [train.py:715] (1/8) Epoch 1, batch 27600, loss[loss=0.1759, simple_loss=0.2371, pruned_loss=0.0573, over 4975.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2447, pruned_loss=0.0581, over 972881.05 frames.], batch size: 24, lr: 8.35e-04
2022-05-04 01:35:47,291 INFO [train.py:715] (1/8) Epoch 1, batch 27650, loss[loss=0.161, simple_loss=0.2337, pruned_loss=0.04419, over 4873.00 frames.], tot_loss[loss=0.181, simple_loss=0.2452, pruned_loss=0.05839, over 973152.88 frames.], batch size: 32, lr: 8.35e-04
2022-05-04 01:36:26,733 INFO [train.py:715] (1/8) Epoch 1, batch 27700, loss[loss=0.1919, simple_loss=0.2549, pruned_loss=0.0644, over 4920.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2447, pruned_loss=0.05819, over 972559.82 frames.], batch size: 39, lr: 8.34e-04
2022-05-04 01:37:07,280 INFO [train.py:715] (1/8) Epoch 1, batch 27750, loss[loss=0.1775, simple_loss=0.235, pruned_loss=0.05999, over 4802.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2449, pruned_loss=0.05825, over 973164.49 frames.], batch size: 25, lr: 8.34e-04
2022-05-04 01:37:47,072 INFO [train.py:715] (1/8) Epoch 1, batch 27800, loss[loss=0.1933, simple_loss=0.2655, pruned_loss=0.06055, over 4813.00 frames.], tot_loss[loss=0.182, simple_loss=0.2464, pruned_loss=0.05876, over 972364.91 frames.], batch size: 25, lr: 8.34e-04
2022-05-04 01:38:26,356 INFO [train.py:715] (1/8) Epoch 1, batch 27850, loss[loss=0.1718, simple_loss=0.2441, pruned_loss=0.04974, over 4886.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2463, pruned_loss=0.05878, over 972082.50 frames.], batch size: 22, lr: 8.33e-04
2022-05-04 01:39:06,463 INFO [train.py:715] (1/8) Epoch 1, batch 27900, loss[loss=0.1829, simple_loss=0.2497, pruned_loss=0.05803, over 4701.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2464, pruned_loss=0.05873, over 972409.13 frames.], batch size: 15, lr: 8.33e-04
2022-05-04 01:39:45,945 INFO [train.py:715] (1/8) Epoch 1, batch 27950, loss[loss=0.1629, simple_loss=0.2319, pruned_loss=0.04693, over 4836.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2463, pruned_loss=0.05877, over 972370.81 frames.], batch size: 15, lr: 8.33e-04
2022-05-04 01:40:25,325 INFO [train.py:715] (1/8) Epoch 1, batch 28000, loss[loss=0.1992, simple_loss=0.2558, pruned_loss=0.07127, over 4785.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2468, pruned_loss=0.05897, over 972892.91 frames.], batch size: 18, lr: 8.32e-04
2022-05-04 01:41:04,104 INFO [train.py:715] (1/8) Epoch 1, batch 28050, loss[loss=0.1605, simple_loss=0.2267, pruned_loss=0.04717, over 4827.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2478, pruned_loss=0.05984, over 972376.48 frames.], batch size: 13, lr: 8.32e-04
2022-05-04 01:41:44,524 INFO [train.py:715] (1/8) Epoch 1, batch 28100, loss[loss=0.1417, simple_loss=0.2203, pruned_loss=0.03157, over 4710.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2474, pruned_loss=0.05979, over 972411.50 frames.], batch size: 15, lr: 8.32e-04
2022-05-04 01:42:23,899 INFO [train.py:715] (1/8) Epoch 1, batch 28150, loss[loss=0.1527, simple_loss=0.2258, pruned_loss=0.03983, over 4944.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2469, pruned_loss=0.05903, over 972098.82 frames.], batch size: 23, lr: 8.31e-04
2022-05-04 01:43:03,287 INFO [train.py:715] (1/8) Epoch 1, batch 28200, loss[loss=0.1797, simple_loss=0.2437, pruned_loss=0.05782, over 4922.00 frames.], tot_loss[loss=0.181, simple_loss=0.2457, pruned_loss=0.05814, over 971899.13 frames.], batch size: 23, lr: 8.31e-04
2022-05-04 01:43:43,968 INFO [train.py:715] (1/8) Epoch 1, batch 28250, loss[loss=0.1774, simple_loss=0.2306, pruned_loss=0.06213, over 4744.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2455, pruned_loss=0.05786, over 971895.48 frames.], batch size: 16, lr: 8.31e-04
2022-05-04 01:44:24,415 INFO [train.py:715] (1/8) Epoch 1, batch 28300, loss[loss=0.199, simple_loss=0.2597, pruned_loss=0.06918, over 4849.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2456, pruned_loss=0.05806, over 972527.67 frames.], batch size: 32, lr: 8.30e-04
2022-05-04 01:45:03,749 INFO [train.py:715] (1/8) Epoch 1, batch 28350, loss[loss=0.2196, simple_loss=0.2711, pruned_loss=0.08403, over 4839.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2472, pruned_loss=0.05912, over 972367.66 frames.], batch size: 26, lr: 8.30e-04
2022-05-04 01:45:42,700 INFO [train.py:715] (1/8) Epoch 1, batch 28400, loss[loss=0.1422, simple_loss=0.2118, pruned_loss=0.03634, over 4686.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2451, pruned_loss=0.0577, over 972929.70 frames.], batch size: 15, lr: 8.30e-04
2022-05-04 01:46:23,130 INFO [train.py:715] (1/8) Epoch 1, batch 28450, loss[loss=0.1811, simple_loss=0.2429, pruned_loss=0.05967, over 4819.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2449, pruned_loss=0.05785, over 972750.43 frames.], batch size: 12, lr: 8.29e-04
2022-05-04 01:47:02,714 INFO [train.py:715] (1/8) Epoch 1, batch 28500, loss[loss=0.1651, simple_loss=0.234, pruned_loss=0.04807, over 4968.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2447, pruned_loss=0.0572, over 972405.71 frames.], batch size: 15, lr: 8.29e-04
2022-05-04 01:47:41,717 INFO [train.py:715] (1/8) Epoch 1, batch 28550, loss[loss=0.1321, simple_loss=0.2039, pruned_loss=0.03017, over 4888.00 frames.], tot_loss[loss=0.1809, simple_loss=0.246, pruned_loss=0.0579, over 973352.11 frames.], batch size: 19, lr: 8.29e-04
2022-05-04 01:48:22,001 INFO [train.py:715] (1/8) Epoch 1, batch 28600, loss[loss=0.1645, simple_loss=0.2342, pruned_loss=0.04738, over 4916.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2456, pruned_loss=0.05774, over 973426.20 frames.], batch size: 18, lr: 8.28e-04
2022-05-04 01:49:01,949 INFO [train.py:715] (1/8) Epoch 1, batch 28650, loss[loss=0.1863, simple_loss=0.2593, pruned_loss=0.05661, over 4953.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2458, pruned_loss=0.05754, over 973924.32 frames.], batch size: 15, lr: 8.28e-04
2022-05-04 01:49:41,102 INFO [train.py:715] (1/8) Epoch 1, batch 28700, loss[loss=0.2011, simple_loss=0.268, pruned_loss=0.06716, over 4789.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2458, pruned_loss=0.05776, over 973530.15 frames.], batch size: 14, lr: 8.28e-04
2022-05-04 01:50:20,242 INFO [train.py:715] (1/8) Epoch 1, batch 28750, loss[loss=0.2195, simple_loss=0.2723, pruned_loss=0.08331, over 4886.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2449, pruned_loss=0.05763, over 973557.87 frames.], batch size: 19, lr: 8.27e-04
2022-05-04 01:51:00,836 INFO [train.py:715] (1/8) Epoch 1, batch 28800, loss[loss=0.1752, simple_loss=0.2379, pruned_loss=0.05623, over 4882.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2445, pruned_loss=0.0574, over 973246.82 frames.], batch size: 16, lr: 8.27e-04
2022-05-04 01:51:40,145 INFO [train.py:715] (1/8) Epoch 1, batch 28850, loss[loss=0.1879, simple_loss=0.2506, pruned_loss=0.06255, over 4955.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2441, pruned_loss=0.0573, over 972054.33 frames.], batch size: 15, lr: 8.27e-04
2022-05-04 01:52:19,908 INFO [train.py:715] (1/8) Epoch 1, batch 28900, loss[loss=0.179, simple_loss=0.2514, pruned_loss=0.05331, over 4868.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2437, pruned_loss=0.05672, over 972623.52 frames.], batch size: 16, lr: 8.27e-04
2022-05-04 01:53:00,601 INFO [train.py:715] (1/8) Epoch 1, batch 28950, loss[loss=0.1741, simple_loss=0.2336, pruned_loss=0.05726, over 4959.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2436, pruned_loss=0.0566, over 971431.20 frames.], batch size: 24, lr: 8.26e-04
2022-05-04 01:53:40,738 INFO [train.py:715] (1/8) Epoch 1, batch 29000, loss[loss=0.2131, simple_loss=0.2688, pruned_loss=0.07865, over 4786.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2434, pruned_loss=0.05662, over 972234.93 frames.], batch size: 18, lr: 8.26e-04
2022-05-04 01:54:19,714 INFO [train.py:715] (1/8) Epoch 1, batch 29050, loss[loss=0.2046, simple_loss=0.2661, pruned_loss=0.07159, over 4914.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2441, pruned_loss=0.05683, over 972215.47 frames.], batch size: 39, lr: 8.26e-04
2022-05-04 01:54:59,584 INFO [train.py:715] (1/8) Epoch 1, batch 29100, loss[loss=0.1885, simple_loss=0.249, pruned_loss=0.06394, over 4858.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2439, pruned_loss=0.05669, over 972821.02 frames.], batch size: 32, lr: 8.25e-04
2022-05-04 01:55:40,261 INFO [train.py:715] (1/8) Epoch 1, batch 29150, loss[loss=0.1986, simple_loss=0.2603, pruned_loss=0.06846, over 4910.00 frames.], tot_loss[loss=0.179, simple_loss=0.2442, pruned_loss=0.05688, over 972177.26 frames.], batch size: 18, lr: 8.25e-04
2022-05-04 01:56:22,368 INFO [train.py:715] (1/8) Epoch 1, batch 29200, loss[loss=0.179, simple_loss=0.2441, pruned_loss=0.05693, over 4989.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2433, pruned_loss=0.05667, over 972252.95 frames.], batch size: 28, lr: 8.25e-04
2022-05-04 01:57:01,390 INFO [train.py:715] (1/8) Epoch 1, batch 29250, loss[loss=0.2157, simple_loss=0.2787, pruned_loss=0.07637, over 4773.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2429, pruned_loss=0.05587, over 971614.24 frames.], batch size: 18, lr: 8.24e-04
2022-05-04 01:57:41,942 INFO [train.py:715] (1/8) Epoch 1, batch 29300, loss[loss=0.1794, simple_loss=0.2332, pruned_loss=0.06282, over 4755.00 frames.], tot_loss[loss=0.1774, simple_loss=0.243, pruned_loss=0.05593, over 970639.07 frames.], batch size: 19, lr: 8.24e-04
2022-05-04 01:58:22,147 INFO [train.py:715] (1/8) Epoch 1, batch 29350, loss[loss=0.1789, simple_loss=0.2376, pruned_loss=0.06017, over 4740.00 frames.], tot_loss[loss=0.179, simple_loss=0.244, pruned_loss=0.05702, over 970619.54 frames.], batch size: 16, lr: 8.24e-04
2022-05-04 01:59:00,690 INFO [train.py:715] (1/8) Epoch 1, batch 29400, loss[loss=0.1907, simple_loss=0.2521, pruned_loss=0.06464, over 4689.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2437, pruned_loss=0.05706, over 969771.90 frames.], batch size: 15, lr: 8.23e-04
2022-05-04 01:59:40,304 INFO [train.py:715] (1/8) Epoch 1, batch 29450, loss[loss=0.1676, simple_loss=0.2392, pruned_loss=0.04803, over 4906.00 frames.], tot_loss[loss=0.1785, simple_loss=0.243, pruned_loss=0.05698, over 969201.44 frames.], batch size: 18, lr: 8.23e-04
2022-05-04 02:00:20,003 INFO [train.py:715] (1/8) Epoch 1, batch 29500, loss[loss=0.1976, simple_loss=0.2506, pruned_loss=0.07226, over 4854.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2439, pruned_loss=0.05776, over 970626.58 frames.], batch size: 34, lr: 8.23e-04
2022-05-04 02:00:59,407 INFO [train.py:715] (1/8) Epoch 1, batch 29550, loss[loss=0.1815, simple_loss=0.2428, pruned_loss=0.06013, over 4799.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2441, pruned_loss=0.05773, over 970697.92 frames.], batch size: 18, lr: 8.22e-04
2022-05-04 02:01:37,990 INFO [train.py:715] (1/8) Epoch 1, batch 29600, loss[loss=0.167, simple_loss=0.2294, pruned_loss=0.05234, over 4788.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2441, pruned_loss=0.05747, over 970563.82 frames.], batch size: 17, lr: 8.22e-04
2022-05-04 02:02:18,238 INFO [train.py:715] (1/8) Epoch 1, batch 29650, loss[loss=0.2009, simple_loss=0.2687, pruned_loss=0.06652, over 4979.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2458, pruned_loss=0.05829, over 971648.85 frames.], batch size: 24, lr: 8.22e-04
2022-05-04 02:02:58,332 INFO [train.py:715] (1/8) Epoch 1, batch 29700, loss[loss=0.1794, simple_loss=0.2516, pruned_loss=0.05361, over 4784.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2453, pruned_loss=0.05805, over 971054.85 frames.], batch size: 17, lr: 8.21e-04
2022-05-04 02:03:36,326 INFO [train.py:715] (1/8) Epoch 1, batch 29750, loss[loss=0.2163, simple_loss=0.2806, pruned_loss=0.07598, over 4746.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2461, pruned_loss=0.05858, over 970890.41 frames.], batch size: 16, lr: 8.21e-04
2022-05-04 02:04:15,638 INFO [train.py:715] (1/8) Epoch 1, batch 29800, loss[loss=0.1633, simple_loss=0.2296, pruned_loss=0.04847, over 4806.00 frames.], tot_loss[loss=0.181, simple_loss=0.2453, pruned_loss=0.05838, over 971207.75 frames.], batch size: 13, lr: 8.21e-04
2022-05-04 02:04:55,050 INFO [train.py:715] (1/8) Epoch 1, batch 29850, loss[loss=0.1738, simple_loss=0.2417, pruned_loss=0.05294, over 4988.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2451, pruned_loss=0.05811, over 971549.69 frames.], batch size: 15, lr: 8.20e-04
2022-05-04 02:05:34,426 INFO [train.py:715] (1/8) Epoch 1, batch 29900, loss[loss=0.1894, simple_loss=0.2495, pruned_loss=0.06464, over 4833.00 frames.], tot_loss[loss=0.18, simple_loss=0.2445, pruned_loss=0.05772, over 972723.36 frames.], batch size: 26, lr: 8.20e-04
2022-05-04 02:06:12,928 INFO [train.py:715] (1/8) Epoch 1, batch 29950, loss[loss=0.1576, simple_loss=0.2308, pruned_loss=0.04222, over 4803.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2441, pruned_loss=0.05775, over 972774.10 frames.], batch size: 21, lr: 8.20e-04
2022-05-04 02:06:52,736 INFO [train.py:715] (1/8) Epoch 1, batch 30000, loss[loss=0.1488, simple_loss=0.2268, pruned_loss=0.03535, over 4816.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2436, pruned_loss=0.05727, over 973029.16 frames.], batch size: 25, lr: 8.20e-04
2022-05-04 02:06:52,736 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 02:07:09,693 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1207, simple_loss=0.2076, pruned_loss=0.01687, over 914524.00 frames.
2022-05-04 02:07:50,180 INFO [train.py:715] (1/8) Epoch 1, batch 30050, loss[loss=0.1849, simple_loss=0.2581, pruned_loss=0.05587, over 4827.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2435, pruned_loss=0.05749, over 973645.80 frames.], batch size: 27, lr: 8.19e-04
2022-05-04 02:08:29,665 INFO [train.py:715] (1/8) Epoch 1, batch 30100, loss[loss=0.2156, simple_loss=0.2666, pruned_loss=0.08231, over 4980.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2436, pruned_loss=0.05733, over 973719.35 frames.], batch size: 39, lr: 8.19e-04
2022-05-04 02:09:09,058 INFO [train.py:715] (1/8) Epoch 1, batch 30150, loss[loss=0.1898, simple_loss=0.2475, pruned_loss=0.06608, over 4772.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2449, pruned_loss=0.05812, over 973496.02 frames.], batch size: 18, lr: 8.19e-04
2022-05-04 02:09:48,369 INFO [train.py:715] (1/8) Epoch 1, batch 30200, loss[loss=0.1691, simple_loss=0.2351, pruned_loss=0.05158, over 4793.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2449, pruned_loss=0.05805, over 973352.82 frames.], batch size: 12, lr: 8.18e-04
2022-05-04 02:10:28,818 INFO [train.py:715] (1/8) Epoch 1, batch 30250, loss[loss=0.1787, simple_loss=0.2439, pruned_loss=0.05677, over 4864.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2442, pruned_loss=0.05742, over 972681.27 frames.], batch size: 39, lr: 8.18e-04
2022-05-04 02:11:08,797 INFO [train.py:715] (1/8) Epoch 1, batch 30300, loss[loss=0.1661, simple_loss=0.2212, pruned_loss=0.05546, over 4807.00 frames.], tot_loss[loss=0.18, simple_loss=0.2445, pruned_loss=0.05771, over 972329.89 frames.], batch size: 24, lr: 8.18e-04
2022-05-04 02:11:47,706 INFO [train.py:715] (1/8) Epoch 1, batch 30350, loss[loss=0.1826, simple_loss=0.2426, pruned_loss=0.06133, over 4801.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2442, pruned_loss=0.05747, over 972451.24 frames.], batch size: 21, lr: 8.17e-04
2022-05-04 02:12:27,774 INFO [train.py:715] (1/8) Epoch 1, batch 30400, loss[loss=0.1765, simple_loss=0.2475, pruned_loss=0.0528, over 4977.00 frames.], tot_loss[loss=0.18, simple_loss=0.2451, pruned_loss=0.05747, over 973283.19 frames.], batch size: 35, lr: 8.17e-04
2022-05-04 02:13:07,261 INFO [train.py:715] (1/8) Epoch 1, batch 30450, loss[loss=0.1988, simple_loss=0.2641, pruned_loss=0.0667, over 4906.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2454, pruned_loss=0.05761, over 973694.25 frames.], batch size: 17, lr: 8.17e-04
2022-05-04 02:13:46,439 INFO [train.py:715] (1/8) Epoch 1, batch 30500, loss[loss=0.1799, simple_loss=0.2457, pruned_loss=0.05709, over 4973.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2457, pruned_loss=0.05794, over 973333.02 frames.], batch size: 15, lr: 8.16e-04
2022-05-04 02:14:25,540 INFO [train.py:715] (1/8) Epoch 1, batch 30550, loss[loss=0.1964, simple_loss=0.2592, pruned_loss=0.06677, over 4768.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2457, pruned_loss=0.05807, over 972088.52 frames.], batch size: 18, lr: 8.16e-04
2022-05-04 02:15:05,339 INFO [train.py:715] (1/8) Epoch 1, batch 30600, loss[loss=0.132, simple_loss=0.1984, pruned_loss=0.03276, over 4806.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2453, pruned_loss=0.05766, over 972838.54 frames.], batch size: 12, lr: 8.16e-04
2022-05-04 02:15:44,806 INFO [train.py:715] (1/8) Epoch 1, batch 30650, loss[loss=0.1852, simple_loss=0.2334, pruned_loss=0.06855, over 4643.00 frames.], tot_loss[loss=0.18, simple_loss=0.2448, pruned_loss=0.05765, over 972257.16 frames.], batch size: 13, lr: 8.15e-04
2022-05-04 02:16:23,386 INFO [train.py:715] (1/8) Epoch 1, batch 30700, loss[loss=0.1741, simple_loss=0.2419, pruned_loss=0.0531, over 4982.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2439, pruned_loss=0.05713, over 971859.36 frames.], batch size: 25, lr: 8.15e-04
2022-05-04 02:17:03,635 INFO [train.py:715] (1/8) Epoch 1, batch 30750, loss[loss=0.1843, simple_loss=0.2564, pruned_loss=0.05605, over 4908.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2446, pruned_loss=0.05735, over 972018.90 frames.], batch size: 17, lr: 8.15e-04
2022-05-04 02:17:43,205 INFO [train.py:715] (1/8) Epoch 1, batch 30800, loss[loss=0.1621, simple_loss=0.2427, pruned_loss=0.04069, over 4795.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2439, pruned_loss=0.05683, over 972404.90 frames.], batch size: 24, lr: 8.15e-04
2022-05-04 02:18:22,128 INFO [train.py:715] (1/8) Epoch 1, batch 30850, loss[loss=0.1766, simple_loss=0.238, pruned_loss=0.05756, over 4973.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2441, pruned_loss=0.05681, over 972688.51 frames.], batch size: 39, lr: 8.14e-04
2022-05-04 02:19:01,714 INFO [train.py:715] (1/8) Epoch 1, batch 30900, loss[loss=0.1467, simple_loss=0.2093, pruned_loss=0.042, over 4751.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2428, pruned_loss=0.05614, over 972714.50 frames.], batch size: 19, lr: 8.14e-04
2022-05-04 02:19:41,342 INFO [train.py:715] (1/8) Epoch 1, batch 30950, loss[loss=0.2034, simple_loss=0.2674, pruned_loss=0.06972, over 4881.00 frames.], tot_loss[loss=0.1787, simple_loss=0.244, pruned_loss=0.05673, over 973003.21 frames.], batch size: 16, lr: 8.14e-04
2022-05-04 02:20:20,851 INFO [train.py:715] (1/8) Epoch 1, batch 31000, loss[loss=0.1769, simple_loss=0.2311, pruned_loss=0.06135, over 4940.00 frames.], tot_loss[loss=0.1798, simple_loss=0.245, pruned_loss=0.05734, over 973803.44 frames.], batch size: 35, lr: 8.13e-04
2022-05-04 02:21:00,355 INFO [train.py:715] (1/8) Epoch 1, batch 31050, loss[loss=0.1794, simple_loss=0.248, pruned_loss=0.05535, over 4784.00 frames.], tot_loss[loss=0.18, simple_loss=0.2449, pruned_loss=0.05758, over 973518.14 frames.], batch size: 18, lr: 8.13e-04
2022-05-04 02:21:40,838 INFO [train.py:715] (1/8) Epoch 1, batch 31100, loss[loss=0.15, simple_loss=0.2225, pruned_loss=0.0387, over 4683.00 frames.], tot_loss[loss=0.1803, simple_loss=0.245, pruned_loss=0.05782, over 973360.20 frames.], batch size: 15, lr: 8.13e-04
2022-05-04 02:22:20,577 INFO [train.py:715] (1/8) Epoch 1, batch 31150, loss[loss=0.1675, simple_loss=0.2424, pruned_loss=0.0463, over 4765.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2458, pruned_loss=0.05827, over 972924.24 frames.], batch size: 19, lr: 8.12e-04
2022-05-04 02:22:59,625 INFO [train.py:715] (1/8) Epoch 1, batch 31200, loss[loss=0.1686, simple_loss=0.2246, pruned_loss=0.05627, over 4969.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2451, pruned_loss=0.05765, over 973142.95 frames.], batch size: 31, lr: 8.12e-04
2022-05-04 02:23:39,855 INFO [train.py:715] (1/8) Epoch 1, batch 31250, loss[loss=0.1647, simple_loss=0.2338, pruned_loss=0.04782, over 4964.00 frames.], tot_loss[loss=0.181, simple_loss=0.2458, pruned_loss=0.05813, over 972943.91 frames.], batch size: 24, lr: 8.12e-04
2022-05-04 02:24:19,620 INFO [train.py:715] (1/8) Epoch 1, batch 31300, loss[loss=0.1594, simple_loss=0.2183, pruned_loss=0.05021, over 4891.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2453, pruned_loss=0.05826, over 972777.52 frames.], batch size: 19, lr: 8.11e-04
2022-05-04 02:24:59,060 INFO [train.py:715] (1/8) Epoch 1, batch 31350, loss[loss=0.2545, simple_loss=0.3072, pruned_loss=0.1009, over 4935.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2453, pruned_loss=0.05796, over 972869.26 frames.], batch size: 23, lr: 8.11e-04
2022-05-04 02:25:38,857 INFO [train.py:715] (1/8) Epoch 1, batch 31400, loss[loss=0.1633, simple_loss=0.2314, pruned_loss=0.04757, over 4798.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2447, pruned_loss=0.05805, over 973675.87 frames.], batch size: 24, lr: 8.11e-04
2022-05-04 02:26:18,864 INFO [train.py:715] (1/8) Epoch 1, batch 31450, loss[loss=0.154, simple_loss=0.2263, pruned_loss=0.04086, over 4962.00 frames.], tot_loss[loss=0.1804, simple_loss=0.245, pruned_loss=0.05796, over 973624.89 frames.], batch size: 24, lr: 8.11e-04
2022-05-04 02:26:58,725 INFO [train.py:715] (1/8) Epoch 1, batch 31500, loss[loss=0.1576, simple_loss=0.2259, pruned_loss=0.04469, over 4779.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2448, pruned_loss=0.05779, over 973113.51 frames.], batch size: 17, lr: 8.10e-04
2022-05-04 02:27:37,229 INFO [train.py:715] (1/8) Epoch 1, batch 31550, loss[loss=0.1764, simple_loss=0.2396, pruned_loss=0.05666, over 4772.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2448, pruned_loss=0.05734, over 972803.70 frames.], batch size: 17, lr: 8.10e-04
2022-05-04 02:28:17,415 INFO [train.py:715] (1/8) Epoch 1, batch 31600, loss[loss=0.1828, simple_loss=0.2541, pruned_loss=0.05578, over 4977.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2441, pruned_loss=0.05676, over 972278.82 frames.], batch size: 35, lr: 8.10e-04
2022-05-04 02:28:57,090 INFO [train.py:715] (1/8) Epoch 1, batch 31650, loss[loss=0.1831, simple_loss=0.2478, pruned_loss=0.05926, over 4901.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2435, pruned_loss=0.05676, over 971911.07 frames.], batch size: 17, lr: 8.09e-04
2022-05-04 02:29:37,000 INFO [train.py:715] (1/8) Epoch 1, batch 31700, loss[loss=0.1891, simple_loss=0.2513, pruned_loss=0.06344, over 4835.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2434, pruned_loss=0.05678, over 972051.74 frames.], batch size: 13, lr: 8.09e-04
2022-05-04 02:30:16,359 INFO [train.py:715] (1/8) Epoch 1, batch 31750, loss[loss=0.1948, simple_loss=0.2556, pruned_loss=0.06695, over 4757.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2438, pruned_loss=0.05725, over 971742.22 frames.], batch size: 16, lr: 8.09e-04
2022-05-04 02:30:56,485 INFO [train.py:715] (1/8) Epoch 1, batch 31800, loss[loss=0.2182, simple_loss=0.2731, pruned_loss=0.08165, over 4873.00 frames.], tot_loss[loss=0.179, simple_loss=0.2437, pruned_loss=0.0571, over 972096.45 frames.], batch size: 22, lr: 8.08e-04
2022-05-04 02:31:36,269 INFO [train.py:715] (1/8) Epoch 1, batch 31850, loss[loss=0.1696, simple_loss=0.2416, pruned_loss=0.04878, over 4992.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2449, pruned_loss=0.05786, over 971798.76 frames.], batch size: 14, lr: 8.08e-04
2022-05-04 02:32:15,744 INFO [train.py:715] (1/8) Epoch 1, batch 31900, loss[loss=0.1828, simple_loss=0.2482, pruned_loss=0.05874, over 4888.00 frames.], tot_loss[loss=0.1792, simple_loss=0.244, pruned_loss=0.05718, over 971559.86 frames.], batch size: 22, lr: 8.08e-04
2022-05-04 02:32:55,106 INFO [train.py:715] (1/8) Epoch 1, batch 31950, loss[loss=0.1502, simple_loss=0.2241, pruned_loss=0.03814, over 4792.00 frames.], tot_loss[loss=0.181, simple_loss=0.2459, pruned_loss=0.05801, over 972101.66 frames.], batch size: 13, lr: 8.08e-04
2022-05-04 02:33:34,636 INFO [train.py:715] (1/8) Epoch 1, batch 32000, loss[loss=0.1728, simple_loss=0.2273, pruned_loss=0.05918, over 4772.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2455, pruned_loss=0.05769, over 972203.53 frames.], batch size: 16, lr: 8.07e-04
2022-05-04 02:34:14,065 INFO [train.py:715] (1/8) Epoch 1, batch 32050, loss[loss=0.1726, simple_loss=0.2323, pruned_loss=0.05641, over 4908.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2448, pruned_loss=0.0574, over 972758.80 frames.], batch size: 18, lr: 8.07e-04
2022-05-04 02:34:53,317 INFO [train.py:715] (1/8) Epoch 1, batch 32100, loss[loss=0.1523, simple_loss=0.2286, pruned_loss=0.038, over 4966.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2439, pruned_loss=0.05696, over 972889.76 frames.], batch size: 39, lr: 8.07e-04
2022-05-04 02:35:32,938 INFO [train.py:715] (1/8) Epoch 1, batch 32150, loss[loss=0.1819, simple_loss=0.2461, pruned_loss=0.05881, over 4918.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2437, pruned_loss=0.05728, over 973058.90 frames.], batch size: 23, lr: 8.06e-04
2022-05-04 02:36:12,936 INFO [train.py:715] (1/8) Epoch 1, batch 32200, loss[loss=0.1635, simple_loss=0.2307, pruned_loss=0.04818, over 4974.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2424, pruned_loss=0.05647, over 972502.10 frames.], batch size: 15, lr: 8.06e-04
2022-05-04 02:36:51,837 INFO [train.py:715] (1/8) Epoch 1, batch 32250, loss[loss=0.1462, simple_loss=0.2132, pruned_loss=0.03962, over 4849.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2422, pruned_loss=0.0563, over 972339.77 frames.], batch size: 13, lr: 8.06e-04
2022-05-04 02:37:31,247 INFO [train.py:715] (1/8) Epoch 1, batch 32300, loss[loss=0.1742, simple_loss=0.2384, pruned_loss=0.05496, over 4954.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2436, pruned_loss=0.05712, over 972945.06 frames.], batch size: 23, lr: 8.05e-04
2022-05-04 02:38:10,686 INFO [train.py:715] (1/8) Epoch 1, batch 32350, loss[loss=0.1801, simple_loss=0.2445, pruned_loss=0.05781, over 4858.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2434, pruned_loss=0.05683, over 973057.41 frames.], batch size: 20, lr: 8.05e-04
2022-05-04 02:38:50,280 INFO [train.py:715] (1/8) Epoch 1, batch 32400, loss[loss=0.1996, simple_loss=0.2701, pruned_loss=0.06454, over 4943.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2425, pruned_loss=0.0561, over 973432.18 frames.], batch size: 21, lr: 8.05e-04
2022-05-04 02:39:29,215 INFO [train.py:715] (1/8) Epoch 1, batch 32450, loss[loss=0.1903, simple_loss=0.258, pruned_loss=0.06126, over 4846.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2427, pruned_loss=0.05646, over 973866.04 frames.], batch size: 20, lr: 8.05e-04
2022-05-04 02:40:08,862 INFO [train.py:715] (1/8) Epoch 1, batch 32500, loss[loss=0.1725, simple_loss=0.255, pruned_loss=0.04504, over 4946.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2424, pruned_loss=0.05635, over 973226.68 frames.], batch size: 21, lr: 8.04e-04
2022-05-04 02:40:48,377 INFO [train.py:715] (1/8) Epoch 1, batch 32550, loss[loss=0.1947, simple_loss=0.2473, pruned_loss=0.07106, over 4823.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2432, pruned_loss=0.05689, over 972912.84 frames.], batch size: 15, lr: 8.04e-04
2022-05-04 02:41:27,296 INFO [train.py:715] (1/8) Epoch 1, batch 32600, loss[loss=0.238, simple_loss=0.2888, pruned_loss=0.0936, over 4933.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2443, pruned_loss=0.05725, over 973810.86 frames.], batch size: 39, lr: 8.04e-04
2022-05-04 02:42:06,684 INFO [train.py:715] (1/8) Epoch 1, batch 32650, loss[loss=0.2059, simple_loss=0.2647, pruned_loss=0.0735, over 4824.00 frames.], tot_loss[loss=0.179, simple_loss=0.2438, pruned_loss=0.05709, over 972954.00 frames.], batch size: 15, lr: 8.03e-04
2022-05-04 02:42:46,232 INFO [train.py:715] (1/8) Epoch 1, batch 32700, loss[loss=0.2002, simple_loss=0.2535, pruned_loss=0.07346, over 4836.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2438, pruned_loss=0.05725, over 973486.38 frames.], batch size: 15, lr: 8.03e-04
2022-05-04 02:43:25,961 INFO [train.py:715] (1/8) Epoch 1, batch 32750, loss[loss=0.1691, simple_loss=0.2461, pruned_loss=0.04602, over 4851.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2437, pruned_loss=0.05679, over 973671.34 frames.], batch size: 20, lr: 8.03e-04
2022-05-04 02:44:05,921 INFO [train.py:715] (1/8) Epoch 1, batch 32800, loss[loss=0.1737, simple_loss=0.2373, pruned_loss=0.05504, over 4947.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2427, pruned_loss=0.05618, over 973943.42 frames.], batch size: 24, lr: 8.02e-04
2022-05-04 02:44:45,555 INFO [train.py:715] (1/8) Epoch 1, batch 32850, loss[loss=0.1666, simple_loss=0.2335, pruned_loss=0.04988, over 4640.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2426, pruned_loss=0.05614, over 973363.09 frames.], batch size: 13, lr: 8.02e-04
2022-05-04 02:45:24,931 INFO [train.py:715] (1/8) Epoch 1, batch 32900, loss[loss=0.1767, simple_loss=0.243, pruned_loss=0.05517, over 4902.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2427, pruned_loss=0.05611, over 972908.49 frames.], batch size: 17, lr: 8.02e-04
2022-05-04 02:46:04,179 INFO [train.py:715] (1/8) Epoch 1, batch 32950, loss[loss=0.1882, simple_loss=0.2446, pruned_loss=0.06587, over 4934.00 frames.], tot_loss[loss=0.1768, simple_loss=0.242, pruned_loss=0.05584, over 973202.32 frames.], batch size: 39, lr: 8.02e-04
2022-05-04 02:46:43,639 INFO [train.py:715] (1/8) Epoch 1, batch 33000, loss[loss=0.1462, simple_loss=0.2268, pruned_loss=0.03284, over 4794.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2423, pruned_loss=0.05578, over 972179.05 frames.], batch size: 21, lr: 8.01e-04
2022-05-04 02:46:43,640 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 02:46:52,425 INFO [train.py:742] (1/8) Epoch 1, validation: loss=0.1208, simple_loss=0.2074, pruned_loss=0.01714, over 914524.00 frames.
2022-05-04 02:47:32,103 INFO [train.py:715] (1/8) Epoch 1, batch 33050, loss[loss=0.16, simple_loss=0.2353, pruned_loss=0.04241, over 4966.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2421, pruned_loss=0.05555, over 972677.55 frames.], batch size: 24, lr: 8.01e-04
2022-05-04 02:48:12,134 INFO [train.py:715] (1/8) Epoch 1, batch 33100, loss[loss=0.1769, simple_loss=0.2337, pruned_loss=0.06007, over 4638.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2407, pruned_loss=0.05522, over 972277.73 frames.], batch size: 13, lr: 8.01e-04
2022-05-04 02:48:51,999 INFO [train.py:715] (1/8) Epoch 1, batch 33150, loss[loss=0.1707, simple_loss=0.2423, pruned_loss=0.04955, over 4856.00 frames.], tot_loss[loss=0.177, simple_loss=0.2419, pruned_loss=0.05606, over 972247.09 frames.], batch size: 20, lr: 8.00e-04
2022-05-04 02:49:31,136 INFO [train.py:715] (1/8) Epoch 1, batch 33200, loss[loss=0.173, simple_loss=0.2349, pruned_loss=0.05551, over 4868.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2422, pruned_loss=0.05628, over 971477.04 frames.], batch size: 32, lr: 8.00e-04
2022-05-04 02:50:11,556 INFO [train.py:715] (1/8) Epoch 1, batch 33250, loss[loss=0.1842, simple_loss=0.2366, pruned_loss=0.0659, over 4827.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2421, pruned_loss=0.05621, over 971428.60 frames.], batch size: 15, lr: 8.00e-04
2022-05-04 02:50:51,589 INFO [train.py:715] (1/8) Epoch 1, batch 33300, loss[loss=0.1613, simple_loss=0.2273, pruned_loss=0.04765, over 4964.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2417, pruned_loss=0.05574, over 972356.64 frames.], batch size: 24, lr: 8.00e-04
2022-05-04 02:51:31,060 INFO [train.py:715] (1/8) Epoch 1, batch 33350, loss[loss=0.1973, simple_loss=0.2558, pruned_loss=0.06935, over 4869.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2424, pruned_loss=0.05603, over 972828.78 frames.], batch size: 16, lr: 7.99e-04
2022-05-04 02:52:11,430 INFO [train.py:715] (1/8) Epoch 1, batch 33400, loss[loss=0.1926, simple_loss=0.2481, pruned_loss=0.06856, over 4934.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2424, pruned_loss=0.05573, over 973468.36 frames.], batch size: 29, lr: 7.99e-04
2022-05-04 02:52:51,301 INFO [train.py:715] (1/8) Epoch 1, batch 33450, loss[loss=0.2008, simple_loss=0.2555, pruned_loss=0.07308, over 4950.00 frames.], tot_loss[loss=0.177, simple_loss=0.2428, pruned_loss=0.05557, over 972638.15 frames.], batch size: 39, lr: 7.99e-04
2022-05-04 02:53:30,404 INFO [train.py:715] (1/8) Epoch 1, batch 33500, loss[loss=0.1936, simple_loss=0.2538, pruned_loss=0.06667, over 4897.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2441, pruned_loss=0.0567, over 972098.59 frames.], batch size: 22, lr: 7.98e-04
2022-05-04 02:54:10,338 INFO [train.py:715] (1/8) Epoch 1, batch 33550, loss[loss=0.1931, simple_loss=0.2676, pruned_loss=0.0593, over 4926.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2433, pruned_loss=0.05647, over 971500.04 frames.], batch size: 18, lr: 7.98e-04
2022-05-04 02:54:50,182 INFO [train.py:715] (1/8) Epoch 1, batch 33600, loss[loss=0.1869, simple_loss=0.2565, pruned_loss=0.05867, over 4978.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2436, pruned_loss=0.05633, over 971524.48 frames.], batch size: 28, lr: 7.98e-04
2022-05-04 02:55:29,607 INFO [train.py:715] (1/8) Epoch 1, batch 33650, loss[loss=0.1647, simple_loss=0.2445, pruned_loss=0.04241, over 4818.00 frames.], tot_loss[loss=0.1782, simple_loss=0.244, pruned_loss=0.05619, over 971370.52 frames.], batch size: 21, lr: 7.97e-04
2022-05-04 02:56:08,651 INFO [train.py:715] (1/8) Epoch 1, batch 33700, loss[loss=0.186, simple_loss=0.2548, pruned_loss=0.05863, over 4932.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2429, pruned_loss=0.05598, over 972086.88 frames.], batch size: 21, lr: 7.97e-04
2022-05-04 02:56:47,806 INFO [train.py:715] (1/8) Epoch 1, batch 33750, loss[loss=0.1742, simple_loss=0.229, pruned_loss=0.05976, over 4856.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2434, pruned_loss=0.05672, over 972619.11 frames.], batch size: 30, lr: 7.97e-04
2022-05-04 02:57:27,452 INFO [train.py:715] (1/8) Epoch 1, batch 33800, loss[loss=0.1722, simple_loss=0.2287, pruned_loss=0.0578, over 4818.00 frames.], tot_loss[loss=0.178, simple_loss=0.243, pruned_loss=0.05652, over 972984.03 frames.], batch size: 25, lr: 7.97e-04
2022-05-04 02:58:06,283 INFO [train.py:715] (1/8) Epoch 1, batch 33850, loss[loss=0.1613, simple_loss=0.2312, pruned_loss=0.04567, over 4804.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2424, pruned_loss=0.05628, over 972678.42 frames.], batch size: 21, lr: 7.96e-04
2022-05-04 02:58:45,802 INFO [train.py:715] (1/8) Epoch 1, batch 33900, loss[loss=0.2113, simple_loss=0.266, pruned_loss=0.07832, over 4810.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2424, pruned_loss=0.05644, over 971936.26 frames.], batch size: 25, lr: 7.96e-04
2022-05-04 02:59:25,363 INFO [train.py:715] (1/8) Epoch 1, batch 33950, loss[loss=0.2025, simple_loss=0.2531, pruned_loss=0.07592, over 4855.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2436, pruned_loss=0.05732, over 972336.17 frames.], batch size: 30, lr: 7.96e-04
2022-05-04 03:00:05,091 INFO [train.py:715] (1/8) Epoch 1, batch 34000, loss[loss=0.1749, simple_loss=0.2507, pruned_loss=0.04953, over 4802.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2438, pruned_loss=0.05725, over 973079.76 frames.], batch size: 21, lr: 7.95e-04
2022-05-04 03:00:44,408 INFO [train.py:715] (1/8) Epoch 1, batch 34050, loss[loss=0.2035, simple_loss=0.2542, pruned_loss=0.07645, over 4749.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2435, pruned_loss=0.05733, over 972757.92 frames.], batch size: 16, lr: 7.95e-04
2022-05-04 03:01:23,796 INFO [train.py:715] (1/8) Epoch 1, batch 34100, loss[loss=0.1744, simple_loss=0.2487, pruned_loss=0.05005, over 4928.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2433, pruned_loss=0.05723, over 973395.71 frames.], batch size: 23, lr: 7.95e-04
2022-05-04 03:02:03,175 INFO [train.py:715] (1/8) Epoch 1, batch 34150, loss[loss=0.1801, simple_loss=0.2452, pruned_loss=0.05747, over 4696.00 frames.], tot_loss[loss=0.1783, simple_loss=0.243, pruned_loss=0.05676, over 973285.02 frames.], batch size: 15, lr: 7.95e-04
2022-05-04 03:02:42,209 INFO [train.py:715] (1/8) Epoch 1, batch 34200, loss[loss=0.1611, simple_loss=0.2294, pruned_loss=0.04642, over 4963.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2436, pruned_loss=0.0568, over 973696.22 frames.], batch size: 24, lr: 7.94e-04
2022-05-04 03:03:21,754 INFO [train.py:715] (1/8) Epoch 1, batch 34250, loss[loss=0.1517, simple_loss=0.2302, pruned_loss=0.0366, over 4911.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2437, pruned_loss=0.05681, over 972410.29 frames.], batch size: 19, lr: 7.94e-04
2022-05-04 03:04:01,436 INFO [train.py:715] (1/8) Epoch 1, batch 34300, loss[loss=0.1945, simple_loss=0.265, pruned_loss=0.06199, over 4908.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2441, pruned_loss=0.05702, over 972439.87 frames.], batch size: 19, lr: 7.94e-04
2022-05-04 03:04:40,846 INFO [train.py:715] (1/8) Epoch 1, batch 34350, loss[loss=0.1963, simple_loss=0.2655, pruned_loss=0.06356, over 4780.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2433, pruned_loss=0.05645, over 972674.60 frames.], batch size: 18, lr: 7.93e-04
2022-05-04 03:05:19,750 INFO [train.py:715] (1/8) Epoch 1, batch 34400, loss[loss=0.1983, simple_loss=0.2596, pruned_loss=0.06851, over 4808.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2439, pruned_loss=0.05719, over 973234.90 frames.], batch size: 21, lr: 7.93e-04
2022-05-04 03:05:59,257 INFO [train.py:715] (1/8) Epoch 1, batch 34450, loss[loss=0.1766, simple_loss=0.2427, pruned_loss=0.05523, over 4841.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2442, pruned_loss=0.05732, over 972335.99 frames.], batch size: 26, lr: 7.93e-04
2022-05-04 03:06:38,478 INFO [train.py:715] (1/8) Epoch 1, batch 34500, loss[loss=0.2174, simple_loss=0.2717, pruned_loss=0.08155, over 4775.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2431, pruned_loss=0.05677, over 972404.37 frames.], batch size: 19, lr: 7.93e-04
2022-05-04 03:07:17,763 INFO [train.py:715] (1/8) Epoch 1, batch 34550, loss[loss=0.1721, simple_loss=0.2433, pruned_loss=0.05049, over 4805.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2438, pruned_loss=0.05725, over 972627.39 frames.], batch size: 25, lr: 7.92e-04
2022-05-04 03:07:57,336 INFO [train.py:715] (1/8) Epoch 1, batch 34600, loss[loss=0.1429, simple_loss=0.2172, pruned_loss=0.0343, over 4851.00 frames.], tot_loss[loss=0.1781, simple_loss=0.243, pruned_loss=0.05663, over 972009.11 frames.], batch size: 30, lr: 7.92e-04
2022-05-04 03:08:37,227 INFO [train.py:715] (1/8) Epoch 1, batch 34650, loss[loss=0.1903, simple_loss=0.2645, pruned_loss=0.05806, over 4950.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2428, pruned_loss=0.05635, over 971981.77 frames.], batch size: 23, lr: 7.92e-04
2022-05-04 03:09:17,426 INFO [train.py:715] (1/8) Epoch 1, batch 34700, loss[loss=0.1662, simple_loss=0.2403, pruned_loss=0.04607, over 4762.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2426, pruned_loss=0.05607, over 972625.60 frames.], batch size: 14, lr: 7.91e-04
2022-05-04 03:09:55,738 INFO [train.py:715] (1/8) Epoch 1, batch 34750, loss[loss=0.201, simple_loss=0.2613, pruned_loss=0.07033, over 4693.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2431, pruned_loss=0.05658, over 971719.84 frames.], batch size: 15, lr: 7.91e-04
2022-05-04 03:10:32,245 INFO [train.py:715] (1/8) Epoch 1, batch 34800, loss[loss=0.1879, simple_loss=0.2552, pruned_loss=0.0603, over 4923.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2433, pruned_loss=0.05686, over 971745.14 frames.], batch size: 23, lr: 7.91e-04
2022-05-04 03:11:25,709 INFO [train.py:715] (1/8) Epoch 2, batch 0, loss[loss=0.1879, simple_loss=0.2495, pruned_loss=0.06315, over 4650.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2495, pruned_loss=0.06315, over 4650.00 frames.], batch size: 13, lr: 7.59e-04
2022-05-04 03:12:05,762 INFO [train.py:715] (1/8) Epoch 2, batch 50, loss[loss=0.1771, simple_loss=0.243, pruned_loss=0.05561, over 4811.00 frames.], tot_loss[loss=0.18, simple_loss=0.2442, pruned_loss=0.05793, over 219775.89 frames.], batch size: 13, lr: 7.59e-04
2022-05-04 03:12:46,585 INFO [train.py:715] (1/8) Epoch 2, batch 100, loss[loss=0.1913, simple_loss=0.2648, pruned_loss=0.05893, over 4982.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2423, pruned_loss=0.05597, over 386377.94 frames.], batch size: 25, lr: 7.59e-04
2022-05-04 03:13:27,197 INFO [train.py:715] (1/8) Epoch 2, batch 150, loss[loss=0.1644, simple_loss=0.2379, pruned_loss=0.04544, over 4758.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2428, pruned_loss=0.05644, over 516884.33 frames.], batch size: 19, lr: 7.59e-04
2022-05-04 03:14:07,251 INFO [train.py:715] (1/8) Epoch 2, batch 200, loss[loss=0.1992, simple_loss=0.2609, pruned_loss=0.06878, over 4878.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2429, pruned_loss=0.05724, over 618190.52 frames.], batch size: 19, lr: 7.58e-04
2022-05-04 03:14:48,003 INFO [train.py:715] (1/8) Epoch 2, batch 250, loss[loss=0.1425, simple_loss=0.2196, pruned_loss=0.03267, over 4934.00 frames.], tot_loss[loss=0.1782, simple_loss=0.243, pruned_loss=0.05667, over 696755.35 frames.], batch size: 29, lr: 7.58e-04
2022-05-04 03:15:29,371 INFO [train.py:715] (1/8) Epoch 2, batch 300, loss[loss=0.1641, simple_loss=0.2304, pruned_loss=0.04887, over 4849.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2424, pruned_loss=0.05597, over 758807.30 frames.], batch size: 32, lr: 7.58e-04
2022-05-04 03:16:10,303 INFO [train.py:715] (1/8) Epoch 2, batch 350, loss[loss=0.162, simple_loss=0.2182, pruned_loss=0.05285, over 4836.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2426, pruned_loss=0.05611, over 806700.29 frames.], batch size: 13, lr: 7.57e-04
2022-05-04 03:16:49,966 INFO [train.py:715] (1/8) Epoch 2, batch 400, loss[loss=0.1619, simple_loss=0.229, pruned_loss=0.04736, over 4807.00 frames.], tot_loss[loss=0.178, simple_loss=0.2432, pruned_loss=0.05639, over 843544.81 frames.], batch size: 24, lr: 7.57e-04
2022-05-04 03:17:30,475 INFO [train.py:715] (1/8) Epoch 2, batch 450, loss[loss=0.2165, simple_loss=0.2794, pruned_loss=0.07675, over 4777.00 frames.], tot_loss[loss=0.18, simple_loss=0.2447, pruned_loss=0.05767, over 871363.60 frames.], batch size: 14, lr: 7.57e-04
2022-05-04 03:18:11,617 INFO [train.py:715] (1/8) Epoch 2, batch 500, loss[loss=0.1792, simple_loss=0.2408, pruned_loss=0.05881, over 4787.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2443, pruned_loss=0.05743, over 893730.32 frames.], batch size: 17, lr: 7.57e-04
2022-05-04 03:18:51,554 INFO [train.py:715] (1/8) Epoch 2, batch 550, loss[loss=0.2307, simple_loss=0.2863, pruned_loss=0.0876, over 4962.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2435, pruned_loss=0.0568, over 911387.59 frames.], batch size: 21, lr: 7.56e-04
2022-05-04 03:19:31,921 INFO [train.py:715] (1/8) Epoch 2, batch 600, loss[loss=0.1838, simple_loss=0.2526, pruned_loss=0.05752, over 4805.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2434, pruned_loss=0.05674, over 924784.73 frames.], batch size: 21, lr: 7.56e-04
2022-05-04 03:20:12,752 INFO [train.py:715] (1/8) Epoch 2, batch 650, loss[loss=0.1664, simple_loss=0.2376, pruned_loss=0.0476, over 4945.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2421, pruned_loss=0.05608, over 935114.98 frames.], batch size: 21, lr: 7.56e-04
2022-05-04 03:20:53,350 INFO [train.py:715] (1/8) Epoch 2, batch 700, loss[loss=0.1522, simple_loss=0.2235, pruned_loss=0.04044, over 4891.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2423, pruned_loss=0.056, over 942061.85 frames.], batch size: 16, lr: 7.56e-04
2022-05-04 03:21:32,905 INFO [train.py:715] (1/8) Epoch 2, batch 750, loss[loss=0.1709, simple_loss=0.2353, pruned_loss=0.05324, over 4970.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2425, pruned_loss=0.05584, over 948972.46 frames.], batch size: 24, lr: 7.55e-04
2022-05-04 03:22:13,351 INFO [train.py:715] (1/8) Epoch 2, batch 800, loss[loss=0.1937, simple_loss=0.2671, pruned_loss=0.06016, over 4917.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2423, pruned_loss=0.05526, over 954284.24 frames.], batch size: 18, lr: 7.55e-04
2022-05-04 03:22:54,005 INFO [train.py:715] (1/8) Epoch 2, batch 850, loss[loss=0.1726, simple_loss=0.2378, pruned_loss=0.05376, over 4950.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2424, pruned_loss=0.05511, over 958305.24 frames.], batch size: 35, lr: 7.55e-04
2022-05-04 03:23:34,291 INFO [train.py:715] (1/8) Epoch 2, batch 900, loss[loss=0.1603, simple_loss=0.2253, pruned_loss=0.04762, over 4873.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2427, pruned_loss=0.05545, over 961191.89 frames.], batch size: 32, lr: 7.55e-04
2022-05-04 03:24:14,714 INFO [train.py:715] (1/8) Epoch 2, batch 950, loss[loss=0.1403, simple_loss=0.1975, pruned_loss=0.0415, over 4986.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2421, pruned_loss=0.05533, over 963956.12 frames.], batch size: 14, lr: 7.54e-04
2022-05-04 03:24:55,405 INFO [train.py:715] (1/8) Epoch 2, batch 1000, loss[loss=0.2003, simple_loss=0.2757, pruned_loss=0.06249, over 4888.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2432, pruned_loss=0.05631, over 965929.53 frames.], batch size: 39, lr: 7.54e-04
2022-05-04 03:25:36,208 INFO [train.py:715] (1/8) Epoch 2, batch 1050, loss[loss=0.1509, simple_loss=0.2326, pruned_loss=0.03463, over 4768.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2427, pruned_loss=0.05599, over 966718.79 frames.], batch size: 16, lr: 7.54e-04
2022-05-04 03:26:15,804 INFO [train.py:715] (1/8) Epoch 2, batch 1100, loss[loss=0.2233, simple_loss=0.2825, pruned_loss=0.08202, over 4854.00 frames.], tot_loss[loss=0.1785, simple_loss=0.244, pruned_loss=0.05653, over 968189.43 frames.], batch size: 20, lr: 7.53e-04
2022-05-04 03:26:56,306 INFO [train.py:715] (1/8) Epoch 2, batch 1150, loss[loss=0.1522, simple_loss=0.2189, pruned_loss=0.04269, over 4919.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2437, pruned_loss=0.05659, over 970272.13 frames.], batch size: 23, lr: 7.53e-04
2022-05-04 03:27:37,639 INFO [train.py:715] (1/8) Epoch 2, batch 1200, loss[loss=0.1271, simple_loss=0.2007, pruned_loss=0.02682, over 4788.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2428, pruned_loss=0.05619, over 970528.87 frames.], batch size: 14, lr: 7.53e-04
2022-05-04 03:28:18,255 INFO [train.py:715] (1/8) Epoch 2, batch 1250, loss[loss=0.1637, simple_loss=0.2337, pruned_loss=0.04687, over 4766.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2431, pruned_loss=0.05599, over 970438.62 frames.], batch size: 12, lr: 7.53e-04
2022-05-04 03:28:57,934 INFO [train.py:715] (1/8) Epoch 2, batch 1300, loss[loss=0.1489, simple_loss=0.2209, pruned_loss=0.03847, over 4831.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2431, pruned_loss=0.05609, over 970561.84 frames.], batch size: 13, lr: 7.52e-04
2022-05-04 03:29:38,477 INFO [train.py:715] (1/8) Epoch 2, batch 1350, loss[loss=0.1608, simple_loss=0.2256, pruned_loss=0.04797, over 4888.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2431, pruned_loss=0.05665, over 970261.80 frames.], batch size: 22, lr: 7.52e-04
2022-05-04 03:30:19,106 INFO [train.py:715] (1/8) Epoch 2, batch 1400, loss[loss=0.1695, simple_loss=0.248, pruned_loss=0.04546, over 4953.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2428, pruned_loss=0.05646, over 970768.50 frames.], batch size: 24, lr: 7.52e-04
2022-05-04 03:30:59,080 INFO [train.py:715] (1/8) Epoch 2, batch 1450, loss[loss=0.1893, simple_loss=0.2601, pruned_loss=0.05925, over 4813.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2421, pruned_loss=0.05588, over 971441.60 frames.], batch size: 21, lr: 7.52e-04
2022-05-04 03:31:39,480 INFO [train.py:715] (1/8) Epoch 2, batch 1500, loss[loss=0.1689, simple_loss=0.2314, pruned_loss=0.05324, over 4756.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2417, pruned_loss=0.05592, over 972626.85 frames.], batch size: 19, lr: 7.51e-04
2022-05-04 03:32:20,466 INFO [train.py:715] (1/8) Epoch 2, batch 1550, loss[loss=0.1792, simple_loss=0.2427, pruned_loss=0.05784, over 4971.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2415, pruned_loss=0.0557, over 973095.31 frames.], batch size: 15, lr: 7.51e-04
2022-05-04 03:33:00,545 INFO [train.py:715] (1/8) Epoch 2, batch 1600, loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.031, over 4915.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2417, pruned_loss=0.05553, over 972832.57 frames.], batch size: 23, lr: 7.51e-04
2022-05-04 03:33:40,361 INFO [train.py:715] (1/8) Epoch 2, batch 1650, loss[loss=0.1589, simple_loss=0.2164, pruned_loss=0.05071, over 4811.00 frames.], tot_loss[loss=0.177, simple_loss=0.2425, pruned_loss=0.05582, over 973290.03 frames.], batch size: 13, lr: 7.51e-04
2022-05-04 03:34:21,229 INFO [train.py:715] (1/8) Epoch 2, batch 1700, loss[loss=0.1734, simple_loss=0.2464, pruned_loss=0.0502, over 4907.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2427, pruned_loss=0.05583, over 973636.01 frames.], batch size: 19, lr: 7.50e-04
2022-05-04 03:35:02,285 INFO [train.py:715] (1/8) Epoch 2, batch 1750, loss[loss=0.1928, simple_loss=0.2464, pruned_loss=0.0696, over 4965.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2437, pruned_loss=0.05677, over 973621.96 frames.], batch size: 33, lr: 7.50e-04
2022-05-04 03:35:42,182 INFO [train.py:715] (1/8) Epoch 2, batch 1800, loss[loss=0.1695, simple_loss=0.2481, pruned_loss=0.04543, over 4825.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2438, pruned_loss=0.05669, over 973731.93 frames.], batch size: 26, lr: 7.50e-04
2022-05-04 03:36:22,547 INFO [train.py:715] (1/8) Epoch 2, batch 1850, loss[loss=0.1539, simple_loss=0.2117, pruned_loss=0.048, over 4860.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2445, pruned_loss=0.05738, over 973384.32 frames.], batch size: 12, lr: 7.50e-04
2022-05-04 03:37:03,509 INFO [train.py:715] (1/8) Epoch 2, batch 1900, loss[loss=0.1761, simple_loss=0.2411, pruned_loss=0.05555, over 4724.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2439, pruned_loss=0.05676, over 973027.67 frames.], batch size: 12, lr: 7.49e-04
2022-05-04 03:37:44,306 INFO [train.py:715] (1/8) Epoch 2, batch 1950, loss[loss=0.1848, simple_loss=0.2549, pruned_loss=0.05736, over 4897.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2432, pruned_loss=0.05587, over 973394.97 frames.], batch size: 39, lr: 7.49e-04
2022-05-04 03:38:24,075 INFO [train.py:715] (1/8) Epoch 2, batch 2000, loss[loss=0.1685, simple_loss=0.2389, pruned_loss=0.04904, over 4783.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2442, pruned_loss=0.0565, over 972409.16 frames.], batch size: 17, lr: 7.49e-04
2022-05-04 03:39:04,264 INFO [train.py:715] (1/8) Epoch 2, batch 2050, loss[loss=0.2, simple_loss=0.2656, pruned_loss=0.06724, over 4822.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2449, pruned_loss=0.05721, over 973273.69 frames.], batch size: 26, lr: 7.48e-04
2022-05-04 03:39:45,391 INFO [train.py:715] (1/8) Epoch 2, batch 2100, loss[loss=0.1587, simple_loss=0.2188, pruned_loss=0.04928, over 4907.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2442, pruned_loss=0.05682, over 973371.84 frames.], batch size: 17, lr: 7.48e-04
2022-05-04 03:40:25,365 INFO [train.py:715] (1/8) Epoch 2, batch 2150, loss[loss=0.1519, simple_loss=0.2332, pruned_loss=0.03531, over 4954.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2434, pruned_loss=0.05635, over 972060.06 frames.], batch size: 21, lr: 7.48e-04
2022-05-04 03:41:04,901 INFO [train.py:715] (1/8) Epoch 2, batch 2200, loss[loss=0.1659, simple_loss=0.2338, pruned_loss=0.04901, over 4936.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2443, pruned_loss=0.05695, over 972201.62 frames.], batch size: 23, lr: 7.48e-04
2022-05-04 03:41:45,613 INFO [train.py:715] (1/8) Epoch 2, batch 2250, loss[loss=0.1836, simple_loss=0.2472, pruned_loss=0.05998, over 4802.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2441, pruned_loss=0.05666, over 971822.94 frames.], batch size: 21, lr: 7.47e-04
2022-05-04 03:42:26,415 INFO [train.py:715] (1/8) Epoch 2, batch 2300, loss[loss=0.1585, simple_loss=0.2291, pruned_loss=0.04394, over 4764.00 frames.], tot_loss[loss=0.177, simple_loss=0.2426, pruned_loss=0.05568, over 971735.95 frames.], batch size: 19, lr: 7.47e-04
2022-05-04 03:43:05,627 INFO [train.py:715] (1/8) Epoch 2, batch 2350, loss[loss=0.1488, simple_loss=0.2101, pruned_loss=0.04377, over 4861.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2427, pruned_loss=0.05557, over 971794.74 frames.], batch size: 16, lr: 7.47e-04
2022-05-04 03:43:48,337 INFO [train.py:715] (1/8) Epoch 2, batch 2400, loss[loss=0.1753, simple_loss=0.2431, pruned_loss=0.05373, over 4751.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2411, pruned_loss=0.05465, over 971661.85 frames.], batch size: 16, lr: 7.47e-04
2022-05-04 03:44:29,322 INFO [train.py:715] (1/8) Epoch 2, batch 2450, loss[loss=0.1717, simple_loss=0.2439, pruned_loss=0.04974, over 4937.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2415, pruned_loss=0.05485, over 972557.20 frames.], batch size: 21, lr: 7.46e-04
2022-05-04 03:45:09,457 INFO [train.py:715] (1/8) Epoch 2, batch 2500, loss[loss=0.1692, simple_loss=0.234, pruned_loss=0.05224, over 4976.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2414, pruned_loss=0.05516, over 972527.93 frames.], batch size: 24, lr: 7.46e-04
2022-05-04 03:45:49,052 INFO [train.py:715] (1/8) Epoch 2, batch 2550, loss[loss=0.2011, simple_loss=0.2451, pruned_loss=0.07857, over 4983.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2414, pruned_loss=0.05562, over 972396.92 frames.], batch size: 14, lr: 7.46e-04
2022-05-04 03:46:29,888 INFO [train.py:715] (1/8) Epoch 2, batch 2600, loss[loss=0.155, simple_loss=0.2148, pruned_loss=0.04755, over 4870.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2407, pruned_loss=0.05506, over 972456.86 frames.], batch size: 20, lr: 7.46e-04
2022-05-04 03:47:10,397 INFO [train.py:715] (1/8) Epoch 2, batch 2650, loss[loss=0.1442, simple_loss=0.2203, pruned_loss=0.03408, over 4775.00 frames.], tot_loss[loss=0.1756, simple_loss=0.241, pruned_loss=0.05514, over 972656.69 frames.], batch size: 18, lr: 7.45e-04
2022-05-04 03:47:49,297 INFO [train.py:715] (1/8) Epoch 2, batch 2700, loss[loss=0.2613, simple_loss=0.3066, pruned_loss=0.108, over 4909.00 frames.], tot_loss[loss=0.174, simple_loss=0.2398, pruned_loss=0.0541, over 972814.97 frames.], batch size: 17, lr: 7.45e-04
2022-05-04 03:48:29,310 INFO [train.py:715] (1/8) Epoch 2, batch 2750, loss[loss=0.1644, simple_loss=0.2304, pruned_loss=0.04923, over 4949.00 frames.], tot_loss[loss=0.175, simple_loss=0.2407, pruned_loss=0.05462, over 972338.25 frames.], batch size: 21, lr: 7.45e-04
2022-05-04 03:49:10,356 INFO [train.py:715] (1/8) Epoch 2, batch 2800, loss[loss=0.186, simple_loss=0.2632, pruned_loss=0.05436, over 4885.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2408, pruned_loss=0.05476, over 972151.58 frames.], batch size: 38, lr: 7.45e-04
2022-05-04 03:49:50,283 INFO [train.py:715] (1/8) Epoch 2, batch 2850, loss[loss=0.1947, simple_loss=0.2638, pruned_loss=0.06282, over 4818.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2407, pruned_loss=0.05473, over 972832.14 frames.], batch size: 25, lr: 7.44e-04
2022-05-04 03:50:29,546 INFO [train.py:715] (1/8) Epoch 2, batch 2900, loss[loss=0.1843, simple_loss=0.2391, pruned_loss=0.06469, over 4893.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2416, pruned_loss=0.0556, over 973392.00 frames.], batch size: 19, lr: 7.44e-04
2022-05-04 03:51:09,906 INFO [train.py:715] (1/8) Epoch 2, batch 2950, loss[loss=0.189, simple_loss=0.2341, pruned_loss=0.07199, over 4833.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2411, pruned_loss=0.05561, over 972481.00 frames.], batch size: 12, lr: 7.44e-04
2022-05-04 03:51:50,594 INFO [train.py:715] (1/8) Epoch 2, batch 3000, loss[loss=0.1817, simple_loss=0.2471, pruned_loss=0.05815, over 4864.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2409, pruned_loss=0.05535, over 971865.57 frames.], batch size: 20, lr: 7.44e-04
2022-05-04 03:51:50,595 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 03:52:00,003 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1191, simple_loss=0.2058, pruned_loss=0.01615, over 914524.00 frames.
2022-05-04 03:52:40,631 INFO [train.py:715] (1/8) Epoch 2, batch 3050, loss[loss=0.1648, simple_loss=0.2298, pruned_loss=0.04984, over 4845.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2418, pruned_loss=0.05547, over 972113.39 frames.], batch size: 20, lr: 7.43e-04
2022-05-04 03:53:19,882 INFO [train.py:715] (1/8) Epoch 2, batch 3100, loss[loss=0.1696, simple_loss=0.2225, pruned_loss=0.05837, over 4920.00 frames.], tot_loss[loss=0.177, simple_loss=0.2419, pruned_loss=0.05608, over 971730.81 frames.], batch size: 18, lr: 7.43e-04
2022-05-04 03:53:59,889 INFO [train.py:715] (1/8) Epoch 2, batch 3150, loss[loss=0.1745, simple_loss=0.253, pruned_loss=0.048, over 4748.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2425, pruned_loss=0.05652, over 971704.24 frames.], batch size: 16, lr: 7.43e-04
2022-05-04 03:54:40,145 INFO [train.py:715] (1/8) Epoch 2, batch 3200, loss[loss=0.1629, simple_loss=0.2275, pruned_loss=0.04913, over 4961.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2423, pruned_loss=0.05639, over 972058.37 frames.], batch size: 35, lr: 7.43e-04
2022-05-04 03:55:19,792 INFO [train.py:715] (1/8) Epoch 2, batch 3250, loss[loss=0.1553, simple_loss=0.2302, pruned_loss=0.04016, over 4819.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2426, pruned_loss=0.05616, over 972031.35 frames.], batch size: 27, lr: 7.42e-04
2022-05-04 03:55:59,356 INFO [train.py:715] (1/8) Epoch 2, batch 3300, loss[loss=0.1482, simple_loss=0.2153, pruned_loss=0.04056, over 4848.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2414, pruned_loss=0.05551, over 972792.87 frames.], batch size: 32, lr: 7.42e-04
2022-05-04 03:56:39,600 INFO [train.py:715] (1/8) Epoch 2, batch 3350, loss[loss=0.2181, simple_loss=0.2706, pruned_loss=0.08283, over 4980.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2415, pruned_loss=0.05547, over 972342.63 frames.], batch size: 14, lr: 7.42e-04
2022-05-04 03:57:20,088 INFO [train.py:715] (1/8) Epoch 2, batch 3400, loss[loss=0.1575, simple_loss=0.2209, pruned_loss=0.04706, over 4873.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2405, pruned_loss=0.05445, over 972219.83 frames.], batch size: 16, lr: 7.42e-04
2022-05-04 03:57:58,916 INFO [train.py:715] (1/8) Epoch 2, batch 3450, loss[loss=0.2028, simple_loss=0.2698, pruned_loss=0.06797, over 4703.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2406, pruned_loss=0.05457, over 972230.11 frames.], batch size: 15, lr: 7.41e-04
2022-05-04 03:58:38,937 INFO [train.py:715] (1/8) Epoch 2, batch 3500, loss[loss=0.1779, simple_loss=0.233, pruned_loss=0.06139, over 4767.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2394, pruned_loss=0.05368, over 972066.32 frames.], batch size: 18, lr: 7.41e-04
2022-05-04 03:59:19,004 INFO [train.py:715] (1/8) Epoch 2, batch 3550, loss[loss=0.1484, simple_loss=0.2003, pruned_loss=0.04823, over 4977.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2399, pruned_loss=0.05433, over 971639.53 frames.], batch size: 14, lr: 7.41e-04
2022-05-04 03:59:58,778 INFO [train.py:715] (1/8) Epoch 2, batch 3600, loss[loss=0.1486, simple_loss=0.2207, pruned_loss=0.03825, over 4796.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2408, pruned_loss=0.05481, over 972106.59 frames.], batch size: 14, lr: 7.41e-04
2022-05-04 04:00:37,768 INFO [train.py:715] (1/8) Epoch 2, batch 3650, loss[loss=0.1979, simple_loss=0.2603, pruned_loss=0.06776, over 4734.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2409, pruned_loss=0.05476, over 973004.69 frames.], batch size: 16, lr: 7.40e-04
2022-05-04 04:01:18,179 INFO [train.py:715] (1/8) Epoch 2, batch 3700, loss[loss=0.1533, simple_loss=0.2239, pruned_loss=0.04134, over 4789.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2407, pruned_loss=0.05449, over 971818.23 frames.], batch size: 14, lr: 7.40e-04
2022-05-04 04:01:58,352 INFO [train.py:715] (1/8) Epoch 2, batch 3750, loss[loss=0.164, simple_loss=0.2384, pruned_loss=0.04484, over 4889.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2411, pruned_loss=0.055, over 971709.68 frames.], batch size: 22, lr: 7.40e-04
2022-05-04 04:02:37,080 INFO [train.py:715] (1/8) Epoch 2, batch 3800, loss[loss=0.1481, simple_loss=0.22, pruned_loss=0.03808, over 4699.00 frames.], tot_loss[loss=0.1764, simple_loss=0.242, pruned_loss=0.05542, over 971420.15 frames.], batch size: 15, lr: 7.40e-04
2022-05-04 04:03:17,275 INFO [train.py:715] (1/8) Epoch 2, batch 3850, loss[loss=0.1633, simple_loss=0.2324, pruned_loss=0.04712, over 4844.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2418, pruned_loss=0.0552, over 971792.08 frames.], batch size: 30, lr: 7.39e-04
2022-05-04 04:03:57,609 INFO [train.py:715] (1/8) Epoch 2, batch 3900, loss[loss=0.1687, simple_loss=0.2296, pruned_loss=0.05383, over 4795.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2414, pruned_loss=0.05482, over 971118.38 frames.], batch size: 12, lr: 7.39e-04
2022-05-04 04:04:36,836 INFO [train.py:715] (1/8) Epoch 2, batch 3950, loss[loss=0.1456, simple_loss=0.2206, pruned_loss=0.0353, over 4778.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2403, pruned_loss=0.0542, over 971039.40 frames.], batch size: 17, lr: 7.39e-04
2022-05-04 04:05:16,465 INFO [train.py:715] (1/8) Epoch 2, batch 4000, loss[loss=0.1456, simple_loss=0.2135, pruned_loss=0.03885, over 4757.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2408, pruned_loss=0.05421, over 971041.36 frames.], batch size: 19, lr: 7.39e-04
2022-05-04 04:05:57,027 INFO [train.py:715] (1/8) Epoch 2, batch 4050, loss[loss=0.2109, simple_loss=0.2781, pruned_loss=0.07186, over 4872.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2409, pruned_loss=0.05423, over 971697.55 frames.], batch size: 16, lr: 7.38e-04
2022-05-04 04:06:37,522 INFO [train.py:715] (1/8) Epoch 2, batch 4100, loss[loss=0.1913, simple_loss=0.2637, pruned_loss=0.05943, over 4786.00 frames.], tot_loss[loss=0.1746, simple_loss=0.241, pruned_loss=0.05411, over 971194.07 frames.], batch size: 17, lr: 7.38e-04
2022-05-04 04:07:16,031 INFO [train.py:715] (1/8) Epoch 2, batch 4150, loss[loss=0.1612, simple_loss=0.2218, pruned_loss=0.05031, over 4868.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2407, pruned_loss=0.05417, over 971886.01 frames.], batch size: 32, lr: 7.38e-04
2022-05-04 04:07:55,385 INFO [train.py:715] (1/8) Epoch 2, batch 4200, loss[loss=0.185, simple_loss=0.2417, pruned_loss=0.06412, over 4842.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2401, pruned_loss=0.05408, over 972299.30 frames.], batch size: 20, lr: 7.38e-04
2022-05-04 04:08:35,829 INFO [train.py:715] (1/8) Epoch 2, batch 4250, loss[loss=0.1811, simple_loss=0.2462, pruned_loss=0.05803, over 4918.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2412, pruned_loss=0.05479, over 972274.45 frames.], batch size: 17, lr: 7.37e-04
2022-05-04 04:09:15,085 INFO [train.py:715] (1/8) Epoch 2, batch 4300, loss[loss=0.1753, simple_loss=0.2492, pruned_loss=0.05066, over 4977.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2411, pruned_loss=0.05493, over 972812.66 frames.], batch size: 25, lr: 7.37e-04
2022-05-04 04:09:54,865 INFO [train.py:715] (1/8) Epoch 2, batch 4350, loss[loss=0.2114, simple_loss=0.2911, pruned_loss=0.06585, over 4699.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2406, pruned_loss=0.05426, over 972722.96 frames.], batch size: 15, lr: 7.37e-04
2022-05-04 04:10:34,722 INFO [train.py:715] (1/8) Epoch 2, batch 4400, loss[loss=0.1909, simple_loss=0.2518, pruned_loss=0.06499, over 4976.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2408, pruned_loss=0.05431, over 972473.16 frames.], batch size: 39, lr: 7.37e-04
2022-05-04 04:11:14,729 INFO [train.py:715] (1/8) Epoch 2, batch 4450, loss[loss=0.1903, simple_loss=0.2541, pruned_loss=0.06326, over 4744.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2415, pruned_loss=0.05451, over 972502.87 frames.], batch size: 16, lr: 7.36e-04
2022-05-04 04:11:53,878 INFO [train.py:715] (1/8) Epoch 2, batch 4500, loss[loss=0.1713, simple_loss=0.2503, pruned_loss=0.04609, over 4813.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2411, pruned_loss=0.05416, over 972524.65 frames.], batch size: 26, lr: 7.36e-04
2022-05-04 04:12:33,895 INFO [train.py:715] (1/8) Epoch 2, batch 4550, loss[loss=0.2088, simple_loss=0.2723, pruned_loss=0.07267, over 4878.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2402, pruned_loss=0.05406, over 972341.62 frames.], batch size: 22, lr: 7.36e-04
2022-05-04 04:13:14,645 INFO [train.py:715] (1/8) Epoch 2, batch 4600, loss[loss=0.186, simple_loss=0.2506, pruned_loss=0.06065, over 4805.00 frames.], tot_loss[loss=0.1751, simple_loss=0.241, pruned_loss=0.05459, over 971460.45 frames.], batch size: 25, lr: 7.36e-04
2022-05-04 04:13:53,699 INFO [train.py:715] (1/8) Epoch 2, batch 4650, loss[loss=0.1608, simple_loss=0.225, pruned_loss=0.04832, over 4918.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2406, pruned_loss=0.05439, over 971986.23 frames.], batch size: 17, lr: 7.35e-04
2022-05-04 04:14:33,004 INFO [train.py:715] (1/8) Epoch 2, batch 4700, loss[loss=0.1875, simple_loss=0.2525, pruned_loss=0.06127, over 4750.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2402, pruned_loss=0.05453, over 972824.45 frames.], batch size: 19, lr: 7.35e-04
2022-05-04 04:15:13,196 INFO [train.py:715] (1/8) Epoch 2, batch 4750, loss[loss=0.1627, simple_loss=0.2349, pruned_loss=0.04522, over 4819.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2402, pruned_loss=0.05419, over 971767.93 frames.], batch size: 26, lr: 7.35e-04
2022-05-04 04:15:53,745 INFO [train.py:715] (1/8) Epoch 2, batch 4800, loss[loss=0.1409, simple_loss=0.2133, pruned_loss=0.03425, over 4889.00 frames.], tot_loss[loss=0.174, simple_loss=0.24, pruned_loss=0.05397, over 972201.22 frames.], batch size: 22, lr: 7.35e-04
2022-05-04 04:16:33,016 INFO [train.py:715] (1/8) Epoch 2, batch 4850, loss[loss=0.1893, simple_loss=0.2523, pruned_loss=0.06311, over 4766.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2406, pruned_loss=0.05441, over 972990.24 frames.], batch size: 17, lr: 7.34e-04
2022-05-04 04:17:12,483 INFO [train.py:715] (1/8) Epoch 2, batch 4900, loss[loss=0.1718, simple_loss=0.2378, pruned_loss=0.05293, over 4911.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2402, pruned_loss=0.05456, over 972525.41 frames.], batch size: 23, lr: 7.34e-04
2022-05-04 04:17:52,927 INFO [train.py:715] (1/8) Epoch 2, batch 4950, loss[loss=0.2776, simple_loss=0.3171, pruned_loss=0.1191, over 4700.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2405, pruned_loss=0.05442, over 972091.32 frames.], batch size: 15, lr: 7.34e-04
2022-05-04 04:18:32,544 INFO [train.py:715] (1/8) Epoch 2, batch 5000, loss[loss=0.1517, simple_loss=0.2272, pruned_loss=0.03808, over 4928.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2406, pruned_loss=0.05449, over 972424.55 frames.], batch size: 23, lr: 7.34e-04
2022-05-04 04:19:12,100 INFO [train.py:715] (1/8) Epoch 2, batch 5050, loss[loss=0.1804, simple_loss=0.2442, pruned_loss=0.05826, over 4940.00 frames.], tot_loss[loss=0.1762, simple_loss=0.242, pruned_loss=0.05518, over 971629.02 frames.], batch size: 21, lr: 7.33e-04
2022-05-04 04:19:53,170 INFO [train.py:715] (1/8) Epoch 2, batch 5100, loss[loss=0.178, simple_loss=0.2491, pruned_loss=0.05345, over 4989.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2433, pruned_loss=0.05607, over 971950.13 frames.], batch size: 20, lr: 7.33e-04
2022-05-04 04:20:34,123 INFO [train.py:715] (1/8) Epoch 2, batch 5150, loss[loss=0.1674, simple_loss=0.2444, pruned_loss=0.04521, over 4983.00 frames.], tot_loss[loss=0.1774, simple_loss=0.243, pruned_loss=0.05586, over 972053.42 frames.], batch size: 25, lr: 7.33e-04
2022-05-04 04:21:13,071 INFO [train.py:715] (1/8) Epoch 2, batch 5200, loss[loss=0.2089, simple_loss=0.2639, pruned_loss=0.0769, over 4907.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2421, pruned_loss=0.05564, over 972261.73 frames.], batch size: 39, lr: 7.33e-04
2022-05-04 04:21:52,854 INFO [train.py:715] (1/8) Epoch 2, batch 5250, loss[loss=0.1663, simple_loss=0.2373, pruned_loss=0.04764, over 4990.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2421, pruned_loss=0.05523, over 973344.57 frames.], batch size: 14, lr: 7.32e-04
2022-05-04 04:22:33,067 INFO [train.py:715] (1/8) Epoch 2, batch 5300, loss[loss=0.17, simple_loss=0.2284, pruned_loss=0.05578, over 4757.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2418, pruned_loss=0.05522, over 973662.83 frames.], batch size: 12, lr: 7.32e-04
2022-05-04 04:23:12,245 INFO [train.py:715] (1/8) Epoch 2, batch 5350, loss[loss=0.1512, simple_loss=0.2077, pruned_loss=0.04735, over 4830.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2409, pruned_loss=0.05465, over 973025.34 frames.], batch size: 13, lr: 7.32e-04
2022-05-04 04:23:51,608 INFO [train.py:715] (1/8) Epoch 2, batch 5400, loss[loss=0.1888, simple_loss=0.2639, pruned_loss=0.05682, over 4809.00 frames.], tot_loss[loss=0.1743, simple_loss=0.24, pruned_loss=0.05429, over 972724.19 frames.], batch size: 25, lr: 7.32e-04
2022-05-04 04:24:32,280 INFO [train.py:715] (1/8) Epoch 2, batch 5450, loss[loss=0.147, simple_loss=0.2174, pruned_loss=0.03827, over 4892.00 frames.], tot_loss[loss=0.175, simple_loss=0.2413, pruned_loss=0.05434, over 972104.88 frames.], batch size: 19, lr: 7.31e-04
2022-05-04 04:25:12,073 INFO [train.py:715] (1/8) Epoch 2, batch 5500, loss[loss=0.1352, simple_loss=0.2027, pruned_loss=0.03381, over 4962.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2409, pruned_loss=0.05404, over 973491.60 frames.], batch size: 14, lr: 7.31e-04
2022-05-04 04:25:51,713 INFO [train.py:715] (1/8) Epoch 2, batch 5550, loss[loss=0.1701, simple_loss=0.2416, pruned_loss=0.04927, over 4957.00 frames.], tot_loss[loss=0.1748, simple_loss=0.241, pruned_loss=0.05432, over 973652.52 frames.], batch size: 15, lr: 7.31e-04
2022-05-04 04:26:32,207 INFO [train.py:715] (1/8) Epoch 2, batch 5600, loss[loss=0.1464, simple_loss=0.2082, pruned_loss=0.04234, over 4833.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2414, pruned_loss=0.05479, over 973183.40 frames.], batch size: 13, lr: 7.31e-04
2022-05-04 04:27:13,265 INFO [train.py:715] (1/8) Epoch 2, batch 5650, loss[loss=0.1792, simple_loss=0.2395, pruned_loss=0.05943, over 4981.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2401, pruned_loss=0.05413, over 972689.92 frames.], batch size: 31, lr: 7.30e-04
2022-05-04 04:27:53,178 INFO [train.py:715] (1/8) Epoch 2, batch 5700, loss[loss=0.1939, simple_loss=0.2479, pruned_loss=0.06988, over 4942.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2404, pruned_loss=0.05415, over 973013.36 frames.], batch size: 35, lr: 7.30e-04
2022-05-04 04:28:33,025 INFO [train.py:715] (1/8) Epoch 2, batch 5750, loss[loss=0.2284, simple_loss=0.2827, pruned_loss=0.08702, over 4872.00 frames.], tot_loss[loss=0.1763, simple_loss=0.242, pruned_loss=0.05529, over 972575.49 frames.], batch size: 30, lr: 7.30e-04
2022-05-04 04:29:13,949 INFO [train.py:715] (1/8) Epoch 2, batch 5800, loss[loss=0.1812, simple_loss=0.2476, pruned_loss=0.05738, over 4769.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2402, pruned_loss=0.0541, over 973457.91 frames.], batch size: 18, lr: 7.30e-04
2022-05-04 04:29:55,087 INFO [train.py:715] (1/8) Epoch 2, batch 5850, loss[loss=0.1941, simple_loss=0.2666, pruned_loss=0.06079, over 4896.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2413, pruned_loss=0.05468, over 973490.37 frames.], batch size: 17, lr: 7.29e-04
2022-05-04 04:30:34,557 INFO [train.py:715] (1/8) Epoch 2, batch 5900, loss[loss=0.1537, simple_loss=0.2188, pruned_loss=0.04434, over 4914.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2414, pruned_loss=0.05539, over 973855.22 frames.], batch size: 18, lr: 7.29e-04
2022-05-04 04:31:15,149 INFO [train.py:715] (1/8) Epoch 2, batch 5950, loss[loss=0.1829, simple_loss=0.2501, pruned_loss=0.0579, over 4827.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2409, pruned_loss=0.05516, over 973764.82 frames.], batch size: 27, lr: 7.29e-04
2022-05-04 04:31:56,152 INFO [train.py:715] (1/8) Epoch 2, batch 6000, loss[loss=0.1609, simple_loss=0.2276, pruned_loss=0.04714, over 4982.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2412, pruned_loss=0.0553, over 973131.43 frames.], batch size: 28, lr: 7.29e-04
2022-05-04 04:31:56,153 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 04:32:04,808 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1188, simple_loss=0.2054, pruned_loss=0.01614, over 914524.00 frames.
2022-05-04 04:32:46,134 INFO [train.py:715] (1/8) Epoch 2, batch 6050, loss[loss=0.1701, simple_loss=0.2408, pruned_loss=0.04975, over 4771.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2407, pruned_loss=0.05471, over 971917.02 frames.], batch size: 17, lr: 7.29e-04
2022-05-04 04:33:25,857 INFO [train.py:715] (1/8) Epoch 2, batch 6100, loss[loss=0.1875, simple_loss=0.2571, pruned_loss=0.05891, over 4775.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2407, pruned_loss=0.05436, over 972352.71 frames.], batch size: 19, lr: 7.28e-04
2022-05-04 04:34:05,819 INFO [train.py:715] (1/8) Epoch 2, batch 6150, loss[loss=0.1469, simple_loss=0.2113, pruned_loss=0.04126, over 4845.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2411, pruned_loss=0.05504, over 973035.49 frames.], batch size: 13, lr: 7.28e-04
2022-05-04 04:34:46,186 INFO [train.py:715] (1/8) Epoch 2, batch 6200, loss[loss=0.1545, simple_loss=0.2174, pruned_loss=0.04581, over 4881.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2401, pruned_loss=0.05415, over 973068.69 frames.], batch size: 16, lr: 7.28e-04
2022-05-04 04:35:26,605 INFO [train.py:715] (1/8) Epoch 2, batch 6250, loss[loss=0.1752, simple_loss=0.2479, pruned_loss=0.05123, over 4809.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2403, pruned_loss=0.05459, over 972571.86 frames.], batch size: 21, lr: 7.28e-04
2022-05-04 04:36:05,778 INFO [train.py:715] (1/8) Epoch 2, batch 6300, loss[loss=0.1796, simple_loss=0.2371, pruned_loss=0.06103, over 4985.00 frames.], tot_loss[loss=0.1754, simple_loss=0.241, pruned_loss=0.05494, over 973080.22 frames.], batch size: 28, lr: 7.27e-04
2022-05-04 04:36:46,020 INFO [train.py:715] (1/8) Epoch 2, batch 6350, loss[loss=0.1675, simple_loss=0.2368, pruned_loss=0.04906, over 4971.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2411, pruned_loss=0.05485, over 973332.98 frames.], batch size: 31, lr: 7.27e-04
2022-05-04 04:37:26,513 INFO [train.py:715] (1/8) Epoch 2, batch 6400, loss[loss=0.2013, simple_loss=0.2639, pruned_loss=0.06938, over 4844.00 frames.], tot_loss[loss=0.175, simple_loss=0.2411, pruned_loss=0.05443, over 973084.66 frames.], batch size: 15, lr: 7.27e-04
2022-05-04 04:38:05,323 INFO [train.py:715] (1/8) Epoch 2, batch 6450, loss[loss=0.2058, simple_loss=0.2584, pruned_loss=0.07658, over 4952.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2413, pruned_loss=0.05411, over 972932.73 frames.], batch size: 39, lr: 7.27e-04
2022-05-04 04:38:44,597 INFO [train.py:715] (1/8) Epoch 2, batch 6500, loss[loss=0.1671, simple_loss=0.227, pruned_loss=0.05357, over 4964.00 frames.], tot_loss[loss=0.1748, simple_loss=0.241, pruned_loss=0.05429, over 972815.27 frames.], batch size: 31, lr: 7.26e-04
2022-05-04 04:39:24,829 INFO [train.py:715] (1/8) Epoch 2, batch 6550, loss[loss=0.1669, simple_loss=0.2447, pruned_loss=0.04461, over 4855.00 frames.], tot_loss[loss=0.174, simple_loss=0.2405, pruned_loss=0.05377, over 971675.91 frames.], batch size: 15, lr: 7.26e-04
2022-05-04 04:40:04,764 INFO [train.py:715] (1/8) Epoch 2, batch 6600, loss[loss=0.1688, simple_loss=0.2365, pruned_loss=0.05055, over 4884.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2398, pruned_loss=0.05381, over 972684.97 frames.], batch size: 22, lr: 7.26e-04
2022-05-04 04:40:43,854 INFO [train.py:715] (1/8) Epoch 2, batch 6650, loss[loss=0.1595, simple_loss=0.2334, pruned_loss=0.04284, over 4950.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2412, pruned_loss=0.05491, over 972868.81 frames.], batch size: 21, lr: 7.26e-04
2022-05-04 04:41:23,366 INFO [train.py:715] (1/8) Epoch 2, batch 6700, loss[loss=0.1459, simple_loss=0.2194, pruned_loss=0.03616, over 4985.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2403, pruned_loss=0.05464, over 972833.53 frames.], batch size: 25, lr: 7.25e-04
2022-05-04 04:42:03,549 INFO [train.py:715] (1/8) Epoch 2, batch 6750, loss[loss=0.1614, simple_loss=0.2297, pruned_loss=0.04658, over 4760.00 frames.], tot_loss[loss=0.1742, simple_loss=0.24, pruned_loss=0.05421, over 972525.83 frames.], batch size: 19, lr: 7.25e-04
2022-05-04 04:42:41,714 INFO [train.py:715] (1/8) Epoch 2, batch 6800, loss[loss=0.1544, simple_loss=0.2225, pruned_loss=0.04318, over 4832.00 frames.], tot_loss[loss=0.174, simple_loss=0.24, pruned_loss=0.05403, over 972284.93 frames.], batch size: 30, lr: 7.25e-04
2022-05-04 04:43:20,936 INFO [train.py:715] (1/8) Epoch 2, batch 6850, loss[loss=0.176, simple_loss=0.2404, pruned_loss=0.05579, over 4788.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2404, pruned_loss=0.05415, over 973092.33 frames.], batch size: 17, lr: 7.25e-04
2022-05-04 04:44:01,036 INFO [train.py:715] (1/8) Epoch 2, batch 6900, loss[loss=0.1775, simple_loss=0.2355, pruned_loss=0.05969, over 4776.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2405, pruned_loss=0.05378, over 972859.26 frames.], batch size: 14, lr: 7.24e-04
2022-05-04 04:44:41,201 INFO [train.py:715] (1/8) Epoch 2, batch 6950, loss[loss=0.1523, simple_loss=0.226, pruned_loss=0.03931, over 4922.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2411, pruned_loss=0.05388, over 972795.16 frames.], batch size: 18, lr: 7.24e-04
2022-05-04 04:45:19,403 INFO [train.py:715] (1/8) Epoch 2, batch 7000, loss[loss=0.1641, simple_loss=0.2244, pruned_loss=0.05186, over 4872.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2411, pruned_loss=0.05416, over 972872.31 frames.], batch size: 20, lr: 7.24e-04
2022-05-04 04:45:59,975 INFO [train.py:715] (1/8) Epoch 2, batch 7050, loss[loss=0.1812, simple_loss=0.2418, pruned_loss=0.06027, over 4984.00 frames.], tot_loss[loss=0.1738, simple_loss=0.24, pruned_loss=0.05375, over 972444.24 frames.], batch size: 14, lr: 7.24e-04
2022-05-04 04:46:40,399 INFO [train.py:715] (1/8) Epoch 2, batch 7100, loss[loss=0.1474, simple_loss=0.2106, pruned_loss=0.04208, over 4779.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2396, pruned_loss=0.05375, over 972770.58 frames.], batch size: 12, lr: 7.24e-04
2022-05-04 04:47:19,786 INFO [train.py:715] (1/8) Epoch 2, batch 7150, loss[loss=0.1676, simple_loss=0.2359, pruned_loss=0.0497, over 4981.00 frames.], tot_loss[loss=0.1737, simple_loss=0.24, pruned_loss=0.05368, over 972161.52 frames.], batch size: 39, lr: 7.23e-04
2022-05-04 04:48:00,084 INFO [train.py:715] (1/8) Epoch 2, batch 7200, loss[loss=0.1984, simple_loss=0.2585, pruned_loss=0.06912, over 4866.00 frames.], tot_loss[loss=0.1734, simple_loss=0.24, pruned_loss=0.05339, over 972014.06 frames.], batch size: 20, lr: 7.23e-04
2022-05-04 04:48:41,274 INFO [train.py:715] (1/8) Epoch 2, batch 7250, loss[loss=0.1835, simple_loss=0.2433, pruned_loss=0.06187, over 4853.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2403, pruned_loss=0.05361, over 971482.62 frames.], batch size: 20, lr: 7.23e-04
2022-05-04 04:49:21,912 INFO [train.py:715] (1/8) Epoch 2, batch 7300, loss[loss=0.1966, simple_loss=0.2541, pruned_loss=0.06955, over 4683.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2416, pruned_loss=0.05499, over 971293.03 frames.], batch size: 15, lr: 7.23e-04
2022-05-04 04:50:01,597 INFO [train.py:715] (1/8) Epoch 2, batch 7350, loss[loss=0.153, simple_loss=0.2238, pruned_loss=0.04105, over 4932.00 frames.], tot_loss[loss=0.1739, simple_loss=0.24, pruned_loss=0.05397, over 970177.47 frames.], batch size: 23, lr: 7.22e-04
2022-05-04 04:50:42,524 INFO [train.py:715] (1/8) Epoch 2, batch 7400, loss[loss=0.223, simple_loss=0.2847, pruned_loss=0.08063, over 4904.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2412, pruned_loss=0.05471, over 970195.76 frames.], batch size: 18, lr: 7.22e-04
2022-05-04 04:51:24,317 INFO [train.py:715] (1/8) Epoch 2, batch 7450, loss[loss=0.1573, simple_loss=0.225, pruned_loss=0.0448, over 4992.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2409, pruned_loss=0.05418, over 970010.85 frames.], batch size: 26, lr: 7.22e-04
2022-05-04 04:52:04,701 INFO [train.py:715] (1/8) Epoch 2, batch 7500, loss[loss=0.1502, simple_loss=0.22, pruned_loss=0.04016, over 4988.00 frames.], tot_loss[loss=0.1752, simple_loss=0.241, pruned_loss=0.05471, over 969842.67 frames.], batch size: 25, lr: 7.22e-04
2022-05-04 04:52:45,155 INFO [train.py:715] (1/8) Epoch 2, batch 7550, loss[loss=0.1795, simple_loss=0.237, pruned_loss=0.06106, over 4916.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2408, pruned_loss=0.05484, over 970841.89 frames.], batch size: 17, lr: 7.21e-04
2022-05-04 04:53:26,931 INFO [train.py:715] (1/8) Epoch 2, batch 7600, loss[loss=0.1869, simple_loss=0.2491, pruned_loss=0.06231, over 4958.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2399, pruned_loss=0.05445, over 970839.12 frames.], batch size: 35, lr: 7.21e-04
2022-05-04 04:54:08,326 INFO [train.py:715] (1/8) Epoch 2, batch 7650, loss[loss=0.1739, simple_loss=0.237, pruned_loss=0.05544, over 4987.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2398, pruned_loss=0.05443, over 970965.76 frames.], batch size: 28, lr: 7.21e-04
2022-05-04 04:54:48,376 INFO [train.py:715] (1/8) Epoch 2, batch 7700, loss[loss=0.1796, simple_loss=0.2479, pruned_loss=0.0556, over 4867.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2399, pruned_loss=0.05432, over 972207.25 frames.], batch size: 20, lr: 7.21e-04
2022-05-04 04:55:29,828 INFO [train.py:715] (1/8) Epoch 2, batch 7750, loss[loss=0.1933, simple_loss=0.2397, pruned_loss=0.07346, over 4745.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2382, pruned_loss=0.0533, over 971891.03 frames.], batch size: 16, lr: 7.21e-04
2022-05-04 04:56:11,494 INFO [train.py:715] (1/8) Epoch 2, batch 7800, loss[loss=0.1906, simple_loss=0.248, pruned_loss=0.06662, over 4977.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2393, pruned_loss=0.05388, over 972278.46 frames.], batch size: 35, lr: 7.20e-04
2022-05-04 04:56:52,000 INFO [train.py:715] (1/8) Epoch 2, batch 7850, loss[loss=0.1391, simple_loss=0.2045, pruned_loss=0.03688, over 4903.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2394, pruned_loss=0.05413, over 972571.92 frames.], batch size: 29, lr: 7.20e-04
2022-05-04 04:57:33,356 INFO [train.py:715] (1/8) Epoch 2, batch 7900, loss[loss=0.1564, simple_loss=0.2346, pruned_loss=0.03905, over 4868.00 frames.], tot_loss[loss=0.1756, simple_loss=0.241, pruned_loss=0.05514, over 972724.65 frames.], batch size: 32, lr: 7.20e-04
2022-05-04 04:58:15,548 INFO [train.py:715] (1/8) Epoch 2, batch 7950, loss[loss=0.1887, simple_loss=0.2487, pruned_loss=0.06434, over 4957.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2405, pruned_loss=0.05525, over 973268.55 frames.], batch size: 39, lr: 7.20e-04
2022-05-04 04:58:57,039 INFO [train.py:715] (1/8) Epoch 2, batch 8000, loss[loss=0.1609, simple_loss=0.2317, pruned_loss=0.04502, over 4989.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2415, pruned_loss=0.05576, over 973399.74 frames.], batch size: 15, lr: 7.19e-04
2022-05-04 04:59:37,239 INFO [train.py:715] (1/8) Epoch 2, batch 8050, loss[loss=0.2167, simple_loss=0.2875, pruned_loss=0.07296, over 4742.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2411, pruned_loss=0.05518, over 973867.98 frames.], batch size: 16, lr: 7.19e-04
2022-05-04 05:00:18,965 INFO [train.py:715] (1/8) Epoch 2, batch 8100, loss[loss=0.1808, simple_loss=0.2484, pruned_loss=0.05663, over 4880.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2412, pruned_loss=0.05534, over 972580.69 frames.], batch size: 38, lr: 7.19e-04
2022-05-04 05:01:00,831 INFO [train.py:715] (1/8) Epoch 2, batch 8150, loss[loss=0.1679, simple_loss=0.2329, pruned_loss=0.05147, over 4837.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2407, pruned_loss=0.05554, over 972547.89 frames.], batch size: 26, lr: 7.19e-04
2022-05-04 05:01:41,270 INFO [train.py:715] (1/8) Epoch 2, batch 8200, loss[loss=0.194, simple_loss=0.247, pruned_loss=0.0705, over 4948.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2406, pruned_loss=0.05546, over 973186.71 frames.], batch size: 39, lr: 7.18e-04
2022-05-04 05:02:22,243 INFO [train.py:715] (1/8) Epoch 2, batch 8250, loss[loss=0.2008, simple_loss=0.2511, pruned_loss=0.07521, over 4867.00 frames.], tot_loss[loss=0.177, simple_loss=0.2414, pruned_loss=0.0563, over 973839.80 frames.], batch size: 32, lr: 7.18e-04
2022-05-04 05:03:04,357 INFO [train.py:715] (1/8) Epoch 2, batch 8300, loss[loss=0.1442, simple_loss=0.2218, pruned_loss=0.03324, over 4815.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2404, pruned_loss=0.05528, over 973135.46 frames.], batch size: 25, lr: 7.18e-04
2022-05-04 05:03:46,075 INFO [train.py:715] (1/8) Epoch 2, batch 8350, loss[loss=0.1581, simple_loss=0.2299, pruned_loss=0.04311, over 4947.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2398, pruned_loss=0.05419, over 973278.47 frames.], batch size: 29, lr: 7.18e-04
2022-05-04 05:04:26,321 INFO [train.py:715] (1/8) Epoch 2, batch 8400, loss[loss=0.1781, simple_loss=0.2518, pruned_loss=0.0522, over 4962.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2393, pruned_loss=0.05365, over 972956.46 frames.], batch size: 21, lr: 7.18e-04
2022-05-04 05:05:07,467 INFO [train.py:715] (1/8) Epoch 2, batch 8450, loss[loss=0.1862, simple_loss=0.2444, pruned_loss=0.06402, over 4777.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2395, pruned_loss=0.05404, over 972010.54 frames.], batch size: 17, lr: 7.17e-04
2022-05-04 05:05:49,559 INFO [train.py:715] (1/8) Epoch 2, batch 8500, loss[loss=0.1875, simple_loss=0.2509, pruned_loss=0.06204, over 4939.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2393, pruned_loss=0.05348, over 972654.37 frames.], batch size: 21, lr: 7.17e-04
2022-05-04 05:06:29,755 INFO [train.py:715] (1/8) Epoch 2, batch 8550, loss[loss=0.1783, simple_loss=0.2426, pruned_loss=0.05697, over 4855.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2393, pruned_loss=0.05388, over 972426.73 frames.], batch size: 20, lr: 7.17e-04
2022-05-04 05:07:10,943 INFO [train.py:715] (1/8) Epoch 2, batch 8600, loss[loss=0.1638, simple_loss=0.2277, pruned_loss=0.04997, over 4840.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2395, pruned_loss=0.0541, over 971590.23 frames.], batch size: 30, lr: 7.17e-04
2022-05-04 05:07:52,990 INFO [train.py:715] (1/8) Epoch 2, batch 8650, loss[loss=0.1854, simple_loss=0.2426, pruned_loss=0.0641, over 4867.00 frames.], tot_loss[loss=0.174, simple_loss=0.2399, pruned_loss=0.05407, over 971555.19 frames.], batch size: 16, lr: 7.16e-04
2022-05-04 05:08:34,282 INFO [train.py:715] (1/8) Epoch 2, batch 8700, loss[loss=0.1727, simple_loss=0.2423, pruned_loss=0.05155, over 4757.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2395, pruned_loss=0.05384, over 972146.06 frames.], batch size: 19, lr: 7.16e-04
2022-05-04 05:09:14,825 INFO [train.py:715] (1/8) Epoch 2, batch 8750, loss[loss=0.1261, simple_loss=0.1921, pruned_loss=0.03004, over 4782.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2394, pruned_loss=0.05368, over 972889.90 frames.], batch size: 14, lr: 7.16e-04
2022-05-04 05:09:56,624 INFO [train.py:715] (1/8) Epoch 2, batch 8800, loss[loss=0.191, simple_loss=0.2495, pruned_loss=0.06625, over 4992.00 frames.], tot_loss[loss=0.1718, simple_loss=0.238, pruned_loss=0.05282, over 973688.09 frames.], batch size: 14, lr: 7.16e-04
2022-05-04 05:10:38,729 INFO [train.py:715] (1/8) Epoch 2, batch 8850, loss[loss=0.1766, simple_loss=0.2429, pruned_loss=0.0552, over 4869.00 frames.], tot_loss[loss=0.1727, simple_loss=0.239, pruned_loss=0.0532, over 972712.18 frames.], batch size: 22, lr: 7.15e-04
2022-05-04 05:11:18,690 INFO [train.py:715] (1/8) Epoch 2, batch 8900, loss[loss=0.1845, simple_loss=0.241, pruned_loss=0.06402, over 4907.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2381, pruned_loss=0.05275, over 972852.37 frames.], batch size: 18, lr: 7.15e-04
2022-05-04 05:12:00,187 INFO [train.py:715] (1/8) Epoch 2, batch 8950, loss[loss=0.1688, simple_loss=0.2371, pruned_loss=0.05023, over 4781.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2388, pruned_loss=0.05325, over 971793.94 frames.], batch size: 17, lr: 7.15e-04
2022-05-04 05:12:42,400 INFO [train.py:715] (1/8) Epoch 2, batch 9000, loss[loss=0.1932, simple_loss=0.2623, pruned_loss=0.06208, over 4893.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2387, pruned_loss=0.05348, over 971384.81 frames.], batch size: 19, lr: 7.15e-04
2022-05-04 05:12:42,401 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 05:12:58,992 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1181, simple_loss=0.2047, pruned_loss=0.01572, over 914524.00 frames.
2022-05-04 05:13:41,059 INFO [train.py:715] (1/8) Epoch 2, batch 9050, loss[loss=0.1577, simple_loss=0.2224, pruned_loss=0.04648, over 4908.00 frames.], tot_loss[loss=0.173, simple_loss=0.239, pruned_loss=0.0535, over 971231.12 frames.], batch size: 23, lr: 7.15e-04
2022-05-04 05:14:21,238 INFO [train.py:715] (1/8) Epoch 2, batch 9100, loss[loss=0.1736, simple_loss=0.2469, pruned_loss=0.05016, over 4904.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2389, pruned_loss=0.05297, over 971368.86 frames.], batch size: 17, lr: 7.14e-04
2022-05-04 05:15:02,333 INFO [train.py:715] (1/8) Epoch 2, batch 9150, loss[loss=0.155, simple_loss=0.2368, pruned_loss=0.03662, over 4799.00 frames.], tot_loss[loss=0.1735, simple_loss=0.24, pruned_loss=0.05347, over 972369.60 frames.], batch size: 21, lr: 7.14e-04
2022-05-04 05:15:43,575 INFO [train.py:715] (1/8) Epoch 2, batch 9200, loss[loss=0.1862, simple_loss=0.2525, pruned_loss=0.05993, over 4703.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2407, pruned_loss=0.05401, over 972588.04 frames.], batch size: 15, lr: 7.14e-04
2022-05-04 05:16:25,082 INFO [train.py:715] (1/8) Epoch 2, batch 9250, loss[loss=0.1498, simple_loss=0.2242, pruned_loss=0.03771, over 4932.00 frames.], tot_loss[loss=0.175, simple_loss=0.2412, pruned_loss=0.05435, over 972393.61 frames.], batch size: 23, lr: 7.14e-04
2022-05-04 05:17:05,066 INFO [train.py:715] (1/8) Epoch 2, batch 9300, loss[loss=0.1613, simple_loss=0.2369, pruned_loss=0.04283, over 4916.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2408, pruned_loss=0.05405, over 972874.05 frames.], batch size: 17, lr: 7.13e-04
2022-05-04 05:17:46,760 INFO [train.py:715] (1/8) Epoch 2, batch 9350, loss[loss=0.1681, simple_loss=0.2316, pruned_loss=0.05235, over 4960.00 frames.], tot_loss[loss=0.173, simple_loss=0.2394, pruned_loss=0.0533, over 972663.36 frames.], batch size: 35, lr: 7.13e-04
2022-05-04 05:18:28,854 INFO [train.py:715] (1/8) Epoch 2, batch 9400, loss[loss=0.1508, simple_loss=0.2146, pruned_loss=0.04346, over 4781.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2384, pruned_loss=0.05294, over 972378.87 frames.], batch size: 18, lr: 7.13e-04
2022-05-04 05:19:08,500 INFO [train.py:715] (1/8) Epoch 2, batch 9450, loss[loss=0.1581, simple_loss=0.219, pruned_loss=0.04856, over 4865.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2379, pruned_loss=0.05264, over 972604.65 frames.], batch size: 16, lr: 7.13e-04
2022-05-04 05:19:48,354 INFO [train.py:715] (1/8) Epoch 2, batch 9500, loss[loss=0.1615, simple_loss=0.2395, pruned_loss=0.04177, over 4916.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2391, pruned_loss=0.05327, over 973441.32 frames.], batch size: 18, lr: 7.13e-04
2022-05-04 05:20:28,628 INFO [train.py:715] (1/8) Epoch 2, batch 9550, loss[loss=0.1782, simple_loss=0.2371, pruned_loss=0.05967, over 4797.00 frames.], tot_loss[loss=0.1728, simple_loss=0.239, pruned_loss=0.05332, over 972465.02 frames.], batch size: 21, lr: 7.12e-04
2022-05-04 05:21:08,637 INFO [train.py:715] (1/8) Epoch 2, batch 9600, loss[loss=0.1495, simple_loss=0.2089, pruned_loss=0.04499, over 4778.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2388, pruned_loss=0.05295, over 971847.57 frames.], batch size: 14, lr: 7.12e-04
2022-05-04 05:21:47,531 INFO [train.py:715] (1/8) Epoch 2, batch 9650, loss[loss=0.1859, simple_loss=0.2439, pruned_loss=0.06396, over 4973.00 frames.], tot_loss[loss=0.1728, simple_loss=0.239, pruned_loss=0.05335, over 972134.36 frames.], batch size: 27, lr: 7.12e-04
2022-05-04 05:22:27,774 INFO [train.py:715] (1/8) Epoch 2, batch 9700, loss[loss=0.1748, simple_loss=0.2478, pruned_loss=0.05091, over 4818.00 frames.], tot_loss[loss=0.173, simple_loss=0.2393, pruned_loss=0.05333, over 972510.06 frames.], batch size: 15, lr: 7.12e-04
2022-05-04 05:23:08,405 INFO [train.py:715] (1/8) Epoch 2, batch 9750, loss[loss=0.201, simple_loss=0.2628, pruned_loss=0.06965, over 4947.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2395, pruned_loss=0.05378, over 971787.05 frames.], batch size: 21, lr: 7.11e-04
2022-05-04 05:23:47,695 INFO [train.py:715] (1/8) Epoch 2, batch 9800, loss[loss=0.1894, simple_loss=0.2583, pruned_loss=0.06027, over 4794.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2398, pruned_loss=0.05347, over 972403.39 frames.], batch size: 21, lr: 7.11e-04
2022-05-04 05:24:26,792 INFO [train.py:715] (1/8) Epoch 2, batch 9850, loss[loss=0.1868, simple_loss=0.2589, pruned_loss=0.05739, over 4936.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2398, pruned_loss=0.05334, over 972996.10 frames.], batch size: 35, lr: 7.11e-04
2022-05-04 05:25:06,813 INFO [train.py:715] (1/8) Epoch 2, batch 9900, loss[loss=0.1629, simple_loss=0.2435, pruned_loss=0.04115, over 4836.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2394, pruned_loss=0.05313, over 972811.34 frames.], batch size: 20, lr: 7.11e-04
2022-05-04 05:25:46,406 INFO [train.py:715] (1/8) Epoch 2, batch 9950, loss[loss=0.1804, simple_loss=0.2474, pruned_loss=0.05671, over 4935.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2394, pruned_loss=0.05288, over 972964.83 frames.], batch size: 23, lr: 7.11e-04
2022-05-04 05:26:25,426 INFO [train.py:715] (1/8) Epoch 2, batch 10000, loss[loss=0.1615, simple_loss=0.2308, pruned_loss=0.04608, over 4913.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2394, pruned_loss=0.05355, over 972870.23 frames.], batch size: 29, lr: 7.10e-04
2022-05-04 05:27:06,101 INFO [train.py:715] (1/8) Epoch 2, batch 10050, loss[loss=0.2101, simple_loss=0.2615, pruned_loss=0.07936, over 4914.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2392, pruned_loss=0.05374, over 971831.21 frames.], batch size: 18, lr: 7.10e-04
2022-05-04 05:27:45,910 INFO [train.py:715] (1/8) Epoch 2, batch 10100, loss[loss=0.1323, simple_loss=0.2098, pruned_loss=0.02739, over 4980.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2383, pruned_loss=0.0535, over 971891.96 frames.], batch size: 24, lr: 7.10e-04
2022-05-04 05:28:25,922 INFO [train.py:715] (1/8) Epoch 2, batch 10150, loss[loss=0.1722, simple_loss=0.2357, pruned_loss=0.05432, over 4814.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2376, pruned_loss=0.05306, over 971442.74 frames.], batch size: 13, lr: 7.10e-04
2022-05-04 05:29:06,179 INFO [train.py:715] (1/8) Epoch 2, batch 10200, loss[loss=0.1577, simple_loss=0.2162, pruned_loss=0.04957, over 4787.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2382, pruned_loss=0.05342, over 971686.38 frames.], batch size: 14, lr: 7.09e-04
2022-05-04 05:29:47,603 INFO [train.py:715] (1/8) Epoch 2, batch 10250, loss[loss=0.1683, simple_loss=0.2393, pruned_loss=0.04871, over 4924.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2383, pruned_loss=0.05314, over 971649.86 frames.], batch size: 23, lr: 7.09e-04
2022-05-04 05:30:27,417 INFO [train.py:715] (1/8) Epoch 2, batch 10300, loss[loss=0.2109, simple_loss=0.2626, pruned_loss=0.07956, over 4871.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2387, pruned_loss=0.05335, over 971829.88 frames.], batch size: 20, lr: 7.09e-04
2022-05-04 05:31:07,041 INFO [train.py:715] (1/8) Epoch 2, batch 10350, loss[loss=0.1887, simple_loss=0.2435, pruned_loss=0.06691, over 4827.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2394, pruned_loss=0.05388, over 972517.43 frames.], batch size: 13, lr: 7.09e-04
2022-05-04 05:31:49,857 INFO [train.py:715] (1/8) Epoch 2, batch 10400, loss[loss=0.1941, simple_loss=0.2585, pruned_loss=0.06482, over 4760.00 frames.], tot_loss[loss=0.174, simple_loss=0.2396, pruned_loss=0.05427, over 972646.26 frames.], batch size: 16, lr: 7.09e-04
2022-05-04 05:32:31,021 INFO [train.py:715] (1/8) Epoch 2, batch 10450, loss[loss=0.1655, simple_loss=0.2368, pruned_loss=0.04711, over 4982.00 frames.], tot_loss[loss=0.1733, simple_loss=0.239, pruned_loss=0.05378, over 972651.78 frames.], batch size: 14, lr: 7.08e-04
2022-05-04 05:33:11,274 INFO [train.py:715] (1/8) Epoch 2, batch 10500, loss[loss=0.1585, simple_loss=0.2189, pruned_loss=0.04901, over 4647.00 frames.], tot_loss[loss=0.1739, simple_loss=0.24, pruned_loss=0.05389, over 972178.61 frames.], batch size: 13, lr: 7.08e-04
2022-05-04 05:33:50,618 INFO [train.py:715] (1/8) Epoch 2, batch 10550, loss[loss=0.1556, simple_loss=0.2345, pruned_loss=0.03834, over 4806.00 frames.], tot_loss[loss=0.174, simple_loss=0.2405, pruned_loss=0.05368, over 973123.99 frames.], batch size: 12, lr: 7.08e-04
2022-05-04 05:34:31,850 INFO [train.py:715] (1/8) Epoch 2, batch 10600, loss[loss=0.1726, simple_loss=0.2369, pruned_loss=0.0542, over 4840.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2414, pruned_loss=0.05409, over 973164.38 frames.], batch size: 25, lr: 7.08e-04
2022-05-04 05:35:12,039 INFO [train.py:715] (1/8) Epoch 2, batch 10650, loss[loss=0.1837, simple_loss=0.2485, pruned_loss=0.05942, over 4754.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2397, pruned_loss=0.05349, over 972453.19 frames.], batch size: 19, lr: 7.07e-04
2022-05-04 05:35:51,941 INFO [train.py:715] (1/8) Epoch 2, batch 10700, loss[loss=0.1804, simple_loss=0.2583, pruned_loss=0.05125, over 4931.00 frames.], tot_loss[loss=0.1737, simple_loss=0.24, pruned_loss=0.05376, over 972577.03 frames.], batch size: 18, lr: 7.07e-04
2022-05-04 05:36:32,505 INFO [train.py:715] (1/8) Epoch 2, batch 10750, loss[loss=0.1627, simple_loss=0.2287, pruned_loss=0.04834, over 4746.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2409, pruned_loss=0.0542, over 972161.66 frames.], batch size: 19, lr: 7.07e-04
2022-05-04 05:37:13,629 INFO [train.py:715] (1/8) Epoch 2, batch 10800, loss[loss=0.1587, simple_loss=0.217, pruned_loss=0.05019, over 4941.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2402, pruned_loss=0.05362, over 971402.12 frames.], batch size: 29, lr: 7.07e-04
2022-05-04 05:37:53,815 INFO [train.py:715] (1/8) Epoch 2, batch 10850, loss[loss=0.15, simple_loss=0.2179, pruned_loss=0.04108, over 4871.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2406, pruned_loss=0.05382, over 972135.28 frames.], batch size: 22, lr: 7.07e-04
2022-05-04 05:38:33,327 INFO [train.py:715] (1/8) Epoch 2, batch 10900, loss[loss=0.1699, simple_loss=0.2357, pruned_loss=0.05207, over 4932.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2406, pruned_loss=0.05415, over 973154.41 frames.], batch size: 23, lr: 7.06e-04
2022-05-04 05:39:14,356 INFO [train.py:715] (1/8) Epoch 2, batch 10950, loss[loss=0.1459, simple_loss=0.2174, pruned_loss=0.03717, over 4988.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2408, pruned_loss=0.05409, over 972585.08 frames.], batch size: 15, lr: 7.06e-04
2022-05-04 05:39:54,164 INFO [train.py:715] (1/8) Epoch 2, batch 11000, loss[loss=0.1714, simple_loss=0.2423, pruned_loss=0.05028, over 4781.00 frames.], tot_loss[loss=0.174, simple_loss=0.2401, pruned_loss=0.05392, over 972378.05 frames.], batch size: 18, lr: 7.06e-04
2022-05-04 05:40:33,761 INFO [train.py:715] (1/8) Epoch 2, batch 11050, loss[loss=0.1389, simple_loss=0.1999, pruned_loss=0.0389, over 4700.00 frames.], tot_loss[loss=0.1738, simple_loss=0.24, pruned_loss=0.05379, over 972665.95 frames.], batch size: 15, lr: 7.06e-04
2022-05-04 05:41:14,435 INFO [train.py:715] (1/8) Epoch 2, batch 11100, loss[loss=0.1648, simple_loss=0.2247, pruned_loss=0.05245, over 4795.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2403, pruned_loss=0.05438, over 973271.72 frames.], batch size: 12, lr: 7.05e-04
2022-05-04 05:41:54,864 INFO [train.py:715] (1/8) Epoch 2, batch 11150, loss[loss=0.1943, simple_loss=0.271, pruned_loss=0.05885, over 4964.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2391, pruned_loss=0.05332, over 973629.02 frames.], batch size: 15, lr: 7.05e-04
2022-05-04 05:42:35,625 INFO [train.py:715] (1/8) Epoch 2, batch 11200, loss[loss=0.1909, simple_loss=0.2683, pruned_loss=0.05674, over 4897.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2394, pruned_loss=0.05347, over 972713.01 frames.], batch size: 39, lr: 7.05e-04
2022-05-04 05:43:15,655 INFO [train.py:715] (1/8) Epoch 2, batch 11250, loss[loss=0.1848, simple_loss=0.2497, pruned_loss=0.05992, over 4749.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2389, pruned_loss=0.05325, over 971223.64 frames.], batch size: 19, lr: 7.05e-04
2022-05-04 05:43:56,718 INFO [train.py:715] (1/8) Epoch 2, batch 11300, loss[loss=0.2091, simple_loss=0.2609, pruned_loss=0.07865, over 4811.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2389, pruned_loss=0.05341, over 972046.28 frames.], batch size: 15, lr: 7.05e-04
2022-05-04 05:44:37,056 INFO [train.py:715] (1/8) Epoch 2, batch 11350, loss[loss=0.1547, simple_loss=0.227, pruned_loss=0.04122, over 4867.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2386, pruned_loss=0.05311, over 971724.07 frames.], batch size: 16, lr: 7.04e-04
2022-05-04 05:45:16,683 INFO [train.py:715] (1/8) Epoch 2, batch 11400, loss[loss=0.1668, simple_loss=0.2376, pruned_loss=0.048, over 4924.00 frames.], tot_loss[loss=0.1728, simple_loss=0.239, pruned_loss=0.05323, over 971489.21 frames.], batch size: 29, lr: 7.04e-04
2022-05-04 05:45:56,736 INFO [train.py:715] (1/8) Epoch 2, batch 11450, loss[loss=0.181, simple_loss=0.2466, pruned_loss=0.05772, over 4929.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2394, pruned_loss=0.05359, over 972290.12 frames.], batch size: 23, lr: 7.04e-04
2022-05-04 05:46:37,329 INFO [train.py:715] (1/8) Epoch 2, batch 11500, loss[loss=0.174, simple_loss=0.2465, pruned_loss=0.05072, over 4902.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2392, pruned_loss=0.05345, over 971998.76 frames.], batch size: 19, lr: 7.04e-04
2022-05-04 05:47:18,055 INFO [train.py:715] (1/8) Epoch 2, batch 11550, loss[loss=0.1812, simple_loss=0.2368, pruned_loss=0.06276, over 4982.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2389, pruned_loss=0.05337, over 972106.73 frames.], batch size: 24, lr: 7.04e-04
2022-05-04 05:47:58,024 INFO [train.py:715] (1/8) Epoch 2, batch 11600, loss[loss=0.1825, simple_loss=0.2571, pruned_loss=0.05398, over 4862.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2384, pruned_loss=0.05307, over 972843.49 frames.], batch size: 20, lr: 7.03e-04
2022-05-04 05:48:39,174 INFO [train.py:715] (1/8) Epoch 2, batch 11650, loss[loss=0.1674, simple_loss=0.2376, pruned_loss=0.04861, over 4855.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2391, pruned_loss=0.05366, over 972135.32 frames.], batch size: 20, lr: 7.03e-04
2022-05-04 05:49:19,424 INFO [train.py:715] (1/8) Epoch 2, batch 11700, loss[loss=0.1399, simple_loss=0.2097, pruned_loss=0.03508, over 4892.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2391, pruned_loss=0.05358, over 973286.35 frames.], batch size: 16, lr: 7.03e-04
2022-05-04 05:49:59,624 INFO [train.py:715] (1/8) Epoch 2, batch 11750, loss[loss=0.1708, simple_loss=0.2431, pruned_loss=0.04929, over 4925.00 frames.], tot_loss[loss=0.1732, simple_loss=0.239, pruned_loss=0.05367, over 972184.91 frames.], batch size: 18, lr: 7.03e-04
2022-05-04 05:50:40,399 INFO [train.py:715] (1/8) Epoch 2, batch 11800, loss[loss=0.1853, simple_loss=0.2516, pruned_loss=0.05957, over 4836.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2374, pruned_loss=0.05289, over 971689.62 frames.], batch size: 26, lr: 7.02e-04
2022-05-04 05:51:20,985 INFO [train.py:715] (1/8) Epoch 2, batch 11850, loss[loss=0.15, simple_loss=0.2253, pruned_loss=0.03732, over 4709.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2377, pruned_loss=0.05267, over 971994.78 frames.], batch size: 15, lr: 7.02e-04
2022-05-04 05:52:00,405 INFO [train.py:715] (1/8) Epoch 2, batch 11900, loss[loss=0.1746, simple_loss=0.2496, pruned_loss=0.04984, over 4828.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2376, pruned_loss=0.05228, over 972207.37 frames.], batch size: 26, lr: 7.02e-04
2022-05-04 05:52:40,334 INFO [train.py:715] (1/8) Epoch 2, batch 11950, loss[loss=0.1623, simple_loss=0.2254, pruned_loss=0.04962, over 4783.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2388, pruned_loss=0.05299, over 972277.28 frames.], batch size: 14, lr: 7.02e-04
2022-05-04 05:53:21,661 INFO [train.py:715] (1/8) Epoch 2, batch 12000, loss[loss=0.1538, simple_loss=0.2146, pruned_loss=0.04654, over 4810.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2386, pruned_loss=0.05255, over 972104.78 frames.], batch size: 12, lr: 7.02e-04
2022-05-04 05:53:21,662 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 05:53:45,624 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1181, simple_loss=0.2049, pruned_loss=0.01568, over 914524.00 frames.
2022-05-04 05:54:27,024 INFO [train.py:715] (1/8) Epoch 2, batch 12050, loss[loss=0.1619, simple_loss=0.2255, pruned_loss=0.04908, over 4926.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2382, pruned_loss=0.0526, over 971017.91 frames.], batch size: 23, lr: 7.01e-04
2022-05-04 05:55:07,116 INFO [train.py:715] (1/8) Epoch 2, batch 12100, loss[loss=0.1546, simple_loss=0.2281, pruned_loss=0.0406, over 4922.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2387, pruned_loss=0.05277, over 971060.65 frames.], batch size: 23, lr: 7.01e-04
2022-05-04 05:55:47,110 INFO [train.py:715] (1/8) Epoch 2, batch 12150, loss[loss=0.1651, simple_loss=0.2334, pruned_loss=0.04839, over 4793.00 frames.], tot_loss[loss=0.173, simple_loss=0.2396, pruned_loss=0.05316, over 970388.09 frames.], batch size: 21, lr: 7.01e-04
2022-05-04 05:56:27,810 INFO [train.py:715] (1/8) Epoch 2, batch 12200, loss[loss=0.1516, simple_loss=0.2229, pruned_loss=0.04016, over 4906.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2394, pruned_loss=0.05315, over 971564.49 frames.], batch size: 19, lr: 7.01e-04
2022-05-04 05:57:07,985 INFO [train.py:715] (1/8) Epoch 2, batch 12250, loss[loss=0.1615, simple_loss=0.2456, pruned_loss=0.03868, over 4799.00 frames.], tot_loss[loss=0.171, simple_loss=0.2378, pruned_loss=0.05213, over 971999.21 frames.], batch size: 24, lr: 7.01e-04
2022-05-04 05:57:48,414 INFO [train.py:715] (1/8) Epoch 2, batch 12300, loss[loss=0.1593, simple_loss=0.2212, pruned_loss=0.04876, over 4778.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2374, pruned_loss=0.05218, over 971894.39 frames.], batch size: 18, lr: 7.00e-04
2022-05-04 05:58:28,536 INFO [train.py:715] (1/8) Epoch 2, batch 12350, loss[loss=0.1748, simple_loss=0.2383, pruned_loss=0.05565, over 4965.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2384, pruned_loss=0.05311, over 972002.47 frames.], batch size: 14, lr: 7.00e-04
2022-05-04 05:59:09,751 INFO [train.py:715] (1/8) Epoch 2, batch 12400, loss[loss=0.1403, simple_loss=0.214, pruned_loss=0.03327, over 4949.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2371, pruned_loss=0.05219, over 973192.87 frames.], batch size: 21, lr: 7.00e-04
2022-05-04 05:59:50,015 INFO [train.py:715] (1/8) Epoch 2, batch 12450, loss[loss=0.1564, simple_loss=0.22, pruned_loss=0.0464, over 4770.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2372, pruned_loss=0.05253, over 973367.20 frames.], batch size: 17, lr: 7.00e-04
2022-05-04 06:00:29,871 INFO [train.py:715] (1/8) Epoch 2, batch 12500, loss[loss=0.156, simple_loss=0.231, pruned_loss=0.04054, over 4799.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2376, pruned_loss=0.05263, over 973266.22 frames.], batch size: 24, lr: 6.99e-04
2022-05-04 06:01:10,535 INFO [train.py:715] (1/8) Epoch 2, batch 12550, loss[loss=0.195, simple_loss=0.2596, pruned_loss=0.06522, over 4863.00 frames.], tot_loss[loss=0.171, simple_loss=0.2374, pruned_loss=0.05229, over 972092.64 frames.], batch size: 20, lr: 6.99e-04
2022-05-04 06:01:50,875 INFO [train.py:715] (1/8) Epoch 2, batch 12600, loss[loss=0.194, simple_loss=0.2503, pruned_loss=0.06887, over 4911.00 frames.], tot_loss[loss=0.171, simple_loss=0.2374, pruned_loss=0.05234, over 972300.75 frames.], batch size: 17, lr: 6.99e-04
2022-05-04 06:02:30,892 INFO [train.py:715] (1/8) Epoch 2, batch 12650, loss[loss=0.1592, simple_loss=0.2218, pruned_loss=0.04826, over 4818.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2384, pruned_loss=0.05273, over 971962.50 frames.], batch size: 25, lr: 6.99e-04
2022-05-04 06:03:11,022 INFO [train.py:715] (1/8) Epoch 2, batch 12700, loss[loss=0.1982, simple_loss=0.2553, pruned_loss=0.07053, over 4704.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2384, pruned_loss=0.05287, over 971152.26 frames.], batch size: 15, lr: 6.99e-04
2022-05-04 06:03:51,751 INFO [train.py:715] (1/8) Epoch 2, batch 12750, loss[loss=0.1579, simple_loss=0.2225, pruned_loss=0.04662, over 4868.00 frames.], tot_loss[loss=0.172, simple_loss=0.2383, pruned_loss=0.05286, over 971493.28 frames.], batch size: 16, lr: 6.98e-04
2022-05-04 06:04:31,918 INFO [train.py:715] (1/8) Epoch 2, batch 12800, loss[loss=0.1587, simple_loss=0.2264, pruned_loss=0.04549, over 4930.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2388, pruned_loss=0.05303, over 971891.79 frames.], batch size: 21, lr: 6.98e-04
2022-05-04 06:05:11,608 INFO [train.py:715] (1/8) Epoch 2, batch 12850, loss[loss=0.1649, simple_loss=0.2294, pruned_loss=0.05024, over 4799.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2391, pruned_loss=0.05305, over 970977.59 frames.], batch size: 21, lr: 6.98e-04
2022-05-04 06:05:52,430 INFO [train.py:715] (1/8) Epoch 2, batch 12900, loss[loss=0.1461, simple_loss=0.2096, pruned_loss=0.04132, over 4990.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2394, pruned_loss=0.05351, over 971378.91 frames.], batch size: 14, lr: 6.98e-04
2022-05-04 06:06:32,856 INFO [train.py:715] (1/8) Epoch 2, batch 12950, loss[loss=0.1644, simple_loss=0.2425, pruned_loss=0.04319, over 4862.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2395, pruned_loss=0.0534, over 972438.29 frames.], batch size: 20, lr: 6.98e-04
2022-05-04 06:07:12,811 INFO [train.py:715] (1/8) Epoch 2, batch 13000, loss[loss=0.1707, simple_loss=0.2307, pruned_loss=0.05531, over 4868.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2396, pruned_loss=0.05374, over 972592.69 frames.], batch size: 32, lr: 6.97e-04
2022-05-04 06:07:53,254 INFO [train.py:715] (1/8) Epoch 2, batch 13050, loss[loss=0.1827, simple_loss=0.2443, pruned_loss=0.06052, over 4958.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2395, pruned_loss=0.05351, over 973946.48 frames.], batch size: 24, lr: 6.97e-04
2022-05-04 06:08:34,485 INFO [train.py:715] (1/8) Epoch 2, batch 13100, loss[loss=0.15, simple_loss=0.2208, pruned_loss=0.03962, over 4942.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2386, pruned_loss=0.05303, over 973575.96 frames.], batch size: 21, lr: 6.97e-04
2022-05-04 06:09:14,672 INFO [train.py:715] (1/8) Epoch 2, batch 13150, loss[loss=0.1794, simple_loss=0.2316, pruned_loss=0.06353, over 4881.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2392, pruned_loss=0.05344, over 972800.68 frames.], batch size: 16, lr: 6.97e-04
2022-05-04 06:09:54,436 INFO [train.py:715] (1/8) Epoch 2, batch 13200, loss[loss=0.1865, simple_loss=0.2579, pruned_loss=0.05755, over 4779.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2383, pruned_loss=0.05312, over 972408.79 frames.], batch size: 14, lr: 6.96e-04
2022-05-04 06:10:35,334 INFO [train.py:715] (1/8) Epoch 2, batch 13250, loss[loss=0.1948, simple_loss=0.2575, pruned_loss=0.06607, over 4782.00 frames.], tot_loss[loss=0.1732, simple_loss=0.239, pruned_loss=0.05366, over 971967.46 frames.], batch size: 12, lr: 6.96e-04
2022-05-04 06:11:15,867 INFO [train.py:715] (1/8) Epoch 2, batch 13300, loss[loss=0.1394, simple_loss=0.219, pruned_loss=0.0299, over 4927.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2393, pruned_loss=0.05383, over 972333.07 frames.], batch size: 18, lr: 6.96e-04
2022-05-04 06:11:55,898 INFO [train.py:715] (1/8) Epoch 2, batch 13350, loss[loss=0.1755, simple_loss=0.2419, pruned_loss=0.05451, over 4756.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2386, pruned_loss=0.05346, over 972519.95 frames.], batch size: 19, lr: 6.96e-04
2022-05-04 06:12:36,498 INFO [train.py:715] (1/8) Epoch 2, batch 13400, loss[loss=0.1583, simple_loss=0.2135, pruned_loss=0.05156, over 4870.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2393, pruned_loss=0.05391, over 972176.81 frames.], batch size: 20, lr: 6.96e-04
2022-05-04 06:13:17,580 INFO [train.py:715] (1/8) Epoch 2, batch 13450, loss[loss=0.1783, simple_loss=0.2603, pruned_loss=0.04814, over 4905.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2379, pruned_loss=0.05319, over 972545.47 frames.], batch size: 18, lr: 6.95e-04
2022-05-04 06:13:57,532 INFO [train.py:715] (1/8) Epoch 2, batch 13500, loss[loss=0.1981, simple_loss=0.2604, pruned_loss=0.06787, over 4850.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2376, pruned_loss=0.05279, over 972448.81 frames.], batch size: 13, lr: 6.95e-04
2022-05-04 06:14:37,535 INFO [train.py:715] (1/8) Epoch 2, batch 13550, loss[loss=0.1459, simple_loss=0.2194, pruned_loss=0.03626, over 4932.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2385, pruned_loss=0.05285, over 973533.81 frames.], batch size: 29, lr: 6.95e-04
2022-05-04 06:15:18,687 INFO [train.py:715] (1/8) Epoch 2, batch 13600, loss[loss=0.2186, simple_loss=0.2757, pruned_loss=0.08073, over 4912.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2384, pruned_loss=0.05269, over 973738.30 frames.], batch size: 19, lr: 6.95e-04
2022-05-04 06:15:59,129 INFO [train.py:715] (1/8) Epoch 2, batch 13650, loss[loss=0.172, simple_loss=0.2363, pruned_loss=0.05386, over 4746.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2388, pruned_loss=0.0527, over 974093.87 frames.], batch size: 16, lr: 6.95e-04
2022-05-04 06:16:38,696 INFO [train.py:715] (1/8) Epoch 2, batch 13700, loss[loss=0.1362, simple_loss=0.2113, pruned_loss=0.03057, over 4772.00 frames.], tot_loss[loss=0.1724, simple_loss=0.239, pruned_loss=0.05291, over 973443.49 frames.], batch size: 14, lr: 6.94e-04
2022-05-04 06:17:19,961 INFO [train.py:715] (1/8) Epoch 2, batch 13750, loss[loss=0.1477, simple_loss=0.2187, pruned_loss=0.03833, over 4865.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2392, pruned_loss=0.05294, over 973524.44 frames.], batch size: 20, lr: 6.94e-04
2022-05-04 06:18:00,038 INFO [train.py:715] (1/8) Epoch 2, batch 13800, loss[loss=0.1538, simple_loss=0.2204, pruned_loss=0.04364, over 4782.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2386, pruned_loss=0.05223, over 972909.07 frames.], batch size: 18, lr: 6.94e-04
2022-05-04 06:18:39,729 INFO [train.py:715] (1/8) Epoch 2, batch 13850, loss[loss=0.1845, simple_loss=0.26, pruned_loss=0.05444, over 4899.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2388, pruned_loss=0.05231, over 972906.74 frames.], batch size: 17, lr: 6.94e-04
2022-05-04 06:19:19,317 INFO [train.py:715] (1/8) Epoch 2, batch 13900, loss[loss=0.153, simple_loss=0.2272, pruned_loss=0.03944, over 4865.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2383, pruned_loss=0.05237, over 972823.30 frames.], batch size: 22, lr: 6.94e-04
2022-05-04 06:20:00,084 INFO [train.py:715] (1/8) Epoch 2, batch 13950, loss[loss=0.1927, simple_loss=0.2603, pruned_loss=0.06252, over 4958.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2373, pruned_loss=0.0519, over 973380.17 frames.], batch size: 15, lr: 6.93e-04
2022-05-04 06:20:40,302 INFO [train.py:715] (1/8) Epoch 2, batch 14000, loss[loss=0.1498, simple_loss=0.2243, pruned_loss=0.03761, over 4828.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2381, pruned_loss=0.05244, over 973602.20 frames.], batch size: 13, lr: 6.93e-04
2022-05-04 06:21:19,543 INFO [train.py:715] (1/8) Epoch 2, batch 14050, loss[loss=0.1557, simple_loss=0.2181, pruned_loss=0.04659, over 4835.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2375, pruned_loss=0.05197, over 972903.55 frames.], batch size: 15, lr: 6.93e-04
2022-05-04 06:22:01,052 INFO [train.py:715] (1/8) Epoch 2, batch 14100, loss[loss=0.1943, simple_loss=0.2537, pruned_loss=0.06749, over 4969.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2387, pruned_loss=0.05272, over 973064.41 frames.], batch size: 15, lr: 6.93e-04
2022-05-04 06:22:41,696 INFO [train.py:715] (1/8) Epoch 2, batch 14150, loss[loss=0.1616, simple_loss=0.231, pruned_loss=0.04614, over 4946.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2377, pruned_loss=0.05269, over 973288.32 frames.], batch size: 39, lr: 6.93e-04
2022-05-04 06:23:21,641 INFO [train.py:715] (1/8) Epoch 2, batch 14200, loss[loss=0.1539, simple_loss=0.2287, pruned_loss=0.0395, over 4814.00 frames.], tot_loss[loss=0.1709, simple_loss=0.237, pruned_loss=0.05238, over 972279.38 frames.], batch size: 25, lr: 6.92e-04
2022-05-04 06:24:01,483 INFO [train.py:715] (1/8) Epoch 2, batch 14250, loss[loss=0.1561, simple_loss=0.2281, pruned_loss=0.04209, over 4731.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2376, pruned_loss=0.05243, over 972733.30 frames.], batch size: 16, lr: 6.92e-04
2022-05-04 06:24:42,096 INFO [train.py:715] (1/8) Epoch 2, batch 14300, loss[loss=0.2029, simple_loss=0.2692, pruned_loss=0.06824, over 4918.00 frames.], tot_loss[loss=0.1708, simple_loss=0.237, pruned_loss=0.05233, over 972409.09 frames.], batch size: 39, lr: 6.92e-04
2022-05-04 06:25:21,657 INFO [train.py:715] (1/8) Epoch 2, batch 14350, loss[loss=0.1721, simple_loss=0.2584, pruned_loss=0.04295, over 4974.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2375, pruned_loss=0.05242, over 973662.17 frames.], batch size: 25, lr: 6.92e-04
2022-05-04 06:26:01,520 INFO [train.py:715] (1/8) Epoch 2, batch 14400, loss[loss=0.1595, simple_loss=0.2298, pruned_loss=0.04461, over 4919.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2375, pruned_loss=0.05185, over 973434.12 frames.], batch size: 18, lr: 6.92e-04
2022-05-04 06:26:41,864 INFO [train.py:715] (1/8) Epoch 2, batch 14450, loss[loss=0.1614, simple_loss=0.2258, pruned_loss=0.04849, over 4820.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2385, pruned_loss=0.05244, over 973358.62 frames.], batch size: 15, lr: 6.91e-04
2022-05-04 06:27:22,095 INFO [train.py:715] (1/8) Epoch 2, batch 14500, loss[loss=0.1489, simple_loss=0.2152, pruned_loss=0.04134, over 4790.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2385, pruned_loss=0.05261, over 972266.81 frames.], batch size: 18, lr: 6.91e-04
2022-05-04 06:28:01,693 INFO [train.py:715] (1/8) Epoch 2, batch 14550, loss[loss=0.1828, simple_loss=0.2508, pruned_loss=0.05744, over 4914.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2387, pruned_loss=0.05294, over 972322.28 frames.], batch size: 17, lr: 6.91e-04
2022-05-04 06:28:42,169 INFO [train.py:715] (1/8) Epoch 2, batch 14600, loss[loss=0.1656, simple_loss=0.2343, pruned_loss=0.04848, over 4826.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2387, pruned_loss=0.05308, over 972904.90 frames.], batch size: 27, lr: 6.91e-04
2022-05-04 06:29:22,667 INFO [train.py:715] (1/8) Epoch 2, batch 14650, loss[loss=0.1534, simple_loss=0.2194, pruned_loss=0.04371, over 4759.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2388, pruned_loss=0.05302, over 971903.18 frames.], batch size: 12, lr: 6.90e-04
2022-05-04 06:30:01,959 INFO [train.py:715] (1/8) Epoch 2, batch 14700, loss[loss=0.175, simple_loss=0.2374, pruned_loss=0.05635, over 4906.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2381, pruned_loss=0.05235, over 971203.26 frames.], batch size: 17, lr: 6.90e-04
2022-05-04 06:30:41,283 INFO [train.py:715] (1/8) Epoch 2, batch 14750, loss[loss=0.1465, simple_loss=0.2254, pruned_loss=0.03384, over 4878.00 frames.], tot_loss[loss=0.1724, simple_loss=0.239, pruned_loss=0.05285, over 970087.89 frames.], batch size: 20, lr: 6.90e-04
2022-05-04 06:31:21,767 INFO [train.py:715] (1/8) Epoch 2, batch 14800, loss[loss=0.195, simple_loss=0.2693, pruned_loss=0.06034, over 4864.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2403, pruned_loss=0.05354, over 970828.17 frames.], batch size: 20, lr: 6.90e-04
2022-05-04 06:32:01,273 INFO [train.py:715] (1/8) Epoch 2, batch 14850, loss[loss=0.1919, simple_loss=0.2496, pruned_loss=0.06712, over 4978.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2395, pruned_loss=0.05303, over 971364.70 frames.], batch size: 14, lr: 6.90e-04
2022-05-04 06:32:40,955 INFO [train.py:715] (1/8) Epoch 2, batch 14900, loss[loss=0.1933, simple_loss=0.2549, pruned_loss=0.06584, over 4919.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2386, pruned_loss=0.05292, over 970913.69 frames.], batch size: 39, lr: 6.89e-04
2022-05-04 06:33:21,118 INFO [train.py:715] (1/8) Epoch 2, batch 14950, loss[loss=0.2146, simple_loss=0.2798, pruned_loss=0.07468, over 4946.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2377, pruned_loss=0.05243, over 971780.44 frames.], batch size: 24, lr: 6.89e-04
2022-05-04 06:34:01,759 INFO [train.py:715] (1/8) Epoch 2, batch 15000, loss[loss=0.169, simple_loss=0.2443, pruned_loss=0.04686, over 4765.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2381, pruned_loss=0.0528, over 971698.81 frames.], batch size: 19, lr: 6.89e-04
2022-05-04 06:34:01,760 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 06:34:11,142 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1176, simple_loss=0.2043, pruned_loss=0.01548, over 914524.00 frames.
2022-05-04 06:34:52,061 INFO [train.py:715] (1/8) Epoch 2, batch 15050, loss[loss=0.1761, simple_loss=0.2503, pruned_loss=0.05099, over 4991.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2374, pruned_loss=0.05259, over 971806.72 frames.], batch size: 20, lr: 6.89e-04
2022-05-04 06:35:31,190 INFO [train.py:715] (1/8) Epoch 2, batch 15100, loss[loss=0.1779, simple_loss=0.2323, pruned_loss=0.06173, over 4749.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2376, pruned_loss=0.05254, over 971713.88 frames.], batch size: 16, lr: 6.89e-04
2022-05-04 06:36:11,672 INFO [train.py:715] (1/8) Epoch 2, batch 15150, loss[loss=0.1952, simple_loss=0.2719, pruned_loss=0.05927, over 4781.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2389, pruned_loss=0.05308, over 971674.11 frames.], batch size: 18, lr: 6.88e-04
2022-05-04 06:36:52,153 INFO [train.py:715] (1/8) Epoch 2, batch 15200, loss[loss=0.1502, simple_loss=0.2146, pruned_loss=0.04288, over 4863.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2382, pruned_loss=0.05252, over 971972.11 frames.], batch size: 32, lr: 6.88e-04
2022-05-04 06:37:31,889 INFO [train.py:715] (1/8) Epoch 2, batch 15250, loss[loss=0.2112, simple_loss=0.2603, pruned_loss=0.081, over 4857.00 frames.], tot_loss[loss=0.1729, simple_loss=0.239, pruned_loss=0.05338, over 971786.97 frames.], batch size: 38, lr: 6.88e-04
2022-05-04 06:38:11,346 INFO [train.py:715] (1/8) Epoch 2, batch 15300, loss[loss=0.2025, simple_loss=0.264, pruned_loss=0.07045, over 4798.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2395, pruned_loss=0.0534, over 971341.04 frames.], batch size: 21, lr: 6.88e-04
2022-05-04 06:38:51,798 INFO [train.py:715] (1/8) Epoch 2, batch 15350, loss[loss=0.1703, simple_loss=0.2347, pruned_loss=0.05292, over 4859.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2392, pruned_loss=0.05348, over 971390.07 frames.], batch size: 20, lr: 6.88e-04
2022-05-04 06:39:32,694 INFO [train.py:715] (1/8) Epoch 2, batch 15400, loss[loss=0.1939, simple_loss=0.2602, pruned_loss=0.06373, over 4797.00 frames.], tot_loss[loss=0.174, simple_loss=0.2404, pruned_loss=0.05383, over 971466.35 frames.], batch size: 21, lr: 6.87e-04
2022-05-04 06:40:11,869 INFO [train.py:715] (1/8) Epoch 2, batch 15450, loss[loss=0.1489, simple_loss=0.2236, pruned_loss=0.03708, over 4857.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2397, pruned_loss=0.05329, over 971810.00 frames.], batch size: 20, lr: 6.87e-04
2022-05-04 06:40:52,377 INFO [train.py:715] (1/8) Epoch 2, batch 15500, loss[loss=0.1381, simple_loss=0.1941, pruned_loss=0.04109, over 4852.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2394, pruned_loss=0.05302, over 971365.09 frames.], batch size: 13, lr: 6.87e-04
2022-05-04 06:41:32,619 INFO [train.py:715] (1/8) Epoch 2, batch 15550, loss[loss=0.1819, simple_loss=0.2452, pruned_loss=0.05936, over 4821.00 frames.], tot_loss[loss=0.1735, simple_loss=0.24, pruned_loss=0.05352, over 972236.76 frames.], batch size: 15, lr: 6.87e-04
2022-05-04 06:42:12,566 INFO [train.py:715] (1/8) Epoch 2, batch 15600, loss[loss=0.196, simple_loss=0.2505, pruned_loss=0.07079, over 4946.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2394, pruned_loss=0.05292, over 972622.67 frames.], batch size: 39, lr: 6.87e-04
2022-05-04 06:42:52,377 INFO [train.py:715] (1/8) Epoch 2, batch 15650, loss[loss=0.1757, simple_loss=0.2442, pruned_loss=0.05361, over 4949.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2388, pruned_loss=0.05268, over 972092.40 frames.], batch size: 21, lr: 6.86e-04
2022-05-04 06:43:33,099 INFO [train.py:715] (1/8) Epoch 2, batch 15700, loss[loss=0.1979, simple_loss=0.2573, pruned_loss=0.06924, over 4932.00 frames.], tot_loss[loss=0.171, simple_loss=0.2377, pruned_loss=0.05213, over 972155.54 frames.], batch size: 21, lr: 6.86e-04
2022-05-04 06:44:13,630 INFO [train.py:715] (1/8) Epoch 2, batch 15750, loss[loss=0.1628, simple_loss=0.2339, pruned_loss=0.04588, over 4879.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2388, pruned_loss=0.05322, over 972355.87 frames.], batch size: 16, lr: 6.86e-04
2022-05-04 06:44:52,974 INFO [train.py:715] (1/8) Epoch 2, batch 15800, loss[loss=0.1605, simple_loss=0.2318, pruned_loss=0.04463, over 4877.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2386, pruned_loss=0.053, over 971993.11 frames.], batch size: 22, lr: 6.86e-04
2022-05-04 06:45:33,632 INFO [train.py:715] (1/8) Epoch 2, batch 15850, loss[loss=0.1573, simple_loss=0.2247, pruned_loss=0.04499, over 4971.00 frames.], tot_loss[loss=0.1731, simple_loss=0.239, pruned_loss=0.05355, over 971771.38 frames.], batch size: 35, lr: 6.86e-04
2022-05-04 06:46:14,113 INFO [train.py:715] (1/8) Epoch 2, batch 15900, loss[loss=0.1737, simple_loss=0.2296, pruned_loss=0.05884, over 4862.00 frames.], tot_loss[loss=0.173, simple_loss=0.2394, pruned_loss=0.05337, over 972092.42 frames.], batch size: 30, lr: 6.85e-04
2022-05-04 06:46:53,879 INFO [train.py:715] (1/8) Epoch 2, batch 15950, loss[loss=0.1776, simple_loss=0.2474, pruned_loss=0.05392, over 4883.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2387, pruned_loss=0.05289, over 971320.38 frames.], batch size: 22, lr: 6.85e-04
2022-05-04 06:47:34,110 INFO [train.py:715] (1/8) Epoch 2, batch 16000, loss[loss=0.1952, simple_loss=0.2548, pruned_loss=0.06777, over 4885.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2385, pruned_loss=0.05264, over 973069.10 frames.], batch size: 22, lr: 6.85e-04
2022-05-04 06:48:14,444 INFO [train.py:715] (1/8) Epoch 2, batch 16050, loss[loss=0.1497, simple_loss=0.2193, pruned_loss=0.04005, over 4814.00 frames.], tot_loss[loss=0.1714, simple_loss=0.238, pruned_loss=0.05243, over 972384.23 frames.], batch size: 26, lr: 6.85e-04
2022-05-04 06:48:54,890 INFO [train.py:715] (1/8) Epoch 2, batch 16100, loss[loss=0.2645, simple_loss=0.2989, pruned_loss=0.115, over 4907.00 frames.], tot_loss[loss=0.172, simple_loss=0.2384, pruned_loss=0.05284, over 972341.51 frames.], batch size: 17, lr: 6.85e-04
2022-05-04 06:49:34,157 INFO [train.py:715] (1/8) Epoch 2, batch 16150, loss[loss=0.1748, simple_loss=0.2385, pruned_loss=0.05555, over 4985.00 frames.], tot_loss[loss=0.173, simple_loss=0.2391, pruned_loss=0.05343, over 972305.59 frames.], batch size: 25, lr: 6.84e-04
2022-05-04 06:50:14,546 INFO [train.py:715] (1/8) Epoch 2, batch 16200, loss[loss=0.2037, simple_loss=0.2761, pruned_loss=0.06564, over 4976.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2392, pruned_loss=0.05361, over 973315.90 frames.], batch size: 31, lr: 6.84e-04
2022-05-04 06:50:54,948 INFO [train.py:715] (1/8) Epoch 2, batch 16250, loss[loss=0.201, simple_loss=0.2567, pruned_loss=0.07265, over 4774.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2394, pruned_loss=0.05346, over 972618.83 frames.], batch size: 17, lr: 6.84e-04
2022-05-04 06:51:34,795 INFO [train.py:715] (1/8) Epoch 2, batch 16300, loss[loss=0.1808, simple_loss=0.2498, pruned_loss=0.05592, over 4853.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2391, pruned_loss=0.05302, over 972708.90 frames.], batch size: 15, lr: 6.84e-04
2022-05-04 06:52:14,671 INFO [train.py:715] (1/8) Epoch 2, batch 16350, loss[loss=0.1957, simple_loss=0.2602, pruned_loss=0.0656, over 4925.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2382, pruned_loss=0.0522, over 971987.09 frames.], batch size: 21, lr: 6.84e-04
2022-05-04 06:52:55,175 INFO [train.py:715] (1/8) Epoch 2, batch 16400, loss[loss=0.1647, simple_loss=0.2307, pruned_loss=0.04935, over 4944.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2374, pruned_loss=0.05155, over 971801.30 frames.], batch size: 21, lr: 6.83e-04
2022-05-04 06:53:35,564 INFO [train.py:715] (1/8) Epoch 2, batch 16450, loss[loss=0.1495, simple_loss=0.2195, pruned_loss=0.03979, over 4981.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2378, pruned_loss=0.0519, over 972292.63 frames.], batch size: 14, lr: 6.83e-04
2022-05-04 06:54:15,147 INFO [train.py:715] (1/8) Epoch 2, batch 16500, loss[loss=0.1722, simple_loss=0.2388, pruned_loss=0.05276, over 4788.00 frames.], tot_loss[loss=0.17, simple_loss=0.2371, pruned_loss=0.05141, over 972587.07 frames.], batch size: 18, lr: 6.83e-04
2022-05-04 06:54:56,126 INFO [train.py:715] (1/8) Epoch 2, batch 16550, loss[loss=0.1751, simple_loss=0.248, pruned_loss=0.05114, over 4833.00 frames.], tot_loss[loss=0.1708, simple_loss=0.238, pruned_loss=0.05176, over 973010.48 frames.], batch size: 15, lr: 6.83e-04
2022-05-04 06:55:36,863 INFO [train.py:715] (1/8) Epoch 2, batch 16600, loss[loss=0.2109, simple_loss=0.2754, pruned_loss=0.0732, over 4943.00 frames.], tot_loss[loss=0.17, simple_loss=0.2371, pruned_loss=0.05146, over 973734.67 frames.], batch size: 35, lr: 6.83e-04
2022-05-04 06:56:16,716 INFO [train.py:715] (1/8) Epoch 2, batch 16650, loss[loss=0.1747, simple_loss=0.2386, pruned_loss=0.05534, over 4840.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2366, pruned_loss=0.05107, over 972618.68 frames.], batch size: 13, lr: 6.82e-04
2022-05-04 06:56:57,161 INFO [train.py:715] (1/8) Epoch 2, batch 16700, loss[loss=0.1529, simple_loss=0.2244, pruned_loss=0.04071, over 4964.00 frames.], tot_loss[loss=0.17, simple_loss=0.2368, pruned_loss=0.05158, over 973554.95 frames.], batch size: 15, lr: 6.82e-04
2022-05-04 06:57:37,913 INFO [train.py:715] (1/8) Epoch 2, batch 16750, loss[loss=0.1546, simple_loss=0.2098, pruned_loss=0.04969, over 4806.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2376, pruned_loss=0.05189, over 973345.33 frames.], batch size: 12, lr: 6.82e-04
2022-05-04 06:58:18,621 INFO [train.py:715] (1/8) Epoch 2, batch 16800, loss[loss=0.1853, simple_loss=0.2562, pruned_loss=0.05726, over 4909.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2373, pruned_loss=0.05158, over 973180.52 frames.], batch size: 23, lr: 6.82e-04
2022-05-04 06:58:58,050 INFO [train.py:715] (1/8) Epoch 2, batch 16850, loss[loss=0.1645, simple_loss=0.24, pruned_loss=0.04445, over 4808.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2378, pruned_loss=0.0522, over 972662.36 frames.], batch size: 25, lr: 6.82e-04
2022-05-04 06:59:39,312 INFO [train.py:715] (1/8) Epoch 2, batch 16900, loss[loss=0.1479, simple_loss=0.2185, pruned_loss=0.03865, over 4984.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2378, pruned_loss=0.0526, over 972567.73 frames.], batch size: 15, lr: 6.81e-04
2022-05-04 07:00:20,137 INFO [train.py:715] (1/8) Epoch 2, batch 16950, loss[loss=0.1724, simple_loss=0.2368, pruned_loss=0.05395, over 4835.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2379, pruned_loss=0.05275, over 972328.66 frames.], batch size: 30, lr: 6.81e-04
2022-05-04 07:00:59,943 INFO [train.py:715] (1/8) Epoch 2, batch 17000, loss[loss=0.1539, simple_loss=0.2283, pruned_loss=0.03975, over 4892.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2381, pruned_loss=0.05274, over 972121.94 frames.], batch size: 19, lr: 6.81e-04
2022-05-04 07:01:40,374 INFO [train.py:715] (1/8) Epoch 2, batch 17050, loss[loss=0.1661, simple_loss=0.2321, pruned_loss=0.05007, over 4768.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2386, pruned_loss=0.05353, over 971985.73 frames.], batch size: 17, lr: 6.81e-04
2022-05-04 07:02:20,959 INFO [train.py:715] (1/8) Epoch 2, batch 17100, loss[loss=0.1501, simple_loss=0.2227, pruned_loss=0.03879, over 4904.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2383, pruned_loss=0.05303, over 972272.47 frames.], batch size: 19, lr: 6.81e-04
2022-05-04 07:03:01,193 INFO [train.py:715] (1/8) Epoch 2, batch 17150, loss[loss=0.1595, simple_loss=0.2275, pruned_loss=0.04569, over 4840.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2376, pruned_loss=0.05242, over 972329.56 frames.], batch size: 13, lr: 6.81e-04
2022-05-04 07:03:40,482 INFO [train.py:715] (1/8) Epoch 2, batch 17200, loss[loss=0.1737, simple_loss=0.2149, pruned_loss=0.06627, over 4816.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2373, pruned_loss=0.0522, over 972143.88 frames.], batch size: 12, lr: 6.80e-04
2022-05-04 07:04:20,887 INFO [train.py:715] (1/8) Epoch 2, batch 17250, loss[loss=0.1641, simple_loss=0.2378, pruned_loss=0.04526, over 4884.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2372, pruned_loss=0.05211, over 972457.26 frames.], batch size: 22, lr: 6.80e-04
2022-05-04 07:05:01,347 INFO [train.py:715] (1/8) Epoch 2, batch 17300, loss[loss=0.2034, simple_loss=0.2662, pruned_loss=0.07036, over 4861.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2383, pruned_loss=0.05294, over 973377.10 frames.], batch size: 30, lr: 6.80e-04
2022-05-04 07:05:40,925 INFO [train.py:715] (1/8) Epoch 2, batch 17350, loss[loss=0.185, simple_loss=0.2473, pruned_loss=0.06137, over 4960.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2372, pruned_loss=0.05221, over 973366.98 frames.], batch size: 15, lr: 6.80e-04
2022-05-04 07:06:20,385 INFO [train.py:715] (1/8) Epoch 2, batch 17400, loss[loss=0.1581, simple_loss=0.2327, pruned_loss=0.04172, over 4944.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2376, pruned_loss=0.05228, over 972764.05 frames.], batch size: 21, lr: 6.80e-04
2022-05-04 07:07:00,339 INFO [train.py:715] (1/8) Epoch 2, batch 17450, loss[loss=0.2065, simple_loss=0.2503, pruned_loss=0.08137, over 4781.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2371, pruned_loss=0.05178, over 972211.13 frames.], batch size: 18, lr: 6.79e-04
2022-05-04 07:07:40,095 INFO [train.py:715] (1/8) Epoch 2, batch 17500, loss[loss=0.1652, simple_loss=0.2343, pruned_loss=0.04809, over 4744.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2372, pruned_loss=0.05186, over 971162.30 frames.], batch size: 16, lr: 6.79e-04
2022-05-04 07:08:18,851 INFO [train.py:715] (1/8) Epoch 2, batch 17550, loss[loss=0.1469, simple_loss=0.2152, pruned_loss=0.03931, over 4976.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2384, pruned_loss=0.05271, over 971427.34 frames.], batch size: 14, lr: 6.79e-04
2022-05-04 07:08:58,966 INFO [train.py:715] (1/8) Epoch 2, batch 17600, loss[loss=0.1711, simple_loss=0.2302, pruned_loss=0.05596, over 4896.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2374, pruned_loss=0.05223, over 971419.33 frames.], batch size: 39, lr: 6.79e-04
2022-05-04 07:09:38,387 INFO [train.py:715] (1/8) Epoch 2, batch 17650, loss[loss=0.1523, simple_loss=0.2267, pruned_loss=0.03896, over 4958.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2377, pruned_loss=0.05231, over 971177.64 frames.], batch size: 15, lr: 6.79e-04
2022-05-04 07:10:17,887 INFO [train.py:715] (1/8) Epoch 2, batch 17700, loss[loss=0.1905, simple_loss=0.2558, pruned_loss=0.06259, over 4984.00 frames.], tot_loss[loss=0.1708, simple_loss=0.237, pruned_loss=0.05235, over 970891.73 frames.], batch size: 27, lr: 6.78e-04
2022-05-04 07:10:57,825 INFO [train.py:715] (1/8) Epoch 2, batch 17750, loss[loss=0.1731, simple_loss=0.2464, pruned_loss=0.04992, over 4911.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2375, pruned_loss=0.05261, over 971064.29 frames.], batch size: 17, lr: 6.78e-04
2022-05-04 07:11:37,691 INFO [train.py:715] (1/8) Epoch 2, batch 17800, loss[loss=0.2027, simple_loss=0.2683, pruned_loss=0.06859, over 4791.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2379, pruned_loss=0.05241, over 971799.01 frames.], batch size: 21, lr: 6.78e-04
2022-05-04 07:12:17,978 INFO [train.py:715] (1/8) Epoch 2, batch 17850, loss[loss=0.1874, simple_loss=0.2637, pruned_loss=0.05557, over 4973.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2383, pruned_loss=0.0525, over 971631.95 frames.], batch size: 15, lr: 6.78e-04
2022-05-04 07:12:56,810 INFO [train.py:715] (1/8) Epoch 2, batch 17900, loss[loss=0.189, simple_loss=0.2551, pruned_loss=0.06142, over 4966.00 frames.], tot_loss[loss=0.171, simple_loss=0.2374, pruned_loss=0.05236, over 971732.86 frames.], batch size: 15, lr: 6.78e-04
2022-05-04 07:13:36,733 INFO [train.py:715] (1/8) Epoch 2, batch 17950, loss[loss=0.1352, simple_loss=0.2048, pruned_loss=0.03277, over 4802.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2366, pruned_loss=0.05195, over 971755.26 frames.], batch size: 12, lr: 6.77e-04
2022-05-04 07:14:16,911 INFO [train.py:715] (1/8) Epoch 2, batch 18000, loss[loss=0.1758, simple_loss=0.2448, pruned_loss=0.05342, over 4964.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2367, pruned_loss=0.05192, over 971875.70 frames.], batch size: 24, lr: 6.77e-04
2022-05-04 07:14:16,911 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 07:14:26,628 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1173, simple_loss=0.2039, pruned_loss=0.01538, over 914524.00 frames.
2022-05-04 07:15:07,352 INFO [train.py:715] (1/8) Epoch 2, batch 18050, loss[loss=0.1773, simple_loss=0.2326, pruned_loss=0.06098, over 4758.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2377, pruned_loss=0.05256, over 971394.02 frames.], batch size: 16, lr: 6.77e-04
2022-05-04 07:15:46,535 INFO [train.py:715] (1/8) Epoch 2, batch 18100, loss[loss=0.148, simple_loss=0.2178, pruned_loss=0.03912, over 4963.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2375, pruned_loss=0.05266, over 971377.20 frames.], batch size: 24, lr: 6.77e-04
2022-05-04 07:16:27,423 INFO [train.py:715] (1/8) Epoch 2, batch 18150, loss[loss=0.1934, simple_loss=0.2689, pruned_loss=0.05891, over 4833.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2379, pruned_loss=0.05261, over 971118.30 frames.], batch size: 26, lr: 6.77e-04
2022-05-04 07:17:08,379 INFO [train.py:715] (1/8) Epoch 2, batch 18200, loss[loss=0.1915, simple_loss=0.2528, pruned_loss=0.06509, over 4974.00 frames.], tot_loss[loss=0.1709, simple_loss=0.237, pruned_loss=0.05237, over 970877.96 frames.], batch size: 24, lr: 6.76e-04
2022-05-04 07:17:49,862 INFO [train.py:715] (1/8) Epoch 2, batch 18250, loss[loss=0.16, simple_loss=0.2279, pruned_loss=0.04605, over 4902.00 frames.], tot_loss[loss=0.171, simple_loss=0.237, pruned_loss=0.05247, over 971061.35 frames.], batch size: 19, lr: 6.76e-04
2022-05-04 07:18:30,278 INFO [train.py:715] (1/8) Epoch 2, batch 18300, loss[loss=0.186, simple_loss=0.2562, pruned_loss=0.05794, over 4883.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2362, pruned_loss=0.05199, over 971029.99 frames.], batch size: 22, lr: 6.76e-04
2022-05-04 07:19:12,143 INFO [train.py:715] (1/8) Epoch 2, batch 18350, loss[loss=0.1723, simple_loss=0.2366, pruned_loss=0.05401, over 4841.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2359, pruned_loss=0.05185, over 971282.89 frames.], batch size: 30, lr: 6.76e-04
2022-05-04 07:19:56,498 INFO [train.py:715] (1/8) Epoch 2, batch 18400, loss[loss=0.1745, simple_loss=0.252, pruned_loss=0.04851, over 4775.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2376, pruned_loss=0.05253, over 971849.97 frames.], batch size: 14, lr: 6.76e-04
2022-05-04 07:20:36,589 INFO [train.py:715] (1/8) Epoch 2, batch 18450, loss[loss=0.1546, simple_loss=0.2243, pruned_loss=0.04243, over 4885.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2377, pruned_loss=0.05235, over 972563.60 frames.], batch size: 16, lr: 6.75e-04
2022-05-04 07:21:18,117 INFO [train.py:715] (1/8) Epoch 2, batch 18500, loss[loss=0.147, simple_loss=0.22, pruned_loss=0.03703, over 4690.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2382, pruned_loss=0.05229, over 972571.08 frames.], batch size: 15, lr: 6.75e-04
2022-05-04 07:21:59,803 INFO [train.py:715] (1/8) Epoch 2, batch 18550, loss[loss=0.1919, simple_loss=0.2478, pruned_loss=0.06795, over 4796.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2372, pruned_loss=0.05173, over 972458.32 frames.], batch size: 24, lr: 6.75e-04
2022-05-04 07:22:41,521 INFO [train.py:715] (1/8) Epoch 2, batch 18600, loss[loss=0.1698, simple_loss=0.2412, pruned_loss=0.04922, over 4872.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2379, pruned_loss=0.05226, over 972681.91 frames.], batch size: 22, lr: 6.75e-04
2022-05-04 07:23:21,826 INFO [train.py:715] (1/8) Epoch 2, batch 18650, loss[loss=0.1639, simple_loss=0.235, pruned_loss=0.04639, over 4821.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2381, pruned_loss=0.05259, over 972637.16 frames.], batch size: 15, lr: 6.75e-04
2022-05-04 07:24:03,474 INFO [train.py:715] (1/8) Epoch 2, batch 18700, loss[loss=0.1641, simple_loss=0.2263, pruned_loss=0.05097, over 4898.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2387, pruned_loss=0.05285, over 971914.66 frames.], batch size: 39, lr: 6.75e-04
2022-05-04 07:24:45,170 INFO [train.py:715] (1/8) Epoch 2, batch 18750, loss[loss=0.1918, simple_loss=0.2578, pruned_loss=0.06286, over 4933.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2373, pruned_loss=0.05192, over 971754.37 frames.], batch size: 29, lr: 6.74e-04
2022-05-04 07:25:25,720 INFO [train.py:715] (1/8) Epoch 2, batch 18800, loss[loss=0.1556, simple_loss=0.2322, pruned_loss=0.03947, over 4815.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2372, pruned_loss=0.0517, over 972000.30 frames.], batch size: 25, lr: 6.74e-04
2022-05-04 07:26:06,681 INFO [train.py:715] (1/8) Epoch 2, batch 18850, loss[loss=0.1855, simple_loss=0.2475, pruned_loss=0.06169, over 4925.00 frames.], tot_loss[loss=0.1711, simple_loss=0.237, pruned_loss=0.05258, over 972328.91 frames.], batch size: 39, lr: 6.74e-04
2022-05-04 07:26:48,081 INFO [train.py:715] (1/8) Epoch 2, batch 18900, loss[loss=0.1656, simple_loss=0.2351, pruned_loss=0.04812, over 4850.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2368, pruned_loss=0.05238, over 972629.10 frames.], batch size: 30, lr: 6.74e-04
2022-05-04 07:27:29,065 INFO [train.py:715] (1/8) Epoch 2, batch 18950, loss[loss=0.186, simple_loss=0.2446, pruned_loss=0.06372, over 4763.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2373, pruned_loss=0.05245, over 972253.49 frames.], batch size: 19, lr: 6.74e-04
2022-05-04 07:28:09,468 INFO [train.py:715] (1/8) Epoch 2, batch 19000, loss[loss=0.1288, simple_loss=0.1989, pruned_loss=0.02938, over 4862.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2374, pruned_loss=0.05243, over 972431.17 frames.], batch size: 16, lr: 6.73e-04
2022-05-04 07:28:50,991 INFO [train.py:715] (1/8) Epoch 2, batch 19050, loss[loss=0.1431, simple_loss=0.2084, pruned_loss=0.03892, over 4992.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2371, pruned_loss=0.05193, over 972949.04 frames.], batch size: 16, lr: 6.73e-04
2022-05-04 07:29:32,581 INFO [train.py:715] (1/8) Epoch 2, batch 19100, loss[loss=0.1605, simple_loss=0.2304, pruned_loss=0.04529, over 4948.00 frames.], tot_loss[loss=0.1708, simple_loss=0.237, pruned_loss=0.05224, over 973537.41 frames.], batch size: 35, lr: 6.73e-04
2022-05-04 07:30:13,188 INFO [train.py:715] (1/8) Epoch 2, batch 19150, loss[loss=0.1647, simple_loss=0.2381, pruned_loss=0.04569, over 4953.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2374, pruned_loss=0.05253, over 973411.25 frames.], batch size: 35, lr: 6.73e-04
2022-05-04 07:30:53,913 INFO [train.py:715] (1/8) Epoch 2, batch 19200, loss[loss=0.1658, simple_loss=0.2398, pruned_loss=0.04589, over 4954.00 frames.], tot_loss[loss=0.1714, simple_loss=0.238, pruned_loss=0.05238, over 972894.73 frames.], batch size: 15, lr: 6.73e-04
2022-05-04 07:31:34,997 INFO [train.py:715] (1/8) Epoch 2, batch 19250, loss[loss=0.1614, simple_loss=0.2416, pruned_loss=0.0406, over 4806.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2375, pruned_loss=0.05203, over 973326.46 frames.], batch size: 24, lr: 6.72e-04
2022-05-04 07:32:15,466 INFO [train.py:715] (1/8) Epoch 2, batch 19300, loss[loss=0.1488, simple_loss=0.2306, pruned_loss=0.03347, over 4789.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2381, pruned_loss=0.05234, over 973884.51 frames.], batch size: 17, lr: 6.72e-04
2022-05-04 07:32:55,617 INFO [train.py:715] (1/8) Epoch 2, batch 19350, loss[loss=0.162, simple_loss=0.231, pruned_loss=0.04651, over 4823.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2377, pruned_loss=0.0523, over 973295.01 frames.], batch size: 25, lr: 6.72e-04
2022-05-04 07:33:36,551 INFO [train.py:715] (1/8) Epoch 2, batch 19400, loss[loss=0.1765, simple_loss=0.2362, pruned_loss=0.05844, over 4910.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2368, pruned_loss=0.05224, over 973898.03 frames.], batch size: 18, lr: 6.72e-04
2022-05-04 07:34:18,482 INFO [train.py:715] (1/8) Epoch 2, batch 19450, loss[loss=0.168, simple_loss=0.2347, pruned_loss=0.05063, over 4806.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2359, pruned_loss=0.05141, over 973043.84 frames.], batch size: 21, lr: 6.72e-04
2022-05-04 07:34:58,691 INFO [train.py:715] (1/8) Epoch 2, batch 19500, loss[loss=0.2208, simple_loss=0.2978, pruned_loss=0.07188, over 4977.00 frames.], tot_loss[loss=0.1707, simple_loss=0.237, pruned_loss=0.05217, over 972668.45 frames.], batch size: 24, lr: 6.72e-04
2022-05-04 07:35:38,977 INFO [train.py:715] (1/8) Epoch 2, batch 19550, loss[loss=0.1549, simple_loss=0.2259, pruned_loss=0.04199, over 4739.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2371, pruned_loss=0.05173, over 971386.82 frames.], batch size: 16, lr: 6.71e-04
2022-05-04 07:36:20,446 INFO [train.py:715] (1/8) Epoch 2, batch 19600, loss[loss=0.1803, simple_loss=0.2442, pruned_loss=0.05818, over 4770.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2377, pruned_loss=0.05243, over 971092.41 frames.], batch size: 18, lr: 6.71e-04
2022-05-04 07:37:01,104 INFO [train.py:715] (1/8) Epoch 2, batch 19650, loss[loss=0.1513, simple_loss=0.2189, pruned_loss=0.04179, over 4791.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2371, pruned_loss=0.05227, over 971586.40 frames.], batch size: 18, lr: 6.71e-04
2022-05-04 07:37:40,940 INFO [train.py:715] (1/8) Epoch 2, batch 19700, loss[loss=0.1492, simple_loss=0.2212, pruned_loss=0.0386, over 4881.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2367, pruned_loss=0.0524, over 971676.63 frames.], batch size: 22, lr: 6.71e-04
2022-05-04 07:38:21,830 INFO [train.py:715] (1/8) Epoch 2, batch 19750, loss[loss=0.2014, simple_loss=0.2683, pruned_loss=0.06718, over 4895.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2374, pruned_loss=0.05268, over 971246.47 frames.], batch size: 22, lr: 6.71e-04
2022-05-04 07:39:02,963 INFO [train.py:715] (1/8) Epoch 2, batch 19800, loss[loss=0.1934, simple_loss=0.2583, pruned_loss=0.06427, over 4954.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2373, pruned_loss=0.05201, over 971037.11 frames.], batch size: 24, lr: 6.70e-04
2022-05-04 07:39:42,762 INFO [train.py:715] (1/8) Epoch 2, batch 19850, loss[loss=0.1449, simple_loss=0.2127, pruned_loss=0.03853, over 4797.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2377, pruned_loss=0.05237, over 970857.94 frames.], batch size: 21, lr: 6.70e-04
2022-05-04 07:40:23,475 INFO [train.py:715] (1/8) Epoch 2, batch 19900, loss[loss=0.1903, simple_loss=0.2515, pruned_loss=0.06457, over 4828.00 frames.], tot_loss[loss=0.1703, simple_loss=0.237, pruned_loss=0.05177, over 971532.42 frames.], batch size: 13, lr: 6.70e-04
2022-05-04 07:41:04,449 INFO [train.py:715] (1/8) Epoch 2, batch 19950, loss[loss=0.2101, simple_loss=0.274, pruned_loss=0.07306, over 4784.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2371, pruned_loss=0.05202, over 970783.77 frames.], batch size: 17, lr: 6.70e-04
2022-05-04 07:41:44,815 INFO [train.py:715] (1/8) Epoch 2, batch 20000, loss[loss=0.1547, simple_loss=0.2237, pruned_loss=0.04289, over 4988.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2365, pruned_loss=0.05116, over 971682.95 frames.], batch size: 24, lr: 6.70e-04
2022-05-04 07:42:25,544 INFO [train.py:715] (1/8) Epoch 2, batch 20050, loss[loss=0.2006, simple_loss=0.2623, pruned_loss=0.06948, over 4934.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2354, pruned_loss=0.05053, over 972601.92 frames.], batch size: 21, lr: 6.69e-04
2022-05-04 07:43:06,858 INFO [train.py:715] (1/8) Epoch 2, batch 20100, loss[loss=0.1981, simple_loss=0.2787, pruned_loss=0.05881, over 4886.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2361, pruned_loss=0.05111, over 972062.66 frames.], batch size: 22, lr: 6.69e-04
2022-05-04 07:43:48,569 INFO [train.py:715] (1/8) Epoch 2, batch 20150, loss[loss=0.2042, simple_loss=0.2683, pruned_loss=0.07007, over 4984.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2366, pruned_loss=0.05107, over 972603.54 frames.], batch size: 25, lr: 6.69e-04
2022-05-04 07:44:28,862 INFO [train.py:715] (1/8) Epoch 2, batch 20200, loss[loss=0.1564, simple_loss=0.2206, pruned_loss=0.04609, over 4746.00 frames.], tot_loss[loss=0.17, simple_loss=0.237, pruned_loss=0.0515, over 972878.70 frames.], batch size: 16, lr: 6.69e-04
2022-05-04 07:45:10,309 INFO [train.py:715] (1/8) Epoch 2, batch 20250, loss[loss=0.2183, simple_loss=0.282, pruned_loss=0.07734, over 4948.00 frames.], tot_loss[loss=0.1699, simple_loss=0.237, pruned_loss=0.05135, over 973116.15 frames.], batch size: 18, lr: 6.69e-04
2022-05-04 07:45:52,268 INFO [train.py:715] (1/8) Epoch 2, batch 20300, loss[loss=0.1789, simple_loss=0.244, pruned_loss=0.05684, over 4856.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2384, pruned_loss=0.05204, over 972635.45 frames.], batch size: 32, lr: 6.69e-04
2022-05-04 07:46:33,100 INFO [train.py:715] (1/8) Epoch 2, batch 20350, loss[loss=0.1367, simple_loss=0.209, pruned_loss=0.03216, over 4968.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2374, pruned_loss=0.05191, over 972626.75 frames.], batch size: 25, lr: 6.68e-04
2022-05-04 07:47:14,052 INFO [train.py:715] (1/8) Epoch 2, batch 20400, loss[loss=0.1845, simple_loss=0.2472, pruned_loss=0.06085, over 4926.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2381, pruned_loss=0.05231, over 971952.33 frames.], batch size: 18, lr: 6.68e-04
2022-05-04 07:47:56,139 INFO [train.py:715] (1/8) Epoch 2, batch 20450, loss[loss=0.1505, simple_loss=0.2255, pruned_loss=0.03779, over 4978.00 frames.], tot_loss[loss=0.171, simple_loss=0.2378, pruned_loss=0.05213, over 971199.97 frames.], batch size: 24, lr: 6.68e-04
2022-05-04 07:48:37,700 INFO [train.py:715] (1/8) Epoch 2, batch 20500, loss[loss=0.1779, simple_loss=0.2422, pruned_loss=0.05673, over 4909.00 frames.], tot_loss[loss=0.1714, simple_loss=0.238, pruned_loss=0.05237, over 971813.09 frames.], batch size: 19, lr: 6.68e-04
2022-05-04 07:49:18,502 INFO [train.py:715] (1/8) Epoch 2, batch 20550, loss[loss=0.1438, simple_loss=0.2231, pruned_loss=0.03226, over 4961.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2379, pruned_loss=0.0522, over 972451.63 frames.], batch size: 21, lr: 6.68e-04
2022-05-04 07:49:59,713 INFO [train.py:715] (1/8) Epoch 2, batch 20600, loss[loss=0.196, simple_loss=0.265, pruned_loss=0.06348, over 4808.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2371, pruned_loss=0.05202, over 972434.93 frames.], batch size: 25, lr: 6.67e-04
2022-05-04 07:50:41,276 INFO [train.py:715] (1/8) Epoch 2, batch 20650, loss[loss=0.1848, simple_loss=0.2358, pruned_loss=0.06686, over 4851.00 frames.], tot_loss[loss=0.1705, simple_loss=0.237, pruned_loss=0.05203, over 972400.79 frames.], batch size: 32, lr: 6.67e-04
2022-05-04 07:51:22,502 INFO [train.py:715] (1/8) Epoch 2, batch 20700, loss[loss=0.1462, simple_loss=0.2233, pruned_loss=0.03448, over 4776.00 frames.], tot_loss[loss=0.1706, simple_loss=0.237, pruned_loss=0.05204, over 972798.24 frames.], batch size: 17, lr: 6.67e-04
2022-05-04 07:52:03,023 INFO [train.py:715] (1/8) Epoch 2, batch 20750, loss[loss=0.1485, simple_loss=0.2153, pruned_loss=0.04083, over 4819.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2382, pruned_loss=0.05249, over 972580.54 frames.], batch size: 25, lr: 6.67e-04
2022-05-04 07:52:44,274 INFO [train.py:715] (1/8) Epoch 2, batch 20800, loss[loss=0.2018, simple_loss=0.2833, pruned_loss=0.0602, over 4987.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2383, pruned_loss=0.0525, over 973154.01 frames.], batch size: 26, lr: 6.67e-04
2022-05-04 07:53:25,478 INFO [train.py:715] (1/8) Epoch 2, batch 20850, loss[loss=0.1861, simple_loss=0.2495, pruned_loss=0.06136, over 4836.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2375, pruned_loss=0.05251, over 973150.44 frames.], batch size: 15, lr: 6.66e-04
2022-05-04 07:54:06,133 INFO [train.py:715] (1/8) Epoch 2, batch 20900, loss[loss=0.1657, simple_loss=0.2414, pruned_loss=0.04501, over 4976.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2376, pruned_loss=0.05272, over 973101.25 frames.], batch size: 35, lr: 6.66e-04
2022-05-04 07:54:47,186 INFO [train.py:715] (1/8) Epoch 2, batch 20950, loss[loss=0.146, simple_loss=0.2185, pruned_loss=0.03669, over 4875.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2371, pruned_loss=0.05236, over 972127.98 frames.], batch size: 22, lr: 6.66e-04
2022-05-04 07:55:28,377 INFO [train.py:715] (1/8) Epoch 2, batch 21000, loss[loss=0.1815, simple_loss=0.2489, pruned_loss=0.05706, over 4827.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2365, pruned_loss=0.0515, over 972823.55 frames.], batch size: 13, lr: 6.66e-04
2022-05-04 07:55:28,378 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 07:55:39,045 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1174, simple_loss=0.2036, pruned_loss=0.01562, over 914524.00 frames.
2022-05-04 07:56:20,523 INFO [train.py:715] (1/8) Epoch 2, batch 21050, loss[loss=0.1635, simple_loss=0.2427, pruned_loss=0.0422, over 4807.00 frames.], tot_loss[loss=0.169, simple_loss=0.2356, pruned_loss=0.05119, over 973092.57 frames.], batch size: 24, lr: 6.66e-04
2022-05-04 07:57:00,978 INFO [train.py:715] (1/8) Epoch 2, batch 21100, loss[loss=0.1939, simple_loss=0.254, pruned_loss=0.06687, over 4875.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2367, pruned_loss=0.05183, over 973805.60 frames.], batch size: 32, lr: 6.66e-04
2022-05-04 07:57:41,484 INFO [train.py:715] (1/8) Epoch 2, batch 21150, loss[loss=0.1637, simple_loss=0.2361, pruned_loss=0.04561, over 4938.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2366, pruned_loss=0.05149, over 973645.02 frames.], batch size: 21, lr: 6.65e-04
2022-05-04 07:58:22,036 INFO [train.py:715] (1/8) Epoch 2, batch 21200, loss[loss=0.1769, simple_loss=0.2271, pruned_loss=0.06331, over 4702.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2363, pruned_loss=0.05177, over 973161.66 frames.], batch size: 15, lr: 6.65e-04
2022-05-04 07:59:02,122 INFO [train.py:715] (1/8) Epoch 2, batch 21250, loss[loss=0.1393, simple_loss=0.2161, pruned_loss=0.03132, over 4979.00 frames.], tot_loss[loss=0.1709, simple_loss=0.237, pruned_loss=0.05245, over 972988.36 frames.], batch size: 31, lr: 6.65e-04
2022-05-04 07:59:42,839 INFO [train.py:715] (1/8) Epoch 2, batch 21300, loss[loss=0.1197, simple_loss=0.1882, pruned_loss=0.02559, over 4734.00 frames.], tot_loss[loss=0.1706, simple_loss=0.237, pruned_loss=0.05209, over 971429.49 frames.], batch size: 12, lr: 6.65e-04
2022-05-04 08:00:23,552 INFO [train.py:715] (1/8) Epoch 2, batch 21350, loss[loss=0.1922, simple_loss=0.2644, pruned_loss=0.05996, over 4747.00 frames.], tot_loss[loss=0.171, simple_loss=0.2376, pruned_loss=0.05226, over 971030.17 frames.], batch size: 19, lr: 6.65e-04
2022-05-04 08:01:04,857 INFO [train.py:715] (1/8) Epoch 2, batch 21400, loss[loss=0.169, simple_loss=0.2357, pruned_loss=0.05116, over 4889.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2375, pruned_loss=0.05243, over 971670.79 frames.], batch size: 22, lr: 6.64e-04
2022-05-04 08:01:45,120 INFO [train.py:715] (1/8) Epoch 2, batch 21450, loss[loss=0.162, simple_loss=0.2164, pruned_loss=0.05375, over 4806.00 frames.], tot_loss[loss=0.17, simple_loss=0.2364, pruned_loss=0.05176, over 971312.79 frames.], batch size: 12, lr: 6.64e-04
2022-05-04 08:02:26,053 INFO [train.py:715] (1/8) Epoch 2, batch 21500, loss[loss=0.1413, simple_loss=0.2239, pruned_loss=0.0293, over 4797.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2372, pruned_loss=0.05212, over 970430.24 frames.], batch size: 24, lr: 6.64e-04
2022-05-04 08:03:07,352 INFO [train.py:715] (1/8) Epoch 2, batch 21550, loss[loss=0.1504, simple_loss=0.2171, pruned_loss=0.04184, over 4788.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2378, pruned_loss=0.0527, over 971860.03 frames.], batch size: 18, lr: 6.64e-04
2022-05-04 08:03:47,337 INFO [train.py:715] (1/8) Epoch 2, batch 21600, loss[loss=0.1635, simple_loss=0.2288, pruned_loss=0.04912, over 4753.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2377, pruned_loss=0.05277, over 971987.20 frames.], batch size: 19, lr: 6.64e-04
2022-05-04 08:04:28,559 INFO [train.py:715] (1/8) Epoch 2, batch 21650, loss[loss=0.1731, simple_loss=0.2345, pruned_loss=0.0559, over 4840.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2368, pruned_loss=0.05231, over 971663.34 frames.], batch size: 15, lr: 6.64e-04
2022-05-04 08:05:10,121 INFO [train.py:715] (1/8) Epoch 2, batch 21700, loss[loss=0.174, simple_loss=0.2339, pruned_loss=0.0571, over 4930.00 frames.], tot_loss[loss=0.1721, simple_loss=0.238, pruned_loss=0.0531, over 971346.93 frames.], batch size: 18, lr: 6.63e-04
2022-05-04 08:05:50,684 INFO [train.py:715] (1/8) Epoch 2, batch 21750, loss[loss=0.1758, simple_loss=0.2434, pruned_loss=0.05414, over 4872.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2374, pruned_loss=0.05277, over 971348.93 frames.], batch size: 38, lr: 6.63e-04
2022-05-04 08:06:31,749 INFO [train.py:715] (1/8) Epoch 2, batch 21800, loss[loss=0.1559, simple_loss=0.2282, pruned_loss=0.04182, over 4910.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2377, pruned_loss=0.05296, over 972952.97 frames.], batch size: 18, lr: 6.63e-04
2022-05-04 08:07:12,171 INFO [train.py:715] (1/8) Epoch 2, batch 21850, loss[loss=0.168, simple_loss=0.2293, pruned_loss=0.05331, over 4844.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2368, pruned_loss=0.05233, over 972452.22 frames.], batch size: 30, lr: 6.63e-04
2022-05-04 08:07:53,265 INFO [train.py:715] (1/8) Epoch 2, batch 21900, loss[loss=0.1801, simple_loss=0.2512, pruned_loss=0.05448, over 4886.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2365, pruned_loss=0.05194, over 972740.94 frames.], batch size: 22, lr: 6.63e-04
2022-05-04 08:08:33,958 INFO [train.py:715] (1/8) Epoch 2, batch 21950, loss[loss=0.1844, simple_loss=0.2399, pruned_loss=0.06444, over 4884.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2359, pruned_loss=0.05166, over 972372.54 frames.], batch size: 32, lr: 6.62e-04
2022-05-04 08:09:15,713 INFO [train.py:715] (1/8) Epoch 2, batch 22000, loss[loss=0.1601, simple_loss=0.2325, pruned_loss=0.04383, over 4964.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2368, pruned_loss=0.0517, over 973526.54 frames.], batch size: 15, lr: 6.62e-04
2022-05-04 08:09:57,813 INFO [train.py:715] (1/8) Epoch 2, batch 22050, loss[loss=0.1834, simple_loss=0.2478, pruned_loss=0.05955, over 4889.00 frames.], tot_loss[loss=0.1693, simple_loss=0.236, pruned_loss=0.05128, over 973517.18 frames.], batch size: 19, lr: 6.62e-04
2022-05-04 08:10:38,624 INFO [train.py:715] (1/8) Epoch 2, batch 22100, loss[loss=0.1474, simple_loss=0.2179, pruned_loss=0.03838, over 4818.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2365, pruned_loss=0.05157, over 972583.30 frames.], batch size: 26, lr: 6.62e-04
2022-05-04 08:11:20,106 INFO [train.py:715] (1/8) Epoch 2, batch 22150, loss[loss=0.1367, simple_loss=0.204, pruned_loss=0.03465, over 4748.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2368, pruned_loss=0.05137, over 972480.56 frames.], batch size: 12, lr: 6.62e-04
2022-05-04 08:12:01,872 INFO [train.py:715] (1/8) Epoch 2, batch 22200, loss[loss=0.1618, simple_loss=0.2331, pruned_loss=0.04528, over 4748.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2359, pruned_loss=0.05098, over 972278.66 frames.], batch size: 16, lr: 6.62e-04
2022-05-04 08:12:43,327 INFO [train.py:715] (1/8) Epoch 2, batch 22250, loss[loss=0.1481, simple_loss=0.2116, pruned_loss=0.04229, over 4805.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2362, pruned_loss=0.05126, over 973604.94 frames.], batch size: 14, lr: 6.61e-04
2022-05-04 08:13:24,129 INFO [train.py:715] (1/8) Epoch 2, batch 22300, loss[loss=0.1694, simple_loss=0.2463, pruned_loss=0.04631, over 4745.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2364, pruned_loss=0.05149, over 973571.69 frames.], batch size: 16, lr: 6.61e-04
2022-05-04 08:14:05,209 INFO [train.py:715] (1/8) Epoch 2, batch 22350, loss[loss=0.1642, simple_loss=0.2282, pruned_loss=0.05005, over 4867.00 frames.], tot_loss[loss=0.1707, simple_loss=0.237, pruned_loss=0.05218, over 973320.75 frames.], batch size: 22, lr: 6.61e-04
2022-05-04 08:14:46,093 INFO [train.py:715] (1/8) Epoch 2, batch 22400, loss[loss=0.1462, simple_loss=0.2231, pruned_loss=0.03469, over 4924.00 frames.], tot_loss[loss=0.17, simple_loss=0.2365, pruned_loss=0.05179, over 972450.27 frames.], batch size: 29, lr: 6.61e-04
2022-05-04 08:15:26,444 INFO [train.py:715] (1/8) Epoch 2, batch 22450, loss[loss=0.1435, simple_loss=0.2141, pruned_loss=0.03649, over 4796.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2356, pruned_loss=0.05105, over 972198.29 frames.], batch size: 13, lr: 6.61e-04
2022-05-04 08:16:07,659 INFO [train.py:715] (1/8) Epoch 2, batch 22500, loss[loss=0.1971, simple_loss=0.245, pruned_loss=0.07459, over 4826.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2353, pruned_loss=0.05069, over 971895.33 frames.], batch size: 15, lr: 6.61e-04
2022-05-04 08:16:48,519 INFO [train.py:715] (1/8) Epoch 2, batch 22550, loss[loss=0.1643, simple_loss=0.2406, pruned_loss=0.04404, over 4757.00 frames.], tot_loss[loss=0.1692, simple_loss=0.236, pruned_loss=0.05119, over 971655.47 frames.], batch size: 19, lr: 6.60e-04
2022-05-04 08:17:29,225 INFO [train.py:715] (1/8) Epoch 2, batch 22600, loss[loss=0.1444, simple_loss=0.2143, pruned_loss=0.03728, over 4831.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2356, pruned_loss=0.05104, over 972138.08 frames.], batch size: 12, lr: 6.60e-04
2022-05-04 08:18:09,992 INFO [train.py:715] (1/8) Epoch 2, batch 22650, loss[loss=0.162, simple_loss=0.2312, pruned_loss=0.04646, over 4814.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2361, pruned_loss=0.0511, over 972292.68 frames.], batch size: 21, lr: 6.60e-04
2022-05-04 08:18:50,671 INFO [train.py:715] (1/8) Epoch 2, batch 22700, loss[loss=0.1851, simple_loss=0.2336, pruned_loss=0.06829, over 4789.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2362, pruned_loss=0.05131, over 972579.17 frames.], batch size: 18, lr: 6.60e-04
2022-05-04 08:19:31,394 INFO [train.py:715] (1/8) Epoch 2, batch 22750, loss[loss=0.1681, simple_loss=0.2419, pruned_loss=0.04718, over 4931.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2371, pruned_loss=0.0513, over 972312.92 frames.], batch size: 23, lr: 6.60e-04
2022-05-04 08:20:12,250 INFO [train.py:715] (1/8) Epoch 2, batch 22800, loss[loss=0.1928, simple_loss=0.2461, pruned_loss=0.06977, over 4874.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2368, pruned_loss=0.05101, over 972236.11 frames.], batch size: 32, lr: 6.59e-04
2022-05-04 08:20:53,295 INFO [train.py:715] (1/8) Epoch 2, batch 22850, loss[loss=0.1438, simple_loss=0.2163, pruned_loss=0.03558, over 4866.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2371, pruned_loss=0.05114, over 971576.58 frames.], batch size: 20, lr: 6.59e-04
2022-05-04 08:21:34,650 INFO [train.py:715] (1/8) Epoch 2, batch 22900, loss[loss=0.1514, simple_loss=0.2188, pruned_loss=0.04207, over 4972.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2368, pruned_loss=0.05096, over 972584.00 frames.], batch size: 28, lr: 6.59e-04
2022-05-04 08:22:15,452 INFO [train.py:715] (1/8) Epoch 2, batch 22950, loss[loss=0.1612, simple_loss=0.2271, pruned_loss=0.04764, over 4761.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2357, pruned_loss=0.05043, over 972638.16 frames.], batch size: 19, lr: 6.59e-04
2022-05-04 08:22:56,045 INFO [train.py:715] (1/8) Epoch 2, batch 23000, loss[loss=0.1735, simple_loss=0.2394, pruned_loss=0.05379, over 4974.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2364, pruned_loss=0.05103, over 972783.51 frames.], batch size: 14, lr: 6.59e-04
2022-05-04 08:23:37,062 INFO [train.py:715] (1/8) Epoch 2, batch 23050, loss[loss=0.1989, simple_loss=0.2564, pruned_loss=0.07069, over 4803.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2366, pruned_loss=0.05124, over 973465.91 frames.], batch size: 17, lr: 6.59e-04
2022-05-04 08:24:17,899 INFO [train.py:715] (1/8) Epoch 2, batch 23100, loss[loss=0.1425, simple_loss=0.213, pruned_loss=0.03594, over 4839.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2351, pruned_loss=0.05027, over 972536.93 frames.], batch size: 27, lr: 6.58e-04
2022-05-04 08:24:58,401 INFO [train.py:715] (1/8) Epoch 2, batch 23150, loss[loss=0.1912, simple_loss=0.2586, pruned_loss=0.06187, over 4867.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2358, pruned_loss=0.05073, over 972604.45 frames.], batch size: 32, lr: 6.58e-04
2022-05-04 08:25:39,720 INFO [train.py:715] (1/8) Epoch 2, batch 23200, loss[loss=0.178, simple_loss=0.2453, pruned_loss=0.05536, over 4910.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2357, pruned_loss=0.05057, over 972540.60 frames.], batch size: 19, lr: 6.58e-04
2022-05-04 08:26:20,393 INFO [train.py:715] (1/8) Epoch 2, batch 23250, loss[loss=0.1639, simple_loss=0.2433, pruned_loss=0.04221, over 4917.00 frames.], tot_loss[loss=0.1688, simple_loss=0.236, pruned_loss=0.05082, over 973015.71 frames.], batch size: 18, lr: 6.58e-04
2022-05-04 08:27:00,745 INFO [train.py:715] (1/8) Epoch 2, batch 23300, loss[loss=0.172, simple_loss=0.2314, pruned_loss=0.05636, over 4687.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2367, pruned_loss=0.05131, over 973567.87 frames.], batch size: 15, lr: 6.58e-04
2022-05-04 08:27:41,447 INFO [train.py:715] (1/8) Epoch 2, batch 23350, loss[loss=0.1311, simple_loss=0.2101, pruned_loss=0.02608, over 4713.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2375, pruned_loss=0.05153, over 973550.38 frames.], batch size: 15, lr: 6.57e-04
2022-05-04 08:28:22,390 INFO [train.py:715] (1/8) Epoch 2, batch 23400, loss[loss=0.1406, simple_loss=0.2155, pruned_loss=0.03286, over 4902.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2373, pruned_loss=0.05173, over 973090.32 frames.], batch size: 19, lr: 6.57e-04
2022-05-04 08:29:03,322 INFO [train.py:715] (1/8) Epoch 2, batch 23450, loss[loss=0.1578, simple_loss=0.2254, pruned_loss=0.0451, over 4813.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2368, pruned_loss=0.0515, over 973144.86 frames.], batch size: 13, lr: 6.57e-04
2022-05-04 08:29:43,618 INFO [train.py:715] (1/8) Epoch 2, batch 23500, loss[loss=0.1607, simple_loss=0.2266, pruned_loss=0.0474, over 4689.00 frames.], tot_loss[loss=0.17, simple_loss=0.2367, pruned_loss=0.05166, over 972730.13 frames.], batch size: 15, lr: 6.57e-04
2022-05-04 08:30:24,810 INFO [train.py:715] (1/8) Epoch 2, batch 23550, loss[loss=0.1915, simple_loss=0.2667, pruned_loss=0.05809, over 4896.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2372, pruned_loss=0.05207, over 972135.72 frames.], batch size: 17, lr: 6.57e-04
2022-05-04 08:31:05,704 INFO [train.py:715] (1/8) Epoch 2, batch 23600, loss[loss=0.1926, simple_loss=0.2616, pruned_loss=0.06179, over 4869.00 frames.], tot_loss[loss=0.1706, simple_loss=0.237, pruned_loss=0.05211, over 972011.36 frames.], batch size: 39, lr: 6.57e-04
2022-05-04 08:31:45,432 INFO [train.py:715] (1/8) Epoch 2, batch 23650, loss[loss=0.1738, simple_loss=0.2445, pruned_loss=0.05151, over 4776.00 frames.], tot_loss[loss=0.171, simple_loss=0.2373, pruned_loss=0.05231, over 971380.97 frames.], batch size: 14, lr: 6.56e-04
2022-05-04 08:32:27,502 INFO [train.py:715] (1/8) Epoch 2, batch 23700, loss[loss=0.2273, simple_loss=0.2861, pruned_loss=0.08425, over 4896.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2379, pruned_loss=0.0523, over 971842.96 frames.], batch size: 18, lr: 6.56e-04
2022-05-04 08:33:07,930 INFO [train.py:715] (1/8) Epoch 2, batch 23750, loss[loss=0.1666, simple_loss=0.2397, pruned_loss=0.04674, over 4693.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2378, pruned_loss=0.05236, over 972714.54 frames.], batch size: 15, lr: 6.56e-04
2022-05-04 08:33:48,794 INFO [train.py:715] (1/8) Epoch 2, batch 23800, loss[loss=0.1423, simple_loss=0.2077, pruned_loss=0.03842, over 4950.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2377, pruned_loss=0.05255, over 972308.37 frames.], batch size: 21, lr: 6.56e-04
2022-05-04 08:34:29,259 INFO [train.py:715] (1/8) Epoch 2, batch 23850, loss[loss=0.1752, simple_loss=0.2474, pruned_loss=0.05151, over 4900.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2379, pruned_loss=0.05236, over 972374.78 frames.], batch size: 19, lr: 6.56e-04
2022-05-04 08:35:10,693 INFO [train.py:715] (1/8) Epoch 2, batch 23900, loss[loss=0.1798, simple_loss=0.2494, pruned_loss=0.05517, over 4817.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2377, pruned_loss=0.05232, over 972303.14 frames.], batch size: 21, lr: 6.56e-04
2022-05-04 08:35:51,717 INFO [train.py:715] (1/8) Epoch 2, batch 23950, loss[loss=0.1835, simple_loss=0.2457, pruned_loss=0.0606, over 4979.00 frames.], tot_loss[loss=0.1712, simple_loss=0.238, pruned_loss=0.05223, over 972356.44 frames.], batch size: 14, lr: 6.55e-04
2022-05-04 08:36:31,646 INFO [train.py:715] (1/8) Epoch 2, batch 24000, loss[loss=0.1609, simple_loss=0.2355, pruned_loss=0.04315, over 4800.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2374, pruned_loss=0.05188, over 972751.85 frames.], batch size: 24, lr: 6.55e-04
2022-05-04 08:36:31,647 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 08:36:40,334 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1168, simple_loss=0.2032, pruned_loss=0.01518, over 914524.00 frames.
2022-05-04 08:37:20,458 INFO [train.py:715] (1/8) Epoch 2, batch 24050, loss[loss=0.1691, simple_loss=0.2426, pruned_loss=0.04781, over 4939.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2365, pruned_loss=0.0515, over 972190.23 frames.], batch size: 23, lr: 6.55e-04
2022-05-04 08:38:01,993 INFO [train.py:715] (1/8) Epoch 2, batch 24100, loss[loss=0.1726, simple_loss=0.2287, pruned_loss=0.05822, over 4851.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2367, pruned_loss=0.05198, over 972261.77 frames.], batch size: 15, lr: 6.55e-04
2022-05-04 08:38:42,990 INFO [train.py:715] (1/8) Epoch 2, batch 24150, loss[loss=0.2266, simple_loss=0.2822, pruned_loss=0.08553, over 4642.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2361, pruned_loss=0.05156, over 972309.43 frames.], batch size: 13, lr: 6.55e-04
2022-05-04 08:39:24,310 INFO [train.py:715] (1/8) Epoch 2, batch 24200, loss[loss=0.1572, simple_loss=0.2202, pruned_loss=0.04708, over 4866.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2356, pruned_loss=0.05124, over 971265.67 frames.], batch size: 30, lr: 6.55e-04
2022-05-04 08:40:05,191 INFO [train.py:715] (1/8) Epoch 2, batch 24250, loss[loss=0.1867, simple_loss=0.2409, pruned_loss=0.06627, over 4854.00 frames.], tot_loss[loss=0.1694, simple_loss=0.236, pruned_loss=0.05143, over 971188.09 frames.], batch size: 32, lr: 6.54e-04
2022-05-04 08:40:46,095 INFO [train.py:715] (1/8) Epoch 2, batch 24300, loss[loss=0.1433, simple_loss=0.2077, pruned_loss=0.03944, over 4855.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2358, pruned_loss=0.0509, over 971429.63 frames.], batch size: 30, lr: 6.54e-04
2022-05-04 08:41:26,660 INFO [train.py:715] (1/8) Epoch 2, batch 24350, loss[loss=0.1525, simple_loss=0.2269, pruned_loss=0.03905, over 4981.00 frames.], tot_loss[loss=0.168, simple_loss=0.2353, pruned_loss=0.05038, over 970797.30 frames.], batch size: 25, lr: 6.54e-04
2022-05-04 08:42:06,545 INFO [train.py:715] (1/8) Epoch 2, batch 24400, loss[loss=0.1621, simple_loss=0.2384, pruned_loss=0.04294, over 4899.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2353, pruned_loss=0.04993, over 970813.40 frames.], batch size: 19, lr: 6.54e-04
2022-05-04 08:42:47,542 INFO [train.py:715] (1/8) Epoch 2, batch 24450, loss[loss=0.181, simple_loss=0.2377, pruned_loss=0.06217, over 4818.00 frames.], tot_loss[loss=0.1689, simple_loss=0.236, pruned_loss=0.05089, over 970993.53 frames.], batch size: 26, lr: 6.54e-04
2022-05-04 08:43:27,490 INFO [train.py:715] (1/8) Epoch 2, batch 24500, loss[loss=0.1784, simple_loss=0.2381, pruned_loss=0.05939, over 4916.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2356, pruned_loss=0.05082, over 970848.94 frames.], batch size: 18, lr: 6.53e-04
2022-05-04 08:44:07,367 INFO [train.py:715] (1/8) Epoch 2, batch 24550, loss[loss=0.1832, simple_loss=0.2633, pruned_loss=0.05148, over 4948.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2355, pruned_loss=0.0506, over 970830.80 frames.], batch size: 21, lr: 6.53e-04
2022-05-04 08:44:46,869 INFO [train.py:715] (1/8) Epoch 2, batch 24600, loss[loss=0.1739, simple_loss=0.2348, pruned_loss=0.05649, over 4776.00 frames.], tot_loss[loss=0.1689, simple_loss=0.236, pruned_loss=0.05094, over 972329.89 frames.], batch size: 14, lr: 6.53e-04
2022-05-04 08:45:27,055 INFO [train.py:715] (1/8) Epoch 2, batch 24650, loss[loss=0.1828, simple_loss=0.2429, pruned_loss=0.0614, over 4959.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2368, pruned_loss=0.05137, over 971674.25 frames.], batch size: 35, lr: 6.53e-04
2022-05-04 08:46:06,408 INFO [train.py:715] (1/8) Epoch 2, batch 24700, loss[loss=0.1665, simple_loss=0.2387, pruned_loss=0.04721, over 4943.00 frames.], tot_loss[loss=0.1701, simple_loss=0.237, pruned_loss=0.05163, over 971339.01 frames.], batch size: 39, lr: 6.53e-04
2022-05-04 08:46:45,152 INFO [train.py:715] (1/8) Epoch 2, batch 24750, loss[loss=0.1626, simple_loss=0.2298, pruned_loss=0.04775, over 4799.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2379, pruned_loss=0.05193, over 971567.23 frames.], batch size: 24, lr: 6.53e-04
2022-05-04 08:47:24,972 INFO [train.py:715] (1/8) Epoch 2, batch 24800, loss[loss=0.1667, simple_loss=0.2351, pruned_loss=0.04917, over 4856.00 frames.], tot_loss[loss=0.1707, simple_loss=0.238, pruned_loss=0.05174, over 972513.69 frames.], batch size: 20, lr: 6.52e-04
2022-05-04 08:48:04,572 INFO [train.py:715] (1/8) Epoch 2, batch 24850, loss[loss=0.1554, simple_loss=0.2275, pruned_loss=0.0417, over 4776.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2368, pruned_loss=0.05113, over 972084.52 frames.], batch size: 17, lr: 6.52e-04
2022-05-04 08:48:43,455 INFO [train.py:715] (1/8) Epoch 2, batch 24900, loss[loss=0.1566, simple_loss=0.22, pruned_loss=0.04656, over 4777.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2377, pruned_loss=0.05194, over 972050.87 frames.], batch size: 12, lr: 6.52e-04
2022-05-04 08:49:22,920 INFO [train.py:715] (1/8) Epoch 2, batch 24950, loss[loss=0.2203, simple_loss=0.2606, pruned_loss=0.09001, over 4902.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2378, pruned_loss=0.05274, over 972065.48 frames.], batch size: 17, lr: 6.52e-04
2022-05-04 08:50:02,462 INFO [train.py:715] (1/8) Epoch 2, batch 25000, loss[loss=0.1666, simple_loss=0.2324, pruned_loss=0.0504, over 4810.00 frames.], tot_loss[loss=0.1712, simple_loss=0.237, pruned_loss=0.05268, over 971896.36 frames.], batch size: 25, lr: 6.52e-04
2022-05-04 08:50:41,254 INFO [train.py:715] (1/8) Epoch 2, batch 25050, loss[loss=0.1783, simple_loss=0.2535, pruned_loss=0.05155, over 4919.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2375, pruned_loss=0.05216, over 971523.09 frames.], batch size: 23, lr: 6.52e-04
2022-05-04 08:51:19,782 INFO [train.py:715] (1/8) Epoch 2, batch 25100, loss[loss=0.1473, simple_loss=0.2172, pruned_loss=0.03864, over 4959.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2378, pruned_loss=0.05216, over 971192.01 frames.], batch size: 14, lr: 6.51e-04
2022-05-04 08:51:59,032 INFO [train.py:715] (1/8) Epoch 2, batch 25150, loss[loss=0.2069, simple_loss=0.2648, pruned_loss=0.07451, over 4900.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2383, pruned_loss=0.0524, over 971033.07 frames.], batch size: 17, lr: 6.51e-04
2022-05-04 08:52:37,844 INFO [train.py:715] (1/8) Epoch 2, batch 25200, loss[loss=0.1547, simple_loss=0.2209, pruned_loss=0.0442, over 4941.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2366, pruned_loss=0.05143, over 970503.46 frames.], batch size: 29, lr: 6.51e-04
2022-05-04 08:53:16,874 INFO [train.py:715] (1/8) Epoch 2, batch 25250, loss[loss=0.1663, simple_loss=0.2482, pruned_loss=0.04224, over 4824.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2371, pruned_loss=0.05163, over 971142.25 frames.], batch size: 15, lr: 6.51e-04
2022-05-04 08:53:55,852 INFO [train.py:715] (1/8) Epoch 2, batch 25300, loss[loss=0.1927, simple_loss=0.2519, pruned_loss=0.06679, over 4822.00 frames.], tot_loss[loss=0.17, simple_loss=0.2367, pruned_loss=0.05164, over 971630.20 frames.], batch size: 12, lr: 6.51e-04
2022-05-04 08:54:35,070 INFO [train.py:715] (1/8) Epoch 2, batch 25350, loss[loss=0.1892, simple_loss=0.2425, pruned_loss=0.06794, over 4942.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2367, pruned_loss=0.05178, over 970848.80 frames.], batch size: 35, lr: 6.51e-04
2022-05-04 08:55:14,140 INFO [train.py:715] (1/8) Epoch 2, batch 25400, loss[loss=0.1669, simple_loss=0.2218, pruned_loss=0.05605, over 4953.00 frames.], tot_loss[loss=0.169, simple_loss=0.2358, pruned_loss=0.05113, over 971114.28 frames.], batch size: 35, lr: 6.50e-04
2022-05-04 08:55:52,994 INFO [train.py:715] (1/8) Epoch 2, batch 25450, loss[loss=0.165, simple_loss=0.2335, pruned_loss=0.04826, over 4817.00 frames.], tot_loss[loss=0.1705, simple_loss=0.237, pruned_loss=0.05196, over 970058.40 frames.], batch size: 27, lr: 6.50e-04
2022-05-04 08:56:32,016 INFO [train.py:715] (1/8) Epoch 2, batch 25500, loss[loss=0.1689, simple_loss=0.2222, pruned_loss=0.05777, over 4829.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2369, pruned_loss=0.05173, over 970697.52 frames.], batch size: 30, lr: 6.50e-04
2022-05-04 08:57:11,298 INFO [train.py:715] (1/8) Epoch 2, batch 25550, loss[loss=0.1696, simple_loss=0.2287, pruned_loss=0.05526, over 4750.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2364, pruned_loss=0.05141, over 970582.63 frames.], batch size: 19, lr: 6.50e-04
2022-05-04 08:57:50,303 INFO [train.py:715] (1/8) Epoch 2, batch 25600, loss[loss=0.188, simple_loss=0.2605, pruned_loss=0.05774, over 4775.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2359, pruned_loss=0.0511, over 970131.03 frames.], batch size: 17, lr: 6.50e-04
2022-05-04 08:58:29,644 INFO [train.py:715] (1/8) Epoch 2, batch 25650, loss[loss=0.1332, simple_loss=0.205, pruned_loss=0.03072, over 4753.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2358, pruned_loss=0.05102, over 971059.35 frames.], batch size: 19, lr: 6.50e-04
2022-05-04 08:59:09,554 INFO [train.py:715] (1/8) Epoch 2, batch 25700, loss[loss=0.1759, simple_loss=0.2375, pruned_loss=0.05717, over 4637.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2354, pruned_loss=0.05056, over 971454.87 frames.], batch size: 13, lr: 6.49e-04
2022-05-04 08:59:48,688 INFO [train.py:715] (1/8) Epoch 2, batch 25750, loss[loss=0.1689, simple_loss=0.2381, pruned_loss=0.04987, over 4790.00 frames.], tot_loss[loss=0.167, simple_loss=0.2343, pruned_loss=0.04986, over 971885.02 frames.], batch size: 17, lr: 6.49e-04
2022-05-04 09:00:27,435 INFO [train.py:715] (1/8) Epoch 2, batch 25800, loss[loss=0.1671, simple_loss=0.2347, pruned_loss=0.04978, over 4884.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2344, pruned_loss=0.05023, over 973125.60 frames.], batch size: 19, lr: 6.49e-04
2022-05-04 09:01:06,417 INFO [train.py:715] (1/8) Epoch 2, batch 25850, loss[loss=0.1755, simple_loss=0.2488, pruned_loss=0.05108, over 4802.00 frames.], tot_loss[loss=0.1681, simple_loss=0.235, pruned_loss=0.05056, over 973097.31 frames.], batch size: 21, lr: 6.49e-04
2022-05-04 09:01:46,180 INFO [train.py:715] (1/8) Epoch 2, batch 25900, loss[loss=0.1631, simple_loss=0.2259, pruned_loss=0.05017, over 4795.00 frames.], tot_loss[loss=0.1692, simple_loss=0.236, pruned_loss=0.05116, over 973048.02 frames.], batch size: 24, lr: 6.49e-04
2022-05-04 09:02:25,984 INFO [train.py:715] (1/8) Epoch 2, batch 25950, loss[loss=0.1718, simple_loss=0.2453, pruned_loss=0.04916, over 4973.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2357, pruned_loss=0.05093, over 973178.22 frames.], batch size: 15, lr: 6.49e-04
2022-05-04 09:03:05,064 INFO [train.py:715] (1/8) Epoch 2, batch 26000, loss[loss=0.1473, simple_loss=0.2188, pruned_loss=0.03787, over 4825.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2346, pruned_loss=0.05077, over 972399.95 frames.], batch size: 27, lr: 6.48e-04
2022-05-04 09:03:44,734 INFO [train.py:715] (1/8) Epoch 2, batch 26050, loss[loss=0.1609, simple_loss=0.2356, pruned_loss=0.04314, over 4780.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2351, pruned_loss=0.05109, over 971967.35 frames.], batch size: 17, lr: 6.48e-04
2022-05-04 09:04:24,301 INFO [train.py:715] (1/8) Epoch 2, batch 26100, loss[loss=0.1855, simple_loss=0.242, pruned_loss=0.06445, over 4817.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2353, pruned_loss=0.05096, over 971012.34 frames.], batch size: 13, lr: 6.48e-04
2022-05-04 09:05:03,478 INFO [train.py:715] (1/8) Epoch 2, batch 26150, loss[loss=0.1685, simple_loss=0.2361, pruned_loss=0.05044, over 4786.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2355, pruned_loss=0.05113, over 971801.18 frames.], batch size: 18, lr: 6.48e-04
2022-05-04 09:05:42,984 INFO [train.py:715] (1/8) Epoch 2, batch 26200, loss[loss=0.158, simple_loss=0.2381, pruned_loss=0.03897, over 4948.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2358, pruned_loss=0.05134, over 972171.26 frames.], batch size: 29, lr: 6.48e-04
2022-05-04 09:06:22,733 INFO [train.py:715] (1/8) Epoch 2, batch 26250, loss[loss=0.1734, simple_loss=0.2319, pruned_loss=0.0575, over 4924.00 frames.], tot_loss[loss=0.1705, simple_loss=0.237, pruned_loss=0.05199, over 971787.11 frames.], batch size: 29, lr: 6.48e-04
2022-05-04 09:07:02,314 INFO [train.py:715] (1/8) Epoch 2, batch 26300, loss[loss=0.1802, simple_loss=0.2487, pruned_loss=0.05582, over 4868.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2361, pruned_loss=0.0513, over 971253.47 frames.], batch size: 32, lr: 6.47e-04
2022-05-04 09:07:40,823 INFO [train.py:715] (1/8) Epoch 2, batch 26350, loss[loss=0.1635, simple_loss=0.2314, pruned_loss=0.04785, over 4891.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2361, pruned_loss=0.05146, over 972106.41 frames.], batch size: 22, lr: 6.47e-04
2022-05-04 09:08:23,928 INFO [train.py:715] (1/8) Epoch 2, batch 26400, loss[loss=0.1931, simple_loss=0.2614, pruned_loss=0.06237, over 4914.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2364, pruned_loss=0.05133, over 972354.96 frames.], batch size: 18, lr: 6.47e-04
2022-05-04 09:09:03,682 INFO [train.py:715] (1/8) Epoch 2, batch 26450, loss[loss=0.2277, simple_loss=0.2839, pruned_loss=0.08572, over 4956.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2364, pruned_loss=0.05113, over 972237.79 frames.], batch size: 21, lr: 6.47e-04
2022-05-04 09:09:42,577 INFO [train.py:715] (1/8) Epoch 2, batch 26500, loss[loss=0.1513, simple_loss=0.2226, pruned_loss=0.03996, over 4898.00 frames.], tot_loss[loss=0.169, simple_loss=0.236, pruned_loss=0.051, over 971872.04 frames.], batch size: 19, lr: 6.47e-04
2022-05-04 09:10:22,391 INFO [train.py:715] (1/8) Epoch 2, batch 26550, loss[loss=0.1546, simple_loss=0.2222, pruned_loss=0.04346, over 4946.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2358, pruned_loss=0.05093, over 971841.63 frames.], batch size: 35, lr: 6.46e-04
2022-05-04 09:11:02,381 INFO [train.py:715] (1/8) Epoch 2, batch 26600, loss[loss=0.1514, simple_loss=0.2235, pruned_loss=0.03965, over 4954.00 frames.], tot_loss[loss=0.168, simple_loss=0.2352, pruned_loss=0.05038, over 972247.71 frames.], batch size: 24, lr: 6.46e-04
2022-05-04 09:11:41,999 INFO [train.py:715] (1/8) Epoch 2, batch 26650, loss[loss=0.197, simple_loss=0.2799, pruned_loss=0.05704, over 4873.00 frames.], tot_loss[loss=0.169, simple_loss=0.2361, pruned_loss=0.05095, over 971966.62 frames.], batch size: 22, lr: 6.46e-04
2022-05-04 09:12:21,003 INFO [train.py:715] (1/8) Epoch 2, batch 26700, loss[loss=0.2289, simple_loss=0.2702, pruned_loss=0.0938, over 4876.00 frames.], tot_loss[loss=0.171, simple_loss=0.2377, pruned_loss=0.05215, over 972182.94 frames.], batch size: 16, lr: 6.46e-04
2022-05-04 09:13:00,963 INFO [train.py:715] (1/8) Epoch 2, batch 26750, loss[loss=0.1536, simple_loss=0.2248, pruned_loss=0.04119, over 4904.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2378, pruned_loss=0.05224, over 972667.93 frames.], batch size: 19, lr: 6.46e-04
2022-05-04 09:13:40,194 INFO [train.py:715] (1/8) Epoch 2, batch 26800, loss[loss=0.1611, simple_loss=0.2331, pruned_loss=0.04458, over 4933.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2369, pruned_loss=0.0514, over 972812.79 frames.], batch size: 18, lr: 6.46e-04
2022-05-04 09:14:19,183 INFO [train.py:715] (1/8) Epoch 2, batch 26850, loss[loss=0.1731, simple_loss=0.2454, pruned_loss=0.05037, over 4932.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2365, pruned_loss=0.05148, over 972515.16 frames.], batch size: 29, lr: 6.45e-04
2022-05-04 09:14:58,118 INFO [train.py:715] (1/8) Epoch 2, batch 26900, loss[loss=0.1655, simple_loss=0.2279, pruned_loss=0.05151, over 4861.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2356, pruned_loss=0.05146, over 972311.41 frames.], batch size: 32, lr: 6.45e-04
2022-05-04 09:15:37,580 INFO [train.py:715] (1/8) Epoch 2, batch 26950, loss[loss=0.1362, simple_loss=0.2052, pruned_loss=0.03355, over 4968.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2359, pruned_loss=0.05178, over 973161.58 frames.], batch size: 14, lr: 6.45e-04
2022-05-04 09:16:16,466 INFO [train.py:715] (1/8) Epoch 2, batch 27000, loss[loss=0.1882, simple_loss=0.2481, pruned_loss=0.06416, over 4789.00 frames.], tot_loss[loss=0.169, simple_loss=0.2353, pruned_loss=0.05134, over 972446.63 frames.], batch size: 24, lr: 6.45e-04
2022-05-04 09:16:16,467 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 09:16:25,255 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1164, simple_loss=0.2027, pruned_loss=0.01502, over 914524.00 frames.
2022-05-04 09:17:03,623 INFO [train.py:715] (1/8) Epoch 2, batch 27050, loss[loss=0.1584, simple_loss=0.2234, pruned_loss=0.04676, over 4809.00 frames.], tot_loss[loss=0.1672, simple_loss=0.234, pruned_loss=0.05024, over 972199.56 frames.], batch size: 27, lr: 6.45e-04
2022-05-04 09:17:42,883 INFO [train.py:715] (1/8) Epoch 2, batch 27100, loss[loss=0.145, simple_loss=0.22, pruned_loss=0.035, over 4782.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2347, pruned_loss=0.05025, over 971185.47 frames.], batch size: 14, lr: 6.45e-04
2022-05-04 09:18:22,880 INFO [train.py:715] (1/8) Epoch 2, batch 27150, loss[loss=0.1845, simple_loss=0.2441, pruned_loss=0.06246, over 4837.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2361, pruned_loss=0.05066, over 970526.86 frames.], batch size: 15, lr: 6.44e-04
2022-05-04 09:19:02,265 INFO [train.py:715] (1/8) Epoch 2, batch 27200, loss[loss=0.1474, simple_loss=0.2126, pruned_loss=0.04105, over 4875.00 frames.], tot_loss[loss=0.1687, simple_loss=0.236, pruned_loss=0.05074, over 971172.53 frames.], batch size: 16, lr: 6.44e-04
2022-05-04 09:19:41,120 INFO [train.py:715] (1/8) Epoch 2, batch 27250, loss[loss=0.135, simple_loss=0.2093, pruned_loss=0.03033, over 4755.00 frames.], tot_loss[loss=0.169, simple_loss=0.2362, pruned_loss=0.05084, over 971364.06 frames.], batch size: 16, lr: 6.44e-04
2022-05-04 09:20:20,689 INFO [train.py:715] (1/8) Epoch 2, batch 27300, loss[loss=0.1655, simple_loss=0.2348, pruned_loss=0.04813, over 4906.00 frames.], tot_loss[loss=0.1685, simple_loss=0.236, pruned_loss=0.05045, over 971756.33 frames.], batch size: 17, lr: 6.44e-04
2022-05-04 09:20:59,723 INFO [train.py:715] (1/8) Epoch 2, batch 27350, loss[loss=0.1475, simple_loss=0.224, pruned_loss=0.03553, over 4893.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2363, pruned_loss=0.0504, over 971839.44 frames.], batch size: 18, lr: 6.44e-04
2022-05-04 09:21:38,802 INFO [train.py:715] (1/8) Epoch 2, batch 27400, loss[loss=0.1608, simple_loss=0.2318, pruned_loss=0.04484, over 4786.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2367, pruned_loss=0.05055, over 972722.90 frames.], batch size: 18, lr: 6.44e-04
2022-05-04 09:22:17,476 INFO [train.py:715] (1/8) Epoch 2, batch 27450, loss[loss=0.179, simple_loss=0.2299, pruned_loss=0.06404, over 4889.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2367, pruned_loss=0.05041, over 972441.97 frames.], batch size: 16, lr: 6.44e-04
2022-05-04 09:22:57,212 INFO [train.py:715] (1/8) Epoch 2, batch 27500, loss[loss=0.1925, simple_loss=0.2394, pruned_loss=0.07281, over 4841.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2372, pruned_loss=0.05077, over 972164.69 frames.], batch size: 32, lr: 6.43e-04
2022-05-04 09:23:37,093 INFO [train.py:715] (1/8) Epoch 2, batch 27550, loss[loss=0.1899, simple_loss=0.2684, pruned_loss=0.05571, over 4982.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2372, pruned_loss=0.05046, over 972533.55 frames.], batch size: 25, lr: 6.43e-04
2022-05-04 09:24:16,421 INFO [train.py:715] (1/8) Epoch 2, batch 27600, loss[loss=0.1784, simple_loss=0.2539, pruned_loss=0.05148, over 4858.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2378, pruned_loss=0.05097, over 972369.40 frames.], batch size: 20, lr: 6.43e-04
2022-05-04 09:24:55,996 INFO [train.py:715] (1/8) Epoch 2, batch 27650, loss[loss=0.1888, simple_loss=0.2582, pruned_loss=0.05975, over 4828.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2371, pruned_loss=0.0508, over 972024.64 frames.], batch size: 15, lr: 6.43e-04
2022-05-04 09:25:36,590 INFO [train.py:715] (1/8) Epoch 2, batch 27700, loss[loss=0.1586, simple_loss=0.2199, pruned_loss=0.04862, over 4699.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2364, pruned_loss=0.05096, over 972643.48 frames.], batch size: 15, lr: 6.43e-04
2022-05-04 09:26:16,917 INFO [train.py:715] (1/8) Epoch 2, batch 27750, loss[loss=0.1797, simple_loss=0.2457, pruned_loss=0.05689, over 4692.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2373, pruned_loss=0.0516, over 972656.86 frames.], batch size: 15, lr: 6.43e-04
2022-05-04 09:26:56,303 INFO [train.py:715] (1/8) Epoch 2, batch 27800, loss[loss=0.1739, simple_loss=0.2373, pruned_loss=0.05523, over 4799.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2362, pruned_loss=0.05124, over 971483.60 frames.], batch size: 21, lr: 6.42e-04
2022-05-04 09:27:36,595 INFO [train.py:715] (1/8) Epoch 2, batch 27850, loss[loss=0.1411, simple_loss=0.2078, pruned_loss=0.03714, over 4814.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2367, pruned_loss=0.05115, over 972459.45 frames.], batch size: 14, lr: 6.42e-04
2022-05-04 09:28:15,905 INFO [train.py:715] (1/8) Epoch 2, batch 27900, loss[loss=0.1559, simple_loss=0.2148, pruned_loss=0.04852, over 4862.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2368, pruned_loss=0.05084, over 971712.94 frames.], batch size: 32, lr: 6.42e-04
2022-05-04 09:28:55,084 INFO [train.py:715] (1/8) Epoch 2, batch 27950, loss[loss=0.1595, simple_loss=0.2308, pruned_loss=0.04409, over 4934.00 frames.], tot_loss[loss=0.1693, simple_loss=0.237, pruned_loss=0.05086, over 972689.06 frames.], batch size: 29, lr: 6.42e-04
2022-05-04 09:29:34,670 INFO [train.py:715] (1/8) Epoch 2, batch 28000, loss[loss=0.1709, simple_loss=0.2391, pruned_loss=0.05139, over 4815.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2366, pruned_loss=0.051, over 972665.69 frames.], batch size: 27, lr: 6.42e-04
2022-05-04 09:30:15,044 INFO [train.py:715] (1/8) Epoch 2, batch 28050, loss[loss=0.1727, simple_loss=0.2286, pruned_loss=0.05837, over 4898.00 frames.], tot_loss[loss=0.169, simple_loss=0.2362, pruned_loss=0.05089, over 972956.22 frames.], batch size: 17, lr: 6.42e-04
2022-05-04 09:30:54,015 INFO [train.py:715] (1/8) Epoch 2, batch 28100, loss[loss=0.2141, simple_loss=0.2756, pruned_loss=0.07629, over 4827.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2365, pruned_loss=0.0507, over 973211.72 frames.], batch size: 15, lr: 6.41e-04
2022-05-04 09:31:33,559 INFO [train.py:715] (1/8) Epoch 2, batch 28150, loss[loss=0.1809, simple_loss=0.2479, pruned_loss=0.05694, over 4986.00 frames.], tot_loss[loss=0.1696, simple_loss=0.237, pruned_loss=0.05106, over 974296.12 frames.], batch size: 28, lr: 6.41e-04
2022-05-04 09:32:13,296 INFO [train.py:715] (1/8) Epoch 2, batch 28200, loss[loss=0.167, simple_loss=0.222, pruned_loss=0.05598, over 4804.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2367, pruned_loss=0.05079, over 973409.26 frames.], batch size: 25, lr: 6.41e-04
2022-05-04 09:32:52,897 INFO [train.py:715] (1/8) Epoch 2, batch 28250, loss[loss=0.1768, simple_loss=0.2437, pruned_loss=0.05499, over 4977.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2373, pruned_loss=0.05112, over 972601.46 frames.], batch size: 15, lr: 6.41e-04
2022-05-04 09:33:31,977 INFO [train.py:715] (1/8) Epoch 2, batch 28300, loss[loss=0.158, simple_loss=0.2416, pruned_loss=0.03721, over 4803.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2371, pruned_loss=0.05123, over 972445.45 frames.], batch size: 25, lr: 6.41e-04
2022-05-04 09:34:11,318 INFO [train.py:715] (1/8) Epoch 2, batch 28350, loss[loss=0.1793, simple_loss=0.2499, pruned_loss=0.05437, over 4872.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2368, pruned_loss=0.05129, over 972163.23 frames.], batch size: 16, lr: 6.41e-04
2022-05-04 09:34:51,511 INFO [train.py:715] (1/8) Epoch 2, batch 28400, loss[loss=0.1778, simple_loss=0.2452, pruned_loss=0.05527, over 4859.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2381, pruned_loss=0.05211, over 972575.15 frames.], batch size: 20, lr: 6.40e-04
2022-05-04 09:35:30,757 INFO [train.py:715] (1/8) Epoch 2, batch 28450, loss[loss=0.1454, simple_loss=0.2187, pruned_loss=0.03609, over 4775.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2383, pruned_loss=0.05255, over 973200.58 frames.], batch size: 19, lr: 6.40e-04
2022-05-04 09:36:10,158 INFO [train.py:715] (1/8) Epoch 2, batch 28500, loss[loss=0.1451, simple_loss=0.2183, pruned_loss=0.03592, over 4783.00 frames.], tot_loss[loss=0.171, simple_loss=0.2377, pruned_loss=0.05214, over 972737.51 frames.], batch size: 23, lr: 6.40e-04
2022-05-04 09:36:50,110 INFO [train.py:715] (1/8) Epoch 2, batch 28550, loss[loss=0.2082, simple_loss=0.2748, pruned_loss=0.07077, over 4829.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2383, pruned_loss=0.05263, over 972166.02 frames.], batch size: 30, lr: 6.40e-04
2022-05-04 09:37:30,232 INFO [train.py:715] (1/8) Epoch 2, batch 28600, loss[loss=0.2194, simple_loss=0.2851, pruned_loss=0.07688, over 4981.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2385, pruned_loss=0.05237, over 972593.00 frames.], batch size: 15, lr: 6.40e-04
2022-05-04 09:38:09,270 INFO [train.py:715] (1/8) Epoch 2, batch 28650, loss[loss=0.1288, simple_loss=0.1924, pruned_loss=0.0326, over 4964.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2377, pruned_loss=0.05192, over 973069.62 frames.], batch size: 14, lr: 6.40e-04
2022-05-04 09:38:49,124 INFO [train.py:715] (1/8) Epoch 2, batch 28700, loss[loss=0.173, simple_loss=0.229, pruned_loss=0.05852, over 4914.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2363, pruned_loss=0.05089, over 973279.45 frames.], batch size: 23, lr: 6.39e-04
2022-05-04 09:39:29,583 INFO [train.py:715] (1/8) Epoch 2, batch 28750, loss[loss=0.1569, simple_loss=0.2229, pruned_loss=0.04545, over 4699.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2358, pruned_loss=0.05036, over 972777.09 frames.], batch size: 15, lr: 6.39e-04
2022-05-04 09:40:08,513 INFO [train.py:715] (1/8) Epoch 2, batch 28800, loss[loss=0.1609, simple_loss=0.23, pruned_loss=0.04589, over 4955.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2349, pruned_loss=0.04996, over 972152.01 frames.], batch size: 24, lr: 6.39e-04
2022-05-04 09:40:48,103 INFO [train.py:715] (1/8) Epoch 2, batch 28850, loss[loss=0.1628, simple_loss=0.237, pruned_loss=0.04436, over 4843.00 frames.], tot_loss[loss=0.1677, simple_loss=0.235, pruned_loss=0.05017, over 972262.56 frames.], batch size: 15, lr: 6.39e-04
2022-05-04 09:41:28,104 INFO [train.py:715] (1/8) Epoch 2, batch 28900, loss[loss=0.168, simple_loss=0.2399, pruned_loss=0.04809, over 4824.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2359, pruned_loss=0.05064, over 972923.29 frames.], batch size: 21, lr: 6.39e-04
2022-05-04 09:42:07,484 INFO [train.py:715] (1/8) Epoch 2, batch 28950, loss[loss=0.1806, simple_loss=0.2574, pruned_loss=0.05196, over 4889.00 frames.], tot_loss[loss=0.169, simple_loss=0.2362, pruned_loss=0.05094, over 973407.02 frames.], batch size: 19, lr: 6.39e-04
2022-05-04 09:42:46,856 INFO [train.py:715] (1/8) Epoch 2, batch 29000, loss[loss=0.1564, simple_loss=0.2302, pruned_loss=0.0413, over 4825.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2355, pruned_loss=0.05086, over 972701.40 frames.], batch size: 26, lr: 6.38e-04
2022-05-04 09:43:26,613 INFO [train.py:715] (1/8) Epoch 2, batch 29050, loss[loss=0.1604, simple_loss=0.2328, pruned_loss=0.04402, over 4981.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2357, pruned_loss=0.05073, over 972186.30 frames.], batch size: 24, lr: 6.38e-04
2022-05-04 09:44:06,287 INFO [train.py:715] (1/8) Epoch 2, batch 29100, loss[loss=0.1894, simple_loss=0.2461, pruned_loss=0.06638, over 4785.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2363, pruned_loss=0.05075, over 971311.49 frames.], batch size: 18, lr: 6.38e-04
2022-05-04 09:44:45,461 INFO [train.py:715] (1/8) Epoch 2, batch 29150, loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.03022, over 4903.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2355, pruned_loss=0.05045, over 972113.29 frames.], batch size: 19, lr: 6.38e-04
2022-05-04 09:45:24,940 INFO [train.py:715] (1/8) Epoch 2, batch 29200, loss[loss=0.1968, simple_loss=0.2689, pruned_loss=0.06237, over 4690.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2352, pruned_loss=0.05016, over 972303.68 frames.], batch size: 15, lr: 6.38e-04
2022-05-04 09:46:05,375 INFO [train.py:715] (1/8) Epoch 2, batch 29250, loss[loss=0.1338, simple_loss=0.1937, pruned_loss=0.03694, over 4778.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2347, pruned_loss=0.05001, over 971828.75 frames.], batch size: 12, lr: 6.38e-04
2022-05-04 09:46:44,477 INFO [train.py:715] (1/8) Epoch 2, batch 29300, loss[loss=0.1733, simple_loss=0.255, pruned_loss=0.04579, over 4817.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2343, pruned_loss=0.05016, over 970995.95 frames.], batch size: 26, lr: 6.37e-04
2022-05-04 09:47:23,247 INFO [train.py:715] (1/8) Epoch 2, batch 29350, loss[loss=0.1523, simple_loss=0.2127, pruned_loss=0.04601, over 4966.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2343, pruned_loss=0.05017, over 971829.15 frames.], batch size: 14, lr: 6.37e-04
2022-05-04 09:48:02,461 INFO [train.py:715] (1/8) Epoch 2, batch 29400, loss[loss=0.1802, simple_loss=0.2393, pruned_loss=0.06055, over 4923.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2347, pruned_loss=0.05089, over 971074.80 frames.], batch size: 39, lr: 6.37e-04
2022-05-04 09:48:41,889 INFO [train.py:715] (1/8) Epoch 2, batch 29450, loss[loss=0.1945, simple_loss=0.2577, pruned_loss=0.06569, over 4879.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2363, pruned_loss=0.05152, over 970999.68 frames.], batch size: 16, lr: 6.37e-04
2022-05-04 09:49:20,755 INFO [train.py:715] (1/8) Epoch 2, batch 29500, loss[loss=0.1509, simple_loss=0.2059, pruned_loss=0.04795, over 4838.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2367, pruned_loss=0.05142, over 971600.36 frames.], batch size: 32, lr: 6.37e-04
2022-05-04 09:49:59,763 INFO [train.py:715] (1/8) Epoch 2, batch 29550, loss[loss=0.1862, simple_loss=0.2536, pruned_loss=0.05935, over 4983.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2361, pruned_loss=0.05121, over 971931.03 frames.], batch size: 24, lr: 6.37e-04
2022-05-04 09:50:39,178 INFO [train.py:715] (1/8) Epoch 2, batch 29600, loss[loss=0.1557, simple_loss=0.2204, pruned_loss=0.04557, over 4924.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2356, pruned_loss=0.05112, over 972959.86 frames.], batch size: 23, lr: 6.37e-04
2022-05-04 09:51:18,365 INFO [train.py:715] (1/8) Epoch 2, batch 29650, loss[loss=0.1532, simple_loss=0.2249, pruned_loss=0.04071, over 4946.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2364, pruned_loss=0.0512, over 973160.78 frames.], batch size: 23, lr: 6.36e-04
2022-05-04 09:51:57,127 INFO [train.py:715] (1/8) Epoch 2, batch 29700, loss[loss=0.1794, simple_loss=0.24, pruned_loss=0.05937, over 4985.00 frames.], tot_loss[loss=0.169, simple_loss=0.2362, pruned_loss=0.0509, over 973107.47 frames.], batch size: 31, lr: 6.36e-04
2022-05-04 09:52:36,251 INFO [train.py:715] (1/8) Epoch 2, batch 29750, loss[loss=0.1763, simple_loss=0.2261, pruned_loss=0.06327, over 4649.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2368, pruned_loss=0.05114, over 973633.01 frames.], batch size: 13, lr: 6.36e-04
2022-05-04 09:53:15,364 INFO [train.py:715] (1/8) Epoch 2, batch 29800, loss[loss=0.1661, simple_loss=0.2298, pruned_loss=0.05123, over 4771.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2365, pruned_loss=0.05109, over 973466.35 frames.], batch size: 18, lr: 6.36e-04
2022-05-04 09:53:53,998 INFO [train.py:715] (1/8) Epoch 2, batch 29850, loss[loss=0.1836, simple_loss=0.2428, pruned_loss=0.06223, over 4916.00 frames.], tot_loss[loss=0.17, simple_loss=0.2366, pruned_loss=0.05168, over 973418.60 frames.], batch size: 29, lr: 6.36e-04
2022-05-04 09:54:33,008 INFO [train.py:715] (1/8) Epoch 2, batch 29900, loss[loss=0.1875, simple_loss=0.2555, pruned_loss=0.05972, over 4984.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2364, pruned_loss=0.05125, over 973121.32 frames.], batch size: 25, lr: 6.36e-04
2022-05-04 09:55:12,828 INFO [train.py:715] (1/8) Epoch 2, batch 29950, loss[loss=0.1396, simple_loss=0.2034, pruned_loss=0.03786, over 4982.00 frames.], tot_loss[loss=0.1677, simple_loss=0.235, pruned_loss=0.05016, over 972833.04 frames.], batch size: 33, lr: 6.35e-04
2022-05-04 09:55:51,631 INFO [train.py:715] (1/8) Epoch 2, batch 30000, loss[loss=0.1697, simple_loss=0.2351, pruned_loss=0.05216, over 4870.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2356, pruned_loss=0.05057, over 973191.11 frames.], batch size: 32, lr: 6.35e-04
2022-05-04 09:55:51,632 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 09:56:00,454 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1166, simple_loss=0.2028, pruned_loss=0.01515, over 914524.00 frames.
2022-05-04 09:56:39,111 INFO [train.py:715] (1/8) Epoch 2, batch 30050, loss[loss=0.1662, simple_loss=0.2272, pruned_loss=0.05261, over 4971.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2367, pruned_loss=0.05142, over 973043.15 frames.], batch size: 15, lr: 6.35e-04
2022-05-04 09:57:18,475 INFO [train.py:715] (1/8) Epoch 2, batch 30100, loss[loss=0.1749, simple_loss=0.248, pruned_loss=0.05089, over 4813.00 frames.], tot_loss[loss=0.1712, simple_loss=0.238, pruned_loss=0.05217, over 972281.78 frames.], batch size: 27, lr: 6.35e-04
2022-05-04 09:57:57,551 INFO [train.py:715] (1/8) Epoch 2, batch 30150, loss[loss=0.2014, simple_loss=0.2447, pruned_loss=0.07899, over 4858.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2373, pruned_loss=0.05209, over 972238.31 frames.], batch size: 32, lr: 6.35e-04
2022-05-04 09:58:37,026 INFO [train.py:715] (1/8) Epoch 2, batch 30200, loss[loss=0.1554, simple_loss=0.2155, pruned_loss=0.04766, over 4787.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2362, pruned_loss=0.05111, over 973035.15 frames.], batch size: 12, lr: 6.35e-04
2022-05-04 09:59:15,773 INFO [train.py:715] (1/8) Epoch 2, batch 30250, loss[loss=0.1448, simple_loss=0.2189, pruned_loss=0.03536, over 4775.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2371, pruned_loss=0.052, over 972752.53 frames.], batch size: 17, lr: 6.34e-04
2022-05-04 09:59:55,025 INFO [train.py:715] (1/8) Epoch 2, batch 30300, loss[loss=0.1279, simple_loss=0.2015, pruned_loss=0.02721, over 4944.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2371, pruned_loss=0.05198, over 972260.71 frames.], batch size: 21, lr: 6.34e-04
2022-05-04 10:00:35,005 INFO [train.py:715] (1/8) Epoch 2, batch 30350, loss[loss=0.1712, simple_loss=0.2284, pruned_loss=0.057, over 4900.00 frames.], tot_loss[loss=0.17, simple_loss=0.2363, pruned_loss=0.05185, over 972829.17 frames.], batch size: 19, lr: 6.34e-04
2022-05-04 10:01:14,087 INFO [train.py:715] (1/8) Epoch 2, batch 30400, loss[loss=0.1835, simple_loss=0.2509, pruned_loss=0.05805, over 4935.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2357, pruned_loss=0.05125, over 972384.05 frames.], batch size: 18, lr: 6.34e-04
2022-05-04 10:01:53,191 INFO [train.py:715] (1/8) Epoch 2, batch 30450, loss[loss=0.1674, simple_loss=0.2309, pruned_loss=0.05199, over 4840.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2356, pruned_loss=0.05152, over 973032.45 frames.], batch size: 30, lr: 6.34e-04
2022-05-04 10:02:32,961 INFO [train.py:715] (1/8) Epoch 2, batch 30500, loss[loss=0.1986, simple_loss=0.258, pruned_loss=0.06956, over 4836.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2361, pruned_loss=0.0522, over 973117.82 frames.], batch size: 30, lr: 6.34e-04
2022-05-04 10:03:12,627 INFO [train.py:715] (1/8) Epoch 2, batch 30550, loss[loss=0.1983, simple_loss=0.2589, pruned_loss=0.06884, over 4801.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2358, pruned_loss=0.05193, over 972612.96 frames.], batch size: 21, lr: 6.33e-04
2022-05-04 10:03:51,362 INFO [train.py:715] (1/8) Epoch 2, batch 30600, loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.03589, over 4944.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2362, pruned_loss=0.05156, over 972905.72 frames.], batch size: 29, lr: 6.33e-04
2022-05-04 10:04:31,216 INFO [train.py:715] (1/8) Epoch 2, batch 30650, loss[loss=0.2018, simple_loss=0.257, pruned_loss=0.07331, over 4745.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2364, pruned_loss=0.05112, over 971730.13 frames.], batch size: 16, lr: 6.33e-04
2022-05-04 10:05:11,277 INFO [train.py:715] (1/8) Epoch 2, batch 30700, loss[loss=0.1667, simple_loss=0.2402, pruned_loss=0.04658, over 4779.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2364, pruned_loss=0.05107, over 971940.11 frames.], batch size: 17, lr: 6.33e-04
2022-05-04 10:05:51,105 INFO [train.py:715] (1/8) Epoch 2, batch 30750, loss[loss=0.2044, simple_loss=0.2688, pruned_loss=0.07002, over 4882.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2369, pruned_loss=0.05135, over 971565.97 frames.], batch size: 22, lr: 6.33e-04
2022-05-04 10:06:30,171 INFO [train.py:715] (1/8) Epoch 2, batch 30800, loss[loss=0.1604, simple_loss=0.2427, pruned_loss=0.03901, over 4882.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2357, pruned_loss=0.05063, over 971481.83 frames.], batch size: 22, lr: 6.33e-04
2022-05-04 10:07:09,679 INFO [train.py:715] (1/8) Epoch 2, batch 30850, loss[loss=0.1898, simple_loss=0.2307, pruned_loss=0.07446, over 4775.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2359, pruned_loss=0.05131, over 970425.93 frames.], batch size: 12, lr: 6.33e-04
2022-05-04 10:07:49,327 INFO [train.py:715] (1/8) Epoch 2, batch 30900, loss[loss=0.1561, simple_loss=0.2301, pruned_loss=0.04107, over 4799.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2354, pruned_loss=0.0509, over 971593.48 frames.], batch size: 21, lr: 6.32e-04
2022-05-04 10:08:27,845 INFO [train.py:715] (1/8) Epoch 2, batch 30950, loss[loss=0.1804, simple_loss=0.2493, pruned_loss=0.05577, over 4933.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2356, pruned_loss=0.05041, over 972552.99 frames.], batch size: 23, lr: 6.32e-04
2022-05-04 10:09:07,768 INFO [train.py:715] (1/8) Epoch 2, batch 31000, loss[loss=0.1742, simple_loss=0.2338, pruned_loss=0.0573, over 4977.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2355, pruned_loss=0.05041, over 972145.87 frames.], batch size: 14, lr: 6.32e-04
2022-05-04 10:09:48,211 INFO [train.py:715] (1/8) Epoch 2, batch 31050, loss[loss=0.1509, simple_loss=0.2264, pruned_loss=0.03767, over 4945.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2359, pruned_loss=0.05077, over 972172.53 frames.], batch size: 29, lr: 6.32e-04
2022-05-04 10:10:27,689 INFO [train.py:715] (1/8) Epoch 2, batch 31100, loss[loss=0.1773, simple_loss=0.2423, pruned_loss=0.05613, over 4833.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2354, pruned_loss=0.05055, over 971605.97 frames.], batch size: 27, lr: 6.32e-04
2022-05-04 10:11:07,480 INFO [train.py:715] (1/8) Epoch 2, batch 31150, loss[loss=0.1634, simple_loss=0.2214, pruned_loss=0.05265, over 4869.00 frames.], tot_loss[loss=0.168, simple_loss=0.2352, pruned_loss=0.05033, over 972180.96 frames.], batch size: 30, lr: 6.32e-04
2022-05-04 10:11:47,656 INFO [train.py:715] (1/8) Epoch 2, batch 31200, loss[loss=0.125, simple_loss=0.1876, pruned_loss=0.03119, over 4765.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2358, pruned_loss=0.05069, over 972449.61 frames.], batch size: 12, lr: 6.31e-04
2022-05-04 10:12:27,441 INFO [train.py:715] (1/8) Epoch 2, batch 31250, loss[loss=0.1918, simple_loss=0.2495, pruned_loss=0.067, over 4834.00 frames.], tot_loss[loss=0.168, simple_loss=0.2353, pruned_loss=0.05038, over 971862.28 frames.], batch size: 15, lr: 6.31e-04
2022-05-04 10:13:06,641 INFO [train.py:715] (1/8) Epoch 2, batch 31300, loss[loss=0.139, simple_loss=0.2084, pruned_loss=0.03477, over 4911.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2345, pruned_loss=0.05023, over 971955.40 frames.], batch size: 17, lr: 6.31e-04
2022-05-04 10:13:46,586 INFO [train.py:715] (1/8) Epoch 2, batch 31350, loss[loss=0.1731, simple_loss=0.2535, pruned_loss=0.04634, over 4827.00 frames.], tot_loss[loss=0.1679, simple_loss=0.235, pruned_loss=0.05041, over 971178.80 frames.], batch size: 25, lr: 6.31e-04
2022-05-04 10:14:26,948 INFO [train.py:715] (1/8) Epoch 2, batch 31400, loss[loss=0.1666, simple_loss=0.2287, pruned_loss=0.05227, over 4696.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2347, pruned_loss=0.0505, over 970754.71 frames.], batch size: 15, lr: 6.31e-04
2022-05-04 10:15:06,595 INFO [train.py:715] (1/8) Epoch 2, batch 31450, loss[loss=0.1889, simple_loss=0.2626, pruned_loss=0.05762, over 4877.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2364, pruned_loss=0.05129, over 971975.14 frames.], batch size: 22, lr: 6.31e-04
2022-05-04 10:15:46,237 INFO [train.py:715] (1/8) Epoch 2, batch 31500, loss[loss=0.2019, simple_loss=0.2666, pruned_loss=0.06859, over 4779.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2373, pruned_loss=0.05188, over 971430.90 frames.], batch size: 17, lr: 6.31e-04
2022-05-04 10:16:26,027 INFO [train.py:715] (1/8) Epoch 2, batch 31550, loss[loss=0.1692, simple_loss=0.2369, pruned_loss=0.05076, over 4851.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2374, pruned_loss=0.05222, over 972225.19 frames.], batch size: 32, lr: 6.30e-04
2022-05-04 10:17:05,439 INFO [train.py:715] (1/8) Epoch 2, batch 31600, loss[loss=0.2352, simple_loss=0.2764, pruned_loss=0.09702, over 4941.00 frames.], tot_loss[loss=0.1702, simple_loss=0.237, pruned_loss=0.05167, over 973276.31 frames.], batch size: 39, lr: 6.30e-04
2022-05-04 10:17:44,223 INFO [train.py:715] (1/8) Epoch 2, batch 31650, loss[loss=0.1701, simple_loss=0.2402, pruned_loss=0.04996, over 4810.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2365, pruned_loss=0.05127, over 973283.33 frames.], batch size: 25, lr: 6.30e-04
2022-05-04 10:18:24,068 INFO [train.py:715] (1/8) Epoch 2, batch 31700, loss[loss=0.1439, simple_loss=0.2134, pruned_loss=0.03716, over 4927.00 frames.], tot_loss[loss=0.1689, simple_loss=0.236, pruned_loss=0.05087, over 973389.34 frames.], batch size: 18, lr: 6.30e-04
2022-05-04 10:19:04,300 INFO [train.py:715] (1/8) Epoch 2, batch 31750, loss[loss=0.1611, simple_loss=0.2375, pruned_loss=0.04235, over 4799.00 frames.], tot_loss[loss=0.168, simple_loss=0.2354, pruned_loss=0.0503, over 973478.59 frames.], batch size: 24, lr: 6.30e-04
2022-05-04 10:19:44,140 INFO [train.py:715] (1/8) Epoch 2, batch 31800, loss[loss=0.1529, simple_loss=0.2322, pruned_loss=0.0368, over 4852.00 frames.], tot_loss[loss=0.1669, simple_loss=0.234, pruned_loss=0.04989, over 972890.78 frames.], batch size: 20, lr: 6.30e-04
2022-05-04 10:20:23,463 INFO [train.py:715] (1/8) Epoch 2, batch 31850, loss[loss=0.199, simple_loss=0.2606, pruned_loss=0.06874, over 4986.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2341, pruned_loss=0.05009, over 972839.24 frames.], batch size: 15, lr: 6.29e-04
2022-05-04 10:21:02,953 INFO [train.py:715] (1/8) Epoch 2, batch 31900, loss[loss=0.1508, simple_loss=0.2115, pruned_loss=0.04506, over 4922.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2337, pruned_loss=0.04976, over 972907.05 frames.], batch size: 21, lr: 6.29e-04
2022-05-04 10:21:42,550 INFO [train.py:715] (1/8) Epoch 2, batch 31950, loss[loss=0.157, simple_loss=0.2381, pruned_loss=0.03794, over 4805.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2338, pruned_loss=0.04978, over 971573.85 frames.], batch size: 25, lr: 6.29e-04
2022-05-04 10:22:21,473 INFO [train.py:715] (1/8) Epoch 2, batch 32000, loss[loss=0.1377, simple_loss=0.1968, pruned_loss=0.03935, over 4965.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2337, pruned_loss=0.04991, over 971302.27 frames.], batch size: 14, lr: 6.29e-04
2022-05-04 10:23:01,107 INFO [train.py:715] (1/8) Epoch 2, batch 32050, loss[loss=0.213, simple_loss=0.2698, pruned_loss=0.07813, over 4769.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2333, pruned_loss=0.04955, over 971258.85 frames.], batch size: 14, lr: 6.29e-04
2022-05-04 10:23:41,015 INFO [train.py:715] (1/8) Epoch 2, batch 32100, loss[loss=0.1545, simple_loss=0.223, pruned_loss=0.04299, over 4706.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2336, pruned_loss=0.04981, over 971169.53 frames.], batch size: 15, lr: 6.29e-04
2022-05-04 10:24:20,304 INFO [train.py:715] (1/8) Epoch 2, batch 32150, loss[loss=0.1505, simple_loss=0.2238, pruned_loss=0.03867, over 4823.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2331, pruned_loss=0.0494, over 970957.50 frames.], batch size: 27, lr: 6.29e-04
2022-05-04 10:24:59,277 INFO [train.py:715] (1/8) Epoch 2, batch 32200, loss[loss=0.1455, simple_loss=0.2171, pruned_loss=0.03689, over 4893.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2329, pruned_loss=0.0493, over 971679.08 frames.], batch size: 19, lr: 6.28e-04
2022-05-04 10:25:39,138 INFO [train.py:715] (1/8) Epoch 2, batch 32250, loss[loss=0.157, simple_loss=0.2287, pruned_loss=0.04262, over 4895.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2334, pruned_loss=0.04964, over 972069.68 frames.], batch size: 19, lr: 6.28e-04
2022-05-04 10:26:18,493 INFO [train.py:715] (1/8) Epoch 2, batch 32300, loss[loss=0.1553, simple_loss=0.222, pruned_loss=0.04431, over 4813.00 frames.], tot_loss[loss=0.166, simple_loss=0.2335, pruned_loss=0.04928, over 970863.26 frames.], batch size: 27, lr: 6.28e-04
2022-05-04 10:26:57,489 INFO [train.py:715] (1/8) Epoch 2, batch 32350, loss[loss=0.1571, simple_loss=0.2285, pruned_loss=0.04284, over 4735.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2343, pruned_loss=0.05017, over 970932.96 frames.], batch size: 16, lr: 6.28e-04
2022-05-04 10:27:37,324 INFO [train.py:715] (1/8) Epoch 2, batch 32400, loss[loss=0.1735, simple_loss=0.2466, pruned_loss=0.05022, over 4938.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2336, pruned_loss=0.04994, over 971201.07 frames.], batch size: 23, lr: 6.28e-04
2022-05-04 10:28:17,091 INFO [train.py:715] (1/8) Epoch 2, batch 32450, loss[loss=0.1459, simple_loss=0.2269, pruned_loss=0.0324, over 4869.00 frames.], tot_loss[loss=0.167, simple_loss=0.2341, pruned_loss=0.04999, over 971802.42 frames.], batch size: 16, lr: 6.28e-04
2022-05-04 10:28:56,074 INFO [train.py:715] (1/8) Epoch 2, batch 32500, loss[loss=0.1934, simple_loss=0.2573, pruned_loss=0.06478, over 4939.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2353, pruned_loss=0.05081, over 972682.49 frames.], batch size: 35, lr: 6.27e-04
2022-05-04 10:29:35,587 INFO [train.py:715] (1/8) Epoch 2, batch 32550, loss[loss=0.1747, simple_loss=0.2444, pruned_loss=0.05248, over 4938.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2347, pruned_loss=0.05042, over 972472.38 frames.], batch size: 29, lr: 6.27e-04
2022-05-04 10:30:15,647 INFO [train.py:715] (1/8) Epoch 2, batch 32600, loss[loss=0.1494, simple_loss=0.2207, pruned_loss=0.03907, over 4874.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2355, pruned_loss=0.0506, over 971907.35 frames.], batch size: 16, lr: 6.27e-04
2022-05-04 10:30:54,905 INFO [train.py:715] (1/8) Epoch 2, batch 32650, loss[loss=0.1816, simple_loss=0.2355, pruned_loss=0.06386, over 4884.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2357, pruned_loss=0.05083, over 972489.40 frames.], batch size: 22, lr: 6.27e-04
2022-05-04 10:31:33,747 INFO [train.py:715] (1/8) Epoch 2, batch 32700, loss[loss=0.1595, simple_loss=0.2358, pruned_loss=0.04162, over 4851.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2344, pruned_loss=0.05027, over 972654.55 frames.], batch size: 20, lr: 6.27e-04
2022-05-04 10:32:13,534 INFO [train.py:715] (1/8) Epoch 2, batch 32750, loss[loss=0.2086, simple_loss=0.2708, pruned_loss=0.07319, over 4880.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2349, pruned_loss=0.05019, over 972214.60 frames.], batch size: 22, lr: 6.27e-04
2022-05-04 10:32:53,518 INFO [train.py:715] (1/8) Epoch 2, batch 32800, loss[loss=0.139, simple_loss=0.2174, pruned_loss=0.03031, over 4982.00 frames.], tot_loss[loss=0.168, simple_loss=0.2348, pruned_loss=0.05061, over 972251.14 frames.], batch size: 14, lr: 6.27e-04
2022-05-04 10:33:32,250 INFO [train.py:715] (1/8) Epoch 2, batch 32850, loss[loss=0.1861, simple_loss=0.2479, pruned_loss=0.06215, over 4855.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2361, pruned_loss=0.05118, over 971855.58 frames.], batch size: 15, lr: 6.26e-04
2022-05-04 10:34:11,596 INFO [train.py:715] (1/8) Epoch 2, batch 32900, loss[loss=0.1853, simple_loss=0.2438, pruned_loss=0.0634, over 4772.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2354, pruned_loss=0.05057, over 972250.89 frames.], batch size: 17, lr: 6.26e-04
2022-05-04 10:34:51,516 INFO [train.py:715] (1/8) Epoch 2, batch 32950, loss[loss=0.18, simple_loss=0.2265, pruned_loss=0.06673, over 4971.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2349, pruned_loss=0.05082, over 972096.98 frames.], batch size: 14, lr: 6.26e-04
2022-05-04 10:35:30,092 INFO [train.py:715] (1/8) Epoch 2, batch 33000, loss[loss=0.142, simple_loss=0.2299, pruned_loss=0.02712, over 4969.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2345, pruned_loss=0.05011, over 973089.73 frames.], batch size: 15, lr: 6.26e-04
2022-05-04 10:35:30,093 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 10:35:38,853 INFO [train.py:742] (1/8) Epoch 2, validation: loss=0.1163, simple_loss=0.2025, pruned_loss=0.01504, over 914524.00 frames.
2022-05-04 10:36:17,842 INFO [train.py:715] (1/8) Epoch 2, batch 33050, loss[loss=0.1594, simple_loss=0.2287, pruned_loss=0.045, over 4968.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2345, pruned_loss=0.04983, over 973392.05 frames.], batch size: 39, lr: 6.26e-04
2022-05-04 10:36:57,378 INFO [train.py:715] (1/8) Epoch 2, batch 33100, loss[loss=0.1814, simple_loss=0.2493, pruned_loss=0.05677, over 4910.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2352, pruned_loss=0.05011, over 974139.63 frames.], batch size: 17, lr: 6.26e-04
2022-05-04 10:37:37,176 INFO [train.py:715] (1/8) Epoch 2, batch 33150, loss[loss=0.1522, simple_loss=0.2249, pruned_loss=0.03973, over 4694.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2357, pruned_loss=0.05065, over 973721.09 frames.], batch size: 15, lr: 6.25e-04
2022-05-04 10:38:16,781 INFO [train.py:715] (1/8) Epoch 2, batch 33200, loss[loss=0.1381, simple_loss=0.2142, pruned_loss=0.03099, over 4970.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2352, pruned_loss=0.05054, over 972683.36 frames.], batch size: 35, lr: 6.25e-04
2022-05-04 10:38:56,316 INFO [train.py:715] (1/8) Epoch 2, batch 33250, loss[loss=0.149, simple_loss=0.2195, pruned_loss=0.03924, over 4915.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2348, pruned_loss=0.05052, over 972655.94 frames.], batch size: 18, lr: 6.25e-04
2022-05-04 10:39:35,517 INFO [train.py:715] (1/8) Epoch 2, batch 33300, loss[loss=0.168, simple_loss=0.2484, pruned_loss=0.0438, over 4818.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2348, pruned_loss=0.05026, over 973503.38 frames.], batch size: 27, lr: 6.25e-04
2022-05-04 10:40:14,693 INFO [train.py:715] (1/8) Epoch 2, batch 33350, loss[loss=0.182, simple_loss=0.2443, pruned_loss=0.05981, over 4896.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2351, pruned_loss=0.05063, over 973156.80 frames.], batch size: 22, lr: 6.25e-04
2022-05-04 10:40:53,961 INFO [train.py:715] (1/8) Epoch 2, batch 33400, loss[loss=0.1623, simple_loss=0.2293, pruned_loss=0.04764, over 4755.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2344, pruned_loss=0.05035, over 973355.67 frames.], batch size: 19, lr: 6.25e-04
2022-05-04 10:41:33,181 INFO [train.py:715] (1/8) Epoch 2, batch 33450, loss[loss=0.1772, simple_loss=0.2359, pruned_loss=0.05932, over 4970.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2349, pruned_loss=0.05048, over 972829.99 frames.], batch size: 14, lr: 6.25e-04
2022-05-04 10:42:13,245 INFO [train.py:715] (1/8) Epoch 2, batch 33500, loss[loss=0.1457, simple_loss=0.2198, pruned_loss=0.03575, over 4804.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2357, pruned_loss=0.05056, over 972267.72 frames.], batch size: 21, lr: 6.24e-04
2022-05-04 10:42:52,006 INFO [train.py:715] (1/8) Epoch 2, batch 33550, loss[loss=0.1551, simple_loss=0.2327, pruned_loss=0.03876, over 4763.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2348, pruned_loss=0.05025, over 972381.47 frames.], batch size: 18, lr: 6.24e-04
2022-05-04 10:43:31,503 INFO [train.py:715] (1/8) Epoch 2, batch 33600, loss[loss=0.1489, simple_loss=0.222, pruned_loss=0.03795, over 4938.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2351, pruned_loss=0.05009, over 971833.81 frames.], batch size: 24, lr: 6.24e-04
2022-05-04 10:44:11,050 INFO [train.py:715] (1/8) Epoch 2, batch 33650, loss[loss=0.1654, simple_loss=0.2484, pruned_loss=0.0412, over 4774.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2356, pruned_loss=0.05035, over 972353.52 frames.], batch size: 18, lr: 6.24e-04
2022-05-04 10:44:50,486 INFO [train.py:715] (1/8) Epoch 2, batch 33700, loss[loss=0.1842, simple_loss=0.2432, pruned_loss=0.06261, over 4864.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2363, pruned_loss=0.05071, over 972565.25 frames.], batch size: 32, lr: 6.24e-04
2022-05-04 10:45:29,906 INFO [train.py:715] (1/8) Epoch 2, batch 33750, loss[loss=0.1733, simple_loss=0.244, pruned_loss=0.05133, over 4972.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2369, pruned_loss=0.05096, over 972828.99 frames.], batch size: 24, lr: 6.24e-04
2022-05-04 10:46:09,308 INFO [train.py:715] (1/8) Epoch 2, batch 33800, loss[loss=0.17, simple_loss=0.2345, pruned_loss=0.05271, over 4944.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2356, pruned_loss=0.05037, over 973539.44 frames.], batch size: 29, lr: 6.23e-04
2022-05-04 10:46:49,486 INFO [train.py:715] (1/8) Epoch 2, batch 33850, loss[loss=0.1284, simple_loss=0.2039, pruned_loss=0.02641, over 4778.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2357, pruned_loss=0.05064, over 973118.19 frames.], batch size: 12, lr: 6.23e-04
2022-05-04 10:47:28,886 INFO [train.py:715] (1/8) Epoch 2, batch 33900, loss[loss=0.1988, simple_loss=0.2635, pruned_loss=0.06708, over 4958.00 frames.], tot_loss[loss=0.168, simple_loss=0.2353, pruned_loss=0.05036, over 972678.98 frames.], batch size: 24, lr: 6.23e-04
2022-05-04 10:48:08,028 INFO [train.py:715] (1/8) Epoch 2, batch 33950, loss[loss=0.1728, simple_loss=0.2316, pruned_loss=0.05699, over 4993.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2351, pruned_loss=0.05015, over 973201.23 frames.], batch size: 14, lr: 6.23e-04
2022-05-04 10:48:47,947 INFO [train.py:715] (1/8) Epoch 2, batch 34000, loss[loss=0.2079, simple_loss=0.2648, pruned_loss=0.07548, over 4775.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2348, pruned_loss=0.05007, over 973115.23 frames.], batch size: 18, lr: 6.23e-04
2022-05-04 10:49:27,581 INFO [train.py:715] (1/8) Epoch 2, batch 34050, loss[loss=0.1475, simple_loss=0.221, pruned_loss=0.03698, over 4770.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2344, pruned_loss=0.04995, over 973590.45 frames.], batch size: 14, lr: 6.23e-04
2022-05-04 10:50:07,046 INFO [train.py:715] (1/8) Epoch 2, batch 34100, loss[loss=0.1912, simple_loss=0.2583, pruned_loss=0.06206, over 4830.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2353, pruned_loss=0.05025, over 973686.89 frames.], batch size: 15, lr: 6.23e-04
2022-05-04 10:50:46,460 INFO [train.py:715] (1/8) Epoch 2, batch 34150, loss[loss=0.1291, simple_loss=0.1966, pruned_loss=0.03081, over 4754.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2361, pruned_loss=0.05072, over 972882.63 frames.], batch size: 12, lr: 6.22e-04
2022-05-04 10:51:26,747 INFO [train.py:715] (1/8) Epoch 2, batch 34200, loss[loss=0.1308, simple_loss=0.2066, pruned_loss=0.02754, over 4905.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2365, pruned_loss=0.05123, over 972226.88 frames.], batch size: 19, lr: 6.22e-04
2022-05-04 10:52:06,318 INFO [train.py:715] (1/8) Epoch 2, batch 34250, loss[loss=0.2094, simple_loss=0.2726, pruned_loss=0.07309, over 4936.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2357, pruned_loss=0.05099, over 973057.44 frames.], batch size: 14, lr: 6.22e-04
2022-05-04 10:52:45,481 INFO [train.py:715] (1/8) Epoch 2, batch 34300, loss[loss=0.2067, simple_loss=0.2739, pruned_loss=0.06976, over 4742.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2355, pruned_loss=0.05055, over 972352.59 frames.], batch size: 16, lr: 6.22e-04
2022-05-04 10:53:25,367 INFO [train.py:715] (1/8) Epoch 2, batch 34350, loss[loss=0.1903, simple_loss=0.268, pruned_loss=0.05631, over 4814.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2347, pruned_loss=0.05033, over 972565.06 frames.], batch size: 21, lr: 6.22e-04
2022-05-04 10:54:07,387 INFO [train.py:715] (1/8) Epoch 2, batch 34400, loss[loss=0.1658, simple_loss=0.2324, pruned_loss=0.04957, over 4785.00 frames.], tot_loss[loss=0.1683, simple_loss=0.235, pruned_loss=0.05085, over 972305.79 frames.], batch size: 18, lr: 6.22e-04
2022-05-04 10:54:46,514 INFO [train.py:715] (1/8) Epoch 2, batch 34450, loss[loss=0.1888, simple_loss=0.2543, pruned_loss=0.06166, over 4983.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2356, pruned_loss=0.05074, over 972020.07 frames.], batch size: 25, lr: 6.22e-04
2022-05-04 10:55:25,438 INFO [train.py:715] (1/8) Epoch 2, batch 34500, loss[loss=0.1882, simple_loss=0.2561, pruned_loss=0.06015, over 4906.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2357, pruned_loss=0.05033, over 972781.06 frames.], batch size: 17, lr: 6.21e-04
2022-05-04 10:56:05,350 INFO [train.py:715] (1/8) Epoch 2, batch 34550, loss[loss=0.1915, simple_loss=0.2525, pruned_loss=0.06525, over 4931.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2359, pruned_loss=0.05021, over 972386.95 frames.], batch size: 21, lr: 6.21e-04
2022-05-04 10:56:44,139 INFO [train.py:715] (1/8) Epoch 2, batch 34600, loss[loss=0.1847, simple_loss=0.247, pruned_loss=0.06122, over 4946.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2356, pruned_loss=0.05041, over 972011.87 frames.], batch size: 29, lr: 6.21e-04
2022-05-04 10:57:23,175 INFO [train.py:715] (1/8) Epoch 2, batch 34650, loss[loss=0.1716, simple_loss=0.2447, pruned_loss=0.04926, over 4953.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2344, pruned_loss=0.05002, over 972539.16 frames.], batch size: 24, lr: 6.21e-04
2022-05-04 10:58:02,534 INFO [train.py:715] (1/8) Epoch 2, batch 34700, loss[loss=0.1819, simple_loss=0.2381, pruned_loss=0.06288, over 4815.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2338, pruned_loss=0.04956, over 971629.75 frames.], batch size: 13, lr: 6.21e-04
2022-05-04 10:58:40,566 INFO [train.py:715] (1/8) Epoch 2, batch 34750, loss[loss=0.1877, simple_loss=0.2591, pruned_loss=0.05815, over 4978.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2347, pruned_loss=0.05016, over 971236.36 frames.], batch size: 24, lr: 6.21e-04
2022-05-04 10:59:17,104 INFO [train.py:715] (1/8) Epoch 2, batch 34800, loss[loss=0.1273, simple_loss=0.1988, pruned_loss=0.02795, over 4797.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2337, pruned_loss=0.0499, over 970477.93 frames.], batch size: 12, lr: 6.20e-04
2022-05-04 11:00:07,064 INFO [train.py:715] (1/8) Epoch 3, batch 0, loss[loss=0.1722, simple_loss=0.2343, pruned_loss=0.0551, over 4934.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2343, pruned_loss=0.0551, over 4934.00 frames.], batch size: 18, lr: 5.87e-04
2022-05-04 11:00:45,743 INFO [train.py:715] (1/8) Epoch 3, batch 50, loss[loss=0.1787, simple_loss=0.2458, pruned_loss=0.05583, over 4780.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2331, pruned_loss=0.04908, over 219528.64 frames.], batch size: 18, lr: 5.87e-04
2022-05-04 11:01:25,678 INFO [train.py:715] (1/8) Epoch 3, batch 100, loss[loss=0.1962, simple_loss=0.2579, pruned_loss=0.06722, over 4925.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2351, pruned_loss=0.05015, over 386727.46 frames.], batch size: 18, lr: 5.87e-04
2022-05-04 11:02:05,233 INFO [train.py:715] (1/8) Epoch 3, batch 150, loss[loss=0.1857, simple_loss=0.2485, pruned_loss=0.06143, over 4871.00 frames.], tot_loss[loss=0.1643, simple_loss=0.232, pruned_loss=0.04836, over 517454.49 frames.], batch size: 32, lr: 5.86e-04
2022-05-04 11:02:44,383 INFO [train.py:715] (1/8) Epoch 3, batch 200, loss[loss=0.1643, simple_loss=0.2166, pruned_loss=0.05601, over 4843.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2343, pruned_loss=0.04918, over 618545.66 frames.], batch size: 12, lr: 5.86e-04
2022-05-04 11:03:23,625 INFO [train.py:715] (1/8) Epoch 3, batch 250, loss[loss=0.1625, simple_loss=0.2256, pruned_loss=0.04975, over 4857.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2346, pruned_loss=0.04883, over 697520.12 frames.], batch size: 32, lr: 5.86e-04
2022-05-04 11:04:03,629 INFO [train.py:715] (1/8) Epoch 3, batch 300, loss[loss=0.1955, simple_loss=0.2647, pruned_loss=0.06318, over 4980.00 frames.], tot_loss[loss=0.1669, simple_loss=0.235, pruned_loss=0.04941, over 758245.94 frames.], batch size: 25, lr: 5.86e-04
2022-05-04 11:04:42,644 INFO [train.py:715] (1/8) Epoch 3, batch 350, loss[loss=0.1646, simple_loss=0.2318, pruned_loss=0.04867, over 4837.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2361, pruned_loss=0.04974, over 805239.43 frames.], batch size: 26, lr: 5.86e-04
2022-05-04 11:05:21,844 INFO [train.py:715] (1/8) Epoch 3, batch 400, loss[loss=0.1833, simple_loss=0.2477, pruned_loss=0.0595, over 4935.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2365, pruned_loss=0.0502, over 842269.53 frames.], batch size: 29, lr: 5.86e-04
2022-05-04 11:06:01,608 INFO [train.py:715] (1/8) Epoch 3, batch 450, loss[loss=0.1715, simple_loss=0.235, pruned_loss=0.05402, over 4769.00 frames.], tot_loss[loss=0.168, simple_loss=0.2361, pruned_loss=0.05001, over 871135.29 frames.], batch size: 19, lr: 5.86e-04
2022-05-04 11:06:41,123 INFO [train.py:715] (1/8) Epoch 3, batch 500, loss[loss=0.1404, simple_loss=0.2219, pruned_loss=0.02941, over 4891.00 frames.], tot_loss[loss=0.167, simple_loss=0.2352, pruned_loss=0.0494, over 893074.32 frames.], batch size: 22, lr: 5.85e-04
2022-05-04 11:07:20,467 INFO [train.py:715] (1/8) Epoch 3, batch 550, loss[loss=0.1749, simple_loss=0.2354, pruned_loss=0.05721, over 4806.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2344, pruned_loss=0.0489, over 910631.36 frames.], batch size: 21, lr: 5.85e-04
2022-05-04 11:07:59,338 INFO [train.py:715] (1/8) Epoch 3, batch 600, loss[loss=0.1568, simple_loss=0.2269, pruned_loss=0.04332, over 4828.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2336, pruned_loss=0.04862, over 924551.81 frames.], batch size: 15, lr: 5.85e-04
2022-05-04 11:08:39,292 INFO [train.py:715] (1/8) Epoch 3, batch 650, loss[loss=0.147, simple_loss=0.219, pruned_loss=0.0375, over 4936.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2336, pruned_loss=0.04865, over 935846.81 frames.], batch size: 23, lr: 5.85e-04
2022-05-04 11:09:18,637 INFO [train.py:715] (1/8) Epoch 3, batch 700, loss[loss=0.1584, simple_loss=0.2394, pruned_loss=0.03869, over 4945.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2346, pruned_loss=0.04915, over 944011.06 frames.], batch size: 21, lr: 5.85e-04
2022-05-04 11:09:57,737 INFO [train.py:715] (1/8) Epoch 3, batch 750, loss[loss=0.1477, simple_loss=0.2182, pruned_loss=0.03861, over 4782.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2357, pruned_loss=0.04967, over 950787.02 frames.], batch size: 17, lr: 5.85e-04
2022-05-04 11:10:37,299 INFO [train.py:715] (1/8) Epoch 3, batch 800, loss[loss=0.1357, simple_loss=0.2068, pruned_loss=0.0323, over 4867.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2358, pruned_loss=0.05003, over 955275.09 frames.], batch size: 32, lr: 5.85e-04
2022-05-04 11:11:17,441 INFO [train.py:715] (1/8) Epoch 3, batch 850, loss[loss=0.155, simple_loss=0.2195, pruned_loss=0.04522, over 4882.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2353, pruned_loss=0.04974, over 960000.71 frames.], batch size: 22, lr: 5.84e-04
2022-05-04 11:11:56,828 INFO [train.py:715] (1/8) Epoch 3, batch 900, loss[loss=0.1397, simple_loss=0.22, pruned_loss=0.02964, over 4820.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2345, pruned_loss=0.04946, over 962243.84 frames.], batch size: 26, lr: 5.84e-04
2022-05-04 11:12:35,441 INFO [train.py:715] (1/8) Epoch 3, batch 950, loss[loss=0.1967, simple_loss=0.2588, pruned_loss=0.06733, over 4781.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2347, pruned_loss=0.0496, over 964564.24 frames.], batch size: 14, lr: 5.84e-04
2022-05-04 11:13:15,424 INFO [train.py:715] (1/8) Epoch 3, batch 1000, loss[loss=0.1762, simple_loss=0.2383, pruned_loss=0.05703, over 4865.00 frames.], tot_loss[loss=0.166, simple_loss=0.2337, pruned_loss=0.04918, over 965577.83 frames.], batch size: 20, lr: 5.84e-04
2022-05-04 11:13:55,092 INFO [train.py:715] (1/8) Epoch 3, batch 1050, loss[loss=0.1554, simple_loss=0.2235, pruned_loss=0.04366, over 4763.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2329, pruned_loss=0.04872, over 966963.34 frames.], batch size: 18, lr: 5.84e-04
2022-05-04 11:14:34,003 INFO [train.py:715] (1/8) Epoch 3, batch 1100, loss[loss=0.1491, simple_loss=0.2183, pruned_loss=0.03991, over 4950.00 frames.], tot_loss[loss=0.1654, simple_loss=0.233, pruned_loss=0.04891, over 969048.21 frames.], batch size: 21, lr: 5.84e-04
2022-05-04 11:15:12,872 INFO [train.py:715] (1/8) Epoch 3, batch 1150, loss[loss=0.1752, simple_loss=0.242, pruned_loss=0.05417, over 4878.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2333, pruned_loss=0.04893, over 969449.46 frames.], batch size: 16, lr: 5.84e-04
2022-05-04 11:15:52,683 INFO [train.py:715] (1/8) Epoch 3, batch 1200, loss[loss=0.1708, simple_loss=0.2377, pruned_loss=0.05189, over 4959.00 frames.], tot_loss[loss=0.1651, simple_loss=0.233, pruned_loss=0.04863, over 969587.44 frames.], batch size: 39, lr: 5.83e-04
2022-05-04 11:16:31,657 INFO [train.py:715] (1/8) Epoch 3, batch 1250, loss[loss=0.2121, simple_loss=0.2928, pruned_loss=0.06569, over 4946.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2328, pruned_loss=0.04828, over 969761.62 frames.], batch size: 29, lr: 5.83e-04
2022-05-04 11:17:10,160 INFO [train.py:715] (1/8) Epoch 3, batch 1300, loss[loss=0.1745, simple_loss=0.2347, pruned_loss=0.05715, over 4839.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2333, pruned_loss=0.04928, over 969442.07 frames.], batch size: 27, lr: 5.83e-04
2022-05-04 11:17:49,722 INFO [train.py:715] (1/8) Epoch 3, batch 1350, loss[loss=0.1395, simple_loss=0.2063, pruned_loss=0.03638, over 4890.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2333, pruned_loss=0.04956, over 969918.06 frames.], batch size: 17, lr: 5.83e-04
2022-05-04 11:18:28,999 INFO [train.py:715] (1/8) Epoch 3, batch 1400, loss[loss=0.196, simple_loss=0.2559, pruned_loss=0.06803, over 4864.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2328, pruned_loss=0.0492, over 970190.26 frames.], batch size: 32, lr: 5.83e-04
2022-05-04 11:19:07,865 INFO [train.py:715] (1/8) Epoch 3, batch 1450, loss[loss=0.1737, simple_loss=0.2494, pruned_loss=0.04902, over 4807.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2323, pruned_loss=0.04866, over 970516.32 frames.], batch size: 25, lr: 5.83e-04
2022-05-04 11:19:46,422 INFO [train.py:715] (1/8) Epoch 3, batch 1500, loss[loss=0.1606, simple_loss=0.2443, pruned_loss=0.03847, over 4946.00 frames.], tot_loss[loss=0.1639, simple_loss=0.232, pruned_loss=0.04788, over 971213.49 frames.], batch size: 29, lr: 5.83e-04
2022-05-04 11:20:26,149 INFO [train.py:715] (1/8) Epoch 3, batch 1550, loss[loss=0.2198, simple_loss=0.2862, pruned_loss=0.0767, over 4945.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2322, pruned_loss=0.04797, over 972055.24 frames.], batch size: 21, lr: 5.83e-04
2022-05-04 11:21:05,415 INFO [train.py:715] (1/8) Epoch 3, batch 1600, loss[loss=0.1499, simple_loss=0.2337, pruned_loss=0.03312, over 4898.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2314, pruned_loss=0.04761, over 972398.56 frames.], batch size: 17, lr: 5.82e-04
2022-05-04 11:21:43,527 INFO [train.py:715] (1/8) Epoch 3, batch 1650, loss[loss=0.1794, simple_loss=0.2425, pruned_loss=0.05819, over 4986.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2323, pruned_loss=0.04814, over 971943.17 frames.], batch size: 40, lr: 5.82e-04
2022-05-04 11:22:22,782 INFO [train.py:715] (1/8) Epoch 3, batch 1700, loss[loss=0.1535, simple_loss=0.2346, pruned_loss=0.03616, over 4824.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2326, pruned_loss=0.04857, over 972678.67 frames.], batch size: 13, lr: 5.82e-04
2022-05-04 11:23:02,319 INFO [train.py:715] (1/8) Epoch 3, batch 1750, loss[loss=0.2294, simple_loss=0.2706, pruned_loss=0.09409, over 4969.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2331, pruned_loss=0.04912, over 973072.76 frames.], batch size: 35, lr: 5.82e-04
2022-05-04 11:23:41,617 INFO [train.py:715] (1/8) Epoch 3, batch 1800, loss[loss=0.169, simple_loss=0.2376, pruned_loss=0.0502, over 4955.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2325, pruned_loss=0.04887, over 972531.87 frames.], batch size: 14, lr: 5.82e-04
2022-05-04 11:24:20,321 INFO [train.py:715] (1/8) Epoch 3, batch 1850, loss[loss=0.1951, simple_loss=0.2602, pruned_loss=0.06497, over 4683.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2328, pruned_loss=0.04914, over 971763.67 frames.], batch size: 15, lr: 5.82e-04
2022-05-04 11:25:00,295 INFO [train.py:715] (1/8) Epoch 3, batch 1900, loss[loss=0.1599, simple_loss=0.2318, pruned_loss=0.04406, over 4780.00 frames.], tot_loss[loss=0.1658, simple_loss=0.233, pruned_loss=0.04933, over 972843.67 frames.], batch size: 18, lr: 5.82e-04
2022-05-04 11:25:39,887 INFO [train.py:715] (1/8) Epoch 3, batch 1950, loss[loss=0.1762, simple_loss=0.2333, pruned_loss=0.05956, over 4852.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2333, pruned_loss=0.04979, over 972750.70 frames.], batch size: 30, lr: 5.81e-04
2022-05-04 11:26:18,805 INFO [train.py:715] (1/8) Epoch 3, batch 2000, loss[loss=0.1795, simple_loss=0.2501, pruned_loss=0.0544, over 4774.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2336, pruned_loss=0.04945, over 973051.81 frames.], batch size: 14, lr: 5.81e-04
2022-05-04 11:26:58,007 INFO [train.py:715] (1/8) Epoch 3, batch 2050, loss[loss=0.1463, simple_loss=0.2128, pruned_loss=0.03988, over 4925.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2338, pruned_loss=0.04957, over 973508.05 frames.], batch size: 18, lr: 5.81e-04
2022-05-04 11:27:37,797 INFO [train.py:715] (1/8) Epoch 3, batch 2100, loss[loss=0.1672, simple_loss=0.227, pruned_loss=0.05366, over 4774.00 frames.], tot_loss[loss=0.166, simple_loss=0.2333, pruned_loss=0.04933, over 973837.90 frames.], batch size: 12, lr: 5.81e-04
2022-05-04 11:28:17,050 INFO [train.py:715] (1/8) Epoch 3, batch 2150, loss[loss=0.1102, simple_loss=0.1858, pruned_loss=0.01734, over 4879.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2335, pruned_loss=0.04965, over 973420.54 frames.], batch size: 22, lr: 5.81e-04
2022-05-04 11:28:55,717 INFO [train.py:715] (1/8) Epoch 3, batch 2200, loss[loss=0.1588, simple_loss=0.2256, pruned_loss=0.04596, over 4806.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2343, pruned_loss=0.05019, over 973346.64 frames.], batch size: 21, lr: 5.81e-04
2022-05-04 11:29:35,103 INFO [train.py:715] (1/8) Epoch 3, batch 2250, loss[loss=0.1695, simple_loss=0.2495, pruned_loss=0.04478, over 4793.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2337, pruned_loss=0.04971, over 972881.76 frames.], batch size: 17, lr: 5.81e-04
2022-05-04 11:30:14,521 INFO [train.py:715] (1/8) Epoch 3, batch 2300, loss[loss=0.1873, simple_loss=0.2438, pruned_loss=0.06544, over 4931.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2336, pruned_loss=0.04929, over 973200.45 frames.], batch size: 23, lr: 5.80e-04
2022-05-04 11:30:53,579 INFO [train.py:715] (1/8) Epoch 3, batch 2350, loss[loss=0.1576, simple_loss=0.2292, pruned_loss=0.04301, over 4984.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2326, pruned_loss=0.04917, over 973158.94 frames.], batch size: 27, lr: 5.80e-04
2022-05-04 11:31:32,373 INFO [train.py:715] (1/8) Epoch 3, batch 2400, loss[loss=0.1559, simple_loss=0.231, pruned_loss=0.04036, over 4873.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2323, pruned_loss=0.04846, over 972798.03 frames.], batch size: 20, lr: 5.80e-04
2022-05-04 11:32:12,611 INFO [train.py:715] (1/8) Epoch 3, batch 2450, loss[loss=0.1738, simple_loss=0.2385, pruned_loss=0.05455, over 4946.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2324, pruned_loss=0.04813, over 972542.98 frames.], batch size: 21, lr: 5.80e-04
2022-05-04 11:32:51,964 INFO [train.py:715] (1/8) Epoch 3, batch 2500, loss[loss=0.1441, simple_loss=0.2232, pruned_loss=0.03249, over 4969.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2328, pruned_loss=0.04879, over 971786.30 frames.], batch size: 21, lr: 5.80e-04
2022-05-04 11:33:30,788 INFO [train.py:715] (1/8) Epoch 3, batch 2550, loss[loss=0.1551, simple_loss=0.2178, pruned_loss=0.04624, over 4776.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2328, pruned_loss=0.04878, over 971435.38 frames.], batch size: 12, lr: 5.80e-04
2022-05-04 11:34:11,447 INFO [train.py:715] (1/8) Epoch 3, batch 2600, loss[loss=0.1924, simple_loss=0.2391, pruned_loss=0.07284, over 4804.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2328, pruned_loss=0.04841, over 971771.75 frames.], batch size: 12, lr: 5.80e-04
2022-05-04 11:34:51,562 INFO [train.py:715] (1/8) Epoch 3, batch 2650, loss[loss=0.1944, simple_loss=0.2549, pruned_loss=0.06698, over 4986.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2337, pruned_loss=0.04926, over 972015.78 frames.], batch size: 25, lr: 5.80e-04
2022-05-04 11:35:30,757 INFO [train.py:715] (1/8) Epoch 3, batch 2700, loss[loss=0.166, simple_loss=0.2526, pruned_loss=0.03969, over 4919.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2344, pruned_loss=0.04943, over 971536.13 frames.], batch size: 18, lr: 5.79e-04
2022-05-04 11:36:10,261 INFO [train.py:715] (1/8) Epoch 3, batch 2750, loss[loss=0.1629, simple_loss=0.2228, pruned_loss=0.05151, over 4789.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2335, pruned_loss=0.04876, over 971180.78 frames.], batch size: 21, lr: 5.79e-04
2022-05-04 11:36:50,508 INFO [train.py:715] (1/8) Epoch 3, batch 2800, loss[loss=0.1364, simple_loss=0.2005, pruned_loss=0.03615, over 4837.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2323, pruned_loss=0.04824, over 971122.27 frames.], batch size: 13, lr: 5.79e-04
2022-05-04 11:37:29,793 INFO [train.py:715] (1/8) Epoch 3, batch 2850, loss[loss=0.1199, simple_loss=0.1869, pruned_loss=0.02642, over 4780.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2329, pruned_loss=0.0487, over 970568.72 frames.], batch size: 12, lr: 5.79e-04
2022-05-04 11:38:08,465 INFO [train.py:715] (1/8) Epoch 3, batch 2900, loss[loss=0.1598, simple_loss=0.2268, pruned_loss=0.04643, over 4887.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2327, pruned_loss=0.04852, over 971246.82 frames.], batch size: 22, lr: 5.79e-04
2022-05-04 11:38:48,428 INFO [train.py:715] (1/8) Epoch 3, batch 2950, loss[loss=0.1894, simple_loss=0.2699, pruned_loss=0.05451, over 4906.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2329, pruned_loss=0.04882, over 971460.43 frames.], batch size: 17, lr: 5.79e-04
2022-05-04 11:39:28,056 INFO [train.py:715] (1/8) Epoch 3, batch 3000, loss[loss=0.2028, simple_loss=0.2717, pruned_loss=0.06689, over 4894.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2334, pruned_loss=0.04871, over 972279.63 frames.], batch size: 22, lr: 5.79e-04
2022-05-04 11:39:28,056 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 11:39:36,790 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1153, simple_loss=0.2015, pruned_loss=0.0146, over 914524.00 frames.
2022-05-04 11:40:16,885 INFO [train.py:715] (1/8) Epoch 3, batch 3050, loss[loss=0.1543, simple_loss=0.2186, pruned_loss=0.04496, over 4772.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2332, pruned_loss=0.04874, over 971988.01 frames.], batch size: 18, lr: 5.78e-04
2022-05-04 11:40:55,668 INFO [train.py:715] (1/8) Epoch 3, batch 3100, loss[loss=0.2233, simple_loss=0.2759, pruned_loss=0.08535, over 4970.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2344, pruned_loss=0.04995, over 971660.88 frames.], batch size: 15, lr: 5.78e-04
2022-05-04 11:41:35,055 INFO [train.py:715] (1/8) Epoch 3, batch 3150, loss[loss=0.1916, simple_loss=0.249, pruned_loss=0.06715, over 4736.00 frames.], tot_loss[loss=0.167, simple_loss=0.2344, pruned_loss=0.04975, over 971527.90 frames.], batch size: 16, lr: 5.78e-04
2022-05-04 11:42:14,855 INFO [train.py:715] (1/8) Epoch 3, batch 3200, loss[loss=0.194, simple_loss=0.2578, pruned_loss=0.06511, over 4784.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2347, pruned_loss=0.04999, over 971380.28 frames.], batch size: 18, lr: 5.78e-04
2022-05-04 11:42:54,656 INFO [train.py:715] (1/8) Epoch 3, batch 3250, loss[loss=0.2025, simple_loss=0.2734, pruned_loss=0.06581, over 4822.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2349, pruned_loss=0.05002, over 971650.13 frames.], batch size: 25, lr: 5.78e-04
2022-05-04 11:43:33,194 INFO [train.py:715] (1/8) Epoch 3, batch 3300, loss[loss=0.1591, simple_loss=0.242, pruned_loss=0.03808, over 4989.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2348, pruned_loss=0.05017, over 972192.15 frames.], batch size: 25, lr: 5.78e-04
2022-05-04 11:44:13,009 INFO [train.py:715] (1/8) Epoch 3, batch 3350, loss[loss=0.1975, simple_loss=0.2634, pruned_loss=0.06574, over 4789.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2348, pruned_loss=0.05005, over 972699.74 frames.], batch size: 24, lr: 5.78e-04
2022-05-04 11:44:52,483 INFO [train.py:715] (1/8) Epoch 3, batch 3400, loss[loss=0.179, simple_loss=0.2449, pruned_loss=0.0566, over 4899.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2357, pruned_loss=0.0504, over 972342.22 frames.], batch size: 22, lr: 5.77e-04
2022-05-04 11:45:31,171 INFO [train.py:715] (1/8) Epoch 3, batch 3450, loss[loss=0.1478, simple_loss=0.2121, pruned_loss=0.04176, over 4862.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2348, pruned_loss=0.04965, over 971572.43 frames.], batch size: 30, lr: 5.77e-04
2022-05-04 11:46:10,503 INFO [train.py:715] (1/8) Epoch 3, batch 3500, loss[loss=0.1746, simple_loss=0.2404, pruned_loss=0.05445, over 4813.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2364, pruned_loss=0.05042, over 972567.56 frames.], batch size: 26, lr: 5.77e-04
2022-05-04 11:46:50,809 INFO [train.py:715] (1/8) Epoch 3, batch 3550, loss[loss=0.1774, simple_loss=0.2483, pruned_loss=0.05323, over 4894.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2352, pruned_loss=0.04987, over 971984.37 frames.], batch size: 19, lr: 5.77e-04
2022-05-04 11:47:30,666 INFO [train.py:715] (1/8) Epoch 3, batch 3600, loss[loss=0.1644, simple_loss=0.2234, pruned_loss=0.05267, over 4959.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2348, pruned_loss=0.0495, over 971297.19 frames.], batch size: 35, lr: 5.77e-04
2022-05-04 11:48:09,900 INFO [train.py:715] (1/8) Epoch 3, batch 3650, loss[loss=0.1563, simple_loss=0.236, pruned_loss=0.0383, over 4902.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2344, pruned_loss=0.04927, over 970852.14 frames.], batch size: 19, lr: 5.77e-04
2022-05-04 11:48:49,624 INFO [train.py:715] (1/8) Epoch 3, batch 3700, loss[loss=0.1502, simple_loss=0.2199, pruned_loss=0.04026, over 4866.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2337, pruned_loss=0.04855, over 970973.39 frames.], batch size: 30, lr: 5.77e-04
2022-05-04 11:49:29,640 INFO [train.py:715] (1/8) Epoch 3, batch 3750, loss[loss=0.1696, simple_loss=0.2347, pruned_loss=0.05226, over 4815.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2326, pruned_loss=0.04794, over 971469.83 frames.], batch size: 25, lr: 5.77e-04
2022-05-04 11:50:09,331 INFO [train.py:715] (1/8) Epoch 3, batch 3800, loss[loss=0.1345, simple_loss=0.2022, pruned_loss=0.03346, over 4778.00 frames.], tot_loss[loss=0.163, simple_loss=0.2315, pruned_loss=0.04723, over 971366.11 frames.], batch size: 14, lr: 5.76e-04
2022-05-04 11:50:48,720 INFO [train.py:715] (1/8) Epoch 3, batch 3850, loss[loss=0.141, simple_loss=0.1996, pruned_loss=0.04122, over 4801.00 frames.], tot_loss[loss=0.1634, simple_loss=0.232, pruned_loss=0.04738, over 972243.95 frames.], batch size: 12, lr: 5.76e-04
2022-05-04 11:51:28,559 INFO [train.py:715] (1/8) Epoch 3, batch 3900, loss[loss=0.2021, simple_loss=0.2636, pruned_loss=0.07031, over 4829.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2322, pruned_loss=0.04798, over 972170.73 frames.], batch size: 15, lr: 5.76e-04
2022-05-04 11:52:08,061 INFO [train.py:715] (1/8) Epoch 3, batch 3950, loss[loss=0.1563, simple_loss=0.2156, pruned_loss=0.04857, over 4781.00 frames.], tot_loss[loss=0.164, simple_loss=0.232, pruned_loss=0.04798, over 972988.00 frames.], batch size: 18, lr: 5.76e-04
2022-05-04 11:52:47,081 INFO [train.py:715] (1/8) Epoch 3, batch 4000, loss[loss=0.1809, simple_loss=0.2514, pruned_loss=0.05525, over 4922.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2325, pruned_loss=0.0487, over 973032.01 frames.], batch size: 23, lr: 5.76e-04
2022-05-04 11:53:26,524 INFO [train.py:715] (1/8) Epoch 3, batch 4050, loss[loss=0.1473, simple_loss=0.2228, pruned_loss=0.03596, over 4693.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2336, pruned_loss=0.04891, over 972873.02 frames.], batch size: 15, lr: 5.76e-04
2022-05-04 11:54:06,704 INFO [train.py:715] (1/8) Epoch 3, batch 4100, loss[loss=0.1813, simple_loss=0.2476, pruned_loss=0.05751, over 4740.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2338, pruned_loss=0.04888, over 972136.25 frames.], batch size: 16, lr: 5.76e-04
2022-05-04 11:54:45,654 INFO [train.py:715] (1/8) Epoch 3, batch 4150, loss[loss=0.1477, simple_loss=0.2262, pruned_loss=0.03461, over 4948.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2337, pruned_loss=0.04883, over 972792.64 frames.], batch size: 21, lr: 5.76e-04
2022-05-04 11:55:24,493 INFO [train.py:715] (1/8) Epoch 3, batch 4200, loss[loss=0.1629, simple_loss=0.2371, pruned_loss=0.04436, over 4930.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2337, pruned_loss=0.04904, over 972384.14 frames.], batch size: 29, lr: 5.75e-04
2022-05-04 11:56:04,947 INFO [train.py:715] (1/8) Epoch 3, batch 4250, loss[loss=0.184, simple_loss=0.2495, pruned_loss=0.05927, over 4778.00 frames.], tot_loss[loss=0.165, simple_loss=0.2325, pruned_loss=0.04876, over 972366.02 frames.], batch size: 18, lr: 5.75e-04
2022-05-04 11:56:44,317 INFO [train.py:715] (1/8) Epoch 3, batch 4300, loss[loss=0.1217, simple_loss=0.1904, pruned_loss=0.02655, over 4760.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2324, pruned_loss=0.04856, over 973487.50 frames.], batch size: 12, lr: 5.75e-04
2022-05-04 11:57:23,798 INFO [train.py:715] (1/8) Epoch 3, batch 4350, loss[loss=0.152, simple_loss=0.2184, pruned_loss=0.04278, over 4924.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2317, pruned_loss=0.04807, over 973293.64 frames.], batch size: 18, lr: 5.75e-04
2022-05-04 11:58:03,479 INFO [train.py:715] (1/8) Epoch 3, batch 4400, loss[loss=0.1469, simple_loss=0.2052, pruned_loss=0.04431, over 4777.00 frames.], tot_loss[loss=0.164, simple_loss=0.2319, pruned_loss=0.04804, over 973431.93 frames.], batch size: 17, lr: 5.75e-04
2022-05-04 11:58:43,516 INFO [train.py:715] (1/8) Epoch 3, batch 4450, loss[loss=0.19, simple_loss=0.2528, pruned_loss=0.06363, over 4966.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2324, pruned_loss=0.04826, over 973124.71 frames.], batch size: 35, lr: 5.75e-04
2022-05-04 11:59:22,566 INFO [train.py:715] (1/8) Epoch 3, batch 4500, loss[loss=0.2033, simple_loss=0.2598, pruned_loss=0.07341, over 4757.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2329, pruned_loss=0.04866, over 972927.97 frames.], batch size: 14, lr: 5.75e-04
2022-05-04 12:00:01,992 INFO [train.py:715] (1/8) Epoch 3, batch 4550, loss[loss=0.1666, simple_loss=0.224, pruned_loss=0.0546, over 4864.00 frames.], tot_loss[loss=0.1642, simple_loss=0.232, pruned_loss=0.0482, over 972902.71 frames.], batch size: 13, lr: 5.74e-04
2022-05-04 12:00:41,741 INFO [train.py:715] (1/8) Epoch 3, batch 4600, loss[loss=0.1498, simple_loss=0.2176, pruned_loss=0.04101, over 4957.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2327, pruned_loss=0.04844, over 973378.95 frames.], batch size: 35, lr: 5.74e-04
2022-05-04 12:01:21,002 INFO [train.py:715] (1/8) Epoch 3, batch 4650, loss[loss=0.1797, simple_loss=0.2397, pruned_loss=0.05987, over 4883.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2321, pruned_loss=0.04864, over 972842.20 frames.], batch size: 16, lr: 5.74e-04
2022-05-04 12:01:59,932 INFO [train.py:715] (1/8) Epoch 3, batch 4700, loss[loss=0.1625, simple_loss=0.2244, pruned_loss=0.05036, over 4828.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2329, pruned_loss=0.04916, over 973117.45 frames.], batch size: 30, lr: 5.74e-04
2022-05-04 12:02:39,135 INFO [train.py:715] (1/8) Epoch 3, batch 4750, loss[loss=0.1401, simple_loss=0.2014, pruned_loss=0.03936, over 4778.00 frames.], tot_loss[loss=0.165, simple_loss=0.2326, pruned_loss=0.04868, over 972510.27 frames.], batch size: 17, lr: 5.74e-04
2022-05-04 12:03:18,735 INFO [train.py:715] (1/8) Epoch 3, batch 4800, loss[loss=0.1434, simple_loss=0.2224, pruned_loss=0.03217, over 4876.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2326, pruned_loss=0.04812, over 972345.50 frames.], batch size: 16, lr: 5.74e-04
2022-05-04 12:03:58,125 INFO [train.py:715] (1/8) Epoch 3, batch 4850, loss[loss=0.2122, simple_loss=0.2698, pruned_loss=0.07731, over 4909.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2323, pruned_loss=0.04833, over 972194.40 frames.], batch size: 17, lr: 5.74e-04
2022-05-04 12:04:36,950 INFO [train.py:715] (1/8) Epoch 3, batch 4900, loss[loss=0.1673, simple_loss=0.241, pruned_loss=0.04676, over 4903.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2327, pruned_loss=0.04844, over 973308.61 frames.], batch size: 17, lr: 5.74e-04
2022-05-04 12:05:16,866 INFO [train.py:715] (1/8) Epoch 3, batch 4950, loss[loss=0.1643, simple_loss=0.2273, pruned_loss=0.05064, over 4965.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2327, pruned_loss=0.04854, over 974171.12 frames.], batch size: 14, lr: 5.73e-04
2022-05-04 12:05:56,318 INFO [train.py:715] (1/8) Epoch 3, batch 5000, loss[loss=0.1269, simple_loss=0.1981, pruned_loss=0.02787, over 4961.00 frames.], tot_loss[loss=0.165, simple_loss=0.2328, pruned_loss=0.04859, over 973601.74 frames.], batch size: 24, lr: 5.73e-04
2022-05-04 12:06:35,119 INFO [train.py:715] (1/8) Epoch 3, batch 5050, loss[loss=0.1581, simple_loss=0.2311, pruned_loss=0.04258, over 4792.00 frames.], tot_loss[loss=0.165, simple_loss=0.2329, pruned_loss=0.04857, over 973031.39 frames.], batch size: 24, lr: 5.73e-04
2022-05-04 12:07:14,483 INFO [train.py:715] (1/8) Epoch 3, batch 5100, loss[loss=0.1371, simple_loss=0.214, pruned_loss=0.03015, over 4830.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2331, pruned_loss=0.0487, over 972933.77 frames.], batch size: 26, lr: 5.73e-04
2022-05-04 12:07:54,243 INFO [train.py:715] (1/8) Epoch 3, batch 5150, loss[loss=0.1698, simple_loss=0.2271, pruned_loss=0.05625, over 4886.00 frames.], tot_loss[loss=0.1641, simple_loss=0.232, pruned_loss=0.04806, over 973061.61 frames.], batch size: 32, lr: 5.73e-04
2022-05-04 12:08:32,991 INFO [train.py:715] (1/8) Epoch 3, batch 5200, loss[loss=0.1663, simple_loss=0.2281, pruned_loss=0.05227, over 4989.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2311, pruned_loss=0.04774, over 971566.80 frames.], batch size: 16, lr: 5.73e-04
2022-05-04 12:09:12,108 INFO [train.py:715] (1/8) Epoch 3, batch 5250, loss[loss=0.1829, simple_loss=0.2515, pruned_loss=0.05717, over 4898.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2314, pruned_loss=0.04819, over 970863.43 frames.], batch size: 19, lr: 5.73e-04
2022-05-04 12:09:52,192 INFO [train.py:715] (1/8) Epoch 3, batch 5300, loss[loss=0.1524, simple_loss=0.2115, pruned_loss=0.04665, over 4793.00 frames.], tot_loss[loss=0.165, simple_loss=0.2324, pruned_loss=0.04878, over 971377.49 frames.], batch size: 17, lr: 5.72e-04
2022-05-04 12:10:31,372 INFO [train.py:715] (1/8) Epoch 3, batch 5350, loss[loss=0.1676, simple_loss=0.2209, pruned_loss=0.05721, over 4801.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2321, pruned_loss=0.04844, over 971828.55 frames.], batch size: 25, lr: 5.72e-04
2022-05-04 12:11:10,304 INFO [train.py:715] (1/8) Epoch 3, batch 5400, loss[loss=0.1664, simple_loss=0.2356, pruned_loss=0.04855, over 4814.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2323, pruned_loss=0.0485, over 972302.90 frames.], batch size: 13, lr: 5.72e-04
2022-05-04 12:11:49,946 INFO [train.py:715] (1/8) Epoch 3, batch 5450, loss[loss=0.1515, simple_loss=0.2261, pruned_loss=0.03849, over 4765.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2338, pruned_loss=0.0488, over 972188.90 frames.], batch size: 19, lr: 5.72e-04
2022-05-04 12:12:30,205 INFO [train.py:715] (1/8) Epoch 3, batch 5500, loss[loss=0.1875, simple_loss=0.2458, pruned_loss=0.06463, over 4877.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2338, pruned_loss=0.04883, over 972723.99 frames.], batch size: 30, lr: 5.72e-04
2022-05-04 12:13:09,478 INFO [train.py:715] (1/8) Epoch 3, batch 5550, loss[loss=0.1569, simple_loss=0.2254, pruned_loss=0.04423, over 4919.00 frames.], tot_loss[loss=0.1651, simple_loss=0.233, pruned_loss=0.04859, over 972443.19 frames.], batch size: 18, lr: 5.72e-04
2022-05-04 12:13:49,878 INFO [train.py:715] (1/8) Epoch 3, batch 5600, loss[loss=0.1861, simple_loss=0.2549, pruned_loss=0.05862, over 4842.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2334, pruned_loss=0.04914, over 972428.33 frames.], batch size: 20, lr: 5.72e-04
2022-05-04 12:14:29,643 INFO [train.py:715] (1/8) Epoch 3, batch 5650, loss[loss=0.138, simple_loss=0.208, pruned_loss=0.03402, over 4849.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2325, pruned_loss=0.04826, over 972837.78 frames.], batch size: 32, lr: 5.72e-04
2022-05-04 12:15:08,734 INFO [train.py:715] (1/8) Epoch 3, batch 5700, loss[loss=0.1336, simple_loss=0.2099, pruned_loss=0.02863, over 4981.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2329, pruned_loss=0.04815, over 972154.49 frames.], batch size: 24, lr: 5.71e-04
2022-05-04 12:15:48,067 INFO [train.py:715] (1/8) Epoch 3, batch 5750, loss[loss=0.1814, simple_loss=0.238, pruned_loss=0.06245, over 4734.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2321, pruned_loss=0.048, over 972307.72 frames.], batch size: 16, lr: 5.71e-04
2022-05-04 12:16:27,888 INFO [train.py:715] (1/8) Epoch 3, batch 5800, loss[loss=0.1653, simple_loss=0.2369, pruned_loss=0.0469, over 4920.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2323, pruned_loss=0.048, over 971508.35 frames.], batch size: 23, lr: 5.71e-04
2022-05-04 12:17:07,629 INFO [train.py:715] (1/8) Epoch 3, batch 5850, loss[loss=0.2024, simple_loss=0.2672, pruned_loss=0.06883, over 4861.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2329, pruned_loss=0.04825, over 971847.03 frames.], batch size: 20, lr: 5.71e-04
2022-05-04 12:17:46,988 INFO [train.py:715] (1/8) Epoch 3, batch 5900, loss[loss=0.1695, simple_loss=0.2338, pruned_loss=0.05265, over 4889.00 frames.], tot_loss[loss=0.1639, simple_loss=0.232, pruned_loss=0.04793, over 971346.80 frames.], batch size: 16, lr: 5.71e-04
2022-05-04 12:18:26,961 INFO [train.py:715] (1/8) Epoch 3, batch 5950, loss[loss=0.1442, simple_loss=0.2166, pruned_loss=0.03592, over 4813.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2318, pruned_loss=0.04826, over 971886.92 frames.], batch size: 27, lr: 5.71e-04
2022-05-04 12:19:06,645 INFO [train.py:715] (1/8) Epoch 3, batch 6000, loss[loss=0.2245, simple_loss=0.286, pruned_loss=0.08148, over 4810.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2324, pruned_loss=0.0487, over 972545.61 frames.], batch size: 17, lr: 5.71e-04
2022-05-04 12:19:06,646 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 12:19:15,397 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1149, simple_loss=0.2013, pruned_loss=0.01424, over 914524.00 frames.
2022-05-04 12:19:55,208 INFO [train.py:715] (1/8) Epoch 3, batch 6050, loss[loss=0.1867, simple_loss=0.2579, pruned_loss=0.05776, over 4903.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2332, pruned_loss=0.04909, over 972652.49 frames.], batch size: 19, lr: 5.71e-04
2022-05-04 12:20:34,637 INFO [train.py:715] (1/8) Epoch 3, batch 6100, loss[loss=0.1512, simple_loss=0.2126, pruned_loss=0.04489, over 4979.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2339, pruned_loss=0.04945, over 972586.03 frames.], batch size: 14, lr: 5.70e-04
2022-05-04 12:21:13,557 INFO [train.py:715] (1/8) Epoch 3, batch 6150, loss[loss=0.1507, simple_loss=0.2244, pruned_loss=0.03848, over 4808.00 frames.], tot_loss[loss=0.166, simple_loss=0.2336, pruned_loss=0.04927, over 972999.32 frames.], batch size: 21, lr: 5.70e-04
2022-05-04 12:21:53,159 INFO [train.py:715] (1/8) Epoch 3, batch 6200, loss[loss=0.1803, simple_loss=0.2498, pruned_loss=0.05546, over 4753.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2341, pruned_loss=0.04926, over 972517.16 frames.], batch size: 16, lr: 5.70e-04
2022-05-04 12:22:33,153 INFO [train.py:715] (1/8) Epoch 3, batch 6250, loss[loss=0.2118, simple_loss=0.2753, pruned_loss=0.07418, over 4899.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2332, pruned_loss=0.0488, over 972001.27 frames.], batch size: 39, lr: 5.70e-04
2022-05-04 12:23:12,500 INFO [train.py:715] (1/8) Epoch 3, batch 6300, loss[loss=0.1549, simple_loss=0.2221, pruned_loss=0.04386, over 4803.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2332, pruned_loss=0.04875, over 972187.91 frames.], batch size: 21, lr: 5.70e-04
2022-05-04 12:23:51,739 INFO [train.py:715] (1/8) Epoch 3, batch 6350, loss[loss=0.1945, simple_loss=0.2541, pruned_loss=0.06748, over 4873.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2326, pruned_loss=0.04834, over 972000.57 frames.], batch size: 38, lr: 5.70e-04
2022-05-04 12:24:31,950 INFO [train.py:715] (1/8) Epoch 3, batch 6400, loss[loss=0.135, simple_loss=0.199, pruned_loss=0.03548, over 4851.00 frames.], tot_loss[loss=0.1638, simple_loss=0.232, pruned_loss=0.04785, over 971871.36 frames.], batch size: 13, lr: 5.70e-04
2022-05-04 12:25:11,497 INFO [train.py:715] (1/8) Epoch 3, batch 6450, loss[loss=0.1505, simple_loss=0.23, pruned_loss=0.03552, over 4813.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2328, pruned_loss=0.04819, over 972860.59 frames.], batch size: 12, lr: 5.70e-04
2022-05-04 12:25:50,482 INFO [train.py:715] (1/8) Epoch 3, batch 6500, loss[loss=0.1505, simple_loss=0.2225, pruned_loss=0.0393, over 4894.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2331, pruned_loss=0.04852, over 972230.44 frames.], batch size: 22, lr: 5.69e-04
2022-05-04 12:26:30,129 INFO [train.py:715] (1/8) Epoch 3, batch 6550, loss[loss=0.1859, simple_loss=0.2471, pruned_loss=0.0624, over 4774.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2322, pruned_loss=0.04801, over 972058.27 frames.], batch size: 14, lr: 5.69e-04
2022-05-04 12:27:09,929 INFO [train.py:715] (1/8) Epoch 3, batch 6600, loss[loss=0.1864, simple_loss=0.2504, pruned_loss=0.06121, over 4858.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2323, pruned_loss=0.04842, over 971866.39 frames.], batch size: 20, lr: 5.69e-04
2022-05-04 12:27:49,184 INFO [train.py:715] (1/8) Epoch 3, batch 6650, loss[loss=0.2048, simple_loss=0.2821, pruned_loss=0.06376, over 4890.00 frames.], tot_loss[loss=0.1652, simple_loss=0.233, pruned_loss=0.04875, over 971483.39 frames.], batch size: 22, lr: 5.69e-04
2022-05-04 12:28:28,361 INFO [train.py:715] (1/8) Epoch 3, batch 6700, loss[loss=0.1831, simple_loss=0.2427, pruned_loss=0.0618, over 4985.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2328, pruned_loss=0.04851, over 971896.84 frames.], batch size: 28, lr: 5.69e-04
2022-05-04 12:29:08,698 INFO [train.py:715] (1/8) Epoch 3, batch 6750, loss[loss=0.1842, simple_loss=0.2527, pruned_loss=0.05785, over 4822.00 frames.], tot_loss[loss=0.1653, simple_loss=0.233, pruned_loss=0.04882, over 973026.07 frames.], batch size: 25, lr: 5.69e-04
2022-05-04 12:29:47,741 INFO [train.py:715] (1/8) Epoch 3, batch 6800, loss[loss=0.1729, simple_loss=0.2455, pruned_loss=0.0502, over 4795.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2332, pruned_loss=0.04865, over 972003.17 frames.], batch size: 24, lr: 5.69e-04
2022-05-04 12:30:27,117 INFO [train.py:715] (1/8) Epoch 3, batch 6850, loss[loss=0.1969, simple_loss=0.2575, pruned_loss=0.06816, over 4883.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2317, pruned_loss=0.04763, over 972381.39 frames.], batch size: 16, lr: 5.68e-04
2022-05-04 12:31:06,814 INFO [train.py:715] (1/8) Epoch 3, batch 6900, loss[loss=0.1543, simple_loss=0.2239, pruned_loss=0.04229, over 4771.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2305, pruned_loss=0.04702, over 973227.99 frames.], batch size: 18, lr: 5.68e-04
2022-05-04 12:31:46,650 INFO [train.py:715] (1/8) Epoch 3, batch 6950, loss[loss=0.1947, simple_loss=0.2538, pruned_loss=0.0678, over 4917.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04772, over 973657.07 frames.], batch size: 19, lr: 5.68e-04
2022-05-04 12:32:25,807 INFO [train.py:715] (1/8) Epoch 3, batch 7000, loss[loss=0.1431, simple_loss=0.2217, pruned_loss=0.03229, over 4802.00 frames.], tot_loss[loss=0.1639, simple_loss=0.232, pruned_loss=0.04795, over 973004.03 frames.], batch size: 25, lr: 5.68e-04
2022-05-04 12:33:05,829 INFO [train.py:715] (1/8) Epoch 3, batch 7050, loss[loss=0.1427, simple_loss=0.2121, pruned_loss=0.03668, over 4745.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2321, pruned_loss=0.04818, over 971945.11 frames.], batch size: 16, lr: 5.68e-04
2022-05-04 12:33:45,716 INFO [train.py:715] (1/8) Epoch 3, batch 7100, loss[loss=0.1557, simple_loss=0.2299, pruned_loss=0.04074, over 4822.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2319, pruned_loss=0.04755, over 971977.66 frames.], batch size: 15, lr: 5.68e-04
2022-05-04 12:34:24,807 INFO [train.py:715] (1/8) Epoch 3, batch 7150, loss[loss=0.1568, simple_loss=0.2202, pruned_loss=0.04674, over 4900.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2325, pruned_loss=0.04737, over 972012.58 frames.], batch size: 17, lr: 5.68e-04
2022-05-04 12:35:04,376 INFO [train.py:715] (1/8) Epoch 3, batch 7200, loss[loss=0.1624, simple_loss=0.2284, pruned_loss=0.0482, over 4830.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2325, pruned_loss=0.04748, over 973106.17 frames.], batch size: 30, lr: 5.68e-04
2022-05-04 12:35:44,149 INFO [train.py:715] (1/8) Epoch 3, batch 7250, loss[loss=0.1583, simple_loss=0.2196, pruned_loss=0.04845, over 4926.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2319, pruned_loss=0.04773, over 973053.66 frames.], batch size: 23, lr: 5.67e-04
2022-05-04 12:36:23,546 INFO [train.py:715] (1/8) Epoch 3, batch 7300, loss[loss=0.1418, simple_loss=0.2155, pruned_loss=0.03409, over 4976.00 frames.], tot_loss[loss=0.1638, simple_loss=0.232, pruned_loss=0.04785, over 973439.21 frames.], batch size: 28, lr: 5.67e-04
2022-05-04 12:37:03,010 INFO [train.py:715] (1/8) Epoch 3, batch 7350, loss[loss=0.1401, simple_loss=0.214, pruned_loss=0.03313, over 4758.00 frames.], tot_loss[loss=0.164, simple_loss=0.2327, pruned_loss=0.04764, over 972841.78 frames.], batch size: 19, lr: 5.67e-04
2022-05-04 12:37:42,378 INFO [train.py:715] (1/8) Epoch 3, batch 7400, loss[loss=0.1242, simple_loss=0.1968, pruned_loss=0.02584, over 4931.00 frames.], tot_loss[loss=0.1637, simple_loss=0.232, pruned_loss=0.04771, over 971986.95 frames.], batch size: 18, lr: 5.67e-04
2022-05-04 12:38:22,632 INFO [train.py:715] (1/8) Epoch 3, batch 7450, loss[loss=0.1535, simple_loss=0.2273, pruned_loss=0.0398, over 4979.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2324, pruned_loss=0.04818, over 972876.38 frames.], batch size: 28, lr: 5.67e-04
2022-05-04 12:39:01,777 INFO [train.py:715] (1/8) Epoch 3, batch 7500, loss[loss=0.1616, simple_loss=0.2424, pruned_loss=0.04038, over 4702.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2323, pruned_loss=0.04806, over 973479.87 frames.], batch size: 15, lr: 5.67e-04
2022-05-04 12:39:41,043 INFO [train.py:715] (1/8) Epoch 3, batch 7550, loss[loss=0.1786, simple_loss=0.2385, pruned_loss=0.05932, over 4878.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2322, pruned_loss=0.04807, over 972071.33 frames.], batch size: 38, lr: 5.67e-04
2022-05-04 12:40:22,796 INFO [train.py:715] (1/8) Epoch 3, batch 7600, loss[loss=0.1497, simple_loss=0.2234, pruned_loss=0.03803, over 4898.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2311, pruned_loss=0.04754, over 971659.04 frames.], batch size: 17, lr: 5.67e-04
2022-05-04 12:41:02,150 INFO [train.py:715] (1/8) Epoch 3, batch 7650, loss[loss=0.1898, simple_loss=0.2643, pruned_loss=0.05768, over 4978.00 frames.], tot_loss[loss=0.165, simple_loss=0.2326, pruned_loss=0.04864, over 972233.87 frames.], batch size: 25, lr: 5.66e-04
2022-05-04 12:41:41,413 INFO [train.py:715] (1/8) Epoch 3, batch 7700, loss[loss=0.1427, simple_loss=0.2119, pruned_loss=0.03671, over 4805.00 frames.], tot_loss[loss=0.1643, simple_loss=0.232, pruned_loss=0.04826, over 972624.38 frames.], batch size: 13, lr: 5.66e-04
2022-05-04 12:42:20,884 INFO [train.py:715] (1/8) Epoch 3, batch 7750, loss[loss=0.1839, simple_loss=0.2477, pruned_loss=0.06, over 4773.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2316, pruned_loss=0.04782, over 972689.77 frames.], batch size: 17, lr: 5.66e-04
2022-05-04 12:43:00,214 INFO [train.py:715] (1/8) Epoch 3, batch 7800, loss[loss=0.1636, simple_loss=0.2315, pruned_loss=0.04785, over 4852.00 frames.], tot_loss[loss=0.1643, simple_loss=0.232, pruned_loss=0.0483, over 972015.95 frames.], batch size: 32, lr: 5.66e-04
2022-05-04 12:43:38,784 INFO [train.py:715] (1/8) Epoch 3, batch 7850, loss[loss=0.1365, simple_loss=0.2116, pruned_loss=0.03071, over 4831.00 frames.], tot_loss[loss=0.165, simple_loss=0.2325, pruned_loss=0.04878, over 971831.62 frames.], batch size: 26, lr: 5.66e-04
2022-05-04 12:44:18,374 INFO [train.py:715] (1/8) Epoch 3, batch 7900, loss[loss=0.1643, simple_loss=0.2347, pruned_loss=0.04692, over 4757.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2317, pruned_loss=0.04804, over 971519.82 frames.], batch size: 16, lr: 5.66e-04
2022-05-04 12:44:58,143 INFO [train.py:715] (1/8) Epoch 3, batch 7950, loss[loss=0.1495, simple_loss=0.2071, pruned_loss=0.0459, over 4825.00 frames.], tot_loss[loss=0.1631, simple_loss=0.231, pruned_loss=0.04766, over 972280.97 frames.], batch size: 15, lr: 5.66e-04
2022-05-04 12:45:36,726 INFO [train.py:715] (1/8) Epoch 3, batch 8000, loss[loss=0.178, simple_loss=0.2426, pruned_loss=0.05671, over 4693.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2313, pruned_loss=0.04776, over 971577.54 frames.], batch size: 15, lr: 5.66e-04
2022-05-04 12:46:14,903 INFO [train.py:715] (1/8) Epoch 3, batch 8050, loss[loss=0.1931, simple_loss=0.2591, pruned_loss=0.06357, over 4784.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2312, pruned_loss=0.04762, over 971456.67 frames.], batch size: 17, lr: 5.65e-04
2022-05-04 12:46:53,632 INFO [train.py:715] (1/8) Epoch 3, batch 8100, loss[loss=0.1771, simple_loss=0.246, pruned_loss=0.05404, over 4932.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2305, pruned_loss=0.04736, over 971472.36 frames.], batch size: 29, lr: 5.65e-04
2022-05-04 12:47:31,938 INFO [train.py:715] (1/8) Epoch 3, batch 8150, loss[loss=0.1814, simple_loss=0.2476, pruned_loss=0.05766, over 4867.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2311, pruned_loss=0.04781, over 971347.17 frames.], batch size: 16, lr: 5.65e-04
2022-05-04 12:48:10,078 INFO [train.py:715] (1/8) Epoch 3, batch 8200, loss[loss=0.1817, simple_loss=0.2489, pruned_loss=0.05722, over 4760.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2319, pruned_loss=0.04846, over 971144.68 frames.], batch size: 19, lr: 5.65e-04
2022-05-04 12:48:49,878 INFO [train.py:715] (1/8) Epoch 3, batch 8250, loss[loss=0.1577, simple_loss=0.2207, pruned_loss=0.04739, over 4796.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2325, pruned_loss=0.04838, over 971835.92 frames.], batch size: 12, lr: 5.65e-04
2022-05-04 12:49:30,618 INFO [train.py:715] (1/8) Epoch 3, batch 8300, loss[loss=0.1651, simple_loss=0.2345, pruned_loss=0.04786, over 4986.00 frames.], tot_loss[loss=0.164, simple_loss=0.2319, pruned_loss=0.04806, over 972142.90 frames.], batch size: 25, lr: 5.65e-04
2022-05-04 12:50:10,668 INFO [train.py:715] (1/8) Epoch 3, batch 8350, loss[loss=0.1241, simple_loss=0.1993, pruned_loss=0.02445, over 4990.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2325, pruned_loss=0.04855, over 972413.97 frames.], batch size: 14, lr: 5.65e-04
2022-05-04 12:50:50,672 INFO [train.py:715] (1/8) Epoch 3, batch 8400, loss[loss=0.1986, simple_loss=0.264, pruned_loss=0.06658, over 4746.00 frames.], tot_loss[loss=0.164, simple_loss=0.2318, pruned_loss=0.04812, over 972348.55 frames.], batch size: 16, lr: 5.65e-04
2022-05-04 12:51:30,653 INFO [train.py:715] (1/8) Epoch 3, batch 8450, loss[loss=0.1753, simple_loss=0.2492, pruned_loss=0.05073, over 4953.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2313, pruned_loss=0.04761, over 972269.66 frames.], batch size: 21, lr: 5.64e-04
2022-05-04 12:52:10,874 INFO [train.py:715] (1/8) Epoch 3, batch 8500, loss[loss=0.1751, simple_loss=0.2509, pruned_loss=0.04967, over 4908.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2311, pruned_loss=0.04736, over 971996.60 frames.], batch size: 19, lr: 5.64e-04
2022-05-04 12:52:49,945 INFO [train.py:715] (1/8) Epoch 3, batch 8550, loss[loss=0.1351, simple_loss=0.2045, pruned_loss=0.03287, over 4902.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2315, pruned_loss=0.04756, over 971725.95 frames.], batch size: 19, lr: 5.64e-04
2022-05-04 12:53:31,547 INFO [train.py:715] (1/8) Epoch 3, batch 8600, loss[loss=0.1654, simple_loss=0.2342, pruned_loss=0.04831, over 4915.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2308, pruned_loss=0.04739, over 972184.64 frames.], batch size: 29, lr: 5.64e-04
2022-05-04 12:54:13,123 INFO [train.py:715] (1/8) Epoch 3, batch 8650, loss[loss=0.1514, simple_loss=0.2234, pruned_loss=0.03971, over 4812.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2312, pruned_loss=0.04767, over 972398.58 frames.], batch size: 26, lr: 5.64e-04
2022-05-04 12:54:53,249 INFO [train.py:715] (1/8) Epoch 3, batch 8700, loss[loss=0.1854, simple_loss=0.2489, pruned_loss=0.06095, over 4870.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2306, pruned_loss=0.0474, over 972505.94 frames.], batch size: 34, lr: 5.64e-04
2022-05-04 12:55:34,488 INFO [train.py:715] (1/8) Epoch 3, batch 8750, loss[loss=0.1613, simple_loss=0.2284, pruned_loss=0.0471, over 4889.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2302, pruned_loss=0.04751, over 972769.52 frames.], batch size: 16, lr: 5.64e-04
2022-05-04 12:56:14,902 INFO [train.py:715] (1/8) Epoch 3, batch 8800, loss[loss=0.1477, simple_loss=0.2138, pruned_loss=0.04085, over 4985.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2307, pruned_loss=0.04829, over 972730.57 frames.], batch size: 14, lr: 5.64e-04
2022-05-04 12:56:55,635 INFO [train.py:715] (1/8) Epoch 3, batch 8850, loss[loss=0.1815, simple_loss=0.2502, pruned_loss=0.05636, over 4934.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2314, pruned_loss=0.0486, over 972635.40 frames.], batch size: 23, lr: 5.63e-04
2022-05-04 12:57:35,624 INFO [train.py:715] (1/8) Epoch 3, batch 8900, loss[loss=0.1349, simple_loss=0.2047, pruned_loss=0.03252, over 4965.00 frames.], tot_loss[loss=0.164, simple_loss=0.2316, pruned_loss=0.04824, over 972862.07 frames.], batch size: 35, lr: 5.63e-04
2022-05-04 12:58:17,385 INFO [train.py:715] (1/8) Epoch 3, batch 8950, loss[loss=0.1365, simple_loss=0.1993, pruned_loss=0.03682, over 4846.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2316, pruned_loss=0.04854, over 972938.98 frames.], batch size: 13, lr: 5.63e-04
2022-05-04 12:58:59,320 INFO [train.py:715] (1/8) Epoch 3, batch 9000, loss[loss=0.1397, simple_loss=0.2038, pruned_loss=0.03783, over 4830.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2315, pruned_loss=0.04797, over 974204.58 frames.], batch size: 12, lr: 5.63e-04
2022-05-04 12:58:59,321 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 12:59:08,110 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1147, simple_loss=0.2006, pruned_loss=0.01442, over 914524.00 frames.
2022-05-04 12:59:49,692 INFO [train.py:715] (1/8) Epoch 3, batch 9050, loss[loss=0.1503, simple_loss=0.2286, pruned_loss=0.036, over 4872.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2321, pruned_loss=0.04806, over 974495.96 frames.], batch size: 22, lr: 5.63e-04
2022-05-04 13:00:30,625 INFO [train.py:715] (1/8) Epoch 3, batch 9100, loss[loss=0.1878, simple_loss=0.2636, pruned_loss=0.05605, over 4963.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2335, pruned_loss=0.04883, over 974462.80 frames.], batch size: 24, lr: 5.63e-04
2022-05-04 13:01:11,927 INFO [train.py:715] (1/8) Epoch 3, batch 9150, loss[loss=0.1596, simple_loss=0.2398, pruned_loss=0.03974, over 4983.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2324, pruned_loss=0.04788, over 974094.06 frames.], batch size: 16, lr: 5.63e-04
2022-05-04 13:01:53,287 INFO [train.py:715] (1/8) Epoch 3, batch 9200, loss[loss=0.1494, simple_loss=0.2155, pruned_loss=0.04164, over 4928.00 frames.], tot_loss[loss=0.1649, simple_loss=0.233, pruned_loss=0.04844, over 974042.59 frames.], batch size: 23, lr: 5.63e-04
2022-05-04 13:02:34,667 INFO [train.py:715] (1/8) Epoch 3, batch 9250, loss[loss=0.1414, simple_loss=0.2069, pruned_loss=0.03794, over 4795.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2332, pruned_loss=0.04831, over 974820.81 frames.], batch size: 14, lr: 5.62e-04
2022-05-04 13:03:15,394 INFO [train.py:715] (1/8) Epoch 3, batch 9300, loss[loss=0.1756, simple_loss=0.2392, pruned_loss=0.05597, over 4958.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2338, pruned_loss=0.04846, over 974398.07 frames.], batch size: 39, lr: 5.62e-04
2022-05-04 13:03:56,627 INFO [train.py:715] (1/8) Epoch 3, batch 9350, loss[loss=0.1301, simple_loss=0.2004, pruned_loss=0.02989, over 4984.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2335, pruned_loss=0.04856, over 973589.09 frames.], batch size: 14, lr: 5.62e-04
2022-05-04 13:04:38,914 INFO [train.py:715] (1/8) Epoch 3, batch 9400, loss[loss=0.1568, simple_loss=0.2343, pruned_loss=0.03969, over 4803.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2328, pruned_loss=0.04804, over 974627.14 frames.], batch size: 24, lr: 5.62e-04
2022-05-04 13:05:19,298 INFO [train.py:715] (1/8) Epoch 3, batch 9450, loss[loss=0.1445, simple_loss=0.2225, pruned_loss=0.03332, over 4936.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2321, pruned_loss=0.04755, over 974467.64 frames.], batch size: 21, lr: 5.62e-04
2022-05-04 13:06:00,823 INFO [train.py:715] (1/8) Epoch 3, batch 9500, loss[loss=0.1521, simple_loss=0.2289, pruned_loss=0.03765, over 4909.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2317, pruned_loss=0.04707, over 973395.83 frames.], batch size: 19, lr: 5.62e-04
2022-05-04 13:06:42,690 INFO [train.py:715] (1/8) Epoch 3, batch 9550, loss[loss=0.1297, simple_loss=0.1943, pruned_loss=0.0325, over 4761.00 frames.], tot_loss[loss=0.1619, simple_loss=0.231, pruned_loss=0.04635, over 974350.60 frames.], batch size: 12, lr: 5.62e-04
2022-05-04 13:07:24,288 INFO [train.py:715] (1/8) Epoch 3, batch 9600, loss[loss=0.1893, simple_loss=0.2529, pruned_loss=0.0629, over 4970.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2312, pruned_loss=0.04673, over 974202.48 frames.], batch size: 21, lr: 5.62e-04
2022-05-04 13:08:05,438 INFO [train.py:715] (1/8) Epoch 3, batch 9650, loss[loss=0.1685, simple_loss=0.2341, pruned_loss=0.05149, over 4831.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2305, pruned_loss=0.04666, over 973215.91 frames.], batch size: 15, lr: 5.61e-04
2022-05-04 13:08:46,929 INFO [train.py:715] (1/8) Epoch 3, batch 9700, loss[loss=0.142, simple_loss=0.2093, pruned_loss=0.03738, over 4873.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2315, pruned_loss=0.04707, over 973843.12 frames.], batch size: 16, lr: 5.61e-04
2022-05-04 13:09:27,942 INFO [train.py:715] (1/8) Epoch 3, batch 9750, loss[loss=0.1639, simple_loss=0.2274, pruned_loss=0.05019, over 4891.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2312, pruned_loss=0.04694, over 973642.90 frames.], batch size: 39, lr: 5.61e-04
2022-05-04 13:10:08,814 INFO [train.py:715] (1/8) Epoch 3, batch 9800, loss[loss=0.1622, simple_loss=0.2294, pruned_loss=0.04746, over 4743.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2317, pruned_loss=0.04701, over 973221.40 frames.], batch size: 16, lr: 5.61e-04
2022-05-04 13:10:50,546 INFO [train.py:715] (1/8) Epoch 3, batch 9850, loss[loss=0.2014, simple_loss=0.2787, pruned_loss=0.0621, over 4947.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2312, pruned_loss=0.0466, over 973841.62 frames.], batch size: 21, lr: 5.61e-04
2022-05-04 13:11:32,495 INFO [train.py:715] (1/8) Epoch 3, batch 9900, loss[loss=0.1471, simple_loss=0.2176, pruned_loss=0.03825, over 4747.00 frames.], tot_loss[loss=0.1628, simple_loss=0.232, pruned_loss=0.0468, over 972380.01 frames.], batch size: 16, lr: 5.61e-04
2022-05-04 13:12:13,018 INFO [train.py:715] (1/8) Epoch 3, batch 9950, loss[loss=0.1975, simple_loss=0.2514, pruned_loss=0.07182, over 4960.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2328, pruned_loss=0.04741, over 972934.84 frames.], batch size: 15, lr: 5.61e-04
2022-05-04 13:12:54,732 INFO [train.py:715] (1/8) Epoch 3, batch 10000, loss[loss=0.1361, simple_loss=0.1994, pruned_loss=0.03637, over 4750.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2315, pruned_loss=0.04696, over 972196.65 frames.], batch size: 16, lr: 5.61e-04
2022-05-04 13:13:36,178 INFO [train.py:715] (1/8) Epoch 3, batch 10050, loss[loss=0.139, simple_loss=0.2005, pruned_loss=0.03871, over 4896.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2318, pruned_loss=0.04752, over 971643.61 frames.], batch size: 17, lr: 5.61e-04
2022-05-04 13:14:17,628 INFO [train.py:715] (1/8) Epoch 3, batch 10100, loss[loss=0.1625, simple_loss=0.2229, pruned_loss=0.05106, over 4871.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2307, pruned_loss=0.04715, over 971939.52 frames.], batch size: 22, lr: 5.60e-04
2022-05-04 13:14:58,623 INFO [train.py:715] (1/8) Epoch 3, batch 10150, loss[loss=0.1515, simple_loss=0.2239, pruned_loss=0.03954, over 4886.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2311, pruned_loss=0.0473, over 972352.75 frames.], batch size: 22, lr: 5.60e-04
2022-05-04 13:15:40,201 INFO [train.py:715] (1/8) Epoch 3, batch 10200, loss[loss=0.1707, simple_loss=0.239, pruned_loss=0.05119, over 4752.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2318, pruned_loss=0.04781, over 972302.05 frames.], batch size: 16, lr: 5.60e-04
2022-05-04 13:16:21,938 INFO [train.py:715] (1/8) Epoch 3, batch 10250, loss[loss=0.1398, simple_loss=0.2105, pruned_loss=0.03457, over 4825.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2323, pruned_loss=0.04741, over 972654.31 frames.], batch size: 26, lr: 5.60e-04
2022-05-04 13:17:01,804 INFO [train.py:715] (1/8) Epoch 3, batch 10300, loss[loss=0.1592, simple_loss=0.2221, pruned_loss=0.04809, over 4868.00 frames.], tot_loss[loss=0.1648, simple_loss=0.233, pruned_loss=0.04832, over 972442.10 frames.], batch size: 16, lr: 5.60e-04
2022-05-04 13:17:42,039 INFO [train.py:715] (1/8) Epoch 3, batch 10350, loss[loss=0.132, simple_loss=0.2076, pruned_loss=0.02822, over 4807.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2337, pruned_loss=0.04881, over 972709.73 frames.], batch size: 21, lr: 5.60e-04
2022-05-04 13:18:22,587 INFO [train.py:715] (1/8) Epoch 3, batch 10400, loss[loss=0.1621, simple_loss=0.2298, pruned_loss=0.04723, over 4978.00 frames.], tot_loss[loss=0.166, simple_loss=0.2341, pruned_loss=0.04901, over 971996.44 frames.], batch size: 14, lr: 5.60e-04
2022-05-04 13:19:03,196 INFO [train.py:715] (1/8) Epoch 3, batch 10450, loss[loss=0.1365, simple_loss=0.2093, pruned_loss=0.03185, over 4950.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2335, pruned_loss=0.04866, over 972569.32 frames.], batch size: 24, lr: 5.60e-04
2022-05-04 13:19:43,632 INFO [train.py:715] (1/8) Epoch 3, batch 10500, loss[loss=0.1602, simple_loss=0.2319, pruned_loss=0.04428, over 4829.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2322, pruned_loss=0.04782, over 972416.38 frames.], batch size: 15, lr: 5.59e-04
2022-05-04 13:20:24,622 INFO [train.py:715] (1/8) Epoch 3, batch 10550, loss[loss=0.151, simple_loss=0.2299, pruned_loss=0.03601, over 4798.00 frames.], tot_loss[loss=0.163, simple_loss=0.2318, pruned_loss=0.04713, over 972587.42 frames.], batch size: 13, lr: 5.59e-04
2022-05-04 13:21:07,135 INFO [train.py:715] (1/8) Epoch 3, batch 10600, loss[loss=0.1968, simple_loss=0.2609, pruned_loss=0.06637, over 4835.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2327, pruned_loss=0.04781, over 972624.70 frames.], batch size: 26, lr: 5.59e-04
2022-05-04 13:21:48,620 INFO [train.py:715] (1/8) Epoch 3, batch 10650, loss[loss=0.1412, simple_loss=0.2098, pruned_loss=0.03632, over 4968.00 frames.], tot_loss[loss=0.1633, simple_loss=0.232, pruned_loss=0.04728, over 971979.51 frames.], batch size: 40, lr: 5.59e-04
2022-05-04 13:22:30,743 INFO [train.py:715] (1/8) Epoch 3, batch 10700, loss[loss=0.1716, simple_loss=0.2345, pruned_loss=0.05429, over 4854.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2317, pruned_loss=0.04723, over 971621.83 frames.], batch size: 32, lr: 5.59e-04
2022-05-04 13:23:13,517 INFO [train.py:715] (1/8) Epoch 3, batch 10750, loss[loss=0.175, simple_loss=0.2348, pruned_loss=0.05755, over 4940.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2319, pruned_loss=0.04757, over 971674.85 frames.], batch size: 21, lr: 5.59e-04
2022-05-04 13:23:56,757 INFO [train.py:715] (1/8) Epoch 3, batch 10800, loss[loss=0.1883, simple_loss=0.2495, pruned_loss=0.06351, over 4973.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2317, pruned_loss=0.04744, over 971520.34 frames.], batch size: 31, lr: 5.59e-04
2022-05-04 13:24:38,548 INFO [train.py:715] (1/8) Epoch 3, batch 10850, loss[loss=0.1804, simple_loss=0.2515, pruned_loss=0.05465, over 4962.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2323, pruned_loss=0.04759, over 972371.37 frames.], batch size: 29, lr: 5.59e-04
2022-05-04 13:25:21,326 INFO [train.py:715] (1/8) Epoch 3, batch 10900, loss[loss=0.1325, simple_loss=0.2088, pruned_loss=0.02807, over 4800.00 frames.], tot_loss[loss=0.1636, simple_loss=0.232, pruned_loss=0.0476, over 973184.41 frames.], batch size: 21, lr: 5.58e-04
2022-05-04 13:26:04,556 INFO [train.py:715] (1/8) Epoch 3, batch 10950, loss[loss=0.1613, simple_loss=0.2281, pruned_loss=0.04722, over 4850.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2319, pruned_loss=0.04746, over 972865.18 frames.], batch size: 30, lr: 5.58e-04
2022-05-04 13:26:46,513 INFO [train.py:715] (1/8) Epoch 3, batch 11000, loss[loss=0.1634, simple_loss=0.2424, pruned_loss=0.04223, over 4818.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2321, pruned_loss=0.04722, over 973141.61 frames.], batch size: 26, lr: 5.58e-04
2022-05-04 13:27:28,081 INFO [train.py:715] (1/8) Epoch 3, batch 11050, loss[loss=0.1356, simple_loss=0.2067, pruned_loss=0.03218, over 4894.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2317, pruned_loss=0.04657, over 972984.68 frames.], batch size: 19, lr: 5.58e-04
2022-05-04 13:28:11,599 INFO [train.py:715] (1/8) Epoch 3, batch 11100, loss[loss=0.1525, simple_loss=0.2179, pruned_loss=0.04352, over 4874.00 frames.], tot_loss[loss=0.163, simple_loss=0.2317, pruned_loss=0.04718, over 971497.69 frames.], batch size: 16, lr: 5.58e-04
2022-05-04 13:28:53,674 INFO [train.py:715] (1/8) Epoch 3, batch 11150, loss[loss=0.1565, simple_loss=0.232, pruned_loss=0.04045, over 4816.00 frames.], tot_loss[loss=0.1623, simple_loss=0.231, pruned_loss=0.04687, over 972169.24 frames.], batch size: 27, lr: 5.58e-04
2022-05-04 13:29:35,736 INFO [train.py:715] (1/8) Epoch 3, batch 11200, loss[loss=0.186, simple_loss=0.2347, pruned_loss=0.06867, over 4958.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2316, pruned_loss=0.04724, over 972068.01 frames.], batch size: 35, lr: 5.58e-04
2022-05-04 13:30:18,278 INFO [train.py:715] (1/8) Epoch 3, batch 11250, loss[loss=0.1864, simple_loss=0.2435, pruned_loss=0.06463, over 4872.00 frames.], tot_loss[loss=0.163, simple_loss=0.2316, pruned_loss=0.04722, over 972087.46 frames.], batch size: 20, lr: 5.58e-04
2022-05-04 13:31:01,498 INFO [train.py:715] (1/8) Epoch 3, batch 11300, loss[loss=0.1586, simple_loss=0.2208, pruned_loss=0.04821, over 4910.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2311, pruned_loss=0.04678, over 971837.59 frames.], batch size: 29, lr: 5.57e-04
2022-05-04 13:31:42,779 INFO [train.py:715] (1/8) Epoch 3, batch 11350, loss[loss=0.1625, simple_loss=0.2237, pruned_loss=0.05063, over 4777.00 frames.], tot_loss[loss=0.163, simple_loss=0.2315, pruned_loss=0.04726, over 971545.61 frames.], batch size: 14, lr: 5.57e-04
2022-05-04 13:32:25,107 INFO [train.py:715] (1/8) Epoch 3, batch 11400, loss[loss=0.1705, simple_loss=0.241, pruned_loss=0.05006, over 4947.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2308, pruned_loss=0.04739, over 971420.40 frames.], batch size: 29, lr: 5.57e-04
2022-05-04 13:33:08,055 INFO [train.py:715] (1/8) Epoch 3, batch 11450, loss[loss=0.1626, simple_loss=0.2314, pruned_loss=0.04696, over 4970.00 frames.], tot_loss[loss=0.163, simple_loss=0.2312, pruned_loss=0.04738, over 971611.07 frames.], batch size: 24, lr: 5.57e-04
2022-05-04 13:33:50,188 INFO [train.py:715] (1/8) Epoch 3, batch 11500, loss[loss=0.1595, simple_loss=0.2155, pruned_loss=0.05173, over 4826.00 frames.], tot_loss[loss=0.163, simple_loss=0.2312, pruned_loss=0.0474, over 971651.36 frames.], batch size: 13, lr: 5.57e-04
2022-05-04 13:34:32,225 INFO [train.py:715] (1/8) Epoch 3, batch 11550, loss[loss=0.1492, simple_loss=0.2236, pruned_loss=0.03739, over 4950.00 frames.], tot_loss[loss=0.163, simple_loss=0.2313, pruned_loss=0.04736, over 972082.96 frames.], batch size: 24, lr: 5.57e-04
2022-05-04 13:35:14,410 INFO [train.py:715] (1/8) Epoch 3, batch 11600, loss[loss=0.1817, simple_loss=0.2554, pruned_loss=0.05395, over 4924.00 frames.], tot_loss[loss=0.163, simple_loss=0.2315, pruned_loss=0.04726, over 971250.33 frames.], batch size: 39, lr: 5.57e-04
2022-05-04 13:35:57,170 INFO [train.py:715] (1/8) Epoch 3, batch 11650, loss[loss=0.1573, simple_loss=0.2258, pruned_loss=0.04441, over 4874.00 frames.], tot_loss[loss=0.163, simple_loss=0.2316, pruned_loss=0.04722, over 970849.66 frames.], batch size: 32, lr: 5.57e-04
2022-05-04 13:36:39,258 INFO [train.py:715] (1/8) Epoch 3, batch 11700, loss[loss=0.1619, simple_loss=0.2217, pruned_loss=0.05104, over 4701.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2316, pruned_loss=0.0477, over 970158.46 frames.], batch size: 15, lr: 5.57e-04
2022-05-04 13:37:21,486 INFO [train.py:715] (1/8) Epoch 3, batch 11750, loss[loss=0.1529, simple_loss=0.2221, pruned_loss=0.04185, over 4881.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2307, pruned_loss=0.04721, over 970708.47 frames.], batch size: 22, lr: 5.56e-04
2022-05-04 13:38:05,282 INFO [train.py:715] (1/8) Epoch 3, batch 11800, loss[loss=0.1833, simple_loss=0.2521, pruned_loss=0.05719, over 4800.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2308, pruned_loss=0.04728, over 971354.42 frames.], batch size: 21, lr: 5.56e-04
2022-05-04 13:38:47,455 INFO [train.py:715] (1/8) Epoch 3, batch 11850, loss[loss=0.1497, simple_loss=0.2128, pruned_loss=0.04327, over 4857.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2305, pruned_loss=0.04667, over 971247.81 frames.], batch size: 20, lr: 5.56e-04
2022-05-04 13:39:29,601 INFO [train.py:715] (1/8) Epoch 3, batch 11900, loss[loss=0.1282, simple_loss=0.2057, pruned_loss=0.02531, over 4800.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2302, pruned_loss=0.04663, over 971834.29 frames.], batch size: 18, lr: 5.56e-04
2022-05-04 13:40:11,706 INFO [train.py:715] (1/8) Epoch 3, batch 11950, loss[loss=0.1399, simple_loss=0.2137, pruned_loss=0.03308, over 4987.00 frames.], tot_loss[loss=0.1628, simple_loss=0.231, pruned_loss=0.04731, over 972288.03 frames.], batch size: 25, lr: 5.56e-04
2022-05-04 13:40:54,204 INFO [train.py:715] (1/8) Epoch 3, batch 12000, loss[loss=0.1612, simple_loss=0.226, pruned_loss=0.04819, over 4899.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2309, pruned_loss=0.04711, over 972138.62 frames.], batch size: 17, lr: 5.56e-04
2022-05-04 13:40:54,204 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 13:41:02,573 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1142, simple_loss=0.2003, pruned_loss=0.01401, over 914524.00 frames.
2022-05-04 13:41:44,680 INFO [train.py:715] (1/8) Epoch 3, batch 12050, loss[loss=0.1363, simple_loss=0.2036, pruned_loss=0.03448, over 4992.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2303, pruned_loss=0.04697, over 973523.40 frames.], batch size: 14, lr: 5.56e-04
2022-05-04 13:42:26,373 INFO [train.py:715] (1/8) Epoch 3, batch 12100, loss[loss=0.1775, simple_loss=0.2429, pruned_loss=0.056, over 4824.00 frames.], tot_loss[loss=0.162, simple_loss=0.2303, pruned_loss=0.04687, over 973665.74 frames.], batch size: 30, lr: 5.56e-04
2022-05-04 13:43:08,783 INFO [train.py:715] (1/8) Epoch 3, batch 12150, loss[loss=0.1655, simple_loss=0.2316, pruned_loss=0.04971, over 4926.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2308, pruned_loss=0.04738, over 973302.97 frames.], batch size: 18, lr: 5.55e-04
2022-05-04 13:43:52,022 INFO [train.py:715] (1/8) Epoch 3, batch 12200, loss[loss=0.1656, simple_loss=0.2319, pruned_loss=0.0496, over 4779.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2307, pruned_loss=0.04736, over 972992.11 frames.], batch size: 17, lr: 5.55e-04
2022-05-04 13:44:33,687 INFO [train.py:715] (1/8) Epoch 3, batch 12250, loss[loss=0.1593, simple_loss=0.2325, pruned_loss=0.043, over 4769.00 frames.], tot_loss[loss=0.1626, simple_loss=0.231, pruned_loss=0.04714, over 973048.57 frames.], batch size: 14, lr: 5.55e-04
2022-05-04 13:45:15,593 INFO [train.py:715] (1/8) Epoch 3, batch 12300, loss[loss=0.1567, simple_loss=0.24, pruned_loss=0.03663, over 4782.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2317, pruned_loss=0.04754, over 972662.66 frames.], batch size: 17, lr: 5.55e-04
2022-05-04 13:45:58,050 INFO [train.py:715] (1/8) Epoch 3, batch 12350, loss[loss=0.163, simple_loss=0.229, pruned_loss=0.04848, over 4984.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2318, pruned_loss=0.04721, over 973206.65 frames.], batch size: 14, lr: 5.55e-04
2022-05-04 13:46:41,402 INFO [train.py:715] (1/8) Epoch 3, batch 12400, loss[loss=0.1763, simple_loss=0.2492, pruned_loss=0.05168, over 4768.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2329, pruned_loss=0.04784, over 973062.08 frames.], batch size: 19, lr: 5.55e-04
2022-05-04 13:47:23,069 INFO [train.py:715] (1/8) Epoch 3, batch 12450, loss[loss=0.2226, simple_loss=0.2904, pruned_loss=0.07737, over 4796.00 frames.], tot_loss[loss=0.1634, simple_loss=0.232, pruned_loss=0.04741, over 972985.47 frames.], batch size: 24, lr: 5.55e-04
2022-05-04 13:48:04,573 INFO [train.py:715] (1/8) Epoch 3, batch 12500, loss[loss=0.1761, simple_loss=0.2479, pruned_loss=0.05212, over 4797.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2317, pruned_loss=0.04762, over 972979.10 frames.], batch size: 21, lr: 5.55e-04
2022-05-04 13:48:47,315 INFO [train.py:715] (1/8) Epoch 3, batch 12550, loss[loss=0.1472, simple_loss=0.2255, pruned_loss=0.03452, over 4757.00 frames.], tot_loss[loss=0.163, simple_loss=0.2315, pruned_loss=0.04726, over 972024.23 frames.], batch size: 19, lr: 5.54e-04
2022-05-04 13:49:29,597 INFO [train.py:715] (1/8) Epoch 3, batch 12600, loss[loss=0.1428, simple_loss=0.2111, pruned_loss=0.03719, over 4906.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2309, pruned_loss=0.04692, over 972957.89 frames.], batch size: 19, lr: 5.54e-04
2022-05-04 13:50:11,359 INFO [train.py:715] (1/8) Epoch 3, batch 12650, loss[loss=0.1436, simple_loss=0.2101, pruned_loss=0.03855, over 4893.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2306, pruned_loss=0.04697, over 972612.86 frames.], batch size: 22, lr: 5.54e-04
2022-05-04 13:50:53,059 INFO [train.py:715] (1/8) Epoch 3, batch 12700, loss[loss=0.1545, simple_loss=0.2234, pruned_loss=0.04283, over 4876.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2308, pruned_loss=0.04692, over 972568.09 frames.], batch size: 16, lr: 5.54e-04
2022-05-04 13:51:35,159 INFO [train.py:715] (1/8) Epoch 3, batch 12750, loss[loss=0.161, simple_loss=0.2152, pruned_loss=0.05341, over 4644.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2312, pruned_loss=0.04712, over 972567.73 frames.], batch size: 13, lr: 5.54e-04
2022-05-04 13:52:17,425 INFO [train.py:715] (1/8) Epoch 3, batch 12800, loss[loss=0.1904, simple_loss=0.2537, pruned_loss=0.06353, over 4884.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2314, pruned_loss=0.04783, over 973103.58 frames.], batch size: 22, lr: 5.54e-04
2022-05-04 13:52:58,258 INFO [train.py:715] (1/8) Epoch 3, batch 12850, loss[loss=0.1349, simple_loss=0.1997, pruned_loss=0.03501, over 4781.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2312, pruned_loss=0.04802, over 972970.15 frames.], batch size: 17, lr: 5.54e-04
2022-05-04 13:53:40,955 INFO [train.py:715] (1/8) Epoch 3, batch 12900, loss[loss=0.1284, simple_loss=0.2001, pruned_loss=0.02839, over 4787.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2302, pruned_loss=0.04767, over 972162.78 frames.], batch size: 14, lr: 5.54e-04
2022-05-04 13:54:23,561 INFO [train.py:715] (1/8) Epoch 3, batch 12950, loss[loss=0.1875, simple_loss=0.2431, pruned_loss=0.06597, over 4852.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2299, pruned_loss=0.04717, over 972947.69 frames.], batch size: 34, lr: 5.54e-04
2022-05-04 13:55:04,923 INFO [train.py:715] (1/8) Epoch 3, batch 13000, loss[loss=0.194, simple_loss=0.2542, pruned_loss=0.06686, over 4796.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2312, pruned_loss=0.04812, over 971947.43 frames.], batch size: 24, lr: 5.53e-04
2022-05-04 13:55:46,799 INFO [train.py:715] (1/8) Epoch 3, batch 13050, loss[loss=0.1282, simple_loss=0.2084, pruned_loss=0.02398, over 4762.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2312, pruned_loss=0.04796, over 972109.96 frames.], batch size: 19, lr: 5.53e-04
2022-05-04 13:56:28,788 INFO [train.py:715] (1/8) Epoch 3, batch 13100, loss[loss=0.1256, simple_loss=0.1956, pruned_loss=0.0278, over 4832.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2304, pruned_loss=0.0476, over 972554.79 frames.], batch size: 12, lr: 5.53e-04
2022-05-04 13:57:10,551 INFO [train.py:715] (1/8) Epoch 3, batch 13150, loss[loss=0.1438, simple_loss=0.2076, pruned_loss=0.03998, over 4983.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2316, pruned_loss=0.04789, over 973095.73 frames.], batch size: 25, lr: 5.53e-04
2022-05-04 13:57:52,121 INFO [train.py:715] (1/8) Epoch 3, batch 13200, loss[loss=0.1666, simple_loss=0.2351, pruned_loss=0.04902, over 4741.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2318, pruned_loss=0.04761, over 973159.26 frames.], batch size: 16, lr: 5.53e-04
2022-05-04 13:58:34,746 INFO [train.py:715] (1/8) Epoch 3, batch 13250, loss[loss=0.1403, simple_loss=0.2074, pruned_loss=0.0366, over 4776.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2312, pruned_loss=0.04752, over 972771.23 frames.], batch size: 17, lr: 5.53e-04
2022-05-04 13:59:17,144 INFO [train.py:715] (1/8) Epoch 3, batch 13300, loss[loss=0.1445, simple_loss=0.2249, pruned_loss=0.03206, over 4984.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2306, pruned_loss=0.04705, over 972463.11 frames.], batch size: 28, lr: 5.53e-04
2022-05-04 13:59:58,631 INFO [train.py:715] (1/8) Epoch 3, batch 13350, loss[loss=0.1781, simple_loss=0.2513, pruned_loss=0.05241, over 4849.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2313, pruned_loss=0.0473, over 971948.38 frames.], batch size: 30, lr: 5.53e-04
2022-05-04 14:00:40,463 INFO [train.py:715] (1/8) Epoch 3, batch 13400, loss[loss=0.1821, simple_loss=0.2538, pruned_loss=0.05526, over 4843.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2305, pruned_loss=0.04682, over 971381.65 frames.], batch size: 30, lr: 5.52e-04
2022-05-04 14:01:23,052 INFO [train.py:715] (1/8) Epoch 3, batch 13450, loss[loss=0.1934, simple_loss=0.2488, pruned_loss=0.06898, over 4924.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2307, pruned_loss=0.04675, over 972265.95 frames.], batch size: 18, lr: 5.52e-04
2022-05-04 14:02:04,520 INFO [train.py:715] (1/8) Epoch 3, batch 13500, loss[loss=0.2054, simple_loss=0.2654, pruned_loss=0.07269, over 4869.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2309, pruned_loss=0.04711, over 972735.10 frames.], batch size: 30, lr: 5.52e-04
2022-05-04 14:02:46,049 INFO [train.py:715] (1/8) Epoch 3, batch 13550, loss[loss=0.1676, simple_loss=0.2377, pruned_loss=0.04878, over 4827.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2305, pruned_loss=0.04692, over 972714.71 frames.], batch size: 15, lr: 5.52e-04
2022-05-04 14:03:28,378 INFO [train.py:715] (1/8) Epoch 3, batch 13600, loss[loss=0.1657, simple_loss=0.233, pruned_loss=0.04915, over 4777.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2301, pruned_loss=0.04662, over 972955.09 frames.], batch size: 14, lr: 5.52e-04
2022-05-04 14:04:10,275 INFO [train.py:715] (1/8) Epoch 3, batch 13650, loss[loss=0.1365, simple_loss=0.2038, pruned_loss=0.0346, over 4975.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2298, pruned_loss=0.04675, over 972701.75 frames.], batch size: 25, lr: 5.52e-04
2022-05-04 14:04:51,700 INFO [train.py:715] (1/8) Epoch 3, batch 13700, loss[loss=0.1712, simple_loss=0.235, pruned_loss=0.05371, over 4975.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2297, pruned_loss=0.04669, over 972270.40 frames.], batch size: 24, lr: 5.52e-04
2022-05-04 14:05:34,460 INFO [train.py:715] (1/8) Epoch 3, batch 13750, loss[loss=0.1721, simple_loss=0.2366, pruned_loss=0.05385, over 4979.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2305, pruned_loss=0.04694, over 972414.51 frames.], batch size: 35, lr: 5.52e-04
2022-05-04 14:06:16,547 INFO [train.py:715] (1/8) Epoch 3, batch 13800, loss[loss=0.1444, simple_loss=0.2129, pruned_loss=0.03795, over 4789.00 frames.], tot_loss[loss=0.161, simple_loss=0.2291, pruned_loss=0.04642, over 972596.27 frames.], batch size: 14, lr: 5.52e-04
2022-05-04 14:06:58,025 INFO [train.py:715] (1/8) Epoch 3, batch 13850, loss[loss=0.1712, simple_loss=0.2347, pruned_loss=0.05383, over 4894.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2298, pruned_loss=0.04671, over 973101.28 frames.], batch size: 19, lr: 5.51e-04
2022-05-04 14:07:39,261 INFO [train.py:715] (1/8) Epoch 3, batch 13900, loss[loss=0.1233, simple_loss=0.198, pruned_loss=0.02429, over 4661.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2304, pruned_loss=0.04693, over 973186.41 frames.], batch size: 13, lr: 5.51e-04
2022-05-04 14:08:21,711 INFO [train.py:715] (1/8) Epoch 3, batch 13950, loss[loss=0.1841, simple_loss=0.2344, pruned_loss=0.06694, over 4844.00 frames.], tot_loss[loss=0.162, simple_loss=0.2306, pruned_loss=0.0467, over 972688.08 frames.], batch size: 30, lr: 5.51e-04
2022-05-04 14:09:04,155 INFO [train.py:715] (1/8) Epoch 3, batch 14000, loss[loss=0.1819, simple_loss=0.2521, pruned_loss=0.05585, over 4717.00 frames.], tot_loss[loss=0.1626, simple_loss=0.231, pruned_loss=0.04709, over 972233.30 frames.], batch size: 16, lr: 5.51e-04
2022-05-04 14:09:45,587 INFO [train.py:715] (1/8) Epoch 3, batch 14050, loss[loss=0.1631, simple_loss=0.229, pruned_loss=0.04857, over 4802.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2307, pruned_loss=0.04733, over 972045.58 frames.], batch size: 21, lr: 5.51e-04
2022-05-04 14:10:28,383 INFO [train.py:715] (1/8) Epoch 3, batch 14100, loss[loss=0.1775, simple_loss=0.2377, pruned_loss=0.05866, over 4880.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2304, pruned_loss=0.04701, over 971531.45 frames.], batch size: 22, lr: 5.51e-04
2022-05-04 14:11:10,222 INFO [train.py:715] (1/8) Epoch 3, batch 14150, loss[loss=0.1289, simple_loss=0.2149, pruned_loss=0.02146, over 4903.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2311, pruned_loss=0.04729, over 971835.74 frames.], batch size: 19, lr: 5.51e-04
2022-05-04 14:11:51,367 INFO [train.py:715] (1/8) Epoch 3, batch 14200, loss[loss=0.1779, simple_loss=0.239, pruned_loss=0.05843, over 4831.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2312, pruned_loss=0.04759, over 972463.43 frames.], batch size: 15, lr: 5.51e-04
2022-05-04 14:12:33,502 INFO [train.py:715] (1/8) Epoch 3, batch 14250, loss[loss=0.1675, simple_loss=0.2343, pruned_loss=0.05036, over 4945.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2308, pruned_loss=0.04729, over 971650.39 frames.], batch size: 21, lr: 5.51e-04
2022-05-04 14:13:15,882 INFO [train.py:715] (1/8) Epoch 3, batch 14300, loss[loss=0.1441, simple_loss=0.2087, pruned_loss=0.03974, over 4791.00 frames.], tot_loss[loss=0.163, simple_loss=0.2312, pruned_loss=0.04745, over 971614.95 frames.], batch size: 24, lr: 5.50e-04
2022-05-04 14:13:58,169 INFO [train.py:715] (1/8) Epoch 3, batch 14350, loss[loss=0.1499, simple_loss=0.2221, pruned_loss=0.03883, over 4907.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2307, pruned_loss=0.04673, over 972373.61 frames.], batch size: 19, lr: 5.50e-04
2022-05-04 14:14:38,947 INFO [train.py:715] (1/8) Epoch 3, batch 14400, loss[loss=0.1508, simple_loss=0.2339, pruned_loss=0.0339, over 4958.00 frames.], tot_loss[loss=0.1625, simple_loss=0.231, pruned_loss=0.04703, over 972013.88 frames.], batch size: 21, lr: 5.50e-04
2022-05-04 14:15:21,403 INFO [train.py:715] (1/8) Epoch 3, batch 14450, loss[loss=0.187, simple_loss=0.236, pruned_loss=0.06898, over 4773.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2315, pruned_loss=0.04732, over 972663.19 frames.], batch size: 14, lr: 5.50e-04
2022-05-04 14:16:03,351 INFO [train.py:715] (1/8) Epoch 3, batch 14500, loss[loss=0.1562, simple_loss=0.2299, pruned_loss=0.04129, over 4799.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2318, pruned_loss=0.04735, over 972065.26 frames.], batch size: 21, lr: 5.50e-04
2022-05-04 14:16:44,524 INFO [train.py:715] (1/8) Epoch 3, batch 14550, loss[loss=0.155, simple_loss=0.2279, pruned_loss=0.04103, over 4928.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2328, pruned_loss=0.04833, over 972114.41 frames.], batch size: 23, lr: 5.50e-04
2022-05-04 14:17:26,978 INFO [train.py:715] (1/8) Epoch 3, batch 14600, loss[loss=0.1296, simple_loss=0.2127, pruned_loss=0.02327, over 4878.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2329, pruned_loss=0.04826, over 972574.50 frames.], batch size: 22, lr: 5.50e-04
2022-05-04 14:18:08,860 INFO [train.py:715] (1/8) Epoch 3, batch 14650, loss[loss=0.137, simple_loss=0.2042, pruned_loss=0.03485, over 4808.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2324, pruned_loss=0.04791, over 972158.74 frames.], batch size: 25, lr: 5.50e-04
2022-05-04 14:18:50,908 INFO [train.py:715] (1/8) Epoch 3, batch 14700, loss[loss=0.1551, simple_loss=0.218, pruned_loss=0.04609, over 4819.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2312, pruned_loss=0.0477, over 972815.35 frames.], batch size: 15, lr: 5.49e-04
2022-05-04 14:19:32,216 INFO [train.py:715] (1/8) Epoch 3, batch 14750, loss[loss=0.1828, simple_loss=0.2594, pruned_loss=0.05315, over 4803.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2308, pruned_loss=0.04713, over 972362.82 frames.], batch size: 24, lr: 5.49e-04
2022-05-04 14:20:14,627 INFO [train.py:715] (1/8) Epoch 3, batch 14800, loss[loss=0.1898, simple_loss=0.2497, pruned_loss=0.06497, over 4901.00 frames.], tot_loss[loss=0.163, simple_loss=0.2313, pruned_loss=0.04737, over 971691.65 frames.], batch size: 17, lr: 5.49e-04
2022-05-04 14:20:56,938 INFO [train.py:715] (1/8) Epoch 3, batch 14850, loss[loss=0.1855, simple_loss=0.2441, pruned_loss=0.06349, over 4923.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04772, over 972219.09 frames.], batch size: 23, lr: 5.49e-04
2022-05-04 14:21:37,857 INFO [train.py:715] (1/8) Epoch 3, batch 14900, loss[loss=0.1654, simple_loss=0.24, pruned_loss=0.04542, over 4696.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04769, over 971814.72 frames.], batch size: 15, lr: 5.49e-04
2022-05-04 14:22:20,812 INFO [train.py:715] (1/8) Epoch 3, batch 14950, loss[loss=0.1554, simple_loss=0.2213, pruned_loss=0.04476, over 4862.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2319, pruned_loss=0.04777, over 971259.40 frames.], batch size: 20, lr: 5.49e-04
2022-05-04 14:23:02,214 INFO [train.py:715] (1/8) Epoch 3, batch 15000, loss[loss=0.1691, simple_loss=0.2416, pruned_loss=0.04825, over 4786.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2317, pruned_loss=0.04728, over 972007.65 frames.], batch size: 18, lr: 5.49e-04
2022-05-04 14:23:02,215 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 14:23:10,879 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1142, simple_loss=0.2003, pruned_loss=0.01402, over 914524.00 frames.
2022-05-04 14:23:52,705 INFO [train.py:715] (1/8) Epoch 3, batch 15050, loss[loss=0.1608, simple_loss=0.2349, pruned_loss=0.04329, over 4849.00 frames.], tot_loss[loss=0.163, simple_loss=0.2316, pruned_loss=0.04714, over 972681.47 frames.], batch size: 32, lr: 5.49e-04
2022-05-04 14:24:34,026 INFO [train.py:715] (1/8) Epoch 3, batch 15100, loss[loss=0.1637, simple_loss=0.2376, pruned_loss=0.04493, over 4837.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2326, pruned_loss=0.04779, over 972513.69 frames.], batch size: 15, lr: 5.49e-04
2022-05-04 14:25:16,182 INFO [train.py:715] (1/8) Epoch 3, batch 15150, loss[loss=0.1624, simple_loss=0.235, pruned_loss=0.04496, over 4794.00 frames.], tot_loss[loss=0.164, simple_loss=0.2323, pruned_loss=0.04785, over 971936.53 frames.], batch size: 14, lr: 5.48e-04
2022-05-04 14:25:57,809 INFO [train.py:715] (1/8) Epoch 3, batch 15200, loss[loss=0.1706, simple_loss=0.2388, pruned_loss=0.05119, over 4979.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2325, pruned_loss=0.04815, over 973035.91 frames.], batch size: 33, lr: 5.48e-04
2022-05-04 14:26:39,369 INFO [train.py:715] (1/8) Epoch 3, batch 15250, loss[loss=0.1701, simple_loss=0.2387, pruned_loss=0.0507, over 4849.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04782, over 972972.30 frames.], batch size: 30, lr: 5.48e-04
2022-05-04 14:27:20,703 INFO [train.py:715] (1/8) Epoch 3, batch 15300, loss[loss=0.1757, simple_loss=0.2288, pruned_loss=0.06128, over 4889.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2316, pruned_loss=0.04771, over 972189.55 frames.], batch size: 16, lr: 5.48e-04
2022-05-04 14:28:02,527 INFO [train.py:715] (1/8) Epoch 3, batch 15350, loss[loss=0.1457, simple_loss=0.2188, pruned_loss=0.0363, over 4818.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2317, pruned_loss=0.04825, over 972451.82 frames.], batch size: 27, lr: 5.48e-04
2022-05-04 14:28:44,647 INFO [train.py:715] (1/8) Epoch 3, batch 15400, loss[loss=0.1263, simple_loss=0.2013, pruned_loss=0.02567, over 4777.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2307, pruned_loss=0.04791, over 973326.75 frames.], batch size: 18, lr: 5.48e-04
2022-05-04 14:29:25,738 INFO [train.py:715] (1/8) Epoch 3, batch 15450, loss[loss=0.1959, simple_loss=0.2493, pruned_loss=0.07131, over 4882.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2305, pruned_loss=0.04759, over 972645.02 frames.], batch size: 16, lr: 5.48e-04
2022-05-04 14:30:08,679 INFO [train.py:715] (1/8) Epoch 3, batch 15500, loss[loss=0.1939, simple_loss=0.2485, pruned_loss=0.06967, over 4958.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2304, pruned_loss=0.04751, over 973273.69 frames.], batch size: 35, lr: 5.48e-04
2022-05-04 14:30:50,507 INFO [train.py:715] (1/8) Epoch 3, batch 15550, loss[loss=0.1854, simple_loss=0.254, pruned_loss=0.05838, over 4945.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2307, pruned_loss=0.0475, over 972591.75 frames.], batch size: 23, lr: 5.48e-04
2022-05-04 14:31:35,088 INFO [train.py:715] (1/8) Epoch 3, batch 15600, loss[loss=0.1545, simple_loss=0.219, pruned_loss=0.04499, over 4896.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2304, pruned_loss=0.04722, over 972643.36 frames.], batch size: 19, lr: 5.47e-04
2022-05-04 14:32:16,096 INFO [train.py:715] (1/8) Epoch 3, batch 15650, loss[loss=0.1561, simple_loss=0.2245, pruned_loss=0.04384, over 4980.00 frames.], tot_loss[loss=0.162, simple_loss=0.2302, pruned_loss=0.04694, over 972302.64 frames.], batch size: 25, lr: 5.47e-04
2022-05-04 14:32:57,687 INFO [train.py:715] (1/8) Epoch 3, batch 15700, loss[loss=0.1448, simple_loss=0.2166, pruned_loss=0.03651, over 4805.00 frames.], tot_loss[loss=0.162, simple_loss=0.2307, pruned_loss=0.04662, over 971776.29 frames.], batch size: 24, lr: 5.47e-04
2022-05-04 14:33:40,521 INFO [train.py:715] (1/8) Epoch 3, batch 15750, loss[loss=0.1496, simple_loss=0.2248, pruned_loss=0.03721, over 4879.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2303, pruned_loss=0.04652, over 972188.99 frames.], batch size: 16, lr: 5.47e-04
2022-05-04 14:34:22,328 INFO [train.py:715] (1/8) Epoch 3, batch 15800, loss[loss=0.1985, simple_loss=0.2549, pruned_loss=0.07104, over 4989.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2312, pruned_loss=0.04686, over 972635.40 frames.], batch size: 35, lr: 5.47e-04
2022-05-04 14:35:03,580 INFO [train.py:715] (1/8) Epoch 3, batch 15850, loss[loss=0.2174, simple_loss=0.2554, pruned_loss=0.08972, over 4791.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2322, pruned_loss=0.04755, over 973790.83 frames.], batch size: 14, lr: 5.47e-04
2022-05-04 14:35:45,955 INFO [train.py:715] (1/8) Epoch 3, batch 15900, loss[loss=0.1868, simple_loss=0.2592, pruned_loss=0.05723, over 4832.00 frames.], tot_loss[loss=0.1639, simple_loss=0.232, pruned_loss=0.04786, over 973835.29 frames.], batch size: 15, lr: 5.47e-04
2022-05-04 14:36:28,594 INFO [train.py:715] (1/8) Epoch 3, batch 15950, loss[loss=0.1655, simple_loss=0.2286, pruned_loss=0.05116, over 4878.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2326, pruned_loss=0.04827, over 973318.65 frames.], batch size: 39, lr: 5.47e-04
2022-05-04 14:37:09,196 INFO [train.py:715] (1/8) Epoch 3, batch 16000, loss[loss=0.2044, simple_loss=0.2677, pruned_loss=0.07052, over 4989.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2325, pruned_loss=0.04818, over 973683.86 frames.], batch size: 15, lr: 5.47e-04
2022-05-04 14:37:50,842 INFO [train.py:715] (1/8) Epoch 3, batch 16050, loss[loss=0.1997, simple_loss=0.2649, pruned_loss=0.06723, over 4779.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2325, pruned_loss=0.04837, over 973743.42 frames.], batch size: 17, lr: 5.46e-04
2022-05-04 14:38:33,471 INFO [train.py:715] (1/8) Epoch 3, batch 16100, loss[loss=0.1909, simple_loss=0.2617, pruned_loss=0.06004, over 4880.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2328, pruned_loss=0.04812, over 972900.64 frames.], batch size: 16, lr: 5.46e-04
2022-05-04 14:39:15,445 INFO [train.py:715] (1/8) Epoch 3, batch 16150, loss[loss=0.1485, simple_loss=0.2187, pruned_loss=0.03912, over 4906.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2325, pruned_loss=0.04808, over 973727.43 frames.], batch size: 17, lr: 5.46e-04
2022-05-04 14:39:56,183 INFO [train.py:715] (1/8) Epoch 3, batch 16200, loss[loss=0.1734, simple_loss=0.239, pruned_loss=0.05385, over 4943.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2314, pruned_loss=0.04716, over 974318.66 frames.], batch size: 39, lr: 5.46e-04
2022-05-04 14:40:38,473 INFO [train.py:715] (1/8) Epoch 3, batch 16250, loss[loss=0.1633, simple_loss=0.244, pruned_loss=0.04131, over 4936.00 frames.], tot_loss[loss=0.1624, simple_loss=0.231, pruned_loss=0.04687, over 974245.27 frames.], batch size: 29, lr: 5.46e-04
2022-05-04 14:41:20,551 INFO [train.py:715] (1/8) Epoch 3, batch 16300, loss[loss=0.1667, simple_loss=0.2316, pruned_loss=0.05088, over 4749.00 frames.], tot_loss[loss=0.162, simple_loss=0.2306, pruned_loss=0.04666, over 973697.69 frames.], batch size: 19, lr: 5.46e-04
2022-05-04 14:42:01,217 INFO [train.py:715] (1/8) Epoch 3, batch 16350, loss[loss=0.1389, simple_loss=0.2114, pruned_loss=0.03317, over 4806.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2312, pruned_loss=0.04716, over 973075.69 frames.], batch size: 21, lr: 5.46e-04
2022-05-04 14:42:43,180 INFO [train.py:715] (1/8) Epoch 3, batch 16400, loss[loss=0.1739, simple_loss=0.2401, pruned_loss=0.0539, over 4981.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2314, pruned_loss=0.04767, over 973794.90 frames.], batch size: 25, lr: 5.46e-04
2022-05-04 14:43:25,719 INFO [train.py:715] (1/8) Epoch 3, batch 16450, loss[loss=0.1533, simple_loss=0.2241, pruned_loss=0.04125, over 4807.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2304, pruned_loss=0.04708, over 973530.21 frames.], batch size: 24, lr: 5.45e-04
2022-05-04 14:44:08,336 INFO [train.py:715] (1/8) Epoch 3, batch 16500, loss[loss=0.1264, simple_loss=0.1905, pruned_loss=0.03114, over 4938.00 frames.], tot_loss[loss=0.1626, simple_loss=0.231, pruned_loss=0.04706, over 973219.44 frames.], batch size: 18, lr: 5.45e-04
2022-05-04 14:44:49,047 INFO [train.py:715] (1/8) Epoch 3, batch 16550, loss[loss=0.2141, simple_loss=0.2761, pruned_loss=0.07602, over 4844.00 frames.], tot_loss[loss=0.1636, simple_loss=0.232, pruned_loss=0.04756, over 973038.90 frames.], batch size: 30, lr: 5.45e-04
2022-05-04 14:45:31,912 INFO [train.py:715] (1/8) Epoch 3, batch 16600, loss[loss=0.1328, simple_loss=0.1977, pruned_loss=0.03393, over 4787.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2329, pruned_loss=0.04822, over 972568.37 frames.], batch size: 14, lr: 5.45e-04
2022-05-04 14:46:14,676 INFO [train.py:715] (1/8) Epoch 3, batch 16650, loss[loss=0.1219, simple_loss=0.1865, pruned_loss=0.02861, over 4782.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2318, pruned_loss=0.04767, over 972487.24 frames.], batch size: 12, lr: 5.45e-04
2022-05-04 14:46:55,373 INFO [train.py:715] (1/8) Epoch 3, batch 16700, loss[loss=0.1503, simple_loss=0.2124, pruned_loss=0.04407, over 4750.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2325, pruned_loss=0.04761, over 971386.23 frames.], batch size: 19, lr: 5.45e-04
2022-05-04 14:47:37,402 INFO [train.py:715] (1/8) Epoch 3, batch 16750, loss[loss=0.1846, simple_loss=0.2523, pruned_loss=0.05851, over 4905.00 frames.], tot_loss[loss=0.164, simple_loss=0.2324, pruned_loss=0.04777, over 971892.19 frames.], batch size: 18, lr: 5.45e-04
2022-05-04 14:48:19,847 INFO [train.py:715] (1/8) Epoch 3, batch 16800, loss[loss=0.1726, simple_loss=0.2437, pruned_loss=0.05076, over 4852.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2315, pruned_loss=0.04747, over 972484.08 frames.], batch size: 32, lr: 5.45e-04
2022-05-04 14:49:01,326 INFO [train.py:715] (1/8) Epoch 3, batch 16850, loss[loss=0.1469, simple_loss=0.2228, pruned_loss=0.03555, over 4765.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2316, pruned_loss=0.04759, over 971830.64 frames.], batch size: 14, lr: 5.45e-04
2022-05-04 14:49:42,737 INFO [train.py:715] (1/8) Epoch 3, batch 16900, loss[loss=0.1463, simple_loss=0.2251, pruned_loss=0.03379, over 4961.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2311, pruned_loss=0.04723, over 971590.43 frames.], batch size: 24, lr: 5.44e-04
2022-05-04 14:50:24,684 INFO [train.py:715] (1/8) Epoch 3, batch 16950, loss[loss=0.144, simple_loss=0.2093, pruned_loss=0.03932, over 4923.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2301, pruned_loss=0.04724, over 972250.58 frames.], batch size: 18, lr: 5.44e-04
2022-05-04 14:51:07,226 INFO [train.py:715] (1/8) Epoch 3, batch 17000, loss[loss=0.148, simple_loss=0.2231, pruned_loss=0.03649, over 4805.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2303, pruned_loss=0.04716, over 972723.52 frames.], batch size: 21, lr: 5.44e-04
2022-05-04 14:51:47,560 INFO [train.py:715] (1/8) Epoch 3, batch 17050, loss[loss=0.2151, simple_loss=0.279, pruned_loss=0.07561, over 4986.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2305, pruned_loss=0.04708, over 973234.01 frames.], batch size: 25, lr: 5.44e-04
2022-05-04 14:52:29,478 INFO [train.py:715] (1/8) Epoch 3, batch 17100, loss[loss=0.1481, simple_loss=0.222, pruned_loss=0.03713, over 4947.00 frames.], tot_loss[loss=0.1625, simple_loss=0.231, pruned_loss=0.04705, over 972488.78 frames.], batch size: 21, lr: 5.44e-04
2022-05-04 14:53:11,178 INFO [train.py:715] (1/8) Epoch 3, batch 17150, loss[loss=0.1426, simple_loss=0.2137, pruned_loss=0.03577, over 4821.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2316, pruned_loss=0.04753, over 973409.51 frames.], batch size: 13, lr: 5.44e-04
2022-05-04 14:53:52,355 INFO [train.py:715] (1/8) Epoch 3, batch 17200, loss[loss=0.1872, simple_loss=0.2471, pruned_loss=0.06363, over 4849.00 frames.], tot_loss[loss=0.163, simple_loss=0.2311, pruned_loss=0.04745, over 973855.08 frames.], batch size: 26, lr: 5.44e-04
2022-05-04 14:54:33,049 INFO [train.py:715] (1/8) Epoch 3, batch 17250, loss[loss=0.1407, simple_loss=0.1985, pruned_loss=0.04142, over 4816.00 frames.], tot_loss[loss=0.163, simple_loss=0.2314, pruned_loss=0.04736, over 972804.52 frames.], batch size: 12, lr: 5.44e-04
2022-05-04 14:55:14,502 INFO [train.py:715] (1/8) Epoch 3, batch 17300, loss[loss=0.1783, simple_loss=0.2519, pruned_loss=0.05237, over 4969.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2315, pruned_loss=0.04704, over 973366.40 frames.], batch size: 15, lr: 5.44e-04
2022-05-04 14:55:56,119 INFO [train.py:715] (1/8) Epoch 3, batch 17350, loss[loss=0.1676, simple_loss=0.2383, pruned_loss=0.04842, over 4987.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2325, pruned_loss=0.04752, over 972981.65 frames.], batch size: 25, lr: 5.43e-04
2022-05-04 14:56:36,181 INFO [train.py:715] (1/8) Epoch 3, batch 17400, loss[loss=0.1665, simple_loss=0.2382, pruned_loss=0.04744, over 4901.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2319, pruned_loss=0.04755, over 971987.39 frames.], batch size: 17, lr: 5.43e-04
2022-05-04 14:57:18,250 INFO [train.py:715] (1/8) Epoch 3, batch 17450, loss[loss=0.2035, simple_loss=0.2737, pruned_loss=0.06663, over 4980.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2322, pruned_loss=0.04775, over 971537.60 frames.], batch size: 24, lr: 5.43e-04
2022-05-04 14:58:00,481 INFO [train.py:715] (1/8) Epoch 3, batch 17500, loss[loss=0.2051, simple_loss=0.2446, pruned_loss=0.0828, over 4958.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2321, pruned_loss=0.04827, over 971067.44 frames.], batch size: 14, lr: 5.43e-04
2022-05-04 14:58:41,508 INFO [train.py:715] (1/8) Epoch 3, batch 17550, loss[loss=0.13, simple_loss=0.2028, pruned_loss=0.02862, over 4909.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2318, pruned_loss=0.04794, over 971142.86 frames.], batch size: 18, lr: 5.43e-04
2022-05-04 14:59:22,854 INFO [train.py:715] (1/8) Epoch 3, batch 17600, loss[loss=0.131, simple_loss=0.2066, pruned_loss=0.02766, over 4982.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2308, pruned_loss=0.0474, over 971731.77 frames.], batch size: 27, lr: 5.43e-04
2022-05-04 15:00:04,539 INFO [train.py:715] (1/8) Epoch 3, batch 17650, loss[loss=0.1349, simple_loss=0.2101, pruned_loss=0.02982, over 4948.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2314, pruned_loss=0.04747, over 972249.62 frames.], batch size: 21, lr: 5.43e-04
2022-05-04 15:00:46,080 INFO [train.py:715] (1/8) Epoch 3, batch 17700, loss[loss=0.1401, simple_loss=0.2113, pruned_loss=0.03442, over 4800.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2309, pruned_loss=0.047, over 972567.25 frames.], batch size: 21, lr: 5.43e-04
2022-05-04 15:01:26,894 INFO [train.py:715] (1/8) Epoch 3, batch 17750, loss[loss=0.184, simple_loss=0.2463, pruned_loss=0.06087, over 4968.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2313, pruned_loss=0.04773, over 972733.21 frames.], batch size: 35, lr: 5.43e-04
2022-05-04 15:02:08,927 INFO [train.py:715] (1/8) Epoch 3, batch 17800, loss[loss=0.1516, simple_loss=0.2216, pruned_loss=0.04078, over 4861.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2314, pruned_loss=0.04801, over 972852.86 frames.], batch size: 20, lr: 5.42e-04
2022-05-04 15:02:50,346 INFO [train.py:715] (1/8) Epoch 3, batch 17850, loss[loss=0.178, simple_loss=0.2538, pruned_loss=0.05109, over 4802.00 frames.], tot_loss[loss=0.164, simple_loss=0.2318, pruned_loss=0.04815, over 972510.37 frames.], batch size: 21, lr: 5.42e-04
2022-05-04 15:03:30,303 INFO [train.py:715] (1/8) Epoch 3, batch 17900, loss[loss=0.1732, simple_loss=0.2439, pruned_loss=0.05127, over 4781.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2312, pruned_loss=0.0472, over 973149.94 frames.], batch size: 18, lr: 5.42e-04
2022-05-04 15:04:12,142 INFO [train.py:715] (1/8) Epoch 3, batch 17950, loss[loss=0.1766, simple_loss=0.2427, pruned_loss=0.05525, over 4984.00 frames.], tot_loss[loss=0.162, simple_loss=0.2307, pruned_loss=0.04665, over 972917.24 frames.], batch size: 28, lr: 5.42e-04
2022-05-04 15:04:53,403 INFO [train.py:715] (1/8) Epoch 3, batch 18000, loss[loss=0.1647, simple_loss=0.2306, pruned_loss=0.04937, over 4839.00 frames.], tot_loss[loss=0.1638, simple_loss=0.232, pruned_loss=0.04781, over 973079.49 frames.], batch size: 12, lr: 5.42e-04
2022-05-04 15:04:53,404 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 15:05:02,070 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1143, simple_loss=0.2002, pruned_loss=0.01414, over 914524.00 frames.
2022-05-04 15:05:43,866 INFO [train.py:715] (1/8) Epoch 3, batch 18050, loss[loss=0.1761, simple_loss=0.2388, pruned_loss=0.05669, over 4786.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2318, pruned_loss=0.04768, over 972628.65 frames.], batch size: 18, lr: 5.42e-04
2022-05-04 15:06:25,502 INFO [train.py:715] (1/8) Epoch 3, batch 18100, loss[loss=0.1552, simple_loss=0.222, pruned_loss=0.04417, over 4695.00 frames.], tot_loss[loss=0.1651, simple_loss=0.233, pruned_loss=0.04858, over 972706.80 frames.], batch size: 15, lr: 5.42e-04
2022-05-04 15:07:06,172 INFO [train.py:715] (1/8) Epoch 3, batch 18150, loss[loss=0.1748, simple_loss=0.248, pruned_loss=0.05083, over 4872.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04781, over 973207.73 frames.], batch size: 16, lr: 5.42e-04
2022-05-04 15:07:47,678 INFO [train.py:715] (1/8) Epoch 3, batch 18200, loss[loss=0.1835, simple_loss=0.2491, pruned_loss=0.05892, over 4786.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2314, pruned_loss=0.0474, over 974058.65 frames.], batch size: 14, lr: 5.42e-04
2022-05-04 15:08:29,470 INFO [train.py:715] (1/8) Epoch 3, batch 18250, loss[loss=0.1869, simple_loss=0.2453, pruned_loss=0.06429, over 4947.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2312, pruned_loss=0.04702, over 974153.66 frames.], batch size: 21, lr: 5.41e-04
2022-05-04 15:09:10,292 INFO [train.py:715] (1/8) Epoch 3, batch 18300, loss[loss=0.1941, simple_loss=0.2614, pruned_loss=0.06341, over 4988.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2307, pruned_loss=0.04643, over 973548.60 frames.], batch size: 26, lr: 5.41e-04
2022-05-04 15:09:51,598 INFO [train.py:715] (1/8) Epoch 3, batch 18350, loss[loss=0.151, simple_loss=0.2123, pruned_loss=0.04486, over 4779.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2305, pruned_loss=0.04664, over 972716.89 frames.], batch size: 18, lr: 5.41e-04
2022-05-04 15:10:33,028 INFO [train.py:715] (1/8) Epoch 3, batch 18400, loss[loss=0.1295, simple_loss=0.2071, pruned_loss=0.02596, over 4994.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2312, pruned_loss=0.04725, over 973383.38 frames.], batch size: 14, lr: 5.41e-04
2022-05-04 15:11:13,983 INFO [train.py:715] (1/8) Epoch 3, batch 18450, loss[loss=0.1785, simple_loss=0.238, pruned_loss=0.05947, over 4903.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2315, pruned_loss=0.04733, over 973191.25 frames.], batch size: 17, lr: 5.41e-04
2022-05-04 15:11:55,022 INFO [train.py:715] (1/8) Epoch 3, batch 18500, loss[loss=0.1761, simple_loss=0.2327, pruned_loss=0.0597, over 4981.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2326, pruned_loss=0.04802, over 973374.19 frames.], batch size: 35, lr: 5.41e-04
2022-05-04 15:12:36,397 INFO [train.py:715] (1/8) Epoch 3, batch 18550, loss[loss=0.2154, simple_loss=0.2787, pruned_loss=0.0761, over 4836.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2329, pruned_loss=0.04809, over 973304.81 frames.], batch size: 27, lr: 5.41e-04
2022-05-04 15:13:18,629 INFO [train.py:715] (1/8) Epoch 3, batch 18600, loss[loss=0.189, simple_loss=0.2529, pruned_loss=0.06259, over 4955.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2331, pruned_loss=0.04796, over 973374.57 frames.], batch size: 14, lr: 5.41e-04
2022-05-04 15:13:58,615 INFO [train.py:715] (1/8) Epoch 3, batch 18650, loss[loss=0.1712, simple_loss=0.2459, pruned_loss=0.04823, over 4772.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2328, pruned_loss=0.04788, over 974035.15 frames.], batch size: 18, lr: 5.41e-04
2022-05-04 15:14:39,319 INFO [train.py:715] (1/8) Epoch 3, batch 18700, loss[loss=0.1566, simple_loss=0.223, pruned_loss=0.04505, over 4870.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2328, pruned_loss=0.04779, over 973450.81 frames.], batch size: 20, lr: 5.40e-04
2022-05-04 15:15:20,415 INFO [train.py:715] (1/8) Epoch 3, batch 18750, loss[loss=0.1682, simple_loss=0.2253, pruned_loss=0.05558, over 4967.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2321, pruned_loss=0.04787, over 972963.33 frames.], batch size: 15, lr: 5.40e-04
2022-05-04 15:16:00,277 INFO [train.py:715] (1/8) Epoch 3, batch 18800, loss[loss=0.1555, simple_loss=0.2243, pruned_loss=0.04335, over 4984.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2313, pruned_loss=0.04712, over 973532.59 frames.], batch size: 15, lr: 5.40e-04
2022-05-04 15:16:41,102 INFO [train.py:715] (1/8) Epoch 3, batch 18850, loss[loss=0.1587, simple_loss=0.2372, pruned_loss=0.04006, over 4738.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2325, pruned_loss=0.04762, over 973076.23 frames.], batch size: 16, lr: 5.40e-04
2022-05-04 15:17:21,053 INFO [train.py:715] (1/8) Epoch 3, batch 18900, loss[loss=0.1809, simple_loss=0.2379, pruned_loss=0.06188, over 4748.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2333, pruned_loss=0.0481, over 973237.37 frames.], batch size: 16, lr: 5.40e-04
2022-05-04 15:18:01,540 INFO [train.py:715] (1/8) Epoch 3, batch 18950, loss[loss=0.1851, simple_loss=0.2457, pruned_loss=0.06224, over 4963.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2332, pruned_loss=0.04822, over 972103.91 frames.], batch size: 15, lr: 5.40e-04
2022-05-04 15:18:40,941 INFO [train.py:715] (1/8) Epoch 3, batch 19000, loss[loss=0.1548, simple_loss=0.2143, pruned_loss=0.04767, over 4780.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2322, pruned_loss=0.04784, over 971963.06 frames.], batch size: 18, lr: 5.40e-04
2022-05-04 15:19:20,768 INFO [train.py:715] (1/8) Epoch 3, batch 19050, loss[loss=0.1566, simple_loss=0.2243, pruned_loss=0.04446, over 4885.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04778, over 972233.23 frames.], batch size: 19, lr: 5.40e-04
2022-05-04 15:20:01,070 INFO [train.py:715] (1/8) Epoch 3, batch 19100, loss[loss=0.1554, simple_loss=0.2241, pruned_loss=0.04339, over 4766.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2319, pruned_loss=0.04761, over 972383.59 frames.], batch size: 12, lr: 5.40e-04
2022-05-04 15:20:40,496 INFO [train.py:715] (1/8) Epoch 3, batch 19150, loss[loss=0.1813, simple_loss=0.2406, pruned_loss=0.06106, over 4906.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2322, pruned_loss=0.048, over 972051.16 frames.], batch size: 39, lr: 5.40e-04
2022-05-04 15:21:20,176 INFO [train.py:715] (1/8) Epoch 3, batch 19200, loss[loss=0.1711, simple_loss=0.2279, pruned_loss=0.05712, over 4979.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2321, pruned_loss=0.048, over 971876.19 frames.], batch size: 35, lr: 5.39e-04
2022-05-04 15:21:59,820 INFO [train.py:715] (1/8) Epoch 3, batch 19250, loss[loss=0.1687, simple_loss=0.2425, pruned_loss=0.04748, over 4945.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2317, pruned_loss=0.04736, over 971695.42 frames.], batch size: 21, lr: 5.39e-04
2022-05-04 15:22:40,129 INFO [train.py:715] (1/8) Epoch 3, batch 19300, loss[loss=0.1406, simple_loss=0.2111, pruned_loss=0.03507, over 4775.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2322, pruned_loss=0.04781, over 971674.30 frames.], batch size: 14, lr: 5.39e-04
2022-05-04 15:23:19,469 INFO [train.py:715] (1/8) Epoch 3, batch 19350, loss[loss=0.2373, simple_loss=0.2913, pruned_loss=0.09162, over 4780.00 frames.], tot_loss[loss=0.163, simple_loss=0.2314, pruned_loss=0.04732, over 971111.77 frames.], batch size: 14, lr: 5.39e-04
2022-05-04 15:23:59,198 INFO [train.py:715] (1/8) Epoch 3, batch 19400, loss[loss=0.1678, simple_loss=0.2304, pruned_loss=0.05257, over 4898.00 frames.], tot_loss[loss=0.163, simple_loss=0.2314, pruned_loss=0.04725, over 971075.88 frames.], batch size: 18, lr: 5.39e-04
2022-05-04 15:24:39,295 INFO [train.py:715] (1/8) Epoch 3, batch 19450, loss[loss=0.1971, simple_loss=0.2408, pruned_loss=0.07671, over 4802.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2318, pruned_loss=0.04745, over 971020.56 frames.], batch size: 13, lr: 5.39e-04
2022-05-04 15:25:18,371 INFO [train.py:715] (1/8) Epoch 3, batch 19500, loss[loss=0.193, simple_loss=0.246, pruned_loss=0.07001, over 4975.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2315, pruned_loss=0.04722, over 971972.10 frames.], batch size: 24, lr: 5.39e-04
2022-05-04 15:25:58,125 INFO [train.py:715] (1/8) Epoch 3, batch 19550, loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03096, over 4940.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2309, pruned_loss=0.04683, over 971687.37 frames.], batch size: 21, lr: 5.39e-04
2022-05-04 15:26:37,669 INFO [train.py:715] (1/8) Epoch 3, batch 19600, loss[loss=0.1348, simple_loss=0.2132, pruned_loss=0.02818, over 4788.00 frames.], tot_loss[loss=0.163, simple_loss=0.2316, pruned_loss=0.04723, over 972044.59 frames.], batch size: 21, lr: 5.39e-04
2022-05-04 15:27:17,570 INFO [train.py:715] (1/8) Epoch 3, batch 19650, loss[loss=0.1581, simple_loss=0.2217, pruned_loss=0.04729, over 4859.00 frames.], tot_loss[loss=0.1624, simple_loss=0.231, pruned_loss=0.0469, over 972351.15 frames.], batch size: 20, lr: 5.38e-04
2022-05-04 15:27:56,471 INFO [train.py:715] (1/8) Epoch 3, batch 19700, loss[loss=0.2333, simple_loss=0.2935, pruned_loss=0.08652, over 4836.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2314, pruned_loss=0.04689, over 972540.05 frames.], batch size: 13, lr: 5.38e-04
2022-05-04 15:28:36,066 INFO [train.py:715] (1/8) Epoch 3, batch 19750, loss[loss=0.22, simple_loss=0.2822, pruned_loss=0.07889, over 4891.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2314, pruned_loss=0.04693, over 971847.56 frames.], batch size: 16, lr: 5.38e-04
2022-05-04 15:29:15,537 INFO [train.py:715] (1/8) Epoch 3, batch 19800, loss[loss=0.1862, simple_loss=0.2506, pruned_loss=0.06092, over 4913.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2313, pruned_loss=0.04684, over 972090.96 frames.], batch size: 18, lr: 5.38e-04
2022-05-04 15:29:55,116 INFO [train.py:715] (1/8) Epoch 3, batch 19850, loss[loss=0.1317, simple_loss=0.2024, pruned_loss=0.0305, over 4872.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2316, pruned_loss=0.04666, over 971978.65 frames.], batch size: 16, lr: 5.38e-04
2022-05-04 15:30:34,812 INFO [train.py:715] (1/8) Epoch 3, batch 19900, loss[loss=0.1362, simple_loss=0.2024, pruned_loss=0.03498, over 4947.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2304, pruned_loss=0.04597, over 972137.56 frames.], batch size: 21, lr: 5.38e-04
2022-05-04 15:31:15,109 INFO [train.py:715] (1/8) Epoch 3, batch 19950, loss[loss=0.1434, simple_loss=0.2088, pruned_loss=0.039, over 4969.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2309, pruned_loss=0.04641, over 972562.59 frames.], batch size: 14, lr: 5.38e-04
2022-05-04 15:31:54,891 INFO [train.py:715] (1/8) Epoch 3, batch 20000, loss[loss=0.134, simple_loss=0.2034, pruned_loss=0.03236, over 4988.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2294, pruned_loss=0.0461, over 973104.15 frames.], batch size: 14, lr: 5.38e-04
2022-05-04 15:32:34,161 INFO [train.py:715] (1/8) Epoch 3, batch 20050, loss[loss=0.1638, simple_loss=0.2336, pruned_loss=0.04696, over 4825.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2292, pruned_loss=0.04608, over 972697.05 frames.], batch size: 26, lr: 5.38e-04
2022-05-04 15:33:14,397 INFO [train.py:715] (1/8) Epoch 3, batch 20100, loss[loss=0.1666, simple_loss=0.2487, pruned_loss=0.04228, over 4958.00 frames.], tot_loss[loss=0.1605, simple_loss=0.229, pruned_loss=0.04597, over 973155.50 frames.], batch size: 24, lr: 5.37e-04
2022-05-04 15:33:54,294 INFO [train.py:715] (1/8) Epoch 3, batch 20150, loss[loss=0.1581, simple_loss=0.2216, pruned_loss=0.04727, over 4802.00 frames.], tot_loss[loss=0.161, simple_loss=0.2294, pruned_loss=0.04633, over 972966.02 frames.], batch size: 17, lr: 5.37e-04
2022-05-04 15:34:33,627 INFO [train.py:715] (1/8) Epoch 3, batch 20200, loss[loss=0.1679, simple_loss=0.2277, pruned_loss=0.05411, over 4984.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2289, pruned_loss=0.04611, over 973151.06 frames.], batch size: 16, lr: 5.37e-04
2022-05-04 15:35:13,290 INFO [train.py:715] (1/8) Epoch 3, batch 20250, loss[loss=0.1444, simple_loss=0.2233, pruned_loss=0.03275, over 4956.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2284, pruned_loss=0.04561, over 972572.34 frames.], batch size: 24, lr: 5.37e-04
2022-05-04 15:35:53,124 INFO [train.py:715] (1/8) Epoch 3, batch 20300, loss[loss=0.1667, simple_loss=0.2301, pruned_loss=0.05167, over 4850.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2291, pruned_loss=0.04614, over 972232.60 frames.], batch size: 34, lr: 5.37e-04
2022-05-04 15:36:33,507 INFO [train.py:715] (1/8) Epoch 3, batch 20350, loss[loss=0.1349, simple_loss=0.1961, pruned_loss=0.03684, over 4824.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2282, pruned_loss=0.04606, over 971395.32 frames.], batch size: 13, lr: 5.37e-04
2022-05-04 15:37:12,082 INFO [train.py:715] (1/8) Epoch 3, batch 20400, loss[loss=0.1493, simple_loss=0.2143, pruned_loss=0.04219, over 4916.00 frames.], tot_loss[loss=0.16, simple_loss=0.2282, pruned_loss=0.04589, over 971520.37 frames.], batch size: 18, lr: 5.37e-04
2022-05-04 15:37:51,789 INFO [train.py:715] (1/8) Epoch 3, batch 20450, loss[loss=0.1827, simple_loss=0.2547, pruned_loss=0.0554, over 4841.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2291, pruned_loss=0.04613, over 972461.20 frames.], batch size: 20, lr: 5.37e-04
2022-05-04 15:38:31,858 INFO [train.py:715] (1/8) Epoch 3, batch 20500, loss[loss=0.1418, simple_loss=0.2045, pruned_loss=0.03953, over 4975.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2297, pruned_loss=0.04655, over 972266.40 frames.], batch size: 31, lr: 5.37e-04
2022-05-04 15:39:10,980 INFO [train.py:715] (1/8) Epoch 3, batch 20550, loss[loss=0.1821, simple_loss=0.2386, pruned_loss=0.06274, over 4786.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2305, pruned_loss=0.0472, over 971997.60 frames.], batch size: 18, lr: 5.36e-04
2022-05-04 15:39:50,430 INFO [train.py:715] (1/8) Epoch 3, batch 20600, loss[loss=0.1602, simple_loss=0.2384, pruned_loss=0.04105, over 4796.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2306, pruned_loss=0.04698, over 973465.45 frames.], batch size: 24, lr: 5.36e-04
2022-05-04 15:40:30,878 INFO [train.py:715] (1/8) Epoch 3, batch 20650, loss[loss=0.1219, simple_loss=0.2002, pruned_loss=0.02176, over 4808.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2297, pruned_loss=0.04648, over 973415.67 frames.], batch size: 25, lr: 5.36e-04
2022-05-04 15:41:10,732 INFO [train.py:715] (1/8) Epoch 3, batch 20700, loss[loss=0.1525, simple_loss=0.2246, pruned_loss=0.04015, over 4782.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2301, pruned_loss=0.04702, over 974014.72 frames.], batch size: 17, lr: 5.36e-04
2022-05-04 15:41:50,203 INFO [train.py:715] (1/8) Epoch 3, batch 20750, loss[loss=0.1429, simple_loss=0.2143, pruned_loss=0.0358, over 4846.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2303, pruned_loss=0.04696, over 973973.43 frames.], batch size: 13, lr: 5.36e-04
2022-05-04 15:42:30,285 INFO [train.py:715] (1/8) Epoch 3, batch 20800, loss[loss=0.1336, simple_loss=0.2001, pruned_loss=0.03356, over 4898.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2305, pruned_loss=0.04744, over 974010.79 frames.], batch size: 17, lr: 5.36e-04
2022-05-04 15:43:11,032 INFO [train.py:715] (1/8) Epoch 3, batch 20850, loss[loss=0.1398, simple_loss=0.218, pruned_loss=0.03079, over 4803.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2303, pruned_loss=0.04725, over 974174.18 frames.], batch size: 25, lr: 5.36e-04
2022-05-04 15:43:50,800 INFO [train.py:715] (1/8) Epoch 3, batch 20900, loss[loss=0.1624, simple_loss=0.2363, pruned_loss=0.04419, over 4854.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2306, pruned_loss=0.04725, over 973694.10 frames.], batch size: 20, lr: 5.36e-04
2022-05-04 15:44:31,206 INFO [train.py:715] (1/8) Epoch 3, batch 20950, loss[loss=0.1382, simple_loss=0.2065, pruned_loss=0.03492, over 4924.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2306, pruned_loss=0.04695, over 972214.28 frames.], batch size: 18, lr: 5.36e-04
2022-05-04 15:45:11,737 INFO [train.py:715] (1/8) Epoch 3, batch 21000, loss[loss=0.1445, simple_loss=0.21, pruned_loss=0.03951, over 4970.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2306, pruned_loss=0.047, over 973279.37 frames.], batch size: 14, lr: 5.36e-04
2022-05-04 15:45:11,738 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 15:45:24,193 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1137, simple_loss=0.1999, pruned_loss=0.01377, over 914524.00 frames.
2022-05-04 15:46:04,595 INFO [train.py:715] (1/8) Epoch 3, batch 21050, loss[loss=0.1482, simple_loss=0.2163, pruned_loss=0.04003, over 4955.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2311, pruned_loss=0.04686, over 972765.42 frames.], batch size: 24, lr: 5.35e-04
2022-05-04 15:46:45,378 INFO [train.py:715] (1/8) Epoch 3, batch 21100, loss[loss=0.1397, simple_loss=0.2098, pruned_loss=0.03485, over 4859.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2307, pruned_loss=0.04695, over 972232.45 frames.], batch size: 20, lr: 5.35e-04
2022-05-04 15:47:25,766 INFO [train.py:715] (1/8) Epoch 3, batch 21150, loss[loss=0.2137, simple_loss=0.2776, pruned_loss=0.07495, over 4906.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2303, pruned_loss=0.04706, over 972241.93 frames.], batch size: 22, lr: 5.35e-04
2022-05-04 15:48:08,584 INFO [train.py:715] (1/8) Epoch 3, batch 21200, loss[loss=0.2079, simple_loss=0.2706, pruned_loss=0.07258, over 4922.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2307, pruned_loss=0.04755, over 972466.23 frames.], batch size: 39, lr: 5.35e-04
2022-05-04 15:48:49,618 INFO [train.py:715] (1/8) Epoch 3, batch 21250, loss[loss=0.1742, simple_loss=0.2405, pruned_loss=0.05389, over 4749.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2314, pruned_loss=0.048, over 972077.68 frames.], batch size: 16, lr: 5.35e-04
2022-05-04 15:49:28,343 INFO [train.py:715] (1/8) Epoch 3, batch 21300, loss[loss=0.1915, simple_loss=0.2562, pruned_loss=0.06337, over 4973.00 frames.], tot_loss[loss=0.163, simple_loss=0.2307, pruned_loss=0.04762, over 971762.08 frames.], batch size: 15, lr: 5.35e-04
2022-05-04 15:50:10,539 INFO [train.py:715] (1/8) Epoch 3, batch 21350, loss[loss=0.148, simple_loss=0.2211, pruned_loss=0.03749, over 4905.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2306, pruned_loss=0.04716, over 972134.73 frames.], batch size: 17, lr: 5.35e-04
2022-05-04 15:50:51,357 INFO [train.py:715] (1/8) Epoch 3, batch 21400, loss[loss=0.2053, simple_loss=0.2582, pruned_loss=0.07615, over 4904.00 frames.], tot_loss[loss=0.163, simple_loss=0.2312, pruned_loss=0.04744, over 971926.34 frames.], batch size: 17, lr: 5.35e-04
2022-05-04 15:51:30,335 INFO [train.py:715] (1/8) Epoch 3, batch 21450, loss[loss=0.1432, simple_loss=0.2251, pruned_loss=0.0306, over 4794.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2314, pruned_loss=0.04752, over 970785.33 frames.], batch size: 24, lr: 5.35e-04
2022-05-04 15:52:08,618 INFO [train.py:715] (1/8) Epoch 3, batch 21500, loss[loss=0.1925, simple_loss=0.2421, pruned_loss=0.07144, over 4858.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2316, pruned_loss=0.04771, over 971724.27 frames.], batch size: 32, lr: 5.34e-04
2022-05-04 15:52:47,660 INFO [train.py:715] (1/8) Epoch 3, batch 21550, loss[loss=0.1616, simple_loss=0.2257, pruned_loss=0.04878, over 4796.00 frames.], tot_loss[loss=0.163, simple_loss=0.2309, pruned_loss=0.04755, over 971462.18 frames.], batch size: 17, lr: 5.34e-04
2022-05-04 15:53:27,195 INFO [train.py:715] (1/8) Epoch 3, batch 21600, loss[loss=0.1628, simple_loss=0.2377, pruned_loss=0.04395, over 4973.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2315, pruned_loss=0.04738, over 971970.48 frames.], batch size: 35, lr: 5.34e-04
2022-05-04 15:54:06,103 INFO [train.py:715] (1/8) Epoch 3, batch 21650, loss[loss=0.1845, simple_loss=0.2515, pruned_loss=0.05872, over 4715.00 frames.], tot_loss[loss=0.1616, simple_loss=0.23, pruned_loss=0.04659, over 971784.94 frames.], batch size: 15, lr: 5.34e-04
2022-05-04 15:54:46,387 INFO [train.py:715] (1/8) Epoch 3, batch 21700, loss[loss=0.1401, simple_loss=0.2136, pruned_loss=0.03326, over 4845.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2307, pruned_loss=0.04687, over 972257.03 frames.], batch size: 15, lr: 5.34e-04
2022-05-04 15:55:26,901 INFO [train.py:715] (1/8) Epoch 3, batch 21750, loss[loss=0.1388, simple_loss=0.2124, pruned_loss=0.03255, over 4927.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2309, pruned_loss=0.04716, over 972092.12 frames.], batch size: 29, lr: 5.34e-04
2022-05-04 15:56:06,022 INFO [train.py:715] (1/8) Epoch 3, batch 21800, loss[loss=0.171, simple_loss=0.2393, pruned_loss=0.05138, over 4817.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2308, pruned_loss=0.047, over 972755.67 frames.], batch size: 27, lr: 5.34e-04
2022-05-04 15:56:44,178 INFO [train.py:715] (1/8) Epoch 3, batch 21850, loss[loss=0.1829, simple_loss=0.241, pruned_loss=0.06246, over 4893.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2306, pruned_loss=0.0466, over 973023.71 frames.], batch size: 22, lr: 5.34e-04
2022-05-04 15:57:22,927 INFO [train.py:715] (1/8) Epoch 3, batch 21900, loss[loss=0.1355, simple_loss=0.2015, pruned_loss=0.0347, over 4865.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2313, pruned_loss=0.04725, over 972930.09 frames.], batch size: 32, lr: 5.34e-04
2022-05-04 15:58:03,616 INFO [train.py:715] (1/8) Epoch 3, batch 21950, loss[loss=0.1687, simple_loss=0.2366, pruned_loss=0.05041, over 4902.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2308, pruned_loss=0.04725, over 972564.79 frames.], batch size: 19, lr: 5.34e-04
2022-05-04 15:58:43,246 INFO [train.py:715] (1/8) Epoch 3, batch 22000, loss[loss=0.1858, simple_loss=0.2502, pruned_loss=0.06067, over 4898.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2303, pruned_loss=0.04669, over 972495.43 frames.], batch size: 22, lr: 5.33e-04
2022-05-04 15:59:23,567 INFO [train.py:715] (1/8) Epoch 3, batch 22050, loss[loss=0.187, simple_loss=0.2477, pruned_loss=0.06311, over 4904.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2305, pruned_loss=0.04702, over 972747.38 frames.], batch size: 17, lr: 5.33e-04
2022-05-04 16:00:04,299 INFO [train.py:715] (1/8) Epoch 3, batch 22100, loss[loss=0.1449, simple_loss=0.2117, pruned_loss=0.03909, over 4870.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2308, pruned_loss=0.04705, over 972354.30 frames.], batch size: 16, lr: 5.33e-04
2022-05-04 16:00:44,821 INFO [train.py:715] (1/8) Epoch 3, batch 22150, loss[loss=0.1645, simple_loss=0.2329, pruned_loss=0.04805, over 4934.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2313, pruned_loss=0.04747, over 972587.90 frames.], batch size: 18, lr: 5.33e-04
2022-05-04 16:01:24,040 INFO [train.py:715] (1/8) Epoch 3, batch 22200, loss[loss=0.1706, simple_loss=0.2304, pruned_loss=0.05538, over 4806.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2307, pruned_loss=0.04725, over 972788.80 frames.], batch size: 12, lr: 5.33e-04
2022-05-04 16:02:04,288 INFO [train.py:715] (1/8) Epoch 3, batch 22250, loss[loss=0.1427, simple_loss=0.2219, pruned_loss=0.03169, over 4811.00 frames.], tot_loss[loss=0.1624, simple_loss=0.231, pruned_loss=0.0469, over 971622.45 frames.], batch size: 26, lr: 5.33e-04
2022-05-04 16:02:45,548 INFO [train.py:715] (1/8) Epoch 3, batch 22300, loss[loss=0.157, simple_loss=0.2232, pruned_loss=0.04547, over 4845.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2312, pruned_loss=0.04705, over 972063.65 frames.], batch size: 30, lr: 5.33e-04
2022-05-04 16:03:24,531 INFO [train.py:715] (1/8) Epoch 3, batch 22350, loss[loss=0.1596, simple_loss=0.2331, pruned_loss=0.04302, over 4814.00 frames.], tot_loss[loss=0.162, simple_loss=0.2305, pruned_loss=0.04671, over 971343.05 frames.], batch size: 25, lr: 5.33e-04
2022-05-04 16:04:04,608 INFO [train.py:715] (1/8) Epoch 3, batch 22400, loss[loss=0.1398, simple_loss=0.2071, pruned_loss=0.03621, over 4898.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2309, pruned_loss=0.04689, over 970715.97 frames.], batch size: 19, lr: 5.33e-04
2022-05-04 16:04:45,515 INFO [train.py:715] (1/8) Epoch 3, batch 22450, loss[loss=0.1396, simple_loss=0.2081, pruned_loss=0.0356, over 4968.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2304, pruned_loss=0.04645, over 971041.71 frames.], batch size: 15, lr: 5.32e-04
2022-05-04 16:05:25,966 INFO [train.py:715] (1/8) Epoch 3, batch 22500, loss[loss=0.1771, simple_loss=0.2446, pruned_loss=0.05474, over 4746.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2305, pruned_loss=0.04659, over 971670.10 frames.], batch size: 19, lr: 5.32e-04
2022-05-04 16:06:05,377 INFO [train.py:715] (1/8) Epoch 3, batch 22550, loss[loss=0.1398, simple_loss=0.2071, pruned_loss=0.03624, over 4871.00 frames.], tot_loss[loss=0.1621, simple_loss=0.231, pruned_loss=0.04655, over 972746.82 frames.], batch size: 22, lr: 5.32e-04
2022-05-04 16:06:45,640 INFO [train.py:715] (1/8) Epoch 3, batch 22600, loss[loss=0.1654, simple_loss=0.2385, pruned_loss=0.04612, over 4941.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2313, pruned_loss=0.04663, over 972026.05 frames.], batch size: 21, lr: 5.32e-04
2022-05-04 16:07:26,480 INFO [train.py:715] (1/8) Epoch 3, batch 22650, loss[loss=0.174, simple_loss=0.2432, pruned_loss=0.05244, over 4733.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2304, pruned_loss=0.04615, over 972013.79 frames.], batch size: 16, lr: 5.32e-04
2022-05-04 16:08:06,296 INFO [train.py:715] (1/8) Epoch 3, batch 22700, loss[loss=0.1109, simple_loss=0.1887, pruned_loss=0.01654, over 4779.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2306, pruned_loss=0.0466, over 972224.28 frames.], batch size: 12, lr: 5.32e-04
2022-05-04 16:08:46,700 INFO [train.py:715] (1/8) Epoch 3, batch 22750, loss[loss=0.175, simple_loss=0.2287, pruned_loss=0.06061, over 4904.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2314, pruned_loss=0.04723, over 973130.65 frames.], batch size: 18, lr: 5.32e-04
2022-05-04 16:09:27,102 INFO [train.py:715] (1/8) Epoch 3, batch 22800, loss[loss=0.1731, simple_loss=0.2355, pruned_loss=0.05538, over 4741.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2311, pruned_loss=0.04708, over 972936.77 frames.], batch size: 16, lr: 5.32e-04
2022-05-04 16:10:07,176 INFO [train.py:715] (1/8) Epoch 3, batch 22850, loss[loss=0.1353, simple_loss=0.1989, pruned_loss=0.03586, over 4744.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2319, pruned_loss=0.04784, over 972954.37 frames.], batch size: 16, lr: 5.32e-04
2022-05-04 16:10:46,895 INFO [train.py:715] (1/8) Epoch 3, batch 22900, loss[loss=0.1386, simple_loss=0.2118, pruned_loss=0.03266, over 4867.00 frames.], tot_loss[loss=0.163, simple_loss=0.2314, pruned_loss=0.04728, over 972719.94 frames.], batch size: 16, lr: 5.32e-04
2022-05-04 16:11:27,323 INFO [train.py:715] (1/8) Epoch 3, batch 22950, loss[loss=0.1654, simple_loss=0.2204, pruned_loss=0.05515, over 4967.00 frames.], tot_loss[loss=0.163, simple_loss=0.2314, pruned_loss=0.04723, over 973041.50 frames.], batch size: 35, lr: 5.31e-04
2022-05-04 16:12:08,435 INFO [train.py:715] (1/8) Epoch 3, batch 23000, loss[loss=0.1618, simple_loss=0.2474, pruned_loss=0.0381, over 4908.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2312, pruned_loss=0.04719, over 973857.50 frames.], batch size: 22, lr: 5.31e-04
2022-05-04 16:12:48,293 INFO [train.py:715] (1/8) Epoch 3, batch 23050, loss[loss=0.1633, simple_loss=0.2321, pruned_loss=0.04724, over 4844.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2304, pruned_loss=0.04651, over 974404.06 frames.], batch size: 34, lr: 5.31e-04
2022-05-04 16:13:28,628 INFO [train.py:715] (1/8) Epoch 3, batch 23100, loss[loss=0.1381, simple_loss=0.2074, pruned_loss=0.03442, over 4965.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2294, pruned_loss=0.04581, over 973912.92 frames.], batch size: 24, lr: 5.31e-04
2022-05-04 16:14:09,393 INFO [train.py:715] (1/8) Epoch 3, batch 23150, loss[loss=0.2105, simple_loss=0.2558, pruned_loss=0.08262, over 4892.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2292, pruned_loss=0.04589, over 974355.42 frames.], batch size: 13, lr: 5.31e-04
2022-05-04 16:14:49,980 INFO [train.py:715] (1/8) Epoch 3, batch 23200, loss[loss=0.1923, simple_loss=0.2593, pruned_loss=0.0626, over 4807.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2299, pruned_loss=0.04629, over 973528.35 frames.], batch size: 21, lr: 5.31e-04
2022-05-04 16:15:29,487 INFO [train.py:715] (1/8) Epoch 3, batch 23250, loss[loss=0.1557, simple_loss=0.2226, pruned_loss=0.04441, over 4950.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2302, pruned_loss=0.04629, over 972324.75 frames.], batch size: 23, lr: 5.31e-04
2022-05-04 16:16:10,259 INFO [train.py:715] (1/8) Epoch 3, batch 23300, loss[loss=0.1612, simple_loss=0.2473, pruned_loss=0.03755, over 4843.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2303, pruned_loss=0.04649, over 973252.17 frames.], batch size: 13, lr: 5.31e-04
2022-05-04 16:16:49,867 INFO [train.py:715] (1/8) Epoch 3, batch 23350, loss[loss=0.2292, simple_loss=0.285, pruned_loss=0.08668, over 4962.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2313, pruned_loss=0.04682, over 972073.61 frames.], batch size: 15, lr: 5.31e-04
2022-05-04 16:17:27,673 INFO [train.py:715] (1/8) Epoch 3, batch 23400, loss[loss=0.16, simple_loss=0.2096, pruned_loss=0.05518, over 4856.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2311, pruned_loss=0.04721, over 972725.14 frames.], batch size: 32, lr: 5.30e-04
2022-05-04 16:18:06,219 INFO [train.py:715] (1/8) Epoch 3, batch 23450, loss[loss=0.1541, simple_loss=0.2295, pruned_loss=0.03935, over 4986.00 frames.], tot_loss[loss=0.1628, simple_loss=0.231, pruned_loss=0.04736, over 973558.89 frames.], batch size: 25, lr: 5.30e-04
2022-05-04 16:18:44,908 INFO [train.py:715] (1/8) Epoch 3, batch 23500, loss[loss=0.1493, simple_loss=0.2221, pruned_loss=0.03829, over 4976.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2306, pruned_loss=0.047, over 973862.42 frames.], batch size: 24, lr: 5.30e-04
2022-05-04 16:19:24,105 INFO [train.py:715] (1/8) Epoch 3, batch 23550, loss[loss=0.1734, simple_loss=0.2393, pruned_loss=0.05379, over 4928.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2307, pruned_loss=0.04701, over 973642.85 frames.], batch size: 23, lr: 5.30e-04
2022-05-04 16:20:05,337 INFO [train.py:715] (1/8) Epoch 3, batch 23600, loss[loss=0.153, simple_loss=0.2274, pruned_loss=0.03927, over 4984.00 frames.], tot_loss[loss=0.162, simple_loss=0.2307, pruned_loss=0.04662, over 973719.94 frames.], batch size: 25, lr: 5.30e-04
2022-05-04 16:20:44,862 INFO [train.py:715] (1/8) Epoch 3, batch 23650, loss[loss=0.1364, simple_loss=0.2114, pruned_loss=0.03065, over 4813.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2298, pruned_loss=0.04664, over 973600.49 frames.], batch size: 25, lr: 5.30e-04
2022-05-04 16:21:24,818 INFO [train.py:715] (1/8) Epoch 3, batch 23700, loss[loss=0.1552, simple_loss=0.2266, pruned_loss=0.04189, over 4845.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2307, pruned_loss=0.04704, over 974300.47 frames.], batch size: 15, lr: 5.30e-04
2022-05-04 16:22:03,569 INFO [train.py:715] (1/8) Epoch 3, batch 23750, loss[loss=0.1344, simple_loss=0.2006, pruned_loss=0.03408, over 4741.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2316, pruned_loss=0.04763, over 973434.72 frames.], batch size: 19, lr: 5.30e-04
2022-05-04 16:22:43,180 INFO [train.py:715] (1/8) Epoch 3, batch 23800, loss[loss=0.165, simple_loss=0.2338, pruned_loss=0.04806, over 4872.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2309, pruned_loss=0.047, over 974182.40 frames.], batch size: 22, lr: 5.30e-04
2022-05-04 16:23:22,778 INFO [train.py:715] (1/8) Epoch 3, batch 23850, loss[loss=0.1491, simple_loss=0.2284, pruned_loss=0.03492, over 4750.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2313, pruned_loss=0.04702, over 973293.82 frames.], batch size: 16, lr: 5.30e-04
2022-05-04 16:24:02,497 INFO [train.py:715] (1/8) Epoch 3, batch 23900, loss[loss=0.1659, simple_loss=0.2321, pruned_loss=0.04986, over 4935.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2299, pruned_loss=0.0462, over 973227.04 frames.], batch size: 18, lr: 5.29e-04
2022-05-04 16:24:41,549 INFO [train.py:715] (1/8) Epoch 3, batch 23950, loss[loss=0.1403, simple_loss=0.2067, pruned_loss=0.03695, over 4689.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2303, pruned_loss=0.04663, over 973213.53 frames.], batch size: 15, lr: 5.29e-04
2022-05-04 16:25:20,395 INFO [train.py:715] (1/8) Epoch 3, batch 24000, loss[loss=0.1501, simple_loss=0.2201, pruned_loss=0.04003, over 4813.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2303, pruned_loss=0.04648, over 973714.14 frames.], batch size: 21, lr: 5.29e-04
2022-05-04 16:25:20,396 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 16:25:32,862 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1132, simple_loss=0.1992, pruned_loss=0.0136, over 914524.00 frames.
