2022-05-03 11:40:27,489 INFO [train.py:775] (2/8) Training started
2022-05-03 11:40:27,489 INFO [train.py:785] (2/8) Device: cuda:2
2022-05-03 11:40:27,491 INFO [train.py:794] (2/8) {'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 11:40:27,492 INFO [train.py:796] (2/8) About to create model
2022-05-03 11:40:27,835 INFO [train.py:800] (2/8) Number of model parameters: 78648040
2022-05-03 11:40:33,425 INFO [train.py:806] (2/8) Using DDP
2022-05-03 11:40:34,136 INFO [asr_datamodule.py:321] (2/8) About to get SPGISpeech train cuts
2022-05-03 11:40:34,139 INFO [asr_datamodule.py:179] (2/8) About to get Musan cuts
2022-05-03 11:40:35,894 INFO [asr_datamodule.py:184] (2/8) Enable MUSAN
2022-05-03 11:40:35,895 INFO [asr_datamodule.py:207] (2/8) Enable SpecAugment
2022-05-03 11:40:35,895 INFO [asr_datamodule.py:208] (2/8) Time warp factor: 80
2022-05-03 11:40:35,895 INFO [asr_datamodule.py:221] (2/8) About to create train dataset
2022-05-03 11:40:35,895 INFO [asr_datamodule.py:234] (2/8) Using DynamicBucketingSampler.
2022-05-03 11:40:36,295 INFO [asr_datamodule.py:242] (2/8) About to create train dataloader
2022-05-03 11:40:36,296 INFO [asr_datamodule.py:326] (2/8) About to get SPGISpeech dev cuts
2022-05-03 11:40:36,297 INFO [asr_datamodule.py:274] (2/8) About to create dev dataset
2022-05-03 11:40:36,447 INFO [asr_datamodule.py:289] (2/8) About to create dev dataloader
2022-05-03 11:41:08,013 INFO [train.py:715] (2/8) Epoch 0, batch 0, loss[loss=3.436, simple_loss=6.872, pruned_loss=5.987, over 4825.00 frames.], tot_loss[loss=3.436, simple_loss=6.872, pruned_loss=5.987, over 4825.00 frames.], batch size: 15, lr: 3.00e-03
2022-05-03 11:41:08,404 INFO [distributed.py:874] (2/8) Reducer buckets have been rebuilt in this iteration.
2022-05-03 11:41:46,309 INFO [train.py:715] (2/8) Epoch 0, batch 50, loss[loss=0.4748, simple_loss=0.9495, pruned_loss=6.829, over 4977.00 frames.], tot_loss[loss=1.323, simple_loss=2.647, pruned_loss=6.493, over 218737.12 frames.], batch size: 15, lr: 3.00e-03
2022-05-03 11:42:25,573 INFO [train.py:715] (2/8) Epoch 0, batch 100, loss[loss=0.3801, simple_loss=0.7602, pruned_loss=6.518, over 4958.00 frames.], tot_loss[loss=0.8188, simple_loss=1.638, pruned_loss=6.588, over 385750.61 frames.], batch size: 35, lr: 3.00e-03
2022-05-03 11:43:04,752 INFO [train.py:715] (2/8) Epoch 0, batch 150, loss[loss=0.3565, simple_loss=0.713, pruned_loss=6.633, over 4747.00 frames.], tot_loss[loss=0.6315, simple_loss=1.263, pruned_loss=6.6, over 516254.21 frames.], batch size: 16, lr: 3.00e-03
2022-05-03 11:43:43,117 INFO [train.py:715] (2/8) Epoch 0, batch 200, loss[loss=0.3196, simple_loss=0.6393, pruned_loss=6.578, over 4822.00 frames.], tot_loss[loss=0.5318, simple_loss=1.064, pruned_loss=6.585, over 617028.52 frames.], batch size: 27, lr: 3.00e-03
2022-05-03 11:44:22,058 INFO [train.py:715] (2/8) Epoch 0, batch 250, loss[loss=0.3263, simple_loss=0.6527, pruned_loss=6.58, over 4905.00 frames.], tot_loss[loss=0.4724, simple_loss=0.9448, pruned_loss=6.6, over 695403.60 frames.], batch size: 17, lr: 3.00e-03
2022-05-03 11:45:01,529 INFO [train.py:715] (2/8) Epoch 0, batch 300, loss[loss=0.3599, simple_loss=0.7199, pruned_loss=6.794, over 4729.00 frames.], tot_loss[loss=0.4315, simple_loss=0.863, pruned_loss=6.603, over 757437.67 frames.], batch size: 16, lr: 3.00e-03
2022-05-03 11:45:41,181 INFO [train.py:715] (2/8) Epoch 0, batch 350, loss[loss=0.3582, simple_loss=0.7165, pruned_loss=6.768, over 4950.00 frames.], tot_loss[loss=0.4037, simple_loss=0.8075, pruned_loss=6.621, over 804274.89 frames.], batch size: 21, lr: 3.00e-03
2022-05-03 11:46:19,545 INFO [train.py:715] (2/8) Epoch 0, batch 400, loss[loss=0.3758, simple_loss=0.7516, pruned_loss=6.72, over 4919.00 frames.], tot_loss[loss=0.3845, simple_loss=0.769, pruned_loss=6.638, over 841941.82 frames.], batch size: 18, lr: 3.00e-03
2022-05-03 11:46:58,903 INFO [train.py:715] (2/8) Epoch 0, batch 450, loss[loss=0.3365, simple_loss=0.673, pruned_loss=6.721, over 4900.00 frames.], tot_loss[loss=0.3696, simple_loss=0.7392, pruned_loss=6.65, over 870567.37 frames.], batch size: 19, lr: 2.99e-03
2022-05-03 11:47:37,995 INFO [train.py:715] (2/8) Epoch 0, batch 500, loss[loss=0.343, simple_loss=0.686, pruned_loss=6.757, over 4783.00 frames.], tot_loss[loss=0.3571, simple_loss=0.7141, pruned_loss=6.651, over 893147.12 frames.], batch size: 18, lr: 2.99e-03
2022-05-03 11:48:17,101 INFO [train.py:715] (2/8) Epoch 0, batch 550, loss[loss=0.3307, simple_loss=0.6615, pruned_loss=6.841, over 4789.00 frames.], tot_loss[loss=0.3461, simple_loss=0.6923, pruned_loss=6.65, over 910535.52