2022-05-04 16:26:12,209 INFO [train.py:715] (1/8) Epoch 3, batch 24050, loss[loss=0.1646, simple_loss=0.2383, pruned_loss=0.04542, over 4982.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2307, pruned_loss=0.04683, over 973880.36 frames.], batch size: 24, lr: 5.29e-04
2022-05-04 16:26:52,062 INFO [train.py:715] (1/8) Epoch 3, batch 24100, loss[loss=0.1539, simple_loss=0.2195, pruned_loss=0.04409, over 4834.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2311, pruned_loss=0.0471, over 973724.79 frames.], batch size: 25, lr: 5.29e-04
2022-05-04 16:27:30,860 INFO [train.py:715] (1/8) Epoch 3, batch 24150, loss[loss=0.1663, simple_loss=0.2314, pruned_loss=0.05061, over 4746.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2312, pruned_loss=0.04706, over 974914.99 frames.], batch size: 19, lr: 5.29e-04
2022-05-04 16:28:10,106 INFO [train.py:715] (1/8) Epoch 3, batch 24200, loss[loss=0.1749, simple_loss=0.2355, pruned_loss=0.05718, over 4707.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2316, pruned_loss=0.0469, over 974170.47 frames.], batch size: 15, lr: 5.29e-04
2022-05-04 16:28:50,504 INFO [train.py:715] (1/8) Epoch 3, batch 24250, loss[loss=0.1625, simple_loss=0.2193, pruned_loss=0.0528, over 4938.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2311, pruned_loss=0.04709, over 972722.69 frames.], batch size: 35, lr: 5.29e-04
2022-05-04 16:29:30,769 INFO [train.py:715] (1/8) Epoch 3, batch 24300, loss[loss=0.1641, simple_loss=0.2366, pruned_loss=0.04586, over 4817.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2309, pruned_loss=0.04707, over 973364.38 frames.], batch size: 27, lr: 5.29e-04
2022-05-04 16:30:10,084 INFO [train.py:715] (1/8) Epoch 3, batch 24350, loss[loss=0.1642, simple_loss=0.2269, pruned_loss=0.0508, over 4877.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2322, pruned_loss=0.04754, over 972979.82 frames.], batch size: 19, lr: 5.29e-04
2022-05-04 16:30:49,734 INFO [train.py:715] (1/8) Epoch 3, batch 24400, loss[loss=0.1369, simple_loss=0.2001, pruned_loss=0.03687, over 4802.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2322, pruned_loss=0.04824, over 972898.92 frames.], batch size: 18, lr: 5.28e-04
2022-05-04 16:31:29,802 INFO [train.py:715] (1/8) Epoch 3, batch 24450, loss[loss=0.1493, simple_loss=0.2086, pruned_loss=0.04503, over 4927.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2321, pruned_loss=0.04819, over 972308.35 frames.], batch size: 18, lr: 5.28e-04
2022-05-04 16:32:09,117 INFO [train.py:715] (1/8) Epoch 3, batch 24500, loss[loss=0.1829, simple_loss=0.2601, pruned_loss=0.05288, over 4767.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2312, pruned_loss=0.04748, over 972578.13 frames.], batch size: 14, lr: 5.28e-04
2022-05-04 16:32:48,511 INFO [train.py:715] (1/8) Epoch 3, batch 24550, loss[loss=0.2007, simple_loss=0.2463, pruned_loss=0.07754, over 4879.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2309, pruned_loss=0.04699, over 971423.15 frames.], batch size: 32, lr: 5.28e-04
2022-05-04 16:33:28,753 INFO [train.py:715] (1/8) Epoch 3, batch 24600, loss[loss=0.1464, simple_loss=0.2131, pruned_loss=0.03989, over 4873.00 frames.], tot_loss[loss=0.1613, simple_loss=0.23, pruned_loss=0.04633, over 971955.74 frames.], batch size: 22, lr: 5.28e-04
2022-05-04 16:34:08,287 INFO [train.py:715] (1/8) Epoch 3, batch 24650, loss[loss=0.1521, simple_loss=0.2197, pruned_loss=0.04227, over 4988.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2299, pruned_loss=0.04622, over 972321.85 frames.], batch size: 25, lr: 5.28e-04
2022-05-04 16:34:47,790 INFO [train.py:715] (1/8) Epoch 3, batch 24700, loss[loss=0.1717, simple_loss=0.2321, pruned_loss=0.05565, over 4841.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2308, pruned_loss=0.04734, over 972065.71 frames.], batch size: 30, lr: 5.28e-04
2022-05-04 16:35:26,410 INFO [train.py:715] (1/8) Epoch 3, batch 24750, loss[loss=0.17, simple_loss=0.2328, pruned_loss=0.05362, over 4993.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2304, pruned_loss=0.04734, over 972396.34 frames.], batch size: 14, lr: 5.28e-04
2022-05-04 16:36:07,061 INFO [train.py:715] (1/8) Epoch 3, batch 24800, loss[loss=0.1533, simple_loss=0.2188, pruned_loss=0.04385, over 4963.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2308, pruned_loss=0.04767, over 972238.32 frames.], batch size: 15, lr: 5.28e-04
2022-05-04 16:36:46,782 INFO [train.py:715] (1/8) Epoch 3, batch 24850, loss[loss=0.1534, simple_loss=0.2273, pruned_loss=0.03976, over 4933.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2307, pruned_loss=0.04753, over 971964.32 frames.], batch size: 21, lr: 5.28e-04
2022-05-04 16:37:25,560 INFO [train.py:715] (1/8) Epoch 3, batch 24900, loss[loss=0.167, simple_loss=0.2349, pruned_loss=0.0495, over 4951.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2312, pruned_loss=0.0475, over 971717.74 frames.], batch size: 21, lr: 5.27e-04
2022-05-04 16:38:05,474 INFO [train.py:715] (1/8) Epoch 3, batch 24950, loss[loss=0.1599, simple_loss=0.2226, pruned_loss=0.04861, over 4767.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2304, pruned_loss=0.04732, over 972522.17 frames.], batch size: 14, lr: 5.27e-04
2022-05-04 16:38:45,653 INFO [train.py:715] (1/8) Epoch 3, batch 25000, loss[loss=0.1549, simple_loss=0.2142, pruned_loss=0.04777, over 4751.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2297, pruned_loss=0.04697, over 972740.75 frames.], batch size: 16, lr: 5.27e-04
2022-05-04 16:39:25,200 INFO [train.py:715] (1/8) Epoch 3, batch 25050, loss[loss=0.1793, simple_loss=0.2308, pruned_loss=0.06387, over 4929.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2308, pruned_loss=0.0471, over 972466.46 frames.], batch size: 18, lr: 5.27e-04
2022-05-04 16:40:04,367 INFO [train.py:715] (1/8) Epoch 3, batch 25100, loss[loss=0.1653, simple_loss=0.235, pruned_loss=0.04783, over 4857.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2316, pruned_loss=0.04751, over 972205.58 frames.], batch size: 20, lr: 5.27e-04
2022-05-04 16:40:44,395 INFO [train.py:715] (1/8) Epoch 3, batch 25150, loss[loss=0.1653, simple_loss=0.2252, pruned_loss=0.05266, over 4837.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2311, pruned_loss=0.0469, over 972310.05 frames.], batch size: 15, lr: 5.27e-04
2022-05-04 16:41:23,891 INFO [train.py:715] (1/8) Epoch 3, batch 25200, loss[loss=0.1548, simple_loss=0.2175, pruned_loss=0.04604, over 4841.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2315, pruned_loss=0.0473, over 972402.08 frames.], batch size: 15, lr: 5.27e-04
2022-05-04 16:42:03,019 INFO [train.py:715] (1/8) Epoch 3, batch 25250, loss[loss=0.1574, simple_loss=0.2313, pruned_loss=0.04173, over 4968.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2316, pruned_loss=0.04729, over 971426.27 frames.], batch size: 24, lr: 5.27e-04
2022-05-04 16:42:43,125 INFO [train.py:715] (1/8) Epoch 3, batch 25300, loss[loss=0.1812, simple_loss=0.2406, pruned_loss=0.06084, over 4923.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2303, pruned_loss=0.04708, over 971616.73 frames.], batch size: 18, lr: 5.27e-04
2022-05-04 16:43:22,952 INFO [train.py:715] (1/8) Epoch 3, batch 25350, loss[loss=0.1837, simple_loss=0.235, pruned_loss=0.06619, over 4769.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2307, pruned_loss=0.04732, over 972275.62 frames.], batch size: 14, lr: 5.26e-04
2022-05-04 16:44:02,967 INFO [train.py:715] (1/8) Epoch 3, batch 25400, loss[loss=0.167, simple_loss=0.2286, pruned_loss=0.05272, over 4862.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2299, pruned_loss=0.04681, over 972081.06 frames.], batch size: 20, lr: 5.26e-04
2022-05-04 16:44:42,162 INFO [train.py:715] (1/8) Epoch 3, batch 25450, loss[loss=0.1351, simple_loss=0.1971, pruned_loss=0.03655, over 4818.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2305, pruned_loss=0.04707, over 971837.23 frames.], batch size: 26, lr: 5.26e-04
2022-05-04 16:45:22,335 INFO [train.py:715] (1/8) Epoch 3, batch 25500, loss[loss=0.1544, simple_loss=0.2331, pruned_loss=0.03786, over 4824.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2313, pruned_loss=0.04747, over 972084.19 frames.], batch size: 27, lr: 5.26e-04
2022-05-04 16:46:02,166 INFO [train.py:715] (1/8) Epoch 3, batch 25550, loss[loss=0.1497, simple_loss=0.2219, pruned_loss=0.03872, over 4925.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2308, pruned_loss=0.04692, over 972029.27 frames.], batch size: 29, lr: 5.26e-04
2022-05-04 16:46:41,622 INFO [train.py:715] (1/8) Epoch 3, batch 25600, loss[loss=0.1899, simple_loss=0.2452, pruned_loss=0.06728, over 4810.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2297, pruned_loss=0.04645, over 971817.76 frames.], batch size: 25, lr: 5.26e-04
2022-05-04 16:47:22,004 INFO [train.py:715] (1/8) Epoch 3, batch 25650, loss[loss=0.13, simple_loss=0.2035, pruned_loss=0.02827, over 4786.00 frames.], tot_loss[loss=0.1617, simple_loss=0.23, pruned_loss=0.04672, over 971711.25 frames.], batch size: 12, lr: 5.26e-04
2022-05-04 16:48:02,209 INFO [train.py:715] (1/8) Epoch 3, batch 25700, loss[loss=0.1358, simple_loss=0.2081, pruned_loss=0.03172, over 4927.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2302, pruned_loss=0.04678, over 970832.91 frames.], batch size: 29, lr: 5.26e-04
2022-05-04 16:48:41,539 INFO [train.py:715] (1/8) Epoch 3, batch 25750, loss[loss=0.1907, simple_loss=0.2575, pruned_loss=0.06194, over 4851.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2307, pruned_loss=0.0473, over 971371.26 frames.], batch size: 20, lr: 5.26e-04
2022-05-04 16:49:21,104 INFO [train.py:715] (1/8) Epoch 3, batch 25800, loss[loss=0.1389, simple_loss=0.2104, pruned_loss=0.03373, over 4905.00 frames.], tot_loss[loss=0.163, simple_loss=0.2309, pruned_loss=0.04753, over 972156.51 frames.], batch size: 19, lr: 5.26e-04
2022-05-04 16:50:01,082 INFO [train.py:715] (1/8) Epoch 3, batch 25850, loss[loss=0.18, simple_loss=0.2423, pruned_loss=0.05885, over 4988.00 frames.], tot_loss[loss=0.1628, simple_loss=0.231, pruned_loss=0.04735, over 972435.02 frames.], batch size: 15, lr: 5.25e-04
2022-05-04 16:50:39,397 INFO [train.py:715] (1/8) Epoch 3, batch 25900, loss[loss=0.1704, simple_loss=0.2354, pruned_loss=0.05273, over 4872.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2306, pruned_loss=0.04704, over 972326.00 frames.], batch size: 32, lr: 5.25e-04
2022-05-04 16:51:18,324 INFO [train.py:715] (1/8) Epoch 3, batch 25950, loss[loss=0.1331, simple_loss=0.2014, pruned_loss=0.03235, over 4881.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2303, pruned_loss=0.04694, over 971691.89 frames.], batch size: 22, lr: 5.25e-04
2022-05-04 16:51:58,433 INFO [train.py:715] (1/8) Epoch 3, batch 26000, loss[loss=0.1823, simple_loss=0.2467, pruned_loss=0.05899, over 4966.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2304, pruned_loss=0.04709, over 971107.49 frames.], batch size: 28, lr: 5.25e-04
2022-05-04 16:52:37,678 INFO [train.py:715] (1/8) Epoch 3, batch 26050, loss[loss=0.1915, simple_loss=0.2491, pruned_loss=0.06698, over 4954.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2295, pruned_loss=0.04668, over 970865.81 frames.], batch size: 21, lr: 5.25e-04
2022-05-04 16:53:16,013 INFO [train.py:715] (1/8) Epoch 3, batch 26100, loss[loss=0.1739, simple_loss=0.2399, pruned_loss=0.054, over 4861.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2298, pruned_loss=0.0467, over 971592.09 frames.], batch size: 16, lr: 5.25e-04
2022-05-04 16:53:55,503 INFO [train.py:715] (1/8) Epoch 3, batch 26150, loss[loss=0.1378, simple_loss=0.2224, pruned_loss=0.02662, over 4833.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2296, pruned_loss=0.04641, over 972008.95 frames.], batch size: 26, lr: 5.25e-04
2022-05-04 16:54:35,542 INFO [train.py:715] (1/8) Epoch 3, batch 26200, loss[loss=0.152, simple_loss=0.2414, pruned_loss=0.0313, over 4809.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2289, pruned_loss=0.0458, over 971825.47 frames.], batch size: 21, lr: 5.25e-04
2022-05-04 16:55:13,646 INFO [train.py:715] (1/8) Epoch 3, batch 26250, loss[loss=0.1765, simple_loss=0.2357, pruned_loss=0.05869, over 4918.00 frames.], tot_loss[loss=0.16, simple_loss=0.2291, pruned_loss=0.04548, over 972485.50 frames.], batch size: 17, lr: 5.25e-04
2022-05-04 16:55:52,855 INFO [train.py:715] (1/8) Epoch 3, batch 26300, loss[loss=0.1815, simple_loss=0.242, pruned_loss=0.06051, over 4874.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2295, pruned_loss=0.04601, over 972683.67 frames.], batch size: 30, lr: 5.25e-04
2022-05-04 16:56:32,818 INFO [train.py:715] (1/8) Epoch 3, batch 26350, loss[loss=0.1375, simple_loss=0.1996, pruned_loss=0.03767, over 4768.00 frames.], tot_loss[loss=0.1614, simple_loss=0.23, pruned_loss=0.04634, over 972730.59 frames.], batch size: 14, lr: 5.24e-04
2022-05-04 16:57:12,181 INFO [train.py:715] (1/8) Epoch 3, batch 26400, loss[loss=0.1792, simple_loss=0.2557, pruned_loss=0.05131, over 4815.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2302, pruned_loss=0.04617, over 973145.61 frames.], batch size: 26, lr: 5.24e-04
2022-05-04 16:57:51,174 INFO [train.py:715] (1/8) Epoch 3, batch 26450, loss[loss=0.1541, simple_loss=0.2215, pruned_loss=0.0434, over 4866.00 frames.], tot_loss[loss=0.161, simple_loss=0.2299, pruned_loss=0.04607, over 973085.60 frames.], batch size: 16, lr: 5.24e-04
2022-05-04 16:58:30,426 INFO [train.py:715] (1/8) Epoch 3, batch 26500, loss[loss=0.1465, simple_loss=0.2202, pruned_loss=0.03635, over 4790.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2301, pruned_loss=0.04652, over 972558.33 frames.], batch size: 18, lr: 5.24e-04
2022-05-04 16:59:09,908 INFO [train.py:715] (1/8) Epoch 3, batch 26550, loss[loss=0.1473, simple_loss=0.2208, pruned_loss=0.03688, over 4945.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2301, pruned_loss=0.04618, over 972397.91 frames.], batch size: 21, lr: 5.24e-04
2022-05-04 16:59:48,109 INFO [train.py:715] (1/8) Epoch 3, batch 26600, loss[loss=0.2055, simple_loss=0.268, pruned_loss=0.07157, over 4892.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2317, pruned_loss=0.04667, over 972814.84 frames.], batch size: 22, lr: 5.24e-04
2022-05-04 17:00:27,327 INFO [train.py:715] (1/8) Epoch 3, batch 26650, loss[loss=0.1632, simple_loss=0.2344, pruned_loss=0.04597, over 4753.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2325, pruned_loss=0.04772, over 972711.52 frames.], batch size: 16, lr: 5.24e-04
2022-05-04 17:01:07,868 INFO [train.py:715] (1/8) Epoch 3, batch 26700, loss[loss=0.1518, simple_loss=0.2324, pruned_loss=0.03559, over 4932.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2323, pruned_loss=0.04742, over 973273.71 frames.], batch size: 21, lr: 5.24e-04
2022-05-04 17:01:47,348 INFO [train.py:715] (1/8) Epoch 3, batch 26750, loss[loss=0.1551, simple_loss=0.2147, pruned_loss=0.04774, over 4766.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2317, pruned_loss=0.04698, over 973430.03 frames.], batch size: 19, lr: 5.24e-04
2022-05-04 17:02:26,596 INFO [train.py:715] (1/8) Epoch 3, batch 26800, loss[loss=0.1783, simple_loss=0.2377, pruned_loss=0.05947, over 4758.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2326, pruned_loss=0.04741, over 973143.19 frames.], batch size: 18, lr: 5.24e-04
2022-05-04 17:03:06,720 INFO [train.py:715] (1/8) Epoch 3, batch 26850, loss[loss=0.1605, simple_loss=0.2154, pruned_loss=0.05278, over 4919.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2308, pruned_loss=0.04647, over 971564.28 frames.], batch size: 18, lr: 5.23e-04
2022-05-04 17:03:47,102 INFO [train.py:715] (1/8) Epoch 3, batch 26900, loss[loss=0.145, simple_loss=0.2195, pruned_loss=0.03524, over 4892.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2301, pruned_loss=0.04616, over 970870.09 frames.], batch size: 17, lr: 5.23e-04
2022-05-04 17:04:26,660 INFO [train.py:715] (1/8) Epoch 3, batch 26950, loss[loss=0.1573, simple_loss=0.225, pruned_loss=0.0448, over 4900.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2303, pruned_loss=0.04609, over 970686.02 frames.], batch size: 19, lr: 5.23e-04
2022-05-04 17:05:05,428 INFO [train.py:715] (1/8) Epoch 3, batch 27000, loss[loss=0.1392, simple_loss=0.2094, pruned_loss=0.03452, over 4984.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2307, pruned_loss=0.04622, over 971074.07 frames.], batch size: 14, lr: 5.23e-04
2022-05-04 17:05:05,428 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 17:05:14,909 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1134, simple_loss=0.1995, pruned_loss=0.01366, over 914524.00 frames.
2022-05-04 17:05:54,551 INFO [train.py:715] (1/8) Epoch 3, batch 27050, loss[loss=0.1802, simple_loss=0.2495, pruned_loss=0.05542, over 4950.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2299, pruned_loss=0.04626, over 971357.03 frames.], batch size: 21, lr: 5.23e-04
2022-05-04 17:06:34,873 INFO [train.py:715] (1/8) Epoch 3, batch 27100, loss[loss=0.1687, simple_loss=0.2396, pruned_loss=0.04891, over 4800.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2307, pruned_loss=0.04686, over 971723.10 frames.], batch size: 21, lr: 5.23e-04
2022-05-04 17:07:14,164 INFO [train.py:715] (1/8) Epoch 3, batch 27150, loss[loss=0.1701, simple_loss=0.2438, pruned_loss=0.04823, over 4930.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2309, pruned_loss=0.04694, over 971455.02 frames.], batch size: 18, lr: 5.23e-04
2022-05-04 17:07:52,931 INFO [train.py:715] (1/8) Epoch 3, batch 27200, loss[loss=0.1668, simple_loss=0.2417, pruned_loss=0.04596, over 4757.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2311, pruned_loss=0.04685, over 970683.14 frames.], batch size: 19, lr: 5.23e-04
2022-05-04 17:08:32,667 INFO [train.py:715] (1/8) Epoch 3, batch 27250, loss[loss=0.1834, simple_loss=0.2288, pruned_loss=0.06895, over 4941.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2303, pruned_loss=0.04647, over 971101.03 frames.], batch size: 35, lr: 5.23e-04
2022-05-04 17:09:12,361 INFO [train.py:715] (1/8) Epoch 3, batch 27300, loss[loss=0.1875, simple_loss=0.259, pruned_loss=0.05805, over 4968.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2303, pruned_loss=0.04622, over 972145.48 frames.], batch size: 28, lr: 5.23e-04
2022-05-04 17:09:51,024 INFO [train.py:715] (1/8) Epoch 3, batch 27350, loss[loss=0.1583, simple_loss=0.2357, pruned_loss=0.04047, over 4951.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2301, pruned_loss=0.04619, over 971973.13 frames.], batch size: 39, lr: 5.22e-04
2022-05-04 17:10:30,266 INFO [train.py:715] (1/8) Epoch 3, batch 27400, loss[loss=0.1677, simple_loss=0.2326, pruned_loss=0.05135, over 4776.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04774, over 972438.67 frames.], batch size: 17, lr: 5.22e-04
2022-05-04 17:11:10,412 INFO [train.py:715] (1/8) Epoch 3, batch 27450, loss[loss=0.1989, simple_loss=0.2654, pruned_loss=0.0662, over 4907.00 frames.], tot_loss[loss=0.1637, simple_loss=0.232, pruned_loss=0.04775, over 973101.26 frames.], batch size: 18, lr: 5.22e-04
2022-05-04 17:11:49,739 INFO [train.py:715] (1/8) Epoch 3, batch 27500, loss[loss=0.1741, simple_loss=0.2405, pruned_loss=0.05381, over 4943.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2316, pruned_loss=0.04757, over 972520.94 frames.], batch size: 23, lr: 5.22e-04
2022-05-04 17:12:28,641 INFO [train.py:715] (1/8) Epoch 3, batch 27550, loss[loss=0.1731, simple_loss=0.2497, pruned_loss=0.04827, over 4798.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2317, pruned_loss=0.04754, over 972182.99 frames.], batch size: 24, lr: 5.22e-04
2022-05-04 17:13:08,354 INFO [train.py:715] (1/8) Epoch 3, batch 27600, loss[loss=0.1693, simple_loss=0.231, pruned_loss=0.05384, over 4773.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2309, pruned_loss=0.04709, over 971677.10 frames.], batch size: 18, lr: 5.22e-04
2022-05-04 17:13:48,000 INFO [train.py:715] (1/8) Epoch 3, batch 27650, loss[loss=0.1838, simple_loss=0.252, pruned_loss=0.05786, over 4893.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2314, pruned_loss=0.04713, over 971764.60 frames.], batch size: 39, lr: 5.22e-04
2022-05-04 17:14:26,623 INFO [train.py:715] (1/8) Epoch 3, batch 27700, loss[loss=0.1649, simple_loss=0.23, pruned_loss=0.04989, over 4910.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2317, pruned_loss=0.04768, over 971811.70 frames.], batch size: 17, lr: 5.22e-04
2022-05-04 17:15:06,394 INFO [train.py:715] (1/8) Epoch 3, batch 27750, loss[loss=0.1948, simple_loss=0.2775, pruned_loss=0.0561, over 4795.00 frames.], tot_loss[loss=0.162, simple_loss=0.2302, pruned_loss=0.04684, over 971627.11 frames.], batch size: 21, lr: 5.22e-04
2022-05-04 17:15:46,346 INFO [train.py:715] (1/8) Epoch 3, batch 27800, loss[loss=0.1422, simple_loss=0.2155, pruned_loss=0.03448, over 4828.00 frames.], tot_loss[loss=0.1616, simple_loss=0.23, pruned_loss=0.04662, over 971822.01 frames.], batch size: 15, lr: 5.22e-04
2022-05-04 17:16:25,739 INFO [train.py:715] (1/8) Epoch 3, batch 27850, loss[loss=0.1655, simple_loss=0.2397, pruned_loss=0.04563, over 4954.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2293, pruned_loss=0.04604, over 971846.50 frames.], batch size: 21, lr: 5.21e-04
2022-05-04 17:17:04,211 INFO [train.py:715] (1/8) Epoch 3, batch 27900, loss[loss=0.1735, simple_loss=0.2418, pruned_loss=0.05258, over 4791.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2289, pruned_loss=0.04632, over 972174.45 frames.], batch size: 14, lr: 5.21e-04
2022-05-04 17:17:43,816 INFO [train.py:715] (1/8) Epoch 3, batch 27950, loss[loss=0.1462, simple_loss=0.2243, pruned_loss=0.03411, over 4981.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2289, pruned_loss=0.04605, over 973044.69 frames.], batch size: 35, lr: 5.21e-04
2022-05-04 17:18:23,715 INFO [train.py:715] (1/8) Epoch 3, batch 28000, loss[loss=0.1255, simple_loss=0.1861, pruned_loss=0.03243, over 4751.00 frames.], tot_loss[loss=0.1609, simple_loss=0.229, pruned_loss=0.04638, over 972087.59 frames.], batch size: 12, lr: 5.21e-04
2022-05-04 17:19:02,276 INFO [train.py:715] (1/8) Epoch 3, batch 28050, loss[loss=0.141, simple_loss=0.2065, pruned_loss=0.03775, over 4742.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2303, pruned_loss=0.04703, over 972299.80 frames.], batch size: 12, lr: 5.21e-04
2022-05-04 17:19:41,710 INFO [train.py:715] (1/8) Epoch 3, batch 28100, loss[loss=0.1946, simple_loss=0.249, pruned_loss=0.07009, over 4913.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2311, pruned_loss=0.0476, over 972183.11 frames.], batch size: 17, lr: 5.21e-04
2022-05-04 17:20:21,588 INFO [train.py:715] (1/8) Epoch 3, batch 28150, loss[loss=0.1573, simple_loss=0.2159, pruned_loss=0.04935, over 4818.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2312, pruned_loss=0.04763, over 972445.03 frames.], batch size: 13, lr: 5.21e-04
2022-05-04 17:21:00,809 INFO [train.py:715] (1/8) Epoch 3, batch 28200, loss[loss=0.152, simple_loss=0.2249, pruned_loss=0.03951, over 4804.00 frames.], tot_loss[loss=0.163, simple_loss=0.2311, pruned_loss=0.04743, over 972524.32 frames.], batch size: 21, lr: 5.21e-04
2022-05-04 17:21:39,658 INFO [train.py:715] (1/8) Epoch 3, batch 28250, loss[loss=0.1567, simple_loss=0.223, pruned_loss=0.04517, over 4816.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2303, pruned_loss=0.04674, over 972665.79 frames.], batch size: 21, lr: 5.21e-04
2022-05-04 17:22:18,998 INFO [train.py:715] (1/8) Epoch 3, batch 28300, loss[loss=0.1593, simple_loss=0.2325, pruned_loss=0.04303, over 4941.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2303, pruned_loss=0.04646, over 972658.43 frames.], batch size: 21, lr: 5.21e-04
2022-05-04 17:22:58,003 INFO [train.py:715] (1/8) Epoch 3, batch 28350, loss[loss=0.1663, simple_loss=0.2287, pruned_loss=0.05198, over 4744.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2302, pruned_loss=0.04601, over 972320.95 frames.], batch size: 16, lr: 5.21e-04
2022-05-04 17:23:37,192 INFO [train.py:715] (1/8) Epoch 3, batch 28400, loss[loss=0.1865, simple_loss=0.244, pruned_loss=0.0645, over 4927.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2303, pruned_loss=0.04625, over 972471.91 frames.], batch size: 18, lr: 5.20e-04
2022-05-04 17:24:15,827 INFO [train.py:715] (1/8) Epoch 3, batch 28450, loss[loss=0.1778, simple_loss=0.2406, pruned_loss=0.05756, over 4987.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2285, pruned_loss=0.04524, over 972934.72 frames.], batch size: 28, lr: 5.20e-04
2022-05-04 17:24:55,564 INFO [train.py:715] (1/8) Epoch 3, batch 28500, loss[loss=0.1725, simple_loss=0.237, pruned_loss=0.05402, over 4915.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2274, pruned_loss=0.04515, over 972731.15 frames.], batch size: 17, lr: 5.20e-04
2022-05-04 17:25:34,507 INFO [train.py:715] (1/8) Epoch 3, batch 28550, loss[loss=0.1954, simple_loss=0.2462, pruned_loss=0.0723, over 4881.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2274, pruned_loss=0.04505, over 972073.23 frames.], batch size: 16, lr: 5.20e-04
2022-05-04 17:26:13,422 INFO [train.py:715] (1/8) Epoch 3, batch 28600, loss[loss=0.1694, simple_loss=0.2392, pruned_loss=0.04983, over 4927.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2281, pruned_loss=0.04519, over 971674.81 frames.], batch size: 23, lr: 5.20e-04
2022-05-04 17:26:53,128 INFO [train.py:715] (1/8) Epoch 3, batch 28650, loss[loss=0.1359, simple_loss=0.207, pruned_loss=0.0324, over 4928.00 frames.], tot_loss[loss=0.1592, simple_loss=0.228, pruned_loss=0.04515, over 972380.75 frames.], batch size: 29, lr: 5.20e-04
2022-05-04 17:27:33,006 INFO [train.py:715] (1/8) Epoch 3, batch 28700, loss[loss=0.1451, simple_loss=0.211, pruned_loss=0.03963, over 4783.00 frames.], tot_loss[loss=0.16, simple_loss=0.2285, pruned_loss=0.04576, over 973034.13 frames.], batch size: 18, lr: 5.20e-04
2022-05-04 17:28:12,158 INFO [train.py:715] (1/8) Epoch 3, batch 28750, loss[loss=0.1366, simple_loss=0.2109, pruned_loss=0.03115, over 4890.00 frames.], tot_loss[loss=0.1594, simple_loss=0.228, pruned_loss=0.04537, over 973337.80 frames.], batch size: 16, lr: 5.20e-04
2022-05-04 17:28:51,999 INFO [train.py:715] (1/8) Epoch 3, batch 28800, loss[loss=0.1838, simple_loss=0.2564, pruned_loss=0.05555, over 4791.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2284, pruned_loss=0.04526, over 973954.10 frames.], batch size: 24, lr: 5.20e-04
2022-05-04 17:29:32,015 INFO [train.py:715] (1/8) Epoch 3, batch 28850, loss[loss=0.1746, simple_loss=0.2405, pruned_loss=0.05433, over 4699.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2291, pruned_loss=0.04569, over 973751.85 frames.], batch size: 15, lr: 5.20e-04
2022-05-04 17:30:11,195 INFO [train.py:715] (1/8) Epoch 3, batch 28900, loss[loss=0.1909, simple_loss=0.2597, pruned_loss=0.06108, over 4820.00 frames.], tot_loss[loss=0.1601, simple_loss=0.229, pruned_loss=0.0456, over 973193.07 frames.], batch size: 15, lr: 5.19e-04
2022-05-04 17:30:50,076 INFO [train.py:715] (1/8) Epoch 3, batch 28950, loss[loss=0.1816, simple_loss=0.2652, pruned_loss=0.04903, over 4966.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2302, pruned_loss=0.04613, over 973125.58 frames.], batch size: 15, lr: 5.19e-04
2022-05-04 17:31:29,812 INFO [train.py:715] (1/8) Epoch 3, batch 29000, loss[loss=0.1508, simple_loss=0.2234, pruned_loss=0.03917, over 4769.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2303, pruned_loss=0.04645, over 972447.93 frames.], batch size: 19, lr: 5.19e-04
2022-05-04 17:32:10,052 INFO [train.py:715] (1/8) Epoch 3, batch 29050, loss[loss=0.1641, simple_loss=0.2222, pruned_loss=0.05298, over 4956.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2301, pruned_loss=0.04653, over 972580.48 frames.], batch size: 24, lr: 5.19e-04
2022-05-04 17:32:48,615 INFO [train.py:715] (1/8) Epoch 3, batch 29100, loss[loss=0.1414, simple_loss=0.2144, pruned_loss=0.03425, over 4942.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2298, pruned_loss=0.04618, over 972194.24 frames.], batch size: 24, lr: 5.19e-04
2022-05-04 17:33:28,200 INFO [train.py:715] (1/8) Epoch 3, batch 29150, loss[loss=0.1567, simple_loss=0.2176, pruned_loss=0.04788, over 4755.00 frames.], tot_loss[loss=0.161, simple_loss=0.2299, pruned_loss=0.04611, over 972196.92 frames.], batch size: 19, lr: 5.19e-04
2022-05-04 17:34:08,092 INFO [train.py:715] (1/8) Epoch 3, batch 29200, loss[loss=0.1262, simple_loss=0.1929, pruned_loss=0.02977, over 4792.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2292, pruned_loss=0.04587, over 972475.84 frames.], batch size: 14, lr: 5.19e-04
2022-05-04 17:34:47,190 INFO [train.py:715] (1/8) Epoch 3, batch 29250, loss[loss=0.1709, simple_loss=0.2412, pruned_loss=0.05031, over 4846.00 frames.], tot_loss[loss=0.16, simple_loss=0.2287, pruned_loss=0.0456, over 971697.30 frames.], batch size: 32, lr: 5.19e-04
2022-05-04 17:35:26,069 INFO [train.py:715] (1/8) Epoch 3, batch 29300, loss[loss=0.1429, simple_loss=0.2195, pruned_loss=0.0331, over 4860.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2292, pruned_loss=0.0459, over 971989.75 frames.], batch size: 34, lr: 5.19e-04
2022-05-04 17:36:06,256 INFO [train.py:715] (1/8) Epoch 3, batch 29350, loss[loss=0.161, simple_loss=0.2227, pruned_loss=0.04962, over 4738.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2281, pruned_loss=0.04526, over 971843.15 frames.], batch size: 16, lr: 5.19e-04
2022-05-04 17:36:45,935 INFO [train.py:715] (1/8) Epoch 3, batch 29400, loss[loss=0.1304, simple_loss=0.2013, pruned_loss=0.02975, over 4803.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2278, pruned_loss=0.04541, over 971193.80 frames.], batch size: 12, lr: 5.18e-04
2022-05-04 17:37:24,691 INFO [train.py:715] (1/8) Epoch 3, batch 29450, loss[loss=0.1948, simple_loss=0.2462, pruned_loss=0.07171, over 4830.00 frames.], tot_loss[loss=0.16, simple_loss=0.2282, pruned_loss=0.04591, over 971981.22 frames.], batch size: 13, lr: 5.18e-04
2022-05-04 17:38:03,871 INFO [train.py:715] (1/8) Epoch 3, batch 29500, loss[loss=0.1477, simple_loss=0.2227, pruned_loss=0.03638, over 4982.00 frames.], tot_loss[loss=0.16, simple_loss=0.2284, pruned_loss=0.0458, over 972466.09 frames.], batch size: 28, lr: 5.18e-04
2022-05-04 17:38:43,451 INFO [train.py:715] (1/8) Epoch 3, batch 29550, loss[loss=0.163, simple_loss=0.2312, pruned_loss=0.04737, over 4928.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2291, pruned_loss=0.04596, over 972417.75 frames.], batch size: 18, lr: 5.18e-04
2022-05-04 17:39:22,772 INFO [train.py:715] (1/8) Epoch 3, batch 29600, loss[loss=0.1768, simple_loss=0.2429, pruned_loss=0.05539, over 4924.00 frames.], tot_loss[loss=0.1604, simple_loss=0.229, pruned_loss=0.04591, over 972605.31 frames.], batch size: 18, lr: 5.18e-04
2022-05-04 17:40:01,837 INFO [train.py:715] (1/8) Epoch 3, batch 29650, loss[loss=0.1505, simple_loss=0.2335, pruned_loss=0.03371, over 4923.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2295, pruned_loss=0.04599, over 972994.35 frames.], batch size: 23, lr: 5.18e-04
2022-05-04 17:40:41,989 INFO [train.py:715] (1/8) Epoch 3, batch 29700, loss[loss=0.1452, simple_loss=0.2132, pruned_loss=0.03859, over 4876.00 frames.], tot_loss[loss=0.161, simple_loss=0.2298, pruned_loss=0.04607, over 972925.32 frames.], batch size: 16, lr: 5.18e-04
2022-05-04 17:41:22,016 INFO [train.py:715] (1/8) Epoch 3, batch 29750, loss[loss=0.1349, simple_loss=0.2011, pruned_loss=0.03433, over 4847.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2299, pruned_loss=0.0464, over 972539.67 frames.], batch size: 30, lr: 5.18e-04
2022-05-04 17:42:00,530 INFO [train.py:715] (1/8) Epoch 3, batch 29800, loss[loss=0.1719, simple_loss=0.2477, pruned_loss=0.04802, over 4800.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2296, pruned_loss=0.04636, over 972435.35 frames.], batch size: 24, lr: 5.18e-04
2022-05-04 17:42:40,517 INFO [train.py:715] (1/8) Epoch 3, batch 29850, loss[loss=0.1893, simple_loss=0.2504, pruned_loss=0.06405, over 4890.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2305, pruned_loss=0.04659, over 972302.49 frames.], batch size: 22, lr: 5.18e-04
2022-05-04 17:43:20,046 INFO [train.py:715] (1/8) Epoch 3, batch 29900, loss[loss=0.172, simple_loss=0.2438, pruned_loss=0.0501, over 4704.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2312, pruned_loss=0.04693, over 971786.00 frames.], batch size: 15, lr: 5.18e-04
2022-05-04 17:43:58,720 INFO [train.py:715] (1/8) Epoch 3, batch 29950, loss[loss=0.1618, simple_loss=0.2313, pruned_loss=0.04614, over 4944.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2307, pruned_loss=0.04652, over 972993.49 frames.], batch size: 35, lr: 5.17e-04
2022-05-04 17:44:37,447 INFO [train.py:715] (1/8) Epoch 3, batch 30000, loss[loss=0.1418, simple_loss=0.2171, pruned_loss=0.03325, over 4800.00 frames.], tot_loss[loss=0.161, simple_loss=0.2301, pruned_loss=0.04596, over 972551.43 frames.], batch size: 24, lr: 5.17e-04
2022-05-04 17:44:37,448 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 17:44:47,857 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1135, simple_loss=0.1993, pruned_loss=0.01381, over 914524.00 frames.
2022-05-04 17:45:26,661 INFO [train.py:715] (1/8) Epoch 3, batch 30050, loss[loss=0.1594, simple_loss=0.2407, pruned_loss=0.03899, over 4735.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2291, pruned_loss=0.04554, over 972709.66 frames.], batch size: 16, lr: 5.17e-04
2022-05-04 17:46:06,300 INFO [train.py:715] (1/8) Epoch 3, batch 30100, loss[loss=0.1447, simple_loss=0.2091, pruned_loss=0.0402, over 4842.00 frames.], tot_loss[loss=0.1598, simple_loss=0.229, pruned_loss=0.04533, over 973062.97 frames.], batch size: 30, lr: 5.17e-04
2022-05-04 17:46:46,369 INFO [train.py:715] (1/8) Epoch 3, batch 30150, loss[loss=0.157, simple_loss=0.2206, pruned_loss=0.04669, over 4840.00 frames.], tot_loss[loss=0.16, simple_loss=0.229, pruned_loss=0.04552, over 972496.68 frames.], batch size: 32, lr: 5.17e-04
2022-05-04 17:47:24,502 INFO [train.py:715] (1/8) Epoch 3, batch 30200, loss[loss=0.1631, simple_loss=0.2312, pruned_loss=0.04752, over 4807.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2299, pruned_loss=0.04611, over 972021.72 frames.], batch size: 21, lr: 5.17e-04
2022-05-04 17:48:04,129 INFO [train.py:715] (1/8) Epoch 3, batch 30250, loss[loss=0.176, simple_loss=0.2383, pruned_loss=0.05689, over 4921.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2299, pruned_loss=0.04592, over 972191.59 frames.], batch size: 23, lr: 5.17e-04
2022-05-04 17:48:44,309 INFO [train.py:715] (1/8) Epoch 3, batch 30300, loss[loss=0.1774, simple_loss=0.2358, pruned_loss=0.05952, over 4831.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2309, pruned_loss=0.04634, over 973466.97 frames.], batch size: 15, lr: 5.17e-04
2022-05-04 17:49:23,079 INFO [train.py:715] (1/8) Epoch 3, batch 30350, loss[loss=0.1338, simple_loss=0.2041, pruned_loss=0.03172, over 4706.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2303, pruned_loss=0.04646, over 973081.54 frames.], batch size: 15, lr: 5.17e-04
2022-05-04 17:50:02,737 INFO [train.py:715] (1/8) Epoch 3, batch 30400, loss[loss=0.1972, simple_loss=0.2595, pruned_loss=0.06745, over 4811.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2304, pruned_loss=0.04636, over 973083.64 frames.], batch size: 21, lr: 5.17e-04
2022-05-04 17:50:42,520 INFO [train.py:715] (1/8) Epoch 3, batch 30450, loss[loss=0.1585, simple_loss=0.2291, pruned_loss=0.0439, over 4980.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04581, over 972690.98 frames.], batch size: 31, lr: 5.16e-04
2022-05-04 17:51:22,933 INFO [train.py:715] (1/8) Epoch 3, batch 30500, loss[loss=0.1919, simple_loss=0.248, pruned_loss=0.06797, over 4862.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2314, pruned_loss=0.04723, over 973145.58 frames.], batch size: 16, lr: 5.16e-04
2022-05-04 17:52:02,151 INFO [train.py:715] (1/8) Epoch 3, batch 30550, loss[loss=0.1752, simple_loss=0.254, pruned_loss=0.04824, over 4810.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2311, pruned_loss=0.04684, over 972641.68 frames.], batch size: 21, lr: 5.16e-04
2022-05-04 17:52:41,687 INFO [train.py:715] (1/8) Epoch 3, batch 30600, loss[loss=0.1396, simple_loss=0.2077, pruned_loss=0.03575, over 4862.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2307, pruned_loss=0.0469, over 972583.30 frames.], batch size: 32, lr: 5.16e-04
2022-05-04 17:53:21,641 INFO [train.py:715] (1/8) Epoch 3, batch 30650, loss[loss=0.1469, simple_loss=0.2174, pruned_loss=0.03816, over 4753.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2295, pruned_loss=0.04641, over 972407.82 frames.], batch size: 16, lr: 5.16e-04
2022-05-04 17:54:00,306 INFO [train.py:715] (1/8) Epoch 3, batch 30700, loss[loss=0.1514, simple_loss=0.2272, pruned_loss=0.03775, over 4979.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2303, pruned_loss=0.04698, over 972636.45 frames.], batch size: 25, lr: 5.16e-04
2022-05-04 17:54:39,863 INFO [train.py:715] (1/8) Epoch 3, batch 30750, loss[loss=0.1619, simple_loss=0.233, pruned_loss=0.04544, over 4846.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2303, pruned_loss=0.04723, over 973452.13 frames.], batch size: 20, lr: 5.16e-04
2022-05-04 17:55:19,270 INFO [train.py:715] (1/8) Epoch 3, batch 30800, loss[loss=0.1802, simple_loss=0.2416, pruned_loss=0.0594, over 4869.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2309, pruned_loss=0.04733, over 973365.71 frames.], batch size: 39, lr: 5.16e-04
2022-05-04 17:55:59,087 INFO [train.py:715] (1/8) Epoch 3, batch 30850, loss[loss=0.1975, simple_loss=0.2653, pruned_loss=0.06484, over 4797.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2303, pruned_loss=0.04713, over 973140.45 frames.], batch size: 14, lr: 5.16e-04
2022-05-04 17:56:37,365 INFO [train.py:715] (1/8) Epoch 3, batch 30900, loss[loss=0.1801, simple_loss=0.2451, pruned_loss=0.05758, over 4767.00 frames.], tot_loss[loss=0.161, simple_loss=0.2293, pruned_loss=0.04636, over 972657.46 frames.], batch size: 18, lr: 5.16e-04
2022-05-04 17:57:16,438 INFO [train.py:715] (1/8) Epoch 3, batch 30950, loss[loss=0.1757, simple_loss=0.2412, pruned_loss=0.05511, over 4982.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2303, pruned_loss=0.04689, over 972891.00 frames.], batch size: 28, lr: 5.15e-04
2022-05-04 17:57:55,765 INFO [train.py:715] (1/8) Epoch 3, batch 31000, loss[loss=0.162, simple_loss=0.2348, pruned_loss=0.04456, over 4886.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2304, pruned_loss=0.04699, over 972475.88 frames.], batch size: 19, lr: 5.15e-04
2022-05-04 17:58:35,033 INFO [train.py:715] (1/8) Epoch 3, batch 31050, loss[loss=0.1958, simple_loss=0.2596, pruned_loss=0.06599, over 4776.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2295, pruned_loss=0.04675, over 972140.62 frames.], batch size: 18, lr: 5.15e-04
2022-05-04 17:59:13,607 INFO [train.py:715] (1/8) Epoch 3, batch 31100, loss[loss=0.1327, simple_loss=0.2091, pruned_loss=0.0281, over 4988.00 frames.], tot_loss[loss=0.163, simple_loss=0.2308, pruned_loss=0.04765, over 972895.43 frames.], batch size: 28, lr: 5.15e-04
2022-05-04 17:59:53,183 INFO [train.py:715] (1/8) Epoch 3, batch 31150, loss[loss=0.1656, simple_loss=0.2333, pruned_loss=0.04898, over 4698.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2308, pruned_loss=0.04707, over 972367.17 frames.], batch size: 15, lr: 5.15e-04
2022-05-04 18:00:32,418 INFO [train.py:715] (1/8) Epoch 3, batch 31200, loss[loss=0.1624, simple_loss=0.236, pruned_loss=0.0444, over 4928.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2307, pruned_loss=0.04658, over 972095.09 frames.], batch size: 18, lr: 5.15e-04
2022-05-04 18:01:11,062 INFO [train.py:715] (1/8) Epoch 3, batch 31250, loss[loss=0.1419, simple_loss=0.2148, pruned_loss=0.0345, over 4812.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2307, pruned_loss=0.04628, over 972236.23 frames.], batch size: 21, lr: 5.15e-04
2022-05-04 18:01:50,133 INFO [train.py:715] (1/8) Epoch 3, batch 31300, loss[loss=0.1249, simple_loss=0.1902, pruned_loss=0.0298, over 4951.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2306, pruned_loss=0.04613, over 972028.73 frames.], batch size: 21, lr: 5.15e-04
2022-05-04 18:02:29,482 INFO [train.py:715] (1/8) Epoch 3, batch 31350, loss[loss=0.1686, simple_loss=0.227, pruned_loss=0.05507, over 4910.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2302, pruned_loss=0.04637, over 972468.62 frames.], batch size: 19, lr: 5.15e-04
2022-05-04 18:03:08,645 INFO [train.py:715] (1/8) Epoch 3, batch 31400, loss[loss=0.1723, simple_loss=0.2443, pruned_loss=0.05014, over 4752.00 frames.], tot_loss[loss=0.1611, simple_loss=0.23, pruned_loss=0.04613, over 971955.15 frames.], batch size: 19, lr: 5.15e-04
2022-05-04 18:03:47,230 INFO [train.py:715] (1/8) Epoch 3, batch 31450, loss[loss=0.1402, simple_loss=0.2161, pruned_loss=0.03216, over 4942.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2306, pruned_loss=0.04608, over 972725.48 frames.], batch size: 23, lr: 5.15e-04
2022-05-04 18:04:26,975 INFO [train.py:715] (1/8) Epoch 3, batch 31500, loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03274, over 4969.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2302, pruned_loss=0.04608, over 973239.78 frames.], batch size: 15, lr: 5.14e-04
2022-05-04 18:05:06,851 INFO [train.py:715] (1/8) Epoch 3, batch 31550, loss[loss=0.1616, simple_loss=0.2203, pruned_loss=0.05147, over 4770.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2306, pruned_loss=0.04639, over 974304.99 frames.], batch size: 17, lr: 5.14e-04
2022-05-04 18:05:47,988 INFO [train.py:715] (1/8) Epoch 3, batch 31600, loss[loss=0.1386, simple_loss=0.2158, pruned_loss=0.0307, over 4887.00 frames.], tot_loss[loss=0.1612, simple_loss=0.23, pruned_loss=0.04621, over 973075.86 frames.], batch size: 39, lr: 5.14e-04
2022-05-04 18:06:26,991 INFO [train.py:715] (1/8) Epoch 3, batch 31650, loss[loss=0.1541, simple_loss=0.2153, pruned_loss=0.04649, over 4791.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2302, pruned_loss=0.04615, over 973028.50 frames.], batch size: 14, lr: 5.14e-04
2022-05-04 18:07:07,172 INFO [train.py:715] (1/8) Epoch 3, batch 31700, loss[loss=0.1801, simple_loss=0.2387, pruned_loss=0.06069, over 4836.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2294, pruned_loss=0.04572, over 972580.24 frames.], batch size: 30, lr: 5.14e-04
2022-05-04 18:07:46,352 INFO [train.py:715] (1/8) Epoch 3, batch 31750, loss[loss=0.1537, simple_loss=0.2228, pruned_loss=0.04228, over 4852.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2296, pruned_loss=0.04594, over 971637.34 frames.], batch size: 30, lr: 5.14e-04
2022-05-04 18:08:24,497 INFO [train.py:715] (1/8) Epoch 3, batch 31800, loss[loss=0.1604, simple_loss=0.2213, pruned_loss=0.04976, over 4759.00 frames.], tot_loss[loss=0.161, simple_loss=0.2301, pruned_loss=0.04597, over 971516.72 frames.], batch size: 19, lr: 5.14e-04
2022-05-04 18:09:04,267 INFO [train.py:715] (1/8) Epoch 3, batch 31850, loss[loss=0.1441, simple_loss=0.22, pruned_loss=0.03409, over 4800.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2303, pruned_loss=0.046, over 971835.70 frames.], batch size: 14, lr: 5.14e-04
2022-05-04 18:09:43,773 INFO [train.py:715] (1/8) Epoch 3, batch 31900, loss[loss=0.1307, simple_loss=0.2014, pruned_loss=0.02997, over 4835.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2298, pruned_loss=0.04566, over 971725.47 frames.], batch size: 15, lr: 5.14e-04
2022-05-04 18:10:22,484 INFO [train.py:715] (1/8) Epoch 3, batch 31950, loss[loss=0.1615, simple_loss=0.2114, pruned_loss=0.05578, over 4750.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2289, pruned_loss=0.04488, over 972191.74 frames.], batch size: 19, lr: 5.14e-04
2022-05-04 18:11:01,411 INFO [train.py:715] (1/8) Epoch 3, batch 32000, loss[loss=0.1603, simple_loss=0.2388, pruned_loss=0.04092, over 4809.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2295, pruned_loss=0.04541, over 972276.02 frames.], batch size: 21, lr: 5.14e-04
2022-05-04 18:11:41,009 INFO [train.py:715] (1/8) Epoch 3, batch 32050, loss[loss=0.1457, simple_loss=0.2135, pruned_loss=0.03897, over 4856.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2299, pruned_loss=0.04588, over 971938.73 frames.], batch size: 20, lr: 5.13e-04
2022-05-04 18:12:19,207 INFO [train.py:715] (1/8) Epoch 3, batch 32100, loss[loss=0.1347, simple_loss=0.2125, pruned_loss=0.02844, over 4891.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2296, pruned_loss=0.04569, over 972542.64 frames.], batch size: 22, lr: 5.13e-04
2022-05-04 18:12:58,308 INFO [train.py:715] (1/8) Epoch 3, batch 32150, loss[loss=0.1087, simple_loss=0.1756, pruned_loss=0.02094, over 4798.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2289, pruned_loss=0.04542, over 972053.94 frames.], batch size: 12, lr: 5.13e-04
2022-05-04 18:13:37,852 INFO [train.py:715] (1/8) Epoch 3, batch 32200, loss[loss=0.1443, simple_loss=0.2214, pruned_loss=0.03361, over 4802.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2287, pruned_loss=0.04538, over 971834.38 frames.], batch size: 21, lr: 5.13e-04
2022-05-04 18:14:16,672 INFO [train.py:715] (1/8) Epoch 3, batch 32250, loss[loss=0.1402, simple_loss=0.2145, pruned_loss=0.03289, over 4957.00 frames.], tot_loss[loss=0.1612, simple_loss=0.23, pruned_loss=0.04613, over 973137.77 frames.], batch size: 21, lr: 5.13e-04
2022-05-04 18:14:55,233 INFO [train.py:715] (1/8) Epoch 3, batch 32300, loss[loss=0.1608, simple_loss=0.2336, pruned_loss=0.04404, over 4950.00 frames.], tot_loss[loss=0.1611, simple_loss=0.23, pruned_loss=0.04605, over 973470.99 frames.], batch size: 21, lr: 5.13e-04
2022-05-04 18:15:34,895 INFO [train.py:715] (1/8) Epoch 3, batch 32350, loss[loss=0.1294, simple_loss=0.2154, pruned_loss=0.02168, over 4778.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2305, pruned_loss=0.04652, over 973556.71 frames.], batch size: 18, lr: 5.13e-04
2022-05-04 18:16:14,618 INFO [train.py:715] (1/8) Epoch 3, batch 32400, loss[loss=0.1389, simple_loss=0.1932, pruned_loss=0.04226, over 4775.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2302, pruned_loss=0.04638, over 973544.81 frames.], batch size: 12, lr: 5.13e-04
2022-05-04 18:16:52,600 INFO [train.py:715] (1/8) Epoch 3, batch 32450, loss[loss=0.179, simple_loss=0.2633, pruned_loss=0.04732, over 4923.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2298, pruned_loss=0.04616, over 972984.35 frames.], batch size: 18, lr: 5.13e-04
2022-05-04 18:17:32,078 INFO [train.py:715] (1/8) Epoch 3, batch 32500, loss[loss=0.1653, simple_loss=0.2306, pruned_loss=0.05, over 4795.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2288, pruned_loss=0.04594, over 973343.99 frames.], batch size: 21, lr: 5.13e-04
2022-05-04 18:18:11,713 INFO [train.py:715] (1/8) Epoch 3, batch 32550, loss[loss=0.1806, simple_loss=0.2426, pruned_loss=0.05927, over 4828.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2289, pruned_loss=0.046, over 973043.50 frames.], batch size: 25, lr: 5.12e-04
2022-05-04 18:18:50,230 INFO [train.py:715] (1/8) Epoch 3, batch 32600, loss[loss=0.1566, simple_loss=0.2333, pruned_loss=0.04, over 4922.00 frames.], tot_loss[loss=0.1604, simple_loss=0.229, pruned_loss=0.04594, over 972855.74 frames.], batch size: 17, lr: 5.12e-04
2022-05-04 18:19:29,062 INFO [train.py:715] (1/8) Epoch 3, batch 32650, loss[loss=0.1928, simple_loss=0.2563, pruned_loss=0.06463, over 4868.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2294, pruned_loss=0.04607, over 972314.88 frames.], batch size: 20, lr: 5.12e-04
2022-05-04 18:20:08,687 INFO [train.py:715] (1/8) Epoch 3, batch 32700, loss[loss=0.137, simple_loss=0.1994, pruned_loss=0.03729, over 4788.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2288, pruned_loss=0.04599, over 972738.51 frames.], batch size: 12, lr: 5.12e-04
2022-05-04 18:20:47,704 INFO [train.py:715] (1/8) Epoch 3, batch 32750, loss[loss=0.1469, simple_loss=0.2132, pruned_loss=0.0403, over 4778.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2283, pruned_loss=0.04573, over 971868.91 frames.], batch size: 17, lr: 5.12e-04
2022-05-04 18:21:26,284 INFO [train.py:715] (1/8) Epoch 3, batch 32800, loss[loss=0.136, simple_loss=0.2006, pruned_loss=0.03572, over 4774.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2291, pruned_loss=0.04563, over 972000.80 frames.], batch size: 14, lr: 5.12e-04
2022-05-04 18:22:05,408 INFO [train.py:715] (1/8) Epoch 3, batch 32850, loss[loss=0.1545, simple_loss=0.2344, pruned_loss=0.03734, over 4905.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2287, pruned_loss=0.04546, over 971914.58 frames.], batch size: 18, lr: 5.12e-04
2022-05-04 18:22:44,588 INFO [train.py:715] (1/8) Epoch 3, batch 32900, loss[loss=0.2251, simple_loss=0.2802, pruned_loss=0.08498, over 4779.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2285, pruned_loss=0.04554, over 972211.05 frames.], batch size: 17, lr: 5.12e-04
2022-05-04 18:23:23,655 INFO [train.py:715] (1/8) Epoch 3, batch 32950, loss[loss=0.1342, simple_loss=0.2004, pruned_loss=0.03402, over 4933.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2281, pruned_loss=0.04505, over 972128.35 frames.], batch size: 21, lr: 5.12e-04
2022-05-04 18:24:02,383 INFO [train.py:715] (1/8) Epoch 3, batch 33000, loss[loss=0.1791, simple_loss=0.2351, pruned_loss=0.06155, over 4978.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2288, pruned_loss=0.04553, over 972293.99 frames.], batch size: 35, lr: 5.12e-04
2022-05-04 18:24:02,384 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 18:24:11,705 INFO [train.py:742] (1/8) Epoch 3, validation: loss=0.1131, simple_loss=0.199, pruned_loss=0.01363, over 914524.00 frames.
2022-05-04 18:24:50,800 INFO [train.py:715] (1/8) Epoch 3, batch 33050, loss[loss=0.1842, simple_loss=0.2386, pruned_loss=0.06491, over 4709.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2284, pruned_loss=0.04568, over 972252.86 frames.], batch size: 15, lr: 5.12e-04
2022-05-04 18:25:30,711 INFO [train.py:715] (1/8) Epoch 3, batch 33100, loss[loss=0.1604, simple_loss=0.2347, pruned_loss=0.04307, over 4866.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2288, pruned_loss=0.0458, over 973069.20 frames.], batch size: 20, lr: 5.11e-04
2022-05-04 18:26:09,588 INFO [train.py:715] (1/8) Epoch 3, batch 33150, loss[loss=0.1434, simple_loss=0.2099, pruned_loss=0.03842, over 4799.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2298, pruned_loss=0.04641, over 973377.95 frames.], batch size: 21, lr: 5.11e-04
2022-05-04 18:26:48,263 INFO [train.py:715] (1/8) Epoch 3, batch 33200, loss[loss=0.1274, simple_loss=0.1934, pruned_loss=0.03065, over 4985.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2281, pruned_loss=0.04608, over 973648.98 frames.], batch size: 25, lr: 5.11e-04
2022-05-04 18:27:28,162 INFO [train.py:715] (1/8) Epoch 3, batch 33250, loss[loss=0.139, simple_loss=0.2133, pruned_loss=0.03237, over 4815.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2291, pruned_loss=0.04601, over 973447.64 frames.], batch size: 27, lr: 5.11e-04
2022-05-04 18:28:07,721 INFO [train.py:715] (1/8) Epoch 3, batch 33300, loss[loss=0.141, simple_loss=0.2097, pruned_loss=0.03613, over 4839.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2293, pruned_loss=0.04619, over 973151.96 frames.], batch size: 15, lr: 5.11e-04
2022-05-04 18:28:46,235 INFO [train.py:715] (1/8) Epoch 3, batch 33350, loss[loss=0.1602, simple_loss=0.2157, pruned_loss=0.05235, over 4941.00 frames.], tot_loss[loss=0.162, simple_loss=0.2308, pruned_loss=0.0466, over 972035.60 frames.], batch size: 39, lr: 5.11e-04
2022-05-04 18:29:25,531 INFO [train.py:715] (1/8) Epoch 3, batch 33400, loss[loss=0.1598, simple_loss=0.23, pruned_loss=0.04479, over 4903.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2305, pruned_loss=0.046, over 972747.34 frames.], batch size: 17, lr: 5.11e-04
2022-05-04 18:30:05,185 INFO [train.py:715] (1/8) Epoch 3, batch 33450, loss[loss=0.2193, simple_loss=0.2769, pruned_loss=0.08083, over 4957.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2298, pruned_loss=0.04546, over 971933.71 frames.], batch size: 15, lr: 5.11e-04
2022-05-04 18:30:44,207 INFO [train.py:715] (1/8) Epoch 3, batch 33500, loss[loss=0.1431, simple_loss=0.2217, pruned_loss=0.03221, over 4967.00 frames.], tot_loss[loss=0.161, simple_loss=0.2305, pruned_loss=0.04578, over 971883.95 frames.], batch size: 35, lr: 5.11e-04
2022-05-04 18:31:23,290 INFO [train.py:715] (1/8) Epoch 3, batch 33550, loss[loss=0.1923, simple_loss=0.2549, pruned_loss=0.06481, over 4761.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2296, pruned_loss=0.04551, over 971295.56 frames.], batch size: 19, lr: 5.11e-04
2022-05-04 18:32:03,650 INFO [train.py:715] (1/8) Epoch 3, batch 33600, loss[loss=0.1685, simple_loss=0.253, pruned_loss=0.04202, over 4822.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2297, pruned_loss=0.04564, over 971570.93 frames.], batch size: 27, lr: 5.11e-04
2022-05-04 18:32:43,012 INFO [train.py:715] (1/8) Epoch 3, batch 33650, loss[loss=0.2222, simple_loss=0.25, pruned_loss=0.0972, over 4734.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2299, pruned_loss=0.0458, over 971253.32 frames.], batch size: 12, lr: 5.10e-04
2022-05-04 18:33:21,657 INFO [train.py:715] (1/8) Epoch 3, batch 33700, loss[loss=0.1862, simple_loss=0.2589, pruned_loss=0.05681, over 4766.00 frames.], tot_loss[loss=0.1602, simple_loss=0.229, pruned_loss=0.04565, over 971154.88 frames.], batch size: 18, lr: 5.10e-04
2022-05-04 18:34:01,450 INFO [train.py:715] (1/8) Epoch 3, batch 33750, loss[loss=0.1608, simple_loss=0.241, pruned_loss=0.04031, over 4920.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2293, pruned_loss=0.04549, over 971655.42 frames.], batch size: 18, lr: 5.10e-04
2022-05-04 18:34:40,932 INFO [train.py:715] (1/8) Epoch 3, batch 33800, loss[loss=0.186, simple_loss=0.2574, pruned_loss=0.05728, over 4939.00 frames.], tot_loss[loss=0.1609, simple_loss=0.23, pruned_loss=0.04588, over 971520.72 frames.], batch size: 23, lr: 5.10e-04
2022-05-04 18:35:19,308 INFO [train.py:715] (1/8) Epoch 3, batch 33850, loss[loss=0.177, simple_loss=0.2697, pruned_loss=0.04217, over 4856.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2294, pruned_loss=0.04579, over 971557.12 frames.], batch size: 38, lr: 5.10e-04
2022-05-04 18:35:58,137 INFO [train.py:715] (1/8) Epoch 3, batch 33900, loss[loss=0.1616, simple_loss=0.2212, pruned_loss=0.05102, over 4822.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2295, pruned_loss=0.04582, over 971719.99 frames.], batch size: 13, lr: 5.10e-04
2022-05-04 18:36:38,299 INFO [train.py:715] (1/8) Epoch 3, batch 33950, loss[loss=0.1684, simple_loss=0.2362, pruned_loss=0.05027, over 4986.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2293, pruned_loss=0.04592, over 971248.37 frames.], batch size: 28, lr: 5.10e-04
2022-05-04 18:37:17,238 INFO [train.py:715] (1/8) Epoch 3, batch 34000, loss[loss=0.1539, simple_loss=0.2145, pruned_loss=0.04661, over 4901.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2301, pruned_loss=0.04665, over 972259.75 frames.], batch size: 18, lr: 5.10e-04
2022-05-04 18:37:55,980 INFO [train.py:715] (1/8) Epoch 3, batch 34050, loss[loss=0.1538, simple_loss=0.2271, pruned_loss=0.04021, over 4840.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2303, pruned_loss=0.04651, over 972213.63 frames.], batch size: 30, lr: 5.10e-04
2022-05-04 18:38:35,311 INFO [train.py:715] (1/8) Epoch 3, batch 34100, loss[loss=0.1301, simple_loss=0.196, pruned_loss=0.03213, over 4832.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2294, pruned_loss=0.04599, over 973767.59 frames.], batch size: 13, lr: 5.10e-04
2022-05-04 18:39:15,275 INFO [train.py:715] (1/8) Epoch 3, batch 34150, loss[loss=0.1535, simple_loss=0.2324, pruned_loss=0.03736, over 4908.00 frames.], tot_loss[loss=0.1606, simple_loss=0.229, pruned_loss=0.04611, over 973521.14 frames.], batch size: 18, lr: 5.10e-04
2022-05-04 18:39:53,556 INFO [train.py:715] (1/8) Epoch 3, batch 34200, loss[loss=0.1744, simple_loss=0.2338, pruned_loss=0.05754, over 4787.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2299, pruned_loss=0.04647, over 973069.40 frames.], batch size: 14, lr: 5.09e-04
2022-05-04 18:40:33,002 INFO [train.py:715] (1/8) Epoch 3, batch 34250, loss[loss=0.1539, simple_loss=0.2265, pruned_loss=0.04068, over 4770.00 frames.], tot_loss[loss=0.161, simple_loss=0.2297, pruned_loss=0.04609, over 972693.10 frames.], batch size: 14, lr: 5.09e-04
2022-05-04 18:41:13,063 INFO [train.py:715] (1/8) Epoch 3, batch 34300, loss[loss=0.1541, simple_loss=0.233, pruned_loss=0.03766, over 4976.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2299, pruned_loss=0.04585, over 973111.05 frames.], batch size: 24, lr: 5.09e-04
2022-05-04 18:41:52,485 INFO [train.py:715] (1/8) Epoch 3, batch 34350, loss[loss=0.1937, simple_loss=0.2544, pruned_loss=0.06651, over 4921.00 frames.], tot_loss[loss=0.1606, simple_loss=0.23, pruned_loss=0.04565, over 973189.18 frames.], batch size: 18, lr: 5.09e-04
2022-05-04 18:42:31,603 INFO [train.py:715] (1/8) Epoch 3, batch 34400, loss[loss=0.1611, simple_loss=0.2439, pruned_loss=0.03918, over 4872.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2299, pruned_loss=0.04542, over 973259.55 frames.], batch size: 22, lr: 5.09e-04
2022-05-04 18:43:11,182 INFO [train.py:715] (1/8) Epoch 3, batch 34450, loss[loss=0.1685, simple_loss=0.238, pruned_loss=0.04955, over 4954.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2308, pruned_loss=0.04592, over 972699.07 frames.], batch size: 29, lr: 5.09e-04
2022-05-04 18:43:51,335 INFO [train.py:715] (1/8) Epoch 3, batch 34500, loss[loss=0.1445, simple_loss=0.2165, pruned_loss=0.03631, over 4923.00 frames.], tot_loss[loss=0.1606, simple_loss=0.23, pruned_loss=0.04555, over 973460.23 frames.], batch size: 18, lr: 5.09e-04
2022-05-04 18:44:29,765 INFO [train.py:715] (1/8) Epoch 3, batch 34550, loss[loss=0.1665, simple_loss=0.2338, pruned_loss=0.04964, over 4796.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2298, pruned_loss=0.04564, over 972331.91 frames.], batch size: 17, lr: 5.09e-04
2022-05-04 18:45:08,812 INFO [train.py:715] (1/8) Epoch 3, batch 34600, loss[loss=0.1399, simple_loss=0.229, pruned_loss=0.02537, over 4775.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2295, pruned_loss=0.04548, over 971479.15 frames.], batch size: 17, lr: 5.09e-04
2022-05-04 18:45:49,190 INFO [train.py:715] (1/8) Epoch 3, batch 34650, loss[loss=0.1322, simple_loss=0.2105, pruned_loss=0.02701, over 4960.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2292, pruned_loss=0.04545, over 971558.26 frames.], batch size: 24, lr: 5.09e-04
2022-05-04 18:46:28,771 INFO [train.py:715] (1/8) Epoch 3, batch 34700, loss[loss=0.1567, simple_loss=0.2351, pruned_loss=0.03917, over 4793.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2297, pruned_loss=0.04553, over 971236.96 frames.], batch size: 14, lr: 5.09e-04
2022-05-04 18:47:07,056 INFO [train.py:715] (1/8) Epoch 3, batch 34750, loss[loss=0.1345, simple_loss=0.2113, pruned_loss=0.02882, over 4946.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2296, pruned_loss=0.04572, over 971116.39 frames.], batch size: 29, lr: 5.08e-04
2022-05-04 18:47:44,743 INFO [train.py:715] (1/8) Epoch 3, batch 34800, loss[loss=0.1571, simple_loss=0.2281, pruned_loss=0.04301, over 4913.00 frames.], tot_loss[loss=0.16, simple_loss=0.2287, pruned_loss=0.04565, over 970862.43 frames.], batch size: 23, lr: 5.08e-04
2022-05-04 18:48:35,134 INFO [train.py:715] (1/8) Epoch 4, batch 0, loss[loss=0.1617, simple_loss=0.2372, pruned_loss=0.04308, over 4954.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2372, pruned_loss=0.04308, over 4954.00 frames.], batch size: 24, lr: 4.78e-04
2022-05-04 18:49:16,502 INFO [train.py:715] (1/8) Epoch 4, batch 50, loss[loss=0.1655, simple_loss=0.228, pruned_loss=0.05145, over 4894.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2271, pruned_loss=0.04536, over 220395.91 frames.], batch size: 32, lr: 4.78e-04
2022-05-04 18:49:57,158 INFO [train.py:715] (1/8) Epoch 4, batch 100, loss[loss=0.1597, simple_loss=0.2351, pruned_loss=0.04214, over 4965.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2271, pruned_loss=0.04557, over 386859.88 frames.], batch size: 35, lr: 4.78e-04
2022-05-04 18:50:37,978 INFO [train.py:715] (1/8) Epoch 4, batch 150, loss[loss=0.1648, simple_loss=0.2373, pruned_loss=0.04611, over 4880.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2274, pruned_loss=0.04501, over 516804.64 frames.], batch size: 22, lr: 4.78e-04
2022-05-04 18:51:19,045 INFO [train.py:715] (1/8) Epoch 4, batch 200, loss[loss=0.1255, simple_loss=0.1981, pruned_loss=0.02649, over 4905.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2274, pruned_loss=0.04494, over 617965.21 frames.], batch size: 18, lr: 4.78e-04
2022-05-04 18:52:00,235 INFO [train.py:715] (1/8) Epoch 4, batch 250, loss[loss=0.1516, simple_loss=0.2321, pruned_loss=0.03551, over 4694.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2282, pruned_loss=0.0454, over 695425.18 frames.], batch size: 15, lr: 4.77e-04
2022-05-04 18:52:41,173 INFO [train.py:715] (1/8) Epoch 4, batch 300, loss[loss=0.1534, simple_loss=0.2198, pruned_loss=0.04348, over 4949.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2279, pruned_loss=0.04551, over 756182.80 frames.], batch size: 21, lr: 4.77e-04
2022-05-04 18:53:22,424 INFO [train.py:715] (1/8) Epoch 4, batch 350, loss[loss=0.1595, simple_loss=0.2341, pruned_loss=0.04248, over 4985.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2295, pruned_loss=0.04588, over 805166.32 frames.], batch size: 26, lr: 4.77e-04
2022-05-04 18:54:04,547 INFO [train.py:715] (1/8) Epoch 4, batch 400, loss[loss=0.2081, simple_loss=0.2619, pruned_loss=0.07717, over 4953.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2299, pruned_loss=0.04643, over 842691.72 frames.], batch size: 35, lr: 4.77e-04
2022-05-04 18:54:45,176 INFO [train.py:715] (1/8) Epoch 4, batch 450, loss[loss=0.1229, simple_loss=0.1882, pruned_loss=0.02884, over 4707.00 frames.], tot_loss[loss=0.1592, simple_loss=0.228, pruned_loss=0.04518, over 871567.05 frames.], batch size: 15, lr: 4.77e-04
2022-05-04 18:55:26,263 INFO [train.py:715] (1/8) Epoch 4, batch 500, loss[loss=0.1576, simple_loss=0.2242, pruned_loss=0.0455, over 4859.00 frames.], tot_loss[loss=0.158, simple_loss=0.2269, pruned_loss=0.04458, over 893941.34 frames.], batch size: 30, lr: 4.77e-04
2022-05-04 18:56:07,506 INFO [train.py:715] (1/8) Epoch 4, batch 550, loss[loss=0.1857, simple_loss=0.2569, pruned_loss=0.05727, over 4903.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2269, pruned_loss=0.04496, over 910481.21 frames.], batch size: 39, lr: 4.77e-04
2022-05-04 18:56:48,412 INFO [train.py:715] (1/8) Epoch 4, batch 600, loss[loss=0.1613, simple_loss=0.2254, pruned_loss=0.04863, over 4989.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2271, pruned_loss=0.04497, over 924351.63 frames.], batch size: 14, lr: 4.77e-04
2022-05-04 18:57:28,915 INFO [train.py:715] (1/8) Epoch 4, batch 650, loss[loss=0.144, simple_loss=0.215, pruned_loss=0.03644, over 4877.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2274, pruned_loss=0.04521, over 935268.75 frames.], batch size: 16, lr: 4.77e-04
2022-05-04 18:58:09,994 INFO [train.py:715] (1/8) Epoch 4, batch 700, loss[loss=0.1616, simple_loss=0.2207, pruned_loss=0.05125, over 4783.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2267, pruned_loss=0.04513, over 942653.78 frames.], batch size: 17, lr: 4.77e-04
2022-05-04 18:58:51,939 INFO [train.py:715] (1/8) Epoch 4, batch 750, loss[loss=0.1404, simple_loss=0.2121, pruned_loss=0.03436, over 4986.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2261, pruned_loss=0.04422, over 949752.37 frames.], batch size: 25, lr: 4.77e-04
2022-05-04 18:59:33,003 INFO [train.py:715] (1/8) Epoch 4, batch 800, loss[loss=0.191, simple_loss=0.2562, pruned_loss=0.06292, over 4906.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2256, pruned_loss=0.04372, over 954744.46 frames.], batch size: 17, lr: 4.77e-04
2022-05-04 19:00:13,430 INFO [train.py:715] (1/8) Epoch 4, batch 850, loss[loss=0.1632, simple_loss=0.2341, pruned_loss=0.04618, over 4928.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2251, pruned_loss=0.04326, over 958872.00 frames.], batch size: 18, lr: 4.76e-04
2022-05-04 19:00:54,495 INFO [train.py:715] (1/8) Epoch 4, batch 900, loss[loss=0.146, simple_loss=0.2163, pruned_loss=0.03783, over 4978.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2258, pruned_loss=0.04364, over 962210.94 frames.], batch size: 24, lr: 4.76e-04
2022-05-04 19:01:35,343 INFO [train.py:715] (1/8) Epoch 4, batch 950, loss[loss=0.1702, simple_loss=0.2384, pruned_loss=0.05103, over 4928.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2274, pruned_loss=0.04466, over 964532.45 frames.], batch size: 23, lr: 4.76e-04
2022-05-04 19:02:16,225 INFO [train.py:715] (1/8) Epoch 4, batch 1000, loss[loss=0.18, simple_loss=0.2322, pruned_loss=0.06395, over 4693.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2281, pruned_loss=0.04522, over 965361.67 frames.], batch size: 15, lr: 4.76e-04
2022-05-04 19:02:56,935 INFO [train.py:715] (1/8) Epoch 4, batch 1050, loss[loss=0.1718, simple_loss=0.2359, pruned_loss=0.05388, over 4868.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2283, pruned_loss=0.04517, over 967060.18 frames.], batch size: 16, lr: 4.76e-04
2022-05-04 19:03:38,139 INFO [train.py:715] (1/8) Epoch 4, batch 1100, loss[loss=0.1503, simple_loss=0.232, pruned_loss=0.03427, over 4800.00 frames.], tot_loss[loss=0.1588, simple_loss=0.228, pruned_loss=0.04483, over 967763.54 frames.], batch size: 21, lr: 4.76e-04
2022-05-04 19:04:18,527 INFO [train.py:715] (1/8) Epoch 4, batch 1150, loss[loss=0.1784, simple_loss=0.243, pruned_loss=0.0569, over 4748.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2278, pruned_loss=0.04457, over 969155.62 frames.], batch size: 19, lr: 4.76e-04
2022-05-04 19:04:58,025 INFO [train.py:715] (1/8) Epoch 4, batch 1200, loss[loss=0.149, simple_loss=0.2305, pruned_loss=0.03376, over 4897.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2281, pruned_loss=0.04457, over 970387.36 frames.], batch size: 17, lr: 4.76e-04
2022-05-04 19:05:38,584 INFO [train.py:715] (1/8) Epoch 4, batch 1250, loss[loss=0.1668, simple_loss=0.2357, pruned_loss=0.04896, over 4821.00 frames.], tot_loss[loss=0.1586, simple_loss=0.228, pruned_loss=0.04462, over 970190.83 frames.], batch size: 27, lr: 4.76e-04
2022-05-04 19:06:19,651 INFO [train.py:715] (1/8) Epoch 4, batch 1300, loss[loss=0.17, simple_loss=0.2522, pruned_loss=0.04385, over 4930.00 frames.], tot_loss[loss=0.158, simple_loss=0.2277, pruned_loss=0.0442, over 970259.20 frames.], batch size: 23, lr: 4.76e-04
2022-05-04 19:06:59,656 INFO [train.py:715] (1/8) Epoch 4, batch 1350, loss[loss=0.1794, simple_loss=0.2413, pruned_loss=0.05878, over 4707.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2273, pruned_loss=0.04424, over 969180.97 frames.], batch size: 15, lr: 4.76e-04
2022-05-04 19:07:40,377 INFO [train.py:715] (1/8) Epoch 4, batch 1400, loss[loss=0.2125, simple_loss=0.2693, pruned_loss=0.07788, over 4869.00 frames.], tot_loss[loss=0.1585, simple_loss=0.228, pruned_loss=0.04445, over 970811.81 frames.], batch size: 16, lr: 4.76e-04
2022-05-04 19:08:21,347 INFO [train.py:715] (1/8) Epoch 4, batch 1450, loss[loss=0.1251, simple_loss=0.1934, pruned_loss=0.02839, over 4823.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2276, pruned_loss=0.04415, over 971346.12 frames.], batch size: 26, lr: 4.75e-04
2022-05-04 19:09:02,420 INFO [train.py:715] (1/8) Epoch 4, batch 1500, loss[loss=0.1648, simple_loss=0.2395, pruned_loss=0.04507, over 4948.00 frames.], tot_loss[loss=0.158, simple_loss=0.2274, pruned_loss=0.04431, over 971751.00 frames.], batch size: 14, lr: 4.75e-04
2022-05-04 19:09:42,042 INFO [train.py:715] (1/8) Epoch 4, batch 1550, loss[loss=0.1385, simple_loss=0.2169, pruned_loss=0.03002, over 4951.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2284, pruned_loss=0.04463, over 971707.29 frames.], batch size: 24, lr: 4.75e-04
2022-05-04 19:10:23,010 INFO [train.py:715] (1/8) Epoch 4, batch 1600, loss[loss=0.127, simple_loss=0.1954, pruned_loss=0.02925, over 4860.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2285, pruned_loss=0.045, over 971457.93 frames.], batch size: 20, lr: 4.75e-04
2022-05-04 19:11:04,741 INFO [train.py:715] (1/8) Epoch 4, batch 1650, loss[loss=0.1643, simple_loss=0.2417, pruned_loss=0.04341, over 4930.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2283, pruned_loss=0.04499, over 972097.72 frames.], batch size: 18, lr: 4.75e-04
2022-05-04 19:11:45,096 INFO [train.py:715] (1/8) Epoch 4, batch 1700, loss[loss=0.1351, simple_loss=0.2108, pruned_loss=0.02976, over 4840.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2274, pruned_loss=0.04491, over 972319.05 frames.], batch size: 20, lr: 4.75e-04
2022-05-04 19:12:25,108 INFO [train.py:715] (1/8) Epoch 4, batch 1750, loss[loss=0.1499, simple_loss=0.2182, pruned_loss=0.04085, over 4963.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2279, pruned_loss=0.04543, over 972200.56 frames.], batch size: 35, lr: 4.75e-04
2022-05-04 19:13:06,309 INFO [train.py:715] (1/8) Epoch 4, batch 1800, loss[loss=0.1255, simple_loss=0.2013, pruned_loss=0.02486, over 4815.00 frames.], tot_loss[loss=0.16, simple_loss=0.2283, pruned_loss=0.04585, over 971849.21 frames.], batch size: 27, lr: 4.75e-04
2022-05-04 19:13:47,663 INFO [train.py:715] (1/8) Epoch 4, batch 1850, loss[loss=0.1572, simple_loss=0.2223, pruned_loss=0.046, over 4771.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2286, pruned_loss=0.04587, over 971813.68 frames.], batch size: 17, lr: 4.75e-04
2022-05-04 19:14:27,698 INFO [train.py:715] (1/8) Epoch 4, batch 1900, loss[loss=0.1587, simple_loss=0.2324, pruned_loss=0.04253, over 4781.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2281, pruned_loss=0.04541, over 972039.60 frames.], batch size: 18, lr: 4.75e-04
2022-05-04 19:15:08,450 INFO [train.py:715] (1/8) Epoch 4, batch 1950, loss[loss=0.1207, simple_loss=0.1927, pruned_loss=0.02434, over 4736.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2291, pruned_loss=0.04569, over 971891.17 frames.], batch size: 12, lr: 4.75e-04
2022-05-04 19:15:48,963 INFO [train.py:715] (1/8) Epoch 4, batch 2000, loss[loss=0.1272, simple_loss=0.1979, pruned_loss=0.02826, over 4762.00 frames.], tot_loss[loss=0.16, simple_loss=0.2291, pruned_loss=0.04543, over 973074.05 frames.], batch size: 12, lr: 4.74e-04
2022-05-04 19:16:28,969 INFO [train.py:715] (1/8) Epoch 4, batch 2050, loss[loss=0.128, simple_loss=0.1972, pruned_loss=0.02944, over 4843.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2289, pruned_loss=0.04559, over 972862.86 frames.], batch size: 32, lr: 4.74e-04
2022-05-04 19:17:08,513 INFO [train.py:715] (1/8) Epoch 4, batch 2100, loss[loss=0.2076, simple_loss=0.2668, pruned_loss=0.07419, over 4799.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2291, pruned_loss=0.04569, over 973727.36 frames.], batch size: 21, lr: 4.74e-04
2022-05-04 19:17:48,266 INFO [train.py:715] (1/8) Epoch 4, batch 2150, loss[loss=0.1756, simple_loss=0.224, pruned_loss=0.06362, over 4916.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2285, pruned_loss=0.04606, over 973397.88 frames.], batch size: 17, lr: 4.74e-04
2022-05-04 19:18:29,061 INFO [train.py:715] (1/8) Epoch 4, batch 2200, loss[loss=0.1242, simple_loss=0.2034, pruned_loss=0.02251, over 4905.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2289, pruned_loss=0.04588, over 973215.96 frames.], batch size: 18, lr: 4.74e-04
2022-05-04 19:19:09,448 INFO [train.py:715] (1/8) Epoch 4, batch 2250, loss[loss=0.1687, simple_loss=0.2396, pruned_loss=0.04894, over 4807.00 frames.], tot_loss[loss=0.1605, simple_loss=0.229, pruned_loss=0.04593, over 972650.14 frames.], batch size: 27, lr: 4.74e-04
2022-05-04 19:19:48,814 INFO [train.py:715] (1/8) Epoch 4, batch 2300, loss[loss=0.1923, simple_loss=0.2678, pruned_loss=0.05844, over 4917.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2294, pruned_loss=0.046, over 972885.58 frames.], batch size: 17, lr: 4.74e-04
2022-05-04 19:20:28,751 INFO [train.py:715] (1/8) Epoch 4, batch 2350, loss[loss=0.1455, simple_loss=0.2205, pruned_loss=0.03525, over 4933.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2286, pruned_loss=0.04499, over 972161.10 frames.], batch size: 21, lr: 4.74e-04
2022-05-04 19:21:08,840 INFO [train.py:715] (1/8) Epoch 4, batch 2400, loss[loss=0.1507, simple_loss=0.227, pruned_loss=0.03717, over 4810.00 frames.], tot_loss[loss=0.158, simple_loss=0.2272, pruned_loss=0.04436, over 971915.26 frames.], batch size: 21, lr: 4.74e-04
2022-05-04 19:21:48,326 INFO [train.py:715] (1/8) Epoch 4, batch 2450, loss[loss=0.1479, simple_loss=0.2287, pruned_loss=0.03351, over 4977.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2279, pruned_loss=0.04437, over 973036.63 frames.], batch size: 25, lr: 4.74e-04
2022-05-04 19:22:28,669 INFO [train.py:715] (1/8) Epoch 4, batch 2500, loss[loss=0.1803, simple_loss=0.2554, pruned_loss=0.05263, over 4808.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2281, pruned_loss=0.04384, over 973920.23 frames.], batch size: 21, lr: 4.74e-04
2022-05-04 19:23:09,578 INFO [train.py:715] (1/8) Epoch 4, batch 2550, loss[loss=0.1805, simple_loss=0.2504, pruned_loss=0.05526, over 4854.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2284, pruned_loss=0.04412, over 972610.37 frames.], batch size: 16, lr: 4.74e-04
2022-05-04 19:23:49,890 INFO [train.py:715] (1/8) Epoch 4, batch 2600, loss[loss=0.1522, simple_loss=0.2161, pruned_loss=0.04413, over 4748.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2287, pruned_loss=0.04431, over 972398.92 frames.], batch size: 16, lr: 4.73e-04
2022-05-04 19:24:29,142 INFO [train.py:715] (1/8) Epoch 4, batch 2650, loss[loss=0.2105, simple_loss=0.2887, pruned_loss=0.06618, over 4741.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2291, pruned_loss=0.04505, over 972207.49 frames.], batch size: 16, lr: 4.73e-04
2022-05-04 19:25:09,505 INFO [train.py:715] (1/8) Epoch 4, batch 2700, loss[loss=0.1592, simple_loss=0.2322, pruned_loss=0.04308, over 4799.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2287, pruned_loss=0.04505, over 972291.38 frames.], batch size: 24, lr: 4.73e-04
2022-05-04 19:25:49,776 INFO [train.py:715] (1/8) Epoch 4, batch 2750, loss[loss=0.1472, simple_loss=0.2183, pruned_loss=0.03807, over 4842.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2282, pruned_loss=0.04457, over 973121.33 frames.], batch size: 15, lr: 4.73e-04
2022-05-04 19:26:29,545 INFO [train.py:715] (1/8) Epoch 4, batch 2800, loss[loss=0.1567, simple_loss=0.238, pruned_loss=0.03768, over 4907.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2275, pruned_loss=0.04396, over 972637.20 frames.], batch size: 23, lr: 4.73e-04
2022-05-04 19:27:08,936 INFO [train.py:715] (1/8) Epoch 4, batch 2850, loss[loss=0.1686, simple_loss=0.2283, pruned_loss=0.05444, over 4755.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2276, pruned_loss=0.04448, over 972618.39 frames.], batch size: 16, lr: 4.73e-04
2022-05-04 19:27:49,251 INFO [train.py:715] (1/8) Epoch 4, batch 2900, loss[loss=0.1585, simple_loss=0.2268, pruned_loss=0.04516, over 4809.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2272, pruned_loss=0.04399, over 972121.79 frames.], batch size: 27, lr: 4.73e-04
2022-05-04 19:28:29,139 INFO [train.py:715] (1/8) Epoch 4, batch 2950, loss[loss=0.2247, simple_loss=0.2751, pruned_loss=0.08711, over 4915.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2276, pruned_loss=0.04439, over 972101.08 frames.], batch size: 18, lr: 4.73e-04
2022-05-04 19:29:08,458 INFO [train.py:715] (1/8) Epoch 4, batch 3000, loss[loss=0.1571, simple_loss=0.2253, pruned_loss=0.04441, over 4908.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2282, pruned_loss=0.04504, over 972717.13 frames.], batch size: 17, lr: 4.73e-04
2022-05-04 19:29:08,459 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 19:29:17,944 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1127, simple_loss=0.1984, pruned_loss=0.01346, over 914524.00 frames.
2022-05-04 19:29:57,096 INFO [train.py:715] (1/8) Epoch 4, batch 3050, loss[loss=0.1992, simple_loss=0.2563, pruned_loss=0.07111, over 4816.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2279, pruned_loss=0.04523, over 972913.78 frames.], batch size: 21, lr: 4.73e-04
2022-05-04 19:30:37,141 INFO [train.py:715] (1/8) Epoch 4, batch 3100, loss[loss=0.1512, simple_loss=0.2264, pruned_loss=0.03804, over 4852.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2277, pruned_loss=0.04499, over 972816.73 frames.], batch size: 20, lr: 4.73e-04
2022-05-04 19:31:17,418 INFO [train.py:715] (1/8) Epoch 4, batch 3150, loss[loss=0.146, simple_loss=0.2137, pruned_loss=0.03915, over 4953.00 frames.], tot_loss[loss=0.159, simple_loss=0.2282, pruned_loss=0.04486, over 972756.94 frames.], batch size: 21, lr: 4.73e-04
2022-05-04 19:31:57,031 INFO [train.py:715] (1/8) Epoch 4, batch 3200, loss[loss=0.1471, simple_loss=0.2124, pruned_loss=0.04094, over 4777.00 frames.], tot_loss[loss=0.159, simple_loss=0.2283, pruned_loss=0.04483, over 972836.07 frames.], batch size: 18, lr: 4.72e-04
2022-05-04 19:32:36,976 INFO [train.py:715] (1/8) Epoch 4, batch 3250, loss[loss=0.1639, simple_loss=0.2321, pruned_loss=0.04791, over 4897.00 frames.], tot_loss[loss=0.1588, simple_loss=0.228, pruned_loss=0.04482, over 972568.49 frames.], batch size: 19, lr: 4.72e-04
2022-05-04 19:33:16,919 INFO [train.py:715] (1/8) Epoch 4, batch 3300, loss[loss=0.1425, simple_loss=0.2069, pruned_loss=0.03907, over 4852.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2282, pruned_loss=0.04506, over 972636.02 frames.], batch size: 15, lr: 4.72e-04
2022-05-04 19:33:56,296 INFO [train.py:715] (1/8) Epoch 4, batch 3350, loss[loss=0.2006, simple_loss=0.2646, pruned_loss=0.06833, over 4749.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2275, pruned_loss=0.04498, over 972421.91 frames.], batch size: 19, lr: 4.72e-04
2022-05-04 19:34:35,335 INFO [train.py:715] (1/8) Epoch 4, batch 3400, loss[loss=0.1497, simple_loss=0.2141, pruned_loss=0.04266, over 4693.00 frames.], tot_loss[loss=0.1594, simple_loss=0.228, pruned_loss=0.04542, over 971546.52 frames.], batch size: 15, lr: 4.72e-04
2022-05-04 19:35:15,782 INFO [train.py:715] (1/8) Epoch 4, batch 3450, loss[loss=0.1483, simple_loss=0.2264, pruned_loss=0.03504, over 4935.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2275, pruned_loss=0.04484, over 971572.45 frames.], batch size: 23, lr: 4.72e-04
2022-05-04 19:35:55,196 INFO [train.py:715] (1/8) Epoch 4, batch 3500, loss[loss=0.1709, simple_loss=0.2491, pruned_loss=0.04637, over 4755.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2271, pruned_loss=0.04453, over 972199.68 frames.], batch size: 19, lr: 4.72e-04
2022-05-04 19:36:34,859 INFO [train.py:715] (1/8) Epoch 4, batch 3550, loss[loss=0.1745, simple_loss=0.2454, pruned_loss=0.05183, over 4880.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2269, pruned_loss=0.04426, over 972762.00 frames.], batch size: 22, lr: 4.72e-04
2022-05-04 19:37:14,703 INFO [train.py:715] (1/8) Epoch 4, batch 3600, loss[loss=0.1638, simple_loss=0.2261, pruned_loss=0.05078, over 4952.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2268, pruned_loss=0.04425, over 972402.61 frames.], batch size: 21, lr: 4.72e-04
2022-05-04 19:37:54,706 INFO [train.py:715] (1/8) Epoch 4, batch 3650, loss[loss=0.1458, simple_loss=0.2183, pruned_loss=0.03665, over 4959.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2263, pruned_loss=0.0442, over 972326.26 frames.], batch size: 24, lr: 4.72e-04
2022-05-04 19:38:34,076 INFO [train.py:715] (1/8) Epoch 4, batch 3700, loss[loss=0.1539, simple_loss=0.2255, pruned_loss=0.04113, over 4977.00 frames.], tot_loss[loss=0.158, simple_loss=0.2271, pruned_loss=0.04443, over 972105.86 frames.], batch size: 28, lr: 4.72e-04
2022-05-04 19:39:13,357 INFO [train.py:715] (1/8) Epoch 4, batch 3750, loss[loss=0.1662, simple_loss=0.2508, pruned_loss=0.0408, over 4905.00 frames.], tot_loss[loss=0.157, simple_loss=0.2263, pruned_loss=0.04382, over 972182.48 frames.], batch size: 17, lr: 4.72e-04
2022-05-04 19:39:53,220 INFO [train.py:715] (1/8) Epoch 4, batch 3800, loss[loss=0.1952, simple_loss=0.2522, pruned_loss=0.06912, over 4733.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2264, pruned_loss=0.04418, over 972656.50 frames.], batch size: 16, lr: 4.72e-04
2022-05-04 19:40:32,941 INFO [train.py:715] (1/8) Epoch 4, batch 3850, loss[loss=0.1325, simple_loss=0.2018, pruned_loss=0.03159, over 4784.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2261, pruned_loss=0.04407, over 972461.47 frames.], batch size: 18, lr: 4.71e-04
2022-05-04 19:41:13,140 INFO [train.py:715] (1/8) Epoch 4, batch 3900, loss[loss=0.1617, simple_loss=0.2334, pruned_loss=0.04499, over 4878.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2257, pruned_loss=0.0441, over 972904.44 frames.], batch size: 16, lr: 4.71e-04
2022-05-04 19:41:53,265 INFO [train.py:715] (1/8) Epoch 4, batch 3950, loss[loss=0.1625, simple_loss=0.229, pruned_loss=0.04798, over 4872.00 frames.], tot_loss[loss=0.157, simple_loss=0.2256, pruned_loss=0.04417, over 973174.73 frames.], batch size: 16, lr: 4.71e-04
2022-05-04 19:42:33,633 INFO [train.py:715] (1/8) Epoch 4, batch 4000, loss[loss=0.166, simple_loss=0.234, pruned_loss=0.04898, over 4983.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2257, pruned_loss=0.04375, over 972984.66 frames.], batch size: 35, lr: 4.71e-04
2022-05-04 19:43:13,674 INFO [train.py:715] (1/8) Epoch 4, batch 4050, loss[loss=0.1753, simple_loss=0.2436, pruned_loss=0.05346, over 4844.00 frames.], tot_loss[loss=0.1572, simple_loss=0.226, pruned_loss=0.04421, over 973743.94 frames.], batch size: 15, lr: 4.71e-04
2022-05-04 19:43:53,247 INFO [train.py:715] (1/8) Epoch 4, batch 4100, loss[loss=0.1619, simple_loss=0.2255, pruned_loss=0.0492, over 4939.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2263, pruned_loss=0.04396, over 973040.11 frames.], batch size: 23, lr: 4.71e-04
2022-05-04 19:44:33,954 INFO [train.py:715] (1/8) Epoch 4, batch 4150, loss[loss=0.1551, simple_loss=0.2214, pruned_loss=0.04441, over 4842.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2273, pruned_loss=0.04462, over 972274.31 frames.], batch size: 30, lr: 4.71e-04
2022-05-04 19:45:13,439 INFO [train.py:715] (1/8) Epoch 4, batch 4200, loss[loss=0.1588, simple_loss=0.2317, pruned_loss=0.04298, over 4989.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2276, pruned_loss=0.04514, over 973551.97 frames.], batch size: 28, lr: 4.71e-04
2022-05-04 19:45:52,931 INFO [train.py:715] (1/8) Epoch 4, batch 4250, loss[loss=0.1249, simple_loss=0.1984, pruned_loss=0.02568, over 4816.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2272, pruned_loss=0.04502, over 972043.79 frames.], batch size: 26, lr: 4.71e-04
2022-05-04 19:46:33,012 INFO [train.py:715] (1/8) Epoch 4, batch 4300, loss[loss=0.1424, simple_loss=0.2159, pruned_loss=0.03441, over 4933.00 frames.], tot_loss[loss=0.159, simple_loss=0.228, pruned_loss=0.04497, over 971554.95 frames.], batch size: 23, lr: 4.71e-04
2022-05-04 19:47:13,039 INFO [train.py:715] (1/8) Epoch 4, batch 4350, loss[loss=0.1493, simple_loss=0.2168, pruned_loss=0.04093, over 4923.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2276, pruned_loss=0.04447, over 970687.94 frames.], batch size: 17, lr: 4.71e-04
2022-05-04 19:47:52,121 INFO [train.py:715] (1/8) Epoch 4, batch 4400, loss[loss=0.1347, simple_loss=0.1997, pruned_loss=0.03486, over 4687.00 frames.], tot_loss[loss=0.1576, simple_loss=0.227, pruned_loss=0.04411, over 971107.88 frames.], batch size: 15, lr: 4.71e-04
2022-05-04 19:48:31,834 INFO [train.py:715] (1/8) Epoch 4, batch 4450, loss[loss=0.1299, simple_loss=0.2049, pruned_loss=0.02747, over 4856.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2265, pruned_loss=0.04413, over 971145.53 frames.], batch size: 20, lr: 4.70e-04
2022-05-04 19:49:12,008 INFO [train.py:715] (1/8) Epoch 4, batch 4500, loss[loss=0.1401, simple_loss=0.1983, pruned_loss=0.04092, over 4982.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2275, pruned_loss=0.04485, over 971739.22 frames.], batch size: 14, lr: 4.70e-04
2022-05-04 19:49:51,277 INFO [train.py:715] (1/8) Epoch 4, batch 4550, loss[loss=0.1807, simple_loss=0.2477, pruned_loss=0.05684, over 4891.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2277, pruned_loss=0.04465, over 970959.35 frames.], batch size: 22, lr: 4.70e-04
2022-05-04 19:50:30,683 INFO [train.py:715] (1/8) Epoch 4, batch 4600, loss[loss=0.1542, simple_loss=0.2237, pruned_loss=0.04232, over 4834.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2277, pruned_loss=0.04469, over 971247.39 frames.], batch size: 13, lr: 4.70e-04
2022-05-04 19:51:10,995 INFO [train.py:715] (1/8) Epoch 4, batch 4650, loss[loss=0.1653, simple_loss=0.2386, pruned_loss=0.04603, over 4764.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2275, pruned_loss=0.04489, over 970610.52 frames.], batch size: 16, lr: 4.70e-04
2022-05-04 19:51:51,343 INFO [train.py:715] (1/8) Epoch 4, batch 4700, loss[loss=0.1673, simple_loss=0.2299, pruned_loss=0.05231, over 4781.00 frames.], tot_loss[loss=0.158, simple_loss=0.2269, pruned_loss=0.04451, over 970932.50 frames.], batch size: 17, lr: 4.70e-04
2022-05-04 19:52:31,255 INFO [train.py:715] (1/8) Epoch 4, batch 4750, loss[loss=0.1733, simple_loss=0.2399, pruned_loss=0.05329, over 4973.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2265, pruned_loss=0.04462, over 970460.93 frames.], batch size: 15, lr: 4.70e-04
2022-05-04 19:53:13,042 INFO [train.py:715] (1/8) Epoch 4, batch 4800, loss[loss=0.1681, simple_loss=0.2431, pruned_loss=0.0466, over 4872.00 frames.], tot_loss[loss=0.158, simple_loss=0.2271, pruned_loss=0.04443, over 970775.12 frames.], batch size: 22, lr: 4.70e-04
2022-05-04 19:53:53,559 INFO [train.py:715] (1/8) Epoch 4, batch 4850, loss[loss=0.177, simple_loss=0.2505, pruned_loss=0.05174, over 4806.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2271, pruned_loss=0.04458, over 971273.47 frames.], batch size: 24, lr: 4.70e-04
2022-05-04 19:54:32,961 INFO [train.py:715] (1/8) Epoch 4, batch 4900, loss[loss=0.1497, simple_loss=0.235, pruned_loss=0.03217, over 4804.00 frames.], tot_loss[loss=0.1592, simple_loss=0.228, pruned_loss=0.04524, over 971354.17 frames.], batch size: 21, lr: 4.70e-04
2022-05-04 19:55:12,354 INFO [train.py:715] (1/8) Epoch 4, batch 4950, loss[loss=0.1275, simple_loss=0.2041, pruned_loss=0.02546, over 4817.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2288, pruned_loss=0.04567, over 971582.51 frames.], batch size: 27, lr: 4.70e-04
2022-05-04 19:55:52,418 INFO [train.py:715] (1/8) Epoch 4, batch 5000, loss[loss=0.1244, simple_loss=0.195, pruned_loss=0.0269, over 4827.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2296, pruned_loss=0.04543, over 972341.25 frames.], batch size: 13, lr: 4.70e-04
2022-05-04 19:56:32,447 INFO [train.py:715] (1/8) Epoch 4, batch 5050, loss[loss=0.1508, simple_loss=0.2224, pruned_loss=0.03961, over 4883.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2297, pruned_loss=0.0448, over 972626.94 frames.], batch size: 22, lr: 4.69e-04
2022-05-04 19:57:12,350 INFO [train.py:715] (1/8) Epoch 4, batch 5100, loss[loss=0.1338, simple_loss=0.2068, pruned_loss=0.03036, over 4765.00 frames.], tot_loss[loss=0.159, simple_loss=0.2291, pruned_loss=0.04446, over 972888.58 frames.], batch size: 19, lr: 4.69e-04
2022-05-04 19:57:51,530 INFO [train.py:715] (1/8) Epoch 4, batch 5150, loss[loss=0.1927, simple_loss=0.2529, pruned_loss=0.06623, over 4984.00 frames.], tot_loss[loss=0.1587, simple_loss=0.228, pruned_loss=0.04468, over 973579.15 frames.], batch size: 14, lr: 4.69e-04
2022-05-04 19:58:31,730 INFO [train.py:715] (1/8) Epoch 4, batch 5200, loss[loss=0.1479, simple_loss=0.228, pruned_loss=0.03392, over 4876.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2284, pruned_loss=0.04493, over 973658.50 frames.], batch size: 22, lr: 4.69e-04
2022-05-04 19:59:11,089 INFO [train.py:715] (1/8) Epoch 4, batch 5250, loss[loss=0.1669, simple_loss=0.2345, pruned_loss=0.04964, over 4877.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2275, pruned_loss=0.0446, over 973530.45 frames.], batch size: 16, lr: 4.69e-04
2022-05-04 19:59:50,718 INFO [train.py:715] (1/8) Epoch 4, batch 5300, loss[loss=0.1371, simple_loss=0.207, pruned_loss=0.03355, over 4981.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2267, pruned_loss=0.0439, over 973588.06 frames.], batch size: 28, lr: 4.69e-04
2022-05-04 20:00:30,980 INFO [train.py:715] (1/8) Epoch 4, batch 5350, loss[loss=0.1713, simple_loss=0.2358, pruned_loss=0.05336, over 4820.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2269, pruned_loss=0.04432, over 973538.16 frames.], batch size: 15, lr: 4.69e-04
2022-05-04 20:01:11,136 INFO [train.py:715] (1/8) Epoch 4, batch 5400, loss[loss=0.1625, simple_loss=0.2271, pruned_loss=0.04897, over 4937.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2268, pruned_loss=0.04427, over 973869.58 frames.], batch size: 23, lr: 4.69e-04
2022-05-04 20:01:51,435 INFO [train.py:715] (1/8) Epoch 4, batch 5450, loss[loss=0.1664, simple_loss=0.2342, pruned_loss=0.04932, over 4955.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2271, pruned_loss=0.04456, over 973125.02 frames.], batch size: 21, lr: 4.69e-04
2022-05-04 20:02:30,843 INFO [train.py:715] (1/8) Epoch 4, batch 5500, loss[loss=0.1725, simple_loss=0.2381, pruned_loss=0.0535, over 4948.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04367, over 972570.55 frames.], batch size: 35, lr: 4.69e-04
2022-05-04 20:03:11,391 INFO [train.py:715] (1/8) Epoch 4, batch 5550, loss[loss=0.1703, simple_loss=0.2367, pruned_loss=0.05193, over 4840.00 frames.], tot_loss[loss=0.1578, simple_loss=0.227, pruned_loss=0.04434, over 972797.48 frames.], batch size: 30, lr: 4.69e-04
2022-05-04 20:03:51,128 INFO [train.py:715] (1/8) Epoch 4, batch 5600, loss[loss=0.1607, simple_loss=0.2271, pruned_loss=0.04719, over 4978.00 frames.], tot_loss[loss=0.1591, simple_loss=0.228, pruned_loss=0.04511, over 972018.18 frames.], batch size: 39, lr: 4.69e-04
2022-05-04 20:04:31,015 INFO [train.py:715] (1/8) Epoch 4, batch 5650, loss[loss=0.1623, simple_loss=0.221, pruned_loss=0.05176, over 4769.00 frames.], tot_loss[loss=0.159, simple_loss=0.2281, pruned_loss=0.0449, over 971585.18 frames.], batch size: 17, lr: 4.68e-04
2022-05-04 20:05:10,997 INFO [train.py:715] (1/8) Epoch 4, batch 5700, loss[loss=0.1515, simple_loss=0.2259, pruned_loss=0.03856, over 4948.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2276, pruned_loss=0.0445, over 972144.42 frames.], batch size: 29, lr: 4.68e-04
2022-05-04 20:05:51,212 INFO [train.py:715] (1/8) Epoch 4, batch 5750, loss[loss=0.1734, simple_loss=0.251, pruned_loss=0.04794, over 4921.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2282, pruned_loss=0.0446, over 972453.44 frames.], batch size: 21, lr: 4.68e-04
2022-05-04 20:06:31,315 INFO [train.py:715] (1/8) Epoch 4, batch 5800, loss[loss=0.1901, simple_loss=0.2506, pruned_loss=0.06475, over 4879.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2285, pruned_loss=0.04519, over 973535.24 frames.], batch size: 16, lr: 4.68e-04
2022-05-04 20:07:10,963 INFO [train.py:715] (1/8) Epoch 4, batch 5850, loss[loss=0.1609, simple_loss=0.2476, pruned_loss=0.03713, over 4930.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2281, pruned_loss=0.04465, over 973362.19 frames.], batch size: 29, lr: 4.68e-04
2022-05-04 20:07:51,263 INFO [train.py:715] (1/8) Epoch 4, batch 5900, loss[loss=0.161, simple_loss=0.2224, pruned_loss=0.04986, over 4834.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2285, pruned_loss=0.04482, over 973824.02 frames.], batch size: 30, lr: 4.68e-04
2022-05-04 20:08:30,938 INFO [train.py:715] (1/8) Epoch 4, batch 5950, loss[loss=0.2074, simple_loss=0.2644, pruned_loss=0.07519, over 4874.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2287, pruned_loss=0.04523, over 972494.08 frames.], batch size: 32, lr: 4.68e-04
2022-05-04 20:09:10,577 INFO [train.py:715] (1/8) Epoch 4, batch 6000, loss[loss=0.1485, simple_loss=0.2246, pruned_loss=0.0362, over 4951.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2278, pruned_loss=0.0446, over 972986.87 frames.], batch size: 21, lr: 4.68e-04
2022-05-04 20:09:10,578 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 20:09:20,452 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1124, simple_loss=0.1981, pruned_loss=0.01337, over 914524.00 frames.
2022-05-04 20:10:00,570 INFO [train.py:715] (1/8) Epoch 4, batch 6050, loss[loss=0.1804, simple_loss=0.2579, pruned_loss=0.05143, over 4784.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2276, pruned_loss=0.04455, over 972969.76 frames.], batch size: 23, lr: 4.68e-04
2022-05-04 20:10:40,773 INFO [train.py:715] (1/8) Epoch 4, batch 6100, loss[loss=0.1725, simple_loss=0.2467, pruned_loss=0.04919, over 4928.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2277, pruned_loss=0.04446, over 973100.39 frames.], batch size: 29, lr: 4.68e-04
2022-05-04 20:11:21,168 INFO [train.py:715] (1/8) Epoch 4, batch 6150, loss[loss=0.1681, simple_loss=0.2388, pruned_loss=0.04873, over 4878.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2283, pruned_loss=0.04479, over 972489.37 frames.], batch size: 20, lr: 4.68e-04
2022-05-04 20:12:01,202 INFO [train.py:715] (1/8) Epoch 4, batch 6200, loss[loss=0.2048, simple_loss=0.2819, pruned_loss=0.06389, over 4976.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2288, pruned_loss=0.04509, over 972732.84 frames.], batch size: 15, lr: 4.68e-04
2022-05-04 20:12:40,832 INFO [train.py:715] (1/8) Epoch 4, batch 6250, loss[loss=0.1722, simple_loss=0.2382, pruned_loss=0.05315, over 4879.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2281, pruned_loss=0.04507, over 972384.42 frames.], batch size: 16, lr: 4.68e-04
2022-05-04 20:13:21,467 INFO [train.py:715] (1/8) Epoch 4, batch 6300, loss[loss=0.141, simple_loss=0.2124, pruned_loss=0.03477, over 4758.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2272, pruned_loss=0.04479, over 971711.56 frames.], batch size: 19, lr: 4.67e-04
2022-05-04 20:14:00,902 INFO [train.py:715] (1/8) Epoch 4, batch 6350, loss[loss=0.1689, simple_loss=0.2474, pruned_loss=0.04517, over 4782.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2279, pruned_loss=0.04499, over 971549.49 frames.], batch size: 14, lr: 4.67e-04
2022-05-04 20:14:41,822 INFO [train.py:715] (1/8) Epoch 4, batch 6400, loss[loss=0.1422, simple_loss=0.211, pruned_loss=0.03674, over 4701.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2279, pruned_loss=0.04476, over 970742.17 frames.], batch size: 15, lr: 4.67e-04
2022-05-04 20:15:21,568 INFO [train.py:715] (1/8) Epoch 4, batch 6450, loss[loss=0.1981, simple_loss=0.266, pruned_loss=0.06509, over 4849.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2284, pruned_loss=0.04518, over 969844.17 frames.], batch size: 15, lr: 4.67e-04
2022-05-04 20:16:01,671 INFO [train.py:715] (1/8) Epoch 4, batch 6500, loss[loss=0.1269, simple_loss=0.2011, pruned_loss=0.02636, over 4767.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2278, pruned_loss=0.04523, over 970554.61 frames.], batch size: 14, lr: 4.67e-04
2022-05-04 20:16:41,339 INFO [train.py:715] (1/8) Epoch 4, batch 6550, loss[loss=0.138, simple_loss=0.2238, pruned_loss=0.02609, over 4753.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2288, pruned_loss=0.04596, over 971420.72 frames.], batch size: 19, lr: 4.67e-04
2022-05-04 20:17:20,652 INFO [train.py:715] (1/8) Epoch 4, batch 6600, loss[loss=0.1602, simple_loss=0.2317, pruned_loss=0.04432, over 4900.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2287, pruned_loss=0.04606, over 971522.06 frames.], batch size: 22, lr: 4.67e-04
2022-05-04 20:18:01,339 INFO [train.py:715] (1/8) Epoch 4, batch 6650, loss[loss=0.1635, simple_loss=0.2388, pruned_loss=0.0441, over 4915.00 frames.], tot_loss[loss=0.1591, simple_loss=0.228, pruned_loss=0.04505, over 972322.59 frames.], batch size: 39, lr: 4.67e-04
2022-05-04 20:18:40,889 INFO [train.py:715] (1/8) Epoch 4, batch 6700, loss[loss=0.132, simple_loss=0.2023, pruned_loss=0.03085, over 4943.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2267, pruned_loss=0.04456, over 973617.97 frames.], batch size: 23, lr: 4.67e-04
2022-05-04 20:19:21,007 INFO [train.py:715] (1/8) Epoch 4, batch 6750, loss[loss=0.1585, simple_loss=0.2228, pruned_loss=0.04711, over 4864.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2276, pruned_loss=0.04468, over 974120.78 frames.], batch size: 30, lr: 4.67e-04
2022-05-04 20:20:00,763 INFO [train.py:715] (1/8) Epoch 4, batch 6800, loss[loss=0.1786, simple_loss=0.2512, pruned_loss=0.05296, over 4798.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2285, pruned_loss=0.04484, over 973642.06 frames.], batch size: 24, lr: 4.67e-04
2022-05-04 20:20:40,798 INFO [train.py:715] (1/8) Epoch 4, batch 6850, loss[loss=0.2027, simple_loss=0.2698, pruned_loss=0.06783, over 4836.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2278, pruned_loss=0.04467, over 974284.99 frames.], batch size: 15, lr: 4.67e-04
2022-05-04 20:21:20,105 INFO [train.py:715] (1/8) Epoch 4, batch 6900, loss[loss=0.141, simple_loss=0.2041, pruned_loss=0.03895, over 4825.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2269, pruned_loss=0.04386, over 973204.60 frames.], batch size: 12, lr: 4.66e-04
2022-05-04 20:21:59,586 INFO [train.py:715] (1/8) Epoch 4, batch 6950, loss[loss=0.1482, simple_loss=0.2182, pruned_loss=0.03908, over 4897.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2265, pruned_loss=0.04347, over 972486.82 frames.], batch size: 17, lr: 4.66e-04
2022-05-04 20:22:39,326 INFO [train.py:715] (1/8) Epoch 4, batch 7000, loss[loss=0.1444, simple_loss=0.212, pruned_loss=0.03838, over 4985.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2262, pruned_loss=0.0432, over 972028.82 frames.], batch size: 28, lr: 4.66e-04
2022-05-04 20:23:19,205 INFO [train.py:715] (1/8) Epoch 4, batch 7050, loss[loss=0.1791, simple_loss=0.2434, pruned_loss=0.05736, over 4986.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2265, pruned_loss=0.04348, over 972276.01 frames.], batch size: 14, lr: 4.66e-04
2022-05-04 20:23:58,927 INFO [train.py:715] (1/8) Epoch 4, batch 7100, loss[loss=0.1903, simple_loss=0.2662, pruned_loss=0.05721, over 4786.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2274, pruned_loss=0.04345, over 972361.13 frames.], batch size: 18, lr: 4.66e-04
2022-05-04 20:24:39,023 INFO [train.py:715] (1/8) Epoch 4, batch 7150, loss[loss=0.147, simple_loss=0.2209, pruned_loss=0.03658, over 4963.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2276, pruned_loss=0.04371, over 972833.78 frames.], batch size: 24, lr: 4.66e-04
2022-05-04 20:25:18,946 INFO [train.py:715] (1/8) Epoch 4, batch 7200, loss[loss=0.1855, simple_loss=0.2455, pruned_loss=0.06272, over 4980.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2284, pruned_loss=0.04489, over 973941.19 frames.], batch size: 25, lr: 4.66e-04
2022-05-04 20:25:59,105 INFO [train.py:715] (1/8) Epoch 4, batch 7250, loss[loss=0.1601, simple_loss=0.2387, pruned_loss=0.04072, over 4969.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2276, pruned_loss=0.04426, over 973331.24 frames.], batch size: 39, lr: 4.66e-04
2022-05-04 20:26:38,426 INFO [train.py:715] (1/8) Epoch 4, batch 7300, loss[loss=0.1427, simple_loss=0.2166, pruned_loss=0.03438, over 4732.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2278, pruned_loss=0.04466, over 973318.48 frames.], batch size: 16, lr: 4.66e-04
2022-05-04 20:27:18,110 INFO [train.py:715] (1/8) Epoch 4, batch 7350, loss[loss=0.1596, simple_loss=0.2224, pruned_loss=0.04842, over 4870.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2276, pruned_loss=0.04488, over 973604.09 frames.], batch size: 16, lr: 4.66e-04
2022-05-04 20:27:58,076 INFO [train.py:715] (1/8) Epoch 4, batch 7400, loss[loss=0.175, simple_loss=0.2469, pruned_loss=0.05154, over 4786.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2287, pruned_loss=0.04541, over 973666.82 frames.], batch size: 18, lr: 4.66e-04
2022-05-04 20:28:38,820 INFO [train.py:715] (1/8) Epoch 4, batch 7450, loss[loss=0.1857, simple_loss=0.2501, pruned_loss=0.06061, over 4873.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2289, pruned_loss=0.04518, over 973762.07 frames.], batch size: 16, lr: 4.66e-04
2022-05-04 20:29:18,223 INFO [train.py:715] (1/8) Epoch 4, batch 7500, loss[loss=0.1573, simple_loss=0.2232, pruned_loss=0.04572, over 4849.00 frames.], tot_loss[loss=0.158, simple_loss=0.2273, pruned_loss=0.04437, over 973646.12 frames.], batch size: 20, lr: 4.66e-04
2022-05-04 20:29:58,248 INFO [train.py:715] (1/8) Epoch 4, batch 7550, loss[loss=0.1406, simple_loss=0.2196, pruned_loss=0.03076, over 4961.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2277, pruned_loss=0.04481, over 973192.90 frames.], batch size: 24, lr: 4.65e-04
2022-05-04 20:30:38,901 INFO [train.py:715] (1/8) Epoch 4, batch 7600, loss[loss=0.1739, simple_loss=0.2459, pruned_loss=0.05097, over 4797.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2277, pruned_loss=0.04484, over 973311.88 frames.], batch size: 21, lr: 4.65e-04
2022-05-04 20:31:18,416 INFO [train.py:715] (1/8) Epoch 4, batch 7650, loss[loss=0.1506, simple_loss=0.2278, pruned_loss=0.03668, over 4907.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2269, pruned_loss=0.04415, over 972784.67 frames.], batch size: 19, lr: 4.65e-04
2022-05-04 20:31:58,077 INFO [train.py:715] (1/8) Epoch 4, batch 7700, loss[loss=0.1482, simple_loss=0.2248, pruned_loss=0.03583, over 4966.00 frames.], tot_loss[loss=0.158, simple_loss=0.2271, pruned_loss=0.04447, over 972663.90 frames.], batch size: 15, lr: 4.65e-04
2022-05-04 20:32:38,176 INFO [train.py:715] (1/8) Epoch 4, batch 7750, loss[loss=0.1304, simple_loss=0.1966, pruned_loss=0.03208, over 4798.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2267, pruned_loss=0.04455, over 971712.95 frames.], batch size: 21, lr: 4.65e-04
2022-05-04 20:33:18,316 INFO [train.py:715] (1/8) Epoch 4, batch 7800, loss[loss=0.139, simple_loss=0.2087, pruned_loss=0.0346, over 4650.00 frames.], tot_loss[loss=0.158, simple_loss=0.2268, pruned_loss=0.04461, over 971994.76 frames.], batch size: 13, lr: 4.65e-04
2022-05-04 20:33:57,317 INFO [train.py:715] (1/8) Epoch 4, batch 7850, loss[loss=0.1725, simple_loss=0.2323, pruned_loss=0.05633, over 4773.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2266, pruned_loss=0.04421, over 972027.44 frames.], batch size: 18, lr: 4.65e-04
2022-05-04 20:34:36,907 INFO [train.py:715] (1/8) Epoch 4, batch 7900, loss[loss=0.1576, simple_loss=0.2302, pruned_loss=0.04255, over 4811.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2275, pruned_loss=0.04463, over 972456.96 frames.], batch size: 25, lr: 4.65e-04
2022-05-04 20:35:16,774 INFO [train.py:715] (1/8) Epoch 4, batch 7950, loss[loss=0.1388, simple_loss=0.2083, pruned_loss=0.03467, over 4816.00 frames.], tot_loss[loss=0.1581, simple_loss=0.227, pruned_loss=0.04453, over 973118.73 frames.], batch size: 27, lr: 4.65e-04
2022-05-04 20:35:56,348 INFO [train.py:715] (1/8) Epoch 4, batch 8000, loss[loss=0.178, simple_loss=0.2374, pruned_loss=0.05926, over 4808.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2275, pruned_loss=0.04458, over 973088.55 frames.], batch size: 13, lr: 4.65e-04
2022-05-04 20:36:36,319 INFO [train.py:715] (1/8) Epoch 4, batch 8050, loss[loss=0.185, simple_loss=0.2633, pruned_loss=0.0533, over 4832.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2284, pruned_loss=0.04455, over 972766.17 frames.], batch size: 15, lr: 4.65e-04
2022-05-04 20:37:16,270 INFO [train.py:715] (1/8) Epoch 4, batch 8100, loss[loss=0.1674, simple_loss=0.2382, pruned_loss=0.04829, over 4809.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2295, pruned_loss=0.04539, over 972714.23 frames.], batch size: 25, lr: 4.65e-04
2022-05-04 20:37:56,514 INFO [train.py:715] (1/8) Epoch 4, batch 8150, loss[loss=0.1843, simple_loss=0.2351, pruned_loss=0.06674, over 4905.00 frames.], tot_loss[loss=0.1599, simple_loss=0.229, pruned_loss=0.04544, over 972723.24 frames.], batch size: 22, lr: 4.65e-04
2022-05-04 20:38:35,999 INFO [train.py:715] (1/8) Epoch 4, batch 8200, loss[loss=0.1685, simple_loss=0.2308, pruned_loss=0.05311, over 4959.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2293, pruned_loss=0.04552, over 973161.92 frames.], batch size: 15, lr: 4.64e-04
2022-05-04 20:39:15,732 INFO [train.py:715] (1/8) Epoch 4, batch 8250, loss[loss=0.1371, simple_loss=0.2103, pruned_loss=0.03196, over 4757.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2291, pruned_loss=0.04526, over 973218.58 frames.], batch size: 19, lr: 4.64e-04
2022-05-04 20:39:55,884 INFO [train.py:715] (1/8) Epoch 4, batch 8300, loss[loss=0.1599, simple_loss=0.227, pruned_loss=0.04637, over 4815.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2286, pruned_loss=0.04522, over 972696.63 frames.], batch size: 25, lr: 4.64e-04
2022-05-04 20:40:35,321 INFO [train.py:715] (1/8) Epoch 4, batch 8350, loss[loss=0.1585, simple_loss=0.227, pruned_loss=0.04501, over 4805.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2289, pruned_loss=0.04489, over 973168.21 frames.], batch size: 21, lr: 4.64e-04
2022-05-04 20:41:15,403 INFO [train.py:715] (1/8) Epoch 4, batch 8400, loss[loss=0.1425, simple_loss=0.2041, pruned_loss=0.04043, over 4685.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2284, pruned_loss=0.04443, over 972448.24 frames.], batch size: 15, lr: 4.64e-04
2022-05-04 20:41:55,752 INFO [train.py:715] (1/8) Epoch 4, batch 8450, loss[loss=0.1621, simple_loss=0.2309, pruned_loss=0.04664, over 4897.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2292, pruned_loss=0.04477, over 972782.39 frames.], batch size: 17, lr: 4.64e-04
2022-05-04 20:42:35,854 INFO [train.py:715] (1/8) Epoch 4, batch 8500, loss[loss=0.135, simple_loss=0.2075, pruned_loss=0.03125, over 4803.00 frames.], tot_loss[loss=0.159, simple_loss=0.2285, pruned_loss=0.04473, over 972815.32 frames.], batch size: 12, lr: 4.64e-04
2022-05-04 20:43:15,269 INFO [train.py:715] (1/8) Epoch 4, batch 8550, loss[loss=0.1699, simple_loss=0.2318, pruned_loss=0.05396, over 4892.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2284, pruned_loss=0.04512, over 972350.68 frames.], batch size: 19, lr: 4.64e-04
2022-05-04 20:43:55,083 INFO [train.py:715] (1/8) Epoch 4, batch 8600, loss[loss=0.1728, simple_loss=0.2464, pruned_loss=0.04965, over 4757.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2281, pruned_loss=0.04508, over 971706.02 frames.], batch size: 16, lr: 4.64e-04
2022-05-04 20:44:35,247 INFO [train.py:715] (1/8) Epoch 4, batch 8650, loss[loss=0.133, simple_loss=0.2079, pruned_loss=0.02902, over 4927.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2283, pruned_loss=0.04524, over 971744.50 frames.], batch size: 29, lr: 4.64e-04
2022-05-04 20:45:14,873 INFO [train.py:715] (1/8) Epoch 4, batch 8700, loss[loss=0.1486, simple_loss=0.2303, pruned_loss=0.03346, over 4953.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04581, over 971955.44 frames.], batch size: 21, lr: 4.64e-04
2022-05-04 20:45:55,174 INFO [train.py:715] (1/8) Epoch 4, batch 8750, loss[loss=0.1218, simple_loss=0.201, pruned_loss=0.02132, over 4963.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2281, pruned_loss=0.04472, over 972428.82 frames.], batch size: 24, lr: 4.64e-04
2022-05-04 20:46:35,399 INFO [train.py:715] (1/8) Epoch 4, batch 8800, loss[loss=0.1573, simple_loss=0.2156, pruned_loss=0.04948, over 4728.00 frames.], tot_loss[loss=0.158, simple_loss=0.2275, pruned_loss=0.04421, over 972782.69 frames.], batch size: 12, lr: 4.63e-04
2022-05-04 20:47:15,435 INFO [train.py:715] (1/8) Epoch 4, batch 8850, loss[loss=0.1356, simple_loss=0.2111, pruned_loss=0.0301, over 4970.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2282, pruned_loss=0.04445, over 973762.94 frames.], batch size: 24, lr: 4.63e-04
2022-05-04 20:47:55,135 INFO [train.py:715] (1/8) Epoch 4, batch 8900, loss[loss=0.1481, simple_loss=0.2078, pruned_loss=0.04419, over 4965.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2273, pruned_loss=0.04402, over 973633.86 frames.], batch size: 35, lr: 4.63e-04
2022-05-04 20:48:34,767 INFO [train.py:715] (1/8) Epoch 4, batch 8950, loss[loss=0.156, simple_loss=0.2229, pruned_loss=0.04455, over 4915.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2271, pruned_loss=0.04369, over 973938.78 frames.], batch size: 17, lr: 4.63e-04
2022-05-04 20:49:15,031 INFO [train.py:715] (1/8) Epoch 4, batch 9000, loss[loss=0.1641, simple_loss=0.222, pruned_loss=0.05313, over 4855.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2261, pruned_loss=0.04382, over 972650.66 frames.], batch size: 20, lr: 4.63e-04
2022-05-04 20:49:15,032 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 20:49:24,977 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1123, simple_loss=0.1979, pruned_loss=0.01336, over 914524.00 frames.
2022-05-04 20:50:05,305 INFO [train.py:715] (1/8) Epoch 4, batch 9050, loss[loss=0.159, simple_loss=0.2357, pruned_loss=0.04116, over 4813.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2269, pruned_loss=0.04397, over 972533.94 frames.], batch size: 27, lr: 4.63e-04
2022-05-04 20:50:45,319 INFO [train.py:715] (1/8) Epoch 4, batch 9100, loss[loss=0.1483, simple_loss=0.2203, pruned_loss=0.03819, over 4963.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2277, pruned_loss=0.04425, over 972976.86 frames.], batch size: 24, lr: 4.63e-04
2022-05-04 20:51:24,716 INFO [train.py:715] (1/8) Epoch 4, batch 9150, loss[loss=0.1519, simple_loss=0.2178, pruned_loss=0.04305, over 4986.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2275, pruned_loss=0.04461, over 974429.17 frames.], batch size: 28, lr: 4.63e-04
2022-05-04 20:52:04,892 INFO [train.py:715] (1/8) Epoch 4, batch 9200, loss[loss=0.1663, simple_loss=0.2311, pruned_loss=0.05071, over 4937.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2279, pruned_loss=0.04495, over 972628.86 frames.], batch size: 21, lr: 4.63e-04
2022-05-04 20:52:45,298 INFO [train.py:715] (1/8) Epoch 4, batch 9250, loss[loss=0.1826, simple_loss=0.2435, pruned_loss=0.06084, over 4780.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2276, pruned_loss=0.04436, over 972847.42 frames.], batch size: 16, lr: 4.63e-04
2022-05-04 20:53:24,547 INFO [train.py:715] (1/8) Epoch 4, batch 9300, loss[loss=0.1747, simple_loss=0.2475, pruned_loss=0.05093, over 4967.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2281, pruned_loss=0.04512, over 973134.53 frames.], batch size: 15, lr: 4.63e-04
2022-05-04 20:54:04,529 INFO [train.py:715] (1/8) Epoch 4, batch 9350, loss[loss=0.1396, simple_loss=0.2037, pruned_loss=0.03778, over 4927.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2271, pruned_loss=0.04431, over 972149.42 frames.], batch size: 29, lr: 4.63e-04
2022-05-04 20:54:44,473 INFO [train.py:715] (1/8) Epoch 4, batch 9400, loss[loss=0.1655, simple_loss=0.2276, pruned_loss=0.05174, over 4785.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2267, pruned_loss=0.04393, over 972429.42 frames.], batch size: 14, lr: 4.63e-04
2022-05-04 20:55:24,005 INFO [train.py:715] (1/8) Epoch 4, batch 9450, loss[loss=0.1801, simple_loss=0.245, pruned_loss=0.05763, over 4826.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2278, pruned_loss=0.04449, over 972427.60 frames.], batch size: 30, lr: 4.62e-04
2022-05-04 20:56:04,099 INFO [train.py:715] (1/8) Epoch 4, batch 9500, loss[loss=0.163, simple_loss=0.2314, pruned_loss=0.04729, over 4957.00 frames.], tot_loss[loss=0.158, simple_loss=0.2274, pruned_loss=0.04425, over 972372.47 frames.], batch size: 14, lr: 4.62e-04
2022-05-04 20:56:44,178 INFO [train.py:715] (1/8) Epoch 4, batch 9550, loss[loss=0.1502, simple_loss=0.2299, pruned_loss=0.03519, over 4899.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2278, pruned_loss=0.0445, over 972488.00 frames.], batch size: 39, lr: 4.62e-04
2022-05-04 20:57:24,671 INFO [train.py:715] (1/8) Epoch 4, batch 9600, loss[loss=0.1553, simple_loss=0.232, pruned_loss=0.03928, over 4810.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2276, pruned_loss=0.04462, over 972067.04 frames.], batch size: 25, lr: 4.62e-04
2022-05-04 20:58:04,101 INFO [train.py:715] (1/8) Epoch 4, batch 9650, loss[loss=0.136, simple_loss=0.2187, pruned_loss=0.02665, over 4900.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2276, pruned_loss=0.04449, over 971633.83 frames.], batch size: 17, lr: 4.62e-04
2022-05-04 20:58:44,665 INFO [train.py:715] (1/8) Epoch 4, batch 9700, loss[loss=0.1701, simple_loss=0.2353, pruned_loss=0.05244, over 4764.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2276, pruned_loss=0.04452, over 971409.91 frames.], batch size: 17, lr: 4.62e-04
2022-05-04 20:59:25,203 INFO [train.py:715] (1/8) Epoch 4, batch 9750, loss[loss=0.1633, simple_loss=0.2229, pruned_loss=0.05189, over 4896.00 frames.], tot_loss[loss=0.159, simple_loss=0.2284, pruned_loss=0.04475, over 972091.14 frames.], batch size: 17, lr: 4.62e-04
2022-05-04 21:00:04,729 INFO [train.py:715] (1/8) Epoch 4, batch 9800, loss[loss=0.1587, simple_loss=0.2092, pruned_loss=0.05408, over 4727.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2287, pruned_loss=0.04524, over 971721.06 frames.], batch size: 16, lr: 4.62e-04
2022-05-04 21:00:43,870 INFO [train.py:715] (1/8) Epoch 4, batch 9850, loss[loss=0.1555, simple_loss=0.2176, pruned_loss=0.04668, over 4848.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2275, pruned_loss=0.04432, over 972492.38 frames.], batch size: 13, lr: 4.62e-04
2022-05-04 21:01:23,907 INFO [train.py:715] (1/8) Epoch 4, batch 9900, loss[loss=0.1763, simple_loss=0.248, pruned_loss=0.05233, over 4909.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2283, pruned_loss=0.04447, over 972735.26 frames.], batch size: 18, lr: 4.62e-04
2022-05-04 21:02:03,386 INFO [train.py:715] (1/8) Epoch 4, batch 9950, loss[loss=0.1735, simple_loss=0.2534, pruned_loss=0.04677, over 4833.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2287, pruned_loss=0.04434, over 972634.20 frames.], batch size: 15, lr: 4.62e-04
2022-05-04 21:02:42,758 INFO [train.py:715] (1/8) Epoch 4, batch 10000, loss[loss=0.1405, simple_loss=0.2165, pruned_loss=0.03221, over 4801.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2278, pruned_loss=0.04414, over 972887.51 frames.], batch size: 21, lr: 4.62e-04
2022-05-04 21:03:22,520 INFO [train.py:715] (1/8) Epoch 4, batch 10050, loss[loss=0.1766, simple_loss=0.2539, pruned_loss=0.04967, over 4812.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2278, pruned_loss=0.04394, over 973435.53 frames.], batch size: 25, lr: 4.62e-04
2022-05-04 21:04:02,316 INFO [train.py:715] (1/8) Epoch 4, batch 10100, loss[loss=0.1518, simple_loss=0.229, pruned_loss=0.03729, over 4925.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2275, pruned_loss=0.04391, over 972926.68 frames.], batch size: 39, lr: 4.61e-04
2022-05-04 21:04:41,563 INFO [train.py:715] (1/8) Epoch 4, batch 10150, loss[loss=0.17, simple_loss=0.2483, pruned_loss=0.04583, over 4806.00 frames.], tot_loss[loss=0.158, simple_loss=0.2278, pruned_loss=0.04412, over 973262.11 frames.], batch size: 26, lr: 4.61e-04
2022-05-04 21:05:21,483 INFO [train.py:715] (1/8) Epoch 4, batch 10200, loss[loss=0.1455, simple_loss=0.214, pruned_loss=0.03849, over 4972.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2269, pruned_loss=0.04344, over 973812.87 frames.], batch size: 14, lr: 4.61e-04
2022-05-04 21:06:02,087 INFO [train.py:715] (1/8) Epoch 4, batch 10250, loss[loss=0.1402, simple_loss=0.2167, pruned_loss=0.03183, over 4927.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2272, pruned_loss=0.04361, over 973720.08 frames.], batch size: 29, lr: 4.61e-04
2022-05-04 21:06:41,871 INFO [train.py:715] (1/8) Epoch 4, batch 10300, loss[loss=0.1382, simple_loss=0.2145, pruned_loss=0.03096, over 4809.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2273, pruned_loss=0.04357, over 973379.53 frames.], batch size: 21, lr: 4.61e-04
2022-05-04 21:07:21,512 INFO [train.py:715] (1/8) Epoch 4, batch 10350, loss[loss=0.169, simple_loss=0.247, pruned_loss=0.04549, over 4938.00 frames.], tot_loss[loss=0.157, simple_loss=0.2271, pruned_loss=0.04347, over 973589.84 frames.], batch size: 23, lr: 4.61e-04
2022-05-04 21:08:01,706 INFO [train.py:715] (1/8) Epoch 4, batch 10400, loss[loss=0.1602, simple_loss=0.2184, pruned_loss=0.05097, over 4795.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2272, pruned_loss=0.04373, over 973605.39 frames.], batch size: 18, lr: 4.61e-04
2022-05-04 21:08:42,302 INFO [train.py:715] (1/8) Epoch 4, batch 10450, loss[loss=0.1139, simple_loss=0.1811, pruned_loss=0.02336, over 4839.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2282, pruned_loss=0.04449, over 972989.80 frames.], batch size: 15, lr: 4.61e-04
2022-05-04 21:09:21,891 INFO [train.py:715] (1/8) Epoch 4, batch 10500, loss[loss=0.1937, simple_loss=0.2661, pruned_loss=0.06066, over 4754.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2278, pruned_loss=0.04434, over 972633.18 frames.], batch size: 14, lr: 4.61e-04
2022-05-04 21:10:02,151 INFO [train.py:715] (1/8) Epoch 4, batch 10550, loss[loss=0.1478, simple_loss=0.2238, pruned_loss=0.03593, over 4929.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2276, pruned_loss=0.04403, over 972362.67 frames.], batch size: 35, lr: 4.61e-04
2022-05-04 21:10:42,499 INFO [train.py:715] (1/8) Epoch 4, batch 10600, loss[loss=0.1883, simple_loss=0.243, pruned_loss=0.06679, over 4776.00 frames.], tot_loss[loss=0.1572, simple_loss=0.227, pruned_loss=0.04368, over 972705.73 frames.], batch size: 18, lr: 4.61e-04
2022-05-04 21:11:22,301 INFO [train.py:715] (1/8) Epoch 4, batch 10650, loss[loss=0.1358, simple_loss=0.2103, pruned_loss=0.03069, over 4853.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2271, pruned_loss=0.04377, over 971895.86 frames.], batch size: 32, lr: 4.61e-04
2022-05-04 21:12:02,346 INFO [train.py:715] (1/8) Epoch 4, batch 10700, loss[loss=0.1758, simple_loss=0.2395, pruned_loss=0.05604, over 4858.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2276, pruned_loss=0.04392, over 972536.83 frames.], batch size: 32, lr: 4.61e-04
2022-05-04 21:12:42,048 INFO [train.py:715] (1/8) Epoch 4, batch 10750, loss[loss=0.1821, simple_loss=0.2589, pruned_loss=0.05268, over 4744.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2282, pruned_loss=0.04424, over 972412.03 frames.], batch size: 16, lr: 4.60e-04
2022-05-04 21:13:22,462 INFO [train.py:715] (1/8) Epoch 4, batch 10800, loss[loss=0.1258, simple_loss=0.2067, pruned_loss=0.02239, over 4966.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2274, pruned_loss=0.04386, over 971935.65 frames.], batch size: 28, lr: 4.60e-04
2022-05-04 21:14:01,783 INFO [train.py:715] (1/8) Epoch 4, batch 10850, loss[loss=0.1515, simple_loss=0.2278, pruned_loss=0.03755, over 4979.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2279, pruned_loss=0.04427, over 972887.63 frames.], batch size: 28, lr: 4.60e-04
2022-05-04 21:14:41,712 INFO [train.py:715] (1/8) Epoch 4, batch 10900, loss[loss=0.1304, simple_loss=0.1982, pruned_loss=0.03128, over 4928.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2274, pruned_loss=0.044, over 972777.73 frames.], batch size: 23, lr: 4.60e-04
2022-05-04 21:15:22,026 INFO [train.py:715] (1/8) Epoch 4, batch 10950, loss[loss=0.1434, simple_loss=0.2073, pruned_loss=0.03973, over 4915.00 frames.], tot_loss[loss=0.1574, simple_loss=0.227, pruned_loss=0.04393, over 973101.17 frames.], batch size: 18, lr: 4.60e-04
2022-05-04 21:16:01,658 INFO [train.py:715] (1/8) Epoch 4, batch 11000, loss[loss=0.1358, simple_loss=0.2043, pruned_loss=0.03362, over 4918.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2272, pruned_loss=0.04389, over 973201.22 frames.], batch size: 18, lr: 4.60e-04
2022-05-04 21:16:44,071 INFO [train.py:715] (1/8) Epoch 4, batch 11050, loss[loss=0.1336, simple_loss=0.2148, pruned_loss=0.0262, over 4799.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2273, pruned_loss=0.04355, over 972507.29 frames.], batch size: 21, lr: 4.60e-04
2022-05-04 21:17:24,553 INFO [train.py:715] (1/8) Epoch 4, batch 11100, loss[loss=0.1353, simple_loss=0.2141, pruned_loss=0.02828, over 4792.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2267, pruned_loss=0.04384, over 971933.43 frames.], batch size: 18, lr: 4.60e-04
2022-05-04 21:18:07,352 INFO [train.py:715] (1/8) Epoch 4, batch 11150, loss[loss=0.1316, simple_loss=0.1969, pruned_loss=0.0331, over 4817.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2275, pruned_loss=0.04435, over 971779.55 frames.], batch size: 12, lr: 4.60e-04
2022-05-04 21:18:49,570 INFO [train.py:715] (1/8) Epoch 4, batch 11200, loss[loss=0.1529, simple_loss=0.2257, pruned_loss=0.04003, over 4795.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2279, pruned_loss=0.0446, over 972760.93 frames.], batch size: 24, lr: 4.60e-04
2022-05-04 21:19:29,978 INFO [train.py:715] (1/8) Epoch 4, batch 11250, loss[loss=0.1539, simple_loss=0.222, pruned_loss=0.04291, over 4923.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2278, pruned_loss=0.04454, over 972903.72 frames.], batch size: 23, lr: 4.60e-04
2022-05-04 21:20:12,902 INFO [train.py:715] (1/8) Epoch 4, batch 11300, loss[loss=0.1552, simple_loss=0.2247, pruned_loss=0.04287, over 4758.00 frames.], tot_loss[loss=0.1578, simple_loss=0.227, pruned_loss=0.04428, over 971854.86 frames.], batch size: 19, lr: 4.60e-04
2022-05-04 21:20:52,361 INFO [train.py:715] (1/8) Epoch 4, batch 11350, loss[loss=0.1605, simple_loss=0.2172, pruned_loss=0.05193, over 4929.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2278, pruned_loss=0.04449, over 971617.59 frames.], batch size: 18, lr: 4.60e-04
2022-05-04 21:21:31,876 INFO [train.py:715] (1/8) Epoch 4, batch 11400, loss[loss=0.171, simple_loss=0.2299, pruned_loss=0.05603, over 4924.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2268, pruned_loss=0.04368, over 972277.39 frames.], batch size: 23, lr: 4.59e-04
2022-05-04 21:22:11,710 INFO [train.py:715] (1/8) Epoch 4, batch 11450, loss[loss=0.1555, simple_loss=0.236, pruned_loss=0.03749, over 4810.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2261, pruned_loss=0.04348, over 972145.83 frames.], batch size: 26, lr: 4.59e-04
2022-05-04 21:22:51,363 INFO [train.py:715] (1/8) Epoch 4, batch 11500, loss[loss=0.1552, simple_loss=0.2232, pruned_loss=0.04356, over 4923.00 frames.], tot_loss[loss=0.1563, simple_loss=0.226, pruned_loss=0.04331, over 972927.91 frames.], batch size: 23, lr: 4.59e-04
2022-05-04 21:23:30,607 INFO [train.py:715] (1/8) Epoch 4, batch 11550, loss[loss=0.1486, simple_loss=0.2189, pruned_loss=0.03916, over 4783.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2263, pruned_loss=0.04365, over 972364.79 frames.], batch size: 14, lr: 4.59e-04
2022-05-04 21:24:09,869 INFO [train.py:715] (1/8) Epoch 4, batch 11600, loss[loss=0.1508, simple_loss=0.2204, pruned_loss=0.04067, over 4918.00 frames.], tot_loss[loss=0.157, simple_loss=0.2264, pruned_loss=0.04382, over 972389.81 frames.], batch size: 17, lr: 4.59e-04
2022-05-04 21:24:50,403 INFO [train.py:715] (1/8) Epoch 4, batch 11650, loss[loss=0.1774, simple_loss=0.2388, pruned_loss=0.05798, over 4834.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2261, pruned_loss=0.04367, over 972516.72 frames.], batch size: 26, lr: 4.59e-04
2022-05-04 21:25:30,283 INFO [train.py:715] (1/8) Epoch 4, batch 11700, loss[loss=0.192, simple_loss=0.2457, pruned_loss=0.06915, over 4813.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2271, pruned_loss=0.0439, over 972742.19 frames.], batch size: 14, lr: 4.59e-04
2022-05-04 21:26:10,253 INFO [train.py:715] (1/8) Epoch 4, batch 11750, loss[loss=0.1627, simple_loss=0.2416, pruned_loss=0.04196, over 4927.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2264, pruned_loss=0.04317, over 972976.22 frames.], batch size: 18, lr: 4.59e-04
2022-05-04 21:26:50,003 INFO [train.py:715] (1/8) Epoch 4, batch 11800, loss[loss=0.1686, simple_loss=0.234, pruned_loss=0.05164, over 4796.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2269, pruned_loss=0.04361, over 973323.36 frames.], batch size: 17, lr: 4.59e-04
2022-05-04 21:27:30,267 INFO [train.py:715] (1/8) Epoch 4, batch 11850, loss[loss=0.1317, simple_loss=0.1994, pruned_loss=0.03201, over 4841.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2274, pruned_loss=0.04349, over 972982.90 frames.], batch size: 32, lr: 4.59e-04
2022-05-04 21:28:09,526 INFO [train.py:715] (1/8) Epoch 4, batch 11900, loss[loss=0.1669, simple_loss=0.2408, pruned_loss=0.04646, over 4796.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2274, pruned_loss=0.04368, over 972597.33 frames.], batch size: 17, lr: 4.59e-04
2022-05-04 21:28:49,305 INFO [train.py:715] (1/8) Epoch 4, batch 11950, loss[loss=0.141, simple_loss=0.208, pruned_loss=0.03704, over 4693.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2268, pruned_loss=0.04348, over 972599.43 frames.], batch size: 15, lr: 4.59e-04
2022-05-04 21:29:29,760 INFO [train.py:715] (1/8) Epoch 4, batch 12000, loss[loss=0.1523, simple_loss=0.2107, pruned_loss=0.04697, over 4846.00 frames.], tot_loss[loss=0.157, simple_loss=0.227, pruned_loss=0.04348, over 972538.84 frames.], batch size: 34, lr: 4.59e-04
2022-05-04 21:29:29,761 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 21:29:49,526 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1122, simple_loss=0.198, pruned_loss=0.01324, over 914524.00 frames.
2022-05-04 21:30:30,061 INFO [train.py:715] (1/8) Epoch 4, batch 12050, loss[loss=0.1494, simple_loss=0.2097, pruned_loss=0.04451, over 4744.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2266, pruned_loss=0.0435, over 972590.77 frames.], batch size: 16, lr: 4.58e-04
2022-05-04 21:31:09,878 INFO [train.py:715] (1/8) Epoch 4, batch 12100, loss[loss=0.1437, simple_loss=0.2164, pruned_loss=0.03554, over 4823.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2265, pruned_loss=0.04349, over 972980.06 frames.], batch size: 15, lr: 4.58e-04
2022-05-04 21:31:50,049 INFO [train.py:715] (1/8) Epoch 4, batch 12150, loss[loss=0.1546, simple_loss=0.2242, pruned_loss=0.04247, over 4818.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2264, pruned_loss=0.04369, over 972124.98 frames.], batch size: 26, lr: 4.58e-04
2022-05-04 21:32:30,105 INFO [train.py:715] (1/8) Epoch 4, batch 12200, loss[loss=0.1903, simple_loss=0.2711, pruned_loss=0.05468, over 4987.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2262, pruned_loss=0.04342, over 971940.20 frames.], batch size: 20, lr: 4.58e-04
2022-05-04 21:33:10,431 INFO [train.py:715] (1/8) Epoch 4, batch 12250, loss[loss=0.1477, simple_loss=0.2242, pruned_loss=0.03558, over 4752.00 frames.], tot_loss[loss=0.157, simple_loss=0.2263, pruned_loss=0.04385, over 971930.22 frames.], batch size: 19, lr: 4.58e-04
2022-05-04 21:33:49,415 INFO [train.py:715] (1/8) Epoch 4, batch 12300, loss[loss=0.1605, simple_loss=0.2358, pruned_loss=0.04255, over 4839.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2258, pruned_loss=0.04332, over 971907.06 frames.], batch size: 26, lr: 4.58e-04
2022-05-04 21:34:29,433 INFO [train.py:715] (1/8) Epoch 4, batch 12350, loss[loss=0.1894, simple_loss=0.2554, pruned_loss=0.06172, over 4911.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.0437, over 972181.81 frames.], batch size: 39, lr: 4.58e-04
2022-05-04 21:35:10,021 INFO [train.py:715] (1/8) Epoch 4, batch 12400, loss[loss=0.1538, simple_loss=0.2246, pruned_loss=0.04151, over 4908.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04364, over 972694.95 frames.], batch size: 19, lr: 4.58e-04
2022-05-04 21:35:49,231 INFO [train.py:715] (1/8) Epoch 4, batch 12450, loss[loss=0.1319, simple_loss=0.2055, pruned_loss=0.02914, over 4829.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2271, pruned_loss=0.0443, over 971923.70 frames.], batch size: 25, lr: 4.58e-04
2022-05-04 21:36:29,196 INFO [train.py:715] (1/8) Epoch 4, batch 12500, loss[loss=0.1637, simple_loss=0.2379, pruned_loss=0.04472, over 4749.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2256, pruned_loss=0.04348, over 971655.05 frames.], batch size: 16, lr: 4.58e-04
2022-05-04 21:37:08,753 INFO [train.py:715] (1/8) Epoch 4, batch 12550, loss[loss=0.1477, simple_loss=0.2098, pruned_loss=0.0428, over 4792.00 frames.], tot_loss[loss=0.156, simple_loss=0.225, pruned_loss=0.04344, over 971966.84 frames.], batch size: 24, lr: 4.58e-04
2022-05-04 21:37:48,537 INFO [train.py:715] (1/8) Epoch 4, batch 12600, loss[loss=0.1264, simple_loss=0.2051, pruned_loss=0.02381, over 4916.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2246, pruned_loss=0.04264, over 972487.04 frames.], batch size: 18, lr: 4.58e-04
2022-05-04 21:38:27,427 INFO [train.py:715] (1/8) Epoch 4, batch 12650, loss[loss=0.1312, simple_loss=0.2088, pruned_loss=0.02682, over 4949.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2254, pruned_loss=0.04373, over 972408.34 frames.], batch size: 35, lr: 4.58e-04
2022-05-04 21:39:07,271 INFO [train.py:715] (1/8) Epoch 4, batch 12700, loss[loss=0.1506, simple_loss=0.2269, pruned_loss=0.03716, over 4984.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2262, pruned_loss=0.04437, over 972603.40 frames.], batch size: 15, lr: 4.58e-04
2022-05-04 21:39:47,343 INFO [train.py:715] (1/8) Epoch 4, batch 12750, loss[loss=0.1529, simple_loss=0.2303, pruned_loss=0.03775, over 4972.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2263, pruned_loss=0.04417, over 972588.04 frames.], batch size: 35, lr: 4.57e-04
2022-05-04 21:40:29,594 INFO [train.py:715] (1/8) Epoch 4, batch 12800, loss[loss=0.1461, simple_loss=0.216, pruned_loss=0.03806, over 4814.00 frames.], tot_loss[loss=0.158, simple_loss=0.227, pruned_loss=0.04452, over 973423.23 frames.], batch size: 13, lr: 4.57e-04
2022-05-04 21:41:08,988 INFO [train.py:715] (1/8) Epoch 4, batch 12850, loss[loss=0.1496, simple_loss=0.225, pruned_loss=0.03713, over 4922.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2268, pruned_loss=0.04446, over 972887.51 frames.], batch size: 29, lr: 4.57e-04
2022-05-04 21:41:49,119 INFO [train.py:715] (1/8) Epoch 4, batch 12900, loss[loss=0.1386, simple_loss=0.2205, pruned_loss=0.0284, over 4939.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2266, pruned_loss=0.0441, over 972753.30 frames.], batch size: 21, lr: 4.57e-04
2022-05-04 21:42:29,043 INFO [train.py:715] (1/8) Epoch 4, batch 12950, loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02892, over 4826.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2267, pruned_loss=0.04396, over 972132.31 frames.], batch size: 15, lr: 4.57e-04
2022-05-04 21:43:07,910 INFO [train.py:715] (1/8) Epoch 4, batch 13000, loss[loss=0.1463, simple_loss=0.2189, pruned_loss=0.03686, over 4740.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04362, over 971784.71 frames.], batch size: 16, lr: 4.57e-04
2022-05-04 21:43:47,498 INFO [train.py:715] (1/8) Epoch 4, batch 13050, loss[loss=0.1623, simple_loss=0.2358, pruned_loss=0.04443, over 4765.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2271, pruned_loss=0.04388, over 971897.59 frames.], batch size: 19, lr: 4.57e-04
2022-05-04 21:44:27,456 INFO [train.py:715] (1/8) Epoch 4, batch 13100, loss[loss=0.2218, simple_loss=0.2878, pruned_loss=0.07795, over 4745.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2266, pruned_loss=0.04347, over 972202.10 frames.], batch size: 16, lr: 4.57e-04
2022-05-04 21:45:06,500 INFO [train.py:715] (1/8) Epoch 4, batch 13150, loss[loss=0.1764, simple_loss=0.2386, pruned_loss=0.05708, over 4868.00 frames.], tot_loss[loss=0.1582, simple_loss=0.228, pruned_loss=0.04421, over 973056.15 frames.], batch size: 32, lr: 4.57e-04
2022-05-04 21:45:46,239 INFO [train.py:715] (1/8) Epoch 4, batch 13200, loss[loss=0.1521, simple_loss=0.2303, pruned_loss=0.03693, over 4785.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2281, pruned_loss=0.04441, over 973081.56 frames.], batch size: 17, lr: 4.57e-04
2022-05-04 21:46:26,562 INFO [train.py:715] (1/8) Epoch 4, batch 13250, loss[loss=0.1878, simple_loss=0.2481, pruned_loss=0.06376, over 4910.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2286, pruned_loss=0.04482, over 972937.33 frames.], batch size: 18, lr: 4.57e-04
2022-05-04 21:47:06,166 INFO [train.py:715] (1/8) Epoch 4, batch 13300, loss[loss=0.1633, simple_loss=0.2397, pruned_loss=0.04343, over 4941.00 frames.], tot_loss[loss=0.159, simple_loss=0.2284, pruned_loss=0.04487, over 974090.53 frames.], batch size: 21, lr: 4.57e-04
2022-05-04 21:47:45,755 INFO [train.py:715] (1/8) Epoch 4, batch 13350, loss[loss=0.1876, simple_loss=0.2497, pruned_loss=0.06268, over 4801.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2284, pruned_loss=0.04487, over 974013.25 frames.], batch size: 21, lr: 4.57e-04
2022-05-04 21:48:25,395 INFO [train.py:715] (1/8) Epoch 4, batch 13400, loss[loss=0.1337, simple_loss=0.2068, pruned_loss=0.03033, over 4804.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2286, pruned_loss=0.04525, over 973569.28 frames.], batch size: 21, lr: 4.56e-04
2022-05-04 21:49:05,439 INFO [train.py:715] (1/8) Epoch 4, batch 13450, loss[loss=0.1684, simple_loss=0.2314, pruned_loss=0.05275, over 4839.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2273, pruned_loss=0.04428, over 973266.16 frames.], batch size: 30, lr: 4.56e-04
2022-05-04 21:49:45,241 INFO [train.py:715] (1/8) Epoch 4, batch 13500, loss[loss=0.1506, simple_loss=0.2145, pruned_loss=0.04334, over 4956.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2275, pruned_loss=0.04437, over 974052.04 frames.], batch size: 15, lr: 4.56e-04
2022-05-04 21:50:27,088 INFO [train.py:715] (1/8) Epoch 4, batch 13550, loss[loss=0.1642, simple_loss=0.227, pruned_loss=0.05066, over 4780.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2273, pruned_loss=0.04424, over 974114.84 frames.], batch size: 18, lr: 4.56e-04
2022-05-04 21:51:07,659 INFO [train.py:715] (1/8) Epoch 4, batch 13600, loss[loss=0.1696, simple_loss=0.2505, pruned_loss=0.04429, over 4695.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2283, pruned_loss=0.04448, over 974077.26 frames.], batch size: 15, lr: 4.56e-04
2022-05-04 21:51:47,210 INFO [train.py:715] (1/8) Epoch 4, batch 13650, loss[loss=0.1884, simple_loss=0.2453, pruned_loss=0.06579, over 4843.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2276, pruned_loss=0.04433, over 973810.54 frames.], batch size: 32, lr: 4.56e-04
2022-05-04 21:52:26,524 INFO [train.py:715] (1/8) Epoch 4, batch 13700, loss[loss=0.1496, simple_loss=0.2154, pruned_loss=0.04193, over 4988.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2273, pruned_loss=0.04403, over 973355.51 frames.], batch size: 15, lr: 4.56e-04
2022-05-04 21:53:06,450 INFO [train.py:715] (1/8) Epoch 4, batch 13750, loss[loss=0.1676, simple_loss=0.2369, pruned_loss=0.04911, over 4791.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2267, pruned_loss=0.04379, over 971977.99 frames.], batch size: 17, lr: 4.56e-04
2022-05-04 21:53:48,110 INFO [train.py:715] (1/8) Epoch 4, batch 13800, loss[loss=0.1572, simple_loss=0.2203, pruned_loss=0.04708, over 4921.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2269, pruned_loss=0.04389, over 971894.46 frames.], batch size: 17, lr: 4.56e-04
2022-05-04 21:54:29,032 INFO [train.py:715] (1/8) Epoch 4, batch 13850, loss[loss=0.1505, simple_loss=0.2269, pruned_loss=0.03706, over 4813.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2253, pruned_loss=0.04286, over 971564.73 frames.], batch size: 21, lr: 4.56e-04
2022-05-04 21:55:10,918 INFO [train.py:715] (1/8) Epoch 4, batch 13900, loss[loss=0.1482, simple_loss=0.2235, pruned_loss=0.03642, over 4785.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2259, pruned_loss=0.04317, over 970530.40 frames.], batch size: 18, lr: 4.56e-04
2022-05-04 21:55:52,329 INFO [train.py:715] (1/8) Epoch 4, batch 13950, loss[loss=0.1442, simple_loss=0.2309, pruned_loss=0.02879, over 4814.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2254, pruned_loss=0.0425, over 971235.80 frames.], batch size: 27, lr: 4.56e-04
2022-05-04 21:56:31,850 INFO [train.py:715] (1/8) Epoch 4, batch 14000, loss[loss=0.1923, simple_loss=0.2452, pruned_loss=0.06975, over 4868.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04361, over 971600.52 frames.], batch size: 16, lr: 4.56e-04
2022-05-04 21:57:12,898 INFO [train.py:715] (1/8) Epoch 4, batch 14050, loss[loss=0.1329, simple_loss=0.1995, pruned_loss=0.03317, over 4828.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2266, pruned_loss=0.04325, over 971688.45 frames.], batch size: 13, lr: 4.55e-04
2022-05-04 21:57:52,560 INFO [train.py:715] (1/8) Epoch 4, batch 14100, loss[loss=0.1368, simple_loss=0.2072, pruned_loss=0.03321, over 4979.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2267, pruned_loss=0.04333, over 972488.29 frames.], batch size: 14, lr: 4.55e-04
2022-05-04 21:58:32,926 INFO [train.py:715] (1/8) Epoch 4, batch 14150, loss[loss=0.1476, simple_loss=0.2151, pruned_loss=0.0401, over 4824.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2273, pruned_loss=0.04391, over 972952.43 frames.], batch size: 15, lr: 4.55e-04
2022-05-04 21:59:12,282 INFO [train.py:715] (1/8) Epoch 4, batch 14200, loss[loss=0.1465, simple_loss=0.2237, pruned_loss=0.03471, over 4703.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2274, pruned_loss=0.04398, over 973021.80 frames.], batch size: 15, lr: 4.55e-04
2022-05-04 21:59:51,972 INFO [train.py:715] (1/8) Epoch 4, batch 14250, loss[loss=0.1767, simple_loss=0.2397, pruned_loss=0.05688, over 4960.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2275, pruned_loss=0.04409, over 972625.05 frames.], batch size: 35, lr: 4.55e-04
2022-05-04 22:00:32,127 INFO [train.py:715] (1/8) Epoch 4, batch 14300, loss[loss=0.1883, simple_loss=0.2447, pruned_loss=0.06592, over 4955.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2276, pruned_loss=0.04413, over 972538.15 frames.], batch size: 23, lr: 4.55e-04
2022-05-04 22:01:10,596 INFO [train.py:715] (1/8) Epoch 4, batch 14350, loss[loss=0.1737, simple_loss=0.2432, pruned_loss=0.05207, over 4789.00 frames.], tot_loss[loss=0.158, simple_loss=0.2275, pruned_loss=0.04427, over 972560.60 frames.], batch size: 24, lr: 4.55e-04
2022-05-04 22:01:50,876 INFO [train.py:715] (1/8) Epoch 4, batch 14400, loss[loss=0.1271, simple_loss=0.1983, pruned_loss=0.02793, over 4827.00 frames.], tot_loss[loss=0.1584, simple_loss=0.228, pruned_loss=0.04438, over 973050.64 frames.], batch size: 13, lr: 4.55e-04
2022-05-04 22:02:30,288 INFO [train.py:715] (1/8) Epoch 4, batch 14450, loss[loss=0.1787, simple_loss=0.2366, pruned_loss=0.06042, over 4743.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2277, pruned_loss=0.04429, over 972231.62 frames.], batch size: 16, lr: 4.55e-04
2022-05-04 22:03:09,287 INFO [train.py:715] (1/8) Epoch 4, batch 14500, loss[loss=0.1638, simple_loss=0.2386, pruned_loss=0.04454, over 4924.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2274, pruned_loss=0.04368, over 973087.35 frames.], batch size: 23, lr: 4.55e-04
2022-05-04 22:03:48,140 INFO [train.py:715] (1/8) Epoch 4, batch 14550, loss[loss=0.162, simple_loss=0.2307, pruned_loss=0.04668, over 4855.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04364, over 973356.95 frames.], batch size: 20, lr: 4.55e-04
2022-05-04 22:04:27,640 INFO [train.py:715] (1/8) Epoch 4, batch 14600, loss[loss=0.1606, simple_loss=0.227, pruned_loss=0.04707, over 4842.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2275, pruned_loss=0.04393, over 974121.76 frames.], batch size: 32, lr: 4.55e-04
2022-05-04 22:05:07,553 INFO [train.py:715] (1/8) Epoch 4, batch 14650, loss[loss=0.1462, simple_loss=0.2187, pruned_loss=0.03683, over 4919.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2277, pruned_loss=0.04396, over 973197.32 frames.], batch size: 18, lr: 4.55e-04
2022-05-04 22:05:46,290 INFO [train.py:715] (1/8) Epoch 4, batch 14700, loss[loss=0.1572, simple_loss=0.2315, pruned_loss=0.04146, over 4885.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2275, pruned_loss=0.04394, over 972393.12 frames.], batch size: 16, lr: 4.55e-04
2022-05-04 22:06:26,150 INFO [train.py:715] (1/8) Epoch 4, batch 14750, loss[loss=0.1514, simple_loss=0.2171, pruned_loss=0.04284, over 4839.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2264, pruned_loss=0.04356, over 971947.79 frames.], batch size: 30, lr: 4.54e-04
2022-05-04 22:07:06,149 INFO [train.py:715] (1/8) Epoch 4, batch 14800, loss[loss=0.1421, simple_loss=0.2199, pruned_loss=0.03216, over 4873.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2265, pruned_loss=0.04389, over 971950.23 frames.], batch size: 20, lr: 4.54e-04
2022-05-04 22:07:51,022 INFO [train.py:715] (1/8) Epoch 4, batch 14850, loss[loss=0.1753, simple_loss=0.2493, pruned_loss=0.0506, over 4913.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2267, pruned_loss=0.04403, over 972540.71 frames.], batch size: 18, lr: 4.54e-04
2022-05-04 22:08:31,239 INFO [train.py:715] (1/8) Epoch 4, batch 14900, loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03207, over 4884.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2269, pruned_loss=0.04416, over 973972.94 frames.], batch size: 19, lr: 4.54e-04
2022-05-04 22:09:11,325 INFO [train.py:715] (1/8) Epoch 4, batch 14950, loss[loss=0.1518, simple_loss=0.213, pruned_loss=0.04528, over 4760.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2265, pruned_loss=0.04434, over 973175.93 frames.], batch size: 14, lr: 4.54e-04
2022-05-04 22:09:51,670 INFO [train.py:715] (1/8) Epoch 4, batch 15000, loss[loss=0.1599, simple_loss=0.2236, pruned_loss=0.04814, over 4812.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2261, pruned_loss=0.04414, over 972295.04 frames.], batch size: 21, lr: 4.54e-04
2022-05-04 22:09:51,670 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 22:10:32,004 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1122, simple_loss=0.1978, pruned_loss=0.01336, over 914524.00 frames.
2022-05-04 22:11:12,735 INFO [train.py:715] (1/8) Epoch 4, batch 15050, loss[loss=0.1173, simple_loss=0.1889, pruned_loss=0.02285, over 4685.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.04348, over 972329.10 frames.], batch size: 15, lr: 4.54e-04
2022-05-04 22:11:52,177 INFO [train.py:715] (1/8) Epoch 4, batch 15100, loss[loss=0.1793, simple_loss=0.2356, pruned_loss=0.06149, over 4764.00 frames.], tot_loss[loss=0.157, simple_loss=0.2265, pruned_loss=0.04376, over 972963.36 frames.], batch size: 16, lr: 4.54e-04
2022-05-04 22:12:32,071 INFO [train.py:715] (1/8) Epoch 4, batch 15150, loss[loss=0.153, simple_loss=0.2367, pruned_loss=0.03462, over 4820.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2271, pruned_loss=0.04373, over 972426.48 frames.], batch size: 26, lr: 4.54e-04
2022-05-04 22:13:12,027 INFO [train.py:715] (1/8) Epoch 4, batch 15200, loss[loss=0.1617, simple_loss=0.2109, pruned_loss=0.0562, over 4894.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2266, pruned_loss=0.04353, over 973331.98 frames.], batch size: 19, lr: 4.54e-04
2022-05-04 22:13:51,744 INFO [train.py:715] (1/8) Epoch 4, batch 15250, loss[loss=0.1625, simple_loss=0.2291, pruned_loss=0.04792, over 4955.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2262, pruned_loss=0.04357, over 974708.16 frames.], batch size: 39, lr: 4.54e-04
2022-05-04 22:14:31,965 INFO [train.py:715] (1/8) Epoch 4, batch 15300, loss[loss=0.117, simple_loss=0.1944, pruned_loss=0.01986, over 4806.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04383, over 974319.26 frames.], batch size: 12, lr: 4.54e-04
2022-05-04 22:15:12,430 INFO [train.py:715] (1/8) Epoch 4, batch 15350, loss[loss=0.1442, simple_loss=0.2147, pruned_loss=0.03686, over 4698.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2272, pruned_loss=0.04405, over 973894.58 frames.], batch size: 15, lr: 4.54e-04
2022-05-04 22:15:52,262 INFO [train.py:715] (1/8) Epoch 4, batch 15400, loss[loss=0.1245, simple_loss=0.1909, pruned_loss=0.02905, over 4826.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2278, pruned_loss=0.04398, over 973917.06 frames.], batch size: 12, lr: 4.53e-04
2022-05-04 22:16:32,477 INFO [train.py:715] (1/8) Epoch 4, batch 15450, loss[loss=0.1527, simple_loss=0.2225, pruned_loss=0.04144, over 4758.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2283, pruned_loss=0.04447, over 973887.67 frames.], batch size: 14, lr: 4.53e-04
2022-05-04 22:17:12,936 INFO [train.py:715] (1/8) Epoch 4, batch 15500, loss[loss=0.165, simple_loss=0.2476, pruned_loss=0.04125, over 4840.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2279, pruned_loss=0.04425, over 973619.71 frames.], batch size: 30, lr: 4.53e-04
2022-05-04 22:17:53,290 INFO [train.py:715] (1/8) Epoch 4, batch 15550, loss[loss=0.1411, simple_loss=0.2093, pruned_loss=0.0365, over 4876.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2286, pruned_loss=0.04448, over 973409.01 frames.], batch size: 20, lr: 4.53e-04
2022-05-04 22:18:32,669 INFO [train.py:715] (1/8) Epoch 4, batch 15600, loss[loss=0.1523, simple_loss=0.2238, pruned_loss=0.04042, over 4964.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2282, pruned_loss=0.04475, over 973540.31 frames.], batch size: 35, lr: 4.53e-04
2022-05-04 22:19:13,501 INFO [train.py:715] (1/8) Epoch 4, batch 15650, loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02955, over 4781.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2275, pruned_loss=0.04406, over 972676.30 frames.], batch size: 12, lr: 4.53e-04
2022-05-04 22:19:53,093 INFO [train.py:715] (1/8) Epoch 4, batch 15700, loss[loss=0.166, simple_loss=0.2408, pruned_loss=0.04558, over 4835.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2275, pruned_loss=0.04372, over 972523.09 frames.], batch size: 32, lr: 4.53e-04
2022-05-04 22:20:33,267 INFO [train.py:715] (1/8) Epoch 4, batch 15750, loss[loss=0.1391, simple_loss=0.2069, pruned_loss=0.03563, over 4695.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2273, pruned_loss=0.04369, over 972305.72 frames.], batch size: 15, lr: 4.53e-04
2022-05-04 22:21:12,815 INFO [train.py:715] (1/8) Epoch 4, batch 15800, loss[loss=0.1518, simple_loss=0.2295, pruned_loss=0.03704, over 4977.00 frames.], tot_loss[loss=0.157, simple_loss=0.227, pruned_loss=0.04351, over 972476.56 frames.], batch size: 24, lr: 4.53e-04
2022-05-04 22:21:53,777 INFO [train.py:715] (1/8) Epoch 4, batch 15850, loss[loss=0.1421, simple_loss=0.2117, pruned_loss=0.03631, over 4836.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2262, pruned_loss=0.04314, over 972766.49 frames.], batch size: 30, lr: 4.53e-04
2022-05-04 22:22:34,973 INFO [train.py:715] (1/8) Epoch 4, batch 15900, loss[loss=0.1499, simple_loss=0.2232, pruned_loss=0.03836, over 4930.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04363, over 973219.59 frames.], batch size: 18, lr: 4.53e-04
2022-05-04 22:23:14,302 INFO [train.py:715] (1/8) Epoch 4, batch 15950, loss[loss=0.1707, simple_loss=0.2439, pruned_loss=0.04875, over 4930.00 frames.], tot_loss[loss=0.1573, simple_loss=0.227, pruned_loss=0.04379, over 973264.36 frames.], batch size: 23, lr: 4.53e-04
2022-05-04 22:23:54,465 INFO [train.py:715] (1/8) Epoch 4, batch 16000, loss[loss=0.1207, simple_loss=0.2019, pruned_loss=0.01973, over 4780.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2268, pruned_loss=0.04372, over 972368.06 frames.], batch size: 18, lr: 4.53e-04
2022-05-04 22:24:34,950 INFO [train.py:715] (1/8) Epoch 4, batch 16050, loss[loss=0.159, simple_loss=0.2407, pruned_loss=0.03861, over 4812.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2263, pruned_loss=0.04325, over 972564.36 frames.], batch size: 25, lr: 4.53e-04
2022-05-04 22:25:14,769 INFO [train.py:715] (1/8) Epoch 4, batch 16100, loss[loss=0.1754, simple_loss=0.245, pruned_loss=0.05291, over 4745.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2258, pruned_loss=0.04297, over 971981.43 frames.], batch size: 16, lr: 4.52e-04
2022-05-04 22:25:54,148 INFO [train.py:715] (1/8) Epoch 4, batch 16150, loss[loss=0.1291, simple_loss=0.2004, pruned_loss=0.02888, over 4826.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2251, pruned_loss=0.04249, over 971370.09 frames.], batch size: 26, lr: 4.52e-04
2022-05-04 22:26:34,757 INFO [train.py:715] (1/8) Epoch 4, batch 16200, loss[loss=0.1585, simple_loss=0.2278, pruned_loss=0.04459, over 4892.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2255, pruned_loss=0.04233, over 971259.06 frames.], batch size: 19, lr: 4.52e-04
2022-05-04 22:27:15,087 INFO [train.py:715] (1/8) Epoch 4, batch 16250, loss[loss=0.1267, simple_loss=0.2001, pruned_loss=0.02668, over 4953.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2258, pruned_loss=0.04297, over 971798.99 frames.], batch size: 24, lr: 4.52e-04
2022-05-04 22:27:54,432 INFO [train.py:715] (1/8) Epoch 4, batch 16300, loss[loss=0.1517, simple_loss=0.2176, pruned_loss=0.04286, over 4812.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2276, pruned_loss=0.0436, over 971613.66 frames.], batch size: 26, lr: 4.52e-04
2022-05-04 22:28:34,992 INFO [train.py:715] (1/8) Epoch 4, batch 16350, loss[loss=0.1598, simple_loss=0.2323, pruned_loss=0.04364, over 4908.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2283, pruned_loss=0.0436, over 971743.50 frames.], batch size: 19, lr: 4.52e-04
2022-05-04 22:29:15,666 INFO [train.py:715] (1/8) Epoch 4, batch 16400, loss[loss=0.1695, simple_loss=0.2376, pruned_loss=0.05071, over 4844.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2288, pruned_loss=0.04381, over 972128.22 frames.], batch size: 20, lr: 4.52e-04
2022-05-04 22:29:56,028 INFO [train.py:715] (1/8) Epoch 4, batch 16450, loss[loss=0.1252, simple_loss=0.1966, pruned_loss=0.02692, over 4886.00 frames.], tot_loss[loss=0.157, simple_loss=0.2272, pruned_loss=0.04344, over 971817.84 frames.], batch size: 22, lr: 4.52e-04
2022-05-04 22:30:35,460 INFO [train.py:715] (1/8) Epoch 4, batch 16500, loss[loss=0.2137, simple_loss=0.2567, pruned_loss=0.08539, over 4833.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2262, pruned_loss=0.04327, over 970199.34 frames.], batch size: 13, lr: 4.52e-04
2022-05-04 22:31:15,350 INFO [train.py:715] (1/8) Epoch 4, batch 16550, loss[loss=0.1462, simple_loss=0.2125, pruned_loss=0.0399, over 4966.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2265, pruned_loss=0.04329, over 971373.38 frames.], batch size: 15, lr: 4.52e-04
2022-05-04 22:31:55,170 INFO [train.py:715] (1/8) Epoch 4, batch 16600, loss[loss=0.1702, simple_loss=0.2262, pruned_loss=0.05707, over 4841.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2257, pruned_loss=0.04306, over 971815.99 frames.], batch size: 30, lr: 4.52e-04
2022-05-04 22:32:33,987 INFO [train.py:715] (1/8) Epoch 4, batch 16650, loss[loss=0.1404, simple_loss=0.2146, pruned_loss=0.03311, over 4982.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2257, pruned_loss=0.04302, over 972442.57 frames.], batch size: 25, lr: 4.52e-04
2022-05-04 22:33:12,847 INFO [train.py:715] (1/8) Epoch 4, batch 16700, loss[loss=0.1795, simple_loss=0.257, pruned_loss=0.05106, over 4780.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2265, pruned_loss=0.04342, over 971687.57 frames.], batch size: 17, lr: 4.52e-04
2022-05-04 22:33:52,192 INFO [train.py:715] (1/8) Epoch 4, batch 16750, loss[loss=0.166, simple_loss=0.2308, pruned_loss=0.05062, over 4957.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2264, pruned_loss=0.04341, over 971724.83 frames.], batch size: 35, lr: 4.52e-04
2022-05-04 22:34:31,642 INFO [train.py:715] (1/8) Epoch 4, batch 16800, loss[loss=0.1361, simple_loss=0.2036, pruned_loss=0.0343, over 4818.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2263, pruned_loss=0.04354, over 971850.72 frames.], batch size: 25, lr: 4.51e-04
2022-05-04 22:35:10,399 INFO [train.py:715] (1/8) Epoch 4, batch 16850, loss[loss=0.1681, simple_loss=0.2306, pruned_loss=0.05282, over 4780.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2267, pruned_loss=0.04379, over 971093.62 frames.], batch size: 18, lr: 4.51e-04
2022-05-04 22:35:50,735 INFO [train.py:715] (1/8) Epoch 4, batch 16900, loss[loss=0.1496, simple_loss=0.2095, pruned_loss=0.04488, over 4897.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2261, pruned_loss=0.04346, over 971874.02 frames.], batch size: 16, lr: 4.51e-04
2022-05-04 22:36:31,084 INFO [train.py:715] (1/8) Epoch 4, batch 16950, loss[loss=0.1764, simple_loss=0.243, pruned_loss=0.05489, over 4795.00 frames.], tot_loss[loss=0.157, simple_loss=0.2265, pruned_loss=0.04381, over 972546.30 frames.], batch size: 13, lr: 4.51e-04
2022-05-04 22:37:10,627 INFO [train.py:715] (1/8) Epoch 4, batch 17000, loss[loss=0.1444, simple_loss=0.2259, pruned_loss=0.03143, over 4991.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2269, pruned_loss=0.04433, over 972342.98 frames.], batch size: 20, lr: 4.51e-04
2022-05-04 22:37:50,453 INFO [train.py:715] (1/8) Epoch 4, batch 17050, loss[loss=0.1681, simple_loss=0.2371, pruned_loss=0.04952, over 4936.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2278, pruned_loss=0.04463, over 973230.83 frames.], batch size: 23, lr: 4.51e-04
2022-05-04 22:38:30,855 INFO [train.py:715] (1/8) Epoch 4, batch 17100, loss[loss=0.1554, simple_loss=0.2284, pruned_loss=0.04125, over 4819.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2276, pruned_loss=0.04447, over 972742.22 frames.], batch size: 25, lr: 4.51e-04
2022-05-04 22:39:10,962 INFO [train.py:715] (1/8) Epoch 4, batch 17150, loss[loss=0.1217, simple_loss=0.1953, pruned_loss=0.02401, over 4845.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2274, pruned_loss=0.04405, over 973160.32 frames.], batch size: 13, lr: 4.51e-04
2022-05-04 22:39:50,103 INFO [train.py:715] (1/8) Epoch 4, batch 17200, loss[loss=0.1521, simple_loss=0.2301, pruned_loss=0.0371, over 4937.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2268, pruned_loss=0.04374, over 974319.96 frames.], batch size: 23, lr: 4.51e-04
2022-05-04 22:40:30,249 INFO [train.py:715] (1/8) Epoch 4, batch 17250, loss[loss=0.1126, simple_loss=0.19, pruned_loss=0.01762, over 4800.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2269, pruned_loss=0.04399, over 973440.80 frames.], batch size: 24, lr: 4.51e-04
2022-05-04 22:41:10,194 INFO [train.py:715] (1/8) Epoch 4, batch 17300, loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.0322, over 4796.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2288, pruned_loss=0.0447, over 972827.57 frames.], batch size: 24, lr: 4.51e-04
2022-05-04 22:41:49,930 INFO [train.py:715] (1/8) Epoch 4, batch 17350, loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03295, over 4927.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2279, pruned_loss=0.04472, over 972980.88 frames.], batch size: 21, lr: 4.51e-04
2022-05-04 22:42:29,447 INFO [train.py:715] (1/8) Epoch 4, batch 17400, loss[loss=0.1632, simple_loss=0.2302, pruned_loss=0.04812, over 4775.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2268, pruned_loss=0.04431, over 973555.81 frames.], batch size: 18, lr: 4.51e-04
2022-05-04 22:43:09,750 INFO [train.py:715] (1/8) Epoch 4, batch 17450, loss[loss=0.1929, simple_loss=0.2663, pruned_loss=0.05974, over 4867.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2268, pruned_loss=0.04408, over 972723.04 frames.], batch size: 20, lr: 4.51e-04
2022-05-04 22:43:50,024 INFO [train.py:715] (1/8) Epoch 4, batch 17500, loss[loss=0.1258, simple_loss=0.1959, pruned_loss=0.02786, over 4969.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2266, pruned_loss=0.044, over 973158.44 frames.], batch size: 28, lr: 4.50e-04
2022-05-04 22:44:29,248 INFO [train.py:715] (1/8) Epoch 4, batch 17550, loss[loss=0.176, simple_loss=0.2429, pruned_loss=0.05455, over 4869.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2268, pruned_loss=0.04392, over 973534.45 frames.], batch size: 32, lr: 4.50e-04
2022-05-04 22:45:09,119 INFO [train.py:715] (1/8) Epoch 4, batch 17600, loss[loss=0.1532, simple_loss=0.2348, pruned_loss=0.03584, over 4894.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2268, pruned_loss=0.04403, over 973147.73 frames.], batch size: 19, lr: 4.50e-04
2022-05-04 22:45:49,512 INFO [train.py:715] (1/8) Epoch 4, batch 17650, loss[loss=0.1573, simple_loss=0.226, pruned_loss=0.04424, over 4798.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2258, pruned_loss=0.04326, over 972664.40 frames.], batch size: 21, lr: 4.50e-04
2022-05-04 22:46:29,567 INFO [train.py:715] (1/8) Epoch 4, batch 17700, loss[loss=0.1361, simple_loss=0.2038, pruned_loss=0.03419, over 4971.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2266, pruned_loss=0.04358, over 972752.93 frames.], batch size: 15, lr: 4.50e-04
2022-05-04 22:47:09,156 INFO [train.py:715] (1/8) Epoch 4, batch 17750, loss[loss=0.1548, simple_loss=0.2274, pruned_loss=0.04112, over 4830.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2263, pruned_loss=0.0435, over 972822.79 frames.], batch size: 26, lr: 4.50e-04
2022-05-04 22:47:49,253 INFO [train.py:715] (1/8) Epoch 4, batch 17800, loss[loss=0.1601, simple_loss=0.2269, pruned_loss=0.04669, over 4879.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2267, pruned_loss=0.04358, over 973494.33 frames.], batch size: 16, lr: 4.50e-04
2022-05-04 22:48:29,923 INFO [train.py:715] (1/8) Epoch 4, batch 17850, loss[loss=0.1346, simple_loss=0.2198, pruned_loss=0.02467, over 4790.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2273, pruned_loss=0.04412, over 973751.55 frames.], batch size: 24, lr: 4.50e-04
2022-05-04 22:49:09,019 INFO [train.py:715] (1/8) Epoch 4, batch 17900, loss[loss=0.1638, simple_loss=0.2249, pruned_loss=0.05134, over 4988.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2272, pruned_loss=0.0441, over 973403.55 frames.], batch size: 31, lr: 4.50e-04
2022-05-04 22:49:49,020 INFO [train.py:715] (1/8) Epoch 4, batch 17950, loss[loss=0.1574, simple_loss=0.2123, pruned_loss=0.05125, over 4827.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2263, pruned_loss=0.04398, over 972398.83 frames.], batch size: 30, lr: 4.50e-04
2022-05-04 22:50:29,179 INFO [train.py:715] (1/8) Epoch 4, batch 18000, loss[loss=0.1502, simple_loss=0.2303, pruned_loss=0.03506, over 4818.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2267, pruned_loss=0.04372, over 972456.70 frames.], batch size: 26, lr: 4.50e-04
2022-05-04 22:50:29,180 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 22:50:38,825 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1119, simple_loss=0.1976, pruned_loss=0.01313, over 914524.00 frames.
2022-05-04 22:51:19,282 INFO [train.py:715] (1/8) Epoch 4, batch 18050, loss[loss=0.149, simple_loss=0.2252, pruned_loss=0.03635, over 4809.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2262, pruned_loss=0.04344, over 971445.49 frames.], batch size: 24, lr: 4.50e-04
2022-05-04 22:51:59,527 INFO [train.py:715] (1/8) Epoch 4, batch 18100, loss[loss=0.1352, simple_loss=0.2044, pruned_loss=0.03297, over 4913.00 frames.], tot_loss[loss=0.1563, simple_loss=0.226, pruned_loss=0.0433, over 972312.31 frames.], batch size: 19, lr: 4.50e-04
2022-05-04 22:52:39,094 INFO [train.py:715] (1/8) Epoch 4, batch 18150, loss[loss=0.1475, simple_loss=0.2166, pruned_loss=0.03921, over 4800.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2268, pruned_loss=0.04387, over 972718.75 frames.], batch size: 24, lr: 4.50e-04
2022-05-04 22:53:19,405 INFO [train.py:715] (1/8) Epoch 4, batch 18200, loss[loss=0.1835, simple_loss=0.2436, pruned_loss=0.06174, over 4878.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2269, pruned_loss=0.04415, over 972655.74 frames.], batch size: 22, lr: 4.49e-04
2022-05-04 22:53:59,870 INFO [train.py:715] (1/8) Epoch 4, batch 18250, loss[loss=0.1585, simple_loss=0.2241, pruned_loss=0.04641, over 4834.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2274, pruned_loss=0.04465, over 972075.21 frames.], batch size: 30, lr: 4.49e-04
2022-05-04 22:54:39,568 INFO [train.py:715] (1/8) Epoch 4, batch 18300, loss[loss=0.1535, simple_loss=0.222, pruned_loss=0.04247, over 4971.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2274, pruned_loss=0.04466, over 971957.51 frames.], batch size: 15, lr: 4.49e-04
2022-05-04 22:55:19,281 INFO [train.py:715] (1/8) Epoch 4, batch 18350, loss[loss=0.1933, simple_loss=0.2613, pruned_loss=0.06261, over 4873.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2271, pruned_loss=0.04422, over 972063.12 frames.], batch size: 22, lr: 4.49e-04
2022-05-04 22:56:00,397 INFO [train.py:715] (1/8) Epoch 4, batch 18400, loss[loss=0.1732, simple_loss=0.2418, pruned_loss=0.05226, over 4945.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2282, pruned_loss=0.04469, over 972645.76 frames.], batch size: 35, lr: 4.49e-04
2022-05-04 22:56:40,811 INFO [train.py:715] (1/8) Epoch 4, batch 18450, loss[loss=0.1532, simple_loss=0.2346, pruned_loss=0.03593, over 4850.00 frames.], tot_loss[loss=0.1589, simple_loss=0.228, pruned_loss=0.04487, over 971882.93 frames.], batch size: 20, lr: 4.49e-04
2022-05-04 22:57:20,894 INFO [train.py:715] (1/8) Epoch 4, batch 18500, loss[loss=0.1596, simple_loss=0.229, pruned_loss=0.04513, over 4744.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2286, pruned_loss=0.04502, over 972246.83 frames.], batch size: 16, lr: 4.49e-04
2022-05-04 22:58:01,187 INFO [train.py:715] (1/8) Epoch 4, batch 18550, loss[loss=0.1387, simple_loss=0.201, pruned_loss=0.03817, over 4769.00 frames.], tot_loss[loss=0.1589, simple_loss=0.228, pruned_loss=0.04493, over 971920.85 frames.], batch size: 12, lr: 4.49e-04
2022-05-04 22:58:41,910 INFO [train.py:715] (1/8) Epoch 4, batch 18600, loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03018, over 4758.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2275, pruned_loss=0.04476, over 970744.81 frames.], batch size: 19, lr: 4.49e-04
2022-05-04 22:59:21,452 INFO [train.py:715] (1/8) Epoch 4, batch 18650, loss[loss=0.1419, simple_loss=0.2136, pruned_loss=0.03511, over 4876.00 frames.], tot_loss[loss=0.1578, simple_loss=0.227, pruned_loss=0.04436, over 970498.36 frames.], batch size: 16, lr: 4.49e-04
2022-05-04 23:00:01,610 INFO [train.py:715] (1/8) Epoch 4, batch 18700, loss[loss=0.1882, simple_loss=0.2545, pruned_loss=0.06094, over 4703.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2273, pruned_loss=0.04438, over 970695.67 frames.], batch size: 15, lr: 4.49e-04
2022-05-04 23:00:42,469 INFO [train.py:715] (1/8) Epoch 4, batch 18750, loss[loss=0.1659, simple_loss=0.2368, pruned_loss=0.04753, over 4983.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2267, pruned_loss=0.04382, over 970752.55 frames.], batch size: 39, lr: 4.49e-04
2022-05-04 23:01:21,941 INFO [train.py:715] (1/8) Epoch 4, batch 18800, loss[loss=0.1373, simple_loss=0.214, pruned_loss=0.03036, over 4751.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2258, pruned_loss=0.04349, over 970989.16 frames.], batch size: 16, lr: 4.49e-04
2022-05-04 23:02:02,026 INFO [train.py:715] (1/8) Epoch 4, batch 18850, loss[loss=0.1541, simple_loss=0.2324, pruned_loss=0.03789, over 4818.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2261, pruned_loss=0.04343, over 971258.11 frames.], batch size: 21, lr: 4.49e-04
2022-05-04 23:02:42,420 INFO [train.py:715] (1/8) Epoch 4, batch 18900, loss[loss=0.197, simple_loss=0.2675, pruned_loss=0.06325, over 4824.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2258, pruned_loss=0.04374, over 971818.26 frames.], batch size: 15, lr: 4.48e-04
2022-05-04 23:03:22,740 INFO [train.py:715] (1/8) Epoch 4, batch 18950, loss[loss=0.1862, simple_loss=0.254, pruned_loss=0.0592, over 4986.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2263, pruned_loss=0.04416, over 971740.38 frames.], batch size: 35, lr: 4.48e-04
2022-05-04 23:04:01,997 INFO [train.py:715] (1/8) Epoch 4, batch 19000, loss[loss=0.1453, simple_loss=0.2092, pruned_loss=0.04067, over 4878.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2256, pruned_loss=0.04347, over 971857.15 frames.], batch size: 22, lr: 4.48e-04
2022-05-04 23:04:42,502 INFO [train.py:715] (1/8) Epoch 4, batch 19050, loss[loss=0.171, simple_loss=0.2443, pruned_loss=0.04885, over 4766.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2256, pruned_loss=0.04333, over 972090.82 frames.], batch size: 19, lr: 4.48e-04
2022-05-04 23:05:23,220 INFO [train.py:715] (1/8) Epoch 4, batch 19100, loss[loss=0.1359, simple_loss=0.2009, pruned_loss=0.03544, over 4881.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2254, pruned_loss=0.0429, over 973016.04 frames.], batch size: 32, lr: 4.48e-04
2022-05-04 23:06:03,171 INFO [train.py:715] (1/8) Epoch 4, batch 19150, loss[loss=0.1505, simple_loss=0.211, pruned_loss=0.04506, over 4901.00 frames.], tot_loss[loss=0.155, simple_loss=0.2247, pruned_loss=0.04262, over 972996.02 frames.], batch size: 19, lr: 4.48e-04
2022-05-04 23:06:43,535 INFO [train.py:715] (1/8) Epoch 4, batch 19200, loss[loss=0.1362, simple_loss=0.2127, pruned_loss=0.02985, over 4884.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2247, pruned_loss=0.04283, over 972248.52 frames.], batch size: 16, lr: 4.48e-04
2022-05-04 23:07:24,302 INFO [train.py:715] (1/8) Epoch 4, batch 19250, loss[loss=0.1468, simple_loss=0.223, pruned_loss=0.03536, over 4807.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2252, pruned_loss=0.043, over 972625.45 frames.], batch size: 26, lr: 4.48e-04
2022-05-04 23:08:04,902 INFO [train.py:715] (1/8) Epoch 4, batch 19300, loss[loss=0.1665, simple_loss=0.2302, pruned_loss=0.05146, over 4780.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2261, pruned_loss=0.04357, over 971719.69 frames.], batch size: 14, lr: 4.48e-04
2022-05-04 23:08:44,076 INFO [train.py:715] (1/8) Epoch 4, batch 19350, loss[loss=0.1573, simple_loss=0.2267, pruned_loss=0.04391, over 4860.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2264, pruned_loss=0.04361, over 971540.07 frames.], batch size: 16, lr: 4.48e-04
2022-05-04 23:09:24,774 INFO [train.py:715] (1/8) Epoch 4, batch 19400, loss[loss=0.1601, simple_loss=0.232, pruned_loss=0.04416, over 4937.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2256, pruned_loss=0.04354, over 971528.53 frames.], batch size: 38, lr: 4.48e-04
2022-05-04 23:10:06,279 INFO [train.py:715] (1/8) Epoch 4, batch 19450, loss[loss=0.1471, simple_loss=0.2224, pruned_loss=0.03584, over 4933.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2261, pruned_loss=0.04361, over 972094.07 frames.], batch size: 29, lr: 4.48e-04
2022-05-04 23:10:47,435 INFO [train.py:715] (1/8) Epoch 4, batch 19500, loss[loss=0.1333, simple_loss=0.2058, pruned_loss=0.03036, over 4800.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2264, pruned_loss=0.04371, over 972182.95 frames.], batch size: 12, lr: 4.48e-04
2022-05-04 23:11:27,083 INFO [train.py:715] (1/8) Epoch 4, batch 19550, loss[loss=0.1548, simple_loss=0.2261, pruned_loss=0.04174, over 4779.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2256, pruned_loss=0.04301, over 972237.94 frames.], batch size: 17, lr: 4.48e-04
2022-05-04 23:12:07,473 INFO [train.py:715] (1/8) Epoch 4, batch 19600, loss[loss=0.1681, simple_loss=0.2236, pruned_loss=0.05633, over 4857.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2255, pruned_loss=0.04313, over 972572.54 frames.], batch size: 32, lr: 4.47e-04
2022-05-04 23:12:47,703 INFO [train.py:715] (1/8) Epoch 4, batch 19650, loss[loss=0.1164, simple_loss=0.1716, pruned_loss=0.03065, over 4772.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2246, pruned_loss=0.04227, over 972168.18 frames.], batch size: 12, lr: 4.47e-04
2022-05-04 23:13:26,467 INFO [train.py:715] (1/8) Epoch 4, batch 19700, loss[loss=0.1425, simple_loss=0.2196, pruned_loss=0.03267, over 4957.00 frames.], tot_loss[loss=0.155, simple_loss=0.2253, pruned_loss=0.04234, over 972027.10 frames.], batch size: 23, lr: 4.47e-04
2022-05-04 23:14:07,143 INFO [train.py:715] (1/8) Epoch 4, batch 19750, loss[loss=0.1775, simple_loss=0.2581, pruned_loss=0.04847, over 4818.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2258, pruned_loss=0.04268, over 971735.75 frames.], batch size: 25, lr: 4.47e-04
2022-05-04 23:14:47,966 INFO [train.py:715] (1/8) Epoch 4, batch 19800, loss[loss=0.1492, simple_loss=0.2199, pruned_loss=0.03925, over 4984.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2255, pruned_loss=0.04252, over 972438.36 frames.], batch size: 33, lr: 4.47e-04
2022-05-04 23:15:27,707 INFO [train.py:715] (1/8) Epoch 4, batch 19850, loss[loss=0.155, simple_loss=0.2198, pruned_loss=0.04508, over 4857.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2247, pruned_loss=0.04271, over 972041.78 frames.], batch size: 32, lr: 4.47e-04
2022-05-04 23:16:07,786 INFO [train.py:715] (1/8) Epoch 4, batch 19900, loss[loss=0.1345, simple_loss=0.2048, pruned_loss=0.03214, over 4938.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2252, pruned_loss=0.04304, over 971990.83 frames.], batch size: 35, lr: 4.47e-04
2022-05-04 23:16:47,893 INFO [train.py:715] (1/8) Epoch 4, batch 19950, loss[loss=0.1644, simple_loss=0.2369, pruned_loss=0.04593, over 4951.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2261, pruned_loss=0.04329, over 972002.11 frames.], batch size: 35, lr: 4.47e-04
2022-05-04 23:17:28,062 INFO [train.py:715] (1/8) Epoch 4, batch 20000, loss[loss=0.1667, simple_loss=0.2371, pruned_loss=0.04821, over 4879.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2262, pruned_loss=0.04373, over 972287.44 frames.], batch size: 22, lr: 4.47e-04
2022-05-04 23:18:06,764 INFO [train.py:715] (1/8) Epoch 4, batch 20050, loss[loss=0.138, simple_loss=0.2059, pruned_loss=0.035, over 4840.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2257, pruned_loss=0.04351, over 972717.79 frames.], batch size: 12, lr: 4.47e-04
2022-05-04 23:18:46,556 INFO [train.py:715] (1/8) Epoch 4, batch 20100, loss[loss=0.1819, simple_loss=0.2561, pruned_loss=0.05385, over 4946.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2264, pruned_loss=0.0434, over 973944.25 frames.], batch size: 21, lr: 4.47e-04
2022-05-04 23:19:26,624 INFO [train.py:715] (1/8) Epoch 4, batch 20150, loss[loss=0.1814, simple_loss=0.2416, pruned_loss=0.06059, over 4902.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2273, pruned_loss=0.044, over 974076.27 frames.], batch size: 39, lr: 4.47e-04
2022-05-04 23:20:06,048 INFO [train.py:715] (1/8) Epoch 4, batch 20200, loss[loss=0.1209, simple_loss=0.1865, pruned_loss=0.02769, over 4822.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2262, pruned_loss=0.0436, over 973748.63 frames.], batch size: 13, lr: 4.47e-04
2022-05-04 23:20:45,796 INFO [train.py:715] (1/8) Epoch 4, batch 20250, loss[loss=0.1267, simple_loss=0.2016, pruned_loss=0.02587, over 4809.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2255, pruned_loss=0.04332, over 973884.49 frames.], batch size: 25, lr: 4.47e-04
2022-05-04 23:21:26,113 INFO [train.py:715] (1/8) Epoch 4, batch 20300, loss[loss=0.1266, simple_loss=0.1955, pruned_loss=0.02884, over 4839.00 frames.], tot_loss[loss=0.1562, simple_loss=0.226, pruned_loss=0.0432, over 973544.23 frames.], batch size: 13, lr: 4.46e-04
2022-05-04 23:22:06,214 INFO [train.py:715] (1/8) Epoch 4, batch 20350, loss[loss=0.1885, simple_loss=0.2467, pruned_loss=0.06516, over 4814.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2267, pruned_loss=0.044, over 972827.40 frames.], batch size: 25, lr: 4.46e-04
2022-05-04 23:22:45,046 INFO [train.py:715] (1/8) Epoch 4, batch 20400, loss[loss=0.1188, simple_loss=0.19, pruned_loss=0.02374, over 4995.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2258, pruned_loss=0.04352, over 973341.20 frames.], batch size: 14, lr: 4.46e-04
2022-05-04 23:23:25,033 INFO [train.py:715] (1/8) Epoch 4, batch 20450, loss[loss=0.1174, simple_loss=0.19, pruned_loss=0.0224, over 4811.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2252, pruned_loss=0.04296, over 973113.61 frames.], batch size: 21, lr: 4.46e-04
2022-05-04 23:24:04,962 INFO [train.py:715] (1/8) Epoch 4, batch 20500, loss[loss=0.2055, simple_loss=0.2562, pruned_loss=0.07741, over 4792.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2258, pruned_loss=0.04337, over 973068.91 frames.], batch size: 17, lr: 4.46e-04
2022-05-04 23:24:44,750 INFO [train.py:715] (1/8) Epoch 4, batch 20550, loss[loss=0.1817, simple_loss=0.2511, pruned_loss=0.05617, over 4751.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2272, pruned_loss=0.04435, over 972227.07 frames.], batch size: 19, lr: 4.46e-04
2022-05-04 23:25:23,719 INFO [train.py:715] (1/8) Epoch 4, batch 20600, loss[loss=0.1435, simple_loss=0.2121, pruned_loss=0.03746, over 4983.00 frames.], tot_loss[loss=0.1566, simple_loss=0.226, pruned_loss=0.04363, over 972043.45 frames.], batch size: 15, lr: 4.46e-04
2022-05-04 23:26:03,659 INFO [train.py:715] (1/8) Epoch 4, batch 20650, loss[loss=0.163, simple_loss=0.2455, pruned_loss=0.04025, over 4879.00 frames.], tot_loss[loss=0.157, simple_loss=0.2266, pruned_loss=0.04367, over 972482.26 frames.], batch size: 20, lr: 4.46e-04
2022-05-04 23:26:44,156 INFO [train.py:715] (1/8) Epoch 4, batch 20700, loss[loss=0.1671, simple_loss=0.2327, pruned_loss=0.05073, over 4870.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2255, pruned_loss=0.04318, over 973415.55 frames.], batch size: 32, lr: 4.46e-04
2022-05-04 23:27:22,808 INFO [train.py:715] (1/8) Epoch 4, batch 20750, loss[loss=0.1246, simple_loss=0.1928, pruned_loss=0.0282, over 4750.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2258, pruned_loss=0.04333, over 973679.89 frames.], batch size: 16, lr: 4.46e-04
2022-05-04 23:28:04,811 INFO [train.py:715] (1/8) Epoch 4, batch 20800, loss[loss=0.1673, simple_loss=0.2249, pruned_loss=0.05487, over 4947.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2259, pruned_loss=0.0434, over 973717.50 frames.], batch size: 21, lr: 4.46e-04
2022-05-04 23:28:44,594 INFO [train.py:715] (1/8) Epoch 4, batch 20850, loss[loss=0.182, simple_loss=0.2492, pruned_loss=0.05744, over 4984.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2256, pruned_loss=0.04306, over 973209.11 frames.], batch size: 15, lr: 4.46e-04
2022-05-04 23:29:24,435 INFO [train.py:715] (1/8) Epoch 4, batch 20900, loss[loss=0.1526, simple_loss=0.2309, pruned_loss=0.0372, over 4920.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2251, pruned_loss=0.04225, over 973332.73 frames.], batch size: 18, lr: 4.46e-04
2022-05-04 23:30:03,467 INFO [train.py:715] (1/8) Epoch 4, batch 20950, loss[loss=0.1583, simple_loss=0.2377, pruned_loss=0.03945, over 4987.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2257, pruned_loss=0.04253, over 973465.62 frames.], batch size: 25, lr: 4.46e-04
2022-05-04 23:30:43,441 INFO [train.py:715] (1/8) Epoch 4, batch 21000, loss[loss=0.1638, simple_loss=0.2369, pruned_loss=0.04535, over 4772.00 frames.], tot_loss[loss=0.1547, simple_loss=0.225, pruned_loss=0.04218, over 973595.55 frames.], batch size: 18, lr: 4.46e-04
2022-05-04 23:30:43,442 INFO [train.py:733] (1/8) Computing validation loss
2022-05-04 23:30:52,895 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1116, simple_loss=0.1973, pruned_loss=0.01293, over 914524.00 frames.
2022-05-04 23:31:33,188 INFO [train.py:715] (1/8) Epoch 4, batch 21050, loss[loss=0.1785, simple_loss=0.2495, pruned_loss=0.05379, over 4813.00 frames.], tot_loss[loss=0.155, simple_loss=0.225, pruned_loss=0.04247, over 972545.19 frames.], batch size: 25, lr: 4.45e-04
2022-05-04 23:32:12,979 INFO [train.py:715] (1/8) Epoch 4, batch 21100, loss[loss=0.1449, simple_loss=0.2266, pruned_loss=0.03156, over 4981.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2256, pruned_loss=0.04286, over 972612.11 frames.], batch size: 24, lr: 4.45e-04
2022-05-04 23:32:52,568 INFO [train.py:715] (1/8) Epoch 4, batch 21150, loss[loss=0.1808, simple_loss=0.2401, pruned_loss=0.06069, over 4948.00 frames.], tot_loss[loss=0.1561, simple_loss=0.226, pruned_loss=0.04311, over 973731.57 frames.], batch size: 15, lr: 4.45e-04
2022-05-04 23:33:32,149 INFO [train.py:715] (1/8) Epoch 4, batch 21200, loss[loss=0.193, simple_loss=0.2568, pruned_loss=0.06461, over 4825.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2256, pruned_loss=0.04273, over 973915.84 frames.], batch size: 26, lr: 4.45e-04
2022-05-04 23:34:12,363 INFO [train.py:715] (1/8) Epoch 4, batch 21250, loss[loss=0.171, simple_loss=0.2367, pruned_loss=0.0527, over 4956.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2254, pruned_loss=0.04258, over 973283.80 frames.], batch size: 35, lr: 4.45e-04
2022-05-04 23:34:51,189 INFO [train.py:715] (1/8) Epoch 4, batch 21300, loss[loss=0.1565, simple_loss=0.2233, pruned_loss=0.0449, over 4968.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2258, pruned_loss=0.04304, over 973486.95 frames.], batch size: 24, lr: 4.45e-04
2022-05-04 23:35:30,244 INFO [train.py:715] (1/8) Epoch 4, batch 21350, loss[loss=0.1642, simple_loss=0.2358, pruned_loss=0.04632, over 4873.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2275, pruned_loss=0.04388, over 972708.30 frames.], batch size: 32, lr: 4.45e-04
2022-05-04 23:36:09,894 INFO [train.py:715] (1/8) Epoch 4, batch 21400, loss[loss=0.15, simple_loss=0.2278, pruned_loss=0.03608, over 4971.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2274, pruned_loss=0.04361, over 973087.12 frames.], batch size: 24, lr: 4.45e-04
2022-05-04 23:36:49,460 INFO [train.py:715] (1/8) Epoch 4, batch 21450, loss[loss=0.1569, simple_loss=0.2377, pruned_loss=0.03806, over 4899.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2265, pruned_loss=0.04288, over 972433.84 frames.], batch size: 17, lr: 4.45e-04
2022-05-04 23:37:28,646 INFO [train.py:715] (1/8) Epoch 4, batch 21500, loss[loss=0.1795, simple_loss=0.2354, pruned_loss=0.06184, over 4918.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2275, pruned_loss=0.04414, over 971544.54 frames.], batch size: 18, lr: 4.45e-04
2022-05-04 23:38:08,480 INFO [train.py:715] (1/8) Epoch 4, batch 21550, loss[loss=0.1535, simple_loss=0.2167, pruned_loss=0.04518, over 4898.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2275, pruned_loss=0.04396, over 972666.99 frames.], batch size: 17, lr: 4.45e-04
2022-05-04 23:38:48,845 INFO [train.py:715] (1/8) Epoch 4, batch 21600, loss[loss=0.1451, simple_loss=0.2148, pruned_loss=0.03768, over 4914.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2273, pruned_loss=0.04423, over 972492.46 frames.], batch size: 18, lr: 4.45e-04
2022-05-04 23:39:28,099 INFO [train.py:715] (1/8) Epoch 4, batch 21650, loss[loss=0.146, simple_loss=0.2151, pruned_loss=0.03848, over 4806.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2268, pruned_loss=0.04388, over 972534.48 frames.], batch size: 12, lr: 4.45e-04
2022-05-04 23:40:08,355 INFO [train.py:715] (1/8) Epoch 4, batch 21700, loss[loss=0.1818, simple_loss=0.2479, pruned_loss=0.05788, over 4902.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2269, pruned_loss=0.0438, over 972243.37 frames.], batch size: 19, lr: 4.45e-04
2022-05-04 23:40:49,366 INFO [train.py:715] (1/8) Epoch 4, batch 21750, loss[loss=0.1527, simple_loss=0.2285, pruned_loss=0.03842, over 4834.00 frames.], tot_loss[loss=0.1572, simple_loss=0.227, pruned_loss=0.04375, over 973430.96 frames.], batch size: 30, lr: 4.44e-04
2022-05-04 23:41:29,011 INFO [train.py:715] (1/8) Epoch 4, batch 21800, loss[loss=0.1599, simple_loss=0.2268, pruned_loss=0.04651, over 4782.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2272, pruned_loss=0.04381, over 972217.47 frames.], batch size: 18, lr: 4.44e-04
2022-05-04 23:42:08,604 INFO [train.py:715] (1/8) Epoch 4, batch 21850, loss[loss=0.1542, simple_loss=0.2273, pruned_loss=0.04059, over 4911.00 frames.], tot_loss[loss=0.156, simple_loss=0.2261, pruned_loss=0.04301, over 972440.11 frames.], batch size: 29, lr: 4.44e-04
2022-05-04 23:42:48,651 INFO [train.py:715] (1/8) Epoch 4, batch 21900, loss[loss=0.1521, simple_loss=0.2444, pruned_loss=0.02986, over 4970.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2267, pruned_loss=0.04304, over 972669.68 frames.], batch size: 24, lr: 4.44e-04
2022-05-04 23:43:29,097 INFO [train.py:715] (1/8) Epoch 4, batch 21950, loss[loss=0.1578, simple_loss=0.2205, pruned_loss=0.04758, over 4884.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2257, pruned_loss=0.04229, over 973020.59 frames.], batch size: 32, lr: 4.44e-04
2022-05-04 23:44:08,295 INFO [train.py:715] (1/8) Epoch 4, batch 22000, loss[loss=0.1452, simple_loss=0.2126, pruned_loss=0.0389, over 4939.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2257, pruned_loss=0.04198, over 973107.32 frames.], batch size: 21, lr: 4.44e-04
2022-05-04 23:44:48,073 INFO [train.py:715] (1/8) Epoch 4, batch 22050, loss[loss=0.1463, simple_loss=0.2135, pruned_loss=0.03954, over 4850.00 frames.], tot_loss[loss=0.155, simple_loss=0.2254, pruned_loss=0.04233, over 972781.51 frames.], batch size: 20, lr: 4.44e-04
2022-05-04 23:45:28,541 INFO [train.py:715] (1/8) Epoch 4, batch 22100, loss[loss=0.1278, simple_loss=0.2055, pruned_loss=0.02502, over 4846.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2254, pruned_loss=0.04245, over 972522.49 frames.], batch size: 27, lr: 4.44e-04
2022-05-04 23:46:08,390 INFO [train.py:715] (1/8) Epoch 4, batch 22150, loss[loss=0.1281, simple_loss=0.1929, pruned_loss=0.03171, over 4747.00 frames.], tot_loss[loss=0.155, simple_loss=0.2251, pruned_loss=0.04245, over 972654.52 frames.], batch size: 12, lr: 4.44e-04
2022-05-04 23:46:47,299 INFO [train.py:715] (1/8) Epoch 4, batch 22200, loss[loss=0.1423, simple_loss=0.2119, pruned_loss=0.03632, over 4881.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2246, pruned_loss=0.04258, over 972759.14 frames.], batch size: 16, lr: 4.44e-04
2022-05-04 23:47:27,363 INFO [train.py:715] (1/8) Epoch 4, batch 22250, loss[loss=0.1823, simple_loss=0.2454, pruned_loss=0.05962, over 4890.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2243, pruned_loss=0.04246, over 972859.15 frames.], batch size: 22, lr: 4.44e-04
2022-05-04 23:48:07,758 INFO [train.py:715] (1/8) Epoch 4, batch 22300, loss[loss=0.1813, simple_loss=0.2527, pruned_loss=0.05494, over 4779.00 frames.], tot_loss[loss=0.1549, simple_loss=0.225, pruned_loss=0.04243, over 973040.89 frames.], batch size: 17, lr: 4.44e-04
2022-05-04 23:48:46,511 INFO [train.py:715] (1/8) Epoch 4, batch 22350, loss[loss=0.1634, simple_loss=0.2299, pruned_loss=0.04844, over 4985.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2262, pruned_loss=0.04309, over 972397.48 frames.], batch size: 28, lr: 4.44e-04
2022-05-04 23:49:25,544 INFO [train.py:715] (1/8) Epoch 4, batch 22400, loss[loss=0.1806, simple_loss=0.2506, pruned_loss=0.05536, over 4766.00 frames.], tot_loss[loss=0.157, simple_loss=0.2265, pruned_loss=0.04371, over 971918.43 frames.], batch size: 18, lr: 4.44e-04
2022-05-04 23:50:06,129 INFO [train.py:715] (1/8) Epoch 4, batch 22450, loss[loss=0.16, simple_loss=0.2303, pruned_loss=0.04486, over 4914.00 frames.], tot_loss[loss=0.1576, simple_loss=0.227, pruned_loss=0.04408, over 972093.35 frames.], batch size: 18, lr: 4.44e-04
2022-05-04 23:50:45,313 INFO [train.py:715] (1/8) Epoch 4, batch 22500, loss[loss=0.1631, simple_loss=0.2299, pruned_loss=0.04818, over 4745.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2268, pruned_loss=0.0437, over 972108.91 frames.], batch size: 16, lr: 4.43e-04
2022-05-04 23:51:24,252 INFO [train.py:715] (1/8) Epoch 4, batch 22550, loss[loss=0.1361, simple_loss=0.2065, pruned_loss=0.03283, over 4788.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2259, pruned_loss=0.04358, over 971808.60 frames.], batch size: 18, lr: 4.43e-04
2022-05-04 23:52:04,195 INFO [train.py:715] (1/8) Epoch 4, batch 22600, loss[loss=0.1466, simple_loss=0.2155, pruned_loss=0.03882, over 4838.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2268, pruned_loss=0.04395, over 972392.91 frames.], batch size: 30, lr: 4.43e-04
2022-05-04 23:52:44,018 INFO [train.py:715] (1/8) Epoch 4, batch 22650, loss[loss=0.1953, simple_loss=0.2574, pruned_loss=0.06655, over 4866.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2276, pruned_loss=0.04432, over 972329.40 frames.], batch size: 32, lr: 4.43e-04
2022-05-04 23:53:22,947 INFO [train.py:715] (1/8) Epoch 4, batch 22700, loss[loss=0.1316, simple_loss=0.1902, pruned_loss=0.03653, over 4764.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2263, pruned_loss=0.04335, over 972105.68 frames.], batch size: 14, lr: 4.43e-04
2022-05-04 23:54:02,343 INFO [train.py:715] (1/8) Epoch 4, batch 22750, loss[loss=0.1595, simple_loss=0.2319, pruned_loss=0.04353, over 4787.00 frames.], tot_loss[loss=0.1571, simple_loss=0.227, pruned_loss=0.04359, over 972461.23 frames.], batch size: 17, lr: 4.43e-04
2022-05-04 23:54:42,053 INFO [train.py:715] (1/8) Epoch 4, batch 22800, loss[loss=0.1652, simple_loss=0.2429, pruned_loss=0.04376, over 4859.00 frames.], tot_loss[loss=0.1574, simple_loss=0.227, pruned_loss=0.04393, over 971904.70 frames.], batch size: 30, lr: 4.43e-04
2022-05-04 23:55:21,180 INFO [train.py:715] (1/8) Epoch 4, batch 22850, loss[loss=0.1607, simple_loss=0.2205, pruned_loss=0.05042, over 4828.00 frames.], tot_loss[loss=0.1578, simple_loss=0.227, pruned_loss=0.04433, over 971784.21 frames.], batch size: 30, lr: 4.43e-04
2022-05-04 23:55:59,902 INFO [train.py:715] (1/8) Epoch 4, batch 22900, loss[loss=0.1738, simple_loss=0.2361, pruned_loss=0.0557, over 4976.00 frames.], tot_loss[loss=0.158, simple_loss=0.2269, pruned_loss=0.04457, over 972873.59 frames.], batch size: 28, lr: 4.43e-04
2022-05-04 23:56:39,570 INFO [train.py:715] (1/8) Epoch 4, batch 22950, loss[loss=0.1911, simple_loss=0.2547, pruned_loss=0.06375, over 4955.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2266, pruned_loss=0.04436, over 972469.09 frames.], batch size: 21, lr: 4.43e-04
2022-05-04 23:57:19,681 INFO [train.py:715] (1/8) Epoch 4, batch 23000, loss[loss=0.1295, simple_loss=0.2073, pruned_loss=0.02582, over 4742.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2258, pruned_loss=0.04389, over 971814.99 frames.], batch size: 16, lr: 4.43e-04
2022-05-04 23:57:58,016 INFO [train.py:715] (1/8) Epoch 4, batch 23050, loss[loss=0.1533, simple_loss=0.2096, pruned_loss=0.04848, over 4870.00 frames.], tot_loss[loss=0.1579, simple_loss=0.227, pruned_loss=0.04446, over 972055.08 frames.], batch size: 13, lr: 4.43e-04
2022-05-04 23:58:37,638 INFO [train.py:715] (1/8) Epoch 4, batch 23100, loss[loss=0.1345, simple_loss=0.2031, pruned_loss=0.03293, over 4866.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2266, pruned_loss=0.04421, over 971307.72 frames.], batch size: 22, lr: 4.43e-04
2022-05-04 23:59:18,009 INFO [train.py:715] (1/8) Epoch 4, batch 23150, loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02928, over 4769.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2259, pruned_loss=0.04377, over 970551.39 frames.], batch size: 17, lr: 4.43e-04
2022-05-04 23:59:57,823 INFO [train.py:715] (1/8) Epoch 4, batch 23200, loss[loss=0.1556, simple_loss=0.2361, pruned_loss=0.03758, over 4777.00 frames.], tot_loss[loss=0.1576, simple_loss=0.227, pruned_loss=0.04408, over 971228.49 frames.], batch size: 18, lr: 4.42e-04
2022-05-05 00:00:36,521 INFO [train.py:715] (1/8) Epoch 4, batch 23250, loss[loss=0.1724, simple_loss=0.2352, pruned_loss=0.05477, over 4839.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2269, pruned_loss=0.04372, over 971267.45 frames.], batch size: 32, lr: 4.42e-04
2022-05-05 00:01:16,402 INFO [train.py:715] (1/8) Epoch 4, batch 23300, loss[loss=0.1582, simple_loss=0.2238, pruned_loss=0.04624, over 4919.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2265, pruned_loss=0.04353, over 972287.88 frames.], batch size: 23, lr: 4.42e-04
2022-05-05 00:01:56,696 INFO [train.py:715] (1/8) Epoch 4, batch 23350, loss[loss=0.1665, simple_loss=0.2335, pruned_loss=0.04974, over 4829.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2272, pruned_loss=0.04399, over 971429.78 frames.], batch size: 27, lr: 4.42e-04
2022-05-05 00:02:35,082 INFO [train.py:715] (1/8) Epoch 4, batch 23400, loss[loss=0.1233, simple_loss=0.1877, pruned_loss=0.02941, over 4880.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2272, pruned_loss=0.04421, over 972031.34 frames.], batch size: 19, lr: 4.42e-04
2022-05-05 00:03:14,416 INFO [train.py:715] (1/8) Epoch 4, batch 23450, loss[loss=0.1485, simple_loss=0.2199, pruned_loss=0.03856, over 4978.00 frames.], tot_loss[loss=0.1578, simple_loss=0.227, pruned_loss=0.0443, over 971962.84 frames.], batch size: 35, lr: 4.42e-04
2022-05-05 00:03:55,003 INFO [train.py:715] (1/8) Epoch 4, batch 23500, loss[loss=0.1573, simple_loss=0.2345, pruned_loss=0.04, over 4843.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04383, over 971867.42 frames.], batch size: 12, lr: 4.42e-04
2022-05-05 00:04:33,370 INFO [train.py:715] (1/8) Epoch 4, batch 23550, loss[loss=0.1852, simple_loss=0.2482, pruned_loss=0.06108, over 4969.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2251, pruned_loss=0.04303, over 971584.30 frames.], batch size: 14, lr: 4.42e-04
2022-05-05 00:05:12,655 INFO [train.py:715] (1/8) Epoch 4, batch 23600, loss[loss=0.1524, simple_loss=0.2274, pruned_loss=0.03864, over 4910.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2261, pruned_loss=0.04363, over 971856.28 frames.], batch size: 22, lr: 4.42e-04
2022-05-05 00:05:53,468 INFO [train.py:715] (1/8) Epoch 4, batch 23650, loss[loss=0.1486, simple_loss=0.2266, pruned_loss=0.0353, over 4777.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2272, pruned_loss=0.04415, over 971695.92 frames.], batch size: 18, lr: 4.42e-04
2022-05-05 00:06:34,859 INFO [train.py:715] (1/8) Epoch 4, batch 23700, loss[loss=0.1568, simple_loss=0.2353, pruned_loss=0.03918, over 4836.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2272, pruned_loss=0.04397, over 971057.29 frames.], batch size: 13, lr: 4.42e-04
2022-05-05 00:07:14,369 INFO [train.py:715] (1/8) Epoch 4, batch 23750, loss[loss=0.1381, simple_loss=0.2141, pruned_loss=0.03105, over 4811.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2282, pruned_loss=0.04443, over 971178.77 frames.], batch size: 26, lr: 4.42e-04
2022-05-05 00:07:53,779 INFO [train.py:715] (1/8) Epoch 4, batch 23800, loss[loss=0.1444, simple_loss=0.2069, pruned_loss=0.04091, over 4842.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2281, pruned_loss=0.04416, over 971917.52 frames.], batch size: 30, lr: 4.42e-04
2022-05-05 00:08:34,382 INFO [train.py:715] (1/8) Epoch 4, batch 23850, loss[loss=0.1308, simple_loss=0.2016, pruned_loss=0.02999, over 4765.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2279, pruned_loss=0.04387, over 972461.61 frames.], batch size: 17, lr: 4.42e-04
2022-05-05 00:09:13,911 INFO [train.py:715] (1/8) Epoch 4, batch 23900, loss[loss=0.1557, simple_loss=0.219, pruned_loss=0.04621, over 4887.00 frames.], tot_loss[loss=0.1569, simple_loss=0.227, pruned_loss=0.04339, over 972664.94 frames.], batch size: 17, lr: 4.42e-04
2022-05-05 00:09:53,737 INFO [train.py:715] (1/8) Epoch 4, batch 23950, loss[loss=0.1687, simple_loss=0.2354, pruned_loss=0.05106, over 4956.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2258, pruned_loss=0.04272, over 971612.60 frames.], batch size: 35, lr: 4.41e-04
2022-05-05 00:10:34,501 INFO [train.py:715] (1/8) Epoch 4, batch 24000, loss[loss=0.1552, simple_loss=0.221, pruned_loss=0.04471, over 4893.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2255, pruned_loss=0.0428, over 971495.18 frames.], batch size: 39, lr: 4.41e-04
2022-05-05 00:10:34,502 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 00:10:44,333 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1115, simple_loss=0.1974, pruned_loss=0.01276, over 914524.00 frames.
2022-05-05 00:11:25,478 INFO [train.py:715] (1/8) Epoch 4, batch 24050, loss[loss=0.1385, simple_loss=0.215, pruned_loss=0.031, over 4898.00 frames.], tot_loss[loss=0.1564, simple_loss=0.226, pruned_loss=0.0434, over 971369.95 frames.], batch size: 19, lr: 4.41e-04
2022-05-05 00:12:06,056 INFO [train.py:715] (1/8) Epoch 4, batch 24100, loss[loss=0.1513, simple_loss=0.2193, pruned_loss=0.04161, over 4851.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2259, pruned_loss=0.04269, over 971526.56 frames.], batch size: 13, lr: 4.41e-04
2022-05-05 00:12:45,929 INFO [train.py:715] (1/8) Epoch 4, batch 24150, loss[loss=0.1462, simple_loss=0.2276, pruned_loss=0.03244, over 4932.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2267, pruned_loss=0.04308, over 971524.55 frames.], batch size: 39, lr: 4.41e-04
2022-05-05 00:13:25,916 INFO [train.py:715] (1/8) Epoch 4, batch 24200, loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.0295, over 4788.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2261, pruned_loss=0.04278, over 971453.94 frames.], batch size: 17, lr: 4.41e-04
2022-05-05 00:14:07,337 INFO [train.py:715] (1/8) Epoch 4, batch 24250, loss[loss=0.1309, simple_loss=0.1986, pruned_loss=0.0316, over 4966.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2259, pruned_loss=0.04278, over 972640.61 frames.], batch size: 24, lr: 4.41e-04
2022-05-05 00:14:46,256 INFO [train.py:715] (1/8) Epoch 4, batch 24300, loss[loss=0.1715, simple_loss=0.2454, pruned_loss=0.04877, over 4868.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2266, pruned_loss=0.04317, over 972657.29 frames.], batch size: 20, lr: 4.41e-04
2022-05-05 00:15:26,714 INFO [train.py:715] (1/8) Epoch 4, batch 24350, loss[loss=0.1489, simple_loss=0.2163, pruned_loss=0.04075, over 4769.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2251, pruned_loss=0.04296, over 972286.17 frames.], batch size: 18, lr: 4.41e-04
2022-05-05 00:16:07,662 INFO [train.py:715] (1/8) Epoch 4, batch 24400, loss[loss=0.1362, simple_loss=0.2044, pruned_loss=0.03404, over 4912.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2251, pruned_loss=0.04282, over 972232.26 frames.], batch size: 29, lr: 4.41e-04
2022-05-05 00:16:47,243 INFO [train.py:715] (1/8) Epoch 4, batch 24450, loss[loss=0.1523, simple_loss=0.2207, pruned_loss=0.04192, over 4790.00 frames.], tot_loss[loss=0.156, simple_loss=0.2255, pruned_loss=0.04322, over 972419.50 frames.], batch size: 14, lr: 4.41e-04
2022-05-05 00:17:27,007 INFO [train.py:715] (1/8) Epoch 4, batch 24500, loss[loss=0.1437, simple_loss=0.2063, pruned_loss=0.04054, over 4651.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2262, pruned_loss=0.04326, over 972995.36 frames.], batch size: 13, lr: 4.41e-04
2022-05-05 00:18:06,876 INFO [train.py:715] (1/8) Epoch 4, batch 24550, loss[loss=0.1487, simple_loss=0.2275, pruned_loss=0.03494, over 4927.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2253, pruned_loss=0.04278, over 973065.41 frames.], batch size: 23, lr: 4.41e-04
2022-05-05 00:18:48,116 INFO [train.py:715] (1/8) Epoch 4, batch 24600, loss[loss=0.1958, simple_loss=0.2621, pruned_loss=0.06473, over 4800.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2262, pruned_loss=0.04309, over 972008.78 frames.], batch size: 14, lr: 4.41e-04
2022-05-05 00:19:27,473 INFO [train.py:715] (1/8) Epoch 4, batch 24650, loss[loss=0.1771, simple_loss=0.2455, pruned_loss=0.05433, over 4916.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2257, pruned_loss=0.04295, over 972548.34 frames.], batch size: 23, lr: 4.41e-04
2022-05-05 00:20:08,195 INFO [train.py:715] (1/8) Epoch 4, batch 24700, loss[loss=0.1434, simple_loss=0.2115, pruned_loss=0.0377, over 4685.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2259, pruned_loss=0.04331, over 971608.67 frames.], batch size: 15, lr: 4.40e-04
2022-05-05 00:20:49,274 INFO [train.py:715] (1/8) Epoch 4, batch 24750, loss[loss=0.2076, simple_loss=0.2727, pruned_loss=0.07122, over 4903.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2252, pruned_loss=0.04304, over 971346.14 frames.], batch size: 17, lr: 4.40e-04
2022-05-05 00:21:28,793 INFO [train.py:715] (1/8) Epoch 4, batch 24800, loss[loss=0.1393, simple_loss=0.2146, pruned_loss=0.03203, over 4806.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.04354, over 971707.58 frames.], batch size: 21, lr: 4.40e-04
2022-05-05 00:22:08,801 INFO [train.py:715] (1/8) Epoch 4, batch 24850, loss[loss=0.1398, simple_loss=0.2083, pruned_loss=0.03567, over 4825.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2263, pruned_loss=0.04369, over 972171.59 frames.], batch size: 13, lr: 4.40e-04
2022-05-05 00:22:49,037 INFO [train.py:715] (1/8) Epoch 4, batch 24900, loss[loss=0.1432, simple_loss=0.2138, pruned_loss=0.03625, over 4938.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2265, pruned_loss=0.04354, over 972720.66 frames.], batch size: 29, lr: 4.40e-04
2022-05-05 00:23:30,185 INFO [train.py:715] (1/8) Epoch 4, batch 24950, loss[loss=0.1476, simple_loss=0.218, pruned_loss=0.03862, over 4934.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2266, pruned_loss=0.04395, over 972034.88 frames.], batch size: 29, lr: 4.40e-04
2022-05-05 00:24:09,091 INFO [train.py:715] (1/8) Epoch 4, batch 25000, loss[loss=0.1446, simple_loss=0.2025, pruned_loss=0.04332, over 4835.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2262, pruned_loss=0.04377, over 972382.69 frames.], batch size: 13, lr: 4.40e-04
2022-05-05 00:24:49,350 INFO [train.py:715] (1/8) Epoch 4, batch 25050, loss[loss=0.1627, simple_loss=0.229, pruned_loss=0.04819, over 4802.00 frames.], tot_loss[loss=0.156, simple_loss=0.2253, pruned_loss=0.04339, over 971916.69 frames.], batch size: 12, lr: 4.40e-04
2022-05-05 00:25:30,445 INFO [train.py:715] (1/8) Epoch 4, batch 25100, loss[loss=0.1706, simple_loss=0.2384, pruned_loss=0.05135, over 4848.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2258, pruned_loss=0.04366, over 973074.42 frames.], batch size: 30, lr: 4.40e-04
2022-05-05 00:26:10,369 INFO [train.py:715] (1/8) Epoch 4, batch 25150, loss[loss=0.1603, simple_loss=0.2343, pruned_loss=0.04314, over 4828.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2255, pruned_loss=0.0436, over 972292.23 frames.], batch size: 21, lr: 4.40e-04
2022-05-05 00:26:49,791 INFO [train.py:715] (1/8) Epoch 4, batch 25200, loss[loss=0.1437, simple_loss=0.216, pruned_loss=0.03575, over 4835.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2257, pruned_loss=0.04372, over 973237.36 frames.], batch size: 13, lr: 4.40e-04
2022-05-05 00:27:30,057 INFO [train.py:715] (1/8) Epoch 4, batch 25250, loss[loss=0.115, simple_loss=0.1899, pruned_loss=0.0201, over 4927.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2265, pruned_loss=0.044, over 972643.53 frames.], batch size: 23, lr: 4.40e-04
2022-05-05 00:28:10,082 INFO [train.py:715] (1/8) Epoch 4, batch 25300, loss[loss=0.1596, simple_loss=0.2179, pruned_loss=0.05071, over 4892.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2258, pruned_loss=0.04354, over 972761.37 frames.], batch size: 19, lr: 4.40e-04
2022-05-05 00:28:47,883 INFO [train.py:715] (1/8) Epoch 4, batch 25350, loss[loss=0.1373, simple_loss=0.2079, pruned_loss=0.03333, over 4914.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2269, pruned_loss=0.04418, over 972534.93 frames.], batch size: 18, lr: 4.40e-04
2022-05-05 00:29:26,726 INFO [train.py:715] (1/8) Epoch 4, batch 25400, loss[loss=0.1642, simple_loss=0.232, pruned_loss=0.04817, over 4982.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2259, pruned_loss=0.0439, over 972812.24 frames.], batch size: 20, lr: 4.40e-04
2022-05-05 00:30:06,396 INFO [train.py:715] (1/8) Epoch 4, batch 25450, loss[loss=0.1433, simple_loss=0.2257, pruned_loss=0.03046, over 4883.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2267, pruned_loss=0.04407, over 973497.76 frames.], batch size: 22, lr: 4.39e-04
2022-05-05 00:30:45,458 INFO [train.py:715] (1/8) Epoch 4, batch 25500, loss[loss=0.1533, simple_loss=0.2258, pruned_loss=0.04037, over 4903.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2257, pruned_loss=0.04349, over 972401.71 frames.], batch size: 18, lr: 4.39e-04
2022-05-05 00:31:25,318 INFO [train.py:715] (1/8) Epoch 4, batch 25550, loss[loss=0.1347, simple_loss=0.2068, pruned_loss=0.03125, over 4872.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2263, pruned_loss=0.04359, over 971939.79 frames.], batch size: 16, lr: 4.39e-04
2022-05-05 00:32:05,298 INFO [train.py:715] (1/8) Epoch 4, batch 25600, loss[loss=0.1756, simple_loss=0.2468, pruned_loss=0.05225, over 4967.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2261, pruned_loss=0.04332, over 971656.84 frames.], batch size: 24, lr: 4.39e-04
2022-05-05 00:32:45,568 INFO [train.py:715] (1/8) Epoch 4, batch 25650, loss[loss=0.1168, simple_loss=0.1992, pruned_loss=0.01719, over 4972.00 frames.], tot_loss[loss=0.1561, simple_loss=0.226, pruned_loss=0.0431, over 972075.09 frames.], batch size: 15, lr: 4.39e-04
2022-05-05 00:33:24,674 INFO [train.py:715] (1/8) Epoch 4, batch 25700, loss[loss=0.1739, simple_loss=0.2401, pruned_loss=0.05386, over 4952.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2266, pruned_loss=0.04336, over 971917.00 frames.], batch size: 35, lr: 4.39e-04
2022-05-05 00:34:04,661 INFO [train.py:715] (1/8) Epoch 4, batch 25750, loss[loss=0.1542, simple_loss=0.2219, pruned_loss=0.04327, over 4930.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2263, pruned_loss=0.04322, over 971830.22 frames.], batch size: 23, lr: 4.39e-04
2022-05-05 00:34:45,102 INFO [train.py:715] (1/8) Epoch 4, batch 25800, loss[loss=0.1572, simple_loss=0.2247, pruned_loss=0.04489, over 4935.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2265, pruned_loss=0.04324, over 971259.82 frames.], batch size: 21, lr: 4.39e-04
2022-05-05 00:35:24,459 INFO [train.py:715] (1/8) Epoch 4, batch 25850, loss[loss=0.1348, simple_loss=0.218, pruned_loss=0.0258, over 4932.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2256, pruned_loss=0.04281, over 971941.77 frames.], batch size: 23, lr: 4.39e-04
2022-05-05 00:36:03,605 INFO [train.py:715] (1/8) Epoch 4, batch 25900, loss[loss=0.1337, simple_loss=0.2008, pruned_loss=0.03329, over 4781.00 frames.], tot_loss[loss=0.1551, simple_loss=0.225, pruned_loss=0.04262, over 972175.55 frames.], batch size: 18, lr: 4.39e-04
2022-05-05 00:36:43,849 INFO [train.py:715] (1/8) Epoch 4, batch 25950, loss[loss=0.1733, simple_loss=0.2525, pruned_loss=0.04702, over 4917.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2253, pruned_loss=0.04291, over 972635.11 frames.], batch size: 29, lr: 4.39e-04
2022-05-05 00:37:24,116 INFO [train.py:715] (1/8) Epoch 4, batch 26000, loss[loss=0.1822, simple_loss=0.2468, pruned_loss=0.05876, over 4937.00 frames.], tot_loss[loss=0.156, simple_loss=0.2254, pruned_loss=0.04323, over 972829.18 frames.], batch size: 29, lr: 4.39e-04
2022-05-05 00:38:02,824 INFO [train.py:715] (1/8) Epoch 4, batch 26050, loss[loss=0.1725, simple_loss=0.2418, pruned_loss=0.05165, over 4878.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2251, pruned_loss=0.04289, over 973582.22 frames.], batch size: 22, lr: 4.39e-04
2022-05-05 00:38:42,230 INFO [train.py:715] (1/8) Epoch 4, batch 26100, loss[loss=0.1433, simple_loss=0.2199, pruned_loss=0.03339, over 4921.00 frames.], tot_loss[loss=0.1553, simple_loss=0.225, pruned_loss=0.04277, over 974050.07 frames.], batch size: 21, lr: 4.39e-04
2022-05-05 00:39:22,685 INFO [train.py:715] (1/8) Epoch 4, batch 26150, loss[loss=0.1782, simple_loss=0.2487, pruned_loss=0.05386, over 4990.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2248, pruned_loss=0.04276, over 973877.55 frames.], batch size: 24, lr: 4.39e-04
2022-05-05 00:40:01,762 INFO [train.py:715] (1/8) Epoch 4, batch 26200, loss[loss=0.1736, simple_loss=0.2329, pruned_loss=0.0572, over 4974.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2245, pruned_loss=0.04288, over 974350.24 frames.], batch size: 15, lr: 4.38e-04
2022-05-05 00:40:41,527 INFO [train.py:715] (1/8) Epoch 4, batch 26250, loss[loss=0.1602, simple_loss=0.229, pruned_loss=0.04564, over 4752.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2251, pruned_loss=0.04332, over 973749.43 frames.], batch size: 19, lr: 4.38e-04
2022-05-05 00:41:21,391 INFO [train.py:715] (1/8) Epoch 4, batch 26300, loss[loss=0.1317, simple_loss=0.2042, pruned_loss=0.0296, over 4806.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.04353, over 973337.32 frames.], batch size: 26, lr: 4.38e-04
2022-05-05 00:42:01,541 INFO [train.py:715] (1/8) Epoch 4, batch 26350, loss[loss=0.1469, simple_loss=0.2229, pruned_loss=0.03546, over 4756.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2261, pruned_loss=0.04331, over 972712.73 frames.], batch size: 18, lr: 4.38e-04
2022-05-05 00:42:40,880 INFO [train.py:715] (1/8) Epoch 4, batch 26400, loss[loss=0.1778, simple_loss=0.23, pruned_loss=0.06281, over 4849.00 frames.], tot_loss[loss=0.156, simple_loss=0.2259, pruned_loss=0.04307, over 972986.00 frames.], batch size: 15, lr: 4.38e-04
2022-05-05 00:43:20,968 INFO [train.py:715] (1/8) Epoch 4, batch 26450, loss[loss=0.1475, simple_loss=0.2144, pruned_loss=0.04026, over 4795.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2259, pruned_loss=0.04324, over 972549.45 frames.], batch size: 12, lr: 4.38e-04
2022-05-05 00:44:01,489 INFO [train.py:715] (1/8) Epoch 4, batch 26500, loss[loss=0.1803, simple_loss=0.2511, pruned_loss=0.05472, over 4704.00 frames.], tot_loss[loss=0.1563, simple_loss=0.226, pruned_loss=0.04327, over 971438.28 frames.], batch size: 15, lr: 4.38e-04
2022-05-05 00:44:40,391 INFO [train.py:715] (1/8) Epoch 4, batch 26550, loss[loss=0.1504, simple_loss=0.2231, pruned_loss=0.03884, over 4945.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2253, pruned_loss=0.04309, over 971750.20 frames.], batch size: 23, lr: 4.38e-04
2022-05-05 00:45:20,038 INFO [train.py:715] (1/8) Epoch 4, batch 26600, loss[loss=0.1803, simple_loss=0.2492, pruned_loss=0.05576, over 4840.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2251, pruned_loss=0.04293, over 971832.65 frames.], batch size: 30, lr: 4.38e-04
2022-05-05 00:46:00,422 INFO [train.py:715] (1/8) Epoch 4, batch 26650, loss[loss=0.1473, simple_loss=0.2259, pruned_loss=0.03434, over 4963.00 frames.], tot_loss[loss=0.1555, simple_loss=0.225, pruned_loss=0.04298, over 971263.19 frames.], batch size: 15, lr: 4.38e-04
2022-05-05 00:46:41,231 INFO [train.py:715] (1/8) Epoch 4, batch 26700, loss[loss=0.1463, simple_loss=0.2217, pruned_loss=0.03544, over 4948.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2248, pruned_loss=0.04273, over 970749.02 frames.], batch size: 24, lr: 4.38e-04
2022-05-05 00:47:20,025 INFO [train.py:715] (1/8) Epoch 4, batch 26750, loss[loss=0.1229, simple_loss=0.2005, pruned_loss=0.02265, over 4928.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2246, pruned_loss=0.04246, over 971742.00 frames.], batch size: 29, lr: 4.38e-04
2022-05-05 00:47:59,600 INFO [train.py:715] (1/8) Epoch 4, batch 26800, loss[loss=0.1359, simple_loss=0.215, pruned_loss=0.02837, over 4817.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2256, pruned_loss=0.043, over 971665.67 frames.], batch size: 27, lr: 4.38e-04
2022-05-05 00:48:39,813 INFO [train.py:715] (1/8) Epoch 4, batch 26850, loss[loss=0.1472, simple_loss=0.2285, pruned_loss=0.03295, over 4784.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2249, pruned_loss=0.04265, over 971570.87 frames.], batch size: 17, lr: 4.38e-04
2022-05-05 00:49:18,743 INFO [train.py:715] (1/8) Epoch 4, batch 26900, loss[loss=0.1315, simple_loss=0.2015, pruned_loss=0.03077, over 4900.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2244, pruned_loss=0.04244, over 970717.43 frames.], batch size: 17, lr: 4.38e-04
2022-05-05 00:49:58,565 INFO [train.py:715] (1/8) Epoch 4, batch 26950, loss[loss=0.1493, simple_loss=0.2224, pruned_loss=0.03808, over 4885.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2243, pruned_loss=0.04224, over 971315.31 frames.], batch size: 19, lr: 4.37e-04
2022-05-05 00:50:38,538 INFO [train.py:715] (1/8) Epoch 4, batch 27000, loss[loss=0.1993, simple_loss=0.2543, pruned_loss=0.07211, over 4757.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2247, pruned_loss=0.04231, over 971475.73 frames.], batch size: 19, lr: 4.37e-04
2022-05-05 00:50:38,539 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 00:50:48,693 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1114, simple_loss=0.197, pruned_loss=0.01284, over 914524.00 frames.
2022-05-05 00:51:28,854 INFO [train.py:715] (1/8) Epoch 4, batch 27050, loss[loss=0.1725, simple_loss=0.2597, pruned_loss=0.04268, over 4892.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2254, pruned_loss=0.04263, over 971329.01 frames.], batch size: 22, lr: 4.37e-04
2022-05-05 00:52:08,425 INFO [train.py:715] (1/8) Epoch 4, batch 27100, loss[loss=0.1639, simple_loss=0.2323, pruned_loss=0.04773, over 4778.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2257, pruned_loss=0.04283, over 970930.17 frames.], batch size: 18, lr: 4.37e-04
2022-05-05 00:52:47,731 INFO [train.py:715] (1/8) Epoch 4, batch 27150, loss[loss=0.1163, simple_loss=0.1885, pruned_loss=0.022, over 4801.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2254, pruned_loss=0.04266, over 971219.93 frames.], batch size: 21, lr: 4.37e-04
2022-05-05 00:53:27,408 INFO [train.py:715] (1/8) Epoch 4, batch 27200, loss[loss=0.151, simple_loss=0.2283, pruned_loss=0.03687, over 4795.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2256, pruned_loss=0.04277, over 971266.15 frames.], batch size: 24, lr: 4.37e-04
2022-05-05 00:54:07,875 INFO [train.py:715] (1/8) Epoch 4, batch 27250, loss[loss=0.1311, simple_loss=0.1969, pruned_loss=0.03259, over 4761.00 frames.], tot_loss[loss=0.156, simple_loss=0.2261, pruned_loss=0.04299, over 970935.36 frames.], batch size: 19, lr: 4.37e-04
2022-05-05 00:54:46,640 INFO [train.py:715] (1/8) Epoch 4, batch 27300, loss[loss=0.132, simple_loss=0.2113, pruned_loss=0.02633, over 4960.00 frames.], tot_loss[loss=0.1559, simple_loss=0.226, pruned_loss=0.04288, over 971531.09 frames.], batch size: 15, lr: 4.37e-04
2022-05-05 00:55:26,636 INFO [train.py:715] (1/8) Epoch 4, batch 27350, loss[loss=0.159, simple_loss=0.2298, pruned_loss=0.04407, over 4759.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2256, pruned_loss=0.04285, over 971292.92 frames.], batch size: 19, lr: 4.37e-04
2022-05-05 00:56:06,590 INFO [train.py:715] (1/8) Epoch 4, batch 27400, loss[loss=0.1255, simple_loss=0.1954, pruned_loss=0.02784, over 4963.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2249, pruned_loss=0.04242, over 972069.27 frames.], batch size: 24, lr: 4.37e-04
2022-05-05 00:56:45,009 INFO [train.py:715] (1/8) Epoch 4, batch 27450, loss[loss=0.1484, simple_loss=0.2225, pruned_loss=0.03719, over 4946.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2236, pruned_loss=0.04153, over 972519.70 frames.], batch size: 21, lr: 4.37e-04
2022-05-05 00:57:24,960 INFO [train.py:715] (1/8) Epoch 4, batch 27500, loss[loss=0.1569, simple_loss=0.2323, pruned_loss=0.04073, over 4816.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2237, pruned_loss=0.04174, over 973705.82 frames.], batch size: 25, lr: 4.37e-04
2022-05-05 00:58:03,983 INFO [train.py:715] (1/8) Epoch 4, batch 27550, loss[loss=0.1545, simple_loss=0.2249, pruned_loss=0.04207, over 4869.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2246, pruned_loss=0.04244, over 974218.53 frames.], batch size: 16, lr: 4.37e-04
2022-05-05 00:58:43,896 INFO [train.py:715] (1/8) Epoch 4, batch 27600, loss[loss=0.1594, simple_loss=0.2151, pruned_loss=0.05178, over 4969.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2249, pruned_loss=0.04241, over 973878.18 frames.], batch size: 15, lr: 4.37e-04
2022-05-05 00:59:22,453 INFO [train.py:715] (1/8) Epoch 4, batch 27650, loss[loss=0.1804, simple_loss=0.2499, pruned_loss=0.0554, over 4920.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2257, pruned_loss=0.04281, over 973460.65 frames.], batch size: 18, lr: 4.37e-04
2022-05-05 01:00:01,786 INFO [train.py:715] (1/8) Epoch 4, batch 27700, loss[loss=0.1451, simple_loss=0.2135, pruned_loss=0.03837, over 4777.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2262, pruned_loss=0.04311, over 973984.98 frames.], batch size: 17, lr: 4.36e-04
2022-05-05 01:00:41,407 INFO [train.py:715] (1/8) Epoch 4, batch 27750, loss[loss=0.1777, simple_loss=0.2434, pruned_loss=0.05599, over 4834.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2262, pruned_loss=0.04339, over 972902.29 frames.], batch size: 15, lr: 4.36e-04
2022-05-05 01:01:20,725 INFO [train.py:715] (1/8) Epoch 4, batch 27800, loss[loss=0.1693, simple_loss=0.2298, pruned_loss=0.05435, over 4985.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2262, pruned_loss=0.04377, over 973007.68 frames.], batch size: 35, lr: 4.36e-04
2022-05-05 01:01:59,775 INFO [train.py:715] (1/8) Epoch 4, batch 27850, loss[loss=0.1695, simple_loss=0.2415, pruned_loss=0.04872, over 4927.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2258, pruned_loss=0.04333, over 973130.25 frames.], batch size: 18, lr: 4.36e-04
2022-05-05 01:02:38,867 INFO [train.py:715] (1/8) Epoch 4, batch 27900, loss[loss=0.179, simple_loss=0.2411, pruned_loss=0.05841, over 4751.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2259, pruned_loss=0.04369, over 972883.58 frames.], batch size: 19, lr: 4.36e-04
2022-05-05 01:03:18,315 INFO [train.py:715] (1/8) Epoch 4, batch 27950, loss[loss=0.1589, simple_loss=0.2391, pruned_loss=0.03937, over 4951.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2254, pruned_loss=0.04301, over 973155.79 frames.], batch size: 21, lr: 4.36e-04
2022-05-05 01:03:57,882 INFO [train.py:715] (1/8) Epoch 4, batch 28000, loss[loss=0.15, simple_loss=0.2214, pruned_loss=0.03931, over 4789.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2246, pruned_loss=0.04233, over 973282.68 frames.], batch size: 17, lr: 4.36e-04
2022-05-05 01:04:37,841 INFO [train.py:715] (1/8) Epoch 4, batch 28050, loss[loss=0.1536, simple_loss=0.224, pruned_loss=0.04161, over 4801.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2249, pruned_loss=0.04228, over 972512.98 frames.], batch size: 13, lr: 4.36e-04
2022-05-05 01:05:17,720 INFO [train.py:715] (1/8) Epoch 4, batch 28100, loss[loss=0.1578, simple_loss=0.2259, pruned_loss=0.0449, over 4774.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2249, pruned_loss=0.04244, over 973093.68 frames.], batch size: 14, lr: 4.36e-04
2022-05-05 01:05:57,317 INFO [train.py:715] (1/8) Epoch 4, batch 28150, loss[loss=0.1372, simple_loss=0.2127, pruned_loss=0.03082, over 4879.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2257, pruned_loss=0.04283, over 972892.74 frames.], batch size: 22, lr: 4.36e-04
2022-05-05 01:06:36,809 INFO [train.py:715] (1/8) Epoch 4, batch 28200, loss[loss=0.14, simple_loss=0.2143, pruned_loss=0.03288, over 4930.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2253, pruned_loss=0.0427, over 972443.34 frames.], batch size: 21, lr: 4.36e-04
2022-05-05 01:07:15,875 INFO [train.py:715] (1/8) Epoch 4, batch 28250, loss[loss=0.1673, simple_loss=0.2412, pruned_loss=0.04676, over 4764.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2258, pruned_loss=0.04289, over 972326.56 frames.], batch size: 19, lr: 4.36e-04
2022-05-05 01:07:55,428 INFO [train.py:715] (1/8) Epoch 4, batch 28300, loss[loss=0.1527, simple_loss=0.2225, pruned_loss=0.04139, over 4816.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2246, pruned_loss=0.04226, over 972439.81 frames.], batch size: 27, lr: 4.36e-04
2022-05-05 01:08:34,756 INFO [train.py:715] (1/8) Epoch 4, batch 28350, loss[loss=0.175, simple_loss=0.2464, pruned_loss=0.05179, over 4782.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2247, pruned_loss=0.04229, over 973129.25 frames.], batch size: 17, lr: 4.36e-04
2022-05-05 01:09:14,661 INFO [train.py:715] (1/8) Epoch 4, batch 28400, loss[loss=0.1403, simple_loss=0.2188, pruned_loss=0.03089, over 4880.00 frames.], tot_loss[loss=0.156, simple_loss=0.2258, pruned_loss=0.04304, over 972624.86 frames.], batch size: 20, lr: 4.36e-04
2022-05-05 01:09:53,875 INFO [train.py:715] (1/8) Epoch 4, batch 28450, loss[loss=0.1384, simple_loss=0.2021, pruned_loss=0.03737, over 4993.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2263, pruned_loss=0.04309, over 972740.55 frames.], batch size: 14, lr: 4.36e-04
2022-05-05 01:10:32,534 INFO [train.py:715] (1/8) Epoch 4, batch 28500, loss[loss=0.146, simple_loss=0.2214, pruned_loss=0.03533, over 4901.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2262, pruned_loss=0.04331, over 972977.53 frames.], batch size: 17, lr: 4.35e-04
2022-05-05 01:11:12,045 INFO [train.py:715] (1/8) Epoch 4, batch 28550, loss[loss=0.1412, simple_loss=0.2134, pruned_loss=0.0345, over 4800.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2253, pruned_loss=0.04263, over 973132.15 frames.], batch size: 21, lr: 4.35e-04
2022-05-05 01:11:51,211 INFO [train.py:715] (1/8) Epoch 4, batch 28600, loss[loss=0.1645, simple_loss=0.2273, pruned_loss=0.05084, over 4690.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2265, pruned_loss=0.04295, over 972232.34 frames.], batch size: 15, lr: 4.35e-04
2022-05-05 01:12:30,868 INFO [train.py:715] (1/8) Epoch 4, batch 28650, loss[loss=0.1583, simple_loss=0.2331, pruned_loss=0.04175, over 4916.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2279, pruned_loss=0.04379, over 972115.01 frames.], batch size: 29, lr: 4.35e-04
2022-05-05 01:13:10,043 INFO [train.py:715] (1/8) Epoch 4, batch 28700, loss[loss=0.1413, simple_loss=0.2035, pruned_loss=0.03955, over 4905.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2279, pruned_loss=0.04366, over 972233.81 frames.], batch size: 17, lr: 4.35e-04
2022-05-05 01:13:49,557 INFO [train.py:715] (1/8) Epoch 4, batch 28750, loss[loss=0.1487, simple_loss=0.2115, pruned_loss=0.04296, over 4886.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2278, pruned_loss=0.04386, over 972334.19 frames.], batch size: 32, lr: 4.35e-04
2022-05-05 01:14:31,719 INFO [train.py:715] (1/8) Epoch 4, batch 28800, loss[loss=0.161, simple_loss=0.2367, pruned_loss=0.04264, over 4890.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2276, pruned_loss=0.04348, over 973102.36 frames.], batch size: 19, lr: 4.35e-04
2022-05-05 01:15:10,520 INFO [train.py:715] (1/8) Epoch 4, batch 28850, loss[loss=0.1903, simple_loss=0.2545, pruned_loss=0.0631, over 4989.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2269, pruned_loss=0.04333, over 973447.68 frames.], batch size: 14, lr: 4.35e-04
2022-05-05 01:15:50,217 INFO [train.py:715] (1/8) Epoch 4, batch 28900, loss[loss=0.1761, simple_loss=0.2544, pruned_loss=0.04891, over 4987.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2265, pruned_loss=0.04304, over 972392.21 frames.], batch size: 28, lr: 4.35e-04
2022-05-05 01:16:29,314 INFO [train.py:715] (1/8) Epoch 4, batch 28950, loss[loss=0.1717, simple_loss=0.2243, pruned_loss=0.05956, over 4854.00 frames.], tot_loss[loss=0.156, simple_loss=0.2262, pruned_loss=0.04292, over 971996.25 frames.], batch size: 13, lr: 4.35e-04
2022-05-05 01:17:08,547 INFO [train.py:715] (1/8) Epoch 4, batch 29000, loss[loss=0.1166, simple_loss=0.1737, pruned_loss=0.02978, over 4851.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2257, pruned_loss=0.04256, over 971822.08 frames.], batch size: 13, lr: 4.35e-04
2022-05-05 01:17:48,165 INFO [train.py:715] (1/8) Epoch 4, batch 29050, loss[loss=0.1461, simple_loss=0.2129, pruned_loss=0.03971, over 4821.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2264, pruned_loss=0.04322, over 972305.10 frames.], batch size: 13, lr: 4.35e-04
2022-05-05 01:18:28,181 INFO [train.py:715] (1/8) Epoch 4, batch 29100, loss[loss=0.2046, simple_loss=0.2645, pruned_loss=0.07232, over 4977.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2255, pruned_loss=0.04268, over 973190.91 frames.], batch size: 14, lr: 4.35e-04
2022-05-05 01:19:07,862 INFO [train.py:715] (1/8) Epoch 4, batch 29150, loss[loss=0.1577, simple_loss=0.2248, pruned_loss=0.04526, over 4886.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2234, pruned_loss=0.04172, over 972501.32 frames.], batch size: 19, lr: 4.35e-04
2022-05-05 01:19:46,741 INFO [train.py:715] (1/8) Epoch 4, batch 29200, loss[loss=0.1884, simple_loss=0.2536, pruned_loss=0.06158, over 4741.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2233, pruned_loss=0.04212, over 972316.23 frames.], batch size: 16, lr: 4.35e-04
2022-05-05 01:20:26,111 INFO [train.py:715] (1/8) Epoch 4, batch 29250, loss[loss=0.1402, simple_loss=0.2144, pruned_loss=0.03301, over 4850.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2236, pruned_loss=0.04194, over 971855.10 frames.], batch size: 30, lr: 4.34e-04
2022-05-05 01:21:05,005 INFO [train.py:715] (1/8) Epoch 4, batch 29300, loss[loss=0.1691, simple_loss=0.2388, pruned_loss=0.04969, over 4822.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2227, pruned_loss=0.04145, over 971577.62 frames.], batch size: 26, lr: 4.34e-04
2022-05-05 01:21:43,985 INFO [train.py:715] (1/8) Epoch 4, batch 29350, loss[loss=0.1646, simple_loss=0.2398, pruned_loss=0.04472, over 4930.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2231, pruned_loss=0.04167, over 971644.90 frames.], batch size: 18, lr: 4.34e-04
2022-05-05 01:22:22,970 INFO [train.py:715] (1/8) Epoch 4, batch 29400, loss[loss=0.1686, simple_loss=0.2435, pruned_loss=0.04681, over 4685.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2239, pruned_loss=0.04221, over 972158.97 frames.], batch size: 15, lr: 4.34e-04
2022-05-05 01:23:02,052 INFO [train.py:715] (1/8) Epoch 4, batch 29450, loss[loss=0.1647, simple_loss=0.2384, pruned_loss=0.04549, over 4698.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2253, pruned_loss=0.04281, over 972868.32 frames.], batch size: 15, lr: 4.34e-04
2022-05-05 01:23:41,628 INFO [train.py:715] (1/8) Epoch 4, batch 29500, loss[loss=0.182, simple_loss=0.2338, pruned_loss=0.06504, over 4983.00 frames.], tot_loss[loss=0.1553, simple_loss=0.225, pruned_loss=0.04281, over 972339.41 frames.], batch size: 35, lr: 4.34e-04
2022-05-05 01:24:20,886 INFO [train.py:715] (1/8) Epoch 4, batch 29550, loss[loss=0.1345, simple_loss=0.1955, pruned_loss=0.03679, over 4636.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2245, pruned_loss=0.04209, over 972022.50 frames.], batch size: 13, lr: 4.34e-04
2022-05-05 01:25:00,168 INFO [train.py:715] (1/8) Epoch 4, batch 29600, loss[loss=0.1457, simple_loss=0.2196, pruned_loss=0.03593, over 4951.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2257, pruned_loss=0.04279, over 970843.30 frames.], batch size: 21, lr: 4.34e-04
2022-05-05 01:25:39,290 INFO [train.py:715] (1/8) Epoch 4, batch 29650, loss[loss=0.1348, simple_loss=0.21, pruned_loss=0.02981, over 4889.00 frames.], tot_loss[loss=0.1548, simple_loss=0.225, pruned_loss=0.04236, over 971437.06 frames.], batch size: 22, lr: 4.34e-04
2022-05-05 01:26:18,060 INFO [train.py:715] (1/8) Epoch 4, batch 29700, loss[loss=0.1582, simple_loss=0.2317, pruned_loss=0.04234, over 4944.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2255, pruned_loss=0.04275, over 971687.71 frames.], batch size: 35, lr: 4.34e-04
2022-05-05 01:26:57,629 INFO [train.py:715] (1/8) Epoch 4, batch 29750, loss[loss=0.1326, simple_loss=0.2113, pruned_loss=0.02698, over 4954.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2253, pruned_loss=0.04303, over 971777.35 frames.], batch size: 21, lr: 4.34e-04
2022-05-05 01:27:36,807 INFO [train.py:715] (1/8) Epoch 4, batch 29800, loss[loss=0.196, simple_loss=0.2586, pruned_loss=0.06668, over 4787.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2257, pruned_loss=0.04284, over 972393.16 frames.], batch size: 17, lr: 4.34e-04
2022-05-05 01:28:16,337 INFO [train.py:715] (1/8) Epoch 4, batch 29850, loss[loss=0.1616, simple_loss=0.2395, pruned_loss=0.04182, over 4851.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2255, pruned_loss=0.04276, over 973033.19 frames.], batch size: 20, lr: 4.34e-04
2022-05-05 01:28:55,205 INFO [train.py:715] (1/8) Epoch 4, batch 29900, loss[loss=0.1398, simple_loss=0.2147, pruned_loss=0.03242, over 4861.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2256, pruned_loss=0.0427, over 972592.49 frames.], batch size: 16, lr: 4.34e-04
2022-05-05 01:29:34,841 INFO [train.py:715] (1/8) Epoch 4, batch 29950, loss[loss=0.1705, simple_loss=0.2417, pruned_loss=0.04971, over 4849.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2264, pruned_loss=0.04272, over 972386.21 frames.], batch size: 34, lr: 4.34e-04
2022-05-05 01:30:13,997 INFO [train.py:715] (1/8) Epoch 4, batch 30000, loss[loss=0.1505, simple_loss=0.2105, pruned_loss=0.04524, over 4835.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2255, pruned_loss=0.04176, over 972760.50 frames.], batch size: 30, lr: 4.34e-04
2022-05-05 01:30:13,997 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 01:30:23,829 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1113, simple_loss=0.1968, pruned_loss=0.01286, over 914524.00 frames.
2022-05-05 01:31:03,989 INFO [train.py:715] (1/8) Epoch 4, batch 30050, loss[loss=0.1584, simple_loss=0.2301, pruned_loss=0.04331, over 4792.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2248, pruned_loss=0.04143, over 972778.01 frames.], batch size: 14, lr: 4.33e-04
2022-05-05 01:31:43,424 INFO [train.py:715] (1/8) Epoch 4, batch 30100, loss[loss=0.157, simple_loss=0.224, pruned_loss=0.04501, over 4960.00 frames.], tot_loss[loss=0.1532, simple_loss=0.224, pruned_loss=0.04123, over 972628.17 frames.], batch size: 35, lr: 4.33e-04
2022-05-05 01:32:23,327 INFO [train.py:715] (1/8) Epoch 4, batch 30150, loss[loss=0.1641, simple_loss=0.2235, pruned_loss=0.05234, over 4651.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2239, pruned_loss=0.04132, over 971887.93 frames.], batch size: 13, lr: 4.33e-04
2022-05-05 01:33:02,795 INFO [train.py:715] (1/8) Epoch 4, batch 30200, loss[loss=0.1539, simple_loss=0.222, pruned_loss=0.04291, over 4976.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2236, pruned_loss=0.04146, over 973097.81 frames.], batch size: 24, lr: 4.33e-04
2022-05-05 01:33:42,431 INFO [train.py:715] (1/8) Epoch 4, batch 30250, loss[loss=0.1656, simple_loss=0.2415, pruned_loss=0.04483, over 4970.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2249, pruned_loss=0.04239, over 973676.57 frames.], batch size: 15, lr: 4.33e-04
2022-05-05 01:34:21,601 INFO [train.py:715] (1/8) Epoch 4, batch 30300, loss[loss=0.1679, simple_loss=0.2345, pruned_loss=0.05064, over 4748.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2251, pruned_loss=0.04275, over 973192.15 frames.], batch size: 16, lr: 4.33e-04
2022-05-05 01:35:01,082 INFO [train.py:715] (1/8) Epoch 4, batch 30350, loss[loss=0.1524, simple_loss=0.213, pruned_loss=0.04595, over 4968.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2252, pruned_loss=0.043, over 972237.97 frames.], batch size: 35, lr: 4.33e-04
2022-05-05 01:35:41,060 INFO [train.py:715] (1/8) Epoch 4, batch 30400, loss[loss=0.173, simple_loss=0.2436, pruned_loss=0.05121, over 4817.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2235, pruned_loss=0.04198, over 971997.86 frames.], batch size: 15, lr: 4.33e-04
2022-05-05 01:36:20,218 INFO [train.py:715] (1/8) Epoch 4, batch 30450, loss[loss=0.201, simple_loss=0.269, pruned_loss=0.06652, over 4875.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2241, pruned_loss=0.04222, over 972208.02 frames.], batch size: 16, lr: 4.33e-04
2022-05-05 01:36:59,979 INFO [train.py:715] (1/8) Epoch 4, batch 30500, loss[loss=0.165, simple_loss=0.2501, pruned_loss=0.03992, over 4721.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2248, pruned_loss=0.04246, over 971836.55 frames.], batch size: 16, lr: 4.33e-04
2022-05-05 01:37:40,031 INFO [train.py:715] (1/8) Epoch 4, batch 30550, loss[loss=0.1158, simple_loss=0.193, pruned_loss=0.01928, over 4966.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2245, pruned_loss=0.04281, over 970837.74 frames.], batch size: 29, lr: 4.33e-04
2022-05-05 01:38:19,337 INFO [train.py:715] (1/8) Epoch 4, batch 30600, loss[loss=0.1384, simple_loss=0.2059, pruned_loss=0.03547, over 4970.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2251, pruned_loss=0.04279, over 970458.89 frames.], batch size: 14, lr: 4.33e-04
2022-05-05 01:38:58,945 INFO [train.py:715] (1/8) Epoch 4, batch 30650, loss[loss=0.1628, simple_loss=0.2235, pruned_loss=0.05106, over 4972.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2257, pruned_loss=0.04295, over 970820.92 frames.], batch size: 15, lr: 4.33e-04
2022-05-05 01:39:38,415 INFO [train.py:715] (1/8) Epoch 4, batch 30700, loss[loss=0.1377, simple_loss=0.2078, pruned_loss=0.03378, over 4824.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2254, pruned_loss=0.04298, over 971523.49 frames.], batch size: 13, lr: 4.33e-04
2022-05-05 01:40:18,148 INFO [train.py:715] (1/8) Epoch 4, batch 30750, loss[loss=0.159, simple_loss=0.2358, pruned_loss=0.04115, over 4985.00 frames.], tot_loss[loss=0.155, simple_loss=0.225, pruned_loss=0.0425, over 971942.37 frames.], batch size: 28, lr: 4.33e-04
2022-05-05 01:40:57,690 INFO [train.py:715] (1/8) Epoch 4, batch 30800, loss[loss=0.1712, simple_loss=0.244, pruned_loss=0.0492, over 4931.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2255, pruned_loss=0.04307, over 972314.15 frames.], batch size: 29, lr: 4.32e-04
2022-05-05 01:41:37,517 INFO [train.py:715] (1/8) Epoch 4, batch 30850, loss[loss=0.145, simple_loss=0.2136, pruned_loss=0.03818, over 4748.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2252, pruned_loss=0.04256, over 972434.10 frames.], batch size: 16, lr: 4.32e-04
2022-05-05 01:42:17,793 INFO [train.py:715] (1/8) Epoch 4, batch 30900, loss[loss=0.1497, simple_loss=0.2165, pruned_loss=0.04148, over 4959.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2248, pruned_loss=0.04248, over 971855.72 frames.], batch size: 24, lr: 4.32e-04
2022-05-05 01:42:57,268 INFO [train.py:715] (1/8) Epoch 4, batch 30950, loss[loss=0.1306, simple_loss=0.2137, pruned_loss=0.02374, over 4817.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2253, pruned_loss=0.04297, over 972370.69 frames.], batch size: 13, lr: 4.32e-04
2022-05-05 01:43:36,638 INFO [train.py:715] (1/8) Epoch 4, batch 31000, loss[loss=0.1393, simple_loss=0.2114, pruned_loss=0.03362, over 4974.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2248, pruned_loss=0.04282, over 972411.28 frames.], batch size: 24, lr: 4.32e-04
2022-05-05 01:44:16,114 INFO [train.py:715] (1/8) Epoch 4, batch 31050, loss[loss=0.1784, simple_loss=0.2472, pruned_loss=0.05482, over 4934.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2247, pruned_loss=0.04275, over 972827.79 frames.], batch size: 18, lr: 4.32e-04
2022-05-05 01:44:55,524 INFO [train.py:715] (1/8) Epoch 4, batch 31100, loss[loss=0.1736, simple_loss=0.2405, pruned_loss=0.05329, over 4857.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2253, pruned_loss=0.04294, over 973040.06 frames.], batch size: 20, lr: 4.32e-04
2022-05-05 01:45:35,035 INFO [train.py:715] (1/8) Epoch 4, batch 31150, loss[loss=0.1563, simple_loss=0.2201, pruned_loss=0.04622, over 4751.00 frames.], tot_loss[loss=0.1559, simple_loss=0.226, pruned_loss=0.04289, over 972936.15 frames.], batch size: 19, lr: 4.32e-04
2022-05-05 01:46:13,907 INFO [train.py:715] (1/8) Epoch 4, batch 31200, loss[loss=0.1687, simple_loss=0.2471, pruned_loss=0.04521, over 4787.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2256, pruned_loss=0.04269, over 972228.06 frames.], batch size: 17, lr: 4.32e-04
2022-05-05 01:46:53,977 INFO [train.py:715] (1/8) Epoch 4, batch 31250, loss[loss=0.1822, simple_loss=0.2458, pruned_loss=0.05932, over 4879.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2272, pruned_loss=0.04379, over 972706.71 frames.], batch size: 32, lr: 4.32e-04
2022-05-05 01:47:33,183 INFO [train.py:715] (1/8) Epoch 4, batch 31300, loss[loss=0.1819, simple_loss=0.2403, pruned_loss=0.06172, over 4841.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2262, pruned_loss=0.04357, over 972511.21 frames.], batch size: 15, lr: 4.32e-04
2022-05-05 01:48:12,193 INFO [train.py:715] (1/8) Epoch 4, batch 31350, loss[loss=0.1733, simple_loss=0.238, pruned_loss=0.05424, over 4760.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2261, pruned_loss=0.04378, over 973393.01 frames.], batch size: 17, lr: 4.32e-04
2022-05-05 01:48:52,076 INFO [train.py:715] (1/8) Epoch 4, batch 31400, loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03503, over 4794.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2257, pruned_loss=0.04298, over 973580.67 frames.], batch size: 14, lr: 4.32e-04
2022-05-05 01:49:31,805 INFO [train.py:715] (1/8) Epoch 4, batch 31450, loss[loss=0.1994, simple_loss=0.2543, pruned_loss=0.07228, over 4781.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2259, pruned_loss=0.0429, over 974087.37 frames.], batch size: 14, lr: 4.32e-04
2022-05-05 01:50:11,374 INFO [train.py:715] (1/8) Epoch 4, batch 31500, loss[loss=0.1663, simple_loss=0.2401, pruned_loss=0.04625, over 4802.00 frames.], tot_loss[loss=0.1564, simple_loss=0.226, pruned_loss=0.04339, over 974184.84 frames.], batch size: 14, lr: 4.32e-04
2022-05-05 01:50:51,742 INFO [train.py:715] (1/8) Epoch 4, batch 31550, loss[loss=0.1522, simple_loss=0.2163, pruned_loss=0.04403, over 4933.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2254, pruned_loss=0.04283, over 974630.33 frames.], batch size: 29, lr: 4.32e-04
2022-05-05 01:51:32,270 INFO [train.py:715] (1/8) Epoch 4, batch 31600, loss[loss=0.154, simple_loss=0.2263, pruned_loss=0.04088, over 4792.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2264, pruned_loss=0.04321, over 974233.76 frames.], batch size: 17, lr: 4.31e-04
2022-05-05 01:52:11,916 INFO [train.py:715] (1/8) Epoch 4, batch 31650, loss[loss=0.1598, simple_loss=0.2268, pruned_loss=0.04639, over 4969.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2263, pruned_loss=0.04336, over 974379.81 frames.], batch size: 39, lr: 4.31e-04
2022-05-05 01:52:51,505 INFO [train.py:715] (1/8) Epoch 4, batch 31700, loss[loss=0.1716, simple_loss=0.253, pruned_loss=0.04506, over 4958.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2262, pruned_loss=0.04326, over 974441.62 frames.], batch size: 21, lr: 4.31e-04
2022-05-05 01:53:31,560 INFO [train.py:715] (1/8) Epoch 4, batch 31750, loss[loss=0.1869, simple_loss=0.2556, pruned_loss=0.05912, over 4860.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2268, pruned_loss=0.04323, over 973646.11 frames.], batch size: 20, lr: 4.31e-04
2022-05-05 01:54:11,604 INFO [train.py:715] (1/8) Epoch 4, batch 31800, loss[loss=0.1479, simple_loss=0.2123, pruned_loss=0.04177, over 4901.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2263, pruned_loss=0.04303, over 973671.02 frames.], batch size: 17, lr: 4.31e-04
2022-05-05 01:54:51,202 INFO [train.py:715] (1/8) Epoch 4, batch 31850, loss[loss=0.1488, simple_loss=0.2195, pruned_loss=0.03904, over 4993.00 frames.], tot_loss[loss=0.157, simple_loss=0.227, pruned_loss=0.04347, over 973562.69 frames.], batch size: 16, lr: 4.31e-04
2022-05-05 01:55:30,808 INFO [train.py:715] (1/8) Epoch 4, batch 31900, loss[loss=0.1657, simple_loss=0.2292, pruned_loss=0.05107, over 4824.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2269, pruned_loss=0.04378, over 973898.96 frames.], batch size: 25, lr: 4.31e-04
2022-05-05 01:56:11,034 INFO [train.py:715] (1/8) Epoch 4, batch 31950, loss[loss=0.1723, simple_loss=0.2461, pruned_loss=0.04924, over 4945.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2272, pruned_loss=0.04412, over 973594.94 frames.], batch size: 21, lr: 4.31e-04
2022-05-05 01:56:50,991 INFO [train.py:715] (1/8) Epoch 4, batch 32000, loss[loss=0.1488, simple_loss=0.2309, pruned_loss=0.03336, over 4938.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2265, pruned_loss=0.04361, over 973817.94 frames.], batch size: 21, lr: 4.31e-04
2022-05-05 01:57:30,375 INFO [train.py:715] (1/8) Epoch 4, batch 32050, loss[loss=0.1607, simple_loss=0.231, pruned_loss=0.04519, over 4825.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2265, pruned_loss=0.04349, over 973791.24 frames.], batch size: 15, lr: 4.31e-04
2022-05-05 01:58:10,944 INFO [train.py:715] (1/8) Epoch 4, batch 32100, loss[loss=0.1502, simple_loss=0.2228, pruned_loss=0.03884, over 4911.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2267, pruned_loss=0.04376, over 973573.96 frames.], batch size: 29, lr: 4.31e-04
2022-05-05 01:58:50,872 INFO [train.py:715] (1/8) Epoch 4, batch 32150, loss[loss=0.1133, simple_loss=0.1961, pruned_loss=0.01522, over 4766.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2278, pruned_loss=0.04387, over 972778.02 frames.], batch size: 19, lr: 4.31e-04
2022-05-05 01:59:30,410 INFO [train.py:715] (1/8) Epoch 4, batch 32200, loss[loss=0.1349, simple_loss=0.213, pruned_loss=0.02836, over 4830.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2265, pruned_loss=0.04291, over 972327.44 frames.], batch size: 26, lr: 4.31e-04
2022-05-05 02:00:10,383 INFO [train.py:715] (1/8) Epoch 4, batch 32250, loss[loss=0.152, simple_loss=0.234, pruned_loss=0.03503, over 4776.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2256, pruned_loss=0.04241, over 972426.39 frames.], batch size: 18, lr: 4.31e-04
2022-05-05 02:00:51,179 INFO [train.py:715] (1/8) Epoch 4, batch 32300, loss[loss=0.1225, simple_loss=0.2011, pruned_loss=0.02195, over 4922.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2247, pruned_loss=0.0416, over 971862.20 frames.], batch size: 29, lr: 4.31e-04
2022-05-05 02:01:31,963 INFO [train.py:715] (1/8) Epoch 4, batch 32350, loss[loss=0.1529, simple_loss=0.2252, pruned_loss=0.04026, over 4840.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2252, pruned_loss=0.04225, over 972066.77 frames.], batch size: 30, lr: 4.31e-04
2022-05-05 02:02:12,296 INFO [train.py:715] (1/8) Epoch 4, batch 32400, loss[loss=0.1599, simple_loss=0.2369, pruned_loss=0.04143, over 4898.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2246, pruned_loss=0.04215, over 972143.31 frames.], batch size: 22, lr: 4.30e-04
2022-05-05 02:02:52,622 INFO [train.py:715] (1/8) Epoch 4, batch 32450, loss[loss=0.143, simple_loss=0.2176, pruned_loss=0.03423, over 4869.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2242, pruned_loss=0.04171, over 971834.01 frames.], batch size: 16, lr: 4.30e-04
2022-05-05 02:03:31,867 INFO [train.py:715] (1/8) Epoch 4, batch 32500, loss[loss=0.1296, simple_loss=0.2013, pruned_loss=0.02894, over 4797.00 frames.], tot_loss[loss=0.1539, simple_loss=0.224, pruned_loss=0.04193, over 971781.22 frames.], batch size: 24, lr: 4.30e-04
2022-05-05 02:04:11,774 INFO [train.py:715] (1/8) Epoch 4, batch 32550, loss[loss=0.1349, simple_loss=0.2092, pruned_loss=0.03031, over 4960.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2241, pruned_loss=0.04177, over 972287.22 frames.], batch size: 24, lr: 4.30e-04
2022-05-05 02:04:50,745 INFO [train.py:715] (1/8) Epoch 4, batch 32600, loss[loss=0.1446, simple_loss=0.2081, pruned_loss=0.04056, over 4984.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2241, pruned_loss=0.04205, over 972145.08 frames.], batch size: 28, lr: 4.30e-04
2022-05-05 02:05:30,808 INFO [train.py:715] (1/8) Epoch 4, batch 32650, loss[loss=0.14, simple_loss=0.2117, pruned_loss=0.03419, over 4817.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2246, pruned_loss=0.04233, over 971538.35 frames.], batch size: 27, lr: 4.30e-04
2022-05-05 02:06:09,921 INFO [train.py:715] (1/8) Epoch 4, batch 32700, loss[loss=0.15, simple_loss=0.2129, pruned_loss=0.04356, over 4910.00 frames.], tot_loss[loss=0.1553, simple_loss=0.225, pruned_loss=0.04279, over 971017.96 frames.], batch size: 39, lr: 4.30e-04
2022-05-05 02:06:49,548 INFO [train.py:715] (1/8) Epoch 4, batch 32750, loss[loss=0.1366, simple_loss=0.2056, pruned_loss=0.03382, over 4727.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2248, pruned_loss=0.04296, over 971204.30 frames.], batch size: 16, lr: 4.30e-04
2022-05-05 02:07:29,265 INFO [train.py:715] (1/8) Epoch 4, batch 32800, loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02983, over 4921.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2254, pruned_loss=0.04319, over 971481.56 frames.], batch size: 29, lr: 4.30e-04
2022-05-05 02:08:09,338 INFO [train.py:715] (1/8) Epoch 4, batch 32850, loss[loss=0.1818, simple_loss=0.2491, pruned_loss=0.05727, over 4733.00 frames.], tot_loss[loss=0.155, simple_loss=0.2247, pruned_loss=0.04266, over 971405.18 frames.], batch size: 16, lr: 4.30e-04
2022-05-05 02:08:49,863 INFO [train.py:715] (1/8) Epoch 4, batch 32900, loss[loss=0.144, simple_loss=0.2118, pruned_loss=0.03804, over 4955.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2247, pruned_loss=0.04258, over 971816.79 frames.], batch size: 24, lr: 4.30e-04
2022-05-05 02:09:30,093 INFO [train.py:715] (1/8) Epoch 4, batch 32950, loss[loss=0.147, simple_loss=0.2263, pruned_loss=0.03386, over 4881.00 frames.], tot_loss[loss=0.155, simple_loss=0.2248, pruned_loss=0.04261, over 970504.94 frames.], batch size: 19, lr: 4.30e-04
2022-05-05 02:10:10,307 INFO [train.py:715] (1/8) Epoch 4, batch 33000, loss[loss=0.1437, simple_loss=0.227, pruned_loss=0.03025, over 4971.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2255, pruned_loss=0.04259, over 970459.05 frames.], batch size: 24, lr: 4.30e-04
2022-05-05 02:10:10,308 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 02:10:20,092 INFO [train.py:742] (1/8) Epoch 4, validation: loss=0.1115, simple_loss=0.197, pruned_loss=0.01298, over 914524.00 frames.
2022-05-05 02:11:00,299 INFO [train.py:715] (1/8) Epoch 4, batch 33050, loss[loss=0.1334, simple_loss=0.1976, pruned_loss=0.03463, over 4781.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2247, pruned_loss=0.04224, over 971568.56 frames.], batch size: 18, lr: 4.30e-04
2022-05-05 02:11:40,010 INFO [train.py:715] (1/8) Epoch 4, batch 33100, loss[loss=0.1487, simple_loss=0.224, pruned_loss=0.03667, over 4967.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2252, pruned_loss=0.04259, over 972951.13 frames.], batch size: 24, lr: 4.30e-04
2022-05-05 02:12:20,048 INFO [train.py:715] (1/8) Epoch 4, batch 33150, loss[loss=0.1549, simple_loss=0.2284, pruned_loss=0.04068, over 4958.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2257, pruned_loss=0.04255, over 972658.89 frames.], batch size: 15, lr: 4.30e-04
2022-05-05 02:13:00,230 INFO [train.py:715] (1/8) Epoch 4, batch 33200, loss[loss=0.1783, simple_loss=0.2477, pruned_loss=0.0544, over 4862.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2254, pruned_loss=0.04242, over 973291.56 frames.], batch size: 20, lr: 4.29e-04
2022-05-05 02:13:40,207 INFO [train.py:715] (1/8) Epoch 4, batch 33250, loss[loss=0.1491, simple_loss=0.2228, pruned_loss=0.03767, over 4758.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2257, pruned_loss=0.04265, over 973440.56 frames.], batch size: 19, lr: 4.29e-04
2022-05-05 02:14:20,217 INFO [train.py:715] (1/8) Epoch 4, batch 33300, loss[loss=0.1441, simple_loss=0.2183, pruned_loss=0.03495, over 4774.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2269, pruned_loss=0.04303, over 973427.13 frames.], batch size: 17, lr: 4.29e-04
2022-05-05 02:14:59,214 INFO [train.py:715] (1/8) Epoch 4, batch 33350, loss[loss=0.161, simple_loss=0.2279, pruned_loss=0.04703, over 4964.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2272, pruned_loss=0.04315, over 973811.71 frames.], batch size: 15, lr: 4.29e-04
2022-05-05 02:15:38,986 INFO [train.py:715] (1/8) Epoch 4, batch 33400, loss[loss=0.1824, simple_loss=0.2449, pruned_loss=0.05991, over 4909.00 frames.], tot_loss[loss=0.157, simple_loss=0.2274, pruned_loss=0.04328, over 973147.45 frames.], batch size: 17, lr: 4.29e-04
2022-05-05 02:16:18,847 INFO [train.py:715] (1/8) Epoch 4, batch 33450, loss[loss=0.1599, simple_loss=0.2282, pruned_loss=0.04579, over 4776.00 frames.], tot_loss[loss=0.1566, simple_loss=0.227, pruned_loss=0.04314, over 972954.35 frames.], batch size: 14, lr: 4.29e-04
2022-05-05 02:16:58,398 INFO [train.py:715] (1/8) Epoch 4, batch 33500, loss[loss=0.143, simple_loss=0.209, pruned_loss=0.03848, over 4821.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2263, pruned_loss=0.04306, over 972699.33 frames.], batch size: 25, lr: 4.29e-04
2022-05-05 02:17:38,204 INFO [train.py:715] (1/8) Epoch 4, batch 33550, loss[loss=0.1427, simple_loss=0.2217, pruned_loss=0.03183, over 4901.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2259, pruned_loss=0.04286, over 971847.52 frames.], batch size: 19, lr: 4.29e-04
2022-05-05 02:18:17,703 INFO [train.py:715] (1/8) Epoch 4, batch 33600, loss[loss=0.1407, simple_loss=0.2108, pruned_loss=0.03532, over 4947.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2259, pruned_loss=0.04257, over 972156.43 frames.], batch size: 21, lr: 4.29e-04
2022-05-05 02:18:57,443 INFO [train.py:715] (1/8) Epoch 4, batch 33650, loss[loss=0.15, simple_loss=0.2185, pruned_loss=0.04076, over 4762.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2262, pruned_loss=0.04256, over 971728.22 frames.], batch size: 19, lr: 4.29e-04
2022-05-05 02:19:36,829 INFO [train.py:715] (1/8) Epoch 4, batch 33700, loss[loss=0.178, simple_loss=0.246, pruned_loss=0.05493, over 4833.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2261, pruned_loss=0.04265, over 971566.12 frames.], batch size: 15, lr: 4.29e-04
2022-05-05 02:20:16,631 INFO [train.py:715] (1/8) Epoch 4, batch 33750, loss[loss=0.2164, simple_loss=0.2677, pruned_loss=0.08255, over 4973.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2267, pruned_loss=0.04314, over 971795.94 frames.], batch size: 15, lr: 4.29e-04
2022-05-05 02:20:56,489 INFO [train.py:715] (1/8) Epoch 4, batch 33800, loss[loss=0.17, simple_loss=0.2462, pruned_loss=0.04687, over 4799.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2263, pruned_loss=0.04301, over 972149.09 frames.], batch size: 21, lr: 4.29e-04
2022-05-05 02:21:35,977 INFO [train.py:715] (1/8) Epoch 4, batch 33850, loss[loss=0.1383, simple_loss=0.2084, pruned_loss=0.03411, over 4880.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2258, pruned_loss=0.04289, over 971585.33 frames.], batch size: 16, lr: 4.29e-04
2022-05-05 02:22:15,612 INFO [train.py:715] (1/8) Epoch 4, batch 33900, loss[loss=0.1534, simple_loss=0.2162, pruned_loss=0.04533, over 4786.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2261, pruned_loss=0.04278, over 971427.90 frames.], batch size: 14, lr: 4.29e-04
2022-05-05 02:22:55,359 INFO [train.py:715] (1/8) Epoch 4, batch 33950, loss[loss=0.1426, simple_loss=0.2144, pruned_loss=0.03539, over 4805.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2254, pruned_loss=0.04237, over 971113.83 frames.], batch size: 14, lr: 4.29e-04
2022-05-05 02:23:35,330 INFO [train.py:715] (1/8) Epoch 4, batch 34000, loss[loss=0.1529, simple_loss=0.2247, pruned_loss=0.04054, over 4928.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2254, pruned_loss=0.04207, over 971985.23 frames.], batch size: 23, lr: 4.28e-04
2022-05-05 02:24:14,855 INFO [train.py:715] (1/8) Epoch 4, batch 34050, loss[loss=0.1352, simple_loss=0.2069, pruned_loss=0.03175, over 4782.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2251, pruned_loss=0.04199, over 972104.88 frames.], batch size: 14, lr: 4.28e-04
2022-05-05 02:24:54,574 INFO [train.py:715] (1/8) Epoch 4, batch 34100, loss[loss=0.1233, simple_loss=0.1987, pruned_loss=0.02396, over 4823.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2241, pruned_loss=0.04127, over 972646.83 frames.], batch size: 13, lr: 4.28e-04
2022-05-05 02:25:34,633 INFO [train.py:715] (1/8) Epoch 4, batch 34150, loss[loss=0.1601, simple_loss=0.2404, pruned_loss=0.03992, over 4779.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2245, pruned_loss=0.04156, over 973436.95 frames.], batch size: 17, lr: 4.28e-04
2022-05-05 02:26:13,490 INFO [train.py:715] (1/8) Epoch 4, batch 34200, loss[loss=0.1509, simple_loss=0.2264, pruned_loss=0.03771, over 4748.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2247, pruned_loss=0.04181, over 973799.76 frames.], batch size: 16, lr: 4.28e-04
2022-05-05 02:26:54,321 INFO [train.py:715] (1/8) Epoch 4, batch 34250, loss[loss=0.148, simple_loss=0.2196, pruned_loss=0.03827, over 4764.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2241, pruned_loss=0.0416, over 973769.27 frames.], batch size: 16, lr: 4.28e-04
2022-05-05 02:27:34,194 INFO [train.py:715] (1/8) Epoch 4, batch 34300, loss[loss=0.1605, simple_loss=0.2283, pruned_loss=0.04638, over 4802.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2241, pruned_loss=0.04209, over 973525.52 frames.], batch size: 21, lr: 4.28e-04
2022-05-05 02:28:13,945 INFO [train.py:715] (1/8) Epoch 4, batch 34350, loss[loss=0.1629, simple_loss=0.2377, pruned_loss=0.04401, over 4714.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2249, pruned_loss=0.04225, over 972945.36 frames.], batch size: 15, lr: 4.28e-04
2022-05-05 02:28:53,980 INFO [train.py:715] (1/8) Epoch 4, batch 34400, loss[loss=0.1671, simple_loss=0.2357, pruned_loss=0.04929, over 4755.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2247, pruned_loss=0.04195, over 972539.77 frames.], batch size: 16, lr: 4.28e-04
2022-05-05 02:29:33,811 INFO [train.py:715] (1/8) Epoch 4, batch 34450, loss[loss=0.1674, simple_loss=0.2283, pruned_loss=0.05319, over 4942.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2247, pruned_loss=0.04195, over 972775.57 frames.], batch size: 35, lr: 4.28e-04
2022-05-05 02:30:14,474 INFO [train.py:715] (1/8) Epoch 4, batch 34500, loss[loss=0.1484, simple_loss=0.2244, pruned_loss=0.03624, over 4962.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2267, pruned_loss=0.04298, over 972984.48 frames.], batch size: 24, lr: 4.28e-04
2022-05-05 02:30:53,319 INFO [train.py:715] (1/8) Epoch 4, batch 34550, loss[loss=0.1322, simple_loss=0.2073, pruned_loss=0.02849, over 4828.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2265, pruned_loss=0.04317, over 973154.38 frames.], batch size: 26, lr: 4.28e-04
2022-05-05 02:31:33,261 INFO [train.py:715] (1/8) Epoch 4, batch 34600, loss[loss=0.1512, simple_loss=0.2316, pruned_loss=0.03546, over 4975.00 frames.], tot_loss[loss=0.1562, simple_loss=0.226, pruned_loss=0.04319, over 973005.16 frames.], batch size: 24, lr: 4.28e-04
2022-05-05 02:32:13,241 INFO [train.py:715] (1/8) Epoch 4, batch 34650, loss[loss=0.1675, simple_loss=0.2368, pruned_loss=0.0491, over 4756.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2257, pruned_loss=0.04308, over 973537.86 frames.], batch size: 16, lr: 4.28e-04
2022-05-05 02:32:52,595 INFO [train.py:715] (1/8) Epoch 4, batch 34700, loss[loss=0.1362, simple_loss=0.1991, pruned_loss=0.03663, over 4895.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2251, pruned_loss=0.04288, over 972821.71 frames.], batch size: 22, lr: 4.28e-04
2022-05-05 02:33:30,875 INFO [train.py:715] (1/8) Epoch 4, batch 34750, loss[loss=0.1442, simple_loss=0.2111, pruned_loss=0.03862, over 4987.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2244, pruned_loss=0.04249, over 972073.66 frames.], batch size: 14, lr: 4.28e-04
2022-05-05 02:34:07,953 INFO [train.py:715] (1/8) Epoch 4, batch 34800, loss[loss=0.18, simple_loss=0.2593, pruned_loss=0.05029, over 4933.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2255, pruned_loss=0.04292, over 973083.36 frames.], batch size: 18, lr: 4.27e-04
2022-05-05 02:34:57,782 INFO [train.py:715] (1/8) Epoch 5, batch 0, loss[loss=0.1291, simple_loss=0.2029, pruned_loss=0.02762, over 4971.00 frames.], tot_loss[loss=0.1291, simple_loss=0.2029, pruned_loss=0.02762, over 4971.00 frames.], batch size: 25, lr: 4.02e-04
2022-05-05 02:35:38,117 INFO [train.py:715] (1/8) Epoch 5, batch 50, loss[loss=0.1409, simple_loss=0.2142, pruned_loss=0.03381, over 4989.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2238, pruned_loss=0.04191, over 220373.63 frames.], batch size: 14, lr: 4.02e-04
2022-05-05 02:36:17,802 INFO [train.py:715] (1/8) Epoch 5, batch 100, loss[loss=0.2234, simple_loss=0.2819, pruned_loss=0.0825, over 4956.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2258, pruned_loss=0.04264, over 387094.25 frames.], batch size: 35, lr: 4.02e-04
2022-05-05 02:36:57,772 INFO [train.py:715] (1/8) Epoch 5, batch 150, loss[loss=0.1385, simple_loss=0.2133, pruned_loss=0.03183, over 4806.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2253, pruned_loss=0.0428, over 517116.94 frames.], batch size: 25, lr: 4.02e-04
2022-05-05 02:37:38,288 INFO [train.py:715] (1/8) Epoch 5, batch 200, loss[loss=0.159, simple_loss=0.2224, pruned_loss=0.04777, over 4904.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2265, pruned_loss=0.04314, over 618258.46 frames.], batch size: 17, lr: 4.02e-04
2022-05-05 02:38:17,739 INFO [train.py:715] (1/8) Epoch 5, batch 250, loss[loss=0.1567, simple_loss=0.2339, pruned_loss=0.03978, over 4919.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2257, pruned_loss=0.04246, over 697232.01 frames.], batch size: 23, lr: 4.02e-04
2022-05-05 02:38:57,167 INFO [train.py:715] (1/8) Epoch 5, batch 300, loss[loss=0.1827, simple_loss=0.2453, pruned_loss=0.06002, over 4775.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2249, pruned_loss=0.04201, over 757630.81 frames.], batch size: 18, lr: 4.01e-04
2022-05-05 02:39:36,899 INFO [train.py:715] (1/8) Epoch 5, batch 350, loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03099, over 4808.00 frames.], tot_loss[loss=0.154, simple_loss=0.2246, pruned_loss=0.04175, over 804723.28 frames.], batch size: 25, lr: 4.01e-04
2022-05-05 02:40:16,665 INFO [train.py:715] (1/8) Epoch 5, batch 400, loss[loss=0.1679, simple_loss=0.2401, pruned_loss=0.04785, over 4971.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2258, pruned_loss=0.04241, over 842090.33 frames.], batch size: 31, lr: 4.01e-04
2022-05-05 02:40:56,052 INFO [train.py:715] (1/8) Epoch 5, batch 450, loss[loss=0.1536, simple_loss=0.2372, pruned_loss=0.03501, over 4847.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2261, pruned_loss=0.04277, over 871113.08 frames.], batch size: 34, lr: 4.01e-04
2022-05-05 02:41:35,803 INFO [train.py:715] (1/8) Epoch 5, batch 500, loss[loss=0.1432, simple_loss=0.2218, pruned_loss=0.03225, over 4808.00 frames.], tot_loss[loss=0.1545, simple_loss=0.225, pruned_loss=0.042, over 893447.34 frames.], batch size: 13, lr: 4.01e-04
2022-05-05 02:42:15,654 INFO [train.py:715] (1/8) Epoch 5, batch 550, loss[loss=0.1836, simple_loss=0.2491, pruned_loss=0.05902, over 4888.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2251, pruned_loss=0.04253, over 910800.90 frames.], batch size: 19, lr: 4.01e-04
2022-05-05 02:42:54,759 INFO [train.py:715] (1/8) Epoch 5, batch 600, loss[loss=0.1573, simple_loss=0.2336, pruned_loss=0.0405, over 4776.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2249, pruned_loss=0.04264, over 925063.91 frames.], batch size: 18, lr: 4.01e-04
2022-05-05 02:43:34,146 INFO [train.py:715] (1/8) Epoch 5, batch 650, loss[loss=0.1467, simple_loss=0.2251, pruned_loss=0.03415, over 4806.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2253, pruned_loss=0.04293, over 935339.69 frames.], batch size: 21, lr: 4.01e-04
2022-05-05 02:44:13,853 INFO [train.py:715] (1/8) Epoch 5, batch 700, loss[loss=0.1627, simple_loss=0.241, pruned_loss=0.0422, over 4776.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2239, pruned_loss=0.04212, over 943551.22 frames.], batch size: 17, lr: 4.01e-04
2022-05-05 02:44:53,918 INFO [train.py:715] (1/8) Epoch 5, batch 750, loss[loss=0.1643, simple_loss=0.2311, pruned_loss=0.04872, over 4749.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2237, pruned_loss=0.04203, over 950226.09 frames.], batch size: 16, lr: 4.01e-04
2022-05-05 02:45:33,289 INFO [train.py:715] (1/8) Epoch 5, batch 800, loss[loss=0.1556, simple_loss=0.2191, pruned_loss=0.04609, over 4766.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2231, pruned_loss=0.04202, over 954919.48 frames.], batch size: 19, lr: 4.01e-04
2022-05-05 02:46:12,789 INFO [train.py:715] (1/8) Epoch 5, batch 850, loss[loss=0.2035, simple_loss=0.2557, pruned_loss=0.07565, over 4873.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2238, pruned_loss=0.04249, over 958172.32 frames.], batch size: 16, lr: 4.01e-04
2022-05-05 02:46:52,361 INFO [train.py:715] (1/8) Epoch 5, batch 900, loss[loss=0.1435, simple_loss=0.2016, pruned_loss=0.04272, over 4974.00 frames.], tot_loss[loss=0.153, simple_loss=0.2225, pruned_loss=0.04173, over 961438.99 frames.], batch size: 15, lr: 4.01e-04
2022-05-05 02:47:31,849 INFO [train.py:715] (1/8) Epoch 5, batch 950, loss[loss=0.1474, simple_loss=0.2073, pruned_loss=0.04376, over 4785.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2232, pruned_loss=0.04258, over 964102.91 frames.], batch size: 17, lr: 4.01e-04
2022-05-05 02:48:11,359 INFO [train.py:715] (1/8) Epoch 5, batch 1000, loss[loss=0.1826, simple_loss=0.2544, pruned_loss=0.05537, over 4756.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2241, pruned_loss=0.04267, over 966933.28 frames.], batch size: 16, lr: 4.01e-04
2022-05-05 02:48:50,621 INFO [train.py:715] (1/8) Epoch 5, batch 1050, loss[loss=0.1567, simple_loss=0.2248, pruned_loss=0.04425, over 4874.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2234, pruned_loss=0.04256, over 968016.06 frames.], batch size: 22, lr: 4.01e-04
2022-05-05 02:49:30,326 INFO [train.py:715] (1/8) Epoch 5, batch 1100, loss[loss=0.1251, simple_loss=0.1928, pruned_loss=0.02874, over 4973.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2235, pruned_loss=0.04276, over 969296.66 frames.], batch size: 14, lr: 4.01e-04
2022-05-05 02:50:09,335 INFO [train.py:715] (1/8) Epoch 5, batch 1150, loss[loss=0.1488, simple_loss=0.2244, pruned_loss=0.03666, over 4875.00 frames.], tot_loss[loss=0.1539, simple_loss=0.223, pruned_loss=0.0424, over 969328.54 frames.], batch size: 32, lr: 4.00e-04
2022-05-05 02:50:49,096 INFO [train.py:715] (1/8) Epoch 5, batch 1200, loss[loss=0.1318, simple_loss=0.2114, pruned_loss=0.02614, over 4894.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2227, pruned_loss=0.04208, over 969052.44 frames.], batch size: 22, lr: 4.00e-04
2022-05-05 02:51:29,247 INFO [train.py:715] (1/8) Epoch 5, batch 1250, loss[loss=0.1384, simple_loss=0.205, pruned_loss=0.03593, over 4681.00 frames.], tot_loss[loss=0.153, simple_loss=0.2222, pruned_loss=0.04195, over 970116.03 frames.], batch size: 15, lr: 4.00e-04
2022-05-05 02:52:08,416 INFO [train.py:715] (1/8) Epoch 5, batch 1300, loss[loss=0.177, simple_loss=0.2498, pruned_loss=0.05207, over 4937.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2224, pruned_loss=0.04173, over 970912.07 frames.], batch size: 23, lr: 4.00e-04
2022-05-05 02:52:48,196 INFO [train.py:715] (1/8) Epoch 5, batch 1350, loss[loss=0.1629, simple_loss=0.229, pruned_loss=0.04835, over 4818.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2231, pruned_loss=0.04225, over 971483.22 frames.], batch size: 27, lr: 4.00e-04
2022-05-05 02:53:27,488 INFO [train.py:715] (1/8) Epoch 5, batch 1400, loss[loss=0.1886, simple_loss=0.257, pruned_loss=0.06014, over 4961.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2224, pruned_loss=0.04216, over 971994.77 frames.], batch size: 15, lr: 4.00e-04
2022-05-05 02:54:07,308 INFO [train.py:715] (1/8) Epoch 5, batch 1450, loss[loss=0.1333, simple_loss=0.2086, pruned_loss=0.02897, over 4931.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2226, pruned_loss=0.04215, over 971353.27 frames.], batch size: 29, lr: 4.00e-04
2022-05-05 02:54:46,735 INFO [train.py:715] (1/8) Epoch 5, batch 1500, loss[loss=0.1663, simple_loss=0.2336, pruned_loss=0.04951, over 4940.00 frames.], tot_loss[loss=0.1535, simple_loss=0.223, pruned_loss=0.04198, over 971568.62 frames.], batch size: 23, lr: 4.00e-04
2022-05-05 02:55:25,730 INFO [train.py:715] (1/8) Epoch 5, batch 1550, loss[loss=0.1713, simple_loss=0.2392, pruned_loss=0.05171, over 4926.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2236, pruned_loss=0.04249, over 972021.90 frames.], batch size: 39, lr: 4.00e-04
2022-05-05 02:56:05,371 INFO [train.py:715] (1/8) Epoch 5, batch 1600, loss[loss=0.1467, simple_loss=0.2199, pruned_loss=0.03676, over 4919.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2231, pruned_loss=0.04235, over 972176.62 frames.], batch size: 29, lr: 4.00e-04
2022-05-05 02:56:45,707 INFO [train.py:715] (1/8) Epoch 5, batch 1650, loss[loss=0.1221, simple_loss=0.1921, pruned_loss=0.02603, over 4984.00 frames.], tot_loss[loss=0.154, simple_loss=0.2234, pruned_loss=0.04227, over 972434.97 frames.], batch size: 14, lr: 4.00e-04
2022-05-05 02:57:24,643 INFO [train.py:715] (1/8) Epoch 5, batch 1700, loss[loss=0.1574, simple_loss=0.2284, pruned_loss=0.04315, over 4927.00 frames.], tot_loss[loss=0.1543, simple_loss=0.224, pruned_loss=0.04233, over 972741.41 frames.], batch size: 18, lr: 4.00e-04
2022-05-05 02:58:05,304 INFO [train.py:715] (1/8) Epoch 5, batch 1750, loss[loss=0.1638, simple_loss=0.2223, pruned_loss=0.05262, over 4813.00 frames.], tot_loss[loss=0.155, simple_loss=0.2244, pruned_loss=0.04277, over 972660.61 frames.], batch size: 24, lr: 4.00e-04
2022-05-05 02:58:45,441 INFO [train.py:715] (1/8) Epoch 5, batch 1800, loss[loss=0.149, simple_loss=0.216, pruned_loss=0.041, over 4983.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2244, pruned_loss=0.0422, over 973588.03 frames.], batch size: 28, lr: 4.00e-04
2022-05-05 02:59:25,898 INFO [train.py:715] (1/8) Epoch 5, batch 1850, loss[loss=0.1385, simple_loss=0.2143, pruned_loss=0.03138, over 4817.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2248, pruned_loss=0.04245, over 973339.98 frames.], batch size: 25, lr: 4.00e-04
2022-05-05 03:00:06,295 INFO [train.py:715] (1/8) Epoch 5, batch 1900, loss[loss=0.1337, simple_loss=0.2128, pruned_loss=0.02725, over 4981.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2243, pruned_loss=0.04241, over 973212.43 frames.], batch size: 25, lr: 4.00e-04
2022-05-05 03:00:46,054 INFO [train.py:715] (1/8) Epoch 5, batch 1950, loss[loss=0.1292, simple_loss=0.2062, pruned_loss=0.02609, over 4891.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2248, pruned_loss=0.04283, over 972893.13 frames.], batch size: 18, lr: 4.00e-04
2022-05-05 03:01:29,141 INFO [train.py:715] (1/8) Epoch 5, batch 2000, loss[loss=0.1511, simple_loss=0.2237, pruned_loss=0.03924, over 4810.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2243, pruned_loss=0.04242, over 972700.89 frames.], batch size: 13, lr: 4.00e-04
2022-05-05 03:02:09,165 INFO [train.py:715] (1/8) Epoch 5, batch 2050, loss[loss=0.157, simple_loss=0.2246, pruned_loss=0.04477, over 4781.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2236, pruned_loss=0.04181, over 972683.01 frames.], batch size: 17, lr: 3.99e-04
2022-05-05 03:02:49,516 INFO [train.py:715] (1/8) Epoch 5, batch 2100, loss[loss=0.1671, simple_loss=0.2336, pruned_loss=0.05033, over 4976.00 frames.], tot_loss[loss=0.153, simple_loss=0.223, pruned_loss=0.04149, over 972983.31 frames.], batch size: 31, lr: 3.99e-04
2022-05-05 03:03:30,096 INFO [train.py:715] (1/8) Epoch 5, batch 2150, loss[loss=0.178, simple_loss=0.2447, pruned_loss=0.05566, over 4692.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2233, pruned_loss=0.04165, over 972418.78 frames.], batch size: 15, lr: 3.99e-04
2022-05-05 03:04:09,685 INFO [train.py:715] (1/8) Epoch 5, batch 2200, loss[loss=0.1342, simple_loss=0.2074, pruned_loss=0.03056, over 4686.00 frames.], tot_loss[loss=0.154, simple_loss=0.2243, pruned_loss=0.04183, over 972917.91 frames.], batch size: 15, lr: 3.99e-04
2022-05-05 03:04:50,064 INFO [train.py:715] (1/8) Epoch 5, batch 2250, loss[loss=0.1323, simple_loss=0.2157, pruned_loss=0.02444, over 4977.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2241, pruned_loss=0.04151, over 971411.57 frames.], batch size: 25, lr: 3.99e-04
2022-05-05 03:05:30,776 INFO [train.py:715] (1/8) Epoch 5, batch 2300, loss[loss=0.1496, simple_loss=0.2268, pruned_loss=0.03618, over 4884.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2235, pruned_loss=0.04109, over 972892.18 frames.], batch size: 22, lr: 3.99e-04
2022-05-05 03:06:10,991 INFO [train.py:715] (1/8) Epoch 5, batch 2350, loss[loss=0.1565, simple_loss=0.2308, pruned_loss=0.04109, over 4797.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2243, pruned_loss=0.04159, over 972876.56 frames.], batch size: 21, lr: 3.99e-04
2022-05-05 03:06:51,195 INFO [train.py:715] (1/8) Epoch 5, batch 2400, loss[loss=0.1779, simple_loss=0.2438, pruned_loss=0.05606, over 4932.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2238, pruned_loss=0.04132, over 973369.57 frames.], batch size: 39, lr: 3.99e-04
2022-05-05 03:07:31,704 INFO [train.py:715] (1/8) Epoch 5, batch 2450, loss[loss=0.1711, simple_loss=0.2411, pruned_loss=0.05055, over 4918.00 frames.], tot_loss[loss=0.1533, simple_loss=0.224, pruned_loss=0.04129, over 973350.15 frames.], batch size: 23, lr: 3.99e-04
2022-05-05 03:08:12,409 INFO [train.py:715] (1/8) Epoch 5, batch 2500, loss[loss=0.1507, simple_loss=0.2264, pruned_loss=0.03748, over 4937.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2238, pruned_loss=0.04083, over 974130.13 frames.], batch size: 18, lr: 3.99e-04
2022-05-05 03:08:52,451 INFO [train.py:715] (1/8) Epoch 5, batch 2550, loss[loss=0.1748, simple_loss=0.2513, pruned_loss=0.04916, over 4951.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2237, pruned_loss=0.04094, over 972456.26 frames.], batch size: 21, lr: 3.99e-04
2022-05-05 03:09:33,372 INFO [train.py:715] (1/8) Epoch 5, batch 2600, loss[loss=0.1515, simple_loss=0.217, pruned_loss=0.04304, over 4956.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2235, pruned_loss=0.04091, over 972226.70 frames.], batch size: 15, lr: 3.99e-04
2022-05-05 03:10:13,558 INFO [train.py:715] (1/8) Epoch 5, batch 2650, loss[loss=0.1584, simple_loss=0.2364, pruned_loss=0.04021, over 4987.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2239, pruned_loss=0.04092, over 973231.41 frames.], batch size: 26, lr: 3.99e-04
2022-05-05 03:10:54,132 INFO [train.py:715] (1/8) Epoch 5, batch 2700, loss[loss=0.145, simple_loss=0.2092, pruned_loss=0.04038, over 4915.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2235, pruned_loss=0.04075, over 973287.49 frames.], batch size: 19, lr: 3.99e-04
2022-05-05 03:11:34,322 INFO [train.py:715] (1/8) Epoch 5, batch 2750, loss[loss=0.1459, simple_loss=0.2228, pruned_loss=0.03445, over 4736.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2238, pruned_loss=0.04089, over 974068.50 frames.], batch size: 16, lr: 3.99e-04
2022-05-05 03:12:14,292 INFO [train.py:715] (1/8) Epoch 5, batch 2800, loss[loss=0.1429, simple_loss=0.2134, pruned_loss=0.03622, over 4861.00 frames.], tot_loss[loss=0.153, simple_loss=0.224, pruned_loss=0.04101, over 974004.68 frames.], batch size: 32, lr: 3.99e-04
2022-05-05 03:12:54,882 INFO [train.py:715] (1/8) Epoch 5, batch 2850, loss[loss=0.1567, simple_loss=0.2232, pruned_loss=0.04508, over 4947.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2233, pruned_loss=0.04062, over 973761.54 frames.], batch size: 21, lr: 3.99e-04
2022-05-05 03:13:35,010 INFO [train.py:715] (1/8) Epoch 5, batch 2900, loss[loss=0.1622, simple_loss=0.2368, pruned_loss=0.04384, over 4820.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2245, pruned_loss=0.04167, over 973990.92 frames.], batch size: 15, lr: 3.99e-04
2022-05-05 03:14:15,393 INFO [train.py:715] (1/8) Epoch 5, batch 2950, loss[loss=0.1552, simple_loss=0.2177, pruned_loss=0.04629, over 4795.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2241, pruned_loss=0.0413, over 974372.30 frames.], batch size: 14, lr: 3.98e-04
2022-05-05 03:14:54,472 INFO [train.py:715] (1/8) Epoch 5, batch 3000, loss[loss=0.1051, simple_loss=0.1733, pruned_loss=0.01849, over 4804.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2243, pruned_loss=0.04157, over 973048.35 frames.], batch size: 12, lr: 3.98e-04
2022-05-05 03:14:54,472 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 03:15:03,921 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1108, simple_loss=0.1962, pruned_loss=0.01274, over 914524.00 frames.
2022-05-05 03:15:42,398 INFO [train.py:715] (1/8) Epoch 5, batch 3050, loss[loss=0.1549, simple_loss=0.2291, pruned_loss=0.0404, over 4845.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2244, pruned_loss=0.04148, over 974036.18 frames.], batch size: 20, lr: 3.98e-04
2022-05-05 03:16:21,560 INFO [train.py:715] (1/8) Epoch 5, batch 3100, loss[loss=0.1644, simple_loss=0.2369, pruned_loss=0.04595, over 4790.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2247, pruned_loss=0.04181, over 973627.50 frames.], batch size: 18, lr: 3.98e-04
2022-05-05 03:17:00,521 INFO [train.py:715] (1/8) Epoch 5, batch 3150, loss[loss=0.2064, simple_loss=0.2642, pruned_loss=0.0743, over 4860.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2254, pruned_loss=0.04195, over 973016.89 frames.], batch size: 30, lr: 3.98e-04
2022-05-05 03:17:40,041 INFO [train.py:715] (1/8) Epoch 5, batch 3200, loss[loss=0.1894, simple_loss=0.2551, pruned_loss=0.06192, over 4926.00 frames.], tot_loss[loss=0.154, simple_loss=0.2249, pruned_loss=0.04155, over 972697.15 frames.], batch size: 18, lr: 3.98e-04
2022-05-05 03:18:19,745 INFO [train.py:715] (1/8) Epoch 5, batch 3250, loss[loss=0.1625, simple_loss=0.2407, pruned_loss=0.04212, over 4812.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2256, pruned_loss=0.04188, over 972430.33 frames.], batch size: 25, lr: 3.98e-04
2022-05-05 03:18:58,962 INFO [train.py:715] (1/8) Epoch 5, batch 3300, loss[loss=0.201, simple_loss=0.2669, pruned_loss=0.06754, over 4902.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2261, pruned_loss=0.04236, over 972512.95 frames.], batch size: 17, lr: 3.98e-04
2022-05-05 03:19:38,240 INFO [train.py:715] (1/8) Epoch 5, batch 3350, loss[loss=0.1235, simple_loss=0.2068, pruned_loss=0.02006, over 4915.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2243, pruned_loss=0.04142, over 972383.79 frames.], batch size: 18, lr: 3.98e-04
2022-05-05 03:20:17,975 INFO [train.py:715] (1/8) Epoch 5, batch 3400, loss[loss=0.1165, simple_loss=0.1931, pruned_loss=0.01993, over 4936.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2242, pruned_loss=0.04146, over 973546.03 frames.], batch size: 29, lr: 3.98e-04
2022-05-05 03:20:57,513 INFO [train.py:715] (1/8) Epoch 5, batch 3450, loss[loss=0.1649, simple_loss=0.2371, pruned_loss=0.0464, over 4772.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2244, pruned_loss=0.04175, over 973513.81 frames.], batch size: 14, lr: 3.98e-04
2022-05-05 03:21:36,811 INFO [train.py:715] (1/8) Epoch 5, batch 3500, loss[loss=0.1459, simple_loss=0.22, pruned_loss=0.03593, over 4831.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2239, pruned_loss=0.04126, over 972638.97 frames.], batch size: 15, lr: 3.98e-04
2022-05-05 03:22:16,035 INFO [train.py:715] (1/8) Epoch 5, batch 3550, loss[loss=0.1581, simple_loss=0.2371, pruned_loss=0.03951, over 4829.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2233, pruned_loss=0.0408, over 972773.62 frames.], batch size: 30, lr: 3.98e-04
2022-05-05 03:22:55,534 INFO [train.py:715] (1/8) Epoch 5, batch 3600, loss[loss=0.18, simple_loss=0.2465, pruned_loss=0.05675, over 4842.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2239, pruned_loss=0.04155, over 972448.90 frames.], batch size: 15, lr: 3.98e-04
2022-05-05 03:23:34,520 INFO [train.py:715] (1/8) Epoch 5, batch 3650, loss[loss=0.1369, simple_loss=0.2112, pruned_loss=0.03129, over 4700.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2237, pruned_loss=0.04162, over 972624.08 frames.], batch size: 15, lr: 3.98e-04
2022-05-05 03:24:13,766 INFO [train.py:715] (1/8) Epoch 5, batch 3700, loss[loss=0.1427, simple_loss=0.2085, pruned_loss=0.03842, over 4754.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2229, pruned_loss=0.04132, over 972530.23 frames.], batch size: 19, lr: 3.98e-04
2022-05-05 03:24:53,926 INFO [train.py:715] (1/8) Epoch 5, batch 3750, loss[loss=0.162, simple_loss=0.2319, pruned_loss=0.04608, over 4800.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2231, pruned_loss=0.04128, over 971875.14 frames.], batch size: 21, lr: 3.98e-04
2022-05-05 03:25:33,699 INFO [train.py:715] (1/8) Epoch 5, batch 3800, loss[loss=0.1564, simple_loss=0.221, pruned_loss=0.04592, over 4858.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2229, pruned_loss=0.04129, over 972460.52 frames.], batch size: 34, lr: 3.97e-04
2022-05-05 03:26:13,096 INFO [train.py:715] (1/8) Epoch 5, batch 3850, loss[loss=0.1244, simple_loss=0.1922, pruned_loss=0.02832, over 4834.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2241, pruned_loss=0.04174, over 972658.85 frames.], batch size: 13, lr: 3.97e-04
2022-05-05 03:26:52,963 INFO [train.py:715] (1/8) Epoch 5, batch 3900, loss[loss=0.2269, simple_loss=0.2824, pruned_loss=0.08571, over 4916.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2241, pruned_loss=0.04209, over 972632.83 frames.], batch size: 19, lr: 3.97e-04
2022-05-05 03:27:32,996 INFO [train.py:715] (1/8) Epoch 5, batch 3950, loss[loss=0.148, simple_loss=0.2188, pruned_loss=0.03864, over 4854.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2243, pruned_loss=0.04195, over 972661.42 frames.], batch size: 20, lr: 3.97e-04
2022-05-05 03:28:13,087 INFO [train.py:715] (1/8) Epoch 5, batch 4000, loss[loss=0.1214, simple_loss=0.1975, pruned_loss=0.02269, over 4912.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2248, pruned_loss=0.04217, over 971864.03 frames.], batch size: 18, lr: 3.97e-04
2022-05-05 03:28:53,742 INFO [train.py:715] (1/8) Epoch 5, batch 4050, loss[loss=0.1705, simple_loss=0.2361, pruned_loss=0.0525, over 4848.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2254, pruned_loss=0.04272, over 972243.54 frames.], batch size: 34, lr: 3.97e-04
2022-05-05 03:29:33,847 INFO [train.py:715] (1/8) Epoch 5, batch 4100, loss[loss=0.1457, simple_loss=0.2225, pruned_loss=0.03448, over 4952.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2251, pruned_loss=0.04289, over 972168.51 frames.], batch size: 24, lr: 3.97e-04
2022-05-05 03:30:14,066 INFO [train.py:715] (1/8) Epoch 5, batch 4150, loss[loss=0.163, simple_loss=0.2279, pruned_loss=0.04906, over 4868.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2265, pruned_loss=0.04353, over 972031.06 frames.], batch size: 32, lr: 3.97e-04
2022-05-05 03:30:53,454 INFO [train.py:715] (1/8) Epoch 5, batch 4200, loss[loss=0.1703, simple_loss=0.2314, pruned_loss=0.05458, over 4839.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2251, pruned_loss=0.04255, over 971762.40 frames.], batch size: 30, lr: 3.97e-04
2022-05-05 03:31:32,796 INFO [train.py:715] (1/8) Epoch 5, batch 4250, loss[loss=0.1459, simple_loss=0.2143, pruned_loss=0.03876, over 4887.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2253, pruned_loss=0.042, over 971694.58 frames.], batch size: 22, lr: 3.97e-04
2022-05-05 03:32:12,491 INFO [train.py:715] (1/8) Epoch 5, batch 4300, loss[loss=0.1321, simple_loss=0.2042, pruned_loss=0.02999, over 4815.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2246, pruned_loss=0.04163, over 971041.09 frames.], batch size: 12, lr: 3.97e-04
2022-05-05 03:32:52,099 INFO [train.py:715] (1/8) Epoch 5, batch 4350, loss[loss=0.1691, simple_loss=0.2319, pruned_loss=0.05319, over 4884.00 frames.], tot_loss[loss=0.1547, simple_loss=0.225, pruned_loss=0.04225, over 971043.21 frames.], batch size: 22, lr: 3.97e-04
2022-05-05 03:33:32,069 INFO [train.py:715] (1/8) Epoch 5, batch 4400, loss[loss=0.178, simple_loss=0.2439, pruned_loss=0.05605, over 4751.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2243, pruned_loss=0.04169, over 970119.61 frames.], batch size: 16, lr: 3.97e-04
2022-05-05 03:34:10,951 INFO [train.py:715] (1/8) Epoch 5, batch 4450, loss[loss=0.1643, simple_loss=0.2189, pruned_loss=0.05481, over 4820.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2228, pruned_loss=0.04123, over 970529.26 frames.], batch size: 12, lr: 3.97e-04
2022-05-05 03:34:50,796 INFO [train.py:715] (1/8) Epoch 5, batch 4500, loss[loss=0.1429, simple_loss=0.2188, pruned_loss=0.03349, over 4821.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2229, pruned_loss=0.04112, over 971062.89 frames.], batch size: 15, lr: 3.97e-04
2022-05-05 03:35:30,127 INFO [train.py:715] (1/8) Epoch 5, batch 4550, loss[loss=0.1975, simple_loss=0.2555, pruned_loss=0.06976, over 4778.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2238, pruned_loss=0.04167, over 970524.28 frames.], batch size: 17, lr: 3.97e-04
2022-05-05 03:36:09,742 INFO [train.py:715] (1/8) Epoch 5, batch 4600, loss[loss=0.1478, simple_loss=0.2067, pruned_loss=0.04444, over 4780.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2242, pruned_loss=0.04203, over 971153.11 frames.], batch size: 12, lr: 3.97e-04
2022-05-05 03:36:50,100 INFO [train.py:715] (1/8) Epoch 5, batch 4650, loss[loss=0.1394, simple_loss=0.2069, pruned_loss=0.03596, over 4742.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2231, pruned_loss=0.04102, over 971379.17 frames.], batch size: 12, lr: 3.97e-04
2022-05-05 03:37:30,440 INFO [train.py:715] (1/8) Epoch 5, batch 4700, loss[loss=0.1524, simple_loss=0.2159, pruned_loss=0.04445, over 4828.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2226, pruned_loss=0.04079, over 970998.70 frames.], batch size: 30, lr: 3.96e-04
2022-05-05 03:38:10,936 INFO [train.py:715] (1/8) Epoch 5, batch 4750, loss[loss=0.1806, simple_loss=0.2556, pruned_loss=0.05279, over 4815.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2235, pruned_loss=0.04145, over 972951.97 frames.], batch size: 25, lr: 3.96e-04
2022-05-05 03:38:50,698 INFO [train.py:715] (1/8) Epoch 5, batch 4800, loss[loss=0.1554, simple_loss=0.219, pruned_loss=0.04591, over 4777.00 frames.], tot_loss[loss=0.153, simple_loss=0.2234, pruned_loss=0.04136, over 972156.96 frames.], batch size: 14, lr: 3.96e-04
2022-05-05 03:39:31,188 INFO [train.py:715] (1/8) Epoch 5, batch 4850, loss[loss=0.1295, simple_loss=0.1977, pruned_loss=0.03064, over 4852.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2231, pruned_loss=0.04131, over 972670.98 frames.], batch size: 32, lr: 3.96e-04
2022-05-05 03:40:11,788 INFO [train.py:715] (1/8) Epoch 5, batch 4900, loss[loss=0.175, simple_loss=0.2318, pruned_loss=0.05912, over 4819.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2241, pruned_loss=0.04206, over 972701.33 frames.], batch size: 25, lr: 3.96e-04
2022-05-05 03:40:51,922 INFO [train.py:715] (1/8) Epoch 5, batch 4950, loss[loss=0.1809, simple_loss=0.2484, pruned_loss=0.05666, over 4744.00 frames.], tot_loss[loss=0.153, simple_loss=0.223, pruned_loss=0.0415, over 972442.02 frames.], batch size: 16, lr: 3.96e-04
2022-05-05 03:41:32,269 INFO [train.py:715] (1/8) Epoch 5, batch 5000, loss[loss=0.1468, simple_loss=0.2213, pruned_loss=0.03613, over 4840.00 frames.], tot_loss[loss=0.153, simple_loss=0.2231, pruned_loss=0.0415, over 972879.64 frames.], batch size: 15, lr: 3.96e-04
2022-05-05 03:42:13,234 INFO [train.py:715] (1/8) Epoch 5, batch 5050, loss[loss=0.1528, simple_loss=0.227, pruned_loss=0.03929, over 4689.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2227, pruned_loss=0.04113, over 973415.04 frames.], batch size: 15, lr: 3.96e-04
2022-05-05 03:42:52,858 INFO [train.py:715] (1/8) Epoch 5, batch 5100, loss[loss=0.1393, simple_loss=0.2061, pruned_loss=0.03628, over 4845.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2239, pruned_loss=0.04146, over 972234.99 frames.], batch size: 30, lr: 3.96e-04
2022-05-05 03:43:32,135 INFO [train.py:715] (1/8) Epoch 5, batch 5150, loss[loss=0.158, simple_loss=0.2285, pruned_loss=0.04377, over 4842.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2237, pruned_loss=0.04161, over 971143.77 frames.], batch size: 30, lr: 3.96e-04
2022-05-05 03:44:11,857 INFO [train.py:715] (1/8) Epoch 5, batch 5200, loss[loss=0.1445, simple_loss=0.2137, pruned_loss=0.03766, over 4914.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2235, pruned_loss=0.04144, over 970658.32 frames.], batch size: 23, lr: 3.96e-04
2022-05-05 03:44:51,644 INFO [train.py:715] (1/8) Epoch 5, batch 5250, loss[loss=0.1465, simple_loss=0.2151, pruned_loss=0.03893, over 4957.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2227, pruned_loss=0.04125, over 971111.45 frames.], batch size: 14, lr: 3.96e-04
2022-05-05 03:45:32,220 INFO [train.py:715] (1/8) Epoch 5, batch 5300, loss[loss=0.1437, simple_loss=0.2218, pruned_loss=0.03284, over 4832.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2233, pruned_loss=0.04182, over 971151.27 frames.], batch size: 13, lr: 3.96e-04
2022-05-05 03:46:12,528 INFO [train.py:715] (1/8) Epoch 5, batch 5350, loss[loss=0.1424, simple_loss=0.2106, pruned_loss=0.03709, over 4824.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2233, pruned_loss=0.04161, over 971871.85 frames.], batch size: 25, lr: 3.96e-04
2022-05-05 03:46:52,870 INFO [train.py:715] (1/8) Epoch 5, batch 5400, loss[loss=0.1583, simple_loss=0.2277, pruned_loss=0.04442, over 4728.00 frames.], tot_loss[loss=0.153, simple_loss=0.2228, pruned_loss=0.04163, over 971650.49 frames.], batch size: 12, lr: 3.96e-04
2022-05-05 03:47:32,590 INFO [train.py:715] (1/8) Epoch 5, batch 5450, loss[loss=0.135, simple_loss=0.2125, pruned_loss=0.02873, over 4792.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2226, pruned_loss=0.04111, over 971297.24 frames.], batch size: 24, lr: 3.96e-04
2022-05-05 03:48:12,697 INFO [train.py:715] (1/8) Epoch 5, batch 5500, loss[loss=0.1295, simple_loss=0.1918, pruned_loss=0.03356, over 4885.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2235, pruned_loss=0.0418, over 972033.08 frames.], batch size: 32, lr: 3.96e-04
2022-05-05 03:48:53,036 INFO [train.py:715] (1/8) Epoch 5, batch 5550, loss[loss=0.1365, simple_loss=0.2151, pruned_loss=0.02895, over 4815.00 frames.], tot_loss[loss=0.153, simple_loss=0.2231, pruned_loss=0.0415, over 972181.54 frames.], batch size: 15, lr: 3.96e-04
2022-05-05 03:49:33,414 INFO [train.py:715] (1/8) Epoch 5, batch 5600, loss[loss=0.1461, simple_loss=0.2125, pruned_loss=0.03985, over 4989.00 frames.], tot_loss[loss=0.154, simple_loss=0.2237, pruned_loss=0.04213, over 973136.02 frames.], batch size: 16, lr: 3.95e-04
2022-05-05 03:50:13,545 INFO [train.py:715] (1/8) Epoch 5, batch 5650, loss[loss=0.1634, simple_loss=0.2415, pruned_loss=0.0427, over 4905.00 frames.], tot_loss[loss=0.154, simple_loss=0.2241, pruned_loss=0.04189, over 972868.89 frames.], batch size: 19, lr: 3.95e-04
2022-05-05 03:50:52,904 INFO [train.py:715] (1/8) Epoch 5, batch 5700, loss[loss=0.1684, simple_loss=0.2347, pruned_loss=0.05102, over 4836.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2232, pruned_loss=0.04162, over 972887.99 frames.], batch size: 15, lr: 3.95e-04
2022-05-05 03:51:33,323 INFO [train.py:715] (1/8) Epoch 5, batch 5750, loss[loss=0.1586, simple_loss=0.2368, pruned_loss=0.04022, over 4898.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2227, pruned_loss=0.04132, over 973420.34 frames.], batch size: 19, lr: 3.95e-04
2022-05-05 03:52:13,232 INFO [train.py:715] (1/8) Epoch 5, batch 5800, loss[loss=0.2073, simple_loss=0.2771, pruned_loss=0.06878, over 4832.00 frames.], tot_loss[loss=0.153, simple_loss=0.2233, pruned_loss=0.0413, over 972403.99 frames.], batch size: 25, lr: 3.95e-04
2022-05-05 03:52:53,768 INFO [train.py:715] (1/8) Epoch 5, batch 5850, loss[loss=0.1665, simple_loss=0.2266, pruned_loss=0.05316, over 4697.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2227, pruned_loss=0.04104, over 971280.13 frames.], batch size: 15, lr: 3.95e-04
2022-05-05 03:53:33,396 INFO [train.py:715] (1/8) Epoch 5, batch 5900, loss[loss=0.1513, simple_loss=0.2255, pruned_loss=0.03862, over 4741.00 frames.], tot_loss[loss=0.152, simple_loss=0.2226, pruned_loss=0.0407, over 971753.41 frames.], batch size: 12, lr: 3.95e-04
2022-05-05 03:54:13,790 INFO [train.py:715] (1/8) Epoch 5, batch 5950, loss[loss=0.1648, simple_loss=0.24, pruned_loss=0.04478, over 4833.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2224, pruned_loss=0.0409, over 972047.50 frames.], batch size: 26, lr: 3.95e-04
2022-05-05 03:54:53,625 INFO [train.py:715] (1/8) Epoch 5, batch 6000, loss[loss=0.1753, simple_loss=0.2349, pruned_loss=0.05788, over 4775.00 frames.], tot_loss[loss=0.153, simple_loss=0.2233, pruned_loss=0.04132, over 971964.89 frames.], batch size: 18, lr: 3.95e-04
2022-05-05 03:54:53,625 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 03:55:03,072 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1106, simple_loss=0.1959, pruned_loss=0.01263, over 914524.00 frames.
2022-05-05 03:55:42,942 INFO [train.py:715] (1/8) Epoch 5, batch 6050, loss[loss=0.1778, simple_loss=0.236, pruned_loss=0.05978, over 4845.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2222, pruned_loss=0.04114, over 972414.75 frames.], batch size: 30, lr: 3.95e-04
2022-05-05 03:56:22,021 INFO [train.py:715] (1/8) Epoch 5, batch 6100, loss[loss=0.1661, simple_loss=0.2316, pruned_loss=0.05035, over 4913.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2234, pruned_loss=0.04189, over 973081.44 frames.], batch size: 23, lr: 3.95e-04
2022-05-05 03:57:01,854 INFO [train.py:715] (1/8) Epoch 5, batch 6150, loss[loss=0.1533, simple_loss=0.2277, pruned_loss=0.03948, over 4954.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2232, pruned_loss=0.04112, over 973419.83 frames.], batch size: 24, lr: 3.95e-04
2022-05-05 03:57:40,840 INFO [train.py:715] (1/8) Epoch 5, batch 6200, loss[loss=0.1336, simple_loss=0.2035, pruned_loss=0.03181, over 4986.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2228, pruned_loss=0.04086, over 972866.42 frames.], batch size: 14, lr: 3.95e-04
2022-05-05 03:58:21,088 INFO [train.py:715] (1/8) Epoch 5, batch 6250, loss[loss=0.1541, simple_loss=0.2276, pruned_loss=0.04035, over 4934.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2221, pruned_loss=0.0406, over 972620.81 frames.], batch size: 39, lr: 3.95e-04
2022-05-05 03:58:59,732 INFO [train.py:715] (1/8) Epoch 5, batch 6300, loss[loss=0.1728, simple_loss=0.2478, pruned_loss=0.0489, over 4774.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2221, pruned_loss=0.04103, over 971362.95 frames.], batch size: 18, lr: 3.95e-04
2022-05-05 03:59:39,538 INFO [train.py:715] (1/8) Epoch 5, batch 6350, loss[loss=0.1434, simple_loss=0.2094, pruned_loss=0.03867, over 4813.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2227, pruned_loss=0.04141, over 970779.61 frames.], batch size: 25, lr: 3.95e-04
2022-05-05 04:00:18,909 INFO [train.py:715] (1/8) Epoch 5, batch 6400, loss[loss=0.1512, simple_loss=0.2202, pruned_loss=0.04107, over 4979.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2223, pruned_loss=0.04167, over 970829.16 frames.], batch size: 35, lr: 3.95e-04
2022-05-05 04:00:57,773 INFO [train.py:715] (1/8) Epoch 5, batch 6450, loss[loss=0.1536, simple_loss=0.2277, pruned_loss=0.03971, over 4863.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2226, pruned_loss=0.04187, over 971259.34 frames.], batch size: 20, lr: 3.95e-04
2022-05-05 04:01:37,237 INFO [train.py:715] (1/8) Epoch 5, batch 6500, loss[loss=0.1166, simple_loss=0.1909, pruned_loss=0.02117, over 4925.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2232, pruned_loss=0.04205, over 970369.96 frames.], batch size: 23, lr: 3.95e-04
2022-05-05 04:02:16,586 INFO [train.py:715] (1/8) Epoch 5, batch 6550, loss[loss=0.1863, simple_loss=0.2566, pruned_loss=0.05802, over 4890.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2235, pruned_loss=0.04206, over 970773.63 frames.], batch size: 17, lr: 3.94e-04
2022-05-05 04:02:55,730 INFO [train.py:715] (1/8) Epoch 5, batch 6600, loss[loss=0.1485, simple_loss=0.2268, pruned_loss=0.03511, over 4908.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2246, pruned_loss=0.04248, over 971418.37 frames.], batch size: 22, lr: 3.94e-04
2022-05-05 04:03:35,253 INFO [train.py:715] (1/8) Epoch 5, batch 6650, loss[loss=0.1301, simple_loss=0.2058, pruned_loss=0.02719, over 4782.00 frames.], tot_loss[loss=0.154, simple_loss=0.2241, pruned_loss=0.04193, over 971834.68 frames.], batch size: 14, lr: 3.94e-04
2022-05-05 04:04:15,808 INFO [train.py:715] (1/8) Epoch 5, batch 6700, loss[loss=0.1618, simple_loss=0.2283, pruned_loss=0.04767, over 4899.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2238, pruned_loss=0.04201, over 971910.52 frames.], batch size: 19, lr: 3.94e-04
2022-05-05 04:04:56,139 INFO [train.py:715] (1/8) Epoch 5, batch 6750, loss[loss=0.1355, simple_loss=0.2013, pruned_loss=0.03488, over 4904.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2248, pruned_loss=0.04233, over 971935.28 frames.], batch size: 17, lr: 3.94e-04
2022-05-05 04:05:36,127 INFO [train.py:715] (1/8) Epoch 5, batch 6800, loss[loss=0.1276, simple_loss=0.2033, pruned_loss=0.02597, over 4934.00 frames.], tot_loss[loss=0.154, simple_loss=0.224, pruned_loss=0.04196, over 971540.49 frames.], batch size: 29, lr: 3.94e-04
2022-05-05 04:06:16,613 INFO [train.py:715] (1/8) Epoch 5, batch 6850, loss[loss=0.1563, simple_loss=0.2387, pruned_loss=0.03698, over 4799.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2244, pruned_loss=0.04215, over 972791.14 frames.], batch size: 21, lr: 3.94e-04
2022-05-05 04:06:56,568 INFO [train.py:715] (1/8) Epoch 5, batch 6900, loss[loss=0.1543, simple_loss=0.2163, pruned_loss=0.04619, over 4863.00 frames.], tot_loss[loss=0.154, simple_loss=0.2241, pruned_loss=0.042, over 972214.26 frames.], batch size: 20, lr: 3.94e-04
2022-05-05 04:07:37,132 INFO [train.py:715] (1/8) Epoch 5, batch 6950, loss[loss=0.1419, simple_loss=0.2239, pruned_loss=0.02998, over 4956.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2235, pruned_loss=0.04157, over 972126.18 frames.], batch size: 15, lr: 3.94e-04
2022-05-05 04:08:16,572 INFO [train.py:715] (1/8) Epoch 5, batch 7000, loss[loss=0.1205, simple_loss=0.1915, pruned_loss=0.02476, over 4647.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2241, pruned_loss=0.04176, over 971698.98 frames.], batch size: 13, lr: 3.94e-04
2022-05-05 04:08:56,469 INFO [train.py:715] (1/8) Epoch 5, batch 7050, loss[loss=0.1492, simple_loss=0.2195, pruned_loss=0.03948, over 4981.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.041, over 972245.33 frames.], batch size: 24, lr: 3.94e-04
2022-05-05 04:09:36,256 INFO [train.py:715] (1/8) Epoch 5, batch 7100, loss[loss=0.1221, simple_loss=0.1979, pruned_loss=0.02318, over 4982.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2226, pruned_loss=0.04077, over 972359.74 frames.], batch size: 15, lr: 3.94e-04
2022-05-05 04:10:15,692 INFO [train.py:715] (1/8) Epoch 5, batch 7150, loss[loss=0.1591, simple_loss=0.2284, pruned_loss=0.04496, over 4689.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04056, over 971686.99 frames.], batch size: 15, lr: 3.94e-04
2022-05-05 04:10:55,647 INFO [train.py:715] (1/8) Epoch 5, batch 7200, loss[loss=0.1711, simple_loss=0.2473, pruned_loss=0.04746, over 4826.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2216, pruned_loss=0.04024, over 971803.29 frames.], batch size: 25, lr: 3.94e-04
2022-05-05 04:11:35,249 INFO [train.py:715] (1/8) Epoch 5, batch 7250, loss[loss=0.1698, simple_loss=0.2358, pruned_loss=0.05188, over 4962.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2221, pruned_loss=0.04034, over 971245.40 frames.], batch size: 15, lr: 3.94e-04
2022-05-05 04:12:15,760 INFO [train.py:715] (1/8) Epoch 5, batch 7300, loss[loss=0.1625, simple_loss=0.2172, pruned_loss=0.05386, over 4916.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2225, pruned_loss=0.04116, over 972308.96 frames.], batch size: 18, lr: 3.94e-04
2022-05-05 04:12:55,318 INFO [train.py:715] (1/8) Epoch 5, batch 7350, loss[loss=0.1427, simple_loss=0.2161, pruned_loss=0.03467, over 4958.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2222, pruned_loss=0.04077, over 972384.77 frames.], batch size: 23, lr: 3.94e-04
2022-05-05 04:13:34,921 INFO [train.py:715] (1/8) Epoch 5, batch 7400, loss[loss=0.1532, simple_loss=0.2226, pruned_loss=0.04187, over 4642.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2226, pruned_loss=0.041, over 972215.87 frames.], batch size: 13, lr: 3.94e-04
2022-05-05 04:14:14,467 INFO [train.py:715] (1/8) Epoch 5, batch 7450, loss[loss=0.1665, simple_loss=0.2341, pruned_loss=0.04947, over 4840.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2226, pruned_loss=0.04076, over 972029.39 frames.], batch size: 30, lr: 3.93e-04
2022-05-05 04:14:53,552 INFO [train.py:715] (1/8) Epoch 5, batch 7500, loss[loss=0.1707, simple_loss=0.2446, pruned_loss=0.04841, over 4917.00 frames.], tot_loss[loss=0.153, simple_loss=0.2232, pruned_loss=0.0414, over 972311.79 frames.], batch size: 29, lr: 3.93e-04
2022-05-05 04:15:33,691 INFO [train.py:715] (1/8) Epoch 5, batch 7550, loss[loss=0.1987, simple_loss=0.253, pruned_loss=0.07221, over 4810.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2221, pruned_loss=0.04125, over 972235.87 frames.], batch size: 15, lr: 3.93e-04
2022-05-05 04:16:13,356 INFO [train.py:715] (1/8) Epoch 5, batch 7600, loss[loss=0.1368, simple_loss=0.2125, pruned_loss=0.03059, over 4823.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2215, pruned_loss=0.04102, over 971796.03 frames.], batch size: 13, lr: 3.93e-04
2022-05-05 04:16:53,611 INFO [train.py:715] (1/8) Epoch 5, batch 7650, loss[loss=0.1367, simple_loss=0.2039, pruned_loss=0.03476, over 4944.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2217, pruned_loss=0.04095, over 972003.13 frames.], batch size: 21, lr: 3.93e-04
2022-05-05 04:17:33,268 INFO [train.py:715] (1/8) Epoch 5, batch 7700, loss[loss=0.1449, simple_loss=0.2151, pruned_loss=0.0373, over 4955.00 frames.], tot_loss[loss=0.152, simple_loss=0.2222, pruned_loss=0.04085, over 971861.88 frames.], batch size: 39, lr: 3.93e-04
2022-05-05 04:18:12,783 INFO [train.py:715] (1/8) Epoch 5, batch 7750, loss[loss=0.1611, simple_loss=0.2227, pruned_loss=0.04976, over 4776.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2235, pruned_loss=0.04165, over 972248.53 frames.], batch size: 14, lr: 3.93e-04
2022-05-05 04:18:52,932 INFO [train.py:715] (1/8) Epoch 5, batch 7800, loss[loss=0.1767, simple_loss=0.2481, pruned_loss=0.05263, over 4763.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2244, pruned_loss=0.0416, over 971936.44 frames.], batch size: 14, lr: 3.93e-04
2022-05-05 04:19:32,135 INFO [train.py:715] (1/8) Epoch 5, batch 7850, loss[loss=0.1548, simple_loss=0.2277, pruned_loss=0.041, over 4964.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2244, pruned_loss=0.04141, over 971927.63 frames.], batch size: 24, lr: 3.93e-04
2022-05-05 04:20:12,363 INFO [train.py:715] (1/8) Epoch 5, batch 7900, loss[loss=0.1593, simple_loss=0.2346, pruned_loss=0.04196, over 4989.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2244, pruned_loss=0.04167, over 971900.15 frames.], batch size: 25, lr: 3.93e-04
2022-05-05 04:20:51,913 INFO [train.py:715] (1/8) Epoch 5, batch 7950, loss[loss=0.1674, simple_loss=0.2385, pruned_loss=0.04808, over 4707.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2242, pruned_loss=0.04206, over 972279.86 frames.], batch size: 15, lr: 3.93e-04
2022-05-05 04:21:32,122 INFO [train.py:715] (1/8) Epoch 5, batch 8000, loss[loss=0.15, simple_loss=0.2244, pruned_loss=0.03777, over 4819.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2247, pruned_loss=0.04229, over 972111.19 frames.], batch size: 25, lr: 3.93e-04
2022-05-05 04:22:11,573 INFO [train.py:715] (1/8) Epoch 5, batch 8050, loss[loss=0.1397, simple_loss=0.2005, pruned_loss=0.03951, over 4822.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2248, pruned_loss=0.04223, over 972169.00 frames.], batch size: 13, lr: 3.93e-04
2022-05-05 04:22:51,024 INFO [train.py:715] (1/8) Epoch 5, batch 8100, loss[loss=0.1561, simple_loss=0.2192, pruned_loss=0.04646, over 4965.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2245, pruned_loss=0.04218, over 972415.38 frames.], batch size: 35, lr: 3.93e-04
2022-05-05 04:23:30,816 INFO [train.py:715] (1/8) Epoch 5, batch 8150, loss[loss=0.1616, simple_loss=0.2222, pruned_loss=0.05048, over 4774.00 frames.], tot_loss[loss=0.154, simple_loss=0.2243, pruned_loss=0.04181, over 972426.20 frames.], batch size: 18, lr: 3.93e-04
2022-05-05 04:24:10,001 INFO [train.py:715] (1/8) Epoch 5, batch 8200, loss[loss=0.1869, simple_loss=0.2555, pruned_loss=0.05908, over 4888.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2241, pruned_loss=0.04206, over 972540.46 frames.], batch size: 22, lr: 3.93e-04
2022-05-05 04:24:50,017 INFO [train.py:715] (1/8) Epoch 5, batch 8250, loss[loss=0.1563, simple_loss=0.2312, pruned_loss=0.04065, over 4953.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2238, pruned_loss=0.04195, over 972300.17 frames.], batch size: 21, lr: 3.93e-04
2022-05-05 04:25:29,488 INFO [train.py:715] (1/8) Epoch 5, batch 8300, loss[loss=0.1213, simple_loss=0.1887, pruned_loss=0.02696, over 4871.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2241, pruned_loss=0.04179, over 972393.48 frames.], batch size: 32, lr: 3.93e-04
2022-05-05 04:26:09,430 INFO [train.py:715] (1/8) Epoch 5, batch 8350, loss[loss=0.133, simple_loss=0.2056, pruned_loss=0.03025, over 4857.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2239, pruned_loss=0.04123, over 972734.61 frames.], batch size: 16, lr: 3.93e-04
2022-05-05 04:26:48,501 INFO [train.py:715] (1/8) Epoch 5, batch 8400, loss[loss=0.1507, simple_loss=0.2301, pruned_loss=0.0357, over 4830.00 frames.], tot_loss[loss=0.153, simple_loss=0.2236, pruned_loss=0.04119, over 973187.98 frames.], batch size: 25, lr: 3.92e-04
2022-05-05 04:27:27,555 INFO [train.py:715] (1/8) Epoch 5, batch 8450, loss[loss=0.1672, simple_loss=0.2327, pruned_loss=0.05086, over 4777.00 frames.], tot_loss[loss=0.1535, simple_loss=0.224, pruned_loss=0.04149, over 972784.13 frames.], batch size: 17, lr: 3.92e-04
2022-05-05 04:28:06,819 INFO [train.py:715] (1/8) Epoch 5, batch 8500, loss[loss=0.1336, simple_loss=0.2151, pruned_loss=0.026, over 4933.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2229, pruned_loss=0.04109, over 973334.78 frames.], batch size: 21, lr: 3.92e-04
2022-05-05 04:28:45,803 INFO [train.py:715] (1/8) Epoch 5, batch 8550, loss[loss=0.149, simple_loss=0.2283, pruned_loss=0.0349, over 4922.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2219, pruned_loss=0.0406, over 973358.38 frames.], batch size: 29, lr: 3.92e-04
2022-05-05 04:29:25,248 INFO [train.py:715] (1/8) Epoch 5, batch 8600, loss[loss=0.1425, simple_loss=0.2033, pruned_loss=0.04085, over 4690.00 frames.], tot_loss[loss=0.1507, simple_loss=0.221, pruned_loss=0.04019, over 972740.30 frames.], batch size: 15, lr: 3.92e-04
2022-05-05 04:30:04,412 INFO [train.py:715] (1/8) Epoch 5, batch 8650, loss[loss=0.16, simple_loss=0.236, pruned_loss=0.04205, over 4989.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2211, pruned_loss=0.04027, over 972543.28 frames.], batch size: 15, lr: 3.92e-04
2022-05-05 04:30:43,891 INFO [train.py:715] (1/8) Epoch 5, batch 8700, loss[loss=0.1676, simple_loss=0.2312, pruned_loss=0.05199, over 4703.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2223, pruned_loss=0.04096, over 973102.21 frames.], batch size: 15, lr: 3.92e-04
2022-05-05 04:31:23,276 INFO [train.py:715] (1/8) Epoch 5, batch 8750, loss[loss=0.1393, simple_loss=0.2088, pruned_loss=0.03493, over 4765.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2224, pruned_loss=0.04096, over 972996.58 frames.], batch size: 12, lr: 3.92e-04
2022-05-05 04:32:02,285 INFO [train.py:715] (1/8) Epoch 5, batch 8800, loss[loss=0.1741, simple_loss=0.2396, pruned_loss=0.05429, over 4866.00 frames.], tot_loss[loss=0.1525, simple_loss=0.223, pruned_loss=0.04101, over 973123.69 frames.], batch size: 20, lr: 3.92e-04
2022-05-05 04:32:42,169 INFO [train.py:715] (1/8) Epoch 5, batch 8850, loss[loss=0.143, simple_loss=0.2158, pruned_loss=0.0351, over 4970.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2238, pruned_loss=0.04163, over 972951.76 frames.], batch size: 15, lr: 3.92e-04
2022-05-05 04:33:20,889 INFO [train.py:715] (1/8) Epoch 5, batch 8900, loss[loss=0.137, simple_loss=0.2048, pruned_loss=0.03462, over 4905.00 frames.], tot_loss[loss=0.153, simple_loss=0.2229, pruned_loss=0.04155, over 973165.34 frames.], batch size: 23, lr: 3.92e-04
2022-05-05 04:33:59,746 INFO [train.py:715] (1/8) Epoch 5, batch 8950, loss[loss=0.1842, simple_loss=0.2448, pruned_loss=0.06181, over 4951.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2236, pruned_loss=0.04187, over 973673.09 frames.], batch size: 15, lr: 3.92e-04
2022-05-05 04:34:39,035 INFO [train.py:715] (1/8) Epoch 5, batch 9000, loss[loss=0.1466, simple_loss=0.2173, pruned_loss=0.03793, over 4935.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2243, pruned_loss=0.04246, over 974521.55 frames.], batch size: 21, lr: 3.92e-04
2022-05-05 04:34:39,036 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 04:34:48,553 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1105, simple_loss=0.196, pruned_loss=0.01252, over 914524.00 frames.
2022-05-05 04:35:28,200 INFO [train.py:715] (1/8) Epoch 5, batch 9050, loss[loss=0.1558, simple_loss=0.2277, pruned_loss=0.04192, over 4959.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2239, pruned_loss=0.04195, over 972574.19 frames.], batch size: 24, lr: 3.92e-04
2022-05-05 04:36:07,677 INFO [train.py:715] (1/8) Epoch 5, batch 9100, loss[loss=0.1284, simple_loss=0.1825, pruned_loss=0.03716, over 4801.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2241, pruned_loss=0.04226, over 971611.15 frames.], batch size: 12, lr: 3.92e-04
2022-05-05 04:36:46,718 INFO [train.py:715] (1/8) Epoch 5, batch 9150, loss[loss=0.167, simple_loss=0.2371, pruned_loss=0.04845, over 4876.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2236, pruned_loss=0.04214, over 971824.75 frames.], batch size: 38, lr: 3.92e-04
2022-05-05 04:37:26,209 INFO [train.py:715] (1/8) Epoch 5, batch 9200, loss[loss=0.1583, simple_loss=0.2291, pruned_loss=0.04378, over 4875.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2229, pruned_loss=0.04203, over 971993.05 frames.], batch size: 16, lr: 3.92e-04
2022-05-05 04:38:06,422 INFO [train.py:715] (1/8) Epoch 5, batch 9250, loss[loss=0.1356, simple_loss=0.2242, pruned_loss=0.02355, over 4802.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2239, pruned_loss=0.04175, over 972981.40 frames.], batch size: 21, lr: 3.92e-04
2022-05-05 04:38:45,295 INFO [train.py:715] (1/8) Epoch 5, batch 9300, loss[loss=0.1372, simple_loss=0.2147, pruned_loss=0.02991, over 4810.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2228, pruned_loss=0.0414, over 972067.25 frames.], batch size: 26, lr: 3.91e-04
2022-05-05 04:39:24,935 INFO [train.py:715] (1/8) Epoch 5, batch 9350, loss[loss=0.1226, simple_loss=0.1927, pruned_loss=0.02627, over 4665.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2226, pruned_loss=0.04101, over 970808.11 frames.], batch size: 13, lr: 3.91e-04
2022-05-05 04:40:04,426 INFO [train.py:715] (1/8) Epoch 5, batch 9400, loss[loss=0.1216, simple_loss=0.1902, pruned_loss=0.02653, over 4839.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2229, pruned_loss=0.04129, over 971257.04 frames.], batch size: 26, lr: 3.91e-04
2022-05-05 04:40:43,714 INFO [train.py:715] (1/8) Epoch 5, batch 9450, loss[loss=0.1581, simple_loss=0.2389, pruned_loss=0.03871, over 4991.00 frames.], tot_loss[loss=0.1521, simple_loss=0.223, pruned_loss=0.04066, over 971873.09 frames.], batch size: 25, lr: 3.91e-04
2022-05-05 04:41:22,597 INFO [train.py:715] (1/8) Epoch 5, batch 9500, loss[loss=0.1097, simple_loss=0.1798, pruned_loss=0.01978, over 4738.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03981, over 972832.04 frames.], batch size: 16, lr: 3.91e-04
2022-05-05 04:42:02,159 INFO [train.py:715] (1/8) Epoch 5, batch 9550, loss[loss=0.1634, simple_loss=0.2257, pruned_loss=0.05059, over 4826.00 frames.], tot_loss[loss=0.1511, simple_loss=0.222, pruned_loss=0.04008, over 972698.09 frames.], batch size: 15, lr: 3.91e-04
2022-05-05 04:42:41,923 INFO [train.py:715] (1/8) Epoch 5, batch 9600, loss[loss=0.1514, simple_loss=0.2283, pruned_loss=0.03723, over 4961.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.04051, over 973535.01 frames.], batch size: 21, lr: 3.91e-04
2022-05-05 04:43:21,159 INFO [train.py:715] (1/8) Epoch 5, batch 9650, loss[loss=0.1523, simple_loss=0.2219, pruned_loss=0.04138, over 4988.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2223, pruned_loss=0.0405, over 973112.21 frames.], batch size: 25, lr: 3.91e-04
2022-05-05 04:44:00,815 INFO [train.py:715] (1/8) Epoch 5, batch 9700, loss[loss=0.1748, simple_loss=0.2368, pruned_loss=0.05643, over 4916.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04056, over 972790.03 frames.], batch size: 18, lr: 3.91e-04
2022-05-05 04:44:40,236 INFO [train.py:715] (1/8) Epoch 5, batch 9750, loss[loss=0.1346, simple_loss=0.2046, pruned_loss=0.03226, over 4895.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2224, pruned_loss=0.04028, over 972006.59 frames.], batch size: 22, lr: 3.91e-04
2022-05-05 04:45:19,139 INFO [train.py:715] (1/8) Epoch 5, batch 9800, loss[loss=0.1261, simple_loss=0.2029, pruned_loss=0.02468, over 4986.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2234, pruned_loss=0.04072, over 971882.75 frames.], batch size: 28, lr: 3.91e-04
2022-05-05 04:45:58,982 INFO [train.py:715] (1/8) Epoch 5, batch 9850, loss[loss=0.1697, simple_loss=0.2279, pruned_loss=0.05573, over 4861.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2224, pruned_loss=0.04029, over 972567.61 frames.], batch size: 20, lr: 3.91e-04
2022-05-05 04:46:38,177 INFO [train.py:715] (1/8) Epoch 5, batch 9900, loss[loss=0.1151, simple_loss=0.1869, pruned_loss=0.02164, over 4782.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2219, pruned_loss=0.03988, over 972253.60 frames.], batch size: 17, lr: 3.91e-04
2022-05-05 04:47:17,943 INFO [train.py:715] (1/8) Epoch 5, batch 9950, loss[loss=0.1472, simple_loss=0.2106, pruned_loss=0.04193, over 4853.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04058, over 972481.55 frames.], batch size: 30, lr: 3.91e-04
2022-05-05 04:47:59,855 INFO [train.py:715] (1/8) Epoch 5, batch 10000, loss[loss=0.1204, simple_loss=0.1919, pruned_loss=0.0245, over 4900.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2225, pruned_loss=0.04091, over 972962.64 frames.], batch size: 19, lr: 3.91e-04
2022-05-05 04:48:39,808 INFO [train.py:715] (1/8) Epoch 5, batch 10050, loss[loss=0.1421, simple_loss=0.2133, pruned_loss=0.03551, over 4748.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04055, over 972215.89 frames.], batch size: 16, lr: 3.91e-04
2022-05-05 04:49:19,420 INFO [train.py:715] (1/8) Epoch 5, batch 10100, loss[loss=0.1508, simple_loss=0.2286, pruned_loss=0.03653, over 4857.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2224, pruned_loss=0.04054, over 971861.22 frames.], batch size: 38, lr: 3.91e-04
2022-05-05 04:49:58,586 INFO [train.py:715] (1/8) Epoch 5, batch 10150, loss[loss=0.1463, simple_loss=0.2145, pruned_loss=0.03908, over 4835.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2222, pruned_loss=0.04078, over 972426.07 frames.], batch size: 13, lr: 3.91e-04
2022-05-05 04:50:38,458 INFO [train.py:715] (1/8) Epoch 5, batch 10200, loss[loss=0.1715, simple_loss=0.2494, pruned_loss=0.04681, over 4899.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2219, pruned_loss=0.04087, over 972158.50 frames.], batch size: 18, lr: 3.91e-04
2022-05-05 04:51:17,796 INFO [train.py:715] (1/8) Epoch 5, batch 10250, loss[loss=0.1516, simple_loss=0.2125, pruned_loss=0.04537, over 4873.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2234, pruned_loss=0.0416, over 972502.52 frames.], batch size: 22, lr: 3.90e-04
2022-05-05 04:51:56,807 INFO [train.py:715] (1/8) Epoch 5, batch 10300, loss[loss=0.16, simple_loss=0.2275, pruned_loss=0.04627, over 4809.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2223, pruned_loss=0.04095, over 971434.17 frames.], batch size: 25, lr: 3.90e-04
2022-05-05 04:52:36,630 INFO [train.py:715] (1/8) Epoch 5, batch 10350, loss[loss=0.1504, simple_loss=0.2301, pruned_loss=0.03538, over 4800.00 frames.], tot_loss[loss=0.1517, simple_loss=0.222, pruned_loss=0.04072, over 971510.25 frames.], batch size: 14, lr: 3.90e-04
2022-05-05 04:53:15,669 INFO [train.py:715] (1/8) Epoch 5, batch 10400, loss[loss=0.1548, simple_loss=0.2289, pruned_loss=0.04037, over 4774.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.04102, over 971812.37 frames.], batch size: 18, lr: 3.90e-04
2022-05-05 04:53:55,617 INFO [train.py:715] (1/8) Epoch 5, batch 10450, loss[loss=0.1683, simple_loss=0.2318, pruned_loss=0.05238, over 4866.00 frames.], tot_loss[loss=0.153, simple_loss=0.2233, pruned_loss=0.04138, over 972019.19 frames.], batch size: 32, lr: 3.90e-04
2022-05-05 04:54:35,518 INFO [train.py:715] (1/8) Epoch 5, batch 10500, loss[loss=0.1129, simple_loss=0.1954, pruned_loss=0.01517, over 4823.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2228, pruned_loss=0.04136, over 971889.43 frames.], batch size: 26, lr: 3.90e-04
2022-05-05 04:55:15,982 INFO [train.py:715] (1/8) Epoch 5, batch 10550, loss[loss=0.1447, simple_loss=0.2105, pruned_loss=0.03946, over 4913.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2232, pruned_loss=0.04122, over 971369.93 frames.], batch size: 17, lr: 3.90e-04
2022-05-05 04:55:55,076 INFO [train.py:715] (1/8) Epoch 5, batch 10600, loss[loss=0.153, simple_loss=0.2112, pruned_loss=0.04746, over 4880.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2232, pruned_loss=0.04102, over 972208.10 frames.], batch size: 16, lr: 3.90e-04
2022-05-05 04:56:34,543 INFO [train.py:715] (1/8) Epoch 5, batch 10650, loss[loss=0.1736, simple_loss=0.2515, pruned_loss=0.04788, over 4826.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2241, pruned_loss=0.04168, over 972113.33 frames.], batch size: 25, lr: 3.90e-04
2022-05-05 04:57:14,070 INFO [train.py:715] (1/8) Epoch 5, batch 10700, loss[loss=0.1712, simple_loss=0.2385, pruned_loss=0.05197, over 4776.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2233, pruned_loss=0.04129, over 971015.66 frames.], batch size: 18, lr: 3.90e-04
2022-05-05 04:57:53,027 INFO [train.py:715] (1/8) Epoch 5, batch 10750, loss[loss=0.1563, simple_loss=0.2406, pruned_loss=0.03598, over 4901.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2241, pruned_loss=0.04181, over 971203.74 frames.], batch size: 19, lr: 3.90e-04
2022-05-05 04:58:32,280 INFO [train.py:715] (1/8) Epoch 5, batch 10800, loss[loss=0.1444, simple_loss=0.2066, pruned_loss=0.0411, over 4984.00 frames.], tot_loss[loss=0.153, simple_loss=0.223, pruned_loss=0.04154, over 971344.55 frames.], batch size: 15, lr: 3.90e-04
2022-05-05 04:59:11,509 INFO [train.py:715] (1/8) Epoch 5, batch 10850, loss[loss=0.1895, simple_loss=0.2515, pruned_loss=0.0638, over 4971.00 frames.], tot_loss[loss=0.153, simple_loss=0.2231, pruned_loss=0.04145, over 971973.32 frames.], batch size: 35, lr: 3.90e-04
2022-05-05 04:59:51,503 INFO [train.py:715] (1/8) Epoch 5, batch 10900, loss[loss=0.1829, simple_loss=0.2639, pruned_loss=0.05098, over 4775.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2237, pruned_loss=0.04132, over 972488.09 frames.], batch size: 14, lr: 3.90e-04
2022-05-05 05:00:30,698 INFO [train.py:715] (1/8) Epoch 5, batch 10950, loss[loss=0.1456, simple_loss=0.222, pruned_loss=0.03458, over 4898.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2239, pruned_loss=0.04125, over 972382.68 frames.], batch size: 17, lr: 3.90e-04
2022-05-05 05:01:10,473 INFO [train.py:715] (1/8) Epoch 5, batch 11000, loss[loss=0.1641, simple_loss=0.2449, pruned_loss=0.04171, over 4816.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2227, pruned_loss=0.04078, over 972451.71 frames.], batch size: 26, lr: 3.90e-04
2022-05-05 05:01:49,964 INFO [train.py:715] (1/8) Epoch 5, batch 11050, loss[loss=0.1377, simple_loss=0.2112, pruned_loss=0.03207, over 4816.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2223, pruned_loss=0.04072, over 972064.78 frames.], batch size: 27, lr: 3.90e-04
2022-05-05 05:02:29,391 INFO [train.py:715] (1/8) Epoch 5, batch 11100, loss[loss=0.1266, simple_loss=0.1989, pruned_loss=0.02717, over 4749.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2219, pruned_loss=0.04062, over 972731.85 frames.], batch size: 16, lr: 3.90e-04
2022-05-05 05:03:08,932 INFO [train.py:715] (1/8) Epoch 5, batch 11150, loss[loss=0.1621, simple_loss=0.2432, pruned_loss=0.0405, over 4969.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2225, pruned_loss=0.04081, over 972600.58 frames.], batch size: 24, lr: 3.90e-04
2022-05-05 05:03:48,023 INFO [train.py:715] (1/8) Epoch 5, batch 11200, loss[loss=0.1891, simple_loss=0.2536, pruned_loss=0.06229, over 4856.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2229, pruned_loss=0.04116, over 971821.08 frames.], batch size: 32, lr: 3.89e-04
2022-05-05 05:04:27,937 INFO [train.py:715] (1/8) Epoch 5, batch 11250, loss[loss=0.1468, simple_loss=0.2188, pruned_loss=0.03736, over 4768.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2232, pruned_loss=0.04121, over 972338.48 frames.], batch size: 19, lr: 3.89e-04
2022-05-05 05:05:07,260 INFO [train.py:715] (1/8) Epoch 5, batch 11300, loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03067, over 4821.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2239, pruned_loss=0.04126, over 971706.52 frames.], batch size: 12, lr: 3.89e-04
2022-05-05 05:05:46,399 INFO [train.py:715] (1/8) Epoch 5, batch 11350, loss[loss=0.1404, simple_loss=0.2138, pruned_loss=0.03351, over 4923.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2237, pruned_loss=0.0409, over 971201.90 frames.], batch size: 23, lr: 3.89e-04
2022-05-05 05:06:27,202 INFO [train.py:715] (1/8) Epoch 5, batch 11400, loss[loss=0.1566, simple_loss=0.2226, pruned_loss=0.04524, over 4841.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2236, pruned_loss=0.04095, over 971175.69 frames.], batch size: 30, lr: 3.89e-04
2022-05-05 05:07:07,359 INFO [train.py:715] (1/8) Epoch 5, batch 11450, loss[loss=0.1632, simple_loss=0.2354, pruned_loss=0.04552, over 4988.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2234, pruned_loss=0.04064, over 971626.15 frames.], batch size: 28, lr: 3.89e-04
2022-05-05 05:07:47,394 INFO [train.py:715] (1/8) Epoch 5, batch 11500, loss[loss=0.1378, simple_loss=0.2059, pruned_loss=0.03484, over 4856.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2237, pruned_loss=0.04091, over 971541.84 frames.], batch size: 32, lr: 3.89e-04
2022-05-05 05:08:27,422 INFO [train.py:715] (1/8) Epoch 5, batch 11550, loss[loss=0.1513, simple_loss=0.2146, pruned_loss=0.04396, over 4753.00 frames.], tot_loss[loss=0.153, simple_loss=0.2235, pruned_loss=0.0412, over 972650.85 frames.], batch size: 19, lr: 3.89e-04
2022-05-05 05:09:07,601 INFO [train.py:715] (1/8) Epoch 5, batch 11600, loss[loss=0.1599, simple_loss=0.2349, pruned_loss=0.04245, over 4803.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2233, pruned_loss=0.04067, over 972868.72 frames.], batch size: 21, lr: 3.89e-04
2022-05-05 05:09:48,306 INFO [train.py:715] (1/8) Epoch 5, batch 11650, loss[loss=0.1261, simple_loss=0.2066, pruned_loss=0.02277, over 4929.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2221, pruned_loss=0.04043, over 972237.82 frames.], batch size: 21, lr: 3.89e-04
2022-05-05 05:10:28,063 INFO [train.py:715] (1/8) Epoch 5, batch 11700, loss[loss=0.1505, simple_loss=0.2238, pruned_loss=0.0386, over 4788.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2217, pruned_loss=0.04049, over 971950.21 frames.], batch size: 18, lr: 3.89e-04
2022-05-05 05:11:08,778 INFO [train.py:715] (1/8) Epoch 5, batch 11750, loss[loss=0.1396, simple_loss=0.2185, pruned_loss=0.0303, over 4837.00 frames.], tot_loss[loss=0.1516, simple_loss=0.222, pruned_loss=0.04062, over 972257.40 frames.], batch size: 25, lr: 3.89e-04
2022-05-05 05:11:48,922 INFO [train.py:715] (1/8) Epoch 5, batch 11800, loss[loss=0.1562, simple_loss=0.2288, pruned_loss=0.04179, over 4922.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2217, pruned_loss=0.04072, over 972246.77 frames.], batch size: 29, lr: 3.89e-04
2022-05-05 05:12:29,043 INFO [train.py:715] (1/8) Epoch 5, batch 11850, loss[loss=0.1826, simple_loss=0.2454, pruned_loss=0.05986, over 4778.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2223, pruned_loss=0.0408, over 971720.81 frames.], batch size: 17, lr: 3.89e-04
2022-05-05 05:13:08,183 INFO [train.py:715] (1/8) Epoch 5, batch 11900, loss[loss=0.1389, simple_loss=0.2137, pruned_loss=0.03199, over 4800.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2221, pruned_loss=0.04046, over 971884.05 frames.], batch size: 21, lr: 3.89e-04
2022-05-05 05:13:47,509 INFO [train.py:715] (1/8) Epoch 5, batch 11950, loss[loss=0.1212, simple_loss=0.1949, pruned_loss=0.02376, over 4924.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2216, pruned_loss=0.04035, over 972212.34 frames.], batch size: 29, lr: 3.89e-04
2022-05-05 05:14:27,518 INFO [train.py:715] (1/8) Epoch 5, batch 12000, loss[loss=0.1706, simple_loss=0.2442, pruned_loss=0.04853, over 4755.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2216, pruned_loss=0.03997, over 972845.97 frames.], batch size: 18, lr: 3.89e-04
2022-05-05 05:14:27,518 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 05:14:37,327 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1103, simple_loss=0.1957, pruned_loss=0.01243, over 914524.00 frames.
2022-05-05 05:15:17,600 INFO [train.py:715] (1/8) Epoch 5, batch 12050, loss[loss=0.1155, simple_loss=0.1875, pruned_loss=0.02177, over 4648.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03976, over 974030.80 frames.], batch size: 13, lr: 3.89e-04
2022-05-05 05:15:57,247 INFO [train.py:715] (1/8) Epoch 5, batch 12100, loss[loss=0.1776, simple_loss=0.2489, pruned_loss=0.05312, over 4915.00 frames.], tot_loss[loss=0.152, simple_loss=0.2225, pruned_loss=0.04074, over 973879.04 frames.], batch size: 18, lr: 3.89e-04
2022-05-05 05:16:36,758 INFO [train.py:715] (1/8) Epoch 5, batch 12150, loss[loss=0.1464, simple_loss=0.2232, pruned_loss=0.03482, over 4936.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2229, pruned_loss=0.04049, over 973609.62 frames.], batch size: 21, lr: 3.88e-04
2022-05-05 05:17:16,023 INFO [train.py:715] (1/8) Epoch 5, batch 12200, loss[loss=0.1452, simple_loss=0.2232, pruned_loss=0.03353, over 4753.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2228, pruned_loss=0.04067, over 972005.07 frames.], batch size: 19, lr: 3.88e-04
2022-05-05 05:17:56,097 INFO [train.py:715] (1/8) Epoch 5, batch 12250, loss[loss=0.1632, simple_loss=0.2188, pruned_loss=0.05373, over 4890.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.0409, over 972492.85 frames.], batch size: 32, lr: 3.88e-04
2022-05-05 05:18:35,381 INFO [train.py:715] (1/8) Epoch 5, batch 12300, loss[loss=0.1309, simple_loss=0.1979, pruned_loss=0.03197, over 4812.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2237, pruned_loss=0.04124, over 972719.87 frames.], batch size: 13, lr: 3.88e-04
2022-05-05 05:19:14,283 INFO [train.py:715] (1/8) Epoch 5, batch 12350, loss[loss=0.1732, simple_loss=0.2553, pruned_loss=0.04558, over 4979.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2245, pruned_loss=0.04143, over 973307.86 frames.], batch size: 24, lr: 3.88e-04
2022-05-05 05:19:53,844 INFO [train.py:715] (1/8) Epoch 5, batch 12400, loss[loss=0.1173, simple_loss=0.1921, pruned_loss=0.02132, over 4925.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2237, pruned_loss=0.04094, over 973214.92 frames.], batch size: 17, lr: 3.88e-04
2022-05-05 05:20:33,431 INFO [train.py:715] (1/8) Epoch 5, batch 12450, loss[loss=0.1331, simple_loss=0.1999, pruned_loss=0.03313, over 4822.00 frames.], tot_loss[loss=0.1524, simple_loss=0.223, pruned_loss=0.04092, over 972353.51 frames.], batch size: 25, lr: 3.88e-04
2022-05-05 05:21:12,660 INFO [train.py:715] (1/8) Epoch 5, batch 12500, loss[loss=0.1551, simple_loss=0.2241, pruned_loss=0.04304, over 4696.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2226, pruned_loss=0.04082, over 972116.39 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:21:51,880 INFO [train.py:715] (1/8) Epoch 5, batch 12550, loss[loss=0.1533, simple_loss=0.2303, pruned_loss=0.03815, over 4976.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2228, pruned_loss=0.041, over 972188.32 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:22:30,629 INFO [train.py:715] (1/8) Epoch 5, batch 12600, loss[loss=0.1375, simple_loss=0.2029, pruned_loss=0.03602, over 4810.00 frames.], tot_loss[loss=0.152, simple_loss=0.2227, pruned_loss=0.04065, over 972653.92 frames.], batch size: 21, lr: 3.88e-04
2022-05-05 05:23:08,932 INFO [train.py:715] (1/8) Epoch 5, batch 12650, loss[loss=0.1534, simple_loss=0.2271, pruned_loss=0.03983, over 4966.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2229, pruned_loss=0.04085, over 972148.74 frames.], batch size: 24, lr: 3.88e-04
2022-05-05 05:23:47,149 INFO [train.py:715] (1/8) Epoch 5, batch 12700, loss[loss=0.192, simple_loss=0.2579, pruned_loss=0.06304, over 4694.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.04111, over 971376.41 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:24:27,046 INFO [train.py:715] (1/8) Epoch 5, batch 12750, loss[loss=0.1284, simple_loss=0.1972, pruned_loss=0.02978, over 4694.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2226, pruned_loss=0.04151, over 971653.41 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:25:06,593 INFO [train.py:715] (1/8) Epoch 5, batch 12800, loss[loss=0.1744, simple_loss=0.2332, pruned_loss=0.05782, over 4853.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2222, pruned_loss=0.04129, over 971629.90 frames.], batch size: 20, lr: 3.88e-04
2022-05-05 05:25:46,752 INFO [train.py:715] (1/8) Epoch 5, batch 12850, loss[loss=0.171, simple_loss=0.2291, pruned_loss=0.05645, over 4964.00 frames.], tot_loss[loss=0.153, simple_loss=0.2228, pruned_loss=0.04163, over 972166.60 frames.], batch size: 31, lr: 3.88e-04
2022-05-05 05:26:26,312 INFO [train.py:715] (1/8) Epoch 5, batch 12900, loss[loss=0.1549, simple_loss=0.2288, pruned_loss=0.04044, over 4824.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2228, pruned_loss=0.04168, over 972325.93 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:27:06,311 INFO [train.py:715] (1/8) Epoch 5, batch 12950, loss[loss=0.1356, simple_loss=0.2004, pruned_loss=0.0354, over 4717.00 frames.], tot_loss[loss=0.152, simple_loss=0.2219, pruned_loss=0.04106, over 971956.72 frames.], batch size: 12, lr: 3.88e-04
2022-05-05 05:27:45,737 INFO [train.py:715] (1/8) Epoch 5, batch 13000, loss[loss=0.1364, simple_loss=0.2234, pruned_loss=0.02475, over 4872.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2215, pruned_loss=0.0405, over 972235.40 frames.], batch size: 16, lr: 3.88e-04
2022-05-05 05:28:25,603 INFO [train.py:715] (1/8) Epoch 5, batch 13050, loss[loss=0.1635, simple_loss=0.2307, pruned_loss=0.04815, over 4695.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2236, pruned_loss=0.04142, over 972122.58 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:29:03,808 INFO [train.py:715] (1/8) Epoch 5, batch 13100, loss[loss=0.1421, simple_loss=0.217, pruned_loss=0.03363, over 4935.00 frames.], tot_loss[loss=0.1523, simple_loss=0.223, pruned_loss=0.0408, over 971901.06 frames.], batch size: 23, lr: 3.87e-04
2022-05-05 05:29:42,388 INFO [train.py:715] (1/8) Epoch 5, batch 13150, loss[loss=0.2014, simple_loss=0.2693, pruned_loss=0.06679, over 4982.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2235, pruned_loss=0.04149, over 972185.56 frames.], batch size: 25, lr: 3.87e-04
2022-05-05 05:30:20,478 INFO [train.py:715] (1/8) Epoch 5, batch 13200, loss[loss=0.1737, simple_loss=0.2543, pruned_loss=0.04657, over 4842.00 frames.], tot_loss[loss=0.1524, simple_loss=0.223, pruned_loss=0.04094, over 971988.64 frames.], batch size: 20, lr: 3.87e-04
2022-05-05 05:30:58,489 INFO [train.py:715] (1/8) Epoch 5, batch 13250, loss[loss=0.1658, simple_loss=0.2367, pruned_loss=0.04751, over 4833.00 frames.], tot_loss[loss=0.1525, simple_loss=0.223, pruned_loss=0.04102, over 971557.93 frames.], batch size: 15, lr: 3.87e-04
2022-05-05 05:31:37,094 INFO [train.py:715] (1/8) Epoch 5, batch 13300, loss[loss=0.1208, simple_loss=0.1898, pruned_loss=0.02595, over 4822.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2231, pruned_loss=0.04134, over 971985.75 frames.], batch size: 13, lr: 3.87e-04
2022-05-05 05:32:14,954 INFO [train.py:715] (1/8) Epoch 5, batch 13350, loss[loss=0.2319, simple_loss=0.2828, pruned_loss=0.09049, over 4687.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2246, pruned_loss=0.04246, over 972058.36 frames.], batch size: 15, lr: 3.87e-04
2022-05-05 05:32:53,086 INFO [train.py:715] (1/8) Epoch 5, batch 13400, loss[loss=0.1516, simple_loss=0.2181, pruned_loss=0.04258, over 4883.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2241, pruned_loss=0.04246, over 972403.48 frames.], batch size: 32, lr: 3.87e-04
2022-05-05 05:33:30,832 INFO [train.py:715] (1/8) Epoch 5, batch 13450, loss[loss=0.1362, simple_loss=0.2132, pruned_loss=0.0296, over 4698.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2236, pruned_loss=0.04229, over 972747.30 frames.], batch size: 15, lr: 3.87e-04
2022-05-05 05:34:09,168 INFO [train.py:715] (1/8) Epoch 5, batch 13500, loss[loss=0.1545, simple_loss=0.2357, pruned_loss=0.03666, over 4967.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2241, pruned_loss=0.04223, over 972546.42 frames.], batch size: 24, lr: 3.87e-04
2022-05-05 05:34:47,072 INFO [train.py:715] (1/8) Epoch 5, batch 13550, loss[loss=0.1776, simple_loss=0.2366, pruned_loss=0.05929, over 4872.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2244, pruned_loss=0.04247, over 971780.39 frames.], batch size: 16, lr: 3.87e-04
2022-05-05 05:35:24,569 INFO [train.py:715] (1/8) Epoch 5, batch 13600, loss[loss=0.1744, simple_loss=0.2413, pruned_loss=0.05377, over 4830.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2246, pruned_loss=0.0428, over 971711.49 frames.], batch size: 30, lr: 3.87e-04
2022-05-05 05:36:03,224 INFO [train.py:715] (1/8) Epoch 5, batch 13650, loss[loss=0.1361, simple_loss=0.2051, pruned_loss=0.0335, over 4760.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2241, pruned_loss=0.04224, over 971532.75 frames.], batch size: 19, lr: 3.87e-04
2022-05-05 05:36:41,018 INFO [train.py:715] (1/8) Epoch 5, batch 13700, loss[loss=0.1169, simple_loss=0.1954, pruned_loss=0.01924, over 4972.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2236, pruned_loss=0.04163, over 971705.05 frames.], batch size: 14, lr: 3.87e-04
2022-05-05 05:37:19,075 INFO [train.py:715] (1/8) Epoch 5, batch 13750, loss[loss=0.1623, simple_loss=0.2302, pruned_loss=0.04725, over 4948.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2234, pruned_loss=0.0416, over 971548.64 frames.], batch size: 21, lr: 3.87e-04
2022-05-05 05:37:56,883 INFO [train.py:715] (1/8) Epoch 5, batch 13800, loss[loss=0.1725, simple_loss=0.2484, pruned_loss=0.04827, over 4974.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2229, pruned_loss=0.04139, over 970778.87 frames.], batch size: 15, lr: 3.87e-04
2022-05-05 05:38:35,346 INFO [train.py:715] (1/8) Epoch 5, batch 13850, loss[loss=0.1303, simple_loss=0.1944, pruned_loss=0.03307, over 4775.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2227, pruned_loss=0.04114, over 971650.10 frames.], batch size: 18, lr: 3.87e-04
2022-05-05 05:39:13,572 INFO [train.py:715] (1/8) Epoch 5, batch 13900, loss[loss=0.1666, simple_loss=0.2336, pruned_loss=0.0498, over 4965.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2221, pruned_loss=0.04077, over 971627.79 frames.], batch size: 35, lr: 3.87e-04
2022-05-05 05:39:51,058 INFO [train.py:715] (1/8) Epoch 5, batch 13950, loss[loss=0.1496, simple_loss=0.2207, pruned_loss=0.03918, over 4916.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2215, pruned_loss=0.04002, over 971906.94 frames.], batch size: 29, lr: 3.87e-04
2022-05-05 05:40:29,789 INFO [train.py:715] (1/8) Epoch 5, batch 14000, loss[loss=0.1578, simple_loss=0.2305, pruned_loss=0.04255, over 4902.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2228, pruned_loss=0.04037, over 971691.64 frames.], batch size: 17, lr: 3.87e-04
2022-05-05 05:41:07,817 INFO [train.py:715] (1/8) Epoch 5, batch 14050, loss[loss=0.1344, simple_loss=0.2091, pruned_loss=0.02983, over 4914.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2221, pruned_loss=0.04026, over 972120.97 frames.], batch size: 18, lr: 3.87e-04
2022-05-05 05:41:45,579 INFO [train.py:715] (1/8) Epoch 5, batch 14100, loss[loss=0.1344, simple_loss=0.2161, pruned_loss=0.02634, over 4986.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2226, pruned_loss=0.04037, over 973258.51 frames.], batch size: 28, lr: 3.86e-04
2022-05-05 05:42:23,457 INFO [train.py:715] (1/8) Epoch 5, batch 14150, loss[loss=0.1588, simple_loss=0.2186, pruned_loss=0.04952, over 4778.00 frames.], tot_loss[loss=0.152, simple_loss=0.2225, pruned_loss=0.04073, over 973795.40 frames.], batch size: 14, lr: 3.86e-04
2022-05-05 05:43:01,800 INFO [train.py:715] (1/8) Epoch 5, batch 14200, loss[loss=0.1607, simple_loss=0.2161, pruned_loss=0.0527, over 4800.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2242, pruned_loss=0.04203, over 973595.53 frames.], batch size: 25, lr: 3.86e-04
2022-05-05 05:43:40,052 INFO [train.py:715] (1/8) Epoch 5, batch 14250, loss[loss=0.1806, simple_loss=0.2566, pruned_loss=0.05235, over 4736.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2237, pruned_loss=0.04171, over 973342.50 frames.], batch size: 16, lr: 3.86e-04
2022-05-05 05:44:18,052 INFO [train.py:715] (1/8) Epoch 5, batch 14300, loss[loss=0.1632, simple_loss=0.228, pruned_loss=0.04923, over 4778.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2224, pruned_loss=0.04102, over 973006.78 frames.], batch size: 18, lr: 3.86e-04
2022-05-05 05:44:56,437 INFO [train.py:715] (1/8) Epoch 5, batch 14350, loss[loss=0.1406, simple_loss=0.2056, pruned_loss=0.03775, over 4888.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2231, pruned_loss=0.04129, over 972140.57 frames.], batch size: 22, lr: 3.86e-04
2022-05-05 05:45:34,232 INFO [train.py:715] (1/8) Epoch 5, batch 14400, loss[loss=0.1461, simple_loss=0.2272, pruned_loss=0.03246, over 4815.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2231, pruned_loss=0.04124, over 972847.42 frames.], batch size: 25, lr: 3.86e-04
2022-05-05 05:46:11,865 INFO [train.py:715] (1/8) Epoch 5, batch 14450, loss[loss=0.1956, simple_loss=0.2686, pruned_loss=0.06127, over 4811.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2231, pruned_loss=0.04124, over 972475.44 frames.], batch size: 15, lr: 3.86e-04
2022-05-05 05:46:49,663 INFO [train.py:715] (1/8) Epoch 5, batch 14500, loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03327, over 4779.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2227, pruned_loss=0.04093, over 971868.00 frames.], batch size: 17, lr: 3.86e-04
2022-05-05 05:47:27,998 INFO [train.py:715] (1/8) Epoch 5, batch 14550, loss[loss=0.1532, simple_loss=0.23, pruned_loss=0.03822, over 4758.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2226, pruned_loss=0.04089, over 972003.32 frames.], batch size: 19, lr: 3.86e-04
2022-05-05 05:48:06,095 INFO [train.py:715] (1/8) Epoch 5, batch 14600, loss[loss=0.1652, simple_loss=0.2376, pruned_loss=0.04634, over 4900.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2235, pruned_loss=0.04137, over 972225.92 frames.], batch size: 18, lr: 3.86e-04
2022-05-05 05:48:44,027 INFO [train.py:715] (1/8) Epoch 5, batch 14650, loss[loss=0.1681, simple_loss=0.2342, pruned_loss=0.051, over 4784.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2243, pruned_loss=0.0421, over 972469.07 frames.], batch size: 18, lr: 3.86e-04
2022-05-05 05:49:22,278 INFO [train.py:715] (1/8) Epoch 5, batch 14700, loss[loss=0.1309, simple_loss=0.2097, pruned_loss=0.026, over 4822.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2241, pruned_loss=0.04226, over 972651.37 frames.], batch size: 25, lr: 3.86e-04
2022-05-05 05:49:59,646 INFO [train.py:715] (1/8) Epoch 5, batch 14750, loss[loss=0.1518, simple_loss=0.2149, pruned_loss=0.04436, over 4875.00 frames.], tot_loss[loss=0.154, simple_loss=0.2238, pruned_loss=0.04209, over 972547.92 frames.], batch size: 20, lr: 3.86e-04
2022-05-05 05:50:37,679 INFO [train.py:715] (1/8) Epoch 5, batch 14800, loss[loss=0.1718, simple_loss=0.2469, pruned_loss=0.04835, over 4775.00 frames.], tot_loss[loss=0.1545, simple_loss=0.224, pruned_loss=0.04252, over 971894.64 frames.], batch size: 14, lr: 3.86e-04
2022-05-05 05:51:15,494 INFO [train.py:715] (1/8) Epoch 5, batch 14850, loss[loss=0.141, simple_loss=0.212, pruned_loss=0.03497, over 4823.00 frames.], tot_loss[loss=0.154, simple_loss=0.2236, pruned_loss=0.04214, over 970987.66 frames.], batch size: 25, lr: 3.86e-04
2022-05-05 05:51:54,110 INFO [train.py:715] (1/8) Epoch 5, batch 14900, loss[loss=0.148, simple_loss=0.2164, pruned_loss=0.03986, over 4849.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2229, pruned_loss=0.04119, over 971421.26 frames.], batch size: 34, lr: 3.86e-04
2022-05-05 05:52:32,766 INFO [train.py:715] (1/8) Epoch 5, batch 14950, loss[loss=0.1345, simple_loss=0.2058, pruned_loss=0.03161, over 4794.00 frames.], tot_loss[loss=0.1528, simple_loss=0.223, pruned_loss=0.04129, over 970376.91 frames.], batch size: 18, lr: 3.86e-04
2022-05-05 05:53:10,827 INFO [train.py:715] (1/8) Epoch 5, batch 15000, loss[loss=0.1354, simple_loss=0.2083, pruned_loss=0.03121, over 4926.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2233, pruned_loss=0.04141, over 971854.39 frames.], batch size: 29, lr: 3.86e-04
2022-05-05 05:53:10,828 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 05:53:21,082 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1105, simple_loss=0.1958, pruned_loss=0.01261, over 914524.00 frames.
2022-05-05 05:53:58,557 INFO [train.py:715] (1/8) Epoch 5, batch 15050, loss[loss=0.1206, simple_loss=0.1822, pruned_loss=0.02949, over 4818.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2234, pruned_loss=0.04141, over 971805.36 frames.], batch size: 12, lr: 3.85e-04
2022-05-05 05:54:37,215 INFO [train.py:715] (1/8) Epoch 5, batch 15100, loss[loss=0.1483, simple_loss=0.2221, pruned_loss=0.03722, over 4918.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2234, pruned_loss=0.04158, over 972653.94 frames.], batch size: 39, lr: 3.85e-04
2022-05-05 05:55:15,137 INFO [train.py:715] (1/8) Epoch 5, batch 15150, loss[loss=0.142, simple_loss=0.2094, pruned_loss=0.03727, over 4876.00 frames.], tot_loss[loss=0.153, simple_loss=0.2234, pruned_loss=0.04126, over 972945.76 frames.], batch size: 20, lr: 3.85e-04
2022-05-05 05:55:53,288 INFO [train.py:715] (1/8) Epoch 5, batch 15200, loss[loss=0.1573, simple_loss=0.2301, pruned_loss=0.04226, over 4892.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2234, pruned_loss=0.0417, over 973894.65 frames.], batch size: 19, lr: 3.85e-04
2022-05-05 05:56:32,205 INFO [train.py:715] (1/8) Epoch 5, batch 15250, loss[loss=0.1404, simple_loss=0.2106, pruned_loss=0.03513, over 4905.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2231, pruned_loss=0.0418, over 972515.61 frames.], batch size: 29, lr: 3.85e-04
2022-05-05 05:57:10,916 INFO [train.py:715] (1/8) Epoch 5, batch 15300, loss[loss=0.1759, simple_loss=0.2279, pruned_loss=0.06196, over 4776.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2234, pruned_loss=0.04201, over 971565.97 frames.], batch size: 17, lr: 3.85e-04
2022-05-05 05:57:50,156 INFO [train.py:715] (1/8) Epoch 5, batch 15350, loss[loss=0.1513, simple_loss=0.2226, pruned_loss=0.04003, over 4976.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2234, pruned_loss=0.042, over 972401.13 frames.], batch size: 24, lr: 3.85e-04
2022-05-05 05:58:28,491 INFO [train.py:715] (1/8) Epoch 5, batch 15400, loss[loss=0.1496, simple_loss=0.2205, pruned_loss=0.03938, over 4986.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2236, pruned_loss=0.04175, over 972865.38 frames.], batch size: 28, lr: 3.85e-04
2022-05-05 05:59:07,537 INFO [train.py:715] (1/8) Epoch 5, batch 15450, loss[loss=0.1601, simple_loss=0.2244, pruned_loss=0.0479, over 4752.00 frames.], tot_loss[loss=0.153, simple_loss=0.2232, pruned_loss=0.04139, over 973166.65 frames.], batch size: 18, lr: 3.85e-04
2022-05-05 05:59:46,050 INFO [train.py:715] (1/8) Epoch 5, batch 15500, loss[loss=0.1625, simple_loss=0.2379, pruned_loss=0.04357, over 4825.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2235, pruned_loss=0.04094, over 971361.42 frames.], batch size: 26, lr: 3.85e-04
2022-05-05 06:00:25,318 INFO [train.py:715] (1/8) Epoch 5, batch 15550, loss[loss=0.1624, simple_loss=0.2343, pruned_loss=0.04524, over 4949.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2233, pruned_loss=0.04097, over 971443.36 frames.], batch size: 21, lr: 3.85e-04
2022-05-05 06:01:03,326 INFO [train.py:715] (1/8) Epoch 5, batch 15600, loss[loss=0.1463, simple_loss=0.2233, pruned_loss=0.03462, over 4840.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2224, pruned_loss=0.04072, over 971629.82 frames.], batch size: 32, lr: 3.85e-04
2022-05-05 06:01:40,925 INFO [train.py:715] (1/8) Epoch 5, batch 15650, loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.02963, over 4943.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2219, pruned_loss=0.04057, over 971555.90 frames.], batch size: 23, lr: 3.85e-04
2022-05-05 06:02:18,448 INFO [train.py:715] (1/8) Epoch 5, batch 15700, loss[loss=0.1543, simple_loss=0.244, pruned_loss=0.03235, over 4752.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2219, pruned_loss=0.04044, over 971918.46 frames.], batch size: 19, lr: 3.85e-04
2022-05-05 06:02:56,464 INFO [train.py:715] (1/8) Epoch 5, batch 15750, loss[loss=0.1387, simple_loss=0.214, pruned_loss=0.03167, over 4956.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2223, pruned_loss=0.04049, over 971340.99 frames.], batch size: 24, lr: 3.85e-04
2022-05-05 06:03:34,889 INFO [train.py:715] (1/8) Epoch 5, batch 15800, loss[loss=0.1648, simple_loss=0.2404, pruned_loss=0.04458, over 4950.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2219, pruned_loss=0.04026, over 972819.94 frames.], batch size: 21, lr: 3.85e-04
2022-05-05 06:04:12,957 INFO [train.py:715] (1/8) Epoch 5, batch 15850, loss[loss=0.1471, simple_loss=0.22, pruned_loss=0.0371, over 4691.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2216, pruned_loss=0.04013, over 973371.11 frames.], batch size: 15, lr: 3.85e-04
2022-05-05 06:04:50,530 INFO [train.py:715] (1/8) Epoch 5, batch 15900, loss[loss=0.133, simple_loss=0.2111, pruned_loss=0.02747, over 4987.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2215, pruned_loss=0.0402, over 973371.63 frames.], batch size: 25, lr: 3.85e-04
2022-05-05 06:05:28,346 INFO [train.py:715] (1/8) Epoch 5, batch 15950, loss[loss=0.1566, simple_loss=0.2241, pruned_loss=0.04452, over 4881.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2212, pruned_loss=0.04021, over 972817.22 frames.], batch size: 22, lr: 3.85e-04
2022-05-05 06:06:05,815 INFO [train.py:715] (1/8) Epoch 5, batch 16000, loss[loss=0.1323, simple_loss=0.2061, pruned_loss=0.0292, over 4982.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2213, pruned_loss=0.04029, over 972207.99 frames.], batch size: 28, lr: 3.85e-04
2022-05-05 06:06:43,537 INFO [train.py:715] (1/8) Epoch 5, batch 16050, loss[loss=0.1321, simple_loss=0.2165, pruned_loss=0.02387, over 4772.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2219, pruned_loss=0.04022, over 972540.19 frames.], batch size: 14, lr: 3.84e-04
2022-05-05 06:07:21,603 INFO [train.py:715] (1/8) Epoch 5, batch 16100, loss[loss=0.1471, simple_loss=0.2282, pruned_loss=0.03296, over 4710.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2221, pruned_loss=0.04018, over 972127.23 frames.], batch size: 15, lr: 3.84e-04
2022-05-05 06:08:00,775 INFO [train.py:715] (1/8) Epoch 5, batch 16150, loss[loss=0.1437, simple_loss=0.2059, pruned_loss=0.04081, over 4862.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2227, pruned_loss=0.04055, over 972735.82 frames.], batch size: 30, lr: 3.84e-04
2022-05-05 06:08:39,730 INFO [train.py:715] (1/8) Epoch 5, batch 16200, loss[loss=0.1848, simple_loss=0.2421, pruned_loss=0.06371, over 4978.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2227, pruned_loss=0.04075, over 973304.24 frames.], batch size: 15, lr: 3.84e-04
2022-05-05 06:09:18,293 INFO [train.py:715] (1/8) Epoch 5, batch 16250, loss[loss=0.1522, simple_loss=0.2245, pruned_loss=0.03994, over 4979.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2229, pruned_loss=0.04076, over 973141.18 frames.], batch size: 15, lr: 3.84e-04
2022-05-05 06:09:56,101 INFO [train.py:715] (1/8) Epoch 5, batch 16300, loss[loss=0.151, simple_loss=0.221, pruned_loss=0.04048, over 4747.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2228, pruned_loss=0.04052, over 972881.16 frames.], batch size: 19, lr: 3.84e-04
2022-05-05 06:10:34,112 INFO [train.py:715] (1/8) Epoch 5, batch 16350, loss[loss=0.1292, simple_loss=0.1983, pruned_loss=0.03, over 4803.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2225, pruned_loss=0.0405, over 972166.86 frames.], batch size: 12, lr: 3.84e-04
2022-05-05 06:11:12,495 INFO [train.py:715] (1/8) Epoch 5, batch 16400, loss[loss=0.1697, simple_loss=0.2388, pruned_loss=0.05034, over 4973.00 frames.], tot_loss[loss=0.1512, simple_loss=0.222, pruned_loss=0.04015, over 972098.40 frames.], batch size: 14, lr: 3.84e-04
2022-05-05 06:11:50,950 INFO [train.py:715] (1/8) Epoch 5, batch 16450, loss[loss=0.1608, simple_loss=0.2335, pruned_loss=0.04402, over 4874.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2225, pruned_loss=0.04017, over 972085.92 frames.], batch size: 22, lr: 3.84e-04
2022-05-05 06:12:30,302 INFO [train.py:715] (1/8) Epoch 5, batch 16500, loss[loss=0.1618, simple_loss=0.2369, pruned_loss=0.04331, over 4922.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2232, pruned_loss=0.04015, over 972683.77 frames.], batch size: 18, lr: 3.84e-04
2022-05-05 06:13:08,221 INFO [train.py:715] (1/8) Epoch 5, batch 16550, loss[loss=0.1322, simple_loss=0.2042, pruned_loss=0.03007, over 4820.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2236, pruned_loss=0.04049, over 972331.09 frames.], batch size: 13, lr: 3.84e-04
2022-05-05 06:13:46,907 INFO [train.py:715] (1/8) Epoch 5, batch 16600, loss[loss=0.1587, simple_loss=0.2291, pruned_loss=0.04414, over 4801.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2242, pruned_loss=0.04122, over 973397.31 frames.], batch size: 21, lr: 3.84e-04
2022-05-05 06:14:25,621 INFO [train.py:715] (1/8) Epoch 5, batch 16650, loss[loss=0.168, simple_loss=0.2415, pruned_loss=0.04727, over 4986.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2237, pruned_loss=0.04103, over 973942.16 frames.], batch size: 14, lr: 3.84e-04
2022-05-05 06:15:04,297 INFO [train.py:715] (1/8) Epoch 5, batch 16700, loss[loss=0.1558, simple_loss=0.2226, pruned_loss=0.04454, over 4837.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2234, pruned_loss=0.04124, over 973753.88 frames.], batch size: 15, lr: 3.84e-04
2022-05-05 06:15:42,486 INFO [train.py:715] (1/8) Epoch 5, batch 16750, loss[loss=0.1329, simple_loss=0.1938, pruned_loss=0.03603, over 4751.00 frames.], tot_loss[loss=0.1529, simple_loss=0.223, pruned_loss=0.04145, over 973353.45 frames.], batch size: 16, lr: 3.84e-04
2022-05-05 06:16:20,937 INFO [train.py:715] (1/8) Epoch 5, batch 16800, loss[loss=0.1251, simple_loss=0.1956, pruned_loss=0.02727, over 4818.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2237, pruned_loss=0.0417, over 973121.00 frames.], batch size: 25, lr: 3.84e-04
2022-05-05 06:17:00,071 INFO [train.py:715] (1/8) Epoch 5, batch 16850, loss[loss=0.1413, simple_loss=0.217, pruned_loss=0.03278, over 4971.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2228, pruned_loss=0.04096, over 973603.54 frames.], batch size: 24, lr: 3.84e-04
2022-05-05 06:17:37,931 INFO [train.py:715] (1/8) Epoch 5, batch 16900, loss[loss=0.136, simple_loss=0.2136, pruned_loss=0.02919, over 4941.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2224, pruned_loss=0.04065, over 973072.07 frames.], batch size: 29, lr: 3.84e-04
2022-05-05 06:18:16,759 INFO [train.py:715] (1/8) Epoch 5, batch 16950, loss[loss=0.148, simple_loss=0.2133, pruned_loss=0.0414, over 4815.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2226, pruned_loss=0.04064, over 973041.39 frames.], batch size: 14, lr: 3.84e-04
2022-05-05 06:18:55,162 INFO [train.py:715] (1/8) Epoch 5, batch 17000, loss[loss=0.1403, simple_loss=0.209, pruned_loss=0.03578, over 4818.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2225, pruned_loss=0.04038, over 973133.33 frames.], batch size: 21, lr: 3.84e-04
2022-05-05 06:19:33,551 INFO [train.py:715] (1/8) Epoch 5, batch 17050, loss[loss=0.1799, simple_loss=0.2429, pruned_loss=0.05841, over 4840.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2227, pruned_loss=0.04071, over 972618.89 frames.], batch size: 32, lr: 3.83e-04
2022-05-05 06:20:11,945 INFO [train.py:715] (1/8) Epoch 5, batch 17100, loss[loss=0.1897, simple_loss=0.2692, pruned_loss=0.05509, over 4752.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2228, pruned_loss=0.04104, over 972877.66 frames.], batch size: 16, lr: 3.83e-04
2022-05-05 06:20:49,751 INFO [train.py:715] (1/8) Epoch 5, batch 17150, loss[loss=0.1519, simple_loss=0.23, pruned_loss=0.03692, over 4884.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2222, pruned_loss=0.04071, over 972509.07 frames.], batch size: 22, lr: 3.83e-04
2022-05-05 06:21:27,634 INFO [train.py:715] (1/8) Epoch 5, batch 17200, loss[loss=0.1534, simple_loss=0.2232, pruned_loss=0.04179, over 4916.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2224, pruned_loss=0.0407, over 971554.69 frames.], batch size: 18, lr: 3.83e-04
2022-05-05 06:22:04,739 INFO [train.py:715] (1/8) Epoch 5, batch 17250, loss[loss=0.1674, simple_loss=0.2403, pruned_loss=0.04719, over 4878.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2221, pruned_loss=0.04051, over 971805.03 frames.], batch size: 16, lr: 3.83e-04
2022-05-05 06:22:42,973 INFO [train.py:715] (1/8) Epoch 5, batch 17300, loss[loss=0.157, simple_loss=0.2244, pruned_loss=0.04478, over 4790.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2233, pruned_loss=0.04111, over 972421.90 frames.], batch size: 17, lr: 3.83e-04
2022-05-05 06:23:22,499 INFO [train.py:715] (1/8) Epoch 5, batch 17350, loss[loss=0.1306, simple_loss=0.2014, pruned_loss=0.02993, over 4717.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2227, pruned_loss=0.04109, over 972282.50 frames.], batch size: 15, lr: 3.83e-04
2022-05-05 06:24:00,865 INFO [train.py:715] (1/8) Epoch 5, batch 17400, loss[loss=0.179, simple_loss=0.2308, pruned_loss=0.06364, over 4933.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.0411, over 972105.18 frames.], batch size: 21, lr: 3.83e-04
2022-05-05 06:24:39,484 INFO [train.py:715] (1/8) Epoch 5, batch 17450, loss[loss=0.1544, simple_loss=0.2241, pruned_loss=0.0423, over 4935.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2222, pruned_loss=0.0407, over 972548.19 frames.], batch size: 29, lr: 3.83e-04
2022-05-05 06:25:17,956 INFO [train.py:715] (1/8) Epoch 5, batch 17500, loss[loss=0.2066, simple_loss=0.263, pruned_loss=0.07511, over 4851.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2231, pruned_loss=0.04127, over 972536.46 frames.], batch size: 32, lr: 3.83e-04
2022-05-05 06:25:56,809 INFO [train.py:715] (1/8) Epoch 5, batch 17550, loss[loss=0.1825, simple_loss=0.2443, pruned_loss=0.06029, over 4992.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2231, pruned_loss=0.04119, over 972495.83 frames.], batch size: 15, lr: 3.83e-04
2022-05-05 06:26:35,442 INFO [train.py:715] (1/8) Epoch 5, batch 17600, loss[loss=0.1746, simple_loss=0.24, pruned_loss=0.05461, over 4775.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2228, pruned_loss=0.04127, over 972152.78 frames.], batch size: 18, lr: 3.83e-04
2022-05-05 06:27:14,154 INFO [train.py:715] (1/8) Epoch 5, batch 17650, loss[loss=0.1442, simple_loss=0.2128, pruned_loss=0.03782, over 4984.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2225, pruned_loss=0.04088, over 972054.19 frames.], batch size: 28, lr: 3.83e-04
2022-05-05 06:27:52,814 INFO [train.py:715] (1/8) Epoch 5, batch 17700, loss[loss=0.1532, simple_loss=0.2187, pruned_loss=0.04387, over 4820.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2221, pruned_loss=0.0406, over 971814.61 frames.], batch size: 15, lr: 3.83e-04
2022-05-05 06:28:31,729 INFO [train.py:715] (1/8) Epoch 5, batch 17750, loss[loss=0.1562, simple_loss=0.2287, pruned_loss=0.04186, over 4916.00 frames.], tot_loss[loss=0.152, simple_loss=0.2226, pruned_loss=0.04072, over 971896.72 frames.], batch size: 39, lr: 3.83e-04
2022-05-05 06:29:09,754 INFO [train.py:715] (1/8) Epoch 5, batch 17800, loss[loss=0.1501, simple_loss=0.2181, pruned_loss=0.04102, over 4982.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2223, pruned_loss=0.04094, over 971527.11 frames.], batch size: 25, lr: 3.83e-04
2022-05-05 06:29:48,585 INFO [train.py:715] (1/8) Epoch 5, batch 17850, loss[loss=0.1683, simple_loss=0.2471, pruned_loss=0.04477, over 4682.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2229, pruned_loss=0.04103, over 971530.71 frames.], batch size: 15, lr: 3.83e-04
2022-05-05 06:30:27,680 INFO [train.py:715] (1/8) Epoch 5, batch 17900, loss[loss=0.1756, simple_loss=0.2563, pruned_loss=0.04749, over 4767.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.04097, over 972438.84 frames.], batch size: 19, lr: 3.83e-04
2022-05-05 06:31:06,335 INFO [train.py:715] (1/8) Epoch 5, batch 17950, loss[loss=0.145, simple_loss=0.2143, pruned_loss=0.03783, over 4954.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.04053, over 972645.95 frames.], batch size: 14, lr: 3.83e-04
2022-05-05 06:31:47,072 INFO [train.py:715] (1/8) Epoch 5, batch 18000, loss[loss=0.1744, simple_loss=0.2413, pruned_loss=0.0538, over 4745.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2231, pruned_loss=0.04079, over 971550.06 frames.], batch size: 16, lr: 3.83e-04
2022-05-05 06:31:47,073 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 06:31:59,753 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1102, simple_loss=0.1955, pruned_loss=0.01245, over 914524.00 frames.
2022-05-05 06:32:38,355 INFO [train.py:715] (1/8) Epoch 5, batch 18050, loss[loss=0.1926, simple_loss=0.2548, pruned_loss=0.06525, over 4933.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.04109, over 971549.08 frames.], batch size: 39, lr: 3.82e-04
2022-05-05 06:33:17,599 INFO [train.py:715] (1/8) Epoch 5, batch 18100, loss[loss=0.1345, simple_loss=0.2008, pruned_loss=0.03409, over 4755.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2234, pruned_loss=0.04124, over 972277.55 frames.], batch size: 12, lr: 3.82e-04
2022-05-05 06:33:56,336 INFO [train.py:715] (1/8) Epoch 5, batch 18150, loss[loss=0.1588, simple_loss=0.2394, pruned_loss=0.0391, over 4911.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2232, pruned_loss=0.04111, over 972251.35 frames.], batch size: 17, lr: 3.82e-04
2022-05-05 06:34:34,860 INFO [train.py:715] (1/8) Epoch 5, batch 18200, loss[loss=0.1719, simple_loss=0.2406, pruned_loss=0.0516, over 4809.00 frames.], tot_loss[loss=0.154, simple_loss=0.2242, pruned_loss=0.04194, over 972602.67 frames.], batch size: 24, lr: 3.82e-04
2022-05-05 06:35:14,237 INFO [train.py:715] (1/8) Epoch 5, batch 18250, loss[loss=0.1845, simple_loss=0.2408, pruned_loss=0.06408, over 4778.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2244, pruned_loss=0.04241, over 972332.86 frames.], batch size: 14, lr: 3.82e-04
2022-05-05 06:35:53,137 INFO [train.py:715] (1/8) Epoch 5, batch 18300, loss[loss=0.1435, simple_loss=0.215, pruned_loss=0.03596, over 4775.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2245, pruned_loss=0.04222, over 972516.54 frames.], batch size: 18, lr: 3.82e-04
2022-05-05 06:36:31,710 INFO [train.py:715] (1/8) Epoch 5, batch 18350, loss[loss=0.1265, simple_loss=0.2009, pruned_loss=0.02608, over 4740.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2241, pruned_loss=0.0419, over 972353.21 frames.], batch size: 16, lr: 3.82e-04
2022-05-05 06:37:10,002 INFO [train.py:715] (1/8) Epoch 5, batch 18400, loss[loss=0.1728, simple_loss=0.2404, pruned_loss=0.05261, over 4900.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2237, pruned_loss=0.0414, over 972238.91 frames.], batch size: 19, lr: 3.82e-04
2022-05-05 06:37:49,158 INFO [train.py:715] (1/8) Epoch 5, batch 18450, loss[loss=0.1487, simple_loss=0.235, pruned_loss=0.03125, over 4832.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2233, pruned_loss=0.04148, over 971870.18 frames.], batch size: 27, lr: 3.82e-04
2022-05-05 06:38:27,822 INFO [train.py:715] (1/8) Epoch 5, batch 18500, loss[loss=0.1257, simple_loss=0.202, pruned_loss=0.0247, over 4917.00 frames.], tot_loss[loss=0.1524, simple_loss=0.223, pruned_loss=0.04096, over 971926.71 frames.], batch size: 29, lr: 3.82e-04
2022-05-05 06:39:06,128 INFO [train.py:715] (1/8) Epoch 5, batch 18550, loss[loss=0.1874, simple_loss=0.2467, pruned_loss=0.06407, over 4704.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2235, pruned_loss=0.04144, over 972868.77 frames.], batch size: 15, lr: 3.82e-04
2022-05-05 06:39:45,171 INFO [train.py:715] (1/8) Epoch 5, batch 18600, loss[loss=0.1588, simple_loss=0.2285, pruned_loss=0.04451, over 4944.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2231, pruned_loss=0.04094, over 972639.36 frames.], batch size: 35, lr: 3.82e-04
2022-05-05 06:40:23,782 INFO [train.py:715] (1/8) Epoch 5, batch 18650, loss[loss=0.1283, simple_loss=0.1886, pruned_loss=0.03398, over 4867.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2225, pruned_loss=0.04086, over 972771.72 frames.], batch size: 30, lr: 3.82e-04
2022-05-05 06:41:01,940 INFO [train.py:715] (1/8) Epoch 5, batch 18700, loss[loss=0.138, simple_loss=0.2078, pruned_loss=0.03407, over 4933.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2232, pruned_loss=0.04102, over 972885.54 frames.], batch size: 23, lr: 3.82e-04
2022-05-05 06:41:40,681 INFO [train.py:715] (1/8) Epoch 5, batch 18750, loss[loss=0.1604, simple_loss=0.2374, pruned_loss=0.04164, over 4827.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2243, pruned_loss=0.04176, over 972795.09 frames.], batch size: 26, lr: 3.82e-04
2022-05-05 06:42:19,957 INFO [train.py:715] (1/8) Epoch 5, batch 18800, loss[loss=0.1708, simple_loss=0.2306, pruned_loss=0.05548, over 4980.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2248, pruned_loss=0.04189, over 972436.32 frames.], batch size: 35, lr: 3.82e-04
2022-05-05 06:42:59,660 INFO [train.py:715] (1/8) Epoch 5, batch 18850, loss[loss=0.1658, simple_loss=0.2304, pruned_loss=0.05056, over 4780.00 frames.], tot_loss[loss=0.153, simple_loss=0.2235, pruned_loss=0.04131, over 971944.67 frames.], batch size: 17, lr: 3.82e-04
2022-05-05 06:43:38,449 INFO [train.py:715] (1/8) Epoch 5, batch 18900, loss[loss=0.1761, simple_loss=0.2414, pruned_loss=0.05541, over 4913.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2241, pruned_loss=0.04141, over 972968.99 frames.], batch size: 19, lr: 3.82e-04
2022-05-05 06:44:16,646 INFO [train.py:715] (1/8) Epoch 5, batch 18950, loss[loss=0.158, simple_loss=0.2296, pruned_loss=0.04322, over 4852.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2256, pruned_loss=0.04206, over 973316.33 frames.], batch size: 32, lr: 3.82e-04
2022-05-05 06:44:56,117 INFO [train.py:715] (1/8) Epoch 5, batch 19000, loss[loss=0.1498, simple_loss=0.2172, pruned_loss=0.04118, over 4809.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2242, pruned_loss=0.04159, over 972084.11 frames.], batch size: 21, lr: 3.82e-04
2022-05-05 06:45:34,091 INFO [train.py:715] (1/8) Epoch 5, batch 19050, loss[loss=0.1466, simple_loss=0.2106, pruned_loss=0.04133, over 4985.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2241, pruned_loss=0.0415, over 971697.40 frames.], batch size: 16, lr: 3.81e-04
2022-05-05 06:46:13,033 INFO [train.py:715] (1/8) Epoch 5, batch 19100, loss[loss=0.1303, simple_loss=0.2017, pruned_loss=0.02947, over 4875.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2236, pruned_loss=0.04102, over 971298.92 frames.], batch size: 22, lr: 3.81e-04
2022-05-05 06:46:52,738 INFO [train.py:715] (1/8) Epoch 5, batch 19150, loss[loss=0.1612, simple_loss=0.2225, pruned_loss=0.04993, over 4846.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2238, pruned_loss=0.04144, over 972138.34 frames.], batch size: 34, lr: 3.81e-04
2022-05-05 06:47:31,316 INFO [train.py:715] (1/8) Epoch 5, batch 19200, loss[loss=0.1642, simple_loss=0.2244, pruned_loss=0.05204, over 4866.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2229, pruned_loss=0.04119, over 972212.65 frames.], batch size: 32, lr: 3.81e-04
2022-05-05 06:48:10,848 INFO [train.py:715] (1/8) Epoch 5, batch 19250, loss[loss=0.1339, simple_loss=0.2045, pruned_loss=0.03168, over 4871.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2235, pruned_loss=0.04159, over 971443.00 frames.], batch size: 20, lr: 3.81e-04
2022-05-05 06:48:48,910 INFO [train.py:715] (1/8) Epoch 5, batch 19300, loss[loss=0.2049, simple_loss=0.279, pruned_loss=0.06542, over 4956.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2236, pruned_loss=0.04152, over 971574.30 frames.], batch size: 24, lr: 3.81e-04
2022-05-05 06:49:28,003 INFO [train.py:715] (1/8) Epoch 5, batch 19350, loss[loss=0.1468, simple_loss=0.2132, pruned_loss=0.04024, over 4933.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.04097, over 971159.42 frames.], batch size: 35, lr: 3.81e-04
2022-05-05 06:50:06,761 INFO [train.py:715] (1/8) Epoch 5, batch 19400, loss[loss=0.2025, simple_loss=0.2525, pruned_loss=0.07625, over 4984.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2224, pruned_loss=0.04084, over 971618.20 frames.], batch size: 33, lr: 3.81e-04
2022-05-05 06:50:45,418 INFO [train.py:715] (1/8) Epoch 5, batch 19450, loss[loss=0.1748, simple_loss=0.2339, pruned_loss=0.05786, over 4802.00 frames.], tot_loss[loss=0.152, simple_loss=0.2221, pruned_loss=0.04094, over 972501.56 frames.], batch size: 25, lr: 3.81e-04
2022-05-05 06:51:25,065 INFO [train.py:715] (1/8) Epoch 5, batch 19500, loss[loss=0.1402, simple_loss=0.2207, pruned_loss=0.02989, over 4974.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.04111, over 973343.51 frames.], batch size: 28, lr: 3.81e-04
2022-05-05 06:52:03,851 INFO [train.py:715] (1/8) Epoch 5, batch 19550, loss[loss=0.1772, simple_loss=0.2376, pruned_loss=0.05839, over 4779.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2224, pruned_loss=0.04139, over 972673.48 frames.], batch size: 17, lr: 3.81e-04
2022-05-05 06:52:42,740 INFO [train.py:715] (1/8) Epoch 5, batch 19600, loss[loss=0.1631, simple_loss=0.2325, pruned_loss=0.04685, over 4937.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2228, pruned_loss=0.04146, over 972389.19 frames.], batch size: 29, lr: 3.81e-04
2022-05-05 06:53:21,192 INFO [train.py:715] (1/8) Epoch 5, batch 19650, loss[loss=0.1604, simple_loss=0.2188, pruned_loss=0.05097, over 4904.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2229, pruned_loss=0.04148, over 972582.98 frames.], batch size: 17, lr: 3.81e-04
2022-05-05 06:54:00,681 INFO [train.py:715] (1/8) Epoch 5, batch 19700, loss[loss=0.1771, simple_loss=0.2464, pruned_loss=0.0539, over 4989.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2231, pruned_loss=0.04157, over 972446.56 frames.], batch size: 26, lr: 3.81e-04
2022-05-05 06:54:39,905 INFO [train.py:715] (1/8) Epoch 5, batch 19750, loss[loss=0.1599, simple_loss=0.223, pruned_loss=0.04838, over 4976.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2238, pruned_loss=0.04197, over 972277.78 frames.], batch size: 14, lr: 3.81e-04
2022-05-05 06:55:17,851 INFO [train.py:715] (1/8) Epoch 5, batch 19800, loss[loss=0.1516, simple_loss=0.221, pruned_loss=0.04111, over 4743.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2232, pruned_loss=0.04148, over 972194.42 frames.], batch size: 16, lr: 3.81e-04
2022-05-05 06:55:56,848 INFO [train.py:715] (1/8) Epoch 5, batch 19850, loss[loss=0.1486, simple_loss=0.2196, pruned_loss=0.03877, over 4877.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2233, pruned_loss=0.04113, over 971843.26 frames.], batch size: 38, lr: 3.81e-04
2022-05-05 06:56:35,749 INFO [train.py:715] (1/8) Epoch 5, batch 19900, loss[loss=0.1631, simple_loss=0.2458, pruned_loss=0.0402, over 4792.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2231, pruned_loss=0.04094, over 972495.67 frames.], batch size: 21, lr: 3.81e-04
2022-05-05 06:57:14,680 INFO [train.py:715] (1/8) Epoch 5, batch 19950, loss[loss=0.1795, simple_loss=0.2372, pruned_loss=0.06092, over 4770.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2224, pruned_loss=0.04049, over 971953.57 frames.], batch size: 17, lr: 3.81e-04
2022-05-05 06:57:53,094 INFO [train.py:715] (1/8) Epoch 5, batch 20000, loss[loss=0.1971, simple_loss=0.2561, pruned_loss=0.06906, over 4886.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2225, pruned_loss=0.04092, over 972022.51 frames.], batch size: 22, lr: 3.81e-04
2022-05-05 06:58:32,599 INFO [train.py:715] (1/8) Epoch 5, batch 20050, loss[loss=0.1581, simple_loss=0.2275, pruned_loss=0.04435, over 4868.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.04108, over 971865.13 frames.], batch size: 32, lr: 3.81e-04
2022-05-05 06:59:12,131 INFO [train.py:715] (1/8) Epoch 5, batch 20100, loss[loss=0.1915, simple_loss=0.2445, pruned_loss=0.06926, over 4889.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2228, pruned_loss=0.04088, over 971196.35 frames.], batch size: 16, lr: 3.80e-04
2022-05-05 06:59:50,439 INFO [train.py:715] (1/8) Epoch 5, batch 20150, loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.03423, over 4797.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2227, pruned_loss=0.04083, over 971254.19 frames.], batch size: 24, lr: 3.80e-04
2022-05-05 07:00:30,260 INFO [train.py:715] (1/8) Epoch 5, batch 20200, loss[loss=0.1446, simple_loss=0.2174, pruned_loss=0.03588, over 4809.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2222, pruned_loss=0.04039, over 972225.50 frames.], batch size: 26, lr: 3.80e-04
2022-05-05 07:01:09,278 INFO [train.py:715] (1/8) Epoch 5, batch 20250, loss[loss=0.1608, simple_loss=0.2299, pruned_loss=0.04589, over 4914.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2218, pruned_loss=0.0403, over 972399.97 frames.], batch size: 18, lr: 3.80e-04
2022-05-05 07:01:47,790 INFO [train.py:715] (1/8) Epoch 5, batch 20300, loss[loss=0.1765, simple_loss=0.2482, pruned_loss=0.05241, over 4932.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2223, pruned_loss=0.04048, over 972670.99 frames.], batch size: 39, lr: 3.80e-04
2022-05-05 07:02:25,751 INFO [train.py:715] (1/8) Epoch 5, batch 20350, loss[loss=0.1338, simple_loss=0.2028, pruned_loss=0.03241, over 4816.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2222, pruned_loss=0.04053, over 973085.00 frames.], batch size: 26, lr: 3.80e-04
2022-05-05 07:03:04,306 INFO [train.py:715] (1/8) Epoch 5, batch 20400, loss[loss=0.1263, simple_loss=0.2014, pruned_loss=0.0256, over 4935.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2231, pruned_loss=0.04088, over 972188.21 frames.], batch size: 23, lr: 3.80e-04
2022-05-05 07:03:43,174 INFO [train.py:715] (1/8) Epoch 5, batch 20450, loss[loss=0.1823, simple_loss=0.2501, pruned_loss=0.05726, over 4779.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.04105, over 972197.86 frames.], batch size: 18, lr: 3.80e-04
2022-05-05 07:04:21,316 INFO [train.py:715] (1/8) Epoch 5, batch 20500, loss[loss=0.1667, simple_loss=0.2446, pruned_loss=0.04442, over 4808.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2223, pruned_loss=0.04081, over 972437.24 frames.], batch size: 25, lr: 3.80e-04
2022-05-05 07:05:00,718 INFO [train.py:715] (1/8) Epoch 5, batch 20550, loss[loss=0.1431, simple_loss=0.2067, pruned_loss=0.03978, over 4982.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2226, pruned_loss=0.04115, over 972810.69 frames.], batch size: 14, lr: 3.80e-04
2022-05-05 07:05:39,979 INFO [train.py:715] (1/8) Epoch 5, batch 20600, loss[loss=0.163, simple_loss=0.2383, pruned_loss=0.04385, over 4841.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2229, pruned_loss=0.04108, over 973284.01 frames.], batch size: 30, lr: 3.80e-04
2022-05-05 07:06:18,975 INFO [train.py:715] (1/8) Epoch 5, batch 20650, loss[loss=0.1583, simple_loss=0.2289, pruned_loss=0.04384, over 4824.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2231, pruned_loss=0.04094, over 972541.52 frames.], batch size: 15, lr: 3.80e-04
2022-05-05 07:06:58,195 INFO [train.py:715] (1/8) Epoch 5, batch 20700, loss[loss=0.1642, simple_loss=0.2349, pruned_loss=0.04681, over 4784.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2215, pruned_loss=0.04012, over 972308.94 frames.], batch size: 17, lr: 3.80e-04
2022-05-05 07:07:36,954 INFO [train.py:715] (1/8) Epoch 5, batch 20750, loss[loss=0.159, simple_loss=0.2348, pruned_loss=0.04167, over 4800.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2231, pruned_loss=0.04072, over 971917.22 frames.], batch size: 21, lr: 3.80e-04
2022-05-05 07:08:16,385 INFO [train.py:715] (1/8) Epoch 5, batch 20800, loss[loss=0.1491, simple_loss=0.2179, pruned_loss=0.04017, over 4926.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2219, pruned_loss=0.04026, over 971607.45 frames.], batch size: 29, lr: 3.80e-04
2022-05-05 07:08:55,025 INFO [train.py:715] (1/8) Epoch 5, batch 20850, loss[loss=0.1681, simple_loss=0.2242, pruned_loss=0.05601, over 4761.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2212, pruned_loss=0.03968, over 971831.29 frames.], batch size: 16, lr: 3.80e-04
2022-05-05 07:09:34,330 INFO [train.py:715] (1/8) Epoch 5, batch 20900, loss[loss=0.1545, simple_loss=0.2247, pruned_loss=0.04217, over 4777.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2208, pruned_loss=0.03949, over 971603.50 frames.], batch size: 17, lr: 3.80e-04
2022-05-05 07:10:12,904 INFO [train.py:715] (1/8) Epoch 5, batch 20950, loss[loss=0.1738, simple_loss=0.2403, pruned_loss=0.05361, over 4752.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2209, pruned_loss=0.03971, over 971688.85 frames.], batch size: 16, lr: 3.80e-04
2022-05-05 07:10:51,488 INFO [train.py:715] (1/8) Epoch 5, batch 21000, loss[loss=0.1315, simple_loss=0.201, pruned_loss=0.03101, over 4824.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2214, pruned_loss=0.0401, over 971844.00 frames.], batch size: 25, lr: 3.80e-04
2022-05-05 07:10:51,488 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 07:11:01,470 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1101, simple_loss=0.1954, pruned_loss=0.01242, over 914524.00 frames.
2022-05-05 07:11:40,515 INFO [train.py:715] (1/8) Epoch 5, batch 21050, loss[loss=0.1439, simple_loss=0.2105, pruned_loss=0.03862, over 4783.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2222, pruned_loss=0.04027, over 972547.71 frames.], batch size: 14, lr: 3.80e-04
2022-05-05 07:12:19,701 INFO [train.py:715] (1/8) Epoch 5, batch 21100, loss[loss=0.1674, simple_loss=0.232, pruned_loss=0.05138, over 4804.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2224, pruned_loss=0.04062, over 972475.35 frames.], batch size: 15, lr: 3.79e-04
2022-05-05 07:12:58,338 INFO [train.py:715] (1/8) Epoch 5, batch 21150, loss[loss=0.1625, simple_loss=0.2243, pruned_loss=0.05031, over 4937.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2218, pruned_loss=0.04035, over 972502.85 frames.], batch size: 23, lr: 3.79e-04
2022-05-05 07:13:37,168 INFO [train.py:715] (1/8) Epoch 5, batch 21200, loss[loss=0.1418, simple_loss=0.2264, pruned_loss=0.02859, over 4823.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2208, pruned_loss=0.0397, over 972676.16 frames.], batch size: 26, lr: 3.79e-04
2022-05-05 07:14:15,843 INFO [train.py:715] (1/8) Epoch 5, batch 21250, loss[loss=0.1263, simple_loss=0.1985, pruned_loss=0.02704, over 4865.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2214, pruned_loss=0.04006, over 972851.87 frames.], batch size: 20, lr: 3.79e-04
2022-05-05 07:14:54,665 INFO [train.py:715] (1/8) Epoch 5, batch 21300, loss[loss=0.165, simple_loss=0.2375, pruned_loss=0.04622, over 4888.00 frames.], tot_loss[loss=0.1514, simple_loss=0.222, pruned_loss=0.04041, over 972530.47 frames.], batch size: 16, lr: 3.79e-04
2022-05-05 07:15:33,337 INFO [train.py:715] (1/8) Epoch 5, batch 21350, loss[loss=0.1556, simple_loss=0.2248, pruned_loss=0.04322, over 4802.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2216, pruned_loss=0.04039, over 971961.32 frames.], batch size: 21, lr: 3.79e-04
2022-05-05 07:16:11,916 INFO [train.py:715] (1/8) Epoch 5, batch 21400, loss[loss=0.1308, simple_loss=0.2132, pruned_loss=0.02419, over 4746.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2218, pruned_loss=0.04032, over 972551.64 frames.], batch size: 19, lr: 3.79e-04
2022-05-05 07:16:50,974 INFO [train.py:715] (1/8) Epoch 5, batch 21450, loss[loss=0.1426, simple_loss=0.2222, pruned_loss=0.03147, over 4826.00 frames.], tot_loss[loss=0.151, simple_loss=0.222, pruned_loss=0.03999, over 971996.76 frames.], batch size: 26, lr: 3.79e-04
2022-05-05 07:17:29,102 INFO [train.py:715] (1/8) Epoch 5, batch 21500, loss[loss=0.1472, simple_loss=0.21, pruned_loss=0.04214, over 4875.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2224, pruned_loss=0.0402, over 972162.12 frames.], batch size: 32, lr: 3.79e-04
2022-05-05 07:18:08,224 INFO [train.py:715] (1/8) Epoch 5, batch 21550, loss[loss=0.1271, simple_loss=0.2058, pruned_loss=0.02419, over 4920.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2223, pruned_loss=0.04036, over 971633.58 frames.], batch size: 23, lr: 3.79e-04
2022-05-05 07:18:46,744 INFO [train.py:715] (1/8) Epoch 5, batch 21600, loss[loss=0.1693, simple_loss=0.2488, pruned_loss=0.04487, over 4958.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2232, pruned_loss=0.04091, over 972369.75 frames.], batch size: 21, lr: 3.79e-04
2022-05-05 07:19:25,824 INFO [train.py:715] (1/8) Epoch 5, batch 21650, loss[loss=0.1495, simple_loss=0.2207, pruned_loss=0.03917, over 4932.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2231, pruned_loss=0.04076, over 972938.36 frames.], batch size: 39, lr: 3.79e-04
2022-05-05 07:20:04,073 INFO [train.py:715] (1/8) Epoch 5, batch 21700, loss[loss=0.1387, simple_loss=0.2113, pruned_loss=0.03302, over 4793.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2227, pruned_loss=0.04073, over 973363.52 frames.], batch size: 17, lr: 3.79e-04
2022-05-05 07:20:42,466 INFO [train.py:715] (1/8) Epoch 5, batch 21750, loss[loss=0.1512, simple_loss=0.211, pruned_loss=0.04574, over 4937.00 frames.], tot_loss[loss=0.1516, simple_loss=0.222, pruned_loss=0.04059, over 973447.69 frames.], batch size: 23, lr: 3.79e-04
2022-05-05 07:21:20,819 INFO [train.py:715] (1/8) Epoch 5, batch 21800, loss[loss=0.1333, simple_loss=0.2169, pruned_loss=0.02483, over 4923.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2226, pruned_loss=0.04082, over 973399.42 frames.], batch size: 23, lr: 3.79e-04
2022-05-05 07:22:00,031 INFO [train.py:715] (1/8) Epoch 5, batch 21850, loss[loss=0.1598, simple_loss=0.2348, pruned_loss=0.04236, over 4954.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2234, pruned_loss=0.04107, over 973079.18 frames.], batch size: 21, lr: 3.79e-04
2022-05-05 07:22:38,263 INFO [train.py:715] (1/8) Epoch 5, batch 21900, loss[loss=0.17, simple_loss=0.223, pruned_loss=0.05855, over 4993.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2238, pruned_loss=0.04121, over 972563.10 frames.], batch size: 14, lr: 3.79e-04
2022-05-05 07:23:16,808 INFO [train.py:715] (1/8) Epoch 5, batch 21950, loss[loss=0.1322, simple_loss=0.2086, pruned_loss=0.02789, over 4960.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2231, pruned_loss=0.04079, over 972808.13 frames.], batch size: 25, lr: 3.79e-04
2022-05-05 07:23:55,218 INFO [train.py:715] (1/8) Epoch 5, batch 22000, loss[loss=0.1203, simple_loss=0.1894, pruned_loss=0.02562, over 4988.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2223, pruned_loss=0.04004, over 973186.59 frames.], batch size: 26, lr: 3.79e-04
2022-05-05 07:24:34,726 INFO [train.py:715] (1/8) Epoch 5, batch 22050, loss[loss=0.1515, simple_loss=0.2153, pruned_loss=0.04383, over 4800.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2218, pruned_loss=0.03994, over 972358.37 frames.], batch size: 24, lr: 3.79e-04
2022-05-05 07:25:13,188 INFO [train.py:715] (1/8) Epoch 5, batch 22100, loss[loss=0.1459, simple_loss=0.223, pruned_loss=0.03441, over 4788.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2223, pruned_loss=0.04005, over 972723.82 frames.], batch size: 12, lr: 3.79e-04
2022-05-05 07:25:52,416 INFO [train.py:715] (1/8) Epoch 5, batch 22150, loss[loss=0.1715, simple_loss=0.2513, pruned_loss=0.04589, over 4690.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2219, pruned_loss=0.04011, over 972532.62 frames.], batch size: 15, lr: 3.78e-04
2022-05-05 07:26:31,448 INFO [train.py:715] (1/8) Epoch 5, batch 22200, loss[loss=0.1229, simple_loss=0.1905, pruned_loss=0.02763, over 4815.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2214, pruned_loss=0.03996, over 972111.21 frames.], batch size: 12, lr: 3.78e-04
2022-05-05 07:27:11,168 INFO [train.py:715] (1/8) Epoch 5, batch 22250, loss[loss=0.2132, simple_loss=0.2609, pruned_loss=0.08273, over 4932.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2226, pruned_loss=0.04058, over 972038.47 frames.], batch size: 39, lr: 3.78e-04
2022-05-05 07:27:50,341 INFO [train.py:715] (1/8) Epoch 5, batch 22300, loss[loss=0.1535, simple_loss=0.228, pruned_loss=0.03951, over 4838.00 frames.], tot_loss[loss=0.153, simple_loss=0.2234, pruned_loss=0.04124, over 971871.30 frames.], batch size: 32, lr: 3.78e-04
2022-05-05 07:28:28,462 INFO [train.py:715] (1/8) Epoch 5, batch 22350, loss[loss=0.1548, simple_loss=0.2262, pruned_loss=0.04174, over 4822.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2238, pruned_loss=0.04145, over 971551.16 frames.], batch size: 13, lr: 3.78e-04
2022-05-05 07:29:06,835 INFO [train.py:715] (1/8) Epoch 5, batch 22400, loss[loss=0.1703, simple_loss=0.244, pruned_loss=0.04828, over 4905.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2237, pruned_loss=0.04147, over 971921.41 frames.], batch size: 17, lr: 3.78e-04
2022-05-05 07:29:45,745 INFO [train.py:715] (1/8) Epoch 5, batch 22450, loss[loss=0.1195, simple_loss=0.188, pruned_loss=0.02554, over 4978.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2233, pruned_loss=0.04109, over 972538.37 frames.], batch size: 35, lr: 3.78e-04
2022-05-05 07:30:25,213 INFO [train.py:715] (1/8) Epoch 5, batch 22500, loss[loss=0.1552, simple_loss=0.2289, pruned_loss=0.04079, over 4966.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.04096, over 972487.74 frames.], batch size: 39, lr: 3.78e-04
2022-05-05 07:31:03,490 INFO [train.py:715] (1/8) Epoch 5, batch 22550, loss[loss=0.1263, simple_loss=0.1916, pruned_loss=0.03049, over 4906.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2221, pruned_loss=0.04069, over 972392.88 frames.], batch size: 19, lr: 3.78e-04
2022-05-05 07:31:42,562 INFO [train.py:715] (1/8) Epoch 5, batch 22600, loss[loss=0.1645, simple_loss=0.2352, pruned_loss=0.0469, over 4689.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2221, pruned_loss=0.04066, over 972386.70 frames.], batch size: 15, lr: 3.78e-04
2022-05-05 07:32:21,689 INFO [train.py:715] (1/8) Epoch 5, batch 22650, loss[loss=0.1493, simple_loss=0.2189, pruned_loss=0.03986, over 4812.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2218, pruned_loss=0.04028, over 972232.01 frames.], batch size: 21, lr: 3.78e-04
2022-05-05 07:33:00,847 INFO [train.py:715] (1/8) Epoch 5, batch 22700, loss[loss=0.1453, simple_loss=0.2183, pruned_loss=0.03616, over 4920.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2221, pruned_loss=0.04041, over 972630.72 frames.], batch size: 18, lr: 3.78e-04
2022-05-05 07:33:39,169 INFO [train.py:715] (1/8) Epoch 5, batch 22750, loss[loss=0.1202, simple_loss=0.1901, pruned_loss=0.02514, over 4782.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2219, pruned_loss=0.0404, over 971909.44 frames.], batch size: 14, lr: 3.78e-04
2022-05-05 07:34:18,367 INFO [train.py:715] (1/8) Epoch 5, batch 22800, loss[loss=0.1557, simple_loss=0.2258, pruned_loss=0.04274, over 4921.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2209, pruned_loss=0.03947, over 971526.28 frames.], batch size: 18, lr: 3.78e-04
2022-05-05 07:34:57,945 INFO [train.py:715] (1/8) Epoch 5, batch 22850, loss[loss=0.1347, simple_loss=0.2115, pruned_loss=0.02897, over 4889.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2225, pruned_loss=0.04092, over 972030.48 frames.], batch size: 32, lr: 3.78e-04
2022-05-05 07:35:36,332 INFO [train.py:715] (1/8) Epoch 5, batch 22900, loss[loss=0.1262, simple_loss=0.1981, pruned_loss=0.02712, over 4816.00 frames.], tot_loss[loss=0.1515, simple_loss=0.222, pruned_loss=0.04049, over 971611.41 frames.], batch size: 27, lr: 3.78e-04
2022-05-05 07:36:15,066 INFO [train.py:715] (1/8) Epoch 5, batch 22950, loss[loss=0.1552, simple_loss=0.2302, pruned_loss=0.04013, over 4932.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2215, pruned_loss=0.04014, over 971948.21 frames.], batch size: 29, lr: 3.78e-04
2022-05-05 07:36:54,411 INFO [train.py:715] (1/8) Epoch 5, batch 23000, loss[loss=0.1703, simple_loss=0.2507, pruned_loss=0.04496, over 4892.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2224, pruned_loss=0.04041, over 972241.60 frames.], batch size: 22, lr: 3.78e-04
2022-05-05 07:37:33,569 INFO [train.py:715] (1/8) Epoch 5, batch 23050, loss[loss=0.1342, simple_loss=0.2091, pruned_loss=0.02961, over 4988.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2213, pruned_loss=0.03974, over 971742.52 frames.], batch size: 28, lr: 3.78e-04
2022-05-05 07:38:12,019 INFO [train.py:715] (1/8) Epoch 5, batch 23100, loss[loss=0.1684, simple_loss=0.2259, pruned_loss=0.05546, over 4945.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2208, pruned_loss=0.03968, over 971739.20 frames.], batch size: 39, lr: 3.78e-04
2022-05-05 07:38:51,180 INFO [train.py:715] (1/8) Epoch 5, batch 23150, loss[loss=0.1547, simple_loss=0.2241, pruned_loss=0.04267, over 4968.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2213, pruned_loss=0.04016, over 972390.76 frames.], batch size: 15, lr: 3.78e-04
2022-05-05 07:39:30,786 INFO [train.py:715] (1/8) Epoch 5, batch 23200, loss[loss=0.1486, simple_loss=0.227, pruned_loss=0.03509, over 4939.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2214, pruned_loss=0.0405, over 972949.46 frames.], batch size: 21, lr: 3.77e-04
2022-05-05 07:40:09,162 INFO [train.py:715] (1/8) Epoch 5, batch 23250, loss[loss=0.1697, simple_loss=0.2385, pruned_loss=0.05044, over 4866.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2217, pruned_loss=0.04039, over 973139.89 frames.], batch size: 38, lr: 3.77e-04
2022-05-05 07:40:47,785 INFO [train.py:715] (1/8) Epoch 5, batch 23300, loss[loss=0.1268, simple_loss=0.1986, pruned_loss=0.02747, over 4795.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2212, pruned_loss=0.04009, over 973440.69 frames.], batch size: 24, lr: 3.77e-04
2022-05-05 07:41:27,177 INFO [train.py:715] (1/8) Epoch 5, batch 23350, loss[loss=0.1599, simple_loss=0.2323, pruned_loss=0.04373, over 4851.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2204, pruned_loss=0.03992, over 974113.26 frames.], batch size: 30, lr: 3.77e-04
2022-05-05 07:42:05,803 INFO [train.py:715] (1/8) Epoch 5, batch 23400, loss[loss=0.1393, simple_loss=0.2117, pruned_loss=0.03349, over 4849.00 frames.], tot_loss[loss=0.15, simple_loss=0.2204, pruned_loss=0.03975, over 974196.80 frames.], batch size: 20, lr: 3.77e-04
2022-05-05 07:42:44,247 INFO [train.py:715] (1/8) Epoch 5, batch 23450, loss[loss=0.1398, simple_loss=0.201, pruned_loss=0.03924, over 4767.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2203, pruned_loss=0.04022, over 973278.01 frames.], batch size: 17, lr: 3.77e-04
2022-05-05 07:43:22,953 INFO [train.py:715] (1/8) Epoch 5, batch 23500, loss[loss=0.1153, simple_loss=0.197, pruned_loss=0.0168, over 4907.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2207, pruned_loss=0.04017, over 972170.64 frames.], batch size: 17, lr: 3.77e-04
2022-05-05 07:44:02,013 INFO [train.py:715] (1/8) Epoch 5, batch 23550, loss[loss=0.161, simple_loss=0.2298, pruned_loss=0.04612, over 4878.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2204, pruned_loss=0.04014, over 971776.94 frames.], batch size: 22, lr: 3.77e-04
2022-05-05 07:44:40,888 INFO [train.py:715] (1/8) Epoch 5, batch 23600, loss[loss=0.1802, simple_loss=0.2514, pruned_loss=0.05453, over 4902.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2212, pruned_loss=0.0407, over 971830.61 frames.], batch size: 19, lr: 3.77e-04
2022-05-05 07:45:19,395 INFO [train.py:715] (1/8) Epoch 5, batch 23650, loss[loss=0.1436, simple_loss=0.2176, pruned_loss=0.0348, over 4925.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04052, over 971767.95 frames.], batch size: 23, lr: 3.77e-04
2022-05-05 07:45:58,899 INFO [train.py:715] (1/8) Epoch 5, batch 23700, loss[loss=0.1736, simple_loss=0.2452, pruned_loss=0.05099, over 4783.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2227, pruned_loss=0.04075, over 972164.59 frames.], batch size: 17, lr: 3.77e-04
2022-05-05 07:46:37,476 INFO [train.py:715] (1/8) Epoch 5, batch 23750, loss[loss=0.1445, simple_loss=0.2114, pruned_loss=0.03886, over 4990.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2237, pruned_loss=0.04178, over 973111.61 frames.], batch size: 28, lr: 3.77e-04
2022-05-05 07:47:16,506 INFO [train.py:715] (1/8) Epoch 5, batch 23800, loss[loss=0.1464, simple_loss=0.2191, pruned_loss=0.03682, over 4969.00 frames.], tot_loss[loss=0.153, simple_loss=0.2232, pruned_loss=0.04142, over 972778.02 frames.], batch size: 24, lr: 3.77e-04
2022-05-05 07:47:55,210 INFO [train.py:715] (1/8) Epoch 5, batch 23850, loss[loss=0.1542, simple_loss=0.2307, pruned_loss=0.03882, over 4950.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2236, pruned_loss=0.04148, over 972832.51 frames.], batch size: 21, lr: 3.77e-04
2022-05-05 07:48:34,439 INFO [train.py:715] (1/8) Epoch 5, batch 23900, loss[loss=0.1318, simple_loss=0.2132, pruned_loss=0.02515, over 4986.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2227, pruned_loss=0.0408, over 971854.93 frames.], batch size: 26, lr: 3.77e-04
2022-05-05 07:49:13,373 INFO [train.py:715] (1/8) Epoch 5, batch 23950, loss[loss=0.1508, simple_loss=0.2208, pruned_loss=0.04037, over 4783.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2223, pruned_loss=0.04065, over 971995.23 frames.], batch size: 18, lr: 3.77e-04
2022-05-05 07:49:51,755 INFO [train.py:715] (1/8) Epoch 5, batch 24000, loss[loss=0.1574, simple_loss=0.2269, pruned_loss=0.04394, over 4800.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.04099, over 971528.41 frames.], batch size: 17, lr: 3.77e-04
2022-05-05 07:49:51,756 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 07:50:02,184 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.11, simple_loss=0.1955, pruned_loss=0.0123, over 914524.00 frames.
2022-05-05 07:50:40,725 INFO [train.py:715] (1/8) Epoch 5, batch 24050, loss[loss=0.1597, simple_loss=0.2314, pruned_loss=0.044, over 4900.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2213, pruned_loss=0.0401, over 971899.76 frames.], batch size: 17, lr: 3.77e-04
2022-05-05 07:51:20,437 INFO [train.py:715] (1/8) Epoch 5, batch 24100, loss[loss=0.1767, simple_loss=0.2378, pruned_loss=0.05777, over 4696.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2219, pruned_loss=0.04026, over 971319.25 frames.], batch size: 15, lr: 3.77e-04
2022-05-05 07:51:59,183 INFO [train.py:715] (1/8) Epoch 5, batch 24150, loss[loss=0.1311, simple_loss=0.2024, pruned_loss=0.02992, over 4837.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2225, pruned_loss=0.04088, over 971255.13 frames.], batch size: 13, lr: 3.77e-04
2022-05-05 07:52:37,493 INFO [train.py:715] (1/8) Epoch 5, batch 24200, loss[loss=0.1482, simple_loss=0.2205, pruned_loss=0.03795, over 4896.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2223, pruned_loss=0.04095, over 970882.25 frames.], batch size: 19, lr: 3.77e-04
2022-05-05 07:53:16,813 INFO [train.py:715] (1/8) Epoch 5, batch 24250, loss[loss=0.1917, simple_loss=0.2462, pruned_loss=0.06856, over 4913.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2213, pruned_loss=0.041, over 971967.73 frames.], batch size: 17, lr: 3.76e-04
2022-05-05 07:53:55,925 INFO [train.py:715] (1/8) Epoch 5, batch 24300, loss[loss=0.1545, simple_loss=0.2209, pruned_loss=0.04405, over 4916.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2206, pruned_loss=0.04061, over 972141.31 frames.], batch size: 17, lr: 3.76e-04
2022-05-05 07:54:34,805 INFO [train.py:715] (1/8) Epoch 5, batch 24350, loss[loss=0.1405, simple_loss=0.2075, pruned_loss=0.03672, over 4955.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2215, pruned_loss=0.04072, over 972302.16 frames.], batch size: 21, lr: 3.76e-04
2022-05-05 07:55:13,058 INFO [train.py:715] (1/8) Epoch 5, batch 24400, loss[loss=0.1541, simple_loss=0.2307, pruned_loss=0.03874, over 4872.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2213, pruned_loss=0.04031, over 971436.98 frames.], batch size: 32, lr: 3.76e-04
2022-05-05 07:55:52,741 INFO [train.py:715] (1/8) Epoch 5, batch 24450, loss[loss=0.1426, simple_loss=0.2109, pruned_loss=0.03716, over 4940.00 frames.], tot_loss[loss=0.1516, simple_loss=0.222, pruned_loss=0.04066, over 971424.23 frames.], batch size: 21, lr: 3.76e-04
2022-05-05 07:56:30,713 INFO [train.py:715] (1/8) Epoch 5, batch 24500, loss[loss=0.1325, simple_loss=0.2098, pruned_loss=0.02762, over 4862.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2224, pruned_loss=0.04095, over 971291.72 frames.], batch size: 20, lr: 3.76e-04
2022-05-05 07:57:09,368 INFO [train.py:715] (1/8) Epoch 5, batch 24550, loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.04093, over 4985.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2239, pruned_loss=0.04179, over 971175.14 frames.], batch size: 39, lr: 3.76e-04
2022-05-05 07:57:48,725 INFO [train.py:715] (1/8) Epoch 5, batch 24600, loss[loss=0.1588, simple_loss=0.2262, pruned_loss=0.04568, over 4788.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2239, pruned_loss=0.04166, over 971518.54 frames.], batch size: 17, lr: 3.76e-04
2022-05-05 07:58:27,793 INFO [train.py:715] (1/8) Epoch 5, batch 24650, loss[loss=0.1244, simple_loss=0.1981, pruned_loss=0.02537, over 4772.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2231, pruned_loss=0.04135, over 970747.71 frames.], batch size: 14, lr: 3.76e-04
2022-05-05 07:59:06,983 INFO [train.py:715] (1/8) Epoch 5, batch 24700, loss[loss=0.1509, simple_loss=0.2271, pruned_loss=0.0374, over 4872.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2232, pruned_loss=0.04153, over 970950.05 frames.], batch size: 22, lr: 3.76e-04
2022-05-05 07:59:45,119 INFO [train.py:715] (1/8) Epoch 5, batch 24750, loss[loss=0.1567, simple_loss=0.2327, pruned_loss=0.04034, over 4878.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.04106, over 970736.91 frames.], batch size: 22, lr: 3.76e-04
2022-05-05 08:00:24,685 INFO [train.py:715] (1/8) Epoch 5, batch 24800, loss[loss=0.1338, simple_loss=0.196, pruned_loss=0.03576, over 4855.00 frames.], tot_loss[loss=0.1522, simple_loss=0.222, pruned_loss=0.04118, over 970332.26 frames.], batch size: 13, lr: 3.76e-04
2022-05-05 08:01:03,113 INFO [train.py:715] (1/8) Epoch 5, batch 24850, loss[loss=0.1467, simple_loss=0.2233, pruned_loss=0.03506, over 4981.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2218, pruned_loss=0.04086, over 970562.74 frames.], batch size: 31, lr: 3.76e-04
2022-05-05 08:01:41,879 INFO [train.py:715] (1/8) Epoch 5, batch 24900, loss[loss=0.2244, simple_loss=0.2701, pruned_loss=0.08936, over 4970.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2225, pruned_loss=0.04115, over 970689.82 frames.], batch size: 15, lr: 3.76e-04
2022-05-05 08:02:21,443 INFO [train.py:715] (1/8) Epoch 5, batch 24950, loss[loss=0.1884, simple_loss=0.251, pruned_loss=0.0629, over 4885.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2228, pruned_loss=0.04118, over 970712.96 frames.], batch size: 22, lr: 3.76e-04
2022-05-05 08:03:00,473 INFO [train.py:715] (1/8) Epoch 5, batch 25000, loss[loss=0.1425, simple_loss=0.2028, pruned_loss=0.04111, over 4968.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2225, pruned_loss=0.04099, over 971169.88 frames.], batch size: 14, lr: 3.76e-04
2022-05-05 08:03:39,040 INFO [train.py:715] (1/8) Epoch 5, batch 25050, loss[loss=0.1348, simple_loss=0.2063, pruned_loss=0.03164, over 4971.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2225, pruned_loss=0.04121, over 971257.10 frames.], batch size: 28, lr: 3.76e-04
2022-05-05 08:04:17,286 INFO [train.py:715] (1/8) Epoch 5, batch 25100, loss[loss=0.1252, simple_loss=0.1974, pruned_loss=0.0265, over 4865.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2216, pruned_loss=0.04032, over 971373.71 frames.], batch size: 32, lr: 3.76e-04
2022-05-05 08:04:57,545 INFO [train.py:715] (1/8) Epoch 5, batch 25150, loss[loss=0.1458, simple_loss=0.2187, pruned_loss=0.03644, over 4919.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2215, pruned_loss=0.04004, over 971463.05 frames.], batch size: 29, lr: 3.76e-04
2022-05-05 08:05:35,728 INFO [train.py:715] (1/8) Epoch 5, batch 25200, loss[loss=0.1642, simple_loss=0.2254, pruned_loss=0.05155, over 4893.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2223, pruned_loss=0.04025, over 970650.25 frames.], batch size: 19, lr: 3.76e-04
2022-05-05 08:06:14,580 INFO [train.py:715] (1/8) Epoch 5, batch 25250, loss[loss=0.1533, simple_loss=0.2314, pruned_loss=0.03758, over 4769.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03981, over 969912.43 frames.], batch size: 19, lr: 3.76e-04
2022-05-05 08:06:53,406 INFO [train.py:715] (1/8) Epoch 5, batch 25300, loss[loss=0.1595, simple_loss=0.2237, pruned_loss=0.04766, over 4701.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2219, pruned_loss=0.04027, over 970479.02 frames.], batch size: 15, lr: 3.75e-04
2022-05-05 08:07:31,749 INFO [train.py:715] (1/8) Epoch 5, batch 25350, loss[loss=0.1105, simple_loss=0.1832, pruned_loss=0.01887, over 4790.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2221, pruned_loss=0.04049, over 970257.29 frames.], batch size: 13, lr: 3.75e-04
2022-05-05 08:08:10,250 INFO [train.py:715] (1/8) Epoch 5, batch 25400, loss[loss=0.158, simple_loss=0.2189, pruned_loss=0.0485, over 4892.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2215, pruned_loss=0.04037, over 970919.15 frames.], batch size: 22, lr: 3.75e-04
2022-05-05 08:08:49,166 INFO [train.py:715] (1/8) Epoch 5, batch 25450, loss[loss=0.1463, simple_loss=0.2146, pruned_loss=0.03906, over 4769.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2217, pruned_loss=0.04019, over 970839.59 frames.], batch size: 18, lr: 3.75e-04
2022-05-05 08:09:28,363 INFO [train.py:715] (1/8) Epoch 5, batch 25500, loss[loss=0.1404, simple_loss=0.2151, pruned_loss=0.03289, over 4802.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2223, pruned_loss=0.04104, over 970958.96 frames.], batch size: 24, lr: 3.75e-04
2022-05-05 08:10:07,145 INFO [train.py:715] (1/8) Epoch 5, batch 25550, loss[loss=0.1585, simple_loss=0.2384, pruned_loss=0.0393, over 4827.00 frames.], tot_loss[loss=0.1528, simple_loss=0.223, pruned_loss=0.04123, over 970878.22 frames.], batch size: 13, lr: 3.75e-04
2022-05-05 08:10:45,634 INFO [train.py:715] (1/8) Epoch 5, batch 25600, loss[loss=0.1465, simple_loss=0.2225, pruned_loss=0.03527, over 4679.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2231, pruned_loss=0.0416, over 971312.55 frames.], batch size: 15, lr: 3.75e-04
2022-05-05 08:11:24,704 INFO [train.py:715] (1/8) Epoch 5, batch 25650, loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02895, over 4988.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2227, pruned_loss=0.04135, over 971612.55 frames.], batch size: 28, lr: 3.75e-04
2022-05-05 08:12:03,096 INFO [train.py:715] (1/8) Epoch 5, batch 25700, loss[loss=0.1676, simple_loss=0.2278, pruned_loss=0.0537, over 4845.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2237, pruned_loss=0.04199, over 971696.61 frames.], batch size: 12, lr: 3.75e-04
2022-05-05 08:12:41,259 INFO [train.py:715] (1/8) Epoch 5, batch 25750, loss[loss=0.1303, simple_loss=0.2094, pruned_loss=0.02564, over 4823.00 frames.], tot_loss[loss=0.154, simple_loss=0.224, pruned_loss=0.04204, over 970866.22 frames.], batch size: 26, lr: 3.75e-04
2022-05-05 08:13:20,743 INFO [train.py:715] (1/8) Epoch 5, batch 25800, loss[loss=0.1562, simple_loss=0.2304, pruned_loss=0.04104, over 4708.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2248, pruned_loss=0.0423, over 971385.41 frames.], batch size: 15, lr: 3.75e-04
2022-05-05 08:13:59,835 INFO [train.py:715] (1/8) Epoch 5, batch 25850, loss[loss=0.146, simple_loss=0.2125, pruned_loss=0.03979, over 4866.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2238, pruned_loss=0.04166, over 971073.50 frames.], batch size: 32, lr: 3.75e-04
2022-05-05 08:14:38,584 INFO [train.py:715] (1/8) Epoch 5, batch 25900, loss[loss=0.1891, simple_loss=0.2651, pruned_loss=0.05658, over 4895.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2238, pruned_loss=0.04121, over 971719.67 frames.], batch size: 22, lr: 3.75e-04
2022-05-05 08:15:17,126 INFO [train.py:715] (1/8) Epoch 5, batch 25950, loss[loss=0.1662, simple_loss=0.2385, pruned_loss=0.04694, over 4832.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2246, pruned_loss=0.04164, over 972242.51 frames.], batch size: 13, lr: 3.75e-04
2022-05-05 08:15:58,604 INFO [train.py:715] (1/8) Epoch 5, batch 26000, loss[loss=0.1338, simple_loss=0.1957, pruned_loss=0.03595, over 4824.00 frames.], tot_loss[loss=0.154, simple_loss=0.2245, pruned_loss=0.04173, over 972736.13 frames.], batch size: 12, lr: 3.75e-04
2022-05-05 08:16:37,297 INFO [train.py:715] (1/8) Epoch 5, batch 26050, loss[loss=0.1373, simple_loss=0.2207, pruned_loss=0.02697, over 4890.00 frames.], tot_loss[loss=0.154, simple_loss=0.2247, pruned_loss=0.04168, over 972301.98 frames.], batch size: 19, lr: 3.75e-04
2022-05-05 08:17:15,757 INFO [train.py:715] (1/8) Epoch 5, batch 26100, loss[loss=0.133, simple_loss=0.2235, pruned_loss=0.02124, over 4931.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2244, pruned_loss=0.04147, over 973191.12 frames.], batch size: 21, lr: 3.75e-04
2022-05-05 08:17:54,717 INFO [train.py:715] (1/8) Epoch 5, batch 26150, loss[loss=0.1424, simple_loss=0.2221, pruned_loss=0.03135, over 4952.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2234, pruned_loss=0.04096, over 973113.26 frames.], batch size: 24, lr: 3.75e-04
2022-05-05 08:18:33,050 INFO [train.py:715] (1/8) Epoch 5, batch 26200, loss[loss=0.1519, simple_loss=0.2351, pruned_loss=0.03432, over 4795.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2228, pruned_loss=0.04077, over 973189.14 frames.], batch size: 21, lr: 3.75e-04
2022-05-05 08:19:12,108 INFO [train.py:715] (1/8) Epoch 5, batch 26250, loss[loss=0.1398, simple_loss=0.2083, pruned_loss=0.03568, over 4769.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2226, pruned_loss=0.04106, over 971793.52 frames.], batch size: 14, lr: 3.75e-04
2022-05-05 08:19:51,346 INFO [train.py:715] (1/8) Epoch 5, batch 26300, loss[loss=0.1606, simple_loss=0.2308, pruned_loss=0.04522, over 4786.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2232, pruned_loss=0.04081, over 972020.36 frames.], batch size: 17, lr: 3.75e-04
2022-05-05 08:20:30,627 INFO [train.py:715] (1/8) Epoch 5, batch 26350, loss[loss=0.2081, simple_loss=0.2589, pruned_loss=0.07867, over 4849.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2225, pruned_loss=0.04055, over 972174.56 frames.], batch size: 34, lr: 3.74e-04
2022-05-05 08:21:09,428 INFO [train.py:715] (1/8) Epoch 5, batch 26400, loss[loss=0.1501, simple_loss=0.225, pruned_loss=0.03755, over 4798.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2232, pruned_loss=0.04057, over 971617.90 frames.], batch size: 21, lr: 3.74e-04
2022-05-05 08:21:48,026 INFO [train.py:715] (1/8) Epoch 5, batch 26450, loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.03016, over 4805.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2225, pruned_loss=0.04014, over 971453.52 frames.], batch size: 21, lr: 3.74e-04
2022-05-05 08:22:26,948 INFO [train.py:715] (1/8) Epoch 5, batch 26500, loss[loss=0.1481, simple_loss=0.2183, pruned_loss=0.03894, over 4948.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2236, pruned_loss=0.0404, over 971734.12 frames.], batch size: 29, lr: 3.74e-04
2022-05-05 08:23:06,043 INFO [train.py:715] (1/8) Epoch 5, batch 26550, loss[loss=0.1885, simple_loss=0.2475, pruned_loss=0.06475, over 4723.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2229, pruned_loss=0.03987, over 971503.47 frames.], batch size: 15, lr: 3.74e-04
2022-05-05 08:23:44,739 INFO [train.py:715] (1/8) Epoch 5, batch 26600, loss[loss=0.1658, simple_loss=0.2305, pruned_loss=0.05051, over 4914.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2226, pruned_loss=0.04003, over 971517.74 frames.], batch size: 18, lr: 3.74e-04
2022-05-05 08:24:24,178 INFO [train.py:715] (1/8) Epoch 5, batch 26650, loss[loss=0.1645, simple_loss=0.2356, pruned_loss=0.0467, over 4873.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2222, pruned_loss=0.03998, over 972242.80 frames.], batch size: 16, lr: 3.74e-04
2022-05-05 08:25:02,987 INFO [train.py:715] (1/8) Epoch 5, batch 26700, loss[loss=0.1593, simple_loss=0.2273, pruned_loss=0.04567, over 4740.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2226, pruned_loss=0.04099, over 971565.42 frames.], batch size: 16, lr: 3.74e-04
2022-05-05 08:25:41,813 INFO [train.py:715] (1/8) Epoch 5, batch 26750, loss[loss=0.1619, simple_loss=0.2294, pruned_loss=0.04722, over 4896.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2219, pruned_loss=0.04044, over 971684.33 frames.], batch size: 17, lr: 3.74e-04
2022-05-05 08:26:20,196 INFO [train.py:715] (1/8) Epoch 5, batch 26800, loss[loss=0.1371, simple_loss=0.2006, pruned_loss=0.03676, over 4869.00 frames.], tot_loss[loss=0.153, simple_loss=0.2231, pruned_loss=0.04146, over 971998.84 frames.], batch size: 32, lr: 3.74e-04
2022-05-05 08:26:59,361 INFO [train.py:715] (1/8) Epoch 5, batch 26850, loss[loss=0.1688, simple_loss=0.2335, pruned_loss=0.05205, over 4770.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2235, pruned_loss=0.04182, over 971895.25 frames.], batch size: 14, lr: 3.74e-04
2022-05-05 08:27:38,338 INFO [train.py:715] (1/8) Epoch 5, batch 26900, loss[loss=0.1651, simple_loss=0.2216, pruned_loss=0.05427, over 4858.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2238, pruned_loss=0.0418, over 973032.38 frames.], batch size: 12, lr: 3.74e-04
2022-05-05 08:28:17,272 INFO [train.py:715] (1/8) Epoch 5, batch 26950, loss[loss=0.1517, simple_loss=0.227, pruned_loss=0.03818, over 4984.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2237, pruned_loss=0.04171, over 972655.46 frames.], batch size: 31, lr: 3.74e-04
2022-05-05 08:28:55,975 INFO [train.py:715] (1/8) Epoch 5, batch 27000, loss[loss=0.1521, simple_loss=0.211, pruned_loss=0.04665, over 4880.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2235, pruned_loss=0.04151, over 973000.10 frames.], batch size: 22, lr: 3.74e-04
2022-05-05 08:28:55,976 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 08:29:05,776 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1098, simple_loss=0.195, pruned_loss=0.01232, over 914524.00 frames.
2022-05-05 08:29:45,281 INFO [train.py:715] (1/8) Epoch 5, batch 27050, loss[loss=0.1756, simple_loss=0.2356, pruned_loss=0.05781, over 4910.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2227, pruned_loss=0.04126, over 973258.89 frames.], batch size: 18, lr: 3.74e-04
2022-05-05 08:30:24,756 INFO [train.py:715] (1/8) Epoch 5, batch 27100, loss[loss=0.1468, simple_loss=0.2124, pruned_loss=0.0406, over 4795.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2227, pruned_loss=0.04154, over 973192.99 frames.], batch size: 24, lr: 3.74e-04
2022-05-05 08:31:04,148 INFO [train.py:715] (1/8) Epoch 5, batch 27150, loss[loss=0.1425, simple_loss=0.2141, pruned_loss=0.03547, over 4898.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2224, pruned_loss=0.04107, over 972222.65 frames.], batch size: 17, lr: 3.74e-04
2022-05-05 08:31:42,964 INFO [train.py:715] (1/8) Epoch 5, batch 27200, loss[loss=0.1662, simple_loss=0.2403, pruned_loss=0.04611, over 4794.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2232, pruned_loss=0.04191, over 972080.88 frames.], batch size: 12, lr: 3.74e-04
2022-05-05 08:32:22,590 INFO [train.py:715] (1/8) Epoch 5, batch 27250, loss[loss=0.154, simple_loss=0.2217, pruned_loss=0.04315, over 4746.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2229, pruned_loss=0.04174, over 971584.88 frames.], batch size: 16, lr: 3.74e-04
2022-05-05 08:33:01,565 INFO [train.py:715] (1/8) Epoch 5, batch 27300, loss[loss=0.1355, simple_loss=0.198, pruned_loss=0.03652, over 4862.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2227, pruned_loss=0.04122, over 972374.02 frames.], batch size: 32, lr: 3.74e-04
2022-05-05 08:33:40,120 INFO [train.py:715] (1/8) Epoch 5, batch 27350, loss[loss=0.1502, simple_loss=0.2189, pruned_loss=0.04078, over 4954.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2229, pruned_loss=0.04145, over 972932.36 frames.], batch size: 21, lr: 3.74e-04
2022-05-05 08:34:19,000 INFO [train.py:715] (1/8) Epoch 5, batch 27400, loss[loss=0.1513, simple_loss=0.2215, pruned_loss=0.04057, over 4866.00 frames.], tot_loss[loss=0.153, simple_loss=0.223, pruned_loss=0.04153, over 973023.29 frames.], batch size: 20, lr: 3.74e-04
2022-05-05 08:34:58,267 INFO [train.py:715] (1/8) Epoch 5, batch 27450, loss[loss=0.1746, simple_loss=0.2496, pruned_loss=0.04977, over 4935.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2231, pruned_loss=0.04128, over 973180.82 frames.], batch size: 21, lr: 3.73e-04
2022-05-05 08:35:38,043 INFO [train.py:715] (1/8) Epoch 5, batch 27500, loss[loss=0.1607, simple_loss=0.2394, pruned_loss=0.04097, over 4959.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2234, pruned_loss=0.04135, over 973601.11 frames.], batch size: 24, lr: 3.73e-04
2022-05-05 08:36:16,529 INFO [train.py:715] (1/8) Epoch 5, batch 27550, loss[loss=0.1208, simple_loss=0.1944, pruned_loss=0.02362, over 4939.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04053, over 973396.31 frames.], batch size: 29, lr: 3.73e-04
2022-05-05 08:36:55,895 INFO [train.py:715] (1/8) Epoch 5, batch 27600, loss[loss=0.1368, simple_loss=0.208, pruned_loss=0.0328, over 4835.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2223, pruned_loss=0.04024, over 973630.15 frames.], batch size: 15, lr: 3.73e-04
2022-05-05 08:37:34,978 INFO [train.py:715] (1/8) Epoch 5, batch 27650, loss[loss=0.1647, simple_loss=0.2465, pruned_loss=0.04141, over 4947.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2222, pruned_loss=0.04028, over 973054.13 frames.], batch size: 21, lr: 3.73e-04
2022-05-05 08:38:13,252 INFO [train.py:715] (1/8) Epoch 5, batch 27700, loss[loss=0.1496, simple_loss=0.2222, pruned_loss=0.03848, over 4981.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2225, pruned_loss=0.04041, over 973203.75 frames.], batch size: 26, lr: 3.73e-04
2022-05-05 08:38:52,848 INFO [train.py:715] (1/8) Epoch 5, batch 27750, loss[loss=0.192, simple_loss=0.257, pruned_loss=0.06353, over 4826.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2221, pruned_loss=0.04046, over 973372.40 frames.], batch size: 13, lr: 3.73e-04
2022-05-05 08:39:32,594 INFO [train.py:715] (1/8) Epoch 5, batch 27800, loss[loss=0.1797, simple_loss=0.2488, pruned_loss=0.05532, over 4944.00 frames.], tot_loss[loss=0.152, simple_loss=0.2226, pruned_loss=0.0407, over 973538.06 frames.], batch size: 39, lr: 3.73e-04
2022-05-05 08:40:11,946 INFO [train.py:715] (1/8) Epoch 5, batch 27850, loss[loss=0.1606, simple_loss=0.2201, pruned_loss=0.05054, over 4988.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2217, pruned_loss=0.04061, over 973484.90 frames.], batch size: 14, lr: 3.73e-04
2022-05-05 08:40:50,651 INFO [train.py:715] (1/8) Epoch 5, batch 27900, loss[loss=0.1292, simple_loss=0.2084, pruned_loss=0.02503, over 4862.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2213, pruned_loss=0.04024, over 973025.34 frames.], batch size: 20, lr: 3.73e-04
2022-05-05 08:41:29,603 INFO [train.py:715] (1/8) Epoch 5, batch 27950, loss[loss=0.1436, simple_loss=0.2198, pruned_loss=0.0337, over 4826.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2212, pruned_loss=0.0401, over 972959.59 frames.], batch size: 25, lr: 3.73e-04
2022-05-05 08:42:09,043 INFO [train.py:715] (1/8) Epoch 5, batch 28000, loss[loss=0.154, simple_loss=0.2308, pruned_loss=0.03857, over 4925.00 frames.], tot_loss[loss=0.1503, simple_loss=0.221, pruned_loss=0.03979, over 972664.37 frames.], batch size: 29, lr: 3.73e-04
2022-05-05 08:42:47,128 INFO [train.py:715] (1/8) Epoch 5, batch 28050, loss[loss=0.1655, simple_loss=0.2348, pruned_loss=0.04807, over 4900.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2218, pruned_loss=0.03985, over 972828.59 frames.], batch size: 19, lr: 3.73e-04
2022-05-05 08:43:25,857 INFO [train.py:715] (1/8) Epoch 5, batch 28100, loss[loss=0.1808, simple_loss=0.2332, pruned_loss=0.06421, over 4991.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2221, pruned_loss=0.04017, over 972453.03 frames.], batch size: 15, lr: 3.73e-04
2022-05-05 08:44:04,995 INFO [train.py:715] (1/8) Epoch 5, batch 28150, loss[loss=0.1487, simple_loss=0.221, pruned_loss=0.03816, over 4938.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2222, pruned_loss=0.04043, over 972310.33 frames.], batch size: 23, lr: 3.73e-04
2022-05-05 08:44:43,938 INFO [train.py:715] (1/8) Epoch 5, batch 28200, loss[loss=0.1535, simple_loss=0.2233, pruned_loss=0.04187, over 4987.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2206, pruned_loss=0.03981, over 972608.20 frames.], batch size: 14, lr: 3.73e-04
2022-05-05 08:45:22,618 INFO [train.py:715] (1/8) Epoch 5, batch 28250, loss[loss=0.1817, simple_loss=0.236, pruned_loss=0.06373, over 4895.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2217, pruned_loss=0.04048, over 972916.28 frames.], batch size: 19, lr: 3.73e-04
2022-05-05 08:46:01,489 INFO [train.py:715] (1/8) Epoch 5, batch 28300, loss[loss=0.1396, simple_loss=0.2044, pruned_loss=0.03739, over 4808.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2226, pruned_loss=0.04095, over 972947.29 frames.], batch size: 13, lr: 3.73e-04
2022-05-05 08:46:39,905 INFO [train.py:715] (1/8) Epoch 5, batch 28350, loss[loss=0.1231, simple_loss=0.1966, pruned_loss=0.02484, over 4981.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2232, pruned_loss=0.04115, over 972670.96 frames.], batch size: 27, lr: 3.73e-04
2022-05-05 08:47:18,561 INFO [train.py:715] (1/8) Epoch 5, batch 28400, loss[loss=0.1338, simple_loss=0.2047, pruned_loss=0.03139, over 4927.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2227, pruned_loss=0.04049, over 973159.47 frames.], batch size: 23, lr: 3.73e-04
2022-05-05 08:47:57,681 INFO [train.py:715] (1/8) Epoch 5, batch 28450, loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03442, over 4834.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2235, pruned_loss=0.04107, over 972991.90 frames.], batch size: 12, lr: 3.73e-04
2022-05-05 08:48:36,729 INFO [train.py:715] (1/8) Epoch 5, batch 28500, loss[loss=0.1313, simple_loss=0.213, pruned_loss=0.02481, over 4739.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2228, pruned_loss=0.04019, over 973765.81 frames.], batch size: 16, lr: 3.72e-04
2022-05-05 08:49:15,939 INFO [train.py:715] (1/8) Epoch 5, batch 28550, loss[loss=0.1724, simple_loss=0.2478, pruned_loss=0.04856, over 4829.00 frames.], tot_loss[loss=0.151, simple_loss=0.222, pruned_loss=0.04002, over 972557.97 frames.], batch size: 30, lr: 3.72e-04
2022-05-05 08:49:54,629 INFO [train.py:715] (1/8) Epoch 5, batch 28600, loss[loss=0.1112, simple_loss=0.1819, pruned_loss=0.02026, over 4814.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2212, pruned_loss=0.03955, over 972143.55 frames.], batch size: 27, lr: 3.72e-04
2022-05-05 08:50:34,063 INFO [train.py:715] (1/8) Epoch 5, batch 28650, loss[loss=0.1622, simple_loss=0.2272, pruned_loss=0.04854, over 4817.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2211, pruned_loss=0.03962, over 972341.41 frames.], batch size: 26, lr: 3.72e-04
2022-05-05 08:51:12,504 INFO [train.py:715] (1/8) Epoch 5, batch 28700, loss[loss=0.1571, simple_loss=0.2219, pruned_loss=0.04614, over 4942.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2216, pruned_loss=0.03969, over 973621.80 frames.], batch size: 21, lr: 3.72e-04
2022-05-05 08:51:51,353 INFO [train.py:715] (1/8) Epoch 5, batch 28750, loss[loss=0.2076, simple_loss=0.2941, pruned_loss=0.06053, over 4916.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2219, pruned_loss=0.03988, over 973931.67 frames.], batch size: 18, lr: 3.72e-04
2022-05-05 08:52:30,123 INFO [train.py:715] (1/8) Epoch 5, batch 28800, loss[loss=0.1416, simple_loss=0.2283, pruned_loss=0.0275, over 4976.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2218, pruned_loss=0.03969, over 973306.98 frames.], batch size: 28, lr: 3.72e-04
2022-05-05 08:53:09,040 INFO [train.py:715] (1/8) Epoch 5, batch 28850, loss[loss=0.1357, simple_loss=0.2044, pruned_loss=0.03351, over 4847.00 frames.], tot_loss[loss=0.151, simple_loss=0.2222, pruned_loss=0.03988, over 973021.74 frames.], batch size: 13, lr: 3.72e-04
2022-05-05 08:53:47,808 INFO [train.py:715] (1/8) Epoch 5, batch 28900, loss[loss=0.1445, simple_loss=0.2179, pruned_loss=0.03555, over 4987.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2224, pruned_loss=0.03998, over 973017.64 frames.], batch size: 35, lr: 3.72e-04
2022-05-05 08:54:26,497 INFO [train.py:715] (1/8) Epoch 5, batch 28950, loss[loss=0.1542, simple_loss=0.2191, pruned_loss=0.04463, over 4821.00 frames.], tot_loss[loss=0.152, simple_loss=0.2231, pruned_loss=0.0404, over 972265.91 frames.], batch size: 25, lr: 3.72e-04
2022-05-05 08:55:05,613 INFO [train.py:715] (1/8) Epoch 5, batch 29000, loss[loss=0.1391, simple_loss=0.2089, pruned_loss=0.03461, over 4852.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2229, pruned_loss=0.03979, over 971861.74 frames.], batch size: 12, lr: 3.72e-04
2022-05-05 08:55:43,855 INFO [train.py:715] (1/8) Epoch 5, batch 29050, loss[loss=0.1662, simple_loss=0.2332, pruned_loss=0.04961, over 4794.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2231, pruned_loss=0.03997, over 971719.30 frames.], batch size: 17, lr: 3.72e-04
2022-05-05 08:56:22,925 INFO [train.py:715] (1/8) Epoch 5, batch 29100, loss[loss=0.121, simple_loss=0.1981, pruned_loss=0.02193, over 4776.00 frames.], tot_loss[loss=0.1504, simple_loss=0.222, pruned_loss=0.0394, over 970593.56 frames.], batch size: 19, lr: 3.72e-04
2022-05-05 08:57:01,742 INFO [train.py:715] (1/8) Epoch 5, batch 29150, loss[loss=0.1689, simple_loss=0.2321, pruned_loss=0.05286, over 4827.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2217, pruned_loss=0.04002, over 970360.48 frames.], batch size: 25, lr: 3.72e-04
2022-05-05 08:57:40,490 INFO [train.py:715] (1/8) Epoch 5, batch 29200, loss[loss=0.1481, simple_loss=0.2198, pruned_loss=0.03822, over 4915.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2221, pruned_loss=0.04062, over 970663.16 frames.], batch size: 29, lr: 3.72e-04
2022-05-05 08:58:19,235 INFO [train.py:715] (1/8) Epoch 5, batch 29250, loss[loss=0.1429, simple_loss=0.2056, pruned_loss=0.04009, over 4787.00 frames.], tot_loss[loss=0.151, simple_loss=0.2214, pruned_loss=0.04034, over 970694.07 frames.], batch size: 14, lr: 3.72e-04
2022-05-05 08:58:57,803 INFO [train.py:715] (1/8) Epoch 5, batch 29300, loss[loss=0.1268, simple_loss=0.2033, pruned_loss=0.02516, over 4845.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2216, pruned_loss=0.04015, over 971448.80 frames.], batch size: 32, lr: 3.72e-04
2022-05-05 08:59:37,059 INFO [train.py:715] (1/8) Epoch 5, batch 29350, loss[loss=0.1567, simple_loss=0.23, pruned_loss=0.04174, over 4801.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2216, pruned_loss=0.0404, over 972066.40 frames.], batch size: 25, lr: 3.72e-04
2022-05-05 09:00:15,740 INFO [train.py:715] (1/8) Epoch 5, batch 29400, loss[loss=0.2072, simple_loss=0.2684, pruned_loss=0.073, over 4917.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2208, pruned_loss=0.03993, over 971955.16 frames.], batch size: 18, lr: 3.72e-04
2022-05-05 09:00:54,490 INFO [train.py:715] (1/8) Epoch 5, batch 29450, loss[loss=0.1439, simple_loss=0.2164, pruned_loss=0.03568, over 4981.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2213, pruned_loss=0.04057, over 971815.13 frames.], batch size: 16, lr: 3.72e-04
2022-05-05 09:01:34,123 INFO [train.py:715] (1/8) Epoch 5, batch 29500, loss[loss=0.1558, simple_loss=0.2341, pruned_loss=0.03874, over 4795.00 frames.], tot_loss[loss=0.1515, simple_loss=0.222, pruned_loss=0.04048, over 972253.56 frames.], batch size: 17, lr: 3.72e-04
2022-05-05 09:02:13,206 INFO [train.py:715] (1/8) Epoch 5, batch 29550, loss[loss=0.198, simple_loss=0.2757, pruned_loss=0.06011, over 4851.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2222, pruned_loss=0.04058, over 971477.96 frames.], batch size: 38, lr: 3.72e-04
2022-05-05 09:02:52,391 INFO [train.py:715] (1/8) Epoch 5, batch 29600, loss[loss=0.181, simple_loss=0.2432, pruned_loss=0.05938, over 4957.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2227, pruned_loss=0.04083, over 971148.66 frames.], batch size: 15, lr: 3.71e-04
2022-05-05 09:03:31,060 INFO [train.py:715] (1/8) Epoch 5, batch 29650, loss[loss=0.1425, simple_loss=0.2099, pruned_loss=0.03756, over 4843.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2222, pruned_loss=0.04041, over 971486.54 frames.], batch size: 30, lr: 3.71e-04
2022-05-05 09:04:09,890 INFO [train.py:715] (1/8) Epoch 5, batch 29700, loss[loss=0.1299, simple_loss=0.2001, pruned_loss=0.02987, over 4778.00 frames.], tot_loss[loss=0.1515, simple_loss=0.222, pruned_loss=0.04049, over 971564.91 frames.], batch size: 17, lr: 3.71e-04
2022-05-05 09:04:48,813 INFO [train.py:715] (1/8) Epoch 5, batch 29750, loss[loss=0.1686, simple_loss=0.2472, pruned_loss=0.04503, over 4863.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2237, pruned_loss=0.04159, over 971640.69 frames.], batch size: 20, lr: 3.71e-04
2022-05-05 09:05:27,384 INFO [train.py:715] (1/8) Epoch 5, batch 29800, loss[loss=0.1436, simple_loss=0.2051, pruned_loss=0.04099, over 4980.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2233, pruned_loss=0.04097, over 972994.56 frames.], batch size: 31, lr: 3.71e-04
2022-05-05 09:06:05,624 INFO [train.py:715] (1/8) Epoch 5, batch 29850, loss[loss=0.1597, simple_loss=0.2299, pruned_loss=0.0448, over 4866.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2213, pruned_loss=0.03985, over 973591.17 frames.], batch size: 16, lr: 3.71e-04
2022-05-05 09:06:44,671 INFO [train.py:715] (1/8) Epoch 5, batch 29900, loss[loss=0.1387, simple_loss=0.214, pruned_loss=0.03164, over 4739.00 frames.], tot_loss[loss=0.151, simple_loss=0.222, pruned_loss=0.03995, over 973470.15 frames.], batch size: 16, lr: 3.71e-04
2022-05-05 09:07:24,017 INFO [train.py:715] (1/8) Epoch 5, batch 29950, loss[loss=0.1356, simple_loss=0.1993, pruned_loss=0.03595, over 4982.00 frames.], tot_loss[loss=0.1512, simple_loss=0.222, pruned_loss=0.04016, over 972641.83 frames.], batch size: 33, lr: 3.71e-04
2022-05-05 09:08:02,568 INFO [train.py:715] (1/8) Epoch 5, batch 30000, loss[loss=0.1327, simple_loss=0.2017, pruned_loss=0.03188, over 4881.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2215, pruned_loss=0.03954, over 972271.38 frames.], batch size: 38, lr: 3.71e-04
2022-05-05 09:08:02,569 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 09:08:12,297 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.11, simple_loss=0.1953, pruned_loss=0.01241, over 914524.00 frames.
2022-05-05 09:08:51,328 INFO [train.py:715] (1/8) Epoch 5, batch 30050, loss[loss=0.1467, simple_loss=0.2193, pruned_loss=0.03701, over 4982.00 frames.], tot_loss[loss=0.15, simple_loss=0.2212, pruned_loss=0.03938, over 972895.32 frames.], batch size: 14, lr: 3.71e-04
2022-05-05 09:09:31,497 INFO [train.py:715] (1/8) Epoch 5, batch 30100, loss[loss=0.1651, simple_loss=0.23, pruned_loss=0.0501, over 4948.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2222, pruned_loss=0.03975, over 972737.29 frames.], batch size: 21, lr: 3.71e-04
2022-05-05 09:10:10,296 INFO [train.py:715] (1/8) Epoch 5, batch 30150, loss[loss=0.1297, simple_loss=0.2006, pruned_loss=0.02939, over 4956.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2218, pruned_loss=0.03959, over 973130.91 frames.], batch size: 21, lr: 3.71e-04
2022-05-05 09:10:48,848 INFO [train.py:715] (1/8) Epoch 5, batch 30200, loss[loss=0.1388, simple_loss=0.2139, pruned_loss=0.03185, over 4771.00 frames.], tot_loss[loss=0.15, simple_loss=0.2214, pruned_loss=0.0393, over 972581.40 frames.], batch size: 18, lr: 3.71e-04
2022-05-05 09:11:27,807 INFO [train.py:715] (1/8) Epoch 5, batch 30250, loss[loss=0.1307, simple_loss=0.1987, pruned_loss=0.03131, over 4787.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2218, pruned_loss=0.03945, over 972825.55 frames.], batch size: 17, lr: 3.71e-04
2022-05-05 09:12:06,784 INFO [train.py:715] (1/8) Epoch 5, batch 30300, loss[loss=0.1653, simple_loss=0.2283, pruned_loss=0.05116, over 4908.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2225, pruned_loss=0.03985, over 974010.11 frames.], batch size: 18, lr: 3.71e-04
2022-05-05 09:12:45,787 INFO [train.py:715] (1/8) Epoch 5, batch 30350, loss[loss=0.1291, simple_loss=0.2035, pruned_loss=0.02741, over 4969.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2226, pruned_loss=0.04003, over 974279.09 frames.], batch size: 28, lr: 3.71e-04
2022-05-05 09:13:24,288 INFO [train.py:715] (1/8) Epoch 5, batch 30400, loss[loss=0.1453, simple_loss=0.2206, pruned_loss=0.03506, over 4679.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2226, pruned_loss=0.03995, over 973512.36 frames.], batch size: 15, lr: 3.71e-04
2022-05-05 09:14:03,375 INFO [train.py:715] (1/8) Epoch 5, batch 30450, loss[loss=0.1411, simple_loss=0.2152, pruned_loss=0.03353, over 4944.00 frames.], tot_loss[loss=0.1508, simple_loss=0.222, pruned_loss=0.03979, over 972758.89 frames.], batch size: 21, lr: 3.71e-04
2022-05-05 09:14:42,253 INFO [train.py:715] (1/8) Epoch 5, batch 30500, loss[loss=0.1842, simple_loss=0.2522, pruned_loss=0.05813, over 4932.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2227, pruned_loss=0.04025, over 972965.30 frames.], batch size: 29, lr: 3.71e-04
2022-05-05 09:15:20,924 INFO [train.py:715] (1/8) Epoch 5, batch 30550, loss[loss=0.1317, simple_loss=0.2058, pruned_loss=0.02884, over 4952.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2229, pruned_loss=0.04027, over 973297.91 frames.], batch size: 21, lr: 3.71e-04
2022-05-05 09:15:58,937 INFO [train.py:715] (1/8) Epoch 5, batch 30600, loss[loss=0.1335, simple_loss=0.2116, pruned_loss=0.02767, over 4881.00 frames.], tot_loss[loss=0.151, simple_loss=0.2221, pruned_loss=0.03988, over 972593.65 frames.], batch size: 22, lr: 3.71e-04
2022-05-05 09:16:37,784 INFO [train.py:715] (1/8) Epoch 5, batch 30650, loss[loss=0.1221, simple_loss=0.1937, pruned_loss=0.02527, over 4934.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03974, over 972497.81 frames.], batch size: 23, lr: 3.71e-04
2022-05-05 09:17:16,921 INFO [train.py:715] (1/8) Epoch 5, batch 30700, loss[loss=0.1541, simple_loss=0.2328, pruned_loss=0.03769, over 4955.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2218, pruned_loss=0.04035, over 972522.87 frames.], batch size: 15, lr: 3.70e-04
2022-05-05 09:17:55,176 INFO [train.py:715] (1/8) Epoch 5, batch 30750, loss[loss=0.1416, simple_loss=0.2153, pruned_loss=0.03394, over 4772.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2218, pruned_loss=0.04036, over 971982.36 frames.], batch size: 17, lr: 3.70e-04
2022-05-05 09:18:33,970 INFO [train.py:715] (1/8) Epoch 5, batch 30800, loss[loss=0.1569, simple_loss=0.2307, pruned_loss=0.04156, over 4706.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2222, pruned_loss=0.04048, over 971372.29 frames.], batch size: 15, lr: 3.70e-04
2022-05-05 09:19:12,988 INFO [train.py:715] (1/8) Epoch 5, batch 30850, loss[loss=0.1425, simple_loss=0.2062, pruned_loss=0.03941, over 4868.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2212, pruned_loss=0.03977, over 971216.39 frames.], batch size: 32, lr: 3.70e-04
2022-05-05 09:19:51,004 INFO [train.py:715] (1/8) Epoch 5, batch 30900, loss[loss=0.1635, simple_loss=0.2302, pruned_loss=0.04842, over 4720.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2219, pruned_loss=0.03984, over 972217.65 frames.], batch size: 16, lr: 3.70e-04
2022-05-05 09:20:29,875 INFO [train.py:715] (1/8) Epoch 5, batch 30950, loss[loss=0.1476, simple_loss=0.219, pruned_loss=0.03809, over 4778.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2219, pruned_loss=0.03978, over 972110.36 frames.], batch size: 12, lr: 3.70e-04
2022-05-05 09:21:09,530 INFO [train.py:715] (1/8) Epoch 5, batch 31000, loss[loss=0.1333, simple_loss=0.2001, pruned_loss=0.03326, over 4735.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2218, pruned_loss=0.0399, over 972170.86 frames.], batch size: 16, lr: 3.70e-04
2022-05-05 09:21:48,996 INFO [train.py:715] (1/8) Epoch 5, batch 31050, loss[loss=0.166, simple_loss=0.2464, pruned_loss=0.04285, over 4928.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2218, pruned_loss=0.04003, over 972804.57 frames.], batch size: 39, lr: 3.70e-04
2022-05-05 09:22:27,595 INFO [train.py:715] (1/8) Epoch 5, batch 31100, loss[loss=0.1627, simple_loss=0.2409, pruned_loss=0.04227, over 4905.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2225, pruned_loss=0.04012, over 973023.29 frames.], batch size: 39, lr: 3.70e-04
2022-05-05 09:23:06,677 INFO [train.py:715] (1/8) Epoch 5, batch 31150, loss[loss=0.1276, simple_loss=0.1925, pruned_loss=0.03129, over 4914.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04055, over 972537.02 frames.], batch size: 17, lr: 3.70e-04
2022-05-05 09:23:45,589 INFO [train.py:715] (1/8) Epoch 5, batch 31200, loss[loss=0.1962, simple_loss=0.2556, pruned_loss=0.06834, over 4930.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2222, pruned_loss=0.04045, over 972203.58 frames.], batch size: 29, lr: 3.70e-04
2022-05-05 09:24:24,058 INFO [train.py:715] (1/8) Epoch 5, batch 31250, loss[loss=0.1424, simple_loss=0.2199, pruned_loss=0.03241, over 4968.00 frames.], tot_loss[loss=0.151, simple_loss=0.2215, pruned_loss=0.0402, over 971489.26 frames.], batch size: 28, lr: 3.70e-04
2022-05-05 09:25:02,649 INFO [train.py:715] (1/8) Epoch 5, batch 31300, loss[loss=0.1424, simple_loss=0.2206, pruned_loss=0.03206, over 4866.00 frames.], tot_loss[loss=0.15, simple_loss=0.2209, pruned_loss=0.03956, over 971957.75 frames.], batch size: 32, lr: 3.70e-04
2022-05-05 09:25:41,534 INFO [train.py:715] (1/8) Epoch 5, batch 31350, loss[loss=0.1483, simple_loss=0.2257, pruned_loss=0.03548, over 4827.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2208, pruned_loss=0.03931, over 971472.82 frames.], batch size: 25, lr: 3.70e-04
2022-05-05 09:26:20,318 INFO [train.py:715] (1/8) Epoch 5, batch 31400, loss[loss=0.1609, simple_loss=0.2398, pruned_loss=0.04097, over 4751.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2215, pruned_loss=0.03947, over 971629.77 frames.], batch size: 16, lr: 3.70e-04
2022-05-05 09:26:59,042 INFO [train.py:715] (1/8) Epoch 5, batch 31450, loss[loss=0.1476, simple_loss=0.2118, pruned_loss=0.04172, over 4891.00 frames.], tot_loss[loss=0.151, simple_loss=0.2222, pruned_loss=0.03995, over 972264.65 frames.], batch size: 16, lr: 3.70e-04
2022-05-05 09:27:37,869 INFO [train.py:715] (1/8) Epoch 5, batch 31500, loss[loss=0.1234, simple_loss=0.1976, pruned_loss=0.02456, over 4798.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2224, pruned_loss=0.04031, over 972677.32 frames.], batch size: 14, lr: 3.70e-04
2022-05-05 09:28:16,785 INFO [train.py:715] (1/8) Epoch 5, batch 31550, loss[loss=0.1431, simple_loss=0.2128, pruned_loss=0.03675, over 4988.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2232, pruned_loss=0.04067, over 972283.20 frames.], batch size: 25, lr: 3.70e-04
2022-05-05 09:28:55,564 INFO [train.py:715] (1/8) Epoch 5, batch 31600, loss[loss=0.1443, simple_loss=0.2133, pruned_loss=0.03765, over 4835.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2235, pruned_loss=0.04106, over 972383.07 frames.], batch size: 26, lr: 3.70e-04
2022-05-05 09:29:34,425 INFO [train.py:715] (1/8) Epoch 5, batch 31650, loss[loss=0.1277, simple_loss=0.2109, pruned_loss=0.02224, over 4991.00 frames.], tot_loss[loss=0.153, simple_loss=0.2234, pruned_loss=0.04134, over 972919.69 frames.], batch size: 28, lr: 3.70e-04
2022-05-05 09:30:13,326 INFO [train.py:715] (1/8) Epoch 5, batch 31700, loss[loss=0.1403, simple_loss=0.2108, pruned_loss=0.0349, over 4801.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2224, pruned_loss=0.04095, over 971772.32 frames.], batch size: 25, lr: 3.70e-04
2022-05-05 09:30:52,060 INFO [train.py:715] (1/8) Epoch 5, batch 31750, loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03359, over 4915.00 frames.], tot_loss[loss=0.152, simple_loss=0.2221, pruned_loss=0.0409, over 972143.90 frames.], batch size: 18, lr: 3.70e-04
2022-05-05 09:31:31,169 INFO [train.py:715] (1/8) Epoch 5, batch 31800, loss[loss=0.1695, simple_loss=0.239, pruned_loss=0.04997, over 4910.00 frames.], tot_loss[loss=0.152, simple_loss=0.2224, pruned_loss=0.04078, over 972933.98 frames.], batch size: 19, lr: 3.69e-04
2022-05-05 09:32:09,901 INFO [train.py:715] (1/8) Epoch 5, batch 31850, loss[loss=0.1612, simple_loss=0.2286, pruned_loss=0.04691, over 4917.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2215, pruned_loss=0.03998, over 973323.31 frames.], batch size: 29, lr: 3.69e-04
2022-05-05 09:32:49,450 INFO [train.py:715] (1/8) Epoch 5, batch 31900, loss[loss=0.1544, simple_loss=0.2189, pruned_loss=0.04494, over 4933.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2213, pruned_loss=0.03997, over 972448.26 frames.], batch size: 23, lr: 3.69e-04
2022-05-05 09:33:28,132 INFO [train.py:715] (1/8) Epoch 5, batch 31950, loss[loss=0.1436, simple_loss=0.216, pruned_loss=0.03561, over 4801.00 frames.], tot_loss[loss=0.151, simple_loss=0.2217, pruned_loss=0.04012, over 971901.58 frames.], batch size: 21, lr: 3.69e-04
2022-05-05 09:34:06,677 INFO [train.py:715] (1/8) Epoch 5, batch 32000, loss[loss=0.1136, simple_loss=0.1852, pruned_loss=0.02104, over 4839.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2213, pruned_loss=0.03969, over 972374.13 frames.], batch size: 13, lr: 3.69e-04
2022-05-05 09:34:45,048 INFO [train.py:715] (1/8) Epoch 5, batch 32050, loss[loss=0.143, simple_loss=0.221, pruned_loss=0.03252, over 4770.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2214, pruned_loss=0.03987, over 971607.71 frames.], batch size: 14, lr: 3.69e-04
2022-05-05 09:35:24,097 INFO [train.py:715] (1/8) Epoch 5, batch 32100, loss[loss=0.1376, simple_loss=0.2171, pruned_loss=0.02903, over 4971.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2215, pruned_loss=0.04041, over 971779.95 frames.], batch size: 15, lr: 3.69e-04
2022-05-05 09:36:02,962 INFO [train.py:715] (1/8) Epoch 5, batch 32150, loss[loss=0.1771, simple_loss=0.2353, pruned_loss=0.05949, over 4919.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2201, pruned_loss=0.03935, over 971448.40 frames.], batch size: 38, lr: 3.69e-04
2022-05-05 09:36:41,524 INFO [train.py:715] (1/8) Epoch 5, batch 32200, loss[loss=0.1244, simple_loss=0.1966, pruned_loss=0.02616, over 4988.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2213, pruned_loss=0.03961, over 972732.12 frames.], batch size: 28, lr: 3.69e-04
2022-05-05 09:37:20,075 INFO [train.py:715] (1/8) Epoch 5, batch 32250, loss[loss=0.1491, simple_loss=0.2207, pruned_loss=0.03869, over 4831.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2218, pruned_loss=0.03983, over 973257.63 frames.], batch size: 25, lr: 3.69e-04
2022-05-05 09:37:59,214 INFO [train.py:715] (1/8) Epoch 5, batch 32300, loss[loss=0.1329, simple_loss=0.2103, pruned_loss=0.02774, over 4764.00 frames.], tot_loss[loss=0.1496, simple_loss=0.221, pruned_loss=0.03908, over 973241.09 frames.], batch size: 16, lr: 3.69e-04
2022-05-05 09:38:37,807 INFO [train.py:715] (1/8) Epoch 5, batch 32350, loss[loss=0.1706, simple_loss=0.2462, pruned_loss=0.04755, over 4782.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2207, pruned_loss=0.03888, over 973312.15 frames.], batch size: 17, lr: 3.69e-04
2022-05-05 09:39:16,504 INFO [train.py:715] (1/8) Epoch 5, batch 32400, loss[loss=0.1589, simple_loss=0.2234, pruned_loss=0.04718, over 4925.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2212, pruned_loss=0.03924, over 972279.60 frames.], batch size: 18, lr: 3.69e-04
2022-05-05 09:39:55,119 INFO [train.py:715] (1/8) Epoch 5, batch 32450, loss[loss=0.132, simple_loss=0.1947, pruned_loss=0.03466, over 4983.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2214, pruned_loss=0.03968, over 972612.48 frames.], batch size: 14, lr: 3.69e-04
2022-05-05 09:40:33,915 INFO [train.py:715] (1/8) Epoch 5, batch 32500, loss[loss=0.1364, simple_loss=0.204, pruned_loss=0.03442, over 4767.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2209, pruned_loss=0.03981, over 972362.84 frames.], batch size: 17, lr: 3.69e-04
2022-05-05 09:41:13,472 INFO [train.py:715] (1/8) Epoch 5, batch 32550, loss[loss=0.1357, simple_loss=0.206, pruned_loss=0.0327, over 4824.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2202, pruned_loss=0.03951, over 972556.40 frames.], batch size: 26, lr: 3.69e-04
2022-05-05 09:41:51,932 INFO [train.py:715] (1/8) Epoch 5, batch 32600, loss[loss=0.1727, simple_loss=0.2294, pruned_loss=0.05804, over 4858.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2203, pruned_loss=0.03962, over 972825.04 frames.], batch size: 32, lr: 3.69e-04
2022-05-05 09:42:30,729 INFO [train.py:715] (1/8) Epoch 5, batch 32650, loss[loss=0.1742, simple_loss=0.2289, pruned_loss=0.05975, over 4719.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2217, pruned_loss=0.04051, over 971726.35 frames.], batch size: 12, lr: 3.69e-04
2022-05-05 09:43:09,274 INFO [train.py:715] (1/8) Epoch 5, batch 32700, loss[loss=0.1614, simple_loss=0.2335, pruned_loss=0.04459, over 4751.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2204, pruned_loss=0.03949, over 971518.80 frames.], batch size: 16, lr: 3.69e-04
2022-05-05 09:43:47,573 INFO [train.py:715] (1/8) Epoch 5, batch 32750, loss[loss=0.1524, simple_loss=0.2173, pruned_loss=0.04378, over 4992.00 frames.], tot_loss[loss=0.151, simple_loss=0.2217, pruned_loss=0.04014, over 971101.96 frames.], batch size: 20, lr: 3.69e-04
2022-05-05 09:44:26,279 INFO [train.py:715] (1/8) Epoch 5, batch 32800, loss[loss=0.1564, simple_loss=0.2182, pruned_loss=0.04728, over 4815.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2214, pruned_loss=0.04018, over 971255.50 frames.], batch size: 27, lr: 3.69e-04
2022-05-05 09:45:05,108 INFO [train.py:715] (1/8) Epoch 5, batch 32850, loss[loss=0.1406, simple_loss=0.2147, pruned_loss=0.03323, over 4703.00 frames.], tot_loss[loss=0.1516, simple_loss=0.222, pruned_loss=0.04055, over 972078.91 frames.], batch size: 15, lr: 3.69e-04
2022-05-05 09:45:44,051 INFO [train.py:715] (1/8) Epoch 5, batch 32900, loss[loss=0.1426, simple_loss=0.2165, pruned_loss=0.03437, over 4965.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2221, pruned_loss=0.04064, over 972381.50 frames.], batch size: 35, lr: 3.69e-04
2022-05-05 09:46:22,919 INFO [train.py:715] (1/8) Epoch 5, batch 32950, loss[loss=0.1975, simple_loss=0.2558, pruned_loss=0.06963, over 4973.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2216, pruned_loss=0.0403, over 972616.16 frames.], batch size: 35, lr: 3.68e-04
2022-05-05 09:47:01,974 INFO [train.py:715] (1/8) Epoch 5, batch 33000, loss[loss=0.1577, simple_loss=0.221, pruned_loss=0.04717, over 4905.00 frames.], tot_loss[loss=0.1514, simple_loss=0.222, pruned_loss=0.04045, over 972930.78 frames.], batch size: 19, lr: 3.68e-04
2022-05-05 09:47:01,975 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 09:47:11,685 INFO [train.py:742] (1/8) Epoch 5, validation: loss=0.1099, simple_loss=0.1951, pruned_loss=0.01236, over 914524.00 frames.
2022-05-05 09:47:50,706 INFO [train.py:715] (1/8) Epoch 5, batch 33050, loss[loss=0.1478, simple_loss=0.2289, pruned_loss=0.03332, over 4746.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2227, pruned_loss=0.04076, over 973265.17 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 09:48:29,616 INFO [train.py:715] (1/8) Epoch 5, batch 33100, loss[loss=0.1278, simple_loss=0.1905, pruned_loss=0.03251, over 4815.00 frames.], tot_loss[loss=0.152, simple_loss=0.2229, pruned_loss=0.04056, over 972045.18 frames.], batch size: 27, lr: 3.68e-04
2022-05-05 09:49:07,626 INFO [train.py:715] (1/8) Epoch 5, batch 33150, loss[loss=0.1411, simple_loss=0.2154, pruned_loss=0.03335, over 4820.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2228, pruned_loss=0.04083, over 971163.31 frames.], batch size: 12, lr: 3.68e-04
2022-05-05 09:49:46,218 INFO [train.py:715] (1/8) Epoch 5, batch 33200, loss[loss=0.1511, simple_loss=0.2307, pruned_loss=0.0357, over 4765.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2228, pruned_loss=0.04077, over 971536.50 frames.], batch size: 18, lr: 3.68e-04
2022-05-05 09:50:25,073 INFO [train.py:715] (1/8) Epoch 5, batch 33250, loss[loss=0.1411, simple_loss=0.2216, pruned_loss=0.03029, over 4836.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04052, over 972399.42 frames.], batch size: 26, lr: 3.68e-04
2022-05-05 09:51:03,572 INFO [train.py:715] (1/8) Epoch 5, batch 33300, loss[loss=0.1553, simple_loss=0.2297, pruned_loss=0.0404, over 4919.00 frames.], tot_loss[loss=0.152, simple_loss=0.2223, pruned_loss=0.04082, over 972169.79 frames.], batch size: 18, lr: 3.68e-04
2022-05-05 09:51:41,940 INFO [train.py:715] (1/8) Epoch 5, batch 33350, loss[loss=0.1549, simple_loss=0.2248, pruned_loss=0.04254, over 4775.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2231, pruned_loss=0.0412, over 972399.55 frames.], batch size: 18, lr: 3.68e-04
2022-05-05 09:52:21,209 INFO [train.py:715] (1/8) Epoch 5, batch 33400, loss[loss=0.1365, simple_loss=0.2136, pruned_loss=0.02973, over 4823.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2223, pruned_loss=0.0407, over 972355.35 frames.], batch size: 26, lr: 3.68e-04
2022-05-05 09:52:59,902 INFO [train.py:715] (1/8) Epoch 5, batch 33450, loss[loss=0.118, simple_loss=0.1961, pruned_loss=0.01995, over 4862.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2222, pruned_loss=0.0404, over 972305.54 frames.], batch size: 20, lr: 3.68e-04
2022-05-05 09:53:38,266 INFO [train.py:715] (1/8) Epoch 5, batch 33500, loss[loss=0.1373, simple_loss=0.2036, pruned_loss=0.03554, over 4696.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2226, pruned_loss=0.04013, over 971044.90 frames.], batch size: 15, lr: 3.68e-04
2022-05-05 09:54:16,986 INFO [train.py:715] (1/8) Epoch 5, batch 33550, loss[loss=0.1505, simple_loss=0.2131, pruned_loss=0.04389, over 4892.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2225, pruned_loss=0.04048, over 971654.41 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 09:54:55,690 INFO [train.py:715] (1/8) Epoch 5, batch 33600, loss[loss=0.1204, simple_loss=0.1908, pruned_loss=0.02499, over 4802.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2225, pruned_loss=0.04037, over 971615.07 frames.], batch size: 12, lr: 3.68e-04
2022-05-05 09:55:34,357 INFO [train.py:715] (1/8) Epoch 5, batch 33650, loss[loss=0.1763, simple_loss=0.2589, pruned_loss=0.04685, over 4894.00 frames.], tot_loss[loss=0.151, simple_loss=0.2221, pruned_loss=0.03995, over 971665.95 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 09:56:12,634 INFO [train.py:715] (1/8) Epoch 5, batch 33700, loss[loss=0.165, simple_loss=0.2284, pruned_loss=0.05081, over 4863.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2214, pruned_loss=0.03983, over 971717.59 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 09:56:51,517 INFO [train.py:715] (1/8) Epoch 5, batch 33750, loss[loss=0.1783, simple_loss=0.2437, pruned_loss=0.0565, over 4788.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2211, pruned_loss=0.0397, over 971406.31 frames.], batch size: 17, lr: 3.68e-04
2022-05-05 09:57:30,195 INFO [train.py:715] (1/8) Epoch 5, batch 33800, loss[loss=0.132, simple_loss=0.2191, pruned_loss=0.02247, over 4925.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2206, pruned_loss=0.03916, over 971578.18 frames.], batch size: 23, lr: 3.68e-04
2022-05-05 09:58:09,142 INFO [train.py:715] (1/8) Epoch 5, batch 33850, loss[loss=0.1508, simple_loss=0.2199, pruned_loss=0.04088, over 4973.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2207, pruned_loss=0.03923, over 970870.92 frames.], batch size: 35, lr: 3.68e-04
2022-05-05 09:58:47,624 INFO [train.py:715] (1/8) Epoch 5, batch 33900, loss[loss=0.1618, simple_loss=0.2364, pruned_loss=0.04355, over 4983.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2201, pruned_loss=0.03915, over 969945.30 frames.], batch size: 25, lr: 3.68e-04
2022-05-05 09:59:25,972 INFO [train.py:715] (1/8) Epoch 5, batch 33950, loss[loss=0.1411, simple_loss=0.2187, pruned_loss=0.03172, over 4709.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2211, pruned_loss=0.03959, over 971190.82 frames.], batch size: 15, lr: 3.68e-04
2022-05-05 10:00:06,982 INFO [train.py:715] (1/8) Epoch 5, batch 34000, loss[loss=0.1417, simple_loss=0.2261, pruned_loss=0.0287, over 4803.00 frames.], tot_loss[loss=0.151, simple_loss=0.2215, pruned_loss=0.04021, over 971233.04 frames.], batch size: 24, lr: 3.68e-04
2022-05-05 10:00:45,231 INFO [train.py:715] (1/8) Epoch 5, batch 34050, loss[loss=0.159, simple_loss=0.2198, pruned_loss=0.04915, over 4921.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2222, pruned_loss=0.04044, over 971481.55 frames.], batch size: 18, lr: 3.67e-04
2022-05-05 10:01:23,921 INFO [train.py:715] (1/8) Epoch 5, batch 34100, loss[loss=0.1359, simple_loss=0.2142, pruned_loss=0.02879, over 4876.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2217, pruned_loss=0.04024, over 971686.89 frames.], batch size: 22, lr: 3.67e-04
2022-05-05 10:02:02,750 INFO [train.py:715] (1/8) Epoch 5, batch 34150, loss[loss=0.1183, simple_loss=0.198, pruned_loss=0.01933, over 4982.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2226, pruned_loss=0.04036, over 972273.72 frames.], batch size: 28, lr: 3.67e-04
2022-05-05 10:02:41,108 INFO [train.py:715] (1/8) Epoch 5, batch 34200, loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03041, over 4816.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2229, pruned_loss=0.04025, over 972595.36 frames.], batch size: 25, lr: 3.67e-04
2022-05-05 10:03:20,096 INFO [train.py:715] (1/8) Epoch 5, batch 34250, loss[loss=0.1502, simple_loss=0.2148, pruned_loss=0.04277, over 4992.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2228, pruned_loss=0.04025, over 971835.45 frames.], batch size: 14, lr: 3.67e-04
2022-05-05 10:03:58,249 INFO [train.py:715] (1/8) Epoch 5, batch 34300, loss[loss=0.1788, simple_loss=0.2456, pruned_loss=0.056, over 4913.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2219, pruned_loss=0.03993, over 972319.62 frames.], batch size: 39, lr: 3.67e-04
2022-05-05 10:04:36,912 INFO [train.py:715] (1/8) Epoch 5, batch 34350, loss[loss=0.1469, simple_loss=0.2199, pruned_loss=0.03697, over 4906.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2224, pruned_loss=0.04014, over 972724.24 frames.], batch size: 18, lr: 3.67e-04
2022-05-05 10:05:14,795 INFO [train.py:715] (1/8) Epoch 5, batch 34400, loss[loss=0.1446, simple_loss=0.2161, pruned_loss=0.03659, over 4917.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2224, pruned_loss=0.04039, over 972326.77 frames.], batch size: 18, lr: 3.67e-04
2022-05-05 10:05:53,765 INFO [train.py:715] (1/8) Epoch 5, batch 34450, loss[loss=0.1715, simple_loss=0.2385, pruned_loss=0.05228, over 4848.00 frames.], tot_loss[loss=0.1521, simple_loss=0.223, pruned_loss=0.04062, over 972324.47 frames.], batch size: 30, lr: 3.67e-04
2022-05-05 10:06:32,733 INFO [train.py:715] (1/8) Epoch 5, batch 34500, loss[loss=0.1345, simple_loss=0.2093, pruned_loss=0.02988, over 4826.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2224, pruned_loss=0.04024, over 972499.21 frames.], batch size: 26, lr: 3.67e-04
2022-05-05 10:07:11,204 INFO [train.py:715] (1/8) Epoch 5, batch 34550, loss[loss=0.1547, simple_loss=0.2328, pruned_loss=0.03828, over 4821.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2221, pruned_loss=0.04013, over 972154.12 frames.], batch size: 25, lr: 3.67e-04
2022-05-05 10:07:49,951 INFO [train.py:715] (1/8) Epoch 5, batch 34600, loss[loss=0.1206, simple_loss=0.1996, pruned_loss=0.0208, over 4927.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2217, pruned_loss=0.03969, over 972394.43 frames.], batch size: 23, lr: 3.67e-04
2022-05-05 10:08:28,655 INFO [train.py:715] (1/8) Epoch 5, batch 34650, loss[loss=0.1466, simple_loss=0.2128, pruned_loss=0.04016, over 4783.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2216, pruned_loss=0.03936, over 972262.58 frames.], batch size: 17, lr: 3.67e-04
2022-05-05 10:09:07,579 INFO [train.py:715] (1/8) Epoch 5, batch 34700, loss[loss=0.1508, simple_loss=0.2229, pruned_loss=0.03941, over 4828.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2215, pruned_loss=0.03954, over 973071.53 frames.], batch size: 15, lr: 3.67e-04
2022-05-05 10:09:44,910 INFO [train.py:715] (1/8) Epoch 5, batch 34750, loss[loss=0.1388, simple_loss=0.2025, pruned_loss=0.03757, over 4792.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2216, pruned_loss=0.03962, over 972758.66 frames.], batch size: 14, lr: 3.67e-04
2022-05-05 10:10:21,602 INFO [train.py:715] (1/8) Epoch 5, batch 34800, loss[loss=0.1461, simple_loss=0.2183, pruned_loss=0.03692, over 4778.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2207, pruned_loss=0.03983, over 970467.27 frames.], batch size: 14, lr: 3.67e-04
2022-05-05 10:11:11,225 INFO [train.py:715] (1/8) Epoch 6, batch 0, loss[loss=0.1536, simple_loss=0.2221, pruned_loss=0.04254, over 4934.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2221, pruned_loss=0.04254, over 4934.00 frames.], batch size: 18, lr: 3.46e-04
2022-05-05 10:11:50,187 INFO [train.py:715] (1/8) Epoch 6, batch 50, loss[loss=0.1398, simple_loss=0.2203, pruned_loss=0.02968, over 4938.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2208, pruned_loss=0.03932, over 219415.05 frames.], batch size: 29, lr: 3.46e-04
2022-05-05 10:12:29,114 INFO [train.py:715] (1/8) Epoch 6, batch 100, loss[loss=0.1371, simple_loss=0.2096, pruned_loss=0.03226, over 4759.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2214, pruned_loss=0.04081, over 387135.43 frames.], batch size: 19, lr: 3.46e-04
2022-05-05 10:13:08,355 INFO [train.py:715] (1/8) Epoch 6, batch 150, loss[loss=0.1344, simple_loss=0.2053, pruned_loss=0.03176, over 4947.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2199, pruned_loss=0.03978, over 516565.81 frames.], batch size: 21, lr: 3.46e-04
2022-05-05 10:13:47,629 INFO [train.py:715] (1/8) Epoch 6, batch 200, loss[loss=0.1434, simple_loss=0.2244, pruned_loss=0.03118, over 4694.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2199, pruned_loss=0.03932, over 617803.62 frames.], batch size: 15, lr: 3.45e-04
2022-05-05 10:14:26,643 INFO [train.py:715] (1/8) Epoch 6, batch 250, loss[loss=0.1367, simple_loss=0.2072, pruned_loss=0.03306, over 4826.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.03955, over 696981.42 frames.], batch size: 13, lr: 3.45e-04
2022-05-05 10:15:05,472 INFO [train.py:715] (1/8) Epoch 6, batch 300, loss[loss=0.1797, simple_loss=0.2462, pruned_loss=0.05661, over 4854.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2215, pruned_loss=0.03956, over 758762.38 frames.], batch size: 34, lr: 3.45e-04
2022-05-05 10:15:44,450 INFO [train.py:715] (1/8) Epoch 6, batch 350, loss[loss=0.1581, simple_loss=0.2275, pruned_loss=0.04438, over 4972.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2226, pruned_loss=0.03992, over 806010.68 frames.], batch size: 14, lr: 3.45e-04
2022-05-05 10:16:23,655 INFO [train.py:715] (1/8) Epoch 6, batch 400, loss[loss=0.148, simple_loss=0.2256, pruned_loss=0.03524, over 4925.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2216, pruned_loss=0.03947, over 843115.12 frames.], batch size: 23, lr: 3.45e-04
2022-05-05 10:17:02,411 INFO [train.py:715] (1/8) Epoch 6, batch 450, loss[loss=0.1668, simple_loss=0.2257, pruned_loss=0.05394, over 4782.00 frames.], tot_loss[loss=0.1506, simple_loss=0.222, pruned_loss=0.0396, over 871406.41 frames.], batch size: 18, lr: 3.45e-04
2022-05-05 10:17:41,010 INFO [train.py:715] (1/8) Epoch 6, batch 500, loss[loss=0.2047, simple_loss=0.2561, pruned_loss=0.07663, over 4952.00 frames.], tot_loss[loss=0.1509, simple_loss=0.222, pruned_loss=0.03988, over 893760.34 frames.], batch size: 15, lr: 3.45e-04
2022-05-05 10:18:20,500 INFO [train.py:715] (1/8) Epoch 6, batch 550, loss[loss=0.2024, simple_loss=0.2472, pruned_loss=0.07878, over 4792.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2207, pruned_loss=0.03921, over 912360.83 frames.], batch size: 14, lr: 3.45e-04
2022-05-05 10:18:59,387 INFO [train.py:715] (1/8) Epoch 6, batch 600, loss[loss=0.1779, simple_loss=0.248, pruned_loss=0.05395, over 4809.00 frames.], tot_loss[loss=0.1501, simple_loss=0.221, pruned_loss=0.0396, over 926549.22 frames.], batch size: 21, lr: 3.45e-04
2022-05-05 10:19:38,403 INFO [train.py:715] (1/8) Epoch 6, batch 650, loss[loss=0.1497, simple_loss=0.2273, pruned_loss=0.03604, over 4983.00 frames.], tot_loss[loss=0.1501, simple_loss=0.221, pruned_loss=0.03961, over 937818.88 frames.], batch size: 35, lr: 3.45e-04
2022-05-05 10:20:17,488 INFO [train.py:715] (1/8) Epoch 6, batch 700, loss[loss=0.1529, simple_loss=0.2257, pruned_loss=0.04001, over 4829.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2204, pruned_loss=0.03942, over 945350.16 frames.], batch size: 26, lr: 3.45e-04
2022-05-05 10:20:57,082 INFO [train.py:715] (1/8) Epoch 6, batch 750, loss[loss=0.1396, simple_loss=0.2156, pruned_loss=0.03182, over 4754.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2204, pruned_loss=0.03949, over 951409.78 frames.], batch size: 16, lr: 3.45e-04
2022-05-05 10:21:35,856 INFO [train.py:715] (1/8) Epoch 6, batch 800, loss[loss=0.1605, simple_loss=0.2269, pruned_loss=0.04701, over 4864.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2214, pruned_loss=0.03981, over 956461.10 frames.], batch size: 22, lr: 3.45e-04
2022-05-05 10:22:14,574 INFO [train.py:715] (1/8) Epoch 6, batch 850, loss[loss=0.1237, simple_loss=0.1981, pruned_loss=0.02463, over 4822.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2213, pruned_loss=0.03994, over 960206.19 frames.], batch size: 25, lr: 3.45e-04
2022-05-05 10:22:54,110 INFO [train.py:715] (1/8) Epoch 6, batch 900, loss[loss=0.158, simple_loss=0.242, pruned_loss=0.03703, over 4929.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2213, pruned_loss=0.0398, over 963768.97 frames.], batch size: 29, lr: 3.45e-04
2022-05-05 10:23:33,406 INFO [train.py:715] (1/8) Epoch 6, batch 950, loss[loss=0.1453, simple_loss=0.2168, pruned_loss=0.03688, over 4896.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2213, pruned_loss=0.03947, over 966009.72 frames.], batch size: 17, lr: 3.45e-04
2022-05-05 10:24:12,119 INFO [train.py:715] (1/8) Epoch 6, batch 1000, loss[loss=0.1818, simple_loss=0.2444, pruned_loss=0.05962, over 4841.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2223, pruned_loss=0.04035, over 967657.06 frames.], batch size: 32, lr: 3.45e-04
2022-05-05 10:24:51,182 INFO [train.py:715] (1/8) Epoch 6, batch 1050, loss[loss=0.1402, simple_loss=0.2167, pruned_loss=0.03181, over 4773.00 frames.], tot_loss[loss=0.151, simple_loss=0.2217, pruned_loss=0.04013, over 969298.76 frames.], batch size: 18, lr: 3.45e-04
2022-05-05 10:25:30,706 INFO [train.py:715] (1/8) Epoch 6, batch 1100, loss[loss=0.1469, simple_loss=0.2285, pruned_loss=0.03266, over 4875.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2212, pruned_loss=0.03959, over 970079.35 frames.], batch size: 16, lr: 3.45e-04
2022-05-05 10:26:09,924 INFO [train.py:715] (1/8) Epoch 6, batch 1150, loss[loss=0.1424, simple_loss=0.2168, pruned_loss=0.03405, over 4878.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2214, pruned_loss=0.03956, over 971053.81 frames.], batch size: 16, lr: 3.45e-04
2022-05-05 10:26:48,497 INFO [train.py:715] (1/8) Epoch 6, batch 1200, loss[loss=0.1383, simple_loss=0.2053, pruned_loss=0.03564, over 4856.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2215, pruned_loss=0.03996, over 971277.96 frames.], batch size: 20, lr: 3.45e-04
2022-05-05 10:27:28,197 INFO [train.py:715] (1/8) Epoch 6, batch 1250, loss[loss=0.1465, simple_loss=0.2238, pruned_loss=0.03457, over 4856.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2202, pruned_loss=0.03922, over 971454.05 frames.], batch size: 30, lr: 3.45e-04
2022-05-05 10:28:07,473 INFO [train.py:715] (1/8) Epoch 6, batch 1300, loss[loss=0.163, simple_loss=0.2287, pruned_loss=0.04865, over 4869.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2202, pruned_loss=0.03966, over 969778.59 frames.], batch size: 30, lr: 3.45e-04
2022-05-05 10:28:46,067 INFO [train.py:715] (1/8) Epoch 6, batch 1350, loss[loss=0.1536, simple_loss=0.2129, pruned_loss=0.0472, over 4911.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2209, pruned_loss=0.04024, over 971234.30 frames.], batch size: 17, lr: 3.45e-04
2022-05-05 10:29:24,991 INFO [train.py:715] (1/8) Epoch 6, batch 1400, loss[loss=0.1586, simple_loss=0.2341, pruned_loss=0.04153, over 4902.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2204, pruned_loss=0.03991, over 971293.32 frames.], batch size: 17, lr: 3.45e-04
2022-05-05 10:30:04,138 INFO [train.py:715] (1/8) Epoch 6, batch 1450, loss[loss=0.1494, simple_loss=0.2288, pruned_loss=0.03506, over 4956.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2208, pruned_loss=0.03977, over 972586.54 frames.], batch size: 21, lr: 3.44e-04
2022-05-05 10:30:42,814 INFO [train.py:715] (1/8) Epoch 6, batch 1500, loss[loss=0.1381, simple_loss=0.2154, pruned_loss=0.03044, over 4790.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2199, pruned_loss=0.03922, over 972798.77 frames.], batch size: 24, lr: 3.44e-04
2022-05-05 10:31:21,208 INFO [train.py:715] (1/8) Epoch 6, batch 1550, loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02988, over 4943.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2199, pruned_loss=0.03933, over 972575.87 frames.], batch size: 29, lr: 3.44e-04
2022-05-05 10:32:00,469 INFO [train.py:715] (1/8) Epoch 6, batch 1600, loss[loss=0.1855, simple_loss=0.2669, pruned_loss=0.05208, over 4868.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2202, pruned_loss=0.03934, over 973022.43 frames.], batch size: 16, lr: 3.44e-04
2022-05-05 10:32:40,017 INFO [train.py:715] (1/8) Epoch 6, batch 1650, loss[loss=0.1655, simple_loss=0.2433, pruned_loss=0.04383, over 4969.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2207, pruned_loss=0.03943, over 972200.44 frames.], batch size: 39, lr: 3.44e-04
2022-05-05 10:33:18,416 INFO [train.py:715] (1/8) Epoch 6, batch 1700, loss[loss=0.1638, simple_loss=0.238, pruned_loss=0.04482, over 4791.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2198, pruned_loss=0.03887, over 972986.97 frames.], batch size: 18, lr: 3.44e-04
2022-05-05 10:33:57,729 INFO [train.py:715] (1/8) Epoch 6, batch 1750, loss[loss=0.1262, simple_loss=0.2047, pruned_loss=0.02391, over 4856.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2191, pruned_loss=0.03856, over 972716.54 frames.], batch size: 20, lr: 3.44e-04
2022-05-05 10:34:37,320 INFO [train.py:715] (1/8) Epoch 6, batch 1800, loss[loss=0.1217, simple_loss=0.1885, pruned_loss=0.02746, over 4805.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2189, pruned_loss=0.03863, over 972583.81 frames.], batch size: 13, lr: 3.44e-04
2022-05-05 10:35:16,404 INFO [train.py:715] (1/8) Epoch 6, batch 1850, loss[loss=0.1493, simple_loss=0.2328, pruned_loss=0.03287, over 4788.00 frames.], tot_loss[loss=0.149, simple_loss=0.22, pruned_loss=0.03902, over 972492.85 frames.], batch size: 17, lr: 3.44e-04
2022-05-05 10:35:54,732 INFO [train.py:715] (1/8) Epoch 6, batch 1900, loss[loss=0.1215, simple_loss=0.1973, pruned_loss=0.02284, over 4842.00 frames.], tot_loss[loss=0.1488, simple_loss=0.22, pruned_loss=0.03876, over 972012.68 frames.], batch size: 32, lr: 3.44e-04
2022-05-05 10:36:34,281 INFO [train.py:715] (1/8) Epoch 6, batch 1950, loss[loss=0.1613, simple_loss=0.2256, pruned_loss=0.04851, over 4650.00 frames.], tot_loss[loss=0.1489, simple_loss=0.22, pruned_loss=0.03886, over 972317.43 frames.], batch size: 13, lr: 3.44e-04
2022-05-05 10:37:13,034 INFO [train.py:715] (1/8) Epoch 6, batch 2000, loss[loss=0.1627, simple_loss=0.2265, pruned_loss=0.04948, over 4831.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2201, pruned_loss=0.03874, over 972427.64 frames.], batch size: 30, lr: 3.44e-04
2022-05-05 10:37:52,081 INFO [train.py:715] (1/8) Epoch 6, batch 2050, loss[loss=0.1215, simple_loss=0.1974, pruned_loss=0.02275, over 4870.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03913, over 972833.91 frames.], batch size: 20, lr: 3.44e-04
2022-05-05 10:38:30,930 INFO [train.py:715] (1/8) Epoch 6, batch 2100, loss[loss=0.1237, simple_loss=0.2012, pruned_loss=0.02307, over 4971.00 frames.], tot_loss[loss=0.148, simple_loss=0.2194, pruned_loss=0.0383, over 972305.53 frames.], batch size: 28, lr: 3.44e-04
2022-05-05 10:39:10,114 INFO [train.py:715] (1/8) Epoch 6, batch 2150, loss[loss=0.1547, simple_loss=0.2274, pruned_loss=0.04099, over 4809.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2189, pruned_loss=0.03776, over 972476.67 frames.], batch size: 25, lr: 3.44e-04
2022-05-05 10:39:49,071 INFO [train.py:715] (1/8) Epoch 6, batch 2200, loss[loss=0.1545, simple_loss=0.2304, pruned_loss=0.0393, over 4981.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2201, pruned_loss=0.03852, over 972798.33 frames.], batch size: 15, lr: 3.44e-04
2022-05-05 10:40:27,529 INFO [train.py:715] (1/8) Epoch 6, batch 2250, loss[loss=0.1713, simple_loss=0.2385, pruned_loss=0.05199, over 4927.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2196, pruned_loss=0.0384, over 973068.41 frames.], batch size: 23, lr: 3.44e-04
2022-05-05 10:41:06,879 INFO [train.py:715] (1/8) Epoch 6, batch 2300, loss[loss=0.152, simple_loss=0.22, pruned_loss=0.04201, over 4829.00 frames.], tot_loss[loss=0.148, simple_loss=0.2194, pruned_loss=0.03827, over 973348.16 frames.], batch size: 15, lr: 3.44e-04
2022-05-05 10:41:45,983 INFO [train.py:715] (1/8) Epoch 6, batch 2350, loss[loss=0.1624, simple_loss=0.2387, pruned_loss=0.04305, over 4835.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2197, pruned_loss=0.03846, over 973165.77 frames.], batch size: 15, lr: 3.44e-04
2022-05-05 10:42:24,704 INFO [train.py:715] (1/8) Epoch 6, batch 2400, loss[loss=0.1393, simple_loss=0.2117, pruned_loss=0.03348, over 4901.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03828, over 973284.51 frames.], batch size: 19, lr: 3.44e-04
2022-05-05 10:43:03,445 INFO [train.py:715] (1/8) Epoch 6, batch 2450, loss[loss=0.1719, simple_loss=0.2439, pruned_loss=0.04994, over 4772.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03865, over 973249.79 frames.], batch size: 18, lr: 3.44e-04
2022-05-05 10:43:42,680 INFO [train.py:715] (1/8) Epoch 6, batch 2500, loss[loss=0.1608, simple_loss=0.2262, pruned_loss=0.04774, over 4980.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2193, pruned_loss=0.03883, over 972884.04 frames.], batch size: 15, lr: 3.44e-04
2022-05-05 10:44:21,880 INFO [train.py:715] (1/8) Epoch 6, batch 2550, loss[loss=0.1663, simple_loss=0.2382, pruned_loss=0.04723, over 4881.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2195, pruned_loss=0.03908, over 973001.47 frames.], batch size: 22, lr: 3.44e-04
2022-05-05 10:45:00,764 INFO [train.py:715] (1/8) Epoch 6, batch 2600, loss[loss=0.1119, simple_loss=0.1877, pruned_loss=0.01801, over 4987.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2195, pruned_loss=0.03892, over 972924.88 frames.], batch size: 28, lr: 3.44e-04
2022-05-05 10:45:40,392 INFO [train.py:715] (1/8) Epoch 6, batch 2650, loss[loss=0.142, simple_loss=0.2204, pruned_loss=0.03184, over 4793.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2187, pruned_loss=0.03841, over 972316.30 frames.], batch size: 24, lr: 3.43e-04
2022-05-05 10:46:19,966 INFO [train.py:715] (1/8) Epoch 6, batch 2700, loss[loss=0.1592, simple_loss=0.2248, pruned_loss=0.04676, over 4776.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2197, pruned_loss=0.03893, over 972179.56 frames.], batch size: 14, lr: 3.43e-04
2022-05-05 10:46:58,108 INFO [train.py:715] (1/8) Epoch 6, batch 2750, loss[loss=0.1266, simple_loss=0.2097, pruned_loss=0.02177, over 4835.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2198, pruned_loss=0.039, over 972354.97 frames.], batch size: 32, lr: 3.43e-04
2022-05-05 10:47:37,114 INFO [train.py:715] (1/8) Epoch 6, batch 2800, loss[loss=0.1422, simple_loss=0.2007, pruned_loss=0.04182, over 4800.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2196, pruned_loss=0.03873, over 973095.70 frames.], batch size: 12, lr: 3.43e-04
2022-05-05 10:48:16,471 INFO [train.py:715] (1/8) Epoch 6, batch 2850, loss[loss=0.1366, simple_loss=0.2109, pruned_loss=0.03113, over 4946.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2189, pruned_loss=0.03869, over 972751.58 frames.], batch size: 35, lr: 3.43e-04
2022-05-05 10:48:55,296 INFO [train.py:715] (1/8) Epoch 6, batch 2900, loss[loss=0.1332, simple_loss=0.2045, pruned_loss=0.03098, over 4756.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2194, pruned_loss=0.03893, over 972637.05 frames.], batch size: 19, lr: 3.43e-04
2022-05-05 10:49:33,641 INFO [train.py:715] (1/8) Epoch 6, batch 2950, loss[loss=0.1185, simple_loss=0.1935, pruned_loss=0.02179, over 4980.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2201, pruned_loss=0.03924, over 972836.45 frames.], batch size: 28, lr: 3.43e-04
2022-05-05 10:50:12,865 INFO [train.py:715] (1/8) Epoch 6, batch 3000, loss[loss=0.1306, simple_loss=0.2064, pruned_loss=0.02741, over 4945.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2204, pruned_loss=0.03911, over 973036.12 frames.], batch size: 14, lr: 3.43e-04
2022-05-05 10:50:12,866 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 10:50:22,539 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1095, simple_loss=0.1945, pruned_loss=0.01223, over 914524.00 frames.
2022-05-05 10:51:02,176 INFO [train.py:715] (1/8) Epoch 6, batch 3050, loss[loss=0.121, simple_loss=0.2008, pruned_loss=0.02063, over 4882.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2207, pruned_loss=0.03936, over 972921.79 frames.], batch size: 22, lr: 3.43e-04
2022-05-05 10:51:41,567 INFO [train.py:715] (1/8) Epoch 6, batch 3100, loss[loss=0.1199, simple_loss=0.1859, pruned_loss=0.02691, over 4790.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2205, pruned_loss=0.03937, over 972400.72 frames.], batch size: 14, lr: 3.43e-04
2022-05-05 10:52:20,136 INFO [train.py:715] (1/8) Epoch 6, batch 3150, loss[loss=0.1922, simple_loss=0.2723, pruned_loss=0.056, over 4780.00 frames.], tot_loss[loss=0.15, simple_loss=0.221, pruned_loss=0.03954, over 972418.73 frames.], batch size: 18, lr: 3.43e-04
2022-05-05 10:52:58,782 INFO [train.py:715] (1/8) Epoch 6, batch 3200, loss[loss=0.1822, simple_loss=0.2446, pruned_loss=0.05993, over 4859.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2211, pruned_loss=0.03963, over 972247.16 frames.], batch size: 32, lr: 3.43e-04
2022-05-05 10:53:38,608 INFO [train.py:715] (1/8) Epoch 6, batch 3250, loss[loss=0.1459, simple_loss=0.2214, pruned_loss=0.03522, over 4992.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2218, pruned_loss=0.0397, over 972140.79 frames.], batch size: 28, lr: 3.43e-04
2022-05-05 10:54:17,332 INFO [train.py:715] (1/8) Epoch 6, batch 3300, loss[loss=0.1357, simple_loss=0.202, pruned_loss=0.03472, over 4785.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2217, pruned_loss=0.03906, over 972391.54 frames.], batch size: 14, lr: 3.43e-04
2022-05-05 10:54:55,863 INFO [train.py:715] (1/8) Epoch 6, batch 3350, loss[loss=0.1857, simple_loss=0.2554, pruned_loss=0.05797, over 4915.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2205, pruned_loss=0.03853, over 972228.54 frames.], batch size: 23, lr: 3.43e-04
2022-05-05 10:55:35,258 INFO [train.py:715] (1/8) Epoch 6, batch 3400, loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03132, over 4784.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2213, pruned_loss=0.03909, over 972684.37 frames.], batch size: 17, lr: 3.43e-04
2022-05-05 10:56:14,436 INFO [train.py:715] (1/8) Epoch 6, batch 3450, loss[loss=0.1619, simple_loss=0.2318, pruned_loss=0.04598, over 4940.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2209, pruned_loss=0.03926, over 972643.48 frames.], batch size: 23, lr: 3.43e-04
2022-05-05 10:56:52,542 INFO [train.py:715] (1/8) Epoch 6, batch 3500, loss[loss=0.1448, simple_loss=0.2175, pruned_loss=0.03606, over 4817.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2203, pruned_loss=0.039, over 972632.06 frames.], batch size: 25, lr: 3.43e-04
2022-05-05 10:57:31,372 INFO [train.py:715] (1/8) Epoch 6, batch 3550, loss[loss=0.1437, simple_loss=0.2154, pruned_loss=0.03602, over 4765.00 frames.], tot_loss[loss=0.1496, simple_loss=0.221, pruned_loss=0.03915, over 971922.67 frames.], batch size: 17, lr: 3.43e-04
2022-05-05 10:58:10,830 INFO [train.py:715] (1/8) Epoch 6, batch 3600, loss[loss=0.1227, simple_loss=0.1991, pruned_loss=0.02312, over 4847.00 frames.], tot_loss[loss=0.149, simple_loss=0.2202, pruned_loss=0.03887, over 972501.03 frames.], batch size: 12, lr: 3.43e-04
2022-05-05 10:58:49,773 INFO [train.py:715] (1/8) Epoch 6, batch 3650, loss[loss=0.1564, simple_loss=0.2345, pruned_loss=0.03916, over 4844.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2204, pruned_loss=0.039, over 972446.87 frames.], batch size: 20, lr: 3.43e-04
2022-05-05 10:59:27,964 INFO [train.py:715] (1/8) Epoch 6, batch 3700, loss[loss=0.1347, simple_loss=0.2171, pruned_loss=0.02612, over 4707.00 frames.], tot_loss[loss=0.1497, simple_loss=0.221, pruned_loss=0.03926, over 972227.77 frames.], batch size: 15, lr: 3.43e-04
2022-05-05 11:00:07,229 INFO [train.py:715] (1/8) Epoch 6, batch 3750, loss[loss=0.1494, simple_loss=0.2158, pruned_loss=0.04152, over 4875.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2211, pruned_loss=0.03938, over 972017.98 frames.], batch size: 32, lr: 3.43e-04
2022-05-05 11:00:46,319 INFO [train.py:715] (1/8) Epoch 6, batch 3800, loss[loss=0.1441, simple_loss=0.2176, pruned_loss=0.03528, over 4916.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2207, pruned_loss=0.03936, over 971828.23 frames.], batch size: 39, lr: 3.43e-04
2022-05-05 11:01:24,436 INFO [train.py:715] (1/8) Epoch 6, batch 3850, loss[loss=0.1511, simple_loss=0.213, pruned_loss=0.04467, over 4896.00 frames.], tot_loss[loss=0.1501, simple_loss=0.221, pruned_loss=0.03963, over 972243.21 frames.], batch size: 22, lr: 3.43e-04
2022-05-05 11:02:03,349 INFO [train.py:715] (1/8) Epoch 6, batch 3900, loss[loss=0.1835, simple_loss=0.2302, pruned_loss=0.0684, over 4791.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2221, pruned_loss=0.0404, over 972733.92 frames.], batch size: 17, lr: 3.42e-04
2022-05-05 11:02:42,647 INFO [train.py:715] (1/8) Epoch 6, batch 3950, loss[loss=0.1613, simple_loss=0.2186, pruned_loss=0.05199, over 4982.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2218, pruned_loss=0.04057, over 972653.27 frames.], batch size: 14, lr: 3.42e-04
2022-05-05 11:03:21,703 INFO [train.py:715] (1/8) Epoch 6, batch 4000, loss[loss=0.1511, simple_loss=0.2212, pruned_loss=0.04049, over 4868.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2208, pruned_loss=0.03969, over 972975.88 frames.], batch size: 20, lr: 3.42e-04
2022-05-05 11:04:00,016 INFO [train.py:715] (1/8) Epoch 6, batch 4050, loss[loss=0.1329, simple_loss=0.2058, pruned_loss=0.02997, over 4907.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2202, pruned_loss=0.0396, over 972626.93 frames.], batch size: 22, lr: 3.42e-04
2022-05-05 11:04:39,118 INFO [train.py:715] (1/8) Epoch 6, batch 4100, loss[loss=0.1391, simple_loss=0.2075, pruned_loss=0.03529, over 4942.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2201, pruned_loss=0.03965, over 971553.04 frames.], batch size: 23, lr: 3.42e-04
2022-05-05 11:05:17,849 INFO [train.py:715] (1/8) Epoch 6, batch 4150, loss[loss=0.1274, simple_loss=0.1953, pruned_loss=0.02974, over 4755.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2198, pruned_loss=0.03931, over 971534.01 frames.], batch size: 16, lr: 3.42e-04
2022-05-05 11:05:56,010 INFO [train.py:715] (1/8) Epoch 6, batch 4200, loss[loss=0.1478, simple_loss=0.2287, pruned_loss=0.03347, over 4970.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2196, pruned_loss=0.03901, over 973064.48 frames.], batch size: 25, lr: 3.42e-04
2022-05-05 11:06:34,726 INFO [train.py:715] (1/8) Epoch 6, batch 4250, loss[loss=0.1413, simple_loss=0.2045, pruned_loss=0.03903, over 4849.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2203, pruned_loss=0.03948, over 972710.89 frames.], batch size: 30, lr: 3.42e-04
2022-05-05 11:07:13,789 INFO [train.py:715] (1/8) Epoch 6, batch 4300, loss[loss=0.1733, simple_loss=0.2462, pruned_loss=0.05027, over 4928.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2211, pruned_loss=0.03962, over 972353.80 frames.], batch size: 18, lr: 3.42e-04
2022-05-05 11:07:52,579 INFO [train.py:715] (1/8) Epoch 6, batch 4350, loss[loss=0.1446, simple_loss=0.2227, pruned_loss=0.03325, over 4779.00 frames.], tot_loss[loss=0.1497, simple_loss=0.221, pruned_loss=0.03914, over 971786.76 frames.], batch size: 18, lr: 3.42e-04
2022-05-05 11:08:30,489 INFO [train.py:715] (1/8) Epoch 6, batch 4400, loss[loss=0.1402, simple_loss=0.2077, pruned_loss=0.03634, over 4795.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2211, pruned_loss=0.03914, over 971981.26 frames.], batch size: 24, lr: 3.42e-04
2022-05-05 11:09:08,937 INFO [train.py:715] (1/8) Epoch 6, batch 4450, loss[loss=0.1623, simple_loss=0.213, pruned_loss=0.05576, over 4906.00 frames.], tot_loss[loss=0.1496, simple_loss=0.221, pruned_loss=0.03916, over 972333.71 frames.], batch size: 17, lr: 3.42e-04
2022-05-05 11:09:48,074 INFO [train.py:715] (1/8) Epoch 6, batch 4500, loss[loss=0.1412, simple_loss=0.2161, pruned_loss=0.03315, over 4938.00 frames.], tot_loss[loss=0.15, simple_loss=0.2211, pruned_loss=0.03947, over 972358.88 frames.], batch size: 23, lr: 3.42e-04
2022-05-05 11:10:26,355 INFO [train.py:715] (1/8) Epoch 6, batch 4550, loss[loss=0.1761, simple_loss=0.2338, pruned_loss=0.05917, over 4966.00 frames.], tot_loss[loss=0.15, simple_loss=0.2211, pruned_loss=0.03943, over 972937.92 frames.], batch size: 24, lr: 3.42e-04
2022-05-05 11:11:04,821 INFO [train.py:715] (1/8) Epoch 6, batch 4600, loss[loss=0.1509, simple_loss=0.2208, pruned_loss=0.0405, over 4944.00 frames.], tot_loss[loss=0.1487, simple_loss=0.22, pruned_loss=0.0387, over 973125.24 frames.], batch size: 23, lr: 3.42e-04
2022-05-05 11:11:44,226 INFO [train.py:715] (1/8) Epoch 6, batch 4650, loss[loss=0.1413, simple_loss=0.2164, pruned_loss=0.03313, over 4814.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2204, pruned_loss=0.03901, over 973618.02 frames.], batch size: 25, lr: 3.42e-04
2022-05-05 11:12:23,352 INFO [train.py:715] (1/8) Epoch 6, batch 4700, loss[loss=0.1986, simple_loss=0.2494, pruned_loss=0.0739, over 4854.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2213, pruned_loss=0.03983, over 973637.56 frames.], batch size: 30, lr: 3.42e-04
2022-05-05 11:13:01,633 INFO [train.py:715] (1/8) Epoch 6, batch 4750, loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.0346, over 4988.00 frames.], tot_loss[loss=0.15, simple_loss=0.2211, pruned_loss=0.0395, over 973177.32 frames.], batch size: 20, lr: 3.42e-04
2022-05-05 11:13:40,647 INFO [train.py:715] (1/8) Epoch 6, batch 4800, loss[loss=0.1351, simple_loss=0.2108, pruned_loss=0.02969, over 4917.00 frames.], tot_loss[loss=0.1501, simple_loss=0.221, pruned_loss=0.03965, over 974188.94 frames.], batch size: 23, lr: 3.42e-04
2022-05-05 11:14:19,742 INFO [train.py:715] (1/8) Epoch 6, batch 4850, loss[loss=0.1574, simple_loss=0.2283, pruned_loss=0.0433, over 4820.00 frames.], tot_loss[loss=0.15, simple_loss=0.221, pruned_loss=0.03951, over 974760.62 frames.], batch size: 25, lr: 3.42e-04
2022-05-05 11:14:58,280 INFO [train.py:715] (1/8) Epoch 6, batch 4900, loss[loss=0.1381, simple_loss=0.2031, pruned_loss=0.03652, over 4776.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2203, pruned_loss=0.03902, over 974882.35 frames.], batch size: 18, lr: 3.42e-04
2022-05-05 11:15:37,165 INFO [train.py:715] (1/8) Epoch 6, batch 4950, loss[loss=0.1813, simple_loss=0.2485, pruned_loss=0.05707, over 4907.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2206, pruned_loss=0.03945, over 974987.04 frames.], batch size: 17, lr: 3.42e-04
2022-05-05 11:16:16,919 INFO [train.py:715] (1/8) Epoch 6, batch 5000, loss[loss=0.1555, simple_loss=0.222, pruned_loss=0.04452, over 4990.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2204, pruned_loss=0.03954, over 974473.87 frames.], batch size: 16, lr: 3.42e-04
2022-05-05 11:16:55,994 INFO [train.py:715] (1/8) Epoch 6, batch 5050, loss[loss=0.1465, simple_loss=0.2251, pruned_loss=0.03392, over 4957.00 frames.], tot_loss[loss=0.1494, simple_loss=0.22, pruned_loss=0.03941, over 974876.07 frames.], batch size: 24, lr: 3.42e-04
2022-05-05 11:17:34,330 INFO [train.py:715] (1/8) Epoch 6, batch 5100, loss[loss=0.1453, simple_loss=0.2195, pruned_loss=0.03557, over 4887.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2202, pruned_loss=0.03921, over 974789.59 frames.], batch size: 19, lr: 3.42e-04
2022-05-05 11:18:13,256 INFO [train.py:715] (1/8) Epoch 6, batch 5150, loss[loss=0.1652, simple_loss=0.2441, pruned_loss=0.04318, over 4973.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2195, pruned_loss=0.03878, over 974658.08 frames.], batch size: 35, lr: 3.41e-04
2022-05-05 11:18:52,363 INFO [train.py:715] (1/8) Epoch 6, batch 5200, loss[loss=0.1854, simple_loss=0.2493, pruned_loss=0.06073, over 4981.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2192, pruned_loss=0.03859, over 973201.92 frames.], batch size: 25, lr: 3.41e-04
2022-05-05 11:19:30,493 INFO [train.py:715] (1/8) Epoch 6, batch 5250, loss[loss=0.1437, simple_loss=0.2225, pruned_loss=0.03244, over 4923.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2205, pruned_loss=0.03899, over 973137.12 frames.], batch size: 39, lr: 3.41e-04
2022-05-05 11:20:09,581 INFO [train.py:715] (1/8) Epoch 6, batch 5300, loss[loss=0.1411, simple_loss=0.2109, pruned_loss=0.03567, over 4780.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2204, pruned_loss=0.03941, over 973431.76 frames.], batch size: 14, lr: 3.41e-04
2022-05-05 11:20:48,896 INFO [train.py:715] (1/8) Epoch 6, batch 5350, loss[loss=0.1554, simple_loss=0.2417, pruned_loss=0.03454, over 4910.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.03871, over 972721.52 frames.], batch size: 17, lr: 3.41e-04
2022-05-05 11:21:27,942 INFO [train.py:715] (1/8) Epoch 6, batch 5400, loss[loss=0.1777, simple_loss=0.2342, pruned_loss=0.06059, over 4979.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2205, pruned_loss=0.03915, over 973280.90 frames.], batch size: 14, lr: 3.41e-04
2022-05-05 11:22:06,518 INFO [train.py:715] (1/8) Epoch 6, batch 5450, loss[loss=0.1678, simple_loss=0.2324, pruned_loss=0.05164, over 4971.00 frames.], tot_loss[loss=0.15, simple_loss=0.221, pruned_loss=0.03948, over 973465.72 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:22:45,326 INFO [train.py:715] (1/8) Epoch 6, batch 5500, loss[loss=0.1649, simple_loss=0.2396, pruned_loss=0.04508, over 4976.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2207, pruned_loss=0.03909, over 974003.09 frames.], batch size: 24, lr: 3.41e-04
2022-05-05 11:23:24,196 INFO [train.py:715] (1/8) Epoch 6, batch 5550, loss[loss=0.1908, simple_loss=0.2545, pruned_loss=0.06355, over 4969.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2208, pruned_loss=0.03921, over 974002.64 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:24:02,782 INFO [train.py:715] (1/8) Epoch 6, batch 5600, loss[loss=0.1723, simple_loss=0.2346, pruned_loss=0.05501, over 4949.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2209, pruned_loss=0.03948, over 973264.20 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:24:42,277 INFO [train.py:715] (1/8) Epoch 6, batch 5650, loss[loss=0.1514, simple_loss=0.2101, pruned_loss=0.0463, over 4796.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2209, pruned_loss=0.03934, over 973122.84 frames.], batch size: 24, lr: 3.41e-04
2022-05-05 11:25:21,628 INFO [train.py:715] (1/8) Epoch 6, batch 5700, loss[loss=0.2018, simple_loss=0.2593, pruned_loss=0.07212, over 4967.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2206, pruned_loss=0.03952, over 973075.10 frames.], batch size: 25, lr: 3.41e-04
2022-05-05 11:26:00,236 INFO [train.py:715] (1/8) Epoch 6, batch 5750, loss[loss=0.1462, simple_loss=0.2272, pruned_loss=0.03262, over 4918.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2209, pruned_loss=0.03962, over 973870.75 frames.], batch size: 29, lr: 3.41e-04
2022-05-05 11:26:38,647 INFO [train.py:715] (1/8) Epoch 6, batch 5800, loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03229, over 4874.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2202, pruned_loss=0.039, over 973695.17 frames.], batch size: 20, lr: 3.41e-04
2022-05-05 11:27:17,532 INFO [train.py:715] (1/8) Epoch 6, batch 5850, loss[loss=0.1456, simple_loss=0.2215, pruned_loss=0.03481, over 4740.00 frames.], tot_loss[loss=0.149, simple_loss=0.2201, pruned_loss=0.03897, over 972684.27 frames.], batch size: 16, lr: 3.41e-04
2022-05-05 11:27:56,997 INFO [train.py:715] (1/8) Epoch 6, batch 5900, loss[loss=0.1597, simple_loss=0.231, pruned_loss=0.04415, over 4876.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2211, pruned_loss=0.03926, over 972198.46 frames.], batch size: 16, lr: 3.41e-04
2022-05-05 11:28:34,911 INFO [train.py:715] (1/8) Epoch 6, batch 5950, loss[loss=0.1564, simple_loss=0.2204, pruned_loss=0.04623, over 4731.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03979, over 972242.63 frames.], batch size: 16, lr: 3.41e-04
2022-05-05 11:29:14,287 INFO [train.py:715] (1/8) Epoch 6, batch 6000, loss[loss=0.1485, simple_loss=0.2088, pruned_loss=0.04411, over 4845.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2219, pruned_loss=0.04035, over 972307.02 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:29:14,287 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 11:29:24,855 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1095, simple_loss=0.1945, pruned_loss=0.01229, over 914524.00 frames.
2022-05-05 11:30:04,471 INFO [train.py:715] (1/8) Epoch 6, batch 6050, loss[loss=0.1775, simple_loss=0.2411, pruned_loss=0.05698, over 4767.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2214, pruned_loss=0.03998, over 972501.10 frames.], batch size: 19, lr: 3.41e-04
2022-05-05 11:30:43,727 INFO [train.py:715] (1/8) Epoch 6, batch 6100, loss[loss=0.1857, simple_loss=0.2576, pruned_loss=0.05693, over 4884.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2222, pruned_loss=0.04027, over 973521.77 frames.], batch size: 20, lr: 3.41e-04
2022-05-05 11:31:23,125 INFO [train.py:715] (1/8) Epoch 6, batch 6150, loss[loss=0.1257, simple_loss=0.2013, pruned_loss=0.02499, over 4747.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2222, pruned_loss=0.04009, over 972764.46 frames.], batch size: 19, lr: 3.41e-04
2022-05-05 11:32:01,616 INFO [train.py:715] (1/8) Epoch 6, batch 6200, loss[loss=0.1309, simple_loss=0.2073, pruned_loss=0.02722, over 4763.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2221, pruned_loss=0.04002, over 972820.15 frames.], batch size: 17, lr: 3.41e-04
2022-05-05 11:32:40,935 INFO [train.py:715] (1/8) Epoch 6, batch 6250, loss[loss=0.2191, simple_loss=0.2655, pruned_loss=0.08637, over 4812.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2224, pruned_loss=0.04047, over 972395.55 frames.], batch size: 13, lr: 3.41e-04
2022-05-05 11:33:20,237 INFO [train.py:715] (1/8) Epoch 6, batch 6300, loss[loss=0.1421, simple_loss=0.2216, pruned_loss=0.03128, over 4824.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2222, pruned_loss=0.04033, over 972423.41 frames.], batch size: 26, lr: 3.41e-04
2022-05-05 11:33:58,708 INFO [train.py:715] (1/8) Epoch 6, batch 6350, loss[loss=0.1558, simple_loss=0.2275, pruned_loss=0.04206, over 4890.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2224, pruned_loss=0.04027, over 971818.15 frames.], batch size: 16, lr: 3.41e-04
2022-05-05 11:34:37,340 INFO [train.py:715] (1/8) Epoch 6, batch 6400, loss[loss=0.1306, simple_loss=0.1999, pruned_loss=0.0306, over 4795.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2221, pruned_loss=0.04012, over 971742.37 frames.], batch size: 14, lr: 3.40e-04
2022-05-05 11:35:16,566 INFO [train.py:715] (1/8) Epoch 6, batch 6450, loss[loss=0.1424, simple_loss=0.2215, pruned_loss=0.03168, over 4895.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2213, pruned_loss=0.04003, over 971256.39 frames.], batch size: 19, lr: 3.40e-04
2022-05-05 11:35:55,390 INFO [train.py:715] (1/8) Epoch 6, batch 6500, loss[loss=0.1107, simple_loss=0.1918, pruned_loss=0.01474, over 4782.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2224, pruned_loss=0.04066, over 971844.36 frames.], batch size: 18, lr: 3.40e-04
2022-05-05 11:36:33,975 INFO [train.py:715] (1/8) Epoch 6, batch 6550, loss[loss=0.1359, simple_loss=0.2077, pruned_loss=0.03203, over 4971.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2225, pruned_loss=0.04068, over 971462.69 frames.], batch size: 24, lr: 3.40e-04
2022-05-05 11:37:12,777 INFO [train.py:715] (1/8) Epoch 6, batch 6600, loss[loss=0.1334, simple_loss=0.2141, pruned_loss=0.02639, over 4991.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2221, pruned_loss=0.04046, over 970562.60 frames.], batch size: 16, lr: 3.40e-04
2022-05-05 11:37:52,974 INFO [train.py:715] (1/8) Epoch 6, batch 6650, loss[loss=0.1371, simple_loss=0.2048, pruned_loss=0.03472, over 4900.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.0405, over 970989.23 frames.], batch size: 19, lr: 3.40e-04
2022-05-05 11:38:31,784 INFO [train.py:715] (1/8) Epoch 6, batch 6700, loss[loss=0.1452, simple_loss=0.2214, pruned_loss=0.03447, over 4927.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2221, pruned_loss=0.04002, over 971782.22 frames.], batch size: 18, lr: 3.40e-04
2022-05-05 11:39:10,522 INFO [train.py:715] (1/8) Epoch 6, batch 6750, loss[loss=0.1311, simple_loss=0.2005, pruned_loss=0.03083, over 4966.00 frames.], tot_loss[loss=0.151, simple_loss=0.2219, pruned_loss=0.04005, over 971820.86 frames.], batch size: 35, lr: 3.40e-04
2022-05-05 11:39:49,801 INFO [train.py:715] (1/8) Epoch 6, batch 6800, loss[loss=0.1408, simple_loss=0.2073, pruned_loss=0.03714, over 4961.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2212, pruned_loss=0.03929, over 971900.25 frames.], batch size: 15, lr: 3.40e-04
2022-05-05 11:40:28,792 INFO [train.py:715] (1/8) Epoch 6, batch 6850, loss[loss=0.135, simple_loss=0.2023, pruned_loss=0.03383, over 4814.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2207, pruned_loss=0.03919, over 972805.69 frames.], batch size: 26, lr: 3.40e-04
2022-05-05 11:41:06,843 INFO [train.py:715] (1/8) Epoch 6, batch 6900, loss[loss=0.1774, simple_loss=0.2498, pruned_loss=0.05252, over 4808.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2202, pruned_loss=0.03915, over 971639.37 frames.], batch size: 25, lr: 3.40e-04
2022-05-05 11:41:45,910 INFO [train.py:715] (1/8) Epoch 6, batch 6950, loss[loss=0.1552, simple_loss=0.2285, pruned_loss=0.04093, over 4745.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2195, pruned_loss=0.03869, over 971130.69 frames.], batch size: 19, lr: 3.40e-04
2022-05-05 11:42:25,621 INFO [train.py:715] (1/8) Epoch 6, batch 7000, loss[loss=0.1698, simple_loss=0.2226, pruned_loss=0.0585, over 4918.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2202, pruned_loss=0.03879, over 971173.00 frames.], batch size: 18, lr: 3.40e-04
2022-05-05 11:43:04,220 INFO [train.py:715] (1/8) Epoch 6, batch 7050, loss[loss=0.1441, simple_loss=0.2196, pruned_loss=0.03431, over 4914.00 frames.], tot_loss[loss=0.148, simple_loss=0.2194, pruned_loss=0.03835, over 970820.82 frames.], batch size: 19, lr: 3.40e-04
2022-05-05 11:43:42,734 INFO [train.py:715] (1/8) Epoch 6, batch 7100, loss[loss=0.1359, simple_loss=0.215, pruned_loss=0.02845, over 4752.00 frames.], tot_loss[loss=0.1496, simple_loss=0.221, pruned_loss=0.03904, over 970397.85 frames.], batch size: 19, lr: 3.40e-04
2022-05-05 11:44:25,534 INFO [train.py:715] (1/8) Epoch 6, batch 7150, loss[loss=0.1561, simple_loss=0.2284, pruned_loss=0.0419, over 4791.00 frames.], tot_loss[loss=0.149, simple_loss=0.2205, pruned_loss=0.03874, over 971072.38 frames.], batch size: 24, lr: 3.40e-04
2022-05-05 11:45:04,234 INFO [train.py:715] (1/8) Epoch 6, batch 7200, loss[loss=0.1504, simple_loss=0.2135, pruned_loss=0.04367, over 4799.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2212, pruned_loss=0.03923, over 971242.24 frames.], batch size: 13, lr: 3.40e-04
2022-05-05 11:45:42,693 INFO [train.py:715] (1/8) Epoch 6, batch 7250, loss[loss=0.1288, simple_loss=0.2097, pruned_loss=0.02392, over 4811.00 frames.], tot_loss[loss=0.149, simple_loss=0.2204, pruned_loss=0.03885, over 970582.54 frames.], batch size: 21, lr: 3.40e-04
2022-05-05 11:46:21,452 INFO [train.py:715] (1/8) Epoch 6, batch 7300, loss[loss=0.1481, simple_loss=0.2176, pruned_loss=0.03927, over 4971.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2206, pruned_loss=0.03884, over 970698.98 frames.], batch size: 35, lr: 3.40e-04
2022-05-05 11:47:01,051 INFO [train.py:715] (1/8) Epoch 6, batch 7350, loss[loss=0.16, simple_loss=0.2303, pruned_loss=0.04481, over 4837.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2207, pruned_loss=0.03885, over 971258.12 frames.], batch size: 20, lr: 3.40e-04
2022-05-05 11:47:38,867 INFO [train.py:715] (1/8) Epoch 6, batch 7400, loss[loss=0.1422, simple_loss=0.215, pruned_loss=0.0347, over 4780.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2202, pruned_loss=0.03868, over 971605.61 frames.], batch size: 18, lr: 3.40e-04
2022-05-05 11:48:18,380 INFO [train.py:715] (1/8) Epoch 6, batch 7450, loss[loss=0.1478, simple_loss=0.2194, pruned_loss=0.03814, over 4899.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2196, pruned_loss=0.03876, over 970999.37 frames.], batch size: 19, lr: 3.40e-04
2022-05-05 11:48:56,997 INFO [train.py:715] (1/8) Epoch 6, batch 7500, loss[loss=0.1396, simple_loss=0.2088, pruned_loss=0.03518, over 4874.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2188, pruned_loss=0.03832, over 970789.55 frames.], batch size: 22, lr: 3.40e-04
2022-05-05 11:49:35,694 INFO [train.py:715] (1/8) Epoch 6, batch 7550, loss[loss=0.1489, simple_loss=0.2127, pruned_loss=0.04255, over 4831.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2196, pruned_loss=0.03889, over 971738.14 frames.], batch size: 15, lr: 3.40e-04
2022-05-05 11:50:14,635 INFO [train.py:715] (1/8) Epoch 6, batch 7600, loss[loss=0.1373, simple_loss=0.2078, pruned_loss=0.03334, over 4807.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2201, pruned_loss=0.03943, over 971473.35 frames.], batch size: 25, lr: 3.40e-04
2022-05-05 11:50:53,763 INFO [train.py:715] (1/8) Epoch 6, batch 7650, loss[loss=0.1539, simple_loss=0.2272, pruned_loss=0.04026, over 4831.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2207, pruned_loss=0.03949, over 971762.30 frames.], batch size: 30, lr: 3.40e-04
2022-05-05 11:51:33,383 INFO [train.py:715] (1/8) Epoch 6, batch 7700, loss[loss=0.1487, simple_loss=0.2315, pruned_loss=0.03294, over 4793.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2209, pruned_loss=0.0397, over 972291.10 frames.], batch size: 21, lr: 3.39e-04
2022-05-05 11:52:11,585 INFO [train.py:715] (1/8) Epoch 6, batch 7750, loss[loss=0.1368, simple_loss=0.2078, pruned_loss=0.0329, over 4972.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2206, pruned_loss=0.03956, over 972141.73 frames.], batch size: 15, lr: 3.39e-04
2022-05-05 11:52:51,085 INFO [train.py:715] (1/8) Epoch 6, batch 7800, loss[loss=0.1221, simple_loss=0.1933, pruned_loss=0.02547, over 4875.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2212, pruned_loss=0.0399, over 972020.11 frames.], batch size: 16, lr: 3.39e-04
2022-05-05 11:53:30,019 INFO [train.py:715] (1/8) Epoch 6, batch 7850, loss[loss=0.1473, simple_loss=0.212, pruned_loss=0.04129, over 4777.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2208, pruned_loss=0.0398, over 971426.88 frames.], batch size: 18, lr: 3.39e-04
2022-05-05 11:54:08,584 INFO [train.py:715] (1/8) Epoch 6, batch 7900, loss[loss=0.1318, simple_loss=0.1971, pruned_loss=0.0333, over 4738.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2213, pruned_loss=0.03982, over 971683.64 frames.], batch size: 12, lr: 3.39e-04
2022-05-05 11:54:47,344 INFO [train.py:715] (1/8) Epoch 6, batch 7950, loss[loss=0.1151, simple_loss=0.1929, pruned_loss=0.01865, over 4836.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2218, pruned_loss=0.04016, over 972307.42 frames.], batch size: 26, lr: 3.39e-04
2022-05-05 11:55:26,518 INFO [train.py:715] (1/8) Epoch 6, batch 8000, loss[loss=0.1333, simple_loss=0.203, pruned_loss=0.03177, over 4820.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2209, pruned_loss=0.03976, over 972757.95 frames.], batch size: 25, lr: 3.39e-04
2022-05-05 11:56:05,898 INFO [train.py:715] (1/8) Epoch 6, batch 8050, loss[loss=0.1217, simple_loss=0.1993, pruned_loss=0.02204, over 4919.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2214, pruned_loss=0.0398, over 973673.35 frames.], batch size: 29, lr: 3.39e-04
2022-05-05 11:56:43,897 INFO [train.py:715] (1/8) Epoch 6, batch 8100, loss[loss=0.2223, simple_loss=0.2718, pruned_loss=0.0864, over 4970.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2218, pruned_loss=0.04018, over 973797.97 frames.], batch size: 15, lr: 3.39e-04
2022-05-05 11:57:22,884 INFO [train.py:715] (1/8) Epoch 6, batch 8150, loss[loss=0.1321, simple_loss=0.2056, pruned_loss=0.02927, over 4951.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2227, pruned_loss=0.04076, over 973355.59 frames.], batch size: 21, lr: 3.39e-04
2022-05-05 11:58:01,958 INFO [train.py:715] (1/8) Epoch 6, batch 8200, loss[loss=0.1648, simple_loss=0.2304, pruned_loss=0.0496, over 4860.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2219, pruned_loss=0.04014, over 972990.76 frames.], batch size: 32, lr: 3.39e-04
2022-05-05 11:58:41,280 INFO [train.py:715] (1/8) Epoch 6, batch 8250, loss[loss=0.1583, simple_loss=0.2239, pruned_loss=0.0463, over 4861.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2216, pruned_loss=0.03977, over 973252.54 frames.], batch size: 16, lr: 3.39e-04
2022-05-05 11:59:19,581 INFO [train.py:715] (1/8) Epoch 6, batch 8300, loss[loss=0.1444, simple_loss=0.2115, pruned_loss=0.03863, over 4862.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2207, pruned_loss=0.0391, over 974117.54 frames.], batch size: 20, lr: 3.39e-04
2022-05-05 11:59:58,760 INFO [train.py:715] (1/8) Epoch 6, batch 8350, loss[loss=0.1206, simple_loss=0.1948, pruned_loss=0.02315, over 4772.00 frames.], tot_loss[loss=0.149, simple_loss=0.2204, pruned_loss=0.03884, over 973702.94 frames.], batch size: 18, lr: 3.39e-04
2022-05-05 12:00:37,621 INFO [train.py:715] (1/8) Epoch 6, batch 8400, loss[loss=0.1436, simple_loss=0.229, pruned_loss=0.02912, over 4937.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2205, pruned_loss=0.03835, over 973209.32 frames.], batch size: 21, lr: 3.39e-04
2022-05-05 12:01:15,842 INFO [train.py:715] (1/8) Epoch 6, batch 8450, loss[loss=0.1678, simple_loss=0.2319, pruned_loss=0.05182, over 4823.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2212, pruned_loss=0.03908, over 972829.91 frames.], batch size: 15, lr: 3.39e-04
2022-05-05 12:01:54,988 INFO [train.py:715] (1/8) Epoch 6, batch 8500, loss[loss=0.1566, simple_loss=0.211, pruned_loss=0.0511, over 4783.00 frames.], tot_loss[loss=0.149, simple_loss=0.2206, pruned_loss=0.03874, over 972956.57 frames.], batch size: 17, lr: 3.39e-04
2022-05-05 12:02:33,549 INFO [train.py:715] (1/8) Epoch 6, batch 8550, loss[loss=0.1782, simple_loss=0.259, pruned_loss=0.04869, over 4805.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2212, pruned_loss=0.03906, over 972943.02 frames.], batch size: 21, lr: 3.39e-04
2022-05-05 12:03:12,438 INFO [train.py:715] (1/8) Epoch 6, batch 8600, loss[loss=0.1227, simple_loss=0.2059, pruned_loss=0.01972, over 4923.00 frames.], tot_loss[loss=0.149, simple_loss=0.2208, pruned_loss=0.03859, over 973160.80 frames.], batch size: 23, lr: 3.39e-04
2022-05-05 12:03:50,310 INFO [train.py:715] (1/8) Epoch 6, batch 8650, loss[loss=0.1397, simple_loss=0.2224, pruned_loss=0.02846, over 4859.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2204, pruned_loss=0.03825, over 973415.38 frames.], batch size: 20, lr: 3.39e-04
2022-05-05 12:04:29,733 INFO [train.py:715] (1/8) Epoch 6, batch 8700, loss[loss=0.155, simple_loss=0.2268, pruned_loss=0.0416, over 4982.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2205, pruned_loss=0.03852, over 972455.31 frames.], batch size: 31, lr: 3.39e-04
2022-05-05 12:05:08,433 INFO [train.py:715] (1/8) Epoch 6, batch 8750, loss[loss=0.1423, simple_loss=0.2101, pruned_loss=0.03723, over 4967.00 frames.], tot_loss[loss=0.1481, simple_loss=0.22, pruned_loss=0.03812, over 972971.14 frames.], batch size: 24, lr: 3.39e-04
2022-05-05 12:05:46,860 INFO [train.py:715] (1/8) Epoch 6, batch 8800, loss[loss=0.1472, simple_loss=0.2142, pruned_loss=0.04003, over 4821.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2203, pruned_loss=0.03832, over 972348.29 frames.], batch size: 14, lr: 3.39e-04
2022-05-05 12:06:25,687 INFO [train.py:715] (1/8) Epoch 6, batch 8850, loss[loss=0.1527, simple_loss=0.2245, pruned_loss=0.04045, over 4876.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2201, pruned_loss=0.03839, over 972746.44 frames.], batch size: 22, lr: 3.39e-04
2022-05-05 12:07:04,758 INFO [train.py:715] (1/8) Epoch 6, batch 8900, loss[loss=0.1527, simple_loss=0.2173, pruned_loss=0.04407, over 4736.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2204, pruned_loss=0.03867, over 972029.82 frames.], batch size: 16, lr: 3.39e-04
2022-05-05 12:07:43,996 INFO [train.py:715] (1/8) Epoch 6, batch 8950, loss[loss=0.1567, simple_loss=0.2267, pruned_loss=0.04335, over 4849.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2209, pruned_loss=0.03879, over 973041.18 frames.], batch size: 20, lr: 3.38e-04
2022-05-05 12:08:22,492 INFO [train.py:715] (1/8) Epoch 6, batch 9000, loss[loss=0.1567, simple_loss=0.2333, pruned_loss=0.04007, over 4762.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2214, pruned_loss=0.0392, over 971630.03 frames.], batch size: 16, lr: 3.38e-04
2022-05-05 12:08:22,493 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 12:08:35,891 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1094, simple_loss=0.1946, pruned_loss=0.01213, over 914524.00 frames.
2022-05-05 12:09:14,901 INFO [train.py:715] (1/8) Epoch 6, batch 9050, loss[loss=0.1583, simple_loss=0.2219, pruned_loss=0.04737, over 4847.00 frames.], tot_loss[loss=0.15, simple_loss=0.2213, pruned_loss=0.0394, over 972450.79 frames.], batch size: 30, lr: 3.38e-04
2022-05-05 12:09:53,935 INFO [train.py:715] (1/8) Epoch 6, batch 9100, loss[loss=0.1327, simple_loss=0.2058, pruned_loss=0.02975, over 4820.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2208, pruned_loss=0.03909, over 972678.08 frames.], batch size: 26, lr: 3.38e-04
2022-05-05 12:10:33,374 INFO [train.py:715] (1/8) Epoch 6, batch 9150, loss[loss=0.1346, simple_loss=0.1984, pruned_loss=0.0354, over 4912.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2201, pruned_loss=0.03853, over 971818.22 frames.], batch size: 18, lr: 3.38e-04
2022-05-05 12:11:11,396 INFO [train.py:715] (1/8) Epoch 6, batch 9200, loss[loss=0.1315, simple_loss=0.1897, pruned_loss=0.03664, over 4807.00 frames.], tot_loss[loss=0.149, simple_loss=0.2206, pruned_loss=0.03875, over 971804.52 frames.], batch size: 12, lr: 3.38e-04
2022-05-05 12:11:50,798 INFO [train.py:715] (1/8) Epoch 6, batch 9250, loss[loss=0.175, simple_loss=0.241, pruned_loss=0.05451, over 4803.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2207, pruned_loss=0.03893, over 971991.93 frames.], batch size: 14, lr: 3.38e-04
2022-05-05 12:12:29,889 INFO [train.py:715] (1/8) Epoch 6, batch 9300, loss[loss=0.1454, simple_loss=0.218, pruned_loss=0.03637, over 4904.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2208, pruned_loss=0.03868, over 971896.82 frames.], batch size: 22, lr: 3.38e-04
2022-05-05 12:13:08,402 INFO [train.py:715] (1/8) Epoch 6, batch 9350, loss[loss=0.1352, simple_loss=0.2103, pruned_loss=0.03008, over 4973.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2202, pruned_loss=0.03846, over 971724.20 frames.], batch size: 24, lr: 3.38e-04
2022-05-05 12:13:47,630 INFO [train.py:715] (1/8) Epoch 6, batch 9400, loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.03446, over 4809.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2203, pruned_loss=0.03852, over 972580.09 frames.], batch size: 25, lr: 3.38e-04
2022-05-05 12:14:26,438 INFO [train.py:715] (1/8) Epoch 6, batch 9450, loss[loss=0.1507, simple_loss=0.232, pruned_loss=0.03469, over 4991.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2204, pruned_loss=0.03848, over 972809.20 frames.], batch size: 25, lr: 3.38e-04
2022-05-05 12:15:05,766 INFO [train.py:715] (1/8) Epoch 6, batch 9500, loss[loss=0.1393, simple_loss=0.2033, pruned_loss=0.0376, over 4882.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2206, pruned_loss=0.03876, over 972452.89 frames.], batch size: 16, lr: 3.38e-04
2022-05-05 12:15:44,437 INFO [train.py:715] (1/8) Epoch 6, batch 9550, loss[loss=0.158, simple_loss=0.239, pruned_loss=0.0385, over 4809.00 frames.], tot_loss[loss=0.149, simple_loss=0.2206, pruned_loss=0.03871, over 973016.03 frames.], batch size: 25, lr: 3.38e-04
2022-05-05 12:16:23,401 INFO [train.py:715] (1/8) Epoch 6, batch 9600, loss[loss=0.1394, simple_loss=0.21, pruned_loss=0.03436, over 4783.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2197, pruned_loss=0.03857, over 973929.67 frames.], batch size: 17, lr: 3.38e-04
2022-05-05 12:17:02,131 INFO [train.py:715] (1/8) Epoch 6, batch 9650, loss[loss=0.1333, simple_loss=0.2051, pruned_loss=0.03081, over 4824.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2198, pruned_loss=0.03833, over 973932.05 frames.], batch size: 13, lr: 3.38e-04
2022-05-05 12:17:40,452 INFO [train.py:715] (1/8) Epoch 6, batch 9700, loss[loss=0.1371, simple_loss=0.2124, pruned_loss=0.03086, over 4922.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2207, pruned_loss=0.03876, over 973142.21 frames.], batch size: 18, lr: 3.38e-04
2022-05-05 12:18:19,760 INFO [train.py:715] (1/8) Epoch 6, batch 9750, loss[loss=0.1762, simple_loss=0.2452, pruned_loss=0.05366, over 4869.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03887, over 973224.15 frames.], batch size: 16, lr: 3.38e-04
2022-05-05 12:18:59,480 INFO [train.py:715] (1/8) Epoch 6, batch 9800, loss[loss=0.1772, simple_loss=0.2643, pruned_loss=0.04507, over 4896.00 frames.], tot_loss[loss=0.1486, simple_loss=0.22, pruned_loss=0.03861, over 973163.04 frames.], batch size: 22, lr: 3.38e-04
2022-05-05 12:19:39,850 INFO [train.py:715] (1/8) Epoch 6, batch 9850, loss[loss=0.1239, simple_loss=0.1975, pruned_loss=0.02512, over 4950.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2188, pruned_loss=0.03808, over 972778.24 frames.], batch size: 21, lr: 3.38e-04
2022-05-05 12:20:19,002 INFO [train.py:715] (1/8) Epoch 6, batch 9900, loss[loss=0.1537, simple_loss=0.2161, pruned_loss=0.04562, over 4789.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2198, pruned_loss=0.03855, over 972637.63 frames.], batch size: 17, lr: 3.38e-04
2022-05-05 12:20:59,136 INFO [train.py:715] (1/8) Epoch 6, batch 9950, loss[loss=0.1552, simple_loss=0.2233, pruned_loss=0.04354, over 4780.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.03868, over 971718.91 frames.], batch size: 17, lr: 3.38e-04
2022-05-05 12:21:39,155 INFO [train.py:715] (1/8) Epoch 6, batch 10000, loss[loss=0.143, simple_loss=0.2124, pruned_loss=0.03681, over 4928.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2197, pruned_loss=0.03885, over 971548.96 frames.], batch size: 29, lr: 3.38e-04
2022-05-05 12:22:17,404 INFO [train.py:715] (1/8) Epoch 6, batch 10050, loss[loss=0.1568, simple_loss=0.2243, pruned_loss=0.04464, over 4970.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2195, pruned_loss=0.03901, over 972298.35 frames.], batch size: 35, lr: 3.38e-04
2022-05-05 12:22:56,770 INFO [train.py:715] (1/8) Epoch 6, batch 10100, loss[loss=0.1662, simple_loss=0.2407, pruned_loss=0.04585, over 4836.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2198, pruned_loss=0.03878, over 972477.22 frames.], batch size: 15, lr: 3.38e-04
2022-05-05 12:23:34,994 INFO [train.py:715] (1/8) Epoch 6, batch 10150, loss[loss=0.1703, simple_loss=0.2223, pruned_loss=0.05918, over 4968.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2206, pruned_loss=0.03932, over 972774.10 frames.], batch size: 15, lr: 3.38e-04
2022-05-05 12:24:14,026 INFO [train.py:715] (1/8) Epoch 6, batch 10200, loss[loss=0.1494, simple_loss=0.223, pruned_loss=0.03791, over 4819.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2208, pruned_loss=0.03939, over 972602.70 frames.], batch size: 25, lr: 3.38e-04
2022-05-05 12:24:52,555 INFO [train.py:715] (1/8) Epoch 6, batch 10250, loss[loss=0.1399, simple_loss=0.2146, pruned_loss=0.03264, over 4774.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2209, pruned_loss=0.03983, over 972562.30 frames.], batch size: 17, lr: 3.37e-04
2022-05-05 12:25:31,644 INFO [train.py:715] (1/8) Epoch 6, batch 10300, loss[loss=0.147, simple_loss=0.2286, pruned_loss=0.03273, over 4900.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2209, pruned_loss=0.04002, over 972781.15 frames.], batch size: 19, lr: 3.37e-04
2022-05-05 12:26:10,145 INFO [train.py:715] (1/8) Epoch 6, batch 10350, loss[loss=0.1857, simple_loss=0.2578, pruned_loss=0.05684, over 4816.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2221, pruned_loss=0.04052, over 972841.52 frames.], batch size: 27, lr: 3.37e-04
2022-05-05 12:26:49,280 INFO [train.py:715] (1/8) Epoch 6, batch 10400, loss[loss=0.1368, simple_loss=0.2089, pruned_loss=0.03236, over 4967.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2219, pruned_loss=0.04041, over 972671.06 frames.], batch size: 15, lr: 3.37e-04
2022-05-05 12:27:27,711 INFO [train.py:715] (1/8) Epoch 6, batch 10450, loss[loss=0.1434, simple_loss=0.2143, pruned_loss=0.0362, over 4963.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2203, pruned_loss=0.0393, over 973184.07 frames.], batch size: 35, lr: 3.37e-04
2022-05-05 12:28:06,365 INFO [train.py:715] (1/8) Epoch 6, batch 10500, loss[loss=0.1616, simple_loss=0.2192, pruned_loss=0.05196, over 4843.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2198, pruned_loss=0.03934, over 973317.17 frames.], batch size: 30, lr: 3.37e-04
2022-05-05 12:28:45,433 INFO [train.py:715] (1/8) Epoch 6, batch 10550, loss[loss=0.1523, simple_loss=0.2183, pruned_loss=0.04314, over 4748.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2194, pruned_loss=0.03938, over 974432.74 frames.], batch size: 16, lr: 3.37e-04
2022-05-05 12:29:23,702 INFO [train.py:715] (1/8) Epoch 6, batch 10600, loss[loss=0.1475, simple_loss=0.2147, pruned_loss=0.04011, over 4885.00 frames.], tot_loss[loss=0.1496, simple_loss=0.22, pruned_loss=0.03963, over 974200.08 frames.], batch size: 32, lr: 3.37e-04
2022-05-05 12:30:02,903 INFO [train.py:715] (1/8) Epoch 6, batch 10650, loss[loss=0.1286, simple_loss=0.2001, pruned_loss=0.02855, over 4919.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2199, pruned_loss=0.03958, over 973659.62 frames.], batch size: 18, lr: 3.37e-04
2022-05-05 12:30:41,619 INFO [train.py:715] (1/8) Epoch 6, batch 10700, loss[loss=0.1343, simple_loss=0.2007, pruned_loss=0.03395, over 4773.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2198, pruned_loss=0.03967, over 973818.63 frames.], batch size: 14, lr: 3.37e-04
2022-05-05 12:31:20,572 INFO [train.py:715] (1/8) Epoch 6, batch 10750, loss[loss=0.1263, simple_loss=0.1992, pruned_loss=0.02674, over 4869.00 frames.], tot_loss[loss=0.1495, simple_loss=0.22, pruned_loss=0.03948, over 972901.09 frames.], batch size: 20, lr: 3.37e-04
2022-05-05 12:31:59,032 INFO [train.py:715] (1/8) Epoch 6, batch 10800, loss[loss=0.1707, simple_loss=0.2423, pruned_loss=0.04954, over 4860.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2204, pruned_loss=0.03952, over 973121.26 frames.], batch size: 32, lr: 3.37e-04
2022-05-05 12:32:37,570 INFO [train.py:715] (1/8) Epoch 6, batch 10850, loss[loss=0.1517, simple_loss=0.2263, pruned_loss=0.03861, over 4821.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2205, pruned_loss=0.03961, over 971220.89 frames.], batch size: 25, lr: 3.37e-04
2022-05-05 12:33:15,995 INFO [train.py:715] (1/8) Epoch 6, batch 10900, loss[loss=0.1283, simple_loss=0.2022, pruned_loss=0.02722, over 4980.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2194, pruned_loss=0.03879, over 972284.03 frames.], batch size: 25, lr: 3.37e-04
2022-05-05 12:33:54,117 INFO [train.py:715] (1/8) Epoch 6, batch 10950, loss[loss=0.1611, simple_loss=0.2351, pruned_loss=0.04358, over 4769.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2192, pruned_loss=0.03847, over 972450.68 frames.], batch size: 17, lr: 3.37e-04
2022-05-05 12:34:33,263 INFO [train.py:715] (1/8) Epoch 6, batch 11000, loss[loss=0.1224, simple_loss=0.1997, pruned_loss=0.02257, over 4937.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2198, pruned_loss=0.0385, over 972305.62 frames.], batch size: 23, lr: 3.37e-04
2022-05-05 12:35:11,626 INFO [train.py:715] (1/8) Epoch 6, batch 11050, loss[loss=0.127, simple_loss=0.212, pruned_loss=0.02097, over 4889.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2198, pruned_loss=0.03848, over 972222.24 frames.], batch size: 22, lr: 3.37e-04
2022-05-05 12:35:50,632 INFO [train.py:715] (1/8) Epoch 6, batch 11100, loss[loss=0.1402, simple_loss=0.2105, pruned_loss=0.03491, over 4762.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2198, pruned_loss=0.03877, over 971928.91 frames.], batch size: 17, lr: 3.37e-04
2022-05-05 12:36:29,030 INFO [train.py:715] (1/8) Epoch 6, batch 11150, loss[loss=0.1554, simple_loss=0.2253, pruned_loss=0.04276, over 4955.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2199, pruned_loss=0.03885, over 972528.93 frames.], batch size: 24, lr: 3.37e-04
2022-05-05 12:37:07,408 INFO [train.py:715] (1/8) Epoch 6, batch 11200, loss[loss=0.139, simple_loss=0.2162, pruned_loss=0.03085, over 4845.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2203, pruned_loss=0.03906, over 972827.45 frames.], batch size: 15, lr: 3.37e-04
2022-05-05 12:37:45,844 INFO [train.py:715] (1/8) Epoch 6, batch 11250, loss[loss=0.1478, simple_loss=0.2208, pruned_loss=0.0374, over 4905.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2195, pruned_loss=0.03867, over 973237.06 frames.], batch size: 18, lr: 3.37e-04
2022-05-05 12:38:24,407 INFO [train.py:715] (1/8) Epoch 6, batch 11300, loss[loss=0.1448, simple_loss=0.2175, pruned_loss=0.03602, over 4926.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2185, pruned_loss=0.03813, over 973512.84 frames.], batch size: 29, lr: 3.37e-04
2022-05-05 12:39:03,682 INFO [train.py:715] (1/8) Epoch 6, batch 11350, loss[loss=0.1773, simple_loss=0.256, pruned_loss=0.04929, over 4941.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2199, pruned_loss=0.03854, over 973830.97 frames.], batch size: 21, lr: 3.37e-04
2022-05-05 12:39:42,623 INFO [train.py:715] (1/8) Epoch 6, batch 11400, loss[loss=0.1881, simple_loss=0.2446, pruned_loss=0.06578, over 4859.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03881, over 974089.94 frames.], batch size: 32, lr: 3.37e-04
2022-05-05 12:40:21,681 INFO [train.py:715] (1/8) Epoch 6, batch 11450, loss[loss=0.1451, simple_loss=0.2118, pruned_loss=0.03923, over 4853.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2209, pruned_loss=0.03919, over 973356.68 frames.], batch size: 20, lr: 3.37e-04
2022-05-05 12:40:59,954 INFO [train.py:715] (1/8) Epoch 6, batch 11500, loss[loss=0.1375, simple_loss=0.2045, pruned_loss=0.03521, over 4856.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03919, over 973647.46 frames.], batch size: 32, lr: 3.37e-04
2022-05-05 12:41:38,302 INFO [train.py:715] (1/8) Epoch 6, batch 11550, loss[loss=0.1242, simple_loss=0.1962, pruned_loss=0.02613, over 4977.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.03869, over 973661.37 frames.], batch size: 28, lr: 3.36e-04
2022-05-05 12:42:17,678 INFO [train.py:715] (1/8) Epoch 6, batch 11600, loss[loss=0.1745, simple_loss=0.2513, pruned_loss=0.04886, over 4903.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03817, over 973353.69 frames.], batch size: 19, lr: 3.36e-04
2022-05-05 12:42:56,132 INFO [train.py:715] (1/8) Epoch 6, batch 11650, loss[loss=0.1597, simple_loss=0.2263, pruned_loss=0.04652, over 4993.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2186, pruned_loss=0.03843, over 972851.83 frames.], batch size: 14, lr: 3.36e-04
2022-05-05 12:43:34,998 INFO [train.py:715] (1/8) Epoch 6, batch 11700, loss[loss=0.1605, simple_loss=0.2368, pruned_loss=0.04208, over 4986.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2194, pruned_loss=0.0384, over 973121.06 frames.], batch size: 31, lr: 3.36e-04
2022-05-05 12:44:13,939 INFO [train.py:715] (1/8) Epoch 6, batch 11750, loss[loss=0.1481, simple_loss=0.2326, pruned_loss=0.03179, over 4811.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2197, pruned_loss=0.03823, over 971957.79 frames.], batch size: 13, lr: 3.36e-04
2022-05-05 12:44:53,167 INFO [train.py:715] (1/8) Epoch 6, batch 11800, loss[loss=0.1301, simple_loss=0.2005, pruned_loss=0.02983, over 4883.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2186, pruned_loss=0.0381, over 971980.49 frames.], batch size: 19, lr: 3.36e-04
2022-05-05 12:45:31,843 INFO [train.py:715] (1/8) Epoch 6, batch 11850, loss[loss=0.1431, simple_loss=0.2086, pruned_loss=0.03882, over 4880.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2185, pruned_loss=0.03808, over 972084.33 frames.], batch size: 22, lr: 3.36e-04
2022-05-05 12:46:10,415 INFO [train.py:715] (1/8) Epoch 6, batch 11900, loss[loss=0.1365, simple_loss=0.2103, pruned_loss=0.03133, over 4805.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2197, pruned_loss=0.03827, over 972697.00 frames.], batch size: 14, lr: 3.36e-04
2022-05-05 12:46:49,726 INFO [train.py:715] (1/8) Epoch 6, batch 11950, loss[loss=0.1178, simple_loss=0.1985, pruned_loss=0.01857, over 4760.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2191, pruned_loss=0.03764, over 972058.82 frames.], batch size: 16, lr: 3.36e-04
2022-05-05 12:47:28,221 INFO [train.py:715] (1/8) Epoch 6, batch 12000, loss[loss=0.1247, simple_loss=0.1942, pruned_loss=0.02758, over 4768.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2183, pruned_loss=0.03742, over 971135.04 frames.], batch size: 14, lr: 3.36e-04
2022-05-05 12:47:28,222 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 12:47:37,945 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1091, simple_loss=0.1942, pruned_loss=0.01199, over 914524.00 frames.
2022-05-05 12:48:16,697 INFO [train.py:715] (1/8) Epoch 6, batch 12050, loss[loss=0.1543, simple_loss=0.2258, pruned_loss=0.04143, over 4835.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2183, pruned_loss=0.0377, over 970890.35 frames.], batch size: 26, lr: 3.36e-04
2022-05-05 12:48:56,377 INFO [train.py:715] (1/8) Epoch 6, batch 12100, loss[loss=0.1292, simple_loss=0.1985, pruned_loss=0.03001, over 4892.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2192, pruned_loss=0.03855, over 971907.28 frames.], batch size: 22, lr: 3.36e-04
2022-05-05 12:49:35,324 INFO [train.py:715] (1/8) Epoch 6, batch 12150, loss[loss=0.1501, simple_loss=0.2218, pruned_loss=0.03923, over 4974.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2195, pruned_loss=0.03911, over 972548.98 frames.], batch size: 24, lr: 3.36e-04
2022-05-05 12:50:14,105 INFO [train.py:715] (1/8) Epoch 6, batch 12200, loss[loss=0.1639, simple_loss=0.2412, pruned_loss=0.0433, over 4974.00 frames.], tot_loss[loss=0.1484, simple_loss=0.219, pruned_loss=0.03887, over 972647.85 frames.], batch size: 35, lr: 3.36e-04
2022-05-05 12:50:53,319 INFO [train.py:715] (1/8) Epoch 6, batch 12250, loss[loss=0.1404, simple_loss=0.2184, pruned_loss=0.0312, over 4896.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2205, pruned_loss=0.03926, over 972483.06 frames.], batch size: 19, lr: 3.36e-04
2022-05-05 12:51:32,112 INFO [train.py:715] (1/8) Epoch 6, batch 12300, loss[loss=0.1327, simple_loss=0.195, pruned_loss=0.03516, over 4799.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2214, pruned_loss=0.03997, over 973006.17 frames.], batch size: 13, lr: 3.36e-04
2022-05-05 12:52:11,890 INFO [train.py:715] (1/8) Epoch 6, batch 12350, loss[loss=0.16, simple_loss=0.229, pruned_loss=0.04552, over 4852.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2221, pruned_loss=0.03991, over 972648.01 frames.], batch size: 32, lr: 3.36e-04
2022-05-05 12:52:50,506 INFO [train.py:715] (1/8) Epoch 6, batch 12400, loss[loss=0.1811, simple_loss=0.2435, pruned_loss=0.05934, over 4742.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2216, pruned_loss=0.03958, over 972293.86 frames.], batch size: 16, lr: 3.36e-04
2022-05-05 12:53:29,628 INFO [train.py:715] (1/8) Epoch 6, batch 12450, loss[loss=0.1361, simple_loss=0.2116, pruned_loss=0.03035, over 4906.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.03959, over 972578.09 frames.], batch size: 18, lr: 3.36e-04
2022-05-05 12:54:08,748 INFO [train.py:715] (1/8) Epoch 6, batch 12500, loss[loss=0.1496, simple_loss=0.2155, pruned_loss=0.04185, over 4790.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2212, pruned_loss=0.03994, over 971062.03 frames.], batch size: 14, lr: 3.36e-04
2022-05-05 12:54:47,051 INFO [train.py:715] (1/8) Epoch 6, batch 12550, loss[loss=0.1664, simple_loss=0.2374, pruned_loss=0.04771, over 4884.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2217, pruned_loss=0.04037, over 970819.95 frames.], batch size: 16, lr: 3.36e-04
2022-05-05 12:55:26,406 INFO [train.py:715] (1/8) Epoch 6, batch 12600, loss[loss=0.1347, simple_loss=0.2116, pruned_loss=0.02887, over 4773.00 frames.], tot_loss[loss=0.151, simple_loss=0.2215, pruned_loss=0.04028, over 971372.05 frames.], batch size: 17, lr: 3.36e-04
2022-05-05 12:56:05,095 INFO [train.py:715] (1/8) Epoch 6, batch 12650, loss[loss=0.155, simple_loss=0.2312, pruned_loss=0.03944, over 4779.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2211, pruned_loss=0.03981, over 971361.41 frames.], batch size: 14, lr: 3.36e-04
2022-05-05 12:56:43,911 INFO [train.py:715] (1/8) Epoch 6, batch 12700, loss[loss=0.1237, simple_loss=0.198, pruned_loss=0.02466, over 4940.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2219, pruned_loss=0.04035, over 971114.03 frames.], batch size: 29, lr: 3.36e-04
2022-05-05 12:57:22,048 INFO [train.py:715] (1/8) Epoch 6, batch 12750, loss[loss=0.1352, simple_loss=0.2143, pruned_loss=0.02808, over 4987.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2213, pruned_loss=0.04027, over 972095.91 frames.], batch size: 25, lr: 3.36e-04
2022-05-05 12:58:01,008 INFO [train.py:715] (1/8) Epoch 6, batch 12800, loss[loss=0.1498, simple_loss=0.2121, pruned_loss=0.04369, over 4935.00 frames.], tot_loss[loss=0.151, simple_loss=0.2214, pruned_loss=0.04031, over 972680.28 frames.], batch size: 35, lr: 3.36e-04
2022-05-05 12:58:39,733 INFO [train.py:715] (1/8) Epoch 6, batch 12850, loss[loss=0.135, simple_loss=0.2055, pruned_loss=0.0323, over 4935.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2211, pruned_loss=0.03987, over 973789.50 frames.], batch size: 23, lr: 3.35e-04
2022-05-05 12:59:18,387 INFO [train.py:715] (1/8) Epoch 6, batch 12900, loss[loss=0.1431, simple_loss=0.2144, pruned_loss=0.03587, over 4976.00 frames.], tot_loss[loss=0.15, simple_loss=0.2209, pruned_loss=0.03954, over 973789.03 frames.], batch size: 15, lr: 3.35e-04
2022-05-05 12:59:58,335 INFO [train.py:715] (1/8) Epoch 6, batch 12950, loss[loss=0.1558, simple_loss=0.2268, pruned_loss=0.04237, over 4872.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2212, pruned_loss=0.03994, over 974192.95 frames.], batch size: 20, lr: 3.35e-04
2022-05-05 13:00:37,485 INFO [train.py:715] (1/8) Epoch 6, batch 13000, loss[loss=0.1523, simple_loss=0.2276, pruned_loss=0.03853, over 4959.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2206, pruned_loss=0.03947, over 974197.07 frames.], batch size: 15, lr: 3.35e-04
2022-05-05 13:01:16,480 INFO [train.py:715] (1/8) Epoch 6, batch 13050, loss[loss=0.1365, simple_loss=0.1947, pruned_loss=0.03915, over 4901.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2198, pruned_loss=0.03892, over 974321.32 frames.], batch size: 18, lr: 3.35e-04
2022-05-05 13:01:54,767 INFO [train.py:715] (1/8) Epoch 6, batch 13100, loss[loss=0.1569, simple_loss=0.2326, pruned_loss=0.04057, over 4752.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2185, pruned_loss=0.03844, over 973786.46 frames.], batch size: 16, lr: 3.35e-04
2022-05-05 13:02:34,347 INFO [train.py:715] (1/8) Epoch 6, batch 13150, loss[loss=0.1592, simple_loss=0.2315, pruned_loss=0.04346, over 4766.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2187, pruned_loss=0.03826, over 973183.13 frames.], batch size: 14, lr: 3.35e-04
2022-05-05 13:03:12,925 INFO [train.py:715] (1/8) Epoch 6, batch 13200, loss[loss=0.1271, simple_loss=0.1981, pruned_loss=0.02805, over 4958.00 frames.], tot_loss[loss=0.1491, simple_loss=0.22, pruned_loss=0.03912, over 973150.16 frames.], batch size: 35, lr: 3.35e-04
2022-05-05 13:03:51,770 INFO [train.py:715] (1/8) Epoch 6, batch 13250, loss[loss=0.1669, simple_loss=0.2445, pruned_loss=0.04467, over 4742.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2211, pruned_loss=0.03962, over 973020.01 frames.], batch size: 16, lr: 3.35e-04
2022-05-05 13:04:30,645 INFO [train.py:715] (1/8) Epoch 6, batch 13300, loss[loss=0.1611, simple_loss=0.2394, pruned_loss=0.04143, over 4925.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2205, pruned_loss=0.03951, over 972104.29 frames.], batch size: 39, lr: 3.35e-04
2022-05-05 13:05:09,759 INFO [train.py:715] (1/8) Epoch 6, batch 13350, loss[loss=0.1412, simple_loss=0.2264, pruned_loss=0.02798, over 4814.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2201, pruned_loss=0.03903, over 971724.99 frames.], batch size: 26, lr: 3.35e-04
2022-05-05 13:05:48,896 INFO [train.py:715] (1/8) Epoch 6, batch 13400, loss[loss=0.158, simple_loss=0.2291, pruned_loss=0.04341, over 4794.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2205, pruned_loss=0.03928, over 971905.77 frames.], batch size: 17, lr: 3.35e-04
2022-05-05 13:06:27,486 INFO [train.py:715] (1/8) Epoch 6, batch 13450, loss[loss=0.1761, simple_loss=0.245, pruned_loss=0.05364, over 4848.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2196, pruned_loss=0.03924, over 973426.81 frames.], batch size: 34, lr: 3.35e-04
2022-05-05 13:07:07,014 INFO [train.py:715] (1/8) Epoch 6, batch 13500, loss[loss=0.1468, simple_loss=0.217, pruned_loss=0.03833, over 4843.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2199, pruned_loss=0.03911, over 973502.87 frames.], batch size: 20, lr: 3.35e-04
2022-05-05 13:07:45,024 INFO [train.py:715] (1/8) Epoch 6, batch 13550, loss[loss=0.1425, simple_loss=0.2192, pruned_loss=0.03288, over 4861.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2188, pruned_loss=0.03834, over 974356.94 frames.], batch size: 30, lr: 3.35e-04
2022-05-05 13:08:23,969 INFO [train.py:715] (1/8) Epoch 6, batch 13600, loss[loss=0.1422, simple_loss=0.2191, pruned_loss=0.03264, over 4930.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2195, pruned_loss=0.03848, over 974069.59 frames.], batch size: 21, lr: 3.35e-04
2022-05-05 13:09:03,113 INFO [train.py:715] (1/8) Epoch 6, batch 13650, loss[loss=0.1436, simple_loss=0.2254, pruned_loss=0.03086, over 4905.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2204, pruned_loss=0.03891, over 974183.03 frames.], batch size: 19, lr: 3.35e-04
2022-05-05 13:09:42,438 INFO [train.py:715] (1/8) Epoch 6, batch 13700, loss[loss=0.2002, simple_loss=0.2687, pruned_loss=0.06584, over 4827.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2206, pruned_loss=0.03901, over 974630.53 frames.], batch size: 15, lr: 3.35e-04
2022-05-05 13:10:21,548 INFO [train.py:715] (1/8) Epoch 6, batch 13750, loss[loss=0.1428, simple_loss=0.2222, pruned_loss=0.03169, over 4968.00 frames.], tot_loss[loss=0.149, simple_loss=0.2201, pruned_loss=0.03892, over 973649.51 frames.], batch size: 15, lr: 3.35e-04
2022-05-05 13:11:00,147 INFO [train.py:715] (1/8) Epoch 6, batch 13800, loss[loss=0.1261, simple_loss=0.201, pruned_loss=0.02555, over 4844.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2194, pruned_loss=0.03883, over 972929.53 frames.], batch size: 20, lr: 3.35e-04
2022-05-05 13:11:40,118 INFO [train.py:715] (1/8) Epoch 6, batch 13850, loss[loss=0.1486, simple_loss=0.2115, pruned_loss=0.04284, over 4982.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2193, pruned_loss=0.03872, over 972896.46 frames.], batch size: 35, lr: 3.35e-04
2022-05-05 13:12:18,449 INFO [train.py:715] (1/8) Epoch 6, batch 13900, loss[loss=0.1449, simple_loss=0.2204, pruned_loss=0.03467, over 4824.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2188, pruned_loss=0.03827, over 972290.11 frames.], batch size: 15, lr: 3.35e-04
2022-05-05 13:12:57,458 INFO [train.py:715] (1/8) Epoch 6, batch 13950, loss[loss=0.149, simple_loss=0.2272, pruned_loss=0.03539, over 4799.00 frames.], tot_loss[loss=0.148, simple_loss=0.2191, pruned_loss=0.03847, over 973174.13 frames.], batch size: 21, lr: 3.35e-04
2022-05-05 13:13:36,064 INFO [train.py:715] (1/8) Epoch 6, batch 14000, loss[loss=0.1283, simple_loss=0.2052, pruned_loss=0.02567, over 4976.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2193, pruned_loss=0.0386, over 973537.01 frames.], batch size: 15, lr: 3.35e-04
2022-05-05 13:14:15,113 INFO [train.py:715] (1/8) Epoch 6, batch 14050, loss[loss=0.1526, simple_loss=0.2205, pruned_loss=0.04237, over 4794.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2188, pruned_loss=0.03809, over 973907.88 frames.], batch size: 14, lr: 3.35e-04
2022-05-05 13:14:53,532 INFO [train.py:715] (1/8) Epoch 6, batch 14100, loss[loss=0.1543, simple_loss=0.2323, pruned_loss=0.03819, over 4863.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2195, pruned_loss=0.03838, over 974243.38 frames.], batch size: 16, lr: 3.35e-04
2022-05-05 13:15:32,014 INFO [train.py:715] (1/8) Epoch 6, batch 14150, loss[loss=0.1504, simple_loss=0.2226, pruned_loss=0.03914, over 4752.00 frames.], tot_loss[loss=0.1482, simple_loss=0.22, pruned_loss=0.03817, over 973975.32 frames.], batch size: 19, lr: 3.35e-04
2022-05-05 13:16:11,445 INFO [train.py:715] (1/8) Epoch 6, batch 14200, loss[loss=0.1755, simple_loss=0.2477, pruned_loss=0.05165, over 4955.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2205, pruned_loss=0.03852, over 973495.52 frames.], batch size: 35, lr: 3.34e-04
2022-05-05 13:16:50,086 INFO [train.py:715] (1/8) Epoch 6, batch 14250, loss[loss=0.1657, simple_loss=0.2288, pruned_loss=0.0513, over 4749.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2205, pruned_loss=0.03843, over 972820.90 frames.], batch size: 19, lr: 3.34e-04
2022-05-05 13:17:29,122 INFO [train.py:715] (1/8) Epoch 6, batch 14300, loss[loss=0.1539, simple_loss=0.2235, pruned_loss=0.04213, over 4847.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2201, pruned_loss=0.03831, over 972308.63 frames.], batch size: 30, lr: 3.34e-04
2022-05-05 13:18:07,581 INFO [train.py:715] (1/8) Epoch 6, batch 14350, loss[loss=0.1792, simple_loss=0.2506, pruned_loss=0.05385, over 4795.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2215, pruned_loss=0.03909, over 971892.77 frames.], batch size: 18, lr: 3.34e-04
2022-05-05 13:18:47,511 INFO [train.py:715] (1/8) Epoch 6, batch 14400, loss[loss=0.173, simple_loss=0.2471, pruned_loss=0.04946, over 4924.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2209, pruned_loss=0.03905, over 971561.15 frames.], batch size: 23, lr: 3.34e-04
2022-05-05 13:19:25,858 INFO [train.py:715] (1/8) Epoch 6, batch 14450, loss[loss=0.1613, simple_loss=0.2327, pruned_loss=0.045, over 4845.00 frames.], tot_loss[loss=0.15, simple_loss=0.2211, pruned_loss=0.03947, over 971707.31 frames.], batch size: 15, lr: 3.34e-04
2022-05-05 13:20:04,247 INFO [train.py:715] (1/8) Epoch 6, batch 14500, loss[loss=0.1452, simple_loss=0.2124, pruned_loss=0.03896, over 4700.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2217, pruned_loss=0.03992, over 971702.43 frames.], batch size: 15, lr: 3.34e-04
2022-05-05 13:20:43,928 INFO [train.py:715] (1/8) Epoch 6, batch 14550, loss[loss=0.1372, simple_loss=0.2087, pruned_loss=0.03278, over 4962.00 frames.], tot_loss[loss=0.1491, simple_loss=0.22, pruned_loss=0.03913, over 972347.25 frames.], batch size: 24, lr: 3.34e-04
2022-05-05 13:21:22,653 INFO [train.py:715] (1/8) Epoch 6, batch 14600, loss[loss=0.1638, simple_loss=0.2172, pruned_loss=0.05522, over 4795.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2212, pruned_loss=0.03952, over 972652.30 frames.], batch size: 14, lr: 3.34e-04
2022-05-05 13:22:01,120 INFO [train.py:715] (1/8) Epoch 6, batch 14650, loss[loss=0.1493, simple_loss=0.2142, pruned_loss=0.0422, over 4946.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2213, pruned_loss=0.03892, over 972246.49 frames.], batch size: 15, lr: 3.34e-04
2022-05-05 13:22:40,131 INFO [train.py:715] (1/8) Epoch 6, batch 14700, loss[loss=0.145, simple_loss=0.2308, pruned_loss=0.02965, over 4910.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2211, pruned_loss=0.03859, over 972208.08 frames.], batch size: 22, lr: 3.34e-04
2022-05-05 13:23:19,675 INFO [train.py:715] (1/8) Epoch 6, batch 14750, loss[loss=0.1521, simple_loss=0.221, pruned_loss=0.04158, over 4908.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2198, pruned_loss=0.03801, over 971714.92 frames.], batch size: 17, lr: 3.34e-04
2022-05-05 13:23:57,830 INFO [train.py:715] (1/8) Epoch 6, batch 14800, loss[loss=0.1393, simple_loss=0.2079, pruned_loss=0.03537, over 4876.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2203, pruned_loss=0.0381, over 973156.83 frames.], batch size: 16, lr: 3.34e-04
2022-05-05 13:24:35,998 INFO [train.py:715] (1/8) Epoch 6, batch 14850, loss[loss=0.1365, simple_loss=0.2092, pruned_loss=0.03194, over 4818.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2211, pruned_loss=0.03929, over 972226.42 frames.], batch size: 25, lr: 3.34e-04
2022-05-05 13:25:15,106 INFO [train.py:715] (1/8) Epoch 6, batch 14900, loss[loss=0.1788, simple_loss=0.2501, pruned_loss=0.05372, over 4839.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2218, pruned_loss=0.03964, over 972291.68 frames.], batch size: 26, lr: 3.34e-04
2022-05-05 13:25:53,361 INFO [train.py:715] (1/8) Epoch 6, batch 14950, loss[loss=0.1481, simple_loss=0.231, pruned_loss=0.03262, over 4824.00 frames.], tot_loss[loss=0.1507, simple_loss=0.222, pruned_loss=0.03971, over 972569.37 frames.], batch size: 26, lr: 3.34e-04
2022-05-05 13:26:32,023 INFO [train.py:715] (1/8) Epoch 6, batch 15000, loss[loss=0.1421, simple_loss=0.2186, pruned_loss=0.03284, over 4955.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2214, pruned_loss=0.03958, over 972285.65 frames.], batch size: 24, lr: 3.34e-04
2022-05-05 13:26:32,023 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 13:26:41,819 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1091, simple_loss=0.1941, pruned_loss=0.01202, over 914524.00 frames.
2022-05-05 13:27:20,605 INFO [train.py:715] (1/8) Epoch 6, batch 15050, loss[loss=0.1834, simple_loss=0.2508, pruned_loss=0.05806, over 4783.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2204, pruned_loss=0.03894, over 971893.59 frames.], batch size: 14, lr: 3.34e-04
2022-05-05 13:27:59,351 INFO [train.py:715] (1/8) Epoch 6, batch 15100, loss[loss=0.1209, simple_loss=0.1901, pruned_loss=0.02586, over 4800.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2206, pruned_loss=0.03904, over 972224.61 frames.], batch size: 24, lr: 3.34e-04
2022-05-05 13:28:41,263 INFO [train.py:715] (1/8) Epoch 6, batch 15150, loss[loss=0.1307, simple_loss=0.2011, pruned_loss=0.0301, over 4814.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2195, pruned_loss=0.0386, over 972518.19 frames.], batch size: 25, lr: 3.34e-04
2022-05-05 13:29:19,833 INFO [train.py:715] (1/8) Epoch 6, batch 15200, loss[loss=0.1579, simple_loss=0.2244, pruned_loss=0.04571, over 4865.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2193, pruned_loss=0.03872, over 971966.83 frames.], batch size: 38, lr: 3.34e-04
2022-05-05 13:29:58,375 INFO [train.py:715] (1/8) Epoch 6, batch 15250, loss[loss=0.1703, simple_loss=0.2364, pruned_loss=0.05212, over 4916.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03822, over 971711.22 frames.], batch size: 17, lr: 3.34e-04
2022-05-05 13:30:37,908 INFO [train.py:715] (1/8) Epoch 6, batch 15300, loss[loss=0.1505, simple_loss=0.2178, pruned_loss=0.04164, over 4831.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.0381, over 971812.61 frames.], batch size: 13, lr: 3.34e-04
2022-05-05 13:31:15,934 INFO [train.py:715] (1/8) Epoch 6, batch 15350, loss[loss=0.1647, simple_loss=0.2447, pruned_loss=0.0424, over 4891.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2188, pruned_loss=0.03777, over 972461.03 frames.], batch size: 22, lr: 3.34e-04
2022-05-05 13:31:54,941 INFO [train.py:715] (1/8) Epoch 6, batch 15400, loss[loss=0.1414, simple_loss=0.2069, pruned_loss=0.03793, over 4969.00 frames.], tot_loss[loss=0.1476, simple_loss=0.219, pruned_loss=0.03809, over 971731.41 frames.], batch size: 35, lr: 3.34e-04
2022-05-05 13:32:33,883 INFO [train.py:715] (1/8) Epoch 6, batch 15450, loss[loss=0.1523, simple_loss=0.2219, pruned_loss=0.0413, over 4798.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2194, pruned_loss=0.03849, over 971589.05 frames.], batch size: 24, lr: 3.34e-04
2022-05-05 13:33:13,327 INFO [train.py:715] (1/8) Epoch 6, batch 15500, loss[loss=0.166, simple_loss=0.2439, pruned_loss=0.04408, over 4779.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2206, pruned_loss=0.03949, over 970308.60 frames.], batch size: 18, lr: 3.34e-04
2022-05-05 13:33:51,505 INFO [train.py:715] (1/8) Epoch 6, batch 15550, loss[loss=0.1669, simple_loss=0.2461, pruned_loss=0.04387, over 4901.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2206, pruned_loss=0.03925, over 970958.28 frames.], batch size: 19, lr: 3.33e-04
2022-05-05 13:34:30,395 INFO [train.py:715] (1/8) Epoch 6, batch 15600, loss[loss=0.1791, simple_loss=0.2528, pruned_loss=0.05266, over 4909.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2213, pruned_loss=0.03908, over 971606.91 frames.], batch size: 17, lr: 3.33e-04
2022-05-05 13:35:09,328 INFO [train.py:715] (1/8) Epoch 6, batch 15650, loss[loss=0.1514, simple_loss=0.2292, pruned_loss=0.03679, over 4877.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2211, pruned_loss=0.03911, over 971299.90 frames.], batch size: 22, lr: 3.33e-04
2022-05-05 13:35:47,372 INFO [train.py:715] (1/8) Epoch 6, batch 15700, loss[loss=0.1548, simple_loss=0.2296, pruned_loss=0.03998, over 4826.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2207, pruned_loss=0.03894, over 971523.58 frames.], batch size: 25, lr: 3.33e-04
2022-05-05 13:36:26,053 INFO [train.py:715] (1/8) Epoch 6, batch 15750, loss[loss=0.1278, simple_loss=0.1906, pruned_loss=0.03249, over 4775.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2205, pruned_loss=0.03896, over 971599.07 frames.], batch size: 12, lr: 3.33e-04
2022-05-05 13:37:04,794 INFO [train.py:715] (1/8) Epoch 6, batch 15800, loss[loss=0.1503, simple_loss=0.2244, pruned_loss=0.0381, over 4812.00 frames.], tot_loss[loss=0.149, simple_loss=0.2202, pruned_loss=0.03888, over 972005.63 frames.], batch size: 21, lr: 3.33e-04
2022-05-05 13:37:43,842 INFO [train.py:715] (1/8) Epoch 6, batch 15850, loss[loss=0.1448, simple_loss=0.2183, pruned_loss=0.03572, over 4778.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2197, pruned_loss=0.03874, over 971836.11 frames.], batch size: 17, lr: 3.33e-04
2022-05-05 13:38:22,284 INFO [train.py:715] (1/8) Epoch 6, batch 15900, loss[loss=0.1669, simple_loss=0.2372, pruned_loss=0.04826, over 4869.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2192, pruned_loss=0.03852, over 971978.42 frames.], batch size: 38, lr: 3.33e-04
2022-05-05 13:39:00,649 INFO [train.py:715] (1/8) Epoch 6, batch 15950, loss[loss=0.1534, simple_loss=0.2352, pruned_loss=0.03583, over 4755.00 frames.], tot_loss[loss=0.1481, simple_loss=0.219, pruned_loss=0.03862, over 971162.55 frames.], batch size: 19, lr: 3.33e-04
2022-05-05 13:39:39,976 INFO [train.py:715] (1/8) Epoch 6, batch 16000, loss[loss=0.149, simple_loss=0.2283, pruned_loss=0.0349, over 4797.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2195, pruned_loss=0.0388, over 971618.29 frames.], batch size: 21, lr: 3.33e-04
2022-05-05 13:40:18,433 INFO [train.py:715] (1/8) Epoch 6, batch 16050, loss[loss=0.1347, simple_loss=0.2025, pruned_loss=0.03348, over 4762.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2202, pruned_loss=0.03897, over 971576.80 frames.], batch size: 17, lr: 3.33e-04
2022-05-05 13:40:56,901 INFO [train.py:715] (1/8) Epoch 6, batch 16100, loss[loss=0.1269, simple_loss=0.2084, pruned_loss=0.02276, over 4816.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2209, pruned_loss=0.03936, over 971251.47 frames.], batch size: 25, lr: 3.33e-04
2022-05-05 13:41:35,294 INFO [train.py:715] (1/8) Epoch 6, batch 16150, loss[loss=0.1373, simple_loss=0.2076, pruned_loss=0.03355, over 4966.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2207, pruned_loss=0.03899, over 971425.01 frames.], batch size: 35, lr: 3.33e-04
2022-05-05 13:42:14,793 INFO [train.py:715] (1/8) Epoch 6, batch 16200, loss[loss=0.1468, simple_loss=0.2163, pruned_loss=0.03866, over 4926.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2204, pruned_loss=0.03912, over 972100.09 frames.], batch size: 29, lr: 3.33e-04
2022-05-05 13:42:53,111 INFO [train.py:715] (1/8) Epoch 6, batch 16250, loss[loss=0.1841, simple_loss=0.2607, pruned_loss=0.05371, over 4731.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2209, pruned_loss=0.03935, over 971851.69 frames.], batch size: 16, lr: 3.33e-04
2022-05-05 13:43:31,728 INFO [train.py:715] (1/8) Epoch 6, batch 16300, loss[loss=0.1449, simple_loss=0.2188, pruned_loss=0.0355, over 4762.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2209, pruned_loss=0.03925, over 972028.81 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:44:11,200 INFO [train.py:715] (1/8) Epoch 6, batch 16350, loss[loss=0.1473, simple_loss=0.2127, pruned_loss=0.04096, over 4658.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2213, pruned_loss=0.03958, over 971992.25 frames.], batch size: 13, lr: 3.33e-04
2022-05-05 13:44:49,508 INFO [train.py:715] (1/8) Epoch 6, batch 16400, loss[loss=0.1734, simple_loss=0.2434, pruned_loss=0.05169, over 4800.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2205, pruned_loss=0.03911, over 972692.73 frames.], batch size: 18, lr: 3.33e-04
2022-05-05 13:45:28,822 INFO [train.py:715] (1/8) Epoch 6, batch 16450, loss[loss=0.1359, simple_loss=0.2054, pruned_loss=0.03318, over 4783.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2201, pruned_loss=0.03874, over 973223.64 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:46:07,628 INFO [train.py:715] (1/8) Epoch 6, batch 16500, loss[loss=0.1594, simple_loss=0.233, pruned_loss=0.04284, over 4966.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2195, pruned_loss=0.03838, over 972680.90 frames.], batch size: 24, lr: 3.33e-04
2022-05-05 13:46:46,578 INFO [train.py:715] (1/8) Epoch 6, batch 16550, loss[loss=0.1627, simple_loss=0.2261, pruned_loss=0.04969, over 4646.00 frames.], tot_loss[loss=0.1485, simple_loss=0.22, pruned_loss=0.03848, over 973626.55 frames.], batch size: 13, lr: 3.33e-04
2022-05-05 13:47:24,409 INFO [train.py:715] (1/8) Epoch 6, batch 16600, loss[loss=0.1566, simple_loss=0.232, pruned_loss=0.04061, over 4966.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2205, pruned_loss=0.03914, over 972815.08 frames.], batch size: 39, lr: 3.33e-04
2022-05-05 13:48:03,149 INFO [train.py:715] (1/8) Epoch 6, batch 16650, loss[loss=0.1544, simple_loss=0.2142, pruned_loss=0.04736, over 4984.00 frames.], tot_loss[loss=0.15, simple_loss=0.2211, pruned_loss=0.03941, over 972212.75 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:48:42,813 INFO [train.py:715] (1/8) Epoch 6, batch 16700, loss[loss=0.164, simple_loss=0.2211, pruned_loss=0.05346, over 4959.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2198, pruned_loss=0.03883, over 972325.07 frames.], batch size: 39, lr: 3.33e-04
2022-05-05 13:49:21,221 INFO [train.py:715] (1/8) Epoch 6, batch 16750, loss[loss=0.126, simple_loss=0.1979, pruned_loss=0.02702, over 4759.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2202, pruned_loss=0.03897, over 971577.23 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:50:00,121 INFO [train.py:715] (1/8) Epoch 6, batch 16800, loss[loss=0.1413, simple_loss=0.2165, pruned_loss=0.03301, over 4866.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2214, pruned_loss=0.03966, over 971584.02 frames.], batch size: 22, lr: 3.33e-04
2022-05-05 13:50:39,327 INFO [train.py:715] (1/8) Epoch 6, batch 16850, loss[loss=0.1491, simple_loss=0.2282, pruned_loss=0.03498, over 4923.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2218, pruned_loss=0.04, over 970919.16 frames.], batch size: 18, lr: 3.33e-04
2022-05-05 13:51:19,121 INFO [train.py:715] (1/8) Epoch 6, batch 16900, loss[loss=0.1656, simple_loss=0.2406, pruned_loss=0.04528, over 4831.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2211, pruned_loss=0.0392, over 971589.89 frames.], batch size: 15, lr: 3.32e-04
2022-05-05 13:51:57,175 INFO [train.py:715] (1/8) Epoch 6, batch 16950, loss[loss=0.1387, simple_loss=0.2162, pruned_loss=0.03059, over 4973.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2211, pruned_loss=0.03936, over 971696.80 frames.], batch size: 15, lr: 3.32e-04
2022-05-05 13:52:36,226 INFO [train.py:715] (1/8) Epoch 6, batch 17000, loss[loss=0.1436, simple_loss=0.2115, pruned_loss=0.03784, over 4763.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2219, pruned_loss=0.03939, over 971299.74 frames.], batch size: 12, lr: 3.32e-04
2022-05-05 13:53:15,745 INFO [train.py:715] (1/8) Epoch 6, batch 17050, loss[loss=0.1546, simple_loss=0.2344, pruned_loss=0.0374, over 4920.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2212, pruned_loss=0.03915, over 971539.56 frames.], batch size: 23, lr: 3.32e-04
2022-05-05 13:53:53,898 INFO [train.py:715] (1/8) Epoch 6, batch 17100, loss[loss=0.168, simple_loss=0.2438, pruned_loss=0.04611, over 4965.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2201, pruned_loss=0.03853, over 971468.56 frames.], batch size: 35, lr: 3.32e-04
2022-05-05 13:54:32,776 INFO [train.py:715] (1/8) Epoch 6, batch 17150, loss[loss=0.1791, simple_loss=0.2526, pruned_loss=0.05276, over 4784.00 frames.], tot_loss[loss=0.148, simple_loss=0.2193, pruned_loss=0.03833, over 971662.88 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 13:55:11,751 INFO [train.py:715] (1/8) Epoch 6, batch 17200, loss[loss=0.1942, simple_loss=0.2529, pruned_loss=0.06769, over 4699.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03837, over 971252.85 frames.], batch size: 15, lr: 3.32e-04
2022-05-05 13:55:51,112 INFO [train.py:715] (1/8) Epoch 6, batch 17250, loss[loss=0.1476, simple_loss=0.2123, pruned_loss=0.04142, over 4768.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2186, pruned_loss=0.03812, over 971984.07 frames.], batch size: 12, lr: 3.32e-04
2022-05-05 13:56:29,077 INFO [train.py:715] (1/8) Epoch 6, batch 17300, loss[loss=0.1357, simple_loss=0.2111, pruned_loss=0.0302, over 4753.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.038, over 971701.35 frames.], batch size: 19, lr: 3.32e-04
2022-05-05 13:57:07,894 INFO [train.py:715] (1/8) Epoch 6, batch 17350, loss[loss=0.1461, simple_loss=0.2184, pruned_loss=0.03691, over 4922.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2197, pruned_loss=0.03874, over 971907.49 frames.], batch size: 29, lr: 3.32e-04
2022-05-05 13:57:47,274 INFO [train.py:715] (1/8) Epoch 6, batch 17400, loss[loss=0.1452, simple_loss=0.2285, pruned_loss=0.03098, over 4905.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03887, over 971511.46 frames.], batch size: 22, lr: 3.32e-04
2022-05-05 13:58:26,214 INFO [train.py:715] (1/8) Epoch 6, batch 17450, loss[loss=0.16, simple_loss=0.2292, pruned_loss=0.04542, over 4845.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03917, over 972140.29 frames.], batch size: 32, lr: 3.32e-04
2022-05-05 13:59:04,833 INFO [train.py:715] (1/8) Epoch 6, batch 17500, loss[loss=0.1763, simple_loss=0.2555, pruned_loss=0.04853, over 4915.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2205, pruned_loss=0.03936, over 971560.69 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 13:59:43,984 INFO [train.py:715] (1/8) Epoch 6, batch 17550, loss[loss=0.1658, simple_loss=0.228, pruned_loss=0.0518, over 4634.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2202, pruned_loss=0.03913, over 971081.57 frames.], batch size: 13, lr: 3.32e-04
2022-05-05 14:00:23,863 INFO [train.py:715] (1/8) Epoch 6, batch 17600, loss[loss=0.1499, simple_loss=0.2031, pruned_loss=0.04833, over 4799.00 frames.], tot_loss[loss=0.15, simple_loss=0.2207, pruned_loss=0.03968, over 970753.03 frames.], batch size: 14, lr: 3.32e-04
2022-05-05 14:01:01,426 INFO [train.py:715] (1/8) Epoch 6, batch 17650, loss[loss=0.1454, simple_loss=0.2139, pruned_loss=0.03846, over 4921.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2203, pruned_loss=0.03946, over 970978.17 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 14:01:40,865 INFO [train.py:715] (1/8) Epoch 6, batch 17700, loss[loss=0.1516, simple_loss=0.2253, pruned_loss=0.03896, over 4859.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2205, pruned_loss=0.03934, over 970782.01 frames.], batch size: 38, lr: 3.32e-04
2022-05-05 14:02:20,254 INFO [train.py:715] (1/8) Epoch 6, batch 17750, loss[loss=0.1414, simple_loss=0.2059, pruned_loss=0.03847, over 4787.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2205, pruned_loss=0.03968, over 971589.97 frames.], batch size: 21, lr: 3.32e-04
2022-05-05 14:02:58,608 INFO [train.py:715] (1/8) Epoch 6, batch 17800, loss[loss=0.1584, simple_loss=0.2206, pruned_loss=0.04811, over 4918.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2209, pruned_loss=0.03973, over 972106.86 frames.], batch size: 39, lr: 3.32e-04
2022-05-05 14:03:37,540 INFO [train.py:715] (1/8) Epoch 6, batch 17850, loss[loss=0.1723, simple_loss=0.252, pruned_loss=0.0463, over 4931.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2211, pruned_loss=0.03996, over 972113.01 frames.], batch size: 23, lr: 3.32e-04
2022-05-05 14:04:16,747 INFO [train.py:715] (1/8) Epoch 6, batch 17900, loss[loss=0.1642, simple_loss=0.2236, pruned_loss=0.05241, over 4849.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2205, pruned_loss=0.03965, over 971087.58 frames.], batch size: 30, lr: 3.32e-04
2022-05-05 14:04:56,311 INFO [train.py:715] (1/8) Epoch 6, batch 17950, loss[loss=0.1973, simple_loss=0.2595, pruned_loss=0.06751, over 4828.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2203, pruned_loss=0.03947, over 970179.77 frames.], batch size: 15, lr: 3.32e-04
2022-05-05 14:05:34,137 INFO [train.py:715] (1/8) Epoch 6, batch 18000, loss[loss=0.1574, simple_loss=0.2325, pruned_loss=0.04116, over 4975.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2205, pruned_loss=0.03948, over 970783.09 frames.], batch size: 24, lr: 3.32e-04
2022-05-05 14:05:34,138 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 14:05:43,884 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1087, simple_loss=0.1939, pruned_loss=0.0118, over 914524.00 frames.
2022-05-05 14:06:22,340 INFO [train.py:715] (1/8) Epoch 6, batch 18050, loss[loss=0.1341, simple_loss=0.1989, pruned_loss=0.03469, over 4784.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2197, pruned_loss=0.03878, over 970771.64 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 14:07:01,821 INFO [train.py:715] (1/8) Epoch 6, batch 18100, loss[loss=0.1381, simple_loss=0.2144, pruned_loss=0.03091, over 4797.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2204, pruned_loss=0.03915, over 971381.62 frames.], batch size: 18, lr: 3.32e-04
2022-05-05 14:07:41,268 INFO [train.py:715] (1/8) Epoch 6, batch 18150, loss[loss=0.1409, simple_loss=0.2105, pruned_loss=0.03561, over 4878.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2217, pruned_loss=0.03962, over 971984.83 frames.], batch size: 16, lr: 3.32e-04
2022-05-05 14:08:19,365 INFO [train.py:715] (1/8) Epoch 6, batch 18200, loss[loss=0.1442, simple_loss=0.225, pruned_loss=0.03167, over 4847.00 frames.], tot_loss[loss=0.149, simple_loss=0.2201, pruned_loss=0.03899, over 972233.75 frames.], batch size: 20, lr: 3.32e-04
2022-05-05 14:08:58,863 INFO [train.py:715] (1/8) Epoch 6, batch 18250, loss[loss=0.1706, simple_loss=0.238, pruned_loss=0.05165, over 4910.00 frames.], tot_loss[loss=0.1491, simple_loss=0.22, pruned_loss=0.03914, over 971766.79 frames.], batch size: 19, lr: 3.31e-04
2022-05-05 14:09:38,214 INFO [train.py:715] (1/8) Epoch 6, batch 18300, loss[loss=0.1197, simple_loss=0.183, pruned_loss=0.02815, over 4787.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2205, pruned_loss=0.03917, over 971441.86 frames.], batch size: 12, lr: 3.31e-04
2022-05-05 14:10:17,261 INFO [train.py:715] (1/8) Epoch 6, batch 18350, loss[loss=0.1451, simple_loss=0.2182, pruned_loss=0.03599, over 4759.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2202, pruned_loss=0.03869, over 971063.23 frames.], batch size: 19, lr: 3.31e-04
2022-05-05 14:10:55,593 INFO [train.py:715] (1/8) Epoch 6, batch 18400, loss[loss=0.1411, simple_loss=0.2042, pruned_loss=0.03901, over 4814.00 frames.], tot_loss[loss=0.149, simple_loss=0.2206, pruned_loss=0.03871, over 970632.51 frames.], batch size: 13, lr: 3.31e-04
2022-05-05 14:11:34,887 INFO [train.py:715] (1/8) Epoch 6, batch 18450, loss[loss=0.1348, simple_loss=0.2106, pruned_loss=0.02954, over 4939.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2203, pruned_loss=0.03841, over 971030.75 frames.], batch size: 21, lr: 3.31e-04
2022-05-05 14:12:14,312 INFO [train.py:715] (1/8) Epoch 6, batch 18500, loss[loss=0.1615, simple_loss=0.2284, pruned_loss=0.04726, over 4975.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2201, pruned_loss=0.03808, over 971235.69 frames.], batch size: 28, lr: 3.31e-04
2022-05-05 14:12:52,320 INFO [train.py:715] (1/8) Epoch 6, batch 18550, loss[loss=0.1817, simple_loss=0.241, pruned_loss=0.06115, over 4774.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2207, pruned_loss=0.0383, over 971958.85 frames.], batch size: 18, lr: 3.31e-04
2022-05-05 14:13:31,755 INFO [train.py:715] (1/8) Epoch 6, batch 18600, loss[loss=0.1486, simple_loss=0.2272, pruned_loss=0.03502, over 4856.00 frames.], tot_loss[loss=0.148, simple_loss=0.2201, pruned_loss=0.03794, over 971944.09 frames.], batch size: 30, lr: 3.31e-04
2022-05-05 14:14:10,850 INFO [train.py:715] (1/8) Epoch 6, batch 18650, loss[loss=0.1363, simple_loss=0.2126, pruned_loss=0.02998, over 4772.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2213, pruned_loss=0.03858, over 972785.50 frames.], batch size: 17, lr: 3.31e-04
2022-05-05 14:14:50,390 INFO [train.py:715] (1/8) Epoch 6, batch 18700, loss[loss=0.147, simple_loss=0.2147, pruned_loss=0.03968, over 4785.00 frames.], tot_loss[loss=0.149, simple_loss=0.221, pruned_loss=0.03849, over 972490.86 frames.], batch size: 14, lr: 3.31e-04
2022-05-05 14:15:28,531 INFO [train.py:715] (1/8) Epoch 6, batch 18750, loss[loss=0.1336, simple_loss=0.1952, pruned_loss=0.03599, over 4700.00 frames.], tot_loss[loss=0.1481, simple_loss=0.22, pruned_loss=0.03809, over 971337.73 frames.], batch size: 15, lr: 3.31e-04
2022-05-05 14:16:07,704 INFO [train.py:715] (1/8) Epoch 6, batch 18800, loss[loss=0.1967, simple_loss=0.2658, pruned_loss=0.06375, over 4875.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2206, pruned_loss=0.03804, over 971996.33 frames.], batch size: 32, lr: 3.31e-04
2022-05-05 14:16:47,208 INFO [train.py:715] (1/8) Epoch 6, batch 18850, loss[loss=0.1768, simple_loss=0.2455, pruned_loss=0.05404, over 4780.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2203, pruned_loss=0.03822, over 971632.95 frames.], batch size: 14, lr: 3.31e-04
2022-05-05 14:17:25,256 INFO [train.py:715] (1/8) Epoch 6, batch 18900, loss[loss=0.1545, simple_loss=0.2252, pruned_loss=0.04188, over 4925.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2202, pruned_loss=0.0382, over 971474.53 frames.], batch size: 18, lr: 3.31e-04
2022-05-05 14:18:04,841 INFO [train.py:715] (1/8) Epoch 6, batch 18950, loss[loss=0.1459, simple_loss=0.2246, pruned_loss=0.03355, over 4957.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2202, pruned_loss=0.03867, over 971748.84 frames.], batch size: 24, lr: 3.31e-04
2022-05-05 14:18:43,968 INFO [train.py:715] (1/8) Epoch 6, batch 19000, loss[loss=0.1238, simple_loss=0.1911, pruned_loss=0.02826, over 4802.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2198, pruned_loss=0.03822, over 972204.65 frames.], batch size: 25, lr: 3.31e-04
2022-05-05 14:19:23,157 INFO [train.py:715] (1/8) Epoch 6, batch 19050, loss[loss=0.1399, simple_loss=0.2138, pruned_loss=0.03305, over 4801.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03821, over 971159.43 frames.], batch size: 21, lr: 3.31e-04
2022-05-05 14:20:01,546 INFO [train.py:715] (1/8) Epoch 6, batch 19100, loss[loss=0.172, simple_loss=0.2455, pruned_loss=0.04931, over 4912.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2191, pruned_loss=0.03812, over 971760.00 frames.], batch size: 18, lr: 3.31e-04
2022-05-05 14:20:40,517 INFO [train.py:715] (1/8) Epoch 6, batch 19150, loss[loss=0.1439, simple_loss=0.2169, pruned_loss=0.0355, over 4824.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2193, pruned_loss=0.0382, over 971635.05 frames.], batch size: 26, lr: 3.31e-04
2022-05-05 14:21:20,172 INFO [train.py:715] (1/8) Epoch 6, batch 19200, loss[loss=0.1429, simple_loss=0.2158, pruned_loss=0.03502, over 4843.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2187, pruned_loss=0.03799, over 971632.34 frames.], batch size: 15, lr: 3.31e-04
2022-05-05 14:21:58,238 INFO [train.py:715] (1/8) Epoch 6, batch 19250, loss[loss=0.1527, simple_loss=0.216, pruned_loss=0.04475, over 4797.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2188, pruned_loss=0.03867, over 971956.04 frames.], batch size: 21, lr: 3.31e-04
2022-05-05 14:22:37,142 INFO [train.py:715] (1/8) Epoch 6, batch 19300, loss[loss=0.1425, simple_loss=0.22, pruned_loss=0.03247, over 4979.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2188, pruned_loss=0.03816, over 972517.58 frames.], batch size: 28, lr: 3.31e-04
2022-05-05 14:23:16,403 INFO [train.py:715] (1/8) Epoch 6, batch 19350, loss[loss=0.1465, simple_loss=0.2211, pruned_loss=0.03597, over 4803.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2195, pruned_loss=0.03846, over 972283.01 frames.], batch size: 26, lr: 3.31e-04
2022-05-05 14:23:54,986 INFO [train.py:715] (1/8) Epoch 6, batch 19400, loss[loss=0.1304, simple_loss=0.2101, pruned_loss=0.02529, over 4893.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03842, over 972420.26 frames.], batch size: 29, lr: 3.31e-04
2022-05-05 14:24:33,672 INFO [train.py:715] (1/8) Epoch 6, batch 19450, loss[loss=0.1084, simple_loss=0.1826, pruned_loss=0.01708, over 4857.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2197, pruned_loss=0.03856, over 972185.85 frames.], batch size: 16, lr: 3.31e-04
2022-05-05 14:25:13,070 INFO [train.py:715] (1/8) Epoch 6, batch 19500, loss[loss=0.1374, simple_loss=0.2093, pruned_loss=0.03273, over 4928.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03827, over 973308.00 frames.], batch size: 23, lr: 3.31e-04
2022-05-05 14:25:51,975 INFO [train.py:715] (1/8) Epoch 6, batch 19550, loss[loss=0.1524, simple_loss=0.2228, pruned_loss=0.04103, over 4817.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2197, pruned_loss=0.03847, over 973569.53 frames.], batch size: 26, lr: 3.31e-04
2022-05-05 14:26:30,330 INFO [train.py:715] (1/8) Epoch 6, batch 19600, loss[loss=0.1329, simple_loss=0.2039, pruned_loss=0.03096, over 4785.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2198, pruned_loss=0.03898, over 973715.33 frames.], batch size: 17, lr: 3.31e-04
2022-05-05 14:27:09,234 INFO [train.py:715] (1/8) Epoch 6, batch 19650, loss[loss=0.1299, simple_loss=0.1927, pruned_loss=0.03356, over 4954.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2198, pruned_loss=0.03901, over 974472.84 frames.], batch size: 35, lr: 3.30e-04
2022-05-05 14:27:48,355 INFO [train.py:715] (1/8) Epoch 6, batch 19700, loss[loss=0.1475, simple_loss=0.2232, pruned_loss=0.03588, over 4929.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2194, pruned_loss=0.03862, over 974383.40 frames.], batch size: 29, lr: 3.30e-04
2022-05-05 14:28:27,136 INFO [train.py:715] (1/8) Epoch 6, batch 19750, loss[loss=0.1617, simple_loss=0.2337, pruned_loss=0.04485, over 4878.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2206, pruned_loss=0.03895, over 974218.48 frames.], batch size: 22, lr: 3.30e-04
2022-05-05 14:29:05,246 INFO [train.py:715] (1/8) Epoch 6, batch 19800, loss[loss=0.1468, simple_loss=0.2133, pruned_loss=0.04015, over 4855.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2211, pruned_loss=0.03874, over 974398.75 frames.], batch size: 32, lr: 3.30e-04
2022-05-05 14:29:44,607 INFO [train.py:715] (1/8) Epoch 6, batch 19850, loss[loss=0.1442, simple_loss=0.2105, pruned_loss=0.03898, over 4989.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2208, pruned_loss=0.03898, over 974430.75 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:30:24,345 INFO [train.py:715] (1/8) Epoch 6, batch 19900, loss[loss=0.1709, simple_loss=0.2462, pruned_loss=0.04781, over 4834.00 frames.], tot_loss[loss=0.149, simple_loss=0.2201, pruned_loss=0.039, over 974255.72 frames.], batch size: 26, lr: 3.30e-04
2022-05-05 14:31:02,427 INFO [train.py:715] (1/8) Epoch 6, batch 19950, loss[loss=0.1473, simple_loss=0.2207, pruned_loss=0.03689, over 4969.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2202, pruned_loss=0.03901, over 972860.21 frames.], batch size: 24, lr: 3.30e-04
2022-05-05 14:31:41,550 INFO [train.py:715] (1/8) Epoch 6, batch 20000, loss[loss=0.1502, simple_loss=0.2088, pruned_loss=0.04579, over 4884.00 frames.], tot_loss[loss=0.149, simple_loss=0.2199, pruned_loss=0.03906, over 972455.82 frames.], batch size: 16, lr: 3.30e-04
2022-05-05 14:32:21,021 INFO [train.py:715] (1/8) Epoch 6, batch 20050, loss[loss=0.139, simple_loss=0.2174, pruned_loss=0.03032, over 4776.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2188, pruned_loss=0.0384, over 972774.26 frames.], batch size: 18, lr: 3.30e-04
2022-05-05 14:32:59,470 INFO [train.py:715] (1/8) Epoch 6, batch 20100, loss[loss=0.1418, simple_loss=0.2204, pruned_loss=0.03158, over 4708.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2193, pruned_loss=0.03872, over 972264.33 frames.], batch size: 15, lr: 3.30e-04
2022-05-05 14:33:38,528 INFO [train.py:715] (1/8) Epoch 6, batch 20150, loss[loss=0.1669, simple_loss=0.2417, pruned_loss=0.04606, over 4807.00 frames.], tot_loss[loss=0.149, simple_loss=0.22, pruned_loss=0.03901, over 971673.15 frames.], batch size: 15, lr: 3.30e-04
2022-05-05 14:34:17,811 INFO [train.py:715] (1/8) Epoch 6, batch 20200, loss[loss=0.1488, simple_loss=0.2203, pruned_loss=0.03861, over 4924.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2193, pruned_loss=0.03896, over 972766.04 frames.], batch size: 23, lr: 3.30e-04
2022-05-05 14:34:56,738 INFO [train.py:715] (1/8) Epoch 6, batch 20250, loss[loss=0.161, simple_loss=0.2286, pruned_loss=0.04667, over 4928.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2212, pruned_loss=0.03974, over 973106.32 frames.], batch size: 23, lr: 3.30e-04
2022-05-05 14:35:35,500 INFO [train.py:715] (1/8) Epoch 6, batch 20300, loss[loss=0.1972, simple_loss=0.2551, pruned_loss=0.06972, over 4901.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2219, pruned_loss=0.0401, over 973111.26 frames.], batch size: 18, lr: 3.30e-04
2022-05-05 14:36:14,862 INFO [train.py:715] (1/8) Epoch 6, batch 20350, loss[loss=0.105, simple_loss=0.1676, pruned_loss=0.0212, over 4855.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2207, pruned_loss=0.03922, over 972891.27 frames.], batch size: 13, lr: 3.30e-04
2022-05-05 14:36:54,310 INFO [train.py:715] (1/8) Epoch 6, batch 20400, loss[loss=0.1565, simple_loss=0.2316, pruned_loss=0.0407, over 4963.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2206, pruned_loss=0.03897, over 973022.98 frames.], batch size: 35, lr: 3.30e-04
2022-05-05 14:37:32,666 INFO [train.py:715] (1/8) Epoch 6, batch 20450, loss[loss=0.1428, simple_loss=0.2083, pruned_loss=0.03869, over 4836.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2205, pruned_loss=0.03866, over 972476.41 frames.], batch size: 13, lr: 3.30e-04
2022-05-05 14:38:11,470 INFO [train.py:715] (1/8) Epoch 6, batch 20500, loss[loss=0.1535, simple_loss=0.2317, pruned_loss=0.03769, over 4780.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2206, pruned_loss=0.03858, over 972614.28 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:38:50,521 INFO [train.py:715] (1/8) Epoch 6, batch 20550, loss[loss=0.1556, simple_loss=0.2354, pruned_loss=0.0379, over 4871.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2211, pruned_loss=0.03894, over 973087.75 frames.], batch size: 16, lr: 3.30e-04
2022-05-05 14:39:29,698 INFO [train.py:715] (1/8) Epoch 6, batch 20600, loss[loss=0.1731, simple_loss=0.2535, pruned_loss=0.0463, over 4889.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2213, pruned_loss=0.03901, over 972445.80 frames.], batch size: 19, lr: 3.30e-04
2022-05-05 14:40:07,965 INFO [train.py:715] (1/8) Epoch 6, batch 20650, loss[loss=0.1432, simple_loss=0.2208, pruned_loss=0.03283, over 4803.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2205, pruned_loss=0.03869, over 972336.74 frames.], batch size: 21, lr: 3.30e-04
2022-05-05 14:40:46,655 INFO [train.py:715] (1/8) Epoch 6, batch 20700, loss[loss=0.1367, simple_loss=0.2029, pruned_loss=0.03529, over 4831.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2207, pruned_loss=0.03884, over 972175.34 frames.], batch size: 15, lr: 3.30e-04
2022-05-05 14:41:25,990 INFO [train.py:715] (1/8) Epoch 6, batch 20750, loss[loss=0.1469, simple_loss=0.2247, pruned_loss=0.03459, over 4899.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2217, pruned_loss=0.03896, over 972698.96 frames.], batch size: 17, lr: 3.30e-04
2022-05-05 14:42:04,389 INFO [train.py:715] (1/8) Epoch 6, batch 20800, loss[loss=0.1492, simple_loss=0.2228, pruned_loss=0.03782, over 4874.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2204, pruned_loss=0.03841, over 972730.29 frames.], batch size: 16, lr: 3.30e-04
2022-05-05 14:42:43,609 INFO [train.py:715] (1/8) Epoch 6, batch 20850, loss[loss=0.1263, simple_loss=0.1959, pruned_loss=0.02835, over 4766.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2195, pruned_loss=0.03814, over 973088.72 frames.], batch size: 12, lr: 3.30e-04
2022-05-05 14:43:22,883 INFO [train.py:715] (1/8) Epoch 6, batch 20900, loss[loss=0.1509, simple_loss=0.217, pruned_loss=0.04241, over 4832.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03866, over 972872.09 frames.], batch size: 32, lr: 3.30e-04
2022-05-05 14:44:02,111 INFO [train.py:715] (1/8) Epoch 6, batch 20950, loss[loss=0.139, simple_loss=0.2103, pruned_loss=0.03385, over 4975.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2187, pruned_loss=0.03816, over 972897.68 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:44:40,093 INFO [train.py:715] (1/8) Epoch 6, batch 21000, loss[loss=0.1661, simple_loss=0.2403, pruned_loss=0.04592, over 4975.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2194, pruned_loss=0.03841, over 973511.53 frames.], batch size: 35, lr: 3.29e-04
2022-05-05 14:44:40,094 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 14:44:51,876 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1089, simple_loss=0.1939, pruned_loss=0.01192, over 914524.00 frames.
2022-05-05 14:45:30,118 INFO [train.py:715] (1/8) Epoch 6, batch 21050, loss[loss=0.1304, simple_loss=0.2075, pruned_loss=0.02666, over 4961.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2202, pruned_loss=0.03881, over 973742.30 frames.], batch size: 24, lr: 3.29e-04
2022-05-05 14:46:09,487 INFO [train.py:715] (1/8) Epoch 6, batch 21100, loss[loss=0.1631, simple_loss=0.2277, pruned_loss=0.04921, over 4934.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2206, pruned_loss=0.03916, over 974289.81 frames.], batch size: 18, lr: 3.29e-04
2022-05-05 14:46:48,886 INFO [train.py:715] (1/8) Epoch 6, batch 21150, loss[loss=0.1593, simple_loss=0.2294, pruned_loss=0.04459, over 4800.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2208, pruned_loss=0.03933, over 973053.16 frames.], batch size: 24, lr: 3.29e-04
2022-05-05 14:47:27,348 INFO [train.py:715] (1/8) Epoch 6, batch 21200, loss[loss=0.1313, simple_loss=0.2053, pruned_loss=0.02864, over 4800.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2212, pruned_loss=0.03958, over 973643.44 frames.], batch size: 21, lr: 3.29e-04
2022-05-05 14:48:06,355 INFO [train.py:715] (1/8) Epoch 6, batch 21250, loss[loss=0.1404, simple_loss=0.2136, pruned_loss=0.03363, over 4985.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2212, pruned_loss=0.03949, over 973678.48 frames.], batch size: 25, lr: 3.29e-04
2022-05-05 14:48:45,979 INFO [train.py:715] (1/8) Epoch 6, batch 21300, loss[loss=0.1945, simple_loss=0.2658, pruned_loss=0.06162, over 4911.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2212, pruned_loss=0.0396, over 973440.24 frames.], batch size: 29, lr: 3.29e-04
2022-05-05 14:49:24,959 INFO [train.py:715] (1/8) Epoch 6, batch 21350, loss[loss=0.1214, simple_loss=0.1916, pruned_loss=0.02563, over 4863.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2207, pruned_loss=0.03916, over 974021.24 frames.], batch size: 32, lr: 3.29e-04
2022-05-05 14:50:03,789 INFO [train.py:715] (1/8) Epoch 6, batch 21400, loss[loss=0.1263, simple_loss=0.1999, pruned_loss=0.0264, over 4901.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2216, pruned_loss=0.03979, over 973582.57 frames.], batch size: 19, lr: 3.29e-04
2022-05-05 14:50:42,550 INFO [train.py:715] (1/8) Epoch 6, batch 21450, loss[loss=0.1456, simple_loss=0.2131, pruned_loss=0.03907, over 4695.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2207, pruned_loss=0.03927, over 973292.18 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 14:51:21,823 INFO [train.py:715] (1/8) Epoch 6, batch 21500, loss[loss=0.1396, simple_loss=0.2047, pruned_loss=0.03724, over 4864.00 frames.], tot_loss[loss=0.148, simple_loss=0.2193, pruned_loss=0.03838, over 973277.05 frames.], batch size: 20, lr: 3.29e-04
2022-05-05 14:52:00,289 INFO [train.py:715] (1/8) Epoch 6, batch 21550, loss[loss=0.1296, simple_loss=0.2029, pruned_loss=0.02817, over 4796.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2187, pruned_loss=0.03804, over 974503.73 frames.], batch size: 24, lr: 3.29e-04
2022-05-05 14:52:39,316 INFO [train.py:715] (1/8) Epoch 6, batch 21600, loss[loss=0.1718, simple_loss=0.2388, pruned_loss=0.05235, over 4773.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2198, pruned_loss=0.03884, over 974384.71 frames.], batch size: 17, lr: 3.29e-04
2022-05-05 14:53:18,465 INFO [train.py:715] (1/8) Epoch 6, batch 21650, loss[loss=0.1235, simple_loss=0.1844, pruned_loss=0.03131, over 4790.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2194, pruned_loss=0.03854, over 974433.73 frames.], batch size: 17, lr: 3.29e-04
2022-05-05 14:53:57,747 INFO [train.py:715] (1/8) Epoch 6, batch 21700, loss[loss=0.1442, simple_loss=0.2136, pruned_loss=0.03737, over 4974.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2203, pruned_loss=0.03925, over 973978.16 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 14:54:36,456 INFO [train.py:715] (1/8) Epoch 6, batch 21750, loss[loss=0.1482, simple_loss=0.2154, pruned_loss=0.0405, over 4799.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2196, pruned_loss=0.03904, over 973700.32 frames.], batch size: 14, lr: 3.29e-04
2022-05-05 14:55:15,315 INFO [train.py:715] (1/8) Epoch 6, batch 21800, loss[loss=0.1626, simple_loss=0.223, pruned_loss=0.05107, over 4851.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2198, pruned_loss=0.03921, over 973976.10 frames.], batch size: 30, lr: 3.29e-04
2022-05-05 14:55:54,107 INFO [train.py:715] (1/8) Epoch 6, batch 21850, loss[loss=0.1794, simple_loss=0.2488, pruned_loss=0.05498, over 4755.00 frames.], tot_loss[loss=0.1492, simple_loss=0.22, pruned_loss=0.0392, over 973343.65 frames.], batch size: 19, lr: 3.29e-04
2022-05-05 14:56:32,648 INFO [train.py:715] (1/8) Epoch 6, batch 21900, loss[loss=0.1299, simple_loss=0.2113, pruned_loss=0.02424, over 4811.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2194, pruned_loss=0.03908, over 973599.28 frames.], batch size: 13, lr: 3.29e-04
2022-05-05 14:57:11,521 INFO [train.py:715] (1/8) Epoch 6, batch 21950, loss[loss=0.1565, simple_loss=0.2305, pruned_loss=0.04128, over 4897.00 frames.], tot_loss[loss=0.1484, simple_loss=0.219, pruned_loss=0.03887, over 973780.54 frames.], batch size: 18, lr: 3.29e-04
2022-05-05 14:57:50,240 INFO [train.py:715] (1/8) Epoch 6, batch 22000, loss[loss=0.1701, simple_loss=0.2435, pruned_loss=0.04832, over 4971.00 frames.], tot_loss[loss=0.1496, simple_loss=0.22, pruned_loss=0.03958, over 974474.42 frames.], batch size: 31, lr: 3.29e-04
2022-05-05 14:58:29,939 INFO [train.py:715] (1/8) Epoch 6, batch 22050, loss[loss=0.167, simple_loss=0.2331, pruned_loss=0.05046, over 4934.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2204, pruned_loss=0.03967, over 973517.74 frames.], batch size: 23, lr: 3.29e-04
2022-05-05 14:59:08,265 INFO [train.py:715] (1/8) Epoch 6, batch 22100, loss[loss=0.1528, simple_loss=0.2256, pruned_loss=0.03996, over 4899.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2197, pruned_loss=0.03948, over 973304.91 frames.], batch size: 22, lr: 3.29e-04
2022-05-05 14:59:47,063 INFO [train.py:715] (1/8) Epoch 6, batch 22150, loss[loss=0.1659, simple_loss=0.2369, pruned_loss=0.04751, over 4981.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2201, pruned_loss=0.03947, over 972719.57 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 15:00:26,257 INFO [train.py:715] (1/8) Epoch 6, batch 22200, loss[loss=0.1325, simple_loss=0.2009, pruned_loss=0.03205, over 4843.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2188, pruned_loss=0.03882, over 972346.12 frames.], batch size: 32, lr: 3.29e-04
2022-05-05 15:01:04,920 INFO [train.py:715] (1/8) Epoch 6, batch 22250, loss[loss=0.1419, simple_loss=0.2154, pruned_loss=0.03419, over 4883.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2208, pruned_loss=0.0395, over 972436.27 frames.], batch size: 22, lr: 3.29e-04
2022-05-05 15:01:43,600 INFO [train.py:715] (1/8) Epoch 6, batch 22300, loss[loss=0.1352, simple_loss=0.2121, pruned_loss=0.02917, over 4820.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2198, pruned_loss=0.03894, over 972182.09 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 15:02:22,659 INFO [train.py:715] (1/8) Epoch 6, batch 22350, loss[loss=0.1482, simple_loss=0.2137, pruned_loss=0.04128, over 4741.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2189, pruned_loss=0.03881, over 971805.37 frames.], batch size: 16, lr: 3.29e-04
2022-05-05 15:03:02,006 INFO [train.py:715] (1/8) Epoch 6, batch 22400, loss[loss=0.1761, simple_loss=0.2398, pruned_loss=0.05621, over 4985.00 frames.], tot_loss[loss=0.148, simple_loss=0.2187, pruned_loss=0.03859, over 971926.08 frames.], batch size: 25, lr: 3.29e-04
2022-05-05 15:03:40,493 INFO [train.py:715] (1/8) Epoch 6, batch 22450, loss[loss=0.1731, simple_loss=0.2357, pruned_loss=0.05531, over 4808.00 frames.], tot_loss[loss=0.149, simple_loss=0.2196, pruned_loss=0.03919, over 972456.04 frames.], batch size: 26, lr: 3.28e-04
2022-05-05 15:04:19,441 INFO [train.py:715] (1/8) Epoch 6, batch 22500, loss[loss=0.1514, simple_loss=0.2412, pruned_loss=0.0308, over 4943.00 frames.], tot_loss[loss=0.1501, simple_loss=0.221, pruned_loss=0.0396, over 972438.62 frames.], batch size: 29, lr: 3.28e-04
2022-05-05 15:04:58,761 INFO [train.py:715] (1/8) Epoch 6, batch 22550, loss[loss=0.1488, simple_loss=0.2279, pruned_loss=0.03483, over 4847.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2199, pruned_loss=0.03888, over 972358.36 frames.], batch size: 20, lr: 3.28e-04
2022-05-05 15:05:37,172 INFO [train.py:715] (1/8) Epoch 6, batch 22600, loss[loss=0.1994, simple_loss=0.2607, pruned_loss=0.06906, over 4872.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.0388, over 972774.03 frames.], batch size: 16, lr: 3.28e-04
2022-05-05 15:06:16,009 INFO [train.py:715] (1/8) Epoch 6, batch 22650, loss[loss=0.143, simple_loss=0.2106, pruned_loss=0.03773, over 4950.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2201, pruned_loss=0.03913, over 972788.83 frames.], batch size: 29, lr: 3.28e-04
2022-05-05 15:06:54,607 INFO [train.py:715] (1/8) Epoch 6, batch 22700, loss[loss=0.1658, simple_loss=0.2195, pruned_loss=0.05608, over 4838.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2192, pruned_loss=0.03873, over 972713.11 frames.], batch size: 26, lr: 3.28e-04
2022-05-05 15:07:33,408 INFO [train.py:715] (1/8) Epoch 6, batch 22750, loss[loss=0.1485, simple_loss=0.2196, pruned_loss=0.03876, over 4981.00 frames.], tot_loss[loss=0.1492, simple_loss=0.22, pruned_loss=0.03919, over 972388.06 frames.], batch size: 31, lr: 3.28e-04
2022-05-05 15:08:11,866 INFO [train.py:715] (1/8) Epoch 6, batch 22800, loss[loss=0.1646, simple_loss=0.2334, pruned_loss=0.04792, over 4694.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2203, pruned_loss=0.039, over 973175.11 frames.], batch size: 15, lr: 3.28e-04
2022-05-05 15:08:50,375 INFO [train.py:715] (1/8) Epoch 6, batch 22850, loss[loss=0.16, simple_loss=0.2381, pruned_loss=0.04095, over 4814.00 frames.], tot_loss[loss=0.1499, simple_loss=0.221, pruned_loss=0.03939, over 972672.33 frames.], batch size: 25, lr: 3.28e-04
2022-05-05 15:09:29,030 INFO [train.py:715] (1/8) Epoch 6, batch 22900, loss[loss=0.1417, simple_loss=0.2132, pruned_loss=0.03513, over 4888.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2216, pruned_loss=0.03949, over 972313.20 frames.], batch size: 19, lr: 3.28e-04
2022-05-05 15:10:08,162 INFO [train.py:715] (1/8) Epoch 6, batch 22950, loss[loss=0.1363, simple_loss=0.2056, pruned_loss=0.03351, over 4833.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2216, pruned_loss=0.03977, over 972276.79 frames.], batch size: 30, lr: 3.28e-04
2022-05-05 15:10:46,574 INFO [train.py:715] (1/8) Epoch 6, batch 23000, loss[loss=0.1331, simple_loss=0.2057, pruned_loss=0.03024, over 4822.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03974, over 972383.26 frames.], batch size: 25, lr: 3.28e-04
2022-05-05 15:11:25,838 INFO [train.py:715] (1/8) Epoch 6, batch 23050, loss[loss=0.1198, simple_loss=0.2008, pruned_loss=0.01945, over 4950.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2216, pruned_loss=0.03978, over 972540.75 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:12:05,302 INFO [train.py:715] (1/8) Epoch 6, batch 23100, loss[loss=0.1328, simple_loss=0.1964, pruned_loss=0.03462, over 4810.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2204, pruned_loss=0.03934, over 972315.95 frames.], batch size: 14, lr: 3.28e-04
2022-05-05 15:12:46,122 INFO [train.py:715] (1/8) Epoch 6, batch 23150, loss[loss=0.13, simple_loss=0.2066, pruned_loss=0.02665, over 4928.00 frames.], tot_loss[loss=0.15, simple_loss=0.2209, pruned_loss=0.03954, over 972856.69 frames.], batch size: 29, lr: 3.28e-04
2022-05-05 15:13:25,468 INFO [train.py:715] (1/8) Epoch 6, batch 23200, loss[loss=0.1361, simple_loss=0.208, pruned_loss=0.03209, over 4811.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2198, pruned_loss=0.03867, over 972594.25 frames.], batch size: 13, lr: 3.28e-04
2022-05-05 15:14:04,868 INFO [train.py:715] (1/8) Epoch 6, batch 23250, loss[loss=0.1436, simple_loss=0.225, pruned_loss=0.03105, over 4792.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2193, pruned_loss=0.03854, over 972958.60 frames.], batch size: 18, lr: 3.28e-04
2022-05-05 15:14:43,527 INFO [train.py:715] (1/8) Epoch 6, batch 23300, loss[loss=0.1822, simple_loss=0.2446, pruned_loss=0.05989, over 4790.00 frames.], tot_loss[loss=0.149, simple_loss=0.22, pruned_loss=0.039, over 972316.47 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:15:21,524 INFO [train.py:715] (1/8) Epoch 6, batch 23350, loss[loss=0.1744, simple_loss=0.2476, pruned_loss=0.05061, over 4959.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2196, pruned_loss=0.03874, over 973260.75 frames.], batch size: 39, lr: 3.28e-04
2022-05-05 15:16:00,568 INFO [train.py:715] (1/8) Epoch 6, batch 23400, loss[loss=0.1501, simple_loss=0.2193, pruned_loss=0.04047, over 4893.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03838, over 973179.01 frames.], batch size: 22, lr: 3.28e-04
2022-05-05 15:16:40,149 INFO [train.py:715] (1/8) Epoch 6, batch 23450, loss[loss=0.1281, simple_loss=0.1942, pruned_loss=0.03105, over 4852.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03823, over 973074.79 frames.], batch size: 34, lr: 3.28e-04
2022-05-05 15:17:19,123 INFO [train.py:715] (1/8) Epoch 6, batch 23500, loss[loss=0.1549, simple_loss=0.2228, pruned_loss=0.04354, over 4858.00 frames.], tot_loss[loss=0.1486, simple_loss=0.22, pruned_loss=0.03861, over 972530.13 frames.], batch size: 30, lr: 3.28e-04
2022-05-05 15:17:58,303 INFO [train.py:715] (1/8) Epoch 6, batch 23550, loss[loss=0.1628, simple_loss=0.2454, pruned_loss=0.04009, over 4916.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2202, pruned_loss=0.03905, over 972774.64 frames.], batch size: 18, lr: 3.28e-04
2022-05-05 15:18:37,516 INFO [train.py:715] (1/8) Epoch 6, batch 23600, loss[loss=0.1742, simple_loss=0.2416, pruned_loss=0.05345, over 4949.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2212, pruned_loss=0.03925, over 972880.36 frames.], batch size: 24, lr: 3.28e-04
2022-05-05 15:19:16,257 INFO [train.py:715] (1/8) Epoch 6, batch 23650, loss[loss=0.1397, simple_loss=0.2077, pruned_loss=0.03588, over 4970.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2212, pruned_loss=0.03894, over 972616.84 frames.], batch size: 24, lr: 3.28e-04
2022-05-05 15:19:54,393 INFO [train.py:715] (1/8) Epoch 6, batch 23700, loss[loss=0.1463, simple_loss=0.218, pruned_loss=0.03729, over 4950.00 frames.], tot_loss[loss=0.1494, simple_loss=0.221, pruned_loss=0.03887, over 973221.91 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:20:33,416 INFO [train.py:715] (1/8) Epoch 6, batch 23750, loss[loss=0.1685, simple_loss=0.2304, pruned_loss=0.05332, over 4849.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2205, pruned_loss=0.03891, over 972921.01 frames.], batch size: 13, lr: 3.28e-04
2022-05-05 15:21:12,838 INFO [train.py:715] (1/8) Epoch 6, batch 23800, loss[loss=0.1284, simple_loss=0.1964, pruned_loss=0.03021, over 4787.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2211, pruned_loss=0.03922, over 973005.26 frames.], batch size: 18, lr: 3.28e-04
2022-05-05 15:21:51,203 INFO [train.py:715] (1/8) Epoch 6, batch 23850, loss[loss=0.1586, simple_loss=0.2325, pruned_loss=0.04233, over 4827.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2206, pruned_loss=0.03899, over 972775.79 frames.], batch size: 15, lr: 3.27e-04
2022-05-05 15:22:29,819 INFO [train.py:715] (1/8) Epoch 6, batch 23900, loss[loss=0.09629, simple_loss=0.1672, pruned_loss=0.01267, over 4714.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2199, pruned_loss=0.03849, over 972621.97 frames.], batch size: 12, lr: 3.27e-04
2022-05-05 15:23:08,547 INFO [train.py:715] (1/8) Epoch 6, batch 23950, loss[loss=0.133, simple_loss=0.1993, pruned_loss=0.03332, over 4839.00 frames.], tot_loss[loss=0.148, simple_loss=0.2195, pruned_loss=0.03826, over 972047.28 frames.], batch size: 15, lr: 3.27e-04
2022-05-05 15:23:47,223 INFO [train.py:715] (1/8) Epoch 6, batch 24000, loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03681, over 4953.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2192, pruned_loss=0.03811, over 972315.72 frames.], batch size: 24, lr: 3.27e-04
2022-05-05 15:23:47,223 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 15:23:58,203 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1089, simple_loss=0.1939, pruned_loss=0.01195, over 914524.00 frames.
2022-05-05 15:24:36,970 INFO [train.py:715] (1/8) Epoch 6, batch 24050, loss[loss=0.1492, simple_loss=0.2147, pruned_loss=0.04189, over 4986.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2194, pruned_loss=0.03843, over 972763.49 frames.], batch size: 28, lr: 3.27e-04
2022-05-05 15:25:15,033 INFO [train.py:715] (1/8) Epoch 6, batch 24100, loss[loss=0.1286, simple_loss=0.2037, pruned_loss=0.0268, over 4965.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2195, pruned_loss=0.03853, over 972764.13 frames.], batch size: 25, lr: 3.27e-04
2022-05-05 15:25:53,709 INFO [train.py:715] (1/8) Epoch 6, batch 24150, loss[loss=0.1295, simple_loss=0.1955, pruned_loss=0.03181, over 4980.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03824, over 973173.22 frames.], batch size: 14, lr: 3.27e-04
2022-05-05 15:26:32,801 INFO [train.py:715] (1/8) Epoch 6, batch 24200, loss[loss=0.1554, simple_loss=0.2129, pruned_loss=0.04895, over 4879.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2188, pruned_loss=0.03803, over 972729.50 frames.], batch size: 32, lr: 3.27e-04
2022-05-05 15:27:10,724 INFO [train.py:715] (1/8) Epoch 6, batch 24250, loss[loss=0.146, simple_loss=0.2115, pruned_loss=0.04029, over 4981.00 frames.], tot_loss[loss=0.1464, simple_loss=0.218, pruned_loss=0.03744, over 972634.04 frames.], batch size: 15, lr: 3.27e-04
2022-05-05 15:27:49,116 INFO [train.py:715] (1/8) Epoch 6, batch 24300, loss[loss=0.1333, simple_loss=0.197, pruned_loss=0.03478, over 4651.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2179, pruned_loss=0.0372, over 971959.34 frames.], batch size: 13, lr: 3.27e-04
2022-05-05 15:28:28,045 INFO [train.py:715] (1/8) Epoch 6, batch 24350, loss[loss=0.1467, simple_loss=0.2217, pruned_loss=0.03586, over 4933.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2195, pruned_loss=0.03791, over 972930.98 frames.], batch size: 23, lr: 3.27e-04
2022-05-05 15:29:07,159 INFO [train.py:715] (1/8) Epoch 6, batch 24400, loss[loss=0.1443, simple_loss=0.2084, pruned_loss=0.04006, over 4776.00 frames.], tot_loss[loss=0.148, simple_loss=0.2194, pruned_loss=0.03827, over 971921.40 frames.], batch size: 17, lr: 3.27e-04
2022-05-05 15:29:45,509 INFO [train.py:715] (1/8) Epoch 6, batch 24450, loss[loss=0.14, simple_loss=0.2142, pruned_loss=0.03291, over 4776.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2193, pruned_loss=0.03781, over 973316.13 frames.], batch size: 18, lr: 3.27e-04
2022-05-05 15:30:24,121 INFO [train.py:715] (1/8) Epoch 6, batch 24500, loss[loss=0.1305, simple_loss=0.2007, pruned_loss=0.03015, over 4900.00 frames.], tot_loss[loss=0.148, simple_loss=0.2197, pruned_loss=0.03812, over 973140.94 frames.], batch size: 19, lr: 3.27e-04
2022-05-05 15:31:03,942 INFO [train.py:715] (1/8) Epoch 6, batch 24550, loss[loss=0.1368, simple_loss=0.2129, pruned_loss=0.03031, over 4752.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2211, pruned_loss=0.03901, over 973064.45 frames.], batch size: 18, lr: 3.27e-04
2022-05-05 15:31:42,163 INFO [train.py:715] (1/8) Epoch 6, batch 24600, loss[loss=0.1432, simple_loss=0.2165, pruned_loss=0.03495, over 4952.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2212, pruned_loss=0.03879, over 973185.30 frames.], batch size: 29, lr: 3.27e-04
2022-05-05 15:32:21,365 INFO [train.py:715] (1/8) Epoch 6, batch 24650, loss[loss=0.1417, simple_loss=0.2179, pruned_loss=0.03275, over 4808.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2215, pruned_loss=0.03889, over 972812.77 frames.], batch size: 21, lr: 3.27e-04
2022-05-05 15:33:00,614 INFO [train.py:715] (1/8) Epoch 6, batch 24700, loss[loss=0.1479, simple_loss=0.225, pruned_loss=0.03541, over 4963.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2215, pruned_loss=0.03899, over 973059.81 frames.], batch size: 24, lr: 3.27e-04
2022-05-05 15:33:39,472 INFO [train.py:715] (1/8) Epoch 6, batch 24750, loss[loss=0.1369, simple_loss=0.2137, pruned_loss=0.03006, over 4845.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2202, pruned_loss=0.03838, over 973051.25 frames.], batch size: 34, lr: 3.27e-04
2022-05-05 15:34:17,835 INFO [train.py:715] (1/8) Epoch 6, batch 24800, loss[loss=0.1175, simple_loss=0.1952, pruned_loss=0.01986, over 4751.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2199, pruned_loss=0.03818, over 972263.37 frames.], batch size: 19, lr: 3.27e-04
2022-05-05 15:34:56,840 INFO [train.py:715] (1/8) Epoch 6, batch 24850, loss[loss=0.1495, simple_loss=0.2244, pruned_loss=0.03733, over 4877.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2194, pruned_loss=0.03812, over 972038.32 frames.], batch size: 16, lr: 3.27e-04
2022-05-05 15:35:36,649 INFO [train.py:715] (1/8) Epoch 6, batch 24900, loss[loss=0.129, simple_loss=0.2048, pruned_loss=0.02662, over 4971.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2184, pruned_loss=0.03769, over 972157.53 frames.], batch size: 24, lr: 3.27e-04
2022-05-05 15:36:14,922 INFO [train.py:715] (1/8) Epoch 6, batch 24950, loss[loss=0.1592, simple_loss=0.2234, pruned_loss=0.04751, over 4980.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2186, pruned_loss=0.03793, over 972600.87 frames.], batch size: 16, lr: 3.27e-04
2022-05-05 15:36:53,554 INFO [train.py:715] (1/8) Epoch 6, batch 25000, loss[loss=0.1686, simple_loss=0.2414, pruned_loss=0.04793, over 4992.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2196, pruned_loss=0.03862, over 972291.16 frames.], batch size: 16, lr: 3.27e-04
2022-05-05 15:37:32,636 INFO [train.py:715] (1/8) Epoch 6, batch 25050, loss[loss=0.1486, simple_loss=0.2213, pruned_loss=0.03797, over 4917.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2191, pruned_loss=0.03857, over 972910.05 frames.], batch size: 17, lr: 3.27e-04
2022-05-05 15:38:11,565 INFO [train.py:715] (1/8) Epoch 6, batch 25100, loss[loss=0.1283, simple_loss=0.201, pruned_loss=0.02782, over 4984.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.0382, over 973253.95 frames.], batch size: 25, lr: 3.27e-04
2022-05-05 15:38:50,120 INFO [train.py:715] (1/8) Epoch 6, batch 25150, loss[loss=0.1146, simple_loss=0.1913, pruned_loss=0.01896, over 4801.00 frames.], tot_loss[loss=0.148, simple_loss=0.2195, pruned_loss=0.03825, over 973839.39 frames.], batch size: 21, lr: 3.27e-04
2022-05-05 15:39:28,930 INFO [train.py:715] (1/8) Epoch 6, batch 25200, loss[loss=0.144, simple_loss=0.2199, pruned_loss=0.03411, over 4768.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2204, pruned_loss=0.03869, over 973960.00 frames.], batch size: 19, lr: 3.27e-04
2022-05-05 15:40:07,781 INFO [train.py:715] (1/8) Epoch 6, batch 25250, loss[loss=0.141, simple_loss=0.203, pruned_loss=0.03945, over 4905.00 frames.], tot_loss[loss=0.149, simple_loss=0.2204, pruned_loss=0.03879, over 973727.37 frames.], batch size: 23, lr: 3.26e-04
2022-05-05 15:40:46,087 INFO [train.py:715] (1/8) Epoch 6, batch 25300, loss[loss=0.1305, simple_loss=0.2043, pruned_loss=0.02836, over 4979.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2206, pruned_loss=0.03927, over 974703.65 frames.], batch size: 28, lr: 3.26e-04
2022-05-05 15:41:24,369 INFO [train.py:715] (1/8) Epoch 6, batch 25350, loss[loss=0.1608, simple_loss=0.2374, pruned_loss=0.04213, over 4904.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2195, pruned_loss=0.03884, over 973875.82 frames.], batch size: 39, lr: 3.26e-04
2022-05-05 15:42:03,176 INFO [train.py:715] (1/8) Epoch 6, batch 25400, loss[loss=0.1349, simple_loss=0.2102, pruned_loss=0.02985, over 4925.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2195, pruned_loss=0.0387, over 973025.10 frames.], batch size: 29, lr: 3.26e-04
2022-05-05 15:42:41,992 INFO [train.py:715] (1/8) Epoch 6, batch 25450, loss[loss=0.1395, simple_loss=0.2096, pruned_loss=0.03471, over 4956.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2201, pruned_loss=0.03877, over 974131.17 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:43:20,086 INFO [train.py:715] (1/8) Epoch 6, batch 25500, loss[loss=0.166, simple_loss=0.2363, pruned_loss=0.0479, over 4882.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2197, pruned_loss=0.03834, over 974646.20 frames.], batch size: 16, lr: 3.26e-04
2022-05-05 15:43:58,584 INFO [train.py:715] (1/8) Epoch 6, batch 25550, loss[loss=0.1506, simple_loss=0.2197, pruned_loss=0.04076, over 4983.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2197, pruned_loss=0.03856, over 973032.76 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:44:37,705 INFO [train.py:715] (1/8) Epoch 6, batch 25600, loss[loss=0.1233, simple_loss=0.1839, pruned_loss=0.03133, over 4864.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2194, pruned_loss=0.03838, over 972545.91 frames.], batch size: 12, lr: 3.26e-04
2022-05-05 15:45:15,937 INFO [train.py:715] (1/8) Epoch 6, batch 25650, loss[loss=0.1477, simple_loss=0.2217, pruned_loss=0.0368, over 4972.00 frames.], tot_loss[loss=0.149, simple_loss=0.2202, pruned_loss=0.03893, over 972891.01 frames.], batch size: 28, lr: 3.26e-04
2022-05-05 15:45:54,741 INFO [train.py:715] (1/8) Epoch 6, batch 25700, loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03012, over 4911.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2192, pruned_loss=0.03828, over 973427.02 frames.], batch size: 17, lr: 3.26e-04
2022-05-05 15:46:34,047 INFO [train.py:715] (1/8) Epoch 6, batch 25750, loss[loss=0.1505, simple_loss=0.2225, pruned_loss=0.03932, over 4689.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2192, pruned_loss=0.03831, over 972831.34 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:47:12,321 INFO [train.py:715] (1/8) Epoch 6, batch 25800, loss[loss=0.1476, simple_loss=0.2215, pruned_loss=0.0369, over 4960.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03867, over 972671.70 frames.], batch size: 24, lr: 3.26e-04
2022-05-05 15:47:50,579 INFO [train.py:715] (1/8) Epoch 6, batch 25850, loss[loss=0.1423, simple_loss=0.2104, pruned_loss=0.03716, over 4788.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2201, pruned_loss=0.03931, over 972985.01 frames.], batch size: 12, lr: 3.26e-04
2022-05-05 15:48:29,221 INFO [train.py:715] (1/8) Epoch 6, batch 25900, loss[loss=0.1529, simple_loss=0.2237, pruned_loss=0.04104, over 4940.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2192, pruned_loss=0.03897, over 973292.37 frames.], batch size: 21, lr: 3.26e-04
2022-05-05 15:49:08,369 INFO [train.py:715] (1/8) Epoch 6, batch 25950, loss[loss=0.1368, simple_loss=0.2045, pruned_loss=0.03451, over 4807.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2203, pruned_loss=0.03937, over 972615.69 frames.], batch size: 27, lr: 3.26e-04
2022-05-05 15:49:46,045 INFO [train.py:715] (1/8) Epoch 6, batch 26000, loss[loss=0.1569, simple_loss=0.2381, pruned_loss=0.03782, over 4877.00 frames.], tot_loss[loss=0.1501, simple_loss=0.221, pruned_loss=0.03956, over 971997.64 frames.], batch size: 22, lr: 3.26e-04
2022-05-05 15:50:24,230 INFO [train.py:715] (1/8) Epoch 6, batch 26050, loss[loss=0.1222, simple_loss=0.1903, pruned_loss=0.02705, over 4847.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2207, pruned_loss=0.03927, over 972205.87 frames.], batch size: 32, lr: 3.26e-04
2022-05-05 15:51:03,213 INFO [train.py:715] (1/8) Epoch 6, batch 26100, loss[loss=0.1478, simple_loss=0.2242, pruned_loss=0.0357, over 4968.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2204, pruned_loss=0.03902, over 971805.01 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:51:41,624 INFO [train.py:715] (1/8) Epoch 6, batch 26150, loss[loss=0.1331, simple_loss=0.2045, pruned_loss=0.03087, over 4989.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03917, over 971616.47 frames.], batch size: 14, lr: 3.26e-04
2022-05-05 15:52:20,122 INFO [train.py:715] (1/8) Epoch 6, batch 26200, loss[loss=0.1197, simple_loss=0.1944, pruned_loss=0.02244, over 4803.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03865, over 972210.45 frames.], batch size: 25, lr: 3.26e-04
2022-05-05 15:52:58,601 INFO [train.py:715] (1/8) Epoch 6, batch 26250, loss[loss=0.1065, simple_loss=0.1801, pruned_loss=0.01641, over 4840.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2181, pruned_loss=0.03785, over 972268.04 frames.], batch size: 26, lr: 3.26e-04
2022-05-05 15:53:37,253 INFO [train.py:715] (1/8) Epoch 6, batch 26300, loss[loss=0.137, simple_loss=0.2089, pruned_loss=0.03251, over 4832.00 frames.], tot_loss[loss=0.147, simple_loss=0.2183, pruned_loss=0.03786, over 972773.32 frames.], batch size: 30, lr: 3.26e-04
2022-05-05 15:54:15,319 INFO [train.py:715] (1/8) Epoch 6, batch 26350, loss[loss=0.126, simple_loss=0.2007, pruned_loss=0.02565, over 4835.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2187, pruned_loss=0.03797, over 972069.10 frames.], batch size: 13, lr: 3.26e-04
2022-05-05 15:54:53,798 INFO [train.py:715] (1/8) Epoch 6, batch 26400, loss[loss=0.1607, simple_loss=0.2366, pruned_loss=0.04238, over 4918.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2191, pruned_loss=0.03818, over 971807.34 frames.], batch size: 39, lr: 3.26e-04
2022-05-05 15:55:33,107 INFO [train.py:715] (1/8) Epoch 6, batch 26450, loss[loss=0.1625, simple_loss=0.2372, pruned_loss=0.04394, over 4855.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2196, pruned_loss=0.03827, over 972036.09 frames.], batch size: 39, lr: 3.26e-04
2022-05-05 15:56:11,695 INFO [train.py:715] (1/8) Epoch 6, batch 26500, loss[loss=0.1179, simple_loss=0.1947, pruned_loss=0.02057, over 4859.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2187, pruned_loss=0.03753, over 972794.03 frames.], batch size: 32, lr: 3.26e-04
2022-05-05 15:56:50,075 INFO [train.py:715] (1/8) Epoch 6, batch 26550, loss[loss=0.1388, simple_loss=0.2066, pruned_loss=0.03547, over 4792.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03739, over 972721.46 frames.], batch size: 14, lr: 3.26e-04
2022-05-05 15:57:28,903 INFO [train.py:715] (1/8) Epoch 6, batch 26600, loss[loss=0.1891, simple_loss=0.2449, pruned_loss=0.0667, over 4824.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2191, pruned_loss=0.03773, over 972763.83 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:58:07,543 INFO [train.py:715] (1/8) Epoch 6, batch 26650, loss[loss=0.1522, simple_loss=0.2278, pruned_loss=0.03832, over 4831.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2195, pruned_loss=0.03783, over 971928.80 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:58:46,267 INFO [train.py:715] (1/8) Epoch 6, batch 26700, loss[loss=0.1789, simple_loss=0.2506, pruned_loss=0.05359, over 4919.00 frames.], tot_loss[loss=0.1471, simple_loss=0.219, pruned_loss=0.03758, over 972361.96 frames.], batch size: 23, lr: 3.25e-04
2022-05-05 15:59:24,556 INFO [train.py:715] (1/8) Epoch 6, batch 26750, loss[loss=0.1379, simple_loss=0.212, pruned_loss=0.03192, over 4926.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2195, pruned_loss=0.03775, over 973127.18 frames.], batch size: 29, lr: 3.25e-04
2022-05-05 16:00:03,787 INFO [train.py:715] (1/8) Epoch 6, batch 26800, loss[loss=0.1292, simple_loss=0.1961, pruned_loss=0.03114, over 4901.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2193, pruned_loss=0.03795, over 972581.79 frames.], batch size: 19, lr: 3.25e-04
2022-05-05 16:00:41,901 INFO [train.py:715] (1/8) Epoch 6, batch 26850, loss[loss=0.1335, simple_loss=0.2169, pruned_loss=0.02505, over 4916.00 frames.], tot_loss[loss=0.147, simple_loss=0.2186, pruned_loss=0.03775, over 972053.35 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:01:20,553 INFO [train.py:715] (1/8) Epoch 6, batch 26900, loss[loss=0.1367, simple_loss=0.2039, pruned_loss=0.03477, over 4772.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2193, pruned_loss=0.03773, over 972495.50 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:01:59,795 INFO [train.py:715] (1/8) Epoch 6, batch 26950, loss[loss=0.1754, simple_loss=0.2535, pruned_loss=0.04867, over 4933.00 frames.], tot_loss[loss=0.149, simple_loss=0.2208, pruned_loss=0.03858, over 971827.49 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:02:39,040 INFO [train.py:715] (1/8) Epoch 6, batch 27000, loss[loss=0.1608, simple_loss=0.2364, pruned_loss=0.04262, over 4931.00 frames.], tot_loss[loss=0.1484, simple_loss=0.22, pruned_loss=0.03841, over 971391.70 frames.], batch size: 23, lr: 3.25e-04
2022-05-05 16:02:39,040 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 16:02:48,795 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1088, simple_loss=0.1938, pruned_loss=0.01188, over 914524.00 frames.
2022-05-05 16:03:28,075 INFO [train.py:715] (1/8) Epoch 6, batch 27050, loss[loss=0.1818, simple_loss=0.2444, pruned_loss=0.05966, over 4772.00 frames.], tot_loss[loss=0.1486, simple_loss=0.22, pruned_loss=0.03858, over 972328.62 frames.], batch size: 14, lr: 3.25e-04
2022-05-05 16:04:06,807 INFO [train.py:715] (1/8) Epoch 6, batch 27100, loss[loss=0.1535, simple_loss=0.2274, pruned_loss=0.03983, over 4840.00 frames.], tot_loss[loss=0.148, simple_loss=0.2199, pruned_loss=0.0381, over 973221.23 frames.], batch size: 13, lr: 3.25e-04
2022-05-05 16:04:45,436 INFO [train.py:715] (1/8) Epoch 6, batch 27150, loss[loss=0.1663, simple_loss=0.2353, pruned_loss=0.04867, over 4814.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2204, pruned_loss=0.03854, over 972735.38 frames.], batch size: 21, lr: 3.25e-04
2022-05-05 16:05:25,174 INFO [train.py:715] (1/8) Epoch 6, batch 27200, loss[loss=0.1445, simple_loss=0.2096, pruned_loss=0.0397, over 4926.00 frames.], tot_loss[loss=0.1483, simple_loss=0.22, pruned_loss=0.03832, over 973316.27 frames.], batch size: 29, lr: 3.25e-04
2022-05-05 16:06:03,414 INFO [train.py:715] (1/8) Epoch 6, batch 27250, loss[loss=0.1343, simple_loss=0.2142, pruned_loss=0.02718, over 4810.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2196, pruned_loss=0.03786, over 973811.99 frames.], batch size: 25, lr: 3.25e-04
2022-05-05 16:06:43,093 INFO [train.py:715] (1/8) Epoch 6, batch 27300, loss[loss=0.1608, simple_loss=0.2219, pruned_loss=0.04982, over 4777.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2193, pruned_loss=0.03748, over 974389.02 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:07:22,062 INFO [train.py:715] (1/8) Epoch 6, batch 27350, loss[loss=0.1556, simple_loss=0.2206, pruned_loss=0.04527, over 4852.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2193, pruned_loss=0.03754, over 974301.84 frames.], batch size: 32, lr: 3.25e-04
2022-05-05 16:08:01,169 INFO [train.py:715] (1/8) Epoch 6, batch 27400, loss[loss=0.153, simple_loss=0.2357, pruned_loss=0.03513, over 4800.00 frames.], tot_loss[loss=0.147, simple_loss=0.2193, pruned_loss=0.03733, over 973774.06 frames.], batch size: 21, lr: 3.25e-04
2022-05-05 16:08:39,771 INFO [train.py:715] (1/8) Epoch 6, batch 27450, loss[loss=0.1542, simple_loss=0.2196, pruned_loss=0.04441, over 4880.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2184, pruned_loss=0.03697, over 973056.41 frames.], batch size: 22, lr: 3.25e-04
2022-05-05 16:09:18,815 INFO [train.py:715] (1/8) Epoch 6, batch 27500, loss[loss=0.1654, simple_loss=0.2302, pruned_loss=0.05031, over 4975.00 frames.], tot_loss[loss=0.146, simple_loss=0.2181, pruned_loss=0.03701, over 973272.35 frames.], batch size: 14, lr: 3.25e-04
2022-05-05 16:09:58,190 INFO [train.py:715] (1/8) Epoch 6, batch 27550, loss[loss=0.1631, simple_loss=0.2216, pruned_loss=0.05227, over 4803.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2187, pruned_loss=0.03751, over 972295.51 frames.], batch size: 21, lr: 3.25e-04
2022-05-05 16:10:36,914 INFO [train.py:715] (1/8) Epoch 6, batch 27600, loss[loss=0.1215, simple_loss=0.1924, pruned_loss=0.02524, over 4986.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2192, pruned_loss=0.03777, over 972248.29 frames.], batch size: 27, lr: 3.25e-04
2022-05-05 16:11:15,427 INFO [train.py:715] (1/8) Epoch 6, batch 27650, loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.03338, over 4961.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2176, pruned_loss=0.03694, over 972090.74 frames.], batch size: 21, lr: 3.25e-04
2022-05-05 16:11:54,439 INFO [train.py:715] (1/8) Epoch 6, batch 27700, loss[loss=0.1682, simple_loss=0.2364, pruned_loss=0.05001, over 4971.00 frames.], tot_loss[loss=0.1467, simple_loss=0.218, pruned_loss=0.03771, over 972425.84 frames.], batch size: 24, lr: 3.25e-04
2022-05-05 16:12:32,980 INFO [train.py:715] (1/8) Epoch 6, batch 27750, loss[loss=0.1387, simple_loss=0.21, pruned_loss=0.03374, over 4882.00 frames.], tot_loss[loss=0.148, simple_loss=0.2189, pruned_loss=0.03858, over 973633.71 frames.], batch size: 22, lr: 3.25e-04
2022-05-05 16:13:12,189 INFO [train.py:715] (1/8) Epoch 6, batch 27800, loss[loss=0.1235, simple_loss=0.1937, pruned_loss=0.02666, over 4928.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2199, pruned_loss=0.03879, over 973091.15 frames.], batch size: 29, lr: 3.25e-04
2022-05-05 16:13:51,235 INFO [train.py:715] (1/8) Epoch 6, batch 27850, loss[loss=0.1644, simple_loss=0.2375, pruned_loss=0.04565, over 4953.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2197, pruned_loss=0.03864, over 973116.34 frames.], batch size: 15, lr: 3.25e-04
2022-05-05 16:14:30,899 INFO [train.py:715] (1/8) Epoch 6, batch 27900, loss[loss=0.1393, simple_loss=0.2103, pruned_loss=0.03416, over 4791.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2187, pruned_loss=0.03813, over 972634.15 frames.], batch size: 17, lr: 3.25e-04
2022-05-05 16:15:09,374 INFO [train.py:715] (1/8) Epoch 6, batch 27950, loss[loss=0.1418, simple_loss=0.203, pruned_loss=0.04029, over 4959.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2184, pruned_loss=0.03807, over 972392.96 frames.], batch size: 14, lr: 3.25e-04
2022-05-05 16:15:48,255 INFO [train.py:715] (1/8) Epoch 6, batch 28000, loss[loss=0.1431, simple_loss=0.2126, pruned_loss=0.03685, over 4979.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2192, pruned_loss=0.03826, over 972076.16 frames.], batch size: 24, lr: 3.25e-04
2022-05-05 16:16:27,388 INFO [train.py:715] (1/8) Epoch 6, batch 28050, loss[loss=0.1476, simple_loss=0.2256, pruned_loss=0.0348, over 4829.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03802, over 971505.36 frames.], batch size: 30, lr: 3.25e-04
2022-05-05 16:17:06,042 INFO [train.py:715] (1/8) Epoch 6, batch 28100, loss[loss=0.1446, simple_loss=0.2226, pruned_loss=0.03333, over 4645.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2189, pruned_loss=0.0381, over 972017.94 frames.], batch size: 13, lr: 3.25e-04
2022-05-05 16:17:44,945 INFO [train.py:715] (1/8) Epoch 6, batch 28150, loss[loss=0.17, simple_loss=0.2361, pruned_loss=0.05194, over 4986.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2192, pruned_loss=0.03826, over 971934.28 frames.], batch size: 35, lr: 3.24e-04
2022-05-05 16:18:24,091 INFO [train.py:715] (1/8) Epoch 6, batch 28200, loss[loss=0.129, simple_loss=0.1973, pruned_loss=0.03036, over 4765.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2189, pruned_loss=0.03834, over 971702.98 frames.], batch size: 14, lr: 3.24e-04
2022-05-05 16:19:03,413 INFO [train.py:715] (1/8) Epoch 6, batch 28250, loss[loss=0.1392, simple_loss=0.2206, pruned_loss=0.02886, over 4905.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2192, pruned_loss=0.03822, over 971829.39 frames.], batch size: 18, lr: 3.24e-04
2022-05-05 16:19:41,793 INFO [train.py:715] (1/8) Epoch 6, batch 28300, loss[loss=0.1621, simple_loss=0.2235, pruned_loss=0.05034, over 4813.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2196, pruned_loss=0.03852, over 972064.67 frames.], batch size: 27, lr: 3.24e-04
2022-05-05 16:20:20,027 INFO [train.py:715] (1/8) Epoch 6, batch 28350, loss[loss=0.1443, simple_loss=0.2197, pruned_loss=0.0344, over 4842.00 frames.], tot_loss[loss=0.149, simple_loss=0.2201, pruned_loss=0.03898, over 971329.24 frames.], batch size: 15, lr: 3.24e-04
2022-05-05 16:20:59,875 INFO [train.py:715] (1/8) Epoch 6, batch 28400, loss[loss=0.1488, simple_loss=0.2237, pruned_loss=0.03694, over 4877.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2195, pruned_loss=0.03864, over 971132.50 frames.], batch size: 16, lr: 3.24e-04
2022-05-05 16:21:38,668 INFO [train.py:715] (1/8) Epoch 6, batch 28450, loss[loss=0.1478, simple_loss=0.2129, pruned_loss=0.04137, over 4843.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2186, pruned_loss=0.03831, over 971376.99 frames.], batch size: 13, lr: 3.24e-04
2022-05-05 16:22:17,506 INFO [train.py:715] (1/8) Epoch 6, batch 28500, loss[loss=0.146, simple_loss=0.2144, pruned_loss=0.03881, over 4792.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2172, pruned_loss=0.03718, over 971806.50 frames.], batch size: 14, lr: 3.24e-04
2022-05-05 16:22:56,651 INFO [train.py:715] (1/8) Epoch 6, batch 28550, loss[loss=0.1438, simple_loss=0.2081, pruned_loss=0.03976, over 4900.00 frames.], tot_loss[loss=0.146, simple_loss=0.2174, pruned_loss=0.03733, over 972676.69 frames.], batch size: 17, lr: 3.24e-04
2022-05-05 16:23:36,091 INFO [train.py:715] (1/8) Epoch 6, batch 28600, loss[loss=0.1644, simple_loss=0.2318, pruned_loss=0.04849, over 4915.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2183, pruned_loss=0.0375, over 972643.21 frames.], batch size: 21, lr: 3.24e-04
2022-05-05 16:24:14,192 INFO [train.py:715] (1/8) Epoch 6, batch 28650, loss[loss=0.1409, simple_loss=0.2101, pruned_loss=0.03589, over 4968.00 frames.], tot_loss[loss=0.1465, simple_loss=0.218, pruned_loss=0.03747, over 973640.00 frames.], batch size: 35, lr: 3.24e-04
2022-05-05 16:24:52,990 INFO [train.py:715] (1/8) Epoch 6, batch 28700, loss[loss=0.17, simple_loss=0.2366, pruned_loss=0.05175, over 4977.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2181, pruned_loss=0.03739, over 973200.10 frames.], batch size: 14, lr: 3.24e-04
2022-05-05 16:25:32,177 INFO [train.py:715] (1/8) Epoch 6, batch 28750, loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03104, over 4830.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2177, pruned_loss=0.03711, over 972752.61 frames.], batch size: 26, lr: 3.24e-04
2022-05-05 16:26:10,898 INFO [train.py:715] (1/8) Epoch 6, batch 28800, loss[loss=0.1808, simple_loss=0.2483, pruned_loss=0.05663, over 4925.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2186, pruned_loss=0.03808, over 972833.01 frames.], batch size: 29, lr: 3.24e-04
2022-05-05 16:26:49,769 INFO [train.py:715] (1/8) Epoch 6, batch 28850, loss[loss=0.1363, simple_loss=0.2034, pruned_loss=0.03456, over 4862.00 frames.], tot_loss[loss=0.1473, simple_loss=0.219, pruned_loss=0.03777, over 972615.65 frames.], batch size: 20, lr: 3.24e-04
2022-05-05 16:27:28,069 INFO [train.py:715] (1/8) Epoch 6, batch 28900, loss[loss=0.1746, simple_loss=0.2375, pruned_loss=0.05582, over 4768.00 frames.], tot_loss[loss=0.148, simple_loss=0.2195, pruned_loss=0.03826, over 972921.65 frames.], batch size: 18, lr: 3.24e-04
2022-05-05 16:28:07,517 INFO [train.py:715] (1/8) Epoch 6, batch 28950, loss[loss=0.1229, simple_loss=0.2011, pruned_loss=0.02228, over 4808.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2191, pruned_loss=0.03825, over 972250.63 frames.], batch size: 25, lr: 3.24e-04
2022-05-05 16:28:45,750 INFO [train.py:715] (1/8) Epoch 6, batch 29000, loss[loss=0.1258, simple_loss=0.2001, pruned_loss=0.02575, over 4799.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2187, pruned_loss=0.03839, over 972660.37 frames.], batch size: 24, lr: 3.24e-04
2022-05-05 16:29:23,908 INFO [train.py:715] (1/8) Epoch 6, batch 29050, loss[loss=0.1529, simple_loss=0.2272, pruned_loss=0.03927, over 4922.00 frames.], tot_loss[loss=0.148, simple_loss=0.219, pruned_loss=0.03846, over 973052.81 frames.], batch size: 39, lr: 3.24e-04
2022-05-05 16:30:02,946 INFO [train.py:715] (1/8) Epoch 6, batch 29100, loss[loss=0.1558, simple_loss=0.2309, pruned_loss=0.04034, over 4784.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2191, pruned_loss=0.03865, over 972665.82 frames.], batch size: 14, lr: 3.24e-04
2022-05-05 16:30:41,839 INFO [train.py:715] (1/8) Epoch 6, batch 29150, loss[loss=0.1457, simple_loss=0.2157, pruned_loss=0.03787, over 4694.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2199, pruned_loss=0.03895, over 972638.08 frames.], batch size: 15, lr: 3.24e-04
2022-05-05 16:31:20,670 INFO [train.py:715] (1/8) Epoch 6, batch 29200, loss[loss=0.1416, simple_loss=0.214, pruned_loss=0.03456, over 4887.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2187, pruned_loss=0.03798, over 972270.24 frames.], batch size: 16, lr: 3.24e-04
2022-05-05 16:31:59,884 INFO [train.py:715] (1/8) Epoch 6, batch 29250, loss[loss=0.1374, simple_loss=0.2116, pruned_loss=0.03165, over 4779.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2191, pruned_loss=0.03802, over 972149.11 frames.], batch size: 18, lr: 3.24e-04
2022-05-05 16:32:39,923 INFO [train.py:715] (1/8) Epoch 6, batch 29300, loss[loss=0.1521, simple_loss=0.2304, pruned_loss=0.0369, over 4773.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2196, pruned_loss=0.0383, over 972956.30 frames.], batch size: 18, lr: 3.24e-04
2022-05-05 16:33:18,208 INFO [train.py:715] (1/8) Epoch 6, batch 29350, loss[loss=0.1666, simple_loss=0.2386, pruned_loss=0.04732, over 4851.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2186, pruned_loss=0.03794, over 973049.64 frames.], batch size: 20, lr: 3.24e-04
2022-05-05 16:33:57,217 INFO [train.py:715] (1/8) Epoch 6, batch 29400, loss[loss=0.1696, simple_loss=0.2568, pruned_loss=0.04114, over 4970.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2193, pruned_loss=0.03861, over 972882.30 frames.], batch size: 39, lr: 3.24e-04
2022-05-05 16:34:36,597 INFO [train.py:715] (1/8) Epoch 6, batch 29450, loss[loss=0.1435, simple_loss=0.1997, pruned_loss=0.04361, over 4984.00 frames.], tot_loss[loss=0.1495, simple_loss=0.22, pruned_loss=0.03951, over 972922.67 frames.], batch size: 14, lr: 3.24e-04
2022-05-05 16:35:15,804 INFO [train.py:715] (1/8) Epoch 6, batch 29500, loss[loss=0.1541, simple_loss=0.2315, pruned_loss=0.03834, over 4960.00 frames.], tot_loss[loss=0.149, simple_loss=0.2199, pruned_loss=0.03906, over 973110.74 frames.], batch size: 39, lr: 3.24e-04
2022-05-05 16:35:53,793 INFO [train.py:715] (1/8) Epoch 6, batch 29550, loss[loss=0.1342, simple_loss=0.213, pruned_loss=0.02775, over 4831.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2196, pruned_loss=0.03865, over 973081.56 frames.], batch size: 15, lr: 3.24e-04
2022-05-05 16:36:33,142 INFO [train.py:715] (1/8) Epoch 6, batch 29600, loss[loss=0.1276, simple_loss=0.1941, pruned_loss=0.03058, over 4984.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2196, pruned_loss=0.03862, over 972996.57 frames.], batch size: 15, lr: 3.24e-04
2022-05-05 16:37:12,533 INFO [train.py:715] (1/8) Epoch 6, batch 29650, loss[loss=0.1673, simple_loss=0.2326, pruned_loss=0.05098, over 4918.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2191, pruned_loss=0.03838, over 973328.61 frames.], batch size: 18, lr: 3.23e-04
2022-05-05 16:37:51,063 INFO [train.py:715] (1/8) Epoch 6, batch 29700, loss[loss=0.1523, simple_loss=0.2236, pruned_loss=0.04051, over 4962.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2189, pruned_loss=0.03845, over 973071.07 frames.], batch size: 24, lr: 3.23e-04
2022-05-05 16:38:29,764 INFO [train.py:715] (1/8) Epoch 6, batch 29750, loss[loss=0.147, simple_loss=0.2218, pruned_loss=0.03605, over 4687.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2193, pruned_loss=0.03846, over 972530.14 frames.], batch size: 15, lr: 3.23e-04
2022-05-05 16:39:08,776 INFO [train.py:715] (1/8) Epoch 6, batch 29800, loss[loss=0.1271, simple_loss=0.204, pruned_loss=0.02507, over 4923.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2196, pruned_loss=0.03847, over 972182.98 frames.], batch size: 23, lr: 3.23e-04
2022-05-05 16:39:48,204 INFO [train.py:715] (1/8) Epoch 6, batch 29850, loss[loss=0.1274, simple_loss=0.1994, pruned_loss=0.02768, over 4966.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2189, pruned_loss=0.03824, over 972211.53 frames.], batch size: 24, lr: 3.23e-04
2022-05-05 16:40:26,715 INFO [train.py:715] (1/8) Epoch 6, batch 29900, loss[loss=0.1518, simple_loss=0.2323, pruned_loss=0.03566, over 4993.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03826, over 971934.40 frames.], batch size: 20, lr: 3.23e-04
2022-05-05 16:41:05,702 INFO [train.py:715] (1/8) Epoch 6, batch 29950, loss[loss=0.1428, simple_loss=0.213, pruned_loss=0.03629, over 4926.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2182, pruned_loss=0.03764, over 972259.41 frames.], batch size: 18, lr: 3.23e-04
2022-05-05 16:41:45,057 INFO [train.py:715] (1/8) Epoch 6, batch 30000, loss[loss=0.119, simple_loss=0.1961, pruned_loss=0.02092, over 4755.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2183, pruned_loss=0.03746, over 972317.47 frames.], batch size: 19, lr: 3.23e-04
2022-05-05 16:41:45,058 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 16:41:54,715 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1088, simple_loss=0.1938, pruned_loss=0.0119, over 914524.00 frames.
2022-05-05 16:42:34,426 INFO [train.py:715] (1/8) Epoch 6, batch 30050, loss[loss=0.1278, simple_loss=0.1978, pruned_loss=0.02889, over 4789.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2173, pruned_loss=0.0371, over 971926.07 frames.], batch size: 12, lr: 3.23e-04
2022-05-05 16:43:12,815 INFO [train.py:715] (1/8) Epoch 6, batch 30100, loss[loss=0.1399, simple_loss=0.1998, pruned_loss=0.03997, over 4835.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2186, pruned_loss=0.03801, over 972514.33 frames.], batch size: 13, lr: 3.23e-04
2022-05-05 16:43:51,561 INFO [train.py:715] (1/8) Epoch 6, batch 30150, loss[loss=0.1459, simple_loss=0.2236, pruned_loss=0.03414, over 4869.00 frames.], tot_loss[loss=0.147, simple_loss=0.2187, pruned_loss=0.03763, over 971805.08 frames.], batch size: 20, lr: 3.23e-04
2022-05-05 16:44:30,968 INFO [train.py:715] (1/8) Epoch 6, batch 30200, loss[loss=0.1239, simple_loss=0.1946, pruned_loss=0.02664, over 4764.00 frames.], tot_loss[loss=0.147, simple_loss=0.2188, pruned_loss=0.03764, over 972337.86 frames.], batch size: 19, lr: 3.23e-04
2022-05-05 16:45:10,341 INFO [train.py:715] (1/8) Epoch 6, batch 30250, loss[loss=0.17, simple_loss=0.2343, pruned_loss=0.05282, over 4779.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2194, pruned_loss=0.03777, over 972667.29 frames.], batch size: 17, lr: 3.23e-04
2022-05-05 16:45:48,513 INFO [train.py:715] (1/8) Epoch 6, batch 30300, loss[loss=0.1676, simple_loss=0.247, pruned_loss=0.04408, over 4876.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2196, pruned_loss=0.03792, over 972190.60 frames.], batch size: 38, lr: 3.23e-04
2022-05-05 16:46:27,515 INFO [train.py:715] (1/8) Epoch 6, batch 30350, loss[loss=0.1476, simple_loss=0.2145, pruned_loss=0.04039, over 4757.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2198, pruned_loss=0.03784, over 971503.58 frames.], batch size: 14, lr: 3.23e-04
2022-05-05 16:47:06,588 INFO [train.py:715] (1/8) Epoch 6, batch 30400, loss[loss=0.1507, simple_loss=0.2194, pruned_loss=0.04102, over 4863.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2203, pruned_loss=0.03835, over 972655.23 frames.], batch size: 32, lr: 3.23e-04
2022-05-05 16:47:45,264 INFO [train.py:715] (1/8) Epoch 6, batch 30450, loss[loss=0.14, simple_loss=0.2133, pruned_loss=0.03337, over 4748.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2201, pruned_loss=0.03838, over 973024.66 frames.], batch size: 16, lr: 3.23e-04
2022-05-05 16:48:23,949 INFO [train.py:715] (1/8) Epoch 6, batch 30500, loss[loss=0.1543, simple_loss=0.2236, pruned_loss=0.04248, over 4865.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2203, pruned_loss=0.03855, over 972597.84 frames.], batch size: 20, lr: 3.23e-04
2022-05-05 16:49:02,697 INFO [train.py:715] (1/8) Epoch 6, batch 30550, loss[loss=0.1567, simple_loss=0.2396, pruned_loss=0.03691, over 4814.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2197, pruned_loss=0.03821, over 971758.20 frames.], batch size: 25, lr: 3.23e-04
2022-05-05 16:49:41,854 INFO [train.py:715] (1/8) Epoch 6, batch 30600, loss[loss=0.1321, simple_loss=0.1979, pruned_loss=0.03319, over 4766.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2198, pruned_loss=0.0384, over 971675.94 frames.], batch size: 14, lr: 3.23e-04
2022-05-05 16:50:20,375 INFO [train.py:715] (1/8) Epoch 6, batch 30650, loss[loss=0.1888, simple_loss=0.2685, pruned_loss=0.05451, over 4845.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2201, pruned_loss=0.03833, over 971745.23 frames.], batch size: 34, lr: 3.23e-04
2022-05-05 16:50:59,234 INFO [train.py:715] (1/8) Epoch 6, batch 30700, loss[loss=0.1092, simple_loss=0.1875, pruned_loss=0.0154, over 4794.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2202, pruned_loss=0.03858, over 970838.58 frames.], batch size: 12, lr: 3.23e-04
2022-05-05 16:51:38,192 INFO [train.py:715] (1/8) Epoch 6, batch 30750, loss[loss=0.1342, simple_loss=0.2049, pruned_loss=0.03176, over 4848.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2192, pruned_loss=0.03774, over 971082.30 frames.], batch size: 34, lr: 3.23e-04
2022-05-05 16:52:17,035 INFO [train.py:715] (1/8) Epoch 6, batch 30800, loss[loss=0.1358, simple_loss=0.2071, pruned_loss=0.0322, over 4848.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2196, pruned_loss=0.03799, over 971352.53 frames.], batch size: 32, lr: 3.23e-04
2022-05-05 16:52:55,433 INFO [train.py:715] (1/8) Epoch 6, batch 30850, loss[loss=0.1392, simple_loss=0.2059, pruned_loss=0.03622, over 4820.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2196, pruned_loss=0.03807, over 971544.87 frames.], batch size: 25, lr: 3.23e-04
2022-05-05 16:53:34,167 INFO [train.py:715] (1/8) Epoch 6, batch 30900, loss[loss=0.1347, simple_loss=0.2036, pruned_loss=0.03284, over 4955.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2198, pruned_loss=0.03833, over 971934.58 frames.], batch size: 24, lr: 3.23e-04
2022-05-05 16:54:13,778 INFO [train.py:715] (1/8) Epoch 6, batch 30950, loss[loss=0.168, simple_loss=0.2284, pruned_loss=0.05381, over 4948.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2187, pruned_loss=0.03824, over 972041.56 frames.], batch size: 35, lr: 3.23e-04
2022-05-05 16:54:51,911 INFO [train.py:715] (1/8) Epoch 6, batch 31000, loss[loss=0.1524, simple_loss=0.2209, pruned_loss=0.04191, over 4687.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2197, pruned_loss=0.03894, over 972381.40 frames.], batch size: 15, lr: 3.23e-04
2022-05-05 16:55:30,914 INFO [train.py:715] (1/8) Epoch 6, batch 31050, loss[loss=0.1322, simple_loss=0.2057, pruned_loss=0.02936, over 4898.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2201, pruned_loss=0.03923, over 971784.77 frames.], batch size: 17, lr: 3.23e-04
2022-05-05 16:56:10,160 INFO [train.py:715] (1/8) Epoch 6, batch 31100, loss[loss=0.1602, simple_loss=0.227, pruned_loss=0.04673, over 4905.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2201, pruned_loss=0.03886, over 972534.27 frames.], batch size: 39, lr: 3.22e-04
2022-05-05 16:56:51,386 INFO [train.py:715] (1/8) Epoch 6, batch 31150, loss[loss=0.16, simple_loss=0.2317, pruned_loss=0.04417, over 4823.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2191, pruned_loss=0.03827, over 972737.52 frames.], batch size: 15, lr: 3.22e-04
2022-05-05 16:57:30,158 INFO [train.py:715] (1/8) Epoch 6, batch 31200, loss[loss=0.1287, simple_loss=0.189, pruned_loss=0.0342, over 4796.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2186, pruned_loss=0.03795, over 972190.05 frames.], batch size: 12, lr: 3.22e-04
2022-05-05 16:58:09,409 INFO [train.py:715] (1/8) Epoch 6, batch 31250, loss[loss=0.141, simple_loss=0.2203, pruned_loss=0.0308, over 4949.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2187, pruned_loss=0.03855, over 972051.54 frames.], batch size: 21, lr: 3.22e-04
2022-05-05 16:58:48,247 INFO [train.py:715] (1/8) Epoch 6, batch 31300, loss[loss=0.1165, simple_loss=0.1919, pruned_loss=0.02056, over 4803.00 frames.], tot_loss[loss=0.149, simple_loss=0.2199, pruned_loss=0.03907, over 972620.55 frames.], batch size: 13, lr: 3.22e-04
2022-05-05 16:59:27,128 INFO [train.py:715] (1/8) Epoch 6, batch 31350, loss[loss=0.1521, simple_loss=0.2253, pruned_loss=0.03951, over 4797.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2202, pruned_loss=0.03918, over 972736.78 frames.], batch size: 18, lr: 3.22e-04
2022-05-05 17:00:06,357 INFO [train.py:715] (1/8) Epoch 6, batch 31400, loss[loss=0.1631, simple_loss=0.2368, pruned_loss=0.04469, over 4770.00 frames.], tot_loss[loss=0.1489, simple_loss=0.22, pruned_loss=0.03889, over 972987.87 frames.], batch size: 17, lr: 3.22e-04
2022-05-05 17:00:45,705 INFO [train.py:715] (1/8) Epoch 6, batch 31450, loss[loss=0.122, simple_loss=0.2027, pruned_loss=0.02063, over 4822.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2185, pruned_loss=0.03786, over 972055.48 frames.], batch size: 26, lr: 3.22e-04
2022-05-05 17:01:23,999 INFO [train.py:715] (1/8) Epoch 6, batch 31500, loss[loss=0.1588, simple_loss=0.2228, pruned_loss=0.04744, over 4918.00 frames.], tot_loss[loss=0.148, simple_loss=0.2196, pruned_loss=0.03819, over 972816.21 frames.], batch size: 29, lr: 3.22e-04
2022-05-05 17:02:02,413 INFO [train.py:715] (1/8) Epoch 6, batch 31550, loss[loss=0.1413, simple_loss=0.2109, pruned_loss=0.03582, over 4884.00 frames.], tot_loss[loss=0.1484, simple_loss=0.22, pruned_loss=0.0384, over 973164.11 frames.], batch size: 19, lr: 3.22e-04
2022-05-05 17:02:41,959 INFO [train.py:715] (1/8) Epoch 6, batch 31600, loss[loss=0.1388, simple_loss=0.2102, pruned_loss=0.03373, over 4953.00 frames.], tot_loss[loss=0.1482, simple_loss=0.22, pruned_loss=0.03816, over 973121.30 frames.], batch size: 23, lr: 3.22e-04
2022-05-05 17:03:21,198 INFO [train.py:715] (1/8) Epoch 6, batch 31650, loss[loss=0.1416, simple_loss=0.2109, pruned_loss=0.03613, over 4916.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2199, pruned_loss=0.03848, over 972792.49 frames.], batch size: 23, lr: 3.22e-04
2022-05-05 17:03:59,731 INFO [train.py:715] (1/8) Epoch 6, batch 31700, loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03174, over 4988.00 frames.], tot_loss[loss=0.1489, simple_loss=0.22, pruned_loss=0.03889, over 972823.82 frames.], batch size: 33, lr: 3.22e-04
2022-05-05 17:04:38,253 INFO [train.py:715] (1/8) Epoch 6, batch 31750, loss[loss=0.135, simple_loss=0.206, pruned_loss=0.03195, over 4978.00 frames.], tot_loss[loss=0.1488, simple_loss=0.22, pruned_loss=0.03879, over 973296.09 frames.], batch size: 28, lr: 3.22e-04
2022-05-05 17:05:17,758 INFO [train.py:715] (1/8) Epoch 6, batch 31800, loss[loss=0.139, simple_loss=0.2103, pruned_loss=0.03389, over 4752.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2197, pruned_loss=0.03847, over 973399.51 frames.], batch size: 16, lr: 3.22e-04
2022-05-05 17:05:56,239 INFO [train.py:715] (1/8) Epoch 6, batch 31850, loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03124, over 4894.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2191, pruned_loss=0.03808, over 973650.20 frames.], batch size: 19, lr: 3.22e-04
2022-05-05 17:06:34,779 INFO [train.py:715] (1/8) Epoch 6, batch 31900, loss[loss=0.1746, simple_loss=0.2464, pruned_loss=0.05138, over 4868.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2185, pruned_loss=0.03789, over 973554.00 frames.], batch size: 32, lr: 3.22e-04
2022-05-05 17:07:13,872 INFO [train.py:715] (1/8) Epoch 6, batch 31950, loss[loss=0.2142, simple_loss=0.2854, pruned_loss=0.07149, over 4909.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2185, pruned_loss=0.03798, over 973721.48 frames.], batch size: 17, lr: 3.22e-04
2022-05-05 17:07:52,488 INFO [train.py:715] (1/8) Epoch 6, batch 32000, loss[loss=0.1395, simple_loss=0.2167, pruned_loss=0.03111, over 4791.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2185, pruned_loss=0.03818, over 973655.20 frames.], batch size: 18, lr: 3.22e-04
2022-05-05 17:08:31,943 INFO [train.py:715] (1/8) Epoch 6, batch 32050, loss[loss=0.1352, simple_loss=0.1977, pruned_loss=0.0364, over 4816.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2186, pruned_loss=0.03854, over 973036.17 frames.], batch size: 13, lr: 3.22e-04
2022-05-05 17:09:11,464 INFO [train.py:715] (1/8) Epoch 6, batch 32100, loss[loss=0.1409, simple_loss=0.2088, pruned_loss=0.03655, over 4821.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2191, pruned_loss=0.03867, over 973126.43 frames.], batch size: 25, lr: 3.22e-04
2022-05-05 17:09:50,454 INFO [train.py:715] (1/8) Epoch 6, batch 32150, loss[loss=0.1486, simple_loss=0.212, pruned_loss=0.04259, over 4809.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2186, pruned_loss=0.03821, over 973670.41 frames.], batch size: 12, lr: 3.22e-04
2022-05-05 17:10:28,949 INFO [train.py:715] (1/8) Epoch 6, batch 32200, loss[loss=0.1782, simple_loss=0.2455, pruned_loss=0.05543, over 4904.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2197, pruned_loss=0.0386, over 973687.66 frames.], batch size: 19, lr: 3.22e-04
2022-05-05 17:11:08,025 INFO [train.py:715] (1/8) Epoch 6, batch 32250, loss[loss=0.1499, simple_loss=0.224, pruned_loss=0.03796, over 4864.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2193, pruned_loss=0.03843, over 973711.49 frames.], batch size: 30, lr: 3.22e-04
2022-05-05 17:11:46,854 INFO [train.py:715] (1/8) Epoch 6, batch 32300, loss[loss=0.1765, simple_loss=0.2474, pruned_loss=0.05281, over 4980.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2192, pruned_loss=0.03789, over 973641.14 frames.], batch size: 15, lr: 3.22e-04
2022-05-05 17:12:26,141 INFO [train.py:715] (1/8) Epoch 6, batch 32350, loss[loss=0.169, simple_loss=0.2402, pruned_loss=0.04894, over 4868.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2201, pruned_loss=0.03878, over 973499.88 frames.], batch size: 20, lr: 3.22e-04
2022-05-05 17:13:04,503 INFO [train.py:715] (1/8) Epoch 6, batch 32400, loss[loss=0.1441, simple_loss=0.2046, pruned_loss=0.04182, over 4839.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2207, pruned_loss=0.03884, over 973779.87 frames.], batch size: 13, lr: 3.22e-04
2022-05-05 17:13:43,924 INFO [train.py:715] (1/8) Epoch 6, batch 32450, loss[loss=0.1573, simple_loss=0.2284, pruned_loss=0.04305, over 4874.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2209, pruned_loss=0.0389, over 972725.17 frames.], batch size: 16, lr: 3.22e-04
2022-05-05 17:14:23,272 INFO [train.py:715] (1/8) Epoch 6, batch 32500, loss[loss=0.1349, simple_loss=0.2063, pruned_loss=0.0318, over 4832.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2209, pruned_loss=0.03877, over 972241.21 frames.], batch size: 26, lr: 3.22e-04
2022-05-05 17:15:01,988 INFO [train.py:715] (1/8) Epoch 6, batch 32550, loss[loss=0.149, simple_loss=0.219, pruned_loss=0.03952, over 4813.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2205, pruned_loss=0.03897, over 972140.84 frames.], batch size: 15, lr: 3.22e-04
2022-05-05 17:15:40,781 INFO [train.py:715] (1/8) Epoch 6, batch 32600, loss[loss=0.134, simple_loss=0.205, pruned_loss=0.03152, over 4970.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.0387, over 972297.64 frames.], batch size: 15, lr: 3.21e-04
2022-05-05 17:16:19,208 INFO [train.py:715] (1/8) Epoch 6, batch 32650, loss[loss=0.1731, simple_loss=0.2434, pruned_loss=0.05137, over 4880.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2204, pruned_loss=0.03874, over 972414.24 frames.], batch size: 22, lr: 3.21e-04
2022-05-05 17:16:57,840 INFO [train.py:715] (1/8) Epoch 6, batch 32700, loss[loss=0.1313, simple_loss=0.2001, pruned_loss=0.03129, over 4868.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2205, pruned_loss=0.039, over 972765.88 frames.], batch size: 39, lr: 3.21e-04
2022-05-05 17:17:35,888 INFO [train.py:715] (1/8) Epoch 6, batch 32750, loss[loss=0.1625, simple_loss=0.2316, pruned_loss=0.04669, over 4926.00 frames.], tot_loss[loss=0.1489, simple_loss=0.22, pruned_loss=0.03889, over 972522.98 frames.], batch size: 18, lr: 3.21e-04
2022-05-05 17:18:14,606 INFO [train.py:715] (1/8) Epoch 6, batch 32800, loss[loss=0.1578, simple_loss=0.2163, pruned_loss=0.04965, over 4859.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2196, pruned_loss=0.03838, over 972807.18 frames.], batch size: 20, lr: 3.21e-04
2022-05-05 17:18:53,199 INFO [train.py:715] (1/8) Epoch 6, batch 32850, loss[loss=0.1456, simple_loss=0.2054, pruned_loss=0.04294, over 4855.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2186, pruned_loss=0.03798, over 972077.78 frames.], batch size: 30, lr: 3.21e-04
2022-05-05 17:19:31,605 INFO [train.py:715] (1/8) Epoch 6, batch 32900, loss[loss=0.1322, simple_loss=0.2076, pruned_loss=0.02841, over 4689.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2184, pruned_loss=0.0379, over 972560.68 frames.], batch size: 15, lr: 3.21e-04
2022-05-05 17:20:09,699 INFO [train.py:715] (1/8) Epoch 6, batch 32950, loss[loss=0.1854, simple_loss=0.2571, pruned_loss=0.05686, over 4685.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2189, pruned_loss=0.03845, over 972131.34 frames.], batch size: 15, lr: 3.21e-04
2022-05-05 17:20:48,508 INFO [train.py:715] (1/8) Epoch 6, batch 33000, loss[loss=0.1359, simple_loss=0.2082, pruned_loss=0.03179, over 4983.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2183, pruned_loss=0.03802, over 973097.65 frames.], batch size: 25, lr: 3.21e-04
2022-05-05 17:20:48,509 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 17:20:58,110 INFO [train.py:742] (1/8) Epoch 6, validation: loss=0.1087, simple_loss=0.1938, pruned_loss=0.01183, over 914524.00 frames.
2022-05-05 17:21:36,676 INFO [train.py:715] (1/8) Epoch 6, batch 33050, loss[loss=0.1483, simple_loss=0.2202, pruned_loss=0.03819, over 4766.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2186, pruned_loss=0.03847, over 972197.66 frames.], batch size: 16, lr: 3.21e-04
2022-05-05 17:22:15,263 INFO [train.py:715] (1/8) Epoch 6, batch 33100, loss[loss=0.1779, simple_loss=0.2433, pruned_loss=0.05622, over 4910.00 frames.], tot_loss[loss=0.148, simple_loss=0.2188, pruned_loss=0.03865, over 972849.24 frames.], batch size: 17, lr: 3.21e-04
2022-05-05 17:22:53,011 INFO [train.py:715] (1/8) Epoch 6, batch 33150, loss[loss=0.1731, simple_loss=0.2518, pruned_loss=0.04724, over 4793.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2194, pruned_loss=0.03885, over 972301.57 frames.], batch size: 21, lr: 3.21e-04
2022-05-05 17:23:31,900 INFO [train.py:715] (1/8) Epoch 6, batch 33200, loss[loss=0.1128, simple_loss=0.1783, pruned_loss=0.02368, over 4838.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2188, pruned_loss=0.0388, over 972493.72 frames.], batch size: 12, lr: 3.21e-04
2022-05-05 17:24:10,787 INFO [train.py:715] (1/8) Epoch 6, batch 33250, loss[loss=0.1546, simple_loss=0.2357, pruned_loss=0.03677, over 4826.00 frames.], tot_loss[loss=0.1486, simple_loss=0.219, pruned_loss=0.03912, over 972058.73 frames.], batch size: 25, lr: 3.21e-04
2022-05-05 17:24:49,864 INFO [train.py:715] (1/8) Epoch 6, batch 33300, loss[loss=0.143, simple_loss=0.228, pruned_loss=0.02901, over 4820.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2191, pruned_loss=0.0389, over 971566.84 frames.], batch size: 27, lr: 3.21e-04
2022-05-05 17:25:28,470 INFO [train.py:715] (1/8) Epoch 6, batch 33350, loss[loss=0.1452, simple_loss=0.2296, pruned_loss=0.03036, over 4950.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2186, pruned_loss=0.03854, over 971347.63 frames.], batch size: 29, lr: 3.21e-04
2022-05-05 17:26:07,938 INFO [train.py:715] (1/8) Epoch 6, batch 33400, loss[loss=0.1529, simple_loss=0.2303, pruned_loss=0.03774, over 4993.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2189, pruned_loss=0.03842, over 971545.51 frames.], batch size: 16, lr: 3.21e-04
2022-05-05 17:26:47,019 INFO [train.py:715] (1/8) Epoch 6, batch 33450, loss[loss=0.1617, simple_loss=0.2263, pruned_loss=0.0486, over 4927.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2193, pruned_loss=0.03856, over 971618.75 frames.], batch size: 18, lr: 3.21e-04
2022-05-05 17:27:25,293 INFO [train.py:715] (1/8) Epoch 6, batch 33500, loss[loss=0.1732, simple_loss=0.2335, pruned_loss=0.05648, over 4947.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2199, pruned_loss=0.03851, over 970990.98 frames.], batch size: 39, lr: 3.21e-04
2022-05-05 17:28:04,317 INFO [train.py:715] (1/8) Epoch 6, batch 33550, loss[loss=0.1362, simple_loss=0.2034, pruned_loss=0.03449, over 4983.00 frames.], tot_loss[loss=0.1474, simple_loss=0.219, pruned_loss=0.03791, over 971181.45 frames.], batch size: 26, lr: 3.21e-04
2022-05-05 17:28:43,725 INFO [train.py:715] (1/8) Epoch 6, batch 33600, loss[loss=0.1469, simple_loss=0.2162, pruned_loss=0.03875, over 4904.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2186, pruned_loss=0.03794, over 970850.56 frames.], batch size: 19, lr: 3.21e-04
2022-05-05 17:29:22,678 INFO [train.py:715] (1/8) Epoch 6, batch 33650, loss[loss=0.129, simple_loss=0.2091, pruned_loss=0.02442, over 4764.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.03769, over 971685.80 frames.], batch size: 19, lr: 3.21e-04
2022-05-05 17:30:01,277 INFO [train.py:715] (1/8) Epoch 6, batch 33700, loss[loss=0.1642, simple_loss=0.2442, pruned_loss=0.04214, over 4934.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2186, pruned_loss=0.0376, over 972938.77 frames.], batch size: 23, lr: 3.21e-04
2022-05-05 17:30:39,887 INFO [train.py:715] (1/8) Epoch 6, batch 33750, loss[loss=0.1278, simple_loss=0.2039, pruned_loss=0.02581, over 4842.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03741, over 972609.21 frames.], batch size: 32, lr: 3.21e-04
2022-05-05 17:31:19,207 INFO [train.py:715] (1/8) Epoch 6, batch 33800, loss[loss=0.1184, simple_loss=0.1887, pruned_loss=0.024, over 4766.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2182, pruned_loss=0.03726, over 972714.71 frames.], batch size: 12, lr: 3.21e-04
2022-05-05 17:31:58,019 INFO [train.py:715] (1/8) Epoch 6, batch 33850, loss[loss=0.1606, simple_loss=0.2404, pruned_loss=0.04036, over 4937.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2187, pruned_loss=0.03748, over 972949.62 frames.], batch size: 23, lr: 3.21e-04
2022-05-05 17:32:36,707 INFO [train.py:715] (1/8) Epoch 6, batch 33900, loss[loss=0.1541, simple_loss=0.2248, pruned_loss=0.04167, over 4709.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2181, pruned_loss=0.03763, over 972953.20 frames.], batch size: 15, lr: 3.21e-04
2022-05-05 17:33:16,052 INFO [train.py:715] (1/8) Epoch 6, batch 33950, loss[loss=0.1397, simple_loss=0.1999, pruned_loss=0.03975, over 4758.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2188, pruned_loss=0.03782, over 972797.25 frames.], batch size: 14, lr: 3.21e-04
2022-05-05 17:33:55,032 INFO [train.py:715] (1/8) Epoch 6, batch 34000, loss[loss=0.1812, simple_loss=0.2615, pruned_loss=0.05047, over 4968.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2195, pruned_loss=0.03798, over 972703.34 frames.], batch size: 24, lr: 3.21e-04
2022-05-05 17:34:33,702 INFO [train.py:715] (1/8) Epoch 6, batch 34050, loss[loss=0.1647, simple_loss=0.2381, pruned_loss=0.04569, over 4794.00 frames.], tot_loss[loss=0.1476, simple_loss=0.219, pruned_loss=0.03805, over 972842.30 frames.], batch size: 24, lr: 3.21e-04
2022-05-05 17:35:12,977 INFO [train.py:715] (1/8) Epoch 6, batch 34100, loss[loss=0.1447, simple_loss=0.215, pruned_loss=0.03722, over 4890.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03819, over 972476.22 frames.], batch size: 39, lr: 3.20e-04
2022-05-05 17:35:51,935 INFO [train.py:715] (1/8) Epoch 6, batch 34150, loss[loss=0.1527, simple_loss=0.2321, pruned_loss=0.03668, over 4974.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2188, pruned_loss=0.03803, over 972222.71 frames.], batch size: 28, lr: 3.20e-04
2022-05-05 17:36:30,537 INFO [train.py:715] (1/8) Epoch 6, batch 34200, loss[loss=0.1525, simple_loss=0.2405, pruned_loss=0.0322, over 4934.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2192, pruned_loss=0.03773, over 972407.62 frames.], batch size: 21, lr: 3.20e-04
2022-05-05 17:37:09,179 INFO [train.py:715] (1/8) Epoch 6, batch 34250, loss[loss=0.1454, simple_loss=0.2202, pruned_loss=0.03533, over 4810.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2197, pruned_loss=0.03837, over 972556.07 frames.], batch size: 24, lr: 3.20e-04
2022-05-05 17:37:48,390 INFO [train.py:715] (1/8) Epoch 6, batch 34300, loss[loss=0.1188, simple_loss=0.1968, pruned_loss=0.02043, over 4827.00 frames.], tot_loss[loss=0.1476, simple_loss=0.219, pruned_loss=0.03809, over 972731.89 frames.], batch size: 26, lr: 3.20e-04
2022-05-05 17:38:26,983 INFO [train.py:715] (1/8) Epoch 6, batch 34350, loss[loss=0.1339, simple_loss=0.2176, pruned_loss=0.02506, over 4806.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2181, pruned_loss=0.03758, over 972458.53 frames.], batch size: 14, lr: 3.20e-04
2022-05-05 17:39:05,620 INFO [train.py:715] (1/8) Epoch 6, batch 34400, loss[loss=0.1664, simple_loss=0.2541, pruned_loss=0.03934, over 4901.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2173, pruned_loss=0.0372, over 972322.60 frames.], batch size: 39, lr: 3.20e-04
2022-05-05 17:39:45,300 INFO [train.py:715] (1/8) Epoch 6, batch 34450, loss[loss=0.1425, simple_loss=0.2139, pruned_loss=0.03554, over 4761.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2173, pruned_loss=0.03715, over 972282.84 frames.], batch size: 16, lr: 3.20e-04
2022-05-05 17:40:24,041 INFO [train.py:715] (1/8) Epoch 6, batch 34500, loss[loss=0.1716, simple_loss=0.2378, pruned_loss=0.05272, over 4790.00 frames.], tot_loss[loss=0.146, simple_loss=0.2177, pruned_loss=0.03717, over 973056.98 frames.], batch size: 18, lr: 3.20e-04
2022-05-05 17:41:02,892 INFO [train.py:715] (1/8) Epoch 6, batch 34550, loss[loss=0.1058, simple_loss=0.1799, pruned_loss=0.01581, over 4751.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2186, pruned_loss=0.03798, over 972972.30 frames.], batch size: 12, lr: 3.20e-04
2022-05-05 17:41:41,800 INFO [train.py:715] (1/8) Epoch 6, batch 34600, loss[loss=0.1369, simple_loss=0.2047, pruned_loss=0.03457, over 4881.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2195, pruned_loss=0.03781, over 973451.77 frames.], batch size: 22, lr: 3.20e-04
2022-05-05 17:42:20,617 INFO [train.py:715] (1/8) Epoch 6, batch 34650, loss[loss=0.1136, simple_loss=0.1887, pruned_loss=0.01927, over 4943.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2194, pruned_loss=0.03756, over 972587.51 frames.], batch size: 23, lr: 3.20e-04
2022-05-05 17:42:59,318 INFO [train.py:715] (1/8) Epoch 6, batch 34700, loss[loss=0.1884, simple_loss=0.2466, pruned_loss=0.06507, over 4941.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2192, pruned_loss=0.03772, over 972532.71 frames.], batch size: 35, lr: 3.20e-04
2022-05-05 17:43:37,142 INFO [train.py:715] (1/8) Epoch 6, batch 34750, loss[loss=0.1371, simple_loss=0.2091, pruned_loss=0.03252, over 4795.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03717, over 973113.53 frames.], batch size: 24, lr: 3.20e-04
2022-05-05 17:44:13,985 INFO [train.py:715] (1/8) Epoch 6, batch 34800, loss[loss=0.1325, simple_loss=0.1958, pruned_loss=0.03455, over 4777.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2174, pruned_loss=0.03752, over 972419.07 frames.], batch size: 12, lr: 3.20e-04
2022-05-05 17:45:04,008 INFO [train.py:715] (1/8) Epoch 7, batch 0, loss[loss=0.1595, simple_loss=0.2312, pruned_loss=0.04394, over 4926.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2312, pruned_loss=0.04394, over 4926.00 frames.], batch size: 29, lr: 3.03e-04
2022-05-05 17:45:42,576 INFO [train.py:715] (1/8) Epoch 7, batch 50, loss[loss=0.1466, simple_loss=0.2106, pruned_loss=0.04134, over 4913.00 frames.], tot_loss[loss=0.149, simple_loss=0.2207, pruned_loss=0.03869, over 219329.87 frames.], batch size: 29, lr: 3.03e-04
2022-05-05 17:46:21,356 INFO [train.py:715] (1/8) Epoch 7, batch 100, loss[loss=0.1773, simple_loss=0.2349, pruned_loss=0.05985, over 4816.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2205, pruned_loss=0.03902, over 386527.80 frames.], batch size: 21, lr: 3.03e-04
2022-05-05 17:47:00,258 INFO [train.py:715] (1/8) Epoch 7, batch 150, loss[loss=0.136, simple_loss=0.2151, pruned_loss=0.02841, over 4769.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2193, pruned_loss=0.03906, over 515728.12 frames.], batch size: 19, lr: 3.03e-04
2022-05-05 17:47:39,938 INFO [train.py:715] (1/8) Epoch 7, batch 200, loss[loss=0.1421, simple_loss=0.208, pruned_loss=0.03809, over 4695.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2186, pruned_loss=0.03814, over 617335.19 frames.], batch size: 15, lr: 3.03e-04
2022-05-05 17:48:18,731 INFO [train.py:715] (1/8) Epoch 7, batch 250, loss[loss=0.1403, simple_loss=0.2147, pruned_loss=0.03298, over 4915.00 frames.], tot_loss[loss=0.147, simple_loss=0.218, pruned_loss=0.038, over 696497.85 frames.], batch size: 29, lr: 3.03e-04
2022-05-05 17:48:58,166 INFO [train.py:715] (1/8) Epoch 7, batch 300, loss[loss=0.1438, simple_loss=0.2334, pruned_loss=0.02707, over 4944.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2169, pruned_loss=0.03719, over 757817.48 frames.], batch size: 29, lr: 3.02e-04
2022-05-05 17:49:36,860 INFO [train.py:715] (1/8) Epoch 7, batch 350, loss[loss=0.1202, simple_loss=0.199, pruned_loss=0.0207, over 4829.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2165, pruned_loss=0.03718, over 804620.99 frames.], batch size: 25, lr: 3.02e-04
2022-05-05 17:50:16,225 INFO [train.py:715] (1/8) Epoch 7, batch 400, loss[loss=0.1585, simple_loss=0.2407, pruned_loss=0.03816, over 4848.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2177, pruned_loss=0.03755, over 841296.47 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 17:50:54,887 INFO [train.py:715] (1/8) Epoch 7, batch 450, loss[loss=0.1412, simple_loss=0.2097, pruned_loss=0.03636, over 4812.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2168, pruned_loss=0.0369, over 870538.62 frames.], batch size: 21, lr: 3.02e-04
2022-05-05 17:51:33,738 INFO [train.py:715] (1/8) Epoch 7, batch 500, loss[loss=0.1352, simple_loss=0.2123, pruned_loss=0.02906, over 4772.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.037, over 893513.38 frames.], batch size: 19, lr: 3.02e-04
2022-05-05 17:52:12,472 INFO [train.py:715] (1/8) Epoch 7, batch 550, loss[loss=0.1572, simple_loss=0.2337, pruned_loss=0.04038, over 4789.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2177, pruned_loss=0.03703, over 910087.35 frames.], batch size: 18, lr: 3.02e-04
2022-05-05 17:52:51,635 INFO [train.py:715] (1/8) Epoch 7, batch 600, loss[loss=0.1647, simple_loss=0.2415, pruned_loss=0.04393, over 4883.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2183, pruned_loss=0.0372, over 924475.39 frames.], batch size: 22, lr: 3.02e-04
2022-05-05 17:53:29,950 INFO [train.py:715] (1/8) Epoch 7, batch 650, loss[loss=0.1291, simple_loss=0.1917, pruned_loss=0.03322, over 4798.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2186, pruned_loss=0.03753, over 934720.94 frames.], batch size: 12, lr: 3.02e-04
2022-05-05 17:54:08,328 INFO [train.py:715] (1/8) Epoch 7, batch 700, loss[loss=0.1455, simple_loss=0.2115, pruned_loss=0.03974, over 4789.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2182, pruned_loss=0.03755, over 943293.23 frames.], batch size: 17, lr: 3.02e-04
2022-05-05 17:54:47,594 INFO [train.py:715] (1/8) Epoch 7, batch 750, loss[loss=0.1225, simple_loss=0.1901, pruned_loss=0.02743, over 4787.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2181, pruned_loss=0.03775, over 949942.00 frames.], batch size: 12, lr: 3.02e-04
2022-05-05 17:55:26,302 INFO [train.py:715] (1/8) Epoch 7, batch 800, loss[loss=0.1746, simple_loss=0.2385, pruned_loss=0.05535, over 4970.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2181, pruned_loss=0.03802, over 954953.38 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 17:56:04,984 INFO [train.py:715] (1/8) Epoch 7, batch 850, loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.032, over 4750.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2177, pruned_loss=0.03784, over 959060.40 frames.], batch size: 16, lr: 3.02e-04
2022-05-05 17:56:44,243 INFO [train.py:715] (1/8) Epoch 7, batch 900, loss[loss=0.1227, simple_loss=0.2043, pruned_loss=0.02055, over 4836.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2183, pruned_loss=0.03817, over 961931.23 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 17:57:23,222 INFO [train.py:715] (1/8) Epoch 7, batch 950, loss[loss=0.1668, simple_loss=0.238, pruned_loss=0.04786, over 4850.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2175, pruned_loss=0.03747, over 963874.03 frames.], batch size: 20, lr: 3.02e-04
2022-05-05 17:58:01,724 INFO [train.py:715] (1/8) Epoch 7, batch 1000, loss[loss=0.1475, simple_loss=0.2098, pruned_loss=0.04258, over 4861.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2172, pruned_loss=0.03763, over 966025.48 frames.], batch size: 30, lr: 3.02e-04
2022-05-05 17:58:40,408 INFO [train.py:715] (1/8) Epoch 7, batch 1050, loss[loss=0.1171, simple_loss=0.1873, pruned_loss=0.02345, over 4784.00 frames.], tot_loss[loss=0.146, simple_loss=0.2168, pruned_loss=0.0376, over 966756.51 frames.], batch size: 14, lr: 3.02e-04
2022-05-05 17:59:19,624 INFO [train.py:715] (1/8) Epoch 7, batch 1100, loss[loss=0.142, simple_loss=0.216, pruned_loss=0.03399, over 4763.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2172, pruned_loss=0.03751, over 968326.26 frames.], batch size: 19, lr: 3.02e-04
2022-05-05 17:59:57,785 INFO [train.py:715] (1/8) Epoch 7, batch 1150, loss[loss=0.167, simple_loss=0.2285, pruned_loss=0.05271, over 4831.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2174, pruned_loss=0.03743, over 968726.01 frames.], batch size: 30, lr: 3.02e-04
2022-05-05 18:00:36,963 INFO [train.py:715] (1/8) Epoch 7, batch 1200, loss[loss=0.1375, simple_loss=0.2088, pruned_loss=0.03314, over 4969.00 frames.], tot_loss[loss=0.146, simple_loss=0.2179, pruned_loss=0.03711, over 969976.14 frames.], batch size: 14, lr: 3.02e-04
2022-05-05 18:01:16,053 INFO [train.py:715] (1/8) Epoch 7, batch 1250, loss[loss=0.1605, simple_loss=0.2304, pruned_loss=0.0453, over 4777.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2183, pruned_loss=0.03744, over 970615.32 frames.], batch size: 18, lr: 3.02e-04
2022-05-05 18:01:55,175 INFO [train.py:715] (1/8) Epoch 7, batch 1300, loss[loss=0.1883, simple_loss=0.2642, pruned_loss=0.05618, over 4869.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2184, pruned_loss=0.03744, over 970883.57 frames.], batch size: 20, lr: 3.02e-04
2022-05-05 18:02:33,767 INFO [train.py:715] (1/8) Epoch 7, batch 1350, loss[loss=0.1472, simple_loss=0.2249, pruned_loss=0.03469, over 4814.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03739, over 970557.48 frames.], batch size: 13, lr: 3.02e-04
2022-05-05 18:03:12,552 INFO [train.py:715] (1/8) Epoch 7, batch 1400, loss[loss=0.1327, simple_loss=0.1994, pruned_loss=0.03304, over 4980.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2192, pruned_loss=0.03789, over 970699.12 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 18:03:51,643 INFO [train.py:715] (1/8) Epoch 7, batch 1450, loss[loss=0.2085, simple_loss=0.2753, pruned_loss=0.07086, over 4889.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2195, pruned_loss=0.03812, over 971547.96 frames.], batch size: 16, lr: 3.02e-04
2022-05-05 18:04:29,772 INFO [train.py:715] (1/8) Epoch 7, batch 1500, loss[loss=0.158, simple_loss=0.2209, pruned_loss=0.04755, over 4958.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2197, pruned_loss=0.03769, over 972525.67 frames.], batch size: 35, lr: 3.02e-04
2022-05-05 18:05:08,980 INFO [train.py:715] (1/8) Epoch 7, batch 1550, loss[loss=0.1483, simple_loss=0.2304, pruned_loss=0.03311, over 4968.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2195, pruned_loss=0.03756, over 972897.61 frames.], batch size: 24, lr: 3.02e-04
2022-05-05 18:05:47,791 INFO [train.py:715] (1/8) Epoch 7, batch 1600, loss[loss=0.1519, simple_loss=0.2223, pruned_loss=0.04074, over 4848.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2191, pruned_loss=0.03756, over 973346.47 frames.], batch size: 30, lr: 3.02e-04
2022-05-05 18:06:26,682 INFO [train.py:715] (1/8) Epoch 7, batch 1650, loss[loss=0.1437, simple_loss=0.2108, pruned_loss=0.03834, over 4760.00 frames.], tot_loss[loss=0.1481, simple_loss=0.22, pruned_loss=0.03805, over 972751.83 frames.], batch size: 19, lr: 3.02e-04
2022-05-05 18:07:05,257 INFO [train.py:715] (1/8) Epoch 7, batch 1700, loss[loss=0.1821, simple_loss=0.2473, pruned_loss=0.05843, over 4971.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2199, pruned_loss=0.03839, over 972464.96 frames.], batch size: 21, lr: 3.02e-04
2022-05-05 18:07:44,162 INFO [train.py:715] (1/8) Epoch 7, batch 1750, loss[loss=0.1391, simple_loss=0.209, pruned_loss=0.03457, over 4988.00 frames.], tot_loss[loss=0.148, simple_loss=0.2193, pruned_loss=0.03839, over 971844.57 frames.], batch size: 14, lr: 3.02e-04
2022-05-05 18:08:24,139 INFO [train.py:715] (1/8) Epoch 7, batch 1800, loss[loss=0.1545, simple_loss=0.222, pruned_loss=0.04353, over 4988.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2197, pruned_loss=0.03842, over 971082.89 frames.], batch size: 20, lr: 3.02e-04
2022-05-05 18:09:03,072 INFO [train.py:715] (1/8) Epoch 7, batch 1850, loss[loss=0.1795, simple_loss=0.2404, pruned_loss=0.05927, over 4912.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2197, pruned_loss=0.03832, over 970465.47 frames.], batch size: 17, lr: 3.02e-04
2022-05-05 18:09:41,927 INFO [train.py:715] (1/8) Epoch 7, batch 1900, loss[loss=0.1375, simple_loss=0.2131, pruned_loss=0.03092, over 4970.00 frames.], tot_loss[loss=0.1484, simple_loss=0.22, pruned_loss=0.03843, over 970597.94 frames.], batch size: 25, lr: 3.01e-04
2022-05-05 18:10:20,113 INFO [train.py:715] (1/8) Epoch 7, batch 1950, loss[loss=0.1215, simple_loss=0.1945, pruned_loss=0.02425, over 4838.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2193, pruned_loss=0.03798, over 970742.46 frames.], batch size: 13, lr: 3.01e-04
2022-05-05 18:10:59,290 INFO [train.py:715] (1/8) Epoch 7, batch 2000, loss[loss=0.1957, simple_loss=0.2543, pruned_loss=0.0685, over 4777.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2201, pruned_loss=0.03871, over 970396.39 frames.], batch size: 14, lr: 3.01e-04
2022-05-05 18:11:37,489 INFO [train.py:715] (1/8) Epoch 7, batch 2050, loss[loss=0.1532, simple_loss=0.2218, pruned_loss=0.04226, over 4808.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2189, pruned_loss=0.03795, over 971332.46 frames.], batch size: 12, lr: 3.01e-04
2022-05-05 18:12:16,139 INFO [train.py:715] (1/8) Epoch 7, batch 2100, loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03018, over 4783.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2179, pruned_loss=0.03737, over 970681.22 frames.], batch size: 17, lr: 3.01e-04
2022-05-05 18:12:54,593 INFO [train.py:715] (1/8) Epoch 7, batch 2150, loss[loss=0.1218, simple_loss=0.1897, pruned_loss=0.02692, over 4746.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2184, pruned_loss=0.03714, over 970552.01 frames.], batch size: 19, lr: 3.01e-04
2022-05-05 18:13:32,805 INFO [train.py:715] (1/8) Epoch 7, batch 2200, loss[loss=0.1494, simple_loss=0.2266, pruned_loss=0.03605, over 4902.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2192, pruned_loss=0.03757, over 970416.13 frames.], batch size: 22, lr: 3.01e-04
2022-05-05 18:14:11,047 INFO [train.py:715] (1/8) Epoch 7, batch 2250, loss[loss=0.1608, simple_loss=0.2353, pruned_loss=0.04316, over 4831.00 frames.], tot_loss[loss=0.1472, simple_loss=0.219, pruned_loss=0.03767, over 970595.10 frames.], batch size: 26, lr: 3.01e-04
2022-05-05 18:14:50,048 INFO [train.py:715] (1/8) Epoch 7, batch 2300, loss[loss=0.1397, simple_loss=0.2084, pruned_loss=0.03545, over 4743.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2186, pruned_loss=0.03736, over 970735.29 frames.], batch size: 16, lr: 3.01e-04
2022-05-05 18:15:29,529 INFO [train.py:715] (1/8) Epoch 7, batch 2350, loss[loss=0.1521, simple_loss=0.222, pruned_loss=0.04113, over 4875.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2188, pruned_loss=0.03725, over 971605.01 frames.], batch size: 22, lr: 3.01e-04
2022-05-05 18:16:08,313 INFO [train.py:715] (1/8) Epoch 7, batch 2400, loss[loss=0.1181, simple_loss=0.1958, pruned_loss=0.02014, over 4901.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2183, pruned_loss=0.03707, over 972498.50 frames.], batch size: 19, lr: 3.01e-04
2022-05-05 18:16:46,790 INFO [train.py:715] (1/8) Epoch 7, batch 2450, loss[loss=0.1523, simple_loss=0.2189, pruned_loss=0.04285, over 4923.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2186, pruned_loss=0.03756, over 972594.67 frames.], batch size: 39, lr: 3.01e-04
2022-05-05 18:17:25,560 INFO [train.py:715] (1/8) Epoch 7, batch 2500, loss[loss=0.1265, simple_loss=0.2055, pruned_loss=0.02372, over 4911.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2181, pruned_loss=0.03733, over 972919.71 frames.], batch size: 17, lr: 3.01e-04
2022-05-05 18:18:03,861 INFO [train.py:715] (1/8) Epoch 7, batch 2550, loss[loss=0.1636, simple_loss=0.2203, pruned_loss=0.05342, over 4949.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2182, pruned_loss=0.03715, over 972753.72 frames.], batch size: 21, lr: 3.01e-04
2022-05-05 18:18:42,384 INFO [train.py:715] (1/8) Epoch 7, batch 2600, loss[loss=0.1468, simple_loss=0.2205, pruned_loss=0.03655, over 4929.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2189, pruned_loss=0.03716, over 973061.10 frames.], batch size: 29, lr: 3.01e-04
2022-05-05 18:19:21,121 INFO [train.py:715] (1/8) Epoch 7, batch 2650, loss[loss=0.1781, simple_loss=0.2507, pruned_loss=0.05273, over 4864.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2187, pruned_loss=0.03723, over 972931.92 frames.], batch size: 20, lr: 3.01e-04
2022-05-05 18:19:59,712 INFO [train.py:715] (1/8) Epoch 7, batch 2700, loss[loss=0.1518, simple_loss=0.2275, pruned_loss=0.03811, over 4840.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.03761, over 972728.51 frames.], batch size: 26, lr: 3.01e-04
2022-05-05 18:20:37,586 INFO [train.py:715] (1/8) Epoch 7, batch 2750, loss[loss=0.1236, simple_loss=0.2012, pruned_loss=0.02297, over 4820.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2197, pruned_loss=0.03805, over 971839.95 frames.], batch size: 27, lr: 3.01e-04
2022-05-05 18:21:16,374 INFO [train.py:715] (1/8) Epoch 7, batch 2800, loss[loss=0.1457, simple_loss=0.2263, pruned_loss=0.03256, over 4741.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2199, pruned_loss=0.0381, over 971953.61 frames.], batch size: 16, lr: 3.01e-04
2022-05-05 18:21:55,735 INFO [train.py:715] (1/8) Epoch 7, batch 2850, loss[loss=0.1299, simple_loss=0.1973, pruned_loss=0.03127, over 4813.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2194, pruned_loss=0.03823, over 971976.52 frames.], batch size: 15, lr: 3.01e-04
2022-05-05 18:22:35,312 INFO [train.py:715] (1/8) Epoch 7, batch 2900, loss[loss=0.1493, simple_loss=0.2298, pruned_loss=0.0344, over 4857.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2195, pruned_loss=0.03796, over 972643.18 frames.], batch size: 32, lr: 3.01e-04
2022-05-05 18:23:14,212 INFO [train.py:715] (1/8) Epoch 7, batch 2950, loss[loss=0.1557, simple_loss=0.2226, pruned_loss=0.04443, over 4951.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2191, pruned_loss=0.03782, over 973331.94 frames.], batch size: 21, lr: 3.01e-04
2022-05-05 18:23:53,380 INFO [train.py:715] (1/8) Epoch 7, batch 3000, loss[loss=0.1341, simple_loss=0.2094, pruned_loss=0.02944, over 4824.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2196, pruned_loss=0.03773, over 973295.62 frames.], batch size: 27, lr: 3.01e-04
2022-05-05 18:23:53,380 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 18:24:04,767 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.1084, simple_loss=0.1933, pruned_loss=0.01171, over 914524.00 frames.
2022-05-05 18:24:44,253 INFO [train.py:715] (1/8) Epoch 7, batch 3050, loss[loss=0.1354, simple_loss=0.2075, pruned_loss=0.03163, over 4743.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2202, pruned_loss=0.03829, over 973080.72 frames.], batch size: 19, lr: 3.01e-04
2022-05-05 18:25:23,056 INFO [train.py:715] (1/8) Epoch 7, batch 3100, loss[loss=0.1316, simple_loss=0.2012, pruned_loss=0.03097, over 4924.00 frames.], tot_loss[loss=0.148, simple_loss=0.2197, pruned_loss=0.03813, over 973508.53 frames.], batch size: 18, lr: 3.01e-04
2022-05-05 18:26:01,760 INFO [train.py:715] (1/8) Epoch 7, batch 3150, loss[loss=0.1543, simple_loss=0.2298, pruned_loss=0.03939, over 4814.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2193, pruned_loss=0.03772, over 973437.79 frames.], batch size: 25, lr: 3.01e-04
2022-05-05 18:26:39,664 INFO [train.py:715] (1/8) Epoch 7, batch 3200, loss[loss=0.1257, simple_loss=0.192, pruned_loss=0.02972, over 4826.00 frames.], tot_loss[loss=0.148, simple_loss=0.2196, pruned_loss=0.03816, over 972885.52 frames.], batch size: 15, lr: 3.01e-04
2022-05-05 18:27:17,883 INFO [train.py:715] (1/8) Epoch 7, batch 3250, loss[loss=0.1284, simple_loss=0.1998, pruned_loss=0.02846, over 4752.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2199, pruned_loss=0.03789, over 971839.11 frames.], batch size: 16, lr: 3.01e-04
2022-05-05 18:27:56,438 INFO [train.py:715] (1/8) Epoch 7, batch 3300, loss[loss=0.1978, simple_loss=0.2675, pruned_loss=0.06406, over 4835.00 frames.], tot_loss[loss=0.1473, simple_loss=0.219, pruned_loss=0.0378, over 971628.75 frames.], batch size: 30, lr: 3.01e-04
2022-05-05 18:28:35,033 INFO [train.py:715] (1/8) Epoch 7, batch 3350, loss[loss=0.1429, simple_loss=0.2184, pruned_loss=0.03373, over 4831.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2192, pruned_loss=0.0377, over 971466.56 frames.], batch size: 15, lr: 3.01e-04
2022-05-05 18:29:13,824 INFO [train.py:715] (1/8) Epoch 7, batch 3400, loss[loss=0.1436, simple_loss=0.2188, pruned_loss=0.03417, over 4857.00 frames.], tot_loss[loss=0.1469, simple_loss=0.219, pruned_loss=0.03741, over 971247.75 frames.], batch size: 20, lr: 3.01e-04
2022-05-05 18:29:52,251 INFO [train.py:715] (1/8) Epoch 7, batch 3450, loss[loss=0.1634, simple_loss=0.2445, pruned_loss=0.04111, over 4690.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2195, pruned_loss=0.03792, over 971618.41 frames.], batch size: 15, lr: 3.01e-04
2022-05-05 18:30:31,306 INFO [train.py:715] (1/8) Epoch 7, batch 3500, loss[loss=0.1207, simple_loss=0.1968, pruned_loss=0.02233, over 4941.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.03721, over 972687.51 frames.], batch size: 21, lr: 3.01e-04
2022-05-05 18:31:09,925 INFO [train.py:715] (1/8) Epoch 7, batch 3550, loss[loss=0.1402, simple_loss=0.1981, pruned_loss=0.04112, over 4753.00 frames.], tot_loss[loss=0.145, simple_loss=0.2164, pruned_loss=0.03675, over 972434.90 frames.], batch size: 12, lr: 3.00e-04
2022-05-05 18:31:48,697 INFO [train.py:715] (1/8) Epoch 7, batch 3600, loss[loss=0.1184, simple_loss=0.1997, pruned_loss=0.01853, over 4904.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2164, pruned_loss=0.03658, over 973273.25 frames.], batch size: 17, lr: 3.00e-04
2022-05-05 18:32:27,427 INFO [train.py:715] (1/8) Epoch 7, batch 3650, loss[loss=0.1442, simple_loss=0.2142, pruned_loss=0.03714, over 4790.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2171, pruned_loss=0.03717, over 973424.93 frames.], batch size: 17, lr: 3.00e-04
2022-05-05 18:33:06,462 INFO [train.py:715] (1/8) Epoch 7, batch 3700, loss[loss=0.1633, simple_loss=0.2266, pruned_loss=0.05002, over 4955.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2191, pruned_loss=0.03813, over 972822.83 frames.], batch size: 24, lr: 3.00e-04
2022-05-05 18:33:45,235 INFO [train.py:715] (1/8) Epoch 7, batch 3750, loss[loss=0.1237, simple_loss=0.1979, pruned_loss=0.0248, over 4831.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2185, pruned_loss=0.0379, over 972144.49 frames.], batch size: 13, lr: 3.00e-04
2022-05-05 18:34:23,492 INFO [train.py:715] (1/8) Epoch 7, batch 3800, loss[loss=0.1552, simple_loss=0.2307, pruned_loss=0.03984, over 4756.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2185, pruned_loss=0.03787, over 971960.74 frames.], batch size: 14, lr: 3.00e-04
2022-05-05 18:35:01,656 INFO [train.py:715] (1/8) Epoch 7, batch 3850, loss[loss=0.1531, simple_loss=0.2331, pruned_loss=0.0365, over 4962.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2176, pruned_loss=0.0376, over 971877.97 frames.], batch size: 21, lr: 3.00e-04
2022-05-05 18:35:39,929 INFO [train.py:715] (1/8) Epoch 7, batch 3900, loss[loss=0.1644, simple_loss=0.232, pruned_loss=0.04843, over 4832.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2183, pruned_loss=0.03801, over 971409.23 frames.], batch size: 30, lr: 3.00e-04
2022-05-05 18:36:18,412 INFO [train.py:715] (1/8) Epoch 7, batch 3950, loss[loss=0.1392, simple_loss=0.2192, pruned_loss=0.02966, over 4816.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2179, pruned_loss=0.0377, over 972331.32 frames.], batch size: 27, lr: 3.00e-04
2022-05-05 18:36:57,042 INFO [train.py:715] (1/8) Epoch 7, batch 4000, loss[loss=0.1546, simple_loss=0.2356, pruned_loss=0.0368, over 4702.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.03808, over 971252.85 frames.], batch size: 15, lr: 3.00e-04
2022-05-05 18:37:35,133 INFO [train.py:715] (1/8) Epoch 7, batch 4050, loss[loss=0.1567, simple_loss=0.2298, pruned_loss=0.04183, over 4975.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2187, pruned_loss=0.03802, over 971353.94 frames.], batch size: 24, lr: 3.00e-04
2022-05-05 18:38:14,043 INFO [train.py:715] (1/8) Epoch 7, batch 4100, loss[loss=0.1446, simple_loss=0.2208, pruned_loss=0.03421, over 4740.00 frames.], tot_loss[loss=0.1482, simple_loss=0.22, pruned_loss=0.03822, over 971300.13 frames.], batch size: 16, lr: 3.00e-04
2022-05-05 18:38:52,563 INFO [train.py:715] (1/8) Epoch 7, batch 4150, loss[loss=0.1192, simple_loss=0.199, pruned_loss=0.01974, over 4728.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03824, over 972141.87 frames.], batch size: 16, lr: 3.00e-04
2022-05-05 18:39:31,261 INFO [train.py:715] (1/8) Epoch 7, batch 4200, loss[loss=0.1408, simple_loss=0.2084, pruned_loss=0.03667, over 4771.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2192, pruned_loss=0.03801, over 972289.56 frames.], batch size: 17, lr: 3.00e-04
2022-05-05 18:40:09,115 INFO [train.py:715] (1/8) Epoch 7, batch 4250, loss[loss=0.1262, simple_loss=0.215, pruned_loss=0.01871, over 4824.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2188, pruned_loss=0.03777, over 971583.20 frames.], batch size: 26, lr: 3.00e-04
2022-05-05 18:40:47,960 INFO [train.py:715] (1/8) Epoch 7, batch 4300, loss[loss=0.1302, simple_loss=0.2068, pruned_loss=0.02681, over 4971.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2182, pruned_loss=0.03764, over 971855.54 frames.], batch size: 28, lr: 3.00e-04
2022-05-05 18:41:28,767 INFO [train.py:715] (1/8) Epoch 7, batch 4350, loss[loss=0.1224, simple_loss=0.1888, pruned_loss=0.02798, over 4826.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2185, pruned_loss=0.03755, over 971514.31 frames.], batch size: 30, lr: 3.00e-04
2022-05-05 18:42:07,271 INFO [train.py:715] (1/8) Epoch 7, batch 4400, loss[loss=0.1231, simple_loss=0.1982, pruned_loss=0.02401, over 4782.00 frames.], tot_loss[loss=0.147, simple_loss=0.2184, pruned_loss=0.0378, over 971102.63 frames.], batch size: 14, lr: 3.00e-04
2022-05-05 18:42:46,327 INFO [train.py:715] (1/8) Epoch 7, batch 4450, loss[loss=0.1353, simple_loss=0.2061, pruned_loss=0.03227, over 4929.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2179, pruned_loss=0.03729, over 971008.65 frames.], batch size: 21, lr: 3.00e-04
2022-05-05 18:43:25,203 INFO [train.py:715] (1/8) Epoch 7, batch 4500, loss[loss=0.131, simple_loss=0.2069, pruned_loss=0.02755, over 4777.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2178, pruned_loss=0.03751, over 971190.18 frames.], batch size: 17, lr: 3.00e-04
2022-05-05 18:44:03,954 INFO [train.py:715] (1/8) Epoch 7, batch 4550, loss[loss=0.1768, simple_loss=0.2438, pruned_loss=0.05489, over 4904.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2183, pruned_loss=0.03752, over 971330.43 frames.], batch size: 39, lr: 3.00e-04
2022-05-05 18:44:42,557 INFO [train.py:715] (1/8) Epoch 7, batch 4600, loss[loss=0.152, simple_loss=0.2209, pruned_loss=0.04157, over 4842.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2181, pruned_loss=0.03762, over 971971.62 frames.], batch size: 32, lr: 3.00e-04
2022-05-05 18:45:21,320 INFO [train.py:715] (1/8) Epoch 7, batch 4650, loss[loss=0.1346, simple_loss=0.2129, pruned_loss=0.02816, over 4940.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2169, pruned_loss=0.03699, over 972444.46 frames.], batch size: 21, lr: 3.00e-04
2022-05-05 18:45:59,786 INFO [train.py:715] (1/8) Epoch 7, batch 4700, loss[loss=0.1442, simple_loss=0.2137, pruned_loss=0.0374, over 4807.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2169, pruned_loss=0.03688, over 973434.23 frames.], batch size: 13, lr: 3.00e-04
2022-05-05 18:46:37,975 INFO [train.py:715] (1/8) Epoch 7, batch 4750, loss[loss=0.1131, simple_loss=0.1874, pruned_loss=0.01941, over 4789.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2169, pruned_loss=0.03691, over 972709.83 frames.], batch size: 12, lr: 3.00e-04
2022-05-05 18:47:17,157 INFO [train.py:715] (1/8) Epoch 7, batch 4800, loss[loss=0.1615, simple_loss=0.2363, pruned_loss=0.04334, over 4689.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2171, pruned_loss=0.03717, over 972076.85 frames.], batch size: 15, lr: 3.00e-04
2022-05-05 18:47:55,563 INFO [train.py:715] (1/8) Epoch 7, batch 4850, loss[loss=0.1469, simple_loss=0.2211, pruned_loss=0.03636, over 4794.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2173, pruned_loss=0.03699, over 972436.47 frames.], batch size: 21, lr: 3.00e-04
2022-05-05 18:48:34,305 INFO [train.py:715] (1/8) Epoch 7, batch 4900, loss[loss=0.1683, simple_loss=0.233, pruned_loss=0.05183, over 4910.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2181, pruned_loss=0.03755, over 972437.78 frames.], batch size: 19, lr: 3.00e-04
2022-05-05 18:49:12,734 INFO [train.py:715] (1/8) Epoch 7, batch 4950, loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03265, over 4885.00 frames.], tot_loss[loss=0.1467, simple_loss=0.218, pruned_loss=0.03768, over 973022.00 frames.], batch size: 32, lr: 3.00e-04
2022-05-05 18:49:51,781 INFO [train.py:715] (1/8) Epoch 7, batch 5000, loss[loss=0.1107, simple_loss=0.1765, pruned_loss=0.0225, over 4800.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2182, pruned_loss=0.03736, over 973067.56 frames.], batch size: 12, lr: 3.00e-04
2022-05-05 18:50:30,776 INFO [train.py:715] (1/8) Epoch 7, batch 5050, loss[loss=0.1553, simple_loss=0.2338, pruned_loss=0.03838, over 4906.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2184, pruned_loss=0.03715, over 973013.25 frames.], batch size: 19, lr: 3.00e-04
2022-05-05 18:51:09,363 INFO [train.py:715] (1/8) Epoch 7, batch 5100, loss[loss=0.134, simple_loss=0.2052, pruned_loss=0.03141, over 4928.00 frames.], tot_loss[loss=0.1461, simple_loss=0.218, pruned_loss=0.03709, over 972623.30 frames.], batch size: 29, lr: 3.00e-04
2022-05-05 18:51:48,429 INFO [train.py:715] (1/8) Epoch 7, batch 5150, loss[loss=0.1507, simple_loss=0.2173, pruned_loss=0.04201, over 4870.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2187, pruned_loss=0.03778, over 972683.77 frames.], batch size: 16, lr: 3.00e-04
2022-05-05 18:52:27,138 INFO [train.py:715] (1/8) Epoch 7, batch 5200, loss[loss=0.1352, simple_loss=0.2118, pruned_loss=0.02933, over 4850.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03735, over 972514.88 frames.], batch size: 32, lr: 2.99e-04
2022-05-05 18:53:06,162 INFO [train.py:715] (1/8) Epoch 7, batch 5250, loss[loss=0.1276, simple_loss=0.1947, pruned_loss=0.03024, over 4991.00 frames.], tot_loss[loss=0.147, simple_loss=0.2189, pruned_loss=0.03754, over 971989.76 frames.], batch size: 14, lr: 2.99e-04
2022-05-05 18:53:44,796 INFO [train.py:715] (1/8) Epoch 7, batch 5300, loss[loss=0.1397, simple_loss=0.2163, pruned_loss=0.03158, over 4796.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.03724, over 971838.90 frames.], batch size: 17, lr: 2.99e-04
2022-05-05 18:54:24,160 INFO [train.py:715] (1/8) Epoch 7, batch 5350, loss[loss=0.1433, simple_loss=0.2107, pruned_loss=0.03801, over 4841.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2182, pruned_loss=0.03729, over 972840.55 frames.], batch size: 30, lr: 2.99e-04
2022-05-05 18:55:02,368 INFO [train.py:715] (1/8) Epoch 7, batch 5400, loss[loss=0.1691, simple_loss=0.2555, pruned_loss=0.0413, over 4771.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03706, over 972167.33 frames.], batch size: 18, lr: 2.99e-04
2022-05-05 18:55:41,208 INFO [train.py:715] (1/8) Epoch 7, batch 5450, loss[loss=0.1533, simple_loss=0.228, pruned_loss=0.03932, over 4886.00 frames.], tot_loss[loss=0.147, simple_loss=0.2187, pruned_loss=0.0377, over 972059.40 frames.], batch size: 16, lr: 2.99e-04
2022-05-05 18:56:20,341 INFO [train.py:715] (1/8) Epoch 7, batch 5500, loss[loss=0.1486, simple_loss=0.2235, pruned_loss=0.03684, over 4864.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2174, pruned_loss=0.03717, over 972114.76 frames.], batch size: 32, lr: 2.99e-04
2022-05-05 18:56:59,125 INFO [train.py:715] (1/8) Epoch 7, batch 5550, loss[loss=0.1145, simple_loss=0.1935, pruned_loss=0.01778, over 4764.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2171, pruned_loss=0.03709, over 972370.58 frames.], batch size: 12, lr: 2.99e-04
2022-05-05 18:57:38,240 INFO [train.py:715] (1/8) Epoch 7, batch 5600, loss[loss=0.1621, simple_loss=0.2463, pruned_loss=0.03901, over 4930.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2178, pruned_loss=0.0368, over 972470.02 frames.], batch size: 29, lr: 2.99e-04
2022-05-05 18:58:17,275 INFO [train.py:715] (1/8) Epoch 7, batch 5650, loss[loss=0.1507, simple_loss=0.2254, pruned_loss=0.03804, over 4940.00 frames.], tot_loss[loss=0.147, simple_loss=0.2189, pruned_loss=0.03753, over 973142.27 frames.], batch size: 21, lr: 2.99e-04
2022-05-05 18:58:56,369 INFO [train.py:715] (1/8) Epoch 7, batch 5700, loss[loss=0.1685, simple_loss=0.2468, pruned_loss=0.04514, over 4824.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2193, pruned_loss=0.0377, over 972712.55 frames.], batch size: 26, lr: 2.99e-04
2022-05-05 18:59:34,743 INFO [train.py:715] (1/8) Epoch 7, batch 5750, loss[loss=0.1497, simple_loss=0.2188, pruned_loss=0.04028, over 4908.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2188, pruned_loss=0.03771, over 973475.97 frames.], batch size: 18, lr: 2.99e-04
2022-05-05 19:00:12,900 INFO [train.py:715] (1/8) Epoch 7, batch 5800, loss[loss=0.1312, simple_loss=0.2094, pruned_loss=0.02649, over 4757.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03734, over 973198.94 frames.], batch size: 19, lr: 2.99e-04
2022-05-05 19:00:52,631 INFO [train.py:715] (1/8) Epoch 7, batch 5850, loss[loss=0.1461, simple_loss=0.2146, pruned_loss=0.03882, over 4949.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03699, over 972636.46 frames.], batch size: 23, lr: 2.99e-04
2022-05-05 19:01:30,925 INFO [train.py:715] (1/8) Epoch 7, batch 5900, loss[loss=0.1644, simple_loss=0.2363, pruned_loss=0.04627, over 4876.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2184, pruned_loss=0.03715, over 972655.21 frames.], batch size: 16, lr: 2.99e-04
2022-05-05 19:02:09,960 INFO [train.py:715] (1/8) Epoch 7, batch 5950, loss[loss=0.1371, simple_loss=0.2034, pruned_loss=0.03543, over 4812.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03711, over 973104.65 frames.], batch size: 21, lr: 2.99e-04
2022-05-05 19:02:48,385 INFO [train.py:715] (1/8) Epoch 7, batch 6000, loss[loss=0.1893, simple_loss=0.244, pruned_loss=0.06734, over 4964.00 frames.], tot_loss[loss=0.1461, simple_loss=0.218, pruned_loss=0.03717, over 972684.86 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:02:48,386 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 19:02:58,048 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.1085, simple_loss=0.1933, pruned_loss=0.0119, over 914524.00 frames.
2022-05-05 19:03:36,918 INFO [train.py:715] (1/8) Epoch 7, batch 6050, loss[loss=0.1343, simple_loss=0.2091, pruned_loss=0.02972, over 4751.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2187, pruned_loss=0.03726, over 972582.34 frames.], batch size: 19, lr: 2.99e-04
2022-05-05 19:04:16,085 INFO [train.py:715] (1/8) Epoch 7, batch 6100, loss[loss=0.154, simple_loss=0.216, pruned_loss=0.04596, over 4689.00 frames.], tot_loss[loss=0.1461, simple_loss=0.218, pruned_loss=0.0371, over 972578.41 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:04:55,381 INFO [train.py:715] (1/8) Epoch 7, batch 6150, loss[loss=0.1375, simple_loss=0.2181, pruned_loss=0.02845, over 4932.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2178, pruned_loss=0.03694, over 972202.46 frames.], batch size: 23, lr: 2.99e-04
2022-05-05 19:05:33,830 INFO [train.py:715] (1/8) Epoch 7, batch 6200, loss[loss=0.1633, simple_loss=0.2306, pruned_loss=0.048, over 4977.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2182, pruned_loss=0.0374, over 972446.86 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:06:13,681 INFO [train.py:715] (1/8) Epoch 7, batch 6250, loss[loss=0.1685, simple_loss=0.2418, pruned_loss=0.04764, over 4769.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2174, pruned_loss=0.03715, over 971663.27 frames.], batch size: 19, lr: 2.99e-04
2022-05-05 19:06:52,573 INFO [train.py:715] (1/8) Epoch 7, batch 6300, loss[loss=0.1864, simple_loss=0.2446, pruned_loss=0.06407, over 4692.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2173, pruned_loss=0.03705, over 971482.61 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:07:30,974 INFO [train.py:715] (1/8) Epoch 7, batch 6350, loss[loss=0.1364, simple_loss=0.2117, pruned_loss=0.03059, over 4858.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2176, pruned_loss=0.03738, over 971833.20 frames.], batch size: 20, lr: 2.99e-04
2022-05-05 19:08:10,035 INFO [train.py:715] (1/8) Epoch 7, batch 6400, loss[loss=0.1267, simple_loss=0.2065, pruned_loss=0.02344, over 4908.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2175, pruned_loss=0.03712, over 971717.56 frames.], batch size: 17, lr: 2.99e-04
2022-05-05 19:08:49,048 INFO [train.py:715] (1/8) Epoch 7, batch 6450, loss[loss=0.154, simple_loss=0.2276, pruned_loss=0.04017, over 4870.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2182, pruned_loss=0.03774, over 972671.00 frames.], batch size: 20, lr: 2.99e-04
2022-05-05 19:09:27,585 INFO [train.py:715] (1/8) Epoch 7, batch 6500, loss[loss=0.1469, simple_loss=0.2173, pruned_loss=0.03822, over 4949.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2184, pruned_loss=0.03809, over 971854.75 frames.], batch size: 29, lr: 2.99e-04
2022-05-05 19:10:06,575 INFO [train.py:715] (1/8) Epoch 7, batch 6550, loss[loss=0.1537, simple_loss=0.2196, pruned_loss=0.04392, over 4829.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2187, pruned_loss=0.03858, over 972283.07 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:10:46,396 INFO [train.py:715] (1/8) Epoch 7, batch 6600, loss[loss=0.1595, simple_loss=0.2362, pruned_loss=0.04144, over 4914.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2192, pruned_loss=0.03885, over 973195.82 frames.], batch size: 19, lr: 2.99e-04
2022-05-05 19:11:25,244 INFO [train.py:715] (1/8) Epoch 7, batch 6650, loss[loss=0.137, simple_loss=0.2089, pruned_loss=0.03259, over 4850.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2188, pruned_loss=0.0382, over 973652.79 frames.], batch size: 34, lr: 2.99e-04
2022-05-05 19:12:04,478 INFO [train.py:715] (1/8) Epoch 7, batch 6700, loss[loss=0.1496, simple_loss=0.2194, pruned_loss=0.0399, over 4834.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2178, pruned_loss=0.03774, over 973565.57 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:12:43,222 INFO [train.py:715] (1/8) Epoch 7, batch 6750, loss[loss=0.1528, simple_loss=0.2253, pruned_loss=0.0402, over 4968.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2172, pruned_loss=0.03747, over 973818.09 frames.], batch size: 14, lr: 2.99e-04
2022-05-05 19:13:22,218 INFO [train.py:715] (1/8) Epoch 7, batch 6800, loss[loss=0.1694, simple_loss=0.2224, pruned_loss=0.05818, over 4960.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2177, pruned_loss=0.03776, over 974255.94 frames.], batch size: 14, lr: 2.99e-04
2022-05-05 19:14:00,582 INFO [train.py:715] (1/8) Epoch 7, batch 6850, loss[loss=0.1501, simple_loss=0.2257, pruned_loss=0.03727, over 4922.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2179, pruned_loss=0.03781, over 973415.37 frames.], batch size: 18, lr: 2.99e-04
2022-05-05 19:14:39,180 INFO [train.py:715] (1/8) Epoch 7, batch 6900, loss[loss=0.1539, simple_loss=0.2265, pruned_loss=0.04071, over 4793.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2177, pruned_loss=0.0376, over 973133.72 frames.], batch size: 21, lr: 2.98e-04
2022-05-05 19:15:18,696 INFO [train.py:715] (1/8) Epoch 7, batch 6950, loss[loss=0.1449, simple_loss=0.216, pruned_loss=0.03689, over 4892.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2181, pruned_loss=0.03727, over 973150.72 frames.], batch size: 19, lr: 2.98e-04
2022-05-05 19:15:56,861 INFO [train.py:715] (1/8) Epoch 7, batch 7000, loss[loss=0.1601, simple_loss=0.2313, pruned_loss=0.04443, over 4830.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2183, pruned_loss=0.03748, over 972560.00 frames.], batch size: 15, lr: 2.98e-04
2022-05-05 19:16:35,555 INFO [train.py:715] (1/8) Epoch 7, batch 7050, loss[loss=0.165, simple_loss=0.2419, pruned_loss=0.04405, over 4927.00 frames.], tot_loss[loss=0.1466, simple_loss=0.218, pruned_loss=0.03757, over 971876.01 frames.], batch size: 23, lr: 2.98e-04
2022-05-05 19:17:14,123 INFO [train.py:715] (1/8) Epoch 7, batch 7100, loss[loss=0.1535, simple_loss=0.2013, pruned_loss=0.0529, over 4813.00 frames.], tot_loss[loss=0.147, simple_loss=0.2184, pruned_loss=0.03783, over 972770.46 frames.], batch size: 12, lr: 2.98e-04
2022-05-05 19:17:52,401 INFO [train.py:715] (1/8) Epoch 7, batch 7150, loss[loss=0.1522, simple_loss=0.2196, pruned_loss=0.04238, over 4963.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.03806, over 973237.01 frames.], batch size: 24, lr: 2.98e-04
2022-05-05 19:18:31,021 INFO [train.py:715] (1/8) Epoch 7, batch 7200, loss[loss=0.1477, simple_loss=0.2079, pruned_loss=0.04376, over 4978.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2182, pruned_loss=0.03746, over 972875.45 frames.], batch size: 35, lr: 2.98e-04
2022-05-05 19:19:10,027 INFO [train.py:715] (1/8) Epoch 7, batch 7250, loss[loss=0.147, simple_loss=0.2144, pruned_loss=0.03977, over 4978.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2176, pruned_loss=0.03698, over 973093.63 frames.], batch size: 25, lr: 2.98e-04
2022-05-05 19:19:49,675 INFO [train.py:715] (1/8) Epoch 7, batch 7300, loss[loss=0.1722, simple_loss=0.2453, pruned_loss=0.04952, over 4858.00 frames.], tot_loss[loss=0.146, simple_loss=0.2179, pruned_loss=0.03704, over 973436.81 frames.], batch size: 20, lr: 2.98e-04
2022-05-05 19:20:28,208 INFO [train.py:715] (1/8) Epoch 7, batch 7350, loss[loss=0.1567, simple_loss=0.2256, pruned_loss=0.04388, over 4919.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2178, pruned_loss=0.037, over 973427.68 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:21:06,665 INFO [train.py:715] (1/8) Epoch 7, batch 7400, loss[loss=0.1249, simple_loss=0.2005, pruned_loss=0.02463, over 4924.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2167, pruned_loss=0.03642, over 973014.97 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:21:45,794 INFO [train.py:715] (1/8) Epoch 7, batch 7450, loss[loss=0.15, simple_loss=0.231, pruned_loss=0.0345, over 4878.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2174, pruned_loss=0.03681, over 973253.10 frames.], batch size: 22, lr: 2.98e-04
2022-05-05 19:22:24,001 INFO [train.py:715] (1/8) Epoch 7, batch 7500, loss[loss=0.1323, simple_loss=0.1918, pruned_loss=0.03639, over 4663.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2182, pruned_loss=0.0375, over 973458.64 frames.], batch size: 13, lr: 2.98e-04
2022-05-05 19:23:02,795 INFO [train.py:715] (1/8) Epoch 7, batch 7550, loss[loss=0.1864, simple_loss=0.2689, pruned_loss=0.05196, over 4961.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2172, pruned_loss=0.03699, over 972891.36 frames.], batch size: 24, lr: 2.98e-04
2022-05-05 19:23:41,664 INFO [train.py:715] (1/8) Epoch 7, batch 7600, loss[loss=0.1278, simple_loss=0.194, pruned_loss=0.03086, over 4839.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2179, pruned_loss=0.03726, over 973020.02 frames.], batch size: 26, lr: 2.98e-04
2022-05-05 19:24:20,770 INFO [train.py:715] (1/8) Epoch 7, batch 7650, loss[loss=0.1508, simple_loss=0.2231, pruned_loss=0.03921, over 4835.00 frames.], tot_loss[loss=0.1463, simple_loss=0.218, pruned_loss=0.0373, over 973741.14 frames.], batch size: 15, lr: 2.98e-04
2022-05-05 19:24:59,083 INFO [train.py:715] (1/8) Epoch 7, batch 7700, loss[loss=0.1402, simple_loss=0.2104, pruned_loss=0.03502, over 4989.00 frames.], tot_loss[loss=0.1452, simple_loss=0.217, pruned_loss=0.03671, over 973364.76 frames.], batch size: 14, lr: 2.98e-04
2022-05-05 19:25:38,047 INFO [train.py:715] (1/8) Epoch 7, batch 7750, loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03534, over 4876.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2163, pruned_loss=0.03619, over 973099.50 frames.], batch size: 16, lr: 2.98e-04
2022-05-05 19:26:17,067 INFO [train.py:715] (1/8) Epoch 7, batch 7800, loss[loss=0.1585, simple_loss=0.2303, pruned_loss=0.04334, over 4939.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03661, over 972904.09 frames.], batch size: 29, lr: 2.98e-04
2022-05-05 19:26:55,231 INFO [train.py:715] (1/8) Epoch 7, batch 7850, loss[loss=0.1287, simple_loss=0.2044, pruned_loss=0.02648, over 4804.00 frames.], tot_loss[loss=0.145, simple_loss=0.2166, pruned_loss=0.03666, over 973250.90 frames.], batch size: 21, lr: 2.98e-04
2022-05-05 19:27:34,448 INFO [train.py:715] (1/8) Epoch 7, batch 7900, loss[loss=0.1241, simple_loss=0.1976, pruned_loss=0.02531, over 4962.00 frames.], tot_loss[loss=0.146, simple_loss=0.2177, pruned_loss=0.03711, over 973031.82 frames.], batch size: 14, lr: 2.98e-04
2022-05-05 19:28:13,175 INFO [train.py:715] (1/8) Epoch 7, batch 7950, loss[loss=0.1677, simple_loss=0.2377, pruned_loss=0.04882, over 4846.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2182, pruned_loss=0.03728, over 973408.56 frames.], batch size: 30, lr: 2.98e-04
2022-05-05 19:28:52,650 INFO [train.py:715] (1/8) Epoch 7, batch 8000, loss[loss=0.1416, simple_loss=0.2188, pruned_loss=0.0322, over 4804.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03697, over 974062.57 frames.], batch size: 15, lr: 2.98e-04
2022-05-05 19:29:30,738 INFO [train.py:715] (1/8) Epoch 7, batch 8050, loss[loss=0.1493, simple_loss=0.2217, pruned_loss=0.03847, over 4960.00 frames.], tot_loss[loss=0.1457, simple_loss=0.218, pruned_loss=0.03675, over 973750.28 frames.], batch size: 24, lr: 2.98e-04
2022-05-05 19:30:09,299 INFO [train.py:715] (1/8) Epoch 7, batch 8100, loss[loss=0.1344, simple_loss=0.2016, pruned_loss=0.03362, over 4909.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2178, pruned_loss=0.03695, over 972555.20 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:30:48,381 INFO [train.py:715] (1/8) Epoch 7, batch 8150, loss[loss=0.1359, simple_loss=0.2133, pruned_loss=0.0292, over 4876.00 frames.], tot_loss[loss=0.1449, simple_loss=0.217, pruned_loss=0.03645, over 972153.50 frames.], batch size: 22, lr: 2.98e-04
2022-05-05 19:31:26,681 INFO [train.py:715] (1/8) Epoch 7, batch 8200, loss[loss=0.1489, simple_loss=0.2261, pruned_loss=0.03586, over 4778.00 frames.], tot_loss[loss=0.145, simple_loss=0.2168, pruned_loss=0.03655, over 972497.17 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:32:05,129 INFO [train.py:715] (1/8) Epoch 7, batch 8250, loss[loss=0.1568, simple_loss=0.2153, pruned_loss=0.0492, over 4976.00 frames.], tot_loss[loss=0.1452, simple_loss=0.217, pruned_loss=0.03668, over 971997.25 frames.], batch size: 15, lr: 2.98e-04
2022-05-05 19:32:43,782 INFO [train.py:715] (1/8) Epoch 7, batch 8300, loss[loss=0.1546, simple_loss=0.2207, pruned_loss=0.04423, over 4934.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2181, pruned_loss=0.03738, over 971971.69 frames.], batch size: 23, lr: 2.98e-04
2022-05-05 19:33:22,691 INFO [train.py:715] (1/8) Epoch 7, batch 8350, loss[loss=0.129, simple_loss=0.216, pruned_loss=0.02098, over 4822.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2181, pruned_loss=0.03765, over 971880.95 frames.], batch size: 26, lr: 2.98e-04
2022-05-05 19:34:00,645 INFO [train.py:715] (1/8) Epoch 7, batch 8400, loss[loss=0.1148, simple_loss=0.1983, pruned_loss=0.01564, over 4985.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2181, pruned_loss=0.03729, over 972555.77 frames.], batch size: 28, lr: 2.98e-04
2022-05-05 19:34:39,721 INFO [train.py:715] (1/8) Epoch 7, batch 8450, loss[loss=0.1388, simple_loss=0.221, pruned_loss=0.02833, over 4783.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2185, pruned_loss=0.03746, over 971496.88 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:35:18,880 INFO [train.py:715] (1/8) Epoch 7, batch 8500, loss[loss=0.1777, simple_loss=0.2346, pruned_loss=0.06037, over 4861.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2182, pruned_loss=0.03698, over 972287.73 frames.], batch size: 30, lr: 2.98e-04
2022-05-05 19:35:58,056 INFO [train.py:715] (1/8) Epoch 7, batch 8550, loss[loss=0.1337, simple_loss=0.2207, pruned_loss=0.0234, over 4954.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2186, pruned_loss=0.03706, over 972923.23 frames.], batch size: 24, lr: 2.97e-04
2022-05-05 19:36:36,296 INFO [train.py:715] (1/8) Epoch 7, batch 8600, loss[loss=0.1312, simple_loss=0.2037, pruned_loss=0.02935, over 4963.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2188, pruned_loss=0.03724, over 972612.50 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:37:14,965 INFO [train.py:715] (1/8) Epoch 7, batch 8650, loss[loss=0.1353, simple_loss=0.2027, pruned_loss=0.03397, over 4709.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2178, pruned_loss=0.03688, over 972304.79 frames.], batch size: 15, lr: 2.97e-04
2022-05-05 19:37:54,310 INFO [train.py:715] (1/8) Epoch 7, batch 8700, loss[loss=0.1365, simple_loss=0.2124, pruned_loss=0.03025, over 4764.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03659, over 972406.93 frames.], batch size: 18, lr: 2.97e-04
2022-05-05 19:38:32,518 INFO [train.py:715] (1/8) Epoch 7, batch 8750, loss[loss=0.1762, simple_loss=0.2465, pruned_loss=0.05291, over 4876.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2176, pruned_loss=0.03688, over 972588.70 frames.], batch size: 22, lr: 2.97e-04
2022-05-05 19:39:11,387 INFO [train.py:715] (1/8) Epoch 7, batch 8800, loss[loss=0.1552, simple_loss=0.2294, pruned_loss=0.04047, over 4763.00 frames.], tot_loss[loss=0.1461, simple_loss=0.218, pruned_loss=0.03717, over 972778.67 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:39:50,321 INFO [train.py:715] (1/8) Epoch 7, batch 8850, loss[loss=0.1619, simple_loss=0.2318, pruned_loss=0.04602, over 4807.00 frames.], tot_loss[loss=0.1463, simple_loss=0.218, pruned_loss=0.03734, over 973139.60 frames.], batch size: 25, lr: 2.97e-04
2022-05-05 19:40:30,009 INFO [train.py:715] (1/8) Epoch 7, batch 8900, loss[loss=0.1673, simple_loss=0.2353, pruned_loss=0.04966, over 4894.00 frames.], tot_loss[loss=0.1466, simple_loss=0.218, pruned_loss=0.03756, over 973865.81 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:41:08,239 INFO [train.py:715] (1/8) Epoch 7, batch 8950, loss[loss=0.1667, simple_loss=0.2535, pruned_loss=0.03991, over 4887.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2182, pruned_loss=0.03755, over 973286.73 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:41:46,838 INFO [train.py:715] (1/8) Epoch 7, batch 9000, loss[loss=0.1427, simple_loss=0.2228, pruned_loss=0.03125, over 4820.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2175, pruned_loss=0.03728, over 973436.19 frames.], batch size: 25, lr: 2.97e-04
2022-05-05 19:41:46,838 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 19:41:56,560 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.1085, simple_loss=0.1932, pruned_loss=0.01192, over 914524.00 frames.
2022-05-05 19:42:35,337 INFO [train.py:715] (1/8) Epoch 7, batch 9050, loss[loss=0.128, simple_loss=0.2113, pruned_loss=0.02233, over 4901.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03672, over 972488.86 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:43:15,395 INFO [train.py:715] (1/8) Epoch 7, batch 9100, loss[loss=0.1386, simple_loss=0.2023, pruned_loss=0.03749, over 4967.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2178, pruned_loss=0.03699, over 971740.97 frames.], batch size: 35, lr: 2.97e-04
2022-05-05 19:43:54,073 INFO [train.py:715] (1/8) Epoch 7, batch 9150, loss[loss=0.1711, simple_loss=0.2427, pruned_loss=0.04973, over 4829.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2181, pruned_loss=0.03718, over 970785.17 frames.], batch size: 15, lr: 2.97e-04
2022-05-05 19:44:32,875 INFO [train.py:715] (1/8) Epoch 7, batch 9200, loss[loss=0.1654, simple_loss=0.2238, pruned_loss=0.05349, over 4969.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2182, pruned_loss=0.03725, over 970547.74 frames.], batch size: 35, lr: 2.97e-04
2022-05-05 19:45:12,204 INFO [train.py:715] (1/8) Epoch 7, batch 9250, loss[loss=0.1602, simple_loss=0.221, pruned_loss=0.04973, over 4867.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2182, pruned_loss=0.03747, over 970373.84 frames.], batch size: 32, lr: 2.97e-04
2022-05-05 19:45:51,292 INFO [train.py:715] (1/8) Epoch 7, batch 9300, loss[loss=0.1349, simple_loss=0.198, pruned_loss=0.03591, over 4803.00 frames.], tot_loss[loss=0.1465, simple_loss=0.218, pruned_loss=0.03753, over 970230.13 frames.], batch size: 25, lr: 2.97e-04
2022-05-05 19:46:30,346 INFO [train.py:715] (1/8) Epoch 7, batch 9350, loss[loss=0.1539, simple_loss=0.2338, pruned_loss=0.03699, over 4984.00 frames.], tot_loss[loss=0.1466, simple_loss=0.218, pruned_loss=0.03763, over 972045.18 frames.], batch size: 28, lr: 2.97e-04
2022-05-05 19:47:08,481 INFO [train.py:715] (1/8) Epoch 7, batch 9400, loss[loss=0.1449, simple_loss=0.2215, pruned_loss=0.03418, over 4891.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2186, pruned_loss=0.03799, over 972738.31 frames.], batch size: 22, lr: 2.97e-04
2022-05-05 19:47:48,273 INFO [train.py:715] (1/8) Epoch 7, batch 9450, loss[loss=0.1653, simple_loss=0.2357, pruned_loss=0.04744, over 4926.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2172, pruned_loss=0.03705, over 973860.30 frames.], batch size: 23, lr: 2.97e-04
2022-05-05 19:48:27,274 INFO [train.py:715] (1/8) Epoch 7, batch 9500, loss[loss=0.1397, simple_loss=0.21, pruned_loss=0.03466, over 4890.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2165, pruned_loss=0.03658, over 973628.57 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:49:05,880 INFO [train.py:715] (1/8) Epoch 7, batch 9550, loss[loss=0.1298, simple_loss=0.2005, pruned_loss=0.02954, over 4930.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2171, pruned_loss=0.03704, over 973436.32 frames.], batch size: 23, lr: 2.97e-04
2022-05-05 19:49:44,836 INFO [train.py:715] (1/8) Epoch 7, batch 9600, loss[loss=0.1849, simple_loss=0.2617, pruned_loss=0.05405, over 4915.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2172, pruned_loss=0.03708, over 972767.37 frames.], batch size: 18, lr: 2.97e-04
2022-05-05 19:50:23,439 INFO [train.py:715] (1/8) Epoch 7, batch 9650, loss[loss=0.1322, simple_loss=0.2105, pruned_loss=0.02688, over 4768.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03693, over 972592.31 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:51:02,959 INFO [train.py:715] (1/8) Epoch 7, batch 9700, loss[loss=0.1683, simple_loss=0.2416, pruned_loss=0.04744, over 4917.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03687, over 972440.14 frames.], batch size: 18, lr: 2.97e-04
2022-05-05 19:51:41,578 INFO [train.py:715] (1/8) Epoch 7, batch 9750, loss[loss=0.1661, simple_loss=0.2451, pruned_loss=0.04356, over 4822.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.03723, over 972393.91 frames.], batch size: 27, lr: 2.97e-04
2022-05-05 19:52:20,963 INFO [train.py:715] (1/8) Epoch 7, batch 9800, loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02981, over 4924.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2194, pruned_loss=0.03761, over 972620.07 frames.], batch size: 18, lr: 2.97e-04
2022-05-05 19:52:59,043 INFO [train.py:715] (1/8) Epoch 7, batch 9850, loss[loss=0.1261, simple_loss=0.1984, pruned_loss=0.02694, over 4812.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2188, pruned_loss=0.03721, over 972340.27 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:53:37,273 INFO [train.py:715] (1/8) Epoch 7, batch 9900, loss[loss=0.1313, simple_loss=0.2188, pruned_loss=0.02186, over 4951.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2177, pruned_loss=0.03655, over 972371.59 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:54:16,175 INFO [train.py:715] (1/8) Epoch 7, batch 9950, loss[loss=0.1444, simple_loss=0.2166, pruned_loss=0.03609, over 4802.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2174, pruned_loss=0.03662, over 972811.36 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:54:55,289 INFO [train.py:715] (1/8) Epoch 7, batch 10000, loss[loss=0.1182, simple_loss=0.1942, pruned_loss=0.02113, over 4693.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2184, pruned_loss=0.03739, over 971726.69 frames.], batch size: 15, lr: 2.97e-04
2022-05-05 19:55:33,943 INFO [train.py:715] (1/8) Epoch 7, batch 10050, loss[loss=0.129, simple_loss=0.2038, pruned_loss=0.02706, over 4879.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03707, over 971600.39 frames.], batch size: 16, lr: 2.97e-04
2022-05-05 19:56:12,507 INFO [train.py:715] (1/8) Epoch 7, batch 10100, loss[loss=0.1301, simple_loss=0.2036, pruned_loss=0.02828, over 4690.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2181, pruned_loss=0.03678, over 971638.05 frames.], batch size: 15, lr: 2.97e-04
2022-05-05 19:56:51,794 INFO [train.py:715] (1/8) Epoch 7, batch 10150, loss[loss=0.1406, simple_loss=0.211, pruned_loss=0.03505, over 4805.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2178, pruned_loss=0.0367, over 971699.02 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:57:30,434 INFO [train.py:715] (1/8) Epoch 7, batch 10200, loss[loss=0.1445, simple_loss=0.2137, pruned_loss=0.03766, over 4798.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2174, pruned_loss=0.03662, over 972134.90 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:58:09,060 INFO [train.py:715] (1/8) Epoch 7, batch 10250, loss[loss=0.1859, simple_loss=0.2475, pruned_loss=0.06219, over 4888.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2173, pruned_loss=0.03685, over 973101.84 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 19:58:48,251 INFO [train.py:715] (1/8) Epoch 7, batch 10300, loss[loss=0.1364, simple_loss=0.204, pruned_loss=0.03442, over 4868.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2177, pruned_loss=0.03754, over 972296.69 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 19:59:26,903 INFO [train.py:715] (1/8) Epoch 7, batch 10350, loss[loss=0.1272, simple_loss=0.2027, pruned_loss=0.02583, over 4764.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2184, pruned_loss=0.03807, over 972443.78 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:00:05,914 INFO [train.py:715] (1/8) Epoch 7, batch 10400, loss[loss=0.1322, simple_loss=0.2001, pruned_loss=0.03211, over 4936.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2182, pruned_loss=0.03798, over 973246.19 frames.], batch size: 29, lr: 2.96e-04
2022-05-05 20:00:44,698 INFO [train.py:715] (1/8) Epoch 7, batch 10450, loss[loss=0.1377, simple_loss=0.2041, pruned_loss=0.03565, over 4766.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2174, pruned_loss=0.03736, over 973239.37 frames.], batch size: 19, lr: 2.96e-04
2022-05-05 20:01:24,299 INFO [train.py:715] (1/8) Epoch 7, batch 10500, loss[loss=0.1565, simple_loss=0.2341, pruned_loss=0.03943, over 4759.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03715, over 972648.18 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:02:03,024 INFO [train.py:715] (1/8) Epoch 7, batch 10550, loss[loss=0.1522, simple_loss=0.2296, pruned_loss=0.03737, over 4866.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03647, over 972734.01 frames.], batch size: 22, lr: 2.96e-04
2022-05-05 20:02:41,165 INFO [train.py:715] (1/8) Epoch 7, batch 10600, loss[loss=0.1579, simple_loss=0.2342, pruned_loss=0.04077, over 4896.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03683, over 973063.43 frames.], batch size: 17, lr: 2.96e-04
2022-05-05 20:03:20,356 INFO [train.py:715] (1/8) Epoch 7, batch 10650, loss[loss=0.1249, simple_loss=0.2035, pruned_loss=0.02316, over 4889.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2172, pruned_loss=0.03689, over 973294.73 frames.], batch size: 22, lr: 2.96e-04
2022-05-05 20:03:59,390 INFO [train.py:715] (1/8) Epoch 7, batch 10700, loss[loss=0.1569, simple_loss=0.2247, pruned_loss=0.04451, over 4924.00 frames.], tot_loss[loss=0.145, simple_loss=0.2167, pruned_loss=0.03666, over 973686.73 frames.], batch size: 23, lr: 2.96e-04
2022-05-05 20:04:38,883 INFO [train.py:715] (1/8) Epoch 7, batch 10750, loss[loss=0.1542, simple_loss=0.2341, pruned_loss=0.03719, over 4814.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2163, pruned_loss=0.0366, over 973838.05 frames.], batch size: 26, lr: 2.96e-04
2022-05-05 20:05:17,666 INFO [train.py:715] (1/8) Epoch 7, batch 10800, loss[loss=0.1565, simple_loss=0.2338, pruned_loss=0.03953, over 4905.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2162, pruned_loss=0.0365, over 973444.35 frames.], batch size: 17, lr: 2.96e-04
2022-05-05 20:05:57,417 INFO [train.py:715] (1/8) Epoch 7, batch 10850, loss[loss=0.1246, simple_loss=0.1942, pruned_loss=0.02754, over 4899.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2149, pruned_loss=0.03629, over 973805.76 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:06:35,667 INFO [train.py:715] (1/8) Epoch 7, batch 10900, loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.03811, over 4845.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2149, pruned_loss=0.03585, over 972825.77 frames.], batch size: 32, lr: 2.96e-04
2022-05-05 20:07:14,751 INFO [train.py:715] (1/8) Epoch 7, batch 10950, loss[loss=0.1989, simple_loss=0.2612, pruned_loss=0.06827, over 4903.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2154, pruned_loss=0.03579, over 972874.90 frames.], batch size: 17, lr: 2.96e-04
2022-05-05 20:07:53,908 INFO [train.py:715] (1/8) Epoch 7, batch 11000, loss[loss=0.1393, simple_loss=0.2134, pruned_loss=0.03256, over 4924.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.03616, over 973417.07 frames.], batch size: 29, lr: 2.96e-04
2022-05-05 20:08:32,747 INFO [train.py:715] (1/8) Epoch 7, batch 11050, loss[loss=0.1531, simple_loss=0.2317, pruned_loss=0.03724, over 4717.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2162, pruned_loss=0.03617, over 973592.24 frames.], batch size: 15, lr: 2.96e-04
2022-05-05 20:09:11,472 INFO [train.py:715] (1/8) Epoch 7, batch 11100, loss[loss=0.2104, simple_loss=0.2769, pruned_loss=0.07198, over 4921.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2162, pruned_loss=0.03621, over 972855.92 frames.], batch size: 39, lr: 2.96e-04
2022-05-05 20:09:50,084 INFO [train.py:715] (1/8) Epoch 7, batch 11150, loss[loss=0.1754, simple_loss=0.2529, pruned_loss=0.04893, over 4986.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2173, pruned_loss=0.03687, over 973064.44 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:10:29,711 INFO [train.py:715] (1/8) Epoch 7, batch 11200, loss[loss=0.1552, simple_loss=0.2235, pruned_loss=0.0434, over 4912.00 frames.], tot_loss[loss=0.1464, simple_loss=0.218, pruned_loss=0.03738, over 973333.11 frames.], batch size: 19, lr: 2.96e-04
2022-05-05 20:11:08,078 INFO [train.py:715] (1/8) Epoch 7, batch 11250, loss[loss=0.1399, simple_loss=0.2092, pruned_loss=0.03527, over 4794.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2168, pruned_loss=0.03691, over 972767.08 frames.], batch size: 21, lr: 2.96e-04
2022-05-05 20:11:46,234 INFO [train.py:715] (1/8) Epoch 7, batch 11300, loss[loss=0.1605, simple_loss=0.2299, pruned_loss=0.04549, over 4979.00 frames.], tot_loss[loss=0.1457, simple_loss=0.217, pruned_loss=0.03723, over 973236.60 frames.], batch size: 35, lr: 2.96e-04
2022-05-05 20:12:25,981 INFO [train.py:715] (1/8) Epoch 7, batch 11350, loss[loss=0.11, simple_loss=0.1801, pruned_loss=0.01995, over 4906.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2175, pruned_loss=0.0374, over 972470.93 frames.], batch size: 22, lr: 2.96e-04
2022-05-05 20:13:04,518 INFO [train.py:715] (1/8) Epoch 7, batch 11400, loss[loss=0.1132, simple_loss=0.1901, pruned_loss=0.01816, over 4989.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2177, pruned_loss=0.03779, over 971699.16 frames.], batch size: 15, lr: 2.96e-04
2022-05-05 20:13:43,555 INFO [train.py:715] (1/8) Epoch 7, batch 11450, loss[loss=0.1413, simple_loss=0.2198, pruned_loss=0.03144, over 4876.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2172, pruned_loss=0.0375, over 971662.27 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:14:22,144 INFO [train.py:715] (1/8) Epoch 7, batch 11500, loss[loss=0.136, simple_loss=0.2114, pruned_loss=0.03024, over 4823.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2171, pruned_loss=0.03736, over 971355.72 frames.], batch size: 26, lr: 2.96e-04
2022-05-05 20:15:01,731 INFO [train.py:715] (1/8) Epoch 7, batch 11550, loss[loss=0.1554, simple_loss=0.2317, pruned_loss=0.03952, over 4897.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2168, pruned_loss=0.03705, over 971822.13 frames.], batch size: 19, lr: 2.96e-04
2022-05-05 20:15:40,000 INFO [train.py:715] (1/8) Epoch 7, batch 11600, loss[loss=0.1515, simple_loss=0.2168, pruned_loss=0.04309, over 4966.00 frames.], tot_loss[loss=0.144, simple_loss=0.2158, pruned_loss=0.03612, over 971749.45 frames.], batch size: 15, lr: 2.96e-04
2022-05-05 20:16:18,811 INFO [train.py:715] (1/8) Epoch 7, batch 11650, loss[loss=0.1354, simple_loss=0.2102, pruned_loss=0.03027, over 4916.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03613, over 971795.91 frames.], batch size: 39, lr: 2.96e-04
2022-05-05 20:16:58,204 INFO [train.py:715] (1/8) Epoch 7, batch 11700, loss[loss=0.1377, simple_loss=0.2149, pruned_loss=0.03028, over 4817.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2177, pruned_loss=0.03677, over 971616.29 frames.], batch size: 26, lr: 2.96e-04
2022-05-05 20:17:36,281 INFO [train.py:715] (1/8) Epoch 7, batch 11750, loss[loss=0.1449, simple_loss=0.2207, pruned_loss=0.03458, over 4961.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03671, over 971829.89 frames.], batch size: 24, lr: 2.96e-04
2022-05-05 20:18:15,078 INFO [train.py:715] (1/8) Epoch 7, batch 11800, loss[loss=0.1247, simple_loss=0.1988, pruned_loss=0.02528, over 4939.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2172, pruned_loss=0.03697, over 972446.42 frames.], batch size: 24, lr: 2.96e-04
2022-05-05 20:18:54,269 INFO [train.py:715] (1/8) Epoch 7, batch 11850, loss[loss=0.1362, simple_loss=0.2168, pruned_loss=0.02776, over 4975.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2178, pruned_loss=0.03732, over 972107.00 frames.], batch size: 14, lr: 2.96e-04
2022-05-05 20:19:32,630 INFO [train.py:715] (1/8) Epoch 7, batch 11900, loss[loss=0.1339, simple_loss=0.2012, pruned_loss=0.03325, over 4764.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03684, over 972512.62 frames.], batch size: 14, lr: 2.96e-04
2022-05-05 20:20:11,923 INFO [train.py:715] (1/8) Epoch 7, batch 11950, loss[loss=0.1307, simple_loss=0.1996, pruned_loss=0.03095, over 4894.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03676, over 972415.16 frames.], batch size: 32, lr: 2.96e-04
2022-05-05 20:20:50,617 INFO [train.py:715] (1/8) Epoch 7, batch 12000, loss[loss=0.1534, simple_loss=0.2091, pruned_loss=0.04882, over 4654.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2181, pruned_loss=0.03719, over 972192.73 frames.], batch size: 13, lr: 2.95e-04
2022-05-05 20:20:50,617 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 20:21:00,228 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.108, simple_loss=0.193, pruned_loss=0.01154, over 914524.00 frames.
2022-05-05 20:21:38,896 INFO [train.py:715] (1/8) Epoch 7, batch 12050, loss[loss=0.1489, simple_loss=0.2199, pruned_loss=0.03894, over 4801.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2166, pruned_loss=0.03636, over 971134.74 frames.], batch size: 21, lr: 2.95e-04
2022-05-05 20:22:18,264 INFO [train.py:715] (1/8) Epoch 7, batch 12100, loss[loss=0.1404, simple_loss=0.2151, pruned_loss=0.03288, over 4753.00 frames.], tot_loss[loss=0.1459, simple_loss=0.218, pruned_loss=0.0369, over 971900.59 frames.], batch size: 19, lr: 2.95e-04
2022-05-05 20:22:56,857 INFO [train.py:715] (1/8) Epoch 7, batch 12150, loss[loss=0.1423, simple_loss=0.2164, pruned_loss=0.03412, over 4939.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2188, pruned_loss=0.03735, over 971931.95 frames.], batch size: 21, lr: 2.95e-04
2022-05-05 20:23:35,619 INFO [train.py:715] (1/8) Epoch 7, batch 12200, loss[loss=0.1688, simple_loss=0.233, pruned_loss=0.05225, over 4826.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2185, pruned_loss=0.03752, over 971733.67 frames.], batch size: 13, lr: 2.95e-04
2022-05-05 20:24:14,744 INFO [train.py:715] (1/8) Epoch 7, batch 12250, loss[loss=0.1395, simple_loss=0.2101, pruned_loss=0.03448, over 4886.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2186, pruned_loss=0.03785, over 971717.34 frames.], batch size: 22, lr: 2.95e-04
2022-05-05 20:24:53,362 INFO [train.py:715] (1/8) Epoch 7, batch 12300, loss[loss=0.1376, simple_loss=0.2007, pruned_loss=0.03726, over 4876.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.03765, over 971906.04 frames.], batch size: 32, lr: 2.95e-04
2022-05-05 20:25:35,090 INFO [train.py:715] (1/8) Epoch 7, batch 12350, loss[loss=0.1162, simple_loss=0.1831, pruned_loss=0.02464, over 4809.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2178, pruned_loss=0.03737, over 971394.05 frames.], batch size: 13, lr: 2.95e-04
2022-05-05 20:26:13,789 INFO [train.py:715] (1/8) Epoch 7, batch 12400, loss[loss=0.1169, simple_loss=0.196, pruned_loss=0.01884, over 4926.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.03726, over 971501.22 frames.], batch size: 23, lr: 2.95e-04
2022-05-05 20:26:53,004 INFO [train.py:715] (1/8) Epoch 7, batch 12450, loss[loss=0.1197, simple_loss=0.1868, pruned_loss=0.02629, over 4944.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2176, pruned_loss=0.03696, over 971989.78 frames.], batch size: 21, lr: 2.95e-04
2022-05-05 20:27:31,403 INFO [train.py:715] (1/8) Epoch 7, batch 12500, loss[loss=0.1309, simple_loss=0.1928, pruned_loss=0.0345, over 4815.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2178, pruned_loss=0.03729, over 972868.86 frames.], batch size: 26, lr: 2.95e-04
2022-05-05 20:28:10,101 INFO [train.py:715] (1/8) Epoch 7, batch 12550, loss[loss=0.1277, simple_loss=0.2004, pruned_loss=0.02755, over 4801.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2185, pruned_loss=0.03798, over 973124.94 frames.], batch size: 25, lr: 2.95e-04
2022-05-05 20:28:49,195 INFO [train.py:715] (1/8) Epoch 7, batch 12600, loss[loss=0.1467, simple_loss=0.2188, pruned_loss=0.03736, over 4967.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2183, pruned_loss=0.03762, over 972769.93 frames.], batch size: 15, lr: 2.95e-04
2022-05-05 20:29:27,377 INFO [train.py:715] (1/8) Epoch 7, batch 12650, loss[loss=0.1558, simple_loss=0.2254, pruned_loss=0.04306, over 4891.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2195, pruned_loss=0.03812, over 972567.04 frames.], batch size: 22, lr: 2.95e-04
2022-05-05 20:30:06,577 INFO [train.py:715] (1/8) Epoch 7, batch 12700, loss[loss=0.1284, simple_loss=0.2049, pruned_loss=0.0259, over 4772.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2182, pruned_loss=0.03763, over 971561.16 frames.], batch size: 18, lr: 2.95e-04
2022-05-05 20:30:44,742 INFO [train.py:715] (1/8) Epoch 7, batch 12750, loss[loss=0.1881, simple_loss=0.2576, pruned_loss=0.05932, over 4919.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2183, pruned_loss=0.03754, over 971815.03 frames.], batch size: 23, lr: 2.95e-04
2022-05-05 20:31:23,969 INFO [train.py:715] (1/8) Epoch 7, batch 12800, loss[loss=0.1302, simple_loss=0.1953, pruned_loss=0.03259, over 4816.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03685, over 972190.75 frames.], batch size: 25, lr: 2.95e-04
2022-05-05 20:32:02,918 INFO [train.py:715] (1/8) Epoch 7, batch 12850, loss[loss=0.1052, simple_loss=0.1704, pruned_loss=0.02002, over 4769.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2173, pruned_loss=0.037, over 972635.58 frames.], batch size: 12, lr: 2.95e-04
2022-05-05 20:32:41,510 INFO [train.py:715] (1/8) Epoch 7, batch 12900, loss[loss=0.145, simple_loss=0.2158, pruned_loss=0.03711, over 4872.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2175, pruned_loss=0.03713, over 971546.80 frames.], batch size: 32, lr: 2.95e-04
2022-05-05 20:33:20,986 INFO [train.py:715] (1/8) Epoch 7, batch 12950, loss[loss=0.1553, simple_loss=0.2314, pruned_loss=0.03958, over 4955.00 frames.], tot_loss[loss=0.146, simple_loss=0.2181, pruned_loss=0.03697, over 971699.34 frames.], batch size: 15, lr: 2.95e-04
2022-05-05 20:33:59,929 INFO [train.py:715] (1/8) Epoch 7, batch 13000, loss[loss=0.1292, simple_loss=0.2057, pruned_loss=0.02636, over 4937.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2176, pruned_loss=0.03655, over 972104.74 frames.], batch size: 23, lr: 2.95e-04
2022-05-05 20:34:38,879 INFO [train.py:715] (1/8) Epoch 7, batch 13050, loss[loss=0.1228, simple_loss=0.2002, pruned_loss=0.02265, over 4823.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2177, pruned_loss=0.03675, over 971996.75 frames.], batch size: 27, lr: 2.95e-04
2022-05-05 20:35:17,658 INFO [train.py:715] (1/8) Epoch 7, batch 13100, loss[loss=0.1537, simple_loss=0.2336, pruned_loss=0.03696, over 4753.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2176, pruned_loss=0.03691, over 972157.08 frames.], batch size: 16, lr: 2.95e-04
2022-05-05 20:35:57,328 INFO [train.py:715] (1/8) Epoch 7, batch 13150, loss[loss=0.1332, simple_loss=0.221, pruned_loss=0.02269, over 4798.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.0371, over 971889.85 frames.], batch size: 18, lr: 2.95e-04
2022-05-05 20:36:35,854 INFO [train.py:715] (1/8) Epoch 7, batch 13200, loss[loss=0.1414, simple_loss=0.2198, pruned_loss=0.03149, over 4724.00 frames.], tot_loss[loss=0.1469, simple_loss=0.219, pruned_loss=0.03738, over 971872.13 frames.], batch size: 16, lr: 2.95e-04
2022-05-05 20:37:15,502 INFO [train.py:715] (1/8) Epoch 7, batch 13250, loss[loss=0.153, simple_loss=0.2256, pruned_loss=0.04014, over 4987.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2182, pruned_loss=0.03705, over 971494.87 frames.], batch size: 25, lr: 2.95e-04
2022-05-05 20:37:54,872 INFO [train.py:715] (1/8) Epoch 7, batch 13300, loss[loss=0.1695, simple_loss=0.2424, pruned_loss=0.04825, over 4786.00 frames.], tot_loss[loss=0.1459, simple_loss=0.218, pruned_loss=0.0369, over 971749.11 frames.], batch size: 14, lr: 2.95e-04
2022-05-05 20:38:33,816 INFO [train.py:715] (1/8) Epoch 7, batch 13350, loss[loss=0.1451, simple_loss=0.2083, pruned_loss=0.04093, over 4944.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2183, pruned_loss=0.03706, over 973258.29 frames.], batch size: 35, lr: 2.95e-04
2022-05-05 20:39:12,815 INFO [train.py:715] (1/8) Epoch 7, batch 13400, loss[loss=0.123, simple_loss=0.1955, pruned_loss=0.02525, over 4975.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.03726, over 973404.47 frames.], batch size: 14, lr: 2.95e-04
2022-05-05 20:39:51,472 INFO [train.py:715] (1/8) Epoch 7, batch 13450, loss[loss=0.1258, simple_loss=0.1897, pruned_loss=0.03091, over 4802.00 frames.], tot_loss[loss=0.146, simple_loss=0.2178, pruned_loss=0.03709, over 973023.12 frames.], batch size: 12, lr: 2.95e-04
2022-05-05 20:40:30,905 INFO [train.py:715] (1/8) Epoch 7, batch 13500, loss[loss=0.149, simple_loss=0.2364, pruned_loss=0.03074, over 4911.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2177, pruned_loss=0.03721, over 973183.96 frames.], batch size: 19, lr: 2.95e-04
2022-05-05 20:41:09,550 INFO [train.py:715] (1/8) Epoch 7, batch 13550, loss[loss=0.1512, simple_loss=0.232, pruned_loss=0.03526, over 4796.00 frames.], tot_loss[loss=0.1463, simple_loss=0.218, pruned_loss=0.03734, over 973321.21 frames.], batch size: 14, lr: 2.95e-04
2022-05-05 20:41:48,026 INFO [train.py:715] (1/8) Epoch 7, batch 13600, loss[loss=0.1464, simple_loss=0.2189, pruned_loss=0.03697, over 4971.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2186, pruned_loss=0.03741, over 973555.83 frames.], batch size: 15, lr: 2.95e-04
2022-05-05 20:42:26,960 INFO [train.py:715] (1/8) Epoch 7, batch 13650, loss[loss=0.128, simple_loss=0.2076, pruned_loss=0.0242, over 4811.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2191, pruned_loss=0.03783, over 972653.41 frames.], batch size: 21, lr: 2.95e-04
2022-05-05 20:43:05,967 INFO [train.py:715] (1/8) Epoch 7, batch 13700, loss[loss=0.1394, simple_loss=0.2089, pruned_loss=0.03493, over 4798.00 frames.], tot_loss[loss=0.147, simple_loss=0.2184, pruned_loss=0.03776, over 972478.90 frames.], batch size: 24, lr: 2.95e-04
2022-05-05 20:43:44,945 INFO [train.py:715] (1/8) Epoch 7, batch 13750, loss[loss=0.1608, simple_loss=0.2195, pruned_loss=0.05105, over 4988.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2175, pruned_loss=0.03703, over 972847.18 frames.], batch size: 26, lr: 2.94e-04
2022-05-05 20:44:23,924 INFO [train.py:715] (1/8) Epoch 7, batch 13800, loss[loss=0.1713, simple_loss=0.2464, pruned_loss=0.04812, over 4905.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2172, pruned_loss=0.03715, over 973180.86 frames.], batch size: 19, lr: 2.94e-04
2022-05-05 20:45:03,236 INFO [train.py:715] (1/8) Epoch 7, batch 13850, loss[loss=0.1671, simple_loss=0.2438, pruned_loss=0.04516, over 4850.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2177, pruned_loss=0.03726, over 972255.23 frames.], batch size: 22, lr: 2.94e-04
2022-05-05 20:45:41,502 INFO [train.py:715] (1/8) Epoch 7, batch 13900, loss[loss=0.1497, simple_loss=0.2251, pruned_loss=0.03714, over 4871.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2182, pruned_loss=0.03769, over 972222.22 frames.], batch size: 20, lr: 2.94e-04
2022-05-05 20:46:20,523 INFO [train.py:715] (1/8) Epoch 7, batch 13950, loss[loss=0.2013, simple_loss=0.2622, pruned_loss=0.0702, over 4854.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2192, pruned_loss=0.03873, over 972178.05 frames.], batch size: 38, lr: 2.94e-04
2022-05-05 20:46:59,564 INFO [train.py:715] (1/8) Epoch 7, batch 14000, loss[loss=0.1558, simple_loss=0.2247, pruned_loss=0.04346, over 4868.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2192, pruned_loss=0.03818, over 972454.66 frames.], batch size: 16, lr: 2.94e-04
2022-05-05 20:47:38,925 INFO [train.py:715] (1/8) Epoch 7, batch 14050, loss[loss=0.1587, simple_loss=0.2291, pruned_loss=0.04416, over 4761.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2188, pruned_loss=0.03793, over 971866.39 frames.], batch size: 14, lr: 2.94e-04
2022-05-05 20:48:18,054 INFO [train.py:715] (1/8) Epoch 7, batch 14100, loss[loss=0.1453, simple_loss=0.2141, pruned_loss=0.03823, over 4868.00 frames.], tot_loss[loss=0.146, simple_loss=0.218, pruned_loss=0.037, over 972318.36 frames.], batch size: 32, lr: 2.94e-04
2022-05-05 20:48:56,863 INFO [train.py:715] (1/8) Epoch 7, batch 14150, loss[loss=0.15, simple_loss=0.2224, pruned_loss=0.03875, over 4891.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03698, over 972708.92 frames.], batch size: 22, lr: 2.94e-04
2022-05-05 20:49:36,153 INFO [train.py:715] (1/8) Epoch 7, batch 14200, loss[loss=0.141, simple_loss=0.2109, pruned_loss=0.03554, over 4940.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2173, pruned_loss=0.03685, over 972254.93 frames.], batch size: 21, lr: 2.94e-04
2022-05-05 20:50:14,410 INFO [train.py:715] (1/8) Epoch 7, batch 14250, loss[loss=0.1487, simple_loss=0.2248, pruned_loss=0.03625, over 4787.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2174, pruned_loss=0.03652, over 972571.19 frames.], batch size: 14, lr: 2.94e-04
2022-05-05 20:50:53,731 INFO [train.py:715] (1/8) Epoch 7, batch 14300, loss[loss=0.1517, simple_loss=0.2149, pruned_loss=0.04427, over 4698.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2178, pruned_loss=0.03672, over 973265.25 frames.], batch size: 15, lr: 2.94e-04
2022-05-05 20:51:33,016 INFO [train.py:715] (1/8) Epoch 7, batch 14350, loss[loss=0.1783, simple_loss=0.2455, pruned_loss=0.05549, over 4976.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2171, pruned_loss=0.03689, over 973644.56 frames.], batch size: 39, lr: 2.94e-04
2022-05-05 20:52:12,045 INFO [train.py:715] (1/8) Epoch 7, batch 14400, loss[loss=0.1128, simple_loss=0.1868, pruned_loss=0.01936, over 4930.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.03677, over 972829.15 frames.], batch size: 18, lr: 2.94e-04
2022-05-05 20:52:50,742 INFO [train.py:715] (1/8) Epoch 7, batch 14450, loss[loss=0.1255, simple_loss=0.1961, pruned_loss=0.02748, over 4875.00 frames.], tot_loss[loss=0.1455, simple_loss=0.217, pruned_loss=0.03703, over 972166.13 frames.], batch size: 22, lr: 2.94e-04
2022-05-05 20:53:29,526 INFO [train.py:715] (1/8) Epoch 7, batch 14500, loss[loss=0.1733, simple_loss=0.2367, pruned_loss=0.05499, over 4900.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2166, pruned_loss=0.03676, over 972182.47 frames.], batch size: 39, lr: 2.94e-04
2022-05-05 20:54:09,120 INFO [train.py:715] (1/8) Epoch 7, batch 14550, loss[loss=0.145, simple_loss=0.2075, pruned_loss=0.04122, over 4767.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03608, over 971945.30 frames.], batch size: 14, lr: 2.94e-04
2022-05-05 20:54:47,912 INFO [train.py:715] (1/8) Epoch 7, batch 14600, loss[loss=0.1328, simple_loss=0.2, pruned_loss=0.03279, over 4847.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2165, pruned_loss=0.03601, over 971730.88 frames.], batch size: 15, lr: 2.94e-04
2022-05-05 20:55:26,850 INFO [train.py:715] (1/8) Epoch 7, batch 14650, loss[loss=0.1748, simple_loss=0.2464, pruned_loss=0.05164, over 4849.00 frames.], tot_loss[loss=0.144, simple_loss=0.2158, pruned_loss=0.03611, over 971749.36 frames.], batch size: 20, lr: 2.94e-04
2022-05-05 20:56:05,814 INFO [train.py:715] (1/8) Epoch 7, batch 14700, loss[loss=0.121, simple_loss=0.1983, pruned_loss=0.02188, over 4940.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2157, pruned_loss=0.03655, over 972343.08 frames.], batch size: 29, lr: 2.94e-04
2022-05-05 20:56:44,945 INFO [train.py:715] (1/8) Epoch 7, batch 14750, loss[loss=0.1377, simple_loss=0.2199, pruned_loss=0.02778, over 4957.00 frames.], tot_loss[loss=0.145, simple_loss=0.2165, pruned_loss=0.03678, over 972118.58 frames.], batch size: 21, lr: 2.94e-04
2022-05-05 20:57:23,495 INFO [train.py:715] (1/8) Epoch 7, batch 14800, loss[loss=0.1574, simple_loss=0.2238, pruned_loss=0.04556, over 4899.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2167, pruned_loss=0.0367, over 973063.92 frames.], batch size: 19, lr: 2.94e-04
2022-05-05 20:58:03,002 INFO [train.py:715] (1/8) Epoch 7, batch 14850, loss[loss=0.172, simple_loss=0.2494, pruned_loss=0.04731, over 4944.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2175, pruned_loss=0.03717, over 971959.66 frames.], batch size: 21, lr: 2.94e-04
2022-05-05 20:58:41,954 INFO [train.py:715] (1/8) Epoch 7, batch 14900, loss[loss=0.1531, simple_loss=0.2184, pruned_loss=0.04389, over 4904.00 frames.], tot_loss[loss=0.147, simple_loss=0.2184, pruned_loss=0.03775, over 972021.00 frames.], batch size: 18, lr: 2.94e-04
2022-05-05 20:59:20,316 INFO [train.py:715] (1/8) Epoch 7, batch 14950, loss[loss=0.156, simple_loss=0.2219, pruned_loss=0.0451, over 4740.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2181, pruned_loss=0.03778, over 971936.02 frames.], batch size: 16, lr: 2.94e-04
2022-05-05 20:59:59,928 INFO [train.py:715] (1/8) Epoch 7, batch 15000, loss[loss=0.1736, simple_loss=0.2465, pruned_loss=0.05037, over 4759.00 frames.], tot_loss[loss=0.1467, simple_loss=0.218, pruned_loss=0.0377, over 971442.16 frames.], batch size: 19, lr: 2.94e-04
2022-05-05 20:59:59,929 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 21:00:14,355 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.1083, simple_loss=0.1931, pruned_loss=0.01175, over 914524.00 frames.
2022-05-05 21:00:53,499 INFO [train.py:715] (1/8) Epoch 7, batch 15050, loss[loss=0.1467, simple_loss=0.2124, pruned_loss=0.04053, over 4856.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2178, pruned_loss=0.03733, over 971740.24 frames.], batch size: 30, lr: 2.94e-04
2022-05-05 21:01:32,730 INFO [train.py:715] (1/8) Epoch 7, batch 15100, loss[loss=0.1232, simple_loss=0.199, pruned_loss=0.02368, over 4942.00 frames.], tot_loss[loss=0.147, simple_loss=0.2186, pruned_loss=0.03769, over 972521.79 frames.], batch size: 35, lr: 2.94e-04
2022-05-05 21:02:11,970 INFO [train.py:715] (1/8) Epoch 7, batch 15150, loss[loss=0.1422, simple_loss=0.2198, pruned_loss=0.03226, over 4947.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2188, pruned_loss=0.03778, over 973283.77 frames.], batch size: 24, lr: 2.94e-04
2022-05-05 21:02:50,725 INFO [train.py:715] (1/8) Epoch 7, batch 15200, loss[loss=0.1453, simple_loss=0.2239, pruned_loss=0.03335, over 4919.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2187, pruned_loss=0.03757, over 973071.79 frames.], batch size: 23, lr: 2.94e-04
2022-05-05 21:03:30,199 INFO [train.py:715] (1/8) Epoch 7, batch 15250, loss[loss=0.1495, simple_loss=0.2267, pruned_loss=0.03613, over 4972.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2183, pruned_loss=0.0376, over 972901.88 frames.], batch size: 35, lr: 2.94e-04
2022-05-05 21:04:09,393 INFO [train.py:715] (1/8) Epoch 7, batch 15300, loss[loss=0.1289, simple_loss=0.2101, pruned_loss=0.02385, over 4958.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2178, pruned_loss=0.03726, over 971982.35 frames.], batch size: 24, lr: 2.94e-04
2022-05-05 21:04:48,397 INFO [train.py:715] (1/8) Epoch 7, batch 15350, loss[loss=0.2043, simple_loss=0.2871, pruned_loss=0.06079, over 4907.00 frames.], tot_loss[loss=0.146, simple_loss=0.2176, pruned_loss=0.03715, over 971692.52 frames.], batch size: 17, lr: 2.94e-04
2022-05-05 21:05:27,507 INFO [train.py:715] (1/8) Epoch 7, batch 15400, loss[loss=0.1168, simple_loss=0.1929, pruned_loss=0.02039, over 4929.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2185, pruned_loss=0.03789, over 972020.02 frames.], batch size: 29, lr: 2.94e-04
2022-05-05 21:06:05,998 INFO [train.py:715] (1/8) Epoch 7, batch 15450, loss[loss=0.1708, simple_loss=0.2507, pruned_loss=0.04543, over 4993.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2181, pruned_loss=0.03761, over 972433.30 frames.], batch size: 14, lr: 2.94e-04
2022-05-05 21:06:45,045 INFO [train.py:715] (1/8) Epoch 7, batch 15500, loss[loss=0.1238, simple_loss=0.2001, pruned_loss=0.02379, over 4799.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2193, pruned_loss=0.03787, over 972534.04 frames.], batch size: 24, lr: 2.93e-04
2022-05-05 21:07:23,168 INFO [train.py:715] (1/8) Epoch 7, batch 15550, loss[loss=0.1529, simple_loss=0.2367, pruned_loss=0.03454, over 4848.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2189, pruned_loss=0.03777, over 973231.20 frames.], batch size: 30, lr: 2.93e-04
2022-05-05 21:08:02,585 INFO [train.py:715] (1/8) Epoch 7, batch 15600, loss[loss=0.1267, simple_loss=0.1992, pruned_loss=0.02704, over 4792.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2193, pruned_loss=0.0379, over 972779.62 frames.], batch size: 21, lr: 2.93e-04
2022-05-05 21:08:42,088 INFO [train.py:715] (1/8) Epoch 7, batch 15650, loss[loss=0.1407, simple_loss=0.2168, pruned_loss=0.03229, over 4972.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03709, over 972599.52 frames.], batch size: 39, lr: 2.93e-04
2022-05-05 21:09:20,365 INFO [train.py:715] (1/8) Epoch 7, batch 15700, loss[loss=0.1614, simple_loss=0.2281, pruned_loss=0.04734, over 4831.00 frames.], tot_loss[loss=0.147, simple_loss=0.2191, pruned_loss=0.03744, over 972738.64 frames.], batch size: 15, lr: 2.93e-04
2022-05-05 21:09:59,354 INFO [train.py:715] (1/8) Epoch 7, batch 15750, loss[loss=0.1417, simple_loss=0.2097, pruned_loss=0.03686, over 4883.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2191, pruned_loss=0.03764, over 971862.42 frames.], batch size: 16, lr: 2.93e-04
2022-05-05 21:10:39,040 INFO [train.py:715] (1/8) Epoch 7, batch 15800, loss[loss=0.1498, simple_loss=0.217, pruned_loss=0.04124, over 4977.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2178, pruned_loss=0.03688, over 971630.07 frames.], batch size: 25, lr: 2.93e-04
2022-05-05 21:11:18,134 INFO [train.py:715] (1/8) Epoch 7, batch 15850, loss[loss=0.1532, simple_loss=0.227, pruned_loss=0.0397, over 4837.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03705, over 971298.66 frames.], batch size: 30, lr: 2.93e-04
2022-05-05 21:11:57,182 INFO [train.py:715] (1/8) Epoch 7, batch 15900, loss[loss=0.1365, simple_loss=0.2116, pruned_loss=0.03071, over 4836.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2183, pruned_loss=0.03699, over 971531.30 frames.], batch size: 32, lr: 2.93e-04
2022-05-05 21:12:36,479 INFO [train.py:715] (1/8) Epoch 7, batch 15950, loss[loss=0.1533, simple_loss=0.216, pruned_loss=0.04525, over 4853.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2186, pruned_loss=0.03694, over 971638.82 frames.], batch size: 13, lr: 2.93e-04
2022-05-05 21:13:15,929 INFO [train.py:715] (1/8) Epoch 7, batch 16000, loss[loss=0.1205, simple_loss=0.1967, pruned_loss=0.02217, over 4776.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.03722, over 971425.06 frames.], batch size: 14, lr: 2.93e-04
2022-05-05 21:13:54,029 INFO [train.py:715] (1/8) Epoch 7, batch 16050, loss[loss=0.1592, simple_loss=0.2337, pruned_loss=0.04231, over 4923.00 frames.], tot_loss[loss=0.1459, simple_loss=0.218, pruned_loss=0.03689, over 971777.67 frames.], batch size: 23, lr: 2.93e-04
2022-05-05 21:14:33,357 INFO [train.py:715] (1/8) Epoch 7, batch 16100, loss[loss=0.1602, simple_loss=0.2401, pruned_loss=0.0401, over 4912.00 frames.], tot_loss[loss=0.146, simple_loss=0.2184, pruned_loss=0.03681, over 971849.21 frames.], batch size: 29, lr: 2.93e-04
2022-05-05 21:15:12,283 INFO [train.py:715] (1/8) Epoch 7, batch 16150, loss[loss=0.1577, simple_loss=0.2311, pruned_loss=0.0421, over 4916.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2182, pruned_loss=0.03718, over 972017.31 frames.], batch size: 17, lr: 2.93e-04
2022-05-05 21:15:50,930 INFO [train.py:715] (1/8) Epoch 7, batch 16200, loss[loss=0.1779, simple_loss=0.2444, pruned_loss=0.0557, over 4966.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2188, pruned_loss=0.03747, over 971492.69 frames.], batch size: 24, lr: 2.93e-04
2022-05-05 21:16:30,081 INFO [train.py:715] (1/8) Epoch 7, batch 16250, loss[loss=0.1311, simple_loss=0.2204, pruned_loss=0.02088, over 4903.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2182, pruned_loss=0.03698, over 971496.94 frames.], batch size: 19, lr: 2.93e-04
2022-05-05 21:17:08,727 INFO [train.py:715] (1/8) Epoch 7, batch 16300, loss[loss=0.1523, simple_loss=0.2255, pruned_loss=0.03953, over 4876.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03736, over 971576.78 frames.], batch size: 22, lr: 2.93e-04
2022-05-05 21:17:48,274 INFO [train.py:715] (1/8) Epoch 7, batch 16350, loss[loss=0.14, simple_loss=0.2284, pruned_loss=0.02576, over 4903.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2176, pruned_loss=0.03673, over 971394.24 frames.], batch size: 19, lr: 2.93e-04
2022-05-05 21:18:26,609 INFO [train.py:715] (1/8) Epoch 7, batch 16400, loss[loss=0.1278, simple_loss=0.2069, pruned_loss=0.02438, over 4864.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2182, pruned_loss=0.03685, over 971422.70 frames.], batch size: 20, lr: 2.93e-04
2022-05-05 21:19:05,502 INFO [train.py:715] (1/8) Epoch 7, batch 16450, loss[loss=0.1524, simple_loss=0.2199, pruned_loss=0.0424, over 4845.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03727, over 970244.04 frames.], batch size: 15, lr: 2.93e-04
2022-05-05 21:19:44,556 INFO [train.py:715] (1/8) Epoch 7, batch 16500, loss[loss=0.1337, simple_loss=0.2099, pruned_loss=0.02875, over 4927.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2184, pruned_loss=0.0369, over 970778.32 frames.], batch size: 23, lr: 2.93e-04
2022-05-05 21:20:22,829 INFO [train.py:715] (1/8) Epoch 7, batch 16550, loss[loss=0.1222, simple_loss=0.1986, pruned_loss=0.02285, over 4981.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.03718, over 971787.49 frames.], batch size: 25, lr: 2.93e-04
2022-05-05 21:21:02,227 INFO [train.py:715] (1/8) Epoch 7, batch 16600, loss[loss=0.1454, simple_loss=0.219, pruned_loss=0.03596, over 4874.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2176, pruned_loss=0.03673, over 972216.19 frames.], batch size: 22, lr: 2.93e-04
2022-05-05 21:21:41,398 INFO [train.py:715] (1/8) Epoch 7, batch 16650, loss[loss=0.15, simple_loss=0.2009, pruned_loss=0.04959, over 4751.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2178, pruned_loss=0.03733, over 972034.45 frames.], batch size: 16, lr: 2.93e-04
2022-05-05 21:22:20,544 INFO [train.py:715] (1/8) Epoch 7, batch 16700, loss[loss=0.1543, simple_loss=0.2187, pruned_loss=0.04491, over 4825.00 frames.], tot_loss[loss=0.1466, simple_loss=0.218, pruned_loss=0.03756, over 971517.75 frames.], batch size: 13, lr: 2.93e-04
2022-05-05 21:22:59,829 INFO [train.py:715] (1/8) Epoch 7, batch 16750, loss[loss=0.1427, simple_loss=0.211, pruned_loss=0.03726, over 4880.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2174, pruned_loss=0.03708, over 972040.98 frames.], batch size: 19, lr: 2.93e-04
2022-05-05 21:23:38,671 INFO [train.py:715] (1/8) Epoch 7, batch 16800, loss[loss=0.1488, simple_loss=0.2143, pruned_loss=0.04164, over 4793.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2168, pruned_loss=0.0369, over 972469.36 frames.], batch size: 14, lr: 2.93e-04
2022-05-05 21:24:17,716 INFO [train.py:715] (1/8) Epoch 7, batch 16850, loss[loss=0.1408, simple_loss=0.2137, pruned_loss=0.03398, over 4892.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2168, pruned_loss=0.03723, over 972416.51 frames.], batch size: 19, lr: 2.93e-04
2022-05-05 21:24:57,001 INFO [train.py:715] (1/8) Epoch 7, batch 16900, loss[loss=0.1662, simple_loss=0.2416, pruned_loss=0.04544, over 4848.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2174, pruned_loss=0.03769, over 971958.88 frames.], batch size: 30, lr: 2.93e-04
2022-05-05 21:25:36,250 INFO [train.py:715] (1/8) Epoch 7, batch 16950, loss[loss=0.1492, simple_loss=0.2169, pruned_loss=0.04071, over 4861.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2179, pruned_loss=0.03764, over 972349.15 frames.], batch size: 32, lr: 2.93e-04
2022-05-05 21:26:14,899 INFO [train.py:715] (1/8) Epoch 7, batch 17000, loss[loss=0.1597, simple_loss=0.2322, pruned_loss=0.04362, over 4965.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2184, pruned_loss=0.03735, over 972969.96 frames.], batch size: 15, lr: 2.93e-04
2022-05-05 21:26:54,054 INFO [train.py:715] (1/8) Epoch 7, batch 17050, loss[loss=0.1625, simple_loss=0.2335, pruned_loss=0.04576, over 4854.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03706, over 973653.78 frames.], batch size: 32, lr: 2.93e-04
2022-05-05 21:27:32,508 INFO [train.py:715] (1/8) Epoch 7, batch 17100, loss[loss=0.136, simple_loss=0.2167, pruned_loss=0.02771, over 4810.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03654, over 973354.80 frames.], batch size: 21, lr: 2.93e-04
2022-05-05 21:28:11,646 INFO [train.py:715] (1/8) Epoch 7, batch 17150, loss[loss=0.1475, simple_loss=0.2154, pruned_loss=0.03985, over 4850.00 frames.], tot_loss[loss=0.146, simple_loss=0.2178, pruned_loss=0.03708, over 973206.39 frames.], batch size: 30, lr: 2.93e-04
2022-05-05 21:28:50,900 INFO [train.py:715] (1/8) Epoch 7, batch 17200, loss[loss=0.1675, simple_loss=0.2408, pruned_loss=0.04706, over 4929.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03679, over 973839.86 frames.], batch size: 39, lr: 2.93e-04
2022-05-05 21:29:29,223 INFO [train.py:715] (1/8) Epoch 7, batch 17250, loss[loss=0.1831, simple_loss=0.248, pruned_loss=0.05914, over 4866.00 frames.], tot_loss[loss=0.1462, simple_loss=0.218, pruned_loss=0.03724, over 974029.40 frames.], batch size: 16, lr: 2.92e-04
2022-05-05 21:30:08,296 INFO [train.py:715] (1/8) Epoch 7, batch 17300, loss[loss=0.1825, simple_loss=0.2438, pruned_loss=0.0606, over 4768.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2178, pruned_loss=0.03757, over 973603.31 frames.], batch size: 14, lr: 2.92e-04
2022-05-05 21:30:46,577 INFO [train.py:715] (1/8) Epoch 7, batch 17350, loss[loss=0.164, simple_loss=0.2292, pruned_loss=0.04941, over 4850.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2179, pruned_loss=0.03768, over 974260.35 frames.], batch size: 30, lr: 2.92e-04
2022-05-05 21:31:25,651 INFO [train.py:715] (1/8) Epoch 7, batch 17400, loss[loss=0.1266, simple_loss=0.1878, pruned_loss=0.03271, over 4789.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2181, pruned_loss=0.03826, over 973854.97 frames.], batch size: 12, lr: 2.92e-04
2022-05-05 21:32:04,442 INFO [train.py:715] (1/8) Epoch 7, batch 17450, loss[loss=0.1756, simple_loss=0.2514, pruned_loss=0.04991, over 4897.00 frames.], tot_loss[loss=0.147, simple_loss=0.2178, pruned_loss=0.03808, over 973271.51 frames.], batch size: 16, lr: 2.92e-04
2022-05-05 21:32:43,222 INFO [train.py:715] (1/8) Epoch 7, batch 17500, loss[loss=0.1607, simple_loss=0.2337, pruned_loss=0.04386, over 4892.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2172, pruned_loss=0.03784, over 972502.72 frames.], batch size: 39, lr: 2.92e-04
2022-05-05 21:33:22,415 INFO [train.py:715] (1/8) Epoch 7, batch 17550, loss[loss=0.1413, simple_loss=0.1954, pruned_loss=0.04362, over 4830.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2166, pruned_loss=0.03721, over 972185.49 frames.], batch size: 12, lr: 2.92e-04
2022-05-05 21:34:00,738 INFO [train.py:715] (1/8) Epoch 7, batch 17600, loss[loss=0.1529, simple_loss=0.2263, pruned_loss=0.0398, over 4840.00 frames.], tot_loss[loss=0.144, simple_loss=0.2153, pruned_loss=0.03634, over 972212.46 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:34:39,811 INFO [train.py:715] (1/8) Epoch 7, batch 17650, loss[loss=0.1606, simple_loss=0.2284, pruned_loss=0.04638, over 4839.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2155, pruned_loss=0.03643, over 973170.58 frames.], batch size: 30, lr: 2.92e-04
2022-05-05 21:35:19,111 INFO [train.py:715] (1/8) Epoch 7, batch 17700, loss[loss=0.1746, simple_loss=0.2335, pruned_loss=0.05784, over 4878.00 frames.], tot_loss[loss=0.1446, simple_loss=0.216, pruned_loss=0.03661, over 973096.93 frames.], batch size: 32, lr: 2.92e-04
2022-05-05 21:35:58,208 INFO [train.py:715] (1/8) Epoch 7, batch 17750, loss[loss=0.1975, simple_loss=0.2645, pruned_loss=0.06522, over 4976.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2168, pruned_loss=0.03684, over 972343.58 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:36:37,515 INFO [train.py:715] (1/8) Epoch 7, batch 17800, loss[loss=0.1229, simple_loss=0.2016, pruned_loss=0.02216, over 4941.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2169, pruned_loss=0.03688, over 972519.72 frames.], batch size: 23, lr: 2.92e-04
2022-05-05 21:37:16,003 INFO [train.py:715] (1/8) Epoch 7, batch 17850, loss[loss=0.1483, simple_loss=0.2221, pruned_loss=0.03732, over 4881.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2169, pruned_loss=0.03678, over 972476.13 frames.], batch size: 22, lr: 2.92e-04
2022-05-05 21:37:55,611 INFO [train.py:715] (1/8) Epoch 7, batch 17900, loss[loss=0.1353, simple_loss=0.2031, pruned_loss=0.03381, over 4790.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2172, pruned_loss=0.03687, over 972344.87 frames.], batch size: 14, lr: 2.92e-04
2022-05-05 21:38:34,077 INFO [train.py:715] (1/8) Epoch 7, batch 17950, loss[loss=0.1473, simple_loss=0.2159, pruned_loss=0.03931, over 4914.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03706, over 972457.56 frames.], batch size: 17, lr: 2.92e-04
2022-05-05 21:39:13,129 INFO [train.py:715] (1/8) Epoch 7, batch 18000, loss[loss=0.171, simple_loss=0.2417, pruned_loss=0.05016, over 4850.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2182, pruned_loss=0.03744, over 972978.20 frames.], batch size: 32, lr: 2.92e-04
2022-05-05 21:39:13,130 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 21:39:22,795 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.1081, simple_loss=0.193, pruned_loss=0.01158, over 914524.00 frames.
2022-05-05 21:40:01,809 INFO [train.py:715] (1/8) Epoch 7, batch 18050, loss[loss=0.1526, simple_loss=0.2178, pruned_loss=0.04364, over 4772.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2173, pruned_loss=0.03707, over 972089.99 frames.], batch size: 17, lr: 2.92e-04
2022-05-05 21:40:41,011 INFO [train.py:715] (1/8) Epoch 7, batch 18100, loss[loss=0.1348, simple_loss=0.2111, pruned_loss=0.02922, over 4833.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2174, pruned_loss=0.0367, over 972762.05 frames.], batch size: 26, lr: 2.92e-04
2022-05-05 21:41:19,570 INFO [train.py:715] (1/8) Epoch 7, batch 18150, loss[loss=0.1288, simple_loss=0.1978, pruned_loss=0.02989, over 4889.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03684, over 971980.67 frames.], batch size: 19, lr: 2.92e-04
2022-05-05 21:41:57,882 INFO [train.py:715] (1/8) Epoch 7, batch 18200, loss[loss=0.1318, simple_loss=0.2056, pruned_loss=0.02897, over 4876.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2181, pruned_loss=0.03718, over 973076.90 frames.], batch size: 20, lr: 2.92e-04
2022-05-05 21:42:36,256 INFO [train.py:715] (1/8) Epoch 7, batch 18250, loss[loss=0.1181, simple_loss=0.1944, pruned_loss=0.02091, over 4824.00 frames.], tot_loss[loss=0.1468, simple_loss=0.219, pruned_loss=0.0373, over 973008.77 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:43:15,545 INFO [train.py:715] (1/8) Epoch 7, batch 18300, loss[loss=0.1217, simple_loss=0.1925, pruned_loss=0.02546, over 4885.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2183, pruned_loss=0.03691, over 973162.01 frames.], batch size: 17, lr: 2.92e-04
2022-05-05 21:43:53,559 INFO [train.py:715] (1/8) Epoch 7, batch 18350, loss[loss=0.1193, simple_loss=0.1894, pruned_loss=0.02458, over 4959.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2182, pruned_loss=0.03678, over 972209.22 frames.], batch size: 28, lr: 2.92e-04
2022-05-05 21:44:31,952 INFO [train.py:715] (1/8) Epoch 7, batch 18400, loss[loss=0.1298, simple_loss=0.2051, pruned_loss=0.02724, over 4907.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2186, pruned_loss=0.03693, over 971681.52 frames.], batch size: 39, lr: 2.92e-04
2022-05-05 21:45:11,791 INFO [train.py:715] (1/8) Epoch 7, batch 18450, loss[loss=0.175, simple_loss=0.2444, pruned_loss=0.05279, over 4879.00 frames.], tot_loss[loss=0.1464, simple_loss=0.219, pruned_loss=0.03693, over 972644.40 frames.], batch size: 22, lr: 2.92e-04
2022-05-05 21:45:50,717 INFO [train.py:715] (1/8) Epoch 7, batch 18500, loss[loss=0.1297, simple_loss=0.1959, pruned_loss=0.03176, over 4768.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2187, pruned_loss=0.03688, over 972536.13 frames.], batch size: 12, lr: 2.92e-04
2022-05-05 21:46:29,380 INFO [train.py:715] (1/8) Epoch 7, batch 18550, loss[loss=0.1293, simple_loss=0.1988, pruned_loss=0.02992, over 4948.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2197, pruned_loss=0.03734, over 971972.82 frames.], batch size: 35, lr: 2.92e-04
2022-05-05 21:47:08,453 INFO [train.py:715] (1/8) Epoch 7, batch 18600, loss[loss=0.1341, simple_loss=0.2148, pruned_loss=0.0267, over 4816.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2189, pruned_loss=0.03718, over 972146.24 frames.], batch size: 27, lr: 2.92e-04
2022-05-05 21:47:47,274 INFO [train.py:715] (1/8) Epoch 7, batch 18650, loss[loss=0.148, simple_loss=0.2199, pruned_loss=0.03807, over 4921.00 frames.], tot_loss[loss=0.147, simple_loss=0.2192, pruned_loss=0.03743, over 972847.80 frames.], batch size: 18, lr: 2.92e-04
2022-05-05 21:48:25,127 INFO [train.py:715] (1/8) Epoch 7, batch 18700, loss[loss=0.1312, simple_loss=0.209, pruned_loss=0.02669, over 4929.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2185, pruned_loss=0.03707, over 973141.36 frames.], batch size: 18, lr: 2.92e-04
2022-05-05 21:49:03,393 INFO [train.py:715] (1/8) Epoch 7, batch 18750, loss[loss=0.1408, simple_loss=0.2166, pruned_loss=0.03247, over 4904.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.03716, over 972123.43 frames.], batch size: 22, lr: 2.92e-04
2022-05-05 21:49:42,763 INFO [train.py:715] (1/8) Epoch 7, batch 18800, loss[loss=0.1708, simple_loss=0.2424, pruned_loss=0.04961, over 4806.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2184, pruned_loss=0.03691, over 972707.08 frames.], batch size: 21, lr: 2.92e-04
2022-05-05 21:50:21,363 INFO [train.py:715] (1/8) Epoch 7, batch 18850, loss[loss=0.1651, simple_loss=0.2306, pruned_loss=0.0498, over 4801.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2176, pruned_loss=0.03656, over 971831.18 frames.], batch size: 24, lr: 2.92e-04
2022-05-05 21:50:59,418 INFO [train.py:715] (1/8) Epoch 7, batch 18900, loss[loss=0.1376, simple_loss=0.2007, pruned_loss=0.0372, over 4987.00 frames.], tot_loss[loss=0.1458, simple_loss=0.218, pruned_loss=0.03685, over 972723.35 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:51:36,462 INFO [train.py:715] (1/8) Epoch 7, batch 18950, loss[loss=0.1612, simple_loss=0.2286, pruned_loss=0.04685, over 4968.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2183, pruned_loss=0.03693, over 972684.47 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:52:14,914 INFO [train.py:715] (1/8) Epoch 7, batch 19000, loss[loss=0.1568, simple_loss=0.2229, pruned_loss=0.04536, over 4892.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2176, pruned_loss=0.03736, over 972720.89 frames.], batch size: 22, lr: 2.92e-04
2022-05-05 21:52:52,515 INFO [train.py:715] (1/8) Epoch 7, batch 19050, loss[loss=0.1328, simple_loss=0.2048, pruned_loss=0.03039, over 4766.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2169, pruned_loss=0.03693, over 972937.61 frames.], batch size: 12, lr: 2.91e-04
2022-05-05 21:53:30,760 INFO [train.py:715] (1/8) Epoch 7, batch 19100, loss[loss=0.1364, simple_loss=0.2174, pruned_loss=0.02772, over 4803.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2153, pruned_loss=0.0362, over 972214.48 frames.], batch size: 21, lr: 2.91e-04
2022-05-05 21:54:09,417 INFO [train.py:715] (1/8) Epoch 7, batch 19150, loss[loss=0.1382, simple_loss=0.2119, pruned_loss=0.03224, over 4862.00 frames.], tot_loss[loss=0.144, simple_loss=0.2155, pruned_loss=0.0363, over 973426.92 frames.], batch size: 13, lr: 2.91e-04
2022-05-05 21:54:47,126 INFO [train.py:715] (1/8) Epoch 7, batch 19200, loss[loss=0.1517, simple_loss=0.2079, pruned_loss=0.04782, over 4929.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2162, pruned_loss=0.03667, over 972422.71 frames.], batch size: 18, lr: 2.91e-04
2022-05-05 21:55:24,842 INFO [train.py:715] (1/8) Epoch 7, batch 19250, loss[loss=0.1795, simple_loss=0.2577, pruned_loss=0.05065, over 4764.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2159, pruned_loss=0.03676, over 972262.47 frames.], batch size: 16, lr: 2.91e-04
2022-05-05 21:56:02,879 INFO [train.py:715] (1/8) Epoch 7, batch 19300, loss[loss=0.1353, simple_loss=0.2111, pruned_loss=0.02979, over 4923.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2157, pruned_loss=0.03621, over 971586.36 frames.], batch size: 23, lr: 2.91e-04
2022-05-05 21:56:41,359 INFO [train.py:715] (1/8) Epoch 7, batch 19350, loss[loss=0.1239, simple_loss=0.2024, pruned_loss=0.02272, over 4917.00 frames.], tot_loss[loss=0.144, simple_loss=0.2159, pruned_loss=0.03608, over 971399.07 frames.], batch size: 18, lr: 2.91e-04
2022-05-05 21:57:18,830 INFO [train.py:715] (1/8) Epoch 7, batch 19400, loss[loss=0.1583, simple_loss=0.2334, pruned_loss=0.04158, over 4752.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2166, pruned_loss=0.03629, over 971751.57 frames.], batch size: 16, lr: 2.91e-04
2022-05-05 21:57:56,265 INFO [train.py:715] (1/8) Epoch 7, batch 19450, loss[loss=0.1676, simple_loss=0.2319, pruned_loss=0.05163, over 4808.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03656, over 971846.33 frames.], batch size: 27, lr: 2.91e-04
2022-05-05 21:58:34,323 INFO [train.py:715] (1/8) Epoch 7, batch 19500, loss[loss=0.1263, simple_loss=0.1993, pruned_loss=0.02664, over 4887.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2172, pruned_loss=0.0367, over 972033.60 frames.], batch size: 19, lr: 2.91e-04
2022-05-05 21:59:11,843 INFO [train.py:715] (1/8) Epoch 7, batch 19550, loss[loss=0.1588, simple_loss=0.23, pruned_loss=0.04374, over 4778.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2179, pruned_loss=0.03742, over 972062.72 frames.], batch size: 17, lr: 2.91e-04
2022-05-05 21:59:49,565 INFO [train.py:715] (1/8) Epoch 7, batch 19600, loss[loss=0.1318, simple_loss=0.2089, pruned_loss=0.02732, over 4791.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03698, over 972687.64 frames.], batch size: 24, lr: 2.91e-04
2022-05-05 22:00:27,124 INFO [train.py:715] (1/8) Epoch 7, batch 19650, loss[loss=0.1433, simple_loss=0.213, pruned_loss=0.03675, over 4772.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2172, pruned_loss=0.03703, over 973118.10 frames.], batch size: 17, lr: 2.91e-04
2022-05-05 22:01:05,540 INFO [train.py:715] (1/8) Epoch 7, batch 19700, loss[loss=0.1124, simple_loss=0.1958, pruned_loss=0.01451, over 4886.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2177, pruned_loss=0.03745, over 973419.57 frames.], batch size: 19, lr: 2.91e-04
2022-05-05 22:01:42,749 INFO [train.py:715] (1/8) Epoch 7, batch 19750, loss[loss=0.1614, simple_loss=0.2408, pruned_loss=0.04098, over 4904.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2179, pruned_loss=0.03776, over 973693.15 frames.], batch size: 17, lr: 2.91e-04
2022-05-05 22:02:20,223 INFO [train.py:715] (1/8) Epoch 7, batch 19800, loss[loss=0.1237, simple_loss=0.197, pruned_loss=0.02517, over 4881.00 frames.], tot_loss[loss=0.1476, simple_loss=0.219, pruned_loss=0.03813, over 973576.16 frames.], batch size: 22, lr: 2.91e-04
2022-05-05 22:02:58,035 INFO [train.py:715] (1/8) Epoch 7, batch 19850, loss[loss=0.1619, simple_loss=0.2239, pruned_loss=0.05, over 4797.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2181, pruned_loss=0.03765, over 973431.62 frames.], batch size: 24, lr: 2.91e-04
2022-05-05 22:03:35,859 INFO [train.py:715] (1/8) Epoch 7, batch 19900, loss[loss=0.2004, simple_loss=0.2569, pruned_loss=0.07197, over 4906.00 frames.], tot_loss[loss=0.147, simple_loss=0.2182, pruned_loss=0.03793, over 973077.32 frames.], batch size: 17, lr: 2.91e-04
2022-05-05 22:04:12,822 INFO [train.py:715] (1/8) Epoch 7, batch 19950, loss[loss=0.1464, simple_loss=0.2242, pruned_loss=0.03432, over 4777.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2175, pruned_loss=0.03764, over 972975.94 frames.], batch size: 17, lr: 2.91e-04
2022-05-05 22:04:50,681 INFO [train.py:715] (1/8) Epoch 7, batch 20000, loss[loss=0.1339, simple_loss=0.2113, pruned_loss=0.02823, over 4978.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2175, pruned_loss=0.03749, over 972806.51 frames.], batch size: 25, lr: 2.91e-04
2022-05-05 22:05:28,968 INFO [train.py:715] (1/8) Epoch 7, batch 20050, loss[loss=0.139, simple_loss=0.2156, pruned_loss=0.03115, over 4904.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2178, pruned_loss=0.03755, over 972601.44 frames.], batch size: 19, lr: 2.91e-04
2022-05-05 22:06:06,298 INFO [train.py:715] (1/8) Epoch 7, batch 20100, loss[loss=0.1248, simple_loss=0.1961, pruned_loss=0.02675, over 4773.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2169, pruned_loss=0.0372, over 973243.50 frames.], batch size: 17, lr: 2.91e-04
2022-05-05 22:06:43,749 INFO [train.py:715] (1/8) Epoch 7, batch 20150, loss[loss=0.1998, simple_loss=0.2672, pruned_loss=0.06623, over 4850.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2177, pruned_loss=0.03735, over 973326.22 frames.], batch size: 20, lr: 2.91e-04
2022-05-05 22:07:21,916 INFO [train.py:715] (1/8) Epoch 7, batch 20200, loss[loss=0.1512, simple_loss=0.2199, pruned_loss=0.04123, over 4972.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2177, pruned_loss=0.0374, over 972640.39 frames.], batch size: 39, lr: 2.91e-04
2022-05-05 22:08:00,055 INFO [train.py:715] (1/8) Epoch 7, batch 20250, loss[loss=0.1442, simple_loss=0.22, pruned_loss=0.03417, over 4767.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2185, pruned_loss=0.03746, over 972427.38 frames.], batch size: 19, lr: 2.91e-04
2022-05-05 22:08:37,466 INFO [train.py:715] (1/8) Epoch 7, batch 20300, loss[loss=0.1292, simple_loss=0.2063, pruned_loss=0.02602, over 4868.00 frames.], tot_loss[loss=0.1459, simple_loss=0.218, pruned_loss=0.03696, over 971847.39 frames.], batch size: 20, lr: 2.91e-04
2022-05-05 22:09:17,217 INFO [train.py:715] (1/8) Epoch 7, batch 20350, loss[loss=0.1588, simple_loss=0.2278, pruned_loss=0.04488, over 4881.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2167, pruned_loss=0.0362, over 971347.13 frames.], batch size: 16, lr: 2.91e-04
2022-05-05 22:09:55,132 INFO [train.py:715] (1/8) Epoch 7, batch 20400, loss[loss=0.1574, simple_loss=0.2242, pruned_loss=0.04527, over 4736.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03558, over 971042.51 frames.], batch size: 16, lr: 2.91e-04
2022-05-05 22:10:33,046 INFO [train.py:715] (1/8) Epoch 7, batch 20450, loss[loss=0.1342, simple_loss=0.2106, pruned_loss=0.02885, over 4930.00 frames.], tot_loss[loss=0.145, simple_loss=0.217, pruned_loss=0.03649, over 971637.82 frames.], batch size: 18, lr: 2.91e-04
2022-05-05 22:11:10,606 INFO [train.py:715] (1/8) Epoch 7, batch 20500, loss[loss=0.1628, simple_loss=0.2338, pruned_loss=0.04592, over 4774.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03676, over 971797.25 frames.], batch size: 18, lr: 2.91e-04
2022-05-05 22:11:48,694 INFO [train.py:715] (1/8) Epoch 7, batch 20550, loss[loss=0.1514, simple_loss=0.2255, pruned_loss=0.0387, over 4878.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2176, pruned_loss=0.03694, over 971752.72 frames.], batch size: 16, lr: 2.91e-04
2022-05-05 22:12:26,842 INFO [train.py:715] (1/8) Epoch 7, batch 20600, loss[loss=0.1265, simple_loss=0.1947, pruned_loss=0.02922, over 4814.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03687, over 973014.58 frames.], batch size: 27, lr: 2.91e-04
2022-05-05 22:13:04,073 INFO [train.py:715] (1/8) Epoch 7, batch 20650, loss[loss=0.1398, simple_loss=0.2083, pruned_loss=0.03566, over 4757.00 frames.], tot_loss[loss=0.1462, simple_loss=0.218, pruned_loss=0.03722, over 973108.26 frames.], batch size: 18, lr: 2.91e-04
2022-05-05 22:13:41,773 INFO [train.py:715] (1/8) Epoch 7, batch 20700, loss[loss=0.14, simple_loss=0.2104, pruned_loss=0.03485, over 4764.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2179, pruned_loss=0.03733, over 973146.14 frames.], batch size: 18, lr: 2.91e-04
2022-05-05 22:14:19,743 INFO [train.py:715] (1/8) Epoch 7, batch 20750, loss[loss=0.1404, simple_loss=0.212, pruned_loss=0.03446, over 4897.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03733, over 973963.27 frames.], batch size: 17, lr: 2.91e-04
2022-05-05 22:14:57,390 INFO [train.py:715] (1/8) Epoch 7, batch 20800, loss[loss=0.1364, simple_loss=0.2025, pruned_loss=0.03516, over 4797.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.03726, over 974272.42 frames.], batch size: 25, lr: 2.91e-04
2022-05-05 22:15:34,693 INFO [train.py:715] (1/8) Epoch 7, batch 20850, loss[loss=0.1439, simple_loss=0.2215, pruned_loss=0.03314, over 4789.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03672, over 973931.29 frames.], batch size: 18, lr: 2.90e-04
2022-05-05 22:16:13,019 INFO [train.py:715] (1/8) Epoch 7, batch 20900, loss[loss=0.1245, simple_loss=0.1895, pruned_loss=0.02978, over 4973.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.0368, over 973218.68 frames.], batch size: 25, lr: 2.90e-04
2022-05-05 22:16:50,912 INFO [train.py:715] (1/8) Epoch 7, batch 20950, loss[loss=0.1362, simple_loss=0.2066, pruned_loss=0.03292, over 4706.00 frames.], tot_loss[loss=0.1455, simple_loss=0.217, pruned_loss=0.03701, over 972497.88 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:17:29,168 INFO [train.py:715] (1/8) Epoch 7, batch 21000, loss[loss=0.1258, simple_loss=0.2011, pruned_loss=0.02521, over 4754.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2167, pruned_loss=0.0369, over 972159.06 frames.], batch size: 19, lr: 2.90e-04
2022-05-05 22:17:29,169 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 22:17:39,072 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.1082, simple_loss=0.193, pruned_loss=0.01169, over 914524.00 frames.
2022-05-05 22:18:17,069 INFO [train.py:715] (1/8) Epoch 7, batch 21050, loss[loss=0.1264, simple_loss=0.1941, pruned_loss=0.02932, over 4852.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2173, pruned_loss=0.0374, over 973034.01 frames.], batch size: 30, lr: 2.90e-04
2022-05-05 22:18:54,966 INFO [train.py:715] (1/8) Epoch 7, batch 21100, loss[loss=0.1245, simple_loss=0.2048, pruned_loss=0.02213, over 4831.00 frames.], tot_loss[loss=0.146, simple_loss=0.217, pruned_loss=0.03748, over 972820.19 frames.], batch size: 26, lr: 2.90e-04
2022-05-05 22:19:32,990 INFO [train.py:715] (1/8) Epoch 7, batch 21150, loss[loss=0.1532, simple_loss=0.223, pruned_loss=0.04172, over 4820.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2168, pruned_loss=0.03715, over 974129.87 frames.], batch size: 26, lr: 2.90e-04
2022-05-05 22:20:10,779 INFO [train.py:715] (1/8) Epoch 7, batch 21200, loss[loss=0.1456, simple_loss=0.219, pruned_loss=0.03608, over 4969.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2164, pruned_loss=0.03665, over 974164.65 frames.], batch size: 24, lr: 2.90e-04
2022-05-05 22:20:49,002 INFO [train.py:715] (1/8) Epoch 7, batch 21250, loss[loss=0.1364, simple_loss=0.2162, pruned_loss=0.02834, over 4691.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03696, over 973670.78 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:21:27,130 INFO [train.py:715] (1/8) Epoch 7, batch 21300, loss[loss=0.136, simple_loss=0.213, pruned_loss=0.02955, over 4901.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2173, pruned_loss=0.0369, over 973861.53 frames.], batch size: 19, lr: 2.90e-04
2022-05-05 22:22:04,501 INFO [train.py:715] (1/8) Epoch 7, batch 21350, loss[loss=0.146, simple_loss=0.2217, pruned_loss=0.03515, over 4846.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03658, over 974110.83 frames.], batch size: 20, lr: 2.90e-04
2022-05-05 22:22:42,287 INFO [train.py:715] (1/8) Epoch 7, batch 21400, loss[loss=0.1427, simple_loss=0.2192, pruned_loss=0.03312, over 4915.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03701, over 975252.34 frames.], batch size: 19, lr: 2.90e-04
2022-05-05 22:23:20,549 INFO [train.py:715] (1/8) Epoch 7, batch 21450, loss[loss=0.1896, simple_loss=0.2596, pruned_loss=0.05976, over 4895.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03733, over 974475.64 frames.], batch size: 17, lr: 2.90e-04
2022-05-05 22:23:58,723 INFO [train.py:715] (1/8) Epoch 7, batch 21500, loss[loss=0.1333, simple_loss=0.2096, pruned_loss=0.02852, over 4942.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2186, pruned_loss=0.03729, over 974293.27 frames.], batch size: 29, lr: 2.90e-04
2022-05-05 22:24:36,576 INFO [train.py:715] (1/8) Epoch 7, batch 21550, loss[loss=0.174, simple_loss=0.2475, pruned_loss=0.05029, over 4892.00 frames.], tot_loss[loss=0.1458, simple_loss=0.218, pruned_loss=0.03682, over 973685.45 frames.], batch size: 19, lr: 2.90e-04
2022-05-05 22:25:14,831 INFO [train.py:715] (1/8) Epoch 7, batch 21600, loss[loss=0.1678, simple_loss=0.2383, pruned_loss=0.0486, over 4827.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2182, pruned_loss=0.03726, over 973339.63 frames.], batch size: 27, lr: 2.90e-04
2022-05-05 22:25:53,302 INFO [train.py:715] (1/8) Epoch 7, batch 21650, loss[loss=0.13, simple_loss=0.2043, pruned_loss=0.02784, over 4984.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03716, over 973191.35 frames.], batch size: 20, lr: 2.90e-04
2022-05-05 22:26:30,668 INFO [train.py:715] (1/8) Epoch 7, batch 21700, loss[loss=0.149, simple_loss=0.2129, pruned_loss=0.04257, over 4809.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03733, over 974015.70 frames.], batch size: 25, lr: 2.90e-04
2022-05-05 22:27:08,759 INFO [train.py:715] (1/8) Epoch 7, batch 21750, loss[loss=0.1349, simple_loss=0.202, pruned_loss=0.03392, over 4869.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03737, over 973109.37 frames.], batch size: 16, lr: 2.90e-04
2022-05-05 22:27:46,872 INFO [train.py:715] (1/8) Epoch 7, batch 21800, loss[loss=0.1802, simple_loss=0.2357, pruned_loss=0.0624, over 4848.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2179, pruned_loss=0.03687, over 972104.25 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:28:24,962 INFO [train.py:715] (1/8) Epoch 7, batch 21850, loss[loss=0.1573, simple_loss=0.2266, pruned_loss=0.04402, over 4850.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03657, over 972565.05 frames.], batch size: 30, lr: 2.90e-04
2022-05-05 22:29:02,874 INFO [train.py:715] (1/8) Epoch 7, batch 21900, loss[loss=0.1732, simple_loss=0.2468, pruned_loss=0.04974, over 4822.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2182, pruned_loss=0.03682, over 972586.00 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:29:40,816 INFO [train.py:715] (1/8) Epoch 7, batch 21950, loss[loss=0.13, simple_loss=0.2027, pruned_loss=0.02865, over 4829.00 frames.], tot_loss[loss=0.1459, simple_loss=0.218, pruned_loss=0.0369, over 972782.33 frames.], batch size: 26, lr: 2.90e-04
2022-05-05 22:30:19,542 INFO [train.py:715] (1/8) Epoch 7, batch 22000, loss[loss=0.1218, simple_loss=0.2068, pruned_loss=0.01841, over 4877.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2169, pruned_loss=0.0367, over 972561.72 frames.], batch size: 22, lr: 2.90e-04
2022-05-05 22:30:57,078 INFO [train.py:715] (1/8) Epoch 7, batch 22050, loss[loss=0.1401, simple_loss=0.2056, pruned_loss=0.03729, over 4741.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03634, over 972867.52 frames.], batch size: 19, lr: 2.90e-04
2022-05-05 22:31:35,219 INFO [train.py:715] (1/8) Epoch 7, batch 22100, loss[loss=0.1487, simple_loss=0.2284, pruned_loss=0.0345, over 4943.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2172, pruned_loss=0.03672, over 973223.48 frames.], batch size: 21, lr: 2.90e-04
2022-05-05 22:32:13,474 INFO [train.py:715] (1/8) Epoch 7, batch 22150, loss[loss=0.1438, simple_loss=0.214, pruned_loss=0.03685, over 4888.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03678, over 972451.30 frames.], batch size: 22, lr: 2.90e-04
2022-05-05 22:32:51,985 INFO [train.py:715] (1/8) Epoch 7, batch 22200, loss[loss=0.1461, simple_loss=0.2167, pruned_loss=0.03771, over 4871.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2165, pruned_loss=0.03646, over 972974.51 frames.], batch size: 16, lr: 2.90e-04
2022-05-05 22:33:29,483 INFO [train.py:715] (1/8) Epoch 7, batch 22250, loss[loss=0.1361, simple_loss=0.2203, pruned_loss=0.02589, over 4969.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03658, over 972723.11 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:34:07,239 INFO [train.py:715] (1/8) Epoch 7, batch 22300, loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.0307, over 4909.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.03672, over 972234.02 frames.], batch size: 39, lr: 2.90e-04
2022-05-05 22:34:45,536 INFO [train.py:715] (1/8) Epoch 7, batch 22350, loss[loss=0.1609, simple_loss=0.2177, pruned_loss=0.05205, over 4840.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2178, pruned_loss=0.03717, over 971662.87 frames.], batch size: 13, lr: 2.90e-04
2022-05-05 22:35:22,814 INFO [train.py:715] (1/8) Epoch 7, batch 22400, loss[loss=0.1672, simple_loss=0.2371, pruned_loss=0.04864, over 4871.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2179, pruned_loss=0.03723, over 971451.19 frames.], batch size: 16, lr: 2.90e-04
2022-05-05 22:36:00,505 INFO [train.py:715] (1/8) Epoch 7, batch 22450, loss[loss=0.1614, simple_loss=0.2352, pruned_loss=0.04383, over 4924.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2178, pruned_loss=0.03717, over 972150.64 frames.], batch size: 29, lr: 2.90e-04
2022-05-05 22:36:38,651 INFO [train.py:715] (1/8) Epoch 7, batch 22500, loss[loss=0.118, simple_loss=0.1977, pruned_loss=0.01918, over 4982.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2176, pruned_loss=0.03691, over 972085.52 frames.], batch size: 28, lr: 2.90e-04
2022-05-05 22:37:16,690 INFO [train.py:715] (1/8) Epoch 7, batch 22550, loss[loss=0.1649, simple_loss=0.2238, pruned_loss=0.05302, over 4847.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.03681, over 972065.99 frames.], batch size: 32, lr: 2.90e-04
2022-05-05 22:37:54,356 INFO [train.py:715] (1/8) Epoch 7, batch 22600, loss[loss=0.1577, simple_loss=0.2317, pruned_loss=0.0418, over 4818.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2174, pruned_loss=0.03679, over 972087.18 frames.], batch size: 27, lr: 2.90e-04
2022-05-05 22:38:32,389 INFO [train.py:715] (1/8) Epoch 7, batch 22650, loss[loss=0.146, simple_loss=0.2078, pruned_loss=0.04213, over 4855.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2176, pruned_loss=0.03687, over 971671.26 frames.], batch size: 30, lr: 2.90e-04
2022-05-05 22:39:10,752 INFO [train.py:715] (1/8) Epoch 7, batch 22700, loss[loss=0.125, simple_loss=0.2052, pruned_loss=0.02241, over 4890.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2185, pruned_loss=0.03703, over 972745.14 frames.], batch size: 22, lr: 2.89e-04
2022-05-05 22:39:48,097 INFO [train.py:715] (1/8) Epoch 7, batch 22750, loss[loss=0.1895, simple_loss=0.2564, pruned_loss=0.06132, over 4882.00 frames.], tot_loss[loss=0.1471, simple_loss=0.219, pruned_loss=0.03764, over 973069.42 frames.], batch size: 16, lr: 2.89e-04
2022-05-05 22:40:25,727 INFO [train.py:715] (1/8) Epoch 7, batch 22800, loss[loss=0.1562, simple_loss=0.2268, pruned_loss=0.04278, over 4975.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2188, pruned_loss=0.03772, over 972004.01 frames.], batch size: 24, lr: 2.89e-04
2022-05-05 22:41:03,918 INFO [train.py:715] (1/8) Epoch 7, batch 22850, loss[loss=0.1394, simple_loss=0.204, pruned_loss=0.03739, over 4855.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2189, pruned_loss=0.03746, over 972560.78 frames.], batch size: 32, lr: 2.89e-04
2022-05-05 22:41:41,492 INFO [train.py:715] (1/8) Epoch 7, batch 22900, loss[loss=0.1742, simple_loss=0.2424, pruned_loss=0.05304, over 4931.00 frames.], tot_loss[loss=0.147, simple_loss=0.2189, pruned_loss=0.03753, over 972575.67 frames.], batch size: 18, lr: 2.89e-04
2022-05-05 22:42:19,140 INFO [train.py:715] (1/8) Epoch 7, batch 22950, loss[loss=0.1783, simple_loss=0.2504, pruned_loss=0.05314, over 4810.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2189, pruned_loss=0.03768, over 972402.43 frames.], batch size: 25, lr: 2.89e-04
2022-05-05 22:42:57,046 INFO [train.py:715] (1/8) Epoch 7, batch 23000, loss[loss=0.1323, simple_loss=0.1858, pruned_loss=0.03934, over 4788.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2187, pruned_loss=0.03791, over 973179.14 frames.], batch size: 12, lr: 2.89e-04
2022-05-05 22:43:35,193 INFO [train.py:715] (1/8) Epoch 7, batch 23050, loss[loss=0.1238, simple_loss=0.1933, pruned_loss=0.02711, over 4750.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2187, pruned_loss=0.03776, over 973484.55 frames.], batch size: 12, lr: 2.89e-04
2022-05-05 22:44:12,641 INFO [train.py:715] (1/8) Epoch 7, batch 23100, loss[loss=0.1822, simple_loss=0.256, pruned_loss=0.05424, over 4937.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2184, pruned_loss=0.03808, over 973014.89 frames.], batch size: 29, lr: 2.89e-04
2022-05-05 22:44:49,937 INFO [train.py:715] (1/8) Epoch 7, batch 23150, loss[loss=0.1549, simple_loss=0.2211, pruned_loss=0.04438, over 4840.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2182, pruned_loss=0.0376, over 973103.91 frames.], batch size: 15, lr: 2.89e-04
2022-05-05 22:45:28,255 INFO [train.py:715] (1/8) Epoch 7, batch 23200, loss[loss=0.1915, simple_loss=0.2566, pruned_loss=0.06316, over 4965.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2184, pruned_loss=0.03788, over 973547.20 frames.], batch size: 39, lr: 2.89e-04
2022-05-05 22:46:06,319 INFO [train.py:715] (1/8) Epoch 7, batch 23250, loss[loss=0.1276, simple_loss=0.1905, pruned_loss=0.0323, over 4982.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2177, pruned_loss=0.03775, over 972857.83 frames.], batch size: 14, lr: 2.89e-04
2022-05-05 22:46:43,803 INFO [train.py:715] (1/8) Epoch 7, batch 23300, loss[loss=0.1515, simple_loss=0.2182, pruned_loss=0.04238, over 4890.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2167, pruned_loss=0.03703, over 972384.02 frames.], batch size: 19, lr: 2.89e-04
2022-05-05 22:47:22,596 INFO [train.py:715] (1/8) Epoch 7, batch 23350, loss[loss=0.1222, simple_loss=0.1927, pruned_loss=0.02581, over 4784.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2166, pruned_loss=0.0371, over 972418.91 frames.], batch size: 18, lr: 2.89e-04
2022-05-05 22:48:01,693 INFO [train.py:715] (1/8) Epoch 7, batch 23400, loss[loss=0.1473, simple_loss=0.2146, pruned_loss=0.04001, over 4889.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2168, pruned_loss=0.03714, over 973051.84 frames.], batch size: 22, lr: 2.89e-04
2022-05-05 22:48:40,125 INFO [train.py:715] (1/8) Epoch 7, batch 23450, loss[loss=0.1603, simple_loss=0.2261, pruned_loss=0.04725, over 4922.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2161, pruned_loss=0.03688, over 971961.45 frames.], batch size: 18, lr: 2.89e-04
2022-05-05 22:49:18,250 INFO [train.py:715] (1/8) Epoch 7, batch 23500, loss[loss=0.135, simple_loss=0.2114, pruned_loss=0.02935, over 4913.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2156, pruned_loss=0.03641, over 972182.48 frames.], batch size: 17, lr: 2.89e-04
2022-05-05 22:49:56,230 INFO [train.py:715] (1/8) Epoch 7, batch 23550, loss[loss=0.1204, simple_loss=0.1874, pruned_loss=0.02666, over 4644.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2149, pruned_loss=0.03625, over 972038.37 frames.], batch size: 13, lr: 2.89e-04
2022-05-05 22:50:34,439 INFO [train.py:715] (1/8) Epoch 7, batch 23600, loss[loss=0.1597, simple_loss=0.229, pruned_loss=0.0452, over 4936.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2154, pruned_loss=0.03611, over 973056.49 frames.], batch size: 23, lr: 2.89e-04
2022-05-05 22:51:11,417 INFO [train.py:715] (1/8) Epoch 7, batch 23650, loss[loss=0.1623, simple_loss=0.222, pruned_loss=0.05132, over 4851.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2157, pruned_loss=0.03631, over 972295.36 frames.], batch size: 30, lr: 2.89e-04
2022-05-05 22:51:49,265 INFO [train.py:715] (1/8) Epoch 7, batch 23700, loss[loss=0.1391, simple_loss=0.2228, pruned_loss=0.02776, over 4956.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2156, pruned_loss=0.03614, over 972446.28 frames.], batch size: 21, lr: 2.89e-04
2022-05-05 22:52:27,397 INFO [train.py:715] (1/8) Epoch 7, batch 23750, loss[loss=0.1341, simple_loss=0.2246, pruned_loss=0.02178, over 4770.00 frames.], tot_loss[loss=0.1449, simple_loss=0.217, pruned_loss=0.03639, over 972290.84 frames.], batch size: 18, lr: 2.89e-04
2022-05-05 22:53:04,578 INFO [train.py:715] (1/8) Epoch 7, batch 23800, loss[loss=0.1366, simple_loss=0.2047, pruned_loss=0.03425, over 4977.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03574, over 972953.72 frames.], batch size: 15, lr: 2.89e-04
2022-05-05 22:53:42,353 INFO [train.py:715] (1/8) Epoch 7, batch 23850, loss[loss=0.1623, simple_loss=0.2349, pruned_loss=0.04487, over 4985.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.03602, over 972704.41 frames.], batch size: 25, lr: 2.89e-04
2022-05-05 22:54:21,021 INFO [train.py:715] (1/8) Epoch 7, batch 23900, loss[loss=0.1174, simple_loss=0.1947, pruned_loss=0.02008, over 4843.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03691, over 972312.16 frames.], batch size: 20, lr: 2.89e-04
2022-05-05 22:54:59,166 INFO [train.py:715] (1/8) Epoch 7, batch 23950, loss[loss=0.1608, simple_loss=0.2252, pruned_loss=0.04816, over 4743.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03734, over 971390.12 frames.], batch size: 16, lr: 2.89e-04
2022-05-05 22:55:36,638 INFO [train.py:715] (1/8) Epoch 7, batch 24000, loss[loss=0.1603, simple_loss=0.2289, pruned_loss=0.04582, over 4939.00 frames.], tot_loss[loss=0.1464, simple_loss=0.218, pruned_loss=0.03738, over 970985.36 frames.], batch size: 21, lr: 2.89e-04
2022-05-05 22:55:36,639 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 22:55:46,187 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.108, simple_loss=0.1929, pruned_loss=0.01156, over 914524.00 frames.
2022-05-05 22:56:23,730 INFO [train.py:715] (1/8) Epoch 7, batch 24050, loss[loss=0.1392, simple_loss=0.213, pruned_loss=0.03273, over 4861.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.03697, over 971193.52 frames.], batch size: 20, lr: 2.89e-04
2022-05-05 22:57:02,034 INFO [train.py:715] (1/8) Epoch 7, batch 24100, loss[loss=0.1381, simple_loss=0.2067, pruned_loss=0.0347, over 4896.00 frames.], tot_loss[loss=0.145, simple_loss=0.217, pruned_loss=0.03651, over 971642.85 frames.], batch size: 22, lr: 2.89e-04
2022-05-05 22:57:40,438 INFO [train.py:715] (1/8) Epoch 7, batch 24150, loss[loss=0.1643, simple_loss=0.2247, pruned_loss=0.05193, over 4656.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.03702, over 971883.51 frames.], batch size: 13, lr: 2.89e-04
2022-05-05 22:58:18,172 INFO [train.py:715] (1/8) Epoch 7, batch 24200, loss[loss=0.1439, simple_loss=0.215, pruned_loss=0.03636, over 4843.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2167, pruned_loss=0.03658, over 971494.12 frames.], batch size: 30, lr: 2.89e-04
2022-05-05 22:58:55,939 INFO [train.py:715] (1/8) Epoch 7, batch 24250, loss[loss=0.1305, simple_loss=0.2034, pruned_loss=0.02882, over 4911.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03704, over 971926.56 frames.], batch size: 17, lr: 2.89e-04
2022-05-05 22:59:34,586 INFO [train.py:715] (1/8) Epoch 7, batch 24300, loss[loss=0.1352, simple_loss=0.2118, pruned_loss=0.02925, over 4855.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.03725, over 971962.99 frames.], batch size: 32, lr: 2.89e-04
2022-05-05 23:00:12,424 INFO [train.py:715] (1/8) Epoch 7, batch 24350, loss[loss=0.1304, simple_loss=0.2022, pruned_loss=0.02929, over 4860.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2187, pruned_loss=0.03745, over 972418.43 frames.], batch size: 12, lr: 2.89e-04
2022-05-05 23:00:50,090 INFO [train.py:715] (1/8) Epoch 7, batch 24400, loss[loss=0.1633, simple_loss=0.2377, pruned_loss=0.04446, over 4750.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.03723, over 971586.81 frames.], batch size: 16, lr: 2.89e-04
2022-05-05 23:01:28,247 INFO [train.py:715] (1/8) Epoch 7, batch 24450, loss[loss=0.1758, simple_loss=0.2524, pruned_loss=0.04959, over 4809.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2178, pruned_loss=0.03678, over 971069.48 frames.], batch size: 25, lr: 2.89e-04
2022-05-05 23:02:06,218 INFO [train.py:715] (1/8) Epoch 7, batch 24500, loss[loss=0.1438, simple_loss=0.2165, pruned_loss=0.03559, over 4831.00 frames.], tot_loss[loss=0.146, simple_loss=0.2181, pruned_loss=0.03695, over 970887.81 frames.], batch size: 26, lr: 2.89e-04
2022-05-05 23:02:43,833 INFO [train.py:715] (1/8) Epoch 7, batch 24550, loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02868, over 4913.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03731, over 970703.96 frames.], batch size: 19, lr: 2.88e-04
2022-05-05 23:03:22,004 INFO [train.py:715] (1/8) Epoch 7, batch 24600, loss[loss=0.1704, simple_loss=0.2297, pruned_loss=0.05556, over 4797.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2185, pruned_loss=0.0369, over 971974.90 frames.], batch size: 14, lr: 2.88e-04
2022-05-05 23:04:01,119 INFO [train.py:715] (1/8) Epoch 7, batch 24650, loss[loss=0.1454, simple_loss=0.2199, pruned_loss=0.03547, over 4926.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03682, over 972033.98 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:04:39,574 INFO [train.py:715] (1/8) Epoch 7, batch 24700, loss[loss=0.1606, simple_loss=0.2339, pruned_loss=0.04365, over 4988.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2179, pruned_loss=0.03689, over 972495.41 frames.], batch size: 16, lr: 2.88e-04
2022-05-05 23:05:17,697 INFO [train.py:715] (1/8) Epoch 7, batch 24750, loss[loss=0.1522, simple_loss=0.2208, pruned_loss=0.04176, over 4976.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2183, pruned_loss=0.0374, over 972886.25 frames.], batch size: 25, lr: 2.88e-04
2022-05-05 23:05:56,161 INFO [train.py:715] (1/8) Epoch 7, batch 24800, loss[loss=0.1505, simple_loss=0.208, pruned_loss=0.04652, over 4912.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2176, pruned_loss=0.03712, over 973904.40 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:06:35,232 INFO [train.py:715] (1/8) Epoch 7, batch 24850, loss[loss=0.1661, simple_loss=0.2291, pruned_loss=0.0515, over 4780.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2171, pruned_loss=0.03706, over 973397.39 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:07:13,827 INFO [train.py:715] (1/8) Epoch 7, batch 24900, loss[loss=0.1589, simple_loss=0.2284, pruned_loss=0.04463, over 4827.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2164, pruned_loss=0.03638, over 973509.37 frames.], batch size: 15, lr: 2.88e-04
2022-05-05 23:07:53,109 INFO [train.py:715] (1/8) Epoch 7, batch 24950, loss[loss=0.1576, simple_loss=0.2247, pruned_loss=0.0452, over 4952.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2162, pruned_loss=0.03622, over 973987.41 frames.], batch size: 35, lr: 2.88e-04
2022-05-05 23:08:32,944 INFO [train.py:715] (1/8) Epoch 7, batch 25000, loss[loss=0.1184, simple_loss=0.1965, pruned_loss=0.02021, over 4814.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.03641, over 972907.65 frames.], batch size: 25, lr: 2.88e-04
2022-05-05 23:09:12,216 INFO [train.py:715] (1/8) Epoch 7, batch 25050, loss[loss=0.1486, simple_loss=0.2182, pruned_loss=0.0395, over 4741.00 frames.], tot_loss[loss=0.145, simple_loss=0.217, pruned_loss=0.03651, over 972328.72 frames.], batch size: 16, lr: 2.88e-04
2022-05-05 23:09:51,235 INFO [train.py:715] (1/8) Epoch 7, batch 25100, loss[loss=0.1266, simple_loss=0.1995, pruned_loss=0.02689, over 4888.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2175, pruned_loss=0.03718, over 972889.24 frames.], batch size: 22, lr: 2.88e-04
2022-05-05 23:10:31,404 INFO [train.py:715] (1/8) Epoch 7, batch 25150, loss[loss=0.1217, simple_loss=0.1797, pruned_loss=0.03183, over 4832.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03649, over 972808.64 frames.], batch size: 12, lr: 2.88e-04
2022-05-05 23:11:11,714 INFO [train.py:715] (1/8) Epoch 7, batch 25200, loss[loss=0.1629, simple_loss=0.2379, pruned_loss=0.04394, over 4782.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2178, pruned_loss=0.03729, over 972861.15 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:11:51,359 INFO [train.py:715] (1/8) Epoch 7, batch 25250, loss[loss=0.1648, simple_loss=0.2374, pruned_loss=0.04613, over 4959.00 frames.], tot_loss[loss=0.146, simple_loss=0.2178, pruned_loss=0.03706, over 972970.55 frames.], batch size: 21, lr: 2.88e-04
2022-05-05 23:12:31,932 INFO [train.py:715] (1/8) Epoch 7, batch 25300, loss[loss=0.1674, simple_loss=0.2277, pruned_loss=0.05354, over 4805.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03704, over 972365.24 frames.], batch size: 13, lr: 2.88e-04
2022-05-05 23:13:13,663 INFO [train.py:715] (1/8) Epoch 7, batch 25350, loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03582, over 4888.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2185, pruned_loss=0.03748, over 972238.79 frames.], batch size: 19, lr: 2.88e-04
2022-05-05 23:13:55,230 INFO [train.py:715] (1/8) Epoch 7, batch 25400, loss[loss=0.1462, simple_loss=0.222, pruned_loss=0.03516, over 4824.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2189, pruned_loss=0.03796, over 972124.99 frames.], batch size: 15, lr: 2.88e-04
2022-05-05 23:14:36,167 INFO [train.py:715] (1/8) Epoch 7, batch 25450, loss[loss=0.1466, simple_loss=0.2153, pruned_loss=0.03896, over 4897.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03717, over 972277.52 frames.], batch size: 19, lr: 2.88e-04
2022-05-05 23:15:18,364 INFO [train.py:715] (1/8) Epoch 7, batch 25500, loss[loss=0.1099, simple_loss=0.1782, pruned_loss=0.02083, over 4792.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2172, pruned_loss=0.03689, over 973062.93 frames.], batch size: 12, lr: 2.88e-04
2022-05-05 23:16:00,247 INFO [train.py:715] (1/8) Epoch 7, batch 25550, loss[loss=0.1604, simple_loss=0.2318, pruned_loss=0.04444, over 4987.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2172, pruned_loss=0.03627, over 973601.30 frames.], batch size: 25, lr: 2.88e-04
2022-05-05 23:16:41,005 INFO [train.py:715] (1/8) Epoch 7, batch 25600, loss[loss=0.1438, simple_loss=0.2102, pruned_loss=0.03867, over 4954.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2175, pruned_loss=0.03672, over 973611.15 frames.], batch size: 15, lr: 2.88e-04
2022-05-05 23:17:22,269 INFO [train.py:715] (1/8) Epoch 7, batch 25650, loss[loss=0.1377, simple_loss=0.1971, pruned_loss=0.03911, over 4730.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2178, pruned_loss=0.03731, over 973081.20 frames.], batch size: 12, lr: 2.88e-04
2022-05-05 23:18:03,668 INFO [train.py:715] (1/8) Epoch 7, batch 25700, loss[loss=0.1296, simple_loss=0.2057, pruned_loss=0.02681, over 4973.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03681, over 973120.76 frames.], batch size: 15, lr: 2.88e-04
2022-05-05 23:18:45,503 INFO [train.py:715] (1/8) Epoch 7, batch 25750, loss[loss=0.1774, simple_loss=0.2571, pruned_loss=0.04888, over 4899.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.03667, over 973273.25 frames.], batch size: 19, lr: 2.88e-04
2022-05-05 23:19:26,138 INFO [train.py:715] (1/8) Epoch 7, batch 25800, loss[loss=0.1266, simple_loss=0.1999, pruned_loss=0.02663, over 4749.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03693, over 974021.89 frames.], batch size: 19, lr: 2.88e-04
2022-05-05 23:20:08,464 INFO [train.py:715] (1/8) Epoch 7, batch 25850, loss[loss=0.1523, simple_loss=0.2262, pruned_loss=0.03918, over 4818.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2175, pruned_loss=0.03697, over 972830.71 frames.], batch size: 15, lr: 2.88e-04
2022-05-05 23:20:50,389 INFO [train.py:715] (1/8) Epoch 7, batch 25900, loss[loss=0.1392, simple_loss=0.2175, pruned_loss=0.0305, over 4796.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2185, pruned_loss=0.0376, over 973224.48 frames.], batch size: 24, lr: 2.88e-04
2022-05-05 23:21:31,302 INFO [train.py:715] (1/8) Epoch 7, batch 25950, loss[loss=0.1693, simple_loss=0.2448, pruned_loss=0.04692, over 4782.00 frames.], tot_loss[loss=0.1473, simple_loss=0.219, pruned_loss=0.03787, over 973160.92 frames.], batch size: 17, lr: 2.88e-04
2022-05-05 23:22:12,749 INFO [train.py:715] (1/8) Epoch 7, batch 26000, loss[loss=0.1155, simple_loss=0.1917, pruned_loss=0.01965, over 4790.00 frames.], tot_loss[loss=0.147, simple_loss=0.2186, pruned_loss=0.03772, over 973548.82 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:22:54,185 INFO [train.py:715] (1/8) Epoch 7, batch 26050, loss[loss=0.1421, simple_loss=0.2208, pruned_loss=0.03172, over 4801.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2182, pruned_loss=0.03744, over 972608.40 frames.], batch size: 21, lr: 2.88e-04
2022-05-05 23:23:36,133 INFO [train.py:715] (1/8) Epoch 7, batch 26100, loss[loss=0.1358, simple_loss=0.2131, pruned_loss=0.02922, over 4976.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2178, pruned_loss=0.03693, over 972241.68 frames.], batch size: 25, lr: 2.88e-04
2022-05-05 23:24:16,474 INFO [train.py:715] (1/8) Epoch 7, batch 26150, loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02856, over 4889.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2174, pruned_loss=0.03668, over 971975.01 frames.], batch size: 16, lr: 2.88e-04
2022-05-05 23:24:57,987 INFO [train.py:715] (1/8) Epoch 7, batch 26200, loss[loss=0.1298, simple_loss=0.1936, pruned_loss=0.03304, over 4829.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03663, over 972485.99 frames.], batch size: 26, lr: 2.88e-04
2022-05-05 23:25:39,232 INFO [train.py:715] (1/8) Epoch 7, batch 26250, loss[loss=0.1453, simple_loss=0.2161, pruned_loss=0.03726, over 4922.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2174, pruned_loss=0.03643, over 972908.75 frames.], batch size: 29, lr: 2.88e-04
2022-05-05 23:26:19,595 INFO [train.py:715] (1/8) Epoch 7, batch 26300, loss[loss=0.1686, simple_loss=0.2505, pruned_loss=0.04337, over 4949.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2172, pruned_loss=0.0365, over 973019.36 frames.], batch size: 23, lr: 2.88e-04
2022-05-05 23:26:59,774 INFO [train.py:715] (1/8) Epoch 7, batch 26350, loss[loss=0.1322, simple_loss=0.2081, pruned_loss=0.0281, over 4770.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2171, pruned_loss=0.03621, over 973796.74 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:27:40,226 INFO [train.py:715] (1/8) Epoch 7, batch 26400, loss[loss=0.1261, simple_loss=0.2004, pruned_loss=0.02592, over 4827.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03663, over 973309.02 frames.], batch size: 26, lr: 2.87e-04
2022-05-05 23:28:20,880 INFO [train.py:715] (1/8) Epoch 7, batch 26450, loss[loss=0.1082, simple_loss=0.1898, pruned_loss=0.01327, over 4792.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2172, pruned_loss=0.03668, over 973338.55 frames.], batch size: 24, lr: 2.87e-04
2022-05-05 23:29:00,622 INFO [train.py:715] (1/8) Epoch 7, batch 26500, loss[loss=0.1255, simple_loss=0.1926, pruned_loss=0.0292, over 4895.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03638, over 972609.71 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:29:40,322 INFO [train.py:715] (1/8) Epoch 7, batch 26550, loss[loss=0.1448, simple_loss=0.2278, pruned_loss=0.03092, over 4755.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03639, over 971727.68 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:30:20,796 INFO [train.py:715] (1/8) Epoch 7, batch 26600, loss[loss=0.1374, simple_loss=0.2129, pruned_loss=0.03094, over 4945.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03629, over 972968.74 frames.], batch size: 39, lr: 2.87e-04
2022-05-05 23:31:00,463 INFO [train.py:715] (1/8) Epoch 7, batch 26650, loss[loss=0.1654, simple_loss=0.2484, pruned_loss=0.04122, over 4897.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2175, pruned_loss=0.03646, over 972511.69 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:31:40,557 INFO [train.py:715] (1/8) Epoch 7, batch 26700, loss[loss=0.1069, simple_loss=0.1663, pruned_loss=0.02375, over 4785.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2164, pruned_loss=0.03614, over 972931.91 frames.], batch size: 12, lr: 2.87e-04
2022-05-05 23:32:21,250 INFO [train.py:715] (1/8) Epoch 7, batch 26750, loss[loss=0.1587, simple_loss=0.227, pruned_loss=0.04521, over 4863.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2174, pruned_loss=0.03678, over 972679.85 frames.], batch size: 32, lr: 2.87e-04
2022-05-05 23:33:01,193 INFO [train.py:715] (1/8) Epoch 7, batch 26800, loss[loss=0.1365, simple_loss=0.2123, pruned_loss=0.0304, over 4895.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2173, pruned_loss=0.03683, over 972585.77 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:33:40,955 INFO [train.py:715] (1/8) Epoch 7, batch 26850, loss[loss=0.158, simple_loss=0.226, pruned_loss=0.04501, over 4989.00 frames.], tot_loss[loss=0.146, simple_loss=0.2174, pruned_loss=0.03734, over 971999.27 frames.], batch size: 14, lr: 2.87e-04
2022-05-05 23:34:21,596 INFO [train.py:715] (1/8) Epoch 7, batch 26900, loss[loss=0.1512, simple_loss=0.2203, pruned_loss=0.04106, over 4760.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2177, pruned_loss=0.03722, over 971484.54 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:35:02,622 INFO [train.py:715] (1/8) Epoch 7, batch 26950, loss[loss=0.1774, simple_loss=0.2494, pruned_loss=0.05273, over 4767.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03719, over 970452.29 frames.], batch size: 18, lr: 2.87e-04
2022-05-05 23:35:42,953 INFO [train.py:715] (1/8) Epoch 7, batch 27000, loss[loss=0.1275, simple_loss=0.1943, pruned_loss=0.03034, over 4779.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2177, pruned_loss=0.03732, over 970328.61 frames.], batch size: 12, lr: 2.87e-04
2022-05-05 23:35:42,954 INFO [train.py:733] (1/8) Computing validation loss
2022-05-05 23:35:52,668 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.108, simple_loss=0.1928, pruned_loss=0.01156, over 914524.00 frames.
2022-05-05 23:36:33,217 INFO [train.py:715] (1/8) Epoch 7, batch 27050, loss[loss=0.1227, simple_loss=0.1965, pruned_loss=0.02447, over 4747.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03687, over 970879.58 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:37:14,392 INFO [train.py:715] (1/8) Epoch 7, batch 27100, loss[loss=0.1163, simple_loss=0.1897, pruned_loss=0.02148, over 4986.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2172, pruned_loss=0.03689, over 970515.32 frames.], batch size: 25, lr: 2.87e-04
2022-05-05 23:37:56,264 INFO [train.py:715] (1/8) Epoch 7, batch 27150, loss[loss=0.1714, simple_loss=0.2313, pruned_loss=0.05574, over 4911.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2178, pruned_loss=0.03737, over 971647.60 frames.], batch size: 18, lr: 2.87e-04
2022-05-05 23:38:37,517 INFO [train.py:715] (1/8) Epoch 7, batch 27200, loss[loss=0.1626, simple_loss=0.2257, pruned_loss=0.04973, over 4856.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2175, pruned_loss=0.03696, over 971940.04 frames.], batch size: 13, lr: 2.87e-04
2022-05-05 23:39:18,979 INFO [train.py:715] (1/8) Epoch 7, batch 27250, loss[loss=0.1518, simple_loss=0.2158, pruned_loss=0.04386, over 4751.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2184, pruned_loss=0.03746, over 972202.40 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:40:00,807 INFO [train.py:715] (1/8) Epoch 7, batch 27300, loss[loss=0.1608, simple_loss=0.2335, pruned_loss=0.04404, over 4852.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03676, over 972133.47 frames.], batch size: 20, lr: 2.87e-04
2022-05-05 23:40:41,775 INFO [train.py:715] (1/8) Epoch 7, batch 27350, loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.03497, over 4760.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2181, pruned_loss=0.03687, over 972052.26 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:41:23,063 INFO [train.py:715] (1/8) Epoch 7, batch 27400, loss[loss=0.1341, simple_loss=0.207, pruned_loss=0.0306, over 4983.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2175, pruned_loss=0.03659, over 972904.43 frames.], batch size: 25, lr: 2.87e-04
2022-05-05 23:42:04,090 INFO [train.py:715] (1/8) Epoch 7, batch 27450, loss[loss=0.1296, simple_loss=0.1992, pruned_loss=0.02997, over 4889.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2175, pruned_loss=0.03664, over 973635.25 frames.], batch size: 16, lr: 2.87e-04
2022-05-05 23:42:45,312 INFO [train.py:715] (1/8) Epoch 7, batch 27500, loss[loss=0.144, simple_loss=0.2084, pruned_loss=0.03977, over 4964.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2176, pruned_loss=0.03661, over 973804.17 frames.], batch size: 15, lr: 2.87e-04
2022-05-05 23:43:25,881 INFO [train.py:715] (1/8) Epoch 7, batch 27550, loss[loss=0.1365, simple_loss=0.2136, pruned_loss=0.0297, over 4792.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2192, pruned_loss=0.03762, over 973160.33 frames.], batch size: 24, lr: 2.87e-04
2022-05-05 23:44:06,407 INFO [train.py:715] (1/8) Epoch 7, batch 27600, loss[loss=0.1273, simple_loss=0.1951, pruned_loss=0.02973, over 4941.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2189, pruned_loss=0.0378, over 972659.25 frames.], batch size: 29, lr: 2.87e-04
2022-05-05 23:44:47,794 INFO [train.py:715] (1/8) Epoch 7, batch 27650, loss[loss=0.1289, simple_loss=0.2029, pruned_loss=0.02744, over 4935.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2186, pruned_loss=0.03777, over 971295.17 frames.], batch size: 21, lr: 2.87e-04
2022-05-05 23:45:28,513 INFO [train.py:715] (1/8) Epoch 7, batch 27700, loss[loss=0.1253, simple_loss=0.2045, pruned_loss=0.02309, over 4742.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2181, pruned_loss=0.03742, over 970986.37 frames.], batch size: 16, lr: 2.87e-04
2022-05-05 23:46:09,257 INFO [train.py:715] (1/8) Epoch 7, batch 27750, loss[loss=0.1922, simple_loss=0.2549, pruned_loss=0.0647, over 4962.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2177, pruned_loss=0.03764, over 971841.38 frames.], batch size: 39, lr: 2.87e-04
2022-05-05 23:46:50,134 INFO [train.py:715] (1/8) Epoch 7, batch 27800, loss[loss=0.1304, simple_loss=0.2087, pruned_loss=0.02611, over 4698.00 frames.], tot_loss[loss=0.1466, simple_loss=0.218, pruned_loss=0.03758, over 970980.09 frames.], batch size: 15, lr: 2.87e-04
2022-05-05 23:47:31,345 INFO [train.py:715] (1/8) Epoch 7, batch 27850, loss[loss=0.1553, simple_loss=0.222, pruned_loss=0.04429, over 4906.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2184, pruned_loss=0.03758, over 971537.43 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:48:11,410 INFO [train.py:715] (1/8) Epoch 7, batch 27900, loss[loss=0.1638, simple_loss=0.2299, pruned_loss=0.04883, over 4868.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2193, pruned_loss=0.03789, over 971200.56 frames.], batch size: 39, lr: 2.87e-04
2022-05-05 23:48:52,371 INFO [train.py:715] (1/8) Epoch 7, batch 27950, loss[loss=0.1494, simple_loss=0.2144, pruned_loss=0.04219, over 4862.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2185, pruned_loss=0.03727, over 970590.12 frames.], batch size: 20, lr: 2.87e-04
2022-05-05 23:49:33,562 INFO [train.py:715] (1/8) Epoch 7, batch 28000, loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03144, over 4771.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03693, over 970634.57 frames.], batch size: 17, lr: 2.87e-04
2022-05-05 23:50:14,250 INFO [train.py:715] (1/8) Epoch 7, batch 28050, loss[loss=0.1631, simple_loss=0.2392, pruned_loss=0.04349, over 4935.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2169, pruned_loss=0.03687, over 970553.46 frames.], batch size: 21, lr: 2.87e-04
2022-05-05 23:50:54,414 INFO [train.py:715] (1/8) Epoch 7, batch 28100, loss[loss=0.1623, simple_loss=0.2294, pruned_loss=0.04757, over 4741.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2172, pruned_loss=0.03731, over 970908.43 frames.], batch size: 16, lr: 2.87e-04
2022-05-05 23:51:35,211 INFO [train.py:715] (1/8) Epoch 7, batch 28150, loss[loss=0.1412, simple_loss=0.2068, pruned_loss=0.0378, over 4881.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2178, pruned_loss=0.03757, over 971879.59 frames.], batch size: 16, lr: 2.87e-04
2022-05-05 23:52:16,651 INFO [train.py:715] (1/8) Epoch 7, batch 28200, loss[loss=0.1168, simple_loss=0.1943, pruned_loss=0.0197, over 4900.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03689, over 972159.71 frames.], batch size: 17, lr: 2.87e-04
2022-05-05 23:52:56,858 INFO [train.py:715] (1/8) Epoch 7, batch 28250, loss[loss=0.1396, simple_loss=0.2045, pruned_loss=0.03736, over 4786.00 frames.], tot_loss[loss=0.1462, simple_loss=0.218, pruned_loss=0.0372, over 971597.07 frames.], batch size: 17, lr: 2.87e-04
2022-05-05 23:53:38,378 INFO [train.py:715] (1/8) Epoch 7, batch 28300, loss[loss=0.1504, simple_loss=0.2285, pruned_loss=0.03615, over 4814.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03701, over 971759.43 frames.], batch size: 25, lr: 2.86e-04
2022-05-05 23:54:21,489 INFO [train.py:715] (1/8) Epoch 7, batch 28350, loss[loss=0.1418, simple_loss=0.2159, pruned_loss=0.03382, over 4947.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2173, pruned_loss=0.0371, over 972318.34 frames.], batch size: 23, lr: 2.86e-04
2022-05-05 23:55:01,301 INFO [train.py:715] (1/8) Epoch 7, batch 28400, loss[loss=0.1684, simple_loss=0.2479, pruned_loss=0.04445, over 4799.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2173, pruned_loss=0.03717, over 972821.74 frames.], batch size: 24, lr: 2.86e-04
2022-05-05 23:55:40,834 INFO [train.py:715] (1/8) Epoch 7, batch 28450, loss[loss=0.1484, simple_loss=0.2217, pruned_loss=0.03755, over 4803.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03702, over 972576.72 frames.], batch size: 14, lr: 2.86e-04
2022-05-05 23:56:20,937 INFO [train.py:715] (1/8) Epoch 7, batch 28500, loss[loss=0.1674, simple_loss=0.2449, pruned_loss=0.045, over 4945.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2172, pruned_loss=0.03699, over 972372.39 frames.], batch size: 21, lr: 2.86e-04
2022-05-05 23:57:01,431 INFO [train.py:715] (1/8) Epoch 7, batch 28550, loss[loss=0.127, simple_loss=0.1967, pruned_loss=0.02858, over 4958.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2167, pruned_loss=0.03672, over 972313.40 frames.], batch size: 14, lr: 2.86e-04
2022-05-05 23:57:41,424 INFO [train.py:715] (1/8) Epoch 7, batch 28600, loss[loss=0.1324, simple_loss=0.208, pruned_loss=0.02838, over 4910.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2167, pruned_loss=0.03672, over 972929.94 frames.], batch size: 17, lr: 2.86e-04
2022-05-05 23:58:21,641 INFO [train.py:715] (1/8) Epoch 7, batch 28650, loss[loss=0.112, simple_loss=0.1917, pruned_loss=0.01615, over 4906.00 frames.], tot_loss[loss=0.144, simple_loss=0.2162, pruned_loss=0.03594, over 973251.78 frames.], batch size: 29, lr: 2.86e-04
2022-05-05 23:59:03,082 INFO [train.py:715] (1/8) Epoch 7, batch 28700, loss[loss=0.1529, simple_loss=0.2228, pruned_loss=0.04152, over 4938.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2168, pruned_loss=0.03634, over 973384.61 frames.], batch size: 21, lr: 2.86e-04
2022-05-05 23:59:43,965 INFO [train.py:715] (1/8) Epoch 7, batch 28750, loss[loss=0.154, simple_loss=0.2191, pruned_loss=0.04449, over 4841.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03636, over 973652.82 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:00:24,198 INFO [train.py:715] (1/8) Epoch 7, batch 28800, loss[loss=0.1537, simple_loss=0.2257, pruned_loss=0.04083, over 4777.00 frames.], tot_loss[loss=0.1449, simple_loss=0.217, pruned_loss=0.03637, over 973290.39 frames.], batch size: 17, lr: 2.86e-04
2022-05-06 00:01:04,809 INFO [train.py:715] (1/8) Epoch 7, batch 28850, loss[loss=0.1544, simple_loss=0.2293, pruned_loss=0.03975, over 4984.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2167, pruned_loss=0.03639, over 972941.59 frames.], batch size: 39, lr: 2.86e-04
2022-05-06 00:01:45,179 INFO [train.py:715] (1/8) Epoch 7, batch 28900, loss[loss=0.1399, simple_loss=0.2201, pruned_loss=0.02986, over 4824.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.03665, over 973027.28 frames.], batch size: 21, lr: 2.86e-04
2022-05-06 00:02:24,704 INFO [train.py:715] (1/8) Epoch 7, batch 28950, loss[loss=0.1477, simple_loss=0.1967, pruned_loss=0.04933, over 4831.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03647, over 972586.52 frames.], batch size: 12, lr: 2.86e-04
2022-05-06 00:03:04,265 INFO [train.py:715] (1/8) Epoch 7, batch 29000, loss[loss=0.1733, simple_loss=0.2289, pruned_loss=0.05886, over 4954.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.03677, over 972783.96 frames.], batch size: 14, lr: 2.86e-04
2022-05-06 00:03:44,917 INFO [train.py:715] (1/8) Epoch 7, batch 29050, loss[loss=0.1692, simple_loss=0.2363, pruned_loss=0.05107, over 4951.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.0366, over 972491.70 frames.], batch size: 39, lr: 2.86e-04
2022-05-06 00:04:24,485 INFO [train.py:715] (1/8) Epoch 7, batch 29100, loss[loss=0.157, simple_loss=0.24, pruned_loss=0.03698, over 4837.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2168, pruned_loss=0.0362, over 972677.78 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:05:04,258 INFO [train.py:715] (1/8) Epoch 7, batch 29150, loss[loss=0.1766, simple_loss=0.2435, pruned_loss=0.05485, over 4839.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2156, pruned_loss=0.03594, over 972561.15 frames.], batch size: 13, lr: 2.86e-04
2022-05-06 00:05:44,151 INFO [train.py:715] (1/8) Epoch 7, batch 29200, loss[loss=0.1306, simple_loss=0.1979, pruned_loss=0.03171, over 4872.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2163, pruned_loss=0.03642, over 972400.68 frames.], batch size: 32, lr: 2.86e-04
2022-05-06 00:06:24,429 INFO [train.py:715] (1/8) Epoch 7, batch 29250, loss[loss=0.1652, simple_loss=0.24, pruned_loss=0.04522, over 4725.00 frames.], tot_loss[loss=0.1451, simple_loss=0.217, pruned_loss=0.03659, over 972976.89 frames.], batch size: 16, lr: 2.86e-04
2022-05-06 00:07:04,336 INFO [train.py:715] (1/8) Epoch 7, batch 29300, loss[loss=0.1483, simple_loss=0.2196, pruned_loss=0.03851, over 4907.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03695, over 973279.49 frames.], batch size: 23, lr: 2.86e-04
2022-05-06 00:07:44,022 INFO [train.py:715] (1/8) Epoch 7, batch 29350, loss[loss=0.138, simple_loss=0.2135, pruned_loss=0.03123, over 4779.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2172, pruned_loss=0.0365, over 973445.55 frames.], batch size: 17, lr: 2.86e-04
2022-05-06 00:08:24,291 INFO [train.py:715] (1/8) Epoch 7, batch 29400, loss[loss=0.1344, simple_loss=0.2106, pruned_loss=0.02908, over 4817.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03662, over 972917.64 frames.], batch size: 27, lr: 2.86e-04
2022-05-06 00:09:03,569 INFO [train.py:715] (1/8) Epoch 7, batch 29450, loss[loss=0.1784, simple_loss=0.2565, pruned_loss=0.05021, over 4815.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.03697, over 972145.01 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:09:43,855 INFO [train.py:715] (1/8) Epoch 7, batch 29500, loss[loss=0.1239, simple_loss=0.2112, pruned_loss=0.0183, over 4698.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2183, pruned_loss=0.03712, over 972163.35 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:10:23,578 INFO [train.py:715] (1/8) Epoch 7, batch 29550, loss[loss=0.1446, simple_loss=0.214, pruned_loss=0.03759, over 4813.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2176, pruned_loss=0.03674, over 972465.10 frames.], batch size: 27, lr: 2.86e-04
2022-05-06 00:11:03,256 INFO [train.py:715] (1/8) Epoch 7, batch 29600, loss[loss=0.1925, simple_loss=0.25, pruned_loss=0.06751, over 4849.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03712, over 972283.09 frames.], batch size: 30, lr: 2.86e-04
2022-05-06 00:11:43,212 INFO [train.py:715] (1/8) Epoch 7, batch 29650, loss[loss=0.1362, simple_loss=0.2104, pruned_loss=0.03102, over 4704.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2185, pruned_loss=0.03751, over 972261.09 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:12:23,008 INFO [train.py:715] (1/8) Epoch 7, batch 29700, loss[loss=0.167, simple_loss=0.2344, pruned_loss=0.04984, over 4843.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2194, pruned_loss=0.03771, over 972224.94 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:13:02,668 INFO [train.py:715] (1/8) Epoch 7, batch 29750, loss[loss=0.1328, simple_loss=0.2104, pruned_loss=0.02757, over 4881.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2187, pruned_loss=0.03732, over 972862.29 frames.], batch size: 22, lr: 2.86e-04
2022-05-06 00:13:42,298 INFO [train.py:715] (1/8) Epoch 7, batch 29800, loss[loss=0.1354, simple_loss=0.2025, pruned_loss=0.03418, over 4924.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2189, pruned_loss=0.03743, over 973452.29 frames.], batch size: 23, lr: 2.86e-04
2022-05-06 00:14:22,418 INFO [train.py:715] (1/8) Epoch 7, batch 29850, loss[loss=0.1478, simple_loss=0.2173, pruned_loss=0.03912, over 4844.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03706, over 973642.30 frames.], batch size: 32, lr: 2.86e-04
2022-05-06 00:15:02,289 INFO [train.py:715] (1/8) Epoch 7, batch 29900, loss[loss=0.1479, simple_loss=0.2117, pruned_loss=0.04204, over 4880.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2183, pruned_loss=0.03741, over 973406.46 frames.], batch size: 16, lr: 2.86e-04
2022-05-06 00:15:41,868 INFO [train.py:715] (1/8) Epoch 7, batch 29950, loss[loss=0.1391, simple_loss=0.2175, pruned_loss=0.0303, over 4803.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2185, pruned_loss=0.03722, over 973514.47 frames.], batch size: 21, lr: 2.86e-04
2022-05-06 00:16:21,230 INFO [train.py:715] (1/8) Epoch 7, batch 30000, loss[loss=0.1536, simple_loss=0.2273, pruned_loss=0.03998, over 4854.00 frames.], tot_loss[loss=0.1457, simple_loss=0.218, pruned_loss=0.03671, over 973673.88 frames.], batch size: 32, lr: 2.86e-04
2022-05-06 00:16:21,231 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 00:16:41,747 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.1081, simple_loss=0.1929, pruned_loss=0.01164, over 914524.00 frames.
2022-05-06 00:17:21,559 INFO [train.py:715] (1/8) Epoch 7, batch 30050, loss[loss=0.1622, simple_loss=0.2381, pruned_loss=0.04318, over 4891.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2175, pruned_loss=0.03643, over 973782.54 frames.], batch size: 19, lr: 2.86e-04
2022-05-06 00:18:00,842 INFO [train.py:715] (1/8) Epoch 7, batch 30100, loss[loss=0.1764, simple_loss=0.2474, pruned_loss=0.05272, over 4707.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.037, over 973743.81 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:18:40,792 INFO [train.py:715] (1/8) Epoch 7, batch 30150, loss[loss=0.1459, simple_loss=0.2233, pruned_loss=0.03422, over 4913.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2187, pruned_loss=0.03784, over 972346.09 frames.], batch size: 29, lr: 2.86e-04
2022-05-06 00:19:20,436 INFO [train.py:715] (1/8) Epoch 7, batch 30200, loss[loss=0.1452, simple_loss=0.2217, pruned_loss=0.03429, over 4948.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2181, pruned_loss=0.0375, over 973196.42 frames.], batch size: 23, lr: 2.85e-04
2022-05-06 00:20:00,696 INFO [train.py:715] (1/8) Epoch 7, batch 30250, loss[loss=0.1382, simple_loss=0.2094, pruned_loss=0.03352, over 4974.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2179, pruned_loss=0.03721, over 973406.82 frames.], batch size: 40, lr: 2.85e-04
2022-05-06 00:20:39,873 INFO [train.py:715] (1/8) Epoch 7, batch 30300, loss[loss=0.1167, simple_loss=0.1838, pruned_loss=0.02487, over 4988.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03711, over 973127.32 frames.], batch size: 25, lr: 2.85e-04
2022-05-06 00:21:19,496 INFO [train.py:715] (1/8) Epoch 7, batch 30350, loss[loss=0.1435, simple_loss=0.2227, pruned_loss=0.03218, over 4981.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2181, pruned_loss=0.03752, over 973196.24 frames.], batch size: 15, lr: 2.85e-04
2022-05-06 00:21:58,991 INFO [train.py:715] (1/8) Epoch 7, batch 30400, loss[loss=0.1241, simple_loss=0.1984, pruned_loss=0.0249, over 4911.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2173, pruned_loss=0.03688, over 972947.00 frames.], batch size: 17, lr: 2.85e-04
2022-05-06 00:22:38,983 INFO [train.py:715] (1/8) Epoch 7, batch 30450, loss[loss=0.16, simple_loss=0.2253, pruned_loss=0.04733, over 4835.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03682, over 972920.71 frames.], batch size: 30, lr: 2.85e-04
2022-05-06 00:23:18,902 INFO [train.py:715] (1/8) Epoch 7, batch 30500, loss[loss=0.1284, simple_loss=0.1992, pruned_loss=0.02877, over 4844.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2171, pruned_loss=0.03693, over 973064.21 frames.], batch size: 30, lr: 2.85e-04
2022-05-06 00:23:58,833 INFO [train.py:715] (1/8) Epoch 7, batch 30550, loss[loss=0.1475, simple_loss=0.2201, pruned_loss=0.03745, over 4950.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03684, over 973743.41 frames.], batch size: 14, lr: 2.85e-04
2022-05-06 00:24:38,537 INFO [train.py:715] (1/8) Epoch 7, batch 30600, loss[loss=0.1314, simple_loss=0.2053, pruned_loss=0.0287, over 4889.00 frames.], tot_loss[loss=0.1451, simple_loss=0.217, pruned_loss=0.03662, over 973481.70 frames.], batch size: 22, lr: 2.85e-04
2022-05-06 00:25:18,174 INFO [train.py:715] (1/8) Epoch 7, batch 30650, loss[loss=0.135, simple_loss=0.2113, pruned_loss=0.02932, over 4956.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03636, over 973222.00 frames.], batch size: 39, lr: 2.85e-04
2022-05-06 00:25:57,792 INFO [train.py:715] (1/8) Epoch 7, batch 30700, loss[loss=0.1292, simple_loss=0.2062, pruned_loss=0.02605, over 4892.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2173, pruned_loss=0.03699, over 973824.11 frames.], batch size: 32, lr: 2.85e-04
2022-05-06 00:26:36,836 INFO [train.py:715] (1/8) Epoch 7, batch 30750, loss[loss=0.1305, simple_loss=0.1975, pruned_loss=0.03179, over 4732.00 frames.], tot_loss[loss=0.146, simple_loss=0.2179, pruned_loss=0.03708, over 973312.18 frames.], batch size: 12, lr: 2.85e-04
2022-05-06 00:27:15,908 INFO [train.py:715] (1/8) Epoch 7, batch 30800, loss[loss=0.1217, simple_loss=0.1946, pruned_loss=0.02439, over 4815.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.0372, over 973016.44 frames.], batch size: 13, lr: 2.85e-04
2022-05-06 00:27:55,696 INFO [train.py:715] (1/8) Epoch 7, batch 30850, loss[loss=0.1556, simple_loss=0.2267, pruned_loss=0.04224, over 4814.00 frames.], tot_loss[loss=0.1462, simple_loss=0.218, pruned_loss=0.03719, over 973104.47 frames.], batch size: 25, lr: 2.85e-04
2022-05-06 00:28:35,196 INFO [train.py:715] (1/8) Epoch 7, batch 30900, loss[loss=0.1431, simple_loss=0.2143, pruned_loss=0.03593, over 4953.00 frames.], tot_loss[loss=0.1463, simple_loss=0.218, pruned_loss=0.03728, over 973619.98 frames.], batch size: 39, lr: 2.85e-04
2022-05-06 00:29:15,595 INFO [train.py:715] (1/8) Epoch 7, batch 30950, loss[loss=0.1421, simple_loss=0.2258, pruned_loss=0.02921, over 4944.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2176, pruned_loss=0.03709, over 973902.24 frames.], batch size: 23, lr: 2.85e-04
2022-05-06 00:29:54,987 INFO [train.py:715] (1/8) Epoch 7, batch 31000, loss[loss=0.1538, simple_loss=0.2161, pruned_loss=0.04576, over 4893.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2178, pruned_loss=0.03727, over 973361.68 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:30:34,545 INFO [train.py:715] (1/8) Epoch 7, batch 31050, loss[loss=0.1367, simple_loss=0.2, pruned_loss=0.03669, over 4753.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2169, pruned_loss=0.03699, over 972637.60 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:31:14,381 INFO [train.py:715] (1/8) Epoch 7, batch 31100, loss[loss=0.15, simple_loss=0.2172, pruned_loss=0.04145, over 4769.00 frames.], tot_loss[loss=0.147, simple_loss=0.2183, pruned_loss=0.03782, over 971678.87 frames.], batch size: 14, lr: 2.85e-04
2022-05-06 00:31:54,496 INFO [train.py:715] (1/8) Epoch 7, batch 31150, loss[loss=0.1501, simple_loss=0.2379, pruned_loss=0.03117, over 4787.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2181, pruned_loss=0.03776, over 972254.59 frames.], batch size: 17, lr: 2.85e-04
2022-05-06 00:32:33,852 INFO [train.py:715] (1/8) Epoch 7, batch 31200, loss[loss=0.1307, simple_loss=0.1925, pruned_loss=0.03449, over 4776.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2173, pruned_loss=0.03748, over 972494.11 frames.], batch size: 17, lr: 2.85e-04
2022-05-06 00:33:13,820 INFO [train.py:715] (1/8) Epoch 7, batch 31250, loss[loss=0.14, simple_loss=0.2221, pruned_loss=0.02893, over 4932.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2165, pruned_loss=0.03708, over 973556.83 frames.], batch size: 23, lr: 2.85e-04
2022-05-06 00:33:54,548 INFO [train.py:715] (1/8) Epoch 7, batch 31300, loss[loss=0.1587, simple_loss=0.2338, pruned_loss=0.04176, over 4825.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2161, pruned_loss=0.03668, over 973770.58 frames.], batch size: 30, lr: 2.85e-04
2022-05-06 00:34:34,123 INFO [train.py:715] (1/8) Epoch 7, batch 31350, loss[loss=0.1239, simple_loss=0.2001, pruned_loss=0.02383, over 4864.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2158, pruned_loss=0.03651, over 973370.00 frames.], batch size: 20, lr: 2.85e-04
2022-05-06 00:35:14,072 INFO [train.py:715] (1/8) Epoch 7, batch 31400, loss[loss=0.1206, simple_loss=0.1831, pruned_loss=0.02901, over 4850.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2159, pruned_loss=0.03641, over 973632.02 frames.], batch size: 12, lr: 2.85e-04
2022-05-06 00:35:53,415 INFO [train.py:715] (1/8) Epoch 7, batch 31450, loss[loss=0.1529, simple_loss=0.2288, pruned_loss=0.03846, over 4967.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2163, pruned_loss=0.03667, over 974219.65 frames.], batch size: 15, lr: 2.85e-04
2022-05-06 00:36:33,193 INFO [train.py:715] (1/8) Epoch 7, batch 31500, loss[loss=0.148, simple_loss=0.2127, pruned_loss=0.04163, over 4898.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2164, pruned_loss=0.03686, over 973783.92 frames.], batch size: 32, lr: 2.85e-04
2022-05-06 00:37:12,322 INFO [train.py:715] (1/8) Epoch 7, batch 31550, loss[loss=0.1486, simple_loss=0.2085, pruned_loss=0.0444, over 4779.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2167, pruned_loss=0.03711, over 972811.22 frames.], batch size: 12, lr: 2.85e-04
2022-05-06 00:37:52,280 INFO [train.py:715] (1/8) Epoch 7, batch 31600, loss[loss=0.1613, simple_loss=0.2353, pruned_loss=0.04371, over 4869.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2165, pruned_loss=0.03689, over 973538.15 frames.], batch size: 22, lr: 2.85e-04
2022-05-06 00:38:32,109 INFO [train.py:715] (1/8) Epoch 7, batch 31650, loss[loss=0.1549, simple_loss=0.218, pruned_loss=0.04589, over 4801.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2166, pruned_loss=0.03687, over 973353.08 frames.], batch size: 17, lr: 2.85e-04
2022-05-06 00:39:11,531 INFO [train.py:715] (1/8) Epoch 7, batch 31700, loss[loss=0.1667, simple_loss=0.2333, pruned_loss=0.05003, over 4917.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2168, pruned_loss=0.03642, over 973582.62 frames.], batch size: 18, lr: 2.85e-04
2022-05-06 00:39:51,234 INFO [train.py:715] (1/8) Epoch 7, batch 31750, loss[loss=0.1301, simple_loss=0.1932, pruned_loss=0.03346, over 4850.00 frames.], tot_loss[loss=0.1451, simple_loss=0.217, pruned_loss=0.03662, over 973019.05 frames.], batch size: 32, lr: 2.85e-04
2022-05-06 00:40:30,495 INFO [train.py:715] (1/8) Epoch 7, batch 31800, loss[loss=0.1242, simple_loss=0.2027, pruned_loss=0.0228, over 4916.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2169, pruned_loss=0.03671, over 973509.94 frames.], batch size: 17, lr: 2.85e-04
2022-05-06 00:41:09,611 INFO [train.py:715] (1/8) Epoch 7, batch 31850, loss[loss=0.1441, simple_loss=0.2095, pruned_loss=0.0393, over 4927.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2158, pruned_loss=0.03599, over 973879.84 frames.], batch size: 18, lr: 2.85e-04
2022-05-06 00:41:49,869 INFO [train.py:715] (1/8) Epoch 7, batch 31900, loss[loss=0.1366, simple_loss=0.2135, pruned_loss=0.02985, over 4835.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.03607, over 973258.23 frames.], batch size: 15, lr: 2.85e-04
2022-05-06 00:42:30,608 INFO [train.py:715] (1/8) Epoch 7, batch 31950, loss[loss=0.1566, simple_loss=0.2276, pruned_loss=0.04274, over 4783.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03578, over 973255.91 frames.], batch size: 18, lr: 2.85e-04
2022-05-06 00:43:11,068 INFO [train.py:715] (1/8) Epoch 7, batch 32000, loss[loss=0.1312, simple_loss=0.1955, pruned_loss=0.03349, over 4781.00 frames.], tot_loss[loss=0.144, simple_loss=0.2162, pruned_loss=0.03588, over 972837.23 frames.], batch size: 18, lr: 2.85e-04
2022-05-06 00:43:50,737 INFO [train.py:715] (1/8) Epoch 7, batch 32050, loss[loss=0.1689, simple_loss=0.2425, pruned_loss=0.04765, over 4914.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.0361, over 973120.51 frames.], batch size: 18, lr: 2.85e-04
2022-05-06 00:44:30,671 INFO [train.py:715] (1/8) Epoch 7, batch 32100, loss[loss=0.1284, simple_loss=0.1956, pruned_loss=0.03063, over 4787.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2164, pruned_loss=0.03591, over 973265.17 frames.], batch size: 18, lr: 2.85e-04
2022-05-06 00:45:10,485 INFO [train.py:715] (1/8) Epoch 7, batch 32150, loss[loss=0.1718, simple_loss=0.2479, pruned_loss=0.04788, over 4817.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2166, pruned_loss=0.03643, over 972794.61 frames.], batch size: 15, lr: 2.84e-04
2022-05-06 00:45:50,039 INFO [train.py:715] (1/8) Epoch 7, batch 32200, loss[loss=0.154, simple_loss=0.2233, pruned_loss=0.04236, over 4942.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2161, pruned_loss=0.03629, over 973529.86 frames.], batch size: 21, lr: 2.84e-04
2022-05-06 00:46:29,888 INFO [train.py:715] (1/8) Epoch 7, batch 32250, loss[loss=0.1517, simple_loss=0.2365, pruned_loss=0.03346, over 4795.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2161, pruned_loss=0.03615, over 973585.64 frames.], batch size: 24, lr: 2.84e-04
2022-05-06 00:47:09,681 INFO [train.py:715] (1/8) Epoch 7, batch 32300, loss[loss=0.1669, simple_loss=0.2386, pruned_loss=0.04762, over 4769.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2173, pruned_loss=0.03688, over 974084.38 frames.], batch size: 18, lr: 2.84e-04
2022-05-06 00:47:50,018 INFO [train.py:715] (1/8) Epoch 7, batch 32350, loss[loss=0.1545, simple_loss=0.2248, pruned_loss=0.04209, over 4802.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2176, pruned_loss=0.03713, over 973956.76 frames.], batch size: 17, lr: 2.84e-04
2022-05-06 00:48:29,380 INFO [train.py:715] (1/8) Epoch 7, batch 32400, loss[loss=0.1308, simple_loss=0.2008, pruned_loss=0.03043, over 4654.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.0371, over 972765.03 frames.], batch size: 13, lr: 2.84e-04
2022-05-06 00:49:09,272 INFO [train.py:715] (1/8) Epoch 7, batch 32450, loss[loss=0.1451, simple_loss=0.2236, pruned_loss=0.03333, over 4923.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2182, pruned_loss=0.03726, over 973004.31 frames.], batch size: 18, lr: 2.84e-04
2022-05-06 00:49:48,745 INFO [train.py:715] (1/8) Epoch 7, batch 32500, loss[loss=0.1549, simple_loss=0.2265, pruned_loss=0.04163, over 4822.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2165, pruned_loss=0.03651, over 972243.25 frames.], batch size: 26, lr: 2.84e-04
2022-05-06 00:50:28,306 INFO [train.py:715] (1/8) Epoch 7, batch 32550, loss[loss=0.1619, simple_loss=0.2236, pruned_loss=0.05005, over 4837.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2162, pruned_loss=0.03671, over 971362.04 frames.], batch size: 26, lr: 2.84e-04
2022-05-06 00:51:08,058 INFO [train.py:715] (1/8) Epoch 7, batch 32600, loss[loss=0.1858, simple_loss=0.2469, pruned_loss=0.06237, over 4902.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2175, pruned_loss=0.03694, over 971600.53 frames.], batch size: 19, lr: 2.84e-04
2022-05-06 00:51:47,571 INFO [train.py:715] (1/8) Epoch 7, batch 32650, loss[loss=0.1276, simple_loss=0.1953, pruned_loss=0.03001, over 4984.00 frames.], tot_loss[loss=0.146, simple_loss=0.2177, pruned_loss=0.03719, over 971666.99 frames.], batch size: 25, lr: 2.84e-04
2022-05-06 00:52:27,388 INFO [train.py:715] (1/8) Epoch 7, batch 32700, loss[loss=0.1332, simple_loss=0.204, pruned_loss=0.03121, over 4810.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03663, over 971495.91 frames.], batch size: 25, lr: 2.84e-04
2022-05-06 00:53:06,825 INFO [train.py:715] (1/8) Epoch 7, batch 32750, loss[loss=0.1673, simple_loss=0.2462, pruned_loss=0.04416, over 4915.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03658, over 971174.93 frames.], batch size: 23, lr: 2.84e-04
2022-05-06 00:53:47,330 INFO [train.py:715] (1/8) Epoch 7, batch 32800, loss[loss=0.1266, simple_loss=0.1993, pruned_loss=0.02696, over 4893.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.03667, over 971272.48 frames.], batch size: 22, lr: 2.84e-04
2022-05-06 00:54:27,998 INFO [train.py:715] (1/8) Epoch 7, batch 32850, loss[loss=0.1619, simple_loss=0.2125, pruned_loss=0.0556, over 4970.00 frames.], tot_loss[loss=0.1452, simple_loss=0.217, pruned_loss=0.0367, over 970796.07 frames.], batch size: 14, lr: 2.84e-04
2022-05-06 00:55:08,138 INFO [train.py:715] (1/8) Epoch 7, batch 32900, loss[loss=0.1564, simple_loss=0.2327, pruned_loss=0.04005, over 4898.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.03674, over 970906.61 frames.], batch size: 19, lr: 2.84e-04
2022-05-06 00:55:48,480 INFO [train.py:715] (1/8) Epoch 7, batch 32950, loss[loss=0.1383, simple_loss=0.2001, pruned_loss=0.03823, over 4894.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2171, pruned_loss=0.03715, over 971646.94 frames.], batch size: 19, lr: 2.84e-04
2022-05-06 00:56:28,437 INFO [train.py:715] (1/8) Epoch 7, batch 33000, loss[loss=0.1703, simple_loss=0.2352, pruned_loss=0.05272, over 4800.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03659, over 970948.66 frames.], batch size: 18, lr: 2.84e-04
2022-05-06 00:56:28,438 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 00:56:38,009 INFO [train.py:742] (1/8) Epoch 7, validation: loss=0.108, simple_loss=0.1927, pruned_loss=0.01164, over 914524.00 frames.
2022-05-06 00:57:17,525 INFO [train.py:715] (1/8) Epoch 7, batch 33050, loss[loss=0.1471, simple_loss=0.2252, pruned_loss=0.03448, over 4810.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2169, pruned_loss=0.03671, over 972054.91 frames.], batch size: 25, lr: 2.84e-04
2022-05-06 00:57:57,510 INFO [train.py:715] (1/8) Epoch 7, batch 33100, loss[loss=0.1234, simple_loss=0.2087, pruned_loss=0.01907, over 4909.00 frames.], tot_loss[loss=0.1457, simple_loss=0.218, pruned_loss=0.03673, over 971804.85 frames.], batch size: 18, lr: 2.84e-04
2022-05-06 00:58:36,961 INFO [train.py:715] (1/8) Epoch 7, batch 33150, loss[loss=0.1226, simple_loss=0.2101, pruned_loss=0.01755, over 4941.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2177, pruned_loss=0.03643, over 972406.34 frames.], batch size: 21, lr: 2.84e-04
2022-05-06 00:59:16,731 INFO [train.py:715] (1/8) Epoch 7, batch 33200, loss[loss=0.1189, simple_loss=0.1963, pruned_loss=0.02079, over 4921.00 frames.], tot_loss[loss=0.145, simple_loss=0.2176, pruned_loss=0.03622, over 972260.96 frames.], batch size: 29, lr: 2.84e-04
2022-05-06 00:59:56,301 INFO [train.py:715] (1/8) Epoch 7, batch 33250, loss[loss=0.1455, simple_loss=0.2252, pruned_loss=0.03292, over 4918.00 frames.], tot_loss[loss=0.1458, simple_loss=0.218, pruned_loss=0.03679, over 972994.59 frames.], batch size: 29, lr: 2.84e-04
2022-05-06 01:00:35,764 INFO [train.py:715] (1/8) Epoch 7, batch 33300, loss[loss=0.1584, simple_loss=0.2252, pruned_loss=0.0458, over 4861.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2185, pruned_loss=0.03705, over 972775.23 frames.], batch size: 30, lr: 2.84e-04
2022-05-06 01:01:15,281 INFO [train.py:715] (1/8) Epoch 7, batch 33350, loss[loss=0.1098, simple_loss=0.1867, pruned_loss=0.01644, over 4922.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2178, pruned_loss=0.0366, over 973114.25 frames.], batch size: 29, lr: 2.84e-04
2022-05-06 01:01:55,578 INFO [train.py:715] (1/8) Epoch 7, batch 33400, loss[loss=0.1443, simple_loss=0.2157, pruned_loss=0.03648, over 4914.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2175, pruned_loss=0.03655, over 973278.09 frames.], batch size: 18, lr: 2.84e-04
2022-05-06 01:02:35,674 INFO [train.py:715] (1/8) Epoch 7, batch 33450, loss[loss=0.1441, simple_loss=0.2193, pruned_loss=0.03448, over 4884.00 frames.], tot_loss[loss=0.145, simple_loss=0.217, pruned_loss=0.03649, over 973137.69 frames.], batch size: 16, lr: 2.84e-04
2022-05-06 01:03:16,258 INFO [train.py:715] (1/8) Epoch 7, batch 33500, loss[loss=0.1354, simple_loss=0.208, pruned_loss=0.0314, over 4839.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2164, pruned_loss=0.0361, over 972261.02 frames.], batch size: 30, lr: 2.84e-04
2022-05-06 01:03:56,836 INFO [train.py:715] (1/8) Epoch 7, batch 33550, loss[loss=0.1413, simple_loss=0.2196, pruned_loss=0.03153, over 4946.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03635, over 971799.48 frames.], batch size: 21, lr: 2.84e-04
2022-05-06 01:04:37,441 INFO [train.py:715] (1/8) Epoch 7, batch 33600, loss[loss=0.1616, simple_loss=0.2316, pruned_loss=0.0458, over 4743.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2176, pruned_loss=0.03682, over 971610.10 frames.], batch size: 16, lr: 2.84e-04
2022-05-06 01:05:17,943 INFO [train.py:715] (1/8) Epoch 7, batch 33650, loss[loss=0.137, simple_loss=0.2147, pruned_loss=0.0296, over 4793.00 frames.], tot_loss[loss=0.145, simple_loss=0.2173, pruned_loss=0.03638, over 971797.84 frames.], batch size: 24, lr: 2.84e-04
2022-05-06 01:05:57,816 INFO [train.py:715] (1/8) Epoch 7, batch 33700, loss[loss=0.1382, simple_loss=0.221, pruned_loss=0.0277, over 4742.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2171, pruned_loss=0.0364, over 971803.08 frames.], batch size: 16, lr: 2.84e-04
2022-05-06 01:06:37,987 INFO [train.py:715] (1/8) Epoch 7, batch 33750, loss[loss=0.1548, simple_loss=0.2224, pruned_loss=0.04355, over 4762.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03653, over 972424.18 frames.], batch size: 19, lr: 2.84e-04
2022-05-06 01:07:17,451 INFO [train.py:715] (1/8) Epoch 7, batch 33800, loss[loss=0.1432, simple_loss=0.2153, pruned_loss=0.03553, over 4865.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03658, over 972998.63 frames.], batch size: 32, lr: 2.84e-04
2022-05-06 01:07:58,050 INFO [train.py:715] (1/8) Epoch 7, batch 33850, loss[loss=0.1562, simple_loss=0.2207, pruned_loss=0.04583, over 4855.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03661, over 972418.76 frames.], batch size: 32, lr: 2.84e-04
2022-05-06 01:08:37,727 INFO [train.py:715] (1/8) Epoch 7, batch 33900, loss[loss=0.1684, simple_loss=0.2377, pruned_loss=0.04953, over 4942.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03629, over 972670.19 frames.], batch size: 39, lr: 2.84e-04
2022-05-06 01:09:17,833 INFO [train.py:715] (1/8) Epoch 7, batch 33950, loss[loss=0.1367, simple_loss=0.2045, pruned_loss=0.03443, over 4814.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2167, pruned_loss=0.03673, over 972651.11 frames.], batch size: 13, lr: 2.84e-04
2022-05-06 01:09:57,293 INFO [train.py:715] (1/8) Epoch 7, batch 34000, loss[loss=0.1695, simple_loss=0.2228, pruned_loss=0.05806, over 4927.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03684, over 973961.36 frames.], batch size: 35, lr: 2.84e-04
2022-05-06 01:10:37,482 INFO [train.py:715] (1/8) Epoch 7, batch 34050, loss[loss=0.1723, simple_loss=0.2422, pruned_loss=0.05126, over 4924.00 frames.], tot_loss[loss=0.145, simple_loss=0.2171, pruned_loss=0.03645, over 973598.03 frames.], batch size: 18, lr: 2.84e-04
2022-05-06 01:11:17,485 INFO [train.py:715] (1/8) Epoch 7, batch 34100, loss[loss=0.1949, simple_loss=0.2363, pruned_loss=0.07674, over 4974.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03651, over 973561.81 frames.], batch size: 15, lr: 2.83e-04
2022-05-06 01:11:56,989 INFO [train.py:715] (1/8) Epoch 7, batch 34150, loss[loss=0.1282, simple_loss=0.1982, pruned_loss=0.0291, over 4830.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.036, over 973684.02 frames.], batch size: 26, lr: 2.83e-04
2022-05-06 01:12:37,410 INFO [train.py:715] (1/8) Epoch 7, batch 34200, loss[loss=0.1474, simple_loss=0.2304, pruned_loss=0.03218, over 4907.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2164, pruned_loss=0.03562, over 973127.45 frames.], batch size: 17, lr: 2.83e-04
2022-05-06 01:13:17,642 INFO [train.py:715] (1/8) Epoch 7, batch 34250, loss[loss=0.1464, simple_loss=0.2163, pruned_loss=0.03827, over 4758.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03528, over 972878.27 frames.], batch size: 19, lr: 2.83e-04
2022-05-06 01:13:58,323 INFO [train.py:715] (1/8) Epoch 7, batch 34300, loss[loss=0.1306, simple_loss=0.2067, pruned_loss=0.02729, over 4845.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.03519, over 972941.45 frames.], batch size: 30, lr: 2.83e-04
2022-05-06 01:14:38,117 INFO [train.py:715] (1/8) Epoch 7, batch 34350, loss[loss=0.1297, simple_loss=0.2034, pruned_loss=0.02797, over 4792.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2151, pruned_loss=0.03564, over 973480.06 frames.], batch size: 24, lr: 2.83e-04
2022-05-06 01:15:18,251 INFO [train.py:715] (1/8) Epoch 7, batch 34400, loss[loss=0.1413, simple_loss=0.2134, pruned_loss=0.03457, over 4956.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.03643, over 973789.62 frames.], batch size: 24, lr: 2.83e-04
2022-05-06 01:15:58,928 INFO [train.py:715] (1/8) Epoch 7, batch 34450, loss[loss=0.1256, simple_loss=0.2029, pruned_loss=0.02414, over 4875.00 frames.], tot_loss[loss=0.1441, simple_loss=0.216, pruned_loss=0.03611, over 973777.72 frames.], batch size: 22, lr: 2.83e-04
2022-05-06 01:16:38,149 INFO [train.py:715] (1/8) Epoch 7, batch 34500, loss[loss=0.147, simple_loss=0.2192, pruned_loss=0.03741, over 4815.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.03597, over 973383.68 frames.], batch size: 25, lr: 2.83e-04
2022-05-06 01:17:18,216 INFO [train.py:715] (1/8) Epoch 7, batch 34550, loss[loss=0.1422, simple_loss=0.2008, pruned_loss=0.04175, over 4764.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2162, pruned_loss=0.03626, over 972960.24 frames.], batch size: 19, lr: 2.83e-04
2022-05-06 01:17:58,855 INFO [train.py:715] (1/8) Epoch 7, batch 34600, loss[loss=0.1471, simple_loss=0.2126, pruned_loss=0.04077, over 4827.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2158, pruned_loss=0.03615, over 972200.15 frames.], batch size: 26, lr: 2.83e-04
2022-05-06 01:18:38,818 INFO [train.py:715] (1/8) Epoch 7, batch 34650, loss[loss=0.1354, simple_loss=0.2053, pruned_loss=0.03273, over 4866.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2165, pruned_loss=0.03681, over 971714.19 frames.], batch size: 30, lr: 2.83e-04
2022-05-06 01:19:19,034 INFO [train.py:715] (1/8) Epoch 7, batch 34700, loss[loss=0.1334, simple_loss=0.2164, pruned_loss=0.02524, over 4922.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2166, pruned_loss=0.03685, over 971311.28 frames.], batch size: 29, lr: 2.83e-04
2022-05-06 01:19:57,510 INFO [train.py:715] (1/8) Epoch 7, batch 34750, loss[loss=0.1427, simple_loss=0.2069, pruned_loss=0.03928, over 4757.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03667, over 971840.80 frames.], batch size: 12, lr: 2.83e-04
2022-05-06 01:20:35,938 INFO [train.py:715] (1/8) Epoch 7, batch 34800, loss[loss=0.1558, simple_loss=0.2372, pruned_loss=0.03725, over 4919.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2151, pruned_loss=0.03627, over 970834.05 frames.], batch size: 18, lr: 2.83e-04
2022-05-06 01:21:27,017 INFO [train.py:715] (1/8) Epoch 8, batch 0, loss[loss=0.1533, simple_loss=0.2321, pruned_loss=0.03728, over 4883.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2321, pruned_loss=0.03728, over 4883.00 frames.], batch size: 22, lr: 2.69e-04
2022-05-06 01:22:06,310 INFO [train.py:715] (1/8) Epoch 8, batch 50, loss[loss=0.159, simple_loss=0.2306, pruned_loss=0.04371, over 4819.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.0352, over 218787.47 frames.], batch size: 26, lr: 2.69e-04
2022-05-06 01:22:47,072 INFO [train.py:715] (1/8) Epoch 8, batch 100, loss[loss=0.1534, simple_loss=0.2307, pruned_loss=0.03801, over 4954.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2142, pruned_loss=0.03399, over 385878.50 frames.], batch size: 21, lr: 2.69e-04
2022-05-06 01:23:26,806 INFO [train.py:715] (1/8) Epoch 8, batch 150, loss[loss=0.1665, simple_loss=0.2275, pruned_loss=0.05278, over 4851.00 frames.], tot_loss[loss=0.1425, simple_loss=0.214, pruned_loss=0.03554, over 514850.92 frames.], batch size: 30, lr: 2.69e-04
2022-05-06 01:24:07,309 INFO [train.py:715] (1/8) Epoch 8, batch 200, loss[loss=0.1559, simple_loss=0.2319, pruned_loss=0.03993, over 4769.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2151, pruned_loss=0.03591, over 616004.64 frames.], batch size: 18, lr: 2.69e-04
2022-05-06 01:24:47,120 INFO [train.py:715] (1/8) Epoch 8, batch 250, loss[loss=0.1411, simple_loss=0.2125, pruned_loss=0.03485, over 4786.00 frames.], tot_loss[loss=0.143, simple_loss=0.215, pruned_loss=0.03556, over 695521.52 frames.], batch size: 21, lr: 2.69e-04
2022-05-06 01:25:27,374 INFO [train.py:715] (1/8) Epoch 8, batch 300, loss[loss=0.1373, simple_loss=0.2194, pruned_loss=0.02755, over 4776.00 frames.], tot_loss[loss=0.143, simple_loss=0.2151, pruned_loss=0.03543, over 757226.80 frames.], batch size: 18, lr: 2.69e-04
2022-05-06 01:26:07,160 INFO [train.py:715] (1/8) Epoch 8, batch 350, loss[loss=0.1651, simple_loss=0.2292, pruned_loss=0.05057, over 4882.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03556, over 804478.29 frames.], batch size: 16, lr: 2.69e-04
2022-05-06 01:26:46,039 INFO [train.py:715] (1/8) Epoch 8, batch 400, loss[loss=0.1546, simple_loss=0.2413, pruned_loss=0.03393, over 4959.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.0354, over 840961.82 frames.], batch size: 24, lr: 2.69e-04
2022-05-06 01:27:26,639 INFO [train.py:715] (1/8) Epoch 8, batch 450, loss[loss=0.1392, simple_loss=0.2057, pruned_loss=0.0363, over 4765.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2156, pruned_loss=0.03592, over 870816.46 frames.], batch size: 18, lr: 2.69e-04
2022-05-06 01:28:06,610 INFO [train.py:715] (1/8) Epoch 8, batch 500, loss[loss=0.1309, simple_loss=0.2036, pruned_loss=0.0291, over 4985.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03588, over 894620.20 frames.], batch size: 25, lr: 2.69e-04
2022-05-06 01:28:47,244 INFO [train.py:715] (1/8) Epoch 8, batch 550, loss[loss=0.1758, simple_loss=0.2445, pruned_loss=0.05361, over 4922.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2157, pruned_loss=0.03591, over 912368.28 frames.], batch size: 18, lr: 2.69e-04
2022-05-06 01:29:26,912 INFO [train.py:715] (1/8) Epoch 8, batch 600, loss[loss=0.1863, simple_loss=0.2433, pruned_loss=0.06467, over 4976.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2157, pruned_loss=0.03638, over 925601.89 frames.], batch size: 39, lr: 2.69e-04
2022-05-06 01:30:07,130 INFO [train.py:715] (1/8) Epoch 8, batch 650, loss[loss=0.1532, simple_loss=0.2334, pruned_loss=0.03647, over 4876.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2161, pruned_loss=0.03654, over 936990.87 frames.], batch size: 16, lr: 2.68e-04
2022-05-06 01:30:47,385 INFO [train.py:715] (1/8) Epoch 8, batch 700, loss[loss=0.1321, simple_loss=0.2114, pruned_loss=0.02644, over 4967.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03609, over 945128.74 frames.], batch size: 24, lr: 2.68e-04
2022-05-06 01:31:27,085 INFO [train.py:715] (1/8) Epoch 8, batch 750, loss[loss=0.1368, simple_loss=0.2062, pruned_loss=0.03369, over 4884.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2164, pruned_loss=0.03632, over 951095.12 frames.], batch size: 19, lr: 2.68e-04
2022-05-06 01:32:07,141 INFO [train.py:715] (1/8) Epoch 8, batch 800, loss[loss=0.1755, simple_loss=0.2348, pruned_loss=0.05815, over 4864.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2167, pruned_loss=0.03688, over 955600.41 frames.], batch size: 22, lr: 2.68e-04
2022-05-06 01:32:47,132 INFO [train.py:715] (1/8) Epoch 8, batch 850, loss[loss=0.1278, simple_loss=0.2007, pruned_loss=0.02743, over 4823.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2169, pruned_loss=0.03743, over 959587.37 frames.], batch size: 26, lr: 2.68e-04
2022-05-06 01:33:28,548 INFO [train.py:715] (1/8) Epoch 8, batch 900, loss[loss=0.1172, simple_loss=0.1963, pruned_loss=0.01907, over 4926.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2169, pruned_loss=0.03732, over 961835.68 frames.], batch size: 39, lr: 2.68e-04
2022-05-06 01:34:08,655 INFO [train.py:715] (1/8) Epoch 8, batch 950, loss[loss=0.1307, simple_loss=0.2077, pruned_loss=0.02683, over 4944.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2172, pruned_loss=0.03735, over 964081.80 frames.], batch size: 23, lr: 2.68e-04
2022-05-06 01:34:49,697 INFO [train.py:715] (1/8) Epoch 8, batch 1000, loss[loss=0.1274, simple_loss=0.1986, pruned_loss=0.02807, over 4822.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2169, pruned_loss=0.03731, over 966192.32 frames.], batch size: 25, lr: 2.68e-04
2022-05-06 01:35:30,785 INFO [train.py:715] (1/8) Epoch 8, batch 1050, loss[loss=0.1402, simple_loss=0.2068, pruned_loss=0.03678, over 4898.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2171, pruned_loss=0.03763, over 967646.49 frames.], batch size: 19, lr: 2.68e-04
2022-05-06 01:36:11,900 INFO [train.py:715] (1/8) Epoch 8, batch 1100, loss[loss=0.1497, simple_loss=0.2151, pruned_loss=0.04213, over 4854.00 frames.], tot_loss[loss=0.146, simple_loss=0.2172, pruned_loss=0.03742, over 968553.30 frames.], batch size: 13, lr: 2.68e-04
2022-05-06 01:36:52,419 INFO [train.py:715] (1/8) Epoch 8, batch 1150, loss[loss=0.1465, simple_loss=0.2131, pruned_loss=0.03991, over 4910.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2169, pruned_loss=0.03702, over 969327.80 frames.], batch size: 19, lr: 2.68e-04
2022-05-06 01:37:33,434 INFO [train.py:715] (1/8) Epoch 8, batch 1200, loss[loss=0.1237, simple_loss=0.1994, pruned_loss=0.02399, over 4853.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2174, pruned_loss=0.03677, over 971286.59 frames.], batch size: 13, lr: 2.68e-04
2022-05-06 01:38:14,760 INFO [train.py:715] (1/8) Epoch 8, batch 1250, loss[loss=0.1573, simple_loss=0.2359, pruned_loss=0.03931, over 4854.00 frames.], tot_loss[loss=0.145, simple_loss=0.2168, pruned_loss=0.03663, over 972010.08 frames.], batch size: 20, lr: 2.68e-04
2022-05-06 01:38:55,091 INFO [train.py:715] (1/8) Epoch 8, batch 1300, loss[loss=0.141, simple_loss=0.2154, pruned_loss=0.03332, over 4866.00 frames.], tot_loss[loss=0.1447, simple_loss=0.216, pruned_loss=0.03667, over 972300.56 frames.], batch size: 16, lr: 2.68e-04
2022-05-06 01:39:36,447 INFO [train.py:715] (1/8) Epoch 8, batch 1350, loss[loss=0.1454, simple_loss=0.2253, pruned_loss=0.03277, over 4891.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2163, pruned_loss=0.03661, over 972012.74 frames.], batch size: 17, lr: 2.68e-04
2022-05-06 01:40:17,096 INFO [train.py:715] (1/8) Epoch 8, batch 1400, loss[loss=0.1537, simple_loss=0.2212, pruned_loss=0.04312, over 4835.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2166, pruned_loss=0.03686, over 972655.56 frames.], batch size: 13, lr: 2.68e-04
2022-05-06 01:40:57,928 INFO [train.py:715] (1/8) Epoch 8, batch 1450, loss[loss=0.1289, simple_loss=0.206, pruned_loss=0.02592, over 4853.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2168, pruned_loss=0.03694, over 972336.70 frames.], batch size: 20, lr: 2.68e-04
2022-05-06 01:41:37,778 INFO [train.py:715] (1/8) Epoch 8, batch 1500, loss[loss=0.1441, simple_loss=0.212, pruned_loss=0.03805, over 4807.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2167, pruned_loss=0.03645, over 972999.34 frames.], batch size: 25, lr: 2.68e-04
2022-05-06 01:42:20,410 INFO [train.py:715] (1/8) Epoch 8, batch 1550, loss[loss=0.1892, simple_loss=0.2496, pruned_loss=0.0644, over 4969.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2162, pruned_loss=0.03646, over 972062.06 frames.], batch size: 35, lr: 2.68e-04
2022-05-06 01:43:00,530 INFO [train.py:715] (1/8) Epoch 8, batch 1600, loss[loss=0.1252, simple_loss=0.1994, pruned_loss=0.02548, over 4873.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2163, pruned_loss=0.03625, over 971995.73 frames.], batch size: 22, lr: 2.68e-04
2022-05-06 01:43:39,974 INFO [train.py:715] (1/8) Epoch 8, batch 1650, loss[loss=0.1823, simple_loss=0.2435, pruned_loss=0.06061, over 4819.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03653, over 971768.12 frames.], batch size: 27, lr: 2.68e-04
2022-05-06 01:44:20,196 INFO [train.py:715] (1/8) Epoch 8, batch 1700, loss[loss=0.1453, simple_loss=0.2274, pruned_loss=0.03155, over 4804.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2166, pruned_loss=0.03633, over 972181.54 frames.], batch size: 25, lr: 2.68e-04
2022-05-06 01:44:59,608 INFO [train.py:715] (1/8) Epoch 8, batch 1750, loss[loss=0.1259, simple_loss=0.1937, pruned_loss=0.02902, over 4764.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2172, pruned_loss=0.037, over 971995.42 frames.], batch size: 12, lr: 2.68e-04
2022-05-06 01:45:39,054 INFO [train.py:715] (1/8) Epoch 8, batch 1800, loss[loss=0.1291, simple_loss=0.1998, pruned_loss=0.02921, over 4883.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2158, pruned_loss=0.03648, over 971490.65 frames.], batch size: 16, lr: 2.68e-04
2022-05-06 01:46:18,113 INFO [train.py:715] (1/8) Epoch 8, batch 1850, loss[loss=0.1642, simple_loss=0.2322, pruned_loss=0.04811, over 4854.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2161, pruned_loss=0.03627, over 971833.81 frames.], batch size: 32, lr: 2.68e-04
2022-05-06 01:46:57,509 INFO [train.py:715] (1/8) Epoch 8, batch 1900, loss[loss=0.123, simple_loss=0.1904, pruned_loss=0.02778, over 4965.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2168, pruned_loss=0.03675, over 972056.08 frames.], batch size: 14, lr: 2.68e-04
2022-05-06 01:47:37,010 INFO [train.py:715] (1/8) Epoch 8, batch 1950, loss[loss=0.1571, simple_loss=0.2353, pruned_loss=0.03946, over 4819.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2159, pruned_loss=0.03621, over 971738.04 frames.], batch size: 26, lr: 2.68e-04
2022-05-06 01:48:16,130 INFO [train.py:715] (1/8) Epoch 8, batch 2000, loss[loss=0.1546, simple_loss=0.2312, pruned_loss=0.03904, over 4978.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.03644, over 971331.04 frames.], batch size: 28, lr: 2.68e-04
2022-05-06 01:48:56,142 INFO [train.py:715] (1/8) Epoch 8, batch 2050, loss[loss=0.168, simple_loss=0.2343, pruned_loss=0.05085, over 4949.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.03646, over 972063.31 frames.], batch size: 29, lr: 2.68e-04
2022-05-06 01:49:35,100 INFO [train.py:715] (1/8) Epoch 8, batch 2100, loss[loss=0.1393, simple_loss=0.2069, pruned_loss=0.03589, over 4890.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2163, pruned_loss=0.03641, over 972092.59 frames.], batch size: 17, lr: 2.68e-04
2022-05-06 01:50:14,047 INFO [train.py:715] (1/8) Epoch 8, batch 2150, loss[loss=0.1719, simple_loss=0.2371, pruned_loss=0.05333, over 4841.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2152, pruned_loss=0.03583, over 972540.37 frames.], batch size: 15, lr: 2.68e-04
2022-05-06 01:50:53,032 INFO [train.py:715] (1/8) Epoch 8, batch 2200, loss[loss=0.1367, simple_loss=0.2093, pruned_loss=0.03202, over 4794.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2153, pruned_loss=0.03593, over 972228.76 frames.], batch size: 21, lr: 2.68e-04
2022-05-06 01:51:32,659 INFO [train.py:715] (1/8) Epoch 8, batch 2250, loss[loss=0.1296, simple_loss=0.2078, pruned_loss=0.0257, over 4894.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2148, pruned_loss=0.03569, over 972406.04 frames.], batch size: 17, lr: 2.68e-04
2022-05-06 01:52:12,074 INFO [train.py:715] (1/8) Epoch 8, batch 2300, loss[loss=0.1438, simple_loss=0.2146, pruned_loss=0.03646, over 4852.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2145, pruned_loss=0.03513, over 973624.17 frames.], batch size: 30, lr: 2.68e-04
2022-05-06 01:52:50,784 INFO [train.py:715] (1/8) Epoch 8, batch 2350, loss[loss=0.134, simple_loss=0.2011, pruned_loss=0.03343, over 4709.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03539, over 973446.36 frames.], batch size: 15, lr: 2.68e-04
2022-05-06 01:53:30,837 INFO [train.py:715] (1/8) Epoch 8, batch 2400, loss[loss=0.1358, simple_loss=0.2034, pruned_loss=0.03416, over 4866.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2148, pruned_loss=0.03551, over 973380.13 frames.], batch size: 20, lr: 2.68e-04
2022-05-06 01:54:10,336 INFO [train.py:715] (1/8) Epoch 8, batch 2450, loss[loss=0.1711, simple_loss=0.2433, pruned_loss=0.04949, over 4881.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03556, over 973281.52 frames.], batch size: 16, lr: 2.68e-04
2022-05-06 01:54:49,892 INFO [train.py:715] (1/8) Epoch 8, batch 2500, loss[loss=0.1464, simple_loss=0.2266, pruned_loss=0.03311, over 4770.00 frames.], tot_loss[loss=0.1435, simple_loss=0.216, pruned_loss=0.03551, over 973444.10 frames.], batch size: 19, lr: 2.68e-04
2022-05-06 01:55:28,675 INFO [train.py:715] (1/8) Epoch 8, batch 2550, loss[loss=0.1468, simple_loss=0.2168, pruned_loss=0.03838, over 4989.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2163, pruned_loss=0.03561, over 973078.58 frames.], batch size: 15, lr: 2.68e-04
2022-05-06 01:56:08,301 INFO [train.py:715] (1/8) Epoch 8, batch 2600, loss[loss=0.1594, simple_loss=0.2223, pruned_loss=0.04825, over 4801.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2158, pruned_loss=0.03536, over 973284.90 frames.], batch size: 13, lr: 2.68e-04
2022-05-06 01:56:47,550 INFO [train.py:715] (1/8) Epoch 8, batch 2650, loss[loss=0.142, simple_loss=0.2224, pruned_loss=0.03078, over 4814.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2157, pruned_loss=0.03511, over 973372.29 frames.], batch size: 14, lr: 2.68e-04
2022-05-06 01:57:27,031 INFO [train.py:715] (1/8) Epoch 8, batch 2700, loss[loss=0.1042, simple_loss=0.1807, pruned_loss=0.0138, over 4900.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03513, over 972546.53 frames.], batch size: 19, lr: 2.68e-04
2022-05-06 01:58:06,369 INFO [train.py:715] (1/8) Epoch 8, batch 2750, loss[loss=0.1399, simple_loss=0.2171, pruned_loss=0.0314, over 4930.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2159, pruned_loss=0.03516, over 972191.92 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 01:58:45,747 INFO [train.py:715] (1/8) Epoch 8, batch 2800, loss[loss=0.1313, simple_loss=0.2074, pruned_loss=0.02758, over 4886.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2166, pruned_loss=0.03543, over 972065.32 frames.], batch size: 22, lr: 2.67e-04
2022-05-06 01:59:24,995 INFO [train.py:715] (1/8) Epoch 8, batch 2850, loss[loss=0.1435, simple_loss=0.2142, pruned_loss=0.03642, over 4922.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.03544, over 972423.58 frames.], batch size: 18, lr: 2.67e-04
2022-05-06 02:00:03,839 INFO [train.py:715] (1/8) Epoch 8, batch 2900, loss[loss=0.1405, simple_loss=0.2108, pruned_loss=0.0351, over 4986.00 frames.], tot_loss[loss=0.1434, simple_loss=0.216, pruned_loss=0.03544, over 972946.53 frames.], batch size: 35, lr: 2.67e-04
2022-05-06 02:00:43,806 INFO [train.py:715] (1/8) Epoch 8, batch 2950, loss[loss=0.1613, simple_loss=0.2384, pruned_loss=0.04216, over 4839.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.03576, over 973495.57 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:01:22,465 INFO [train.py:715] (1/8) Epoch 8, batch 3000, loss[loss=0.1341, simple_loss=0.2205, pruned_loss=0.02389, over 4785.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2156, pruned_loss=0.03513, over 973066.59 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 02:01:22,466 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 02:01:32,130 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1076, simple_loss=0.1923, pruned_loss=0.0115, over 914524.00 frames.
2022-05-06 02:02:11,362 INFO [train.py:715] (1/8) Epoch 8, batch 3050, loss[loss=0.1597, simple_loss=0.2183, pruned_loss=0.05054, over 4963.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2158, pruned_loss=0.03591, over 972241.08 frames.], batch size: 35, lr: 2.67e-04
2022-05-06 02:02:50,367 INFO [train.py:715] (1/8) Epoch 8, batch 3100, loss[loss=0.192, simple_loss=0.2438, pruned_loss=0.07013, over 4885.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2167, pruned_loss=0.03619, over 971049.34 frames.], batch size: 32, lr: 2.67e-04
2022-05-06 02:03:29,322 INFO [train.py:715] (1/8) Epoch 8, batch 3150, loss[loss=0.1168, simple_loss=0.1859, pruned_loss=0.02383, over 4822.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03611, over 970415.85 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:04:09,013 INFO [train.py:715] (1/8) Epoch 8, batch 3200, loss[loss=0.1629, simple_loss=0.2316, pruned_loss=0.04707, over 4973.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2171, pruned_loss=0.03665, over 970483.45 frames.], batch size: 14, lr: 2.67e-04
2022-05-06 02:04:48,445 INFO [train.py:715] (1/8) Epoch 8, batch 3250, loss[loss=0.1494, simple_loss=0.2248, pruned_loss=0.03695, over 4943.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.03611, over 971118.11 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 02:05:28,478 INFO [train.py:715] (1/8) Epoch 8, batch 3300, loss[loss=0.1427, simple_loss=0.2162, pruned_loss=0.03467, over 4793.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2161, pruned_loss=0.03627, over 971230.44 frames.], batch size: 24, lr: 2.67e-04
2022-05-06 02:06:08,832 INFO [train.py:715] (1/8) Epoch 8, batch 3350, loss[loss=0.1499, simple_loss=0.2168, pruned_loss=0.04151, over 4805.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2159, pruned_loss=0.03618, over 971129.38 frames.], batch size: 21, lr: 2.67e-04
2022-05-06 02:06:49,934 INFO [train.py:715] (1/8) Epoch 8, batch 3400, loss[loss=0.1247, simple_loss=0.2006, pruned_loss=0.02438, over 4954.00 frames.], tot_loss[loss=0.145, simple_loss=0.2166, pruned_loss=0.03667, over 971123.63 frames.], batch size: 24, lr: 2.67e-04
2022-05-06 02:07:30,801 INFO [train.py:715] (1/8) Epoch 8, batch 3450, loss[loss=0.1175, simple_loss=0.1891, pruned_loss=0.02297, over 4980.00 frames.], tot_loss[loss=0.1451, simple_loss=0.217, pruned_loss=0.03657, over 971443.29 frames.], batch size: 28, lr: 2.67e-04
2022-05-06 02:08:11,009 INFO [train.py:715] (1/8) Epoch 8, batch 3500, loss[loss=0.153, simple_loss=0.2285, pruned_loss=0.03875, over 4971.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03696, over 971769.29 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:08:52,350 INFO [train.py:715] (1/8) Epoch 8, batch 3550, loss[loss=0.1421, simple_loss=0.2201, pruned_loss=0.03202, over 4964.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2183, pruned_loss=0.03699, over 971250.12 frames.], batch size: 24, lr: 2.67e-04
2022-05-06 02:09:33,201 INFO [train.py:715] (1/8) Epoch 8, batch 3600, loss[loss=0.1451, simple_loss=0.2242, pruned_loss=0.03299, over 4844.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2176, pruned_loss=0.03707, over 970944.07 frames.], batch size: 30, lr: 2.67e-04
2022-05-06 02:10:13,455 INFO [train.py:715] (1/8) Epoch 8, batch 3650, loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.0309, over 4975.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2178, pruned_loss=0.03724, over 971362.21 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:10:53,935 INFO [train.py:715] (1/8) Epoch 8, batch 3700, loss[loss=0.1742, simple_loss=0.2378, pruned_loss=0.0553, over 4790.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2174, pruned_loss=0.03709, over 971539.07 frames.], batch size: 14, lr: 2.67e-04
2022-05-06 02:11:34,277 INFO [train.py:715] (1/8) Epoch 8, batch 3750, loss[loss=0.1263, simple_loss=0.2018, pruned_loss=0.02537, over 4949.00 frames.], tot_loss[loss=0.1441, simple_loss=0.216, pruned_loss=0.03615, over 972274.74 frames.], batch size: 21, lr: 2.67e-04
2022-05-06 02:12:13,640 INFO [train.py:715] (1/8) Epoch 8, batch 3800, loss[loss=0.1407, simple_loss=0.2161, pruned_loss=0.03261, over 4785.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2159, pruned_loss=0.03588, over 971130.69 frames.], batch size: 14, lr: 2.67e-04
2022-05-06 02:12:54,045 INFO [train.py:715] (1/8) Epoch 8, batch 3850, loss[loss=0.1335, simple_loss=0.1937, pruned_loss=0.0366, over 4903.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2149, pruned_loss=0.03534, over 971416.28 frames.], batch size: 19, lr: 2.67e-04
2022-05-06 02:13:34,220 INFO [train.py:715] (1/8) Epoch 8, batch 3900, loss[loss=0.1413, simple_loss=0.2172, pruned_loss=0.03268, over 4878.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2155, pruned_loss=0.03546, over 971556.64 frames.], batch size: 39, lr: 2.67e-04
2022-05-06 02:14:14,988 INFO [train.py:715] (1/8) Epoch 8, batch 3950, loss[loss=0.1544, simple_loss=0.2192, pruned_loss=0.04476, over 4977.00 frames.], tot_loss[loss=0.143, simple_loss=0.2152, pruned_loss=0.0354, over 970851.81 frames.], batch size: 39, lr: 2.67e-04
2022-05-06 02:14:54,901 INFO [train.py:715] (1/8) Epoch 8, batch 4000, loss[loss=0.1437, simple_loss=0.2176, pruned_loss=0.03484, over 4877.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2151, pruned_loss=0.03553, over 971116.76 frames.], batch size: 16, lr: 2.67e-04
2022-05-06 02:15:35,362 INFO [train.py:715] (1/8) Epoch 8, batch 4050, loss[loss=0.1231, simple_loss=0.1949, pruned_loss=0.02562, over 4993.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2152, pruned_loss=0.03551, over 970881.16 frames.], batch size: 14, lr: 2.67e-04
2022-05-06 02:16:16,173 INFO [train.py:715] (1/8) Epoch 8, batch 4100, loss[loss=0.1419, simple_loss=0.2035, pruned_loss=0.04013, over 4958.00 frames.], tot_loss[loss=0.1443, simple_loss=0.216, pruned_loss=0.03625, over 971402.48 frames.], batch size: 21, lr: 2.67e-04
2022-05-06 02:16:55,925 INFO [train.py:715] (1/8) Epoch 8, batch 4150, loss[loss=0.1569, simple_loss=0.2136, pruned_loss=0.05006, over 4792.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2155, pruned_loss=0.03577, over 971711.14 frames.], batch size: 14, lr: 2.67e-04
2022-05-06 02:17:35,664 INFO [train.py:715] (1/8) Epoch 8, batch 4200, loss[loss=0.1652, simple_loss=0.2365, pruned_loss=0.04692, over 4831.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2162, pruned_loss=0.03635, over 971808.99 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:18:15,236 INFO [train.py:715] (1/8) Epoch 8, batch 4250, loss[loss=0.1625, simple_loss=0.2332, pruned_loss=0.04589, over 4788.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2172, pruned_loss=0.03668, over 972363.84 frames.], batch size: 17, lr: 2.67e-04
2022-05-06 02:18:54,993 INFO [train.py:715] (1/8) Epoch 8, batch 4300, loss[loss=0.1576, simple_loss=0.2317, pruned_loss=0.0418, over 4869.00 frames.], tot_loss[loss=0.1448, simple_loss=0.217, pruned_loss=0.0363, over 972785.55 frames.], batch size: 20, lr: 2.67e-04
2022-05-06 02:19:34,156 INFO [train.py:715] (1/8) Epoch 8, batch 4350, loss[loss=0.148, simple_loss=0.2184, pruned_loss=0.03884, over 4935.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2164, pruned_loss=0.03633, over 972767.67 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 02:20:13,548 INFO [train.py:715] (1/8) Epoch 8, batch 4400, loss[loss=0.1772, simple_loss=0.2479, pruned_loss=0.05326, over 4701.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2167, pruned_loss=0.03646, over 972395.13 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:20:53,472 INFO [train.py:715] (1/8) Epoch 8, batch 4450, loss[loss=0.1351, simple_loss=0.2145, pruned_loss=0.02782, over 4878.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03663, over 972163.29 frames.], batch size: 16, lr: 2.67e-04
2022-05-06 02:21:33,243 INFO [train.py:715] (1/8) Epoch 8, batch 4500, loss[loss=0.1386, simple_loss=0.2144, pruned_loss=0.03142, over 4819.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2175, pruned_loss=0.03673, over 972385.99 frames.], batch size: 25, lr: 2.67e-04
2022-05-06 02:22:12,210 INFO [train.py:715] (1/8) Epoch 8, batch 4550, loss[loss=0.1458, simple_loss=0.2163, pruned_loss=0.03763, over 4780.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2171, pruned_loss=0.0368, over 972158.52 frames.], batch size: 12, lr: 2.67e-04
2022-05-06 02:22:52,193 INFO [train.py:715] (1/8) Epoch 8, batch 4600, loss[loss=0.1359, simple_loss=0.208, pruned_loss=0.03194, over 4979.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2174, pruned_loss=0.03665, over 972135.02 frames.], batch size: 24, lr: 2.67e-04
2022-05-06 02:23:31,741 INFO [train.py:715] (1/8) Epoch 8, batch 4650, loss[loss=0.1502, simple_loss=0.221, pruned_loss=0.03971, over 4780.00 frames.], tot_loss[loss=0.1457, simple_loss=0.218, pruned_loss=0.03667, over 971741.42 frames.], batch size: 14, lr: 2.67e-04
2022-05-06 02:24:11,304 INFO [train.py:715] (1/8) Epoch 8, batch 4700, loss[loss=0.1284, simple_loss=0.2059, pruned_loss=0.02549, over 4881.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2175, pruned_loss=0.03669, over 971283.19 frames.], batch size: 22, lr: 2.67e-04
2022-05-06 02:24:50,834 INFO [train.py:715] (1/8) Epoch 8, batch 4750, loss[loss=0.1575, simple_loss=0.2268, pruned_loss=0.04415, over 4919.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2179, pruned_loss=0.03678, over 971279.07 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 02:25:30,494 INFO [train.py:715] (1/8) Epoch 8, batch 4800, loss[loss=0.1621, simple_loss=0.2407, pruned_loss=0.04171, over 4802.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2177, pruned_loss=0.0367, over 970469.03 frames.], batch size: 21, lr: 2.67e-04
2022-05-06 02:26:10,395 INFO [train.py:715] (1/8) Epoch 8, batch 4850, loss[loss=0.1508, simple_loss=0.2232, pruned_loss=0.03918, over 4933.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2172, pruned_loss=0.03629, over 971246.83 frames.], batch size: 23, lr: 2.66e-04
2022-05-06 02:26:49,518 INFO [train.py:715] (1/8) Epoch 8, batch 4900, loss[loss=0.1319, simple_loss=0.2128, pruned_loss=0.02552, over 4947.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2164, pruned_loss=0.03586, over 970867.68 frames.], batch size: 21, lr: 2.66e-04
2022-05-06 02:27:29,283 INFO [train.py:715] (1/8) Epoch 8, batch 4950, loss[loss=0.134, simple_loss=0.2061, pruned_loss=0.03096, over 4964.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2163, pruned_loss=0.03566, over 970735.99 frames.], batch size: 24, lr: 2.66e-04
2022-05-06 02:28:08,948 INFO [train.py:715] (1/8) Epoch 8, batch 5000, loss[loss=0.1228, simple_loss=0.1935, pruned_loss=0.02604, over 4985.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2167, pruned_loss=0.03591, over 970641.45 frames.], batch size: 31, lr: 2.66e-04
2022-05-06 02:28:47,819 INFO [train.py:715] (1/8) Epoch 8, batch 5050, loss[loss=0.145, simple_loss=0.2063, pruned_loss=0.04183, over 4873.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2169, pruned_loss=0.0362, over 971262.58 frames.], batch size: 32, lr: 2.66e-04
2022-05-06 02:29:26,969 INFO [train.py:715] (1/8) Epoch 8, batch 5100, loss[loss=0.1463, simple_loss=0.2104, pruned_loss=0.0411, over 4741.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03608, over 972156.85 frames.], batch size: 16, lr: 2.66e-04
2022-05-06 02:30:06,432 INFO [train.py:715] (1/8) Epoch 8, batch 5150, loss[loss=0.1475, simple_loss=0.2148, pruned_loss=0.04009, over 4751.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03524, over 972098.26 frames.], batch size: 16, lr: 2.66e-04
2022-05-06 02:30:45,336 INFO [train.py:715] (1/8) Epoch 8, batch 5200, loss[loss=0.162, simple_loss=0.2329, pruned_loss=0.04557, over 4907.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03542, over 970906.40 frames.], batch size: 39, lr: 2.66e-04
2022-05-06 02:31:24,031 INFO [train.py:715] (1/8) Epoch 8, batch 5250, loss[loss=0.1683, simple_loss=0.2327, pruned_loss=0.05194, over 4695.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2155, pruned_loss=0.03519, over 971434.56 frames.], batch size: 15, lr: 2.66e-04
2022-05-06 02:32:04,139 INFO [train.py:715] (1/8) Epoch 8, batch 5300, loss[loss=0.1566, simple_loss=0.2266, pruned_loss=0.04335, over 4890.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03538, over 972126.05 frames.], batch size: 16, lr: 2.66e-04
2022-05-06 02:32:43,763 INFO [train.py:715] (1/8) Epoch 8, batch 5350, loss[loss=0.1343, simple_loss=0.2193, pruned_loss=0.02465, over 4931.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03502, over 972338.40 frames.], batch size: 18, lr: 2.66e-04
2022-05-06 02:33:23,698 INFO [train.py:715] (1/8) Epoch 8, batch 5400, loss[loss=0.1511, simple_loss=0.2267, pruned_loss=0.03778, over 4880.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03479, over 973266.12 frames.], batch size: 22, lr: 2.66e-04
2022-05-06 02:34:04,187 INFO [train.py:715] (1/8) Epoch 8, batch 5450, loss[loss=0.1216, simple_loss=0.1897, pruned_loss=0.02675, over 4756.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2145, pruned_loss=0.03502, over 972409.07 frames.], batch size: 16, lr: 2.66e-04
2022-05-06 02:34:44,682 INFO [train.py:715] (1/8) Epoch 8, batch 5500, loss[loss=0.157, simple_loss=0.2321, pruned_loss=0.04091, over 4838.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03497, over 972217.61 frames.], batch size: 30, lr: 2.66e-04
2022-05-06 02:35:24,976 INFO [train.py:715] (1/8) Epoch 8, batch 5550, loss[loss=0.1228, simple_loss=0.2018, pruned_loss=0.02185, over 4817.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2149, pruned_loss=0.03542, over 972153.77 frames.], batch size: 26, lr: 2.66e-04
2022-05-06 02:36:04,817 INFO [train.py:715] (1/8) Epoch 8, batch 5600, loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.03675, over 4926.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03566, over 973066.72 frames.], batch size: 29, lr: 2.66e-04
2022-05-06 02:36:44,882 INFO [train.py:715] (1/8) Epoch 8, batch 5650, loss[loss=0.1291, simple_loss=0.2055, pruned_loss=0.02634, over 4914.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03565, over 973027.65 frames.], batch size: 29, lr: 2.66e-04
2022-05-06 02:37:24,007 INFO [train.py:715] (1/8) Epoch 8, batch 5700, loss[loss=0.1651, simple_loss=0.2365, pruned_loss=0.04689, over 4822.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03567, over 973136.07 frames.], batch size: 27, lr: 2.66e-04
2022-05-06 02:38:03,521 INFO [train.py:715] (1/8) Epoch 8, batch 5750, loss[loss=0.1572, simple_loss=0.2093, pruned_loss=0.05257, over 4967.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03543, over 974032.31 frames.], batch size: 35, lr: 2.66e-04
2022-05-06 02:38:42,306 INFO [train.py:715] (1/8) Epoch 8, batch 5800, loss[loss=0.1407, simple_loss=0.218, pruned_loss=0.03164, over 4969.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2161, pruned_loss=0.03549, over 973371.53 frames.], batch size: 39, lr: 2.66e-04
2022-05-06 02:39:21,804 INFO [train.py:715] (1/8) Epoch 8, batch 5850, loss[loss=0.1207, simple_loss=0.1863, pruned_loss=0.02758, over 4790.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2168, pruned_loss=0.03573, over 973538.84 frames.], batch size: 14, lr: 2.66e-04
2022-05-06 02:40:00,574 INFO [train.py:715] (1/8) Epoch 8, batch 5900, loss[loss=0.1478, simple_loss=0.2016, pruned_loss=0.04697, over 4871.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03572, over 973253.22 frames.], batch size: 32, lr: 2.66e-04
2022-05-06 02:40:40,156 INFO [train.py:715] (1/8) Epoch 8, batch 5950, loss[loss=0.1325, simple_loss=0.2078, pruned_loss=0.02857, over 4966.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.03596, over 973324.57 frames.], batch size: 24, lr: 2.66e-04
2022-05-06 02:41:20,039 INFO [train.py:715] (1/8) Epoch 8, batch 6000, loss[loss=0.1314, simple_loss=0.2073, pruned_loss=0.02777, over 4914.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.03614, over 972635.61 frames.], batch size: 29, lr: 2.66e-04
2022-05-06 02:41:20,040 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 02:41:29,608 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1075, simple_loss=0.1921, pruned_loss=0.01146, over 914524.00 frames.
2022-05-06 02:42:09,076 INFO [train.py:715] (1/8) Epoch 8, batch 6050, loss[loss=0.1129, simple_loss=0.1771, pruned_loss=0.02431, over 4817.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2163, pruned_loss=0.03624, over 973422.09 frames.], batch size: 12, lr: 2.66e-04
2022-05-06 02:42:48,773 INFO [train.py:715] (1/8) Epoch 8, batch 6100, loss[loss=0.1149, simple_loss=0.1889, pruned_loss=0.02043, over 4771.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2164, pruned_loss=0.03642, over 972953.22 frames.], batch size: 17, lr: 2.66e-04
2022-05-06 02:43:28,439 INFO [train.py:715] (1/8) Epoch 8, batch 6150, loss[loss=0.1501, simple_loss=0.2234, pruned_loss=0.03836, over 4824.00 frames.], tot_loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.03592, over 973330.54 frames.], batch size: 26, lr: 2.66e-04
2022-05-06 02:44:08,988 INFO [train.py:715] (1/8) Epoch 8, batch 6200, loss[loss=0.1376, simple_loss=0.208, pruned_loss=0.03357, over 4949.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2156, pruned_loss=0.03601, over 972620.93 frames.], batch size: 15, lr: 2.66e-04
2022-05-06 02:44:49,477 INFO [train.py:715] (1/8) Epoch 8, batch 6250, loss[loss=0.125, simple_loss=0.1929, pruned_loss=0.02853, over 4752.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2149, pruned_loss=0.03561, over 971975.90 frames.], batch size: 19, lr: 2.66e-04
2022-05-06 02:45:29,147 INFO [train.py:715] (1/8) Epoch 8, batch 6300, loss[loss=0.1412, simple_loss=0.2223, pruned_loss=0.03009, over 4809.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2147, pruned_loss=0.03552, over 972666.96 frames.], batch size: 24, lr: 2.66e-04
2022-05-06 02:46:08,067 INFO [train.py:715] (1/8) Epoch 8, batch 6350, loss[loss=0.1267, simple_loss=0.2009, pruned_loss=0.02622, over 4760.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2149, pruned_loss=0.03547, over 972084.22 frames.], batch size: 19, lr: 2.66e-04
2022-05-06 02:46:47,836 INFO [train.py:715] (1/8) Epoch 8, batch 6400, loss[loss=0.1267, simple_loss=0.206, pruned_loss=0.02371, over 4830.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2153, pruned_loss=0.0355, over 971844.13 frames.], batch size: 26, lr: 2.66e-04
2022-05-06 02:47:27,071 INFO [train.py:715] (1/8) Epoch 8, batch 6450, loss[loss=0.1245, simple_loss=0.1947, pruned_loss=0.02712, over 4969.00 frames.], tot_loss[loss=0.143, simple_loss=0.2151, pruned_loss=0.03545, over 972451.33 frames.], batch size: 14, lr: 2.66e-04
2022-05-06 02:48:06,524 INFO [train.py:715] (1/8) Epoch 8, batch 6500, loss[loss=0.1306, simple_loss=0.2084, pruned_loss=0.02639, over 4835.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2149, pruned_loss=0.03541, over 972630.49 frames.], batch size: 26, lr: 2.66e-04
2022-05-06 02:48:45,645 INFO [train.py:715] (1/8) Epoch 8, batch 6550, loss[loss=0.144, simple_loss=0.2159, pruned_loss=0.03606, over 4991.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03555, over 971821.07 frames.], batch size: 16, lr: 2.66e-04
2022-05-06 02:49:25,320 INFO [train.py:715] (1/8) Epoch 8, batch 6600, loss[loss=0.1682, simple_loss=0.2433, pruned_loss=0.04658, over 4790.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.03595, over 971857.27 frames.], batch size: 24, lr: 2.66e-04
2022-05-06 02:50:04,627 INFO [train.py:715] (1/8) Epoch 8, batch 6650, loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03467, over 4687.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03578, over 971650.17 frames.], batch size: 15, lr: 2.66e-04
2022-05-06 02:50:43,410 INFO [train.py:715] (1/8) Epoch 8, batch 6700, loss[loss=0.137, simple_loss=0.2191, pruned_loss=0.02745, over 4885.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03667, over 972132.24 frames.], batch size: 19, lr: 2.66e-04
2022-05-06 02:51:23,636 INFO [train.py:715] (1/8) Epoch 8, batch 6750, loss[loss=0.1256, simple_loss=0.1976, pruned_loss=0.02682, over 4839.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2164, pruned_loss=0.03639, over 972269.64 frames.], batch size: 13, lr: 2.66e-04
2022-05-06 02:52:03,060 INFO [train.py:715] (1/8) Epoch 8, batch 6800, loss[loss=0.161, simple_loss=0.2177, pruned_loss=0.05217, over 4843.00 frames.], tot_loss[loss=0.145, simple_loss=0.2167, pruned_loss=0.03668, over 973181.17 frames.], batch size: 13, lr: 2.66e-04
2022-05-06 02:52:42,036 INFO [train.py:715] (1/8) Epoch 8, batch 6850, loss[loss=0.1485, simple_loss=0.2117, pruned_loss=0.04267, over 4963.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2163, pruned_loss=0.03625, over 972558.56 frames.], batch size: 24, lr: 2.66e-04
2022-05-06 02:53:21,953 INFO [train.py:715] (1/8) Epoch 8, batch 6900, loss[loss=0.1572, simple_loss=0.2329, pruned_loss=0.04076, over 4826.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2161, pruned_loss=0.0357, over 972235.68 frames.], batch size: 15, lr: 2.66e-04
2022-05-06 02:54:02,361 INFO [train.py:715] (1/8) Epoch 8, batch 6950, loss[loss=0.1429, simple_loss=0.2117, pruned_loss=0.03705, over 4848.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03561, over 972125.44 frames.], batch size: 30, lr: 2.66e-04
2022-05-06 02:54:42,181 INFO [train.py:715] (1/8) Epoch 8, batch 7000, loss[loss=0.1402, simple_loss=0.2237, pruned_loss=0.02838, over 4783.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2151, pruned_loss=0.03507, over 971445.70 frames.], batch size: 17, lr: 2.65e-04
2022-05-06 02:55:21,788 INFO [train.py:715] (1/8) Epoch 8, batch 7050, loss[loss=0.1418, simple_loss=0.2255, pruned_loss=0.02902, over 4934.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.0352, over 972276.36 frames.], batch size: 23, lr: 2.65e-04
2022-05-06 02:56:01,483 INFO [train.py:715] (1/8) Epoch 8, batch 7100, loss[loss=0.1647, simple_loss=0.2389, pruned_loss=0.04522, over 4825.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03566, over 972313.34 frames.], batch size: 27, lr: 2.65e-04
2022-05-06 02:56:41,148 INFO [train.py:715] (1/8) Epoch 8, batch 7150, loss[loss=0.1425, simple_loss=0.226, pruned_loss=0.02954, over 4892.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03578, over 973634.30 frames.], batch size: 22, lr: 2.65e-04
2022-05-06 02:57:20,449 INFO [train.py:715] (1/8) Epoch 8, batch 7200, loss[loss=0.1493, simple_loss=0.2294, pruned_loss=0.03464, over 4965.00 frames.], tot_loss[loss=0.1437, simple_loss=0.216, pruned_loss=0.03569, over 973170.33 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 02:57:59,454 INFO [train.py:715] (1/8) Epoch 8, batch 7250, loss[loss=0.1469, simple_loss=0.2239, pruned_loss=0.03491, over 4836.00 frames.], tot_loss[loss=0.144, simple_loss=0.2162, pruned_loss=0.03589, over 973696.84 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 02:58:39,562 INFO [train.py:715] (1/8) Epoch 8, batch 7300, loss[loss=0.1935, simple_loss=0.2497, pruned_loss=0.06863, over 4863.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.03564, over 973338.45 frames.], batch size: 30, lr: 2.65e-04
2022-05-06 02:59:18,937 INFO [train.py:715] (1/8) Epoch 8, batch 7350, loss[loss=0.164, simple_loss=0.2324, pruned_loss=0.04785, over 4909.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03566, over 973227.00 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 02:59:58,525 INFO [train.py:715] (1/8) Epoch 8, batch 7400, loss[loss=0.1619, simple_loss=0.2247, pruned_loss=0.04951, over 4976.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2154, pruned_loss=0.03594, over 972562.59 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:00:38,458 INFO [train.py:715] (1/8) Epoch 8, batch 7450, loss[loss=0.144, simple_loss=0.2257, pruned_loss=0.03109, over 4906.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03605, over 972975.76 frames.], batch size: 17, lr: 2.65e-04
2022-05-06 03:01:18,182 INFO [train.py:715] (1/8) Epoch 8, batch 7500, loss[loss=0.1292, simple_loss=0.1961, pruned_loss=0.0311, over 4823.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2163, pruned_loss=0.03622, over 972852.20 frames.], batch size: 27, lr: 2.65e-04
2022-05-06 03:01:57,874 INFO [train.py:715] (1/8) Epoch 8, batch 7550, loss[loss=0.1407, simple_loss=0.2115, pruned_loss=0.03496, over 4840.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2167, pruned_loss=0.03644, over 972840.61 frames.], batch size: 30, lr: 2.65e-04
2022-05-06 03:02:37,824 INFO [train.py:715] (1/8) Epoch 8, batch 7600, loss[loss=0.1179, simple_loss=0.2028, pruned_loss=0.01651, over 4800.00 frames.], tot_loss[loss=0.145, simple_loss=0.2174, pruned_loss=0.03636, over 972983.94 frames.], batch size: 25, lr: 2.65e-04
2022-05-06 03:03:17,993 INFO [train.py:715] (1/8) Epoch 8, batch 7650, loss[loss=0.145, simple_loss=0.2286, pruned_loss=0.03076, over 4642.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03659, over 972954.37 frames.], batch size: 13, lr: 2.65e-04
2022-05-06 03:03:57,443 INFO [train.py:715] (1/8) Epoch 8, batch 7700, loss[loss=0.1676, simple_loss=0.2449, pruned_loss=0.04517, over 4758.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2171, pruned_loss=0.03631, over 972826.75 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 03:04:36,611 INFO [train.py:715] (1/8) Epoch 8, batch 7750, loss[loss=0.1424, simple_loss=0.2042, pruned_loss=0.04034, over 4855.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.03576, over 973859.64 frames.], batch size: 13, lr: 2.65e-04
2022-05-06 03:05:16,803 INFO [train.py:715] (1/8) Epoch 8, batch 7800, loss[loss=0.1143, simple_loss=0.1959, pruned_loss=0.01636, over 4876.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03581, over 973179.35 frames.], batch size: 20, lr: 2.65e-04
2022-05-06 03:05:56,862 INFO [train.py:715] (1/8) Epoch 8, batch 7850, loss[loss=0.1317, simple_loss=0.2156, pruned_loss=0.02393, over 4889.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2153, pruned_loss=0.03545, over 972747.86 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 03:06:35,520 INFO [train.py:715] (1/8) Epoch 8, batch 7900, loss[loss=0.1294, simple_loss=0.2005, pruned_loss=0.0292, over 4859.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03636, over 972487.89 frames.], batch size: 16, lr: 2.65e-04
2022-05-06 03:07:15,010 INFO [train.py:715] (1/8) Epoch 8, batch 7950, loss[loss=0.1434, simple_loss=0.2206, pruned_loss=0.0331, over 4936.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03616, over 972602.05 frames.], batch size: 21, lr: 2.65e-04
2022-05-06 03:07:54,696 INFO [train.py:715] (1/8) Epoch 8, batch 8000, loss[loss=0.1515, simple_loss=0.2153, pruned_loss=0.04384, over 4824.00 frames.], tot_loss[loss=0.145, simple_loss=0.217, pruned_loss=0.03651, over 973199.53 frames.], batch size: 25, lr: 2.65e-04
2022-05-06 03:08:33,651 INFO [train.py:715] (1/8) Epoch 8, batch 8050, loss[loss=0.1728, simple_loss=0.2394, pruned_loss=0.05307, over 4857.00 frames.], tot_loss[loss=0.144, simple_loss=0.216, pruned_loss=0.036, over 972846.63 frames.], batch size: 32, lr: 2.65e-04
2022-05-06 03:09:12,023 INFO [train.py:715] (1/8) Epoch 8, batch 8100, loss[loss=0.1269, simple_loss=0.208, pruned_loss=0.02293, over 4869.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.0356, over 972808.93 frames.], batch size: 20, lr: 2.65e-04
2022-05-06 03:09:51,250 INFO [train.py:715] (1/8) Epoch 8, batch 8150, loss[loss=0.1397, simple_loss=0.2051, pruned_loss=0.03716, over 4794.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03572, over 973012.20 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:10:31,282 INFO [train.py:715] (1/8) Epoch 8, batch 8200, loss[loss=0.1425, simple_loss=0.214, pruned_loss=0.03553, over 4817.00 frames.], tot_loss[loss=0.143, simple_loss=0.215, pruned_loss=0.03546, over 973364.39 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:11:09,921 INFO [train.py:715] (1/8) Epoch 8, batch 8250, loss[loss=0.1464, simple_loss=0.2246, pruned_loss=0.03406, over 4776.00 frames.], tot_loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.03595, over 972526.34 frames.], batch size: 17, lr: 2.65e-04
2022-05-06 03:11:48,877 INFO [train.py:715] (1/8) Epoch 8, batch 8300, loss[loss=0.1472, simple_loss=0.2124, pruned_loss=0.04096, over 4841.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03552, over 973279.29 frames.], batch size: 32, lr: 2.65e-04
2022-05-06 03:12:28,300 INFO [train.py:715] (1/8) Epoch 8, batch 8350, loss[loss=0.1276, simple_loss=0.2013, pruned_loss=0.02694, over 4857.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2156, pruned_loss=0.03582, over 972844.21 frames.], batch size: 16, lr: 2.65e-04
2022-05-06 03:13:07,316 INFO [train.py:715] (1/8) Epoch 8, batch 8400, loss[loss=0.1982, simple_loss=0.2495, pruned_loss=0.0734, over 4842.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03555, over 971865.12 frames.], batch size: 30, lr: 2.65e-04
2022-05-06 03:13:45,975 INFO [train.py:715] (1/8) Epoch 8, batch 8450, loss[loss=0.1383, simple_loss=0.2149, pruned_loss=0.03086, over 4863.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03529, over 971928.91 frames.], batch size: 22, lr: 2.65e-04
2022-05-06 03:14:25,534 INFO [train.py:715] (1/8) Epoch 8, batch 8500, loss[loss=0.1256, simple_loss=0.192, pruned_loss=0.02962, over 4820.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2155, pruned_loss=0.03542, over 972367.93 frames.], batch size: 12, lr: 2.65e-04
2022-05-06 03:15:05,502 INFO [train.py:715] (1/8) Epoch 8, batch 8550, loss[loss=0.1895, simple_loss=0.2528, pruned_loss=0.06307, over 4769.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2167, pruned_loss=0.03622, over 972518.35 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:15:44,167 INFO [train.py:715] (1/8) Epoch 8, batch 8600, loss[loss=0.1422, simple_loss=0.2161, pruned_loss=0.03411, over 4851.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.03597, over 971574.50 frames.], batch size: 20, lr: 2.65e-04
2022-05-06 03:16:23,287 INFO [train.py:715] (1/8) Epoch 8, batch 8650, loss[loss=0.1457, simple_loss=0.2142, pruned_loss=0.03859, over 4927.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2163, pruned_loss=0.03606, over 970863.62 frames.], batch size: 29, lr: 2.65e-04
2022-05-06 03:17:02,907 INFO [train.py:715] (1/8) Epoch 8, batch 8700, loss[loss=0.1536, simple_loss=0.2324, pruned_loss=0.03743, over 4859.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2155, pruned_loss=0.03562, over 971384.77 frames.], batch size: 20, lr: 2.65e-04
2022-05-06 03:17:41,706 INFO [train.py:715] (1/8) Epoch 8, batch 8750, loss[loss=0.1458, simple_loss=0.2184, pruned_loss=0.03657, over 4834.00 frames.], tot_loss[loss=0.145, simple_loss=0.217, pruned_loss=0.03648, over 970437.07 frames.], batch size: 30, lr: 2.65e-04
2022-05-06 03:18:20,679 INFO [train.py:715] (1/8) Epoch 8, batch 8800, loss[loss=0.1215, simple_loss=0.1929, pruned_loss=0.02499, over 4807.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03634, over 971098.38 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:19:00,222 INFO [train.py:715] (1/8) Epoch 8, batch 8850, loss[loss=0.1334, simple_loss=0.2062, pruned_loss=0.03026, over 4970.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2166, pruned_loss=0.03635, over 972212.38 frames.], batch size: 35, lr: 2.65e-04
2022-05-06 03:19:39,733 INFO [train.py:715] (1/8) Epoch 8, batch 8900, loss[loss=0.1373, simple_loss=0.2142, pruned_loss=0.03022, over 4971.00 frames.], tot_loss[loss=0.1448, simple_loss=0.217, pruned_loss=0.03628, over 972455.00 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:20:18,232 INFO [train.py:715] (1/8) Epoch 8, batch 8950, loss[loss=0.1571, simple_loss=0.2372, pruned_loss=0.03847, over 4851.00 frames.], tot_loss[loss=0.145, simple_loss=0.2172, pruned_loss=0.03646, over 973058.67 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:20:57,342 INFO [train.py:715] (1/8) Epoch 8, batch 9000, loss[loss=0.151, simple_loss=0.2305, pruned_loss=0.03576, over 4858.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03672, over 972224.63 frames.], batch size: 20, lr: 2.65e-04
2022-05-06 03:20:57,342 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 03:21:06,881 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1075, simple_loss=0.1922, pruned_loss=0.01144, over 914524.00 frames.
2022-05-06 03:21:46,750 INFO [train.py:715] (1/8) Epoch 8, batch 9050, loss[loss=0.153, simple_loss=0.2259, pruned_loss=0.04003, over 4981.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03657, over 972712.12 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:22:26,225 INFO [train.py:715] (1/8) Epoch 8, batch 9100, loss[loss=0.1302, simple_loss=0.2071, pruned_loss=0.02667, over 4813.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2163, pruned_loss=0.03594, over 973044.87 frames.], batch size: 25, lr: 2.65e-04
2022-05-06 03:23:05,926 INFO [train.py:715] (1/8) Epoch 8, batch 9150, loss[loss=0.1669, simple_loss=0.2297, pruned_loss=0.05201, over 4903.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2168, pruned_loss=0.03609, over 972622.61 frames.], batch size: 19, lr: 2.64e-04
2022-05-06 03:23:44,125 INFO [train.py:715] (1/8) Epoch 8, batch 9200, loss[loss=0.1402, simple_loss=0.1957, pruned_loss=0.04233, over 4985.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2165, pruned_loss=0.03565, over 972491.21 frames.], batch size: 14, lr: 2.64e-04
2022-05-06 03:24:23,673 INFO [train.py:715] (1/8) Epoch 8, batch 9250, loss[loss=0.1934, simple_loss=0.2486, pruned_loss=0.06904, over 4915.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2167, pruned_loss=0.03609, over 971934.77 frames.], batch size: 17, lr: 2.64e-04
2022-05-06 03:25:03,205 INFO [train.py:715] (1/8) Epoch 8, batch 9300, loss[loss=0.1285, simple_loss=0.2041, pruned_loss=0.02647, over 4804.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2162, pruned_loss=0.03554, over 971849.75 frames.], batch size: 21, lr: 2.64e-04
2022-05-06 03:25:42,061 INFO [train.py:715] (1/8) Epoch 8, batch 9350, loss[loss=0.1311, simple_loss=0.2001, pruned_loss=0.03101, over 4639.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03577, over 972655.32 frames.], batch size: 13, lr: 2.64e-04
2022-05-06 03:26:20,919 INFO [train.py:715] (1/8) Epoch 8, batch 9400, loss[loss=0.1499, simple_loss=0.2249, pruned_loss=0.03745, over 4835.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2153, pruned_loss=0.03577, over 973456.22 frames.], batch size: 13, lr: 2.64e-04
2022-05-06 03:27:00,379 INFO [train.py:715] (1/8) Epoch 8, batch 9450, loss[loss=0.1719, simple_loss=0.2252, pruned_loss=0.05928, over 4778.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2152, pruned_loss=0.0359, over 973504.22 frames.], batch size: 19, lr: 2.64e-04
2022-05-06 03:27:40,544 INFO [train.py:715] (1/8) Epoch 8, batch 9500, loss[loss=0.1317, simple_loss=0.2044, pruned_loss=0.02948, over 4980.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2152, pruned_loss=0.03575, over 973804.65 frames.], batch size: 28, lr: 2.64e-04
2022-05-06 03:28:21,703 INFO [train.py:715] (1/8) Epoch 8, batch 9550, loss[loss=0.1146, simple_loss=0.1957, pruned_loss=0.01677, over 4983.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.03644, over 973606.87 frames.], batch size: 27, lr: 2.64e-04
2022-05-06 03:29:01,737 INFO [train.py:715] (1/8) Epoch 8, batch 9600, loss[loss=0.1335, simple_loss=0.2069, pruned_loss=0.03008, over 4698.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2162, pruned_loss=0.03624, over 973383.72 frames.], batch size: 15, lr: 2.64e-04
2022-05-06 03:29:41,774 INFO [train.py:715] (1/8) Epoch 8, batch 9650, loss[loss=0.1562, simple_loss=0.2294, pruned_loss=0.0415, over 4781.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2148, pruned_loss=0.03575, over 972346.28 frames.], batch size: 17, lr: 2.64e-04
2022-05-06 03:30:21,100 INFO [train.py:715] (1/8) Epoch 8, batch 9700, loss[loss=0.1154, simple_loss=0.1923, pruned_loss=0.01922, over 4978.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2147, pruned_loss=0.03526, over 972837.33 frames.], batch size: 28, lr: 2.64e-04
2022-05-06 03:30:59,872 INFO [train.py:715] (1/8) Epoch 8, batch 9750, loss[loss=0.1242, simple_loss=0.1993, pruned_loss=0.02459, over 4929.00 frames.], tot_loss[loss=0.142, simple_loss=0.2143, pruned_loss=0.03486, over 972780.10 frames.], batch size: 21, lr: 2.64e-04
2022-05-06 03:31:39,486 INFO [train.py:715] (1/8) Epoch 8, batch 9800, loss[loss=0.1455, simple_loss=0.2111, pruned_loss=0.03996, over 4857.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.03554, over 972028.31 frames.], batch size: 32, lr: 2.64e-04
2022-05-06 03:32:18,975 INFO [train.py:715] (1/8) Epoch 8, batch 9850, loss[loss=0.1202, simple_loss=0.1877, pruned_loss=0.02634, over 4820.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03542, over 972866.24 frames.], batch size: 26, lr: 2.64e-04
2022-05-06 03:32:58,280 INFO [train.py:715] (1/8) Epoch 8, batch 9900, loss[loss=0.1534, simple_loss=0.2144, pruned_loss=0.04624, over 4815.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2163, pruned_loss=0.03623, over 972922.95 frames.], batch size: 15, lr: 2.64e-04
2022-05-06 03:33:37,625 INFO [train.py:715] (1/8) Epoch 8, batch 9950, loss[loss=0.1515, simple_loss=0.2246, pruned_loss=0.03925, over 4895.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.03601, over 972917.75 frames.], batch size: 22, lr: 2.64e-04
2022-05-06 03:34:17,537 INFO [train.py:715] (1/8) Epoch 8, batch 10000, loss[loss=0.1318, simple_loss=0.1982, pruned_loss=0.03269, over 4772.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2161, pruned_loss=0.03611, over 972786.76 frames.], batch size: 17, lr: 2.64e-04
2022-05-06 03:34:56,517 INFO [train.py:715] (1/8) Epoch 8, batch 10050, loss[loss=0.115, simple_loss=0.1745, pruned_loss=0.02772, over 4769.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2169, pruned_loss=0.0365, over 973376.39 frames.], batch size: 12, lr: 2.64e-04
2022-05-06 03:35:35,063 INFO [train.py:715] (1/8) Epoch 8, batch 10100, loss[loss=0.1424, simple_loss=0.2198, pruned_loss=0.03254, over 4989.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2161, pruned_loss=0.03568, over 973599.75 frames.], batch size: 28, lr: 2.64e-04
2022-05-06 03:36:15,142 INFO [train.py:715] (1/8) Epoch 8, batch 10150, loss[loss=0.1578, simple_loss=0.2316, pruned_loss=0.04202, over 4927.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03531, over 973753.89 frames.], batch size: 23, lr: 2.64e-04
2022-05-06 03:36:55,128 INFO [train.py:715] (1/8) Epoch 8, batch 10200, loss[loss=0.1562, simple_loss=0.2304, pruned_loss=0.04096, over 4857.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.03529, over 973826.61 frames.], batch size: 38, lr: 2.64e-04
2022-05-06 03:37:34,629 INFO [train.py:715] (1/8) Epoch 8, batch 10250, loss[loss=0.1231, simple_loss=0.1988, pruned_loss=0.02372, over 4829.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.0354, over 974146.54 frames.], batch size: 25, lr: 2.64e-04
2022-05-06 03:38:14,434 INFO [train.py:715] (1/8) Epoch 8, batch 10300, loss[loss=0.128, simple_loss=0.205, pruned_loss=0.02557, over 4814.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03536, over 972855.71 frames.], batch size: 27, lr: 2.64e-04
2022-05-06 03:38:53,951 INFO [train.py:715] (1/8) Epoch 8, batch 10350, loss[loss=0.1372, simple_loss=0.2094, pruned_loss=0.03253, over 4854.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03538, over 973245.12 frames.], batch size: 20, lr: 2.64e-04
2022-05-06 03:39:32,643 INFO [train.py:715] (1/8) Epoch 8, batch 10400, loss[loss=0.1329, simple_loss=0.2135, pruned_loss=0.02614, over 4986.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03519, over 973391.61 frames.], batch size: 25, lr: 2.64e-04
2022-05-06 03:40:12,244 INFO [train.py:715] (1/8) Epoch 8, batch 10450, loss[loss=0.1369, simple_loss=0.2153, pruned_loss=0.02932, over 4798.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03497, over 973729.04 frames.], batch size: 21, lr: 2.64e-04
2022-05-06 03:40:51,309 INFO [train.py:715] (1/8) Epoch 8, batch 10500, loss[loss=0.1279, simple_loss=0.1935, pruned_loss=0.03113, over 4873.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03514, over 973459.69 frames.], batch size: 22, lr: 2.64e-04
2022-05-06 03:41:30,158 INFO [train.py:715] (1/8) Epoch 8, batch 10550, loss[loss=0.1348, simple_loss=0.2183, pruned_loss=0.02559, over 4916.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03498, over 973658.28 frames.], batch size: 17, lr: 2.64e-04
2022-05-06 03:42:08,773 INFO [train.py:715] (1/8) Epoch 8, batch 10600, loss[loss=0.1376, simple_loss=0.2123, pruned_loss=0.03142, over 4919.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2152, pruned_loss=0.03493, over 973555.74 frames.], batch size: 17, lr: 2.64e-04
2022-05-06 03:42:48,076 INFO [train.py:715] (1/8) Epoch 8, batch 10650, loss[loss=0.1632, simple_loss=0.243, pruned_loss=0.04168, over 4753.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2152, pruned_loss=0.03497, over 973764.95 frames.], batch size: 19, lr: 2.64e-04
2022-05-06 03:43:27,263 INFO [train.py:715] (1/8) Epoch 8, batch 10700, loss[loss=0.1536, simple_loss=0.2321, pruned_loss=0.03756, over 4990.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03493, over 973465.87 frames.], batch size: 25, lr: 2.64e-04
2022-05-06 03:44:06,359 INFO [train.py:715] (1/8) Epoch 8, batch 10750, loss[loss=0.108, simple_loss=0.1875, pruned_loss=0.01429, over 4819.00 frames.], tot_loss[loss=0.1433, simple_loss=0.216, pruned_loss=0.03533, over 973713.97 frames.], batch size: 26, lr: 2.64e-04
2022-05-06 03:44:46,296 INFO [train.py:715] (1/8) Epoch 8, batch 10800, loss[loss=0.1647, simple_loss=0.2393, pruned_loss=0.04504, over 4924.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03585, over 973368.71 frames.], batch size: 39, lr: 2.64e-04
2022-05-06 03:45:26,108 INFO [train.py:715] (1/8) Epoch 8, batch 10850, loss[loss=0.1315, simple_loss=0.1977, pruned_loss=0.03262, over 4881.00 frames.], tot_loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.03601, over 973430.89 frames.], batch size: 19, lr: 2.64e-04
2022-05-06 03:46:05,372 INFO [train.py:715] (1/8) Epoch 8, batch 10900, loss[loss=0.138, simple_loss=0.2127, pruned_loss=0.03165, over 4993.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2167, pruned_loss=0.03643, over 972143.49 frames.], batch size: 14, lr: 2.64e-04
2022-05-06 03:46:44,378 INFO [train.py:715] (1/8) Epoch 8, batch 10950, loss[loss=0.1413, simple_loss=0.2192, pruned_loss=0.03172, over 4965.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03617, over 973489.20 frames.], batch size: 24, lr: 2.64e-04
2022-05-06 03:47:24,380 INFO [train.py:715] (1/8) Epoch 8, batch 11000, loss[loss=0.1174, simple_loss=0.1863, pruned_loss=0.02425, over 4800.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.0356, over 973641.66 frames.], batch size: 14, lr: 2.64e-04
2022-05-06 03:48:03,914 INFO [train.py:715] (1/8) Epoch 8, batch 11050, loss[loss=0.1545, simple_loss=0.2194, pruned_loss=0.04475, over 4942.00 frames.], tot_loss[loss=0.144, simple_loss=0.2168, pruned_loss=0.03564, over 973441.25 frames.], batch size: 23, lr: 2.64e-04
2022-05-06 03:48:42,673 INFO [train.py:715] (1/8) Epoch 8, batch 11100, loss[loss=0.1566, simple_loss=0.2209, pruned_loss=0.04615, over 4741.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2161, pruned_loss=0.03551, over 972515.83 frames.], batch size: 16, lr: 2.64e-04
2022-05-06 03:49:22,148 INFO [train.py:715] (1/8) Epoch 8, batch 11150, loss[loss=0.1378, simple_loss=0.2089, pruned_loss=0.03336, over 4733.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03543, over 971936.89 frames.], batch size: 16, lr: 2.64e-04
2022-05-06 03:50:01,947 INFO [train.py:715] (1/8) Epoch 8, batch 11200, loss[loss=0.1338, simple_loss=0.2126, pruned_loss=0.02744, over 4777.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03527, over 971682.90 frames.], batch size: 18, lr: 2.64e-04
2022-05-06 03:50:40,572 INFO [train.py:715] (1/8) Epoch 8, batch 11250, loss[loss=0.1323, simple_loss=0.196, pruned_loss=0.03428, over 4811.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2161, pruned_loss=0.03583, over 971843.43 frames.], batch size: 25, lr: 2.64e-04
2022-05-06 03:51:19,596 INFO [train.py:715] (1/8) Epoch 8, batch 11300, loss[loss=0.1321, simple_loss=0.2092, pruned_loss=0.02747, over 4778.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03508, over 971475.64 frames.], batch size: 18, lr: 2.64e-04
2022-05-06 03:51:58,930 INFO [train.py:715] (1/8) Epoch 8, batch 11350, loss[loss=0.1818, simple_loss=0.2679, pruned_loss=0.04789, over 4781.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03531, over 972232.21 frames.], batch size: 18, lr: 2.63e-04
2022-05-06 03:52:37,408 INFO [train.py:715] (1/8) Epoch 8, batch 11400, loss[loss=0.1472, simple_loss=0.2233, pruned_loss=0.03553, over 4810.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03518, over 971174.82 frames.], batch size: 26, lr: 2.63e-04
2022-05-06 03:53:16,054 INFO [train.py:715] (1/8) Epoch 8, batch 11450, loss[loss=0.1351, simple_loss=0.2186, pruned_loss=0.02574, over 4925.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03515, over 972355.23 frames.], batch size: 23, lr: 2.63e-04
2022-05-06 03:53:55,354 INFO [train.py:715] (1/8) Epoch 8, batch 11500, loss[loss=0.1328, simple_loss=0.2126, pruned_loss=0.0265, over 4920.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03559, over 972787.37 frames.], batch size: 19, lr: 2.63e-04
2022-05-06 03:54:34,458 INFO [train.py:715] (1/8) Epoch 8, batch 11550, loss[loss=0.1414, simple_loss=0.2237, pruned_loss=0.02952, over 4892.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03573, over 972993.46 frames.], batch size: 16, lr: 2.63e-04
2022-05-06 03:55:13,514 INFO [train.py:715] (1/8) Epoch 8, batch 11600, loss[loss=0.1519, simple_loss=0.2187, pruned_loss=0.04253, over 4891.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2166, pruned_loss=0.03595, over 973763.81 frames.], batch size: 22, lr: 2.63e-04
2022-05-06 03:55:53,447 INFO [train.py:715] (1/8) Epoch 8, batch 11650, loss[loss=0.1562, simple_loss=0.2359, pruned_loss=0.03822, over 4839.00 frames.], tot_loss[loss=0.1444, simple_loss=0.217, pruned_loss=0.03587, over 972502.24 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 03:56:33,837 INFO [train.py:715] (1/8) Epoch 8, batch 11700, loss[loss=0.1326, simple_loss=0.1999, pruned_loss=0.03268, over 4825.00 frames.], tot_loss[loss=0.1446, simple_loss=0.217, pruned_loss=0.03604, over 972396.89 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 03:57:13,269 INFO [train.py:715] (1/8) Epoch 8, batch 11750, loss[loss=0.1568, simple_loss=0.2224, pruned_loss=0.04559, over 4685.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2166, pruned_loss=0.03609, over 972060.53 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 03:57:52,307 INFO [train.py:715] (1/8) Epoch 8, batch 11800, loss[loss=0.1826, simple_loss=0.2345, pruned_loss=0.06535, over 4787.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2168, pruned_loss=0.03585, over 971643.93 frames.], batch size: 17, lr: 2.63e-04
2022-05-06 03:58:32,052 INFO [train.py:715] (1/8) Epoch 8, batch 11850, loss[loss=0.1204, simple_loss=0.19, pruned_loss=0.02546, over 4974.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03566, over 971952.84 frames.], batch size: 14, lr: 2.63e-04
2022-05-06 03:59:11,745 INFO [train.py:715] (1/8) Epoch 8, batch 11900, loss[loss=0.1398, simple_loss=0.2177, pruned_loss=0.03095, over 4965.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03534, over 972407.51 frames.], batch size: 29, lr: 2.63e-04
2022-05-06 03:59:51,350 INFO [train.py:715] (1/8) Epoch 8, batch 11950, loss[loss=0.1459, simple_loss=0.2129, pruned_loss=0.03941, over 4792.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03546, over 971297.09 frames.], batch size: 12, lr: 2.63e-04
2022-05-06 04:00:30,531 INFO [train.py:715] (1/8) Epoch 8, batch 12000, loss[loss=0.1578, simple_loss=0.2216, pruned_loss=0.04702, over 4835.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2167, pruned_loss=0.03581, over 971730.55 frames.], batch size: 30, lr: 2.63e-04
2022-05-06 04:00:30,531 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 04:00:40,092 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1076, simple_loss=0.1923, pruned_loss=0.0115, over 914524.00 frames.
2022-05-06 04:01:19,845 INFO [train.py:715] (1/8) Epoch 8, batch 12050, loss[loss=0.1349, simple_loss=0.2101, pruned_loss=0.02985, over 4988.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03575, over 972233.78 frames.], batch size: 25, lr: 2.63e-04
2022-05-06 04:01:59,449 INFO [train.py:715] (1/8) Epoch 8, batch 12100, loss[loss=0.1177, simple_loss=0.1868, pruned_loss=0.02431, over 4794.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03508, over 973211.05 frames.], batch size: 12, lr: 2.63e-04
2022-05-06 04:02:38,525 INFO [train.py:715] (1/8) Epoch 8, batch 12150, loss[loss=0.153, simple_loss=0.2194, pruned_loss=0.04331, over 4978.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03532, over 972532.93 frames.], batch size: 31, lr: 2.63e-04
2022-05-06 04:03:17,596 INFO [train.py:715] (1/8) Epoch 8, batch 12200, loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.03423, over 4828.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.03511, over 972375.18 frames.], batch size: 26, lr: 2.63e-04
2022-05-06 04:03:57,165 INFO [train.py:715] (1/8) Epoch 8, batch 12250, loss[loss=0.1307, simple_loss=0.195, pruned_loss=0.03325, over 4864.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2149, pruned_loss=0.03539, over 971736.78 frames.], batch size: 16, lr: 2.63e-04
2022-05-06 04:04:36,398 INFO [train.py:715] (1/8) Epoch 8, batch 12300, loss[loss=0.1404, simple_loss=0.216, pruned_loss=0.03237, over 4890.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2153, pruned_loss=0.03554, over 971835.76 frames.], batch size: 16, lr: 2.63e-04
2022-05-06 04:05:15,240 INFO [train.py:715] (1/8) Epoch 8, batch 12350, loss[loss=0.1137, simple_loss=0.1799, pruned_loss=0.02374, over 4955.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2155, pruned_loss=0.03579, over 972103.09 frames.], batch size: 29, lr: 2.63e-04
2022-05-06 04:05:54,662 INFO [train.py:715] (1/8) Epoch 8, batch 12400, loss[loss=0.1187, simple_loss=0.1964, pruned_loss=0.02047, over 4766.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.03557, over 972673.31 frames.], batch size: 18, lr: 2.63e-04
2022-05-06 04:06:34,256 INFO [train.py:715] (1/8) Epoch 8, batch 12450, loss[loss=0.1424, simple_loss=0.212, pruned_loss=0.0364, over 4846.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.03553, over 973485.30 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 04:07:13,259 INFO [train.py:715] (1/8) Epoch 8, batch 12500, loss[loss=0.1443, simple_loss=0.2199, pruned_loss=0.03434, over 4941.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03581, over 972759.00 frames.], batch size: 39, lr: 2.63e-04
2022-05-06 04:07:52,129 INFO [train.py:715] (1/8) Epoch 8, batch 12550, loss[loss=0.1335, simple_loss=0.2048, pruned_loss=0.0311, over 4817.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2147, pruned_loss=0.03541, over 971617.55 frames.], batch size: 26, lr: 2.63e-04
2022-05-06 04:08:31,834 INFO [train.py:715] (1/8) Epoch 8, batch 12600, loss[loss=0.1256, simple_loss=0.2074, pruned_loss=0.02194, over 4850.00 frames.], tot_loss[loss=0.142, simple_loss=0.2142, pruned_loss=0.03491, over 972703.75 frames.], batch size: 20, lr: 2.63e-04
2022-05-06 04:09:10,880 INFO [train.py:715] (1/8) Epoch 8, batch 12650, loss[loss=0.1351, simple_loss=0.2044, pruned_loss=0.03291, over 4975.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2136, pruned_loss=0.03455, over 971651.34 frames.], batch size: 25, lr: 2.63e-04
2022-05-06 04:09:50,740 INFO [train.py:715] (1/8) Epoch 8, batch 12700, loss[loss=0.1406, simple_loss=0.2003, pruned_loss=0.04044, over 4973.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03456, over 971952.84 frames.], batch size: 14, lr: 2.63e-04
2022-05-06 04:10:30,127 INFO [train.py:715] (1/8) Epoch 8, batch 12750, loss[loss=0.1719, simple_loss=0.2512, pruned_loss=0.04626, over 4690.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2141, pruned_loss=0.03473, over 972351.54 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 04:11:10,325 INFO [train.py:715] (1/8) Epoch 8, batch 12800, loss[loss=0.1747, simple_loss=0.2463, pruned_loss=0.0516, over 4884.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03525, over 971683.65 frames.], batch size: 22, lr: 2.63e-04
2022-05-06 04:11:48,984 INFO [train.py:715] (1/8) Epoch 8, batch 12850, loss[loss=0.1256, simple_loss=0.2037, pruned_loss=0.02373, over 4922.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2152, pruned_loss=0.03564, over 971361.47 frames.], batch size: 29, lr: 2.63e-04
2022-05-06 04:12:28,016 INFO [train.py:715] (1/8) Epoch 8, batch 12900, loss[loss=0.1433, simple_loss=0.2152, pruned_loss=0.03573, over 4739.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2164, pruned_loss=0.03638, over 970961.30 frames.], batch size: 19, lr: 2.63e-04
2022-05-06 04:13:07,529 INFO [train.py:715] (1/8) Epoch 8, batch 12950, loss[loss=0.1285, simple_loss=0.1969, pruned_loss=0.03005, over 4852.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2154, pruned_loss=0.03583, over 971320.47 frames.], batch size: 20, lr: 2.63e-04
2022-05-06 04:13:46,917 INFO [train.py:715] (1/8) Epoch 8, batch 13000, loss[loss=0.1502, simple_loss=0.2243, pruned_loss=0.038, over 4808.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2156, pruned_loss=0.0358, over 971080.86 frames.], batch size: 21, lr: 2.63e-04
2022-05-06 04:14:26,217 INFO [train.py:715] (1/8) Epoch 8, batch 13050, loss[loss=0.1486, simple_loss=0.2187, pruned_loss=0.03928, over 4898.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.0361, over 972294.68 frames.], batch size: 39, lr: 2.63e-04
2022-05-06 04:15:05,643 INFO [train.py:715] (1/8) Epoch 8, batch 13100, loss[loss=0.2215, simple_loss=0.2855, pruned_loss=0.07872, over 4639.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2163, pruned_loss=0.03602, over 973187.05 frames.], batch size: 13, lr: 2.63e-04
2022-05-06 04:15:45,374 INFO [train.py:715] (1/8) Epoch 8, batch 13150, loss[loss=0.1422, simple_loss=0.2192, pruned_loss=0.03264, over 4756.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.03619, over 972180.32 frames.], batch size: 16, lr: 2.63e-04
2022-05-06 04:16:24,331 INFO [train.py:715] (1/8) Epoch 8, batch 13200, loss[loss=0.1212, simple_loss=0.1846, pruned_loss=0.02894, over 4653.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2168, pruned_loss=0.03645, over 971522.63 frames.], batch size: 13, lr: 2.63e-04
2022-05-06 04:17:03,720 INFO [train.py:715] (1/8) Epoch 8, batch 13250, loss[loss=0.1344, simple_loss=0.2112, pruned_loss=0.02879, over 4891.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2168, pruned_loss=0.03603, over 971602.33 frames.], batch size: 22, lr: 2.63e-04
2022-05-06 04:17:43,339 INFO [train.py:715] (1/8) Epoch 8, batch 13300, loss[loss=0.1269, simple_loss=0.1971, pruned_loss=0.02832, over 4821.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2162, pruned_loss=0.03628, over 972259.88 frames.], batch size: 13, lr: 2.63e-04
2022-05-06 04:18:22,361 INFO [train.py:715] (1/8) Epoch 8, batch 13350, loss[loss=0.1514, simple_loss=0.2222, pruned_loss=0.04037, over 4773.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2155, pruned_loss=0.03588, over 972860.65 frames.], batch size: 18, lr: 2.63e-04
2022-05-06 04:19:01,005 INFO [train.py:715] (1/8) Epoch 8, batch 13400, loss[loss=0.1449, simple_loss=0.2289, pruned_loss=0.03048, over 4812.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.03566, over 972581.88 frames.], batch size: 13, lr: 2.63e-04
2022-05-06 04:19:39,804 INFO [train.py:715] (1/8) Epoch 8, batch 13450, loss[loss=0.1517, simple_loss=0.2213, pruned_loss=0.04101, over 4970.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03552, over 972957.39 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 04:20:19,857 INFO [train.py:715] (1/8) Epoch 8, batch 13500, loss[loss=0.1463, simple_loss=0.2178, pruned_loss=0.03741, over 4890.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.03565, over 971658.34 frames.], batch size: 22, lr: 2.63e-04
2022-05-06 04:20:58,647 INFO [train.py:715] (1/8) Epoch 8, batch 13550, loss[loss=0.1458, simple_loss=0.2209, pruned_loss=0.03535, over 4896.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03563, over 972245.39 frames.], batch size: 19, lr: 2.62e-04
2022-05-06 04:21:37,840 INFO [train.py:715] (1/8) Epoch 8, batch 13600, loss[loss=0.1469, simple_loss=0.2146, pruned_loss=0.03962, over 4783.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03574, over 972280.31 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:22:16,980 INFO [train.py:715] (1/8) Epoch 8, batch 13650, loss[loss=0.1539, simple_loss=0.2244, pruned_loss=0.04164, over 4755.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.03603, over 971449.48 frames.], batch size: 19, lr: 2.62e-04
2022-05-06 04:22:56,130 INFO [train.py:715] (1/8) Epoch 8, batch 13700, loss[loss=0.1371, simple_loss=0.2183, pruned_loss=0.0279, over 4817.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2157, pruned_loss=0.03593, over 971601.02 frames.], batch size: 25, lr: 2.62e-04
2022-05-06 04:23:34,772 INFO [train.py:715] (1/8) Epoch 8, batch 13750, loss[loss=0.1384, simple_loss=0.2149, pruned_loss=0.03097, over 4816.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2155, pruned_loss=0.03609, over 972468.08 frames.], batch size: 26, lr: 2.62e-04
2022-05-06 04:24:13,492 INFO [train.py:715] (1/8) Epoch 8, batch 13800, loss[loss=0.1503, simple_loss=0.2265, pruned_loss=0.03706, over 4740.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2162, pruned_loss=0.03658, over 972802.78 frames.], batch size: 16, lr: 2.62e-04
2022-05-06 04:24:52,950 INFO [train.py:715] (1/8) Epoch 8, batch 13850, loss[loss=0.134, simple_loss=0.2026, pruned_loss=0.03267, over 4864.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2164, pruned_loss=0.03669, over 972739.69 frames.], batch size: 20, lr: 2.62e-04
2022-05-06 04:25:31,245 INFO [train.py:715] (1/8) Epoch 8, batch 13900, loss[loss=0.1456, simple_loss=0.2116, pruned_loss=0.03981, over 4954.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2154, pruned_loss=0.03584, over 973328.19 frames.], batch size: 35, lr: 2.62e-04
2022-05-06 04:26:10,341 INFO [train.py:715] (1/8) Epoch 8, batch 13950, loss[loss=0.1237, simple_loss=0.2, pruned_loss=0.02368, over 4779.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03599, over 972817.81 frames.], batch size: 18, lr: 2.62e-04
2022-05-06 04:26:49,435 INFO [train.py:715] (1/8) Epoch 8, batch 14000, loss[loss=0.1261, simple_loss=0.2041, pruned_loss=0.02408, over 4735.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2151, pruned_loss=0.03536, over 971822.91 frames.], batch size: 12, lr: 2.62e-04
2022-05-06 04:27:28,486 INFO [train.py:715] (1/8) Epoch 8, batch 14050, loss[loss=0.1662, simple_loss=0.238, pruned_loss=0.04716, over 4970.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2157, pruned_loss=0.03592, over 971621.99 frames.], batch size: 28, lr: 2.62e-04
2022-05-06 04:28:06,684 INFO [train.py:715] (1/8) Epoch 8, batch 14100, loss[loss=0.1645, simple_loss=0.2244, pruned_loss=0.0523, over 4857.00 frames.], tot_loss[loss=0.145, simple_loss=0.2169, pruned_loss=0.03652, over 972291.97 frames.], batch size: 30, lr: 2.62e-04
2022-05-06 04:28:45,330 INFO [train.py:715] (1/8) Epoch 8, batch 14150, loss[loss=0.165, simple_loss=0.2196, pruned_loss=0.05522, over 4953.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2172, pruned_loss=0.03654, over 972264.76 frames.], batch size: 21, lr: 2.62e-04
2022-05-06 04:29:25,593 INFO [train.py:715] (1/8) Epoch 8, batch 14200, loss[loss=0.1266, simple_loss=0.1987, pruned_loss=0.02727, over 4876.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2175, pruned_loss=0.03692, over 972096.91 frames.], batch size: 22, lr: 2.62e-04
2022-05-06 04:30:04,165 INFO [train.py:715] (1/8) Epoch 8, batch 14250, loss[loss=0.1302, simple_loss=0.2045, pruned_loss=0.02791, over 4784.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2181, pruned_loss=0.03731, over 971956.82 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:30:44,073 INFO [train.py:715] (1/8) Epoch 8, batch 14300, loss[loss=0.1764, simple_loss=0.2504, pruned_loss=0.05117, over 4792.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2173, pruned_loss=0.03648, over 971885.93 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:31:23,537 INFO [train.py:715] (1/8) Epoch 8, batch 14350, loss[loss=0.1678, simple_loss=0.2234, pruned_loss=0.05607, over 4777.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2178, pruned_loss=0.03661, over 971788.05 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:32:02,827 INFO [train.py:715] (1/8) Epoch 8, batch 14400, loss[loss=0.1695, simple_loss=0.249, pruned_loss=0.04503, over 4962.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2179, pruned_loss=0.03672, over 971282.99 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:32:41,521 INFO [train.py:715] (1/8) Epoch 8, batch 14450, loss[loss=0.1661, simple_loss=0.241, pruned_loss=0.04564, over 4803.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2172, pruned_loss=0.03648, over 971126.70 frames.], batch size: 24, lr: 2.62e-04
2022-05-06 04:33:20,780 INFO [train.py:715] (1/8) Epoch 8, batch 14500, loss[loss=0.1315, simple_loss=0.1965, pruned_loss=0.03323, over 4769.00 frames.], tot_loss[loss=0.1452, simple_loss=0.217, pruned_loss=0.03667, over 970654.95 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:34:00,258 INFO [train.py:715] (1/8) Epoch 8, batch 14550, loss[loss=0.1164, simple_loss=0.1957, pruned_loss=0.01852, over 4919.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2165, pruned_loss=0.03622, over 970440.14 frames.], batch size: 23, lr: 2.62e-04
2022-05-06 04:34:38,296 INFO [train.py:715] (1/8) Epoch 8, batch 14600, loss[loss=0.1358, simple_loss=0.211, pruned_loss=0.03024, over 4782.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03561, over 971437.88 frames.], batch size: 13, lr: 2.62e-04
2022-05-06 04:35:17,881 INFO [train.py:715] (1/8) Epoch 8, batch 14650, loss[loss=0.1297, simple_loss=0.209, pruned_loss=0.02522, over 4796.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2154, pruned_loss=0.03609, over 971203.40 frames.], batch size: 21, lr: 2.62e-04
2022-05-06 04:35:57,141 INFO [train.py:715] (1/8) Epoch 8, batch 14700, loss[loss=0.1331, simple_loss=0.2198, pruned_loss=0.02317, over 4884.00 frames.], tot_loss[loss=0.1429, simple_loss=0.215, pruned_loss=0.03541, over 971507.53 frames.], batch size: 22, lr: 2.62e-04
2022-05-06 04:36:35,959 INFO [train.py:715] (1/8) Epoch 8, batch 14750, loss[loss=0.2097, simple_loss=0.2434, pruned_loss=0.08803, over 4969.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2158, pruned_loss=0.0362, over 971440.25 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:37:14,356 INFO [train.py:715] (1/8) Epoch 8, batch 14800, loss[loss=0.1835, simple_loss=0.2528, pruned_loss=0.05716, over 4782.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2163, pruned_loss=0.03642, over 970480.80 frames.], batch size: 18, lr: 2.62e-04
2022-05-06 04:37:54,170 INFO [train.py:715] (1/8) Epoch 8, batch 14850, loss[loss=0.1694, simple_loss=0.2525, pruned_loss=0.04316, over 4911.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.03603, over 970608.84 frames.], batch size: 23, lr: 2.62e-04
2022-05-06 04:38:33,090 INFO [train.py:715] (1/8) Epoch 8, batch 14900, loss[loss=0.1835, simple_loss=0.2411, pruned_loss=0.06295, over 4830.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03606, over 970729.86 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:39:11,873 INFO [train.py:715] (1/8) Epoch 8, batch 14950, loss[loss=0.1551, simple_loss=0.2161, pruned_loss=0.04708, over 4838.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2155, pruned_loss=0.03584, over 970940.88 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:39:51,071 INFO [train.py:715] (1/8) Epoch 8, batch 15000, loss[loss=0.1315, simple_loss=0.2093, pruned_loss=0.0268, over 4932.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.03592, over 971059.32 frames.], batch size: 23, lr: 2.62e-04
2022-05-06 04:39:51,072 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 04:40:00,792 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1076, simple_loss=0.1921, pruned_loss=0.01153, over 914524.00 frames.
2022-05-06 04:40:40,560 INFO [train.py:715] (1/8) Epoch 8, batch 15050, loss[loss=0.1724, simple_loss=0.2346, pruned_loss=0.05509, over 4856.00 frames.], tot_loss[loss=0.144, simple_loss=0.2162, pruned_loss=0.03591, over 971199.38 frames.], batch size: 30, lr: 2.62e-04
2022-05-06 04:41:19,880 INFO [train.py:715] (1/8) Epoch 8, batch 15100, loss[loss=0.1267, simple_loss=0.1984, pruned_loss=0.02745, over 4977.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03578, over 971479.97 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:41:59,417 INFO [train.py:715] (1/8) Epoch 8, batch 15150, loss[loss=0.1525, simple_loss=0.2276, pruned_loss=0.03873, over 4868.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03611, over 970818.69 frames.], batch size: 22, lr: 2.62e-04
2022-05-06 04:42:38,837 INFO [train.py:715] (1/8) Epoch 8, batch 15200, loss[loss=0.1175, simple_loss=0.192, pruned_loss=0.02151, over 4762.00 frames.], tot_loss[loss=0.1437, simple_loss=0.216, pruned_loss=0.03573, over 970051.61 frames.], batch size: 18, lr: 2.62e-04
2022-05-06 04:43:18,564 INFO [train.py:715] (1/8) Epoch 8, batch 15250, loss[loss=0.1377, simple_loss=0.2092, pruned_loss=0.0331, over 4883.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03522, over 969957.52 frames.], batch size: 22, lr: 2.62e-04
2022-05-06 04:43:58,534 INFO [train.py:715] (1/8) Epoch 8, batch 15300, loss[loss=0.1259, simple_loss=0.1984, pruned_loss=0.02668, over 4831.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03602, over 970429.06 frames.], batch size: 13, lr: 2.62e-04
2022-05-06 04:44:37,106 INFO [train.py:715] (1/8) Epoch 8, batch 15350, loss[loss=0.1303, simple_loss=0.1988, pruned_loss=0.03089, over 4840.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03603, over 971129.53 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:45:16,999 INFO [train.py:715] (1/8) Epoch 8, batch 15400, loss[loss=0.1567, simple_loss=0.2295, pruned_loss=0.04198, over 4791.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2162, pruned_loss=0.03624, over 970951.74 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:45:55,987 INFO [train.py:715] (1/8) Epoch 8, batch 15450, loss[loss=0.1313, simple_loss=0.207, pruned_loss=0.02776, over 4876.00 frames.], tot_loss[loss=0.144, simple_loss=0.2157, pruned_loss=0.03619, over 970291.66 frames.], batch size: 32, lr: 2.62e-04
2022-05-06 04:46:34,946 INFO [train.py:715] (1/8) Epoch 8, batch 15500, loss[loss=0.1333, simple_loss=0.1952, pruned_loss=0.03568, over 4826.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2157, pruned_loss=0.03628, over 969967.85 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:47:13,678 INFO [train.py:715] (1/8) Epoch 8, batch 15550, loss[loss=0.1316, simple_loss=0.2169, pruned_loss=0.02312, over 4863.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2156, pruned_loss=0.03625, over 970399.80 frames.], batch size: 20, lr: 2.62e-04
2022-05-06 04:47:52,419 INFO [train.py:715] (1/8) Epoch 8, batch 15600, loss[loss=0.147, simple_loss=0.2259, pruned_loss=0.03402, over 4903.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.0361, over 970574.92 frames.], batch size: 17, lr: 2.62e-04
2022-05-06 04:48:32,602 INFO [train.py:715] (1/8) Epoch 8, batch 15650, loss[loss=0.1295, simple_loss=0.2041, pruned_loss=0.0275, over 4980.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2165, pruned_loss=0.03623, over 970291.32 frames.], batch size: 25, lr: 2.62e-04
2022-05-06 04:49:11,091 INFO [train.py:715] (1/8) Epoch 8, batch 15700, loss[loss=0.1643, simple_loss=0.2251, pruned_loss=0.05177, over 4810.00 frames.], tot_loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.036, over 970791.43 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:49:50,931 INFO [train.py:715] (1/8) Epoch 8, batch 15750, loss[loss=0.149, simple_loss=0.2274, pruned_loss=0.03525, over 4831.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2166, pruned_loss=0.03628, over 971695.27 frames.], batch size: 26, lr: 2.62e-04
2022-05-06 04:50:30,394 INFO [train.py:715] (1/8) Epoch 8, batch 15800, loss[loss=0.1546, simple_loss=0.2362, pruned_loss=0.03653, over 4938.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2177, pruned_loss=0.03675, over 971722.20 frames.], batch size: 39, lr: 2.61e-04
2022-05-06 04:51:09,455 INFO [train.py:715] (1/8) Epoch 8, batch 15850, loss[loss=0.1415, simple_loss=0.2186, pruned_loss=0.0322, over 4690.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2179, pruned_loss=0.03644, over 972072.66 frames.], batch size: 15, lr: 2.61e-04
2022-05-06 04:51:48,558 INFO [train.py:715] (1/8) Epoch 8, batch 15900, loss[loss=0.1669, simple_loss=0.2417, pruned_loss=0.04609, over 4857.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2183, pruned_loss=0.03675, over 972335.40 frames.], batch size: 20, lr: 2.61e-04
2022-05-06 04:52:27,778 INFO [train.py:715] (1/8) Epoch 8, batch 15950, loss[loss=0.1509, simple_loss=0.223, pruned_loss=0.03943, over 4861.00 frames.], tot_loss[loss=0.145, simple_loss=0.217, pruned_loss=0.03647, over 972410.58 frames.], batch size: 32, lr: 2.61e-04
2022-05-06 04:53:07,059 INFO [train.py:715] (1/8) Epoch 8, batch 16000, loss[loss=0.1238, simple_loss=0.1964, pruned_loss=0.02557, over 4926.00 frames.], tot_loss[loss=0.145, simple_loss=0.217, pruned_loss=0.03648, over 971997.71 frames.], batch size: 23, lr: 2.61e-04
2022-05-06 04:53:45,659 INFO [train.py:715] (1/8) Epoch 8, batch 16050, loss[loss=0.1724, simple_loss=0.2507, pruned_loss=0.04708, over 4851.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2174, pruned_loss=0.03623, over 972072.63 frames.], batch size: 30, lr: 2.61e-04
2022-05-06 04:54:25,529 INFO [train.py:715] (1/8) Epoch 8, batch 16100, loss[loss=0.141, simple_loss=0.2171, pruned_loss=0.03247, over 4933.00 frames.], tot_loss[loss=0.145, simple_loss=0.2174, pruned_loss=0.03633, over 971840.49 frames.], batch size: 23, lr: 2.61e-04
2022-05-06 04:55:04,007 INFO [train.py:715] (1/8) Epoch 8, batch 16150, loss[loss=0.1414, simple_loss=0.2155, pruned_loss=0.03364, over 4794.00 frames.], tot_loss[loss=0.145, simple_loss=0.2175, pruned_loss=0.0363, over 971804.95 frames.], batch size: 24, lr: 2.61e-04
2022-05-06 04:55:43,549 INFO [train.py:715] (1/8) Epoch 8, batch 16200, loss[loss=0.1505, simple_loss=0.2286, pruned_loss=0.03627, over 4750.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2162, pruned_loss=0.03552, over 972516.18 frames.], batch size: 16, lr: 2.61e-04
2022-05-06 04:56:21,935 INFO [train.py:715] (1/8) Epoch 8, batch 16250, loss[loss=0.1608, simple_loss=0.2297, pruned_loss=0.04598, over 4943.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2165, pruned_loss=0.03585, over 972623.55 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 04:57:01,395 INFO [train.py:715] (1/8) Epoch 8, batch 16300, loss[loss=0.1571, simple_loss=0.2177, pruned_loss=0.04819, over 4763.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03579, over 972591.77 frames.], batch size: 19, lr: 2.61e-04
2022-05-06 04:57:40,825 INFO [train.py:715] (1/8) Epoch 8, batch 16350, loss[loss=0.1289, simple_loss=0.1916, pruned_loss=0.03308, over 4740.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03545, over 972208.26 frames.], batch size: 12, lr: 2.61e-04
2022-05-06 04:58:19,601 INFO [train.py:715] (1/8) Epoch 8, batch 16400, loss[loss=0.1143, simple_loss=0.191, pruned_loss=0.01881, over 4806.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.0355, over 971856.03 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 04:58:58,716 INFO [train.py:715] (1/8) Epoch 8, batch 16450, loss[loss=0.1202, simple_loss=0.1839, pruned_loss=0.02826, over 4987.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03578, over 972772.26 frames.], batch size: 31, lr: 2.61e-04
2022-05-06 04:59:37,564 INFO [train.py:715] (1/8) Epoch 8, batch 16500, loss[loss=0.1357, simple_loss=0.2115, pruned_loss=0.02997, over 4944.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03516, over 973132.61 frames.], batch size: 23, lr: 2.61e-04
2022-05-06 05:00:17,265 INFO [train.py:715] (1/8) Epoch 8, batch 16550, loss[loss=0.1631, simple_loss=0.2281, pruned_loss=0.04903, over 4833.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.03504, over 972593.03 frames.], batch size: 26, lr: 2.61e-04
2022-05-06 05:00:56,285 INFO [train.py:715] (1/8) Epoch 8, batch 16600, loss[loss=0.162, simple_loss=0.2197, pruned_loss=0.05218, over 4826.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2145, pruned_loss=0.035, over 972292.02 frames.], batch size: 13, lr: 2.61e-04
2022-05-06 05:01:35,315 INFO [train.py:715] (1/8) Epoch 8, batch 16650, loss[loss=0.1802, simple_loss=0.2531, pruned_loss=0.05362, over 4952.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03532, over 972687.14 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:02:14,560 INFO [train.py:715] (1/8) Epoch 8, batch 16700, loss[loss=0.2196, simple_loss=0.271, pruned_loss=0.08413, over 4779.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03516, over 972870.55 frames.], batch size: 18, lr: 2.61e-04
2022-05-06 05:02:53,476 INFO [train.py:715] (1/8) Epoch 8, batch 16750, loss[loss=0.1638, simple_loss=0.2371, pruned_loss=0.04526, over 4965.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2152, pruned_loss=0.03552, over 972469.73 frames.], batch size: 15, lr: 2.61e-04
2022-05-06 05:03:33,071 INFO [train.py:715] (1/8) Epoch 8, batch 16800, loss[loss=0.1471, simple_loss=0.2165, pruned_loss=0.03888, over 4803.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2161, pruned_loss=0.03615, over 972959.98 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:04:12,048 INFO [train.py:715] (1/8) Epoch 8, batch 16850, loss[loss=0.1306, simple_loss=0.2017, pruned_loss=0.0297, over 4803.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2158, pruned_loss=0.03572, over 972742.61 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 05:04:51,958 INFO [train.py:715] (1/8) Epoch 8, batch 16900, loss[loss=0.1849, simple_loss=0.2511, pruned_loss=0.05935, over 4807.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2167, pruned_loss=0.03615, over 972376.40 frames.], batch size: 14, lr: 2.61e-04
2022-05-06 05:05:30,455 INFO [train.py:715] (1/8) Epoch 8, batch 16950, loss[loss=0.1396, simple_loss=0.199, pruned_loss=0.0401, over 4709.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03573, over 971927.97 frames.], batch size: 12, lr: 2.61e-04
2022-05-06 05:06:10,152 INFO [train.py:715] (1/8) Epoch 8, batch 17000, loss[loss=0.1357, simple_loss=0.2112, pruned_loss=0.03013, over 4955.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.03602, over 972780.59 frames.], batch size: 39, lr: 2.61e-04
2022-05-06 05:06:49,665 INFO [train.py:715] (1/8) Epoch 8, batch 17050, loss[loss=0.1475, simple_loss=0.2244, pruned_loss=0.0353, over 4821.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03548, over 973050.96 frames.], batch size: 26, lr: 2.61e-04
2022-05-06 05:07:28,342 INFO [train.py:715] (1/8) Epoch 8, batch 17100, loss[loss=0.1184, simple_loss=0.1975, pruned_loss=0.01965, over 4836.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03534, over 972645.84 frames.], batch size: 30, lr: 2.61e-04
2022-05-06 05:08:08,035 INFO [train.py:715] (1/8) Epoch 8, batch 17150, loss[loss=0.1398, simple_loss=0.2137, pruned_loss=0.03295, over 4849.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03491, over 972598.41 frames.], batch size: 15, lr: 2.61e-04
2022-05-06 05:08:47,210 INFO [train.py:715] (1/8) Epoch 8, batch 17200, loss[loss=0.1473, simple_loss=0.2178, pruned_loss=0.03839, over 4752.00 frames.], tot_loss[loss=0.142, simple_loss=0.215, pruned_loss=0.03453, over 971441.92 frames.], batch size: 16, lr: 2.61e-04
2022-05-06 05:09:26,330 INFO [train.py:715] (1/8) Epoch 8, batch 17250, loss[loss=0.1767, simple_loss=0.2474, pruned_loss=0.05296, over 4816.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2154, pruned_loss=0.03483, over 971351.41 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 05:10:04,661 INFO [train.py:715] (1/8) Epoch 8, batch 17300, loss[loss=0.1456, simple_loss=0.2194, pruned_loss=0.03593, over 4695.00 frames.], tot_loss[loss=0.143, simple_loss=0.2157, pruned_loss=0.03519, over 970893.29 frames.], batch size: 15, lr: 2.61e-04
2022-05-06 05:10:44,500 INFO [train.py:715] (1/8) Epoch 8, batch 17350, loss[loss=0.1462, simple_loss=0.2103, pruned_loss=0.04104, over 4798.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03525, over 970480.05 frames.], batch size: 14, lr: 2.61e-04
2022-05-06 05:11:23,601 INFO [train.py:715] (1/8) Epoch 8, batch 17400, loss[loss=0.1439, simple_loss=0.2175, pruned_loss=0.03512, over 4791.00 frames.], tot_loss[loss=0.143, simple_loss=0.2156, pruned_loss=0.03515, over 970148.67 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:12:02,694 INFO [train.py:715] (1/8) Epoch 8, batch 17450, loss[loss=0.1109, simple_loss=0.1924, pruned_loss=0.01468, over 4961.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03516, over 969541.97 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:12:42,127 INFO [train.py:715] (1/8) Epoch 8, batch 17500, loss[loss=0.1362, simple_loss=0.2076, pruned_loss=0.03236, over 4814.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03491, over 969960.97 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 05:13:23,168 INFO [train.py:715] (1/8) Epoch 8, batch 17550, loss[loss=0.1407, simple_loss=0.2178, pruned_loss=0.03178, over 4973.00 frames.], tot_loss[loss=0.143, simple_loss=0.2158, pruned_loss=0.03509, over 970236.77 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 05:14:02,978 INFO [train.py:715] (1/8) Epoch 8, batch 17600, loss[loss=0.1324, simple_loss=0.213, pruned_loss=0.02585, over 4776.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03553, over 970704.73 frames.], batch size: 18, lr: 2.61e-04
2022-05-06 05:14:41,722 INFO [train.py:715] (1/8) Epoch 8, batch 17650, loss[loss=0.1492, simple_loss=0.2211, pruned_loss=0.03871, over 4793.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2153, pruned_loss=0.03539, over 971779.89 frames.], batch size: 14, lr: 2.61e-04
2022-05-06 05:15:22,864 INFO [train.py:715] (1/8) Epoch 8, batch 17700, loss[loss=0.1364, simple_loss=0.2168, pruned_loss=0.02801, over 4931.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2161, pruned_loss=0.03558, over 971880.38 frames.], batch size: 23, lr: 2.61e-04
2022-05-06 05:16:02,821 INFO [train.py:715] (1/8) Epoch 8, batch 17750, loss[loss=0.1754, simple_loss=0.2395, pruned_loss=0.05561, over 4766.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.03572, over 970465.51 frames.], batch size: 16, lr: 2.61e-04
2022-05-06 05:16:43,290 INFO [train.py:715] (1/8) Epoch 8, batch 17800, loss[loss=0.1191, simple_loss=0.2009, pruned_loss=0.01865, over 4978.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2168, pruned_loss=0.03602, over 971377.58 frames.], batch size: 28, lr: 2.61e-04
2022-05-06 05:17:23,946 INFO [train.py:715] (1/8) Epoch 8, batch 17850, loss[loss=0.1182, simple_loss=0.1819, pruned_loss=0.02726, over 4839.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2169, pruned_loss=0.03628, over 971156.82 frames.], batch size: 13, lr: 2.61e-04
2022-05-06 05:18:04,812 INFO [train.py:715] (1/8) Epoch 8, batch 17900, loss[loss=0.1372, simple_loss=0.2178, pruned_loss=0.02829, over 4961.00 frames.], tot_loss[loss=0.1443, simple_loss=0.217, pruned_loss=0.03582, over 971741.97 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:18:46,219 INFO [train.py:715] (1/8) Epoch 8, batch 17950, loss[loss=0.1211, simple_loss=0.1956, pruned_loss=0.02333, over 4805.00 frames.], tot_loss[loss=0.145, simple_loss=0.2175, pruned_loss=0.03623, over 972312.90 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:19:26,621 INFO [train.py:715] (1/8) Epoch 8, batch 18000, loss[loss=0.1375, simple_loss=0.206, pruned_loss=0.03455, over 4806.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2164, pruned_loss=0.03591, over 972022.71 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:19:26,622 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 05:19:36,399 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1073, simple_loss=0.1919, pruned_loss=0.01138, over 914524.00 frames.
2022-05-06 05:20:17,009 INFO [train.py:715] (1/8) Epoch 8, batch 18050, loss[loss=0.1209, simple_loss=0.1975, pruned_loss=0.02217, over 4745.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03565, over 971787.59 frames.], batch size: 12, lr: 2.60e-04
2022-05-06 05:20:59,056 INFO [train.py:715] (1/8) Epoch 8, batch 18100, loss[loss=0.1369, simple_loss=0.2047, pruned_loss=0.03449, over 4749.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03537, over 971807.09 frames.], batch size: 12, lr: 2.60e-04
2022-05-06 05:21:40,104 INFO [train.py:715] (1/8) Epoch 8, batch 18150, loss[loss=0.132, simple_loss=0.2035, pruned_loss=0.03025, over 4890.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03561, over 971492.72 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:22:21,013 INFO [train.py:715] (1/8) Epoch 8, batch 18200, loss[loss=0.1613, simple_loss=0.2223, pruned_loss=0.05011, over 4909.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03489, over 971497.37 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:23:02,794 INFO [train.py:715] (1/8) Epoch 8, batch 18250, loss[loss=0.1761, simple_loss=0.2474, pruned_loss=0.05235, over 4875.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.03528, over 971662.57 frames.], batch size: 32, lr: 2.60e-04
2022-05-06 05:23:43,811 INFO [train.py:715] (1/8) Epoch 8, batch 18300, loss[loss=0.1378, simple_loss=0.206, pruned_loss=0.03477, over 4776.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.03546, over 972278.32 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:24:25,292 INFO [train.py:715] (1/8) Epoch 8, batch 18350, loss[loss=0.1424, simple_loss=0.2128, pruned_loss=0.03602, over 4752.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03531, over 971837.38 frames.], batch size: 16, lr: 2.60e-04
2022-05-06 05:25:06,144 INFO [train.py:715] (1/8) Epoch 8, batch 18400, loss[loss=0.1357, simple_loss=0.2104, pruned_loss=0.03047, over 4973.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03509, over 971956.16 frames.], batch size: 24, lr: 2.60e-04
2022-05-06 05:25:47,836 INFO [train.py:715] (1/8) Epoch 8, batch 18450, loss[loss=0.113, simple_loss=0.1788, pruned_loss=0.02366, over 4894.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03553, over 971875.75 frames.], batch size: 19, lr: 2.60e-04
2022-05-06 05:26:28,561 INFO [train.py:715] (1/8) Epoch 8, batch 18500, loss[loss=0.1505, simple_loss=0.2263, pruned_loss=0.03739, over 4805.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2159, pruned_loss=0.03525, over 972051.61 frames.], batch size: 21, lr: 2.60e-04
2022-05-06 05:27:08,963 INFO [train.py:715] (1/8) Epoch 8, batch 18550, loss[loss=0.1311, simple_loss=0.1963, pruned_loss=0.03296, over 4781.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03522, over 972372.52 frames.], batch size: 12, lr: 2.60e-04
2022-05-06 05:27:50,214 INFO [train.py:715] (1/8) Epoch 8, batch 18600, loss[loss=0.1528, simple_loss=0.2185, pruned_loss=0.04356, over 4892.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03519, over 972670.96 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:28:30,415 INFO [train.py:715] (1/8) Epoch 8, batch 18650, loss[loss=0.1674, simple_loss=0.238, pruned_loss=0.0484, over 4772.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2142, pruned_loss=0.03513, over 972531.29 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:29:09,933 INFO [train.py:715] (1/8) Epoch 8, batch 18700, loss[loss=0.1241, simple_loss=0.1966, pruned_loss=0.02577, over 4817.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.035, over 973331.89 frames.], batch size: 26, lr: 2.60e-04
2022-05-06 05:29:49,896 INFO [train.py:715] (1/8) Epoch 8, batch 18750, loss[loss=0.1265, simple_loss=0.197, pruned_loss=0.02794, over 4950.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2144, pruned_loss=0.03513, over 972887.36 frames.], batch size: 24, lr: 2.60e-04
2022-05-06 05:30:30,991 INFO [train.py:715] (1/8) Epoch 8, batch 18800, loss[loss=0.1426, simple_loss=0.2081, pruned_loss=0.03855, over 4981.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2145, pruned_loss=0.03527, over 973172.21 frames.], batch size: 14, lr: 2.60e-04
2022-05-06 05:31:10,616 INFO [train.py:715] (1/8) Epoch 8, batch 18850, loss[loss=0.1207, simple_loss=0.2011, pruned_loss=0.02012, over 4923.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.0347, over 973757.89 frames.], batch size: 29, lr: 2.60e-04
2022-05-06 05:31:50,020 INFO [train.py:715] (1/8) Epoch 8, batch 18900, loss[loss=0.1417, simple_loss=0.2099, pruned_loss=0.03676, over 4988.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03519, over 973776.67 frames.], batch size: 14, lr: 2.60e-04
2022-05-06 05:32:30,291 INFO [train.py:715] (1/8) Epoch 8, batch 18950, loss[loss=0.122, simple_loss=0.1938, pruned_loss=0.02512, over 4806.00 frames.], tot_loss[loss=0.144, simple_loss=0.2165, pruned_loss=0.03575, over 973042.91 frames.], batch size: 21, lr: 2.60e-04
2022-05-06 05:33:10,181 INFO [train.py:715] (1/8) Epoch 8, batch 19000, loss[loss=0.1465, simple_loss=0.2164, pruned_loss=0.0383, over 4801.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03541, over 972194.38 frames.], batch size: 25, lr: 2.60e-04
2022-05-06 05:33:50,114 INFO [train.py:715] (1/8) Epoch 8, batch 19050, loss[loss=0.1537, simple_loss=0.2405, pruned_loss=0.03348, over 4909.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2159, pruned_loss=0.03532, over 971406.45 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:34:31,418 INFO [train.py:715] (1/8) Epoch 8, batch 19100, loss[loss=0.1595, simple_loss=0.2401, pruned_loss=0.03942, over 4834.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03467, over 971492.74 frames.], batch size: 15, lr: 2.60e-04
2022-05-06 05:35:13,333 INFO [train.py:715] (1/8) Epoch 8, batch 19150, loss[loss=0.1401, simple_loss=0.2246, pruned_loss=0.02778, over 4966.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03525, over 971405.57 frames.], batch size: 15, lr: 2.60e-04
2022-05-06 05:35:55,002 INFO [train.py:715] (1/8) Epoch 8, batch 19200, loss[loss=0.1018, simple_loss=0.1733, pruned_loss=0.01515, over 4779.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.03554, over 970691.14 frames.], batch size: 12, lr: 2.60e-04
2022-05-06 05:36:35,263 INFO [train.py:715] (1/8) Epoch 8, batch 19250, loss[loss=0.141, simple_loss=0.2106, pruned_loss=0.03571, over 4840.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03522, over 970935.97 frames.], batch size: 26, lr: 2.60e-04
2022-05-06 05:37:17,454 INFO [train.py:715] (1/8) Epoch 8, batch 19300, loss[loss=0.1406, simple_loss=0.2097, pruned_loss=0.03575, over 4810.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.0352, over 970756.93 frames.], batch size: 25, lr: 2.60e-04
2022-05-06 05:37:58,612 INFO [train.py:715] (1/8) Epoch 8, batch 19350, loss[loss=0.1287, simple_loss=0.1981, pruned_loss=0.02966, over 4886.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2155, pruned_loss=0.0355, over 971089.86 frames.], batch size: 22, lr: 2.60e-04
2022-05-06 05:38:39,847 INFO [train.py:715] (1/8) Epoch 8, batch 19400, loss[loss=0.1574, simple_loss=0.2211, pruned_loss=0.04683, over 4943.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2151, pruned_loss=0.035, over 971692.47 frames.], batch size: 35, lr: 2.60e-04
2022-05-06 05:39:21,793 INFO [train.py:715] (1/8) Epoch 8, batch 19450, loss[loss=0.1635, simple_loss=0.2343, pruned_loss=0.04634, over 4698.00 frames.], tot_loss[loss=0.1444, simple_loss=0.217, pruned_loss=0.03584, over 971728.38 frames.], batch size: 15, lr: 2.60e-04
2022-05-06 05:40:03,276 INFO [train.py:715] (1/8) Epoch 8, batch 19500, loss[loss=0.1916, simple_loss=0.2611, pruned_loss=0.06102, over 4794.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2172, pruned_loss=0.03594, over 971626.50 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:40:44,564 INFO [train.py:715] (1/8) Epoch 8, batch 19550, loss[loss=0.1382, simple_loss=0.2158, pruned_loss=0.03029, over 4770.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2163, pruned_loss=0.03537, over 971918.96 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:41:25,036 INFO [train.py:715] (1/8) Epoch 8, batch 19600, loss[loss=0.1327, simple_loss=0.1877, pruned_loss=0.03884, over 4808.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2159, pruned_loss=0.03527, over 972640.42 frames.], batch size: 12, lr: 2.60e-04
2022-05-06 05:42:06,545 INFO [train.py:715] (1/8) Epoch 8, batch 19650, loss[loss=0.1462, simple_loss=0.2228, pruned_loss=0.03483, over 4944.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2154, pruned_loss=0.03573, over 972775.25 frames.], batch size: 39, lr: 2.60e-04
2022-05-06 05:42:47,227 INFO [train.py:715] (1/8) Epoch 8, batch 19700, loss[loss=0.1159, simple_loss=0.1934, pruned_loss=0.01916, over 4904.00 frames.], tot_loss[loss=0.1431, simple_loss=0.215, pruned_loss=0.03554, over 972742.85 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:43:28,189 INFO [train.py:715] (1/8) Epoch 8, batch 19750, loss[loss=0.13, simple_loss=0.2052, pruned_loss=0.02742, over 4851.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2158, pruned_loss=0.03622, over 972678.17 frames.], batch size: 13, lr: 2.60e-04
2022-05-06 05:44:09,861 INFO [train.py:715] (1/8) Epoch 8, batch 19800, loss[loss=0.1328, simple_loss=0.2057, pruned_loss=0.02991, over 4939.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2162, pruned_loss=0.03638, over 972058.84 frames.], batch size: 23, lr: 2.60e-04
2022-05-06 05:44:50,902 INFO [train.py:715] (1/8) Epoch 8, batch 19850, loss[loss=0.1663, simple_loss=0.243, pruned_loss=0.04479, over 4912.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03676, over 971973.07 frames.], batch size: 39, lr: 2.60e-04
2022-05-06 05:45:31,223 INFO [train.py:715] (1/8) Epoch 8, batch 19900, loss[loss=0.1608, simple_loss=0.233, pruned_loss=0.04429, over 4906.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03684, over 972261.15 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:46:10,998 INFO [train.py:715] (1/8) Epoch 8, batch 19950, loss[loss=0.1545, simple_loss=0.2194, pruned_loss=0.04478, over 4755.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2165, pruned_loss=0.03688, over 972601.69 frames.], batch size: 19, lr: 2.60e-04
2022-05-06 05:46:51,592 INFO [train.py:715] (1/8) Epoch 8, batch 20000, loss[loss=0.1819, simple_loss=0.2498, pruned_loss=0.05703, over 4890.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2158, pruned_loss=0.03655, over 973010.40 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:47:32,112 INFO [train.py:715] (1/8) Epoch 8, batch 20050, loss[loss=0.1719, simple_loss=0.2379, pruned_loss=0.05297, over 4871.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2159, pruned_loss=0.03689, over 973839.41 frames.], batch size: 32, lr: 2.60e-04
2022-05-06 05:48:12,624 INFO [train.py:715] (1/8) Epoch 8, batch 20100, loss[loss=0.1405, simple_loss=0.2088, pruned_loss=0.03608, over 4736.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2156, pruned_loss=0.03664, over 973735.77 frames.], batch size: 16, lr: 2.60e-04
2022-05-06 05:48:53,762 INFO [train.py:715] (1/8) Epoch 8, batch 20150, loss[loss=0.139, simple_loss=0.2054, pruned_loss=0.03626, over 4977.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2163, pruned_loss=0.03657, over 974134.85 frames.], batch size: 14, lr: 2.60e-04
2022-05-06 05:49:34,567 INFO [train.py:715] (1/8) Epoch 8, batch 20200, loss[loss=0.1539, simple_loss=0.2336, pruned_loss=0.03705, over 4954.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2155, pruned_loss=0.03616, over 973001.58 frames.], batch size: 29, lr: 2.60e-04
2022-05-06 05:50:15,437 INFO [train.py:715] (1/8) Epoch 8, batch 20250, loss[loss=0.1342, simple_loss=0.214, pruned_loss=0.02724, over 4931.00 frames.], tot_loss[loss=0.1434, simple_loss=0.215, pruned_loss=0.03591, over 973661.79 frames.], batch size: 29, lr: 2.60e-04
2022-05-06 05:50:56,705 INFO [train.py:715] (1/8) Epoch 8, batch 20300, loss[loss=0.1494, simple_loss=0.226, pruned_loss=0.03639, over 4955.00 frames.], tot_loss[loss=0.143, simple_loss=0.2149, pruned_loss=0.03557, over 974520.25 frames.], batch size: 21, lr: 2.60e-04
2022-05-06 05:51:37,706 INFO [train.py:715] (1/8) Epoch 8, batch 20350, loss[loss=0.1471, simple_loss=0.2199, pruned_loss=0.03718, over 4909.00 frames.], tot_loss[loss=0.143, simple_loss=0.2147, pruned_loss=0.03571, over 973538.96 frames.], batch size: 19, lr: 2.59e-04
2022-05-06 05:52:18,260 INFO [train.py:715] (1/8) Epoch 8, batch 20400, loss[loss=0.1343, simple_loss=0.2134, pruned_loss=0.02762, over 4765.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2151, pruned_loss=0.03563, over 973364.74 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 05:52:58,518 INFO [train.py:715] (1/8) Epoch 8, batch 20450, loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02977, over 4953.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2151, pruned_loss=0.03524, over 972920.64 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 05:53:39,594 INFO [train.py:715] (1/8) Epoch 8, batch 20500, loss[loss=0.1361, simple_loss=0.1969, pruned_loss=0.03767, over 4785.00 frames.], tot_loss[loss=0.1415, simple_loss=0.214, pruned_loss=0.03449, over 972295.33 frames.], batch size: 12, lr: 2.59e-04
2022-05-06 05:54:20,098 INFO [train.py:715] (1/8) Epoch 8, batch 20550, loss[loss=0.1308, simple_loss=0.2032, pruned_loss=0.02917, over 4805.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.0352, over 973288.36 frames.], batch size: 21, lr: 2.59e-04
2022-05-06 05:55:00,452 INFO [train.py:715] (1/8) Epoch 8, batch 20600, loss[loss=0.1344, simple_loss=0.2102, pruned_loss=0.02927, over 4828.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03496, over 972358.81 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 05:55:41,408 INFO [train.py:715] (1/8) Epoch 8, batch 20650, loss[loss=0.1329, simple_loss=0.2111, pruned_loss=0.02738, over 4947.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.0348, over 971806.56 frames.], batch size: 21, lr: 2.59e-04
2022-05-06 05:56:22,560 INFO [train.py:715] (1/8) Epoch 8, batch 20700, loss[loss=0.1223, simple_loss=0.1875, pruned_loss=0.02858, over 4775.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03491, over 972155.71 frames.], batch size: 18, lr: 2.59e-04
2022-05-06 05:57:02,757 INFO [train.py:715] (1/8) Epoch 8, batch 20750, loss[loss=0.1599, simple_loss=0.2324, pruned_loss=0.04375, over 4804.00 frames.], tot_loss[loss=0.1424, simple_loss=0.215, pruned_loss=0.03488, over 972503.01 frames.], batch size: 21, lr: 2.59e-04
2022-05-06 05:57:42,964 INFO [train.py:715] (1/8) Epoch 8, batch 20800, loss[loss=0.1318, simple_loss=0.2101, pruned_loss=0.02674, over 4838.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03535, over 972075.22 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 05:58:24,020 INFO [train.py:715] (1/8) Epoch 8, batch 20850, loss[loss=0.1682, simple_loss=0.2347, pruned_loss=0.05082, over 4700.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03492, over 971706.44 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 05:59:04,429 INFO [train.py:715] (1/8) Epoch 8, batch 20900, loss[loss=0.1411, simple_loss=0.2104, pruned_loss=0.03592, over 4937.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03518, over 972380.83 frames.], batch size: 23, lr: 2.59e-04
2022-05-06 05:59:43,022 INFO [train.py:715] (1/8) Epoch 8, batch 20950, loss[loss=0.1234, simple_loss=0.1971, pruned_loss=0.0248, over 4951.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2138, pruned_loss=0.03484, over 972815.04 frames.], batch size: 21, lr: 2.59e-04
2022-05-06 06:00:22,703 INFO [train.py:715] (1/8) Epoch 8, batch 21000, loss[loss=0.161, simple_loss=0.2376, pruned_loss=0.04221, over 4978.00 frames.], tot_loss[loss=0.1416, simple_loss=0.214, pruned_loss=0.03462, over 973378.87 frames.], batch size: 39, lr: 2.59e-04
2022-05-06 06:00:22,704 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 06:00:32,254 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1072, simple_loss=0.1919, pruned_loss=0.01129, over 914524.00 frames.
2022-05-06 06:01:12,646 INFO [train.py:715] (1/8) Epoch 8, batch 21050, loss[loss=0.1587, simple_loss=0.2247, pruned_loss=0.04638, over 4692.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03482, over 972893.33 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 06:01:52,988 INFO [train.py:715] (1/8) Epoch 8, batch 21100, loss[loss=0.1624, simple_loss=0.2312, pruned_loss=0.04687, over 4981.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03497, over 972850.63 frames.], batch size: 24, lr: 2.59e-04
2022-05-06 06:02:31,461 INFO [train.py:715] (1/8) Epoch 8, batch 21150, loss[loss=0.1399, simple_loss=0.2171, pruned_loss=0.03129, over 4878.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2158, pruned_loss=0.03517, over 973278.56 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 06:03:10,262 INFO [train.py:715] (1/8) Epoch 8, batch 21200, loss[loss=0.1628, simple_loss=0.2367, pruned_loss=0.04444, over 4916.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2167, pruned_loss=0.03559, over 971854.87 frames.], batch size: 18, lr: 2.59e-04
2022-05-06 06:03:49,966 INFO [train.py:715] (1/8) Epoch 8, batch 21250, loss[loss=0.1243, simple_loss=0.207, pruned_loss=0.02079, over 4814.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03556, over 970775.66 frames.], batch size: 21, lr: 2.59e-04
2022-05-06 06:04:29,228 INFO [train.py:715] (1/8) Epoch 8, batch 21300, loss[loss=0.1663, simple_loss=0.2519, pruned_loss=0.0403, over 4844.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03561, over 971120.68 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 06:05:07,758 INFO [train.py:715] (1/8) Epoch 8, batch 21350, loss[loss=0.1291, simple_loss=0.2117, pruned_loss=0.02322, over 4847.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2163, pruned_loss=0.03555, over 971208.22 frames.], batch size: 12, lr: 2.59e-04
2022-05-06 06:05:47,404 INFO [train.py:715] (1/8) Epoch 8, batch 21400, loss[loss=0.1203, simple_loss=0.1844, pruned_loss=0.02814, over 4773.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.03557, over 971316.66 frames.], batch size: 12, lr: 2.59e-04
2022-05-06 06:06:27,494 INFO [train.py:715] (1/8) Epoch 8, batch 21450, loss[loss=0.1274, simple_loss=0.1959, pruned_loss=0.02948, over 4908.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2158, pruned_loss=0.03541, over 972619.32 frames.], batch size: 18, lr: 2.59e-04
2022-05-06 06:07:06,788 INFO [train.py:715] (1/8) Epoch 8, batch 21500, loss[loss=0.1463, simple_loss=0.2165, pruned_loss=0.03799, over 4825.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2163, pruned_loss=0.03561, over 972934.46 frames.], batch size: 25, lr: 2.59e-04
2022-05-06 06:07:45,789 INFO [train.py:715] (1/8) Epoch 8, batch 21550, loss[loss=0.164, simple_loss=0.2395, pruned_loss=0.04422, over 4781.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2165, pruned_loss=0.03569, over 972452.21 frames.], batch size: 14, lr: 2.59e-04
2022-05-06 06:08:25,814 INFO [train.py:715] (1/8) Epoch 8, batch 21600, loss[loss=0.153, simple_loss=0.226, pruned_loss=0.04, over 4940.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2163, pruned_loss=0.03558, over 971535.25 frames.], batch size: 29, lr: 2.59e-04
2022-05-06 06:09:04,794 INFO [train.py:715] (1/8) Epoch 8, batch 21650, loss[loss=0.1581, simple_loss=0.2235, pruned_loss=0.04632, over 4689.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2166, pruned_loss=0.0355, over 971267.74 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 06:09:43,491 INFO [train.py:715] (1/8) Epoch 8, batch 21700, loss[loss=0.1517, simple_loss=0.2209, pruned_loss=0.04131, over 4987.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2161, pruned_loss=0.0354, over 971908.41 frames.], batch size: 25, lr: 2.59e-04
2022-05-06 06:10:23,858 INFO [train.py:715] (1/8) Epoch 8, batch 21750, loss[loss=0.1524, simple_loss=0.2264, pruned_loss=0.03923, over 4976.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.0358, over 972393.86 frames.], batch size: 24, lr: 2.59e-04
2022-05-06 06:11:03,697 INFO [train.py:715] (1/8) Epoch 8, batch 21800, loss[loss=0.1457, simple_loss=0.2128, pruned_loss=0.03928, over 4854.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2155, pruned_loss=0.03573, over 972401.20 frames.], batch size: 30, lr: 2.59e-04
2022-05-06 06:11:42,810 INFO [train.py:715] (1/8) Epoch 8, batch 21850, loss[loss=0.1414, simple_loss=0.2218, pruned_loss=0.03049, over 4985.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03605, over 971411.48 frames.], batch size: 24, lr: 2.59e-04
2022-05-06 06:12:21,177 INFO [train.py:715] (1/8) Epoch 8, batch 21900, loss[loss=0.148, simple_loss=0.2176, pruned_loss=0.03923, over 4874.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2159, pruned_loss=0.03617, over 971874.43 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 06:13:00,617 INFO [train.py:715] (1/8) Epoch 8, batch 21950, loss[loss=0.1561, simple_loss=0.2254, pruned_loss=0.04344, over 4832.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2161, pruned_loss=0.03632, over 973199.08 frames.], batch size: 26, lr: 2.59e-04
2022-05-06 06:13:39,700 INFO [train.py:715] (1/8) Epoch 8, batch 22000, loss[loss=0.1537, simple_loss=0.2302, pruned_loss=0.03863, over 4776.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2155, pruned_loss=0.03587, over 973171.22 frames.], batch size: 18, lr: 2.59e-04
2022-05-06 06:14:18,326 INFO [train.py:715] (1/8) Epoch 8, batch 22050, loss[loss=0.1504, simple_loss=0.2159, pruned_loss=0.04241, over 4890.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03554, over 973446.40 frames.], batch size: 19, lr: 2.59e-04
2022-05-06 06:14:58,044 INFO [train.py:715] (1/8) Epoch 8, batch 22100, loss[loss=0.1216, simple_loss=0.1973, pruned_loss=0.02293, over 4857.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03536, over 973001.51 frames.], batch size: 20, lr: 2.59e-04
2022-05-06 06:15:37,419 INFO [train.py:715] (1/8) Epoch 8, batch 22150, loss[loss=0.152, simple_loss=0.2342, pruned_loss=0.03486, over 4847.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2154, pruned_loss=0.03591, over 973216.42 frames.], batch size: 13, lr: 2.59e-04
2022-05-06 06:16:16,517 INFO [train.py:715] (1/8) Epoch 8, batch 22200, loss[loss=0.1316, simple_loss=0.196, pruned_loss=0.03355, over 4749.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03562, over 972679.33 frames.], batch size: 19, lr: 2.59e-04
2022-05-06 06:16:55,347 INFO [train.py:715] (1/8) Epoch 8, batch 22250, loss[loss=0.1545, simple_loss=0.2245, pruned_loss=0.04226, over 4783.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2163, pruned_loss=0.03591, over 972501.80 frames.], batch size: 14, lr: 2.59e-04
2022-05-06 06:17:34,563 INFO [train.py:715] (1/8) Epoch 8, batch 22300, loss[loss=0.1304, simple_loss=0.1991, pruned_loss=0.03084, over 4946.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2169, pruned_loss=0.03589, over 972842.80 frames.], batch size: 21, lr: 2.59e-04
2022-05-06 06:18:13,307 INFO [train.py:715] (1/8) Epoch 8, batch 22350, loss[loss=0.1734, simple_loss=0.2472, pruned_loss=0.04982, over 4700.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2163, pruned_loss=0.03527, over 972739.02 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 06:18:51,902 INFO [train.py:715] (1/8) Epoch 8, batch 22400, loss[loss=0.1251, simple_loss=0.1872, pruned_loss=0.03144, over 4650.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2161, pruned_loss=0.03538, over 972821.93 frames.], batch size: 13, lr: 2.59e-04
2022-05-06 06:19:31,232 INFO [train.py:715] (1/8) Epoch 8, batch 22450, loss[loss=0.1516, simple_loss=0.228, pruned_loss=0.03757, over 4941.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03546, over 972735.38 frames.], batch size: 21, lr: 2.59e-04
2022-05-06 06:20:10,733 INFO [train.py:715] (1/8) Epoch 8, batch 22500, loss[loss=0.122, simple_loss=0.2015, pruned_loss=0.02122, over 4886.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03526, over 972527.72 frames.], batch size: 22, lr: 2.59e-04
2022-05-06 06:20:49,332 INFO [train.py:715] (1/8) Epoch 8, batch 22550, loss[loss=0.129, simple_loss=0.19, pruned_loss=0.03396, over 4862.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03546, over 972959.97 frames.], batch size: 32, lr: 2.59e-04
2022-05-06 06:21:28,250 INFO [train.py:715] (1/8) Epoch 8, batch 22600, loss[loss=0.1758, simple_loss=0.2441, pruned_loss=0.05373, over 4968.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03577, over 972418.54 frames.], batch size: 28, lr: 2.59e-04
2022-05-06 06:22:07,734 INFO [train.py:715] (1/8) Epoch 8, batch 22650, loss[loss=0.1323, simple_loss=0.2005, pruned_loss=0.03206, over 4853.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2144, pruned_loss=0.03506, over 971627.85 frames.], batch size: 20, lr: 2.58e-04
2022-05-06 06:22:46,456 INFO [train.py:715] (1/8) Epoch 8, batch 22700, loss[loss=0.1555, simple_loss=0.2293, pruned_loss=0.04082, over 4941.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03539, over 972086.44 frames.], batch size: 39, lr: 2.58e-04
2022-05-06 06:23:24,775 INFO [train.py:715] (1/8) Epoch 8, batch 22750, loss[loss=0.1401, simple_loss=0.2273, pruned_loss=0.0264, over 4748.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03572, over 972151.81 frames.], batch size: 16, lr: 2.58e-04
2022-05-06 06:24:04,594 INFO [train.py:715] (1/8) Epoch 8, batch 22800, loss[loss=0.1524, simple_loss=0.2188, pruned_loss=0.04299, over 4954.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03525, over 973000.97 frames.], batch size: 14, lr: 2.58e-04
2022-05-06 06:24:43,768 INFO [train.py:715] (1/8) Epoch 8, batch 22850, loss[loss=0.1431, simple_loss=0.2133, pruned_loss=0.0364, over 4977.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03539, over 972788.80 frames.], batch size: 24, lr: 2.58e-04
2022-05-06 06:25:22,843 INFO [train.py:715] (1/8) Epoch 8, batch 22900, loss[loss=0.1605, simple_loss=0.2217, pruned_loss=0.04966, over 4828.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03538, over 973397.51 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:26:01,955 INFO [train.py:715] (1/8) Epoch 8, batch 22950, loss[loss=0.1414, simple_loss=0.2121, pruned_loss=0.03538, over 4784.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03511, over 972961.92 frames.], batch size: 14, lr: 2.58e-04
2022-05-06 06:26:41,731 INFO [train.py:715] (1/8) Epoch 8, batch 23000, loss[loss=0.1234, simple_loss=0.2047, pruned_loss=0.02103, over 4818.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03506, over 973231.32 frames.], batch size: 27, lr: 2.58e-04
2022-05-06 06:27:20,524 INFO [train.py:715] (1/8) Epoch 8, batch 23050, loss[loss=0.1558, simple_loss=0.2272, pruned_loss=0.04221, over 4970.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2145, pruned_loss=0.03515, over 973127.39 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:27:59,219 INFO [train.py:715] (1/8) Epoch 8, batch 23100, loss[loss=0.1441, simple_loss=0.217, pruned_loss=0.0356, over 4809.00 frames.], tot_loss[loss=0.1418, simple_loss=0.214, pruned_loss=0.03482, over 973401.55 frames.], batch size: 12, lr: 2.58e-04
2022-05-06 06:28:39,374 INFO [train.py:715] (1/8) Epoch 8, batch 23150, loss[loss=0.1386, simple_loss=0.2069, pruned_loss=0.03516, over 4760.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2141, pruned_loss=0.03501, over 973068.48 frames.], batch size: 12, lr: 2.58e-04
2022-05-06 06:29:18,750 INFO [train.py:715] (1/8) Epoch 8, batch 23200, loss[loss=0.1527, simple_loss=0.219, pruned_loss=0.04322, over 4922.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2146, pruned_loss=0.03542, over 972877.76 frames.], batch size: 18, lr: 2.58e-04
2022-05-06 06:29:57,395 INFO [train.py:715] (1/8) Epoch 8, batch 23250, loss[loss=0.158, simple_loss=0.2339, pruned_loss=0.04108, over 4822.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03519, over 973528.44 frames.], batch size: 27, lr: 2.58e-04
2022-05-06 06:30:36,511 INFO [train.py:715] (1/8) Epoch 8, batch 23300, loss[loss=0.1648, simple_loss=0.2511, pruned_loss=0.0393, over 4963.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03556, over 972800.85 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:31:16,262 INFO [train.py:715] (1/8) Epoch 8, batch 23350, loss[loss=0.1203, simple_loss=0.1937, pruned_loss=0.02343, over 4913.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03525, over 973073.60 frames.], batch size: 17, lr: 2.58e-04
2022-05-06 06:31:55,024 INFO [train.py:715] (1/8) Epoch 8, batch 23400, loss[loss=0.165, simple_loss=0.2337, pruned_loss=0.04815, over 4747.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2143, pruned_loss=0.03509, over 972491.35 frames.], batch size: 16, lr: 2.58e-04
2022-05-06 06:32:33,887 INFO [train.py:715] (1/8) Epoch 8, batch 23450, loss[loss=0.1303, simple_loss=0.202, pruned_loss=0.02933, over 4768.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2149, pruned_loss=0.0353, over 971084.64 frames.], batch size: 17, lr: 2.58e-04
2022-05-06 06:33:13,363 INFO [train.py:715] (1/8) Epoch 8, batch 23500, loss[loss=0.1472, simple_loss=0.2237, pruned_loss=0.03532, over 4860.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03531, over 971669.53 frames.], batch size: 20, lr: 2.58e-04
2022-05-06 06:33:52,529 INFO [train.py:715] (1/8) Epoch 8, batch 23550, loss[loss=0.1462, simple_loss=0.2214, pruned_loss=0.03545, over 4905.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2144, pruned_loss=0.0349, over 972423.62 frames.], batch size: 19, lr: 2.58e-04
2022-05-06 06:34:31,315 INFO [train.py:715] (1/8) Epoch 8, batch 23600, loss[loss=0.1323, simple_loss=0.2027, pruned_loss=0.03093, over 4832.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.03503, over 972181.49 frames.], batch size: 12, lr: 2.58e-04
2022-05-06 06:35:10,239 INFO [train.py:715] (1/8) Epoch 8, batch 23650, loss[loss=0.13, simple_loss=0.1926, pruned_loss=0.03374, over 4799.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2148, pruned_loss=0.0354, over 972160.09 frames.], batch size: 13, lr: 2.58e-04
2022-05-06 06:35:50,044 INFO [train.py:715] (1/8) Epoch 8, batch 23700, loss[loss=0.1275, simple_loss=0.1927, pruned_loss=0.03111, over 4751.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2149, pruned_loss=0.03584, over 972276.99 frames.], batch size: 16, lr: 2.58e-04
2022-05-06 06:36:28,663 INFO [train.py:715] (1/8) Epoch 8, batch 23750, loss[loss=0.1472, simple_loss=0.2203, pruned_loss=0.03705, over 4870.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2152, pruned_loss=0.03547, over 972673.75 frames.], batch size: 20, lr: 2.58e-04
2022-05-06 06:37:07,512 INFO [train.py:715] (1/8) Epoch 8, batch 23800, loss[loss=0.1451, simple_loss=0.2183, pruned_loss=0.03597, over 4986.00 frames.], tot_loss[loss=0.144, simple_loss=0.2162, pruned_loss=0.03585, over 973552.43 frames.], batch size: 39, lr: 2.58e-04
2022-05-06 06:37:46,984 INFO [train.py:715] (1/8) Epoch 8, batch 23850, loss[loss=0.1243, simple_loss=0.2041, pruned_loss=0.02218, over 4811.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2172, pruned_loss=0.03601, over 973368.06 frames.], batch size: 25, lr: 2.58e-04
2022-05-06 06:38:26,638 INFO [train.py:715] (1/8) Epoch 8, batch 23900, loss[loss=0.1245, simple_loss=0.1997, pruned_loss=0.02463, over 4868.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2168, pruned_loss=0.03623, over 973318.97 frames.], batch size: 32, lr: 2.58e-04
2022-05-06 06:39:05,505 INFO [train.py:715] (1/8) Epoch 8, batch 23950, loss[loss=0.1503, simple_loss=0.2168, pruned_loss=0.04194, over 4844.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03597, over 973669.50 frames.], batch size: 32, lr: 2.58e-04
2022-05-06 06:39:44,885 INFO [train.py:715] (1/8) Epoch 8, batch 24000, loss[loss=0.1264, simple_loss=0.205, pruned_loss=0.02389, over 4759.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2155, pruned_loss=0.03576, over 974190.13 frames.], batch size: 14, lr: 2.58e-04
2022-05-06 06:39:44,886 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 06:39:54,532 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1075, simple_loss=0.192, pruned_loss=0.01146, over 914524.00 frames.
2022-05-06 06:40:33,717 INFO [train.py:715] (1/8) Epoch 8, batch 24050, loss[loss=0.1649, simple_loss=0.2327, pruned_loss=0.04852, over 4706.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03586, over 973472.68 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:41:13,147 INFO [train.py:715] (1/8) Epoch 8, batch 24100, loss[loss=0.1304, simple_loss=0.2038, pruned_loss=0.02852, over 4824.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2161, pruned_loss=0.03602, over 972641.70 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:41:52,115 INFO [train.py:715] (1/8) Epoch 8, batch 24150, loss[loss=0.1559, simple_loss=0.2287, pruned_loss=0.0416, over 4987.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03635, over 972957.94 frames.], batch size: 25, lr: 2.58e-04
2022-05-06 06:42:31,047 INFO [train.py:715] (1/8) Epoch 8, batch 24200, loss[loss=0.1511, simple_loss=0.2181, pruned_loss=0.042, over 4951.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.03599, over 972950.90 frames.], batch size: 14, lr: 2.58e-04
2022-05-06 06:43:11,236 INFO [train.py:715] (1/8) Epoch 8, batch 24250, loss[loss=0.1702, simple_loss=0.231, pruned_loss=0.05464, over 4942.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2154, pruned_loss=0.03565, over 972808.66 frames.], batch size: 21, lr: 2.58e-04
2022-05-06 06:43:50,601 INFO [train.py:715] (1/8) Epoch 8, batch 24300, loss[loss=0.1259, simple_loss=0.1994, pruned_loss=0.02622, over 4737.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03592, over 971667.18 frames.], batch size: 16, lr: 2.58e-04
2022-05-06 06:44:29,312 INFO [train.py:715] (1/8) Epoch 8, batch 24350, loss[loss=0.1422, simple_loss=0.2165, pruned_loss=0.03389, over 4912.00 frames.], tot_loss[loss=0.1442, simple_loss=0.216, pruned_loss=0.03617, over 971917.68 frames.], batch size: 18, lr: 2.58e-04
2022-05-06 06:45:08,113 INFO [train.py:715] (1/8) Epoch 8, batch 24400, loss[loss=0.1439, simple_loss=0.2139, pruned_loss=0.03698, over 4908.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03632, over 973251.52 frames.], batch size: 17, lr: 2.58e-04
2022-05-06 06:45:47,149 INFO [train.py:715] (1/8) Epoch 8, batch 24450, loss[loss=0.1401, simple_loss=0.2165, pruned_loss=0.03178, over 4840.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2154, pruned_loss=0.03594, over 972557.45 frames.], batch size: 30, lr: 2.58e-04
2022-05-06 06:46:26,135 INFO [train.py:715] (1/8) Epoch 8, batch 24500, loss[loss=0.1415, simple_loss=0.2158, pruned_loss=0.03359, over 4793.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2161, pruned_loss=0.03635, over 972772.78 frames.], batch size: 24, lr: 2.58e-04
2022-05-06 06:47:04,985 INFO [train.py:715] (1/8) Epoch 8, batch 24550, loss[loss=0.1433, simple_loss=0.219, pruned_loss=0.03384, over 4744.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03584, over 972856.36 frames.], batch size: 16, lr: 2.58e-04
2022-05-06 06:47:44,928 INFO [train.py:715] (1/8) Epoch 8, batch 24600, loss[loss=0.1208, simple_loss=0.2005, pruned_loss=0.02055, over 4927.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03542, over 972448.09 frames.], batch size: 29, lr: 2.58e-04
2022-05-06 06:48:24,238 INFO [train.py:715] (1/8) Epoch 8, batch 24650, loss[loss=0.1491, simple_loss=0.219, pruned_loss=0.03962, over 4958.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2166, pruned_loss=0.03625, over 972050.41 frames.], batch size: 24, lr: 2.58e-04
2022-05-06 06:49:02,875 INFO [train.py:715] (1/8) Epoch 8, batch 24700, loss[loss=0.1943, simple_loss=0.2526, pruned_loss=0.06793, over 4823.00 frames.], tot_loss[loss=0.146, simple_loss=0.2171, pruned_loss=0.03741, over 972524.94 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:49:42,047 INFO [train.py:715] (1/8) Epoch 8, batch 24750, loss[loss=0.1451, simple_loss=0.2271, pruned_loss=0.03159, over 4766.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2166, pruned_loss=0.03704, over 973034.95 frames.], batch size: 18, lr: 2.58e-04
2022-05-06 06:50:21,620 INFO [train.py:715] (1/8) Epoch 8, batch 24800, loss[loss=0.1157, simple_loss=0.1985, pruned_loss=0.01647, over 4920.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2167, pruned_loss=0.0369, over 973875.81 frames.], batch size: 23, lr: 2.58e-04
2022-05-06 06:51:00,471 INFO [train.py:715] (1/8) Epoch 8, batch 24850, loss[loss=0.13, simple_loss=0.2074, pruned_loss=0.0263, over 4961.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.03643, over 973028.14 frames.], batch size: 24, lr: 2.58e-04
2022-05-06 06:51:39,143 INFO [train.py:715] (1/8) Epoch 8, batch 24900, loss[loss=0.2083, simple_loss=0.2647, pruned_loss=0.07592, over 4691.00 frames.], tot_loss[loss=0.1442, simple_loss=0.216, pruned_loss=0.03622, over 972495.54 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:52:19,143 INFO [train.py:715] (1/8) Epoch 8, batch 24950, loss[loss=0.1497, simple_loss=0.2123, pruned_loss=0.04358, over 4886.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03656, over 972815.95 frames.], batch size: 22, lr: 2.58e-04
2022-05-06 06:52:58,628 INFO [train.py:715] (1/8) Epoch 8, batch 25000, loss[loss=0.1204, simple_loss=0.2011, pruned_loss=0.01985, over 4799.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.03589, over 972579.76 frames.], batch size: 21, lr: 2.57e-04
2022-05-06 06:53:37,562 INFO [train.py:715] (1/8) Epoch 8, batch 25050, loss[loss=0.1444, simple_loss=0.2129, pruned_loss=0.03792, over 4921.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03555, over 972324.92 frames.], batch size: 18, lr: 2.57e-04
2022-05-06 06:54:16,390 INFO [train.py:715] (1/8) Epoch 8, batch 25100, loss[loss=0.1555, simple_loss=0.2352, pruned_loss=0.03789, over 4687.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03573, over 972122.65 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 06:54:55,804 INFO [train.py:715] (1/8) Epoch 8, batch 25150, loss[loss=0.1593, simple_loss=0.2259, pruned_loss=0.04632, over 4822.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03558, over 972153.53 frames.], batch size: 12, lr: 2.57e-04
2022-05-06 06:55:34,829 INFO [train.py:715] (1/8) Epoch 8, batch 25200, loss[loss=0.1457, simple_loss=0.2261, pruned_loss=0.03267, over 4842.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03578, over 972524.94 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 06:56:13,820 INFO [train.py:715] (1/8) Epoch 8, batch 25250, loss[loss=0.1291, simple_loss=0.2085, pruned_loss=0.02484, over 4939.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03537, over 972170.49 frames.], batch size: 24, lr: 2.57e-04
2022-05-06 06:56:53,388 INFO [train.py:715] (1/8) Epoch 8, batch 25300, loss[loss=0.1462, simple_loss=0.2078, pruned_loss=0.04226, over 4909.00 frames.], tot_loss[loss=0.1426, simple_loss=0.215, pruned_loss=0.03511, over 972566.75 frames.], batch size: 23, lr: 2.57e-04
2022-05-06 06:57:32,352 INFO [train.py:715] (1/8) Epoch 8, batch 25350, loss[loss=0.1523, simple_loss=0.2347, pruned_loss=0.03496, over 4982.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2151, pruned_loss=0.03536, over 972747.27 frames.], batch size: 25, lr: 2.57e-04
2022-05-06 06:58:11,170 INFO [train.py:715] (1/8) Epoch 8, batch 25400, loss[loss=0.1358, simple_loss=0.2193, pruned_loss=0.02619, over 4762.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03559, over 972186.97 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 06:58:50,227 INFO [train.py:715] (1/8) Epoch 8, batch 25450, loss[loss=0.1356, simple_loss=0.2102, pruned_loss=0.03049, over 4906.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2153, pruned_loss=0.03552, over 972396.34 frames.], batch size: 19, lr: 2.57e-04
2022-05-06 06:59:30,371 INFO [train.py:715] (1/8) Epoch 8, batch 25500, loss[loss=0.1401, simple_loss=0.2093, pruned_loss=0.03541, over 4773.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.0356, over 972211.53 frames.], batch size: 14, lr: 2.57e-04
2022-05-06 07:00:12,379 INFO [train.py:715] (1/8) Epoch 8, batch 25550, loss[loss=0.1604, simple_loss=0.2251, pruned_loss=0.04788, over 4872.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03575, over 970817.10 frames.], batch size: 39, lr: 2.57e-04
2022-05-06 07:00:51,652 INFO [train.py:715] (1/8) Epoch 8, batch 25600, loss[loss=0.1526, simple_loss=0.2201, pruned_loss=0.04258, over 4693.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2167, pruned_loss=0.03633, over 970213.53 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:01:30,733 INFO [train.py:715] (1/8) Epoch 8, batch 25650, loss[loss=0.1357, simple_loss=0.2056, pruned_loss=0.03292, over 4972.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2161, pruned_loss=0.03636, over 970711.81 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:02:09,695 INFO [train.py:715] (1/8) Epoch 8, batch 25700, loss[loss=0.1441, simple_loss=0.2173, pruned_loss=0.03543, over 4905.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2156, pruned_loss=0.03604, over 971414.07 frames.], batch size: 18, lr: 2.57e-04
2022-05-06 07:02:48,862 INFO [train.py:715] (1/8) Epoch 8, batch 25750, loss[loss=0.1416, simple_loss=0.2106, pruned_loss=0.03632, over 4806.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2159, pruned_loss=0.03583, over 971781.48 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:03:27,687 INFO [train.py:715] (1/8) Epoch 8, batch 25800, loss[loss=0.1441, simple_loss=0.2134, pruned_loss=0.03735, over 4783.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2165, pruned_loss=0.0359, over 972118.97 frames.], batch size: 12, lr: 2.57e-04
2022-05-06 07:04:06,652 INFO [train.py:715] (1/8) Epoch 8, batch 25850, loss[loss=0.1728, simple_loss=0.2491, pruned_loss=0.04826, over 4792.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2166, pruned_loss=0.03579, over 971776.68 frames.], batch size: 17, lr: 2.57e-04
2022-05-06 07:04:45,938 INFO [train.py:715] (1/8) Epoch 8, batch 25900, loss[loss=0.1398, simple_loss=0.2063, pruned_loss=0.03664, over 4864.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03566, over 971752.94 frames.], batch size: 32, lr: 2.57e-04
2022-05-06 07:05:24,606 INFO [train.py:715] (1/8) Epoch 8, batch 25950, loss[loss=0.1398, simple_loss=0.2157, pruned_loss=0.03195, over 4769.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2159, pruned_loss=0.03533, over 970948.28 frames.], batch size: 18, lr: 2.57e-04
2022-05-06 07:06:03,741 INFO [train.py:715] (1/8) Epoch 8, batch 26000, loss[loss=0.1567, simple_loss=0.2296, pruned_loss=0.04188, over 4884.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03555, over 970678.57 frames.], batch size: 22, lr: 2.57e-04
2022-05-06 07:06:42,906 INFO [train.py:715] (1/8) Epoch 8, batch 26050, loss[loss=0.1406, simple_loss=0.2036, pruned_loss=0.03883, over 4823.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.0355, over 970247.89 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:07:21,666 INFO [train.py:715] (1/8) Epoch 8, batch 26100, loss[loss=0.1696, simple_loss=0.2349, pruned_loss=0.05212, over 4837.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03546, over 970503.57 frames.], batch size: 30, lr: 2.57e-04
2022-05-06 07:08:01,299 INFO [train.py:715] (1/8) Epoch 8, batch 26150, loss[loss=0.1465, simple_loss=0.2165, pruned_loss=0.03828, over 4846.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03556, over 969915.97 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:08:40,492 INFO [train.py:715] (1/8) Epoch 8, batch 26200, loss[loss=0.1858, simple_loss=0.2504, pruned_loss=0.06056, over 4949.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03637, over 969853.85 frames.], batch size: 23, lr: 2.57e-04
2022-05-06 07:09:19,621 INFO [train.py:715] (1/8) Epoch 8, batch 26250, loss[loss=0.1454, simple_loss=0.2254, pruned_loss=0.03272, over 4864.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2154, pruned_loss=0.03592, over 970620.67 frames.], batch size: 32, lr: 2.57e-04
2022-05-06 07:09:57,935 INFO [train.py:715] (1/8) Epoch 8, batch 26300, loss[loss=0.1388, simple_loss=0.2018, pruned_loss=0.03787, over 4829.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03606, over 970598.91 frames.], batch size: 13, lr: 2.57e-04
2022-05-06 07:10:37,572 INFO [train.py:715] (1/8) Epoch 8, batch 26350, loss[loss=0.1362, simple_loss=0.2115, pruned_loss=0.03043, over 4886.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03561, over 971557.95 frames.], batch size: 22, lr: 2.57e-04
2022-05-06 07:11:16,886 INFO [train.py:715] (1/8) Epoch 8, batch 26400, loss[loss=0.1307, simple_loss=0.1998, pruned_loss=0.03078, over 4881.00 frames.], tot_loss[loss=0.1447, simple_loss=0.217, pruned_loss=0.03614, over 972608.37 frames.], batch size: 12, lr: 2.57e-04
2022-05-06 07:11:55,836 INFO [train.py:715] (1/8) Epoch 8, batch 26450, loss[loss=0.1317, simple_loss=0.2035, pruned_loss=0.02992, over 4827.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2171, pruned_loss=0.03592, over 971842.00 frames.], batch size: 30, lr: 2.57e-04
2022-05-06 07:12:34,671 INFO [train.py:715] (1/8) Epoch 8, batch 26500, loss[loss=0.1607, simple_loss=0.2361, pruned_loss=0.04264, over 4933.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2161, pruned_loss=0.0357, over 971613.36 frames.], batch size: 21, lr: 2.57e-04
2022-05-06 07:13:13,274 INFO [train.py:715] (1/8) Epoch 8, batch 26550, loss[loss=0.1165, simple_loss=0.1925, pruned_loss=0.02027, over 4808.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03575, over 971607.40 frames.], batch size: 25, lr: 2.57e-04
2022-05-06 07:13:52,657 INFO [train.py:715] (1/8) Epoch 8, batch 26600, loss[loss=0.1468, simple_loss=0.22, pruned_loss=0.03681, over 4751.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.03568, over 971603.36 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 07:14:30,714 INFO [train.py:715] (1/8) Epoch 8, batch 26650, loss[loss=0.1722, simple_loss=0.2339, pruned_loss=0.05523, over 4760.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2156, pruned_loss=0.03579, over 971871.76 frames.], batch size: 19, lr: 2.57e-04
2022-05-06 07:15:10,076 INFO [train.py:715] (1/8) Epoch 8, batch 26700, loss[loss=0.1657, simple_loss=0.2314, pruned_loss=0.05001, over 4806.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2152, pruned_loss=0.03562, over 971932.89 frames.], batch size: 12, lr: 2.57e-04
2022-05-06 07:15:49,148 INFO [train.py:715] (1/8) Epoch 8, batch 26750, loss[loss=0.1755, simple_loss=0.2495, pruned_loss=0.0507, over 4899.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2153, pruned_loss=0.03546, over 972410.74 frames.], batch size: 39, lr: 2.57e-04
2022-05-06 07:16:27,929 INFO [train.py:715] (1/8) Epoch 8, batch 26800, loss[loss=0.1328, simple_loss=0.2077, pruned_loss=0.02896, over 4813.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03518, over 972100.89 frames.], batch size: 27, lr: 2.57e-04
2022-05-06 07:17:07,163 INFO [train.py:715] (1/8) Epoch 8, batch 26850, loss[loss=0.1636, simple_loss=0.2344, pruned_loss=0.04643, over 4765.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03534, over 972158.87 frames.], batch size: 19, lr: 2.57e-04
2022-05-06 07:17:46,412 INFO [train.py:715] (1/8) Epoch 8, batch 26900, loss[loss=0.1366, simple_loss=0.2053, pruned_loss=0.03392, over 4840.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2151, pruned_loss=0.03561, over 972917.21 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:18:25,463 INFO [train.py:715] (1/8) Epoch 8, batch 26950, loss[loss=0.1453, simple_loss=0.2132, pruned_loss=0.0387, over 4932.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03519, over 973407.32 frames.], batch size: 39, lr: 2.57e-04
2022-05-06 07:19:04,350 INFO [train.py:715] (1/8) Epoch 8, batch 27000, loss[loss=0.1524, simple_loss=0.2319, pruned_loss=0.03648, over 4765.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2147, pruned_loss=0.0351, over 973130.70 frames.], batch size: 18, lr: 2.57e-04
2022-05-06 07:19:04,351 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 07:19:13,679 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1072, simple_loss=0.1919, pruned_loss=0.01129, over 914524.00 frames.
2022-05-06 07:19:52,524 INFO [train.py:715] (1/8) Epoch 8, batch 27050, loss[loss=0.1496, simple_loss=0.2284, pruned_loss=0.03535, over 4938.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2152, pruned_loss=0.0356, over 973288.93 frames.], batch size: 21, lr: 2.57e-04
2022-05-06 07:20:31,869 INFO [train.py:715] (1/8) Epoch 8, batch 27100, loss[loss=0.1345, simple_loss=0.1971, pruned_loss=0.0359, over 4774.00 frames.], tot_loss[loss=0.143, simple_loss=0.2149, pruned_loss=0.03555, over 973102.84 frames.], batch size: 12, lr: 2.57e-04
2022-05-06 07:21:10,968 INFO [train.py:715] (1/8) Epoch 8, batch 27150, loss[loss=0.1079, simple_loss=0.1728, pruned_loss=0.02153, over 4882.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2153, pruned_loss=0.03574, over 972527.97 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 07:21:49,180 INFO [train.py:715] (1/8) Epoch 8, batch 27200, loss[loss=0.1472, simple_loss=0.208, pruned_loss=0.04315, over 4873.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2156, pruned_loss=0.03573, over 972633.92 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 07:22:28,508 INFO [train.py:715] (1/8) Epoch 8, batch 27250, loss[loss=0.161, simple_loss=0.2464, pruned_loss=0.03778, over 4934.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03583, over 972968.86 frames.], batch size: 18, lr: 2.57e-04
2022-05-06 07:23:07,825 INFO [train.py:715] (1/8) Epoch 8, batch 27300, loss[loss=0.1971, simple_loss=0.2715, pruned_loss=0.0614, over 4899.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2158, pruned_loss=0.03586, over 972332.01 frames.], batch size: 22, lr: 2.57e-04
2022-05-06 07:23:46,491 INFO [train.py:715] (1/8) Epoch 8, batch 27350, loss[loss=0.1389, simple_loss=0.2141, pruned_loss=0.03188, over 4971.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03544, over 972039.32 frames.], batch size: 28, lr: 2.57e-04
2022-05-06 07:24:25,180 INFO [train.py:715] (1/8) Epoch 8, batch 27400, loss[loss=0.1193, simple_loss=0.1902, pruned_loss=0.02414, over 4784.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03532, over 971787.86 frames.], batch size: 14, lr: 2.56e-04
2022-05-06 07:25:04,319 INFO [train.py:715] (1/8) Epoch 8, batch 27450, loss[loss=0.164, simple_loss=0.23, pruned_loss=0.04905, over 4854.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2161, pruned_loss=0.03586, over 972395.62 frames.], batch size: 32, lr: 2.56e-04
2022-05-06 07:25:43,016 INFO [train.py:715] (1/8) Epoch 8, batch 27500, loss[loss=0.1463, simple_loss=0.2205, pruned_loss=0.03609, over 4782.00 frames.], tot_loss[loss=0.143, simple_loss=0.2151, pruned_loss=0.03548, over 972207.28 frames.], batch size: 18, lr: 2.56e-04
2022-05-06 07:26:21,671 INFO [train.py:715] (1/8) Epoch 8, batch 27550, loss[loss=0.1336, simple_loss=0.2056, pruned_loss=0.03075, over 4937.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.0355, over 972037.07 frames.], batch size: 21, lr: 2.56e-04
2022-05-06 07:27:01,332 INFO [train.py:715] (1/8) Epoch 8, batch 27600, loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.03121, over 4922.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03557, over 972190.89 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:27:40,424 INFO [train.py:715] (1/8) Epoch 8, batch 27650, loss[loss=0.1088, simple_loss=0.18, pruned_loss=0.01876, over 4984.00 frames.], tot_loss[loss=0.143, simple_loss=0.2157, pruned_loss=0.03521, over 972728.51 frames.], batch size: 15, lr: 2.56e-04
2022-05-06 07:28:19,090 INFO [train.py:715] (1/8) Epoch 8, batch 27700, loss[loss=0.1317, simple_loss=0.2126, pruned_loss=0.0254, over 4815.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2161, pruned_loss=0.03541, over 971963.72 frames.], batch size: 27, lr: 2.56e-04
2022-05-06 07:28:58,321 INFO [train.py:715] (1/8) Epoch 8, batch 27750, loss[loss=0.1839, simple_loss=0.2629, pruned_loss=0.05242, over 4892.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2161, pruned_loss=0.03568, over 971896.76 frames.], batch size: 16, lr: 2.56e-04
2022-05-06 07:29:38,018 INFO [train.py:715] (1/8) Epoch 8, batch 27800, loss[loss=0.1171, simple_loss=0.1993, pruned_loss=0.01745, over 4802.00 frames.], tot_loss[loss=0.1434, simple_loss=0.216, pruned_loss=0.0354, over 971500.75 frames.], batch size: 21, lr: 2.56e-04
2022-05-06 07:30:16,790 INFO [train.py:715] (1/8) Epoch 8, batch 27850, loss[loss=0.125, simple_loss=0.207, pruned_loss=0.02152, over 4867.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2167, pruned_loss=0.03597, over 971600.00 frames.], batch size: 20, lr: 2.56e-04
2022-05-06 07:30:54,913 INFO [train.py:715] (1/8) Epoch 8, batch 27900, loss[loss=0.1372, simple_loss=0.2047, pruned_loss=0.03484, over 4970.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2166, pruned_loss=0.03627, over 972103.70 frames.], batch size: 24, lr: 2.56e-04
2022-05-06 07:31:34,147 INFO [train.py:715] (1/8) Epoch 8, batch 27950, loss[loss=0.1337, simple_loss=0.2116, pruned_loss=0.02788, over 4953.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2156, pruned_loss=0.03597, over 972319.52 frames.], batch size: 24, lr: 2.56e-04
2022-05-06 07:32:13,472 INFO [train.py:715] (1/8) Epoch 8, batch 28000, loss[loss=0.1388, simple_loss=0.2159, pruned_loss=0.0309, over 4781.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03578, over 972408.18 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:32:51,690 INFO [train.py:715] (1/8) Epoch 8, batch 28050, loss[loss=0.1225, simple_loss=0.2097, pruned_loss=0.01771, over 4885.00 frames.], tot_loss[loss=0.144, simple_loss=0.2163, pruned_loss=0.03588, over 972941.05 frames.], batch size: 22, lr: 2.56e-04
2022-05-06 07:33:31,443 INFO [train.py:715] (1/8) Epoch 8, batch 28100, loss[loss=0.1553, simple_loss=0.2274, pruned_loss=0.04159, over 4778.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2164, pruned_loss=0.03595, over 973300.47 frames.], batch size: 18, lr: 2.56e-04
2022-05-06 07:34:10,511 INFO [train.py:715] (1/8) Epoch 8, batch 28150, loss[loss=0.1501, simple_loss=0.216, pruned_loss=0.04206, over 4794.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.0361, over 972747.05 frames.], batch size: 21, lr: 2.56e-04
2022-05-06 07:34:49,968 INFO [train.py:715] (1/8) Epoch 8, batch 28200, loss[loss=0.1351, simple_loss=0.2, pruned_loss=0.03509, over 4708.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03571, over 972812.77 frames.], batch size: 15, lr: 2.56e-04
2022-05-06 07:35:29,402 INFO [train.py:715] (1/8) Epoch 8, batch 28250, loss[loss=0.1553, simple_loss=0.2223, pruned_loss=0.04414, over 4695.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03562, over 973064.97 frames.], batch size: 15, lr: 2.56e-04
2022-05-06 07:36:09,670 INFO [train.py:715] (1/8) Epoch 8, batch 28300, loss[loss=0.1464, simple_loss=0.2148, pruned_loss=0.03894, over 4948.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2167, pruned_loss=0.0364, over 973238.42 frames.], batch size: 23, lr: 2.56e-04
2022-05-06 07:36:49,587 INFO [train.py:715] (1/8) Epoch 8, batch 28350, loss[loss=0.1172, simple_loss=0.1795, pruned_loss=0.02744, over 4966.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2167, pruned_loss=0.03614, over 973239.37 frames.], batch size: 14, lr: 2.56e-04
2022-05-06 07:37:28,934 INFO [train.py:715] (1/8) Epoch 8, batch 28400, loss[loss=0.1285, simple_loss=0.2026, pruned_loss=0.02719, over 4965.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2163, pruned_loss=0.03609, over 973455.32 frames.], batch size: 24, lr: 2.56e-04
2022-05-06 07:38:08,992 INFO [train.py:715] (1/8) Epoch 8, batch 28450, loss[loss=0.1389, simple_loss=0.213, pruned_loss=0.03243, over 4921.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2158, pruned_loss=0.03589, over 973211.20 frames.], batch size: 39, lr: 2.56e-04
2022-05-06 07:38:48,156 INFO [train.py:715] (1/8) Epoch 8, batch 28500, loss[loss=0.1396, simple_loss=0.2185, pruned_loss=0.03033, over 4947.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03554, over 973323.51 frames.], batch size: 24, lr: 2.56e-04
2022-05-06 07:39:26,863 INFO [train.py:715] (1/8) Epoch 8, batch 28550, loss[loss=0.1407, simple_loss=0.2177, pruned_loss=0.03192, over 4971.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03544, over 973700.80 frames.], batch size: 25, lr: 2.56e-04
2022-05-06 07:40:05,724 INFO [train.py:715] (1/8) Epoch 8, batch 28600, loss[loss=0.1175, simple_loss=0.1879, pruned_loss=0.0236, over 4967.00 frames.], tot_loss[loss=0.1446, simple_loss=0.217, pruned_loss=0.03616, over 973492.54 frames.], batch size: 28, lr: 2.56e-04
2022-05-06 07:40:45,398 INFO [train.py:715] (1/8) Epoch 8, batch 28650, loss[loss=0.1329, simple_loss=0.2017, pruned_loss=0.03205, over 4897.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2168, pruned_loss=0.03621, over 973759.70 frames.], batch size: 39, lr: 2.56e-04
2022-05-06 07:41:24,250 INFO [train.py:715] (1/8) Epoch 8, batch 28700, loss[loss=0.1347, simple_loss=0.2004, pruned_loss=0.03452, over 4871.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.03578, over 974244.41 frames.], batch size: 32, lr: 2.56e-04
2022-05-06 07:42:02,598 INFO [train.py:715] (1/8) Epoch 8, batch 28750, loss[loss=0.1523, simple_loss=0.227, pruned_loss=0.03886, over 4982.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2154, pruned_loss=0.03569, over 973672.16 frames.], batch size: 31, lr: 2.56e-04
2022-05-06 07:42:42,142 INFO [train.py:715] (1/8) Epoch 8, batch 28800, loss[loss=0.1532, simple_loss=0.219, pruned_loss=0.04368, over 4841.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03517, over 973137.53 frames.], batch size: 15, lr: 2.56e-04
2022-05-06 07:43:21,536 INFO [train.py:715] (1/8) Epoch 8, batch 28850, loss[loss=0.1666, simple_loss=0.2314, pruned_loss=0.05084, over 4815.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03533, over 972309.12 frames.], batch size: 25, lr: 2.56e-04
2022-05-06 07:44:00,547 INFO [train.py:715] (1/8) Epoch 8, batch 28900, loss[loss=0.1596, simple_loss=0.2296, pruned_loss=0.04482, over 4796.00 frames.], tot_loss[loss=0.143, simple_loss=0.2157, pruned_loss=0.03518, over 972324.34 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:44:39,168 INFO [train.py:715] (1/8) Epoch 8, batch 28950, loss[loss=0.1232, simple_loss=0.2012, pruned_loss=0.02261, over 4938.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2157, pruned_loss=0.03499, over 972694.76 frames.], batch size: 29, lr: 2.56e-04
2022-05-06 07:45:18,517 INFO [train.py:715] (1/8) Epoch 8, batch 29000, loss[loss=0.1363, simple_loss=0.216, pruned_loss=0.02832, over 4965.00 frames.], tot_loss[loss=0.142, simple_loss=0.2148, pruned_loss=0.03455, over 972986.32 frames.], batch size: 24, lr: 2.56e-04
2022-05-06 07:45:57,175 INFO [train.py:715] (1/8) Epoch 8, batch 29050, loss[loss=0.1646, simple_loss=0.2377, pruned_loss=0.04574, over 4823.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03439, over 972275.61 frames.], batch size: 15, lr: 2.56e-04
2022-05-06 07:46:36,416 INFO [train.py:715] (1/8) Epoch 8, batch 29100, loss[loss=0.1562, simple_loss=0.2208, pruned_loss=0.0458, over 4785.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03492, over 972719.64 frames.], batch size: 18, lr: 2.56e-04
2022-05-06 07:47:14,941 INFO [train.py:715] (1/8) Epoch 8, batch 29150, loss[loss=0.1118, simple_loss=0.1815, pruned_loss=0.02109, over 4636.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2152, pruned_loss=0.03486, over 972591.98 frames.], batch size: 13, lr: 2.56e-04
2022-05-06 07:47:54,239 INFO [train.py:715] (1/8) Epoch 8, batch 29200, loss[loss=0.1397, simple_loss=0.213, pruned_loss=0.0332, over 4767.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.03455, over 972633.23 frames.], batch size: 18, lr: 2.56e-04
2022-05-06 07:48:32,867 INFO [train.py:715] (1/8) Epoch 8, batch 29250, loss[loss=0.1323, simple_loss=0.2015, pruned_loss=0.03157, over 4951.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03469, over 972868.89 frames.], batch size: 14, lr: 2.56e-04
2022-05-06 07:49:11,136 INFO [train.py:715] (1/8) Epoch 8, batch 29300, loss[loss=0.1742, simple_loss=0.2604, pruned_loss=0.04396, over 4980.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.0354, over 972344.68 frames.], batch size: 25, lr: 2.56e-04
2022-05-06 07:49:50,319 INFO [train.py:715] (1/8) Epoch 8, batch 29350, loss[loss=0.1099, simple_loss=0.1864, pruned_loss=0.01673, over 4799.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.03556, over 972293.28 frames.], batch size: 12, lr: 2.56e-04
2022-05-06 07:50:29,149 INFO [train.py:715] (1/8) Epoch 8, batch 29400, loss[loss=0.1546, simple_loss=0.2267, pruned_loss=0.04124, over 4929.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03505, over 973015.34 frames.], batch size: 39, lr: 2.56e-04
2022-05-06 07:51:08,795 INFO [train.py:715] (1/8) Epoch 8, batch 29450, loss[loss=0.1361, simple_loss=0.2087, pruned_loss=0.03178, over 4950.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2141, pruned_loss=0.03502, over 973521.44 frames.], batch size: 21, lr: 2.56e-04
2022-05-06 07:51:48,077 INFO [train.py:715] (1/8) Epoch 8, batch 29500, loss[loss=0.1171, simple_loss=0.1971, pruned_loss=0.01855, over 4749.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03514, over 973840.41 frames.], batch size: 16, lr: 2.56e-04
2022-05-06 07:52:27,547 INFO [train.py:715] (1/8) Epoch 8, batch 29550, loss[loss=0.1276, simple_loss=0.2048, pruned_loss=0.02517, over 4848.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2151, pruned_loss=0.03535, over 973726.85 frames.], batch size: 15, lr: 2.56e-04
2022-05-06 07:53:06,111 INFO [train.py:715] (1/8) Epoch 8, batch 29600, loss[loss=0.1475, simple_loss=0.2125, pruned_loss=0.04127, over 4789.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03524, over 973429.45 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:53:45,380 INFO [train.py:715] (1/8) Epoch 8, batch 29650, loss[loss=0.1441, simple_loss=0.2185, pruned_loss=0.03488, over 4938.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2155, pruned_loss=0.03585, over 973278.75 frames.], batch size: 29, lr: 2.56e-04
2022-05-06 07:54:24,982 INFO [train.py:715] (1/8) Epoch 8, batch 29700, loss[loss=0.1281, simple_loss=0.201, pruned_loss=0.02765, over 4976.00 frames.], tot_loss[loss=0.143, simple_loss=0.215, pruned_loss=0.03555, over 973424.27 frames.], batch size: 14, lr: 2.56e-04
2022-05-06 07:55:03,538 INFO [train.py:715] (1/8) Epoch 8, batch 29750, loss[loss=0.1518, simple_loss=0.2144, pruned_loss=0.04458, over 4772.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2158, pruned_loss=0.03619, over 972492.18 frames.], batch size: 18, lr: 2.56e-04
2022-05-06 07:55:42,375 INFO [train.py:715] (1/8) Epoch 8, batch 29800, loss[loss=0.1438, simple_loss=0.2089, pruned_loss=0.03934, over 4923.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2154, pruned_loss=0.03617, over 972358.64 frames.], batch size: 18, lr: 2.55e-04
2022-05-06 07:56:21,282 INFO [train.py:715] (1/8) Epoch 8, batch 29850, loss[loss=0.131, simple_loss=0.2047, pruned_loss=0.0286, over 4869.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2157, pruned_loss=0.03597, over 973338.62 frames.], batch size: 22, lr: 2.55e-04
2022-05-06 07:57:00,651 INFO [train.py:715] (1/8) Epoch 8, batch 29900, loss[loss=0.1633, simple_loss=0.2398, pruned_loss=0.04346, over 4864.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03544, over 972449.94 frames.], batch size: 30, lr: 2.55e-04
2022-05-06 07:57:39,545 INFO [train.py:715] (1/8) Epoch 8, batch 29950, loss[loss=0.1112, simple_loss=0.1826, pruned_loss=0.01989, over 4826.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03488, over 970950.79 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 07:58:18,654 INFO [train.py:715] (1/8) Epoch 8, batch 30000, loss[loss=0.1728, simple_loss=0.242, pruned_loss=0.05179, over 4784.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03521, over 971472.94 frames.], batch size: 17, lr: 2.55e-04
2022-05-06 07:58:18,654 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 07:58:28,241 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1073, simple_loss=0.1918, pruned_loss=0.01141, over 914524.00 frames.
2022-05-06 07:59:07,023 INFO [train.py:715] (1/8) Epoch 8, batch 30050, loss[loss=0.141, simple_loss=0.2156, pruned_loss=0.03321, over 4791.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03531, over 971634.88 frames.], batch size: 18, lr: 2.55e-04
2022-05-06 07:59:46,355 INFO [train.py:715] (1/8) Epoch 8, batch 30100, loss[loss=0.1648, simple_loss=0.2428, pruned_loss=0.04342, over 4848.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03483, over 971699.91 frames.], batch size: 13, lr: 2.55e-04
2022-05-06 08:00:25,656 INFO [train.py:715] (1/8) Epoch 8, batch 30150, loss[loss=0.1666, simple_loss=0.2303, pruned_loss=0.05142, over 4854.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03503, over 971966.04 frames.], batch size: 32, lr: 2.55e-04
2022-05-06 08:01:04,249 INFO [train.py:715] (1/8) Epoch 8, batch 30200, loss[loss=0.1476, simple_loss=0.2163, pruned_loss=0.03945, over 4959.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.03558, over 972389.42 frames.], batch size: 35, lr: 2.55e-04
2022-05-06 08:01:43,180 INFO [train.py:715] (1/8) Epoch 8, batch 30250, loss[loss=0.1794, simple_loss=0.2389, pruned_loss=0.05995, over 4780.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03581, over 972668.36 frames.], batch size: 18, lr: 2.55e-04
2022-05-06 08:02:22,869 INFO [train.py:715] (1/8) Epoch 8, batch 30300, loss[loss=0.1564, simple_loss=0.2204, pruned_loss=0.04621, over 4979.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.03589, over 973032.57 frames.], batch size: 31, lr: 2.55e-04
2022-05-06 08:03:01,866 INFO [train.py:715] (1/8) Epoch 8, batch 30350, loss[loss=0.1432, simple_loss=0.2121, pruned_loss=0.03717, over 4971.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.0357, over 973360.25 frames.], batch size: 14, lr: 2.55e-04
2022-05-06 08:03:40,562 INFO [train.py:715] (1/8) Epoch 8, batch 30400, loss[loss=0.1734, simple_loss=0.2454, pruned_loss=0.05065, over 4816.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.03603, over 973262.62 frames.], batch size: 21, lr: 2.55e-04
2022-05-06 08:04:19,868 INFO [train.py:715] (1/8) Epoch 8, batch 30450, loss[loss=0.1554, simple_loss=0.2239, pruned_loss=0.04345, over 4967.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2155, pruned_loss=0.03551, over 973219.99 frames.], batch size: 35, lr: 2.55e-04
2022-05-06 08:04:58,850 INFO [train.py:715] (1/8) Epoch 8, batch 30500, loss[loss=0.1412, simple_loss=0.1972, pruned_loss=0.0426, over 4953.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03579, over 973616.61 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 08:05:37,496 INFO [train.py:715] (1/8) Epoch 8, batch 30550, loss[loss=0.1386, simple_loss=0.2038, pruned_loss=0.03666, over 4913.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2153, pruned_loss=0.03546, over 973602.42 frames.], batch size: 39, lr: 2.55e-04
2022-05-06 08:06:16,535 INFO [train.py:715] (1/8) Epoch 8, batch 30600, loss[loss=0.1108, simple_loss=0.1837, pruned_loss=0.01896, over 4705.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.03553, over 973253.22 frames.], batch size: 12, lr: 2.55e-04
2022-05-06 08:06:56,251 INFO [train.py:715] (1/8) Epoch 8, batch 30650, loss[loss=0.1065, simple_loss=0.1811, pruned_loss=0.01595, over 4834.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03504, over 972955.04 frames.], batch size: 13, lr: 2.55e-04
2022-05-06 08:07:35,434 INFO [train.py:715] (1/8) Epoch 8, batch 30700, loss[loss=0.1393, simple_loss=0.2212, pruned_loss=0.02871, over 4947.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2141, pruned_loss=0.03469, over 973312.77 frames.], batch size: 21, lr: 2.55e-04
2022-05-06 08:08:15,305 INFO [train.py:715] (1/8) Epoch 8, batch 30750, loss[loss=0.1542, simple_loss=0.2382, pruned_loss=0.0351, over 4900.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2151, pruned_loss=0.03521, over 973283.42 frames.], batch size: 19, lr: 2.55e-04
2022-05-06 08:08:55,422 INFO [train.py:715] (1/8) Epoch 8, batch 30800, loss[loss=0.1475, simple_loss=0.2153, pruned_loss=0.03982, over 4863.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2149, pruned_loss=0.03534, over 973577.34 frames.], batch size: 20, lr: 2.55e-04
2022-05-06 08:09:33,881 INFO [train.py:715] (1/8) Epoch 8, batch 30850, loss[loss=0.127, simple_loss=0.1852, pruned_loss=0.03437, over 4788.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03556, over 973895.42 frames.], batch size: 12, lr: 2.55e-04
2022-05-06 08:10:12,782 INFO [train.py:715] (1/8) Epoch 8, batch 30900, loss[loss=0.1167, simple_loss=0.1915, pruned_loss=0.02099, over 4898.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.03553, over 973281.08 frames.], batch size: 22, lr: 2.55e-04
2022-05-06 08:10:52,538 INFO [train.py:715] (1/8) Epoch 8, batch 30950, loss[loss=0.1353, simple_loss=0.2147, pruned_loss=0.02798, over 4869.00 frames.], tot_loss[loss=0.1442, simple_loss=0.216, pruned_loss=0.0362, over 973352.55 frames.], batch size: 20, lr: 2.55e-04
2022-05-06 08:11:32,557 INFO [train.py:715] (1/8) Epoch 8, batch 31000, loss[loss=0.1731, simple_loss=0.2371, pruned_loss=0.05458, over 4689.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2173, pruned_loss=0.03709, over 972761.20 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 08:12:11,800 INFO [train.py:715] (1/8) Epoch 8, batch 31050, loss[loss=0.1434, simple_loss=0.216, pruned_loss=0.03541, over 4792.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2167, pruned_loss=0.03686, over 972239.79 frames.], batch size: 21, lr: 2.55e-04
2022-05-06 08:12:51,401 INFO [train.py:715] (1/8) Epoch 8, batch 31100, loss[loss=0.1186, simple_loss=0.1886, pruned_loss=0.0243, over 4785.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2165, pruned_loss=0.03664, over 972551.67 frames.], batch size: 18, lr: 2.55e-04
2022-05-06 08:13:30,935 INFO [train.py:715] (1/8) Epoch 8, batch 31150, loss[loss=0.1454, simple_loss=0.2282, pruned_loss=0.03135, over 4764.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03628, over 972397.34 frames.], batch size: 19, lr: 2.55e-04
2022-05-06 08:14:09,970 INFO [train.py:715] (1/8) Epoch 8, batch 31200, loss[loss=0.1788, simple_loss=0.2431, pruned_loss=0.05722, over 4794.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2169, pruned_loss=0.03679, over 972065.87 frames.], batch size: 14, lr: 2.55e-04
2022-05-06 08:14:48,710 INFO [train.py:715] (1/8) Epoch 8, batch 31250, loss[loss=0.13, simple_loss=0.2, pruned_loss=0.03001, over 4748.00 frames.], tot_loss[loss=0.1449, simple_loss=0.217, pruned_loss=0.03634, over 972070.28 frames.], batch size: 16, lr: 2.55e-04
2022-05-06 08:15:28,180 INFO [train.py:715] (1/8) Epoch 8, batch 31300, loss[loss=0.1439, simple_loss=0.2136, pruned_loss=0.03716, over 4755.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03631, over 971920.71 frames.], batch size: 19, lr: 2.55e-04
2022-05-06 08:16:07,662 INFO [train.py:715] (1/8) Epoch 8, batch 31350, loss[loss=0.1225, simple_loss=0.1973, pruned_loss=0.0239, over 4821.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03578, over 971555.74 frames.], batch size: 27, lr: 2.55e-04
2022-05-06 08:16:46,293 INFO [train.py:715] (1/8) Epoch 8, batch 31400, loss[loss=0.1194, simple_loss=0.1885, pruned_loss=0.02514, over 4833.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2158, pruned_loss=0.03566, over 971783.27 frames.], batch size: 12, lr: 2.55e-04
2022-05-06 08:17:25,748 INFO [train.py:715] (1/8) Epoch 8, batch 31450, loss[loss=0.1208, simple_loss=0.1924, pruned_loss=0.02457, over 4798.00 frames.], tot_loss[loss=0.1441, simple_loss=0.216, pruned_loss=0.03612, over 971844.45 frames.], batch size: 21, lr: 2.55e-04
2022-05-06 08:18:05,871 INFO [train.py:715] (1/8) Epoch 8, batch 31500, loss[loss=0.1977, simple_loss=0.2504, pruned_loss=0.07254, over 4969.00 frames.], tot_loss[loss=0.145, simple_loss=0.2168, pruned_loss=0.03661, over 972125.98 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 08:18:45,116 INFO [train.py:715] (1/8) Epoch 8, batch 31550, loss[loss=0.1323, simple_loss=0.1982, pruned_loss=0.03319, over 4853.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2166, pruned_loss=0.03646, over 971157.56 frames.], batch size: 20, lr: 2.55e-04
2022-05-06 08:19:24,099 INFO [train.py:715] (1/8) Epoch 8, batch 31600, loss[loss=0.1736, simple_loss=0.2409, pruned_loss=0.05314, over 4869.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2163, pruned_loss=0.03607, over 971918.46 frames.], batch size: 16, lr: 2.55e-04
2022-05-06 08:20:03,753 INFO [train.py:715] (1/8) Epoch 8, batch 31650, loss[loss=0.145, simple_loss=0.2186, pruned_loss=0.03576, over 4991.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03581, over 972351.80 frames.], batch size: 28, lr: 2.55e-04
2022-05-06 08:20:43,074 INFO [train.py:715] (1/8) Epoch 8, batch 31700, loss[loss=0.1469, simple_loss=0.2183, pruned_loss=0.03779, over 4895.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03568, over 972153.50 frames.], batch size: 17, lr: 2.55e-04
2022-05-06 08:21:22,754 INFO [train.py:715] (1/8) Epoch 8, batch 31750, loss[loss=0.146, simple_loss=0.2173, pruned_loss=0.03736, over 4989.00 frames.], tot_loss[loss=0.1428, simple_loss=0.215, pruned_loss=0.03533, over 973659.11 frames.], batch size: 14, lr: 2.55e-04
2022-05-06 08:22:01,967 INFO [train.py:715] (1/8) Epoch 8, batch 31800, loss[loss=0.1404, simple_loss=0.2154, pruned_loss=0.0327, over 4926.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.0352, over 973865.82 frames.], batch size: 29, lr: 2.55e-04
2022-05-06 08:22:41,011 INFO [train.py:715] (1/8) Epoch 8, batch 31850, loss[loss=0.1438, simple_loss=0.2143, pruned_loss=0.03665, over 4968.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03523, over 973112.36 frames.], batch size: 40, lr: 2.55e-04
2022-05-06 08:23:19,921 INFO [train.py:715] (1/8) Epoch 8, batch 31900, loss[loss=0.133, simple_loss=0.2095, pruned_loss=0.02819, over 4774.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.035, over 972820.18 frames.], batch size: 18, lr: 2.55e-04
2022-05-06 08:23:58,318 INFO [train.py:715] (1/8) Epoch 8, batch 31950, loss[loss=0.1274, simple_loss=0.19, pruned_loss=0.03239, over 4754.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2141, pruned_loss=0.0348, over 973468.58 frames.], batch size: 12, lr: 2.55e-04
2022-05-06 08:24:37,605 INFO [train.py:715] (1/8) Epoch 8, batch 32000, loss[loss=0.1489, simple_loss=0.2165, pruned_loss=0.04065, over 4852.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2149, pruned_loss=0.03488, over 973281.33 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 08:25:17,169 INFO [train.py:715] (1/8) Epoch 8, batch 32050, loss[loss=0.1117, simple_loss=0.18, pruned_loss=0.02171, over 4861.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03487, over 973153.60 frames.], batch size: 13, lr: 2.55e-04
2022-05-06 08:25:55,736 INFO [train.py:715] (1/8) Epoch 8, batch 32100, loss[loss=0.1409, simple_loss=0.2189, pruned_loss=0.03147, over 4964.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.0352, over 972599.39 frames.], batch size: 24, lr: 2.55e-04
2022-05-06 08:26:34,467 INFO [train.py:715] (1/8) Epoch 8, batch 32150, loss[loss=0.1721, simple_loss=0.2336, pruned_loss=0.05527, over 4784.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03501, over 972257.49 frames.], batch size: 17, lr: 2.55e-04
2022-05-06 08:27:14,043 INFO [train.py:715] (1/8) Epoch 8, batch 32200, loss[loss=0.1122, simple_loss=0.1782, pruned_loss=0.02308, over 4687.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03523, over 972908.83 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:27:52,862 INFO [train.py:715] (1/8) Epoch 8, batch 32250, loss[loss=0.1103, simple_loss=0.1881, pruned_loss=0.01623, over 4989.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03521, over 973694.93 frames.], batch size: 26, lr: 2.54e-04
2022-05-06 08:28:32,330 INFO [train.py:715] (1/8) Epoch 8, batch 32300, loss[loss=0.1495, simple_loss=0.2186, pruned_loss=0.04022, over 4918.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03514, over 972877.48 frames.], batch size: 18, lr: 2.54e-04
2022-05-06 08:29:11,538 INFO [train.py:715] (1/8) Epoch 8, batch 32350, loss[loss=0.1273, simple_loss=0.2016, pruned_loss=0.02646, over 4755.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2154, pruned_loss=0.03508, over 972834.69 frames.], batch size: 16, lr: 2.54e-04
2022-05-06 08:29:51,455 INFO [train.py:715] (1/8) Epoch 8, batch 32400, loss[loss=0.1078, simple_loss=0.1721, pruned_loss=0.02179, over 4755.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03556, over 972977.13 frames.], batch size: 12, lr: 2.54e-04
2022-05-06 08:30:30,383 INFO [train.py:715] (1/8) Epoch 8, batch 32450, loss[loss=0.1426, simple_loss=0.2225, pruned_loss=0.03134, over 4854.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2163, pruned_loss=0.0359, over 973323.43 frames.], batch size: 20, lr: 2.54e-04
2022-05-06 08:31:09,404 INFO [train.py:715] (1/8) Epoch 8, batch 32500, loss[loss=0.1959, simple_loss=0.2664, pruned_loss=0.0627, over 4697.00 frames.], tot_loss[loss=0.1434, simple_loss=0.216, pruned_loss=0.0354, over 972451.69 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:31:48,951 INFO [train.py:715] (1/8) Epoch 8, batch 32550, loss[loss=0.149, simple_loss=0.2225, pruned_loss=0.03773, over 4938.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03541, over 972741.97 frames.], batch size: 29, lr: 2.54e-04
2022-05-06 08:32:27,499 INFO [train.py:715] (1/8) Epoch 8, batch 32600, loss[loss=0.1861, simple_loss=0.2522, pruned_loss=0.05994, over 4706.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2164, pruned_loss=0.03588, over 971556.86 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:33:06,726 INFO [train.py:715] (1/8) Epoch 8, batch 32650, loss[loss=0.1317, simple_loss=0.2078, pruned_loss=0.02784, over 4954.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2164, pruned_loss=0.0361, over 971246.94 frames.], batch size: 24, lr: 2.54e-04
2022-05-06 08:33:45,980 INFO [train.py:715] (1/8) Epoch 8, batch 32700, loss[loss=0.1409, simple_loss=0.2101, pruned_loss=0.03585, over 4926.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2159, pruned_loss=0.03584, over 971347.66 frames.], batch size: 29, lr: 2.54e-04
2022-05-06 08:34:26,176 INFO [train.py:715] (1/8) Epoch 8, batch 32750, loss[loss=0.1467, simple_loss=0.2201, pruned_loss=0.03666, over 4896.00 frames.], tot_loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.03597, over 970632.10 frames.], batch size: 22, lr: 2.54e-04
2022-05-06 08:35:04,663 INFO [train.py:715] (1/8) Epoch 8, batch 32800, loss[loss=0.1427, simple_loss=0.2138, pruned_loss=0.03579, over 4921.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2165, pruned_loss=0.03588, over 971276.07 frames.], batch size: 29, lr: 2.54e-04
2022-05-06 08:35:43,304 INFO [train.py:715] (1/8) Epoch 8, batch 32850, loss[loss=0.1525, simple_loss=0.2272, pruned_loss=0.03885, over 4706.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03489, over 972433.71 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:36:22,462 INFO [train.py:715] (1/8) Epoch 8, batch 32900, loss[loss=0.177, simple_loss=0.2404, pruned_loss=0.0568, over 4775.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03511, over 972441.20 frames.], batch size: 18, lr: 2.54e-04
2022-05-06 08:37:00,741 INFO [train.py:715] (1/8) Epoch 8, batch 32950, loss[loss=0.1377, simple_loss=0.213, pruned_loss=0.03122, over 4825.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03532, over 972262.40 frames.], batch size: 25, lr: 2.54e-04
2022-05-06 08:37:39,627 INFO [train.py:715] (1/8) Epoch 8, batch 33000, loss[loss=0.134, simple_loss=0.2213, pruned_loss=0.0233, over 4990.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2163, pruned_loss=0.03566, over 972747.50 frames.], batch size: 26, lr: 2.54e-04
2022-05-06 08:37:39,628 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 08:37:52,641 INFO [train.py:742] (1/8) Epoch 8, validation: loss=0.1071, simple_loss=0.1917, pruned_loss=0.01126, over 914524.00 frames.
2022-05-06 08:38:31,969 INFO [train.py:715] (1/8) Epoch 8, batch 33050, loss[loss=0.1334, simple_loss=0.1999, pruned_loss=0.03346, over 4809.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2168, pruned_loss=0.03597, over 973007.76 frames.], batch size: 25, lr: 2.54e-04
2022-05-06 08:39:10,826 INFO [train.py:715] (1/8) Epoch 8, batch 33100, loss[loss=0.1564, simple_loss=0.2225, pruned_loss=0.04512, over 4777.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2161, pruned_loss=0.0355, over 971813.76 frames.], batch size: 18, lr: 2.54e-04
2022-05-06 08:39:50,122 INFO [train.py:715] (1/8) Epoch 8, batch 33150, loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03112, over 4849.00 frames.], tot_loss[loss=0.144, simple_loss=0.2163, pruned_loss=0.0358, over 971319.42 frames.], batch size: 30, lr: 2.54e-04
2022-05-06 08:40:28,831 INFO [train.py:715] (1/8) Epoch 8, batch 33200, loss[loss=0.1533, simple_loss=0.2237, pruned_loss=0.04147, over 4931.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.03595, over 972047.09 frames.], batch size: 39, lr: 2.54e-04
2022-05-06 08:41:08,505 INFO [train.py:715] (1/8) Epoch 8, batch 33250, loss[loss=0.1588, simple_loss=0.2319, pruned_loss=0.04279, over 4816.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2166, pruned_loss=0.0363, over 972307.11 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:41:48,101 INFO [train.py:715] (1/8) Epoch 8, batch 33300, loss[loss=0.1319, simple_loss=0.2094, pruned_loss=0.02718, over 4955.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2161, pruned_loss=0.03586, over 972425.15 frames.], batch size: 21, lr: 2.54e-04
2022-05-06 08:42:26,897 INFO [train.py:715] (1/8) Epoch 8, batch 33350, loss[loss=0.141, simple_loss=0.2117, pruned_loss=0.0352, over 4749.00 frames.], tot_loss[loss=0.1436, simple_loss=0.216, pruned_loss=0.03558, over 972499.64 frames.], batch size: 16, lr: 2.54e-04
2022-05-06 08:43:06,261 INFO [train.py:715] (1/8) Epoch 8, batch 33400, loss[loss=0.1418, simple_loss=0.2075, pruned_loss=0.03806, over 4978.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2161, pruned_loss=0.03529, over 972529.08 frames.], batch size: 14, lr: 2.54e-04
2022-05-06 08:43:45,173 INFO [train.py:715] (1/8) Epoch 8, batch 33450, loss[loss=0.1654, simple_loss=0.2294, pruned_loss=0.05071, over 4811.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2162, pruned_loss=0.03545, over 971666.20 frames.], batch size: 21, lr: 2.54e-04
2022-05-06 08:44:24,006 INFO [train.py:715] (1/8) Epoch 8, batch 33500, loss[loss=0.1213, simple_loss=0.1965, pruned_loss=0.02302, over 4725.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2166, pruned_loss=0.0354, over 971520.09 frames.], batch size: 12, lr: 2.54e-04
2022-05-06 08:45:05,008 INFO [train.py:715] (1/8) Epoch 8, batch 33550, loss[loss=0.1361, simple_loss=0.2034, pruned_loss=0.03435, over 4925.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2164, pruned_loss=0.03541, over 971927.39 frames.], batch size: 35, lr: 2.54e-04
2022-05-06 08:45:44,462 INFO [train.py:715] (1/8) Epoch 8, batch 33600, loss[loss=0.1433, simple_loss=0.2158, pruned_loss=0.03535, over 4984.00 frames.], tot_loss[loss=0.1431, simple_loss=0.216, pruned_loss=0.03516, over 972448.53 frames.], batch size: 35, lr: 2.54e-04
2022-05-06 08:46:23,907 INFO [train.py:715] (1/8) Epoch 8, batch 33650, loss[loss=0.205, simple_loss=0.2722, pruned_loss=0.06896, over 4968.00 frames.], tot_loss[loss=0.143, simple_loss=0.2159, pruned_loss=0.03505, over 972243.15 frames.], batch size: 14, lr: 2.54e-04
2022-05-06 08:47:02,971 INFO [train.py:715] (1/8) Epoch 8, batch 33700, loss[loss=0.1341, simple_loss=0.211, pruned_loss=0.02866, over 4817.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03478, over 971817.98 frames.], batch size: 26, lr: 2.54e-04
2022-05-06 08:47:41,960 INFO [train.py:715] (1/8) Epoch 8, batch 33750, loss[loss=0.1246, simple_loss=0.1884, pruned_loss=0.03043, over 4930.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2146, pruned_loss=0.03441, over 972428.29 frames.], batch size: 21, lr: 2.54e-04
2022-05-06 08:48:20,684 INFO [train.py:715] (1/8) Epoch 8, batch 33800, loss[loss=0.1469, simple_loss=0.2124, pruned_loss=0.04066, over 4926.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.03448, over 972449.68 frames.], batch size: 23, lr: 2.54e-04
2022-05-06 08:48:59,309 INFO [train.py:715] (1/8) Epoch 8, batch 33850, loss[loss=0.1529, simple_loss=0.222, pruned_loss=0.0419, over 4974.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2149, pruned_loss=0.03466, over 971943.01 frames.], batch size: 35, lr: 2.54e-04
2022-05-06 08:49:38,116 INFO [train.py:715] (1/8) Epoch 8, batch 33900, loss[loss=0.1232, simple_loss=0.1874, pruned_loss=0.02949, over 4912.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.0349, over 972509.65 frames.], batch size: 17, lr: 2.54e-04
2022-05-06 08:50:17,035 INFO [train.py:715] (1/8) Epoch 8, batch 33950, loss[loss=0.1376, simple_loss=0.2074, pruned_loss=0.03391, over 4694.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03479, over 972849.15 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:50:56,632 INFO [train.py:715] (1/8) Epoch 8, batch 34000, loss[loss=0.1435, simple_loss=0.2247, pruned_loss=0.03112, over 4993.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2151, pruned_loss=0.03463, over 973614.02 frames.], batch size: 14, lr: 2.54e-04
2022-05-06 08:51:35,548 INFO [train.py:715] (1/8) Epoch 8, batch 34050, loss[loss=0.1276, simple_loss=0.2081, pruned_loss=0.02357, over 4797.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2151, pruned_loss=0.03451, over 973753.30 frames.], batch size: 25, lr: 2.54e-04
2022-05-06 08:52:14,815 INFO [train.py:715] (1/8) Epoch 8, batch 34100, loss[loss=0.1434, simple_loss=0.2061, pruned_loss=0.04033, over 4839.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2154, pruned_loss=0.03467, over 973178.74 frames.], batch size: 30, lr: 2.54e-04
2022-05-06 08:52:53,780 INFO [train.py:715] (1/8) Epoch 8, batch 34150, loss[loss=0.1334, simple_loss=0.2144, pruned_loss=0.02619, over 4974.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2147, pruned_loss=0.03433, over 972676.91 frames.], batch size: 24, lr: 2.54e-04
2022-05-06 08:53:32,396 INFO [train.py:715] (1/8) Epoch 8, batch 34200, loss[loss=0.1257, simple_loss=0.2003, pruned_loss=0.0256, over 4644.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03458, over 972655.19 frames.], batch size: 13, lr: 2.54e-04
2022-05-06 08:54:11,302 INFO [train.py:715] (1/8) Epoch 8, batch 34250, loss[loss=0.1712, simple_loss=0.2473, pruned_loss=0.04757, over 4824.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03495, over 972515.08 frames.], batch size: 13, lr: 2.54e-04
2022-05-06 08:54:50,272 INFO [train.py:715] (1/8) Epoch 8, batch 34300, loss[loss=0.111, simple_loss=0.1799, pruned_loss=0.02102, over 4751.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2155, pruned_loss=0.03577, over 972632.65 frames.], batch size: 19, lr: 2.54e-04
2022-05-06 08:55:29,024 INFO [train.py:715] (1/8) Epoch 8, batch 34350, loss[loss=0.1532, simple_loss=0.217, pruned_loss=0.04465, over 4949.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2158, pruned_loss=0.03596, over 972557.62 frames.], batch size: 21, lr: 2.54e-04
2022-05-06 08:56:07,453 INFO [train.py:715] (1/8) Epoch 8, batch 34400, loss[loss=0.1373, simple_loss=0.2065, pruned_loss=0.03404, over 4810.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2161, pruned_loss=0.03562, over 972825.08 frames.], batch size: 13, lr: 2.54e-04
2022-05-06 08:56:46,675 INFO [train.py:715] (1/8) Epoch 8, batch 34450, loss[loss=0.1346, simple_loss=0.2123, pruned_loss=0.02847, over 4979.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2159, pruned_loss=0.03535, over 972959.09 frames.], batch size: 26, lr: 2.54e-04
2022-05-06 08:57:26,047 INFO [train.py:715] (1/8) Epoch 8, batch 34500, loss[loss=0.1295, simple_loss=0.2067, pruned_loss=0.02621, over 4795.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2155, pruned_loss=0.035, over 972594.68 frames.], batch size: 21, lr: 2.54e-04
2022-05-06 08:58:04,289 INFO [train.py:715] (1/8) Epoch 8, batch 34550, loss[loss=0.1367, simple_loss=0.2084, pruned_loss=0.03253, over 4919.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2153, pruned_loss=0.03474, over 972548.92 frames.], batch size: 18, lr: 2.54e-04
2022-05-06 08:58:42,924 INFO [train.py:715] (1/8) Epoch 8, batch 34600, loss[loss=0.1381, simple_loss=0.2153, pruned_loss=0.03051, over 4905.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2151, pruned_loss=0.03485, over 972277.81 frames.], batch size: 23, lr: 2.54e-04
2022-05-06 08:59:21,846 INFO [train.py:715] (1/8) Epoch 8, batch 34650, loss[loss=0.2167, simple_loss=0.269, pruned_loss=0.08215, over 4766.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03498, over 972533.97 frames.], batch size: 19, lr: 2.53e-04
2022-05-06 09:00:01,501 INFO [train.py:715] (1/8) Epoch 8, batch 34700, loss[loss=0.1251, simple_loss=0.1954, pruned_loss=0.02734, over 4819.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2145, pruned_loss=0.03512, over 973164.75 frames.], batch size: 27, lr: 2.53e-04
2022-05-06 09:00:38,661 INFO [train.py:715] (1/8) Epoch 8, batch 34750, loss[loss=0.1222, simple_loss=0.2001, pruned_loss=0.02212, over 4917.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.035, over 972606.64 frames.], batch size: 23, lr: 2.53e-04
2022-05-06 09:01:15,260 INFO [train.py:715] (1/8) Epoch 8, batch 34800, loss[loss=0.1701, simple_loss=0.2472, pruned_loss=0.04646, over 4915.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2143, pruned_loss=0.03533, over 972613.85 frames.], batch size: 18, lr: 2.53e-04
2022-05-06 09:02:04,640 INFO [train.py:715] (1/8) Epoch 9, batch 0, loss[loss=0.148, simple_loss=0.2165, pruned_loss=0.03977, over 4772.00 frames.], tot_loss[loss=0.148, simple_loss=0.2165, pruned_loss=0.03977, over 4772.00 frames.], batch size: 17, lr: 2.42e-04
2022-05-06 09:02:43,973 INFO [train.py:715] (1/8) Epoch 9, batch 50, loss[loss=0.1466, simple_loss=0.2236, pruned_loss=0.03485, over 4787.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03506, over 219286.65 frames.], batch size: 21, lr: 2.41e-04
2022-05-06 09:03:23,609 INFO [train.py:715] (1/8) Epoch 9, batch 100, loss[loss=0.1392, simple_loss=0.2115, pruned_loss=0.03348, over 4864.00 frames.], tot_loss[loss=0.143, simple_loss=0.2159, pruned_loss=0.03508, over 387024.03 frames.], batch size: 20, lr: 2.41e-04
2022-05-06 09:04:02,101 INFO [train.py:715] (1/8) Epoch 9, batch 150, loss[loss=0.1595, simple_loss=0.2292, pruned_loss=0.04486, over 4834.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03532, over 516313.72 frames.], batch size: 27, lr: 2.41e-04
2022-05-06 09:04:42,539 INFO [train.py:715] (1/8) Epoch 9, batch 200, loss[loss=0.158, simple_loss=0.2315, pruned_loss=0.0423, over 4848.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03531, over 617946.23 frames.], batch size: 30, lr: 2.41e-04
2022-05-06 09:05:21,803 INFO [train.py:715] (1/8) Epoch 9, batch 250, loss[loss=0.1473, simple_loss=0.2182, pruned_loss=0.03816, over 4946.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.03519, over 696317.30 frames.], batch size: 35, lr: 2.41e-04
2022-05-06 09:06:01,094 INFO [train.py:715] (1/8) Epoch 9, batch 300, loss[loss=0.1632, simple_loss=0.2221, pruned_loss=0.05221, over 4817.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2146, pruned_loss=0.03524, over 756580.90 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:06:40,654 INFO [train.py:715] (1/8) Epoch 9, batch 350, loss[loss=0.1702, simple_loss=0.2427, pruned_loss=0.0489, over 4789.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2151, pruned_loss=0.03508, over 804231.34 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:07:20,400 INFO [train.py:715] (1/8) Epoch 9, batch 400, loss[loss=0.1632, simple_loss=0.2396, pruned_loss=0.04339, over 4985.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03492, over 841129.39 frames.], batch size: 24, lr: 2.41e-04
2022-05-06 09:07:59,734 INFO [train.py:715] (1/8) Epoch 9, batch 450, loss[loss=0.1302, simple_loss=0.2052, pruned_loss=0.02764, over 4902.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03496, over 871037.55 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:08:38,885 INFO [train.py:715] (1/8) Epoch 9, batch 500, loss[loss=0.1259, simple_loss=0.1945, pruned_loss=0.02865, over 4838.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2131, pruned_loss=0.03437, over 892954.84 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:09:19,203 INFO [train.py:715] (1/8) Epoch 9, batch 550, loss[loss=0.1301, simple_loss=0.2045, pruned_loss=0.02779, over 4888.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03483, over 910469.73 frames.], batch size: 22, lr: 2.41e-04
2022-05-06 09:09:58,806 INFO [train.py:715] (1/8) Epoch 9, batch 600, loss[loss=0.1324, simple_loss=0.2123, pruned_loss=0.02628, over 4969.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2149, pruned_loss=0.03484, over 924475.25 frames.], batch size: 14, lr: 2.41e-04
2022-05-06 09:10:37,825 INFO [train.py:715] (1/8) Epoch 9, batch 650, loss[loss=0.1116, simple_loss=0.1889, pruned_loss=0.01713, over 4797.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2148, pruned_loss=0.03457, over 934593.66 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:11:16,916 INFO [train.py:715] (1/8) Epoch 9, batch 700, loss[loss=0.1341, simple_loss=0.2053, pruned_loss=0.0314, over 4990.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2149, pruned_loss=0.03475, over 943732.23 frames.], batch size: 28, lr: 2.41e-04
2022-05-06 09:11:56,396 INFO [train.py:715] (1/8) Epoch 9, batch 750, loss[loss=0.184, simple_loss=0.2535, pruned_loss=0.05726, over 4776.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03526, over 950254.38 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:12:35,539 INFO [train.py:715] (1/8) Epoch 9, batch 800, loss[loss=0.1427, simple_loss=0.2148, pruned_loss=0.03536, over 4905.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2151, pruned_loss=0.03506, over 955626.40 frames.], batch size: 19, lr: 2.41e-04
2022-05-06 09:13:14,319 INFO [train.py:715] (1/8) Epoch 9, batch 850, loss[loss=0.1374, simple_loss=0.2039, pruned_loss=0.0355, over 4866.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03491, over 959626.07 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:13:53,318 INFO [train.py:715] (1/8) Epoch 9, batch 900, loss[loss=0.1378, simple_loss=0.2088, pruned_loss=0.03335, over 4906.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03444, over 962996.80 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:14:32,595 INFO [train.py:715] (1/8) Epoch 9, batch 950, loss[loss=0.1858, simple_loss=0.268, pruned_loss=0.05177, over 4971.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03415, over 964289.84 frames.], batch size: 24, lr: 2.41e-04
2022-05-06 09:15:12,209 INFO [train.py:715] (1/8) Epoch 9, batch 1000, loss[loss=0.1348, simple_loss=0.2079, pruned_loss=0.03082, over 4844.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.03427, over 966598.33 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:15:50,366 INFO [train.py:715] (1/8) Epoch 9, batch 1050, loss[loss=0.1098, simple_loss=0.1868, pruned_loss=0.01638, over 4806.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2147, pruned_loss=0.03438, over 967202.34 frames.], batch size: 25, lr: 2.41e-04
2022-05-06 09:16:30,509 INFO [train.py:715] (1/8) Epoch 9, batch 1100, loss[loss=0.1368, simple_loss=0.2025, pruned_loss=0.03553, over 4944.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03484, over 968227.07 frames.], batch size: 21, lr: 2.41e-04
2022-05-06 09:17:10,343 INFO [train.py:715] (1/8) Epoch 9, batch 1150, loss[loss=0.1449, simple_loss=0.2296, pruned_loss=0.03012, over 4867.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03488, over 970059.70 frames.], batch size: 22, lr: 2.41e-04
2022-05-06 09:17:49,482 INFO [train.py:715] (1/8) Epoch 9, batch 1200, loss[loss=0.1144, simple_loss=0.1953, pruned_loss=0.01675, over 4974.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2149, pruned_loss=0.03466, over 970037.54 frames.], batch size: 24, lr: 2.41e-04
2022-05-06 09:18:28,817 INFO [train.py:715] (1/8) Epoch 9, batch 1250, loss[loss=0.1397, simple_loss=0.2089, pruned_loss=0.03524, over 4786.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2154, pruned_loss=0.03464, over 969875.65 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:19:08,560 INFO [train.py:715] (1/8) Epoch 9, batch 1300, loss[loss=0.1534, simple_loss=0.2158, pruned_loss=0.04555, over 4842.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2147, pruned_loss=0.03424, over 970479.22 frames.], batch size: 32, lr: 2.41e-04
2022-05-06 09:19:48,098 INFO [train.py:715] (1/8) Epoch 9, batch 1350, loss[loss=0.1225, simple_loss=0.1987, pruned_loss=0.02319, over 4872.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2148, pruned_loss=0.03454, over 971162.25 frames.], batch size: 20, lr: 2.41e-04
2022-05-06 09:20:26,894 INFO [train.py:715] (1/8) Epoch 9, batch 1400, loss[loss=0.1458, simple_loss=0.2139, pruned_loss=0.03884, over 4790.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03478, over 970833.36 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:21:06,499 INFO [train.py:715] (1/8) Epoch 9, batch 1450, loss[loss=0.138, simple_loss=0.2037, pruned_loss=0.03614, over 4980.00 frames.], tot_loss[loss=0.1426, simple_loss=0.215, pruned_loss=0.03511, over 971026.26 frames.], batch size: 28, lr: 2.41e-04
2022-05-06 09:21:45,307 INFO [train.py:715] (1/8) Epoch 9, batch 1500, loss[loss=0.1218, simple_loss=0.1887, pruned_loss=0.02742, over 4852.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03467, over 971363.22 frames.], batch size: 12, lr: 2.41e-04
2022-05-06 09:22:24,142 INFO [train.py:715] (1/8) Epoch 9, batch 1550, loss[loss=0.1698, simple_loss=0.2439, pruned_loss=0.04783, over 4902.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2141, pruned_loss=0.03463, over 972100.39 frames.], batch size: 32, lr: 2.41e-04
2022-05-06 09:23:03,176 INFO [train.py:715] (1/8) Epoch 9, batch 1600, loss[loss=0.1417, simple_loss=0.2205, pruned_loss=0.03141, over 4764.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2145, pruned_loss=0.03502, over 971308.45 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:23:42,081 INFO [train.py:715] (1/8) Epoch 9, batch 1650, loss[loss=0.1259, simple_loss=0.215, pruned_loss=0.01847, over 4983.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2147, pruned_loss=0.03521, over 972378.24 frames.], batch size: 26, lr: 2.41e-04
2022-05-06 09:24:21,073 INFO [train.py:715] (1/8) Epoch 9, batch 1700, loss[loss=0.1363, simple_loss=0.2075, pruned_loss=0.03258, over 4826.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2146, pruned_loss=0.03523, over 972128.67 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:25:00,144 INFO [train.py:715] (1/8) Epoch 9, batch 1750, loss[loss=0.1287, simple_loss=0.193, pruned_loss=0.03219, over 4769.00 frames.], tot_loss[loss=0.143, simple_loss=0.215, pruned_loss=0.03548, over 972835.50 frames.], batch size: 14, lr: 2.41e-04
2022-05-06 09:25:39,670 INFO [train.py:715] (1/8) Epoch 9, batch 1800, loss[loss=0.1349, simple_loss=0.2092, pruned_loss=0.03028, over 4890.00 frames.], tot_loss[loss=0.143, simple_loss=0.2148, pruned_loss=0.03556, over 972213.31 frames.], batch size: 19, lr: 2.41e-04
2022-05-06 09:26:18,852 INFO [train.py:715] (1/8) Epoch 9, batch 1850, loss[loss=0.15, simple_loss=0.2303, pruned_loss=0.03489, over 4967.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2151, pruned_loss=0.03565, over 972223.46 frames.], batch size: 24, lr: 2.41e-04
2022-05-06 09:26:57,983 INFO [train.py:715] (1/8) Epoch 9, batch 1900, loss[loss=0.1471, simple_loss=0.2248, pruned_loss=0.03471, over 4909.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03491, over 972613.57 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:27:37,984 INFO [train.py:715] (1/8) Epoch 9, batch 1950, loss[loss=0.1388, simple_loss=0.2112, pruned_loss=0.03325, over 4773.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.035, over 971500.28 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:28:17,646 INFO [train.py:715] (1/8) Epoch 9, batch 2000, loss[loss=0.1432, simple_loss=0.2277, pruned_loss=0.02936, over 4793.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2142, pruned_loss=0.03495, over 971341.84 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:28:56,801 INFO [train.py:715] (1/8) Epoch 9, batch 2050, loss[loss=0.1464, simple_loss=0.2195, pruned_loss=0.03662, over 4887.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03479, over 971797.38 frames.], batch size: 38, lr: 2.41e-04
2022-05-06 09:29:35,321 INFO [train.py:715] (1/8) Epoch 9, batch 2100, loss[loss=0.1352, simple_loss=0.2223, pruned_loss=0.02401, over 4965.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2138, pruned_loss=0.03435, over 971797.86 frames.], batch size: 24, lr: 2.41e-04
2022-05-06 09:30:14,644 INFO [train.py:715] (1/8) Epoch 9, batch 2150, loss[loss=0.1326, simple_loss=0.2089, pruned_loss=0.0281, over 4977.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2136, pruned_loss=0.03425, over 971917.73 frames.], batch size: 28, lr: 2.41e-04
2022-05-06 09:30:53,732 INFO [train.py:715] (1/8) Epoch 9, batch 2200, loss[loss=0.1182, simple_loss=0.1932, pruned_loss=0.02162, over 4811.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2138, pruned_loss=0.03416, over 972011.14 frames.], batch size: 24, lr: 2.41e-04
2022-05-06 09:31:32,489 INFO [train.py:715] (1/8) Epoch 9, batch 2250, loss[loss=0.151, simple_loss=0.2173, pruned_loss=0.04233, over 4966.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03458, over 971566.54 frames.], batch size: 31, lr: 2.41e-04
2022-05-06 09:32:11,655 INFO [train.py:715] (1/8) Epoch 9, batch 2300, loss[loss=0.1236, simple_loss=0.1922, pruned_loss=0.02751, over 4754.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2134, pruned_loss=0.03451, over 972078.96 frames.], batch size: 19, lr: 2.41e-04
2022-05-06 09:32:50,733 INFO [train.py:715] (1/8) Epoch 9, batch 2350, loss[loss=0.1584, simple_loss=0.2323, pruned_loss=0.0423, over 4973.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2131, pruned_loss=0.03432, over 971114.66 frames.], batch size: 24, lr: 2.41e-04
2022-05-06 09:33:30,100 INFO [train.py:715] (1/8) Epoch 9, batch 2400, loss[loss=0.1621, simple_loss=0.2257, pruned_loss=0.04921, over 4904.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2128, pruned_loss=0.03424, over 971245.03 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:34:08,885 INFO [train.py:715] (1/8) Epoch 9, batch 2450, loss[loss=0.1328, simple_loss=0.1972, pruned_loss=0.03421, over 4866.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2124, pruned_loss=0.03393, over 971432.71 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:34:48,497 INFO [train.py:715] (1/8) Epoch 9, batch 2500, loss[loss=0.1309, simple_loss=0.2004, pruned_loss=0.03073, over 4754.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2122, pruned_loss=0.03385, over 971502.81 frames.], batch size: 19, lr: 2.41e-04
2022-05-06 09:35:27,020 INFO [train.py:715] (1/8) Epoch 9, batch 2550, loss[loss=0.104, simple_loss=0.1738, pruned_loss=0.01712, over 4751.00 frames.], tot_loss[loss=0.1405, simple_loss=0.213, pruned_loss=0.03405, over 971166.62 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:36:06,037 INFO [train.py:715] (1/8) Epoch 9, batch 2600, loss[loss=0.1282, simple_loss=0.2056, pruned_loss=0.02535, over 4776.00 frames.], tot_loss[loss=0.141, simple_loss=0.2136, pruned_loss=0.03419, over 972659.48 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:36:45,106 INFO [train.py:715] (1/8) Epoch 9, batch 2650, loss[loss=0.1481, simple_loss=0.2103, pruned_loss=0.04296, over 4946.00 frames.], tot_loss[loss=0.141, simple_loss=0.2136, pruned_loss=0.03424, over 973141.98 frames.], batch size: 35, lr: 2.41e-04
2022-05-06 09:37:24,472 INFO [train.py:715] (1/8) Epoch 9, batch 2700, loss[loss=0.139, simple_loss=0.2038, pruned_loss=0.03711, over 4781.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2144, pruned_loss=0.03488, over 973080.28 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 09:38:03,292 INFO [train.py:715] (1/8) Epoch 9, batch 2750, loss[loss=0.1687, simple_loss=0.241, pruned_loss=0.0482, over 4971.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.03514, over 973130.56 frames.], batch size: 15, lr: 2.40e-04
2022-05-06 09:38:42,264 INFO [train.py:715] (1/8) Epoch 9, batch 2800, loss[loss=0.1378, simple_loss=0.21, pruned_loss=0.03273, over 4865.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03469, over 972813.01 frames.], batch size: 30, lr: 2.40e-04
2022-05-06 09:39:21,838 INFO [train.py:715] (1/8) Epoch 9, batch 2850, loss[loss=0.1151, simple_loss=0.1912, pruned_loss=0.01947, over 4808.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03519, over 972438.97 frames.], batch size: 12, lr: 2.40e-04
2022-05-06 09:40:00,908 INFO [train.py:715] (1/8) Epoch 9, batch 2900, loss[loss=0.1231, simple_loss=0.2056, pruned_loss=0.02032, over 4801.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.03522, over 972933.91 frames.], batch size: 21, lr: 2.40e-04
2022-05-06 09:40:39,676 INFO [train.py:715] (1/8) Epoch 9, batch 2950, loss[loss=0.1588, simple_loss=0.2362, pruned_loss=0.04075, over 4955.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2142, pruned_loss=0.03511, over 972866.42 frames.], batch size: 15, lr: 2.40e-04
2022-05-06 09:41:18,902 INFO [train.py:715] (1/8) Epoch 9, batch 3000, loss[loss=0.1295, simple_loss=0.1993, pruned_loss=0.02989, over 4972.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2152, pruned_loss=0.03549, over 973251.70 frames.], batch size: 35, lr: 2.40e-04
2022-05-06 09:41:18,903 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 09:41:28,535 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.1069, simple_loss=0.1915, pruned_loss=0.01118, over 914524.00 frames.
2022-05-06 09:42:08,247 INFO [train.py:715] (1/8) Epoch 9, batch 3050, loss[loss=0.1271, simple_loss=0.2042, pruned_loss=0.02503, over 4766.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03503, over 973576.49 frames.], batch size: 14, lr: 2.40e-04
2022-05-06 09:42:47,734 INFO [train.py:715] (1/8) Epoch 9, batch 3100, loss[loss=0.1216, simple_loss=0.1976, pruned_loss=0.02283, over 4767.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2151, pruned_loss=0.03509, over 973146.23 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 09:43:27,211 INFO [train.py:715] (1/8) Epoch 9, batch 3150, loss[loss=0.1385, simple_loss=0.2196, pruned_loss=0.02874, over 4828.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.03471, over 972454.67 frames.], batch size: 26, lr: 2.40e-04
2022-05-06 09:44:06,424 INFO [train.py:715] (1/8) Epoch 9, batch 3200, loss[loss=0.1374, simple_loss=0.2123, pruned_loss=0.03122, over 4892.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2141, pruned_loss=0.03459, over 972429.04 frames.], batch size: 22, lr: 2.40e-04
2022-05-06 09:44:45,578 INFO [train.py:715] (1/8) Epoch 9, batch 3250, loss[loss=0.1492, simple_loss=0.2142, pruned_loss=0.04215, over 4704.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.03454, over 971822.74 frames.], batch size: 15, lr: 2.40e-04
2022-05-06 09:45:24,836 INFO [train.py:715] (1/8) Epoch 9, batch 3300, loss[loss=0.1292, simple_loss=0.1982, pruned_loss=0.03014, over 4918.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2132, pruned_loss=0.03407, over 972471.78 frames.], batch size: 23, lr: 2.40e-04
2022-05-06 09:46:03,658 INFO [train.py:715] (1/8) Epoch 9, batch 3350, loss[loss=0.1518, simple_loss=0.2235, pruned_loss=0.04012, over 4934.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03399, over 972611.36 frames.], batch size: 39, lr: 2.40e-04
2022-05-06 09:46:42,960 INFO [train.py:715] (1/8) Epoch 9, batch 3400, loss[loss=0.1517, simple_loss=0.2201, pruned_loss=0.04163, over 4779.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03475, over 973568.63 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 09:47:22,068 INFO [train.py:715] (1/8) Epoch 9, batch 3450, loss[loss=0.1415, simple_loss=0.225, pruned_loss=0.02906, over 4834.00 frames.], tot_loss[loss=0.143, simple_loss=0.2152, pruned_loss=0.03537, over 973262.66 frames.], batch size: 15, lr: 2.40e-04
2022-05-06 09:48:00,719 INFO [train.py:715] (1/8) Epoch 9, batch 3500, loss[loss=0.1335, simple_loss=0.2001, pruned_loss=0.03345, over 4804.00 frames.], tot_loss[loss=0.143, simple_loss=0.2152, pruned_loss=0.0354, over 973136.24 frames.], batch size: 13, lr: 2.40e-04
2022-05-06 09:48:40,283 INFO [train.py:715] (1/8) Epoch 9, batch 3550, loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02862, over 4885.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2153, pruned_loss=0.03547, over 972849.94 frames.], batch size: 16, lr: 2.40e-04
2022-05-06 09:49:19,723 INFO [train.py:715] (1/8) Epoch 9, batch 3600, loss[loss=0.1216, simple_loss=0.2024, pruned_loss=0.02042, over 4860.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03521, over 972354.86 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 09:49:59,013 INFO [train.py:715] (1/8) Epoch 9, batch 3650, loss[loss=0.1843, simple_loss=0.2565, pruned_loss=0.05606, over 4783.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.03552, over 972937.67 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 09:50:37,659 INFO [train.py:715] (1/8) Epoch 9, batch 3700, loss[loss=0.1881, simple_loss=0.2422, pruned_loss=0.06696, over 4763.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.03549, over 972198.75 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 09:51:17,145 INFO [train.py:715] (1/8) Epoch 9, batch 3750, loss[loss=0.1251, simple_loss=0.2088, pruned_loss=0.02074, over 4952.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03524, over 972540.00 frames.], batch size: 24, lr: 2.40e-04
2022-05-06 09:51:56,916 INFO [train.py:715] (1/8) Epoch 9, batch 3800, loss[loss=0.1533, simple_loss=0.2369, pruned_loss=0.0349, over 4799.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2141, pruned_loss=0.03437, over 972818.02 frames.], batch size: 21, lr: 2.40e-04
2022-05-06 09:52:35,336 INFO [train.py:715] (1/8) Epoch 9, batch 3850, loss[loss=0.2269, simple_loss=0.2993, pruned_loss=0.07729, over 4810.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03466, over 972285.43 frames.], batch size: 24, lr: 2.40e-04
2022-05-06 09:53:14,339 INFO [train.py:715] (1/8) Epoch 9, batch 3900, loss[loss=0.169, simple_loss=0.2421, pruned_loss=0.04796, over 4933.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03542, over 972379.31 frames.], batch size: 21, lr: 2.40e-04
2022-05-06 09:53:53,823 INFO [train.py:715] (1/8) Epoch 9, batch 3950, loss[loss=0.1219, simple_loss=0.194, pruned_loss=0.02487, over 4916.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2156, pruned_loss=0.03536, over 972428.01 frames.], batch size: 17, lr: 2.40e-04
2022-05-06 09:54:33,399 INFO [train.py:715] (1/8) Epoch 9, batch 4000, loss[loss=0.1617, simple_loss=0.2233, pruned_loss=0.05009, over 4936.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03533, over 970996.18 frames.], batch size: 35, lr: 2.40e-04
2022-05-06 09:55:12,125 INFO [train.py:715] (1/8) Epoch 9, batch 4050, loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.031, over 4905.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.035, over 971567.07 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 09:55:52,105 INFO [train.py:715] (1/8) Epoch 9, batch 4100, loss[loss=0.1404, simple_loss=0.2122, pruned_loss=0.03431, over 4981.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2143, pruned_loss=0.03477, over 971355.80 frames.], batch size: 28, lr: 2.40e-04
2022-05-06 09:56:30,804 INFO [train.py:715] (1/8) Epoch 9, batch 4150, loss[loss=0.1176, simple_loss=0.2004, pruned_loss=0.01742, over 4803.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2149, pruned_loss=0.03446, over 972340.92 frames.], batch size: 25, lr: 2.40e-04
2022-05-06 09:57:10,156 INFO [train.py:715] (1/8) Epoch 9, batch 4200, loss[loss=0.1394, simple_loss=0.2049, pruned_loss=0.03694, over 4771.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2148, pruned_loss=0.03445, over 972647.52 frames.], batch size: 17, lr: 2.40e-04
2022-05-06 09:57:49,719 INFO [train.py:715] (1/8) Epoch 9, batch 4250, loss[loss=0.1137, simple_loss=0.192, pruned_loss=0.0177, over 4904.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2146, pruned_loss=0.03442, over 972826.55 frames.], batch size: 29, lr: 2.40e-04
2022-05-06 09:58:29,616 INFO [train.py:715] (1/8) Epoch 9, batch 4300, loss[loss=0.1375, simple_loss=0.2031, pruned_loss=0.03598, over 4959.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03466, over 973126.14 frames.], batch size: 14, lr: 2.40e-04
2022-05-06 09:59:09,596 INFO [train.py:715] (1/8) Epoch 9, batch 4350, loss[loss=0.1435, simple_loss=0.2192, pruned_loss=0.03392, over 4926.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2146, pruned_loss=0.03455, over 973517.89 frames.], batch size: 23, lr: 2.40e-04
2022-05-06 09:59:48,192 INFO [train.py:715] (1/8) Epoch 9, batch 4400, loss[loss=0.1501, simple_loss=0.2283, pruned_loss=0.03594, over 4777.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03462, over 973155.20 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 10:00:27,690 INFO [train.py:715] (1/8) Epoch 9, batch 4450, loss[loss=0.1356, simple_loss=0.2147, pruned_loss=0.02821, over 4971.00 frames.], tot_loss[loss=0.1416, simple_loss=0.214, pruned_loss=0.03464, over 973284.86 frames.], batch size: 24, lr: 2.40e-04
2022-05-06 10:01:06,481 INFO [train.py:715] (1/8) Epoch 9, batch 4500, loss[loss=0.1382, simple_loss=0.2082, pruned_loss=0.03408, over 4838.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03483, over 973142.44 frames.], batch size: 15, lr: 2.40e-04
2022-05-06 10:01:45,448 INFO [train.py:715] (1/8) Epoch 9, batch 4550, loss[loss=0.1331, simple_loss=0.2068, pruned_loss=0.02972, over 4855.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03493, over 973133.26 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 10:02:24,724 INFO [train.py:715] (1/8) Epoch 9, batch 4600, loss[loss=0.1222, simple_loss=0.1901, pruned_loss=0.02716, over 4824.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03471, over 972480.61 frames.], batch size: 13, lr: 2.40e-04
2022-05-06 10:03:04,292 INFO [train.py:715] (1/8) Epoch 9, batch 4650, loss[loss=0.1424, simple_loss=0.2207, pruned_loss=0.03205, over 4861.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.0351, over 971810.72 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 10:03:43,900 INFO [train.py:715] (1/8) Epoch 9, batch 4700, loss[loss=0.1498, simple_loss=0.2204, pruned_loss=0.03957, over 4776.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2155, pruned_loss=0.03511, over 972455.33 frames.], batch size: 14, lr: 2.40e-04
2022-05-06 10:04:22,845 INFO [train.py:715] (1/8) Epoch 9, batch 4750, loss[loss=0.1443, simple_loss=0.2191, pruned_loss=0.03476, over 4803.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03505, over 971804.35 frames.], batch size: 21, lr: 2.40e-04
2022-05-06 10:05:02,421 INFO [train.py:715] (1/8) Epoch 9, batch 4800, loss[loss=0.1408, simple_loss=0.2163, pruned_loss=0.03266, over 4842.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03474, over 972029.90 frames.], batch size: 15, lr: 2.40e-04
2022-05-06 10:05:41,418 INFO [train.py:715] (1/8) Epoch 9, batch 4850, loss[loss=0.1252, simple_loss=0.1903, pruned_loss=0.03009, over 4697.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.03458, over 971787.76 frames.], batch size: 15, lr: 2.40e-04
2022-05-06 10:06:20,851 INFO [train.py:715] (1/8) Epoch 9, batch 4900, loss[loss=0.1352, simple_loss=0.1989, pruned_loss=0.03571, over 4977.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2141, pruned_loss=0.03432, over 971894.27 frames.], batch size: 14, lr: 2.40e-04
2022-05-06 10:06:59,739 INFO [train.py:715] (1/8) Epoch 9, batch 4950, loss[loss=0.1181, simple_loss=0.1823, pruned_loss=0.0269, over 4783.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03413, over 971247.22 frames.], batch size: 12, lr: 2.40e-04
2022-05-06 10:07:39,113 INFO [train.py:715] (1/8) Epoch 9, batch 5000, loss[loss=0.1573, simple_loss=0.2342, pruned_loss=0.04016, over 4768.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03386, over 971555.05 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 10:08:18,414 INFO [train.py:715] (1/8) Epoch 9, batch 5050, loss[loss=0.1318, simple_loss=0.2047, pruned_loss=0.02951, over 4835.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2142, pruned_loss=0.03449, over 971729.24 frames.], batch size: 13, lr: 2.40e-04
2022-05-06 10:08:57,173 INFO [train.py:715] (1/8) Epoch 9, batch 5100, loss[loss=0.1321, simple_loss=0.2085, pruned_loss=0.0278, over 4989.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2139, pruned_loss=0.03448, over 971802.42 frames.], batch size: 25, lr: 2.40e-04
2022-05-06 10:09:36,558 INFO [train.py:715] (1/8) Epoch 9, batch 5150, loss[loss=0.1406, simple_loss=0.221, pruned_loss=0.03008, over 4907.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03446, over 972501.47 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 10:10:15,463 INFO [train.py:715] (1/8) Epoch 9, batch 5200, loss[loss=0.1276, simple_loss=0.203, pruned_loss=0.02613, over 4846.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.03467, over 972557.49 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 10:10:54,748 INFO [train.py:715] (1/8) Epoch 9, batch 5250, loss[loss=0.1401, simple_loss=0.2121, pruned_loss=0.03403, over 4984.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03473, over 973583.35 frames.], batch size: 15, lr: 2.40e-04
2022-05-06 10:11:33,952 INFO [train.py:715] (1/8) Epoch 9, batch 5300, loss[loss=0.1149, simple_loss=0.1894, pruned_loss=0.02018, over 4832.00 frames.], tot_loss[loss=0.142, simple_loss=0.2148, pruned_loss=0.03458, over 974088.62 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:12:13,443 INFO [train.py:715] (1/8) Epoch 9, batch 5350, loss[loss=0.1662, simple_loss=0.2452, pruned_loss=0.04358, over 4830.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03418, over 973568.72 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:12:52,102 INFO [train.py:715] (1/8) Epoch 9, batch 5400, loss[loss=0.1267, simple_loss=0.1966, pruned_loss=0.02844, over 4937.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2145, pruned_loss=0.03446, over 972745.88 frames.], batch size: 29, lr: 2.39e-04
2022-05-06 10:13:30,896 INFO [train.py:715] (1/8) Epoch 9, batch 5450, loss[loss=0.1117, simple_loss=0.1823, pruned_loss=0.02059, over 4698.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03479, over 973036.14 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:14:10,208 INFO [train.py:715] (1/8) Epoch 9, batch 5500, loss[loss=0.1277, simple_loss=0.2116, pruned_loss=0.02193, over 4763.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03486, over 972038.03 frames.], batch size: 19, lr: 2.39e-04
2022-05-06 10:14:49,298 INFO [train.py:715] (1/8) Epoch 9, batch 5550, loss[loss=0.1436, simple_loss=0.2152, pruned_loss=0.03604, over 4925.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.03417, over 971538.82 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:15:28,464 INFO [train.py:715] (1/8) Epoch 9, batch 5600, loss[loss=0.131, simple_loss=0.2076, pruned_loss=0.02723, over 4908.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.03433, over 971394.52 frames.], batch size: 18, lr: 2.39e-04
2022-05-06 10:16:07,454 INFO [train.py:715] (1/8) Epoch 9, batch 5650, loss[loss=0.1384, simple_loss=0.2099, pruned_loss=0.03345, over 4843.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03431, over 972095.12 frames.], batch size: 30, lr: 2.39e-04
2022-05-06 10:16:47,096 INFO [train.py:715] (1/8) Epoch 9, batch 5700, loss[loss=0.1429, simple_loss=0.2176, pruned_loss=0.03408, over 4802.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2137, pruned_loss=0.03423, over 971545.38 frames.], batch size: 21, lr: 2.39e-04
2022-05-06 10:17:26,139 INFO [train.py:715] (1/8) Epoch 9, batch 5750, loss[loss=0.1517, simple_loss=0.2243, pruned_loss=0.03958, over 4920.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03447, over 971290.68 frames.], batch size: 18, lr: 2.39e-04
2022-05-06 10:18:04,784 INFO [train.py:715] (1/8) Epoch 9, batch 5800, loss[loss=0.1087, simple_loss=0.1838, pruned_loss=0.01687, over 4799.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2137, pruned_loss=0.03432, over 970837.28 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:18:44,311 INFO [train.py:715] (1/8) Epoch 9, batch 5850, loss[loss=0.1345, simple_loss=0.2154, pruned_loss=0.02684, over 4808.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03377, over 971139.88 frames.], batch size: 26, lr: 2.39e-04
2022-05-06 10:19:23,128 INFO [train.py:715] (1/8) Epoch 9, batch 5900, loss[loss=0.1294, simple_loss=0.2115, pruned_loss=0.0237, over 4802.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03382, over 971297.84 frames.], batch size: 21, lr: 2.39e-04
2022-05-06 10:20:02,775 INFO [train.py:715] (1/8) Epoch 9, batch 5950, loss[loss=0.1287, simple_loss=0.2029, pruned_loss=0.02723, over 4799.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2132, pruned_loss=0.03387, over 970756.46 frames.], batch size: 25, lr: 2.39e-04
2022-05-06 10:20:41,533 INFO [train.py:715] (1/8) Epoch 9, batch 6000, loss[loss=0.1427, simple_loss=0.2121, pruned_loss=0.03666, over 4851.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2135, pruned_loss=0.03433, over 970581.96 frames.], batch size: 30, lr: 2.39e-04
2022-05-06 10:20:41,534 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 10:20:51,194 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.107, simple_loss=0.1914, pruned_loss=0.0113, over 914524.00 frames.
2022-05-06 10:21:30,882 INFO [train.py:715] (1/8) Epoch 9, batch 6050, loss[loss=0.1636, simple_loss=0.2461, pruned_loss=0.04048, over 4760.00 frames.], tot_loss[loss=0.141, simple_loss=0.2134, pruned_loss=0.03428, over 970845.16 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:22:10,752 INFO [train.py:715] (1/8) Epoch 9, batch 6100, loss[loss=0.1186, simple_loss=0.1957, pruned_loss=0.02073, over 4846.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.03417, over 972040.87 frames.], batch size: 13, lr: 2.39e-04
2022-05-06 10:22:49,971 INFO [train.py:715] (1/8) Epoch 9, batch 6150, loss[loss=0.1283, simple_loss=0.2029, pruned_loss=0.02688, over 4903.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03427, over 972136.03 frames.], batch size: 23, lr: 2.39e-04
2022-05-06 10:23:28,782 INFO [train.py:715] (1/8) Epoch 9, batch 6200, loss[loss=0.1477, simple_loss=0.2183, pruned_loss=0.03851, over 4856.00 frames.], tot_loss[loss=0.1411, simple_loss=0.214, pruned_loss=0.03409, over 971968.70 frames.], batch size: 30, lr: 2.39e-04
2022-05-06 10:24:08,418 INFO [train.py:715] (1/8) Epoch 9, batch 6250, loss[loss=0.1273, simple_loss=0.2073, pruned_loss=0.02363, over 4758.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03411, over 971866.64 frames.], batch size: 16, lr: 2.39e-04
2022-05-06 10:24:47,199 INFO [train.py:715] (1/8) Epoch 9, batch 6300, loss[loss=0.1247, simple_loss=0.202, pruned_loss=0.02367, over 4901.00 frames.], tot_loss[loss=0.141, simple_loss=0.2135, pruned_loss=0.03427, over 971854.28 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:25:26,318 INFO [train.py:715] (1/8) Epoch 9, batch 6350, loss[loss=0.1178, simple_loss=0.182, pruned_loss=0.02682, over 4799.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.0344, over 972705.06 frames.], batch size: 14, lr: 2.39e-04
2022-05-06 10:26:05,950 INFO [train.py:715] (1/8) Epoch 9, batch 6400, loss[loss=0.1451, simple_loss=0.2235, pruned_loss=0.03339, over 4867.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.03459, over 972275.78 frames.], batch size: 20, lr: 2.39e-04
2022-05-06 10:26:46,098 INFO [train.py:715] (1/8) Epoch 9, batch 6450, loss[loss=0.1309, simple_loss=0.216, pruned_loss=0.02289, over 4800.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2145, pruned_loss=0.03444, over 972776.65 frames.], batch size: 25, lr: 2.39e-04
2022-05-06 10:27:25,422 INFO [train.py:715] (1/8) Epoch 9, batch 6500, loss[loss=0.1697, simple_loss=0.2369, pruned_loss=0.05127, over 4978.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03516, over 973198.45 frames.], batch size: 35, lr: 2.39e-04
2022-05-06 10:28:04,256 INFO [train.py:715] (1/8) Epoch 9, batch 6550, loss[loss=0.1316, simple_loss=0.2068, pruned_loss=0.0282, over 4945.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03492, over 972325.03 frames.], batch size: 21, lr: 2.39e-04
2022-05-06 10:28:44,036 INFO [train.py:715] (1/8) Epoch 9, batch 6600, loss[loss=0.1672, simple_loss=0.2381, pruned_loss=0.04816, over 4907.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03482, over 971743.26 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:29:23,591 INFO [train.py:715] (1/8) Epoch 9, batch 6650, loss[loss=0.135, simple_loss=0.2075, pruned_loss=0.0313, over 4928.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03427, over 972583.72 frames.], batch size: 21, lr: 2.39e-04
2022-05-06 10:30:02,746 INFO [train.py:715] (1/8) Epoch 9, batch 6700, loss[loss=0.1796, simple_loss=0.2414, pruned_loss=0.05894, over 4779.00 frames.], tot_loss[loss=0.142, simple_loss=0.2143, pruned_loss=0.03482, over 971901.06 frames.], batch size: 18, lr: 2.39e-04
2022-05-06 10:30:44,170 INFO [train.py:715] (1/8) Epoch 9, batch 6750, loss[loss=0.1793, simple_loss=0.2508, pruned_loss=0.05387, over 4779.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.0351, over 972012.04 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:31:23,599 INFO [train.py:715] (1/8) Epoch 9, batch 6800, loss[loss=0.14, simple_loss=0.2152, pruned_loss=0.03235, over 4883.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.035, over 971281.64 frames.], batch size: 19, lr: 2.39e-04
2022-05-06 10:32:02,556 INFO [train.py:715] (1/8) Epoch 9, batch 6850, loss[loss=0.1482, simple_loss=0.224, pruned_loss=0.0362, over 4976.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03487, over 970847.54 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:32:40,752 INFO [train.py:715] (1/8) Epoch 9, batch 6900, loss[loss=0.1298, simple_loss=0.1954, pruned_loss=0.03208, over 4859.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.03506, over 971612.39 frames.], batch size: 30, lr: 2.39e-04
2022-05-06 10:33:20,057 INFO [train.py:715] (1/8) Epoch 9, batch 6950, loss[loss=0.1933, simple_loss=0.2678, pruned_loss=0.0594, over 4927.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03489, over 972033.42 frames.], batch size: 18, lr: 2.39e-04
2022-05-06 10:33:59,862 INFO [train.py:715] (1/8) Epoch 9, batch 7000, loss[loss=0.1353, simple_loss=0.1952, pruned_loss=0.0377, over 4816.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03494, over 971921.50 frames.], batch size: 27, lr: 2.39e-04
2022-05-06 10:34:38,723 INFO [train.py:715] (1/8) Epoch 9, batch 7050, loss[loss=0.1482, simple_loss=0.2241, pruned_loss=0.03614, over 4797.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.03526, over 971591.19 frames.], batch size: 24, lr: 2.39e-04
2022-05-06 10:35:17,349 INFO [train.py:715] (1/8) Epoch 9, batch 7100, loss[loss=0.1386, simple_loss=0.2041, pruned_loss=0.03652, over 4948.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2154, pruned_loss=0.03515, over 972461.69 frames.], batch size: 24, lr: 2.39e-04
2022-05-06 10:35:56,808 INFO [train.py:715] (1/8) Epoch 9, batch 7150, loss[loss=0.1449, simple_loss=0.2089, pruned_loss=0.04043, over 4757.00 frames.], tot_loss[loss=0.1415, simple_loss=0.214, pruned_loss=0.03452, over 973052.94 frames.], batch size: 16, lr: 2.39e-04
2022-05-06 10:36:35,505 INFO [train.py:715] (1/8) Epoch 9, batch 7200, loss[loss=0.1473, simple_loss=0.2197, pruned_loss=0.03745, over 4896.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03464, over 973205.76 frames.], batch size: 19, lr: 2.39e-04
2022-05-06 10:37:14,248 INFO [train.py:715] (1/8) Epoch 9, batch 7250, loss[loss=0.1923, simple_loss=0.2618, pruned_loss=0.06138, over 4959.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03461, over 973016.91 frames.], batch size: 21, lr: 2.39e-04
2022-05-06 10:37:53,495 INFO [train.py:715] (1/8) Epoch 9, batch 7300, loss[loss=0.1363, simple_loss=0.2094, pruned_loss=0.03163, over 4971.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2145, pruned_loss=0.03426, over 973678.04 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:38:32,799 INFO [train.py:715] (1/8) Epoch 9, batch 7350, loss[loss=0.1302, simple_loss=0.2023, pruned_loss=0.02903, over 4822.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2149, pruned_loss=0.03479, over 973461.06 frames.], batch size: 13, lr: 2.39e-04
2022-05-06 10:39:11,300 INFO [train.py:715] (1/8) Epoch 9, batch 7400, loss[loss=0.1171, simple_loss=0.189, pruned_loss=0.02255, over 4806.00 frames.], tot_loss[loss=0.1419, simple_loss=0.215, pruned_loss=0.03437, over 972721.00 frames.], batch size: 12, lr: 2.39e-04
2022-05-06 10:39:50,257 INFO [train.py:715] (1/8) Epoch 9, batch 7450, loss[loss=0.1848, simple_loss=0.246, pruned_loss=0.06178, over 4878.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2159, pruned_loss=0.03494, over 973249.89 frames.], batch size: 16, lr: 2.39e-04
2022-05-06 10:40:30,202 INFO [train.py:715] (1/8) Epoch 9, batch 7500, loss[loss=0.09983, simple_loss=0.1722, pruned_loss=0.01371, over 4811.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03533, over 973853.22 frames.], batch size: 12, lr: 2.39e-04
2022-05-06 10:41:09,244 INFO [train.py:715] (1/8) Epoch 9, batch 7550, loss[loss=0.1185, simple_loss=0.1936, pruned_loss=0.02175, over 4897.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03531, over 973522.66 frames.], batch size: 19, lr: 2.39e-04
2022-05-06 10:41:48,086 INFO [train.py:715] (1/8) Epoch 9, batch 7600, loss[loss=0.1234, simple_loss=0.1999, pruned_loss=0.02345, over 4912.00 frames.], tot_loss[loss=0.142, simple_loss=0.2148, pruned_loss=0.03462, over 972809.10 frames.], batch size: 23, lr: 2.39e-04
2022-05-06 10:42:27,541 INFO [train.py:715] (1/8) Epoch 9, batch 7650, loss[loss=0.1452, simple_loss=0.2085, pruned_loss=0.04094, over 4849.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03512, over 973786.55 frames.], batch size: 20, lr: 2.39e-04
2022-05-06 10:43:06,740 INFO [train.py:715] (1/8) Epoch 9, batch 7700, loss[loss=0.1304, simple_loss=0.2062, pruned_loss=0.0273, over 4812.00 frames.], tot_loss[loss=0.1415, simple_loss=0.214, pruned_loss=0.03454, over 972982.00 frames.], batch size: 21, lr: 2.39e-04
2022-05-06 10:43:45,562 INFO [train.py:715] (1/8) Epoch 9, batch 7750, loss[loss=0.1524, simple_loss=0.2237, pruned_loss=0.04053, over 4846.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2138, pruned_loss=0.03405, over 972435.42 frames.], batch size: 13, lr: 2.39e-04
2022-05-06 10:44:24,374 INFO [train.py:715] (1/8) Epoch 9, batch 7800, loss[loss=0.1546, simple_loss=0.2209, pruned_loss=0.04415, over 4785.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2148, pruned_loss=0.03466, over 972676.28 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:45:04,414 INFO [train.py:715] (1/8) Epoch 9, batch 7850, loss[loss=0.1409, simple_loss=0.2194, pruned_loss=0.03115, over 4810.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.0345, over 972902.67 frames.], batch size: 26, lr: 2.39e-04
2022-05-06 10:45:43,395 INFO [train.py:715] (1/8) Epoch 9, batch 7900, loss[loss=0.1752, simple_loss=0.2431, pruned_loss=0.05368, over 4695.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2162, pruned_loss=0.03507, over 972530.40 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:46:21,524 INFO [train.py:715] (1/8) Epoch 9, batch 7950, loss[loss=0.1265, simple_loss=0.2078, pruned_loss=0.02258, over 4742.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2159, pruned_loss=0.03517, over 972232.40 frames.], batch size: 16, lr: 2.39e-04
2022-05-06 10:47:00,913 INFO [train.py:715] (1/8) Epoch 9, batch 8000, loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.037, over 4884.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2155, pruned_loss=0.03511, over 972450.10 frames.], batch size: 39, lr: 2.38e-04
2022-05-06 10:47:39,931 INFO [train.py:715] (1/8) Epoch 9, batch 8050, loss[loss=0.1735, simple_loss=0.2412, pruned_loss=0.05295, over 4786.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2152, pruned_loss=0.03501, over 971576.10 frames.], batch size: 17, lr: 2.38e-04
2022-05-06 10:48:18,558 INFO [train.py:715] (1/8) Epoch 9, batch 8100, loss[loss=0.1268, simple_loss=0.1999, pruned_loss=0.02686, over 4783.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2158, pruned_loss=0.03468, over 971285.68 frames.], batch size: 18, lr: 2.38e-04
2022-05-06 10:48:57,106 INFO [train.py:715] (1/8) Epoch 9, batch 8150, loss[loss=0.1477, simple_loss=0.22, pruned_loss=0.03768, over 4919.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2164, pruned_loss=0.03474, over 971654.02 frames.], batch size: 29, lr: 2.38e-04
2022-05-06 10:49:36,458 INFO [train.py:715] (1/8) Epoch 9, batch 8200, loss[loss=0.1684, simple_loss=0.2393, pruned_loss=0.04878, over 4756.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2166, pruned_loss=0.03479, over 972368.14 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 10:50:15,123 INFO [train.py:715] (1/8) Epoch 9, batch 8250, loss[loss=0.1416, simple_loss=0.2059, pruned_loss=0.03861, over 4808.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2151, pruned_loss=0.03428, over 972583.09 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 10:50:53,694 INFO [train.py:715] (1/8) Epoch 9, batch 8300, loss[loss=0.1575, simple_loss=0.2299, pruned_loss=0.04248, over 4844.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2144, pruned_loss=0.03403, over 972677.32 frames.], batch size: 20, lr: 2.38e-04
2022-05-06 10:51:32,737 INFO [train.py:715] (1/8) Epoch 9, batch 8350, loss[loss=0.1225, simple_loss=0.1966, pruned_loss=0.02421, over 4766.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2145, pruned_loss=0.03424, over 972029.59 frames.], batch size: 18, lr: 2.38e-04
2022-05-06 10:52:12,412 INFO [train.py:715] (1/8) Epoch 9, batch 8400, loss[loss=0.1366, simple_loss=0.2138, pruned_loss=0.02972, over 4760.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2138, pruned_loss=0.03417, over 971973.69 frames.], batch size: 16, lr: 2.38e-04
2022-05-06 10:52:50,770 INFO [train.py:715] (1/8) Epoch 9, batch 8450, loss[loss=0.1413, simple_loss=0.2106, pruned_loss=0.03602, over 4931.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2141, pruned_loss=0.03479, over 972185.78 frames.], batch size: 23, lr: 2.38e-04
2022-05-06 10:53:29,410 INFO [train.py:715] (1/8) Epoch 9, batch 8500, loss[loss=0.1241, simple_loss=0.1918, pruned_loss=0.02818, over 4791.00 frames.], tot_loss[loss=0.1418, simple_loss=0.214, pruned_loss=0.03483, over 971762.95 frames.], batch size: 24, lr: 2.38e-04
2022-05-06 10:54:08,961 INFO [train.py:715] (1/8) Epoch 9, batch 8550, loss[loss=0.1752, simple_loss=0.2589, pruned_loss=0.04574, over 4804.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.03517, over 971724.45 frames.], batch size: 26, lr: 2.38e-04
2022-05-06 10:54:48,126 INFO [train.py:715] (1/8) Epoch 9, batch 8600, loss[loss=0.1433, simple_loss=0.2109, pruned_loss=0.03787, over 4844.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2147, pruned_loss=0.03523, over 971971.12 frames.], batch size: 13, lr: 2.38e-04
2022-05-06 10:55:26,981 INFO [train.py:715] (1/8) Epoch 9, batch 8650, loss[loss=0.1504, simple_loss=0.2268, pruned_loss=0.03703, over 4955.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.0352, over 972348.86 frames.], batch size: 24, lr: 2.38e-04
2022-05-06 10:56:06,796 INFO [train.py:715] (1/8) Epoch 9, batch 8700, loss[loss=0.147, simple_loss=0.2245, pruned_loss=0.03477, over 4785.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03516, over 972323.75 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 10:56:46,700 INFO [train.py:715] (1/8) Epoch 9, batch 8750, loss[loss=0.1327, simple_loss=0.2045, pruned_loss=0.03045, over 4831.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2139, pruned_loss=0.03487, over 972113.17 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 10:57:25,011 INFO [train.py:715] (1/8) Epoch 9, batch 8800, loss[loss=0.1607, simple_loss=0.2366, pruned_loss=0.04245, over 4906.00 frames.], tot_loss[loss=0.1421, simple_loss=0.214, pruned_loss=0.03506, over 972691.80 frames.], batch size: 18, lr: 2.38e-04
2022-05-06 10:58:04,390 INFO [train.py:715] (1/8) Epoch 9, batch 8850, loss[loss=0.1414, simple_loss=0.2243, pruned_loss=0.02923, over 4763.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03499, over 972999.84 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 10:58:43,836 INFO [train.py:715] (1/8) Epoch 9, batch 8900, loss[loss=0.1463, simple_loss=0.2287, pruned_loss=0.03191, over 4773.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03505, over 972728.31 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 10:59:22,964 INFO [train.py:715] (1/8) Epoch 9, batch 8950, loss[loss=0.1128, simple_loss=0.187, pruned_loss=0.01929, over 4782.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03479, over 971657.50 frames.], batch size: 17, lr: 2.38e-04
2022-05-06 11:00:01,616 INFO [train.py:715] (1/8) Epoch 9, batch 9000, loss[loss=0.1318, simple_loss=0.2104, pruned_loss=0.02655, over 4948.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03503, over 972042.86 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:00:01,617 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 11:00:11,232 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.107, simple_loss=0.1914, pruned_loss=0.0113, over 914524.00 frames.
2022-05-06 11:00:49,916 INFO [train.py:715] (1/8) Epoch 9, batch 9050, loss[loss=0.1543, simple_loss=0.2265, pruned_loss=0.04108, over 4945.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03514, over 972086.29 frames.], batch size: 23, lr: 2.38e-04
2022-05-06 11:01:30,081 INFO [train.py:715] (1/8) Epoch 9, batch 9100, loss[loss=0.1431, simple_loss=0.2167, pruned_loss=0.03469, over 4769.00 frames.], tot_loss[loss=0.1437, simple_loss=0.216, pruned_loss=0.03567, over 972628.10 frames.], batch size: 18, lr: 2.38e-04
2022-05-06 11:02:09,671 INFO [train.py:715] (1/8) Epoch 9, batch 9150, loss[loss=0.1399, simple_loss=0.2045, pruned_loss=0.0376, over 4686.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03531, over 972342.12 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:02:48,632 INFO [train.py:715] (1/8) Epoch 9, batch 9200, loss[loss=0.1572, simple_loss=0.2184, pruned_loss=0.04804, over 4823.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.0357, over 972939.74 frames.], batch size: 13, lr: 2.38e-04
2022-05-06 11:03:28,185 INFO [train.py:715] (1/8) Epoch 9, batch 9250, loss[loss=0.1621, simple_loss=0.2343, pruned_loss=0.04497, over 4923.00 frames.], tot_loss[loss=0.1435, simple_loss=0.216, pruned_loss=0.03546, over 972982.38 frames.], batch size: 17, lr: 2.38e-04
2022-05-06 11:04:07,598 INFO [train.py:715] (1/8) Epoch 9, batch 9300, loss[loss=0.1443, simple_loss=0.218, pruned_loss=0.03531, over 4842.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2159, pruned_loss=0.03513, over 972396.43 frames.], batch size: 32, lr: 2.38e-04
2022-05-06 11:04:46,768 INFO [train.py:715] (1/8) Epoch 9, batch 9350, loss[loss=0.1302, simple_loss=0.2006, pruned_loss=0.02995, over 4956.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03543, over 972854.58 frames.], batch size: 29, lr: 2.38e-04
2022-05-06 11:05:25,230 INFO [train.py:715] (1/8) Epoch 9, batch 9400, loss[loss=0.1475, simple_loss=0.2033, pruned_loss=0.04585, over 4899.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.03516, over 972606.65 frames.], batch size: 17, lr: 2.38e-04
2022-05-06 11:06:05,135 INFO [train.py:715] (1/8) Epoch 9, batch 9450, loss[loss=0.137, simple_loss=0.2032, pruned_loss=0.03543, over 4740.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2142, pruned_loss=0.03449, over 972277.80 frames.], batch size: 16, lr: 2.38e-04
2022-05-06 11:06:44,277 INFO [train.py:715] (1/8) Epoch 9, batch 9500, loss[loss=0.1443, simple_loss=0.2114, pruned_loss=0.03865, over 4847.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2138, pruned_loss=0.03455, over 972316.17 frames.], batch size: 32, lr: 2.38e-04
2022-05-06 11:07:22,930 INFO [train.py:715] (1/8) Epoch 9, batch 9550, loss[loss=0.1108, simple_loss=0.1882, pruned_loss=0.01666, over 4849.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.03445, over 972597.96 frames.], batch size: 30, lr: 2.38e-04
2022-05-06 11:08:02,129 INFO [train.py:715] (1/8) Epoch 9, batch 9600, loss[loss=0.1402, simple_loss=0.2196, pruned_loss=0.03045, over 4810.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03465, over 972257.62 frames.], batch size: 25, lr: 2.38e-04
2022-05-06 11:08:41,397 INFO [train.py:715] (1/8) Epoch 9, batch 9650, loss[loss=0.1061, simple_loss=0.1794, pruned_loss=0.01644, over 4983.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03462, over 971702.93 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 11:09:20,425 INFO [train.py:715] (1/8) Epoch 9, batch 9700, loss[loss=0.1483, simple_loss=0.2191, pruned_loss=0.0387, over 4966.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03506, over 970957.00 frames.], batch size: 35, lr: 2.38e-04
2022-05-06 11:09:58,455 INFO [train.py:715] (1/8) Epoch 9, batch 9750, loss[loss=0.1265, simple_loss=0.1997, pruned_loss=0.02668, over 4879.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03507, over 971945.50 frames.], batch size: 16, lr: 2.38e-04
2022-05-06 11:10:38,590 INFO [train.py:715] (1/8) Epoch 9, batch 9800, loss[loss=0.1416, simple_loss=0.2178, pruned_loss=0.0327, over 4874.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03544, over 972466.83 frames.], batch size: 22, lr: 2.38e-04
2022-05-06 11:11:18,277 INFO [train.py:715] (1/8) Epoch 9, batch 9850, loss[loss=0.1552, simple_loss=0.2269, pruned_loss=0.04179, over 4890.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2161, pruned_loss=0.03552, over 973024.38 frames.], batch size: 17, lr: 2.38e-04
2022-05-06 11:11:56,606 INFO [train.py:715] (1/8) Epoch 9, batch 9900, loss[loss=0.1196, simple_loss=0.1958, pruned_loss=0.02168, over 4840.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2155, pruned_loss=0.03519, over 973012.23 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:12:35,815 INFO [train.py:715] (1/8) Epoch 9, batch 9950, loss[loss=0.1727, simple_loss=0.2413, pruned_loss=0.05208, over 4847.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2163, pruned_loss=0.03539, over 973342.19 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:13:15,754 INFO [train.py:715] (1/8) Epoch 9, batch 10000, loss[loss=0.131, simple_loss=0.191, pruned_loss=0.03546, over 4839.00 frames.], tot_loss[loss=0.144, simple_loss=0.2167, pruned_loss=0.03565, over 974095.73 frames.], batch size: 13, lr: 2.38e-04
2022-05-06 11:13:55,092 INFO [train.py:715] (1/8) Epoch 9, batch 10050, loss[loss=0.1308, simple_loss=0.2104, pruned_loss=0.02556, over 4968.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2168, pruned_loss=0.03542, over 974312.78 frames.], batch size: 24, lr: 2.38e-04
2022-05-06 11:14:33,373 INFO [train.py:715] (1/8) Epoch 9, batch 10100, loss[loss=0.1253, simple_loss=0.1987, pruned_loss=0.02598, over 4826.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2161, pruned_loss=0.03513, over 974297.84 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:15:12,909 INFO [train.py:715] (1/8) Epoch 9, batch 10150, loss[loss=0.163, simple_loss=0.2445, pruned_loss=0.04077, over 4782.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2156, pruned_loss=0.03471, over 972766.97 frames.], batch size: 17, lr: 2.38e-04
2022-05-06 11:15:52,568 INFO [train.py:715] (1/8) Epoch 9, batch 10200, loss[loss=0.1541, simple_loss=0.2366, pruned_loss=0.03578, over 4818.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2153, pruned_loss=0.0347, over 973108.68 frames.], batch size: 25, lr: 2.38e-04
2022-05-06 11:16:31,359 INFO [train.py:715] (1/8) Epoch 9, batch 10250, loss[loss=0.1297, simple_loss=0.2082, pruned_loss=0.02557, over 4968.00 frames.], tot_loss[loss=0.1422, simple_loss=0.215, pruned_loss=0.03472, over 972731.66 frames.], batch size: 35, lr: 2.38e-04
2022-05-06 11:17:10,101 INFO [train.py:715] (1/8) Epoch 9, batch 10300, loss[loss=0.1205, simple_loss=0.1919, pruned_loss=0.02452, over 4845.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03491, over 972560.92 frames.], batch size: 30, lr: 2.38e-04
2022-05-06 11:17:49,722 INFO [train.py:715] (1/8) Epoch 9, batch 10350, loss[loss=0.1417, simple_loss=0.2201, pruned_loss=0.03166, over 4783.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03411, over 973147.18 frames.], batch size: 17, lr: 2.38e-04
2022-05-06 11:18:28,418 INFO [train.py:715] (1/8) Epoch 9, batch 10400, loss[loss=0.1378, simple_loss=0.2143, pruned_loss=0.03065, over 4947.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2145, pruned_loss=0.03444, over 972958.71 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:19:06,741 INFO [train.py:715] (1/8) Epoch 9, batch 10450, loss[loss=0.1523, simple_loss=0.2333, pruned_loss=0.03571, over 4927.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03419, over 972890.45 frames.], batch size: 23, lr: 2.38e-04
2022-05-06 11:19:45,851 INFO [train.py:715] (1/8) Epoch 9, batch 10500, loss[loss=0.143, simple_loss=0.2117, pruned_loss=0.0372, over 4957.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2148, pruned_loss=0.03443, over 973138.43 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 11:20:25,282 INFO [train.py:715] (1/8) Epoch 9, batch 10550, loss[loss=0.1377, simple_loss=0.2074, pruned_loss=0.03401, over 4867.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2154, pruned_loss=0.03453, over 972946.67 frames.], batch size: 16, lr: 2.38e-04
2022-05-06 11:21:04,100 INFO [train.py:715] (1/8) Epoch 9, batch 10600, loss[loss=0.15, simple_loss=0.2136, pruned_loss=0.04323, over 4821.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03433, over 972432.91 frames.], batch size: 13, lr: 2.38e-04
2022-05-06 11:21:42,612 INFO [train.py:715] (1/8) Epoch 9, batch 10650, loss[loss=0.1749, simple_loss=0.2465, pruned_loss=0.05165, over 4898.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03433, over 972211.96 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:22:21,911 INFO [train.py:715] (1/8) Epoch 9, batch 10700, loss[loss=0.1467, simple_loss=0.2312, pruned_loss=0.03116, over 4725.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2148, pruned_loss=0.03437, over 972608.89 frames.], batch size: 16, lr: 2.37e-04
2022-05-06 11:23:01,946 INFO [train.py:715] (1/8) Epoch 9, batch 10750, loss[loss=0.1553, simple_loss=0.2281, pruned_loss=0.04126, over 4900.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.0349, over 972290.12 frames.], batch size: 22, lr: 2.37e-04
2022-05-06 11:23:40,543 INFO [train.py:715] (1/8) Epoch 9, batch 10800, loss[loss=0.145, simple_loss=0.2266, pruned_loss=0.0317, over 4975.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03487, over 972068.18 frames.], batch size: 25, lr: 2.37e-04
2022-05-06 11:24:20,015 INFO [train.py:715] (1/8) Epoch 9, batch 10850, loss[loss=0.1491, simple_loss=0.2244, pruned_loss=0.0369, over 4821.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2147, pruned_loss=0.03451, over 971999.03 frames.], batch size: 26, lr: 2.37e-04
2022-05-06 11:24:59,846 INFO [train.py:715] (1/8) Epoch 9, batch 10900, loss[loss=0.1752, simple_loss=0.2375, pruned_loss=0.05643, over 4872.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2147, pruned_loss=0.03447, over 972569.19 frames.], batch size: 20, lr: 2.37e-04
2022-05-06 11:25:40,139 INFO [train.py:715] (1/8) Epoch 9, batch 10950, loss[loss=0.1442, simple_loss=0.2191, pruned_loss=0.03463, over 4696.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2143, pruned_loss=0.03415, over 972766.29 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:26:20,015 INFO [train.py:715] (1/8) Epoch 9, batch 11000, loss[loss=0.13, simple_loss=0.1965, pruned_loss=0.03169, over 4807.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03433, over 972537.13 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:27:00,849 INFO [train.py:715] (1/8) Epoch 9, batch 11050, loss[loss=0.1148, simple_loss=0.1891, pruned_loss=0.0202, over 4984.00 frames.], tot_loss[loss=0.141, simple_loss=0.2141, pruned_loss=0.03397, over 972514.70 frames.], batch size: 33, lr: 2.37e-04
2022-05-06 11:27:42,115 INFO [train.py:715] (1/8) Epoch 9, batch 11100, loss[loss=0.1272, simple_loss=0.1997, pruned_loss=0.02736, over 4981.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03363, over 972354.06 frames.], batch size: 25, lr: 2.37e-04
2022-05-06 11:28:22,782 INFO [train.py:715] (1/8) Epoch 9, batch 11150, loss[loss=0.1633, simple_loss=0.2314, pruned_loss=0.04758, over 4882.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.0339, over 972469.76 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:29:03,601 INFO [train.py:715] (1/8) Epoch 9, batch 11200, loss[loss=0.1395, simple_loss=0.2037, pruned_loss=0.03767, over 4858.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2136, pruned_loss=0.03409, over 973057.73 frames.], batch size: 16, lr: 2.37e-04
2022-05-06 11:29:45,085 INFO [train.py:715] (1/8) Epoch 9, batch 11250, loss[loss=0.1257, simple_loss=0.2006, pruned_loss=0.02538, over 4911.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03461, over 973089.68 frames.], batch size: 17, lr: 2.37e-04
2022-05-06 11:30:26,200 INFO [train.py:715] (1/8) Epoch 9, batch 11300, loss[loss=0.1336, simple_loss=0.219, pruned_loss=0.02416, over 4889.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2134, pruned_loss=0.03417, over 973338.99 frames.], batch size: 22, lr: 2.37e-04
2022-05-06 11:31:06,651 INFO [train.py:715] (1/8) Epoch 9, batch 11350, loss[loss=0.1564, simple_loss=0.2278, pruned_loss=0.0425, over 4941.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2128, pruned_loss=0.03382, over 972468.36 frames.], batch size: 23, lr: 2.37e-04
2022-05-06 11:31:47,929 INFO [train.py:715] (1/8) Epoch 9, batch 11400, loss[loss=0.1239, simple_loss=0.1877, pruned_loss=0.03006, over 4774.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2126, pruned_loss=0.03363, over 972485.09 frames.], batch size: 18, lr: 2.37e-04
2022-05-06 11:32:29,502 INFO [train.py:715] (1/8) Epoch 9, batch 11450, loss[loss=0.1516, simple_loss=0.2219, pruned_loss=0.04069, over 4972.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.03419, over 973062.37 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:33:10,078 INFO [train.py:715] (1/8) Epoch 9, batch 11500, loss[loss=0.1322, simple_loss=0.2125, pruned_loss=0.02594, over 4848.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.03396, over 973165.56 frames.], batch size: 20, lr: 2.37e-04
2022-05-06 11:33:50,773 INFO [train.py:715] (1/8) Epoch 9, batch 11550, loss[loss=0.1139, simple_loss=0.1823, pruned_loss=0.02278, over 4746.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2129, pruned_loss=0.03375, over 973241.18 frames.], batch size: 12, lr: 2.37e-04
2022-05-06 11:34:32,085 INFO [train.py:715] (1/8) Epoch 9, batch 11600, loss[loss=0.1307, simple_loss=0.1963, pruned_loss=0.03259, over 4780.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03383, over 972904.42 frames.], batch size: 18, lr: 2.37e-04
2022-05-06 11:35:13,600 INFO [train.py:715] (1/8) Epoch 9, batch 11650, loss[loss=0.1363, simple_loss=0.2167, pruned_loss=0.02797, over 4880.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2137, pruned_loss=0.03397, over 972925.66 frames.], batch size: 16, lr: 2.37e-04
2022-05-06 11:35:53,524 INFO [train.py:715] (1/8) Epoch 9, batch 11700, loss[loss=0.1238, simple_loss=0.1927, pruned_loss=0.02747, over 4692.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.03435, over 972640.92 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:36:34,969 INFO [train.py:715] (1/8) Epoch 9, batch 11750, loss[loss=0.1451, simple_loss=0.2215, pruned_loss=0.03435, over 4989.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03477, over 971792.51 frames.], batch size: 28, lr: 2.37e-04
2022-05-06 11:37:16,471 INFO [train.py:715] (1/8) Epoch 9, batch 11800, loss[loss=0.1425, simple_loss=0.2133, pruned_loss=0.03581, over 4930.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03447, over 972459.29 frames.], batch size: 23, lr: 2.37e-04
2022-05-06 11:37:56,813 INFO [train.py:715] (1/8) Epoch 9, batch 11850, loss[loss=0.1688, simple_loss=0.2398, pruned_loss=0.0489, over 4708.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2149, pruned_loss=0.03435, over 972105.61 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:38:37,230 INFO [train.py:715] (1/8) Epoch 9, batch 11900, loss[loss=0.1161, simple_loss=0.1976, pruned_loss=0.01731, over 4977.00 frames.], tot_loss[loss=0.141, simple_loss=0.2141, pruned_loss=0.03401, over 971830.85 frames.], batch size: 25, lr: 2.37e-04
2022-05-06 11:39:18,264 INFO [train.py:715] (1/8) Epoch 9, batch 11950, loss[loss=0.137, simple_loss=0.2177, pruned_loss=0.02813, over 4931.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2144, pruned_loss=0.0342, over 971566.95 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:39:59,370 INFO [train.py:715] (1/8) Epoch 9, batch 12000, loss[loss=0.1366, simple_loss=0.2037, pruned_loss=0.03476, over 4898.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2138, pruned_loss=0.03389, over 971855.99 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:39:59,371 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 11:40:09,082 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.107, simple_loss=0.1913, pruned_loss=0.01136, over 914524.00 frames.
2022-05-06 11:40:50,129 INFO [train.py:715] (1/8) Epoch 9, batch 12050, loss[loss=0.1147, simple_loss=0.1828, pruned_loss=0.02333, over 4793.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03477, over 970751.37 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:41:29,624 INFO [train.py:715] (1/8) Epoch 9, batch 12100, loss[loss=0.1792, simple_loss=0.2411, pruned_loss=0.05863, over 4963.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.0351, over 971808.80 frames.], batch size: 35, lr: 2.37e-04
2022-05-06 11:42:10,007 INFO [train.py:715] (1/8) Epoch 9, batch 12150, loss[loss=0.1087, simple_loss=0.1655, pruned_loss=0.02595, over 4646.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03444, over 971825.57 frames.], batch size: 13, lr: 2.37e-04
2022-05-06 11:42:50,010 INFO [train.py:715] (1/8) Epoch 9, batch 12200, loss[loss=0.1616, simple_loss=0.2277, pruned_loss=0.04777, over 4769.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.0346, over 972164.20 frames.], batch size: 17, lr: 2.37e-04
2022-05-06 11:43:29,262 INFO [train.py:715] (1/8) Epoch 9, batch 12250, loss[loss=0.1367, simple_loss=0.2168, pruned_loss=0.02829, over 4783.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03485, over 971561.18 frames.], batch size: 18, lr: 2.37e-04
2022-05-06 11:44:08,227 INFO [train.py:715] (1/8) Epoch 9, batch 12300, loss[loss=0.1778, simple_loss=0.2532, pruned_loss=0.05121, over 4914.00 frames.], tot_loss[loss=0.142, simple_loss=0.2144, pruned_loss=0.03479, over 971164.68 frames.], batch size: 39, lr: 2.37e-04
2022-05-06 11:44:47,994 INFO [train.py:715] (1/8) Epoch 9, batch 12350, loss[loss=0.1273, simple_loss=0.1994, pruned_loss=0.02761, over 4906.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2137, pruned_loss=0.03403, over 972033.85 frames.], batch size: 22, lr: 2.37e-04
2022-05-06 11:45:28,044 INFO [train.py:715] (1/8) Epoch 9, batch 12400, loss[loss=0.1457, simple_loss=0.222, pruned_loss=0.03464, over 4898.00 frames.], tot_loss[loss=0.141, simple_loss=0.2135, pruned_loss=0.03428, over 972217.32 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:46:07,550 INFO [train.py:715] (1/8) Epoch 9, batch 12450, loss[loss=0.1319, simple_loss=0.196, pruned_loss=0.03388, over 4909.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2131, pruned_loss=0.03411, over 972001.97 frames.], batch size: 17, lr: 2.37e-04
2022-05-06 11:46:47,600 INFO [train.py:715] (1/8) Epoch 9, batch 12500, loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.02798, over 4917.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2131, pruned_loss=0.0342, over 971129.93 frames.], batch size: 29, lr: 2.37e-04
2022-05-06 11:47:27,737 INFO [train.py:715] (1/8) Epoch 9, batch 12550, loss[loss=0.159, simple_loss=0.2244, pruned_loss=0.04682, over 4751.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03458, over 970815.57 frames.], batch size: 16, lr: 2.37e-04
2022-05-06 11:48:07,699 INFO [train.py:715] (1/8) Epoch 9, batch 12600, loss[loss=0.1437, simple_loss=0.2107, pruned_loss=0.03833, over 4816.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2135, pruned_loss=0.03433, over 971181.25 frames.], batch size: 26, lr: 2.37e-04
2022-05-06 11:48:46,466 INFO [train.py:715] (1/8) Epoch 9, batch 12650, loss[loss=0.116, simple_loss=0.1927, pruned_loss=0.01963, over 4802.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03418, over 971520.26 frames.], batch size: 13, lr: 2.37e-04
2022-05-06 11:49:26,603 INFO [train.py:715] (1/8) Epoch 9, batch 12700, loss[loss=0.1639, simple_loss=0.2348, pruned_loss=0.04654, over 4848.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2134, pruned_loss=0.03421, over 971850.19 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:50:06,594 INFO [train.py:715] (1/8) Epoch 9, batch 12750, loss[loss=0.1388, simple_loss=0.2199, pruned_loss=0.02884, over 4931.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03359, over 971882.74 frames.], batch size: 23, lr: 2.37e-04
2022-05-06 11:50:45,762 INFO [train.py:715] (1/8) Epoch 9, batch 12800, loss[loss=0.1508, simple_loss=0.2193, pruned_loss=0.04109, over 4909.00 frames.], tot_loss[loss=0.141, simple_loss=0.2134, pruned_loss=0.03431, over 972562.68 frames.], batch size: 39, lr: 2.37e-04
2022-05-06 11:51:25,612 INFO [train.py:715] (1/8) Epoch 9, batch 12850, loss[loss=0.1663, simple_loss=0.2345, pruned_loss=0.04905, over 4892.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2133, pruned_loss=0.03455, over 972970.10 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:52:05,505 INFO [train.py:715] (1/8) Epoch 9, batch 12900, loss[loss=0.1455, simple_loss=0.2175, pruned_loss=0.03679, over 4882.00 frames.], tot_loss[loss=0.142, simple_loss=0.2143, pruned_loss=0.0349, over 973021.41 frames.], batch size: 39, lr: 2.37e-04
2022-05-06 11:52:45,481 INFO [train.py:715] (1/8) Epoch 9, batch 12950, loss[loss=0.1317, simple_loss=0.2095, pruned_loss=0.02697, over 4978.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.03508, over 972857.05 frames.], batch size: 24, lr: 2.37e-04
2022-05-06 11:53:24,509 INFO [train.py:715] (1/8) Epoch 9, batch 13000, loss[loss=0.1291, simple_loss=0.2077, pruned_loss=0.02521, over 4765.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.0349, over 972111.42 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:54:04,861 INFO [train.py:715] (1/8) Epoch 9, batch 13050, loss[loss=0.125, simple_loss=0.2079, pruned_loss=0.02105, over 4870.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.0348, over 972187.65 frames.], batch size: 32, lr: 2.37e-04
2022-05-06 11:54:44,630 INFO [train.py:715] (1/8) Epoch 9, batch 13100, loss[loss=0.1345, simple_loss=0.2023, pruned_loss=0.03336, over 4807.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03463, over 972306.67 frames.], batch size: 25, lr: 2.37e-04
2022-05-06 11:55:23,873 INFO [train.py:715] (1/8) Epoch 9, batch 13150, loss[loss=0.1423, simple_loss=0.2186, pruned_loss=0.03298, over 4765.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.03469, over 971511.51 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:56:03,856 INFO [train.py:715] (1/8) Epoch 9, batch 13200, loss[loss=0.1279, simple_loss=0.2118, pruned_loss=0.02203, over 4853.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03493, over 971408.30 frames.], batch size: 32, lr: 2.37e-04
2022-05-06 11:56:44,172 INFO [train.py:715] (1/8) Epoch 9, batch 13250, loss[loss=0.1504, simple_loss=0.2222, pruned_loss=0.03927, over 4973.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03479, over 971547.85 frames.], batch size: 28, lr: 2.37e-04
2022-05-06 11:57:23,745 INFO [train.py:715] (1/8) Epoch 9, batch 13300, loss[loss=0.1334, simple_loss=0.2035, pruned_loss=0.0317, over 4988.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03446, over 971574.00 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:58:03,452 INFO [train.py:715] (1/8) Epoch 9, batch 13350, loss[loss=0.1677, simple_loss=0.2426, pruned_loss=0.04643, over 4825.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.03427, over 971864.76 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:58:43,523 INFO [train.py:715] (1/8) Epoch 9, batch 13400, loss[loss=0.1178, simple_loss=0.2009, pruned_loss=0.01733, over 4961.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03389, over 972849.91 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:59:23,795 INFO [train.py:715] (1/8) Epoch 9, batch 13450, loss[loss=0.1274, simple_loss=0.1992, pruned_loss=0.02773, over 4841.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2135, pruned_loss=0.03379, over 972722.83 frames.], batch size: 13, lr: 2.36e-04
2022-05-06 12:00:02,977 INFO [train.py:715] (1/8) Epoch 9, batch 13500, loss[loss=0.164, simple_loss=0.2418, pruned_loss=0.04307, over 4945.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.034, over 972508.58 frames.], batch size: 39, lr: 2.36e-04
2022-05-06 12:00:42,984 INFO [train.py:715] (1/8) Epoch 9, batch 13550, loss[loss=0.1147, simple_loss=0.191, pruned_loss=0.01922, over 4975.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2138, pruned_loss=0.03429, over 972588.29 frames.], batch size: 28, lr: 2.36e-04
2022-05-06 12:01:22,501 INFO [train.py:715] (1/8) Epoch 9, batch 13600, loss[loss=0.1868, simple_loss=0.246, pruned_loss=0.06381, over 4811.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.03398, over 972877.93 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:02:01,625 INFO [train.py:715] (1/8) Epoch 9, batch 13650, loss[loss=0.128, simple_loss=0.2054, pruned_loss=0.02533, over 4890.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2138, pruned_loss=0.0339, over 973560.07 frames.], batch size: 19, lr: 2.36e-04
2022-05-06 12:02:40,851 INFO [train.py:715] (1/8) Epoch 9, batch 13700, loss[loss=0.1764, simple_loss=0.2387, pruned_loss=0.05709, over 4980.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03468, over 973226.68 frames.], batch size: 35, lr: 2.36e-04
2022-05-06 12:03:20,739 INFO [train.py:715] (1/8) Epoch 9, batch 13750, loss[loss=0.0917, simple_loss=0.1676, pruned_loss=0.007878, over 4814.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.03464, over 972576.93 frames.], batch size: 27, lr: 2.36e-04
2022-05-06 12:03:59,885 INFO [train.py:715] (1/8) Epoch 9, batch 13800, loss[loss=0.1338, simple_loss=0.2066, pruned_loss=0.03049, over 4907.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.0355, over 972391.09 frames.], batch size: 17, lr: 2.36e-04
2022-05-06 12:04:38,383 INFO [train.py:715] (1/8) Epoch 9, batch 13850, loss[loss=0.2095, simple_loss=0.2875, pruned_loss=0.06571, over 4988.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03474, over 973088.65 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:05:17,837 INFO [train.py:715] (1/8) Epoch 9, batch 13900, loss[loss=0.1238, simple_loss=0.1978, pruned_loss=0.02489, over 4796.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2138, pruned_loss=0.0347, over 972943.05 frames.], batch size: 12, lr: 2.36e-04
2022-05-06 12:05:57,958 INFO [train.py:715] (1/8) Epoch 9, batch 13950, loss[loss=0.1174, simple_loss=0.1766, pruned_loss=0.02912, over 4644.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2131, pruned_loss=0.03418, over 972798.97 frames.], batch size: 13, lr: 2.36e-04
2022-05-06 12:06:36,916 INFO [train.py:715] (1/8) Epoch 9, batch 14000, loss[loss=0.1184, simple_loss=0.1835, pruned_loss=0.02668, over 4825.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03445, over 972428.73 frames.], batch size: 13, lr: 2.36e-04
2022-05-06 12:07:16,025 INFO [train.py:715] (1/8) Epoch 9, batch 14050, loss[loss=0.1653, simple_loss=0.2433, pruned_loss=0.04364, over 4829.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2137, pruned_loss=0.03429, over 972195.70 frames.], batch size: 26, lr: 2.36e-04
2022-05-06 12:07:55,562 INFO [train.py:715] (1/8) Epoch 9, batch 14100, loss[loss=0.1272, simple_loss=0.1946, pruned_loss=0.02996, over 4696.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03454, over 971782.00 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:08:35,128 INFO [train.py:715] (1/8) Epoch 9, batch 14150, loss[loss=0.1615, simple_loss=0.2356, pruned_loss=0.04368, over 4838.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2138, pruned_loss=0.03433, over 972290.05 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:09:14,477 INFO [train.py:715] (1/8) Epoch 9, batch 14200, loss[loss=0.1638, simple_loss=0.2396, pruned_loss=0.04398, over 4757.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03465, over 972194.59 frames.], batch size: 14, lr: 2.36e-04
2022-05-06 12:09:53,801 INFO [train.py:715] (1/8) Epoch 9, batch 14250, loss[loss=0.1493, simple_loss=0.2142, pruned_loss=0.04219, over 4762.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03458, over 971803.63 frames.], batch size: 12, lr: 2.36e-04
2022-05-06 12:10:33,295 INFO [train.py:715] (1/8) Epoch 9, batch 14300, loss[loss=0.1279, simple_loss=0.2059, pruned_loss=0.02498, over 4907.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03502, over 971921.11 frames.], batch size: 19, lr: 2.36e-04
2022-05-06 12:11:11,971 INFO [train.py:715] (1/8) Epoch 9, batch 14350, loss[loss=0.1516, simple_loss=0.2305, pruned_loss=0.03631, over 4989.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2158, pruned_loss=0.03567, over 972378.89 frames.], batch size: 16, lr: 2.36e-04
2022-05-06 12:11:50,595 INFO [train.py:715] (1/8) Epoch 9, batch 14400, loss[loss=0.1414, simple_loss=0.2033, pruned_loss=0.03974, over 4965.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2165, pruned_loss=0.03583, over 972171.34 frames.], batch size: 24, lr: 2.36e-04
2022-05-06 12:12:30,354 INFO [train.py:715] (1/8) Epoch 9, batch 14450, loss[loss=0.1155, simple_loss=0.1927, pruned_loss=0.01915, over 4781.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.03523, over 972162.75 frames.], batch size: 18, lr: 2.36e-04
2022-05-06 12:13:09,686 INFO [train.py:715] (1/8) Epoch 9, batch 14500, loss[loss=0.1267, simple_loss=0.2048, pruned_loss=0.02434, over 4978.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03533, over 972553.25 frames.], batch size: 28, lr: 2.36e-04
2022-05-06 12:13:48,631 INFO [train.py:715] (1/8) Epoch 9, batch 14550, loss[loss=0.1234, simple_loss=0.1959, pruned_loss=0.02547, over 4834.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2152, pruned_loss=0.03474, over 974070.65 frames.], batch size: 13, lr: 2.36e-04
2022-05-06 12:14:27,681 INFO [train.py:715] (1/8) Epoch 9, batch 14600, loss[loss=0.1325, simple_loss=0.2015, pruned_loss=0.03172, over 4943.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2146, pruned_loss=0.03427, over 973301.65 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:15:07,384 INFO [train.py:715] (1/8) Epoch 9, batch 14650, loss[loss=0.1354, simple_loss=0.2152, pruned_loss=0.0278, over 4796.00 frames.], tot_loss[loss=0.1409, simple_loss=0.214, pruned_loss=0.03386, over 972475.87 frames.], batch size: 24, lr: 2.36e-04
2022-05-06 12:15:45,917 INFO [train.py:715] (1/8) Epoch 9, batch 14700, loss[loss=0.1312, simple_loss=0.2044, pruned_loss=0.02904, over 4884.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2142, pruned_loss=0.03375, over 972144.85 frames.], batch size: 19, lr: 2.36e-04
2022-05-06 12:16:27,516 INFO [train.py:715] (1/8) Epoch 9, batch 14750, loss[loss=0.134, simple_loss=0.206, pruned_loss=0.03101, over 4917.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2138, pruned_loss=0.03357, over 973331.65 frames.], batch size: 18, lr: 2.36e-04
2022-05-06 12:17:06,569 INFO [train.py:715] (1/8) Epoch 9, batch 14800, loss[loss=0.1234, simple_loss=0.1969, pruned_loss=0.02499, over 4806.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2136, pruned_loss=0.03372, over 972648.06 frames.], batch size: 13, lr: 2.36e-04
2022-05-06 12:17:45,493 INFO [train.py:715] (1/8) Epoch 9, batch 14850, loss[loss=0.1617, simple_loss=0.2312, pruned_loss=0.04608, over 4838.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03361, over 972010.40 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:18:24,544 INFO [train.py:715] (1/8) Epoch 9, batch 14900, loss[loss=0.1755, simple_loss=0.2421, pruned_loss=0.05444, over 4943.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.03427, over 972485.94 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:19:03,080 INFO [train.py:715] (1/8) Epoch 9, batch 14950, loss[loss=0.1227, simple_loss=0.1983, pruned_loss=0.02352, over 4957.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.03383, over 973166.82 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:19:42,677 INFO [train.py:715] (1/8) Epoch 9, batch 15000, loss[loss=0.1873, simple_loss=0.2619, pruned_loss=0.05634, over 4865.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2141, pruned_loss=0.03438, over 972861.63 frames.], batch size: 20, lr: 2.36e-04
2022-05-06 12:19:42,678 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 12:19:52,344 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.1071, simple_loss=0.1915, pruned_loss=0.01139, over 914524.00 frames.
2022-05-06 12:20:32,094 INFO [train.py:715] (1/8) Epoch 9, batch 15050, loss[loss=0.1105, simple_loss=0.1872, pruned_loss=0.01686, over 4814.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03466, over 972816.93 frames.], batch size: 25, lr: 2.36e-04
2022-05-06 12:21:11,098 INFO [train.py:715] (1/8) Epoch 9, batch 15100, loss[loss=0.1418, simple_loss=0.2178, pruned_loss=0.03289, over 4870.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03474, over 973196.66 frames.], batch size: 22, lr: 2.36e-04
2022-05-06 12:21:50,196 INFO [train.py:715] (1/8) Epoch 9, batch 15150, loss[loss=0.1549, simple_loss=0.2348, pruned_loss=0.0375, over 4847.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03438, over 972781.55 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:22:30,007 INFO [train.py:715] (1/8) Epoch 9, batch 15200, loss[loss=0.1419, simple_loss=0.2099, pruned_loss=0.03696, over 4856.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.0348, over 972796.97 frames.], batch size: 38, lr: 2.36e-04
2022-05-06 12:23:09,319 INFO [train.py:715] (1/8) Epoch 9, batch 15250, loss[loss=0.1239, simple_loss=0.1946, pruned_loss=0.02661, over 4856.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03438, over 971750.03 frames.], batch size: 20, lr: 2.36e-04
2022-05-06 12:23:48,031 INFO [train.py:715] (1/8) Epoch 9, batch 15300, loss[loss=0.1208, simple_loss=0.1935, pruned_loss=0.02411, over 4974.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2145, pruned_loss=0.03428, over 972547.02 frames.], batch size: 28, lr: 2.36e-04
2022-05-06 12:24:27,147 INFO [train.py:715] (1/8) Epoch 9, batch 15350, loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.02813, over 4928.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2143, pruned_loss=0.03414, over 973147.98 frames.], batch size: 29, lr: 2.36e-04
2022-05-06 12:25:06,185 INFO [train.py:715] (1/8) Epoch 9, batch 15400, loss[loss=0.1794, simple_loss=0.2495, pruned_loss=0.05462, over 4986.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2137, pruned_loss=0.03404, over 973736.54 frames.], batch size: 25, lr: 2.36e-04
2022-05-06 12:25:44,959 INFO [train.py:715] (1/8) Epoch 9, batch 15450, loss[loss=0.1821, simple_loss=0.246, pruned_loss=0.05906, over 4714.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03461, over 972876.43 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:26:23,384 INFO [train.py:715] (1/8) Epoch 9, batch 15500, loss[loss=0.1246, simple_loss=0.2016, pruned_loss=0.02374, over 4979.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03492, over 972426.65 frames.], batch size: 14, lr: 2.36e-04
2022-05-06 12:27:03,110 INFO [train.py:715] (1/8) Epoch 9, batch 15550, loss[loss=0.1313, simple_loss=0.2039, pruned_loss=0.0293, over 4822.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2147, pruned_loss=0.03521, over 971252.61 frames.], batch size: 26, lr: 2.36e-04
2022-05-06 12:27:41,870 INFO [train.py:715] (1/8) Epoch 9, batch 15600, loss[loss=0.1363, simple_loss=0.2071, pruned_loss=0.03274, over 4947.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03501, over 971786.08 frames.], batch size: 29, lr: 2.36e-04
2022-05-06 12:28:20,221 INFO [train.py:715] (1/8) Epoch 9, batch 15650, loss[loss=0.1748, simple_loss=0.2473, pruned_loss=0.0511, over 4953.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.03437, over 972238.72 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:28:59,313 INFO [train.py:715] (1/8) Epoch 9, batch 15700, loss[loss=0.1323, simple_loss=0.2053, pruned_loss=0.02962, over 4971.00 frames.], tot_loss[loss=0.1416, simple_loss=0.214, pruned_loss=0.03457, over 972430.07 frames.], batch size: 35, lr: 2.36e-04
2022-05-06 12:29:39,070 INFO [train.py:715] (1/8) Epoch 9, batch 15750, loss[loss=0.1149, simple_loss=0.1882, pruned_loss=0.02084, over 4787.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.03433, over 972620.48 frames.], batch size: 17, lr: 2.36e-04
2022-05-06 12:30:17,865 INFO [train.py:715] (1/8) Epoch 9, batch 15800, loss[loss=0.1392, simple_loss=0.214, pruned_loss=0.03223, over 4913.00 frames.], tot_loss[loss=0.142, simple_loss=0.2149, pruned_loss=0.03454, over 972913.87 frames.], batch size: 18, lr: 2.36e-04
2022-05-06 12:30:56,795 INFO [train.py:715] (1/8) Epoch 9, batch 15850, loss[loss=0.1917, simple_loss=0.2544, pruned_loss=0.06449, over 4866.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2142, pruned_loss=0.03431, over 972625.98 frames.], batch size: 38, lr: 2.36e-04
2022-05-06 12:31:36,397 INFO [train.py:715] (1/8) Epoch 9, batch 15900, loss[loss=0.1674, simple_loss=0.2457, pruned_loss=0.04448, over 4980.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2148, pruned_loss=0.0344, over 972761.35 frames.], batch size: 25, lr: 2.36e-04
2022-05-06 12:32:15,972 INFO [train.py:715] (1/8) Epoch 9, batch 15950, loss[loss=0.1353, simple_loss=0.2132, pruned_loss=0.02868, over 4875.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2141, pruned_loss=0.03437, over 973069.59 frames.], batch size: 20, lr: 2.36e-04
2022-05-06 12:32:54,615 INFO [train.py:715] (1/8) Epoch 9, batch 16000, loss[loss=0.1432, simple_loss=0.2195, pruned_loss=0.0334, over 4878.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2134, pruned_loss=0.03421, over 972616.29 frames.], batch size: 16, lr: 2.36e-04
2022-05-06 12:33:33,294 INFO [train.py:715] (1/8) Epoch 9, batch 16050, loss[loss=0.1466, simple_loss=0.2177, pruned_loss=0.03771, over 4934.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03452, over 972594.74 frames.], batch size: 18, lr: 2.36e-04
2022-05-06 12:34:12,504 INFO [train.py:715] (1/8) Epoch 9, batch 16100, loss[loss=0.1594, simple_loss=0.2333, pruned_loss=0.04278, over 4894.00 frames.], tot_loss[loss=0.142, simple_loss=0.2141, pruned_loss=0.03489, over 972406.47 frames.], batch size: 19, lr: 2.36e-04
2022-05-06 12:34:51,589 INFO [train.py:715] (1/8) Epoch 9, batch 16150, loss[loss=0.1552, simple_loss=0.2223, pruned_loss=0.04403, over 4796.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03461, over 972666.02 frames.], batch size: 24, lr: 2.36e-04
2022-05-06 12:35:30,766 INFO [train.py:715] (1/8) Epoch 9, batch 16200, loss[loss=0.1253, simple_loss=0.1999, pruned_loss=0.02537, over 4816.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2136, pruned_loss=0.0343, over 972852.24 frames.], batch size: 26, lr: 2.36e-04
2022-05-06 12:36:10,108 INFO [train.py:715] (1/8) Epoch 9, batch 16250, loss[loss=0.1265, simple_loss=0.1962, pruned_loss=0.02842, over 4960.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2136, pruned_loss=0.0345, over 972254.65 frames.], batch size: 24, lr: 2.35e-04
2022-05-06 12:36:49,785 INFO [train.py:715] (1/8) Epoch 9, batch 16300, loss[loss=0.1472, simple_loss=0.2254, pruned_loss=0.03446, over 4814.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2139, pruned_loss=0.03475, over 972297.25 frames.], batch size: 25, lr: 2.35e-04
2022-05-06 12:37:27,726 INFO [train.py:715] (1/8) Epoch 9, batch 16350, loss[loss=0.1635, simple_loss=0.2439, pruned_loss=0.04156, over 4868.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03476, over 972055.17 frames.], batch size: 20, lr: 2.35e-04
2022-05-06 12:38:07,158 INFO [train.py:715] (1/8) Epoch 9, batch 16400, loss[loss=0.1642, simple_loss=0.2355, pruned_loss=0.04645, over 4922.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.0345, over 971982.88 frames.], batch size: 29, lr: 2.35e-04
2022-05-06 12:38:47,051 INFO [train.py:715] (1/8) Epoch 9, batch 16450, loss[loss=0.1024, simple_loss=0.1727, pruned_loss=0.01602, over 4813.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2136, pruned_loss=0.03471, over 972090.72 frames.], batch size: 13, lr: 2.35e-04
2022-05-06 12:39:25,802 INFO [train.py:715] (1/8) Epoch 9, batch 16500, loss[loss=0.1379, simple_loss=0.2214, pruned_loss=0.02718, over 4893.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2139, pruned_loss=0.03491, over 972257.29 frames.], batch size: 19, lr: 2.35e-04
2022-05-06 12:40:04,400 INFO [train.py:715] (1/8) Epoch 9, batch 16550, loss[loss=0.1488, simple_loss=0.223, pruned_loss=0.03733, over 4845.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2139, pruned_loss=0.03479, over 972112.14 frames.], batch size: 30, lr: 2.35e-04
2022-05-06 12:40:43,849 INFO [train.py:715] (1/8) Epoch 9, batch 16600, loss[loss=0.1469, simple_loss=0.2133, pruned_loss=0.04024, over 4821.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2149, pruned_loss=0.0355, over 972114.26 frames.], batch size: 13, lr: 2.35e-04
2022-05-06 12:41:23,435 INFO [train.py:715] (1/8) Epoch 9, batch 16650, loss[loss=0.1489, simple_loss=0.219, pruned_loss=0.03942, over 4907.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2145, pruned_loss=0.03503, over 971428.35 frames.], batch size: 38, lr: 2.35e-04
2022-05-06 12:42:02,348 INFO [train.py:715] (1/8) Epoch 9, batch 16700, loss[loss=0.1415, simple_loss=0.2123, pruned_loss=0.03533, over 4852.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2145, pruned_loss=0.03492, over 970512.93 frames.], batch size: 20, lr: 2.35e-04
2022-05-06 12:42:41,612 INFO [train.py:715] (1/8) Epoch 9, batch 16750, loss[loss=0.1245, simple_loss=0.2085, pruned_loss=0.02025, over 4961.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.0356, over 971817.76 frames.], batch size: 24, lr: 2.35e-04
2022-05-06 12:43:21,410 INFO [train.py:715] (1/8) Epoch 9, batch 16800, loss[loss=0.1418, simple_loss=0.2212, pruned_loss=0.03118, over 4840.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.0358, over 971196.06 frames.], batch size: 26, lr: 2.35e-04
2022-05-06 12:44:01,037 INFO [train.py:715] (1/8) Epoch 9, batch 16850, loss[loss=0.1611, simple_loss=0.2366, pruned_loss=0.04275, over 4909.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2149, pruned_loss=0.03534, over 971132.56 frames.], batch size: 17, lr: 2.35e-04
2022-05-06 12:44:40,455 INFO [train.py:715] (1/8) Epoch 9, batch 16900, loss[loss=0.159, simple_loss=0.2222, pruned_loss=0.04786, over 4898.00 frames.], tot_loss[loss=0.142, simple_loss=0.2144, pruned_loss=0.03476, over 971512.49 frames.], batch size: 19, lr: 2.35e-04
2022-05-06 12:45:20,530 INFO [train.py:715] (1/8) Epoch 9, batch 16950, loss[loss=0.1215, simple_loss=0.1953, pruned_loss=0.02382, over 4974.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2135, pruned_loss=0.03447, over 971312.83 frames.], batch size: 35, lr: 2.35e-04
2022-05-06 12:46:00,233 INFO [train.py:715] (1/8) Epoch 9, batch 17000, loss[loss=0.1355, simple_loss=0.2076, pruned_loss=0.03169, over 4781.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.03434, over 972084.38 frames.], batch size: 14, lr: 2.35e-04
2022-05-06 12:46:38,803 INFO [train.py:715] (1/8) Epoch 9, batch 17050, loss[loss=0.1566, simple_loss=0.2275, pruned_loss=0.04288, over 4956.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2138, pruned_loss=0.03422, over 972522.15 frames.], batch size: 35, lr: 2.35e-04
2022-05-06 12:47:18,384 INFO [train.py:715] (1/8) Epoch 9, batch 17100, loss[loss=0.1249, simple_loss=0.1862, pruned_loss=0.03184, over 4797.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03463, over 972568.34 frames.], batch size: 14, lr: 2.35e-04
2022-05-06 12:47:58,061 INFO [train.py:715] (1/8) Epoch 9, batch 17150, loss[loss=0.1276, simple_loss=0.2078, pruned_loss=0.0237, over 4862.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2138, pruned_loss=0.03425, over 972004.12 frames.], batch size: 20, lr: 2.35e-04
2022-05-06 12:48:37,317 INFO [train.py:715] (1/8) Epoch 9, batch 17200, loss[loss=0.1279, simple_loss=0.2079, pruned_loss=0.02394, over 4775.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2138, pruned_loss=0.03464, over 971859.05 frames.], batch size: 17, lr: 2.35e-04
2022-05-06 12:49:15,988 INFO [train.py:715] (1/8) Epoch 9, batch 17250, loss[loss=0.1977, simple_loss=0.2726, pruned_loss=0.06143, over 4787.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2138, pruned_loss=0.03465, over 972144.24 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 12:49:54,883 INFO [train.py:715] (1/8) Epoch 9, batch 17300, loss[loss=0.1162, simple_loss=0.1961, pruned_loss=0.0182, over 4780.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03468, over 972501.95 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 12:50:33,969 INFO [train.py:715] (1/8) Epoch 9, batch 17350, loss[loss=0.2111, simple_loss=0.2804, pruned_loss=0.07087, over 4684.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03495, over 972479.68 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 12:51:13,076 INFO [train.py:715] (1/8) Epoch 9, batch 17400, loss[loss=0.132, simple_loss=0.2077, pruned_loss=0.02818, over 4841.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03475, over 972148.12 frames.], batch size: 30, lr: 2.35e-04
2022-05-06 12:51:52,390 INFO [train.py:715] (1/8) Epoch 9, batch 17450, loss[loss=0.1372, simple_loss=0.2099, pruned_loss=0.03226, over 4927.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.0349, over 973281.58 frames.], batch size: 21, lr: 2.35e-04
2022-05-06 12:52:31,599 INFO [train.py:715] (1/8) Epoch 9, batch 17500, loss[loss=0.1499, simple_loss=0.2206, pruned_loss=0.03953, over 4922.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03458, over 973211.62 frames.], batch size: 39, lr: 2.35e-04
2022-05-06 12:53:10,810 INFO [train.py:715] (1/8) Epoch 9, batch 17550, loss[loss=0.1242, simple_loss=0.1923, pruned_loss=0.0281, over 4830.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03425, over 973213.54 frames.], batch size: 12, lr: 2.35e-04
2022-05-06 12:53:49,889 INFO [train.py:715] (1/8) Epoch 9, batch 17600, loss[loss=0.1515, simple_loss=0.2269, pruned_loss=0.03807, over 4776.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2137, pruned_loss=0.03402, over 972965.40 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 12:54:29,584 INFO [train.py:715] (1/8) Epoch 9, batch 17650, loss[loss=0.1323, simple_loss=0.1974, pruned_loss=0.03361, over 4748.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2138, pruned_loss=0.03414, over 972213.91 frames.], batch size: 16, lr: 2.35e-04
2022-05-06 12:55:08,475 INFO [train.py:715] (1/8) Epoch 9, batch 17700, loss[loss=0.1666, simple_loss=0.2497, pruned_loss=0.04173, over 4694.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03321, over 971419.01 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 12:55:47,740 INFO [train.py:715] (1/8) Epoch 9, batch 17750, loss[loss=0.1377, simple_loss=0.2237, pruned_loss=0.02591, over 4866.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.03355, over 971850.08 frames.], batch size: 16, lr: 2.35e-04
2022-05-06 12:56:27,545 INFO [train.py:715] (1/8) Epoch 9, batch 17800, loss[loss=0.1305, simple_loss=0.2047, pruned_loss=0.02816, over 4974.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2139, pruned_loss=0.03387, over 971665.78 frames.], batch size: 21, lr: 2.35e-04
2022-05-06 12:57:06,520 INFO [train.py:715] (1/8) Epoch 9, batch 17850, loss[loss=0.157, simple_loss=0.2305, pruned_loss=0.04179, over 4712.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2142, pruned_loss=0.0341, over 971632.37 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 12:57:45,748 INFO [train.py:715] (1/8) Epoch 9, batch 17900, loss[loss=0.1455, simple_loss=0.2171, pruned_loss=0.03701, over 4772.00 frames.], tot_loss[loss=0.1418, simple_loss=0.214, pruned_loss=0.03474, over 971861.02 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 12:58:25,610 INFO [train.py:715] (1/8) Epoch 9, batch 17950, loss[loss=0.1408, simple_loss=0.2189, pruned_loss=0.03134, over 4766.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03515, over 972003.38 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 12:59:04,969 INFO [train.py:715] (1/8) Epoch 9, batch 18000, loss[loss=0.1294, simple_loss=0.208, pruned_loss=0.02538, over 4792.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03509, over 971937.58 frames.], batch size: 17, lr: 2.35e-04
2022-05-06 12:59:04,969 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 12:59:14,503 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.1068, simple_loss=0.1912, pruned_loss=0.01121, over 914524.00 frames.
2022-05-06 12:59:53,952 INFO [train.py:715] (1/8) Epoch 9, batch 18050, loss[loss=0.1414, simple_loss=0.209, pruned_loss=0.03685, over 4921.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03465, over 972826.55 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 13:00:33,771 INFO [train.py:715] (1/8) Epoch 9, batch 18100, loss[loss=0.1438, simple_loss=0.226, pruned_loss=0.03083, over 4915.00 frames.], tot_loss[loss=0.1424, simple_loss=0.215, pruned_loss=0.03489, over 972356.88 frames.], batch size: 19, lr: 2.35e-04
2022-05-06 13:01:13,060 INFO [train.py:715] (1/8) Epoch 9, batch 18150, loss[loss=0.1245, simple_loss=0.1969, pruned_loss=0.02601, over 4765.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2159, pruned_loss=0.03533, over 972553.88 frames.], batch size: 14, lr: 2.35e-04
2022-05-06 13:01:52,670 INFO [train.py:715] (1/8) Epoch 9, batch 18200, loss[loss=0.1376, simple_loss=0.2125, pruned_loss=0.03133, over 4781.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03462, over 972701.06 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 13:02:31,900 INFO [train.py:715] (1/8) Epoch 9, batch 18250, loss[loss=0.1742, simple_loss=0.2419, pruned_loss=0.05322, over 4840.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03505, over 972481.78 frames.], batch size: 30, lr: 2.35e-04
2022-05-06 13:03:11,072 INFO [train.py:715] (1/8) Epoch 9, batch 18300, loss[loss=0.1295, simple_loss=0.2036, pruned_loss=0.02764, over 4902.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.0347, over 972778.95 frames.], batch size: 19, lr: 2.35e-04
2022-05-06 13:03:50,427 INFO [train.py:715] (1/8) Epoch 9, batch 18350, loss[loss=0.1198, simple_loss=0.1925, pruned_loss=0.02356, over 4900.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2142, pruned_loss=0.03428, over 972134.76 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 13:04:29,592 INFO [train.py:715] (1/8) Epoch 9, batch 18400, loss[loss=0.1402, simple_loss=0.221, pruned_loss=0.02973, over 4790.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2151, pruned_loss=0.03461, over 972823.77 frames.], batch size: 24, lr: 2.35e-04
2022-05-06 13:05:08,635 INFO [train.py:715] (1/8) Epoch 9, batch 18450, loss[loss=0.102, simple_loss=0.1752, pruned_loss=0.01441, over 4851.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2153, pruned_loss=0.03443, over 973312.89 frames.], batch size: 13, lr: 2.35e-04
2022-05-06 13:05:47,600 INFO [train.py:715] (1/8) Epoch 9, batch 18500, loss[loss=0.1263, simple_loss=0.2034, pruned_loss=0.02464, over 4924.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2152, pruned_loss=0.03431, over 973404.14 frames.], batch size: 29, lr: 2.35e-04
2022-05-06 13:06:26,993 INFO [train.py:715] (1/8) Epoch 9, batch 18550, loss[loss=0.1242, simple_loss=0.1997, pruned_loss=0.0243, over 4977.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2145, pruned_loss=0.03396, over 972230.49 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 13:07:06,065 INFO [train.py:715] (1/8) Epoch 9, batch 18600, loss[loss=0.1409, simple_loss=0.2071, pruned_loss=0.03737, over 4842.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03439, over 972461.78 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 13:07:44,914 INFO [train.py:715] (1/8) Epoch 9, batch 18650, loss[loss=0.1409, simple_loss=0.2155, pruned_loss=0.03309, over 4808.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.03444, over 973058.90 frames.], batch size: 26, lr: 2.35e-04
2022-05-06 13:08:24,472 INFO [train.py:715] (1/8) Epoch 9, batch 18700, loss[loss=0.1443, simple_loss=0.218, pruned_loss=0.0353, over 4758.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.03439, over 973343.85 frames.], batch size: 16, lr: 2.35e-04
2022-05-06 13:09:03,184 INFO [train.py:715] (1/8) Epoch 9, batch 18750, loss[loss=0.1503, simple_loss=0.2264, pruned_loss=0.03714, over 4765.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03463, over 973705.57 frames.], batch size: 17, lr: 2.35e-04
2022-05-06 13:09:42,757 INFO [train.py:715] (1/8) Epoch 9, batch 18800, loss[loss=0.1249, simple_loss=0.1941, pruned_loss=0.02781, over 4925.00 frames.], tot_loss[loss=0.1415, simple_loss=0.214, pruned_loss=0.03444, over 973111.90 frames.], batch size: 29, lr: 2.35e-04
2022-05-06 13:10:21,583 INFO [train.py:715] (1/8) Epoch 9, batch 18850, loss[loss=0.1128, simple_loss=0.186, pruned_loss=0.01982, over 4803.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03481, over 973566.89 frames.], batch size: 24, lr: 2.35e-04
2022-05-06 13:11:00,814 INFO [train.py:715] (1/8) Epoch 9, batch 18900, loss[loss=0.1411, simple_loss=0.21, pruned_loss=0.03605, over 4813.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03476, over 972744.77 frames.], batch size: 21, lr: 2.35e-04
2022-05-06 13:11:40,161 INFO [train.py:715] (1/8) Epoch 9, batch 18950, loss[loss=0.124, simple_loss=0.1923, pruned_loss=0.02782, over 4704.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2147, pruned_loss=0.03441, over 972484.10 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 13:12:18,867 INFO [train.py:715] (1/8) Epoch 9, batch 19000, loss[loss=0.1853, simple_loss=0.2445, pruned_loss=0.06306, over 4941.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03494, over 971909.70 frames.], batch size: 14, lr: 2.35e-04
2022-05-06 13:12:58,958 INFO [train.py:715] (1/8) Epoch 9, batch 19050, loss[loss=0.1451, simple_loss=0.2178, pruned_loss=0.03621, over 4845.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2157, pruned_loss=0.03477, over 972096.80 frames.], batch size: 30, lr: 2.34e-04
2022-05-06 13:13:38,425 INFO [train.py:715] (1/8) Epoch 9, batch 19100, loss[loss=0.1551, simple_loss=0.2311, pruned_loss=0.03955, over 4816.00 frames.], tot_loss[loss=0.143, simple_loss=0.2159, pruned_loss=0.03501, over 971875.83 frames.], batch size: 25, lr: 2.34e-04
2022-05-06 13:14:17,257 INFO [train.py:715] (1/8) Epoch 9, batch 19150, loss[loss=0.1779, simple_loss=0.2284, pruned_loss=0.06364, over 4979.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03483, over 971509.95 frames.], batch size: 35, lr: 2.34e-04
2022-05-06 13:14:57,085 INFO [train.py:715] (1/8) Epoch 9, batch 19200, loss[loss=0.1408, simple_loss=0.2218, pruned_loss=0.02995, over 4700.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2154, pruned_loss=0.03506, over 971806.26 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:15:36,587 INFO [train.py:715] (1/8) Epoch 9, batch 19250, loss[loss=0.1316, simple_loss=0.2012, pruned_loss=0.03097, over 4748.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03483, over 971948.99 frames.], batch size: 16, lr: 2.34e-04
2022-05-06 13:16:15,483 INFO [train.py:715] (1/8) Epoch 9, batch 19300, loss[loss=0.1498, simple_loss=0.2249, pruned_loss=0.03735, over 4922.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2155, pruned_loss=0.03509, over 971801.67 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:16:54,059 INFO [train.py:715] (1/8) Epoch 9, batch 19350, loss[loss=0.1462, simple_loss=0.2231, pruned_loss=0.03469, over 4855.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03517, over 971512.58 frames.], batch size: 13, lr: 2.34e-04
2022-05-06 13:17:34,089 INFO [train.py:715] (1/8) Epoch 9, batch 19400, loss[loss=0.1416, simple_loss=0.2152, pruned_loss=0.03395, over 4833.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03544, over 971068.23 frames.], batch size: 26, lr: 2.34e-04
2022-05-06 13:18:13,120 INFO [train.py:715] (1/8) Epoch 9, batch 19450, loss[loss=0.1447, simple_loss=0.2147, pruned_loss=0.03737, over 4812.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.03512, over 970322.74 frames.], batch size: 26, lr: 2.34e-04
2022-05-06 13:18:51,809 INFO [train.py:715] (1/8) Epoch 9, batch 19500, loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.0312, over 4980.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03495, over 971376.15 frames.], batch size: 28, lr: 2.34e-04
2022-05-06 13:19:30,940 INFO [train.py:715] (1/8) Epoch 9, batch 19550, loss[loss=0.1581, simple_loss=0.2222, pruned_loss=0.04699, over 4844.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2138, pruned_loss=0.03467, over 971280.34 frames.], batch size: 30, lr: 2.34e-04
2022-05-06 13:20:10,203 INFO [train.py:715] (1/8) Epoch 9, batch 19600, loss[loss=0.1639, simple_loss=0.2223, pruned_loss=0.0528, over 4871.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03496, over 971468.49 frames.], batch size: 32, lr: 2.34e-04
2022-05-06 13:20:48,778 INFO [train.py:715] (1/8) Epoch 9, batch 19650, loss[loss=0.114, simple_loss=0.1923, pruned_loss=0.01789, over 4746.00 frames.], tot_loss[loss=0.143, simple_loss=0.215, pruned_loss=0.03547, over 971767.12 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:21:27,267 INFO [train.py:715] (1/8) Epoch 9, batch 19700, loss[loss=0.1541, simple_loss=0.2256, pruned_loss=0.04131, over 4962.00 frames.], tot_loss[loss=0.1419, simple_loss=0.214, pruned_loss=0.03491, over 972142.31 frames.], batch size: 35, lr: 2.34e-04
2022-05-06 13:22:07,180 INFO [train.py:715] (1/8) Epoch 9, batch 19750, loss[loss=0.1295, simple_loss=0.1913, pruned_loss=0.03389, over 4895.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2142, pruned_loss=0.03506, over 972260.77 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:22:46,852 INFO [train.py:715] (1/8) Epoch 9, batch 19800, loss[loss=0.1125, simple_loss=0.189, pruned_loss=0.01796, over 4789.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2138, pruned_loss=0.03469, over 972195.58 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:23:26,647 INFO [train.py:715] (1/8) Epoch 9, batch 19850, loss[loss=0.1279, simple_loss=0.2061, pruned_loss=0.02483, over 4759.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.03439, over 972616.96 frames.], batch size: 16, lr: 2.34e-04
2022-05-06 13:24:06,289 INFO [train.py:715] (1/8) Epoch 9, batch 19900, loss[loss=0.1329, simple_loss=0.2165, pruned_loss=0.02461, over 4984.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03446, over 972164.73 frames.], batch size: 28, lr: 2.34e-04
2022-05-06 13:24:45,452 INFO [train.py:715] (1/8) Epoch 9, batch 19950, loss[loss=0.1381, simple_loss=0.2019, pruned_loss=0.03713, over 4784.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03496, over 972654.22 frames.], batch size: 14, lr: 2.34e-04
2022-05-06 13:25:24,504 INFO [train.py:715] (1/8) Epoch 9, batch 20000, loss[loss=0.1656, simple_loss=0.2287, pruned_loss=0.05132, over 4690.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03454, over 972092.87 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:26:02,949 INFO [train.py:715] (1/8) Epoch 9, batch 20050, loss[loss=0.1343, simple_loss=0.2037, pruned_loss=0.03246, over 4859.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2138, pruned_loss=0.0349, over 972760.70 frames.], batch size: 20, lr: 2.34e-04
2022-05-06 13:26:42,419 INFO [train.py:715] (1/8) Epoch 9, batch 20100, loss[loss=0.1256, simple_loss=0.1995, pruned_loss=0.02587, over 4980.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2131, pruned_loss=0.03476, over 973389.52 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:27:21,485 INFO [train.py:715] (1/8) Epoch 9, batch 20150, loss[loss=0.1272, simple_loss=0.2034, pruned_loss=0.02549, over 4928.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2133, pruned_loss=0.03449, over 973175.99 frames.], batch size: 23, lr: 2.34e-04
2022-05-06 13:27:59,969 INFO [train.py:715] (1/8) Epoch 9, batch 20200, loss[loss=0.1233, simple_loss=0.1946, pruned_loss=0.02603, over 4795.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2132, pruned_loss=0.03414, over 973281.59 frames.], batch size: 14, lr: 2.34e-04
2022-05-06 13:28:39,473 INFO [train.py:715] (1/8) Epoch 9, batch 20250, loss[loss=0.1633, simple_loss=0.2226, pruned_loss=0.05196, over 4950.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2141, pruned_loss=0.03438, over 974011.94 frames.], batch size: 35, lr: 2.34e-04
2022-05-06 13:29:18,327 INFO [train.py:715] (1/8) Epoch 9, batch 20300, loss[loss=0.1494, simple_loss=0.2266, pruned_loss=0.0361, over 4826.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03484, over 973658.73 frames.], batch size: 30, lr: 2.34e-04
2022-05-06 13:29:57,719 INFO [train.py:715] (1/8) Epoch 9, batch 20350, loss[loss=0.1184, simple_loss=0.1837, pruned_loss=0.02651, over 4791.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03456, over 973234.70 frames.], batch size: 14, lr: 2.34e-04
2022-05-06 13:30:37,200 INFO [train.py:715] (1/8) Epoch 9, batch 20400, loss[loss=0.1308, simple_loss=0.2037, pruned_loss=0.02897, over 4948.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2134, pruned_loss=0.03438, over 973233.72 frames.], batch size: 35, lr: 2.34e-04
2022-05-06 13:31:17,089 INFO [train.py:715] (1/8) Epoch 9, batch 20450, loss[loss=0.1262, simple_loss=0.1916, pruned_loss=0.0304, over 4840.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2133, pruned_loss=0.03462, over 973523.51 frames.], batch size: 13, lr: 2.34e-04
2022-05-06 13:31:56,597 INFO [train.py:715] (1/8) Epoch 9, batch 20500, loss[loss=0.1441, simple_loss=0.2225, pruned_loss=0.03284, over 4880.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2134, pruned_loss=0.03459, over 973470.68 frames.], batch size: 22, lr: 2.34e-04
2022-05-06 13:32:35,668 INFO [train.py:715] (1/8) Epoch 9, batch 20550, loss[loss=0.1262, simple_loss=0.2141, pruned_loss=0.01922, over 4864.00 frames.], tot_loss[loss=0.141, simple_loss=0.2133, pruned_loss=0.03434, over 973392.16 frames.], batch size: 20, lr: 2.34e-04
2022-05-06 13:33:14,860 INFO [train.py:715] (1/8) Epoch 9, batch 20600, loss[loss=0.1327, simple_loss=0.2037, pruned_loss=0.0309, over 4968.00 frames.], tot_loss[loss=0.1408, simple_loss=0.213, pruned_loss=0.03432, over 973705.54 frames.], batch size: 24, lr: 2.34e-04
2022-05-06 13:33:53,310 INFO [train.py:715] (1/8) Epoch 9, batch 20650, loss[loss=0.1866, simple_loss=0.2454, pruned_loss=0.06387, over 4975.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2128, pruned_loss=0.03426, over 973208.58 frames.], batch size: 35, lr: 2.34e-04
2022-05-06 13:34:32,412 INFO [train.py:715] (1/8) Epoch 9, batch 20700, loss[loss=0.1211, simple_loss=0.1911, pruned_loss=0.02555, over 4801.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2137, pruned_loss=0.03473, over 973516.41 frames.], batch size: 21, lr: 2.34e-04
2022-05-06 13:35:11,244 INFO [train.py:715] (1/8) Epoch 9, batch 20750, loss[loss=0.1806, simple_loss=0.2418, pruned_loss=0.05966, over 4815.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03494, over 973881.47 frames.], batch size: 21, lr: 2.34e-04
2022-05-06 13:35:50,837 INFO [train.py:715] (1/8) Epoch 9, batch 20800, loss[loss=0.1602, simple_loss=0.2335, pruned_loss=0.04346, over 4779.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03477, over 973144.25 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:36:30,208 INFO [train.py:715] (1/8) Epoch 9, batch 20850, loss[loss=0.124, simple_loss=0.2018, pruned_loss=0.02308, over 4901.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03458, over 972627.08 frames.], batch size: 23, lr: 2.34e-04
2022-05-06 13:37:09,646 INFO [train.py:715] (1/8) Epoch 9, batch 20900, loss[loss=0.1192, simple_loss=0.1904, pruned_loss=0.02398, over 4843.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.03426, over 973324.86 frames.], batch size: 30, lr: 2.34e-04
2022-05-06 13:37:49,141 INFO [train.py:715] (1/8) Epoch 9, batch 20950, loss[loss=0.1671, simple_loss=0.2413, pruned_loss=0.04643, over 4989.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.03466, over 972890.62 frames.], batch size: 25, lr: 2.34e-04
2022-05-06 13:38:28,444 INFO [train.py:715] (1/8) Epoch 9, batch 21000, loss[loss=0.1253, simple_loss=0.1953, pruned_loss=0.02767, over 4810.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2139, pruned_loss=0.03446, over 972806.77 frames.], batch size: 25, lr: 2.34e-04
2022-05-06 13:38:28,445 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 13:38:38,083 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.1069, simple_loss=0.1912, pruned_loss=0.01129, over 914524.00 frames.
2022-05-06 13:39:17,238 INFO [train.py:715] (1/8) Epoch 9, batch 21050, loss[loss=0.153, simple_loss=0.2271, pruned_loss=0.03948, over 4859.00 frames.], tot_loss[loss=0.141, simple_loss=0.2131, pruned_loss=0.03446, over 972413.87 frames.], batch size: 20, lr: 2.34e-04
2022-05-06 13:39:56,154 INFO [train.py:715] (1/8) Epoch 9, batch 21100, loss[loss=0.1131, simple_loss=0.1859, pruned_loss=0.02018, over 4637.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2138, pruned_loss=0.0347, over 972273.82 frames.], batch size: 13, lr: 2.34e-04
2022-05-06 13:40:35,520 INFO [train.py:715] (1/8) Epoch 9, batch 21150, loss[loss=0.162, simple_loss=0.2404, pruned_loss=0.0418, over 4860.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2129, pruned_loss=0.03413, over 972008.47 frames.], batch size: 20, lr: 2.34e-04
2022-05-06 13:41:14,528 INFO [train.py:715] (1/8) Epoch 9, batch 21200, loss[loss=0.1914, simple_loss=0.2488, pruned_loss=0.06701, over 4738.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2132, pruned_loss=0.03432, over 972134.74 frames.], batch size: 16, lr: 2.34e-04
2022-05-06 13:41:54,102 INFO [train.py:715] (1/8) Epoch 9, batch 21250, loss[loss=0.1361, simple_loss=0.2053, pruned_loss=0.03341, over 4983.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2138, pruned_loss=0.0343, over 972274.28 frames.], batch size: 31, lr: 2.34e-04
2022-05-06 13:42:32,486 INFO [train.py:715] (1/8) Epoch 9, batch 21300, loss[loss=0.1501, simple_loss=0.2193, pruned_loss=0.04046, over 4893.00 frames.], tot_loss[loss=0.1415, simple_loss=0.214, pruned_loss=0.03449, over 972197.92 frames.], batch size: 16, lr: 2.34e-04
2022-05-06 13:43:11,099 INFO [train.py:715] (1/8) Epoch 9, batch 21350, loss[loss=0.1618, simple_loss=0.2352, pruned_loss=0.04422, over 4841.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2137, pruned_loss=0.03396, over 972335.72 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:43:50,025 INFO [train.py:715] (1/8) Epoch 9, batch 21400, loss[loss=0.1266, simple_loss=0.2045, pruned_loss=0.02436, over 4928.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2138, pruned_loss=0.03404, over 972247.23 frames.], batch size: 29, lr: 2.34e-04
2022-05-06 13:44:28,769 INFO [train.py:715] (1/8) Epoch 9, batch 21450, loss[loss=0.15, simple_loss=0.2236, pruned_loss=0.03815, over 4802.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2149, pruned_loss=0.03442, over 972527.55 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:45:07,164 INFO [train.py:715] (1/8) Epoch 9, batch 21500, loss[loss=0.1266, simple_loss=0.1963, pruned_loss=0.0284, over 4819.00 frames.], tot_loss[loss=0.1422, simple_loss=0.215, pruned_loss=0.03475, over 972321.83 frames.], batch size: 12, lr: 2.34e-04
2022-05-06 13:45:46,283 INFO [train.py:715] (1/8) Epoch 9, batch 21550, loss[loss=0.1348, simple_loss=0.212, pruned_loss=0.02879, over 4975.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03451, over 973368.10 frames.], batch size: 25, lr: 2.34e-04
2022-05-06 13:46:24,998 INFO [train.py:715] (1/8) Epoch 9, batch 21600, loss[loss=0.1548, simple_loss=0.2327, pruned_loss=0.03844, over 4932.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2152, pruned_loss=0.03461, over 972261.58 frames.], batch size: 23, lr: 2.34e-04
2022-05-06 13:47:04,088 INFO [train.py:715] (1/8) Epoch 9, batch 21650, loss[loss=0.2324, simple_loss=0.2986, pruned_loss=0.08305, over 4736.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03554, over 972327.66 frames.], batch size: 16, lr: 2.34e-04
2022-05-06 13:47:43,362 INFO [train.py:715] (1/8) Epoch 9, batch 21700, loss[loss=0.1507, simple_loss=0.2211, pruned_loss=0.04015, over 4889.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2153, pruned_loss=0.03478, over 973279.82 frames.], batch size: 17, lr: 2.34e-04
2022-05-06 13:48:22,453 INFO [train.py:715] (1/8) Epoch 9, batch 21750, loss[loss=0.1551, simple_loss=0.2319, pruned_loss=0.03912, over 4927.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2152, pruned_loss=0.03476, over 973125.66 frames.], batch size: 23, lr: 2.34e-04
2022-05-06 13:49:01,560 INFO [train.py:715] (1/8) Epoch 9, batch 21800, loss[loss=0.128, simple_loss=0.2017, pruned_loss=0.02722, over 4900.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.03442, over 973325.35 frames.], batch size: 17, lr: 2.34e-04
2022-05-06 13:49:41,088 INFO [train.py:715] (1/8) Epoch 9, batch 21850, loss[loss=0.1425, simple_loss=0.2157, pruned_loss=0.03464, over 4801.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.03427, over 973535.74 frames.], batch size: 21, lr: 2.34e-04
2022-05-06 13:50:20,437 INFO [train.py:715] (1/8) Epoch 9, batch 21900, loss[loss=0.1415, simple_loss=0.213, pruned_loss=0.03501, over 4981.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2145, pruned_loss=0.0344, over 973077.57 frames.], batch size: 14, lr: 2.33e-04
2022-05-06 13:50:59,009 INFO [train.py:715] (1/8) Epoch 9, batch 21950, loss[loss=0.1226, simple_loss=0.1871, pruned_loss=0.02904, over 4755.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03384, over 971998.65 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 13:51:37,909 INFO [train.py:715] (1/8) Epoch 9, batch 22000, loss[loss=0.1395, simple_loss=0.2063, pruned_loss=0.03638, over 4972.00 frames.], tot_loss[loss=0.141, simple_loss=0.2136, pruned_loss=0.03422, over 971863.71 frames.], batch size: 14, lr: 2.33e-04
2022-05-06 13:52:16,811 INFO [train.py:715] (1/8) Epoch 9, batch 22050, loss[loss=0.1421, simple_loss=0.218, pruned_loss=0.03306, over 4967.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.03442, over 971966.82 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 13:52:56,513 INFO [train.py:715] (1/8) Epoch 9, batch 22100, loss[loss=0.1445, simple_loss=0.2102, pruned_loss=0.03938, over 4859.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2135, pruned_loss=0.03486, over 972249.07 frames.], batch size: 32, lr: 2.33e-04
2022-05-06 13:53:35,798 INFO [train.py:715] (1/8) Epoch 9, batch 22150, loss[loss=0.1558, simple_loss=0.2239, pruned_loss=0.04387, over 4930.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2134, pruned_loss=0.0347, over 972839.21 frames.], batch size: 39, lr: 2.33e-04
2022-05-06 13:54:14,967 INFO [train.py:715] (1/8) Epoch 9, batch 22200, loss[loss=0.1286, simple_loss=0.2099, pruned_loss=0.02367, over 4787.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2136, pruned_loss=0.03479, over 973728.41 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 13:54:54,446 INFO [train.py:715] (1/8) Epoch 9, batch 22250, loss[loss=0.1467, simple_loss=0.2305, pruned_loss=0.03143, over 4934.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2135, pruned_loss=0.0344, over 973820.84 frames.], batch size: 18, lr: 2.33e-04
2022-05-06 13:55:33,231 INFO [train.py:715] (1/8) Epoch 9, batch 22300, loss[loss=0.1546, simple_loss=0.2177, pruned_loss=0.0457, over 4843.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2132, pruned_loss=0.03406, over 973519.58 frames.], batch size: 30, lr: 2.33e-04
2022-05-06 13:56:11,828 INFO [train.py:715] (1/8) Epoch 9, batch 22350, loss[loss=0.1224, simple_loss=0.1958, pruned_loss=0.02451, over 4779.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03447, over 972304.69 frames.], batch size: 18, lr: 2.33e-04
2022-05-06 13:56:50,719 INFO [train.py:715] (1/8) Epoch 9, batch 22400, loss[loss=0.1382, simple_loss=0.2291, pruned_loss=0.02371, over 4754.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03457, over 972146.43 frames.], batch size: 19, lr: 2.33e-04
2022-05-06 13:57:29,425 INFO [train.py:715] (1/8) Epoch 9, batch 22450, loss[loss=0.1199, simple_loss=0.1945, pruned_loss=0.02261, over 4744.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03483, over 971651.67 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 13:58:08,125 INFO [train.py:715] (1/8) Epoch 9, batch 22500, loss[loss=0.1335, simple_loss=0.21, pruned_loss=0.02853, over 4973.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2144, pruned_loss=0.03473, over 971498.98 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 13:58:47,014 INFO [train.py:715] (1/8) Epoch 9, batch 22550, loss[loss=0.1356, simple_loss=0.2174, pruned_loss=0.02691, over 4880.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2139, pruned_loss=0.03465, over 971778.98 frames.], batch size: 22, lr: 2.33e-04
2022-05-06 13:59:26,034 INFO [train.py:715] (1/8) Epoch 9, batch 22600, loss[loss=0.1407, simple_loss=0.217, pruned_loss=0.0322, over 4890.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2144, pruned_loss=0.03494, over 972016.17 frames.], batch size: 22, lr: 2.33e-04
2022-05-06 14:00:05,203 INFO [train.py:715] (1/8) Epoch 9, batch 22650, loss[loss=0.16, simple_loss=0.2145, pruned_loss=0.05281, over 4958.00 frames.], tot_loss[loss=0.1415, simple_loss=0.214, pruned_loss=0.03453, over 971542.09 frames.], batch size: 14, lr: 2.33e-04
2022-05-06 14:00:44,240 INFO [train.py:715] (1/8) Epoch 9, batch 22700, loss[loss=0.1575, simple_loss=0.2272, pruned_loss=0.04395, over 4867.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2142, pruned_loss=0.03447, over 972287.11 frames.], batch size: 32, lr: 2.33e-04
2022-05-06 14:01:26,076 INFO [train.py:715] (1/8) Epoch 9, batch 22750, loss[loss=0.1775, simple_loss=0.2293, pruned_loss=0.06284, over 4864.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2138, pruned_loss=0.03448, over 972148.68 frames.], batch size: 13, lr: 2.33e-04
2022-05-06 14:02:04,850 INFO [train.py:715] (1/8) Epoch 9, batch 22800, loss[loss=0.1565, simple_loss=0.225, pruned_loss=0.04401, over 4989.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03382, over 972192.13 frames.], batch size: 28, lr: 2.33e-04
2022-05-06 14:02:44,150 INFO [train.py:715] (1/8) Epoch 9, batch 22850, loss[loss=0.1461, simple_loss=0.2273, pruned_loss=0.03239, over 4910.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03463, over 972591.70 frames.], batch size: 17, lr: 2.33e-04
2022-05-06 14:03:22,716 INFO [train.py:715] (1/8) Epoch 9, batch 22900, loss[loss=0.1355, simple_loss=0.2118, pruned_loss=0.02957, over 4976.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.03442, over 972310.74 frames.], batch size: 35, lr: 2.33e-04
2022-05-06 14:04:01,801 INFO [train.py:715] (1/8) Epoch 9, batch 22950, loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02981, over 4919.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03493, over 972275.18 frames.], batch size: 18, lr: 2.33e-04
2022-05-06 14:04:40,854 INFO [train.py:715] (1/8) Epoch 9, batch 23000, loss[loss=0.1132, simple_loss=0.1913, pruned_loss=0.01757, over 4782.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03488, over 971410.59 frames.], batch size: 17, lr: 2.33e-04
2022-05-06 14:05:20,246 INFO [train.py:715] (1/8) Epoch 9, batch 23050, loss[loss=0.131, simple_loss=0.2028, pruned_loss=0.02963, over 4903.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03538, over 971773.21 frames.], batch size: 39, lr: 2.33e-04
2022-05-06 14:05:59,520 INFO [train.py:715] (1/8) Epoch 9, batch 23100, loss[loss=0.1447, simple_loss=0.2188, pruned_loss=0.03524, over 4971.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03555, over 972193.69 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 14:06:38,544 INFO [train.py:715] (1/8) Epoch 9, batch 23150, loss[loss=0.1614, simple_loss=0.2237, pruned_loss=0.04954, over 4843.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03489, over 972257.27 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 14:07:18,157 INFO [train.py:715] (1/8) Epoch 9, batch 23200, loss[loss=0.1161, simple_loss=0.1853, pruned_loss=0.02343, over 4814.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03513, over 972617.81 frames.], batch size: 26, lr: 2.33e-04
2022-05-06 14:07:57,914 INFO [train.py:715] (1/8) Epoch 9, batch 23250, loss[loss=0.1196, simple_loss=0.1953, pruned_loss=0.022, over 4963.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03491, over 972998.99 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 14:08:37,685 INFO [train.py:715] (1/8) Epoch 9, batch 23300, loss[loss=0.1684, simple_loss=0.243, pruned_loss=0.04691, over 4872.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2151, pruned_loss=0.0348, over 973106.96 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:09:17,439 INFO [train.py:715] (1/8) Epoch 9, batch 23350, loss[loss=0.1221, simple_loss=0.2009, pruned_loss=0.02167, over 4762.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2141, pruned_loss=0.03435, over 973000.78 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:09:56,737 INFO [train.py:715] (1/8) Epoch 9, batch 23400, loss[loss=0.1682, simple_loss=0.2448, pruned_loss=0.0458, over 4918.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03463, over 973260.75 frames.], batch size: 18, lr: 2.33e-04
2022-05-06 14:10:35,590 INFO [train.py:715] (1/8) Epoch 9, batch 23450, loss[loss=0.1348, simple_loss=0.2075, pruned_loss=0.0311, over 4981.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.03519, over 972875.94 frames.], batch size: 14, lr: 2.33e-04
2022-05-06 14:11:14,353 INFO [train.py:715] (1/8) Epoch 9, batch 23500, loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.0294, over 4959.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2148, pruned_loss=0.0347, over 972863.00 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 14:11:52,878 INFO [train.py:715] (1/8) Epoch 9, batch 23550, loss[loss=0.1372, simple_loss=0.2152, pruned_loss=0.02962, over 4862.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03404, over 972249.70 frames.], batch size: 20, lr: 2.33e-04
2022-05-06 14:12:32,345 INFO [train.py:715] (1/8) Epoch 9, batch 23600, loss[loss=0.1174, simple_loss=0.1883, pruned_loss=0.02325, over 4794.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.0338, over 972396.26 frames.], batch size: 12, lr: 2.33e-04
2022-05-06 14:13:11,497 INFO [train.py:715] (1/8) Epoch 9, batch 23650, loss[loss=0.1221, simple_loss=0.1966, pruned_loss=0.02376, over 4808.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2137, pruned_loss=0.03359, over 971942.36 frames.], batch size: 25, lr: 2.33e-04
2022-05-06 14:13:50,876 INFO [train.py:715] (1/8) Epoch 9, batch 23700, loss[loss=0.1639, simple_loss=0.2245, pruned_loss=0.05163, over 4973.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03422, over 972728.77 frames.], batch size: 35, lr: 2.33e-04
2022-05-06 14:14:30,047 INFO [train.py:715] (1/8) Epoch 9, batch 23750, loss[loss=0.1486, simple_loss=0.2191, pruned_loss=0.03904, over 4901.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03485, over 972935.99 frames.], batch size: 17, lr: 2.33e-04
2022-05-06 14:15:09,284 INFO [train.py:715] (1/8) Epoch 9, batch 23800, loss[loss=0.1683, simple_loss=0.2446, pruned_loss=0.04603, over 4886.00 frames.], tot_loss[loss=0.1432, simple_loss=0.216, pruned_loss=0.03515, over 972982.36 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:15:48,389 INFO [train.py:715] (1/8) Epoch 9, batch 23850, loss[loss=0.1668, simple_loss=0.2408, pruned_loss=0.04644, over 4948.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2158, pruned_loss=0.03482, over 973931.35 frames.], batch size: 21, lr: 2.33e-04
2022-05-06 14:16:27,642 INFO [train.py:715] (1/8) Epoch 9, batch 23900, loss[loss=0.113, simple_loss=0.1877, pruned_loss=0.01912, over 4810.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2161, pruned_loss=0.03516, over 974479.06 frames.], batch size: 25, lr: 2.33e-04
2022-05-06 14:17:06,533 INFO [train.py:715] (1/8) Epoch 9, batch 23950, loss[loss=0.1176, simple_loss=0.1868, pruned_loss=0.02418, over 4794.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03512, over 973316.22 frames.], batch size: 12, lr: 2.33e-04
2022-05-06 14:17:45,501 INFO [train.py:715] (1/8) Epoch 9, batch 24000, loss[loss=0.1341, simple_loss=0.2128, pruned_loss=0.02768, over 4894.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03478, over 972831.60 frames.], batch size: 17, lr: 2.33e-04
2022-05-06 14:17:45,502 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 14:17:55,356 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.1069, simple_loss=0.1913, pruned_loss=0.01128, over 914524.00 frames.
2022-05-06 14:18:34,688 INFO [train.py:715] (1/8) Epoch 9, batch 24050, loss[loss=0.1126, simple_loss=0.1929, pruned_loss=0.01613, over 4777.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2149, pruned_loss=0.0348, over 973507.77 frames.], batch size: 18, lr: 2.33e-04
2022-05-06 14:19:14,960 INFO [train.py:715] (1/8) Epoch 9, batch 24100, loss[loss=0.1207, simple_loss=0.1928, pruned_loss=0.02432, over 4827.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03417, over 973203.99 frames.], batch size: 13, lr: 2.33e-04
2022-05-06 14:19:54,473 INFO [train.py:715] (1/8) Epoch 9, batch 24150, loss[loss=0.1527, simple_loss=0.2218, pruned_loss=0.04178, over 4895.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2142, pruned_loss=0.03445, over 973257.77 frames.], batch size: 22, lr: 2.33e-04
2022-05-06 14:20:33,557 INFO [train.py:715] (1/8) Epoch 9, batch 24200, loss[loss=0.1681, simple_loss=0.2361, pruned_loss=0.05007, over 4857.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03478, over 973696.61 frames.], batch size: 20, lr: 2.33e-04
2022-05-06 14:21:12,481 INFO [train.py:715] (1/8) Epoch 9, batch 24250, loss[loss=0.1368, simple_loss=0.2041, pruned_loss=0.0347, over 4789.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2149, pruned_loss=0.0346, over 973006.59 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 14:21:52,134 INFO [train.py:715] (1/8) Epoch 9, batch 24300, loss[loss=0.1337, simple_loss=0.2232, pruned_loss=0.02213, over 4864.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03472, over 972813.18 frames.], batch size: 13, lr: 2.33e-04
2022-05-06 14:22:31,315 INFO [train.py:715] (1/8) Epoch 9, batch 24350, loss[loss=0.1676, simple_loss=0.2396, pruned_loss=0.04786, over 4835.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03497, over 973246.13 frames.], batch size: 13, lr: 2.33e-04
2022-05-06 14:23:10,724 INFO [train.py:715] (1/8) Epoch 9, batch 24400, loss[loss=0.142, simple_loss=0.2061, pruned_loss=0.03888, over 4776.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2145, pruned_loss=0.03509, over 973741.18 frames.], batch size: 17, lr: 2.33e-04
2022-05-06 14:23:50,611 INFO [train.py:715] (1/8) Epoch 9, batch 24450, loss[loss=0.114, simple_loss=0.188, pruned_loss=0.02002, over 4760.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03471, over 972824.77 frames.], batch size: 18, lr: 2.33e-04
2022-05-06 14:24:30,637 INFO [train.py:715] (1/8) Epoch 9, batch 24500, loss[loss=0.121, simple_loss=0.1839, pruned_loss=0.02901, over 4731.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03484, over 972905.10 frames.], batch size: 12, lr: 2.33e-04
2022-05-06 14:25:10,996 INFO [train.py:715] (1/8) Epoch 9, batch 24550, loss[loss=0.1122, simple_loss=0.1879, pruned_loss=0.01824, over 4987.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03451, over 972711.14 frames.], batch size: 25, lr: 2.33e-04
2022-05-06 14:25:50,742 INFO [train.py:715] (1/8) Epoch 9, batch 24600, loss[loss=0.1365, simple_loss=0.2194, pruned_loss=0.02683, over 4931.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2134, pruned_loss=0.03447, over 973328.05 frames.], batch size: 39, lr: 2.33e-04
2022-05-06 14:26:30,712 INFO [train.py:715] (1/8) Epoch 9, batch 24650, loss[loss=0.1226, simple_loss=0.2067, pruned_loss=0.01925, over 4759.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2132, pruned_loss=0.03421, over 972084.52 frames.], batch size: 19, lr: 2.33e-04
2022-05-06 14:27:09,790 INFO [train.py:715] (1/8) Epoch 9, batch 24700, loss[loss=0.1264, simple_loss=0.2005, pruned_loss=0.02615, over 4982.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.03343, over 972129.22 frames.], batch size: 25, lr: 2.33e-04
2022-05-06 14:27:48,505 INFO [train.py:715] (1/8) Epoch 9, batch 24750, loss[loss=0.1245, simple_loss=0.2024, pruned_loss=0.02327, over 4759.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2132, pruned_loss=0.03348, over 972052.00 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:28:28,023 INFO [train.py:715] (1/8) Epoch 9, batch 24800, loss[loss=0.147, simple_loss=0.2198, pruned_loss=0.03712, over 4876.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2131, pruned_loss=0.03392, over 972225.06 frames.], batch size: 38, lr: 2.32e-04
2022-05-06 14:29:07,568 INFO [train.py:715] (1/8) Epoch 9, batch 24850, loss[loss=0.144, simple_loss=0.2171, pruned_loss=0.03548, over 4884.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2129, pruned_loss=0.03391, over 972667.80 frames.], batch size: 22, lr: 2.32e-04
2022-05-06 14:29:46,969 INFO [train.py:715] (1/8) Epoch 9, batch 24900, loss[loss=0.1193, simple_loss=0.2006, pruned_loss=0.01895, over 4820.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2131, pruned_loss=0.03405, over 972839.60 frames.], batch size: 27, lr: 2.32e-04
2022-05-06 14:30:26,385 INFO [train.py:715] (1/8) Epoch 9, batch 24950, loss[loss=0.1357, simple_loss=0.2122, pruned_loss=0.02961, over 4893.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03409, over 973121.02 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 14:31:06,082 INFO [train.py:715] (1/8) Epoch 9, batch 25000, loss[loss=0.1266, simple_loss=0.201, pruned_loss=0.0261, over 4985.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2135, pruned_loss=0.03367, over 972809.57 frames.], batch size: 26, lr: 2.32e-04
2022-05-06 14:31:44,918 INFO [train.py:715] (1/8) Epoch 9, batch 25050, loss[loss=0.1395, simple_loss=0.2039, pruned_loss=0.03751, over 4898.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2149, pruned_loss=0.03464, over 972941.02 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 14:32:24,414 INFO [train.py:715] (1/8) Epoch 9, batch 25100, loss[loss=0.1259, simple_loss=0.1986, pruned_loss=0.02664, over 4891.00 frames.], tot_loss[loss=0.1421, simple_loss=0.215, pruned_loss=0.03461, over 973456.79 frames.], batch size: 19, lr: 2.32e-04
2022-05-06 14:33:03,521 INFO [train.py:715] (1/8) Epoch 9, batch 25150, loss[loss=0.1276, simple_loss=0.2014, pruned_loss=0.02691, over 4831.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2153, pruned_loss=0.03458, over 974118.53 frames.], batch size: 26, lr: 2.32e-04
2022-05-06 14:33:42,579 INFO [train.py:715] (1/8) Epoch 9, batch 25200, loss[loss=0.1591, simple_loss=0.2371, pruned_loss=0.04051, over 4735.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2149, pruned_loss=0.03446, over 973943.91 frames.], batch size: 16, lr: 2.32e-04
2022-05-06 14:34:21,837 INFO [train.py:715] (1/8) Epoch 9, batch 25250, loss[loss=0.1151, simple_loss=0.1804, pruned_loss=0.02494, over 4781.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2145, pruned_loss=0.03419, over 972715.37 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 14:35:00,581 INFO [train.py:715] (1/8) Epoch 9, batch 25300, loss[loss=0.1232, simple_loss=0.1991, pruned_loss=0.02368, over 4798.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2151, pruned_loss=0.03459, over 972652.41 frames.], batch size: 24, lr: 2.32e-04
2022-05-06 14:35:40,267 INFO [train.py:715] (1/8) Epoch 9, batch 25350, loss[loss=0.1225, simple_loss=0.1903, pruned_loss=0.02737, over 4797.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03503, over 972019.21 frames.], batch size: 14, lr: 2.32e-04
2022-05-06 14:36:20,104 INFO [train.py:715] (1/8) Epoch 9, batch 25400, loss[loss=0.1297, simple_loss=0.2026, pruned_loss=0.02843, over 4921.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03476, over 972743.37 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 14:37:00,342 INFO [train.py:715] (1/8) Epoch 9, batch 25450, loss[loss=0.1428, simple_loss=0.2145, pruned_loss=0.03553, over 4936.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03534, over 973270.61 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 14:37:38,910 INFO [train.py:715] (1/8) Epoch 9, batch 25500, loss[loss=0.1475, simple_loss=0.2254, pruned_loss=0.03481, over 4825.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2157, pruned_loss=0.03507, over 973255.35 frames.], batch size: 26, lr: 2.32e-04
2022-05-06 14:38:18,070 INFO [train.py:715] (1/8) Epoch 9, batch 25550, loss[loss=0.1286, simple_loss=0.2001, pruned_loss=0.0285, over 4855.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2151, pruned_loss=0.03508, over 974083.79 frames.], batch size: 16, lr: 2.32e-04
2022-05-06 14:38:57,224 INFO [train.py:715] (1/8) Epoch 9, batch 25600, loss[loss=0.1629, simple_loss=0.2292, pruned_loss=0.04833, over 4925.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.03425, over 973238.02 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 14:39:36,161 INFO [train.py:715] (1/8) Epoch 9, batch 25650, loss[loss=0.1324, simple_loss=0.208, pruned_loss=0.02843, over 4924.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03367, over 973418.85 frames.], batch size: 23, lr: 2.32e-04
2022-05-06 14:40:15,294 INFO [train.py:715] (1/8) Epoch 9, batch 25700, loss[loss=0.1272, simple_loss=0.2096, pruned_loss=0.02236, over 4835.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2131, pruned_loss=0.03406, over 973144.09 frames.], batch size: 27, lr: 2.32e-04
2022-05-06 14:40:54,414 INFO [train.py:715] (1/8) Epoch 9, batch 25750, loss[loss=0.1174, simple_loss=0.1923, pruned_loss=0.02129, over 4937.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2133, pruned_loss=0.03419, over 973143.09 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 14:41:33,408 INFO [train.py:715] (1/8) Epoch 9, batch 25800, loss[loss=0.1136, simple_loss=0.179, pruned_loss=0.02406, over 4782.00 frames.], tot_loss[loss=0.1407, simple_loss=0.213, pruned_loss=0.03425, over 973221.27 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 14:42:13,625 INFO [train.py:715] (1/8) Epoch 9, batch 25850, loss[loss=0.1545, simple_loss=0.2308, pruned_loss=0.03908, over 4912.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.03439, over 973225.40 frames.], batch size: 18, lr: 2.32e-04
2022-05-06 14:42:53,070 INFO [train.py:715] (1/8) Epoch 9, batch 25900, loss[loss=0.1299, simple_loss=0.2009, pruned_loss=0.02947, over 4954.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2133, pruned_loss=0.0341, over 973035.18 frames.], batch size: 35, lr: 2.32e-04
2022-05-06 14:43:32,745 INFO [train.py:715] (1/8) Epoch 9, batch 25950, loss[loss=0.1373, simple_loss=0.2155, pruned_loss=0.02953, over 4922.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2135, pruned_loss=0.03456, over 973286.35 frames.], batch size: 23, lr: 2.32e-04
2022-05-06 14:44:11,975 INFO [train.py:715] (1/8) Epoch 9, batch 26000, loss[loss=0.1501, simple_loss=0.2164, pruned_loss=0.04192, over 4812.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2142, pruned_loss=0.03525, over 973476.33 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:44:51,304 INFO [train.py:715] (1/8) Epoch 9, batch 26050, loss[loss=0.1464, simple_loss=0.2179, pruned_loss=0.03748, over 4711.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2146, pruned_loss=0.03548, over 972546.44 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 14:45:30,098 INFO [train.py:715] (1/8) Epoch 9, batch 26100, loss[loss=0.1262, simple_loss=0.206, pruned_loss=0.0232, over 4810.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2145, pruned_loss=0.035, over 972876.71 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:46:09,803 INFO [train.py:715] (1/8) Epoch 9, batch 26150, loss[loss=0.1129, simple_loss=0.1797, pruned_loss=0.02309, over 4777.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2141, pruned_loss=0.03478, over 972334.00 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 14:46:50,050 INFO [train.py:715] (1/8) Epoch 9, batch 26200, loss[loss=0.1192, simple_loss=0.1902, pruned_loss=0.02411, over 4957.00 frames.], tot_loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03436, over 971991.26 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 14:47:29,915 INFO [train.py:715] (1/8) Epoch 9, batch 26250, loss[loss=0.15, simple_loss=0.2212, pruned_loss=0.03938, over 4768.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2131, pruned_loss=0.03389, over 971126.04 frames.], batch size: 18, lr: 2.32e-04
2022-05-06 14:48:09,835 INFO [train.py:715] (1/8) Epoch 9, batch 26300, loss[loss=0.1597, simple_loss=0.242, pruned_loss=0.03868, over 4889.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03327, over 970771.41 frames.], batch size: 19, lr: 2.32e-04
2022-05-06 14:48:49,364 INFO [train.py:715] (1/8) Epoch 9, batch 26350, loss[loss=0.1475, simple_loss=0.2223, pruned_loss=0.03632, over 4818.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2135, pruned_loss=0.03356, over 970867.19 frames.], batch size: 25, lr: 2.32e-04
2022-05-06 14:49:28,721 INFO [train.py:715] (1/8) Epoch 9, batch 26400, loss[loss=0.1206, simple_loss=0.1941, pruned_loss=0.02356, over 4902.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03325, over 971218.52 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 14:50:07,638 INFO [train.py:715] (1/8) Epoch 9, batch 26450, loss[loss=0.1604, simple_loss=0.2325, pruned_loss=0.04419, over 4928.00 frames.], tot_loss[loss=0.1396, simple_loss=0.213, pruned_loss=0.03312, over 971130.66 frames.], batch size: 39, lr: 2.32e-04
2022-05-06 14:50:46,954 INFO [train.py:715] (1/8) Epoch 9, batch 26500, loss[loss=0.1477, simple_loss=0.2062, pruned_loss=0.04456, over 4792.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03343, over 971323.58 frames.], batch size: 14, lr: 2.32e-04
2022-05-06 14:51:26,795 INFO [train.py:715] (1/8) Epoch 9, batch 26550, loss[loss=0.1269, simple_loss=0.2107, pruned_loss=0.02157, over 4924.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03385, over 972044.87 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:52:06,133 INFO [train.py:715] (1/8) Epoch 9, batch 26600, loss[loss=0.1582, simple_loss=0.2424, pruned_loss=0.03693, over 4747.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.03389, over 972064.89 frames.], batch size: 19, lr: 2.32e-04
2022-05-06 14:52:46,086 INFO [train.py:715] (1/8) Epoch 9, batch 26650, loss[loss=0.1352, simple_loss=0.2073, pruned_loss=0.03154, over 4847.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2143, pruned_loss=0.03431, over 972550.11 frames.], batch size: 20, lr: 2.32e-04
2022-05-06 14:53:25,378 INFO [train.py:715] (1/8) Epoch 9, batch 26700, loss[loss=0.1376, simple_loss=0.2033, pruned_loss=0.03594, over 4837.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2133, pruned_loss=0.03422, over 972782.57 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 14:54:04,741 INFO [train.py:715] (1/8) Epoch 9, batch 26750, loss[loss=0.1405, simple_loss=0.2232, pruned_loss=0.02883, over 4843.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03455, over 972327.44 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 14:54:43,924 INFO [train.py:715] (1/8) Epoch 9, batch 26800, loss[loss=0.1225, simple_loss=0.1951, pruned_loss=0.02494, over 4786.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2153, pruned_loss=0.03476, over 973192.01 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 14:55:22,919 INFO [train.py:715] (1/8) Epoch 9, batch 26850, loss[loss=0.1237, simple_loss=0.2017, pruned_loss=0.02287, over 4976.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03449, over 972649.52 frames.], batch size: 28, lr: 2.32e-04
2022-05-06 14:56:02,399 INFO [train.py:715] (1/8) Epoch 9, batch 26900, loss[loss=0.1304, simple_loss=0.2125, pruned_loss=0.02415, over 4789.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03469, over 972212.79 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 14:56:42,225 INFO [train.py:715] (1/8) Epoch 9, batch 26950, loss[loss=0.1501, simple_loss=0.2198, pruned_loss=0.04019, over 4787.00 frames.], tot_loss[loss=0.1424, simple_loss=0.215, pruned_loss=0.03493, over 972831.59 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 14:57:21,393 INFO [train.py:715] (1/8) Epoch 9, batch 27000, loss[loss=0.1344, simple_loss=0.2069, pruned_loss=0.03095, over 4963.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03449, over 972713.45 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 14:57:21,394 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 14:57:30,963 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.1068, simple_loss=0.1912, pruned_loss=0.01121, over 914524.00 frames.
2022-05-06 14:58:10,505 INFO [train.py:715] (1/8) Epoch 9, batch 27050, loss[loss=0.1424, simple_loss=0.2209, pruned_loss=0.03191, over 4874.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2142, pruned_loss=0.03375, over 972243.58 frames.], batch size: 16, lr: 2.32e-04
2022-05-06 14:58:50,063 INFO [train.py:715] (1/8) Epoch 9, batch 27100, loss[loss=0.1692, simple_loss=0.2443, pruned_loss=0.047, over 4918.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2136, pruned_loss=0.03356, over 973116.93 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 14:59:30,120 INFO [train.py:715] (1/8) Epoch 9, batch 27150, loss[loss=0.1467, simple_loss=0.2188, pruned_loss=0.0373, over 4808.00 frames.], tot_loss[loss=0.141, simple_loss=0.2144, pruned_loss=0.03378, over 973316.10 frames.], batch size: 25, lr: 2.32e-04
2022-05-06 15:00:09,246 INFO [train.py:715] (1/8) Epoch 9, batch 27200, loss[loss=0.1477, simple_loss=0.2163, pruned_loss=0.03956, over 4917.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.0343, over 973194.09 frames.], batch size: 18, lr: 2.32e-04
2022-05-06 15:00:48,162 INFO [train.py:715] (1/8) Epoch 9, batch 27250, loss[loss=0.1207, simple_loss=0.196, pruned_loss=0.02266, over 4783.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2132, pruned_loss=0.034, over 973145.15 frames.], batch size: 14, lr: 2.32e-04
2022-05-06 15:01:27,380 INFO [train.py:715] (1/8) Epoch 9, batch 27300, loss[loss=0.1282, simple_loss=0.2017, pruned_loss=0.02737, over 4804.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.03401, over 972990.98 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 15:02:06,269 INFO [train.py:715] (1/8) Epoch 9, batch 27350, loss[loss=0.1348, simple_loss=0.2108, pruned_loss=0.02943, over 4981.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03385, over 973069.07 frames.], batch size: 25, lr: 2.32e-04
2022-05-06 15:02:45,300 INFO [train.py:715] (1/8) Epoch 9, batch 27400, loss[loss=0.144, simple_loss=0.2246, pruned_loss=0.03168, over 4892.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03401, over 973160.20 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 15:03:24,465 INFO [train.py:715] (1/8) Epoch 9, batch 27450, loss[loss=0.1587, simple_loss=0.2277, pruned_loss=0.04482, over 4698.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2141, pruned_loss=0.03472, over 973296.13 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 15:04:03,431 INFO [train.py:715] (1/8) Epoch 9, batch 27500, loss[loss=0.1482, simple_loss=0.2216, pruned_loss=0.03741, over 4950.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03492, over 973134.20 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 15:04:42,461 INFO [train.py:715] (1/8) Epoch 9, batch 27550, loss[loss=0.1645, simple_loss=0.2438, pruned_loss=0.04264, over 4883.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2144, pruned_loss=0.03438, over 972987.89 frames.], batch size: 22, lr: 2.32e-04
2022-05-06 15:05:21,386 INFO [train.py:715] (1/8) Epoch 9, batch 27600, loss[loss=0.1123, simple_loss=0.1856, pruned_loss=0.0195, over 4828.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03451, over 973498.38 frames.], batch size: 13, lr: 2.32e-04
2022-05-06 15:06:00,160 INFO [train.py:715] (1/8) Epoch 9, batch 27650, loss[loss=0.1545, simple_loss=0.2269, pruned_loss=0.041, over 4756.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.0347, over 973483.63 frames.], batch size: 19, lr: 2.32e-04
2022-05-06 15:06:39,015 INFO [train.py:715] (1/8) Epoch 9, batch 27700, loss[loss=0.176, simple_loss=0.2491, pruned_loss=0.0515, over 4943.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03506, over 973290.78 frames.], batch size: 23, lr: 2.32e-04
2022-05-06 15:07:18,262 INFO [train.py:715] (1/8) Epoch 9, batch 27750, loss[loss=0.1318, simple_loss=0.2047, pruned_loss=0.02951, over 4815.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2138, pruned_loss=0.0345, over 973325.48 frames.], batch size: 27, lr: 2.31e-04
2022-05-06 15:07:57,613 INFO [train.py:715] (1/8) Epoch 9, batch 27800, loss[loss=0.1696, simple_loss=0.2498, pruned_loss=0.04466, over 4906.00 frames.], tot_loss[loss=0.142, simple_loss=0.2142, pruned_loss=0.03488, over 972631.74 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:08:36,546 INFO [train.py:715] (1/8) Epoch 9, batch 27850, loss[loss=0.169, simple_loss=0.2421, pruned_loss=0.04796, over 4981.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03481, over 972794.69 frames.], batch size: 39, lr: 2.31e-04
2022-05-06 15:09:16,411 INFO [train.py:715] (1/8) Epoch 9, batch 27900, loss[loss=0.1424, simple_loss=0.2134, pruned_loss=0.03571, over 4957.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03487, over 972612.87 frames.], batch size: 21, lr: 2.31e-04
2022-05-06 15:09:54,910 INFO [train.py:715] (1/8) Epoch 9, batch 27950, loss[loss=0.1735, simple_loss=0.247, pruned_loss=0.04999, over 4879.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03466, over 973067.15 frames.], batch size: 16, lr: 2.31e-04
2022-05-06 15:10:34,264 INFO [train.py:715] (1/8) Epoch 9, batch 28000, loss[loss=0.1234, simple_loss=0.2025, pruned_loss=0.02221, over 4959.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.03444, over 974146.72 frames.], batch size: 24, lr: 2.31e-04
2022-05-06 15:11:13,570 INFO [train.py:715] (1/8) Epoch 9, batch 28050, loss[loss=0.1632, simple_loss=0.2398, pruned_loss=0.04325, over 4810.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2134, pruned_loss=0.03438, over 974162.93 frames.], batch size: 21, lr: 2.31e-04
2022-05-06 15:11:52,639 INFO [train.py:715] (1/8) Epoch 9, batch 28100, loss[loss=0.1212, simple_loss=0.1936, pruned_loss=0.02434, over 4930.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2125, pruned_loss=0.034, over 974284.32 frames.], batch size: 21, lr: 2.31e-04
2022-05-06 15:12:31,905 INFO [train.py:715] (1/8) Epoch 9, batch 28150, loss[loss=0.1319, simple_loss=0.2139, pruned_loss=0.02498, over 4974.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2126, pruned_loss=0.034, over 974270.00 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:13:10,814 INFO [train.py:715] (1/8) Epoch 9, batch 28200, loss[loss=0.1436, simple_loss=0.2199, pruned_loss=0.03364, over 4935.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2132, pruned_loss=0.03436, over 973357.77 frames.], batch size: 23, lr: 2.31e-04
2022-05-06 15:13:50,245 INFO [train.py:715] (1/8) Epoch 9, batch 28250, loss[loss=0.136, simple_loss=0.2139, pruned_loss=0.02905, over 4818.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2141, pruned_loss=0.03432, over 973045.12 frames.], batch size: 13, lr: 2.31e-04
2022-05-06 15:14:28,525 INFO [train.py:715] (1/8) Epoch 9, batch 28300, loss[loss=0.1338, simple_loss=0.206, pruned_loss=0.03082, over 4844.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.03439, over 972207.91 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:15:07,473 INFO [train.py:715] (1/8) Epoch 9, batch 28350, loss[loss=0.1244, simple_loss=0.2037, pruned_loss=0.02254, over 4989.00 frames.], tot_loss[loss=0.142, simple_loss=0.215, pruned_loss=0.03449, over 973013.35 frames.], batch size: 25, lr: 2.31e-04
2022-05-06 15:15:46,872 INFO [train.py:715] (1/8) Epoch 9, batch 28400, loss[loss=0.1414, simple_loss=0.2206, pruned_loss=0.03103, over 4903.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.0345, over 973260.08 frames.], batch size: 17, lr: 2.31e-04
2022-05-06 15:16:25,950 INFO [train.py:715] (1/8) Epoch 9, batch 28450, loss[loss=0.1306, simple_loss=0.2095, pruned_loss=0.02581, over 4749.00 frames.], tot_loss[loss=0.141, simple_loss=0.2133, pruned_loss=0.03431, over 972743.87 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:17:04,385 INFO [train.py:715] (1/8) Epoch 9, batch 28500, loss[loss=0.1198, simple_loss=0.1965, pruned_loss=0.02156, over 4827.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2128, pruned_loss=0.03405, over 971816.38 frames.], batch size: 26, lr: 2.31e-04
2022-05-06 15:17:43,519 INFO [train.py:715] (1/8) Epoch 9, batch 28550, loss[loss=0.1533, simple_loss=0.2309, pruned_loss=0.03786, over 4984.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2132, pruned_loss=0.03403, over 972044.44 frames.], batch size: 25, lr: 2.31e-04
2022-05-06 15:18:22,912 INFO [train.py:715] (1/8) Epoch 9, batch 28600, loss[loss=0.1481, simple_loss=0.224, pruned_loss=0.03614, over 4856.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03407, over 971026.45 frames.], batch size: 20, lr: 2.31e-04
2022-05-06 15:19:01,329 INFO [train.py:715] (1/8) Epoch 9, batch 28650, loss[loss=0.1236, simple_loss=0.2024, pruned_loss=0.02244, over 4821.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03391, over 971137.87 frames.], batch size: 25, lr: 2.31e-04
2022-05-06 15:19:40,169 INFO [train.py:715] (1/8) Epoch 9, batch 28700, loss[loss=0.1491, simple_loss=0.2185, pruned_loss=0.03989, over 4958.00 frames.], tot_loss[loss=0.1419, simple_loss=0.214, pruned_loss=0.03485, over 971122.89 frames.], batch size: 24, lr: 2.31e-04
2022-05-06 15:20:19,621 INFO [train.py:715] (1/8) Epoch 9, batch 28750, loss[loss=0.1361, simple_loss=0.2163, pruned_loss=0.02794, over 4774.00 frames.], tot_loss[loss=0.142, simple_loss=0.2142, pruned_loss=0.03493, over 970839.61 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:20:58,321 INFO [train.py:715] (1/8) Epoch 9, batch 28800, loss[loss=0.1499, simple_loss=0.2372, pruned_loss=0.03129, over 4777.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2135, pruned_loss=0.03449, over 971387.95 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:21:36,721 INFO [train.py:715] (1/8) Epoch 9, batch 28850, loss[loss=0.1424, simple_loss=0.2106, pruned_loss=0.03708, over 4810.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03448, over 971819.16 frames.], batch size: 21, lr: 2.31e-04
2022-05-06 15:22:16,101 INFO [train.py:715] (1/8) Epoch 9, batch 28900, loss[loss=0.1342, simple_loss=0.2088, pruned_loss=0.02978, over 4882.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.03421, over 972405.74 frames.], batch size: 39, lr: 2.31e-04
2022-05-06 15:22:55,365 INFO [train.py:715] (1/8) Epoch 9, batch 28950, loss[loss=0.1423, simple_loss=0.2162, pruned_loss=0.03421, over 4849.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.0345, over 973605.59 frames.], batch size: 32, lr: 2.31e-04
2022-05-06 15:23:33,680 INFO [train.py:715] (1/8) Epoch 9, batch 29000, loss[loss=0.1342, simple_loss=0.2123, pruned_loss=0.02807, over 4874.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.03396, over 973450.01 frames.], batch size: 22, lr: 2.31e-04
2022-05-06 15:24:12,156 INFO [train.py:715] (1/8) Epoch 9, batch 29050, loss[loss=0.1098, simple_loss=0.1847, pruned_loss=0.01743, over 4811.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2139, pruned_loss=0.03389, over 973663.93 frames.], batch size: 26, lr: 2.31e-04
2022-05-06 15:24:51,097 INFO [train.py:715] (1/8) Epoch 9, batch 29100, loss[loss=0.1327, simple_loss=0.2176, pruned_loss=0.02392, over 4688.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2148, pruned_loss=0.03444, over 972691.30 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:25:30,246 INFO [train.py:715] (1/8) Epoch 9, batch 29150, loss[loss=0.1274, simple_loss=0.1901, pruned_loss=0.0323, over 4773.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2155, pruned_loss=0.035, over 972171.05 frames.], batch size: 17, lr: 2.31e-04
2022-05-06 15:26:09,094 INFO [train.py:715] (1/8) Epoch 9, batch 29200, loss[loss=0.1754, simple_loss=0.2349, pruned_loss=0.05792, over 4960.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03535, over 971906.36 frames.], batch size: 35, lr: 2.31e-04
2022-05-06 15:26:48,459 INFO [train.py:715] (1/8) Epoch 9, batch 29250, loss[loss=0.1123, simple_loss=0.1913, pruned_loss=0.01666, over 4834.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.03519, over 971691.36 frames.], batch size: 25, lr: 2.31e-04
2022-05-06 15:27:27,198 INFO [train.py:715] (1/8) Epoch 9, batch 29300, loss[loss=0.1085, simple_loss=0.1797, pruned_loss=0.0187, over 4800.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03498, over 971939.55 frames.], batch size: 12, lr: 2.31e-04
2022-05-06 15:28:06,265 INFO [train.py:715] (1/8) Epoch 9, batch 29350, loss[loss=0.1299, simple_loss=0.2007, pruned_loss=0.0295, over 4831.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03473, over 971481.75 frames.], batch size: 13, lr: 2.31e-04
2022-05-06 15:28:45,210 INFO [train.py:715] (1/8) Epoch 9, batch 29400, loss[loss=0.1208, simple_loss=0.1942, pruned_loss=0.02374, over 4815.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.03436, over 971782.78 frames.], batch size: 27, lr: 2.31e-04
2022-05-06 15:29:23,945 INFO [train.py:715] (1/8) Epoch 9, batch 29450, loss[loss=0.1192, simple_loss=0.1968, pruned_loss=0.02081, over 4972.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2148, pruned_loss=0.0347, over 971899.01 frames.], batch size: 35, lr: 2.31e-04
2022-05-06 15:30:02,403 INFO [train.py:715] (1/8) Epoch 9, batch 29500, loss[loss=0.1353, simple_loss=0.2048, pruned_loss=0.03292, over 4689.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03448, over 971300.10 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:30:41,335 INFO [train.py:715] (1/8) Epoch 9, batch 29550, loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03245, over 4927.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2143, pruned_loss=0.03471, over 971899.60 frames.], batch size: 23, lr: 2.31e-04
2022-05-06 15:31:20,275 INFO [train.py:715] (1/8) Epoch 9, batch 29600, loss[loss=0.1549, simple_loss=0.2236, pruned_loss=0.04305, over 4906.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2148, pruned_loss=0.03527, over 971821.65 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:31:59,539 INFO [train.py:715] (1/8) Epoch 9, batch 29650, loss[loss=0.1562, simple_loss=0.233, pruned_loss=0.03972, over 4749.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2141, pruned_loss=0.0348, over 972365.05 frames.], batch size: 16, lr: 2.31e-04
2022-05-06 15:32:39,144 INFO [train.py:715] (1/8) Epoch 9, batch 29700, loss[loss=0.1608, simple_loss=0.2493, pruned_loss=0.03615, over 4734.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03479, over 972520.22 frames.], batch size: 16, lr: 2.31e-04
2022-05-06 15:33:17,089 INFO [train.py:715] (1/8) Epoch 9, batch 29750, loss[loss=0.1332, simple_loss=0.2039, pruned_loss=0.03126, over 4734.00 frames.], tot_loss[loss=0.142, simple_loss=0.2144, pruned_loss=0.03479, over 972447.38 frames.], batch size: 12, lr: 2.31e-04
2022-05-06 15:33:55,770 INFO [train.py:715] (1/8) Epoch 9, batch 29800, loss[loss=0.1596, simple_loss=0.2348, pruned_loss=0.04225, over 4955.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.03519, over 971682.99 frames.], batch size: 21, lr: 2.31e-04
2022-05-06 15:34:34,893 INFO [train.py:715] (1/8) Epoch 9, batch 29850, loss[loss=0.1339, simple_loss=0.1958, pruned_loss=0.03603, over 4823.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03508, over 972158.23 frames.], batch size: 13, lr: 2.31e-04
2022-05-06 15:35:13,058 INFO [train.py:715] (1/8) Epoch 9, batch 29900, loss[loss=0.1515, simple_loss=0.2154, pruned_loss=0.04378, over 4786.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03514, over 972201.08 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:35:52,534 INFO [train.py:715] (1/8) Epoch 9, batch 29950, loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03503, over 4966.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03515, over 972948.78 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:36:31,405 INFO [train.py:715] (1/8) Epoch 9, batch 30000, loss[loss=0.1588, simple_loss=0.227, pruned_loss=0.04528, over 4862.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2139, pruned_loss=0.03462, over 973144.32 frames.], batch size: 30, lr: 2.31e-04
2022-05-06 15:36:31,406 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 15:36:40,919 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.1068, simple_loss=0.1911, pruned_loss=0.01124, over 914524.00 frames.
2022-05-06 15:37:20,162 INFO [train.py:715] (1/8) Epoch 9, batch 30050, loss[loss=0.1478, simple_loss=0.2257, pruned_loss=0.03491, over 4975.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.03527, over 973108.00 frames.], batch size: 25, lr: 2.31e-04
2022-05-06 15:37:58,805 INFO [train.py:715] (1/8) Epoch 9, batch 30100, loss[loss=0.1535, simple_loss=0.2281, pruned_loss=0.03948, over 4766.00 frames.], tot_loss[loss=0.144, simple_loss=0.2162, pruned_loss=0.03593, over 972864.17 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:38:38,124 INFO [train.py:715] (1/8) Epoch 9, batch 30150, loss[loss=0.1221, simple_loss=0.1835, pruned_loss=0.03039, over 4966.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.03552, over 973000.29 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:39:17,503 INFO [train.py:715] (1/8) Epoch 9, batch 30200, loss[loss=0.1515, simple_loss=0.2252, pruned_loss=0.03897, over 4909.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.0351, over 973083.16 frames.], batch size: 17, lr: 2.31e-04
2022-05-06 15:39:56,686 INFO [train.py:715] (1/8) Epoch 9, batch 30250, loss[loss=0.1384, simple_loss=0.2151, pruned_loss=0.03089, over 4899.00 frames.], tot_loss[loss=0.1422, simple_loss=0.215, pruned_loss=0.03473, over 972428.96 frames.], batch size: 39, lr: 2.31e-04
2022-05-06 15:40:35,242 INFO [train.py:715] (1/8) Epoch 9, batch 30300, loss[loss=0.1228, simple_loss=0.1973, pruned_loss=0.02419, over 4988.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2139, pruned_loss=0.03388, over 972204.70 frames.], batch size: 25, lr: 2.31e-04
2022-05-06 15:41:14,055 INFO [train.py:715] (1/8) Epoch 9, batch 30350, loss[loss=0.1425, simple_loss=0.2161, pruned_loss=0.03441, over 4885.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2142, pruned_loss=0.03383, over 971912.91 frames.], batch size: 16, lr: 2.31e-04
2022-05-06 15:41:53,482 INFO [train.py:715] (1/8) Epoch 9, batch 30400, loss[loss=0.1181, simple_loss=0.1985, pruned_loss=0.01883, over 4812.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2144, pruned_loss=0.03403, over 971262.06 frames.], batch size: 25, lr: 2.31e-04
2022-05-06 15:42:32,289 INFO [train.py:715] (1/8) Epoch 9, batch 30450, loss[loss=0.1665, simple_loss=0.2275, pruned_loss=0.05273, over 4967.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2141, pruned_loss=0.03388, over 970714.76 frames.], batch size: 24, lr: 2.31e-04
2022-05-06 15:43:10,913 INFO [train.py:715] (1/8) Epoch 9, batch 30500, loss[loss=0.1565, simple_loss=0.2245, pruned_loss=0.0443, over 4924.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2145, pruned_loss=0.03403, over 971089.75 frames.], batch size: 23, lr: 2.31e-04
2022-05-06 15:43:49,983 INFO [train.py:715] (1/8) Epoch 9, batch 30550, loss[loss=0.1177, simple_loss=0.19, pruned_loss=0.02276, over 4949.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2142, pruned_loss=0.03379, over 971876.17 frames.], batch size: 21, lr: 2.31e-04
2022-05-06 15:44:28,846 INFO [train.py:715] (1/8) Epoch 9, batch 30600, loss[loss=0.1694, simple_loss=0.2301, pruned_loss=0.05439, over 4834.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2139, pruned_loss=0.03361, over 972057.94 frames.], batch size: 30, lr: 2.31e-04
2022-05-06 15:45:06,877 INFO [train.py:715] (1/8) Epoch 9, batch 30650, loss[loss=0.1618, simple_loss=0.2227, pruned_loss=0.05045, over 4984.00 frames.], tot_loss[loss=0.1407, simple_loss=0.214, pruned_loss=0.03374, over 971934.47 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:45:45,878 INFO [train.py:715] (1/8) Epoch 9, batch 30700, loss[loss=0.1547, simple_loss=0.2314, pruned_loss=0.039, over 4790.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2135, pruned_loss=0.03343, over 972374.72 frames.], batch size: 17, lr: 2.30e-04
2022-05-06 15:46:27,568 INFO [train.py:715] (1/8) Epoch 9, batch 30750, loss[loss=0.1353, simple_loss=0.2114, pruned_loss=0.02954, over 4898.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.0333, over 973017.40 frames.], batch size: 17, lr: 2.30e-04
2022-05-06 15:47:06,252 INFO [train.py:715] (1/8) Epoch 9, batch 30800, loss[loss=0.1399, simple_loss=0.2006, pruned_loss=0.03962, over 4941.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.03344, over 972604.89 frames.], batch size: 29, lr: 2.30e-04
2022-05-06 15:47:44,602 INFO [train.py:715] (1/8) Epoch 9, batch 30850, loss[loss=0.1209, simple_loss=0.2061, pruned_loss=0.01786, over 4931.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03411, over 973092.82 frames.], batch size: 29, lr: 2.30e-04
2022-05-06 15:48:23,854 INFO [train.py:715] (1/8) Epoch 9, batch 30900, loss[loss=0.1301, simple_loss=0.2112, pruned_loss=0.02453, over 4972.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03444, over 973150.45 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 15:49:03,043 INFO [train.py:715] (1/8) Epoch 9, batch 30950, loss[loss=0.1837, simple_loss=0.2489, pruned_loss=0.05921, over 4967.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.03431, over 972912.79 frames.], batch size: 24, lr: 2.30e-04
2022-05-06 15:49:41,532 INFO [train.py:715] (1/8) Epoch 9, batch 31000, loss[loss=0.1465, simple_loss=0.2169, pruned_loss=0.0381, over 4944.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03465, over 973231.65 frames.], batch size: 21, lr: 2.30e-04
2022-05-06 15:50:20,505 INFO [train.py:715] (1/8) Epoch 9, batch 31050, loss[loss=0.163, simple_loss=0.2481, pruned_loss=0.03891, over 4775.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2142, pruned_loss=0.035, over 971980.67 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 15:50:59,763 INFO [train.py:715] (1/8) Epoch 9, batch 31100, loss[loss=0.1319, simple_loss=0.2157, pruned_loss=0.02408, over 4782.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2139, pruned_loss=0.0348, over 972174.82 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 15:51:38,432 INFO [train.py:715] (1/8) Epoch 9, batch 31150, loss[loss=0.1219, simple_loss=0.1918, pruned_loss=0.02605, over 4944.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2135, pruned_loss=0.03446, over 973192.23 frames.], batch size: 39, lr: 2.30e-04
2022-05-06 15:52:17,015 INFO [train.py:715] (1/8) Epoch 9, batch 31200, loss[loss=0.1472, simple_loss=0.2279, pruned_loss=0.03325, over 4887.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03502, over 972834.40 frames.], batch size: 22, lr: 2.30e-04
2022-05-06 15:52:56,548 INFO [train.py:715] (1/8) Epoch 9, batch 31250, loss[loss=0.1246, simple_loss=0.1859, pruned_loss=0.03163, over 4977.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03484, over 972141.80 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 15:53:35,997 INFO [train.py:715] (1/8) Epoch 9, batch 31300, loss[loss=0.1698, simple_loss=0.2407, pruned_loss=0.04947, over 4869.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.0348, over 972187.16 frames.], batch size: 38, lr: 2.30e-04
2022-05-06 15:54:14,967 INFO [train.py:715] (1/8) Epoch 9, batch 31350, loss[loss=0.1237, simple_loss=0.1981, pruned_loss=0.02462, over 4977.00 frames.], tot_loss[loss=0.1422, simple_loss=0.215, pruned_loss=0.03464, over 972089.55 frames.], batch size: 24, lr: 2.30e-04
2022-05-06 15:54:53,755 INFO [train.py:715] (1/8) Epoch 9, batch 31400, loss[loss=0.1658, simple_loss=0.2449, pruned_loss=0.04337, over 4897.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2156, pruned_loss=0.03488, over 972213.40 frames.], batch size: 39, lr: 2.30e-04
2022-05-06 15:55:32,698 INFO [train.py:715] (1/8) Epoch 9, batch 31450, loss[loss=0.1559, simple_loss=0.2229, pruned_loss=0.04445, over 4962.00 frames.], tot_loss[loss=0.1431, simple_loss=0.216, pruned_loss=0.03513, over 972534.06 frames.], batch size: 39, lr: 2.30e-04
2022-05-06 15:56:11,770 INFO [train.py:715] (1/8) Epoch 9, batch 31500, loss[loss=0.1418, simple_loss=0.2123, pruned_loss=0.03563, over 4926.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2152, pruned_loss=0.03484, over 972285.39 frames.], batch size: 23, lr: 2.30e-04
2022-05-06 15:56:50,180 INFO [train.py:715] (1/8) Epoch 9, batch 31550, loss[loss=0.1281, simple_loss=0.2085, pruned_loss=0.02384, over 4786.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03517, over 972370.45 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 15:57:29,717 INFO [train.py:715] (1/8) Epoch 9, batch 31600, loss[loss=0.1365, simple_loss=0.2141, pruned_loss=0.02945, over 4894.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2151, pruned_loss=0.03482, over 972535.61 frames.], batch size: 22, lr: 2.30e-04
2022-05-06 15:58:09,719 INFO [train.py:715] (1/8) Epoch 9, batch 31650, loss[loss=0.1591, simple_loss=0.2382, pruned_loss=0.03995, over 4853.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03496, over 973363.89 frames.], batch size: 20, lr: 2.30e-04
2022-05-06 15:58:48,440 INFO [train.py:715] (1/8) Epoch 9, batch 31700, loss[loss=0.1114, simple_loss=0.1855, pruned_loss=0.01862, over 4808.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.0348, over 972000.09 frames.], batch size: 26, lr: 2.30e-04
2022-05-06 15:59:27,450 INFO [train.py:715] (1/8) Epoch 9, batch 31750, loss[loss=0.1376, simple_loss=0.2085, pruned_loss=0.03332, over 4763.00 frames.], tot_loss[loss=0.1426, simple_loss=0.215, pruned_loss=0.03506, over 972244.74 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:00:06,077 INFO [train.py:715] (1/8) Epoch 9, batch 31800, loss[loss=0.1385, simple_loss=0.2101, pruned_loss=0.03341, over 4793.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2141, pruned_loss=0.03481, over 971915.15 frames.], batch size: 21, lr: 2.30e-04
2022-05-06 16:00:45,141 INFO [train.py:715] (1/8) Epoch 9, batch 31850, loss[loss=0.1474, simple_loss=0.2166, pruned_loss=0.03912, over 4946.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2132, pruned_loss=0.03418, over 973084.26 frames.], batch size: 29, lr: 2.30e-04
2022-05-06 16:01:23,635 INFO [train.py:715] (1/8) Epoch 9, batch 31900, loss[loss=0.1619, simple_loss=0.2366, pruned_loss=0.04357, over 4793.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2152, pruned_loss=0.03502, over 972622.20 frames.], batch size: 21, lr: 2.30e-04
2022-05-06 16:02:02,943 INFO [train.py:715] (1/8) Epoch 9, batch 31950, loss[loss=0.1579, simple_loss=0.2297, pruned_loss=0.04308, over 4948.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2151, pruned_loss=0.03471, over 973025.04 frames.], batch size: 31, lr: 2.30e-04
2022-05-06 16:02:42,217 INFO [train.py:715] (1/8) Epoch 9, batch 32000, loss[loss=0.1364, simple_loss=0.2058, pruned_loss=0.03346, over 4820.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.03414, over 972423.45 frames.], batch size: 27, lr: 2.30e-04
2022-05-06 16:03:20,776 INFO [train.py:715] (1/8) Epoch 9, batch 32050, loss[loss=0.1576, simple_loss=0.231, pruned_loss=0.04207, over 4700.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2141, pruned_loss=0.03401, over 971890.12 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:03:59,266 INFO [train.py:715] (1/8) Epoch 9, batch 32100, loss[loss=0.1449, simple_loss=0.2179, pruned_loss=0.036, over 4686.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.03394, over 971251.07 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:04:38,258 INFO [train.py:715] (1/8) Epoch 9, batch 32150, loss[loss=0.1546, simple_loss=0.2203, pruned_loss=0.0444, over 4872.00 frames.], tot_loss[loss=0.14, simple_loss=0.2127, pruned_loss=0.03359, over 971623.16 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 16:05:17,698 INFO [train.py:715] (1/8) Epoch 9, batch 32200, loss[loss=0.1569, simple_loss=0.2248, pruned_loss=0.04444, over 4781.00 frames.], tot_loss[loss=0.14, simple_loss=0.2124, pruned_loss=0.03378, over 972286.42 frames.], batch size: 17, lr: 2.30e-04
2022-05-06 16:05:55,459 INFO [train.py:715] (1/8) Epoch 9, batch 32250, loss[loss=0.1292, simple_loss=0.2108, pruned_loss=0.0238, over 4795.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2138, pruned_loss=0.03428, over 972456.90 frames.], batch size: 21, lr: 2.30e-04
2022-05-06 16:06:34,662 INFO [train.py:715] (1/8) Epoch 9, batch 32300, loss[loss=0.1122, simple_loss=0.1789, pruned_loss=0.02274, over 4959.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.0341, over 972803.02 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:07:13,840 INFO [train.py:715] (1/8) Epoch 9, batch 32350, loss[loss=0.1339, simple_loss=0.2015, pruned_loss=0.03314, over 4981.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2145, pruned_loss=0.03406, over 972509.76 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:07:52,329 INFO [train.py:715] (1/8) Epoch 9, batch 32400, loss[loss=0.1277, simple_loss=0.1918, pruned_loss=0.03178, over 4796.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2145, pruned_loss=0.03422, over 971516.87 frames.], batch size: 12, lr: 2.30e-04
2022-05-06 16:08:31,412 INFO [train.py:715] (1/8) Epoch 9, batch 32450, loss[loss=0.131, simple_loss=0.2039, pruned_loss=0.02902, over 4844.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03396, over 971074.27 frames.], batch size: 30, lr: 2.30e-04
2022-05-06 16:09:10,515 INFO [train.py:715] (1/8) Epoch 9, batch 32500, loss[loss=0.1677, simple_loss=0.2298, pruned_loss=0.05285, over 4796.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03392, over 971636.98 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 16:09:49,356 INFO [train.py:715] (1/8) Epoch 9, batch 32550, loss[loss=0.1643, simple_loss=0.2345, pruned_loss=0.04702, over 4910.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.03452, over 971750.60 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:10:27,860 INFO [train.py:715] (1/8) Epoch 9, batch 32600, loss[loss=0.113, simple_loss=0.1869, pruned_loss=0.01959, over 4802.00 frames.], tot_loss[loss=0.142, simple_loss=0.2148, pruned_loss=0.03459, over 971125.66 frames.], batch size: 24, lr: 2.30e-04
2022-05-06 16:11:06,890 INFO [train.py:715] (1/8) Epoch 9, batch 32650, loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02836, over 4982.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03415, over 970643.90 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 16:11:45,869 INFO [train.py:715] (1/8) Epoch 9, batch 32700, loss[loss=0.1205, simple_loss=0.1892, pruned_loss=0.02595, over 4987.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03449, over 971992.40 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 16:12:24,792 INFO [train.py:715] (1/8) Epoch 9, batch 32750, loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03819, over 4860.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.03416, over 971896.50 frames.], batch size: 32, lr: 2.30e-04
2022-05-06 16:13:03,518 INFO [train.py:715] (1/8) Epoch 9, batch 32800, loss[loss=0.1826, simple_loss=0.2521, pruned_loss=0.05653, over 4966.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2141, pruned_loss=0.03377, over 972099.36 frames.], batch size: 24, lr: 2.30e-04
2022-05-06 16:13:42,559 INFO [train.py:715] (1/8) Epoch 9, batch 32850, loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03337, over 4752.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2134, pruned_loss=0.03341, over 972280.81 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:14:21,300 INFO [train.py:715] (1/8) Epoch 9, batch 32900, loss[loss=0.1367, simple_loss=0.2083, pruned_loss=0.03261, over 4887.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.0336, over 972594.76 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:14:59,681 INFO [train.py:715] (1/8) Epoch 9, batch 32950, loss[loss=0.1269, simple_loss=0.1992, pruned_loss=0.02733, over 4948.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03457, over 972773.47 frames.], batch size: 35, lr: 2.30e-04
2022-05-06 16:15:38,638 INFO [train.py:715] (1/8) Epoch 9, batch 33000, loss[loss=0.1641, simple_loss=0.2392, pruned_loss=0.0445, over 4969.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03526, over 972311.82 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:15:38,639 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 16:15:48,001 INFO [train.py:742] (1/8) Epoch 9, validation: loss=0.1068, simple_loss=0.1913, pruned_loss=0.01119, over 914524.00 frames.
2022-05-06 16:16:27,262 INFO [train.py:715] (1/8) Epoch 9, batch 33050, loss[loss=0.1197, simple_loss=0.1923, pruned_loss=0.02357, over 4872.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03501, over 972643.65 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 16:17:06,451 INFO [train.py:715] (1/8) Epoch 9, batch 33100, loss[loss=0.1509, simple_loss=0.2198, pruned_loss=0.04098, over 4984.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2134, pruned_loss=0.03424, over 972208.69 frames.], batch size: 31, lr: 2.30e-04
2022-05-06 16:17:45,623 INFO [train.py:715] (1/8) Epoch 9, batch 33150, loss[loss=0.1473, simple_loss=0.2253, pruned_loss=0.03466, over 4983.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.03437, over 972673.23 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:18:25,448 INFO [train.py:715] (1/8) Epoch 9, batch 33200, loss[loss=0.1617, simple_loss=0.2333, pruned_loss=0.04512, over 4813.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.03437, over 972882.10 frames.], batch size: 25, lr: 2.30e-04
2022-05-06 16:19:04,993 INFO [train.py:715] (1/8) Epoch 9, batch 33250, loss[loss=0.1412, simple_loss=0.2191, pruned_loss=0.03169, over 4697.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.03444, over 971750.78 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:19:44,050 INFO [train.py:715] (1/8) Epoch 9, batch 33300, loss[loss=0.1252, simple_loss=0.1871, pruned_loss=0.03159, over 4772.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03471, over 970724.58 frames.], batch size: 17, lr: 2.30e-04
2022-05-06 16:20:23,550 INFO [train.py:715] (1/8) Epoch 9, batch 33350, loss[loss=0.1337, simple_loss=0.2056, pruned_loss=0.03091, over 4890.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03424, over 971334.06 frames.], batch size: 22, lr: 2.30e-04
2022-05-06 16:21:03,297 INFO [train.py:715] (1/8) Epoch 9, batch 33400, loss[loss=0.108, simple_loss=0.1819, pruned_loss=0.01707, over 4929.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2132, pruned_loss=0.03408, over 972283.29 frames.], batch size: 29, lr: 2.30e-04
2022-05-06 16:21:43,052 INFO [train.py:715] (1/8) Epoch 9, batch 33450, loss[loss=0.1364, simple_loss=0.2148, pruned_loss=0.029, over 4893.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03398, over 972562.17 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:22:22,072 INFO [train.py:715] (1/8) Epoch 9, batch 33500, loss[loss=0.1296, simple_loss=0.2128, pruned_loss=0.02317, over 4916.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03353, over 972518.28 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 16:23:00,823 INFO [train.py:715] (1/8) Epoch 9, batch 33550, loss[loss=0.1617, simple_loss=0.2348, pruned_loss=0.04428, over 4787.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03352, over 972845.94 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 16:23:40,546 INFO [train.py:715] (1/8) Epoch 9, batch 33600, loss[loss=0.1293, simple_loss=0.1954, pruned_loss=0.03162, over 4831.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2119, pruned_loss=0.0332, over 972810.61 frames.], batch size: 26, lr: 2.30e-04
2022-05-06 16:24:19,317 INFO [train.py:715] (1/8) Epoch 9, batch 33650, loss[loss=0.1155, simple_loss=0.1994, pruned_loss=0.01576, over 4919.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2125, pruned_loss=0.03359, over 971918.81 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 16:24:58,233 INFO [train.py:715] (1/8) Epoch 9, batch 33700, loss[loss=0.1231, simple_loss=0.1964, pruned_loss=0.02496, over 4640.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2127, pruned_loss=0.03394, over 971957.09 frames.], batch size: 13, lr: 2.29e-04
2022-05-06 16:25:37,405 INFO [train.py:715] (1/8) Epoch 9, batch 33750, loss[loss=0.1443, simple_loss=0.2091, pruned_loss=0.03976, over 4772.00 frames.], tot_loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03436, over 972503.00 frames.], batch size: 14, lr: 2.29e-04
2022-05-06 16:26:16,194 INFO [train.py:715] (1/8) Epoch 9, batch 33800, loss[loss=0.1397, simple_loss=0.2085, pruned_loss=0.03545, over 4926.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2128, pruned_loss=0.03404, over 972329.91 frames.], batch size: 23, lr: 2.29e-04
2022-05-06 16:26:54,909 INFO [train.py:715] (1/8) Epoch 9, batch 33850, loss[loss=0.1051, simple_loss=0.1868, pruned_loss=0.01169, over 4897.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03418, over 972258.33 frames.], batch size: 22, lr: 2.29e-04
2022-05-06 16:27:33,752 INFO [train.py:715] (1/8) Epoch 9, batch 33900, loss[loss=0.1228, simple_loss=0.1974, pruned_loss=0.02414, over 4964.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2148, pruned_loss=0.03438, over 972326.25 frames.], batch size: 28, lr: 2.29e-04
2022-05-06 16:28:13,482 INFO [train.py:715] (1/8) Epoch 9, batch 33950, loss[loss=0.1161, simple_loss=0.1932, pruned_loss=0.0195, over 4871.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2145, pruned_loss=0.0341, over 972702.77 frames.], batch size: 20, lr: 2.29e-04
2022-05-06 16:28:52,280 INFO [train.py:715] (1/8) Epoch 9, batch 34000, loss[loss=0.1241, simple_loss=0.1903, pruned_loss=0.02897, over 4792.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2138, pruned_loss=0.03402, over 972401.74 frames.], batch size: 14, lr: 2.29e-04
2022-05-06 16:29:31,509 INFO [train.py:715] (1/8) Epoch 9, batch 34050, loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03065, over 4888.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.03388, over 972069.24 frames.], batch size: 19, lr: 2.29e-04
2022-05-06 16:30:09,972 INFO [train.py:715] (1/8) Epoch 9, batch 34100, loss[loss=0.1327, simple_loss=0.2017, pruned_loss=0.03187, over 4833.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2148, pruned_loss=0.03445, over 971163.57 frames.], batch size: 15, lr: 2.29e-04
2022-05-06 16:30:49,072 INFO [train.py:715] (1/8) Epoch 9, batch 34150, loss[loss=0.139, simple_loss=0.2273, pruned_loss=0.02536, over 4948.00 frames.], tot_loss[loss=0.142, simple_loss=0.2149, pruned_loss=0.03458, over 971439.55 frames.], batch size: 29, lr: 2.29e-04
2022-05-06 16:31:27,538 INFO [train.py:715] (1/8) Epoch 9, batch 34200, loss[loss=0.1363, simple_loss=0.2085, pruned_loss=0.03205, over 4960.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03462, over 971099.97 frames.], batch size: 24, lr: 2.29e-04
2022-05-06 16:32:05,777 INFO [train.py:715] (1/8) Epoch 9, batch 34250, loss[loss=0.1205, simple_loss=0.1953, pruned_loss=0.02292, over 4765.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03465, over 971876.21 frames.], batch size: 16, lr: 2.29e-04
2022-05-06 16:32:45,090 INFO [train.py:715] (1/8) Epoch 9, batch 34300, loss[loss=0.1824, simple_loss=0.2585, pruned_loss=0.05311, over 4690.00 frames.], tot_loss[loss=0.1424, simple_loss=0.215, pruned_loss=0.03487, over 971735.06 frames.], batch size: 15, lr: 2.29e-04
2022-05-06 16:33:23,847 INFO [train.py:715] (1/8) Epoch 9, batch 34350, loss[loss=0.1196, simple_loss=0.1902, pruned_loss=0.0245, over 4927.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03458, over 972219.10 frames.], batch size: 29, lr: 2.29e-04
2022-05-06 16:34:02,522 INFO [train.py:715] (1/8) Epoch 9, batch 34400, loss[loss=0.1445, simple_loss=0.2129, pruned_loss=0.03806, over 4858.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2143, pruned_loss=0.03473, over 972536.52 frames.], batch size: 20, lr: 2.29e-04
2022-05-06 16:34:41,422 INFO [train.py:715] (1/8) Epoch 9, batch 34450, loss[loss=0.1391, simple_loss=0.2175, pruned_loss=0.03038, over 4790.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03458, over 971895.96 frames.], batch size: 18, lr: 2.29e-04
2022-05-06 16:35:20,340 INFO [train.py:715] (1/8) Epoch 9, batch 34500, loss[loss=0.1246, simple_loss=0.205, pruned_loss=0.02213, over 4802.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03478, over 971771.53 frames.], batch size: 24, lr: 2.29e-04
2022-05-06 16:35:59,372 INFO [train.py:715] (1/8) Epoch 9, batch 34550, loss[loss=0.1225, simple_loss=0.197, pruned_loss=0.024, over 4786.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03534, over 972036.34 frames.], batch size: 17, lr: 2.29e-04
2022-05-06 16:36:38,002 INFO [train.py:715] (1/8) Epoch 9, batch 34600, loss[loss=0.1278, simple_loss=0.1977, pruned_loss=0.02897, over 4894.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2151, pruned_loss=0.0348, over 972237.03 frames.], batch size: 19, lr: 2.29e-04
2022-05-06 16:37:17,104 INFO [train.py:715] (1/8) Epoch 9, batch 34650, loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03182, over 4927.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03489, over 973480.92 frames.], batch size: 17, lr: 2.29e-04
2022-05-06 16:37:56,492 INFO [train.py:715] (1/8) Epoch 9, batch 34700, loss[loss=0.1418, simple_loss=0.2119, pruned_loss=0.03585, over 4937.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2156, pruned_loss=0.03514, over 973167.79 frames.], batch size: 23, lr: 2.29e-04
2022-05-06 16:38:34,783 INFO [train.py:715] (1/8) Epoch 9, batch 34750, loss[loss=0.1501, simple_loss=0.2285, pruned_loss=0.03578, over 4793.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2153, pruned_loss=0.03474, over 972990.86 frames.], batch size: 14, lr: 2.29e-04
2022-05-06 16:39:12,242 INFO [train.py:715] (1/8) Epoch 9, batch 34800, loss[loss=0.1442, simple_loss=0.2093, pruned_loss=0.03957, over 4916.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03425, over 972568.06 frames.], batch size: 18, lr: 2.29e-04
2022-05-06 16:40:01,174 INFO [train.py:715] (1/8) Epoch 10, batch 0, loss[loss=0.1638, simple_loss=0.2463, pruned_loss=0.04065, over 4955.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2463, pruned_loss=0.04065, over 4955.00 frames.], batch size: 35, lr: 2.19e-04
2022-05-06 16:40:41,027 INFO [train.py:715] (1/8) Epoch 10, batch 50, loss[loss=0.1435, simple_loss=0.2221, pruned_loss=0.03245, over 4872.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2163, pruned_loss=0.0352, over 219212.00 frames.], batch size: 20, lr: 2.19e-04
2022-05-06 16:41:20,750 INFO [train.py:715] (1/8) Epoch 10, batch 100, loss[loss=0.1681, simple_loss=0.2357, pruned_loss=0.05026, over 4957.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03478, over 386804.18 frames.], batch size: 35, lr: 2.19e-04
2022-05-06 16:42:00,748 INFO [train.py:715] (1/8) Epoch 10, batch 150, loss[loss=0.1575, simple_loss=0.2393, pruned_loss=0.03786, over 4945.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03376, over 516305.24 frames.], batch size: 23, lr: 2.19e-04
2022-05-06 16:42:41,341 INFO [train.py:715] (1/8) Epoch 10, batch 200, loss[loss=0.1493, simple_loss=0.2275, pruned_loss=0.03553, over 4888.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03395, over 617919.63 frames.], batch size: 19, lr: 2.19e-04
2022-05-06 16:43:22,395 INFO [train.py:715] (1/8) Epoch 10, batch 250, loss[loss=0.1816, simple_loss=0.2448, pruned_loss=0.05917, over 4967.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03498, over 695870.27 frames.], batch size: 15, lr: 2.19e-04
2022-05-06 16:44:03,219 INFO [train.py:715] (1/8) Epoch 10, batch 300, loss[loss=0.1459, simple_loss=0.2056, pruned_loss=0.04314, over 4841.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03544, over 757422.90 frames.], batch size: 15, lr: 2.19e-04
2022-05-06 16:44:43,666 INFO [train.py:715] (1/8) Epoch 10, batch 350, loss[loss=0.1418, simple_loss=0.2075, pruned_loss=0.03808, over 4766.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03519, over 805159.12 frames.], batch size: 12, lr: 2.19e-04
2022-05-06 16:45:25,022 INFO [train.py:715] (1/8) Epoch 10, batch 400, loss[loss=0.126, simple_loss=0.1947, pruned_loss=0.02869, over 4844.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2155, pruned_loss=0.03516, over 842522.40 frames.], batch size: 13, lr: 2.19e-04
2022-05-06 16:46:06,714 INFO [train.py:715] (1/8) Epoch 10, batch 450, loss[loss=0.1136, simple_loss=0.1912, pruned_loss=0.01804, over 4969.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2156, pruned_loss=0.03513, over 872438.75 frames.], batch size: 24, lr: 2.19e-04
2022-05-06 16:46:47,445 INFO [train.py:715] (1/8) Epoch 10, batch 500, loss[loss=0.1179, simple_loss=0.1902, pruned_loss=0.02285, over 4903.00 frames.], tot_loss[loss=0.142, simple_loss=0.2141, pruned_loss=0.03491, over 895935.01 frames.], batch size: 17, lr: 2.19e-04
2022-05-06 16:47:28,877 INFO [train.py:715] (1/8) Epoch 10, batch 550, loss[loss=0.1121, simple_loss=0.1844, pruned_loss=0.0199, over 4902.00 frames.], tot_loss[loss=0.142, simple_loss=0.2143, pruned_loss=0.0349, over 913240.34 frames.], batch size: 18, lr: 2.19e-04
2022-05-06 16:48:10,018 INFO [train.py:715] (1/8) Epoch 10, batch 600, loss[loss=0.129, simple_loss=0.2012, pruned_loss=0.0284, over 4860.00 frames.], tot_loss[loss=0.1418, simple_loss=0.214, pruned_loss=0.03486, over 926181.01 frames.], batch size: 20, lr: 2.19e-04
2022-05-06 16:48:50,534 INFO [train.py:715] (1/8) Epoch 10, batch 650, loss[loss=0.1563, simple_loss=0.2365, pruned_loss=0.03806, over 4880.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2138, pruned_loss=0.03419, over 935742.89 frames.], batch size: 22, lr: 2.19e-04
2022-05-06 16:49:31,184 INFO [train.py:715] (1/8) Epoch 10, batch 700, loss[loss=0.166, simple_loss=0.2322, pruned_loss=0.04986, over 4949.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.0344, over 943568.33 frames.], batch size: 21, lr: 2.19e-04
2022-05-06 16:50:12,722 INFO [train.py:715] (1/8) Epoch 10, batch 750, loss[loss=0.1182, simple_loss=0.1878, pruned_loss=0.02432, over 4788.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.0343, over 949777.64 frames.], batch size: 24, lr: 2.19e-04
2022-05-06 16:50:54,023 INFO [train.py:715] (1/8) Epoch 10, batch 800, loss[loss=0.1285, simple_loss=0.2113, pruned_loss=0.02281, over 4924.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2135, pruned_loss=0.03429, over 954565.77 frames.], batch size: 29, lr: 2.19e-04
2022-05-06 16:51:34,422 INFO [train.py:715] (1/8) Epoch 10, batch 850, loss[loss=0.1901, simple_loss=0.2659, pruned_loss=0.05709, over 4868.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2139, pruned_loss=0.03421, over 958489.63 frames.], batch size: 32, lr: 2.19e-04
2022-05-06 16:52:15,217 INFO [train.py:715] (1/8) Epoch 10, batch 900, loss[loss=0.1337, simple_loss=0.2058, pruned_loss=0.03083, over 4986.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03384, over 962160.59 frames.], batch size: 35, lr: 2.19e-04
2022-05-06 16:52:55,735 INFO [train.py:715] (1/8) Epoch 10, batch 950, loss[loss=0.1421, simple_loss=0.2122, pruned_loss=0.036, over 4808.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.03357, over 964391.25 frames.], batch size: 21, lr: 2.19e-04
2022-05-06 16:53:35,730 INFO [train.py:715] (1/8) Epoch 10, batch 1000, loss[loss=0.1315, simple_loss=0.1996, pruned_loss=0.0317, over 4970.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03407, over 965981.19 frames.], batch size: 24, lr: 2.19e-04
2022-05-06 16:54:14,956 INFO [train.py:715] (1/8) Epoch 10, batch 1050, loss[loss=0.1274, simple_loss=0.2056, pruned_loss=0.02459, over 4752.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03409, over 966466.07 frames.], batch size: 19, lr: 2.19e-04
2022-05-06 16:54:55,329 INFO [train.py:715] (1/8) Epoch 10, batch 1100, loss[loss=0.1384, simple_loss=0.21, pruned_loss=0.03343, over 4824.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.0333, over 967372.84 frames.], batch size: 30, lr: 2.19e-04
2022-05-06 16:55:34,628 INFO [train.py:715] (1/8) Epoch 10, batch 1150, loss[loss=0.1363, simple_loss=0.2134, pruned_loss=0.02956, over 4927.00 frames.], tot_loss[loss=0.1389, simple_loss=0.212, pruned_loss=0.03289, over 968071.39 frames.], batch size: 21, lr: 2.19e-04
2022-05-06 16:56:13,827 INFO [train.py:715] (1/8) Epoch 10, batch 1200, loss[loss=0.1317, simple_loss=0.2039, pruned_loss=0.02974, over 4833.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03273, over 968892.70 frames.], batch size: 30, lr: 2.19e-04
2022-05-06 16:56:53,598 INFO [train.py:715] (1/8) Epoch 10, batch 1250, loss[loss=0.1479, simple_loss=0.2266, pruned_loss=0.03458, over 4963.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.03317, over 970029.08 frames.], batch size: 39, lr: 2.19e-04
2022-05-06 16:57:32,223 INFO [train.py:715] (1/8) Epoch 10, batch 1300, loss[loss=0.1491, simple_loss=0.2199, pruned_loss=0.0391, over 4789.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03281, over 970676.42 frames.], batch size: 18, lr: 2.19e-04
2022-05-06 16:58:11,017 INFO [train.py:715] (1/8) Epoch 10, batch 1350, loss[loss=0.142, simple_loss=0.2248, pruned_loss=0.02959, over 4825.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2126, pruned_loss=0.03282, over 971423.90 frames.], batch size: 25, lr: 2.19e-04
2022-05-06 16:58:49,193 INFO [train.py:715] (1/8) Epoch 10, batch 1400, loss[loss=0.1422, simple_loss=0.2057, pruned_loss=0.03936, over 4760.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2133, pruned_loss=0.03348, over 971411.83 frames.], batch size: 14, lr: 2.19e-04
2022-05-06 16:59:28,743 INFO [train.py:715] (1/8) Epoch 10, batch 1450, loss[loss=0.172, simple_loss=0.2368, pruned_loss=0.05357, over 4907.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03349, over 970515.72 frames.], batch size: 17, lr: 2.19e-04
2022-05-06 17:00:07,715 INFO [train.py:715] (1/8) Epoch 10, batch 1500, loss[loss=0.1438, simple_loss=0.2225, pruned_loss=0.0326, over 4969.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2139, pruned_loss=0.03357, over 970353.61 frames.], batch size: 24, lr: 2.19e-04
2022-05-06 17:00:46,474 INFO [train.py:715] (1/8) Epoch 10, batch 1550, loss[loss=0.1329, simple_loss=0.2035, pruned_loss=0.03114, over 4806.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2142, pruned_loss=0.03371, over 970986.42 frames.], batch size: 13, lr: 2.19e-04
2022-05-06 17:01:25,569 INFO [train.py:715] (1/8) Epoch 10, batch 1600, loss[loss=0.1592, simple_loss=0.2241, pruned_loss=0.04717, over 4964.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2144, pruned_loss=0.03388, over 971653.73 frames.], batch size: 14, lr: 2.19e-04
2022-05-06 17:02:04,988 INFO [train.py:715] (1/8) Epoch 10, batch 1650, loss[loss=0.1419, simple_loss=0.2118, pruned_loss=0.03603, over 4987.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2141, pruned_loss=0.03378, over 971595.66 frames.], batch size: 14, lr: 2.19e-04
2022-05-06 17:02:43,706 INFO [train.py:715] (1/8) Epoch 10, batch 1700, loss[loss=0.1515, simple_loss=0.2306, pruned_loss=0.03617, over 4894.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2135, pruned_loss=0.03336, over 971317.13 frames.], batch size: 39, lr: 2.19e-04
2022-05-06 17:03:22,052 INFO [train.py:715] (1/8) Epoch 10, batch 1750, loss[loss=0.1385, simple_loss=0.2239, pruned_loss=0.02659, over 4971.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.03352, over 971532.31 frames.], batch size: 24, lr: 2.19e-04
2022-05-06 17:04:02,175 INFO [train.py:715] (1/8) Epoch 10, batch 1800, loss[loss=0.1469, simple_loss=0.2239, pruned_loss=0.035, over 4976.00 frames.], tot_loss[loss=0.1399, simple_loss=0.213, pruned_loss=0.03343, over 971366.86 frames.], batch size: 15, lr: 2.19e-04
2022-05-06 17:04:41,813 INFO [train.py:715] (1/8) Epoch 10, batch 1850, loss[loss=0.119, simple_loss=0.1977, pruned_loss=0.02016, over 4834.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2122, pruned_loss=0.0333, over 972063.53 frames.], batch size: 30, lr: 2.19e-04
2022-05-06 17:05:20,550 INFO [train.py:715] (1/8) Epoch 10, batch 1900, loss[loss=0.1131, simple_loss=0.1852, pruned_loss=0.02047, over 4827.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2125, pruned_loss=0.03342, over 972396.62 frames.], batch size: 27, lr: 2.19e-04
2022-05-06 17:05:59,509 INFO [train.py:715] (1/8) Epoch 10, batch 1950, loss[loss=0.1542, simple_loss=0.2336, pruned_loss=0.03744, over 4916.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2123, pruned_loss=0.03361, over 972624.48 frames.], batch size: 19, lr: 2.18e-04
2022-05-06 17:06:39,836 INFO [train.py:715] (1/8) Epoch 10, batch 2000, loss[loss=0.1255, simple_loss=0.2019, pruned_loss=0.02456, over 4970.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.03358, over 972604.10 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:07:19,135 INFO [train.py:715] (1/8) Epoch 10, batch 2050, loss[loss=0.124, simple_loss=0.2054, pruned_loss=0.02133, over 4814.00 frames.], tot_loss[loss=0.1399, simple_loss=0.213, pruned_loss=0.03341, over 972728.90 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:07:57,714 INFO [train.py:715] (1/8) Epoch 10, batch 2100, loss[loss=0.1398, simple_loss=0.2104, pruned_loss=0.0346, over 4831.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03362, over 972815.28 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:08:37,348 INFO [train.py:715] (1/8) Epoch 10, batch 2150, loss[loss=0.1325, simple_loss=0.217, pruned_loss=0.02397, over 4937.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.03391, over 972998.13 frames.], batch size: 23, lr: 2.18e-04
2022-05-06 17:09:16,486 INFO [train.py:715] (1/8) Epoch 10, batch 2200, loss[loss=0.1346, simple_loss=0.2114, pruned_loss=0.02888, over 4783.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.03387, over 972226.37 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:09:55,192 INFO [train.py:715] (1/8) Epoch 10, batch 2250, loss[loss=0.1272, simple_loss=0.1974, pruned_loss=0.02844, over 4826.00 frames.], tot_loss[loss=0.142, simple_loss=0.2148, pruned_loss=0.0346, over 971377.29 frames.], batch size: 26, lr: 2.18e-04
2022-05-06 17:10:33,967 INFO [train.py:715] (1/8) Epoch 10, batch 2300, loss[loss=0.1147, simple_loss=0.1934, pruned_loss=0.01804, over 4832.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2149, pruned_loss=0.03446, over 971865.62 frames.], batch size: 13, lr: 2.18e-04
2022-05-06 17:11:13,692 INFO [train.py:715] (1/8) Epoch 10, batch 2350, loss[loss=0.1473, simple_loss=0.2167, pruned_loss=0.0389, over 4700.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03402, over 972574.53 frames.], batch size: 15, lr: 2.18e-04
2022-05-06 17:11:52,500 INFO [train.py:715] (1/8) Epoch 10, batch 2400, loss[loss=0.1276, simple_loss=0.1951, pruned_loss=0.03011, over 4930.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03421, over 972688.02 frames.], batch size: 35, lr: 2.18e-04
2022-05-06 17:12:31,235 INFO [train.py:715] (1/8) Epoch 10, batch 2450, loss[loss=0.1339, simple_loss=0.2152, pruned_loss=0.02626, over 4860.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.03397, over 972340.31 frames.], batch size: 20, lr: 2.18e-04
2022-05-06 17:13:10,535 INFO [train.py:715] (1/8) Epoch 10, batch 2500, loss[loss=0.1365, simple_loss=0.2164, pruned_loss=0.02827, over 4871.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03396, over 972488.93 frames.], batch size: 16, lr: 2.18e-04
2022-05-06 17:13:49,919 INFO [train.py:715] (1/8) Epoch 10, batch 2550, loss[loss=0.1253, simple_loss=0.2049, pruned_loss=0.02283, over 4951.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03392, over 972264.51 frames.], batch size: 29, lr: 2.18e-04
2022-05-06 17:14:29,342 INFO [train.py:715] (1/8) Epoch 10, batch 2600, loss[loss=0.1363, simple_loss=0.208, pruned_loss=0.03232, over 4825.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03384, over 972316.78 frames.], batch size: 13, lr: 2.18e-04
2022-05-06 17:15:08,457 INFO [train.py:715] (1/8) Epoch 10, batch 2650, loss[loss=0.1427, simple_loss=0.2084, pruned_loss=0.03853, over 4918.00 frames.], tot_loss[loss=0.1404, simple_loss=0.213, pruned_loss=0.03391, over 972756.20 frames.], batch size: 23, lr: 2.18e-04
2022-05-06 17:15:47,657 INFO [train.py:715] (1/8) Epoch 10, batch 2700, loss[loss=0.1291, simple_loss=0.2036, pruned_loss=0.0273, over 4776.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03379, over 972440.72 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:16:26,374 INFO [train.py:715] (1/8) Epoch 10, batch 2750, loss[loss=0.1425, simple_loss=0.2142, pruned_loss=0.03538, over 4855.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.03372, over 971665.52 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:17:05,077 INFO [train.py:715] (1/8) Epoch 10, batch 2800, loss[loss=0.1497, simple_loss=0.236, pruned_loss=0.0317, over 4864.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2131, pruned_loss=0.0341, over 971437.88 frames.], batch size: 20, lr: 2.18e-04
2022-05-06 17:17:43,817 INFO [train.py:715] (1/8) Epoch 10, batch 2850, loss[loss=0.1413, simple_loss=0.206, pruned_loss=0.03829, over 4754.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2122, pruned_loss=0.03343, over 972093.64 frames.], batch size: 16, lr: 2.18e-04
2022-05-06 17:18:23,062 INFO [train.py:715] (1/8) Epoch 10, batch 2900, loss[loss=0.1167, simple_loss=0.194, pruned_loss=0.01974, over 4993.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2119, pruned_loss=0.03353, over 972386.16 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:19:02,253 INFO [train.py:715] (1/8) Epoch 10, batch 2950, loss[loss=0.1471, simple_loss=0.2206, pruned_loss=0.0368, over 4911.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2128, pruned_loss=0.03398, over 972174.96 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:19:40,630 INFO [train.py:715] (1/8) Epoch 10, batch 3000, loss[loss=0.1271, simple_loss=0.1977, pruned_loss=0.02831, over 4795.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2137, pruned_loss=0.03458, over 971936.30 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:19:40,632 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 17:19:50,102 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1065, simple_loss=0.1908, pruned_loss=0.01113, over 914524.00 frames.
2022-05-06 17:20:28,625 INFO [train.py:715] (1/8) Epoch 10, batch 3050, loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02897, over 4785.00 frames.], tot_loss[loss=0.142, simple_loss=0.2142, pruned_loss=0.03495, over 972248.26 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:21:07,567 INFO [train.py:715] (1/8) Epoch 10, batch 3100, loss[loss=0.1234, simple_loss=0.199, pruned_loss=0.02396, over 4933.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2142, pruned_loss=0.03497, over 972685.97 frames.], batch size: 29, lr: 2.18e-04
2022-05-06 17:21:46,718 INFO [train.py:715] (1/8) Epoch 10, batch 3150, loss[loss=0.1624, simple_loss=0.2305, pruned_loss=0.0471, over 4792.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2141, pruned_loss=0.03503, over 971830.55 frames.], batch size: 17, lr: 2.18e-04
2022-05-06 17:22:25,531 INFO [train.py:715] (1/8) Epoch 10, batch 3200, loss[loss=0.1571, simple_loss=0.2255, pruned_loss=0.04437, over 4802.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2139, pruned_loss=0.03451, over 971129.01 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:23:03,964 INFO [train.py:715] (1/8) Epoch 10, batch 3250, loss[loss=0.1599, simple_loss=0.2339, pruned_loss=0.04295, over 4784.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2139, pruned_loss=0.03443, over 971182.56 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:23:44,489 INFO [train.py:715] (1/8) Epoch 10, batch 3300, loss[loss=0.165, simple_loss=0.2271, pruned_loss=0.05143, over 4837.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03467, over 971661.85 frames.], batch size: 15, lr: 2.18e-04
2022-05-06 17:24:24,203 INFO [train.py:715] (1/8) Epoch 10, batch 3350, loss[loss=0.1424, simple_loss=0.2084, pruned_loss=0.03819, over 4985.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03397, over 972189.83 frames.], batch size: 35, lr: 2.18e-04
2022-05-06 17:25:04,068 INFO [train.py:715] (1/8) Epoch 10, batch 3400, loss[loss=0.1297, simple_loss=0.2051, pruned_loss=0.02715, over 4843.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03397, over 972486.43 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:25:44,884 INFO [train.py:715] (1/8) Epoch 10, batch 3450, loss[loss=0.1406, simple_loss=0.2034, pruned_loss=0.03889, over 4946.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03484, over 973349.63 frames.], batch size: 35, lr: 2.18e-04
2022-05-06 17:26:26,600 INFO [train.py:715] (1/8) Epoch 10, batch 3500, loss[loss=0.146, simple_loss=0.2267, pruned_loss=0.03266, over 4951.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2146, pruned_loss=0.03443, over 972489.86 frames.], batch size: 29, lr: 2.18e-04
2022-05-06 17:27:07,260 INFO [train.py:715] (1/8) Epoch 10, batch 3550, loss[loss=0.1355, simple_loss=0.2172, pruned_loss=0.02689, over 4956.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2143, pruned_loss=0.03433, over 973052.99 frames.], batch size: 39, lr: 2.18e-04
2022-05-06 17:27:48,542 INFO [train.py:715] (1/8) Epoch 10, batch 3600, loss[loss=0.1566, simple_loss=0.2269, pruned_loss=0.0431, over 4964.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03391, over 973386.38 frames.], batch size: 35, lr: 2.18e-04
2022-05-06 17:28:29,171 INFO [train.py:715] (1/8) Epoch 10, batch 3650, loss[loss=0.1273, simple_loss=0.1922, pruned_loss=0.03121, over 4795.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03386, over 973053.64 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:29:10,563 INFO [train.py:715] (1/8) Epoch 10, batch 3700, loss[loss=0.1414, simple_loss=0.2083, pruned_loss=0.03723, over 4936.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03369, over 973079.32 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:29:51,151 INFO [train.py:715] (1/8) Epoch 10, batch 3750, loss[loss=0.1407, simple_loss=0.2142, pruned_loss=0.03367, over 4773.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03309, over 972439.43 frames.], batch size: 17, lr: 2.18e-04
2022-05-06 17:30:32,378 INFO [train.py:715] (1/8) Epoch 10, batch 3800, loss[loss=0.111, simple_loss=0.1838, pruned_loss=0.01907, over 4931.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.03251, over 971761.76 frames.], batch size: 29, lr: 2.18e-04
2022-05-06 17:31:13,744 INFO [train.py:715] (1/8) Epoch 10, batch 3850, loss[loss=0.1051, simple_loss=0.1744, pruned_loss=0.01788, over 4827.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.0329, over 971649.63 frames.], batch size: 12, lr: 2.18e-04
2022-05-06 17:31:54,693 INFO [train.py:715] (1/8) Epoch 10, batch 3900, loss[loss=0.1416, simple_loss=0.2167, pruned_loss=0.03322, over 4943.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2121, pruned_loss=0.03259, over 972149.46 frames.], batch size: 23, lr: 2.18e-04
2022-05-06 17:32:36,891 INFO [train.py:715] (1/8) Epoch 10, batch 3950, loss[loss=0.131, simple_loss=0.2023, pruned_loss=0.02981, over 4974.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03269, over 972899.63 frames.], batch size: 35, lr: 2.18e-04
2022-05-06 17:33:16,171 INFO [train.py:715] (1/8) Epoch 10, batch 4000, loss[loss=0.1358, simple_loss=0.2119, pruned_loss=0.02992, over 4744.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2121, pruned_loss=0.03233, over 972999.55 frames.], batch size: 16, lr: 2.18e-04
2022-05-06 17:33:55,835 INFO [train.py:715] (1/8) Epoch 10, batch 4050, loss[loss=0.1742, simple_loss=0.2306, pruned_loss=0.05893, over 4832.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2126, pruned_loss=0.03285, over 972227.34 frames.], batch size: 15, lr: 2.18e-04
2022-05-06 17:34:34,554 INFO [train.py:715] (1/8) Epoch 10, batch 4100, loss[loss=0.128, simple_loss=0.1922, pruned_loss=0.03194, over 4831.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2124, pruned_loss=0.03271, over 972522.17 frames.], batch size: 26, lr: 2.18e-04
2022-05-06 17:35:13,433 INFO [train.py:715] (1/8) Epoch 10, batch 4150, loss[loss=0.1384, simple_loss=0.2244, pruned_loss=0.02624, over 4931.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2125, pruned_loss=0.03344, over 972533.90 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:35:52,990 INFO [train.py:715] (1/8) Epoch 10, batch 4200, loss[loss=0.1377, simple_loss=0.2126, pruned_loss=0.03141, over 4788.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2124, pruned_loss=0.03357, over 972670.41 frames.], batch size: 24, lr: 2.18e-04
2022-05-06 17:36:31,675 INFO [train.py:715] (1/8) Epoch 10, batch 4250, loss[loss=0.1184, simple_loss=0.2047, pruned_loss=0.01609, over 4944.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2127, pruned_loss=0.03384, over 972302.93 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:37:10,487 INFO [train.py:715] (1/8) Epoch 10, batch 4300, loss[loss=0.1282, simple_loss=0.2059, pruned_loss=0.02524, over 4969.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.0335, over 973436.69 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:37:49,692 INFO [train.py:715] (1/8) Epoch 10, batch 4350, loss[loss=0.1437, simple_loss=0.2134, pruned_loss=0.03698, over 4909.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03304, over 972205.60 frames.], batch size: 19, lr: 2.18e-04
2022-05-06 17:38:28,648 INFO [train.py:715] (1/8) Epoch 10, batch 4400, loss[loss=0.1202, simple_loss=0.1827, pruned_loss=0.02882, over 4789.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2127, pruned_loss=0.03302, over 972218.96 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:39:07,612 INFO [train.py:715] (1/8) Epoch 10, batch 4450, loss[loss=0.1318, simple_loss=0.2129, pruned_loss=0.02533, over 4968.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03369, over 972249.15 frames.], batch size: 15, lr: 2.18e-04
2022-05-06 17:39:46,321 INFO [train.py:715] (1/8) Epoch 10, batch 4500, loss[loss=0.1212, simple_loss=0.2027, pruned_loss=0.01986, over 4925.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.03339, over 972981.51 frames.], batch size: 23, lr: 2.18e-04
2022-05-06 17:40:25,796 INFO [train.py:715] (1/8) Epoch 10, batch 4550, loss[loss=0.1318, simple_loss=0.2, pruned_loss=0.03181, over 4726.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2126, pruned_loss=0.03357, over 972897.64 frames.], batch size: 16, lr: 2.18e-04
2022-05-06 17:41:04,676 INFO [train.py:715] (1/8) Epoch 10, batch 4600, loss[loss=0.1409, simple_loss=0.2117, pruned_loss=0.03509, over 4776.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03399, over 972453.81 frames.], batch size: 12, lr: 2.18e-04
2022-05-06 17:41:43,575 INFO [train.py:715] (1/8) Epoch 10, batch 4650, loss[loss=0.13, simple_loss=0.2087, pruned_loss=0.02567, over 4945.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.03454, over 972480.15 frames.], batch size: 29, lr: 2.18e-04
2022-05-06 17:42:23,830 INFO [train.py:715] (1/8) Epoch 10, batch 4700, loss[loss=0.1391, simple_loss=0.2166, pruned_loss=0.03083, over 4796.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03352, over 972649.56 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:43:03,973 INFO [train.py:715] (1/8) Epoch 10, batch 4750, loss[loss=0.1513, simple_loss=0.2262, pruned_loss=0.03815, over 4890.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2124, pruned_loss=0.03358, over 972983.08 frames.], batch size: 39, lr: 2.18e-04
2022-05-06 17:43:43,162 INFO [train.py:715] (1/8) Epoch 10, batch 4800, loss[loss=0.147, simple_loss=0.2335, pruned_loss=0.03023, over 4818.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03359, over 972823.06 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:44:22,995 INFO [train.py:715] (1/8) Epoch 10, batch 4850, loss[loss=0.1492, simple_loss=0.2288, pruned_loss=0.03484, over 4905.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03347, over 972090.90 frames.], batch size: 17, lr: 2.18e-04
2022-05-06 17:45:02,946 INFO [train.py:715] (1/8) Epoch 10, batch 4900, loss[loss=0.1423, simple_loss=0.2119, pruned_loss=0.03634, over 4912.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03385, over 972618.60 frames.], batch size: 23, lr: 2.18e-04
2022-05-06 17:45:42,395 INFO [train.py:715] (1/8) Epoch 10, batch 4950, loss[loss=0.1795, simple_loss=0.2322, pruned_loss=0.06343, over 4994.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03445, over 973124.04 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:46:21,434 INFO [train.py:715] (1/8) Epoch 10, batch 5000, loss[loss=0.1347, simple_loss=0.2081, pruned_loss=0.03067, over 4922.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2133, pruned_loss=0.03413, over 972491.96 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:47:00,595 INFO [train.py:715] (1/8) Epoch 10, batch 5050, loss[loss=0.151, simple_loss=0.2194, pruned_loss=0.04135, over 4949.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2132, pruned_loss=0.03394, over 973477.07 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:47:39,526 INFO [train.py:715] (1/8) Epoch 10, batch 5100, loss[loss=0.182, simple_loss=0.2398, pruned_loss=0.06214, over 4696.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2132, pruned_loss=0.03428, over 973199.14 frames.], batch size: 15, lr: 2.18e-04
2022-05-06 17:48:18,799 INFO [train.py:715] (1/8) Epoch 10, batch 5150, loss[loss=0.1393, simple_loss=0.2224, pruned_loss=0.02806, over 4958.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2126, pruned_loss=0.03362, over 973624.26 frames.], batch size: 24, lr: 2.18e-04
2022-05-06 17:48:58,636 INFO [train.py:715] (1/8) Epoch 10, batch 5200, loss[loss=0.1267, simple_loss=0.213, pruned_loss=0.02019, over 4825.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03311, over 974097.92 frames.], batch size: 26, lr: 2.17e-04
2022-05-06 17:49:38,470 INFO [train.py:715] (1/8) Epoch 10, batch 5250, loss[loss=0.1221, simple_loss=0.2, pruned_loss=0.02212, over 4986.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.0329, over 974556.24 frames.], batch size: 28, lr: 2.17e-04
2022-05-06 17:50:17,849 INFO [train.py:715] (1/8) Epoch 10, batch 5300, loss[loss=0.1956, simple_loss=0.25, pruned_loss=0.07062, over 4841.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.03311, over 974037.63 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 17:50:57,191 INFO [train.py:715] (1/8) Epoch 10, batch 5350, loss[loss=0.1409, simple_loss=0.213, pruned_loss=0.0344, over 4821.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2122, pruned_loss=0.03278, over 973797.84 frames.], batch size: 26, lr: 2.17e-04
2022-05-06 17:51:37,021 INFO [train.py:715] (1/8) Epoch 10, batch 5400, loss[loss=0.1422, simple_loss=0.2094, pruned_loss=0.03753, over 4764.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2125, pruned_loss=0.03269, over 973010.36 frames.], batch size: 18, lr: 2.17e-04
2022-05-06 17:52:16,936 INFO [train.py:715] (1/8) Epoch 10, batch 5450, loss[loss=0.1343, simple_loss=0.2065, pruned_loss=0.03112, over 4781.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03291, over 972648.75 frames.], batch size: 12, lr: 2.17e-04
2022-05-06 17:52:56,342 INFO [train.py:715] (1/8) Epoch 10, batch 5500, loss[loss=0.1349, simple_loss=0.1976, pruned_loss=0.03613, over 4752.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03291, over 972271.25 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 17:53:36,102 INFO [train.py:715] (1/8) Epoch 10, batch 5550, loss[loss=0.1794, simple_loss=0.2467, pruned_loss=0.05603, over 4848.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2129, pruned_loss=0.03377, over 972220.66 frames.], batch size: 20, lr: 2.17e-04
2022-05-06 17:54:16,051 INFO [train.py:715] (1/8) Epoch 10, batch 5600, loss[loss=0.1577, simple_loss=0.2304, pruned_loss=0.04255, over 4860.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03357, over 972235.97 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 17:54:55,812 INFO [train.py:715] (1/8) Epoch 10, batch 5650, loss[loss=0.1484, simple_loss=0.2121, pruned_loss=0.04238, over 4840.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03361, over 971310.55 frames.], batch size: 32, lr: 2.17e-04
2022-05-06 17:55:34,979 INFO [train.py:715] (1/8) Epoch 10, batch 5700, loss[loss=0.1505, simple_loss=0.2231, pruned_loss=0.03897, over 4792.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.03415, over 970928.01 frames.], batch size: 18, lr: 2.17e-04
2022-05-06 17:56:15,021 INFO [train.py:715] (1/8) Epoch 10, batch 5750, loss[loss=0.1415, simple_loss=0.2179, pruned_loss=0.03249, over 4786.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03419, over 971141.18 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 17:56:54,687 INFO [train.py:715] (1/8) Epoch 10, batch 5800, loss[loss=0.1447, simple_loss=0.2146, pruned_loss=0.03739, over 4767.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2125, pruned_loss=0.03368, over 970410.85 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 17:57:34,209 INFO [train.py:715] (1/8) Epoch 10, batch 5850, loss[loss=0.1296, simple_loss=0.1979, pruned_loss=0.03068, over 4777.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2134, pruned_loss=0.03438, over 970932.14 frames.], batch size: 18, lr: 2.17e-04
2022-05-06 17:58:14,027 INFO [train.py:715] (1/8) Epoch 10, batch 5900, loss[loss=0.1127, simple_loss=0.1858, pruned_loss=0.01977, over 4984.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03465, over 971136.51 frames.], batch size: 39, lr: 2.17e-04
2022-05-06 17:58:53,763 INFO [train.py:715] (1/8) Epoch 10, batch 5950, loss[loss=0.1526, simple_loss=0.2344, pruned_loss=0.03538, over 4867.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.03449, over 970986.22 frames.], batch size: 32, lr: 2.17e-04
2022-05-06 17:59:33,429 INFO [train.py:715] (1/8) Epoch 10, batch 6000, loss[loss=0.145, simple_loss=0.228, pruned_loss=0.03105, over 4811.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.03426, over 971559.75 frames.], batch size: 25, lr: 2.17e-04
2022-05-06 17:59:33,429 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 17:59:42,753 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1067, simple_loss=0.1909, pruned_loss=0.01126, over 914524.00 frames.
2022-05-06 18:00:22,323 INFO [train.py:715] (1/8) Epoch 10, batch 6050, loss[loss=0.128, simple_loss=0.1996, pruned_loss=0.02817, over 4845.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2143, pruned_loss=0.03422, over 972506.16 frames.], batch size: 30, lr: 2.17e-04
2022-05-06 18:01:00,747 INFO [train.py:715] (1/8) Epoch 10, batch 6100, loss[loss=0.1653, simple_loss=0.2441, pruned_loss=0.04326, over 4880.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2141, pruned_loss=0.0339, over 973094.99 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 18:01:40,209 INFO [train.py:715] (1/8) Epoch 10, batch 6150, loss[loss=0.1277, simple_loss=0.2037, pruned_loss=0.02583, over 4915.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2136, pruned_loss=0.03351, over 973036.95 frames.], batch size: 23, lr: 2.17e-04
2022-05-06 18:02:20,068 INFO [train.py:715] (1/8) Epoch 10, batch 6200, loss[loss=0.1395, simple_loss=0.2154, pruned_loss=0.03178, over 4812.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2135, pruned_loss=0.03341, over 973391.81 frames.], batch size: 13, lr: 2.17e-04
2022-05-06 18:02:59,939 INFO [train.py:715] (1/8) Epoch 10, batch 6250, loss[loss=0.1482, simple_loss=0.2149, pruned_loss=0.0408, over 4873.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2132, pruned_loss=0.03313, over 974013.32 frames.], batch size: 32, lr: 2.17e-04
2022-05-06 18:03:39,470 INFO [train.py:715] (1/8) Epoch 10, batch 6300, loss[loss=0.1335, simple_loss=0.2027, pruned_loss=0.03219, over 4814.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03272, over 973277.68 frames.], batch size: 25, lr: 2.17e-04
2022-05-06 18:04:19,277 INFO [train.py:715] (1/8) Epoch 10, batch 6350, loss[loss=0.1332, simple_loss=0.2093, pruned_loss=0.02849, over 4759.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03287, over 972892.63 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 18:04:58,325 INFO [train.py:715] (1/8) Epoch 10, batch 6400, loss[loss=0.1666, simple_loss=0.2386, pruned_loss=0.0473, over 4815.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03276, over 973171.65 frames.], batch size: 27, lr: 2.17e-04
2022-05-06 18:05:36,734 INFO [train.py:715] (1/8) Epoch 10, batch 6450, loss[loss=0.1535, simple_loss=0.2233, pruned_loss=0.04185, over 4788.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.03345, over 972819.65 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 18:06:15,658 INFO [train.py:715] (1/8) Epoch 10, batch 6500, loss[loss=0.1241, simple_loss=0.2013, pruned_loss=0.02343, over 4697.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2131, pruned_loss=0.03396, over 971985.26 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:06:54,774 INFO [train.py:715] (1/8) Epoch 10, batch 6550, loss[loss=0.1391, simple_loss=0.206, pruned_loss=0.03615, over 4874.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03415, over 972804.74 frames.], batch size: 32, lr: 2.17e-04
2022-05-06 18:07:33,912 INFO [train.py:715] (1/8) Epoch 10, batch 6600, loss[loss=0.1371, simple_loss=0.2123, pruned_loss=0.03097, over 4779.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2151, pruned_loss=0.03469, over 972486.74 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:08:12,465 INFO [train.py:715] (1/8) Epoch 10, batch 6650, loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.0329, over 4741.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.0344, over 972313.29 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 18:08:52,618 INFO [train.py:715] (1/8) Epoch 10, batch 6700, loss[loss=0.1182, simple_loss=0.2048, pruned_loss=0.01578, over 4948.00 frames.], tot_loss[loss=0.1409, simple_loss=0.214, pruned_loss=0.03385, over 972172.54 frames.], batch size: 21, lr: 2.17e-04
2022-05-06 18:09:31,852 INFO [train.py:715] (1/8) Epoch 10, batch 6750, loss[loss=0.1251, simple_loss=0.194, pruned_loss=0.02807, over 4962.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03393, over 971837.90 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:10:10,543 INFO [train.py:715] (1/8) Epoch 10, batch 6800, loss[loss=0.1387, simple_loss=0.2015, pruned_loss=0.03798, over 4891.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2137, pruned_loss=0.03357, over 971507.28 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:10:50,410 INFO [train.py:715] (1/8) Epoch 10, batch 6850, loss[loss=0.1623, simple_loss=0.2426, pruned_loss=0.041, over 4878.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2136, pruned_loss=0.03343, over 971104.92 frames.], batch size: 20, lr: 2.17e-04
2022-05-06 18:11:29,654 INFO [train.py:715] (1/8) Epoch 10, batch 6900, loss[loss=0.1576, simple_loss=0.2227, pruned_loss=0.04627, over 4951.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2142, pruned_loss=0.03338, over 971882.28 frames.], batch size: 29, lr: 2.17e-04
2022-05-06 18:12:08,733 INFO [train.py:715] (1/8) Epoch 10, batch 6950, loss[loss=0.1524, simple_loss=0.2246, pruned_loss=0.04009, over 4862.00 frames.], tot_loss[loss=0.14, simple_loss=0.2137, pruned_loss=0.03311, over 971264.13 frames.], batch size: 20, lr: 2.17e-04
2022-05-06 18:12:48,639 INFO [train.py:715] (1/8) Epoch 10, batch 7000, loss[loss=0.1369, simple_loss=0.2164, pruned_loss=0.02872, over 4973.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2146, pruned_loss=0.03386, over 972234.75 frames.], batch size: 24, lr: 2.17e-04
2022-05-06 18:13:28,547 INFO [train.py:715] (1/8) Epoch 10, batch 7050, loss[loss=0.1215, simple_loss=0.1943, pruned_loss=0.02435, over 4880.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2137, pruned_loss=0.03329, over 973162.68 frames.], batch size: 39, lr: 2.17e-04
2022-05-06 18:14:07,742 INFO [train.py:715] (1/8) Epoch 10, batch 7100, loss[loss=0.1536, simple_loss=0.2316, pruned_loss=0.03779, over 4818.00 frames.], tot_loss[loss=0.14, simple_loss=0.2135, pruned_loss=0.03328, over 973337.07 frames.], batch size: 25, lr: 2.17e-04
2022-05-06 18:14:46,903 INFO [train.py:715] (1/8) Epoch 10, batch 7150, loss[loss=0.1525, simple_loss=0.2123, pruned_loss=0.04637, over 4767.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2129, pruned_loss=0.03295, over 972827.69 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:15:26,296 INFO [train.py:715] (1/8) Epoch 10, batch 7200, loss[loss=0.1122, simple_loss=0.1909, pruned_loss=0.01673, over 4753.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2135, pruned_loss=0.03333, over 972737.91 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 18:16:05,418 INFO [train.py:715] (1/8) Epoch 10, batch 7250, loss[loss=0.1257, simple_loss=0.2133, pruned_loss=0.01905, over 4915.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2136, pruned_loss=0.03362, over 973597.68 frames.], batch size: 29, lr: 2.17e-04
2022-05-06 18:16:44,408 INFO [train.py:715] (1/8) Epoch 10, batch 7300, loss[loss=0.1492, simple_loss=0.23, pruned_loss=0.03424, over 4843.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2142, pruned_loss=0.03371, over 973416.71 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:17:23,326 INFO [train.py:715] (1/8) Epoch 10, batch 7350, loss[loss=0.1212, simple_loss=0.1865, pruned_loss=0.02793, over 4975.00 frames.], tot_loss[loss=0.1397, simple_loss=0.213, pruned_loss=0.03323, over 973360.23 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 18:18:02,741 INFO [train.py:715] (1/8) Epoch 10, batch 7400, loss[loss=0.1314, simple_loss=0.1863, pruned_loss=0.03823, over 4826.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03294, over 973305.11 frames.], batch size: 13, lr: 2.17e-04
2022-05-06 18:18:41,888 INFO [train.py:715] (1/8) Epoch 10, batch 7450, loss[loss=0.117, simple_loss=0.1984, pruned_loss=0.01784, over 4911.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.0328, over 972768.67 frames.], batch size: 29, lr: 2.17e-04
2022-05-06 18:19:20,014 INFO [train.py:715] (1/8) Epoch 10, batch 7500, loss[loss=0.1478, simple_loss=0.225, pruned_loss=0.03534, over 4871.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2122, pruned_loss=0.03277, over 972328.52 frames.], batch size: 32, lr: 2.17e-04
2022-05-06 18:19:59,641 INFO [train.py:715] (1/8) Epoch 10, batch 7550, loss[loss=0.1328, simple_loss=0.2076, pruned_loss=0.02902, over 4837.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03311, over 972349.68 frames.], batch size: 26, lr: 2.17e-04
2022-05-06 18:20:38,456 INFO [train.py:715] (1/8) Epoch 10, batch 7600, loss[loss=0.1234, simple_loss=0.2003, pruned_loss=0.02326, over 4799.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03283, over 972042.42 frames.], batch size: 21, lr: 2.17e-04
2022-05-06 18:21:17,037 INFO [train.py:715] (1/8) Epoch 10, batch 7650, loss[loss=0.1329, simple_loss=0.2059, pruned_loss=0.0299, over 4750.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2122, pruned_loss=0.03334, over 972698.39 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 18:21:56,435 INFO [train.py:715] (1/8) Epoch 10, batch 7700, loss[loss=0.1935, simple_loss=0.2439, pruned_loss=0.07149, over 4788.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2122, pruned_loss=0.03357, over 971843.19 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:22:35,792 INFO [train.py:715] (1/8) Epoch 10, batch 7750, loss[loss=0.1662, simple_loss=0.2367, pruned_loss=0.04784, over 4973.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2123, pruned_loss=0.03355, over 972609.31 frames.], batch size: 35, lr: 2.17e-04
2022-05-06 18:23:15,171 INFO [train.py:715] (1/8) Epoch 10, batch 7800, loss[loss=0.1204, simple_loss=0.19, pruned_loss=0.02545, over 4986.00 frames.], tot_loss[loss=0.139, simple_loss=0.2113, pruned_loss=0.03335, over 972281.05 frames.], batch size: 25, lr: 2.17e-04
2022-05-06 18:23:53,545 INFO [train.py:715] (1/8) Epoch 10, batch 7850, loss[loss=0.1197, simple_loss=0.2041, pruned_loss=0.01765, over 4936.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2109, pruned_loss=0.03322, over 972465.66 frames.], batch size: 21, lr: 2.17e-04
2022-05-06 18:24:33,021 INFO [train.py:715] (1/8) Epoch 10, batch 7900, loss[loss=0.1435, simple_loss=0.2144, pruned_loss=0.03632, over 4766.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2109, pruned_loss=0.03318, over 971470.85 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 18:25:12,543 INFO [train.py:715] (1/8) Epoch 10, batch 7950, loss[loss=0.1098, simple_loss=0.1792, pruned_loss=0.02022, over 4949.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2105, pruned_loss=0.03308, over 971109.22 frames.], batch size: 21, lr: 2.17e-04
2022-05-06 18:25:51,356 INFO [train.py:715] (1/8) Epoch 10, batch 8000, loss[loss=0.1373, simple_loss=0.2071, pruned_loss=0.03376, over 4855.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2112, pruned_loss=0.03308, over 971756.01 frames.], batch size: 30, lr: 2.17e-04
2022-05-06 18:26:30,788 INFO [train.py:715] (1/8) Epoch 10, batch 8050, loss[loss=0.1187, simple_loss=0.1989, pruned_loss=0.0193, over 4846.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03355, over 971876.70 frames.], batch size: 20, lr: 2.17e-04
2022-05-06 18:27:10,407 INFO [train.py:715] (1/8) Epoch 10, batch 8100, loss[loss=0.124, simple_loss=0.1974, pruned_loss=0.02532, over 4913.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03333, over 972526.15 frames.], batch size: 18, lr: 2.17e-04
2022-05-06 18:27:49,300 INFO [train.py:715] (1/8) Epoch 10, batch 8150, loss[loss=0.1516, simple_loss=0.2198, pruned_loss=0.04175, over 4764.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2132, pruned_loss=0.03333, over 972306.01 frames.], batch size: 18, lr: 2.17e-04
2022-05-06 18:28:27,914 INFO [train.py:715] (1/8) Epoch 10, batch 8200, loss[loss=0.1702, simple_loss=0.2268, pruned_loss=0.05681, over 4959.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2132, pruned_loss=0.0335, over 972602.91 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:29:07,588 INFO [train.py:715] (1/8) Epoch 10, batch 8250, loss[loss=0.1223, simple_loss=0.2034, pruned_loss=0.02062, over 4891.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03329, over 972044.36 frames.], batch size: 22, lr: 2.17e-04
2022-05-06 18:29:46,986 INFO [train.py:715] (1/8) Epoch 10, batch 8300, loss[loss=0.1578, simple_loss=0.2259, pruned_loss=0.04483, over 4794.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2123, pruned_loss=0.03339, over 972288.23 frames.], batch size: 24, lr: 2.17e-04
2022-05-06 18:30:25,733 INFO [train.py:715] (1/8) Epoch 10, batch 8350, loss[loss=0.1508, simple_loss=0.2151, pruned_loss=0.04325, over 4771.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2123, pruned_loss=0.03331, over 970951.81 frames.], batch size: 18, lr: 2.17e-04
2022-05-06 18:31:05,467 INFO [train.py:715] (1/8) Epoch 10, batch 8400, loss[loss=0.1585, simple_loss=0.2326, pruned_loss=0.04219, over 4904.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.03432, over 971592.06 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:31:44,982 INFO [train.py:715] (1/8) Epoch 10, batch 8450, loss[loss=0.1586, simple_loss=0.2317, pruned_loss=0.04276, over 4921.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03472, over 972478.70 frames.], batch size: 18, lr: 2.16e-04
2022-05-06 18:32:23,259 INFO [train.py:715] (1/8) Epoch 10, batch 8500, loss[loss=0.1599, simple_loss=0.2346, pruned_loss=0.04265, over 4854.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03469, over 972563.82 frames.], batch size: 30, lr: 2.16e-04
2022-05-06 18:33:02,050 INFO [train.py:715] (1/8) Epoch 10, batch 8550, loss[loss=0.1406, simple_loss=0.2097, pruned_loss=0.03573, over 4911.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03482, over 973125.55 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 18:33:41,299 INFO [train.py:715] (1/8) Epoch 10, batch 8600, loss[loss=0.1595, simple_loss=0.2224, pruned_loss=0.04826, over 4963.00 frames.], tot_loss[loss=0.142, simple_loss=0.2142, pruned_loss=0.03494, over 972547.64 frames.], batch size: 14, lr: 2.16e-04
2022-05-06 18:34:19,981 INFO [train.py:715] (1/8) Epoch 10, batch 8650, loss[loss=0.1243, simple_loss=0.2042, pruned_loss=0.02223, over 4920.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.0347, over 972662.88 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 18:34:58,631 INFO [train.py:715] (1/8) Epoch 10, batch 8700, loss[loss=0.1517, simple_loss=0.2239, pruned_loss=0.03979, over 4944.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2139, pruned_loss=0.03462, over 972121.33 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 18:35:37,456 INFO [train.py:715] (1/8) Epoch 10, batch 8750, loss[loss=0.1324, simple_loss=0.2017, pruned_loss=0.03159, over 4690.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2123, pruned_loss=0.03378, over 972385.09 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:36:15,828 INFO [train.py:715] (1/8) Epoch 10, batch 8800, loss[loss=0.1243, simple_loss=0.1938, pruned_loss=0.02744, over 4866.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2116, pruned_loss=0.0334, over 972342.66 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 18:36:54,705 INFO [train.py:715] (1/8) Epoch 10, batch 8850, loss[loss=0.1453, simple_loss=0.2271, pruned_loss=0.03173, over 4745.00 frames.], tot_loss[loss=0.14, simple_loss=0.2124, pruned_loss=0.03378, over 972330.53 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:37:34,276 INFO [train.py:715] (1/8) Epoch 10, batch 8900, loss[loss=0.1537, simple_loss=0.2185, pruned_loss=0.04441, over 4728.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2118, pruned_loss=0.03375, over 972506.81 frames.], batch size: 16, lr: 2.16e-04
2022-05-06 18:38:13,788 INFO [train.py:715] (1/8) Epoch 10, batch 8950, loss[loss=0.1479, simple_loss=0.216, pruned_loss=0.03986, over 4878.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2125, pruned_loss=0.03358, over 972477.10 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 18:38:53,302 INFO [train.py:715] (1/8) Epoch 10, batch 9000, loss[loss=0.1071, simple_loss=0.1756, pruned_loss=0.01928, over 4774.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2128, pruned_loss=0.03386, over 972720.31 frames.], batch size: 12, lr: 2.16e-04
2022-05-06 18:38:53,303 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 18:39:02,858 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1064, simple_loss=0.1907, pruned_loss=0.01106, over 914524.00 frames.
2022-05-06 18:39:42,084 INFO [train.py:715] (1/8) Epoch 10, batch 9050, loss[loss=0.1272, simple_loss=0.2067, pruned_loss=0.02388, over 4922.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2128, pruned_loss=0.03389, over 971905.06 frames.], batch size: 29, lr: 2.16e-04
2022-05-06 18:40:21,148 INFO [train.py:715] (1/8) Epoch 10, batch 9100, loss[loss=0.175, simple_loss=0.2364, pruned_loss=0.05681, over 4914.00 frames.], tot_loss[loss=0.1404, simple_loss=0.213, pruned_loss=0.03393, over 972196.13 frames.], batch size: 18, lr: 2.16e-04
2022-05-06 18:41:01,479 INFO [train.py:715] (1/8) Epoch 10, batch 9150, loss[loss=0.1549, simple_loss=0.2225, pruned_loss=0.04364, over 4753.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03317, over 972717.33 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:41:40,995 INFO [train.py:715] (1/8) Epoch 10, batch 9200, loss[loss=0.1332, simple_loss=0.1941, pruned_loss=0.03616, over 4970.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.03335, over 972311.92 frames.], batch size: 35, lr: 2.16e-04
2022-05-06 18:42:20,444 INFO [train.py:715] (1/8) Epoch 10, batch 9250, loss[loss=0.1455, simple_loss=0.2214, pruned_loss=0.03485, over 4702.00 frames.], tot_loss[loss=0.141, simple_loss=0.2136, pruned_loss=0.03417, over 971464.35 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:43:00,262 INFO [train.py:715] (1/8) Epoch 10, batch 9300, loss[loss=0.1287, simple_loss=0.2069, pruned_loss=0.0253, over 4701.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2134, pruned_loss=0.03407, over 971399.34 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:43:39,883 INFO [train.py:715] (1/8) Epoch 10, batch 9350, loss[loss=0.145, simple_loss=0.2172, pruned_loss=0.03637, over 4812.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2129, pruned_loss=0.03401, over 971897.95 frames.], batch size: 25, lr: 2.16e-04
2022-05-06 18:44:19,391 INFO [train.py:715] (1/8) Epoch 10, batch 9400, loss[loss=0.1267, simple_loss=0.1971, pruned_loss=0.02812, over 4929.00 frames.], tot_loss[loss=0.1408, simple_loss=0.213, pruned_loss=0.03429, over 971590.13 frames.], batch size: 29, lr: 2.16e-04
2022-05-06 18:44:58,979 INFO [train.py:715] (1/8) Epoch 10, batch 9450, loss[loss=0.1291, simple_loss=0.2067, pruned_loss=0.02581, over 4690.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2125, pruned_loss=0.03367, over 972256.96 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:45:38,373 INFO [train.py:715] (1/8) Epoch 10, batch 9500, loss[loss=0.1132, simple_loss=0.189, pruned_loss=0.01874, over 4792.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2129, pruned_loss=0.03406, over 972167.29 frames.], batch size: 24, lr: 2.16e-04
2022-05-06 18:46:17,353 INFO [train.py:715] (1/8) Epoch 10, batch 9550, loss[loss=0.1272, simple_loss=0.2081, pruned_loss=0.02313, over 4779.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2128, pruned_loss=0.03415, over 971428.21 frames.], batch size: 14, lr: 2.16e-04
2022-05-06 18:46:55,766 INFO [train.py:715] (1/8) Epoch 10, batch 9600, loss[loss=0.1167, simple_loss=0.1922, pruned_loss=0.02063, over 4954.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2124, pruned_loss=0.03386, over 973059.57 frames.], batch size: 35, lr: 2.16e-04
2022-05-06 18:47:34,907 INFO [train.py:715] (1/8) Epoch 10, batch 9650, loss[loss=0.1166, simple_loss=0.1935, pruned_loss=0.01984, over 4897.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2114, pruned_loss=0.03337, over 972709.88 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 18:48:14,559 INFO [train.py:715] (1/8) Epoch 10, batch 9700, loss[loss=0.1458, simple_loss=0.2229, pruned_loss=0.0343, over 4825.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2111, pruned_loss=0.03334, over 972040.76 frames.], batch size: 26, lr: 2.16e-04
2022-05-06 18:48:52,978 INFO [train.py:715] (1/8) Epoch 10, batch 9750, loss[loss=0.1522, simple_loss=0.2095, pruned_loss=0.04743, over 4990.00 frames.], tot_loss[loss=0.1387, simple_loss=0.211, pruned_loss=0.03323, over 971687.11 frames.], batch size: 14, lr: 2.16e-04
2022-05-06 18:49:32,213 INFO [train.py:715] (1/8) Epoch 10, batch 9800, loss[loss=0.1417, simple_loss=0.2145, pruned_loss=0.03446, over 4916.00 frames.], tot_loss[loss=0.1385, simple_loss=0.211, pruned_loss=0.03302, over 970877.71 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:50:11,747 INFO [train.py:715] (1/8) Epoch 10, batch 9850, loss[loss=0.1167, simple_loss=0.1892, pruned_loss=0.02207, over 4797.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2116, pruned_loss=0.03329, over 971263.58 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 18:50:51,055 INFO [train.py:715] (1/8) Epoch 10, batch 9900, loss[loss=0.139, simple_loss=0.2102, pruned_loss=0.03387, over 4747.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2117, pruned_loss=0.03324, over 971521.88 frames.], batch size: 16, lr: 2.16e-04
2022-05-06 18:51:30,045 INFO [train.py:715] (1/8) Epoch 10, batch 9950, loss[loss=0.1523, simple_loss=0.2347, pruned_loss=0.03489, over 4889.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2129, pruned_loss=0.03403, over 971271.04 frames.], batch size: 16, lr: 2.16e-04
2022-05-06 18:52:10,242 INFO [train.py:715] (1/8) Epoch 10, batch 10000, loss[loss=0.1525, simple_loss=0.2176, pruned_loss=0.04366, over 4754.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2124, pruned_loss=0.03394, over 971414.84 frames.], batch size: 12, lr: 2.16e-04
2022-05-06 18:52:49,841 INFO [train.py:715] (1/8) Epoch 10, batch 10050, loss[loss=0.1581, simple_loss=0.2277, pruned_loss=0.04425, over 4907.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2128, pruned_loss=0.03402, over 972132.03 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 18:53:27,865 INFO [train.py:715] (1/8) Epoch 10, batch 10100, loss[loss=0.1338, simple_loss=0.2039, pruned_loss=0.03179, over 4812.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2127, pruned_loss=0.03393, over 972085.21 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 18:54:06,607 INFO [train.py:715] (1/8) Epoch 10, batch 10150, loss[loss=0.1225, simple_loss=0.2001, pruned_loss=0.02242, over 4653.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2124, pruned_loss=0.03366, over 971598.26 frames.], batch size: 13, lr: 2.16e-04
2022-05-06 18:54:46,538 INFO [train.py:715] (1/8) Epoch 10, batch 10200, loss[loss=0.1643, simple_loss=0.2345, pruned_loss=0.04703, over 4975.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03325, over 972082.95 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:55:25,657 INFO [train.py:715] (1/8) Epoch 10, batch 10250, loss[loss=0.1328, simple_loss=0.2123, pruned_loss=0.02665, over 4766.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2131, pruned_loss=0.03376, over 972460.46 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:56:04,511 INFO [train.py:715] (1/8) Epoch 10, batch 10300, loss[loss=0.127, simple_loss=0.1966, pruned_loss=0.02868, over 4851.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2141, pruned_loss=0.03403, over 971994.96 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 18:56:44,439 INFO [train.py:715] (1/8) Epoch 10, batch 10350, loss[loss=0.1525, simple_loss=0.2304, pruned_loss=0.0373, over 4926.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2138, pruned_loss=0.03403, over 972599.64 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:57:24,435 INFO [train.py:715] (1/8) Epoch 10, batch 10400, loss[loss=0.134, simple_loss=0.2141, pruned_loss=0.02695, over 4914.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2146, pruned_loss=0.03441, over 972455.63 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:58:02,843 INFO [train.py:715] (1/8) Epoch 10, batch 10450, loss[loss=0.1538, simple_loss=0.2251, pruned_loss=0.04122, over 4837.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2151, pruned_loss=0.03455, over 972210.49 frames.], batch size: 30, lr: 2.16e-04
2022-05-06 18:58:41,110 INFO [train.py:715] (1/8) Epoch 10, batch 10500, loss[loss=0.1427, simple_loss=0.221, pruned_loss=0.03222, over 4986.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2148, pruned_loss=0.03434, over 972071.53 frames.], batch size: 28, lr: 2.16e-04
2022-05-06 18:59:20,243 INFO [train.py:715] (1/8) Epoch 10, batch 10550, loss[loss=0.1609, simple_loss=0.2395, pruned_loss=0.04109, over 4875.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2143, pruned_loss=0.03407, over 971803.88 frames.], batch size: 22, lr: 2.16e-04
2022-05-06 18:59:59,208 INFO [train.py:715] (1/8) Epoch 10, batch 10600, loss[loss=0.1354, simple_loss=0.211, pruned_loss=0.02994, over 4826.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2143, pruned_loss=0.03432, over 972173.85 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 19:00:37,419 INFO [train.py:715] (1/8) Epoch 10, batch 10650, loss[loss=0.1555, simple_loss=0.228, pruned_loss=0.04151, over 4854.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2147, pruned_loss=0.03424, over 972889.41 frames.], batch size: 20, lr: 2.16e-04
2022-05-06 19:01:16,839 INFO [train.py:715] (1/8) Epoch 10, batch 10700, loss[loss=0.1809, simple_loss=0.2321, pruned_loss=0.0649, over 4787.00 frames.], tot_loss[loss=0.142, simple_loss=0.2155, pruned_loss=0.03423, over 971476.33 frames.], batch size: 12, lr: 2.16e-04
2022-05-06 19:01:56,164 INFO [train.py:715] (1/8) Epoch 10, batch 10750, loss[loss=0.1333, simple_loss=0.2058, pruned_loss=0.03044, over 4984.00 frames.], tot_loss[loss=0.1415, simple_loss=0.215, pruned_loss=0.03403, over 972616.14 frames.], batch size: 28, lr: 2.16e-04
2022-05-06 19:02:34,988 INFO [train.py:715] (1/8) Epoch 10, batch 10800, loss[loss=0.1639, simple_loss=0.2456, pruned_loss=0.04112, over 4769.00 frames.], tot_loss[loss=0.142, simple_loss=0.2152, pruned_loss=0.0344, over 973005.61 frames.], batch size: 14, lr: 2.16e-04
2022-05-06 19:03:13,434 INFO [train.py:715] (1/8) Epoch 10, batch 10850, loss[loss=0.1488, simple_loss=0.2161, pruned_loss=0.0408, over 4868.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2145, pruned_loss=0.03421, over 973487.65 frames.], batch size: 20, lr: 2.16e-04
2022-05-06 19:03:52,877 INFO [train.py:715] (1/8) Epoch 10, batch 10900, loss[loss=0.1091, simple_loss=0.1749, pruned_loss=0.02167, over 4822.00 frames.], tot_loss[loss=0.142, simple_loss=0.2151, pruned_loss=0.03446, over 973837.65 frames.], batch size: 27, lr: 2.16e-04
2022-05-06 19:04:31,792 INFO [train.py:715] (1/8) Epoch 10, batch 10950, loss[loss=0.1116, simple_loss=0.1796, pruned_loss=0.02184, over 4832.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03418, over 973960.97 frames.], batch size: 26, lr: 2.16e-04
2022-05-06 19:05:10,345 INFO [train.py:715] (1/8) Epoch 10, batch 11000, loss[loss=0.1456, simple_loss=0.215, pruned_loss=0.03814, over 4924.00 frames.], tot_loss[loss=0.141, simple_loss=0.2141, pruned_loss=0.03397, over 973871.13 frames.], batch size: 29, lr: 2.16e-04
2022-05-06 19:05:49,496 INFO [train.py:715] (1/8) Epoch 10, batch 11050, loss[loss=0.1503, simple_loss=0.2241, pruned_loss=0.03821, over 4807.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03403, over 973220.51 frames.], batch size: 25, lr: 2.16e-04
2022-05-06 19:06:29,281 INFO [train.py:715] (1/8) Epoch 10, batch 11100, loss[loss=0.1294, simple_loss=0.206, pruned_loss=0.02642, over 4962.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2136, pruned_loss=0.03412, over 973268.49 frames.], batch size: 29, lr: 2.16e-04
2022-05-06 19:07:07,078 INFO [train.py:715] (1/8) Epoch 10, batch 11150, loss[loss=0.1534, simple_loss=0.2242, pruned_loss=0.04131, over 4883.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2128, pruned_loss=0.03397, over 973208.73 frames.], batch size: 16, lr: 2.16e-04
2022-05-06 19:07:46,334 INFO [train.py:715] (1/8) Epoch 10, batch 11200, loss[loss=0.1478, simple_loss=0.2128, pruned_loss=0.04146, over 4872.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2131, pruned_loss=0.03419, over 972935.49 frames.], batch size: 30, lr: 2.16e-04
2022-05-06 19:08:25,401 INFO [train.py:715] (1/8) Epoch 10, batch 11250, loss[loss=0.1281, simple_loss=0.1972, pruned_loss=0.02951, over 4819.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2132, pruned_loss=0.03409, over 973155.03 frames.], batch size: 26, lr: 2.16e-04
2022-05-06 19:09:03,753 INFO [train.py:715] (1/8) Epoch 10, batch 11300, loss[loss=0.1373, simple_loss=0.2122, pruned_loss=0.03113, over 4818.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03346, over 973045.63 frames.], batch size: 13, lr: 2.16e-04
2022-05-06 19:09:42,490 INFO [train.py:715] (1/8) Epoch 10, batch 11350, loss[loss=0.1139, simple_loss=0.1891, pruned_loss=0.0193, over 4985.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03382, over 972841.74 frames.], batch size: 25, lr: 2.16e-04
2022-05-06 19:10:21,469 INFO [train.py:715] (1/8) Epoch 10, batch 11400, loss[loss=0.1397, simple_loss=0.2131, pruned_loss=0.03319, over 4948.00 frames.], tot_loss[loss=0.1402, simple_loss=0.213, pruned_loss=0.0337, over 972831.05 frames.], batch size: 23, lr: 2.16e-04
2022-05-06 19:11:00,936 INFO [train.py:715] (1/8) Epoch 10, batch 11450, loss[loss=0.1332, simple_loss=0.2114, pruned_loss=0.02747, over 4878.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.03411, over 972398.76 frames.], batch size: 16, lr: 2.16e-04
2022-05-06 19:11:38,820 INFO [train.py:715] (1/8) Epoch 10, batch 11500, loss[loss=0.1324, simple_loss=0.2091, pruned_loss=0.02784, over 4784.00 frames.], tot_loss[loss=0.1399, simple_loss=0.213, pruned_loss=0.03344, over 972869.77 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 19:12:17,870 INFO [train.py:715] (1/8) Epoch 10, batch 11550, loss[loss=0.1234, simple_loss=0.1943, pruned_loss=0.02622, over 4906.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03311, over 972819.12 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 19:12:57,423 INFO [train.py:715] (1/8) Epoch 10, batch 11600, loss[loss=0.1175, simple_loss=0.192, pruned_loss=0.02154, over 4782.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03328, over 972788.37 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 19:13:35,824 INFO [train.py:715] (1/8) Epoch 10, batch 11650, loss[loss=0.1501, simple_loss=0.2119, pruned_loss=0.04419, over 4864.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03317, over 972811.45 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 19:14:14,880 INFO [train.py:715] (1/8) Epoch 10, batch 11700, loss[loss=0.1603, simple_loss=0.2215, pruned_loss=0.04955, over 4976.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03303, over 972634.67 frames.], batch size: 35, lr: 2.16e-04
2022-05-06 19:14:53,451 INFO [train.py:715] (1/8) Epoch 10, batch 11750, loss[loss=0.1634, simple_loss=0.2435, pruned_loss=0.04165, over 4939.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.03235, over 973058.49 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:15:32,369 INFO [train.py:715] (1/8) Epoch 10, batch 11800, loss[loss=0.1332, simple_loss=0.2194, pruned_loss=0.02354, over 4853.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.03218, over 973046.31 frames.], batch size: 20, lr: 2.15e-04
2022-05-06 19:16:10,394 INFO [train.py:715] (1/8) Epoch 10, batch 11850, loss[loss=0.1135, simple_loss=0.1878, pruned_loss=0.01954, over 4981.00 frames.], tot_loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.03221, over 973145.45 frames.], batch size: 28, lr: 2.15e-04
2022-05-06 19:16:49,161 INFO [train.py:715] (1/8) Epoch 10, batch 11900, loss[loss=0.1229, simple_loss=0.2034, pruned_loss=0.02114, over 4844.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2121, pruned_loss=0.03272, over 972713.81 frames.], batch size: 12, lr: 2.15e-04
2022-05-06 19:17:30,482 INFO [train.py:715] (1/8) Epoch 10, batch 11950, loss[loss=0.1144, simple_loss=0.1916, pruned_loss=0.01862, over 4786.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03325, over 973500.98 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:18:09,370 INFO [train.py:715] (1/8) Epoch 10, batch 12000, loss[loss=0.1241, simple_loss=0.2008, pruned_loss=0.02367, over 4749.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03318, over 973300.72 frames.], batch size: 16, lr: 2.15e-04
2022-05-06 19:18:09,370 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 19:18:19,017 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1065, simple_loss=0.1908, pruned_loss=0.01105, over 914524.00 frames.
2022-05-06 19:18:57,897 INFO [train.py:715] (1/8) Epoch 10, batch 12050, loss[loss=0.1433, simple_loss=0.2219, pruned_loss=0.03234, over 4871.00 frames.], tot_loss[loss=0.139, simple_loss=0.2117, pruned_loss=0.0332, over 972385.44 frames.], batch size: 22, lr: 2.15e-04
2022-05-06 19:19:37,114 INFO [train.py:715] (1/8) Epoch 10, batch 12100, loss[loss=0.1235, simple_loss=0.1951, pruned_loss=0.02591, over 4817.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2117, pruned_loss=0.03351, over 972452.08 frames.], batch size: 12, lr: 2.15e-04
2022-05-06 19:20:16,372 INFO [train.py:715] (1/8) Epoch 10, batch 12150, loss[loss=0.1326, simple_loss=0.2162, pruned_loss=0.02453, over 4954.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2128, pruned_loss=0.03376, over 973069.60 frames.], batch size: 24, lr: 2.15e-04
2022-05-06 19:20:55,541 INFO [train.py:715] (1/8) Epoch 10, batch 12200, loss[loss=0.1325, simple_loss=0.2089, pruned_loss=0.02808, over 4943.00 frames.], tot_loss[loss=0.14, simple_loss=0.2124, pruned_loss=0.03375, over 973300.88 frames.], batch size: 21, lr: 2.15e-04
2022-05-06 19:21:34,085 INFO [train.py:715] (1/8) Epoch 10, batch 12250, loss[loss=0.1528, simple_loss=0.2155, pruned_loss=0.04502, over 4838.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2125, pruned_loss=0.03361, over 973335.53 frames.], batch size: 30, lr: 2.15e-04
2022-05-06 19:22:13,027 INFO [train.py:715] (1/8) Epoch 10, batch 12300, loss[loss=0.1405, simple_loss=0.217, pruned_loss=0.03206, over 4878.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2128, pruned_loss=0.03377, over 972603.82 frames.], batch size: 22, lr: 2.15e-04
2022-05-06 19:22:51,956 INFO [train.py:715] (1/8) Epoch 10, batch 12350, loss[loss=0.154, simple_loss=0.2186, pruned_loss=0.04471, over 4848.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2131, pruned_loss=0.03373, over 971675.95 frames.], batch size: 32, lr: 2.15e-04
2022-05-06 19:23:30,786 INFO [train.py:715] (1/8) Epoch 10, batch 12400, loss[loss=0.1287, simple_loss=0.2096, pruned_loss=0.02394, over 4927.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03389, over 971877.36 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:24:09,216 INFO [train.py:715] (1/8) Epoch 10, batch 12450, loss[loss=0.1263, simple_loss=0.1989, pruned_loss=0.02689, over 4966.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03396, over 972851.44 frames.], batch size: 35, lr: 2.15e-04
2022-05-06 19:24:48,246 INFO [train.py:715] (1/8) Epoch 10, batch 12500, loss[loss=0.1393, simple_loss=0.2143, pruned_loss=0.03211, over 4949.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03369, over 972646.87 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:25:27,024 INFO [train.py:715] (1/8) Epoch 10, batch 12550, loss[loss=0.1349, simple_loss=0.2024, pruned_loss=0.03372, over 4921.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03394, over 973096.58 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:26:05,179 INFO [train.py:715] (1/8) Epoch 10, batch 12600, loss[loss=0.1472, simple_loss=0.214, pruned_loss=0.04016, over 4863.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03364, over 972036.17 frames.], batch size: 32, lr: 2.15e-04
2022-05-06 19:26:43,469 INFO [train.py:715] (1/8) Epoch 10, batch 12650, loss[loss=0.1458, simple_loss=0.2091, pruned_loss=0.0412, over 4828.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03366, over 971846.95 frames.], batch size: 13, lr: 2.15e-04
2022-05-06 19:27:22,407 INFO [train.py:715] (1/8) Epoch 10, batch 12700, loss[loss=0.1405, simple_loss=0.2174, pruned_loss=0.03181, over 4825.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03386, over 971733.89 frames.], batch size: 27, lr: 2.15e-04
2022-05-06 19:28:00,770 INFO [train.py:715] (1/8) Epoch 10, batch 12750, loss[loss=0.1611, simple_loss=0.2329, pruned_loss=0.04463, over 4974.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2128, pruned_loss=0.03396, over 971290.86 frames.], batch size: 39, lr: 2.15e-04
2022-05-06 19:28:39,214 INFO [train.py:715] (1/8) Epoch 10, batch 12800, loss[loss=0.1461, simple_loss=0.2176, pruned_loss=0.03731, over 4991.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2125, pruned_loss=0.03391, over 971223.50 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:29:18,665 INFO [train.py:715] (1/8) Epoch 10, batch 12850, loss[loss=0.09729, simple_loss=0.1633, pruned_loss=0.01561, over 4769.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2123, pruned_loss=0.0336, over 971912.37 frames.], batch size: 12, lr: 2.15e-04
2022-05-06 19:29:57,810 INFO [train.py:715] (1/8) Epoch 10, batch 12900, loss[loss=0.1495, simple_loss=0.2196, pruned_loss=0.03972, over 4823.00 frames.], tot_loss[loss=0.14, simple_loss=0.2125, pruned_loss=0.03369, over 970846.04 frames.], batch size: 26, lr: 2.15e-04
2022-05-06 19:30:36,228 INFO [train.py:715] (1/8) Epoch 10, batch 12950, loss[loss=0.1168, simple_loss=0.186, pruned_loss=0.02386, over 4763.00 frames.], tot_loss[loss=0.14, simple_loss=0.2126, pruned_loss=0.03374, over 971970.76 frames.], batch size: 12, lr: 2.15e-04
2022-05-06 19:31:14,797 INFO [train.py:715] (1/8) Epoch 10, batch 13000, loss[loss=0.1442, simple_loss=0.2087, pruned_loss=0.03982, over 4933.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2127, pruned_loss=0.03403, over 972082.65 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:31:54,374 INFO [train.py:715] (1/8) Epoch 10, batch 13050, loss[loss=0.162, simple_loss=0.233, pruned_loss=0.04554, over 4900.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2137, pruned_loss=0.03454, over 972969.40 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:32:32,855 INFO [train.py:715] (1/8) Epoch 10, batch 13100, loss[loss=0.1199, simple_loss=0.2051, pruned_loss=0.01729, over 4908.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2133, pruned_loss=0.03423, over 973248.85 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:33:11,982 INFO [train.py:715] (1/8) Epoch 10, batch 13150, loss[loss=0.1473, simple_loss=0.2187, pruned_loss=0.03793, over 4984.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2129, pruned_loss=0.03376, over 972530.65 frames.], batch size: 35, lr: 2.15e-04
2022-05-06 19:33:51,010 INFO [train.py:715] (1/8) Epoch 10, batch 13200, loss[loss=0.1389, simple_loss=0.2136, pruned_loss=0.03215, over 4914.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03394, over 972312.88 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:34:30,048 INFO [train.py:715] (1/8) Epoch 10, batch 13250, loss[loss=0.1504, simple_loss=0.2263, pruned_loss=0.03723, over 4833.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03393, over 971237.99 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:35:08,698 INFO [train.py:715] (1/8) Epoch 10, batch 13300, loss[loss=0.1323, simple_loss=0.2087, pruned_loss=0.02793, over 4808.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03385, over 971416.91 frames.], batch size: 25, lr: 2.15e-04
2022-05-06 19:35:47,099 INFO [train.py:715] (1/8) Epoch 10, batch 13350, loss[loss=0.1565, simple_loss=0.2323, pruned_loss=0.04035, over 4774.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03365, over 971841.46 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:36:26,371 INFO [train.py:715] (1/8) Epoch 10, batch 13400, loss[loss=0.1236, simple_loss=0.2016, pruned_loss=0.02286, over 4769.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03328, over 972124.44 frames.], batch size: 17, lr: 2.15e-04
2022-05-06 19:37:04,724 INFO [train.py:715] (1/8) Epoch 10, batch 13450, loss[loss=0.1641, simple_loss=0.2409, pruned_loss=0.04368, over 4922.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03311, over 974266.79 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:37:42,964 INFO [train.py:715] (1/8) Epoch 10, batch 13500, loss[loss=0.1241, simple_loss=0.1908, pruned_loss=0.02868, over 4918.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2126, pruned_loss=0.03359, over 973972.86 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:38:22,031 INFO [train.py:715] (1/8) Epoch 10, batch 13550, loss[loss=0.1346, simple_loss=0.201, pruned_loss=0.03409, over 4851.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2129, pruned_loss=0.03335, over 973323.51 frames.], batch size: 30, lr: 2.15e-04
2022-05-06 19:39:00,605 INFO [train.py:715] (1/8) Epoch 10, batch 13600, loss[loss=0.1442, simple_loss=0.226, pruned_loss=0.03121, over 4817.00 frames.], tot_loss[loss=0.1398, simple_loss=0.213, pruned_loss=0.03333, over 972872.05 frames.], batch size: 25, lr: 2.15e-04
2022-05-06 19:39:39,003 INFO [train.py:715] (1/8) Epoch 10, batch 13650, loss[loss=0.1322, simple_loss=0.2077, pruned_loss=0.02831, over 4865.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.03365, over 973412.28 frames.], batch size: 16, lr: 2.15e-04
2022-05-06 19:40:17,577 INFO [train.py:715] (1/8) Epoch 10, batch 13700, loss[loss=0.1208, simple_loss=0.1954, pruned_loss=0.02312, over 4883.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03331, over 973491.81 frames.], batch size: 22, lr: 2.15e-04
2022-05-06 19:40:57,642 INFO [train.py:715] (1/8) Epoch 10, batch 13750, loss[loss=0.1264, simple_loss=0.206, pruned_loss=0.0234, over 4994.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03342, over 973191.86 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:41:37,005 INFO [train.py:715] (1/8) Epoch 10, batch 13800, loss[loss=0.1403, simple_loss=0.219, pruned_loss=0.03081, over 4762.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03308, over 972874.58 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:42:15,509 INFO [train.py:715] (1/8) Epoch 10, batch 13850, loss[loss=0.1463, simple_loss=0.219, pruned_loss=0.03675, over 4814.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03346, over 972951.14 frames.], batch size: 26, lr: 2.15e-04
2022-05-06 19:42:55,147 INFO [train.py:715] (1/8) Epoch 10, batch 13900, loss[loss=0.1199, simple_loss=0.1914, pruned_loss=0.02417, over 4911.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2113, pruned_loss=0.03273, over 972747.75 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:43:33,823 INFO [train.py:715] (1/8) Epoch 10, batch 13950, loss[loss=0.1133, simple_loss=0.1964, pruned_loss=0.0151, over 4907.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03298, over 973279.93 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:44:12,830 INFO [train.py:715] (1/8) Epoch 10, batch 14000, loss[loss=0.1378, simple_loss=0.2103, pruned_loss=0.0327, over 4958.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2128, pruned_loss=0.03289, over 973728.58 frames.], batch size: 21, lr: 2.15e-04
2022-05-06 19:44:51,235 INFO [train.py:715] (1/8) Epoch 10, batch 14050, loss[loss=0.123, simple_loss=0.2021, pruned_loss=0.02198, over 4866.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2135, pruned_loss=0.03381, over 973771.32 frames.], batch size: 20, lr: 2.15e-04
2022-05-06 19:45:30,767 INFO [train.py:715] (1/8) Epoch 10, batch 14100, loss[loss=0.1259, simple_loss=0.2017, pruned_loss=0.02506, over 4883.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2138, pruned_loss=0.03401, over 973592.61 frames.], batch size: 22, lr: 2.15e-04
2022-05-06 19:46:09,123 INFO [train.py:715] (1/8) Epoch 10, batch 14150, loss[loss=0.1336, simple_loss=0.2128, pruned_loss=0.02719, over 4941.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.0338, over 974284.76 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:46:47,032 INFO [train.py:715] (1/8) Epoch 10, batch 14200, loss[loss=0.1478, simple_loss=0.2179, pruned_loss=0.03887, over 4743.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.03348, over 973610.73 frames.], batch size: 16, lr: 2.15e-04
2022-05-06 19:47:26,633 INFO [train.py:715] (1/8) Epoch 10, batch 14250, loss[loss=0.1438, simple_loss=0.2155, pruned_loss=0.03602, over 4859.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03319, over 972703.28 frames.], batch size: 30, lr: 2.15e-04
2022-05-06 19:48:05,008 INFO [train.py:715] (1/8) Epoch 10, batch 14300, loss[loss=0.1263, simple_loss=0.1955, pruned_loss=0.02857, over 4965.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03293, over 973218.45 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:48:43,126 INFO [train.py:715] (1/8) Epoch 10, batch 14350, loss[loss=0.1199, simple_loss=0.1891, pruned_loss=0.0253, over 4742.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03279, over 972733.54 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:49:21,564 INFO [train.py:715] (1/8) Epoch 10, batch 14400, loss[loss=0.1671, simple_loss=0.2264, pruned_loss=0.05393, over 4855.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2127, pruned_loss=0.03303, over 972509.02 frames.], batch size: 32, lr: 2.15e-04
2022-05-06 19:50:01,190 INFO [train.py:715] (1/8) Epoch 10, batch 14450, loss[loss=0.1226, simple_loss=0.196, pruned_loss=0.02462, over 4781.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03261, over 972712.88 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:50:39,558 INFO [train.py:715] (1/8) Epoch 10, batch 14500, loss[loss=0.1485, simple_loss=0.2206, pruned_loss=0.03814, over 4825.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03311, over 972319.18 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:51:17,696 INFO [train.py:715] (1/8) Epoch 10, batch 14550, loss[loss=0.1492, simple_loss=0.224, pruned_loss=0.03715, over 4908.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03327, over 972106.54 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:51:57,345 INFO [train.py:715] (1/8) Epoch 10, batch 14600, loss[loss=0.1254, simple_loss=0.1923, pruned_loss=0.0292, over 4642.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.03288, over 972128.55 frames.], batch size: 13, lr: 2.15e-04
2022-05-06 19:52:35,978 INFO [train.py:715] (1/8) Epoch 10, batch 14650, loss[loss=0.1174, simple_loss=0.1841, pruned_loss=0.02535, over 4658.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2111, pruned_loss=0.03257, over 972336.12 frames.], batch size: 13, lr: 2.15e-04
2022-05-06 19:53:14,366 INFO [train.py:715] (1/8) Epoch 10, batch 14700, loss[loss=0.1808, simple_loss=0.2531, pruned_loss=0.05426, over 4868.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03295, over 972445.19 frames.], batch size: 30, lr: 2.15e-04
2022-05-06 19:53:53,324 INFO [train.py:715] (1/8) Epoch 10, batch 14750, loss[loss=0.11, simple_loss=0.1837, pruned_loss=0.01816, over 4810.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03288, over 972151.55 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:54:33,130 INFO [train.py:715] (1/8) Epoch 10, batch 14800, loss[loss=0.1499, simple_loss=0.2219, pruned_loss=0.03894, over 4641.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.03342, over 972049.20 frames.], batch size: 13, lr: 2.15e-04
2022-05-06 19:55:12,157 INFO [train.py:715] (1/8) Epoch 10, batch 14850, loss[loss=0.1215, simple_loss=0.1962, pruned_loss=0.02341, over 4903.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2125, pruned_loss=0.03387, over 972136.00 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:55:50,172 INFO [train.py:715] (1/8) Epoch 10, batch 14900, loss[loss=0.1372, simple_loss=0.2125, pruned_loss=0.03099, over 4738.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2131, pruned_loss=0.03376, over 972301.17 frames.], batch size: 12, lr: 2.15e-04
2022-05-06 19:56:30,291 INFO [train.py:715] (1/8) Epoch 10, batch 14950, loss[loss=0.158, simple_loss=0.2306, pruned_loss=0.04267, over 4959.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03364, over 972066.31 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:57:09,813 INFO [train.py:715] (1/8) Epoch 10, batch 15000, loss[loss=0.1601, simple_loss=0.2421, pruned_loss=0.03911, over 4975.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2135, pruned_loss=0.03376, over 972560.14 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:57:09,814 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 19:57:19,462 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1065, simple_loss=0.1909, pruned_loss=0.01111, over 914524.00 frames.
2022-05-06 19:57:59,086 INFO [train.py:715] (1/8) Epoch 10, batch 15050, loss[loss=0.1514, simple_loss=0.2242, pruned_loss=0.03931, over 4778.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.0339, over 972483.46 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:58:38,143 INFO [train.py:715] (1/8) Epoch 10, batch 15100, loss[loss=0.1447, simple_loss=0.2246, pruned_loss=0.03239, over 4964.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2137, pruned_loss=0.03375, over 972653.31 frames.], batch size: 39, lr: 2.15e-04
2022-05-06 19:59:17,366 INFO [train.py:715] (1/8) Epoch 10, batch 15150, loss[loss=0.1235, simple_loss=0.1978, pruned_loss=0.02457, over 4796.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.03333, over 971588.92 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 19:59:56,358 INFO [train.py:715] (1/8) Epoch 10, batch 15200, loss[loss=0.1369, simple_loss=0.2165, pruned_loss=0.02865, over 4975.00 frames.], tot_loss[loss=0.139, simple_loss=0.2123, pruned_loss=0.03287, over 973044.09 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 20:00:35,740 INFO [train.py:715] (1/8) Epoch 10, batch 15250, loss[loss=0.1477, simple_loss=0.2194, pruned_loss=0.03802, over 4919.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2133, pruned_loss=0.03324, over 973253.70 frames.], batch size: 29, lr: 2.14e-04
2022-05-06 20:01:14,787 INFO [train.py:715] (1/8) Epoch 10, batch 15300, loss[loss=0.1358, simple_loss=0.2113, pruned_loss=0.03015, over 4701.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.0335, over 973361.03 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:01:54,056 INFO [train.py:715] (1/8) Epoch 10, batch 15350, loss[loss=0.1229, simple_loss=0.1938, pruned_loss=0.026, over 4800.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.0333, over 973439.89 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:02:34,120 INFO [train.py:715] (1/8) Epoch 10, batch 15400, loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02939, over 4795.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.0338, over 973638.79 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:03:13,390 INFO [train.py:715] (1/8) Epoch 10, batch 15450, loss[loss=0.1146, simple_loss=0.1764, pruned_loss=0.02643, over 4756.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.03441, over 972823.67 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:03:53,462 INFO [train.py:715] (1/8) Epoch 10, batch 15500, loss[loss=0.1753, simple_loss=0.2431, pruned_loss=0.05377, over 4786.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2134, pruned_loss=0.03455, over 973320.71 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:04:32,468 INFO [train.py:715] (1/8) Epoch 10, batch 15550, loss[loss=0.1313, simple_loss=0.205, pruned_loss=0.02875, over 4773.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03386, over 972981.22 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:05:11,885 INFO [train.py:715] (1/8) Epoch 10, batch 15600, loss[loss=0.1252, simple_loss=0.1905, pruned_loss=0.02996, over 4828.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03362, over 972458.86 frames.], batch size: 13, lr: 2.14e-04
2022-05-06 20:05:50,240 INFO [train.py:715] (1/8) Epoch 10, batch 15650, loss[loss=0.1329, simple_loss=0.2032, pruned_loss=0.03133, over 4738.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03317, over 971507.71 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:06:28,929 INFO [train.py:715] (1/8) Epoch 10, batch 15700, loss[loss=0.1265, simple_loss=0.2049, pruned_loss=0.02404, over 4770.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03373, over 971601.72 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:07:08,403 INFO [train.py:715] (1/8) Epoch 10, batch 15750, loss[loss=0.1266, simple_loss=0.2005, pruned_loss=0.02638, over 4778.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03386, over 971825.80 frames.], batch size: 17, lr: 2.14e-04
2022-05-06 20:07:46,970 INFO [train.py:715] (1/8) Epoch 10, batch 15800, loss[loss=0.1314, simple_loss=0.1973, pruned_loss=0.03274, over 4775.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03345, over 972246.31 frames.], batch size: 17, lr: 2.14e-04
2022-05-06 20:08:26,767 INFO [train.py:715] (1/8) Epoch 10, batch 15850, loss[loss=0.1063, simple_loss=0.1787, pruned_loss=0.01695, over 4872.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2128, pruned_loss=0.03319, over 972142.51 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:09:05,637 INFO [train.py:715] (1/8) Epoch 10, batch 15900, loss[loss=0.1171, simple_loss=0.1873, pruned_loss=0.02348, over 4930.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03355, over 971630.14 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:09:44,835 INFO [train.py:715] (1/8) Epoch 10, batch 15950, loss[loss=0.1437, simple_loss=0.2106, pruned_loss=0.03834, over 4733.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.03378, over 971705.02 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:10:23,753 INFO [train.py:715] (1/8) Epoch 10, batch 16000, loss[loss=0.1667, simple_loss=0.2382, pruned_loss=0.04761, over 4823.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2122, pruned_loss=0.0337, over 972196.48 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:11:02,642 INFO [train.py:715] (1/8) Epoch 10, batch 16050, loss[loss=0.1562, simple_loss=0.2207, pruned_loss=0.04588, over 4778.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03356, over 972225.94 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:11:41,914 INFO [train.py:715] (1/8) Epoch 10, batch 16100, loss[loss=0.1103, simple_loss=0.1849, pruned_loss=0.01786, over 4823.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.0334, over 971644.97 frames.], batch size: 26, lr: 2.14e-04
2022-05-06 20:12:21,125 INFO [train.py:715] (1/8) Epoch 10, batch 16150, loss[loss=0.1255, simple_loss=0.1846, pruned_loss=0.03322, over 4789.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.03307, over 971526.83 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:13:01,095 INFO [train.py:715] (1/8) Epoch 10, batch 16200, loss[loss=0.1273, simple_loss=0.2049, pruned_loss=0.02482, over 4782.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2118, pruned_loss=0.03328, over 972353.57 frames.], batch size: 18, lr: 2.14e-04
2022-05-06 20:13:40,632 INFO [train.py:715] (1/8) Epoch 10, batch 16250, loss[loss=0.1459, simple_loss=0.2198, pruned_loss=0.03596, over 4811.00 frames.], tot_loss[loss=0.14, simple_loss=0.2127, pruned_loss=0.03366, over 972370.12 frames.], batch size: 25, lr: 2.14e-04
2022-05-06 20:14:19,843 INFO [train.py:715] (1/8) Epoch 10, batch 16300, loss[loss=0.1335, simple_loss=0.2086, pruned_loss=0.02918, over 4960.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03326, over 972689.68 frames.], batch size: 35, lr: 2.14e-04
2022-05-06 20:14:59,849 INFO [train.py:715] (1/8) Epoch 10, batch 16350, loss[loss=0.1153, simple_loss=0.1992, pruned_loss=0.01574, over 4807.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.0338, over 972689.39 frames.], batch size: 25, lr: 2.14e-04
2022-05-06 20:15:39,242 INFO [train.py:715] (1/8) Epoch 10, batch 16400, loss[loss=0.1666, simple_loss=0.2408, pruned_loss=0.04623, over 4763.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2136, pruned_loss=0.03412, over 973376.48 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:16:18,980 INFO [train.py:715] (1/8) Epoch 10, batch 16450, loss[loss=0.1173, simple_loss=0.1992, pruned_loss=0.01773, over 4981.00 frames.], tot_loss[loss=0.141, simple_loss=0.2136, pruned_loss=0.03419, over 972712.34 frames.], batch size: 25, lr: 2.14e-04
2022-05-06 20:16:57,468 INFO [train.py:715] (1/8) Epoch 10, batch 16500, loss[loss=0.1862, simple_loss=0.2497, pruned_loss=0.06134, over 4761.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03461, over 973396.10 frames.], batch size: 18, lr: 2.14e-04
2022-05-06 20:17:36,174 INFO [train.py:715] (1/8) Epoch 10, batch 16550, loss[loss=0.1704, simple_loss=0.2282, pruned_loss=0.05633, over 4782.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2132, pruned_loss=0.03422, over 972487.04 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:18:15,834 INFO [train.py:715] (1/8) Epoch 10, batch 16600, loss[loss=0.1498, simple_loss=0.2175, pruned_loss=0.04107, over 4989.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2134, pruned_loss=0.03434, over 972682.41 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:18:54,011 INFO [train.py:715] (1/8) Epoch 10, batch 16650, loss[loss=0.1171, simple_loss=0.1915, pruned_loss=0.02135, over 4810.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.03443, over 973108.80 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:19:33,368 INFO [train.py:715] (1/8) Epoch 10, batch 16700, loss[loss=0.1259, simple_loss=0.1923, pruned_loss=0.02968, over 4775.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2143, pruned_loss=0.03476, over 972232.03 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:20:12,354 INFO [train.py:715] (1/8) Epoch 10, batch 16750, loss[loss=0.1233, simple_loss=0.1985, pruned_loss=0.02405, over 4804.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.0348, over 971997.50 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 20:20:52,510 INFO [train.py:715] (1/8) Epoch 10, batch 16800, loss[loss=0.1146, simple_loss=0.1939, pruned_loss=0.01765, over 4904.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2138, pruned_loss=0.03433, over 972775.17 frames.], batch size: 23, lr: 2.14e-04
2022-05-06 20:21:31,829 INFO [train.py:715] (1/8) Epoch 10, batch 16850, loss[loss=0.1289, simple_loss=0.2008, pruned_loss=0.02851, over 4738.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2135, pruned_loss=0.03446, over 972246.53 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:22:11,632 INFO [train.py:715] (1/8) Epoch 10, batch 16900, loss[loss=0.1424, simple_loss=0.2109, pruned_loss=0.03701, over 4936.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2123, pruned_loss=0.03406, over 972755.78 frames.], batch size: 23, lr: 2.14e-04
2022-05-06 20:22:51,672 INFO [train.py:715] (1/8) Epoch 10, batch 16950, loss[loss=0.1461, simple_loss=0.233, pruned_loss=0.02956, over 4783.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2122, pruned_loss=0.03366, over 972573.50 frames.], batch size: 17, lr: 2.14e-04
2022-05-06 20:23:29,921 INFO [train.py:715] (1/8) Epoch 10, batch 17000, loss[loss=0.1306, simple_loss=0.198, pruned_loss=0.0316, over 4790.00 frames.], tot_loss[loss=0.1398, simple_loss=0.212, pruned_loss=0.03375, over 972795.65 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:24:09,509 INFO [train.py:715] (1/8) Epoch 10, batch 17050, loss[loss=0.1369, simple_loss=0.2041, pruned_loss=0.03484, over 4834.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2121, pruned_loss=0.03369, over 972318.56 frames.], batch size: 13, lr: 2.14e-04
2022-05-06 20:24:48,208 INFO [train.py:715] (1/8) Epoch 10, batch 17100, loss[loss=0.1437, simple_loss=0.2088, pruned_loss=0.03927, over 4779.00 frames.], tot_loss[loss=0.1398, simple_loss=0.212, pruned_loss=0.03383, over 972067.79 frames.], batch size: 18, lr: 2.14e-04
2022-05-06 20:25:27,434 INFO [train.py:715] (1/8) Epoch 10, batch 17150, loss[loss=0.1237, simple_loss=0.207, pruned_loss=0.02016, over 4982.00 frames.], tot_loss[loss=0.1398, simple_loss=0.212, pruned_loss=0.03377, over 971882.25 frames.], batch size: 27, lr: 2.14e-04
2022-05-06 20:26:07,395 INFO [train.py:715] (1/8) Epoch 10, batch 17200, loss[loss=0.1507, simple_loss=0.2194, pruned_loss=0.04101, over 4920.00 frames.], tot_loss[loss=0.14, simple_loss=0.2124, pruned_loss=0.03379, over 971680.48 frames.], batch size: 18, lr: 2.14e-04
2022-05-06 20:26:47,003 INFO [train.py:715] (1/8) Epoch 10, batch 17250, loss[loss=0.1462, simple_loss=0.2183, pruned_loss=0.03705, over 4642.00 frames.], tot_loss[loss=0.14, simple_loss=0.2124, pruned_loss=0.03384, over 971228.77 frames.], batch size: 13, lr: 2.14e-04
2022-05-06 20:27:26,659 INFO [train.py:715] (1/8) Epoch 10, batch 17300, loss[loss=0.1398, simple_loss=0.212, pruned_loss=0.03373, over 4824.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2123, pruned_loss=0.03352, over 971344.33 frames.], batch size: 25, lr: 2.14e-04
2022-05-06 20:28:05,424 INFO [train.py:715] (1/8) Epoch 10, batch 17350, loss[loss=0.1319, simple_loss=0.2054, pruned_loss=0.02915, over 4977.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2134, pruned_loss=0.03412, over 970929.18 frames.], batch size: 35, lr: 2.14e-04
2022-05-06 20:28:44,825 INFO [train.py:715] (1/8) Epoch 10, batch 17400, loss[loss=0.137, simple_loss=0.2152, pruned_loss=0.0294, over 4948.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2129, pruned_loss=0.03392, over 972051.94 frames.], batch size: 23, lr: 2.14e-04
2022-05-06 20:29:24,004 INFO [train.py:715] (1/8) Epoch 10, batch 17450, loss[loss=0.1299, simple_loss=0.1985, pruned_loss=0.03071, over 4872.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2121, pruned_loss=0.03344, over 973106.43 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:30:02,978 INFO [train.py:715] (1/8) Epoch 10, batch 17500, loss[loss=0.1386, simple_loss=0.217, pruned_loss=0.03008, over 4892.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.0336, over 973476.79 frames.], batch size: 39, lr: 2.14e-04
2022-05-06 20:30:42,973 INFO [train.py:715] (1/8) Epoch 10, batch 17550, loss[loss=0.1256, simple_loss=0.1959, pruned_loss=0.02764, over 4917.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03324, over 973738.25 frames.], batch size: 17, lr: 2.14e-04
2022-05-06 20:31:21,945 INFO [train.py:715] (1/8) Epoch 10, batch 17600, loss[loss=0.1141, simple_loss=0.1902, pruned_loss=0.01903, over 4827.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.0333, over 974140.15 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:32:01,504 INFO [train.py:715] (1/8) Epoch 10, batch 17650, loss[loss=0.1427, simple_loss=0.213, pruned_loss=0.03624, over 4865.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2113, pruned_loss=0.03274, over 973970.51 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:32:40,267 INFO [train.py:715] (1/8) Epoch 10, batch 17700, loss[loss=0.1167, simple_loss=0.2015, pruned_loss=0.016, over 4916.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2121, pruned_loss=0.03353, over 973334.21 frames.], batch size: 29, lr: 2.14e-04
2022-05-06 20:33:20,044 INFO [train.py:715] (1/8) Epoch 10, batch 17750, loss[loss=0.1205, simple_loss=0.1975, pruned_loss=0.02175, over 4863.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2122, pruned_loss=0.03368, over 972870.91 frames.], batch size: 20, lr: 2.14e-04
2022-05-06 20:33:59,767 INFO [train.py:715] (1/8) Epoch 10, batch 17800, loss[loss=0.1414, simple_loss=0.2194, pruned_loss=0.03169, over 4792.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03349, over 972446.50 frames.], batch size: 17, lr: 2.14e-04
2022-05-06 20:34:38,716 INFO [train.py:715] (1/8) Epoch 10, batch 17850, loss[loss=0.1202, simple_loss=0.199, pruned_loss=0.02073, over 4922.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03314, over 972047.22 frames.], batch size: 29, lr: 2.14e-04
2022-05-06 20:35:18,470 INFO [train.py:715] (1/8) Epoch 10, batch 17900, loss[loss=0.1489, simple_loss=0.2123, pruned_loss=0.04276, over 4960.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03311, over 972150.28 frames.], batch size: 35, lr: 2.14e-04
2022-05-06 20:35:57,404 INFO [train.py:715] (1/8) Epoch 10, batch 17950, loss[loss=0.1325, simple_loss=0.2126, pruned_loss=0.02621, over 4926.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2116, pruned_loss=0.03331, over 971610.33 frames.], batch size: 23, lr: 2.14e-04
2022-05-06 20:36:36,021 INFO [train.py:715] (1/8) Epoch 10, batch 18000, loss[loss=0.1399, simple_loss=0.2114, pruned_loss=0.0342, over 4879.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2117, pruned_loss=0.03349, over 971254.75 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:36:36,021 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 20:36:45,530 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1064, simple_loss=0.1906, pruned_loss=0.01104, over 914524.00 frames.
2022-05-06 20:37:24,882 INFO [train.py:715] (1/8) Epoch 10, batch 18050, loss[loss=0.1218, simple_loss=0.193, pruned_loss=0.02532, over 4872.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2119, pruned_loss=0.03389, over 970471.56 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:38:03,975 INFO [train.py:715] (1/8) Epoch 10, batch 18100, loss[loss=0.1548, simple_loss=0.2207, pruned_loss=0.04452, over 4866.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2121, pruned_loss=0.03402, over 970694.14 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:38:43,262 INFO [train.py:715] (1/8) Epoch 10, batch 18150, loss[loss=0.152, simple_loss=0.224, pruned_loss=0.04, over 4767.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2119, pruned_loss=0.03381, over 971581.54 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:39:21,939 INFO [train.py:715] (1/8) Epoch 10, batch 18200, loss[loss=0.1374, simple_loss=0.2039, pruned_loss=0.03546, over 4853.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2131, pruned_loss=0.03436, over 971372.31 frames.], batch size: 30, lr: 2.14e-04
2022-05-06 20:40:00,616 INFO [train.py:715] (1/8) Epoch 10, batch 18250, loss[loss=0.156, simple_loss=0.2299, pruned_loss=0.04099, over 4918.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2133, pruned_loss=0.03409, over 971900.17 frames.], batch size: 39, lr: 2.14e-04
2022-05-06 20:40:40,108 INFO [train.py:715] (1/8) Epoch 10, batch 18300, loss[loss=0.1089, simple_loss=0.1833, pruned_loss=0.01727, over 4777.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03403, over 972158.38 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:41:19,471 INFO [train.py:715] (1/8) Epoch 10, batch 18350, loss[loss=0.1618, simple_loss=0.2272, pruned_loss=0.04818, over 4893.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03469, over 972071.18 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:41:57,958 INFO [train.py:715] (1/8) Epoch 10, batch 18400, loss[loss=0.1422, simple_loss=0.2324, pruned_loss=0.02603, over 4798.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.0347, over 971681.90 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:42:37,148 INFO [train.py:715] (1/8) Epoch 10, batch 18450, loss[loss=0.1396, simple_loss=0.22, pruned_loss=0.02962, over 4802.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03405, over 972103.86 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 20:43:16,004 INFO [train.py:715] (1/8) Epoch 10, batch 18500, loss[loss=0.1304, simple_loss=0.2112, pruned_loss=0.0248, over 4809.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03362, over 972937.89 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:43:55,528 INFO [train.py:715] (1/8) Epoch 10, batch 18550, loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03736, over 4855.00 frames.], tot_loss[loss=0.1402, simple_loss=0.213, pruned_loss=0.03372, over 972821.38 frames.], batch size: 20, lr: 2.13e-04
2022-05-06 20:44:33,840 INFO [train.py:715] (1/8) Epoch 10, batch 18600, loss[loss=0.1212, simple_loss=0.2006, pruned_loss=0.02094, over 4751.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03378, over 972151.55 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 20:45:13,252 INFO [train.py:715] (1/8) Epoch 10, batch 18650, loss[loss=0.1465, simple_loss=0.2273, pruned_loss=0.03282, over 4855.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2131, pruned_loss=0.03397, over 972228.39 frames.], batch size: 20, lr: 2.13e-04
2022-05-06 20:45:52,993 INFO [train.py:715] (1/8) Epoch 10, batch 18700, loss[loss=0.1404, simple_loss=0.2171, pruned_loss=0.03183, over 4971.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03377, over 971850.31 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 20:46:31,252 INFO [train.py:715] (1/8) Epoch 10, batch 18750, loss[loss=0.1206, simple_loss=0.1955, pruned_loss=0.02284, over 4763.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03305, over 971436.70 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 20:47:10,632 INFO [train.py:715] (1/8) Epoch 10, batch 18800, loss[loss=0.1498, simple_loss=0.2236, pruned_loss=0.03794, over 4824.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03373, over 971748.86 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 20:47:50,109 INFO [train.py:715] (1/8) Epoch 10, batch 18850, loss[loss=0.1272, simple_loss=0.1874, pruned_loss=0.03349, over 4875.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03342, over 971303.74 frames.], batch size: 32, lr: 2.13e-04
2022-05-06 20:48:29,010 INFO [train.py:715] (1/8) Epoch 10, batch 18900, loss[loss=0.1529, simple_loss=0.2317, pruned_loss=0.03708, over 4736.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2125, pruned_loss=0.03343, over 971804.27 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 20:49:08,062 INFO [train.py:715] (1/8) Epoch 10, batch 18950, loss[loss=0.141, simple_loss=0.2087, pruned_loss=0.0366, over 4928.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.03346, over 972534.58 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 20:49:48,333 INFO [train.py:715] (1/8) Epoch 10, batch 19000, loss[loss=0.135, simple_loss=0.2095, pruned_loss=0.03022, over 4778.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2128, pruned_loss=0.03368, over 971912.77 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 20:50:27,638 INFO [train.py:715] (1/8) Epoch 10, batch 19050, loss[loss=0.1484, simple_loss=0.2249, pruned_loss=0.03599, over 4778.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03398, over 971300.55 frames.], batch size: 17, lr: 2.13e-04
2022-05-06 20:51:06,450 INFO [train.py:715] (1/8) Epoch 10, batch 19100, loss[loss=0.1819, simple_loss=0.2634, pruned_loss=0.05019, over 4983.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2139, pruned_loss=0.03397, over 971226.08 frames.], batch size: 28, lr: 2.13e-04
2022-05-06 20:51:46,325 INFO [train.py:715] (1/8) Epoch 10, batch 19150, loss[loss=0.1483, simple_loss=0.2213, pruned_loss=0.03768, over 4943.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2134, pruned_loss=0.03358, over 971939.63 frames.], batch size: 35, lr: 2.13e-04
2022-05-06 20:52:26,494 INFO [train.py:715] (1/8) Epoch 10, batch 19200, loss[loss=0.1407, simple_loss=0.2097, pruned_loss=0.03586, over 4788.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03319, over 973576.78 frames.], batch size: 17, lr: 2.13e-04
2022-05-06 20:53:06,170 INFO [train.py:715] (1/8) Epoch 10, batch 19250, loss[loss=0.1058, simple_loss=0.1791, pruned_loss=0.01621, over 4779.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2121, pruned_loss=0.03336, over 973535.29 frames.], batch size: 12, lr: 2.13e-04
2022-05-06 20:53:46,066 INFO [train.py:715] (1/8) Epoch 10, batch 19300, loss[loss=0.1609, simple_loss=0.2267, pruned_loss=0.04752, over 4799.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2116, pruned_loss=0.03327, over 972869.86 frames.], batch size: 21, lr: 2.13e-04
2022-05-06 20:54:26,471 INFO [train.py:715] (1/8) Epoch 10, batch 19350, loss[loss=0.1585, simple_loss=0.23, pruned_loss=0.0435, over 4809.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2116, pruned_loss=0.0336, over 971273.99 frames.], batch size: 25, lr: 2.13e-04
2022-05-06 20:55:06,648 INFO [train.py:715] (1/8) Epoch 10, batch 19400, loss[loss=0.1085, simple_loss=0.1861, pruned_loss=0.01546, over 4921.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2124, pruned_loss=0.03359, over 971519.42 frames.], batch size: 21, lr: 2.13e-04
2022-05-06 20:55:45,792 INFO [train.py:715] (1/8) Epoch 10, batch 19450, loss[loss=0.1418, simple_loss=0.2057, pruned_loss=0.03898, over 4977.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2122, pruned_loss=0.0335, over 971626.54 frames.], batch size: 14, lr: 2.13e-04
2022-05-06 20:56:25,408 INFO [train.py:715] (1/8) Epoch 10, batch 19500, loss[loss=0.15, simple_loss=0.2141, pruned_loss=0.04295, over 4962.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03336, over 971425.75 frames.], batch size: 35, lr: 2.13e-04
2022-05-06 20:57:04,607 INFO [train.py:715] (1/8) Epoch 10, batch 19550, loss[loss=0.1486, simple_loss=0.2145, pruned_loss=0.04131, over 4968.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2125, pruned_loss=0.03346, over 971372.13 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 20:57:43,329 INFO [train.py:715] (1/8) Epoch 10, batch 19600, loss[loss=0.1311, simple_loss=0.2096, pruned_loss=0.02629, over 4697.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2128, pruned_loss=0.03371, over 971402.49 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 20:58:22,306 INFO [train.py:715] (1/8) Epoch 10, batch 19650, loss[loss=0.1257, simple_loss=0.1987, pruned_loss=0.0263, over 4880.00 frames.], tot_loss[loss=0.14, simple_loss=0.2126, pruned_loss=0.03366, over 971980.89 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 20:59:01,941 INFO [train.py:715] (1/8) Epoch 10, batch 19700, loss[loss=0.1381, simple_loss=0.2178, pruned_loss=0.02927, over 4817.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2128, pruned_loss=0.03383, over 973205.38 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 20:59:41,297 INFO [train.py:715] (1/8) Epoch 10, batch 19750, loss[loss=0.143, simple_loss=0.2245, pruned_loss=0.03079, over 4752.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03375, over 972686.08 frames.], batch size: 14, lr: 2.13e-04
2022-05-06 21:00:19,602 INFO [train.py:715] (1/8) Epoch 10, batch 19800, loss[loss=0.144, simple_loss=0.2018, pruned_loss=0.04304, over 4833.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2128, pruned_loss=0.03373, over 972889.86 frames.], batch size: 30, lr: 2.13e-04
2022-05-06 21:00:59,239 INFO [train.py:715] (1/8) Epoch 10, batch 19850, loss[loss=0.1476, simple_loss=0.2196, pruned_loss=0.03777, over 4922.00 frames.], tot_loss[loss=0.1405, simple_loss=0.213, pruned_loss=0.03398, over 972888.40 frames.], batch size: 39, lr: 2.13e-04
2022-05-06 21:01:38,756 INFO [train.py:715] (1/8) Epoch 10, batch 19900, loss[loss=0.1266, simple_loss=0.2028, pruned_loss=0.02519, over 4977.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2131, pruned_loss=0.0341, over 972975.11 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 21:02:19,872 INFO [train.py:715] (1/8) Epoch 10, batch 19950, loss[loss=0.1092, simple_loss=0.182, pruned_loss=0.01819, over 4779.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03408, over 972796.27 frames.], batch size: 14, lr: 2.13e-04
2022-05-06 21:02:58,927 INFO [train.py:715] (1/8) Epoch 10, batch 20000, loss[loss=0.1831, simple_loss=0.2602, pruned_loss=0.05305, over 4983.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.0339, over 972042.02 frames.], batch size: 28, lr: 2.13e-04
2022-05-06 21:03:37,945 INFO [train.py:715] (1/8) Epoch 10, batch 20050, loss[loss=0.1169, simple_loss=0.1941, pruned_loss=0.0198, over 4972.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03342, over 972514.32 frames.], batch size: 25, lr: 2.13e-04
2022-05-06 21:04:17,426 INFO [train.py:715] (1/8) Epoch 10, batch 20100, loss[loss=0.1492, simple_loss=0.2254, pruned_loss=0.03651, over 4837.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.03354, over 972128.99 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:04:55,528 INFO [train.py:715] (1/8) Epoch 10, batch 20150, loss[loss=0.1272, simple_loss=0.1997, pruned_loss=0.02741, over 4834.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03381, over 972054.90 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 21:05:34,940 INFO [train.py:715] (1/8) Epoch 10, batch 20200, loss[loss=0.1422, simple_loss=0.2244, pruned_loss=0.03001, over 4849.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2122, pruned_loss=0.03348, over 973028.48 frames.], batch size: 32, lr: 2.13e-04
2022-05-06 21:06:13,958 INFO [train.py:715] (1/8) Epoch 10, batch 20250, loss[loss=0.1199, simple_loss=0.1952, pruned_loss=0.02228, over 4833.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.03339, over 972111.32 frames.], batch size: 30, lr: 2.13e-04
2022-05-06 21:06:52,617 INFO [train.py:715] (1/8) Epoch 10, batch 20300, loss[loss=0.156, simple_loss=0.2276, pruned_loss=0.04223, over 4979.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03321, over 972106.69 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 21:07:31,400 INFO [train.py:715] (1/8) Epoch 10, batch 20350, loss[loss=0.116, simple_loss=0.1906, pruned_loss=0.02065, over 4826.00 frames.], tot_loss[loss=0.139, simple_loss=0.2116, pruned_loss=0.03317, over 972004.23 frames.], batch size: 13, lr: 2.13e-04
2022-05-06 21:08:10,510 INFO [train.py:715] (1/8) Epoch 10, batch 20400, loss[loss=0.1423, simple_loss=0.2272, pruned_loss=0.02871, over 4832.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.03304, over 971805.55 frames.], batch size: 27, lr: 2.13e-04
2022-05-06 21:08:49,424 INFO [train.py:715] (1/8) Epoch 10, batch 20450, loss[loss=0.1601, simple_loss=0.227, pruned_loss=0.04662, over 4826.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2118, pruned_loss=0.03275, over 971903.27 frames.], batch size: 21, lr: 2.13e-04
2022-05-06 21:09:27,878 INFO [train.py:715] (1/8) Epoch 10, batch 20500, loss[loss=0.1335, simple_loss=0.2058, pruned_loss=0.03062, over 4766.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03264, over 972088.12 frames.], batch size: 14, lr: 2.13e-04
2022-05-06 21:10:06,950 INFO [train.py:715] (1/8) Epoch 10, batch 20550, loss[loss=0.1248, simple_loss=0.2046, pruned_loss=0.02248, over 4971.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03316, over 972560.43 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 21:10:46,031 INFO [train.py:715] (1/8) Epoch 10, batch 20600, loss[loss=0.1661, simple_loss=0.242, pruned_loss=0.04514, over 4921.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.03375, over 973301.61 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:11:25,461 INFO [train.py:715] (1/8) Epoch 10, batch 20650, loss[loss=0.1448, simple_loss=0.2075, pruned_loss=0.04108, over 4836.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03346, over 974081.04 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:12:04,255 INFO [train.py:715] (1/8) Epoch 10, batch 20700, loss[loss=0.1301, simple_loss=0.2131, pruned_loss=0.02354, over 4908.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03339, over 974579.10 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:12:44,598 INFO [train.py:715] (1/8) Epoch 10, batch 20750, loss[loss=0.1561, simple_loss=0.2311, pruned_loss=0.04057, over 4809.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.0334, over 974800.85 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 21:13:24,576 INFO [train.py:715] (1/8) Epoch 10, batch 20800, loss[loss=0.1185, simple_loss=0.1955, pruned_loss=0.02074, over 4923.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03315, over 974206.28 frames.], batch size: 29, lr: 2.13e-04
2022-05-06 21:14:03,351 INFO [train.py:715] (1/8) Epoch 10, batch 20850, loss[loss=0.14, simple_loss=0.209, pruned_loss=0.03547, over 4769.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.0329, over 974040.23 frames.], batch size: 12, lr: 2.13e-04
2022-05-06 21:14:43,291 INFO [train.py:715] (1/8) Epoch 10, batch 20900, loss[loss=0.1872, simple_loss=0.2471, pruned_loss=0.06361, over 4821.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03321, over 973243.23 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:15:23,753 INFO [train.py:715] (1/8) Epoch 10, batch 20950, loss[loss=0.1417, simple_loss=0.2133, pruned_loss=0.0351, over 4915.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2122, pruned_loss=0.0333, over 973076.43 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 21:16:02,697 INFO [train.py:715] (1/8) Epoch 10, batch 21000, loss[loss=0.1395, simple_loss=0.209, pruned_loss=0.03503, over 4839.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03304, over 973089.85 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 21:16:02,698 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 21:16:12,203 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1065, simple_loss=0.1909, pruned_loss=0.01111, over 914524.00 frames.
2022-05-06 21:16:51,723 INFO [train.py:715] (1/8) Epoch 10, batch 21050, loss[loss=0.1314, simple_loss=0.2094, pruned_loss=0.02671, over 4743.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03313, over 972935.26 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 21:17:32,531 INFO [train.py:715] (1/8) Epoch 10, batch 21100, loss[loss=0.1186, simple_loss=0.1933, pruned_loss=0.02195, over 4773.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03288, over 973357.29 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:18:14,008 INFO [train.py:715] (1/8) Epoch 10, batch 21150, loss[loss=0.1608, simple_loss=0.2197, pruned_loss=0.05093, over 4896.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2118, pruned_loss=0.03281, over 973599.48 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:18:55,109 INFO [train.py:715] (1/8) Epoch 10, batch 21200, loss[loss=0.1363, simple_loss=0.226, pruned_loss=0.02325, over 4913.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03282, over 973829.20 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 21:19:35,763 INFO [train.py:715] (1/8) Epoch 10, batch 21250, loss[loss=0.1883, simple_loss=0.2685, pruned_loss=0.05401, over 4732.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03291, over 972777.16 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 21:20:17,438 INFO [train.py:715] (1/8) Epoch 10, batch 21300, loss[loss=0.1532, simple_loss=0.2191, pruned_loss=0.04362, over 4706.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.03294, over 973087.14 frames.], batch size: 12, lr: 2.13e-04
2022-05-06 21:20:58,695 INFO [train.py:715] (1/8) Epoch 10, batch 21350, loss[loss=0.1246, simple_loss=0.2021, pruned_loss=0.02358, over 4905.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2111, pruned_loss=0.03293, over 972523.04 frames.], batch size: 17, lr: 2.13e-04
2022-05-06 21:21:39,101 INFO [train.py:715] (1/8) Epoch 10, batch 21400, loss[loss=0.1409, simple_loss=0.2167, pruned_loss=0.03252, over 4924.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2108, pruned_loss=0.03273, over 972722.14 frames.], batch size: 29, lr: 2.13e-04
2022-05-06 21:22:20,533 INFO [train.py:715] (1/8) Epoch 10, batch 21450, loss[loss=0.1412, simple_loss=0.2099, pruned_loss=0.03619, over 4950.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.03302, over 972958.84 frames.], batch size: 21, lr: 2.13e-04
2022-05-06 21:23:02,350 INFO [train.py:715] (1/8) Epoch 10, batch 21500, loss[loss=0.1235, simple_loss=0.1941, pruned_loss=0.02649, over 4755.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.033, over 972480.57 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 21:23:43,372 INFO [train.py:715] (1/8) Epoch 10, batch 21550, loss[loss=0.1371, simple_loss=0.2154, pruned_loss=0.02941, over 4979.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03322, over 972288.85 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:24:24,293 INFO [train.py:715] (1/8) Epoch 10, batch 21600, loss[loss=0.1349, simple_loss=0.2108, pruned_loss=0.02949, over 4971.00 frames.], tot_loss[loss=0.1395, simple_loss=0.212, pruned_loss=0.03346, over 971857.53 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 21:25:06,222 INFO [train.py:715] (1/8) Epoch 10, batch 21650, loss[loss=0.1289, simple_loss=0.2009, pruned_loss=0.02846, over 4882.00 frames.], tot_loss[loss=0.1396, simple_loss=0.212, pruned_loss=0.03361, over 972504.50 frames.], batch size: 22, lr: 2.13e-04
2022-05-06 21:25:47,752 INFO [train.py:715] (1/8) Epoch 10, batch 21700, loss[loss=0.1994, simple_loss=0.255, pruned_loss=0.07192, over 4862.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2121, pruned_loss=0.0339, over 973479.95 frames.], batch size: 20, lr: 2.13e-04
2022-05-06 21:26:28,009 INFO [train.py:715] (1/8) Epoch 10, batch 21750, loss[loss=0.1669, simple_loss=0.2422, pruned_loss=0.04581, over 4763.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2127, pruned_loss=0.03388, over 973294.29 frames.], batch size: 12, lr: 2.13e-04
2022-05-06 21:27:08,994 INFO [train.py:715] (1/8) Epoch 10, batch 21800, loss[loss=0.1686, simple_loss=0.2272, pruned_loss=0.055, over 4961.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2127, pruned_loss=0.03409, over 973156.05 frames.], batch size: 35, lr: 2.13e-04
2022-05-06 21:27:50,695 INFO [train.py:715] (1/8) Epoch 10, batch 21850, loss[loss=0.127, simple_loss=0.1946, pruned_loss=0.0297, over 4863.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03409, over 973713.33 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:28:31,165 INFO [train.py:715] (1/8) Epoch 10, batch 21900, loss[loss=0.1125, simple_loss=0.1959, pruned_loss=0.01457, over 4799.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2133, pruned_loss=0.03403, over 974222.16 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 21:29:11,913 INFO [train.py:715] (1/8) Epoch 10, batch 21950, loss[loss=0.163, simple_loss=0.2397, pruned_loss=0.04311, over 4926.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2139, pruned_loss=0.03401, over 973376.26 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:29:53,131 INFO [train.py:715] (1/8) Epoch 10, batch 22000, loss[loss=0.1605, simple_loss=0.2354, pruned_loss=0.04276, over 4867.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2131, pruned_loss=0.03338, over 972307.28 frames.], batch size: 16, lr: 2.12e-04
2022-05-06 21:30:33,466 INFO [train.py:715] (1/8) Epoch 10, batch 22050, loss[loss=0.1582, simple_loss=0.235, pruned_loss=0.04067, over 4868.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03313, over 972616.41 frames.], batch size: 22, lr: 2.12e-04
2022-05-06 21:31:14,082 INFO [train.py:715] (1/8) Epoch 10, batch 22100, loss[loss=0.1166, simple_loss=0.1914, pruned_loss=0.02092, over 4871.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.03309, over 972292.81 frames.], batch size: 30, lr: 2.12e-04
2022-05-06 21:31:54,934 INFO [train.py:715] (1/8) Epoch 10, batch 22150, loss[loss=0.1567, simple_loss=0.2258, pruned_loss=0.04377, over 4934.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.0336, over 971631.28 frames.], batch size: 29, lr: 2.12e-04
2022-05-06 21:32:35,990 INFO [train.py:715] (1/8) Epoch 10, batch 22200, loss[loss=0.133, simple_loss=0.2095, pruned_loss=0.02823, over 4915.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03345, over 972449.58 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:33:16,085 INFO [train.py:715] (1/8) Epoch 10, batch 22250, loss[loss=0.1382, simple_loss=0.21, pruned_loss=0.03319, over 4977.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.0332, over 972325.73 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 21:33:56,742 INFO [train.py:715] (1/8) Epoch 10, batch 22300, loss[loss=0.1443, simple_loss=0.2107, pruned_loss=0.03898, over 4943.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2129, pruned_loss=0.03329, over 973225.50 frames.], batch size: 35, lr: 2.12e-04
2022-05-06 21:34:37,776 INFO [train.py:715] (1/8) Epoch 10, batch 22350, loss[loss=0.1285, simple_loss=0.1986, pruned_loss=0.02924, over 4805.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2127, pruned_loss=0.03308, over 972216.66 frames.], batch size: 13, lr: 2.12e-04
2022-05-06 21:35:17,627 INFO [train.py:715] (1/8) Epoch 10, batch 22400, loss[loss=0.1407, simple_loss=0.2189, pruned_loss=0.03121, over 4936.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.03343, over 972228.63 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 21:35:56,791 INFO [train.py:715] (1/8) Epoch 10, batch 22450, loss[loss=0.1143, simple_loss=0.1907, pruned_loss=0.01897, over 4826.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.0338, over 972155.47 frames.], batch size: 27, lr: 2.12e-04
2022-05-06 21:36:36,738 INFO [train.py:715] (1/8) Epoch 10, batch 22500, loss[loss=0.1388, simple_loss=0.2154, pruned_loss=0.03117, over 4903.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2129, pruned_loss=0.03329, over 971780.89 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 21:37:17,615 INFO [train.py:715] (1/8) Epoch 10, batch 22550, loss[loss=0.1298, simple_loss=0.1972, pruned_loss=0.03121, over 4873.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03303, over 971650.38 frames.], batch size: 20, lr: 2.12e-04
2022-05-06 21:37:56,434 INFO [train.py:715] (1/8) Epoch 10, batch 22600, loss[loss=0.1194, simple_loss=0.2037, pruned_loss=0.01757, over 4827.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2126, pruned_loss=0.033, over 972088.32 frames.], batch size: 27, lr: 2.12e-04
2022-05-06 21:38:37,518 INFO [train.py:715] (1/8) Epoch 10, batch 22650, loss[loss=0.1159, simple_loss=0.191, pruned_loss=0.02042, over 4826.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2131, pruned_loss=0.03294, over 972788.28 frames.], batch size: 12, lr: 2.12e-04
2022-05-06 21:39:19,371 INFO [train.py:715] (1/8) Epoch 10, batch 22700, loss[loss=0.1552, simple_loss=0.2247, pruned_loss=0.04281, over 4877.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2137, pruned_loss=0.03345, over 973326.24 frames.], batch size: 30, lr: 2.12e-04
2022-05-06 21:40:00,104 INFO [train.py:715] (1/8) Epoch 10, batch 22750, loss[loss=0.133, simple_loss=0.2114, pruned_loss=0.0273, over 4891.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2142, pruned_loss=0.03402, over 973700.27 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 21:40:41,328 INFO [train.py:715] (1/8) Epoch 10, batch 22800, loss[loss=0.1295, simple_loss=0.2072, pruned_loss=0.02594, over 4693.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2137, pruned_loss=0.03377, over 973736.03 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 21:41:22,883 INFO [train.py:715] (1/8) Epoch 10, batch 22850, loss[loss=0.1364, simple_loss=0.2104, pruned_loss=0.03117, over 4812.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03358, over 974134.79 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 21:42:04,583 INFO [train.py:715] (1/8) Epoch 10, batch 22900, loss[loss=0.1758, simple_loss=0.2399, pruned_loss=0.0559, over 4979.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2136, pruned_loss=0.03377, over 974433.48 frames.], batch size: 33, lr: 2.12e-04
2022-05-06 21:42:45,060 INFO [train.py:715] (1/8) Epoch 10, batch 22950, loss[loss=0.1386, simple_loss=0.2187, pruned_loss=0.02926, over 4858.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.03394, over 974110.54 frames.], batch size: 20, lr: 2.12e-04
2022-05-06 21:43:27,084 INFO [train.py:715] (1/8) Epoch 10, batch 23000, loss[loss=0.1576, simple_loss=0.2338, pruned_loss=0.04068, over 4772.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2136, pruned_loss=0.03411, over 972871.77 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:44:09,154 INFO [train.py:715] (1/8) Epoch 10, batch 23050, loss[loss=0.1516, simple_loss=0.2244, pruned_loss=0.03943, over 4812.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2133, pruned_loss=0.03416, over 973238.28 frames.], batch size: 13, lr: 2.12e-04
2022-05-06 21:44:49,663 INFO [train.py:715] (1/8) Epoch 10, batch 23100, loss[loss=0.1411, simple_loss=0.2166, pruned_loss=0.03283, over 4793.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2136, pruned_loss=0.03381, over 972188.60 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 21:45:30,875 INFO [train.py:715] (1/8) Epoch 10, batch 23150, loss[loss=0.1293, simple_loss=0.2072, pruned_loss=0.02566, over 4901.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03356, over 972579.72 frames.], batch size: 22, lr: 2.12e-04
2022-05-06 21:46:12,881 INFO [train.py:715] (1/8) Epoch 10, batch 23200, loss[loss=0.141, simple_loss=0.2214, pruned_loss=0.03033, over 4890.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03371, over 972133.70 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 21:46:54,167 INFO [train.py:715] (1/8) Epoch 10, batch 23250, loss[loss=0.1019, simple_loss=0.1696, pruned_loss=0.01708, over 4780.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2132, pruned_loss=0.03347, over 972081.33 frames.], batch size: 12, lr: 2.12e-04
2022-05-06 21:47:34,834 INFO [train.py:715] (1/8) Epoch 10, batch 23300, loss[loss=0.1221, simple_loss=0.1963, pruned_loss=0.02392, over 4746.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2132, pruned_loss=0.03349, over 971725.92 frames.], batch size: 16, lr: 2.12e-04
2022-05-06 21:48:16,728 INFO [train.py:715] (1/8) Epoch 10, batch 23350, loss[loss=0.1455, simple_loss=0.2141, pruned_loss=0.03845, over 4931.00 frames.], tot_loss[loss=0.1398, simple_loss=0.213, pruned_loss=0.03332, over 971278.18 frames.], batch size: 29, lr: 2.12e-04
2022-05-06 21:48:58,867 INFO [train.py:715] (1/8) Epoch 10, batch 23400, loss[loss=0.1582, simple_loss=0.2194, pruned_loss=0.04851, over 4768.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03321, over 972889.52 frames.], batch size: 12, lr: 2.12e-04
2022-05-06 21:49:39,777 INFO [train.py:715] (1/8) Epoch 10, batch 23450, loss[loss=0.1645, simple_loss=0.2284, pruned_loss=0.05034, over 4830.00 frames.], tot_loss[loss=0.1411, simple_loss=0.214, pruned_loss=0.03411, over 972593.08 frames.], batch size: 30, lr: 2.12e-04
2022-05-06 21:50:20,138 INFO [train.py:715] (1/8) Epoch 10, batch 23500, loss[loss=0.1358, simple_loss=0.2109, pruned_loss=0.03033, over 4843.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2134, pruned_loss=0.03356, over 972317.23 frames.], batch size: 13, lr: 2.12e-04
2022-05-06 21:51:02,211 INFO [train.py:715] (1/8) Epoch 10, batch 23550, loss[loss=0.1511, simple_loss=0.2271, pruned_loss=0.03751, over 4837.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2132, pruned_loss=0.03362, over 971951.14 frames.], batch size: 30, lr: 2.12e-04
2022-05-06 21:51:43,372 INFO [train.py:715] (1/8) Epoch 10, batch 23600, loss[loss=0.1702, simple_loss=0.2445, pruned_loss=0.04797, over 4784.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03329, over 972093.20 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 21:52:23,135 INFO [train.py:715] (1/8) Epoch 10, batch 23650, loss[loss=0.1516, simple_loss=0.2273, pruned_loss=0.03791, over 4782.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.03336, over 972305.64 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:53:03,644 INFO [train.py:715] (1/8) Epoch 10, batch 23700, loss[loss=0.1329, simple_loss=0.2118, pruned_loss=0.02697, over 4817.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03381, over 972644.85 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 21:53:44,222 INFO [train.py:715] (1/8) Epoch 10, batch 23750, loss[loss=0.1349, simple_loss=0.2159, pruned_loss=0.02696, over 4831.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.03383, over 972061.93 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 21:54:24,368 INFO [train.py:715] (1/8) Epoch 10, batch 23800, loss[loss=0.1699, simple_loss=0.2404, pruned_loss=0.04965, over 4929.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.03354, over 971763.36 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 21:55:04,952 INFO [train.py:715] (1/8) Epoch 10, batch 23850, loss[loss=0.1153, simple_loss=0.1871, pruned_loss=0.02174, over 4761.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03331, over 972203.22 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 21:55:46,219 INFO [train.py:715] (1/8) Epoch 10, batch 23900, loss[loss=0.1332, simple_loss=0.2053, pruned_loss=0.03056, over 4871.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03362, over 972502.89 frames.], batch size: 32, lr: 2.12e-04
2022-05-06 21:56:25,836 INFO [train.py:715] (1/8) Epoch 10, batch 23950, loss[loss=0.1335, simple_loss=0.2123, pruned_loss=0.02734, over 4878.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.0338, over 972673.22 frames.], batch size: 22, lr: 2.12e-04
2022-05-06 21:57:06,218 INFO [train.py:715] (1/8) Epoch 10, batch 24000, loss[loss=0.1341, simple_loss=0.2058, pruned_loss=0.03119, over 4914.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03353, over 972962.69 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 21:57:06,219 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 21:57:15,894 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1061, simple_loss=0.1905, pruned_loss=0.01087, over 914524.00 frames.
2022-05-06 21:57:55,802 INFO [train.py:715] (1/8) Epoch 10, batch 24050, loss[loss=0.1344, simple_loss=0.219, pruned_loss=0.02489, over 4898.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03346, over 972116.57 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 21:58:36,846 INFO [train.py:715] (1/8) Epoch 10, batch 24100, loss[loss=0.132, simple_loss=0.2039, pruned_loss=0.0301, over 4802.00 frames.], tot_loss[loss=0.1404, simple_loss=0.213, pruned_loss=0.03392, over 972074.76 frames.], batch size: 13, lr: 2.12e-04
2022-05-06 21:59:18,107 INFO [train.py:715] (1/8) Epoch 10, batch 24150, loss[loss=0.1756, simple_loss=0.2394, pruned_loss=0.0559, over 4962.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2124, pruned_loss=0.03367, over 972137.45 frames.], batch size: 35, lr: 2.12e-04
2022-05-06 21:59:57,434 INFO [train.py:715] (1/8) Epoch 10, batch 24200, loss[loss=0.1072, simple_loss=0.1791, pruned_loss=0.01768, over 4778.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2125, pruned_loss=0.03351, over 972860.65 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 22:00:38,180 INFO [train.py:715] (1/8) Epoch 10, batch 24250, loss[loss=0.1473, simple_loss=0.2129, pruned_loss=0.0409, over 4749.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2119, pruned_loss=0.03356, over 972497.95 frames.], batch size: 16, lr: 2.12e-04
2022-05-06 22:01:19,295 INFO [train.py:715] (1/8) Epoch 10, batch 24300, loss[loss=0.134, simple_loss=0.1961, pruned_loss=0.03598, over 4859.00 frames.], tot_loss[loss=0.14, simple_loss=0.2126, pruned_loss=0.03371, over 973212.27 frames.], batch size: 20, lr: 2.12e-04
2022-05-06 22:01:59,414 INFO [train.py:715] (1/8) Epoch 10, batch 24350, loss[loss=0.138, simple_loss=0.2132, pruned_loss=0.03142, over 4836.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.03344, over 972041.62 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:02:39,456 INFO [train.py:715] (1/8) Epoch 10, batch 24400, loss[loss=0.1159, simple_loss=0.1893, pruned_loss=0.02125, over 4804.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.0332, over 971880.95 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 22:03:20,172 INFO [train.py:715] (1/8) Epoch 10, batch 24450, loss[loss=0.1241, simple_loss=0.1972, pruned_loss=0.02546, over 4826.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03286, over 972377.51 frames.], batch size: 25, lr: 2.12e-04
2022-05-06 22:04:01,140 INFO [train.py:715] (1/8) Epoch 10, batch 24500, loss[loss=0.141, simple_loss=0.2027, pruned_loss=0.03966, over 4770.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03327, over 972146.98 frames.], batch size: 14, lr: 2.12e-04
2022-05-06 22:04:40,219 INFO [train.py:715] (1/8) Epoch 10, batch 24550, loss[loss=0.1303, simple_loss=0.2042, pruned_loss=0.02822, over 4872.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2114, pruned_loss=0.03304, over 972056.14 frames.], batch size: 20, lr: 2.12e-04
2022-05-06 22:05:20,208 INFO [train.py:715] (1/8) Epoch 10, batch 24600, loss[loss=0.1224, simple_loss=0.1931, pruned_loss=0.0259, over 4859.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2116, pruned_loss=0.03314, over 971797.08 frames.], batch size: 32, lr: 2.12e-04
2022-05-06 22:06:00,571 INFO [train.py:715] (1/8) Epoch 10, batch 24650, loss[loss=0.1417, simple_loss=0.2138, pruned_loss=0.03478, over 4981.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03288, over 972181.34 frames.], batch size: 28, lr: 2.12e-04
2022-05-06 22:06:39,589 INFO [train.py:715] (1/8) Epoch 10, batch 24700, loss[loss=0.1137, simple_loss=0.1912, pruned_loss=0.0181, over 4958.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03301, over 971168.00 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 22:07:18,187 INFO [train.py:715] (1/8) Epoch 10, batch 24750, loss[loss=0.1329, simple_loss=0.2088, pruned_loss=0.02848, over 4692.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2128, pruned_loss=0.03323, over 970663.19 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:07:57,676 INFO [train.py:715] (1/8) Epoch 10, batch 24800, loss[loss=0.1397, simple_loss=0.2228, pruned_loss=0.02826, over 4960.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2127, pruned_loss=0.03283, over 971407.87 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:08:36,829 INFO [train.py:715] (1/8) Epoch 10, batch 24850, loss[loss=0.1288, simple_loss=0.2021, pruned_loss=0.02773, over 4856.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2121, pruned_loss=0.03259, over 971337.53 frames.], batch size: 20, lr: 2.12e-04
2022-05-06 22:09:14,900 INFO [train.py:715] (1/8) Epoch 10, batch 24900, loss[loss=0.1589, simple_loss=0.2477, pruned_loss=0.03508, over 4962.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2122, pruned_loss=0.03281, over 971754.69 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 22:09:54,525 INFO [train.py:715] (1/8) Epoch 10, batch 24950, loss[loss=0.1075, simple_loss=0.1724, pruned_loss=0.02129, over 4822.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2122, pruned_loss=0.03283, over 972213.68 frames.], batch size: 12, lr: 2.12e-04
2022-05-06 22:10:34,378 INFO [train.py:715] (1/8) Epoch 10, batch 25000, loss[loss=0.1455, simple_loss=0.2262, pruned_loss=0.03238, over 4813.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03304, over 971863.81 frames.], batch size: 26, lr: 2.12e-04
2022-05-06 22:11:13,231 INFO [train.py:715] (1/8) Epoch 10, batch 25050, loss[loss=0.1173, simple_loss=0.1889, pruned_loss=0.0229, over 4898.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.0335, over 971254.90 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 22:11:52,699 INFO [train.py:715] (1/8) Epoch 10, batch 25100, loss[loss=0.1108, simple_loss=0.1792, pruned_loss=0.02119, over 4808.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2112, pruned_loss=0.03288, over 972081.48 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 22:12:32,739 INFO [train.py:715] (1/8) Epoch 10, batch 25150, loss[loss=0.1512, simple_loss=0.214, pruned_loss=0.04415, over 4891.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03263, over 972258.26 frames.], batch size: 22, lr: 2.12e-04
2022-05-06 22:13:12,213 INFO [train.py:715] (1/8) Epoch 10, batch 25200, loss[loss=0.1469, simple_loss=0.2268, pruned_loss=0.03349, over 4842.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2118, pruned_loss=0.03277, over 972667.36 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:13:50,342 INFO [train.py:715] (1/8) Epoch 10, batch 25250, loss[loss=0.1059, simple_loss=0.1728, pruned_loss=0.0195, over 4986.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03289, over 973179.53 frames.], batch size: 14, lr: 2.12e-04
2022-05-06 22:14:29,220 INFO [train.py:715] (1/8) Epoch 10, batch 25300, loss[loss=0.1283, simple_loss=0.205, pruned_loss=0.02583, over 4790.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03307, over 972878.94 frames.], batch size: 12, lr: 2.12e-04
2022-05-06 22:15:08,866 INFO [train.py:715] (1/8) Epoch 10, batch 25350, loss[loss=0.1692, simple_loss=0.2484, pruned_loss=0.04499, over 4776.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2111, pruned_loss=0.0327, over 972129.35 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 22:15:47,383 INFO [train.py:715] (1/8) Epoch 10, batch 25400, loss[loss=0.1642, simple_loss=0.2412, pruned_loss=0.04362, over 4976.00 frames.], tot_loss[loss=0.138, simple_loss=0.2109, pruned_loss=0.03252, over 971752.49 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:16:26,239 INFO [train.py:715] (1/8) Epoch 10, batch 25450, loss[loss=0.1466, simple_loss=0.2184, pruned_loss=0.0374, over 4975.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03317, over 972529.98 frames.], batch size: 31, lr: 2.12e-04
2022-05-06 22:17:06,160 INFO [train.py:715] (1/8) Epoch 10, batch 25500, loss[loss=0.157, simple_loss=0.2253, pruned_loss=0.04432, over 4775.00 frames.], tot_loss[loss=0.1399, simple_loss=0.213, pruned_loss=0.03342, over 972198.96 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:17:45,979 INFO [train.py:715] (1/8) Epoch 10, batch 25550, loss[loss=0.1635, simple_loss=0.2353, pruned_loss=0.0459, over 4875.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03359, over 972475.75 frames.], batch size: 16, lr: 2.11e-04
2022-05-06 22:18:24,960 INFO [train.py:715] (1/8) Epoch 10, batch 25600, loss[loss=0.119, simple_loss=0.1915, pruned_loss=0.02326, over 4909.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2123, pruned_loss=0.03356, over 972706.34 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:19:05,117 INFO [train.py:715] (1/8) Epoch 10, batch 25650, loss[loss=0.141, simple_loss=0.206, pruned_loss=0.03801, over 4770.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2122, pruned_loss=0.03364, over 972469.10 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:19:45,490 INFO [train.py:715] (1/8) Epoch 10, batch 25700, loss[loss=0.1359, simple_loss=0.2102, pruned_loss=0.0308, over 4934.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.03373, over 972188.61 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:20:25,354 INFO [train.py:715] (1/8) Epoch 10, batch 25750, loss[loss=0.1605, simple_loss=0.2332, pruned_loss=0.04392, over 4885.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03345, over 972125.32 frames.], batch size: 38, lr: 2.11e-04
2022-05-06 22:21:04,755 INFO [train.py:715] (1/8) Epoch 10, batch 25800, loss[loss=0.1266, simple_loss=0.2041, pruned_loss=0.0246, over 4939.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03318, over 971332.47 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 22:21:45,293 INFO [train.py:715] (1/8) Epoch 10, batch 25850, loss[loss=0.138, simple_loss=0.2145, pruned_loss=0.03077, over 4987.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03303, over 971727.08 frames.], batch size: 25, lr: 2.11e-04
2022-05-06 22:22:25,228 INFO [train.py:715] (1/8) Epoch 10, batch 25900, loss[loss=0.1537, simple_loss=0.2275, pruned_loss=0.03994, over 4902.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.0332, over 971539.77 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:23:03,946 INFO [train.py:715] (1/8) Epoch 10, batch 25950, loss[loss=0.1106, simple_loss=0.1772, pruned_loss=0.02201, over 4824.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03259, over 971820.74 frames.], batch size: 13, lr: 2.11e-04
2022-05-06 22:23:42,721 INFO [train.py:715] (1/8) Epoch 10, batch 26000, loss[loss=0.1504, simple_loss=0.2271, pruned_loss=0.03683, over 4914.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03274, over 972655.54 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:24:21,993 INFO [train.py:715] (1/8) Epoch 10, batch 26050, loss[loss=0.1294, simple_loss=0.1997, pruned_loss=0.02952, over 4886.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03273, over 972552.81 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 22:25:00,978 INFO [train.py:715] (1/8) Epoch 10, batch 26100, loss[loss=0.1189, simple_loss=0.1847, pruned_loss=0.02657, over 4810.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03267, over 972224.31 frames.], batch size: 13, lr: 2.11e-04
2022-05-06 22:25:40,340 INFO [train.py:715] (1/8) Epoch 10, batch 26150, loss[loss=0.1427, simple_loss=0.214, pruned_loss=0.03565, over 4965.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2113, pruned_loss=0.03283, over 971906.34 frames.], batch size: 24, lr: 2.11e-04
2022-05-06 22:26:21,099 INFO [train.py:715] (1/8) Epoch 10, batch 26200, loss[loss=0.1423, simple_loss=0.2165, pruned_loss=0.0341, over 4649.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2109, pruned_loss=0.03301, over 971319.27 frames.], batch size: 13, lr: 2.11e-04
2022-05-06 22:27:00,356 INFO [train.py:715] (1/8) Epoch 10, batch 26250, loss[loss=0.1353, simple_loss=0.2073, pruned_loss=0.03166, over 4908.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2126, pruned_loss=0.03379, over 971309.29 frames.], batch size: 39, lr: 2.11e-04
2022-05-06 22:27:40,001 INFO [train.py:715] (1/8) Epoch 10, batch 26300, loss[loss=0.1506, simple_loss=0.2244, pruned_loss=0.03837, over 4874.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2122, pruned_loss=0.03339, over 971317.00 frames.], batch size: 22, lr: 2.11e-04
2022-05-06 22:28:19,567 INFO [train.py:715] (1/8) Epoch 10, batch 26350, loss[loss=0.1616, simple_loss=0.2356, pruned_loss=0.04383, over 4821.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2129, pruned_loss=0.03377, over 971303.03 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:28:59,169 INFO [train.py:715] (1/8) Epoch 10, batch 26400, loss[loss=0.1297, simple_loss=0.2047, pruned_loss=0.02732, over 4887.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03339, over 971118.66 frames.], batch size: 22, lr: 2.11e-04
2022-05-06 22:29:38,876 INFO [train.py:715] (1/8) Epoch 10, batch 26450, loss[loss=0.143, simple_loss=0.22, pruned_loss=0.03295, over 4839.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03334, over 970764.41 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:30:18,685 INFO [train.py:715] (1/8) Epoch 10, batch 26500, loss[loss=0.1589, simple_loss=0.2239, pruned_loss=0.04691, over 4987.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03354, over 971209.53 frames.], batch size: 35, lr: 2.11e-04
2022-05-06 22:30:59,102 INFO [train.py:715] (1/8) Epoch 10, batch 26550, loss[loss=0.1383, simple_loss=0.2067, pruned_loss=0.03498, over 4856.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03316, over 971485.70 frames.], batch size: 32, lr: 2.11e-04
2022-05-06 22:31:37,642 INFO [train.py:715] (1/8) Epoch 10, batch 26600, loss[loss=0.1395, simple_loss=0.2068, pruned_loss=0.03606, over 4805.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03293, over 972048.62 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:32:17,166 INFO [train.py:715] (1/8) Epoch 10, batch 26650, loss[loss=0.1395, simple_loss=0.2121, pruned_loss=0.03344, over 4686.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2112, pruned_loss=0.03263, over 971083.21 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:32:56,676 INFO [train.py:715] (1/8) Epoch 10, batch 26700, loss[loss=0.161, simple_loss=0.2272, pruned_loss=0.04743, over 4781.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2116, pruned_loss=0.03298, over 972043.93 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:33:36,181 INFO [train.py:715] (1/8) Epoch 10, batch 26750, loss[loss=0.1377, simple_loss=0.2034, pruned_loss=0.03597, over 4843.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03255, over 972252.87 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:34:14,869 INFO [train.py:715] (1/8) Epoch 10, batch 26800, loss[loss=0.1336, simple_loss=0.2117, pruned_loss=0.0278, over 4755.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03271, over 972081.55 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 22:34:54,622 INFO [train.py:715] (1/8) Epoch 10, batch 26850, loss[loss=0.1397, simple_loss=0.221, pruned_loss=0.0292, over 4767.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03294, over 972225.37 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 22:35:34,133 INFO [train.py:715] (1/8) Epoch 10, batch 26900, loss[loss=0.136, simple_loss=0.2067, pruned_loss=0.03259, over 4794.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03271, over 972167.47 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:36:12,945 INFO [train.py:715] (1/8) Epoch 10, batch 26950, loss[loss=0.1578, simple_loss=0.2363, pruned_loss=0.0397, over 4888.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03259, over 972400.49 frames.], batch size: 16, lr: 2.11e-04
2022-05-06 22:36:51,897 INFO [train.py:715] (1/8) Epoch 10, batch 27000, loss[loss=0.1348, simple_loss=0.2025, pruned_loss=0.03352, over 4964.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.03316, over 971978.61 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:36:51,897 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 22:37:01,643 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1063, simple_loss=0.1906, pruned_loss=0.01104, over 914524.00 frames.
2022-05-06 22:37:41,042 INFO [train.py:715] (1/8) Epoch 10, batch 27050, loss[loss=0.1546, simple_loss=0.2254, pruned_loss=0.0419, over 4926.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03293, over 972473.21 frames.], batch size: 29, lr: 2.11e-04
2022-05-06 22:38:21,000 INFO [train.py:715] (1/8) Epoch 10, batch 27100, loss[loss=0.1454, simple_loss=0.2237, pruned_loss=0.03353, over 4972.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03309, over 971942.93 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 22:38:59,619 INFO [train.py:715] (1/8) Epoch 10, batch 27150, loss[loss=0.1446, simple_loss=0.2118, pruned_loss=0.03869, over 4917.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03281, over 972224.73 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:39:38,792 INFO [train.py:715] (1/8) Epoch 10, batch 27200, loss[loss=0.1171, simple_loss=0.1953, pruned_loss=0.01951, over 4894.00 frames.], tot_loss[loss=0.1389, simple_loss=0.212, pruned_loss=0.03288, over 972855.89 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:40:18,814 INFO [train.py:715] (1/8) Epoch 10, batch 27250, loss[loss=0.1204, simple_loss=0.197, pruned_loss=0.02188, over 4791.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03254, over 972795.06 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:40:58,232 INFO [train.py:715] (1/8) Epoch 10, batch 27300, loss[loss=0.1258, simple_loss=0.2048, pruned_loss=0.02344, over 4954.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03304, over 972516.56 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:41:36,437 INFO [train.py:715] (1/8) Epoch 10, batch 27350, loss[loss=0.1534, simple_loss=0.2324, pruned_loss=0.03719, over 4826.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03293, over 971760.39 frames.], batch size: 26, lr: 2.11e-04
2022-05-06 22:42:15,733 INFO [train.py:715] (1/8) Epoch 10, batch 27400, loss[loss=0.1766, simple_loss=0.2412, pruned_loss=0.05603, over 4894.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03276, over 972389.56 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:42:55,897 INFO [train.py:715] (1/8) Epoch 10, batch 27450, loss[loss=0.1504, simple_loss=0.2155, pruned_loss=0.04267, over 4983.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2128, pruned_loss=0.03319, over 971522.26 frames.], batch size: 25, lr: 2.11e-04
2022-05-06 22:43:34,162 INFO [train.py:715] (1/8) Epoch 10, batch 27500, loss[loss=0.1608, simple_loss=0.2396, pruned_loss=0.041, over 4933.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2137, pruned_loss=0.03358, over 971708.95 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:44:13,419 INFO [train.py:715] (1/8) Epoch 10, batch 27550, loss[loss=0.1419, simple_loss=0.2094, pruned_loss=0.03719, over 4796.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2134, pruned_loss=0.03344, over 972409.57 frames.], batch size: 13, lr: 2.11e-04
2022-05-06 22:44:52,784 INFO [train.py:715] (1/8) Epoch 10, batch 27600, loss[loss=0.1468, simple_loss=0.2204, pruned_loss=0.03658, over 4904.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2129, pruned_loss=0.03329, over 972002.65 frames.], batch size: 22, lr: 2.11e-04
2022-05-06 22:45:32,120 INFO [train.py:715] (1/8) Epoch 10, batch 27650, loss[loss=0.1678, simple_loss=0.2315, pruned_loss=0.05206, over 4981.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03293, over 971826.25 frames.], batch size: 33, lr: 2.11e-04
2022-05-06 22:46:11,037 INFO [train.py:715] (1/8) Epoch 10, batch 27700, loss[loss=0.1253, simple_loss=0.2064, pruned_loss=0.02211, over 4789.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2128, pruned_loss=0.03317, over 972046.87 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:46:51,031 INFO [train.py:715] (1/8) Epoch 10, batch 27750, loss[loss=0.1446, simple_loss=0.2298, pruned_loss=0.02969, over 4911.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03282, over 973042.26 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:47:31,110 INFO [train.py:715] (1/8) Epoch 10, batch 27800, loss[loss=0.1403, simple_loss=0.2168, pruned_loss=0.03195, over 4827.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03283, over 971903.10 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:48:10,305 INFO [train.py:715] (1/8) Epoch 10, batch 27850, loss[loss=0.1638, simple_loss=0.2228, pruned_loss=0.05238, over 4917.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03294, over 971892.59 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:48:50,685 INFO [train.py:715] (1/8) Epoch 10, batch 27900, loss[loss=0.1361, simple_loss=0.2119, pruned_loss=0.03013, over 4792.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03293, over 971409.40 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:49:34,041 INFO [train.py:715] (1/8) Epoch 10, batch 27950, loss[loss=0.125, simple_loss=0.1997, pruned_loss=0.02517, over 4970.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03292, over 971624.80 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:50:13,537 INFO [train.py:715] (1/8) Epoch 10, batch 28000, loss[loss=0.14, simple_loss=0.2158, pruned_loss=0.0321, over 4941.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2122, pruned_loss=0.03275, over 970819.15 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 22:50:53,595 INFO [train.py:715] (1/8) Epoch 10, batch 28050, loss[loss=0.1458, simple_loss=0.2168, pruned_loss=0.0374, over 4985.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03256, over 972116.24 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:51:34,468 INFO [train.py:715] (1/8) Epoch 10, batch 28100, loss[loss=0.1251, simple_loss=0.1954, pruned_loss=0.02741, over 4898.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03258, over 973059.70 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:52:15,137 INFO [train.py:715] (1/8) Epoch 10, batch 28150, loss[loss=0.14, simple_loss=0.2263, pruned_loss=0.02686, over 4955.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03286, over 973489.54 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 22:52:54,872 INFO [train.py:715] (1/8) Epoch 10, batch 28200, loss[loss=0.1283, simple_loss=0.2068, pruned_loss=0.02491, over 4932.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03291, over 972554.59 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:53:35,218 INFO [train.py:715] (1/8) Epoch 10, batch 28250, loss[loss=0.1241, simple_loss=0.2003, pruned_loss=0.02402, over 4697.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03303, over 972471.17 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:54:16,802 INFO [train.py:715] (1/8) Epoch 10, batch 28300, loss[loss=0.1211, simple_loss=0.1954, pruned_loss=0.0234, over 4816.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03265, over 972179.96 frames.], batch size: 13, lr: 2.11e-04
2022-05-06 22:54:56,895 INFO [train.py:715] (1/8) Epoch 10, batch 28350, loss[loss=0.1361, simple_loss=0.2057, pruned_loss=0.03323, over 4935.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03297, over 972023.74 frames.], batch size: 29, lr: 2.11e-04
2022-05-06 22:55:37,454 INFO [train.py:715] (1/8) Epoch 10, batch 28400, loss[loss=0.1501, simple_loss=0.2285, pruned_loss=0.0359, over 4901.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2121, pruned_loss=0.0328, over 972949.32 frames.], batch size: 38, lr: 2.11e-04
2022-05-06 22:56:19,123 INFO [train.py:715] (1/8) Epoch 10, batch 28450, loss[loss=0.1295, simple_loss=0.1995, pruned_loss=0.0297, over 4810.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03266, over 972606.20 frames.], batch size: 26, lr: 2.11e-04
2022-05-06 22:57:00,137 INFO [train.py:715] (1/8) Epoch 10, batch 28500, loss[loss=0.1513, simple_loss=0.2149, pruned_loss=0.04385, over 4916.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03299, over 972802.93 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:57:40,570 INFO [train.py:715] (1/8) Epoch 10, batch 28550, loss[loss=0.1598, simple_loss=0.2418, pruned_loss=0.03893, over 4788.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2114, pruned_loss=0.03308, over 972451.53 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:58:21,443 INFO [train.py:715] (1/8) Epoch 10, batch 28600, loss[loss=0.1125, simple_loss=0.1811, pruned_loss=0.022, over 4898.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.0328, over 972717.30 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 22:59:03,592 INFO [train.py:715] (1/8) Epoch 10, batch 28650, loss[loss=0.1531, simple_loss=0.2241, pruned_loss=0.04106, over 4878.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03252, over 972610.26 frames.], batch size: 16, lr: 2.11e-04
2022-05-06 22:59:43,745 INFO [train.py:715] (1/8) Epoch 10, batch 28700, loss[loss=0.127, simple_loss=0.2044, pruned_loss=0.02477, over 4923.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03256, over 973265.16 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 23:00:24,818 INFO [train.py:715] (1/8) Epoch 10, batch 28750, loss[loss=0.1718, simple_loss=0.244, pruned_loss=0.04975, over 4833.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03276, over 972791.65 frames.], batch size: 30, lr: 2.11e-04
2022-05-06 23:01:05,926 INFO [train.py:715] (1/8) Epoch 10, batch 28800, loss[loss=0.1372, simple_loss=0.2101, pruned_loss=0.03216, over 4781.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03254, over 972858.62 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 23:01:46,831 INFO [train.py:715] (1/8) Epoch 10, batch 28850, loss[loss=0.127, simple_loss=0.1986, pruned_loss=0.02772, over 4892.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.0325, over 972253.61 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 23:02:27,345 INFO [train.py:715] (1/8) Epoch 10, batch 28900, loss[loss=0.1297, simple_loss=0.2043, pruned_loss=0.02755, over 4822.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2122, pruned_loss=0.03279, over 972001.11 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 23:03:08,219 INFO [train.py:715] (1/8) Epoch 10, batch 28950, loss[loss=0.1193, simple_loss=0.1926, pruned_loss=0.02301, over 4947.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03281, over 972125.34 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 23:03:49,288 INFO [train.py:715] (1/8) Epoch 10, batch 29000, loss[loss=0.128, simple_loss=0.1984, pruned_loss=0.02883, over 4770.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03281, over 972478.19 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 23:04:28,432 INFO [train.py:715] (1/8) Epoch 10, batch 29050, loss[loss=0.1241, simple_loss=0.1951, pruned_loss=0.02658, over 4787.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2118, pruned_loss=0.03273, over 971923.08 frames.], batch size: 12, lr: 2.10e-04
2022-05-06 23:05:07,309 INFO [train.py:715] (1/8) Epoch 10, batch 29100, loss[loss=0.1228, simple_loss=0.1979, pruned_loss=0.02385, over 4915.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03331, over 972503.06 frames.], batch size: 23, lr: 2.10e-04
2022-05-06 23:05:47,498 INFO [train.py:715] (1/8) Epoch 10, batch 29150, loss[loss=0.1321, simple_loss=0.2121, pruned_loss=0.02606, over 4988.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03284, over 972192.05 frames.], batch size: 28, lr: 2.10e-04
2022-05-06 23:06:27,797 INFO [train.py:715] (1/8) Epoch 10, batch 29200, loss[loss=0.1198, simple_loss=0.1972, pruned_loss=0.0212, over 4972.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2129, pruned_loss=0.03274, over 971607.26 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:07:06,679 INFO [train.py:715] (1/8) Epoch 10, batch 29250, loss[loss=0.1602, simple_loss=0.2269, pruned_loss=0.04678, over 4987.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2127, pruned_loss=0.03284, over 972413.01 frames.], batch size: 35, lr: 2.10e-04
2022-05-06 23:07:46,923 INFO [train.py:715] (1/8) Epoch 10, batch 29300, loss[loss=0.192, simple_loss=0.2507, pruned_loss=0.06668, over 4859.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2123, pruned_loss=0.03332, over 972357.01 frames.], batch size: 32, lr: 2.10e-04
2022-05-06 23:08:27,017 INFO [train.py:715] (1/8) Epoch 10, batch 29350, loss[loss=0.1373, simple_loss=0.2151, pruned_loss=0.02975, over 4755.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03337, over 971712.45 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:09:06,029 INFO [train.py:715] (1/8) Epoch 10, batch 29400, loss[loss=0.1314, simple_loss=0.2009, pruned_loss=0.03099, over 4887.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03304, over 972654.28 frames.], batch size: 32, lr: 2.10e-04
2022-05-06 23:09:45,809 INFO [train.py:715] (1/8) Epoch 10, batch 29450, loss[loss=0.1378, simple_loss=0.2029, pruned_loss=0.03633, over 4791.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03252, over 972138.12 frames.], batch size: 18, lr: 2.10e-04
2022-05-06 23:10:26,006 INFO [train.py:715] (1/8) Epoch 10, batch 29500, loss[loss=0.1422, simple_loss=0.2141, pruned_loss=0.03509, over 4879.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2113, pruned_loss=0.03281, over 972641.04 frames.], batch size: 22, lr: 2.10e-04
2022-05-06 23:11:05,711 INFO [train.py:715] (1/8) Epoch 10, batch 29550, loss[loss=0.1539, simple_loss=0.2139, pruned_loss=0.04691, over 4803.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03357, over 973035.17 frames.], batch size: 13, lr: 2.10e-04
2022-05-06 23:11:44,349 INFO [train.py:715] (1/8) Epoch 10, batch 29600, loss[loss=0.1302, simple_loss=0.2009, pruned_loss=0.02976, over 4928.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.03377, over 972850.59 frames.], batch size: 29, lr: 2.10e-04
2022-05-06 23:12:23,998 INFO [train.py:715] (1/8) Epoch 10, batch 29650, loss[loss=0.1284, simple_loss=0.1992, pruned_loss=0.02883, over 4858.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03294, over 972841.65 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:13:03,436 INFO [train.py:715] (1/8) Epoch 10, batch 29700, loss[loss=0.1464, simple_loss=0.2168, pruned_loss=0.03798, over 4914.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2112, pruned_loss=0.03278, over 973170.34 frames.], batch size: 18, lr: 2.10e-04
2022-05-06 23:13:42,106 INFO [train.py:715] (1/8) Epoch 10, batch 29750, loss[loss=0.1252, simple_loss=0.1929, pruned_loss=0.02875, over 4916.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.03252, over 973134.70 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:14:21,084 INFO [train.py:715] (1/8) Epoch 10, batch 29800, loss[loss=0.1223, simple_loss=0.1998, pruned_loss=0.02237, over 4921.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03228, over 972838.96 frames.], batch size: 23, lr: 2.10e-04
2022-05-06 23:15:00,558 INFO [train.py:715] (1/8) Epoch 10, batch 29850, loss[loss=0.1466, simple_loss=0.2182, pruned_loss=0.03744, over 4931.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.03231, over 973785.14 frames.], batch size: 21, lr: 2.10e-04
2022-05-06 23:15:39,442 INFO [train.py:715] (1/8) Epoch 10, batch 29900, loss[loss=0.1206, simple_loss=0.1982, pruned_loss=0.02157, over 4816.00 frames.], tot_loss[loss=0.1383, simple_loss=0.212, pruned_loss=0.03233, over 974053.50 frames.], batch size: 26, lr: 2.10e-04
2022-05-06 23:16:17,903 INFO [train.py:715] (1/8) Epoch 10, batch 29950, loss[loss=0.144, simple_loss=0.2109, pruned_loss=0.03858, over 4917.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2108, pruned_loss=0.03209, over 973662.20 frames.], batch size: 29, lr: 2.10e-04
2022-05-06 23:16:57,116 INFO [train.py:715] (1/8) Epoch 10, batch 30000, loss[loss=0.1293, simple_loss=0.191, pruned_loss=0.0338, over 4795.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03168, over 973329.20 frames.], batch size: 13, lr: 2.10e-04
2022-05-06 23:16:57,117 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 23:17:06,542 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1063, simple_loss=0.1906, pruned_loss=0.01106, over 914524.00 frames.
2022-05-06 23:17:46,311 INFO [train.py:715] (1/8) Epoch 10, batch 30050, loss[loss=0.1513, simple_loss=0.2242, pruned_loss=0.03916, over 4702.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03177, over 972326.97 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:18:25,808 INFO [train.py:715] (1/8) Epoch 10, batch 30100, loss[loss=0.1408, simple_loss=0.2172, pruned_loss=0.03219, over 4868.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.0316, over 971947.36 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:19:04,202 INFO [train.py:715] (1/8) Epoch 10, batch 30150, loss[loss=0.1383, simple_loss=0.2169, pruned_loss=0.02984, over 4812.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03218, over 972346.30 frames.], batch size: 26, lr: 2.10e-04
2022-05-06 23:19:44,551 INFO [train.py:715] (1/8) Epoch 10, batch 30200, loss[loss=0.1726, simple_loss=0.2452, pruned_loss=0.05004, over 4953.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03276, over 972135.54 frames.], batch size: 35, lr: 2.10e-04
2022-05-06 23:20:24,573 INFO [train.py:715] (1/8) Epoch 10, batch 30250, loss[loss=0.1254, simple_loss=0.2037, pruned_loss=0.02353, over 4979.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2115, pruned_loss=0.03237, over 972028.07 frames.], batch size: 28, lr: 2.10e-04
2022-05-06 23:21:02,967 INFO [train.py:715] (1/8) Epoch 10, batch 30300, loss[loss=0.1493, simple_loss=0.2293, pruned_loss=0.03462, over 4854.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2121, pruned_loss=0.03268, over 972276.66 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:21:41,383 INFO [train.py:715] (1/8) Epoch 10, batch 30350, loss[loss=0.1541, simple_loss=0.2298, pruned_loss=0.03915, over 4846.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.0327, over 972237.72 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:22:21,182 INFO [train.py:715] (1/8) Epoch 10, batch 30400, loss[loss=0.1111, simple_loss=0.188, pruned_loss=0.0171, over 4769.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03256, over 972520.06 frames.], batch size: 12, lr: 2.10e-04
2022-05-06 23:23:00,551 INFO [train.py:715] (1/8) Epoch 10, batch 30450, loss[loss=0.1463, simple_loss=0.2098, pruned_loss=0.04141, over 4978.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.03321, over 972680.15 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:23:38,712 INFO [train.py:715] (1/8) Epoch 10, batch 30500, loss[loss=0.131, simple_loss=0.2009, pruned_loss=0.03059, over 4828.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2119, pruned_loss=0.0333, over 972544.30 frames.], batch size: 13, lr: 2.10e-04
2022-05-06 23:24:18,308 INFO [train.py:715] (1/8) Epoch 10, batch 30550, loss[loss=0.1379, simple_loss=0.215, pruned_loss=0.03044, over 4768.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03321, over 972820.79 frames.], batch size: 18, lr: 2.10e-04
2022-05-06 23:24:57,947 INFO [train.py:715] (1/8) Epoch 10, batch 30600, loss[loss=0.1227, simple_loss=0.1908, pruned_loss=0.02737, over 4957.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03367, over 972747.31 frames.], batch size: 24, lr: 2.10e-04
2022-05-06 23:25:36,411 INFO [train.py:715] (1/8) Epoch 10, batch 30650, loss[loss=0.1261, simple_loss=0.2006, pruned_loss=0.02581, over 4952.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03352, over 973227.70 frames.], batch size: 29, lr: 2.10e-04
2022-05-06 23:26:15,887 INFO [train.py:715] (1/8) Epoch 10, batch 30700, loss[loss=0.1219, simple_loss=0.1954, pruned_loss=0.02424, over 4840.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03334, over 973144.16 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:26:55,010 INFO [train.py:715] (1/8) Epoch 10, batch 30750, loss[loss=0.1411, simple_loss=0.2069, pruned_loss=0.03769, over 4898.00 frames.], tot_loss[loss=0.1392, simple_loss=0.212, pruned_loss=0.03317, over 972916.76 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:27:33,920 INFO [train.py:715] (1/8) Epoch 10, batch 30800, loss[loss=0.1319, simple_loss=0.2122, pruned_loss=0.02577, over 4986.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2122, pruned_loss=0.03345, over 973542.18 frames.], batch size: 14, lr: 2.10e-04
2022-05-06 23:28:12,414 INFO [train.py:715] (1/8) Epoch 10, batch 30850, loss[loss=0.1324, simple_loss=0.2112, pruned_loss=0.02679, over 4873.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2112, pruned_loss=0.03286, over 973187.19 frames.], batch size: 22, lr: 2.10e-04
2022-05-06 23:28:52,168 INFO [train.py:715] (1/8) Epoch 10, batch 30900, loss[loss=0.1138, simple_loss=0.1885, pruned_loss=0.01954, over 4850.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03338, over 973343.30 frames.], batch size: 13, lr: 2.10e-04
2022-05-06 23:29:32,118 INFO [train.py:715] (1/8) Epoch 10, batch 30950, loss[loss=0.1569, simple_loss=0.2282, pruned_loss=0.04279, over 4911.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2127, pruned_loss=0.03399, over 973121.18 frames.], batch size: 39, lr: 2.10e-04
2022-05-06 23:30:11,648 INFO [train.py:715] (1/8) Epoch 10, batch 31000, loss[loss=0.1516, simple_loss=0.2185, pruned_loss=0.04233, over 4765.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.0334, over 972266.31 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:30:50,322 INFO [train.py:715] (1/8) Epoch 10, batch 31050, loss[loss=0.1524, simple_loss=0.2231, pruned_loss=0.04083, over 4782.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2131, pruned_loss=0.03352, over 973045.55 frames.], batch size: 14, lr: 2.10e-04
2022-05-06 23:31:29,595 INFO [train.py:715] (1/8) Epoch 10, batch 31100, loss[loss=0.1398, simple_loss=0.2114, pruned_loss=0.0341, over 4762.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.03387, over 973571.02 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:32:09,329 INFO [train.py:715] (1/8) Epoch 10, batch 31150, loss[loss=0.1225, simple_loss=0.1944, pruned_loss=0.02534, over 4968.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03358, over 972280.10 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:32:47,337 INFO [train.py:715] (1/8) Epoch 10, batch 31200, loss[loss=0.1306, simple_loss=0.1961, pruned_loss=0.03258, over 4959.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2125, pruned_loss=0.03341, over 973610.28 frames.], batch size: 24, lr: 2.10e-04
2022-05-06 23:33:26,833 INFO [train.py:715] (1/8) Epoch 10, batch 31250, loss[loss=0.1265, simple_loss=0.2099, pruned_loss=0.02152, over 4781.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2129, pruned_loss=0.03333, over 974151.81 frames.], batch size: 17, lr: 2.10e-04
2022-05-06 23:34:06,257 INFO [train.py:715] (1/8) Epoch 10, batch 31300, loss[loss=0.1338, simple_loss=0.2062, pruned_loss=0.0307, over 4843.00 frames.], tot_loss[loss=0.1398, simple_loss=0.213, pruned_loss=0.03333, over 973465.41 frames.], batch size: 32, lr: 2.10e-04
2022-05-06 23:34:45,244 INFO [train.py:715] (1/8) Epoch 10, batch 31350, loss[loss=0.1328, simple_loss=0.2135, pruned_loss=0.02608, over 4782.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03315, over 973794.01 frames.], batch size: 14, lr: 2.10e-04
2022-05-06 23:35:23,743 INFO [train.py:715] (1/8) Epoch 10, batch 31400, loss[loss=0.1142, simple_loss=0.1853, pruned_loss=0.0215, over 4844.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2122, pruned_loss=0.03333, over 972775.81 frames.], batch size: 13, lr: 2.10e-04
2022-05-06 23:36:02,748 INFO [train.py:715] (1/8) Epoch 10, batch 31450, loss[loss=0.1579, simple_loss=0.2357, pruned_loss=0.04006, over 4870.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.03312, over 972932.81 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:36:42,179 INFO [train.py:715] (1/8) Epoch 10, batch 31500, loss[loss=0.1596, simple_loss=0.2453, pruned_loss=0.03702, over 4835.00 frames.], tot_loss[loss=0.1396, simple_loss=0.213, pruned_loss=0.03312, over 973201.61 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:37:19,858 INFO [train.py:715] (1/8) Epoch 10, batch 31550, loss[loss=0.1312, simple_loss=0.2062, pruned_loss=0.02807, over 4830.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2134, pruned_loss=0.03341, over 972088.83 frames.], batch size: 25, lr: 2.10e-04
2022-05-06 23:37:58,957 INFO [train.py:715] (1/8) Epoch 10, batch 31600, loss[loss=0.1485, simple_loss=0.2239, pruned_loss=0.03648, over 4941.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2139, pruned_loss=0.03362, over 971821.99 frames.], batch size: 39, lr: 2.10e-04
2022-05-06 23:38:38,099 INFO [train.py:715] (1/8) Epoch 10, batch 31650, loss[loss=0.1134, simple_loss=0.1914, pruned_loss=0.01775, over 4967.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2131, pruned_loss=0.03324, over 971338.51 frames.], batch size: 24, lr: 2.10e-04
2022-05-06 23:39:17,247 INFO [train.py:715] (1/8) Epoch 10, batch 31700, loss[loss=0.1571, simple_loss=0.2326, pruned_loss=0.04082, over 4993.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2128, pruned_loss=0.03268, over 970803.37 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:39:55,915 INFO [train.py:715] (1/8) Epoch 10, batch 31750, loss[loss=0.1826, simple_loss=0.2689, pruned_loss=0.04815, over 4858.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2136, pruned_loss=0.03342, over 970747.86 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:40:34,954 INFO [train.py:715] (1/8) Epoch 10, batch 31800, loss[loss=0.1446, simple_loss=0.218, pruned_loss=0.03561, over 4881.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2131, pruned_loss=0.03301, over 971017.18 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:41:14,311 INFO [train.py:715] (1/8) Epoch 10, batch 31850, loss[loss=0.1299, simple_loss=0.2112, pruned_loss=0.02426, over 4956.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2133, pruned_loss=0.03313, over 971079.39 frames.], batch size: 21, lr: 2.10e-04
2022-05-06 23:41:52,375 INFO [train.py:715] (1/8) Epoch 10, batch 31900, loss[loss=0.1382, simple_loss=0.2156, pruned_loss=0.03042, over 4869.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2135, pruned_loss=0.03284, over 970584.49 frames.], batch size: 32, lr: 2.10e-04
2022-05-06 23:42:31,531 INFO [train.py:715] (1/8) Epoch 10, batch 31950, loss[loss=0.1199, simple_loss=0.1987, pruned_loss=0.02051, over 4896.00 frames.], tot_loss[loss=0.14, simple_loss=0.2138, pruned_loss=0.0331, over 970961.22 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:43:10,933 INFO [train.py:715] (1/8) Epoch 10, batch 32000, loss[loss=0.1171, simple_loss=0.1806, pruned_loss=0.02682, over 4824.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2132, pruned_loss=0.03354, over 970539.88 frames.], batch size: 12, lr: 2.10e-04
2022-05-06 23:43:49,606 INFO [train.py:715] (1/8) Epoch 10, batch 32050, loss[loss=0.1373, simple_loss=0.2224, pruned_loss=0.02611, over 4976.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03334, over 971269.00 frames.], batch size: 28, lr: 2.10e-04
2022-05-06 23:44:27,921 INFO [train.py:715] (1/8) Epoch 10, batch 32100, loss[loss=0.1514, simple_loss=0.2147, pruned_loss=0.04403, over 4804.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03341, over 971672.33 frames.], batch size: 12, lr: 2.10e-04
2022-05-06 23:45:06,918 INFO [train.py:715] (1/8) Epoch 10, batch 32150, loss[loss=0.1453, simple_loss=0.2157, pruned_loss=0.03741, over 4764.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2133, pruned_loss=0.03329, over 972363.12 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:45:45,857 INFO [train.py:715] (1/8) Epoch 10, batch 32200, loss[loss=0.1216, simple_loss=0.1858, pruned_loss=0.02872, over 4786.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.03318, over 972371.60 frames.], batch size: 12, lr: 2.10e-04
2022-05-06 23:46:23,729 INFO [train.py:715] (1/8) Epoch 10, batch 32250, loss[loss=0.1448, simple_loss=0.2187, pruned_loss=0.03549, over 4796.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2138, pruned_loss=0.03368, over 972637.83 frames.], batch size: 17, lr: 2.10e-04
2022-05-06 23:47:02,889 INFO [train.py:715] (1/8) Epoch 10, batch 32300, loss[loss=0.1403, simple_loss=0.2164, pruned_loss=0.03208, over 4845.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.0337, over 973267.81 frames.], batch size: 26, lr: 2.10e-04
2022-05-06 23:47:42,104 INFO [train.py:715] (1/8) Epoch 10, batch 32350, loss[loss=0.1695, simple_loss=0.2302, pruned_loss=0.0544, over 4976.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.03383, over 973426.55 frames.], batch size: 35, lr: 2.10e-04
2022-05-06 23:48:20,911 INFO [train.py:715] (1/8) Epoch 10, batch 32400, loss[loss=0.139, simple_loss=0.2207, pruned_loss=0.02868, over 4980.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2138, pruned_loss=0.03347, over 974232.56 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:48:59,317 INFO [train.py:715] (1/8) Epoch 10, batch 32450, loss[loss=0.1422, simple_loss=0.2211, pruned_loss=0.03166, over 4772.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2132, pruned_loss=0.03314, over 973467.05 frames.], batch size: 18, lr: 2.10e-04
2022-05-06 23:49:38,637 INFO [train.py:715] (1/8) Epoch 10, batch 32500, loss[loss=0.1281, simple_loss=0.1991, pruned_loss=0.0285, over 4877.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2138, pruned_loss=0.03348, over 972551.41 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:50:18,350 INFO [train.py:715] (1/8) Epoch 10, batch 32550, loss[loss=0.1635, simple_loss=0.222, pruned_loss=0.0525, over 4890.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2136, pruned_loss=0.03359, over 972238.14 frames.], batch size: 32, lr: 2.10e-04
2022-05-06 23:50:56,263 INFO [train.py:715] (1/8) Epoch 10, batch 32600, loss[loss=0.1295, simple_loss=0.1991, pruned_loss=0.02991, over 4788.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03347, over 971889.68 frames.], batch size: 14, lr: 2.10e-04
2022-05-06 23:51:35,799 INFO [train.py:715] (1/8) Epoch 10, batch 32650, loss[loss=0.1209, simple_loss=0.1939, pruned_loss=0.02397, over 4692.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2129, pruned_loss=0.03396, over 971087.52 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:52:15,571 INFO [train.py:715] (1/8) Epoch 10, batch 32700, loss[loss=0.1549, simple_loss=0.2198, pruned_loss=0.045, over 4907.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2139, pruned_loss=0.03496, over 972047.92 frames.], batch size: 17, lr: 2.09e-04
2022-05-06 23:52:53,838 INFO [train.py:715] (1/8) Epoch 10, batch 32750, loss[loss=0.1126, simple_loss=0.1859, pruned_loss=0.01968, over 4795.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2133, pruned_loss=0.03458, over 972261.01 frames.], batch size: 14, lr: 2.09e-04
2022-05-06 23:53:34,530 INFO [train.py:715] (1/8) Epoch 10, batch 32800, loss[loss=0.1343, simple_loss=0.2074, pruned_loss=0.03057, over 4809.00 frames.], tot_loss[loss=0.142, simple_loss=0.2143, pruned_loss=0.03481, over 971775.47 frames.], batch size: 25, lr: 2.09e-04
2022-05-06 23:54:14,796 INFO [train.py:715] (1/8) Epoch 10, batch 32850, loss[loss=0.1303, simple_loss=0.2042, pruned_loss=0.02821, over 4783.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2137, pruned_loss=0.03461, over 971732.27 frames.], batch size: 18, lr: 2.09e-04
2022-05-06 23:54:54,904 INFO [train.py:715] (1/8) Epoch 10, batch 32900, loss[loss=0.1193, simple_loss=0.1874, pruned_loss=0.02561, over 4794.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03395, over 972254.51 frames.], batch size: 18, lr: 2.09e-04
2022-05-06 23:55:34,248 INFO [train.py:715] (1/8) Epoch 10, batch 32950, loss[loss=0.1376, simple_loss=0.2162, pruned_loss=0.02951, over 4821.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.03379, over 972875.60 frames.], batch size: 26, lr: 2.09e-04
2022-05-06 23:56:14,927 INFO [train.py:715] (1/8) Epoch 10, batch 33000, loss[loss=0.1313, simple_loss=0.2004, pruned_loss=0.03106, over 4880.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03344, over 972608.03 frames.], batch size: 22, lr: 2.09e-04
2022-05-06 23:56:14,927 INFO [train.py:733] (1/8) Computing validation loss
2022-05-06 23:56:24,576 INFO [train.py:742] (1/8) Epoch 10, validation: loss=0.1063, simple_loss=0.1905, pruned_loss=0.01103, over 914524.00 frames.
2022-05-06 23:57:03,967 INFO [train.py:715] (1/8) Epoch 10, batch 33050, loss[loss=0.1629, simple_loss=0.2365, pruned_loss=0.04468, over 4978.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03332, over 972691.65 frames.], batch size: 26, lr: 2.09e-04
2022-05-06 23:57:43,742 INFO [train.py:715] (1/8) Epoch 10, batch 33100, loss[loss=0.1556, simple_loss=0.2237, pruned_loss=0.04379, over 4839.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.0332, over 973062.95 frames.], batch size: 15, lr: 2.09e-04
2022-05-06 23:58:21,696 INFO [train.py:715] (1/8) Epoch 10, batch 33150, loss[loss=0.1489, simple_loss=0.2126, pruned_loss=0.0426, over 4742.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2131, pruned_loss=0.03409, over 973558.70 frames.], batch size: 19, lr: 2.09e-04
2022-05-06 23:59:00,827 INFO [train.py:715] (1/8) Epoch 10, batch 33200, loss[loss=0.1464, simple_loss=0.2126, pruned_loss=0.04008, over 4854.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.03331, over 973869.23 frames.], batch size: 32, lr: 2.09e-04
2022-05-06 23:59:40,448 INFO [train.py:715] (1/8) Epoch 10, batch 33250, loss[loss=0.1554, simple_loss=0.2247, pruned_loss=0.04299, over 4836.00 frames.], tot_loss[loss=0.1406, simple_loss=0.213, pruned_loss=0.0341, over 973339.33 frames.], batch size: 15, lr: 2.09e-04
2022-05-07 00:00:18,363 INFO [train.py:715] (1/8) Epoch 10, batch 33300, loss[loss=0.1837, simple_loss=0.249, pruned_loss=0.0592, over 4872.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03379, over 973692.79 frames.], batch size: 39, lr: 2.09e-04
2022-05-07 00:00:57,775 INFO [train.py:715] (1/8) Epoch 10, batch 33350, loss[loss=0.1477, simple_loss=0.2086, pruned_loss=0.04341, over 4849.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03357, over 972553.73 frames.], batch size: 32, lr: 2.09e-04
2022-05-07 00:01:37,024 INFO [train.py:715] (1/8) Epoch 10, batch 33400, loss[loss=0.1391, simple_loss=0.2076, pruned_loss=0.0353, over 4801.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2144, pruned_loss=0.03417, over 972923.36 frames.], batch size: 21, lr: 2.09e-04
2022-05-07 00:02:16,548 INFO [train.py:715] (1/8) Epoch 10, batch 33450, loss[loss=0.1169, simple_loss=0.1934, pruned_loss=0.02022, over 4895.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.03359, over 973277.90 frames.], batch size: 22, lr: 2.09e-04
2022-05-07 00:02:54,369 INFO [train.py:715] (1/8) Epoch 10, batch 33500, loss[loss=0.1452, simple_loss=0.2013, pruned_loss=0.04449, over 4966.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2138, pruned_loss=0.03389, over 972484.20 frames.], batch size: 39, lr: 2.09e-04
2022-05-07 00:03:33,963 INFO [train.py:715] (1/8) Epoch 10, batch 33550, loss[loss=0.1409, simple_loss=0.2054, pruned_loss=0.03813, over 4798.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2141, pruned_loss=0.03406, over 973103.11 frames.], batch size: 24, lr: 2.09e-04
2022-05-07 00:04:13,566 INFO [train.py:715] (1/8) Epoch 10, batch 33600, loss[loss=0.1432, simple_loss=0.2141, pruned_loss=0.0361, over 4831.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03344, over 972285.25 frames.], batch size: 13, lr: 2.09e-04
2022-05-07 00:04:52,117 INFO [train.py:715] (1/8) Epoch 10, batch 33650, loss[loss=0.1246, simple_loss=0.1978, pruned_loss=0.02566, over 4930.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.03369, over 971842.79 frames.], batch size: 29, lr: 2.09e-04
2022-05-07 00:05:30,845 INFO [train.py:715] (1/8) Epoch 10, batch 33700, loss[loss=0.104, simple_loss=0.1698, pruned_loss=0.01909, over 4875.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.03334, over 971602.44 frames.], batch size: 32, lr: 2.09e-04
2022-05-07 00:06:10,499 INFO [train.py:715] (1/8) Epoch 10, batch 33750, loss[loss=0.1589, simple_loss=0.2386, pruned_loss=0.03962, over 4825.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2127, pruned_loss=0.03287, over 971321.95 frames.], batch size: 15, lr: 2.09e-04
2022-05-07 00:06:50,188 INFO [train.py:715] (1/8) Epoch 10, batch 33800, loss[loss=0.1516, simple_loss=0.2296, pruned_loss=0.03677, over 4855.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2128, pruned_loss=0.03283, over 972082.10 frames.], batch size: 22, lr: 2.09e-04
2022-05-07 00:07:29,176 INFO [train.py:715] (1/8) Epoch 10, batch 33850, loss[loss=0.1276, simple_loss=0.2007, pruned_loss=0.02729, over 4802.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2128, pruned_loss=0.03294, over 971650.74 frames.], batch size: 14, lr: 2.09e-04
2022-05-07 00:08:08,844 INFO [train.py:715] (1/8) Epoch 10, batch 33900, loss[loss=0.1523, simple_loss=0.2306, pruned_loss=0.03694, over 4903.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.03371, over 972511.44 frames.], batch size: 19, lr: 2.09e-04
2022-05-07 00:08:48,751 INFO [train.py:715] (1/8) Epoch 10, batch 33950, loss[loss=0.1283, simple_loss=0.2017, pruned_loss=0.02747, over 4866.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2132, pruned_loss=0.0336, over 971627.52 frames.], batch size: 22, lr: 2.09e-04
2022-05-07 00:09:27,310 INFO [train.py:715] (1/8) Epoch 10, batch 34000, loss[loss=0.1448, simple_loss=0.2221, pruned_loss=0.03375, over 4933.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2129, pruned_loss=0.03317, over 970810.30 frames.], batch size: 21, lr: 2.09e-04
2022-05-07 00:10:06,619 INFO [train.py:715] (1/8) Epoch 10, batch 34050, loss[loss=0.145, simple_loss=0.2201, pruned_loss=0.03497, over 4913.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03325, over 970625.92 frames.], batch size: 18, lr: 2.09e-04
2022-05-07 00:10:45,881 INFO [train.py:715] (1/8) Epoch 10, batch 34100, loss[loss=0.1547, simple_loss=0.2258, pruned_loss=0.04176, over 4900.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2129, pruned_loss=0.0338, over 970949.55 frames.], batch size: 39, lr: 2.09e-04
2022-05-07 00:11:25,365 INFO [train.py:715] (1/8) Epoch 10, batch 34150, loss[loss=0.1301, simple_loss=0.2074, pruned_loss=0.02643, over 4875.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03314, over 971089.23 frames.], batch size: 19, lr: 2.09e-04
2022-05-07 00:12:04,927 INFO [train.py:715] (1/8) Epoch 10, batch 34200, loss[loss=0.1533, simple_loss=0.2332, pruned_loss=0.03663, over 4911.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2131, pruned_loss=0.0335, over 971785.46 frames.], batch size: 17, lr: 2.09e-04
2022-05-07 00:12:44,149 INFO [train.py:715] (1/8) Epoch 10, batch 34250, loss[loss=0.1316, simple_loss=0.1975, pruned_loss=0.03284, over 4871.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03313, over 972532.31 frames.], batch size: 32, lr: 2.09e-04
2022-05-07 00:13:23,665 INFO [train.py:715] (1/8) Epoch 10, batch 34300, loss[loss=0.1231, simple_loss=0.1993, pruned_loss=0.02346, over 4916.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2125, pruned_loss=0.0329, over 973164.03 frames.], batch size: 17, lr: 2.09e-04
2022-05-07 00:14:03,574 INFO [train.py:715] (1/8) Epoch 10, batch 34350, loss[loss=0.1398, simple_loss=0.2165, pruned_loss=0.03156, over 4776.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03261, over 973722.90 frames.], batch size: 14, lr: 2.09e-04
2022-05-07 00:14:43,438 INFO [train.py:715] (1/8) Epoch 10, batch 34400, loss[loss=0.1237, simple_loss=0.1966, pruned_loss=0.02537, over 4882.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03257, over 973831.15 frames.], batch size: 22, lr: 2.09e-04
2022-05-07 00:15:23,586 INFO [train.py:715] (1/8) Epoch 10, batch 34450, loss[loss=0.1137, simple_loss=0.1795, pruned_loss=0.024, over 4754.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03251, over 973487.80 frames.], batch size: 16, lr: 2.09e-04
2022-05-07 00:16:03,653 INFO [train.py:715] (1/8) Epoch 10, batch 34500, loss[loss=0.1342, simple_loss=0.2027, pruned_loss=0.03286, over 4814.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2129, pruned_loss=0.03308, over 973410.27 frames.], batch size: 13, lr: 2.09e-04
2022-05-07 00:16:42,854 INFO [train.py:715] (1/8) Epoch 10, batch 34550, loss[loss=0.1221, simple_loss=0.2015, pruned_loss=0.02136, over 4976.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03309, over 973681.16 frames.], batch size: 25, lr: 2.09e-04
2022-05-07 00:17:23,159 INFO [train.py:715] (1/8) Epoch 10, batch 34600, loss[loss=0.1398, simple_loss=0.2168, pruned_loss=0.03141, over 4821.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03296, over 973154.19 frames.], batch size: 25, lr: 2.09e-04
2022-05-07 00:18:03,614 INFO [train.py:715] (1/8) Epoch 10, batch 34650, loss[loss=0.1425, simple_loss=0.2117, pruned_loss=0.03662, over 4844.00 frames.], tot_loss[loss=0.139, simple_loss=0.2123, pruned_loss=0.03283, over 972870.35 frames.], batch size: 15, lr: 2.09e-04
2022-05-07 00:18:42,655 INFO [train.py:715] (1/8) Epoch 10, batch 34700, loss[loss=0.1376, simple_loss=0.2016, pruned_loss=0.03678, over 4911.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03279, over 972131.65 frames.], batch size: 17, lr: 2.09e-04
2022-05-07 00:19:21,235 INFO [train.py:715] (1/8) Epoch 10, batch 34750, loss[loss=0.1294, simple_loss=0.2021, pruned_loss=0.02829, over 4819.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03322, over 971211.64 frames.], batch size: 27, lr: 2.09e-04
2022-05-07 00:19:57,692 INFO [train.py:715] (1/8) Epoch 10, batch 34800, loss[loss=0.1311, simple_loss=0.2, pruned_loss=0.0311, over 4799.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2114, pruned_loss=0.03304, over 970853.29 frames.], batch size: 12, lr: 2.09e-04
2022-05-07 00:20:47,594 INFO [train.py:715] (1/8) Epoch 11, batch 0, loss[loss=0.1659, simple_loss=0.2352, pruned_loss=0.04829, over 4984.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2352, pruned_loss=0.04829, over 4984.00 frames.], batch size: 14, lr: 2.00e-04
2022-05-07 00:21:26,502 INFO [train.py:715] (1/8) Epoch 11, batch 50, loss[loss=0.1465, simple_loss=0.2213, pruned_loss=0.03584, over 4761.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2183, pruned_loss=0.03679, over 218859.97 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:22:06,403 INFO [train.py:715] (1/8) Epoch 11, batch 100, loss[loss=0.1726, simple_loss=0.2419, pruned_loss=0.0517, over 4669.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2139, pruned_loss=0.03511, over 385006.47 frames.], batch size: 13, lr: 2.00e-04
2022-05-07 00:22:46,281 INFO [train.py:715] (1/8) Epoch 11, batch 150, loss[loss=0.1411, simple_loss=0.2238, pruned_loss=0.02919, over 4837.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2128, pruned_loss=0.03386, over 514737.58 frames.], batch size: 27, lr: 2.00e-04
2022-05-07 00:23:26,828 INFO [train.py:715] (1/8) Epoch 11, batch 200, loss[loss=0.1645, simple_loss=0.2363, pruned_loss=0.04633, over 4875.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2143, pruned_loss=0.03427, over 616115.41 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:24:06,703 INFO [train.py:715] (1/8) Epoch 11, batch 250, loss[loss=0.1294, simple_loss=0.1992, pruned_loss=0.02983, over 4821.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.0344, over 694911.61 frames.], batch size: 26, lr: 2.00e-04
2022-05-07 00:24:45,523 INFO [train.py:715] (1/8) Epoch 11, batch 300, loss[loss=0.1457, simple_loss=0.2208, pruned_loss=0.03524, over 4876.00 frames.], tot_loss[loss=0.142, simple_loss=0.215, pruned_loss=0.03451, over 755805.16 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:25:26,111 INFO [train.py:715] (1/8) Epoch 11, batch 350, loss[loss=0.1197, simple_loss=0.1955, pruned_loss=0.02195, over 4811.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2139, pruned_loss=0.03398, over 803679.94 frames.], batch size: 26, lr: 2.00e-04
2022-05-07 00:26:05,774 INFO [train.py:715] (1/8) Epoch 11, batch 400, loss[loss=0.1315, simple_loss=0.2107, pruned_loss=0.02616, over 4977.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03364, over 841963.92 frames.], batch size: 28, lr: 2.00e-04
2022-05-07 00:26:46,461 INFO [train.py:715] (1/8) Epoch 11, batch 450, loss[loss=0.1412, simple_loss=0.2077, pruned_loss=0.03737, over 4812.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03365, over 871640.88 frames.], batch size: 21, lr: 2.00e-04
2022-05-07 00:27:27,783 INFO [train.py:715] (1/8) Epoch 11, batch 500, loss[loss=0.1104, simple_loss=0.186, pruned_loss=0.0174, over 4903.00 frames.], tot_loss[loss=0.1396, simple_loss=0.213, pruned_loss=0.03307, over 894037.93 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:28:09,385 INFO [train.py:715] (1/8) Epoch 11, batch 550, loss[loss=0.1387, simple_loss=0.2066, pruned_loss=0.03542, over 4905.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2133, pruned_loss=0.03292, over 912186.37 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:28:50,711 INFO [train.py:715] (1/8) Epoch 11, batch 600, loss[loss=0.12, simple_loss=0.1969, pruned_loss=0.0216, over 4838.00 frames.], tot_loss[loss=0.139, simple_loss=0.2126, pruned_loss=0.03269, over 926239.51 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:29:32,039 INFO [train.py:715] (1/8) Epoch 11, batch 650, loss[loss=0.1097, simple_loss=0.1936, pruned_loss=0.01288, over 4816.00 frames.], tot_loss[loss=0.1385, simple_loss=0.212, pruned_loss=0.03245, over 936543.97 frames.], batch size: 26, lr: 2.00e-04
2022-05-07 00:30:13,299 INFO [train.py:715] (1/8) Epoch 11, batch 700, loss[loss=0.1495, simple_loss=0.2197, pruned_loss=0.03967, over 4907.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2121, pruned_loss=0.03279, over 943811.32 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:30:54,878 INFO [train.py:715] (1/8) Epoch 11, batch 750, loss[loss=0.1649, simple_loss=0.2348, pruned_loss=0.04755, over 4888.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03241, over 950088.28 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:31:36,031 INFO [train.py:715] (1/8) Epoch 11, batch 800, loss[loss=0.1586, simple_loss=0.2256, pruned_loss=0.04575, over 4965.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.03263, over 955671.70 frames.], batch size: 31, lr: 2.00e-04
2022-05-07 00:32:16,760 INFO [train.py:715] (1/8) Epoch 11, batch 850, loss[loss=0.1621, simple_loss=0.2229, pruned_loss=0.05067, over 4931.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.03294, over 959536.22 frames.], batch size: 21, lr: 2.00e-04
2022-05-07 00:32:58,368 INFO [train.py:715] (1/8) Epoch 11, batch 900, loss[loss=0.1312, simple_loss=0.2033, pruned_loss=0.02959, over 4986.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2107, pruned_loss=0.03238, over 962108.08 frames.], batch size: 35, lr: 2.00e-04
2022-05-07 00:33:38,987 INFO [train.py:715] (1/8) Epoch 11, batch 950, loss[loss=0.1461, simple_loss=0.2147, pruned_loss=0.03879, over 4990.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2105, pruned_loss=0.03236, over 965214.88 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:34:19,481 INFO [train.py:715] (1/8) Epoch 11, batch 1000, loss[loss=0.131, simple_loss=0.2121, pruned_loss=0.02501, over 4921.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03253, over 966801.31 frames.], batch size: 23, lr: 2.00e-04
2022-05-07 00:34:58,897 INFO [train.py:715] (1/8) Epoch 11, batch 1050, loss[loss=0.1307, simple_loss=0.2059, pruned_loss=0.02777, over 4790.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2111, pruned_loss=0.03296, over 967652.66 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:35:41,052 INFO [train.py:715] (1/8) Epoch 11, batch 1100, loss[loss=0.1186, simple_loss=0.1945, pruned_loss=0.02134, over 4815.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2112, pruned_loss=0.03277, over 968866.76 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:36:20,725 INFO [train.py:715] (1/8) Epoch 11, batch 1150, loss[loss=0.1105, simple_loss=0.1836, pruned_loss=0.01866, over 4938.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2112, pruned_loss=0.03291, over 970413.68 frames.], batch size: 29, lr: 2.00e-04
2022-05-07 00:37:00,333 INFO [train.py:715] (1/8) Epoch 11, batch 1200, loss[loss=0.1421, simple_loss=0.2231, pruned_loss=0.0305, over 4767.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03304, over 970762.50 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:37:39,166 INFO [train.py:715] (1/8) Epoch 11, batch 1250, loss[loss=0.1154, simple_loss=0.1779, pruned_loss=0.02647, over 4650.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2122, pruned_loss=0.03307, over 970606.35 frames.], batch size: 13, lr: 2.00e-04
2022-05-07 00:38:18,010 INFO [train.py:715] (1/8) Epoch 11, batch 1300, loss[loss=0.1349, simple_loss=0.2072, pruned_loss=0.03125, over 4784.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03329, over 970575.23 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:38:56,867 INFO [train.py:715] (1/8) Epoch 11, batch 1350, loss[loss=0.1459, simple_loss=0.2211, pruned_loss=0.03537, over 4893.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03334, over 971080.36 frames.], batch size: 22, lr: 2.00e-04
2022-05-07 00:39:35,886 INFO [train.py:715] (1/8) Epoch 11, batch 1400, loss[loss=0.1442, simple_loss=0.211, pruned_loss=0.03868, over 4841.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.0331, over 971306.39 frames.], batch size: 30, lr: 2.00e-04
2022-05-07 00:40:14,718 INFO [train.py:715] (1/8) Epoch 11, batch 1450, loss[loss=0.1313, simple_loss=0.1998, pruned_loss=0.03144, over 4849.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03349, over 970470.43 frames.], batch size: 30, lr: 2.00e-04
2022-05-07 00:40:53,349 INFO [train.py:715] (1/8) Epoch 11, batch 1500, loss[loss=0.1213, simple_loss=0.1918, pruned_loss=0.02541, over 4772.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2131, pruned_loss=0.03356, over 970974.50 frames.], batch size: 19, lr: 2.00e-04
2022-05-07 00:41:31,716 INFO [train.py:715] (1/8) Epoch 11, batch 1550, loss[loss=0.1438, simple_loss=0.215, pruned_loss=0.03629, over 4859.00 frames.], tot_loss[loss=0.1402, simple_loss=0.213, pruned_loss=0.03368, over 971220.89 frames.], batch size: 30, lr: 2.00e-04
2022-05-07 00:42:10,774 INFO [train.py:715] (1/8) Epoch 11, batch 1600, loss[loss=0.1609, simple_loss=0.23, pruned_loss=0.04588, over 4774.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03322, over 971297.22 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:42:49,743 INFO [train.py:715] (1/8) Epoch 11, batch 1650, loss[loss=0.1309, simple_loss=0.2025, pruned_loss=0.02965, over 4969.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03319, over 972108.06 frames.], batch size: 35, lr: 2.00e-04
2022-05-07 00:43:28,112 INFO [train.py:715] (1/8) Epoch 11, batch 1700, loss[loss=0.14, simple_loss=0.2175, pruned_loss=0.03129, over 4874.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03283, over 972517.90 frames.], batch size: 32, lr: 2.00e-04
2022-05-07 00:44:07,383 INFO [train.py:715] (1/8) Epoch 11, batch 1750, loss[loss=0.1454, simple_loss=0.223, pruned_loss=0.03389, over 4889.00 frames.], tot_loss[loss=0.139, simple_loss=0.2123, pruned_loss=0.03286, over 972935.11 frames.], batch size: 22, lr: 2.00e-04
2022-05-07 00:44:46,272 INFO [train.py:715] (1/8) Epoch 11, batch 1800, loss[loss=0.1744, simple_loss=0.2389, pruned_loss=0.05493, over 4836.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2114, pruned_loss=0.03276, over 971767.13 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:45:25,308 INFO [train.py:715] (1/8) Epoch 11, batch 1850, loss[loss=0.1664, simple_loss=0.226, pruned_loss=0.05336, over 4787.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03317, over 972016.43 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:46:04,491 INFO [train.py:715] (1/8) Epoch 11, batch 1900, loss[loss=0.1179, simple_loss=0.1919, pruned_loss=0.02193, over 4941.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2121, pruned_loss=0.03336, over 971472.62 frames.], batch size: 21, lr: 2.00e-04
2022-05-07 00:46:43,768 INFO [train.py:715] (1/8) Epoch 11, batch 1950, loss[loss=0.1326, simple_loss=0.2109, pruned_loss=0.02719, over 4880.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2114, pruned_loss=0.0328, over 971580.47 frames.], batch size: 22, lr: 2.00e-04
2022-05-07 00:47:23,302 INFO [train.py:715] (1/8) Epoch 11, batch 2000, loss[loss=0.1349, simple_loss=0.2034, pruned_loss=0.03317, over 4793.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2119, pruned_loss=0.0333, over 972185.89 frames.], batch size: 21, lr: 2.00e-04
2022-05-07 00:48:01,935 INFO [train.py:715] (1/8) Epoch 11, batch 2050, loss[loss=0.1023, simple_loss=0.1699, pruned_loss=0.01735, over 4753.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2111, pruned_loss=0.03297, over 972627.61 frames.], batch size: 12, lr: 2.00e-04
2022-05-07 00:48:41,073 INFO [train.py:715] (1/8) Epoch 11, batch 2100, loss[loss=0.1306, simple_loss=0.2098, pruned_loss=0.02566, over 4962.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.03308, over 973086.08 frames.], batch size: 24, lr: 2.00e-04
2022-05-07 00:49:20,365 INFO [train.py:715] (1/8) Epoch 11, batch 2150, loss[loss=0.1151, simple_loss=0.1864, pruned_loss=0.02186, over 4916.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03321, over 973231.25 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:49:59,567 INFO [train.py:715] (1/8) Epoch 11, batch 2200, loss[loss=0.1242, simple_loss=0.1992, pruned_loss=0.02459, over 4790.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03296, over 973060.59 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:50:38,225 INFO [train.py:715] (1/8) Epoch 11, batch 2250, loss[loss=0.1249, simple_loss=0.1902, pruned_loss=0.02982, over 4791.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03274, over 972986.80 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:51:17,284 INFO [train.py:715] (1/8) Epoch 11, batch 2300, loss[loss=0.1454, simple_loss=0.2181, pruned_loss=0.03639, over 4806.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03299, over 972290.06 frames.], batch size: 25, lr: 2.00e-04
2022-05-07 00:51:56,684 INFO [train.py:715] (1/8) Epoch 11, batch 2350, loss[loss=0.1321, simple_loss=0.2033, pruned_loss=0.03051, over 4979.00 frames.], tot_loss[loss=0.1381, simple_loss=0.211, pruned_loss=0.03262, over 972134.49 frames.], batch size: 24, lr: 2.00e-04
2022-05-07 00:52:35,088 INFO [train.py:715] (1/8) Epoch 11, batch 2400, loss[loss=0.1716, simple_loss=0.2351, pruned_loss=0.05404, over 4918.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2111, pruned_loss=0.0328, over 971644.63 frames.], batch size: 39, lr: 2.00e-04
2022-05-07 00:53:14,461 INFO [train.py:715] (1/8) Epoch 11, batch 2450, loss[loss=0.1402, simple_loss=0.2086, pruned_loss=0.03586, over 4778.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2113, pruned_loss=0.03292, over 970839.39 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:53:54,034 INFO [train.py:715] (1/8) Epoch 11, batch 2500, loss[loss=0.1404, simple_loss=0.2157, pruned_loss=0.03257, over 4826.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2112, pruned_loss=0.03275, over 970355.83 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:54:33,186 INFO [train.py:715] (1/8) Epoch 11, batch 2550, loss[loss=0.1299, simple_loss=0.2055, pruned_loss=0.0271, over 4903.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.03267, over 970827.68 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:55:12,425 INFO [train.py:715] (1/8) Epoch 11, batch 2600, loss[loss=0.1491, simple_loss=0.2253, pruned_loss=0.03647, over 4872.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03288, over 970333.62 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:55:51,268 INFO [train.py:715] (1/8) Epoch 11, batch 2650, loss[loss=0.1318, simple_loss=0.204, pruned_loss=0.02976, over 4950.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.03364, over 970160.25 frames.], batch size: 24, lr: 2.00e-04
2022-05-07 00:56:30,350 INFO [train.py:715] (1/8) Epoch 11, batch 2700, loss[loss=0.1307, simple_loss=0.2138, pruned_loss=0.02385, over 4813.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2126, pruned_loss=0.03359, over 969672.56 frames.], batch size: 25, lr: 2.00e-04
2022-05-07 00:57:09,095 INFO [train.py:715] (1/8) Epoch 11, batch 2750, loss[loss=0.1511, simple_loss=0.2244, pruned_loss=0.03894, over 4679.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2117, pruned_loss=0.03324, over 968975.90 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:57:48,074 INFO [train.py:715] (1/8) Epoch 11, batch 2800, loss[loss=0.1482, simple_loss=0.2222, pruned_loss=0.03715, over 4690.00 frames.], tot_loss[loss=0.139, simple_loss=0.2116, pruned_loss=0.03318, over 969330.56 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:58:27,252 INFO [train.py:715] (1/8) Epoch 11, batch 2850, loss[loss=0.1236, simple_loss=0.1955, pruned_loss=0.02586, over 4775.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03263, over 970222.24 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:59:05,713 INFO [train.py:715] (1/8) Epoch 11, batch 2900, loss[loss=0.1229, simple_loss=0.1861, pruned_loss=0.02985, over 4748.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03263, over 971349.22 frames.], batch size: 12, lr: 2.00e-04
2022-05-07 00:59:45,169 INFO [train.py:715] (1/8) Epoch 11, batch 2950, loss[loss=0.1314, simple_loss=0.1995, pruned_loss=0.03163, over 4853.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03284, over 970618.85 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 01:00:25,033 INFO [train.py:715] (1/8) Epoch 11, batch 3000, loss[loss=0.1244, simple_loss=0.1888, pruned_loss=0.02999, over 4815.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03297, over 971149.48 frames.], batch size: 12, lr: 2.00e-04
2022-05-07 01:00:25,033 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 01:00:34,772 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.1061, simple_loss=0.1902, pruned_loss=0.01097, over 914524.00 frames.
2022-05-07 01:01:14,749 INFO [train.py:715] (1/8) Epoch 11, batch 3050, loss[loss=0.1401, simple_loss=0.2039, pruned_loss=0.03816, over 4761.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03323, over 971001.60 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 01:01:54,017 INFO [train.py:715] (1/8) Epoch 11, batch 3100, loss[loss=0.1255, simple_loss=0.2005, pruned_loss=0.02527, over 4751.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03311, over 971583.99 frames.], batch size: 19, lr: 2.00e-04
2022-05-07 01:02:34,092 INFO [train.py:715] (1/8) Epoch 11, batch 3150, loss[loss=0.123, simple_loss=0.2095, pruned_loss=0.01826, over 4832.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03309, over 971614.28 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 01:03:13,130 INFO [train.py:715] (1/8) Epoch 11, batch 3200, loss[loss=0.17, simple_loss=0.2399, pruned_loss=0.05004, over 4799.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03304, over 971784.75 frames.], batch size: 14, lr: 2.00e-04
2022-05-07 01:03:52,804 INFO [train.py:715] (1/8) Epoch 11, batch 3250, loss[loss=0.1567, simple_loss=0.2479, pruned_loss=0.03275, over 4812.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2131, pruned_loss=0.03337, over 971696.04 frames.], batch size: 25, lr: 2.00e-04
2022-05-07 01:04:31,534 INFO [train.py:715] (1/8) Epoch 11, batch 3300, loss[loss=0.1422, simple_loss=0.225, pruned_loss=0.02975, over 4914.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2132, pruned_loss=0.03348, over 972186.63 frames.], batch size: 39, lr: 2.00e-04
2022-05-07 01:05:10,792 INFO [train.py:715] (1/8) Epoch 11, batch 3350, loss[loss=0.1446, simple_loss=0.2246, pruned_loss=0.03229, over 4918.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03324, over 972509.50 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 01:05:50,447 INFO [train.py:715] (1/8) Epoch 11, batch 3400, loss[loss=0.129, simple_loss=0.1965, pruned_loss=0.03069, over 4878.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03264, over 972929.76 frames.], batch size: 22, lr: 2.00e-04
2022-05-07 01:06:29,439 INFO [train.py:715] (1/8) Epoch 11, batch 3450, loss[loss=0.1199, simple_loss=0.1974, pruned_loss=0.02123, over 4841.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03262, over 973023.52 frames.], batch size: 26, lr: 2.00e-04
2022-05-07 01:07:08,300 INFO [train.py:715] (1/8) Epoch 11, batch 3500, loss[loss=0.1348, simple_loss=0.2102, pruned_loss=0.02976, over 4943.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2128, pruned_loss=0.03294, over 973389.63 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:07:47,576 INFO [train.py:715] (1/8) Epoch 11, batch 3550, loss[loss=0.1033, simple_loss=0.1748, pruned_loss=0.01594, over 4806.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2119, pruned_loss=0.03234, over 973430.96 frames.], batch size: 12, lr: 1.99e-04
2022-05-07 01:08:27,197 INFO [train.py:715] (1/8) Epoch 11, batch 3600, loss[loss=0.1327, simple_loss=0.2049, pruned_loss=0.03021, over 4961.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2124, pruned_loss=0.03249, over 972921.31 frames.], batch size: 39, lr: 1.99e-04
2022-05-07 01:09:05,518 INFO [train.py:715] (1/8) Epoch 11, batch 3650, loss[loss=0.1469, simple_loss=0.2285, pruned_loss=0.0326, over 4767.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2121, pruned_loss=0.03221, over 971955.43 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:09:45,171 INFO [train.py:715] (1/8) Epoch 11, batch 3700, loss[loss=0.1334, simple_loss=0.2124, pruned_loss=0.0272, over 4889.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2118, pruned_loss=0.0324, over 972540.12 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:10:24,607 INFO [train.py:715] (1/8) Epoch 11, batch 3750, loss[loss=0.1442, simple_loss=0.2171, pruned_loss=0.03567, over 4763.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2114, pruned_loss=0.03277, over 972017.41 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:11:03,053 INFO [train.py:715] (1/8) Epoch 11, batch 3800, loss[loss=0.1167, simple_loss=0.1818, pruned_loss=0.02582, over 4842.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03269, over 971953.03 frames.], batch size: 13, lr: 1.99e-04
2022-05-07 01:11:42,116 INFO [train.py:715] (1/8) Epoch 11, batch 3850, loss[loss=0.1649, simple_loss=0.2331, pruned_loss=0.04838, over 4879.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03317, over 972018.70 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:12:21,426 INFO [train.py:715] (1/8) Epoch 11, batch 3900, loss[loss=0.1183, simple_loss=0.183, pruned_loss=0.02679, over 4777.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03291, over 971608.93 frames.], batch size: 12, lr: 1.99e-04
2022-05-07 01:13:01,151 INFO [train.py:715] (1/8) Epoch 11, batch 3950, loss[loss=0.1444, simple_loss=0.2175, pruned_loss=0.03569, over 4826.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03268, over 971615.49 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:13:39,998 INFO [train.py:715] (1/8) Epoch 11, batch 4000, loss[loss=0.1413, simple_loss=0.2108, pruned_loss=0.03596, over 4968.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03295, over 970846.27 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:14:19,841 INFO [train.py:715] (1/8) Epoch 11, batch 4050, loss[loss=0.1373, simple_loss=0.2095, pruned_loss=0.03252, over 4969.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2126, pruned_loss=0.03302, over 971260.90 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:14:59,481 INFO [train.py:715] (1/8) Epoch 11, batch 4100, loss[loss=0.1153, simple_loss=0.176, pruned_loss=0.02727, over 4777.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03301, over 970806.08 frames.], batch size: 12, lr: 1.99e-04
2022-05-07 01:15:38,034 INFO [train.py:715] (1/8) Epoch 11, batch 4150, loss[loss=0.1535, simple_loss=0.2272, pruned_loss=0.03988, over 4953.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03305, over 971091.37 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:16:16,420 INFO [train.py:715] (1/8) Epoch 11, batch 4200, loss[loss=0.1448, simple_loss=0.2246, pruned_loss=0.0325, over 4930.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03336, over 971332.47 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:16:56,666 INFO [train.py:715] (1/8) Epoch 11, batch 4250, loss[loss=0.1599, simple_loss=0.2398, pruned_loss=0.04, over 4936.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03323, over 972200.77 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:17:36,661 INFO [train.py:715] (1/8) Epoch 11, batch 4300, loss[loss=0.1282, simple_loss=0.2016, pruned_loss=0.02736, over 4932.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2139, pruned_loss=0.03376, over 973013.34 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:18:15,827 INFO [train.py:715] (1/8) Epoch 11, batch 4350, loss[loss=0.1621, simple_loss=0.235, pruned_loss=0.04458, over 4705.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2141, pruned_loss=0.03382, over 972201.35 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:18:56,186 INFO [train.py:715] (1/8) Epoch 11, batch 4400, loss[loss=0.1367, simple_loss=0.209, pruned_loss=0.03218, over 4813.00 frames.], tot_loss[loss=0.1407, simple_loss=0.214, pruned_loss=0.03372, over 972274.51 frames.], batch size: 26, lr: 1.99e-04
2022-05-07 01:19:36,300 INFO [train.py:715] (1/8) Epoch 11, batch 4450, loss[loss=0.1334, simple_loss=0.2038, pruned_loss=0.03152, over 4817.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2134, pruned_loss=0.03324, over 972344.02 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:20:15,926 INFO [train.py:715] (1/8) Epoch 11, batch 4500, loss[loss=0.1332, simple_loss=0.218, pruned_loss=0.0242, over 4969.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03343, over 972141.21 frames.], batch size: 24, lr: 1.99e-04
2022-05-07 01:20:55,946 INFO [train.py:715] (1/8) Epoch 11, batch 4550, loss[loss=0.1297, simple_loss=0.2089, pruned_loss=0.02525, over 4825.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03316, over 972772.83 frames.], batch size: 26, lr: 1.99e-04
2022-05-07 01:21:35,998 INFO [train.py:715] (1/8) Epoch 11, batch 4600, loss[loss=0.1183, simple_loss=0.19, pruned_loss=0.02324, over 4830.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03296, over 972984.61 frames.], batch size: 13, lr: 1.99e-04
2022-05-07 01:22:15,465 INFO [train.py:715] (1/8) Epoch 11, batch 4650, loss[loss=0.1381, simple_loss=0.2101, pruned_loss=0.03307, over 4816.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.0331, over 973169.20 frames.], batch size: 26, lr: 1.99e-04
2022-05-07 01:22:55,186 INFO [train.py:715] (1/8) Epoch 11, batch 4700, loss[loss=0.1467, simple_loss=0.2168, pruned_loss=0.03828, over 4841.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03316, over 972950.50 frames.], batch size: 30, lr: 1.99e-04
2022-05-07 01:23:35,353 INFO [train.py:715] (1/8) Epoch 11, batch 4750, loss[loss=0.1474, simple_loss=0.2153, pruned_loss=0.03973, over 4821.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03298, over 973146.67 frames.], batch size: 26, lr: 1.99e-04
2022-05-07 01:24:15,523 INFO [train.py:715] (1/8) Epoch 11, batch 4800, loss[loss=0.1411, simple_loss=0.2088, pruned_loss=0.03668, over 4779.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.0327, over 972240.48 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:24:55,134 INFO [train.py:715] (1/8) Epoch 11, batch 4850, loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03373, over 4756.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03251, over 972732.20 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:25:34,925 INFO [train.py:715] (1/8) Epoch 11, batch 4900, loss[loss=0.1403, simple_loss=0.2117, pruned_loss=0.0345, over 4968.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2106, pruned_loss=0.03264, over 973090.92 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:26:14,645 INFO [train.py:715] (1/8) Epoch 11, batch 4950, loss[loss=0.1197, simple_loss=0.2043, pruned_loss=0.01754, over 4892.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2109, pruned_loss=0.03241, over 973545.57 frames.], batch size: 22, lr: 1.99e-04
2022-05-07 01:26:53,442 INFO [train.py:715] (1/8) Epoch 11, batch 5000, loss[loss=0.1735, simple_loss=0.2377, pruned_loss=0.05468, over 4794.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2118, pruned_loss=0.03257, over 972861.97 frames.], batch size: 24, lr: 1.99e-04
2022-05-07 01:27:31,885 INFO [train.py:715] (1/8) Epoch 11, batch 5050, loss[loss=0.1453, simple_loss=0.2057, pruned_loss=0.04246, over 4880.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03311, over 972878.20 frames.], batch size: 32, lr: 1.99e-04
2022-05-07 01:28:11,147 INFO [train.py:715] (1/8) Epoch 11, batch 5100, loss[loss=0.1681, simple_loss=0.2303, pruned_loss=0.0529, over 4927.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03327, over 973320.29 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:28:50,275 INFO [train.py:715] (1/8) Epoch 11, batch 5150, loss[loss=0.1143, simple_loss=0.1917, pruned_loss=0.01846, over 4883.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03301, over 973955.03 frames.], batch size: 13, lr: 1.99e-04
2022-05-07 01:29:29,207 INFO [train.py:715] (1/8) Epoch 11, batch 5200, loss[loss=0.1407, simple_loss=0.2228, pruned_loss=0.02928, over 4981.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03306, over 973146.65 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:30:08,612 INFO [train.py:715] (1/8) Epoch 11, batch 5250, loss[loss=0.1541, simple_loss=0.2142, pruned_loss=0.04704, over 4972.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03304, over 972517.11 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:30:48,294 INFO [train.py:715] (1/8) Epoch 11, batch 5300, loss[loss=0.1143, simple_loss=0.1949, pruned_loss=0.01684, over 4911.00 frames.], tot_loss[loss=0.1399, simple_loss=0.213, pruned_loss=0.0334, over 972872.07 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:31:27,448 INFO [train.py:715] (1/8) Epoch 11, batch 5350, loss[loss=0.1919, simple_loss=0.2325, pruned_loss=0.07568, over 4860.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03341, over 972672.44 frames.], batch size: 30, lr: 1.99e-04
2022-05-07 01:32:06,514 INFO [train.py:715] (1/8) Epoch 11, batch 5400, loss[loss=0.1475, simple_loss=0.2239, pruned_loss=0.03552, over 4910.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2131, pruned_loss=0.03328, over 973647.85 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:32:45,902 INFO [train.py:715] (1/8) Epoch 11, batch 5450, loss[loss=0.1329, simple_loss=0.2135, pruned_loss=0.02615, over 4901.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03345, over 973534.85 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:33:25,404 INFO [train.py:715] (1/8) Epoch 11, batch 5500, loss[loss=0.1293, simple_loss=0.2033, pruned_loss=0.0277, over 4787.00 frames.], tot_loss[loss=0.1404, simple_loss=0.213, pruned_loss=0.03393, over 973914.48 frames.], batch size: 13, lr: 1.99e-04
2022-05-07 01:34:04,257 INFO [train.py:715] (1/8) Epoch 11, batch 5550, loss[loss=0.1381, simple_loss=0.2249, pruned_loss=0.02563, over 4772.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2116, pruned_loss=0.03359, over 973115.22 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:34:42,711 INFO [train.py:715] (1/8) Epoch 11, batch 5600, loss[loss=0.119, simple_loss=0.1916, pruned_loss=0.02322, over 4899.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2115, pruned_loss=0.03348, over 973475.89 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:35:22,177 INFO [train.py:715] (1/8) Epoch 11, batch 5650, loss[loss=0.1214, simple_loss=0.2041, pruned_loss=0.01933, over 4823.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2111, pruned_loss=0.033, over 973034.86 frames.], batch size: 21, lr: 1.99e-04
2022-05-07 01:36:01,617 INFO [train.py:715] (1/8) Epoch 11, batch 5700, loss[loss=0.1433, simple_loss=0.2082, pruned_loss=0.03923, over 4983.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2104, pruned_loss=0.03265, over 972587.96 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:36:40,403 INFO [train.py:715] (1/8) Epoch 11, batch 5750, loss[loss=0.1293, simple_loss=0.21, pruned_loss=0.02428, over 4779.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2104, pruned_loss=0.03261, over 971907.82 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:37:19,381 INFO [train.py:715] (1/8) Epoch 11, batch 5800, loss[loss=0.1554, simple_loss=0.2326, pruned_loss=0.03908, over 4989.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2102, pruned_loss=0.03223, over 972378.15 frames.], batch size: 20, lr: 1.99e-04
2022-05-07 01:37:58,490 INFO [train.py:715] (1/8) Epoch 11, batch 5850, loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.02788, over 4920.00 frames.], tot_loss[loss=0.138, simple_loss=0.2107, pruned_loss=0.03264, over 972571.56 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:38:37,498 INFO [train.py:715] (1/8) Epoch 11, batch 5900, loss[loss=0.1226, simple_loss=0.2082, pruned_loss=0.01856, over 4956.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2102, pruned_loss=0.0322, over 971938.84 frames.], batch size: 21, lr: 1.99e-04
2022-05-07 01:39:16,659 INFO [train.py:715] (1/8) Epoch 11, batch 5950, loss[loss=0.1335, simple_loss=0.2091, pruned_loss=0.02895, over 4923.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03272, over 971974.06 frames.], batch size: 21, lr: 1.99e-04
2022-05-07 01:39:56,452 INFO [train.py:715] (1/8) Epoch 11, batch 6000, loss[loss=0.154, simple_loss=0.2262, pruned_loss=0.0409, over 4893.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2119, pruned_loss=0.03323, over 972289.01 frames.], batch size: 39, lr: 1.99e-04
2022-05-07 01:39:56,452 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 01:40:06,015 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.1059, simple_loss=0.1901, pruned_loss=0.01082, over 914524.00 frames.
2022-05-07 01:40:45,578 INFO [train.py:715] (1/8) Epoch 11, batch 6050, loss[loss=0.1302, simple_loss=0.2036, pruned_loss=0.02838, over 4985.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03358, over 972265.84 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:41:24,994 INFO [train.py:715] (1/8) Epoch 11, batch 6100, loss[loss=0.1468, simple_loss=0.2118, pruned_loss=0.0409, over 4805.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03368, over 972296.58 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:42:03,740 INFO [train.py:715] (1/8) Epoch 11, batch 6150, loss[loss=0.1196, simple_loss=0.1908, pruned_loss=0.02425, over 4747.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03313, over 972312.21 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:42:43,202 INFO [train.py:715] (1/8) Epoch 11, batch 6200, loss[loss=0.144, simple_loss=0.2284, pruned_loss=0.0298, over 4809.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.03298, over 971679.04 frames.], batch size: 13, lr: 1.99e-04
2022-05-07 01:43:22,228 INFO [train.py:715] (1/8) Epoch 11, batch 6250, loss[loss=0.1295, simple_loss=0.1991, pruned_loss=0.02996, over 4922.00 frames.], tot_loss[loss=0.1383, simple_loss=0.211, pruned_loss=0.03278, over 971609.53 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:44:01,020 INFO [train.py:715] (1/8) Epoch 11, batch 6300, loss[loss=0.1579, simple_loss=0.2324, pruned_loss=0.04174, over 4805.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03271, over 971542.36 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:44:39,696 INFO [train.py:715] (1/8) Epoch 11, batch 6350, loss[loss=0.1695, simple_loss=0.2399, pruned_loss=0.0495, over 4860.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03264, over 971880.62 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:45:20,275 INFO [train.py:715] (1/8) Epoch 11, batch 6400, loss[loss=0.1443, simple_loss=0.2243, pruned_loss=0.03215, over 4709.00 frames.], tot_loss[loss=0.1389, simple_loss=0.212, pruned_loss=0.03288, over 971949.50 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:45:59,619 INFO [train.py:715] (1/8) Epoch 11, batch 6450, loss[loss=0.1345, simple_loss=0.2115, pruned_loss=0.02874, over 4935.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03299, over 972674.78 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:46:38,695 INFO [train.py:715] (1/8) Epoch 11, batch 6500, loss[loss=0.1381, simple_loss=0.2046, pruned_loss=0.03576, over 4916.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03258, over 972134.42 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:47:18,041 INFO [train.py:715] (1/8) Epoch 11, batch 6550, loss[loss=0.1161, simple_loss=0.1892, pruned_loss=0.02154, over 4834.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03225, over 971572.26 frames.], batch size: 27, lr: 1.99e-04
2022-05-07 01:47:58,220 INFO [train.py:715] (1/8) Epoch 11, batch 6600, loss[loss=0.1479, simple_loss=0.2203, pruned_loss=0.03777, over 4802.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03271, over 971545.36 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:48:38,349 INFO [train.py:715] (1/8) Epoch 11, batch 6650, loss[loss=0.1597, simple_loss=0.2353, pruned_loss=0.04205, over 4774.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2121, pruned_loss=0.03261, over 971946.85 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:49:17,554 INFO [train.py:715] (1/8) Epoch 11, batch 6700, loss[loss=0.1131, simple_loss=0.1921, pruned_loss=0.01708, over 4876.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.03319, over 973031.92 frames.], batch size: 22, lr: 1.99e-04
2022-05-07 01:49:57,808 INFO [train.py:715] (1/8) Epoch 11, batch 6750, loss[loss=0.1333, simple_loss=0.2005, pruned_loss=0.03303, over 4947.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.0334, over 973644.41 frames.], batch size: 23, lr: 1.99e-04
2022-05-07 01:50:37,611 INFO [train.py:715] (1/8) Epoch 11, batch 6800, loss[loss=0.1242, simple_loss=0.1958, pruned_loss=0.02627, over 4933.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03333, over 973212.11 frames.], batch size: 23, lr: 1.99e-04
2022-05-07 01:51:16,480 INFO [train.py:715] (1/8) Epoch 11, batch 6850, loss[loss=0.1698, simple_loss=0.2359, pruned_loss=0.05187, over 4936.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2132, pruned_loss=0.03393, over 973144.30 frames.], batch size: 21, lr: 1.99e-04
2022-05-07 01:51:55,547 INFO [train.py:715] (1/8) Epoch 11, batch 6900, loss[loss=0.1401, simple_loss=0.2114, pruned_loss=0.03441, over 4878.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03382, over 972498.77 frames.], batch size: 22, lr: 1.99e-04
2022-05-07 01:52:34,238 INFO [train.py:715] (1/8) Epoch 11, batch 6950, loss[loss=0.1466, simple_loss=0.2155, pruned_loss=0.03883, over 4773.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2132, pruned_loss=0.03399, over 972293.32 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:53:13,694 INFO [train.py:715] (1/8) Epoch 11, batch 7000, loss[loss=0.1547, simple_loss=0.2224, pruned_loss=0.04353, over 4837.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2137, pruned_loss=0.03428, over 972654.63 frames.], batch size: 30, lr: 1.99e-04
2022-05-07 01:53:52,258 INFO [train.py:715] (1/8) Epoch 11, batch 7050, loss[loss=0.1267, simple_loss=0.2042, pruned_loss=0.02462, over 4967.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.03426, over 973077.00 frames.], batch size: 35, lr: 1.99e-04
2022-05-07 01:54:31,700 INFO [train.py:715] (1/8) Epoch 11, batch 7100, loss[loss=0.1499, simple_loss=0.2309, pruned_loss=0.03447, over 4905.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2142, pruned_loss=0.03396, over 973335.89 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:55:10,750 INFO [train.py:715] (1/8) Epoch 11, batch 7150, loss[loss=0.1548, simple_loss=0.2217, pruned_loss=0.04396, over 4822.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2136, pruned_loss=0.03362, over 972397.32 frames.], batch size: 13, lr: 1.99e-04
2022-05-07 01:55:49,512 INFO [train.py:715] (1/8) Epoch 11, batch 7200, loss[loss=0.1244, simple_loss=0.2055, pruned_loss=0.02166, over 4926.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.03378, over 972432.78 frames.], batch size: 23, lr: 1.99e-04
2022-05-07 01:56:28,455 INFO [train.py:715] (1/8) Epoch 11, batch 7250, loss[loss=0.1711, simple_loss=0.2409, pruned_loss=0.05064, over 4775.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.03388, over 972327.92 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:57:07,432 INFO [train.py:715] (1/8) Epoch 11, batch 7300, loss[loss=0.1273, simple_loss=0.1923, pruned_loss=0.03113, over 4815.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2129, pruned_loss=0.03375, over 972785.82 frames.], batch size: 12, lr: 1.99e-04
2022-05-07 01:57:46,560 INFO [train.py:715] (1/8) Epoch 11, batch 7350, loss[loss=0.1055, simple_loss=0.1666, pruned_loss=0.02227, over 4782.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2125, pruned_loss=0.03358, over 972232.26 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:58:25,307 INFO [train.py:715] (1/8) Epoch 11, batch 7400, loss[loss=0.1084, simple_loss=0.1796, pruned_loss=0.01861, over 4841.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03351, over 973040.13 frames.], batch size: 30, lr: 1.98e-04
2022-05-07 01:59:04,707 INFO [train.py:715] (1/8) Epoch 11, batch 7450, loss[loss=0.1501, simple_loss=0.2202, pruned_loss=0.03999, over 4842.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.03336, over 972913.45 frames.], batch size: 32, lr: 1.98e-04
2022-05-07 01:59:43,844 INFO [train.py:715] (1/8) Epoch 11, batch 7500, loss[loss=0.1512, simple_loss=0.2228, pruned_loss=0.03986, over 4847.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03338, over 972175.75 frames.], batch size: 20, lr: 1.98e-04
2022-05-07 02:00:23,097 INFO [train.py:715] (1/8) Epoch 11, batch 7550, loss[loss=0.1415, simple_loss=0.2183, pruned_loss=0.03235, over 4898.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03334, over 971876.31 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:01:02,848 INFO [train.py:715] (1/8) Epoch 11, batch 7600, loss[loss=0.1641, simple_loss=0.2331, pruned_loss=0.04753, over 4871.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.03348, over 971154.31 frames.], batch size: 20, lr: 1.98e-04
2022-05-07 02:01:42,518 INFO [train.py:715] (1/8) Epoch 11, batch 7650, loss[loss=0.2045, simple_loss=0.2671, pruned_loss=0.07095, over 4821.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03408, over 971142.91 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:02:22,058 INFO [train.py:715] (1/8) Epoch 11, batch 7700, loss[loss=0.154, simple_loss=0.2221, pruned_loss=0.04294, over 4867.00 frames.], tot_loss[loss=0.14, simple_loss=0.2126, pruned_loss=0.03366, over 971433.52 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:03:01,234 INFO [train.py:715] (1/8) Epoch 11, batch 7750, loss[loss=0.1366, simple_loss=0.2126, pruned_loss=0.03026, over 4822.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2125, pruned_loss=0.03347, over 972459.77 frames.], batch size: 25, lr: 1.98e-04
2022-05-07 02:03:40,569 INFO [train.py:715] (1/8) Epoch 11, batch 7800, loss[loss=0.1533, simple_loss=0.2273, pruned_loss=0.03967, over 4735.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2125, pruned_loss=0.03389, over 973109.31 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:04:19,857 INFO [train.py:715] (1/8) Epoch 11, batch 7850, loss[loss=0.1257, simple_loss=0.1951, pruned_loss=0.02816, over 4754.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2121, pruned_loss=0.03352, over 972706.71 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:04:58,996 INFO [train.py:715] (1/8) Epoch 11, batch 7900, loss[loss=0.1416, simple_loss=0.209, pruned_loss=0.03714, over 4823.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2123, pruned_loss=0.03333, over 971440.66 frames.], batch size: 12, lr: 1.98e-04
2022-05-07 02:05:37,734 INFO [train.py:715] (1/8) Epoch 11, batch 7950, loss[loss=0.1221, simple_loss=0.2025, pruned_loss=0.02079, over 4813.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03384, over 972033.79 frames.], batch size: 21, lr: 1.98e-04
2022-05-07 02:06:18,364 INFO [train.py:715] (1/8) Epoch 11, batch 8000, loss[loss=0.1662, simple_loss=0.2397, pruned_loss=0.04638, over 4882.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03379, over 972088.25 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:06:57,624 INFO [train.py:715] (1/8) Epoch 11, batch 8050, loss[loss=0.1189, simple_loss=0.1892, pruned_loss=0.02424, over 4830.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03372, over 972170.89 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:07:37,873 INFO [train.py:715] (1/8) Epoch 11, batch 8100, loss[loss=0.1214, simple_loss=0.1979, pruned_loss=0.02251, over 4921.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.03422, over 972120.82 frames.], batch size: 23, lr: 1.98e-04
2022-05-07 02:08:17,872 INFO [train.py:715] (1/8) Epoch 11, batch 8150, loss[loss=0.1406, simple_loss=0.2168, pruned_loss=0.03221, over 4890.00 frames.], tot_loss[loss=0.141, simple_loss=0.2142, pruned_loss=0.03388, over 971803.81 frames.], batch size: 22, lr: 1.98e-04
2022-05-07 02:08:57,402 INFO [train.py:715] (1/8) Epoch 11, batch 8200, loss[loss=0.1612, simple_loss=0.2257, pruned_loss=0.04836, over 4860.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2142, pruned_loss=0.03408, over 972061.31 frames.], batch size: 32, lr: 1.98e-04
2022-05-07 02:09:36,727 INFO [train.py:715] (1/8) Epoch 11, batch 8250, loss[loss=0.1521, simple_loss=0.2152, pruned_loss=0.04445, over 4969.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.03392, over 971498.21 frames.], batch size: 35, lr: 1.98e-04
2022-05-07 02:10:15,065 INFO [train.py:715] (1/8) Epoch 11, batch 8300, loss[loss=0.1437, simple_loss=0.2076, pruned_loss=0.03995, over 4854.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03399, over 972781.37 frames.], batch size: 34, lr: 1.98e-04
2022-05-07 02:10:54,964 INFO [train.py:715] (1/8) Epoch 11, batch 8350, loss[loss=0.137, simple_loss=0.2149, pruned_loss=0.02954, over 4801.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.0334, over 972769.29 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:11:34,531 INFO [train.py:715] (1/8) Epoch 11, batch 8400, loss[loss=0.1456, simple_loss=0.2102, pruned_loss=0.04054, over 4975.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03342, over 972857.15 frames.], batch size: 35, lr: 1.98e-04
2022-05-07 02:12:13,507 INFO [train.py:715] (1/8) Epoch 11, batch 8450, loss[loss=0.09925, simple_loss=0.1717, pruned_loss=0.01338, over 4883.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03326, over 972298.89 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:12:52,197 INFO [train.py:715] (1/8) Epoch 11, batch 8500, loss[loss=0.1431, simple_loss=0.2158, pruned_loss=0.03523, over 4792.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03363, over 972844.32 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:13:32,011 INFO [train.py:715] (1/8) Epoch 11, batch 8550, loss[loss=0.1308, simple_loss=0.2032, pruned_loss=0.02925, over 4974.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2122, pruned_loss=0.03351, over 973652.98 frames.], batch size: 35, lr: 1.98e-04
2022-05-07 02:14:11,215 INFO [train.py:715] (1/8) Epoch 11, batch 8600, loss[loss=0.1211, simple_loss=0.1896, pruned_loss=0.02631, over 4828.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2118, pruned_loss=0.0334, over 972318.51 frames.], batch size: 12, lr: 1.98e-04
2022-05-07 02:14:49,545 INFO [train.py:715] (1/8) Epoch 11, batch 8650, loss[loss=0.1502, simple_loss=0.2217, pruned_loss=0.03937, over 4780.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2132, pruned_loss=0.03408, over 972706.61 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:15:29,406 INFO [train.py:715] (1/8) Epoch 11, batch 8700, loss[loss=0.1429, simple_loss=0.2189, pruned_loss=0.03345, over 4898.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.03428, over 972476.37 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:16:08,725 INFO [train.py:715] (1/8) Epoch 11, batch 8750, loss[loss=0.1462, simple_loss=0.2028, pruned_loss=0.04482, over 4893.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03366, over 972358.07 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:16:47,707 INFO [train.py:715] (1/8) Epoch 11, batch 8800, loss[loss=0.145, simple_loss=0.2135, pruned_loss=0.03825, over 4734.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03351, over 971220.99 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:17:26,841 INFO [train.py:715] (1/8) Epoch 11, batch 8850, loss[loss=0.1464, simple_loss=0.2262, pruned_loss=0.03331, over 4983.00 frames.], tot_loss[loss=0.1398, simple_loss=0.213, pruned_loss=0.03327, over 971003.90 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:18:06,536 INFO [train.py:715] (1/8) Epoch 11, batch 8900, loss[loss=0.1304, simple_loss=0.1902, pruned_loss=0.03536, over 4827.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.0334, over 970808.09 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:18:46,170 INFO [train.py:715] (1/8) Epoch 11, batch 8950, loss[loss=0.1349, simple_loss=0.2046, pruned_loss=0.03264, over 4851.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03275, over 970672.97 frames.], batch size: 30, lr: 1.98e-04
2022-05-07 02:19:25,280 INFO [train.py:715] (1/8) Epoch 11, batch 9000, loss[loss=0.161, simple_loss=0.229, pruned_loss=0.04647, over 4745.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03244, over 970872.64 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:19:25,280 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 02:19:34,857 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.1061, simple_loss=0.1903, pruned_loss=0.011, over 914524.00 frames.
2022-05-07 02:20:13,755 INFO [train.py:715] (1/8) Epoch 11, batch 9050, loss[loss=0.1353, simple_loss=0.2211, pruned_loss=0.02469, over 4983.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2117, pruned_loss=0.03207, over 970705.19 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:20:55,921 INFO [train.py:715] (1/8) Epoch 11, batch 9100, loss[loss=0.1994, simple_loss=0.2595, pruned_loss=0.06967, over 4751.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2114, pruned_loss=0.03195, over 970383.79 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:21:35,544 INFO [train.py:715] (1/8) Epoch 11, batch 9150, loss[loss=0.131, simple_loss=0.2048, pruned_loss=0.02863, over 4936.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03211, over 971158.60 frames.], batch size: 21, lr: 1.98e-04
2022-05-07 02:22:15,058 INFO [train.py:715] (1/8) Epoch 11, batch 9200, loss[loss=0.1508, simple_loss=0.2233, pruned_loss=0.03912, over 4753.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03202, over 971267.68 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:22:54,640 INFO [train.py:715] (1/8) Epoch 11, batch 9250, loss[loss=0.1473, simple_loss=0.22, pruned_loss=0.03729, over 4986.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2124, pruned_loss=0.03267, over 971780.02 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:23:33,879 INFO [train.py:715] (1/8) Epoch 11, batch 9300, loss[loss=0.1152, simple_loss=0.184, pruned_loss=0.02319, over 4841.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2135, pruned_loss=0.03332, over 972698.20 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:24:12,714 INFO [train.py:715] (1/8) Epoch 11, batch 9350, loss[loss=0.1293, simple_loss=0.2097, pruned_loss=0.02443, over 4909.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2144, pruned_loss=0.03365, over 972410.94 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:24:51,488 INFO [train.py:715] (1/8) Epoch 11, batch 9400, loss[loss=0.1845, simple_loss=0.2523, pruned_loss=0.05838, over 4759.00 frames.], tot_loss[loss=0.1403, simple_loss=0.214, pruned_loss=0.03327, over 972471.16 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:25:31,004 INFO [train.py:715] (1/8) Epoch 11, batch 9450, loss[loss=0.1617, simple_loss=0.2328, pruned_loss=0.04532, over 4871.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2133, pruned_loss=0.03341, over 971554.96 frames.], batch size: 39, lr: 1.98e-04
2022-05-07 02:26:10,041 INFO [train.py:715] (1/8) Epoch 11, batch 9500, loss[loss=0.1422, simple_loss=0.2186, pruned_loss=0.03287, over 4801.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2137, pruned_loss=0.03324, over 972142.40 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:26:48,577 INFO [train.py:715] (1/8) Epoch 11, batch 9550, loss[loss=0.1005, simple_loss=0.1679, pruned_loss=0.01658, over 4762.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2128, pruned_loss=0.03304, over 972046.56 frames.], batch size: 12, lr: 1.98e-04
2022-05-07 02:27:28,242 INFO [train.py:715] (1/8) Epoch 11, batch 9600, loss[loss=0.1646, simple_loss=0.2412, pruned_loss=0.04399, over 4792.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2128, pruned_loss=0.03301, over 971307.48 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:28:07,061 INFO [train.py:715] (1/8) Epoch 11, batch 9650, loss[loss=0.1517, simple_loss=0.2317, pruned_loss=0.03586, over 4830.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03334, over 971665.03 frames.], batch size: 25, lr: 1.98e-04
2022-05-07 02:28:45,590 INFO [train.py:715] (1/8) Epoch 11, batch 9700, loss[loss=0.1436, simple_loss=0.2179, pruned_loss=0.03468, over 4861.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.03343, over 972203.34 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:29:24,592 INFO [train.py:715] (1/8) Epoch 11, batch 9750, loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02925, over 4775.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.0332, over 972497.51 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:30:03,699 INFO [train.py:715] (1/8) Epoch 11, batch 9800, loss[loss=0.1427, simple_loss=0.2186, pruned_loss=0.03339, over 4641.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.03323, over 971633.03 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:30:43,329 INFO [train.py:715] (1/8) Epoch 11, batch 9850, loss[loss=0.1406, simple_loss=0.2138, pruned_loss=0.03374, over 4751.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03338, over 971926.10 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:31:22,289 INFO [train.py:715] (1/8) Epoch 11, batch 9900, loss[loss=0.1165, simple_loss=0.1939, pruned_loss=0.01954, over 4876.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.03303, over 972567.96 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:32:02,527 INFO [train.py:715] (1/8) Epoch 11, batch 9950, loss[loss=0.1405, simple_loss=0.2194, pruned_loss=0.03073, over 4869.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03312, over 973104.32 frames.], batch size: 20, lr: 1.98e-04
2022-05-07 02:32:41,860 INFO [train.py:715] (1/8) Epoch 11, batch 10000, loss[loss=0.1237, simple_loss=0.1974, pruned_loss=0.02503, over 4805.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2114, pruned_loss=0.0328, over 973113.49 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:33:21,583 INFO [train.py:715] (1/8) Epoch 11, batch 10050, loss[loss=0.1289, simple_loss=0.2022, pruned_loss=0.02776, over 4942.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.0323, over 973651.16 frames.], batch size: 23, lr: 1.98e-04
2022-05-07 02:33:59,725 INFO [train.py:715] (1/8) Epoch 11, batch 10100, loss[loss=0.1302, simple_loss=0.2087, pruned_loss=0.02586, over 4976.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03273, over 973460.57 frames.], batch size: 28, lr: 1.98e-04
2022-05-07 02:34:38,759 INFO [train.py:715] (1/8) Epoch 11, batch 10150, loss[loss=0.1423, simple_loss=0.2154, pruned_loss=0.03457, over 4867.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03316, over 972717.24 frames.], batch size: 34, lr: 1.98e-04
2022-05-07 02:35:17,190 INFO [train.py:715] (1/8) Epoch 11, batch 10200, loss[loss=0.1466, simple_loss=0.2164, pruned_loss=0.03846, over 4777.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2119, pruned_loss=0.0337, over 972556.91 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:35:55,360 INFO [train.py:715] (1/8) Epoch 11, batch 10250, loss[loss=0.1411, simple_loss=0.2118, pruned_loss=0.03518, over 4973.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2129, pruned_loss=0.03395, over 972891.61 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:36:34,758 INFO [train.py:715] (1/8) Epoch 11, batch 10300, loss[loss=0.1657, simple_loss=0.2231, pruned_loss=0.05413, over 4866.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2122, pruned_loss=0.03356, over 972367.63 frames.], batch size: 30, lr: 1.98e-04
2022-05-07 02:37:13,489 INFO [train.py:715] (1/8) Epoch 11, batch 10350, loss[loss=0.1146, simple_loss=0.1965, pruned_loss=0.01632, over 4845.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03288, over 972857.83 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:37:52,309 INFO [train.py:715] (1/8) Epoch 11, batch 10400, loss[loss=0.1166, simple_loss=0.1874, pruned_loss=0.02293, over 4970.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2117, pruned_loss=0.03243, over 972807.19 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:38:30,791 INFO [train.py:715] (1/8) Epoch 11, batch 10450, loss[loss=0.1343, simple_loss=0.2151, pruned_loss=0.0268, over 4735.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03271, over 972110.95 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:39:09,432 INFO [train.py:715] (1/8) Epoch 11, batch 10500, loss[loss=0.1431, simple_loss=0.2084, pruned_loss=0.03894, over 4801.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03307, over 972800.67 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:39:48,491 INFO [train.py:715] (1/8) Epoch 11, batch 10550, loss[loss=0.1245, simple_loss=0.1952, pruned_loss=0.02685, over 4766.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03346, over 973105.19 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:40:27,839 INFO [train.py:715] (1/8) Epoch 11, batch 10600, loss[loss=0.13, simple_loss=0.2023, pruned_loss=0.02886, over 4815.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03352, over 972565.36 frames.], batch size: 25, lr: 1.98e-04
2022-05-07 02:41:06,629 INFO [train.py:715] (1/8) Epoch 11, batch 10650, loss[loss=0.1569, simple_loss=0.2187, pruned_loss=0.04753, over 4854.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03327, over 973481.93 frames.], batch size: 30, lr: 1.98e-04
2022-05-07 02:41:45,854 INFO [train.py:715] (1/8) Epoch 11, batch 10700, loss[loss=0.1591, simple_loss=0.232, pruned_loss=0.04315, over 4770.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2128, pruned_loss=0.0331, over 972852.46 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:42:25,057 INFO [train.py:715] (1/8) Epoch 11, batch 10750, loss[loss=0.1512, simple_loss=0.2322, pruned_loss=0.03511, over 4796.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2131, pruned_loss=0.03309, over 972522.55 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:43:03,975 INFO [train.py:715] (1/8) Epoch 11, batch 10800, loss[loss=0.1326, simple_loss=0.2101, pruned_loss=0.02758, over 4864.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03285, over 972394.15 frames.], batch size: 22, lr: 1.98e-04
2022-05-07 02:43:43,680 INFO [train.py:715] (1/8) Epoch 11, batch 10850, loss[loss=0.1317, simple_loss=0.2069, pruned_loss=0.0282, over 4783.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2128, pruned_loss=0.03292, over 973384.99 frames.], batch size: 12, lr: 1.98e-04
2022-05-07 02:44:23,473 INFO [train.py:715] (1/8) Epoch 11, batch 10900, loss[loss=0.1372, simple_loss=0.2037, pruned_loss=0.03537, over 4908.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.0325, over 972870.91 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:45:02,836 INFO [train.py:715] (1/8) Epoch 11, batch 10950, loss[loss=0.1224, simple_loss=0.1865, pruned_loss=0.02918, over 4687.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2117, pruned_loss=0.03244, over 972530.72 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:45:42,050 INFO [train.py:715] (1/8) Epoch 11, batch 11000, loss[loss=0.1645, simple_loss=0.2424, pruned_loss=0.04326, over 4804.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03264, over 971446.96 frames.], batch size: 26, lr: 1.98e-04
2022-05-07 02:46:21,455 INFO [train.py:715] (1/8) Epoch 11, batch 11050, loss[loss=0.1263, simple_loss=0.1935, pruned_loss=0.02958, over 4757.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03287, over 969551.47 frames.], batch size: 12, lr: 1.98e-04
2022-05-07 02:47:00,460 INFO [train.py:715] (1/8) Epoch 11, batch 11100, loss[loss=0.172, simple_loss=0.2333, pruned_loss=0.05537, over 4985.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03257, over 971288.59 frames.], batch size: 35, lr: 1.98e-04
2022-05-07 02:47:39,071 INFO [train.py:715] (1/8) Epoch 11, batch 11150, loss[loss=0.1466, simple_loss=0.2286, pruned_loss=0.03228, over 4978.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.0323, over 971220.60 frames.], batch size: 25, lr: 1.98e-04
2022-05-07 02:48:18,475 INFO [train.py:715] (1/8) Epoch 11, batch 11200, loss[loss=0.1412, simple_loss=0.2159, pruned_loss=0.03328, over 4838.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03259, over 971259.26 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:48:57,592 INFO [train.py:715] (1/8) Epoch 11, batch 11250, loss[loss=0.1489, simple_loss=0.2206, pruned_loss=0.0386, over 4934.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03234, over 971576.24 frames.], batch size: 35, lr: 1.98e-04
2022-05-07 02:49:35,932 INFO [train.py:715] (1/8) Epoch 11, batch 11300, loss[loss=0.1572, simple_loss=0.2327, pruned_loss=0.04087, over 4982.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03232, over 972103.43 frames.], batch size: 35, lr: 1.98e-04
2022-05-07 02:50:14,828 INFO [train.py:715] (1/8) Epoch 11, batch 11350, loss[loss=0.1262, simple_loss=0.1992, pruned_loss=0.02663, over 4890.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03258, over 972248.52 frames.], batch size: 16, lr: 1.97e-04
2022-05-07 02:50:54,376 INFO [train.py:715] (1/8) Epoch 11, batch 11400, loss[loss=0.1405, simple_loss=0.2043, pruned_loss=0.03837, over 4985.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03204, over 972536.24 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 02:51:32,953 INFO [train.py:715] (1/8) Epoch 11, batch 11450, loss[loss=0.1461, simple_loss=0.2226, pruned_loss=0.03477, over 4984.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.03188, over 972826.27 frames.], batch size: 28, lr: 1.97e-04
2022-05-07 02:52:11,283 INFO [train.py:715] (1/8) Epoch 11, batch 11500, loss[loss=0.1341, simple_loss=0.1998, pruned_loss=0.03416, over 4831.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2113, pruned_loss=0.03185, over 972781.82 frames.], batch size: 30, lr: 1.97e-04
2022-05-07 02:52:50,116 INFO [train.py:715] (1/8) Epoch 11, batch 11550, loss[loss=0.1134, simple_loss=0.182, pruned_loss=0.02243, over 4787.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03179, over 972510.87 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 02:53:29,306 INFO [train.py:715] (1/8) Epoch 11, batch 11600, loss[loss=0.1316, simple_loss=0.2076, pruned_loss=0.02782, over 4989.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.0318, over 972727.87 frames.], batch size: 27, lr: 1.97e-04
2022-05-07 02:54:08,237 INFO [train.py:715] (1/8) Epoch 11, batch 11650, loss[loss=0.1379, simple_loss=0.2085, pruned_loss=0.03367, over 4800.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.03168, over 972825.73 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 02:54:46,497 INFO [train.py:715] (1/8) Epoch 11, batch 11700, loss[loss=0.1348, simple_loss=0.2052, pruned_loss=0.03214, over 4777.00 frames.], tot_loss[loss=0.1369, simple_loss=0.21, pruned_loss=0.03188, over 972877.80 frames.], batch size: 12, lr: 1.97e-04
2022-05-07 02:55:25,414 INFO [train.py:715] (1/8) Epoch 11, batch 11750, loss[loss=0.1435, simple_loss=0.2193, pruned_loss=0.03384, over 4967.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.03173, over 972783.63 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 02:56:04,630 INFO [train.py:715] (1/8) Epoch 11, batch 11800, loss[loss=0.1584, simple_loss=0.2274, pruned_loss=0.04472, over 4936.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2115, pruned_loss=0.0324, over 972887.51 frames.], batch size: 23, lr: 1.97e-04
2022-05-07 02:56:43,717 INFO [train.py:715] (1/8) Epoch 11, batch 11850, loss[loss=0.1304, simple_loss=0.2107, pruned_loss=0.02501, over 4762.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03286, over 972491.72 frames.], batch size: 16, lr: 1.97e-04
2022-05-07 02:57:23,418 INFO [train.py:715] (1/8) Epoch 11, batch 11900, loss[loss=0.158, simple_loss=0.2314, pruned_loss=0.04228, over 4910.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03229, over 973125.83 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 02:58:03,756 INFO [train.py:715] (1/8) Epoch 11, batch 11950, loss[loss=0.1236, simple_loss=0.2018, pruned_loss=0.0227, over 4902.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03217, over 973618.30 frames.], batch size: 19, lr: 1.97e-04
2022-05-07 02:58:43,550 INFO [train.py:715] (1/8) Epoch 11, batch 12000, loss[loss=0.1325, simple_loss=0.2115, pruned_loss=0.02679, over 4930.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.0322, over 973261.66 frames.], batch size: 23, lr: 1.97e-04
2022-05-07 02:58:43,551 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 02:58:53,276 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.1061, simple_loss=0.1902, pruned_loss=0.01096, over 914524.00 frames.
2022-05-07 02:59:33,220 INFO [train.py:715] (1/8) Epoch 11, batch 12050, loss[loss=0.1258, simple_loss=0.2015, pruned_loss=0.02506, over 4841.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.03185, over 973009.80 frames.], batch size: 20, lr: 1.97e-04
2022-05-07 03:00:12,653 INFO [train.py:715] (1/8) Epoch 11, batch 12100, loss[loss=0.1172, simple_loss=0.1858, pruned_loss=0.02434, over 4920.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2113, pruned_loss=0.03203, over 972320.37 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 03:00:51,877 INFO [train.py:715] (1/8) Epoch 11, batch 12150, loss[loss=0.1491, simple_loss=0.2309, pruned_loss=0.03364, over 4887.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03246, over 972691.69 frames.], batch size: 19, lr: 1.97e-04
2022-05-07 03:01:31,409 INFO [train.py:715] (1/8) Epoch 11, batch 12200, loss[loss=0.1253, simple_loss=0.2016, pruned_loss=0.02453, over 4980.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03215, over 973489.83 frames.], batch size: 28, lr: 1.97e-04
2022-05-07 03:02:09,906 INFO [train.py:715] (1/8) Epoch 11, batch 12250, loss[loss=0.1514, simple_loss=0.2293, pruned_loss=0.03672, over 4873.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03216, over 973351.28 frames.], batch size: 22, lr: 1.97e-04
2022-05-07 03:02:49,518 INFO [train.py:715] (1/8) Epoch 11, batch 12300, loss[loss=0.1574, simple_loss=0.2306, pruned_loss=0.04205, over 4984.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2119, pruned_loss=0.03224, over 973681.25 frames.], batch size: 28, lr: 1.97e-04
2022-05-07 03:03:29,341 INFO [train.py:715] (1/8) Epoch 11, batch 12350, loss[loss=0.1382, simple_loss=0.2079, pruned_loss=0.03427, over 4955.00 frames.], tot_loss[loss=0.1384, simple_loss=0.212, pruned_loss=0.03238, over 974194.56 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:04:08,698 INFO [train.py:715] (1/8) Epoch 11, batch 12400, loss[loss=0.1381, simple_loss=0.2037, pruned_loss=0.03622, over 4751.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2122, pruned_loss=0.03248, over 974125.70 frames.], batch size: 19, lr: 1.97e-04
2022-05-07 03:04:46,935 INFO [train.py:715] (1/8) Epoch 11, batch 12450, loss[loss=0.136, simple_loss=0.2113, pruned_loss=0.03034, over 4751.00 frames.], tot_loss[loss=0.1387, simple_loss=0.212, pruned_loss=0.03265, over 974328.76 frames.], batch size: 19, lr: 1.97e-04
2022-05-07 03:05:26,170 INFO [train.py:715] (1/8) Epoch 11, batch 12500, loss[loss=0.1625, simple_loss=0.2291, pruned_loss=0.0479, over 4918.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03264, over 973615.41 frames.], batch size: 35, lr: 1.97e-04
2022-05-07 03:06:05,440 INFO [train.py:715] (1/8) Epoch 11, batch 12550, loss[loss=0.1338, simple_loss=0.1994, pruned_loss=0.03405, over 4692.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03257, over 973121.90 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:06:44,096 INFO [train.py:715] (1/8) Epoch 11, batch 12600, loss[loss=0.1218, simple_loss=0.199, pruned_loss=0.02228, over 4972.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03237, over 973056.36 frames.], batch size: 24, lr: 1.97e-04
2022-05-07 03:07:23,082 INFO [train.py:715] (1/8) Epoch 11, batch 12650, loss[loss=0.1174, simple_loss=0.1916, pruned_loss=0.02154, over 4782.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03241, over 972452.71 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 03:08:02,196 INFO [train.py:715] (1/8) Epoch 11, batch 12700, loss[loss=0.1683, simple_loss=0.2386, pruned_loss=0.049, over 4872.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2119, pruned_loss=0.03246, over 971995.08 frames.], batch size: 22, lr: 1.97e-04
2022-05-07 03:08:40,892 INFO [train.py:715] (1/8) Epoch 11, batch 12750, loss[loss=0.1289, simple_loss=0.198, pruned_loss=0.0299, over 4968.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.0322, over 972019.15 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:09:19,304 INFO [train.py:715] (1/8) Epoch 11, batch 12800, loss[loss=0.1364, simple_loss=0.2173, pruned_loss=0.02768, over 4704.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03265, over 971814.55 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:09:58,881 INFO [train.py:715] (1/8) Epoch 11, batch 12850, loss[loss=0.1237, simple_loss=0.191, pruned_loss=0.02817, over 4779.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03243, over 971657.35 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:10:38,291 INFO [train.py:715] (1/8) Epoch 11, batch 12900, loss[loss=0.1558, simple_loss=0.2193, pruned_loss=0.04615, over 4883.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03265, over 971557.90 frames.], batch size: 22, lr: 1.97e-04
2022-05-07 03:11:17,935 INFO [train.py:715] (1/8) Epoch 11, batch 12950, loss[loss=0.137, simple_loss=0.2071, pruned_loss=0.03346, over 4776.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2116, pruned_loss=0.03284, over 972218.20 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:11:56,714 INFO [train.py:715] (1/8) Epoch 11, batch 13000, loss[loss=0.138, simple_loss=0.2089, pruned_loss=0.03354, over 4990.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03266, over 971111.02 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 03:12:36,381 INFO [train.py:715] (1/8) Epoch 11, batch 13050, loss[loss=0.1188, simple_loss=0.2007, pruned_loss=0.01842, over 4974.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03269, over 971459.82 frames.], batch size: 24, lr: 1.97e-04
2022-05-07 03:13:15,476 INFO [train.py:715] (1/8) Epoch 11, batch 13100, loss[loss=0.1206, simple_loss=0.1899, pruned_loss=0.02569, over 4840.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2112, pruned_loss=0.03284, over 971297.67 frames.], batch size: 13, lr: 1.97e-04
2022-05-07 03:13:53,590 INFO [train.py:715] (1/8) Epoch 11, batch 13150, loss[loss=0.1598, simple_loss=0.2315, pruned_loss=0.0441, over 4908.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03288, over 971279.87 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:14:32,702 INFO [train.py:715] (1/8) Epoch 11, batch 13200, loss[loss=0.1106, simple_loss=0.1805, pruned_loss=0.02037, over 4933.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03299, over 970877.76 frames.], batch size: 23, lr: 1.97e-04
2022-05-07 03:15:11,060 INFO [train.py:715] (1/8) Epoch 11, batch 13250, loss[loss=0.144, simple_loss=0.2079, pruned_loss=0.04011, over 4986.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03265, over 971294.79 frames.], batch size: 31, lr: 1.97e-04
2022-05-07 03:15:50,455 INFO [train.py:715] (1/8) Epoch 11, batch 13300, loss[loss=0.1597, simple_loss=0.2304, pruned_loss=0.0445, over 4818.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2107, pruned_loss=0.03243, over 971734.75 frames.], batch size: 13, lr: 1.97e-04
2022-05-07 03:16:29,353 INFO [train.py:715] (1/8) Epoch 11, batch 13350, loss[loss=0.174, simple_loss=0.2452, pruned_loss=0.05142, over 4756.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.03287, over 971597.26 frames.], batch size: 19, lr: 1.97e-04
2022-05-07 03:17:08,605 INFO [train.py:715] (1/8) Epoch 11, batch 13400, loss[loss=0.1386, simple_loss=0.2131, pruned_loss=0.03209, over 4770.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03207, over 971698.12 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 03:17:47,311 INFO [train.py:715] (1/8) Epoch 11, batch 13450, loss[loss=0.145, simple_loss=0.2254, pruned_loss=0.03229, over 4805.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03224, over 971764.49 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:18:26,313 INFO [train.py:715] (1/8) Epoch 11, batch 13500, loss[loss=0.148, simple_loss=0.2288, pruned_loss=0.03364, over 4809.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03147, over 971015.23 frames.], batch size: 24, lr: 1.97e-04
2022-05-07 03:19:05,026 INFO [train.py:715] (1/8) Epoch 11, batch 13550, loss[loss=0.1404, simple_loss=0.2169, pruned_loss=0.03201, over 4792.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03194, over 971389.74 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:19:44,151 INFO [train.py:715] (1/8) Epoch 11, batch 13600, loss[loss=0.1107, simple_loss=0.1808, pruned_loss=0.0203, over 4791.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03184, over 971126.00 frames.], batch size: 12, lr: 1.97e-04
2022-05-07 03:20:22,540 INFO [train.py:715] (1/8) Epoch 11, batch 13650, loss[loss=0.1456, simple_loss=0.222, pruned_loss=0.03462, over 4836.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2109, pruned_loss=0.03164, over 971680.51 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:21:00,723 INFO [train.py:715] (1/8) Epoch 11, batch 13700, loss[loss=0.1234, simple_loss=0.2042, pruned_loss=0.02127, over 4883.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2111, pruned_loss=0.03187, over 972179.81 frames.], batch size: 16, lr: 1.97e-04
2022-05-07 03:21:39,802 INFO [train.py:715] (1/8) Epoch 11, batch 13750, loss[loss=0.1103, simple_loss=0.185, pruned_loss=0.01776, over 4822.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03199, over 971935.10 frames.], batch size: 27, lr: 1.97e-04
2022-05-07 03:22:19,176 INFO [train.py:715] (1/8) Epoch 11, batch 13800, loss[loss=0.1405, simple_loss=0.2176, pruned_loss=0.03174, over 4970.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.03233, over 972526.19 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:22:57,644 INFO [train.py:715] (1/8) Epoch 11, batch 13850, loss[loss=0.1434, simple_loss=0.2138, pruned_loss=0.03648, over 4826.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03251, over 972366.61 frames.], batch size: 26, lr: 1.97e-04
2022-05-07 03:23:37,059 INFO [train.py:715] (1/8) Epoch 11, batch 13900, loss[loss=0.1538, simple_loss=0.2255, pruned_loss=0.04106, over 4738.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03255, over 971856.69 frames.], batch size: 16, lr: 1.97e-04
2022-05-07 03:24:15,994 INFO [train.py:715] (1/8) Epoch 11, batch 13950, loss[loss=0.1299, simple_loss=0.1924, pruned_loss=0.03366, over 4884.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03222, over 971131.97 frames.], batch size: 32, lr: 1.97e-04
2022-05-07 03:24:55,165 INFO [train.py:715] (1/8) Epoch 11, batch 14000, loss[loss=0.1745, simple_loss=0.2377, pruned_loss=0.05564, over 4915.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2113, pruned_loss=0.03313, over 971578.61 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 03:25:34,613 INFO [train.py:715] (1/8) Epoch 11, batch 14050, loss[loss=0.1144, simple_loss=0.1886, pruned_loss=0.02013, over 4877.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2115, pruned_loss=0.03305, over 971998.93 frames.], batch size: 22, lr: 1.97e-04
2022-05-07 03:26:14,334 INFO [train.py:715] (1/8) Epoch 11, batch 14100, loss[loss=0.1322, simple_loss=0.1969, pruned_loss=0.03371, over 4849.00 frames.], tot_loss[loss=0.1392, simple_loss=0.212, pruned_loss=0.03315, over 971679.48 frames.], batch size: 13, lr: 1.97e-04
2022-05-07 03:26:53,601 INFO [train.py:715] (1/8) Epoch 11, batch 14150, loss[loss=0.1408, simple_loss=0.2157, pruned_loss=0.03293, over 4686.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03356, over 972120.35 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:27:32,872 INFO [train.py:715] (1/8) Epoch 11, batch 14200, loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03012, over 4852.00 frames.], tot_loss[loss=0.1389, simple_loss=0.212, pruned_loss=0.03295, over 972157.29 frames.], batch size: 20, lr: 1.97e-04
2022-05-07 03:28:13,021 INFO [train.py:715] (1/8) Epoch 11, batch 14250, loss[loss=0.1366, simple_loss=0.2114, pruned_loss=0.03088, over 4872.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2106, pruned_loss=0.03225, over 972270.31 frames.], batch size: 22, lr: 1.97e-04
2022-05-07 03:28:53,025 INFO [train.py:715] (1/8) Epoch 11, batch 14300, loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.03192, over 4686.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03258, over 972930.58 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:29:32,290 INFO [train.py:715] (1/8) Epoch 11, batch 14350, loss[loss=0.1612, simple_loss=0.2297, pruned_loss=0.0464, over 4830.00 frames.], tot_loss[loss=0.1385, simple_loss=0.212, pruned_loss=0.03253, over 972889.64 frames.], batch size: 26, lr: 1.97e-04
2022-05-07 03:30:12,240 INFO [train.py:715] (1/8) Epoch 11, batch 14400, loss[loss=0.1175, simple_loss=0.1911, pruned_loss=0.02199, over 4925.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2122, pruned_loss=0.03269, over 972831.40 frames.], batch size: 29, lr: 1.97e-04
2022-05-07 03:30:52,511 INFO [train.py:715] (1/8) Epoch 11, batch 14450, loss[loss=0.1151, simple_loss=0.196, pruned_loss=0.01711, over 4795.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03247, over 972376.60 frames.], batch size: 24, lr: 1.97e-04
2022-05-07 03:31:31,925 INFO [train.py:715] (1/8) Epoch 11, batch 14500, loss[loss=0.1237, simple_loss=0.1931, pruned_loss=0.02717, over 4986.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03319, over 972203.08 frames.], batch size: 28, lr: 1.97e-04
2022-05-07 03:32:11,429 INFO [train.py:715] (1/8) Epoch 11, batch 14550, loss[loss=0.1411, simple_loss=0.2097, pruned_loss=0.03626, over 4690.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.033, over 972382.34 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:32:51,265 INFO [train.py:715] (1/8) Epoch 11, batch 14600, loss[loss=0.1343, simple_loss=0.212, pruned_loss=0.0283, over 4876.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03283, over 972822.89 frames.], batch size: 22, lr: 1.97e-04
2022-05-07 03:33:30,641 INFO [train.py:715] (1/8) Epoch 11, batch 14650, loss[loss=0.1186, simple_loss=0.1975, pruned_loss=0.01983, over 4867.00 frames.], tot_loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.03228, over 972758.34 frames.], batch size: 20, lr: 1.97e-04
2022-05-07 03:34:09,059 INFO [train.py:715] (1/8) Epoch 11, batch 14700, loss[loss=0.1652, simple_loss=0.2382, pruned_loss=0.0461, over 4773.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.03225, over 973172.95 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:34:48,555 INFO [train.py:715] (1/8) Epoch 11, batch 14750, loss[loss=0.1122, simple_loss=0.1801, pruned_loss=0.02214, over 4782.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2108, pruned_loss=0.03212, over 971328.89 frames.], batch size: 12, lr: 1.97e-04
2022-05-07 03:35:27,685 INFO [train.py:715] (1/8) Epoch 11, batch 14800, loss[loss=0.1388, simple_loss=0.2145, pruned_loss=0.03148, over 4947.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.03162, over 971720.79 frames.], batch size: 39, lr: 1.97e-04
2022-05-07 03:36:06,361 INFO [train.py:715] (1/8) Epoch 11, batch 14850, loss[loss=0.1469, simple_loss=0.2165, pruned_loss=0.03866, over 4957.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03229, over 971555.99 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:36:45,865 INFO [train.py:715] (1/8) Epoch 11, batch 14900, loss[loss=0.148, simple_loss=0.2271, pruned_loss=0.03442, over 4939.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2132, pruned_loss=0.03324, over 971827.71 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:37:25,089 INFO [train.py:715] (1/8) Epoch 11, batch 14950, loss[loss=0.1128, simple_loss=0.1937, pruned_loss=0.0159, over 4948.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2129, pruned_loss=0.0329, over 971723.22 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:38:03,595 INFO [train.py:715] (1/8) Epoch 11, batch 15000, loss[loss=0.1383, simple_loss=0.2153, pruned_loss=0.0307, over 4767.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2126, pruned_loss=0.03298, over 971768.84 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 03:38:03,595 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 03:38:13,229 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.106, simple_loss=0.1901, pruned_loss=0.01091, over 914524.00 frames.
2022-05-07 03:38:52,006 INFO [train.py:715] (1/8) Epoch 11, batch 15050, loss[loss=0.1504, simple_loss=0.2157, pruned_loss=0.04255, over 4990.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03318, over 972612.72 frames.], batch size: 20, lr: 1.97e-04
2022-05-07 03:39:30,961 INFO [train.py:715] (1/8) Epoch 11, batch 15100, loss[loss=0.1501, simple_loss=0.2158, pruned_loss=0.04217, over 4851.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03281, over 971971.64 frames.], batch size: 32, lr: 1.97e-04
2022-05-07 03:40:10,672 INFO [train.py:715] (1/8) Epoch 11, batch 15150, loss[loss=0.1168, simple_loss=0.184, pruned_loss=0.02476, over 4842.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03298, over 971680.95 frames.], batch size: 34, lr: 1.97e-04
2022-05-07 03:40:49,843 INFO [train.py:715] (1/8) Epoch 11, batch 15200, loss[loss=0.1444, simple_loss=0.2236, pruned_loss=0.03256, over 4798.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03306, over 972259.29 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:41:28,411 INFO [train.py:715] (1/8) Epoch 11, batch 15250, loss[loss=0.133, simple_loss=0.1972, pruned_loss=0.03444, over 4768.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03331, over 971813.71 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 03:42:07,673 INFO [train.py:715] (1/8) Epoch 11, batch 15300, loss[loss=0.1114, simple_loss=0.1893, pruned_loss=0.01679, over 4803.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.03345, over 971536.62 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 03:42:46,995 INFO [train.py:715] (1/8) Epoch 11, batch 15350, loss[loss=0.1352, simple_loss=0.214, pruned_loss=0.0282, over 4803.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2139, pruned_loss=0.03372, over 970720.62 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 03:43:25,867 INFO [train.py:715] (1/8) Epoch 11, batch 15400, loss[loss=0.1533, simple_loss=0.2223, pruned_loss=0.0422, over 4906.00 frames.], tot_loss[loss=0.1405, simple_loss=0.214, pruned_loss=0.03347, over 971742.74 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 03:44:04,610 INFO [train.py:715] (1/8) Epoch 11, batch 15450, loss[loss=0.1282, simple_loss=0.2045, pruned_loss=0.02588, over 4957.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2135, pruned_loss=0.03313, over 971260.35 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 03:44:44,030 INFO [train.py:715] (1/8) Epoch 11, batch 15500, loss[loss=0.1555, simple_loss=0.2352, pruned_loss=0.0379, over 4875.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2138, pruned_loss=0.03331, over 970812.33 frames.], batch size: 32, lr: 1.96e-04
2022-05-07 03:45:23,174 INFO [train.py:715] (1/8) Epoch 11, batch 15550, loss[loss=0.1256, simple_loss=0.1926, pruned_loss=0.02934, over 4973.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2138, pruned_loss=0.03335, over 971344.50 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 03:46:01,711 INFO [train.py:715] (1/8) Epoch 11, batch 15600, loss[loss=0.1377, simple_loss=0.205, pruned_loss=0.0352, over 4880.00 frames.], tot_loss[loss=0.139, simple_loss=0.2126, pruned_loss=0.03268, over 971099.18 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 03:46:40,885 INFO [train.py:715] (1/8) Epoch 11, batch 15650, loss[loss=0.1667, simple_loss=0.2419, pruned_loss=0.04575, over 4957.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2118, pruned_loss=0.03216, over 971287.85 frames.], batch size: 24, lr: 1.96e-04
2022-05-07 03:47:19,846 INFO [train.py:715] (1/8) Epoch 11, batch 15700, loss[loss=0.1457, simple_loss=0.2115, pruned_loss=0.03998, over 4971.00 frames.], tot_loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.03222, over 970736.48 frames.], batch size: 35, lr: 1.96e-04
2022-05-07 03:47:58,646 INFO [train.py:715] (1/8) Epoch 11, batch 15750, loss[loss=0.1353, simple_loss=0.2027, pruned_loss=0.03395, over 4782.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03205, over 970837.50 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 03:48:37,398 INFO [train.py:715] (1/8) Epoch 11, batch 15800, loss[loss=0.157, simple_loss=0.2358, pruned_loss=0.03908, over 4950.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.03216, over 970954.54 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 03:49:16,761 INFO [train.py:715] (1/8) Epoch 11, batch 15850, loss[loss=0.1122, simple_loss=0.1839, pruned_loss=0.02022, over 4852.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03233, over 972153.72 frames.], batch size: 34, lr: 1.96e-04
2022-05-07 03:49:55,699 INFO [train.py:715] (1/8) Epoch 11, batch 15900, loss[loss=0.137, simple_loss=0.2116, pruned_loss=0.03123, over 4640.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03234, over 971756.06 frames.], batch size: 13, lr: 1.96e-04
2022-05-07 03:50:34,612 INFO [train.py:715] (1/8) Epoch 11, batch 15950, loss[loss=0.1309, simple_loss=0.1985, pruned_loss=0.03169, over 4916.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03211, over 971940.57 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 03:51:13,829 INFO [train.py:715] (1/8) Epoch 11, batch 16000, loss[loss=0.1714, simple_loss=0.2409, pruned_loss=0.05089, over 4803.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2104, pruned_loss=0.03227, over 971309.59 frames.], batch size: 12, lr: 1.96e-04
2022-05-07 03:51:53,253 INFO [train.py:715] (1/8) Epoch 11, batch 16050, loss[loss=0.1654, simple_loss=0.2438, pruned_loss=0.04348, over 4822.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03226, over 972543.63 frames.], batch size: 26, lr: 1.96e-04
2022-05-07 03:52:31,942 INFO [train.py:715] (1/8) Epoch 11, batch 16100, loss[loss=0.1307, simple_loss=0.2004, pruned_loss=0.03051, over 4757.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03228, over 972300.53 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 03:53:10,820 INFO [train.py:715] (1/8) Epoch 11, batch 16150, loss[loss=0.1423, simple_loss=0.209, pruned_loss=0.03779, over 4872.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03231, over 973117.88 frames.], batch size: 32, lr: 1.96e-04
2022-05-07 03:53:50,408 INFO [train.py:715] (1/8) Epoch 11, batch 16200, loss[loss=0.1359, simple_loss=0.2128, pruned_loss=0.02946, over 4866.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03244, over 973398.41 frames.], batch size: 20, lr: 1.96e-04
2022-05-07 03:54:29,891 INFO [train.py:715] (1/8) Epoch 11, batch 16250, loss[loss=0.1469, simple_loss=0.2218, pruned_loss=0.03602, over 4989.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03258, over 973481.67 frames.], batch size: 25, lr: 1.96e-04
2022-05-07 03:55:08,240 INFO [train.py:715] (1/8) Epoch 11, batch 16300, loss[loss=0.1338, simple_loss=0.2071, pruned_loss=0.03027, over 4778.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2107, pruned_loss=0.03248, over 972853.31 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 03:55:47,437 INFO [train.py:715] (1/8) Epoch 11, batch 16350, loss[loss=0.1257, simple_loss=0.2078, pruned_loss=0.02176, over 4862.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03226, over 973241.47 frames.], batch size: 20, lr: 1.96e-04
2022-05-07 03:56:26,686 INFO [train.py:715] (1/8) Epoch 11, batch 16400, loss[loss=0.1293, simple_loss=0.1966, pruned_loss=0.03096, over 4862.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03154, over 973415.06 frames.], batch size: 32, lr: 1.96e-04
2022-05-07 03:57:05,185 INFO [train.py:715] (1/8) Epoch 11, batch 16450, loss[loss=0.1569, simple_loss=0.231, pruned_loss=0.04143, over 4749.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03183, over 973397.49 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 03:57:44,154 INFO [train.py:715] (1/8) Epoch 11, batch 16500, loss[loss=0.1052, simple_loss=0.1706, pruned_loss=0.01991, over 4979.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.0317, over 973039.28 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 03:58:23,676 INFO [train.py:715] (1/8) Epoch 11, batch 16550, loss[loss=0.1467, simple_loss=0.2217, pruned_loss=0.03588, over 4907.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03221, over 973267.58 frames.], batch size: 29, lr: 1.96e-04
2022-05-07 03:59:02,828 INFO [train.py:715] (1/8) Epoch 11, batch 16600, loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03202, over 4954.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03282, over 972433.43 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 03:59:41,215 INFO [train.py:715] (1/8) Epoch 11, batch 16650, loss[loss=0.1339, simple_loss=0.2229, pruned_loss=0.02242, over 4767.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2118, pruned_loss=0.03273, over 972744.39 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 04:00:20,434 INFO [train.py:715] (1/8) Epoch 11, batch 16700, loss[loss=0.1274, simple_loss=0.1977, pruned_loss=0.02853, over 4801.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.03299, over 971772.30 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 04:00:59,418 INFO [train.py:715] (1/8) Epoch 11, batch 16750, loss[loss=0.1311, simple_loss=0.2051, pruned_loss=0.02854, over 4985.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2122, pruned_loss=0.03284, over 971583.92 frames.], batch size: 26, lr: 1.96e-04
2022-05-07 04:01:38,343 INFO [train.py:715] (1/8) Epoch 11, batch 16800, loss[loss=0.1324, simple_loss=0.2074, pruned_loss=0.02869, over 4805.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03286, over 971595.43 frames.], batch size: 25, lr: 1.96e-04
2022-05-07 04:02:18,000 INFO [train.py:715] (1/8) Epoch 11, batch 16850, loss[loss=0.1198, simple_loss=0.1853, pruned_loss=0.02716, over 4809.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.03253, over 971745.83 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:02:57,563 INFO [train.py:715] (1/8) Epoch 11, batch 16900, loss[loss=0.1687, simple_loss=0.2352, pruned_loss=0.05106, over 4778.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.03267, over 972444.08 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 04:03:37,033 INFO [train.py:715] (1/8) Epoch 11, batch 16950, loss[loss=0.1313, simple_loss=0.1954, pruned_loss=0.03358, over 4861.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2113, pruned_loss=0.03296, over 972227.39 frames.], batch size: 30, lr: 1.96e-04
2022-05-07 04:04:15,768 INFO [train.py:715] (1/8) Epoch 11, batch 17000, loss[loss=0.1186, simple_loss=0.1886, pruned_loss=0.02428, over 4788.00 frames.], tot_loss[loss=0.138, simple_loss=0.2109, pruned_loss=0.03253, over 971937.11 frames.], batch size: 17, lr: 1.96e-04
2022-05-07 04:04:55,516 INFO [train.py:715] (1/8) Epoch 11, batch 17050, loss[loss=0.1577, simple_loss=0.2264, pruned_loss=0.04447, over 4900.00 frames.], tot_loss[loss=0.138, simple_loss=0.2107, pruned_loss=0.03262, over 972544.87 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 04:05:38,138 INFO [train.py:715] (1/8) Epoch 11, batch 17100, loss[loss=0.228, simple_loss=0.3025, pruned_loss=0.07673, over 4778.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03262, over 972515.85 frames.], batch size: 17, lr: 1.96e-04
2022-05-07 04:06:17,135 INFO [train.py:715] (1/8) Epoch 11, batch 17150, loss[loss=0.124, simple_loss=0.2037, pruned_loss=0.02212, over 4862.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03267, over 972718.12 frames.], batch size: 20, lr: 1.96e-04
2022-05-07 04:06:56,400 INFO [train.py:715] (1/8) Epoch 11, batch 17200, loss[loss=0.1224, simple_loss=0.2045, pruned_loss=0.02015, over 4962.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03229, over 973180.09 frames.], batch size: 24, lr: 1.96e-04
2022-05-07 04:07:35,869 INFO [train.py:715] (1/8) Epoch 11, batch 17250, loss[loss=0.1201, simple_loss=0.1945, pruned_loss=0.02283, over 4902.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03231, over 972715.12 frames.], batch size: 17, lr: 1.96e-04
2022-05-07 04:08:14,921 INFO [train.py:715] (1/8) Epoch 11, batch 17300, loss[loss=0.1447, simple_loss=0.2243, pruned_loss=0.03254, over 4764.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2118, pruned_loss=0.03285, over 972721.84 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 04:08:53,666 INFO [train.py:715] (1/8) Epoch 11, batch 17350, loss[loss=0.1283, simple_loss=0.2015, pruned_loss=0.02749, over 4776.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03283, over 972789.95 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:09:33,995 INFO [train.py:715] (1/8) Epoch 11, batch 17400, loss[loss=0.1521, simple_loss=0.2191, pruned_loss=0.04261, over 4852.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03305, over 972302.96 frames.], batch size: 32, lr: 1.96e-04
2022-05-07 04:10:14,492 INFO [train.py:715] (1/8) Epoch 11, batch 17450, loss[loss=0.1123, simple_loss=0.1986, pruned_loss=0.013, over 4880.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03291, over 971850.50 frames.], batch size: 22, lr: 1.96e-04
2022-05-07 04:10:53,806 INFO [train.py:715] (1/8) Epoch 11, batch 17500, loss[loss=0.143, simple_loss=0.2078, pruned_loss=0.03912, over 4826.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2128, pruned_loss=0.03312, over 972064.41 frames.], batch size: 13, lr: 1.96e-04
2022-05-07 04:11:33,226 INFO [train.py:715] (1/8) Epoch 11, batch 17550, loss[loss=0.1602, simple_loss=0.228, pruned_loss=0.04626, over 4909.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2133, pruned_loss=0.03318, over 972239.61 frames.], batch size: 17, lr: 1.96e-04
2022-05-07 04:12:12,580 INFO [train.py:715] (1/8) Epoch 11, batch 17600, loss[loss=0.1508, simple_loss=0.2322, pruned_loss=0.03472, over 4808.00 frames.], tot_loss[loss=0.14, simple_loss=0.2136, pruned_loss=0.03322, over 973332.04 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 04:12:51,733 INFO [train.py:715] (1/8) Epoch 11, batch 17650, loss[loss=0.141, simple_loss=0.2197, pruned_loss=0.03119, over 4922.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2132, pruned_loss=0.03285, over 972852.39 frames.], batch size: 29, lr: 1.96e-04
2022-05-07 04:13:29,974 INFO [train.py:715] (1/8) Epoch 11, batch 17700, loss[loss=0.1344, simple_loss=0.2061, pruned_loss=0.03138, over 4866.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2132, pruned_loss=0.03328, over 972812.58 frames.], batch size: 32, lr: 1.96e-04
2022-05-07 04:14:09,458 INFO [train.py:715] (1/8) Epoch 11, batch 17750, loss[loss=0.1424, simple_loss=0.2086, pruned_loss=0.03813, over 4695.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03303, over 971851.67 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:14:49,018 INFO [train.py:715] (1/8) Epoch 11, batch 17800, loss[loss=0.1161, simple_loss=0.1934, pruned_loss=0.01937, over 4961.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03296, over 971405.72 frames.], batch size: 24, lr: 1.96e-04
2022-05-07 04:15:27,266 INFO [train.py:715] (1/8) Epoch 11, batch 17850, loss[loss=0.1283, simple_loss=0.2002, pruned_loss=0.02816, over 4903.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03271, over 970996.51 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 04:16:06,260 INFO [train.py:715] (1/8) Epoch 11, batch 17900, loss[loss=0.1573, simple_loss=0.2335, pruned_loss=0.04053, over 4705.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03284, over 971022.20 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:16:45,883 INFO [train.py:715] (1/8) Epoch 11, batch 17950, loss[loss=0.1286, simple_loss=0.2112, pruned_loss=0.02299, over 4771.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03304, over 971440.42 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 04:17:24,878 INFO [train.py:715] (1/8) Epoch 11, batch 18000, loss[loss=0.1209, simple_loss=0.1964, pruned_loss=0.02269, over 4827.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2119, pruned_loss=0.03321, over 971800.26 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:17:24,879 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 04:17:34,463 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.1061, simple_loss=0.1903, pruned_loss=0.01092, over 914524.00 frames.
2022-05-07 04:18:14,160 INFO [train.py:715] (1/8) Epoch 11, batch 18050, loss[loss=0.1459, simple_loss=0.2141, pruned_loss=0.03883, over 4843.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03355, over 971959.54 frames.], batch size: 13, lr: 1.96e-04
2022-05-07 04:18:53,411 INFO [train.py:715] (1/8) Epoch 11, batch 18100, loss[loss=0.1077, simple_loss=0.182, pruned_loss=0.01672, over 4907.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2128, pruned_loss=0.03367, over 972184.22 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:19:32,618 INFO [train.py:715] (1/8) Epoch 11, batch 18150, loss[loss=0.1565, simple_loss=0.2432, pruned_loss=0.03491, over 4797.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03349, over 972095.53 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 04:20:12,196 INFO [train.py:715] (1/8) Epoch 11, batch 18200, loss[loss=0.1094, simple_loss=0.1822, pruned_loss=0.01828, over 4795.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2114, pruned_loss=0.03277, over 972355.59 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:20:50,629 INFO [train.py:715] (1/8) Epoch 11, batch 18250, loss[loss=0.1186, simple_loss=0.1924, pruned_loss=0.0224, over 4818.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.03265, over 971738.34 frames.], batch size: 25, lr: 1.96e-04
2022-05-07 04:21:29,930 INFO [train.py:715] (1/8) Epoch 11, batch 18300, loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03047, over 4942.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.03254, over 971795.50 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 04:22:09,178 INFO [train.py:715] (1/8) Epoch 11, batch 18350, loss[loss=0.1567, simple_loss=0.2273, pruned_loss=0.04308, over 4906.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2108, pruned_loss=0.03283, over 972341.50 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 04:22:47,574 INFO [train.py:715] (1/8) Epoch 11, batch 18400, loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03608, over 4640.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2107, pruned_loss=0.03239, over 972456.64 frames.], batch size: 13, lr: 1.96e-04
2022-05-07 04:23:25,990 INFO [train.py:715] (1/8) Epoch 11, batch 18450, loss[loss=0.1543, simple_loss=0.2292, pruned_loss=0.03966, over 4820.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.033, over 971183.51 frames.], batch size: 27, lr: 1.96e-04
2022-05-07 04:24:05,025 INFO [train.py:715] (1/8) Epoch 11, batch 18500, loss[loss=0.1255, simple_loss=0.2021, pruned_loss=0.02445, over 4896.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2114, pruned_loss=0.03293, over 971959.97 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 04:24:44,463 INFO [train.py:715] (1/8) Epoch 11, batch 18550, loss[loss=0.1569, simple_loss=0.2309, pruned_loss=0.04145, over 4815.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2117, pruned_loss=0.03319, over 971928.69 frames.], batch size: 25, lr: 1.96e-04
2022-05-07 04:25:22,567 INFO [train.py:715] (1/8) Epoch 11, batch 18600, loss[loss=0.1522, simple_loss=0.2148, pruned_loss=0.04484, over 4964.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.033, over 971611.05 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:26:01,412 INFO [train.py:715] (1/8) Epoch 11, batch 18650, loss[loss=0.1347, simple_loss=0.2111, pruned_loss=0.02913, over 4763.00 frames.], tot_loss[loss=0.139, simple_loss=0.2118, pruned_loss=0.03308, over 971479.71 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 04:26:40,670 INFO [train.py:715] (1/8) Epoch 11, batch 18700, loss[loss=0.1467, simple_loss=0.2226, pruned_loss=0.03541, over 4754.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03306, over 971199.09 frames.], batch size: 12, lr: 1.96e-04
2022-05-07 04:27:18,913 INFO [train.py:715] (1/8) Epoch 11, batch 18750, loss[loss=0.133, simple_loss=0.2152, pruned_loss=0.02542, over 4813.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03303, over 971612.26 frames.], batch size: 26, lr: 1.96e-04
2022-05-07 04:27:57,981 INFO [train.py:715] (1/8) Epoch 11, batch 18800, loss[loss=0.1346, simple_loss=0.207, pruned_loss=0.03113, over 4750.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03294, over 971308.78 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 04:28:36,595 INFO [train.py:715] (1/8) Epoch 11, batch 18850, loss[loss=0.1399, simple_loss=0.2125, pruned_loss=0.03364, over 4938.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03275, over 971551.26 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 04:29:16,504 INFO [train.py:715] (1/8) Epoch 11, batch 18900, loss[loss=0.1427, simple_loss=0.2068, pruned_loss=0.03933, over 4879.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2121, pruned_loss=0.03217, over 972152.16 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 04:29:55,268 INFO [train.py:715] (1/8) Epoch 11, batch 18950, loss[loss=0.1399, simple_loss=0.2022, pruned_loss=0.03886, over 4773.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03239, over 972611.61 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:30:34,363 INFO [train.py:715] (1/8) Epoch 11, batch 19000, loss[loss=0.1588, simple_loss=0.2368, pruned_loss=0.04039, over 4833.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03252, over 972196.05 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:31:13,458 INFO [train.py:715] (1/8) Epoch 11, batch 19050, loss[loss=0.1254, simple_loss=0.2008, pruned_loss=0.02503, over 4827.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2122, pruned_loss=0.03279, over 972194.00 frames.], batch size: 13, lr: 1.96e-04
2022-05-07 04:31:52,059 INFO [train.py:715] (1/8) Epoch 11, batch 19100, loss[loss=0.1267, simple_loss=0.1983, pruned_loss=0.02748, over 4854.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03249, over 971294.61 frames.], batch size: 13, lr: 1.96e-04
2022-05-07 04:32:31,181 INFO [train.py:715] (1/8) Epoch 11, batch 19150, loss[loss=0.1528, simple_loss=0.2292, pruned_loss=0.03816, over 4953.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03197, over 971870.18 frames.], batch size: 24, lr: 1.96e-04
2022-05-07 04:33:10,080 INFO [train.py:715] (1/8) Epoch 11, batch 19200, loss[loss=0.1423, simple_loss=0.2277, pruned_loss=0.02842, over 4937.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03197, over 972279.06 frames.], batch size: 23, lr: 1.96e-04
2022-05-07 04:33:49,487 INFO [train.py:715] (1/8) Epoch 11, batch 19250, loss[loss=0.1398, simple_loss=0.2165, pruned_loss=0.0315, over 4784.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03154, over 972567.74 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:34:27,830 INFO [train.py:715] (1/8) Epoch 11, batch 19300, loss[loss=0.1434, simple_loss=0.211, pruned_loss=0.0379, over 4688.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03186, over 972291.81 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:35:06,983 INFO [train.py:715] (1/8) Epoch 11, batch 19350, loss[loss=0.17, simple_loss=0.2402, pruned_loss=0.04987, over 4947.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03179, over 970855.38 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 04:35:46,163 INFO [train.py:715] (1/8) Epoch 11, batch 19400, loss[loss=0.1291, simple_loss=0.2033, pruned_loss=0.0275, over 4926.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03195, over 971049.82 frames.], batch size: 23, lr: 1.96e-04
2022-05-07 04:36:24,113 INFO [train.py:715] (1/8) Epoch 11, batch 19450, loss[loss=0.1429, simple_loss=0.2171, pruned_loss=0.03435, over 4879.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03173, over 971639.04 frames.], batch size: 16, lr: 1.95e-04
2022-05-07 04:37:03,254 INFO [train.py:715] (1/8) Epoch 11, batch 19500, loss[loss=0.1453, simple_loss=0.2091, pruned_loss=0.04076, over 4641.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03182, over 971104.89 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 04:37:42,220 INFO [train.py:715] (1/8) Epoch 11, batch 19550, loss[loss=0.1436, simple_loss=0.2123, pruned_loss=0.03739, over 4939.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03195, over 971406.55 frames.], batch size: 29, lr: 1.95e-04
2022-05-07 04:38:20,966 INFO [train.py:715] (1/8) Epoch 11, batch 19600, loss[loss=0.1658, simple_loss=0.2343, pruned_loss=0.04868, over 4695.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03221, over 971466.43 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 04:38:59,550 INFO [train.py:715] (1/8) Epoch 11, batch 19650, loss[loss=0.1264, simple_loss=0.2006, pruned_loss=0.02606, over 4901.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2108, pruned_loss=0.03216, over 971990.28 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 04:39:38,339 INFO [train.py:715] (1/8) Epoch 11, batch 19700, loss[loss=0.1387, simple_loss=0.2111, pruned_loss=0.03312, over 4737.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03175, over 973128.69 frames.], batch size: 16, lr: 1.95e-04
2022-05-07 04:40:17,423 INFO [train.py:715] (1/8) Epoch 11, batch 19750, loss[loss=0.1452, simple_loss=0.2243, pruned_loss=0.03306, over 4802.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2109, pruned_loss=0.03172, over 972632.27 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 04:40:55,514 INFO [train.py:715] (1/8) Epoch 11, batch 19800, loss[loss=0.1383, simple_loss=0.2079, pruned_loss=0.03433, over 4834.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03258, over 973245.46 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 04:41:35,009 INFO [train.py:715] (1/8) Epoch 11, batch 19850, loss[loss=0.1273, simple_loss=0.2039, pruned_loss=0.02533, over 4814.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03245, over 972666.40 frames.], batch size: 26, lr: 1.95e-04
2022-05-07 04:42:14,378 INFO [train.py:715] (1/8) Epoch 11, batch 19900, loss[loss=0.1122, simple_loss=0.1849, pruned_loss=0.01973, over 4833.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2109, pruned_loss=0.03267, over 973453.65 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 04:42:53,606 INFO [train.py:715] (1/8) Epoch 11, batch 19950, loss[loss=0.1182, simple_loss=0.19, pruned_loss=0.02319, over 4876.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2112, pruned_loss=0.03274, over 973994.67 frames.], batch size: 22, lr: 1.95e-04
2022-05-07 04:43:32,807 INFO [train.py:715] (1/8) Epoch 11, batch 20000, loss[loss=0.1544, simple_loss=0.2208, pruned_loss=0.04395, over 4779.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.03301, over 973491.23 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 04:44:11,788 INFO [train.py:715] (1/8) Epoch 11, batch 20050, loss[loss=0.1299, simple_loss=0.2021, pruned_loss=0.02882, over 4988.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2114, pruned_loss=0.03286, over 973635.96 frames.], batch size: 25, lr: 1.95e-04
2022-05-07 04:44:51,034 INFO [train.py:715] (1/8) Epoch 11, batch 20100, loss[loss=0.15, simple_loss=0.2278, pruned_loss=0.03607, over 4958.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.0326, over 973576.86 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 04:45:29,363 INFO [train.py:715] (1/8) Epoch 11, batch 20150, loss[loss=0.1215, simple_loss=0.208, pruned_loss=0.01753, over 4932.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2118, pruned_loss=0.03222, over 972935.86 frames.], batch size: 29, lr: 1.95e-04
2022-05-07 04:46:08,146 INFO [train.py:715] (1/8) Epoch 11, batch 20200, loss[loss=0.1252, simple_loss=0.1918, pruned_loss=0.02923, over 4878.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2117, pruned_loss=0.03245, over 972118.35 frames.], batch size: 22, lr: 1.95e-04
2022-05-07 04:46:46,985 INFO [train.py:715] (1/8) Epoch 11, batch 20250, loss[loss=0.1177, simple_loss=0.2042, pruned_loss=0.01563, over 4931.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2116, pruned_loss=0.0321, over 972642.28 frames.], batch size: 29, lr: 1.95e-04
2022-05-07 04:47:25,725 INFO [train.py:715] (1/8) Epoch 11, batch 20300, loss[loss=0.1586, simple_loss=0.2355, pruned_loss=0.04085, over 4976.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2129, pruned_loss=0.03306, over 973006.85 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 04:48:04,825 INFO [train.py:715] (1/8) Epoch 11, batch 20350, loss[loss=0.1201, simple_loss=0.1982, pruned_loss=0.021, over 4763.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03322, over 973090.93 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 04:48:43,802 INFO [train.py:715] (1/8) Epoch 11, batch 20400, loss[loss=0.135, simple_loss=0.2044, pruned_loss=0.03283, over 4976.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.0329, over 972730.11 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 04:49:23,229 INFO [train.py:715] (1/8) Epoch 11, batch 20450, loss[loss=0.1696, simple_loss=0.2455, pruned_loss=0.04692, over 4776.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2129, pruned_loss=0.03317, over 972165.81 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 04:50:01,766 INFO [train.py:715] (1/8) Epoch 11, batch 20500, loss[loss=0.1189, simple_loss=0.1896, pruned_loss=0.02409, over 4987.00 frames.], tot_loss[loss=0.1395, simple_loss=0.213, pruned_loss=0.03303, over 973018.66 frames.], batch size: 20, lr: 1.95e-04
2022-05-07 04:50:41,082 INFO [train.py:715] (1/8) Epoch 11, batch 20550, loss[loss=0.1445, simple_loss=0.222, pruned_loss=0.03349, over 4812.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2137, pruned_loss=0.03347, over 972574.94 frames.], batch size: 27, lr: 1.95e-04
2022-05-07 04:51:19,715 INFO [train.py:715] (1/8) Epoch 11, batch 20600, loss[loss=0.1223, simple_loss=0.2005, pruned_loss=0.02207, over 4794.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2138, pruned_loss=0.03369, over 972155.92 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 04:51:57,494 INFO [train.py:715] (1/8) Epoch 11, batch 20650, loss[loss=0.1182, simple_loss=0.1966, pruned_loss=0.01994, over 4907.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2139, pruned_loss=0.03399, over 973128.65 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 04:52:36,872 INFO [train.py:715] (1/8) Epoch 11, batch 20700, loss[loss=0.1464, simple_loss=0.2222, pruned_loss=0.03531, over 4778.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2137, pruned_loss=0.03353, over 973709.73 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 04:53:16,100 INFO [train.py:715] (1/8) Epoch 11, batch 20750, loss[loss=0.1785, simple_loss=0.2527, pruned_loss=0.05217, over 4858.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2137, pruned_loss=0.03344, over 973880.91 frames.], batch size: 32, lr: 1.95e-04
2022-05-07 04:53:54,801 INFO [train.py:715] (1/8) Epoch 11, batch 20800, loss[loss=0.1225, simple_loss=0.1973, pruned_loss=0.02379, over 4949.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2135, pruned_loss=0.03351, over 973842.83 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 04:54:33,173 INFO [train.py:715] (1/8) Epoch 11, batch 20850, loss[loss=0.1168, simple_loss=0.1961, pruned_loss=0.01873, over 4775.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03302, over 972777.26 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 04:55:12,421 INFO [train.py:715] (1/8) Epoch 11, batch 20900, loss[loss=0.1359, simple_loss=0.2072, pruned_loss=0.03231, over 4849.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2134, pruned_loss=0.03344, over 972273.03 frames.], batch size: 20, lr: 1.95e-04
2022-05-07 04:55:52,033 INFO [train.py:715] (1/8) Epoch 11, batch 20950, loss[loss=0.1362, simple_loss=0.2072, pruned_loss=0.0326, over 4874.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.0333, over 971971.44 frames.], batch size: 16, lr: 1.95e-04
2022-05-07 04:56:30,995 INFO [train.py:715] (1/8) Epoch 11, batch 21000, loss[loss=0.1313, simple_loss=0.2164, pruned_loss=0.02306, over 4917.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2124, pruned_loss=0.03296, over 972612.31 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 04:56:30,995 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 04:56:40,630 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.106, simple_loss=0.19, pruned_loss=0.01097, over 914524.00 frames.
2022-05-07 04:57:20,113 INFO [train.py:715] (1/8) Epoch 11, batch 21050, loss[loss=0.1493, simple_loss=0.2131, pruned_loss=0.04275, over 4903.00 frames.], tot_loss[loss=0.139, simple_loss=0.2125, pruned_loss=0.03274, over 972925.69 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 04:57:59,829 INFO [train.py:715] (1/8) Epoch 11, batch 21100, loss[loss=0.1392, simple_loss=0.1991, pruned_loss=0.03966, over 4803.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03287, over 972362.73 frames.], batch size: 12, lr: 1.95e-04
2022-05-07 04:58:38,868 INFO [train.py:715] (1/8) Epoch 11, batch 21150, loss[loss=0.1252, simple_loss=0.2041, pruned_loss=0.02312, over 4885.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03262, over 972289.77 frames.], batch size: 22, lr: 1.95e-04
2022-05-07 04:59:18,203 INFO [train.py:715] (1/8) Epoch 11, batch 21200, loss[loss=0.1433, simple_loss=0.2213, pruned_loss=0.03267, over 4847.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.0325, over 973143.94 frames.], batch size: 30, lr: 1.95e-04
2022-05-07 04:59:56,328 INFO [train.py:715] (1/8) Epoch 11, batch 21250, loss[loss=0.1595, simple_loss=0.2299, pruned_loss=0.04455, over 4638.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03283, over 973440.13 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 05:00:35,643 INFO [train.py:715] (1/8) Epoch 11, batch 21300, loss[loss=0.1244, simple_loss=0.1947, pruned_loss=0.0271, over 4901.00 frames.], tot_loss[loss=0.1381, simple_loss=0.211, pruned_loss=0.0326, over 973007.60 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 05:01:15,032 INFO [train.py:715] (1/8) Epoch 11, batch 21350, loss[loss=0.1447, simple_loss=0.2084, pruned_loss=0.04044, over 4828.00 frames.], tot_loss[loss=0.1381, simple_loss=0.211, pruned_loss=0.03256, over 973346.10 frames.], batch size: 26, lr: 1.95e-04
2022-05-07 05:01:53,537 INFO [train.py:715] (1/8) Epoch 11, batch 21400, loss[loss=0.1528, simple_loss=0.2236, pruned_loss=0.04097, over 4987.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2108, pruned_loss=0.03234, over 974023.15 frames.], batch size: 25, lr: 1.95e-04
2022-05-07 05:02:32,175 INFO [train.py:715] (1/8) Epoch 11, batch 21450, loss[loss=0.1553, simple_loss=0.2275, pruned_loss=0.04157, over 4878.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2127, pruned_loss=0.033, over 974039.82 frames.], batch size: 39, lr: 1.95e-04
2022-05-07 05:03:11,027 INFO [train.py:715] (1/8) Epoch 11, batch 21500, loss[loss=0.1295, simple_loss=0.205, pruned_loss=0.02698, over 4882.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03306, over 973167.45 frames.], batch size: 16, lr: 1.95e-04
2022-05-07 05:03:50,392 INFO [train.py:715] (1/8) Epoch 11, batch 21550, loss[loss=0.1358, simple_loss=0.2073, pruned_loss=0.03217, over 4906.00 frames.], tot_loss[loss=0.1393, simple_loss=0.212, pruned_loss=0.03329, over 972767.03 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:04:28,684 INFO [train.py:715] (1/8) Epoch 11, batch 21600, loss[loss=0.1388, simple_loss=0.2069, pruned_loss=0.03534, over 4892.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.03311, over 972960.31 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 05:05:07,533 INFO [train.py:715] (1/8) Epoch 11, batch 21650, loss[loss=0.1542, simple_loss=0.2273, pruned_loss=0.04052, over 4817.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03274, over 972511.12 frames.], batch size: 26, lr: 1.95e-04
2022-05-07 05:05:47,600 INFO [train.py:715] (1/8) Epoch 11, batch 21700, loss[loss=0.1078, simple_loss=0.1849, pruned_loss=0.01537, over 4656.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03264, over 973368.59 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 05:06:26,892 INFO [train.py:715] (1/8) Epoch 11, batch 21750, loss[loss=0.1403, simple_loss=0.225, pruned_loss=0.02782, over 4787.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03282, over 973622.29 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:07:07,078 INFO [train.py:715] (1/8) Epoch 11, batch 21800, loss[loss=0.1591, simple_loss=0.2287, pruned_loss=0.04473, over 4780.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2116, pruned_loss=0.03282, over 973203.58 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 05:07:46,754 INFO [train.py:715] (1/8) Epoch 11, batch 21850, loss[loss=0.1319, simple_loss=0.202, pruned_loss=0.03091, over 4877.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03293, over 972676.85 frames.], batch size: 32, lr: 1.95e-04
2022-05-07 05:08:27,244 INFO [train.py:715] (1/8) Epoch 11, batch 21900, loss[loss=0.127, simple_loss=0.1948, pruned_loss=0.0296, over 4935.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03291, over 972449.15 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 05:09:06,467 INFO [train.py:715] (1/8) Epoch 11, batch 21950, loss[loss=0.133, simple_loss=0.2062, pruned_loss=0.0299, over 4899.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03201, over 972571.80 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:09:46,785 INFO [train.py:715] (1/8) Epoch 11, batch 22000, loss[loss=0.1302, simple_loss=0.2001, pruned_loss=0.03013, over 4937.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03207, over 972911.69 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 05:10:27,248 INFO [train.py:715] (1/8) Epoch 11, batch 22050, loss[loss=0.1397, simple_loss=0.2155, pruned_loss=0.032, over 4993.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2119, pruned_loss=0.03219, over 972770.32 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 05:11:05,504 INFO [train.py:715] (1/8) Epoch 11, batch 22100, loss[loss=0.1552, simple_loss=0.2153, pruned_loss=0.0476, over 4872.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.03178, over 972938.24 frames.], batch size: 20, lr: 1.95e-04
2022-05-07 05:11:45,101 INFO [train.py:715] (1/8) Epoch 11, batch 22150, loss[loss=0.1307, simple_loss=0.2059, pruned_loss=0.02779, over 4870.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.0322, over 972954.25 frames.], batch size: 20, lr: 1.95e-04
2022-05-07 05:12:24,694 INFO [train.py:715] (1/8) Epoch 11, batch 22200, loss[loss=0.1348, simple_loss=0.2133, pruned_loss=0.02818, over 4911.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2121, pruned_loss=0.03256, over 972654.07 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 05:13:03,454 INFO [train.py:715] (1/8) Epoch 11, batch 22250, loss[loss=0.1681, simple_loss=0.2306, pruned_loss=0.05284, over 4829.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2125, pruned_loss=0.0326, over 973122.78 frames.], batch size: 30, lr: 1.95e-04
2022-05-07 05:13:41,894 INFO [train.py:715] (1/8) Epoch 11, batch 22300, loss[loss=0.1203, simple_loss=0.1939, pruned_loss=0.02334, over 4844.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2124, pruned_loss=0.03249, over 972968.35 frames.], batch size: 27, lr: 1.95e-04
2022-05-07 05:14:21,106 INFO [train.py:715] (1/8) Epoch 11, batch 22350, loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03032, over 4820.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2131, pruned_loss=0.03304, over 973542.40 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 05:15:00,559 INFO [train.py:715] (1/8) Epoch 11, batch 22400, loss[loss=0.1865, simple_loss=0.2514, pruned_loss=0.0608, over 4903.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03279, over 972916.60 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:15:38,493 INFO [train.py:715] (1/8) Epoch 11, batch 22450, loss[loss=0.143, simple_loss=0.2126, pruned_loss=0.03672, over 4777.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03306, over 972481.75 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:16:18,410 INFO [train.py:715] (1/8) Epoch 11, batch 22500, loss[loss=0.1557, simple_loss=0.2249, pruned_loss=0.04323, over 4815.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03313, over 972694.12 frames.], batch size: 25, lr: 1.95e-04
2022-05-07 05:16:57,485 INFO [train.py:715] (1/8) Epoch 11, batch 22550, loss[loss=0.1106, simple_loss=0.1835, pruned_loss=0.01887, over 4818.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.03254, over 973140.48 frames.], batch size: 27, lr: 1.95e-04
2022-05-07 05:17:36,658 INFO [train.py:715] (1/8) Epoch 11, batch 22600, loss[loss=0.1183, simple_loss=0.193, pruned_loss=0.0218, over 4810.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03235, over 972656.69 frames.], batch size: 26, lr: 1.95e-04
2022-05-07 05:18:15,031 INFO [train.py:715] (1/8) Epoch 11, batch 22650, loss[loss=0.1546, simple_loss=0.2316, pruned_loss=0.03877, over 4803.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.0317, over 972187.55 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 05:18:54,217 INFO [train.py:715] (1/8) Epoch 11, batch 22700, loss[loss=0.1181, simple_loss=0.2007, pruned_loss=0.01773, over 4935.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03189, over 972985.95 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 05:19:34,099 INFO [train.py:715] (1/8) Epoch 11, batch 22750, loss[loss=0.1471, simple_loss=0.2167, pruned_loss=0.03871, over 4838.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03234, over 973028.93 frames.], batch size: 32, lr: 1.95e-04
2022-05-07 05:20:12,498 INFO [train.py:715] (1/8) Epoch 11, batch 22800, loss[loss=0.1193, simple_loss=0.1956, pruned_loss=0.02152, over 4977.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03209, over 972371.29 frames.], batch size: 25, lr: 1.95e-04
2022-05-07 05:20:52,299 INFO [train.py:715] (1/8) Epoch 11, batch 22850, loss[loss=0.1385, simple_loss=0.2145, pruned_loss=0.03132, over 4901.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2107, pruned_loss=0.03248, over 972699.81 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:21:31,226 INFO [train.py:715] (1/8) Epoch 11, batch 22900, loss[loss=0.142, simple_loss=0.2264, pruned_loss=0.02882, over 4891.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03301, over 972891.34 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 05:22:10,212 INFO [train.py:715] (1/8) Epoch 11, batch 22950, loss[loss=0.1416, simple_loss=0.2192, pruned_loss=0.03203, over 4800.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03305, over 972585.09 frames.], batch size: 24, lr: 1.95e-04
2022-05-07 05:22:48,362 INFO [train.py:715] (1/8) Epoch 11, batch 23000, loss[loss=0.1432, simple_loss=0.2086, pruned_loss=0.03892, over 4859.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03264, over 971923.24 frames.], batch size: 32, lr: 1.95e-04
2022-05-07 05:23:27,333 INFO [train.py:715] (1/8) Epoch 11, batch 23050, loss[loss=0.1652, simple_loss=0.2342, pruned_loss=0.04806, over 4832.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03272, over 971946.11 frames.], batch size: 30, lr: 1.95e-04
2022-05-07 05:24:06,663 INFO [train.py:715] (1/8) Epoch 11, batch 23100, loss[loss=0.1381, simple_loss=0.2091, pruned_loss=0.03357, over 4757.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.0322, over 972337.14 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 05:24:44,410 INFO [train.py:715] (1/8) Epoch 11, batch 23150, loss[loss=0.1451, simple_loss=0.2199, pruned_loss=0.03515, over 4916.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03212, over 972461.54 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:25:23,981 INFO [train.py:715] (1/8) Epoch 11, batch 23200, loss[loss=0.1441, simple_loss=0.2104, pruned_loss=0.03896, over 4816.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03217, over 972492.94 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 05:26:02,913 INFO [train.py:715] (1/8) Epoch 11, batch 23250, loss[loss=0.1414, simple_loss=0.2077, pruned_loss=0.03754, over 4829.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03195, over 972190.68 frames.], batch size: 26, lr: 1.95e-04
2022-05-07 05:26:41,984 INFO [train.py:715] (1/8) Epoch 11, batch 23300, loss[loss=0.1469, simple_loss=0.2162, pruned_loss=0.03877, over 4790.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.03177, over 972010.91 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 05:27:20,072 INFO [train.py:715] (1/8) Epoch 11, batch 23350, loss[loss=0.1406, simple_loss=0.2075, pruned_loss=0.03686, over 4776.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03209, over 971682.16 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 05:27:59,127 INFO [train.py:715] (1/8) Epoch 11, batch 23400, loss[loss=0.1464, simple_loss=0.2211, pruned_loss=0.03583, over 4912.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.032, over 972069.40 frames.], batch size: 23, lr: 1.95e-04
2022-05-07 05:28:38,748 INFO [train.py:715] (1/8) Epoch 11, batch 23450, loss[loss=0.1061, simple_loss=0.1813, pruned_loss=0.01547, over 4975.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03166, over 972485.53 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 05:29:16,873 INFO [train.py:715] (1/8) Epoch 11, batch 23500, loss[loss=0.157, simple_loss=0.2217, pruned_loss=0.04614, over 4962.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03199, over 973063.77 frames.], batch size: 31, lr: 1.95e-04
2022-05-07 05:29:55,785 INFO [train.py:715] (1/8) Epoch 11, batch 23550, loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02813, over 4977.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03229, over 972865.20 frames.], batch size: 25, lr: 1.95e-04
2022-05-07 05:30:34,768 INFO [train.py:715] (1/8) Epoch 11, batch 23600, loss[loss=0.1308, simple_loss=0.2132, pruned_loss=0.02417, over 4950.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03251, over 973334.29 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 05:31:14,121 INFO [train.py:715] (1/8) Epoch 11, batch 23650, loss[loss=0.1257, simple_loss=0.1931, pruned_loss=0.02918, over 4972.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03309, over 972874.14 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 05:31:51,830 INFO [train.py:715] (1/8) Epoch 11, batch 23700, loss[loss=0.1454, simple_loss=0.2143, pruned_loss=0.03828, over 4976.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03289, over 972623.43 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 05:32:30,814 INFO [train.py:715] (1/8) Epoch 11, batch 23750, loss[loss=0.1506, simple_loss=0.2202, pruned_loss=0.04046, over 4772.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03303, over 971676.28 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:33:09,310 INFO [train.py:715] (1/8) Epoch 11, batch 23800, loss[loss=0.1366, simple_loss=0.2179, pruned_loss=0.02767, over 4774.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03275, over 971963.40 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 05:33:46,740 INFO [train.py:715] (1/8) Epoch 11, batch 23850, loss[loss=0.1173, simple_loss=0.1907, pruned_loss=0.02197, over 4960.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03292, over 972378.52 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:34:24,311 INFO [train.py:715] (1/8) Epoch 11, batch 23900, loss[loss=0.1376, simple_loss=0.2168, pruned_loss=0.02922, over 4719.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2131, pruned_loss=0.03291, over 972815.30 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 05:35:01,657 INFO [train.py:715] (1/8) Epoch 11, batch 23950, loss[loss=0.163, simple_loss=0.2425, pruned_loss=0.04177, over 4889.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2128, pruned_loss=0.03293, over 973370.57 frames.], batch size: 39, lr: 1.94e-04
2022-05-07 05:35:39,343 INFO [train.py:715] (1/8) Epoch 11, batch 24000, loss[loss=0.1277, simple_loss=0.2078, pruned_loss=0.02382, over 4941.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2119, pruned_loss=0.03225, over 973484.69 frames.], batch size: 39, lr: 1.94e-04
2022-05-07 05:35:39,343 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 05:35:48,814 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.1059, simple_loss=0.19, pruned_loss=0.01092, over 914524.00 frames.
2022-05-07 05:36:27,141 INFO [train.py:715] (1/8) Epoch 11, batch 24050, loss[loss=0.122, simple_loss=0.2053, pruned_loss=0.01936, over 4744.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2119, pruned_loss=0.03197, over 972924.71 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 05:37:04,269 INFO [train.py:715] (1/8) Epoch 11, batch 24100, loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03443, over 4804.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2117, pruned_loss=0.03202, over 972876.65 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 05:37:42,097 INFO [train.py:715] (1/8) Epoch 11, batch 24150, loss[loss=0.1103, simple_loss=0.1849, pruned_loss=0.01781, over 4991.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2118, pruned_loss=0.03221, over 973143.37 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:38:20,369 INFO [train.py:715] (1/8) Epoch 11, batch 24200, loss[loss=0.1229, simple_loss=0.1953, pruned_loss=0.02525, over 4785.00 frames.], tot_loss[loss=0.1383, simple_loss=0.212, pruned_loss=0.0323, over 973096.50 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:38:57,454 INFO [train.py:715] (1/8) Epoch 11, batch 24250, loss[loss=0.145, simple_loss=0.218, pruned_loss=0.03599, over 4729.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03158, over 972455.00 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 05:39:35,485 INFO [train.py:715] (1/8) Epoch 11, batch 24300, loss[loss=0.1455, simple_loss=0.2103, pruned_loss=0.04038, over 4972.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.0323, over 972779.01 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:40:13,074 INFO [train.py:715] (1/8) Epoch 11, batch 24350, loss[loss=0.138, simple_loss=0.2091, pruned_loss=0.03342, over 4921.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2113, pruned_loss=0.03269, over 972769.77 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 05:40:50,679 INFO [train.py:715] (1/8) Epoch 11, batch 24400, loss[loss=0.14, simple_loss=0.215, pruned_loss=0.03253, over 4878.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2109, pruned_loss=0.03241, over 973214.56 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 05:41:28,273 INFO [train.py:715] (1/8) Epoch 11, batch 24450, loss[loss=0.1568, simple_loss=0.2394, pruned_loss=0.03707, over 4912.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03218, over 972763.22 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 05:42:06,387 INFO [train.py:715] (1/8) Epoch 11, batch 24500, loss[loss=0.1543, simple_loss=0.2217, pruned_loss=0.04342, over 4934.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03196, over 973066.81 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 05:42:45,028 INFO [train.py:715] (1/8) Epoch 11, batch 24550, loss[loss=0.1311, simple_loss=0.2111, pruned_loss=0.02556, over 4752.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03194, over 973044.41 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 05:43:23,052 INFO [train.py:715] (1/8) Epoch 11, batch 24600, loss[loss=0.129, simple_loss=0.2008, pruned_loss=0.02863, over 4849.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.0319, over 972323.94 frames.], batch size: 20, lr: 1.94e-04
2022-05-07 05:44:01,528 INFO [train.py:715] (1/8) Epoch 11, batch 24650, loss[loss=0.1245, simple_loss=0.2112, pruned_loss=0.01888, over 4820.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2114, pruned_loss=0.03201, over 971863.28 frames.], batch size: 27, lr: 1.94e-04
2022-05-07 05:44:39,862 INFO [train.py:715] (1/8) Epoch 11, batch 24700, loss[loss=0.1356, simple_loss=0.2161, pruned_loss=0.02761, over 4919.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03201, over 971707.54 frames.], batch size: 29, lr: 1.94e-04
2022-05-07 05:45:18,489 INFO [train.py:715] (1/8) Epoch 11, batch 24750, loss[loss=0.119, simple_loss=0.1913, pruned_loss=0.02339, over 4973.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03228, over 972098.84 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:45:56,384 INFO [train.py:715] (1/8) Epoch 11, batch 24800, loss[loss=0.168, simple_loss=0.2371, pruned_loss=0.04939, over 4858.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03211, over 972088.79 frames.], batch size: 20, lr: 1.94e-04
2022-05-07 05:46:34,704 INFO [train.py:715] (1/8) Epoch 11, batch 24850, loss[loss=0.1506, simple_loss=0.2198, pruned_loss=0.04066, over 4787.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03233, over 972241.57 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:47:13,630 INFO [train.py:715] (1/8) Epoch 11, batch 24900, loss[loss=0.1263, simple_loss=0.2053, pruned_loss=0.02361, over 4937.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03219, over 971928.06 frames.], batch size: 29, lr: 1.94e-04
2022-05-07 05:47:51,695 INFO [train.py:715] (1/8) Epoch 11, batch 24950, loss[loss=0.1543, simple_loss=0.2326, pruned_loss=0.03797, over 4873.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2121, pruned_loss=0.03202, over 972437.25 frames.], batch size: 20, lr: 1.94e-04
2022-05-07 05:48:30,024 INFO [train.py:715] (1/8) Epoch 11, batch 25000, loss[loss=0.1365, simple_loss=0.2158, pruned_loss=0.02859, over 4988.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2125, pruned_loss=0.03265, over 972388.77 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 05:49:08,320 INFO [train.py:715] (1/8) Epoch 11, batch 25050, loss[loss=0.1683, simple_loss=0.2348, pruned_loss=0.05096, over 4771.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03262, over 972507.91 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 05:49:49,681 INFO [train.py:715] (1/8) Epoch 11, batch 25100, loss[loss=0.1251, simple_loss=0.198, pruned_loss=0.02606, over 4755.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03254, over 972765.13 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:50:27,840 INFO [train.py:715] (1/8) Epoch 11, batch 25150, loss[loss=0.1457, simple_loss=0.2254, pruned_loss=0.03296, over 4974.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03259, over 973033.00 frames.], batch size: 28, lr: 1.94e-04
2022-05-07 05:51:06,432 INFO [train.py:715] (1/8) Epoch 11, batch 25200, loss[loss=0.1296, simple_loss=0.2007, pruned_loss=0.0293, over 4747.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.03249, over 972094.97 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 05:51:45,285 INFO [train.py:715] (1/8) Epoch 11, batch 25250, loss[loss=0.15, simple_loss=0.2372, pruned_loss=0.03142, over 4877.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2118, pruned_loss=0.0324, over 971498.81 frames.], batch size: 22, lr: 1.94e-04
2022-05-07 05:52:23,563 INFO [train.py:715] (1/8) Epoch 11, batch 25300, loss[loss=0.1299, simple_loss=0.202, pruned_loss=0.02892, over 4889.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2103, pruned_loss=0.03216, over 971724.88 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 05:53:01,963 INFO [train.py:715] (1/8) Epoch 11, batch 25350, loss[loss=0.1166, simple_loss=0.1996, pruned_loss=0.01679, over 4788.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2107, pruned_loss=0.03229, over 971616.06 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:53:40,602 INFO [train.py:715] (1/8) Epoch 11, batch 25400, loss[loss=0.166, simple_loss=0.2336, pruned_loss=0.04919, over 4989.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03247, over 971817.50 frames.], batch size: 20, lr: 1.94e-04
2022-05-07 05:54:19,419 INFO [train.py:715] (1/8) Epoch 11, batch 25450, loss[loss=0.138, simple_loss=0.2188, pruned_loss=0.02861, over 4930.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03284, over 972571.86 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 05:54:57,472 INFO [train.py:715] (1/8) Epoch 11, batch 25500, loss[loss=0.1517, simple_loss=0.2185, pruned_loss=0.04245, over 4838.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03321, over 972443.39 frames.], batch size: 30, lr: 1.94e-04
2022-05-07 05:55:36,082 INFO [train.py:715] (1/8) Epoch 11, batch 25550, loss[loss=0.1474, simple_loss=0.2196, pruned_loss=0.03761, over 4768.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.03336, over 972998.57 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 05:56:15,316 INFO [train.py:715] (1/8) Epoch 11, batch 25600, loss[loss=0.1465, simple_loss=0.2125, pruned_loss=0.04022, over 4818.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2124, pruned_loss=0.03301, over 972241.14 frames.], batch size: 25, lr: 1.94e-04
2022-05-07 05:56:53,595 INFO [train.py:715] (1/8) Epoch 11, batch 25650, loss[loss=0.1307, simple_loss=0.2132, pruned_loss=0.02406, over 4920.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2125, pruned_loss=0.03291, over 972699.71 frames.], batch size: 29, lr: 1.94e-04
2022-05-07 05:57:31,751 INFO [train.py:715] (1/8) Epoch 11, batch 25700, loss[loss=0.1386, simple_loss=0.2136, pruned_loss=0.03181, over 4887.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2134, pruned_loss=0.03336, over 972410.58 frames.], batch size: 22, lr: 1.94e-04
2022-05-07 05:58:10,583 INFO [train.py:715] (1/8) Epoch 11, batch 25750, loss[loss=0.1465, simple_loss=0.2116, pruned_loss=0.04072, over 4806.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.0337, over 973073.72 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 05:58:48,901 INFO [train.py:715] (1/8) Epoch 11, batch 25800, loss[loss=0.1829, simple_loss=0.258, pruned_loss=0.0539, over 4778.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.0333, over 972536.16 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 05:59:26,904 INFO [train.py:715] (1/8) Epoch 11, batch 25850, loss[loss=0.1282, simple_loss=0.1977, pruned_loss=0.02935, over 4750.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.03325, over 972609.60 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 06:00:05,563 INFO [train.py:715] (1/8) Epoch 11, batch 25900, loss[loss=0.1634, simple_loss=0.2428, pruned_loss=0.04199, over 4945.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03318, over 972117.88 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 06:00:44,270 INFO [train.py:715] (1/8) Epoch 11, batch 25950, loss[loss=0.1441, simple_loss=0.2206, pruned_loss=0.0338, over 4807.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03292, over 971172.70 frames.], batch size: 25, lr: 1.94e-04
2022-05-07 06:01:22,320 INFO [train.py:715] (1/8) Epoch 11, batch 26000, loss[loss=0.176, simple_loss=0.2372, pruned_loss=0.05739, over 4642.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03334, over 971004.38 frames.], batch size: 13, lr: 1.94e-04
2022-05-07 06:02:00,400 INFO [train.py:715] (1/8) Epoch 11, batch 26050, loss[loss=0.1412, simple_loss=0.2196, pruned_loss=0.03145, over 4817.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03347, over 971272.05 frames.], batch size: 27, lr: 1.94e-04
2022-05-07 06:02:38,955 INFO [train.py:715] (1/8) Epoch 11, batch 26100, loss[loss=0.1581, simple_loss=0.2329, pruned_loss=0.0417, over 4969.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03331, over 971481.50 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 06:03:17,345 INFO [train.py:715] (1/8) Epoch 11, batch 26150, loss[loss=0.1174, simple_loss=0.2001, pruned_loss=0.01739, over 4742.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2126, pruned_loss=0.03303, over 972800.33 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 06:03:55,314 INFO [train.py:715] (1/8) Epoch 11, batch 26200, loss[loss=0.1311, simple_loss=0.1983, pruned_loss=0.03193, over 4809.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03288, over 971974.64 frames.], batch size: 13, lr: 1.94e-04
2022-05-07 06:04:32,932 INFO [train.py:715] (1/8) Epoch 11, batch 26250, loss[loss=0.1188, simple_loss=0.1942, pruned_loss=0.02175, over 4961.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03219, over 971442.48 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 06:05:10,976 INFO [train.py:715] (1/8) Epoch 11, batch 26300, loss[loss=0.1353, simple_loss=0.2063, pruned_loss=0.03221, over 4921.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.03217, over 971683.44 frames.], batch size: 23, lr: 1.94e-04
2022-05-07 06:05:48,409 INFO [train.py:715] (1/8) Epoch 11, batch 26350, loss[loss=0.1308, simple_loss=0.1978, pruned_loss=0.03187, over 4694.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03213, over 971095.68 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:06:25,428 INFO [train.py:715] (1/8) Epoch 11, batch 26400, loss[loss=0.1488, simple_loss=0.2202, pruned_loss=0.03866, over 4847.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03228, over 970497.47 frames.], batch size: 32, lr: 1.94e-04
2022-05-07 06:07:03,857 INFO [train.py:715] (1/8) Epoch 11, batch 26450, loss[loss=0.1193, simple_loss=0.1972, pruned_loss=0.02069, over 4866.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03264, over 969625.61 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 06:07:41,341 INFO [train.py:715] (1/8) Epoch 11, batch 26500, loss[loss=0.1565, simple_loss=0.231, pruned_loss=0.04101, over 4704.00 frames.], tot_loss[loss=0.1381, simple_loss=0.211, pruned_loss=0.0326, over 970300.44 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:08:19,085 INFO [train.py:715] (1/8) Epoch 11, batch 26550, loss[loss=0.1355, simple_loss=0.2099, pruned_loss=0.03052, over 4769.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.0328, over 971033.87 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 06:08:56,822 INFO [train.py:715] (1/8) Epoch 11, batch 26600, loss[loss=0.1176, simple_loss=0.1969, pruned_loss=0.01913, over 4754.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03287, over 970862.68 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 06:09:34,848 INFO [train.py:715] (1/8) Epoch 11, batch 26650, loss[loss=0.1312, simple_loss=0.2207, pruned_loss=0.02087, over 4991.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2127, pruned_loss=0.03278, over 970572.68 frames.], batch size: 27, lr: 1.94e-04
2022-05-07 06:10:12,913 INFO [train.py:715] (1/8) Epoch 11, batch 26700, loss[loss=0.127, simple_loss=0.2008, pruned_loss=0.02658, over 4916.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2121, pruned_loss=0.03256, over 971093.31 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 06:10:49,900 INFO [train.py:715] (1/8) Epoch 11, batch 26750, loss[loss=0.1272, simple_loss=0.2001, pruned_loss=0.02717, over 4835.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2123, pruned_loss=0.03229, over 970965.82 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:11:28,543 INFO [train.py:715] (1/8) Epoch 11, batch 26800, loss[loss=0.1287, simple_loss=0.199, pruned_loss=0.02919, over 4817.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2122, pruned_loss=0.03226, over 970928.88 frames.], batch size: 13, lr: 1.94e-04
2022-05-07 06:12:06,145 INFO [train.py:715] (1/8) Epoch 11, batch 26850, loss[loss=0.1256, simple_loss=0.1952, pruned_loss=0.02798, over 4977.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2115, pruned_loss=0.03186, over 971432.63 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:12:43,633 INFO [train.py:715] (1/8) Epoch 11, batch 26900, loss[loss=0.1504, simple_loss=0.2237, pruned_loss=0.03854, over 4980.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03202, over 972018.22 frames.], batch size: 25, lr: 1.94e-04
2022-05-07 06:13:21,273 INFO [train.py:715] (1/8) Epoch 11, batch 26950, loss[loss=0.1607, simple_loss=0.2326, pruned_loss=0.04435, over 4773.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2126, pruned_loss=0.03284, over 971710.13 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 06:13:59,655 INFO [train.py:715] (1/8) Epoch 11, batch 27000, loss[loss=0.15, simple_loss=0.2244, pruned_loss=0.03776, over 4688.00 frames.], tot_loss[loss=0.1395, simple_loss=0.213, pruned_loss=0.03298, over 970897.71 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:13:59,655 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 06:14:09,118 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.1059, simple_loss=0.19, pruned_loss=0.01084, over 914524.00 frames.
2022-05-07 06:14:47,548 INFO [train.py:715] (1/8) Epoch 11, batch 27050, loss[loss=0.1382, simple_loss=0.2146, pruned_loss=0.03086, over 4751.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2118, pruned_loss=0.03237, over 970120.27 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:15:25,151 INFO [train.py:715] (1/8) Epoch 11, batch 27100, loss[loss=0.1448, simple_loss=0.2265, pruned_loss=0.03156, over 4762.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03221, over 968979.52 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:16:02,384 INFO [train.py:715] (1/8) Epoch 11, batch 27150, loss[loss=0.1216, simple_loss=0.2016, pruned_loss=0.02079, over 4855.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.0328, over 969615.62 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:16:41,025 INFO [train.py:715] (1/8) Epoch 11, batch 27200, loss[loss=0.131, simple_loss=0.207, pruned_loss=0.02753, over 4914.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2118, pruned_loss=0.03235, over 970067.17 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 06:17:18,730 INFO [train.py:715] (1/8) Epoch 11, batch 27250, loss[loss=0.125, simple_loss=0.1971, pruned_loss=0.02642, over 4945.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03231, over 971079.76 frames.], batch size: 29, lr: 1.94e-04
2022-05-07 06:17:56,619 INFO [train.py:715] (1/8) Epoch 11, batch 27300, loss[loss=0.1201, simple_loss=0.1965, pruned_loss=0.02189, over 4804.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.0322, over 971313.07 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 06:18:34,289 INFO [train.py:715] (1/8) Epoch 11, batch 27350, loss[loss=0.1402, simple_loss=0.2058, pruned_loss=0.03725, over 4856.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03221, over 971021.79 frames.], batch size: 30, lr: 1.94e-04
2022-05-07 06:19:13,065 INFO [train.py:715] (1/8) Epoch 11, batch 27400, loss[loss=0.1421, simple_loss=0.2109, pruned_loss=0.03661, over 4907.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2107, pruned_loss=0.03238, over 970894.93 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:19:50,856 INFO [train.py:715] (1/8) Epoch 11, batch 27450, loss[loss=0.134, simple_loss=0.2166, pruned_loss=0.02574, over 4904.00 frames.], tot_loss[loss=0.1368, simple_loss=0.21, pruned_loss=0.03181, over 971309.54 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:20:28,121 INFO [train.py:715] (1/8) Epoch 11, batch 27500, loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.0331, over 4911.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03147, over 972158.03 frames.], batch size: 23, lr: 1.94e-04
2022-05-07 06:21:07,287 INFO [train.py:715] (1/8) Epoch 11, batch 27550, loss[loss=0.1428, simple_loss=0.2182, pruned_loss=0.03366, over 4886.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2111, pruned_loss=0.03271, over 972846.41 frames.], batch size: 22, lr: 1.94e-04
2022-05-07 06:21:45,753 INFO [train.py:715] (1/8) Epoch 11, batch 27600, loss[loss=0.1159, simple_loss=0.193, pruned_loss=0.01938, over 4795.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03235, over 972122.65 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 06:22:23,477 INFO [train.py:715] (1/8) Epoch 11, batch 27650, loss[loss=0.1212, simple_loss=0.1996, pruned_loss=0.02142, over 4829.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03239, over 972380.65 frames.], batch size: 25, lr: 1.94e-04
2022-05-07 06:23:01,300 INFO [train.py:715] (1/8) Epoch 11, batch 27700, loss[loss=0.1339, simple_loss=0.2072, pruned_loss=0.03026, over 4887.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03236, over 972311.19 frames.], batch size: 32, lr: 1.94e-04
2022-05-07 06:23:39,627 INFO [train.py:715] (1/8) Epoch 11, batch 27750, loss[loss=0.1429, simple_loss=0.2181, pruned_loss=0.03385, over 4815.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03238, over 972611.67 frames.], batch size: 13, lr: 1.94e-04
2022-05-07 06:24:17,574 INFO [train.py:715] (1/8) Epoch 11, batch 27800, loss[loss=0.1487, simple_loss=0.216, pruned_loss=0.04076, over 4882.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.0324, over 973278.77 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 06:24:54,558 INFO [train.py:715] (1/8) Epoch 11, batch 27850, loss[loss=0.1329, simple_loss=0.2155, pruned_loss=0.02514, over 4893.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03237, over 973773.95 frames.], batch size: 22, lr: 1.93e-04
2022-05-07 06:25:32,920 INFO [train.py:715] (1/8) Epoch 11, batch 27900, loss[loss=0.1478, simple_loss=0.2182, pruned_loss=0.03872, over 4754.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03209, over 973556.33 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:26:10,975 INFO [train.py:715] (1/8) Epoch 11, batch 27950, loss[loss=0.1317, simple_loss=0.2018, pruned_loss=0.0308, over 4834.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03229, over 973781.45 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:26:48,584 INFO [train.py:715] (1/8) Epoch 11, batch 28000, loss[loss=0.1283, simple_loss=0.2098, pruned_loss=0.02343, over 4982.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.03192, over 973643.00 frames.], batch size: 28, lr: 1.93e-04
2022-05-07 06:27:26,141 INFO [train.py:715] (1/8) Epoch 11, batch 28050, loss[loss=0.1259, simple_loss=0.1973, pruned_loss=0.02728, over 4888.00 frames.], tot_loss[loss=0.1385, simple_loss=0.212, pruned_loss=0.03249, over 972445.39 frames.], batch size: 22, lr: 1.93e-04
2022-05-07 06:28:04,144 INFO [train.py:715] (1/8) Epoch 11, batch 28100, loss[loss=0.1187, simple_loss=0.1933, pruned_loss=0.02204, over 4927.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2121, pruned_loss=0.0324, over 972344.38 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:28:41,423 INFO [train.py:715] (1/8) Epoch 11, batch 28150, loss[loss=0.1119, simple_loss=0.1868, pruned_loss=0.01849, over 4981.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03184, over 972419.85 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 06:29:18,871 INFO [train.py:715] (1/8) Epoch 11, batch 28200, loss[loss=0.1529, simple_loss=0.2239, pruned_loss=0.04102, over 4812.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03175, over 972900.92 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 06:29:57,425 INFO [train.py:715] (1/8) Epoch 11, batch 28250, loss[loss=0.1151, simple_loss=0.197, pruned_loss=0.01666, over 4695.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03206, over 972380.79 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:30:34,927 INFO [train.py:715] (1/8) Epoch 11, batch 28300, loss[loss=0.1404, simple_loss=0.2202, pruned_loss=0.03027, over 4858.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03228, over 971329.05 frames.], batch size: 20, lr: 1.93e-04
2022-05-07 06:31:12,833 INFO [train.py:715] (1/8) Epoch 11, batch 28350, loss[loss=0.1299, simple_loss=0.2007, pruned_loss=0.02958, over 4785.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.0328, over 972348.10 frames.], batch size: 12, lr: 1.93e-04
2022-05-07 06:31:50,529 INFO [train.py:715] (1/8) Epoch 11, batch 28400, loss[loss=0.1402, simple_loss=0.2137, pruned_loss=0.03331, over 4985.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2125, pruned_loss=0.03348, over 972548.62 frames.], batch size: 28, lr: 1.93e-04
2022-05-07 06:32:28,903 INFO [train.py:715] (1/8) Epoch 11, batch 28450, loss[loss=0.1464, simple_loss=0.225, pruned_loss=0.03394, over 4913.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03327, over 972415.83 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 06:33:06,942 INFO [train.py:715] (1/8) Epoch 11, batch 28500, loss[loss=0.1136, simple_loss=0.1948, pruned_loss=0.0162, over 4978.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03299, over 972352.71 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 06:33:44,634 INFO [train.py:715] (1/8) Epoch 11, batch 28550, loss[loss=0.1325, simple_loss=0.2028, pruned_loss=0.03111, over 4860.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03262, over 971873.18 frames.], batch size: 30, lr: 1.93e-04
2022-05-07 06:34:23,474 INFO [train.py:715] (1/8) Epoch 11, batch 28600, loss[loss=0.1226, simple_loss=0.1923, pruned_loss=0.02646, over 4967.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03258, over 972453.97 frames.], batch size: 28, lr: 1.93e-04
2022-05-07 06:35:01,438 INFO [train.py:715] (1/8) Epoch 11, batch 28650, loss[loss=0.1285, simple_loss=0.2044, pruned_loss=0.02635, over 4810.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2117, pruned_loss=0.03225, over 972938.96 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 06:35:39,423 INFO [train.py:715] (1/8) Epoch 11, batch 28700, loss[loss=0.1215, simple_loss=0.1981, pruned_loss=0.02243, over 4897.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.0318, over 972545.00 frames.], batch size: 19, lr: 1.93e-04
2022-05-07 06:36:17,176 INFO [train.py:715] (1/8) Epoch 11, batch 28750, loss[loss=0.1283, simple_loss=0.2071, pruned_loss=0.0248, over 4896.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03154, over 972929.83 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 06:36:55,927 INFO [train.py:715] (1/8) Epoch 11, batch 28800, loss[loss=0.1279, simple_loss=0.2084, pruned_loss=0.02371, over 4943.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.0313, over 972895.19 frames.], batch size: 29, lr: 1.93e-04
2022-05-07 06:37:33,393 INFO [train.py:715] (1/8) Epoch 11, batch 28850, loss[loss=0.1582, simple_loss=0.2342, pruned_loss=0.04115, over 4945.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03134, over 973044.68 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 06:38:10,807 INFO [train.py:715] (1/8) Epoch 11, batch 28900, loss[loss=0.1503, simple_loss=0.2273, pruned_loss=0.03661, over 4948.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03165, over 972968.09 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 06:38:49,570 INFO [train.py:715] (1/8) Epoch 11, batch 28950, loss[loss=0.134, simple_loss=0.205, pruned_loss=0.03152, over 4843.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03194, over 973185.29 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 06:39:27,040 INFO [train.py:715] (1/8) Epoch 11, batch 29000, loss[loss=0.1625, simple_loss=0.2415, pruned_loss=0.0418, over 4791.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03205, over 972155.72 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 06:40:04,961 INFO [train.py:715] (1/8) Epoch 11, batch 29050, loss[loss=0.1327, simple_loss=0.2013, pruned_loss=0.03208, over 4883.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03183, over 971909.82 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:40:42,753 INFO [train.py:715] (1/8) Epoch 11, batch 29100, loss[loss=0.1346, simple_loss=0.2058, pruned_loss=0.03166, over 4785.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.03216, over 972133.02 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:41:21,084 INFO [train.py:715] (1/8) Epoch 11, batch 29150, loss[loss=0.1275, simple_loss=0.1978, pruned_loss=0.02863, over 4890.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2116, pruned_loss=0.03212, over 972664.22 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 06:41:58,822 INFO [train.py:715] (1/8) Epoch 11, batch 29200, loss[loss=0.1429, simple_loss=0.2213, pruned_loss=0.03219, over 4814.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03205, over 973937.50 frames.], batch size: 26, lr: 1.93e-04
2022-05-07 06:42:36,370 INFO [train.py:715] (1/8) Epoch 11, batch 29250, loss[loss=0.171, simple_loss=0.2438, pruned_loss=0.04908, over 4774.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03202, over 974307.08 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 06:43:15,064 INFO [train.py:715] (1/8) Epoch 11, batch 29300, loss[loss=0.1348, simple_loss=0.2008, pruned_loss=0.03444, over 4843.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03195, over 973569.55 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 06:43:53,137 INFO [train.py:715] (1/8) Epoch 11, batch 29350, loss[loss=0.1383, simple_loss=0.2124, pruned_loss=0.0321, over 4956.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2117, pruned_loss=0.03225, over 973261.38 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 06:44:30,903 INFO [train.py:715] (1/8) Epoch 11, batch 29400, loss[loss=0.1133, simple_loss=0.1828, pruned_loss=0.02185, over 4828.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.03254, over 972844.18 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:45:08,812 INFO [train.py:715] (1/8) Epoch 11, batch 29450, loss[loss=0.1376, simple_loss=0.2121, pruned_loss=0.03151, over 4784.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2118, pruned_loss=0.03284, over 972343.94 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:45:46,709 INFO [train.py:715] (1/8) Epoch 11, batch 29500, loss[loss=0.1501, simple_loss=0.2244, pruned_loss=0.0379, over 4849.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03265, over 972083.50 frames.], batch size: 30, lr: 1.93e-04
2022-05-07 06:46:25,302 INFO [train.py:715] (1/8) Epoch 11, batch 29550, loss[loss=0.1689, simple_loss=0.2324, pruned_loss=0.05271, over 4956.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03234, over 972268.73 frames.], batch size: 35, lr: 1.93e-04
2022-05-07 06:47:02,906 INFO [train.py:715] (1/8) Epoch 11, batch 29600, loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03524, over 4785.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03259, over 972774.93 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:47:41,475 INFO [train.py:715] (1/8) Epoch 11, batch 29650, loss[loss=0.1275, simple_loss=0.1996, pruned_loss=0.02772, over 4970.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03252, over 972830.60 frames.], batch size: 31, lr: 1.93e-04
2022-05-07 06:48:19,468 INFO [train.py:715] (1/8) Epoch 11, batch 29700, loss[loss=0.1299, simple_loss=0.2009, pruned_loss=0.02951, over 4976.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03257, over 972952.60 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 06:48:57,628 INFO [train.py:715] (1/8) Epoch 11, batch 29750, loss[loss=0.1439, simple_loss=0.2117, pruned_loss=0.03809, over 4781.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2114, pruned_loss=0.03203, over 972801.14 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 06:49:35,439 INFO [train.py:715] (1/8) Epoch 11, batch 29800, loss[loss=0.1251, simple_loss=0.2054, pruned_loss=0.02246, over 4822.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03213, over 972401.98 frames.], batch size: 26, lr: 1.93e-04
2022-05-07 06:50:13,830 INFO [train.py:715] (1/8) Epoch 11, batch 29850, loss[loss=0.1424, simple_loss=0.2114, pruned_loss=0.03667, over 4880.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03253, over 972595.00 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:50:52,366 INFO [train.py:715] (1/8) Epoch 11, batch 29900, loss[loss=0.1193, simple_loss=0.1905, pruned_loss=0.0241, over 4833.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.03264, over 972637.42 frames.], batch size: 30, lr: 1.93e-04
2022-05-07 06:51:29,993 INFO [train.py:715] (1/8) Epoch 11, batch 29950, loss[loss=0.1507, simple_loss=0.2114, pruned_loss=0.04502, over 4750.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03281, over 973105.41 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:52:08,181 INFO [train.py:715] (1/8) Epoch 11, batch 30000, loss[loss=0.1437, simple_loss=0.2145, pruned_loss=0.03646, over 4944.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2114, pruned_loss=0.03286, over 973008.19 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 06:52:08,182 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 06:52:17,626 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.106, simple_loss=0.19, pruned_loss=0.01095, over 914524.00 frames.
2022-05-07 06:52:56,515 INFO [train.py:715] (1/8) Epoch 11, batch 30050, loss[loss=0.1626, simple_loss=0.2303, pruned_loss=0.04746, over 4771.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2113, pruned_loss=0.03297, over 972979.51 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 06:53:34,390 INFO [train.py:715] (1/8) Epoch 11, batch 30100, loss[loss=0.1186, simple_loss=0.2052, pruned_loss=0.01603, over 4921.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03275, over 973004.41 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:54:13,053 INFO [train.py:715] (1/8) Epoch 11, batch 30150, loss[loss=0.1497, simple_loss=0.2264, pruned_loss=0.03649, over 4786.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03291, over 972911.72 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:54:50,401 INFO [train.py:715] (1/8) Epoch 11, batch 30200, loss[loss=0.1703, simple_loss=0.2329, pruned_loss=0.05384, over 4986.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03285, over 972751.40 frames.], batch size: 35, lr: 1.93e-04
2022-05-07 06:55:29,246 INFO [train.py:715] (1/8) Epoch 11, batch 30250, loss[loss=0.1245, simple_loss=0.2004, pruned_loss=0.02434, over 4962.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03274, over 972402.78 frames.], batch size: 24, lr: 1.93e-04
2022-05-07 06:56:07,229 INFO [train.py:715] (1/8) Epoch 11, batch 30300, loss[loss=0.1308, simple_loss=0.2096, pruned_loss=0.02601, over 4814.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03301, over 972221.15 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 06:56:45,179 INFO [train.py:715] (1/8) Epoch 11, batch 30350, loss[loss=0.1331, simple_loss=0.208, pruned_loss=0.02906, over 4766.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03253, over 972697.15 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 06:57:23,262 INFO [train.py:715] (1/8) Epoch 11, batch 30400, loss[loss=0.1505, simple_loss=0.2209, pruned_loss=0.04003, over 4807.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03248, over 971478.34 frames.], batch size: 26, lr: 1.93e-04
2022-05-07 06:58:01,503 INFO [train.py:715] (1/8) Epoch 11, batch 30450, loss[loss=0.1413, simple_loss=0.2131, pruned_loss=0.0348, over 4973.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03223, over 971511.21 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:58:39,334 INFO [train.py:715] (1/8) Epoch 11, batch 30500, loss[loss=0.1288, simple_loss=0.2056, pruned_loss=0.02605, over 4880.00 frames.], tot_loss[loss=0.1382, simple_loss=0.212, pruned_loss=0.03215, over 972344.98 frames.], batch size: 22, lr: 1.93e-04
2022-05-07 06:59:17,161 INFO [train.py:715] (1/8) Epoch 11, batch 30550, loss[loss=0.129, simple_loss=0.2114, pruned_loss=0.02327, over 4970.00 frames.], tot_loss[loss=0.139, simple_loss=0.2127, pruned_loss=0.0326, over 972401.69 frames.], batch size: 31, lr: 1.93e-04
2022-05-07 06:59:56,423 INFO [train.py:715] (1/8) Epoch 11, batch 30600, loss[loss=0.1128, simple_loss=0.1836, pruned_loss=0.02096, over 4954.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03221, over 972513.86 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 07:00:35,051 INFO [train.py:715] (1/8) Epoch 11, batch 30650, loss[loss=0.1286, simple_loss=0.2041, pruned_loss=0.02658, over 4749.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03218, over 972505.64 frames.], batch size: 19, lr: 1.93e-04
2022-05-07 07:01:13,847 INFO [train.py:715] (1/8) Epoch 11, batch 30700, loss[loss=0.123, simple_loss=0.1977, pruned_loss=0.02413, over 4825.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2104, pruned_loss=0.03222, over 972332.71 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 07:01:52,352 INFO [train.py:715] (1/8) Epoch 11, batch 30750, loss[loss=0.1822, simple_loss=0.2669, pruned_loss=0.0488, over 4935.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03268, over 972545.37 frames.], batch size: 23, lr: 1.93e-04
2022-05-07 07:02:30,968 INFO [train.py:715] (1/8) Epoch 11, batch 30800, loss[loss=0.1168, simple_loss=0.1932, pruned_loss=0.02015, over 4864.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03226, over 971779.40 frames.], batch size: 20, lr: 1.93e-04
2022-05-07 07:03:09,726 INFO [train.py:715] (1/8) Epoch 11, batch 30850, loss[loss=0.1314, simple_loss=0.2002, pruned_loss=0.03124, over 4911.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03185, over 971963.91 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 07:03:48,284 INFO [train.py:715] (1/8) Epoch 11, batch 30900, loss[loss=0.1445, simple_loss=0.2281, pruned_loss=0.03044, over 4877.00 frames.], tot_loss[loss=0.1368, simple_loss=0.21, pruned_loss=0.03179, over 972065.55 frames.], batch size: 22, lr: 1.93e-04
2022-05-07 07:04:27,092 INFO [train.py:715] (1/8) Epoch 11, batch 30950, loss[loss=0.1229, simple_loss=0.1899, pruned_loss=0.02795, over 4801.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03169, over 972826.33 frames.], batch size: 24, lr: 1.93e-04
2022-05-07 07:05:06,009 INFO [train.py:715] (1/8) Epoch 11, batch 31000, loss[loss=0.1459, simple_loss=0.2255, pruned_loss=0.0331, over 4782.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.0322, over 972515.23 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 07:05:44,523 INFO [train.py:715] (1/8) Epoch 11, batch 31050, loss[loss=0.153, simple_loss=0.2232, pruned_loss=0.04142, over 4756.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03181, over 972770.42 frames.], batch size: 19, lr: 1.93e-04
2022-05-07 07:06:23,343 INFO [train.py:715] (1/8) Epoch 11, batch 31100, loss[loss=0.1215, simple_loss=0.1949, pruned_loss=0.02402, over 4769.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03203, over 972762.88 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 07:07:01,744 INFO [train.py:715] (1/8) Epoch 11, batch 31150, loss[loss=0.1278, simple_loss=0.1985, pruned_loss=0.02856, over 4772.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03179, over 973139.80 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 07:07:39,381 INFO [train.py:715] (1/8) Epoch 11, batch 31200, loss[loss=0.1196, simple_loss=0.1971, pruned_loss=0.02106, over 4842.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03186, over 972089.49 frames.], batch size: 26, lr: 1.93e-04
2022-05-07 07:08:17,484 INFO [train.py:715] (1/8) Epoch 11, batch 31250, loss[loss=0.1036, simple_loss=0.1643, pruned_loss=0.02147, over 4829.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03147, over 971888.59 frames.], batch size: 13, lr: 1.93e-04
2022-05-07 07:08:55,766 INFO [train.py:715] (1/8) Epoch 11, batch 31300, loss[loss=0.1408, simple_loss=0.2068, pruned_loss=0.0374, over 4959.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03173, over 971129.72 frames.], batch size: 29, lr: 1.93e-04
2022-05-07 07:09:33,562 INFO [train.py:715] (1/8) Epoch 11, batch 31350, loss[loss=0.1627, simple_loss=0.2496, pruned_loss=0.03792, over 4743.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03253, over 971729.24 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 07:10:10,910 INFO [train.py:715] (1/8) Epoch 11, batch 31400, loss[loss=0.1405, simple_loss=0.2147, pruned_loss=0.0331, over 4791.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.0323, over 971814.15 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 07:10:48,407 INFO [train.py:715] (1/8) Epoch 11, batch 31450, loss[loss=0.1287, simple_loss=0.2013, pruned_loss=0.02811, over 4801.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.0319, over 972133.17 frames.], batch size: 24, lr: 1.93e-04
2022-05-07 07:11:26,023 INFO [train.py:715] (1/8) Epoch 11, batch 31500, loss[loss=0.1408, simple_loss=0.2165, pruned_loss=0.0326, over 4961.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03132, over 972627.88 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 07:12:03,670 INFO [train.py:715] (1/8) Epoch 11, batch 31550, loss[loss=0.1588, simple_loss=0.2283, pruned_loss=0.04468, over 4942.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03167, over 972886.04 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 07:12:41,673 INFO [train.py:715] (1/8) Epoch 11, batch 31600, loss[loss=0.154, simple_loss=0.2245, pruned_loss=0.0417, over 4772.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.03146, over 972694.30 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 07:13:19,758 INFO [train.py:715] (1/8) Epoch 11, batch 31650, loss[loss=0.1231, simple_loss=0.204, pruned_loss=0.02112, over 4825.00 frames.], tot_loss[loss=0.1372, simple_loss=0.211, pruned_loss=0.03174, over 973073.14 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 07:13:57,689 INFO [train.py:715] (1/8) Epoch 11, batch 31700, loss[loss=0.1591, simple_loss=0.2314, pruned_loss=0.04334, over 4977.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.0324, over 973167.74 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 07:14:35,212 INFO [train.py:715] (1/8) Epoch 11, batch 31750, loss[loss=0.1605, simple_loss=0.2423, pruned_loss=0.03933, over 4864.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2126, pruned_loss=0.03281, over 972783.48 frames.], batch size: 20, lr: 1.93e-04
2022-05-07 07:15:14,062 INFO [train.py:715] (1/8) Epoch 11, batch 31800, loss[loss=0.1142, simple_loss=0.1903, pruned_loss=0.01906, over 4769.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03251, over 972219.75 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 07:15:52,639 INFO [train.py:715] (1/8) Epoch 11, batch 31850, loss[loss=0.1576, simple_loss=0.2302, pruned_loss=0.0425, over 4930.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2125, pruned_loss=0.0329, over 972326.85 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 07:16:30,871 INFO [train.py:715] (1/8) Epoch 11, batch 31900, loss[loss=0.122, simple_loss=0.1973, pruned_loss=0.02335, over 4978.00 frames.], tot_loss[loss=0.1395, simple_loss=0.213, pruned_loss=0.03303, over 973036.41 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 07:17:09,162 INFO [train.py:715] (1/8) Epoch 11, batch 31950, loss[loss=0.1504, simple_loss=0.2382, pruned_loss=0.03127, over 4950.00 frames.], tot_loss[loss=0.139, simple_loss=0.2126, pruned_loss=0.03269, over 972620.40 frames.], batch size: 23, lr: 1.93e-04
2022-05-07 07:17:47,945 INFO [train.py:715] (1/8) Epoch 11, batch 32000, loss[loss=0.1767, simple_loss=0.2348, pruned_loss=0.05926, over 4952.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03256, over 972886.25 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 07:18:26,170 INFO [train.py:715] (1/8) Epoch 11, batch 32050, loss[loss=0.1299, simple_loss=0.2009, pruned_loss=0.02946, over 4777.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03273, over 973216.05 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 07:19:04,556 INFO [train.py:715] (1/8) Epoch 11, batch 32100, loss[loss=0.1135, simple_loss=0.1913, pruned_loss=0.0178, over 4844.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03297, over 973424.18 frames.], batch size: 20, lr: 1.92e-04
2022-05-07 07:19:42,574 INFO [train.py:715] (1/8) Epoch 11, batch 32150, loss[loss=0.1597, simple_loss=0.2234, pruned_loss=0.04806, over 4884.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2118, pruned_loss=0.03253, over 973378.42 frames.], batch size: 22, lr: 1.92e-04
2022-05-07 07:20:19,992 INFO [train.py:715] (1/8) Epoch 11, batch 32200, loss[loss=0.1071, simple_loss=0.1763, pruned_loss=0.01901, over 4789.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03235, over 972514.86 frames.], batch size: 12, lr: 1.92e-04
2022-05-07 07:20:57,516 INFO [train.py:715] (1/8) Epoch 11, batch 32250, loss[loss=0.1516, simple_loss=0.2284, pruned_loss=0.03738, over 4849.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03219, over 971788.31 frames.], batch size: 20, lr: 1.92e-04
2022-05-07 07:21:35,350 INFO [train.py:715] (1/8) Epoch 11, batch 32300, loss[loss=0.1282, simple_loss=0.2057, pruned_loss=0.02537, over 4913.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.03192, over 971932.52 frames.], batch size: 29, lr: 1.92e-04
2022-05-07 07:22:13,998 INFO [train.py:715] (1/8) Epoch 11, batch 32350, loss[loss=0.1205, simple_loss=0.2013, pruned_loss=0.01988, over 4852.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03177, over 972104.61 frames.], batch size: 20, lr: 1.92e-04
2022-05-07 07:22:51,417 INFO [train.py:715] (1/8) Epoch 11, batch 32400, loss[loss=0.131, simple_loss=0.2107, pruned_loss=0.02561, over 4940.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03157, over 972881.51 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:23:29,419 INFO [train.py:715] (1/8) Epoch 11, batch 32450, loss[loss=0.1275, simple_loss=0.2115, pruned_loss=0.02172, over 4936.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2106, pruned_loss=0.0314, over 973126.92 frames.], batch size: 23, lr: 1.92e-04
2022-05-07 07:24:07,459 INFO [train.py:715] (1/8) Epoch 11, batch 32500, loss[loss=0.1108, simple_loss=0.1791, pruned_loss=0.02127, over 4971.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03112, over 973276.16 frames.], batch size: 15, lr: 1.92e-04
2022-05-07 07:24:45,518 INFO [train.py:715] (1/8) Epoch 11, batch 32550, loss[loss=0.1267, simple_loss=0.1961, pruned_loss=0.02863, over 4775.00 frames.], tot_loss[loss=0.1358, simple_loss=0.209, pruned_loss=0.03126, over 972737.11 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:25:23,171 INFO [train.py:715] (1/8) Epoch 11, batch 32600, loss[loss=0.1161, simple_loss=0.1886, pruned_loss=0.02182, over 4931.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03155, over 972420.24 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:26:01,258 INFO [train.py:715] (1/8) Epoch 11, batch 32650, loss[loss=0.1142, simple_loss=0.1932, pruned_loss=0.01761, over 4956.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03189, over 972299.09 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:26:39,442 INFO [train.py:715] (1/8) Epoch 11, batch 32700, loss[loss=0.1385, simple_loss=0.22, pruned_loss=0.02847, over 4942.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03171, over 972626.67 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:27:16,891 INFO [train.py:715] (1/8) Epoch 11, batch 32750, loss[loss=0.1407, simple_loss=0.218, pruned_loss=0.03169, over 4938.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03135, over 972402.44 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:27:55,659 INFO [train.py:715] (1/8) Epoch 11, batch 32800, loss[loss=0.1322, simple_loss=0.2102, pruned_loss=0.0271, over 4761.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03158, over 972349.86 frames.], batch size: 19, lr: 1.92e-04
2022-05-07 07:28:35,368 INFO [train.py:715] (1/8) Epoch 11, batch 32850, loss[loss=0.1173, simple_loss=0.1965, pruned_loss=0.01904, over 4951.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03173, over 972277.03 frames.], batch size: 23, lr: 1.92e-04
2022-05-07 07:29:13,921 INFO [train.py:715] (1/8) Epoch 11, batch 32900, loss[loss=0.1268, simple_loss=0.1977, pruned_loss=0.02798, over 4774.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03227, over 971616.46 frames.], batch size: 18, lr: 1.92e-04
2022-05-07 07:29:52,133 INFO [train.py:715] (1/8) Epoch 11, batch 32950, loss[loss=0.134, simple_loss=0.204, pruned_loss=0.03204, over 4965.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03211, over 972418.07 frames.], batch size: 24, lr: 1.92e-04
2022-05-07 07:30:31,050 INFO [train.py:715] (1/8) Epoch 11, batch 33000, loss[loss=0.1402, simple_loss=0.2148, pruned_loss=0.03273, over 4885.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03202, over 972887.68 frames.], batch size: 32, lr: 1.92e-04
2022-05-07 07:30:31,050 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 07:30:40,494 INFO [train.py:742] (1/8) Epoch 11, validation: loss=0.1059, simple_loss=0.1899, pruned_loss=0.0109, over 914524.00 frames.
2022-05-07 07:31:19,432 INFO [train.py:715] (1/8) Epoch 11, batch 33050, loss[loss=0.1169, simple_loss=0.1906, pruned_loss=0.02158, over 4809.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03176, over 973242.90 frames.], batch size: 25, lr: 1.92e-04
2022-05-07 07:32:00,931 INFO [train.py:715] (1/8) Epoch 11, batch 33100, loss[loss=0.1564, simple_loss=0.2282, pruned_loss=0.04227, over 4750.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2122, pruned_loss=0.03235, over 973000.18 frames.], batch size: 16, lr: 1.92e-04
2022-05-07 07:32:38,894 INFO [train.py:715] (1/8) Epoch 11, batch 33150, loss[loss=0.1006, simple_loss=0.1666, pruned_loss=0.01729, over 4785.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2129, pruned_loss=0.03304, over 972467.44 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:33:17,511 INFO [train.py:715] (1/8) Epoch 11, batch 33200, loss[loss=0.1574, simple_loss=0.2293, pruned_loss=0.04277, over 4778.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2132, pruned_loss=0.03324, over 972553.16 frames.], batch size: 18, lr: 1.92e-04
2022-05-07 07:33:56,635 INFO [train.py:715] (1/8) Epoch 11, batch 33250, loss[loss=0.1427, simple_loss=0.2161, pruned_loss=0.03461, over 4883.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03264, over 972474.85 frames.], batch size: 19, lr: 1.92e-04
2022-05-07 07:34:35,411 INFO [train.py:715] (1/8) Epoch 11, batch 33300, loss[loss=0.1587, simple_loss=0.2251, pruned_loss=0.04611, over 4879.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2125, pruned_loss=0.03263, over 973290.22 frames.], batch size: 16, lr: 1.92e-04
2022-05-07 07:35:13,285 INFO [train.py:715] (1/8) Epoch 11, batch 33350, loss[loss=0.1441, simple_loss=0.212, pruned_loss=0.03804, over 4993.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2123, pruned_loss=0.03264, over 973275.55 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:35:51,706 INFO [train.py:715] (1/8) Epoch 11, batch 33400, loss[loss=0.1491, simple_loss=0.2218, pruned_loss=0.03825, over 4872.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.0331, over 972622.24 frames.], batch size: 32, lr: 1.92e-04
2022-05-07 07:36:30,393 INFO [train.py:715] (1/8) Epoch 11, batch 33450, loss[loss=0.1247, simple_loss=0.2064, pruned_loss=0.02148, over 4939.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2125, pruned_loss=0.03261, over 972592.25 frames.], batch size: 23, lr: 1.92e-04
2022-05-07 07:37:08,731 INFO [train.py:715] (1/8) Epoch 11, batch 33500, loss[loss=0.1432, simple_loss=0.2134, pruned_loss=0.03645, over 4858.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2125, pruned_loss=0.03241, over 972139.53 frames.], batch size: 32, lr: 1.92e-04
2022-05-07 07:37:47,175 INFO [train.py:715] (1/8) Epoch 11, batch 33550, loss[loss=0.1278, simple_loss=0.1948, pruned_loss=0.03039, over 4774.00 frames.], tot_loss[loss=0.139, simple_loss=0.2127, pruned_loss=0.03261, over 971433.00 frames.], batch size: 12, lr: 1.92e-04
2022-05-07 07:38:25,762 INFO [train.py:715] (1/8) Epoch 11, batch 33600, loss[loss=0.1427, simple_loss=0.2135, pruned_loss=0.03595, over 4887.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2118, pruned_loss=0.0323, over 972108.93 frames.], batch size: 22, lr: 1.92e-04
2022-05-07 07:39:04,166 INFO [train.py:715] (1/8) Epoch 11, batch 33650, loss[loss=0.1228, simple_loss=0.2022, pruned_loss=0.02172, over 4935.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2114, pruned_loss=0.03185, over 971756.30 frames.], batch size: 23, lr: 1.92e-04
2022-05-07 07:39:42,291 INFO [train.py:715] (1/8) Epoch 11, batch 33700, loss[loss=0.1385, simple_loss=0.206, pruned_loss=0.03546, over 4982.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03196, over 972800.00 frames.], batch size: 35, lr: 1.92e-04
2022-05-07 07:40:20,579 INFO [train.py:715] (1/8) Epoch 11, batch 33750, loss[loss=0.131, simple_loss=0.2043, pruned_loss=0.02889, over 4856.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2116, pruned_loss=0.03212, over 972235.12 frames.], batch size: 30, lr: 1.92e-04
2022-05-07 07:40:59,160 INFO [train.py:715] (1/8) Epoch 11, batch 33800, loss[loss=0.1296, simple_loss=0.2066, pruned_loss=0.02635, over 4795.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.032, over 972275.66 frames.], batch size: 12, lr: 1.92e-04
2022-05-07 07:41:37,148 INFO [train.py:715] (1/8) Epoch 11, batch 33850, loss[loss=0.1137, simple_loss=0.1896, pruned_loss=0.01893, over 4807.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03171, over 971633.22 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:42:15,182 INFO [train.py:715] (1/8) Epoch 11, batch 33900, loss[loss=0.1439, simple_loss=0.213, pruned_loss=0.0374, over 4986.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03202, over 972325.12 frames.], batch size: 33, lr: 1.92e-04
2022-05-07 07:42:53,935 INFO [train.py:715] (1/8) Epoch 11, batch 33950, loss[loss=0.1424, simple_loss=0.2118, pruned_loss=0.03653, over 4776.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2124, pruned_loss=0.03238, over 971932.16 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:43:32,253 INFO [train.py:715] (1/8) Epoch 11, batch 34000, loss[loss=0.1479, simple_loss=0.2101, pruned_loss=0.04287, over 4971.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03314, over 972159.94 frames.], batch size: 15, lr: 1.92e-04
2022-05-07 07:44:10,352 INFO [train.py:715] (1/8) Epoch 11, batch 34050, loss[loss=0.1675, simple_loss=0.2365, pruned_loss=0.04923, over 4954.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03278, over 971645.06 frames.], batch size: 39, lr: 1.92e-04
2022-05-07 07:44:48,874 INFO [train.py:715] (1/8) Epoch 11, batch 34100, loss[loss=0.1461, simple_loss=0.222, pruned_loss=0.03507, over 4979.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03217, over 972070.04 frames.], batch size: 28, lr: 1.92e-04
2022-05-07 07:45:27,615 INFO [train.py:715] (1/8) Epoch 11, batch 34150, loss[loss=0.1164, simple_loss=0.1943, pruned_loss=0.01921, over 4894.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03199, over 971604.16 frames.], batch size: 22, lr: 1.92e-04
2022-05-07 07:46:05,718 INFO [train.py:715] (1/8) Epoch 11, batch 34200, loss[loss=0.111, simple_loss=0.1878, pruned_loss=0.01706, over 4913.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03166, over 971687.95 frames.], batch size: 18, lr: 1.92e-04
2022-05-07 07:46:44,143 INFO [train.py:715] (1/8) Epoch 11, batch 34250, loss[loss=0.185, simple_loss=0.2637, pruned_loss=0.05318, over 4883.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2118, pruned_loss=0.03205, over 972101.08 frames.], batch size: 16, lr: 1.92e-04
2022-05-07 07:47:23,305 INFO [train.py:715] (1/8) Epoch 11, batch 34300, loss[loss=0.1135, simple_loss=0.1923, pruned_loss=0.01739, over 4868.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03227, over 972234.47 frames.], batch size: 20, lr: 1.92e-04
2022-05-07 07:48:01,583 INFO [train.py:715] (1/8) Epoch 11, batch 34350, loss[loss=0.1359, simple_loss=0.2057, pruned_loss=0.03302, over 4826.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03217, over 972459.89 frames.], batch size: 15, lr: 1.92e-04
2022-05-07 07:48:40,040 INFO [train.py:715] (1/8) Epoch 11, batch 34400, loss[loss=0.1091, simple_loss=0.1876, pruned_loss=0.01526, over 4740.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2112, pruned_loss=0.03208, over 972352.58 frames.], batch size: 16, lr: 1.92e-04
2022-05-07 07:49:18,692 INFO [train.py:715] (1/8) Epoch 11, batch 34450, loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02891, over 4829.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2116, pruned_loss=0.03206, over 972765.22 frames.], batch size: 15, lr: 1.92e-04
2022-05-07 07:49:57,868 INFO [train.py:715] (1/8) Epoch 11, batch 34500, loss[loss=0.1605, simple_loss=0.2268, pruned_loss=0.04705, over 4659.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03231, over 972810.71 frames.], batch size: 13, lr: 1.92e-04
2022-05-07 07:50:35,958 INFO [train.py:715] (1/8) Epoch 11, batch 34550, loss[loss=0.1344, simple_loss=0.2091, pruned_loss=0.02984, over 4817.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2118, pruned_loss=0.03226, over 971992.56 frames.], batch size: 13, lr: 1.92e-04
2022-05-07 07:51:12,744 INFO [train.py:715] (1/8) Epoch 11, batch 34600, loss[loss=0.1511, simple_loss=0.2272, pruned_loss=0.03745, over 4984.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.0322, over 972411.10 frames.], batch size: 25, lr: 1.92e-04
2022-05-07 07:51:50,533 INFO [train.py:715] (1/8) Epoch 11, batch 34650, loss[loss=0.1448, simple_loss=0.2225, pruned_loss=0.03351, over 4989.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03202, over 971608.26 frames.], batch size: 20, lr: 1.92e-04
2022-05-07 07:52:27,800 INFO [train.py:715] (1/8) Epoch 11, batch 34700, loss[loss=0.1304, simple_loss=0.2019, pruned_loss=0.02944, over 4825.00 frames.], tot_loss[loss=0.1368, simple_loss=0.21, pruned_loss=0.03176, over 971799.06 frames.], batch size: 13, lr: 1.92e-04
2022-05-07 07:53:04,319 INFO [train.py:715] (1/8) Epoch 11, batch 34750, loss[loss=0.1295, simple_loss=0.2106, pruned_loss=0.02418, over 4908.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03211, over 972871.57 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:53:39,314 INFO [train.py:715] (1/8) Epoch 11, batch 34800, loss[loss=0.1361, simple_loss=0.2122, pruned_loss=0.02999, over 4924.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03239, over 972023.40 frames.], batch size: 18, lr: 1.92e-04
2022-05-07 07:54:26,267 INFO [train.py:715] (1/8) Epoch 12, batch 0, loss[loss=0.1131, simple_loss=0.1894, pruned_loss=0.01835, over 4971.00 frames.], tot_loss[loss=0.1131, simple_loss=0.1894, pruned_loss=0.01835, over 4971.00 frames.], batch size: 25, lr: 1.85e-04
2022-05-07 07:55:04,629 INFO [train.py:715] (1/8) Epoch 12, batch 50, loss[loss=0.1477, simple_loss=0.2238, pruned_loss=0.03582, over 4960.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03315, over 218381.44 frames.], batch size: 24, lr: 1.85e-04
2022-05-07 07:55:42,694 INFO [train.py:715] (1/8) Epoch 12, batch 100, loss[loss=0.1296, simple_loss=0.2058, pruned_loss=0.02668, over 4797.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03224, over 385599.62 frames.], batch size: 21, lr: 1.85e-04
2022-05-07 07:56:21,321 INFO [train.py:715] (1/8) Epoch 12, batch 150, loss[loss=0.1206, simple_loss=0.1944, pruned_loss=0.02344, over 4957.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03241, over 515666.82 frames.], batch size: 29, lr: 1.85e-04
2022-05-07 07:56:59,067 INFO [train.py:715] (1/8) Epoch 12, batch 200, loss[loss=0.1449, simple_loss=0.219, pruned_loss=0.03538, over 4818.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03248, over 616980.67 frames.], batch size: 13, lr: 1.85e-04
2022-05-07 07:57:38,281 INFO [train.py:715] (1/8) Epoch 12, batch 250, loss[loss=0.1121, simple_loss=0.1813, pruned_loss=0.02141, over 4802.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2118, pruned_loss=0.03251, over 695466.39 frames.], batch size: 12, lr: 1.85e-04
2022-05-07 07:58:16,543 INFO [train.py:715] (1/8) Epoch 12, batch 300, loss[loss=0.1247, simple_loss=0.2027, pruned_loss=0.02342, over 4831.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2109, pruned_loss=0.03247, over 756805.92 frames.], batch size: 27, lr: 1.84e-04
2022-05-07 07:58:54,473 INFO [train.py:715] (1/8) Epoch 12, batch 350, loss[loss=0.122, simple_loss=0.2006, pruned_loss=0.02173, over 4839.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.03197, over 805622.04 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 07:59:32,946 INFO [train.py:715] (1/8) Epoch 12, batch 400, loss[loss=0.1561, simple_loss=0.2159, pruned_loss=0.0481, over 4946.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03257, over 841858.14 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:00:10,577 INFO [train.py:715] (1/8) Epoch 12, batch 450, loss[loss=0.1351, simple_loss=0.2066, pruned_loss=0.03179, over 4875.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03227, over 869943.65 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:00:48,790 INFO [train.py:715] (1/8) Epoch 12, batch 500, loss[loss=0.1351, simple_loss=0.2017, pruned_loss=0.03432, over 4981.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03231, over 892564.99 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:01:26,252 INFO [train.py:715] (1/8) Epoch 12, batch 550, loss[loss=0.167, simple_loss=0.2246, pruned_loss=0.05474, over 4915.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2129, pruned_loss=0.03284, over 910513.73 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:02:04,575 INFO [train.py:715] (1/8) Epoch 12, batch 600, loss[loss=0.1301, simple_loss=0.2116, pruned_loss=0.02426, over 4878.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2133, pruned_loss=0.03316, over 924616.24 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:02:41,619 INFO [train.py:715] (1/8) Epoch 12, batch 650, loss[loss=0.133, simple_loss=0.2001, pruned_loss=0.03292, over 4988.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03289, over 935340.29 frames.], batch size: 31, lr: 1.84e-04
2022-05-07 08:03:20,189 INFO [train.py:715] (1/8) Epoch 12, batch 700, loss[loss=0.1306, simple_loss=0.2087, pruned_loss=0.02628, over 4785.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03221, over 943456.49 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:03:58,810 INFO [train.py:715] (1/8) Epoch 12, batch 750, loss[loss=0.167, simple_loss=0.2404, pruned_loss=0.0468, over 4808.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2111, pruned_loss=0.03176, over 949617.03 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:04:37,571 INFO [train.py:715] (1/8) Epoch 12, batch 800, loss[loss=0.1604, simple_loss=0.2338, pruned_loss=0.04348, over 4949.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03244, over 955509.50 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:05:16,070 INFO [train.py:715] (1/8) Epoch 12, batch 850, loss[loss=0.1298, simple_loss=0.2118, pruned_loss=0.0239, over 4938.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03175, over 959327.45 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:05:54,154 INFO [train.py:715] (1/8) Epoch 12, batch 900, loss[loss=0.1437, simple_loss=0.2176, pruned_loss=0.03496, over 4952.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03134, over 962780.71 frames.], batch size: 23, lr: 1.84e-04
2022-05-07 08:06:32,489 INFO [train.py:715] (1/8) Epoch 12, batch 950, loss[loss=0.1352, simple_loss=0.2171, pruned_loss=0.02672, over 4826.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03164, over 965080.98 frames.], batch size: 25, lr: 1.84e-04
2022-05-07 08:07:09,850 INFO [train.py:715] (1/8) Epoch 12, batch 1000, loss[loss=0.1166, simple_loss=0.1904, pruned_loss=0.0214, over 4941.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2108, pruned_loss=0.03228, over 966683.32 frames.], batch size: 23, lr: 1.84e-04
2022-05-07 08:07:47,344 INFO [train.py:715] (1/8) Epoch 12, batch 1050, loss[loss=0.1602, simple_loss=0.2307, pruned_loss=0.04483, over 4816.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03244, over 967764.87 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:08:25,195 INFO [train.py:715] (1/8) Epoch 12, batch 1100, loss[loss=0.1377, simple_loss=0.2078, pruned_loss=0.03378, over 4714.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2107, pruned_loss=0.0323, over 968678.83 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:09:03,089 INFO [train.py:715] (1/8) Epoch 12, batch 1150, loss[loss=0.1227, simple_loss=0.1916, pruned_loss=0.02691, over 4820.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03186, over 968890.87 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:09:41,432 INFO [train.py:715] (1/8) Epoch 12, batch 1200, loss[loss=0.1292, simple_loss=0.2025, pruned_loss=0.02791, over 4796.00 frames.], tot_loss[loss=0.137, simple_loss=0.21, pruned_loss=0.03202, over 969304.37 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:10:18,739 INFO [train.py:715] (1/8) Epoch 12, batch 1250, loss[loss=0.1394, simple_loss=0.2014, pruned_loss=0.03869, over 4776.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03168, over 969623.09 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:10:56,844 INFO [train.py:715] (1/8) Epoch 12, batch 1300, loss[loss=0.1689, simple_loss=0.2395, pruned_loss=0.04915, over 4870.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03184, over 969770.17 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:11:33,988 INFO [train.py:715] (1/8) Epoch 12, batch 1350, loss[loss=0.1093, simple_loss=0.1837, pruned_loss=0.01745, over 4912.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03134, over 970001.28 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:12:12,111 INFO [train.py:715] (1/8) Epoch 12, batch 1400, loss[loss=0.1469, simple_loss=0.2175, pruned_loss=0.03809, over 4773.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03173, over 969526.18 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:12:49,762 INFO [train.py:715] (1/8) Epoch 12, batch 1450, loss[loss=0.1484, simple_loss=0.2246, pruned_loss=0.03607, over 4877.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.0316, over 970727.84 frames.], batch size: 32, lr: 1.84e-04
2022-05-07 08:13:27,680 INFO [train.py:715] (1/8) Epoch 12, batch 1500, loss[loss=0.1492, simple_loss=0.2285, pruned_loss=0.03493, over 4934.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03133, over 971012.93 frames.], batch size: 39, lr: 1.84e-04
2022-05-07 08:14:05,278 INFO [train.py:715] (1/8) Epoch 12, batch 1550, loss[loss=0.1115, simple_loss=0.1908, pruned_loss=0.01609, over 4779.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03148, over 972158.14 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:14:42,472 INFO [train.py:715] (1/8) Epoch 12, batch 1600, loss[loss=0.1139, simple_loss=0.1864, pruned_loss=0.02072, over 4763.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.03216, over 972582.06 frames.], batch size: 12, lr: 1.84e-04
2022-05-07 08:15:20,491 INFO [train.py:715] (1/8) Epoch 12, batch 1650, loss[loss=0.1316, simple_loss=0.1935, pruned_loss=0.0349, over 4845.00 frames.], tot_loss[loss=0.1372, simple_loss=0.211, pruned_loss=0.03172, over 973276.83 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:15:57,869 INFO [train.py:715] (1/8) Epoch 12, batch 1700, loss[loss=0.167, simple_loss=0.2372, pruned_loss=0.04846, over 4835.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03245, over 972450.12 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:16:35,313 INFO [train.py:715] (1/8) Epoch 12, batch 1750, loss[loss=0.1462, simple_loss=0.218, pruned_loss=0.03723, over 4965.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.0328, over 973289.90 frames.], batch size: 39, lr: 1.84e-04
2022-05-07 08:17:12,473 INFO [train.py:715] (1/8) Epoch 12, batch 1800, loss[loss=0.1217, simple_loss=0.1895, pruned_loss=0.02697, over 4976.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03271, over 973604.34 frames.], batch size: 24, lr: 1.84e-04
2022-05-07 08:17:50,169 INFO [train.py:715] (1/8) Epoch 12, batch 1850, loss[loss=0.1198, simple_loss=0.1954, pruned_loss=0.02212, over 4836.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03268, over 973437.91 frames.], batch size: 20, lr: 1.84e-04
2022-05-07 08:18:27,661 INFO [train.py:715] (1/8) Epoch 12, batch 1900, loss[loss=0.145, simple_loss=0.2192, pruned_loss=0.0354, over 4901.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03235, over 972598.74 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:19:05,287 INFO [train.py:715] (1/8) Epoch 12, batch 1950, loss[loss=0.1761, simple_loss=0.2484, pruned_loss=0.05194, over 4798.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03187, over 971407.59 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:19:43,104 INFO [train.py:715] (1/8) Epoch 12, batch 2000, loss[loss=0.1686, simple_loss=0.2315, pruned_loss=0.05286, over 4978.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03168, over 971835.99 frames.], batch size: 35, lr: 1.84e-04
2022-05-07 08:20:21,265 INFO [train.py:715] (1/8) Epoch 12, batch 2050, loss[loss=0.1245, simple_loss=0.2028, pruned_loss=0.02314, over 4822.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03212, over 972315.74 frames.], batch size: 26, lr: 1.84e-04
2022-05-07 08:20:59,324 INFO [train.py:715] (1/8) Epoch 12, batch 2100, loss[loss=0.1675, simple_loss=0.2325, pruned_loss=0.05127, over 4753.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03273, over 971714.56 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:21:36,625 INFO [train.py:715] (1/8) Epoch 12, batch 2150, loss[loss=0.1216, simple_loss=0.1998, pruned_loss=0.02175, over 4754.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2112, pruned_loss=0.03266, over 971958.98 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:22:14,598 INFO [train.py:715] (1/8) Epoch 12, batch 2200, loss[loss=0.1492, simple_loss=0.2289, pruned_loss=0.03471, over 4872.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03222, over 971743.20 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:22:52,523 INFO [train.py:715] (1/8) Epoch 12, batch 2250, loss[loss=0.1388, simple_loss=0.2152, pruned_loss=0.03119, over 4860.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2099, pruned_loss=0.03197, over 971862.14 frames.], batch size: 20, lr: 1.84e-04
2022-05-07 08:23:30,601 INFO [train.py:715] (1/8) Epoch 12, batch 2300, loss[loss=0.1447, simple_loss=0.2212, pruned_loss=0.03409, over 4794.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2089, pruned_loss=0.03131, over 970948.77 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:24:07,788 INFO [train.py:715] (1/8) Epoch 12, batch 2350, loss[loss=0.1284, simple_loss=0.2007, pruned_loss=0.02803, over 4925.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2087, pruned_loss=0.0314, over 971172.87 frames.], batch size: 29, lr: 1.84e-04
2022-05-07 08:24:45,332 INFO [train.py:715] (1/8) Epoch 12, batch 2400, loss[loss=0.1441, simple_loss=0.2105, pruned_loss=0.03881, over 4876.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2095, pruned_loss=0.03176, over 972061.65 frames.], batch size: 22, lr: 1.84e-04
2022-05-07 08:25:23,253 INFO [train.py:715] (1/8) Epoch 12, batch 2450, loss[loss=0.1054, simple_loss=0.172, pruned_loss=0.01939, over 4785.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2091, pruned_loss=0.03123, over 970811.12 frames.], batch size: 12, lr: 1.84e-04
2022-05-07 08:26:00,045 INFO [train.py:715] (1/8) Epoch 12, batch 2500, loss[loss=0.1469, simple_loss=0.2326, pruned_loss=0.03061, over 4968.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03175, over 972400.97 frames.], batch size: 39, lr: 1.84e-04
2022-05-07 08:26:38,143 INFO [train.py:715] (1/8) Epoch 12, batch 2550, loss[loss=0.168, simple_loss=0.2347, pruned_loss=0.05061, over 4868.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2094, pruned_loss=0.03147, over 972966.17 frames.], batch size: 20, lr: 1.84e-04
2022-05-07 08:27:15,554 INFO [train.py:715] (1/8) Epoch 12, batch 2600, loss[loss=0.1651, simple_loss=0.2358, pruned_loss=0.04721, over 4847.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03121, over 972797.41 frames.], batch size: 32, lr: 1.84e-04
2022-05-07 08:27:54,390 INFO [train.py:715] (1/8) Epoch 12, batch 2650, loss[loss=0.1291, simple_loss=0.2027, pruned_loss=0.02771, over 4803.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03148, over 972372.03 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:28:32,761 INFO [train.py:715] (1/8) Epoch 12, batch 2700, loss[loss=0.1569, simple_loss=0.2292, pruned_loss=0.04228, over 4799.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03165, over 971990.00 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:29:11,552 INFO [train.py:715] (1/8) Epoch 12, batch 2750, loss[loss=0.1452, simple_loss=0.2144, pruned_loss=0.03798, over 4749.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03113, over 972045.25 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:29:50,428 INFO [train.py:715] (1/8) Epoch 12, batch 2800, loss[loss=0.1433, simple_loss=0.2234, pruned_loss=0.0316, over 4935.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2107, pruned_loss=0.03136, over 972279.99 frames.], batch size: 29, lr: 1.84e-04
2022-05-07 08:30:28,439 INFO [train.py:715] (1/8) Epoch 12, batch 2850, loss[loss=0.12, simple_loss=0.1916, pruned_loss=0.02417, over 4862.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03174, over 972915.88 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:31:07,107 INFO [train.py:715] (1/8) Epoch 12, batch 2900, loss[loss=0.1216, simple_loss=0.204, pruned_loss=0.01965, over 4943.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2103, pruned_loss=0.03131, over 973446.67 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:31:45,581 INFO [train.py:715] (1/8) Epoch 12, batch 2950, loss[loss=0.1351, simple_loss=0.2028, pruned_loss=0.0337, over 4870.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03139, over 973027.05 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:32:24,293 INFO [train.py:715] (1/8) Epoch 12, batch 3000, loss[loss=0.1135, simple_loss=0.1775, pruned_loss=0.02481, over 4816.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03147, over 973504.84 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:32:24,294 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 08:32:33,757 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1056, simple_loss=0.1896, pruned_loss=0.01082, over 914524.00 frames.
2022-05-07 08:33:11,809 INFO [train.py:715] (1/8) Epoch 12, batch 3050, loss[loss=0.1302, simple_loss=0.2133, pruned_loss=0.02349, over 4815.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03149, over 972908.84 frames.], batch size: 27, lr: 1.84e-04
2022-05-07 08:33:49,495 INFO [train.py:715] (1/8) Epoch 12, batch 3100, loss[loss=0.1481, simple_loss=0.2336, pruned_loss=0.03129, over 4806.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03189, over 972124.13 frames.], batch size: 25, lr: 1.84e-04
2022-05-07 08:34:27,408 INFO [train.py:715] (1/8) Epoch 12, batch 3150, loss[loss=0.1809, simple_loss=0.2528, pruned_loss=0.0545, over 4848.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.03193, over 972381.44 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:35:05,546 INFO [train.py:715] (1/8) Epoch 12, batch 3200, loss[loss=0.151, simple_loss=0.2294, pruned_loss=0.03627, over 4976.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03203, over 973449.26 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:35:43,250 INFO [train.py:715] (1/8) Epoch 12, batch 3250, loss[loss=0.1484, simple_loss=0.2319, pruned_loss=0.03246, over 4786.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03176, over 973674.42 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:36:21,486 INFO [train.py:715] (1/8) Epoch 12, batch 3300, loss[loss=0.127, simple_loss=0.2107, pruned_loss=0.02166, over 4944.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03181, over 973533.80 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:36:59,238 INFO [train.py:715] (1/8) Epoch 12, batch 3350, loss[loss=0.1304, simple_loss=0.2081, pruned_loss=0.02637, over 4863.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03167, over 972502.20 frames.], batch size: 32, lr: 1.84e-04
2022-05-07 08:37:37,377 INFO [train.py:715] (1/8) Epoch 12, batch 3400, loss[loss=0.132, simple_loss=0.2043, pruned_loss=0.0299, over 4783.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03183, over 972288.14 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:38:14,962 INFO [train.py:715] (1/8) Epoch 12, batch 3450, loss[loss=0.1326, simple_loss=0.2123, pruned_loss=0.02647, over 4758.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03167, over 972533.69 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:38:52,885 INFO [train.py:715] (1/8) Epoch 12, batch 3500, loss[loss=0.1501, simple_loss=0.233, pruned_loss=0.03358, over 4906.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03152, over 973093.64 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:39:31,089 INFO [train.py:715] (1/8) Epoch 12, batch 3550, loss[loss=0.1277, simple_loss=0.2007, pruned_loss=0.02738, over 4694.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03141, over 972537.90 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:40:08,796 INFO [train.py:715] (1/8) Epoch 12, batch 3600, loss[loss=0.1204, simple_loss=0.1936, pruned_loss=0.02361, over 4979.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03169, over 972600.67 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:40:46,535 INFO [train.py:715] (1/8) Epoch 12, batch 3650, loss[loss=0.1083, simple_loss=0.1766, pruned_loss=0.02004, over 4837.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03154, over 972438.65 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:41:24,471 INFO [train.py:715] (1/8) Epoch 12, batch 3700, loss[loss=0.1537, simple_loss=0.2353, pruned_loss=0.03603, over 4822.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03199, over 971870.09 frames.], batch size: 25, lr: 1.84e-04
2022-05-07 08:42:02,375 INFO [train.py:715] (1/8) Epoch 12, batch 3750, loss[loss=0.1302, simple_loss=0.2023, pruned_loss=0.02909, over 4758.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.0318, over 971803.49 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:42:40,470 INFO [train.py:715] (1/8) Epoch 12, batch 3800, loss[loss=0.1465, simple_loss=0.2222, pruned_loss=0.03538, over 4793.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03206, over 971678.60 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:43:18,090 INFO [train.py:715] (1/8) Epoch 12, batch 3850, loss[loss=0.1253, simple_loss=0.2093, pruned_loss=0.02061, over 4851.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03234, over 972279.12 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:43:55,567 INFO [train.py:715] (1/8) Epoch 12, batch 3900, loss[loss=0.1553, simple_loss=0.2184, pruned_loss=0.04609, over 4867.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03228, over 972387.10 frames.], batch size: 39, lr: 1.84e-04
2022-05-07 08:44:33,442 INFO [train.py:715] (1/8) Epoch 12, batch 3950, loss[loss=0.1118, simple_loss=0.1922, pruned_loss=0.01572, over 4777.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03222, over 972558.69 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:45:11,214 INFO [train.py:715] (1/8) Epoch 12, batch 4000, loss[loss=0.1451, simple_loss=0.2159, pruned_loss=0.03717, over 4901.00 frames.], tot_loss[loss=0.138, simple_loss=0.2109, pruned_loss=0.03251, over 972643.83 frames.], batch size: 38, lr: 1.84e-04
2022-05-07 08:45:49,155 INFO [train.py:715] (1/8) Epoch 12, batch 4050, loss[loss=0.1311, simple_loss=0.2099, pruned_loss=0.02619, over 4850.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.0322, over 973716.69 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:46:27,046 INFO [train.py:715] (1/8) Epoch 12, batch 4100, loss[loss=0.1249, simple_loss=0.2096, pruned_loss=0.02013, over 4737.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03195, over 973031.04 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:47:05,071 INFO [train.py:715] (1/8) Epoch 12, batch 4150, loss[loss=0.1224, simple_loss=0.1916, pruned_loss=0.02663, over 4870.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03205, over 973341.19 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:47:43,034 INFO [train.py:715] (1/8) Epoch 12, batch 4200, loss[loss=0.1517, simple_loss=0.224, pruned_loss=0.03975, over 4959.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.03213, over 973432.82 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:48:20,659 INFO [train.py:715] (1/8) Epoch 12, batch 4250, loss[loss=0.1264, simple_loss=0.2059, pruned_loss=0.02342, over 4701.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03264, over 973508.28 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:48:58,348 INFO [train.py:715] (1/8) Epoch 12, batch 4300, loss[loss=0.12, simple_loss=0.1953, pruned_loss=0.02236, over 4775.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2103, pruned_loss=0.03219, over 973700.39 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:49:37,516 INFO [train.py:715] (1/8) Epoch 12, batch 4350, loss[loss=0.1145, simple_loss=0.1821, pruned_loss=0.0235, over 4891.00 frames.], tot_loss[loss=0.137, simple_loss=0.21, pruned_loss=0.03197, over 972651.22 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:50:16,265 INFO [train.py:715] (1/8) Epoch 12, batch 4400, loss[loss=0.1264, simple_loss=0.2002, pruned_loss=0.0263, over 4857.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.03167, over 972041.31 frames.], batch size: 32, lr: 1.84e-04
2022-05-07 08:50:54,766 INFO [train.py:715] (1/8) Epoch 12, batch 4450, loss[loss=0.133, simple_loss=0.2018, pruned_loss=0.03209, over 4874.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03178, over 971939.18 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:51:33,204 INFO [train.py:715] (1/8) Epoch 12, batch 4500, loss[loss=0.1182, simple_loss=0.1944, pruned_loss=0.02099, over 4847.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03186, over 972211.20 frames.], batch size: 20, lr: 1.84e-04
2022-05-07 08:52:12,282 INFO [train.py:715] (1/8) Epoch 12, batch 4550, loss[loss=0.1297, simple_loss=0.1973, pruned_loss=0.03104, over 4789.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03211, over 971152.67 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:52:50,492 INFO [train.py:715] (1/8) Epoch 12, batch 4600, loss[loss=0.1567, simple_loss=0.2235, pruned_loss=0.04501, over 4982.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03226, over 970982.89 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:53:29,040 INFO [train.py:715] (1/8) Epoch 12, batch 4650, loss[loss=0.1261, simple_loss=0.2, pruned_loss=0.02608, over 4822.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03215, over 972117.67 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:54:07,731 INFO [train.py:715] (1/8) Epoch 12, batch 4700, loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02928, over 4808.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03152, over 972045.06 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:54:46,300 INFO [train.py:715] (1/8) Epoch 12, batch 4750, loss[loss=0.1318, simple_loss=0.1927, pruned_loss=0.03545, over 4927.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03135, over 972153.11 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:55:25,000 INFO [train.py:715] (1/8) Epoch 12, batch 4800, loss[loss=0.1576, simple_loss=0.237, pruned_loss=0.03914, over 4802.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.0315, over 972194.97 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:56:03,563 INFO [train.py:715] (1/8) Epoch 12, batch 4850, loss[loss=0.1494, simple_loss=0.2239, pruned_loss=0.03747, over 4794.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03156, over 973205.90 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:56:42,595 INFO [train.py:715] (1/8) Epoch 12, batch 4900, loss[loss=0.1163, simple_loss=0.1922, pruned_loss=0.02014, over 4883.00 frames.], tot_loss[loss=0.1368, simple_loss=0.211, pruned_loss=0.03132, over 972841.67 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 08:57:20,602 INFO [train.py:715] (1/8) Epoch 12, batch 4950, loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02911, over 4918.00 frames.], tot_loss[loss=0.136, simple_loss=0.2101, pruned_loss=0.03095, over 973096.21 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 08:57:58,208 INFO [train.py:715] (1/8) Epoch 12, batch 5000, loss[loss=0.1414, simple_loss=0.2157, pruned_loss=0.03348, over 4836.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2108, pruned_loss=0.03103, over 972696.30 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 08:58:36,393 INFO [train.py:715] (1/8) Epoch 12, batch 5050, loss[loss=0.1392, simple_loss=0.2079, pruned_loss=0.03527, over 4992.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2113, pruned_loss=0.03148, over 972328.39 frames.], batch size: 28, lr: 1.83e-04
2022-05-07 08:59:13,985 INFO [train.py:715] (1/8) Epoch 12, batch 5100, loss[loss=0.1358, simple_loss=0.2146, pruned_loss=0.02848, over 4824.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2109, pruned_loss=0.03142, over 972467.21 frames.], batch size: 26, lr: 1.83e-04
2022-05-07 08:59:52,114 INFO [train.py:715] (1/8) Epoch 12, batch 5150, loss[loss=0.1379, simple_loss=0.2072, pruned_loss=0.03434, over 4991.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2104, pruned_loss=0.03108, over 972458.26 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:00:30,013 INFO [train.py:715] (1/8) Epoch 12, batch 5200, loss[loss=0.1308, simple_loss=0.2114, pruned_loss=0.02508, over 4831.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03142, over 973629.09 frames.], batch size: 25, lr: 1.83e-04
2022-05-07 09:01:08,128 INFO [train.py:715] (1/8) Epoch 12, batch 5250, loss[loss=0.1478, simple_loss=0.2101, pruned_loss=0.0428, over 4870.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2107, pruned_loss=0.03125, over 973514.88 frames.], batch size: 32, lr: 1.83e-04
2022-05-07 09:01:45,994 INFO [train.py:715] (1/8) Epoch 12, batch 5300, loss[loss=0.1288, simple_loss=0.205, pruned_loss=0.02634, over 4836.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2103, pruned_loss=0.03104, over 973637.45 frames.], batch size: 26, lr: 1.83e-04
2022-05-07 09:02:24,111 INFO [train.py:715] (1/8) Epoch 12, batch 5350, loss[loss=0.1178, simple_loss=0.1969, pruned_loss=0.01932, over 4981.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2105, pruned_loss=0.03133, over 973322.72 frames.], batch size: 24, lr: 1.83e-04
2022-05-07 09:03:02,673 INFO [train.py:715] (1/8) Epoch 12, batch 5400, loss[loss=0.1137, simple_loss=0.1837, pruned_loss=0.02182, over 4935.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03169, over 973368.66 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:03:40,515 INFO [train.py:715] (1/8) Epoch 12, batch 5450, loss[loss=0.1572, simple_loss=0.2297, pruned_loss=0.04235, over 4968.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03149, over 973412.67 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:04:18,719 INFO [train.py:715] (1/8) Epoch 12, batch 5500, loss[loss=0.1326, simple_loss=0.2, pruned_loss=0.03266, over 4761.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03125, over 973518.85 frames.], batch size: 12, lr: 1.83e-04
2022-05-07 09:04:56,509 INFO [train.py:715] (1/8) Epoch 12, batch 5550, loss[loss=0.1406, simple_loss=0.2194, pruned_loss=0.03093, over 4942.00 frames.], tot_loss[loss=0.1369, simple_loss=0.21, pruned_loss=0.03191, over 974297.92 frames.], batch size: 21, lr: 1.83e-04
2022-05-07 09:05:35,155 INFO [train.py:715] (1/8) Epoch 12, batch 5600, loss[loss=0.1402, simple_loss=0.2211, pruned_loss=0.02965, over 4962.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2099, pruned_loss=0.03191, over 974180.83 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:06:12,951 INFO [train.py:715] (1/8) Epoch 12, batch 5650, loss[loss=0.1115, simple_loss=0.1941, pruned_loss=0.01441, over 4988.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2098, pruned_loss=0.03174, over 973640.02 frames.], batch size: 28, lr: 1.83e-04
2022-05-07 09:06:50,902 INFO [train.py:715] (1/8) Epoch 12, batch 5700, loss[loss=0.1247, simple_loss=0.2078, pruned_loss=0.02075, over 4808.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03169, over 973261.33 frames.], batch size: 21, lr: 1.83e-04
2022-05-07 09:07:29,809 INFO [train.py:715] (1/8) Epoch 12, batch 5750, loss[loss=0.1402, simple_loss=0.2145, pruned_loss=0.033, over 4783.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03218, over 973555.75 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:08:07,981 INFO [train.py:715] (1/8) Epoch 12, batch 5800, loss[loss=0.1469, simple_loss=0.2213, pruned_loss=0.03625, over 4701.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03249, over 973456.60 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:08:46,181 INFO [train.py:715] (1/8) Epoch 12, batch 5850, loss[loss=0.1138, simple_loss=0.1962, pruned_loss=0.01568, over 4800.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03236, over 973135.51 frames.], batch size: 21, lr: 1.83e-04
2022-05-07 09:09:24,396 INFO [train.py:715] (1/8) Epoch 12, batch 5900, loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03139, over 4753.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2108, pruned_loss=0.03211, over 972757.75 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:10:02,494 INFO [train.py:715] (1/8) Epoch 12, batch 5950, loss[loss=0.1094, simple_loss=0.188, pruned_loss=0.01534, over 4975.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03199, over 972279.77 frames.], batch size: 25, lr: 1.83e-04
2022-05-07 09:10:40,377 INFO [train.py:715] (1/8) Epoch 12, batch 6000, loss[loss=0.1748, simple_loss=0.2422, pruned_loss=0.05365, over 4829.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03241, over 972815.25 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:10:40,377 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 09:10:49,853 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1057, simple_loss=0.1897, pruned_loss=0.01086, over 914524.00 frames.
2022-05-07 09:11:28,484 INFO [train.py:715] (1/8) Epoch 12, batch 6050, loss[loss=0.1576, simple_loss=0.2281, pruned_loss=0.04352, over 4968.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03231, over 973609.45 frames.], batch size: 24, lr: 1.83e-04
2022-05-07 09:12:07,188 INFO [train.py:715] (1/8) Epoch 12, batch 6100, loss[loss=0.1613, simple_loss=0.2223, pruned_loss=0.05016, over 4845.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.0325, over 973469.84 frames.], batch size: 30, lr: 1.83e-04
2022-05-07 09:12:46,265 INFO [train.py:715] (1/8) Epoch 12, batch 6150, loss[loss=0.1179, simple_loss=0.1907, pruned_loss=0.02255, over 4816.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03206, over 974147.31 frames.], batch size: 27, lr: 1.83e-04
2022-05-07 09:13:24,047 INFO [train.py:715] (1/8) Epoch 12, batch 6200, loss[loss=0.1304, simple_loss=0.198, pruned_loss=0.0314, over 4903.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03248, over 974168.22 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:14:02,112 INFO [train.py:715] (1/8) Epoch 12, batch 6250, loss[loss=0.1434, simple_loss=0.2155, pruned_loss=0.03567, over 4941.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.0326, over 973796.64 frames.], batch size: 23, lr: 1.83e-04
2022-05-07 09:14:42,632 INFO [train.py:715] (1/8) Epoch 12, batch 6300, loss[loss=0.1487, simple_loss=0.2102, pruned_loss=0.04365, over 4969.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03276, over 973449.06 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:15:20,413 INFO [train.py:715] (1/8) Epoch 12, batch 6350, loss[loss=0.1474, simple_loss=0.2254, pruned_loss=0.03467, over 4837.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2121, pruned_loss=0.03268, over 973787.93 frames.], batch size: 26, lr: 1.83e-04
2022-05-07 09:15:58,261 INFO [train.py:715] (1/8) Epoch 12, batch 6400, loss[loss=0.1262, simple_loss=0.1993, pruned_loss=0.02653, over 4707.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.0323, over 972409.63 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:16:36,190 INFO [train.py:715] (1/8) Epoch 12, batch 6450, loss[loss=0.1487, simple_loss=0.215, pruned_loss=0.04114, over 4966.00 frames.], tot_loss[loss=0.1385, simple_loss=0.212, pruned_loss=0.03249, over 973176.85 frames.], batch size: 35, lr: 1.83e-04
2022-05-07 09:17:14,183 INFO [train.py:715] (1/8) Epoch 12, batch 6500, loss[loss=0.1526, simple_loss=0.2263, pruned_loss=0.03952, over 4812.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2118, pruned_loss=0.03251, over 972625.57 frames.], batch size: 25, lr: 1.83e-04
2022-05-07 09:17:51,826 INFO [train.py:715] (1/8) Epoch 12, batch 6550, loss[loss=0.1252, simple_loss=0.1949, pruned_loss=0.02777, over 4920.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03242, over 972290.77 frames.], batch size: 35, lr: 1.83e-04
2022-05-07 09:18:29,934 INFO [train.py:715] (1/8) Epoch 12, batch 6600, loss[loss=0.1718, simple_loss=0.2437, pruned_loss=0.04993, over 4776.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03246, over 972738.56 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:19:08,081 INFO [train.py:715] (1/8) Epoch 12, batch 6650, loss[loss=0.1314, simple_loss=0.2097, pruned_loss=0.02652, over 4864.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2113, pruned_loss=0.03244, over 972117.10 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:19:46,572 INFO [train.py:715] (1/8) Epoch 12, batch 6700, loss[loss=0.1269, simple_loss=0.2024, pruned_loss=0.0257, over 4747.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03186, over 971774.12 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:20:24,048 INFO [train.py:715] (1/8) Epoch 12, batch 6750, loss[loss=0.1038, simple_loss=0.1766, pruned_loss=0.01551, over 4779.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03154, over 972180.88 frames.], batch size: 12, lr: 1.83e-04
2022-05-07 09:21:02,176 INFO [train.py:715] (1/8) Epoch 12, batch 6800, loss[loss=0.1468, simple_loss=0.2271, pruned_loss=0.03322, over 4983.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03185, over 971891.78 frames.], batch size: 28, lr: 1.83e-04
2022-05-07 09:21:40,241 INFO [train.py:715] (1/8) Epoch 12, batch 6850, loss[loss=0.1117, simple_loss=0.1915, pruned_loss=0.01596, over 4888.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03169, over 972691.23 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:22:18,036 INFO [train.py:715] (1/8) Epoch 12, batch 6900, loss[loss=0.1108, simple_loss=0.1804, pruned_loss=0.0206, over 4834.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03113, over 972722.65 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:22:56,144 INFO [train.py:715] (1/8) Epoch 12, batch 6950, loss[loss=0.1218, simple_loss=0.1993, pruned_loss=0.02221, over 4986.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03126, over 972357.08 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:23:34,139 INFO [train.py:715] (1/8) Epoch 12, batch 7000, loss[loss=0.1293, simple_loss=0.2018, pruned_loss=0.02846, over 4803.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03133, over 972386.36 frames.], batch size: 21, lr: 1.83e-04
2022-05-07 09:24:12,558 INFO [train.py:715] (1/8) Epoch 12, batch 7050, loss[loss=0.1424, simple_loss=0.2093, pruned_loss=0.03778, over 4780.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03187, over 972484.84 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:24:50,038 INFO [train.py:715] (1/8) Epoch 12, batch 7100, loss[loss=0.1232, simple_loss=0.1921, pruned_loss=0.02709, over 4969.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03176, over 972676.10 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:25:28,614 INFO [train.py:715] (1/8) Epoch 12, batch 7150, loss[loss=0.1182, simple_loss=0.1839, pruned_loss=0.02626, over 4640.00 frames.], tot_loss[loss=0.1358, simple_loss=0.209, pruned_loss=0.03131, over 972253.17 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:26:06,442 INFO [train.py:715] (1/8) Epoch 12, batch 7200, loss[loss=0.1614, simple_loss=0.2358, pruned_loss=0.04348, over 4768.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2091, pruned_loss=0.03126, over 972515.37 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:26:44,293 INFO [train.py:715] (1/8) Epoch 12, batch 7250, loss[loss=0.1462, simple_loss=0.2159, pruned_loss=0.0383, over 4782.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03204, over 971591.23 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:27:22,554 INFO [train.py:715] (1/8) Epoch 12, batch 7300, loss[loss=0.1484, simple_loss=0.2275, pruned_loss=0.03462, over 4974.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03224, over 972453.59 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:28:00,294 INFO [train.py:715] (1/8) Epoch 12, batch 7350, loss[loss=0.1395, simple_loss=0.2189, pruned_loss=0.03008, over 4944.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2107, pruned_loss=0.03234, over 972326.60 frames.], batch size: 23, lr: 1.83e-04
2022-05-07 09:28:38,318 INFO [train.py:715] (1/8) Epoch 12, batch 7400, loss[loss=0.1275, simple_loss=0.2052, pruned_loss=0.0249, over 4855.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03195, over 971651.01 frames.], batch size: 32, lr: 1.83e-04
2022-05-07 09:29:16,073 INFO [train.py:715] (1/8) Epoch 12, batch 7450, loss[loss=0.1198, simple_loss=0.1876, pruned_loss=0.02601, over 4990.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2103, pruned_loss=0.03213, over 972427.54 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:29:54,164 INFO [train.py:715] (1/8) Epoch 12, batch 7500, loss[loss=0.1707, simple_loss=0.2335, pruned_loss=0.05396, over 4952.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2094, pruned_loss=0.03158, over 971268.60 frames.], batch size: 39, lr: 1.83e-04
2022-05-07 09:30:32,183 INFO [train.py:715] (1/8) Epoch 12, batch 7550, loss[loss=0.1289, simple_loss=0.2131, pruned_loss=0.02236, over 4971.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03165, over 971763.23 frames.], batch size: 24, lr: 1.83e-04
2022-05-07 09:31:10,033 INFO [train.py:715] (1/8) Epoch 12, batch 7600, loss[loss=0.1872, simple_loss=0.2581, pruned_loss=0.05821, over 4855.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03168, over 972069.76 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:31:48,262 INFO [train.py:715] (1/8) Epoch 12, batch 7650, loss[loss=0.1143, simple_loss=0.188, pruned_loss=0.02025, over 4771.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.0315, over 971818.23 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:32:26,440 INFO [train.py:715] (1/8) Epoch 12, batch 7700, loss[loss=0.1443, simple_loss=0.2191, pruned_loss=0.03478, over 4909.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03161, over 972063.29 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:33:04,637 INFO [train.py:715] (1/8) Epoch 12, batch 7750, loss[loss=0.107, simple_loss=0.1779, pruned_loss=0.01805, over 4773.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03156, over 971865.20 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:33:42,404 INFO [train.py:715] (1/8) Epoch 12, batch 7800, loss[loss=0.1312, simple_loss=0.2064, pruned_loss=0.02802, over 4868.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03143, over 971639.84 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:34:20,597 INFO [train.py:715] (1/8) Epoch 12, batch 7850, loss[loss=0.1553, simple_loss=0.2264, pruned_loss=0.04203, over 4961.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.0312, over 972419.60 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:34:58,403 INFO [train.py:715] (1/8) Epoch 12, batch 7900, loss[loss=0.1378, simple_loss=0.2143, pruned_loss=0.03058, over 4956.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03138, over 972852.02 frames.], batch size: 35, lr: 1.83e-04
2022-05-07 09:35:36,654 INFO [train.py:715] (1/8) Epoch 12, batch 7950, loss[loss=0.1533, simple_loss=0.233, pruned_loss=0.0368, over 4838.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03159, over 972429.73 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:36:14,626 INFO [train.py:715] (1/8) Epoch 12, batch 8000, loss[loss=0.1451, simple_loss=0.2214, pruned_loss=0.03436, over 4934.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03195, over 972318.56 frames.], batch size: 29, lr: 1.83e-04
2022-05-07 09:36:53,083 INFO [train.py:715] (1/8) Epoch 12, batch 8050, loss[loss=0.1301, simple_loss=0.2051, pruned_loss=0.02755, over 4863.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.0319, over 971653.46 frames.], batch size: 32, lr: 1.83e-04
2022-05-07 09:37:31,434 INFO [train.py:715] (1/8) Epoch 12, batch 8100, loss[loss=0.149, simple_loss=0.2213, pruned_loss=0.0384, over 4940.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03153, over 971007.41 frames.], batch size: 23, lr: 1.83e-04
2022-05-07 09:38:09,023 INFO [train.py:715] (1/8) Epoch 12, batch 8150, loss[loss=0.1701, simple_loss=0.237, pruned_loss=0.0516, over 4797.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03195, over 970794.96 frames.], batch size: 21, lr: 1.83e-04
2022-05-07 09:38:47,286 INFO [train.py:715] (1/8) Epoch 12, batch 8200, loss[loss=0.1318, simple_loss=0.2038, pruned_loss=0.02993, over 4770.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03229, over 972088.87 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:39:25,282 INFO [train.py:715] (1/8) Epoch 12, batch 8250, loss[loss=0.1442, simple_loss=0.2141, pruned_loss=0.03717, over 4832.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2101, pruned_loss=0.032, over 972052.80 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:40:03,014 INFO [train.py:715] (1/8) Epoch 12, batch 8300, loss[loss=0.1245, simple_loss=0.2018, pruned_loss=0.02362, over 4738.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2105, pruned_loss=0.0324, over 972350.00 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:40:41,115 INFO [train.py:715] (1/8) Epoch 12, batch 8350, loss[loss=0.12, simple_loss=0.1898, pruned_loss=0.02514, over 4821.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2106, pruned_loss=0.03224, over 971844.48 frames.], batch size: 26, lr: 1.83e-04
2022-05-07 09:41:19,302 INFO [train.py:715] (1/8) Epoch 12, batch 8400, loss[loss=0.1104, simple_loss=0.184, pruned_loss=0.01835, over 4799.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03182, over 972381.89 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:41:57,374 INFO [train.py:715] (1/8) Epoch 12, batch 8450, loss[loss=0.1266, simple_loss=0.2064, pruned_loss=0.02337, over 4846.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03154, over 972148.78 frames.], batch size: 26, lr: 1.83e-04
2022-05-07 09:42:34,903 INFO [train.py:715] (1/8) Epoch 12, batch 8500, loss[loss=0.1286, simple_loss=0.1901, pruned_loss=0.0336, over 4894.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.0315, over 972057.52 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:43:13,189 INFO [train.py:715] (1/8) Epoch 12, batch 8550, loss[loss=0.145, simple_loss=0.2184, pruned_loss=0.03577, over 4866.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03187, over 971559.94 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:43:51,189 INFO [train.py:715] (1/8) Epoch 12, batch 8600, loss[loss=0.1539, simple_loss=0.2194, pruned_loss=0.04413, over 4823.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2124, pruned_loss=0.03267, over 971489.39 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:44:28,880 INFO [train.py:715] (1/8) Epoch 12, batch 8650, loss[loss=0.1543, simple_loss=0.2353, pruned_loss=0.03666, over 4910.00 frames.], tot_loss[loss=0.139, simple_loss=0.2123, pruned_loss=0.03284, over 970899.16 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:45:07,105 INFO [train.py:715] (1/8) Epoch 12, batch 8700, loss[loss=0.1355, simple_loss=0.1947, pruned_loss=0.03817, over 4825.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.03305, over 972157.96 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:45:45,272 INFO [train.py:715] (1/8) Epoch 12, batch 8750, loss[loss=0.1278, simple_loss=0.2015, pruned_loss=0.0271, over 4920.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03267, over 972292.54 frames.], batch size: 29, lr: 1.83e-04
2022-05-07 09:46:23,701 INFO [train.py:715] (1/8) Epoch 12, batch 8800, loss[loss=0.0989, simple_loss=0.1628, pruned_loss=0.01749, over 4787.00 frames.], tot_loss[loss=0.1384, simple_loss=0.212, pruned_loss=0.03241, over 972100.53 frames.], batch size: 12, lr: 1.83e-04
2022-05-07 09:47:01,615 INFO [train.py:715] (1/8) Epoch 12, batch 8850, loss[loss=0.133, simple_loss=0.2002, pruned_loss=0.0329, over 4859.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03219, over 972991.28 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:47:40,622 INFO [train.py:715] (1/8) Epoch 12, batch 8900, loss[loss=0.154, simple_loss=0.2348, pruned_loss=0.03658, over 4885.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.0316, over 972831.94 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:48:20,160 INFO [train.py:715] (1/8) Epoch 12, batch 8950, loss[loss=0.1549, simple_loss=0.2209, pruned_loss=0.04442, over 4857.00 frames.], tot_loss[loss=0.138, simple_loss=0.2117, pruned_loss=0.03213, over 973134.92 frames.], batch size: 32, lr: 1.83e-04
2022-05-07 09:48:58,104 INFO [train.py:715] (1/8) Epoch 12, batch 9000, loss[loss=0.1312, simple_loss=0.2078, pruned_loss=0.02723, over 4859.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2123, pruned_loss=0.03224, over 973998.55 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:48:58,105 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 09:49:07,573 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1057, simple_loss=0.1898, pruned_loss=0.01084, over 914524.00 frames.
2022-05-07 09:49:45,346 INFO [train.py:715] (1/8) Epoch 12, batch 9050, loss[loss=0.1338, simple_loss=0.2156, pruned_loss=0.02598, over 4806.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03243, over 974347.03 frames.], batch size: 25, lr: 1.83e-04
2022-05-07 09:50:23,565 INFO [train.py:715] (1/8) Epoch 12, batch 9100, loss[loss=0.1222, simple_loss=0.2006, pruned_loss=0.02196, over 4866.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03232, over 974354.50 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:51:01,824 INFO [train.py:715] (1/8) Epoch 12, batch 9150, loss[loss=0.1417, simple_loss=0.2246, pruned_loss=0.02941, over 4889.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2122, pruned_loss=0.03255, over 974101.91 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:51:39,542 INFO [train.py:715] (1/8) Epoch 12, batch 9200, loss[loss=0.1199, simple_loss=0.1949, pruned_loss=0.02242, over 4819.00 frames.], tot_loss[loss=0.1384, simple_loss=0.212, pruned_loss=0.03239, over 973586.08 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:52:17,395 INFO [train.py:715] (1/8) Epoch 12, batch 9250, loss[loss=0.1521, simple_loss=0.2199, pruned_loss=0.04211, over 4751.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03221, over 973100.57 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:52:55,475 INFO [train.py:715] (1/8) Epoch 12, batch 9300, loss[loss=0.1369, simple_loss=0.2157, pruned_loss=0.02906, over 4919.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.0324, over 973611.75 frames.], batch size: 23, lr: 1.83e-04
2022-05-07 09:53:33,066 INFO [train.py:715] (1/8) Epoch 12, batch 9350, loss[loss=0.1253, simple_loss=0.1957, pruned_loss=0.02747, over 4822.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03226, over 972283.81 frames.], batch size: 12, lr: 1.83e-04
2022-05-07 09:54:10,845 INFO [train.py:715] (1/8) Epoch 12, batch 9400, loss[loss=0.1376, simple_loss=0.2158, pruned_loss=0.02969, over 4879.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.03234, over 972404.00 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:54:48,556 INFO [train.py:715] (1/8) Epoch 12, batch 9450, loss[loss=0.1175, simple_loss=0.199, pruned_loss=0.01806, over 4811.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.0323, over 971331.17 frames.], batch size: 26, lr: 1.83e-04
2022-05-07 09:55:26,598 INFO [train.py:715] (1/8) Epoch 12, batch 9500, loss[loss=0.1269, simple_loss=0.1949, pruned_loss=0.0295, over 4978.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03216, over 971946.87 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:56:04,151 INFO [train.py:715] (1/8) Epoch 12, batch 9550, loss[loss=0.1284, simple_loss=0.2078, pruned_loss=0.0245, over 4838.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03227, over 972290.10 frames.], batch size: 26, lr: 1.82e-04
2022-05-07 09:56:41,642 INFO [train.py:715] (1/8) Epoch 12, batch 9600, loss[loss=0.1288, simple_loss=0.2194, pruned_loss=0.01914, over 4770.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03178, over 972379.54 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 09:57:19,885 INFO [train.py:715] (1/8) Epoch 12, batch 9650, loss[loss=0.1235, simple_loss=0.1967, pruned_loss=0.02513, over 4763.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.03162, over 971991.68 frames.], batch size: 12, lr: 1.82e-04
2022-05-07 09:57:57,757 INFO [train.py:715] (1/8) Epoch 12, batch 9700, loss[loss=0.1472, simple_loss=0.2182, pruned_loss=0.03807, over 4904.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03125, over 972200.32 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 09:58:35,536 INFO [train.py:715] (1/8) Epoch 12, batch 9750, loss[loss=0.1327, simple_loss=0.2115, pruned_loss=0.02692, over 4683.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03182, over 972810.45 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 09:59:13,485 INFO [train.py:715] (1/8) Epoch 12, batch 9800, loss[loss=0.1093, simple_loss=0.1801, pruned_loss=0.01924, over 4958.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.0318, over 973199.20 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 09:59:52,002 INFO [train.py:715] (1/8) Epoch 12, batch 9850, loss[loss=0.1679, simple_loss=0.2342, pruned_loss=0.05081, over 4946.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03202, over 972754.73 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:00:29,634 INFO [train.py:715] (1/8) Epoch 12, batch 9900, loss[loss=0.1359, simple_loss=0.2125, pruned_loss=0.02967, over 4895.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03184, over 973586.61 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:01:07,865 INFO [train.py:715] (1/8) Epoch 12, batch 9950, loss[loss=0.1338, simple_loss=0.2229, pruned_loss=0.02235, over 4951.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.0318, over 973398.83 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 10:01:46,619 INFO [train.py:715] (1/8) Epoch 12, batch 10000, loss[loss=0.1512, simple_loss=0.2267, pruned_loss=0.03789, over 4834.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2106, pruned_loss=0.03191, over 973574.38 frames.], batch size: 26, lr: 1.82e-04
2022-05-07 10:02:25,151 INFO [train.py:715] (1/8) Epoch 12, batch 10050, loss[loss=0.1334, simple_loss=0.2168, pruned_loss=0.02503, over 4797.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03175, over 973361.31 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:03:03,489 INFO [train.py:715] (1/8) Epoch 12, batch 10100, loss[loss=0.1175, simple_loss=0.1893, pruned_loss=0.02281, over 4995.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03182, over 973552.64 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:03:41,897 INFO [train.py:715] (1/8) Epoch 12, batch 10150, loss[loss=0.1503, simple_loss=0.2269, pruned_loss=0.03683, over 4811.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03138, over 973159.57 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:04:20,551 INFO [train.py:715] (1/8) Epoch 12, batch 10200, loss[loss=0.1569, simple_loss=0.2272, pruned_loss=0.04334, over 4875.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03152, over 972694.59 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:04:57,864 INFO [train.py:715] (1/8) Epoch 12, batch 10250, loss[loss=0.1043, simple_loss=0.1703, pruned_loss=0.01912, over 4837.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.0315, over 972765.68 frames.], batch size: 13, lr: 1.82e-04
2022-05-07 10:05:36,038 INFO [train.py:715] (1/8) Epoch 12, batch 10300, loss[loss=0.122, simple_loss=0.2026, pruned_loss=0.02064, over 4798.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03171, over 972521.63 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:06:14,199 INFO [train.py:715] (1/8) Epoch 12, batch 10350, loss[loss=0.1273, simple_loss=0.2082, pruned_loss=0.0232, over 4896.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03165, over 972050.69 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:06:52,243 INFO [train.py:715] (1/8) Epoch 12, batch 10400, loss[loss=0.1331, simple_loss=0.1968, pruned_loss=0.03471, over 4738.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03187, over 972221.21 frames.], batch size: 12, lr: 1.82e-04
2022-05-07 10:07:29,800 INFO [train.py:715] (1/8) Epoch 12, batch 10450, loss[loss=0.145, simple_loss=0.2233, pruned_loss=0.03333, over 4788.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2113, pruned_loss=0.03208, over 972269.53 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:08:07,730 INFO [train.py:715] (1/8) Epoch 12, batch 10500, loss[loss=0.1489, simple_loss=0.2229, pruned_loss=0.0374, over 4750.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.03222, over 972900.91 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:08:46,138 INFO [train.py:715] (1/8) Epoch 12, batch 10550, loss[loss=0.1548, simple_loss=0.2335, pruned_loss=0.03806, over 4811.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2105, pruned_loss=0.03129, over 972589.17 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:09:23,511 INFO [train.py:715] (1/8) Epoch 12, batch 10600, loss[loss=0.123, simple_loss=0.1976, pruned_loss=0.02417, over 4843.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2118, pruned_loss=0.03188, over 972526.29 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:10:01,494 INFO [train.py:715] (1/8) Epoch 12, batch 10650, loss[loss=0.1432, simple_loss=0.2236, pruned_loss=0.03143, over 4986.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2115, pruned_loss=0.032, over 972461.64 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:10:39,356 INFO [train.py:715] (1/8) Epoch 12, batch 10700, loss[loss=0.1244, simple_loss=0.2003, pruned_loss=0.02422, over 4814.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2114, pruned_loss=0.03196, over 972853.41 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:11:16,859 INFO [train.py:715] (1/8) Epoch 12, batch 10750, loss[loss=0.1556, simple_loss=0.2209, pruned_loss=0.04515, over 4784.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03211, over 973778.67 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:11:54,746 INFO [train.py:715] (1/8) Epoch 12, batch 10800, loss[loss=0.1516, simple_loss=0.2242, pruned_loss=0.03954, over 4961.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2103, pruned_loss=0.03209, over 972994.46 frames.], batch size: 39, lr: 1.82e-04
2022-05-07 10:12:32,736 INFO [train.py:715] (1/8) Epoch 12, batch 10850, loss[loss=0.1284, simple_loss=0.1917, pruned_loss=0.03257, over 4852.00 frames.], tot_loss[loss=0.138, simple_loss=0.2109, pruned_loss=0.03254, over 973015.94 frames.], batch size: 20, lr: 1.82e-04
2022-05-07 10:13:11,527 INFO [train.py:715] (1/8) Epoch 12, batch 10900, loss[loss=0.1653, simple_loss=0.2409, pruned_loss=0.04488, over 4817.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2109, pruned_loss=0.03243, over 972759.83 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:13:48,733 INFO [train.py:715] (1/8) Epoch 12, batch 10950, loss[loss=0.1411, simple_loss=0.2157, pruned_loss=0.03331, over 4799.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03252, over 972255.88 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:14:26,878 INFO [train.py:715] (1/8) Epoch 12, batch 11000, loss[loss=0.1578, simple_loss=0.2137, pruned_loss=0.05096, over 4863.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2108, pruned_loss=0.03236, over 971585.74 frames.], batch size: 30, lr: 1.82e-04
2022-05-07 10:15:05,148 INFO [train.py:715] (1/8) Epoch 12, batch 11050, loss[loss=0.1221, simple_loss=0.1968, pruned_loss=0.02369, over 4800.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03191, over 971408.69 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 10:15:42,772 INFO [train.py:715] (1/8) Epoch 12, batch 11100, loss[loss=0.147, simple_loss=0.2178, pruned_loss=0.0381, over 4780.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03198, over 971089.41 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:16:21,274 INFO [train.py:715] (1/8) Epoch 12, batch 11150, loss[loss=0.1291, simple_loss=0.2069, pruned_loss=0.02563, over 4778.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03225, over 970593.37 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:16:58,892 INFO [train.py:715] (1/8) Epoch 12, batch 11200, loss[loss=0.1116, simple_loss=0.183, pruned_loss=0.02005, over 4955.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2096, pruned_loss=0.03177, over 970986.75 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:17:36,990 INFO [train.py:715] (1/8) Epoch 12, batch 11250, loss[loss=0.126, simple_loss=0.2064, pruned_loss=0.02281, over 4935.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.03234, over 971950.54 frames.], batch size: 29, lr: 1.82e-04
2022-05-07 10:18:14,709 INFO [train.py:715] (1/8) Epoch 12, batch 11300, loss[loss=0.1774, simple_loss=0.2438, pruned_loss=0.05549, over 4948.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03242, over 972554.27 frames.], batch size: 35, lr: 1.82e-04
2022-05-07 10:18:51,981 INFO [train.py:715] (1/8) Epoch 12, batch 11350, loss[loss=0.1584, simple_loss=0.2349, pruned_loss=0.04094, over 4962.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03208, over 973375.33 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 10:19:30,202 INFO [train.py:715] (1/8) Epoch 12, batch 11400, loss[loss=0.1221, simple_loss=0.1948, pruned_loss=0.02469, over 4816.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.032, over 973920.96 frames.], batch size: 26, lr: 1.82e-04
2022-05-07 10:20:07,742 INFO [train.py:715] (1/8) Epoch 12, batch 11450, loss[loss=0.1591, simple_loss=0.2309, pruned_loss=0.04367, over 4977.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03152, over 973798.74 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:20:45,261 INFO [train.py:715] (1/8) Epoch 12, batch 11500, loss[loss=0.1321, simple_loss=0.2041, pruned_loss=0.03001, over 4964.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03156, over 973223.93 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 10:21:23,004 INFO [train.py:715] (1/8) Epoch 12, batch 11550, loss[loss=0.1553, simple_loss=0.2273, pruned_loss=0.04163, over 4832.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03121, over 972775.42 frames.], batch size: 12, lr: 1.82e-04
2022-05-07 10:22:01,395 INFO [train.py:715] (1/8) Epoch 12, batch 11600, loss[loss=0.1314, simple_loss=0.1997, pruned_loss=0.03149, over 4813.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03153, over 972837.83 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:22:38,880 INFO [train.py:715] (1/8) Epoch 12, batch 11650, loss[loss=0.1441, simple_loss=0.2136, pruned_loss=0.03725, over 4873.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03165, over 971978.52 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:23:16,093 INFO [train.py:715] (1/8) Epoch 12, batch 11700, loss[loss=0.1356, simple_loss=0.2055, pruned_loss=0.0329, over 4784.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.0317, over 972511.69 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:23:53,749 INFO [train.py:715] (1/8) Epoch 12, batch 11750, loss[loss=0.1515, simple_loss=0.2253, pruned_loss=0.03882, over 4902.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2089, pruned_loss=0.03142, over 972083.79 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:24:31,084 INFO [train.py:715] (1/8) Epoch 12, batch 11800, loss[loss=0.1652, simple_loss=0.2357, pruned_loss=0.04734, over 4895.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03172, over 971905.06 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:25:08,778 INFO [train.py:715] (1/8) Epoch 12, batch 11850, loss[loss=0.1132, simple_loss=0.1866, pruned_loss=0.01993, over 4783.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03136, over 971009.31 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:25:46,625 INFO [train.py:715] (1/8) Epoch 12, batch 11900, loss[loss=0.1425, simple_loss=0.2227, pruned_loss=0.03114, over 4981.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03122, over 971251.00 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:26:24,517 INFO [train.py:715] (1/8) Epoch 12, batch 11950, loss[loss=0.1209, simple_loss=0.2021, pruned_loss=0.01988, over 4857.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.0314, over 970774.84 frames.], batch size: 20, lr: 1.82e-04
2022-05-07 10:27:01,979 INFO [train.py:715] (1/8) Epoch 12, batch 12000, loss[loss=0.1309, simple_loss=0.2071, pruned_loss=0.02737, over 4888.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03147, over 970838.66 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:27:01,979 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 10:27:11,324 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1058, simple_loss=0.1897, pruned_loss=0.01095, over 914524.00 frames.
2022-05-07 10:27:50,032 INFO [train.py:715] (1/8) Epoch 12, batch 12050, loss[loss=0.1484, simple_loss=0.2141, pruned_loss=0.04136, over 4959.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2104, pruned_loss=0.03226, over 970770.70 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 10:28:29,110 INFO [train.py:715] (1/8) Epoch 12, batch 12100, loss[loss=0.1235, simple_loss=0.2043, pruned_loss=0.0214, over 4978.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.032, over 972017.95 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 10:29:08,865 INFO [train.py:715] (1/8) Epoch 12, batch 12150, loss[loss=0.1458, simple_loss=0.2191, pruned_loss=0.03628, over 4850.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2105, pruned_loss=0.0325, over 973065.78 frames.], batch size: 32, lr: 1.82e-04
2022-05-07 10:29:47,130 INFO [train.py:715] (1/8) Epoch 12, batch 12200, loss[loss=0.1461, simple_loss=0.2213, pruned_loss=0.03541, over 4950.00 frames.], tot_loss[loss=0.1373, simple_loss=0.21, pruned_loss=0.03227, over 972365.14 frames.], batch size: 39, lr: 1.82e-04
2022-05-07 10:30:25,402 INFO [train.py:715] (1/8) Epoch 12, batch 12250, loss[loss=0.1313, simple_loss=0.1988, pruned_loss=0.03191, over 4981.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2094, pruned_loss=0.03188, over 971522.93 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 10:31:04,255 INFO [train.py:715] (1/8) Epoch 12, batch 12300, loss[loss=0.144, simple_loss=0.2098, pruned_loss=0.03911, over 4966.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.03206, over 972069.38 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:31:42,833 INFO [train.py:715] (1/8) Epoch 12, batch 12350, loss[loss=0.1116, simple_loss=0.1905, pruned_loss=0.01637, over 4829.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.03254, over 971502.23 frames.], batch size: 26, lr: 1.82e-04
2022-05-07 10:32:20,262 INFO [train.py:715] (1/8) Epoch 12, batch 12400, loss[loss=0.1477, simple_loss=0.2192, pruned_loss=0.03814, over 4886.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2104, pruned_loss=0.03222, over 971553.36 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:32:57,989 INFO [train.py:715] (1/8) Epoch 12, batch 12450, loss[loss=0.1484, simple_loss=0.2245, pruned_loss=0.03619, over 4824.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03188, over 972351.84 frames.], batch size: 27, lr: 1.82e-04
2022-05-07 10:33:36,210 INFO [train.py:715] (1/8) Epoch 12, batch 12500, loss[loss=0.1057, simple_loss=0.186, pruned_loss=0.0127, over 4902.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03219, over 971904.21 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:34:13,318 INFO [train.py:715] (1/8) Epoch 12, batch 12550, loss[loss=0.1376, simple_loss=0.2018, pruned_loss=0.03671, over 4829.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2106, pruned_loss=0.03195, over 971706.13 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:34:51,156 INFO [train.py:715] (1/8) Epoch 12, batch 12600, loss[loss=0.1297, simple_loss=0.2075, pruned_loss=0.02588, over 4918.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03206, over 972262.30 frames.], batch size: 29, lr: 1.82e-04
2022-05-07 10:35:28,921 INFO [train.py:715] (1/8) Epoch 12, batch 12650, loss[loss=0.1428, simple_loss=0.2244, pruned_loss=0.0306, over 4878.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03229, over 971891.46 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:36:06,675 INFO [train.py:715] (1/8) Epoch 12, batch 12700, loss[loss=0.1129, simple_loss=0.1959, pruned_loss=0.01493, over 4885.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.03223, over 971386.35 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:36:44,126 INFO [train.py:715] (1/8) Epoch 12, batch 12750, loss[loss=0.177, simple_loss=0.2484, pruned_loss=0.05283, over 4775.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2112, pruned_loss=0.03164, over 971489.51 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:37:22,155 INFO [train.py:715] (1/8) Epoch 12, batch 12800, loss[loss=0.119, simple_loss=0.1902, pruned_loss=0.02392, over 4932.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03117, over 971659.01 frames.], batch size: 35, lr: 1.82e-04
2022-05-07 10:38:00,582 INFO [train.py:715] (1/8) Epoch 12, batch 12850, loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02944, over 4982.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03058, over 972131.96 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:38:37,911 INFO [train.py:715] (1/8) Epoch 12, batch 12900, loss[loss=0.1271, simple_loss=0.1993, pruned_loss=0.0274, over 4935.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03059, over 971913.19 frames.], batch size: 23, lr: 1.82e-04
2022-05-07 10:39:15,002 INFO [train.py:715] (1/8) Epoch 12, batch 12950, loss[loss=0.13, simple_loss=0.2142, pruned_loss=0.02287, over 4814.00 frames.], tot_loss[loss=0.1357, simple_loss=0.21, pruned_loss=0.03073, over 971443.89 frames.], batch size: 26, lr: 1.82e-04
2022-05-07 10:39:52,999 INFO [train.py:715] (1/8) Epoch 12, batch 13000, loss[loss=0.1127, simple_loss=0.1919, pruned_loss=0.01682, over 4777.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2104, pruned_loss=0.03108, over 971827.45 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:40:30,780 INFO [train.py:715] (1/8) Epoch 12, batch 13050, loss[loss=0.1241, simple_loss=0.189, pruned_loss=0.02957, over 4778.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2105, pruned_loss=0.03118, over 972065.84 frames.], batch size: 12, lr: 1.82e-04
2022-05-07 10:41:08,533 INFO [train.py:715] (1/8) Epoch 12, batch 13100, loss[loss=0.1501, simple_loss=0.2314, pruned_loss=0.03438, over 4929.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2109, pruned_loss=0.03143, over 971804.59 frames.], batch size: 23, lr: 1.82e-04
2022-05-07 10:41:46,123 INFO [train.py:715] (1/8) Epoch 12, batch 13150, loss[loss=0.1417, simple_loss=0.2172, pruned_loss=0.03313, over 4938.00 frames.], tot_loss[loss=0.137, simple_loss=0.2108, pruned_loss=0.03155, over 971526.64 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:42:23,791 INFO [train.py:715] (1/8) Epoch 12, batch 13200, loss[loss=0.1428, simple_loss=0.2163, pruned_loss=0.03468, over 4894.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03181, over 972370.34 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:43:01,014 INFO [train.py:715] (1/8) Epoch 12, batch 13250, loss[loss=0.1648, simple_loss=0.2279, pruned_loss=0.05079, over 4815.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.0322, over 972081.22 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:43:38,189 INFO [train.py:715] (1/8) Epoch 12, batch 13300, loss[loss=0.1547, simple_loss=0.23, pruned_loss=0.03974, over 4915.00 frames.], tot_loss[loss=0.1382, simple_loss=0.212, pruned_loss=0.03226, over 971701.62 frames.], batch size: 23, lr: 1.82e-04
2022-05-07 10:44:16,078 INFO [train.py:715] (1/8) Epoch 12, batch 13350, loss[loss=0.1484, simple_loss=0.2144, pruned_loss=0.04122, over 4934.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2126, pruned_loss=0.0325, over 972796.03 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:44:54,317 INFO [train.py:715] (1/8) Epoch 12, batch 13400, loss[loss=0.1561, simple_loss=0.2194, pruned_loss=0.04637, over 4806.00 frames.], tot_loss[loss=0.138, simple_loss=0.2117, pruned_loss=0.03213, over 971586.68 frames.], batch size: 13, lr: 1.82e-04
2022-05-07 10:45:31,693 INFO [train.py:715] (1/8) Epoch 12, batch 13450, loss[loss=0.1274, simple_loss=0.1961, pruned_loss=0.02932, over 4791.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03251, over 971393.62 frames.], batch size: 12, lr: 1.82e-04
2022-05-07 10:46:09,031 INFO [train.py:715] (1/8) Epoch 12, batch 13500, loss[loss=0.1261, simple_loss=0.2016, pruned_loss=0.02528, over 4707.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2118, pruned_loss=0.03242, over 971716.10 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:46:47,475 INFO [train.py:715] (1/8) Epoch 12, batch 13550, loss[loss=0.1265, simple_loss=0.1993, pruned_loss=0.0269, over 4699.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03189, over 972181.70 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:47:24,688 INFO [train.py:715] (1/8) Epoch 12, batch 13600, loss[loss=0.1472, simple_loss=0.2231, pruned_loss=0.03563, over 4768.00 frames.], tot_loss[loss=0.137, simple_loss=0.2109, pruned_loss=0.03158, over 972802.60 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:48:02,574 INFO [train.py:715] (1/8) Epoch 12, batch 13650, loss[loss=0.1299, simple_loss=0.2109, pruned_loss=0.02444, over 4968.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2106, pruned_loss=0.03115, over 971913.23 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:48:40,711 INFO [train.py:715] (1/8) Epoch 12, batch 13700, loss[loss=0.1294, simple_loss=0.2165, pruned_loss=0.02114, over 4908.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03134, over 972283.47 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:49:18,441 INFO [train.py:715] (1/8) Epoch 12, batch 13750, loss[loss=0.1315, simple_loss=0.1896, pruned_loss=0.03667, over 4774.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03149, over 971267.79 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:49:56,511 INFO [train.py:715] (1/8) Epoch 12, batch 13800, loss[loss=0.1725, simple_loss=0.2423, pruned_loss=0.05137, over 4876.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03159, over 971593.48 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:50:34,449 INFO [train.py:715] (1/8) Epoch 12, batch 13850, loss[loss=0.1389, simple_loss=0.2138, pruned_loss=0.03199, over 4779.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03179, over 971851.88 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:51:12,973 INFO [train.py:715] (1/8) Epoch 12, batch 13900, loss[loss=0.1286, simple_loss=0.2075, pruned_loss=0.02481, over 4771.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03173, over 971700.24 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:51:50,186 INFO [train.py:715] (1/8) Epoch 12, batch 13950, loss[loss=0.1289, simple_loss=0.203, pruned_loss=0.02747, over 4913.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03131, over 972010.57 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:52:28,378 INFO [train.py:715] (1/8) Epoch 12, batch 14000, loss[loss=0.1259, simple_loss=0.195, pruned_loss=0.02844, over 4928.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03152, over 972240.96 frames.], batch size: 23, lr: 1.82e-04
2022-05-07 10:53:06,890 INFO [train.py:715] (1/8) Epoch 12, batch 14050, loss[loss=0.13, simple_loss=0.2094, pruned_loss=0.02531, over 4852.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03176, over 972452.61 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:53:44,260 INFO [train.py:715] (1/8) Epoch 12, batch 14100, loss[loss=0.1159, simple_loss=0.18, pruned_loss=0.02588, over 4876.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2116, pruned_loss=0.03182, over 972327.10 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:54:21,696 INFO [train.py:715] (1/8) Epoch 12, batch 14150, loss[loss=0.1365, simple_loss=0.2128, pruned_loss=0.03013, over 4959.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.03186, over 971420.00 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:55:00,103 INFO [train.py:715] (1/8) Epoch 12, batch 14200, loss[loss=0.1503, simple_loss=0.2219, pruned_loss=0.03934, over 4908.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03193, over 971541.84 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:55:38,425 INFO [train.py:715] (1/8) Epoch 12, batch 14250, loss[loss=0.1575, simple_loss=0.2299, pruned_loss=0.04255, over 4805.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03191, over 971142.55 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 10:56:18,100 INFO [train.py:715] (1/8) Epoch 12, batch 14300, loss[loss=0.1475, simple_loss=0.2225, pruned_loss=0.03627, over 4841.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03209, over 972023.31 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 10:56:56,598 INFO [train.py:715] (1/8) Epoch 12, batch 14350, loss[loss=0.1407, simple_loss=0.2115, pruned_loss=0.03494, over 4927.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03221, over 972561.28 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 10:57:35,984 INFO [train.py:715] (1/8) Epoch 12, batch 14400, loss[loss=0.1487, simple_loss=0.2242, pruned_loss=0.03667, over 4943.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03159, over 972784.69 frames.], batch size: 21, lr: 1.81e-04
2022-05-07 10:58:14,128 INFO [train.py:715] (1/8) Epoch 12, batch 14450, loss[loss=0.171, simple_loss=0.2369, pruned_loss=0.05253, over 4956.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.03175, over 971530.26 frames.], batch size: 35, lr: 1.81e-04
2022-05-07 10:58:53,050 INFO [train.py:715] (1/8) Epoch 12, batch 14500, loss[loss=0.1349, simple_loss=0.2024, pruned_loss=0.03377, over 4914.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03159, over 971005.62 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 10:59:32,162 INFO [train.py:715] (1/8) Epoch 12, batch 14550, loss[loss=0.1398, simple_loss=0.2111, pruned_loss=0.03425, over 4792.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03202, over 970746.54 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:00:11,046 INFO [train.py:715] (1/8) Epoch 12, batch 14600, loss[loss=0.1324, simple_loss=0.2132, pruned_loss=0.02578, over 4736.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2116, pruned_loss=0.03199, over 971336.66 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:00:49,664 INFO [train.py:715] (1/8) Epoch 12, batch 14650, loss[loss=0.137, simple_loss=0.208, pruned_loss=0.03302, over 4848.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2112, pruned_loss=0.03168, over 971468.07 frames.], batch size: 30, lr: 1.81e-04
2022-05-07 11:01:27,545 INFO [train.py:715] (1/8) Epoch 12, batch 14700, loss[loss=0.1591, simple_loss=0.2255, pruned_loss=0.04635, over 4952.00 frames.], tot_loss[loss=0.137, simple_loss=0.2108, pruned_loss=0.0316, over 971544.46 frames.], batch size: 24, lr: 1.81e-04
2022-05-07 11:02:06,067 INFO [train.py:715] (1/8) Epoch 12, batch 14750, loss[loss=0.127, simple_loss=0.2077, pruned_loss=0.02312, over 4933.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03214, over 971777.70 frames.], batch size: 23, lr: 1.81e-04
2022-05-07 11:02:43,583 INFO [train.py:715] (1/8) Epoch 12, batch 14800, loss[loss=0.1269, simple_loss=0.199, pruned_loss=0.02738, over 4644.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03213, over 971349.23 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:03:21,328 INFO [train.py:715] (1/8) Epoch 12, batch 14850, loss[loss=0.1622, simple_loss=0.2308, pruned_loss=0.04676, over 4694.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03212, over 970645.80 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:03:59,689 INFO [train.py:715] (1/8) Epoch 12, batch 14900, loss[loss=0.1347, simple_loss=0.2116, pruned_loss=0.02885, over 4851.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03148, over 970931.55 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 11:04:38,249 INFO [train.py:715] (1/8) Epoch 12, batch 14950, loss[loss=0.1137, simple_loss=0.1851, pruned_loss=0.0212, over 4986.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03165, over 971544.83 frames.], batch size: 28, lr: 1.81e-04
2022-05-07 11:05:15,441 INFO [train.py:715] (1/8) Epoch 12, batch 15000, loss[loss=0.1519, simple_loss=0.2211, pruned_loss=0.04131, over 4976.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03189, over 971478.88 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:05:15,442 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 11:05:25,070 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1057, simple_loss=0.1897, pruned_loss=0.01083, over 914524.00 frames.
2022-05-07 11:06:02,919 INFO [train.py:715] (1/8) Epoch 12, batch 15050, loss[loss=0.1284, simple_loss=0.2077, pruned_loss=0.02459, over 4741.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03219, over 971827.35 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:06:41,209 INFO [train.py:715] (1/8) Epoch 12, batch 15100, loss[loss=0.1489, simple_loss=0.2179, pruned_loss=0.03996, over 4815.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03257, over 971554.93 frames.], batch size: 26, lr: 1.81e-04
2022-05-07 11:07:20,389 INFO [train.py:715] (1/8) Epoch 12, batch 15150, loss[loss=0.1191, simple_loss=0.1926, pruned_loss=0.02274, over 4910.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03225, over 971808.94 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:07:58,862 INFO [train.py:715] (1/8) Epoch 12, batch 15200, loss[loss=0.1285, simple_loss=0.1955, pruned_loss=0.03075, over 4848.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03241, over 971236.38 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 11:08:37,649 INFO [train.py:715] (1/8) Epoch 12, batch 15250, loss[loss=0.1658, simple_loss=0.2288, pruned_loss=0.05138, over 4823.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.0324, over 971103.22 frames.], batch size: 26, lr: 1.81e-04
2022-05-07 11:09:16,357 INFO [train.py:715] (1/8) Epoch 12, batch 15300, loss[loss=0.1472, simple_loss=0.2271, pruned_loss=0.03369, over 4774.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03191, over 971609.50 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:09:54,569 INFO [train.py:715] (1/8) Epoch 12, batch 15350, loss[loss=0.1153, simple_loss=0.1894, pruned_loss=0.02058, over 4845.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03158, over 972221.62 frames.], batch size: 30, lr: 1.81e-04
2022-05-07 11:10:31,953 INFO [train.py:715] (1/8) Epoch 12, batch 15400, loss[loss=0.1274, simple_loss=0.207, pruned_loss=0.0239, over 4954.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03233, over 972768.36 frames.], batch size: 29, lr: 1.81e-04
2022-05-07 11:11:09,690 INFO [train.py:715] (1/8) Epoch 12, batch 15450, loss[loss=0.1072, simple_loss=0.1896, pruned_loss=0.01241, over 4936.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03242, over 972273.97 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:11:48,439 INFO [train.py:715] (1/8) Epoch 12, batch 15500, loss[loss=0.1028, simple_loss=0.1799, pruned_loss=0.01287, over 4793.00 frames.], tot_loss[loss=0.137, simple_loss=0.2101, pruned_loss=0.03197, over 972349.28 frames.], batch size: 24, lr: 1.81e-04
2022-05-07 11:12:26,567 INFO [train.py:715] (1/8) Epoch 12, batch 15550, loss[loss=0.175, simple_loss=0.2452, pruned_loss=0.05245, over 4767.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.03209, over 972759.38 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:13:04,460 INFO [train.py:715] (1/8) Epoch 12, batch 15600, loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02897, over 4867.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.0319, over 972275.04 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 11:13:42,239 INFO [train.py:715] (1/8) Epoch 12, batch 15650, loss[loss=0.1466, simple_loss=0.2211, pruned_loss=0.03601, over 4788.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03216, over 972259.04 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:14:20,675 INFO [train.py:715] (1/8) Epoch 12, batch 15700, loss[loss=0.1549, simple_loss=0.2251, pruned_loss=0.04233, over 4860.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03178, over 971960.56 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 11:14:58,387 INFO [train.py:715] (1/8) Epoch 12, batch 15750, loss[loss=0.1238, simple_loss=0.1918, pruned_loss=0.02788, over 4787.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03169, over 972435.05 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:15:36,107 INFO [train.py:715] (1/8) Epoch 12, batch 15800, loss[loss=0.1115, simple_loss=0.1857, pruned_loss=0.01868, over 4988.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2096, pruned_loss=0.03177, over 973223.58 frames.], batch size: 20, lr: 1.81e-04
2022-05-07 11:16:14,197 INFO [train.py:715] (1/8) Epoch 12, batch 15850, loss[loss=0.1518, simple_loss=0.2249, pruned_loss=0.03937, over 4920.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.0313, over 973021.77 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:16:51,696 INFO [train.py:715] (1/8) Epoch 12, batch 15900, loss[loss=0.1294, simple_loss=0.2068, pruned_loss=0.02594, over 4944.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03169, over 973369.87 frames.], batch size: 29, lr: 1.81e-04
2022-05-07 11:17:29,509 INFO [train.py:715] (1/8) Epoch 12, batch 15950, loss[loss=0.1628, simple_loss=0.2265, pruned_loss=0.04957, over 4961.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.0317, over 972717.98 frames.], batch size: 21, lr: 1.81e-04
2022-05-07 11:18:07,571 INFO [train.py:715] (1/8) Epoch 12, batch 16000, loss[loss=0.1269, simple_loss=0.1975, pruned_loss=0.02809, over 4909.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2095, pruned_loss=0.03155, over 972821.62 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:18:47,329 INFO [train.py:715] (1/8) Epoch 12, batch 16050, loss[loss=0.1645, simple_loss=0.2326, pruned_loss=0.04818, over 4966.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2095, pruned_loss=0.03181, over 973078.71 frames.], batch size: 39, lr: 1.81e-04
2022-05-07 11:19:25,280 INFO [train.py:715] (1/8) Epoch 12, batch 16100, loss[loss=0.1365, simple_loss=0.217, pruned_loss=0.02803, over 4854.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03152, over 972606.94 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:20:04,190 INFO [train.py:715] (1/8) Epoch 12, batch 16150, loss[loss=0.1364, simple_loss=0.2105, pruned_loss=0.03114, over 4938.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.0314, over 972067.01 frames.], batch size: 29, lr: 1.81e-04
2022-05-07 11:20:43,070 INFO [train.py:715] (1/8) Epoch 12, batch 16200, loss[loss=0.1182, simple_loss=0.175, pruned_loss=0.03069, over 4647.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03171, over 973089.16 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:21:21,842 INFO [train.py:715] (1/8) Epoch 12, batch 16250, loss[loss=0.1165, simple_loss=0.1945, pruned_loss=0.01925, over 4860.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.0315, over 973886.13 frames.], batch size: 20, lr: 1.81e-04
2022-05-07 11:21:59,698 INFO [train.py:715] (1/8) Epoch 12, batch 16300, loss[loss=0.1254, simple_loss=0.204, pruned_loss=0.02345, over 4779.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03137, over 973382.41 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:22:37,476 INFO [train.py:715] (1/8) Epoch 12, batch 16350, loss[loss=0.1494, simple_loss=0.221, pruned_loss=0.03893, over 4805.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.03108, over 973729.74 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:23:16,255 INFO [train.py:715] (1/8) Epoch 12, batch 16400, loss[loss=0.1236, simple_loss=0.2034, pruned_loss=0.0219, over 4871.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2114, pruned_loss=0.03175, over 973735.19 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 11:23:54,212 INFO [train.py:715] (1/8) Epoch 12, batch 16450, loss[loss=0.1461, simple_loss=0.217, pruned_loss=0.03758, over 4778.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2117, pruned_loss=0.03182, over 973030.65 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:24:33,103 INFO [train.py:715] (1/8) Epoch 12, batch 16500, loss[loss=0.1235, simple_loss=0.199, pruned_loss=0.02404, over 4823.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03124, over 972138.28 frames.], batch size: 27, lr: 1.81e-04
2022-05-07 11:25:12,188 INFO [train.py:715] (1/8) Epoch 12, batch 16550, loss[loss=0.1177, simple_loss=0.1842, pruned_loss=0.02563, over 4961.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2105, pruned_loss=0.0315, over 973286.62 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:25:51,325 INFO [train.py:715] (1/8) Epoch 12, batch 16600, loss[loss=0.1148, simple_loss=0.1916, pruned_loss=0.01903, over 4874.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03138, over 973151.07 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 11:26:29,847 INFO [train.py:715] (1/8) Epoch 12, batch 16650, loss[loss=0.1379, simple_loss=0.2074, pruned_loss=0.03414, over 4974.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.0315, over 973281.74 frames.], batch size: 31, lr: 1.81e-04
2022-05-07 11:27:08,908 INFO [train.py:715] (1/8) Epoch 12, batch 16700, loss[loss=0.1168, simple_loss=0.1919, pruned_loss=0.02084, over 4944.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03149, over 973244.69 frames.], batch size: 35, lr: 1.81e-04
2022-05-07 11:27:48,112 INFO [train.py:715] (1/8) Epoch 12, batch 16750, loss[loss=0.1246, simple_loss=0.2014, pruned_loss=0.02386, over 4753.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.0317, over 973221.34 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:28:26,500 INFO [train.py:715] (1/8) Epoch 12, batch 16800, loss[loss=0.1343, simple_loss=0.2066, pruned_loss=0.03098, over 4913.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03205, over 973395.75 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:29:05,272 INFO [train.py:715] (1/8) Epoch 12, batch 16850, loss[loss=0.129, simple_loss=0.2002, pruned_loss=0.02884, over 4894.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03142, over 973672.54 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:29:44,426 INFO [train.py:715] (1/8) Epoch 12, batch 16900, loss[loss=0.139, simple_loss=0.2071, pruned_loss=0.03548, over 4844.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03124, over 974114.36 frames.], batch size: 12, lr: 1.81e-04
2022-05-07 11:30:24,203 INFO [train.py:715] (1/8) Epoch 12, batch 16950, loss[loss=0.1309, simple_loss=0.1986, pruned_loss=0.0316, over 4887.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03066, over 972988.24 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:31:02,693 INFO [train.py:715] (1/8) Epoch 12, batch 17000, loss[loss=0.1286, simple_loss=0.2047, pruned_loss=0.02618, over 4963.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03072, over 972465.08 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:31:40,879 INFO [train.py:715] (1/8) Epoch 12, batch 17050, loss[loss=0.1751, simple_loss=0.2413, pruned_loss=0.05449, over 4854.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03136, over 972487.46 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 11:32:19,765 INFO [train.py:715] (1/8) Epoch 12, batch 17100, loss[loss=0.1331, simple_loss=0.2098, pruned_loss=0.02817, over 4819.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03162, over 972950.79 frames.], batch size: 25, lr: 1.81e-04
2022-05-07 11:32:58,566 INFO [train.py:715] (1/8) Epoch 12, batch 17150, loss[loss=0.1326, simple_loss=0.209, pruned_loss=0.02811, over 4916.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03185, over 972688.27 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:33:37,596 INFO [train.py:715] (1/8) Epoch 12, batch 17200, loss[loss=0.1249, simple_loss=0.2019, pruned_loss=0.024, over 4947.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03114, over 972149.46 frames.], batch size: 24, lr: 1.81e-04
2022-05-07 11:34:16,031 INFO [train.py:715] (1/8) Epoch 12, batch 17250, loss[loss=0.1277, simple_loss=0.2041, pruned_loss=0.02561, over 4866.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03144, over 971970.79 frames.], batch size: 20, lr: 1.81e-04
2022-05-07 11:34:54,496 INFO [train.py:715] (1/8) Epoch 12, batch 17300, loss[loss=0.1127, simple_loss=0.1893, pruned_loss=0.01801, over 4896.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03131, over 972747.63 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 11:35:32,128 INFO [train.py:715] (1/8) Epoch 12, batch 17350, loss[loss=0.1742, simple_loss=0.2319, pruned_loss=0.05826, over 4873.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03163, over 973291.79 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 11:36:10,079 INFO [train.py:715] (1/8) Epoch 12, batch 17400, loss[loss=0.1127, simple_loss=0.1875, pruned_loss=0.01889, over 4989.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03132, over 972919.84 frames.], batch size: 26, lr: 1.81e-04
2022-05-07 11:36:47,848 INFO [train.py:715] (1/8) Epoch 12, batch 17450, loss[loss=0.1367, simple_loss=0.2136, pruned_loss=0.02988, over 4994.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.0317, over 973107.17 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:37:26,179 INFO [train.py:715] (1/8) Epoch 12, batch 17500, loss[loss=0.1344, simple_loss=0.1994, pruned_loss=0.03472, over 4874.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03166, over 972854.60 frames.], batch size: 20, lr: 1.81e-04
2022-05-07 11:38:04,043 INFO [train.py:715] (1/8) Epoch 12, batch 17550, loss[loss=0.1292, simple_loss=0.2054, pruned_loss=0.02648, over 4985.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2111, pruned_loss=0.03149, over 971918.67 frames.], batch size: 25, lr: 1.81e-04
2022-05-07 11:38:42,240 INFO [train.py:715] (1/8) Epoch 12, batch 17600, loss[loss=0.1664, simple_loss=0.233, pruned_loss=0.04994, over 4915.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03149, over 972622.62 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:39:19,887 INFO [train.py:715] (1/8) Epoch 12, batch 17650, loss[loss=0.1171, simple_loss=0.1841, pruned_loss=0.02503, over 4877.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03126, over 972163.37 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:39:57,992 INFO [train.py:715] (1/8) Epoch 12, batch 17700, loss[loss=0.1408, simple_loss=0.2157, pruned_loss=0.03297, over 4775.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03173, over 971875.86 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:40:36,850 INFO [train.py:715] (1/8) Epoch 12, batch 17750, loss[loss=0.1109, simple_loss=0.1805, pruned_loss=0.02068, over 4993.00 frames.], tot_loss[loss=0.137, simple_loss=0.2101, pruned_loss=0.03193, over 972847.99 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:41:15,691 INFO [train.py:715] (1/8) Epoch 12, batch 17800, loss[loss=0.1295, simple_loss=0.2011, pruned_loss=0.02895, over 4984.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.03168, over 973384.82 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:41:54,191 INFO [train.py:715] (1/8) Epoch 12, batch 17850, loss[loss=0.1435, simple_loss=0.2077, pruned_loss=0.03958, over 4968.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2093, pruned_loss=0.03175, over 973069.08 frames.], batch size: 24, lr: 1.81e-04
2022-05-07 11:42:32,959 INFO [train.py:715] (1/8) Epoch 12, batch 17900, loss[loss=0.1388, simple_loss=0.2035, pruned_loss=0.03706, over 4888.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03151, over 972939.90 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 11:43:10,439 INFO [train.py:715] (1/8) Epoch 12, batch 17950, loss[loss=0.1324, simple_loss=0.2038, pruned_loss=0.03051, over 4859.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03162, over 973817.41 frames.], batch size: 30, lr: 1.81e-04
2022-05-07 11:43:48,630 INFO [train.py:715] (1/8) Epoch 12, batch 18000, loss[loss=0.1488, simple_loss=0.2207, pruned_loss=0.03845, over 4938.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03209, over 973479.10 frames.], batch size: 35, lr: 1.81e-04
2022-05-07 11:43:48,631 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 11:43:58,183 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.106, simple_loss=0.19, pruned_loss=0.011, over 914524.00 frames.
2022-05-07 11:44:36,610 INFO [train.py:715] (1/8) Epoch 12, batch 18050, loss[loss=0.1456, simple_loss=0.2113, pruned_loss=0.03996, over 4791.00 frames.], tot_loss[loss=0.138, simple_loss=0.2108, pruned_loss=0.03265, over 972931.13 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:45:14,477 INFO [train.py:715] (1/8) Epoch 12, batch 18100, loss[loss=0.1163, simple_loss=0.201, pruned_loss=0.01581, over 4775.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2105, pruned_loss=0.0325, over 972789.93 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:45:52,626 INFO [train.py:715] (1/8) Epoch 12, batch 18150, loss[loss=0.147, simple_loss=0.2203, pruned_loss=0.0369, over 4836.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2094, pruned_loss=0.03185, over 972668.69 frames.], batch size: 30, lr: 1.81e-04
2022-05-07 11:46:30,451 INFO [train.py:715] (1/8) Epoch 12, batch 18200, loss[loss=0.1427, simple_loss=0.209, pruned_loss=0.03823, over 4749.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2097, pruned_loss=0.03226, over 972433.41 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:47:08,252 INFO [train.py:715] (1/8) Epoch 12, batch 18250, loss[loss=0.1407, simple_loss=0.2213, pruned_loss=0.03007, over 4792.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2102, pruned_loss=0.0325, over 972854.34 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:47:46,409 INFO [train.py:715] (1/8) Epoch 12, batch 18300, loss[loss=0.1233, simple_loss=0.1968, pruned_loss=0.02488, over 4957.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2094, pruned_loss=0.03193, over 973352.79 frames.], batch size: 35, lr: 1.81e-04
2022-05-07 11:48:24,295 INFO [train.py:715] (1/8) Epoch 12, batch 18350, loss[loss=0.1129, simple_loss=0.1852, pruned_loss=0.02034, over 4808.00 frames.], tot_loss[loss=0.137, simple_loss=0.21, pruned_loss=0.03202, over 973539.70 frames.], batch size: 26, lr: 1.81e-04
2022-05-07 11:49:02,250 INFO [train.py:715] (1/8) Epoch 12, batch 18400, loss[loss=0.1546, simple_loss=0.2226, pruned_loss=0.04333, over 4772.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03183, over 972588.23 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:49:39,751 INFO [train.py:715] (1/8) Epoch 12, batch 18450, loss[loss=0.1083, simple_loss=0.1761, pruned_loss=0.02026, over 4808.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03202, over 972313.49 frames.], batch size: 12, lr: 1.81e-04
2022-05-07 11:50:17,855 INFO [train.py:715] (1/8) Epoch 12, batch 18500, loss[loss=0.1417, simple_loss=0.2205, pruned_loss=0.03144, over 4754.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03185, over 971914.67 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:50:55,667 INFO [train.py:715] (1/8) Epoch 12, batch 18550, loss[loss=0.1505, simple_loss=0.2278, pruned_loss=0.03664, over 4800.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03216, over 972155.80 frames.], batch size: 21, lr: 1.81e-04
2022-05-07 11:51:33,503 INFO [train.py:715] (1/8) Epoch 12, batch 18600, loss[loss=0.1371, simple_loss=0.2033, pruned_loss=0.03541, over 4918.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03219, over 972119.08 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:52:11,114 INFO [train.py:715] (1/8) Epoch 12, batch 18650, loss[loss=0.1341, simple_loss=0.1932, pruned_loss=0.03749, over 4992.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.0321, over 972187.78 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:52:48,679 INFO [train.py:715] (1/8) Epoch 12, batch 18700, loss[loss=0.1528, simple_loss=0.2336, pruned_loss=0.03603, over 4833.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03199, over 971298.58 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:53:26,074 INFO [train.py:715] (1/8) Epoch 12, batch 18750, loss[loss=0.1485, simple_loss=0.221, pruned_loss=0.03795, over 4855.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.03195, over 971565.49 frames.], batch size: 20, lr: 1.81e-04
2022-05-07 11:54:04,016 INFO [train.py:715] (1/8) Epoch 12, batch 18800, loss[loss=0.1282, simple_loss=0.2012, pruned_loss=0.02766, over 4956.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03183, over 972756.84 frames.], batch size: 21, lr: 1.81e-04
2022-05-07 11:54:41,891 INFO [train.py:715] (1/8) Epoch 12, batch 18850, loss[loss=0.1192, simple_loss=0.1874, pruned_loss=0.02552, over 4742.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03198, over 972183.63 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:55:19,707 INFO [train.py:715] (1/8) Epoch 12, batch 18900, loss[loss=0.155, simple_loss=0.2438, pruned_loss=0.0331, over 4895.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03179, over 972955.69 frames.], batch size: 39, lr: 1.81e-04
2022-05-07 11:55:57,999 INFO [train.py:715] (1/8) Epoch 12, batch 18950, loss[loss=0.1329, simple_loss=0.2155, pruned_loss=0.02521, over 4837.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03194, over 972424.98 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:56:35,810 INFO [train.py:715] (1/8) Epoch 12, batch 19000, loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02881, over 4745.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03175, over 972848.68 frames.], batch size: 12, lr: 1.81e-04
2022-05-07 11:57:13,291 INFO [train.py:715] (1/8) Epoch 12, batch 19050, loss[loss=0.1401, simple_loss=0.2081, pruned_loss=0.03604, over 4872.00 frames.], tot_loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.03218, over 973163.09 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 11:57:50,570 INFO [train.py:715] (1/8) Epoch 12, batch 19100, loss[loss=0.146, simple_loss=0.2143, pruned_loss=0.03883, over 4780.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2111, pruned_loss=0.03159, over 973187.22 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 11:58:28,559 INFO [train.py:715] (1/8) Epoch 12, batch 19150, loss[loss=0.1523, simple_loss=0.2182, pruned_loss=0.04323, over 4647.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03133, over 973275.89 frames.], batch size: 13, lr: 1.80e-04
2022-05-07 11:59:07,194 INFO [train.py:715] (1/8) Epoch 12, batch 19200, loss[loss=0.1287, simple_loss=0.1899, pruned_loss=0.03375, over 4848.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03105, over 973320.08 frames.], batch size: 34, lr: 1.80e-04
2022-05-07 11:59:45,242 INFO [train.py:715] (1/8) Epoch 12, batch 19250, loss[loss=0.1258, simple_loss=0.197, pruned_loss=0.02733, over 4875.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2106, pruned_loss=0.03133, over 973314.08 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 12:00:23,724 INFO [train.py:715] (1/8) Epoch 12, batch 19300, loss[loss=0.1713, simple_loss=0.2272, pruned_loss=0.05773, over 4777.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03172, over 972713.73 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:01:01,908 INFO [train.py:715] (1/8) Epoch 12, batch 19350, loss[loss=0.145, simple_loss=0.2264, pruned_loss=0.03174, over 4797.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2119, pruned_loss=0.03241, over 972486.08 frames.], batch size: 21, lr: 1.80e-04
2022-05-07 12:01:39,914 INFO [train.py:715] (1/8) Epoch 12, batch 19400, loss[loss=0.1358, simple_loss=0.2072, pruned_loss=0.03216, over 4781.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03268, over 971823.49 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:02:17,954 INFO [train.py:715] (1/8) Epoch 12, batch 19450, loss[loss=0.1714, simple_loss=0.2272, pruned_loss=0.05778, over 4826.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2104, pruned_loss=0.03233, over 972296.80 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:02:56,774 INFO [train.py:715] (1/8) Epoch 12, batch 19500, loss[loss=0.1272, simple_loss=0.21, pruned_loss=0.02223, over 4890.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03206, over 971681.66 frames.], batch size: 16, lr: 1.80e-04
2022-05-07 12:03:35,596 INFO [train.py:715] (1/8) Epoch 12, batch 19550, loss[loss=0.1308, simple_loss=0.2032, pruned_loss=0.02924, over 4849.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2099, pruned_loss=0.03218, over 971897.88 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:04:14,318 INFO [train.py:715] (1/8) Epoch 12, batch 19600, loss[loss=0.1452, simple_loss=0.2204, pruned_loss=0.03497, over 4964.00 frames.], tot_loss[loss=0.137, simple_loss=0.2096, pruned_loss=0.03217, over 972161.71 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:04:53,461 INFO [train.py:715] (1/8) Epoch 12, batch 19650, loss[loss=0.1348, simple_loss=0.2144, pruned_loss=0.0276, over 4797.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2096, pruned_loss=0.03202, over 971884.93 frames.], batch size: 21, lr: 1.80e-04
2022-05-07 12:05:32,634 INFO [train.py:715] (1/8) Epoch 12, batch 19700, loss[loss=0.1535, simple_loss=0.2226, pruned_loss=0.0422, over 4884.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03256, over 971284.41 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 12:06:12,002 INFO [train.py:715] (1/8) Epoch 12, batch 19750, loss[loss=0.1764, simple_loss=0.2462, pruned_loss=0.05336, over 4941.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03272, over 971846.45 frames.], batch size: 39, lr: 1.80e-04
2022-05-07 12:06:52,638 INFO [train.py:715] (1/8) Epoch 12, batch 19800, loss[loss=0.1696, simple_loss=0.2392, pruned_loss=0.05005, over 4825.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03274, over 972121.48 frames.], batch size: 27, lr: 1.80e-04
2022-05-07 12:07:33,085 INFO [train.py:715] (1/8) Epoch 12, batch 19850, loss[loss=0.1392, simple_loss=0.2087, pruned_loss=0.03484, over 4823.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03299, over 972208.60 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:08:14,261 INFO [train.py:715] (1/8) Epoch 12, batch 19900, loss[loss=0.1394, simple_loss=0.2099, pruned_loss=0.03448, over 4959.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03256, over 972869.59 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:08:54,591 INFO [train.py:715] (1/8) Epoch 12, batch 19950, loss[loss=0.1378, simple_loss=0.2056, pruned_loss=0.03504, over 4940.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03242, over 973590.98 frames.], batch size: 21, lr: 1.80e-04
2022-05-07 12:09:35,215 INFO [train.py:715] (1/8) Epoch 12, batch 20000, loss[loss=0.1191, simple_loss=0.1993, pruned_loss=0.01943, over 4919.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03237, over 973371.04 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:10:15,444 INFO [train.py:715] (1/8) Epoch 12, batch 20050, loss[loss=0.1459, simple_loss=0.2152, pruned_loss=0.03835, over 4935.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03196, over 973499.15 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:10:55,695 INFO [train.py:715] (1/8) Epoch 12, batch 20100, loss[loss=0.1518, simple_loss=0.2329, pruned_loss=0.03536, over 4753.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03163, over 973198.92 frames.], batch size: 19, lr: 1.80e-04
2022-05-07 12:11:35,656 INFO [train.py:715] (1/8) Epoch 12, batch 20150, loss[loss=0.1293, simple_loss=0.2015, pruned_loss=0.02858, over 4895.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03143, over 973532.46 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:12:16,048 INFO [train.py:715] (1/8) Epoch 12, batch 20200, loss[loss=0.1488, simple_loss=0.2223, pruned_loss=0.03766, over 4944.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.0317, over 974203.96 frames.], batch size: 21, lr: 1.80e-04
2022-05-07 12:12:56,152 INFO [train.py:715] (1/8) Epoch 12, batch 20250, loss[loss=0.1265, simple_loss=0.1952, pruned_loss=0.02885, over 4866.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03125, over 973846.46 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 12:13:36,221 INFO [train.py:715] (1/8) Epoch 12, batch 20300, loss[loss=0.1358, simple_loss=0.2181, pruned_loss=0.02675, over 4986.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03132, over 973689.68 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:14:16,796 INFO [train.py:715] (1/8) Epoch 12, batch 20350, loss[loss=0.1198, simple_loss=0.1883, pruned_loss=0.02567, over 4950.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2094, pruned_loss=0.0317, over 973478.01 frames.], batch size: 23, lr: 1.80e-04
2022-05-07 12:14:56,466 INFO [train.py:715] (1/8) Epoch 12, batch 20400, loss[loss=0.1656, simple_loss=0.2407, pruned_loss=0.04523, over 4832.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03192, over 974138.62 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:15:36,229 INFO [train.py:715] (1/8) Epoch 12, batch 20450, loss[loss=0.1311, simple_loss=0.2034, pruned_loss=0.02936, over 4833.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03212, over 973494.15 frames.], batch size: 30, lr: 1.80e-04
2022-05-07 12:16:15,878 INFO [train.py:715] (1/8) Epoch 12, batch 20500, loss[loss=0.1405, simple_loss=0.2186, pruned_loss=0.03118, over 4874.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03174, over 972124.36 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 12:16:56,324 INFO [train.py:715] (1/8) Epoch 12, batch 20550, loss[loss=0.1395, simple_loss=0.218, pruned_loss=0.03046, over 4811.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03212, over 971971.54 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:17:36,247 INFO [train.py:715] (1/8) Epoch 12, batch 20600, loss[loss=0.1392, simple_loss=0.2156, pruned_loss=0.03137, over 4987.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03209, over 972834.27 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:18:15,191 INFO [train.py:715] (1/8) Epoch 12, batch 20650, loss[loss=0.1248, simple_loss=0.1903, pruned_loss=0.0297, over 4746.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03201, over 972400.59 frames.], batch size: 12, lr: 1.80e-04
2022-05-07 12:18:54,299 INFO [train.py:715] (1/8) Epoch 12, batch 20700, loss[loss=0.1448, simple_loss=0.2203, pruned_loss=0.03466, over 4859.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03189, over 972272.00 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:19:32,275 INFO [train.py:715] (1/8) Epoch 12, batch 20750, loss[loss=0.1541, simple_loss=0.2183, pruned_loss=0.04489, over 4974.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03198, over 971960.04 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:20:10,578 INFO [train.py:715] (1/8) Epoch 12, batch 20800, loss[loss=0.1263, simple_loss=0.202, pruned_loss=0.02532, over 4980.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2113, pruned_loss=0.03178, over 971931.18 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:20:48,330 INFO [train.py:715] (1/8) Epoch 12, batch 20850, loss[loss=0.1215, simple_loss=0.1965, pruned_loss=0.02329, over 4810.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.03181, over 971758.28 frames.], batch size: 13, lr: 1.80e-04
2022-05-07 12:21:26,469 INFO [train.py:715] (1/8) Epoch 12, batch 20900, loss[loss=0.1515, simple_loss=0.23, pruned_loss=0.03645, over 4815.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03171, over 971492.07 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:22:04,013 INFO [train.py:715] (1/8) Epoch 12, batch 20950, loss[loss=0.1166, simple_loss=0.1965, pruned_loss=0.01831, over 4985.00 frames.], tot_loss[loss=0.137, simple_loss=0.2109, pruned_loss=0.03153, over 972402.04 frames.], batch size: 28, lr: 1.80e-04
2022-05-07 12:22:41,376 INFO [train.py:715] (1/8) Epoch 12, batch 21000, loss[loss=0.1798, simple_loss=0.2485, pruned_loss=0.05562, over 4843.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03165, over 972783.36 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:22:41,377 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 12:22:50,900 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1056, simple_loss=0.1896, pruned_loss=0.01081, over 914524.00 frames.
2022-05-07 12:23:28,724 INFO [train.py:715] (1/8) Epoch 12, batch 21050, loss[loss=0.1496, simple_loss=0.2231, pruned_loss=0.03811, over 4908.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.0316, over 972749.84 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:24:06,829 INFO [train.py:715] (1/8) Epoch 12, batch 21100, loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03207, over 4886.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2109, pruned_loss=0.03159, over 971868.63 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 12:24:44,629 INFO [train.py:715] (1/8) Epoch 12, batch 21150, loss[loss=0.1591, simple_loss=0.229, pruned_loss=0.04462, over 4872.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03222, over 972605.46 frames.], batch size: 16, lr: 1.80e-04
2022-05-07 12:25:22,420 INFO [train.py:715] (1/8) Epoch 12, batch 21200, loss[loss=0.1418, simple_loss=0.2059, pruned_loss=0.03889, over 4959.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03198, over 972583.75 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:26:00,701 INFO [train.py:715] (1/8) Epoch 12, batch 21250, loss[loss=0.1226, simple_loss=0.1959, pruned_loss=0.0246, over 4806.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03188, over 972500.53 frames.], batch size: 12, lr: 1.80e-04
2022-05-07 12:26:39,506 INFO [train.py:715] (1/8) Epoch 12, batch 21300, loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03467, over 4893.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03227, over 971762.15 frames.], batch size: 16, lr: 1.80e-04
2022-05-07 12:27:17,267 INFO [train.py:715] (1/8) Epoch 12, batch 21350, loss[loss=0.1344, simple_loss=0.2032, pruned_loss=0.03282, over 4855.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03187, over 970976.90 frames.], batch size: 30, lr: 1.80e-04
2022-05-07 12:27:56,361 INFO [train.py:715] (1/8) Epoch 12, batch 21400, loss[loss=0.1661, simple_loss=0.2262, pruned_loss=0.05296, over 4896.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03161, over 970895.14 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 12:28:35,915 INFO [train.py:715] (1/8) Epoch 12, batch 21450, loss[loss=0.1584, simple_loss=0.2429, pruned_loss=0.03692, over 4887.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03169, over 971271.09 frames.], batch size: 19, lr: 1.80e-04
2022-05-07 12:29:14,513 INFO [train.py:715] (1/8) Epoch 12, batch 21500, loss[loss=0.1369, simple_loss=0.2046, pruned_loss=0.03465, over 4695.00 frames.], tot_loss[loss=0.137, simple_loss=0.2101, pruned_loss=0.03189, over 970917.48 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:29:53,101 INFO [train.py:715] (1/8) Epoch 12, batch 21550, loss[loss=0.1706, simple_loss=0.242, pruned_loss=0.04956, over 4979.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2098, pruned_loss=0.03189, over 970902.01 frames.], batch size: 28, lr: 1.80e-04
2022-05-07 12:30:31,275 INFO [train.py:715] (1/8) Epoch 12, batch 21600, loss[loss=0.1532, simple_loss=0.2316, pruned_loss=0.03743, over 4817.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2095, pruned_loss=0.03154, over 971363.62 frames.], batch size: 27, lr: 1.80e-04
2022-05-07 12:31:09,734 INFO [train.py:715] (1/8) Epoch 12, batch 21650, loss[loss=0.1457, simple_loss=0.2226, pruned_loss=0.03438, over 4834.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03157, over 971796.41 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:31:46,935 INFO [train.py:715] (1/8) Epoch 12, batch 21700, loss[loss=0.1196, simple_loss=0.1849, pruned_loss=0.02711, over 4925.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03193, over 972296.02 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:32:25,498 INFO [train.py:715] (1/8) Epoch 12, batch 21750, loss[loss=0.1539, simple_loss=0.2297, pruned_loss=0.03911, over 4795.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.03186, over 972034.04 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:33:04,220 INFO [train.py:715] (1/8) Epoch 12, batch 21800, loss[loss=0.1419, simple_loss=0.216, pruned_loss=0.03389, over 4857.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2116, pruned_loss=0.03203, over 972225.96 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:33:42,113 INFO [train.py:715] (1/8) Epoch 12, batch 21850, loss[loss=0.1545, simple_loss=0.227, pruned_loss=0.04099, over 4908.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03175, over 971689.24 frames.], batch size: 19, lr: 1.80e-04
2022-05-07 12:34:19,729 INFO [train.py:715] (1/8) Epoch 12, batch 21900, loss[loss=0.1239, simple_loss=0.1933, pruned_loss=0.02728, over 4883.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.0319, over 972468.07 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 12:34:58,478 INFO [train.py:715] (1/8) Epoch 12, batch 21950, loss[loss=0.1472, simple_loss=0.2246, pruned_loss=0.03483, over 4794.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03186, over 972445.05 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:35:37,484 INFO [train.py:715] (1/8) Epoch 12, batch 22000, loss[loss=0.1365, simple_loss=0.2044, pruned_loss=0.03434, over 4867.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03162, over 973244.53 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:36:15,712 INFO [train.py:715] (1/8) Epoch 12, batch 22050, loss[loss=0.1358, simple_loss=0.2138, pruned_loss=0.02896, over 4989.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03161, over 972497.52 frames.], batch size: 28, lr: 1.80e-04
2022-05-07 12:36:54,708 INFO [train.py:715] (1/8) Epoch 12, batch 22100, loss[loss=0.1395, simple_loss=0.217, pruned_loss=0.031, over 4914.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.0312, over 972859.89 frames.], batch size: 39, lr: 1.80e-04
2022-05-07 12:37:33,662 INFO [train.py:715] (1/8) Epoch 12, batch 22150, loss[loss=0.1256, simple_loss=0.2102, pruned_loss=0.02055, over 4850.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03124, over 973271.24 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:38:11,937 INFO [train.py:715] (1/8) Epoch 12, batch 22200, loss[loss=0.1088, simple_loss=0.185, pruned_loss=0.01632, over 4787.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03072, over 972905.73 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:38:49,702 INFO [train.py:715] (1/8) Epoch 12, batch 22250, loss[loss=0.1323, simple_loss=0.2029, pruned_loss=0.03086, over 4762.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2097, pruned_loss=0.03084, over 972742.52 frames.], batch size: 19, lr: 1.80e-04
2022-05-07 12:39:30,403 INFO [train.py:715] (1/8) Epoch 12, batch 22300, loss[loss=0.1506, simple_loss=0.2233, pruned_loss=0.03896, over 4850.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.0313, over 971712.04 frames.], batch size: 30, lr: 1.80e-04
2022-05-07 12:40:08,654 INFO [train.py:715] (1/8) Epoch 12, batch 22350, loss[loss=0.1328, simple_loss=0.206, pruned_loss=0.0298, over 4828.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.0306, over 971469.73 frames.], batch size: 12, lr: 1.80e-04
2022-05-07 12:40:46,767 INFO [train.py:715] (1/8) Epoch 12, batch 22400, loss[loss=0.1552, simple_loss=0.2218, pruned_loss=0.04428, over 4811.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.0307, over 971763.39 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:41:25,347 INFO [train.py:715] (1/8) Epoch 12, batch 22450, loss[loss=0.1493, simple_loss=0.2299, pruned_loss=0.03438, over 4837.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03095, over 972136.25 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:42:03,785 INFO [train.py:715] (1/8) Epoch 12, batch 22500, loss[loss=0.1331, simple_loss=0.2107, pruned_loss=0.02776, over 4939.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03121, over 972674.19 frames.], batch size: 21, lr: 1.80e-04
2022-05-07 12:42:42,489 INFO [train.py:715] (1/8) Epoch 12, batch 22550, loss[loss=0.1317, simple_loss=0.2041, pruned_loss=0.02966, over 4891.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.031, over 973358.57 frames.], batch size: 19, lr: 1.80e-04
2022-05-07 12:43:20,635 INFO [train.py:715] (1/8) Epoch 12, batch 22600, loss[loss=0.1327, simple_loss=0.2002, pruned_loss=0.0326, over 4991.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.0313, over 974018.22 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:43:58,690 INFO [train.py:715] (1/8) Epoch 12, batch 22650, loss[loss=0.1135, simple_loss=0.1933, pruned_loss=0.01687, over 4804.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.03166, over 973154.42 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:44:36,596 INFO [train.py:715] (1/8) Epoch 12, batch 22700, loss[loss=0.1421, simple_loss=0.2222, pruned_loss=0.031, over 4925.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03175, over 972510.56 frames.], batch size: 29, lr: 1.80e-04
2022-05-07 12:45:14,815 INFO [train.py:715] (1/8) Epoch 12, batch 22750, loss[loss=0.1585, simple_loss=0.2394, pruned_loss=0.03882, over 4820.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03178, over 972007.65 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:45:53,322 INFO [train.py:715] (1/8) Epoch 12, batch 22800, loss[loss=0.1335, simple_loss=0.2104, pruned_loss=0.02827, over 4979.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03185, over 972190.48 frames.], batch size: 39, lr: 1.80e-04
2022-05-07 12:46:32,316 INFO [train.py:715] (1/8) Epoch 12, batch 22850, loss[loss=0.1423, simple_loss=0.2005, pruned_loss=0.04204, over 4947.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.03224, over 971634.37 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:47:10,526 INFO [train.py:715] (1/8) Epoch 12, batch 22900, loss[loss=0.1138, simple_loss=0.197, pruned_loss=0.01531, over 4950.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03144, over 971864.98 frames.], batch size: 29, lr: 1.80e-04
2022-05-07 12:47:48,405 INFO [train.py:715] (1/8) Epoch 12, batch 22950, loss[loss=0.1284, simple_loss=0.2042, pruned_loss=0.02627, over 4796.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.0309, over 971339.89 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:48:26,680 INFO [train.py:715] (1/8) Epoch 12, batch 23000, loss[loss=0.1329, simple_loss=0.2035, pruned_loss=0.03113, over 4809.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03077, over 971541.57 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:49:04,952 INFO [train.py:715] (1/8) Epoch 12, batch 23050, loss[loss=0.1127, simple_loss=0.184, pruned_loss=0.02072, over 4789.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03113, over 972068.15 frames.], batch size: 21, lr: 1.80e-04
2022-05-07 12:49:43,054 INFO [train.py:715] (1/8) Epoch 12, batch 23100, loss[loss=0.1096, simple_loss=0.1844, pruned_loss=0.01737, over 4899.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03147, over 971709.70 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:50:21,957 INFO [train.py:715] (1/8) Epoch 12, batch 23150, loss[loss=0.1172, simple_loss=0.1955, pruned_loss=0.0194, over 4790.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03143, over 971503.37 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:51:01,026 INFO [train.py:715] (1/8) Epoch 12, batch 23200, loss[loss=0.1439, simple_loss=0.2076, pruned_loss=0.0401, over 4779.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.0313, over 971413.92 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:51:39,418 INFO [train.py:715] (1/8) Epoch 12, batch 23250, loss[loss=0.1726, simple_loss=0.2446, pruned_loss=0.05032, over 4840.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03188, over 972022.07 frames.], batch size: 30, lr: 1.80e-04
2022-05-07 12:52:17,114 INFO [train.py:715] (1/8) Epoch 12, batch 23300, loss[loss=0.1244, simple_loss=0.2009, pruned_loss=0.02391, over 4844.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2103, pruned_loss=0.03217, over 972226.24 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:52:55,812 INFO [train.py:715] (1/8) Epoch 12, batch 23350, loss[loss=0.1294, simple_loss=0.2079, pruned_loss=0.02543, over 4827.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03183, over 972262.38 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:53:33,839 INFO [train.py:715] (1/8) Epoch 12, batch 23400, loss[loss=0.1273, simple_loss=0.2069, pruned_loss=0.02385, over 4986.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03206, over 972868.34 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:54:11,389 INFO [train.py:715] (1/8) Epoch 12, batch 23450, loss[loss=0.1499, simple_loss=0.2231, pruned_loss=0.0384, over 4947.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03184, over 973497.85 frames.], batch size: 21, lr: 1.80e-04
2022-05-07 12:54:49,573 INFO [train.py:715] (1/8) Epoch 12, batch 23500, loss[loss=0.1337, simple_loss=0.2167, pruned_loss=0.02537, over 4967.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.0316, over 973143.09 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:55:28,414 INFO [train.py:715] (1/8) Epoch 12, batch 23550, loss[loss=0.1264, simple_loss=0.2086, pruned_loss=0.02208, over 4935.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03152, over 973266.43 frames.], batch size: 29, lr: 1.80e-04
2022-05-07 12:56:07,100 INFO [train.py:715] (1/8) Epoch 12, batch 23600, loss[loss=0.1508, simple_loss=0.2184, pruned_loss=0.04157, over 4889.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03133, over 973192.15 frames.], batch size: 22, lr: 1.80e-04
2022-05-07 12:56:45,804 INFO [train.py:715] (1/8) Epoch 12, batch 23650, loss[loss=0.1165, simple_loss=0.186, pruned_loss=0.02354, over 4834.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03141, over 973536.12 frames.], batch size: 13, lr: 1.80e-04
2022-05-07 12:57:24,212 INFO [train.py:715] (1/8) Epoch 12, batch 23700, loss[loss=0.1323, simple_loss=0.2108, pruned_loss=0.02687, over 4853.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03153, over 972783.39 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:58:02,492 INFO [train.py:715] (1/8) Epoch 12, batch 23750, loss[loss=0.1293, simple_loss=0.2056, pruned_loss=0.02654, over 4988.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03167, over 973245.04 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:58:41,207 INFO [train.py:715] (1/8) Epoch 12, batch 23800, loss[loss=0.1277, simple_loss=0.1995, pruned_loss=0.02795, over 4825.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.0318, over 971235.37 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:59:20,125 INFO [train.py:715] (1/8) Epoch 12, batch 23850, loss[loss=0.1511, simple_loss=0.2274, pruned_loss=0.03743, over 4841.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2098, pruned_loss=0.03182, over 971785.94 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:59:59,700 INFO [train.py:715] (1/8) Epoch 12, batch 23900, loss[loss=0.1555, simple_loss=0.2226, pruned_loss=0.04424, over 4905.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03165, over 972149.63 frames.], batch size: 19, lr: 1.80e-04
2022-05-07 13:00:39,417 INFO [train.py:715] (1/8) Epoch 12, batch 23950, loss[loss=0.1545, simple_loss=0.2317, pruned_loss=0.03867, over 4843.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03211, over 971686.13 frames.], batch size: 32, lr: 1.79e-04
2022-05-07 13:01:18,255 INFO [train.py:715] (1/8) Epoch 12, batch 24000, loss[loss=0.1175, simple_loss=0.1956, pruned_loss=0.01971, over 4981.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03263, over 972892.41 frames.], batch size: 28, lr: 1.79e-04
2022-05-07 13:01:18,256 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 13:01:27,805 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1054, simple_loss=0.1895, pruned_loss=0.01071, over 914524.00 frames.
2022-05-07 13:02:06,834 INFO [train.py:715] (1/8) Epoch 12, batch 24050, loss[loss=0.1054, simple_loss=0.1804, pruned_loss=0.01519, over 4824.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03262, over 973088.22 frames.], batch size: 26, lr: 1.79e-04
2022-05-07 13:02:47,368 INFO [train.py:715] (1/8) Epoch 12, batch 24100, loss[loss=0.1423, simple_loss=0.2355, pruned_loss=0.02457, over 4821.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2129, pruned_loss=0.0331, over 973443.51 frames.], batch size: 26, lr: 1.79e-04
2022-05-07 13:03:27,826 INFO [train.py:715] (1/8) Epoch 12, batch 24150, loss[loss=0.138, simple_loss=0.2145, pruned_loss=0.03078, over 4923.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.0322, over 973680.05 frames.], batch size: 23, lr: 1.79e-04
2022-05-07 13:04:07,863 INFO [train.py:715] (1/8) Epoch 12, batch 24200, loss[loss=0.1455, simple_loss=0.2227, pruned_loss=0.03416, over 4783.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03203, over 973771.62 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:04:47,987 INFO [train.py:715] (1/8) Epoch 12, batch 24250, loss[loss=0.1357, simple_loss=0.2194, pruned_loss=0.02605, over 4956.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03156, over 974740.63 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:05:28,030 INFO [train.py:715] (1/8) Epoch 12, batch 24300, loss[loss=0.1415, simple_loss=0.2179, pruned_loss=0.03251, over 4949.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2108, pruned_loss=0.03206, over 974171.08 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:06:07,758 INFO [train.py:715] (1/8) Epoch 12, batch 24350, loss[loss=0.1184, simple_loss=0.1977, pruned_loss=0.01953, over 4823.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03197, over 973801.27 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:06:47,578 INFO [train.py:715] (1/8) Epoch 12, batch 24400, loss[loss=0.1302, simple_loss=0.2039, pruned_loss=0.02826, over 4845.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03186, over 973415.90 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:07:27,539 INFO [train.py:715] (1/8) Epoch 12, batch 24450, loss[loss=0.1486, simple_loss=0.2303, pruned_loss=0.03345, over 4811.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03136, over 973051.44 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:08:07,311 INFO [train.py:715] (1/8) Epoch 12, batch 24500, loss[loss=0.1349, simple_loss=0.2128, pruned_loss=0.02846, over 4873.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03157, over 972625.63 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:08:46,554 INFO [train.py:715] (1/8) Epoch 12, batch 24550, loss[loss=0.1654, simple_loss=0.2417, pruned_loss=0.04455, over 4904.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03132, over 971994.55 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:09:26,207 INFO [train.py:715] (1/8) Epoch 12, batch 24600, loss[loss=0.1249, simple_loss=0.1947, pruned_loss=0.02753, over 4796.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03145, over 973296.55 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:10:05,927 INFO [train.py:715] (1/8) Epoch 12, batch 24650, loss[loss=0.1419, simple_loss=0.2139, pruned_loss=0.03495, over 4771.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03138, over 972027.91 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:10:45,628 INFO [train.py:715] (1/8) Epoch 12, batch 24700, loss[loss=0.1238, simple_loss=0.2084, pruned_loss=0.01957, over 4965.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03143, over 971778.08 frames.], batch size: 28, lr: 1.79e-04
2022-05-07 13:11:24,796 INFO [train.py:715] (1/8) Epoch 12, batch 24750, loss[loss=0.1265, simple_loss=0.2116, pruned_loss=0.02069, over 4933.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2105, pruned_loss=0.03136, over 971760.39 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 13:12:05,000 INFO [train.py:715] (1/8) Epoch 12, batch 24800, loss[loss=0.2036, simple_loss=0.2765, pruned_loss=0.06535, over 4987.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2111, pruned_loss=0.03157, over 972230.91 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:12:44,865 INFO [train.py:715] (1/8) Epoch 12, batch 24850, loss[loss=0.1529, simple_loss=0.2349, pruned_loss=0.03544, over 4883.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2101, pruned_loss=0.03107, over 972452.30 frames.], batch size: 32, lr: 1.79e-04
2022-05-07 13:13:24,119 INFO [train.py:715] (1/8) Epoch 12, batch 24900, loss[loss=0.1377, simple_loss=0.2151, pruned_loss=0.03021, over 4841.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2108, pruned_loss=0.0315, over 972699.04 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:14:03,443 INFO [train.py:715] (1/8) Epoch 12, batch 24950, loss[loss=0.1557, simple_loss=0.2269, pruned_loss=0.04224, over 4901.00 frames.], tot_loss[loss=0.137, simple_loss=0.2109, pruned_loss=0.03156, over 972400.03 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:14:42,358 INFO [train.py:715] (1/8) Epoch 12, batch 25000, loss[loss=0.1349, simple_loss=0.2141, pruned_loss=0.02787, over 4780.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.03122, over 971754.49 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:15:20,417 INFO [train.py:715] (1/8) Epoch 12, batch 25050, loss[loss=0.1598, simple_loss=0.2445, pruned_loss=0.03757, over 4781.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03104, over 971175.79 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:15:58,462 INFO [train.py:715] (1/8) Epoch 12, batch 25100, loss[loss=0.1339, simple_loss=0.1937, pruned_loss=0.03705, over 4967.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03175, over 971192.78 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:16:36,854 INFO [train.py:715] (1/8) Epoch 12, batch 25150, loss[loss=0.1179, simple_loss=0.1899, pruned_loss=0.02298, over 4977.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03139, over 972415.36 frames.], batch size: 28, lr: 1.79e-04
2022-05-07 13:17:15,107 INFO [train.py:715] (1/8) Epoch 12, batch 25200, loss[loss=0.1448, simple_loss=0.226, pruned_loss=0.03177, over 4891.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.03157, over 972382.57 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 13:17:52,727 INFO [train.py:715] (1/8) Epoch 12, batch 25250, loss[loss=0.1051, simple_loss=0.1747, pruned_loss=0.01773, over 4986.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03105, over 972449.67 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:18:30,736 INFO [train.py:715] (1/8) Epoch 12, batch 25300, loss[loss=0.172, simple_loss=0.2365, pruned_loss=0.05372, over 4927.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03144, over 971682.93 frames.], batch size: 39, lr: 1.79e-04
2022-05-07 13:19:08,815 INFO [train.py:715] (1/8) Epoch 12, batch 25350, loss[loss=0.1338, simple_loss=0.2151, pruned_loss=0.02626, over 4897.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03153, over 971461.19 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:19:47,797 INFO [train.py:715] (1/8) Epoch 12, batch 25400, loss[loss=0.1369, simple_loss=0.2143, pruned_loss=0.02973, over 4875.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03124, over 971292.54 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:20:26,685 INFO [train.py:715] (1/8) Epoch 12, batch 25450, loss[loss=0.136, simple_loss=0.2128, pruned_loss=0.02959, over 4915.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03155, over 970592.10 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:21:06,568 INFO [train.py:715] (1/8) Epoch 12, batch 25500, loss[loss=0.1364, simple_loss=0.2118, pruned_loss=0.03053, over 4977.00 frames.], tot_loss[loss=0.137, simple_loss=0.2101, pruned_loss=0.03192, over 970768.43 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:21:45,634 INFO [train.py:715] (1/8) Epoch 12, batch 25550, loss[loss=0.1203, simple_loss=0.2015, pruned_loss=0.01961, over 4947.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03214, over 971153.39 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 13:22:23,736 INFO [train.py:715] (1/8) Epoch 12, batch 25600, loss[loss=0.1564, simple_loss=0.2184, pruned_loss=0.04721, over 4847.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.03212, over 971358.24 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:23:02,066 INFO [train.py:715] (1/8) Epoch 12, batch 25650, loss[loss=0.1725, simple_loss=0.2455, pruned_loss=0.04974, over 4702.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03175, over 971145.70 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:23:40,759 INFO [train.py:715] (1/8) Epoch 12, batch 25700, loss[loss=0.135, simple_loss=0.2138, pruned_loss=0.02814, over 4745.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03183, over 971229.29 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:24:19,545 INFO [train.py:715] (1/8) Epoch 12, batch 25750, loss[loss=0.1395, simple_loss=0.2119, pruned_loss=0.03353, over 4795.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03187, over 971479.42 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 13:24:58,012 INFO [train.py:715] (1/8) Epoch 12, batch 25800, loss[loss=0.1344, simple_loss=0.2117, pruned_loss=0.02853, over 4986.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03206, over 972330.15 frames.], batch size: 26, lr: 1.79e-04
2022-05-07 13:25:36,924 INFO [train.py:715] (1/8) Epoch 12, batch 25850, loss[loss=0.1261, simple_loss=0.1941, pruned_loss=0.02907, over 4914.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03191, over 972637.49 frames.], batch size: 29, lr: 1.79e-04
2022-05-07 13:26:15,481 INFO [train.py:715] (1/8) Epoch 12, batch 25900, loss[loss=0.1432, simple_loss=0.2186, pruned_loss=0.03386, over 4951.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.0326, over 973201.10 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:26:53,774 INFO [train.py:715] (1/8) Epoch 12, batch 25950, loss[loss=0.12, simple_loss=0.1963, pruned_loss=0.0218, over 4873.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.03235, over 973887.45 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 13:27:31,256 INFO [train.py:715] (1/8) Epoch 12, batch 26000, loss[loss=0.1355, simple_loss=0.2074, pruned_loss=0.03181, over 4959.00 frames.], tot_loss[loss=0.138, simple_loss=0.2117, pruned_loss=0.03213, over 974024.07 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 13:28:09,533 INFO [train.py:715] (1/8) Epoch 12, batch 26050, loss[loss=0.1321, simple_loss=0.2119, pruned_loss=0.02614, over 4775.00 frames.], tot_loss[loss=0.137, simple_loss=0.2108, pruned_loss=0.03158, over 973920.24 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:28:48,389 INFO [train.py:715] (1/8) Epoch 12, batch 26100, loss[loss=0.1615, simple_loss=0.2235, pruned_loss=0.04973, over 4860.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2107, pruned_loss=0.03133, over 972916.83 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:29:27,206 INFO [train.py:715] (1/8) Epoch 12, batch 26150, loss[loss=0.1499, simple_loss=0.2216, pruned_loss=0.03906, over 4846.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.0316, over 973454.61 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:30:06,156 INFO [train.py:715] (1/8) Epoch 12, batch 26200, loss[loss=0.1083, simple_loss=0.183, pruned_loss=0.0168, over 4753.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03149, over 974062.05 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:30:44,509 INFO [train.py:715] (1/8) Epoch 12, batch 26250, loss[loss=0.1436, simple_loss=0.217, pruned_loss=0.03508, over 4812.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03154, over 973007.90 frames.], batch size: 25, lr: 1.79e-04
2022-05-07 13:31:23,012 INFO [train.py:715] (1/8) Epoch 12, batch 26300, loss[loss=0.1363, simple_loss=0.2052, pruned_loss=0.0337, over 4771.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03096, over 973002.53 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:32:02,156 INFO [train.py:715] (1/8) Epoch 12, batch 26350, loss[loss=0.1358, simple_loss=0.2028, pruned_loss=0.03444, over 4801.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03086, over 972635.06 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:32:40,223 INFO [train.py:715] (1/8) Epoch 12, batch 26400, loss[loss=0.1548, simple_loss=0.2289, pruned_loss=0.04039, over 4877.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03092, over 973599.08 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:33:18,357 INFO [train.py:715] (1/8) Epoch 12, batch 26450, loss[loss=0.1961, simple_loss=0.2746, pruned_loss=0.05883, over 4787.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2112, pruned_loss=0.03152, over 973454.41 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:33:56,295 INFO [train.py:715] (1/8) Epoch 12, batch 26500, loss[loss=0.1128, simple_loss=0.1884, pruned_loss=0.01861, over 4913.00 frames.], tot_loss[loss=0.1369, simple_loss=0.211, pruned_loss=0.03142, over 972526.80 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:34:34,594 INFO [train.py:715] (1/8) Epoch 12, batch 26550, loss[loss=0.1366, simple_loss=0.2109, pruned_loss=0.03112, over 4908.00 frames.], tot_loss[loss=0.1371, simple_loss=0.211, pruned_loss=0.03163, over 972077.58 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:35:12,935 INFO [train.py:715] (1/8) Epoch 12, batch 26600, loss[loss=0.1428, simple_loss=0.2158, pruned_loss=0.03487, over 4814.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2112, pruned_loss=0.03159, over 971459.68 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:35:51,458 INFO [train.py:715] (1/8) Epoch 12, batch 26650, loss[loss=0.1451, simple_loss=0.2149, pruned_loss=0.03764, over 4927.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2109, pruned_loss=0.03126, over 972166.40 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:36:30,042 INFO [train.py:715] (1/8) Epoch 12, batch 26700, loss[loss=0.1478, simple_loss=0.2262, pruned_loss=0.03468, over 4919.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2125, pruned_loss=0.03204, over 972026.77 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:37:08,439 INFO [train.py:715] (1/8) Epoch 12, batch 26750, loss[loss=0.1632, simple_loss=0.2287, pruned_loss=0.04883, over 4795.00 frames.], tot_loss[loss=0.138, simple_loss=0.2117, pruned_loss=0.03216, over 971483.04 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:37:47,893 INFO [train.py:715] (1/8) Epoch 12, batch 26800, loss[loss=0.1173, simple_loss=0.1874, pruned_loss=0.02367, over 4848.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03214, over 971686.45 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:38:27,723 INFO [train.py:715] (1/8) Epoch 12, batch 26850, loss[loss=0.1418, simple_loss=0.2091, pruned_loss=0.03719, over 4748.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.03178, over 971877.71 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:39:07,111 INFO [train.py:715] (1/8) Epoch 12, batch 26900, loss[loss=0.1353, simple_loss=0.2095, pruned_loss=0.03057, over 4867.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03169, over 971258.64 frames.], batch size: 20, lr: 1.79e-04
2022-05-07 13:39:45,937 INFO [train.py:715] (1/8) Epoch 12, batch 26950, loss[loss=0.1503, simple_loss=0.2224, pruned_loss=0.03905, over 4643.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2106, pruned_loss=0.03189, over 971930.75 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:40:25,469 INFO [train.py:715] (1/8) Epoch 12, batch 27000, loss[loss=0.1227, simple_loss=0.2015, pruned_loss=0.02192, over 4812.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03144, over 972028.78 frames.], batch size: 25, lr: 1.79e-04
2022-05-07 13:40:25,469 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 13:40:37,912 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1054, simple_loss=0.1894, pruned_loss=0.01072, over 914524.00 frames.
2022-05-07 13:41:17,234 INFO [train.py:715] (1/8) Epoch 12, batch 27050, loss[loss=0.1169, simple_loss=0.1908, pruned_loss=0.02152, over 4903.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03121, over 972119.11 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:41:55,465 INFO [train.py:715] (1/8) Epoch 12, batch 27100, loss[loss=0.1105, simple_loss=0.1837, pruned_loss=0.01865, over 4809.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03137, over 973000.97 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:42:33,821 INFO [train.py:715] (1/8) Epoch 12, batch 27150, loss[loss=0.1233, simple_loss=0.1889, pruned_loss=0.02882, over 4782.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2106, pruned_loss=0.03193, over 972072.65 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:43:12,678 INFO [train.py:715] (1/8) Epoch 12, batch 27200, loss[loss=0.1355, simple_loss=0.212, pruned_loss=0.02953, over 4917.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2108, pruned_loss=0.0315, over 972272.04 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:43:50,979 INFO [train.py:715] (1/8) Epoch 12, batch 27250, loss[loss=0.1175, simple_loss=0.2041, pruned_loss=0.0155, over 4976.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.0315, over 972156.08 frames.], batch size: 28, lr: 1.79e-04
2022-05-07 13:44:29,602 INFO [train.py:715] (1/8) Epoch 12, batch 27300, loss[loss=0.1212, simple_loss=0.1841, pruned_loss=0.02912, over 4852.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03163, over 972462.69 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:45:08,187 INFO [train.py:715] (1/8) Epoch 12, batch 27350, loss[loss=0.1069, simple_loss=0.1812, pruned_loss=0.01633, over 4774.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03196, over 972041.95 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:45:47,172 INFO [train.py:715] (1/8) Epoch 12, batch 27400, loss[loss=0.1308, simple_loss=0.2043, pruned_loss=0.02865, over 4804.00 frames.], tot_loss[loss=0.1369, simple_loss=0.21, pruned_loss=0.03184, over 972391.69 frames.], batch size: 26, lr: 1.79e-04
2022-05-07 13:46:25,848 INFO [train.py:715] (1/8) Epoch 12, batch 27450, loss[loss=0.1669, simple_loss=0.2267, pruned_loss=0.05356, over 4982.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.03234, over 973249.46 frames.], batch size: 35, lr: 1.79e-04
2022-05-07 13:47:04,345 INFO [train.py:715] (1/8) Epoch 12, batch 27500, loss[loss=0.1305, simple_loss=0.2157, pruned_loss=0.02258, over 4981.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03213, over 973254.25 frames.], batch size: 25, lr: 1.79e-04
2022-05-07 13:47:43,119 INFO [train.py:715] (1/8) Epoch 12, batch 27550, loss[loss=0.1255, simple_loss=0.2005, pruned_loss=0.02528, over 4921.00 frames.], tot_loss[loss=0.1371, simple_loss=0.21, pruned_loss=0.03208, over 973800.54 frames.], batch size: 23, lr: 1.79e-04
2022-05-07 13:48:21,800 INFO [train.py:715] (1/8) Epoch 12, batch 27600, loss[loss=0.1209, simple_loss=0.211, pruned_loss=0.01538, over 4877.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03183, over 972915.56 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 13:49:00,950 INFO [train.py:715] (1/8) Epoch 12, batch 27650, loss[loss=0.1418, simple_loss=0.2087, pruned_loss=0.03747, over 4774.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03225, over 972666.43 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:49:39,561 INFO [train.py:715] (1/8) Epoch 12, batch 27700, loss[loss=0.1203, simple_loss=0.1908, pruned_loss=0.02486, over 4794.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03203, over 972316.51 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:50:18,425 INFO [train.py:715] (1/8) Epoch 12, batch 27750, loss[loss=0.1184, simple_loss=0.196, pruned_loss=0.02045, over 4953.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03169, over 972705.74 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:50:56,330 INFO [train.py:715] (1/8) Epoch 12, batch 27800, loss[loss=0.1418, simple_loss=0.2213, pruned_loss=0.03115, over 4778.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2096, pruned_loss=0.03184, over 972652.08 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:51:34,005 INFO [train.py:715] (1/8) Epoch 12, batch 27850, loss[loss=0.129, simple_loss=0.2086, pruned_loss=0.02472, over 4811.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03097, over 972018.52 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 13:52:12,425 INFO [train.py:715] (1/8) Epoch 12, batch 27900, loss[loss=0.1153, simple_loss=0.189, pruned_loss=0.02082, over 4960.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.0308, over 971485.51 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:52:50,386 INFO [train.py:715] (1/8) Epoch 12, batch 27950, loss[loss=0.1307, simple_loss=0.2126, pruned_loss=0.02445, over 4883.00 frames.], tot_loss[loss=0.136, simple_loss=0.2088, pruned_loss=0.03162, over 971269.11 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:53:28,678 INFO [train.py:715] (1/8) Epoch 12, batch 28000, loss[loss=0.1484, simple_loss=0.2207, pruned_loss=0.0381, over 4961.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2092, pruned_loss=0.03166, over 973216.47 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:54:06,333 INFO [train.py:715] (1/8) Epoch 12, batch 28050, loss[loss=0.1666, simple_loss=0.2404, pruned_loss=0.04639, over 4897.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2106, pruned_loss=0.03218, over 973324.49 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:54:44,517 INFO [train.py:715] (1/8) Epoch 12, batch 28100, loss[loss=0.1266, simple_loss=0.2087, pruned_loss=0.02229, over 4882.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03185, over 972598.78 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:55:22,261 INFO [train.py:715] (1/8) Epoch 12, batch 28150, loss[loss=0.131, simple_loss=0.2097, pruned_loss=0.02611, over 4882.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.0319, over 972321.18 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 13:56:00,668 INFO [train.py:715] (1/8) Epoch 12, batch 28200, loss[loss=0.1352, simple_loss=0.2096, pruned_loss=0.03038, over 4905.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03196, over 972682.37 frames.], batch size: 39, lr: 1.79e-04
2022-05-07 13:56:39,079 INFO [train.py:715] (1/8) Epoch 12, batch 28250, loss[loss=0.1148, simple_loss=0.1795, pruned_loss=0.02509, over 4774.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2108, pruned_loss=0.03245, over 972319.77 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:57:17,046 INFO [train.py:715] (1/8) Epoch 12, batch 28300, loss[loss=0.1291, simple_loss=0.2076, pruned_loss=0.02532, over 4805.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03227, over 971822.32 frames.], batch size: 26, lr: 1.79e-04
2022-05-07 13:57:55,849 INFO [train.py:715] (1/8) Epoch 12, batch 28350, loss[loss=0.1462, simple_loss=0.2111, pruned_loss=0.0406, over 4870.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03251, over 972431.72 frames.], batch size: 32, lr: 1.79e-04
2022-05-07 13:58:33,888 INFO [train.py:715] (1/8) Epoch 12, batch 28400, loss[loss=0.1314, simple_loss=0.2084, pruned_loss=0.02717, over 4799.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03229, over 972130.48 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 13:59:12,074 INFO [train.py:715] (1/8) Epoch 12, batch 28450, loss[loss=0.1364, simple_loss=0.2049, pruned_loss=0.03392, over 4774.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03171, over 972825.35 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:59:49,935 INFO [train.py:715] (1/8) Epoch 12, batch 28500, loss[loss=0.1337, simple_loss=0.2155, pruned_loss=0.02597, over 4897.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03225, over 972164.18 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 14:00:27,902 INFO [train.py:715] (1/8) Epoch 12, batch 28550, loss[loss=0.1268, simple_loss=0.2035, pruned_loss=0.02506, over 4992.00 frames.], tot_loss[loss=0.137, simple_loss=0.2099, pruned_loss=0.032, over 972872.04 frames.], batch size: 28, lr: 1.79e-04
2022-05-07 14:01:06,321 INFO [train.py:715] (1/8) Epoch 12, batch 28600, loss[loss=0.1323, simple_loss=0.1964, pruned_loss=0.03404, over 4852.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2103, pruned_loss=0.03211, over 973105.67 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 14:01:44,217 INFO [train.py:715] (1/8) Epoch 12, batch 28650, loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03229, over 4752.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.0318, over 972753.29 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 14:02:23,316 INFO [train.py:715] (1/8) Epoch 12, batch 28700, loss[loss=0.1511, simple_loss=0.2381, pruned_loss=0.03207, over 4775.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03131, over 972217.09 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 14:03:01,841 INFO [train.py:715] (1/8) Epoch 12, batch 28750, loss[loss=0.1297, simple_loss=0.2034, pruned_loss=0.02799, over 4925.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03139, over 972240.00 frames.], batch size: 29, lr: 1.79e-04
2022-05-07 14:03:40,808 INFO [train.py:715] (1/8) Epoch 12, batch 28800, loss[loss=0.1237, simple_loss=0.1972, pruned_loss=0.02513, over 4757.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.03155, over 972895.34 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 14:04:18,679 INFO [train.py:715] (1/8) Epoch 12, batch 28850, loss[loss=0.137, simple_loss=0.2115, pruned_loss=0.03127, over 4971.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03139, over 972165.27 frames.], batch size: 28, lr: 1.79e-04
2022-05-07 14:04:57,034 INFO [train.py:715] (1/8) Epoch 12, batch 28900, loss[loss=0.1193, simple_loss=0.1903, pruned_loss=0.02415, over 4913.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03148, over 971849.35 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:05:35,792 INFO [train.py:715] (1/8) Epoch 12, batch 28950, loss[loss=0.1426, simple_loss=0.2017, pruned_loss=0.04178, over 4777.00 frames.], tot_loss[loss=0.1359, simple_loss=0.209, pruned_loss=0.03141, over 972238.59 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:06:14,146 INFO [train.py:715] (1/8) Epoch 12, batch 29000, loss[loss=0.1218, simple_loss=0.1966, pruned_loss=0.02344, over 4790.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03102, over 972428.57 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:06:53,419 INFO [train.py:715] (1/8) Epoch 12, batch 29050, loss[loss=0.1385, simple_loss=0.2125, pruned_loss=0.03227, over 4710.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03131, over 971836.20 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:07:31,885 INFO [train.py:715] (1/8) Epoch 12, batch 29100, loss[loss=0.1486, simple_loss=0.2243, pruned_loss=0.03644, over 4792.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.03125, over 972080.94 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:08:10,554 INFO [train.py:715] (1/8) Epoch 12, batch 29150, loss[loss=0.1185, simple_loss=0.1937, pruned_loss=0.02161, over 4977.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.0312, over 972269.95 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 14:08:48,982 INFO [train.py:715] (1/8) Epoch 12, batch 29200, loss[loss=0.1459, simple_loss=0.2165, pruned_loss=0.03766, over 4905.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03117, over 972815.76 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:09:27,677 INFO [train.py:715] (1/8) Epoch 12, batch 29250, loss[loss=0.129, simple_loss=0.2086, pruned_loss=0.02476, over 4816.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03147, over 972918.74 frames.], batch size: 25, lr: 1.78e-04
2022-05-07 14:10:05,811 INFO [train.py:715] (1/8) Epoch 12, batch 29300, loss[loss=0.125, simple_loss=0.2017, pruned_loss=0.02412, over 4957.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03149, over 972022.35 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:10:43,236 INFO [train.py:715] (1/8) Epoch 12, batch 29350, loss[loss=0.1411, simple_loss=0.2196, pruned_loss=0.03134, over 4955.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03123, over 972317.69 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:11:22,340 INFO [train.py:715] (1/8) Epoch 12, batch 29400, loss[loss=0.1378, simple_loss=0.2172, pruned_loss=0.02918, over 4698.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03149, over 972007.33 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:12:00,594 INFO [train.py:715] (1/8) Epoch 12, batch 29450, loss[loss=0.1496, simple_loss=0.2319, pruned_loss=0.03368, over 4964.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03146, over 972492.85 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:12:38,753 INFO [train.py:715] (1/8) Epoch 12, batch 29500, loss[loss=0.1292, simple_loss=0.201, pruned_loss=0.02868, over 4977.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2113, pruned_loss=0.03185, over 972412.91 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:13:16,879 INFO [train.py:715] (1/8) Epoch 12, batch 29550, loss[loss=0.1485, simple_loss=0.2217, pruned_loss=0.03762, over 4981.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03147, over 973153.45 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 14:13:55,810 INFO [train.py:715] (1/8) Epoch 12, batch 29600, loss[loss=0.1434, simple_loss=0.2219, pruned_loss=0.03244, over 4769.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03177, over 973247.93 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:14:34,034 INFO [train.py:715] (1/8) Epoch 12, batch 29650, loss[loss=0.1271, simple_loss=0.2026, pruned_loss=0.02582, over 4795.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03157, over 973653.86 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 14:15:11,743 INFO [train.py:715] (1/8) Epoch 12, batch 29700, loss[loss=0.1429, simple_loss=0.2147, pruned_loss=0.03556, over 4963.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03127, over 974188.28 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:15:51,300 INFO [train.py:715] (1/8) Epoch 12, batch 29750, loss[loss=0.1341, simple_loss=0.2056, pruned_loss=0.03132, over 4828.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.03166, over 974344.34 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:16:30,411 INFO [train.py:715] (1/8) Epoch 12, batch 29800, loss[loss=0.1477, simple_loss=0.2171, pruned_loss=0.03919, over 4963.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03134, over 974082.62 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 14:17:09,226 INFO [train.py:715] (1/8) Epoch 12, batch 29850, loss[loss=0.1862, simple_loss=0.2674, pruned_loss=0.05252, over 4887.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03173, over 973750.79 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:17:47,535 INFO [train.py:715] (1/8) Epoch 12, batch 29900, loss[loss=0.1554, simple_loss=0.2268, pruned_loss=0.04204, over 4690.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03236, over 973865.56 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:18:26,387 INFO [train.py:715] (1/8) Epoch 12, batch 29950, loss[loss=0.1236, simple_loss=0.1943, pruned_loss=0.02648, over 4923.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03288, over 973804.45 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:19:04,511 INFO [train.py:715] (1/8) Epoch 12, batch 30000, loss[loss=0.1551, simple_loss=0.2242, pruned_loss=0.04305, over 4953.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03287, over 974798.28 frames.], batch size: 35, lr: 1.78e-04
2022-05-07 14:19:04,511 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 14:19:14,013 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1054, simple_loss=0.1894, pruned_loss=0.01072, over 914524.00 frames.
2022-05-07 14:19:52,928 INFO [train.py:715] (1/8) Epoch 12, batch 30050, loss[loss=0.1541, simple_loss=0.2232, pruned_loss=0.04248, over 4837.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2114, pruned_loss=0.03279, over 974608.06 frames.], batch size: 30, lr: 1.78e-04
2022-05-07 14:20:31,331 INFO [train.py:715] (1/8) Epoch 12, batch 30100, loss[loss=0.1358, simple_loss=0.1999, pruned_loss=0.0359, over 4821.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03262, over 974236.38 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:21:10,497 INFO [train.py:715] (1/8) Epoch 12, batch 30150, loss[loss=0.1267, simple_loss=0.2086, pruned_loss=0.02242, over 4894.00 frames.], tot_loss[loss=0.1381, simple_loss=0.211, pruned_loss=0.03259, over 974234.58 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:21:48,960 INFO [train.py:715] (1/8) Epoch 12, batch 30200, loss[loss=0.1014, simple_loss=0.1692, pruned_loss=0.01677, over 4773.00 frames.], tot_loss[loss=0.138, simple_loss=0.2108, pruned_loss=0.03258, over 974056.28 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 14:22:28,441 INFO [train.py:715] (1/8) Epoch 12, batch 30250, loss[loss=0.13, simple_loss=0.2084, pruned_loss=0.02578, over 4799.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03199, over 973318.71 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:23:07,605 INFO [train.py:715] (1/8) Epoch 12, batch 30300, loss[loss=0.1397, simple_loss=0.2013, pruned_loss=0.03907, over 4772.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.0319, over 972712.99 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 14:23:45,575 INFO [train.py:715] (1/8) Epoch 12, batch 30350, loss[loss=0.1331, simple_loss=0.2096, pruned_loss=0.02823, over 4975.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03152, over 973736.24 frames.], batch size: 31, lr: 1.78e-04
2022-05-07 14:24:23,564 INFO [train.py:715] (1/8) Epoch 12, batch 30400, loss[loss=0.1649, simple_loss=0.2426, pruned_loss=0.04358, over 4969.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.0312, over 972642.48 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:25:01,299 INFO [train.py:715] (1/8) Epoch 12, batch 30450, loss[loss=0.1274, simple_loss=0.2007, pruned_loss=0.02707, over 4967.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.0312, over 972968.65 frames.], batch size: 28, lr: 1.78e-04
2022-05-07 14:25:39,285 INFO [train.py:715] (1/8) Epoch 12, batch 30500, loss[loss=0.1313, simple_loss=0.2035, pruned_loss=0.02952, over 4814.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03128, over 973921.47 frames.], batch size: 26, lr: 1.78e-04
2022-05-07 14:26:17,291 INFO [train.py:715] (1/8) Epoch 12, batch 30550, loss[loss=0.1357, simple_loss=0.2097, pruned_loss=0.03081, over 4960.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03121, over 973454.76 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:26:55,238 INFO [train.py:715] (1/8) Epoch 12, batch 30600, loss[loss=0.1261, simple_loss=0.2041, pruned_loss=0.02405, over 4974.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03087, over 973605.93 frames.], batch size: 28, lr: 1.78e-04
2022-05-07 14:27:32,192 INFO [train.py:715] (1/8) Epoch 12, batch 30650, loss[loss=0.1641, simple_loss=0.2381, pruned_loss=0.04499, over 4885.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.03095, over 972964.88 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 14:28:10,733 INFO [train.py:715] (1/8) Epoch 12, batch 30700, loss[loss=0.1221, simple_loss=0.1978, pruned_loss=0.02319, over 4744.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2101, pruned_loss=0.03109, over 972547.77 frames.], batch size: 16, lr: 1.78e-04
2022-05-07 14:28:48,656 INFO [train.py:715] (1/8) Epoch 12, batch 30750, loss[loss=0.1178, simple_loss=0.2005, pruned_loss=0.01757, over 4896.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03127, over 972765.46 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:29:27,169 INFO [train.py:715] (1/8) Epoch 12, batch 30800, loss[loss=0.1699, simple_loss=0.2303, pruned_loss=0.05473, over 4888.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03162, over 973481.53 frames.], batch size: 16, lr: 1.78e-04
2022-05-07 14:30:05,817 INFO [train.py:715] (1/8) Epoch 12, batch 30850, loss[loss=0.1267, simple_loss=0.207, pruned_loss=0.02314, over 4806.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03163, over 972780.60 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 14:30:45,016 INFO [train.py:715] (1/8) Epoch 12, batch 30900, loss[loss=0.1407, simple_loss=0.2111, pruned_loss=0.03512, over 4931.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03125, over 972673.56 frames.], batch size: 23, lr: 1.78e-04
2022-05-07 14:31:23,360 INFO [train.py:715] (1/8) Epoch 12, batch 30950, loss[loss=0.1679, simple_loss=0.2384, pruned_loss=0.04873, over 4916.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03148, over 972659.24 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 14:32:02,067 INFO [train.py:715] (1/8) Epoch 12, batch 31000, loss[loss=0.1214, simple_loss=0.1972, pruned_loss=0.02275, over 4962.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03168, over 972748.06 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:32:41,211 INFO [train.py:715] (1/8) Epoch 12, batch 31050, loss[loss=0.1651, simple_loss=0.2284, pruned_loss=0.05089, over 4774.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03199, over 972266.67 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:33:19,692 INFO [train.py:715] (1/8) Epoch 12, batch 31100, loss[loss=0.1353, simple_loss=0.2099, pruned_loss=0.03038, over 4940.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03209, over 971186.32 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:33:57,508 INFO [train.py:715] (1/8) Epoch 12, batch 31150, loss[loss=0.1229, simple_loss=0.196, pruned_loss=0.02487, over 4833.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2105, pruned_loss=0.0325, over 971008.86 frames.], batch size: 26, lr: 1.78e-04
2022-05-07 14:34:36,522 INFO [train.py:715] (1/8) Epoch 12, batch 31200, loss[loss=0.1446, simple_loss=0.2132, pruned_loss=0.03802, over 4990.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2104, pruned_loss=0.03258, over 970875.37 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 14:35:15,361 INFO [train.py:715] (1/8) Epoch 12, batch 31250, loss[loss=0.119, simple_loss=0.1953, pruned_loss=0.02137, over 4881.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03167, over 971007.08 frames.], batch size: 16, lr: 1.78e-04
2022-05-07 14:35:54,066 INFO [train.py:715] (1/8) Epoch 12, batch 31300, loss[loss=0.12, simple_loss=0.1941, pruned_loss=0.02299, over 4921.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2103, pruned_loss=0.03197, over 971530.69 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:36:32,586 INFO [train.py:715] (1/8) Epoch 12, batch 31350, loss[loss=0.1198, simple_loss=0.1918, pruned_loss=0.02387, over 4795.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.0316, over 971748.66 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:37:11,753 INFO [train.py:715] (1/8) Epoch 12, batch 31400, loss[loss=0.1475, simple_loss=0.2223, pruned_loss=0.03638, over 4763.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03163, over 971272.78 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:37:50,156 INFO [train.py:715] (1/8) Epoch 12, batch 31450, loss[loss=0.1615, simple_loss=0.2328, pruned_loss=0.04507, over 4917.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03159, over 971166.57 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:38:28,381 INFO [train.py:715] (1/8) Epoch 12, batch 31500, loss[loss=0.1535, simple_loss=0.2263, pruned_loss=0.04032, over 4895.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2109, pruned_loss=0.03169, over 971877.85 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 14:39:06,659 INFO [train.py:715] (1/8) Epoch 12, batch 31550, loss[loss=0.1287, simple_loss=0.2108, pruned_loss=0.0233, over 4954.00 frames.], tot_loss[loss=0.138, simple_loss=0.2119, pruned_loss=0.03209, over 971537.37 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:39:45,223 INFO [train.py:715] (1/8) Epoch 12, batch 31600, loss[loss=0.1248, simple_loss=0.2071, pruned_loss=0.02127, over 4909.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2117, pruned_loss=0.03193, over 971816.29 frames.], batch size: 22, lr: 1.78e-04
2022-05-07 14:40:22,892 INFO [train.py:715] (1/8) Epoch 12, batch 31650, loss[loss=0.1625, simple_loss=0.2353, pruned_loss=0.04489, over 4747.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03256, over 971508.30 frames.], batch size: 16, lr: 1.78e-04
2022-05-07 14:41:00,520 INFO [train.py:715] (1/8) Epoch 12, batch 31700, loss[loss=0.111, simple_loss=0.1818, pruned_loss=0.02008, over 4796.00 frames.], tot_loss[loss=0.1383, simple_loss=0.212, pruned_loss=0.03232, over 972305.08 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 14:41:38,634 INFO [train.py:715] (1/8) Epoch 12, batch 31750, loss[loss=0.1105, simple_loss=0.1871, pruned_loss=0.01698, over 4914.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2125, pruned_loss=0.03259, over 972882.95 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:42:16,748 INFO [train.py:715] (1/8) Epoch 12, batch 31800, loss[loss=0.1191, simple_loss=0.1953, pruned_loss=0.02141, over 4780.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03225, over 972099.50 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:42:54,717 INFO [train.py:715] (1/8) Epoch 12, batch 31850, loss[loss=0.1027, simple_loss=0.1682, pruned_loss=0.01862, over 4822.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03223, over 971336.96 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:43:32,405 INFO [train.py:715] (1/8) Epoch 12, batch 31900, loss[loss=0.1449, simple_loss=0.2084, pruned_loss=0.04067, over 4968.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03212, over 971686.06 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:44:10,699 INFO [train.py:715] (1/8) Epoch 12, batch 31950, loss[loss=0.1172, simple_loss=0.1921, pruned_loss=0.02113, over 4967.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.03198, over 971093.51 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:44:48,297 INFO [train.py:715] (1/8) Epoch 12, batch 32000, loss[loss=0.1509, simple_loss=0.2184, pruned_loss=0.04167, over 4890.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.03173, over 970566.89 frames.], batch size: 32, lr: 1.78e-04
2022-05-07 14:45:26,166 INFO [train.py:715] (1/8) Epoch 12, batch 32050, loss[loss=0.1237, simple_loss=0.1969, pruned_loss=0.02524, over 4817.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03159, over 971345.90 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 14:46:04,024 INFO [train.py:715] (1/8) Epoch 12, batch 32100, loss[loss=0.1265, simple_loss=0.1946, pruned_loss=0.02919, over 4830.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03155, over 971106.86 frames.], batch size: 30, lr: 1.78e-04
2022-05-07 14:46:42,430 INFO [train.py:715] (1/8) Epoch 12, batch 32150, loss[loss=0.1281, simple_loss=0.1973, pruned_loss=0.02942, over 4885.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03132, over 971076.74 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:47:20,026 INFO [train.py:715] (1/8) Epoch 12, batch 32200, loss[loss=0.1276, simple_loss=0.1981, pruned_loss=0.02849, over 4918.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03129, over 971456.08 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:47:58,175 INFO [train.py:715] (1/8) Epoch 12, batch 32250, loss[loss=0.1385, simple_loss=0.2143, pruned_loss=0.03139, over 4782.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03134, over 971604.80 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:48:36,825 INFO [train.py:715] (1/8) Epoch 12, batch 32300, loss[loss=0.1432, simple_loss=0.2155, pruned_loss=0.03545, over 4785.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03186, over 971505.06 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 14:49:14,384 INFO [train.py:715] (1/8) Epoch 12, batch 32350, loss[loss=0.1404, simple_loss=0.2176, pruned_loss=0.03162, over 4989.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.0316, over 971361.21 frames.], batch size: 25, lr: 1.78e-04
2022-05-07 14:49:52,728 INFO [train.py:715] (1/8) Epoch 12, batch 32400, loss[loss=0.1345, simple_loss=0.2188, pruned_loss=0.02508, over 4983.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.0316, over 972224.20 frames.], batch size: 27, lr: 1.78e-04
2022-05-07 14:50:30,828 INFO [train.py:715] (1/8) Epoch 12, batch 32450, loss[loss=0.1432, simple_loss=0.2255, pruned_loss=0.03047, over 4788.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2111, pruned_loss=0.03174, over 972252.44 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:51:09,335 INFO [train.py:715] (1/8) Epoch 12, batch 32500, loss[loss=0.1334, simple_loss=0.2007, pruned_loss=0.03309, over 4899.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2108, pruned_loss=0.03154, over 972017.65 frames.], batch size: 22, lr: 1.78e-04
2022-05-07 14:51:46,832 INFO [train.py:715] (1/8) Epoch 12, batch 32550, loss[loss=0.1573, simple_loss=0.2399, pruned_loss=0.03732, over 4834.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2105, pruned_loss=0.03126, over 971804.18 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:52:25,072 INFO [train.py:715] (1/8) Epoch 12, batch 32600, loss[loss=0.1663, simple_loss=0.2327, pruned_loss=0.04996, over 4910.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03154, over 971855.59 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:53:03,209 INFO [train.py:715] (1/8) Epoch 12, batch 32650, loss[loss=0.1314, simple_loss=0.2042, pruned_loss=0.02928, over 4858.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03148, over 972726.89 frames.], batch size: 20, lr: 1.78e-04
2022-05-07 14:53:40,738 INFO [train.py:715] (1/8) Epoch 12, batch 32700, loss[loss=0.1611, simple_loss=0.2471, pruned_loss=0.03752, over 4985.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03141, over 973793.35 frames.], batch size: 20, lr: 1.78e-04
2022-05-07 14:54:18,462 INFO [train.py:715] (1/8) Epoch 12, batch 32750, loss[loss=0.1378, simple_loss=0.202, pruned_loss=0.03677, over 4778.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.03201, over 974004.13 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:54:56,867 INFO [train.py:715] (1/8) Epoch 12, batch 32800, loss[loss=0.1327, simple_loss=0.206, pruned_loss=0.02966, over 4834.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.0322, over 972718.88 frames.], batch size: 30, lr: 1.78e-04
2022-05-07 14:55:35,237 INFO [train.py:715] (1/8) Epoch 12, batch 32850, loss[loss=0.129, simple_loss=0.2015, pruned_loss=0.02821, over 4838.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03184, over 972111.73 frames.], batch size: 30, lr: 1.78e-04
2022-05-07 14:56:12,924 INFO [train.py:715] (1/8) Epoch 12, batch 32900, loss[loss=0.1382, simple_loss=0.2125, pruned_loss=0.03198, over 4968.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03181, over 972527.92 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:56:51,023 INFO [train.py:715] (1/8) Epoch 12, batch 32950, loss[loss=0.1617, simple_loss=0.2355, pruned_loss=0.04389, over 4905.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.03176, over 972774.55 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:57:29,168 INFO [train.py:715] (1/8) Epoch 12, batch 33000, loss[loss=0.127, simple_loss=0.2038, pruned_loss=0.0251, over 4959.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03151, over 973883.15 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:57:29,169 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 14:57:38,690 INFO [train.py:742] (1/8) Epoch 12, validation: loss=0.1057, simple_loss=0.1896, pruned_loss=0.01085, over 914524.00 frames.
2022-05-07 14:58:18,190 INFO [train.py:715] (1/8) Epoch 12, batch 33050, loss[loss=0.1604, simple_loss=0.2231, pruned_loss=0.04884, over 4746.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03154, over 973237.90 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:58:56,559 INFO [train.py:715] (1/8) Epoch 12, batch 33100, loss[loss=0.1198, simple_loss=0.1914, pruned_loss=0.02416, over 4638.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.03149, over 972336.75 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:59:34,844 INFO [train.py:715] (1/8) Epoch 12, batch 33150, loss[loss=0.138, simple_loss=0.2236, pruned_loss=0.02616, over 4954.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.0312, over 972982.81 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 15:00:12,870 INFO [train.py:715] (1/8) Epoch 12, batch 33200, loss[loss=0.153, simple_loss=0.225, pruned_loss=0.04047, over 4765.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03116, over 973267.18 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 15:00:51,451 INFO [train.py:715] (1/8) Epoch 12, batch 33250, loss[loss=0.1185, simple_loss=0.1955, pruned_loss=0.02075, over 4944.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03122, over 973207.89 frames.], batch size: 23, lr: 1.78e-04
2022-05-07 15:01:29,593 INFO [train.py:715] (1/8) Epoch 12, batch 33300, loss[loss=0.1562, simple_loss=0.2291, pruned_loss=0.04169, over 4913.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03135, over 972910.40 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 15:02:07,723 INFO [train.py:715] (1/8) Epoch 12, batch 33350, loss[loss=0.152, simple_loss=0.2236, pruned_loss=0.04016, over 4919.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03117, over 973094.23 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 15:02:46,384 INFO [train.py:715] (1/8) Epoch 12, batch 33400, loss[loss=0.1253, simple_loss=0.198, pruned_loss=0.02631, over 4980.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03084, over 973364.54 frames.], batch size: 25, lr: 1.78e-04
2022-05-07 15:03:25,038 INFO [train.py:715] (1/8) Epoch 12, batch 33450, loss[loss=0.1002, simple_loss=0.1698, pruned_loss=0.01528, over 4718.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03039, over 972702.87 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 15:04:03,395 INFO [train.py:715] (1/8) Epoch 12, batch 33500, loss[loss=0.1081, simple_loss=0.1777, pruned_loss=0.01928, over 4788.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03095, over 972600.36 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 15:04:42,494 INFO [train.py:715] (1/8) Epoch 12, batch 33550, loss[loss=0.1257, simple_loss=0.2063, pruned_loss=0.02254, over 4932.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03067, over 972585.27 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 15:05:21,128 INFO [train.py:715] (1/8) Epoch 12, batch 33600, loss[loss=0.1208, simple_loss=0.1914, pruned_loss=0.0251, over 4831.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.03065, over 971015.91 frames.], batch size: 26, lr: 1.78e-04
2022-05-07 15:05:59,985 INFO [train.py:715] (1/8) Epoch 12, batch 33650, loss[loss=0.1335, simple_loss=0.2059, pruned_loss=0.03051, over 4824.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03105, over 971578.55 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 15:06:38,064 INFO [train.py:715] (1/8) Epoch 12, batch 33700, loss[loss=0.1174, simple_loss=0.1937, pruned_loss=0.02053, over 4994.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.0313, over 972018.74 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 15:07:16,847 INFO [train.py:715] (1/8) Epoch 12, batch 33750, loss[loss=0.1308, simple_loss=0.2152, pruned_loss=0.02317, over 4706.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03084, over 971489.96 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 15:07:55,115 INFO [train.py:715] (1/8) Epoch 12, batch 33800, loss[loss=0.1338, simple_loss=0.2102, pruned_loss=0.02871, over 4879.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03115, over 971407.74 frames.], batch size: 22, lr: 1.78e-04
2022-05-07 15:08:32,478 INFO [train.py:715] (1/8) Epoch 12, batch 33850, loss[loss=0.1459, simple_loss=0.2124, pruned_loss=0.03964, over 4899.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2106, pruned_loss=0.03144, over 971658.44 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 15:09:10,657 INFO [train.py:715] (1/8) Epoch 12, batch 33900, loss[loss=0.1505, simple_loss=0.2276, pruned_loss=0.03667, over 4835.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03205, over 971364.65 frames.], batch size: 30, lr: 1.78e-04
2022-05-07 15:09:47,910 INFO [train.py:715] (1/8) Epoch 12, batch 33950, loss[loss=0.1293, simple_loss=0.2037, pruned_loss=0.0274, over 4979.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03196, over 972281.16 frames.], batch size: 15, lr: 1.77e-04
2022-05-07 15:10:26,072 INFO [train.py:715] (1/8) Epoch 12, batch 34000, loss[loss=0.126, simple_loss=0.1922, pruned_loss=0.02992, over 4707.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2107, pruned_loss=0.03246, over 972557.80 frames.], batch size: 15, lr: 1.77e-04
2022-05-07 15:11:03,703 INFO [train.py:715] (1/8) Epoch 12, batch 34050, loss[loss=0.1341, simple_loss=0.1927, pruned_loss=0.03773, over 4709.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.0323, over 972717.97 frames.], batch size: 15, lr: 1.77e-04
2022-05-07 15:11:41,638 INFO [train.py:715] (1/8) Epoch 12, batch 34100, loss[loss=0.1232, simple_loss=0.199, pruned_loss=0.02371, over 4990.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03218, over 973201.51 frames.], batch size: 14, lr: 1.77e-04
2022-05-07 15:12:19,674 INFO [train.py:715] (1/8) Epoch 12, batch 34150, loss[loss=0.1497, simple_loss=0.2309, pruned_loss=0.03423, over 4870.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03173, over 972646.14 frames.], batch size: 38, lr: 1.77e-04
2022-05-07 15:12:57,181 INFO [train.py:715] (1/8) Epoch 12, batch 34200, loss[loss=0.1071, simple_loss=0.1817, pruned_loss=0.01621, over 4789.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03107, over 972366.76 frames.], batch size: 24, lr: 1.77e-04
2022-05-07 15:13:35,451 INFO [train.py:715] (1/8) Epoch 12, batch 34250, loss[loss=0.126, simple_loss=0.1874, pruned_loss=0.0323, over 4829.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03101, over 972653.26 frames.], batch size: 26, lr: 1.77e-04
2022-05-07 15:14:12,820 INFO [train.py:715] (1/8) Epoch 12, batch 34300, loss[loss=0.1363, simple_loss=0.2035, pruned_loss=0.03459, over 4844.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2089, pruned_loss=0.0311, over 972681.73 frames.], batch size: 30, lr: 1.77e-04
2022-05-07 15:14:51,108 INFO [train.py:715] (1/8) Epoch 12, batch 34350, loss[loss=0.1178, simple_loss=0.193, pruned_loss=0.0213, over 4881.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03095, over 972089.18 frames.], batch size: 16, lr: 1.77e-04
2022-05-07 15:15:28,882 INFO [train.py:715] (1/8) Epoch 12, batch 34400, loss[loss=0.1347, simple_loss=0.2072, pruned_loss=0.03106, over 4962.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03115, over 972654.45 frames.], batch size: 24, lr: 1.77e-04
2022-05-07 15:16:07,248 INFO [train.py:715] (1/8) Epoch 12, batch 34450, loss[loss=0.1377, simple_loss=0.2067, pruned_loss=0.0343, over 4930.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03118, over 972174.60 frames.], batch size: 29, lr: 1.77e-04
2022-05-07 15:16:45,352 INFO [train.py:715] (1/8) Epoch 12, batch 34500, loss[loss=0.1238, simple_loss=0.2145, pruned_loss=0.0166, over 4777.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03155, over 971516.10 frames.], batch size: 14, lr: 1.77e-04
2022-05-07 15:17:23,597 INFO [train.py:715] (1/8) Epoch 12, batch 34550, loss[loss=0.1509, simple_loss=0.2372, pruned_loss=0.03232, over 4786.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03223, over 971861.75 frames.], batch size: 18, lr: 1.77e-04
2022-05-07 15:18:02,263 INFO [train.py:715] (1/8) Epoch 12, batch 34600, loss[loss=0.1562, simple_loss=0.2195, pruned_loss=0.04643, over 4976.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03229, over 972743.65 frames.], batch size: 25, lr: 1.77e-04
2022-05-07 15:18:41,676 INFO [train.py:715] (1/8) Epoch 12, batch 34650, loss[loss=0.1278, simple_loss=0.2098, pruned_loss=0.02287, over 4959.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03252, over 972251.04 frames.], batch size: 28, lr: 1.77e-04
2022-05-07 15:19:21,044 INFO [train.py:715] (1/8) Epoch 12, batch 34700, loss[loss=0.1101, simple_loss=0.1866, pruned_loss=0.01683, over 4919.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03263, over 971893.00 frames.], batch size: 29, lr: 1.77e-04
2022-05-07 15:19:58,686 INFO [train.py:715] (1/8) Epoch 12, batch 34750, loss[loss=0.1536, simple_loss=0.2322, pruned_loss=0.03753, over 4761.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03214, over 971971.30 frames.], batch size: 19, lr: 1.77e-04
2022-05-07 15:20:34,682 INFO [train.py:715] (1/8) Epoch 12, batch 34800, loss[loss=0.1306, simple_loss=0.2041, pruned_loss=0.02854, over 4935.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03234, over 971202.04 frames.], batch size: 18, lr: 1.77e-04
2022-05-07 15:21:23,127 INFO [train.py:715] (1/8) Epoch 13, batch 0, loss[loss=0.1213, simple_loss=0.2006, pruned_loss=0.021, over 4954.00 frames.], tot_loss[loss=0.1213, simple_loss=0.2006, pruned_loss=0.021, over 4954.00 frames.], batch size: 24, lr: 1.71e-04
2022-05-07 15:22:01,156 INFO [train.py:715] (1/8) Epoch 13, batch 50, loss[loss=0.1478, simple_loss=0.2152, pruned_loss=0.04018, over 4851.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2099, pruned_loss=0.03323, over 220030.69 frames.], batch size: 30, lr: 1.71e-04
2022-05-07 15:22:39,465 INFO [train.py:715] (1/8) Epoch 13, batch 100, loss[loss=0.1548, simple_loss=0.2263, pruned_loss=0.04166, over 4896.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2111, pruned_loss=0.0328, over 387481.02 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:23:17,863 INFO [train.py:715] (1/8) Epoch 13, batch 150, loss[loss=0.137, simple_loss=0.21, pruned_loss=0.03201, over 4975.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2106, pruned_loss=0.03227, over 517867.66 frames.], batch size: 27, lr: 1.71e-04
2022-05-07 15:23:57,326 INFO [train.py:715] (1/8) Epoch 13, batch 200, loss[loss=0.1352, simple_loss=0.2019, pruned_loss=0.03422, over 4777.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2096, pruned_loss=0.03203, over 618531.13 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:24:35,739 INFO [train.py:715] (1/8) Epoch 13, batch 250, loss[loss=0.1069, simple_loss=0.1764, pruned_loss=0.0187, over 4828.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2086, pruned_loss=0.03141, over 696454.84 frames.], batch size: 13, lr: 1.71e-04
2022-05-07 15:25:15,234 INFO [train.py:715] (1/8) Epoch 13, batch 300, loss[loss=0.126, simple_loss=0.2045, pruned_loss=0.02377, over 4885.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2072, pruned_loss=0.03087, over 757615.80 frames.], batch size: 19, lr: 1.71e-04
2022-05-07 15:25:53,994 INFO [train.py:715] (1/8) Epoch 13, batch 350, loss[loss=0.1329, simple_loss=0.2015, pruned_loss=0.03211, over 4766.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2071, pruned_loss=0.03071, over 805300.67 frames.], batch size: 12, lr: 1.71e-04
2022-05-07 15:26:33,533 INFO [train.py:715] (1/8) Epoch 13, batch 400, loss[loss=0.136, simple_loss=0.2111, pruned_loss=0.03049, over 4982.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2085, pruned_loss=0.03102, over 841713.60 frames.], batch size: 25, lr: 1.71e-04
2022-05-07 15:27:13,024 INFO [train.py:715] (1/8) Epoch 13, batch 450, loss[loss=0.1548, simple_loss=0.2316, pruned_loss=0.03901, over 4842.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03174, over 870784.17 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:27:53,172 INFO [train.py:715] (1/8) Epoch 13, batch 500, loss[loss=0.1116, simple_loss=0.1795, pruned_loss=0.02183, over 4863.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2083, pruned_loss=0.03122, over 893322.97 frames.], batch size: 13, lr: 1.71e-04
2022-05-07 15:28:33,645 INFO [train.py:715] (1/8) Epoch 13, batch 550, loss[loss=0.1398, simple_loss=0.2222, pruned_loss=0.02872, over 4774.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2082, pruned_loss=0.03115, over 910279.45 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:29:12,912 INFO [train.py:715] (1/8) Epoch 13, batch 600, loss[loss=0.1537, simple_loss=0.2305, pruned_loss=0.03846, over 4822.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2088, pruned_loss=0.03141, over 924301.87 frames.], batch size: 26, lr: 1.71e-04
2022-05-07 15:29:53,386 INFO [train.py:715] (1/8) Epoch 13, batch 650, loss[loss=0.1603, simple_loss=0.2358, pruned_loss=0.04238, over 4883.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2094, pruned_loss=0.03171, over 935577.11 frames.], batch size: 22, lr: 1.71e-04
2022-05-07 15:30:33,369 INFO [train.py:715] (1/8) Epoch 13, batch 700, loss[loss=0.1303, simple_loss=0.2054, pruned_loss=0.02763, over 4744.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2092, pruned_loss=0.03165, over 944200.17 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:31:13,980 INFO [train.py:715] (1/8) Epoch 13, batch 750, loss[loss=0.1186, simple_loss=0.1846, pruned_loss=0.02633, over 4739.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2082, pruned_loss=0.03131, over 950999.96 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:31:53,298 INFO [train.py:715] (1/8) Epoch 13, batch 800, loss[loss=0.1358, simple_loss=0.2122, pruned_loss=0.02972, over 4882.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2095, pruned_loss=0.03179, over 956330.13 frames.], batch size: 22, lr: 1.71e-04
2022-05-07 15:32:32,562 INFO [train.py:715] (1/8) Epoch 13, batch 850, loss[loss=0.1598, simple_loss=0.2315, pruned_loss=0.04407, over 4939.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03217, over 960562.06 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:33:12,800 INFO [train.py:715] (1/8) Epoch 13, batch 900, loss[loss=0.1532, simple_loss=0.2252, pruned_loss=0.04062, over 4852.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2098, pruned_loss=0.0318, over 963093.91 frames.], batch size: 20, lr: 1.71e-04
2022-05-07 15:33:52,197 INFO [train.py:715] (1/8) Epoch 13, batch 950, loss[loss=0.1253, simple_loss=0.2053, pruned_loss=0.02269, over 4815.00 frames.], tot_loss[loss=0.1358, simple_loss=0.209, pruned_loss=0.0313, over 966091.69 frames.], batch size: 26, lr: 1.71e-04
2022-05-07 15:34:32,776 INFO [train.py:715] (1/8) Epoch 13, batch 1000, loss[loss=0.1348, simple_loss=0.2072, pruned_loss=0.03118, over 4882.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2088, pruned_loss=0.03169, over 966940.60 frames.], batch size: 22, lr: 1.71e-04
2022-05-07 15:35:12,239 INFO [train.py:715] (1/8) Epoch 13, batch 1050, loss[loss=0.1319, simple_loss=0.2069, pruned_loss=0.02849, over 4985.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2093, pruned_loss=0.03206, over 967920.92 frames.], batch size: 26, lr: 1.71e-04
2022-05-07 15:35:52,557 INFO [train.py:715] (1/8) Epoch 13, batch 1100, loss[loss=0.1362, simple_loss=0.2153, pruned_loss=0.02857, over 4789.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2094, pruned_loss=0.03167, over 969030.19 frames.], batch size: 14, lr: 1.71e-04
2022-05-07 15:36:32,011 INFO [train.py:715] (1/8) Epoch 13, batch 1150, loss[loss=0.1202, simple_loss=0.2034, pruned_loss=0.01853, over 4797.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03151, over 969376.29 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:37:11,799 INFO [train.py:715] (1/8) Epoch 13, batch 1200, loss[loss=0.1383, simple_loss=0.1993, pruned_loss=0.0386, over 4877.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.0318, over 970304.07 frames.], batch size: 32, lr: 1.71e-04
2022-05-07 15:37:52,138 INFO [train.py:715] (1/8) Epoch 13, batch 1250, loss[loss=0.1353, simple_loss=0.21, pruned_loss=0.03027, over 4910.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2094, pruned_loss=0.03156, over 970659.12 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:38:31,100 INFO [train.py:715] (1/8) Epoch 13, batch 1300, loss[loss=0.1472, simple_loss=0.2246, pruned_loss=0.03494, over 4852.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03162, over 971245.75 frames.], batch size: 38, lr: 1.71e-04
2022-05-07 15:39:11,010 INFO [train.py:715] (1/8) Epoch 13, batch 1350, loss[loss=0.1229, simple_loss=0.1965, pruned_loss=0.02463, over 4898.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03174, over 971102.78 frames.], batch size: 22, lr: 1.71e-04
2022-05-07 15:39:49,773 INFO [train.py:715] (1/8) Epoch 13, batch 1400, loss[loss=0.1193, simple_loss=0.1944, pruned_loss=0.02213, over 4917.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2098, pruned_loss=0.03179, over 971453.27 frames.], batch size: 23, lr: 1.71e-04
2022-05-07 15:40:28,864 INFO [train.py:715] (1/8) Epoch 13, batch 1450, loss[loss=0.1255, simple_loss=0.2046, pruned_loss=0.02317, over 4839.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03122, over 971572.56 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:41:06,535 INFO [train.py:715] (1/8) Epoch 13, batch 1500, loss[loss=0.1258, simple_loss=0.2118, pruned_loss=0.0199, over 4798.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.0315, over 971366.33 frames.], batch size: 24, lr: 1.71e-04
2022-05-07 15:41:44,154 INFO [train.py:715] (1/8) Epoch 13, batch 1550, loss[loss=0.1301, simple_loss=0.2019, pruned_loss=0.02918, over 4806.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03141, over 971736.32 frames.], batch size: 13, lr: 1.71e-04
2022-05-07 15:42:22,725 INFO [train.py:715] (1/8) Epoch 13, batch 1600, loss[loss=0.1214, simple_loss=0.2035, pruned_loss=0.01965, over 4796.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03092, over 971984.29 frames.], batch size: 12, lr: 1.71e-04
2022-05-07 15:43:00,641 INFO [train.py:715] (1/8) Epoch 13, batch 1650, loss[loss=0.1146, simple_loss=0.1912, pruned_loss=0.01898, over 4818.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03073, over 972050.86 frames.], batch size: 13, lr: 1.71e-04
2022-05-07 15:43:39,379 INFO [train.py:715] (1/8) Epoch 13, batch 1700, loss[loss=0.134, simple_loss=0.2007, pruned_loss=0.03366, over 4921.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03088, over 973199.97 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:44:17,663 INFO [train.py:715] (1/8) Epoch 13, batch 1750, loss[loss=0.1342, simple_loss=0.1991, pruned_loss=0.03467, over 4776.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03124, over 972851.46 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:44:57,091 INFO [train.py:715] (1/8) Epoch 13, batch 1800, loss[loss=0.1567, simple_loss=0.2384, pruned_loss=0.03746, over 4920.00 frames.], tot_loss[loss=0.135, simple_loss=0.2086, pruned_loss=0.03074, over 972284.64 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:45:35,169 INFO [train.py:715] (1/8) Epoch 13, batch 1850, loss[loss=0.139, simple_loss=0.2273, pruned_loss=0.02534, over 4802.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03104, over 971085.82 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:46:13,431 INFO [train.py:715] (1/8) Epoch 13, batch 1900, loss[loss=0.1772, simple_loss=0.2465, pruned_loss=0.05395, over 4954.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2094, pruned_loss=0.03166, over 971645.70 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:46:52,090 INFO [train.py:715] (1/8) Epoch 13, batch 1950, loss[loss=0.1735, simple_loss=0.2381, pruned_loss=0.05444, over 4918.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2097, pruned_loss=0.03188, over 970814.02 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:47:30,465 INFO [train.py:715] (1/8) Epoch 13, batch 2000, loss[loss=0.1283, simple_loss=0.2023, pruned_loss=0.02715, over 4930.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2103, pruned_loss=0.03196, over 971450.30 frames.], batch size: 23, lr: 1.71e-04
2022-05-07 15:48:09,019 INFO [train.py:715] (1/8) Epoch 13, batch 2050, loss[loss=0.1242, simple_loss=0.1965, pruned_loss=0.02595, over 4791.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03152, over 972411.06 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:48:47,026 INFO [train.py:715] (1/8) Epoch 13, batch 2100, loss[loss=0.1124, simple_loss=0.1958, pruned_loss=0.01445, over 4695.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2095, pruned_loss=0.03166, over 971675.59 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:49:26,191 INFO [train.py:715] (1/8) Epoch 13, batch 2150, loss[loss=0.1236, simple_loss=0.1998, pruned_loss=0.02372, over 4775.00 frames.], tot_loss[loss=0.137, simple_loss=0.2101, pruned_loss=0.03199, over 971431.58 frames.], batch size: 19, lr: 1.71e-04
2022-05-07 15:50:04,034 INFO [train.py:715] (1/8) Epoch 13, batch 2200, loss[loss=0.1475, simple_loss=0.213, pruned_loss=0.04097, over 4778.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2095, pruned_loss=0.03162, over 971036.25 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:50:42,243 INFO [train.py:715] (1/8) Epoch 13, batch 2250, loss[loss=0.1294, simple_loss=0.2136, pruned_loss=0.02263, over 4789.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03141, over 971507.64 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:51:20,494 INFO [train.py:715] (1/8) Epoch 13, batch 2300, loss[loss=0.1497, simple_loss=0.2192, pruned_loss=0.04017, over 4693.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2087, pruned_loss=0.0312, over 971360.82 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:51:59,649 INFO [train.py:715] (1/8) Epoch 13, batch 2350, loss[loss=0.1382, simple_loss=0.2149, pruned_loss=0.03076, over 4931.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03101, over 972378.41 frames.], batch size: 23, lr: 1.71e-04
2022-05-07 15:52:38,011 INFO [train.py:715] (1/8) Epoch 13, batch 2400, loss[loss=0.1322, simple_loss=0.2163, pruned_loss=0.02403, over 4987.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03137, over 972781.49 frames.], batch size: 25, lr: 1.71e-04
2022-05-07 15:53:16,751 INFO [train.py:715] (1/8) Epoch 13, batch 2450, loss[loss=0.1333, simple_loss=0.212, pruned_loss=0.02732, over 4881.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03127, over 973326.45 frames.], batch size: 22, lr: 1.71e-04
2022-05-07 15:53:55,655 INFO [train.py:715] (1/8) Epoch 13, batch 2500, loss[loss=0.1528, simple_loss=0.2258, pruned_loss=0.03989, over 4886.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03152, over 972733.95 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:54:34,064 INFO [train.py:715] (1/8) Epoch 13, batch 2550, loss[loss=0.1175, simple_loss=0.1929, pruned_loss=0.02109, over 4879.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03119, over 972058.84 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:55:12,161 INFO [train.py:715] (1/8) Epoch 13, batch 2600, loss[loss=0.1461, simple_loss=0.226, pruned_loss=0.03303, over 4979.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03068, over 972200.92 frames.], batch size: 25, lr: 1.71e-04
2022-05-07 15:55:50,587 INFO [train.py:715] (1/8) Epoch 13, batch 2650, loss[loss=0.143, simple_loss=0.2132, pruned_loss=0.03636, over 4818.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03042, over 972379.86 frames.], batch size: 27, lr: 1.71e-04
2022-05-07 15:56:28,665 INFO [train.py:715] (1/8) Epoch 13, batch 2700, loss[loss=0.1202, simple_loss=0.2019, pruned_loss=0.01927, over 4733.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2093, pruned_loss=0.03045, over 972107.89 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 15:57:06,441 INFO [train.py:715] (1/8) Epoch 13, batch 2750, loss[loss=0.1522, simple_loss=0.2277, pruned_loss=0.03831, over 4770.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2101, pruned_loss=0.03067, over 972198.22 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 15:57:43,969 INFO [train.py:715] (1/8) Epoch 13, batch 2800, loss[loss=0.1748, simple_loss=0.2422, pruned_loss=0.05368, over 4924.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2112, pruned_loss=0.03098, over 971746.93 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 15:58:22,563 INFO [train.py:715] (1/8) Epoch 13, batch 2850, loss[loss=0.1471, simple_loss=0.218, pruned_loss=0.03808, over 4982.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2106, pruned_loss=0.03079, over 972310.13 frames.], batch size: 28, lr: 1.70e-04
2022-05-07 15:59:00,071 INFO [train.py:715] (1/8) Epoch 13, batch 2900, loss[loss=0.1601, simple_loss=0.2427, pruned_loss=0.03871, over 4763.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2107, pruned_loss=0.0311, over 972151.88 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 15:59:37,968 INFO [train.py:715] (1/8) Epoch 13, batch 2950, loss[loss=0.126, simple_loss=0.2051, pruned_loss=0.02343, over 4908.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2107, pruned_loss=0.03113, over 971284.01 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:00:15,988 INFO [train.py:715] (1/8) Epoch 13, batch 3000, loss[loss=0.1215, simple_loss=0.1933, pruned_loss=0.02488, over 4814.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2103, pruned_loss=0.0311, over 971343.09 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:00:15,989 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 16:00:25,447 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1052, simple_loss=0.1893, pruned_loss=0.01058, over 914524.00 frames.
2022-05-07 16:01:03,672 INFO [train.py:715] (1/8) Epoch 13, batch 3050, loss[loss=0.1157, simple_loss=0.1989, pruned_loss=0.01628, over 4847.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03125, over 971650.81 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:01:42,204 INFO [train.py:715] (1/8) Epoch 13, batch 3100, loss[loss=0.127, simple_loss=0.2175, pruned_loss=0.01829, over 4855.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03155, over 972312.65 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:02:19,763 INFO [train.py:715] (1/8) Epoch 13, batch 3150, loss[loss=0.1355, simple_loss=0.2165, pruned_loss=0.02729, over 4844.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03117, over 972413.06 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:02:57,077 INFO [train.py:715] (1/8) Epoch 13, batch 3200, loss[loss=0.1445, simple_loss=0.2157, pruned_loss=0.03666, over 4990.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03118, over 972886.77 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:03:35,542 INFO [train.py:715] (1/8) Epoch 13, batch 3250, loss[loss=0.1482, simple_loss=0.2146, pruned_loss=0.04086, over 4985.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03106, over 972973.85 frames.], batch size: 28, lr: 1.70e-04
2022-05-07 16:04:13,565 INFO [train.py:715] (1/8) Epoch 13, batch 3300, loss[loss=0.1157, simple_loss=0.1885, pruned_loss=0.02149, over 4785.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.0311, over 973177.18 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 16:04:51,382 INFO [train.py:715] (1/8) Epoch 13, batch 3350, loss[loss=0.1336, simple_loss=0.1982, pruned_loss=0.03448, over 4862.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03156, over 972312.40 frames.], batch size: 30, lr: 1.70e-04
2022-05-07 16:05:29,078 INFO [train.py:715] (1/8) Epoch 13, batch 3400, loss[loss=0.1242, simple_loss=0.1991, pruned_loss=0.02458, over 4889.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2093, pruned_loss=0.03157, over 972204.07 frames.], batch size: 22, lr: 1.70e-04
2022-05-07 16:06:07,373 INFO [train.py:715] (1/8) Epoch 13, batch 3450, loss[loss=0.1253, simple_loss=0.202, pruned_loss=0.02435, over 4852.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03061, over 971450.35 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:06:47,673 INFO [train.py:715] (1/8) Epoch 13, batch 3500, loss[loss=0.1364, simple_loss=0.2108, pruned_loss=0.03098, over 4956.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03088, over 971194.96 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:07:25,033 INFO [train.py:715] (1/8) Epoch 13, batch 3550, loss[loss=0.1423, simple_loss=0.2174, pruned_loss=0.03358, over 4859.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03098, over 971120.27 frames.], batch size: 38, lr: 1.70e-04
2022-05-07 16:08:03,491 INFO [train.py:715] (1/8) Epoch 13, batch 3600, loss[loss=0.1555, simple_loss=0.232, pruned_loss=0.03947, over 4840.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03035, over 972061.65 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:08:41,281 INFO [train.py:715] (1/8) Epoch 13, batch 3650, loss[loss=0.1686, simple_loss=0.2519, pruned_loss=0.04267, over 4766.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03088, over 972239.05 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:09:18,852 INFO [train.py:715] (1/8) Epoch 13, batch 3700, loss[loss=0.1199, simple_loss=0.1903, pruned_loss=0.02475, over 4940.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.0308, over 972022.59 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:09:56,571 INFO [train.py:715] (1/8) Epoch 13, batch 3750, loss[loss=0.1307, simple_loss=0.2087, pruned_loss=0.02635, over 4783.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03079, over 971595.41 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:10:34,805 INFO [train.py:715] (1/8) Epoch 13, batch 3800, loss[loss=0.1291, simple_loss=0.2074, pruned_loss=0.0254, over 4752.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03081, over 971758.21 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:11:11,948 INFO [train.py:715] (1/8) Epoch 13, batch 3850, loss[loss=0.1423, simple_loss=0.2183, pruned_loss=0.03312, over 4916.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03104, over 972053.65 frames.], batch size: 39, lr: 1.70e-04
2022-05-07 16:11:49,246 INFO [train.py:715] (1/8) Epoch 13, batch 3900, loss[loss=0.1347, simple_loss=0.2207, pruned_loss=0.02435, over 4751.00 frames.], tot_loss[loss=0.135, simple_loss=0.2086, pruned_loss=0.03074, over 972103.99 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:12:27,133 INFO [train.py:715] (1/8) Epoch 13, batch 3950, loss[loss=0.1589, simple_loss=0.2158, pruned_loss=0.05106, over 4977.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03083, over 972987.66 frames.], batch size: 35, lr: 1.70e-04
2022-05-07 16:13:05,302 INFO [train.py:715] (1/8) Epoch 13, batch 4000, loss[loss=0.103, simple_loss=0.1766, pruned_loss=0.01467, over 4923.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03086, over 973432.29 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:13:42,998 INFO [train.py:715] (1/8) Epoch 13, batch 4050, loss[loss=0.149, simple_loss=0.2192, pruned_loss=0.03935, over 4813.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.03089, over 973791.19 frames.], batch size: 27, lr: 1.70e-04
2022-05-07 16:14:20,645 INFO [train.py:715] (1/8) Epoch 13, batch 4100, loss[loss=0.188, simple_loss=0.2584, pruned_loss=0.05884, over 4858.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03125, over 973592.26 frames.], batch size: 30, lr: 1.70e-04
2022-05-07 16:14:59,188 INFO [train.py:715] (1/8) Epoch 13, batch 4150, loss[loss=0.1479, simple_loss=0.2211, pruned_loss=0.03736, over 4869.00 frames.], tot_loss[loss=0.1361, simple_loss=0.209, pruned_loss=0.03159, over 973165.06 frames.], batch size: 32, lr: 1.70e-04
2022-05-07 16:15:36,531 INFO [train.py:715] (1/8) Epoch 13, batch 4200, loss[loss=0.1241, simple_loss=0.1825, pruned_loss=0.0329, over 4778.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2089, pruned_loss=0.03141, over 973904.73 frames.], batch size: 12, lr: 1.70e-04
2022-05-07 16:16:14,503 INFO [train.py:715] (1/8) Epoch 13, batch 4250, loss[loss=0.1553, simple_loss=0.2197, pruned_loss=0.0454, over 4843.00 frames.], tot_loss[loss=0.136, simple_loss=0.2089, pruned_loss=0.03156, over 973490.24 frames.], batch size: 32, lr: 1.70e-04
2022-05-07 16:16:52,601 INFO [train.py:715] (1/8) Epoch 13, batch 4300, loss[loss=0.1308, simple_loss=0.2043, pruned_loss=0.02862, over 4864.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03162, over 973397.58 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:17:30,607 INFO [train.py:715] (1/8) Epoch 13, batch 4350, loss[loss=0.1226, simple_loss=0.197, pruned_loss=0.02405, over 4981.00 frames.], tot_loss[loss=0.137, simple_loss=0.2098, pruned_loss=0.03208, over 973356.44 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:18:08,280 INFO [train.py:715] (1/8) Epoch 13, batch 4400, loss[loss=0.1157, simple_loss=0.1922, pruned_loss=0.01963, over 4749.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2095, pruned_loss=0.03179, over 972940.13 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:18:46,446 INFO [train.py:715] (1/8) Epoch 13, batch 4450, loss[loss=0.1348, simple_loss=0.2261, pruned_loss=0.02178, over 4877.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2095, pruned_loss=0.03194, over 971821.29 frames.], batch size: 38, lr: 1.70e-04
2022-05-07 16:19:25,669 INFO [train.py:715] (1/8) Epoch 13, batch 4500, loss[loss=0.1256, simple_loss=0.1982, pruned_loss=0.02649, over 4770.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2094, pruned_loss=0.0318, over 971965.03 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 16:20:03,833 INFO [train.py:715] (1/8) Epoch 13, batch 4550, loss[loss=0.1363, simple_loss=0.2076, pruned_loss=0.03255, over 4824.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03165, over 971785.39 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:20:40,818 INFO [train.py:715] (1/8) Epoch 13, batch 4600, loss[loss=0.1344, simple_loss=0.2182, pruned_loss=0.0253, over 4831.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03126, over 971517.20 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:21:19,534 INFO [train.py:715] (1/8) Epoch 13, batch 4650, loss[loss=0.1673, simple_loss=0.2404, pruned_loss=0.04713, over 4757.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03169, over 971009.47 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:21:57,442 INFO [train.py:715] (1/8) Epoch 13, batch 4700, loss[loss=0.1661, simple_loss=0.2277, pruned_loss=0.05219, over 4868.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.03167, over 971587.30 frames.], batch size: 32, lr: 1.70e-04
2022-05-07 16:22:35,615 INFO [train.py:715] (1/8) Epoch 13, batch 4750, loss[loss=0.1153, simple_loss=0.1885, pruned_loss=0.02106, over 4949.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03143, over 972136.80 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:23:13,890 INFO [train.py:715] (1/8) Epoch 13, batch 4800, loss[loss=0.1565, simple_loss=0.2322, pruned_loss=0.04041, over 4987.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03131, over 972530.42 frames.], batch size: 25, lr: 1.70e-04
2022-05-07 16:23:53,169 INFO [train.py:715] (1/8) Epoch 13, batch 4850, loss[loss=0.1738, simple_loss=0.2455, pruned_loss=0.05109, over 4794.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03147, over 972251.01 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:24:31,293 INFO [train.py:715] (1/8) Epoch 13, batch 4900, loss[loss=0.1289, simple_loss=0.1922, pruned_loss=0.03279, over 4736.00 frames.], tot_loss[loss=0.1369, simple_loss=0.21, pruned_loss=0.0319, over 972934.71 frames.], batch size: 12, lr: 1.70e-04
2022-05-07 16:25:10,154 INFO [train.py:715] (1/8) Epoch 13, batch 4950, loss[loss=0.1346, simple_loss=0.1984, pruned_loss=0.03543, over 4789.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.03196, over 972596.21 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:25:49,562 INFO [train.py:715] (1/8) Epoch 13, batch 5000, loss[loss=0.1146, simple_loss=0.1916, pruned_loss=0.01877, over 4868.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2093, pruned_loss=0.03154, over 972226.74 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:26:28,896 INFO [train.py:715] (1/8) Epoch 13, batch 5050, loss[loss=0.09763, simple_loss=0.1693, pruned_loss=0.013, over 4818.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2089, pruned_loss=0.03137, over 972236.40 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:27:07,532 INFO [train.py:715] (1/8) Epoch 13, batch 5100, loss[loss=0.1399, simple_loss=0.2136, pruned_loss=0.0331, over 4933.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2094, pruned_loss=0.03159, over 972681.67 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:27:46,961 INFO [train.py:715] (1/8) Epoch 13, batch 5150, loss[loss=0.1434, simple_loss=0.2148, pruned_loss=0.03603, over 4963.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2091, pruned_loss=0.03122, over 971973.43 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:28:26,704 INFO [train.py:715] (1/8) Epoch 13, batch 5200, loss[loss=0.1578, simple_loss=0.2343, pruned_loss=0.0406, over 4772.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03151, over 972077.38 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:29:06,561 INFO [train.py:715] (1/8) Epoch 13, batch 5250, loss[loss=0.1366, simple_loss=0.2138, pruned_loss=0.02969, over 4917.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03103, over 971788.80 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:29:45,239 INFO [train.py:715] (1/8) Epoch 13, batch 5300, loss[loss=0.1279, simple_loss=0.1963, pruned_loss=0.0298, over 4882.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03067, over 972266.14 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:30:25,371 INFO [train.py:715] (1/8) Epoch 13, batch 5350, loss[loss=0.1321, simple_loss=0.2025, pruned_loss=0.0308, over 4958.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03078, over 972167.48 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:31:05,471 INFO [train.py:715] (1/8) Epoch 13, batch 5400, loss[loss=0.1012, simple_loss=0.1764, pruned_loss=0.01304, over 4984.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03076, over 971598.66 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:31:45,407 INFO [train.py:715] (1/8) Epoch 13, batch 5450, loss[loss=0.1177, simple_loss=0.1889, pruned_loss=0.02319, over 4882.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03078, over 972309.40 frames.], batch size: 22, lr: 1.70e-04
2022-05-07 16:32:24,989 INFO [train.py:715] (1/8) Epoch 13, batch 5500, loss[loss=0.1279, simple_loss=0.201, pruned_loss=0.02736, over 4776.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03099, over 972478.58 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:33:04,814 INFO [train.py:715] (1/8) Epoch 13, batch 5550, loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02856, over 4829.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2089, pruned_loss=0.03126, over 973143.24 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:33:44,070 INFO [train.py:715] (1/8) Epoch 13, batch 5600, loss[loss=0.1271, simple_loss=0.2054, pruned_loss=0.02437, over 4762.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03042, over 972928.60 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:34:23,527 INFO [train.py:715] (1/8) Epoch 13, batch 5650, loss[loss=0.131, simple_loss=0.2097, pruned_loss=0.02613, over 4989.00 frames.], tot_loss[loss=0.1356, simple_loss=0.209, pruned_loss=0.03103, over 972992.50 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:35:03,784 INFO [train.py:715] (1/8) Epoch 13, batch 5700, loss[loss=0.1456, simple_loss=0.222, pruned_loss=0.03458, over 4796.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03094, over 972086.54 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:35:43,895 INFO [train.py:715] (1/8) Epoch 13, batch 5750, loss[loss=0.1251, simple_loss=0.2019, pruned_loss=0.02422, over 4738.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03079, over 971951.80 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:36:22,746 INFO [train.py:715] (1/8) Epoch 13, batch 5800, loss[loss=0.1384, simple_loss=0.2163, pruned_loss=0.0303, over 4754.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03086, over 971763.51 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:37:02,235 INFO [train.py:715] (1/8) Epoch 13, batch 5850, loss[loss=0.1668, simple_loss=0.2417, pruned_loss=0.04598, over 4844.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03092, over 970893.07 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:37:42,374 INFO [train.py:715] (1/8) Epoch 13, batch 5900, loss[loss=0.1607, simple_loss=0.2262, pruned_loss=0.04761, over 4806.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03124, over 970169.76 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:38:21,742 INFO [train.py:715] (1/8) Epoch 13, batch 5950, loss[loss=0.127, simple_loss=0.1993, pruned_loss=0.02734, over 4855.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03099, over 970893.98 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:39:01,219 INFO [train.py:715] (1/8) Epoch 13, batch 6000, loss[loss=0.1654, simple_loss=0.2379, pruned_loss=0.04644, over 4919.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03096, over 970726.49 frames.], batch size: 29, lr: 1.70e-04
2022-05-07 16:39:01,220 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 16:39:10,780 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1054, simple_loss=0.1893, pruned_loss=0.01078, over 914524.00 frames.
2022-05-07 16:39:50,261 INFO [train.py:715] (1/8) Epoch 13, batch 6050, loss[loss=0.1619, simple_loss=0.2389, pruned_loss=0.04245, over 4780.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03114, over 971091.81 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 16:40:29,776 INFO [train.py:715] (1/8) Epoch 13, batch 6100, loss[loss=0.1086, simple_loss=0.1771, pruned_loss=0.02008, over 4871.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2077, pruned_loss=0.03046, over 971375.84 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:41:09,345 INFO [train.py:715] (1/8) Epoch 13, batch 6150, loss[loss=0.1124, simple_loss=0.1772, pruned_loss=0.02384, over 4731.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03059, over 971571.26 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:41:47,243 INFO [train.py:715] (1/8) Epoch 13, batch 6200, loss[loss=0.136, simple_loss=0.2188, pruned_loss=0.02661, over 4837.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2082, pruned_loss=0.03074, over 972393.70 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:42:26,292 INFO [train.py:715] (1/8) Epoch 13, batch 6250, loss[loss=0.1164, simple_loss=0.1877, pruned_loss=0.02252, over 4780.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2082, pruned_loss=0.03071, over 972524.69 frames.], batch size: 12, lr: 1.70e-04
2022-05-07 16:43:05,825 INFO [train.py:715] (1/8) Epoch 13, batch 6300, loss[loss=0.1415, simple_loss=0.2081, pruned_loss=0.03744, over 4650.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03077, over 972321.31 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 16:43:44,414 INFO [train.py:715] (1/8) Epoch 13, batch 6350, loss[loss=0.1692, simple_loss=0.2449, pruned_loss=0.04673, over 4971.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03097, over 972821.97 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:44:24,231 INFO [train.py:715] (1/8) Epoch 13, batch 6400, loss[loss=0.1436, simple_loss=0.2133, pruned_loss=0.03693, over 4856.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03093, over 973313.43 frames.], batch size: 32, lr: 1.70e-04
2022-05-07 16:45:04,049 INFO [train.py:715] (1/8) Epoch 13, batch 6450, loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02905, over 4830.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03134, over 972700.30 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 16:45:44,145 INFO [train.py:715] (1/8) Epoch 13, batch 6500, loss[loss=0.146, simple_loss=0.2224, pruned_loss=0.03478, over 4777.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03145, over 972421.39 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:46:23,314 INFO [train.py:715] (1/8) Epoch 13, batch 6550, loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03125, over 4846.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03148, over 972028.24 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:47:02,653 INFO [train.py:715] (1/8) Epoch 13, batch 6600, loss[loss=0.1174, simple_loss=0.1865, pruned_loss=0.0242, over 4797.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03164, over 972422.39 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 16:47:42,042 INFO [train.py:715] (1/8) Epoch 13, batch 6650, loss[loss=0.1556, simple_loss=0.2259, pruned_loss=0.04259, over 4854.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.0311, over 971157.03 frames.], batch size: 34, lr: 1.70e-04
2022-05-07 16:48:20,277 INFO [train.py:715] (1/8) Epoch 13, batch 6700, loss[loss=0.1693, simple_loss=0.236, pruned_loss=0.05136, over 4936.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03151, over 970959.21 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:48:58,717 INFO [train.py:715] (1/8) Epoch 13, batch 6750, loss[loss=0.1334, simple_loss=0.2021, pruned_loss=0.03229, over 4813.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03183, over 971702.93 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:49:37,984 INFO [train.py:715] (1/8) Epoch 13, batch 6800, loss[loss=0.1525, simple_loss=0.2302, pruned_loss=0.03742, over 4952.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.03129, over 972836.36 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:50:17,435 INFO [train.py:715] (1/8) Epoch 13, batch 6850, loss[loss=0.1199, simple_loss=0.1953, pruned_loss=0.0223, over 4745.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03083, over 972621.32 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:50:55,369 INFO [train.py:715] (1/8) Epoch 13, batch 6900, loss[loss=0.1282, simple_loss=0.2101, pruned_loss=0.0231, over 4774.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2096, pruned_loss=0.03057, over 972155.46 frames.], batch size: 12, lr: 1.70e-04
2022-05-07 16:51:33,404 INFO [train.py:715] (1/8) Epoch 13, batch 6950, loss[loss=0.1416, simple_loss=0.2122, pruned_loss=0.03549, over 4888.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03046, over 971306.47 frames.], batch size: 32, lr: 1.70e-04
2022-05-07 16:52:12,641 INFO [train.py:715] (1/8) Epoch 13, batch 7000, loss[loss=0.1586, simple_loss=0.2327, pruned_loss=0.04222, over 4758.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03044, over 971305.77 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:52:51,283 INFO [train.py:715] (1/8) Epoch 13, batch 7050, loss[loss=0.1396, simple_loss=0.2069, pruned_loss=0.03614, over 4870.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03052, over 971268.22 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:53:30,247 INFO [train.py:715] (1/8) Epoch 13, batch 7100, loss[loss=0.1323, simple_loss=0.2215, pruned_loss=0.02159, over 4807.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03079, over 971511.69 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:54:09,707 INFO [train.py:715] (1/8) Epoch 13, batch 7150, loss[loss=0.1231, simple_loss=0.1934, pruned_loss=0.02641, over 4763.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03077, over 970294.75 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:54:49,407 INFO [train.py:715] (1/8) Epoch 13, batch 7200, loss[loss=0.1437, simple_loss=0.2203, pruned_loss=0.03357, over 4795.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03153, over 970043.49 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:55:27,556 INFO [train.py:715] (1/8) Epoch 13, batch 7250, loss[loss=0.1362, simple_loss=0.2207, pruned_loss=0.02588, over 4956.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03104, over 970875.52 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:56:05,826 INFO [train.py:715] (1/8) Epoch 13, batch 7300, loss[loss=0.1485, simple_loss=0.2219, pruned_loss=0.03758, over 4786.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2103, pruned_loss=0.03114, over 971298.93 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:56:45,077 INFO [train.py:715] (1/8) Epoch 13, batch 7350, loss[loss=0.124, simple_loss=0.1973, pruned_loss=0.02538, over 4764.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.0307, over 971253.11 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:57:23,720 INFO [train.py:715] (1/8) Epoch 13, batch 7400, loss[loss=0.154, simple_loss=0.2263, pruned_loss=0.04083, over 4793.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03096, over 971369.29 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:58:01,546 INFO [train.py:715] (1/8) Epoch 13, batch 7450, loss[loss=0.1517, simple_loss=0.208, pruned_loss=0.04767, over 4965.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03091, over 972452.03 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:58:40,997 INFO [train.py:715] (1/8) Epoch 13, batch 7500, loss[loss=0.1358, simple_loss=0.1997, pruned_loss=0.03592, over 4868.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03153, over 972082.43 frames.], batch size: 32, lr: 1.70e-04
2022-05-07 16:59:20,237 INFO [train.py:715] (1/8) Epoch 13, batch 7550, loss[loss=0.1586, simple_loss=0.2182, pruned_loss=0.04947, over 4769.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03113, over 971545.59 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:59:57,841 INFO [train.py:715] (1/8) Epoch 13, batch 7600, loss[loss=0.1192, simple_loss=0.1957, pruned_loss=0.02133, over 4807.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03102, over 972122.33 frames.], batch size: 25, lr: 1.70e-04
2022-05-07 17:00:36,718 INFO [train.py:715] (1/8) Epoch 13, batch 7650, loss[loss=0.1218, simple_loss=0.1993, pruned_loss=0.02214, over 4827.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03092, over 971743.19 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 17:01:15,688 INFO [train.py:715] (1/8) Epoch 13, batch 7700, loss[loss=0.168, simple_loss=0.2358, pruned_loss=0.05009, over 4787.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03116, over 971996.49 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 17:01:54,652 INFO [train.py:715] (1/8) Epoch 13, batch 7750, loss[loss=0.131, simple_loss=0.2004, pruned_loss=0.03086, over 4788.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03144, over 971437.83 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 17:02:32,579 INFO [train.py:715] (1/8) Epoch 13, batch 7800, loss[loss=0.1433, simple_loss=0.2275, pruned_loss=0.0295, over 4789.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03148, over 972115.08 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 17:03:11,065 INFO [train.py:715] (1/8) Epoch 13, batch 7850, loss[loss=0.1262, simple_loss=0.2063, pruned_loss=0.02307, over 4907.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03117, over 972717.64 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 17:03:50,704 INFO [train.py:715] (1/8) Epoch 13, batch 7900, loss[loss=0.122, simple_loss=0.2006, pruned_loss=0.02165, over 4812.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03128, over 972663.74 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 17:04:28,754 INFO [train.py:715] (1/8) Epoch 13, batch 7950, loss[loss=0.1498, simple_loss=0.2297, pruned_loss=0.03492, over 4988.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2105, pruned_loss=0.03152, over 972658.09 frames.], batch size: 35, lr: 1.70e-04
2022-05-07 17:05:07,216 INFO [train.py:715] (1/8) Epoch 13, batch 8000, loss[loss=0.1781, simple_loss=0.2478, pruned_loss=0.05423, over 4740.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2109, pruned_loss=0.03165, over 973281.16 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 17:05:45,984 INFO [train.py:715] (1/8) Epoch 13, batch 8050, loss[loss=0.1335, simple_loss=0.2099, pruned_loss=0.02854, over 4820.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03177, over 973388.83 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 17:06:24,533 INFO [train.py:715] (1/8) Epoch 13, batch 8100, loss[loss=0.125, simple_loss=0.2038, pruned_loss=0.02313, over 4954.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03113, over 973951.22 frames.], batch size: 23, lr: 1.69e-04
2022-05-07 17:07:02,517 INFO [train.py:715] (1/8) Epoch 13, batch 8150, loss[loss=0.1559, simple_loss=0.2422, pruned_loss=0.03476, over 4801.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03138, over 973228.44 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 17:07:40,995 INFO [train.py:715] (1/8) Epoch 13, batch 8200, loss[loss=0.1482, simple_loss=0.2153, pruned_loss=0.04058, over 4924.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2108, pruned_loss=0.03203, over 971945.64 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:08:20,206 INFO [train.py:715] (1/8) Epoch 13, batch 8250, loss[loss=0.1153, simple_loss=0.1961, pruned_loss=0.01723, over 4753.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.03196, over 972067.73 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:08:58,114 INFO [train.py:715] (1/8) Epoch 13, batch 8300, loss[loss=0.1163, simple_loss=0.1893, pruned_loss=0.02168, over 4929.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03247, over 971730.46 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:09:36,543 INFO [train.py:715] (1/8) Epoch 13, batch 8350, loss[loss=0.178, simple_loss=0.2418, pruned_loss=0.05713, over 4772.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.03281, over 972055.67 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:10:15,708 INFO [train.py:715] (1/8) Epoch 13, batch 8400, loss[loss=0.1207, simple_loss=0.192, pruned_loss=0.02474, over 4865.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2112, pruned_loss=0.03269, over 972075.03 frames.], batch size: 30, lr: 1.69e-04
2022-05-07 17:10:54,565 INFO [train.py:715] (1/8) Epoch 13, batch 8450, loss[loss=0.1454, simple_loss=0.2104, pruned_loss=0.0402, over 4976.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03192, over 972581.90 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:11:32,556 INFO [train.py:715] (1/8) Epoch 13, batch 8500, loss[loss=0.144, simple_loss=0.2262, pruned_loss=0.03096, over 4953.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03147, over 972208.60 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:12:11,699 INFO [train.py:715] (1/8) Epoch 13, batch 8550, loss[loss=0.126, simple_loss=0.1978, pruned_loss=0.02712, over 4968.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03146, over 972457.02 frames.], batch size: 35, lr: 1.69e-04
2022-05-07 17:12:50,648 INFO [train.py:715] (1/8) Epoch 13, batch 8600, loss[loss=0.1167, simple_loss=0.1841, pruned_loss=0.02466, over 4973.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03171, over 973296.73 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:13:28,885 INFO [train.py:715] (1/8) Epoch 13, batch 8650, loss[loss=0.1708, simple_loss=0.2335, pruned_loss=0.05409, over 4919.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03191, over 972434.96 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:14:07,228 INFO [train.py:715] (1/8) Epoch 13, batch 8700, loss[loss=0.147, simple_loss=0.224, pruned_loss=0.03503, over 4747.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03127, over 972545.59 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:14:45,869 INFO [train.py:715] (1/8) Epoch 13, batch 8750, loss[loss=0.1572, simple_loss=0.2228, pruned_loss=0.04582, over 4973.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.0314, over 972538.80 frames.], batch size: 25, lr: 1.69e-04
2022-05-07 17:15:24,585 INFO [train.py:715] (1/8) Epoch 13, batch 8800, loss[loss=0.1475, simple_loss=0.2245, pruned_loss=0.03519, over 4952.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03142, over 972888.62 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:16:02,887 INFO [train.py:715] (1/8) Epoch 13, batch 8850, loss[loss=0.1208, simple_loss=0.1956, pruned_loss=0.02302, over 4786.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03151, over 973231.44 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 17:16:40,975 INFO [train.py:715] (1/8) Epoch 13, batch 8900, loss[loss=0.1296, simple_loss=0.2034, pruned_loss=0.02789, over 4771.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03171, over 971890.87 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:17:19,700 INFO [train.py:715] (1/8) Epoch 13, batch 8950, loss[loss=0.1623, simple_loss=0.2266, pruned_loss=0.04903, over 4873.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03111, over 971990.34 frames.], batch size: 32, lr: 1.69e-04
2022-05-07 17:17:57,828 INFO [train.py:715] (1/8) Epoch 13, batch 9000, loss[loss=0.1293, simple_loss=0.2105, pruned_loss=0.02404, over 4685.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03123, over 972216.76 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:17:57,829 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 17:18:07,453 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1055, simple_loss=0.1893, pruned_loss=0.01084, over 914524.00 frames.
2022-05-07 17:18:45,502 INFO [train.py:715] (1/8) Epoch 13, batch 9050, loss[loss=0.1506, simple_loss=0.2287, pruned_loss=0.03622, over 4895.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03145, over 972678.77 frames.], batch size: 22, lr: 1.69e-04
2022-05-07 17:19:23,916 INFO [train.py:715] (1/8) Epoch 13, batch 9100, loss[loss=0.1111, simple_loss=0.1911, pruned_loss=0.01552, over 4909.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03097, over 972829.01 frames.], batch size: 29, lr: 1.69e-04
2022-05-07 17:20:03,104 INFO [train.py:715] (1/8) Epoch 13, batch 9150, loss[loss=0.1569, simple_loss=0.226, pruned_loss=0.04393, over 4959.00 frames.], tot_loss[loss=0.136, simple_loss=0.209, pruned_loss=0.03151, over 971793.50 frames.], batch size: 35, lr: 1.69e-04
2022-05-07 17:20:42,099 INFO [train.py:715] (1/8) Epoch 13, batch 9200, loss[loss=0.1287, simple_loss=0.2041, pruned_loss=0.02666, over 4908.00 frames.], tot_loss[loss=0.135, simple_loss=0.2086, pruned_loss=0.0307, over 971746.23 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 17:21:20,014 INFO [train.py:715] (1/8) Epoch 13, batch 9250, loss[loss=0.1503, simple_loss=0.2177, pruned_loss=0.0415, over 4973.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03081, over 972017.39 frames.], batch size: 24, lr: 1.69e-04
2022-05-07 17:21:58,916 INFO [train.py:715] (1/8) Epoch 13, batch 9300, loss[loss=0.1346, simple_loss=0.2147, pruned_loss=0.0272, over 4969.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.0305, over 971726.55 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:22:37,770 INFO [train.py:715] (1/8) Epoch 13, batch 9350, loss[loss=0.1386, simple_loss=0.2137, pruned_loss=0.03173, over 4810.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03021, over 972134.12 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:23:15,584 INFO [train.py:715] (1/8) Epoch 13, batch 9400, loss[loss=0.1441, simple_loss=0.219, pruned_loss=0.0346, over 4798.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03053, over 972543.86 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:23:54,037 INFO [train.py:715] (1/8) Epoch 13, batch 9450, loss[loss=0.12, simple_loss=0.1944, pruned_loss=0.02277, over 4925.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03054, over 972606.71 frames.], batch size: 29, lr: 1.69e-04
2022-05-07 17:24:32,858 INFO [train.py:715] (1/8) Epoch 13, batch 9500, loss[loss=0.1241, simple_loss=0.1969, pruned_loss=0.02564, over 4903.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03099, over 973483.01 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:25:11,115 INFO [train.py:715] (1/8) Epoch 13, batch 9550, loss[loss=0.1322, simple_loss=0.1978, pruned_loss=0.03327, over 4779.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03116, over 972697.23 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:25:49,081 INFO [train.py:715] (1/8) Epoch 13, batch 9600, loss[loss=0.1055, simple_loss=0.1755, pruned_loss=0.01772, over 4892.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03118, over 972077.65 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:26:28,029 INFO [train.py:715] (1/8) Epoch 13, batch 9650, loss[loss=0.1399, simple_loss=0.2095, pruned_loss=0.03514, over 4793.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03126, over 971093.79 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:27:06,439 INFO [train.py:715] (1/8) Epoch 13, batch 9700, loss[loss=0.1127, simple_loss=0.1848, pruned_loss=0.02027, over 4801.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03143, over 971433.07 frames.], batch size: 25, lr: 1.69e-04
2022-05-07 17:27:44,983 INFO [train.py:715] (1/8) Epoch 13, batch 9750, loss[loss=0.1092, simple_loss=0.1784, pruned_loss=0.01995, over 4984.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2095, pruned_loss=0.03149, over 971689.38 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:28:23,842 INFO [train.py:715] (1/8) Epoch 13, batch 9800, loss[loss=0.1146, simple_loss=0.1817, pruned_loss=0.02377, over 4942.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03142, over 971990.90 frames.], batch size: 23, lr: 1.69e-04
2022-05-07 17:29:03,036 INFO [train.py:715] (1/8) Epoch 13, batch 9850, loss[loss=0.1343, simple_loss=0.2105, pruned_loss=0.02906, over 4835.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03097, over 970936.28 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:29:41,588 INFO [train.py:715] (1/8) Epoch 13, batch 9900, loss[loss=0.1154, simple_loss=0.197, pruned_loss=0.01692, over 4962.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03102, over 971404.51 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:30:19,824 INFO [train.py:715] (1/8) Epoch 13, batch 9950, loss[loss=0.1338, simple_loss=0.2046, pruned_loss=0.0315, over 4954.00 frames.], tot_loss[loss=0.135, simple_loss=0.2086, pruned_loss=0.03069, over 972202.19 frames.], batch size: 35, lr: 1.69e-04
2022-05-07 17:30:58,618 INFO [train.py:715] (1/8) Epoch 13, batch 10000, loss[loss=0.1302, simple_loss=0.2042, pruned_loss=0.02808, over 4801.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03089, over 971361.63 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:31:37,788 INFO [train.py:715] (1/8) Epoch 13, batch 10050, loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02911, over 4788.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03116, over 970552.83 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:32:16,725 INFO [train.py:715] (1/8) Epoch 13, batch 10100, loss[loss=0.1615, simple_loss=0.2287, pruned_loss=0.04718, over 4770.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03135, over 970950.94 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:32:54,969 INFO [train.py:715] (1/8) Epoch 13, batch 10150, loss[loss=0.1095, simple_loss=0.1778, pruned_loss=0.02057, over 4987.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03124, over 970923.11 frames.], batch size: 28, lr: 1.69e-04
2022-05-07 17:33:34,003 INFO [train.py:715] (1/8) Epoch 13, batch 10200, loss[loss=0.1367, simple_loss=0.2089, pruned_loss=0.03222, over 4865.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03117, over 971422.29 frames.], batch size: 32, lr: 1.69e-04
2022-05-07 17:34:13,397 INFO [train.py:715] (1/8) Epoch 13, batch 10250, loss[loss=0.1314, simple_loss=0.1963, pruned_loss=0.03329, over 4768.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03132, over 972525.10 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 17:34:52,088 INFO [train.py:715] (1/8) Epoch 13, batch 10300, loss[loss=0.1433, simple_loss=0.2265, pruned_loss=0.03005, over 4703.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.0313, over 972910.40 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:35:31,129 INFO [train.py:715] (1/8) Epoch 13, batch 10350, loss[loss=0.1188, simple_loss=0.1909, pruned_loss=0.02338, over 4792.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.0311, over 973081.52 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:36:10,307 INFO [train.py:715] (1/8) Epoch 13, batch 10400, loss[loss=0.1354, simple_loss=0.2114, pruned_loss=0.02971, over 4834.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03051, over 973285.84 frames.], batch size: 30, lr: 1.69e-04
2022-05-07 17:36:49,253 INFO [train.py:715] (1/8) Epoch 13, batch 10450, loss[loss=0.1656, simple_loss=0.232, pruned_loss=0.04953, over 4749.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03025, over 972355.21 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:37:26,681 INFO [train.py:715] (1/8) Epoch 13, batch 10500, loss[loss=0.1342, simple_loss=0.2048, pruned_loss=0.03178, over 4866.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.0311, over 973187.28 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 17:38:05,568 INFO [train.py:715] (1/8) Epoch 13, batch 10550, loss[loss=0.1258, simple_loss=0.2068, pruned_loss=0.02244, over 4941.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03081, over 973069.27 frames.], batch size: 39, lr: 1.69e-04
2022-05-07 17:38:44,488 INFO [train.py:715] (1/8) Epoch 13, batch 10600, loss[loss=0.122, simple_loss=0.1926, pruned_loss=0.02574, over 4982.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03121, over 972064.50 frames.], batch size: 25, lr: 1.69e-04
2022-05-07 17:39:22,613 INFO [train.py:715] (1/8) Epoch 13, batch 10650, loss[loss=0.1158, simple_loss=0.1894, pruned_loss=0.02109, over 4837.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03101, over 973122.41 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:40:01,765 INFO [train.py:715] (1/8) Epoch 13, batch 10700, loss[loss=0.1083, simple_loss=0.1857, pruned_loss=0.0155, over 4889.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03118, over 973281.67 frames.], batch size: 22, lr: 1.69e-04
2022-05-07 17:40:41,063 INFO [train.py:715] (1/8) Epoch 13, batch 10750, loss[loss=0.1213, simple_loss=0.2, pruned_loss=0.0213, over 4687.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03067, over 972914.11 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:41:19,860 INFO [train.py:715] (1/8) Epoch 13, batch 10800, loss[loss=0.1683, simple_loss=0.2313, pruned_loss=0.05266, over 4868.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03019, over 972802.70 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 17:41:57,882 INFO [train.py:715] (1/8) Epoch 13, batch 10850, loss[loss=0.1146, simple_loss=0.1918, pruned_loss=0.01864, over 4983.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03044, over 972710.76 frames.], batch size: 28, lr: 1.69e-04
2022-05-07 17:42:37,035 INFO [train.py:715] (1/8) Epoch 13, batch 10900, loss[loss=0.1376, simple_loss=0.2133, pruned_loss=0.03093, over 4775.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03041, over 972740.64 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 17:43:16,894 INFO [train.py:715] (1/8) Epoch 13, batch 10950, loss[loss=0.1227, simple_loss=0.2017, pruned_loss=0.02189, over 4786.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03082, over 972832.88 frames.], batch size: 12, lr: 1.69e-04
2022-05-07 17:43:56,323 INFO [train.py:715] (1/8) Epoch 13, batch 11000, loss[loss=0.1611, simple_loss=0.2149, pruned_loss=0.05367, over 4830.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03092, over 972971.71 frames.], batch size: 26, lr: 1.69e-04
2022-05-07 17:44:34,955 INFO [train.py:715] (1/8) Epoch 13, batch 11050, loss[loss=0.1397, simple_loss=0.2221, pruned_loss=0.02868, over 4972.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03081, over 973469.09 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:45:14,251 INFO [train.py:715] (1/8) Epoch 13, batch 11100, loss[loss=0.147, simple_loss=0.2222, pruned_loss=0.03594, over 4906.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03099, over 973975.25 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:45:53,237 INFO [train.py:715] (1/8) Epoch 13, batch 11150, loss[loss=0.1282, simple_loss=0.2072, pruned_loss=0.0246, over 4820.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03089, over 973656.00 frames.], batch size: 25, lr: 1.69e-04
2022-05-07 17:46:30,991 INFO [train.py:715] (1/8) Epoch 13, batch 11200, loss[loss=0.1204, simple_loss=0.1927, pruned_loss=0.02399, over 4832.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2088, pruned_loss=0.0312, over 973735.39 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:47:09,194 INFO [train.py:715] (1/8) Epoch 13, batch 11250, loss[loss=0.1348, simple_loss=0.2101, pruned_loss=0.02977, over 4870.00 frames.], tot_loss[loss=0.1359, simple_loss=0.209, pruned_loss=0.0314, over 973437.48 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 17:47:48,141 INFO [train.py:715] (1/8) Epoch 13, batch 11300, loss[loss=0.1523, simple_loss=0.2206, pruned_loss=0.04201, over 4817.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2084, pruned_loss=0.03109, over 972655.40 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:48:27,089 INFO [train.py:715] (1/8) Epoch 13, batch 11350, loss[loss=0.1353, simple_loss=0.2112, pruned_loss=0.02967, over 4795.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03097, over 971927.53 frames.], batch size: 24, lr: 1.69e-04
2022-05-07 17:49:05,293 INFO [train.py:715] (1/8) Epoch 13, batch 11400, loss[loss=0.1342, simple_loss=0.2139, pruned_loss=0.02726, over 4880.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03091, over 971646.80 frames.], batch size: 22, lr: 1.69e-04
2022-05-07 17:49:44,159 INFO [train.py:715] (1/8) Epoch 13, batch 11450, loss[loss=0.115, simple_loss=0.1837, pruned_loss=0.02313, over 4993.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03124, over 972210.62 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 17:50:25,719 INFO [train.py:715] (1/8) Epoch 13, batch 11500, loss[loss=0.1358, simple_loss=0.202, pruned_loss=0.03479, over 4812.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03104, over 972117.25 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:51:03,651 INFO [train.py:715] (1/8) Epoch 13, batch 11550, loss[loss=0.1272, simple_loss=0.207, pruned_loss=0.02372, over 4949.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2078, pruned_loss=0.03102, over 972258.49 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:51:42,317 INFO [train.py:715] (1/8) Epoch 13, batch 11600, loss[loss=0.1184, simple_loss=0.1962, pruned_loss=0.02031, over 4791.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2085, pruned_loss=0.03128, over 972138.86 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:52:21,601 INFO [train.py:715] (1/8) Epoch 13, batch 11650, loss[loss=0.1078, simple_loss=0.1831, pruned_loss=0.01626, over 4944.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2089, pruned_loss=0.03123, over 972765.99 frames.], batch size: 29, lr: 1.69e-04
2022-05-07 17:53:00,311 INFO [train.py:715] (1/8) Epoch 13, batch 11700, loss[loss=0.143, simple_loss=0.2176, pruned_loss=0.03415, over 4923.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03144, over 973043.01 frames.], batch size: 39, lr: 1.69e-04
2022-05-07 17:53:38,279 INFO [train.py:715] (1/8) Epoch 13, batch 11750, loss[loss=0.1145, simple_loss=0.1955, pruned_loss=0.01675, over 4752.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2091, pruned_loss=0.03151, over 972081.56 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:54:16,756 INFO [train.py:715] (1/8) Epoch 13, batch 11800, loss[loss=0.1384, simple_loss=0.2104, pruned_loss=0.03315, over 4835.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03175, over 972050.32 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:54:55,481 INFO [train.py:715] (1/8) Epoch 13, batch 11850, loss[loss=0.1408, simple_loss=0.2145, pruned_loss=0.03357, over 4683.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03172, over 971554.02 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:55:32,889 INFO [train.py:715] (1/8) Epoch 13, batch 11900, loss[loss=0.121, simple_loss=0.2076, pruned_loss=0.01723, over 4754.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03126, over 970642.74 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:56:11,547 INFO [train.py:715] (1/8) Epoch 13, batch 11950, loss[loss=0.1538, simple_loss=0.2426, pruned_loss=0.03254, over 4973.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03148, over 970984.45 frames.], batch size: 25, lr: 1.69e-04
2022-05-07 17:56:50,612 INFO [train.py:715] (1/8) Epoch 13, batch 12000, loss[loss=0.1187, simple_loss=0.1855, pruned_loss=0.02593, over 4654.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.03134, over 971313.87 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 17:56:50,613 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 17:57:00,357 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1055, simple_loss=0.1893, pruned_loss=0.01081, over 914524.00 frames.
2022-05-07 17:57:40,026 INFO [train.py:715] (1/8) Epoch 13, batch 12050, loss[loss=0.1314, simple_loss=0.2082, pruned_loss=0.02728, over 4901.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2091, pruned_loss=0.03157, over 971665.58 frames.], batch size: 22, lr: 1.69e-04
2022-05-07 17:58:18,318 INFO [train.py:715] (1/8) Epoch 13, batch 12100, loss[loss=0.1508, simple_loss=0.2191, pruned_loss=0.04125, over 4937.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03146, over 971696.89 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:58:56,074 INFO [train.py:715] (1/8) Epoch 13, batch 12150, loss[loss=0.1368, simple_loss=0.2142, pruned_loss=0.02968, over 4946.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03129, over 972428.95 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:59:34,974 INFO [train.py:715] (1/8) Epoch 13, batch 12200, loss[loss=0.143, simple_loss=0.2248, pruned_loss=0.03063, over 4901.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2091, pruned_loss=0.03132, over 971885.07 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 18:00:13,890 INFO [train.py:715] (1/8) Epoch 13, batch 12250, loss[loss=0.1359, simple_loss=0.1985, pruned_loss=0.03669, over 4750.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03171, over 971323.76 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 18:00:52,460 INFO [train.py:715] (1/8) Epoch 13, batch 12300, loss[loss=0.1283, simple_loss=0.2039, pruned_loss=0.02631, over 4859.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03149, over 971815.32 frames.], batch size: 32, lr: 1.69e-04
2022-05-07 18:01:30,136 INFO [train.py:715] (1/8) Epoch 13, batch 12350, loss[loss=0.1342, simple_loss=0.2059, pruned_loss=0.03128, over 4700.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03186, over 971059.86 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:02:09,069 INFO [train.py:715] (1/8) Epoch 13, batch 12400, loss[loss=0.1587, simple_loss=0.2493, pruned_loss=0.03407, over 4989.00 frames.], tot_loss[loss=0.1371, simple_loss=0.211, pruned_loss=0.03156, over 971699.00 frames.], batch size: 28, lr: 1.69e-04
2022-05-07 18:02:47,460 INFO [train.py:715] (1/8) Epoch 13, batch 12450, loss[loss=0.1402, simple_loss=0.2116, pruned_loss=0.03445, over 4904.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.03116, over 972217.89 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 18:03:24,475 INFO [train.py:715] (1/8) Epoch 13, batch 12500, loss[loss=0.1329, simple_loss=0.2021, pruned_loss=0.03184, over 4782.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2109, pruned_loss=0.0316, over 972882.99 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 18:04:03,263 INFO [train.py:715] (1/8) Epoch 13, batch 12550, loss[loss=0.1394, simple_loss=0.2171, pruned_loss=0.03088, over 4954.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2109, pruned_loss=0.03175, over 973060.58 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:04:41,884 INFO [train.py:715] (1/8) Epoch 13, batch 12600, loss[loss=0.1428, simple_loss=0.2189, pruned_loss=0.03331, over 4983.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2115, pruned_loss=0.03191, over 972509.99 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:05:20,412 INFO [train.py:715] (1/8) Epoch 13, batch 12650, loss[loss=0.1375, simple_loss=0.2144, pruned_loss=0.03037, over 4857.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2104, pruned_loss=0.03115, over 972455.11 frames.], batch size: 30, lr: 1.69e-04
2022-05-07 18:05:58,207 INFO [train.py:715] (1/8) Epoch 13, batch 12700, loss[loss=0.1455, simple_loss=0.2134, pruned_loss=0.03883, over 4651.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2108, pruned_loss=0.03115, over 972234.60 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 18:06:37,496 INFO [train.py:715] (1/8) Epoch 13, batch 12750, loss[loss=0.127, simple_loss=0.1973, pruned_loss=0.02835, over 4785.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2106, pruned_loss=0.03105, over 972393.16 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 18:07:16,118 INFO [train.py:715] (1/8) Epoch 13, batch 12800, loss[loss=0.1311, simple_loss=0.2065, pruned_loss=0.02782, over 4690.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2106, pruned_loss=0.03117, over 971324.70 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:07:53,802 INFO [train.py:715] (1/8) Epoch 13, batch 12850, loss[loss=0.1346, simple_loss=0.2049, pruned_loss=0.03218, over 4878.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2109, pruned_loss=0.03175, over 971514.38 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 18:08:32,304 INFO [train.py:715] (1/8) Epoch 13, batch 12900, loss[loss=0.1139, simple_loss=0.189, pruned_loss=0.01938, over 4779.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.0317, over 971490.20 frames.], batch size: 12, lr: 1.69e-04
2022-05-07 18:09:10,903 INFO [train.py:715] (1/8) Epoch 13, batch 12950, loss[loss=0.1329, simple_loss=0.2053, pruned_loss=0.03026, over 4707.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03099, over 971643.72 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:09:48,857 INFO [train.py:715] (1/8) Epoch 13, batch 13000, loss[loss=0.116, simple_loss=0.1973, pruned_loss=0.01736, over 4818.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.0311, over 972056.00 frames.], batch size: 26, lr: 1.69e-04
2022-05-07 18:10:26,258 INFO [train.py:715] (1/8) Epoch 13, batch 13050, loss[loss=0.1239, simple_loss=0.1997, pruned_loss=0.02405, over 4917.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.0312, over 971692.65 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 18:11:05,303 INFO [train.py:715] (1/8) Epoch 13, batch 13100, loss[loss=0.1646, simple_loss=0.227, pruned_loss=0.05114, over 4954.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03137, over 971898.91 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:11:43,997 INFO [train.py:715] (1/8) Epoch 13, batch 13150, loss[loss=0.1851, simple_loss=0.2536, pruned_loss=0.0583, over 4696.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03134, over 971459.84 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:12:21,746 INFO [train.py:715] (1/8) Epoch 13, batch 13200, loss[loss=0.139, simple_loss=0.211, pruned_loss=0.0335, over 4774.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.0312, over 971686.81 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 18:13:00,179 INFO [train.py:715] (1/8) Epoch 13, batch 13250, loss[loss=0.1251, simple_loss=0.1985, pruned_loss=0.02584, over 4870.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2088, pruned_loss=0.03146, over 971122.68 frames.], batch size: 22, lr: 1.69e-04
2022-05-07 18:13:38,869 INFO [train.py:715] (1/8) Epoch 13, batch 13300, loss[loss=0.1367, simple_loss=0.2188, pruned_loss=0.02724, over 4756.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2089, pruned_loss=0.03136, over 971255.00 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 18:14:17,605 INFO [train.py:715] (1/8) Epoch 13, batch 13350, loss[loss=0.1575, simple_loss=0.2334, pruned_loss=0.04081, over 4761.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03114, over 971652.54 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 18:14:55,894 INFO [train.py:715] (1/8) Epoch 13, batch 13400, loss[loss=0.1472, simple_loss=0.2284, pruned_loss=0.03306, over 4903.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03118, over 972431.61 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 18:15:35,683 INFO [train.py:715] (1/8) Epoch 13, batch 13450, loss[loss=0.1347, simple_loss=0.2118, pruned_loss=0.02885, over 4820.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03113, over 972257.33 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:16:14,411 INFO [train.py:715] (1/8) Epoch 13, batch 13500, loss[loss=0.1803, simple_loss=0.249, pruned_loss=0.05584, over 4803.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03133, over 971799.71 frames.], batch size: 24, lr: 1.69e-04
2022-05-07 18:16:52,067 INFO [train.py:715] (1/8) Epoch 13, batch 13550, loss[loss=0.1141, simple_loss=0.1873, pruned_loss=0.02048, over 4856.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03113, over 972169.45 frames.], batch size: 34, lr: 1.69e-04
2022-05-07 18:17:29,855 INFO [train.py:715] (1/8) Epoch 13, batch 13600, loss[loss=0.1424, simple_loss=0.2168, pruned_loss=0.03401, over 4747.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03103, over 972780.23 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:18:08,972 INFO [train.py:715] (1/8) Epoch 13, batch 13650, loss[loss=0.1248, simple_loss=0.1942, pruned_loss=0.02769, over 4787.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03119, over 972693.82 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:18:47,088 INFO [train.py:715] (1/8) Epoch 13, batch 13700, loss[loss=0.1701, simple_loss=0.2373, pruned_loss=0.05152, over 4843.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2091, pruned_loss=0.03155, over 972717.68 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:19:24,726 INFO [train.py:715] (1/8) Epoch 13, batch 13750, loss[loss=0.1433, simple_loss=0.2276, pruned_loss=0.02948, over 4778.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03137, over 972366.44 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:20:03,322 INFO [train.py:715] (1/8) Epoch 13, batch 13800, loss[loss=0.1285, simple_loss=0.1977, pruned_loss=0.02965, over 4765.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03148, over 972776.98 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:20:41,461 INFO [train.py:715] (1/8) Epoch 13, batch 13850, loss[loss=0.1099, simple_loss=0.1771, pruned_loss=0.02138, over 4824.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2081, pruned_loss=0.03077, over 972029.71 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 18:21:19,891 INFO [train.py:715] (1/8) Epoch 13, batch 13900, loss[loss=0.1293, simple_loss=0.1998, pruned_loss=0.02941, over 4931.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03078, over 972854.86 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:21:58,634 INFO [train.py:715] (1/8) Epoch 13, batch 13950, loss[loss=0.1365, simple_loss=0.2058, pruned_loss=0.0336, over 4980.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.03084, over 972003.53 frames.], batch size: 31, lr: 1.68e-04
2022-05-07 18:22:37,442 INFO [train.py:715] (1/8) Epoch 13, batch 14000, loss[loss=0.1361, simple_loss=0.2052, pruned_loss=0.03349, over 4797.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03091, over 971951.86 frames.], batch size: 24, lr: 1.68e-04
2022-05-07 18:23:15,662 INFO [train.py:715] (1/8) Epoch 13, batch 14050, loss[loss=0.1236, simple_loss=0.2047, pruned_loss=0.02129, over 4888.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03102, over 972118.24 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:23:53,254 INFO [train.py:715] (1/8) Epoch 13, batch 14100, loss[loss=0.1263, simple_loss=0.1954, pruned_loss=0.02855, over 4951.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03072, over 972616.02 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:24:32,484 INFO [train.py:715] (1/8) Epoch 13, batch 14150, loss[loss=0.1689, simple_loss=0.2367, pruned_loss=0.05059, over 4940.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03121, over 972141.89 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:25:10,631 INFO [train.py:715] (1/8) Epoch 13, batch 14200, loss[loss=0.09507, simple_loss=0.1678, pruned_loss=0.01115, over 4826.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03144, over 972143.48 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:25:48,511 INFO [train.py:715] (1/8) Epoch 13, batch 14250, loss[loss=0.1332, simple_loss=0.2065, pruned_loss=0.02996, over 4820.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03123, over 972272.06 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:26:26,777 INFO [train.py:715] (1/8) Epoch 13, batch 14300, loss[loss=0.1467, simple_loss=0.2188, pruned_loss=0.03732, over 4829.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03165, over 973192.44 frames.], batch size: 30, lr: 1.68e-04
2022-05-07 18:27:06,171 INFO [train.py:715] (1/8) Epoch 13, batch 14350, loss[loss=0.1579, simple_loss=0.2151, pruned_loss=0.05033, over 4955.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03128, over 972984.92 frames.], batch size: 35, lr: 1.68e-04
2022-05-07 18:27:44,510 INFO [train.py:715] (1/8) Epoch 13, batch 14400, loss[loss=0.1362, simple_loss=0.2116, pruned_loss=0.0304, over 4780.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03113, over 972434.67 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:28:22,438 INFO [train.py:715] (1/8) Epoch 13, batch 14450, loss[loss=0.1565, simple_loss=0.2342, pruned_loss=0.03936, over 4932.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03122, over 972372.02 frames.], batch size: 23, lr: 1.68e-04
2022-05-07 18:29:01,543 INFO [train.py:715] (1/8) Epoch 13, batch 14500, loss[loss=0.1309, simple_loss=0.2052, pruned_loss=0.02832, over 4810.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.0306, over 972661.00 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 18:29:40,347 INFO [train.py:715] (1/8) Epoch 13, batch 14550, loss[loss=0.1614, simple_loss=0.2395, pruned_loss=0.04168, over 4936.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2098, pruned_loss=0.03077, over 972558.64 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:30:18,694 INFO [train.py:715] (1/8) Epoch 13, batch 14600, loss[loss=0.1231, simple_loss=0.1936, pruned_loss=0.02633, over 4825.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2106, pruned_loss=0.03129, over 972612.07 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 18:30:57,059 INFO [train.py:715] (1/8) Epoch 13, batch 14650, loss[loss=0.1455, simple_loss=0.2261, pruned_loss=0.03248, over 4786.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2107, pruned_loss=0.03118, over 972215.05 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:31:35,710 INFO [train.py:715] (1/8) Epoch 13, batch 14700, loss[loss=0.1353, simple_loss=0.2119, pruned_loss=0.02939, over 4771.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03173, over 972005.96 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:32:13,646 INFO [train.py:715] (1/8) Epoch 13, batch 14750, loss[loss=0.1326, simple_loss=0.215, pruned_loss=0.0251, over 4890.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.03144, over 972021.86 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:32:50,801 INFO [train.py:715] (1/8) Epoch 13, batch 14800, loss[loss=0.1382, simple_loss=0.2079, pruned_loss=0.03426, over 4865.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.031, over 971689.03 frames.], batch size: 30, lr: 1.68e-04
2022-05-07 18:33:29,891 INFO [train.py:715] (1/8) Epoch 13, batch 14850, loss[loss=0.1314, simple_loss=0.1976, pruned_loss=0.03261, over 4880.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03081, over 972055.05 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:34:08,571 INFO [train.py:715] (1/8) Epoch 13, batch 14900, loss[loss=0.1547, simple_loss=0.232, pruned_loss=0.03868, over 4686.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.0313, over 971761.89 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:34:46,495 INFO [train.py:715] (1/8) Epoch 13, batch 14950, loss[loss=0.1432, simple_loss=0.2192, pruned_loss=0.03364, over 4989.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03141, over 972722.20 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:35:24,996 INFO [train.py:715] (1/8) Epoch 13, batch 15000, loss[loss=0.1253, simple_loss=0.1967, pruned_loss=0.02699, over 4972.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03099, over 971938.39 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:35:24,996 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 18:35:34,567 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1052, simple_loss=0.189, pruned_loss=0.01074, over 914524.00 frames.
2022-05-07 18:36:13,160 INFO [train.py:715] (1/8) Epoch 13, batch 15050, loss[loss=0.1334, simple_loss=0.2091, pruned_loss=0.02887, over 4875.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03084, over 973299.04 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:36:52,714 INFO [train.py:715] (1/8) Epoch 13, batch 15100, loss[loss=0.1395, simple_loss=0.2142, pruned_loss=0.03239, over 4760.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03104, over 972890.26 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:37:31,194 INFO [train.py:715] (1/8) Epoch 13, batch 15150, loss[loss=0.1293, simple_loss=0.2196, pruned_loss=0.01948, over 4760.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2099, pruned_loss=0.03078, over 971728.27 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:38:09,447 INFO [train.py:715] (1/8) Epoch 13, batch 15200, loss[loss=0.141, simple_loss=0.2104, pruned_loss=0.03573, over 4886.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03086, over 972707.66 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 18:38:49,231 INFO [train.py:715] (1/8) Epoch 13, batch 15250, loss[loss=0.1214, simple_loss=0.1942, pruned_loss=0.02434, over 4785.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03104, over 972876.30 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 18:39:27,976 INFO [train.py:715] (1/8) Epoch 13, batch 15300, loss[loss=0.1239, simple_loss=0.2007, pruned_loss=0.02357, over 4923.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03142, over 972877.21 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:40:06,015 INFO [train.py:715] (1/8) Epoch 13, batch 15350, loss[loss=0.1203, simple_loss=0.1916, pruned_loss=0.02448, over 4750.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2095, pruned_loss=0.03178, over 972433.52 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:40:45,014 INFO [train.py:715] (1/8) Epoch 13, batch 15400, loss[loss=0.1331, simple_loss=0.2059, pruned_loss=0.0301, over 4775.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.0318, over 972066.90 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:41:23,910 INFO [train.py:715] (1/8) Epoch 13, batch 15450, loss[loss=0.143, simple_loss=0.2118, pruned_loss=0.03712, over 4783.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2093, pruned_loss=0.03163, over 971586.13 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:42:03,716 INFO [train.py:715] (1/8) Epoch 13, batch 15500, loss[loss=0.114, simple_loss=0.1928, pruned_loss=0.01766, over 4983.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2089, pruned_loss=0.03144, over 971683.99 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:42:41,963 INFO [train.py:715] (1/8) Epoch 13, batch 15550, loss[loss=0.1834, simple_loss=0.2448, pruned_loss=0.061, over 4918.00 frames.], tot_loss[loss=0.136, simple_loss=0.2091, pruned_loss=0.03146, over 972012.41 frames.], batch size: 39, lr: 1.68e-04
2022-05-07 18:43:21,700 INFO [train.py:715] (1/8) Epoch 13, batch 15600, loss[loss=0.1274, simple_loss=0.2017, pruned_loss=0.02657, over 4924.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03157, over 972702.99 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:44:01,140 INFO [train.py:715] (1/8) Epoch 13, batch 15650, loss[loss=0.1446, simple_loss=0.2177, pruned_loss=0.03568, over 4772.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.0318, over 972682.38 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:44:39,652 INFO [train.py:715] (1/8) Epoch 13, batch 15700, loss[loss=0.1308, simple_loss=0.2121, pruned_loss=0.02474, over 4861.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03168, over 972196.71 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:45:18,633 INFO [train.py:715] (1/8) Epoch 13, batch 15750, loss[loss=0.1296, simple_loss=0.2051, pruned_loss=0.02699, over 4697.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03198, over 972645.02 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:45:57,412 INFO [train.py:715] (1/8) Epoch 13, batch 15800, loss[loss=0.1551, simple_loss=0.2246, pruned_loss=0.04274, over 4956.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03116, over 971421.01 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:46:35,695 INFO [train.py:715] (1/8) Epoch 13, batch 15850, loss[loss=0.1347, simple_loss=0.2218, pruned_loss=0.02378, over 4815.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2087, pruned_loss=0.03121, over 971530.45 frames.], batch size: 27, lr: 1.68e-04
2022-05-07 18:47:13,601 INFO [train.py:715] (1/8) Epoch 13, batch 15900, loss[loss=0.1627, simple_loss=0.2336, pruned_loss=0.04589, over 4942.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03102, over 972683.04 frames.], batch size: 39, lr: 1.68e-04
2022-05-07 18:47:52,839 INFO [train.py:715] (1/8) Epoch 13, batch 15950, loss[loss=0.1432, simple_loss=0.2171, pruned_loss=0.03466, over 4926.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03109, over 972882.24 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:48:31,350 INFO [train.py:715] (1/8) Epoch 13, batch 16000, loss[loss=0.1481, simple_loss=0.2337, pruned_loss=0.03125, over 4936.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03127, over 972988.17 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:49:09,603 INFO [train.py:715] (1/8) Epoch 13, batch 16050, loss[loss=0.1599, simple_loss=0.2453, pruned_loss=0.03723, over 4821.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03138, over 972424.14 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:49:48,081 INFO [train.py:715] (1/8) Epoch 13, batch 16100, loss[loss=0.1623, simple_loss=0.2521, pruned_loss=0.03629, over 4815.00 frames.], tot_loss[loss=0.1359, simple_loss=0.21, pruned_loss=0.0309, over 973331.72 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:50:27,337 INFO [train.py:715] (1/8) Epoch 13, batch 16150, loss[loss=0.1619, simple_loss=0.2178, pruned_loss=0.053, over 4817.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2105, pruned_loss=0.03131, over 972413.07 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 18:51:05,994 INFO [train.py:715] (1/8) Epoch 13, batch 16200, loss[loss=0.1213, simple_loss=0.1964, pruned_loss=0.02313, over 4759.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2097, pruned_loss=0.03084, over 971475.76 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 18:51:42,926 INFO [train.py:715] (1/8) Epoch 13, batch 16250, loss[loss=0.1362, simple_loss=0.203, pruned_loss=0.03468, over 4867.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03075, over 971181.19 frames.], batch size: 20, lr: 1.68e-04
2022-05-07 18:52:22,102 INFO [train.py:715] (1/8) Epoch 13, batch 16300, loss[loss=0.122, simple_loss=0.1995, pruned_loss=0.02226, over 4797.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03025, over 970789.85 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:53:00,699 INFO [train.py:715] (1/8) Epoch 13, batch 16350, loss[loss=0.1306, simple_loss=0.2075, pruned_loss=0.02683, over 4741.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03082, over 970956.25 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:53:39,046 INFO [train.py:715] (1/8) Epoch 13, batch 16400, loss[loss=0.1348, simple_loss=0.2169, pruned_loss=0.02635, over 4830.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03096, over 971680.25 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 18:54:18,191 INFO [train.py:715] (1/8) Epoch 13, batch 16450, loss[loss=0.1234, simple_loss=0.2009, pruned_loss=0.023, over 4781.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.0313, over 971576.33 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 18:54:57,404 INFO [train.py:715] (1/8) Epoch 13, batch 16500, loss[loss=0.1333, simple_loss=0.2082, pruned_loss=0.02924, over 4782.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.031, over 970655.12 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 18:55:36,544 INFO [train.py:715] (1/8) Epoch 13, batch 16550, loss[loss=0.1144, simple_loss=0.1881, pruned_loss=0.02038, over 4744.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03081, over 971248.50 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:56:13,926 INFO [train.py:715] (1/8) Epoch 13, batch 16600, loss[loss=0.1312, simple_loss=0.2168, pruned_loss=0.02287, over 4864.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03079, over 971544.95 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 18:56:53,168 INFO [train.py:715] (1/8) Epoch 13, batch 16650, loss[loss=0.12, simple_loss=0.1999, pruned_loss=0.02002, over 4971.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03105, over 971691.65 frames.], batch size: 24, lr: 1.68e-04
2022-05-07 18:57:31,702 INFO [train.py:715] (1/8) Epoch 13, batch 16700, loss[loss=0.1099, simple_loss=0.1779, pruned_loss=0.02094, over 4869.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03126, over 970894.24 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 18:58:09,692 INFO [train.py:715] (1/8) Epoch 13, batch 16750, loss[loss=0.1389, simple_loss=0.2109, pruned_loss=0.03348, over 4779.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03159, over 971278.50 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:58:48,290 INFO [train.py:715] (1/8) Epoch 13, batch 16800, loss[loss=0.1282, simple_loss=0.2053, pruned_loss=0.02552, over 4831.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03154, over 971750.51 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:59:27,922 INFO [train.py:715] (1/8) Epoch 13, batch 16850, loss[loss=0.1218, simple_loss=0.188, pruned_loss=0.02786, over 4816.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03162, over 972634.72 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 19:00:06,298 INFO [train.py:715] (1/8) Epoch 13, batch 16900, loss[loss=0.1249, simple_loss=0.2059, pruned_loss=0.02199, over 4844.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03163, over 972789.75 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:00:44,804 INFO [train.py:715] (1/8) Epoch 13, batch 16950, loss[loss=0.1195, simple_loss=0.1961, pruned_loss=0.0215, over 4840.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03154, over 972813.73 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 19:01:23,721 INFO [train.py:715] (1/8) Epoch 13, batch 17000, loss[loss=0.1468, simple_loss=0.2352, pruned_loss=0.02925, over 4752.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03146, over 972786.37 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:02:02,419 INFO [train.py:715] (1/8) Epoch 13, batch 17050, loss[loss=0.1327, simple_loss=0.2007, pruned_loss=0.03234, over 4925.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03167, over 972793.95 frames.], batch size: 29, lr: 1.68e-04
2022-05-07 19:02:40,533 INFO [train.py:715] (1/8) Epoch 13, batch 17100, loss[loss=0.1307, simple_loss=0.1994, pruned_loss=0.03101, over 4710.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.03144, over 972313.42 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:03:19,263 INFO [train.py:715] (1/8) Epoch 13, batch 17150, loss[loss=0.1149, simple_loss=0.1893, pruned_loss=0.02024, over 4848.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03113, over 972979.38 frames.], batch size: 20, lr: 1.68e-04
2022-05-07 19:03:58,106 INFO [train.py:715] (1/8) Epoch 13, batch 17200, loss[loss=0.1624, simple_loss=0.2377, pruned_loss=0.04354, over 4899.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.0311, over 972723.73 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 19:04:36,809 INFO [train.py:715] (1/8) Epoch 13, batch 17250, loss[loss=0.141, simple_loss=0.2153, pruned_loss=0.03337, over 4946.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03077, over 973530.88 frames.], batch size: 35, lr: 1.68e-04
2022-05-07 19:05:14,781 INFO [train.py:715] (1/8) Epoch 13, batch 17300, loss[loss=0.1436, simple_loss=0.209, pruned_loss=0.0391, over 4914.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03111, over 973714.57 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 19:05:53,539 INFO [train.py:715] (1/8) Epoch 13, batch 17350, loss[loss=0.1465, simple_loss=0.2175, pruned_loss=0.03778, over 4775.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2107, pruned_loss=0.03137, over 973721.27 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 19:06:32,453 INFO [train.py:715] (1/8) Epoch 13, batch 17400, loss[loss=0.172, simple_loss=0.2506, pruned_loss=0.04668, over 4949.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2113, pruned_loss=0.03164, over 974371.45 frames.], batch size: 24, lr: 1.68e-04
2022-05-07 19:07:10,069 INFO [train.py:715] (1/8) Epoch 13, batch 17450, loss[loss=0.1158, simple_loss=0.1805, pruned_loss=0.02549, over 4822.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03135, over 973616.58 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 19:07:48,570 INFO [train.py:715] (1/8) Epoch 13, batch 17500, loss[loss=0.1437, simple_loss=0.2133, pruned_loss=0.03708, over 4986.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03107, over 972914.77 frames.], batch size: 39, lr: 1.68e-04
2022-05-07 19:08:27,656 INFO [train.py:715] (1/8) Epoch 13, batch 17550, loss[loss=0.1265, simple_loss=0.2002, pruned_loss=0.02644, over 4755.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03108, over 971981.80 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:09:06,327 INFO [train.py:715] (1/8) Epoch 13, batch 17600, loss[loss=0.1339, simple_loss=0.2062, pruned_loss=0.03084, over 4824.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03138, over 971482.19 frames.], batch size: 26, lr: 1.68e-04
2022-05-07 19:09:43,953 INFO [train.py:715] (1/8) Epoch 13, batch 17650, loss[loss=0.1514, simple_loss=0.2211, pruned_loss=0.04086, over 4924.00 frames.], tot_loss[loss=0.1356, simple_loss=0.209, pruned_loss=0.03115, over 972529.18 frames.], batch size: 39, lr: 1.68e-04
2022-05-07 19:10:23,207 INFO [train.py:715] (1/8) Epoch 13, batch 17700, loss[loss=0.1142, simple_loss=0.183, pruned_loss=0.02275, over 4867.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03115, over 972503.86 frames.], batch size: 38, lr: 1.68e-04
2022-05-07 19:11:02,064 INFO [train.py:715] (1/8) Epoch 13, batch 17750, loss[loss=0.1425, simple_loss=0.22, pruned_loss=0.03245, over 4736.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03128, over 972209.04 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 19:11:39,682 INFO [train.py:715] (1/8) Epoch 13, batch 17800, loss[loss=0.1326, simple_loss=0.2079, pruned_loss=0.0287, over 4913.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03078, over 972621.02 frames.], batch size: 23, lr: 1.68e-04
2022-05-07 19:12:18,455 INFO [train.py:715] (1/8) Epoch 13, batch 17850, loss[loss=0.1369, simple_loss=0.2089, pruned_loss=0.03246, over 4833.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2104, pruned_loss=0.03093, over 973285.92 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 19:12:57,286 INFO [train.py:715] (1/8) Epoch 13, batch 17900, loss[loss=0.1336, simple_loss=0.192, pruned_loss=0.03762, over 4831.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2102, pruned_loss=0.03064, over 972933.27 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 19:13:35,477 INFO [train.py:715] (1/8) Epoch 13, batch 17950, loss[loss=0.1229, simple_loss=0.1867, pruned_loss=0.02959, over 4823.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2102, pruned_loss=0.03073, over 973126.40 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 19:14:13,539 INFO [train.py:715] (1/8) Epoch 13, batch 18000, loss[loss=0.1238, simple_loss=0.1956, pruned_loss=0.02606, over 4856.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2094, pruned_loss=0.03018, over 973495.27 frames.], batch size: 30, lr: 1.68e-04
2022-05-07 19:14:13,540 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 19:14:23,028 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1055, simple_loss=0.1892, pruned_loss=0.01083, over 914524.00 frames.
2022-05-07 19:15:00,700 INFO [train.py:715] (1/8) Epoch 13, batch 18050, loss[loss=0.1314, simple_loss=0.2114, pruned_loss=0.02568, over 4762.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2091, pruned_loss=0.03011, over 973400.22 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 19:15:39,774 INFO [train.py:715] (1/8) Epoch 13, batch 18100, loss[loss=0.1322, simple_loss=0.209, pruned_loss=0.02769, over 4903.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02991, over 972475.05 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 19:16:18,125 INFO [train.py:715] (1/8) Epoch 13, batch 18150, loss[loss=0.1131, simple_loss=0.1885, pruned_loss=0.01886, over 4819.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03059, over 972518.59 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 19:16:55,372 INFO [train.py:715] (1/8) Epoch 13, batch 18200, loss[loss=0.1547, simple_loss=0.2289, pruned_loss=0.04021, over 4709.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2097, pruned_loss=0.03082, over 972443.94 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:17:33,693 INFO [train.py:715] (1/8) Epoch 13, batch 18250, loss[loss=0.1342, simple_loss=0.2043, pruned_loss=0.03206, over 4977.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03106, over 972472.94 frames.], batch size: 28, lr: 1.68e-04
2022-05-07 19:18:12,484 INFO [train.py:715] (1/8) Epoch 13, batch 18300, loss[loss=0.1288, simple_loss=0.2019, pruned_loss=0.02783, over 4932.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03074, over 972498.85 frames.], batch size: 29, lr: 1.68e-04
2022-05-07 19:18:51,123 INFO [train.py:715] (1/8) Epoch 13, batch 18350, loss[loss=0.124, simple_loss=0.2027, pruned_loss=0.0227, over 4871.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03076, over 972293.39 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 19:19:29,020 INFO [train.py:715] (1/8) Epoch 13, batch 18400, loss[loss=0.1309, simple_loss=0.2089, pruned_loss=0.02649, over 4887.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03087, over 971909.54 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:20:07,827 INFO [train.py:715] (1/8) Epoch 13, batch 18450, loss[loss=0.1378, simple_loss=0.2174, pruned_loss=0.02911, over 4829.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03123, over 971660.78 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 19:20:46,497 INFO [train.py:715] (1/8) Epoch 13, batch 18500, loss[loss=0.1518, simple_loss=0.212, pruned_loss=0.0458, over 4780.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03122, over 971424.95 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 19:21:23,938 INFO [train.py:715] (1/8) Epoch 13, batch 18550, loss[loss=0.1352, simple_loss=0.2074, pruned_loss=0.03152, over 4787.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03138, over 971626.58 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 19:22:01,961 INFO [train.py:715] (1/8) Epoch 13, batch 18600, loss[loss=0.1278, simple_loss=0.211, pruned_loss=0.0223, over 4811.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03112, over 970978.03 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 19:22:40,557 INFO [train.py:715] (1/8) Epoch 13, batch 18650, loss[loss=0.1542, simple_loss=0.2262, pruned_loss=0.04109, over 4695.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03106, over 971602.52 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:23:18,503 INFO [train.py:715] (1/8) Epoch 13, batch 18700, loss[loss=0.1369, simple_loss=0.2084, pruned_loss=0.03272, over 4889.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.0313, over 971663.10 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 19:23:56,288 INFO [train.py:715] (1/8) Epoch 13, batch 18750, loss[loss=0.1327, simple_loss=0.2117, pruned_loss=0.0269, over 4922.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03109, over 972034.10 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 19:24:35,597 INFO [train.py:715] (1/8) Epoch 13, batch 18800, loss[loss=0.1136, simple_loss=0.1899, pruned_loss=0.01859, over 4886.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.0311, over 972623.17 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 19:25:14,016 INFO [train.py:715] (1/8) Epoch 13, batch 18850, loss[loss=0.1083, simple_loss=0.1818, pruned_loss=0.01739, over 4978.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03092, over 973080.69 frames.], batch size: 24, lr: 1.68e-04
2022-05-07 19:25:52,021 INFO [train.py:715] (1/8) Epoch 13, batch 18900, loss[loss=0.1204, simple_loss=0.1983, pruned_loss=0.02127, over 4810.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.031, over 972672.08 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 19:26:30,885 INFO [train.py:715] (1/8) Epoch 13, batch 18950, loss[loss=0.1294, simple_loss=0.1942, pruned_loss=0.03237, over 4764.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03061, over 972535.81 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:27:09,769 INFO [train.py:715] (1/8) Epoch 13, batch 19000, loss[loss=0.1372, simple_loss=0.2072, pruned_loss=0.03362, over 4890.00 frames.], tot_loss[loss=0.1351, simple_loss=0.209, pruned_loss=0.03058, over 971944.34 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 19:27:48,115 INFO [train.py:715] (1/8) Epoch 13, batch 19050, loss[loss=0.1632, simple_loss=0.2383, pruned_loss=0.04399, over 4694.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03026, over 971919.82 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:28:26,439 INFO [train.py:715] (1/8) Epoch 13, batch 19100, loss[loss=0.1154, simple_loss=0.186, pruned_loss=0.02239, over 4772.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03037, over 971166.08 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 19:29:05,442 INFO [train.py:715] (1/8) Epoch 13, batch 19150, loss[loss=0.1374, simple_loss=0.2081, pruned_loss=0.03341, over 4817.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.03043, over 971711.68 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 19:29:44,108 INFO [train.py:715] (1/8) Epoch 13, batch 19200, loss[loss=0.128, simple_loss=0.1982, pruned_loss=0.02888, over 4866.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03089, over 971834.87 frames.], batch size: 32, lr: 1.67e-04
2022-05-07 19:30:21,503 INFO [train.py:715] (1/8) Epoch 13, batch 19250, loss[loss=0.1358, simple_loss=0.2107, pruned_loss=0.03043, over 4909.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03019, over 972283.41 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 19:31:00,080 INFO [train.py:715] (1/8) Epoch 13, batch 19300, loss[loss=0.1636, simple_loss=0.2337, pruned_loss=0.0467, over 4787.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03076, over 972136.00 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 19:31:39,543 INFO [train.py:715] (1/8) Epoch 13, batch 19350, loss[loss=0.1642, simple_loss=0.2376, pruned_loss=0.04538, over 4936.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03089, over 972585.04 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 19:32:18,083 INFO [train.py:715] (1/8) Epoch 13, batch 19400, loss[loss=0.1242, simple_loss=0.2006, pruned_loss=0.02389, over 4896.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.03062, over 972717.85 frames.], batch size: 22, lr: 1.67e-04
2022-05-07 19:32:56,517 INFO [train.py:715] (1/8) Epoch 13, batch 19450, loss[loss=0.1337, simple_loss=0.2108, pruned_loss=0.02826, over 4959.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03029, over 972937.51 frames.], batch size: 24, lr: 1.67e-04
2022-05-07 19:33:37,810 INFO [train.py:715] (1/8) Epoch 13, batch 19500, loss[loss=0.1403, simple_loss=0.2141, pruned_loss=0.03324, over 4854.00 frames.], tot_loss[loss=0.1343, simple_loss=0.208, pruned_loss=0.03036, over 971647.90 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 19:34:16,751 INFO [train.py:715] (1/8) Epoch 13, batch 19550, loss[loss=0.1305, simple_loss=0.2042, pruned_loss=0.02839, over 4932.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03081, over 971137.29 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 19:34:54,321 INFO [train.py:715] (1/8) Epoch 13, batch 19600, loss[loss=0.1387, simple_loss=0.2215, pruned_loss=0.02798, over 4920.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.03047, over 971940.99 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 19:35:32,451 INFO [train.py:715] (1/8) Epoch 13, batch 19650, loss[loss=0.1333, simple_loss=0.2004, pruned_loss=0.03306, over 4777.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03063, over 971381.24 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 19:36:11,257 INFO [train.py:715] (1/8) Epoch 13, batch 19700, loss[loss=0.1245, simple_loss=0.2011, pruned_loss=0.02393, over 4920.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03064, over 970589.81 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 19:36:49,086 INFO [train.py:715] (1/8) Epoch 13, batch 19750, loss[loss=0.1264, simple_loss=0.2017, pruned_loss=0.02558, over 4814.00 frames.], tot_loss[loss=0.1359, simple_loss=0.21, pruned_loss=0.03093, over 971429.05 frames.], batch size: 27, lr: 1.67e-04
2022-05-07 19:37:26,941 INFO [train.py:715] (1/8) Epoch 13, batch 19800, loss[loss=0.127, simple_loss=0.2029, pruned_loss=0.02553, over 4828.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2102, pruned_loss=0.03097, over 971999.72 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:38:05,612 INFO [train.py:715] (1/8) Epoch 13, batch 19850, loss[loss=0.1211, simple_loss=0.1955, pruned_loss=0.02329, over 4830.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2099, pruned_loss=0.03083, over 972410.18 frames.], batch size: 13, lr: 1.67e-04
2022-05-07 19:38:44,245 INFO [train.py:715] (1/8) Epoch 13, batch 19900, loss[loss=0.1325, simple_loss=0.1981, pruned_loss=0.0334, over 4792.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03102, over 972163.83 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 19:39:22,442 INFO [train.py:715] (1/8) Epoch 13, batch 19950, loss[loss=0.1291, simple_loss=0.2017, pruned_loss=0.02825, over 4789.00 frames.], tot_loss[loss=0.1356, simple_loss=0.209, pruned_loss=0.0311, over 972534.70 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 19:40:01,315 INFO [train.py:715] (1/8) Epoch 13, batch 20000, loss[loss=0.1367, simple_loss=0.2157, pruned_loss=0.02888, over 4832.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03139, over 972472.76 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:40:39,761 INFO [train.py:715] (1/8) Epoch 13, batch 20050, loss[loss=0.1407, simple_loss=0.2153, pruned_loss=0.03303, over 4809.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.0316, over 972404.96 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 19:41:16,931 INFO [train.py:715] (1/8) Epoch 13, batch 20100, loss[loss=0.1074, simple_loss=0.1761, pruned_loss=0.01934, over 4979.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.0313, over 972766.59 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 19:41:54,382 INFO [train.py:715] (1/8) Epoch 13, batch 20150, loss[loss=0.1162, simple_loss=0.1977, pruned_loss=0.01737, over 4781.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03131, over 972740.79 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 19:42:33,108 INFO [train.py:715] (1/8) Epoch 13, batch 20200, loss[loss=0.1423, simple_loss=0.2254, pruned_loss=0.02956, over 4879.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03094, over 972922.99 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 19:43:11,186 INFO [train.py:715] (1/8) Epoch 13, batch 20250, loss[loss=0.125, simple_loss=0.1938, pruned_loss=0.02806, over 4932.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03091, over 973745.31 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 19:43:48,892 INFO [train.py:715] (1/8) Epoch 13, batch 20300, loss[loss=0.125, simple_loss=0.1981, pruned_loss=0.02593, over 4903.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2103, pruned_loss=0.03125, over 973971.25 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 19:44:26,995 INFO [train.py:715] (1/8) Epoch 13, batch 20350, loss[loss=0.1463, simple_loss=0.2142, pruned_loss=0.03924, over 4849.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.03107, over 973882.66 frames.], batch size: 30, lr: 1.67e-04
2022-05-07 19:45:05,764 INFO [train.py:715] (1/8) Epoch 13, batch 20400, loss[loss=0.1207, simple_loss=0.1997, pruned_loss=0.02088, over 4806.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2104, pruned_loss=0.03116, over 973238.67 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 19:45:43,493 INFO [train.py:715] (1/8) Epoch 13, batch 20450, loss[loss=0.1179, simple_loss=0.1925, pruned_loss=0.02163, over 4820.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03095, over 972109.17 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:46:21,266 INFO [train.py:715] (1/8) Epoch 13, batch 20500, loss[loss=0.1342, simple_loss=0.2124, pruned_loss=0.02803, over 4837.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03167, over 971319.66 frames.], batch size: 30, lr: 1.67e-04
2022-05-07 19:46:59,825 INFO [train.py:715] (1/8) Epoch 13, batch 20550, loss[loss=0.1444, simple_loss=0.2231, pruned_loss=0.03281, over 4878.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2113, pruned_loss=0.03187, over 971448.85 frames.], batch size: 22, lr: 1.67e-04
2022-05-07 19:47:37,481 INFO [train.py:715] (1/8) Epoch 13, batch 20600, loss[loss=0.1286, simple_loss=0.2092, pruned_loss=0.02401, over 4821.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2109, pruned_loss=0.03165, over 971605.36 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 19:48:15,108 INFO [train.py:715] (1/8) Epoch 13, batch 20650, loss[loss=0.1456, simple_loss=0.21, pruned_loss=0.04059, over 4759.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03128, over 971639.42 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 19:48:52,914 INFO [train.py:715] (1/8) Epoch 13, batch 20700, loss[loss=0.1468, simple_loss=0.2184, pruned_loss=0.03763, over 4966.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03118, over 971971.23 frames.], batch size: 39, lr: 1.67e-04
2022-05-07 19:49:31,351 INFO [train.py:715] (1/8) Epoch 13, batch 20750, loss[loss=0.1634, simple_loss=0.2389, pruned_loss=0.04391, over 4985.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03167, over 971794.20 frames.], batch size: 24, lr: 1.67e-04
2022-05-07 19:50:08,697 INFO [train.py:715] (1/8) Epoch 13, batch 20800, loss[loss=0.141, simple_loss=0.2121, pruned_loss=0.03491, over 4971.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2095, pruned_loss=0.03172, over 972032.25 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 19:50:46,282 INFO [train.py:715] (1/8) Epoch 13, batch 20850, loss[loss=0.1572, simple_loss=0.232, pruned_loss=0.04122, over 4749.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2098, pruned_loss=0.03191, over 972015.78 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 19:51:24,971 INFO [train.py:715] (1/8) Epoch 13, batch 20900, loss[loss=0.1406, simple_loss=0.2198, pruned_loss=0.03071, over 4835.00 frames.], tot_loss[loss=0.1368, simple_loss=0.21, pruned_loss=0.03179, over 972712.12 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 19:52:03,244 INFO [train.py:715] (1/8) Epoch 13, batch 20950, loss[loss=0.1312, simple_loss=0.1968, pruned_loss=0.03274, over 4971.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03147, over 972775.22 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 19:52:40,749 INFO [train.py:715] (1/8) Epoch 13, batch 21000, loss[loss=0.1271, simple_loss=0.2023, pruned_loss=0.02595, over 4826.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03128, over 972617.60 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:52:40,749 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 19:52:50,264 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1054, simple_loss=0.1891, pruned_loss=0.01084, over 914524.00 frames.
2022-05-07 19:53:28,437 INFO [train.py:715] (1/8) Epoch 13, batch 21050, loss[loss=0.1556, simple_loss=0.2201, pruned_loss=0.04556, over 4784.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03142, over 972880.34 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 19:54:06,970 INFO [train.py:715] (1/8) Epoch 13, batch 21100, loss[loss=0.1255, simple_loss=0.1902, pruned_loss=0.03037, over 4835.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03154, over 973590.93 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:54:46,058 INFO [train.py:715] (1/8) Epoch 13, batch 21150, loss[loss=0.1183, simple_loss=0.188, pruned_loss=0.02431, over 4943.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03196, over 973364.02 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 19:55:23,881 INFO [train.py:715] (1/8) Epoch 13, batch 21200, loss[loss=0.1538, simple_loss=0.2331, pruned_loss=0.03725, over 4986.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03176, over 972826.94 frames.], batch size: 28, lr: 1.67e-04
2022-05-07 19:56:02,467 INFO [train.py:715] (1/8) Epoch 13, batch 21250, loss[loss=0.1044, simple_loss=0.1849, pruned_loss=0.012, over 4903.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03132, over 972851.36 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 19:56:41,279 INFO [train.py:715] (1/8) Epoch 13, batch 21300, loss[loss=0.1775, simple_loss=0.2405, pruned_loss=0.05719, over 4886.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03128, over 972568.83 frames.], batch size: 32, lr: 1.67e-04
2022-05-07 19:57:19,156 INFO [train.py:715] (1/8) Epoch 13, batch 21350, loss[loss=0.1408, simple_loss=0.2228, pruned_loss=0.0294, over 4934.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03145, over 972239.27 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 19:57:57,084 INFO [train.py:715] (1/8) Epoch 13, batch 21400, loss[loss=0.1437, simple_loss=0.2239, pruned_loss=0.03172, over 4991.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03162, over 972507.63 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 19:58:35,345 INFO [train.py:715] (1/8) Epoch 13, batch 21450, loss[loss=0.1352, simple_loss=0.2047, pruned_loss=0.03287, over 4942.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03136, over 971660.42 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 19:59:14,502 INFO [train.py:715] (1/8) Epoch 13, batch 21500, loss[loss=0.124, simple_loss=0.2033, pruned_loss=0.02233, over 4956.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.03168, over 971852.53 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 19:59:52,266 INFO [train.py:715] (1/8) Epoch 13, batch 21550, loss[loss=0.1474, simple_loss=0.2107, pruned_loss=0.04202, over 4938.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03195, over 972090.61 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:00:30,900 INFO [train.py:715] (1/8) Epoch 13, batch 21600, loss[loss=0.1136, simple_loss=0.184, pruned_loss=0.02156, over 4981.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03132, over 971474.80 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 20:01:09,864 INFO [train.py:715] (1/8) Epoch 13, batch 21650, loss[loss=0.1593, simple_loss=0.2267, pruned_loss=0.046, over 4735.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03101, over 971341.72 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:01:48,624 INFO [train.py:715] (1/8) Epoch 13, batch 21700, loss[loss=0.1077, simple_loss=0.1865, pruned_loss=0.01446, over 4799.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03131, over 971318.10 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 20:02:27,467 INFO [train.py:715] (1/8) Epoch 13, batch 21750, loss[loss=0.1284, simple_loss=0.2068, pruned_loss=0.02494, over 4787.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03121, over 971891.78 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 20:03:06,115 INFO [train.py:715] (1/8) Epoch 13, batch 21800, loss[loss=0.1331, simple_loss=0.1995, pruned_loss=0.03339, over 4820.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03169, over 971606.82 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:03:45,415 INFO [train.py:715] (1/8) Epoch 13, batch 21850, loss[loss=0.1415, simple_loss=0.2184, pruned_loss=0.03227, over 4907.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03181, over 972198.33 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:04:23,523 INFO [train.py:715] (1/8) Epoch 13, batch 21900, loss[loss=0.1328, simple_loss=0.2106, pruned_loss=0.02748, over 4937.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03192, over 972862.86 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 20:05:01,705 INFO [train.py:715] (1/8) Epoch 13, batch 21950, loss[loss=0.1592, simple_loss=0.2262, pruned_loss=0.04607, over 4913.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.0315, over 972799.53 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:05:40,178 INFO [train.py:715] (1/8) Epoch 13, batch 22000, loss[loss=0.1296, simple_loss=0.202, pruned_loss=0.02857, over 4795.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03099, over 972503.30 frames.], batch size: 24, lr: 1.67e-04
2022-05-07 20:06:17,896 INFO [train.py:715] (1/8) Epoch 13, batch 22050, loss[loss=0.1143, simple_loss=0.1904, pruned_loss=0.01905, over 4942.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03109, over 972620.51 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 20:06:55,943 INFO [train.py:715] (1/8) Epoch 13, batch 22100, loss[loss=0.1391, simple_loss=0.2197, pruned_loss=0.02926, over 4861.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03136, over 972849.73 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:07:33,696 INFO [train.py:715] (1/8) Epoch 13, batch 22150, loss[loss=0.1192, simple_loss=0.2035, pruned_loss=0.01749, over 4858.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03127, over 972686.25 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:08:12,651 INFO [train.py:715] (1/8) Epoch 13, batch 22200, loss[loss=0.1274, simple_loss=0.2068, pruned_loss=0.02397, over 4977.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03109, over 973422.37 frames.], batch size: 28, lr: 1.67e-04
2022-05-07 20:08:50,196 INFO [train.py:715] (1/8) Epoch 13, batch 22250, loss[loss=0.1321, simple_loss=0.2005, pruned_loss=0.03187, over 4818.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03117, over 973533.66 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:09:28,959 INFO [train.py:715] (1/8) Epoch 13, batch 22300, loss[loss=0.1311, simple_loss=0.1996, pruned_loss=0.03125, over 4839.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03127, over 973213.89 frames.], batch size: 13, lr: 1.67e-04
2022-05-07 20:10:07,705 INFO [train.py:715] (1/8) Epoch 13, batch 22350, loss[loss=0.144, simple_loss=0.2156, pruned_loss=0.03624, over 4783.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.0309, over 973270.86 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 20:10:45,731 INFO [train.py:715] (1/8) Epoch 13, batch 22400, loss[loss=0.1375, simple_loss=0.2072, pruned_loss=0.03385, over 4830.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03085, over 973386.60 frames.], batch size: 30, lr: 1.67e-04
2022-05-07 20:11:23,413 INFO [train.py:715] (1/8) Epoch 13, batch 22450, loss[loss=0.1312, simple_loss=0.2071, pruned_loss=0.02767, over 4829.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03115, over 972272.90 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:12:01,255 INFO [train.py:715] (1/8) Epoch 13, batch 22500, loss[loss=0.1312, simple_loss=0.2039, pruned_loss=0.02926, over 4968.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.0308, over 972362.69 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:12:39,614 INFO [train.py:715] (1/8) Epoch 13, batch 22550, loss[loss=0.142, simple_loss=0.212, pruned_loss=0.03595, over 4811.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03056, over 972161.02 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:13:16,805 INFO [train.py:715] (1/8) Epoch 13, batch 22600, loss[loss=0.1552, simple_loss=0.232, pruned_loss=0.03924, over 4798.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03134, over 972131.13 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 20:13:54,712 INFO [train.py:715] (1/8) Epoch 13, batch 22650, loss[loss=0.1277, simple_loss=0.2055, pruned_loss=0.02492, over 4982.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.03189, over 972834.39 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 20:14:32,805 INFO [train.py:715] (1/8) Epoch 13, batch 22700, loss[loss=0.1184, simple_loss=0.194, pruned_loss=0.02145, over 4901.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03167, over 972566.03 frames.], batch size: 22, lr: 1.67e-04
2022-05-07 20:15:11,035 INFO [train.py:715] (1/8) Epoch 13, batch 22750, loss[loss=0.1358, simple_loss=0.2068, pruned_loss=0.03236, over 4976.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03182, over 972849.72 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:15:49,015 INFO [train.py:715] (1/8) Epoch 13, batch 22800, loss[loss=0.1436, simple_loss=0.2139, pruned_loss=0.03665, over 4701.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03156, over 972580.24 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:16:27,580 INFO [train.py:715] (1/8) Epoch 13, batch 22850, loss[loss=0.1416, simple_loss=0.211, pruned_loss=0.03616, over 4961.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2113, pruned_loss=0.0321, over 972108.09 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:17:06,830 INFO [train.py:715] (1/8) Epoch 13, batch 22900, loss[loss=0.1255, simple_loss=0.2024, pruned_loss=0.02426, over 4881.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03257, over 971621.13 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:17:44,515 INFO [train.py:715] (1/8) Epoch 13, batch 22950, loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03409, over 4808.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03302, over 971947.39 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 20:18:23,097 INFO [train.py:715] (1/8) Epoch 13, batch 23000, loss[loss=0.1277, simple_loss=0.1968, pruned_loss=0.02935, over 4819.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03279, over 971746.43 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:19:01,745 INFO [train.py:715] (1/8) Epoch 13, batch 23050, loss[loss=0.14, simple_loss=0.215, pruned_loss=0.03247, over 4847.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03236, over 971746.96 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:19:40,070 INFO [train.py:715] (1/8) Epoch 13, batch 23100, loss[loss=0.1248, simple_loss=0.1999, pruned_loss=0.02484, over 4984.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03213, over 971444.55 frames.], batch size: 28, lr: 1.67e-04
2022-05-07 20:20:17,987 INFO [train.py:715] (1/8) Epoch 13, batch 23150, loss[loss=0.1644, simple_loss=0.2368, pruned_loss=0.04606, over 4768.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03214, over 971039.76 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:20:56,165 INFO [train.py:715] (1/8) Epoch 13, batch 23200, loss[loss=0.1504, simple_loss=0.2264, pruned_loss=0.03715, over 4986.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2117, pruned_loss=0.03189, over 972018.22 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:21:34,318 INFO [train.py:715] (1/8) Epoch 13, batch 23250, loss[loss=0.1284, simple_loss=0.2156, pruned_loss=0.02055, over 4872.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2116, pruned_loss=0.03188, over 972969.89 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:22:11,787 INFO [train.py:715] (1/8) Epoch 13, batch 23300, loss[loss=0.1567, simple_loss=0.2234, pruned_loss=0.04494, over 4900.00 frames.], tot_loss[loss=0.138, simple_loss=0.212, pruned_loss=0.03197, over 972691.15 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 20:22:50,103 INFO [train.py:715] (1/8) Epoch 13, batch 23350, loss[loss=0.1163, simple_loss=0.191, pruned_loss=0.02081, over 4928.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2109, pruned_loss=0.03135, over 972863.87 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 20:23:28,678 INFO [train.py:715] (1/8) Epoch 13, batch 23400, loss[loss=0.1441, simple_loss=0.1998, pruned_loss=0.0442, over 4819.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2112, pruned_loss=0.03157, over 972493.83 frames.], batch size: 12, lr: 1.67e-04
2022-05-07 20:24:06,994 INFO [train.py:715] (1/8) Epoch 13, batch 23450, loss[loss=0.1315, simple_loss=0.2078, pruned_loss=0.02762, over 4813.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03129, over 972003.16 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:24:45,014 INFO [train.py:715] (1/8) Epoch 13, batch 23500, loss[loss=0.1361, simple_loss=0.216, pruned_loss=0.02812, over 4982.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.03148, over 972199.77 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 20:25:23,769 INFO [train.py:715] (1/8) Epoch 13, batch 23550, loss[loss=0.1175, simple_loss=0.2, pruned_loss=0.01745, over 4917.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03146, over 971831.65 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 20:26:02,269 INFO [train.py:715] (1/8) Epoch 13, batch 23600, loss[loss=0.1273, simple_loss=0.1973, pruned_loss=0.02867, over 4813.00 frames.], tot_loss[loss=0.1359, simple_loss=0.209, pruned_loss=0.03139, over 972664.99 frames.], batch size: 24, lr: 1.67e-04
2022-05-07 20:26:39,841 INFO [train.py:715] (1/8) Epoch 13, batch 23650, loss[loss=0.1507, simple_loss=0.2203, pruned_loss=0.04053, over 4863.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2098, pruned_loss=0.03193, over 973131.67 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:27:18,104 INFO [train.py:715] (1/8) Epoch 13, batch 23700, loss[loss=0.1613, simple_loss=0.233, pruned_loss=0.04474, over 4917.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.0315, over 972278.23 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 20:27:56,589 INFO [train.py:715] (1/8) Epoch 13, batch 23750, loss[loss=0.1229, simple_loss=0.1981, pruned_loss=0.02382, over 4817.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03171, over 972997.20 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:28:34,761 INFO [train.py:715] (1/8) Epoch 13, batch 23800, loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03095, over 4929.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03204, over 972510.77 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 20:29:12,136 INFO [train.py:715] (1/8) Epoch 13, batch 23850, loss[loss=0.1446, simple_loss=0.2269, pruned_loss=0.03115, over 4972.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2105, pruned_loss=0.03126, over 972681.31 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:29:51,250 INFO [train.py:715] (1/8) Epoch 13, batch 23900, loss[loss=0.1532, simple_loss=0.2328, pruned_loss=0.03686, over 4929.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2113, pruned_loss=0.03144, over 972117.79 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 20:30:29,202 INFO [train.py:715] (1/8) Epoch 13, batch 23950, loss[loss=0.1284, simple_loss=0.2061, pruned_loss=0.02538, over 4906.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2107, pruned_loss=0.03124, over 972691.18 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 20:31:06,579 INFO [train.py:715] (1/8) Epoch 13, batch 24000, loss[loss=0.1072, simple_loss=0.188, pruned_loss=0.01316, over 4934.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03163, over 972804.63 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 20:31:06,580 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 20:31:16,110 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1053, simple_loss=0.1891, pruned_loss=0.01069, over 914524.00 frames.
2022-05-07 20:31:53,723 INFO [train.py:715] (1/8) Epoch 13, batch 24050, loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02854, over 4966.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03151, over 971932.36 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 20:32:31,542 INFO [train.py:715] (1/8) Epoch 13, batch 24100, loss[loss=0.1618, simple_loss=0.2444, pruned_loss=0.03962, over 4746.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03137, over 971532.80 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:33:10,918 INFO [train.py:715] (1/8) Epoch 13, batch 24150, loss[loss=0.1245, simple_loss=0.1975, pruned_loss=0.02575, over 4889.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03099, over 972447.85 frames.], batch size: 22, lr: 1.67e-04
2022-05-07 20:33:49,885 INFO [train.py:715] (1/8) Epoch 13, batch 24200, loss[loss=0.1622, simple_loss=0.229, pruned_loss=0.04769, over 4977.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.0315, over 973346.25 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:34:28,088 INFO [train.py:715] (1/8) Epoch 13, batch 24250, loss[loss=0.1216, simple_loss=0.1948, pruned_loss=0.02413, over 4827.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03108, over 972876.92 frames.], batch size: 27, lr: 1.67e-04
2022-05-07 20:35:06,953 INFO [train.py:715] (1/8) Epoch 13, batch 24300, loss[loss=0.1305, simple_loss=0.206, pruned_loss=0.0275, over 4881.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2095, pruned_loss=0.03163, over 972279.70 frames.], batch size: 38, lr: 1.67e-04
2022-05-07 20:35:45,651 INFO [train.py:715] (1/8) Epoch 13, batch 24350, loss[loss=0.1355, simple_loss=0.206, pruned_loss=0.03247, over 4902.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03151, over 972516.84 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:36:23,177 INFO [train.py:715] (1/8) Epoch 13, batch 24400, loss[loss=0.1316, simple_loss=0.2132, pruned_loss=0.02503, over 4829.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03093, over 972439.11 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:37:01,584 INFO [train.py:715] (1/8) Epoch 13, batch 24450, loss[loss=0.1086, simple_loss=0.1823, pruned_loss=0.01745, over 4810.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03084, over 972220.98 frames.], batch size: 24, lr: 1.67e-04
2022-05-07 20:37:40,242 INFO [train.py:715] (1/8) Epoch 13, batch 24500, loss[loss=0.1361, simple_loss=0.2075, pruned_loss=0.03236, over 4834.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03073, over 972088.85 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:38:18,538 INFO [train.py:715] (1/8) Epoch 13, batch 24550, loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02875, over 4776.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03068, over 972371.75 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 20:38:56,903 INFO [train.py:715] (1/8) Epoch 13, batch 24600, loss[loss=0.113, simple_loss=0.1892, pruned_loss=0.01846, over 4789.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2091, pruned_loss=0.03011, over 972400.23 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 20:39:36,095 INFO [train.py:715] (1/8) Epoch 13, batch 24650, loss[loss=0.1401, simple_loss=0.214, pruned_loss=0.03315, over 4804.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2099, pruned_loss=0.03046, over 972831.65 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 20:40:14,991 INFO [train.py:715] (1/8) Epoch 13, batch 24700, loss[loss=0.1261, simple_loss=0.2101, pruned_loss=0.02107, over 4807.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2105, pruned_loss=0.03086, over 972233.17 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 20:40:52,895 INFO [train.py:715] (1/8) Epoch 13, batch 24750, loss[loss=0.1354, simple_loss=0.2026, pruned_loss=0.03409, over 4888.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2096, pruned_loss=0.0306, over 972208.23 frames.], batch size: 39, lr: 1.67e-04
2022-05-07 20:41:31,286 INFO [train.py:715] (1/8) Epoch 13, batch 24800, loss[loss=0.1262, simple_loss=0.2019, pruned_loss=0.0253, over 4885.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03085, over 971927.46 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:42:10,094 INFO [train.py:715] (1/8) Epoch 13, batch 24850, loss[loss=0.1391, simple_loss=0.2087, pruned_loss=0.03475, over 4927.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03068, over 972103.29 frames.], batch size: 29, lr: 1.66e-04
2022-05-07 20:42:48,218 INFO [train.py:715] (1/8) Epoch 13, batch 24900, loss[loss=0.1493, simple_loss=0.2083, pruned_loss=0.04511, over 4902.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03046, over 972930.60 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 20:43:26,337 INFO [train.py:715] (1/8) Epoch 13, batch 24950, loss[loss=0.1319, simple_loss=0.2003, pruned_loss=0.03172, over 4990.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03055, over 972953.23 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 20:44:04,945 INFO [train.py:715] (1/8) Epoch 13, batch 25000, loss[loss=0.1384, simple_loss=0.2136, pruned_loss=0.03158, over 4863.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03067, over 973337.19 frames.], batch size: 30, lr: 1.66e-04
2022-05-07 20:44:43,240 INFO [train.py:715] (1/8) Epoch 13, batch 25050, loss[loss=0.1379, simple_loss=0.2156, pruned_loss=0.03012, over 4977.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03053, over 972917.86 frames.], batch size: 35, lr: 1.66e-04
2022-05-07 20:45:20,927 INFO [train.py:715] (1/8) Epoch 13, batch 25100, loss[loss=0.1032, simple_loss=0.1807, pruned_loss=0.01287, over 4798.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03042, over 972288.50 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 20:46:00,046 INFO [train.py:715] (1/8) Epoch 13, batch 25150, loss[loss=0.129, simple_loss=0.2065, pruned_loss=0.02574, over 4972.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03021, over 972982.53 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 20:46:38,592 INFO [train.py:715] (1/8) Epoch 13, batch 25200, loss[loss=0.1208, simple_loss=0.2009, pruned_loss=0.02038, over 4887.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03085, over 972339.21 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 20:47:17,721 INFO [train.py:715] (1/8) Epoch 13, batch 25250, loss[loss=0.1438, simple_loss=0.2218, pruned_loss=0.03284, over 4875.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2105, pruned_loss=0.03123, over 971982.30 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 20:47:55,933 INFO [train.py:715] (1/8) Epoch 13, batch 25300, loss[loss=0.1466, simple_loss=0.2006, pruned_loss=0.04636, over 4925.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2103, pruned_loss=0.03139, over 971741.73 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 20:48:34,494 INFO [train.py:715] (1/8) Epoch 13, batch 25350, loss[loss=0.1264, simple_loss=0.202, pruned_loss=0.02536, over 4819.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03151, over 971262.78 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 20:49:13,703 INFO [train.py:715] (1/8) Epoch 13, batch 25400, loss[loss=0.1143, simple_loss=0.1877, pruned_loss=0.02052, over 4985.00 frames.], tot_loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03123, over 971603.00 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 20:49:51,568 INFO [train.py:715] (1/8) Epoch 13, batch 25450, loss[loss=0.1145, simple_loss=0.1954, pruned_loss=0.01679, over 4871.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2103, pruned_loss=0.03108, over 972143.61 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 20:50:30,632 INFO [train.py:715] (1/8) Epoch 13, batch 25500, loss[loss=0.1282, simple_loss=0.2068, pruned_loss=0.02486, over 4818.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03081, over 971655.47 frames.], batch size: 27, lr: 1.66e-04
2022-05-07 20:51:09,205 INFO [train.py:715] (1/8) Epoch 13, batch 25550, loss[loss=0.1259, simple_loss=0.1938, pruned_loss=0.02902, over 4931.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03056, over 971551.28 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 20:51:47,751 INFO [train.py:715] (1/8) Epoch 13, batch 25600, loss[loss=0.1515, simple_loss=0.2306, pruned_loss=0.03617, over 4874.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03088, over 971969.46 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 20:52:25,801 INFO [train.py:715] (1/8) Epoch 13, batch 25650, loss[loss=0.1447, simple_loss=0.2138, pruned_loss=0.0378, over 4953.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03114, over 972609.07 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 20:53:05,139 INFO [train.py:715] (1/8) Epoch 13, batch 25700, loss[loss=0.1461, simple_loss=0.2233, pruned_loss=0.03448, over 4776.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03074, over 971494.09 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 20:53:43,488 INFO [train.py:715] (1/8) Epoch 13, batch 25750, loss[loss=0.1427, simple_loss=0.2092, pruned_loss=0.03806, over 4988.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03086, over 970901.53 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 20:54:21,675 INFO [train.py:715] (1/8) Epoch 13, batch 25800, loss[loss=0.1299, simple_loss=0.204, pruned_loss=0.0279, over 4853.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03079, over 972614.81 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 20:55:00,567 INFO [train.py:715] (1/8) Epoch 13, batch 25850, loss[loss=0.1499, simple_loss=0.2271, pruned_loss=0.03638, over 4921.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03109, over 972744.60 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 20:55:39,360 INFO [train.py:715] (1/8) Epoch 13, batch 25900, loss[loss=0.1315, simple_loss=0.2092, pruned_loss=0.02694, over 4796.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03109, over 972483.92 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 20:56:18,183 INFO [train.py:715] (1/8) Epoch 13, batch 25950, loss[loss=0.1264, simple_loss=0.2132, pruned_loss=0.01974, over 4985.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03136, over 972385.27 frames.], batch size: 28, lr: 1.66e-04
2022-05-07 20:56:57,182 INFO [train.py:715] (1/8) Epoch 13, batch 26000, loss[loss=0.1387, simple_loss=0.2083, pruned_loss=0.03459, over 4933.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03134, over 972197.50 frames.], batch size: 29, lr: 1.66e-04
2022-05-07 20:57:36,562 INFO [train.py:715] (1/8) Epoch 13, batch 26050, loss[loss=0.1373, simple_loss=0.2067, pruned_loss=0.03391, over 4854.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.0311, over 972155.50 frames.], batch size: 32, lr: 1.66e-04
2022-05-07 20:58:15,757 INFO [train.py:715] (1/8) Epoch 13, batch 26100, loss[loss=0.1274, simple_loss=0.1991, pruned_loss=0.0279, over 4986.00 frames.], tot_loss[loss=0.136, simple_loss=0.2091, pruned_loss=0.03145, over 972179.69 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 20:58:54,141 INFO [train.py:715] (1/8) Epoch 13, batch 26150, loss[loss=0.112, simple_loss=0.1796, pruned_loss=0.02219, over 4985.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2085, pruned_loss=0.03138, over 972819.07 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 20:59:33,346 INFO [train.py:715] (1/8) Epoch 13, batch 26200, loss[loss=0.1258, simple_loss=0.2092, pruned_loss=0.02117, over 4905.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03093, over 972403.31 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:00:12,172 INFO [train.py:715] (1/8) Epoch 13, batch 26250, loss[loss=0.1068, simple_loss=0.1768, pruned_loss=0.01834, over 4743.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2082, pruned_loss=0.0307, over 972622.23 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:00:50,347 INFO [train.py:715] (1/8) Epoch 13, batch 26300, loss[loss=0.1938, simple_loss=0.2611, pruned_loss=0.06326, over 4941.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03123, over 974092.00 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:01:28,299 INFO [train.py:715] (1/8) Epoch 13, batch 26350, loss[loss=0.1353, simple_loss=0.2111, pruned_loss=0.02975, over 4774.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03144, over 973390.99 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 21:02:07,167 INFO [train.py:715] (1/8) Epoch 13, batch 26400, loss[loss=0.1405, simple_loss=0.225, pruned_loss=0.02804, over 4987.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03129, over 973198.00 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 21:02:46,108 INFO [train.py:715] (1/8) Epoch 13, batch 26450, loss[loss=0.1349, simple_loss=0.217, pruned_loss=0.02636, over 4891.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03145, over 973356.16 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:03:24,280 INFO [train.py:715] (1/8) Epoch 13, batch 26500, loss[loss=0.1221, simple_loss=0.2056, pruned_loss=0.01927, over 4977.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2112, pruned_loss=0.03172, over 972855.37 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 21:04:03,401 INFO [train.py:715] (1/8) Epoch 13, batch 26550, loss[loss=0.1317, simple_loss=0.2048, pruned_loss=0.02933, over 4768.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2113, pruned_loss=0.03159, over 972448.84 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 21:04:41,841 INFO [train.py:715] (1/8) Epoch 13, batch 26600, loss[loss=0.1181, simple_loss=0.1906, pruned_loss=0.02278, over 4880.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2112, pruned_loss=0.03156, over 972326.65 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:05:20,069 INFO [train.py:715] (1/8) Epoch 13, batch 26650, loss[loss=0.1203, simple_loss=0.1888, pruned_loss=0.02591, over 4781.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2105, pruned_loss=0.03121, over 972342.44 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:05:58,319 INFO [train.py:715] (1/8) Epoch 13, batch 26700, loss[loss=0.1329, simple_loss=0.2004, pruned_loss=0.03264, over 4976.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2099, pruned_loss=0.0309, over 973159.16 frames.], batch size: 31, lr: 1.66e-04
2022-05-07 21:06:37,487 INFO [train.py:715] (1/8) Epoch 13, batch 26750, loss[loss=0.1646, simple_loss=0.2343, pruned_loss=0.04745, over 4945.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2107, pruned_loss=0.03111, over 973538.92 frames.], batch size: 35, lr: 1.66e-04
2022-05-07 21:07:15,992 INFO [train.py:715] (1/8) Epoch 13, batch 26800, loss[loss=0.1475, simple_loss=0.2186, pruned_loss=0.03816, over 4801.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2109, pruned_loss=0.0313, over 972338.06 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:07:54,605 INFO [train.py:715] (1/8) Epoch 13, batch 26850, loss[loss=0.1096, simple_loss=0.178, pruned_loss=0.02064, over 4820.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2111, pruned_loss=0.03178, over 972389.24 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:08:33,349 INFO [train.py:715] (1/8) Epoch 13, batch 26900, loss[loss=0.1422, simple_loss=0.2107, pruned_loss=0.03687, over 4782.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03199, over 971919.06 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 21:09:11,797 INFO [train.py:715] (1/8) Epoch 13, batch 26950, loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03215, over 4918.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03168, over 972504.82 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:09:50,379 INFO [train.py:715] (1/8) Epoch 13, batch 27000, loss[loss=0.1175, simple_loss=0.1938, pruned_loss=0.02055, over 4910.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03171, over 971714.21 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:09:50,379 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 21:09:59,936 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1053, simple_loss=0.1891, pruned_loss=0.01077, over 914524.00 frames.
2022-05-07 21:10:39,030 INFO [train.py:715] (1/8) Epoch 13, batch 27050, loss[loss=0.1209, simple_loss=0.1926, pruned_loss=0.0246, over 4740.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.0318, over 971949.78 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:11:17,914 INFO [train.py:715] (1/8) Epoch 13, batch 27100, loss[loss=0.1259, simple_loss=0.2005, pruned_loss=0.02569, over 4871.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03177, over 972252.81 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:11:57,148 INFO [train.py:715] (1/8) Epoch 13, batch 27150, loss[loss=0.1252, simple_loss=0.2041, pruned_loss=0.02313, over 4821.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03155, over 971589.16 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 21:12:36,129 INFO [train.py:715] (1/8) Epoch 13, batch 27200, loss[loss=0.1127, simple_loss=0.1884, pruned_loss=0.01851, over 4989.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03113, over 971726.78 frames.], batch size: 28, lr: 1.66e-04
2022-05-07 21:13:14,932 INFO [train.py:715] (1/8) Epoch 13, batch 27250, loss[loss=0.1137, simple_loss=0.1907, pruned_loss=0.01837, over 4962.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03082, over 972444.00 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 21:13:54,930 INFO [train.py:715] (1/8) Epoch 13, batch 27300, loss[loss=0.1432, simple_loss=0.2094, pruned_loss=0.03852, over 4868.00 frames.], tot_loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03117, over 972675.80 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:14:33,866 INFO [train.py:715] (1/8) Epoch 13, batch 27350, loss[loss=0.1208, simple_loss=0.2022, pruned_loss=0.01974, over 4847.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2108, pruned_loss=0.03126, over 973014.19 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:15:11,634 INFO [train.py:715] (1/8) Epoch 13, batch 27400, loss[loss=0.1398, simple_loss=0.2165, pruned_loss=0.03157, over 4836.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2107, pruned_loss=0.03099, over 971785.64 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:15:49,749 INFO [train.py:715] (1/8) Epoch 13, batch 27450, loss[loss=0.1448, simple_loss=0.2159, pruned_loss=0.03689, over 4917.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2099, pruned_loss=0.03059, over 972018.02 frames.], batch size: 39, lr: 1.66e-04
2022-05-07 21:16:30,592 INFO [train.py:715] (1/8) Epoch 13, batch 27500, loss[loss=0.1401, simple_loss=0.2203, pruned_loss=0.02993, over 4959.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2101, pruned_loss=0.03088, over 972198.27 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 21:17:08,830 INFO [train.py:715] (1/8) Epoch 13, batch 27550, loss[loss=0.1386, simple_loss=0.2092, pruned_loss=0.034, over 4829.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2103, pruned_loss=0.0311, over 973158.40 frames.], batch size: 30, lr: 1.66e-04
2022-05-07 21:17:46,779 INFO [train.py:715] (1/8) Epoch 13, batch 27600, loss[loss=0.1285, simple_loss=0.2064, pruned_loss=0.02531, over 4871.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.03091, over 973474.85 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:18:25,961 INFO [train.py:715] (1/8) Epoch 13, batch 27650, loss[loss=0.1468, simple_loss=0.2139, pruned_loss=0.03982, over 4979.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03205, over 973223.74 frames.], batch size: 33, lr: 1.66e-04
2022-05-07 21:19:03,879 INFO [train.py:715] (1/8) Epoch 13, batch 27700, loss[loss=0.1531, simple_loss=0.2222, pruned_loss=0.04203, over 4845.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03153, over 972605.68 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:19:42,899 INFO [train.py:715] (1/8) Epoch 13, batch 27750, loss[loss=0.1521, simple_loss=0.2145, pruned_loss=0.04488, over 4930.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03141, over 972715.45 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:20:21,395 INFO [train.py:715] (1/8) Epoch 13, batch 27800, loss[loss=0.1425, simple_loss=0.2274, pruned_loss=0.0288, over 4978.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.03135, over 972198.02 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 21:21:00,122 INFO [train.py:715] (1/8) Epoch 13, batch 27850, loss[loss=0.1321, simple_loss=0.2084, pruned_loss=0.02792, over 4918.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03101, over 972193.72 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 21:21:38,317 INFO [train.py:715] (1/8) Epoch 13, batch 27900, loss[loss=0.1338, simple_loss=0.2094, pruned_loss=0.0291, over 4890.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03141, over 971252.77 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 21:22:16,099 INFO [train.py:715] (1/8) Epoch 13, batch 27950, loss[loss=0.1346, simple_loss=0.199, pruned_loss=0.0351, over 4969.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.0318, over 971511.54 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:22:55,057 INFO [train.py:715] (1/8) Epoch 13, batch 28000, loss[loss=0.1679, simple_loss=0.2287, pruned_loss=0.05355, over 4823.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.03093, over 971003.15 frames.], batch size: 26, lr: 1.66e-04
2022-05-07 21:23:33,525 INFO [train.py:715] (1/8) Epoch 13, batch 28050, loss[loss=0.1397, simple_loss=0.208, pruned_loss=0.03567, over 4775.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.0306, over 971491.93 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 21:24:11,557 INFO [train.py:715] (1/8) Epoch 13, batch 28100, loss[loss=0.1257, simple_loss=0.2057, pruned_loss=0.02286, over 4865.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03065, over 971261.56 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:24:49,601 INFO [train.py:715] (1/8) Epoch 13, batch 28150, loss[loss=0.1381, simple_loss=0.2132, pruned_loss=0.03146, over 4986.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03074, over 971792.49 frames.], batch size: 31, lr: 1.66e-04
2022-05-07 21:25:28,816 INFO [train.py:715] (1/8) Epoch 13, batch 28200, loss[loss=0.1244, simple_loss=0.2104, pruned_loss=0.01917, over 4935.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03072, over 972142.59 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:26:06,614 INFO [train.py:715] (1/8) Epoch 13, batch 28250, loss[loss=0.09933, simple_loss=0.1752, pruned_loss=0.01172, over 4779.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2092, pruned_loss=0.03049, over 972957.67 frames.], batch size: 12, lr: 1.66e-04
2022-05-07 21:26:44,776 INFO [train.py:715] (1/8) Epoch 13, batch 28300, loss[loss=0.1606, simple_loss=0.2278, pruned_loss=0.04669, over 4896.00 frames.], tot_loss[loss=0.135, simple_loss=0.2092, pruned_loss=0.03045, over 972619.30 frames.], batch size: 39, lr: 1.66e-04
2022-05-07 21:27:23,480 INFO [train.py:715] (1/8) Epoch 13, batch 28350, loss[loss=0.1764, simple_loss=0.2446, pruned_loss=0.05415, over 4845.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03081, over 973182.21 frames.], batch size: 30, lr: 1.66e-04
2022-05-07 21:28:01,613 INFO [train.py:715] (1/8) Epoch 13, batch 28400, loss[loss=0.1332, simple_loss=0.2086, pruned_loss=0.02894, over 4863.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03091, over 972663.13 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:28:40,048 INFO [train.py:715] (1/8) Epoch 13, batch 28450, loss[loss=0.1359, simple_loss=0.2, pruned_loss=0.0359, over 4882.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03035, over 972021.78 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:29:18,393 INFO [train.py:715] (1/8) Epoch 13, batch 28500, loss[loss=0.1291, simple_loss=0.2102, pruned_loss=0.02398, over 4983.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03002, over 971669.61 frames.], batch size: 28, lr: 1.66e-04
2022-05-07 21:29:57,062 INFO [train.py:715] (1/8) Epoch 13, batch 28550, loss[loss=0.1379, simple_loss=0.2157, pruned_loss=0.03007, over 4789.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03006, over 972639.44 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 21:30:35,262 INFO [train.py:715] (1/8) Epoch 13, batch 28600, loss[loss=0.1412, simple_loss=0.2145, pruned_loss=0.03398, over 4908.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.03039, over 972741.45 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 21:31:13,618 INFO [train.py:715] (1/8) Epoch 13, batch 28650, loss[loss=0.1431, simple_loss=0.2075, pruned_loss=0.03936, over 4791.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03034, over 973223.29 frames.], batch size: 12, lr: 1.66e-04
2022-05-07 21:31:52,264 INFO [train.py:715] (1/8) Epoch 13, batch 28700, loss[loss=0.1362, simple_loss=0.212, pruned_loss=0.03023, over 4853.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03041, over 973890.57 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:32:30,333 INFO [train.py:715] (1/8) Epoch 13, batch 28750, loss[loss=0.1521, simple_loss=0.2205, pruned_loss=0.04184, over 4938.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03036, over 973908.23 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:33:08,640 INFO [train.py:715] (1/8) Epoch 13, batch 28800, loss[loss=0.1462, simple_loss=0.2121, pruned_loss=0.04015, over 4880.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03042, over 974254.60 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:33:47,849 INFO [train.py:715] (1/8) Epoch 13, batch 28850, loss[loss=0.137, simple_loss=0.2142, pruned_loss=0.02988, over 4813.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03094, over 974055.59 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 21:34:26,369 INFO [train.py:715] (1/8) Epoch 13, batch 28900, loss[loss=0.1238, simple_loss=0.2012, pruned_loss=0.02323, over 4840.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03099, over 973664.80 frames.], batch size: 26, lr: 1.66e-04
2022-05-07 21:35:04,282 INFO [train.py:715] (1/8) Epoch 13, batch 28950, loss[loss=0.1475, simple_loss=0.2153, pruned_loss=0.03983, over 4875.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.0317, over 973875.41 frames.], batch size: 32, lr: 1.66e-04
2022-05-07 21:35:42,446 INFO [train.py:715] (1/8) Epoch 13, batch 29000, loss[loss=0.1478, simple_loss=0.2091, pruned_loss=0.0433, over 4973.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03213, over 973683.26 frames.], batch size: 35, lr: 1.66e-04
2022-05-07 21:36:21,642 INFO [train.py:715] (1/8) Epoch 13, batch 29050, loss[loss=0.13, simple_loss=0.1946, pruned_loss=0.03274, over 4971.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2106, pruned_loss=0.03219, over 973873.35 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:37:00,160 INFO [train.py:715] (1/8) Epoch 13, batch 29100, loss[loss=0.1563, simple_loss=0.2201, pruned_loss=0.04623, over 4956.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2098, pruned_loss=0.03196, over 973937.99 frames.], batch size: 35, lr: 1.66e-04
2022-05-07 21:37:38,206 INFO [train.py:715] (1/8) Epoch 13, batch 29150, loss[loss=0.1238, simple_loss=0.2015, pruned_loss=0.02301, over 4860.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03144, over 973870.43 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:38:16,964 INFO [train.py:715] (1/8) Epoch 13, batch 29200, loss[loss=0.1628, simple_loss=0.2278, pruned_loss=0.04889, over 4872.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.03136, over 973775.11 frames.], batch size: 32, lr: 1.66e-04
2022-05-07 21:38:55,229 INFO [train.py:715] (1/8) Epoch 13, batch 29250, loss[loss=0.13, simple_loss=0.2009, pruned_loss=0.02949, over 4849.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03168, over 973924.36 frames.], batch size: 34, lr: 1.66e-04
2022-05-07 21:39:34,056 INFO [train.py:715] (1/8) Epoch 13, batch 29300, loss[loss=0.1345, simple_loss=0.2148, pruned_loss=0.0271, over 4813.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.0311, over 973188.24 frames.], batch size: 27, lr: 1.66e-04
2022-05-07 21:40:12,822 INFO [train.py:715] (1/8) Epoch 13, batch 29350, loss[loss=0.1195, simple_loss=0.1935, pruned_loss=0.02273, over 4898.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03077, over 972509.37 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 21:40:51,677 INFO [train.py:715] (1/8) Epoch 13, batch 29400, loss[loss=0.122, simple_loss=0.1953, pruned_loss=0.02442, over 4829.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03073, over 972720.08 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 21:41:29,698 INFO [train.py:715] (1/8) Epoch 13, batch 29450, loss[loss=0.1368, simple_loss=0.2171, pruned_loss=0.02824, over 4815.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03025, over 971888.84 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:42:08,739 INFO [train.py:715] (1/8) Epoch 13, batch 29500, loss[loss=0.1428, simple_loss=0.217, pruned_loss=0.0343, over 4842.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03022, over 972408.58 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:42:47,372 INFO [train.py:715] (1/8) Epoch 13, batch 29550, loss[loss=0.1299, simple_loss=0.2113, pruned_loss=0.02424, over 4878.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02996, over 971889.73 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:43:25,737 INFO [train.py:715] (1/8) Epoch 13, batch 29600, loss[loss=0.1548, simple_loss=0.232, pruned_loss=0.03879, over 4902.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03047, over 972184.50 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 21:44:03,485 INFO [train.py:715] (1/8) Epoch 13, batch 29650, loss[loss=0.1232, simple_loss=0.2038, pruned_loss=0.0213, over 4936.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03103, over 972277.49 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:44:41,769 INFO [train.py:715] (1/8) Epoch 13, batch 29700, loss[loss=0.1453, simple_loss=0.218, pruned_loss=0.0363, over 4876.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03087, over 973080.42 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:45:20,122 INFO [train.py:715] (1/8) Epoch 13, batch 29750, loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02844, over 4919.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2094, pruned_loss=0.03152, over 973211.84 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:45:59,492 INFO [train.py:715] (1/8) Epoch 13, batch 29800, loss[loss=0.1515, simple_loss=0.2294, pruned_loss=0.03686, over 4864.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03134, over 971433.16 frames.], batch size: 30, lr: 1.66e-04
2022-05-07 21:46:38,721 INFO [train.py:715] (1/8) Epoch 13, batch 29850, loss[loss=0.1445, simple_loss=0.219, pruned_loss=0.03504, over 4892.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03133, over 971677.85 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:47:18,328 INFO [train.py:715] (1/8) Epoch 13, batch 29900, loss[loss=0.1211, simple_loss=0.188, pruned_loss=0.02709, over 4825.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03134, over 971780.85 frames.], batch size: 13, lr: 1.66e-04
2022-05-07 21:47:57,740 INFO [train.py:715] (1/8) Epoch 13, batch 29950, loss[loss=0.134, simple_loss=0.21, pruned_loss=0.02897, over 4820.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03125, over 972330.01 frames.], batch size: 27, lr: 1.66e-04
2022-05-07 21:48:36,359 INFO [train.py:715] (1/8) Epoch 13, batch 30000, loss[loss=0.1202, simple_loss=0.1998, pruned_loss=0.0203, over 4942.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03144, over 973413.01 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:48:36,359 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 21:48:45,863 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1054, simple_loss=0.1891, pruned_loss=0.01083, over 914524.00 frames.
2022-05-07 21:49:25,291 INFO [train.py:715] (1/8) Epoch 13, batch 30050, loss[loss=0.1533, simple_loss=0.2189, pruned_loss=0.04385, over 4842.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03116, over 973692.47 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:50:05,122 INFO [train.py:715] (1/8) Epoch 13, batch 30100, loss[loss=0.1151, simple_loss=0.1856, pruned_loss=0.02226, over 4802.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03118, over 973606.29 frames.], batch size: 12, lr: 1.66e-04
2022-05-07 21:50:44,579 INFO [train.py:715] (1/8) Epoch 13, batch 30150, loss[loss=0.1467, simple_loss=0.2191, pruned_loss=0.03718, over 4861.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03079, over 973513.07 frames.], batch size: 32, lr: 1.66e-04
2022-05-07 21:51:23,152 INFO [train.py:715] (1/8) Epoch 13, batch 30200, loss[loss=0.129, simple_loss=0.2045, pruned_loss=0.02677, over 4846.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03091, over 973579.62 frames.], batch size: 30, lr: 1.66e-04
2022-05-07 21:52:02,988 INFO [train.py:715] (1/8) Epoch 13, batch 30250, loss[loss=0.1291, simple_loss=0.2042, pruned_loss=0.02707, over 4924.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2088, pruned_loss=0.0311, over 973441.97 frames.], batch size: 29, lr: 1.66e-04
2022-05-07 21:52:42,788 INFO [train.py:715] (1/8) Epoch 13, batch 30300, loss[loss=0.1623, simple_loss=0.2414, pruned_loss=0.04159, over 4955.00 frames.], tot_loss[loss=0.136, simple_loss=0.2089, pruned_loss=0.03156, over 973787.53 frames.], batch size: 39, lr: 1.66e-04
2022-05-07 21:53:22,299 INFO [train.py:715] (1/8) Epoch 13, batch 30350, loss[loss=0.124, simple_loss=0.1975, pruned_loss=0.02525, over 4779.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03102, over 972701.30 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:54:01,876 INFO [train.py:715] (1/8) Epoch 13, batch 30400, loss[loss=0.1247, simple_loss=0.196, pruned_loss=0.02669, over 4907.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03113, over 972122.22 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 21:54:42,508 INFO [train.py:715] (1/8) Epoch 13, batch 30450, loss[loss=0.134, simple_loss=0.2028, pruned_loss=0.0326, over 4823.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03106, over 972534.20 frames.], batch size: 13, lr: 1.66e-04
2022-05-07 21:55:22,611 INFO [train.py:715] (1/8) Epoch 13, batch 30500, loss[loss=0.1197, simple_loss=0.1933, pruned_loss=0.02307, over 4895.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03108, over 972531.31 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 21:56:02,392 INFO [train.py:715] (1/8) Epoch 13, batch 30550, loss[loss=0.111, simple_loss=0.1861, pruned_loss=0.01796, over 4955.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03132, over 972609.75 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:56:43,835 INFO [train.py:715] (1/8) Epoch 13, batch 30600, loss[loss=0.1308, simple_loss=0.2041, pruned_loss=0.0288, over 4925.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03132, over 972765.02 frames.], batch size: 35, lr: 1.66e-04
2022-05-07 21:57:24,951 INFO [train.py:715] (1/8) Epoch 13, batch 30650, loss[loss=0.1385, simple_loss=0.216, pruned_loss=0.03052, over 4929.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03183, over 972015.12 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 21:58:05,363 INFO [train.py:715] (1/8) Epoch 13, batch 30700, loss[loss=0.1652, simple_loss=0.2312, pruned_loss=0.04958, over 4904.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03183, over 971948.69 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 21:58:45,823 INFO [train.py:715] (1/8) Epoch 13, batch 30750, loss[loss=0.1096, simple_loss=0.1838, pruned_loss=0.01769, over 4980.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03189, over 972192.20 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 21:59:26,816 INFO [train.py:715] (1/8) Epoch 13, batch 30800, loss[loss=0.1365, simple_loss=0.2112, pruned_loss=0.03087, over 4818.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.03195, over 972676.67 frames.], batch size: 26, lr: 1.65e-04
2022-05-07 22:00:07,578 INFO [train.py:715] (1/8) Epoch 13, batch 30850, loss[loss=0.1334, simple_loss=0.2037, pruned_loss=0.03153, over 4848.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03157, over 972357.68 frames.], batch size: 34, lr: 1.65e-04
2022-05-07 22:00:48,211 INFO [train.py:715] (1/8) Epoch 13, batch 30900, loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03018, over 4876.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03118, over 972254.64 frames.], batch size: 16, lr: 1.65e-04
2022-05-07 22:01:29,254 INFO [train.py:715] (1/8) Epoch 13, batch 30950, loss[loss=0.1542, simple_loss=0.2273, pruned_loss=0.04053, over 4915.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03105, over 972997.33 frames.], batch size: 39, lr: 1.65e-04
2022-05-07 22:02:09,963 INFO [train.py:715] (1/8) Epoch 13, batch 31000, loss[loss=0.1398, simple_loss=0.22, pruned_loss=0.02979, over 4829.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.031, over 972500.59 frames.], batch size: 27, lr: 1.65e-04
2022-05-07 22:02:50,142 INFO [train.py:715] (1/8) Epoch 13, batch 31050, loss[loss=0.1688, simple_loss=0.2253, pruned_loss=0.05611, over 4726.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03138, over 972309.24 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:03:30,744 INFO [train.py:715] (1/8) Epoch 13, batch 31100, loss[loss=0.1467, simple_loss=0.2239, pruned_loss=0.03474, over 4809.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03164, over 972771.24 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:04:11,679 INFO [train.py:715] (1/8) Epoch 13, batch 31150, loss[loss=0.1476, simple_loss=0.2367, pruned_loss=0.02921, over 4950.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03147, over 973254.56 frames.], batch size: 21, lr: 1.65e-04
2022-05-07 22:04:52,783 INFO [train.py:715] (1/8) Epoch 13, batch 31200, loss[loss=0.1097, simple_loss=0.1822, pruned_loss=0.01862, over 4842.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03198, over 973654.66 frames.], batch size: 30, lr: 1.65e-04
2022-05-07 22:05:32,911 INFO [train.py:715] (1/8) Epoch 13, batch 31250, loss[loss=0.1425, simple_loss=0.2152, pruned_loss=0.0349, over 4754.00 frames.], tot_loss[loss=0.137, simple_loss=0.2108, pruned_loss=0.03163, over 973678.02 frames.], batch size: 16, lr: 1.65e-04
2022-05-07 22:06:13,236 INFO [train.py:715] (1/8) Epoch 13, batch 31300, loss[loss=0.117, simple_loss=0.1961, pruned_loss=0.019, over 4930.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03126, over 973284.75 frames.], batch size: 29, lr: 1.65e-04
2022-05-07 22:06:53,514 INFO [train.py:715] (1/8) Epoch 13, batch 31350, loss[loss=0.1289, simple_loss=0.2047, pruned_loss=0.02652, over 4740.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03173, over 972999.31 frames.], batch size: 16, lr: 1.65e-04
2022-05-07 22:07:33,262 INFO [train.py:715] (1/8) Epoch 13, batch 31400, loss[loss=0.126, simple_loss=0.198, pruned_loss=0.027, over 4789.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03168, over 974292.02 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:08:13,735 INFO [train.py:715] (1/8) Epoch 13, batch 31450, loss[loss=0.1231, simple_loss=0.2073, pruned_loss=0.01948, over 4987.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03167, over 974092.75 frames.], batch size: 28, lr: 1.65e-04
2022-05-07 22:08:54,096 INFO [train.py:715] (1/8) Epoch 13, batch 31500, loss[loss=0.1565, simple_loss=0.2315, pruned_loss=0.04077, over 4937.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.03161, over 973605.80 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:09:33,923 INFO [train.py:715] (1/8) Epoch 13, batch 31550, loss[loss=0.1409, simple_loss=0.21, pruned_loss=0.03591, over 4839.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.03166, over 973099.31 frames.], batch size: 32, lr: 1.65e-04
2022-05-07 22:10:14,441 INFO [train.py:715] (1/8) Epoch 13, batch 31600, loss[loss=0.1585, simple_loss=0.2278, pruned_loss=0.04459, over 4828.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.03204, over 972685.26 frames.], batch size: 30, lr: 1.65e-04
2022-05-07 22:10:55,011 INFO [train.py:715] (1/8) Epoch 13, batch 31650, loss[loss=0.129, simple_loss=0.2075, pruned_loss=0.02526, over 4968.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03182, over 972478.98 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:11:35,404 INFO [train.py:715] (1/8) Epoch 13, batch 31700, loss[loss=0.1349, simple_loss=0.2097, pruned_loss=0.03007, over 4925.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03145, over 972529.91 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:12:15,838 INFO [train.py:715] (1/8) Epoch 13, batch 31750, loss[loss=0.1401, simple_loss=0.2254, pruned_loss=0.02736, over 4804.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03162, over 971711.21 frames.], batch size: 21, lr: 1.65e-04
2022-05-07 22:12:56,367 INFO [train.py:715] (1/8) Epoch 13, batch 31800, loss[loss=0.1426, simple_loss=0.2073, pruned_loss=0.03898, over 4768.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.0316, over 972612.13 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:13:37,276 INFO [train.py:715] (1/8) Epoch 13, batch 31850, loss[loss=0.1557, simple_loss=0.2244, pruned_loss=0.0435, over 4965.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03162, over 973273.73 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:14:18,122 INFO [train.py:715] (1/8) Epoch 13, batch 31900, loss[loss=0.1147, simple_loss=0.1946, pruned_loss=0.01743, over 4804.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03165, over 972417.09 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:14:59,146 INFO [train.py:715] (1/8) Epoch 13, batch 31950, loss[loss=0.128, simple_loss=0.202, pruned_loss=0.02695, over 4903.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03129, over 972699.15 frames.], batch size: 39, lr: 1.65e-04
2022-05-07 22:15:39,544 INFO [train.py:715] (1/8) Epoch 13, batch 32000, loss[loss=0.1158, simple_loss=0.1904, pruned_loss=0.02059, over 4830.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03139, over 972894.33 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:16:20,152 INFO [train.py:715] (1/8) Epoch 13, batch 32050, loss[loss=0.1561, simple_loss=0.2147, pruned_loss=0.04876, over 4989.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.031, over 973313.84 frames.], batch size: 31, lr: 1.65e-04
2022-05-07 22:17:00,693 INFO [train.py:715] (1/8) Epoch 13, batch 32100, loss[loss=0.1282, simple_loss=0.2058, pruned_loss=0.02527, over 4788.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03105, over 972937.21 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:17:41,702 INFO [train.py:715] (1/8) Epoch 13, batch 32150, loss[loss=0.1562, simple_loss=0.2285, pruned_loss=0.04193, over 4946.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03147, over 972853.24 frames.], batch size: 39, lr: 1.65e-04
2022-05-07 22:18:22,397 INFO [train.py:715] (1/8) Epoch 13, batch 32200, loss[loss=0.1272, simple_loss=0.2081, pruned_loss=0.02317, over 4979.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03103, over 971587.06 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:19:03,052 INFO [train.py:715] (1/8) Epoch 13, batch 32250, loss[loss=0.1064, simple_loss=0.1744, pruned_loss=0.01921, over 4800.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03059, over 971928.20 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:19:43,878 INFO [train.py:715] (1/8) Epoch 13, batch 32300, loss[loss=0.1335, simple_loss=0.2004, pruned_loss=0.03331, over 4864.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03054, over 971801.41 frames.], batch size: 30, lr: 1.65e-04
2022-05-07 22:20:24,952 INFO [train.py:715] (1/8) Epoch 13, batch 32350, loss[loss=0.09711, simple_loss=0.1727, pruned_loss=0.01075, over 4766.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03137, over 971588.57 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:21:06,368 INFO [train.py:715] (1/8) Epoch 13, batch 32400, loss[loss=0.1212, simple_loss=0.2078, pruned_loss=0.01725, over 4965.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03135, over 971284.89 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:21:47,428 INFO [train.py:715] (1/8) Epoch 13, batch 32450, loss[loss=0.1138, simple_loss=0.1811, pruned_loss=0.02324, over 4784.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03117, over 970518.19 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:22:28,214 INFO [train.py:715] (1/8) Epoch 13, batch 32500, loss[loss=0.1175, simple_loss=0.1926, pruned_loss=0.02122, over 4779.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.031, over 971465.79 frames.], batch size: 19, lr: 1.65e-04
2022-05-07 22:23:09,251 INFO [train.py:715] (1/8) Epoch 13, batch 32550, loss[loss=0.1127, simple_loss=0.1953, pruned_loss=0.01504, over 4933.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03045, over 972381.16 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:23:49,650 INFO [train.py:715] (1/8) Epoch 13, batch 32600, loss[loss=0.1444, simple_loss=0.2183, pruned_loss=0.0352, over 4943.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.0304, over 972037.78 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:24:30,001 INFO [train.py:715] (1/8) Epoch 13, batch 32650, loss[loss=0.1151, simple_loss=0.1942, pruned_loss=0.01798, over 4766.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03026, over 971618.55 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:25:10,592 INFO [train.py:715] (1/8) Epoch 13, batch 32700, loss[loss=0.117, simple_loss=0.1886, pruned_loss=0.02269, over 4823.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03079, over 971212.39 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:25:50,910 INFO [train.py:715] (1/8) Epoch 13, batch 32750, loss[loss=0.1269, simple_loss=0.2075, pruned_loss=0.02309, over 4821.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.03056, over 971367.24 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:26:31,936 INFO [train.py:715] (1/8) Epoch 13, batch 32800, loss[loss=0.1172, simple_loss=0.1916, pruned_loss=0.02136, over 4902.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03079, over 971762.95 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:27:12,672 INFO [train.py:715] (1/8) Epoch 13, batch 32850, loss[loss=0.1631, simple_loss=0.234, pruned_loss=0.04613, over 4832.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03086, over 971585.49 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:27:53,751 INFO [train.py:715] (1/8) Epoch 13, batch 32900, loss[loss=0.1041, simple_loss=0.1734, pruned_loss=0.01744, over 4953.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03052, over 971468.99 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:28:33,956 INFO [train.py:715] (1/8) Epoch 13, batch 32950, loss[loss=0.1551, simple_loss=0.2335, pruned_loss=0.03831, over 4919.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03107, over 971472.05 frames.], batch size: 29, lr: 1.65e-04
2022-05-07 22:29:14,626 INFO [train.py:715] (1/8) Epoch 13, batch 33000, loss[loss=0.1526, simple_loss=0.2127, pruned_loss=0.04621, over 4703.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03087, over 971690.07 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:29:14,626 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 22:29:24,503 INFO [train.py:742] (1/8) Epoch 13, validation: loss=0.1054, simple_loss=0.1892, pruned_loss=0.01081, over 914524.00 frames.
2022-05-07 22:30:05,557 INFO [train.py:715] (1/8) Epoch 13, batch 33050, loss[loss=0.1303, simple_loss=0.207, pruned_loss=0.02681, over 4835.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03077, over 971729.24 frames.], batch size: 30, lr: 1.65e-04
2022-05-07 22:30:45,207 INFO [train.py:715] (1/8) Epoch 13, batch 33100, loss[loss=0.1329, simple_loss=0.1967, pruned_loss=0.03451, over 4969.00 frames.], tot_loss[loss=0.135, simple_loss=0.2086, pruned_loss=0.0307, over 971660.45 frames.], batch size: 35, lr: 1.65e-04
2022-05-07 22:31:25,148 INFO [train.py:715] (1/8) Epoch 13, batch 33150, loss[loss=0.1416, simple_loss=0.2154, pruned_loss=0.03387, over 4783.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.03128, over 972487.77 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:32:05,571 INFO [train.py:715] (1/8) Epoch 13, batch 33200, loss[loss=0.1112, simple_loss=0.1797, pruned_loss=0.02136, over 4899.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03108, over 972156.24 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:32:46,035 INFO [train.py:715] (1/8) Epoch 13, batch 33250, loss[loss=0.1228, simple_loss=0.1953, pruned_loss=0.02514, over 4785.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.0317, over 972434.18 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:33:26,589 INFO [train.py:715] (1/8) Epoch 13, batch 33300, loss[loss=0.1505, simple_loss=0.2143, pruned_loss=0.04333, over 4908.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.0308, over 971881.61 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:34:07,016 INFO [train.py:715] (1/8) Epoch 13, batch 33350, loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02954, over 4952.00 frames.], tot_loss[loss=0.1356, simple_loss=0.209, pruned_loss=0.03104, over 971598.70 frames.], batch size: 29, lr: 1.65e-04
2022-05-07 22:34:47,638 INFO [train.py:715] (1/8) Epoch 13, batch 33400, loss[loss=0.1366, simple_loss=0.2144, pruned_loss=0.0294, over 4788.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03089, over 971407.66 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:35:28,235 INFO [train.py:715] (1/8) Epoch 13, batch 33450, loss[loss=0.1519, simple_loss=0.2315, pruned_loss=0.03615, over 4896.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03119, over 971646.07 frames.], batch size: 19, lr: 1.65e-04
2022-05-07 22:36:08,958 INFO [train.py:715] (1/8) Epoch 13, batch 33500, loss[loss=0.1454, simple_loss=0.2215, pruned_loss=0.0346, over 4762.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03099, over 971661.60 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:36:49,609 INFO [train.py:715] (1/8) Epoch 13, batch 33550, loss[loss=0.1396, simple_loss=0.2207, pruned_loss=0.02922, over 4918.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2086, pruned_loss=0.03108, over 971224.35 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:37:30,304 INFO [train.py:715] (1/8) Epoch 13, batch 33600, loss[loss=0.1138, simple_loss=0.1859, pruned_loss=0.02085, over 4788.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03116, over 971464.63 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:38:10,834 INFO [train.py:715] (1/8) Epoch 13, batch 33650, loss[loss=0.1484, simple_loss=0.2209, pruned_loss=0.03799, over 4881.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03146, over 971401.03 frames.], batch size: 22, lr: 1.65e-04
2022-05-07 22:38:51,061 INFO [train.py:715] (1/8) Epoch 13, batch 33700, loss[loss=0.1381, simple_loss=0.2012, pruned_loss=0.03745, over 4974.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03153, over 972150.93 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:39:32,034 INFO [train.py:715] (1/8) Epoch 13, batch 33750, loss[loss=0.1678, simple_loss=0.2535, pruned_loss=0.04108, over 4888.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03128, over 972893.70 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:40:12,830 INFO [train.py:715] (1/8) Epoch 13, batch 33800, loss[loss=0.159, simple_loss=0.2214, pruned_loss=0.04831, over 4849.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03068, over 972602.03 frames.], batch size: 20, lr: 1.65e-04
2022-05-07 22:40:53,573 INFO [train.py:715] (1/8) Epoch 13, batch 33850, loss[loss=0.1314, simple_loss=0.1974, pruned_loss=0.03275, over 4743.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.0305, over 972236.47 frames.], batch size: 16, lr: 1.65e-04
2022-05-07 22:41:34,021 INFO [train.py:715] (1/8) Epoch 13, batch 33900, loss[loss=0.1419, simple_loss=0.2161, pruned_loss=0.0339, over 4844.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03084, over 973265.74 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:42:15,284 INFO [train.py:715] (1/8) Epoch 13, batch 33950, loss[loss=0.1566, simple_loss=0.2298, pruned_loss=0.04173, over 4850.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03092, over 972854.27 frames.], batch size: 20, lr: 1.65e-04
2022-05-07 22:42:56,289 INFO [train.py:715] (1/8) Epoch 13, batch 34000, loss[loss=0.1413, simple_loss=0.2076, pruned_loss=0.03751, over 4779.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03111, over 972281.56 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:43:36,858 INFO [train.py:715] (1/8) Epoch 13, batch 34050, loss[loss=0.1468, simple_loss=0.2306, pruned_loss=0.03149, over 4958.00 frames.], tot_loss[loss=0.136, simple_loss=0.2099, pruned_loss=0.03103, over 971163.72 frames.], batch size: 21, lr: 1.65e-04
2022-05-07 22:44:17,681 INFO [train.py:715] (1/8) Epoch 13, batch 34100, loss[loss=0.167, simple_loss=0.2426, pruned_loss=0.0457, over 4956.00 frames.], tot_loss[loss=0.137, simple_loss=0.211, pruned_loss=0.03151, over 972297.94 frames.], batch size: 21, lr: 1.65e-04
2022-05-07 22:44:57,557 INFO [train.py:715] (1/8) Epoch 13, batch 34150, loss[loss=0.131, simple_loss=0.1979, pruned_loss=0.03203, over 4780.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03147, over 972395.34 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:45:38,243 INFO [train.py:715] (1/8) Epoch 13, batch 34200, loss[loss=0.1422, simple_loss=0.2245, pruned_loss=0.02998, over 4808.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03092, over 972337.75 frames.], batch size: 21, lr: 1.65e-04
2022-05-07 22:46:18,599 INFO [train.py:715] (1/8) Epoch 13, batch 34250, loss[loss=0.1515, simple_loss=0.2281, pruned_loss=0.03745, over 4942.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.0308, over 972688.06 frames.], batch size: 21, lr: 1.65e-04
2022-05-07 22:46:59,525 INFO [train.py:715] (1/8) Epoch 13, batch 34300, loss[loss=0.1462, simple_loss=0.2155, pruned_loss=0.03843, over 4847.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2103, pruned_loss=0.03096, over 972268.09 frames.], batch size: 30, lr: 1.65e-04
2022-05-07 22:47:39,593 INFO [train.py:715] (1/8) Epoch 13, batch 34350, loss[loss=0.1521, simple_loss=0.233, pruned_loss=0.0356, over 4991.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.03093, over 972610.36 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:48:20,188 INFO [train.py:715] (1/8) Epoch 13, batch 34400, loss[loss=0.1298, simple_loss=0.2067, pruned_loss=0.02647, over 4943.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2102, pruned_loss=0.03108, over 972537.83 frames.], batch size: 21, lr: 1.65e-04
2022-05-07 22:49:01,282 INFO [train.py:715] (1/8) Epoch 13, batch 34450, loss[loss=0.1642, simple_loss=0.2352, pruned_loss=0.04659, over 4905.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03127, over 972539.60 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:49:41,686 INFO [train.py:715] (1/8) Epoch 13, batch 34500, loss[loss=0.115, simple_loss=0.1781, pruned_loss=0.02596, over 4765.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.0319, over 972570.61 frames.], batch size: 19, lr: 1.65e-04
2022-05-07 22:50:21,491 INFO [train.py:715] (1/8) Epoch 13, batch 34550, loss[loss=0.1096, simple_loss=0.177, pruned_loss=0.02115, over 4817.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03158, over 972444.74 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:51:01,498 INFO [train.py:715] (1/8) Epoch 13, batch 34600, loss[loss=0.1408, simple_loss=0.2161, pruned_loss=0.03272, over 4799.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03147, over 972654.42 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:51:40,865 INFO [train.py:715] (1/8) Epoch 13, batch 34650, loss[loss=0.1015, simple_loss=0.1725, pruned_loss=0.01527, over 4685.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03121, over 972601.82 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:52:20,400 INFO [train.py:715] (1/8) Epoch 13, batch 34700, loss[loss=0.143, simple_loss=0.2101, pruned_loss=0.03795, over 4921.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.0309, over 972657.44 frames.], batch size: 35, lr: 1.65e-04
2022-05-07 22:52:59,357 INFO [train.py:715] (1/8) Epoch 13, batch 34750, loss[loss=0.1495, simple_loss=0.2135, pruned_loss=0.04271, over 4899.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2098, pruned_loss=0.03085, over 971951.91 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:53:36,104 INFO [train.py:715] (1/8) Epoch 13, batch 34800, loss[loss=0.1374, simple_loss=0.2197, pruned_loss=0.02753, over 4928.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03028, over 971894.45 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:54:25,043 INFO [train.py:715] (1/8) Epoch 14, batch 0, loss[loss=0.1497, simple_loss=0.2146, pruned_loss=0.04245, over 4772.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2146, pruned_loss=0.04245, over 4772.00 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 22:55:04,009 INFO [train.py:715] (1/8) Epoch 14, batch 50, loss[loss=0.1374, simple_loss=0.2167, pruned_loss=0.0291, over 4827.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03115, over 219137.48 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 22:55:42,419 INFO [train.py:715] (1/8) Epoch 14, batch 100, loss[loss=0.1469, simple_loss=0.2183, pruned_loss=0.0377, over 4875.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03125, over 386547.72 frames.], batch size: 22, lr: 1.59e-04
2022-05-07 22:56:21,299 INFO [train.py:715] (1/8) Epoch 14, batch 150, loss[loss=0.1217, simple_loss=0.1947, pruned_loss=0.02436, over 4880.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2063, pruned_loss=0.03068, over 516527.89 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 22:56:59,872 INFO [train.py:715] (1/8) Epoch 14, batch 200, loss[loss=0.1574, simple_loss=0.2423, pruned_loss=0.03626, over 4864.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2077, pruned_loss=0.0308, over 617708.96 frames.], batch size: 20, lr: 1.59e-04
2022-05-07 22:57:38,465 INFO [train.py:715] (1/8) Epoch 14, batch 250, loss[loss=0.1392, simple_loss=0.2145, pruned_loss=0.03192, over 4888.00 frames.], tot_loss[loss=0.135, simple_loss=0.2077, pruned_loss=0.03111, over 695704.76 frames.], batch size: 22, lr: 1.59e-04
2022-05-07 22:58:17,246 INFO [train.py:715] (1/8) Epoch 14, batch 300, loss[loss=0.1714, simple_loss=0.2357, pruned_loss=0.05351, over 4821.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2094, pruned_loss=0.03174, over 757026.27 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 22:58:56,803 INFO [train.py:715] (1/8) Epoch 14, batch 350, loss[loss=0.1237, simple_loss=0.1962, pruned_loss=0.02567, over 4781.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2091, pruned_loss=0.03155, over 805046.17 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 22:59:35,345 INFO [train.py:715] (1/8) Epoch 14, batch 400, loss[loss=0.1232, simple_loss=0.1922, pruned_loss=0.02707, over 4865.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03142, over 841986.37 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:00:14,777 INFO [train.py:715] (1/8) Epoch 14, batch 450, loss[loss=0.1311, simple_loss=0.2088, pruned_loss=0.02671, over 4796.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03129, over 870855.75 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:00:54,064 INFO [train.py:715] (1/8) Epoch 14, batch 500, loss[loss=0.139, simple_loss=0.215, pruned_loss=0.03151, over 4769.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03094, over 893417.76 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:01:33,691 INFO [train.py:715] (1/8) Epoch 14, batch 550, loss[loss=0.1097, simple_loss=0.1865, pruned_loss=0.01642, over 4750.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03082, over 911005.38 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:02:12,463 INFO [train.py:715] (1/8) Epoch 14, batch 600, loss[loss=0.1372, simple_loss=0.2055, pruned_loss=0.0345, over 4788.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03076, over 923591.67 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:02:51,131 INFO [train.py:715] (1/8) Epoch 14, batch 650, loss[loss=0.1469, simple_loss=0.2259, pruned_loss=0.03398, over 4875.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03054, over 933985.55 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:03:32,630 INFO [train.py:715] (1/8) Epoch 14, batch 700, loss[loss=0.1446, simple_loss=0.2114, pruned_loss=0.03891, over 4819.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03053, over 942623.32 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:04:11,049 INFO [train.py:715] (1/8) Epoch 14, batch 750, loss[loss=0.1225, simple_loss=0.1994, pruned_loss=0.02279, over 4785.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03021, over 949057.07 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:04:51,153 INFO [train.py:715] (1/8) Epoch 14, batch 800, loss[loss=0.1387, simple_loss=0.2185, pruned_loss=0.02942, over 4939.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03051, over 954222.83 frames.], batch size: 29, lr: 1.59e-04
2022-05-07 23:05:30,246 INFO [train.py:715] (1/8) Epoch 14, batch 850, loss[loss=0.136, simple_loss=0.2044, pruned_loss=0.03383, over 4789.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03017, over 957714.79 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:06:09,704 INFO [train.py:715] (1/8) Epoch 14, batch 900, loss[loss=0.1426, simple_loss=0.2115, pruned_loss=0.03682, over 4804.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2079, pruned_loss=0.03052, over 961361.99 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:06:48,330 INFO [train.py:715] (1/8) Epoch 14, batch 950, loss[loss=0.1513, simple_loss=0.2269, pruned_loss=0.03785, over 4926.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03078, over 964254.44 frames.], batch size: 23, lr: 1.59e-04
2022-05-07 23:07:27,887 INFO [train.py:715] (1/8) Epoch 14, batch 1000, loss[loss=0.1392, simple_loss=0.2156, pruned_loss=0.03142, over 4836.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03083, over 965742.29 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:08:07,948 INFO [train.py:715] (1/8) Epoch 14, batch 1050, loss[loss=0.1229, simple_loss=0.2041, pruned_loss=0.02089, over 4694.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.03086, over 966426.93 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:08:47,267 INFO [train.py:715] (1/8) Epoch 14, batch 1100, loss[loss=0.132, simple_loss=0.2019, pruned_loss=0.03105, over 4832.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03073, over 967904.63 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:09:26,968 INFO [train.py:715] (1/8) Epoch 14, batch 1150, loss[loss=0.1159, simple_loss=0.182, pruned_loss=0.02489, over 4846.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03123, over 968695.72 frames.], batch size: 30, lr: 1.59e-04
2022-05-07 23:10:07,039 INFO [train.py:715] (1/8) Epoch 14, batch 1200, loss[loss=0.1363, simple_loss=0.218, pruned_loss=0.02733, over 4814.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03112, over 969388.03 frames.], batch size: 27, lr: 1.59e-04
2022-05-07 23:10:47,180 INFO [train.py:715] (1/8) Epoch 14, batch 1250, loss[loss=0.1361, simple_loss=0.2132, pruned_loss=0.0295, over 4811.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.031, over 969777.09 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:11:26,196 INFO [train.py:715] (1/8) Epoch 14, batch 1300, loss[loss=0.1453, simple_loss=0.2083, pruned_loss=0.04112, over 4860.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03156, over 969839.77 frames.], batch size: 30, lr: 1.59e-04
2022-05-07 23:12:05,713 INFO [train.py:715] (1/8) Epoch 14, batch 1350, loss[loss=0.1429, simple_loss=0.2241, pruned_loss=0.03081, over 4958.00 frames.], tot_loss[loss=0.1359, simple_loss=0.209, pruned_loss=0.03143, over 970425.66 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:12:45,092 INFO [train.py:715] (1/8) Epoch 14, batch 1400, loss[loss=0.1969, simple_loss=0.2592, pruned_loss=0.06725, over 4883.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2088, pruned_loss=0.03115, over 971301.31 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:13:24,657 INFO [train.py:715] (1/8) Epoch 14, batch 1450, loss[loss=0.1404, simple_loss=0.217, pruned_loss=0.03192, over 4702.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03153, over 972606.61 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:14:04,628 INFO [train.py:715] (1/8) Epoch 14, batch 1500, loss[loss=0.1388, simple_loss=0.206, pruned_loss=0.03578, over 4941.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03104, over 973498.47 frames.], batch size: 29, lr: 1.59e-04
2022-05-07 23:14:44,304 INFO [train.py:715] (1/8) Epoch 14, batch 1550, loss[loss=0.1116, simple_loss=0.1911, pruned_loss=0.01607, over 4923.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2094, pruned_loss=0.03152, over 973438.14 frames.], batch size: 23, lr: 1.59e-04
2022-05-07 23:15:24,197 INFO [train.py:715] (1/8) Epoch 14, batch 1600, loss[loss=0.1219, simple_loss=0.1991, pruned_loss=0.02232, over 4809.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.0313, over 973146.70 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:16:03,422 INFO [train.py:715] (1/8) Epoch 14, batch 1650, loss[loss=0.1545, simple_loss=0.2341, pruned_loss=0.0375, over 4857.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03147, over 972680.47 frames.], batch size: 20, lr: 1.59e-04
2022-05-07 23:16:43,088 INFO [train.py:715] (1/8) Epoch 14, batch 1700, loss[loss=0.15, simple_loss=0.2135, pruned_loss=0.04328, over 4962.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03152, over 972311.15 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:17:22,573 INFO [train.py:715] (1/8) Epoch 14, batch 1750, loss[loss=0.1443, simple_loss=0.215, pruned_loss=0.03686, over 4765.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03076, over 972012.57 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:18:02,283 INFO [train.py:715] (1/8) Epoch 14, batch 1800, loss[loss=0.1222, simple_loss=0.1982, pruned_loss=0.02308, over 4980.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03071, over 973017.83 frames.], batch size: 33, lr: 1.59e-04
2022-05-07 23:18:40,624 INFO [train.py:715] (1/8) Epoch 14, batch 1850, loss[loss=0.1317, simple_loss=0.2179, pruned_loss=0.02278, over 4796.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2078, pruned_loss=0.03045, over 973930.61 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:19:19,857 INFO [train.py:715] (1/8) Epoch 14, batch 1900, loss[loss=0.1234, simple_loss=0.1969, pruned_loss=0.02495, over 4930.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2083, pruned_loss=0.03075, over 973251.68 frames.], batch size: 23, lr: 1.59e-04
2022-05-07 23:19:59,664 INFO [train.py:715] (1/8) Epoch 14, batch 1950, loss[loss=0.11, simple_loss=0.1833, pruned_loss=0.0184, over 4985.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03063, over 973328.01 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:20:39,803 INFO [train.py:715] (1/8) Epoch 14, batch 2000, loss[loss=0.1247, simple_loss=0.1981, pruned_loss=0.02565, over 4781.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03039, over 973312.37 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:21:19,090 INFO [train.py:715] (1/8) Epoch 14, batch 2050, loss[loss=0.1128, simple_loss=0.1888, pruned_loss=0.01839, over 4833.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02981, over 973655.47 frames.], batch size: 26, lr: 1.59e-04
2022-05-07 23:21:58,528 INFO [train.py:715] (1/8) Epoch 14, batch 2100, loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03195, over 4915.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03035, over 973779.01 frames.], batch size: 23, lr: 1.59e-04
2022-05-07 23:22:38,245 INFO [train.py:715] (1/8) Epoch 14, batch 2150, loss[loss=0.1181, simple_loss=0.2051, pruned_loss=0.01556, over 4820.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03029, over 973717.34 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:23:16,935 INFO [train.py:715] (1/8) Epoch 14, batch 2200, loss[loss=0.1394, simple_loss=0.2066, pruned_loss=0.03611, over 4801.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2082, pruned_loss=0.03068, over 973763.43 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:23:55,888 INFO [train.py:715] (1/8) Epoch 14, batch 2250, loss[loss=0.1302, simple_loss=0.2163, pruned_loss=0.02206, over 4940.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03092, over 973147.30 frames.], batch size: 29, lr: 1.59e-04
2022-05-07 23:24:34,961 INFO [train.py:715] (1/8) Epoch 14, batch 2300, loss[loss=0.1499, simple_loss=0.2186, pruned_loss=0.04059, over 4960.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03056, over 972453.62 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:25:14,124 INFO [train.py:715] (1/8) Epoch 14, batch 2350, loss[loss=0.1443, simple_loss=0.2176, pruned_loss=0.03547, over 4971.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03088, over 971978.29 frames.], batch size: 35, lr: 1.59e-04
2022-05-07 23:25:53,239 INFO [train.py:715] (1/8) Epoch 14, batch 2400, loss[loss=0.1211, simple_loss=0.2007, pruned_loss=0.02077, over 4858.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03072, over 972595.78 frames.], batch size: 30, lr: 1.59e-04
2022-05-07 23:26:32,284 INFO [train.py:715] (1/8) Epoch 14, batch 2450, loss[loss=0.1416, simple_loss=0.2223, pruned_loss=0.03043, over 4979.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03085, over 972744.11 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:27:11,604 INFO [train.py:715] (1/8) Epoch 14, batch 2500, loss[loss=0.1451, simple_loss=0.2232, pruned_loss=0.03348, over 4830.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03079, over 972536.31 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:27:50,104 INFO [train.py:715] (1/8) Epoch 14, batch 2550, loss[loss=0.147, simple_loss=0.2141, pruned_loss=0.03998, over 4704.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.03064, over 971802.57 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:28:29,684 INFO [train.py:715] (1/8) Epoch 14, batch 2600, loss[loss=0.1239, simple_loss=0.1994, pruned_loss=0.02416, over 4921.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03087, over 971425.01 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:29:09,134 INFO [train.py:715] (1/8) Epoch 14, batch 2650, loss[loss=0.1346, simple_loss=0.2037, pruned_loss=0.03277, over 4883.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03146, over 972703.32 frames.], batch size: 39, lr: 1.59e-04
2022-05-07 23:29:48,488 INFO [train.py:715] (1/8) Epoch 14, batch 2700, loss[loss=0.1388, simple_loss=0.2168, pruned_loss=0.03039, over 4813.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03078, over 973217.92 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:30:27,052 INFO [train.py:715] (1/8) Epoch 14, batch 2750, loss[loss=0.1631, simple_loss=0.2303, pruned_loss=0.04794, over 4893.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2079, pruned_loss=0.03043, over 973984.44 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:31:06,230 INFO [train.py:715] (1/8) Epoch 14, batch 2800, loss[loss=0.1177, simple_loss=0.1862, pruned_loss=0.02458, over 4973.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.03034, over 973915.52 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:31:45,880 INFO [train.py:715] (1/8) Epoch 14, batch 2850, loss[loss=0.148, simple_loss=0.2263, pruned_loss=0.03489, over 4919.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02999, over 974634.06 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:32:24,322 INFO [train.py:715] (1/8) Epoch 14, batch 2900, loss[loss=0.1451, simple_loss=0.213, pruned_loss=0.03859, over 4979.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02992, over 975744.50 frames.], batch size: 35, lr: 1.59e-04
2022-05-07 23:33:06,133 INFO [train.py:715] (1/8) Epoch 14, batch 2950, loss[loss=0.1334, simple_loss=0.2025, pruned_loss=0.03221, over 4825.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03005, over 974719.05 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:33:45,667 INFO [train.py:715] (1/8) Epoch 14, batch 3000, loss[loss=0.159, simple_loss=0.221, pruned_loss=0.04853, over 4912.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03034, over 974247.37 frames.], batch size: 23, lr: 1.59e-04
2022-05-07 23:33:45,668 INFO [train.py:733] (1/8) Computing validation loss
2022-05-07 23:33:55,240 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1052, simple_loss=0.1891, pruned_loss=0.01067, over 914524.00 frames.
2022-05-07 23:34:34,251 INFO [train.py:715] (1/8) Epoch 14, batch 3050, loss[loss=0.1243, simple_loss=0.1951, pruned_loss=0.02674, over 4981.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03017, over 972958.63 frames.], batch size: 28, lr: 1.59e-04
2022-05-07 23:35:14,227 INFO [train.py:715] (1/8) Epoch 14, batch 3100, loss[loss=0.136, simple_loss=0.2014, pruned_loss=0.0353, over 4843.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03089, over 973624.02 frames.], batch size: 30, lr: 1.59e-04
2022-05-07 23:35:53,794 INFO [train.py:715] (1/8) Epoch 14, batch 3150, loss[loss=0.1339, simple_loss=0.2093, pruned_loss=0.02923, over 4788.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03113, over 973648.40 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:36:33,467 INFO [train.py:715] (1/8) Epoch 14, batch 3200, loss[loss=0.122, simple_loss=0.194, pruned_loss=0.02495, over 4739.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03101, over 972908.61 frames.], batch size: 12, lr: 1.59e-04
2022-05-07 23:37:14,496 INFO [train.py:715] (1/8) Epoch 14, batch 3250, loss[loss=0.1544, simple_loss=0.2223, pruned_loss=0.04323, over 4961.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03136, over 972819.91 frames.], batch size: 39, lr: 1.59e-04
2022-05-07 23:37:54,317 INFO [train.py:715] (1/8) Epoch 14, batch 3300, loss[loss=0.1483, simple_loss=0.2103, pruned_loss=0.04318, over 4916.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03099, over 973210.21 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:38:34,444 INFO [train.py:715] (1/8) Epoch 14, batch 3350, loss[loss=0.124, simple_loss=0.1931, pruned_loss=0.02746, over 4901.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03087, over 972694.05 frames.], batch size: 19, lr: 1.59e-04
2022-05-07 23:39:15,382 INFO [train.py:715] (1/8) Epoch 14, batch 3400, loss[loss=0.1648, simple_loss=0.2374, pruned_loss=0.04609, over 4942.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03063, over 972250.30 frames.], batch size: 39, lr: 1.59e-04
2022-05-07 23:39:56,039 INFO [train.py:715] (1/8) Epoch 14, batch 3450, loss[loss=0.1424, simple_loss=0.2267, pruned_loss=0.02909, over 4924.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03064, over 971399.35 frames.], batch size: 23, lr: 1.59e-04
2022-05-07 23:40:35,912 INFO [train.py:715] (1/8) Epoch 14, batch 3500, loss[loss=0.1307, simple_loss=0.1976, pruned_loss=0.03189, over 4747.00 frames.], tot_loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03121, over 972443.80 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:41:15,986 INFO [train.py:715] (1/8) Epoch 14, batch 3550, loss[loss=0.1344, simple_loss=0.2146, pruned_loss=0.02705, over 4802.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03104, over 972592.14 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:41:56,129 INFO [train.py:715] (1/8) Epoch 14, batch 3600, loss[loss=0.1254, simple_loss=0.1965, pruned_loss=0.02719, over 4791.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.0312, over 972862.64 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:42:36,135 INFO [train.py:715] (1/8) Epoch 14, batch 3650, loss[loss=0.1331, simple_loss=0.2087, pruned_loss=0.02877, over 4900.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03109, over 973003.53 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:43:16,039 INFO [train.py:715] (1/8) Epoch 14, batch 3700, loss[loss=0.1432, simple_loss=0.2231, pruned_loss=0.0317, over 4872.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2106, pruned_loss=0.03127, over 972966.27 frames.], batch size: 20, lr: 1.59e-04
2022-05-07 23:43:56,771 INFO [train.py:715] (1/8) Epoch 14, batch 3750, loss[loss=0.1392, simple_loss=0.2167, pruned_loss=0.03086, over 4687.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.03096, over 972747.53 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:44:36,930 INFO [train.py:715] (1/8) Epoch 14, batch 3800, loss[loss=0.1689, simple_loss=0.2381, pruned_loss=0.04984, over 4706.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03081, over 972768.96 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:45:16,171 INFO [train.py:715] (1/8) Epoch 14, batch 3850, loss[loss=0.1529, simple_loss=0.216, pruned_loss=0.04489, over 4964.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.0305, over 972084.39 frames.], batch size: 39, lr: 1.59e-04
2022-05-07 23:45:56,647 INFO [train.py:715] (1/8) Epoch 14, batch 3900, loss[loss=0.1171, simple_loss=0.1812, pruned_loss=0.0265, over 4791.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03035, over 971762.95 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:46:37,884 INFO [train.py:715] (1/8) Epoch 14, batch 3950, loss[loss=0.1328, simple_loss=0.2036, pruned_loss=0.03095, over 4981.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03094, over 971900.89 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:47:18,823 INFO [train.py:715] (1/8) Epoch 14, batch 4000, loss[loss=0.1555, simple_loss=0.2178, pruned_loss=0.04661, over 4940.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2094, pruned_loss=0.03152, over 971103.16 frames.], batch size: 29, lr: 1.59e-04
2022-05-07 23:47:59,307 INFO [train.py:715] (1/8) Epoch 14, batch 4050, loss[loss=0.1316, simple_loss=0.211, pruned_loss=0.02612, over 4703.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03089, over 971329.39 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:48:40,126 INFO [train.py:715] (1/8) Epoch 14, batch 4100, loss[loss=0.1275, simple_loss=0.2047, pruned_loss=0.02515, over 4771.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03087, over 971524.17 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:49:21,558 INFO [train.py:715] (1/8) Epoch 14, batch 4150, loss[loss=0.1424, simple_loss=0.222, pruned_loss=0.03137, over 4910.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03043, over 972131.83 frames.], batch size: 19, lr: 1.59e-04
2022-05-07 23:50:02,216 INFO [train.py:715] (1/8) Epoch 14, batch 4200, loss[loss=0.1443, simple_loss=0.2144, pruned_loss=0.03708, over 4841.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03049, over 972681.70 frames.], batch size: 30, lr: 1.59e-04
2022-05-07 23:50:43,290 INFO [train.py:715] (1/8) Epoch 14, batch 4250, loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03099, over 4977.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2092, pruned_loss=0.03049, over 972245.88 frames.], batch size: 35, lr: 1.59e-04
2022-05-07 23:51:25,173 INFO [train.py:715] (1/8) Epoch 14, batch 4300, loss[loss=0.1346, simple_loss=0.2061, pruned_loss=0.03158, over 4863.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.0308, over 972850.53 frames.], batch size: 30, lr: 1.59e-04
2022-05-07 23:52:06,420 INFO [train.py:715] (1/8) Epoch 14, batch 4350, loss[loss=0.1379, simple_loss=0.2062, pruned_loss=0.03482, over 4848.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2109, pruned_loss=0.03176, over 972299.88 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:52:46,956 INFO [train.py:715] (1/8) Epoch 14, batch 4400, loss[loss=0.12, simple_loss=0.1905, pruned_loss=0.0247, over 4858.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03162, over 971981.47 frames.], batch size: 20, lr: 1.59e-04
2022-05-07 23:53:27,641 INFO [train.py:715] (1/8) Epoch 14, batch 4450, loss[loss=0.1636, simple_loss=0.2369, pruned_loss=0.04522, over 4969.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2103, pruned_loss=0.03125, over 973012.19 frames.], batch size: 31, lr: 1.59e-04
2022-05-07 23:54:08,633 INFO [train.py:715] (1/8) Epoch 14, batch 4500, loss[loss=0.162, simple_loss=0.219, pruned_loss=0.05251, over 4844.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03161, over 972565.22 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:54:48,679 INFO [train.py:715] (1/8) Epoch 14, batch 4550, loss[loss=0.1465, simple_loss=0.2206, pruned_loss=0.03619, over 4854.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03129, over 971958.82 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:55:27,630 INFO [train.py:715] (1/8) Epoch 14, batch 4600, loss[loss=0.1144, simple_loss=0.1984, pruned_loss=0.01517, over 4821.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03105, over 972857.56 frames.], batch size: 27, lr: 1.59e-04
2022-05-07 23:56:08,471 INFO [train.py:715] (1/8) Epoch 14, batch 4650, loss[loss=0.1331, simple_loss=0.2066, pruned_loss=0.02986, over 4869.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03134, over 972146.47 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:56:48,196 INFO [train.py:715] (1/8) Epoch 14, batch 4700, loss[loss=0.1229, simple_loss=0.2026, pruned_loss=0.02155, over 4971.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03115, over 972559.11 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:57:26,877 INFO [train.py:715] (1/8) Epoch 14, batch 4750, loss[loss=0.1428, simple_loss=0.2114, pruned_loss=0.03705, over 4935.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2091, pruned_loss=0.03133, over 972513.75 frames.], batch size: 35, lr: 1.58e-04
2022-05-07 23:58:06,245 INFO [train.py:715] (1/8) Epoch 14, batch 4800, loss[loss=0.1294, simple_loss=0.1991, pruned_loss=0.0299, over 4738.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03158, over 972366.25 frames.], batch size: 16, lr: 1.58e-04
2022-05-07 23:58:46,077 INFO [train.py:715] (1/8) Epoch 14, batch 4850, loss[loss=0.1168, simple_loss=0.1958, pruned_loss=0.01886, over 4741.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2086, pruned_loss=0.03107, over 972901.96 frames.], batch size: 16, lr: 1.58e-04
2022-05-07 23:59:25,003 INFO [train.py:715] (1/8) Epoch 14, batch 4900, loss[loss=0.1823, simple_loss=0.2523, pruned_loss=0.05621, over 4801.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03143, over 973411.36 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:00:04,153 INFO [train.py:715] (1/8) Epoch 14, batch 4950, loss[loss=0.138, simple_loss=0.2241, pruned_loss=0.02602, over 4747.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2085, pruned_loss=0.03106, over 973671.22 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:00:44,230 INFO [train.py:715] (1/8) Epoch 14, batch 5000, loss[loss=0.129, simple_loss=0.1995, pruned_loss=0.02928, over 4739.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2089, pruned_loss=0.03111, over 973381.21 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:01:23,509 INFO [train.py:715] (1/8) Epoch 14, batch 5050, loss[loss=0.1483, simple_loss=0.2115, pruned_loss=0.04249, over 4819.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.0313, over 973253.48 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:02:02,196 INFO [train.py:715] (1/8) Epoch 14, batch 5100, loss[loss=0.1428, simple_loss=0.2171, pruned_loss=0.03422, over 4938.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03174, over 973483.76 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 00:02:41,795 INFO [train.py:715] (1/8) Epoch 14, batch 5150, loss[loss=0.1295, simple_loss=0.2129, pruned_loss=0.0231, over 4975.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03169, over 973897.18 frames.], batch size: 24, lr: 1.58e-04
2022-05-08 00:03:21,422 INFO [train.py:715] (1/8) Epoch 14, batch 5200, loss[loss=0.1218, simple_loss=0.1933, pruned_loss=0.02516, over 4987.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2087, pruned_loss=0.03105, over 973528.22 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:03:59,947 INFO [train.py:715] (1/8) Epoch 14, batch 5250, loss[loss=0.1537, simple_loss=0.2396, pruned_loss=0.03395, over 4924.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.03013, over 973065.81 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:04:38,439 INFO [train.py:715] (1/8) Epoch 14, batch 5300, loss[loss=0.1527, simple_loss=0.2148, pruned_loss=0.04536, over 4971.00 frames.], tot_loss[loss=0.1346, simple_loss=0.208, pruned_loss=0.03058, over 973906.91 frames.], batch size: 33, lr: 1.58e-04
2022-05-08 00:05:17,621 INFO [train.py:715] (1/8) Epoch 14, batch 5350, loss[loss=0.1423, simple_loss=0.213, pruned_loss=0.03583, over 4790.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03105, over 972929.63 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:05:56,194 INFO [train.py:715] (1/8) Epoch 14, batch 5400, loss[loss=0.1254, simple_loss=0.1956, pruned_loss=0.02767, over 4775.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03066, over 972024.91 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:06:34,699 INFO [train.py:715] (1/8) Epoch 14, batch 5450, loss[loss=0.1631, simple_loss=0.2306, pruned_loss=0.04777, over 4841.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03042, over 971996.76 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 00:07:13,536 INFO [train.py:715] (1/8) Epoch 14, batch 5500, loss[loss=0.1509, simple_loss=0.2204, pruned_loss=0.04068, over 4903.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03076, over 972538.74 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:07:53,213 INFO [train.py:715] (1/8) Epoch 14, batch 5550, loss[loss=0.1189, simple_loss=0.1942, pruned_loss=0.02181, over 4817.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03102, over 971863.77 frames.], batch size: 26, lr: 1.58e-04
2022-05-08 00:08:31,524 INFO [train.py:715] (1/8) Epoch 14, batch 5600, loss[loss=0.1381, simple_loss=0.2185, pruned_loss=0.02891, over 4944.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2101, pruned_loss=0.03106, over 971742.71 frames.], batch size: 35, lr: 1.58e-04
2022-05-08 00:09:10,029 INFO [train.py:715] (1/8) Epoch 14, batch 5650, loss[loss=0.1282, simple_loss=0.1972, pruned_loss=0.02957, over 4780.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03078, over 971938.02 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:09:49,139 INFO [train.py:715] (1/8) Epoch 14, batch 5700, loss[loss=0.1694, simple_loss=0.2398, pruned_loss=0.04953, over 4944.00 frames.], tot_loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03122, over 972388.95 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:10:27,412 INFO [train.py:715] (1/8) Epoch 14, batch 5750, loss[loss=0.1335, simple_loss=0.1956, pruned_loss=0.03566, over 4876.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2103, pruned_loss=0.03128, over 972237.39 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:11:05,794 INFO [train.py:715] (1/8) Epoch 14, batch 5800, loss[loss=0.135, simple_loss=0.2242, pruned_loss=0.02287, over 4915.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03091, over 971982.38 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:11:44,413 INFO [train.py:715] (1/8) Epoch 14, batch 5850, loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.033, over 4865.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2086, pruned_loss=0.03129, over 972292.37 frames.], batch size: 20, lr: 1.58e-04
2022-05-08 00:12:23,189 INFO [train.py:715] (1/8) Epoch 14, batch 5900, loss[loss=0.1241, simple_loss=0.1944, pruned_loss=0.0269, over 4988.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2087, pruned_loss=0.03117, over 973936.69 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 00:13:02,948 INFO [train.py:715] (1/8) Epoch 14, batch 5950, loss[loss=0.1842, simple_loss=0.2382, pruned_loss=0.06507, over 4955.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03098, over 973060.28 frames.], batch size: 35, lr: 1.58e-04
2022-05-08 00:13:42,645 INFO [train.py:715] (1/8) Epoch 14, batch 6000, loss[loss=0.1238, simple_loss=0.1941, pruned_loss=0.02673, over 4973.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.0306, over 973365.42 frames.], batch size: 24, lr: 1.58e-04
2022-05-08 00:13:42,646 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 00:13:52,504 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.105, simple_loss=0.1888, pruned_loss=0.01057, over 914524.00 frames.
2022-05-08 00:14:31,603 INFO [train.py:715] (1/8) Epoch 14, batch 6050, loss[loss=0.1266, simple_loss=0.1941, pruned_loss=0.02953, over 4974.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03051, over 974124.11 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:15:10,778 INFO [train.py:715] (1/8) Epoch 14, batch 6100, loss[loss=0.131, simple_loss=0.204, pruned_loss=0.02905, over 4704.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.03033, over 973828.06 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:15:50,792 INFO [train.py:715] (1/8) Epoch 14, batch 6150, loss[loss=0.1199, simple_loss=0.1838, pruned_loss=0.028, over 4775.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03024, over 973874.63 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:16:30,388 INFO [train.py:715] (1/8) Epoch 14, batch 6200, loss[loss=0.1348, simple_loss=0.2036, pruned_loss=0.03305, over 4850.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03025, over 972853.52 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 00:17:10,262 INFO [train.py:715] (1/8) Epoch 14, batch 6250, loss[loss=0.129, simple_loss=0.214, pruned_loss=0.022, over 4957.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2089, pruned_loss=0.03024, over 972905.43 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:17:49,629 INFO [train.py:715] (1/8) Epoch 14, batch 6300, loss[loss=0.1258, simple_loss=0.2044, pruned_loss=0.02355, over 4917.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03005, over 972349.66 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 00:18:29,658 INFO [train.py:715] (1/8) Epoch 14, batch 6350, loss[loss=0.1253, simple_loss=0.2076, pruned_loss=0.02149, over 4899.00 frames.], tot_loss[loss=0.135, simple_loss=0.2092, pruned_loss=0.03037, over 972403.63 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:19:09,434 INFO [train.py:715] (1/8) Epoch 14, batch 6400, loss[loss=0.1195, simple_loss=0.1946, pruned_loss=0.0222, over 4782.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03067, over 972238.82 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:19:49,526 INFO [train.py:715] (1/8) Epoch 14, batch 6450, loss[loss=0.1495, simple_loss=0.2192, pruned_loss=0.03991, over 4889.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2101, pruned_loss=0.03089, over 972008.79 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:20:29,513 INFO [train.py:715] (1/8) Epoch 14, batch 6500, loss[loss=0.1156, simple_loss=0.1967, pruned_loss=0.01722, over 4765.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2105, pruned_loss=0.03136, over 971985.98 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:21:09,167 INFO [train.py:715] (1/8) Epoch 14, batch 6550, loss[loss=0.1325, simple_loss=0.2024, pruned_loss=0.03125, over 4959.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2104, pruned_loss=0.03108, over 972082.53 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:21:49,050 INFO [train.py:715] (1/8) Epoch 14, batch 6600, loss[loss=0.131, simple_loss=0.1989, pruned_loss=0.03154, over 4844.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.031, over 971484.31 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 00:22:29,224 INFO [train.py:715] (1/8) Epoch 14, batch 6650, loss[loss=0.1506, simple_loss=0.2199, pruned_loss=0.04065, over 4773.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03057, over 970721.68 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:23:08,957 INFO [train.py:715] (1/8) Epoch 14, batch 6700, loss[loss=0.1634, simple_loss=0.2351, pruned_loss=0.04583, over 4958.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03104, over 971212.47 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:23:48,840 INFO [train.py:715] (1/8) Epoch 14, batch 6750, loss[loss=0.1281, simple_loss=0.211, pruned_loss=0.02257, over 4899.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03133, over 971174.26 frames.], batch size: 22, lr: 1.58e-04
2022-05-08 00:24:28,833 INFO [train.py:715] (1/8) Epoch 14, batch 6800, loss[loss=0.1427, simple_loss=0.2087, pruned_loss=0.03835, over 4854.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03141, over 971022.52 frames.], batch size: 30, lr: 1.58e-04
2022-05-08 00:25:08,845 INFO [train.py:715] (1/8) Epoch 14, batch 6850, loss[loss=0.1358, simple_loss=0.2152, pruned_loss=0.02823, over 4784.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03154, over 971558.81 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:25:48,262 INFO [train.py:715] (1/8) Epoch 14, batch 6900, loss[loss=0.1618, simple_loss=0.2303, pruned_loss=0.04664, over 4761.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03159, over 971525.57 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:26:28,456 INFO [train.py:715] (1/8) Epoch 14, batch 6950, loss[loss=0.1437, simple_loss=0.2186, pruned_loss=0.03443, over 4977.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03113, over 971313.16 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:27:08,560 INFO [train.py:715] (1/8) Epoch 14, batch 7000, loss[loss=0.1694, simple_loss=0.2397, pruned_loss=0.04959, over 4980.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03113, over 972020.02 frames.], batch size: 35, lr: 1.58e-04
2022-05-08 00:27:48,544 INFO [train.py:715] (1/8) Epoch 14, batch 7050, loss[loss=0.1819, simple_loss=0.2531, pruned_loss=0.05533, over 4919.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03091, over 972437.39 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:28:27,871 INFO [train.py:715] (1/8) Epoch 14, batch 7100, loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03171, over 4890.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03075, over 972389.89 frames.], batch size: 22, lr: 1.58e-04
2022-05-08 00:29:07,961 INFO [train.py:715] (1/8) Epoch 14, batch 7150, loss[loss=0.1127, simple_loss=0.1878, pruned_loss=0.01876, over 4969.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.03085, over 972263.65 frames.], batch size: 24, lr: 1.58e-04
2022-05-08 00:29:48,162 INFO [train.py:715] (1/8) Epoch 14, batch 7200, loss[loss=0.1423, simple_loss=0.2109, pruned_loss=0.0369, over 4791.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03068, over 972868.21 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:30:28,009 INFO [train.py:715] (1/8) Epoch 14, batch 7250, loss[loss=0.1299, simple_loss=0.2056, pruned_loss=0.02714, over 4782.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03059, over 971713.39 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:31:08,156 INFO [train.py:715] (1/8) Epoch 14, batch 7300, loss[loss=0.1555, simple_loss=0.2347, pruned_loss=0.03818, over 4833.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03079, over 972243.80 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:31:48,259 INFO [train.py:715] (1/8) Epoch 14, batch 7350, loss[loss=0.1454, simple_loss=0.219, pruned_loss=0.03586, over 4921.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03129, over 972374.97 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:32:28,612 INFO [train.py:715] (1/8) Epoch 14, batch 7400, loss[loss=0.1171, simple_loss=0.1953, pruned_loss=0.01943, over 4642.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03119, over 971781.16 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:33:08,061 INFO [train.py:715] (1/8) Epoch 14, batch 7450, loss[loss=0.1371, simple_loss=0.2201, pruned_loss=0.02708, over 4906.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03113, over 971950.98 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:33:47,752 INFO [train.py:715] (1/8) Epoch 14, batch 7500, loss[loss=0.1323, simple_loss=0.2148, pruned_loss=0.02487, over 4914.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2104, pruned_loss=0.03122, over 972328.02 frames.], batch size: 23, lr: 1.58e-04
2022-05-08 00:34:27,408 INFO [train.py:715] (1/8) Epoch 14, batch 7550, loss[loss=0.123, simple_loss=0.1976, pruned_loss=0.0242, over 4793.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03099, over 972329.88 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 00:35:06,409 INFO [train.py:715] (1/8) Epoch 14, batch 7600, loss[loss=0.1182, simple_loss=0.1888, pruned_loss=0.02377, over 4985.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03083, over 972682.06 frames.], batch size: 31, lr: 1.58e-04
2022-05-08 00:35:46,361 INFO [train.py:715] (1/8) Epoch 14, batch 7650, loss[loss=0.1508, simple_loss=0.2187, pruned_loss=0.04138, over 4776.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03118, over 972491.21 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:36:25,263 INFO [train.py:715] (1/8) Epoch 14, batch 7700, loss[loss=0.1322, simple_loss=0.1998, pruned_loss=0.03229, over 4760.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03076, over 972008.01 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 00:37:05,576 INFO [train.py:715] (1/8) Epoch 14, batch 7750, loss[loss=0.1744, simple_loss=0.2488, pruned_loss=0.04996, over 4756.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2104, pruned_loss=0.03137, over 971559.42 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:37:44,375 INFO [train.py:715] (1/8) Epoch 14, batch 7800, loss[loss=0.1447, simple_loss=0.2169, pruned_loss=0.03622, over 4809.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2116, pruned_loss=0.03153, over 972137.80 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:38:23,470 INFO [train.py:715] (1/8) Epoch 14, batch 7850, loss[loss=0.1121, simple_loss=0.1862, pruned_loss=0.01895, over 4815.00 frames.], tot_loss[loss=0.1365, simple_loss=0.211, pruned_loss=0.03105, over 972045.10 frames.], batch size: 12, lr: 1.58e-04
2022-05-08 00:39:03,276 INFO [train.py:715] (1/8) Epoch 14, batch 7900, loss[loss=0.116, simple_loss=0.1989, pruned_loss=0.01648, over 4761.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2111, pruned_loss=0.03102, over 972057.39 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:39:42,080 INFO [train.py:715] (1/8) Epoch 14, batch 7950, loss[loss=0.1219, simple_loss=0.2076, pruned_loss=0.01804, over 4806.00 frames.], tot_loss[loss=0.1363, simple_loss=0.211, pruned_loss=0.03079, over 972634.04 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:40:21,686 INFO [train.py:715] (1/8) Epoch 14, batch 8000, loss[loss=0.1233, simple_loss=0.2077, pruned_loss=0.01948, over 4871.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2097, pruned_loss=0.03054, over 972652.01 frames.], batch size: 22, lr: 1.58e-04
2022-05-08 00:41:00,523 INFO [train.py:715] (1/8) Epoch 14, batch 8050, loss[loss=0.1453, simple_loss=0.2187, pruned_loss=0.03594, over 4895.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2093, pruned_loss=0.03043, over 972642.17 frames.], batch size: 22, lr: 1.58e-04
2022-05-08 00:41:40,052 INFO [train.py:715] (1/8) Epoch 14, batch 8100, loss[loss=0.1497, simple_loss=0.2217, pruned_loss=0.03884, over 4797.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03037, over 972118.52 frames.], batch size: 24, lr: 1.58e-04
2022-05-08 00:42:18,788 INFO [train.py:715] (1/8) Epoch 14, batch 8150, loss[loss=0.1506, simple_loss=0.2268, pruned_loss=0.03719, over 4974.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03017, over 972523.18 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:42:58,282 INFO [train.py:715] (1/8) Epoch 14, batch 8200, loss[loss=0.1663, simple_loss=0.2359, pruned_loss=0.04834, over 4919.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02994, over 973133.21 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:43:37,715 INFO [train.py:715] (1/8) Epoch 14, batch 8250, loss[loss=0.1497, simple_loss=0.2259, pruned_loss=0.03669, over 4643.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03001, over 972464.50 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:44:17,181 INFO [train.py:715] (1/8) Epoch 14, batch 8300, loss[loss=0.1226, simple_loss=0.1871, pruned_loss=0.02904, over 4799.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03029, over 972908.83 frames.], batch size: 12, lr: 1.58e-04
2022-05-08 00:44:56,127 INFO [train.py:715] (1/8) Epoch 14, batch 8350, loss[loss=0.1614, simple_loss=0.2311, pruned_loss=0.04583, over 4993.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03014, over 973519.55 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:45:35,322 INFO [train.py:715] (1/8) Epoch 14, batch 8400, loss[loss=0.1169, simple_loss=0.1894, pruned_loss=0.02224, over 4796.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2078, pruned_loss=0.03088, over 973183.52 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:46:14,802 INFO [train.py:715] (1/8) Epoch 14, batch 8450, loss[loss=0.1492, simple_loss=0.2309, pruned_loss=0.03374, over 4762.00 frames.], tot_loss[loss=0.135, simple_loss=0.2081, pruned_loss=0.03097, over 972207.99 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:46:53,356 INFO [train.py:715] (1/8) Epoch 14, batch 8500, loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02971, over 4731.00 frames.], tot_loss[loss=0.135, simple_loss=0.2082, pruned_loss=0.03094, over 973069.31 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:47:32,485 INFO [train.py:715] (1/8) Epoch 14, batch 8550, loss[loss=0.1335, simple_loss=0.1999, pruned_loss=0.03357, over 4801.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03087, over 973096.90 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:48:13,439 INFO [train.py:715] (1/8) Epoch 14, batch 8600, loss[loss=0.1197, simple_loss=0.1976, pruned_loss=0.02088, over 4950.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03053, over 972973.39 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 00:48:52,732 INFO [train.py:715] (1/8) Epoch 14, batch 8650, loss[loss=0.1113, simple_loss=0.1833, pruned_loss=0.01967, over 4835.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03103, over 971732.93 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:49:34,154 INFO [train.py:715] (1/8) Epoch 14, batch 8700, loss[loss=0.146, simple_loss=0.2152, pruned_loss=0.03846, over 4928.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03116, over 971587.91 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:50:13,522 INFO [train.py:715] (1/8) Epoch 14, batch 8750, loss[loss=0.1249, simple_loss=0.197, pruned_loss=0.02637, over 4821.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03108, over 971323.91 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 00:50:53,248 INFO [train.py:715] (1/8) Epoch 14, batch 8800, loss[loss=0.1191, simple_loss=0.1934, pruned_loss=0.02244, over 4904.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.03087, over 972339.07 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:51:32,822 INFO [train.py:715] (1/8) Epoch 14, batch 8850, loss[loss=0.167, simple_loss=0.2313, pruned_loss=0.05131, over 4878.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.0305, over 973002.97 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:52:13,343 INFO [train.py:715] (1/8) Epoch 14, batch 8900, loss[loss=0.119, simple_loss=0.1956, pruned_loss=0.02118, over 4883.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2075, pruned_loss=0.03037, over 972824.12 frames.], batch size: 38, lr: 1.58e-04
2022-05-08 00:52:53,207 INFO [train.py:715] (1/8) Epoch 14, batch 8950, loss[loss=0.1387, simple_loss=0.2101, pruned_loss=0.0336, over 4960.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03062, over 972445.88 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:53:33,015 INFO [train.py:715] (1/8) Epoch 14, batch 9000, loss[loss=0.108, simple_loss=0.1807, pruned_loss=0.01772, over 4941.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03028, over 971350.72 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:53:33,016 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 00:53:47,941 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1052, simple_loss=0.189, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 00:54:27,479 INFO [train.py:715] (1/8) Epoch 14, batch 9050, loss[loss=0.1403, simple_loss=0.2177, pruned_loss=0.0314, over 4926.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02998, over 972226.68 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:55:07,807 INFO [train.py:715] (1/8) Epoch 14, batch 9100, loss[loss=0.1493, simple_loss=0.2337, pruned_loss=0.03238, over 4904.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02983, over 971869.55 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:55:47,311 INFO [train.py:715] (1/8) Epoch 14, batch 9150, loss[loss=0.1482, simple_loss=0.2247, pruned_loss=0.03581, over 4796.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2089, pruned_loss=0.03023, over 972093.73 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:56:27,171 INFO [train.py:715] (1/8) Epoch 14, batch 9200, loss[loss=0.1386, simple_loss=0.2228, pruned_loss=0.02721, over 4935.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.03038, over 971853.10 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:57:06,885 INFO [train.py:715] (1/8) Epoch 14, batch 9250, loss[loss=0.1281, simple_loss=0.2115, pruned_loss=0.02235, over 4889.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03042, over 970717.18 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:57:46,605 INFO [train.py:715] (1/8) Epoch 14, batch 9300, loss[loss=0.1312, simple_loss=0.2034, pruned_loss=0.02948, over 4958.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03059, over 970831.45 frames.], batch size: 35, lr: 1.58e-04
2022-05-08 00:58:26,526 INFO [train.py:715] (1/8) Epoch 14, batch 9350, loss[loss=0.1355, simple_loss=0.2135, pruned_loss=0.02878, over 4942.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03055, over 971704.33 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:59:06,672 INFO [train.py:715] (1/8) Epoch 14, batch 9400, loss[loss=0.1119, simple_loss=0.1799, pruned_loss=0.02194, over 4772.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03102, over 971834.29 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:59:46,293 INFO [train.py:715] (1/8) Epoch 14, batch 9450, loss[loss=0.142, simple_loss=0.2221, pruned_loss=0.03098, over 4790.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2097, pruned_loss=0.03087, over 971162.51 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 01:00:26,046 INFO [train.py:715] (1/8) Epoch 14, batch 9500, loss[loss=0.2066, simple_loss=0.2641, pruned_loss=0.07457, over 4872.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03094, over 971649.47 frames.], batch size: 38, lr: 1.58e-04
2022-05-08 01:01:05,839 INFO [train.py:715] (1/8) Epoch 14, batch 9550, loss[loss=0.1101, simple_loss=0.1843, pruned_loss=0.01791, over 4985.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2096, pruned_loss=0.03058, over 972573.08 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:01:45,996 INFO [train.py:715] (1/8) Epoch 14, batch 9600, loss[loss=0.133, simple_loss=0.2115, pruned_loss=0.02722, over 4937.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2094, pruned_loss=0.03053, over 972899.60 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 01:02:25,430 INFO [train.py:715] (1/8) Epoch 14, batch 9650, loss[loss=0.1257, simple_loss=0.2071, pruned_loss=0.0222, over 4933.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03072, over 972217.32 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 01:03:05,453 INFO [train.py:715] (1/8) Epoch 14, batch 9700, loss[loss=0.118, simple_loss=0.1906, pruned_loss=0.02268, over 4826.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03058, over 972432.60 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:03:45,039 INFO [train.py:715] (1/8) Epoch 14, batch 9750, loss[loss=0.136, simple_loss=0.218, pruned_loss=0.027, over 4810.00 frames.], tot_loss[loss=0.136, simple_loss=0.2099, pruned_loss=0.031, over 972393.78 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:04:25,340 INFO [train.py:715] (1/8) Epoch 14, batch 9800, loss[loss=0.1457, simple_loss=0.2176, pruned_loss=0.03689, over 4812.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03148, over 971664.45 frames.], batch size: 24, lr: 1.58e-04
2022-05-08 01:05:04,562 INFO [train.py:715] (1/8) Epoch 14, batch 9850, loss[loss=0.09735, simple_loss=0.1709, pruned_loss=0.0119, over 4821.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03134, over 971284.75 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 01:05:44,640 INFO [train.py:715] (1/8) Epoch 14, batch 9900, loss[loss=0.1317, simple_loss=0.1999, pruned_loss=0.03179, over 4833.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.03157, over 972118.09 frames.], batch size: 26, lr: 1.58e-04
2022-05-08 01:06:24,618 INFO [train.py:715] (1/8) Epoch 14, batch 9950, loss[loss=0.1143, simple_loss=0.1937, pruned_loss=0.01742, over 4748.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03168, over 972122.52 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 01:07:03,943 INFO [train.py:715] (1/8) Epoch 14, batch 10000, loss[loss=0.1355, simple_loss=0.2126, pruned_loss=0.02921, over 4809.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.03165, over 972997.21 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 01:07:43,989 INFO [train.py:715] (1/8) Epoch 14, batch 10050, loss[loss=0.1305, simple_loss=0.2064, pruned_loss=0.02727, over 4786.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03169, over 972295.70 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 01:08:23,510 INFO [train.py:715] (1/8) Epoch 14, batch 10100, loss[loss=0.1385, simple_loss=0.2073, pruned_loss=0.03485, over 4788.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03106, over 972304.65 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 01:09:03,292 INFO [train.py:715] (1/8) Epoch 14, batch 10150, loss[loss=0.1223, simple_loss=0.1942, pruned_loss=0.0252, over 4785.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03086, over 971951.23 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 01:09:42,484 INFO [train.py:715] (1/8) Epoch 14, batch 10200, loss[loss=0.1055, simple_loss=0.1861, pruned_loss=0.01251, over 4785.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03075, over 972131.91 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 01:10:22,732 INFO [train.py:715] (1/8) Epoch 14, batch 10250, loss[loss=0.1299, simple_loss=0.2052, pruned_loss=0.02735, over 4818.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03126, over 972567.83 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:11:02,452 INFO [train.py:715] (1/8) Epoch 14, batch 10300, loss[loss=0.1411, simple_loss=0.2017, pruned_loss=0.04025, over 4845.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2109, pruned_loss=0.03174, over 971813.05 frames.], batch size: 30, lr: 1.58e-04
2022-05-08 01:11:41,908 INFO [train.py:715] (1/8) Epoch 14, batch 10350, loss[loss=0.1037, simple_loss=0.1798, pruned_loss=0.01381, over 4815.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03132, over 971799.05 frames.], batch size: 26, lr: 1.58e-04
2022-05-08 01:12:22,106 INFO [train.py:715] (1/8) Epoch 14, batch 10400, loss[loss=0.1483, simple_loss=0.2265, pruned_loss=0.03506, over 4907.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03122, over 971714.98 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 01:13:01,498 INFO [train.py:715] (1/8) Epoch 14, batch 10450, loss[loss=0.09957, simple_loss=0.1656, pruned_loss=0.01676, over 4639.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03074, over 971704.61 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 01:13:41,725 INFO [train.py:715] (1/8) Epoch 14, batch 10500, loss[loss=0.14, simple_loss=0.2111, pruned_loss=0.03447, over 4907.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03034, over 971637.98 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 01:14:21,000 INFO [train.py:715] (1/8) Epoch 14, batch 10550, loss[loss=0.1302, simple_loss=0.2003, pruned_loss=0.03007, over 4881.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03057, over 972210.01 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 01:15:01,260 INFO [train.py:715] (1/8) Epoch 14, batch 10600, loss[loss=0.1376, simple_loss=0.201, pruned_loss=0.03712, over 4880.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03109, over 973491.86 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 01:15:40,585 INFO [train.py:715] (1/8) Epoch 14, batch 10650, loss[loss=0.1212, simple_loss=0.1939, pruned_loss=0.02426, over 4789.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03132, over 972454.52 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 01:16:19,711 INFO [train.py:715] (1/8) Epoch 14, batch 10700, loss[loss=0.1378, simple_loss=0.2127, pruned_loss=0.03142, over 4808.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03118, over 972324.56 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:16:58,897 INFO [train.py:715] (1/8) Epoch 14, batch 10750, loss[loss=0.1674, simple_loss=0.2354, pruned_loss=0.04973, over 4847.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03121, over 972423.31 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 01:17:38,324 INFO [train.py:715] (1/8) Epoch 14, batch 10800, loss[loss=0.1338, simple_loss=0.213, pruned_loss=0.0273, over 4841.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03136, over 971532.35 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 01:18:17,863 INFO [train.py:715] (1/8) Epoch 14, batch 10850, loss[loss=0.1593, simple_loss=0.2402, pruned_loss=0.03919, over 4812.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03127, over 972048.42 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:18:56,524 INFO [train.py:715] (1/8) Epoch 14, batch 10900, loss[loss=0.1579, simple_loss=0.2247, pruned_loss=0.04551, over 4981.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03122, over 972359.94 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:19:36,719 INFO [train.py:715] (1/8) Epoch 14, batch 10950, loss[loss=0.1252, simple_loss=0.2041, pruned_loss=0.02315, over 4984.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.03133, over 972119.30 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 01:20:17,496 INFO [train.py:715] (1/8) Epoch 14, batch 11000, loss[loss=0.1245, simple_loss=0.2088, pruned_loss=0.02005, over 4824.00 frames.], tot_loss[loss=0.134, simple_loss=0.2074, pruned_loss=0.03035, over 972736.62 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:20:56,618 INFO [train.py:715] (1/8) Epoch 14, batch 11050, loss[loss=0.1223, simple_loss=0.1935, pruned_loss=0.02558, over 4784.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03091, over 972151.97 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 01:21:37,655 INFO [train.py:715] (1/8) Epoch 14, batch 11100, loss[loss=0.1363, simple_loss=0.2046, pruned_loss=0.03396, over 4836.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2084, pruned_loss=0.03115, over 971588.31 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:22:18,215 INFO [train.py:715] (1/8) Epoch 14, batch 11150, loss[loss=0.1163, simple_loss=0.1865, pruned_loss=0.02302, over 4820.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2081, pruned_loss=0.03101, over 971447.12 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 01:22:58,448 INFO [train.py:715] (1/8) Epoch 14, batch 11200, loss[loss=0.1033, simple_loss=0.1791, pruned_loss=0.01373, over 4818.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2075, pruned_loss=0.03064, over 971509.04 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 01:23:37,884 INFO [train.py:715] (1/8) Epoch 14, batch 11250, loss[loss=0.1678, simple_loss=0.24, pruned_loss=0.04779, over 4777.00 frames.], tot_loss[loss=0.1349, simple_loss=0.208, pruned_loss=0.03095, over 972132.01 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 01:24:18,310 INFO [train.py:715] (1/8) Epoch 14, batch 11300, loss[loss=0.1256, simple_loss=0.1983, pruned_loss=0.02642, over 4798.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2073, pruned_loss=0.03069, over 971751.94 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 01:24:58,565 INFO [train.py:715] (1/8) Epoch 14, batch 11350, loss[loss=0.117, simple_loss=0.1897, pruned_loss=0.02217, over 4965.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2068, pruned_loss=0.03007, over 971991.17 frames.], batch size: 25, lr: 1.57e-04
2022-05-08 01:25:37,721 INFO [train.py:715] (1/8) Epoch 14, batch 11400, loss[loss=0.1215, simple_loss=0.1949, pruned_loss=0.02408, over 4854.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03026, over 972820.12 frames.], batch size: 30, lr: 1.57e-04
2022-05-08 01:26:18,732 INFO [train.py:715] (1/8) Epoch 14, batch 11450, loss[loss=0.1501, simple_loss=0.22, pruned_loss=0.04006, over 4970.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.0301, over 972398.14 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 01:26:59,102 INFO [train.py:715] (1/8) Epoch 14, batch 11500, loss[loss=0.1392, simple_loss=0.2083, pruned_loss=0.03506, over 4970.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03024, over 971097.54 frames.], batch size: 24, lr: 1.57e-04
2022-05-08 01:27:39,025 INFO [train.py:715] (1/8) Epoch 14, batch 11550, loss[loss=0.1116, simple_loss=0.1944, pruned_loss=0.01438, over 4828.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.0301, over 972490.56 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 01:28:18,471 INFO [train.py:715] (1/8) Epoch 14, batch 11600, loss[loss=0.1635, simple_loss=0.2309, pruned_loss=0.048, over 4855.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02967, over 973326.64 frames.], batch size: 39, lr: 1.57e-04
2022-05-08 01:28:58,176 INFO [train.py:715] (1/8) Epoch 14, batch 11650, loss[loss=0.1493, simple_loss=0.2145, pruned_loss=0.04203, over 4967.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2064, pruned_loss=0.02964, over 973317.13 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 01:29:37,886 INFO [train.py:715] (1/8) Epoch 14, batch 11700, loss[loss=0.1353, simple_loss=0.2018, pruned_loss=0.03437, over 4985.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2064, pruned_loss=0.02973, over 972928.47 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:30:17,155 INFO [train.py:715] (1/8) Epoch 14, batch 11750, loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03335, over 4755.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03, over 972516.99 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 01:30:56,857 INFO [train.py:715] (1/8) Epoch 14, batch 11800, loss[loss=0.1295, simple_loss=0.2105, pruned_loss=0.02426, over 4880.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03061, over 972866.27 frames.], batch size: 22, lr: 1.57e-04
2022-05-08 01:31:35,979 INFO [train.py:715] (1/8) Epoch 14, batch 11850, loss[loss=0.1293, simple_loss=0.2034, pruned_loss=0.02757, over 4983.00 frames.], tot_loss[loss=0.136, simple_loss=0.2102, pruned_loss=0.03093, over 972299.06 frames.], batch size: 39, lr: 1.57e-04
2022-05-08 01:32:14,892 INFO [train.py:715] (1/8) Epoch 14, batch 11900, loss[loss=0.1071, simple_loss=0.1843, pruned_loss=0.01499, over 4941.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.0301, over 972312.55 frames.], batch size: 39, lr: 1.57e-04
2022-05-08 01:32:54,217 INFO [train.py:715] (1/8) Epoch 14, batch 11950, loss[loss=0.1256, simple_loss=0.1979, pruned_loss=0.02662, over 4988.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02974, over 972350.47 frames.], batch size: 25, lr: 1.57e-04
2022-05-08 01:33:33,584 INFO [train.py:715] (1/8) Epoch 14, batch 12000, loss[loss=0.1327, simple_loss=0.1978, pruned_loss=0.03376, over 4867.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02992, over 971542.14 frames.], batch size: 16, lr: 1.57e-04
2022-05-08 01:33:33,584 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 01:33:43,199 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1051, simple_loss=0.1889, pruned_loss=0.01067, over 914524.00 frames.
2022-05-08 01:34:22,502 INFO [train.py:715] (1/8) Epoch 14, batch 12050, loss[loss=0.162, simple_loss=0.2302, pruned_loss=0.04686, over 4785.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03022, over 972086.85 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 01:35:01,856 INFO [train.py:715] (1/8) Epoch 14, batch 12100, loss[loss=0.1233, simple_loss=0.2028, pruned_loss=0.02193, over 4882.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02993, over 971756.66 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:35:41,274 INFO [train.py:715] (1/8) Epoch 14, batch 12150, loss[loss=0.1039, simple_loss=0.1635, pruned_loss=0.02218, over 4646.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02979, over 971320.35 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 01:36:20,619 INFO [train.py:715] (1/8) Epoch 14, batch 12200, loss[loss=0.1201, simple_loss=0.1892, pruned_loss=0.02549, over 4813.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2069, pruned_loss=0.02968, over 971272.31 frames.], batch size: 27, lr: 1.57e-04
2022-05-08 01:37:00,487 INFO [train.py:715] (1/8) Epoch 14, batch 12250, loss[loss=0.1127, simple_loss=0.1981, pruned_loss=0.01365, over 4971.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03, over 971530.72 frames.], batch size: 24, lr: 1.57e-04
2022-05-08 01:37:39,678 INFO [train.py:715] (1/8) Epoch 14, batch 12300, loss[loss=0.1497, simple_loss=0.2189, pruned_loss=0.04021, over 4918.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03008, over 972309.53 frames.], batch size: 39, lr: 1.57e-04
2022-05-08 01:38:19,194 INFO [train.py:715] (1/8) Epoch 14, batch 12350, loss[loss=0.167, simple_loss=0.2442, pruned_loss=0.04492, over 4817.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.03037, over 972318.45 frames.], batch size: 25, lr: 1.57e-04
2022-05-08 01:38:58,799 INFO [train.py:715] (1/8) Epoch 14, batch 12400, loss[loss=0.1206, simple_loss=0.1981, pruned_loss=0.02155, over 4864.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03015, over 971523.59 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 01:39:37,833 INFO [train.py:715] (1/8) Epoch 14, batch 12450, loss[loss=0.1303, simple_loss=0.2109, pruned_loss=0.02486, over 4819.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03076, over 972440.66 frames.], batch size: 27, lr: 1.57e-04
2022-05-08 01:40:17,253 INFO [train.py:715] (1/8) Epoch 14, batch 12500, loss[loss=0.1344, simple_loss=0.2029, pruned_loss=0.03297, over 4960.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03068, over 973413.19 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 01:40:57,007 INFO [train.py:715] (1/8) Epoch 14, batch 12550, loss[loss=0.159, simple_loss=0.2266, pruned_loss=0.04567, over 4776.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03085, over 972647.50 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:41:36,645 INFO [train.py:715] (1/8) Epoch 14, batch 12600, loss[loss=0.1325, simple_loss=0.2097, pruned_loss=0.02764, over 4813.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03034, over 971962.51 frames.], batch size: 27, lr: 1.57e-04
2022-05-08 01:42:15,595 INFO [train.py:715] (1/8) Epoch 14, batch 12650, loss[loss=0.1186, simple_loss=0.189, pruned_loss=0.02414, over 4807.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02984, over 971794.78 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:42:55,480 INFO [train.py:715] (1/8) Epoch 14, batch 12700, loss[loss=0.1296, simple_loss=0.2146, pruned_loss=0.02231, over 4700.00 frames.], tot_loss[loss=0.133, simple_loss=0.2068, pruned_loss=0.02962, over 971964.70 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 01:43:35,535 INFO [train.py:715] (1/8) Epoch 14, batch 12750, loss[loss=0.1526, simple_loss=0.226, pruned_loss=0.03956, over 4929.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02989, over 972443.39 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 01:44:15,513 INFO [train.py:715] (1/8) Epoch 14, batch 12800, loss[loss=0.1384, simple_loss=0.2126, pruned_loss=0.03214, over 4807.00 frames.], tot_loss[loss=0.134, simple_loss=0.2075, pruned_loss=0.03021, over 972533.40 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:44:55,316 INFO [train.py:715] (1/8) Epoch 14, batch 12850, loss[loss=0.1229, simple_loss=0.1938, pruned_loss=0.026, over 4792.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2072, pruned_loss=0.0303, over 973223.18 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:45:35,519 INFO [train.py:715] (1/8) Epoch 14, batch 12900, loss[loss=0.1934, simple_loss=0.2554, pruned_loss=0.06567, over 4883.00 frames.], tot_loss[loss=0.1346, simple_loss=0.208, pruned_loss=0.03057, over 973197.18 frames.], batch size: 16, lr: 1.57e-04
2022-05-08 01:46:15,877 INFO [train.py:715] (1/8) Epoch 14, batch 12950, loss[loss=0.1516, simple_loss=0.2339, pruned_loss=0.03466, over 4825.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.03053, over 973674.11 frames.], batch size: 27, lr: 1.57e-04
2022-05-08 01:46:55,832 INFO [train.py:715] (1/8) Epoch 14, batch 13000, loss[loss=0.17, simple_loss=0.2318, pruned_loss=0.05414, over 4784.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03067, over 973307.43 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:47:36,084 INFO [train.py:715] (1/8) Epoch 14, batch 13050, loss[loss=0.1372, simple_loss=0.2064, pruned_loss=0.03403, over 4783.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03103, over 973642.63 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:48:16,095 INFO [train.py:715] (1/8) Epoch 14, batch 13100, loss[loss=0.1212, simple_loss=0.1986, pruned_loss=0.02191, over 4772.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.0314, over 973219.38 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:48:56,290 INFO [train.py:715] (1/8) Epoch 14, batch 13150, loss[loss=0.1363, simple_loss=0.2189, pruned_loss=0.02685, over 4976.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03167, over 973222.25 frames.], batch size: 24, lr: 1.57e-04
2022-05-08 01:49:36,386 INFO [train.py:715] (1/8) Epoch 14, batch 13200, loss[loss=0.1527, simple_loss=0.2227, pruned_loss=0.04138, over 4932.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03097, over 973008.58 frames.], batch size: 29, lr: 1.57e-04
2022-05-08 01:50:16,590 INFO [train.py:715] (1/8) Epoch 14, batch 13250, loss[loss=0.1182, simple_loss=0.1851, pruned_loss=0.02567, over 4683.00 frames.], tot_loss[loss=0.1343, simple_loss=0.208, pruned_loss=0.03027, over 972801.65 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 01:50:56,843 INFO [train.py:715] (1/8) Epoch 14, batch 13300, loss[loss=0.1364, simple_loss=0.1989, pruned_loss=0.03692, over 4821.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03114, over 972707.83 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 01:51:36,430 INFO [train.py:715] (1/8) Epoch 14, batch 13350, loss[loss=0.1379, simple_loss=0.2142, pruned_loss=0.03076, over 4914.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03065, over 971689.11 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 01:52:15,921 INFO [train.py:715] (1/8) Epoch 14, batch 13400, loss[loss=0.1508, simple_loss=0.2259, pruned_loss=0.03785, over 4918.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.03096, over 972088.57 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:52:55,516 INFO [train.py:715] (1/8) Epoch 14, batch 13450, loss[loss=0.1532, simple_loss=0.2351, pruned_loss=0.03567, over 4900.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.0304, over 972360.48 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 01:53:35,077 INFO [train.py:715] (1/8) Epoch 14, batch 13500, loss[loss=0.1551, simple_loss=0.2103, pruned_loss=0.04991, over 4855.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03061, over 972911.25 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:54:14,283 INFO [train.py:715] (1/8) Epoch 14, batch 13550, loss[loss=0.1418, simple_loss=0.2275, pruned_loss=0.02809, over 4922.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03098, over 973054.46 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 01:54:53,677 INFO [train.py:715] (1/8) Epoch 14, batch 13600, loss[loss=0.1241, simple_loss=0.192, pruned_loss=0.02812, over 4784.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03104, over 972895.28 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:55:32,973 INFO [train.py:715] (1/8) Epoch 14, batch 13650, loss[loss=0.1517, simple_loss=0.2305, pruned_loss=0.03645, over 4807.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03168, over 973214.06 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 01:56:12,541 INFO [train.py:715] (1/8) Epoch 14, batch 13700, loss[loss=0.1382, simple_loss=0.209, pruned_loss=0.03369, over 4900.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03125, over 973512.53 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 01:56:51,587 INFO [train.py:715] (1/8) Epoch 14, batch 13750, loss[loss=0.139, simple_loss=0.2084, pruned_loss=0.03486, over 4769.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03051, over 972621.57 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:57:30,922 INFO [train.py:715] (1/8) Epoch 14, batch 13800, loss[loss=0.1206, simple_loss=0.1955, pruned_loss=0.02286, over 4852.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.0304, over 972957.05 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 01:58:12,499 INFO [train.py:715] (1/8) Epoch 14, batch 13850, loss[loss=0.1483, simple_loss=0.2305, pruned_loss=0.03303, over 4814.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2079, pruned_loss=0.03056, over 972558.24 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 01:58:51,820 INFO [train.py:715] (1/8) Epoch 14, batch 13900, loss[loss=0.1512, simple_loss=0.2212, pruned_loss=0.04056, over 4933.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2088, pruned_loss=0.03108, over 972381.87 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 01:59:31,444 INFO [train.py:715] (1/8) Epoch 14, batch 13950, loss[loss=0.1175, simple_loss=0.1861, pruned_loss=0.02444, over 4764.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03126, over 972292.54 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:00:10,942 INFO [train.py:715] (1/8) Epoch 14, batch 14000, loss[loss=0.1285, simple_loss=0.2093, pruned_loss=0.02387, over 4816.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03112, over 972212.72 frames.], batch size: 27, lr: 1.57e-04
2022-05-08 02:00:50,381 INFO [train.py:715] (1/8) Epoch 14, batch 14050, loss[loss=0.1312, simple_loss=0.1898, pruned_loss=0.03627, over 4643.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03142, over 971869.14 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:01:30,046 INFO [train.py:715] (1/8) Epoch 14, batch 14100, loss[loss=0.1322, simple_loss=0.2042, pruned_loss=0.03012, over 4989.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03105, over 972758.25 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 02:02:09,597 INFO [train.py:715] (1/8) Epoch 14, batch 14150, loss[loss=0.1169, simple_loss=0.1933, pruned_loss=0.02025, over 4900.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03104, over 972557.77 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:02:49,095 INFO [train.py:715] (1/8) Epoch 14, batch 14200, loss[loss=0.1444, simple_loss=0.202, pruned_loss=0.04338, over 4855.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2096, pruned_loss=0.03072, over 973030.34 frames.], batch size: 34, lr: 1.57e-04
2022-05-08 02:03:28,350 INFO [train.py:715] (1/8) Epoch 14, batch 14250, loss[loss=0.1513, simple_loss=0.2169, pruned_loss=0.0429, over 4891.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03014, over 973054.43 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:04:08,206 INFO [train.py:715] (1/8) Epoch 14, batch 14300, loss[loss=0.1076, simple_loss=0.1783, pruned_loss=0.01846, over 4952.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03017, over 973401.22 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:04:47,398 INFO [train.py:715] (1/8) Epoch 14, batch 14350, loss[loss=0.1013, simple_loss=0.1732, pruned_loss=0.01473, over 4833.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03034, over 972968.97 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:05:26,848 INFO [train.py:715] (1/8) Epoch 14, batch 14400, loss[loss=0.1429, simple_loss=0.2002, pruned_loss=0.04278, over 4790.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.0309, over 972178.32 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:06:06,345 INFO [train.py:715] (1/8) Epoch 14, batch 14450, loss[loss=0.1221, simple_loss=0.1963, pruned_loss=0.02395, over 4884.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2091, pruned_loss=0.0312, over 972179.80 frames.], batch size: 22, lr: 1.57e-04
2022-05-08 02:06:45,902 INFO [train.py:715] (1/8) Epoch 14, batch 14500, loss[loss=0.1368, simple_loss=0.2112, pruned_loss=0.03116, over 4838.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03073, over 971506.92 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:07:25,176 INFO [train.py:715] (1/8) Epoch 14, batch 14550, loss[loss=0.1216, simple_loss=0.1968, pruned_loss=0.02318, over 4930.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03087, over 970562.14 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 02:08:04,456 INFO [train.py:715] (1/8) Epoch 14, batch 14600, loss[loss=0.1274, simple_loss=0.1912, pruned_loss=0.0318, over 4772.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03058, over 970226.66 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:08:44,678 INFO [train.py:715] (1/8) Epoch 14, batch 14650, loss[loss=0.1118, simple_loss=0.1771, pruned_loss=0.02329, over 4756.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03001, over 969371.98 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 02:09:24,104 INFO [train.py:715] (1/8) Epoch 14, batch 14700, loss[loss=0.1324, simple_loss=0.1993, pruned_loss=0.03276, over 4902.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2071, pruned_loss=0.02988, over 970185.88 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:10:03,917 INFO [train.py:715] (1/8) Epoch 14, batch 14750, loss[loss=0.1087, simple_loss=0.1847, pruned_loss=0.01632, over 4939.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.0299, over 970771.82 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:10:43,094 INFO [train.py:715] (1/8) Epoch 14, batch 14800, loss[loss=0.1103, simple_loss=0.1894, pruned_loss=0.01563, over 4839.00 frames.], tot_loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.02965, over 971532.32 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 02:11:23,009 INFO [train.py:715] (1/8) Epoch 14, batch 14850, loss[loss=0.1144, simple_loss=0.1844, pruned_loss=0.02224, over 4795.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2072, pruned_loss=0.02996, over 970461.34 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 02:12:02,548 INFO [train.py:715] (1/8) Epoch 14, batch 14900, loss[loss=0.1394, simple_loss=0.2178, pruned_loss=0.03046, over 4778.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02989, over 970860.09 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:12:41,995 INFO [train.py:715] (1/8) Epoch 14, batch 14950, loss[loss=0.126, simple_loss=0.1908, pruned_loss=0.03056, over 4935.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02981, over 971710.94 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 02:13:22,054 INFO [train.py:715] (1/8) Epoch 14, batch 15000, loss[loss=0.1569, simple_loss=0.2366, pruned_loss=0.03856, over 4847.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.0302, over 971838.10 frames.], batch size: 34, lr: 1.57e-04
2022-05-08 02:13:22,055 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 02:13:31,707 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1052, simple_loss=0.1889, pruned_loss=0.01079, over 914524.00 frames.
2022-05-08 02:14:12,552 INFO [train.py:715] (1/8) Epoch 14, batch 15050, loss[loss=0.1086, simple_loss=0.1793, pruned_loss=0.01892, over 4967.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.03, over 971773.70 frames.], batch size: 24, lr: 1.57e-04
2022-05-08 02:14:52,640 INFO [train.py:715] (1/8) Epoch 14, batch 15100, loss[loss=0.1237, simple_loss=0.1976, pruned_loss=0.02487, over 4871.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02996, over 972004.61 frames.], batch size: 22, lr: 1.57e-04
2022-05-08 02:15:33,284 INFO [train.py:715] (1/8) Epoch 14, batch 15150, loss[loss=0.1347, simple_loss=0.2063, pruned_loss=0.03151, over 4807.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03028, over 971985.15 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:16:13,414 INFO [train.py:715] (1/8) Epoch 14, batch 15200, loss[loss=0.1236, simple_loss=0.1922, pruned_loss=0.02753, over 4863.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03016, over 972406.52 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 02:16:54,052 INFO [train.py:715] (1/8) Epoch 14, batch 15250, loss[loss=0.1554, simple_loss=0.2238, pruned_loss=0.04355, over 4810.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03012, over 971820.55 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:17:33,931 INFO [train.py:715] (1/8) Epoch 14, batch 15300, loss[loss=0.1536, simple_loss=0.223, pruned_loss=0.04208, over 4985.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2098, pruned_loss=0.03082, over 972307.96 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:18:13,471 INFO [train.py:715] (1/8) Epoch 14, batch 15350, loss[loss=0.148, simple_loss=0.2249, pruned_loss=0.03549, over 4926.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03127, over 972194.65 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 02:18:53,583 INFO [train.py:715] (1/8) Epoch 14, batch 15400, loss[loss=0.157, simple_loss=0.2284, pruned_loss=0.04281, over 4945.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03177, over 972102.03 frames.], batch size: 39, lr: 1.57e-04
2022-05-08 02:19:32,971 INFO [train.py:715] (1/8) Epoch 14, batch 15450, loss[loss=0.1278, simple_loss=0.2126, pruned_loss=0.02151, over 4938.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03147, over 972782.38 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:20:12,213 INFO [train.py:715] (1/8) Epoch 14, batch 15500, loss[loss=0.165, simple_loss=0.2428, pruned_loss=0.04363, over 4925.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03193, over 973180.50 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 02:20:51,547 INFO [train.py:715] (1/8) Epoch 14, batch 15550, loss[loss=0.1502, simple_loss=0.228, pruned_loss=0.03615, over 4978.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03212, over 972653.18 frames.], batch size: 39, lr: 1.57e-04
2022-05-08 02:21:31,493 INFO [train.py:715] (1/8) Epoch 14, batch 15600, loss[loss=0.1058, simple_loss=0.174, pruned_loss=0.01879, over 4747.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03133, over 972236.87 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:22:10,936 INFO [train.py:715] (1/8) Epoch 14, batch 15650, loss[loss=0.1124, simple_loss=0.1848, pruned_loss=0.01998, over 4902.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03066, over 971963.92 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:22:49,322 INFO [train.py:715] (1/8) Epoch 14, batch 15700, loss[loss=0.1498, simple_loss=0.2225, pruned_loss=0.0386, over 4914.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.03075, over 972252.71 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:23:29,530 INFO [train.py:715] (1/8) Epoch 14, batch 15750, loss[loss=0.1443, simple_loss=0.2093, pruned_loss=0.03966, over 4855.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03116, over 971945.15 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 02:24:09,056 INFO [train.py:715] (1/8) Epoch 14, batch 15800, loss[loss=0.1514, simple_loss=0.2292, pruned_loss=0.03676, over 4683.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03138, over 971263.92 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:24:48,296 INFO [train.py:715] (1/8) Epoch 14, batch 15850, loss[loss=0.1392, simple_loss=0.2158, pruned_loss=0.03131, over 4916.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03137, over 972422.64 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 02:25:27,577 INFO [train.py:715] (1/8) Epoch 14, batch 15900, loss[loss=0.1362, simple_loss=0.2124, pruned_loss=0.02994, over 4961.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2095, pruned_loss=0.03162, over 972399.19 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:26:07,618 INFO [train.py:715] (1/8) Epoch 14, batch 15950, loss[loss=0.1199, simple_loss=0.1947, pruned_loss=0.02256, over 4896.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03123, over 972142.29 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:26:47,031 INFO [train.py:715] (1/8) Epoch 14, batch 16000, loss[loss=0.1457, simple_loss=0.2179, pruned_loss=0.0368, over 4925.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03091, over 972177.86 frames.], batch size: 29, lr: 1.57e-04
2022-05-08 02:27:25,750 INFO [train.py:715] (1/8) Epoch 14, batch 16050, loss[loss=0.1566, simple_loss=0.2411, pruned_loss=0.03608, over 4880.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03066, over 972097.24 frames.], batch size: 16, lr: 1.57e-04
2022-05-08 02:28:04,466 INFO [train.py:715] (1/8) Epoch 14, batch 16100, loss[loss=0.1211, simple_loss=0.1851, pruned_loss=0.02852, over 4872.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03073, over 972069.25 frames.], batch size: 16, lr: 1.57e-04
2022-05-08 02:28:42,589 INFO [train.py:715] (1/8) Epoch 14, batch 16150, loss[loss=0.1432, simple_loss=0.2127, pruned_loss=0.03681, over 4942.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.0311, over 971716.72 frames.], batch size: 29, lr: 1.57e-04
2022-05-08 02:29:20,833 INFO [train.py:715] (1/8) Epoch 14, batch 16200, loss[loss=0.1369, simple_loss=0.2097, pruned_loss=0.03202, over 4827.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03084, over 971472.39 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:29:59,441 INFO [train.py:715] (1/8) Epoch 14, batch 16250, loss[loss=0.1615, simple_loss=0.2362, pruned_loss=0.04341, over 4771.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03104, over 971773.13 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:30:38,583 INFO [train.py:715] (1/8) Epoch 14, batch 16300, loss[loss=0.1404, simple_loss=0.2148, pruned_loss=0.03304, over 4965.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03086, over 971758.22 frames.], batch size: 25, lr: 1.57e-04
2022-05-08 02:31:16,533 INFO [train.py:715] (1/8) Epoch 14, batch 16350, loss[loss=0.1364, simple_loss=0.2113, pruned_loss=0.03079, over 4903.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03097, over 971610.13 frames.], batch size: 22, lr: 1.57e-04
2022-05-08 02:31:55,705 INFO [train.py:715] (1/8) Epoch 14, batch 16400, loss[loss=0.1329, simple_loss=0.2008, pruned_loss=0.03246, over 4806.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.03124, over 971996.67 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:32:35,419 INFO [train.py:715] (1/8) Epoch 14, batch 16450, loss[loss=0.1251, simple_loss=0.1989, pruned_loss=0.02567, over 4947.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03094, over 971543.37 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:33:14,859 INFO [train.py:715] (1/8) Epoch 14, batch 16500, loss[loss=0.1414, simple_loss=0.2155, pruned_loss=0.03359, over 4872.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03096, over 971773.51 frames.], batch size: 34, lr: 1.57e-04
2022-05-08 02:33:53,748 INFO [train.py:715] (1/8) Epoch 14, batch 16550, loss[loss=0.1269, simple_loss=0.2015, pruned_loss=0.02621, over 4933.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03152, over 972072.75 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 02:34:34,127 INFO [train.py:715] (1/8) Epoch 14, batch 16600, loss[loss=0.1204, simple_loss=0.1993, pruned_loss=0.02073, over 4925.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03152, over 971987.21 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 02:35:13,399 INFO [train.py:715] (1/8) Epoch 14, batch 16650, loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03075, over 4802.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03149, over 971928.68 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:35:55,025 INFO [train.py:715] (1/8) Epoch 14, batch 16700, loss[loss=0.1173, simple_loss=0.1915, pruned_loss=0.02153, over 4828.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2103, pruned_loss=0.03122, over 971902.71 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:36:34,911 INFO [train.py:715] (1/8) Epoch 14, batch 16750, loss[loss=0.1195, simple_loss=0.1928, pruned_loss=0.0231, over 4935.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2113, pruned_loss=0.03147, over 971200.50 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:37:15,261 INFO [train.py:715] (1/8) Epoch 14, batch 16800, loss[loss=0.1169, simple_loss=0.1929, pruned_loss=0.02051, over 4825.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2113, pruned_loss=0.0316, over 971109.24 frames.], batch size: 30, lr: 1.57e-04
2022-05-08 02:37:54,770 INFO [train.py:715] (1/8) Epoch 14, batch 16850, loss[loss=0.1612, simple_loss=0.234, pruned_loss=0.04419, over 4931.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2107, pruned_loss=0.03099, over 971028.55 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:38:34,398 INFO [train.py:715] (1/8) Epoch 14, batch 16900, loss[loss=0.1081, simple_loss=0.1784, pruned_loss=0.01886, over 4844.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2101, pruned_loss=0.03065, over 971570.81 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 02:39:15,366 INFO [train.py:715] (1/8) Epoch 14, batch 16950, loss[loss=0.1196, simple_loss=0.1941, pruned_loss=0.02256, over 4754.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03075, over 971336.36 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:39:56,920 INFO [train.py:715] (1/8) Epoch 14, batch 17000, loss[loss=0.153, simple_loss=0.2207, pruned_loss=0.04264, over 4906.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03081, over 971130.30 frames.], batch size: 39, lr: 1.57e-04
2022-05-08 02:40:37,816 INFO [train.py:715] (1/8) Epoch 14, batch 17050, loss[loss=0.1482, simple_loss=0.2273, pruned_loss=0.0346, over 4965.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03069, over 971748.34 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 02:41:18,914 INFO [train.py:715] (1/8) Epoch 14, batch 17100, loss[loss=0.1512, simple_loss=0.2251, pruned_loss=0.03869, over 4751.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03095, over 971223.92 frames.], batch size: 16, lr: 1.57e-04
2022-05-08 02:42:01,002 INFO [train.py:715] (1/8) Epoch 14, batch 17150, loss[loss=0.1346, simple_loss=0.2075, pruned_loss=0.03087, over 4925.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03098, over 972181.99 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 02:42:41,745 INFO [train.py:715] (1/8) Epoch 14, batch 17200, loss[loss=0.1647, simple_loss=0.2423, pruned_loss=0.04354, over 4876.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03096, over 972264.12 frames.], batch size: 22, lr: 1.57e-04
2022-05-08 02:43:22,730 INFO [train.py:715] (1/8) Epoch 14, batch 17250, loss[loss=0.131, simple_loss=0.2017, pruned_loss=0.03012, over 4897.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03139, over 972341.34 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:44:04,209 INFO [train.py:715] (1/8) Epoch 14, batch 17300, loss[loss=0.1489, simple_loss=0.2194, pruned_loss=0.03926, over 4745.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03125, over 971479.95 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:44:45,867 INFO [train.py:715] (1/8) Epoch 14, batch 17350, loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03166, over 4935.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03094, over 971009.35 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:45:26,235 INFO [train.py:715] (1/8) Epoch 14, batch 17400, loss[loss=0.1278, simple_loss=0.1991, pruned_loss=0.02824, over 4841.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03124, over 971414.92 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 02:46:07,496 INFO [train.py:715] (1/8) Epoch 14, batch 17450, loss[loss=0.1265, simple_loss=0.1935, pruned_loss=0.02972, over 4804.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03117, over 970941.32 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 02:46:49,065 INFO [train.py:715] (1/8) Epoch 14, batch 17500, loss[loss=0.1089, simple_loss=0.1837, pruned_loss=0.01706, over 4989.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2091, pruned_loss=0.03122, over 971555.66 frames.], batch size: 28, lr: 1.56e-04
2022-05-08 02:47:29,798 INFO [train.py:715] (1/8) Epoch 14, batch 17550, loss[loss=0.1292, simple_loss=0.2036, pruned_loss=0.02745, over 4900.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03075, over 971765.83 frames.], batch size: 39, lr: 1.56e-04
2022-05-08 02:48:10,331 INFO [train.py:715] (1/8) Epoch 14, batch 17600, loss[loss=0.1601, simple_loss=0.2241, pruned_loss=0.04806, over 4912.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03101, over 972156.06 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 02:48:52,028 INFO [train.py:715] (1/8) Epoch 14, batch 17650, loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03463, over 4798.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03093, over 971884.16 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 02:49:33,167 INFO [train.py:715] (1/8) Epoch 14, batch 17700, loss[loss=0.1209, simple_loss=0.1918, pruned_loss=0.02499, over 4862.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03105, over 971627.58 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 02:50:13,650 INFO [train.py:715] (1/8) Epoch 14, batch 17750, loss[loss=0.1194, simple_loss=0.1881, pruned_loss=0.02533, over 4819.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03117, over 972193.08 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 02:50:55,021 INFO [train.py:715] (1/8) Epoch 14, batch 17800, loss[loss=0.1099, simple_loss=0.1876, pruned_loss=0.01614, over 4965.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03113, over 973312.72 frames.], batch size: 28, lr: 1.56e-04
2022-05-08 02:51:35,968 INFO [train.py:715] (1/8) Epoch 14, batch 17850, loss[loss=0.1275, simple_loss=0.2096, pruned_loss=0.02272, over 4872.00 frames.], tot_loss[loss=0.136, simple_loss=0.2102, pruned_loss=0.03096, over 973505.02 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 02:52:16,729 INFO [train.py:715] (1/8) Epoch 14, batch 17900, loss[loss=0.1626, simple_loss=0.2281, pruned_loss=0.04861, over 4969.00 frames.], tot_loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03117, over 974085.82 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 02:52:57,225 INFO [train.py:715] (1/8) Epoch 14, batch 17950, loss[loss=0.1354, simple_loss=0.1996, pruned_loss=0.0356, over 4852.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.03122, over 974061.71 frames.], batch size: 32, lr: 1.56e-04
2022-05-08 02:53:38,601 INFO [train.py:715] (1/8) Epoch 14, batch 18000, loss[loss=0.1652, simple_loss=0.2296, pruned_loss=0.05039, over 4983.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.03121, over 974095.12 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 02:53:38,602 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 02:53:48,448 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1052, simple_loss=0.1889, pruned_loss=0.01075, over 914524.00 frames.
2022-05-08 02:54:29,841 INFO [train.py:715] (1/8) Epoch 14, batch 18050, loss[loss=0.1402, simple_loss=0.2073, pruned_loss=0.03653, over 4807.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.03124, over 973701.77 frames.], batch size: 27, lr: 1.56e-04
2022-05-08 02:55:10,985 INFO [train.py:715] (1/8) Epoch 14, batch 18100, loss[loss=0.1626, simple_loss=0.2541, pruned_loss=0.03553, over 4923.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03125, over 973929.61 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 02:55:52,582 INFO [train.py:715] (1/8) Epoch 14, batch 18150, loss[loss=0.1069, simple_loss=0.1712, pruned_loss=0.02131, over 4741.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03111, over 973603.27 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 02:56:33,502 INFO [train.py:715] (1/8) Epoch 14, batch 18200, loss[loss=0.1443, simple_loss=0.2138, pruned_loss=0.0374, over 4966.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03131, over 973685.45 frames.], batch size: 35, lr: 1.56e-04
2022-05-08 02:57:15,448 INFO [train.py:715] (1/8) Epoch 14, batch 18250, loss[loss=0.1267, simple_loss=0.1973, pruned_loss=0.02802, over 4972.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03114, over 973607.52 frames.], batch size: 28, lr: 1.56e-04
2022-05-08 02:57:56,892 INFO [train.py:715] (1/8) Epoch 14, batch 18300, loss[loss=0.17, simple_loss=0.2475, pruned_loss=0.04628, over 4984.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2096, pruned_loss=0.0307, over 974062.12 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 02:58:36,497 INFO [train.py:715] (1/8) Epoch 14, batch 18350, loss[loss=0.117, simple_loss=0.1884, pruned_loss=0.0228, over 4819.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03051, over 973981.91 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 02:59:17,360 INFO [train.py:715] (1/8) Epoch 14, batch 18400, loss[loss=0.1357, simple_loss=0.2031, pruned_loss=0.03411, over 4698.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03029, over 973527.29 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 02:59:57,998 INFO [train.py:715] (1/8) Epoch 14, batch 18450, loss[loss=0.1293, simple_loss=0.2135, pruned_loss=0.02251, over 4912.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.0303, over 973525.68 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:00:38,229 INFO [train.py:715] (1/8) Epoch 14, batch 18500, loss[loss=0.1722, simple_loss=0.2365, pruned_loss=0.05397, over 4979.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03037, over 974155.06 frames.], batch size: 33, lr: 1.56e-04
2022-05-08 03:01:18,697 INFO [train.py:715] (1/8) Epoch 14, batch 18550, loss[loss=0.1523, simple_loss=0.2289, pruned_loss=0.03787, over 4815.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03087, over 973823.91 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 03:01:59,558 INFO [train.py:715] (1/8) Epoch 14, batch 18600, loss[loss=0.1155, simple_loss=0.1767, pruned_loss=0.02716, over 4958.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.03058, over 973196.72 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:02:39,860 INFO [train.py:715] (1/8) Epoch 14, batch 18650, loss[loss=0.1318, simple_loss=0.196, pruned_loss=0.03374, over 4824.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02997, over 974278.74 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:03:20,564 INFO [train.py:715] (1/8) Epoch 14, batch 18700, loss[loss=0.1303, simple_loss=0.1984, pruned_loss=0.03104, over 4912.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03046, over 973824.44 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:04:01,153 INFO [train.py:715] (1/8) Epoch 14, batch 18750, loss[loss=0.1483, simple_loss=0.2078, pruned_loss=0.04437, over 4830.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03073, over 973071.01 frames.], batch size: 30, lr: 1.56e-04
2022-05-08 03:04:41,112 INFO [train.py:715] (1/8) Epoch 14, batch 18800, loss[loss=0.1264, simple_loss=0.2043, pruned_loss=0.02429, over 4937.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03077, over 972785.76 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:05:21,081 INFO [train.py:715] (1/8) Epoch 14, batch 18850, loss[loss=0.1167, simple_loss=0.2027, pruned_loss=0.01531, over 4877.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03033, over 972708.50 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:06:01,827 INFO [train.py:715] (1/8) Epoch 14, batch 18900, loss[loss=0.136, simple_loss=0.2123, pruned_loss=0.02984, over 4801.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03048, over 972524.18 frames.], batch size: 26, lr: 1.56e-04
2022-05-08 03:06:42,896 INFO [train.py:715] (1/8) Epoch 14, batch 18950, loss[loss=0.1158, simple_loss=0.1824, pruned_loss=0.02456, over 4941.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03062, over 972588.68 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:07:23,138 INFO [train.py:715] (1/8) Epoch 14, batch 19000, loss[loss=0.1699, simple_loss=0.2551, pruned_loss=0.04241, over 4830.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03069, over 972053.56 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:08:04,062 INFO [train.py:715] (1/8) Epoch 14, batch 19050, loss[loss=0.1512, simple_loss=0.2198, pruned_loss=0.04133, over 4833.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03098, over 972371.66 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:08:45,080 INFO [train.py:715] (1/8) Epoch 14, batch 19100, loss[loss=0.1286, simple_loss=0.212, pruned_loss=0.02253, over 4873.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03038, over 972893.60 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 03:09:25,463 INFO [train.py:715] (1/8) Epoch 14, batch 19150, loss[loss=0.1488, simple_loss=0.2197, pruned_loss=0.03899, over 4837.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2073, pruned_loss=0.0302, over 972380.81 frames.], batch size: 30, lr: 1.56e-04
2022-05-08 03:10:04,868 INFO [train.py:715] (1/8) Epoch 14, batch 19200, loss[loss=0.1443, simple_loss=0.2098, pruned_loss=0.0394, over 4926.00 frames.], tot_loss[loss=0.1336, simple_loss=0.207, pruned_loss=0.03009, over 971920.75 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:10:45,984 INFO [train.py:715] (1/8) Epoch 14, batch 19250, loss[loss=0.1107, simple_loss=0.1811, pruned_loss=0.02014, over 4793.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2069, pruned_loss=0.02994, over 972145.28 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:11:26,901 INFO [train.py:715] (1/8) Epoch 14, batch 19300, loss[loss=0.123, simple_loss=0.2069, pruned_loss=0.0196, over 4937.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02968, over 972318.71 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:12:06,948 INFO [train.py:715] (1/8) Epoch 14, batch 19350, loss[loss=0.1229, simple_loss=0.1964, pruned_loss=0.02468, over 4980.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02961, over 972544.47 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 03:12:47,192 INFO [train.py:715] (1/8) Epoch 14, batch 19400, loss[loss=0.1507, simple_loss=0.2324, pruned_loss=0.03446, over 4810.00 frames.], tot_loss[loss=0.135, simple_loss=0.2095, pruned_loss=0.03023, over 972167.38 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 03:13:28,643 INFO [train.py:715] (1/8) Epoch 14, batch 19450, loss[loss=0.1639, simple_loss=0.2239, pruned_loss=0.05199, over 4966.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2094, pruned_loss=0.03025, over 972523.02 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:14:08,956 INFO [train.py:715] (1/8) Epoch 14, batch 19500, loss[loss=0.1328, simple_loss=0.2052, pruned_loss=0.03024, over 4769.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.03026, over 972341.50 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:14:49,602 INFO [train.py:715] (1/8) Epoch 14, batch 19550, loss[loss=0.1334, simple_loss=0.2021, pruned_loss=0.03235, over 4955.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03041, over 972436.05 frames.], batch size: 35, lr: 1.56e-04
2022-05-08 03:15:30,059 INFO [train.py:715] (1/8) Epoch 14, batch 19600, loss[loss=0.1645, simple_loss=0.2385, pruned_loss=0.04522, over 4751.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03073, over 972393.73 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 03:16:10,994 INFO [train.py:715] (1/8) Epoch 14, batch 19650, loss[loss=0.1632, simple_loss=0.2228, pruned_loss=0.05182, over 4927.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.03088, over 972474.77 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:16:51,969 INFO [train.py:715] (1/8) Epoch 14, batch 19700, loss[loss=0.1311, simple_loss=0.2091, pruned_loss=0.02658, over 4792.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03056, over 972812.40 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:17:32,726 INFO [train.py:715] (1/8) Epoch 14, batch 19750, loss[loss=0.1094, simple_loss=0.1745, pruned_loss=0.02214, over 4850.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03032, over 973062.51 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:18:13,648 INFO [train.py:715] (1/8) Epoch 14, batch 19800, loss[loss=0.1043, simple_loss=0.1885, pruned_loss=0.01008, over 4975.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.03015, over 973182.98 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:18:54,278 INFO [train.py:715] (1/8) Epoch 14, batch 19850, loss[loss=0.1399, simple_loss=0.2027, pruned_loss=0.03853, over 4959.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03036, over 973010.65 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:19:35,278 INFO [train.py:715] (1/8) Epoch 14, batch 19900, loss[loss=0.1241, simple_loss=0.191, pruned_loss=0.02857, over 4853.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03066, over 972479.48 frames.], batch size: 30, lr: 1.56e-04
2022-05-08 03:20:15,396 INFO [train.py:715] (1/8) Epoch 14, batch 19950, loss[loss=0.124, simple_loss=0.1925, pruned_loss=0.02777, over 4803.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03045, over 972044.32 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:20:55,689 INFO [train.py:715] (1/8) Epoch 14, batch 20000, loss[loss=0.1128, simple_loss=0.1856, pruned_loss=0.02002, over 4986.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03101, over 973329.14 frames.], batch size: 28, lr: 1.56e-04
2022-05-08 03:21:35,501 INFO [train.py:715] (1/8) Epoch 14, batch 20050, loss[loss=0.1381, simple_loss=0.2188, pruned_loss=0.02871, over 4902.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03112, over 973232.37 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:22:15,342 INFO [train.py:715] (1/8) Epoch 14, batch 20100, loss[loss=0.1365, simple_loss=0.2118, pruned_loss=0.03057, over 4942.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2081, pruned_loss=0.03063, over 974000.22 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:22:55,786 INFO [train.py:715] (1/8) Epoch 14, batch 20150, loss[loss=0.1178, simple_loss=0.2032, pruned_loss=0.01617, over 4792.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2077, pruned_loss=0.03056, over 973341.98 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:23:35,888 INFO [train.py:715] (1/8) Epoch 14, batch 20200, loss[loss=0.1221, simple_loss=0.2009, pruned_loss=0.02165, over 4801.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2075, pruned_loss=0.03044, over 972939.99 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:24:16,362 INFO [train.py:715] (1/8) Epoch 14, batch 20250, loss[loss=0.1197, simple_loss=0.1967, pruned_loss=0.02132, over 4752.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2075, pruned_loss=0.03051, over 973199.37 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:24:56,525 INFO [train.py:715] (1/8) Epoch 14, batch 20300, loss[loss=0.1375, simple_loss=0.2236, pruned_loss=0.0257, over 4746.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2084, pruned_loss=0.03112, over 972590.31 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 03:25:37,281 INFO [train.py:715] (1/8) Epoch 14, batch 20350, loss[loss=0.1241, simple_loss=0.2161, pruned_loss=0.01605, over 4790.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03033, over 972875.57 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 03:26:17,618 INFO [train.py:715] (1/8) Epoch 14, batch 20400, loss[loss=0.1378, simple_loss=0.2161, pruned_loss=0.02979, over 4783.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02996, over 972044.43 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:26:58,066 INFO [train.py:715] (1/8) Epoch 14, batch 20450, loss[loss=0.1576, simple_loss=0.2193, pruned_loss=0.0479, over 4786.00 frames.], tot_loss[loss=0.1347, simple_loss=0.208, pruned_loss=0.0307, over 971383.06 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:27:39,219 INFO [train.py:715] (1/8) Epoch 14, batch 20500, loss[loss=0.1461, simple_loss=0.2239, pruned_loss=0.03413, over 4774.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2081, pruned_loss=0.0307, over 971782.81 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:28:19,575 INFO [train.py:715] (1/8) Epoch 14, batch 20550, loss[loss=0.1682, simple_loss=0.2403, pruned_loss=0.04804, over 4861.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03128, over 971529.26 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:29:00,484 INFO [train.py:715] (1/8) Epoch 14, batch 20600, loss[loss=0.143, simple_loss=0.2101, pruned_loss=0.03799, over 4989.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03086, over 971901.81 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 03:29:41,273 INFO [train.py:715] (1/8) Epoch 14, batch 20650, loss[loss=0.1473, simple_loss=0.2093, pruned_loss=0.04265, over 4790.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03061, over 972351.30 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 03:30:22,920 INFO [train.py:715] (1/8) Epoch 14, batch 20700, loss[loss=0.1341, simple_loss=0.2103, pruned_loss=0.02898, over 4989.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03055, over 971984.50 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:31:03,263 INFO [train.py:715] (1/8) Epoch 14, batch 20750, loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03421, over 4747.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03064, over 972291.74 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:31:43,461 INFO [train.py:715] (1/8) Epoch 14, batch 20800, loss[loss=0.1432, simple_loss=0.22, pruned_loss=0.03318, over 4772.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03074, over 971702.83 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:32:24,163 INFO [train.py:715] (1/8) Epoch 14, batch 20850, loss[loss=0.1355, simple_loss=0.2173, pruned_loss=0.02683, over 4964.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03073, over 971514.24 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 03:33:04,695 INFO [train.py:715] (1/8) Epoch 14, batch 20900, loss[loss=0.1192, simple_loss=0.1922, pruned_loss=0.02315, over 4908.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03049, over 971136.74 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 03:33:45,375 INFO [train.py:715] (1/8) Epoch 14, batch 20950, loss[loss=0.1249, simple_loss=0.2032, pruned_loss=0.02328, over 4921.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03044, over 971739.96 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 03:34:25,919 INFO [train.py:715] (1/8) Epoch 14, batch 21000, loss[loss=0.1282, simple_loss=0.204, pruned_loss=0.0262, over 4814.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03047, over 971727.70 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:34:25,920 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 03:34:37,000 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1051, simple_loss=0.1889, pruned_loss=0.0107, over 914524.00 frames.
2022-05-08 03:35:17,922 INFO [train.py:715] (1/8) Epoch 14, batch 21050, loss[loss=0.1605, simple_loss=0.2298, pruned_loss=0.04559, over 4959.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.0309, over 971897.96 frames.], batch size: 35, lr: 1.56e-04
2022-05-08 03:35:58,611 INFO [train.py:715] (1/8) Epoch 14, batch 21100, loss[loss=0.1403, simple_loss=0.2144, pruned_loss=0.03313, over 4754.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03082, over 971554.74 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:36:39,424 INFO [train.py:715] (1/8) Epoch 14, batch 21150, loss[loss=0.1128, simple_loss=0.1906, pruned_loss=0.0175, over 4924.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03015, over 971807.77 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 03:37:18,916 INFO [train.py:715] (1/8) Epoch 14, batch 21200, loss[loss=0.1362, simple_loss=0.2152, pruned_loss=0.02858, over 4839.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.0308, over 972436.07 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:37:59,333 INFO [train.py:715] (1/8) Epoch 14, batch 21250, loss[loss=0.1415, simple_loss=0.2154, pruned_loss=0.03385, over 4928.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03098, over 973140.58 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:38:39,035 INFO [train.py:715] (1/8) Epoch 14, batch 21300, loss[loss=0.1257, simple_loss=0.2025, pruned_loss=0.02448, over 4857.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03105, over 972496.56 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:39:17,959 INFO [train.py:715] (1/8) Epoch 14, batch 21350, loss[loss=0.1488, simple_loss=0.2421, pruned_loss=0.02777, over 4942.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03114, over 972237.63 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:39:58,405 INFO [train.py:715] (1/8) Epoch 14, batch 21400, loss[loss=0.1372, simple_loss=0.2043, pruned_loss=0.03502, over 4924.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2089, pruned_loss=0.03131, over 972227.87 frames.], batch size: 39, lr: 1.56e-04
2022-05-08 03:40:38,647 INFO [train.py:715] (1/8) Epoch 14, batch 21450, loss[loss=0.1111, simple_loss=0.1881, pruned_loss=0.01709, over 4950.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.031, over 971928.34 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 03:41:18,066 INFO [train.py:715] (1/8) Epoch 14, batch 21500, loss[loss=0.1192, simple_loss=0.1918, pruned_loss=0.0233, over 4826.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03062, over 972810.47 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:41:57,087 INFO [train.py:715] (1/8) Epoch 14, batch 21550, loss[loss=0.1498, simple_loss=0.2343, pruned_loss=0.03269, over 4825.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03029, over 972742.28 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:42:37,079 INFO [train.py:715] (1/8) Epoch 14, batch 21600, loss[loss=0.122, simple_loss=0.1976, pruned_loss=0.02322, over 4784.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.0304, over 972497.88 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:43:16,858 INFO [train.py:715] (1/8) Epoch 14, batch 21650, loss[loss=0.1311, simple_loss=0.2149, pruned_loss=0.02371, over 4797.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03023, over 972205.18 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:43:55,952 INFO [train.py:715] (1/8) Epoch 14, batch 21700, loss[loss=0.1122, simple_loss=0.1845, pruned_loss=0.01996, over 4770.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03051, over 972003.09 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 03:44:36,362 INFO [train.py:715] (1/8) Epoch 14, batch 21750, loss[loss=0.1415, simple_loss=0.2028, pruned_loss=0.04009, over 4787.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03049, over 972208.51 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:45:16,761 INFO [train.py:715] (1/8) Epoch 14, batch 21800, loss[loss=0.1489, simple_loss=0.2085, pruned_loss=0.04467, over 4804.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.03064, over 973232.30 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:45:56,155 INFO [train.py:715] (1/8) Epoch 14, batch 21850, loss[loss=0.1424, simple_loss=0.2101, pruned_loss=0.03737, over 4773.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03049, over 972500.30 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:46:35,763 INFO [train.py:715] (1/8) Epoch 14, batch 21900, loss[loss=0.1786, simple_loss=0.2454, pruned_loss=0.05585, over 4819.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.03064, over 972433.90 frames.], batch size: 26, lr: 1.56e-04
2022-05-08 03:47:16,030 INFO [train.py:715] (1/8) Epoch 14, batch 21950, loss[loss=0.1224, simple_loss=0.1948, pruned_loss=0.02507, over 4776.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03026, over 972451.91 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:47:55,293 INFO [train.py:715] (1/8) Epoch 14, batch 22000, loss[loss=0.1259, simple_loss=0.2084, pruned_loss=0.02165, over 4816.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03027, over 972401.42 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 03:48:34,008 INFO [train.py:715] (1/8) Epoch 14, batch 22050, loss[loss=0.1403, simple_loss=0.2268, pruned_loss=0.0269, over 4752.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03007, over 973089.24 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 03:49:14,114 INFO [train.py:715] (1/8) Epoch 14, batch 22100, loss[loss=0.1465, simple_loss=0.2168, pruned_loss=0.03807, over 4959.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03005, over 972901.64 frames.], batch size: 39, lr: 1.56e-04
2022-05-08 03:49:53,836 INFO [train.py:715] (1/8) Epoch 14, batch 22150, loss[loss=0.1386, simple_loss=0.216, pruned_loss=0.03055, over 4911.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03031, over 972245.67 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:50:32,851 INFO [train.py:715] (1/8) Epoch 14, batch 22200, loss[loss=0.1332, simple_loss=0.2118, pruned_loss=0.02732, over 4889.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03058, over 972472.79 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 03:51:12,595 INFO [train.py:715] (1/8) Epoch 14, batch 22250, loss[loss=0.1544, simple_loss=0.2238, pruned_loss=0.04247, over 4936.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03068, over 972644.62 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:51:52,771 INFO [train.py:715] (1/8) Epoch 14, batch 22300, loss[loss=0.1235, simple_loss=0.2014, pruned_loss=0.02283, over 4823.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03058, over 972741.34 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:52:32,253 INFO [train.py:715] (1/8) Epoch 14, batch 22350, loss[loss=0.1423, simple_loss=0.2194, pruned_loss=0.03257, over 4858.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.0308, over 972518.55 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:53:11,410 INFO [train.py:715] (1/8) Epoch 14, batch 22400, loss[loss=0.132, simple_loss=0.2085, pruned_loss=0.0277, over 4774.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03094, over 972161.51 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:53:51,757 INFO [train.py:715] (1/8) Epoch 14, batch 22450, loss[loss=0.1134, simple_loss=0.1855, pruned_loss=0.02065, over 4908.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03099, over 972591.04 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:54:31,167 INFO [train.py:715] (1/8) Epoch 14, batch 22500, loss[loss=0.1427, simple_loss=0.2259, pruned_loss=0.02977, over 4987.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03077, over 973091.04 frames.], batch size: 28, lr: 1.56e-04
2022-05-08 03:55:10,462 INFO [train.py:715] (1/8) Epoch 14, batch 22550, loss[loss=0.1219, simple_loss=0.199, pruned_loss=0.02242, over 4800.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.03159, over 972494.61 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 03:55:50,819 INFO [train.py:715] (1/8) Epoch 14, batch 22600, loss[loss=0.1484, simple_loss=0.2223, pruned_loss=0.03725, over 4788.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03106, over 971986.40 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:56:31,697 INFO [train.py:715] (1/8) Epoch 14, batch 22650, loss[loss=0.1565, simple_loss=0.2295, pruned_loss=0.04172, over 4926.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.031, over 972809.70 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 03:57:11,539 INFO [train.py:715] (1/8) Epoch 14, batch 22700, loss[loss=0.1217, simple_loss=0.2055, pruned_loss=0.01898, over 4863.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03082, over 973267.62 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:57:50,671 INFO [train.py:715] (1/8) Epoch 14, batch 22750, loss[loss=0.1231, simple_loss=0.1969, pruned_loss=0.02463, over 4875.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03125, over 974055.38 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:58:32,006 INFO [train.py:715] (1/8) Epoch 14, batch 22800, loss[loss=0.118, simple_loss=0.1837, pruned_loss=0.02616, over 4809.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03104, over 972887.70 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:59:12,926 INFO [train.py:715] (1/8) Epoch 14, batch 22850, loss[loss=0.143, simple_loss=0.2135, pruned_loss=0.03622, over 4923.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03081, over 972992.47 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 03:59:53,212 INFO [train.py:715] (1/8) Epoch 14, batch 22900, loss[loss=0.1619, simple_loss=0.2424, pruned_loss=0.04066, over 4888.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03102, over 973435.89 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 04:00:33,085 INFO [train.py:715] (1/8) Epoch 14, batch 22950, loss[loss=0.104, simple_loss=0.1751, pruned_loss=0.01642, over 4892.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03068, over 973797.24 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 04:01:13,586 INFO [train.py:715] (1/8) Epoch 14, batch 23000, loss[loss=0.1344, simple_loss=0.1985, pruned_loss=0.03521, over 4901.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2096, pruned_loss=0.03049, over 972890.41 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 04:01:53,106 INFO [train.py:715] (1/8) Epoch 14, batch 23050, loss[loss=0.153, simple_loss=0.219, pruned_loss=0.04349, over 4774.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2095, pruned_loss=0.03018, over 972890.99 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 04:02:32,421 INFO [train.py:715] (1/8) Epoch 14, batch 23100, loss[loss=0.1298, simple_loss=0.2065, pruned_loss=0.02656, over 4817.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2089, pruned_loss=0.03021, over 973479.90 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 04:03:13,068 INFO [train.py:715] (1/8) Epoch 14, batch 23150, loss[loss=0.1273, simple_loss=0.2063, pruned_loss=0.02416, over 4775.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03021, over 972484.77 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 04:03:54,328 INFO [train.py:715] (1/8) Epoch 14, batch 23200, loss[loss=0.1436, simple_loss=0.2149, pruned_loss=0.03613, over 4831.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03008, over 972259.22 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 04:04:33,072 INFO [train.py:715] (1/8) Epoch 14, batch 23250, loss[loss=0.1322, simple_loss=0.205, pruned_loss=0.02972, over 4952.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03019, over 972863.00 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 04:05:13,473 INFO [train.py:715] (1/8) Epoch 14, batch 23300, loss[loss=0.1228, simple_loss=0.1935, pruned_loss=0.026, over 4805.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2079, pruned_loss=0.03049, over 972592.90 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 04:05:54,169 INFO [train.py:715] (1/8) Epoch 14, batch 23350, loss[loss=0.1723, simple_loss=0.2369, pruned_loss=0.05383, over 4833.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2077, pruned_loss=0.03062, over 973244.48 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 04:06:33,757 INFO [train.py:715] (1/8) Epoch 14, batch 23400, loss[loss=0.1319, simple_loss=0.2075, pruned_loss=0.02817, over 4961.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2077, pruned_loss=0.03049, over 972423.18 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 04:07:12,808 INFO [train.py:715] (1/8) Epoch 14, batch 23450, loss[loss=0.1344, simple_loss=0.2171, pruned_loss=0.0259, over 4852.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2073, pruned_loss=0.03044, over 972425.58 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 04:07:53,399 INFO [train.py:715] (1/8) Epoch 14, batch 23500, loss[loss=0.123, simple_loss=0.2107, pruned_loss=0.01765, over 4917.00 frames.], tot_loss[loss=0.1348, simple_loss=0.208, pruned_loss=0.03076, over 971870.40 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 04:08:34,067 INFO [train.py:715] (1/8) Epoch 14, batch 23550, loss[loss=0.1226, simple_loss=0.2019, pruned_loss=0.02167, over 4778.00 frames.], tot_loss[loss=0.135, simple_loss=0.2083, pruned_loss=0.03087, over 971491.42 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 04:09:13,326 INFO [train.py:715] (1/8) Epoch 14, batch 23600, loss[loss=0.1542, simple_loss=0.236, pruned_loss=0.03625, over 4808.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2079, pruned_loss=0.03079, over 972437.08 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 04:09:52,600 INFO [train.py:715] (1/8) Epoch 14, batch 23650, loss[loss=0.1325, simple_loss=0.2074, pruned_loss=0.02883, over 4816.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03049, over 971253.33 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 04:10:32,144 INFO [train.py:715] (1/8) Epoch 14, batch 23700, loss[loss=0.1217, simple_loss=0.195, pruned_loss=0.02419, over 4745.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02999, over 971231.79 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 04:11:11,207 INFO [train.py:715] (1/8) Epoch 14, batch 23750, loss[loss=0.1129, simple_loss=0.1894, pruned_loss=0.01827, over 4825.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03026, over 970770.60 frames.], batch size: 27, lr: 1.56e-04
2022-05-08 04:11:50,485 INFO [train.py:715] (1/8) Epoch 14, batch 23800, loss[loss=0.1568, simple_loss=0.24, pruned_loss=0.03677, over 4922.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03021, over 970804.28 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 04:12:30,662 INFO [train.py:715] (1/8) Epoch 14, batch 23850, loss[loss=0.1479, simple_loss=0.2162, pruned_loss=0.03976, over 4742.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03008, over 970584.02 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 04:13:10,496 INFO [train.py:715] (1/8) Epoch 14, batch 23900, loss[loss=0.1472, simple_loss=0.2212, pruned_loss=0.03662, over 4888.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03048, over 970576.60 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 04:13:49,744 INFO [train.py:715] (1/8) Epoch 14, batch 23950, loss[loss=0.1445, simple_loss=0.2219, pruned_loss=0.03356, over 4773.00 frames.], tot_loss[loss=0.1351, simple_loss=0.209, pruned_loss=0.03063, over 971514.48 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:14:30,066 INFO [train.py:715] (1/8) Epoch 14, batch 24000, loss[loss=0.1106, simple_loss=0.187, pruned_loss=0.0171, over 4980.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.0307, over 972748.78 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 04:14:30,067 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 04:14:41,438 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1052, simple_loss=0.1889, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 04:15:21,389 INFO [train.py:715] (1/8) Epoch 14, batch 24050, loss[loss=0.1465, simple_loss=0.2135, pruned_loss=0.0398, over 4894.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03064, over 972269.51 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:16:02,442 INFO [train.py:715] (1/8) Epoch 14, batch 24100, loss[loss=0.1351, simple_loss=0.2201, pruned_loss=0.02505, over 4893.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03064, over 972268.07 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:16:41,520 INFO [train.py:715] (1/8) Epoch 14, batch 24150, loss[loss=0.1093, simple_loss=0.1862, pruned_loss=0.01618, over 4971.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03071, over 973024.56 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 04:17:21,110 INFO [train.py:715] (1/8) Epoch 14, batch 24200, loss[loss=0.1471, simple_loss=0.2122, pruned_loss=0.04103, over 4834.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03094, over 971990.91 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:18:01,401 INFO [train.py:715] (1/8) Epoch 14, batch 24250, loss[loss=0.1633, simple_loss=0.238, pruned_loss=0.04427, over 4912.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03113, over 972455.60 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 04:18:41,649 INFO [train.py:715] (1/8) Epoch 14, batch 24300, loss[loss=0.1166, simple_loss=0.1913, pruned_loss=0.02097, over 4920.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03091, over 972750.65 frames.], batch size: 29, lr: 1.55e-04
2022-05-08 04:19:20,611 INFO [train.py:715] (1/8) Epoch 14, batch 24350, loss[loss=0.1347, simple_loss=0.2005, pruned_loss=0.0344, over 4860.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03082, over 972605.86 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 04:20:01,377 INFO [train.py:715] (1/8) Epoch 14, batch 24400, loss[loss=0.1446, simple_loss=0.2177, pruned_loss=0.03572, over 4870.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03099, over 972754.05 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 04:20:43,002 INFO [train.py:715] (1/8) Epoch 14, batch 24450, loss[loss=0.1429, simple_loss=0.2251, pruned_loss=0.03036, over 4815.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2099, pruned_loss=0.03071, over 973123.83 frames.], batch size: 27, lr: 1.55e-04
2022-05-08 04:21:22,339 INFO [train.py:715] (1/8) Epoch 14, batch 24500, loss[loss=0.1236, simple_loss=0.1977, pruned_loss=0.02473, over 4977.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2092, pruned_loss=0.03029, over 972761.39 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 04:22:02,604 INFO [train.py:715] (1/8) Epoch 14, batch 24550, loss[loss=0.1376, simple_loss=0.2071, pruned_loss=0.034, over 4993.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03025, over 972633.48 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 04:22:43,756 INFO [train.py:715] (1/8) Epoch 14, batch 24600, loss[loss=0.1379, simple_loss=0.201, pruned_loss=0.03736, over 4774.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.0303, over 971454.96 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:23:25,383 INFO [train.py:715] (1/8) Epoch 14, batch 24650, loss[loss=0.122, simple_loss=0.2034, pruned_loss=0.02028, over 4902.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03079, over 972441.73 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:24:07,559 INFO [train.py:715] (1/8) Epoch 14, batch 24700, loss[loss=0.1143, simple_loss=0.1995, pruned_loss=0.0146, over 4938.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.03071, over 972013.79 frames.], batch size: 29, lr: 1.55e-04
2022-05-08 04:24:48,459 INFO [train.py:715] (1/8) Epoch 14, batch 24750, loss[loss=0.1362, simple_loss=0.2106, pruned_loss=0.03089, over 4822.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03062, over 972344.79 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:25:30,055 INFO [train.py:715] (1/8) Epoch 14, batch 24800, loss[loss=0.1243, simple_loss=0.2038, pruned_loss=0.02235, over 4807.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03056, over 972277.31 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 04:26:10,632 INFO [train.py:715] (1/8) Epoch 14, batch 24850, loss[loss=0.1097, simple_loss=0.1908, pruned_loss=0.0143, over 4934.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03024, over 971696.45 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 04:26:50,223 INFO [train.py:715] (1/8) Epoch 14, batch 24900, loss[loss=0.1547, simple_loss=0.2253, pruned_loss=0.04205, over 4927.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03072, over 971930.84 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 04:27:31,154 INFO [train.py:715] (1/8) Epoch 14, batch 24950, loss[loss=0.1619, simple_loss=0.2449, pruned_loss=0.03938, over 4790.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03067, over 971721.59 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:28:12,058 INFO [train.py:715] (1/8) Epoch 14, batch 25000, loss[loss=0.1312, simple_loss=0.2047, pruned_loss=0.02887, over 4750.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03062, over 972193.98 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:28:51,321 INFO [train.py:715] (1/8) Epoch 14, batch 25050, loss[loss=0.1102, simple_loss=0.1828, pruned_loss=0.01881, over 4650.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03074, over 972245.81 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 04:29:32,179 INFO [train.py:715] (1/8) Epoch 14, batch 25100, loss[loss=0.1292, simple_loss=0.2149, pruned_loss=0.02177, over 4711.00 frames.], tot_loss[loss=0.135, simple_loss=0.2093, pruned_loss=0.03038, over 972178.10 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:30:13,142 INFO [train.py:715] (1/8) Epoch 14, batch 25150, loss[loss=0.1162, simple_loss=0.187, pruned_loss=0.02268, over 4970.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03058, over 972737.04 frames.], batch size: 31, lr: 1.55e-04
2022-05-08 04:30:53,341 INFO [train.py:715] (1/8) Epoch 14, batch 25200, loss[loss=0.1365, simple_loss=0.2067, pruned_loss=0.03317, over 4949.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03033, over 972700.41 frames.], batch size: 35, lr: 1.55e-04
2022-05-08 04:31:31,965 INFO [train.py:715] (1/8) Epoch 14, batch 25250, loss[loss=0.1137, simple_loss=0.1879, pruned_loss=0.01971, over 4786.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.03063, over 972579.23 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:32:12,611 INFO [train.py:715] (1/8) Epoch 14, batch 25300, loss[loss=0.1409, simple_loss=0.2159, pruned_loss=0.03293, over 4929.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2098, pruned_loss=0.0306, over 972319.96 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 04:32:53,034 INFO [train.py:715] (1/8) Epoch 14, batch 25350, loss[loss=0.1755, simple_loss=0.2423, pruned_loss=0.05436, over 4887.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03079, over 972529.58 frames.], batch size: 38, lr: 1.55e-04
2022-05-08 04:33:31,612 INFO [train.py:715] (1/8) Epoch 14, batch 25400, loss[loss=0.1162, simple_loss=0.1886, pruned_loss=0.02195, over 4825.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2098, pruned_loss=0.03076, over 972407.04 frames.], batch size: 27, lr: 1.55e-04
2022-05-08 04:34:11,989 INFO [train.py:715] (1/8) Epoch 14, batch 25450, loss[loss=0.1327, simple_loss=0.2034, pruned_loss=0.03096, over 4769.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03094, over 971859.12 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 04:34:52,385 INFO [train.py:715] (1/8) Epoch 14, batch 25500, loss[loss=0.1519, simple_loss=0.2197, pruned_loss=0.04203, over 4785.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03112, over 971780.08 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:35:31,824 INFO [train.py:715] (1/8) Epoch 14, batch 25550, loss[loss=0.121, simple_loss=0.1959, pruned_loss=0.02299, over 4925.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03108, over 971712.34 frames.], batch size: 29, lr: 1.55e-04
2022-05-08 04:36:10,565 INFO [train.py:715] (1/8) Epoch 14, batch 25600, loss[loss=0.1419, simple_loss=0.2165, pruned_loss=0.03362, over 4969.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03048, over 972338.01 frames.], batch size: 35, lr: 1.55e-04
2022-05-08 04:36:50,637 INFO [train.py:715] (1/8) Epoch 14, batch 25650, loss[loss=0.1229, simple_loss=0.2033, pruned_loss=0.02121, over 4764.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03063, over 971992.82 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:37:30,752 INFO [train.py:715] (1/8) Epoch 14, batch 25700, loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02925, over 4982.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03053, over 972461.20 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:38:09,220 INFO [train.py:715] (1/8) Epoch 14, batch 25750, loss[loss=0.1496, simple_loss=0.2279, pruned_loss=0.0357, over 4706.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03014, over 972284.68 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:38:48,535 INFO [train.py:715] (1/8) Epoch 14, batch 25800, loss[loss=0.1316, simple_loss=0.2065, pruned_loss=0.02833, over 4960.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03039, over 971992.16 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 04:39:28,744 INFO [train.py:715] (1/8) Epoch 14, batch 25850, loss[loss=0.143, simple_loss=0.2223, pruned_loss=0.03185, over 4778.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03009, over 972655.40 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 04:40:07,967 INFO [train.py:715] (1/8) Epoch 14, batch 25900, loss[loss=0.1307, simple_loss=0.21, pruned_loss=0.0257, over 4747.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02996, over 973714.24 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:40:46,747 INFO [train.py:715] (1/8) Epoch 14, batch 25950, loss[loss=0.1263, simple_loss=0.1873, pruned_loss=0.03263, over 4991.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03032, over 973871.98 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 04:41:26,885 INFO [train.py:715] (1/8) Epoch 14, batch 26000, loss[loss=0.1485, simple_loss=0.2249, pruned_loss=0.03607, over 4834.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03053, over 973959.54 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:42:06,875 INFO [train.py:715] (1/8) Epoch 14, batch 26050, loss[loss=0.1413, simple_loss=0.2086, pruned_loss=0.03694, over 4815.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03043, over 973563.52 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 04:42:44,785 INFO [train.py:715] (1/8) Epoch 14, batch 26100, loss[loss=0.1476, simple_loss=0.2258, pruned_loss=0.03465, over 4919.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03058, over 972213.52 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 04:43:24,720 INFO [train.py:715] (1/8) Epoch 14, batch 26150, loss[loss=0.1102, simple_loss=0.183, pruned_loss=0.0187, over 4951.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03078, over 972481.94 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 04:44:05,204 INFO [train.py:715] (1/8) Epoch 14, batch 26200, loss[loss=0.14, simple_loss=0.2192, pruned_loss=0.03041, over 4893.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03019, over 972896.54 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:44:44,009 INFO [train.py:715] (1/8) Epoch 14, batch 26250, loss[loss=0.139, simple_loss=0.203, pruned_loss=0.03743, over 4951.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03044, over 972075.23 frames.], batch size: 35, lr: 1.55e-04
2022-05-08 04:45:23,190 INFO [train.py:715] (1/8) Epoch 14, batch 26300, loss[loss=0.1249, simple_loss=0.1947, pruned_loss=0.02756, over 4634.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03039, over 972008.83 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 04:46:03,666 INFO [train.py:715] (1/8) Epoch 14, batch 26350, loss[loss=0.1347, simple_loss=0.212, pruned_loss=0.02865, over 4884.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03041, over 971560.71 frames.], batch size: 39, lr: 1.55e-04
2022-05-08 04:46:43,197 INFO [train.py:715] (1/8) Epoch 14, batch 26400, loss[loss=0.119, simple_loss=0.1932, pruned_loss=0.02236, over 4834.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2103, pruned_loss=0.03092, over 971392.93 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:47:21,826 INFO [train.py:715] (1/8) Epoch 14, batch 26450, loss[loss=0.1283, simple_loss=0.2092, pruned_loss=0.02372, over 4900.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2098, pruned_loss=0.03064, over 971044.72 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:48:02,186 INFO [train.py:715] (1/8) Epoch 14, batch 26500, loss[loss=0.131, simple_loss=0.2129, pruned_loss=0.02452, over 4952.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03043, over 971849.46 frames.], batch size: 39, lr: 1.55e-04
2022-05-08 04:48:42,606 INFO [train.py:715] (1/8) Epoch 14, batch 26550, loss[loss=0.1762, simple_loss=0.2465, pruned_loss=0.05295, over 4954.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03049, over 971703.58 frames.], batch size: 39, lr: 1.55e-04
2022-05-08 04:49:21,901 INFO [train.py:715] (1/8) Epoch 14, batch 26600, loss[loss=0.1191, simple_loss=0.1919, pruned_loss=0.02318, over 4857.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03037, over 971933.95 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 04:50:00,873 INFO [train.py:715] (1/8) Epoch 14, batch 26650, loss[loss=0.1713, simple_loss=0.245, pruned_loss=0.04877, over 4895.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2094, pruned_loss=0.03046, over 972403.42 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:50:41,185 INFO [train.py:715] (1/8) Epoch 14, batch 26700, loss[loss=0.1241, simple_loss=0.1892, pruned_loss=0.02944, over 4981.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03035, over 972494.17 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 04:51:21,687 INFO [train.py:715] (1/8) Epoch 14, batch 26750, loss[loss=0.1658, simple_loss=0.2268, pruned_loss=0.05246, over 4920.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.0304, over 972803.08 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 04:52:00,693 INFO [train.py:715] (1/8) Epoch 14, batch 26800, loss[loss=0.1541, simple_loss=0.2168, pruned_loss=0.04575, over 4959.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03035, over 972696.32 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 04:52:40,486 INFO [train.py:715] (1/8) Epoch 14, batch 26850, loss[loss=0.1449, simple_loss=0.2225, pruned_loss=0.0337, over 4870.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03016, over 971956.25 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 04:53:20,920 INFO [train.py:715] (1/8) Epoch 14, batch 26900, loss[loss=0.1266, simple_loss=0.206, pruned_loss=0.02361, over 4756.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03019, over 971734.26 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:54:00,765 INFO [train.py:715] (1/8) Epoch 14, batch 26950, loss[loss=0.1409, simple_loss=0.2155, pruned_loss=0.03311, over 4832.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.0302, over 971683.25 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:54:39,971 INFO [train.py:715] (1/8) Epoch 14, batch 27000, loss[loss=0.1337, simple_loss=0.2124, pruned_loss=0.02757, over 4813.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02952, over 971672.88 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:54:39,972 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 04:54:49,614 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1049, simple_loss=0.1886, pruned_loss=0.01053, over 914524.00 frames.
2022-05-08 04:55:29,151 INFO [train.py:715] (1/8) Epoch 14, batch 27050, loss[loss=0.1465, simple_loss=0.2132, pruned_loss=0.03994, over 4898.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02996, over 972252.19 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:56:09,810 INFO [train.py:715] (1/8) Epoch 14, batch 27100, loss[loss=0.1573, simple_loss=0.2233, pruned_loss=0.04569, over 4812.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.0299, over 973194.90 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 04:56:50,327 INFO [train.py:715] (1/8) Epoch 14, batch 27150, loss[loss=0.1378, simple_loss=0.2188, pruned_loss=0.02838, over 4949.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02973, over 973359.18 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 04:57:29,057 INFO [train.py:715] (1/8) Epoch 14, batch 27200, loss[loss=0.1182, simple_loss=0.1828, pruned_loss=0.02679, over 4776.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03007, over 972313.05 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:58:08,436 INFO [train.py:715] (1/8) Epoch 14, batch 27250, loss[loss=0.1358, simple_loss=0.214, pruned_loss=0.02873, over 4874.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03001, over 971453.06 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 04:58:48,581 INFO [train.py:715] (1/8) Epoch 14, batch 27300, loss[loss=0.156, simple_loss=0.2351, pruned_loss=0.0385, over 4909.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03035, over 972161.03 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 04:59:28,198 INFO [train.py:715] (1/8) Epoch 14, batch 27350, loss[loss=0.1286, simple_loss=0.2054, pruned_loss=0.02592, over 4927.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.0301, over 971714.59 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 05:00:06,595 INFO [train.py:715] (1/8) Epoch 14, batch 27400, loss[loss=0.1351, simple_loss=0.2039, pruned_loss=0.03311, over 4783.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03002, over 971990.63 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 05:00:46,872 INFO [train.py:715] (1/8) Epoch 14, batch 27450, loss[loss=0.1227, simple_loss=0.1986, pruned_loss=0.02342, over 4818.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03008, over 971744.16 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 05:01:26,696 INFO [train.py:715] (1/8) Epoch 14, batch 27500, loss[loss=0.1368, simple_loss=0.211, pruned_loss=0.03132, over 4826.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.03056, over 972155.92 frames.], batch size: 26, lr: 1.55e-04
2022-05-08 05:02:05,462 INFO [train.py:715] (1/8) Epoch 14, batch 27550, loss[loss=0.1685, simple_loss=0.2347, pruned_loss=0.05109, over 4769.00 frames.], tot_loss[loss=0.1357, simple_loss=0.21, pruned_loss=0.03066, over 972604.00 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:02:45,165 INFO [train.py:715] (1/8) Epoch 14, batch 27600, loss[loss=0.1282, simple_loss=0.2011, pruned_loss=0.02768, over 4888.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03067, over 971930.33 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:03:25,496 INFO [train.py:715] (1/8) Epoch 14, batch 27650, loss[loss=0.1519, simple_loss=0.2318, pruned_loss=0.03599, over 4896.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.0308, over 971554.88 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 05:04:04,759 INFO [train.py:715] (1/8) Epoch 14, batch 27700, loss[loss=0.1606, simple_loss=0.2416, pruned_loss=0.03982, over 4689.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03075, over 972132.86 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:04:43,287 INFO [train.py:715] (1/8) Epoch 14, batch 27750, loss[loss=0.118, simple_loss=0.1915, pruned_loss=0.02226, over 4900.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03046, over 972746.16 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:05:23,451 INFO [train.py:715] (1/8) Epoch 14, batch 27800, loss[loss=0.1324, simple_loss=0.2074, pruned_loss=0.02875, over 4810.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03093, over 972866.01 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 05:06:03,192 INFO [train.py:715] (1/8) Epoch 14, batch 27850, loss[loss=0.1344, simple_loss=0.2105, pruned_loss=0.02917, over 4962.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03115, over 972268.79 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:06:41,709 INFO [train.py:715] (1/8) Epoch 14, batch 27900, loss[loss=0.1305, simple_loss=0.1975, pruned_loss=0.03179, over 4837.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03033, over 972079.16 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 05:07:21,721 INFO [train.py:715] (1/8) Epoch 14, batch 27950, loss[loss=0.1388, simple_loss=0.2091, pruned_loss=0.03428, over 4987.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03018, over 972605.78 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:08:01,583 INFO [train.py:715] (1/8) Epoch 14, batch 28000, loss[loss=0.1402, simple_loss=0.2135, pruned_loss=0.03342, over 4799.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03063, over 972642.18 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 05:08:40,625 INFO [train.py:715] (1/8) Epoch 14, batch 28050, loss[loss=0.1229, simple_loss=0.1993, pruned_loss=0.02323, over 4940.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03067, over 972871.89 frames.], batch size: 29, lr: 1.55e-04
2022-05-08 05:09:19,685 INFO [train.py:715] (1/8) Epoch 14, batch 28100, loss[loss=0.1291, simple_loss=0.2012, pruned_loss=0.02851, over 4960.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03078, over 973469.97 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 05:10:00,236 INFO [train.py:715] (1/8) Epoch 14, batch 28150, loss[loss=0.1199, simple_loss=0.1927, pruned_loss=0.02357, over 4900.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03058, over 973580.69 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 05:10:39,947 INFO [train.py:715] (1/8) Epoch 14, batch 28200, loss[loss=0.1584, simple_loss=0.2256, pruned_loss=0.04563, over 4866.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2083, pruned_loss=0.03073, over 972650.67 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:11:17,987 INFO [train.py:715] (1/8) Epoch 14, batch 28250, loss[loss=0.1664, simple_loss=0.2313, pruned_loss=0.05079, over 4891.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03124, over 973229.96 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:11:58,130 INFO [train.py:715] (1/8) Epoch 14, batch 28300, loss[loss=0.1625, simple_loss=0.2312, pruned_loss=0.04689, over 4960.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03172, over 972348.80 frames.], batch size: 35, lr: 1.55e-04
2022-05-08 05:12:38,008 INFO [train.py:715] (1/8) Epoch 14, batch 28350, loss[loss=0.1139, simple_loss=0.1918, pruned_loss=0.018, over 4946.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.0315, over 973295.89 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:13:16,556 INFO [train.py:715] (1/8) Epoch 14, batch 28400, loss[loss=0.1215, simple_loss=0.1946, pruned_loss=0.02425, over 4883.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03131, over 973490.36 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 05:13:56,142 INFO [train.py:715] (1/8) Epoch 14, batch 28450, loss[loss=0.1331, simple_loss=0.21, pruned_loss=0.02813, over 4820.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03101, over 973265.26 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:14:36,400 INFO [train.py:715] (1/8) Epoch 14, batch 28500, loss[loss=0.1378, simple_loss=0.2161, pruned_loss=0.02973, over 4879.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.0304, over 972863.49 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 05:15:15,667 INFO [train.py:715] (1/8) Epoch 14, batch 28550, loss[loss=0.1381, simple_loss=0.2194, pruned_loss=0.02842, over 4902.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03015, over 973172.28 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 05:15:54,186 INFO [train.py:715] (1/8) Epoch 14, batch 28600, loss[loss=0.1272, simple_loss=0.1953, pruned_loss=0.02953, over 4711.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03044, over 973148.68 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:16:34,507 INFO [train.py:715] (1/8) Epoch 14, batch 28650, loss[loss=0.1187, simple_loss=0.1969, pruned_loss=0.02023, over 4817.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02998, over 973711.19 frames.], batch size: 26, lr: 1.55e-04
2022-05-08 05:17:14,556 INFO [train.py:715] (1/8) Epoch 14, batch 28700, loss[loss=0.1333, simple_loss=0.2061, pruned_loss=0.03028, over 4897.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03017, over 974209.12 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 05:17:52,658 INFO [train.py:715] (1/8) Epoch 14, batch 28750, loss[loss=0.1428, simple_loss=0.2067, pruned_loss=0.03943, over 4940.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02996, over 974501.44 frames.], batch size: 35, lr: 1.55e-04
2022-05-08 05:18:32,378 INFO [train.py:715] (1/8) Epoch 14, batch 28800, loss[loss=0.1297, simple_loss=0.1987, pruned_loss=0.03035, over 4751.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02997, over 974487.80 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:19:12,487 INFO [train.py:715] (1/8) Epoch 14, batch 28850, loss[loss=0.1605, simple_loss=0.2329, pruned_loss=0.0441, over 4759.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02993, over 973855.58 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:19:52,380 INFO [train.py:715] (1/8) Epoch 14, batch 28900, loss[loss=0.1162, simple_loss=0.1926, pruned_loss=0.01991, over 4816.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02999, over 973180.57 frames.], batch size: 26, lr: 1.55e-04
2022-05-08 05:20:30,231 INFO [train.py:715] (1/8) Epoch 14, batch 28950, loss[loss=0.1414, simple_loss=0.2055, pruned_loss=0.03862, over 4778.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.03019, over 973887.88 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:21:10,708 INFO [train.py:715] (1/8) Epoch 14, batch 29000, loss[loss=0.1517, simple_loss=0.2214, pruned_loss=0.04102, over 4855.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2093, pruned_loss=0.03005, over 974077.51 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 05:21:50,340 INFO [train.py:715] (1/8) Epoch 14, batch 29050, loss[loss=0.1435, simple_loss=0.2199, pruned_loss=0.03359, over 4893.00 frames.], tot_loss[loss=0.1346, simple_loss=0.209, pruned_loss=0.0301, over 973248.52 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 05:22:29,110 INFO [train.py:715] (1/8) Epoch 14, batch 29100, loss[loss=0.1449, simple_loss=0.2108, pruned_loss=0.03952, over 4866.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03036, over 973843.77 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 05:23:08,501 INFO [train.py:715] (1/8) Epoch 14, batch 29150, loss[loss=0.1393, simple_loss=0.2094, pruned_loss=0.03457, over 4912.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03063, over 973328.97 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:23:48,537 INFO [train.py:715] (1/8) Epoch 14, batch 29200, loss[loss=0.1344, simple_loss=0.2147, pruned_loss=0.02705, over 4876.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.0302, over 972664.47 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 05:24:28,399 INFO [train.py:715] (1/8) Epoch 14, batch 29250, loss[loss=0.1273, simple_loss=0.2081, pruned_loss=0.02324, over 4885.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03057, over 973040.30 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:25:06,495 INFO [train.py:715] (1/8) Epoch 14, batch 29300, loss[loss=0.135, simple_loss=0.2163, pruned_loss=0.02684, over 4773.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03046, over 972116.97 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:25:46,615 INFO [train.py:715] (1/8) Epoch 14, batch 29350, loss[loss=0.1268, simple_loss=0.2006, pruned_loss=0.02652, over 4945.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03041, over 972520.43 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 05:26:26,522 INFO [train.py:715] (1/8) Epoch 14, batch 29400, loss[loss=0.1142, simple_loss=0.1878, pruned_loss=0.02035, over 4787.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03051, over 972272.31 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:27:05,396 INFO [train.py:715] (1/8) Epoch 14, batch 29450, loss[loss=0.1555, simple_loss=0.2281, pruned_loss=0.04145, over 4863.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03077, over 972179.10 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 05:27:45,247 INFO [train.py:715] (1/8) Epoch 14, batch 29500, loss[loss=0.1232, simple_loss=0.1915, pruned_loss=0.02741, over 4791.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03044, over 972059.51 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 05:28:25,583 INFO [train.py:715] (1/8) Epoch 14, batch 29550, loss[loss=0.1519, simple_loss=0.2309, pruned_loss=0.03647, over 4792.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2082, pruned_loss=0.03075, over 971615.85 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 05:29:05,394 INFO [train.py:715] (1/8) Epoch 14, batch 29600, loss[loss=0.1224, simple_loss=0.2068, pruned_loss=0.01901, over 4800.00 frames.], tot_loss[loss=0.1347, simple_loss=0.208, pruned_loss=0.03074, over 971604.35 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 05:29:44,404 INFO [train.py:715] (1/8) Epoch 14, batch 29650, loss[loss=0.1155, simple_loss=0.1797, pruned_loss=0.02567, over 4771.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2076, pruned_loss=0.03069, over 972083.95 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 05:30:25,203 INFO [train.py:715] (1/8) Epoch 14, batch 29700, loss[loss=0.1145, simple_loss=0.2007, pruned_loss=0.01415, over 4810.00 frames.], tot_loss[loss=0.135, simple_loss=0.2081, pruned_loss=0.03089, over 971311.29 frames.], batch size: 26, lr: 1.55e-04
2022-05-08 05:31:06,278 INFO [train.py:715] (1/8) Epoch 14, batch 29750, loss[loss=0.113, simple_loss=0.191, pruned_loss=0.01752, over 4946.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2079, pruned_loss=0.03074, over 972455.52 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:31:45,878 INFO [train.py:715] (1/8) Epoch 14, batch 29800, loss[loss=0.1479, simple_loss=0.2098, pruned_loss=0.04299, over 4791.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2081, pruned_loss=0.03104, over 972899.28 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 05:32:26,701 INFO [train.py:715] (1/8) Epoch 14, batch 29850, loss[loss=0.14, simple_loss=0.2105, pruned_loss=0.03471, over 4838.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2072, pruned_loss=0.03023, over 972573.21 frames.], batch size: 32, lr: 1.55e-04
2022-05-08 05:33:06,681 INFO [train.py:715] (1/8) Epoch 14, batch 29900, loss[loss=0.1563, simple_loss=0.2234, pruned_loss=0.04458, over 4868.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2077, pruned_loss=0.03034, over 973046.60 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 05:33:46,350 INFO [train.py:715] (1/8) Epoch 14, batch 29950, loss[loss=0.1307, simple_loss=0.208, pruned_loss=0.02674, over 4973.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2075, pruned_loss=0.0305, over 973171.41 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:34:25,088 INFO [train.py:715] (1/8) Epoch 14, batch 30000, loss[loss=0.141, simple_loss=0.2129, pruned_loss=0.03459, over 4957.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2089, pruned_loss=0.03113, over 972531.64 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:34:25,088 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 05:34:42,243 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1052, simple_loss=0.189, pruned_loss=0.01075, over 914524.00 frames.
2022-05-08 05:35:21,218 INFO [train.py:715] (1/8) Epoch 14, batch 30050, loss[loss=0.1597, simple_loss=0.2346, pruned_loss=0.0424, over 4885.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03123, over 973006.88 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:36:01,206 INFO [train.py:715] (1/8) Epoch 14, batch 30100, loss[loss=0.1354, simple_loss=0.2074, pruned_loss=0.03164, over 4818.00 frames.], tot_loss[loss=0.136, simple_loss=0.2091, pruned_loss=0.03147, over 972646.76 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:36:42,322 INFO [train.py:715] (1/8) Epoch 14, batch 30150, loss[loss=0.1314, simple_loss=0.2152, pruned_loss=0.02382, over 4920.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2092, pruned_loss=0.03157, over 972283.43 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:37:21,238 INFO [train.py:715] (1/8) Epoch 14, batch 30200, loss[loss=0.1425, simple_loss=0.2105, pruned_loss=0.03721, over 4946.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03169, over 973061.19 frames.], batch size: 39, lr: 1.55e-04
2022-05-08 05:38:01,195 INFO [train.py:715] (1/8) Epoch 14, batch 30250, loss[loss=0.1436, simple_loss=0.2125, pruned_loss=0.03739, over 4831.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03142, over 973526.59 frames.], batch size: 30, lr: 1.55e-04
2022-05-08 05:38:41,858 INFO [train.py:715] (1/8) Epoch 14, batch 30300, loss[loss=0.1431, simple_loss=0.2034, pruned_loss=0.04138, over 4958.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2088, pruned_loss=0.03127, over 974624.38 frames.], batch size: 35, lr: 1.55e-04
2022-05-08 05:39:21,379 INFO [train.py:715] (1/8) Epoch 14, batch 30350, loss[loss=0.1126, simple_loss=0.1939, pruned_loss=0.01567, over 4784.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03091, over 973690.43 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 05:40:00,595 INFO [train.py:715] (1/8) Epoch 14, batch 30400, loss[loss=0.1413, simple_loss=0.2106, pruned_loss=0.03599, over 4872.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03055, over 973880.30 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 05:40:40,493 INFO [train.py:715] (1/8) Epoch 14, batch 30450, loss[loss=0.1424, simple_loss=0.2182, pruned_loss=0.03333, over 4764.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03057, over 973353.80 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:41:20,817 INFO [train.py:715] (1/8) Epoch 14, batch 30500, loss[loss=0.1348, simple_loss=0.2014, pruned_loss=0.03414, over 4750.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.0305, over 974144.11 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:41:59,764 INFO [train.py:715] (1/8) Epoch 14, batch 30550, loss[loss=0.1504, simple_loss=0.2178, pruned_loss=0.04145, over 4863.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2081, pruned_loss=0.03069, over 973613.00 frames.], batch size: 38, lr: 1.54e-04
2022-05-08 05:42:39,650 INFO [train.py:715] (1/8) Epoch 14, batch 30600, loss[loss=0.1748, simple_loss=0.2481, pruned_loss=0.05075, over 4886.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03076, over 972741.42 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 05:43:20,416 INFO [train.py:715] (1/8) Epoch 14, batch 30650, loss[loss=0.1257, simple_loss=0.204, pruned_loss=0.02376, over 4913.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03139, over 972580.51 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 05:43:59,988 INFO [train.py:715] (1/8) Epoch 14, batch 30700, loss[loss=0.1249, simple_loss=0.1937, pruned_loss=0.02802, over 4685.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03103, over 973267.63 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 05:44:39,753 INFO [train.py:715] (1/8) Epoch 14, batch 30750, loss[loss=0.1495, simple_loss=0.2184, pruned_loss=0.04032, over 4929.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03085, over 973526.00 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 05:45:19,651 INFO [train.py:715] (1/8) Epoch 14, batch 30800, loss[loss=0.1315, simple_loss=0.2155, pruned_loss=0.02378, over 4795.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03053, over 973255.73 frames.], batch size: 24, lr: 1.54e-04
2022-05-08 05:46:00,445 INFO [train.py:715] (1/8) Epoch 14, batch 30850, loss[loss=0.1346, simple_loss=0.2066, pruned_loss=0.03128, over 4858.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03047, over 973087.20 frames.], batch size: 20, lr: 1.54e-04
2022-05-08 05:46:39,516 INFO [train.py:715] (1/8) Epoch 14, batch 30900, loss[loss=0.155, simple_loss=0.2284, pruned_loss=0.04079, over 4969.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03051, over 972879.49 frames.], batch size: 27, lr: 1.54e-04
2022-05-08 05:47:18,033 INFO [train.py:715] (1/8) Epoch 14, batch 30950, loss[loss=0.132, simple_loss=0.2074, pruned_loss=0.02831, over 4987.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2102, pruned_loss=0.03101, over 974031.75 frames.], batch size: 28, lr: 1.54e-04
2022-05-08 05:47:57,811 INFO [train.py:715] (1/8) Epoch 14, batch 31000, loss[loss=0.1354, simple_loss=0.2151, pruned_loss=0.02788, over 4796.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2106, pruned_loss=0.031, over 974127.88 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 05:48:37,495 INFO [train.py:715] (1/8) Epoch 14, batch 31050, loss[loss=0.1224, simple_loss=0.2033, pruned_loss=0.02079, over 4922.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2096, pruned_loss=0.03075, over 973181.88 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 05:49:17,862 INFO [train.py:715] (1/8) Epoch 14, batch 31100, loss[loss=0.1469, simple_loss=0.2303, pruned_loss=0.03177, over 4812.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03086, over 972819.95 frames.], batch size: 27, lr: 1.54e-04
2022-05-08 05:49:58,982 INFO [train.py:715] (1/8) Epoch 14, batch 31150, loss[loss=0.1726, simple_loss=0.2456, pruned_loss=0.04983, over 4986.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2102, pruned_loss=0.03068, over 972515.86 frames.], batch size: 25, lr: 1.54e-04
2022-05-08 05:50:40,137 INFO [train.py:715] (1/8) Epoch 14, batch 31200, loss[loss=0.1276, simple_loss=0.1975, pruned_loss=0.02887, over 4982.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.031, over 973091.50 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 05:51:19,928 INFO [train.py:715] (1/8) Epoch 14, batch 31250, loss[loss=0.1241, simple_loss=0.1993, pruned_loss=0.02444, over 4785.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03092, over 972590.55 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 05:52:00,322 INFO [train.py:715] (1/8) Epoch 14, batch 31300, loss[loss=0.1427, simple_loss=0.21, pruned_loss=0.03775, over 4972.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03049, over 973441.41 frames.], batch size: 35, lr: 1.54e-04
2022-05-08 05:52:41,149 INFO [train.py:715] (1/8) Epoch 14, batch 31350, loss[loss=0.1349, simple_loss=0.2056, pruned_loss=0.03213, over 4825.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03066, over 973109.11 frames.], batch size: 26, lr: 1.54e-04
2022-05-08 05:53:21,033 INFO [train.py:715] (1/8) Epoch 14, batch 31400, loss[loss=0.1227, simple_loss=0.1884, pruned_loss=0.0285, over 4769.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03073, over 972664.82 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 05:54:00,705 INFO [train.py:715] (1/8) Epoch 14, batch 31450, loss[loss=0.1292, simple_loss=0.2012, pruned_loss=0.02854, over 4926.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03074, over 972634.74 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 05:54:40,746 INFO [train.py:715] (1/8) Epoch 14, batch 31500, loss[loss=0.1499, simple_loss=0.2347, pruned_loss=0.03254, over 4835.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03088, over 972851.23 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 05:55:21,330 INFO [train.py:715] (1/8) Epoch 14, batch 31550, loss[loss=0.1393, simple_loss=0.2129, pruned_loss=0.0328, over 4978.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03023, over 972558.89 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 05:56:01,191 INFO [train.py:715] (1/8) Epoch 14, batch 31600, loss[loss=0.1165, simple_loss=0.1874, pruned_loss=0.02278, over 4986.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03011, over 972423.98 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 05:56:40,686 INFO [train.py:715] (1/8) Epoch 14, batch 31650, loss[loss=0.1424, simple_loss=0.2145, pruned_loss=0.03511, over 4889.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03029, over 972245.24 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 05:57:21,068 INFO [train.py:715] (1/8) Epoch 14, batch 31700, loss[loss=0.1131, simple_loss=0.1864, pruned_loss=0.01987, over 4830.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03023, over 972644.14 frames.], batch size: 13, lr: 1.54e-04
2022-05-08 05:58:00,666 INFO [train.py:715] (1/8) Epoch 14, batch 31750, loss[loss=0.1315, simple_loss=0.1994, pruned_loss=0.0318, over 4976.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03044, over 972619.44 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 05:58:40,567 INFO [train.py:715] (1/8) Epoch 14, batch 31800, loss[loss=0.1183, simple_loss=0.1913, pruned_loss=0.02263, over 4828.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2081, pruned_loss=0.03068, over 972328.02 frames.], batch size: 13, lr: 1.54e-04
2022-05-08 05:59:20,873 INFO [train.py:715] (1/8) Epoch 14, batch 31850, loss[loss=0.16, simple_loss=0.229, pruned_loss=0.04547, over 4773.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.0306, over 972606.33 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:00:01,587 INFO [train.py:715] (1/8) Epoch 14, batch 31900, loss[loss=0.1476, simple_loss=0.2188, pruned_loss=0.03816, over 4861.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03117, over 973028.92 frames.], batch size: 13, lr: 1.54e-04
2022-05-08 06:00:40,983 INFO [train.py:715] (1/8) Epoch 14, batch 31950, loss[loss=0.1822, simple_loss=0.243, pruned_loss=0.06066, over 4947.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03105, over 972627.71 frames.], batch size: 35, lr: 1.54e-04
2022-05-08 06:01:20,558 INFO [train.py:715] (1/8) Epoch 14, batch 32000, loss[loss=0.144, simple_loss=0.2204, pruned_loss=0.03381, over 4922.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03062, over 973277.02 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:02:01,140 INFO [train.py:715] (1/8) Epoch 14, batch 32050, loss[loss=0.1325, simple_loss=0.2119, pruned_loss=0.02652, over 4944.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03035, over 973107.63 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:02:40,615 INFO [train.py:715] (1/8) Epoch 14, batch 32100, loss[loss=0.1301, simple_loss=0.198, pruned_loss=0.03112, over 4969.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03021, over 973392.59 frames.], batch size: 35, lr: 1.54e-04
2022-05-08 06:03:20,379 INFO [train.py:715] (1/8) Epoch 14, batch 32150, loss[loss=0.1186, simple_loss=0.1865, pruned_loss=0.02528, over 4810.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2067, pruned_loss=0.0299, over 972961.38 frames.], batch size: 26, lr: 1.54e-04
2022-05-08 06:04:00,804 INFO [train.py:715] (1/8) Epoch 14, batch 32200, loss[loss=0.1365, simple_loss=0.2134, pruned_loss=0.02982, over 4945.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2072, pruned_loss=0.03014, over 973057.70 frames.], batch size: 29, lr: 1.54e-04
2022-05-08 06:04:41,242 INFO [train.py:715] (1/8) Epoch 14, batch 32250, loss[loss=0.1295, simple_loss=0.2068, pruned_loss=0.02603, over 4861.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2076, pruned_loss=0.03029, over 973159.67 frames.], batch size: 30, lr: 1.54e-04
2022-05-08 06:05:20,518 INFO [train.py:715] (1/8) Epoch 14, batch 32300, loss[loss=0.1434, simple_loss=0.2103, pruned_loss=0.0383, over 4935.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03063, over 973572.01 frames.], batch size: 21, lr: 1.54e-04
2022-05-08 06:06:00,156 INFO [train.py:715] (1/8) Epoch 14, batch 32350, loss[loss=0.1399, simple_loss=0.2189, pruned_loss=0.03046, over 4913.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.0307, over 973114.62 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:06:40,253 INFO [train.py:715] (1/8) Epoch 14, batch 32400, loss[loss=0.1363, simple_loss=0.2074, pruned_loss=0.03265, over 4789.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03029, over 972729.73 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:07:19,944 INFO [train.py:715] (1/8) Epoch 14, batch 32450, loss[loss=0.128, simple_loss=0.201, pruned_loss=0.02747, over 4985.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03081, over 972809.22 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:07:59,620 INFO [train.py:715] (1/8) Epoch 14, batch 32500, loss[loss=0.1382, simple_loss=0.2193, pruned_loss=0.0286, over 4822.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03056, over 971947.34 frames.], batch size: 26, lr: 1.54e-04
2022-05-08 06:08:39,981 INFO [train.py:715] (1/8) Epoch 14, batch 32550, loss[loss=0.1168, simple_loss=0.1849, pruned_loss=0.0244, over 4781.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03026, over 971565.85 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:09:20,726 INFO [train.py:715] (1/8) Epoch 14, batch 32600, loss[loss=0.1533, simple_loss=0.2256, pruned_loss=0.04053, over 4945.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03018, over 972119.82 frames.], batch size: 35, lr: 1.54e-04
2022-05-08 06:10:00,327 INFO [train.py:715] (1/8) Epoch 14, batch 32650, loss[loss=0.1681, simple_loss=0.2439, pruned_loss=0.04613, over 4698.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.0305, over 972197.07 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:10:43,598 INFO [train.py:715] (1/8) Epoch 14, batch 32700, loss[loss=0.1224, simple_loss=0.1972, pruned_loss=0.02378, over 4848.00 frames.], tot_loss[loss=0.1347, simple_loss=0.209, pruned_loss=0.03017, over 972412.92 frames.], batch size: 20, lr: 1.54e-04
2022-05-08 06:11:24,760 INFO [train.py:715] (1/8) Epoch 14, batch 32750, loss[loss=0.1223, simple_loss=0.1951, pruned_loss=0.0248, over 4855.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.0296, over 972558.19 frames.], batch size: 20, lr: 1.54e-04
2022-05-08 06:12:05,075 INFO [train.py:715] (1/8) Epoch 14, batch 32800, loss[loss=0.1332, simple_loss=0.2056, pruned_loss=0.03044, over 4711.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02942, over 972493.49 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:12:45,521 INFO [train.py:715] (1/8) Epoch 14, batch 32850, loss[loss=0.1401, simple_loss=0.2207, pruned_loss=0.02976, over 4885.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2086, pruned_loss=0.02993, over 972566.34 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 06:13:26,800 INFO [train.py:715] (1/8) Epoch 14, batch 32900, loss[loss=0.1492, simple_loss=0.2392, pruned_loss=0.02956, over 4751.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2088, pruned_loss=0.02995, over 972301.60 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 06:14:07,964 INFO [train.py:715] (1/8) Epoch 14, batch 32950, loss[loss=0.1217, simple_loss=0.1998, pruned_loss=0.02177, over 4792.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2096, pruned_loss=0.03047, over 972645.34 frames.], batch size: 21, lr: 1.54e-04
2022-05-08 06:14:47,662 INFO [train.py:715] (1/8) Epoch 14, batch 33000, loss[loss=0.1347, simple_loss=0.2075, pruned_loss=0.03097, over 4942.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03085, over 973168.31 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:14:47,663 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 06:15:25,560 INFO [train.py:742] (1/8) Epoch 14, validation: loss=0.1051, simple_loss=0.1889, pruned_loss=0.01071, over 914524.00 frames.
2022-05-08 06:16:05,283 INFO [train.py:715] (1/8) Epoch 14, batch 33050, loss[loss=0.1244, simple_loss=0.2087, pruned_loss=0.02005, over 4767.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03051, over 972209.37 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 06:16:46,134 INFO [train.py:715] (1/8) Epoch 14, batch 33100, loss[loss=0.1543, simple_loss=0.226, pruned_loss=0.04126, over 4797.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2076, pruned_loss=0.03025, over 972544.43 frames.], batch size: 21, lr: 1.54e-04
2022-05-08 06:17:27,356 INFO [train.py:715] (1/8) Epoch 14, batch 33150, loss[loss=0.1352, simple_loss=0.2062, pruned_loss=0.03213, over 4840.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2071, pruned_loss=0.02998, over 971215.01 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:18:07,422 INFO [train.py:715] (1/8) Epoch 14, batch 33200, loss[loss=0.1459, simple_loss=0.2145, pruned_loss=0.03864, over 4695.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03061, over 971119.36 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:18:47,768 INFO [train.py:715] (1/8) Epoch 14, batch 33250, loss[loss=0.154, simple_loss=0.2308, pruned_loss=0.03867, over 4766.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2082, pruned_loss=0.03065, over 971728.98 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 06:19:28,521 INFO [train.py:715] (1/8) Epoch 14, batch 33300, loss[loss=0.1524, simple_loss=0.2222, pruned_loss=0.04133, over 4863.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03083, over 971710.49 frames.], batch size: 32, lr: 1.54e-04
2022-05-08 06:20:09,719 INFO [train.py:715] (1/8) Epoch 14, batch 33350, loss[loss=0.1301, simple_loss=0.2071, pruned_loss=0.02659, over 4882.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03048, over 971568.13 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 06:20:49,888 INFO [train.py:715] (1/8) Epoch 14, batch 33400, loss[loss=0.1345, simple_loss=0.2022, pruned_loss=0.03344, over 4839.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03054, over 971585.67 frames.], batch size: 32, lr: 1.54e-04
2022-05-08 06:21:30,268 INFO [train.py:715] (1/8) Epoch 14, batch 33450, loss[loss=0.1252, simple_loss=0.2159, pruned_loss=0.01727, over 4804.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03042, over 971229.75 frames.], batch size: 25, lr: 1.54e-04
2022-05-08 06:22:11,499 INFO [train.py:715] (1/8) Epoch 14, batch 33500, loss[loss=0.1353, simple_loss=0.2076, pruned_loss=0.0315, over 4874.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03098, over 971908.03 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 06:22:51,806 INFO [train.py:715] (1/8) Epoch 14, batch 33550, loss[loss=0.1714, simple_loss=0.2393, pruned_loss=0.05177, over 4958.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03047, over 972082.17 frames.], batch size: 35, lr: 1.54e-04
2022-05-08 06:23:33,013 INFO [train.py:715] (1/8) Epoch 14, batch 33600, loss[loss=0.1435, simple_loss=0.2138, pruned_loss=0.03658, over 4929.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03041, over 973214.76 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:24:14,057 INFO [train.py:715] (1/8) Epoch 14, batch 33650, loss[loss=0.1468, simple_loss=0.2288, pruned_loss=0.03246, over 4812.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2097, pruned_loss=0.03083, over 973240.87 frames.], batch size: 25, lr: 1.54e-04
2022-05-08 06:24:54,965 INFO [train.py:715] (1/8) Epoch 14, batch 33700, loss[loss=0.1149, simple_loss=0.1847, pruned_loss=0.02251, over 4834.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03015, over 973033.84 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:25:35,108 INFO [train.py:715] (1/8) Epoch 14, batch 33750, loss[loss=0.1254, simple_loss=0.2049, pruned_loss=0.0229, over 4931.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2088, pruned_loss=0.02998, over 973564.41 frames.], batch size: 29, lr: 1.54e-04
2022-05-08 06:26:15,686 INFO [train.py:715] (1/8) Epoch 14, batch 33800, loss[loss=0.1386, simple_loss=0.2128, pruned_loss=0.03225, over 4700.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03032, over 973753.90 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:26:56,927 INFO [train.py:715] (1/8) Epoch 14, batch 33850, loss[loss=0.1331, simple_loss=0.2101, pruned_loss=0.02804, over 4880.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02992, over 973379.01 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 06:27:37,002 INFO [train.py:715] (1/8) Epoch 14, batch 33900, loss[loss=0.1403, simple_loss=0.1986, pruned_loss=0.04095, over 4975.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03005, over 972538.70 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:28:17,544 INFO [train.py:715] (1/8) Epoch 14, batch 33950, loss[loss=0.1384, simple_loss=0.2161, pruned_loss=0.03033, over 4770.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03055, over 972029.15 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:28:58,266 INFO [train.py:715] (1/8) Epoch 14, batch 34000, loss[loss=0.1596, simple_loss=0.2371, pruned_loss=0.04103, over 4847.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03053, over 972618.56 frames.], batch size: 30, lr: 1.54e-04
2022-05-08 06:29:39,242 INFO [train.py:715] (1/8) Epoch 14, batch 34050, loss[loss=0.1422, simple_loss=0.2183, pruned_loss=0.03308, over 4958.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03069, over 972210.29 frames.], batch size: 24, lr: 1.54e-04
2022-05-08 06:30:19,201 INFO [train.py:715] (1/8) Epoch 14, batch 34100, loss[loss=0.1422, simple_loss=0.2225, pruned_loss=0.03092, over 4840.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03046, over 971632.20 frames.], batch size: 30, lr: 1.54e-04
2022-05-08 06:30:59,693 INFO [train.py:715] (1/8) Epoch 14, batch 34150, loss[loss=0.145, simple_loss=0.214, pruned_loss=0.03799, over 4688.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03009, over 970884.14 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:31:40,127 INFO [train.py:715] (1/8) Epoch 14, batch 34200, loss[loss=0.1521, simple_loss=0.2251, pruned_loss=0.03961, over 4763.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03056, over 971565.08 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 06:32:20,291 INFO [train.py:715] (1/8) Epoch 14, batch 34250, loss[loss=0.1395, simple_loss=0.2171, pruned_loss=0.03092, over 4818.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03035, over 972005.74 frames.], batch size: 21, lr: 1.54e-04
2022-05-08 06:33:00,831 INFO [train.py:715] (1/8) Epoch 14, batch 34300, loss[loss=0.1448, simple_loss=0.2221, pruned_loss=0.03379, over 4797.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03041, over 972024.58 frames.], batch size: 24, lr: 1.54e-04
2022-05-08 06:33:41,481 INFO [train.py:715] (1/8) Epoch 14, batch 34350, loss[loss=0.1311, simple_loss=0.2074, pruned_loss=0.02741, over 4975.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03066, over 972624.82 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:34:22,181 INFO [train.py:715] (1/8) Epoch 14, batch 34400, loss[loss=0.1277, simple_loss=0.2104, pruned_loss=0.02249, over 4808.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2095, pruned_loss=0.03044, over 971914.29 frames.], batch size: 21, lr: 1.54e-04
2022-05-08 06:35:01,767 INFO [train.py:715] (1/8) Epoch 14, batch 34450, loss[loss=0.1386, simple_loss=0.2086, pruned_loss=0.03431, over 4967.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2094, pruned_loss=0.03046, over 971427.26 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:35:42,622 INFO [train.py:715] (1/8) Epoch 14, batch 34500, loss[loss=0.1276, simple_loss=0.2027, pruned_loss=0.02623, over 4758.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.0303, over 971816.50 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 06:36:23,319 INFO [train.py:715] (1/8) Epoch 14, batch 34550, loss[loss=0.1228, simple_loss=0.1868, pruned_loss=0.02945, over 4696.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03053, over 971636.17 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:37:03,432 INFO [train.py:715] (1/8) Epoch 14, batch 34600, loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02879, over 4933.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2092, pruned_loss=0.03046, over 972306.10 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:37:43,644 INFO [train.py:715] (1/8) Epoch 14, batch 34650, loss[loss=0.1114, simple_loss=0.1864, pruned_loss=0.01825, over 4947.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.0303, over 972531.40 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:38:24,396 INFO [train.py:715] (1/8) Epoch 14, batch 34700, loss[loss=0.1305, simple_loss=0.2004, pruned_loss=0.03032, over 4752.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03057, over 971761.52 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 06:39:03,262 INFO [train.py:715] (1/8) Epoch 14, batch 34750, loss[loss=0.162, simple_loss=0.23, pruned_loss=0.04695, over 4787.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03107, over 971528.22 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:39:40,047 INFO [train.py:715] (1/8) Epoch 14, batch 34800, loss[loss=0.1568, simple_loss=0.2314, pruned_loss=0.04112, over 4788.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.0312, over 971938.46 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:40:33,607 INFO [train.py:715] (1/8) Epoch 15, batch 0, loss[loss=0.1408, simple_loss=0.2162, pruned_loss=0.03265, over 4967.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2162, pruned_loss=0.03265, over 4967.00 frames.], batch size: 39, lr: 1.49e-04
2022-05-08 06:41:12,922 INFO [train.py:715] (1/8) Epoch 15, batch 50, loss[loss=0.1511, simple_loss=0.2198, pruned_loss=0.04113, over 4897.00 frames.], tot_loss[loss=0.1368, simple_loss=0.211, pruned_loss=0.0313, over 219424.24 frames.], batch size: 19, lr: 1.49e-04
2022-05-08 06:41:54,160 INFO [train.py:715] (1/8) Epoch 15, batch 100, loss[loss=0.1446, simple_loss=0.2075, pruned_loss=0.04085, over 4912.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2073, pruned_loss=0.03088, over 386023.03 frames.], batch size: 39, lr: 1.49e-04
2022-05-08 06:42:35,664 INFO [train.py:715] (1/8) Epoch 15, batch 150, loss[loss=0.1225, simple_loss=0.1912, pruned_loss=0.02692, over 4846.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02973, over 517187.70 frames.], batch size: 13, lr: 1.49e-04
2022-05-08 06:43:15,916 INFO [train.py:715] (1/8) Epoch 15, batch 200, loss[loss=0.1345, simple_loss=0.2059, pruned_loss=0.03155, over 4955.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02918, over 617256.23 frames.], batch size: 29, lr: 1.49e-04
2022-05-08 06:43:56,377 INFO [train.py:715] (1/8) Epoch 15, batch 250, loss[loss=0.1284, simple_loss=0.21, pruned_loss=0.02335, over 4799.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02932, over 694810.06 frames.], batch size: 12, lr: 1.49e-04
2022-05-08 06:44:37,767 INFO [train.py:715] (1/8) Epoch 15, batch 300, loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02864, over 4946.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02992, over 756449.13 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 06:45:18,785 INFO [train.py:715] (1/8) Epoch 15, batch 350, loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03258, over 4922.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.0301, over 804241.05 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 06:45:58,465 INFO [train.py:715] (1/8) Epoch 15, batch 400, loss[loss=0.1165, simple_loss=0.1787, pruned_loss=0.02716, over 4724.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03131, over 842316.71 frames.], batch size: 12, lr: 1.49e-04
2022-05-08 06:46:39,360 INFO [train.py:715] (1/8) Epoch 15, batch 450, loss[loss=0.145, simple_loss=0.2047, pruned_loss=0.04267, over 4916.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03106, over 871111.51 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 06:47:20,091 INFO [train.py:715] (1/8) Epoch 15, batch 500, loss[loss=0.1407, simple_loss=0.2142, pruned_loss=0.03353, over 4956.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03073, over 893826.60 frames.], batch size: 35, lr: 1.49e-04
2022-05-08 06:48:00,509 INFO [train.py:715] (1/8) Epoch 15, batch 550, loss[loss=0.1569, simple_loss=0.2404, pruned_loss=0.03666, over 4927.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.0303, over 912148.34 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 06:48:40,052 INFO [train.py:715] (1/8) Epoch 15, batch 600, loss[loss=0.1311, simple_loss=0.2084, pruned_loss=0.02687, over 4798.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03084, over 924642.27 frames.], batch size: 25, lr: 1.49e-04
2022-05-08 06:49:21,143 INFO [train.py:715] (1/8) Epoch 15, batch 650, loss[loss=0.1354, simple_loss=0.2153, pruned_loss=0.02769, over 4940.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03073, over 936830.43 frames.], batch size: 35, lr: 1.49e-04
2022-05-08 06:50:01,503 INFO [train.py:715] (1/8) Epoch 15, batch 700, loss[loss=0.1298, simple_loss=0.1983, pruned_loss=0.03064, over 4779.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03054, over 943754.06 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 06:50:41,519 INFO [train.py:715] (1/8) Epoch 15, batch 750, loss[loss=0.1158, simple_loss=0.185, pruned_loss=0.02329, over 4865.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03073, over 949160.45 frames.], batch size: 20, lr: 1.49e-04
2022-05-08 06:51:21,998 INFO [train.py:715] (1/8) Epoch 15, batch 800, loss[loss=0.1337, simple_loss=0.205, pruned_loss=0.03123, over 4782.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03064, over 953803.71 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 06:52:02,781 INFO [train.py:715] (1/8) Epoch 15, batch 850, loss[loss=0.1475, simple_loss=0.2193, pruned_loss=0.03785, over 4929.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03059, over 957232.65 frames.], batch size: 35, lr: 1.49e-04
2022-05-08 06:52:43,859 INFO [train.py:715] (1/8) Epoch 15, batch 900, loss[loss=0.1467, simple_loss=0.2245, pruned_loss=0.03446, over 4764.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03078, over 960920.84 frames.], batch size: 16, lr: 1.49e-04
2022-05-08 06:53:23,523 INFO [train.py:715] (1/8) Epoch 15, batch 950, loss[loss=0.1299, simple_loss=0.1983, pruned_loss=0.03077, over 4964.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03043, over 963020.25 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:54:04,065 INFO [train.py:715] (1/8) Epoch 15, batch 1000, loss[loss=0.1305, simple_loss=0.2082, pruned_loss=0.02642, over 4818.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03034, over 965283.82 frames.], batch size: 27, lr: 1.49e-04
2022-05-08 06:54:44,291 INFO [train.py:715] (1/8) Epoch 15, batch 1050, loss[loss=0.1207, simple_loss=0.1846, pruned_loss=0.02842, over 4778.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.03032, over 965470.20 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 06:55:23,572 INFO [train.py:715] (1/8) Epoch 15, batch 1100, loss[loss=0.1368, simple_loss=0.205, pruned_loss=0.03428, over 4992.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03029, over 967263.75 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:56:04,676 INFO [train.py:715] (1/8) Epoch 15, batch 1150, loss[loss=0.1088, simple_loss=0.1823, pruned_loss=0.01766, over 4867.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03028, over 968926.13 frames.], batch size: 13, lr: 1.49e-04
2022-05-08 06:56:45,823 INFO [train.py:715] (1/8) Epoch 15, batch 1200, loss[loss=0.1582, simple_loss=0.2301, pruned_loss=0.04308, over 4982.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03032, over 969152.68 frames.], batch size: 28, lr: 1.49e-04
2022-05-08 06:57:26,536 INFO [train.py:715] (1/8) Epoch 15, batch 1250, loss[loss=0.1394, simple_loss=0.2061, pruned_loss=0.03634, over 4646.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02994, over 969033.46 frames.], batch size: 13, lr: 1.49e-04
2022-05-08 06:58:05,999 INFO [train.py:715] (1/8) Epoch 15, batch 1300, loss[loss=0.123, simple_loss=0.1893, pruned_loss=0.02832, over 4950.00 frames.], tot_loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03019, over 970017.18 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 06:58:46,680 INFO [train.py:715] (1/8) Epoch 15, batch 1350, loss[loss=0.1378, simple_loss=0.2057, pruned_loss=0.0349, over 4688.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03009, over 971266.45 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:59:27,341 INFO [train.py:715] (1/8) Epoch 15, batch 1400, loss[loss=0.1244, simple_loss=0.1947, pruned_loss=0.02707, over 4787.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2072, pruned_loss=0.03022, over 971502.44 frames.], batch size: 14, lr: 1.49e-04
2022-05-08 07:00:07,533 INFO [train.py:715] (1/8) Epoch 15, batch 1450, loss[loss=0.1521, simple_loss=0.2189, pruned_loss=0.04261, over 4828.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2072, pruned_loss=0.0301, over 971922.64 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 07:00:47,336 INFO [train.py:715] (1/8) Epoch 15, batch 1500, loss[loss=0.1107, simple_loss=0.1907, pruned_loss=0.01528, over 4951.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02971, over 972409.72 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 07:01:28,504 INFO [train.py:715] (1/8) Epoch 15, batch 1550, loss[loss=0.1644, simple_loss=0.233, pruned_loss=0.04789, over 4787.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02997, over 972593.29 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:02:08,728 INFO [train.py:715] (1/8) Epoch 15, batch 1600, loss[loss=0.1297, simple_loss=0.199, pruned_loss=0.03023, over 4914.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03008, over 974013.41 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 07:02:47,766 INFO [train.py:715] (1/8) Epoch 15, batch 1650, loss[loss=0.1304, simple_loss=0.206, pruned_loss=0.02737, over 4864.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03016, over 973190.07 frames.], batch size: 39, lr: 1.49e-04
2022-05-08 07:03:28,296 INFO [train.py:715] (1/8) Epoch 15, batch 1700, loss[loss=0.1669, simple_loss=0.246, pruned_loss=0.04394, over 4933.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2072, pruned_loss=0.03013, over 973716.43 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 07:04:08,877 INFO [train.py:715] (1/8) Epoch 15, batch 1750, loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03064, over 4821.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03029, over 973284.27 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 07:04:48,958 INFO [train.py:715] (1/8) Epoch 15, batch 1800, loss[loss=0.1374, simple_loss=0.2159, pruned_loss=0.02943, over 4778.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03029, over 972406.69 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 07:05:28,936 INFO [train.py:715] (1/8) Epoch 15, batch 1850, loss[loss=0.1214, simple_loss=0.1973, pruned_loss=0.02278, over 4789.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03001, over 972032.80 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:06:09,783 INFO [train.py:715] (1/8) Epoch 15, batch 1900, loss[loss=0.1274, simple_loss=0.2001, pruned_loss=0.02731, over 4850.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03033, over 971026.18 frames.], batch size: 30, lr: 1.49e-04
2022-05-08 07:06:50,230 INFO [train.py:715] (1/8) Epoch 15, batch 1950, loss[loss=0.1275, simple_loss=0.2032, pruned_loss=0.02587, over 4933.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.03047, over 971204.08 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 07:07:29,402 INFO [train.py:715] (1/8) Epoch 15, batch 2000, loss[loss=0.1271, simple_loss=0.1997, pruned_loss=0.02723, over 4958.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03089, over 972181.35 frames.], batch size: 35, lr: 1.49e-04
2022-05-08 07:08:10,504 INFO [train.py:715] (1/8) Epoch 15, batch 2050, loss[loss=0.1287, simple_loss=0.2003, pruned_loss=0.02858, over 4872.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03122, over 972711.74 frames.], batch size: 13, lr: 1.49e-04
2022-05-08 07:08:50,814 INFO [train.py:715] (1/8) Epoch 15, batch 2100, loss[loss=0.128, simple_loss=0.2029, pruned_loss=0.02658, over 4891.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03155, over 972901.55 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:09:30,720 INFO [train.py:715] (1/8) Epoch 15, batch 2150, loss[loss=0.1204, simple_loss=0.1936, pruned_loss=0.02355, over 4771.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03097, over 972102.18 frames.], batch size: 19, lr: 1.49e-04
2022-05-08 07:10:10,973 INFO [train.py:715] (1/8) Epoch 15, batch 2200, loss[loss=0.1592, simple_loss=0.2369, pruned_loss=0.04074, over 4985.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03094, over 971796.68 frames.], batch size: 25, lr: 1.49e-04
2022-05-08 07:10:51,405 INFO [train.py:715] (1/8) Epoch 15, batch 2250, loss[loss=0.1248, simple_loss=0.198, pruned_loss=0.02581, over 4646.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03089, over 971758.16 frames.], batch size: 13, lr: 1.49e-04
2022-05-08 07:11:31,545 INFO [train.py:715] (1/8) Epoch 15, batch 2300, loss[loss=0.1488, simple_loss=0.2329, pruned_loss=0.03231, over 4791.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03092, over 971892.27 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 07:12:11,044 INFO [train.py:715] (1/8) Epoch 15, batch 2350, loss[loss=0.1346, simple_loss=0.2056, pruned_loss=0.03175, over 4763.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03096, over 972845.99 frames.], batch size: 16, lr: 1.49e-04
2022-05-08 07:12:51,319 INFO [train.py:715] (1/8) Epoch 15, batch 2400, loss[loss=0.1466, simple_loss=0.2243, pruned_loss=0.03451, over 4856.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03067, over 973001.86 frames.], batch size: 20, lr: 1.49e-04
2022-05-08 07:13:31,555 INFO [train.py:715] (1/8) Epoch 15, batch 2450, loss[loss=0.1373, simple_loss=0.1989, pruned_loss=0.03787, over 4804.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03079, over 972373.87 frames.], batch size: 12, lr: 1.49e-04
2022-05-08 07:14:11,483 INFO [train.py:715] (1/8) Epoch 15, batch 2500, loss[loss=0.1947, simple_loss=0.2487, pruned_loss=0.07038, over 4970.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03099, over 972877.80 frames.], batch size: 39, lr: 1.49e-04
2022-05-08 07:14:50,602 INFO [train.py:715] (1/8) Epoch 15, batch 2550, loss[loss=0.1847, simple_loss=0.25, pruned_loss=0.05965, over 4841.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03097, over 973116.25 frames.], batch size: 30, lr: 1.49e-04
2022-05-08 07:15:31,410 INFO [train.py:715] (1/8) Epoch 15, batch 2600, loss[loss=0.13, simple_loss=0.2023, pruned_loss=0.02881, over 4755.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03102, over 972782.89 frames.], batch size: 19, lr: 1.49e-04
2022-05-08 07:16:12,098 INFO [train.py:715] (1/8) Epoch 15, batch 2650, loss[loss=0.148, simple_loss=0.2158, pruned_loss=0.04003, over 4850.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2083, pruned_loss=0.03096, over 972070.12 frames.], batch size: 20, lr: 1.49e-04
2022-05-08 07:16:51,588 INFO [train.py:715] (1/8) Epoch 15, batch 2700, loss[loss=0.1645, simple_loss=0.23, pruned_loss=0.04943, over 4768.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03106, over 972185.09 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:17:33,113 INFO [train.py:715] (1/8) Epoch 15, batch 2750, loss[loss=0.1479, simple_loss=0.2237, pruned_loss=0.03603, over 4881.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2087, pruned_loss=0.03126, over 972656.40 frames.], batch size: 22, lr: 1.49e-04
2022-05-08 07:18:14,182 INFO [train.py:715] (1/8) Epoch 15, batch 2800, loss[loss=0.1242, simple_loss=0.2026, pruned_loss=0.0229, over 4923.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2081, pruned_loss=0.03105, over 973279.42 frames.], batch size: 23, lr: 1.49e-04
2022-05-08 07:18:54,879 INFO [train.py:715] (1/8) Epoch 15, batch 2850, loss[loss=0.1259, simple_loss=0.2027, pruned_loss=0.02457, over 4931.00 frames.], tot_loss[loss=0.1346, simple_loss=0.208, pruned_loss=0.03061, over 973588.56 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 07:19:34,213 INFO [train.py:715] (1/8) Epoch 15, batch 2900, loss[loss=0.1245, simple_loss=0.1907, pruned_loss=0.02912, over 4774.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2078, pruned_loss=0.03102, over 973126.08 frames.], batch size: 12, lr: 1.49e-04
2022-05-08 07:20:14,828 INFO [train.py:715] (1/8) Epoch 15, batch 2950, loss[loss=0.1342, simple_loss=0.2126, pruned_loss=0.02789, over 4814.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2082, pruned_loss=0.03084, over 972553.42 frames.], batch size: 25, lr: 1.49e-04
2022-05-08 07:20:55,610 INFO [train.py:715] (1/8) Epoch 15, batch 3000, loss[loss=0.1333, simple_loss=0.2044, pruned_loss=0.03116, over 4919.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.03094, over 973189.65 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:20:55,611 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 07:21:13,097 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1049, simple_loss=0.1887, pruned_loss=0.01057, over 914524.00 frames.
2022-05-08 07:21:54,019 INFO [train.py:715] (1/8) Epoch 15, batch 3050, loss[loss=0.13, simple_loss=0.2117, pruned_loss=0.02418, over 4775.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2076, pruned_loss=0.03045, over 973270.96 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 07:22:33,939 INFO [train.py:715] (1/8) Epoch 15, batch 3100, loss[loss=0.1094, simple_loss=0.188, pruned_loss=0.01539, over 4885.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2079, pruned_loss=0.03053, over 972389.68 frames.], batch size: 22, lr: 1.49e-04
2022-05-08 07:23:14,667 INFO [train.py:715] (1/8) Epoch 15, batch 3150, loss[loss=0.1157, simple_loss=0.1904, pruned_loss=0.0205, over 4912.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2088, pruned_loss=0.03128, over 973219.05 frames.], batch size: 19, lr: 1.49e-04
2022-05-08 07:23:55,196 INFO [train.py:715] (1/8) Epoch 15, batch 3200, loss[loss=0.1622, simple_loss=0.2357, pruned_loss=0.04436, over 4841.00 frames.], tot_loss[loss=0.1358, simple_loss=0.209, pruned_loss=0.03129, over 973453.63 frames.], batch size: 30, lr: 1.49e-04
2022-05-08 07:24:35,381 INFO [train.py:715] (1/8) Epoch 15, batch 3250, loss[loss=0.1301, simple_loss=0.2039, pruned_loss=0.02816, over 4873.00 frames.], tot_loss[loss=0.1358, simple_loss=0.209, pruned_loss=0.0313, over 973077.37 frames.], batch size: 22, lr: 1.49e-04
2022-05-08 07:25:15,318 INFO [train.py:715] (1/8) Epoch 15, batch 3300, loss[loss=0.1423, simple_loss=0.2189, pruned_loss=0.03287, over 4791.00 frames.], tot_loss[loss=0.1346, simple_loss=0.208, pruned_loss=0.03064, over 973255.65 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 07:25:56,108 INFO [train.py:715] (1/8) Epoch 15, batch 3350, loss[loss=0.1276, simple_loss=0.2016, pruned_loss=0.02684, over 4807.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03067, over 972560.80 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:26:36,431 INFO [train.py:715] (1/8) Epoch 15, batch 3400, loss[loss=0.1364, simple_loss=0.2039, pruned_loss=0.03441, over 4903.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03105, over 973310.51 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:27:16,651 INFO [train.py:715] (1/8) Epoch 15, batch 3450, loss[loss=0.1418, simple_loss=0.2186, pruned_loss=0.0325, over 4798.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.031, over 973589.80 frames.], batch size: 14, lr: 1.49e-04
2022-05-08 07:27:56,922 INFO [train.py:715] (1/8) Epoch 15, batch 3500, loss[loss=0.1372, simple_loss=0.2135, pruned_loss=0.0305, over 4877.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2089, pruned_loss=0.03109, over 973771.96 frames.], batch size: 22, lr: 1.49e-04
2022-05-08 07:28:37,334 INFO [train.py:715] (1/8) Epoch 15, batch 3550, loss[loss=0.1544, simple_loss=0.2392, pruned_loss=0.03481, over 4945.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2083, pruned_loss=0.03071, over 974359.60 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 07:29:17,839 INFO [train.py:715] (1/8) Epoch 15, batch 3600, loss[loss=0.1455, simple_loss=0.221, pruned_loss=0.03501, over 4918.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2078, pruned_loss=0.03021, over 974389.91 frames.], batch size: 39, lr: 1.49e-04
2022-05-08 07:29:57,636 INFO [train.py:715] (1/8) Epoch 15, batch 3650, loss[loss=0.1353, simple_loss=0.2045, pruned_loss=0.03307, over 4974.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02992, over 974267.67 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 07:30:38,278 INFO [train.py:715] (1/8) Epoch 15, batch 3700, loss[loss=0.1534, simple_loss=0.2274, pruned_loss=0.03972, over 4926.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2065, pruned_loss=0.02963, over 974191.07 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:31:19,136 INFO [train.py:715] (1/8) Epoch 15, batch 3750, loss[loss=0.1649, simple_loss=0.2338, pruned_loss=0.04803, over 4936.00 frames.], tot_loss[loss=0.1334, simple_loss=0.207, pruned_loss=0.02984, over 973357.65 frames.], batch size: 35, lr: 1.48e-04
2022-05-08 07:31:58,790 INFO [train.py:715] (1/8) Epoch 15, batch 3800, loss[loss=0.1461, simple_loss=0.2152, pruned_loss=0.03851, over 4961.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02943, over 972974.83 frames.], batch size: 35, lr: 1.48e-04
2022-05-08 07:32:38,792 INFO [train.py:715] (1/8) Epoch 15, batch 3850, loss[loss=0.1362, simple_loss=0.2124, pruned_loss=0.03002, over 4984.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02914, over 972936.70 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 07:33:19,078 INFO [train.py:715] (1/8) Epoch 15, batch 3900, loss[loss=0.1321, simple_loss=0.1986, pruned_loss=0.03283, over 4919.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.0298, over 973050.46 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 07:33:58,247 INFO [train.py:715] (1/8) Epoch 15, batch 3950, loss[loss=0.1466, simple_loss=0.2272, pruned_loss=0.03298, over 4903.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02985, over 972695.96 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 07:34:37,990 INFO [train.py:715] (1/8) Epoch 15, batch 4000, loss[loss=0.1226, simple_loss=0.2, pruned_loss=0.02256, over 4990.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02978, over 973197.70 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 07:35:17,771 INFO [train.py:715] (1/8) Epoch 15, batch 4050, loss[loss=0.1456, simple_loss=0.2238, pruned_loss=0.03371, over 4921.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.02999, over 973710.92 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 07:35:58,779 INFO [train.py:715] (1/8) Epoch 15, batch 4100, loss[loss=0.1345, simple_loss=0.2124, pruned_loss=0.02825, over 4862.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03022, over 974361.38 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 07:36:37,613 INFO [train.py:715] (1/8) Epoch 15, batch 4150, loss[loss=0.1609, simple_loss=0.2282, pruned_loss=0.04679, over 4824.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03019, over 973743.09 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 07:37:17,773 INFO [train.py:715] (1/8) Epoch 15, batch 4200, loss[loss=0.1295, simple_loss=0.2075, pruned_loss=0.02578, over 4818.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2079, pruned_loss=0.0304, over 974208.74 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:37:58,196 INFO [train.py:715] (1/8) Epoch 15, batch 4250, loss[loss=0.1138, simple_loss=0.1953, pruned_loss=0.01617, over 4945.00 frames.], tot_loss[loss=0.1346, simple_loss=0.208, pruned_loss=0.03055, over 972616.87 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 07:38:38,203 INFO [train.py:715] (1/8) Epoch 15, batch 4300, loss[loss=0.1235, simple_loss=0.2043, pruned_loss=0.02138, over 4756.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03066, over 972092.00 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 07:39:18,219 INFO [train.py:715] (1/8) Epoch 15, batch 4350, loss[loss=0.1285, simple_loss=0.2011, pruned_loss=0.02796, over 4835.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03069, over 973031.41 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 07:39:58,272 INFO [train.py:715] (1/8) Epoch 15, batch 4400, loss[loss=0.1266, simple_loss=0.2019, pruned_loss=0.02562, over 4846.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2077, pruned_loss=0.03039, over 972253.33 frames.], batch size: 32, lr: 1.48e-04
2022-05-08 07:40:38,801 INFO [train.py:715] (1/8) Epoch 15, batch 4450, loss[loss=0.1258, simple_loss=0.1933, pruned_loss=0.0292, over 4828.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03013, over 972680.59 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 07:41:18,471 INFO [train.py:715] (1/8) Epoch 15, batch 4500, loss[loss=0.1109, simple_loss=0.1774, pruned_loss=0.02219, over 4951.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2073, pruned_loss=0.03024, over 971728.23 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:41:58,877 INFO [train.py:715] (1/8) Epoch 15, batch 4550, loss[loss=0.1366, simple_loss=0.2017, pruned_loss=0.03573, over 4912.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03031, over 972744.82 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 07:42:39,496 INFO [train.py:715] (1/8) Epoch 15, batch 4600, loss[loss=0.1517, simple_loss=0.2203, pruned_loss=0.04149, over 4886.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03012, over 973277.51 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 07:43:19,668 INFO [train.py:715] (1/8) Epoch 15, batch 4650, loss[loss=0.157, simple_loss=0.235, pruned_loss=0.03952, over 4909.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03117, over 972640.18 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 07:43:59,054 INFO [train.py:715] (1/8) Epoch 15, batch 4700, loss[loss=0.1399, simple_loss=0.2095, pruned_loss=0.03518, over 4904.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03065, over 973220.11 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 07:44:39,326 INFO [train.py:715] (1/8) Epoch 15, batch 4750, loss[loss=0.1332, simple_loss=0.2121, pruned_loss=0.02714, over 4794.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03033, over 973417.69 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 07:45:20,568 INFO [train.py:715] (1/8) Epoch 15, batch 4800, loss[loss=0.1254, simple_loss=0.1992, pruned_loss=0.02583, over 4870.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.0301, over 972910.10 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 07:46:00,526 INFO [train.py:715] (1/8) Epoch 15, batch 4850, loss[loss=0.1537, simple_loss=0.2258, pruned_loss=0.04084, over 4847.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2071, pruned_loss=0.03004, over 972900.78 frames.], batch size: 30, lr: 1.48e-04
2022-05-08 07:46:41,248 INFO [train.py:715] (1/8) Epoch 15, batch 4900, loss[loss=0.1268, simple_loss=0.2032, pruned_loss=0.0252, over 4962.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2073, pruned_loss=0.03015, over 972820.74 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:47:21,697 INFO [train.py:715] (1/8) Epoch 15, batch 4950, loss[loss=0.1453, simple_loss=0.216, pruned_loss=0.0373, over 4878.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03018, over 973307.63 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 07:48:02,263 INFO [train.py:715] (1/8) Epoch 15, batch 5000, loss[loss=0.1436, simple_loss=0.2239, pruned_loss=0.03161, over 4929.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02973, over 973389.23 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 07:48:41,754 INFO [train.py:715] (1/8) Epoch 15, batch 5050, loss[loss=0.1341, simple_loss=0.2105, pruned_loss=0.02882, over 4937.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.0299, over 972831.12 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 07:49:21,840 INFO [train.py:715] (1/8) Epoch 15, batch 5100, loss[loss=0.1566, simple_loss=0.2342, pruned_loss=0.0395, over 4973.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03012, over 973132.79 frames.], batch size: 28, lr: 1.48e-04
2022-05-08 07:50:02,143 INFO [train.py:715] (1/8) Epoch 15, batch 5150, loss[loss=0.1687, simple_loss=0.2455, pruned_loss=0.04597, over 4828.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03007, over 973326.54 frames.], batch size: 26, lr: 1.48e-04
2022-05-08 07:50:42,065 INFO [train.py:715] (1/8) Epoch 15, batch 5200, loss[loss=0.1166, simple_loss=0.1869, pruned_loss=0.02321, over 4802.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02981, over 972560.29 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 07:51:22,086 INFO [train.py:715] (1/8) Epoch 15, batch 5250, loss[loss=0.1444, simple_loss=0.2095, pruned_loss=0.03965, over 4964.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02941, over 971958.08 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 07:52:03,630 INFO [train.py:715] (1/8) Epoch 15, batch 5300, loss[loss=0.161, simple_loss=0.2281, pruned_loss=0.04692, over 4922.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.0293, over 971236.55 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 07:52:45,866 INFO [train.py:715] (1/8) Epoch 15, batch 5350, loss[loss=0.17, simple_loss=0.236, pruned_loss=0.05196, over 4938.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02908, over 972303.55 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:53:26,856 INFO [train.py:715] (1/8) Epoch 15, batch 5400, loss[loss=0.1223, simple_loss=0.1921, pruned_loss=0.0263, over 4959.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02941, over 972418.04 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:54:08,833 INFO [train.py:715] (1/8) Epoch 15, batch 5450, loss[loss=0.1231, simple_loss=0.193, pruned_loss=0.02657, over 4984.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02955, over 971659.86 frames.], batch size: 26, lr: 1.48e-04
2022-05-08 07:54:50,478 INFO [train.py:715] (1/8) Epoch 15, batch 5500, loss[loss=0.1161, simple_loss=0.2021, pruned_loss=0.01504, over 4802.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03017, over 972189.88 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 07:55:32,153 INFO [train.py:715] (1/8) Epoch 15, batch 5550, loss[loss=0.1044, simple_loss=0.1736, pruned_loss=0.01761, over 4892.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03025, over 972641.44 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 07:56:12,920 INFO [train.py:715] (1/8) Epoch 15, batch 5600, loss[loss=0.1273, simple_loss=0.2006, pruned_loss=0.02701, over 4886.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.03006, over 971826.14 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 07:56:54,780 INFO [train.py:715] (1/8) Epoch 15, batch 5650, loss[loss=0.1143, simple_loss=0.1781, pruned_loss=0.02522, over 4853.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2078, pruned_loss=0.03022, over 970709.21 frames.], batch size: 32, lr: 1.48e-04
2022-05-08 07:57:37,314 INFO [train.py:715] (1/8) Epoch 15, batch 5700, loss[loss=0.1409, simple_loss=0.221, pruned_loss=0.03034, over 4923.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03041, over 971154.23 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 07:58:18,545 INFO [train.py:715] (1/8) Epoch 15, batch 5750, loss[loss=0.1509, simple_loss=0.2167, pruned_loss=0.04261, over 4876.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03064, over 971094.04 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 07:58:59,972 INFO [train.py:715] (1/8) Epoch 15, batch 5800, loss[loss=0.1265, simple_loss=0.1958, pruned_loss=0.02855, over 4871.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.03075, over 972155.91 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 07:59:41,229 INFO [train.py:715] (1/8) Epoch 15, batch 5850, loss[loss=0.1259, simple_loss=0.2026, pruned_loss=0.02463, over 4800.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03042, over 972035.73 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:00:25,529 INFO [train.py:715] (1/8) Epoch 15, batch 5900, loss[loss=0.1362, simple_loss=0.2113, pruned_loss=0.03055, over 4847.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.0304, over 971191.75 frames.], batch size: 30, lr: 1.48e-04
2022-05-08 08:01:06,125 INFO [train.py:715] (1/8) Epoch 15, batch 5950, loss[loss=0.1164, simple_loss=0.1983, pruned_loss=0.01723, over 4934.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03041, over 971318.35 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:01:47,586 INFO [train.py:715] (1/8) Epoch 15, batch 6000, loss[loss=0.1403, simple_loss=0.2155, pruned_loss=0.03256, over 4891.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.03076, over 972345.61 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:01:47,587 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 08:01:57,158 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1051, simple_loss=0.1887, pruned_loss=0.01077, over 914524.00 frames.
2022-05-08 08:02:38,332 INFO [train.py:715] (1/8) Epoch 15, batch 6050, loss[loss=0.1376, simple_loss=0.2176, pruned_loss=0.02883, over 4978.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03104, over 972434.68 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 08:03:20,377 INFO [train.py:715] (1/8) Epoch 15, batch 6100, loss[loss=0.1358, simple_loss=0.2028, pruned_loss=0.03442, over 4945.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.03135, over 971369.65 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:04:00,139 INFO [train.py:715] (1/8) Epoch 15, batch 6150, loss[loss=0.1469, simple_loss=0.2209, pruned_loss=0.03642, over 4909.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03101, over 971153.90 frames.], batch size: 39, lr: 1.48e-04
2022-05-08 08:04:41,013 INFO [train.py:715] (1/8) Epoch 15, batch 6200, loss[loss=0.1491, simple_loss=0.2232, pruned_loss=0.03753, over 4832.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03059, over 971071.33 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:05:20,639 INFO [train.py:715] (1/8) Epoch 15, batch 6250, loss[loss=0.126, simple_loss=0.1942, pruned_loss=0.02891, over 4968.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03073, over 971807.09 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 08:06:01,402 INFO [train.py:715] (1/8) Epoch 15, batch 6300, loss[loss=0.1414, simple_loss=0.2224, pruned_loss=0.0302, over 4955.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.03091, over 971432.58 frames.], batch size: 39, lr: 1.48e-04
2022-05-08 08:06:41,232 INFO [train.py:715] (1/8) Epoch 15, batch 6350, loss[loss=0.1213, simple_loss=0.1993, pruned_loss=0.02164, over 4932.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2077, pruned_loss=0.03056, over 971659.09 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:07:21,276 INFO [train.py:715] (1/8) Epoch 15, batch 6400, loss[loss=0.1286, simple_loss=0.2062, pruned_loss=0.02545, over 4890.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03102, over 971637.09 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:08:01,847 INFO [train.py:715] (1/8) Epoch 15, batch 6450, loss[loss=0.116, simple_loss=0.1977, pruned_loss=0.01716, over 4753.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03073, over 971846.49 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:08:41,392 INFO [train.py:715] (1/8) Epoch 15, batch 6500, loss[loss=0.1369, simple_loss=0.221, pruned_loss=0.02642, over 4986.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2087, pruned_loss=0.03122, over 971281.37 frames.], batch size: 28, lr: 1.48e-04
2022-05-08 08:09:21,818 INFO [train.py:715] (1/8) Epoch 15, batch 6550, loss[loss=0.1928, simple_loss=0.2623, pruned_loss=0.0617, over 4925.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03131, over 972100.75 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:10:02,128 INFO [train.py:715] (1/8) Epoch 15, batch 6600, loss[loss=0.1169, simple_loss=0.1833, pruned_loss=0.02524, over 4873.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03098, over 972696.01 frames.], batch size: 32, lr: 1.48e-04
2022-05-08 08:10:42,817 INFO [train.py:715] (1/8) Epoch 15, batch 6650, loss[loss=0.1592, simple_loss=0.2347, pruned_loss=0.0418, over 4697.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03113, over 972941.09 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:11:22,257 INFO [train.py:715] (1/8) Epoch 15, batch 6700, loss[loss=0.1519, simple_loss=0.2224, pruned_loss=0.04066, over 4829.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03096, over 972757.43 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:12:02,760 INFO [train.py:715] (1/8) Epoch 15, batch 6750, loss[loss=0.141, simple_loss=0.2038, pruned_loss=0.03907, over 4839.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03081, over 972454.04 frames.], batch size: 30, lr: 1.48e-04
2022-05-08 08:12:44,125 INFO [train.py:715] (1/8) Epoch 15, batch 6800, loss[loss=0.1137, simple_loss=0.1945, pruned_loss=0.01648, over 4976.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03062, over 972499.64 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:13:23,944 INFO [train.py:715] (1/8) Epoch 15, batch 6850, loss[loss=0.1276, simple_loss=0.2035, pruned_loss=0.02585, over 4848.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03023, over 972932.11 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:14:03,541 INFO [train.py:715] (1/8) Epoch 15, batch 6900, loss[loss=0.1505, simple_loss=0.2205, pruned_loss=0.04026, over 4980.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03035, over 973551.72 frames.], batch size: 31, lr: 1.48e-04
2022-05-08 08:14:44,363 INFO [train.py:715] (1/8) Epoch 15, batch 6950, loss[loss=0.1341, simple_loss=0.2126, pruned_loss=0.02775, over 4770.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03029, over 972982.72 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:15:25,013 INFO [train.py:715] (1/8) Epoch 15, batch 7000, loss[loss=0.1485, simple_loss=0.2219, pruned_loss=0.03758, over 4796.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03031, over 972262.03 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:16:03,974 INFO [train.py:715] (1/8) Epoch 15, batch 7050, loss[loss=0.151, simple_loss=0.2295, pruned_loss=0.03625, over 4814.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03007, over 972760.92 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:16:44,707 INFO [train.py:715] (1/8) Epoch 15, batch 7100, loss[loss=0.1438, simple_loss=0.2133, pruned_loss=0.03716, over 4919.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02988, over 971951.34 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:17:25,231 INFO [train.py:715] (1/8) Epoch 15, batch 7150, loss[loss=0.1312, simple_loss=0.194, pruned_loss=0.0342, over 4813.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02979, over 971329.85 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 08:18:05,131 INFO [train.py:715] (1/8) Epoch 15, batch 7200, loss[loss=0.1049, simple_loss=0.1801, pruned_loss=0.01486, over 4789.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02991, over 971751.10 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:18:44,342 INFO [train.py:715] (1/8) Epoch 15, batch 7250, loss[loss=0.1426, simple_loss=0.2144, pruned_loss=0.0354, over 4690.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02953, over 972435.68 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:19:25,107 INFO [train.py:715] (1/8) Epoch 15, batch 7300, loss[loss=0.1133, simple_loss=0.1828, pruned_loss=0.02194, over 4784.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02944, over 972179.10 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:20:06,080 INFO [train.py:715] (1/8) Epoch 15, batch 7350, loss[loss=0.1392, simple_loss=0.2151, pruned_loss=0.03163, over 4756.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.0299, over 972227.32 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:20:45,517 INFO [train.py:715] (1/8) Epoch 15, batch 7400, loss[loss=0.1063, simple_loss=0.1805, pruned_loss=0.01606, over 4742.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03019, over 971531.62 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:21:26,000 INFO [train.py:715] (1/8) Epoch 15, batch 7450, loss[loss=0.1289, simple_loss=0.2009, pruned_loss=0.02849, over 4801.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03015, over 971794.50 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:22:06,378 INFO [train.py:715] (1/8) Epoch 15, batch 7500, loss[loss=0.1197, simple_loss=0.1928, pruned_loss=0.02329, over 4788.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.0302, over 972373.47 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:22:46,662 INFO [train.py:715] (1/8) Epoch 15, batch 7550, loss[loss=0.1268, simple_loss=0.1987, pruned_loss=0.02746, over 4777.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02982, over 972288.56 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:23:25,902 INFO [train.py:715] (1/8) Epoch 15, batch 7600, loss[loss=0.1323, simple_loss=0.2017, pruned_loss=0.03151, over 4745.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.0291, over 972294.56 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:24:05,910 INFO [train.py:715] (1/8) Epoch 15, batch 7650, loss[loss=0.1331, simple_loss=0.2045, pruned_loss=0.03083, over 4867.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2087, pruned_loss=0.02958, over 972794.10 frames.], batch size: 32, lr: 1.48e-04
2022-05-08 08:24:45,955 INFO [train.py:715] (1/8) Epoch 15, batch 7700, loss[loss=0.1209, simple_loss=0.1864, pruned_loss=0.02773, over 4817.00 frames.], tot_loss[loss=0.1342, simple_loss=0.209, pruned_loss=0.02971, over 972719.02 frames.], batch size: 27, lr: 1.48e-04
2022-05-08 08:25:24,893 INFO [train.py:715] (1/8) Epoch 15, batch 7750, loss[loss=0.1499, simple_loss=0.229, pruned_loss=0.03535, over 4912.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2092, pruned_loss=0.02978, over 972309.33 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 08:26:04,532 INFO [train.py:715] (1/8) Epoch 15, batch 7800, loss[loss=0.1445, simple_loss=0.2109, pruned_loss=0.03909, over 4871.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2098, pruned_loss=0.03018, over 972252.36 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:26:43,769 INFO [train.py:715] (1/8) Epoch 15, batch 7850, loss[loss=0.1333, simple_loss=0.2031, pruned_loss=0.03177, over 4760.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2109, pruned_loss=0.03101, over 972513.47 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:27:23,769 INFO [train.py:715] (1/8) Epoch 15, batch 7900, loss[loss=0.1127, simple_loss=0.1767, pruned_loss=0.02437, over 4744.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2103, pruned_loss=0.03092, over 972516.99 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:28:01,920 INFO [train.py:715] (1/8) Epoch 15, batch 7950, loss[loss=0.142, simple_loss=0.2222, pruned_loss=0.03094, over 4964.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03105, over 972767.07 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:28:41,239 INFO [train.py:715] (1/8) Epoch 15, batch 8000, loss[loss=0.1184, simple_loss=0.2043, pruned_loss=0.01625, over 4946.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03094, over 972363.11 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 08:29:20,798 INFO [train.py:715] (1/8) Epoch 15, batch 8050, loss[loss=0.1623, simple_loss=0.2417, pruned_loss=0.04149, over 4901.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.031, over 972668.90 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:29:59,766 INFO [train.py:715] (1/8) Epoch 15, batch 8100, loss[loss=0.1396, simple_loss=0.2081, pruned_loss=0.03554, over 4976.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03155, over 972104.14 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:30:38,764 INFO [train.py:715] (1/8) Epoch 15, batch 8150, loss[loss=0.1267, simple_loss=0.2049, pruned_loss=0.02423, over 4844.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03116, over 972195.24 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:31:18,842 INFO [train.py:715] (1/8) Epoch 15, batch 8200, loss[loss=0.1163, simple_loss=0.1885, pruned_loss=0.02204, over 4970.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03096, over 972143.82 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:31:57,573 INFO [train.py:715] (1/8) Epoch 15, batch 8250, loss[loss=0.1193, simple_loss=0.2076, pruned_loss=0.0155, over 4888.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03044, over 972045.52 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:32:36,548 INFO [train.py:715] (1/8) Epoch 15, batch 8300, loss[loss=0.1603, simple_loss=0.2345, pruned_loss=0.04306, over 4866.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03052, over 971580.92 frames.], batch size: 32, lr: 1.48e-04
2022-05-08 08:33:15,773 INFO [train.py:715] (1/8) Epoch 15, batch 8350, loss[loss=0.1243, simple_loss=0.1956, pruned_loss=0.02652, over 4857.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03102, over 971914.29 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:33:55,968 INFO [train.py:715] (1/8) Epoch 15, batch 8400, loss[loss=0.13, simple_loss=0.2074, pruned_loss=0.02627, over 4886.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03047, over 972581.60 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 08:34:35,507 INFO [train.py:715] (1/8) Epoch 15, batch 8450, loss[loss=0.123, simple_loss=0.1957, pruned_loss=0.02512, over 4983.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03047, over 972403.62 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:35:14,647 INFO [train.py:715] (1/8) Epoch 15, batch 8500, loss[loss=0.1261, simple_loss=0.1927, pruned_loss=0.02974, over 4770.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03081, over 971595.36 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:35:54,837 INFO [train.py:715] (1/8) Epoch 15, batch 8550, loss[loss=0.1191, simple_loss=0.1993, pruned_loss=0.01946, over 4926.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03049, over 972083.42 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:36:33,510 INFO [train.py:715] (1/8) Epoch 15, batch 8600, loss[loss=0.1347, simple_loss=0.2032, pruned_loss=0.03316, over 4838.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03074, over 972141.57 frames.], batch size: 30, lr: 1.48e-04
2022-05-08 08:37:12,323 INFO [train.py:715] (1/8) Epoch 15, batch 8650, loss[loss=0.1475, simple_loss=0.2199, pruned_loss=0.0375, over 4804.00 frames.], tot_loss[loss=0.135, simple_loss=0.2086, pruned_loss=0.03067, over 971820.71 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:37:51,177 INFO [train.py:715] (1/8) Epoch 15, batch 8700, loss[loss=0.1288, simple_loss=0.1973, pruned_loss=0.03012, over 4871.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03078, over 971993.70 frames.], batch size: 32, lr: 1.48e-04
2022-05-08 08:38:30,425 INFO [train.py:715] (1/8) Epoch 15, batch 8750, loss[loss=0.133, simple_loss=0.2124, pruned_loss=0.02681, over 4915.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03061, over 972023.42 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 08:39:08,911 INFO [train.py:715] (1/8) Epoch 15, batch 8800, loss[loss=0.1598, simple_loss=0.2421, pruned_loss=0.0387, over 4901.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.0312, over 972155.70 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:39:47,401 INFO [train.py:715] (1/8) Epoch 15, batch 8850, loss[loss=0.1175, simple_loss=0.2005, pruned_loss=0.01724, over 4919.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03063, over 972511.65 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:40:26,825 INFO [train.py:715] (1/8) Epoch 15, batch 8900, loss[loss=0.1435, simple_loss=0.2129, pruned_loss=0.03705, over 4767.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03063, over 972827.31 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:41:06,361 INFO [train.py:715] (1/8) Epoch 15, batch 8950, loss[loss=0.1421, simple_loss=0.213, pruned_loss=0.03561, over 4863.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03077, over 972900.89 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:41:45,474 INFO [train.py:715] (1/8) Epoch 15, batch 9000, loss[loss=0.1373, simple_loss=0.2184, pruned_loss=0.02812, over 4951.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03055, over 973416.63 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:41:45,475 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 08:42:05,029 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1051, simple_loss=0.1887, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 08:42:44,047 INFO [train.py:715] (1/8) Epoch 15, batch 9050, loss[loss=0.1295, simple_loss=0.1981, pruned_loss=0.03046, over 4773.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03039, over 972211.57 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:43:23,567 INFO [train.py:715] (1/8) Epoch 15, batch 9100, loss[loss=0.1144, simple_loss=0.19, pruned_loss=0.01941, over 4789.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.0299, over 971058.68 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:44:03,261 INFO [train.py:715] (1/8) Epoch 15, batch 9150, loss[loss=0.1136, simple_loss=0.1894, pruned_loss=0.01895, over 4827.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2086, pruned_loss=0.02991, over 971455.97 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 08:44:42,057 INFO [train.py:715] (1/8) Epoch 15, batch 9200, loss[loss=0.1782, simple_loss=0.2652, pruned_loss=0.0456, over 4970.00 frames.], tot_loss[loss=0.1346, simple_loss=0.209, pruned_loss=0.03015, over 970838.20 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:45:21,337 INFO [train.py:715] (1/8) Epoch 15, batch 9250, loss[loss=0.1254, simple_loss=0.1922, pruned_loss=0.02933, over 4864.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03027, over 970981.58 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:46:01,233 INFO [train.py:715] (1/8) Epoch 15, batch 9300, loss[loss=0.1373, simple_loss=0.2117, pruned_loss=0.03148, over 4878.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03026, over 971513.22 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 08:46:41,142 INFO [train.py:715] (1/8) Epoch 15, batch 9350, loss[loss=0.1279, simple_loss=0.2094, pruned_loss=0.02317, over 4942.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03041, over 971726.96 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 08:47:19,985 INFO [train.py:715] (1/8) Epoch 15, batch 9400, loss[loss=0.1222, simple_loss=0.1983, pruned_loss=0.023, over 4853.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03041, over 972529.73 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:47:59,300 INFO [train.py:715] (1/8) Epoch 15, batch 9450, loss[loss=0.1408, simple_loss=0.2092, pruned_loss=0.03614, over 4877.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.0307, over 971895.45 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:48:38,583 INFO [train.py:715] (1/8) Epoch 15, batch 9500, loss[loss=0.1367, simple_loss=0.214, pruned_loss=0.02971, over 4795.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.0299, over 971773.83 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 08:49:16,967 INFO [train.py:715] (1/8) Epoch 15, batch 9550, loss[loss=0.1289, simple_loss=0.2034, pruned_loss=0.02724, over 4810.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.0301, over 971867.34 frames.], batch size: 26, lr: 1.48e-04
2022-05-08 08:49:56,303 INFO [train.py:715] (1/8) Epoch 15, batch 9600, loss[loss=0.1129, simple_loss=0.1901, pruned_loss=0.01785, over 4932.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02998, over 971947.81 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 08:50:35,898 INFO [train.py:715] (1/8) Epoch 15, batch 9650, loss[loss=0.1495, simple_loss=0.2268, pruned_loss=0.03605, over 4787.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02992, over 971090.25 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:51:15,440 INFO [train.py:715] (1/8) Epoch 15, batch 9700, loss[loss=0.1222, simple_loss=0.2007, pruned_loss=0.02184, over 4761.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03, over 971113.73 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:51:53,983 INFO [train.py:715] (1/8) Epoch 15, batch 9750, loss[loss=0.1447, simple_loss=0.219, pruned_loss=0.03517, over 4985.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03052, over 972030.84 frames.], batch size: 28, lr: 1.48e-04
2022-05-08 08:52:33,225 INFO [train.py:715] (1/8) Epoch 15, batch 9800, loss[loss=0.1498, simple_loss=0.218, pruned_loss=0.04081, over 4950.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03017, over 971653.01 frames.], batch size: 39, lr: 1.48e-04
2022-05-08 08:53:12,404 INFO [train.py:715] (1/8) Epoch 15, batch 9850, loss[loss=0.1173, simple_loss=0.1786, pruned_loss=0.02804, over 4805.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03033, over 971923.93 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:53:50,990 INFO [train.py:715] (1/8) Epoch 15, batch 9900, loss[loss=0.1332, simple_loss=0.2139, pruned_loss=0.0263, over 4809.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02998, over 971878.97 frames.], batch size: 26, lr: 1.48e-04
2022-05-08 08:54:30,412 INFO [train.py:715] (1/8) Epoch 15, batch 9950, loss[loss=0.1431, simple_loss=0.2074, pruned_loss=0.03939, over 4908.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.0303, over 972166.56 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:55:09,392 INFO [train.py:715] (1/8) Epoch 15, batch 10000, loss[loss=0.1124, simple_loss=0.1914, pruned_loss=0.01671, over 4933.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02995, over 972468.99 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:55:48,596 INFO [train.py:715] (1/8) Epoch 15, batch 10050, loss[loss=0.1458, simple_loss=0.2126, pruned_loss=0.0395, over 4981.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02965, over 972676.32 frames.], batch size: 31, lr: 1.48e-04
2022-05-08 08:56:26,957 INFO [train.py:715] (1/8) Epoch 15, batch 10100, loss[loss=0.1558, simple_loss=0.2344, pruned_loss=0.03856, over 4930.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02993, over 972672.61 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:57:05,755 INFO [train.py:715] (1/8) Epoch 15, batch 10150, loss[loss=0.1413, simple_loss=0.2256, pruned_loss=0.02845, over 4956.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03009, over 973425.93 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 08:57:45,599 INFO [train.py:715] (1/8) Epoch 15, batch 10200, loss[loss=0.1302, simple_loss=0.1947, pruned_loss=0.0329, over 4967.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.0297, over 973613.24 frames.], batch size: 35, lr: 1.48e-04
2022-05-08 08:58:23,919 INFO [train.py:715] (1/8) Epoch 15, batch 10250, loss[loss=0.1337, simple_loss=0.2038, pruned_loss=0.03178, over 4970.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02943, over 973339.25 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:59:03,215 INFO [train.py:715] (1/8) Epoch 15, batch 10300, loss[loss=0.1212, simple_loss=0.1951, pruned_loss=0.0237, over 4883.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.02929, over 973056.26 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 08:59:42,676 INFO [train.py:715] (1/8) Epoch 15, batch 10350, loss[loss=0.1159, simple_loss=0.1952, pruned_loss=0.01832, over 4923.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02936, over 973249.16 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 09:00:21,802 INFO [train.py:715] (1/8) Epoch 15, batch 10400, loss[loss=0.1198, simple_loss=0.1951, pruned_loss=0.02222, over 4802.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.0291, over 972262.79 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 09:00:59,816 INFO [train.py:715] (1/8) Epoch 15, batch 10450, loss[loss=0.1466, simple_loss=0.2086, pruned_loss=0.04223, over 4901.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02915, over 971987.58 frames.], batch size: 39, lr: 1.48e-04
2022-05-08 09:01:38,799 INFO [train.py:715] (1/8) Epoch 15, batch 10500, loss[loss=0.1338, simple_loss=0.2192, pruned_loss=0.02424, over 4765.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02962, over 971219.34 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 09:02:18,514 INFO [train.py:715] (1/8) Epoch 15, batch 10550, loss[loss=0.1416, simple_loss=0.2212, pruned_loss=0.03099, over 4941.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02955, over 971756.48 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 09:02:56,677 INFO [train.py:715] (1/8) Epoch 15, batch 10600, loss[loss=0.1174, simple_loss=0.194, pruned_loss=0.02044, over 4947.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2083, pruned_loss=0.02957, over 971933.40 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 09:03:35,332 INFO [train.py:715] (1/8) Epoch 15, batch 10650, loss[loss=0.1142, simple_loss=0.1869, pruned_loss=0.02075, over 4812.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02961, over 972381.86 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 09:04:14,411 INFO [train.py:715] (1/8) Epoch 15, batch 10700, loss[loss=0.117, simple_loss=0.1896, pruned_loss=0.02216, over 4760.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02997, over 972941.32 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 09:04:53,621 INFO [train.py:715] (1/8) Epoch 15, batch 10750, loss[loss=0.1426, simple_loss=0.2104, pruned_loss=0.03741, over 4856.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02989, over 972369.14 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 09:05:31,570 INFO [train.py:715] (1/8) Epoch 15, batch 10800, loss[loss=0.1379, simple_loss=0.2089, pruned_loss=0.03344, over 4837.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02967, over 972832.29 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 09:06:11,103 INFO [train.py:715] (1/8) Epoch 15, batch 10850, loss[loss=0.1319, simple_loss=0.213, pruned_loss=0.02538, over 4956.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02932, over 972918.86 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:06:50,382 INFO [train.py:715] (1/8) Epoch 15, batch 10900, loss[loss=0.1252, simple_loss=0.1959, pruned_loss=0.02723, over 4761.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02907, over 972613.21 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:07:28,751 INFO [train.py:715] (1/8) Epoch 15, batch 10950, loss[loss=0.1418, simple_loss=0.2197, pruned_loss=0.03191, over 4942.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02905, over 972127.62 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 09:08:06,753 INFO [train.py:715] (1/8) Epoch 15, batch 11000, loss[loss=0.156, simple_loss=0.2436, pruned_loss=0.03418, over 4902.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02917, over 972204.46 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:08:45,834 INFO [train.py:715] (1/8) Epoch 15, batch 11050, loss[loss=0.1355, simple_loss=0.2168, pruned_loss=0.02712, over 4787.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02897, over 971768.93 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:09:25,314 INFO [train.py:715] (1/8) Epoch 15, batch 11100, loss[loss=0.1116, simple_loss=0.1949, pruned_loss=0.01412, over 4824.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02874, over 971459.44 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:10:03,219 INFO [train.py:715] (1/8) Epoch 15, batch 11150, loss[loss=0.1344, simple_loss=0.2014, pruned_loss=0.0337, over 4786.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02898, over 971416.50 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:10:41,863 INFO [train.py:715] (1/8) Epoch 15, batch 11200, loss[loss=0.1288, simple_loss=0.2069, pruned_loss=0.02536, over 4824.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.0288, over 971579.97 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:11:20,807 INFO [train.py:715] (1/8) Epoch 15, batch 11250, loss[loss=0.1496, simple_loss=0.2178, pruned_loss=0.04069, over 4692.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02949, over 971915.15 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:11:59,327 INFO [train.py:715] (1/8) Epoch 15, batch 11300, loss[loss=0.1329, simple_loss=0.2189, pruned_loss=0.02346, over 4986.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02928, over 972219.60 frames.], batch size: 28, lr: 1.47e-04
2022-05-08 09:12:37,831 INFO [train.py:715] (1/8) Epoch 15, batch 11350, loss[loss=0.1487, simple_loss=0.2201, pruned_loss=0.03867, over 4839.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02971, over 971948.49 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 09:13:17,186 INFO [train.py:715] (1/8) Epoch 15, batch 11400, loss[loss=0.1365, simple_loss=0.1995, pruned_loss=0.03676, over 4976.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02985, over 971378.12 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 09:13:55,477 INFO [train.py:715] (1/8) Epoch 15, batch 11450, loss[loss=0.1294, simple_loss=0.2104, pruned_loss=0.02417, over 4926.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02963, over 971212.07 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:14:34,169 INFO [train.py:715] (1/8) Epoch 15, batch 11500, loss[loss=0.1509, simple_loss=0.2206, pruned_loss=0.04059, over 4876.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.0299, over 972249.25 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:15:13,130 INFO [train.py:715] (1/8) Epoch 15, batch 11550, loss[loss=0.1256, simple_loss=0.1871, pruned_loss=0.03201, over 4815.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03054, over 971466.88 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 09:15:52,413 INFO [train.py:715] (1/8) Epoch 15, batch 11600, loss[loss=0.1458, simple_loss=0.2223, pruned_loss=0.03462, over 4965.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03077, over 972308.64 frames.], batch size: 24, lr: 1.47e-04
2022-05-08 09:16:30,747 INFO [train.py:715] (1/8) Epoch 15, batch 11650, loss[loss=0.1466, simple_loss=0.2119, pruned_loss=0.04065, over 4961.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.03036, over 972106.10 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 09:17:09,226 INFO [train.py:715] (1/8) Epoch 15, batch 11700, loss[loss=0.1386, simple_loss=0.1995, pruned_loss=0.03886, over 4852.00 frames.], tot_loss[loss=0.135, simple_loss=0.2083, pruned_loss=0.03085, over 971859.64 frames.], batch size: 34, lr: 1.47e-04
2022-05-08 09:17:48,439 INFO [train.py:715] (1/8) Epoch 15, batch 11750, loss[loss=0.1295, simple_loss=0.2138, pruned_loss=0.02258, over 4871.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2081, pruned_loss=0.03072, over 971405.36 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:18:27,440 INFO [train.py:715] (1/8) Epoch 15, batch 11800, loss[loss=0.1363, simple_loss=0.2132, pruned_loss=0.0297, over 4808.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03127, over 971272.14 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:19:05,508 INFO [train.py:715] (1/8) Epoch 15, batch 11850, loss[loss=0.1111, simple_loss=0.1905, pruned_loss=0.01586, over 4935.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03074, over 971747.46 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:19:45,034 INFO [train.py:715] (1/8) Epoch 15, batch 11900, loss[loss=0.1385, simple_loss=0.2185, pruned_loss=0.02925, over 4939.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03064, over 971666.22 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:20:25,114 INFO [train.py:715] (1/8) Epoch 15, batch 11950, loss[loss=0.1509, simple_loss=0.2254, pruned_loss=0.03821, over 4857.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03023, over 971133.32 frames.], batch size: 39, lr: 1.47e-04
2022-05-08 09:21:03,704 INFO [train.py:715] (1/8) Epoch 15, batch 12000, loss[loss=0.1344, simple_loss=0.2177, pruned_loss=0.02553, over 4828.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03013, over 971533.86 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 09:21:03,705 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 09:21:20,396 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.105, simple_loss=0.1887, pruned_loss=0.01066, over 914524.00 frames.
2022-05-08 09:21:59,107 INFO [train.py:715] (1/8) Epoch 15, batch 12050, loss[loss=0.1481, simple_loss=0.2248, pruned_loss=0.03573, over 4801.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.02983, over 970484.74 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:22:38,255 INFO [train.py:715] (1/8) Epoch 15, batch 12100, loss[loss=0.1398, simple_loss=0.2185, pruned_loss=0.03053, over 4923.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02974, over 969878.06 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:23:17,963 INFO [train.py:715] (1/8) Epoch 15, batch 12150, loss[loss=0.1474, simple_loss=0.2283, pruned_loss=0.03323, over 4819.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03034, over 969966.53 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 09:23:56,411 INFO [train.py:715] (1/8) Epoch 15, batch 12200, loss[loss=0.1388, simple_loss=0.2041, pruned_loss=0.03676, over 4804.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02998, over 971079.20 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 09:24:35,177 INFO [train.py:715] (1/8) Epoch 15, batch 12250, loss[loss=0.1282, simple_loss=0.197, pruned_loss=0.02974, over 4825.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.0298, over 970972.21 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:25:14,191 INFO [train.py:715] (1/8) Epoch 15, batch 12300, loss[loss=0.1353, simple_loss=0.2126, pruned_loss=0.029, over 4874.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.02998, over 970571.93 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 09:25:54,055 INFO [train.py:715] (1/8) Epoch 15, batch 12350, loss[loss=0.1284, simple_loss=0.2082, pruned_loss=0.02435, over 4979.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2087, pruned_loss=0.02988, over 971376.83 frames.], batch size: 28, lr: 1.47e-04
2022-05-08 09:26:32,317 INFO [train.py:715] (1/8) Epoch 15, batch 12400, loss[loss=0.1265, simple_loss=0.1935, pruned_loss=0.02975, over 4918.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.0296, over 971178.35 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:27:11,076 INFO [train.py:715] (1/8) Epoch 15, batch 12450, loss[loss=0.1648, simple_loss=0.2308, pruned_loss=0.0494, over 4875.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03003, over 972070.26 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:27:51,101 INFO [train.py:715] (1/8) Epoch 15, batch 12500, loss[loss=0.1297, simple_loss=0.1922, pruned_loss=0.03359, over 4767.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02947, over 971743.40 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:28:29,284 INFO [train.py:715] (1/8) Epoch 15, batch 12550, loss[loss=0.1634, simple_loss=0.2289, pruned_loss=0.04898, over 4847.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02938, over 972307.36 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 09:29:08,343 INFO [train.py:715] (1/8) Epoch 15, batch 12600, loss[loss=0.1282, simple_loss=0.2014, pruned_loss=0.02743, over 4770.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02943, over 971712.80 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:29:46,869 INFO [train.py:715] (1/8) Epoch 15, batch 12650, loss[loss=0.1509, simple_loss=0.2172, pruned_loss=0.04229, over 4804.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.03011, over 970617.69 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:30:26,461 INFO [train.py:715] (1/8) Epoch 15, batch 12700, loss[loss=0.1376, simple_loss=0.2065, pruned_loss=0.03436, over 4955.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03038, over 969771.27 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 09:31:04,796 INFO [train.py:715] (1/8) Epoch 15, batch 12750, loss[loss=0.1436, simple_loss=0.2137, pruned_loss=0.03673, over 4870.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03029, over 970060.49 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:31:43,648 INFO [train.py:715] (1/8) Epoch 15, batch 12800, loss[loss=0.1372, simple_loss=0.2037, pruned_loss=0.03534, over 4880.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.0302, over 971004.38 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:32:23,162 INFO [train.py:715] (1/8) Epoch 15, batch 12850, loss[loss=0.1386, simple_loss=0.2009, pruned_loss=0.03815, over 4957.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02995, over 972068.88 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 09:33:01,777 INFO [train.py:715] (1/8) Epoch 15, batch 12900, loss[loss=0.142, simple_loss=0.2205, pruned_loss=0.03174, over 4960.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02992, over 972407.34 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:33:40,810 INFO [train.py:715] (1/8) Epoch 15, batch 12950, loss[loss=0.1584, simple_loss=0.2301, pruned_loss=0.04333, over 4842.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.03002, over 971396.55 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:34:20,120 INFO [train.py:715] (1/8) Epoch 15, batch 13000, loss[loss=0.1107, simple_loss=0.1828, pruned_loss=0.01933, over 4888.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02987, over 972229.47 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:34:59,665 INFO [train.py:715] (1/8) Epoch 15, batch 13050, loss[loss=0.138, simple_loss=0.2041, pruned_loss=0.03591, over 4901.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03038, over 972066.76 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:35:38,158 INFO [train.py:715] (1/8) Epoch 15, batch 13100, loss[loss=0.117, simple_loss=0.1896, pruned_loss=0.02224, over 4836.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03003, over 972078.27 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 09:36:17,621 INFO [train.py:715] (1/8) Epoch 15, batch 13150, loss[loss=0.1614, simple_loss=0.224, pruned_loss=0.04944, over 4937.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03011, over 972096.93 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 09:36:57,401 INFO [train.py:715] (1/8) Epoch 15, batch 13200, loss[loss=0.1374, simple_loss=0.2117, pruned_loss=0.03151, over 4688.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03076, over 970987.99 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:37:35,200 INFO [train.py:715] (1/8) Epoch 15, batch 13250, loss[loss=0.1504, simple_loss=0.2188, pruned_loss=0.041, over 4984.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03041, over 970798.65 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:38:14,326 INFO [train.py:715] (1/8) Epoch 15, batch 13300, loss[loss=0.1557, simple_loss=0.2215, pruned_loss=0.04493, over 4898.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03039, over 971101.31 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:38:53,945 INFO [train.py:715] (1/8) Epoch 15, batch 13350, loss[loss=0.1116, simple_loss=0.1803, pruned_loss=0.02144, over 4766.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03005, over 971607.09 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 09:39:34,556 INFO [train.py:715] (1/8) Epoch 15, batch 13400, loss[loss=0.1477, simple_loss=0.2255, pruned_loss=0.03495, over 4882.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.0303, over 972008.13 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:40:13,178 INFO [train.py:715] (1/8) Epoch 15, batch 13450, loss[loss=0.1354, simple_loss=0.212, pruned_loss=0.0294, over 4810.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2077, pruned_loss=0.03034, over 972920.67 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:40:51,764 INFO [train.py:715] (1/8) Epoch 15, batch 13500, loss[loss=0.1631, simple_loss=0.2412, pruned_loss=0.04252, over 4945.00 frames.], tot_loss[loss=0.1343, simple_loss=0.208, pruned_loss=0.03034, over 973970.39 frames.], batch size: 39, lr: 1.47e-04
2022-05-08 09:41:31,300 INFO [train.py:715] (1/8) Epoch 15, batch 13550, loss[loss=0.1268, simple_loss=0.2075, pruned_loss=0.02304, over 4694.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03042, over 973711.29 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:42:09,580 INFO [train.py:715] (1/8) Epoch 15, batch 13600, loss[loss=0.1025, simple_loss=0.1746, pruned_loss=0.01517, over 4779.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03038, over 973936.51 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 09:42:48,560 INFO [train.py:715] (1/8) Epoch 15, batch 13650, loss[loss=0.1166, simple_loss=0.1885, pruned_loss=0.02238, over 4879.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2073, pruned_loss=0.03023, over 973932.34 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:43:27,803 INFO [train.py:715] (1/8) Epoch 15, batch 13700, loss[loss=0.1296, simple_loss=0.1993, pruned_loss=0.03, over 4855.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2075, pruned_loss=0.03031, over 973548.63 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 09:44:06,255 INFO [train.py:715] (1/8) Epoch 15, batch 13750, loss[loss=0.1156, simple_loss=0.1892, pruned_loss=0.02105, over 4691.00 frames.], tot_loss[loss=0.1346, simple_loss=0.208, pruned_loss=0.03063, over 973346.68 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:44:44,978 INFO [train.py:715] (1/8) Epoch 15, batch 13800, loss[loss=0.1319, simple_loss=0.2087, pruned_loss=0.02758, over 4939.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2081, pruned_loss=0.03073, over 973182.68 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:45:23,193 INFO [train.py:715] (1/8) Epoch 15, batch 13850, loss[loss=0.1141, simple_loss=0.1937, pruned_loss=0.01729, over 4935.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03075, over 973410.37 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:46:05,197 INFO [train.py:715] (1/8) Epoch 15, batch 13900, loss[loss=0.1396, simple_loss=0.2016, pruned_loss=0.03877, over 4757.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03075, over 972642.41 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 09:46:43,308 INFO [train.py:715] (1/8) Epoch 15, batch 13950, loss[loss=0.1255, simple_loss=0.2031, pruned_loss=0.024, over 4965.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03015, over 973074.03 frames.], batch size: 24, lr: 1.47e-04
2022-05-08 09:47:21,601 INFO [train.py:715] (1/8) Epoch 15, batch 14000, loss[loss=0.1205, simple_loss=0.1988, pruned_loss=0.02111, over 4987.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02986, over 971828.45 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:48:00,879 INFO [train.py:715] (1/8) Epoch 15, batch 14050, loss[loss=0.149, simple_loss=0.2165, pruned_loss=0.04074, over 4690.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02968, over 971990.35 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:48:38,845 INFO [train.py:715] (1/8) Epoch 15, batch 14100, loss[loss=0.1179, simple_loss=0.1921, pruned_loss=0.02184, over 4802.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02952, over 972022.94 frames.], batch size: 24, lr: 1.47e-04
2022-05-08 09:49:17,894 INFO [train.py:715] (1/8) Epoch 15, batch 14150, loss[loss=0.1563, simple_loss=0.217, pruned_loss=0.0478, over 4913.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02949, over 972230.56 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:49:56,536 INFO [train.py:715] (1/8) Epoch 15, batch 14200, loss[loss=0.1061, simple_loss=0.1828, pruned_loss=0.01472, over 4869.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02995, over 971613.93 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:50:35,494 INFO [train.py:715] (1/8) Epoch 15, batch 14250, loss[loss=0.1083, simple_loss=0.1715, pruned_loss=0.02257, over 4813.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02981, over 972036.65 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:51:13,320 INFO [train.py:715] (1/8) Epoch 15, batch 14300, loss[loss=0.1272, simple_loss=0.192, pruned_loss=0.03118, over 4853.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03021, over 971038.69 frames.], batch size: 30, lr: 1.47e-04
2022-05-08 09:51:51,761 INFO [train.py:715] (1/8) Epoch 15, batch 14350, loss[loss=0.1182, simple_loss=0.1954, pruned_loss=0.02045, over 4952.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03028, over 971012.79 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:52:30,863 INFO [train.py:715] (1/8) Epoch 15, batch 14400, loss[loss=0.1576, simple_loss=0.2304, pruned_loss=0.04238, over 4878.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03024, over 971500.41 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:53:08,605 INFO [train.py:715] (1/8) Epoch 15, batch 14450, loss[loss=0.1398, simple_loss=0.2144, pruned_loss=0.03262, over 4876.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.0304, over 971214.08 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:53:47,586 INFO [train.py:715] (1/8) Epoch 15, batch 14500, loss[loss=0.1621, simple_loss=0.2235, pruned_loss=0.05035, over 4805.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03048, over 972196.67 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:54:25,850 INFO [train.py:715] (1/8) Epoch 15, batch 14550, loss[loss=0.1238, simple_loss=0.202, pruned_loss=0.02277, over 4985.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2091, pruned_loss=0.03026, over 973328.98 frames.], batch size: 28, lr: 1.47e-04
2022-05-08 09:55:04,847 INFO [train.py:715] (1/8) Epoch 15, batch 14600, loss[loss=0.1451, simple_loss=0.2107, pruned_loss=0.03977, over 4850.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2085, pruned_loss=0.02986, over 973626.37 frames.], batch size: 30, lr: 1.47e-04
2022-05-08 09:55:42,673 INFO [train.py:715] (1/8) Epoch 15, batch 14650, loss[loss=0.172, simple_loss=0.2515, pruned_loss=0.04618, over 4729.00 frames.], tot_loss[loss=0.1355, simple_loss=0.21, pruned_loss=0.03044, over 972119.07 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:56:20,651 INFO [train.py:715] (1/8) Epoch 15, batch 14700, loss[loss=0.1224, simple_loss=0.199, pruned_loss=0.02288, over 4846.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2097, pruned_loss=0.03037, over 972457.17 frames.], batch size: 34, lr: 1.47e-04
2022-05-08 09:56:59,719 INFO [train.py:715] (1/8) Epoch 15, batch 14750, loss[loss=0.101, simple_loss=0.1849, pruned_loss=0.008497, over 4801.00 frames.], tot_loss[loss=0.1343, simple_loss=0.209, pruned_loss=0.02986, over 972794.33 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 09:57:37,353 INFO [train.py:715] (1/8) Epoch 15, batch 14800, loss[loss=0.1312, simple_loss=0.2108, pruned_loss=0.02582, over 4878.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2085, pruned_loss=0.02948, over 971635.61 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:58:16,194 INFO [train.py:715] (1/8) Epoch 15, batch 14850, loss[loss=0.1513, simple_loss=0.2318, pruned_loss=0.03534, over 4937.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2089, pruned_loss=0.03006, over 970809.71 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:58:55,098 INFO [train.py:715] (1/8) Epoch 15, batch 14900, loss[loss=0.1257, simple_loss=0.1928, pruned_loss=0.02933, over 4841.00 frames.], tot_loss[loss=0.1346, simple_loss=0.209, pruned_loss=0.0301, over 971118.89 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 09:59:33,267 INFO [train.py:715] (1/8) Epoch 15, batch 14950, loss[loss=0.1501, simple_loss=0.2251, pruned_loss=0.03757, over 4702.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03004, over 971748.10 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:00:11,564 INFO [train.py:715] (1/8) Epoch 15, batch 15000, loss[loss=0.1386, simple_loss=0.2104, pruned_loss=0.03346, over 4661.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03002, over 971337.24 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:00:11,564 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 10:00:26,345 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1051, simple_loss=0.1887, pruned_loss=0.01077, over 914524.00 frames.
2022-05-08 10:01:05,817 INFO [train.py:715] (1/8) Epoch 15, batch 15050, loss[loss=0.1329, simple_loss=0.2126, pruned_loss=0.02656, over 4828.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.03015, over 971937.02 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 10:01:43,979 INFO [train.py:715] (1/8) Epoch 15, batch 15100, loss[loss=0.1306, simple_loss=0.2073, pruned_loss=0.02696, over 4962.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03052, over 971630.72 frames.], batch size: 39, lr: 1.47e-04
2022-05-08 10:02:23,330 INFO [train.py:715] (1/8) Epoch 15, batch 15150, loss[loss=0.1381, simple_loss=0.2092, pruned_loss=0.03353, over 4749.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2095, pruned_loss=0.03043, over 971726.04 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:03:01,053 INFO [train.py:715] (1/8) Epoch 15, batch 15200, loss[loss=0.1122, simple_loss=0.1769, pruned_loss=0.02372, over 4790.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2103, pruned_loss=0.03094, over 972689.12 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:03:39,352 INFO [train.py:715] (1/8) Epoch 15, batch 15250, loss[loss=0.1303, simple_loss=0.2125, pruned_loss=0.02407, over 4892.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2106, pruned_loss=0.03113, over 972174.50 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 10:04:18,902 INFO [train.py:715] (1/8) Epoch 15, batch 15300, loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02857, over 4806.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2112, pruned_loss=0.0313, over 972503.99 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 10:04:56,985 INFO [train.py:715] (1/8) Epoch 15, batch 15350, loss[loss=0.1607, simple_loss=0.2306, pruned_loss=0.04537, over 4882.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2102, pruned_loss=0.0308, over 972388.50 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 10:05:35,894 INFO [train.py:715] (1/8) Epoch 15, batch 15400, loss[loss=0.1207, simple_loss=0.1864, pruned_loss=0.02746, over 4846.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.03025, over 972661.85 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:06:13,986 INFO [train.py:715] (1/8) Epoch 15, batch 15450, loss[loss=0.159, simple_loss=0.2269, pruned_loss=0.04549, over 4979.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03104, over 973037.36 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:06:52,882 INFO [train.py:715] (1/8) Epoch 15, batch 15500, loss[loss=0.1104, simple_loss=0.1897, pruned_loss=0.01562, over 4944.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03089, over 973241.49 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:07:31,440 INFO [train.py:715] (1/8) Epoch 15, batch 15550, loss[loss=0.1503, simple_loss=0.2232, pruned_loss=0.03872, over 4965.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.03122, over 972585.83 frames.], batch size: 24, lr: 1.47e-04
2022-05-08 10:08:10,329 INFO [train.py:715] (1/8) Epoch 15, batch 15600, loss[loss=0.1553, simple_loss=0.225, pruned_loss=0.04283, over 4782.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03149, over 973091.49 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:08:49,135 INFO [train.py:715] (1/8) Epoch 15, batch 15650, loss[loss=0.1361, simple_loss=0.2186, pruned_loss=0.02674, over 4902.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.03093, over 972005.68 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 10:09:27,218 INFO [train.py:715] (1/8) Epoch 15, batch 15700, loss[loss=0.1155, simple_loss=0.1927, pruned_loss=0.01919, over 4968.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03094, over 972630.86 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:10:05,788 INFO [train.py:715] (1/8) Epoch 15, batch 15750, loss[loss=0.1148, simple_loss=0.1866, pruned_loss=0.02148, over 4787.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.031, over 972911.07 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 10:10:44,348 INFO [train.py:715] (1/8) Epoch 15, batch 15800, loss[loss=0.1198, simple_loss=0.1992, pruned_loss=0.02017, over 4930.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03082, over 972810.01 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 10:11:23,020 INFO [train.py:715] (1/8) Epoch 15, batch 15850, loss[loss=0.1213, simple_loss=0.1989, pruned_loss=0.02184, over 4921.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03056, over 973293.68 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 10:12:01,147 INFO [train.py:715] (1/8) Epoch 15, batch 15900, loss[loss=0.1441, simple_loss=0.2321, pruned_loss=0.02803, over 4734.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03059, over 973517.04 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:12:39,300 INFO [train.py:715] (1/8) Epoch 15, batch 15950, loss[loss=0.1348, simple_loss=0.212, pruned_loss=0.0288, over 4870.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03042, over 973967.21 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:13:18,371 INFO [train.py:715] (1/8) Epoch 15, batch 16000, loss[loss=0.1304, simple_loss=0.2097, pruned_loss=0.02554, over 4792.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.03062, over 973588.14 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:13:56,003 INFO [train.py:715] (1/8) Epoch 15, batch 16050, loss[loss=0.146, simple_loss=0.2265, pruned_loss=0.03275, over 4938.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.0306, over 973082.86 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 10:14:34,596 INFO [train.py:715] (1/8) Epoch 15, batch 16100, loss[loss=0.1349, simple_loss=0.2179, pruned_loss=0.02589, over 4912.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03096, over 973240.55 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 10:15:13,038 INFO [train.py:715] (1/8) Epoch 15, batch 16150, loss[loss=0.134, simple_loss=0.2037, pruned_loss=0.03215, over 4832.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2101, pruned_loss=0.03106, over 972920.12 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:15:51,556 INFO [train.py:715] (1/8) Epoch 15, batch 16200, loss[loss=0.1213, simple_loss=0.1949, pruned_loss=0.02389, over 4865.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03132, over 972715.33 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 10:16:29,839 INFO [train.py:715] (1/8) Epoch 15, batch 16250, loss[loss=0.1608, simple_loss=0.2322, pruned_loss=0.04466, over 4869.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03135, over 973036.09 frames.], batch size: 38, lr: 1.47e-04
2022-05-08 10:17:08,216 INFO [train.py:715] (1/8) Epoch 15, batch 16300, loss[loss=0.1348, simple_loss=0.2098, pruned_loss=0.02993, over 4705.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03097, over 973299.17 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:17:46,752 INFO [train.py:715] (1/8) Epoch 15, batch 16350, loss[loss=0.1065, simple_loss=0.1824, pruned_loss=0.01526, over 4770.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2093, pruned_loss=0.03031, over 973014.94 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:18:24,621 INFO [train.py:715] (1/8) Epoch 15, batch 16400, loss[loss=0.111, simple_loss=0.1862, pruned_loss=0.01792, over 4956.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.0301, over 973277.43 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:19:03,504 INFO [train.py:715] (1/8) Epoch 15, batch 16450, loss[loss=0.1444, simple_loss=0.2152, pruned_loss=0.03676, over 4833.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03028, over 973553.46 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:19:41,759 INFO [train.py:715] (1/8) Epoch 15, batch 16500, loss[loss=0.162, simple_loss=0.218, pruned_loss=0.05297, over 4965.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03035, over 973148.68 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:20:20,130 INFO [train.py:715] (1/8) Epoch 15, batch 16550, loss[loss=0.1436, simple_loss=0.2173, pruned_loss=0.03497, over 4986.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2095, pruned_loss=0.03048, over 973011.44 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:20:58,278 INFO [train.py:715] (1/8) Epoch 15, batch 16600, loss[loss=0.1197, simple_loss=0.2066, pruned_loss=0.01641, over 4825.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2092, pruned_loss=0.0305, over 973397.45 frames.], batch size: 27, lr: 1.47e-04
2022-05-08 10:21:37,034 INFO [train.py:715] (1/8) Epoch 15, batch 16650, loss[loss=0.1552, simple_loss=0.2137, pruned_loss=0.04833, over 4979.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.0301, over 974243.16 frames.], batch size: 39, lr: 1.47e-04
2022-05-08 10:22:16,802 INFO [train.py:715] (1/8) Epoch 15, batch 16700, loss[loss=0.1115, simple_loss=0.1849, pruned_loss=0.01908, over 4933.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.02997, over 973553.91 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 10:22:55,523 INFO [train.py:715] (1/8) Epoch 15, batch 16750, loss[loss=0.1129, simple_loss=0.1855, pruned_loss=0.02017, over 4964.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03038, over 971799.83 frames.], batch size: 31, lr: 1.47e-04
2022-05-08 10:23:34,513 INFO [train.py:715] (1/8) Epoch 15, batch 16800, loss[loss=0.1118, simple_loss=0.181, pruned_loss=0.02136, over 4930.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02996, over 971409.08 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 10:24:13,669 INFO [train.py:715] (1/8) Epoch 15, batch 16850, loss[loss=0.1324, simple_loss=0.2044, pruned_loss=0.03024, over 4978.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03014, over 971945.33 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:24:52,754 INFO [train.py:715] (1/8) Epoch 15, batch 16900, loss[loss=0.1324, simple_loss=0.2043, pruned_loss=0.03022, over 4785.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03047, over 972647.03 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:25:31,720 INFO [train.py:715] (1/8) Epoch 15, batch 16950, loss[loss=0.1272, simple_loss=0.2124, pruned_loss=0.02106, over 4944.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03046, over 972850.23 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 10:26:10,072 INFO [train.py:715] (1/8) Epoch 15, batch 17000, loss[loss=0.1395, simple_loss=0.2178, pruned_loss=0.03058, over 4805.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03054, over 972808.16 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:26:49,334 INFO [train.py:715] (1/8) Epoch 15, batch 17050, loss[loss=0.1506, simple_loss=0.2198, pruned_loss=0.04068, over 4881.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03081, over 972179.79 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:27:27,262 INFO [train.py:715] (1/8) Epoch 15, batch 17100, loss[loss=0.1303, simple_loss=0.2077, pruned_loss=0.02645, over 4873.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03059, over 971671.04 frames.], batch size: 39, lr: 1.47e-04
2022-05-08 10:28:06,075 INFO [train.py:715] (1/8) Epoch 15, batch 17150, loss[loss=0.1193, simple_loss=0.2034, pruned_loss=0.01763, over 4974.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03039, over 972317.62 frames.], batch size: 24, lr: 1.47e-04
2022-05-08 10:28:44,478 INFO [train.py:715] (1/8) Epoch 15, batch 17200, loss[loss=0.1269, simple_loss=0.2066, pruned_loss=0.02359, over 4965.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03075, over 972904.59 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:29:23,148 INFO [train.py:715] (1/8) Epoch 15, batch 17250, loss[loss=0.1191, simple_loss=0.1834, pruned_loss=0.02742, over 4815.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03035, over 971309.33 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:30:01,722 INFO [train.py:715] (1/8) Epoch 15, batch 17300, loss[loss=0.1451, simple_loss=0.2134, pruned_loss=0.03833, over 4948.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03025, over 971664.66 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 10:30:40,361 INFO [train.py:715] (1/8) Epoch 15, batch 17350, loss[loss=0.1391, simple_loss=0.2061, pruned_loss=0.03606, over 4818.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03057, over 971217.44 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:31:19,950 INFO [train.py:715] (1/8) Epoch 15, batch 17400, loss[loss=0.1365, simple_loss=0.2117, pruned_loss=0.03061, over 4785.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03057, over 971323.60 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 10:31:57,892 INFO [train.py:715] (1/8) Epoch 15, batch 17450, loss[loss=0.1339, simple_loss=0.2186, pruned_loss=0.02462, over 4935.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03021, over 971626.94 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:32:36,870 INFO [train.py:715] (1/8) Epoch 15, batch 17500, loss[loss=0.1441, simple_loss=0.1962, pruned_loss=0.046, over 4992.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03046, over 971782.90 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:33:15,844 INFO [train.py:715] (1/8) Epoch 15, batch 17550, loss[loss=0.1151, simple_loss=0.1917, pruned_loss=0.01928, over 4810.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.03001, over 972262.83 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 10:33:54,434 INFO [train.py:715] (1/8) Epoch 15, batch 17600, loss[loss=0.1584, simple_loss=0.2254, pruned_loss=0.04566, over 4831.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03049, over 972070.28 frames.], batch size: 30, lr: 1.47e-04
2022-05-08 10:34:32,814 INFO [train.py:715] (1/8) Epoch 15, batch 17650, loss[loss=0.1151, simple_loss=0.1794, pruned_loss=0.02544, over 4891.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03042, over 972001.67 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 10:35:11,434 INFO [train.py:715] (1/8) Epoch 15, batch 17700, loss[loss=0.1407, simple_loss=0.2232, pruned_loss=0.02908, over 4796.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2079, pruned_loss=0.03054, over 971544.40 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:35:50,323 INFO [train.py:715] (1/8) Epoch 15, batch 17750, loss[loss=0.1109, simple_loss=0.1959, pruned_loss=0.01292, over 4942.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02989, over 971987.58 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 10:36:28,684 INFO [train.py:715] (1/8) Epoch 15, batch 17800, loss[loss=0.1204, simple_loss=0.1983, pruned_loss=0.02127, over 4825.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03006, over 972055.46 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 10:37:07,668 INFO [train.py:715] (1/8) Epoch 15, batch 17850, loss[loss=0.1857, simple_loss=0.2521, pruned_loss=0.05968, over 4931.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02987, over 971982.32 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 10:37:46,657 INFO [train.py:715] (1/8) Epoch 15, batch 17900, loss[loss=0.1212, simple_loss=0.2136, pruned_loss=0.01438, over 4852.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02954, over 971973.11 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 10:38:25,488 INFO [train.py:715] (1/8) Epoch 15, batch 17950, loss[loss=0.1418, simple_loss=0.221, pruned_loss=0.0313, over 4838.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02935, over 971956.64 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:39:03,819 INFO [train.py:715] (1/8) Epoch 15, batch 18000, loss[loss=0.1304, simple_loss=0.2118, pruned_loss=0.0245, over 4862.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02879, over 971981.05 frames.], batch size: 32, lr: 1.47e-04
2022-05-08 10:39:03,820 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 10:39:13,330 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1048, simple_loss=0.1885, pruned_loss=0.01059, over 914524.00 frames.
2022-05-08 10:39:51,811 INFO [train.py:715] (1/8) Epoch 15, batch 18050, loss[loss=0.1262, simple_loss=0.2096, pruned_loss=0.02142, over 4781.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02922, over 971945.79 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:40:30,478 INFO [train.py:715] (1/8) Epoch 15, batch 18100, loss[loss=0.111, simple_loss=0.1828, pruned_loss=0.01956, over 4745.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02906, over 971800.44 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 10:41:09,235 INFO [train.py:715] (1/8) Epoch 15, batch 18150, loss[loss=0.138, simple_loss=0.2218, pruned_loss=0.02712, over 4817.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02882, over 971431.32 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 10:41:47,121 INFO [train.py:715] (1/8) Epoch 15, batch 18200, loss[loss=0.1605, simple_loss=0.2302, pruned_loss=0.04536, over 4973.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2061, pruned_loss=0.02933, over 971793.81 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 10:42:25,782 INFO [train.py:715] (1/8) Epoch 15, batch 18250, loss[loss=0.1526, simple_loss=0.2382, pruned_loss=0.03352, over 4858.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.0296, over 971167.07 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 10:43:04,443 INFO [train.py:715] (1/8) Epoch 15, batch 18300, loss[loss=0.1625, simple_loss=0.2284, pruned_loss=0.04831, over 4916.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02995, over 970754.35 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 10:43:42,535 INFO [train.py:715] (1/8) Epoch 15, batch 18350, loss[loss=0.1395, simple_loss=0.2158, pruned_loss=0.03161, over 4957.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03005, over 972237.31 frames.], batch size: 28, lr: 1.46e-04
2022-05-08 10:44:21,121 INFO [train.py:715] (1/8) Epoch 15, batch 18400, loss[loss=0.1459, simple_loss=0.2347, pruned_loss=0.02852, over 4798.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03022, over 972303.40 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 10:44:59,620 INFO [train.py:715] (1/8) Epoch 15, batch 18450, loss[loss=0.1308, simple_loss=0.2158, pruned_loss=0.02287, over 4801.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03028, over 971410.98 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 10:45:38,879 INFO [train.py:715] (1/8) Epoch 15, batch 18500, loss[loss=0.1167, simple_loss=0.189, pruned_loss=0.02222, over 4806.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2067, pruned_loss=0.02957, over 971441.32 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 10:46:17,374 INFO [train.py:715] (1/8) Epoch 15, batch 18550, loss[loss=0.1232, simple_loss=0.204, pruned_loss=0.02122, over 4786.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2072, pruned_loss=0.02986, over 971113.84 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 10:46:55,964 INFO [train.py:715] (1/8) Epoch 15, batch 18600, loss[loss=0.1297, simple_loss=0.2063, pruned_loss=0.02654, over 4821.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02961, over 971422.83 frames.], batch size: 26, lr: 1.46e-04
2022-05-08 10:47:34,881 INFO [train.py:715] (1/8) Epoch 15, batch 18650, loss[loss=0.1425, simple_loss=0.2157, pruned_loss=0.03466, over 4778.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02963, over 971110.67 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 10:48:13,537 INFO [train.py:715] (1/8) Epoch 15, batch 18700, loss[loss=0.139, simple_loss=0.2042, pruned_loss=0.03692, over 4837.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02997, over 971899.32 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 10:48:52,386 INFO [train.py:715] (1/8) Epoch 15, batch 18750, loss[loss=0.1254, simple_loss=0.1959, pruned_loss=0.02747, over 4821.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03007, over 971897.06 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 10:49:31,673 INFO [train.py:715] (1/8) Epoch 15, batch 18800, loss[loss=0.1306, simple_loss=0.2019, pruned_loss=0.02969, over 4976.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2097, pruned_loss=0.03058, over 971858.29 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 10:50:10,917 INFO [train.py:715] (1/8) Epoch 15, batch 18850, loss[loss=0.1491, simple_loss=0.229, pruned_loss=0.03458, over 4756.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03014, over 971476.34 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 10:50:49,338 INFO [train.py:715] (1/8) Epoch 15, batch 18900, loss[loss=0.1287, simple_loss=0.2045, pruned_loss=0.02645, over 4803.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.0305, over 971213.32 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 10:51:28,550 INFO [train.py:715] (1/8) Epoch 15, batch 18950, loss[loss=0.1428, simple_loss=0.221, pruned_loss=0.03232, over 4756.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03035, over 970994.57 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 10:52:07,862 INFO [train.py:715] (1/8) Epoch 15, batch 19000, loss[loss=0.1192, simple_loss=0.191, pruned_loss=0.02367, over 4875.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02998, over 971731.78 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 10:52:46,219 INFO [train.py:715] (1/8) Epoch 15, batch 19050, loss[loss=0.1432, simple_loss=0.2193, pruned_loss=0.03352, over 4780.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.0301, over 971920.51 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 10:53:25,396 INFO [train.py:715] (1/8) Epoch 15, batch 19100, loss[loss=0.1261, simple_loss=0.2001, pruned_loss=0.02609, over 4951.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02988, over 972217.32 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 10:54:03,699 INFO [train.py:715] (1/8) Epoch 15, batch 19150, loss[loss=0.116, simple_loss=0.2011, pruned_loss=0.0154, over 4947.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03027, over 971173.82 frames.], batch size: 29, lr: 1.46e-04
2022-05-08 10:54:41,933 INFO [train.py:715] (1/8) Epoch 15, batch 19200, loss[loss=0.118, simple_loss=0.2014, pruned_loss=0.01726, over 4943.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03018, over 971385.86 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 10:55:19,947 INFO [train.py:715] (1/8) Epoch 15, batch 19250, loss[loss=0.1394, simple_loss=0.2133, pruned_loss=0.03278, over 4908.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03039, over 971666.29 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 10:55:58,064 INFO [train.py:715] (1/8) Epoch 15, batch 19300, loss[loss=0.1688, simple_loss=0.2555, pruned_loss=0.04108, over 4959.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03051, over 972701.80 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 10:56:36,949 INFO [train.py:715] (1/8) Epoch 15, batch 19350, loss[loss=0.1066, simple_loss=0.1744, pruned_loss=0.01939, over 4748.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.0304, over 972870.15 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 10:57:14,728 INFO [train.py:715] (1/8) Epoch 15, batch 19400, loss[loss=0.139, simple_loss=0.2028, pruned_loss=0.03755, over 4958.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03051, over 972763.67 frames.], batch size: 35, lr: 1.46e-04
2022-05-08 10:57:53,615 INFO [train.py:715] (1/8) Epoch 15, batch 19450, loss[loss=0.1247, simple_loss=0.2026, pruned_loss=0.02337, over 4822.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03037, over 972240.70 frames.], batch size: 27, lr: 1.46e-04
2022-05-08 10:58:31,636 INFO [train.py:715] (1/8) Epoch 15, batch 19500, loss[loss=0.1295, simple_loss=0.2136, pruned_loss=0.02274, over 4814.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03011, over 972145.38 frames.], batch size: 26, lr: 1.46e-04
2022-05-08 10:59:09,770 INFO [train.py:715] (1/8) Epoch 15, batch 19550, loss[loss=0.1536, simple_loss=0.2201, pruned_loss=0.0435, over 4778.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03034, over 972080.35 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 10:59:48,218 INFO [train.py:715] (1/8) Epoch 15, batch 19600, loss[loss=0.1303, simple_loss=0.2051, pruned_loss=0.02779, over 4934.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02996, over 972820.69 frames.], batch size: 29, lr: 1.46e-04
2022-05-08 11:00:26,255 INFO [train.py:715] (1/8) Epoch 15, batch 19650, loss[loss=0.1589, simple_loss=0.2231, pruned_loss=0.04735, over 4803.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03001, over 972219.58 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:01:05,291 INFO [train.py:715] (1/8) Epoch 15, batch 19700, loss[loss=0.1443, simple_loss=0.2291, pruned_loss=0.02975, over 4985.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02981, over 972264.75 frames.], batch size: 28, lr: 1.46e-04
2022-05-08 11:01:42,981 INFO [train.py:715] (1/8) Epoch 15, batch 19750, loss[loss=0.1461, simple_loss=0.2187, pruned_loss=0.03671, over 4968.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03045, over 972603.09 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:02:21,392 INFO [train.py:715] (1/8) Epoch 15, batch 19800, loss[loss=0.1609, simple_loss=0.2256, pruned_loss=0.04813, over 4804.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03063, over 972239.78 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:02:59,698 INFO [train.py:715] (1/8) Epoch 15, batch 19850, loss[loss=0.105, simple_loss=0.1773, pruned_loss=0.01632, over 4767.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03024, over 971661.86 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 11:03:37,788 INFO [train.py:715] (1/8) Epoch 15, batch 19900, loss[loss=0.1187, simple_loss=0.1884, pruned_loss=0.02453, over 4953.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.0301, over 971879.03 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 11:04:16,974 INFO [train.py:715] (1/8) Epoch 15, batch 19950, loss[loss=0.1265, simple_loss=0.2044, pruned_loss=0.02434, over 4898.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03015, over 971950.40 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:04:55,167 INFO [train.py:715] (1/8) Epoch 15, batch 20000, loss[loss=0.1185, simple_loss=0.195, pruned_loss=0.02104, over 4881.00 frames.], tot_loss[loss=0.1343, simple_loss=0.208, pruned_loss=0.03034, over 971910.13 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:05:33,567 INFO [train.py:715] (1/8) Epoch 15, batch 20050, loss[loss=0.1456, simple_loss=0.2212, pruned_loss=0.03497, over 4871.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03059, over 972108.19 frames.], batch size: 32, lr: 1.46e-04
2022-05-08 11:06:11,839 INFO [train.py:715] (1/8) Epoch 15, batch 20100, loss[loss=0.1083, simple_loss=0.1891, pruned_loss=0.01374, over 4805.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02974, over 972372.85 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:06:50,117 INFO [train.py:715] (1/8) Epoch 15, batch 20150, loss[loss=0.1421, simple_loss=0.224, pruned_loss=0.03013, over 4984.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2063, pruned_loss=0.02943, over 972166.42 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 11:07:28,126 INFO [train.py:715] (1/8) Epoch 15, batch 20200, loss[loss=0.1465, simple_loss=0.2093, pruned_loss=0.04191, over 4967.00 frames.], tot_loss[loss=0.132, simple_loss=0.2058, pruned_loss=0.02908, over 972562.60 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:08:05,821 INFO [train.py:715] (1/8) Epoch 15, batch 20250, loss[loss=0.1144, simple_loss=0.1947, pruned_loss=0.017, over 4831.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.02929, over 973174.13 frames.], batch size: 26, lr: 1.46e-04
2022-05-08 11:08:44,518 INFO [train.py:715] (1/8) Epoch 15, batch 20300, loss[loss=0.1723, simple_loss=0.2622, pruned_loss=0.0412, over 4927.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02984, over 973323.72 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 11:09:22,706 INFO [train.py:715] (1/8) Epoch 15, batch 20350, loss[loss=0.1148, simple_loss=0.1814, pruned_loss=0.02411, over 4865.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02975, over 972656.27 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:10:01,087 INFO [train.py:715] (1/8) Epoch 15, batch 20400, loss[loss=0.1309, simple_loss=0.2112, pruned_loss=0.02526, over 4809.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02992, over 971936.20 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 11:10:38,947 INFO [train.py:715] (1/8) Epoch 15, batch 20450, loss[loss=0.1075, simple_loss=0.1853, pruned_loss=0.01489, over 4951.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03005, over 971336.80 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 11:11:17,698 INFO [train.py:715] (1/8) Epoch 15, batch 20500, loss[loss=0.1172, simple_loss=0.1842, pruned_loss=0.02514, over 4780.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03076, over 970881.68 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 11:11:55,871 INFO [train.py:715] (1/8) Epoch 15, batch 20550, loss[loss=0.1467, simple_loss=0.2212, pruned_loss=0.0361, over 4987.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03077, over 971553.58 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:12:33,920 INFO [train.py:715] (1/8) Epoch 15, batch 20600, loss[loss=0.1511, simple_loss=0.2225, pruned_loss=0.03986, over 4942.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03042, over 971618.72 frames.], batch size: 35, lr: 1.46e-04
2022-05-08 11:13:12,980 INFO [train.py:715] (1/8) Epoch 15, batch 20650, loss[loss=0.1232, simple_loss=0.1984, pruned_loss=0.02396, over 4833.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.02994, over 971250.95 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:13:51,739 INFO [train.py:715] (1/8) Epoch 15, batch 20700, loss[loss=0.1247, simple_loss=0.196, pruned_loss=0.02668, over 4958.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2087, pruned_loss=0.0299, over 972170.08 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 11:14:31,083 INFO [train.py:715] (1/8) Epoch 15, batch 20750, loss[loss=0.1289, simple_loss=0.1972, pruned_loss=0.03032, over 4819.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02968, over 971856.79 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 11:15:09,387 INFO [train.py:715] (1/8) Epoch 15, batch 20800, loss[loss=0.1267, simple_loss=0.2006, pruned_loss=0.0264, over 4949.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02949, over 971751.87 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 11:15:48,762 INFO [train.py:715] (1/8) Epoch 15, batch 20850, loss[loss=0.1316, simple_loss=0.2154, pruned_loss=0.02389, over 4787.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02959, over 971992.35 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:16:27,992 INFO [train.py:715] (1/8) Epoch 15, batch 20900, loss[loss=0.1604, simple_loss=0.2361, pruned_loss=0.04232, over 4881.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02992, over 972210.12 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 11:17:06,242 INFO [train.py:715] (1/8) Epoch 15, batch 20950, loss[loss=0.1305, simple_loss=0.1941, pruned_loss=0.03341, over 4849.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03077, over 972002.50 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:17:45,527 INFO [train.py:715] (1/8) Epoch 15, batch 21000, loss[loss=0.113, simple_loss=0.1786, pruned_loss=0.02374, over 4786.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03062, over 972302.13 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:17:45,528 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 11:17:56,038 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1051, simple_loss=0.1887, pruned_loss=0.01075, over 914524.00 frames.
2022-05-08 11:18:35,286 INFO [train.py:715] (1/8) Epoch 15, batch 21050, loss[loss=0.1355, simple_loss=0.1973, pruned_loss=0.03686, over 4771.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03039, over 972327.04 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:19:14,769 INFO [train.py:715] (1/8) Epoch 15, batch 21100, loss[loss=0.1583, simple_loss=0.2236, pruned_loss=0.04648, over 4971.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03012, over 973769.95 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 11:19:53,786 INFO [train.py:715] (1/8) Epoch 15, batch 21150, loss[loss=0.1497, simple_loss=0.2292, pruned_loss=0.03504, over 4762.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02998, over 973250.41 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:20:32,268 INFO [train.py:715] (1/8) Epoch 15, batch 21200, loss[loss=0.1434, simple_loss=0.2166, pruned_loss=0.03516, over 4897.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02997, over 972970.31 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:21:11,105 INFO [train.py:715] (1/8) Epoch 15, batch 21250, loss[loss=0.1519, simple_loss=0.2309, pruned_loss=0.03639, over 4904.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03034, over 972846.33 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:21:49,144 INFO [train.py:715] (1/8) Epoch 15, batch 21300, loss[loss=0.1543, simple_loss=0.2212, pruned_loss=0.04368, over 4870.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03011, over 972756.05 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 11:22:26,790 INFO [train.py:715] (1/8) Epoch 15, batch 21350, loss[loss=0.1666, simple_loss=0.2319, pruned_loss=0.05065, over 4832.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03064, over 972754.92 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:23:05,097 INFO [train.py:715] (1/8) Epoch 15, batch 21400, loss[loss=0.126, simple_loss=0.1971, pruned_loss=0.02744, over 4924.00 frames.], tot_loss[loss=0.1347, simple_loss=0.208, pruned_loss=0.03071, over 972211.74 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 11:23:43,353 INFO [train.py:715] (1/8) Epoch 15, batch 21450, loss[loss=0.131, simple_loss=0.2138, pruned_loss=0.02412, over 4760.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.0304, over 972842.98 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:24:21,357 INFO [train.py:715] (1/8) Epoch 15, batch 21500, loss[loss=0.1354, simple_loss=0.2081, pruned_loss=0.03135, over 4831.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03001, over 972498.58 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:24:59,648 INFO [train.py:715] (1/8) Epoch 15, batch 21550, loss[loss=0.1215, simple_loss=0.1964, pruned_loss=0.02329, over 4862.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2088, pruned_loss=0.02998, over 972259.85 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 11:25:38,153 INFO [train.py:715] (1/8) Epoch 15, batch 21600, loss[loss=0.124, simple_loss=0.2028, pruned_loss=0.02259, over 4782.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2089, pruned_loss=0.02984, over 972458.21 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 11:26:16,022 INFO [train.py:715] (1/8) Epoch 15, batch 21650, loss[loss=0.1291, simple_loss=0.2055, pruned_loss=0.02631, over 4871.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03017, over 973546.58 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 11:26:54,239 INFO [train.py:715] (1/8) Epoch 15, batch 21700, loss[loss=0.1488, simple_loss=0.2172, pruned_loss=0.04026, over 4745.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03033, over 972538.96 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:27:32,377 INFO [train.py:715] (1/8) Epoch 15, batch 21750, loss[loss=0.1232, simple_loss=0.1989, pruned_loss=0.02378, over 4836.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03066, over 973068.53 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:28:10,477 INFO [train.py:715] (1/8) Epoch 15, batch 21800, loss[loss=0.1274, simple_loss=0.201, pruned_loss=0.02693, over 4783.00 frames.], tot_loss[loss=0.1347, simple_loss=0.208, pruned_loss=0.0307, over 972839.42 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 11:28:48,404 INFO [train.py:715] (1/8) Epoch 15, batch 21850, loss[loss=0.1358, simple_loss=0.2081, pruned_loss=0.0317, over 4834.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02971, over 972803.10 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:29:29,585 INFO [train.py:715] (1/8) Epoch 15, batch 21900, loss[loss=0.1395, simple_loss=0.2083, pruned_loss=0.03537, over 4851.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03031, over 972765.22 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:30:08,735 INFO [train.py:715] (1/8) Epoch 15, batch 21950, loss[loss=0.1397, simple_loss=0.2103, pruned_loss=0.03449, over 4806.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.0303, over 972419.15 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 11:30:47,269 INFO [train.py:715] (1/8) Epoch 15, batch 22000, loss[loss=0.1193, simple_loss=0.1879, pruned_loss=0.02537, over 4839.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03051, over 972024.75 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:31:25,790 INFO [train.py:715] (1/8) Epoch 15, batch 22050, loss[loss=0.1178, simple_loss=0.1957, pruned_loss=0.01994, over 4828.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03003, over 972645.11 frames.], batch size: 26, lr: 1.46e-04
2022-05-08 11:32:05,113 INFO [train.py:715] (1/8) Epoch 15, batch 22100, loss[loss=0.1292, simple_loss=0.2027, pruned_loss=0.02782, over 4887.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02967, over 972318.90 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:32:43,915 INFO [train.py:715] (1/8) Epoch 15, batch 22150, loss[loss=0.1256, simple_loss=0.198, pruned_loss=0.02661, over 4695.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02971, over 972032.67 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:33:22,285 INFO [train.py:715] (1/8) Epoch 15, batch 22200, loss[loss=0.1336, simple_loss=0.2168, pruned_loss=0.02524, over 4883.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02947, over 971407.31 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 11:34:01,327 INFO [train.py:715] (1/8) Epoch 15, batch 22250, loss[loss=0.14, simple_loss=0.2139, pruned_loss=0.03307, over 4977.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02993, over 971984.52 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 11:34:40,259 INFO [train.py:715] (1/8) Epoch 15, batch 22300, loss[loss=0.1286, simple_loss=0.2071, pruned_loss=0.02501, over 4932.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02969, over 971827.18 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 11:35:18,795 INFO [train.py:715] (1/8) Epoch 15, batch 22350, loss[loss=0.1047, simple_loss=0.1749, pruned_loss=0.01724, over 4849.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02985, over 972715.81 frames.], batch size: 32, lr: 1.46e-04
2022-05-08 11:35:57,367 INFO [train.py:715] (1/8) Epoch 15, batch 22400, loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02959, over 4771.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02988, over 972025.47 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:36:36,629 INFO [train.py:715] (1/8) Epoch 15, batch 22450, loss[loss=0.1476, simple_loss=0.2268, pruned_loss=0.03418, over 4970.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2078, pruned_loss=0.03014, over 971473.13 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:37:15,517 INFO [train.py:715] (1/8) Epoch 15, batch 22500, loss[loss=0.1114, simple_loss=0.1905, pruned_loss=0.01612, over 4796.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02991, over 971970.64 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:37:54,243 INFO [train.py:715] (1/8) Epoch 15, batch 22550, loss[loss=0.1317, simple_loss=0.2164, pruned_loss=0.02347, over 4780.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03004, over 971279.67 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:38:32,797 INFO [train.py:715] (1/8) Epoch 15, batch 22600, loss[loss=0.1232, simple_loss=0.2117, pruned_loss=0.01732, over 4906.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.03004, over 971765.32 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:39:11,719 INFO [train.py:715] (1/8) Epoch 15, batch 22650, loss[loss=0.1372, simple_loss=0.2115, pruned_loss=0.03143, over 4914.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.03002, over 972121.50 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 11:39:50,375 INFO [train.py:715] (1/8) Epoch 15, batch 22700, loss[loss=0.1254, simple_loss=0.207, pruned_loss=0.02192, over 4984.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03016, over 971548.95 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:40:29,114 INFO [train.py:715] (1/8) Epoch 15, batch 22750, loss[loss=0.126, simple_loss=0.2091, pruned_loss=0.02148, over 4786.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03034, over 971818.03 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 11:41:08,455 INFO [train.py:715] (1/8) Epoch 15, batch 22800, loss[loss=0.1457, simple_loss=0.2276, pruned_loss=0.03194, over 4804.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03097, over 972702.39 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 11:41:47,280 INFO [train.py:715] (1/8) Epoch 15, batch 22850, loss[loss=0.1337, simple_loss=0.21, pruned_loss=0.02873, over 4856.00 frames.], tot_loss[loss=0.136, simple_loss=0.2101, pruned_loss=0.03093, over 972425.53 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 11:42:26,023 INFO [train.py:715] (1/8) Epoch 15, batch 22900, loss[loss=0.1426, simple_loss=0.2163, pruned_loss=0.03441, over 4742.00 frames.], tot_loss[loss=0.136, simple_loss=0.2102, pruned_loss=0.03087, over 971489.85 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:43:05,210 INFO [train.py:715] (1/8) Epoch 15, batch 22950, loss[loss=0.119, simple_loss=0.2046, pruned_loss=0.01667, over 4759.00 frames.], tot_loss[loss=0.135, simple_loss=0.2092, pruned_loss=0.03043, over 971220.22 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:43:43,827 INFO [train.py:715] (1/8) Epoch 15, batch 23000, loss[loss=0.1094, simple_loss=0.1896, pruned_loss=0.01454, over 4716.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03017, over 970463.87 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:44:22,239 INFO [train.py:715] (1/8) Epoch 15, batch 23050, loss[loss=0.1291, simple_loss=0.2075, pruned_loss=0.02539, over 4809.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2089, pruned_loss=0.03, over 970963.00 frames.], batch size: 27, lr: 1.46e-04
2022-05-08 11:45:00,626 INFO [train.py:715] (1/8) Epoch 15, batch 23100, loss[loss=0.1168, simple_loss=0.197, pruned_loss=0.01829, over 4755.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2096, pruned_loss=0.03042, over 971299.02 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:45:39,479 INFO [train.py:715] (1/8) Epoch 15, batch 23150, loss[loss=0.1489, simple_loss=0.2196, pruned_loss=0.03908, over 4694.00 frames.], tot_loss[loss=0.1359, simple_loss=0.21, pruned_loss=0.03091, over 970793.10 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:46:17,453 INFO [train.py:715] (1/8) Epoch 15, batch 23200, loss[loss=0.1255, simple_loss=0.2053, pruned_loss=0.02281, over 4931.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03083, over 971011.84 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 11:46:55,708 INFO [train.py:715] (1/8) Epoch 15, batch 23250, loss[loss=0.1358, simple_loss=0.2086, pruned_loss=0.03148, over 4957.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03079, over 970600.27 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:47:34,386 INFO [train.py:715] (1/8) Epoch 15, batch 23300, loss[loss=0.1565, simple_loss=0.218, pruned_loss=0.04751, over 4731.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03084, over 970800.37 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:48:12,430 INFO [train.py:715] (1/8) Epoch 15, batch 23350, loss[loss=0.1294, simple_loss=0.2065, pruned_loss=0.02618, over 4966.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.03064, over 970880.78 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:48:50,739 INFO [train.py:715] (1/8) Epoch 15, batch 23400, loss[loss=0.1463, simple_loss=0.2198, pruned_loss=0.03641, over 4768.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03049, over 971456.21 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:49:28,562 INFO [train.py:715] (1/8) Epoch 15, batch 23450, loss[loss=0.1307, simple_loss=0.203, pruned_loss=0.02919, over 4795.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02992, over 972427.37 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:50:07,086 INFO [train.py:715] (1/8) Epoch 15, batch 23500, loss[loss=0.1352, simple_loss=0.2207, pruned_loss=0.02484, over 4788.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02978, over 972391.78 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:50:44,860 INFO [train.py:715] (1/8) Epoch 15, batch 23550, loss[loss=0.1385, simple_loss=0.2137, pruned_loss=0.03169, over 4878.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02959, over 972145.99 frames.], batch size: 32, lr: 1.46e-04
2022-05-08 11:51:22,854 INFO [train.py:715] (1/8) Epoch 15, batch 23600, loss[loss=0.1465, simple_loss=0.2217, pruned_loss=0.03564, over 4744.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02995, over 972151.23 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:52:01,279 INFO [train.py:715] (1/8) Epoch 15, batch 23650, loss[loss=0.1251, simple_loss=0.205, pruned_loss=0.02259, over 4818.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02968, over 971500.95 frames.], batch size: 26, lr: 1.46e-04
2022-05-08 11:52:39,167 INFO [train.py:715] (1/8) Epoch 15, batch 23700, loss[loss=0.1106, simple_loss=0.1841, pruned_loss=0.01851, over 4909.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02967, over 971660.18 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:53:17,232 INFO [train.py:715] (1/8) Epoch 15, batch 23750, loss[loss=0.1667, simple_loss=0.2301, pruned_loss=0.05167, over 4894.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03013, over 971400.22 frames.], batch size: 38, lr: 1.46e-04
2022-05-08 11:53:55,059 INFO [train.py:715] (1/8) Epoch 15, batch 23800, loss[loss=0.1341, simple_loss=0.201, pruned_loss=0.03358, over 4901.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02996, over 971405.83 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:54:33,046 INFO [train.py:715] (1/8) Epoch 15, batch 23850, loss[loss=0.1661, simple_loss=0.2273, pruned_loss=0.05241, over 4966.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03013, over 971154.55 frames.], batch size: 35, lr: 1.46e-04
2022-05-08 11:55:11,363 INFO [train.py:715] (1/8) Epoch 15, batch 23900, loss[loss=0.1365, simple_loss=0.1993, pruned_loss=0.03687, over 4928.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03033, over 971246.68 frames.], batch size: 35, lr: 1.46e-04
2022-05-08 11:55:48,903 INFO [train.py:715] (1/8) Epoch 15, batch 23950, loss[loss=0.1595, simple_loss=0.2403, pruned_loss=0.03931, over 4907.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03025, over 971579.32 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 11:56:27,443 INFO [train.py:715] (1/8) Epoch 15, batch 24000, loss[loss=0.1287, simple_loss=0.2054, pruned_loss=0.02604, over 4955.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03017, over 971229.93 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:56:27,444 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 11:56:37,034 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.105, simple_loss=0.1886, pruned_loss=0.01071, over 914524.00 frames.
2022-05-08 11:57:15,622 INFO [train.py:715] (1/8) Epoch 15, batch 24050, loss[loss=0.1452, simple_loss=0.2204, pruned_loss=0.03499, over 4903.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03004, over 972151.29 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:57:54,186 INFO [train.py:715] (1/8) Epoch 15, batch 24100, loss[loss=0.1326, simple_loss=0.2087, pruned_loss=0.02822, over 4925.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02988, over 972149.21 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:58:32,191 INFO [train.py:715] (1/8) Epoch 15, batch 24150, loss[loss=0.135, simple_loss=0.2083, pruned_loss=0.03086, over 4747.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02939, over 971949.09 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:59:10,407 INFO [train.py:715] (1/8) Epoch 15, batch 24200, loss[loss=0.1359, simple_loss=0.2263, pruned_loss=0.02276, over 4971.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02998, over 972794.13 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:59:48,422 INFO [train.py:715] (1/8) Epoch 15, batch 24250, loss[loss=0.1331, simple_loss=0.2192, pruned_loss=0.02346, over 4763.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02967, over 972560.33 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 12:00:26,747 INFO [train.py:715] (1/8) Epoch 15, batch 24300, loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02958, over 4890.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02974, over 972463.88 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 12:01:03,899 INFO [train.py:715] (1/8) Epoch 15, batch 24350, loss[loss=0.1469, simple_loss=0.2285, pruned_loss=0.0326, over 4768.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02943, over 971801.74 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 12:01:42,321 INFO [train.py:715] (1/8) Epoch 15, batch 24400, loss[loss=0.1256, simple_loss=0.2046, pruned_loss=0.02326, over 4785.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.0291, over 972101.99 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 12:02:20,845 INFO [train.py:715] (1/8) Epoch 15, batch 24450, loss[loss=0.1429, simple_loss=0.2114, pruned_loss=0.03725, over 4977.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02903, over 971240.54 frames.], batch size: 28, lr: 1.46e-04
2022-05-08 12:02:58,826 INFO [train.py:715] (1/8) Epoch 15, batch 24500, loss[loss=0.1337, simple_loss=0.215, pruned_loss=0.02622, over 4862.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02953, over 972861.44 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 12:03:36,485 INFO [train.py:715] (1/8) Epoch 15, batch 24550, loss[loss=0.1297, simple_loss=0.2013, pruned_loss=0.02909, over 4986.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03021, over 973014.41 frames.], batch size: 31, lr: 1.46e-04
2022-05-08 12:04:14,727 INFO [train.py:715] (1/8) Epoch 15, batch 24600, loss[loss=0.1111, simple_loss=0.177, pruned_loss=0.02259, over 4791.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02982, over 973499.56 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 12:04:53,495 INFO [train.py:715] (1/8) Epoch 15, batch 24650, loss[loss=0.1054, simple_loss=0.1751, pruned_loss=0.01783, over 4798.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02947, over 973391.68 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 12:05:31,175 INFO [train.py:715] (1/8) Epoch 15, batch 24700, loss[loss=0.1125, simple_loss=0.1976, pruned_loss=0.01368, over 4922.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02912, over 972428.09 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 12:06:09,581 INFO [train.py:715] (1/8) Epoch 15, batch 24750, loss[loss=0.1336, simple_loss=0.2107, pruned_loss=0.02826, over 4931.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02928, over 972126.73 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 12:06:47,908 INFO [train.py:715] (1/8) Epoch 15, batch 24800, loss[loss=0.1308, simple_loss=0.2037, pruned_loss=0.02896, over 4871.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02891, over 973139.37 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 12:07:25,660 INFO [train.py:715] (1/8) Epoch 15, batch 24850, loss[loss=0.1239, simple_loss=0.206, pruned_loss=0.02092, over 4948.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02931, over 973307.81 frames.], batch size: 29, lr: 1.46e-04
2022-05-08 12:08:03,594 INFO [train.py:715] (1/8) Epoch 15, batch 24900, loss[loss=0.1283, simple_loss=0.209, pruned_loss=0.02377, over 4958.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02899, over 973776.46 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 12:08:41,841 INFO [train.py:715] (1/8) Epoch 15, batch 24950, loss[loss=0.1408, simple_loss=0.2131, pruned_loss=0.03426, over 4766.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02934, over 973210.85 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 12:09:20,947 INFO [train.py:715] (1/8) Epoch 15, batch 25000, loss[loss=0.129, simple_loss=0.2071, pruned_loss=0.02545, over 4983.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.0298, over 973119.51 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 12:09:58,499 INFO [train.py:715] (1/8) Epoch 15, batch 25050, loss[loss=0.1134, simple_loss=0.1991, pruned_loss=0.01386, over 4895.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02992, over 972629.88 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 12:10:36,537 INFO [train.py:715] (1/8) Epoch 15, batch 25100, loss[loss=0.1052, simple_loss=0.1767, pruned_loss=0.01687, over 4807.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02979, over 973068.40 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 12:11:14,987 INFO [train.py:715] (1/8) Epoch 15, batch 25150, loss[loss=0.1147, simple_loss=0.1918, pruned_loss=0.01883, over 4954.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2089, pruned_loss=0.03029, over 971958.56 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 12:11:53,004 INFO [train.py:715] (1/8) Epoch 15, batch 25200, loss[loss=0.1286, simple_loss=0.2003, pruned_loss=0.0284, over 4936.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02993, over 972588.52 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 12:12:30,794 INFO [train.py:715] (1/8) Epoch 15, batch 25250, loss[loss=0.1206, simple_loss=0.1881, pruned_loss=0.02656, over 4732.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.03004, over 971726.06 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 12:13:09,119 INFO [train.py:715] (1/8) Epoch 15, batch 25300, loss[loss=0.13, simple_loss=0.1913, pruned_loss=0.0344, over 4761.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02989, over 972275.85 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 12:13:47,201 INFO [train.py:715] (1/8) Epoch 15, batch 25350, loss[loss=0.1332, simple_loss=0.2033, pruned_loss=0.03155, over 4776.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03027, over 972423.73 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 12:14:24,745 INFO [train.py:715] (1/8) Epoch 15, batch 25400, loss[loss=0.1332, simple_loss=0.2161, pruned_loss=0.02515, over 4900.00 frames.], tot_loss[loss=0.1334, simple_loss=0.207, pruned_loss=0.0299, over 973460.22 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 12:15:02,819 INFO [train.py:715] (1/8) Epoch 15, batch 25450, loss[loss=0.1214, simple_loss=0.1961, pruned_loss=0.0233, over 4810.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02991, over 973427.89 frames.], batch size: 26, lr: 1.46e-04
2022-05-08 12:15:41,206 INFO [train.py:715] (1/8) Epoch 15, batch 25500, loss[loss=0.1293, simple_loss=0.2029, pruned_loss=0.0278, over 4931.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03009, over 973058.35 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 12:16:18,763 INFO [train.py:715] (1/8) Epoch 15, batch 25550, loss[loss=0.16, simple_loss=0.2233, pruned_loss=0.04837, over 4860.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03033, over 972932.49 frames.], batch size: 30, lr: 1.45e-04
2022-05-08 12:16:56,916 INFO [train.py:715] (1/8) Epoch 15, batch 25600, loss[loss=0.1398, simple_loss=0.2091, pruned_loss=0.03526, over 4849.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03038, over 971824.34 frames.], batch size: 30, lr: 1.45e-04
2022-05-08 12:17:35,537 INFO [train.py:715] (1/8) Epoch 15, batch 25650, loss[loss=0.1602, simple_loss=0.2347, pruned_loss=0.04286, over 4843.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03024, over 971807.45 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:18:13,816 INFO [train.py:715] (1/8) Epoch 15, batch 25700, loss[loss=0.1364, simple_loss=0.2076, pruned_loss=0.03258, over 4798.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03025, over 971782.27 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 12:18:51,202 INFO [train.py:715] (1/8) Epoch 15, batch 25750, loss[loss=0.136, simple_loss=0.2113, pruned_loss=0.03037, over 4866.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03034, over 972264.69 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 12:19:29,352 INFO [train.py:715] (1/8) Epoch 15, batch 25800, loss[loss=0.1416, simple_loss=0.2173, pruned_loss=0.03294, over 4953.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03051, over 972269.31 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:20:07,976 INFO [train.py:715] (1/8) Epoch 15, batch 25850, loss[loss=0.1262, simple_loss=0.2007, pruned_loss=0.02582, over 4994.00 frames.], tot_loss[loss=0.134, simple_loss=0.2073, pruned_loss=0.03032, over 973010.71 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:20:45,417 INFO [train.py:715] (1/8) Epoch 15, batch 25900, loss[loss=0.1858, simple_loss=0.2693, pruned_loss=0.05117, over 4832.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03043, over 973159.80 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:21:24,014 INFO [train.py:715] (1/8) Epoch 15, batch 25950, loss[loss=0.1401, simple_loss=0.2301, pruned_loss=0.02506, over 4893.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2078, pruned_loss=0.03048, over 973264.40 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:22:02,167 INFO [train.py:715] (1/8) Epoch 15, batch 26000, loss[loss=0.1259, simple_loss=0.2002, pruned_loss=0.02578, over 4972.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2079, pruned_loss=0.0305, over 973062.01 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:22:39,852 INFO [train.py:715] (1/8) Epoch 15, batch 26050, loss[loss=0.1215, simple_loss=0.1961, pruned_loss=0.02345, over 4801.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03061, over 972884.41 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 12:23:17,646 INFO [train.py:715] (1/8) Epoch 15, batch 26100, loss[loss=0.112, simple_loss=0.1877, pruned_loss=0.01814, over 4986.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03077, over 972268.76 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:23:56,077 INFO [train.py:715] (1/8) Epoch 15, batch 26150, loss[loss=0.1567, simple_loss=0.2292, pruned_loss=0.04215, over 4771.00 frames.], tot_loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03017, over 972611.67 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:24:33,867 INFO [train.py:715] (1/8) Epoch 15, batch 26200, loss[loss=0.127, simple_loss=0.1957, pruned_loss=0.02911, over 4778.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2074, pruned_loss=0.03007, over 973384.88 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:25:11,685 INFO [train.py:715] (1/8) Epoch 15, batch 26250, loss[loss=0.1363, simple_loss=0.2127, pruned_loss=0.02991, over 4686.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02994, over 971900.71 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:25:50,004 INFO [train.py:715] (1/8) Epoch 15, batch 26300, loss[loss=0.1238, simple_loss=0.2045, pruned_loss=0.02152, over 4967.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03003, over 972272.80 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 12:26:28,462 INFO [train.py:715] (1/8) Epoch 15, batch 26350, loss[loss=0.145, simple_loss=0.2219, pruned_loss=0.03412, over 4738.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03029, over 972061.39 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 12:27:06,220 INFO [train.py:715] (1/8) Epoch 15, batch 26400, loss[loss=0.132, simple_loss=0.2134, pruned_loss=0.02533, over 4812.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02965, over 971999.57 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 12:27:44,351 INFO [train.py:715] (1/8) Epoch 15, batch 26450, loss[loss=0.1189, simple_loss=0.2004, pruned_loss=0.01877, over 4882.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02932, over 971470.45 frames.], batch size: 22, lr: 1.45e-04
2022-05-08 12:28:22,637 INFO [train.py:715] (1/8) Epoch 15, batch 26500, loss[loss=0.1296, simple_loss=0.2103, pruned_loss=0.02445, over 4896.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02936, over 972259.35 frames.], batch size: 38, lr: 1.45e-04
2022-05-08 12:29:00,419 INFO [train.py:715] (1/8) Epoch 15, batch 26550, loss[loss=0.1389, simple_loss=0.2173, pruned_loss=0.03027, over 4961.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.0294, over 971774.95 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 12:29:38,155 INFO [train.py:715] (1/8) Epoch 15, batch 26600, loss[loss=0.1331, simple_loss=0.1936, pruned_loss=0.03627, over 4771.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02983, over 971825.76 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 12:30:16,188 INFO [train.py:715] (1/8) Epoch 15, batch 26650, loss[loss=0.1698, simple_loss=0.2321, pruned_loss=0.05377, over 4927.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2067, pruned_loss=0.0298, over 971806.88 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 12:30:54,319 INFO [train.py:715] (1/8) Epoch 15, batch 26700, loss[loss=0.1169, simple_loss=0.1922, pruned_loss=0.02079, over 4786.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2069, pruned_loss=0.02998, over 972769.24 frames.], batch size: 12, lr: 1.45e-04
2022-05-08 12:31:31,944 INFO [train.py:715] (1/8) Epoch 15, batch 26750, loss[loss=0.1603, simple_loss=0.2337, pruned_loss=0.04347, over 4988.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02984, over 972933.77 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:32:10,363 INFO [train.py:715] (1/8) Epoch 15, batch 26800, loss[loss=0.1225, simple_loss=0.2017, pruned_loss=0.02169, over 4990.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03014, over 973208.39 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:32:48,685 INFO [train.py:715] (1/8) Epoch 15, batch 26850, loss[loss=0.1347, simple_loss=0.2107, pruned_loss=0.0293, over 4688.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02975, over 973483.04 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:33:26,764 INFO [train.py:715] (1/8) Epoch 15, batch 26900, loss[loss=0.1437, simple_loss=0.2187, pruned_loss=0.0343, over 4792.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03043, over 972567.31 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:34:04,501 INFO [train.py:715] (1/8) Epoch 15, batch 26950, loss[loss=0.1294, simple_loss=0.1963, pruned_loss=0.03129, over 4751.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03055, over 972129.27 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 12:34:42,583 INFO [train.py:715] (1/8) Epoch 15, batch 27000, loss[loss=0.1442, simple_loss=0.2146, pruned_loss=0.03689, over 4870.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03014, over 971996.64 frames.], batch size: 22, lr: 1.45e-04
2022-05-08 12:34:42,584 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 12:34:52,204 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1049, simple_loss=0.1884, pruned_loss=0.01064, over 914524.00 frames.
2022-05-08 12:35:31,296 INFO [train.py:715] (1/8) Epoch 15, batch 27050, loss[loss=0.1384, simple_loss=0.2173, pruned_loss=0.02971, over 4946.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02991, over 971845.12 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 12:36:10,019 INFO [train.py:715] (1/8) Epoch 15, batch 27100, loss[loss=0.1542, simple_loss=0.2268, pruned_loss=0.04077, over 4964.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.03006, over 971928.15 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:36:48,674 INFO [train.py:715] (1/8) Epoch 15, batch 27150, loss[loss=0.1354, simple_loss=0.2051, pruned_loss=0.03285, over 4704.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.0303, over 971894.01 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:37:26,865 INFO [train.py:715] (1/8) Epoch 15, batch 27200, loss[loss=0.115, simple_loss=0.1864, pruned_loss=0.02182, over 4790.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.0299, over 970737.19 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:38:05,903 INFO [train.py:715] (1/8) Epoch 15, batch 27250, loss[loss=0.134, simple_loss=0.2084, pruned_loss=0.0298, over 4984.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02974, over 971357.67 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:38:43,694 INFO [train.py:715] (1/8) Epoch 15, batch 27300, loss[loss=0.1253, simple_loss=0.1974, pruned_loss=0.02664, over 4667.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.0295, over 971551.59 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 12:39:21,922 INFO [train.py:715] (1/8) Epoch 15, batch 27350, loss[loss=0.1369, simple_loss=0.2094, pruned_loss=0.03221, over 4690.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02977, over 971521.20 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:40:00,099 INFO [train.py:715] (1/8) Epoch 15, batch 27400, loss[loss=0.1243, simple_loss=0.1988, pruned_loss=0.0249, over 4958.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02962, over 971651.61 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:40:38,412 INFO [train.py:715] (1/8) Epoch 15, batch 27450, loss[loss=0.1147, simple_loss=0.1786, pruned_loss=0.02537, over 4822.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03041, over 971574.64 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 12:41:16,676 INFO [train.py:715] (1/8) Epoch 15, batch 27500, loss[loss=0.1478, simple_loss=0.2235, pruned_loss=0.03603, over 4815.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03046, over 971935.04 frames.], batch size: 27, lr: 1.45e-04
2022-05-08 12:41:54,850 INFO [train.py:715] (1/8) Epoch 15, batch 27550, loss[loss=0.1735, simple_loss=0.25, pruned_loss=0.04848, over 4879.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03043, over 972233.05 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 12:42:33,403 INFO [train.py:715] (1/8) Epoch 15, batch 27600, loss[loss=0.1147, simple_loss=0.1898, pruned_loss=0.01982, over 4948.00 frames.], tot_loss[loss=0.1343, simple_loss=0.208, pruned_loss=0.03035, over 972154.89 frames.], batch size: 29, lr: 1.45e-04
2022-05-08 12:43:10,763 INFO [train.py:715] (1/8) Epoch 15, batch 27650, loss[loss=0.1343, simple_loss=0.2069, pruned_loss=0.03081, over 4892.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03031, over 972434.79 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:43:49,459 INFO [train.py:715] (1/8) Epoch 15, batch 27700, loss[loss=0.1256, simple_loss=0.205, pruned_loss=0.02311, over 4986.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02981, over 972826.65 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:44:27,755 INFO [train.py:715] (1/8) Epoch 15, batch 27750, loss[loss=0.1316, simple_loss=0.2068, pruned_loss=0.02822, over 4780.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02958, over 972911.70 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 12:45:06,221 INFO [train.py:715] (1/8) Epoch 15, batch 27800, loss[loss=0.1121, simple_loss=0.1882, pruned_loss=0.01795, over 4914.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02947, over 972073.50 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:45:44,233 INFO [train.py:715] (1/8) Epoch 15, batch 27850, loss[loss=0.1077, simple_loss=0.1843, pruned_loss=0.01556, over 4945.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.0293, over 971824.94 frames.], batch size: 29, lr: 1.45e-04
2022-05-08 12:46:21,974 INFO [train.py:715] (1/8) Epoch 15, batch 27900, loss[loss=0.1292, simple_loss=0.2053, pruned_loss=0.02654, over 4922.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02921, over 972213.42 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 12:47:00,798 INFO [train.py:715] (1/8) Epoch 15, batch 27950, loss[loss=0.116, simple_loss=0.198, pruned_loss=0.01698, over 4892.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02947, over 972663.28 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 12:47:38,667 INFO [train.py:715] (1/8) Epoch 15, batch 28000, loss[loss=0.1454, simple_loss=0.2198, pruned_loss=0.03551, over 4977.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03033, over 973051.30 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:48:16,880 INFO [train.py:715] (1/8) Epoch 15, batch 28050, loss[loss=0.1252, simple_loss=0.1967, pruned_loss=0.02688, over 4876.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03043, over 974045.94 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 12:48:55,112 INFO [train.py:715] (1/8) Epoch 15, batch 28100, loss[loss=0.1396, simple_loss=0.2139, pruned_loss=0.03264, over 4900.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03027, over 973818.73 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:49:33,363 INFO [train.py:715] (1/8) Epoch 15, batch 28150, loss[loss=0.1287, simple_loss=0.2005, pruned_loss=0.02849, over 4889.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03056, over 973718.19 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:50:11,124 INFO [train.py:715] (1/8) Epoch 15, batch 28200, loss[loss=0.1334, simple_loss=0.2191, pruned_loss=0.02379, over 4945.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03028, over 973993.36 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 12:50:49,027 INFO [train.py:715] (1/8) Epoch 15, batch 28250, loss[loss=0.1646, simple_loss=0.2479, pruned_loss=0.04064, over 4925.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02984, over 973646.02 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 12:51:28,196 INFO [train.py:715] (1/8) Epoch 15, batch 28300, loss[loss=0.1552, simple_loss=0.2315, pruned_loss=0.03943, over 4981.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02969, over 972794.46 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:52:05,677 INFO [train.py:715] (1/8) Epoch 15, batch 28350, loss[loss=0.1435, simple_loss=0.2145, pruned_loss=0.03631, over 4956.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03012, over 973822.82 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 12:52:43,906 INFO [train.py:715] (1/8) Epoch 15, batch 28400, loss[loss=0.1349, simple_loss=0.2147, pruned_loss=0.02753, over 4971.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03021, over 973517.94 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:53:22,225 INFO [train.py:715] (1/8) Epoch 15, batch 28450, loss[loss=0.1279, simple_loss=0.2037, pruned_loss=0.02611, over 4949.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03003, over 973169.59 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 12:54:00,370 INFO [train.py:715] (1/8) Epoch 15, batch 28500, loss[loss=0.15, simple_loss=0.2304, pruned_loss=0.03484, over 4740.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03069, over 972420.65 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 12:54:38,503 INFO [train.py:715] (1/8) Epoch 15, batch 28550, loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.0281, over 4766.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03009, over 972122.82 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:55:16,673 INFO [train.py:715] (1/8) Epoch 15, batch 28600, loss[loss=0.1263, simple_loss=0.1946, pruned_loss=0.02899, over 4870.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.03005, over 972274.61 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 12:55:55,090 INFO [train.py:715] (1/8) Epoch 15, batch 28650, loss[loss=0.1262, simple_loss=0.2029, pruned_loss=0.02475, over 4891.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02962, over 971422.96 frames.], batch size: 22, lr: 1.45e-04
2022-05-08 12:56:32,943 INFO [train.py:715] (1/8) Epoch 15, batch 28700, loss[loss=0.1387, simple_loss=0.2068, pruned_loss=0.03528, over 4986.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03004, over 970988.98 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 12:57:11,385 INFO [train.py:715] (1/8) Epoch 15, batch 28750, loss[loss=0.1265, simple_loss=0.1993, pruned_loss=0.02684, over 4980.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02995, over 971119.35 frames.], batch size: 31, lr: 1.45e-04
2022-05-08 12:57:50,113 INFO [train.py:715] (1/8) Epoch 15, batch 28800, loss[loss=0.1303, simple_loss=0.2009, pruned_loss=0.02989, over 4833.00 frames.], tot_loss[loss=0.135, simple_loss=0.2094, pruned_loss=0.03033, over 971836.25 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:58:28,474 INFO [train.py:715] (1/8) Epoch 15, batch 28850, loss[loss=0.1203, simple_loss=0.1833, pruned_loss=0.02868, over 4830.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03027, over 971804.01 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 12:59:06,966 INFO [train.py:715] (1/8) Epoch 15, batch 28900, loss[loss=0.1243, simple_loss=0.1966, pruned_loss=0.02605, over 4771.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03017, over 971897.04 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 12:59:45,682 INFO [train.py:715] (1/8) Epoch 15, batch 28950, loss[loss=0.1189, simple_loss=0.1975, pruned_loss=0.02012, over 4908.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02979, over 971300.63 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:00:24,854 INFO [train.py:715] (1/8) Epoch 15, batch 29000, loss[loss=0.1508, simple_loss=0.2177, pruned_loss=0.04193, over 4985.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02949, over 971837.48 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 13:01:03,428 INFO [train.py:715] (1/8) Epoch 15, batch 29050, loss[loss=0.1361, simple_loss=0.2132, pruned_loss=0.02948, over 4959.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.03001, over 972759.32 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:01:42,352 INFO [train.py:715] (1/8) Epoch 15, batch 29100, loss[loss=0.1379, simple_loss=0.2155, pruned_loss=0.0301, over 4876.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2069, pruned_loss=0.02988, over 972631.58 frames.], batch size: 22, lr: 1.45e-04
2022-05-08 13:02:21,519 INFO [train.py:715] (1/8) Epoch 15, batch 29150, loss[loss=0.1292, simple_loss=0.2147, pruned_loss=0.02192, over 4832.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02987, over 972109.28 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:03:00,530 INFO [train.py:715] (1/8) Epoch 15, batch 29200, loss[loss=0.1192, simple_loss=0.1867, pruned_loss=0.02581, over 4804.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02926, over 972415.36 frames.], batch size: 12, lr: 1.45e-04
2022-05-08 13:03:38,942 INFO [train.py:715] (1/8) Epoch 15, batch 29250, loss[loss=0.1234, simple_loss=0.2001, pruned_loss=0.02333, over 4941.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02929, over 972566.23 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:04:18,000 INFO [train.py:715] (1/8) Epoch 15, batch 29300, loss[loss=0.1114, simple_loss=0.1888, pruned_loss=0.01705, over 4903.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02892, over 973188.58 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:04:56,888 INFO [train.py:715] (1/8) Epoch 15, batch 29350, loss[loss=0.1272, simple_loss=0.2102, pruned_loss=0.02206, over 4766.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02921, over 973401.86 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:05:35,476 INFO [train.py:715] (1/8) Epoch 15, batch 29400, loss[loss=0.1307, simple_loss=0.2132, pruned_loss=0.02412, over 4985.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02948, over 973478.67 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 13:06:14,533 INFO [train.py:715] (1/8) Epoch 15, batch 29450, loss[loss=0.1266, simple_loss=0.2009, pruned_loss=0.02611, over 4761.00 frames.], tot_loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02976, over 972830.87 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:06:53,809 INFO [train.py:715] (1/8) Epoch 15, batch 29500, loss[loss=0.1028, simple_loss=0.1703, pruned_loss=0.01764, over 4864.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02974, over 973187.07 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 13:07:31,956 INFO [train.py:715] (1/8) Epoch 15, batch 29550, loss[loss=0.1623, simple_loss=0.2395, pruned_loss=0.04252, over 4830.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02925, over 972231.61 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:08:09,733 INFO [train.py:715] (1/8) Epoch 15, batch 29600, loss[loss=0.1264, simple_loss=0.207, pruned_loss=0.02289, over 4775.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02936, over 971612.06 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:08:48,780 INFO [train.py:715] (1/8) Epoch 15, batch 29650, loss[loss=0.1385, simple_loss=0.2088, pruned_loss=0.0341, over 4980.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02884, over 971423.08 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:09:27,543 INFO [train.py:715] (1/8) Epoch 15, batch 29700, loss[loss=0.1423, simple_loss=0.2303, pruned_loss=0.02712, over 4842.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2078, pruned_loss=0.02936, over 971432.80 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:10:05,828 INFO [train.py:715] (1/8) Epoch 15, batch 29750, loss[loss=0.116, simple_loss=0.1909, pruned_loss=0.02059, over 4653.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2085, pruned_loss=0.02952, over 971560.45 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 13:10:43,495 INFO [train.py:715] (1/8) Epoch 15, batch 29800, loss[loss=0.1278, simple_loss=0.1909, pruned_loss=0.0323, over 4783.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2093, pruned_loss=0.02987, over 971544.68 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:11:22,789 INFO [train.py:715] (1/8) Epoch 15, batch 29850, loss[loss=0.1174, simple_loss=0.1894, pruned_loss=0.02272, over 4972.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2093, pruned_loss=0.03005, over 971677.11 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 13:12:04,498 INFO [train.py:715] (1/8) Epoch 15, batch 29900, loss[loss=0.1256, simple_loss=0.2068, pruned_loss=0.02227, over 4989.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2085, pruned_loss=0.0296, over 972654.00 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 13:12:43,061 INFO [train.py:715] (1/8) Epoch 15, batch 29950, loss[loss=0.1578, simple_loss=0.2267, pruned_loss=0.04445, over 4978.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02911, over 971917.75 frames.], batch size: 33, lr: 1.45e-04
2022-05-08 13:13:21,407 INFO [train.py:715] (1/8) Epoch 15, batch 30000, loss[loss=0.1357, simple_loss=0.2115, pruned_loss=0.02991, over 4694.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02904, over 971605.66 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:13:21,407 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 13:13:30,916 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1049, simple_loss=0.1885, pruned_loss=0.01066, over 914524.00 frames.
2022-05-08 13:14:09,986 INFO [train.py:715] (1/8) Epoch 15, batch 30050, loss[loss=0.1221, simple_loss=0.2016, pruned_loss=0.02135, over 4878.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02958, over 971711.36 frames.], batch size: 22, lr: 1.45e-04
2022-05-08 13:14:49,076 INFO [train.py:715] (1/8) Epoch 15, batch 30100, loss[loss=0.1142, simple_loss=0.1928, pruned_loss=0.01782, over 4859.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2087, pruned_loss=0.02991, over 971145.79 frames.], batch size: 30, lr: 1.45e-04
2022-05-08 13:15:28,233 INFO [train.py:715] (1/8) Epoch 15, batch 30150, loss[loss=0.1331, simple_loss=0.199, pruned_loss=0.03358, over 4651.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03019, over 970617.98 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 13:16:07,083 INFO [train.py:715] (1/8) Epoch 15, batch 30200, loss[loss=0.1349, simple_loss=0.1982, pruned_loss=0.03582, over 4984.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02985, over 971735.21 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:16:46,374 INFO [train.py:715] (1/8) Epoch 15, batch 30250, loss[loss=0.1232, simple_loss=0.2056, pruned_loss=0.02043, over 4919.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.0297, over 971477.77 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:17:25,198 INFO [train.py:715] (1/8) Epoch 15, batch 30300, loss[loss=0.1024, simple_loss=0.1758, pruned_loss=0.01447, over 4949.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02964, over 972268.32 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:18:03,168 INFO [train.py:715] (1/8) Epoch 15, batch 30350, loss[loss=0.1294, simple_loss=0.2006, pruned_loss=0.02915, over 4970.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02964, over 972310.00 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:18:42,390 INFO [train.py:715] (1/8) Epoch 15, batch 30400, loss[loss=0.1473, simple_loss=0.2131, pruned_loss=0.04069, over 4853.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2069, pruned_loss=0.02967, over 972078.91 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 13:19:21,253 INFO [train.py:715] (1/8) Epoch 15, batch 30450, loss[loss=0.1482, simple_loss=0.221, pruned_loss=0.03771, over 4903.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02993, over 972576.65 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:20:00,133 INFO [train.py:715] (1/8) Epoch 15, batch 30500, loss[loss=0.1326, simple_loss=0.2043, pruned_loss=0.03045, over 4749.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02992, over 972288.33 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 13:20:38,343 INFO [train.py:715] (1/8) Epoch 15, batch 30550, loss[loss=0.1253, simple_loss=0.1939, pruned_loss=0.02832, over 4742.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03055, over 971527.01 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 13:21:17,388 INFO [train.py:715] (1/8) Epoch 15, batch 30600, loss[loss=0.1139, simple_loss=0.191, pruned_loss=0.01839, over 4885.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2082, pruned_loss=0.03071, over 971931.19 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 13:21:56,194 INFO [train.py:715] (1/8) Epoch 15, batch 30650, loss[loss=0.1452, simple_loss=0.2143, pruned_loss=0.03802, over 4796.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03076, over 971449.47 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:22:34,336 INFO [train.py:715] (1/8) Epoch 15, batch 30700, loss[loss=0.1506, simple_loss=0.2333, pruned_loss=0.03395, over 4938.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03038, over 971896.14 frames.], batch size: 29, lr: 1.45e-04
2022-05-08 13:23:13,405 INFO [train.py:715] (1/8) Epoch 15, batch 30750, loss[loss=0.1349, simple_loss=0.203, pruned_loss=0.03334, over 4769.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03066, over 971895.15 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:23:52,075 INFO [train.py:715] (1/8) Epoch 15, batch 30800, loss[loss=0.1172, simple_loss=0.1986, pruned_loss=0.01785, over 4795.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03052, over 972297.01 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 13:24:30,181 INFO [train.py:715] (1/8) Epoch 15, batch 30850, loss[loss=0.128, simple_loss=0.201, pruned_loss=0.02751, over 4913.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03046, over 972354.71 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:25:08,417 INFO [train.py:715] (1/8) Epoch 15, batch 30900, loss[loss=0.1379, simple_loss=0.2129, pruned_loss=0.03145, over 4930.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03053, over 972062.64 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:25:46,855 INFO [train.py:715] (1/8) Epoch 15, batch 30950, loss[loss=0.1491, simple_loss=0.2171, pruned_loss=0.04055, over 4979.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.03111, over 972734.07 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:26:25,013 INFO [train.py:715] (1/8) Epoch 15, batch 31000, loss[loss=0.1515, simple_loss=0.2197, pruned_loss=0.04167, over 4897.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.031, over 972288.83 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:27:02,425 INFO [train.py:715] (1/8) Epoch 15, batch 31050, loss[loss=0.1299, simple_loss=0.2041, pruned_loss=0.02784, over 4794.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2094, pruned_loss=0.03054, over 973069.06 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:27:40,735 INFO [train.py:715] (1/8) Epoch 15, batch 31100, loss[loss=0.1321, simple_loss=0.2097, pruned_loss=0.02725, over 4904.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02983, over 973210.21 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:28:18,889 INFO [train.py:715] (1/8) Epoch 15, batch 31150, loss[loss=0.1399, simple_loss=0.2191, pruned_loss=0.03034, over 4865.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02961, over 972340.32 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:28:57,278 INFO [train.py:715] (1/8) Epoch 15, batch 31200, loss[loss=0.1158, simple_loss=0.1977, pruned_loss=0.017, over 4914.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02995, over 972481.71 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:29:34,877 INFO [train.py:715] (1/8) Epoch 15, batch 31250, loss[loss=0.1292, simple_loss=0.2002, pruned_loss=0.02907, over 4773.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03014, over 972429.45 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:30:13,198 INFO [train.py:715] (1/8) Epoch 15, batch 31300, loss[loss=0.1406, simple_loss=0.2118, pruned_loss=0.0347, over 4810.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.0299, over 971630.97 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 13:30:51,250 INFO [train.py:715] (1/8) Epoch 15, batch 31350, loss[loss=0.1011, simple_loss=0.1739, pruned_loss=0.0141, over 4770.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02967, over 971245.27 frames.], batch size: 12, lr: 1.45e-04
2022-05-08 13:31:28,511 INFO [train.py:715] (1/8) Epoch 15, batch 31400, loss[loss=0.1497, simple_loss=0.224, pruned_loss=0.03769, over 4857.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02943, over 971380.95 frames.], batch size: 30, lr: 1.45e-04
2022-05-08 13:32:06,856 INFO [train.py:715] (1/8) Epoch 15, batch 31450, loss[loss=0.1329, simple_loss=0.2013, pruned_loss=0.03224, over 4787.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02989, over 971458.37 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:32:45,120 INFO [train.py:715] (1/8) Epoch 15, batch 31500, loss[loss=0.1244, simple_loss=0.1956, pruned_loss=0.02662, over 4945.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.0299, over 971440.01 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 13:33:23,446 INFO [train.py:715] (1/8) Epoch 15, batch 31550, loss[loss=0.111, simple_loss=0.1842, pruned_loss=0.01893, over 4986.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03064, over 971287.03 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 13:34:01,213 INFO [train.py:715] (1/8) Epoch 15, batch 31600, loss[loss=0.1302, simple_loss=0.2007, pruned_loss=0.02989, over 4773.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03071, over 970941.98 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:34:39,670 INFO [train.py:715] (1/8) Epoch 15, batch 31650, loss[loss=0.1106, simple_loss=0.184, pruned_loss=0.01861, over 4936.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03023, over 971747.92 frames.], batch size: 29, lr: 1.45e-04
2022-05-08 13:35:18,004 INFO [train.py:715] (1/8) Epoch 15, batch 31700, loss[loss=0.1297, simple_loss=0.2136, pruned_loss=0.02287, over 4834.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02993, over 972026.48 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:35:55,499 INFO [train.py:715] (1/8) Epoch 15, batch 31750, loss[loss=0.142, simple_loss=0.2107, pruned_loss=0.03672, over 4843.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02989, over 971944.42 frames.], batch size: 30, lr: 1.45e-04
2022-05-08 13:36:34,397 INFO [train.py:715] (1/8) Epoch 15, batch 31800, loss[loss=0.147, simple_loss=0.215, pruned_loss=0.03948, over 4809.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02986, over 971473.39 frames.], batch size: 26, lr: 1.45e-04
2022-05-08 13:37:12,867 INFO [train.py:715] (1/8) Epoch 15, batch 31850, loss[loss=0.1497, simple_loss=0.2221, pruned_loss=0.03868, over 4956.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03004, over 972412.96 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 13:37:52,391 INFO [train.py:715] (1/8) Epoch 15, batch 31900, loss[loss=0.1232, simple_loss=0.1998, pruned_loss=0.02332, over 4851.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03046, over 972477.60 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:38:29,678 INFO [train.py:715] (1/8) Epoch 15, batch 31950, loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03103, over 4809.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02996, over 972701.03 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 13:39:08,342 INFO [train.py:715] (1/8) Epoch 15, batch 32000, loss[loss=0.1382, simple_loss=0.2031, pruned_loss=0.0367, over 4837.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02995, over 972554.20 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:39:46,519 INFO [train.py:715] (1/8) Epoch 15, batch 32050, loss[loss=0.1184, simple_loss=0.1984, pruned_loss=0.01918, over 4827.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.0297, over 972155.95 frames.], batch size: 27, lr: 1.45e-04
2022-05-08 13:40:23,949 INFO [train.py:715] (1/8) Epoch 15, batch 32100, loss[loss=0.1375, simple_loss=0.2154, pruned_loss=0.02984, over 4945.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.0295, over 971709.78 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 13:41:02,344 INFO [train.py:715] (1/8) Epoch 15, batch 32150, loss[loss=0.1702, simple_loss=0.2396, pruned_loss=0.0504, over 4962.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02947, over 971892.06 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:41:40,506 INFO [train.py:715] (1/8) Epoch 15, batch 32200, loss[loss=0.1392, simple_loss=0.2141, pruned_loss=0.03208, over 4981.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02959, over 972451.38 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 13:42:19,025 INFO [train.py:715] (1/8) Epoch 15, batch 32250, loss[loss=0.1634, simple_loss=0.233, pruned_loss=0.0469, over 4920.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02954, over 972204.85 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:42:56,893 INFO [train.py:715] (1/8) Epoch 15, batch 32300, loss[loss=0.1644, simple_loss=0.2446, pruned_loss=0.04205, over 4770.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2079, pruned_loss=0.02937, over 972139.37 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:43:35,759 INFO [train.py:715] (1/8) Epoch 15, batch 32350, loss[loss=0.1709, simple_loss=0.2366, pruned_loss=0.05258, over 4825.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2082, pruned_loss=0.02925, over 972943.11 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:44:14,156 INFO [train.py:715] (1/8) Epoch 15, batch 32400, loss[loss=0.1512, simple_loss=0.2228, pruned_loss=0.03982, over 4964.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2086, pruned_loss=0.02937, over 972782.27 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 13:44:51,909 INFO [train.py:715] (1/8) Epoch 15, batch 32450, loss[loss=0.1209, simple_loss=0.2022, pruned_loss=0.01985, over 4950.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2081, pruned_loss=0.02949, over 973016.93 frames.], batch size: 29, lr: 1.45e-04
2022-05-08 13:45:30,473 INFO [train.py:715] (1/8) Epoch 15, batch 32500, loss[loss=0.1399, simple_loss=0.2113, pruned_loss=0.03429, over 4655.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02943, over 971764.63 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 13:46:08,936 INFO [train.py:715] (1/8) Epoch 15, batch 32550, loss[loss=0.1294, simple_loss=0.2106, pruned_loss=0.02412, over 4648.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02986, over 971940.02 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 13:46:47,813 INFO [train.py:715] (1/8) Epoch 15, batch 32600, loss[loss=0.1318, simple_loss=0.2113, pruned_loss=0.02612, over 4814.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.0297, over 972300.02 frames.], batch size: 27, lr: 1.45e-04
2022-05-08 13:47:26,422 INFO [train.py:715] (1/8) Epoch 15, batch 32650, loss[loss=0.1685, simple_loss=0.2387, pruned_loss=0.04913, over 4795.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02976, over 972631.27 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:48:05,086 INFO [train.py:715] (1/8) Epoch 15, batch 32700, loss[loss=0.1191, simple_loss=0.1934, pruned_loss=0.02238, over 4970.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02942, over 972803.25 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 13:48:43,313 INFO [train.py:715] (1/8) Epoch 15, batch 32750, loss[loss=0.1276, simple_loss=0.2048, pruned_loss=0.02519, over 4752.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02922, over 972945.55 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:49:21,522 INFO [train.py:715] (1/8) Epoch 15, batch 32800, loss[loss=0.1197, simple_loss=0.201, pruned_loss=0.01921, over 4820.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02925, over 972546.34 frames.], batch size: 26, lr: 1.45e-04
2022-05-08 13:49:59,268 INFO [train.py:715] (1/8) Epoch 15, batch 32850, loss[loss=0.153, simple_loss=0.2197, pruned_loss=0.04319, over 4863.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02903, over 972834.40 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:50:37,489 INFO [train.py:715] (1/8) Epoch 15, batch 32900, loss[loss=0.1462, simple_loss=0.2219, pruned_loss=0.03523, over 4815.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02956, over 972638.74 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:51:16,080 INFO [train.py:715] (1/8) Epoch 15, batch 32950, loss[loss=0.1258, simple_loss=0.2078, pruned_loss=0.02197, over 4794.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02952, over 972584.98 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 13:51:54,466 INFO [train.py:715] (1/8) Epoch 15, batch 33000, loss[loss=0.121, simple_loss=0.1952, pruned_loss=0.02341, over 4987.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02966, over 973177.93 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 13:51:54,467 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 13:52:03,987 INFO [train.py:742] (1/8) Epoch 15, validation: loss=0.1052, simple_loss=0.1886, pruned_loss=0.01088, over 914524.00 frames.
2022-05-08 13:52:42,032 INFO [train.py:715] (1/8) Epoch 15, batch 33050, loss[loss=0.1029, simple_loss=0.1689, pruned_loss=0.01844, over 4787.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02962, over 973570.84 frames.], batch size: 12, lr: 1.45e-04
2022-05-08 13:53:20,377 INFO [train.py:715] (1/8) Epoch 15, batch 33100, loss[loss=0.1378, simple_loss=0.2188, pruned_loss=0.02839, over 4831.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02951, over 972880.78 frames.], batch size: 30, lr: 1.45e-04
2022-05-08 13:53:58,081 INFO [train.py:715] (1/8) Epoch 15, batch 33150, loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03039, over 4971.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02933, over 972175.34 frames.], batch size: 24, lr: 1.44e-04
2022-05-08 13:54:37,162 INFO [train.py:715] (1/8) Epoch 15, batch 33200, loss[loss=0.1309, simple_loss=0.209, pruned_loss=0.02644, over 4944.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02938, over 972535.59 frames.], batch size: 24, lr: 1.44e-04
2022-05-08 13:55:15,596 INFO [train.py:715] (1/8) Epoch 15, batch 33250, loss[loss=0.1076, simple_loss=0.1868, pruned_loss=0.01413, over 4895.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02907, over 972295.89 frames.], batch size: 17, lr: 1.44e-04
2022-05-08 13:55:53,705 INFO [train.py:715] (1/8) Epoch 15, batch 33300, loss[loss=0.1344, simple_loss=0.2222, pruned_loss=0.02334, over 4972.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02904, over 972580.80 frames.], batch size: 31, lr: 1.44e-04
2022-05-08 13:56:31,676 INFO [train.py:715] (1/8) Epoch 15, batch 33350, loss[loss=0.1952, simple_loss=0.2683, pruned_loss=0.06105, over 4839.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02933, over 972558.46 frames.], batch size: 15, lr: 1.44e-04
2022-05-08 13:57:09,333 INFO [train.py:715] (1/8) Epoch 15, batch 33400, loss[loss=0.1368, simple_loss=0.2001, pruned_loss=0.0368, over 4769.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02944, over 972031.61 frames.], batch size: 14, lr: 1.44e-04
2022-05-08 13:57:47,386 INFO [train.py:715] (1/8) Epoch 15, batch 33450, loss[loss=0.1197, simple_loss=0.1917, pruned_loss=0.02389, over 4785.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02956, over 971881.46 frames.], batch size: 18, lr: 1.44e-04
2022-05-08 13:58:25,101 INFO [train.py:715] (1/8) Epoch 15, batch 33500, loss[loss=0.1163, simple_loss=0.201, pruned_loss=0.01581, over 4788.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02965, over 971731.76 frames.], batch size: 14, lr: 1.44e-04
2022-05-08 13:59:02,924 INFO [train.py:715] (1/8) Epoch 15, batch 33550, loss[loss=0.1049, simple_loss=0.1809, pruned_loss=0.01446, over 4803.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02965, over 971238.17 frames.], batch size: 21, lr: 1.44e-04
2022-05-08 13:59:40,602 INFO [train.py:715] (1/8) Epoch 15, batch 33600, loss[loss=0.1078, simple_loss=0.1857, pruned_loss=0.01501, over 4927.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02934, over 971189.84 frames.], batch size: 29, lr: 1.44e-04
2022-05-08 14:00:18,652 INFO [train.py:715] (1/8) Epoch 15, batch 33650, loss[loss=0.1319, simple_loss=0.2019, pruned_loss=0.03092, over 4855.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2075, pruned_loss=0.02898, over 972048.01 frames.], batch size: 13, lr: 1.44e-04
2022-05-08 14:00:56,119 INFO [train.py:715] (1/8) Epoch 15, batch 33700, loss[loss=0.1259, simple_loss=0.2029, pruned_loss=0.02447, over 4830.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02925, over 971416.88 frames.], batch size: 13, lr: 1.44e-04
2022-05-08 14:01:33,645 INFO [train.py:715] (1/8) Epoch 15, batch 33750, loss[loss=0.1251, simple_loss=0.1965, pruned_loss=0.02682, over 4909.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02915, over 971397.94 frames.], batch size: 18, lr: 1.44e-04
2022-05-08 14:02:11,483 INFO [train.py:715] (1/8) Epoch 15, batch 33800, loss[loss=0.132, simple_loss=0.2115, pruned_loss=0.02621, over 4860.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02905, over 971506.21 frames.], batch size: 16, lr: 1.44e-04
2022-05-08 14:02:48,674 INFO [train.py:715] (1/8) Epoch 15, batch 33850, loss[loss=0.1232, simple_loss=0.2022, pruned_loss=0.02203, over 4882.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02897, over 971672.59 frames.], batch size: 16, lr: 1.44e-04
2022-05-08 14:03:26,485 INFO [train.py:715] (1/8) Epoch 15, batch 33900, loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.0299, over 4709.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02926, over 971465.67 frames.], batch size: 15, lr: 1.44e-04
2022-05-08 14:04:04,822 INFO [train.py:715] (1/8) Epoch 15, batch 33950, loss[loss=0.1438, simple_loss=0.2166, pruned_loss=0.0355, over 4944.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02947, over 972282.87 frames.], batch size: 21, lr: 1.44e-04
2022-05-08 14:04:42,873 INFO [train.py:715] (1/8) Epoch 15, batch 34000, loss[loss=0.1319, simple_loss=0.2207, pruned_loss=0.02148, over 4818.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02953, over 971237.52 frames.], batch size: 26, lr: 1.44e-04
2022-05-08 14:05:20,769 INFO [train.py:715] (1/8) Epoch 15, batch 34050, loss[loss=0.1376, simple_loss=0.2098, pruned_loss=0.03272, over 4978.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02952, over 972023.72 frames.], batch size: 15, lr: 1.44e-04
2022-05-08 14:05:58,930 INFO [train.py:715] (1/8) Epoch 15, batch 34100, loss[loss=0.1297, simple_loss=0.2016, pruned_loss=0.02884, over 4930.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02963, over 973003.21 frames.], batch size: 29, lr: 1.44e-04
2022-05-08 14:06:37,189 INFO [train.py:715] (1/8) Epoch 15, batch 34150, loss[loss=0.1467, simple_loss=0.2244, pruned_loss=0.03454, over 4942.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02944, over 973676.70 frames.], batch size: 23, lr: 1.44e-04
2022-05-08 14:07:14,888 INFO [train.py:715] (1/8) Epoch 15, batch 34200, loss[loss=0.1233, simple_loss=0.2043, pruned_loss=0.02112, over 4891.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02941, over 973929.06 frames.], batch size: 22, lr: 1.44e-04
2022-05-08 14:07:52,718 INFO [train.py:715] (1/8) Epoch 15, batch 34250, loss[loss=0.1151, simple_loss=0.194, pruned_loss=0.01812, over 4850.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2076, pruned_loss=0.0293, over 973689.18 frames.], batch size: 20, lr: 1.44e-04
2022-05-08 14:08:30,684 INFO [train.py:715] (1/8) Epoch 15, batch 34300, loss[loss=0.1229, simple_loss=0.2024, pruned_loss=0.02168, over 4765.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02881, over 972761.95 frames.], batch size: 19, lr: 1.44e-04
2022-05-08 14:09:08,611 INFO [train.py:715] (1/8) Epoch 15, batch 34350, loss[loss=0.132, simple_loss=0.1962, pruned_loss=0.03388, over 4869.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.0292, over 973049.30 frames.], batch size: 32, lr: 1.44e-04
2022-05-08 14:09:45,971 INFO [train.py:715] (1/8) Epoch 15, batch 34400, loss[loss=0.1648, simple_loss=0.2293, pruned_loss=0.05015, over 4902.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02884, over 973008.96 frames.], batch size: 17, lr: 1.44e-04
2022-05-08 14:10:24,172 INFO [train.py:715] (1/8) Epoch 15, batch 34450, loss[loss=0.1116, simple_loss=0.189, pruned_loss=0.01715, over 4770.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02883, over 973163.75 frames.], batch size: 14, lr: 1.44e-04
2022-05-08 14:11:02,053 INFO [train.py:715] (1/8) Epoch 15, batch 34500, loss[loss=0.1352, simple_loss=0.211, pruned_loss=0.02968, over 4932.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02863, over 973508.03 frames.], batch size: 35, lr: 1.44e-04
2022-05-08 14:11:39,389 INFO [train.py:715] (1/8) Epoch 15, batch 34550, loss[loss=0.1343, simple_loss=0.203, pruned_loss=0.03282, over 4970.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2081, pruned_loss=0.02921, over 972389.12 frames.], batch size: 28, lr: 1.44e-04
2022-05-08 14:12:17,004 INFO [train.py:715] (1/8) Epoch 15, batch 34600, loss[loss=0.1858, simple_loss=0.2475, pruned_loss=0.06203, over 4964.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2095, pruned_loss=0.0305, over 972898.52 frames.], batch size: 14, lr: 1.44e-04
2022-05-08 14:12:54,930 INFO [train.py:715] (1/8) Epoch 15, batch 34650, loss[loss=0.1495, simple_loss=0.2234, pruned_loss=0.03781, over 4737.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03014, over 973186.66 frames.], batch size: 16, lr: 1.44e-04
2022-05-08 14:13:32,474 INFO [train.py:715] (1/8) Epoch 15, batch 34700, loss[loss=0.1345, simple_loss=0.212, pruned_loss=0.02849, over 4685.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03043, over 972501.22 frames.], batch size: 15, lr: 1.44e-04
2022-05-08 14:14:09,602 INFO [train.py:715] (1/8) Epoch 15, batch 34750, loss[loss=0.1246, simple_loss=0.1898, pruned_loss=0.02972, over 4795.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2091, pruned_loss=0.03018, over 971853.87 frames.], batch size: 17, lr: 1.44e-04
2022-05-08 14:14:44,841 INFO [train.py:715] (1/8) Epoch 15, batch 34800, loss[loss=0.1485, simple_loss=0.2214, pruned_loss=0.03784, over 4924.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03001, over 972160.78 frames.], batch size: 18, lr: 1.44e-04
2022-05-08 14:15:33,454 INFO [train.py:715] (1/8) Epoch 16, batch 0, loss[loss=0.1341, simple_loss=0.2149, pruned_loss=0.02672, over 4761.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2149, pruned_loss=0.02672, over 4761.00 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:16:11,643 INFO [train.py:715] (1/8) Epoch 16, batch 50, loss[loss=0.1248, simple_loss=0.1994, pruned_loss=0.02516, over 4890.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02892, over 219468.33 frames.], batch size: 22, lr: 1.40e-04
2022-05-08 14:16:50,215 INFO [train.py:715] (1/8) Epoch 16, batch 100, loss[loss=0.1108, simple_loss=0.1846, pruned_loss=0.01848, over 4833.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02995, over 386197.26 frames.], batch size: 26, lr: 1.40e-04
2022-05-08 14:17:27,946 INFO [train.py:715] (1/8) Epoch 16, batch 150, loss[loss=0.1215, simple_loss=0.197, pruned_loss=0.02299, over 4795.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03057, over 516503.12 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:18:06,152 INFO [train.py:715] (1/8) Epoch 16, batch 200, loss[loss=0.1324, simple_loss=0.2099, pruned_loss=0.02746, over 4813.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2093, pruned_loss=0.0305, over 617836.88 frames.], batch size: 26, lr: 1.40e-04
2022-05-08 14:18:44,297 INFO [train.py:715] (1/8) Epoch 16, batch 250, loss[loss=0.1334, simple_loss=0.2093, pruned_loss=0.02878, over 4878.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03018, over 696800.55 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:19:22,617 INFO [train.py:715] (1/8) Epoch 16, batch 300, loss[loss=0.153, simple_loss=0.2212, pruned_loss=0.04241, over 4944.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03006, over 758357.75 frames.], batch size: 39, lr: 1.40e-04
2022-05-08 14:20:01,043 INFO [train.py:715] (1/8) Epoch 16, batch 350, loss[loss=0.1593, simple_loss=0.2289, pruned_loss=0.04488, over 4917.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2072, pruned_loss=0.03007, over 805630.32 frames.], batch size: 17, lr: 1.40e-04
2022-05-08 14:20:38,725 INFO [train.py:715] (1/8) Epoch 16, batch 400, loss[loss=0.1161, simple_loss=0.1826, pruned_loss=0.02484, over 4823.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02971, over 842045.33 frames.], batch size: 12, lr: 1.40e-04
2022-05-08 14:21:17,414 INFO [train.py:715] (1/8) Epoch 16, batch 450, loss[loss=0.1284, simple_loss=0.2067, pruned_loss=0.02505, over 4817.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03004, over 870786.80 frames.], batch size: 13, lr: 1.40e-04
2022-05-08 14:21:55,828 INFO [train.py:715] (1/8) Epoch 16, batch 500, loss[loss=0.1453, simple_loss=0.2213, pruned_loss=0.03467, over 4946.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2067, pruned_loss=0.02974, over 892259.33 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:22:33,537 INFO [train.py:715] (1/8) Epoch 16, batch 550, loss[loss=0.1354, simple_loss=0.213, pruned_loss=0.02889, over 4959.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02953, over 909181.45 frames.], batch size: 39, lr: 1.40e-04
2022-05-08 14:23:12,214 INFO [train.py:715] (1/8) Epoch 16, batch 600, loss[loss=0.1241, simple_loss=0.2064, pruned_loss=0.02088, over 4989.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2058, pruned_loss=0.02922, over 923348.90 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 14:23:50,880 INFO [train.py:715] (1/8) Epoch 16, batch 650, loss[loss=0.1465, simple_loss=0.2161, pruned_loss=0.03845, over 4736.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02945, over 934346.83 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:24:28,548 INFO [train.py:715] (1/8) Epoch 16, batch 700, loss[loss=0.1361, simple_loss=0.2087, pruned_loss=0.03172, over 4851.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02898, over 942462.38 frames.], batch size: 30, lr: 1.40e-04
2022-05-08 14:25:06,447 INFO [train.py:715] (1/8) Epoch 16, batch 750, loss[loss=0.1364, simple_loss=0.218, pruned_loss=0.02735, over 4803.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02929, over 947776.16 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 14:25:45,233 INFO [train.py:715] (1/8) Epoch 16, batch 800, loss[loss=0.159, simple_loss=0.2387, pruned_loss=0.03964, over 4867.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02969, over 953229.22 frames.], batch size: 20, lr: 1.40e-04
2022-05-08 14:26:23,533 INFO [train.py:715] (1/8) Epoch 16, batch 850, loss[loss=0.1098, simple_loss=0.1888, pruned_loss=0.01544, over 4992.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02968, over 959138.36 frames.], batch size: 14, lr: 1.40e-04
2022-05-08 14:27:01,593 INFO [train.py:715] (1/8) Epoch 16, batch 900, loss[loss=0.1253, simple_loss=0.1984, pruned_loss=0.02611, over 4795.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02977, over 961875.30 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:27:39,693 INFO [train.py:715] (1/8) Epoch 16, batch 950, loss[loss=0.1311, simple_loss=0.2013, pruned_loss=0.03045, over 4986.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02982, over 963666.81 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 14:28:18,129 INFO [train.py:715] (1/8) Epoch 16, batch 1000, loss[loss=0.1217, simple_loss=0.2003, pruned_loss=0.02155, over 4865.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02976, over 964045.71 frames.], batch size: 13, lr: 1.40e-04
2022-05-08 14:28:55,786 INFO [train.py:715] (1/8) Epoch 16, batch 1050, loss[loss=0.1206, simple_loss=0.1882, pruned_loss=0.02646, over 4970.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02975, over 966347.58 frames.], batch size: 14, lr: 1.40e-04
2022-05-08 14:29:33,186 INFO [train.py:715] (1/8) Epoch 16, batch 1100, loss[loss=0.1302, simple_loss=0.2023, pruned_loss=0.02908, over 4755.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02933, over 967224.38 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:30:11,813 INFO [train.py:715] (1/8) Epoch 16, batch 1150, loss[loss=0.1289, simple_loss=0.2106, pruned_loss=0.02364, over 4875.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02908, over 968216.49 frames.], batch size: 22, lr: 1.40e-04
2022-05-08 14:30:49,882 INFO [train.py:715] (1/8) Epoch 16, batch 1200, loss[loss=0.118, simple_loss=0.1926, pruned_loss=0.02175, over 4958.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02933, over 969391.24 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:31:27,246 INFO [train.py:715] (1/8) Epoch 16, batch 1250, loss[loss=0.1303, simple_loss=0.2058, pruned_loss=0.02745, over 4927.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02937, over 970020.20 frames.], batch size: 23, lr: 1.40e-04
2022-05-08 14:32:05,204 INFO [train.py:715] (1/8) Epoch 16, batch 1300, loss[loss=0.1208, simple_loss=0.2042, pruned_loss=0.01874, over 4972.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02972, over 970591.78 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:32:43,365 INFO [train.py:715] (1/8) Epoch 16, batch 1350, loss[loss=0.1682, simple_loss=0.2462, pruned_loss=0.04507, over 4704.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2073, pruned_loss=0.03003, over 971415.94 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:33:21,097 INFO [train.py:715] (1/8) Epoch 16, batch 1400, loss[loss=0.1342, simple_loss=0.2164, pruned_loss=0.02605, over 4911.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02983, over 971253.84 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:33:59,220 INFO [train.py:715] (1/8) Epoch 16, batch 1450, loss[loss=0.1446, simple_loss=0.2147, pruned_loss=0.03722, over 4921.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.0303, over 972270.55 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:34:37,201 INFO [train.py:715] (1/8) Epoch 16, batch 1500, loss[loss=0.1499, simple_loss=0.2214, pruned_loss=0.0392, over 4786.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03058, over 972427.72 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:35:14,922 INFO [train.py:715] (1/8) Epoch 16, batch 1550, loss[loss=0.1537, simple_loss=0.2257, pruned_loss=0.04087, over 4758.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03077, over 973268.71 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:35:52,771 INFO [train.py:715] (1/8) Epoch 16, batch 1600, loss[loss=0.1323, simple_loss=0.2098, pruned_loss=0.0274, over 4796.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03057, over 973098.68 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:36:30,170 INFO [train.py:715] (1/8) Epoch 16, batch 1650, loss[loss=0.1614, simple_loss=0.23, pruned_loss=0.04636, over 4868.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.0306, over 973392.64 frames.], batch size: 32, lr: 1.40e-04
2022-05-08 14:37:07,996 INFO [train.py:715] (1/8) Epoch 16, batch 1700, loss[loss=0.141, simple_loss=0.2146, pruned_loss=0.03366, over 4868.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.0305, over 973398.82 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:37:46,155 INFO [train.py:715] (1/8) Epoch 16, batch 1750, loss[loss=0.1387, simple_loss=0.2013, pruned_loss=0.03802, over 4846.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03037, over 972855.64 frames.], batch size: 32, lr: 1.40e-04
2022-05-08 14:38:24,069 INFO [train.py:715] (1/8) Epoch 16, batch 1800, loss[loss=0.1218, simple_loss=0.2028, pruned_loss=0.02044, over 4880.00 frames.], tot_loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03024, over 972999.40 frames.], batch size: 22, lr: 1.40e-04
2022-05-08 14:39:02,377 INFO [train.py:715] (1/8) Epoch 16, batch 1850, loss[loss=0.1258, simple_loss=0.206, pruned_loss=0.02274, over 4828.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.0301, over 972743.19 frames.], batch size: 26, lr: 1.40e-04
2022-05-08 14:39:41,006 INFO [train.py:715] (1/8) Epoch 16, batch 1900, loss[loss=0.1602, simple_loss=0.2243, pruned_loss=0.04809, over 4892.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.02998, over 972445.04 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:40:18,875 INFO [train.py:715] (1/8) Epoch 16, batch 1950, loss[loss=0.1158, simple_loss=0.1907, pruned_loss=0.02042, over 4951.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2074, pruned_loss=0.03006, over 972470.42 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:40:57,049 INFO [train.py:715] (1/8) Epoch 16, batch 2000, loss[loss=0.1599, simple_loss=0.2313, pruned_loss=0.04419, over 4857.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.03007, over 972911.46 frames.], batch size: 30, lr: 1.40e-04
2022-05-08 14:41:35,847 INFO [train.py:715] (1/8) Epoch 16, batch 2050, loss[loss=0.1631, simple_loss=0.2361, pruned_loss=0.04505, over 4773.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02999, over 973395.51 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:42:14,574 INFO [train.py:715] (1/8) Epoch 16, batch 2100, loss[loss=0.1369, simple_loss=0.2056, pruned_loss=0.03415, over 4866.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02998, over 973365.01 frames.], batch size: 32, lr: 1.40e-04
2022-05-08 14:42:52,430 INFO [train.py:715] (1/8) Epoch 16, batch 2150, loss[loss=0.1175, simple_loss=0.1931, pruned_loss=0.02094, over 4824.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02975, over 972173.29 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:43:31,558 INFO [train.py:715] (1/8) Epoch 16, batch 2200, loss[loss=0.108, simple_loss=0.1844, pruned_loss=0.01581, over 4834.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02943, over 972156.02 frames.], batch size: 26, lr: 1.40e-04
2022-05-08 14:44:09,851 INFO [train.py:715] (1/8) Epoch 16, batch 2250, loss[loss=0.139, simple_loss=0.2248, pruned_loss=0.02663, over 4926.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02915, over 972088.84 frames.], batch size: 29, lr: 1.40e-04
2022-05-08 14:44:47,486 INFO [train.py:715] (1/8) Epoch 16, batch 2300, loss[loss=0.1479, simple_loss=0.2248, pruned_loss=0.03548, over 4919.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02895, over 971001.60 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:45:25,055 INFO [train.py:715] (1/8) Epoch 16, batch 2350, loss[loss=0.1342, simple_loss=0.2037, pruned_loss=0.03238, over 4809.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02943, over 971020.47 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:46:03,346 INFO [train.py:715] (1/8) Epoch 16, batch 2400, loss[loss=0.1493, simple_loss=0.2303, pruned_loss=0.03415, over 4797.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2064, pruned_loss=0.02938, over 970382.73 frames.], batch size: 12, lr: 1.40e-04
2022-05-08 14:46:41,420 INFO [train.py:715] (1/8) Epoch 16, batch 2450, loss[loss=0.1195, simple_loss=0.1865, pruned_loss=0.02628, over 4768.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2067, pruned_loss=0.02956, over 970633.06 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:47:18,881 INFO [train.py:715] (1/8) Epoch 16, batch 2500, loss[loss=0.1177, simple_loss=0.198, pruned_loss=0.01873, over 4752.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02949, over 971497.59 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:47:57,274 INFO [train.py:715] (1/8) Epoch 16, batch 2550, loss[loss=0.1344, simple_loss=0.2068, pruned_loss=0.03101, over 4790.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.0294, over 971699.46 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:48:35,429 INFO [train.py:715] (1/8) Epoch 16, batch 2600, loss[loss=0.1387, simple_loss=0.2139, pruned_loss=0.03169, over 4941.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2068, pruned_loss=0.02983, over 972962.06 frames.], batch size: 39, lr: 1.40e-04
2022-05-08 14:49:13,163 INFO [train.py:715] (1/8) Epoch 16, batch 2650, loss[loss=0.1481, simple_loss=0.2271, pruned_loss=0.03453, over 4847.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2059, pruned_loss=0.02925, over 972581.50 frames.], batch size: 30, lr: 1.40e-04
2022-05-08 14:49:51,050 INFO [train.py:715] (1/8) Epoch 16, batch 2700, loss[loss=0.1322, simple_loss=0.2031, pruned_loss=0.03067, over 4934.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2058, pruned_loss=0.02926, over 972836.87 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:50:29,661 INFO [train.py:715] (1/8) Epoch 16, batch 2750, loss[loss=0.1204, simple_loss=0.1994, pruned_loss=0.02066, over 4814.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02969, over 973048.56 frames.], batch size: 26, lr: 1.40e-04
2022-05-08 14:51:08,571 INFO [train.py:715] (1/8) Epoch 16, batch 2800, loss[loss=0.1208, simple_loss=0.1949, pruned_loss=0.02335, over 4970.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02991, over 973073.93 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:51:46,949 INFO [train.py:715] (1/8) Epoch 16, batch 2850, loss[loss=0.1209, simple_loss=0.1933, pruned_loss=0.02422, over 4794.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03015, over 972733.69 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:52:24,999 INFO [train.py:715] (1/8) Epoch 16, batch 2900, loss[loss=0.1072, simple_loss=0.1792, pruned_loss=0.0176, over 4969.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02988, over 972790.29 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:53:03,775 INFO [train.py:715] (1/8) Epoch 16, batch 2950, loss[loss=0.1101, simple_loss=0.1834, pruned_loss=0.01838, over 4689.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02968, over 972679.33 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:53:41,758 INFO [train.py:715] (1/8) Epoch 16, batch 3000, loss[loss=0.1143, simple_loss=0.1822, pruned_loss=0.02323, over 4989.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02965, over 973687.17 frames.], batch size: 14, lr: 1.40e-04
2022-05-08 14:53:41,759 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 14:53:51,191 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.105, simple_loss=0.1885, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 14:54:29,009 INFO [train.py:715] (1/8) Epoch 16, batch 3050, loss[loss=0.1193, simple_loss=0.1997, pruned_loss=0.0194, over 4879.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02956, over 973070.65 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:55:09,458 INFO [train.py:715] (1/8) Epoch 16, batch 3100, loss[loss=0.1421, simple_loss=0.2092, pruned_loss=0.03755, over 4943.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02938, over 972017.42 frames.], batch size: 14, lr: 1.40e-04
2022-05-08 14:55:47,850 INFO [train.py:715] (1/8) Epoch 16, batch 3150, loss[loss=0.1491, simple_loss=0.2235, pruned_loss=0.03736, over 4983.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02928, over 972985.76 frames.], batch size: 33, lr: 1.40e-04
2022-05-08 14:56:26,006 INFO [train.py:715] (1/8) Epoch 16, batch 3200, loss[loss=0.1408, simple_loss=0.2162, pruned_loss=0.03271, over 4898.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02936, over 973506.70 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:57:04,240 INFO [train.py:715] (1/8) Epoch 16, batch 3250, loss[loss=0.1159, simple_loss=0.1931, pruned_loss=0.01936, over 4956.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.0295, over 973432.69 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:57:42,064 INFO [train.py:715] (1/8) Epoch 16, batch 3300, loss[loss=0.1229, simple_loss=0.2007, pruned_loss=0.02249, over 4691.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02942, over 974361.53 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:58:20,054 INFO [train.py:715] (1/8) Epoch 16, batch 3350, loss[loss=0.09852, simple_loss=0.1652, pruned_loss=0.0159, over 4809.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2078, pruned_loss=0.02903, over 974900.86 frames.], batch size: 12, lr: 1.40e-04
2022-05-08 14:58:57,931 INFO [train.py:715] (1/8) Epoch 16, batch 3400, loss[loss=0.1484, simple_loss=0.2188, pruned_loss=0.03902, over 4776.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.02959, over 974314.21 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:59:35,866 INFO [train.py:715] (1/8) Epoch 16, batch 3450, loss[loss=0.1279, simple_loss=0.2037, pruned_loss=0.02602, over 4943.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02979, over 974220.33 frames.], batch size: 23, lr: 1.40e-04
2022-05-08 15:00:13,955 INFO [train.py:715] (1/8) Epoch 16, batch 3500, loss[loss=0.1244, simple_loss=0.2046, pruned_loss=0.02207, over 4817.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02931, over 973459.42 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 15:00:51,757 INFO [train.py:715] (1/8) Epoch 16, batch 3550, loss[loss=0.1388, simple_loss=0.2095, pruned_loss=0.03402, over 4865.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02976, over 972719.12 frames.], batch size: 32, lr: 1.40e-04
2022-05-08 15:01:30,179 INFO [train.py:715] (1/8) Epoch 16, batch 3600, loss[loss=0.1621, simple_loss=0.2505, pruned_loss=0.03684, over 4794.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02998, over 973069.96 frames.], batch size: 17, lr: 1.40e-04
2022-05-08 15:02:07,899 INFO [train.py:715] (1/8) Epoch 16, batch 3650, loss[loss=0.1309, simple_loss=0.2094, pruned_loss=0.02618, over 4763.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02984, over 972245.68 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 15:02:46,544 INFO [train.py:715] (1/8) Epoch 16, batch 3700, loss[loss=0.1487, simple_loss=0.2148, pruned_loss=0.04134, over 4928.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.03042, over 972256.55 frames.], batch size: 23, lr: 1.40e-04
2022-05-08 15:03:25,030 INFO [train.py:715] (1/8) Epoch 16, batch 3750, loss[loss=0.1397, simple_loss=0.2082, pruned_loss=0.03563, over 4807.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03019, over 972809.74 frames.], batch size: 13, lr: 1.40e-04
2022-05-08 15:04:03,392 INFO [train.py:715] (1/8) Epoch 16, batch 3800, loss[loss=0.1168, simple_loss=0.1905, pruned_loss=0.02157, over 4935.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.0301, over 972712.36 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 15:04:42,257 INFO [train.py:715] (1/8) Epoch 16, batch 3850, loss[loss=0.1202, simple_loss=0.2003, pruned_loss=0.02003, over 4959.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.0296, over 972534.58 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 15:05:21,016 INFO [train.py:715] (1/8) Epoch 16, batch 3900, loss[loss=0.1275, simple_loss=0.2094, pruned_loss=0.02278, over 4919.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2078, pruned_loss=0.02934, over 972459.96 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 15:05:58,861 INFO [train.py:715] (1/8) Epoch 16, batch 3950, loss[loss=0.135, simple_loss=0.2066, pruned_loss=0.0317, over 4765.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02919, over 972135.04 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:06:36,787 INFO [train.py:715] (1/8) Epoch 16, batch 4000, loss[loss=0.1336, simple_loss=0.2161, pruned_loss=0.02558, over 4915.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02912, over 973289.33 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 15:07:14,746 INFO [train.py:715] (1/8) Epoch 16, batch 4050, loss[loss=0.1585, simple_loss=0.233, pruned_loss=0.04203, over 4811.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02953, over 972623.79 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 15:07:52,149 INFO [train.py:715] (1/8) Epoch 16, batch 4100, loss[loss=0.1304, simple_loss=0.2135, pruned_loss=0.02362, over 4812.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02981, over 972271.39 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 15:08:29,800 INFO [train.py:715] (1/8) Epoch 16, batch 4150, loss[loss=0.1076, simple_loss=0.1828, pruned_loss=0.01625, over 4967.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02973, over 971931.68 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 15:09:07,466 INFO [train.py:715] (1/8) Epoch 16, batch 4200, loss[loss=0.1427, simple_loss=0.2133, pruned_loss=0.03605, over 4853.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02962, over 971950.38 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:09:45,635 INFO [train.py:715] (1/8) Epoch 16, batch 4250, loss[loss=0.1303, simple_loss=0.2061, pruned_loss=0.02725, over 4857.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2069, pruned_loss=0.0297, over 971972.60 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 15:10:23,343 INFO [train.py:715] (1/8) Epoch 16, batch 4300, loss[loss=0.1495, simple_loss=0.2213, pruned_loss=0.03884, over 4967.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02952, over 971919.07 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:11:01,197 INFO [train.py:715] (1/8) Epoch 16, batch 4350, loss[loss=0.1575, simple_loss=0.2315, pruned_loss=0.04174, over 4779.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02983, over 972136.20 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:11:39,307 INFO [train.py:715] (1/8) Epoch 16, batch 4400, loss[loss=0.1404, simple_loss=0.2216, pruned_loss=0.02964, over 4910.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.0297, over 972181.87 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:12:17,135 INFO [train.py:715] (1/8) Epoch 16, batch 4450, loss[loss=0.1653, simple_loss=0.2415, pruned_loss=0.04456, over 4693.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03025, over 971788.27 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:12:54,753 INFO [train.py:715] (1/8) Epoch 16, batch 4500, loss[loss=0.1398, simple_loss=0.2078, pruned_loss=0.03592, over 4970.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03084, over 971762.50 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 15:13:32,862 INFO [train.py:715] (1/8) Epoch 16, batch 4550, loss[loss=0.1129, simple_loss=0.1899, pruned_loss=0.01795, over 4906.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03, over 971701.23 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:14:11,256 INFO [train.py:715] (1/8) Epoch 16, batch 4600, loss[loss=0.1264, simple_loss=0.2022, pruned_loss=0.02525, over 4986.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03005, over 972035.47 frames.], batch size: 28, lr: 1.39e-04
2022-05-08 15:14:49,232 INFO [train.py:715] (1/8) Epoch 16, batch 4650, loss[loss=0.1204, simple_loss=0.2001, pruned_loss=0.02037, over 4939.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03007, over 972926.08 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 15:15:27,632 INFO [train.py:715] (1/8) Epoch 16, batch 4700, loss[loss=0.1272, simple_loss=0.2039, pruned_loss=0.02524, over 4948.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02958, over 972509.44 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 15:16:06,227 INFO [train.py:715] (1/8) Epoch 16, batch 4750, loss[loss=0.1465, simple_loss=0.225, pruned_loss=0.03398, over 4889.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03021, over 972319.55 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 15:16:44,832 INFO [train.py:715] (1/8) Epoch 16, batch 4800, loss[loss=0.1194, simple_loss=0.1969, pruned_loss=0.02092, over 4894.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.0302, over 972974.44 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:17:23,110 INFO [train.py:715] (1/8) Epoch 16, batch 4850, loss[loss=0.1667, simple_loss=0.2476, pruned_loss=0.04286, over 4971.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02955, over 973286.46 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:18:01,810 INFO [train.py:715] (1/8) Epoch 16, batch 4900, loss[loss=0.1102, simple_loss=0.1831, pruned_loss=0.01867, over 4764.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02968, over 973122.52 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 15:18:40,682 INFO [train.py:715] (1/8) Epoch 16, batch 4950, loss[loss=0.1459, simple_loss=0.2132, pruned_loss=0.03932, over 4880.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02963, over 973093.43 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:19:18,926 INFO [train.py:715] (1/8) Epoch 16, batch 5000, loss[loss=0.1432, simple_loss=0.2189, pruned_loss=0.03372, over 4917.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.0297, over 972543.51 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 15:19:57,139 INFO [train.py:715] (1/8) Epoch 16, batch 5050, loss[loss=0.08852, simple_loss=0.1496, pruned_loss=0.01369, over 4767.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2088, pruned_loss=0.03004, over 972566.49 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 15:20:35,446 INFO [train.py:715] (1/8) Epoch 16, batch 5100, loss[loss=0.1246, simple_loss=0.2059, pruned_loss=0.02166, over 4925.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2088, pruned_loss=0.02981, over 971903.98 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 15:21:13,350 INFO [train.py:715] (1/8) Epoch 16, batch 5150, loss[loss=0.1696, simple_loss=0.2479, pruned_loss=0.04566, over 4978.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03005, over 971506.36 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 15:21:50,909 INFO [train.py:715] (1/8) Epoch 16, batch 5200, loss[loss=0.1321, simple_loss=0.1992, pruned_loss=0.03251, over 4840.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03012, over 971795.24 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:22:28,869 INFO [train.py:715] (1/8) Epoch 16, batch 5250, loss[loss=0.09812, simple_loss=0.1756, pruned_loss=0.01031, over 4861.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02951, over 972017.08 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 15:23:07,105 INFO [train.py:715] (1/8) Epoch 16, batch 5300, loss[loss=0.1439, simple_loss=0.2104, pruned_loss=0.03869, over 4751.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02968, over 971415.94 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:23:45,228 INFO [train.py:715] (1/8) Epoch 16, batch 5350, loss[loss=0.1473, simple_loss=0.2229, pruned_loss=0.03583, over 4897.00 frames.], tot_loss[loss=0.135, simple_loss=0.2094, pruned_loss=0.03028, over 971844.17 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:24:23,038 INFO [train.py:715] (1/8) Epoch 16, batch 5400, loss[loss=0.1148, simple_loss=0.1949, pruned_loss=0.01733, over 4962.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03023, over 971777.18 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 15:25:00,888 INFO [train.py:715] (1/8) Epoch 16, batch 5450, loss[loss=0.1227, simple_loss=0.1946, pruned_loss=0.02544, over 4880.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03038, over 972777.05 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:25:38,708 INFO [train.py:715] (1/8) Epoch 16, batch 5500, loss[loss=0.1716, simple_loss=0.2461, pruned_loss=0.04857, over 4872.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03023, over 973482.46 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 15:26:16,329 INFO [train.py:715] (1/8) Epoch 16, batch 5550, loss[loss=0.1271, simple_loss=0.2058, pruned_loss=0.02422, over 4951.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.0304, over 973498.13 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 15:26:54,076 INFO [train.py:715] (1/8) Epoch 16, batch 5600, loss[loss=0.1194, simple_loss=0.185, pruned_loss=0.02693, over 4790.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03061, over 973282.69 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 15:27:32,730 INFO [train.py:715] (1/8) Epoch 16, batch 5650, loss[loss=0.1384, simple_loss=0.2096, pruned_loss=0.03359, over 4910.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.0302, over 973602.45 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:28:10,548 INFO [train.py:715] (1/8) Epoch 16, batch 5700, loss[loss=0.1362, simple_loss=0.207, pruned_loss=0.03266, over 4786.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.03039, over 972812.62 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:28:48,370 INFO [train.py:715] (1/8) Epoch 16, batch 5750, loss[loss=0.1249, simple_loss=0.2077, pruned_loss=0.02099, over 4990.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03051, over 972436.30 frames.], batch size: 28, lr: 1.39e-04
2022-05-08 15:29:26,214 INFO [train.py:715] (1/8) Epoch 16, batch 5800, loss[loss=0.1413, simple_loss=0.2154, pruned_loss=0.03357, over 4929.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03029, over 972206.39 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 15:30:04,478 INFO [train.py:715] (1/8) Epoch 16, batch 5850, loss[loss=0.123, simple_loss=0.1992, pruned_loss=0.02343, over 4760.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02959, over 971516.37 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:30:42,018 INFO [train.py:715] (1/8) Epoch 16, batch 5900, loss[loss=0.163, simple_loss=0.2348, pruned_loss=0.04555, over 4751.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02987, over 971790.37 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:31:19,663 INFO [train.py:715] (1/8) Epoch 16, batch 5950, loss[loss=0.1973, simple_loss=0.2784, pruned_loss=0.05811, over 4981.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.0299, over 972302.12 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 15:31:58,429 INFO [train.py:715] (1/8) Epoch 16, batch 6000, loss[loss=0.1277, simple_loss=0.2036, pruned_loss=0.02595, over 4958.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.0297, over 972263.24 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 15:31:58,429 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 15:32:07,946 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.105, simple_loss=0.1885, pruned_loss=0.01082, over 914524.00 frames.
2022-05-08 15:32:46,979 INFO [train.py:715] (1/8) Epoch 16, batch 6050, loss[loss=0.1596, simple_loss=0.2284, pruned_loss=0.04534, over 4829.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.0298, over 972788.81 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:33:25,024 INFO [train.py:715] (1/8) Epoch 16, batch 6100, loss[loss=0.1356, simple_loss=0.2049, pruned_loss=0.03316, over 4849.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03034, over 972858.32 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:34:02,793 INFO [train.py:715] (1/8) Epoch 16, batch 6150, loss[loss=0.1371, simple_loss=0.208, pruned_loss=0.03313, over 4986.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2101, pruned_loss=0.03088, over 972719.42 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:34:40,934 INFO [train.py:715] (1/8) Epoch 16, batch 6200, loss[loss=0.1433, simple_loss=0.2075, pruned_loss=0.03954, over 4845.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2093, pruned_loss=0.03019, over 972114.86 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:35:19,472 INFO [train.py:715] (1/8) Epoch 16, batch 6250, loss[loss=0.1062, simple_loss=0.1785, pruned_loss=0.0169, over 4842.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02968, over 971939.40 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:35:57,115 INFO [train.py:715] (1/8) Epoch 16, batch 6300, loss[loss=0.1264, simple_loss=0.1981, pruned_loss=0.02735, over 4747.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.0301, over 971274.95 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:36:34,886 INFO [train.py:715] (1/8) Epoch 16, batch 6350, loss[loss=0.1274, simple_loss=0.2034, pruned_loss=0.02571, over 4811.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02991, over 970532.82 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:37:13,403 INFO [train.py:715] (1/8) Epoch 16, batch 6400, loss[loss=0.1284, simple_loss=0.1962, pruned_loss=0.03026, over 4882.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.0298, over 970327.67 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 15:37:51,670 INFO [train.py:715] (1/8) Epoch 16, batch 6450, loss[loss=0.1456, simple_loss=0.2198, pruned_loss=0.03571, over 4840.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03002, over 969988.96 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 15:38:29,442 INFO [train.py:715] (1/8) Epoch 16, batch 6500, loss[loss=0.1114, simple_loss=0.1933, pruned_loss=0.01478, over 4940.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2092, pruned_loss=0.0301, over 970194.06 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 15:39:07,581 INFO [train.py:715] (1/8) Epoch 16, batch 6550, loss[loss=0.1274, simple_loss=0.2007, pruned_loss=0.02704, over 4705.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2092, pruned_loss=0.02972, over 971133.28 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:39:46,028 INFO [train.py:715] (1/8) Epoch 16, batch 6600, loss[loss=0.1707, simple_loss=0.2532, pruned_loss=0.04411, over 4857.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2091, pruned_loss=0.02955, over 971222.15 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 15:40:23,829 INFO [train.py:715] (1/8) Epoch 16, batch 6650, loss[loss=0.1524, simple_loss=0.2315, pruned_loss=0.03669, over 4850.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2093, pruned_loss=0.0299, over 971598.60 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 15:41:01,686 INFO [train.py:715] (1/8) Epoch 16, batch 6700, loss[loss=0.1256, simple_loss=0.2048, pruned_loss=0.02317, over 4973.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2085, pruned_loss=0.02943, over 972097.99 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 15:41:39,714 INFO [train.py:715] (1/8) Epoch 16, batch 6750, loss[loss=0.1333, simple_loss=0.2045, pruned_loss=0.03109, over 4773.00 frames.], tot_loss[loss=0.1342, simple_loss=0.209, pruned_loss=0.02969, over 972040.08 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:42:17,832 INFO [train.py:715] (1/8) Epoch 16, batch 6800, loss[loss=0.1229, simple_loss=0.1986, pruned_loss=0.02356, over 4833.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2085, pruned_loss=0.02957, over 973104.92 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 15:42:54,814 INFO [train.py:715] (1/8) Epoch 16, batch 6850, loss[loss=0.139, simple_loss=0.2157, pruned_loss=0.03119, over 4806.00 frames.], tot_loss[loss=0.134, simple_loss=0.2089, pruned_loss=0.02952, over 973058.16 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 15:43:32,598 INFO [train.py:715] (1/8) Epoch 16, batch 6900, loss[loss=0.121, simple_loss=0.1878, pruned_loss=0.0271, over 4900.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2089, pruned_loss=0.02986, over 973349.30 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:44:10,714 INFO [train.py:715] (1/8) Epoch 16, batch 6950, loss[loss=0.1799, simple_loss=0.253, pruned_loss=0.05344, over 4967.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2089, pruned_loss=0.02977, over 972898.58 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:44:48,420 INFO [train.py:715] (1/8) Epoch 16, batch 7000, loss[loss=0.1308, simple_loss=0.2112, pruned_loss=0.02515, over 4972.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2093, pruned_loss=0.03013, over 972501.26 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:45:26,357 INFO [train.py:715] (1/8) Epoch 16, batch 7050, loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03608, over 4971.00 frames.], tot_loss[loss=0.1345, simple_loss=0.209, pruned_loss=0.02998, over 972257.84 frames.], batch size: 35, lr: 1.39e-04
2022-05-08 15:46:04,191 INFO [train.py:715] (1/8) Epoch 16, batch 7100, loss[loss=0.1457, simple_loss=0.2186, pruned_loss=0.03637, over 4987.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2081, pruned_loss=0.02939, over 972201.80 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:46:42,648 INFO [train.py:715] (1/8) Epoch 16, batch 7150, loss[loss=0.1436, simple_loss=0.2214, pruned_loss=0.03293, over 4795.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2088, pruned_loss=0.0294, over 971766.31 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:47:19,962 INFO [train.py:715] (1/8) Epoch 16, batch 7200, loss[loss=0.1736, simple_loss=0.2348, pruned_loss=0.05619, over 4879.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2095, pruned_loss=0.02974, over 971609.27 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:47:57,929 INFO [train.py:715] (1/8) Epoch 16, batch 7250, loss[loss=0.1149, simple_loss=0.1931, pruned_loss=0.01834, over 4819.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2088, pruned_loss=0.02954, over 972154.21 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 15:48:36,999 INFO [train.py:715] (1/8) Epoch 16, batch 7300, loss[loss=0.1491, simple_loss=0.2225, pruned_loss=0.03785, over 4751.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2091, pruned_loss=0.02992, over 971908.13 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:49:15,796 INFO [train.py:715] (1/8) Epoch 16, batch 7350, loss[loss=0.1335, simple_loss=0.2102, pruned_loss=0.02841, over 4871.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2081, pruned_loss=0.02948, over 971947.25 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 15:49:55,250 INFO [train.py:715] (1/8) Epoch 16, batch 7400, loss[loss=0.1509, simple_loss=0.2227, pruned_loss=0.03948, over 4979.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02967, over 972184.21 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:50:34,969 INFO [train.py:715] (1/8) Epoch 16, batch 7450, loss[loss=0.1223, simple_loss=0.1951, pruned_loss=0.02475, over 4977.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03016, over 971659.01 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:51:14,636 INFO [train.py:715] (1/8) Epoch 16, batch 7500, loss[loss=0.1353, simple_loss=0.2169, pruned_loss=0.02683, over 4885.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.0298, over 971682.03 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:51:53,676 INFO [train.py:715] (1/8) Epoch 16, batch 7550, loss[loss=0.1346, simple_loss=0.2173, pruned_loss=0.02592, over 4779.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02959, over 971745.03 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:52:33,694 INFO [train.py:715] (1/8) Epoch 16, batch 7600, loss[loss=0.1392, simple_loss=0.2164, pruned_loss=0.03101, over 4871.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02951, over 970998.30 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:53:14,076 INFO [train.py:715] (1/8) Epoch 16, batch 7650, loss[loss=0.1512, simple_loss=0.2347, pruned_loss=0.03382, over 4831.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02932, over 972146.82 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 15:53:54,208 INFO [train.py:715] (1/8) Epoch 16, batch 7700, loss[loss=0.1395, simple_loss=0.2201, pruned_loss=0.02939, over 4907.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02906, over 972206.53 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:54:33,729 INFO [train.py:715] (1/8) Epoch 16, batch 7750, loss[loss=0.1228, simple_loss=0.2074, pruned_loss=0.01904, over 4696.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02907, over 972703.73 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:55:13,917 INFO [train.py:715] (1/8) Epoch 16, batch 7800, loss[loss=0.1284, simple_loss=0.2072, pruned_loss=0.02479, over 4794.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02901, over 973224.05 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 15:55:54,761 INFO [train.py:715] (1/8) Epoch 16, batch 7850, loss[loss=0.1502, simple_loss=0.2206, pruned_loss=0.03991, over 4931.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2071, pruned_loss=0.02892, over 972461.68 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 15:56:34,169 INFO [train.py:715] (1/8) Epoch 16, batch 7900, loss[loss=0.1441, simple_loss=0.2188, pruned_loss=0.03471, over 4956.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.0293, over 973546.25 frames.], batch size: 35, lr: 1.39e-04
2022-05-08 15:57:14,054 INFO [train.py:715] (1/8) Epoch 16, batch 7950, loss[loss=0.1497, simple_loss=0.2105, pruned_loss=0.04449, over 4874.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02951, over 973152.64 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 15:57:54,557 INFO [train.py:715] (1/8) Epoch 16, batch 8000, loss[loss=0.156, simple_loss=0.2281, pruned_loss=0.04192, over 4812.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02973, over 972594.03 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:58:34,601 INFO [train.py:715] (1/8) Epoch 16, batch 8050, loss[loss=0.1233, simple_loss=0.201, pruned_loss=0.02281, over 4874.00 frames.], tot_loss[loss=0.134, simple_loss=0.2085, pruned_loss=0.0298, over 972170.70 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 15:59:14,244 INFO [train.py:715] (1/8) Epoch 16, batch 8100, loss[loss=0.1263, simple_loss=0.2046, pruned_loss=0.02398, over 4937.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2095, pruned_loss=0.03056, over 971544.64 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 15:59:54,667 INFO [train.py:715] (1/8) Epoch 16, batch 8150, loss[loss=0.1219, simple_loss=0.202, pruned_loss=0.02094, over 4885.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03054, over 970771.12 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:00:35,742 INFO [train.py:715] (1/8) Epoch 16, batch 8200, loss[loss=0.1536, simple_loss=0.2196, pruned_loss=0.04379, over 4866.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03034, over 970664.93 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 16:01:15,831 INFO [train.py:715] (1/8) Epoch 16, batch 8250, loss[loss=0.1753, simple_loss=0.2334, pruned_loss=0.05864, over 4781.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03023, over 970700.85 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:01:55,578 INFO [train.py:715] (1/8) Epoch 16, batch 8300, loss[loss=0.124, simple_loss=0.2043, pruned_loss=0.02183, over 4767.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02985, over 970418.91 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:02:36,299 INFO [train.py:715] (1/8) Epoch 16, batch 8350, loss[loss=0.1094, simple_loss=0.1841, pruned_loss=0.01735, over 4947.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02984, over 971336.61 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:03:16,606 INFO [train.py:715] (1/8) Epoch 16, batch 8400, loss[loss=0.1355, simple_loss=0.2028, pruned_loss=0.03413, over 4754.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03014, over 972173.22 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:03:55,140 INFO [train.py:715] (1/8) Epoch 16, batch 8450, loss[loss=0.1436, simple_loss=0.2314, pruned_loss=0.02792, over 4948.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03017, over 972791.39 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:04:34,542 INFO [train.py:715] (1/8) Epoch 16, batch 8500, loss[loss=0.143, simple_loss=0.2274, pruned_loss=0.02934, over 4814.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02992, over 973459.74 frames.], batch size: 27, lr: 1.39e-04
2022-05-08 16:05:13,260 INFO [train.py:715] (1/8) Epoch 16, batch 8550, loss[loss=0.1395, simple_loss=0.2132, pruned_loss=0.03288, over 4884.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02979, over 973475.38 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:05:51,577 INFO [train.py:715] (1/8) Epoch 16, batch 8600, loss[loss=0.1265, simple_loss=0.2046, pruned_loss=0.02422, over 4903.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03018, over 973171.58 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:06:29,598 INFO [train.py:715] (1/8) Epoch 16, batch 8650, loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03066, over 4768.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02986, over 972831.87 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:07:08,668 INFO [train.py:715] (1/8) Epoch 16, batch 8700, loss[loss=0.1171, simple_loss=0.192, pruned_loss=0.0211, over 4820.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.0297, over 972950.61 frames.], batch size: 27, lr: 1.39e-04
2022-05-08 16:07:47,718 INFO [train.py:715] (1/8) Epoch 16, batch 8750, loss[loss=0.1359, simple_loss=0.206, pruned_loss=0.03295, over 4933.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02989, over 973589.16 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 16:08:26,279 INFO [train.py:715] (1/8) Epoch 16, batch 8800, loss[loss=0.1262, simple_loss=0.2078, pruned_loss=0.02227, over 4858.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02992, over 973494.45 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 16:09:04,985 INFO [train.py:715] (1/8) Epoch 16, batch 8850, loss[loss=0.1351, simple_loss=0.2114, pruned_loss=0.02943, over 4868.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02995, over 972920.97 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 16:09:44,466 INFO [train.py:715] (1/8) Epoch 16, batch 8900, loss[loss=0.1262, simple_loss=0.2051, pruned_loss=0.02365, over 4882.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03008, over 972144.34 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:10:22,906 INFO [train.py:715] (1/8) Epoch 16, batch 8950, loss[loss=0.1729, simple_loss=0.2372, pruned_loss=0.05426, over 4984.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02988, over 971756.85 frames.], batch size: 33, lr: 1.39e-04
2022-05-08 16:11:01,137 INFO [train.py:715] (1/8) Epoch 16, batch 9000, loss[loss=0.153, simple_loss=0.2328, pruned_loss=0.03665, over 4751.00 frames.], tot_loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.02972, over 971529.65 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:11:01,137 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 16:11:23,894 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.105, simple_loss=0.1884, pruned_loss=0.01076, over 914524.00 frames.
2022-05-08 16:12:02,817 INFO [train.py:715] (1/8) Epoch 16, batch 9050, loss[loss=0.1396, simple_loss=0.2215, pruned_loss=0.02879, over 4746.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2063, pruned_loss=0.02962, over 971600.69 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:12:41,947 INFO [train.py:715] (1/8) Epoch 16, batch 9100, loss[loss=0.1056, simple_loss=0.1806, pruned_loss=0.01526, over 4652.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02954, over 972383.10 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 16:13:20,956 INFO [train.py:715] (1/8) Epoch 16, batch 9150, loss[loss=0.1362, simple_loss=0.2066, pruned_loss=0.03287, over 4968.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02937, over 972724.26 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:13:58,479 INFO [train.py:715] (1/8) Epoch 16, batch 9200, loss[loss=0.1719, simple_loss=0.2315, pruned_loss=0.05616, over 4875.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02989, over 972488.48 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:14:37,129 INFO [train.py:715] (1/8) Epoch 16, batch 9250, loss[loss=0.1194, simple_loss=0.1976, pruned_loss=0.02061, over 4939.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03017, over 971844.36 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:15:16,083 INFO [train.py:715] (1/8) Epoch 16, batch 9300, loss[loss=0.1386, simple_loss=0.2156, pruned_loss=0.03079, over 4752.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02994, over 972496.39 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:15:54,778 INFO [train.py:715] (1/8) Epoch 16, batch 9350, loss[loss=0.1341, simple_loss=0.2143, pruned_loss=0.02698, over 4912.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02969, over 972288.28 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 16:16:33,102 INFO [train.py:715] (1/8) Epoch 16, batch 9400, loss[loss=0.1457, simple_loss=0.2293, pruned_loss=0.0311, over 4911.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.02995, over 971782.86 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:17:11,621 INFO [train.py:715] (1/8) Epoch 16, batch 9450, loss[loss=0.1143, simple_loss=0.191, pruned_loss=0.01877, over 4950.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02997, over 971095.46 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 16:17:50,526 INFO [train.py:715] (1/8) Epoch 16, batch 9500, loss[loss=0.1507, simple_loss=0.2192, pruned_loss=0.04113, over 4981.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02962, over 971253.44 frames.], batch size: 33, lr: 1.39e-04
2022-05-08 16:18:28,785 INFO [train.py:715] (1/8) Epoch 16, batch 9550, loss[loss=0.1247, simple_loss=0.206, pruned_loss=0.02167, over 4686.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02993, over 971270.39 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:19:08,108 INFO [train.py:715] (1/8) Epoch 16, batch 9600, loss[loss=0.1201, simple_loss=0.1998, pruned_loss=0.02025, over 4967.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02943, over 971161.09 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 16:19:47,956 INFO [train.py:715] (1/8) Epoch 16, batch 9650, loss[loss=0.1325, simple_loss=0.2101, pruned_loss=0.02748, over 4900.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02951, over 972340.75 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 16:20:27,598 INFO [train.py:715] (1/8) Epoch 16, batch 9700, loss[loss=0.1219, simple_loss=0.2018, pruned_loss=0.02103, over 4827.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02941, over 972589.40 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 16:21:08,018 INFO [train.py:715] (1/8) Epoch 16, batch 9750, loss[loss=0.1537, simple_loss=0.2306, pruned_loss=0.03838, over 4867.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02953, over 972930.42 frames.], batch size: 38, lr: 1.39e-04
2022-05-08 16:21:49,081 INFO [train.py:715] (1/8) Epoch 16, batch 9800, loss[loss=0.1377, simple_loss=0.2007, pruned_loss=0.03733, over 4983.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2087, pruned_loss=0.02994, over 972904.07 frames.], batch size: 33, lr: 1.39e-04
2022-05-08 16:22:29,511 INFO [train.py:715] (1/8) Epoch 16, batch 9850, loss[loss=0.1481, simple_loss=0.2254, pruned_loss=0.03536, over 4920.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02975, over 973078.67 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:23:09,297 INFO [train.py:715] (1/8) Epoch 16, batch 9900, loss[loss=0.1145, simple_loss=0.1877, pruned_loss=0.02063, over 4827.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.0299, over 972367.42 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 16:23:49,481 INFO [train.py:715] (1/8) Epoch 16, batch 9950, loss[loss=0.1617, simple_loss=0.2388, pruned_loss=0.04228, over 4832.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.02965, over 973699.47 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:24:30,459 INFO [train.py:715] (1/8) Epoch 16, batch 10000, loss[loss=0.1267, simple_loss=0.2006, pruned_loss=0.02641, over 4821.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02938, over 973390.49 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 16:25:09,384 INFO [train.py:715] (1/8) Epoch 16, batch 10050, loss[loss=0.1415, simple_loss=0.2133, pruned_loss=0.03483, over 4904.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2087, pruned_loss=0.02987, over 972776.53 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 16:25:49,617 INFO [train.py:715] (1/8) Epoch 16, batch 10100, loss[loss=0.1557, simple_loss=0.2268, pruned_loss=0.0423, over 4923.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2084, pruned_loss=0.0297, over 972725.62 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:26:30,391 INFO [train.py:715] (1/8) Epoch 16, batch 10150, loss[loss=0.1321, simple_loss=0.207, pruned_loss=0.02857, over 4950.00 frames.], tot_loss[loss=0.1334, simple_loss=0.208, pruned_loss=0.02944, over 971827.34 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:27:10,598 INFO [train.py:715] (1/8) Epoch 16, batch 10200, loss[loss=0.1398, simple_loss=0.2131, pruned_loss=0.03323, over 4922.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02937, over 972045.90 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:27:49,596 INFO [train.py:715] (1/8) Epoch 16, batch 10250, loss[loss=0.1148, simple_loss=0.1864, pruned_loss=0.02163, over 4734.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02961, over 971965.66 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:28:29,501 INFO [train.py:715] (1/8) Epoch 16, batch 10300, loss[loss=0.1173, simple_loss=0.1961, pruned_loss=0.01918, over 4950.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02953, over 972036.96 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 16:29:09,152 INFO [train.py:715] (1/8) Epoch 16, batch 10350, loss[loss=0.1392, simple_loss=0.2173, pruned_loss=0.03058, over 4919.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02946, over 972292.30 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:29:47,476 INFO [train.py:715] (1/8) Epoch 16, batch 10400, loss[loss=0.1413, simple_loss=0.2114, pruned_loss=0.03562, over 4955.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02943, over 971079.25 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:30:26,269 INFO [train.py:715] (1/8) Epoch 16, batch 10450, loss[loss=0.1243, simple_loss=0.2032, pruned_loss=0.02267, over 4859.00 frames.], tot_loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02977, over 970794.31 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 16:31:05,204 INFO [train.py:715] (1/8) Epoch 16, batch 10500, loss[loss=0.1439, simple_loss=0.2221, pruned_loss=0.0329, over 4963.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02969, over 971421.04 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:31:44,639 INFO [train.py:715] (1/8) Epoch 16, batch 10550, loss[loss=0.1701, simple_loss=0.2383, pruned_loss=0.05096, over 4849.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02929, over 971662.97 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 16:32:22,612 INFO [train.py:715] (1/8) Epoch 16, batch 10600, loss[loss=0.1445, simple_loss=0.2155, pruned_loss=0.03674, over 4758.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02974, over 972214.58 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:33:01,319 INFO [train.py:715] (1/8) Epoch 16, batch 10650, loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03872, over 4908.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02992, over 972469.09 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:33:40,768 INFO [train.py:715] (1/8) Epoch 16, batch 10700, loss[loss=0.1052, simple_loss=0.1844, pruned_loss=0.01298, over 4794.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03003, over 972780.51 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:34:19,602 INFO [train.py:715] (1/8) Epoch 16, batch 10750, loss[loss=0.1548, simple_loss=0.2357, pruned_loss=0.03697, over 4787.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02993, over 972632.06 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:34:58,502 INFO [train.py:715] (1/8) Epoch 16, batch 10800, loss[loss=0.1353, simple_loss=0.2013, pruned_loss=0.03461, over 4762.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03015, over 972318.43 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:35:37,667 INFO [train.py:715] (1/8) Epoch 16, batch 10850, loss[loss=0.1376, simple_loss=0.2037, pruned_loss=0.03578, over 4858.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02999, over 972404.65 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 16:36:17,326 INFO [train.py:715] (1/8) Epoch 16, batch 10900, loss[loss=0.1143, simple_loss=0.196, pruned_loss=0.01634, over 4939.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02985, over 972622.06 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 16:36:55,552 INFO [train.py:715] (1/8) Epoch 16, batch 10950, loss[loss=0.1346, simple_loss=0.1971, pruned_loss=0.03601, over 4799.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03016, over 972171.72 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 16:37:34,522 INFO [train.py:715] (1/8) Epoch 16, batch 11000, loss[loss=0.1708, simple_loss=0.256, pruned_loss=0.04284, over 4766.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03061, over 971718.89 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:38:13,976 INFO [train.py:715] (1/8) Epoch 16, batch 11050, loss[loss=0.1496, simple_loss=0.2252, pruned_loss=0.03701, over 4931.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03091, over 972391.00 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:38:55,217 INFO [train.py:715] (1/8) Epoch 16, batch 11100, loss[loss=0.1218, simple_loss=0.1839, pruned_loss=0.0299, over 4787.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03089, over 971543.72 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:39:33,650 INFO [train.py:715] (1/8) Epoch 16, batch 11150, loss[loss=0.1278, simple_loss=0.2033, pruned_loss=0.02616, over 4970.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03018, over 971847.35 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:40:12,897 INFO [train.py:715] (1/8) Epoch 16, batch 11200, loss[loss=0.1666, simple_loss=0.2394, pruned_loss=0.04692, over 4874.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02996, over 971681.80 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:40:51,685 INFO [train.py:715] (1/8) Epoch 16, batch 11250, loss[loss=0.1322, simple_loss=0.2076, pruned_loss=0.02843, over 4827.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02943, over 971613.65 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:41:29,876 INFO [train.py:715] (1/8) Epoch 16, batch 11300, loss[loss=0.1128, simple_loss=0.1926, pruned_loss=0.01651, over 4831.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02929, over 972110.05 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 16:42:08,151 INFO [train.py:715] (1/8) Epoch 16, batch 11350, loss[loss=0.1381, simple_loss=0.2107, pruned_loss=0.03278, over 4887.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.0296, over 972472.28 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 16:42:47,130 INFO [train.py:715] (1/8) Epoch 16, batch 11400, loss[loss=0.1387, simple_loss=0.2084, pruned_loss=0.0345, over 4991.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02967, over 971481.08 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:43:25,157 INFO [train.py:715] (1/8) Epoch 16, batch 11450, loss[loss=0.1517, simple_loss=0.2236, pruned_loss=0.03984, over 4856.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02929, over 972041.10 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 16:44:03,088 INFO [train.py:715] (1/8) Epoch 16, batch 11500, loss[loss=0.1489, simple_loss=0.2174, pruned_loss=0.04026, over 4844.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02929, over 971327.62 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 16:44:41,778 INFO [train.py:715] (1/8) Epoch 16, batch 11550, loss[loss=0.1294, simple_loss=0.1934, pruned_loss=0.03267, over 4756.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02907, over 971903.12 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:45:20,367 INFO [train.py:715] (1/8) Epoch 16, batch 11600, loss[loss=0.15, simple_loss=0.2239, pruned_loss=0.03802, over 4770.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02975, over 972214.37 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:45:57,962 INFO [train.py:715] (1/8) Epoch 16, batch 11650, loss[loss=0.1408, simple_loss=0.2049, pruned_loss=0.03835, over 4974.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02907, over 972232.32 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:46:36,441 INFO [train.py:715] (1/8) Epoch 16, batch 11700, loss[loss=0.1484, simple_loss=0.2126, pruned_loss=0.0421, over 4927.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2064, pruned_loss=0.02936, over 972258.69 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 16:47:15,523 INFO [train.py:715] (1/8) Epoch 16, batch 11750, loss[loss=0.1289, simple_loss=0.1978, pruned_loss=0.03006, over 4801.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02937, over 971788.39 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 16:47:53,674 INFO [train.py:715] (1/8) Epoch 16, batch 11800, loss[loss=0.09833, simple_loss=0.1745, pruned_loss=0.01107, over 4902.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02897, over 973015.29 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:48:31,494 INFO [train.py:715] (1/8) Epoch 16, batch 11850, loss[loss=0.1299, simple_loss=0.1999, pruned_loss=0.02994, over 4702.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02912, over 972605.62 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:49:10,176 INFO [train.py:715] (1/8) Epoch 16, batch 11900, loss[loss=0.1129, simple_loss=0.1793, pruned_loss=0.02328, over 4791.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02879, over 972297.08 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 16:49:48,596 INFO [train.py:715] (1/8) Epoch 16, batch 11950, loss[loss=0.1456, simple_loss=0.2156, pruned_loss=0.03776, over 4819.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02866, over 972607.17 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 16:50:26,418 INFO [train.py:715] (1/8) Epoch 16, batch 12000, loss[loss=0.1969, simple_loss=0.2466, pruned_loss=0.07358, over 4958.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02919, over 972284.32 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 16:50:26,418 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 16:50:37,201 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.1049, simple_loss=0.1884, pruned_loss=0.01072, over 914524.00 frames.
2022-05-08 16:51:16,064 INFO [train.py:715] (1/8) Epoch 16, batch 12050, loss[loss=0.1569, simple_loss=0.2342, pruned_loss=0.0398, over 4925.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02905, over 972487.93 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 16:51:55,271 INFO [train.py:715] (1/8) Epoch 16, batch 12100, loss[loss=0.1956, simple_loss=0.2703, pruned_loss=0.06047, over 4853.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02941, over 971959.27 frames.], batch size: 20, lr: 1.38e-04
2022-05-08 16:52:34,708 INFO [train.py:715] (1/8) Epoch 16, batch 12150, loss[loss=0.146, simple_loss=0.2269, pruned_loss=0.03258, over 4741.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02969, over 971297.94 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 16:53:12,372 INFO [train.py:715] (1/8) Epoch 16, batch 12200, loss[loss=0.1277, simple_loss=0.2021, pruned_loss=0.02661, over 4880.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02938, over 970276.63 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 16:53:50,653 INFO [train.py:715] (1/8) Epoch 16, batch 12250, loss[loss=0.1637, simple_loss=0.2323, pruned_loss=0.04755, over 4875.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02933, over 970118.43 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 16:54:29,691 INFO [train.py:715] (1/8) Epoch 16, batch 12300, loss[loss=0.1196, simple_loss=0.1958, pruned_loss=0.02171, over 4829.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02944, over 970017.41 frames.], batch size: 26, lr: 1.38e-04
2022-05-08 16:55:08,774 INFO [train.py:715] (1/8) Epoch 16, batch 12350, loss[loss=0.1313, simple_loss=0.2158, pruned_loss=0.02339, over 4888.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02964, over 970672.92 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 16:55:47,032 INFO [train.py:715] (1/8) Epoch 16, batch 12400, loss[loss=0.1103, simple_loss=0.1985, pruned_loss=0.01099, over 4812.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02949, over 970036.52 frames.], batch size: 27, lr: 1.38e-04
2022-05-08 16:56:26,145 INFO [train.py:715] (1/8) Epoch 16, batch 12450, loss[loss=0.1247, simple_loss=0.1919, pruned_loss=0.02875, over 4974.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.0294, over 970435.06 frames.], batch size: 33, lr: 1.38e-04
2022-05-08 16:57:06,010 INFO [train.py:715] (1/8) Epoch 16, batch 12500, loss[loss=0.1379, simple_loss=0.2149, pruned_loss=0.03046, over 4950.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02986, over 971435.29 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 16:57:44,612 INFO [train.py:715] (1/8) Epoch 16, batch 12550, loss[loss=0.1209, simple_loss=0.1979, pruned_loss=0.02195, over 4895.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2088, pruned_loss=0.03001, over 971238.22 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 16:58:23,183 INFO [train.py:715] (1/8) Epoch 16, batch 12600, loss[loss=0.1385, simple_loss=0.2189, pruned_loss=0.02904, over 4777.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2086, pruned_loss=0.02988, over 970742.53 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 16:59:01,905 INFO [train.py:715] (1/8) Epoch 16, batch 12650, loss[loss=0.1312, simple_loss=0.2127, pruned_loss=0.02485, over 4771.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02957, over 970656.36 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 16:59:40,537 INFO [train.py:715] (1/8) Epoch 16, batch 12700, loss[loss=0.1371, simple_loss=0.2237, pruned_loss=0.0253, over 4888.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02957, over 971527.09 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:00:18,087 INFO [train.py:715] (1/8) Epoch 16, batch 12750, loss[loss=0.1532, simple_loss=0.2342, pruned_loss=0.03608, over 4815.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02984, over 971374.34 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:00:57,706 INFO [train.py:715] (1/8) Epoch 16, batch 12800, loss[loss=0.1101, simple_loss=0.1896, pruned_loss=0.01528, over 4750.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02912, over 972230.36 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:01:36,684 INFO [train.py:715] (1/8) Epoch 16, batch 12850, loss[loss=0.1545, simple_loss=0.2234, pruned_loss=0.04279, over 4919.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02935, over 972365.97 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:02:15,045 INFO [train.py:715] (1/8) Epoch 16, batch 12900, loss[loss=0.11, simple_loss=0.1786, pruned_loss=0.02065, over 4913.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02932, over 972250.64 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:02:53,759 INFO [train.py:715] (1/8) Epoch 16, batch 12950, loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03154, over 4881.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02904, over 972350.49 frames.], batch size: 20, lr: 1.38e-04
2022-05-08 17:03:32,779 INFO [train.py:715] (1/8) Epoch 16, batch 13000, loss[loss=0.1129, simple_loss=0.1946, pruned_loss=0.01557, over 4988.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02893, over 972684.70 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:04:11,282 INFO [train.py:715] (1/8) Epoch 16, batch 13050, loss[loss=0.1408, simple_loss=0.218, pruned_loss=0.03183, over 4820.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02906, over 972132.34 frames.], batch size: 26, lr: 1.38e-04
2022-05-08 17:04:49,802 INFO [train.py:715] (1/8) Epoch 16, batch 13100, loss[loss=0.1243, simple_loss=0.2, pruned_loss=0.02429, over 4801.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.0293, over 971633.18 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:05:28,950 INFO [train.py:715] (1/8) Epoch 16, batch 13150, loss[loss=0.1352, simple_loss=0.2063, pruned_loss=0.03207, over 4832.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.0293, over 971915.08 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:06:08,073 INFO [train.py:715] (1/8) Epoch 16, batch 13200, loss[loss=0.1316, simple_loss=0.2034, pruned_loss=0.02995, over 4783.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.02936, over 972203.15 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:06:46,154 INFO [train.py:715] (1/8) Epoch 16, batch 13250, loss[loss=0.1238, simple_loss=0.2005, pruned_loss=0.02355, over 4793.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02911, over 972355.57 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:07:25,007 INFO [train.py:715] (1/8) Epoch 16, batch 13300, loss[loss=0.1351, simple_loss=0.2104, pruned_loss=0.02988, over 4945.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2069, pruned_loss=0.02967, over 972468.96 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 17:08:04,361 INFO [train.py:715] (1/8) Epoch 16, batch 13350, loss[loss=0.1755, simple_loss=0.2534, pruned_loss=0.04882, over 4683.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03005, over 972539.58 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:08:42,685 INFO [train.py:715] (1/8) Epoch 16, batch 13400, loss[loss=0.1578, simple_loss=0.2311, pruned_loss=0.04222, over 4769.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03021, over 972726.55 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:09:21,146 INFO [train.py:715] (1/8) Epoch 16, batch 13450, loss[loss=0.1103, simple_loss=0.1743, pruned_loss=0.02313, over 4820.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02938, over 972704.56 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 17:10:00,928 INFO [train.py:715] (1/8) Epoch 16, batch 13500, loss[loss=0.1337, simple_loss=0.2047, pruned_loss=0.03134, over 4983.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02927, over 973693.04 frames.], batch size: 31, lr: 1.38e-04
2022-05-08 17:10:39,239 INFO [train.py:715] (1/8) Epoch 16, batch 13550, loss[loss=0.1431, simple_loss=0.2131, pruned_loss=0.03652, over 4913.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02944, over 974964.00 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:11:17,354 INFO [train.py:715] (1/8) Epoch 16, batch 13600, loss[loss=0.1375, simple_loss=0.1984, pruned_loss=0.03832, over 4752.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02942, over 974568.61 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:11:56,186 INFO [train.py:715] (1/8) Epoch 16, batch 13650, loss[loss=0.1287, simple_loss=0.1957, pruned_loss=0.03085, over 4769.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02965, over 973509.86 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:12:35,109 INFO [train.py:715] (1/8) Epoch 16, batch 13700, loss[loss=0.146, simple_loss=0.2032, pruned_loss=0.04434, over 4794.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02974, over 973464.50 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:13:13,501 INFO [train.py:715] (1/8) Epoch 16, batch 13750, loss[loss=0.1203, simple_loss=0.1829, pruned_loss=0.02887, over 4790.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02984, over 973082.24 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 17:13:52,007 INFO [train.py:715] (1/8) Epoch 16, batch 13800, loss[loss=0.1322, simple_loss=0.2038, pruned_loss=0.03035, over 4921.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02964, over 972593.97 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:14:30,651 INFO [train.py:715] (1/8) Epoch 16, batch 13850, loss[loss=0.109, simple_loss=0.1872, pruned_loss=0.01547, over 4869.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02919, over 972423.15 frames.], batch size: 20, lr: 1.38e-04
2022-05-08 17:15:08,623 INFO [train.py:715] (1/8) Epoch 16, batch 13900, loss[loss=0.127, simple_loss=0.1999, pruned_loss=0.02702, over 4753.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02956, over 972418.08 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:15:46,308 INFO [train.py:715] (1/8) Epoch 16, batch 13950, loss[loss=0.1249, simple_loss=0.1891, pruned_loss=0.03033, over 4701.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02934, over 971778.29 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:16:24,662 INFO [train.py:715] (1/8) Epoch 16, batch 14000, loss[loss=0.1092, simple_loss=0.1762, pruned_loss=0.02114, over 4822.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02934, over 971478.95 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 17:17:03,282 INFO [train.py:715] (1/8) Epoch 16, batch 14050, loss[loss=0.121, simple_loss=0.2037, pruned_loss=0.01917, over 4771.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02948, over 971754.96 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:17:41,057 INFO [train.py:715] (1/8) Epoch 16, batch 14100, loss[loss=0.1236, simple_loss=0.2037, pruned_loss=0.02175, over 4946.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02963, over 971851.60 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:18:18,777 INFO [train.py:715] (1/8) Epoch 16, batch 14150, loss[loss=0.1257, simple_loss=0.1983, pruned_loss=0.02658, over 4973.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.02979, over 971199.20 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:18:57,315 INFO [train.py:715] (1/8) Epoch 16, batch 14200, loss[loss=0.1496, simple_loss=0.2228, pruned_loss=0.03817, over 4913.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.02982, over 971197.76 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:19:36,007 INFO [train.py:715] (1/8) Epoch 16, batch 14250, loss[loss=0.1362, simple_loss=0.1991, pruned_loss=0.03661, over 4953.00 frames.], tot_loss[loss=0.1347, simple_loss=0.209, pruned_loss=0.03024, over 972193.57 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:20:14,628 INFO [train.py:715] (1/8) Epoch 16, batch 14300, loss[loss=0.1397, simple_loss=0.22, pruned_loss=0.02967, over 4963.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2102, pruned_loss=0.03081, over 972235.96 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 17:20:53,330 INFO [train.py:715] (1/8) Epoch 16, batch 14350, loss[loss=0.147, simple_loss=0.2186, pruned_loss=0.03772, over 4856.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03067, over 972409.48 frames.], batch size: 20, lr: 1.38e-04
2022-05-08 17:21:32,523 INFO [train.py:715] (1/8) Epoch 16, batch 14400, loss[loss=0.112, simple_loss=0.1945, pruned_loss=0.01472, over 4823.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03023, over 972441.70 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:22:10,263 INFO [train.py:715] (1/8) Epoch 16, batch 14450, loss[loss=0.1291, simple_loss=0.2039, pruned_loss=0.0271, over 4978.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.02984, over 972841.22 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:22:49,092 INFO [train.py:715] (1/8) Epoch 16, batch 14500, loss[loss=0.1895, simple_loss=0.2603, pruned_loss=0.05939, over 4689.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02967, over 971949.75 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:23:28,026 INFO [train.py:715] (1/8) Epoch 16, batch 14550, loss[loss=0.1026, simple_loss=0.1767, pruned_loss=0.01422, over 4816.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02991, over 972743.17 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:24:06,692 INFO [train.py:715] (1/8) Epoch 16, batch 14600, loss[loss=0.1317, simple_loss=0.203, pruned_loss=0.03018, over 4968.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03039, over 972917.19 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:24:44,963 INFO [train.py:715] (1/8) Epoch 16, batch 14650, loss[loss=0.1063, simple_loss=0.1824, pruned_loss=0.01506, over 4745.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02998, over 973034.46 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:25:23,542 INFO [train.py:715] (1/8) Epoch 16, batch 14700, loss[loss=0.1126, simple_loss=0.1852, pruned_loss=0.02004, over 4915.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02934, over 973581.71 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:26:02,838 INFO [train.py:715] (1/8) Epoch 16, batch 14750, loss[loss=0.1292, simple_loss=0.1973, pruned_loss=0.03053, over 4762.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02935, over 972267.47 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:26:40,633 INFO [train.py:715] (1/8) Epoch 16, batch 14800, loss[loss=0.1281, simple_loss=0.1955, pruned_loss=0.0304, over 4989.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03007, over 972295.52 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:27:19,700 INFO [train.py:715] (1/8) Epoch 16, batch 14850, loss[loss=0.1149, simple_loss=0.188, pruned_loss=0.02094, over 4874.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03008, over 972716.10 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 17:27:58,609 INFO [train.py:715] (1/8) Epoch 16, batch 14900, loss[loss=0.138, simple_loss=0.2153, pruned_loss=0.03029, over 4915.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02973, over 972226.61 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:28:37,079 INFO [train.py:715] (1/8) Epoch 16, batch 14950, loss[loss=0.156, simple_loss=0.2185, pruned_loss=0.04676, over 4910.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02976, over 972595.38 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:29:16,132 INFO [train.py:715] (1/8) Epoch 16, batch 15000, loss[loss=0.1192, simple_loss=0.1996, pruned_loss=0.0194, over 4783.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02965, over 971670.62 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:29:16,133 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 17:29:25,727 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.1049, simple_loss=0.1884, pruned_loss=0.01069, over 914524.00 frames.
2022-05-08 17:30:04,000 INFO [train.py:715] (1/8) Epoch 16, batch 15050, loss[loss=0.1266, simple_loss=0.2143, pruned_loss=0.01942, over 4797.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02946, over 971395.19 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:30:42,065 INFO [train.py:715] (1/8) Epoch 16, batch 15100, loss[loss=0.1228, simple_loss=0.1987, pruned_loss=0.02344, over 4805.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02941, over 972594.19 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:31:20,869 INFO [train.py:715] (1/8) Epoch 16, batch 15150, loss[loss=0.1292, simple_loss=0.2054, pruned_loss=0.0265, over 4879.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02931, over 972245.61 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 17:31:58,566 INFO [train.py:715] (1/8) Epoch 16, batch 15200, loss[loss=0.1381, simple_loss=0.2153, pruned_loss=0.03049, over 4977.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02908, over 972674.40 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:32:36,118 INFO [train.py:715] (1/8) Epoch 16, batch 15250, loss[loss=0.1534, simple_loss=0.2163, pruned_loss=0.04528, over 4821.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02885, over 972884.45 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:33:14,310 INFO [train.py:715] (1/8) Epoch 16, batch 15300, loss[loss=0.1216, simple_loss=0.1942, pruned_loss=0.02453, over 4788.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02911, over 973148.29 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:33:52,457 INFO [train.py:715] (1/8) Epoch 16, batch 15350, loss[loss=0.1367, simple_loss=0.2073, pruned_loss=0.03302, over 4865.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02904, over 973181.21 frames.], batch size: 38, lr: 1.38e-04
2022-05-08 17:34:30,725 INFO [train.py:715] (1/8) Epoch 16, batch 15400, loss[loss=0.1255, simple_loss=0.2016, pruned_loss=0.02469, over 4960.00 frames.], tot_loss[loss=0.133, simple_loss=0.2078, pruned_loss=0.02911, over 972509.05 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:35:08,738 INFO [train.py:715] (1/8) Epoch 16, batch 15450, loss[loss=0.1307, simple_loss=0.2068, pruned_loss=0.02731, over 4802.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02925, over 972480.99 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:35:47,169 INFO [train.py:715] (1/8) Epoch 16, batch 15500, loss[loss=0.117, simple_loss=0.1935, pruned_loss=0.02023, over 4986.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2075, pruned_loss=0.02883, over 972399.63 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:36:24,799 INFO [train.py:715] (1/8) Epoch 16, batch 15550, loss[loss=0.1046, simple_loss=0.1746, pruned_loss=0.01732, over 4989.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2071, pruned_loss=0.02894, over 972644.28 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:37:02,465 INFO [train.py:715] (1/8) Epoch 16, batch 15600, loss[loss=0.142, simple_loss=0.2141, pruned_loss=0.03492, over 4920.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02959, over 973267.84 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:37:41,083 INFO [train.py:715] (1/8) Epoch 16, batch 15650, loss[loss=0.1547, simple_loss=0.2298, pruned_loss=0.03982, over 4841.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02986, over 973120.52 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:38:19,116 INFO [train.py:715] (1/8) Epoch 16, batch 15700, loss[loss=0.1257, simple_loss=0.1996, pruned_loss=0.02589, over 4982.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02983, over 972966.45 frames.], batch size: 33, lr: 1.38e-04
2022-05-08 17:38:56,852 INFO [train.py:715] (1/8) Epoch 16, batch 15750, loss[loss=0.1475, simple_loss=0.2247, pruned_loss=0.03513, over 4750.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02976, over 972516.01 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 17:39:34,741 INFO [train.py:715] (1/8) Epoch 16, batch 15800, loss[loss=0.1171, simple_loss=0.189, pruned_loss=0.02264, over 4794.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02917, over 972248.11 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:40:13,084 INFO [train.py:715] (1/8) Epoch 16, batch 15850, loss[loss=0.1189, simple_loss=0.1994, pruned_loss=0.01916, over 4874.00 frames.], tot_loss[loss=0.1334, simple_loss=0.208, pruned_loss=0.02941, over 971946.12 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 17:40:50,715 INFO [train.py:715] (1/8) Epoch 16, batch 15900, loss[loss=0.1466, simple_loss=0.2173, pruned_loss=0.038, over 4789.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02985, over 972599.80 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:41:28,317 INFO [train.py:715] (1/8) Epoch 16, batch 15950, loss[loss=0.1125, simple_loss=0.181, pruned_loss=0.02204, over 4818.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02934, over 971998.01 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:42:06,731 INFO [train.py:715] (1/8) Epoch 16, batch 16000, loss[loss=0.1433, simple_loss=0.2223, pruned_loss=0.03216, over 4947.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.02885, over 972121.60 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 17:42:44,834 INFO [train.py:715] (1/8) Epoch 16, batch 16050, loss[loss=0.1739, simple_loss=0.2404, pruned_loss=0.05376, over 4772.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02913, over 971247.22 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:43:22,460 INFO [train.py:715] (1/8) Epoch 16, batch 16100, loss[loss=0.1117, simple_loss=0.1747, pruned_loss=0.02441, over 4802.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02931, over 971644.15 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 17:43:59,973 INFO [train.py:715] (1/8) Epoch 16, batch 16150, loss[loss=0.1407, simple_loss=0.2125, pruned_loss=0.03442, over 4884.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02935, over 972098.92 frames.], batch size: 38, lr: 1.38e-04
2022-05-08 17:44:38,353 INFO [train.py:715] (1/8) Epoch 16, batch 16200, loss[loss=0.1274, simple_loss=0.1963, pruned_loss=0.02932, over 4967.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.02926, over 973338.61 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 17:45:15,920 INFO [train.py:715] (1/8) Epoch 16, batch 16250, loss[loss=0.1279, simple_loss=0.1978, pruned_loss=0.02896, over 4953.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.0291, over 973933.37 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:45:53,549 INFO [train.py:715] (1/8) Epoch 16, batch 16300, loss[loss=0.1173, simple_loss=0.1968, pruned_loss=0.0189, over 4923.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02887, over 973910.47 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 17:46:31,875 INFO [train.py:715] (1/8) Epoch 16, batch 16350, loss[loss=0.1138, simple_loss=0.1892, pruned_loss=0.01924, over 4928.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02921, over 973357.74 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:47:10,534 INFO [train.py:715] (1/8) Epoch 16, batch 16400, loss[loss=0.1355, simple_loss=0.2076, pruned_loss=0.03166, over 4741.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.029, over 973583.71 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:47:47,573 INFO [train.py:715] (1/8) Epoch 16, batch 16450, loss[loss=0.1658, simple_loss=0.2275, pruned_loss=0.05204, over 4852.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.0292, over 973578.61 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 17:48:25,522 INFO [train.py:715] (1/8) Epoch 16, batch 16500, loss[loss=0.1383, simple_loss=0.2155, pruned_loss=0.03058, over 4966.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02905, over 972709.40 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:49:04,092 INFO [train.py:715] (1/8) Epoch 16, batch 16550, loss[loss=0.1072, simple_loss=0.1876, pruned_loss=0.01337, over 4811.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02901, over 972589.94 frames.], batch size: 26, lr: 1.38e-04
2022-05-08 17:49:41,517 INFO [train.py:715] (1/8) Epoch 16, batch 16600, loss[loss=0.1126, simple_loss=0.1962, pruned_loss=0.01452, over 4978.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02889, over 973296.38 frames.], batch size: 28, lr: 1.38e-04
2022-05-08 17:50:19,533 INFO [train.py:715] (1/8) Epoch 16, batch 16650, loss[loss=0.1584, simple_loss=0.2294, pruned_loss=0.04374, over 4906.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02916, over 973462.10 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:50:57,795 INFO [train.py:715] (1/8) Epoch 16, batch 16700, loss[loss=0.103, simple_loss=0.1835, pruned_loss=0.01123, over 4832.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02913, over 973669.15 frames.], batch size: 27, lr: 1.38e-04
2022-05-08 17:51:35,938 INFO [train.py:715] (1/8) Epoch 16, batch 16750, loss[loss=0.1265, simple_loss=0.1958, pruned_loss=0.02858, over 4859.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02916, over 973919.39 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:52:13,455 INFO [train.py:715] (1/8) Epoch 16, batch 16800, loss[loss=0.1337, simple_loss=0.198, pruned_loss=0.03471, over 4802.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02959, over 973904.97 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:52:51,538 INFO [train.py:715] (1/8) Epoch 16, batch 16850, loss[loss=0.1267, simple_loss=0.2173, pruned_loss=0.01803, over 4824.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02937, over 972896.46 frames.], batch size: 27, lr: 1.38e-04
2022-05-08 17:53:30,001 INFO [train.py:715] (1/8) Epoch 16, batch 16900, loss[loss=0.1453, simple_loss=0.2193, pruned_loss=0.03567, over 4988.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02973, over 972848.26 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:54:07,593 INFO [train.py:715] (1/8) Epoch 16, batch 16950, loss[loss=0.1698, simple_loss=0.2397, pruned_loss=0.04994, over 4762.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02984, over 972825.20 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:54:45,479 INFO [train.py:715] (1/8) Epoch 16, batch 17000, loss[loss=0.114, simple_loss=0.1842, pruned_loss=0.02188, over 4838.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02919, over 972818.52 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:55:23,674 INFO [train.py:715] (1/8) Epoch 16, batch 17050, loss[loss=0.129, simple_loss=0.1981, pruned_loss=0.02992, over 4798.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02948, over 971853.50 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:56:02,258 INFO [train.py:715] (1/8) Epoch 16, batch 17100, loss[loss=0.1246, simple_loss=0.2084, pruned_loss=0.02041, over 4777.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02932, over 972064.21 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:56:39,333 INFO [train.py:715] (1/8) Epoch 16, batch 17150, loss[loss=0.1316, simple_loss=0.2032, pruned_loss=0.03, over 4961.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.0298, over 972164.78 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:57:17,465 INFO [train.py:715] (1/8) Epoch 16, batch 17200, loss[loss=0.1321, simple_loss=0.2087, pruned_loss=0.0278, over 4891.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03039, over 971916.65 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:57:56,362 INFO [train.py:715] (1/8) Epoch 16, batch 17250, loss[loss=0.132, simple_loss=0.2174, pruned_loss=0.02334, over 4723.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03025, over 971685.18 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:58:33,737 INFO [train.py:715] (1/8) Epoch 16, batch 17300, loss[loss=0.1144, simple_loss=0.1815, pruned_loss=0.02371, over 4939.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2072, pruned_loss=0.02991, over 971366.83 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:59:11,267 INFO [train.py:715] (1/8) Epoch 16, batch 17350, loss[loss=0.1324, simple_loss=0.2118, pruned_loss=0.02652, over 4694.00 frames.], tot_loss[loss=0.1336, simple_loss=0.207, pruned_loss=0.0301, over 971449.06 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:59:49,076 INFO [train.py:715] (1/8) Epoch 16, batch 17400, loss[loss=0.1251, simple_loss=0.1991, pruned_loss=0.02551, over 4778.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2079, pruned_loss=0.0306, over 971127.21 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:00:27,745 INFO [train.py:715] (1/8) Epoch 16, batch 17450, loss[loss=0.1054, simple_loss=0.1807, pruned_loss=0.01508, over 4815.00 frames.], tot_loss[loss=0.135, simple_loss=0.2081, pruned_loss=0.03092, over 971141.51 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 18:01:04,506 INFO [train.py:715] (1/8) Epoch 16, batch 17500, loss[loss=0.151, simple_loss=0.2246, pruned_loss=0.03868, over 4770.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03099, over 971567.78 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:01:42,655 INFO [train.py:715] (1/8) Epoch 16, batch 17550, loss[loss=0.1361, simple_loss=0.2059, pruned_loss=0.03319, over 4993.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03087, over 971907.34 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 18:02:21,342 INFO [train.py:715] (1/8) Epoch 16, batch 17600, loss[loss=0.1375, simple_loss=0.21, pruned_loss=0.03247, over 4750.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03072, over 972474.38 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:02:58,693 INFO [train.py:715] (1/8) Epoch 16, batch 17650, loss[loss=0.1556, simple_loss=0.2284, pruned_loss=0.04143, over 4904.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03068, over 972482.80 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 18:03:36,637 INFO [train.py:715] (1/8) Epoch 16, batch 17700, loss[loss=0.1394, simple_loss=0.2137, pruned_loss=0.03254, over 4994.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03039, over 972195.46 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 18:04:15,004 INFO [train.py:715] (1/8) Epoch 16, batch 17750, loss[loss=0.131, simple_loss=0.2, pruned_loss=0.03103, over 4948.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03029, over 973105.27 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 18:04:53,068 INFO [train.py:715] (1/8) Epoch 16, batch 17800, loss[loss=0.181, simple_loss=0.233, pruned_loss=0.06449, over 4909.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.0298, over 973450.08 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:05:30,279 INFO [train.py:715] (1/8) Epoch 16, batch 17850, loss[loss=0.1503, simple_loss=0.2297, pruned_loss=0.03543, over 4871.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.03002, over 973146.52 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:06:08,450 INFO [train.py:715] (1/8) Epoch 16, batch 17900, loss[loss=0.1355, simple_loss=0.2151, pruned_loss=0.02797, over 4906.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02968, over 973559.28 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:06:46,891 INFO [train.py:715] (1/8) Epoch 16, batch 17950, loss[loss=0.1357, simple_loss=0.2015, pruned_loss=0.035, over 4751.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02991, over 973317.43 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 18:07:24,274 INFO [train.py:715] (1/8) Epoch 16, batch 18000, loss[loss=0.1165, simple_loss=0.1989, pruned_loss=0.01706, over 4935.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02991, over 973461.19 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 18:07:24,275 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 18:07:33,812 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.105, simple_loss=0.1884, pruned_loss=0.01082, over 914524.00 frames.
2022-05-08 18:08:11,767 INFO [train.py:715] (1/8) Epoch 16, batch 18050, loss[loss=0.1211, simple_loss=0.1936, pruned_loss=0.02432, over 4739.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02997, over 972209.28 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:08:50,179 INFO [train.py:715] (1/8) Epoch 16, batch 18100, loss[loss=0.1086, simple_loss=0.1836, pruned_loss=0.01681, over 4916.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.0301, over 973207.26 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:09:28,824 INFO [train.py:715] (1/8) Epoch 16, batch 18150, loss[loss=0.1249, simple_loss=0.2007, pruned_loss=0.02453, over 4805.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03052, over 973343.96 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 18:10:07,473 INFO [train.py:715] (1/8) Epoch 16, batch 18200, loss[loss=0.1212, simple_loss=0.1858, pruned_loss=0.02825, over 4863.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.03072, over 972421.91 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 18:10:45,083 INFO [train.py:715] (1/8) Epoch 16, batch 18250, loss[loss=0.1229, simple_loss=0.2059, pruned_loss=0.01989, over 4810.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03045, over 972073.14 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 18:11:23,847 INFO [train.py:715] (1/8) Epoch 16, batch 18300, loss[loss=0.142, simple_loss=0.2098, pruned_loss=0.0371, over 4867.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03012, over 972913.24 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:12:02,948 INFO [train.py:715] (1/8) Epoch 16, batch 18350, loss[loss=0.1375, simple_loss=0.2157, pruned_loss=0.02965, over 4814.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02972, over 973443.72 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:12:40,720 INFO [train.py:715] (1/8) Epoch 16, batch 18400, loss[loss=0.1556, simple_loss=0.2253, pruned_loss=0.04292, over 4918.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02988, over 972881.69 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 18:13:19,244 INFO [train.py:715] (1/8) Epoch 16, batch 18450, loss[loss=0.1401, simple_loss=0.2224, pruned_loss=0.02893, over 4943.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2076, pruned_loss=0.02927, over 972571.77 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 18:13:57,852 INFO [train.py:715] (1/8) Epoch 16, batch 18500, loss[loss=0.147, simple_loss=0.2172, pruned_loss=0.03841, over 4928.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02969, over 971699.54 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:14:36,372 INFO [train.py:715] (1/8) Epoch 16, batch 18550, loss[loss=0.1252, simple_loss=0.2098, pruned_loss=0.02028, over 4781.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02964, over 972083.81 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:15:13,855 INFO [train.py:715] (1/8) Epoch 16, batch 18600, loss[loss=0.1405, simple_loss=0.2121, pruned_loss=0.03446, over 4836.00 frames.], tot_loss[loss=0.134, simple_loss=0.2084, pruned_loss=0.02981, over 971750.76 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 18:15:52,138 INFO [train.py:715] (1/8) Epoch 16, batch 18650, loss[loss=0.1283, simple_loss=0.2098, pruned_loss=0.02341, over 4877.00 frames.], tot_loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02973, over 972612.96 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 18:16:30,641 INFO [train.py:715] (1/8) Epoch 16, batch 18700, loss[loss=0.1385, simple_loss=0.2178, pruned_loss=0.02959, over 4876.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02984, over 972642.71 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:17:08,139 INFO [train.py:715] (1/8) Epoch 16, batch 18750, loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03203, over 4801.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.0297, over 972903.06 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:17:45,513 INFO [train.py:715] (1/8) Epoch 16, batch 18800, loss[loss=0.1377, simple_loss=0.2125, pruned_loss=0.03142, over 4985.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02944, over 973402.08 frames.], batch size: 26, lr: 1.38e-04
2022-05-08 18:18:23,822 INFO [train.py:715] (1/8) Epoch 16, batch 18850, loss[loss=0.1317, simple_loss=0.1989, pruned_loss=0.03224, over 4953.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02993, over 972772.88 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 18:19:02,091 INFO [train.py:715] (1/8) Epoch 16, batch 18900, loss[loss=0.1313, simple_loss=0.2005, pruned_loss=0.03107, over 4969.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02994, over 971843.88 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 18:19:39,523 INFO [train.py:715] (1/8) Epoch 16, batch 18950, loss[loss=0.1232, simple_loss=0.2039, pruned_loss=0.02125, over 4801.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03017, over 971925.49 frames.], batch size: 26, lr: 1.38e-04
2022-05-08 18:20:17,361 INFO [train.py:715] (1/8) Epoch 16, batch 19000, loss[loss=0.1666, simple_loss=0.2274, pruned_loss=0.05289, over 4984.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.0304, over 971664.24 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 18:20:55,965 INFO [train.py:715] (1/8) Epoch 16, batch 19050, loss[loss=0.1507, simple_loss=0.2233, pruned_loss=0.03904, over 4978.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2094, pruned_loss=0.03041, over 971880.87 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 18:21:36,429 INFO [train.py:715] (1/8) Epoch 16, batch 19100, loss[loss=0.1196, simple_loss=0.1989, pruned_loss=0.02013, over 4883.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03024, over 972447.12 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 18:22:14,090 INFO [train.py:715] (1/8) Epoch 16, batch 19150, loss[loss=0.1331, simple_loss=0.2045, pruned_loss=0.03081, over 4965.00 frames.], tot_loss[loss=0.135, simple_loss=0.2086, pruned_loss=0.03071, over 973088.33 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 18:22:52,371 INFO [train.py:715] (1/8) Epoch 16, batch 19200, loss[loss=0.1296, simple_loss=0.2018, pruned_loss=0.02868, over 4887.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03045, over 972653.92 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:23:31,022 INFO [train.py:715] (1/8) Epoch 16, batch 19250, loss[loss=0.1399, simple_loss=0.2133, pruned_loss=0.03324, over 4907.00 frames.], tot_loss[loss=0.1351, simple_loss=0.209, pruned_loss=0.03055, over 973312.41 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 18:24:08,555 INFO [train.py:715] (1/8) Epoch 16, batch 19300, loss[loss=0.1254, simple_loss=0.2013, pruned_loss=0.02474, over 4778.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03012, over 972793.70 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:24:46,584 INFO [train.py:715] (1/8) Epoch 16, batch 19350, loss[loss=0.1385, simple_loss=0.2161, pruned_loss=0.03047, over 4747.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2087, pruned_loss=0.02979, over 971536.30 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:25:25,228 INFO [train.py:715] (1/8) Epoch 16, batch 19400, loss[loss=0.1341, simple_loss=0.1963, pruned_loss=0.03598, over 4748.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02977, over 971424.66 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:26:03,260 INFO [train.py:715] (1/8) Epoch 16, batch 19450, loss[loss=0.1182, simple_loss=0.2, pruned_loss=0.01822, over 4802.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02973, over 971369.02 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 18:26:40,799 INFO [train.py:715] (1/8) Epoch 16, batch 19500, loss[loss=0.1356, simple_loss=0.2262, pruned_loss=0.02247, over 4922.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03015, over 971697.59 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 18:27:18,958 INFO [train.py:715] (1/8) Epoch 16, batch 19550, loss[loss=0.1199, simple_loss=0.2046, pruned_loss=0.01762, over 4964.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03021, over 970694.00 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 18:27:57,193 INFO [train.py:715] (1/8) Epoch 16, batch 19600, loss[loss=0.1386, simple_loss=0.2194, pruned_loss=0.02892, over 4692.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03005, over 970881.03 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 18:28:34,601 INFO [train.py:715] (1/8) Epoch 16, batch 19650, loss[loss=0.1084, simple_loss=0.1791, pruned_loss=0.01883, over 4798.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03017, over 970787.47 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 18:29:12,875 INFO [train.py:715] (1/8) Epoch 16, batch 19700, loss[loss=0.1256, simple_loss=0.2003, pruned_loss=0.02542, over 4886.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03044, over 970966.41 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 18:29:51,095 INFO [train.py:715] (1/8) Epoch 16, batch 19750, loss[loss=0.1381, simple_loss=0.2092, pruned_loss=0.03354, over 4776.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03026, over 971008.72 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:30:28,917 INFO [train.py:715] (1/8) Epoch 16, batch 19800, loss[loss=0.1577, simple_loss=0.2266, pruned_loss=0.04444, over 4954.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03014, over 970842.73 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 18:31:06,637 INFO [train.py:715] (1/8) Epoch 16, batch 19850, loss[loss=0.1173, simple_loss=0.1832, pruned_loss=0.0257, over 4848.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03039, over 970755.71 frames.], batch size: 30, lr: 1.38e-04
2022-05-08 18:31:44,941 INFO [train.py:715] (1/8) Epoch 16, batch 19900, loss[loss=0.1368, simple_loss=0.2074, pruned_loss=0.03312, over 4783.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02958, over 971038.93 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:32:22,973 INFO [train.py:715] (1/8) Epoch 16, batch 19950, loss[loss=0.1315, simple_loss=0.1914, pruned_loss=0.03579, over 4786.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02956, over 970899.64 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 18:33:00,614 INFO [train.py:715] (1/8) Epoch 16, batch 20000, loss[loss=0.1294, simple_loss=0.195, pruned_loss=0.03194, over 4803.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2063, pruned_loss=0.0294, over 971454.09 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 18:33:38,892 INFO [train.py:715] (1/8) Epoch 16, batch 20050, loss[loss=0.1184, simple_loss=0.1951, pruned_loss=0.02088, over 4805.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2061, pruned_loss=0.02934, over 971118.58 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:34:17,306 INFO [train.py:715] (1/8) Epoch 16, batch 20100, loss[loss=0.1722, simple_loss=0.2437, pruned_loss=0.05035, over 4796.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.02931, over 971467.57 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:34:54,671 INFO [train.py:715] (1/8) Epoch 16, batch 20150, loss[loss=0.1256, simple_loss=0.2091, pruned_loss=0.02102, over 4971.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02896, over 971653.39 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 18:35:32,577 INFO [train.py:715] (1/8) Epoch 16, batch 20200, loss[loss=0.1284, simple_loss=0.1959, pruned_loss=0.03047, over 4912.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02901, over 971570.10 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:36:10,896 INFO [train.py:715] (1/8) Epoch 16, batch 20250, loss[loss=0.1744, simple_loss=0.236, pruned_loss=0.05641, over 4939.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02875, over 971832.63 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 18:36:49,191 INFO [train.py:715] (1/8) Epoch 16, batch 20300, loss[loss=0.1333, simple_loss=0.212, pruned_loss=0.02729, over 4935.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02857, over 972774.43 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 18:37:27,017 INFO [train.py:715] (1/8) Epoch 16, batch 20350, loss[loss=0.1402, simple_loss=0.2109, pruned_loss=0.0348, over 4779.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02865, over 971945.45 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 18:38:05,175 INFO [train.py:715] (1/8) Epoch 16, batch 20400, loss[loss=0.1406, simple_loss=0.2161, pruned_loss=0.03253, over 4636.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02893, over 971536.22 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 18:38:43,166 INFO [train.py:715] (1/8) Epoch 16, batch 20450, loss[loss=0.1459, simple_loss=0.2183, pruned_loss=0.03677, over 4991.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02921, over 972225.48 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 18:39:21,071 INFO [train.py:715] (1/8) Epoch 16, batch 20500, loss[loss=0.1541, simple_loss=0.2298, pruned_loss=0.03924, over 4860.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2062, pruned_loss=0.02946, over 971063.77 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 18:39:58,716 INFO [train.py:715] (1/8) Epoch 16, batch 20550, loss[loss=0.1439, simple_loss=0.2086, pruned_loss=0.03964, over 4651.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02929, over 970962.63 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 18:40:37,508 INFO [train.py:715] (1/8) Epoch 16, batch 20600, loss[loss=0.1407, simple_loss=0.2084, pruned_loss=0.03653, over 4777.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02955, over 970741.89 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 18:41:15,474 INFO [train.py:715] (1/8) Epoch 16, batch 20650, loss[loss=0.1506, simple_loss=0.2122, pruned_loss=0.0445, over 4798.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.0294, over 970745.62 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 18:41:52,934 INFO [train.py:715] (1/8) Epoch 16, batch 20700, loss[loss=0.1449, simple_loss=0.2203, pruned_loss=0.03475, over 4954.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02916, over 971439.94 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 18:42:31,440 INFO [train.py:715] (1/8) Epoch 16, batch 20750, loss[loss=0.1291, simple_loss=0.1999, pruned_loss=0.02913, over 4757.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02892, over 972200.55 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 18:43:09,457 INFO [train.py:715] (1/8) Epoch 16, batch 20800, loss[loss=0.1181, simple_loss=0.1841, pruned_loss=0.02608, over 4740.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02914, over 972209.85 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 18:43:47,990 INFO [train.py:715] (1/8) Epoch 16, batch 20850, loss[loss=0.1278, simple_loss=0.2085, pruned_loss=0.02355, over 4670.00 frames.], tot_loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02976, over 971314.86 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 18:44:25,955 INFO [train.py:715] (1/8) Epoch 16, batch 20900, loss[loss=0.1108, simple_loss=0.1894, pruned_loss=0.01609, over 4821.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02961, over 970885.68 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 18:45:05,225 INFO [train.py:715] (1/8) Epoch 16, batch 20950, loss[loss=0.1273, simple_loss=0.2075, pruned_loss=0.02352, over 4806.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02946, over 970351.87 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 18:45:43,435 INFO [train.py:715] (1/8) Epoch 16, batch 21000, loss[loss=0.09591, simple_loss=0.1601, pruned_loss=0.01587, over 4983.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02977, over 970772.56 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 18:45:43,436 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 18:45:53,029 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.1047, simple_loss=0.1882, pruned_loss=0.0106, over 914524.00 frames.
2022-05-08 18:46:31,913 INFO [train.py:715] (1/8) Epoch 16, batch 21050, loss[loss=0.1337, simple_loss=0.2073, pruned_loss=0.03006, over 4779.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02983, over 970717.70 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 18:47:10,476 INFO [train.py:715] (1/8) Epoch 16, batch 21100, loss[loss=0.1635, simple_loss=0.2335, pruned_loss=0.04674, over 4735.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02979, over 970947.64 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 18:47:49,071 INFO [train.py:715] (1/8) Epoch 16, batch 21150, loss[loss=0.1221, simple_loss=0.1959, pruned_loss=0.02415, over 4818.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02966, over 971652.84 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 18:48:27,790 INFO [train.py:715] (1/8) Epoch 16, batch 21200, loss[loss=0.1387, simple_loss=0.2199, pruned_loss=0.02875, over 4886.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.0295, over 971891.17 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 18:49:06,848 INFO [train.py:715] (1/8) Epoch 16, batch 21250, loss[loss=0.1419, simple_loss=0.2254, pruned_loss=0.02918, over 4732.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2085, pruned_loss=0.02983, over 972472.37 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 18:49:44,919 INFO [train.py:715] (1/8) Epoch 16, batch 21300, loss[loss=0.1117, simple_loss=0.1874, pruned_loss=0.01799, over 4692.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02958, over 971076.38 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:50:23,522 INFO [train.py:715] (1/8) Epoch 16, batch 21350, loss[loss=0.1335, simple_loss=0.2015, pruned_loss=0.03277, over 4806.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2062, pruned_loss=0.02915, over 971277.32 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 18:51:01,537 INFO [train.py:715] (1/8) Epoch 16, batch 21400, loss[loss=0.1061, simple_loss=0.1832, pruned_loss=0.01447, over 4887.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02931, over 971005.28 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 18:51:39,055 INFO [train.py:715] (1/8) Epoch 16, batch 21450, loss[loss=0.1372, simple_loss=0.212, pruned_loss=0.03125, over 4907.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02954, over 971984.92 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 18:52:17,450 INFO [train.py:715] (1/8) Epoch 16, batch 21500, loss[loss=0.1309, simple_loss=0.2107, pruned_loss=0.02554, over 4932.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.0295, over 971759.39 frames.], batch size: 23, lr: 1.37e-04
2022-05-08 18:52:55,412 INFO [train.py:715] (1/8) Epoch 16, batch 21550, loss[loss=0.1218, simple_loss=0.2101, pruned_loss=0.0167, over 4699.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02946, over 972225.92 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:53:33,003 INFO [train.py:715] (1/8) Epoch 16, batch 21600, loss[loss=0.124, simple_loss=0.1995, pruned_loss=0.02425, over 4703.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02953, over 972585.56 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:54:11,336 INFO [train.py:715] (1/8) Epoch 16, batch 21650, loss[loss=0.1305, simple_loss=0.2054, pruned_loss=0.02781, over 4781.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2089, pruned_loss=0.02989, over 972843.30 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 18:54:49,122 INFO [train.py:715] (1/8) Epoch 16, batch 21700, loss[loss=0.1358, simple_loss=0.2058, pruned_loss=0.03291, over 4941.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.0297, over 973137.06 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 18:55:27,326 INFO [train.py:715] (1/8) Epoch 16, batch 21750, loss[loss=0.131, simple_loss=0.2025, pruned_loss=0.02978, over 4791.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02973, over 972628.91 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 18:56:04,820 INFO [train.py:715] (1/8) Epoch 16, batch 21800, loss[loss=0.1158, simple_loss=0.1945, pruned_loss=0.01858, over 4773.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02998, over 971969.35 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 18:56:42,922 INFO [train.py:715] (1/8) Epoch 16, batch 21850, loss[loss=0.1259, simple_loss=0.1983, pruned_loss=0.02669, over 4872.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2078, pruned_loss=0.03015, over 972342.03 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 18:57:20,564 INFO [train.py:715] (1/8) Epoch 16, batch 21900, loss[loss=0.1487, simple_loss=0.2287, pruned_loss=0.0343, over 4984.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02996, over 972535.43 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 18:57:57,979 INFO [train.py:715] (1/8) Epoch 16, batch 21950, loss[loss=0.1329, simple_loss=0.2081, pruned_loss=0.02883, over 4851.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03032, over 972429.39 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 18:58:36,387 INFO [train.py:715] (1/8) Epoch 16, batch 22000, loss[loss=0.1629, simple_loss=0.2332, pruned_loss=0.04626, over 4973.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03046, over 972782.60 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:59:14,000 INFO [train.py:715] (1/8) Epoch 16, batch 22050, loss[loss=0.1312, simple_loss=0.2211, pruned_loss=0.0207, over 4862.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03014, over 972847.13 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 18:59:52,239 INFO [train.py:715] (1/8) Epoch 16, batch 22100, loss[loss=0.1139, simple_loss=0.19, pruned_loss=0.01885, over 4937.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02997, over 972673.41 frames.], batch size: 29, lr: 1.37e-04
2022-05-08 19:00:29,952 INFO [train.py:715] (1/8) Epoch 16, batch 22150, loss[loss=0.1227, simple_loss=0.2019, pruned_loss=0.02177, over 4828.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02986, over 972077.84 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 19:01:08,388 INFO [train.py:715] (1/8) Epoch 16, batch 22200, loss[loss=0.119, simple_loss=0.1906, pruned_loss=0.02368, over 4649.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02965, over 971960.11 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:01:46,152 INFO [train.py:715] (1/8) Epoch 16, batch 22250, loss[loss=0.1471, simple_loss=0.21, pruned_loss=0.04214, over 4850.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2086, pruned_loss=0.02989, over 972144.15 frames.], batch size: 30, lr: 1.37e-04
2022-05-08 19:02:24,238 INFO [train.py:715] (1/8) Epoch 16, batch 22300, loss[loss=0.1177, simple_loss=0.2016, pruned_loss=0.01694, over 4826.00 frames.], tot_loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02973, over 972360.00 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 19:03:02,796 INFO [train.py:715] (1/8) Epoch 16, batch 22350, loss[loss=0.1248, simple_loss=0.2045, pruned_loss=0.02249, over 4789.00 frames.], tot_loss[loss=0.135, simple_loss=0.2097, pruned_loss=0.03022, over 972089.80 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:03:40,844 INFO [train.py:715] (1/8) Epoch 16, batch 22400, loss[loss=0.1003, simple_loss=0.1615, pruned_loss=0.01959, over 4804.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2093, pruned_loss=0.03014, over 971239.25 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:04:19,200 INFO [train.py:715] (1/8) Epoch 16, batch 22450, loss[loss=0.163, simple_loss=0.235, pruned_loss=0.0455, over 4980.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.03065, over 971674.52 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 19:04:57,329 INFO [train.py:715] (1/8) Epoch 16, batch 22500, loss[loss=0.1319, simple_loss=0.2026, pruned_loss=0.03062, over 4923.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03083, over 972402.78 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 19:05:35,515 INFO [train.py:715] (1/8) Epoch 16, batch 22550, loss[loss=0.1143, simple_loss=0.1857, pruned_loss=0.02142, over 4771.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03075, over 972422.32 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:06:13,253 INFO [train.py:715] (1/8) Epoch 16, batch 22600, loss[loss=0.1395, simple_loss=0.2031, pruned_loss=0.03799, over 4780.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02985, over 972568.17 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 19:06:50,941 INFO [train.py:715] (1/8) Epoch 16, batch 22650, loss[loss=0.1335, simple_loss=0.2066, pruned_loss=0.03017, over 4871.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02977, over 972060.79 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:07:29,634 INFO [train.py:715] (1/8) Epoch 16, batch 22700, loss[loss=0.1159, simple_loss=0.1922, pruned_loss=0.01976, over 4817.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03007, over 972352.92 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 19:08:07,679 INFO [train.py:715] (1/8) Epoch 16, batch 22750, loss[loss=0.1203, simple_loss=0.2004, pruned_loss=0.0201, over 4975.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02971, over 972490.63 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 19:08:45,790 INFO [train.py:715] (1/8) Epoch 16, batch 22800, loss[loss=0.1395, simple_loss=0.2291, pruned_loss=0.02499, over 4771.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.0296, over 971645.09 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:09:23,700 INFO [train.py:715] (1/8) Epoch 16, batch 22850, loss[loss=0.1433, simple_loss=0.2275, pruned_loss=0.02953, over 4891.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02966, over 971546.40 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:10:01,848 INFO [train.py:715] (1/8) Epoch 16, batch 22900, loss[loss=0.1384, simple_loss=0.2133, pruned_loss=0.03175, over 4861.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02986, over 972021.30 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:10:39,884 INFO [train.py:715] (1/8) Epoch 16, batch 22950, loss[loss=0.1222, simple_loss=0.1995, pruned_loss=0.02248, over 4957.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02972, over 972284.77 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:11:17,832 INFO [train.py:715] (1/8) Epoch 16, batch 23000, loss[loss=0.1376, simple_loss=0.2169, pruned_loss=0.02919, over 4874.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02979, over 972596.18 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 19:11:56,368 INFO [train.py:715] (1/8) Epoch 16, batch 23050, loss[loss=0.158, simple_loss=0.2347, pruned_loss=0.04067, over 4923.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02946, over 971737.33 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:12:34,518 INFO [train.py:715] (1/8) Epoch 16, batch 23100, loss[loss=0.1346, simple_loss=0.2122, pruned_loss=0.02848, over 4760.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02933, over 971727.00 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:13:12,452 INFO [train.py:715] (1/8) Epoch 16, batch 23150, loss[loss=0.1187, simple_loss=0.1988, pruned_loss=0.0193, over 4925.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02951, over 971832.43 frames.], batch size: 23, lr: 1.37e-04
2022-05-08 19:13:50,196 INFO [train.py:715] (1/8) Epoch 16, batch 23200, loss[loss=0.1485, simple_loss=0.2244, pruned_loss=0.03635, over 4905.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02925, over 971261.27 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:14:28,511 INFO [train.py:715] (1/8) Epoch 16, batch 23250, loss[loss=0.1381, simple_loss=0.2158, pruned_loss=0.03023, over 4645.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02908, over 970516.60 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:15:06,177 INFO [train.py:715] (1/8) Epoch 16, batch 23300, loss[loss=0.1389, simple_loss=0.2103, pruned_loss=0.03382, over 4909.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02917, over 970953.95 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:15:44,248 INFO [train.py:715] (1/8) Epoch 16, batch 23350, loss[loss=0.1518, simple_loss=0.2154, pruned_loss=0.04408, over 4854.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02914, over 971470.93 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:16:21,896 INFO [train.py:715] (1/8) Epoch 16, batch 23400, loss[loss=0.1709, simple_loss=0.2466, pruned_loss=0.04762, over 4774.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02922, over 971408.80 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:16:59,785 INFO [train.py:715] (1/8) Epoch 16, batch 23450, loss[loss=0.1544, simple_loss=0.2209, pruned_loss=0.04395, over 4939.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02899, over 972394.10 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 19:17:37,690 INFO [train.py:715] (1/8) Epoch 16, batch 23500, loss[loss=0.1366, simple_loss=0.1991, pruned_loss=0.03709, over 4827.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02896, over 972011.44 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:18:15,673 INFO [train.py:715] (1/8) Epoch 16, batch 23550, loss[loss=0.1329, simple_loss=0.2104, pruned_loss=0.02769, over 4949.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.0288, over 971883.75 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:18:54,222 INFO [train.py:715] (1/8) Epoch 16, batch 23600, loss[loss=0.1411, simple_loss=0.2039, pruned_loss=0.0392, over 4852.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02856, over 972592.04 frames.], batch size: 30, lr: 1.37e-04
2022-05-08 19:19:31,588 INFO [train.py:715] (1/8) Epoch 16, batch 23650, loss[loss=0.1289, simple_loss=0.2147, pruned_loss=0.02152, over 4766.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02913, over 972370.66 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:20:09,501 INFO [train.py:715] (1/8) Epoch 16, batch 23700, loss[loss=0.1183, simple_loss=0.1945, pruned_loss=0.02105, over 4865.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02932, over 973301.50 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:20:47,876 INFO [train.py:715] (1/8) Epoch 16, batch 23750, loss[loss=0.1504, simple_loss=0.2389, pruned_loss=0.03091, over 4943.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02966, over 973956.31 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:21:25,951 INFO [train.py:715] (1/8) Epoch 16, batch 23800, loss[loss=0.1304, simple_loss=0.2028, pruned_loss=0.02896, over 4954.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2084, pruned_loss=0.02965, over 973859.68 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:22:04,206 INFO [train.py:715] (1/8) Epoch 16, batch 23850, loss[loss=0.1207, simple_loss=0.1966, pruned_loss=0.02242, over 4876.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2084, pruned_loss=0.0296, over 973854.48 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:22:42,142 INFO [train.py:715] (1/8) Epoch 16, batch 23900, loss[loss=0.129, simple_loss=0.1969, pruned_loss=0.03057, over 4789.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.0296, over 972709.15 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:23:20,420 INFO [train.py:715] (1/8) Epoch 16, batch 23950, loss[loss=0.1282, simple_loss=0.2041, pruned_loss=0.02619, over 4985.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02904, over 972601.06 frames.], batch size: 28, lr: 1.37e-04
2022-05-08 19:23:57,817 INFO [train.py:715] (1/8) Epoch 16, batch 24000, loss[loss=0.1406, simple_loss=0.2084, pruned_loss=0.03647, over 4856.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02963, over 972512.79 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:23:57,818 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 19:24:07,636 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.1049, simple_loss=0.1883, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 19:24:46,421 INFO [train.py:715] (1/8) Epoch 16, batch 24050, loss[loss=0.1311, simple_loss=0.2033, pruned_loss=0.02944, over 4978.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02907, over 972948.11 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:25:24,730 INFO [train.py:715] (1/8) Epoch 16, batch 24100, loss[loss=0.123, simple_loss=0.1999, pruned_loss=0.02307, over 4810.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02925, over 973574.65 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 19:26:03,115 INFO [train.py:715] (1/8) Epoch 16, batch 24150, loss[loss=0.1465, simple_loss=0.2148, pruned_loss=0.03912, over 4972.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02914, over 973344.62 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:26:40,870 INFO [train.py:715] (1/8) Epoch 16, batch 24200, loss[loss=0.136, simple_loss=0.2118, pruned_loss=0.03011, over 4937.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02942, over 972418.03 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:27:19,229 INFO [train.py:715] (1/8) Epoch 16, batch 24250, loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02859, over 4802.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02967, over 973225.76 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:27:57,174 INFO [train.py:715] (1/8) Epoch 16, batch 24300, loss[loss=0.1259, simple_loss=0.1992, pruned_loss=0.02629, over 4874.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.02996, over 973140.23 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 19:28:35,672 INFO [train.py:715] (1/8) Epoch 16, batch 24350, loss[loss=0.1499, simple_loss=0.2301, pruned_loss=0.03479, over 4712.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02973, over 972854.20 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:29:13,226 INFO [train.py:715] (1/8) Epoch 16, batch 24400, loss[loss=0.1286, simple_loss=0.2025, pruned_loss=0.02739, over 4883.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02948, over 972914.96 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 19:29:50,784 INFO [train.py:715] (1/8) Epoch 16, batch 24450, loss[loss=0.1572, simple_loss=0.2309, pruned_loss=0.04174, over 4921.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02955, over 972922.97 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 19:30:28,699 INFO [train.py:715] (1/8) Epoch 16, batch 24500, loss[loss=0.1419, simple_loss=0.2169, pruned_loss=0.03346, over 4898.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02992, over 972412.85 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:31:06,550 INFO [train.py:715] (1/8) Epoch 16, batch 24550, loss[loss=0.125, simple_loss=0.2075, pruned_loss=0.02127, over 4986.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02957, over 972298.55 frames.], batch size: 28, lr: 1.37e-04
2022-05-08 19:31:43,994 INFO [train.py:715] (1/8) Epoch 16, batch 24600, loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02829, over 4943.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02979, over 972281.00 frames.], batch size: 23, lr: 1.37e-04
2022-05-08 19:32:21,349 INFO [train.py:715] (1/8) Epoch 16, batch 24650, loss[loss=0.1433, simple_loss=0.2168, pruned_loss=0.03491, over 4799.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02967, over 971837.85 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:32:59,496 INFO [train.py:715] (1/8) Epoch 16, batch 24700, loss[loss=0.1286, simple_loss=0.2056, pruned_loss=0.02575, over 4808.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02984, over 971791.73 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 19:33:37,067 INFO [train.py:715] (1/8) Epoch 16, batch 24750, loss[loss=0.1496, simple_loss=0.2164, pruned_loss=0.0414, over 4892.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2077, pruned_loss=0.03044, over 971788.02 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:34:14,867 INFO [train.py:715] (1/8) Epoch 16, batch 24800, loss[loss=0.1424, simple_loss=0.2125, pruned_loss=0.03617, over 4918.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03033, over 971823.53 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 19:34:52,612 INFO [train.py:715] (1/8) Epoch 16, batch 24850, loss[loss=0.117, simple_loss=0.1873, pruned_loss=0.02334, over 4644.00 frames.], tot_loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03015, over 971507.71 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:35:30,370 INFO [train.py:715] (1/8) Epoch 16, batch 24900, loss[loss=0.1587, simple_loss=0.2214, pruned_loss=0.04804, over 4917.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02967, over 971250.67 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 19:36:08,067 INFO [train.py:715] (1/8) Epoch 16, batch 24950, loss[loss=0.1248, simple_loss=0.1989, pruned_loss=0.02533, over 4767.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2072, pruned_loss=0.02986, over 971423.63 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:36:45,486 INFO [train.py:715] (1/8) Epoch 16, batch 25000, loss[loss=0.1495, simple_loss=0.2178, pruned_loss=0.04061, over 4913.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02994, over 971546.24 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:37:23,738 INFO [train.py:715] (1/8) Epoch 16, batch 25050, loss[loss=0.1226, simple_loss=0.1984, pruned_loss=0.02341, over 4890.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02961, over 972288.97 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 19:38:02,502 INFO [train.py:715] (1/8) Epoch 16, batch 25100, loss[loss=0.1336, simple_loss=0.2092, pruned_loss=0.02904, over 4729.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02942, over 971625.82 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:38:40,222 INFO [train.py:715] (1/8) Epoch 16, batch 25150, loss[loss=0.1466, simple_loss=0.2169, pruned_loss=0.03811, over 4685.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02902, over 972600.33 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:39:18,061 INFO [train.py:715] (1/8) Epoch 16, batch 25200, loss[loss=0.1425, simple_loss=0.2051, pruned_loss=0.03992, over 4738.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02901, over 973171.85 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:39:56,035 INFO [train.py:715] (1/8) Epoch 16, batch 25250, loss[loss=0.1552, simple_loss=0.2331, pruned_loss=0.03864, over 4985.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.02964, over 972652.95 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 19:40:33,646 INFO [train.py:715] (1/8) Epoch 16, batch 25300, loss[loss=0.1051, simple_loss=0.1792, pruned_loss=0.01549, over 4931.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02964, over 973327.22 frames.], batch size: 23, lr: 1.37e-04
2022-05-08 19:41:10,911 INFO [train.py:715] (1/8) Epoch 16, batch 25350, loss[loss=0.1251, simple_loss=0.1912, pruned_loss=0.02949, over 4782.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2076, pruned_loss=0.02931, over 974211.46 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:41:49,015 INFO [train.py:715] (1/8) Epoch 16, batch 25400, loss[loss=0.1172, simple_loss=0.1898, pruned_loss=0.02234, over 4687.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02955, over 973623.93 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:42:27,348 INFO [train.py:715] (1/8) Epoch 16, batch 25450, loss[loss=0.1362, simple_loss=0.2172, pruned_loss=0.02763, over 4791.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.0298, over 972753.41 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:43:04,844 INFO [train.py:715] (1/8) Epoch 16, batch 25500, loss[loss=0.1241, simple_loss=0.1988, pruned_loss=0.02472, over 4769.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02992, over 972650.11 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:43:42,849 INFO [train.py:715] (1/8) Epoch 16, batch 25550, loss[loss=0.1295, simple_loss=0.2048, pruned_loss=0.02706, over 4815.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03036, over 972473.81 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 19:44:21,360 INFO [train.py:715] (1/8) Epoch 16, batch 25600, loss[loss=0.123, simple_loss=0.2055, pruned_loss=0.02023, over 4936.00 frames.], tot_loss[loss=0.135, simple_loss=0.2092, pruned_loss=0.0304, over 971763.47 frames.], batch size: 23, lr: 1.37e-04
2022-05-08 19:45:00,144 INFO [train.py:715] (1/8) Epoch 16, batch 25650, loss[loss=0.1191, simple_loss=0.201, pruned_loss=0.01859, over 4935.00 frames.], tot_loss[loss=0.135, simple_loss=0.2093, pruned_loss=0.03034, over 971973.05 frames.], batch size: 29, lr: 1.37e-04
2022-05-08 19:45:38,373 INFO [train.py:715] (1/8) Epoch 16, batch 25700, loss[loss=0.1028, simple_loss=0.1728, pruned_loss=0.01641, over 4902.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03003, over 971838.40 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:46:16,990 INFO [train.py:715] (1/8) Epoch 16, batch 25750, loss[loss=0.1157, simple_loss=0.1933, pruned_loss=0.01906, over 4860.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03023, over 972297.86 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 19:46:55,625 INFO [train.py:715] (1/8) Epoch 16, batch 25800, loss[loss=0.1356, simple_loss=0.2005, pruned_loss=0.03537, over 4865.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2091, pruned_loss=0.03006, over 971654.26 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:47:34,229 INFO [train.py:715] (1/8) Epoch 16, batch 25850, loss[loss=0.1544, simple_loss=0.2229, pruned_loss=0.04295, over 4764.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2091, pruned_loss=0.03018, over 972172.68 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:48:13,045 INFO [train.py:715] (1/8) Epoch 16, batch 25900, loss[loss=0.138, simple_loss=0.2132, pruned_loss=0.03139, over 4961.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2089, pruned_loss=0.02995, over 971930.98 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:48:52,468 INFO [train.py:715] (1/8) Epoch 16, batch 25950, loss[loss=0.1354, simple_loss=0.2035, pruned_loss=0.03368, over 4754.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2086, pruned_loss=0.02922, over 972768.91 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:49:32,210 INFO [train.py:715] (1/8) Epoch 16, batch 26000, loss[loss=0.1318, simple_loss=0.2036, pruned_loss=0.02995, over 4819.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02914, over 972482.00 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 19:50:11,560 INFO [train.py:715] (1/8) Epoch 16, batch 26050, loss[loss=0.1096, simple_loss=0.1925, pruned_loss=0.01337, over 4794.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2073, pruned_loss=0.02889, over 971903.81 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:50:50,797 INFO [train.py:715] (1/8) Epoch 16, batch 26100, loss[loss=0.1007, simple_loss=0.1749, pruned_loss=0.01325, over 4840.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02875, over 972187.95 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:51:30,061 INFO [train.py:715] (1/8) Epoch 16, batch 26150, loss[loss=0.1163, simple_loss=0.1813, pruned_loss=0.02571, over 4800.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.029, over 971344.22 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 19:52:08,699 INFO [train.py:715] (1/8) Epoch 16, batch 26200, loss[loss=0.1648, simple_loss=0.2273, pruned_loss=0.05115, over 4836.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.0293, over 971492.91 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:52:48,176 INFO [train.py:715] (1/8) Epoch 16, batch 26250, loss[loss=0.1175, simple_loss=0.1847, pruned_loss=0.02514, over 4996.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02986, over 972022.06 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:53:27,350 INFO [train.py:715] (1/8) Epoch 16, batch 26300, loss[loss=0.1263, simple_loss=0.195, pruned_loss=0.02875, over 4873.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2081, pruned_loss=0.02974, over 971691.49 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:54:07,005 INFO [train.py:715] (1/8) Epoch 16, batch 26350, loss[loss=0.1033, simple_loss=0.1759, pruned_loss=0.01532, over 4989.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2091, pruned_loss=0.03027, over 972147.82 frames.], batch size: 28, lr: 1.37e-04
2022-05-08 19:54:46,283 INFO [train.py:715] (1/8) Epoch 16, batch 26400, loss[loss=0.1317, simple_loss=0.2179, pruned_loss=0.02277, over 4839.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03061, over 972283.15 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:55:26,166 INFO [train.py:715] (1/8) Epoch 16, batch 26450, loss[loss=0.1387, simple_loss=0.209, pruned_loss=0.03416, over 4791.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03049, over 972022.88 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:56:05,128 INFO [train.py:715] (1/8) Epoch 16, batch 26500, loss[loss=0.1304, simple_loss=0.1993, pruned_loss=0.03078, over 4927.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03003, over 971504.52 frames.], batch size: 29, lr: 1.37e-04
2022-05-08 19:56:44,032 INFO [train.py:715] (1/8) Epoch 16, batch 26550, loss[loss=0.1411, simple_loss=0.2108, pruned_loss=0.03572, over 4837.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2065, pruned_loss=0.02961, over 971433.08 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:57:23,102 INFO [train.py:715] (1/8) Epoch 16, batch 26600, loss[loss=0.1375, simple_loss=0.2125, pruned_loss=0.03124, over 4968.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.0296, over 972620.10 frames.], batch size: 28, lr: 1.37e-04
2022-05-08 19:58:02,096 INFO [train.py:715] (1/8) Epoch 16, batch 26650, loss[loss=0.1595, simple_loss=0.2307, pruned_loss=0.04414, over 4846.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02967, over 972616.87 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:58:41,433 INFO [train.py:715] (1/8) Epoch 16, batch 26700, loss[loss=0.1312, simple_loss=0.2066, pruned_loss=0.02792, over 4665.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02984, over 972558.72 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:59:20,668 INFO [train.py:715] (1/8) Epoch 16, batch 26750, loss[loss=0.1228, simple_loss=0.1992, pruned_loss=0.02321, over 4775.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02958, over 972229.53 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 20:00:00,472 INFO [train.py:715] (1/8) Epoch 16, batch 26800, loss[loss=0.1143, simple_loss=0.1867, pruned_loss=0.02094, over 4853.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.0296, over 972818.62 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 20:00:39,348 INFO [train.py:715] (1/8) Epoch 16, batch 26850, loss[loss=0.1172, simple_loss=0.1948, pruned_loss=0.01974, over 4981.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02945, over 972627.75 frames.], batch size: 28, lr: 1.37e-04
2022-05-08 20:01:18,830 INFO [train.py:715] (1/8) Epoch 16, batch 26900, loss[loss=0.1265, simple_loss=0.1996, pruned_loss=0.02668, over 4823.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03053, over 971649.34 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 20:01:58,326 INFO [train.py:715] (1/8) Epoch 16, batch 26950, loss[loss=0.1193, simple_loss=0.1946, pruned_loss=0.02205, over 4695.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.0307, over 971353.68 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 20:02:37,507 INFO [train.py:715] (1/8) Epoch 16, batch 27000, loss[loss=0.1251, simple_loss=0.2031, pruned_loss=0.02355, over 4909.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03053, over 971867.37 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 20:02:37,508 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 20:02:47,201 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.1048, simple_loss=0.1883, pruned_loss=0.01067, over 914524.00 frames.
2022-05-08 20:03:26,296 INFO [train.py:715] (1/8) Epoch 16, batch 27050, loss[loss=0.1445, simple_loss=0.2206, pruned_loss=0.03425, over 4874.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03026, over 972661.00 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 20:04:08,233 INFO [train.py:715] (1/8) Epoch 16, batch 27100, loss[loss=0.1164, simple_loss=0.2005, pruned_loss=0.01617, over 4903.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02996, over 973336.01 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 20:04:47,173 INFO [train.py:715] (1/8) Epoch 16, batch 27150, loss[loss=0.1464, simple_loss=0.2281, pruned_loss=0.03238, over 4891.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03002, over 972853.00 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 20:05:26,611 INFO [train.py:715] (1/8) Epoch 16, batch 27200, loss[loss=0.149, simple_loss=0.2177, pruned_loss=0.04015, over 4774.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03005, over 973351.96 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 20:06:05,792 INFO [train.py:715] (1/8) Epoch 16, batch 27250, loss[loss=0.1514, simple_loss=0.2424, pruned_loss=0.03016, over 4764.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02968, over 972364.18 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 20:06:45,177 INFO [train.py:715] (1/8) Epoch 16, batch 27300, loss[loss=0.1288, simple_loss=0.2015, pruned_loss=0.02803, over 4840.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02947, over 972533.80 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 20:07:24,245 INFO [train.py:715] (1/8) Epoch 16, batch 27350, loss[loss=0.1217, simple_loss=0.1931, pruned_loss=0.0251, over 4948.00 frames.], tot_loss[loss=0.133, simple_loss=0.2077, pruned_loss=0.02919, over 973198.40 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 20:08:03,614 INFO [train.py:715] (1/8) Epoch 16, batch 27400, loss[loss=0.1454, simple_loss=0.2265, pruned_loss=0.03219, over 4793.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2082, pruned_loss=0.02942, over 972220.58 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 20:08:42,908 INFO [train.py:715] (1/8) Epoch 16, batch 27450, loss[loss=0.1558, simple_loss=0.2269, pruned_loss=0.04236, over 4828.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02986, over 971730.85 frames.], batch size: 27, lr: 1.37e-04
2022-05-08 20:09:21,911 INFO [train.py:715] (1/8) Epoch 16, batch 27500, loss[loss=0.151, simple_loss=0.2272, pruned_loss=0.03737, over 4796.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02935, over 971700.24 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 20:10:01,275 INFO [train.py:715] (1/8) Epoch 16, batch 27550, loss[loss=0.1072, simple_loss=0.1862, pruned_loss=0.01413, over 4907.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02929, over 972385.39 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 20:10:41,138 INFO [train.py:715] (1/8) Epoch 16, batch 27600, loss[loss=0.126, simple_loss=0.189, pruned_loss=0.03148, over 4928.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02925, over 972995.18 frames.], batch size: 29, lr: 1.37e-04
2022-05-08 20:11:20,152 INFO [train.py:715] (1/8) Epoch 16, batch 27650, loss[loss=0.1245, simple_loss=0.189, pruned_loss=0.02994, over 4937.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02932, over 972941.24 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 20:11:59,674 INFO [train.py:715] (1/8) Epoch 16, batch 27700, loss[loss=0.1215, simple_loss=0.1982, pruned_loss=0.02235, over 4813.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02905, over 973126.44 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 20:12:38,980 INFO [train.py:715] (1/8) Epoch 16, batch 27750, loss[loss=0.1148, simple_loss=0.1877, pruned_loss=0.02099, over 4932.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2057, pruned_loss=0.02875, over 973729.19 frames.], batch size: 29, lr: 1.37e-04
2022-05-08 20:13:18,199 INFO [train.py:715] (1/8) Epoch 16, batch 27800, loss[loss=0.1195, simple_loss=0.1992, pruned_loss=0.01992, over 4779.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02927, over 972198.40 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 20:13:57,558 INFO [train.py:715] (1/8) Epoch 16, batch 27850, loss[loss=0.1553, simple_loss=0.2393, pruned_loss=0.03562, over 4965.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02883, over 972025.85 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 20:14:36,988 INFO [train.py:715] (1/8) Epoch 16, batch 27900, loss[loss=0.1465, simple_loss=0.2208, pruned_loss=0.03613, over 4829.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02841, over 972317.16 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 20:15:16,668 INFO [train.py:715] (1/8) Epoch 16, batch 27950, loss[loss=0.136, simple_loss=0.2089, pruned_loss=0.03154, over 4878.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02825, over 973020.35 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 20:15:55,985 INFO [train.py:715] (1/8) Epoch 16, batch 28000, loss[loss=0.1296, simple_loss=0.2052, pruned_loss=0.027, over 4823.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.0282, over 972349.97 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 20:16:35,542 INFO [train.py:715] (1/8) Epoch 16, batch 28050, loss[loss=0.1387, simple_loss=0.2118, pruned_loss=0.03281, over 4794.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02923, over 972804.05 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 20:17:15,211 INFO [train.py:715] (1/8) Epoch 16, batch 28100, loss[loss=0.1406, simple_loss=0.2212, pruned_loss=0.03, over 4959.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02931, over 971683.33 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 20:17:54,193 INFO [train.py:715] (1/8) Epoch 16, batch 28150, loss[loss=0.1351, simple_loss=0.2129, pruned_loss=0.02868, over 4978.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02971, over 972204.12 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 20:18:33,943 INFO [train.py:715] (1/8) Epoch 16, batch 28200, loss[loss=0.1321, simple_loss=0.2047, pruned_loss=0.02976, over 4963.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02976, over 973174.25 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 20:19:13,272 INFO [train.py:715] (1/8) Epoch 16, batch 28250, loss[loss=0.1456, simple_loss=0.2183, pruned_loss=0.0364, over 4972.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02968, over 973054.36 frames.], batch size: 28, lr: 1.37e-04
2022-05-08 20:19:51,894 INFO [train.py:715] (1/8) Epoch 16, batch 28300, loss[loss=0.1222, simple_loss=0.2045, pruned_loss=0.01995, over 4804.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02967, over 973160.16 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 20:20:31,609 INFO [train.py:715] (1/8) Epoch 16, batch 28350, loss[loss=0.1243, simple_loss=0.1915, pruned_loss=0.02855, over 4772.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03016, over 972039.29 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 20:21:11,561 INFO [train.py:715] (1/8) Epoch 16, batch 28400, loss[loss=0.1358, simple_loss=0.1919, pruned_loss=0.0399, over 4843.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.0304, over 972703.58 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 20:21:51,025 INFO [train.py:715] (1/8) Epoch 16, batch 28450, loss[loss=0.1229, simple_loss=0.1975, pruned_loss=0.02416, over 4989.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.03, over 973941.61 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 20:22:29,723 INFO [train.py:715] (1/8) Epoch 16, batch 28500, loss[loss=0.1122, simple_loss=0.1886, pruned_loss=0.01786, over 4684.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03003, over 973310.46 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 20:23:09,904 INFO [train.py:715] (1/8) Epoch 16, batch 28550, loss[loss=0.1112, simple_loss=0.1802, pruned_loss=0.02112, over 4795.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.03, over 973206.33 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 20:23:49,361 INFO [train.py:715] (1/8) Epoch 16, batch 28600, loss[loss=0.1466, simple_loss=0.2249, pruned_loss=0.03418, over 4977.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02986, over 973009.81 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 20:24:28,954 INFO [train.py:715] (1/8) Epoch 16, batch 28650, loss[loss=0.1368, simple_loss=0.2192, pruned_loss=0.02722, over 4842.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02949, over 972511.42 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 20:25:08,100 INFO [train.py:715] (1/8) Epoch 16, batch 28700, loss[loss=0.1133, simple_loss=0.1855, pruned_loss=0.02056, over 4793.00 frames.], tot_loss[loss=0.133, simple_loss=0.2076, pruned_loss=0.0292, over 973135.46 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 20:25:47,664 INFO [train.py:715] (1/8) Epoch 16, batch 28750, loss[loss=0.1245, simple_loss=0.1882, pruned_loss=0.03044, over 4785.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02904, over 973562.40 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 20:26:27,379 INFO [train.py:715] (1/8) Epoch 16, batch 28800, loss[loss=0.1054, simple_loss=0.1839, pruned_loss=0.01345, over 4815.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02904, over 972780.25 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 20:27:06,543 INFO [train.py:715] (1/8) Epoch 16, batch 28850, loss[loss=0.1303, simple_loss=0.2103, pruned_loss=0.02517, over 4821.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02939, over 972583.29 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 20:27:46,357 INFO [train.py:715] (1/8) Epoch 16, batch 28900, loss[loss=0.1298, simple_loss=0.212, pruned_loss=0.02382, over 4808.00 frames.], tot_loss[loss=0.133, simple_loss=0.2068, pruned_loss=0.02956, over 972425.19 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 20:28:25,961 INFO [train.py:715] (1/8) Epoch 16, batch 28950, loss[loss=0.1554, simple_loss=0.2332, pruned_loss=0.03876, over 4696.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02963, over 971748.90 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:29:05,874 INFO [train.py:715] (1/8) Epoch 16, batch 29000, loss[loss=0.1326, simple_loss=0.2045, pruned_loss=0.03034, over 4903.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02928, over 971442.52 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 20:29:45,343 INFO [train.py:715] (1/8) Epoch 16, batch 29050, loss[loss=0.1089, simple_loss=0.1755, pruned_loss=0.02114, over 4858.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02911, over 971688.92 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 20:30:25,183 INFO [train.py:715] (1/8) Epoch 16, batch 29100, loss[loss=0.1751, simple_loss=0.2549, pruned_loss=0.04761, over 4883.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02932, over 971971.05 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 20:31:06,236 INFO [train.py:715] (1/8) Epoch 16, batch 29150, loss[loss=0.0902, simple_loss=0.1636, pruned_loss=0.008414, over 4851.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02932, over 971891.04 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 20:31:46,265 INFO [train.py:715] (1/8) Epoch 16, batch 29200, loss[loss=0.1293, simple_loss=0.209, pruned_loss=0.02483, over 4766.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02956, over 971300.02 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 20:32:27,444 INFO [train.py:715] (1/8) Epoch 16, batch 29250, loss[loss=0.1125, simple_loss=0.1886, pruned_loss=0.01822, over 4817.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02963, over 971505.73 frames.], batch size: 27, lr: 1.36e-04
2022-05-08 20:33:08,441 INFO [train.py:715] (1/8) Epoch 16, batch 29300, loss[loss=0.1267, simple_loss=0.2009, pruned_loss=0.0263, over 4963.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02952, over 971617.63 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 20:33:49,835 INFO [train.py:715] (1/8) Epoch 16, batch 29350, loss[loss=0.1504, simple_loss=0.2265, pruned_loss=0.03709, over 4807.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02958, over 971807.67 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 20:34:30,964 INFO [train.py:715] (1/8) Epoch 16, batch 29400, loss[loss=0.1397, simple_loss=0.2147, pruned_loss=0.03231, over 4869.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.0297, over 972104.71 frames.], batch size: 30, lr: 1.36e-04
2022-05-08 20:35:12,718 INFO [train.py:715] (1/8) Epoch 16, batch 29450, loss[loss=0.1429, simple_loss=0.2243, pruned_loss=0.03068, over 4751.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02971, over 972033.95 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 20:35:54,215 INFO [train.py:715] (1/8) Epoch 16, batch 29500, loss[loss=0.1339, simple_loss=0.2136, pruned_loss=0.0271, over 4925.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.0298, over 972716.83 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:36:36,042 INFO [train.py:715] (1/8) Epoch 16, batch 29550, loss[loss=0.1102, simple_loss=0.1878, pruned_loss=0.01624, over 4822.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02971, over 972482.06 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 20:37:17,258 INFO [train.py:715] (1/8) Epoch 16, batch 29600, loss[loss=0.1249, simple_loss=0.1935, pruned_loss=0.02821, over 4969.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.03001, over 972840.49 frames.], batch size: 28, lr: 1.36e-04
2022-05-08 20:37:59,049 INFO [train.py:715] (1/8) Epoch 16, batch 29650, loss[loss=0.1694, simple_loss=0.2324, pruned_loss=0.05323, over 4960.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2072, pruned_loss=0.03004, over 972592.60 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 20:38:40,542 INFO [train.py:715] (1/8) Epoch 16, batch 29700, loss[loss=0.1122, simple_loss=0.1763, pruned_loss=0.02407, over 4857.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.02998, over 972464.97 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 20:39:21,784 INFO [train.py:715] (1/8) Epoch 16, batch 29750, loss[loss=0.1443, simple_loss=0.2082, pruned_loss=0.04023, over 4772.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03017, over 972611.54 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:40:02,897 INFO [train.py:715] (1/8) Epoch 16, batch 29800, loss[loss=0.1427, simple_loss=0.2253, pruned_loss=0.03003, over 4765.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.02968, over 972429.63 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 20:40:44,775 INFO [train.py:715] (1/8) Epoch 16, batch 29850, loss[loss=0.1256, simple_loss=0.1971, pruned_loss=0.02704, over 4816.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2085, pruned_loss=0.02971, over 971911.16 frames.], batch size: 27, lr: 1.36e-04
2022-05-08 20:41:26,317 INFO [train.py:715] (1/8) Epoch 16, batch 29900, loss[loss=0.1306, simple_loss=0.1983, pruned_loss=0.03146, over 4748.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03002, over 971511.68 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 20:42:07,626 INFO [train.py:715] (1/8) Epoch 16, batch 29950, loss[loss=0.1346, simple_loss=0.2106, pruned_loss=0.02934, over 4831.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03007, over 971121.46 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:42:50,224 INFO [train.py:715] (1/8) Epoch 16, batch 30000, loss[loss=0.1167, simple_loss=0.187, pruned_loss=0.02319, over 4944.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03002, over 970879.43 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 20:42:50,225 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 20:43:01,793 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.1047, simple_loss=0.1883, pruned_loss=0.01058, over 914524.00 frames.
2022-05-08 20:43:44,294 INFO [train.py:715] (1/8) Epoch 16, batch 30050, loss[loss=0.1458, simple_loss=0.2218, pruned_loss=0.03487, over 4780.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03013, over 970056.43 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:44:26,133 INFO [train.py:715] (1/8) Epoch 16, batch 30100, loss[loss=0.1167, simple_loss=0.1901, pruned_loss=0.02168, over 4926.00 frames.], tot_loss[loss=0.1346, simple_loss=0.209, pruned_loss=0.03013, over 970658.31 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:45:06,930 INFO [train.py:715] (1/8) Epoch 16, batch 30150, loss[loss=0.1289, simple_loss=0.192, pruned_loss=0.03292, over 4847.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03021, over 971049.68 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:45:48,625 INFO [train.py:715] (1/8) Epoch 16, batch 30200, loss[loss=0.1792, simple_loss=0.246, pruned_loss=0.05622, over 4972.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03028, over 971234.62 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:46:29,894 INFO [train.py:715] (1/8) Epoch 16, batch 30250, loss[loss=0.1635, simple_loss=0.2262, pruned_loss=0.05044, over 4896.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03041, over 971343.31 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 20:47:09,988 INFO [train.py:715] (1/8) Epoch 16, batch 30300, loss[loss=0.133, simple_loss=0.2128, pruned_loss=0.02661, over 4924.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03054, over 972281.56 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:47:50,188 INFO [train.py:715] (1/8) Epoch 16, batch 30350, loss[loss=0.1316, simple_loss=0.2016, pruned_loss=0.0308, over 4780.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.0299, over 971966.40 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:48:30,616 INFO [train.py:715] (1/8) Epoch 16, batch 30400, loss[loss=0.1246, simple_loss=0.202, pruned_loss=0.02362, over 4926.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.0297, over 972045.43 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 20:49:10,252 INFO [train.py:715] (1/8) Epoch 16, batch 30450, loss[loss=0.1106, simple_loss=0.1953, pruned_loss=0.01293, over 4819.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02967, over 971959.23 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:49:49,411 INFO [train.py:715] (1/8) Epoch 16, batch 30500, loss[loss=0.1466, simple_loss=0.2207, pruned_loss=0.03627, over 4958.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03012, over 972997.79 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 20:50:29,342 INFO [train.py:715] (1/8) Epoch 16, batch 30550, loss[loss=0.109, simple_loss=0.1834, pruned_loss=0.01732, over 4775.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.0304, over 972806.62 frames.], batch size: 12, lr: 1.36e-04
2022-05-08 20:51:09,803 INFO [train.py:715] (1/8) Epoch 16, batch 30600, loss[loss=0.1188, simple_loss=0.1891, pruned_loss=0.02429, over 4957.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03069, over 972239.13 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 20:51:49,048 INFO [train.py:715] (1/8) Epoch 16, batch 30650, loss[loss=0.1332, simple_loss=0.1921, pruned_loss=0.0371, over 4865.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03019, over 972081.94 frames.], batch size: 30, lr: 1.36e-04
2022-05-08 20:52:28,809 INFO [train.py:715] (1/8) Epoch 16, batch 30700, loss[loss=0.1363, simple_loss=0.2076, pruned_loss=0.03253, over 4894.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02984, over 973364.89 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 20:53:10,035 INFO [train.py:715] (1/8) Epoch 16, batch 30750, loss[loss=0.1262, simple_loss=0.1889, pruned_loss=0.03171, over 4916.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02963, over 974013.06 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 20:53:49,630 INFO [train.py:715] (1/8) Epoch 16, batch 30800, loss[loss=0.1415, simple_loss=0.2129, pruned_loss=0.03504, over 4853.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02934, over 973536.79 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 20:54:28,450 INFO [train.py:715] (1/8) Epoch 16, batch 30850, loss[loss=0.1258, simple_loss=0.203, pruned_loss=0.0243, over 4763.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02904, over 973341.22 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 20:55:08,445 INFO [train.py:715] (1/8) Epoch 16, batch 30900, loss[loss=0.1948, simple_loss=0.2507, pruned_loss=0.06943, over 4855.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02935, over 972038.12 frames.], batch size: 30, lr: 1.36e-04
2022-05-08 20:55:47,893 INFO [train.py:715] (1/8) Epoch 16, batch 30950, loss[loss=0.1234, simple_loss=0.1924, pruned_loss=0.02722, over 4967.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02947, over 972218.92 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:56:26,909 INFO [train.py:715] (1/8) Epoch 16, batch 31000, loss[loss=0.143, simple_loss=0.205, pruned_loss=0.04051, over 4791.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02956, over 972068.39 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 20:57:06,089 INFO [train.py:715] (1/8) Epoch 16, batch 31050, loss[loss=0.1326, simple_loss=0.2048, pruned_loss=0.03021, over 4853.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02959, over 971686.40 frames.], batch size: 34, lr: 1.36e-04
2022-05-08 20:57:45,842 INFO [train.py:715] (1/8) Epoch 16, batch 31100, loss[loss=0.1457, simple_loss=0.2218, pruned_loss=0.03485, over 4962.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02954, over 971291.14 frames.], batch size: 39, lr: 1.36e-04
2022-05-08 20:58:25,691 INFO [train.py:715] (1/8) Epoch 16, batch 31150, loss[loss=0.139, simple_loss=0.2148, pruned_loss=0.03163, over 4769.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03018, over 971501.74 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 20:59:04,401 INFO [train.py:715] (1/8) Epoch 16, batch 31200, loss[loss=0.1162, simple_loss=0.1933, pruned_loss=0.01952, over 4953.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03009, over 971543.16 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 20:59:44,071 INFO [train.py:715] (1/8) Epoch 16, batch 31250, loss[loss=0.1444, simple_loss=0.2175, pruned_loss=0.03564, over 4874.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2072, pruned_loss=0.02989, over 972820.12 frames.], batch size: 22, lr: 1.36e-04
2022-05-08 21:00:23,617 INFO [train.py:715] (1/8) Epoch 16, batch 31300, loss[loss=0.1356, simple_loss=0.2122, pruned_loss=0.02953, over 4760.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02988, over 972983.85 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:01:03,212 INFO [train.py:715] (1/8) Epoch 16, batch 31350, loss[loss=0.1346, simple_loss=0.2006, pruned_loss=0.03432, over 4985.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02958, over 972352.52 frames.], batch size: 28, lr: 1.36e-04
2022-05-08 21:01:42,656 INFO [train.py:715] (1/8) Epoch 16, batch 31400, loss[loss=0.1203, simple_loss=0.1912, pruned_loss=0.02476, over 4745.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02943, over 971993.16 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:02:22,723 INFO [train.py:715] (1/8) Epoch 16, batch 31450, loss[loss=0.1648, simple_loss=0.2359, pruned_loss=0.04689, over 4793.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.0297, over 972428.42 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 21:03:01,702 INFO [train.py:715] (1/8) Epoch 16, batch 31500, loss[loss=0.1143, simple_loss=0.1892, pruned_loss=0.01968, over 4752.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2075, pruned_loss=0.03012, over 972261.38 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:03:40,546 INFO [train.py:715] (1/8) Epoch 16, batch 31550, loss[loss=0.1325, simple_loss=0.2195, pruned_loss=0.02276, over 4939.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02985, over 972661.89 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 21:04:19,811 INFO [train.py:715] (1/8) Epoch 16, batch 31600, loss[loss=0.1175, simple_loss=0.1983, pruned_loss=0.01835, over 4905.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2076, pruned_loss=0.03026, over 972460.24 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 21:04:58,924 INFO [train.py:715] (1/8) Epoch 16, batch 31650, loss[loss=0.1361, simple_loss=0.2062, pruned_loss=0.03302, over 4828.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03032, over 972731.45 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:05:37,862 INFO [train.py:715] (1/8) Epoch 16, batch 31700, loss[loss=0.1323, simple_loss=0.2042, pruned_loss=0.03026, over 4775.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03009, over 972707.13 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:06:17,109 INFO [train.py:715] (1/8) Epoch 16, batch 31750, loss[loss=0.1361, simple_loss=0.2001, pruned_loss=0.03606, over 4836.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02975, over 972033.56 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 21:06:56,945 INFO [train.py:715] (1/8) Epoch 16, batch 31800, loss[loss=0.1355, simple_loss=0.2019, pruned_loss=0.0346, over 4828.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02944, over 972849.41 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 21:07:36,870 INFO [train.py:715] (1/8) Epoch 16, batch 31850, loss[loss=0.1155, simple_loss=0.1905, pruned_loss=0.02025, over 4952.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02919, over 972546.02 frames.], batch size: 35, lr: 1.36e-04
2022-05-08 21:08:15,872 INFO [train.py:715] (1/8) Epoch 16, batch 31900, loss[loss=0.1508, simple_loss=0.2282, pruned_loss=0.03671, over 4974.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02953, over 972314.41 frames.], batch size: 28, lr: 1.36e-04
2022-05-08 21:08:55,066 INFO [train.py:715] (1/8) Epoch 16, batch 31950, loss[loss=0.1532, simple_loss=0.224, pruned_loss=0.04121, over 4871.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02953, over 972279.29 frames.], batch size: 20, lr: 1.36e-04
2022-05-08 21:09:34,444 INFO [train.py:715] (1/8) Epoch 16, batch 32000, loss[loss=0.1511, simple_loss=0.2231, pruned_loss=0.0396, over 4939.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02936, over 972482.46 frames.], batch size: 35, lr: 1.36e-04
2022-05-08 21:10:13,551 INFO [train.py:715] (1/8) Epoch 16, batch 32050, loss[loss=0.1241, simple_loss=0.201, pruned_loss=0.02354, over 4838.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02963, over 971735.63 frames.], batch size: 30, lr: 1.36e-04
2022-05-08 21:10:53,100 INFO [train.py:715] (1/8) Epoch 16, batch 32100, loss[loss=0.126, simple_loss=0.2121, pruned_loss=0.01992, over 4739.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02969, over 971084.23 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:11:32,626 INFO [train.py:715] (1/8) Epoch 16, batch 32150, loss[loss=0.1153, simple_loss=0.1916, pruned_loss=0.01948, over 4981.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.0292, over 970998.52 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:12:12,706 INFO [train.py:715] (1/8) Epoch 16, batch 32200, loss[loss=0.1345, simple_loss=0.1954, pruned_loss=0.03682, over 4834.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02921, over 971391.31 frames.], batch size: 32, lr: 1.36e-04
2022-05-08 21:12:51,835 INFO [train.py:715] (1/8) Epoch 16, batch 32250, loss[loss=0.1489, simple_loss=0.2291, pruned_loss=0.03432, over 4977.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02946, over 971512.13 frames.], batch size: 39, lr: 1.36e-04
2022-05-08 21:13:31,328 INFO [train.py:715] (1/8) Epoch 16, batch 32300, loss[loss=0.1104, simple_loss=0.1898, pruned_loss=0.01554, over 4830.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02979, over 971707.52 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 21:14:11,348 INFO [train.py:715] (1/8) Epoch 16, batch 32350, loss[loss=0.1341, simple_loss=0.2041, pruned_loss=0.03207, over 4980.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.0296, over 972175.68 frames.], batch size: 40, lr: 1.36e-04
2022-05-08 21:14:50,974 INFO [train.py:715] (1/8) Epoch 16, batch 32400, loss[loss=0.1197, simple_loss=0.1983, pruned_loss=0.0205, over 4760.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02876, over 972647.47 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:15:30,004 INFO [train.py:715] (1/8) Epoch 16, batch 32450, loss[loss=0.1217, simple_loss=0.205, pruned_loss=0.01925, over 4748.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02866, over 973418.84 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:16:10,039 INFO [train.py:715] (1/8) Epoch 16, batch 32500, loss[loss=0.156, simple_loss=0.2269, pruned_loss=0.04252, over 4951.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02884, over 973200.71 frames.], batch size: 35, lr: 1.36e-04
2022-05-08 21:16:49,333 INFO [train.py:715] (1/8) Epoch 16, batch 32550, loss[loss=0.121, simple_loss=0.1982, pruned_loss=0.02187, over 4691.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02929, over 972344.84 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:17:28,291 INFO [train.py:715] (1/8) Epoch 16, batch 32600, loss[loss=0.1519, simple_loss=0.2224, pruned_loss=0.04075, over 4769.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02959, over 972756.51 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 21:18:07,205 INFO [train.py:715] (1/8) Epoch 16, batch 32650, loss[loss=0.1444, simple_loss=0.2097, pruned_loss=0.0395, over 4982.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.0296, over 973550.07 frames.], batch size: 31, lr: 1.36e-04
2022-05-08 21:18:46,397 INFO [train.py:715] (1/8) Epoch 16, batch 32700, loss[loss=0.1288, simple_loss=0.2007, pruned_loss=0.02848, over 4835.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02993, over 972874.79 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:19:25,741 INFO [train.py:715] (1/8) Epoch 16, batch 32750, loss[loss=0.1322, simple_loss=0.2084, pruned_loss=0.02796, over 4794.00 frames.], tot_loss[loss=0.134, simple_loss=0.2085, pruned_loss=0.02973, over 973213.44 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:20:05,465 INFO [train.py:715] (1/8) Epoch 16, batch 32800, loss[loss=0.1208, simple_loss=0.2037, pruned_loss=0.01895, over 4786.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2087, pruned_loss=0.02974, over 973095.10 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:20:44,828 INFO [train.py:715] (1/8) Epoch 16, batch 32850, loss[loss=0.1522, simple_loss=0.2111, pruned_loss=0.04668, over 4822.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.02962, over 972723.36 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:21:24,456 INFO [train.py:715] (1/8) Epoch 16, batch 32900, loss[loss=0.1649, simple_loss=0.2417, pruned_loss=0.04407, over 4842.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02947, over 972931.00 frames.], batch size: 32, lr: 1.36e-04
2022-05-08 21:22:03,433 INFO [train.py:715] (1/8) Epoch 16, batch 32950, loss[loss=0.1427, simple_loss=0.2208, pruned_loss=0.03229, over 4884.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02959, over 972812.89 frames.], batch size: 20, lr: 1.36e-04
2022-05-08 21:22:42,575 INFO [train.py:715] (1/8) Epoch 16, batch 33000, loss[loss=0.1339, simple_loss=0.2061, pruned_loss=0.03089, over 4695.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02969, over 971870.47 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:22:42,575 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 21:22:55,771 INFO [train.py:742] (1/8) Epoch 16, validation: loss=0.105, simple_loss=0.1884, pruned_loss=0.01078, over 914524.00 frames.
2022-05-08 21:23:35,556 INFO [train.py:715] (1/8) Epoch 16, batch 33050, loss[loss=0.1152, simple_loss=0.1892, pruned_loss=0.02054, over 4902.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02958, over 971260.79 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 21:24:14,886 INFO [train.py:715] (1/8) Epoch 16, batch 33100, loss[loss=0.1417, simple_loss=0.2138, pruned_loss=0.03478, over 4790.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02966, over 972412.57 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:24:54,246 INFO [train.py:715] (1/8) Epoch 16, batch 33150, loss[loss=0.1169, simple_loss=0.1939, pruned_loss=0.01989, over 4895.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02947, over 972781.16 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:25:34,102 INFO [train.py:715] (1/8) Epoch 16, batch 33200, loss[loss=0.1179, simple_loss=0.1853, pruned_loss=0.02521, over 4814.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02914, over 972913.62 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 21:26:13,851 INFO [train.py:715] (1/8) Epoch 16, batch 33250, loss[loss=0.1269, simple_loss=0.2059, pruned_loss=0.02398, over 4794.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02959, over 971317.56 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 21:26:53,438 INFO [train.py:715] (1/8) Epoch 16, batch 33300, loss[loss=0.1312, simple_loss=0.2151, pruned_loss=0.02359, over 4948.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02908, over 972057.12 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 21:27:32,753 INFO [train.py:715] (1/8) Epoch 16, batch 33350, loss[loss=0.1496, simple_loss=0.2211, pruned_loss=0.0391, over 4940.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02914, over 971995.94 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 21:28:12,220 INFO [train.py:715] (1/8) Epoch 16, batch 33400, loss[loss=0.1442, simple_loss=0.2225, pruned_loss=0.0329, over 4645.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02922, over 971820.54 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 21:28:51,459 INFO [train.py:715] (1/8) Epoch 16, batch 33450, loss[loss=0.1174, simple_loss=0.1947, pruned_loss=0.02006, over 4761.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02954, over 971724.76 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 21:29:30,508 INFO [train.py:715] (1/8) Epoch 16, batch 33500, loss[loss=0.1205, simple_loss=0.1987, pruned_loss=0.0212, over 4810.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2076, pruned_loss=0.02933, over 972095.00 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 21:30:09,462 INFO [train.py:715] (1/8) Epoch 16, batch 33550, loss[loss=0.1192, simple_loss=0.1987, pruned_loss=0.01979, over 4938.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02944, over 972300.57 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 21:30:49,233 INFO [train.py:715] (1/8) Epoch 16, batch 33600, loss[loss=0.1453, simple_loss=0.2237, pruned_loss=0.0335, over 4884.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02983, over 972238.71 frames.], batch size: 22, lr: 1.36e-04
2022-05-08 21:31:28,204 INFO [train.py:715] (1/8) Epoch 16, batch 33650, loss[loss=0.1459, simple_loss=0.2124, pruned_loss=0.0397, over 4982.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.0293, over 973238.77 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:32:07,849 INFO [train.py:715] (1/8) Epoch 16, batch 33700, loss[loss=0.1213, simple_loss=0.1909, pruned_loss=0.02581, over 4884.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02897, over 972882.34 frames.], batch size: 22, lr: 1.36e-04
2022-05-08 21:32:46,799 INFO [train.py:715] (1/8) Epoch 16, batch 33750, loss[loss=0.1477, simple_loss=0.2262, pruned_loss=0.03465, over 4694.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02907, over 971706.15 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:33:25,791 INFO [train.py:715] (1/8) Epoch 16, batch 33800, loss[loss=0.1506, simple_loss=0.2115, pruned_loss=0.04482, over 4815.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2077, pruned_loss=0.02899, over 971799.07 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 21:34:05,046 INFO [train.py:715] (1/8) Epoch 16, batch 33850, loss[loss=0.1414, simple_loss=0.2132, pruned_loss=0.0348, over 4751.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2071, pruned_loss=0.0289, over 971208.15 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:34:44,275 INFO [train.py:715] (1/8) Epoch 16, batch 33900, loss[loss=0.13, simple_loss=0.2002, pruned_loss=0.02989, over 4791.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02925, over 970418.10 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 21:35:24,624 INFO [train.py:715] (1/8) Epoch 16, batch 33950, loss[loss=0.1339, simple_loss=0.2183, pruned_loss=0.02474, over 4864.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.0291, over 971007.03 frames.], batch size: 20, lr: 1.36e-04
2022-05-08 21:36:03,122 INFO [train.py:715] (1/8) Epoch 16, batch 34000, loss[loss=0.1425, simple_loss=0.2277, pruned_loss=0.02869, over 4754.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02957, over 969708.66 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 21:36:43,151 INFO [train.py:715] (1/8) Epoch 16, batch 34050, loss[loss=0.1226, simple_loss=0.2018, pruned_loss=0.02166, over 4697.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02938, over 969301.99 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:37:22,556 INFO [train.py:715] (1/8) Epoch 16, batch 34100, loss[loss=0.1551, simple_loss=0.2254, pruned_loss=0.04235, over 4832.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02919, over 969978.79 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:38:01,722 INFO [train.py:715] (1/8) Epoch 16, batch 34150, loss[loss=0.125, simple_loss=0.2021, pruned_loss=0.02397, over 4971.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02953, over 969057.08 frames.], batch size: 31, lr: 1.36e-04
2022-05-08 21:38:41,102 INFO [train.py:715] (1/8) Epoch 16, batch 34200, loss[loss=0.1252, simple_loss=0.2048, pruned_loss=0.02274, over 4696.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02932, over 969611.09 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:39:20,455 INFO [train.py:715] (1/8) Epoch 16, batch 34250, loss[loss=0.1225, simple_loss=0.1939, pruned_loss=0.02556, over 4843.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02924, over 969923.37 frames.], batch size: 30, lr: 1.36e-04
2022-05-08 21:40:00,521 INFO [train.py:715] (1/8) Epoch 16, batch 34300, loss[loss=0.1357, simple_loss=0.2114, pruned_loss=0.03001, over 4897.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02955, over 970765.40 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 21:40:39,498 INFO [train.py:715] (1/8) Epoch 16, batch 34350, loss[loss=0.1484, simple_loss=0.2216, pruned_loss=0.03764, over 4891.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02948, over 970920.63 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:41:18,850 INFO [train.py:715] (1/8) Epoch 16, batch 34400, loss[loss=0.1292, simple_loss=0.2047, pruned_loss=0.02687, over 4770.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02943, over 971519.15 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 21:41:58,454 INFO [train.py:715] (1/8) Epoch 16, batch 34450, loss[loss=0.1365, simple_loss=0.206, pruned_loss=0.03347, over 4818.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02997, over 970886.82 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:42:37,731 INFO [train.py:715] (1/8) Epoch 16, batch 34500, loss[loss=0.1101, simple_loss=0.1881, pruned_loss=0.01608, over 4788.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02943, over 971464.13 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:43:17,127 INFO [train.py:715] (1/8) Epoch 16, batch 34550, loss[loss=0.1285, simple_loss=0.1995, pruned_loss=0.02878, over 4828.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02951, over 971590.27 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 21:43:56,248 INFO [train.py:715] (1/8) Epoch 16, batch 34600, loss[loss=0.1252, simple_loss=0.2165, pruned_loss=0.01701, over 4950.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02965, over 971523.86 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 21:44:36,203 INFO [train.py:715] (1/8) Epoch 16, batch 34650, loss[loss=0.1119, simple_loss=0.1892, pruned_loss=0.01729, over 4948.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02992, over 971975.56 frames.], batch size: 29, lr: 1.36e-04
2022-05-08 21:45:15,712 INFO [train.py:715] (1/8) Epoch 16, batch 34700, loss[loss=0.1304, simple_loss=0.2085, pruned_loss=0.02621, over 4811.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.0299, over 971345.67 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:45:54,807 INFO [train.py:715] (1/8) Epoch 16, batch 34750, loss[loss=0.1756, simple_loss=0.2255, pruned_loss=0.06284, over 4854.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2073, pruned_loss=0.03002, over 971299.11 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 21:46:32,022 INFO [train.py:715] (1/8) Epoch 16, batch 34800, loss[loss=0.126, simple_loss=0.2054, pruned_loss=0.0233, over 4867.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2066, pruned_loss=0.02963, over 971571.50 frames.], batch size: 20, lr: 1.36e-04
2022-05-08 21:47:23,867 INFO [train.py:715] (1/8) Epoch 17, batch 0, loss[loss=0.1403, simple_loss=0.2163, pruned_loss=0.03218, over 4935.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2163, pruned_loss=0.03218, over 4935.00 frames.], batch size: 21, lr: 1.32e-04
2022-05-08 21:48:03,326 INFO [train.py:715] (1/8) Epoch 17, batch 50, loss[loss=0.1244, simple_loss=0.2075, pruned_loss=0.02063, over 4920.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2099, pruned_loss=0.03051, over 219845.28 frames.], batch size: 23, lr: 1.32e-04
2022-05-08 21:48:44,382 INFO [train.py:715] (1/8) Epoch 17, batch 100, loss[loss=0.1226, simple_loss=0.1977, pruned_loss=0.02374, over 4783.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2108, pruned_loss=0.03139, over 386636.47 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 21:49:25,325 INFO [train.py:715] (1/8) Epoch 17, batch 150, loss[loss=0.1312, simple_loss=0.1976, pruned_loss=0.03237, over 4690.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03059, over 517429.09 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 21:50:06,398 INFO [train.py:715] (1/8) Epoch 17, batch 200, loss[loss=0.1295, simple_loss=0.2078, pruned_loss=0.02556, over 4892.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03072, over 619191.58 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 21:50:49,382 INFO [train.py:715] (1/8) Epoch 17, batch 250, loss[loss=0.1171, simple_loss=0.1916, pruned_loss=0.02126, over 4955.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2094, pruned_loss=0.03043, over 698082.74 frames.], batch size: 39, lr: 1.32e-04
2022-05-08 21:51:30,995 INFO [train.py:715] (1/8) Epoch 17, batch 300, loss[loss=0.1383, simple_loss=0.2034, pruned_loss=0.03663, over 4771.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.03001, over 758396.08 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 21:52:11,852 INFO [train.py:715] (1/8) Epoch 17, batch 350, loss[loss=0.1257, simple_loss=0.2012, pruned_loss=0.02504, over 4762.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02952, over 805358.46 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 21:52:52,803 INFO [train.py:715] (1/8) Epoch 17, batch 400, loss[loss=0.1501, simple_loss=0.2173, pruned_loss=0.04147, over 4915.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.02964, over 842065.10 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 21:53:33,713 INFO [train.py:715] (1/8) Epoch 17, batch 450, loss[loss=0.1262, simple_loss=0.1954, pruned_loss=0.02853, over 4898.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.03002, over 871143.67 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 21:54:14,773 INFO [train.py:715] (1/8) Epoch 17, batch 500, loss[loss=0.1461, simple_loss=0.2288, pruned_loss=0.03176, over 4887.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02974, over 893774.72 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 21:54:56,775 INFO [train.py:715] (1/8) Epoch 17, batch 550, loss[loss=0.1136, simple_loss=0.1909, pruned_loss=0.01816, over 4749.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2094, pruned_loss=0.03047, over 911276.09 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 21:55:37,896 INFO [train.py:715] (1/8) Epoch 17, batch 600, loss[loss=0.122, simple_loss=0.1916, pruned_loss=0.0262, over 4936.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03038, over 925429.41 frames.], batch size: 29, lr: 1.32e-04
2022-05-08 21:56:20,088 INFO [train.py:715] (1/8) Epoch 17, batch 650, loss[loss=0.1151, simple_loss=0.1912, pruned_loss=0.0195, over 4954.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02947, over 935685.56 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 21:57:01,712 INFO [train.py:715] (1/8) Epoch 17, batch 700, loss[loss=0.1298, simple_loss=0.2017, pruned_loss=0.02894, over 4889.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02933, over 944550.10 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 21:57:42,593 INFO [train.py:715] (1/8) Epoch 17, batch 750, loss[loss=0.1205, simple_loss=0.1929, pruned_loss=0.02403, over 4935.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02918, over 949969.30 frames.], batch size: 29, lr: 1.32e-04
2022-05-08 21:58:23,375 INFO [train.py:715] (1/8) Epoch 17, batch 800, loss[loss=0.1462, simple_loss=0.2195, pruned_loss=0.03647, over 4883.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2074, pruned_loss=0.029, over 955090.58 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 21:59:03,979 INFO [train.py:715] (1/8) Epoch 17, batch 850, loss[loss=0.1425, simple_loss=0.2078, pruned_loss=0.03862, over 4847.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02924, over 958727.24 frames.], batch size: 26, lr: 1.32e-04
2022-05-08 21:59:45,397 INFO [train.py:715] (1/8) Epoch 17, batch 900, loss[loss=0.1168, simple_loss=0.1921, pruned_loss=0.02071, over 4892.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02935, over 961213.23 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:00:26,263 INFO [train.py:715] (1/8) Epoch 17, batch 950, loss[loss=0.1336, simple_loss=0.2063, pruned_loss=0.03041, over 4835.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02944, over 963737.91 frames.], batch size: 26, lr: 1.32e-04
2022-05-08 22:01:07,650 INFO [train.py:715] (1/8) Epoch 17, batch 1000, loss[loss=0.1219, simple_loss=0.202, pruned_loss=0.02089, over 4816.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2066, pruned_loss=0.02983, over 965216.23 frames.], batch size: 13, lr: 1.32e-04
2022-05-08 22:01:48,889 INFO [train.py:715] (1/8) Epoch 17, batch 1050, loss[loss=0.1219, simple_loss=0.1934, pruned_loss=0.02521, over 4774.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02951, over 967347.64 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:02:29,884 INFO [train.py:715] (1/8) Epoch 17, batch 1100, loss[loss=0.1252, simple_loss=0.1971, pruned_loss=0.02662, over 4947.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02903, over 968930.30 frames.], batch size: 35, lr: 1.32e-04
2022-05-08 22:03:10,393 INFO [train.py:715] (1/8) Epoch 17, batch 1150, loss[loss=0.1333, simple_loss=0.2126, pruned_loss=0.02696, over 4824.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02914, over 969627.95 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:03:51,863 INFO [train.py:715] (1/8) Epoch 17, batch 1200, loss[loss=0.1501, simple_loss=0.2132, pruned_loss=0.04348, over 4874.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02893, over 970541.21 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 22:04:32,825 INFO [train.py:715] (1/8) Epoch 17, batch 1250, loss[loss=0.1242, simple_loss=0.1999, pruned_loss=0.02422, over 4964.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02882, over 970860.88 frames.], batch size: 35, lr: 1.32e-04
2022-05-08 22:05:13,888 INFO [train.py:715] (1/8) Epoch 17, batch 1300, loss[loss=0.1249, simple_loss=0.1839, pruned_loss=0.03302, over 4839.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02852, over 970686.67 frames.], batch size: 12, lr: 1.32e-04
2022-05-08 22:05:55,247 INFO [train.py:715] (1/8) Epoch 17, batch 1350, loss[loss=0.1305, simple_loss=0.2016, pruned_loss=0.02971, over 4808.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.0286, over 971536.09 frames.], batch size: 26, lr: 1.32e-04
2022-05-08 22:06:36,195 INFO [train.py:715] (1/8) Epoch 17, batch 1400, loss[loss=0.1246, simple_loss=0.2045, pruned_loss=0.02232, over 4842.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02869, over 971573.81 frames.], batch size: 20, lr: 1.32e-04
2022-05-08 22:07:16,803 INFO [train.py:715] (1/8) Epoch 17, batch 1450, loss[loss=0.1536, simple_loss=0.2206, pruned_loss=0.04324, over 4935.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02897, over 971722.80 frames.], batch size: 29, lr: 1.32e-04
2022-05-08 22:07:57,514 INFO [train.py:715] (1/8) Epoch 17, batch 1500, loss[loss=0.1317, simple_loss=0.2116, pruned_loss=0.02588, over 4752.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02915, over 972184.52 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:08:39,019 INFO [train.py:715] (1/8) Epoch 17, batch 1550, loss[loss=0.125, simple_loss=0.2011, pruned_loss=0.02444, over 4931.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02937, over 972843.87 frames.], batch size: 39, lr: 1.32e-04
2022-05-08 22:09:20,352 INFO [train.py:715] (1/8) Epoch 17, batch 1600, loss[loss=0.1632, simple_loss=0.2247, pruned_loss=0.05081, over 4796.00 frames.], tot_loss[loss=0.133, simple_loss=0.2076, pruned_loss=0.02918, over 973409.98 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:10:01,032 INFO [train.py:715] (1/8) Epoch 17, batch 1650, loss[loss=0.1244, simple_loss=0.1961, pruned_loss=0.02635, over 4969.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2077, pruned_loss=0.02882, over 973865.15 frames.], batch size: 35, lr: 1.32e-04
2022-05-08 22:10:42,369 INFO [train.py:715] (1/8) Epoch 17, batch 1700, loss[loss=0.1291, simple_loss=0.2032, pruned_loss=0.02752, over 4817.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2071, pruned_loss=0.0286, over 973271.39 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:11:23,610 INFO [train.py:715] (1/8) Epoch 17, batch 1750, loss[loss=0.1136, simple_loss=0.1906, pruned_loss=0.01824, over 4818.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02922, over 972988.44 frames.], batch size: 13, lr: 1.32e-04
2022-05-08 22:12:04,542 INFO [train.py:715] (1/8) Epoch 17, batch 1800, loss[loss=0.1357, simple_loss=0.2064, pruned_loss=0.03249, over 4832.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02917, over 972353.54 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:12:45,643 INFO [train.py:715] (1/8) Epoch 17, batch 1850, loss[loss=0.1164, simple_loss=0.188, pruned_loss=0.02238, over 4985.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02919, over 972345.89 frames.], batch size: 24, lr: 1.32e-04
2022-05-08 22:13:27,333 INFO [train.py:715] (1/8) Epoch 17, batch 1900, loss[loss=0.1555, simple_loss=0.2291, pruned_loss=0.04099, over 4945.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02927, over 972367.84 frames.], batch size: 39, lr: 1.32e-04
2022-05-08 22:14:08,488 INFO [train.py:715] (1/8) Epoch 17, batch 1950, loss[loss=0.1334, simple_loss=0.1954, pruned_loss=0.03575, over 4957.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02964, over 972852.99 frames.], batch size: 35, lr: 1.32e-04
2022-05-08 22:14:49,362 INFO [train.py:715] (1/8) Epoch 17, batch 2000, loss[loss=0.1695, simple_loss=0.2294, pruned_loss=0.05475, over 4856.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02945, over 972920.11 frames.], batch size: 38, lr: 1.32e-04
2022-05-08 22:15:30,343 INFO [train.py:715] (1/8) Epoch 17, batch 2050, loss[loss=0.1376, simple_loss=0.2146, pruned_loss=0.03024, over 4794.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02931, over 972075.67 frames.], batch size: 24, lr: 1.32e-04
2022-05-08 22:16:11,457 INFO [train.py:715] (1/8) Epoch 17, batch 2100, loss[loss=0.1457, simple_loss=0.217, pruned_loss=0.03718, over 4883.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02928, over 972883.98 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 22:16:52,789 INFO [train.py:715] (1/8) Epoch 17, batch 2150, loss[loss=0.1269, simple_loss=0.1877, pruned_loss=0.03301, over 4793.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02908, over 973056.79 frames.], batch size: 12, lr: 1.32e-04
2022-05-08 22:17:34,188 INFO [train.py:715] (1/8) Epoch 17, batch 2200, loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03216, over 4750.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02924, over 973683.00 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:18:15,309 INFO [train.py:715] (1/8) Epoch 17, batch 2250, loss[loss=0.155, simple_loss=0.2314, pruned_loss=0.03924, over 4738.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02944, over 974070.43 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:18:56,045 INFO [train.py:715] (1/8) Epoch 17, batch 2300, loss[loss=0.1383, simple_loss=0.2163, pruned_loss=0.03018, over 4936.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02917, over 974252.12 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:19:36,478 INFO [train.py:715] (1/8) Epoch 17, batch 2350, loss[loss=0.1184, simple_loss=0.1913, pruned_loss=0.02276, over 4770.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02936, over 973609.92 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:20:17,323 INFO [train.py:715] (1/8) Epoch 17, batch 2400, loss[loss=0.1325, simple_loss=0.2061, pruned_loss=0.02946, over 4884.00 frames.], tot_loss[loss=0.1321, simple_loss=0.206, pruned_loss=0.02908, over 973145.17 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 22:20:58,234 INFO [train.py:715] (1/8) Epoch 17, batch 2450, loss[loss=0.1291, simple_loss=0.1962, pruned_loss=0.03098, over 4956.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02915, over 972819.60 frames.], batch size: 21, lr: 1.32e-04
2022-05-08 22:21:39,037 INFO [train.py:715] (1/8) Epoch 17, batch 2500, loss[loss=0.1405, simple_loss=0.2272, pruned_loss=0.02696, over 4751.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02879, over 972744.16 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:22:20,000 INFO [train.py:715] (1/8) Epoch 17, batch 2550, loss[loss=0.1446, simple_loss=0.2182, pruned_loss=0.03551, over 4772.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2063, pruned_loss=0.02945, over 972544.14 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:23:00,961 INFO [train.py:715] (1/8) Epoch 17, batch 2600, loss[loss=0.1475, simple_loss=0.2255, pruned_loss=0.0348, over 4934.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02918, over 972500.96 frames.], batch size: 23, lr: 1.32e-04
2022-05-08 22:23:42,204 INFO [train.py:715] (1/8) Epoch 17, batch 2650, loss[loss=0.1245, simple_loss=0.1954, pruned_loss=0.02682, over 4814.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02945, over 972073.24 frames.], batch size: 13, lr: 1.32e-04
2022-05-08 22:24:22,852 INFO [train.py:715] (1/8) Epoch 17, batch 2700, loss[loss=0.1193, simple_loss=0.1902, pruned_loss=0.0242, over 4810.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02943, over 972021.03 frames.], batch size: 21, lr: 1.32e-04
2022-05-08 22:25:04,067 INFO [train.py:715] (1/8) Epoch 17, batch 2750, loss[loss=0.123, simple_loss=0.1949, pruned_loss=0.02549, over 4789.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2062, pruned_loss=0.02917, over 971984.21 frames.], batch size: 21, lr: 1.32e-04
2022-05-08 22:25:44,578 INFO [train.py:715] (1/8) Epoch 17, batch 2800, loss[loss=0.1326, simple_loss=0.216, pruned_loss=0.02458, over 4818.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02936, over 972048.11 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:26:25,555 INFO [train.py:715] (1/8) Epoch 17, batch 2850, loss[loss=0.1162, simple_loss=0.1891, pruned_loss=0.02169, over 4755.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02911, over 972269.46 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:27:06,300 INFO [train.py:715] (1/8) Epoch 17, batch 2900, loss[loss=0.1259, simple_loss=0.1979, pruned_loss=0.02697, over 4747.00 frames.], tot_loss[loss=0.132, simple_loss=0.2057, pruned_loss=0.02914, over 972601.10 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:27:47,271 INFO [train.py:715] (1/8) Epoch 17, batch 2950, loss[loss=0.1288, simple_loss=0.2072, pruned_loss=0.0252, over 4887.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02917, over 972565.22 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:28:28,416 INFO [train.py:715] (1/8) Epoch 17, batch 3000, loss[loss=0.1149, simple_loss=0.1839, pruned_loss=0.02292, over 4857.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2063, pruned_loss=0.02954, over 972602.57 frames.], batch size: 20, lr: 1.32e-04
2022-05-08 22:28:28,416 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 22:28:43,494 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1047, simple_loss=0.1882, pruned_loss=0.01063, over 914524.00 frames.
2022-05-08 22:29:24,681 INFO [train.py:715] (1/8) Epoch 17, batch 3050, loss[loss=0.1512, simple_loss=0.2229, pruned_loss=0.03972, over 4918.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2063, pruned_loss=0.02955, over 972712.55 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:30:05,287 INFO [train.py:715] (1/8) Epoch 17, batch 3100, loss[loss=0.1407, simple_loss=0.2289, pruned_loss=0.02621, over 4974.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02991, over 972327.22 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:30:46,415 INFO [train.py:715] (1/8) Epoch 17, batch 3150, loss[loss=0.1334, simple_loss=0.206, pruned_loss=0.03045, over 4942.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2065, pruned_loss=0.02966, over 973487.35 frames.], batch size: 29, lr: 1.32e-04
2022-05-08 22:31:26,340 INFO [train.py:715] (1/8) Epoch 17, batch 3200, loss[loss=0.1184, simple_loss=0.2055, pruned_loss=0.01567, over 4818.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02946, over 973189.46 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:32:07,676 INFO [train.py:715] (1/8) Epoch 17, batch 3250, loss[loss=0.1109, simple_loss=0.1863, pruned_loss=0.01774, over 4811.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02933, over 973033.07 frames.], batch size: 26, lr: 1.32e-04
2022-05-08 22:32:47,754 INFO [train.py:715] (1/8) Epoch 17, batch 3300, loss[loss=0.1492, simple_loss=0.2232, pruned_loss=0.03767, over 4928.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02972, over 973483.23 frames.], batch size: 23, lr: 1.32e-04
2022-05-08 22:33:28,456 INFO [train.py:715] (1/8) Epoch 17, batch 3350, loss[loss=0.1245, simple_loss=0.2024, pruned_loss=0.02327, over 4905.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.0296, over 972542.71 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:34:09,185 INFO [train.py:715] (1/8) Epoch 17, batch 3400, loss[loss=0.1402, simple_loss=0.2247, pruned_loss=0.02785, over 4759.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02969, over 973680.79 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:34:50,591 INFO [train.py:715] (1/8) Epoch 17, batch 3450, loss[loss=0.1378, simple_loss=0.2097, pruned_loss=0.03288, over 4831.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.0297, over 973697.57 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:35:30,942 INFO [train.py:715] (1/8) Epoch 17, batch 3500, loss[loss=0.1182, simple_loss=0.1957, pruned_loss=0.02034, over 4708.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02928, over 972980.97 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:36:11,165 INFO [train.py:715] (1/8) Epoch 17, batch 3550, loss[loss=0.126, simple_loss=0.1936, pruned_loss=0.02925, over 4801.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02905, over 972770.40 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 22:36:52,130 INFO [train.py:715] (1/8) Epoch 17, batch 3600, loss[loss=0.1222, simple_loss=0.202, pruned_loss=0.02126, over 4780.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02926, over 972700.79 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:37:31,754 INFO [train.py:715] (1/8) Epoch 17, batch 3650, loss[loss=0.1638, simple_loss=0.2336, pruned_loss=0.04694, over 4788.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.02927, over 973229.44 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:38:11,920 INFO [train.py:715] (1/8) Epoch 17, batch 3700, loss[loss=0.1562, simple_loss=0.2302, pruned_loss=0.04107, over 4826.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02882, over 973995.54 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:38:52,844 INFO [train.py:715] (1/8) Epoch 17, batch 3750, loss[loss=0.1222, simple_loss=0.1901, pruned_loss=0.02715, over 4909.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02857, over 973220.04 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:39:33,617 INFO [train.py:715] (1/8) Epoch 17, batch 3800, loss[loss=0.1473, simple_loss=0.2273, pruned_loss=0.03369, over 4972.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02835, over 972974.47 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:40:14,218 INFO [train.py:715] (1/8) Epoch 17, batch 3850, loss[loss=0.1288, simple_loss=0.1988, pruned_loss=0.02939, over 4853.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02885, over 972328.11 frames.], batch size: 32, lr: 1.32e-04
2022-05-08 22:40:54,278 INFO [train.py:715] (1/8) Epoch 17, batch 3900, loss[loss=0.1456, simple_loss=0.2048, pruned_loss=0.04323, over 4927.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02854, over 971403.68 frames.], batch size: 23, lr: 1.32e-04
2022-05-08 22:41:35,757 INFO [train.py:715] (1/8) Epoch 17, batch 3950, loss[loss=0.1515, simple_loss=0.2198, pruned_loss=0.04158, over 4683.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02865, over 970922.13 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:42:15,631 INFO [train.py:715] (1/8) Epoch 17, batch 4000, loss[loss=0.1028, simple_loss=0.1723, pruned_loss=0.01668, over 4988.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02876, over 971385.99 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 22:42:56,129 INFO [train.py:715] (1/8) Epoch 17, batch 4050, loss[loss=0.1228, simple_loss=0.2001, pruned_loss=0.02272, over 4948.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02874, over 971990.56 frames.], batch size: 23, lr: 1.32e-04
2022-05-08 22:43:36,610 INFO [train.py:715] (1/8) Epoch 17, batch 4100, loss[loss=0.1318, simple_loss=0.2093, pruned_loss=0.0272, over 4772.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.0291, over 972655.39 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:44:17,664 INFO [train.py:715] (1/8) Epoch 17, batch 4150, loss[loss=0.1425, simple_loss=0.214, pruned_loss=0.03549, over 4785.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02919, over 972396.77 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:44:56,902 INFO [train.py:715] (1/8) Epoch 17, batch 4200, loss[loss=0.1343, simple_loss=0.2164, pruned_loss=0.0261, over 4748.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.0291, over 972089.98 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:45:36,942 INFO [train.py:715] (1/8) Epoch 17, batch 4250, loss[loss=0.1209, simple_loss=0.2031, pruned_loss=0.01936, over 4810.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02904, over 972309.77 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:46:18,117 INFO [train.py:715] (1/8) Epoch 17, batch 4300, loss[loss=0.1293, simple_loss=0.1928, pruned_loss=0.0329, over 4971.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02904, over 971501.12 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 22:46:58,166 INFO [train.py:715] (1/8) Epoch 17, batch 4350, loss[loss=0.16, simple_loss=0.2264, pruned_loss=0.04684, over 4945.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02896, over 971535.51 frames.], batch size: 21, lr: 1.31e-04
2022-05-08 22:47:38,037 INFO [train.py:715] (1/8) Epoch 17, batch 4400, loss[loss=0.1659, simple_loss=0.2376, pruned_loss=0.04715, over 4981.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.0292, over 972330.78 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 22:48:18,893 INFO [train.py:715] (1/8) Epoch 17, batch 4450, loss[loss=0.1197, simple_loss=0.1891, pruned_loss=0.02517, over 4893.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02941, over 972475.97 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 22:48:59,883 INFO [train.py:715] (1/8) Epoch 17, batch 4500, loss[loss=0.1206, simple_loss=0.1964, pruned_loss=0.02234, over 4820.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02902, over 971922.90 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 22:49:39,756 INFO [train.py:715] (1/8) Epoch 17, batch 4550, loss[loss=0.1361, simple_loss=0.2125, pruned_loss=0.02985, over 4879.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02893, over 972516.19 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 22:50:20,189 INFO [train.py:715] (1/8) Epoch 17, batch 4600, loss[loss=0.148, simple_loss=0.2164, pruned_loss=0.0398, over 4653.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02922, over 971880.88 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 22:51:01,209 INFO [train.py:715] (1/8) Epoch 17, batch 4650, loss[loss=0.1002, simple_loss=0.1778, pruned_loss=0.01125, over 4808.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02908, over 972070.80 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 22:51:41,122 INFO [train.py:715] (1/8) Epoch 17, batch 4700, loss[loss=0.1344, simple_loss=0.2133, pruned_loss=0.02776, over 4786.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02922, over 972339.16 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 22:52:21,064 INFO [train.py:715] (1/8) Epoch 17, batch 4750, loss[loss=0.1417, simple_loss=0.2104, pruned_loss=0.03647, over 4983.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02904, over 971961.16 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 22:53:02,043 INFO [train.py:715] (1/8) Epoch 17, batch 4800, loss[loss=0.1317, simple_loss=0.2006, pruned_loss=0.03141, over 4941.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.0294, over 972630.87 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 22:53:42,799 INFO [train.py:715] (1/8) Epoch 17, batch 4850, loss[loss=0.1532, simple_loss=0.2234, pruned_loss=0.04147, over 4841.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02896, over 972510.78 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 22:54:22,668 INFO [train.py:715] (1/8) Epoch 17, batch 4900, loss[loss=0.1284, simple_loss=0.1987, pruned_loss=0.02906, over 4937.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02845, over 972678.75 frames.], batch size: 29, lr: 1.31e-04
2022-05-08 22:55:03,095 INFO [train.py:715] (1/8) Epoch 17, batch 4950, loss[loss=0.1639, simple_loss=0.248, pruned_loss=0.03984, over 4900.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02865, over 972840.87 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 22:55:44,139 INFO [train.py:715] (1/8) Epoch 17, batch 5000, loss[loss=0.1413, simple_loss=0.2073, pruned_loss=0.03768, over 4776.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02897, over 973230.95 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 22:56:24,630 INFO [train.py:715] (1/8) Epoch 17, batch 5050, loss[loss=0.1448, simple_loss=0.211, pruned_loss=0.03929, over 4917.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02935, over 973566.49 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 22:57:04,162 INFO [train.py:715] (1/8) Epoch 17, batch 5100, loss[loss=0.1731, simple_loss=0.2493, pruned_loss=0.04841, over 4764.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02894, over 972523.88 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 22:57:44,979 INFO [train.py:715] (1/8) Epoch 17, batch 5150, loss[loss=0.1504, simple_loss=0.2241, pruned_loss=0.03833, over 4830.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02872, over 972993.11 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 22:58:26,125 INFO [train.py:715] (1/8) Epoch 17, batch 5200, loss[loss=0.1408, simple_loss=0.2214, pruned_loss=0.03012, over 4932.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02873, over 973074.12 frames.], batch size: 23, lr: 1.31e-04
2022-05-08 22:59:05,341 INFO [train.py:715] (1/8) Epoch 17, batch 5250, loss[loss=0.1475, simple_loss=0.2249, pruned_loss=0.03511, over 4964.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02956, over 973276.34 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 22:59:44,887 INFO [train.py:715] (1/8) Epoch 17, batch 5300, loss[loss=0.1445, simple_loss=0.22, pruned_loss=0.03448, over 4844.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02986, over 973157.25 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 23:00:25,446 INFO [train.py:715] (1/8) Epoch 17, batch 5350, loss[loss=0.1132, simple_loss=0.1884, pruned_loss=0.01901, over 4741.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02952, over 973537.52 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:01:06,235 INFO [train.py:715] (1/8) Epoch 17, batch 5400, loss[loss=0.1277, simple_loss=0.1931, pruned_loss=0.03115, over 4978.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02933, over 973536.77 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:01:45,346 INFO [train.py:715] (1/8) Epoch 17, batch 5450, loss[loss=0.1343, simple_loss=0.2057, pruned_loss=0.03144, over 4913.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.0295, over 973465.83 frames.], batch size: 39, lr: 1.31e-04
2022-05-08 23:02:26,547 INFO [train.py:715] (1/8) Epoch 17, batch 5500, loss[loss=0.1509, simple_loss=0.2258, pruned_loss=0.03804, over 4782.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02951, over 972858.68 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:03:07,877 INFO [train.py:715] (1/8) Epoch 17, batch 5550, loss[loss=0.1116, simple_loss=0.1869, pruned_loss=0.0182, over 4828.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02975, over 972465.13 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:03:46,991 INFO [train.py:715] (1/8) Epoch 17, batch 5600, loss[loss=0.145, simple_loss=0.2135, pruned_loss=0.03826, over 4922.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2068, pruned_loss=0.02978, over 973428.89 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:04:27,249 INFO [train.py:715] (1/8) Epoch 17, batch 5650, loss[loss=0.1223, simple_loss=0.1975, pruned_loss=0.02355, over 4669.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02955, over 973358.64 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 23:05:08,282 INFO [train.py:715] (1/8) Epoch 17, batch 5700, loss[loss=0.1223, simple_loss=0.2018, pruned_loss=0.02139, over 4796.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2057, pruned_loss=0.0289, over 972880.83 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 23:05:48,475 INFO [train.py:715] (1/8) Epoch 17, batch 5750, loss[loss=0.1465, simple_loss=0.2068, pruned_loss=0.04312, over 4967.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2067, pruned_loss=0.02958, over 973154.33 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:06:27,749 INFO [train.py:715] (1/8) Epoch 17, batch 5800, loss[loss=0.1358, simple_loss=0.2044, pruned_loss=0.03363, over 4688.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2065, pruned_loss=0.02967, over 972286.84 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:07:08,770 INFO [train.py:715] (1/8) Epoch 17, batch 5850, loss[loss=0.1221, simple_loss=0.1905, pruned_loss=0.02684, over 4968.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02951, over 974011.30 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:07:49,077 INFO [train.py:715] (1/8) Epoch 17, batch 5900, loss[loss=0.1337, simple_loss=0.2092, pruned_loss=0.02911, over 4941.00 frames.], tot_loss[loss=0.1322, simple_loss=0.206, pruned_loss=0.02918, over 973807.91 frames.], batch size: 39, lr: 1.31e-04
2022-05-08 23:08:29,702 INFO [train.py:715] (1/8) Epoch 17, batch 5950, loss[loss=0.1232, simple_loss=0.2031, pruned_loss=0.02165, over 4782.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02944, over 973652.25 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:09:09,144 INFO [train.py:715] (1/8) Epoch 17, batch 6000, loss[loss=0.1371, simple_loss=0.2149, pruned_loss=0.02964, over 4773.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2062, pruned_loss=0.02956, over 972824.81 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:09:09,145 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 23:09:23,455 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1047, simple_loss=0.1881, pruned_loss=0.01069, over 914524.00 frames.
2022-05-08 23:10:02,837 INFO [train.py:715] (1/8) Epoch 17, batch 6050, loss[loss=0.1165, simple_loss=0.1964, pruned_loss=0.0183, over 4930.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02947, over 973007.82 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:10:43,308 INFO [train.py:715] (1/8) Epoch 17, batch 6100, loss[loss=0.1491, simple_loss=0.2267, pruned_loss=0.03572, over 4984.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02961, over 973167.86 frames.], batch size: 25, lr: 1.31e-04
2022-05-08 23:11:22,425 INFO [train.py:715] (1/8) Epoch 17, batch 6150, loss[loss=0.1418, simple_loss=0.2208, pruned_loss=0.03135, over 4901.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02947, over 973256.33 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:12:02,005 INFO [train.py:715] (1/8) Epoch 17, batch 6200, loss[loss=0.1353, simple_loss=0.2099, pruned_loss=0.03034, over 4807.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02944, over 973340.70 frames.], batch size: 25, lr: 1.31e-04
2022-05-08 23:12:42,481 INFO [train.py:715] (1/8) Epoch 17, batch 6250, loss[loss=0.1305, simple_loss=0.1994, pruned_loss=0.03081, over 4835.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03001, over 973269.41 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 23:13:22,265 INFO [train.py:715] (1/8) Epoch 17, batch 6300, loss[loss=0.1191, simple_loss=0.1946, pruned_loss=0.0218, over 4700.00 frames.], tot_loss[loss=0.133, simple_loss=0.2068, pruned_loss=0.02967, over 972671.19 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:14:01,681 INFO [train.py:715] (1/8) Epoch 17, batch 6350, loss[loss=0.1356, simple_loss=0.2034, pruned_loss=0.03393, over 4840.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02946, over 971948.97 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:14:41,497 INFO [train.py:715] (1/8) Epoch 17, batch 6400, loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.03368, over 4805.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02979, over 971143.67 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 23:15:21,777 INFO [train.py:715] (1/8) Epoch 17, batch 6450, loss[loss=0.1418, simple_loss=0.2068, pruned_loss=0.03841, over 4754.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02982, over 972434.92 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:16:01,144 INFO [train.py:715] (1/8) Epoch 17, batch 6500, loss[loss=0.1309, simple_loss=0.2095, pruned_loss=0.02616, over 4980.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02947, over 972437.20 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 23:16:40,473 INFO [train.py:715] (1/8) Epoch 17, batch 6550, loss[loss=0.1347, simple_loss=0.222, pruned_loss=0.02368, over 4738.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02959, over 971860.52 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:17:20,843 INFO [train.py:715] (1/8) Epoch 17, batch 6600, loss[loss=0.1519, simple_loss=0.228, pruned_loss=0.03793, over 4828.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02984, over 971687.53 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 23:18:01,034 INFO [train.py:715] (1/8) Epoch 17, batch 6650, loss[loss=0.1304, simple_loss=0.2131, pruned_loss=0.0238, over 4928.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02964, over 971884.17 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:18:40,482 INFO [train.py:715] (1/8) Epoch 17, batch 6700, loss[loss=0.1401, simple_loss=0.2108, pruned_loss=0.03468, over 4892.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02962, over 972727.77 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:19:20,726 INFO [train.py:715] (1/8) Epoch 17, batch 6750, loss[loss=0.1695, simple_loss=0.2396, pruned_loss=0.0497, over 4878.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02985, over 972356.13 frames.], batch size: 39, lr: 1.31e-04
2022-05-08 23:20:00,497 INFO [train.py:715] (1/8) Epoch 17, batch 6800, loss[loss=0.1442, simple_loss=0.2219, pruned_loss=0.03324, over 4952.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02973, over 972568.15 frames.], batch size: 39, lr: 1.31e-04
2022-05-08 23:20:41,163 INFO [train.py:715] (1/8) Epoch 17, batch 6850, loss[loss=0.1095, simple_loss=0.1844, pruned_loss=0.0173, over 4964.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02935, over 973165.18 frames.], batch size: 25, lr: 1.31e-04
2022-05-08 23:21:20,237 INFO [train.py:715] (1/8) Epoch 17, batch 6900, loss[loss=0.117, simple_loss=0.1979, pruned_loss=0.01806, over 4747.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02938, over 972464.47 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:22:00,930 INFO [train.py:715] (1/8) Epoch 17, batch 6950, loss[loss=0.1202, simple_loss=0.185, pruned_loss=0.02774, over 4858.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02896, over 971899.23 frames.], batch size: 32, lr: 1.31e-04
2022-05-08 23:22:40,660 INFO [train.py:715] (1/8) Epoch 17, batch 7000, loss[loss=0.1431, simple_loss=0.2202, pruned_loss=0.03301, over 4962.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02908, over 972515.27 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:23:20,233 INFO [train.py:715] (1/8) Epoch 17, batch 7050, loss[loss=0.1099, simple_loss=0.1813, pruned_loss=0.01923, over 4886.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02939, over 973655.00 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 23:24:00,498 INFO [train.py:715] (1/8) Epoch 17, batch 7100, loss[loss=0.122, simple_loss=0.2001, pruned_loss=0.02195, over 4857.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02972, over 973210.74 frames.], batch size: 20, lr: 1.31e-04
2022-05-08 23:24:40,019 INFO [train.py:715] (1/8) Epoch 17, batch 7150, loss[loss=0.1078, simple_loss=0.1862, pruned_loss=0.01465, over 4978.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02953, over 973006.65 frames.], batch size: 28, lr: 1.31e-04
2022-05-08 23:25:19,631 INFO [train.py:715] (1/8) Epoch 17, batch 7200, loss[loss=0.1523, simple_loss=0.2203, pruned_loss=0.04211, over 4915.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03019, over 972888.36 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:25:58,579 INFO [train.py:715] (1/8) Epoch 17, batch 7250, loss[loss=0.1279, simple_loss=0.2108, pruned_loss=0.02246, over 4858.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02942, over 973288.52 frames.], batch size: 20, lr: 1.31e-04
2022-05-08 23:26:39,073 INFO [train.py:715] (1/8) Epoch 17, batch 7300, loss[loss=0.1151, simple_loss=0.2, pruned_loss=0.01512, over 4994.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02918, over 972446.21 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:27:18,023 INFO [train.py:715] (1/8) Epoch 17, batch 7350, loss[loss=0.1298, simple_loss=0.1987, pruned_loss=0.03041, over 4962.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02944, over 972761.37 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:27:56,384 INFO [train.py:715] (1/8) Epoch 17, batch 7400, loss[loss=0.1659, simple_loss=0.2357, pruned_loss=0.0481, over 4793.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02957, over 971863.38 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:28:36,426 INFO [train.py:715] (1/8) Epoch 17, batch 7450, loss[loss=0.16, simple_loss=0.2283, pruned_loss=0.04583, over 4866.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02952, over 971453.75 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 23:29:15,430 INFO [train.py:715] (1/8) Epoch 17, batch 7500, loss[loss=0.1623, simple_loss=0.2401, pruned_loss=0.04226, over 4976.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02996, over 971949.86 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:29:55,162 INFO [train.py:715] (1/8) Epoch 17, batch 7550, loss[loss=0.1399, simple_loss=0.2121, pruned_loss=0.0338, over 4948.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02941, over 972088.62 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:30:34,489 INFO [train.py:715] (1/8) Epoch 17, batch 7600, loss[loss=0.1347, simple_loss=0.2162, pruned_loss=0.02662, over 4977.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02925, over 971076.29 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:31:14,611 INFO [train.py:715] (1/8) Epoch 17, batch 7650, loss[loss=0.1385, simple_loss=0.2178, pruned_loss=0.02954, over 4917.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02948, over 970596.99 frames.], batch size: 23, lr: 1.31e-04
2022-05-08 23:31:54,494 INFO [train.py:715] (1/8) Epoch 17, batch 7700, loss[loss=0.1048, simple_loss=0.1719, pruned_loss=0.01883, over 4814.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02898, over 971163.51 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 23:32:33,792 INFO [train.py:715] (1/8) Epoch 17, batch 7750, loss[loss=0.1057, simple_loss=0.1864, pruned_loss=0.0125, over 4980.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.0293, over 972845.65 frames.], batch size: 20, lr: 1.31e-04
2022-05-08 23:33:14,390 INFO [train.py:715] (1/8) Epoch 17, batch 7800, loss[loss=0.1015, simple_loss=0.1752, pruned_loss=0.01395, over 4818.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02963, over 972603.49 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 23:33:54,605 INFO [train.py:715] (1/8) Epoch 17, batch 7850, loss[loss=0.1238, simple_loss=0.202, pruned_loss=0.0228, over 4750.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02983, over 972237.01 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:34:34,853 INFO [train.py:715] (1/8) Epoch 17, batch 7900, loss[loss=0.1503, simple_loss=0.2216, pruned_loss=0.03951, over 4934.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02958, over 972490.39 frames.], batch size: 23, lr: 1.31e-04
2022-05-08 23:35:13,812 INFO [train.py:715] (1/8) Epoch 17, batch 7950, loss[loss=0.1246, simple_loss=0.2019, pruned_loss=0.02366, over 4936.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02961, over 971981.66 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:35:53,562 INFO [train.py:715] (1/8) Epoch 17, batch 8000, loss[loss=0.1144, simple_loss=0.1916, pruned_loss=0.01864, over 4974.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02926, over 971823.05 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:36:33,456 INFO [train.py:715] (1/8) Epoch 17, batch 8050, loss[loss=0.1262, simple_loss=0.2042, pruned_loss=0.02412, over 4910.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02945, over 972397.35 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:37:12,790 INFO [train.py:715] (1/8) Epoch 17, batch 8100, loss[loss=0.1366, simple_loss=0.2186, pruned_loss=0.02726, over 4795.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02965, over 972007.98 frames.], batch size: 21, lr: 1.31e-04
2022-05-08 23:37:52,686 INFO [train.py:715] (1/8) Epoch 17, batch 8150, loss[loss=0.1489, simple_loss=0.22, pruned_loss=0.03892, over 4700.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03042, over 971997.19 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:38:32,360 INFO [train.py:715] (1/8) Epoch 17, batch 8200, loss[loss=0.126, simple_loss=0.2081, pruned_loss=0.02192, over 4815.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.03004, over 971913.91 frames.], batch size: 27, lr: 1.31e-04
2022-05-08 23:39:14,692 INFO [train.py:715] (1/8) Epoch 17, batch 8250, loss[loss=0.1432, simple_loss=0.2149, pruned_loss=0.03571, over 4928.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02992, over 972304.49 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:39:53,905 INFO [train.py:715] (1/8) Epoch 17, batch 8300, loss[loss=0.1639, simple_loss=0.2391, pruned_loss=0.0443, over 4890.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02966, over 972428.40 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:40:33,618 INFO [train.py:715] (1/8) Epoch 17, batch 8350, loss[loss=0.1267, simple_loss=0.2071, pruned_loss=0.02319, over 4819.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2085, pruned_loss=0.02955, over 973223.12 frames.], batch size: 25, lr: 1.31e-04
2022-05-08 23:41:13,218 INFO [train.py:715] (1/8) Epoch 17, batch 8400, loss[loss=0.1378, simple_loss=0.2043, pruned_loss=0.03561, over 4841.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02972, over 973382.29 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 23:41:52,761 INFO [train.py:715] (1/8) Epoch 17, batch 8450, loss[loss=0.1355, simple_loss=0.2165, pruned_loss=0.02727, over 4983.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02952, over 973567.96 frames.], batch size: 39, lr: 1.31e-04
2022-05-08 23:42:32,328 INFO [train.py:715] (1/8) Epoch 17, batch 8500, loss[loss=0.1452, simple_loss=0.2245, pruned_loss=0.03296, over 4856.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02976, over 973909.59 frames.], batch size: 20, lr: 1.31e-04
2022-05-08 23:43:12,146 INFO [train.py:715] (1/8) Epoch 17, batch 8550, loss[loss=0.1347, simple_loss=0.2019, pruned_loss=0.03371, over 4789.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.02997, over 972654.23 frames.], batch size: 21, lr: 1.31e-04
2022-05-08 23:43:52,009 INFO [train.py:715] (1/8) Epoch 17, batch 8600, loss[loss=0.137, simple_loss=0.206, pruned_loss=0.03405, over 4960.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02936, over 973525.88 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:44:31,014 INFO [train.py:715] (1/8) Epoch 17, batch 8650, loss[loss=0.143, simple_loss=0.2211, pruned_loss=0.03248, over 4786.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02936, over 973383.31 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:45:10,885 INFO [train.py:715] (1/8) Epoch 17, batch 8700, loss[loss=0.1342, simple_loss=0.2143, pruned_loss=0.027, over 4935.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02926, over 973384.81 frames.], batch size: 21, lr: 1.31e-04
2022-05-08 23:45:50,293 INFO [train.py:715] (1/8) Epoch 17, batch 8750, loss[loss=0.12, simple_loss=0.1972, pruned_loss=0.0214, over 4783.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02951, over 972750.18 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:46:29,854 INFO [train.py:715] (1/8) Epoch 17, batch 8800, loss[loss=0.11, simple_loss=0.1816, pruned_loss=0.01917, over 4975.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.0291, over 972556.82 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:47:09,587 INFO [train.py:715] (1/8) Epoch 17, batch 8850, loss[loss=0.1305, simple_loss=0.2092, pruned_loss=0.02597, over 4963.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02958, over 973074.06 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:47:48,797 INFO [train.py:715] (1/8) Epoch 17, batch 8900, loss[loss=0.1081, simple_loss=0.1812, pruned_loss=0.01743, over 4973.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02953, over 972708.72 frames.], batch size: 28, lr: 1.31e-04
2022-05-08 23:48:28,442 INFO [train.py:715] (1/8) Epoch 17, batch 8950, loss[loss=0.1434, simple_loss=0.2228, pruned_loss=0.032, over 4947.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02914, over 972376.18 frames.], batch size: 29, lr: 1.31e-04
2022-05-08 23:49:07,469 INFO [train.py:715] (1/8) Epoch 17, batch 9000, loss[loss=0.1454, simple_loss=0.212, pruned_loss=0.03941, over 4907.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02924, over 972189.47 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:49:07,469 INFO [train.py:733] (1/8) Computing validation loss
2022-05-08 23:49:17,247 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1048, simple_loss=0.1882, pruned_loss=0.01072, over 914524.00 frames.
2022-05-08 23:49:56,408 INFO [train.py:715] (1/8) Epoch 17, batch 9050, loss[loss=0.1779, simple_loss=0.2601, pruned_loss=0.0479, over 4847.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02915, over 972544.15 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:50:36,246 INFO [train.py:715] (1/8) Epoch 17, batch 9100, loss[loss=0.1208, simple_loss=0.1989, pruned_loss=0.02139, over 4868.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02948, over 972256.53 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:51:15,867 INFO [train.py:715] (1/8) Epoch 17, batch 9150, loss[loss=0.1295, simple_loss=0.2091, pruned_loss=0.02495, over 4909.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02948, over 971275.19 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:51:54,744 INFO [train.py:715] (1/8) Epoch 17, batch 9200, loss[loss=0.1427, simple_loss=0.2244, pruned_loss=0.03055, over 4976.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02919, over 969926.56 frames.], batch size: 28, lr: 1.31e-04
2022-05-08 23:52:34,932 INFO [train.py:715] (1/8) Epoch 17, batch 9250, loss[loss=0.122, simple_loss=0.1948, pruned_loss=0.02461, over 4745.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02931, over 970366.59 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:53:14,614 INFO [train.py:715] (1/8) Epoch 17, batch 9300, loss[loss=0.1329, simple_loss=0.2138, pruned_loss=0.02601, over 4685.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02893, over 971332.86 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:53:53,949 INFO [train.py:715] (1/8) Epoch 17, batch 9350, loss[loss=0.1208, simple_loss=0.1956, pruned_loss=0.02296, over 4929.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02852, over 971291.59 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:54:33,279 INFO [train.py:715] (1/8) Epoch 17, batch 9400, loss[loss=0.1279, simple_loss=0.2026, pruned_loss=0.0266, over 4928.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02868, over 971617.29 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:55:13,699 INFO [train.py:715] (1/8) Epoch 17, batch 9450, loss[loss=0.1321, simple_loss=0.2073, pruned_loss=0.02851, over 4774.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02862, over 971545.03 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 23:55:53,690 INFO [train.py:715] (1/8) Epoch 17, batch 9500, loss[loss=0.1334, simple_loss=0.2084, pruned_loss=0.02925, over 4904.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02861, over 972101.81 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 23:56:32,924 INFO [train.py:715] (1/8) Epoch 17, batch 9550, loss[loss=0.1371, simple_loss=0.2087, pruned_loss=0.0327, over 4930.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02918, over 971895.07 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:57:12,481 INFO [train.py:715] (1/8) Epoch 17, batch 9600, loss[loss=0.1151, simple_loss=0.1888, pruned_loss=0.02072, over 4904.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02898, over 971948.14 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:57:52,755 INFO [train.py:715] (1/8) Epoch 17, batch 9650, loss[loss=0.127, simple_loss=0.2021, pruned_loss=0.02597, over 4891.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02986, over 971617.85 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:58:31,947 INFO [train.py:715] (1/8) Epoch 17, batch 9700, loss[loss=0.1422, simple_loss=0.211, pruned_loss=0.0367, over 4849.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02946, over 973072.47 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 23:59:11,712 INFO [train.py:715] (1/8) Epoch 17, batch 9750, loss[loss=0.1443, simple_loss=0.2092, pruned_loss=0.03973, over 4793.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02938, over 973022.27 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:59:51,455 INFO [train.py:715] (1/8) Epoch 17, batch 9800, loss[loss=0.1247, simple_loss=0.192, pruned_loss=0.02875, over 4781.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02941, over 971853.10 frames.], batch size: 17, lr: 1.31e-04
2022-05-09 00:00:31,044 INFO [train.py:715] (1/8) Epoch 17, batch 9850, loss[loss=0.1296, simple_loss=0.2022, pruned_loss=0.02851, over 4840.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02939, over 971976.90 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:01:10,439 INFO [train.py:715] (1/8) Epoch 17, batch 9900, loss[loss=0.1311, simple_loss=0.2033, pruned_loss=0.0294, over 4966.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02927, over 971917.12 frames.], batch size: 35, lr: 1.31e-04
2022-05-09 00:01:49,847 INFO [train.py:715] (1/8) Epoch 17, batch 9950, loss[loss=0.118, simple_loss=0.1944, pruned_loss=0.02078, over 4847.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02887, over 971926.10 frames.], batch size: 13, lr: 1.31e-04
2022-05-09 00:02:30,138 INFO [train.py:715] (1/8) Epoch 17, batch 10000, loss[loss=0.1427, simple_loss=0.2131, pruned_loss=0.03619, over 4787.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2078, pruned_loss=0.0293, over 972066.91 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:03:09,388 INFO [train.py:715] (1/8) Epoch 17, batch 10050, loss[loss=0.1195, simple_loss=0.2045, pruned_loss=0.01719, over 4811.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02906, over 972112.81 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:03:48,273 INFO [train.py:715] (1/8) Epoch 17, batch 10100, loss[loss=0.1281, simple_loss=0.2046, pruned_loss=0.02577, over 4940.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02885, over 972013.98 frames.], batch size: 29, lr: 1.31e-04
2022-05-09 00:04:27,590 INFO [train.py:715] (1/8) Epoch 17, batch 10150, loss[loss=0.1337, simple_loss=0.205, pruned_loss=0.03118, over 4872.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02899, over 971313.16 frames.], batch size: 32, lr: 1.31e-04
2022-05-09 00:05:06,928 INFO [train.py:715] (1/8) Epoch 17, batch 10200, loss[loss=0.1002, simple_loss=0.1767, pruned_loss=0.01187, over 4640.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02899, over 971553.18 frames.], batch size: 13, lr: 1.31e-04
2022-05-09 00:05:44,868 INFO [train.py:715] (1/8) Epoch 17, batch 10250, loss[loss=0.1192, simple_loss=0.1945, pruned_loss=0.02201, over 4944.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02866, over 972380.80 frames.], batch size: 35, lr: 1.31e-04
2022-05-09 00:06:24,646 INFO [train.py:715] (1/8) Epoch 17, batch 10300, loss[loss=0.14, simple_loss=0.2164, pruned_loss=0.03181, over 4762.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2084, pruned_loss=0.02957, over 972548.74 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:07:04,573 INFO [train.py:715] (1/8) Epoch 17, batch 10350, loss[loss=0.1331, simple_loss=0.206, pruned_loss=0.03007, over 4990.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.03004, over 972782.00 frames.], batch size: 14, lr: 1.31e-04
2022-05-09 00:07:43,241 INFO [train.py:715] (1/8) Epoch 17, batch 10400, loss[loss=0.1744, simple_loss=0.2466, pruned_loss=0.05107, over 4749.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02972, over 971284.78 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:08:22,340 INFO [train.py:715] (1/8) Epoch 17, batch 10450, loss[loss=0.1265, simple_loss=0.1958, pruned_loss=0.02858, over 4861.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02925, over 970958.20 frames.], batch size: 32, lr: 1.31e-04
2022-05-09 00:09:02,375 INFO [train.py:715] (1/8) Epoch 17, batch 10500, loss[loss=0.1408, simple_loss=0.2065, pruned_loss=0.03761, over 4837.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02984, over 971425.63 frames.], batch size: 30, lr: 1.31e-04
2022-05-09 00:09:41,411 INFO [train.py:715] (1/8) Epoch 17, batch 10550, loss[loss=0.1506, simple_loss=0.233, pruned_loss=0.03409, over 4854.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02946, over 971423.91 frames.], batch size: 20, lr: 1.31e-04
2022-05-09 00:10:19,761 INFO [train.py:715] (1/8) Epoch 17, batch 10600, loss[loss=0.1244, simple_loss=0.1995, pruned_loss=0.02464, over 4941.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02918, over 971838.64 frames.], batch size: 24, lr: 1.31e-04
2022-05-09 00:10:59,063 INFO [train.py:715] (1/8) Epoch 17, batch 10650, loss[loss=0.1429, simple_loss=0.2246, pruned_loss=0.03063, over 4832.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02892, over 972225.30 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:11:38,574 INFO [train.py:715] (1/8) Epoch 17, batch 10700, loss[loss=0.1443, simple_loss=0.2336, pruned_loss=0.02746, over 4754.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02905, over 971751.87 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:12:17,254 INFO [train.py:715] (1/8) Epoch 17, batch 10750, loss[loss=0.1174, simple_loss=0.201, pruned_loss=0.01696, over 4824.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02923, over 971254.20 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:12:56,255 INFO [train.py:715] (1/8) Epoch 17, batch 10800, loss[loss=0.1283, simple_loss=0.2074, pruned_loss=0.02463, over 4879.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.0292, over 971153.19 frames.], batch size: 22, lr: 1.31e-04
2022-05-09 00:13:36,019 INFO [train.py:715] (1/8) Epoch 17, batch 10850, loss[loss=0.1172, simple_loss=0.1886, pruned_loss=0.02293, over 4901.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02874, over 972833.51 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:14:15,587 INFO [train.py:715] (1/8) Epoch 17, batch 10900, loss[loss=0.1102, simple_loss=0.1837, pruned_loss=0.01832, over 4861.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02827, over 972964.14 frames.], batch size: 32, lr: 1.31e-04
2022-05-09 00:14:53,757 INFO [train.py:715] (1/8) Epoch 17, batch 10950, loss[loss=0.1149, simple_loss=0.1814, pruned_loss=0.02422, over 4844.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02816, over 973596.24 frames.], batch size: 12, lr: 1.31e-04
2022-05-09 00:15:33,874 INFO [train.py:715] (1/8) Epoch 17, batch 11000, loss[loss=0.1303, simple_loss=0.1993, pruned_loss=0.03067, over 4736.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02881, over 973742.67 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:16:13,743 INFO [train.py:715] (1/8) Epoch 17, batch 11050, loss[loss=0.1144, simple_loss=0.182, pruned_loss=0.02344, over 4987.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2051, pruned_loss=0.02884, over 973669.82 frames.], batch size: 14, lr: 1.31e-04
2022-05-09 00:16:52,422 INFO [train.py:715] (1/8) Epoch 17, batch 11100, loss[loss=0.1367, simple_loss=0.2064, pruned_loss=0.03345, over 4806.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2053, pruned_loss=0.02877, over 973081.27 frames.], batch size: 24, lr: 1.31e-04
2022-05-09 00:17:31,477 INFO [train.py:715] (1/8) Epoch 17, batch 11150, loss[loss=0.1222, simple_loss=0.2032, pruned_loss=0.02062, over 4804.00 frames.], tot_loss[loss=0.131, simple_loss=0.2051, pruned_loss=0.02843, over 973641.07 frames.], batch size: 21, lr: 1.31e-04
2022-05-09 00:18:11,484 INFO [train.py:715] (1/8) Epoch 17, batch 11200, loss[loss=0.1217, simple_loss=0.1971, pruned_loss=0.02316, over 4714.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2055, pruned_loss=0.02905, over 973085.73 frames.], batch size: 12, lr: 1.31e-04
2022-05-09 00:18:51,602 INFO [train.py:715] (1/8) Epoch 17, batch 11250, loss[loss=0.1366, simple_loss=0.2084, pruned_loss=0.03244, over 4895.00 frames.], tot_loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.02968, over 973577.08 frames.], batch size: 17, lr: 1.31e-04
2022-05-09 00:19:29,832 INFO [train.py:715] (1/8) Epoch 17, batch 11300, loss[loss=0.1417, simple_loss=0.2202, pruned_loss=0.03159, over 4754.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2063, pruned_loss=0.02949, over 972506.79 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:20:09,301 INFO [train.py:715] (1/8) Epoch 17, batch 11350, loss[loss=0.1139, simple_loss=0.1923, pruned_loss=0.01771, over 4943.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2063, pruned_loss=0.02961, over 971576.93 frames.], batch size: 29, lr: 1.31e-04
2022-05-09 00:20:49,484 INFO [train.py:715] (1/8) Epoch 17, batch 11400, loss[loss=0.1202, simple_loss=0.1849, pruned_loss=0.02769, over 4654.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2061, pruned_loss=0.02984, over 971518.06 frames.], batch size: 13, lr: 1.31e-04
2022-05-09 00:21:28,499 INFO [train.py:715] (1/8) Epoch 17, batch 11450, loss[loss=0.1231, simple_loss=0.1966, pruned_loss=0.02485, over 4903.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2062, pruned_loss=0.02939, over 971592.30 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:22:07,510 INFO [train.py:715] (1/8) Epoch 17, batch 11500, loss[loss=0.1244, simple_loss=0.2008, pruned_loss=0.02404, over 4827.00 frames.], tot_loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.02967, over 972323.94 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:22:47,220 INFO [train.py:715] (1/8) Epoch 17, batch 11550, loss[loss=0.1306, simple_loss=0.2037, pruned_loss=0.02874, over 4753.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2062, pruned_loss=0.02935, over 972203.17 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:23:27,158 INFO [train.py:715] (1/8) Epoch 17, batch 11600, loss[loss=0.1117, simple_loss=0.1808, pruned_loss=0.02126, over 4813.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2057, pruned_loss=0.02908, over 971797.70 frames.], batch size: 25, lr: 1.31e-04
2022-05-09 00:24:05,126 INFO [train.py:715] (1/8) Epoch 17, batch 11650, loss[loss=0.1279, simple_loss=0.2036, pruned_loss=0.02613, over 4821.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.02932, over 972476.13 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:24:44,952 INFO [train.py:715] (1/8) Epoch 17, batch 11700, loss[loss=0.1375, simple_loss=0.2115, pruned_loss=0.03176, over 4694.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2056, pruned_loss=0.02912, over 972489.97 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:25:24,933 INFO [train.py:715] (1/8) Epoch 17, batch 11750, loss[loss=0.1077, simple_loss=0.1841, pruned_loss=0.0156, over 4809.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2055, pruned_loss=0.02896, over 971439.96 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:26:03,882 INFO [train.py:715] (1/8) Epoch 17, batch 11800, loss[loss=0.1303, simple_loss=0.2, pruned_loss=0.03033, over 4906.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.0291, over 971638.86 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:26:42,873 INFO [train.py:715] (1/8) Epoch 17, batch 11850, loss[loss=0.1213, simple_loss=0.2044, pruned_loss=0.01916, over 4940.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02919, over 973044.01 frames.], batch size: 23, lr: 1.31e-04
2022-05-09 00:27:22,143 INFO [train.py:715] (1/8) Epoch 17, batch 11900, loss[loss=0.1158, simple_loss=0.1943, pruned_loss=0.01865, over 4913.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02911, over 973327.46 frames.], batch size: 17, lr: 1.31e-04
2022-05-09 00:28:01,949 INFO [train.py:715] (1/8) Epoch 17, batch 11950, loss[loss=0.1503, simple_loss=0.227, pruned_loss=0.03681, over 4780.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02915, over 974003.60 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:28:40,962 INFO [train.py:715] (1/8) Epoch 17, batch 12000, loss[loss=0.09633, simple_loss=0.1688, pruned_loss=0.01194, over 4768.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2055, pruned_loss=0.02886, over 973412.70 frames.], batch size: 12, lr: 1.31e-04
2022-05-09 00:28:40,963 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 00:28:52,719 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1048, simple_loss=0.1882, pruned_loss=0.0107, over 914524.00 frames.
2022-05-09 00:29:31,823 INFO [train.py:715] (1/8) Epoch 17, batch 12050, loss[loss=0.1449, simple_loss=0.225, pruned_loss=0.03238, over 4921.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02897, over 973907.71 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:30:10,916 INFO [train.py:715] (1/8) Epoch 17, batch 12100, loss[loss=0.1292, simple_loss=0.2062, pruned_loss=0.02608, over 4987.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02946, over 973826.25 frames.], batch size: 31, lr: 1.31e-04
2022-05-09 00:30:50,928 INFO [train.py:715] (1/8) Epoch 17, batch 12150, loss[loss=0.1504, simple_loss=0.223, pruned_loss=0.03892, over 4963.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02936, over 972743.68 frames.], batch size: 39, lr: 1.31e-04
2022-05-09 00:31:29,660 INFO [train.py:715] (1/8) Epoch 17, batch 12200, loss[loss=0.1276, simple_loss=0.192, pruned_loss=0.03163, over 4989.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.02899, over 972625.45 frames.], batch size: 14, lr: 1.31e-04
2022-05-09 00:32:08,194 INFO [train.py:715] (1/8) Epoch 17, batch 12250, loss[loss=0.1259, simple_loss=0.2028, pruned_loss=0.02447, over 4843.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02881, over 973773.17 frames.], batch size: 13, lr: 1.31e-04
2022-05-09 00:32:47,684 INFO [train.py:715] (1/8) Epoch 17, batch 12300, loss[loss=0.1424, simple_loss=0.1964, pruned_loss=0.04415, over 4976.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02866, over 974331.87 frames.], batch size: 14, lr: 1.31e-04
2022-05-09 00:33:26,858 INFO [train.py:715] (1/8) Epoch 17, batch 12350, loss[loss=0.128, simple_loss=0.1977, pruned_loss=0.0292, over 4978.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2053, pruned_loss=0.02841, over 974376.01 frames.], batch size: 35, lr: 1.31e-04
2022-05-09 00:34:05,590 INFO [train.py:715] (1/8) Epoch 17, batch 12400, loss[loss=0.1269, simple_loss=0.2131, pruned_loss=0.02036, over 4796.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2046, pruned_loss=0.02798, over 973558.76 frames.], batch size: 17, lr: 1.31e-04
2022-05-09 00:34:44,614 INFO [train.py:715] (1/8) Epoch 17, batch 12450, loss[loss=0.15, simple_loss=0.2261, pruned_loss=0.03694, over 4869.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02808, over 973714.80 frames.], batch size: 34, lr: 1.31e-04
2022-05-09 00:35:24,996 INFO [train.py:715] (1/8) Epoch 17, batch 12500, loss[loss=0.1357, simple_loss=0.2105, pruned_loss=0.03044, over 4873.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02848, over 973310.20 frames.], batch size: 22, lr: 1.31e-04
2022-05-09 00:36:03,574 INFO [train.py:715] (1/8) Epoch 17, batch 12550, loss[loss=0.1304, simple_loss=0.2072, pruned_loss=0.02683, over 4811.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02861, over 973787.64 frames.], batch size: 25, lr: 1.31e-04
2022-05-09 00:36:42,924 INFO [train.py:715] (1/8) Epoch 17, batch 12600, loss[loss=0.1362, simple_loss=0.2137, pruned_loss=0.02935, over 4941.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02887, over 973282.77 frames.], batch size: 21, lr: 1.31e-04
2022-05-09 00:37:22,856 INFO [train.py:715] (1/8) Epoch 17, batch 12650, loss[loss=0.1443, simple_loss=0.2201, pruned_loss=0.0342, over 4850.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02946, over 973298.58 frames.], batch size: 34, lr: 1.31e-04
2022-05-09 00:38:02,850 INFO [train.py:715] (1/8) Epoch 17, batch 12700, loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03083, over 4964.00 frames.], tot_loss[loss=0.1333, simple_loss=0.208, pruned_loss=0.02934, over 972932.71 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:38:42,157 INFO [train.py:715] (1/8) Epoch 17, batch 12750, loss[loss=0.1904, simple_loss=0.2724, pruned_loss=0.05418, over 4779.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2082, pruned_loss=0.02936, over 973056.04 frames.], batch size: 17, lr: 1.31e-04
2022-05-09 00:39:20,959 INFO [train.py:715] (1/8) Epoch 17, batch 12800, loss[loss=0.1109, simple_loss=0.18, pruned_loss=0.02092, over 4909.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02906, over 972858.14 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:40:00,596 INFO [train.py:715] (1/8) Epoch 17, batch 12850, loss[loss=0.1417, simple_loss=0.2251, pruned_loss=0.0292, over 4870.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02887, over 973093.54 frames.], batch size: 32, lr: 1.31e-04
2022-05-09 00:40:39,037 INFO [train.py:715] (1/8) Epoch 17, batch 12900, loss[loss=0.1219, simple_loss=0.2054, pruned_loss=0.0192, over 4769.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02873, over 973541.06 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:41:18,424 INFO [train.py:715] (1/8) Epoch 17, batch 12950, loss[loss=0.1249, simple_loss=0.2039, pruned_loss=0.02292, over 4934.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02892, over 973194.55 frames.], batch size: 23, lr: 1.31e-04
2022-05-09 00:41:57,019 INFO [train.py:715] (1/8) Epoch 17, batch 13000, loss[loss=0.1545, simple_loss=0.2296, pruned_loss=0.03967, over 4805.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02902, over 971916.23 frames.], batch size: 21, lr: 1.31e-04
2022-05-09 00:42:36,100 INFO [train.py:715] (1/8) Epoch 17, batch 13050, loss[loss=0.1261, simple_loss=0.1967, pruned_loss=0.02776, over 4750.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.0295, over 972822.87 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:43:15,220 INFO [train.py:715] (1/8) Epoch 17, batch 13100, loss[loss=0.1438, simple_loss=0.2108, pruned_loss=0.03846, over 4969.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02957, over 972117.36 frames.], batch size: 35, lr: 1.31e-04
2022-05-09 00:43:54,021 INFO [train.py:715] (1/8) Epoch 17, batch 13150, loss[loss=0.1223, simple_loss=0.2098, pruned_loss=0.01738, over 4822.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.0295, over 971698.57 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:44:33,796 INFO [train.py:715] (1/8) Epoch 17, batch 13200, loss[loss=0.1333, simple_loss=0.2051, pruned_loss=0.0307, over 4776.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02944, over 971828.65 frames.], batch size: 17, lr: 1.31e-04
2022-05-09 00:45:12,324 INFO [train.py:715] (1/8) Epoch 17, batch 13250, loss[loss=0.1461, simple_loss=0.2169, pruned_loss=0.03764, over 4961.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02966, over 972363.67 frames.], batch size: 35, lr: 1.31e-04
2022-05-09 00:45:51,623 INFO [train.py:715] (1/8) Epoch 17, batch 13300, loss[loss=0.1479, simple_loss=0.2197, pruned_loss=0.03799, over 4705.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02931, over 973441.42 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:46:30,707 INFO [train.py:715] (1/8) Epoch 17, batch 13350, loss[loss=0.1206, simple_loss=0.1978, pruned_loss=0.02165, over 4787.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02983, over 972790.68 frames.], batch size: 17, lr: 1.31e-04
2022-05-09 00:47:09,939 INFO [train.py:715] (1/8) Epoch 17, batch 13400, loss[loss=0.1182, simple_loss=0.1937, pruned_loss=0.02132, over 4948.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02972, over 972123.52 frames.], batch size: 21, lr: 1.31e-04
2022-05-09 00:47:49,249 INFO [train.py:715] (1/8) Epoch 17, batch 13450, loss[loss=0.105, simple_loss=0.1793, pruned_loss=0.01534, over 4829.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02937, over 972976.34 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 00:48:27,709 INFO [train.py:715] (1/8) Epoch 17, batch 13500, loss[loss=0.1356, simple_loss=0.2116, pruned_loss=0.02981, over 4954.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02939, over 973050.15 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 00:49:07,418 INFO [train.py:715] (1/8) Epoch 17, batch 13550, loss[loss=0.1234, simple_loss=0.1949, pruned_loss=0.02596, over 4840.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02921, over 973781.11 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 00:49:45,770 INFO [train.py:715] (1/8) Epoch 17, batch 13600, loss[loss=0.1145, simple_loss=0.1919, pruned_loss=0.01854, over 4986.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02908, over 973684.63 frames.], batch size: 28, lr: 1.30e-04
2022-05-09 00:50:24,813 INFO [train.py:715] (1/8) Epoch 17, batch 13650, loss[loss=0.1211, simple_loss=0.201, pruned_loss=0.02056, over 4892.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02945, over 973540.53 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 00:51:04,637 INFO [train.py:715] (1/8) Epoch 17, batch 13700, loss[loss=0.1253, simple_loss=0.207, pruned_loss=0.02181, over 4800.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02916, over 973314.61 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 00:51:43,953 INFO [train.py:715] (1/8) Epoch 17, batch 13750, loss[loss=0.1701, simple_loss=0.2413, pruned_loss=0.0495, over 4833.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2074, pruned_loss=0.02901, over 973579.92 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 00:52:24,098 INFO [train.py:715] (1/8) Epoch 17, batch 13800, loss[loss=0.1272, simple_loss=0.1955, pruned_loss=0.02942, over 4943.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02915, over 972622.98 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 00:53:03,511 INFO [train.py:715] (1/8) Epoch 17, batch 13850, loss[loss=0.1252, simple_loss=0.1991, pruned_loss=0.02566, over 4923.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02872, over 973113.23 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 00:53:43,316 INFO [train.py:715] (1/8) Epoch 17, batch 13900, loss[loss=0.1286, simple_loss=0.2113, pruned_loss=0.02299, over 4937.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02862, over 973674.65 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 00:54:22,806 INFO [train.py:715] (1/8) Epoch 17, batch 13950, loss[loss=0.1533, simple_loss=0.2317, pruned_loss=0.0375, over 4877.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02888, over 973148.40 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 00:55:02,838 INFO [train.py:715] (1/8) Epoch 17, batch 14000, loss[loss=0.1409, simple_loss=0.2063, pruned_loss=0.03777, over 4723.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02891, over 972448.55 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 00:55:41,999 INFO [train.py:715] (1/8) Epoch 17, batch 14050, loss[loss=0.1243, simple_loss=0.2024, pruned_loss=0.02304, over 4981.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02928, over 972910.59 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 00:56:21,074 INFO [train.py:715] (1/8) Epoch 17, batch 14100, loss[loss=0.1199, simple_loss=0.1983, pruned_loss=0.02079, over 4791.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02914, over 973189.27 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 00:57:01,246 INFO [train.py:715] (1/8) Epoch 17, batch 14150, loss[loss=0.1383, simple_loss=0.2068, pruned_loss=0.03495, over 4845.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2061, pruned_loss=0.02946, over 972315.74 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 00:57:40,316 INFO [train.py:715] (1/8) Epoch 17, batch 14200, loss[loss=0.1591, simple_loss=0.2364, pruned_loss=0.04087, over 4838.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02939, over 972188.95 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 00:58:19,830 INFO [train.py:715] (1/8) Epoch 17, batch 14250, loss[loss=0.1313, simple_loss=0.2176, pruned_loss=0.02249, over 4819.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02921, over 970964.26 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 00:58:58,997 INFO [train.py:715] (1/8) Epoch 17, batch 14300, loss[loss=0.1291, simple_loss=0.1981, pruned_loss=0.03009, over 4816.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02932, over 970961.74 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 00:59:38,849 INFO [train.py:715] (1/8) Epoch 17, batch 14350, loss[loss=0.1376, simple_loss=0.2123, pruned_loss=0.03149, over 4815.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02908, over 971113.20 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:00:17,886 INFO [train.py:715] (1/8) Epoch 17, batch 14400, loss[loss=0.1549, simple_loss=0.2286, pruned_loss=0.04057, over 4972.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02902, over 971744.20 frames.], batch size: 31, lr: 1.30e-04
2022-05-09 01:00:56,578 INFO [train.py:715] (1/8) Epoch 17, batch 14450, loss[loss=0.1488, simple_loss=0.2219, pruned_loss=0.03789, over 4864.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02891, over 972377.04 frames.], batch size: 38, lr: 1.30e-04
2022-05-09 01:01:36,311 INFO [train.py:715] (1/8) Epoch 17, batch 14500, loss[loss=0.129, simple_loss=0.1976, pruned_loss=0.03022, over 4955.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02843, over 972516.15 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 01:02:15,669 INFO [train.py:715] (1/8) Epoch 17, batch 14550, loss[loss=0.111, simple_loss=0.1878, pruned_loss=0.01711, over 4825.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02849, over 972244.74 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 01:02:54,143 INFO [train.py:715] (1/8) Epoch 17, batch 14600, loss[loss=0.1375, simple_loss=0.2048, pruned_loss=0.03512, over 4871.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02837, over 972640.10 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:03:33,784 INFO [train.py:715] (1/8) Epoch 17, batch 14650, loss[loss=0.1187, simple_loss=0.1898, pruned_loss=0.02377, over 4868.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02873, over 973001.50 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:04:13,439 INFO [train.py:715] (1/8) Epoch 17, batch 14700, loss[loss=0.1286, simple_loss=0.2025, pruned_loss=0.02732, over 4756.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02895, over 972268.10 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:04:52,648 INFO [train.py:715] (1/8) Epoch 17, batch 14750, loss[loss=0.1388, simple_loss=0.2148, pruned_loss=0.0314, over 4927.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.029, over 972196.31 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:05:31,531 INFO [train.py:715] (1/8) Epoch 17, batch 14800, loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03152, over 4960.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02899, over 972188.26 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 01:06:11,599 INFO [train.py:715] (1/8) Epoch 17, batch 14850, loss[loss=0.1589, simple_loss=0.2234, pruned_loss=0.0472, over 4962.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2062, pruned_loss=0.0292, over 972418.13 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 01:06:50,382 INFO [train.py:715] (1/8) Epoch 17, batch 14900, loss[loss=0.1449, simple_loss=0.2113, pruned_loss=0.03922, over 4946.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02937, over 973042.86 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 01:07:29,327 INFO [train.py:715] (1/8) Epoch 17, batch 14950, loss[loss=0.1174, simple_loss=0.1945, pruned_loss=0.02016, over 4953.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.0292, over 973053.34 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:08:09,011 INFO [train.py:715] (1/8) Epoch 17, batch 15000, loss[loss=0.1128, simple_loss=0.1859, pruned_loss=0.01989, over 4798.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02957, over 973058.36 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:08:09,012 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 01:08:19,082 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1046, simple_loss=0.1881, pruned_loss=0.01059, over 914524.00 frames.
2022-05-09 01:08:59,143 INFO [train.py:715] (1/8) Epoch 17, batch 15050, loss[loss=0.1334, simple_loss=0.2141, pruned_loss=0.02638, over 4824.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02954, over 972643.86 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:09:38,649 INFO [train.py:715] (1/8) Epoch 17, batch 15100, loss[loss=0.143, simple_loss=0.2186, pruned_loss=0.03369, over 4856.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02915, over 972828.00 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:10:17,573 INFO [train.py:715] (1/8) Epoch 17, batch 15150, loss[loss=0.09987, simple_loss=0.1712, pruned_loss=0.01427, over 4936.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02904, over 973200.44 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:10:56,609 INFO [train.py:715] (1/8) Epoch 17, batch 15200, loss[loss=0.1159, simple_loss=0.1964, pruned_loss=0.01775, over 4928.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02899, over 972763.87 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 01:11:36,233 INFO [train.py:715] (1/8) Epoch 17, batch 15250, loss[loss=0.1446, simple_loss=0.2154, pruned_loss=0.03686, over 4930.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02915, over 972878.74 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:12:15,602 INFO [train.py:715] (1/8) Epoch 17, batch 15300, loss[loss=0.138, simple_loss=0.2157, pruned_loss=0.03018, over 4753.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02936, over 973462.45 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:12:53,856 INFO [train.py:715] (1/8) Epoch 17, batch 15350, loss[loss=0.1143, simple_loss=0.1875, pruned_loss=0.02049, over 4932.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.02894, over 972857.73 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:13:33,402 INFO [train.py:715] (1/8) Epoch 17, batch 15400, loss[loss=0.1554, simple_loss=0.2346, pruned_loss=0.03807, over 4902.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02912, over 971865.34 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 01:14:12,474 INFO [train.py:715] (1/8) Epoch 17, batch 15450, loss[loss=0.1311, simple_loss=0.2064, pruned_loss=0.02792, over 4826.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2058, pruned_loss=0.02895, over 971648.06 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 01:14:51,816 INFO [train.py:715] (1/8) Epoch 17, batch 15500, loss[loss=0.1314, simple_loss=0.2035, pruned_loss=0.02969, over 4978.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02945, over 972070.71 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:15:30,647 INFO [train.py:715] (1/8) Epoch 17, batch 15550, loss[loss=0.1064, simple_loss=0.1837, pruned_loss=0.01454, over 4893.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02949, over 972214.80 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 01:16:10,368 INFO [train.py:715] (1/8) Epoch 17, batch 15600, loss[loss=0.1115, simple_loss=0.1851, pruned_loss=0.01899, over 4914.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02939, over 972622.26 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:16:49,786 INFO [train.py:715] (1/8) Epoch 17, batch 15650, loss[loss=0.1633, simple_loss=0.2393, pruned_loss=0.04362, over 4906.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03011, over 972797.93 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:17:27,915 INFO [train.py:715] (1/8) Epoch 17, batch 15700, loss[loss=0.1391, simple_loss=0.2095, pruned_loss=0.03431, over 4938.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03036, over 973134.77 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:18:07,727 INFO [train.py:715] (1/8) Epoch 17, batch 15750, loss[loss=0.1231, simple_loss=0.1992, pruned_loss=0.0235, over 4792.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03009, over 972727.07 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:18:47,131 INFO [train.py:715] (1/8) Epoch 17, batch 15800, loss[loss=0.1332, simple_loss=0.2005, pruned_loss=0.03296, over 4738.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.0303, over 971960.06 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:19:26,079 INFO [train.py:715] (1/8) Epoch 17, batch 15850, loss[loss=0.1164, simple_loss=0.1841, pruned_loss=0.02432, over 4827.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03003, over 972374.97 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:20:04,716 INFO [train.py:715] (1/8) Epoch 17, batch 15900, loss[loss=0.145, simple_loss=0.2314, pruned_loss=0.02929, over 4971.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02973, over 972277.96 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 01:20:44,126 INFO [train.py:715] (1/8) Epoch 17, batch 15950, loss[loss=0.1361, simple_loss=0.2118, pruned_loss=0.03018, over 4872.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02934, over 971319.12 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:21:23,627 INFO [train.py:715] (1/8) Epoch 17, batch 16000, loss[loss=0.1188, simple_loss=0.1875, pruned_loss=0.02509, over 4971.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02924, over 971637.59 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:22:02,014 INFO [train.py:715] (1/8) Epoch 17, batch 16050, loss[loss=0.1364, simple_loss=0.2192, pruned_loss=0.02677, over 4899.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02907, over 971137.69 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:22:42,058 INFO [train.py:715] (1/8) Epoch 17, batch 16100, loss[loss=0.1462, simple_loss=0.2143, pruned_loss=0.0391, over 4870.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02975, over 971664.32 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 01:23:21,959 INFO [train.py:715] (1/8) Epoch 17, batch 16150, loss[loss=0.1125, simple_loss=0.1771, pruned_loss=0.0239, over 4765.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02913, over 972581.87 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:24:01,720 INFO [train.py:715] (1/8) Epoch 17, batch 16200, loss[loss=0.1435, simple_loss=0.2238, pruned_loss=0.03165, over 4980.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02903, over 972830.32 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:24:43,126 INFO [train.py:715] (1/8) Epoch 17, batch 16250, loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03001, over 4984.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2054, pruned_loss=0.02868, over 973041.24 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 01:25:23,138 INFO [train.py:715] (1/8) Epoch 17, batch 16300, loss[loss=0.1357, simple_loss=0.2084, pruned_loss=0.03149, over 4887.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2056, pruned_loss=0.02888, over 972656.84 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 01:26:02,217 INFO [train.py:715] (1/8) Epoch 17, batch 16350, loss[loss=0.1367, simple_loss=0.2066, pruned_loss=0.03343, over 4871.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02932, over 972217.00 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 01:26:40,869 INFO [train.py:715] (1/8) Epoch 17, batch 16400, loss[loss=0.1352, simple_loss=0.2146, pruned_loss=0.02792, over 4912.00 frames.], tot_loss[loss=0.1321, simple_loss=0.206, pruned_loss=0.02905, over 973202.80 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:27:20,585 INFO [train.py:715] (1/8) Epoch 17, batch 16450, loss[loss=0.1374, simple_loss=0.2088, pruned_loss=0.03303, over 4905.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.0289, over 973321.68 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 01:28:00,548 INFO [train.py:715] (1/8) Epoch 17, batch 16500, loss[loss=0.1431, simple_loss=0.2279, pruned_loss=0.02916, over 4942.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02947, over 972417.78 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:28:39,570 INFO [train.py:715] (1/8) Epoch 17, batch 16550, loss[loss=0.1274, simple_loss=0.2006, pruned_loss=0.02709, over 4942.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02916, over 971174.70 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 01:29:18,072 INFO [train.py:715] (1/8) Epoch 17, batch 16600, loss[loss=0.1171, simple_loss=0.1881, pruned_loss=0.02308, over 4818.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02944, over 970979.82 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 01:29:58,260 INFO [train.py:715] (1/8) Epoch 17, batch 16650, loss[loss=0.09849, simple_loss=0.1743, pruned_loss=0.01136, over 4819.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02914, over 971025.10 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:30:38,043 INFO [train.py:715] (1/8) Epoch 17, batch 16700, loss[loss=0.117, simple_loss=0.1978, pruned_loss=0.0181, over 4821.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02902, over 971921.09 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 01:31:16,487 INFO [train.py:715] (1/8) Epoch 17, batch 16750, loss[loss=0.1527, simple_loss=0.228, pruned_loss=0.03871, over 4863.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02865, over 971876.05 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:31:56,306 INFO [train.py:715] (1/8) Epoch 17, batch 16800, loss[loss=0.1133, simple_loss=0.1812, pruned_loss=0.02266, over 4748.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02915, over 971752.60 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:32:35,730 INFO [train.py:715] (1/8) Epoch 17, batch 16850, loss[loss=0.1376, simple_loss=0.2013, pruned_loss=0.03695, over 4709.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02908, over 971783.93 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:33:15,641 INFO [train.py:715] (1/8) Epoch 17, batch 16900, loss[loss=0.1119, simple_loss=0.1855, pruned_loss=0.01918, over 4845.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02926, over 972104.48 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:33:53,863 INFO [train.py:715] (1/8) Epoch 17, batch 16950, loss[loss=0.1352, simple_loss=0.2115, pruned_loss=0.02941, over 4856.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.0299, over 972394.68 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:34:33,422 INFO [train.py:715] (1/8) Epoch 17, batch 17000, loss[loss=0.1146, simple_loss=0.1878, pruned_loss=0.02069, over 4874.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02984, over 972404.66 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:35:12,910 INFO [train.py:715] (1/8) Epoch 17, batch 17050, loss[loss=0.1313, simple_loss=0.2154, pruned_loss=0.02364, over 4850.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02972, over 972151.11 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:35:51,173 INFO [train.py:715] (1/8) Epoch 17, batch 17100, loss[loss=0.1307, simple_loss=0.2095, pruned_loss=0.02597, over 4944.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02947, over 972149.18 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 01:36:30,679 INFO [train.py:715] (1/8) Epoch 17, batch 17150, loss[loss=0.1186, simple_loss=0.1842, pruned_loss=0.02648, over 4763.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.02928, over 972020.49 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:37:10,049 INFO [train.py:715] (1/8) Epoch 17, batch 17200, loss[loss=0.1478, simple_loss=0.215, pruned_loss=0.04026, over 4856.00 frames.], tot_loss[loss=0.132, simple_loss=0.2059, pruned_loss=0.02906, over 972443.34 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:37:48,559 INFO [train.py:715] (1/8) Epoch 17, batch 17250, loss[loss=0.1182, simple_loss=0.196, pruned_loss=0.02023, over 4970.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2049, pruned_loss=0.02881, over 972710.95 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:38:26,820 INFO [train.py:715] (1/8) Epoch 17, batch 17300, loss[loss=0.1393, simple_loss=0.2147, pruned_loss=0.03191, over 4916.00 frames.], tot_loss[loss=0.131, simple_loss=0.2045, pruned_loss=0.02871, over 972467.87 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:39:06,134 INFO [train.py:715] (1/8) Epoch 17, batch 17350, loss[loss=0.1242, simple_loss=0.1964, pruned_loss=0.02602, over 4859.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2048, pruned_loss=0.02917, over 972296.55 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:39:45,335 INFO [train.py:715] (1/8) Epoch 17, batch 17400, loss[loss=0.1392, simple_loss=0.2092, pruned_loss=0.03456, over 4961.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2044, pruned_loss=0.02868, over 972565.51 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 01:40:23,318 INFO [train.py:715] (1/8) Epoch 17, batch 17450, loss[loss=0.1194, simple_loss=0.184, pruned_loss=0.02737, over 4738.00 frames.], tot_loss[loss=0.131, simple_loss=0.2045, pruned_loss=0.02875, over 971663.19 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:41:03,008 INFO [train.py:715] (1/8) Epoch 17, batch 17500, loss[loss=0.1538, simple_loss=0.2372, pruned_loss=0.03522, over 4790.00 frames.], tot_loss[loss=0.1312, simple_loss=0.205, pruned_loss=0.02869, over 971816.08 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:41:42,132 INFO [train.py:715] (1/8) Epoch 17, batch 17550, loss[loss=0.1201, simple_loss=0.1902, pruned_loss=0.02501, over 4938.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.02876, over 972825.03 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:42:20,887 INFO [train.py:715] (1/8) Epoch 17, batch 17600, loss[loss=0.1484, simple_loss=0.2283, pruned_loss=0.03424, over 4941.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02915, over 972670.46 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:42:59,399 INFO [train.py:715] (1/8) Epoch 17, batch 17650, loss[loss=0.1243, simple_loss=0.1968, pruned_loss=0.02595, over 4964.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2059, pruned_loss=0.02938, over 972821.77 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:43:38,881 INFO [train.py:715] (1/8) Epoch 17, batch 17700, loss[loss=0.1138, simple_loss=0.1841, pruned_loss=0.02177, over 4794.00 frames.], tot_loss[loss=0.132, simple_loss=0.2058, pruned_loss=0.02906, over 972565.08 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:44:17,597 INFO [train.py:715] (1/8) Epoch 17, batch 17750, loss[loss=0.1335, simple_loss=0.2084, pruned_loss=0.02931, over 4858.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02949, over 972154.12 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 01:44:56,089 INFO [train.py:715] (1/8) Epoch 17, batch 17800, loss[loss=0.1196, simple_loss=0.1856, pruned_loss=0.02681, over 4645.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02956, over 971638.37 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:45:35,674 INFO [train.py:715] (1/8) Epoch 17, batch 17850, loss[loss=0.1375, simple_loss=0.2201, pruned_loss=0.02746, over 4951.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02932, over 972224.40 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:46:14,669 INFO [train.py:715] (1/8) Epoch 17, batch 17900, loss[loss=0.1157, simple_loss=0.1864, pruned_loss=0.02252, over 4799.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02904, over 972519.92 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:46:54,011 INFO [train.py:715] (1/8) Epoch 17, batch 17950, loss[loss=0.124, simple_loss=0.1982, pruned_loss=0.0249, over 4799.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02947, over 972608.69 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:47:32,272 INFO [train.py:715] (1/8) Epoch 17, batch 18000, loss[loss=0.1416, simple_loss=0.2071, pruned_loss=0.03805, over 4973.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02964, over 973401.14 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:47:32,273 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 01:47:42,061 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1047, simple_loss=0.1881, pruned_loss=0.01066, over 914524.00 frames.
2022-05-09 01:48:20,783 INFO [train.py:715] (1/8) Epoch 17, batch 18050, loss[loss=0.1142, simple_loss=0.1819, pruned_loss=0.02328, over 4823.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02943, over 972302.18 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:49:00,410 INFO [train.py:715] (1/8) Epoch 17, batch 18100, loss[loss=0.1066, simple_loss=0.1787, pruned_loss=0.01722, over 4781.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02954, over 971498.27 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:49:39,797 INFO [train.py:715] (1/8) Epoch 17, batch 18150, loss[loss=0.1693, simple_loss=0.2269, pruned_loss=0.05582, over 4950.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02961, over 971109.03 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 01:50:17,777 INFO [train.py:715] (1/8) Epoch 17, batch 18200, loss[loss=0.121, simple_loss=0.1952, pruned_loss=0.02342, over 4918.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02955, over 971774.66 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:50:57,528 INFO [train.py:715] (1/8) Epoch 17, batch 18250, loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03154, over 4982.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02956, over 971926.84 frames.], batch size: 28, lr: 1.30e-04
2022-05-09 01:51:37,058 INFO [train.py:715] (1/8) Epoch 17, batch 18300, loss[loss=0.1224, simple_loss=0.1943, pruned_loss=0.02526, over 4778.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02952, over 971508.53 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:52:15,572 INFO [train.py:715] (1/8) Epoch 17, batch 18350, loss[loss=0.1292, simple_loss=0.2105, pruned_loss=0.02396, over 4944.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.03, over 971320.95 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:52:55,002 INFO [train.py:715] (1/8) Epoch 17, batch 18400, loss[loss=0.1362, simple_loss=0.2202, pruned_loss=0.02614, over 4986.00 frames.], tot_loss[loss=0.134, simple_loss=0.2085, pruned_loss=0.02977, over 972575.65 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:53:33,898 INFO [train.py:715] (1/8) Epoch 17, batch 18450, loss[loss=0.1363, simple_loss=0.2213, pruned_loss=0.02567, over 4760.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02978, over 972807.66 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:54:13,083 INFO [train.py:715] (1/8) Epoch 17, batch 18500, loss[loss=0.1352, simple_loss=0.2131, pruned_loss=0.02867, over 4766.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02967, over 972638.67 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 01:54:51,412 INFO [train.py:715] (1/8) Epoch 17, batch 18550, loss[loss=0.1354, simple_loss=0.2181, pruned_loss=0.02642, over 4800.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02934, over 972343.01 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:55:30,372 INFO [train.py:715] (1/8) Epoch 17, batch 18600, loss[loss=0.1185, simple_loss=0.2021, pruned_loss=0.01743, over 4841.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02987, over 972466.56 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 01:56:09,531 INFO [train.py:715] (1/8) Epoch 17, batch 18650, loss[loss=0.1138, simple_loss=0.1925, pruned_loss=0.01757, over 4918.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02984, over 973175.61 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 01:56:47,375 INFO [train.py:715] (1/8) Epoch 17, batch 18700, loss[loss=0.1403, simple_loss=0.2066, pruned_loss=0.03697, over 4851.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02957, over 973439.54 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:57:27,053 INFO [train.py:715] (1/8) Epoch 17, batch 18750, loss[loss=0.134, simple_loss=0.2073, pruned_loss=0.03032, over 4907.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02945, over 972915.28 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:58:06,641 INFO [train.py:715] (1/8) Epoch 17, batch 18800, loss[loss=0.1713, simple_loss=0.2445, pruned_loss=0.04899, over 4829.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2084, pruned_loss=0.02946, over 972557.78 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:58:45,347 INFO [train.py:715] (1/8) Epoch 17, batch 18850, loss[loss=0.1285, simple_loss=0.1993, pruned_loss=0.0288, over 4763.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2077, pruned_loss=0.02898, over 972500.01 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:59:23,450 INFO [train.py:715] (1/8) Epoch 17, batch 18900, loss[loss=0.1323, simple_loss=0.2027, pruned_loss=0.03095, over 4826.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02992, over 972153.22 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 02:00:02,548 INFO [train.py:715] (1/8) Epoch 17, batch 18950, loss[loss=0.1307, simple_loss=0.2031, pruned_loss=0.02912, over 4876.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2071, pruned_loss=0.02899, over 972740.17 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 02:00:41,831 INFO [train.py:715] (1/8) Epoch 17, batch 19000, loss[loss=0.1187, simple_loss=0.2055, pruned_loss=0.01591, over 4863.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2074, pruned_loss=0.02895, over 972350.73 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 02:01:20,325 INFO [train.py:715] (1/8) Epoch 17, batch 19050, loss[loss=0.1306, simple_loss=0.2043, pruned_loss=0.02847, over 4923.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2078, pruned_loss=0.02903, over 971466.27 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 02:01:59,754 INFO [train.py:715] (1/8) Epoch 17, batch 19100, loss[loss=0.1247, simple_loss=0.2069, pruned_loss=0.02122, over 4976.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2077, pruned_loss=0.02894, over 970851.19 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:02:38,885 INFO [train.py:715] (1/8) Epoch 17, batch 19150, loss[loss=0.1363, simple_loss=0.2219, pruned_loss=0.02539, over 4785.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2082, pruned_loss=0.02902, over 971501.18 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 02:03:17,327 INFO [train.py:715] (1/8) Epoch 17, batch 19200, loss[loss=0.1283, simple_loss=0.2111, pruned_loss=0.02279, over 4987.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2081, pruned_loss=0.02915, over 970965.10 frames.], batch size: 28, lr: 1.30e-04
2022-05-09 02:03:56,163 INFO [train.py:715] (1/8) Epoch 17, batch 19250, loss[loss=0.1438, simple_loss=0.2191, pruned_loss=0.03422, over 4876.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2085, pruned_loss=0.02905, over 970594.80 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 02:04:35,737 INFO [train.py:715] (1/8) Epoch 17, batch 19300, loss[loss=0.1092, simple_loss=0.1829, pruned_loss=0.01774, over 4816.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2082, pruned_loss=0.02924, over 970816.13 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 02:05:15,461 INFO [train.py:715] (1/8) Epoch 17, batch 19350, loss[loss=0.1416, simple_loss=0.2194, pruned_loss=0.03194, over 4918.00 frames.], tot_loss[loss=0.1331, simple_loss=0.208, pruned_loss=0.02907, over 971539.18 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 02:05:54,626 INFO [train.py:715] (1/8) Epoch 17, batch 19400, loss[loss=0.1396, simple_loss=0.2145, pruned_loss=0.03235, over 4740.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2079, pruned_loss=0.02934, over 971951.22 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 02:06:34,192 INFO [train.py:715] (1/8) Epoch 17, batch 19450, loss[loss=0.1313, simple_loss=0.1941, pruned_loss=0.0342, over 4887.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02981, over 972426.67 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 02:07:13,756 INFO [train.py:715] (1/8) Epoch 17, batch 19500, loss[loss=0.1168, simple_loss=0.1932, pruned_loss=0.02017, over 4806.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.0294, over 971444.91 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 02:07:53,344 INFO [train.py:715] (1/8) Epoch 17, batch 19550, loss[loss=0.1377, simple_loss=0.2037, pruned_loss=0.03584, over 4810.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02955, over 972208.23 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 02:08:31,622 INFO [train.py:715] (1/8) Epoch 17, batch 19600, loss[loss=0.1812, simple_loss=0.2662, pruned_loss=0.04814, over 4702.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02937, over 971469.93 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:09:11,586 INFO [train.py:715] (1/8) Epoch 17, batch 19650, loss[loss=0.1528, simple_loss=0.228, pruned_loss=0.03883, over 4980.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.0291, over 971522.11 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 02:09:51,448 INFO [train.py:715] (1/8) Epoch 17, batch 19700, loss[loss=0.113, simple_loss=0.1879, pruned_loss=0.01909, over 4791.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02933, over 971814.20 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:10:30,060 INFO [train.py:715] (1/8) Epoch 17, batch 19750, loss[loss=0.1298, simple_loss=0.203, pruned_loss=0.02834, over 4988.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.0294, over 971854.89 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 02:11:09,367 INFO [train.py:715] (1/8) Epoch 17, batch 19800, loss[loss=0.1443, simple_loss=0.2122, pruned_loss=0.03822, over 4888.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02962, over 971591.97 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 02:11:47,961 INFO [train.py:715] (1/8) Epoch 17, batch 19850, loss[loss=0.149, simple_loss=0.2143, pruned_loss=0.04187, over 4889.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02995, over 970903.45 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 02:12:26,930 INFO [train.py:715] (1/8) Epoch 17, batch 19900, loss[loss=0.1273, simple_loss=0.1969, pruned_loss=0.02883, over 4800.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02987, over 971578.32 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 02:13:05,188 INFO [train.py:715] (1/8) Epoch 17, batch 19950, loss[loss=0.1102, simple_loss=0.1775, pruned_loss=0.0214, over 4708.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02966, over 971555.22 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:13:44,427 INFO [train.py:715] (1/8) Epoch 17, batch 20000, loss[loss=0.1167, simple_loss=0.1947, pruned_loss=0.01932, over 4796.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02931, over 972343.31 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 02:14:24,054 INFO [train.py:715] (1/8) Epoch 17, batch 20050, loss[loss=0.135, simple_loss=0.2016, pruned_loss=0.03426, over 4858.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.029, over 972199.67 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 02:15:03,203 INFO [train.py:715] (1/8) Epoch 17, batch 20100, loss[loss=0.124, simple_loss=0.2028, pruned_loss=0.02258, over 4651.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02841, over 971176.42 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 02:15:42,009 INFO [train.py:715] (1/8) Epoch 17, batch 20150, loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02962, over 4795.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02835, over 971811.48 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 02:16:22,282 INFO [train.py:715] (1/8) Epoch 17, batch 20200, loss[loss=0.1355, simple_loss=0.2033, pruned_loss=0.03385, over 4788.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02856, over 971370.43 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:17:02,696 INFO [train.py:715] (1/8) Epoch 17, batch 20250, loss[loss=0.1311, simple_loss=0.213, pruned_loss=0.02461, over 4765.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02808, over 971946.95 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:17:40,775 INFO [train.py:715] (1/8) Epoch 17, batch 20300, loss[loss=0.1189, simple_loss=0.1958, pruned_loss=0.02097, over 4957.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02884, over 972412.31 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 02:18:20,508 INFO [train.py:715] (1/8) Epoch 17, batch 20350, loss[loss=0.1359, simple_loss=0.2114, pruned_loss=0.03024, over 4836.00 frames.], tot_loss[loss=0.1321, simple_loss=0.207, pruned_loss=0.02861, over 972308.64 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:19:00,634 INFO [train.py:715] (1/8) Epoch 17, batch 20400, loss[loss=0.1564, simple_loss=0.2311, pruned_loss=0.04084, over 4863.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02853, over 972714.28 frames.], batch size: 38, lr: 1.30e-04
2022-05-09 02:19:39,221 INFO [train.py:715] (1/8) Epoch 17, batch 20450, loss[loss=0.1631, simple_loss=0.2316, pruned_loss=0.0473, over 4762.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02874, over 972613.32 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:20:17,923 INFO [train.py:715] (1/8) Epoch 17, batch 20500, loss[loss=0.127, simple_loss=0.2012, pruned_loss=0.02641, over 4815.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02916, over 971739.64 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:20:57,776 INFO [train.py:715] (1/8) Epoch 17, batch 20550, loss[loss=0.1369, simple_loss=0.2045, pruned_loss=0.03467, over 4879.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02871, over 971315.68 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 02:21:36,910 INFO [train.py:715] (1/8) Epoch 17, batch 20600, loss[loss=0.1179, simple_loss=0.193, pruned_loss=0.02143, over 4782.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02848, over 971916.63 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:22:15,099 INFO [train.py:715] (1/8) Epoch 17, batch 20650, loss[loss=0.1551, simple_loss=0.2406, pruned_loss=0.03477, over 4839.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02881, over 972522.90 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:22:54,068 INFO [train.py:715] (1/8) Epoch 17, batch 20700, loss[loss=0.1528, simple_loss=0.2223, pruned_loss=0.04163, over 4960.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02857, over 972500.53 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 02:23:33,730 INFO [train.py:715] (1/8) Epoch 17, batch 20750, loss[loss=0.1308, simple_loss=0.2116, pruned_loss=0.02494, over 4768.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02886, over 972833.27 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:24:12,679 INFO [train.py:715] (1/8) Epoch 17, batch 20800, loss[loss=0.1254, simple_loss=0.2019, pruned_loss=0.02447, over 4718.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02897, over 972711.44 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:24:51,252 INFO [train.py:715] (1/8) Epoch 17, batch 20850, loss[loss=0.1404, simple_loss=0.2126, pruned_loss=0.03412, over 4843.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02864, over 972426.32 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 02:25:30,263 INFO [train.py:715] (1/8) Epoch 17, batch 20900, loss[loss=0.1275, simple_loss=0.2007, pruned_loss=0.0271, over 4795.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02833, over 972503.01 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:26:10,245 INFO [train.py:715] (1/8) Epoch 17, batch 20950, loss[loss=0.1323, simple_loss=0.208, pruned_loss=0.02835, over 4981.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02839, over 973430.69 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 02:26:48,265 INFO [train.py:715] (1/8) Epoch 17, batch 21000, loss[loss=0.1326, simple_loss=0.2119, pruned_loss=0.02661, over 4857.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02845, over 973096.94 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 02:26:48,266 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 02:27:00,912 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1049, simple_loss=0.1882, pruned_loss=0.01077, over 914524.00 frames.
2022-05-09 02:27:38,926 INFO [train.py:715] (1/8) Epoch 17, batch 21050, loss[loss=0.1274, simple_loss=0.195, pruned_loss=0.02986, over 4893.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02892, over 972339.80 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 02:28:18,318 INFO [train.py:715] (1/8) Epoch 17, batch 21100, loss[loss=0.1271, simple_loss=0.209, pruned_loss=0.02262, over 4847.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02863, over 972900.28 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 02:28:58,368 INFO [train.py:715] (1/8) Epoch 17, batch 21150, loss[loss=0.1289, simple_loss=0.2006, pruned_loss=0.02864, over 4856.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02914, over 972492.62 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 02:29:37,026 INFO [train.py:715] (1/8) Epoch 17, batch 21200, loss[loss=0.1352, simple_loss=0.2112, pruned_loss=0.0296, over 4784.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02954, over 972341.30 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:30:15,710 INFO [train.py:715] (1/8) Epoch 17, batch 21250, loss[loss=0.1201, simple_loss=0.1947, pruned_loss=0.02274, over 4646.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02924, over 972379.24 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 02:30:55,575 INFO [train.py:715] (1/8) Epoch 17, batch 21300, loss[loss=0.1191, simple_loss=0.192, pruned_loss=0.02315, over 4849.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.0295, over 971833.53 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 02:31:35,365 INFO [train.py:715] (1/8) Epoch 17, batch 21350, loss[loss=0.1415, simple_loss=0.2113, pruned_loss=0.03591, over 4964.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02954, over 971941.89 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 02:32:13,590 INFO [train.py:715] (1/8) Epoch 17, batch 21400, loss[loss=0.1304, simple_loss=0.2122, pruned_loss=0.02432, over 4889.00 frames.], tot_loss[loss=0.1334, simple_loss=0.208, pruned_loss=0.02943, over 971484.94 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:32:53,758 INFO [train.py:715] (1/8) Epoch 17, batch 21450, loss[loss=0.1484, simple_loss=0.2258, pruned_loss=0.03555, over 4743.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2081, pruned_loss=0.02937, over 971443.57 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:33:33,548 INFO [train.py:715] (1/8) Epoch 17, batch 21500, loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03256, over 4785.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02948, over 971554.24 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:34:12,044 INFO [train.py:715] (1/8) Epoch 17, batch 21550, loss[loss=0.1173, simple_loss=0.1895, pruned_loss=0.02255, over 4855.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02939, over 972578.66 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 02:34:51,492 INFO [train.py:715] (1/8) Epoch 17, batch 21600, loss[loss=0.1564, simple_loss=0.2388, pruned_loss=0.03701, over 4798.00 frames.], tot_loss[loss=0.1332, simple_loss=0.208, pruned_loss=0.02919, over 971633.68 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:35:31,958 INFO [train.py:715] (1/8) Epoch 17, batch 21650, loss[loss=0.1111, simple_loss=0.1795, pruned_loss=0.02137, over 4755.00 frames.], tot_loss[loss=0.1334, simple_loss=0.208, pruned_loss=0.02937, over 971731.23 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 02:36:11,046 INFO [train.py:715] (1/8) Epoch 17, batch 21700, loss[loss=0.1362, simple_loss=0.2143, pruned_loss=0.02909, over 4906.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2083, pruned_loss=0.02922, over 972809.06 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:36:49,698 INFO [train.py:715] (1/8) Epoch 17, batch 21750, loss[loss=0.1274, simple_loss=0.2013, pruned_loss=0.02675, over 4765.00 frames.], tot_loss[loss=0.134, simple_loss=0.2087, pruned_loss=0.02967, over 973237.41 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:37:29,247 INFO [train.py:715] (1/8) Epoch 17, batch 21800, loss[loss=0.1102, simple_loss=0.1741, pruned_loss=0.02316, over 4937.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2083, pruned_loss=0.02938, over 972753.37 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:38:08,209 INFO [train.py:715] (1/8) Epoch 17, batch 21850, loss[loss=0.1271, simple_loss=0.204, pruned_loss=0.02506, over 4918.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02855, over 973213.81 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 02:38:47,457 INFO [train.py:715] (1/8) Epoch 17, batch 21900, loss[loss=0.1189, simple_loss=0.1916, pruned_loss=0.02307, over 4814.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.0285, over 972962.12 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 02:39:25,950 INFO [train.py:715] (1/8) Epoch 17, batch 21950, loss[loss=0.1486, simple_loss=0.2185, pruned_loss=0.03934, over 4832.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02872, over 973200.28 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 02:40:05,670 INFO [train.py:715] (1/8) Epoch 17, batch 22000, loss[loss=0.1135, simple_loss=0.1877, pruned_loss=0.01968, over 4910.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02862, over 972946.33 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 02:40:45,436 INFO [train.py:715] (1/8) Epoch 17, batch 22050, loss[loss=0.1504, simple_loss=0.2171, pruned_loss=0.04181, over 4972.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02873, over 973198.08 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:41:23,862 INFO [train.py:715] (1/8) Epoch 17, batch 22100, loss[loss=0.1183, simple_loss=0.1914, pruned_loss=0.02262, over 4853.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02876, over 973289.12 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 02:42:03,595 INFO [train.py:715] (1/8) Epoch 17, batch 22150, loss[loss=0.1215, simple_loss=0.196, pruned_loss=0.02352, over 4757.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.02876, over 973727.73 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 02:42:43,496 INFO [train.py:715] (1/8) Epoch 17, batch 22200, loss[loss=0.1238, simple_loss=0.1969, pruned_loss=0.02533, over 4970.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2053, pruned_loss=0.02862, over 974420.23 frames.], batch size: 28, lr: 1.30e-04
2022-05-09 02:43:22,388 INFO [train.py:715] (1/8) Epoch 17, batch 22250, loss[loss=0.1201, simple_loss=0.2021, pruned_loss=0.01904, over 4960.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02955, over 973799.52 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 02:44:01,341 INFO [train.py:715] (1/8) Epoch 17, batch 22300, loss[loss=0.1058, simple_loss=0.1693, pruned_loss=0.0212, over 4836.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02912, over 973707.63 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:44:41,265 INFO [train.py:715] (1/8) Epoch 17, batch 22350, loss[loss=0.1263, simple_loss=0.2023, pruned_loss=0.02518, over 4867.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02915, over 973371.33 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 02:45:20,835 INFO [train.py:715] (1/8) Epoch 17, batch 22400, loss[loss=0.1292, simple_loss=0.2093, pruned_loss=0.02453, over 4818.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02917, over 972924.54 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 02:45:59,650 INFO [train.py:715] (1/8) Epoch 17, batch 22450, loss[loss=0.1309, simple_loss=0.2005, pruned_loss=0.03068, over 4801.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02935, over 972414.32 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:46:38,624 INFO [train.py:715] (1/8) Epoch 17, batch 22500, loss[loss=0.1144, simple_loss=0.1974, pruned_loss=0.01568, over 4805.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02839, over 972727.73 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 02:47:18,393 INFO [train.py:715] (1/8) Epoch 17, batch 22550, loss[loss=0.1807, simple_loss=0.2427, pruned_loss=0.0594, over 4849.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.02876, over 971703.95 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:47:56,725 INFO [train.py:715] (1/8) Epoch 17, batch 22600, loss[loss=0.1203, simple_loss=0.2003, pruned_loss=0.02012, over 4795.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2053, pruned_loss=0.02897, over 971831.16 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:48:36,267 INFO [train.py:715] (1/8) Epoch 17, batch 22650, loss[loss=0.1554, simple_loss=0.2291, pruned_loss=0.04081, over 4828.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02943, over 971939.23 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:49:15,728 INFO [train.py:715] (1/8) Epoch 17, batch 22700, loss[loss=0.113, simple_loss=0.1858, pruned_loss=0.02009, over 4790.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02985, over 973150.79 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 02:49:54,665 INFO [train.py:715] (1/8) Epoch 17, batch 22750, loss[loss=0.1132, simple_loss=0.1883, pruned_loss=0.01901, over 4796.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.0295, over 972940.43 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:50:33,047 INFO [train.py:715] (1/8) Epoch 17, batch 22800, loss[loss=0.1381, simple_loss=0.2174, pruned_loss=0.02947, over 4897.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02956, over 973204.73 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 02:51:12,436 INFO [train.py:715] (1/8) Epoch 17, batch 22850, loss[loss=0.1223, simple_loss=0.1979, pruned_loss=0.02337, over 4751.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02951, over 972642.56 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 02:51:52,340 INFO [train.py:715] (1/8) Epoch 17, batch 22900, loss[loss=0.1219, simple_loss=0.1835, pruned_loss=0.03014, over 4766.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02969, over 972302.87 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 02:52:30,188 INFO [train.py:715] (1/8) Epoch 17, batch 22950, loss[loss=0.1356, simple_loss=0.1964, pruned_loss=0.03742, over 4806.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02973, over 971862.64 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 02:53:10,087 INFO [train.py:715] (1/8) Epoch 17, batch 23000, loss[loss=0.1355, simple_loss=0.2026, pruned_loss=0.03425, over 4985.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02991, over 971469.75 frames.], batch size: 28, lr: 1.29e-04
2022-05-09 02:53:50,343 INFO [train.py:715] (1/8) Epoch 17, batch 23050, loss[loss=0.129, simple_loss=0.194, pruned_loss=0.03203, over 4792.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02982, over 972544.52 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 02:54:29,510 INFO [train.py:715] (1/8) Epoch 17, batch 23100, loss[loss=0.1456, simple_loss=0.203, pruned_loss=0.04414, over 4755.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03005, over 971679.00 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 02:55:07,922 INFO [train.py:715] (1/8) Epoch 17, batch 23150, loss[loss=0.1392, simple_loss=0.217, pruned_loss=0.03065, over 4975.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03008, over 972134.15 frames.], batch size: 28, lr: 1.29e-04
2022-05-09 02:55:47,702 INFO [train.py:715] (1/8) Epoch 17, batch 23200, loss[loss=0.1376, simple_loss=0.218, pruned_loss=0.02857, over 4807.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02978, over 972476.79 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 02:56:27,704 INFO [train.py:715] (1/8) Epoch 17, batch 23250, loss[loss=0.1589, simple_loss=0.2366, pruned_loss=0.04054, over 4954.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.0295, over 972141.03 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 02:57:05,636 INFO [train.py:715] (1/8) Epoch 17, batch 23300, loss[loss=0.1234, simple_loss=0.2022, pruned_loss=0.02231, over 4817.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02892, over 971448.77 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 02:57:44,992 INFO [train.py:715] (1/8) Epoch 17, batch 23350, loss[loss=0.1059, simple_loss=0.1745, pruned_loss=0.01864, over 4993.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02881, over 971791.20 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 02:58:25,086 INFO [train.py:715] (1/8) Epoch 17, batch 23400, loss[loss=0.1253, simple_loss=0.1981, pruned_loss=0.02626, over 4809.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02938, over 971922.12 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 02:59:03,868 INFO [train.py:715] (1/8) Epoch 17, batch 23450, loss[loss=0.1431, simple_loss=0.2162, pruned_loss=0.03499, over 4961.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02947, over 970835.09 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 02:59:42,960 INFO [train.py:715] (1/8) Epoch 17, batch 23500, loss[loss=0.1331, simple_loss=0.211, pruned_loss=0.02758, over 4985.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02954, over 970243.57 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:00:22,277 INFO [train.py:715] (1/8) Epoch 17, batch 23550, loss[loss=0.1224, simple_loss=0.1914, pruned_loss=0.02667, over 4826.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02945, over 969581.60 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:01:01,966 INFO [train.py:715] (1/8) Epoch 17, batch 23600, loss[loss=0.1186, simple_loss=0.2013, pruned_loss=0.01793, over 4871.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02949, over 970503.12 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 03:01:40,303 INFO [train.py:715] (1/8) Epoch 17, batch 23650, loss[loss=0.1403, simple_loss=0.2153, pruned_loss=0.03269, over 4849.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02957, over 970673.31 frames.], batch size: 34, lr: 1.29e-04
2022-05-09 03:02:19,919 INFO [train.py:715] (1/8) Epoch 17, batch 23700, loss[loss=0.1531, simple_loss=0.2349, pruned_loss=0.03561, over 4742.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.0296, over 970866.08 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:02:59,507 INFO [train.py:715] (1/8) Epoch 17, batch 23750, loss[loss=0.1248, simple_loss=0.1964, pruned_loss=0.02659, over 4848.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02905, over 970995.55 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:03:38,380 INFO [train.py:715] (1/8) Epoch 17, batch 23800, loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.0287, over 4987.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02936, over 971201.43 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:04:16,662 INFO [train.py:715] (1/8) Epoch 17, batch 23850, loss[loss=0.1119, simple_loss=0.1928, pruned_loss=0.01552, over 4932.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02921, over 972256.97 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 03:04:56,710 INFO [train.py:715] (1/8) Epoch 17, batch 23900, loss[loss=0.1573, simple_loss=0.2269, pruned_loss=0.04384, over 4974.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02955, over 971659.05 frames.], batch size: 39, lr: 1.29e-04
2022-05-09 03:05:35,870 INFO [train.py:715] (1/8) Epoch 17, batch 23950, loss[loss=0.1246, simple_loss=0.2042, pruned_loss=0.02244, over 4815.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2076, pruned_loss=0.02903, over 971921.27 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:06:14,198 INFO [train.py:715] (1/8) Epoch 17, batch 24000, loss[loss=0.122, simple_loss=0.2, pruned_loss=0.022, over 4814.00 frames.], tot_loss[loss=0.133, simple_loss=0.2077, pruned_loss=0.02919, over 971350.28 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:06:14,199 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 03:06:24,068 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1047, simple_loss=0.1881, pruned_loss=0.01067, over 914524.00 frames.
2022-05-09 03:07:02,576 INFO [train.py:715] (1/8) Epoch 17, batch 24050, loss[loss=0.1392, simple_loss=0.2079, pruned_loss=0.03526, over 4736.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02916, over 971042.66 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:07:41,973 INFO [train.py:715] (1/8) Epoch 17, batch 24100, loss[loss=0.1439, simple_loss=0.2266, pruned_loss=0.0306, over 4978.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02913, over 970895.45 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:08:22,149 INFO [train.py:715] (1/8) Epoch 17, batch 24150, loss[loss=0.1485, simple_loss=0.2219, pruned_loss=0.03754, over 4942.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02904, over 971487.98 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:09:00,901 INFO [train.py:715] (1/8) Epoch 17, batch 24200, loss[loss=0.108, simple_loss=0.179, pruned_loss=0.01855, over 4804.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02845, over 971479.21 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 03:09:42,450 INFO [train.py:715] (1/8) Epoch 17, batch 24250, loss[loss=0.1631, simple_loss=0.234, pruned_loss=0.0461, over 4828.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02886, over 972740.37 frames.], batch size: 27, lr: 1.29e-04
2022-05-09 03:10:23,058 INFO [train.py:715] (1/8) Epoch 17, batch 24300, loss[loss=0.1059, simple_loss=0.1722, pruned_loss=0.01976, over 4993.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2073, pruned_loss=0.02881, over 972476.26 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:11:02,610 INFO [train.py:715] (1/8) Epoch 17, batch 24350, loss[loss=0.1334, simple_loss=0.2066, pruned_loss=0.03008, over 4814.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02835, over 971974.77 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:11:41,988 INFO [train.py:715] (1/8) Epoch 17, batch 24400, loss[loss=0.1434, simple_loss=0.2122, pruned_loss=0.03729, over 4943.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02838, over 972426.22 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 03:12:21,137 INFO [train.py:715] (1/8) Epoch 17, batch 24450, loss[loss=0.1046, simple_loss=0.1898, pruned_loss=0.00969, over 4865.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02823, over 972389.82 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:13:01,328 INFO [train.py:715] (1/8) Epoch 17, batch 24500, loss[loss=0.1211, simple_loss=0.2055, pruned_loss=0.01838, over 4916.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02885, over 972267.77 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 03:13:40,452 INFO [train.py:715] (1/8) Epoch 17, batch 24550, loss[loss=0.181, simple_loss=0.2446, pruned_loss=0.05872, over 4897.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02918, over 971012.45 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:14:19,283 INFO [train.py:715] (1/8) Epoch 17, batch 24600, loss[loss=0.1423, simple_loss=0.2228, pruned_loss=0.03087, over 4880.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02842, over 971404.10 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:14:59,437 INFO [train.py:715] (1/8) Epoch 17, batch 24650, loss[loss=0.1234, simple_loss=0.1951, pruned_loss=0.02587, over 4772.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02881, over 971678.43 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:15:39,740 INFO [train.py:715] (1/8) Epoch 17, batch 24700, loss[loss=0.1403, simple_loss=0.2129, pruned_loss=0.03386, over 4649.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02886, over 970700.96 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:16:18,263 INFO [train.py:715] (1/8) Epoch 17, batch 24750, loss[loss=0.1339, simple_loss=0.2194, pruned_loss=0.0242, over 4916.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02878, over 971204.61 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 03:16:58,092 INFO [train.py:715] (1/8) Epoch 17, batch 24800, loss[loss=0.09913, simple_loss=0.1762, pruned_loss=0.01101, over 4880.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02878, over 971771.97 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 03:17:37,933 INFO [train.py:715] (1/8) Epoch 17, batch 24850, loss[loss=0.1133, simple_loss=0.1826, pruned_loss=0.02204, over 4980.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02905, over 972400.49 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:18:17,564 INFO [train.py:715] (1/8) Epoch 17, batch 24900, loss[loss=0.1092, simple_loss=0.188, pruned_loss=0.01515, over 4919.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2057, pruned_loss=0.02856, over 972684.32 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 03:18:56,111 INFO [train.py:715] (1/8) Epoch 17, batch 24950, loss[loss=0.121, simple_loss=0.1974, pruned_loss=0.02233, over 4849.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02872, over 972326.23 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 03:19:35,618 INFO [train.py:715] (1/8) Epoch 17, batch 25000, loss[loss=0.1615, simple_loss=0.2319, pruned_loss=0.04555, over 4981.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02873, over 972176.31 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:20:14,000 INFO [train.py:715] (1/8) Epoch 17, batch 25050, loss[loss=0.1255, simple_loss=0.1982, pruned_loss=0.02638, over 4962.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02889, over 971917.04 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:20:52,997 INFO [train.py:715] (1/8) Epoch 17, batch 25100, loss[loss=0.1201, simple_loss=0.1995, pruned_loss=0.02033, over 4940.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02899, over 972340.51 frames.], batch size: 29, lr: 1.29e-04
2022-05-09 03:21:32,981 INFO [train.py:715] (1/8) Epoch 17, batch 25150, loss[loss=0.1146, simple_loss=0.1923, pruned_loss=0.0185, over 4989.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02912, over 972622.02 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:22:12,872 INFO [train.py:715] (1/8) Epoch 17, batch 25200, loss[loss=0.1279, simple_loss=0.1949, pruned_loss=0.03047, over 4983.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02976, over 972724.35 frames.], batch size: 39, lr: 1.29e-04
2022-05-09 03:22:51,914 INFO [train.py:715] (1/8) Epoch 17, batch 25250, loss[loss=0.1308, simple_loss=0.2046, pruned_loss=0.02844, over 4919.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.0298, over 973313.60 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 03:23:31,035 INFO [train.py:715] (1/8) Epoch 17, batch 25300, loss[loss=0.1416, simple_loss=0.2102, pruned_loss=0.03648, over 4798.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2075, pruned_loss=0.03017, over 973685.04 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 03:24:11,038 INFO [train.py:715] (1/8) Epoch 17, batch 25350, loss[loss=0.1239, simple_loss=0.2037, pruned_loss=0.02208, over 4812.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03019, over 973588.55 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:24:49,785 INFO [train.py:715] (1/8) Epoch 17, batch 25400, loss[loss=0.1347, simple_loss=0.2211, pruned_loss=0.02409, over 4762.00 frames.], tot_loss[loss=0.1334, simple_loss=0.207, pruned_loss=0.02986, over 972412.20 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:25:28,939 INFO [train.py:715] (1/8) Epoch 17, batch 25450, loss[loss=0.149, simple_loss=0.2158, pruned_loss=0.04106, over 4957.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2071, pruned_loss=0.02995, over 972905.81 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 03:26:08,064 INFO [train.py:715] (1/8) Epoch 17, batch 25500, loss[loss=0.1068, simple_loss=0.168, pruned_loss=0.02283, over 4802.00 frames.], tot_loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.02971, over 972224.03 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:26:47,840 INFO [train.py:715] (1/8) Epoch 17, batch 25550, loss[loss=0.1245, simple_loss=0.1996, pruned_loss=0.0247, over 4910.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2066, pruned_loss=0.0296, over 972373.76 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:27:26,923 INFO [train.py:715] (1/8) Epoch 17, batch 25600, loss[loss=0.1333, simple_loss=0.1997, pruned_loss=0.03348, over 4890.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02934, over 972881.15 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 03:28:05,435 INFO [train.py:715] (1/8) Epoch 17, batch 25650, loss[loss=0.103, simple_loss=0.1799, pruned_loss=0.01301, over 4817.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02922, over 972246.69 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:28:45,199 INFO [train.py:715] (1/8) Epoch 17, batch 25700, loss[loss=0.1533, simple_loss=0.2295, pruned_loss=0.03859, over 4769.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02982, over 972804.10 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:29:24,286 INFO [train.py:715] (1/8) Epoch 17, batch 25750, loss[loss=0.1438, simple_loss=0.2229, pruned_loss=0.03235, over 4814.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02989, over 972615.00 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 03:30:03,677 INFO [train.py:715] (1/8) Epoch 17, batch 25800, loss[loss=0.1485, simple_loss=0.22, pruned_loss=0.03849, over 4879.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03058, over 972920.50 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 03:30:43,162 INFO [train.py:715] (1/8) Epoch 17, batch 25850, loss[loss=0.1226, simple_loss=0.202, pruned_loss=0.0216, over 4798.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03018, over 971811.36 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:31:22,524 INFO [train.py:715] (1/8) Epoch 17, batch 25900, loss[loss=0.1177, simple_loss=0.1913, pruned_loss=0.02205, over 4977.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02991, over 972716.79 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 03:32:01,046 INFO [train.py:715] (1/8) Epoch 17, batch 25950, loss[loss=0.1345, simple_loss=0.2129, pruned_loss=0.02807, over 4810.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02967, over 972553.59 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:32:39,477 INFO [train.py:715] (1/8) Epoch 17, batch 26000, loss[loss=0.1198, simple_loss=0.2038, pruned_loss=0.01786, over 4945.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02972, over 971674.18 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:33:19,120 INFO [train.py:715] (1/8) Epoch 17, batch 26050, loss[loss=0.1527, simple_loss=0.2182, pruned_loss=0.04356, over 4925.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02973, over 971058.97 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 03:33:57,728 INFO [train.py:715] (1/8) Epoch 17, batch 26100, loss[loss=0.1055, simple_loss=0.1759, pruned_loss=0.01757, over 4773.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03022, over 971003.56 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:34:37,130 INFO [train.py:715] (1/8) Epoch 17, batch 26150, loss[loss=0.1272, simple_loss=0.198, pruned_loss=0.02823, over 4844.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02982, over 971416.21 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:35:16,506 INFO [train.py:715] (1/8) Epoch 17, batch 26200, loss[loss=0.1557, simple_loss=0.2274, pruned_loss=0.04203, over 4705.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02953, over 970761.60 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:35:56,478 INFO [train.py:715] (1/8) Epoch 17, batch 26250, loss[loss=0.1621, simple_loss=0.2302, pruned_loss=0.04702, over 4859.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02917, over 971528.00 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 03:36:35,142 INFO [train.py:715] (1/8) Epoch 17, batch 26300, loss[loss=0.1583, simple_loss=0.23, pruned_loss=0.04328, over 4883.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02971, over 971395.93 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:37:13,920 INFO [train.py:715] (1/8) Epoch 17, batch 26350, loss[loss=0.1161, simple_loss=0.1929, pruned_loss=0.01971, over 4835.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02913, over 971662.01 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:37:53,865 INFO [train.py:715] (1/8) Epoch 17, batch 26400, loss[loss=0.1362, simple_loss=0.2066, pruned_loss=0.03292, over 4812.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02961, over 971166.98 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:38:32,576 INFO [train.py:715] (1/8) Epoch 17, batch 26450, loss[loss=0.1381, simple_loss=0.2175, pruned_loss=0.0293, over 4865.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2084, pruned_loss=0.02958, over 970493.52 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 03:39:11,785 INFO [train.py:715] (1/8) Epoch 17, batch 26500, loss[loss=0.1392, simple_loss=0.2094, pruned_loss=0.03456, over 4875.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2085, pruned_loss=0.02961, over 970801.14 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:39:51,008 INFO [train.py:715] (1/8) Epoch 17, batch 26550, loss[loss=0.1592, simple_loss=0.2217, pruned_loss=0.04838, over 4699.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02975, over 969965.17 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:40:29,942 INFO [train.py:715] (1/8) Epoch 17, batch 26600, loss[loss=0.1245, simple_loss=0.2028, pruned_loss=0.02306, over 4798.00 frames.], tot_loss[loss=0.1333, simple_loss=0.208, pruned_loss=0.02937, over 969988.64 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 03:41:08,347 INFO [train.py:715] (1/8) Epoch 17, batch 26650, loss[loss=0.1218, simple_loss=0.1916, pruned_loss=0.02603, over 4902.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2075, pruned_loss=0.02893, over 970975.48 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:41:47,380 INFO [train.py:715] (1/8) Epoch 17, batch 26700, loss[loss=0.1292, simple_loss=0.2068, pruned_loss=0.02575, over 4885.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02874, over 971028.74 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:42:26,784 INFO [train.py:715] (1/8) Epoch 17, batch 26750, loss[loss=0.1179, simple_loss=0.1959, pruned_loss=0.01991, over 4888.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.0284, over 970281.40 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:43:05,132 INFO [train.py:715] (1/8) Epoch 17, batch 26800, loss[loss=0.1097, simple_loss=0.1819, pruned_loss=0.01881, over 4886.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02814, over 971098.70 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:43:43,931 INFO [train.py:715] (1/8) Epoch 17, batch 26850, loss[loss=0.139, simple_loss=0.2074, pruned_loss=0.03529, over 4910.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02876, over 971574.35 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:44:23,812 INFO [train.py:715] (1/8) Epoch 17, batch 26900, loss[loss=0.1367, simple_loss=0.2039, pruned_loss=0.03473, over 4870.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02915, over 971296.94 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 03:45:02,976 INFO [train.py:715] (1/8) Epoch 17, batch 26950, loss[loss=0.1364, simple_loss=0.2197, pruned_loss=0.0266, over 4954.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02887, over 971043.30 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 03:45:41,689 INFO [train.py:715] (1/8) Epoch 17, batch 27000, loss[loss=0.1295, simple_loss=0.1909, pruned_loss=0.03405, over 4743.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02919, over 971148.42 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:45:41,690 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 03:45:51,480 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1047, simple_loss=0.188, pruned_loss=0.0107, over 914524.00 frames.
2022-05-09 03:46:30,441 INFO [train.py:715] (1/8) Epoch 17, batch 27050, loss[loss=0.1355, simple_loss=0.2037, pruned_loss=0.0336, over 4957.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02932, over 970962.34 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:47:09,960 INFO [train.py:715] (1/8) Epoch 17, batch 27100, loss[loss=0.1496, simple_loss=0.2189, pruned_loss=0.04015, over 4812.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02947, over 971099.43 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:47:49,460 INFO [train.py:715] (1/8) Epoch 17, batch 27150, loss[loss=0.1416, simple_loss=0.222, pruned_loss=0.03062, over 4784.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02898, over 970824.11 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 03:48:27,665 INFO [train.py:715] (1/8) Epoch 17, batch 27200, loss[loss=0.1279, simple_loss=0.2009, pruned_loss=0.0275, over 4778.00 frames.], tot_loss[loss=0.1309, simple_loss=0.205, pruned_loss=0.02834, over 971142.39 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:49:06,446 INFO [train.py:715] (1/8) Epoch 17, batch 27250, loss[loss=0.1434, simple_loss=0.2247, pruned_loss=0.03109, over 4771.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2049, pruned_loss=0.0282, over 970089.80 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 03:49:46,071 INFO [train.py:715] (1/8) Epoch 17, batch 27300, loss[loss=0.1107, simple_loss=0.1957, pruned_loss=0.01288, over 4890.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2059, pruned_loss=0.028, over 970413.92 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:50:25,158 INFO [train.py:715] (1/8) Epoch 17, batch 27350, loss[loss=0.127, simple_loss=0.2002, pruned_loss=0.02696, over 4938.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02808, over 971077.27 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:51:04,593 INFO [train.py:715] (1/8) Epoch 17, batch 27400, loss[loss=0.1564, simple_loss=0.2309, pruned_loss=0.04098, over 4921.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2074, pruned_loss=0.0289, over 971385.89 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 03:51:43,497 INFO [train.py:715] (1/8) Epoch 17, batch 27450, loss[loss=0.1216, simple_loss=0.1978, pruned_loss=0.0227, over 4738.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2068, pruned_loss=0.02853, over 972258.22 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:52:23,143 INFO [train.py:715] (1/8) Epoch 17, batch 27500, loss[loss=0.136, simple_loss=0.2203, pruned_loss=0.02581, over 4825.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02844, over 972145.75 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:53:01,811 INFO [train.py:715] (1/8) Epoch 17, batch 27550, loss[loss=0.1258, simple_loss=0.2053, pruned_loss=0.02313, over 4860.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2082, pruned_loss=0.02924, over 971825.94 frames.], batch size: 22, lr: 1.29e-04
2022-05-09 03:53:40,302 INFO [train.py:715] (1/8) Epoch 17, batch 27600, loss[loss=0.1565, simple_loss=0.232, pruned_loss=0.0405, over 4819.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02918, over 972440.26 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:54:19,257 INFO [train.py:715] (1/8) Epoch 17, batch 27650, loss[loss=0.1267, simple_loss=0.2069, pruned_loss=0.02328, over 4824.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.0292, over 971711.77 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:54:57,848 INFO [train.py:715] (1/8) Epoch 17, batch 27700, loss[loss=0.1479, simple_loss=0.2229, pruned_loss=0.03641, over 4799.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.0296, over 971914.31 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:55:37,181 INFO [train.py:715] (1/8) Epoch 17, batch 27750, loss[loss=0.1319, simple_loss=0.2069, pruned_loss=0.02844, over 4831.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02937, over 972988.87 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 03:56:16,913 INFO [train.py:715] (1/8) Epoch 17, batch 27800, loss[loss=0.1544, simple_loss=0.2268, pruned_loss=0.04098, over 4758.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.0293, over 972781.07 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:56:57,478 INFO [train.py:715] (1/8) Epoch 17, batch 27850, loss[loss=0.1406, simple_loss=0.214, pruned_loss=0.03365, over 4865.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.0296, over 973371.96 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 03:57:37,273 INFO [train.py:715] (1/8) Epoch 17, batch 27900, loss[loss=0.1464, simple_loss=0.2185, pruned_loss=0.03721, over 4890.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02964, over 973240.55 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:58:16,548 INFO [train.py:715] (1/8) Epoch 17, batch 27950, loss[loss=0.1513, simple_loss=0.2234, pruned_loss=0.03959, over 4691.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02955, over 973481.01 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:58:56,514 INFO [train.py:715] (1/8) Epoch 17, batch 28000, loss[loss=0.1186, simple_loss=0.1994, pruned_loss=0.0189, over 4804.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02948, over 972882.49 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:59:36,518 INFO [train.py:715] (1/8) Epoch 17, batch 28050, loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02919, over 4777.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02935, over 972809.11 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:00:15,246 INFO [train.py:715] (1/8) Epoch 17, batch 28100, loss[loss=0.124, simple_loss=0.1918, pruned_loss=0.02809, over 4793.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02967, over 972344.18 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:00:54,611 INFO [train.py:715] (1/8) Epoch 17, batch 28150, loss[loss=0.1267, simple_loss=0.197, pruned_loss=0.02819, over 4975.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02944, over 972341.37 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:01:33,612 INFO [train.py:715] (1/8) Epoch 17, batch 28200, loss[loss=0.1362, simple_loss=0.2094, pruned_loss=0.03153, over 4847.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02924, over 972676.95 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 04:02:12,001 INFO [train.py:715] (1/8) Epoch 17, batch 28250, loss[loss=0.1214, simple_loss=0.2001, pruned_loss=0.02134, over 4943.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02989, over 972395.63 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:02:50,446 INFO [train.py:715] (1/8) Epoch 17, batch 28300, loss[loss=0.1278, simple_loss=0.2109, pruned_loss=0.02235, over 4851.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02994, over 972399.64 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:03:29,617 INFO [train.py:715] (1/8) Epoch 17, batch 28350, loss[loss=0.1472, simple_loss=0.2113, pruned_loss=0.04159, over 4860.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2078, pruned_loss=0.03014, over 972396.14 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:04:09,194 INFO [train.py:715] (1/8) Epoch 17, batch 28400, loss[loss=0.1219, simple_loss=0.2043, pruned_loss=0.01977, over 4948.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03065, over 973051.50 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:04:48,215 INFO [train.py:715] (1/8) Epoch 17, batch 28450, loss[loss=0.1082, simple_loss=0.191, pruned_loss=0.01272, over 4792.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02968, over 972553.49 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:05:26,442 INFO [train.py:715] (1/8) Epoch 17, batch 28500, loss[loss=0.1406, simple_loss=0.2085, pruned_loss=0.03639, over 4774.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02934, over 972367.84 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 04:06:06,460 INFO [train.py:715] (1/8) Epoch 17, batch 28550, loss[loss=0.1362, simple_loss=0.2052, pruned_loss=0.03362, over 4709.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02924, over 971633.37 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:06:45,100 INFO [train.py:715] (1/8) Epoch 17, batch 28600, loss[loss=0.1467, simple_loss=0.216, pruned_loss=0.0387, over 4821.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02922, over 972487.06 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:07:23,871 INFO [train.py:715] (1/8) Epoch 17, batch 28650, loss[loss=0.1374, simple_loss=0.2092, pruned_loss=0.03278, over 4747.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02937, over 972108.66 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:08:02,255 INFO [train.py:715] (1/8) Epoch 17, batch 28700, loss[loss=0.1367, simple_loss=0.2169, pruned_loss=0.02821, over 4940.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02975, over 972946.37 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:08:41,576 INFO [train.py:715] (1/8) Epoch 17, batch 28750, loss[loss=0.1081, simple_loss=0.1787, pruned_loss=0.01873, over 4855.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02968, over 972687.14 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 04:09:20,208 INFO [train.py:715] (1/8) Epoch 17, batch 28800, loss[loss=0.1643, simple_loss=0.2324, pruned_loss=0.04805, over 4854.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02952, over 973123.96 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 04:09:58,906 INFO [train.py:715] (1/8) Epoch 17, batch 28850, loss[loss=0.1469, simple_loss=0.2179, pruned_loss=0.03795, over 4847.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02921, over 972754.84 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 04:10:37,988 INFO [train.py:715] (1/8) Epoch 17, batch 28900, loss[loss=0.1599, simple_loss=0.234, pruned_loss=0.0429, over 4736.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02934, over 972284.67 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:11:16,518 INFO [train.py:715] (1/8) Epoch 17, batch 28950, loss[loss=0.1089, simple_loss=0.1816, pruned_loss=0.01807, over 4912.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02914, over 972860.54 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:11:54,922 INFO [train.py:715] (1/8) Epoch 17, batch 29000, loss[loss=0.1096, simple_loss=0.1813, pruned_loss=0.01897, over 4796.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02869, over 971956.77 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:12:33,660 INFO [train.py:715] (1/8) Epoch 17, batch 29050, loss[loss=0.1484, simple_loss=0.2128, pruned_loss=0.04203, over 4691.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02882, over 971875.67 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:13:13,011 INFO [train.py:715] (1/8) Epoch 17, batch 29100, loss[loss=0.1226, simple_loss=0.2033, pruned_loss=0.02094, over 4886.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02874, over 971263.62 frames.], batch size: 22, lr: 1.29e-04
2022-05-09 04:13:51,909 INFO [train.py:715] (1/8) Epoch 17, batch 29150, loss[loss=0.1106, simple_loss=0.1907, pruned_loss=0.01531, over 4774.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02873, over 971723.52 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 04:14:30,016 INFO [train.py:715] (1/8) Epoch 17, batch 29200, loss[loss=0.1094, simple_loss=0.1915, pruned_loss=0.01367, over 4844.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.029, over 971042.46 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 04:15:09,515 INFO [train.py:715] (1/8) Epoch 17, batch 29250, loss[loss=0.1326, simple_loss=0.2015, pruned_loss=0.03188, over 4804.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02908, over 971413.83 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:15:49,146 INFO [train.py:715] (1/8) Epoch 17, batch 29300, loss[loss=0.1211, simple_loss=0.2025, pruned_loss=0.01983, over 4812.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02894, over 971264.90 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:16:27,569 INFO [train.py:715] (1/8) Epoch 17, batch 29350, loss[loss=0.1721, simple_loss=0.2483, pruned_loss=0.04798, over 4879.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02963, over 971175.40 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:17:06,154 INFO [train.py:715] (1/8) Epoch 17, batch 29400, loss[loss=0.1315, simple_loss=0.2106, pruned_loss=0.0262, over 4772.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02948, over 971390.39 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 04:17:45,837 INFO [train.py:715] (1/8) Epoch 17, batch 29450, loss[loss=0.1377, simple_loss=0.2206, pruned_loss=0.02736, over 4950.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.02889, over 972974.18 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:18:24,962 INFO [train.py:715] (1/8) Epoch 17, batch 29500, loss[loss=0.1502, simple_loss=0.2212, pruned_loss=0.0396, over 4970.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02895, over 972448.22 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:19:03,881 INFO [train.py:715] (1/8) Epoch 17, batch 29550, loss[loss=0.1626, simple_loss=0.2465, pruned_loss=0.03938, over 4887.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02875, over 973063.60 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:19:43,164 INFO [train.py:715] (1/8) Epoch 17, batch 29600, loss[loss=0.1527, simple_loss=0.2332, pruned_loss=0.0361, over 4798.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02895, over 972303.53 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:20:22,741 INFO [train.py:715] (1/8) Epoch 17, batch 29650, loss[loss=0.1413, simple_loss=0.2108, pruned_loss=0.03591, over 4915.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02912, over 973655.37 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 04:21:01,514 INFO [train.py:715] (1/8) Epoch 17, batch 29700, loss[loss=0.1368, simple_loss=0.2049, pruned_loss=0.03433, over 4840.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02933, over 973134.52 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 04:21:40,462 INFO [train.py:715] (1/8) Epoch 17, batch 29750, loss[loss=0.1323, simple_loss=0.2053, pruned_loss=0.02965, over 4894.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02956, over 972737.34 frames.], batch size: 39, lr: 1.29e-04
2022-05-09 04:22:20,619 INFO [train.py:715] (1/8) Epoch 17, batch 29800, loss[loss=0.1519, simple_loss=0.2253, pruned_loss=0.03923, over 4806.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02968, over 972185.72 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:22:59,614 INFO [train.py:715] (1/8) Epoch 17, batch 29850, loss[loss=0.129, simple_loss=0.2126, pruned_loss=0.02273, over 4983.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02911, over 973287.78 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:23:38,912 INFO [train.py:715] (1/8) Epoch 17, batch 29900, loss[loss=0.1149, simple_loss=0.1984, pruned_loss=0.01569, over 4976.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02922, over 973329.14 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:24:18,619 INFO [train.py:715] (1/8) Epoch 17, batch 29950, loss[loss=0.13, simple_loss=0.2111, pruned_loss=0.0244, over 4969.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02892, over 973620.04 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:24:58,023 INFO [train.py:715] (1/8) Epoch 17, batch 30000, loss[loss=0.1243, simple_loss=0.1926, pruned_loss=0.02801, over 4781.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02926, over 972535.30 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 04:24:58,024 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 04:25:08,262 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1047, simple_loss=0.188, pruned_loss=0.01065, over 914524.00 frames.
2022-05-09 04:25:48,087 INFO [train.py:715] (1/8) Epoch 17, batch 30050, loss[loss=0.1402, simple_loss=0.2166, pruned_loss=0.0319, over 4831.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2083, pruned_loss=0.02953, over 972452.90 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:26:27,728 INFO [train.py:715] (1/8) Epoch 17, batch 30100, loss[loss=0.1394, simple_loss=0.2176, pruned_loss=0.0306, over 4968.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2081, pruned_loss=0.02937, over 973093.07 frames.], batch size: 28, lr: 1.29e-04
2022-05-09 04:27:06,810 INFO [train.py:715] (1/8) Epoch 17, batch 30150, loss[loss=0.1299, simple_loss=0.2129, pruned_loss=0.02348, over 4951.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02938, over 972954.76 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:27:46,308 INFO [train.py:715] (1/8) Epoch 17, batch 30200, loss[loss=0.1428, simple_loss=0.226, pruned_loss=0.02979, over 4756.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.0294, over 972448.46 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:28:25,424 INFO [train.py:715] (1/8) Epoch 17, batch 30250, loss[loss=0.1194, simple_loss=0.2066, pruned_loss=0.01612, over 4835.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02906, over 972308.14 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 04:29:04,415 INFO [train.py:715] (1/8) Epoch 17, batch 30300, loss[loss=0.1307, simple_loss=0.2084, pruned_loss=0.02644, over 4987.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02933, over 972304.97 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:29:44,185 INFO [train.py:715] (1/8) Epoch 17, batch 30350, loss[loss=0.1124, simple_loss=0.1778, pruned_loss=0.02349, over 4782.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.0296, over 972168.21 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:30:23,366 INFO [train.py:715] (1/8) Epoch 17, batch 30400, loss[loss=0.1772, simple_loss=0.2563, pruned_loss=0.04909, over 4823.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.0295, over 972764.77 frames.], batch size: 27, lr: 1.29e-04
2022-05-09 04:31:02,093 INFO [train.py:715] (1/8) Epoch 17, batch 30450, loss[loss=0.121, simple_loss=0.185, pruned_loss=0.02852, over 4874.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02923, over 971975.49 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:31:41,824 INFO [train.py:715] (1/8) Epoch 17, batch 30500, loss[loss=0.09322, simple_loss=0.1534, pruned_loss=0.01651, over 4753.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02935, over 972181.64 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 04:32:21,635 INFO [train.py:715] (1/8) Epoch 17, batch 30550, loss[loss=0.1212, simple_loss=0.2058, pruned_loss=0.01824, over 4971.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02887, over 972190.96 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:33:01,421 INFO [train.py:715] (1/8) Epoch 17, batch 30600, loss[loss=0.1222, simple_loss=0.1975, pruned_loss=0.02344, over 4932.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02864, over 972176.65 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:33:40,314 INFO [train.py:715] (1/8) Epoch 17, batch 30650, loss[loss=0.1247, simple_loss=0.209, pruned_loss=0.02021, over 4798.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02887, over 972875.47 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:34:20,057 INFO [train.py:715] (1/8) Epoch 17, batch 30700, loss[loss=0.1195, simple_loss=0.1921, pruned_loss=0.02341, over 4860.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02829, over 973342.83 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:34:59,085 INFO [train.py:715] (1/8) Epoch 17, batch 30750, loss[loss=0.1355, simple_loss=0.2081, pruned_loss=0.03144, over 4987.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02824, over 973668.07 frames.], batch size: 33, lr: 1.29e-04
2022-05-09 04:35:38,913 INFO [train.py:715] (1/8) Epoch 17, batch 30800, loss[loss=0.1505, simple_loss=0.2354, pruned_loss=0.03276, over 4784.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02883, over 972061.43 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:36:18,139 INFO [train.py:715] (1/8) Epoch 17, batch 30850, loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02937, over 4948.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02897, over 972214.78 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:36:58,358 INFO [train.py:715] (1/8) Epoch 17, batch 30900, loss[loss=0.1322, simple_loss=0.2113, pruned_loss=0.02659, over 4838.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02899, over 972109.61 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 04:37:38,028 INFO [train.py:715] (1/8) Epoch 17, batch 30950, loss[loss=0.1123, simple_loss=0.1788, pruned_loss=0.02292, over 4688.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02848, over 972534.94 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:38:17,299 INFO [train.py:715] (1/8) Epoch 17, batch 31000, loss[loss=0.1326, simple_loss=0.2118, pruned_loss=0.02672, over 4985.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02882, over 971997.56 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:38:57,010 INFO [train.py:715] (1/8) Epoch 17, batch 31050, loss[loss=0.1075, simple_loss=0.1921, pruned_loss=0.0115, over 4960.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02863, over 972077.09 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:39:36,077 INFO [train.py:715] (1/8) Epoch 17, batch 31100, loss[loss=0.1185, simple_loss=0.1962, pruned_loss=0.02042, over 4987.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02877, over 972276.29 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:40:15,209 INFO [train.py:715] (1/8) Epoch 17, batch 31150, loss[loss=0.1376, simple_loss=0.2149, pruned_loss=0.03016, over 4772.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.02892, over 972159.43 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:40:54,494 INFO [train.py:715] (1/8) Epoch 17, batch 31200, loss[loss=0.1422, simple_loss=0.22, pruned_loss=0.03224, over 4779.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02902, over 971807.28 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:41:34,593 INFO [train.py:715] (1/8) Epoch 17, batch 31250, loss[loss=0.1415, simple_loss=0.2296, pruned_loss=0.02667, over 4871.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02908, over 971868.32 frames.], batch size: 22, lr: 1.29e-04
2022-05-09 04:42:13,892 INFO [train.py:715] (1/8) Epoch 17, batch 31300, loss[loss=0.1211, simple_loss=0.1926, pruned_loss=0.02477, over 4944.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02932, over 972077.47 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:42:53,278 INFO [train.py:715] (1/8) Epoch 17, batch 31350, loss[loss=0.1134, simple_loss=0.1859, pruned_loss=0.02045, over 4792.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02921, over 971642.21 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:43:32,644 INFO [train.py:715] (1/8) Epoch 17, batch 31400, loss[loss=0.1234, simple_loss=0.2042, pruned_loss=0.02127, over 4901.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.0293, over 971437.77 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 04:44:11,253 INFO [train.py:715] (1/8) Epoch 17, batch 31450, loss[loss=0.1368, simple_loss=0.216, pruned_loss=0.0288, over 4896.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02888, over 971719.09 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:44:51,211 INFO [train.py:715] (1/8) Epoch 17, batch 31500, loss[loss=0.141, simple_loss=0.2173, pruned_loss=0.03239, over 4867.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02909, over 971932.14 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:45:29,936 INFO [train.py:715] (1/8) Epoch 17, batch 31550, loss[loss=0.1199, simple_loss=0.1988, pruned_loss=0.02046, over 4969.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02966, over 971872.23 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:46:09,494 INFO [train.py:715] (1/8) Epoch 17, batch 31600, loss[loss=0.1601, simple_loss=0.2164, pruned_loss=0.05195, over 4885.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02926, over 972063.85 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:46:48,897 INFO [train.py:715] (1/8) Epoch 17, batch 31650, loss[loss=0.1191, simple_loss=0.1868, pruned_loss=0.02565, over 4940.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02921, over 972729.84 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:47:28,179 INFO [train.py:715] (1/8) Epoch 17, batch 31700, loss[loss=0.1307, simple_loss=0.1981, pruned_loss=0.0316, over 4822.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02926, over 973036.26 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:48:07,936 INFO [train.py:715] (1/8) Epoch 17, batch 31750, loss[loss=0.1551, simple_loss=0.2275, pruned_loss=0.04134, over 4811.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2086, pruned_loss=0.0299, over 972844.75 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 04:48:47,177 INFO [train.py:715] (1/8) Epoch 17, batch 31800, loss[loss=0.1355, simple_loss=0.2163, pruned_loss=0.02739, over 4803.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2084, pruned_loss=0.02974, over 973554.45 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:49:27,379 INFO [train.py:715] (1/8) Epoch 17, batch 31850, loss[loss=0.1517, simple_loss=0.2139, pruned_loss=0.04469, over 4749.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02922, over 972780.62 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:50:06,508 INFO [train.py:715] (1/8) Epoch 17, batch 31900, loss[loss=0.1232, simple_loss=0.1967, pruned_loss=0.02483, over 4958.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02953, over 973310.23 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:50:45,987 INFO [train.py:715] (1/8) Epoch 17, batch 31950, loss[loss=0.1383, simple_loss=0.211, pruned_loss=0.03279, over 4971.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02917, over 974246.45 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 04:51:25,760 INFO [train.py:715] (1/8) Epoch 17, batch 32000, loss[loss=0.1223, simple_loss=0.2085, pruned_loss=0.01802, over 4772.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02909, over 973743.74 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:52:04,645 INFO [train.py:715] (1/8) Epoch 17, batch 32050, loss[loss=0.1306, simple_loss=0.2067, pruned_loss=0.02722, over 4880.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02884, over 973359.02 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:52:44,367 INFO [train.py:715] (1/8) Epoch 17, batch 32100, loss[loss=0.1313, simple_loss=0.2166, pruned_loss=0.02297, over 4864.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02919, over 972538.56 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 04:53:23,403 INFO [train.py:715] (1/8) Epoch 17, batch 32150, loss[loss=0.1462, simple_loss=0.2242, pruned_loss=0.0341, over 4702.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02938, over 972481.56 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:54:02,756 INFO [train.py:715] (1/8) Epoch 17, batch 32200, loss[loss=0.1483, simple_loss=0.2172, pruned_loss=0.03965, over 4868.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02962, over 972815.89 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:54:45,061 INFO [train.py:715] (1/8) Epoch 17, batch 32250, loss[loss=0.1422, simple_loss=0.2159, pruned_loss=0.0343, over 4847.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02947, over 972793.64 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:55:24,421 INFO [train.py:715] (1/8) Epoch 17, batch 32300, loss[loss=0.1315, simple_loss=0.2068, pruned_loss=0.02806, over 4773.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02942, over 973334.48 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:56:04,322 INFO [train.py:715] (1/8) Epoch 17, batch 32350, loss[loss=0.1402, simple_loss=0.2267, pruned_loss=0.02688, over 4950.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02917, over 972926.01 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:56:43,386 INFO [train.py:715] (1/8) Epoch 17, batch 32400, loss[loss=0.1502, simple_loss=0.2302, pruned_loss=0.03516, over 4693.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02951, over 972899.12 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:57:22,530 INFO [train.py:715] (1/8) Epoch 17, batch 32450, loss[loss=0.1383, simple_loss=0.2176, pruned_loss=0.0295, over 4840.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02946, over 973291.20 frames.], batch size: 26, lr: 1.28e-04
2022-05-09 04:58:02,556 INFO [train.py:715] (1/8) Epoch 17, batch 32500, loss[loss=0.1644, simple_loss=0.2309, pruned_loss=0.04895, over 4923.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02983, over 973844.68 frames.], batch size: 39, lr: 1.28e-04
2022-05-09 04:58:41,967 INFO [train.py:715] (1/8) Epoch 17, batch 32550, loss[loss=0.1445, simple_loss=0.2144, pruned_loss=0.03734, over 4986.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02979, over 973607.95 frames.], batch size: 28, lr: 1.28e-04
2022-05-09 04:59:21,558 INFO [train.py:715] (1/8) Epoch 17, batch 32600, loss[loss=0.1768, simple_loss=0.2429, pruned_loss=0.0553, over 4834.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2067, pruned_loss=0.02988, over 973170.76 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:00:01,071 INFO [train.py:715] (1/8) Epoch 17, batch 32650, loss[loss=0.1164, simple_loss=0.1864, pruned_loss=0.0232, over 4927.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2063, pruned_loss=0.02962, over 973363.38 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:00:39,806 INFO [train.py:715] (1/8) Epoch 17, batch 32700, loss[loss=0.09903, simple_loss=0.1659, pruned_loss=0.01607, over 4775.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2056, pruned_loss=0.0291, over 972495.89 frames.], batch size: 12, lr: 1.28e-04
2022-05-09 05:01:19,988 INFO [train.py:715] (1/8) Epoch 17, batch 32750, loss[loss=0.1239, simple_loss=0.1974, pruned_loss=0.02521, over 4965.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2058, pruned_loss=0.02888, over 973595.02 frames.], batch size: 24, lr: 1.28e-04
2022-05-09 05:01:59,335 INFO [train.py:715] (1/8) Epoch 17, batch 32800, loss[loss=0.1345, simple_loss=0.2036, pruned_loss=0.03269, over 4872.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02895, over 973505.62 frames.], batch size: 30, lr: 1.28e-04
2022-05-09 05:02:38,973 INFO [train.py:715] (1/8) Epoch 17, batch 32850, loss[loss=0.1487, simple_loss=0.2265, pruned_loss=0.03539, over 4814.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02929, over 973054.95 frames.], batch size: 26, lr: 1.28e-04
2022-05-09 05:03:18,518 INFO [train.py:715] (1/8) Epoch 17, batch 32900, loss[loss=0.1462, simple_loss=0.2077, pruned_loss=0.04239, over 4953.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02884, over 972372.28 frames.], batch size: 39, lr: 1.28e-04
2022-05-09 05:03:58,022 INFO [train.py:715] (1/8) Epoch 17, batch 32950, loss[loss=0.1482, simple_loss=0.2158, pruned_loss=0.04025, over 4841.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02846, over 972394.56 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:04:36,960 INFO [train.py:715] (1/8) Epoch 17, batch 33000, loss[loss=0.1392, simple_loss=0.2102, pruned_loss=0.03417, over 4842.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02854, over 972945.94 frames.], batch size: 30, lr: 1.28e-04
2022-05-09 05:04:36,960 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 05:04:49,645 INFO [train.py:742] (1/8) Epoch 17, validation: loss=0.1049, simple_loss=0.1881, pruned_loss=0.0108, over 914524.00 frames.
2022-05-09 05:05:28,987 INFO [train.py:715] (1/8) Epoch 17, batch 33050, loss[loss=0.1365, simple_loss=0.2042, pruned_loss=0.03443, over 4893.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02868, over 973391.36 frames.], batch size: 39, lr: 1.28e-04
2022-05-09 05:06:08,147 INFO [train.py:715] (1/8) Epoch 17, batch 33100, loss[loss=0.1351, simple_loss=0.1957, pruned_loss=0.03724, over 4834.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.0289, over 973998.96 frames.], batch size: 13, lr: 1.28e-04
2022-05-09 05:06:47,448 INFO [train.py:715] (1/8) Epoch 17, batch 33150, loss[loss=0.1551, simple_loss=0.2269, pruned_loss=0.04166, over 4967.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02906, over 973538.02 frames.], batch size: 24, lr: 1.28e-04
2022-05-09 05:07:27,180 INFO [train.py:715] (1/8) Epoch 17, batch 33200, loss[loss=0.1129, simple_loss=0.1823, pruned_loss=0.02177, over 4794.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2071, pruned_loss=0.02879, over 973356.97 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:08:06,792 INFO [train.py:715] (1/8) Epoch 17, batch 33250, loss[loss=0.1782, simple_loss=0.2575, pruned_loss=0.04948, over 4777.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02906, over 973479.10 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:08:46,104 INFO [train.py:715] (1/8) Epoch 17, batch 33300, loss[loss=0.1399, simple_loss=0.2078, pruned_loss=0.03599, over 4735.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.0292, over 971681.25 frames.], batch size: 16, lr: 1.28e-04
2022-05-09 05:09:25,682 INFO [train.py:715] (1/8) Epoch 17, batch 33350, loss[loss=0.1095, simple_loss=0.1893, pruned_loss=0.01485, over 4763.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2074, pruned_loss=0.02881, over 972121.19 frames.], batch size: 12, lr: 1.28e-04
2022-05-09 05:10:05,483 INFO [train.py:715] (1/8) Epoch 17, batch 33400, loss[loss=0.1208, simple_loss=0.1975, pruned_loss=0.02205, over 4807.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2088, pruned_loss=0.02955, over 972217.81 frames.], batch size: 24, lr: 1.28e-04
2022-05-09 05:10:44,824 INFO [train.py:715] (1/8) Epoch 17, batch 33450, loss[loss=0.1245, simple_loss=0.2063, pruned_loss=0.0214, over 4769.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02951, over 971552.23 frames.], batch size: 17, lr: 1.28e-04
2022-05-09 05:11:24,374 INFO [train.py:715] (1/8) Epoch 17, batch 33500, loss[loss=0.1159, simple_loss=0.1829, pruned_loss=0.02439, over 4824.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2085, pruned_loss=0.02931, over 971482.31 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:12:04,583 INFO [train.py:715] (1/8) Epoch 17, batch 33550, loss[loss=0.133, simple_loss=0.2085, pruned_loss=0.02872, over 4799.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2088, pruned_loss=0.02954, over 971804.53 frames.], batch size: 17, lr: 1.28e-04
2022-05-09 05:12:44,744 INFO [train.py:715] (1/8) Epoch 17, batch 33600, loss[loss=0.1631, simple_loss=0.2278, pruned_loss=0.04921, over 4779.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.0292, over 971491.62 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:13:23,720 INFO [train.py:715] (1/8) Epoch 17, batch 33650, loss[loss=0.1125, simple_loss=0.1905, pruned_loss=0.01727, over 4810.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2073, pruned_loss=0.0286, over 971086.25 frames.], batch size: 27, lr: 1.28e-04
2022-05-09 05:14:03,357 INFO [train.py:715] (1/8) Epoch 17, batch 33700, loss[loss=0.1441, simple_loss=0.2134, pruned_loss=0.03741, over 4952.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2062, pruned_loss=0.02822, over 971432.34 frames.], batch size: 24, lr: 1.28e-04
2022-05-09 05:14:42,580 INFO [train.py:715] (1/8) Epoch 17, batch 33750, loss[loss=0.1552, simple_loss=0.2186, pruned_loss=0.0459, over 4785.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2071, pruned_loss=0.02896, over 972148.74 frames.], batch size: 17, lr: 1.28e-04
2022-05-09 05:15:21,392 INFO [train.py:715] (1/8) Epoch 17, batch 33800, loss[loss=0.1468, simple_loss=0.2177, pruned_loss=0.0379, over 4797.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02899, over 972229.61 frames.], batch size: 24, lr: 1.28e-04
2022-05-09 05:16:01,529 INFO [train.py:715] (1/8) Epoch 17, batch 33850, loss[loss=0.1544, simple_loss=0.2324, pruned_loss=0.03822, over 4958.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02908, over 972847.04 frames.], batch size: 39, lr: 1.28e-04
2022-05-09 05:16:41,834 INFO [train.py:715] (1/8) Epoch 17, batch 33900, loss[loss=0.1125, simple_loss=0.1885, pruned_loss=0.01824, over 4767.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.0291, over 972356.60 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:17:21,087 INFO [train.py:715] (1/8) Epoch 17, batch 33950, loss[loss=0.1499, simple_loss=0.234, pruned_loss=0.03289, over 4810.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02887, over 972031.29 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:18:00,090 INFO [train.py:715] (1/8) Epoch 17, batch 34000, loss[loss=0.1213, simple_loss=0.1919, pruned_loss=0.02531, over 4795.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02911, over 971905.05 frames.], batch size: 21, lr: 1.28e-04
2022-05-09 05:18:39,510 INFO [train.py:715] (1/8) Epoch 17, batch 34050, loss[loss=0.1253, simple_loss=0.2044, pruned_loss=0.02311, over 4899.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02959, over 971961.39 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:19:19,502 INFO [train.py:715] (1/8) Epoch 17, batch 34100, loss[loss=0.1321, simple_loss=0.207, pruned_loss=0.02859, over 4787.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02978, over 972571.36 frames.], batch size: 21, lr: 1.28e-04
2022-05-09 05:19:58,306 INFO [train.py:715] (1/8) Epoch 17, batch 34150, loss[loss=0.1347, simple_loss=0.2114, pruned_loss=0.02904, over 4877.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02962, over 972275.52 frames.], batch size: 32, lr: 1.28e-04
2022-05-09 05:20:37,450 INFO [train.py:715] (1/8) Epoch 17, batch 34200, loss[loss=0.1463, simple_loss=0.2207, pruned_loss=0.03592, over 4764.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02929, over 971183.37 frames.], batch size: 17, lr: 1.28e-04
2022-05-09 05:21:16,559 INFO [train.py:715] (1/8) Epoch 17, batch 34250, loss[loss=0.1082, simple_loss=0.181, pruned_loss=0.01767, over 4831.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02937, over 971028.37 frames.], batch size: 13, lr: 1.28e-04
2022-05-09 05:21:55,279 INFO [train.py:715] (1/8) Epoch 17, batch 34300, loss[loss=0.1323, simple_loss=0.205, pruned_loss=0.02979, over 4694.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02954, over 971233.16 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:22:34,164 INFO [train.py:715] (1/8) Epoch 17, batch 34350, loss[loss=0.1384, simple_loss=0.2056, pruned_loss=0.03559, over 4913.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02917, over 971217.08 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:23:13,524 INFO [train.py:715] (1/8) Epoch 17, batch 34400, loss[loss=0.1332, simple_loss=0.2028, pruned_loss=0.03178, over 4747.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02931, over 970744.44 frames.], batch size: 19, lr: 1.28e-04
2022-05-09 05:23:52,513 INFO [train.py:715] (1/8) Epoch 17, batch 34450, loss[loss=0.149, simple_loss=0.2175, pruned_loss=0.04031, over 4837.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.0297, over 971428.83 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:24:30,968 INFO [train.py:715] (1/8) Epoch 17, batch 34500, loss[loss=0.1273, simple_loss=0.1995, pruned_loss=0.02755, over 4988.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02952, over 972020.57 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:25:09,841 INFO [train.py:715] (1/8) Epoch 17, batch 34550, loss[loss=0.114, simple_loss=0.1945, pruned_loss=0.01672, over 4976.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02897, over 972287.41 frames.], batch size: 35, lr: 1.28e-04
2022-05-09 05:25:48,993 INFO [train.py:715] (1/8) Epoch 17, batch 34600, loss[loss=0.1112, simple_loss=0.1862, pruned_loss=0.0181, over 4798.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2076, pruned_loss=0.02901, over 972334.92 frames.], batch size: 21, lr: 1.28e-04
2022-05-09 05:26:27,693 INFO [train.py:715] (1/8) Epoch 17, batch 34650, loss[loss=0.1374, simple_loss=0.2155, pruned_loss=0.02967, over 4957.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2079, pruned_loss=0.02913, over 972653.33 frames.], batch size: 39, lr: 1.28e-04
2022-05-09 05:27:06,960 INFO [train.py:715] (1/8) Epoch 17, batch 34700, loss[loss=0.1366, simple_loss=0.204, pruned_loss=0.03458, over 4752.00 frames.], tot_loss[loss=0.133, simple_loss=0.2076, pruned_loss=0.02923, over 971761.75 frames.], batch size: 16, lr: 1.28e-04
2022-05-09 05:27:45,508 INFO [train.py:715] (1/8) Epoch 17, batch 34750, loss[loss=0.1163, simple_loss=0.1893, pruned_loss=0.02169, over 4804.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02916, over 971820.53 frames.], batch size: 17, lr: 1.28e-04
2022-05-09 05:28:22,197 INFO [train.py:715] (1/8) Epoch 17, batch 34800, loss[loss=0.1263, simple_loss=0.1978, pruned_loss=0.02743, over 4799.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02884, over 971116.11 frames.], batch size: 12, lr: 1.28e-04
2022-05-09 05:29:12,360 INFO [train.py:715] (1/8) Epoch 18, batch 0, loss[loss=0.1212, simple_loss=0.1853, pruned_loss=0.02851, over 4839.00 frames.], tot_loss[loss=0.1212, simple_loss=0.1853, pruned_loss=0.02851, over 4839.00 frames.], batch size: 30, lr: 1.25e-04
2022-05-09 05:29:51,054 INFO [train.py:715] (1/8) Epoch 18, batch 50, loss[loss=0.151, simple_loss=0.2245, pruned_loss=0.03874, over 4792.00 frames.], tot_loss[loss=0.1354, simple_loss=0.21, pruned_loss=0.03037, over 218790.02 frames.], batch size: 12, lr: 1.25e-04
2022-05-09 05:30:31,041 INFO [train.py:715] (1/8) Epoch 18, batch 100, loss[loss=0.1103, simple_loss=0.1892, pruned_loss=0.01569, over 4915.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2075, pruned_loss=0.03036, over 385187.51 frames.], batch size: 29, lr: 1.25e-04
2022-05-09 05:31:10,957 INFO [train.py:715] (1/8) Epoch 18, batch 150, loss[loss=0.142, simple_loss=0.2059, pruned_loss=0.03901, over 4702.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2059, pruned_loss=0.02956, over 515131.33 frames.], batch size: 15, lr: 1.25e-04
2022-05-09 05:31:50,256 INFO [train.py:715] (1/8) Epoch 18, batch 200, loss[loss=0.1217, simple_loss=0.2011, pruned_loss=0.02109, over 4955.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2048, pruned_loss=0.0287, over 617045.10 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 05:32:29,109 INFO [train.py:715] (1/8) Epoch 18, batch 250, loss[loss=0.1129, simple_loss=0.18, pruned_loss=0.02286, over 4803.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2055, pruned_loss=0.02867, over 694392.88 frames.], batch size: 12, lr: 1.25e-04
2022-05-09 05:33:08,560 INFO [train.py:715] (1/8) Epoch 18, batch 300, loss[loss=0.1279, simple_loss=0.1967, pruned_loss=0.02953, over 4865.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02885, over 755835.37 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:33:48,411 INFO [train.py:715] (1/8) Epoch 18, batch 350, loss[loss=0.222, simple_loss=0.2967, pruned_loss=0.07364, over 4798.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02943, over 803901.64 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 05:34:27,358 INFO [train.py:715] (1/8) Epoch 18, batch 400, loss[loss=0.1304, simple_loss=0.2017, pruned_loss=0.02958, over 4828.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02992, over 841056.20 frames.], batch size: 26, lr: 1.25e-04
2022-05-09 05:35:07,143 INFO [train.py:715] (1/8) Epoch 18, batch 450, loss[loss=0.1287, simple_loss=0.2077, pruned_loss=0.02483, over 4981.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03015, over 870482.91 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:35:47,324 INFO [train.py:715] (1/8) Epoch 18, batch 500, loss[loss=0.1169, simple_loss=0.1911, pruned_loss=0.02131, over 4936.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.0296, over 893725.50 frames.], batch size: 29, lr: 1.25e-04
2022-05-09 05:36:27,089 INFO [train.py:715] (1/8) Epoch 18, batch 550, loss[loss=0.1182, simple_loss=0.1912, pruned_loss=0.02263, over 4939.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02979, over 911231.39 frames.], batch size: 23, lr: 1.25e-04
2022-05-09 05:37:06,104 INFO [train.py:715] (1/8) Epoch 18, batch 600, loss[loss=0.196, simple_loss=0.2568, pruned_loss=0.06764, over 4983.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2074, pruned_loss=0.03017, over 925540.55 frames.], batch size: 31, lr: 1.25e-04
2022-05-09 05:37:45,636 INFO [train.py:715] (1/8) Epoch 18, batch 650, loss[loss=0.1257, simple_loss=0.1981, pruned_loss=0.02661, over 4812.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02966, over 935270.81 frames.], batch size: 13, lr: 1.25e-04
2022-05-09 05:38:25,473 INFO [train.py:715] (1/8) Epoch 18, batch 700, loss[loss=0.1518, simple_loss=0.2266, pruned_loss=0.03852, over 4684.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02955, over 943781.32 frames.], batch size: 15, lr: 1.25e-04
2022-05-09 05:39:04,427 INFO [train.py:715] (1/8) Epoch 18, batch 750, loss[loss=0.1677, simple_loss=0.227, pruned_loss=0.0542, over 4915.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2085, pruned_loss=0.02989, over 950567.66 frames.], batch size: 18, lr: 1.25e-04
2022-05-09 05:39:43,253 INFO [train.py:715] (1/8) Epoch 18, batch 800, loss[loss=0.1613, simple_loss=0.2126, pruned_loss=0.05506, over 4872.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.03005, over 955196.91 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:40:22,746 INFO [train.py:715] (1/8) Epoch 18, batch 850, loss[loss=0.1249, simple_loss=0.2012, pruned_loss=0.02429, over 4864.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02998, over 960310.81 frames.], batch size: 22, lr: 1.25e-04
2022-05-09 05:41:02,297 INFO [train.py:715] (1/8) Epoch 18, batch 900, loss[loss=0.1157, simple_loss=0.1879, pruned_loss=0.0217, over 4924.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02997, over 961887.79 frames.], batch size: 18, lr: 1.25e-04
2022-05-09 05:41:41,276 INFO [train.py:715] (1/8) Epoch 18, batch 950, loss[loss=0.1317, simple_loss=0.2018, pruned_loss=0.03077, over 4730.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02986, over 963977.47 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:42:20,886 INFO [train.py:715] (1/8) Epoch 18, batch 1000, loss[loss=0.1415, simple_loss=0.2272, pruned_loss=0.02795, over 4826.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2069, pruned_loss=0.02999, over 965737.54 frames.], batch size: 15, lr: 1.25e-04
2022-05-09 05:43:00,525 INFO [train.py:715] (1/8) Epoch 18, batch 1050, loss[loss=0.1375, simple_loss=0.2081, pruned_loss=0.03348, over 4827.00 frames.], tot_loss[loss=0.1334, simple_loss=0.207, pruned_loss=0.02992, over 967645.55 frames.], batch size: 13, lr: 1.25e-04
2022-05-09 05:43:39,929 INFO [train.py:715] (1/8) Epoch 18, batch 1100, loss[loss=0.1336, simple_loss=0.2041, pruned_loss=0.03154, over 4723.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2073, pruned_loss=0.0301, over 968449.48 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:44:18,723 INFO [train.py:715] (1/8) Epoch 18, batch 1150, loss[loss=0.1135, simple_loss=0.1956, pruned_loss=0.01574, over 4918.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.0295, over 969831.69 frames.], batch size: 29, lr: 1.25e-04
2022-05-09 05:44:58,549 INFO [train.py:715] (1/8) Epoch 18, batch 1200, loss[loss=0.1123, simple_loss=0.1785, pruned_loss=0.0231, over 4785.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02935, over 969687.52 frames.], batch size: 14, lr: 1.25e-04
2022-05-09 05:45:38,520 INFO [train.py:715] (1/8) Epoch 18, batch 1250, loss[loss=0.1284, simple_loss=0.21, pruned_loss=0.02343, over 4988.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.0297, over 970237.32 frames.], batch size: 14, lr: 1.25e-04
2022-05-09 05:46:17,550 INFO [train.py:715] (1/8) Epoch 18, batch 1300, loss[loss=0.1396, simple_loss=0.2152, pruned_loss=0.03202, over 4800.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2069, pruned_loss=0.02968, over 971428.35 frames.], batch size: 25, lr: 1.25e-04
2022-05-09 05:46:56,370 INFO [train.py:715] (1/8) Epoch 18, batch 1350, loss[loss=0.1403, simple_loss=0.2082, pruned_loss=0.03623, over 4741.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02949, over 971644.44 frames.], batch size: 12, lr: 1.25e-04
2022-05-09 05:47:35,778 INFO [train.py:715] (1/8) Epoch 18, batch 1400, loss[loss=0.1413, simple_loss=0.2078, pruned_loss=0.03737, over 4985.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2061, pruned_loss=0.02945, over 972700.58 frames.], batch size: 15, lr: 1.25e-04
2022-05-09 05:48:15,003 INFO [train.py:715] (1/8) Epoch 18, batch 1450, loss[loss=0.1502, simple_loss=0.2332, pruned_loss=0.03363, over 4813.00 frames.], tot_loss[loss=0.132, simple_loss=0.2056, pruned_loss=0.02917, over 973554.93 frames.], batch size: 25, lr: 1.25e-04
2022-05-09 05:48:53,401 INFO [train.py:715] (1/8) Epoch 18, batch 1500, loss[loss=0.1298, simple_loss=0.2172, pruned_loss=0.02116, over 4878.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02889, over 972868.78 frames.], batch size: 22, lr: 1.25e-04
2022-05-09 05:49:32,904 INFO [train.py:715] (1/8) Epoch 18, batch 1550, loss[loss=0.1435, simple_loss=0.212, pruned_loss=0.03749, over 4931.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02879, over 972864.26 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:50:12,319 INFO [train.py:715] (1/8) Epoch 18, batch 1600, loss[loss=0.1324, simple_loss=0.2082, pruned_loss=0.02826, over 4951.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02868, over 972704.82 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:50:51,518 INFO [train.py:715] (1/8) Epoch 18, batch 1650, loss[loss=0.1344, simple_loss=0.2019, pruned_loss=0.03347, over 4647.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2058, pruned_loss=0.02883, over 973045.89 frames.], batch size: 13, lr: 1.25e-04
2022-05-09 05:51:30,465 INFO [train.py:715] (1/8) Epoch 18, batch 1700, loss[loss=0.1301, simple_loss=0.2052, pruned_loss=0.02751, over 4756.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2057, pruned_loss=0.02873, over 972691.59 frames.], batch size: 19, lr: 1.25e-04
2022-05-09 05:52:09,881 INFO [train.py:715] (1/8) Epoch 18, batch 1750, loss[loss=0.1373, simple_loss=0.2095, pruned_loss=0.03252, over 4980.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2056, pruned_loss=0.029, over 972249.40 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:52:49,170 INFO [train.py:715] (1/8) Epoch 18, batch 1800, loss[loss=0.1468, simple_loss=0.2204, pruned_loss=0.03656, over 4829.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2053, pruned_loss=0.02865, over 972346.29 frames.], batch size: 30, lr: 1.25e-04
2022-05-09 05:53:27,453 INFO [train.py:715] (1/8) Epoch 18, batch 1850, loss[loss=0.1189, simple_loss=0.1906, pruned_loss=0.0236, over 4851.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2053, pruned_loss=0.02849, over 971096.17 frames.], batch size: 20, lr: 1.25e-04
2022-05-09 05:54:06,242 INFO [train.py:715] (1/8) Epoch 18, batch 1900, loss[loss=0.1147, simple_loss=0.1855, pruned_loss=0.02196, over 4836.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02882, over 972158.31 frames.], batch size: 26, lr: 1.25e-04
2022-05-09 05:54:45,621 INFO [train.py:715] (1/8) Epoch 18, batch 1950, loss[loss=0.1437, simple_loss=0.2123, pruned_loss=0.03758, over 4841.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02882, over 973055.85 frames.], batch size: 15, lr: 1.25e-04
2022-05-09 05:55:24,352 INFO [train.py:715] (1/8) Epoch 18, batch 2000, loss[loss=0.09114, simple_loss=0.1631, pruned_loss=0.009602, over 4802.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02926, over 973036.49 frames.], batch size: 12, lr: 1.25e-04
2022-05-09 05:56:02,840 INFO [train.py:715] (1/8) Epoch 18, batch 2050, loss[loss=0.1238, simple_loss=0.2006, pruned_loss=0.02347, over 4941.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02911, over 973454.32 frames.], batch size: 35, lr: 1.25e-04
2022-05-09 05:56:42,078 INFO [train.py:715] (1/8) Epoch 18, batch 2100, loss[loss=0.138, simple_loss=0.2134, pruned_loss=0.03129, over 4879.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02938, over 973689.63 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:57:21,524 INFO [train.py:715] (1/8) Epoch 18, batch 2150, loss[loss=0.167, simple_loss=0.2358, pruned_loss=0.04907, over 4973.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02912, over 973479.80 frames.], batch size: 35, lr: 1.25e-04
2022-05-09 05:57:59,830 INFO [train.py:715] (1/8) Epoch 18, batch 2200, loss[loss=0.1365, simple_loss=0.209, pruned_loss=0.03204, over 4970.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2058, pruned_loss=0.02898, over 973359.69 frames.], batch size: 24, lr: 1.25e-04
2022-05-09 05:58:39,477 INFO [train.py:715] (1/8) Epoch 18, batch 2250, loss[loss=0.1518, simple_loss=0.2243, pruned_loss=0.03963, over 4794.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02895, over 972737.50 frames.], batch size: 14, lr: 1.25e-04
2022-05-09 05:59:18,828 INFO [train.py:715] (1/8) Epoch 18, batch 2300, loss[loss=0.1155, simple_loss=0.1866, pruned_loss=0.02219, over 4799.00 frames.], tot_loss[loss=0.131, simple_loss=0.2052, pruned_loss=0.0284, over 972035.58 frames.], batch size: 14, lr: 1.25e-04
2022-05-09 05:59:57,617 INFO [train.py:715] (1/8) Epoch 18, batch 2350, loss[loss=0.1422, simple_loss=0.2175, pruned_loss=0.03346, over 4988.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2053, pruned_loss=0.02841, over 972515.40 frames.], batch size: 25, lr: 1.25e-04
2022-05-09 06:00:36,228 INFO [train.py:715] (1/8) Epoch 18, batch 2400, loss[loss=0.1054, simple_loss=0.1761, pruned_loss=0.01735, over 4958.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2053, pruned_loss=0.02791, over 972523.79 frames.], batch size: 35, lr: 1.25e-04
2022-05-09 06:01:15,690 INFO [train.py:715] (1/8) Epoch 18, batch 2450, loss[loss=0.1316, simple_loss=0.2111, pruned_loss=0.02608, over 4758.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02828, over 972307.44 frames.], batch size: 19, lr: 1.25e-04
2022-05-09 06:01:55,086 INFO [train.py:715] (1/8) Epoch 18, batch 2500, loss[loss=0.1238, simple_loss=0.1979, pruned_loss=0.02483, over 4934.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.0289, over 972218.63 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 06:02:33,093 INFO [train.py:715] (1/8) Epoch 18, batch 2550, loss[loss=0.1185, simple_loss=0.1923, pruned_loss=0.0223, over 4987.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02909, over 972182.30 frames.], batch size: 28, lr: 1.25e-04
2022-05-09 06:03:11,866 INFO [train.py:715] (1/8) Epoch 18, batch 2600, loss[loss=0.1349, simple_loss=0.2007, pruned_loss=0.03456, over 4785.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02906, over 972465.94 frames.], batch size: 17, lr: 1.25e-04
2022-05-09 06:03:51,785 INFO [train.py:715] (1/8) Epoch 18, batch 2650, loss[loss=0.1236, simple_loss=0.2039, pruned_loss=0.02165, over 4849.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02942, over 972981.45 frames.], batch size: 15, lr: 1.25e-04
2022-05-09 06:04:30,526 INFO [train.py:715] (1/8) Epoch 18, batch 2700, loss[loss=0.09509, simple_loss=0.1661, pruned_loss=0.01203, over 4780.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02906, over 972669.22 frames.], batch size: 12, lr: 1.25e-04
2022-05-09 06:05:08,884 INFO [train.py:715] (1/8) Epoch 18, batch 2750, loss[loss=0.1054, simple_loss=0.1836, pruned_loss=0.01359, over 4910.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02878, over 972093.84 frames.], batch size: 17, lr: 1.25e-04
2022-05-09 06:05:47,972 INFO [train.py:715] (1/8) Epoch 18, batch 2800, loss[loss=0.1401, simple_loss=0.2237, pruned_loss=0.02824, over 4814.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02872, over 971927.70 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 06:06:27,518 INFO [train.py:715] (1/8) Epoch 18, batch 2850, loss[loss=0.1189, simple_loss=0.1883, pruned_loss=0.02472, over 4807.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02894, over 971713.17 frames.], batch size: 26, lr: 1.25e-04
2022-05-09 06:07:06,088 INFO [train.py:715] (1/8) Epoch 18, batch 2900, loss[loss=0.1329, simple_loss=0.212, pruned_loss=0.02692, over 4879.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02895, over 971987.40 frames.], batch size: 22, lr: 1.25e-04
2022-05-09 06:07:44,915 INFO [train.py:715] (1/8) Epoch 18, batch 2950, loss[loss=0.1221, simple_loss=0.2005, pruned_loss=0.02186, over 4816.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02926, over 972949.56 frames.], batch size: 26, lr: 1.25e-04
2022-05-09 06:08:24,282 INFO [train.py:715] (1/8) Epoch 18, batch 3000, loss[loss=0.1413, simple_loss=0.2236, pruned_loss=0.0295, over 4801.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2066, pruned_loss=0.0296, over 972876.78 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 06:08:24,282 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 06:08:34,097 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1047, simple_loss=0.1881, pruned_loss=0.01065, over 914524.00 frames.
2022-05-09 06:09:14,106 INFO [train.py:715] (1/8) Epoch 18, batch 3050, loss[loss=0.1389, simple_loss=0.2042, pruned_loss=0.03678, over 4812.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02936, over 972718.17 frames.], batch size: 12, lr: 1.25e-04
2022-05-09 06:09:52,622 INFO [train.py:715] (1/8) Epoch 18, batch 3100, loss[loss=0.1164, simple_loss=0.2003, pruned_loss=0.01627, over 4950.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02911, over 972576.20 frames.], batch size: 29, lr: 1.25e-04
2022-05-09 06:10:31,510 INFO [train.py:715] (1/8) Epoch 18, batch 3150, loss[loss=0.1425, simple_loss=0.2241, pruned_loss=0.03048, over 4748.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02914, over 972563.61 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 06:11:10,546 INFO [train.py:715] (1/8) Epoch 18, batch 3200, loss[loss=0.1243, simple_loss=0.2001, pruned_loss=0.02425, over 4760.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02926, over 972530.69 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:11:50,029 INFO [train.py:715] (1/8) Epoch 18, batch 3250, loss[loss=0.166, simple_loss=0.232, pruned_loss=0.05003, over 4913.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02963, over 972508.31 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 06:12:28,191 INFO [train.py:715] (1/8) Epoch 18, batch 3300, loss[loss=0.1188, simple_loss=0.1854, pruned_loss=0.02608, over 4910.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02964, over 972150.56 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 06:13:07,648 INFO [train.py:715] (1/8) Epoch 18, batch 3350, loss[loss=0.175, simple_loss=0.2406, pruned_loss=0.05464, over 4843.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2099, pruned_loss=0.03034, over 972618.17 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 06:13:47,788 INFO [train.py:715] (1/8) Epoch 18, batch 3400, loss[loss=0.1214, simple_loss=0.1946, pruned_loss=0.02412, over 4934.00 frames.], tot_loss[loss=0.1343, simple_loss=0.209, pruned_loss=0.02985, over 972690.14 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 06:14:26,393 INFO [train.py:715] (1/8) Epoch 18, batch 3450, loss[loss=0.1381, simple_loss=0.2074, pruned_loss=0.03441, over 4957.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2085, pruned_loss=0.02954, over 973128.08 frames.], batch size: 40, lr: 1.24e-04
2022-05-09 06:15:05,249 INFO [train.py:715] (1/8) Epoch 18, batch 3500, loss[loss=0.1229, simple_loss=0.2027, pruned_loss=0.02159, over 4828.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.0294, over 972852.25 frames.], batch size: 27, lr: 1.24e-04
2022-05-09 06:15:45,331 INFO [train.py:715] (1/8) Epoch 18, batch 3550, loss[loss=0.1461, simple_loss=0.218, pruned_loss=0.03706, over 4931.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02944, over 972193.24 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 06:16:24,508 INFO [train.py:715] (1/8) Epoch 18, batch 3600, loss[loss=0.1526, simple_loss=0.2317, pruned_loss=0.03681, over 4864.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02947, over 972326.81 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 06:17:03,259 INFO [train.py:715] (1/8) Epoch 18, batch 3650, loss[loss=0.1234, simple_loss=0.1992, pruned_loss=0.02378, over 4813.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02931, over 972586.30 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 06:17:42,725 INFO [train.py:715] (1/8) Epoch 18, batch 3700, loss[loss=0.1151, simple_loss=0.194, pruned_loss=0.0181, over 4930.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02951, over 972265.31 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 06:18:21,997 INFO [train.py:715] (1/8) Epoch 18, batch 3750, loss[loss=0.1113, simple_loss=0.1842, pruned_loss=0.01926, over 4637.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02928, over 972235.91 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 06:18:59,950 INFO [train.py:715] (1/8) Epoch 18, batch 3800, loss[loss=0.1457, simple_loss=0.223, pruned_loss=0.03423, over 4838.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2064, pruned_loss=0.02935, over 972196.77 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:19:39,328 INFO [train.py:715] (1/8) Epoch 18, batch 3850, loss[loss=0.1402, simple_loss=0.2159, pruned_loss=0.03227, over 4848.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.0293, over 972029.14 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:20:19,350 INFO [train.py:715] (1/8) Epoch 18, batch 3900, loss[loss=0.1194, simple_loss=0.1976, pruned_loss=0.02062, over 4987.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02947, over 972249.47 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:20:57,824 INFO [train.py:715] (1/8) Epoch 18, batch 3950, loss[loss=0.1332, simple_loss=0.2078, pruned_loss=0.02932, over 4844.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02928, over 972275.80 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 06:21:37,236 INFO [train.py:715] (1/8) Epoch 18, batch 4000, loss[loss=0.1564, simple_loss=0.2319, pruned_loss=0.04048, over 4970.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02966, over 973070.86 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:22:16,731 INFO [train.py:715] (1/8) Epoch 18, batch 4050, loss[loss=0.1411, simple_loss=0.2304, pruned_loss=0.02586, over 4794.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02942, over 973815.96 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:22:56,009 INFO [train.py:715] (1/8) Epoch 18, batch 4100, loss[loss=0.1457, simple_loss=0.2168, pruned_loss=0.03734, over 4936.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02925, over 973248.59 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:23:34,759 INFO [train.py:715] (1/8) Epoch 18, batch 4150, loss[loss=0.1387, simple_loss=0.2109, pruned_loss=0.03324, over 4854.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02896, over 972782.50 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 06:24:14,197 INFO [train.py:715] (1/8) Epoch 18, batch 4200, loss[loss=0.1126, simple_loss=0.1812, pruned_loss=0.02194, over 4896.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02848, over 972270.57 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:24:53,577 INFO [train.py:715] (1/8) Epoch 18, batch 4250, loss[loss=0.1242, simple_loss=0.2078, pruned_loss=0.02027, over 4981.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2066, pruned_loss=0.02811, over 973177.69 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:25:32,487 INFO [train.py:715] (1/8) Epoch 18, batch 4300, loss[loss=0.1363, simple_loss=0.2072, pruned_loss=0.0327, over 4919.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02857, over 973681.09 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 06:26:12,599 INFO [train.py:715] (1/8) Epoch 18, batch 4350, loss[loss=0.134, simple_loss=0.2128, pruned_loss=0.02762, over 4827.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02854, over 973129.67 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 06:26:52,056 INFO [train.py:715] (1/8) Epoch 18, batch 4400, loss[loss=0.1389, simple_loss=0.2166, pruned_loss=0.03062, over 4825.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02841, over 973391.37 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:27:31,549 INFO [train.py:715] (1/8) Epoch 18, batch 4450, loss[loss=0.1243, simple_loss=0.2015, pruned_loss=0.02356, over 4950.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.02794, over 972983.05 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:28:09,898 INFO [train.py:715] (1/8) Epoch 18, batch 4500, loss[loss=0.134, simple_loss=0.2108, pruned_loss=0.02862, over 4910.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2053, pruned_loss=0.0279, over 972678.05 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:28:49,167 INFO [train.py:715] (1/8) Epoch 18, batch 4550, loss[loss=0.1328, simple_loss=0.2166, pruned_loss=0.02449, over 4810.00 frames.], tot_loss[loss=0.131, simple_loss=0.2056, pruned_loss=0.02818, over 973179.93 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 06:29:29,011 INFO [train.py:715] (1/8) Epoch 18, batch 4600, loss[loss=0.1221, simple_loss=0.21, pruned_loss=0.01712, over 4980.00 frames.], tot_loss[loss=0.131, simple_loss=0.2057, pruned_loss=0.02815, over 972454.35 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 06:30:07,898 INFO [train.py:715] (1/8) Epoch 18, batch 4650, loss[loss=0.1352, simple_loss=0.2211, pruned_loss=0.02467, over 4977.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2053, pruned_loss=0.02797, over 972004.51 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 06:30:47,011 INFO [train.py:715] (1/8) Epoch 18, batch 4700, loss[loss=0.1135, simple_loss=0.1895, pruned_loss=0.01874, over 4911.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02855, over 972039.43 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:31:26,064 INFO [train.py:715] (1/8) Epoch 18, batch 4750, loss[loss=0.1436, simple_loss=0.2158, pruned_loss=0.03567, over 4973.00 frames.], tot_loss[loss=0.1321, simple_loss=0.207, pruned_loss=0.0286, over 972976.83 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:32:06,181 INFO [train.py:715] (1/8) Epoch 18, batch 4800, loss[loss=0.1277, simple_loss=0.1978, pruned_loss=0.02882, over 4987.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2067, pruned_loss=0.0284, over 972824.66 frames.], batch size: 33, lr: 1.24e-04
2022-05-09 06:32:44,917 INFO [train.py:715] (1/8) Epoch 18, batch 4850, loss[loss=0.1531, simple_loss=0.2323, pruned_loss=0.03699, over 4822.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2054, pruned_loss=0.02759, over 972924.85 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:33:24,373 INFO [train.py:715] (1/8) Epoch 18, batch 4900, loss[loss=0.1429, simple_loss=0.2221, pruned_loss=0.03189, over 4764.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02827, over 972832.84 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 06:34:04,558 INFO [train.py:715] (1/8) Epoch 18, batch 4950, loss[loss=0.09055, simple_loss=0.1647, pruned_loss=0.008192, over 4756.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02861, over 972457.29 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 06:34:43,673 INFO [train.py:715] (1/8) Epoch 18, batch 5000, loss[loss=0.1224, simple_loss=0.1939, pruned_loss=0.02552, over 4747.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.0287, over 972613.55 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:35:22,353 INFO [train.py:715] (1/8) Epoch 18, batch 5050, loss[loss=0.1263, simple_loss=0.2015, pruned_loss=0.0256, over 4916.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02909, over 972696.48 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 06:36:01,524 INFO [train.py:715] (1/8) Epoch 18, batch 5100, loss[loss=0.1176, simple_loss=0.193, pruned_loss=0.02114, over 4978.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02877, over 972466.38 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:36:41,085 INFO [train.py:715] (1/8) Epoch 18, batch 5150, loss[loss=0.1623, simple_loss=0.2224, pruned_loss=0.05104, over 4847.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02867, over 971920.80 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 06:37:19,651 INFO [train.py:715] (1/8) Epoch 18, batch 5200, loss[loss=0.1551, simple_loss=0.2346, pruned_loss=0.03783, over 4972.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.0289, over 972412.61 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:37:59,022 INFO [train.py:715] (1/8) Epoch 18, batch 5250, loss[loss=0.1321, simple_loss=0.2076, pruned_loss=0.0283, over 4924.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.0284, over 972204.48 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 06:38:38,938 INFO [train.py:715] (1/8) Epoch 18, batch 5300, loss[loss=0.1335, simple_loss=0.2098, pruned_loss=0.02858, over 4922.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02831, over 972038.86 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:39:18,962 INFO [train.py:715] (1/8) Epoch 18, batch 5350, loss[loss=0.1245, simple_loss=0.2081, pruned_loss=0.02049, over 4910.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02849, over 971739.29 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:39:57,057 INFO [train.py:715] (1/8) Epoch 18, batch 5400, loss[loss=0.1376, simple_loss=0.2082, pruned_loss=0.03353, over 4994.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02853, over 971226.37 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:40:38,714 INFO [train.py:715] (1/8) Epoch 18, batch 5450, loss[loss=0.1341, simple_loss=0.1927, pruned_loss=0.0378, over 4896.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.0286, over 971199.94 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:41:19,095 INFO [train.py:715] (1/8) Epoch 18, batch 5500, loss[loss=0.1174, simple_loss=0.1956, pruned_loss=0.01958, over 4956.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02843, over 970943.38 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:41:58,075 INFO [train.py:715] (1/8) Epoch 18, batch 5550, loss[loss=0.1143, simple_loss=0.189, pruned_loss=0.01984, over 4959.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02875, over 970984.23 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:42:36,880 INFO [train.py:715] (1/8) Epoch 18, batch 5600, loss[loss=0.1197, simple_loss=0.1962, pruned_loss=0.02159, over 4927.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02878, over 970379.30 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 06:43:15,922 INFO [train.py:715] (1/8) Epoch 18, batch 5650, loss[loss=0.1323, simple_loss=0.2006, pruned_loss=0.03205, over 4943.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02891, over 969846.42 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 06:43:55,545 INFO [train.py:715] (1/8) Epoch 18, batch 5700, loss[loss=0.1492, simple_loss=0.2269, pruned_loss=0.03577, over 4789.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02888, over 970361.87 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:44:33,666 INFO [train.py:715] (1/8) Epoch 18, batch 5750, loss[loss=0.1284, simple_loss=0.2073, pruned_loss=0.02478, over 4779.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2073, pruned_loss=0.02876, over 971398.51 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:45:12,596 INFO [train.py:715] (1/8) Epoch 18, batch 5800, loss[loss=0.1301, simple_loss=0.2062, pruned_loss=0.027, over 4804.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02854, over 971721.42 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:45:52,337 INFO [train.py:715] (1/8) Epoch 18, batch 5850, loss[loss=0.1228, simple_loss=0.192, pruned_loss=0.02679, over 4836.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02835, over 971838.93 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 06:46:31,451 INFO [train.py:715] (1/8) Epoch 18, batch 5900, loss[loss=0.1324, simple_loss=0.209, pruned_loss=0.02791, over 4793.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02873, over 971578.62 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:47:10,140 INFO [train.py:715] (1/8) Epoch 18, batch 5950, loss[loss=0.1177, simple_loss=0.1937, pruned_loss=0.02084, over 4962.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02906, over 972094.89 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:47:49,546 INFO [train.py:715] (1/8) Epoch 18, batch 6000, loss[loss=0.1457, simple_loss=0.2219, pruned_loss=0.03474, over 4829.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.0295, over 971104.27 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:47:49,547 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 06:47:59,476 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1047, simple_loss=0.188, pruned_loss=0.01075, over 914524.00 frames.
2022-05-09 06:48:39,108 INFO [train.py:715] (1/8) Epoch 18, batch 6050, loss[loss=0.1134, simple_loss=0.1866, pruned_loss=0.02004, over 4820.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02931, over 971345.20 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 06:49:18,283 INFO [train.py:715] (1/8) Epoch 18, batch 6100, loss[loss=0.1345, simple_loss=0.201, pruned_loss=0.03394, over 4785.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02884, over 971943.13 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:49:56,625 INFO [train.py:715] (1/8) Epoch 18, batch 6150, loss[loss=0.1108, simple_loss=0.1901, pruned_loss=0.01576, over 4854.00 frames.], tot_loss[loss=0.133, simple_loss=0.2076, pruned_loss=0.02914, over 972361.95 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 06:50:35,918 INFO [train.py:715] (1/8) Epoch 18, batch 6200, loss[loss=0.1407, simple_loss=0.2103, pruned_loss=0.03554, over 4984.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02938, over 972517.06 frames.], batch size: 33, lr: 1.24e-04
2022-05-09 06:51:15,497 INFO [train.py:715] (1/8) Epoch 18, batch 6250, loss[loss=0.141, simple_loss=0.2173, pruned_loss=0.03236, over 4886.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2087, pruned_loss=0.02993, over 972620.32 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 06:51:54,529 INFO [train.py:715] (1/8) Epoch 18, batch 6300, loss[loss=0.1148, simple_loss=0.1808, pruned_loss=0.02445, over 4830.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02933, over 973484.53 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 06:52:33,701 INFO [train.py:715] (1/8) Epoch 18, batch 6350, loss[loss=0.1235, simple_loss=0.2037, pruned_loss=0.02165, over 4797.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02898, over 972945.27 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 06:53:12,892 INFO [train.py:715] (1/8) Epoch 18, batch 6400, loss[loss=0.1247, simple_loss=0.1922, pruned_loss=0.02856, over 4942.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02878, over 972814.42 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:53:52,073 INFO [train.py:715] (1/8) Epoch 18, batch 6450, loss[loss=0.1167, simple_loss=0.1951, pruned_loss=0.01913, over 4989.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.0288, over 972092.54 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 06:54:30,355 INFO [train.py:715] (1/8) Epoch 18, batch 6500, loss[loss=0.1124, simple_loss=0.1902, pruned_loss=0.01729, over 4800.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02851, over 972389.77 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 06:55:08,635 INFO [train.py:715] (1/8) Epoch 18, batch 6550, loss[loss=0.1327, simple_loss=0.2127, pruned_loss=0.02636, over 4763.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02822, over 972481.21 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:55:48,100 INFO [train.py:715] (1/8) Epoch 18, batch 6600, loss[loss=0.1458, simple_loss=0.2136, pruned_loss=0.03902, over 4961.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02839, over 973428.69 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:56:27,457 INFO [train.py:715] (1/8) Epoch 18, batch 6650, loss[loss=0.1292, simple_loss=0.2094, pruned_loss=0.02444, over 4884.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02872, over 972847.00 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 06:57:05,500 INFO [train.py:715] (1/8) Epoch 18, batch 6700, loss[loss=0.1283, simple_loss=0.2098, pruned_loss=0.02337, over 4951.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.0283, over 973198.58 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:57:44,487 INFO [train.py:715] (1/8) Epoch 18, batch 6750, loss[loss=0.1741, simple_loss=0.2455, pruned_loss=0.05132, over 4943.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02898, over 973217.43 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 06:58:23,844 INFO [train.py:715] (1/8) Epoch 18, batch 6800, loss[loss=0.1229, simple_loss=0.1992, pruned_loss=0.02328, over 4756.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02894, over 973334.57 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:59:02,551 INFO [train.py:715] (1/8) Epoch 18, batch 6850, loss[loss=0.1268, simple_loss=0.1952, pruned_loss=0.02924, over 4951.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2069, pruned_loss=0.02832, over 973781.19 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:59:40,720 INFO [train.py:715] (1/8) Epoch 18, batch 6900, loss[loss=0.1185, simple_loss=0.2008, pruned_loss=0.0181, over 4855.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2066, pruned_loss=0.02827, over 974107.59 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 07:00:20,315 INFO [train.py:715] (1/8) Epoch 18, batch 6950, loss[loss=0.1339, simple_loss=0.2195, pruned_loss=0.0242, over 4934.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2072, pruned_loss=0.02854, over 972606.09 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 07:00:59,038 INFO [train.py:715] (1/8) Epoch 18, batch 7000, loss[loss=0.1258, simple_loss=0.1978, pruned_loss=0.0269, over 4878.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2068, pruned_loss=0.02847, over 972400.12 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 07:01:37,420 INFO [train.py:715] (1/8) Epoch 18, batch 7050, loss[loss=0.1524, simple_loss=0.2109, pruned_loss=0.04695, over 4874.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02838, over 971921.57 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:02:16,623 INFO [train.py:715] (1/8) Epoch 18, batch 7100, loss[loss=0.1314, simple_loss=0.2163, pruned_loss=0.0233, over 4780.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02824, over 972189.88 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:02:56,201 INFO [train.py:715] (1/8) Epoch 18, batch 7150, loss[loss=0.1565, simple_loss=0.2362, pruned_loss=0.03839, over 4746.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02804, over 972342.98 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 07:03:34,828 INFO [train.py:715] (1/8) Epoch 18, batch 7200, loss[loss=0.1371, simple_loss=0.2112, pruned_loss=0.0315, over 4785.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02835, over 972213.20 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:04:13,058 INFO [train.py:715] (1/8) Epoch 18, batch 7250, loss[loss=0.1657, simple_loss=0.2271, pruned_loss=0.05212, over 4705.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02844, over 972816.74 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:04:52,159 INFO [train.py:715] (1/8) Epoch 18, batch 7300, loss[loss=0.1194, simple_loss=0.1965, pruned_loss=0.02113, over 4761.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02832, over 972948.08 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 07:05:31,282 INFO [train.py:715] (1/8) Epoch 18, batch 7350, loss[loss=0.1392, simple_loss=0.226, pruned_loss=0.02622, over 4821.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02855, over 973176.18 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:06:09,355 INFO [train.py:715] (1/8) Epoch 18, batch 7400, loss[loss=0.1693, simple_loss=0.2445, pruned_loss=0.04705, over 4987.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2056, pruned_loss=0.02785, over 972793.34 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 07:06:48,510 INFO [train.py:715] (1/8) Epoch 18, batch 7450, loss[loss=0.1181, simple_loss=0.1883, pruned_loss=0.02394, over 4778.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02816, over 972561.59 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:07:27,758 INFO [train.py:715] (1/8) Epoch 18, batch 7500, loss[loss=0.1663, simple_loss=0.2394, pruned_loss=0.04658, over 4954.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02836, over 972343.35 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 07:08:05,370 INFO [train.py:715] (1/8) Epoch 18, batch 7550, loss[loss=0.1315, simple_loss=0.2133, pruned_loss=0.0248, over 4986.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02841, over 972897.01 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 07:08:43,909 INFO [train.py:715] (1/8) Epoch 18, batch 7600, loss[loss=0.1269, simple_loss=0.2014, pruned_loss=0.02621, over 4832.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02836, over 972972.53 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:09:23,637 INFO [train.py:715] (1/8) Epoch 18, batch 7650, loss[loss=0.1144, simple_loss=0.1928, pruned_loss=0.01807, over 4820.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2049, pruned_loss=0.02822, over 972031.46 frames.], batch size: 27, lr: 1.24e-04
2022-05-09 07:10:02,903 INFO [train.py:715] (1/8) Epoch 18, batch 7700, loss[loss=0.1282, simple_loss=0.2036, pruned_loss=0.02639, over 4705.00 frames.], tot_loss[loss=0.13, simple_loss=0.2042, pruned_loss=0.02786, over 972144.61 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:10:41,608 INFO [train.py:715] (1/8) Epoch 18, batch 7750, loss[loss=0.1318, simple_loss=0.2105, pruned_loss=0.02658, over 4827.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2046, pruned_loss=0.02799, over 972636.53 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 07:11:21,213 INFO [train.py:715] (1/8) Epoch 18, batch 7800, loss[loss=0.1166, simple_loss=0.1879, pruned_loss=0.02263, over 4833.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02811, over 972207.57 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:12:01,094 INFO [train.py:715] (1/8) Epoch 18, batch 7850, loss[loss=0.1262, simple_loss=0.2117, pruned_loss=0.02028, over 4944.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02838, over 972257.84 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 07:12:40,474 INFO [train.py:715] (1/8) Epoch 18, batch 7900, loss[loss=0.1313, simple_loss=0.2082, pruned_loss=0.02721, over 4940.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02844, over 972333.99 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 07:13:19,673 INFO [train.py:715] (1/8) Epoch 18, batch 7950, loss[loss=0.1445, simple_loss=0.2152, pruned_loss=0.03687, over 4753.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02857, over 972512.18 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:13:59,113 INFO [train.py:715] (1/8) Epoch 18, batch 8000, loss[loss=0.1305, simple_loss=0.2129, pruned_loss=0.02408, over 4975.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2052, pruned_loss=0.02833, over 972593.95 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:14:38,127 INFO [train.py:715] (1/8) Epoch 18, batch 8050, loss[loss=0.1348, simple_loss=0.2234, pruned_loss=0.02314, over 4975.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2058, pruned_loss=0.02894, over 972776.95 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:15:16,607 INFO [train.py:715] (1/8) Epoch 18, batch 8100, loss[loss=0.1121, simple_loss=0.1935, pruned_loss=0.0154, over 4889.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2056, pruned_loss=0.02889, over 972625.45 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 07:15:55,248 INFO [train.py:715] (1/8) Epoch 18, batch 8150, loss[loss=0.156, simple_loss=0.2358, pruned_loss=0.03809, over 4748.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2053, pruned_loss=0.02905, over 971526.06 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:16:34,307 INFO [train.py:715] (1/8) Epoch 18, batch 8200, loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.0323, over 4924.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2057, pruned_loss=0.02937, over 971384.81 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:17:12,925 INFO [train.py:715] (1/8) Epoch 18, batch 8250, loss[loss=0.12, simple_loss=0.1951, pruned_loss=0.02241, over 4992.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02903, over 971845.68 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:17:51,218 INFO [train.py:715] (1/8) Epoch 18, batch 8300, loss[loss=0.1403, simple_loss=0.2228, pruned_loss=0.02884, over 4905.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03007, over 972372.98 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 07:18:31,281 INFO [train.py:715] (1/8) Epoch 18, batch 8350, loss[loss=0.1083, simple_loss=0.1856, pruned_loss=0.01552, over 4934.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02966, over 972752.62 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 07:19:10,480 INFO [train.py:715] (1/8) Epoch 18, batch 8400, loss[loss=0.1278, simple_loss=0.2006, pruned_loss=0.02744, over 4842.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02982, over 972703.00 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 07:19:48,917 INFO [train.py:715] (1/8) Epoch 18, batch 8450, loss[loss=0.12, simple_loss=0.1987, pruned_loss=0.02069, over 4760.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.0296, over 972405.54 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:20:28,154 INFO [train.py:715] (1/8) Epoch 18, batch 8500, loss[loss=0.1437, simple_loss=0.2179, pruned_loss=0.03472, over 4966.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.02935, over 972109.25 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:21:07,333 INFO [train.py:715] (1/8) Epoch 18, batch 8550, loss[loss=0.12, simple_loss=0.1957, pruned_loss=0.02215, over 4838.00 frames.], tot_loss[loss=0.133, simple_loss=0.2064, pruned_loss=0.02977, over 972361.13 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 07:21:46,032 INFO [train.py:715] (1/8) Epoch 18, batch 8600, loss[loss=0.1332, simple_loss=0.2065, pruned_loss=0.02998, over 4852.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2072, pruned_loss=0.03, over 972305.23 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:22:24,236 INFO [train.py:715] (1/8) Epoch 18, batch 8650, loss[loss=0.1266, simple_loss=0.211, pruned_loss=0.02114, over 4955.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.02999, over 972700.41 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:23:03,807 INFO [train.py:715] (1/8) Epoch 18, batch 8700, loss[loss=0.1093, simple_loss=0.1864, pruned_loss=0.01614, over 4923.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2073, pruned_loss=0.03019, over 972039.78 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 07:23:43,632 INFO [train.py:715] (1/8) Epoch 18, batch 8750, loss[loss=0.124, simple_loss=0.1972, pruned_loss=0.02539, over 4987.00 frames.], tot_loss[loss=0.134, simple_loss=0.2072, pruned_loss=0.03042, over 973439.23 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 07:24:23,134 INFO [train.py:715] (1/8) Epoch 18, batch 8800, loss[loss=0.1473, simple_loss=0.2191, pruned_loss=0.0377, over 4788.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2074, pruned_loss=0.03058, over 973564.16 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:25:01,505 INFO [train.py:715] (1/8) Epoch 18, batch 8850, loss[loss=0.1524, simple_loss=0.2279, pruned_loss=0.03844, over 4848.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2072, pruned_loss=0.02989, over 973987.08 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:25:41,120 INFO [train.py:715] (1/8) Epoch 18, batch 8900, loss[loss=0.1386, simple_loss=0.2133, pruned_loss=0.03197, over 4882.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2063, pruned_loss=0.02955, over 973489.40 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 07:26:19,640 INFO [train.py:715] (1/8) Epoch 18, batch 8950, loss[loss=0.1176, simple_loss=0.1974, pruned_loss=0.01895, over 4964.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2058, pruned_loss=0.02899, over 973308.74 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:26:58,101 INFO [train.py:715] (1/8) Epoch 18, batch 9000, loss[loss=0.1335, simple_loss=0.2062, pruned_loss=0.03046, over 4935.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2053, pruned_loss=0.02901, over 972591.16 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 07:26:58,102 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 07:27:08,040 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1045, simple_loss=0.1879, pruned_loss=0.01057, over 914524.00 frames.
2022-05-09 07:27:46,931 INFO [train.py:715] (1/8) Epoch 18, batch 9050, loss[loss=0.1475, simple_loss=0.2103, pruned_loss=0.04233, over 4983.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.0288, over 972983.27 frames.], batch size: 31, lr: 1.24e-04
2022-05-09 07:28:26,540 INFO [train.py:715] (1/8) Epoch 18, batch 9100, loss[loss=0.152, simple_loss=0.2267, pruned_loss=0.03861, over 4739.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02904, over 972121.22 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:29:05,670 INFO [train.py:715] (1/8) Epoch 18, batch 9150, loss[loss=0.1256, simple_loss=0.2025, pruned_loss=0.02432, over 4794.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02922, over 972953.69 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:29:43,359 INFO [train.py:715] (1/8) Epoch 18, batch 9200, loss[loss=0.1558, simple_loss=0.2357, pruned_loss=0.03798, over 4697.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.0293, over 971894.54 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:30:22,559 INFO [train.py:715] (1/8) Epoch 18, batch 9250, loss[loss=0.1656, simple_loss=0.2387, pruned_loss=0.04625, over 4905.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02887, over 972622.97 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 07:31:01,719 INFO [train.py:715] (1/8) Epoch 18, batch 9300, loss[loss=0.1564, simple_loss=0.2204, pruned_loss=0.04619, over 4836.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02895, over 972471.45 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:31:39,923 INFO [train.py:715] (1/8) Epoch 18, batch 9350, loss[loss=0.1229, simple_loss=0.1922, pruned_loss=0.02679, over 4788.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02908, over 972284.38 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:32:18,512 INFO [train.py:715] (1/8) Epoch 18, batch 9400, loss[loss=0.1444, simple_loss=0.2149, pruned_loss=0.03692, over 4927.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02911, over 972977.38 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:32:58,076 INFO [train.py:715] (1/8) Epoch 18, batch 9450, loss[loss=0.154, simple_loss=0.2411, pruned_loss=0.03346, over 4777.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02907, over 972206.08 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:33:36,477 INFO [train.py:715] (1/8) Epoch 18, batch 9500, loss[loss=0.1674, simple_loss=0.2245, pruned_loss=0.05514, over 4738.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02899, over 971897.76 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:34:14,736 INFO [train.py:715] (1/8) Epoch 18, batch 9550, loss[loss=0.127, simple_loss=0.1954, pruned_loss=0.02932, over 4897.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.0289, over 971965.40 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:34:53,871 INFO [train.py:715] (1/8) Epoch 18, batch 9600, loss[loss=0.1518, simple_loss=0.2311, pruned_loss=0.03627, over 4807.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02869, over 971834.80 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:35:33,427 INFO [train.py:715] (1/8) Epoch 18, batch 9650, loss[loss=0.09235, simple_loss=0.1557, pruned_loss=0.01449, over 4982.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02843, over 972233.76 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:36:12,258 INFO [train.py:715] (1/8) Epoch 18, batch 9700, loss[loss=0.106, simple_loss=0.1832, pruned_loss=0.01443, over 4978.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.0283, over 972811.80 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 07:36:50,931 INFO [train.py:715] (1/8) Epoch 18, batch 9750, loss[loss=0.1425, simple_loss=0.2093, pruned_loss=0.03787, over 4805.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2063, pruned_loss=0.0283, over 972939.99 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:37:31,028 INFO [train.py:715] (1/8) Epoch 18, batch 9800, loss[loss=0.1186, simple_loss=0.1979, pruned_loss=0.01964, over 4935.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02798, over 973068.52 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 07:38:09,632 INFO [train.py:715] (1/8) Epoch 18, batch 9850, loss[loss=0.1036, simple_loss=0.1797, pruned_loss=0.01378, over 4755.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2056, pruned_loss=0.02811, over 972327.10 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:38:47,996 INFO [train.py:715] (1/8) Epoch 18, batch 9900, loss[loss=0.1262, simple_loss=0.1963, pruned_loss=0.02805, over 4848.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02839, over 971458.64 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:39:27,316 INFO [train.py:715] (1/8) Epoch 18, batch 9950, loss[loss=0.1202, simple_loss=0.1958, pruned_loss=0.02236, over 4946.00 frames.], tot_loss[loss=0.131, simple_loss=0.2056, pruned_loss=0.02818, over 972729.62 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 07:40:06,405 INFO [train.py:715] (1/8) Epoch 18, batch 10000, loss[loss=0.117, simple_loss=0.196, pruned_loss=0.01902, over 4766.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.0282, over 971946.12 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:40:45,255 INFO [train.py:715] (1/8) Epoch 18, batch 10050, loss[loss=0.1311, simple_loss=0.1986, pruned_loss=0.03183, over 4826.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2065, pruned_loss=0.02834, over 972937.20 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:41:23,495 INFO [train.py:715] (1/8) Epoch 18, batch 10100, loss[loss=0.1312, simple_loss=0.2078, pruned_loss=0.02735, over 4964.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2064, pruned_loss=0.02825, over 973325.75 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:42:02,484 INFO [train.py:715] (1/8) Epoch 18, batch 10150, loss[loss=0.108, simple_loss=0.1831, pruned_loss=0.01644, over 4806.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2065, pruned_loss=0.02811, over 974394.62 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:42:41,660 INFO [train.py:715] (1/8) Epoch 18, batch 10200, loss[loss=0.1254, simple_loss=0.2024, pruned_loss=0.02424, over 4914.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2073, pruned_loss=0.02865, over 974650.18 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 07:43:20,199 INFO [train.py:715] (1/8) Epoch 18, batch 10250, loss[loss=0.1505, simple_loss=0.2219, pruned_loss=0.03954, over 4787.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2075, pruned_loss=0.02878, over 973684.58 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:43:59,313 INFO [train.py:715] (1/8) Epoch 18, batch 10300, loss[loss=0.1213, simple_loss=0.1966, pruned_loss=0.023, over 4926.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2078, pruned_loss=0.02886, over 973515.67 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 07:44:39,637 INFO [train.py:715] (1/8) Epoch 18, batch 10350, loss[loss=0.103, simple_loss=0.1794, pruned_loss=0.01336, over 4826.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2077, pruned_loss=0.02888, over 973358.65 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 07:45:18,119 INFO [train.py:715] (1/8) Epoch 18, batch 10400, loss[loss=0.1033, simple_loss=0.1781, pruned_loss=0.0143, over 4987.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2077, pruned_loss=0.02902, over 973270.88 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 07:45:56,567 INFO [train.py:715] (1/8) Epoch 18, batch 10450, loss[loss=0.1135, simple_loss=0.1849, pruned_loss=0.02098, over 4986.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.0287, over 972234.07 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:46:36,298 INFO [train.py:715] (1/8) Epoch 18, batch 10500, loss[loss=0.1395, simple_loss=0.2035, pruned_loss=0.03779, over 4765.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02892, over 971915.72 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:47:15,164 INFO [train.py:715] (1/8) Epoch 18, batch 10550, loss[loss=0.157, simple_loss=0.2298, pruned_loss=0.0421, over 4845.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02903, over 972082.59 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 07:47:53,897 INFO [train.py:715] (1/8) Epoch 18, batch 10600, loss[loss=0.1774, simple_loss=0.2577, pruned_loss=0.04857, over 4961.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02936, over 971187.99 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:48:33,500 INFO [train.py:715] (1/8) Epoch 18, batch 10650, loss[loss=0.142, simple_loss=0.2123, pruned_loss=0.03592, over 4849.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02917, over 970363.44 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 07:49:13,191 INFO [train.py:715] (1/8) Epoch 18, batch 10700, loss[loss=0.1306, simple_loss=0.2028, pruned_loss=0.02921, over 4852.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02891, over 971490.88 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 07:49:52,110 INFO [train.py:715] (1/8) Epoch 18, batch 10750, loss[loss=0.1491, simple_loss=0.2269, pruned_loss=0.03561, over 4909.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02856, over 971731.77 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:50:31,124 INFO [train.py:715] (1/8) Epoch 18, batch 10800, loss[loss=0.1238, simple_loss=0.1998, pruned_loss=0.02395, over 4796.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02844, over 972472.87 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:51:10,555 INFO [train.py:715] (1/8) Epoch 18, batch 10850, loss[loss=0.1234, simple_loss=0.2015, pruned_loss=0.02267, over 4890.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2066, pruned_loss=0.02832, over 972217.33 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:51:49,055 INFO [train.py:715] (1/8) Epoch 18, batch 10900, loss[loss=0.1307, simple_loss=0.2039, pruned_loss=0.0288, over 4983.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2065, pruned_loss=0.02819, over 973173.80 frames.], batch size: 27, lr: 1.24e-04
2022-05-09 07:52:27,635 INFO [train.py:715] (1/8) Epoch 18, batch 10950, loss[loss=0.1391, simple_loss=0.2047, pruned_loss=0.03677, over 4874.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2055, pruned_loss=0.0278, over 972983.12 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 07:53:07,687 INFO [train.py:715] (1/8) Epoch 18, batch 11000, loss[loss=0.1409, simple_loss=0.2225, pruned_loss=0.02968, over 4968.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2062, pruned_loss=0.02805, over 973400.94 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:53:46,744 INFO [train.py:715] (1/8) Epoch 18, batch 11050, loss[loss=0.1235, simple_loss=0.1932, pruned_loss=0.02688, over 4812.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.0284, over 972596.40 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 07:54:26,296 INFO [train.py:715] (1/8) Epoch 18, batch 11100, loss[loss=0.1456, simple_loss=0.2211, pruned_loss=0.03509, over 4974.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02886, over 972953.17 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:55:05,194 INFO [train.py:715] (1/8) Epoch 18, batch 11150, loss[loss=0.1545, simple_loss=0.2214, pruned_loss=0.04383, over 4813.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02864, over 973082.57 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:55:44,746 INFO [train.py:715] (1/8) Epoch 18, batch 11200, loss[loss=0.1386, simple_loss=0.205, pruned_loss=0.03612, over 4884.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02893, over 972674.24 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 07:56:23,189 INFO [train.py:715] (1/8) Epoch 18, batch 11250, loss[loss=0.1138, simple_loss=0.1867, pruned_loss=0.0205, over 4805.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02857, over 973283.74 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:57:01,928 INFO [train.py:715] (1/8) Epoch 18, batch 11300, loss[loss=0.1339, simple_loss=0.1997, pruned_loss=0.03405, over 4990.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02859, over 972876.58 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:57:41,017 INFO [train.py:715] (1/8) Epoch 18, batch 11350, loss[loss=0.1396, simple_loss=0.2079, pruned_loss=0.03567, over 4916.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02856, over 973027.32 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 07:58:20,187 INFO [train.py:715] (1/8) Epoch 18, batch 11400, loss[loss=0.1315, simple_loss=0.2052, pruned_loss=0.0289, over 4836.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02832, over 973935.72 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:58:59,554 INFO [train.py:715] (1/8) Epoch 18, batch 11450, loss[loss=0.1268, simple_loss=0.2045, pruned_loss=0.02461, over 4986.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02833, over 974141.68 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:59:38,057 INFO [train.py:715] (1/8) Epoch 18, batch 11500, loss[loss=0.1404, simple_loss=0.2139, pruned_loss=0.03349, over 4833.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2045, pruned_loss=0.02782, over 973627.18 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 08:00:17,723 INFO [train.py:715] (1/8) Epoch 18, batch 11550, loss[loss=0.1844, simple_loss=0.2349, pruned_loss=0.06699, over 4921.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2049, pruned_loss=0.0284, over 974103.17 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 08:00:57,124 INFO [train.py:715] (1/8) Epoch 18, batch 11600, loss[loss=0.1398, simple_loss=0.2241, pruned_loss=0.0277, over 4949.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2046, pruned_loss=0.0285, over 973809.75 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 08:01:35,946 INFO [train.py:715] (1/8) Epoch 18, batch 11650, loss[loss=0.1408, simple_loss=0.2107, pruned_loss=0.03544, over 4989.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02877, over 973289.08 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:02:15,651 INFO [train.py:715] (1/8) Epoch 18, batch 11700, loss[loss=0.1032, simple_loss=0.1797, pruned_loss=0.01339, over 4774.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2048, pruned_loss=0.02836, over 973332.94 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 08:02:54,929 INFO [train.py:715] (1/8) Epoch 18, batch 11750, loss[loss=0.1206, simple_loss=0.1989, pruned_loss=0.02115, over 4950.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2043, pruned_loss=0.02845, over 973577.85 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 08:03:34,973 INFO [train.py:715] (1/8) Epoch 18, batch 11800, loss[loss=0.1283, simple_loss=0.1921, pruned_loss=0.0323, over 4893.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2046, pruned_loss=0.02822, over 973750.11 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 08:04:13,540 INFO [train.py:715] (1/8) Epoch 18, batch 11850, loss[loss=0.1172, simple_loss=0.1757, pruned_loss=0.02934, over 4859.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2047, pruned_loss=0.02819, over 974309.58 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 08:04:53,376 INFO [train.py:715] (1/8) Epoch 18, batch 11900, loss[loss=0.1306, simple_loss=0.2083, pruned_loss=0.02642, over 4913.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2048, pruned_loss=0.02842, over 974086.02 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 08:05:32,228 INFO [train.py:715] (1/8) Epoch 18, batch 11950, loss[loss=0.1182, simple_loss=0.1905, pruned_loss=0.02293, over 4918.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2048, pruned_loss=0.02826, over 973494.78 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 08:06:10,822 INFO [train.py:715] (1/8) Epoch 18, batch 12000, loss[loss=0.1504, simple_loss=0.2295, pruned_loss=0.03571, over 4793.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02867, over 973203.19 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 08:06:10,823 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 08:06:20,738 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1046, simple_loss=0.188, pruned_loss=0.01063, over 914524.00 frames.
2022-05-09 08:07:00,011 INFO [train.py:715] (1/8) Epoch 18, batch 12050, loss[loss=0.1157, simple_loss=0.1848, pruned_loss=0.02334, over 4950.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2057, pruned_loss=0.02858, over 972741.06 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 08:07:39,517 INFO [train.py:715] (1/8) Epoch 18, batch 12100, loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02896, over 4877.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.02814, over 973485.07 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 08:08:19,046 INFO [train.py:715] (1/8) Epoch 18, batch 12150, loss[loss=0.113, simple_loss=0.1841, pruned_loss=0.02093, over 4969.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02825, over 973348.47 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:08:59,339 INFO [train.py:715] (1/8) Epoch 18, batch 12200, loss[loss=0.1606, simple_loss=0.2222, pruned_loss=0.04946, over 4969.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.0287, over 973198.12 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:09:38,274 INFO [train.py:715] (1/8) Epoch 18, batch 12250, loss[loss=0.1318, simple_loss=0.2107, pruned_loss=0.02643, over 4834.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02858, over 972709.26 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:10:18,803 INFO [train.py:715] (1/8) Epoch 18, batch 12300, loss[loss=0.1377, simple_loss=0.2099, pruned_loss=0.03275, over 4911.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02858, over 973257.51 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 08:10:58,224 INFO [train.py:715] (1/8) Epoch 18, batch 12350, loss[loss=0.117, simple_loss=0.1858, pruned_loss=0.02414, over 4910.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02823, over 972452.78 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 08:11:37,140 INFO [train.py:715] (1/8) Epoch 18, batch 12400, loss[loss=0.1101, simple_loss=0.1864, pruned_loss=0.01686, over 4963.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.0281, over 972714.07 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 08:12:16,681 INFO [train.py:715] (1/8) Epoch 18, batch 12450, loss[loss=0.1306, simple_loss=0.1999, pruned_loss=0.03064, over 4696.00 frames.], tot_loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02832, over 972450.12 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:12:55,934 INFO [train.py:715] (1/8) Epoch 18, batch 12500, loss[loss=0.1124, simple_loss=0.1867, pruned_loss=0.01899, over 4841.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2052, pruned_loss=0.02846, over 973445.26 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 08:13:36,318 INFO [train.py:715] (1/8) Epoch 18, batch 12550, loss[loss=0.1192, simple_loss=0.1903, pruned_loss=0.02401, over 4841.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02888, over 973855.86 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 08:14:14,818 INFO [train.py:715] (1/8) Epoch 18, batch 12600, loss[loss=0.1266, simple_loss=0.1922, pruned_loss=0.03052, over 4969.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02909, over 973865.44 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 08:14:54,512 INFO [train.py:715] (1/8) Epoch 18, batch 12650, loss[loss=0.1538, simple_loss=0.2206, pruned_loss=0.0435, over 4883.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02903, over 974184.54 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 08:15:33,310 INFO [train.py:715] (1/8) Epoch 18, batch 12700, loss[loss=0.145, simple_loss=0.2151, pruned_loss=0.0374, over 4900.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02954, over 973443.35 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 08:16:12,927 INFO [train.py:715] (1/8) Epoch 18, batch 12750, loss[loss=0.1438, simple_loss=0.2138, pruned_loss=0.03686, over 4948.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2058, pruned_loss=0.02886, over 973831.53 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 08:16:52,481 INFO [train.py:715] (1/8) Epoch 18, batch 12800, loss[loss=0.1385, simple_loss=0.2177, pruned_loss=0.02969, over 4829.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2062, pruned_loss=0.02953, over 974027.82 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:17:31,835 INFO [train.py:715] (1/8) Epoch 18, batch 12850, loss[loss=0.1318, simple_loss=0.2082, pruned_loss=0.02763, over 4892.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02937, over 974060.56 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 08:18:11,706 INFO [train.py:715] (1/8) Epoch 18, batch 12900, loss[loss=0.1502, simple_loss=0.2326, pruned_loss=0.03386, over 4746.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02921, over 972956.30 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 08:18:50,195 INFO [train.py:715] (1/8) Epoch 18, batch 12950, loss[loss=0.1433, simple_loss=0.2323, pruned_loss=0.02717, over 4890.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02914, over 973197.23 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 08:19:30,193 INFO [train.py:715] (1/8) Epoch 18, batch 13000, loss[loss=0.1218, simple_loss=0.2034, pruned_loss=0.02013, over 4884.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02911, over 973295.04 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 08:20:09,527 INFO [train.py:715] (1/8) Epoch 18, batch 13050, loss[loss=0.1412, simple_loss=0.225, pruned_loss=0.02872, over 4767.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02873, over 973381.08 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 08:20:48,609 INFO [train.py:715] (1/8) Epoch 18, batch 13100, loss[loss=0.1327, simple_loss=0.202, pruned_loss=0.03173, over 4769.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02851, over 973224.69 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 08:21:28,135 INFO [train.py:715] (1/8) Epoch 18, batch 13150, loss[loss=0.1166, simple_loss=0.1951, pruned_loss=0.01911, over 4980.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02839, over 972870.87 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 08:22:07,407 INFO [train.py:715] (1/8) Epoch 18, batch 13200, loss[loss=0.1376, simple_loss=0.2096, pruned_loss=0.03274, over 4820.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02852, over 972701.27 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 08:22:47,220 INFO [train.py:715] (1/8) Epoch 18, batch 13250, loss[loss=0.1204, simple_loss=0.1835, pruned_loss=0.02866, over 4805.00 frames.], tot_loss[loss=0.1309, simple_loss=0.205, pruned_loss=0.02841, over 971789.43 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 08:23:25,807 INFO [train.py:715] (1/8) Epoch 18, batch 13300, loss[loss=0.1557, simple_loss=0.2216, pruned_loss=0.0449, over 4877.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02881, over 972401.10 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 08:24:05,547 INFO [train.py:715] (1/8) Epoch 18, batch 13350, loss[loss=0.1439, simple_loss=0.2139, pruned_loss=0.03698, over 4735.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2054, pruned_loss=0.02873, over 971983.77 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 08:24:44,550 INFO [train.py:715] (1/8) Epoch 18, batch 13400, loss[loss=0.09976, simple_loss=0.1765, pruned_loss=0.0115, over 4861.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02876, over 972251.56 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 08:25:25,444 INFO [train.py:715] (1/8) Epoch 18, batch 13450, loss[loss=0.1447, simple_loss=0.2229, pruned_loss=0.03323, over 4776.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2055, pruned_loss=0.0285, over 972004.74 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:26:05,135 INFO [train.py:715] (1/8) Epoch 18, batch 13500, loss[loss=0.1354, simple_loss=0.2017, pruned_loss=0.03457, over 4906.00 frames.], tot_loss[loss=0.131, simple_loss=0.2052, pruned_loss=0.02844, over 972800.28 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 08:26:44,080 INFO [train.py:715] (1/8) Epoch 18, batch 13550, loss[loss=0.1243, simple_loss=0.2029, pruned_loss=0.0228, over 4796.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02822, over 973208.14 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 08:27:23,341 INFO [train.py:715] (1/8) Epoch 18, batch 13600, loss[loss=0.1299, simple_loss=0.2088, pruned_loss=0.02545, over 4764.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02823, over 972950.13 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 08:28:02,152 INFO [train.py:715] (1/8) Epoch 18, batch 13650, loss[loss=0.1423, simple_loss=0.208, pruned_loss=0.03827, over 4838.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02857, over 972344.68 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 08:28:41,561 INFO [train.py:715] (1/8) Epoch 18, batch 13700, loss[loss=0.1455, simple_loss=0.2197, pruned_loss=0.03561, over 4822.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02867, over 972441.87 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 08:29:20,646 INFO [train.py:715] (1/8) Epoch 18, batch 13750, loss[loss=0.1246, simple_loss=0.1952, pruned_loss=0.02701, over 4768.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02882, over 972401.96 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:29:59,710 INFO [train.py:715] (1/8) Epoch 18, batch 13800, loss[loss=0.1319, simple_loss=0.2098, pruned_loss=0.02702, over 4811.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02892, over 972467.21 frames.], batch size: 26, lr: 1.23e-04
2022-05-09 08:30:39,480 INFO [train.py:715] (1/8) Epoch 18, batch 13850, loss[loss=0.1188, simple_loss=0.1927, pruned_loss=0.02248, over 4894.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.0286, over 972002.23 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 08:31:18,290 INFO [train.py:715] (1/8) Epoch 18, batch 13900, loss[loss=0.1334, simple_loss=0.1994, pruned_loss=0.0337, over 4788.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02843, over 972950.35 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:31:57,750 INFO [train.py:715] (1/8) Epoch 18, batch 13950, loss[loss=0.1331, simple_loss=0.2054, pruned_loss=0.03045, over 4759.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2057, pruned_loss=0.02851, over 973364.10 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:32:37,348 INFO [train.py:715] (1/8) Epoch 18, batch 14000, loss[loss=0.1154, simple_loss=0.1904, pruned_loss=0.02013, over 4928.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02877, over 973150.23 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:33:17,111 INFO [train.py:715] (1/8) Epoch 18, batch 14050, loss[loss=0.1164, simple_loss=0.1881, pruned_loss=0.02229, over 4872.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02907, over 972892.95 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:33:56,294 INFO [train.py:715] (1/8) Epoch 18, batch 14100, loss[loss=0.1523, simple_loss=0.2175, pruned_loss=0.04358, over 4788.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02891, over 972485.72 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 08:34:35,395 INFO [train.py:715] (1/8) Epoch 18, batch 14150, loss[loss=0.1183, simple_loss=0.1912, pruned_loss=0.02268, over 4750.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02922, over 973209.33 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:35:14,781 INFO [train.py:715] (1/8) Epoch 18, batch 14200, loss[loss=0.1499, simple_loss=0.2239, pruned_loss=0.03789, over 4808.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02907, over 972725.95 frames.], batch size: 26, lr: 1.23e-04
2022-05-09 08:35:54,058 INFO [train.py:715] (1/8) Epoch 18, batch 14250, loss[loss=0.1274, simple_loss=0.2064, pruned_loss=0.0242, over 4814.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02904, over 972816.95 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 08:36:33,985 INFO [train.py:715] (1/8) Epoch 18, batch 14300, loss[loss=0.1152, simple_loss=0.2004, pruned_loss=0.01496, over 4760.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02904, over 973703.85 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 08:37:13,313 INFO [train.py:715] (1/8) Epoch 18, batch 14350, loss[loss=0.1387, simple_loss=0.2258, pruned_loss=0.02577, over 4803.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02919, over 973048.97 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 08:37:52,855 INFO [train.py:715] (1/8) Epoch 18, batch 14400, loss[loss=0.1162, simple_loss=0.1921, pruned_loss=0.02016, over 4901.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02956, over 973312.32 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 08:38:32,499 INFO [train.py:715] (1/8) Epoch 18, batch 14450, loss[loss=0.1173, simple_loss=0.2039, pruned_loss=0.01531, over 4850.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02954, over 972418.92 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 08:39:11,248 INFO [train.py:715] (1/8) Epoch 18, batch 14500, loss[loss=0.1122, simple_loss=0.1738, pruned_loss=0.02533, over 4941.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.0294, over 972255.72 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 08:39:50,386 INFO [train.py:715] (1/8) Epoch 18, batch 14550, loss[loss=0.127, simple_loss=0.2129, pruned_loss=0.02061, over 4982.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02898, over 972941.00 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 08:40:29,520 INFO [train.py:715] (1/8) Epoch 18, batch 14600, loss[loss=0.147, simple_loss=0.2124, pruned_loss=0.04085, over 4863.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02937, over 971876.09 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 08:41:09,221 INFO [train.py:715] (1/8) Epoch 18, batch 14650, loss[loss=0.09704, simple_loss=0.1619, pruned_loss=0.0161, over 4763.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02903, over 971727.30 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 08:41:48,677 INFO [train.py:715] (1/8) Epoch 18, batch 14700, loss[loss=0.1293, simple_loss=0.1988, pruned_loss=0.02988, over 4879.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02906, over 972504.67 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:42:28,035 INFO [train.py:715] (1/8) Epoch 18, batch 14750, loss[loss=0.1195, simple_loss=0.2065, pruned_loss=0.01625, over 4756.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.0291, over 971941.03 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 08:43:07,464 INFO [train.py:715] (1/8) Epoch 18, batch 14800, loss[loss=0.1445, simple_loss=0.212, pruned_loss=0.03851, over 4762.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02894, over 971510.77 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:43:46,219 INFO [train.py:715] (1/8) Epoch 18, batch 14850, loss[loss=0.193, simple_loss=0.2503, pruned_loss=0.06792, over 4696.00 frames.], tot_loss[loss=0.1321, simple_loss=0.206, pruned_loss=0.02907, over 971115.52 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 08:44:25,878 INFO [train.py:715] (1/8) Epoch 18, batch 14900, loss[loss=0.1321, simple_loss=0.2163, pruned_loss=0.02395, over 4942.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02936, over 971021.92 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 08:45:05,547 INFO [train.py:715] (1/8) Epoch 18, batch 14950, loss[loss=0.1284, simple_loss=0.2041, pruned_loss=0.02632, over 4874.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02915, over 971217.91 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 08:45:44,812 INFO [train.py:715] (1/8) Epoch 18, batch 15000, loss[loss=0.1192, simple_loss=0.1845, pruned_loss=0.02694, over 4927.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.0289, over 972004.15 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 08:45:44,813 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 08:45:54,766 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1048, simple_loss=0.1881, pruned_loss=0.01071, over 914524.00 frames.
2022-05-09 08:46:34,345 INFO [train.py:715] (1/8) Epoch 18, batch 15050, loss[loss=0.1177, simple_loss=0.1943, pruned_loss=0.02057, over 4933.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02937, over 971731.41 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 08:47:13,521 INFO [train.py:715] (1/8) Epoch 18, batch 15100, loss[loss=0.1508, simple_loss=0.2258, pruned_loss=0.03783, over 4790.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02872, over 971871.02 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:47:53,251 INFO [train.py:715] (1/8) Epoch 18, batch 15150, loss[loss=0.1131, simple_loss=0.1915, pruned_loss=0.01731, over 4949.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2053, pruned_loss=0.02866, over 971725.13 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 08:48:32,387 INFO [train.py:715] (1/8) Epoch 18, batch 15200, loss[loss=0.1392, simple_loss=0.2063, pruned_loss=0.03599, over 4842.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.0289, over 972070.91 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 08:49:11,926 INFO [train.py:715] (1/8) Epoch 18, batch 15250, loss[loss=0.1255, simple_loss=0.2017, pruned_loss=0.02469, over 4905.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.0291, over 972626.39 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 08:49:51,788 INFO [train.py:715] (1/8) Epoch 18, batch 15300, loss[loss=0.1158, simple_loss=0.1866, pruned_loss=0.02247, over 4976.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2053, pruned_loss=0.029, over 972378.29 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 08:50:31,161 INFO [train.py:715] (1/8) Epoch 18, batch 15350, loss[loss=0.122, simple_loss=0.1956, pruned_loss=0.02417, over 4916.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2047, pruned_loss=0.02842, over 971849.04 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 08:51:10,079 INFO [train.py:715] (1/8) Epoch 18, batch 15400, loss[loss=0.1439, simple_loss=0.212, pruned_loss=0.0379, over 4830.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02884, over 972353.72 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 08:51:49,370 INFO [train.py:715] (1/8) Epoch 18, batch 15450, loss[loss=0.1496, simple_loss=0.2179, pruned_loss=0.04067, over 4799.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02937, over 971747.74 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:52:28,994 INFO [train.py:715] (1/8) Epoch 18, batch 15500, loss[loss=0.1151, simple_loss=0.1901, pruned_loss=0.02002, over 4827.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02956, over 971153.47 frames.], batch size: 27, lr: 1.23e-04
2022-05-09 08:53:08,161 INFO [train.py:715] (1/8) Epoch 18, batch 15550, loss[loss=0.1127, simple_loss=0.1778, pruned_loss=0.02374, over 4989.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02869, over 971753.39 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 08:53:47,888 INFO [train.py:715] (1/8) Epoch 18, batch 15600, loss[loss=0.1534, simple_loss=0.2246, pruned_loss=0.0411, over 4917.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02909, over 971144.99 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 08:54:28,009 INFO [train.py:715] (1/8) Epoch 18, batch 15650, loss[loss=0.1119, simple_loss=0.1836, pruned_loss=0.02005, over 4968.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02881, over 971860.61 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 08:55:07,610 INFO [train.py:715] (1/8) Epoch 18, batch 15700, loss[loss=0.1708, simple_loss=0.253, pruned_loss=0.04431, over 4739.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02902, over 971339.41 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:55:46,514 INFO [train.py:715] (1/8) Epoch 18, batch 15750, loss[loss=0.1147, simple_loss=0.1977, pruned_loss=0.01588, over 4867.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02912, over 970672.66 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 08:56:25,956 INFO [train.py:715] (1/8) Epoch 18, batch 15800, loss[loss=0.1182, simple_loss=0.186, pruned_loss=0.02517, over 4987.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02909, over 971762.75 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 08:57:05,865 INFO [train.py:715] (1/8) Epoch 18, batch 15850, loss[loss=0.1393, simple_loss=0.1989, pruned_loss=0.03981, over 4840.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02955, over 972137.97 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 08:57:45,096 INFO [train.py:715] (1/8) Epoch 18, batch 15900, loss[loss=0.1351, simple_loss=0.2171, pruned_loss=0.02653, over 4865.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02921, over 972297.27 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:58:24,410 INFO [train.py:715] (1/8) Epoch 18, batch 15950, loss[loss=0.1304, simple_loss=0.2176, pruned_loss=0.02162, over 4989.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02894, over 972645.24 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 08:59:04,888 INFO [train.py:715] (1/8) Epoch 18, batch 16000, loss[loss=0.1437, simple_loss=0.2189, pruned_loss=0.03431, over 4936.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02908, over 972409.01 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 08:59:45,376 INFO [train.py:715] (1/8) Epoch 18, batch 16050, loss[loss=0.1353, simple_loss=0.2024, pruned_loss=0.03408, over 4851.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02894, over 972822.26 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 09:00:24,417 INFO [train.py:715] (1/8) Epoch 18, batch 16100, loss[loss=0.1362, simple_loss=0.2226, pruned_loss=0.02491, over 4755.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02911, over 971562.85 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:01:03,599 INFO [train.py:715] (1/8) Epoch 18, batch 16150, loss[loss=0.1329, simple_loss=0.2108, pruned_loss=0.02755, over 4848.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02863, over 971020.88 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:01:43,692 INFO [train.py:715] (1/8) Epoch 18, batch 16200, loss[loss=0.1352, simple_loss=0.2154, pruned_loss=0.02757, over 4904.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2066, pruned_loss=0.0284, over 971643.15 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:02:22,641 INFO [train.py:715] (1/8) Epoch 18, batch 16250, loss[loss=0.139, simple_loss=0.2103, pruned_loss=0.03381, over 4841.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2067, pruned_loss=0.02832, over 971172.52 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:03:01,671 INFO [train.py:715] (1/8) Epoch 18, batch 16300, loss[loss=0.1513, simple_loss=0.2286, pruned_loss=0.03697, over 4807.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02827, over 971286.31 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:03:41,210 INFO [train.py:715] (1/8) Epoch 18, batch 16350, loss[loss=0.1327, simple_loss=0.2137, pruned_loss=0.02579, over 4934.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02882, over 971851.27 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 09:04:20,327 INFO [train.py:715] (1/8) Epoch 18, batch 16400, loss[loss=0.1356, simple_loss=0.2123, pruned_loss=0.02942, over 4926.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02887, over 971865.14 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:04:59,284 INFO [train.py:715] (1/8) Epoch 18, batch 16450, loss[loss=0.1317, simple_loss=0.2047, pruned_loss=0.02931, over 4927.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02829, over 971572.45 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:05:38,806 INFO [train.py:715] (1/8) Epoch 18, batch 16500, loss[loss=0.1203, simple_loss=0.191, pruned_loss=0.02481, over 4849.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2059, pruned_loss=0.02794, over 971208.07 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 09:06:18,646 INFO [train.py:715] (1/8) Epoch 18, batch 16550, loss[loss=0.1383, simple_loss=0.226, pruned_loss=0.02528, over 4685.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2065, pruned_loss=0.02804, over 971492.77 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:06:57,075 INFO [train.py:715] (1/8) Epoch 18, batch 16600, loss[loss=0.1272, simple_loss=0.2004, pruned_loss=0.027, over 4868.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2061, pruned_loss=0.02802, over 972913.79 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 09:07:36,511 INFO [train.py:715] (1/8) Epoch 18, batch 16650, loss[loss=0.1387, simple_loss=0.2124, pruned_loss=0.03253, over 4860.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2064, pruned_loss=0.02795, over 972497.85 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:08:15,857 INFO [train.py:715] (1/8) Epoch 18, batch 16700, loss[loss=0.112, simple_loss=0.1926, pruned_loss=0.01575, over 4914.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2068, pruned_loss=0.02819, over 972285.50 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 09:08:55,196 INFO [train.py:715] (1/8) Epoch 18, batch 16750, loss[loss=0.1556, simple_loss=0.2129, pruned_loss=0.04919, over 4833.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2068, pruned_loss=0.02834, over 971797.89 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 09:09:34,643 INFO [train.py:715] (1/8) Epoch 18, batch 16800, loss[loss=0.1125, simple_loss=0.1828, pruned_loss=0.02113, over 4836.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02856, over 971782.79 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 09:10:13,847 INFO [train.py:715] (1/8) Epoch 18, batch 16850, loss[loss=0.1387, simple_loss=0.2164, pruned_loss=0.03048, over 4836.00 frames.], tot_loss[loss=0.1321, simple_loss=0.207, pruned_loss=0.02864, over 972262.27 frames.], batch size: 27, lr: 1.23e-04
2022-05-09 09:10:53,308 INFO [train.py:715] (1/8) Epoch 18, batch 16900, loss[loss=0.1437, simple_loss=0.2099, pruned_loss=0.03873, over 4962.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.0286, over 971727.45 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:11:32,152 INFO [train.py:715] (1/8) Epoch 18, batch 16950, loss[loss=0.1457, simple_loss=0.2187, pruned_loss=0.03632, over 4795.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02883, over 971545.88 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:12:11,610 INFO [train.py:715] (1/8) Epoch 18, batch 17000, loss[loss=0.1495, simple_loss=0.2193, pruned_loss=0.03992, over 4794.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02836, over 972065.04 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:12:51,060 INFO [train.py:715] (1/8) Epoch 18, batch 17050, loss[loss=0.1414, simple_loss=0.2206, pruned_loss=0.03115, over 4800.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.0286, over 972064.53 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:13:30,535 INFO [train.py:715] (1/8) Epoch 18, batch 17100, loss[loss=0.1475, simple_loss=0.2183, pruned_loss=0.03834, over 4836.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02851, over 971306.48 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:14:10,113 INFO [train.py:715] (1/8) Epoch 18, batch 17150, loss[loss=0.1422, simple_loss=0.2212, pruned_loss=0.03155, over 4784.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02837, over 971117.04 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:14:49,237 INFO [train.py:715] (1/8) Epoch 18, batch 17200, loss[loss=0.1349, simple_loss=0.21, pruned_loss=0.02989, over 4983.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02838, over 971179.15 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:15:28,957 INFO [train.py:715] (1/8) Epoch 18, batch 17250, loss[loss=0.1155, simple_loss=0.1816, pruned_loss=0.02473, over 4806.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02833, over 971579.19 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 09:16:08,220 INFO [train.py:715] (1/8) Epoch 18, batch 17300, loss[loss=0.117, simple_loss=0.1918, pruned_loss=0.02111, over 4917.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02837, over 971491.39 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 09:16:48,151 INFO [train.py:715] (1/8) Epoch 18, batch 17350, loss[loss=0.1302, simple_loss=0.2081, pruned_loss=0.02611, over 4928.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02872, over 972186.72 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:17:27,212 INFO [train.py:715] (1/8) Epoch 18, batch 17400, loss[loss=0.1326, simple_loss=0.2093, pruned_loss=0.02792, over 4920.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02863, over 972740.43 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 09:18:07,003 INFO [train.py:715] (1/8) Epoch 18, batch 17450, loss[loss=0.1343, simple_loss=0.2045, pruned_loss=0.032, over 4834.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02812, over 971891.71 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:18:46,083 INFO [train.py:715] (1/8) Epoch 18, batch 17500, loss[loss=0.1388, simple_loss=0.1992, pruned_loss=0.03924, over 4796.00 frames.], tot_loss[loss=0.13, simple_loss=0.2048, pruned_loss=0.02762, over 971726.87 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:19:24,710 INFO [train.py:715] (1/8) Epoch 18, batch 17550, loss[loss=0.1289, simple_loss=0.2059, pruned_loss=0.02598, over 4983.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2053, pruned_loss=0.02787, over 971467.82 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 09:20:04,278 INFO [train.py:715] (1/8) Epoch 18, batch 17600, loss[loss=0.1294, simple_loss=0.2028, pruned_loss=0.02802, over 4957.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02843, over 971579.53 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 09:20:43,545 INFO [train.py:715] (1/8) Epoch 18, batch 17650, loss[loss=0.1423, simple_loss=0.2137, pruned_loss=0.03548, over 4692.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02926, over 972539.45 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:21:22,849 INFO [train.py:715] (1/8) Epoch 18, batch 17700, loss[loss=0.1247, simple_loss=0.2081, pruned_loss=0.02065, over 4791.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02862, over 972735.03 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:22:01,946 INFO [train.py:715] (1/8) Epoch 18, batch 17750, loss[loss=0.1504, simple_loss=0.2227, pruned_loss=0.03902, over 4687.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02877, over 972116.50 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:22:41,547 INFO [train.py:715] (1/8) Epoch 18, batch 17800, loss[loss=0.1308, simple_loss=0.2095, pruned_loss=0.02605, over 4840.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02867, over 972570.64 frames.], batch size: 26, lr: 1.23e-04
2022-05-09 09:23:20,833 INFO [train.py:715] (1/8) Epoch 18, batch 17850, loss[loss=0.1437, simple_loss=0.2172, pruned_loss=0.03512, over 4892.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02869, over 972884.87 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:23:59,344 INFO [train.py:715] (1/8) Epoch 18, batch 17900, loss[loss=0.1165, simple_loss=0.1903, pruned_loss=0.02136, over 4990.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02857, over 973143.94 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:24:39,457 INFO [train.py:715] (1/8) Epoch 18, batch 17950, loss[loss=0.1117, simple_loss=0.1827, pruned_loss=0.02038, over 4991.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02859, over 973407.81 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 09:25:18,519 INFO [train.py:715] (1/8) Epoch 18, batch 18000, loss[loss=0.1301, simple_loss=0.1987, pruned_loss=0.0308, over 4954.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.0289, over 973218.45 frames.], batch size: 39, lr: 1.23e-04
2022-05-09 09:25:18,520 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 09:25:28,383 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1046, simple_loss=0.1878, pruned_loss=0.01063, over 914524.00 frames.
2022-05-09 09:26:07,769 INFO [train.py:715] (1/8) Epoch 18, batch 18050, loss[loss=0.1361, simple_loss=0.206, pruned_loss=0.03308, over 4987.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02871, over 973707.64 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:26:47,162 INFO [train.py:715] (1/8) Epoch 18, batch 18100, loss[loss=0.1412, simple_loss=0.219, pruned_loss=0.03173, over 4932.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02889, over 973524.03 frames.], batch size: 39, lr: 1.23e-04
2022-05-09 09:27:26,267 INFO [train.py:715] (1/8) Epoch 18, batch 18150, loss[loss=0.1238, simple_loss=0.2037, pruned_loss=0.02192, over 4991.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02909, over 973215.84 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 09:28:06,060 INFO [train.py:715] (1/8) Epoch 18, batch 18200, loss[loss=0.1432, simple_loss=0.2184, pruned_loss=0.034, over 4920.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02875, over 973357.62 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 09:28:45,777 INFO [train.py:715] (1/8) Epoch 18, batch 18250, loss[loss=0.1316, simple_loss=0.1959, pruned_loss=0.03367, over 4705.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02896, over 973120.49 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:29:24,149 INFO [train.py:715] (1/8) Epoch 18, batch 18300, loss[loss=0.1288, simple_loss=0.206, pruned_loss=0.02583, over 4833.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02905, over 972927.14 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:30:03,822 INFO [train.py:715] (1/8) Epoch 18, batch 18350, loss[loss=0.122, simple_loss=0.1987, pruned_loss=0.02271, over 4780.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02925, over 972947.68 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:30:43,379 INFO [train.py:715] (1/8) Epoch 18, batch 18400, loss[loss=0.1581, simple_loss=0.2275, pruned_loss=0.0443, over 4917.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02918, over 973371.77 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:31:22,380 INFO [train.py:715] (1/8) Epoch 18, batch 18450, loss[loss=0.1395, simple_loss=0.22, pruned_loss=0.02947, over 4825.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02884, over 972848.91 frames.], batch size: 27, lr: 1.23e-04
2022-05-09 09:32:01,510 INFO [train.py:715] (1/8) Epoch 18, batch 18500, loss[loss=0.1339, simple_loss=0.2069, pruned_loss=0.03048, over 4733.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2074, pruned_loss=0.02881, over 972305.40 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 09:32:40,861 INFO [train.py:715] (1/8) Epoch 18, batch 18550, loss[loss=0.1351, simple_loss=0.2255, pruned_loss=0.02231, over 4857.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2077, pruned_loss=0.02885, over 971299.34 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 09:33:20,072 INFO [train.py:715] (1/8) Epoch 18, batch 18600, loss[loss=0.1285, simple_loss=0.2159, pruned_loss=0.02055, over 4804.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02859, over 971527.64 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:33:58,710 INFO [train.py:715] (1/8) Epoch 18, batch 18650, loss[loss=0.1374, simple_loss=0.2073, pruned_loss=0.03372, over 4814.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02831, over 972155.67 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:34:38,205 INFO [train.py:715] (1/8) Epoch 18, batch 18700, loss[loss=0.1299, simple_loss=0.2, pruned_loss=0.02988, over 4802.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02858, over 971778.91 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:35:17,418 INFO [train.py:715] (1/8) Epoch 18, batch 18750, loss[loss=0.1078, simple_loss=0.1726, pruned_loss=0.0215, over 4960.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02925, over 971890.59 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 09:35:56,634 INFO [train.py:715] (1/8) Epoch 18, batch 18800, loss[loss=0.1661, simple_loss=0.2246, pruned_loss=0.05387, over 4981.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02943, over 972122.43 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 09:36:35,994 INFO [train.py:715] (1/8) Epoch 18, batch 18850, loss[loss=0.129, simple_loss=0.2128, pruned_loss=0.02258, over 4803.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02904, over 971714.34 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:37:15,843 INFO [train.py:715] (1/8) Epoch 18, batch 18900, loss[loss=0.1161, simple_loss=0.1888, pruned_loss=0.02165, over 4906.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02876, over 970843.24 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:37:54,908 INFO [train.py:715] (1/8) Epoch 18, batch 18950, loss[loss=0.142, simple_loss=0.211, pruned_loss=0.03651, over 4976.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02867, over 971394.96 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:38:33,353 INFO [train.py:715] (1/8) Epoch 18, batch 19000, loss[loss=0.1609, simple_loss=0.2419, pruned_loss=0.03995, over 4683.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02877, over 970921.52 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:39:12,865 INFO [train.py:715] (1/8) Epoch 18, batch 19050, loss[loss=0.1149, simple_loss=0.1842, pruned_loss=0.0228, over 4837.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02886, over 970923.45 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:39:51,871 INFO [train.py:715] (1/8) Epoch 18, batch 19100, loss[loss=0.1228, simple_loss=0.1906, pruned_loss=0.02743, over 4875.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02885, over 972660.70 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 09:40:31,187 INFO [train.py:715] (1/8) Epoch 18, batch 19150, loss[loss=0.1228, simple_loss=0.2025, pruned_loss=0.02156, over 4901.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02861, over 972432.44 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 09:41:11,053 INFO [train.py:715] (1/8) Epoch 18, batch 19200, loss[loss=0.1494, simple_loss=0.2305, pruned_loss=0.03421, over 4793.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02848, over 971101.32 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:41:50,579 INFO [train.py:715] (1/8) Epoch 18, batch 19250, loss[loss=0.1666, simple_loss=0.2481, pruned_loss=0.04258, over 4823.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.0289, over 971510.51 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:42:29,655 INFO [train.py:715] (1/8) Epoch 18, batch 19300, loss[loss=0.1208, simple_loss=0.1941, pruned_loss=0.02373, over 4886.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02856, over 971588.95 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 09:43:08,116 INFO [train.py:715] (1/8) Epoch 18, batch 19350, loss[loss=0.1359, simple_loss=0.2075, pruned_loss=0.03217, over 4761.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2045, pruned_loss=0.02809, over 971569.72 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:43:47,522 INFO [train.py:715] (1/8) Epoch 18, batch 19400, loss[loss=0.1116, simple_loss=0.1832, pruned_loss=0.01999, over 4797.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2045, pruned_loss=0.02799, over 971503.95 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:44:26,730 INFO [train.py:715] (1/8) Epoch 18, batch 19450, loss[loss=0.1253, simple_loss=0.1982, pruned_loss=0.02618, over 4899.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2046, pruned_loss=0.02816, over 971866.21 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:45:05,481 INFO [train.py:715] (1/8) Epoch 18, batch 19500, loss[loss=0.133, simple_loss=0.2084, pruned_loss=0.02883, over 4821.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2038, pruned_loss=0.02788, over 971594.23 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:45:44,652 INFO [train.py:715] (1/8) Epoch 18, batch 19550, loss[loss=0.1106, simple_loss=0.1851, pruned_loss=0.01806, over 4749.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2041, pruned_loss=0.02787, over 971832.50 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:46:24,060 INFO [train.py:715] (1/8) Epoch 18, batch 19600, loss[loss=0.1445, simple_loss=0.2244, pruned_loss=0.0323, over 4786.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02846, over 971757.49 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:47:02,883 INFO [train.py:715] (1/8) Epoch 18, batch 19650, loss[loss=0.1909, simple_loss=0.2734, pruned_loss=0.05422, over 4944.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.0285, over 972173.61 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 09:47:41,710 INFO [train.py:715] (1/8) Epoch 18, batch 19700, loss[loss=0.1774, simple_loss=0.256, pruned_loss=0.04943, over 4934.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02894, over 971496.67 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 09:48:21,728 INFO [train.py:715] (1/8) Epoch 18, batch 19750, loss[loss=0.1295, simple_loss=0.2051, pruned_loss=0.0269, over 4767.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02921, over 971170.52 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:49:01,596 INFO [train.py:715] (1/8) Epoch 18, batch 19800, loss[loss=0.1357, simple_loss=0.2087, pruned_loss=0.03136, over 4961.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02896, over 972247.18 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:49:40,673 INFO [train.py:715] (1/8) Epoch 18, batch 19850, loss[loss=0.1221, simple_loss=0.2049, pruned_loss=0.01962, over 4941.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.0288, over 972316.29 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 09:50:20,122 INFO [train.py:715] (1/8) Epoch 18, batch 19900, loss[loss=0.1329, simple_loss=0.2089, pruned_loss=0.02847, over 4642.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02838, over 971680.12 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 09:50:59,801 INFO [train.py:715] (1/8) Epoch 18, batch 19950, loss[loss=0.123, simple_loss=0.2065, pruned_loss=0.0197, over 4876.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2069, pruned_loss=0.02823, over 971067.42 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 09:51:39,045 INFO [train.py:715] (1/8) Epoch 18, batch 20000, loss[loss=0.1239, simple_loss=0.1972, pruned_loss=0.02532, over 4940.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2065, pruned_loss=0.02827, over 972565.75 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:52:18,796 INFO [train.py:715] (1/8) Epoch 18, batch 20050, loss[loss=0.1199, simple_loss=0.1891, pruned_loss=0.02539, over 4978.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02794, over 973258.22 frames.], batch size: 31, lr: 1.23e-04
2022-05-09 09:52:59,016 INFO [train.py:715] (1/8) Epoch 18, batch 20100, loss[loss=0.1276, simple_loss=0.2064, pruned_loss=0.02445, over 4834.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02822, over 972929.77 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:53:39,144 INFO [train.py:715] (1/8) Epoch 18, batch 20150, loss[loss=0.1346, simple_loss=0.2091, pruned_loss=0.03007, over 4868.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.02806, over 972825.30 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:54:18,208 INFO [train.py:715] (1/8) Epoch 18, batch 20200, loss[loss=0.1332, simple_loss=0.1947, pruned_loss=0.03579, over 4823.00 frames.], tot_loss[loss=0.131, simple_loss=0.2056, pruned_loss=0.02818, over 973629.27 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:54:57,192 INFO [train.py:715] (1/8) Epoch 18, batch 20250, loss[loss=0.1239, simple_loss=0.194, pruned_loss=0.02684, over 4686.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02844, over 973476.33 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:55:36,871 INFO [train.py:715] (1/8) Epoch 18, batch 20300, loss[loss=0.1247, simple_loss=0.21, pruned_loss=0.01963, over 4817.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02867, over 973323.34 frames.], batch size: 27, lr: 1.23e-04
2022-05-09 09:56:16,002 INFO [train.py:715] (1/8) Epoch 18, batch 20350, loss[loss=0.1522, simple_loss=0.2187, pruned_loss=0.04281, over 4699.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02924, over 972484.19 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:56:55,258 INFO [train.py:715] (1/8) Epoch 18, batch 20400, loss[loss=0.1087, simple_loss=0.185, pruned_loss=0.01618, over 4755.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.0291, over 972309.01 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:57:34,098 INFO [train.py:715] (1/8) Epoch 18, batch 20450, loss[loss=0.1234, simple_loss=0.1861, pruned_loss=0.03029, over 4984.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02903, over 971434.96 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 09:58:14,206 INFO [train.py:715] (1/8) Epoch 18, batch 20500, loss[loss=0.1269, simple_loss=0.1984, pruned_loss=0.02772, over 4703.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02914, over 972057.81 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:58:52,921 INFO [train.py:715] (1/8) Epoch 18, batch 20550, loss[loss=0.1417, simple_loss=0.2213, pruned_loss=0.03104, over 4792.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.0292, over 972010.70 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:59:31,852 INFO [train.py:715] (1/8) Epoch 18, batch 20600, loss[loss=0.1304, simple_loss=0.2095, pruned_loss=0.02564, over 4853.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02943, over 971456.01 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 10:00:10,868 INFO [train.py:715] (1/8) Epoch 18, batch 20650, loss[loss=0.1161, simple_loss=0.1946, pruned_loss=0.01877, over 4785.00 frames.], tot_loss[loss=0.133, simple_loss=0.2068, pruned_loss=0.02955, over 971885.29 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:00:50,419 INFO [train.py:715] (1/8) Epoch 18, batch 20700, loss[loss=0.1171, simple_loss=0.2006, pruned_loss=0.01683, over 4805.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02839, over 972131.66 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:01:28,857 INFO [train.py:715] (1/8) Epoch 18, batch 20750, loss[loss=0.1518, simple_loss=0.2311, pruned_loss=0.03623, over 4945.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02867, over 972967.50 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 10:02:08,329 INFO [train.py:715] (1/8) Epoch 18, batch 20800, loss[loss=0.1558, simple_loss=0.2236, pruned_loss=0.04403, over 4894.00 frames.], tot_loss[loss=0.1321, simple_loss=0.206, pruned_loss=0.02907, over 973021.41 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:02:47,764 INFO [train.py:715] (1/8) Epoch 18, batch 20850, loss[loss=0.1302, simple_loss=0.2152, pruned_loss=0.02263, over 4729.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02872, over 972669.48 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 10:03:26,620 INFO [train.py:715] (1/8) Epoch 18, batch 20900, loss[loss=0.1311, simple_loss=0.204, pruned_loss=0.02909, over 4838.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2057, pruned_loss=0.02878, over 972203.61 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 10:04:05,319 INFO [train.py:715] (1/8) Epoch 18, batch 20950, loss[loss=0.1411, simple_loss=0.2096, pruned_loss=0.03629, over 4979.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2052, pruned_loss=0.0286, over 971720.46 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 10:04:44,841 INFO [train.py:715] (1/8) Epoch 18, batch 21000, loss[loss=0.1259, simple_loss=0.2027, pruned_loss=0.02449, over 4758.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2048, pruned_loss=0.02837, over 972035.16 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:04:44,842 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 10:04:54,817 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1046, simple_loss=0.1879, pruned_loss=0.01059, over 914524.00 frames.
2022-05-09 10:05:34,562 INFO [train.py:715] (1/8) Epoch 18, batch 21050, loss[loss=0.1586, simple_loss=0.2341, pruned_loss=0.04159, over 4966.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.02879, over 972320.30 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 10:06:14,352 INFO [train.py:715] (1/8) Epoch 18, batch 21100, loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02922, over 4872.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02887, over 972680.57 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 10:06:53,519 INFO [train.py:715] (1/8) Epoch 18, batch 21150, loss[loss=0.134, simple_loss=0.202, pruned_loss=0.03294, over 4739.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02906, over 971898.63 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 10:07:33,000 INFO [train.py:715] (1/8) Epoch 18, batch 21200, loss[loss=0.1327, simple_loss=0.201, pruned_loss=0.03222, over 4848.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02893, over 971829.60 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 10:08:12,705 INFO [train.py:715] (1/8) Epoch 18, batch 21250, loss[loss=0.115, simple_loss=0.1893, pruned_loss=0.02035, over 4855.00 frames.], tot_loss[loss=0.132, simple_loss=0.207, pruned_loss=0.02855, over 971880.23 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 10:08:51,643 INFO [train.py:715] (1/8) Epoch 18, batch 21300, loss[loss=0.1473, simple_loss=0.229, pruned_loss=0.03283, over 4865.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02904, over 971772.18 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 10:09:30,190 INFO [train.py:715] (1/8) Epoch 18, batch 21350, loss[loss=0.1474, simple_loss=0.222, pruned_loss=0.03637, over 4866.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02925, over 972298.87 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 10:10:09,580 INFO [train.py:715] (1/8) Epoch 18, batch 21400, loss[loss=0.1186, simple_loss=0.1948, pruned_loss=0.02118, over 4841.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2074, pruned_loss=0.02872, over 972313.96 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 10:10:51,759 INFO [train.py:715] (1/8) Epoch 18, batch 21450, loss[loss=0.1215, simple_loss=0.1925, pruned_loss=0.0252, over 4757.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2076, pruned_loss=0.02905, over 972608.27 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:11:30,940 INFO [train.py:715] (1/8) Epoch 18, batch 21500, loss[loss=0.146, simple_loss=0.2168, pruned_loss=0.03763, over 4875.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02917, over 972858.43 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 10:12:09,690 INFO [train.py:715] (1/8) Epoch 18, batch 21550, loss[loss=0.1323, simple_loss=0.2072, pruned_loss=0.02869, over 4875.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.02893, over 973137.76 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 10:12:49,086 INFO [train.py:715] (1/8) Epoch 18, batch 21600, loss[loss=0.1079, simple_loss=0.1868, pruned_loss=0.01447, over 4991.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02945, over 971586.96 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 10:13:28,300 INFO [train.py:715] (1/8) Epoch 18, batch 21650, loss[loss=0.1318, simple_loss=0.2096, pruned_loss=0.02704, over 4783.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02911, over 971580.88 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:14:06,692 INFO [train.py:715] (1/8) Epoch 18, batch 21700, loss[loss=0.1179, simple_loss=0.1907, pruned_loss=0.02251, over 4767.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02873, over 971023.59 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:14:45,677 INFO [train.py:715] (1/8) Epoch 18, batch 21750, loss[loss=0.1607, simple_loss=0.2305, pruned_loss=0.04546, over 4768.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02851, over 971769.18 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:15:24,819 INFO [train.py:715] (1/8) Epoch 18, batch 21800, loss[loss=0.1614, simple_loss=0.2272, pruned_loss=0.04781, over 4928.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02902, over 971982.58 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 10:16:04,132 INFO [train.py:715] (1/8) Epoch 18, batch 21850, loss[loss=0.1176, simple_loss=0.2015, pruned_loss=0.01687, over 4887.00 frames.], tot_loss[loss=0.132, simple_loss=0.2058, pruned_loss=0.02912, over 972113.06 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 10:16:43,558 INFO [train.py:715] (1/8) Epoch 18, batch 21900, loss[loss=0.1207, simple_loss=0.1917, pruned_loss=0.02487, over 4979.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02871, over 972137.42 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:17:23,078 INFO [train.py:715] (1/8) Epoch 18, batch 21950, loss[loss=0.1205, simple_loss=0.1987, pruned_loss=0.02116, over 4742.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2055, pruned_loss=0.02864, over 972148.97 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 10:18:02,132 INFO [train.py:715] (1/8) Epoch 18, batch 22000, loss[loss=0.1334, simple_loss=0.2243, pruned_loss=0.02127, over 4754.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2049, pruned_loss=0.0283, over 971923.37 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:18:41,240 INFO [train.py:715] (1/8) Epoch 18, batch 22050, loss[loss=0.1233, simple_loss=0.1983, pruned_loss=0.02409, over 4810.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2047, pruned_loss=0.02797, over 972489.16 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:19:20,730 INFO [train.py:715] (1/8) Epoch 18, batch 22100, loss[loss=0.1555, simple_loss=0.2188, pruned_loss=0.04608, over 4873.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.0288, over 971649.50 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 10:19:59,601 INFO [train.py:715] (1/8) Epoch 18, batch 22150, loss[loss=0.1382, simple_loss=0.1998, pruned_loss=0.03824, over 4784.00 frames.], tot_loss[loss=0.131, simple_loss=0.2051, pruned_loss=0.02848, over 971531.61 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:20:39,095 INFO [train.py:715] (1/8) Epoch 18, batch 22200, loss[loss=0.1053, simple_loss=0.1873, pruned_loss=0.01161, over 4784.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.0285, over 971296.06 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:21:17,770 INFO [train.py:715] (1/8) Epoch 18, batch 22250, loss[loss=0.1404, simple_loss=0.2104, pruned_loss=0.0352, over 4787.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02895, over 971703.78 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 10:21:57,017 INFO [train.py:715] (1/8) Epoch 18, batch 22300, loss[loss=0.1037, simple_loss=0.1796, pruned_loss=0.01388, over 4980.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02909, over 970992.21 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:22:35,718 INFO [train.py:715] (1/8) Epoch 18, batch 22350, loss[loss=0.1266, simple_loss=0.1926, pruned_loss=0.03034, over 4811.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02925, over 971999.60 frames.], batch size: 26, lr: 1.23e-04
2022-05-09 10:23:14,492 INFO [train.py:715] (1/8) Epoch 18, batch 22400, loss[loss=0.1353, simple_loss=0.2283, pruned_loss=0.02118, over 4774.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02954, over 971008.14 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 10:23:53,396 INFO [train.py:715] (1/8) Epoch 18, batch 22450, loss[loss=0.1151, simple_loss=0.1885, pruned_loss=0.0209, over 4795.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02884, over 970504.52 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:24:32,482 INFO [train.py:715] (1/8) Epoch 18, batch 22500, loss[loss=0.1342, simple_loss=0.2088, pruned_loss=0.02974, over 4852.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02872, over 971208.02 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 10:25:11,261 INFO [train.py:715] (1/8) Epoch 18, batch 22550, loss[loss=0.1251, simple_loss=0.211, pruned_loss=0.01964, over 4806.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02897, over 972088.36 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 10:25:50,055 INFO [train.py:715] (1/8) Epoch 18, batch 22600, loss[loss=0.1078, simple_loss=0.1822, pruned_loss=0.01664, over 4789.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02855, over 971830.06 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:26:29,077 INFO [train.py:715] (1/8) Epoch 18, batch 22650, loss[loss=0.1043, simple_loss=0.1755, pruned_loss=0.01655, over 4771.00 frames.], tot_loss[loss=0.1308, simple_loss=0.205, pruned_loss=0.0283, over 972979.61 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:27:07,865 INFO [train.py:715] (1/8) Epoch 18, batch 22700, loss[loss=0.1268, simple_loss=0.1974, pruned_loss=0.02809, over 4890.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.0288, over 974218.54 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:27:46,834 INFO [train.py:715] (1/8) Epoch 18, batch 22750, loss[loss=0.1343, simple_loss=0.2017, pruned_loss=0.0335, over 4917.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02899, over 974433.86 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 10:28:26,215 INFO [train.py:715] (1/8) Epoch 18, batch 22800, loss[loss=0.157, simple_loss=0.2411, pruned_loss=0.03643, over 4828.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2069, pruned_loss=0.02843, over 974418.64 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:29:04,920 INFO [train.py:715] (1/8) Epoch 18, batch 22850, loss[loss=0.1175, simple_loss=0.2011, pruned_loss=0.01697, over 4892.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2068, pruned_loss=0.02843, over 973969.08 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:29:43,877 INFO [train.py:715] (1/8) Epoch 18, batch 22900, loss[loss=0.1353, simple_loss=0.2157, pruned_loss=0.02747, over 4793.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02853, over 973170.19 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 10:30:22,780 INFO [train.py:715] (1/8) Epoch 18, batch 22950, loss[loss=0.1305, simple_loss=0.2096, pruned_loss=0.02574, over 4981.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02878, over 973218.96 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 10:31:02,202 INFO [train.py:715] (1/8) Epoch 18, batch 23000, loss[loss=0.1462, simple_loss=0.2276, pruned_loss=0.0324, over 4918.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02856, over 972334.91 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:31:40,969 INFO [train.py:715] (1/8) Epoch 18, batch 23050, loss[loss=0.1117, simple_loss=0.1846, pruned_loss=0.01934, over 4868.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02886, over 971931.39 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 10:32:20,092 INFO [train.py:715] (1/8) Epoch 18, batch 23100, loss[loss=0.1257, simple_loss=0.187, pruned_loss=0.03222, over 4693.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02887, over 972674.21 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:32:59,653 INFO [train.py:715] (1/8) Epoch 18, batch 23150, loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.041, over 4839.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02847, over 972821.13 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:33:38,764 INFO [train.py:715] (1/8) Epoch 18, batch 23200, loss[loss=0.1226, simple_loss=0.196, pruned_loss=0.02458, over 4905.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02901, over 972933.43 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:34:17,629 INFO [train.py:715] (1/8) Epoch 18, batch 23250, loss[loss=0.1302, simple_loss=0.202, pruned_loss=0.02919, over 4828.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02865, over 972315.78 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 10:34:56,933 INFO [train.py:715] (1/8) Epoch 18, batch 23300, loss[loss=0.117, simple_loss=0.1934, pruned_loss=0.02035, over 4865.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02887, over 972342.00 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 10:35:36,580 INFO [train.py:715] (1/8) Epoch 18, batch 23350, loss[loss=0.1426, simple_loss=0.2219, pruned_loss=0.0317, over 4693.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02879, over 971475.21 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:36:15,525 INFO [train.py:715] (1/8) Epoch 18, batch 23400, loss[loss=0.1337, simple_loss=0.2139, pruned_loss=0.02671, over 4833.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02859, over 971260.44 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:36:54,045 INFO [train.py:715] (1/8) Epoch 18, batch 23450, loss[loss=0.1494, simple_loss=0.2265, pruned_loss=0.03618, over 4963.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.0286, over 971737.84 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:37:33,548 INFO [train.py:715] (1/8) Epoch 18, batch 23500, loss[loss=0.121, simple_loss=0.1901, pruned_loss=0.02595, over 4841.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2049, pruned_loss=0.02837, over 971929.44 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 10:38:12,434 INFO [train.py:715] (1/8) Epoch 18, batch 23550, loss[loss=0.1296, simple_loss=0.1947, pruned_loss=0.03224, over 4752.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2045, pruned_loss=0.02855, over 971842.57 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 10:38:51,085 INFO [train.py:715] (1/8) Epoch 18, batch 23600, loss[loss=0.1499, simple_loss=0.2302, pruned_loss=0.03478, over 4857.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02857, over 971445.78 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 10:39:30,019 INFO [train.py:715] (1/8) Epoch 18, batch 23650, loss[loss=0.1178, simple_loss=0.177, pruned_loss=0.02935, over 4723.00 frames.], tot_loss[loss=0.1312, simple_loss=0.205, pruned_loss=0.02869, over 971966.90 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 10:40:08,660 INFO [train.py:715] (1/8) Epoch 18, batch 23700, loss[loss=0.1338, simple_loss=0.2139, pruned_loss=0.02679, over 4936.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2057, pruned_loss=0.02892, over 971963.80 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 10:40:47,462 INFO [train.py:715] (1/8) Epoch 18, batch 23750, loss[loss=0.1107, simple_loss=0.1777, pruned_loss=0.02187, over 4790.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2057, pruned_loss=0.02879, over 972088.84 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:41:26,879 INFO [train.py:715] (1/8) Epoch 18, batch 23800, loss[loss=0.1435, simple_loss=0.2229, pruned_loss=0.03206, over 4810.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02869, over 972245.21 frames.], batch size: 27, lr: 1.23e-04
2022-05-09 10:42:06,533 INFO [train.py:715] (1/8) Epoch 18, batch 23850, loss[loss=0.1079, simple_loss=0.1811, pruned_loss=0.01736, over 4987.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2058, pruned_loss=0.02887, over 973416.50 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 10:42:45,347 INFO [train.py:715] (1/8) Epoch 18, batch 23900, loss[loss=0.16, simple_loss=0.226, pruned_loss=0.04696, over 4760.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02883, over 973252.34 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:43:24,099 INFO [train.py:715] (1/8) Epoch 18, batch 23950, loss[loss=0.1455, simple_loss=0.2273, pruned_loss=0.03187, over 4974.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02878, over 972994.71 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 10:44:03,428 INFO [train.py:715] (1/8) Epoch 18, batch 24000, loss[loss=0.1326, simple_loss=0.1938, pruned_loss=0.03567, over 4806.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.0293, over 972824.78 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 10:44:03,429 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 10:44:13,351 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1045, simple_loss=0.1878, pruned_loss=0.01057, over 914524.00 frames.
2022-05-09 10:44:52,988 INFO [train.py:715] (1/8) Epoch 18, batch 24050, loss[loss=0.1242, simple_loss=0.2094, pruned_loss=0.01953, over 4797.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.0296, over 972848.65 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 10:45:31,814 INFO [train.py:715] (1/8) Epoch 18, batch 24100, loss[loss=0.1569, simple_loss=0.2429, pruned_loss=0.03542, over 4821.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02953, over 973297.33 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 10:46:10,744 INFO [train.py:715] (1/8) Epoch 18, batch 24150, loss[loss=0.1097, simple_loss=0.1776, pruned_loss=0.02086, over 4859.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02948, over 973584.75 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 10:46:50,168 INFO [train.py:715] (1/8) Epoch 18, batch 24200, loss[loss=0.1128, simple_loss=0.1923, pruned_loss=0.01667, over 4831.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02908, over 972520.00 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 10:47:29,223 INFO [train.py:715] (1/8) Epoch 18, batch 24250, loss[loss=0.1244, simple_loss=0.2012, pruned_loss=0.02375, over 4884.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02893, over 973040.52 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 10:48:08,101 INFO [train.py:715] (1/8) Epoch 18, batch 24300, loss[loss=0.1264, simple_loss=0.2093, pruned_loss=0.02174, over 4973.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02883, over 973354.92 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 10:48:46,575 INFO [train.py:715] (1/8) Epoch 18, batch 24350, loss[loss=0.1309, simple_loss=0.2151, pruned_loss=0.02334, over 4688.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02912, over 973746.58 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 10:49:25,637 INFO [train.py:715] (1/8) Epoch 18, batch 24400, loss[loss=0.1252, simple_loss=0.1931, pruned_loss=0.02859, over 4881.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02916, over 972553.79 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 10:50:04,245 INFO [train.py:715] (1/8) Epoch 18, batch 24450, loss[loss=0.1175, simple_loss=0.1787, pruned_loss=0.02815, over 4877.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02915, over 971508.02 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 10:50:42,844 INFO [train.py:715] (1/8) Epoch 18, batch 24500, loss[loss=0.1317, simple_loss=0.2152, pruned_loss=0.02411, over 4945.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02923, over 971739.41 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 10:51:22,301 INFO [train.py:715] (1/8) Epoch 18, batch 24550, loss[loss=0.1021, simple_loss=0.1803, pruned_loss=0.01191, over 4823.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02901, over 972659.66 frames.], batch size: 27, lr: 1.22e-04
2022-05-09 10:52:01,505 INFO [train.py:715] (1/8) Epoch 18, batch 24600, loss[loss=0.1377, simple_loss=0.2137, pruned_loss=0.03079, over 4695.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02919, over 973259.48 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 10:52:40,235 INFO [train.py:715] (1/8) Epoch 18, batch 24650, loss[loss=0.1543, simple_loss=0.2213, pruned_loss=0.04369, over 4689.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02909, over 973351.27 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 10:53:18,838 INFO [train.py:715] (1/8) Epoch 18, batch 24700, loss[loss=0.1165, simple_loss=0.1876, pruned_loss=0.02264, over 4763.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02926, over 973188.37 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 10:53:58,061 INFO [train.py:715] (1/8) Epoch 18, batch 24750, loss[loss=0.1044, simple_loss=0.1837, pruned_loss=0.01255, over 4921.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02874, over 973682.04 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 10:54:37,026 INFO [train.py:715] (1/8) Epoch 18, batch 24800, loss[loss=0.1312, simple_loss=0.2098, pruned_loss=0.02627, over 4954.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02823, over 973823.98 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 10:55:16,441 INFO [train.py:715] (1/8) Epoch 18, batch 24850, loss[loss=0.1281, simple_loss=0.2036, pruned_loss=0.02631, over 4845.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02872, over 972728.67 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 10:55:55,498 INFO [train.py:715] (1/8) Epoch 18, batch 24900, loss[loss=0.123, simple_loss=0.1965, pruned_loss=0.02472, over 4780.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02857, over 972645.47 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 10:56:35,063 INFO [train.py:715] (1/8) Epoch 18, batch 24950, loss[loss=0.1209, simple_loss=0.2001, pruned_loss=0.0208, over 4812.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02869, over 971894.42 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 10:57:14,186 INFO [train.py:715] (1/8) Epoch 18, batch 25000, loss[loss=0.1262, simple_loss=0.1987, pruned_loss=0.02686, over 4873.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02874, over 971608.35 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 10:57:52,840 INFO [train.py:715] (1/8) Epoch 18, batch 25050, loss[loss=0.1168, simple_loss=0.1867, pruned_loss=0.02344, over 4743.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2066, pruned_loss=0.02841, over 971136.42 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 10:58:32,133 INFO [train.py:715] (1/8) Epoch 18, batch 25100, loss[loss=0.128, simple_loss=0.2177, pruned_loss=0.01919, over 4785.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2074, pruned_loss=0.02876, over 971810.65 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 10:59:11,688 INFO [train.py:715] (1/8) Epoch 18, batch 25150, loss[loss=0.1744, simple_loss=0.2462, pruned_loss=0.0513, over 4921.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2073, pruned_loss=0.0286, over 972174.10 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 10:59:50,259 INFO [train.py:715] (1/8) Epoch 18, batch 25200, loss[loss=0.1261, simple_loss=0.1931, pruned_loss=0.02955, over 4976.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.0286, over 972946.59 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:00:29,819 INFO [train.py:715] (1/8) Epoch 18, batch 25250, loss[loss=0.1477, simple_loss=0.224, pruned_loss=0.03571, over 4799.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02834, over 972659.37 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:01:09,545 INFO [train.py:715] (1/8) Epoch 18, batch 25300, loss[loss=0.1365, simple_loss=0.2162, pruned_loss=0.02839, over 4960.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02884, over 972697.48 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:01:48,675 INFO [train.py:715] (1/8) Epoch 18, batch 25350, loss[loss=0.1254, simple_loss=0.1978, pruned_loss=0.0265, over 4831.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02857, over 972298.53 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 11:02:27,380 INFO [train.py:715] (1/8) Epoch 18, batch 25400, loss[loss=0.1371, simple_loss=0.208, pruned_loss=0.03313, over 4869.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02907, over 972696.53 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 11:03:06,959 INFO [train.py:715] (1/8) Epoch 18, batch 25450, loss[loss=0.1415, simple_loss=0.2161, pruned_loss=0.03343, over 4918.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02928, over 972386.87 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:03:45,936 INFO [train.py:715] (1/8) Epoch 18, batch 25500, loss[loss=0.1622, simple_loss=0.2438, pruned_loss=0.04031, over 4757.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02947, over 971514.00 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:04:24,920 INFO [train.py:715] (1/8) Epoch 18, batch 25550, loss[loss=0.09462, simple_loss=0.1632, pruned_loss=0.01303, over 4771.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02897, over 971672.57 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 11:05:04,555 INFO [train.py:715] (1/8) Epoch 18, batch 25600, loss[loss=0.1255, simple_loss=0.211, pruned_loss=0.02003, over 4747.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02925, over 971519.68 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:05:44,103 INFO [train.py:715] (1/8) Epoch 18, batch 25650, loss[loss=0.1419, simple_loss=0.2251, pruned_loss=0.02936, over 4690.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.0291, over 971206.38 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:06:23,309 INFO [train.py:715] (1/8) Epoch 18, batch 25700, loss[loss=0.1262, simple_loss=0.1967, pruned_loss=0.02785, over 4968.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02881, over 971623.41 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:07:02,568 INFO [train.py:715] (1/8) Epoch 18, batch 25750, loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02968, over 4959.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02895, over 972500.80 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:07:41,973 INFO [train.py:715] (1/8) Epoch 18, batch 25800, loss[loss=0.1385, simple_loss=0.2192, pruned_loss=0.02888, over 4921.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.0291, over 972614.90 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:08:20,795 INFO [train.py:715] (1/8) Epoch 18, batch 25850, loss[loss=0.1429, simple_loss=0.2202, pruned_loss=0.03277, over 4843.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02916, over 972325.71 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:08:59,118 INFO [train.py:715] (1/8) Epoch 18, batch 25900, loss[loss=0.1113, simple_loss=0.1881, pruned_loss=0.01727, over 4802.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2055, pruned_loss=0.02903, over 972507.08 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:09:38,435 INFO [train.py:715] (1/8) Epoch 18, batch 25950, loss[loss=0.1125, simple_loss=0.193, pruned_loss=0.01598, over 4819.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02897, over 972111.98 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 11:10:17,512 INFO [train.py:715] (1/8) Epoch 18, batch 26000, loss[loss=0.1303, simple_loss=0.2101, pruned_loss=0.02527, over 4955.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2052, pruned_loss=0.02864, over 972077.21 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:10:56,983 INFO [train.py:715] (1/8) Epoch 18, batch 26050, loss[loss=0.1322, simple_loss=0.2089, pruned_loss=0.0277, over 4878.00 frames.], tot_loss[loss=0.1311, simple_loss=0.205, pruned_loss=0.02855, over 971485.72 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:11:36,112 INFO [train.py:715] (1/8) Epoch 18, batch 26100, loss[loss=0.125, simple_loss=0.198, pruned_loss=0.02594, over 4905.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2051, pruned_loss=0.0287, over 971994.15 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:12:15,688 INFO [train.py:715] (1/8) Epoch 18, batch 26150, loss[loss=0.1123, simple_loss=0.1963, pruned_loss=0.01413, over 4939.00 frames.], tot_loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02839, over 972305.40 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:12:54,902 INFO [train.py:715] (1/8) Epoch 18, batch 26200, loss[loss=0.1136, simple_loss=0.1915, pruned_loss=0.01787, over 4828.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2047, pruned_loss=0.02817, over 972392.15 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 11:13:33,233 INFO [train.py:715] (1/8) Epoch 18, batch 26250, loss[loss=0.1573, simple_loss=0.2306, pruned_loss=0.04205, over 4895.00 frames.], tot_loss[loss=0.1307, simple_loss=0.205, pruned_loss=0.02819, over 971810.46 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:14:12,857 INFO [train.py:715] (1/8) Epoch 18, batch 26300, loss[loss=0.1176, simple_loss=0.191, pruned_loss=0.02211, over 4806.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2048, pruned_loss=0.02803, over 972213.68 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 11:14:51,541 INFO [train.py:715] (1/8) Epoch 18, batch 26350, loss[loss=0.1275, simple_loss=0.2044, pruned_loss=0.02535, over 4955.00 frames.], tot_loss[loss=0.1305, simple_loss=0.205, pruned_loss=0.02795, over 972685.23 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:15:30,591 INFO [train.py:715] (1/8) Epoch 18, batch 26400, loss[loss=0.1169, simple_loss=0.1949, pruned_loss=0.01947, over 4827.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2056, pruned_loss=0.02809, over 972860.15 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 11:16:09,483 INFO [train.py:715] (1/8) Epoch 18, batch 26450, loss[loss=0.1292, simple_loss=0.191, pruned_loss=0.03375, over 4690.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02828, over 972959.45 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:16:49,037 INFO [train.py:715] (1/8) Epoch 18, batch 26500, loss[loss=0.1499, simple_loss=0.2146, pruned_loss=0.04254, over 4700.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02833, over 972774.34 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:17:28,068 INFO [train.py:715] (1/8) Epoch 18, batch 26550, loss[loss=0.1226, simple_loss=0.2039, pruned_loss=0.02064, over 4921.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02828, over 973190.99 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 11:18:06,862 INFO [train.py:715] (1/8) Epoch 18, batch 26600, loss[loss=0.1413, simple_loss=0.2223, pruned_loss=0.03013, over 4914.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02848, over 972713.76 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:18:46,128 INFO [train.py:715] (1/8) Epoch 18, batch 26650, loss[loss=0.1178, simple_loss=0.1905, pruned_loss=0.02256, over 4821.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02906, over 972309.90 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 11:19:25,269 INFO [train.py:715] (1/8) Epoch 18, batch 26700, loss[loss=0.1453, simple_loss=0.2248, pruned_loss=0.03293, over 4919.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02913, over 972036.05 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 11:20:05,263 INFO [train.py:715] (1/8) Epoch 18, batch 26750, loss[loss=0.1038, simple_loss=0.1815, pruned_loss=0.01307, over 4834.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02899, over 972012.56 frames.], batch size: 27, lr: 1.22e-04
2022-05-09 11:20:43,661 INFO [train.py:715] (1/8) Epoch 18, batch 26800, loss[loss=0.1338, simple_loss=0.2101, pruned_loss=0.02875, over 4764.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02917, over 972124.11 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:21:23,697 INFO [train.py:715] (1/8) Epoch 18, batch 26850, loss[loss=0.1302, simple_loss=0.2095, pruned_loss=0.02542, over 4811.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02939, over 973216.65 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 11:22:03,377 INFO [train.py:715] (1/8) Epoch 18, batch 26900, loss[loss=0.1235, simple_loss=0.2002, pruned_loss=0.02334, over 4738.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02936, over 972825.66 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:22:41,420 INFO [train.py:715] (1/8) Epoch 18, batch 26950, loss[loss=0.1186, simple_loss=0.2041, pruned_loss=0.01658, over 4976.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02931, over 973070.01 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 11:23:20,804 INFO [train.py:715] (1/8) Epoch 18, batch 27000, loss[loss=0.1587, simple_loss=0.2397, pruned_loss=0.0389, over 4804.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02891, over 972705.24 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:23:20,805 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 11:23:30,798 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1044, simple_loss=0.1877, pruned_loss=0.01055, over 914524.00 frames.
2022-05-09 11:24:11,106 INFO [train.py:715] (1/8) Epoch 18, batch 27050, loss[loss=0.1262, simple_loss=0.2052, pruned_loss=0.02364, over 4872.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.0289, over 972766.44 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 11:24:50,010 INFO [train.py:715] (1/8) Epoch 18, batch 27100, loss[loss=0.1243, simple_loss=0.1834, pruned_loss=0.03257, over 4778.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02893, over 972932.51 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:25:29,323 INFO [train.py:715] (1/8) Epoch 18, batch 27150, loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.02823, over 4962.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2071, pruned_loss=0.02866, over 973085.20 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:26:08,684 INFO [train.py:715] (1/8) Epoch 18, batch 27200, loss[loss=0.1098, simple_loss=0.1751, pruned_loss=0.02221, over 4786.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2066, pruned_loss=0.02842, over 972856.37 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 11:26:47,915 INFO [train.py:715] (1/8) Epoch 18, batch 27250, loss[loss=0.1106, simple_loss=0.1811, pruned_loss=0.02008, over 4973.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02862, over 972441.73 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:27:26,981 INFO [train.py:715] (1/8) Epoch 18, batch 27300, loss[loss=0.1359, simple_loss=0.2144, pruned_loss=0.02871, over 4911.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2075, pruned_loss=0.02869, over 972311.24 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:28:05,821 INFO [train.py:715] (1/8) Epoch 18, batch 27350, loss[loss=0.138, simple_loss=0.2151, pruned_loss=0.03043, over 4831.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.0284, over 972091.56 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:28:46,001 INFO [train.py:715] (1/8) Epoch 18, batch 27400, loss[loss=0.1399, simple_loss=0.2079, pruned_loss=0.03594, over 4852.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02908, over 970928.28 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 11:29:25,403 INFO [train.py:715] (1/8) Epoch 18, batch 27450, loss[loss=0.1104, simple_loss=0.1896, pruned_loss=0.01561, over 4765.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02868, over 972129.76 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:30:04,443 INFO [train.py:715] (1/8) Epoch 18, batch 27500, loss[loss=0.1265, simple_loss=0.2052, pruned_loss=0.02396, over 4966.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.0289, over 972164.96 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:30:44,159 INFO [train.py:715] (1/8) Epoch 18, batch 27550, loss[loss=0.1266, simple_loss=0.206, pruned_loss=0.02359, over 4768.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.0288, over 972120.92 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:31:23,278 INFO [train.py:715] (1/8) Epoch 18, batch 27600, loss[loss=0.1291, simple_loss=0.205, pruned_loss=0.02666, over 4948.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02867, over 971652.55 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:32:01,946 INFO [train.py:715] (1/8) Epoch 18, batch 27650, loss[loss=0.1167, simple_loss=0.1936, pruned_loss=0.01994, over 4961.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02871, over 971677.25 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:32:40,856 INFO [train.py:715] (1/8) Epoch 18, batch 27700, loss[loss=0.1256, simple_loss=0.2135, pruned_loss=0.01884, over 4832.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02912, over 972289.72 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:33:20,155 INFO [train.py:715] (1/8) Epoch 18, batch 27750, loss[loss=0.1488, simple_loss=0.2266, pruned_loss=0.03548, over 4836.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02885, over 972411.31 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 11:33:59,614 INFO [train.py:715] (1/8) Epoch 18, batch 27800, loss[loss=0.1448, simple_loss=0.2126, pruned_loss=0.03852, over 4867.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02855, over 972871.26 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 11:34:38,868 INFO [train.py:715] (1/8) Epoch 18, batch 27850, loss[loss=0.1303, simple_loss=0.2026, pruned_loss=0.02904, over 4778.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02825, over 972376.92 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:35:18,479 INFO [train.py:715] (1/8) Epoch 18, batch 27900, loss[loss=0.1409, simple_loss=0.2194, pruned_loss=0.03117, over 4895.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02835, over 972275.11 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 11:35:57,736 INFO [train.py:715] (1/8) Epoch 18, batch 27950, loss[loss=0.1106, simple_loss=0.1826, pruned_loss=0.01937, over 4940.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.0287, over 971865.70 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 11:36:36,980 INFO [train.py:715] (1/8) Epoch 18, batch 28000, loss[loss=0.1308, simple_loss=0.2049, pruned_loss=0.02833, over 4771.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02856, over 971404.50 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:37:16,529 INFO [train.py:715] (1/8) Epoch 18, batch 28050, loss[loss=0.1202, simple_loss=0.1929, pruned_loss=0.02379, over 4772.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02833, over 971977.25 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:37:56,319 INFO [train.py:715] (1/8) Epoch 18, batch 28100, loss[loss=0.1236, simple_loss=0.2017, pruned_loss=0.02274, over 4846.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2068, pruned_loss=0.02841, over 971659.45 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 11:38:35,509 INFO [train.py:715] (1/8) Epoch 18, batch 28150, loss[loss=0.1182, simple_loss=0.1921, pruned_loss=0.02215, over 4910.00 frames.], tot_loss[loss=0.131, simple_loss=0.2057, pruned_loss=0.0281, over 972501.73 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:39:13,842 INFO [train.py:715] (1/8) Epoch 18, batch 28200, loss[loss=0.1166, simple_loss=0.1893, pruned_loss=0.02189, over 4775.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02839, over 972732.86 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:39:53,464 INFO [train.py:715] (1/8) Epoch 18, batch 28250, loss[loss=0.1311, simple_loss=0.2115, pruned_loss=0.02534, over 4791.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2066, pruned_loss=0.02846, over 973052.21 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:40:32,293 INFO [train.py:715] (1/8) Epoch 18, batch 28300, loss[loss=0.1193, simple_loss=0.1891, pruned_loss=0.02475, over 4848.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2068, pruned_loss=0.02845, over 972277.22 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 11:41:11,194 INFO [train.py:715] (1/8) Epoch 18, batch 28350, loss[loss=0.1275, simple_loss=0.1946, pruned_loss=0.03014, over 4891.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2073, pruned_loss=0.02849, over 973114.43 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:41:50,492 INFO [train.py:715] (1/8) Epoch 18, batch 28400, loss[loss=0.1362, simple_loss=0.2065, pruned_loss=0.03291, over 4857.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2076, pruned_loss=0.02887, over 973193.00 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 11:42:29,770 INFO [train.py:715] (1/8) Epoch 18, batch 28450, loss[loss=0.1163, simple_loss=0.1889, pruned_loss=0.02186, over 4782.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02876, over 973033.16 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:43:08,843 INFO [train.py:715] (1/8) Epoch 18, batch 28500, loss[loss=0.1395, simple_loss=0.2214, pruned_loss=0.02885, over 4988.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2074, pruned_loss=0.02845, over 973843.15 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:43:47,953 INFO [train.py:715] (1/8) Epoch 18, batch 28550, loss[loss=0.128, simple_loss=0.199, pruned_loss=0.02847, over 4781.00 frames.], tot_loss[loss=0.1319, simple_loss=0.207, pruned_loss=0.02841, over 973941.66 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:44:27,956 INFO [train.py:715] (1/8) Epoch 18, batch 28600, loss[loss=0.1266, simple_loss=0.2062, pruned_loss=0.02352, over 4695.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2066, pruned_loss=0.02827, over 972949.80 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:45:06,659 INFO [train.py:715] (1/8) Epoch 18, batch 28650, loss[loss=0.1332, simple_loss=0.2085, pruned_loss=0.02898, over 4786.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2065, pruned_loss=0.02814, over 973614.17 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:45:45,608 INFO [train.py:715] (1/8) Epoch 18, batch 28700, loss[loss=0.1192, simple_loss=0.1893, pruned_loss=0.02456, over 4681.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02867, over 973767.03 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:46:25,176 INFO [train.py:715] (1/8) Epoch 18, batch 28750, loss[loss=0.137, simple_loss=0.2141, pruned_loss=0.02998, over 4918.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02851, over 973907.57 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:47:04,220 INFO [train.py:715] (1/8) Epoch 18, batch 28800, loss[loss=0.1294, simple_loss=0.1942, pruned_loss=0.03235, over 4815.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02826, over 974279.11 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:47:43,075 INFO [train.py:715] (1/8) Epoch 18, batch 28850, loss[loss=0.1209, simple_loss=0.1994, pruned_loss=0.0212, over 4863.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2066, pruned_loss=0.02808, over 973705.58 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 11:48:21,621 INFO [train.py:715] (1/8) Epoch 18, batch 28900, loss[loss=0.1249, simple_loss=0.1955, pruned_loss=0.02717, over 4974.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2064, pruned_loss=0.02821, over 973162.35 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 11:49:01,741 INFO [train.py:715] (1/8) Epoch 18, batch 28950, loss[loss=0.1354, simple_loss=0.2113, pruned_loss=0.02981, over 4943.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02855, over 972864.02 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:49:40,558 INFO [train.py:715] (1/8) Epoch 18, batch 29000, loss[loss=0.1246, simple_loss=0.2045, pruned_loss=0.02236, over 4842.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2068, pruned_loss=0.02836, over 973002.17 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:50:19,732 INFO [train.py:715] (1/8) Epoch 18, batch 29050, loss[loss=0.1521, simple_loss=0.2208, pruned_loss=0.04167, over 4843.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2082, pruned_loss=0.02902, over 972958.10 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:50:59,123 INFO [train.py:715] (1/8) Epoch 18, batch 29100, loss[loss=0.1338, simple_loss=0.2011, pruned_loss=0.03325, over 4858.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2081, pruned_loss=0.02863, over 971989.82 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 11:51:38,415 INFO [train.py:715] (1/8) Epoch 18, batch 29150, loss[loss=0.1654, simple_loss=0.2388, pruned_loss=0.04601, over 4802.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2075, pruned_loss=0.0289, over 972558.74 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:52:17,126 INFO [train.py:715] (1/8) Epoch 18, batch 29200, loss[loss=0.1344, simple_loss=0.2149, pruned_loss=0.02698, over 4899.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2082, pruned_loss=0.02938, over 972692.72 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:52:55,643 INFO [train.py:715] (1/8) Epoch 18, batch 29250, loss[loss=0.1553, simple_loss=0.2189, pruned_loss=0.0459, over 4988.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02954, over 972723.22 frames.], batch size: 33, lr: 1.22e-04
2022-05-09 11:53:35,210 INFO [train.py:715] (1/8) Epoch 18, batch 29300, loss[loss=0.1338, simple_loss=0.2044, pruned_loss=0.03162, over 4785.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02889, over 972194.96 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:54:13,911 INFO [train.py:715] (1/8) Epoch 18, batch 29350, loss[loss=0.1216, simple_loss=0.1942, pruned_loss=0.02456, over 4805.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02877, over 972336.38 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:54:52,606 INFO [train.py:715] (1/8) Epoch 18, batch 29400, loss[loss=0.1309, simple_loss=0.2087, pruned_loss=0.02654, over 4913.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02897, over 972211.12 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:55:33,950 INFO [train.py:715] (1/8) Epoch 18, batch 29450, loss[loss=0.1512, simple_loss=0.225, pruned_loss=0.03866, over 4940.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02853, over 972513.02 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 11:56:12,976 INFO [train.py:715] (1/8) Epoch 18, batch 29500, loss[loss=0.1234, simple_loss=0.1968, pruned_loss=0.02498, over 4880.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02821, over 971583.39 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:56:52,073 INFO [train.py:715] (1/8) Epoch 18, batch 29550, loss[loss=0.1217, simple_loss=0.1994, pruned_loss=0.02203, over 4753.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02838, over 971208.50 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:57:30,047 INFO [train.py:715] (1/8) Epoch 18, batch 29600, loss[loss=0.1262, simple_loss=0.2019, pruned_loss=0.02521, over 4887.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02866, over 970975.03 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:58:09,261 INFO [train.py:715] (1/8) Epoch 18, batch 29650, loss[loss=0.1384, simple_loss=0.2138, pruned_loss=0.03147, over 4938.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02844, over 970934.42 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 11:58:48,231 INFO [train.py:715] (1/8) Epoch 18, batch 29700, loss[loss=0.1565, simple_loss=0.2345, pruned_loss=0.03929, over 4930.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02815, over 970936.04 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:59:26,596 INFO [train.py:715] (1/8) Epoch 18, batch 29750, loss[loss=0.1374, simple_loss=0.2158, pruned_loss=0.02952, over 4892.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02883, over 970354.56 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:00:05,914 INFO [train.py:715] (1/8) Epoch 18, batch 29800, loss[loss=0.1465, simple_loss=0.2256, pruned_loss=0.03373, over 4989.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02854, over 970950.67 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:00:45,625 INFO [train.py:715] (1/8) Epoch 18, batch 29850, loss[loss=0.1308, simple_loss=0.2131, pruned_loss=0.0243, over 4791.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02847, over 971506.30 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:01:24,703 INFO [train.py:715] (1/8) Epoch 18, batch 29900, loss[loss=0.136, simple_loss=0.2225, pruned_loss=0.0247, over 4752.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.0287, over 970822.04 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:02:03,296 INFO [train.py:715] (1/8) Epoch 18, batch 29950, loss[loss=0.1475, simple_loss=0.2208, pruned_loss=0.03709, over 4862.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02859, over 971495.62 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 12:02:43,058 INFO [train.py:715] (1/8) Epoch 18, batch 30000, loss[loss=0.119, simple_loss=0.1998, pruned_loss=0.01914, over 4793.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2054, pruned_loss=0.02805, over 971313.81 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:02:43,059 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 12:02:52,969 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1047, simple_loss=0.188, pruned_loss=0.01071, over 914524.00 frames.
2022-05-09 12:03:33,188 INFO [train.py:715] (1/8) Epoch 18, batch 30050, loss[loss=0.133, simple_loss=0.2124, pruned_loss=0.02678, over 4789.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02831, over 971109.73 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:04:12,315 INFO [train.py:715] (1/8) Epoch 18, batch 30100, loss[loss=0.1569, simple_loss=0.226, pruned_loss=0.0439, over 4649.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02831, over 971416.84 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 12:04:50,501 INFO [train.py:715] (1/8) Epoch 18, batch 30150, loss[loss=0.1098, simple_loss=0.1801, pruned_loss=0.01974, over 4854.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02826, over 971767.00 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:05:29,935 INFO [train.py:715] (1/8) Epoch 18, batch 30200, loss[loss=0.1348, simple_loss=0.2004, pruned_loss=0.03455, over 4857.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02885, over 971936.40 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:06:09,179 INFO [train.py:715] (1/8) Epoch 18, batch 30250, loss[loss=0.1648, simple_loss=0.2289, pruned_loss=0.05034, over 4972.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02903, over 971728.50 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 12:06:48,868 INFO [train.py:715] (1/8) Epoch 18, batch 30300, loss[loss=0.1248, simple_loss=0.2025, pruned_loss=0.02354, over 4908.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02906, over 971847.97 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 12:07:27,505 INFO [train.py:715] (1/8) Epoch 18, batch 30350, loss[loss=0.1137, simple_loss=0.1981, pruned_loss=0.01463, over 4855.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02869, over 971573.17 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 12:08:07,405 INFO [train.py:715] (1/8) Epoch 18, batch 30400, loss[loss=0.1452, simple_loss=0.2268, pruned_loss=0.03186, over 4783.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02864, over 972244.59 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:08:46,434 INFO [train.py:715] (1/8) Epoch 18, batch 30450, loss[loss=0.1364, simple_loss=0.2074, pruned_loss=0.03274, over 4774.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.0289, over 971968.85 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:09:24,921 INFO [train.py:715] (1/8) Epoch 18, batch 30500, loss[loss=0.1147, simple_loss=0.1969, pruned_loss=0.01625, over 4921.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02899, over 972755.32 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 12:10:04,131 INFO [train.py:715] (1/8) Epoch 18, batch 30550, loss[loss=0.1253, simple_loss=0.1965, pruned_loss=0.02709, over 4764.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02878, over 972410.16 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:10:42,812 INFO [train.py:715] (1/8) Epoch 18, batch 30600, loss[loss=0.1378, simple_loss=0.2062, pruned_loss=0.03476, over 4856.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02896, over 972718.60 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 12:11:21,479 INFO [train.py:715] (1/8) Epoch 18, batch 30650, loss[loss=0.1667, simple_loss=0.2417, pruned_loss=0.04583, over 4857.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02875, over 972490.89 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 12:12:00,152 INFO [train.py:715] (1/8) Epoch 18, batch 30700, loss[loss=0.1473, simple_loss=0.2184, pruned_loss=0.03809, over 4776.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02849, over 972493.38 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 12:12:39,280 INFO [train.py:715] (1/8) Epoch 18, batch 30750, loss[loss=0.1475, simple_loss=0.2212, pruned_loss=0.03697, over 4764.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02894, over 972519.72 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:13:18,033 INFO [train.py:715] (1/8) Epoch 18, batch 30800, loss[loss=0.1664, simple_loss=0.2475, pruned_loss=0.0427, over 4891.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02903, over 972323.45 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:13:56,471 INFO [train.py:715] (1/8) Epoch 18, batch 30850, loss[loss=0.1411, simple_loss=0.227, pruned_loss=0.02757, over 4786.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02892, over 971905.30 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:14:35,505 INFO [train.py:715] (1/8) Epoch 18, batch 30900, loss[loss=0.1406, simple_loss=0.2138, pruned_loss=0.03372, over 4979.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02853, over 972025.77 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:15:14,124 INFO [train.py:715] (1/8) Epoch 18, batch 30950, loss[loss=0.1275, simple_loss=0.2103, pruned_loss=0.02229, over 4930.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02839, over 971890.36 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 12:15:52,432 INFO [train.py:715] (1/8) Epoch 18, batch 31000, loss[loss=0.1194, simple_loss=0.2005, pruned_loss=0.01918, over 4931.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02843, over 972016.22 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 12:16:31,404 INFO [train.py:715] (1/8) Epoch 18, batch 31050, loss[loss=0.123, simple_loss=0.2068, pruned_loss=0.01965, over 4866.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02851, over 972804.61 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:17:10,957 INFO [train.py:715] (1/8) Epoch 18, batch 31100, loss[loss=0.1275, simple_loss=0.2166, pruned_loss=0.01918, over 4877.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2054, pruned_loss=0.02869, over 973018.41 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:17:49,896 INFO [train.py:715] (1/8) Epoch 18, batch 31150, loss[loss=0.1175, simple_loss=0.1892, pruned_loss=0.0229, over 4955.00 frames.], tot_loss[loss=0.132, simple_loss=0.2056, pruned_loss=0.02914, over 972586.17 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:18:28,834 INFO [train.py:715] (1/8) Epoch 18, batch 31200, loss[loss=0.147, simple_loss=0.2246, pruned_loss=0.03473, over 4787.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2059, pruned_loss=0.02945, over 972327.46 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:19:08,211 INFO [train.py:715] (1/8) Epoch 18, batch 31250, loss[loss=0.1231, simple_loss=0.1977, pruned_loss=0.02425, over 4809.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2068, pruned_loss=0.02966, over 971789.63 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:19:47,254 INFO [train.py:715] (1/8) Epoch 18, batch 31300, loss[loss=0.1551, simple_loss=0.2261, pruned_loss=0.04201, over 4940.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02917, over 972943.66 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 12:20:25,873 INFO [train.py:715] (1/8) Epoch 18, batch 31350, loss[loss=0.1472, simple_loss=0.224, pruned_loss=0.03523, over 4967.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.0293, over 972929.54 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:21:05,050 INFO [train.py:715] (1/8) Epoch 18, batch 31400, loss[loss=0.1577, simple_loss=0.2144, pruned_loss=0.0505, over 4840.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02946, over 973272.46 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:21:44,552 INFO [train.py:715] (1/8) Epoch 18, batch 31450, loss[loss=0.1377, simple_loss=0.2127, pruned_loss=0.03129, over 4879.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02915, over 973133.18 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:22:23,384 INFO [train.py:715] (1/8) Epoch 18, batch 31500, loss[loss=0.1131, simple_loss=0.1856, pruned_loss=0.02034, over 4978.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02863, over 973405.04 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 12:23:01,620 INFO [train.py:715] (1/8) Epoch 18, batch 31550, loss[loss=0.1158, simple_loss=0.1868, pruned_loss=0.0224, over 4898.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02912, over 973658.83 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:23:41,438 INFO [train.py:715] (1/8) Epoch 18, batch 31600, loss[loss=0.1089, simple_loss=0.1962, pruned_loss=0.01079, over 4932.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02883, over 973168.30 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 12:24:20,709 INFO [train.py:715] (1/8) Epoch 18, batch 31650, loss[loss=0.1405, simple_loss=0.2208, pruned_loss=0.0301, over 4780.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02877, over 972936.11 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:24:59,683 INFO [train.py:715] (1/8) Epoch 18, batch 31700, loss[loss=0.1317, simple_loss=0.2023, pruned_loss=0.03053, over 4853.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02892, over 972794.22 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:25:38,809 INFO [train.py:715] (1/8) Epoch 18, batch 31750, loss[loss=0.1644, simple_loss=0.2337, pruned_loss=0.04756, over 4856.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02896, over 972882.73 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 12:26:18,650 INFO [train.py:715] (1/8) Epoch 18, batch 31800, loss[loss=0.1401, simple_loss=0.2258, pruned_loss=0.02718, over 4896.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02875, over 973909.76 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:26:58,018 INFO [train.py:715] (1/8) Epoch 18, batch 31850, loss[loss=0.1395, simple_loss=0.2153, pruned_loss=0.0319, over 4855.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02871, over 973189.99 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 12:27:36,975 INFO [train.py:715] (1/8) Epoch 18, batch 31900, loss[loss=0.113, simple_loss=0.1848, pruned_loss=0.0206, over 4985.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02878, over 973473.01 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:28:16,142 INFO [train.py:715] (1/8) Epoch 18, batch 31950, loss[loss=0.1372, simple_loss=0.2154, pruned_loss=0.02947, over 4936.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02878, over 973564.32 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 12:28:54,455 INFO [train.py:715] (1/8) Epoch 18, batch 32000, loss[loss=0.1219, simple_loss=0.2058, pruned_loss=0.01897, over 4747.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02851, over 973498.95 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:29:32,616 INFO [train.py:715] (1/8) Epoch 18, batch 32050, loss[loss=0.1416, simple_loss=0.2124, pruned_loss=0.03545, over 4751.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2067, pruned_loss=0.02843, over 973204.00 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:30:11,873 INFO [train.py:715] (1/8) Epoch 18, batch 32100, loss[loss=0.1449, simple_loss=0.2162, pruned_loss=0.03681, over 4767.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.0289, over 971897.41 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:30:51,371 INFO [train.py:715] (1/8) Epoch 18, batch 32150, loss[loss=0.1494, simple_loss=0.2287, pruned_loss=0.03506, over 4965.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02901, over 971510.84 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:31:30,530 INFO [train.py:715] (1/8) Epoch 18, batch 32200, loss[loss=0.1013, simple_loss=0.1769, pruned_loss=0.01282, over 4802.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02925, over 971169.94 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:32:08,909 INFO [train.py:715] (1/8) Epoch 18, batch 32250, loss[loss=0.1325, simple_loss=0.2117, pruned_loss=0.02669, over 4756.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02883, over 971899.85 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:32:48,155 INFO [train.py:715] (1/8) Epoch 18, batch 32300, loss[loss=0.1399, simple_loss=0.2148, pruned_loss=0.03246, over 4894.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02906, over 972612.99 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:33:26,711 INFO [train.py:715] (1/8) Epoch 18, batch 32350, loss[loss=0.1112, simple_loss=0.19, pruned_loss=0.01621, over 4828.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02867, over 972084.51 frames.], batch size: 27, lr: 1.22e-04
2022-05-09 12:34:05,358 INFO [train.py:715] (1/8) Epoch 18, batch 32400, loss[loss=0.1214, simple_loss=0.1969, pruned_loss=0.02291, over 4922.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02843, over 971858.01 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:34:44,777 INFO [train.py:715] (1/8) Epoch 18, batch 32450, loss[loss=0.1276, simple_loss=0.2089, pruned_loss=0.02311, over 4977.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02882, over 971866.98 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 12:35:23,648 INFO [train.py:715] (1/8) Epoch 18, batch 32500, loss[loss=0.09656, simple_loss=0.1789, pruned_loss=0.007111, over 4894.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02848, over 971918.80 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:36:02,850 INFO [train.py:715] (1/8) Epoch 18, batch 32550, loss[loss=0.1553, simple_loss=0.24, pruned_loss=0.03529, over 4980.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02849, over 971707.59 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 12:36:42,022 INFO [train.py:715] (1/8) Epoch 18, batch 32600, loss[loss=0.1347, simple_loss=0.2099, pruned_loss=0.02977, over 4939.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.0286, over 971767.87 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:37:21,451 INFO [train.py:715] (1/8) Epoch 18, batch 32650, loss[loss=0.1342, simple_loss=0.2032, pruned_loss=0.03258, over 4807.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02879, over 971113.81 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:37:59,892 INFO [train.py:715] (1/8) Epoch 18, batch 32700, loss[loss=0.1341, simple_loss=0.1935, pruned_loss=0.03737, over 4831.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.0286, over 970780.71 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:38:38,639 INFO [train.py:715] (1/8) Epoch 18, batch 32750, loss[loss=0.1283, simple_loss=0.1982, pruned_loss=0.0292, over 4794.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.0285, over 971319.64 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:39:17,958 INFO [train.py:715] (1/8) Epoch 18, batch 32800, loss[loss=0.1344, simple_loss=0.2127, pruned_loss=0.02808, over 4853.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02882, over 971266.37 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 12:39:57,147 INFO [train.py:715] (1/8) Epoch 18, batch 32850, loss[loss=0.1456, simple_loss=0.2199, pruned_loss=0.03569, over 4777.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02877, over 971753.85 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:40:35,664 INFO [train.py:715] (1/8) Epoch 18, batch 32900, loss[loss=0.1226, simple_loss=0.2003, pruned_loss=0.02248, over 4781.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.0291, over 972290.02 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:41:14,762 INFO [train.py:715] (1/8) Epoch 18, batch 32950, loss[loss=0.18, simple_loss=0.2526, pruned_loss=0.05367, over 4978.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02931, over 972462.37 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 12:41:53,956 INFO [train.py:715] (1/8) Epoch 18, batch 33000, loss[loss=0.1165, simple_loss=0.1886, pruned_loss=0.02221, over 4785.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02883, over 971929.16 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:41:53,957 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 12:42:03,827 INFO [train.py:742] (1/8) Epoch 18, validation: loss=0.1046, simple_loss=0.1878, pruned_loss=0.01068, over 914524.00 frames.
2022-05-09 12:42:43,653 INFO [train.py:715] (1/8) Epoch 18, batch 33050, loss[loss=0.1304, simple_loss=0.2056, pruned_loss=0.02756, over 4981.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02873, over 970908.46 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:43:22,615 INFO [train.py:715] (1/8) Epoch 18, batch 33100, loss[loss=0.1492, simple_loss=0.225, pruned_loss=0.03668, over 4959.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02832, over 971435.37 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:44:02,102 INFO [train.py:715] (1/8) Epoch 18, batch 33150, loss[loss=0.135, simple_loss=0.2029, pruned_loss=0.0336, over 4779.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02849, over 971101.79 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:44:41,943 INFO [train.py:715] (1/8) Epoch 18, batch 33200, loss[loss=0.1284, simple_loss=0.211, pruned_loss=0.02286, over 4911.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2054, pruned_loss=0.02797, over 971057.58 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 12:45:20,899 INFO [train.py:715] (1/8) Epoch 18, batch 33250, loss[loss=0.1346, simple_loss=0.2118, pruned_loss=0.02865, over 4986.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2055, pruned_loss=0.02747, over 971738.92 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:45:59,527 INFO [train.py:715] (1/8) Epoch 18, batch 33300, loss[loss=0.1478, simple_loss=0.2375, pruned_loss=0.02906, over 4986.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2064, pruned_loss=0.02799, over 972744.07 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:46:38,974 INFO [train.py:715] (1/8) Epoch 18, batch 33350, loss[loss=0.1645, simple_loss=0.257, pruned_loss=0.03599, over 4977.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2069, pruned_loss=0.02825, over 972151.52 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 12:47:18,350 INFO [train.py:715] (1/8) Epoch 18, batch 33400, loss[loss=0.1206, simple_loss=0.199, pruned_loss=0.02116, over 4777.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2065, pruned_loss=0.02798, over 971291.45 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:47:57,076 INFO [train.py:715] (1/8) Epoch 18, batch 33450, loss[loss=0.1423, simple_loss=0.2138, pruned_loss=0.0354, over 4845.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2064, pruned_loss=0.02793, over 972087.36 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:48:36,021 INFO [train.py:715] (1/8) Epoch 18, batch 33500, loss[loss=0.1307, simple_loss=0.2168, pruned_loss=0.02237, over 4767.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2067, pruned_loss=0.02808, over 972325.12 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:49:15,395 INFO [train.py:715] (1/8) Epoch 18, batch 33550, loss[loss=0.1217, simple_loss=0.2001, pruned_loss=0.02164, over 4845.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2063, pruned_loss=0.02812, over 972816.57 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 12:49:54,435 INFO [train.py:715] (1/8) Epoch 18, batch 33600, loss[loss=0.129, simple_loss=0.2003, pruned_loss=0.0288, over 4866.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.028, over 973300.44 frames.], batch size: 38, lr: 1.22e-04
2022-05-09 12:50:32,499 INFO [train.py:715] (1/8) Epoch 18, batch 33650, loss[loss=0.1333, simple_loss=0.204, pruned_loss=0.03134, over 4956.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02831, over 972652.47 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:51:11,944 INFO [train.py:715] (1/8) Epoch 18, batch 33700, loss[loss=0.1134, simple_loss=0.189, pruned_loss=0.01888, over 4775.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2052, pruned_loss=0.02802, over 972239.48 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:51:51,112 INFO [train.py:715] (1/8) Epoch 18, batch 33750, loss[loss=0.1289, simple_loss=0.2018, pruned_loss=0.02803, over 4903.00 frames.], tot_loss[loss=0.1305, simple_loss=0.205, pruned_loss=0.02801, over 970656.41 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 12:52:30,427 INFO [train.py:715] (1/8) Epoch 18, batch 33800, loss[loss=0.153, simple_loss=0.225, pruned_loss=0.04052, over 4807.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.0287, over 970997.66 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:53:09,705 INFO [train.py:715] (1/8) Epoch 18, batch 33850, loss[loss=0.124, simple_loss=0.1891, pruned_loss=0.0295, over 4749.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02854, over 970868.32 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:53:49,535 INFO [train.py:715] (1/8) Epoch 18, batch 33900, loss[loss=0.1248, simple_loss=0.198, pruned_loss=0.02583, over 4769.00 frames.], tot_loss[loss=0.131, simple_loss=0.2056, pruned_loss=0.02821, over 970529.17 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:54:28,736 INFO [train.py:715] (1/8) Epoch 18, batch 33950, loss[loss=0.138, simple_loss=0.2063, pruned_loss=0.03479, over 4910.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2052, pruned_loss=0.02786, over 970221.73 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:55:07,056 INFO [train.py:715] (1/8) Epoch 18, batch 34000, loss[loss=0.1125, simple_loss=0.1883, pruned_loss=0.01834, over 4976.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2062, pruned_loss=0.02822, over 970393.71 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 12:55:46,476 INFO [train.py:715] (1/8) Epoch 18, batch 34050, loss[loss=0.135, simple_loss=0.2201, pruned_loss=0.02499, over 4840.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02876, over 970416.79 frames.], batch size: 27, lr: 1.22e-04
2022-05-09 12:56:25,889 INFO [train.py:715] (1/8) Epoch 18, batch 34100, loss[loss=0.1175, simple_loss=0.1878, pruned_loss=0.0236, over 4941.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02877, over 970622.93 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 12:57:05,030 INFO [train.py:715] (1/8) Epoch 18, batch 34150, loss[loss=0.129, simple_loss=0.2085, pruned_loss=0.02472, over 4974.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02886, over 970985.53 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:57:44,079 INFO [train.py:715] (1/8) Epoch 18, batch 34200, loss[loss=0.1316, simple_loss=0.2052, pruned_loss=0.02906, over 4822.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02918, over 970774.71 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 12:58:23,225 INFO [train.py:715] (1/8) Epoch 18, batch 34250, loss[loss=0.1521, simple_loss=0.22, pruned_loss=0.04212, over 4806.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02899, over 971245.97 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:59:02,030 INFO [train.py:715] (1/8) Epoch 18, batch 34300, loss[loss=0.1501, simple_loss=0.2164, pruned_loss=0.04192, over 4960.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02918, over 972381.93 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:59:40,331 INFO [train.py:715] (1/8) Epoch 18, batch 34350, loss[loss=0.1326, simple_loss=0.2135, pruned_loss=0.02584, over 4704.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02891, over 971865.66 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 13:00:19,858 INFO [train.py:715] (1/8) Epoch 18, batch 34400, loss[loss=0.1414, simple_loss=0.2103, pruned_loss=0.03625, over 4881.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02929, over 971993.80 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 13:00:59,441 INFO [train.py:715] (1/8) Epoch 18, batch 34450, loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03427, over 4949.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02903, over 972017.02 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 13:01:39,369 INFO [train.py:715] (1/8) Epoch 18, batch 34500, loss[loss=0.1363, simple_loss=0.2043, pruned_loss=0.0341, over 4839.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02898, over 972488.42 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 13:02:18,892 INFO [train.py:715] (1/8) Epoch 18, batch 34550, loss[loss=0.1127, simple_loss=0.1845, pruned_loss=0.02046, over 4932.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02973, over 973143.93 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 13:02:58,570 INFO [train.py:715] (1/8) Epoch 18, batch 34600, loss[loss=0.1375, simple_loss=0.2159, pruned_loss=0.02954, over 4891.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02954, over 973481.46 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 13:03:37,752 INFO [train.py:715] (1/8) Epoch 18, batch 34650, loss[loss=0.1094, simple_loss=0.1821, pruned_loss=0.01837, over 4735.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02926, over 973235.27 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 13:04:17,378 INFO [train.py:715] (1/8) Epoch 18, batch 34700, loss[loss=0.1128, simple_loss=0.1961, pruned_loss=0.01478, over 4870.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02829, over 972535.62 frames.], batch size: 20, lr: 1.21e-04
2022-05-09 13:04:56,515 INFO [train.py:715] (1/8) Epoch 18, batch 34750, loss[loss=0.1446, simple_loss=0.2136, pruned_loss=0.03781, over 4910.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02863, over 972428.31 frames.], batch size: 18, lr: 1.21e-04
2022-05-09 13:05:34,141 INFO [train.py:715] (1/8) Epoch 18, batch 34800, loss[loss=0.1297, simple_loss=0.1941, pruned_loss=0.0327, over 4762.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2054, pruned_loss=0.0289, over 969935.09 frames.], batch size: 12, lr: 1.21e-04
2022-05-09 13:06:24,912 INFO [train.py:715] (1/8) Epoch 19, batch 0, loss[loss=0.1337, simple_loss=0.2142, pruned_loss=0.02656, over 4955.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2142, pruned_loss=0.02656, over 4955.00 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 13:07:03,500 INFO [train.py:715] (1/8) Epoch 19, batch 50, loss[loss=0.1088, simple_loss=0.1868, pruned_loss=0.01536, over 4972.00 frames.], tot_loss[loss=0.1259, simple_loss=0.1998, pruned_loss=0.02597, over 219473.49 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 13:07:43,522 INFO [train.py:715] (1/8) Epoch 19, batch 100, loss[loss=0.1449, simple_loss=0.2133, pruned_loss=0.03824, over 4964.00 frames.], tot_loss[loss=0.1292, simple_loss=0.2025, pruned_loss=0.02794, over 387068.66 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:08:23,943 INFO [train.py:715] (1/8) Epoch 19, batch 150, loss[loss=0.1257, simple_loss=0.2011, pruned_loss=0.0252, over 4775.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2038, pruned_loss=0.02784, over 516550.67 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:09:04,145 INFO [train.py:715] (1/8) Epoch 19, batch 200, loss[loss=0.1196, simple_loss=0.1768, pruned_loss=0.03122, over 4931.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2041, pruned_loss=0.02807, over 617508.14 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:09:44,074 INFO [train.py:715] (1/8) Epoch 19, batch 250, loss[loss=0.1285, simple_loss=0.1982, pruned_loss=0.0294, over 4843.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2043, pruned_loss=0.02805, over 695551.46 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 13:10:24,213 INFO [train.py:715] (1/8) Epoch 19, batch 300, loss[loss=0.1346, simple_loss=0.216, pruned_loss=0.02658, over 4788.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02847, over 756734.23 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:11:04,668 INFO [train.py:715] (1/8) Epoch 19, batch 350, loss[loss=0.1409, simple_loss=0.2179, pruned_loss=0.03193, over 4771.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02837, over 804083.70 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:11:43,714 INFO [train.py:715] (1/8) Epoch 19, batch 400, loss[loss=0.1383, simple_loss=0.2125, pruned_loss=0.03207, over 4880.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2048, pruned_loss=0.0277, over 841630.23 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:12:24,046 INFO [train.py:715] (1/8) Epoch 19, batch 450, loss[loss=0.1213, simple_loss=0.1993, pruned_loss=0.02164, over 4983.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02834, over 870643.59 frames.], batch size: 31, lr: 1.18e-04
2022-05-09 13:13:04,618 INFO [train.py:715] (1/8) Epoch 19, batch 500, loss[loss=0.1502, simple_loss=0.2229, pruned_loss=0.03876, over 4977.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.0284, over 893772.91 frames.], batch size: 31, lr: 1.18e-04
2022-05-09 13:13:44,277 INFO [train.py:715] (1/8) Epoch 19, batch 550, loss[loss=0.1075, simple_loss=0.1795, pruned_loss=0.0177, over 4900.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2048, pruned_loss=0.02782, over 911142.42 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:14:24,232 INFO [train.py:715] (1/8) Epoch 19, batch 600, loss[loss=0.1495, simple_loss=0.2197, pruned_loss=0.0397, over 4815.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02811, over 924751.85 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 13:15:04,544 INFO [train.py:715] (1/8) Epoch 19, batch 650, loss[loss=0.1406, simple_loss=0.2153, pruned_loss=0.03299, over 4954.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02831, over 936113.58 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 13:15:44,875 INFO [train.py:715] (1/8) Epoch 19, batch 700, loss[loss=0.1406, simple_loss=0.2181, pruned_loss=0.03154, over 4975.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02857, over 944722.77 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:16:24,131 INFO [train.py:715] (1/8) Epoch 19, batch 750, loss[loss=0.136, simple_loss=0.2167, pruned_loss=0.02759, over 4925.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02811, over 951948.04 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:17:03,937 INFO [train.py:715] (1/8) Epoch 19, batch 800, loss[loss=0.13, simple_loss=0.2056, pruned_loss=0.02722, over 4820.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02892, over 955959.81 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:17:44,199 INFO [train.py:715] (1/8) Epoch 19, batch 850, loss[loss=0.1443, simple_loss=0.2193, pruned_loss=0.03468, over 4906.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02876, over 959945.07 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 13:18:24,379 INFO [train.py:715] (1/8) Epoch 19, batch 900, loss[loss=0.1431, simple_loss=0.2221, pruned_loss=0.032, over 4939.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02858, over 963640.22 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:19:03,892 INFO [train.py:715] (1/8) Epoch 19, batch 950, loss[loss=0.1478, simple_loss=0.2177, pruned_loss=0.03899, over 4836.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02854, over 965687.13 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:19:43,252 INFO [train.py:715] (1/8) Epoch 19, batch 1000, loss[loss=0.1145, simple_loss=0.1923, pruned_loss=0.0184, over 4916.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02848, over 967787.86 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:20:23,197 INFO [train.py:715] (1/8) Epoch 19, batch 1050, loss[loss=0.1525, simple_loss=0.2364, pruned_loss=0.03429, over 4940.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.0287, over 969131.05 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 13:21:02,189 INFO [train.py:715] (1/8) Epoch 19, batch 1100, loss[loss=0.1456, simple_loss=0.2163, pruned_loss=0.03739, over 4875.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.0289, over 969411.31 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 13:21:42,015 INFO [train.py:715] (1/8) Epoch 19, batch 1150, loss[loss=0.1067, simple_loss=0.182, pruned_loss=0.01571, over 4944.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02874, over 970115.91 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:22:21,963 INFO [train.py:715] (1/8) Epoch 19, batch 1200, loss[loss=0.1188, simple_loss=0.1997, pruned_loss=0.01895, over 4924.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02852, over 969672.52 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:23:01,714 INFO [train.py:715] (1/8) Epoch 19, batch 1250, loss[loss=0.1419, simple_loss=0.2204, pruned_loss=0.03171, over 4942.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02854, over 969615.02 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 13:23:41,060 INFO [train.py:715] (1/8) Epoch 19, batch 1300, loss[loss=0.1309, simple_loss=0.2029, pruned_loss=0.02945, over 4896.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02871, over 969677.70 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:24:20,595 INFO [train.py:715] (1/8) Epoch 19, batch 1350, loss[loss=0.1479, simple_loss=0.2213, pruned_loss=0.03729, over 4853.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02935, over 970043.86 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 13:25:00,617 INFO [train.py:715] (1/8) Epoch 19, batch 1400, loss[loss=0.145, simple_loss=0.2211, pruned_loss=0.03446, over 4642.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02919, over 970446.13 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:25:39,919 INFO [train.py:715] (1/8) Epoch 19, batch 1450, loss[loss=0.1445, simple_loss=0.2217, pruned_loss=0.03367, over 4917.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02954, over 971351.48 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:26:20,229 INFO [train.py:715] (1/8) Epoch 19, batch 1500, loss[loss=0.1213, simple_loss=0.2044, pruned_loss=0.01911, over 4972.00 frames.], tot_loss[loss=0.133, simple_loss=0.2076, pruned_loss=0.02922, over 970979.09 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:27:00,279 INFO [train.py:715] (1/8) Epoch 19, batch 1550, loss[loss=0.1131, simple_loss=0.181, pruned_loss=0.02255, over 4732.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2077, pruned_loss=0.029, over 971836.76 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 13:27:40,365 INFO [train.py:715] (1/8) Epoch 19, batch 1600, loss[loss=0.1085, simple_loss=0.1773, pruned_loss=0.01986, over 4796.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02872, over 971718.76 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 13:28:19,703 INFO [train.py:715] (1/8) Epoch 19, batch 1650, loss[loss=0.1251, simple_loss=0.2064, pruned_loss=0.02184, over 4902.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02871, over 972362.51 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 13:28:59,071 INFO [train.py:715] (1/8) Epoch 19, batch 1700, loss[loss=0.107, simple_loss=0.1887, pruned_loss=0.01263, over 4919.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02854, over 972781.31 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 13:29:39,054 INFO [train.py:715] (1/8) Epoch 19, batch 1750, loss[loss=0.1233, simple_loss=0.1996, pruned_loss=0.02348, over 4805.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02856, over 972895.64 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 13:30:18,170 INFO [train.py:715] (1/8) Epoch 19, batch 1800, loss[loss=0.1198, simple_loss=0.1962, pruned_loss=0.02167, over 4815.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.02822, over 973216.48 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 13:30:57,612 INFO [train.py:715] (1/8) Epoch 19, batch 1850, loss[loss=0.1321, simple_loss=0.2109, pruned_loss=0.02667, over 4798.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02823, over 973004.75 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:31:36,856 INFO [train.py:715] (1/8) Epoch 19, batch 1900, loss[loss=0.142, simple_loss=0.2176, pruned_loss=0.03324, over 4875.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02832, over 972816.95 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 13:32:16,774 INFO [train.py:715] (1/8) Epoch 19, batch 1950, loss[loss=0.1366, simple_loss=0.2065, pruned_loss=0.03333, over 4697.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.02806, over 973178.23 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:32:55,072 INFO [train.py:715] (1/8) Epoch 19, batch 2000, loss[loss=0.121, simple_loss=0.2003, pruned_loss=0.02084, over 4964.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2052, pruned_loss=0.02783, over 973500.50 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:33:34,212 INFO [train.py:715] (1/8) Epoch 19, batch 2050, loss[loss=0.131, simple_loss=0.2012, pruned_loss=0.03041, over 4792.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2047, pruned_loss=0.02804, over 973190.01 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:34:13,312 INFO [train.py:715] (1/8) Epoch 19, batch 2100, loss[loss=0.1269, simple_loss=0.1978, pruned_loss=0.028, over 4973.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2041, pruned_loss=0.02767, over 972784.75 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:34:52,130 INFO [train.py:715] (1/8) Epoch 19, batch 2150, loss[loss=0.1511, simple_loss=0.2153, pruned_loss=0.0435, over 4960.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.0283, over 973519.34 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:35:31,124 INFO [train.py:715] (1/8) Epoch 19, batch 2200, loss[loss=0.1238, simple_loss=0.2068, pruned_loss=0.02038, over 4827.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02833, over 972565.14 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:36:09,821 INFO [train.py:715] (1/8) Epoch 19, batch 2250, loss[loss=0.143, simple_loss=0.2233, pruned_loss=0.03139, over 4812.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02849, over 971210.16 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 13:36:49,415 INFO [train.py:715] (1/8) Epoch 19, batch 2300, loss[loss=0.1296, simple_loss=0.2158, pruned_loss=0.02176, over 4754.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.0286, over 970332.88 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 13:37:28,006 INFO [train.py:715] (1/8) Epoch 19, batch 2350, loss[loss=0.1304, simple_loss=0.2006, pruned_loss=0.03013, over 4899.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02852, over 970978.01 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:38:07,165 INFO [train.py:715] (1/8) Epoch 19, batch 2400, loss[loss=0.1247, simple_loss=0.2044, pruned_loss=0.02255, over 4814.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02883, over 972071.57 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 13:38:46,611 INFO [train.py:715] (1/8) Epoch 19, batch 2450, loss[loss=0.1375, simple_loss=0.2051, pruned_loss=0.0349, over 4841.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02862, over 972021.22 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:39:25,448 INFO [train.py:715] (1/8) Epoch 19, batch 2500, loss[loss=0.1638, simple_loss=0.2278, pruned_loss=0.04983, over 4867.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02858, over 971410.38 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 13:40:04,476 INFO [train.py:715] (1/8) Epoch 19, batch 2550, loss[loss=0.1174, simple_loss=0.1953, pruned_loss=0.0198, over 4965.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02855, over 971136.96 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:40:44,005 INFO [train.py:715] (1/8) Epoch 19, batch 2600, loss[loss=0.1242, simple_loss=0.214, pruned_loss=0.01721, over 4823.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02852, over 971200.20 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:41:26,470 INFO [train.py:715] (1/8) Epoch 19, batch 2650, loss[loss=0.1658, simple_loss=0.255, pruned_loss=0.03828, over 4795.00 frames.], tot_loss[loss=0.1308, simple_loss=0.205, pruned_loss=0.0283, over 971131.85 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:42:05,377 INFO [train.py:715] (1/8) Epoch 19, batch 2700, loss[loss=0.1076, simple_loss=0.175, pruned_loss=0.02011, over 4989.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2049, pruned_loss=0.02823, over 971671.04 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:42:44,052 INFO [train.py:715] (1/8) Epoch 19, batch 2750, loss[loss=0.1026, simple_loss=0.1725, pruned_loss=0.01641, over 4813.00 frames.], tot_loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02831, over 972104.04 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 13:43:23,787 INFO [train.py:715] (1/8) Epoch 19, batch 2800, loss[loss=0.1622, simple_loss=0.2189, pruned_loss=0.05278, over 4810.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02878, over 972562.97 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:44:03,072 INFO [train.py:715] (1/8) Epoch 19, batch 2850, loss[loss=0.1247, simple_loss=0.2014, pruned_loss=0.024, over 4817.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.02825, over 972972.40 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 13:44:41,999 INFO [train.py:715] (1/8) Epoch 19, batch 2900, loss[loss=0.1235, simple_loss=0.2062, pruned_loss=0.02045, over 4905.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.02809, over 972896.93 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 13:45:20,759 INFO [train.py:715] (1/8) Epoch 19, batch 2950, loss[loss=0.1122, simple_loss=0.1866, pruned_loss=0.01893, over 4879.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2046, pruned_loss=0.02763, over 971361.99 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 13:46:00,072 INFO [train.py:715] (1/8) Epoch 19, batch 3000, loss[loss=0.1515, simple_loss=0.2357, pruned_loss=0.03363, over 4966.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02818, over 971732.18 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:46:00,073 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 13:46:10,051 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1045, simple_loss=0.1877, pruned_loss=0.01062, over 914524.00 frames.
2022-05-09 13:46:50,339 INFO [train.py:715] (1/8) Epoch 19, batch 3050, loss[loss=0.1419, simple_loss=0.2123, pruned_loss=0.03573, over 4805.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02816, over 972687.85 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 13:47:29,686 INFO [train.py:715] (1/8) Epoch 19, batch 3100, loss[loss=0.1257, simple_loss=0.2048, pruned_loss=0.02329, over 4814.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02826, over 972659.00 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 13:48:08,830 INFO [train.py:715] (1/8) Epoch 19, batch 3150, loss[loss=0.1411, simple_loss=0.2125, pruned_loss=0.0348, over 4813.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2072, pruned_loss=0.0283, over 972176.15 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:48:48,668 INFO [train.py:715] (1/8) Epoch 19, batch 3200, loss[loss=0.1437, simple_loss=0.216, pruned_loss=0.03568, over 4740.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2076, pruned_loss=0.02831, over 972830.34 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:49:27,689 INFO [train.py:715] (1/8) Epoch 19, batch 3250, loss[loss=0.1535, simple_loss=0.2241, pruned_loss=0.04148, over 4947.00 frames.], tot_loss[loss=0.1317, simple_loss=0.207, pruned_loss=0.02825, over 972393.04 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:50:07,131 INFO [train.py:715] (1/8) Epoch 19, batch 3300, loss[loss=0.1504, simple_loss=0.2202, pruned_loss=0.04032, over 4955.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2069, pruned_loss=0.02808, over 972027.31 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:50:46,358 INFO [train.py:715] (1/8) Epoch 19, batch 3350, loss[loss=0.1302, simple_loss=0.1991, pruned_loss=0.03065, over 4693.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2059, pruned_loss=0.02792, over 971407.09 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:51:26,502 INFO [train.py:715] (1/8) Epoch 19, batch 3400, loss[loss=0.1188, simple_loss=0.1785, pruned_loss=0.02956, over 4642.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.0289, over 972167.81 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:52:05,356 INFO [train.py:715] (1/8) Epoch 19, batch 3450, loss[loss=0.15, simple_loss=0.2172, pruned_loss=0.04141, over 4787.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02916, over 972114.98 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:52:44,610 INFO [train.py:715] (1/8) Epoch 19, batch 3500, loss[loss=0.1499, simple_loss=0.2072, pruned_loss=0.04626, over 4860.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2059, pruned_loss=0.02929, over 971955.62 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 13:53:23,730 INFO [train.py:715] (1/8) Epoch 19, batch 3550, loss[loss=0.1245, simple_loss=0.2068, pruned_loss=0.02111, over 4757.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02863, over 971944.12 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 13:54:02,618 INFO [train.py:715] (1/8) Epoch 19, batch 3600, loss[loss=0.11, simple_loss=0.1898, pruned_loss=0.01507, over 4812.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2044, pruned_loss=0.02865, over 972313.07 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:54:42,249 INFO [train.py:715] (1/8) Epoch 19, batch 3650, loss[loss=0.1375, simple_loss=0.2056, pruned_loss=0.03465, over 4834.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2047, pruned_loss=0.02879, over 972810.02 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 13:55:21,393 INFO [train.py:715] (1/8) Epoch 19, batch 3700, loss[loss=0.1153, simple_loss=0.1969, pruned_loss=0.01678, over 4934.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2046, pruned_loss=0.02829, over 973045.32 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:56:01,848 INFO [train.py:715] (1/8) Epoch 19, batch 3750, loss[loss=0.1594, simple_loss=0.2316, pruned_loss=0.04358, over 4694.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2038, pruned_loss=0.02789, over 972641.48 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:56:40,834 INFO [train.py:715] (1/8) Epoch 19, batch 3800, loss[loss=0.115, simple_loss=0.18, pruned_loss=0.02497, over 4861.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2041, pruned_loss=0.02814, over 973193.90 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 13:57:19,814 INFO [train.py:715] (1/8) Epoch 19, batch 3850, loss[loss=0.1684, simple_loss=0.2325, pruned_loss=0.0521, over 4692.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2044, pruned_loss=0.02845, over 972316.82 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:57:59,510 INFO [train.py:715] (1/8) Epoch 19, batch 3900, loss[loss=0.1253, simple_loss=0.194, pruned_loss=0.02833, over 4982.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2045, pruned_loss=0.02854, over 972240.58 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:58:38,557 INFO [train.py:715] (1/8) Epoch 19, batch 3950, loss[loss=0.1552, simple_loss=0.2257, pruned_loss=0.04239, over 4917.00 frames.], tot_loss[loss=0.131, simple_loss=0.2046, pruned_loss=0.02876, over 971971.52 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:59:17,192 INFO [train.py:715] (1/8) Epoch 19, batch 4000, loss[loss=0.107, simple_loss=0.1841, pruned_loss=0.01495, over 4816.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2051, pruned_loss=0.02902, over 972283.05 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 13:59:56,643 INFO [train.py:715] (1/8) Epoch 19, batch 4050, loss[loss=0.1196, simple_loss=0.1956, pruned_loss=0.02176, over 4796.00 frames.], tot_loss[loss=0.131, simple_loss=0.205, pruned_loss=0.02856, over 971789.04 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:00:36,774 INFO [train.py:715] (1/8) Epoch 19, batch 4100, loss[loss=0.1091, simple_loss=0.1844, pruned_loss=0.01683, over 4787.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02886, over 972154.03 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:01:15,966 INFO [train.py:715] (1/8) Epoch 19, batch 4150, loss[loss=0.1075, simple_loss=0.1856, pruned_loss=0.01465, over 4971.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.0286, over 971046.38 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 14:01:54,738 INFO [train.py:715] (1/8) Epoch 19, batch 4200, loss[loss=0.1206, simple_loss=0.1925, pruned_loss=0.02435, over 4836.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02889, over 970858.37 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 14:02:33,997 INFO [train.py:715] (1/8) Epoch 19, batch 4250, loss[loss=0.1441, simple_loss=0.2244, pruned_loss=0.03195, over 4906.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02865, over 970473.00 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:03:13,058 INFO [train.py:715] (1/8) Epoch 19, batch 4300, loss[loss=0.1307, simple_loss=0.2152, pruned_loss=0.02314, over 4810.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02854, over 970902.35 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 14:03:52,543 INFO [train.py:715] (1/8) Epoch 19, batch 4350, loss[loss=0.1175, simple_loss=0.1868, pruned_loss=0.02411, over 4864.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02821, over 971138.79 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 14:04:31,610 INFO [train.py:715] (1/8) Epoch 19, batch 4400, loss[loss=0.157, simple_loss=0.2249, pruned_loss=0.04462, over 4642.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02814, over 970790.43 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 14:05:11,662 INFO [train.py:715] (1/8) Epoch 19, batch 4450, loss[loss=0.1141, simple_loss=0.1713, pruned_loss=0.02841, over 4802.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02871, over 971753.91 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:05:50,507 INFO [train.py:715] (1/8) Epoch 19, batch 4500, loss[loss=0.1249, simple_loss=0.1957, pruned_loss=0.02698, over 4961.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02902, over 972381.99 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 14:06:29,203 INFO [train.py:715] (1/8) Epoch 19, batch 4550, loss[loss=0.1289, simple_loss=0.1982, pruned_loss=0.02979, over 4780.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.0286, over 972392.83 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 14:07:08,887 INFO [train.py:715] (1/8) Epoch 19, batch 4600, loss[loss=0.1185, simple_loss=0.1899, pruned_loss=0.02358, over 4945.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02855, over 972950.69 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:07:48,136 INFO [train.py:715] (1/8) Epoch 19, batch 4650, loss[loss=0.1048, simple_loss=0.1787, pruned_loss=0.01542, over 4849.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2063, pruned_loss=0.02829, over 973097.47 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 14:08:27,121 INFO [train.py:715] (1/8) Epoch 19, batch 4700, loss[loss=0.1359, simple_loss=0.2125, pruned_loss=0.02964, over 4849.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2066, pruned_loss=0.02845, over 972994.54 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 14:09:06,335 INFO [train.py:715] (1/8) Epoch 19, batch 4750, loss[loss=0.1038, simple_loss=0.1698, pruned_loss=0.01889, over 4777.00 frames.], tot_loss[loss=0.131, simple_loss=0.2061, pruned_loss=0.028, over 973170.24 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:09:46,292 INFO [train.py:715] (1/8) Epoch 19, batch 4800, loss[loss=0.1177, simple_loss=0.1971, pruned_loss=0.01917, over 4762.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2061, pruned_loss=0.02819, over 972732.82 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:10:25,675 INFO [train.py:715] (1/8) Epoch 19, batch 4850, loss[loss=0.136, simple_loss=0.2038, pruned_loss=0.03412, over 4929.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02823, over 973839.70 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:11:04,337 INFO [train.py:715] (1/8) Epoch 19, batch 4900, loss[loss=0.1206, simple_loss=0.196, pruned_loss=0.02267, over 4828.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2055, pruned_loss=0.0279, over 974209.83 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 14:11:44,091 INFO [train.py:715] (1/8) Epoch 19, batch 4950, loss[loss=0.1415, simple_loss=0.2238, pruned_loss=0.02955, over 4819.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02812, over 974380.84 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:12:23,738 INFO [train.py:715] (1/8) Epoch 19, batch 5000, loss[loss=0.14, simple_loss=0.2239, pruned_loss=0.02801, over 4779.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2056, pruned_loss=0.02814, over 974490.07 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:13:02,750 INFO [train.py:715] (1/8) Epoch 19, batch 5050, loss[loss=0.1219, simple_loss=0.2047, pruned_loss=0.01953, over 4825.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.0286, over 973726.89 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:13:41,113 INFO [train.py:715] (1/8) Epoch 19, batch 5100, loss[loss=0.1105, simple_loss=0.1888, pruned_loss=0.0161, over 4799.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02903, over 973354.59 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:14:21,158 INFO [train.py:715] (1/8) Epoch 19, batch 5150, loss[loss=0.14, simple_loss=0.222, pruned_loss=0.02906, over 4917.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02904, over 973791.64 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:15:00,190 INFO [train.py:715] (1/8) Epoch 19, batch 5200, loss[loss=0.1423, simple_loss=0.2187, pruned_loss=0.03297, over 4972.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02845, over 973407.16 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 14:15:38,853 INFO [train.py:715] (1/8) Epoch 19, batch 5250, loss[loss=0.1205, simple_loss=0.1929, pruned_loss=0.02411, over 4835.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.0286, over 972885.65 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 14:16:18,534 INFO [train.py:715] (1/8) Epoch 19, batch 5300, loss[loss=0.1218, simple_loss=0.186, pruned_loss=0.02883, over 4928.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2057, pruned_loss=0.02852, over 972214.08 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:16:58,482 INFO [train.py:715] (1/8) Epoch 19, batch 5350, loss[loss=0.1392, simple_loss=0.2216, pruned_loss=0.02838, over 4809.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02834, over 972759.69 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:17:38,564 INFO [train.py:715] (1/8) Epoch 19, batch 5400, loss[loss=0.1222, simple_loss=0.2061, pruned_loss=0.0192, over 4767.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02807, over 972263.40 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 14:18:17,824 INFO [train.py:715] (1/8) Epoch 19, batch 5450, loss[loss=0.128, simple_loss=0.203, pruned_loss=0.02654, over 4951.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02813, over 973096.30 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:18:58,016 INFO [train.py:715] (1/8) Epoch 19, batch 5500, loss[loss=0.1558, simple_loss=0.2347, pruned_loss=0.03843, over 4925.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2059, pruned_loss=0.02814, over 973542.52 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 14:19:37,199 INFO [train.py:715] (1/8) Epoch 19, batch 5550, loss[loss=0.1164, simple_loss=0.1936, pruned_loss=0.01962, over 4987.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02807, over 973746.68 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 14:20:16,790 INFO [train.py:715] (1/8) Epoch 19, batch 5600, loss[loss=0.1382, simple_loss=0.2147, pruned_loss=0.03087, over 4821.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02827, over 973402.98 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:20:56,100 INFO [train.py:715] (1/8) Epoch 19, batch 5650, loss[loss=0.1314, simple_loss=0.1995, pruned_loss=0.0316, over 4813.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02813, over 973249.50 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 14:21:35,827 INFO [train.py:715] (1/8) Epoch 19, batch 5700, loss[loss=0.1419, simple_loss=0.2227, pruned_loss=0.03054, over 4861.00 frames.], tot_loss[loss=0.131, simple_loss=0.2056, pruned_loss=0.02816, over 972285.90 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 14:22:15,334 INFO [train.py:715] (1/8) Epoch 19, batch 5750, loss[loss=0.1298, simple_loss=0.2091, pruned_loss=0.02521, over 4976.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02832, over 971499.11 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 14:22:53,917 INFO [train.py:715] (1/8) Epoch 19, batch 5800, loss[loss=0.1371, simple_loss=0.2066, pruned_loss=0.03377, over 4841.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02912, over 971756.31 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:23:33,191 INFO [train.py:715] (1/8) Epoch 19, batch 5850, loss[loss=0.1291, simple_loss=0.2029, pruned_loss=0.02767, over 4817.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02929, over 971750.60 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 14:24:11,657 INFO [train.py:715] (1/8) Epoch 19, batch 5900, loss[loss=0.1391, simple_loss=0.2234, pruned_loss=0.0274, over 4743.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02888, over 972007.72 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 14:24:51,076 INFO [train.py:715] (1/8) Epoch 19, batch 5950, loss[loss=0.1197, simple_loss=0.1983, pruned_loss=0.0206, over 4888.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02894, over 972277.57 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:25:30,278 INFO [train.py:715] (1/8) Epoch 19, batch 6000, loss[loss=0.1108, simple_loss=0.1959, pruned_loss=0.01287, over 4971.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02815, over 972696.42 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:25:30,279 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 14:25:40,197 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1046, simple_loss=0.1878, pruned_loss=0.01067, over 914524.00 frames.
2022-05-09 14:26:19,489 INFO [train.py:715] (1/8) Epoch 19, batch 6050, loss[loss=0.135, simple_loss=0.2054, pruned_loss=0.03234, over 4795.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02804, over 972665.62 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:26:58,345 INFO [train.py:715] (1/8) Epoch 19, batch 6100, loss[loss=0.1348, simple_loss=0.198, pruned_loss=0.03584, over 4969.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2052, pruned_loss=0.02829, over 973587.71 frames.], batch size: 31, lr: 1.18e-04
2022-05-09 14:27:37,408 INFO [train.py:715] (1/8) Epoch 19, batch 6150, loss[loss=0.1206, simple_loss=0.2039, pruned_loss=0.0187, over 4805.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2053, pruned_loss=0.02842, over 973076.05 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:28:15,611 INFO [train.py:715] (1/8) Epoch 19, batch 6200, loss[loss=0.1217, simple_loss=0.1907, pruned_loss=0.02632, over 4926.00 frames.], tot_loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.02789, over 972856.98 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:28:55,851 INFO [train.py:715] (1/8) Epoch 19, batch 6250, loss[loss=0.131, simple_loss=0.2071, pruned_loss=0.02742, over 4884.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.02812, over 973159.59 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 14:29:35,035 INFO [train.py:715] (1/8) Epoch 19, batch 6300, loss[loss=0.1443, simple_loss=0.2206, pruned_loss=0.03397, over 4924.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02844, over 974194.22 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:30:14,720 INFO [train.py:715] (1/8) Epoch 19, batch 6350, loss[loss=0.1588, simple_loss=0.2267, pruned_loss=0.04539, over 4951.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02865, over 972936.13 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:30:54,198 INFO [train.py:715] (1/8) Epoch 19, batch 6400, loss[loss=0.1118, simple_loss=0.1905, pruned_loss=0.01655, over 4940.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02823, over 973492.93 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:31:33,475 INFO [train.py:715] (1/8) Epoch 19, batch 6450, loss[loss=0.14, simple_loss=0.2181, pruned_loss=0.03096, over 4991.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02872, over 972428.45 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 14:32:12,982 INFO [train.py:715] (1/8) Epoch 19, batch 6500, loss[loss=0.1183, simple_loss=0.1839, pruned_loss=0.02634, over 4863.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02834, over 972142.48 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 14:32:51,555 INFO [train.py:715] (1/8) Epoch 19, batch 6550, loss[loss=0.1342, simple_loss=0.2216, pruned_loss=0.02337, over 4780.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02861, over 972683.83 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:33:31,039 INFO [train.py:715] (1/8) Epoch 19, batch 6600, loss[loss=0.164, simple_loss=0.2309, pruned_loss=0.04857, over 4808.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02871, over 972934.63 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:34:10,193 INFO [train.py:715] (1/8) Epoch 19, batch 6650, loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02947, over 4918.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02895, over 973481.59 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:34:48,933 INFO [train.py:715] (1/8) Epoch 19, batch 6700, loss[loss=0.116, simple_loss=0.1867, pruned_loss=0.0227, over 4782.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02863, over 972918.91 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:35:28,062 INFO [train.py:715] (1/8) Epoch 19, batch 6750, loss[loss=0.1414, simple_loss=0.2185, pruned_loss=0.03212, over 4951.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02899, over 973197.91 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:36:07,536 INFO [train.py:715] (1/8) Epoch 19, batch 6800, loss[loss=0.1157, simple_loss=0.1869, pruned_loss=0.02222, over 4852.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02886, over 973736.34 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 14:36:46,929 INFO [train.py:715] (1/8) Epoch 19, batch 6850, loss[loss=0.1292, simple_loss=0.2022, pruned_loss=0.02814, over 4868.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02844, over 972751.79 frames.], batch size: 34, lr: 1.18e-04
2022-05-09 14:37:25,088 INFO [train.py:715] (1/8) Epoch 19, batch 6900, loss[loss=0.1534, simple_loss=0.2254, pruned_loss=0.04065, over 4678.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02829, over 971967.66 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:38:04,131 INFO [train.py:715] (1/8) Epoch 19, batch 6950, loss[loss=0.1054, simple_loss=0.1838, pruned_loss=0.01351, over 4977.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02852, over 971782.18 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 14:38:43,601 INFO [train.py:715] (1/8) Epoch 19, batch 7000, loss[loss=0.121, simple_loss=0.1957, pruned_loss=0.02316, over 4941.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02858, over 971505.14 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 14:39:22,850 INFO [train.py:715] (1/8) Epoch 19, batch 7050, loss[loss=0.1332, simple_loss=0.1984, pruned_loss=0.03401, over 4770.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02841, over 970638.02 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:40:02,430 INFO [train.py:715] (1/8) Epoch 19, batch 7100, loss[loss=0.1126, simple_loss=0.1837, pruned_loss=0.02073, over 4877.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02824, over 972341.98 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 14:40:42,071 INFO [train.py:715] (1/8) Epoch 19, batch 7150, loss[loss=0.1344, simple_loss=0.2178, pruned_loss=0.02553, over 4840.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2051, pruned_loss=0.02787, over 971654.27 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:41:20,976 INFO [train.py:715] (1/8) Epoch 19, batch 7200, loss[loss=0.1646, simple_loss=0.2469, pruned_loss=0.04116, over 4816.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02835, over 972062.01 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:41:59,732 INFO [train.py:715] (1/8) Epoch 19, batch 7250, loss[loss=0.1124, simple_loss=0.1745, pruned_loss=0.02512, over 4901.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02838, over 972163.11 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:42:39,094 INFO [train.py:715] (1/8) Epoch 19, batch 7300, loss[loss=0.106, simple_loss=0.1836, pruned_loss=0.01416, over 4764.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2051, pruned_loss=0.02828, over 972528.28 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:43:18,258 INFO [train.py:715] (1/8) Epoch 19, batch 7350, loss[loss=0.1226, simple_loss=0.1986, pruned_loss=0.02324, over 4934.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02837, over 973224.84 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 14:43:57,158 INFO [train.py:715] (1/8) Epoch 19, batch 7400, loss[loss=0.1249, simple_loss=0.1987, pruned_loss=0.02557, over 4891.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02872, over 973267.84 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 14:44:37,620 INFO [train.py:715] (1/8) Epoch 19, batch 7450, loss[loss=0.1293, simple_loss=0.2001, pruned_loss=0.02928, over 4858.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02912, over 973147.37 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 14:45:17,478 INFO [train.py:715] (1/8) Epoch 19, batch 7500, loss[loss=0.1185, simple_loss=0.192, pruned_loss=0.02252, over 4904.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.029, over 974141.57 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:45:56,700 INFO [train.py:715] (1/8) Epoch 19, batch 7550, loss[loss=0.1507, simple_loss=0.2235, pruned_loss=0.03896, over 4685.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02911, over 972743.97 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:46:36,050 INFO [train.py:715] (1/8) Epoch 19, batch 7600, loss[loss=0.1581, simple_loss=0.2305, pruned_loss=0.04285, over 4917.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.02936, over 973329.54 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 14:47:16,831 INFO [train.py:715] (1/8) Epoch 19, batch 7650, loss[loss=0.148, simple_loss=0.22, pruned_loss=0.03805, over 4934.00 frames.], tot_loss[loss=0.1334, simple_loss=0.207, pruned_loss=0.0299, over 973694.95 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 14:47:56,150 INFO [train.py:715] (1/8) Epoch 19, batch 7700, loss[loss=0.1196, simple_loss=0.2, pruned_loss=0.01956, over 4812.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03014, over 974568.43 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:48:34,961 INFO [train.py:715] (1/8) Epoch 19, batch 7750, loss[loss=0.09249, simple_loss=0.1634, pruned_loss=0.01077, over 4780.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02975, over 974563.90 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:49:14,657 INFO [train.py:715] (1/8) Epoch 19, batch 7800, loss[loss=0.1476, simple_loss=0.2136, pruned_loss=0.04081, over 4851.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02971, over 974551.50 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 14:49:54,092 INFO [train.py:715] (1/8) Epoch 19, batch 7850, loss[loss=0.1318, simple_loss=0.2016, pruned_loss=0.03095, over 4973.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02993, over 974446.39 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 14:50:33,360 INFO [train.py:715] (1/8) Epoch 19, batch 7900, loss[loss=0.1254, simple_loss=0.209, pruned_loss=0.0209, over 4835.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03015, over 974459.45 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 14:51:11,761 INFO [train.py:715] (1/8) Epoch 19, batch 7950, loss[loss=0.1209, simple_loss=0.197, pruned_loss=0.02242, over 4942.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.0299, over 974349.80 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 14:51:51,088 INFO [train.py:715] (1/8) Epoch 19, batch 8000, loss[loss=0.1423, simple_loss=0.2164, pruned_loss=0.03407, over 4948.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02999, over 974483.88 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 14:52:30,288 INFO [train.py:715] (1/8) Epoch 19, batch 8050, loss[loss=0.1287, simple_loss=0.2067, pruned_loss=0.0254, over 4904.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02973, over 974222.60 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:53:08,820 INFO [train.py:715] (1/8) Epoch 19, batch 8100, loss[loss=0.1222, simple_loss=0.1989, pruned_loss=0.02272, over 4811.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02912, over 974110.84 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 14:53:48,272 INFO [train.py:715] (1/8) Epoch 19, batch 8150, loss[loss=0.1368, simple_loss=0.2118, pruned_loss=0.0309, over 4871.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02869, over 974846.11 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 14:54:27,920 INFO [train.py:715] (1/8) Epoch 19, batch 8200, loss[loss=0.1292, simple_loss=0.203, pruned_loss=0.0277, over 4759.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2054, pruned_loss=0.02882, over 974106.48 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:55:06,906 INFO [train.py:715] (1/8) Epoch 19, batch 8250, loss[loss=0.1217, simple_loss=0.2021, pruned_loss=0.02066, over 4983.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02888, over 973738.36 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:55:45,532 INFO [train.py:715] (1/8) Epoch 19, batch 8300, loss[loss=0.1507, simple_loss=0.2205, pruned_loss=0.0405, over 4934.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02882, over 973064.97 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 14:56:25,221 INFO [train.py:715] (1/8) Epoch 19, batch 8350, loss[loss=0.1134, simple_loss=0.1885, pruned_loss=0.01916, over 4797.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02906, over 972605.09 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 14:57:04,444 INFO [train.py:715] (1/8) Epoch 19, batch 8400, loss[loss=0.1169, simple_loss=0.1935, pruned_loss=0.0202, over 4841.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02939, over 972917.22 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 14:57:43,449 INFO [train.py:715] (1/8) Epoch 19, batch 8450, loss[loss=0.1482, simple_loss=0.2201, pruned_loss=0.03818, over 4878.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02885, over 973422.69 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 14:58:23,230 INFO [train.py:715] (1/8) Epoch 19, batch 8500, loss[loss=0.13, simple_loss=0.2174, pruned_loss=0.02133, over 4849.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02879, over 972767.78 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 14:59:01,923 INFO [train.py:715] (1/8) Epoch 19, batch 8550, loss[loss=0.1222, simple_loss=0.1975, pruned_loss=0.02347, over 4853.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02916, over 973115.74 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 14:59:41,019 INFO [train.py:715] (1/8) Epoch 19, batch 8600, loss[loss=0.1197, simple_loss=0.1862, pruned_loss=0.02655, over 4756.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02903, over 972456.84 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 15:00:20,523 INFO [train.py:715] (1/8) Epoch 19, batch 8650, loss[loss=0.1398, simple_loss=0.2159, pruned_loss=0.03182, over 4934.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.0288, over 972303.81 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 15:01:00,037 INFO [train.py:715] (1/8) Epoch 19, batch 8700, loss[loss=0.1214, simple_loss=0.1971, pruned_loss=0.02288, over 4850.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02852, over 972553.35 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 15:01:39,200 INFO [train.py:715] (1/8) Epoch 19, batch 8750, loss[loss=0.1449, simple_loss=0.2235, pruned_loss=0.03317, over 4820.00 frames.], tot_loss[loss=0.131, simple_loss=0.2051, pruned_loss=0.02846, over 973042.14 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 15:02:17,954 INFO [train.py:715] (1/8) Epoch 19, batch 8800, loss[loss=0.1189, simple_loss=0.1906, pruned_loss=0.02361, over 4979.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02843, over 973151.49 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 15:02:57,606 INFO [train.py:715] (1/8) Epoch 19, batch 8850, loss[loss=0.123, simple_loss=0.1964, pruned_loss=0.02485, over 4945.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02899, over 972623.31 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 15:03:36,661 INFO [train.py:715] (1/8) Epoch 19, batch 8900, loss[loss=0.1063, simple_loss=0.1824, pruned_loss=0.0151, over 4961.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.0287, over 972512.21 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 15:04:16,003 INFO [train.py:715] (1/8) Epoch 19, batch 8950, loss[loss=0.1149, simple_loss=0.1937, pruned_loss=0.01807, over 4945.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02847, over 972585.47 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 15:04:54,899 INFO [train.py:715] (1/8) Epoch 19, batch 9000, loss[loss=0.1336, simple_loss=0.2143, pruned_loss=0.0265, over 4960.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.0284, over 972071.55 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 15:04:54,900 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 15:05:04,820 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1047, simple_loss=0.1879, pruned_loss=0.01072, over 914524.00 frames.
2022-05-09 15:05:44,264 INFO [train.py:715] (1/8) Epoch 19, batch 9050, loss[loss=0.1252, simple_loss=0.1952, pruned_loss=0.02757, over 4841.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02886, over 971665.24 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 15:06:23,987 INFO [train.py:715] (1/8) Epoch 19, batch 9100, loss[loss=0.1116, simple_loss=0.1822, pruned_loss=0.02049, over 4742.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02877, over 972613.34 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 15:07:03,252 INFO [train.py:715] (1/8) Epoch 19, batch 9150, loss[loss=0.1332, simple_loss=0.2032, pruned_loss=0.03155, over 4925.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02844, over 973373.68 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 15:07:42,029 INFO [train.py:715] (1/8) Epoch 19, batch 9200, loss[loss=0.1157, simple_loss=0.1932, pruned_loss=0.01911, over 4815.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02815, over 973651.43 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 15:08:21,753 INFO [train.py:715] (1/8) Epoch 19, batch 9250, loss[loss=0.1171, simple_loss=0.1916, pruned_loss=0.02134, over 4944.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02802, over 973395.09 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 15:09:00,950 INFO [train.py:715] (1/8) Epoch 19, batch 9300, loss[loss=0.1315, simple_loss=0.2135, pruned_loss=0.0247, over 4896.00 frames.], tot_loss[loss=0.131, simple_loss=0.2059, pruned_loss=0.02806, over 972637.86 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 15:09:39,862 INFO [train.py:715] (1/8) Epoch 19, batch 9350, loss[loss=0.1221, simple_loss=0.1878, pruned_loss=0.02818, over 4743.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.0284, over 971969.27 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 15:10:19,954 INFO [train.py:715] (1/8) Epoch 19, batch 9400, loss[loss=0.1408, simple_loss=0.2309, pruned_loss=0.02531, over 4922.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02829, over 972171.80 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 15:11:00,057 INFO [train.py:715] (1/8) Epoch 19, batch 9450, loss[loss=0.1155, simple_loss=0.1934, pruned_loss=0.01883, over 4967.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2061, pruned_loss=0.02814, over 971836.97 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 15:11:38,882 INFO [train.py:715] (1/8) Epoch 19, batch 9500, loss[loss=0.1377, simple_loss=0.2137, pruned_loss=0.03085, over 4871.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02853, over 971869.02 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 15:12:18,092 INFO [train.py:715] (1/8) Epoch 19, batch 9550, loss[loss=0.1307, simple_loss=0.2024, pruned_loss=0.02945, over 4803.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2061, pruned_loss=0.02817, over 971877.92 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 15:12:57,470 INFO [train.py:715] (1/8) Epoch 19, batch 9600, loss[loss=0.1418, simple_loss=0.215, pruned_loss=0.03426, over 4967.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02831, over 972285.37 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 15:13:36,646 INFO [train.py:715] (1/8) Epoch 19, batch 9650, loss[loss=0.1114, simple_loss=0.1771, pruned_loss=0.02279, over 4761.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02812, over 971851.92 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 15:14:14,973 INFO [train.py:715] (1/8) Epoch 19, batch 9700, loss[loss=0.1282, simple_loss=0.194, pruned_loss=0.03119, over 4838.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2052, pruned_loss=0.02798, over 972759.00 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 15:14:54,701 INFO [train.py:715] (1/8) Epoch 19, batch 9750, loss[loss=0.1438, simple_loss=0.2261, pruned_loss=0.03078, over 4817.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2052, pruned_loss=0.02789, over 972044.21 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 15:15:34,782 INFO [train.py:715] (1/8) Epoch 19, batch 9800, loss[loss=0.1346, simple_loss=0.2063, pruned_loss=0.03145, over 4873.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2055, pruned_loss=0.02845, over 972204.02 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 15:16:14,502 INFO [train.py:715] (1/8) Epoch 19, batch 9850, loss[loss=0.1225, simple_loss=0.2014, pruned_loss=0.02178, over 4978.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.0284, over 972259.81 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 15:16:53,380 INFO [train.py:715] (1/8) Epoch 19, batch 9900, loss[loss=0.1643, simple_loss=0.2287, pruned_loss=0.04998, over 4828.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02886, over 972824.85 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 15:17:33,333 INFO [train.py:715] (1/8) Epoch 19, batch 9950, loss[loss=0.1338, simple_loss=0.2003, pruned_loss=0.03367, over 4903.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02838, over 974305.66 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 15:18:12,858 INFO [train.py:715] (1/8) Epoch 19, batch 10000, loss[loss=0.1315, simple_loss=0.2114, pruned_loss=0.02579, over 4923.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02849, over 974477.08 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 15:18:51,540 INFO [train.py:715] (1/8) Epoch 19, batch 10050, loss[loss=0.1445, simple_loss=0.2113, pruned_loss=0.03891, over 4829.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02847, over 973477.97 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 15:19:31,275 INFO [train.py:715] (1/8) Epoch 19, batch 10100, loss[loss=0.1359, simple_loss=0.2066, pruned_loss=0.03262, over 4818.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.0287, over 972988.21 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 15:20:10,774 INFO [train.py:715] (1/8) Epoch 19, batch 10150, loss[loss=0.1109, simple_loss=0.1806, pruned_loss=0.02057, over 4824.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02828, over 973056.70 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 15:20:49,755 INFO [train.py:715] (1/8) Epoch 19, batch 10200, loss[loss=0.1175, simple_loss=0.1874, pruned_loss=0.02378, over 4815.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2044, pruned_loss=0.02763, over 972901.21 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 15:21:29,135 INFO [train.py:715] (1/8) Epoch 19, batch 10250, loss[loss=0.1084, simple_loss=0.18, pruned_loss=0.01842, over 4963.00 frames.], tot_loss[loss=0.1305, simple_loss=0.205, pruned_loss=0.02799, over 972521.86 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 15:22:09,263 INFO [train.py:715] (1/8) Epoch 19, batch 10300, loss[loss=0.1337, simple_loss=0.2113, pruned_loss=0.028, over 4944.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02793, over 972622.99 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 15:22:48,848 INFO [train.py:715] (1/8) Epoch 19, batch 10350, loss[loss=0.1259, simple_loss=0.2016, pruned_loss=0.02504, over 4850.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2047, pruned_loss=0.0278, over 972549.47 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 15:23:27,532 INFO [train.py:715] (1/8) Epoch 19, batch 10400, loss[loss=0.1342, simple_loss=0.2133, pruned_loss=0.02755, over 4739.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02797, over 973258.37 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:24:07,289 INFO [train.py:715] (1/8) Epoch 19, batch 10450, loss[loss=0.1268, simple_loss=0.2114, pruned_loss=0.02115, over 4917.00 frames.], tot_loss[loss=0.13, simple_loss=0.2049, pruned_loss=0.02758, over 972602.69 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 15:24:47,015 INFO [train.py:715] (1/8) Epoch 19, batch 10500, loss[loss=0.1354, simple_loss=0.2188, pruned_loss=0.02597, over 4837.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2052, pruned_loss=0.02766, over 972810.81 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:25:25,923 INFO [train.py:715] (1/8) Epoch 19, batch 10550, loss[loss=0.1355, simple_loss=0.211, pruned_loss=0.03002, over 4772.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2048, pruned_loss=0.0274, over 972800.63 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:26:04,897 INFO [train.py:715] (1/8) Epoch 19, batch 10600, loss[loss=0.1297, simple_loss=0.2092, pruned_loss=0.02511, over 4826.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2047, pruned_loss=0.02734, over 973420.71 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:26:47,167 INFO [train.py:715] (1/8) Epoch 19, batch 10650, loss[loss=0.1339, simple_loss=0.1995, pruned_loss=0.03413, over 4812.00 frames.], tot_loss[loss=0.1301, simple_loss=0.205, pruned_loss=0.02756, over 972305.35 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 15:27:26,342 INFO [train.py:715] (1/8) Epoch 19, batch 10700, loss[loss=0.1422, simple_loss=0.2087, pruned_loss=0.03781, over 4898.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2052, pruned_loss=0.02767, over 972169.37 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:28:05,701 INFO [train.py:715] (1/8) Epoch 19, batch 10750, loss[loss=0.117, simple_loss=0.2007, pruned_loss=0.01669, over 4688.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2057, pruned_loss=0.02764, over 972183.51 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:28:45,278 INFO [train.py:715] (1/8) Epoch 19, batch 10800, loss[loss=0.1661, simple_loss=0.2519, pruned_loss=0.04019, over 4810.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2054, pruned_loss=0.02801, over 972405.59 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 15:29:25,025 INFO [train.py:715] (1/8) Epoch 19, batch 10850, loss[loss=0.1332, simple_loss=0.203, pruned_loss=0.03167, over 4908.00 frames.], tot_loss[loss=0.1302, simple_loss=0.205, pruned_loss=0.02774, over 972494.97 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:30:03,630 INFO [train.py:715] (1/8) Epoch 19, batch 10900, loss[loss=0.1207, simple_loss=0.2023, pruned_loss=0.01953, over 4744.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02847, over 971811.88 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:30:42,647 INFO [train.py:715] (1/8) Epoch 19, batch 10950, loss[loss=0.1248, simple_loss=0.206, pruned_loss=0.02181, over 4766.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2066, pruned_loss=0.02838, over 972386.40 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 15:31:22,351 INFO [train.py:715] (1/8) Epoch 19, batch 11000, loss[loss=0.118, simple_loss=0.1837, pruned_loss=0.02612, over 4830.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2063, pruned_loss=0.02826, over 972052.05 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 15:32:02,206 INFO [train.py:715] (1/8) Epoch 19, batch 11050, loss[loss=0.1262, simple_loss=0.1927, pruned_loss=0.02984, over 4809.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02837, over 972404.62 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 15:32:40,672 INFO [train.py:715] (1/8) Epoch 19, batch 11100, loss[loss=0.1324, simple_loss=0.2141, pruned_loss=0.02538, over 4785.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02827, over 972027.77 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:33:20,041 INFO [train.py:715] (1/8) Epoch 19, batch 11150, loss[loss=0.1252, simple_loss=0.1981, pruned_loss=0.02612, over 4977.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02829, over 971517.65 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 15:33:59,451 INFO [train.py:715] (1/8) Epoch 19, batch 11200, loss[loss=0.1624, simple_loss=0.2229, pruned_loss=0.05094, over 4958.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02868, over 972313.53 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 15:34:38,831 INFO [train.py:715] (1/8) Epoch 19, batch 11250, loss[loss=0.1429, simple_loss=0.2226, pruned_loss=0.03165, over 4942.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02826, over 972147.28 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 15:35:18,201 INFO [train.py:715] (1/8) Epoch 19, batch 11300, loss[loss=0.1449, simple_loss=0.2221, pruned_loss=0.0338, over 4785.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2058, pruned_loss=0.0278, over 972202.47 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 15:35:56,983 INFO [train.py:715] (1/8) Epoch 19, batch 11350, loss[loss=0.1242, simple_loss=0.205, pruned_loss=0.02174, over 4753.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2055, pruned_loss=0.02794, over 972021.77 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:36:36,597 INFO [train.py:715] (1/8) Epoch 19, batch 11400, loss[loss=0.1246, simple_loss=0.2004, pruned_loss=0.02444, over 4861.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2046, pruned_loss=0.02754, over 971772.91 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 15:37:16,204 INFO [train.py:715] (1/8) Epoch 19, batch 11450, loss[loss=0.1163, simple_loss=0.1854, pruned_loss=0.02358, over 4850.00 frames.], tot_loss[loss=0.1293, simple_loss=0.2042, pruned_loss=0.02722, over 971445.86 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 15:37:56,152 INFO [train.py:715] (1/8) Epoch 19, batch 11500, loss[loss=0.1546, simple_loss=0.2172, pruned_loss=0.04603, over 4891.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2043, pruned_loss=0.02775, over 972141.42 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 15:38:35,579 INFO [train.py:715] (1/8) Epoch 19, batch 11550, loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03075, over 4850.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2041, pruned_loss=0.0278, over 971720.42 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 15:39:14,554 INFO [train.py:715] (1/8) Epoch 19, batch 11600, loss[loss=0.1251, simple_loss=0.2074, pruned_loss=0.0214, over 4813.00 frames.], tot_loss[loss=0.131, simple_loss=0.2051, pruned_loss=0.02841, over 972117.03 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 15:39:54,375 INFO [train.py:715] (1/8) Epoch 19, batch 11650, loss[loss=0.1425, simple_loss=0.2201, pruned_loss=0.03248, over 4932.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02814, over 971800.30 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 15:40:33,464 INFO [train.py:715] (1/8) Epoch 19, batch 11700, loss[loss=0.1425, simple_loss=0.2184, pruned_loss=0.03334, over 4794.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.02813, over 971129.93 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 15:41:13,001 INFO [train.py:715] (1/8) Epoch 19, batch 11750, loss[loss=0.1307, simple_loss=0.214, pruned_loss=0.02364, over 4803.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2046, pruned_loss=0.02798, over 971001.35 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 15:41:52,541 INFO [train.py:715] (1/8) Epoch 19, batch 11800, loss[loss=0.1262, simple_loss=0.2083, pruned_loss=0.02202, over 4842.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2043, pruned_loss=0.02765, over 971471.16 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:42:32,200 INFO [train.py:715] (1/8) Epoch 19, batch 11850, loss[loss=0.1401, simple_loss=0.2089, pruned_loss=0.03569, over 4817.00 frames.], tot_loss[loss=0.1295, simple_loss=0.2038, pruned_loss=0.02764, over 972422.60 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 15:43:11,790 INFO [train.py:715] (1/8) Epoch 19, batch 11900, loss[loss=0.1205, simple_loss=0.2012, pruned_loss=0.01991, over 4929.00 frames.], tot_loss[loss=0.1294, simple_loss=0.2037, pruned_loss=0.02758, over 972434.05 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 15:43:51,290 INFO [train.py:715] (1/8) Epoch 19, batch 11950, loss[loss=0.129, simple_loss=0.2055, pruned_loss=0.0263, over 4811.00 frames.], tot_loss[loss=0.1293, simple_loss=0.2036, pruned_loss=0.02747, over 972040.94 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 15:44:30,453 INFO [train.py:715] (1/8) Epoch 19, batch 12000, loss[loss=0.1431, simple_loss=0.2123, pruned_loss=0.03693, over 4911.00 frames.], tot_loss[loss=0.1291, simple_loss=0.2032, pruned_loss=0.02751, over 971679.49 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:44:30,454 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 15:44:40,312 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1044, simple_loss=0.1877, pruned_loss=0.01054, over 914524.00 frames.
2022-05-09 15:45:20,289 INFO [train.py:715] (1/8) Epoch 19, batch 12050, loss[loss=0.1108, simple_loss=0.1811, pruned_loss=0.02028, over 4698.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2038, pruned_loss=0.02789, over 971786.27 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:46:00,174 INFO [train.py:715] (1/8) Epoch 19, batch 12100, loss[loss=0.1369, simple_loss=0.2075, pruned_loss=0.03322, over 4857.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2042, pruned_loss=0.02799, over 972480.54 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 15:46:39,303 INFO [train.py:715] (1/8) Epoch 19, batch 12150, loss[loss=0.1149, simple_loss=0.1891, pruned_loss=0.02038, over 4814.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2041, pruned_loss=0.02761, over 971624.89 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 15:47:18,768 INFO [train.py:715] (1/8) Epoch 19, batch 12200, loss[loss=0.1476, simple_loss=0.2322, pruned_loss=0.03146, over 4949.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2042, pruned_loss=0.02763, over 971251.94 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 15:47:58,230 INFO [train.py:715] (1/8) Epoch 19, batch 12250, loss[loss=0.1245, simple_loss=0.207, pruned_loss=0.02096, over 4969.00 frames.], tot_loss[loss=0.1296, simple_loss=0.2041, pruned_loss=0.02757, over 971420.37 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 15:48:37,867 INFO [train.py:715] (1/8) Epoch 19, batch 12300, loss[loss=0.1373, simple_loss=0.2078, pruned_loss=0.03343, over 4761.00 frames.], tot_loss[loss=0.13, simple_loss=0.2044, pruned_loss=0.02775, over 970807.56 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:49:17,545 INFO [train.py:715] (1/8) Epoch 19, batch 12350, loss[loss=0.1399, simple_loss=0.2174, pruned_loss=0.03114, over 4778.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.0281, over 971674.08 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:49:56,103 INFO [train.py:715] (1/8) Epoch 19, batch 12400, loss[loss=0.1274, simple_loss=0.1971, pruned_loss=0.02884, over 4898.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2044, pruned_loss=0.02794, over 971531.24 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:50:35,575 INFO [train.py:715] (1/8) Epoch 19, batch 12450, loss[loss=0.114, simple_loss=0.1853, pruned_loss=0.0214, over 4765.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2045, pruned_loss=0.02819, over 971686.50 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 15:51:14,298 INFO [train.py:715] (1/8) Epoch 19, batch 12500, loss[loss=0.1096, simple_loss=0.1891, pruned_loss=0.01506, over 4796.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2047, pruned_loss=0.02835, over 972122.18 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 15:51:53,637 INFO [train.py:715] (1/8) Epoch 19, batch 12550, loss[loss=0.1683, simple_loss=0.2475, pruned_loss=0.04454, over 4887.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2047, pruned_loss=0.02812, over 972492.16 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 15:52:33,120 INFO [train.py:715] (1/8) Epoch 19, batch 12600, loss[loss=0.127, simple_loss=0.1931, pruned_loss=0.03043, over 4897.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2049, pruned_loss=0.0284, over 972827.42 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:53:12,607 INFO [train.py:715] (1/8) Epoch 19, batch 12650, loss[loss=0.1236, simple_loss=0.2065, pruned_loss=0.02037, over 4830.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2048, pruned_loss=0.02828, over 973089.21 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:53:51,566 INFO [train.py:715] (1/8) Epoch 19, batch 12700, loss[loss=0.131, simple_loss=0.2016, pruned_loss=0.03025, over 4823.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2051, pruned_loss=0.02831, over 972379.60 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 15:54:30,741 INFO [train.py:715] (1/8) Epoch 19, batch 12750, loss[loss=0.118, simple_loss=0.2018, pruned_loss=0.01708, over 4957.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02848, over 972833.58 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 15:55:10,385 INFO [train.py:715] (1/8) Epoch 19, batch 12800, loss[loss=0.1286, simple_loss=0.2045, pruned_loss=0.02631, over 4914.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02889, over 972569.69 frames.], batch size: 39, lr: 1.17e-04
2022-05-09 15:55:49,786 INFO [train.py:715] (1/8) Epoch 19, batch 12850, loss[loss=0.1147, simple_loss=0.1823, pruned_loss=0.02359, over 4917.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02861, over 972262.03 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 15:56:28,728 INFO [train.py:715] (1/8) Epoch 19, batch 12900, loss[loss=0.1238, simple_loss=0.198, pruned_loss=0.0248, over 4784.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2052, pruned_loss=0.02847, over 971283.29 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 15:57:08,290 INFO [train.py:715] (1/8) Epoch 19, batch 12950, loss[loss=0.1423, simple_loss=0.2041, pruned_loss=0.04026, over 4836.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.02821, over 971649.54 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 15:57:47,529 INFO [train.py:715] (1/8) Epoch 19, batch 13000, loss[loss=0.1503, simple_loss=0.2271, pruned_loss=0.03669, over 4966.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02823, over 971859.05 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 15:58:26,676 INFO [train.py:715] (1/8) Epoch 19, batch 13050, loss[loss=0.1153, simple_loss=0.1838, pruned_loss=0.02338, over 4786.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.0284, over 972321.51 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 15:59:05,569 INFO [train.py:715] (1/8) Epoch 19, batch 13100, loss[loss=0.1345, simple_loss=0.2175, pruned_loss=0.02573, over 4945.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02873, over 971893.05 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 15:59:44,831 INFO [train.py:715] (1/8) Epoch 19, batch 13150, loss[loss=0.1646, simple_loss=0.248, pruned_loss=0.04064, over 4788.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02869, over 972166.10 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:00:24,453 INFO [train.py:715] (1/8) Epoch 19, batch 13200, loss[loss=0.1284, simple_loss=0.2112, pruned_loss=0.02277, over 4867.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02833, over 972611.59 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 16:01:03,676 INFO [train.py:715] (1/8) Epoch 19, batch 13250, loss[loss=0.1174, simple_loss=0.1971, pruned_loss=0.01883, over 4810.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.0282, over 971350.35 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:01:42,977 INFO [train.py:715] (1/8) Epoch 19, batch 13300, loss[loss=0.1547, simple_loss=0.2223, pruned_loss=0.04357, over 4983.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02856, over 971263.88 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 16:02:22,611 INFO [train.py:715] (1/8) Epoch 19, batch 13350, loss[loss=0.1438, simple_loss=0.2229, pruned_loss=0.03238, over 4927.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02891, over 970953.81 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:03:01,276 INFO [train.py:715] (1/8) Epoch 19, batch 13400, loss[loss=0.1432, simple_loss=0.2144, pruned_loss=0.036, over 4865.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02846, over 971774.67 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:03:40,771 INFO [train.py:715] (1/8) Epoch 19, batch 13450, loss[loss=0.1083, simple_loss=0.1887, pruned_loss=0.01389, over 4793.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02851, over 970855.63 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:04:20,043 INFO [train.py:715] (1/8) Epoch 19, batch 13500, loss[loss=0.1378, simple_loss=0.2117, pruned_loss=0.0319, over 4869.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02839, over 971393.59 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:04:59,468 INFO [train.py:715] (1/8) Epoch 19, batch 13550, loss[loss=0.1199, simple_loss=0.2044, pruned_loss=0.01768, over 4971.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2057, pruned_loss=0.02849, over 971390.79 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:05:38,404 INFO [train.py:715] (1/8) Epoch 19, batch 13600, loss[loss=0.1344, simple_loss=0.2153, pruned_loss=0.02679, over 4860.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02844, over 971928.28 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:06:17,569 INFO [train.py:715] (1/8) Epoch 19, batch 13650, loss[loss=0.1404, simple_loss=0.214, pruned_loss=0.03337, over 4792.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02828, over 972204.80 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:06:57,011 INFO [train.py:715] (1/8) Epoch 19, batch 13700, loss[loss=0.1266, simple_loss=0.2093, pruned_loss=0.02195, over 4818.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02839, over 972506.01 frames.], batch size: 27, lr: 1.17e-04
2022-05-09 16:07:35,729 INFO [train.py:715] (1/8) Epoch 19, batch 13750, loss[loss=0.1409, simple_loss=0.2149, pruned_loss=0.03341, over 4758.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02821, over 971843.74 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:08:14,996 INFO [train.py:715] (1/8) Epoch 19, batch 13800, loss[loss=0.1414, simple_loss=0.2092, pruned_loss=0.03676, over 4961.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02847, over 972026.75 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:08:55,056 INFO [train.py:715] (1/8) Epoch 19, batch 13850, loss[loss=0.1317, simple_loss=0.2046, pruned_loss=0.02941, over 4990.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02869, over 972874.15 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:09:34,675 INFO [train.py:715] (1/8) Epoch 19, batch 13900, loss[loss=0.1326, simple_loss=0.209, pruned_loss=0.0281, over 4860.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02816, over 972939.42 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:10:14,440 INFO [train.py:715] (1/8) Epoch 19, batch 13950, loss[loss=0.1239, simple_loss=0.2006, pruned_loss=0.02361, over 4818.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02814, over 971695.95 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:10:53,214 INFO [train.py:715] (1/8) Epoch 19, batch 14000, loss[loss=0.1292, simple_loss=0.2027, pruned_loss=0.02787, over 4887.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.02884, over 972553.22 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:11:32,643 INFO [train.py:715] (1/8) Epoch 19, batch 14050, loss[loss=0.1293, simple_loss=0.214, pruned_loss=0.02232, over 4811.00 frames.], tot_loss[loss=0.132, simple_loss=0.2058, pruned_loss=0.02908, over 972513.87 frames.], batch size: 27, lr: 1.17e-04
2022-05-09 16:12:11,842 INFO [train.py:715] (1/8) Epoch 19, batch 14100, loss[loss=0.1243, simple_loss=0.2052, pruned_loss=0.02171, over 4986.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.02894, over 972116.06 frames.], batch size: 28, lr: 1.17e-04
2022-05-09 16:12:51,001 INFO [train.py:715] (1/8) Epoch 19, batch 14150, loss[loss=0.1499, simple_loss=0.2209, pruned_loss=0.03941, over 4764.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02897, over 972671.17 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:13:30,120 INFO [train.py:715] (1/8) Epoch 19, batch 14200, loss[loss=0.1426, simple_loss=0.2182, pruned_loss=0.03345, over 4916.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.029, over 972622.53 frames.], batch size: 39, lr: 1.17e-04
2022-05-09 16:14:08,937 INFO [train.py:715] (1/8) Epoch 19, batch 14250, loss[loss=0.1319, simple_loss=0.2089, pruned_loss=0.02743, over 4891.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.0292, over 973177.80 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 16:14:48,113 INFO [train.py:715] (1/8) Epoch 19, batch 14300, loss[loss=0.1447, simple_loss=0.2246, pruned_loss=0.03239, over 4792.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.02894, over 973455.10 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:15:27,230 INFO [train.py:715] (1/8) Epoch 19, batch 14350, loss[loss=0.1079, simple_loss=0.1891, pruned_loss=0.01336, over 4812.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02897, over 972698.62 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 16:16:06,783 INFO [train.py:715] (1/8) Epoch 19, batch 14400, loss[loss=0.1202, simple_loss=0.198, pruned_loss=0.02119, over 4803.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02864, over 972323.30 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 16:16:45,663 INFO [train.py:715] (1/8) Epoch 19, batch 14450, loss[loss=0.1502, simple_loss=0.2099, pruned_loss=0.04531, over 4984.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02853, over 971927.97 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:17:24,667 INFO [train.py:715] (1/8) Epoch 19, batch 14500, loss[loss=0.1109, simple_loss=0.187, pruned_loss=0.01742, over 4989.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02827, over 973028.82 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 16:18:03,477 INFO [train.py:715] (1/8) Epoch 19, batch 14550, loss[loss=0.1445, simple_loss=0.2149, pruned_loss=0.03708, over 4944.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02848, over 973807.76 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 16:18:43,153 INFO [train.py:715] (1/8) Epoch 19, batch 14600, loss[loss=0.1457, simple_loss=0.2169, pruned_loss=0.03724, over 4938.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2066, pruned_loss=0.02852, over 973142.53 frames.], batch size: 39, lr: 1.17e-04
2022-05-09 16:19:22,222 INFO [train.py:715] (1/8) Epoch 19, batch 14650, loss[loss=0.1386, simple_loss=0.216, pruned_loss=0.03062, over 4993.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2068, pruned_loss=0.02848, over 972982.84 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:20:01,164 INFO [train.py:715] (1/8) Epoch 19, batch 14700, loss[loss=0.1291, simple_loss=0.201, pruned_loss=0.02861, over 4874.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02837, over 972560.42 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:20:40,519 INFO [train.py:715] (1/8) Epoch 19, batch 14750, loss[loss=0.1152, simple_loss=0.1943, pruned_loss=0.01806, over 4878.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2056, pruned_loss=0.0281, over 971977.17 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 16:21:19,780 INFO [train.py:715] (1/8) Epoch 19, batch 14800, loss[loss=0.1222, simple_loss=0.1962, pruned_loss=0.02407, over 4931.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2053, pruned_loss=0.02777, over 972621.42 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:21:58,084 INFO [train.py:715] (1/8) Epoch 19, batch 14850, loss[loss=0.15, simple_loss=0.218, pruned_loss=0.04102, over 4932.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02801, over 972382.98 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:22:37,371 INFO [train.py:715] (1/8) Epoch 19, batch 14900, loss[loss=0.1159, simple_loss=0.2, pruned_loss=0.01588, over 4863.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2053, pruned_loss=0.02785, over 972105.31 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:23:16,321 INFO [train.py:715] (1/8) Epoch 19, batch 14950, loss[loss=0.1032, simple_loss=0.1767, pruned_loss=0.01487, over 4767.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.0283, over 972461.29 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 16:23:55,092 INFO [train.py:715] (1/8) Epoch 19, batch 15000, loss[loss=0.1313, simple_loss=0.195, pruned_loss=0.03381, over 4947.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02836, over 972287.19 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:23:55,093 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 16:24:07,488 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1045, simple_loss=0.1877, pruned_loss=0.01064, over 914524.00 frames.
2022-05-09 16:24:46,705 INFO [train.py:715] (1/8) Epoch 19, batch 15050, loss[loss=0.1312, simple_loss=0.2086, pruned_loss=0.02694, over 4950.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02827, over 970990.24 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 16:25:26,173 INFO [train.py:715] (1/8) Epoch 19, batch 15100, loss[loss=0.1514, simple_loss=0.2171, pruned_loss=0.04287, over 4781.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2065, pruned_loss=0.02815, over 971272.19 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:26:05,804 INFO [train.py:715] (1/8) Epoch 19, batch 15150, loss[loss=0.1294, simple_loss=0.2082, pruned_loss=0.0253, over 4853.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.0279, over 971269.74 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 16:26:45,267 INFO [train.py:715] (1/8) Epoch 19, batch 15200, loss[loss=0.1276, simple_loss=0.2082, pruned_loss=0.02348, over 4827.00 frames.], tot_loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.02796, over 971935.21 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 16:27:24,249 INFO [train.py:715] (1/8) Epoch 19, batch 15250, loss[loss=0.1298, simple_loss=0.1967, pruned_loss=0.03141, over 4986.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2046, pruned_loss=0.02788, over 971553.38 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 16:28:04,180 INFO [train.py:715] (1/8) Epoch 19, batch 15300, loss[loss=0.1113, simple_loss=0.1869, pruned_loss=0.01788, over 4803.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02794, over 971187.91 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:28:43,717 INFO [train.py:715] (1/8) Epoch 19, batch 15350, loss[loss=0.133, simple_loss=0.208, pruned_loss=0.02898, over 4979.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02816, over 972223.05 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:29:23,529 INFO [train.py:715] (1/8) Epoch 19, batch 15400, loss[loss=0.1378, simple_loss=0.2135, pruned_loss=0.03105, over 4815.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02859, over 971974.92 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:30:03,011 INFO [train.py:715] (1/8) Epoch 19, batch 15450, loss[loss=0.1352, simple_loss=0.195, pruned_loss=0.03772, over 4989.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02873, over 972076.09 frames.], batch size: 31, lr: 1.17e-04
2022-05-09 16:30:42,451 INFO [train.py:715] (1/8) Epoch 19, batch 15500, loss[loss=0.1155, simple_loss=0.1871, pruned_loss=0.02192, over 4829.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02855, over 972579.58 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 16:31:21,336 INFO [train.py:715] (1/8) Epoch 19, batch 15550, loss[loss=0.1393, simple_loss=0.2148, pruned_loss=0.03187, over 4980.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02854, over 972626.57 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:32:00,418 INFO [train.py:715] (1/8) Epoch 19, batch 15600, loss[loss=0.1144, simple_loss=0.1895, pruned_loss=0.01968, over 4857.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02882, over 972291.25 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 16:32:40,088 INFO [train.py:715] (1/8) Epoch 19, batch 15650, loss[loss=0.1195, simple_loss=0.1998, pruned_loss=0.01964, over 4806.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02842, over 971788.62 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:33:19,038 INFO [train.py:715] (1/8) Epoch 19, batch 15700, loss[loss=0.1151, simple_loss=0.1918, pruned_loss=0.01926, over 4932.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02891, over 971472.08 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 16:33:59,135 INFO [train.py:715] (1/8) Epoch 19, batch 15750, loss[loss=0.1097, simple_loss=0.1818, pruned_loss=0.01884, over 4967.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02867, over 971750.85 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:34:38,393 INFO [train.py:715] (1/8) Epoch 19, batch 15800, loss[loss=0.1197, simple_loss=0.1984, pruned_loss=0.02053, over 4977.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02833, over 972040.02 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:35:17,528 INFO [train.py:715] (1/8) Epoch 19, batch 15850, loss[loss=0.1475, simple_loss=0.226, pruned_loss=0.03446, over 4741.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.0282, over 972956.13 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:35:56,470 INFO [train.py:715] (1/8) Epoch 19, batch 15900, loss[loss=0.1478, simple_loss=0.2249, pruned_loss=0.03538, over 4866.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02844, over 972280.36 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 16:36:35,618 INFO [train.py:715] (1/8) Epoch 19, batch 15950, loss[loss=0.1237, simple_loss=0.2026, pruned_loss=0.02239, over 4757.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02823, over 972032.12 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:37:15,269 INFO [train.py:715] (1/8) Epoch 19, batch 16000, loss[loss=0.1372, simple_loss=0.2166, pruned_loss=0.02894, over 4873.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02841, over 971275.10 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 16:37:53,931 INFO [train.py:715] (1/8) Epoch 19, batch 16050, loss[loss=0.1119, simple_loss=0.1911, pruned_loss=0.01629, over 4822.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02897, over 971275.43 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 16:38:33,242 INFO [train.py:715] (1/8) Epoch 19, batch 16100, loss[loss=0.1602, simple_loss=0.2368, pruned_loss=0.04178, over 4765.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2058, pruned_loss=0.02882, over 971363.32 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:39:12,539 INFO [train.py:715] (1/8) Epoch 19, batch 16150, loss[loss=0.1201, simple_loss=0.1834, pruned_loss=0.02839, over 4882.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02873, over 971066.01 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:39:51,594 INFO [train.py:715] (1/8) Epoch 19, batch 16200, loss[loss=0.1116, simple_loss=0.1831, pruned_loss=0.02003, over 4862.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02864, over 971231.17 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 16:40:29,888 INFO [train.py:715] (1/8) Epoch 19, batch 16250, loss[loss=0.1603, simple_loss=0.2351, pruned_loss=0.04277, over 4785.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02869, over 971464.20 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:41:08,935 INFO [train.py:715] (1/8) Epoch 19, batch 16300, loss[loss=0.1236, simple_loss=0.2076, pruned_loss=0.01981, over 4984.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02841, over 971889.48 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:41:48,389 INFO [train.py:715] (1/8) Epoch 19, batch 16350, loss[loss=0.1265, simple_loss=0.2064, pruned_loss=0.02333, over 4891.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02851, over 971769.74 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 16:42:26,933 INFO [train.py:715] (1/8) Epoch 19, batch 16400, loss[loss=0.1247, simple_loss=0.196, pruned_loss=0.02674, over 4928.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.0287, over 971429.55 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:43:05,774 INFO [train.py:715] (1/8) Epoch 19, batch 16450, loss[loss=0.1341, simple_loss=0.207, pruned_loss=0.03064, over 4929.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02914, over 972225.60 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 16:43:44,322 INFO [train.py:715] (1/8) Epoch 19, batch 16500, loss[loss=0.1385, simple_loss=0.2143, pruned_loss=0.03135, over 4783.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02847, over 971486.85 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:44:23,743 INFO [train.py:715] (1/8) Epoch 19, batch 16550, loss[loss=0.1112, simple_loss=0.1862, pruned_loss=0.01813, over 4989.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02903, over 971460.80 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:45:02,745 INFO [train.py:715] (1/8) Epoch 19, batch 16600, loss[loss=0.1172, simple_loss=0.183, pruned_loss=0.02566, over 4839.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02861, over 972006.46 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 16:45:41,758 INFO [train.py:715] (1/8) Epoch 19, batch 16650, loss[loss=0.1077, simple_loss=0.1796, pruned_loss=0.01788, over 4794.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02834, over 972362.57 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 16:46:21,729 INFO [train.py:715] (1/8) Epoch 19, batch 16700, loss[loss=0.1044, simple_loss=0.1847, pruned_loss=0.01205, over 4819.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2046, pruned_loss=0.02782, over 972773.51 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:47:00,842 INFO [train.py:715] (1/8) Epoch 19, batch 16750, loss[loss=0.1301, simple_loss=0.1996, pruned_loss=0.03026, over 4810.00 frames.], tot_loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.02795, over 972021.49 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:47:40,567 INFO [train.py:715] (1/8) Epoch 19, batch 16800, loss[loss=0.1016, simple_loss=0.172, pruned_loss=0.01556, over 4973.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02829, over 971967.74 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:48:19,962 INFO [train.py:715] (1/8) Epoch 19, batch 16850, loss[loss=0.1352, simple_loss=0.2104, pruned_loss=0.02997, over 4862.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02851, over 972568.71 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 16:48:59,490 INFO [train.py:715] (1/8) Epoch 19, batch 16900, loss[loss=0.1391, simple_loss=0.2113, pruned_loss=0.03345, over 4789.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02849, over 973310.01 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:49:38,103 INFO [train.py:715] (1/8) Epoch 19, batch 16950, loss[loss=0.1573, simple_loss=0.227, pruned_loss=0.04377, over 4875.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02836, over 972798.44 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 16:50:17,668 INFO [train.py:715] (1/8) Epoch 19, batch 17000, loss[loss=0.1957, simple_loss=0.2491, pruned_loss=0.07116, over 4812.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02836, over 972495.86 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 16:50:57,094 INFO [train.py:715] (1/8) Epoch 19, batch 17050, loss[loss=0.1403, simple_loss=0.1923, pruned_loss=0.04421, over 4990.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.02882, over 972449.98 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:51:36,153 INFO [train.py:715] (1/8) Epoch 19, batch 17100, loss[loss=0.1191, simple_loss=0.1942, pruned_loss=0.02202, over 4713.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2056, pruned_loss=0.02887, over 972424.13 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:52:15,335 INFO [train.py:715] (1/8) Epoch 19, batch 17150, loss[loss=0.1149, simple_loss=0.1829, pruned_loss=0.02344, over 4854.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2046, pruned_loss=0.02821, over 972609.64 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 16:52:54,352 INFO [train.py:715] (1/8) Epoch 19, batch 17200, loss[loss=0.1373, simple_loss=0.2163, pruned_loss=0.0292, over 4809.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2043, pruned_loss=0.02793, over 972757.23 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:53:33,089 INFO [train.py:715] (1/8) Epoch 19, batch 17250, loss[loss=0.137, simple_loss=0.2056, pruned_loss=0.03426, over 4749.00 frames.], tot_loss[loss=0.13, simple_loss=0.2044, pruned_loss=0.0278, over 972575.74 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:54:12,076 INFO [train.py:715] (1/8) Epoch 19, batch 17300, loss[loss=0.1412, simple_loss=0.2169, pruned_loss=0.03277, over 4791.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2047, pruned_loss=0.0278, over 972938.39 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:54:51,724 INFO [train.py:715] (1/8) Epoch 19, batch 17350, loss[loss=0.1359, simple_loss=0.2056, pruned_loss=0.0331, over 4892.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2051, pruned_loss=0.02823, over 972607.65 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:55:31,272 INFO [train.py:715] (1/8) Epoch 19, batch 17400, loss[loss=0.1377, simple_loss=0.2245, pruned_loss=0.02547, over 4910.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02842, over 972559.40 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:56:10,515 INFO [train.py:715] (1/8) Epoch 19, batch 17450, loss[loss=0.1612, simple_loss=0.2215, pruned_loss=0.05041, over 4969.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.0282, over 972658.47 frames.], batch size: 40, lr: 1.17e-04
2022-05-09 16:56:49,863 INFO [train.py:715] (1/8) Epoch 19, batch 17500, loss[loss=0.1324, simple_loss=0.2103, pruned_loss=0.0272, over 4787.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02861, over 972121.48 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 16:57:29,143 INFO [train.py:715] (1/8) Epoch 19, batch 17550, loss[loss=0.09767, simple_loss=0.1789, pruned_loss=0.008206, over 4956.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02848, over 973009.12 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 16:58:08,751 INFO [train.py:715] (1/8) Epoch 19, batch 17600, loss[loss=0.09666, simple_loss=0.1697, pruned_loss=0.0118, over 4789.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02815, over 972844.69 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:58:47,928 INFO [train.py:715] (1/8) Epoch 19, batch 17650, loss[loss=0.1146, simple_loss=0.199, pruned_loss=0.01511, over 4765.00 frames.], tot_loss[loss=0.131, simple_loss=0.2059, pruned_loss=0.02805, over 973044.20 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:59:27,072 INFO [train.py:715] (1/8) Epoch 19, batch 17700, loss[loss=0.1128, simple_loss=0.1909, pruned_loss=0.01736, over 4894.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2071, pruned_loss=0.0287, over 972088.70 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 17:00:06,647 INFO [train.py:715] (1/8) Epoch 19, batch 17750, loss[loss=0.1587, simple_loss=0.2236, pruned_loss=0.04694, over 4977.00 frames.], tot_loss[loss=0.1319, simple_loss=0.207, pruned_loss=0.02841, over 972913.14 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 17:00:45,236 INFO [train.py:715] (1/8) Epoch 19, batch 17800, loss[loss=0.1289, simple_loss=0.2104, pruned_loss=0.02368, over 4787.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2077, pruned_loss=0.02875, over 973597.17 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:01:24,009 INFO [train.py:715] (1/8) Epoch 19, batch 17850, loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02892, over 4864.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02833, over 974345.52 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 17:02:03,484 INFO [train.py:715] (1/8) Epoch 19, batch 17900, loss[loss=0.127, simple_loss=0.2106, pruned_loss=0.02172, over 4834.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02862, over 973202.91 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:02:41,967 INFO [train.py:715] (1/8) Epoch 19, batch 17950, loss[loss=0.1174, simple_loss=0.1959, pruned_loss=0.01942, over 4822.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02897, over 973521.64 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:03:21,252 INFO [train.py:715] (1/8) Epoch 19, batch 18000, loss[loss=0.1259, simple_loss=0.1964, pruned_loss=0.02773, over 4900.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02865, over 973749.80 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 17:03:21,254 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 17:03:31,130 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1046, simple_loss=0.1877, pruned_loss=0.01074, over 914524.00 frames.
2022-05-09 17:04:10,639 INFO [train.py:715] (1/8) Epoch 19, batch 18050, loss[loss=0.125, simple_loss=0.1996, pruned_loss=0.02521, over 4768.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02851, over 973548.67 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 17:04:50,206 INFO [train.py:715] (1/8) Epoch 19, batch 18100, loss[loss=0.1419, simple_loss=0.2221, pruned_loss=0.03087, over 4944.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2067, pruned_loss=0.02845, over 973211.71 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:05:30,065 INFO [train.py:715] (1/8) Epoch 19, batch 18150, loss[loss=0.1396, simple_loss=0.2172, pruned_loss=0.03099, over 4930.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2068, pruned_loss=0.02843, over 973093.07 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 17:06:09,189 INFO [train.py:715] (1/8) Epoch 19, batch 18200, loss[loss=0.1326, simple_loss=0.2058, pruned_loss=0.02969, over 4777.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02854, over 972932.36 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:06:48,108 INFO [train.py:715] (1/8) Epoch 19, batch 18250, loss[loss=0.1446, simple_loss=0.2224, pruned_loss=0.03342, over 4980.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02879, over 972767.26 frames.], batch size: 39, lr: 1.17e-04
2022-05-09 17:07:28,072 INFO [train.py:715] (1/8) Epoch 19, batch 18300, loss[loss=0.1224, simple_loss=0.1928, pruned_loss=0.02605, over 4839.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.0282, over 972379.35 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 17:08:07,528 INFO [train.py:715] (1/8) Epoch 19, batch 18350, loss[loss=0.1079, simple_loss=0.1823, pruned_loss=0.01679, over 4844.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2054, pruned_loss=0.02805, over 972412.76 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 17:08:47,416 INFO [train.py:715] (1/8) Epoch 19, batch 18400, loss[loss=0.1257, simple_loss=0.1994, pruned_loss=0.02603, over 4915.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02823, over 972671.91 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 17:09:26,673 INFO [train.py:715] (1/8) Epoch 19, batch 18450, loss[loss=0.1111, simple_loss=0.1898, pruned_loss=0.01624, over 4937.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02808, over 971817.99 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 17:10:06,106 INFO [train.py:715] (1/8) Epoch 19, batch 18500, loss[loss=0.1008, simple_loss=0.1713, pruned_loss=0.01514, over 4770.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02822, over 972365.08 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 17:10:45,343 INFO [train.py:715] (1/8) Epoch 19, batch 18550, loss[loss=0.1256, simple_loss=0.2008, pruned_loss=0.02515, over 4868.00 frames.], tot_loss[loss=0.1309, simple_loss=0.206, pruned_loss=0.02795, over 973036.29 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 17:11:24,396 INFO [train.py:715] (1/8) Epoch 19, batch 18600, loss[loss=0.1206, simple_loss=0.1978, pruned_loss=0.02169, over 4775.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2054, pruned_loss=0.02772, over 972759.99 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:12:06,301 INFO [train.py:715] (1/8) Epoch 19, batch 18650, loss[loss=0.1225, simple_loss=0.1979, pruned_loss=0.02355, over 4846.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2051, pruned_loss=0.02761, over 972261.39 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 17:12:45,150 INFO [train.py:715] (1/8) Epoch 19, batch 18700, loss[loss=0.1386, simple_loss=0.2073, pruned_loss=0.03493, over 4846.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2056, pruned_loss=0.02771, over 972470.04 frames.], batch size: 34, lr: 1.17e-04
2022-05-09 17:13:24,440 INFO [train.py:715] (1/8) Epoch 19, batch 18750, loss[loss=0.1346, simple_loss=0.2154, pruned_loss=0.02688, over 4889.00 frames.], tot_loss[loss=0.1309, simple_loss=0.206, pruned_loss=0.02788, over 971925.85 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 17:14:04,377 INFO [train.py:715] (1/8) Epoch 19, batch 18800, loss[loss=0.1494, simple_loss=0.2334, pruned_loss=0.03269, over 4806.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02791, over 971646.59 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:14:44,259 INFO [train.py:715] (1/8) Epoch 19, batch 18850, loss[loss=0.1486, simple_loss=0.2282, pruned_loss=0.03448, over 4832.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02809, over 972274.78 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:15:23,448 INFO [train.py:715] (1/8) Epoch 19, batch 18900, loss[loss=0.1388, simple_loss=0.2074, pruned_loss=0.03515, over 4777.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.0285, over 971991.88 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 17:16:02,831 INFO [train.py:715] (1/8) Epoch 19, batch 18950, loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02819, over 4950.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2059, pruned_loss=0.02812, over 972557.25 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 17:16:42,871 INFO [train.py:715] (1/8) Epoch 19, batch 19000, loss[loss=0.1518, simple_loss=0.2281, pruned_loss=0.03776, over 4920.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2065, pruned_loss=0.0283, over 971501.24 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:17:22,369 INFO [train.py:715] (1/8) Epoch 19, batch 19050, loss[loss=0.1383, simple_loss=0.2117, pruned_loss=0.03244, over 4863.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2061, pruned_loss=0.02818, over 971621.37 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 17:18:01,435 INFO [train.py:715] (1/8) Epoch 19, batch 19100, loss[loss=0.1245, simple_loss=0.1935, pruned_loss=0.02772, over 4875.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02857, over 971305.59 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:18:41,052 INFO [train.py:715] (1/8) Epoch 19, batch 19150, loss[loss=0.1219, simple_loss=0.2003, pruned_loss=0.02178, over 4830.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02868, over 971724.71 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 17:19:20,396 INFO [train.py:715] (1/8) Epoch 19, batch 19200, loss[loss=0.1302, simple_loss=0.2126, pruned_loss=0.02389, over 4795.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.0285, over 971311.89 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 17:19:59,881 INFO [train.py:715] (1/8) Epoch 19, batch 19250, loss[loss=0.1499, simple_loss=0.2293, pruned_loss=0.03527, over 4814.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02847, over 971531.08 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:20:39,155 INFO [train.py:715] (1/8) Epoch 19, batch 19300, loss[loss=0.1263, simple_loss=0.1905, pruned_loss=0.03107, over 4869.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02868, over 971110.77 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 17:21:19,518 INFO [train.py:715] (1/8) Epoch 19, batch 19350, loss[loss=0.1351, simple_loss=0.21, pruned_loss=0.03009, over 4774.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02868, over 971008.62 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:21:58,943 INFO [train.py:715] (1/8) Epoch 19, batch 19400, loss[loss=0.1706, simple_loss=0.2435, pruned_loss=0.04887, over 4829.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2073, pruned_loss=0.02883, over 971748.67 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 17:22:38,649 INFO [train.py:715] (1/8) Epoch 19, batch 19450, loss[loss=0.1434, simple_loss=0.2186, pruned_loss=0.03407, over 4927.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02828, over 971880.77 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 17:23:18,388 INFO [train.py:715] (1/8) Epoch 19, batch 19500, loss[loss=0.1243, simple_loss=0.2031, pruned_loss=0.02271, over 4742.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02896, over 972087.81 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:23:57,780 INFO [train.py:715] (1/8) Epoch 19, batch 19550, loss[loss=0.1349, simple_loss=0.2054, pruned_loss=0.03223, over 4828.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02894, over 972558.66 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 17:24:36,947 INFO [train.py:715] (1/8) Epoch 19, batch 19600, loss[loss=0.1291, simple_loss=0.2212, pruned_loss=0.01845, over 4913.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02864, over 972449.63 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 17:25:17,641 INFO [train.py:715] (1/8) Epoch 19, batch 19650, loss[loss=0.1112, simple_loss=0.1912, pruned_loss=0.01563, over 4756.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.0281, over 972017.98 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:25:56,966 INFO [train.py:715] (1/8) Epoch 19, batch 19700, loss[loss=0.1267, simple_loss=0.2007, pruned_loss=0.02639, over 4859.00 frames.], tot_loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02833, over 971952.20 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:26:35,806 INFO [train.py:715] (1/8) Epoch 19, batch 19750, loss[loss=0.1177, simple_loss=0.2117, pruned_loss=0.01188, over 4886.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02849, over 971967.88 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 17:27:16,059 INFO [train.py:715] (1/8) Epoch 19, batch 19800, loss[loss=0.1659, simple_loss=0.239, pruned_loss=0.04637, over 4904.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02902, over 972066.26 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 17:27:55,917 INFO [train.py:715] (1/8) Epoch 19, batch 19850, loss[loss=0.1275, simple_loss=0.2028, pruned_loss=0.02607, over 4817.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02856, over 972286.92 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 17:28:35,200 INFO [train.py:715] (1/8) Epoch 19, batch 19900, loss[loss=0.1247, simple_loss=0.196, pruned_loss=0.02667, over 4950.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02868, over 972736.27 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 17:29:13,879 INFO [train.py:715] (1/8) Epoch 19, batch 19950, loss[loss=0.1252, simple_loss=0.2042, pruned_loss=0.0231, over 4977.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02836, over 973757.12 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:29:53,614 INFO [train.py:715] (1/8) Epoch 19, batch 20000, loss[loss=0.1496, simple_loss=0.2239, pruned_loss=0.03766, over 4760.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2048, pruned_loss=0.02822, over 973144.43 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:30:33,006 INFO [train.py:715] (1/8) Epoch 19, batch 20050, loss[loss=0.1271, simple_loss=0.1939, pruned_loss=0.03008, over 4706.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2042, pruned_loss=0.02761, over 972886.55 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:31:12,639 INFO [train.py:715] (1/8) Epoch 19, batch 20100, loss[loss=0.1283, simple_loss=0.207, pruned_loss=0.02481, over 4838.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.02823, over 972440.08 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 17:31:52,170 INFO [train.py:715] (1/8) Epoch 19, batch 20150, loss[loss=0.1346, simple_loss=0.2175, pruned_loss=0.0259, over 4901.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.02806, over 972181.81 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:32:31,814 INFO [train.py:715] (1/8) Epoch 19, batch 20200, loss[loss=0.1323, simple_loss=0.2083, pruned_loss=0.02817, over 4882.00 frames.], tot_loss[loss=0.1294, simple_loss=0.2037, pruned_loss=0.02748, over 972406.11 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:33:11,345 INFO [train.py:715] (1/8) Epoch 19, batch 20250, loss[loss=0.1096, simple_loss=0.1894, pruned_loss=0.01492, over 4910.00 frames.], tot_loss[loss=0.1289, simple_loss=0.2034, pruned_loss=0.02719, over 971800.89 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:33:50,681 INFO [train.py:715] (1/8) Epoch 19, batch 20300, loss[loss=0.1301, simple_loss=0.2103, pruned_loss=0.02494, over 4986.00 frames.], tot_loss[loss=0.1294, simple_loss=0.2041, pruned_loss=0.02738, over 971569.25 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:34:30,222 INFO [train.py:715] (1/8) Epoch 19, batch 20350, loss[loss=0.112, simple_loss=0.1835, pruned_loss=0.02026, over 4703.00 frames.], tot_loss[loss=0.1295, simple_loss=0.204, pruned_loss=0.02754, over 971771.50 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:35:09,429 INFO [train.py:715] (1/8) Epoch 19, batch 20400, loss[loss=0.1394, simple_loss=0.2226, pruned_loss=0.02815, over 4974.00 frames.], tot_loss[loss=0.13, simple_loss=0.2047, pruned_loss=0.02761, over 971639.83 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:35:48,299 INFO [train.py:715] (1/8) Epoch 19, batch 20450, loss[loss=0.1223, simple_loss=0.1982, pruned_loss=0.02318, over 4966.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02813, over 971630.27 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 17:36:28,042 INFO [train.py:715] (1/8) Epoch 19, batch 20500, loss[loss=0.1417, simple_loss=0.2112, pruned_loss=0.03607, over 4992.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02837, over 971301.49 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 17:37:07,743 INFO [train.py:715] (1/8) Epoch 19, batch 20550, loss[loss=0.1407, simple_loss=0.2207, pruned_loss=0.03041, over 4785.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.0283, over 971407.81 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:37:46,557 INFO [train.py:715] (1/8) Epoch 19, batch 20600, loss[loss=0.1148, simple_loss=0.1962, pruned_loss=0.0167, over 4821.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2057, pruned_loss=0.02789, over 971342.67 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 17:38:26,009 INFO [train.py:715] (1/8) Epoch 19, batch 20650, loss[loss=0.1188, simple_loss=0.1984, pruned_loss=0.01963, over 4975.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.0283, over 971509.19 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:39:05,341 INFO [train.py:715] (1/8) Epoch 19, batch 20700, loss[loss=0.1339, simple_loss=0.1954, pruned_loss=0.03621, over 4831.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02812, over 971176.28 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:39:44,819 INFO [train.py:715] (1/8) Epoch 19, batch 20750, loss[loss=0.1549, simple_loss=0.2278, pruned_loss=0.04098, over 4749.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02833, over 970355.78 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:40:23,533 INFO [train.py:715] (1/8) Epoch 19, batch 20800, loss[loss=0.1262, simple_loss=0.2016, pruned_loss=0.02543, over 4699.00 frames.], tot_loss[loss=0.131, simple_loss=0.2057, pruned_loss=0.02812, over 970766.95 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:41:02,812 INFO [train.py:715] (1/8) Epoch 19, batch 20850, loss[loss=0.1361, simple_loss=0.2033, pruned_loss=0.03441, over 4763.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02826, over 971085.79 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:41:42,478 INFO [train.py:715] (1/8) Epoch 19, batch 20900, loss[loss=0.1398, simple_loss=0.2259, pruned_loss=0.02683, over 4864.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02821, over 971634.52 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 17:42:21,287 INFO [train.py:715] (1/8) Epoch 19, batch 20950, loss[loss=0.1338, simple_loss=0.2156, pruned_loss=0.02594, over 4912.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02824, over 973020.88 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 17:43:01,032 INFO [train.py:715] (1/8) Epoch 19, batch 21000, loss[loss=0.1091, simple_loss=0.1865, pruned_loss=0.01584, over 4690.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2052, pruned_loss=0.02798, over 973361.40 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:43:01,033 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 17:43:11,506 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1045, simple_loss=0.1878, pruned_loss=0.01062, over 914524.00 frames.
2022-05-09 17:43:51,334 INFO [train.py:715] (1/8) Epoch 19, batch 21050, loss[loss=0.1206, simple_loss=0.1932, pruned_loss=0.02402, over 4989.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02803, over 973216.23 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:44:31,300 INFO [train.py:715] (1/8) Epoch 19, batch 21100, loss[loss=0.1274, simple_loss=0.2064, pruned_loss=0.02417, over 4955.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.02793, over 972752.90 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 17:45:10,115 INFO [train.py:715] (1/8) Epoch 19, batch 21150, loss[loss=0.1347, simple_loss=0.2115, pruned_loss=0.02899, over 4831.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02802, over 972554.43 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:45:49,701 INFO [train.py:715] (1/8) Epoch 19, batch 21200, loss[loss=0.1215, simple_loss=0.2031, pruned_loss=0.01997, over 4899.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2062, pruned_loss=0.02813, over 973191.61 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:46:28,949 INFO [train.py:715] (1/8) Epoch 19, batch 21250, loss[loss=0.1397, simple_loss=0.2188, pruned_loss=0.03031, over 4807.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02857, over 973965.67 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:47:07,991 INFO [train.py:715] (1/8) Epoch 19, batch 21300, loss[loss=0.1272, simple_loss=0.2124, pruned_loss=0.02094, over 4970.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02849, over 973206.24 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:47:46,804 INFO [train.py:715] (1/8) Epoch 19, batch 21350, loss[loss=0.1447, simple_loss=0.2174, pruned_loss=0.03603, over 4937.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02848, over 973857.47 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 17:48:26,338 INFO [train.py:715] (1/8) Epoch 19, batch 21400, loss[loss=0.1214, simple_loss=0.1992, pruned_loss=0.02182, over 4840.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2057, pruned_loss=0.02781, over 974357.34 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:49:05,852 INFO [train.py:715] (1/8) Epoch 19, batch 21450, loss[loss=0.1185, simple_loss=0.1961, pruned_loss=0.02039, over 4912.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2055, pruned_loss=0.02801, over 973566.10 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 17:49:44,644 INFO [train.py:715] (1/8) Epoch 19, batch 21500, loss[loss=0.1231, simple_loss=0.1999, pruned_loss=0.02317, over 4797.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.0285, over 972571.43 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 17:50:24,353 INFO [train.py:715] (1/8) Epoch 19, batch 21550, loss[loss=0.1225, simple_loss=0.1986, pruned_loss=0.02316, over 4822.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02852, over 972303.37 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 17:51:04,079 INFO [train.py:715] (1/8) Epoch 19, batch 21600, loss[loss=0.1196, simple_loss=0.1986, pruned_loss=0.02028, over 4901.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02874, over 972443.36 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:51:43,837 INFO [train.py:715] (1/8) Epoch 19, batch 21650, loss[loss=0.1251, simple_loss=0.1997, pruned_loss=0.02525, over 4833.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2073, pruned_loss=0.02856, over 972116.23 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 17:52:22,732 INFO [train.py:715] (1/8) Epoch 19, batch 21700, loss[loss=0.1476, simple_loss=0.2126, pruned_loss=0.04126, over 4782.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2073, pruned_loss=0.02854, over 971746.76 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 17:53:02,142 INFO [train.py:715] (1/8) Epoch 19, batch 21750, loss[loss=0.09577, simple_loss=0.1777, pruned_loss=0.006921, over 4881.00 frames.], tot_loss[loss=0.131, simple_loss=0.206, pruned_loss=0.02799, over 972864.52 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 17:53:43,112 INFO [train.py:715] (1/8) Epoch 19, batch 21800, loss[loss=0.1396, simple_loss=0.2197, pruned_loss=0.02975, over 4892.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.02805, over 972171.37 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 17:54:22,932 INFO [train.py:715] (1/8) Epoch 19, batch 21850, loss[loss=0.1438, simple_loss=0.2184, pruned_loss=0.03467, over 4831.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02799, over 972082.16 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 17:55:03,312 INFO [train.py:715] (1/8) Epoch 19, batch 21900, loss[loss=0.122, simple_loss=0.1961, pruned_loss=0.024, over 4825.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.028, over 972403.61 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 17:55:43,305 INFO [train.py:715] (1/8) Epoch 19, batch 21950, loss[loss=0.1251, simple_loss=0.1943, pruned_loss=0.02796, over 4898.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02813, over 972075.42 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 17:56:22,524 INFO [train.py:715] (1/8) Epoch 19, batch 22000, loss[loss=0.1376, simple_loss=0.2181, pruned_loss=0.02859, over 4891.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2049, pruned_loss=0.02806, over 972050.26 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 17:57:01,816 INFO [train.py:715] (1/8) Epoch 19, batch 22050, loss[loss=0.1274, simple_loss=0.199, pruned_loss=0.02791, over 4750.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2048, pruned_loss=0.02766, over 972229.96 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 17:57:41,387 INFO [train.py:715] (1/8) Epoch 19, batch 22100, loss[loss=0.1201, simple_loss=0.1939, pruned_loss=0.02318, over 4940.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2051, pruned_loss=0.02796, over 971965.69 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 17:58:21,388 INFO [train.py:715] (1/8) Epoch 19, batch 22150, loss[loss=0.1169, simple_loss=0.1918, pruned_loss=0.021, over 4809.00 frames.], tot_loss[loss=0.1303, simple_loss=0.205, pruned_loss=0.02776, over 971331.52 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 17:59:00,686 INFO [train.py:715] (1/8) Epoch 19, batch 22200, loss[loss=0.1274, simple_loss=0.2072, pruned_loss=0.02384, over 4977.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2048, pruned_loss=0.02773, over 971517.42 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 17:59:40,598 INFO [train.py:715] (1/8) Epoch 19, batch 22250, loss[loss=0.1241, simple_loss=0.2071, pruned_loss=0.02057, over 4991.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02786, over 972723.99 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:00:20,344 INFO [train.py:715] (1/8) Epoch 19, batch 22300, loss[loss=0.125, simple_loss=0.1997, pruned_loss=0.0251, over 4896.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2043, pruned_loss=0.02767, over 972434.95 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:00:59,297 INFO [train.py:715] (1/8) Epoch 19, batch 22350, loss[loss=0.1303, simple_loss=0.2171, pruned_loss=0.02174, over 4889.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02791, over 971246.90 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 18:01:38,317 INFO [train.py:715] (1/8) Epoch 19, batch 22400, loss[loss=0.1473, simple_loss=0.2255, pruned_loss=0.03455, over 4796.00 frames.], tot_loss[loss=0.13, simple_loss=0.2043, pruned_loss=0.0279, over 971446.37 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:02:17,606 INFO [train.py:715] (1/8) Epoch 19, batch 22450, loss[loss=0.1198, simple_loss=0.1873, pruned_loss=0.02614, over 4962.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2048, pruned_loss=0.02783, over 971697.57 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:02:57,542 INFO [train.py:715] (1/8) Epoch 19, batch 22500, loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02976, over 4842.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2046, pruned_loss=0.02784, over 971917.50 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 18:03:36,397 INFO [train.py:715] (1/8) Epoch 19, batch 22550, loss[loss=0.1472, simple_loss=0.2201, pruned_loss=0.03717, over 4903.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02799, over 972762.92 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 18:04:16,056 INFO [train.py:715] (1/8) Epoch 19, batch 22600, loss[loss=0.1492, simple_loss=0.2276, pruned_loss=0.03543, over 4986.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2055, pruned_loss=0.02849, over 972923.41 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:04:55,708 INFO [train.py:715] (1/8) Epoch 19, batch 22650, loss[loss=0.1219, simple_loss=0.1911, pruned_loss=0.0264, over 4922.00 frames.], tot_loss[loss=0.131, simple_loss=0.2054, pruned_loss=0.02832, over 971667.53 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 18:05:34,682 INFO [train.py:715] (1/8) Epoch 19, batch 22700, loss[loss=0.1317, simple_loss=0.1977, pruned_loss=0.03283, over 4811.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02844, over 972264.66 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:06:13,668 INFO [train.py:715] (1/8) Epoch 19, batch 22750, loss[loss=0.1663, simple_loss=0.2199, pruned_loss=0.05636, over 4864.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02845, over 972126.10 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:06:53,392 INFO [train.py:715] (1/8) Epoch 19, batch 22800, loss[loss=0.1219, simple_loss=0.2013, pruned_loss=0.02125, over 4891.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02876, over 971759.56 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:07:33,726 INFO [train.py:715] (1/8) Epoch 19, batch 22850, loss[loss=0.1099, simple_loss=0.1887, pruned_loss=0.01555, over 4714.00 frames.], tot_loss[loss=0.1331, simple_loss=0.208, pruned_loss=0.02912, over 972372.73 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:08:11,744 INFO [train.py:715] (1/8) Epoch 19, batch 22900, loss[loss=0.08895, simple_loss=0.1601, pruned_loss=0.008897, over 4842.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02866, over 971387.36 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:08:51,140 INFO [train.py:715] (1/8) Epoch 19, batch 22950, loss[loss=0.1345, simple_loss=0.2102, pruned_loss=0.02938, over 4893.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02848, over 971803.28 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:09:31,738 INFO [train.py:715] (1/8) Epoch 19, batch 23000, loss[loss=0.1274, simple_loss=0.2023, pruned_loss=0.02624, over 4814.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02837, over 971758.06 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:10:12,240 INFO [train.py:715] (1/8) Epoch 19, batch 23050, loss[loss=0.138, simple_loss=0.2093, pruned_loss=0.03336, over 4870.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02869, over 971820.22 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 18:10:52,465 INFO [train.py:715] (1/8) Epoch 19, batch 23100, loss[loss=0.1279, simple_loss=0.2039, pruned_loss=0.02593, over 4817.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02796, over 972075.65 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:11:33,199 INFO [train.py:715] (1/8) Epoch 19, batch 23150, loss[loss=0.1197, simple_loss=0.1886, pruned_loss=0.02545, over 4782.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2045, pruned_loss=0.02803, over 972091.32 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:12:14,187 INFO [train.py:715] (1/8) Epoch 19, batch 23200, loss[loss=0.1395, simple_loss=0.2006, pruned_loss=0.03922, over 4859.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2043, pruned_loss=0.02779, over 972955.84 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 18:12:53,607 INFO [train.py:715] (1/8) Epoch 19, batch 23250, loss[loss=0.1157, simple_loss=0.191, pruned_loss=0.0202, over 4896.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2046, pruned_loss=0.02782, over 972770.38 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:13:34,362 INFO [train.py:715] (1/8) Epoch 19, batch 23300, loss[loss=0.1378, simple_loss=0.2142, pruned_loss=0.0307, over 4760.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.0281, over 972761.00 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:14:16,106 INFO [train.py:715] (1/8) Epoch 19, batch 23350, loss[loss=0.1269, simple_loss=0.2002, pruned_loss=0.0268, over 4953.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02819, over 973077.81 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 18:14:56,711 INFO [train.py:715] (1/8) Epoch 19, batch 23400, loss[loss=0.1142, simple_loss=0.1869, pruned_loss=0.02072, over 4916.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02807, over 973064.50 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:15:37,870 INFO [train.py:715] (1/8) Epoch 19, batch 23450, loss[loss=0.1431, simple_loss=0.2222, pruned_loss=0.032, over 4971.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02811, over 973262.88 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:16:19,139 INFO [train.py:715] (1/8) Epoch 19, batch 23500, loss[loss=0.1246, simple_loss=0.2061, pruned_loss=0.02152, over 4906.00 frames.], tot_loss[loss=0.131, simple_loss=0.2051, pruned_loss=0.0284, over 973093.27 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:17:00,517 INFO [train.py:715] (1/8) Epoch 19, batch 23550, loss[loss=0.139, simple_loss=0.2006, pruned_loss=0.03873, over 4750.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02857, over 972432.07 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:17:41,322 INFO [train.py:715] (1/8) Epoch 19, batch 23600, loss[loss=0.1243, simple_loss=0.1952, pruned_loss=0.02669, over 4969.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.0284, over 972915.27 frames.], batch size: 28, lr: 1.16e-04
2022-05-09 18:18:22,137 INFO [train.py:715] (1/8) Epoch 19, batch 23650, loss[loss=0.1254, simple_loss=0.2066, pruned_loss=0.02205, over 4746.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02875, over 973346.37 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:19:04,123 INFO [train.py:715] (1/8) Epoch 19, batch 23700, loss[loss=0.1345, simple_loss=0.1985, pruned_loss=0.0352, over 4818.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02841, over 973224.76 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:19:44,516 INFO [train.py:715] (1/8) Epoch 19, batch 23750, loss[loss=0.1302, simple_loss=0.2029, pruned_loss=0.02871, over 4907.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02832, over 972939.77 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:20:24,718 INFO [train.py:715] (1/8) Epoch 19, batch 23800, loss[loss=0.1239, simple_loss=0.2053, pruned_loss=0.0212, over 4922.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.02816, over 972924.14 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 18:21:05,129 INFO [train.py:715] (1/8) Epoch 19, batch 23850, loss[loss=0.1483, simple_loss=0.2225, pruned_loss=0.03701, over 4835.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02828, over 972787.85 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:21:45,586 INFO [train.py:715] (1/8) Epoch 19, batch 23900, loss[loss=0.1416, simple_loss=0.2067, pruned_loss=0.03825, over 4847.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.0284, over 973150.80 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:22:24,880 INFO [train.py:715] (1/8) Epoch 19, batch 23950, loss[loss=0.1626, simple_loss=0.2171, pruned_loss=0.05402, over 4848.00 frames.], tot_loss[loss=0.131, simple_loss=0.2052, pruned_loss=0.02844, over 972610.17 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:23:05,241 INFO [train.py:715] (1/8) Epoch 19, batch 24000, loss[loss=0.175, simple_loss=0.2525, pruned_loss=0.04871, over 4822.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2049, pruned_loss=0.02844, over 972177.29 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 18:23:05,242 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 18:23:15,158 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1046, simple_loss=0.1878, pruned_loss=0.01073, over 914524.00 frames.
2022-05-09 18:23:55,483 INFO [train.py:715] (1/8) Epoch 19, batch 24050, loss[loss=0.142, simple_loss=0.2106, pruned_loss=0.03663, over 4852.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2046, pruned_loss=0.02826, over 971510.12 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 18:24:36,267 INFO [train.py:715] (1/8) Epoch 19, batch 24100, loss[loss=0.1462, simple_loss=0.2102, pruned_loss=0.04112, over 4873.00 frames.], tot_loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02834, over 971041.29 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 18:25:16,108 INFO [train.py:715] (1/8) Epoch 19, batch 24150, loss[loss=0.1402, simple_loss=0.2104, pruned_loss=0.03503, over 4837.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02795, over 971774.97 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:25:56,278 INFO [train.py:715] (1/8) Epoch 19, batch 24200, loss[loss=0.1266, simple_loss=0.2109, pruned_loss=0.02116, over 4810.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.028, over 971942.84 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:26:36,609 INFO [train.py:715] (1/8) Epoch 19, batch 24250, loss[loss=0.164, simple_loss=0.2429, pruned_loss=0.0425, over 4800.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2047, pruned_loss=0.02815, over 972426.22 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 18:27:17,345 INFO [train.py:715] (1/8) Epoch 19, batch 24300, loss[loss=0.151, simple_loss=0.199, pruned_loss=0.05154, over 4816.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2045, pruned_loss=0.02799, over 971818.53 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 18:27:56,387 INFO [train.py:715] (1/8) Epoch 19, batch 24350, loss[loss=0.1156, simple_loss=0.1876, pruned_loss=0.02178, over 4900.00 frames.], tot_loss[loss=0.1303, simple_loss=0.205, pruned_loss=0.02779, over 972379.72 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 18:28:36,031 INFO [train.py:715] (1/8) Epoch 19, batch 24400, loss[loss=0.1391, simple_loss=0.2198, pruned_loss=0.02921, over 4925.00 frames.], tot_loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.02786, over 972956.26 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:29:16,434 INFO [train.py:715] (1/8) Epoch 19, batch 24450, loss[loss=0.1129, simple_loss=0.1856, pruned_loss=0.02006, over 4822.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2046, pruned_loss=0.0282, over 972563.65 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 18:29:55,848 INFO [train.py:715] (1/8) Epoch 19, batch 24500, loss[loss=0.1428, simple_loss=0.2227, pruned_loss=0.03144, over 4704.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2042, pruned_loss=0.02768, over 971981.43 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:30:34,371 INFO [train.py:715] (1/8) Epoch 19, batch 24550, loss[loss=0.1361, simple_loss=0.2214, pruned_loss=0.02545, over 4780.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2048, pruned_loss=0.02781, over 972677.32 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:31:13,259 INFO [train.py:715] (1/8) Epoch 19, batch 24600, loss[loss=0.1093, simple_loss=0.1918, pruned_loss=0.01338, over 4924.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2049, pruned_loss=0.02787, over 973262.85 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 18:31:52,754 INFO [train.py:715] (1/8) Epoch 19, batch 24650, loss[loss=0.1359, simple_loss=0.2084, pruned_loss=0.03176, over 4960.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2049, pruned_loss=0.02781, over 973692.61 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:32:31,483 INFO [train.py:715] (1/8) Epoch 19, batch 24700, loss[loss=0.1282, simple_loss=0.2107, pruned_loss=0.02282, over 4967.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.02806, over 973514.95 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 18:33:10,025 INFO [train.py:715] (1/8) Epoch 19, batch 24750, loss[loss=0.1317, simple_loss=0.1997, pruned_loss=0.03189, over 4848.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02832, over 972814.49 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 18:33:50,378 INFO [train.py:715] (1/8) Epoch 19, batch 24800, loss[loss=0.1375, simple_loss=0.2222, pruned_loss=0.02638, over 4778.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02838, over 972791.88 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:34:30,019 INFO [train.py:715] (1/8) Epoch 19, batch 24850, loss[loss=0.1406, simple_loss=0.2092, pruned_loss=0.036, over 4976.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2054, pruned_loss=0.02884, over 973514.21 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 18:35:09,083 INFO [train.py:715] (1/8) Epoch 19, batch 24900, loss[loss=0.1626, simple_loss=0.2359, pruned_loss=0.04469, over 4956.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02879, over 972885.70 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 18:35:48,549 INFO [train.py:715] (1/8) Epoch 19, batch 24950, loss[loss=0.1003, simple_loss=0.1692, pruned_loss=0.01568, over 4855.00 frames.], tot_loss[loss=0.131, simple_loss=0.2051, pruned_loss=0.02846, over 973157.85 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 18:36:28,346 INFO [train.py:715] (1/8) Epoch 19, batch 25000, loss[loss=0.139, simple_loss=0.2172, pruned_loss=0.03041, over 4901.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02856, over 972986.39 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 18:37:07,185 INFO [train.py:715] (1/8) Epoch 19, batch 25050, loss[loss=0.1199, simple_loss=0.1996, pruned_loss=0.02013, over 4772.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.0285, over 971906.95 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:37:46,484 INFO [train.py:715] (1/8) Epoch 19, batch 25100, loss[loss=0.112, simple_loss=0.185, pruned_loss=0.01945, over 4798.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02895, over 971618.38 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:38:26,081 INFO [train.py:715] (1/8) Epoch 19, batch 25150, loss[loss=0.1274, simple_loss=0.1985, pruned_loss=0.02809, over 4760.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02907, over 972057.58 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:39:05,716 INFO [train.py:715] (1/8) Epoch 19, batch 25200, loss[loss=0.09796, simple_loss=0.1604, pruned_loss=0.01774, over 4993.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02869, over 973624.58 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:39:44,330 INFO [train.py:715] (1/8) Epoch 19, batch 25250, loss[loss=0.1221, simple_loss=0.198, pruned_loss=0.02314, over 4880.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02857, over 972809.11 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:40:23,571 INFO [train.py:715] (1/8) Epoch 19, batch 25300, loss[loss=0.132, simple_loss=0.2088, pruned_loss=0.02759, over 4781.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2054, pruned_loss=0.02794, over 972656.36 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:41:03,214 INFO [train.py:715] (1/8) Epoch 19, batch 25350, loss[loss=0.1614, simple_loss=0.2397, pruned_loss=0.04152, over 4755.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02858, over 972144.85 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:41:42,424 INFO [train.py:715] (1/8) Epoch 19, batch 25400, loss[loss=0.1183, simple_loss=0.1852, pruned_loss=0.02572, over 4953.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02845, over 972135.52 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 18:42:21,493 INFO [train.py:715] (1/8) Epoch 19, batch 25450, loss[loss=0.1131, simple_loss=0.1879, pruned_loss=0.01911, over 4854.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.0283, over 971550.37 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 18:43:00,712 INFO [train.py:715] (1/8) Epoch 19, batch 25500, loss[loss=0.1099, simple_loss=0.1911, pruned_loss=0.01434, over 4802.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2055, pruned_loss=0.0277, over 971253.31 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:43:39,822 INFO [train.py:715] (1/8) Epoch 19, batch 25550, loss[loss=0.1476, simple_loss=0.2181, pruned_loss=0.03857, over 4979.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02864, over 971525.34 frames.], batch size: 31, lr: 1.16e-04
2022-05-09 18:44:18,027 INFO [train.py:715] (1/8) Epoch 19, batch 25600, loss[loss=0.1482, simple_loss=0.2331, pruned_loss=0.03166, over 4896.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02901, over 971987.41 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:44:56,957 INFO [train.py:715] (1/8) Epoch 19, batch 25650, loss[loss=0.1247, simple_loss=0.2066, pruned_loss=0.02137, over 4829.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02849, over 972490.97 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 18:45:36,000 INFO [train.py:715] (1/8) Epoch 19, batch 25700, loss[loss=0.1198, simple_loss=0.1998, pruned_loss=0.0199, over 4932.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02854, over 972195.47 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:46:14,561 INFO [train.py:715] (1/8) Epoch 19, batch 25750, loss[loss=0.1208, simple_loss=0.1988, pruned_loss=0.02144, over 4808.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02876, over 973002.33 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:46:53,569 INFO [train.py:715] (1/8) Epoch 19, batch 25800, loss[loss=0.1176, simple_loss=0.1992, pruned_loss=0.01801, over 4746.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02877, over 972001.00 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:47:32,965 INFO [train.py:715] (1/8) Epoch 19, batch 25850, loss[loss=0.1283, simple_loss=0.2014, pruned_loss=0.0276, over 4769.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.0283, over 972068.25 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:48:12,295 INFO [train.py:715] (1/8) Epoch 19, batch 25900, loss[loss=0.1277, simple_loss=0.1967, pruned_loss=0.02928, over 4949.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02813, over 972999.77 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:48:50,876 INFO [train.py:715] (1/8) Epoch 19, batch 25950, loss[loss=0.1736, simple_loss=0.2559, pruned_loss=0.04563, over 4987.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02844, over 973267.52 frames.], batch size: 31, lr: 1.16e-04
2022-05-09 18:49:30,500 INFO [train.py:715] (1/8) Epoch 19, batch 26000, loss[loss=0.1316, simple_loss=0.2051, pruned_loss=0.029, over 4808.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02843, over 972661.65 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:50:10,471 INFO [train.py:715] (1/8) Epoch 19, batch 26050, loss[loss=0.1481, simple_loss=0.2366, pruned_loss=0.02986, over 4944.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02836, over 972917.20 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 18:50:49,154 INFO [train.py:715] (1/8) Epoch 19, batch 26100, loss[loss=0.1369, simple_loss=0.2148, pruned_loss=0.02949, over 4911.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02879, over 972921.56 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:51:28,553 INFO [train.py:715] (1/8) Epoch 19, batch 26150, loss[loss=0.1376, simple_loss=0.2145, pruned_loss=0.03033, over 4780.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02865, over 972556.75 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:52:07,553 INFO [train.py:715] (1/8) Epoch 19, batch 26200, loss[loss=0.1404, simple_loss=0.221, pruned_loss=0.02988, over 4787.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02834, over 971908.47 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:52:47,075 INFO [train.py:715] (1/8) Epoch 19, batch 26250, loss[loss=0.1384, simple_loss=0.2146, pruned_loss=0.03107, over 4857.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02835, over 972018.16 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 18:53:25,458 INFO [train.py:715] (1/8) Epoch 19, batch 26300, loss[loss=0.1074, simple_loss=0.1799, pruned_loss=0.01749, over 4919.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02866, over 972377.71 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:54:04,837 INFO [train.py:715] (1/8) Epoch 19, batch 26350, loss[loss=0.1365, simple_loss=0.2106, pruned_loss=0.03119, over 4941.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02856, over 972439.38 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 18:54:44,060 INFO [train.py:715] (1/8) Epoch 19, batch 26400, loss[loss=0.1389, simple_loss=0.2187, pruned_loss=0.02957, over 4793.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02832, over 972055.64 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:55:23,156 INFO [train.py:715] (1/8) Epoch 19, batch 26450, loss[loss=0.1299, simple_loss=0.2077, pruned_loss=0.02602, over 4887.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02847, over 971984.94 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:56:02,212 INFO [train.py:715] (1/8) Epoch 19, batch 26500, loss[loss=0.1199, simple_loss=0.1961, pruned_loss=0.02182, over 4755.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.02809, over 970978.24 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:56:40,880 INFO [train.py:715] (1/8) Epoch 19, batch 26550, loss[loss=0.1299, simple_loss=0.2077, pruned_loss=0.02608, over 4827.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02836, over 970863.63 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:57:21,641 INFO [train.py:715] (1/8) Epoch 19, batch 26600, loss[loss=0.1509, simple_loss=0.2143, pruned_loss=0.0437, over 4866.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02838, over 971882.33 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 18:58:02,778 INFO [train.py:715] (1/8) Epoch 19, batch 26650, loss[loss=0.1252, simple_loss=0.206, pruned_loss=0.02219, over 4790.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.0287, over 972294.13 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:58:41,692 INFO [train.py:715] (1/8) Epoch 19, batch 26700, loss[loss=0.1038, simple_loss=0.183, pruned_loss=0.01231, over 4922.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2057, pruned_loss=0.02855, over 971805.47 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 18:59:21,008 INFO [train.py:715] (1/8) Epoch 19, batch 26750, loss[loss=0.1513, simple_loss=0.2401, pruned_loss=0.03126, over 4968.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02902, over 971754.30 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:00:00,951 INFO [train.py:715] (1/8) Epoch 19, batch 26800, loss[loss=0.1265, simple_loss=0.2029, pruned_loss=0.02504, over 4855.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02954, over 970840.20 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 19:00:41,174 INFO [train.py:715] (1/8) Epoch 19, batch 26850, loss[loss=0.1453, simple_loss=0.2138, pruned_loss=0.03839, over 4901.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02928, over 970399.16 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:01:20,363 INFO [train.py:715] (1/8) Epoch 19, batch 26900, loss[loss=0.1488, simple_loss=0.2112, pruned_loss=0.04318, over 4809.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2062, pruned_loss=0.02935, over 971047.10 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 19:02:00,252 INFO [train.py:715] (1/8) Epoch 19, batch 26950, loss[loss=0.1291, simple_loss=0.1972, pruned_loss=0.03054, over 4967.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02905, over 971151.67 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 19:02:39,712 INFO [train.py:715] (1/8) Epoch 19, batch 27000, loss[loss=0.1444, simple_loss=0.2234, pruned_loss=0.03272, over 4884.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02912, over 972310.96 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:02:39,713 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 19:02:49,600 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1047, simple_loss=0.1878, pruned_loss=0.0108, over 914524.00 frames.
2022-05-09 19:03:29,469 INFO [train.py:715] (1/8) Epoch 19, batch 27050, loss[loss=0.131, simple_loss=0.2153, pruned_loss=0.02333, over 4871.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02888, over 972795.01 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:04:09,786 INFO [train.py:715] (1/8) Epoch 19, batch 27100, loss[loss=0.1155, simple_loss=0.1876, pruned_loss=0.02168, over 4806.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02893, over 972421.47 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 19:04:50,658 INFO [train.py:715] (1/8) Epoch 19, batch 27150, loss[loss=0.121, simple_loss=0.198, pruned_loss=0.02197, over 4894.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02881, over 972740.56 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:05:30,589 INFO [train.py:715] (1/8) Epoch 19, batch 27200, loss[loss=0.137, simple_loss=0.2149, pruned_loss=0.02955, over 4992.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02924, over 973589.16 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:06:11,126 INFO [train.py:715] (1/8) Epoch 19, batch 27250, loss[loss=0.1256, simple_loss=0.2008, pruned_loss=0.02522, over 4951.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02937, over 973182.62 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:06:52,920 INFO [train.py:715] (1/8) Epoch 19, batch 27300, loss[loss=0.09998, simple_loss=0.1739, pruned_loss=0.01301, over 4880.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02919, over 972854.49 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 19:07:33,646 INFO [train.py:715] (1/8) Epoch 19, batch 27350, loss[loss=0.1364, simple_loss=0.2107, pruned_loss=0.03109, over 4905.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02874, over 972678.24 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:08:14,908 INFO [train.py:715] (1/8) Epoch 19, batch 27400, loss[loss=0.126, simple_loss=0.2132, pruned_loss=0.01946, over 4919.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.0286, over 973666.86 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:08:54,853 INFO [train.py:715] (1/8) Epoch 19, batch 27450, loss[loss=0.1305, simple_loss=0.2024, pruned_loss=0.0293, over 4899.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02864, over 972862.99 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:09:36,480 INFO [train.py:715] (1/8) Epoch 19, batch 27500, loss[loss=0.1212, simple_loss=0.1921, pruned_loss=0.02518, over 4953.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2058, pruned_loss=0.02882, over 972546.62 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 19:10:17,082 INFO [train.py:715] (1/8) Epoch 19, batch 27550, loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02842, over 4885.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02832, over 971973.89 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 19:10:57,703 INFO [train.py:715] (1/8) Epoch 19, batch 27600, loss[loss=0.1147, simple_loss=0.1826, pruned_loss=0.02343, over 4782.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02854, over 971262.71 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:11:38,776 INFO [train.py:715] (1/8) Epoch 19, batch 27650, loss[loss=0.113, simple_loss=0.1854, pruned_loss=0.02025, over 4905.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.0283, over 971284.11 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 19:12:19,402 INFO [train.py:715] (1/8) Epoch 19, batch 27700, loss[loss=0.09526, simple_loss=0.1652, pruned_loss=0.01264, over 4960.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02812, over 971894.79 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:13:00,032 INFO [train.py:715] (1/8) Epoch 19, batch 27750, loss[loss=0.1197, simple_loss=0.2034, pruned_loss=0.01801, over 4917.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02841, over 972763.79 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:13:40,108 INFO [train.py:715] (1/8) Epoch 19, batch 27800, loss[loss=0.1178, simple_loss=0.2004, pruned_loss=0.01761, over 4744.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02851, over 972744.21 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:14:21,131 INFO [train.py:715] (1/8) Epoch 19, batch 27850, loss[loss=0.1467, simple_loss=0.2164, pruned_loss=0.0385, over 4847.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02888, over 972165.32 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 19:15:01,164 INFO [train.py:715] (1/8) Epoch 19, batch 27900, loss[loss=0.1267, simple_loss=0.2081, pruned_loss=0.0226, over 4946.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02931, over 972537.27 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:15:41,279 INFO [train.py:715] (1/8) Epoch 19, batch 27950, loss[loss=0.1281, simple_loss=0.2017, pruned_loss=0.02723, over 4980.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02873, over 973343.51 frames.], batch size: 28, lr: 1.16e-04
2022-05-09 19:16:21,258 INFO [train.py:715] (1/8) Epoch 19, batch 28000, loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02911, over 4823.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02851, over 972734.08 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 19:17:02,123 INFO [train.py:715] (1/8) Epoch 19, batch 28050, loss[loss=0.1362, simple_loss=0.2145, pruned_loss=0.02895, over 4750.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02843, over 972696.46 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:17:42,527 INFO [train.py:715] (1/8) Epoch 19, batch 28100, loss[loss=0.09876, simple_loss=0.1806, pruned_loss=0.008444, over 4960.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02816, over 972265.60 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:18:22,476 INFO [train.py:715] (1/8) Epoch 19, batch 28150, loss[loss=0.1602, simple_loss=0.2254, pruned_loss=0.04744, over 4979.00 frames.], tot_loss[loss=0.1309, simple_loss=0.205, pruned_loss=0.02839, over 972804.23 frames.], batch size: 31, lr: 1.16e-04
2022-05-09 19:19:02,902 INFO [train.py:715] (1/8) Epoch 19, batch 28200, loss[loss=0.1243, simple_loss=0.1996, pruned_loss=0.02451, over 4968.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2051, pruned_loss=0.02827, over 973257.59 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:19:42,615 INFO [train.py:715] (1/8) Epoch 19, batch 28250, loss[loss=0.111, simple_loss=0.1903, pruned_loss=0.01587, over 4810.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02834, over 974007.25 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:20:22,518 INFO [train.py:715] (1/8) Epoch 19, batch 28300, loss[loss=0.1435, simple_loss=0.2218, pruned_loss=0.03254, over 4964.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02854, over 973298.91 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 19:21:02,194 INFO [train.py:715] (1/8) Epoch 19, batch 28350, loss[loss=0.1476, simple_loss=0.2298, pruned_loss=0.03268, over 4858.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02861, over 973652.79 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:21:42,215 INFO [train.py:715] (1/8) Epoch 19, batch 28400, loss[loss=0.1125, simple_loss=0.1904, pruned_loss=0.0173, over 4907.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02833, over 973344.72 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 19:22:22,327 INFO [train.py:715] (1/8) Epoch 19, batch 28450, loss[loss=0.1046, simple_loss=0.1758, pruned_loss=0.01668, over 4873.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.02795, over 972689.99 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 19:23:02,153 INFO [train.py:715] (1/8) Epoch 19, batch 28500, loss[loss=0.138, simple_loss=0.2134, pruned_loss=0.03129, over 4983.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02857, over 973077.92 frames.], batch size: 28, lr: 1.16e-04
2022-05-09 19:23:42,828 INFO [train.py:715] (1/8) Epoch 19, batch 28550, loss[loss=0.1484, simple_loss=0.2208, pruned_loss=0.03798, over 4931.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02871, over 972588.01 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 19:24:22,309 INFO [train.py:715] (1/8) Epoch 19, batch 28600, loss[loss=0.1019, simple_loss=0.175, pruned_loss=0.01436, over 4787.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02843, over 972838.57 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 19:25:02,348 INFO [train.py:715] (1/8) Epoch 19, batch 28650, loss[loss=0.1387, simple_loss=0.2145, pruned_loss=0.0314, over 4912.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02835, over 973210.32 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:25:43,120 INFO [train.py:715] (1/8) Epoch 19, batch 28700, loss[loss=0.113, simple_loss=0.1911, pruned_loss=0.01744, over 4695.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2048, pruned_loss=0.02817, over 972233.04 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:26:22,653 INFO [train.py:715] (1/8) Epoch 19, batch 28750, loss[loss=0.1069, simple_loss=0.1834, pruned_loss=0.01515, over 4810.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2048, pruned_loss=0.02831, over 972621.40 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:27:02,559 INFO [train.py:715] (1/8) Epoch 19, batch 28800, loss[loss=0.1556, simple_loss=0.2286, pruned_loss=0.04132, over 4699.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.0281, over 972509.68 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:27:41,936 INFO [train.py:715] (1/8) Epoch 19, batch 28850, loss[loss=0.1128, simple_loss=0.1973, pruned_loss=0.01415, over 4759.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2045, pruned_loss=0.02812, over 972030.87 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:28:21,324 INFO [train.py:715] (1/8) Epoch 19, batch 28900, loss[loss=0.1197, simple_loss=0.1964, pruned_loss=0.02153, over 4925.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2041, pruned_loss=0.02779, over 971567.39 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 19:28:59,435 INFO [train.py:715] (1/8) Epoch 19, batch 28950, loss[loss=0.1271, simple_loss=0.2071, pruned_loss=0.02359, over 4844.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.02804, over 971987.28 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 19:29:38,324 INFO [train.py:715] (1/8) Epoch 19, batch 29000, loss[loss=0.1364, simple_loss=0.2185, pruned_loss=0.02714, over 4778.00 frames.], tot_loss[loss=0.131, simple_loss=0.2057, pruned_loss=0.02816, over 972054.87 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:30:17,557 INFO [train.py:715] (1/8) Epoch 19, batch 29050, loss[loss=0.1396, simple_loss=0.2194, pruned_loss=0.02992, over 4821.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2052, pruned_loss=0.02795, over 972371.51 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 19:30:56,436 INFO [train.py:715] (1/8) Epoch 19, batch 29100, loss[loss=0.1218, simple_loss=0.1968, pruned_loss=0.02345, over 4785.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2048, pruned_loss=0.02783, over 972697.18 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:31:35,381 INFO [train.py:715] (1/8) Epoch 19, batch 29150, loss[loss=0.1397, simple_loss=0.2146, pruned_loss=0.03239, over 4895.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2049, pruned_loss=0.02809, over 972067.34 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:32:14,163 INFO [train.py:715] (1/8) Epoch 19, batch 29200, loss[loss=0.1283, simple_loss=0.1975, pruned_loss=0.02959, over 4984.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2046, pruned_loss=0.02798, over 972704.99 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:32:53,529 INFO [train.py:715] (1/8) Epoch 19, batch 29250, loss[loss=0.1182, simple_loss=0.1928, pruned_loss=0.02183, over 4775.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2045, pruned_loss=0.02784, over 972201.82 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:33:32,154 INFO [train.py:715] (1/8) Epoch 19, batch 29300, loss[loss=0.1184, simple_loss=0.1933, pruned_loss=0.0217, over 4920.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02839, over 971300.27 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 19:34:11,672 INFO [train.py:715] (1/8) Epoch 19, batch 29350, loss[loss=0.145, simple_loss=0.2138, pruned_loss=0.03809, over 4927.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02865, over 971696.27 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 19:34:50,601 INFO [train.py:715] (1/8) Epoch 19, batch 29400, loss[loss=0.1754, simple_loss=0.253, pruned_loss=0.04891, over 4854.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02843, over 971624.43 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:35:29,738 INFO [train.py:715] (1/8) Epoch 19, batch 29450, loss[loss=0.122, simple_loss=0.1963, pruned_loss=0.0238, over 4823.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.0281, over 971939.37 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 19:36:09,169 INFO [train.py:715] (1/8) Epoch 19, batch 29500, loss[loss=0.118, simple_loss=0.1902, pruned_loss=0.02289, over 4828.00 frames.], tot_loss[loss=0.1294, simple_loss=0.2038, pruned_loss=0.02752, over 971819.25 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 19:36:48,558 INFO [train.py:715] (1/8) Epoch 19, batch 29550, loss[loss=0.1457, simple_loss=0.2117, pruned_loss=0.03988, over 4890.00 frames.], tot_loss[loss=0.13, simple_loss=0.2045, pruned_loss=0.02776, over 971948.60 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:37:28,166 INFO [train.py:715] (1/8) Epoch 19, batch 29600, loss[loss=0.1083, simple_loss=0.1759, pruned_loss=0.02039, over 4988.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2046, pruned_loss=0.02756, over 972640.90 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:38:07,299 INFO [train.py:715] (1/8) Epoch 19, batch 29650, loss[loss=0.135, simple_loss=0.2094, pruned_loss=0.03036, over 4968.00 frames.], tot_loss[loss=0.1293, simple_loss=0.204, pruned_loss=0.02726, over 972735.41 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:38:47,450 INFO [train.py:715] (1/8) Epoch 19, batch 29700, loss[loss=0.1173, simple_loss=0.1952, pruned_loss=0.01971, over 4971.00 frames.], tot_loss[loss=0.1292, simple_loss=0.2039, pruned_loss=0.02723, over 973350.56 frames.], batch size: 28, lr: 1.16e-04
2022-05-09 19:39:26,740 INFO [train.py:715] (1/8) Epoch 19, batch 29750, loss[loss=0.1521, simple_loss=0.219, pruned_loss=0.04262, over 4860.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02795, over 972700.88 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:40:06,088 INFO [train.py:715] (1/8) Epoch 19, batch 29800, loss[loss=0.1515, simple_loss=0.2267, pruned_loss=0.03816, over 4958.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2046, pruned_loss=0.02776, over 972568.38 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 19:40:45,393 INFO [train.py:715] (1/8) Epoch 19, batch 29850, loss[loss=0.1421, simple_loss=0.2217, pruned_loss=0.03126, over 4962.00 frames.], tot_loss[loss=0.13, simple_loss=0.2046, pruned_loss=0.02769, over 973331.49 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:41:24,804 INFO [train.py:715] (1/8) Epoch 19, batch 29900, loss[loss=0.1414, simple_loss=0.207, pruned_loss=0.03787, over 4983.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2059, pruned_loss=0.02818, over 973204.16 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 19:42:04,766 INFO [train.py:715] (1/8) Epoch 19, batch 29950, loss[loss=0.1372, simple_loss=0.2101, pruned_loss=0.0321, over 4981.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.0283, over 973039.80 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:42:43,615 INFO [train.py:715] (1/8) Epoch 19, batch 30000, loss[loss=0.15, simple_loss=0.2167, pruned_loss=0.04169, over 4694.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02857, over 971742.67 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:42:43,616 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 19:42:53,508 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1045, simple_loss=0.1877, pruned_loss=0.01067, over 914524.00 frames.
2022-05-09 19:43:32,624 INFO [train.py:715] (1/8) Epoch 19, batch 30050, loss[loss=0.1086, simple_loss=0.1798, pruned_loss=0.01872, over 4787.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02896, over 972499.09 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 19:44:12,190 INFO [train.py:715] (1/8) Epoch 19, batch 30100, loss[loss=0.1275, simple_loss=0.2058, pruned_loss=0.02458, over 4778.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02892, over 972178.58 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:44:51,309 INFO [train.py:715] (1/8) Epoch 19, batch 30150, loss[loss=0.1219, simple_loss=0.1949, pruned_loss=0.02448, over 4793.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02869, over 972176.35 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:45:31,083 INFO [train.py:715] (1/8) Epoch 19, batch 30200, loss[loss=0.1066, simple_loss=0.1742, pruned_loss=0.01952, over 4870.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02896, over 971650.57 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:46:09,570 INFO [train.py:715] (1/8) Epoch 19, batch 30250, loss[loss=0.1364, simple_loss=0.2176, pruned_loss=0.0276, over 4935.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02925, over 971193.35 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:46:48,893 INFO [train.py:715] (1/8) Epoch 19, batch 30300, loss[loss=0.1262, simple_loss=0.2046, pruned_loss=0.02392, over 4891.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02857, over 972742.62 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:47:28,482 INFO [train.py:715] (1/8) Epoch 19, batch 30350, loss[loss=0.1382, simple_loss=0.2018, pruned_loss=0.03734, over 4854.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.02812, over 971985.98 frames.], batch size: 34, lr: 1.16e-04
2022-05-09 19:48:08,086 INFO [train.py:715] (1/8) Epoch 19, batch 30400, loss[loss=0.1177, simple_loss=0.1923, pruned_loss=0.02154, over 4873.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2047, pruned_loss=0.02789, over 971724.92 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:48:47,850 INFO [train.py:715] (1/8) Epoch 19, batch 30450, loss[loss=0.1578, simple_loss=0.2313, pruned_loss=0.0422, over 4990.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2047, pruned_loss=0.02792, over 971648.81 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 19:49:26,656 INFO [train.py:715] (1/8) Epoch 19, batch 30500, loss[loss=0.133, simple_loss=0.206, pruned_loss=0.03002, over 4806.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02799, over 972792.28 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:50:06,591 INFO [train.py:715] (1/8) Epoch 19, batch 30550, loss[loss=0.1454, simple_loss=0.2223, pruned_loss=0.03425, over 4940.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2042, pruned_loss=0.02765, over 973031.67 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:50:45,746 INFO [train.py:715] (1/8) Epoch 19, batch 30600, loss[loss=0.1647, simple_loss=0.2414, pruned_loss=0.04404, over 4925.00 frames.], tot_loss[loss=0.1288, simple_loss=0.2036, pruned_loss=0.02697, over 972978.33 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 19:51:25,827 INFO [train.py:715] (1/8) Epoch 19, batch 30650, loss[loss=0.1381, simple_loss=0.1993, pruned_loss=0.03847, over 4904.00 frames.], tot_loss[loss=0.1288, simple_loss=0.2035, pruned_loss=0.02704, over 972757.48 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:52:05,609 INFO [train.py:715] (1/8) Epoch 19, batch 30700, loss[loss=0.1319, simple_loss=0.2148, pruned_loss=0.02446, over 4825.00 frames.], tot_loss[loss=0.129, simple_loss=0.2036, pruned_loss=0.02721, over 973110.27 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:52:45,189 INFO [train.py:715] (1/8) Epoch 19, batch 30750, loss[loss=0.133, simple_loss=0.2091, pruned_loss=0.0284, over 4817.00 frames.], tot_loss[loss=0.1292, simple_loss=0.2036, pruned_loss=0.02738, over 972835.61 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 19:53:25,711 INFO [train.py:715] (1/8) Epoch 19, batch 30800, loss[loss=0.1398, simple_loss=0.2141, pruned_loss=0.03274, over 4752.00 frames.], tot_loss[loss=0.1293, simple_loss=0.2036, pruned_loss=0.02752, over 972107.24 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:54:05,655 INFO [train.py:715] (1/8) Epoch 19, batch 30850, loss[loss=0.154, simple_loss=0.2331, pruned_loss=0.03744, over 4816.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2047, pruned_loss=0.02797, over 971620.23 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 19:54:46,445 INFO [train.py:715] (1/8) Epoch 19, batch 30900, loss[loss=0.1483, simple_loss=0.228, pruned_loss=0.03424, over 4872.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2046, pruned_loss=0.02803, over 971877.89 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 19:55:26,513 INFO [train.py:715] (1/8) Epoch 19, batch 30950, loss[loss=0.1275, simple_loss=0.1992, pruned_loss=0.02792, over 4691.00 frames.], tot_loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02837, over 972948.73 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:56:07,122 INFO [train.py:715] (1/8) Epoch 19, batch 31000, loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03089, over 4766.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02831, over 972160.74 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:56:47,788 INFO [train.py:715] (1/8) Epoch 19, batch 31050, loss[loss=0.1375, simple_loss=0.2135, pruned_loss=0.03076, over 4845.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.0281, over 972091.30 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:57:28,127 INFO [train.py:715] (1/8) Epoch 19, batch 31100, loss[loss=0.1214, simple_loss=0.1937, pruned_loss=0.02454, over 4702.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.02792, over 972578.31 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:58:08,816 INFO [train.py:715] (1/8) Epoch 19, batch 31150, loss[loss=0.125, simple_loss=0.1948, pruned_loss=0.0276, over 4883.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02867, over 972497.21 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:58:49,191 INFO [train.py:715] (1/8) Epoch 19, batch 31200, loss[loss=0.1236, simple_loss=0.2056, pruned_loss=0.02081, over 4817.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02856, over 972125.16 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 19:59:30,188 INFO [train.py:715] (1/8) Epoch 19, batch 31250, loss[loss=0.1095, simple_loss=0.1866, pruned_loss=0.01621, over 4973.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2065, pruned_loss=0.0284, over 972805.47 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 20:00:09,923 INFO [train.py:715] (1/8) Epoch 19, batch 31300, loss[loss=0.1283, simple_loss=0.2005, pruned_loss=0.0281, over 4930.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2053, pruned_loss=0.02792, over 972626.26 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:00:50,570 INFO [train.py:715] (1/8) Epoch 19, batch 31350, loss[loss=0.1359, simple_loss=0.2175, pruned_loss=0.02715, over 4814.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2056, pruned_loss=0.02786, over 971955.65 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:01:31,161 INFO [train.py:715] (1/8) Epoch 19, batch 31400, loss[loss=0.1347, simple_loss=0.2164, pruned_loss=0.02647, over 4749.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02821, over 971192.12 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 20:02:11,466 INFO [train.py:715] (1/8) Epoch 19, batch 31450, loss[loss=0.1302, simple_loss=0.2073, pruned_loss=0.02653, over 4900.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02825, over 971563.45 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 20:02:52,729 INFO [train.py:715] (1/8) Epoch 19, batch 31500, loss[loss=0.1128, simple_loss=0.1929, pruned_loss=0.01635, over 4926.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02808, over 972185.46 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 20:03:32,848 INFO [train.py:715] (1/8) Epoch 19, batch 31550, loss[loss=0.1244, simple_loss=0.198, pruned_loss=0.02539, over 4826.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2056, pruned_loss=0.02808, over 973040.06 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:04:13,436 INFO [train.py:715] (1/8) Epoch 19, batch 31600, loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03073, over 4685.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2054, pruned_loss=0.02778, over 973197.67 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:04:53,553 INFO [train.py:715] (1/8) Epoch 19, batch 31650, loss[loss=0.1052, simple_loss=0.1883, pruned_loss=0.01105, over 4820.00 frames.], tot_loss[loss=0.1295, simple_loss=0.2047, pruned_loss=0.02711, over 973377.48 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 20:05:33,936 INFO [train.py:715] (1/8) Epoch 19, batch 31700, loss[loss=0.1269, simple_loss=0.2026, pruned_loss=0.02562, over 4685.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2048, pruned_loss=0.02729, over 973168.84 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:06:14,402 INFO [train.py:715] (1/8) Epoch 19, batch 31750, loss[loss=0.1115, simple_loss=0.1735, pruned_loss=0.02471, over 4820.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2049, pruned_loss=0.02775, over 972933.31 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:06:54,585 INFO [train.py:715] (1/8) Epoch 19, batch 31800, loss[loss=0.156, simple_loss=0.2327, pruned_loss=0.03966, over 4916.00 frames.], tot_loss[loss=0.1303, simple_loss=0.205, pruned_loss=0.02785, over 972830.38 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 20:07:35,678 INFO [train.py:715] (1/8) Epoch 19, batch 31850, loss[loss=0.1376, simple_loss=0.2122, pruned_loss=0.03148, over 4840.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2048, pruned_loss=0.02776, over 973051.44 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:08:15,975 INFO [train.py:715] (1/8) Epoch 19, batch 31900, loss[loss=0.1199, simple_loss=0.1938, pruned_loss=0.02302, over 4788.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02832, over 972166.39 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:08:56,574 INFO [train.py:715] (1/8) Epoch 19, batch 31950, loss[loss=0.1111, simple_loss=0.1784, pruned_loss=0.02192, over 4897.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2065, pruned_loss=0.02834, over 971708.37 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 20:09:36,645 INFO [train.py:715] (1/8) Epoch 19, batch 32000, loss[loss=0.1151, simple_loss=0.1862, pruned_loss=0.02204, over 4858.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.029, over 972128.07 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 20:10:16,974 INFO [train.py:715] (1/8) Epoch 19, batch 32050, loss[loss=0.1339, simple_loss=0.2112, pruned_loss=0.02827, over 4927.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02936, over 973140.55 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 20:10:57,304 INFO [train.py:715] (1/8) Epoch 19, batch 32100, loss[loss=0.1443, simple_loss=0.2268, pruned_loss=0.03086, over 4748.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02871, over 972387.75 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 20:11:37,102 INFO [train.py:715] (1/8) Epoch 19, batch 32150, loss[loss=0.1296, simple_loss=0.2048, pruned_loss=0.02713, over 4912.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2057, pruned_loss=0.02862, over 972337.00 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 20:12:18,360 INFO [train.py:715] (1/8) Epoch 19, batch 32200, loss[loss=0.129, simple_loss=0.2095, pruned_loss=0.02427, over 4945.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02841, over 973293.87 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:12:58,107 INFO [train.py:715] (1/8) Epoch 19, batch 32250, loss[loss=0.128, simple_loss=0.202, pruned_loss=0.02704, over 4852.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02875, over 972918.20 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 20:13:38,489 INFO [train.py:715] (1/8) Epoch 19, batch 32300, loss[loss=0.1225, simple_loss=0.2048, pruned_loss=0.02005, over 4941.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02858, over 972131.14 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 20:14:19,662 INFO [train.py:715] (1/8) Epoch 19, batch 32350, loss[loss=0.1405, simple_loss=0.2129, pruned_loss=0.03408, over 4886.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02819, over 971794.11 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 20:15:00,204 INFO [train.py:715] (1/8) Epoch 19, batch 32400, loss[loss=0.1464, simple_loss=0.2138, pruned_loss=0.0395, over 4724.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02828, over 971154.03 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 20:15:40,800 INFO [train.py:715] (1/8) Epoch 19, batch 32450, loss[loss=0.1848, simple_loss=0.2517, pruned_loss=0.059, over 4744.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02852, over 970423.76 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 20:16:20,797 INFO [train.py:715] (1/8) Epoch 19, batch 32500, loss[loss=0.1212, simple_loss=0.2076, pruned_loss=0.01743, over 4808.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02821, over 970821.64 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 20:17:01,566 INFO [train.py:715] (1/8) Epoch 19, batch 32550, loss[loss=0.1413, simple_loss=0.2196, pruned_loss=0.03148, over 4913.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02796, over 970886.29 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 20:17:41,579 INFO [train.py:715] (1/8) Epoch 19, batch 32600, loss[loss=0.1507, simple_loss=0.2224, pruned_loss=0.03947, over 4981.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2042, pruned_loss=0.02775, over 971043.49 frames.], batch size: 28, lr: 1.16e-04
2022-05-09 20:18:21,655 INFO [train.py:715] (1/8) Epoch 19, batch 32650, loss[loss=0.1126, simple_loss=0.2001, pruned_loss=0.01259, over 4938.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.02808, over 971327.47 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:19:02,300 INFO [train.py:715] (1/8) Epoch 19, batch 32700, loss[loss=0.1283, simple_loss=0.2048, pruned_loss=0.02594, over 4816.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2047, pruned_loss=0.02794, over 971495.44 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:19:42,119 INFO [train.py:715] (1/8) Epoch 19, batch 32750, loss[loss=0.1569, simple_loss=0.2283, pruned_loss=0.04274, over 4834.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02837, over 972200.02 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:20:21,838 INFO [train.py:715] (1/8) Epoch 19, batch 32800, loss[loss=0.1303, simple_loss=0.207, pruned_loss=0.02674, over 4819.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02856, over 972336.43 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 20:21:00,653 INFO [train.py:715] (1/8) Epoch 19, batch 32850, loss[loss=0.1143, simple_loss=0.1855, pruned_loss=0.02152, over 4793.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02903, over 971862.95 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:21:39,681 INFO [train.py:715] (1/8) Epoch 19, batch 32900, loss[loss=0.1322, simple_loss=0.2131, pruned_loss=0.02565, over 4916.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02889, over 972005.02 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 20:22:18,346 INFO [train.py:715] (1/8) Epoch 19, batch 32950, loss[loss=0.1236, simple_loss=0.2009, pruned_loss=0.02313, over 4976.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02872, over 972100.20 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 20:22:57,659 INFO [train.py:715] (1/8) Epoch 19, batch 33000, loss[loss=0.1325, simple_loss=0.2107, pruned_loss=0.0272, over 4956.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.02895, over 971468.58 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 20:22:57,660 INFO [train.py:733] (1/8) Computing validation loss
2022-05-09 20:23:07,492 INFO [train.py:742] (1/8) Epoch 19, validation: loss=0.1048, simple_loss=0.1878, pruned_loss=0.01088, over 914524.00 frames.
2022-05-09 20:23:46,767 INFO [train.py:715] (1/8) Epoch 19, batch 33050, loss[loss=0.121, simple_loss=0.1976, pruned_loss=0.02215, over 4990.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02915, over 971197.18 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 20:24:26,210 INFO [train.py:715] (1/8) Epoch 19, batch 33100, loss[loss=0.1586, simple_loss=0.2268, pruned_loss=0.04524, over 4976.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02919, over 972776.15 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 20:25:05,025 INFO [train.py:715] (1/8) Epoch 19, batch 33150, loss[loss=0.1375, simple_loss=0.215, pruned_loss=0.03001, over 4983.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02926, over 973077.76 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:25:44,207 INFO [train.py:715] (1/8) Epoch 19, batch 33200, loss[loss=0.1194, simple_loss=0.206, pruned_loss=0.01636, over 4965.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.0286, over 972329.76 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 20:26:23,768 INFO [train.py:715] (1/8) Epoch 19, batch 33250, loss[loss=0.1346, simple_loss=0.2112, pruned_loss=0.02901, over 4794.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02839, over 972778.18 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:27:03,188 INFO [train.py:715] (1/8) Epoch 19, batch 33300, loss[loss=0.1731, simple_loss=0.2425, pruned_loss=0.05187, over 4779.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02875, over 972639.49 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 20:27:42,907 INFO [train.py:715] (1/8) Epoch 19, batch 33350, loss[loss=0.1226, simple_loss=0.2005, pruned_loss=0.02232, over 4765.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2063, pruned_loss=0.02822, over 972471.09 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 20:28:22,074 INFO [train.py:715] (1/8) Epoch 19, batch 33400, loss[loss=0.1218, simple_loss=0.1976, pruned_loss=0.02301, over 4922.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02842, over 972280.49 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 20:29:01,051 INFO [train.py:715] (1/8) Epoch 19, batch 33450, loss[loss=0.1267, simple_loss=0.2027, pruned_loss=0.02532, over 4759.00 frames.], tot_loss[loss=0.1309, simple_loss=0.206, pruned_loss=0.02791, over 972202.33 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 20:29:40,018 INFO [train.py:715] (1/8) Epoch 19, batch 33500, loss[loss=0.1329, simple_loss=0.2108, pruned_loss=0.02748, over 4836.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02829, over 971492.52 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 20:30:18,901 INFO [train.py:715] (1/8) Epoch 19, batch 33550, loss[loss=0.1373, simple_loss=0.2061, pruned_loss=0.03425, over 4823.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02836, over 971754.52 frames.], batch size: 13, lr: 1.15e-04
2022-05-09 20:30:58,227 INFO [train.py:715] (1/8) Epoch 19, batch 33600, loss[loss=0.1239, simple_loss=0.2015, pruned_loss=0.02317, over 4752.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02869, over 971755.52 frames.], batch size: 16, lr: 1.15e-04
2022-05-09 20:31:37,212 INFO [train.py:715] (1/8) Epoch 19, batch 33650, loss[loss=0.1583, simple_loss=0.2398, pruned_loss=0.03837, over 4905.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2056, pruned_loss=0.02789, over 971610.41 frames.], batch size: 17, lr: 1.15e-04
2022-05-09 20:32:16,613 INFO [train.py:715] (1/8) Epoch 19, batch 33700, loss[loss=0.1213, simple_loss=0.1962, pruned_loss=0.02325, over 4937.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2044, pruned_loss=0.02759, over 971419.39 frames.], batch size: 21, lr: 1.15e-04
2022-05-09 20:32:55,330 INFO [train.py:715] (1/8) Epoch 19, batch 33750, loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.03227, over 4965.00 frames.], tot_loss[loss=0.1296, simple_loss=0.204, pruned_loss=0.02766, over 972097.35 frames.], batch size: 24, lr: 1.15e-04
2022-05-09 20:33:34,122 INFO [train.py:715] (1/8) Epoch 19, batch 33800, loss[loss=0.1457, simple_loss=0.2228, pruned_loss=0.03425, over 4878.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2043, pruned_loss=0.02771, over 971682.17 frames.], batch size: 22, lr: 1.15e-04
2022-05-09 20:34:12,731 INFO [train.py:715] (1/8) Epoch 19, batch 33850, loss[loss=0.1362, simple_loss=0.2132, pruned_loss=0.02958, over 4965.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2049, pruned_loss=0.02845, over 971323.56 frames.], batch size: 24, lr: 1.15e-04
2022-05-09 20:34:51,526 INFO [train.py:715] (1/8) Epoch 19, batch 33900, loss[loss=0.1396, simple_loss=0.2193, pruned_loss=0.02989, over 4773.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02874, over 971539.01 frames.], batch size: 18, lr: 1.15e-04
2022-05-09 20:35:31,241 INFO [train.py:715] (1/8) Epoch 19, batch 33950, loss[loss=0.1544, simple_loss=0.2185, pruned_loss=0.04516, over 4645.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02905, over 971402.33 frames.], batch size: 13, lr: 1.15e-04
2022-05-09 20:36:10,889 INFO [train.py:715] (1/8) Epoch 19, batch 34000, loss[loss=0.1182, simple_loss=0.1949, pruned_loss=0.0208, over 4961.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02842, over 971842.59 frames.], batch size: 24, lr: 1.15e-04
2022-05-09 20:36:50,177 INFO [train.py:715] (1/8) Epoch 19, batch 34050, loss[loss=0.1474, simple_loss=0.2218, pruned_loss=0.03655, over 4921.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02887, over 972205.29 frames.], batch size: 18, lr: 1.15e-04
2022-05-09 20:37:28,966 INFO [train.py:715] (1/8) Epoch 19, batch 34100, loss[loss=0.1508, simple_loss=0.2285, pruned_loss=0.03652, over 4738.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02837, over 971800.21 frames.], batch size: 16, lr: 1.15e-04
2022-05-09 20:38:08,478 INFO [train.py:715] (1/8) Epoch 19, batch 34150, loss[loss=0.116, simple_loss=0.1917, pruned_loss=0.02016, over 4927.00 frames.], tot_loss[loss=0.131, simple_loss=0.2055, pruned_loss=0.02831, over 971584.45 frames.], batch size: 29, lr: 1.15e-04
2022-05-09 20:38:48,053 INFO [train.py:715] (1/8) Epoch 19, batch 34200, loss[loss=0.1316, simple_loss=0.2042, pruned_loss=0.02947, over 4950.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.02811, over 972113.76 frames.], batch size: 29, lr: 1.15e-04
2022-05-09 20:39:27,598 INFO [train.py:715] (1/8) Epoch 19, batch 34250, loss[loss=0.1407, simple_loss=0.2119, pruned_loss=0.03472, over 4872.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02798, over 972083.65 frames.], batch size: 20, lr: 1.15e-04
2022-05-09 20:40:06,923 INFO [train.py:715] (1/8) Epoch 19, batch 34300, loss[loss=0.1154, simple_loss=0.1984, pruned_loss=0.01619, over 4908.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2051, pruned_loss=0.028, over 972498.03 frames.], batch size: 17, lr: 1.15e-04
2022-05-09 20:40:46,125 INFO [train.py:715] (1/8) Epoch 19, batch 34350, loss[loss=0.1221, simple_loss=0.186, pruned_loss=0.02914, over 4825.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2047, pruned_loss=0.02791, over 971791.78 frames.], batch size: 13, lr: 1.15e-04
2022-05-09 20:41:25,882 INFO [train.py:715] (1/8) Epoch 19, batch 34400, loss[loss=0.08943, simple_loss=0.1506, pruned_loss=0.01413, over 4794.00 frames.], tot_loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.0279, over 971769.28 frames.], batch size: 12, lr: 1.15e-04
2022-05-09 20:42:05,039 INFO [train.py:715] (1/8) Epoch 19, batch 34450, loss[loss=0.1563, simple_loss=0.2214, pruned_loss=0.04555, over 4982.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02808, over 972352.26 frames.], batch size: 27, lr: 1.15e-04
2022-05-09 20:42:44,558 INFO [train.py:715] (1/8) Epoch 19, batch 34500, loss[loss=0.1563, simple_loss=0.219, pruned_loss=0.0468, over 4879.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.02811, over 972424.28 frames.], batch size: 16, lr: 1.15e-04
2022-05-09 20:43:24,264 INFO [train.py:715] (1/8) Epoch 19, batch 34550, loss[loss=0.1653, simple_loss=0.2469, pruned_loss=0.04183, over 4797.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02869, over 971883.01 frames.], batch size: 21, lr: 1.15e-04
2022-05-09 20:44:03,125 INFO [train.py:715] (1/8) Epoch 19, batch 34600, loss[loss=0.1504, simple_loss=0.2205, pruned_loss=0.04017, over 4829.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.029, over 971884.68 frames.], batch size: 15, lr: 1.15e-04
2022-05-09 20:44:45,162 INFO [train.py:715] (1/8) Epoch 19, batch 34650, loss[loss=0.1156, simple_loss=0.178, pruned_loss=0.02661, over 4986.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02861, over 972092.63 frames.], batch size: 16, lr: 1.15e-04
2022-05-09 20:45:24,628 INFO [train.py:715] (1/8) Epoch 19, batch 34700, loss[loss=0.1123, simple_loss=0.1885, pruned_loss=0.01803, over 4876.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.0284, over 972521.96 frames.], batch size: 22, lr: 1.15e-04
2022-05-09 20:46:02,677 INFO [train.py:715] (1/8) Epoch 19, batch 34750, loss[loss=0.1446, simple_loss=0.222, pruned_loss=0.03362, over 4918.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.02808, over 972515.02 frames.], batch size: 17, lr: 1.15e-04
2022-05-09 20:46:39,957 INFO [train.py:715] (1/8) Epoch 19, batch 34800, loss[loss=0.1378, simple_loss=0.2144, pruned_loss=0.03059, over 4889.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2049, pruned_loss=0.02812, over 973116.93 frames.], batch size: 19, lr: 1.15e-04
2022-05-09 20:46:48,560 INFO [train.py:915] (1/8) Done!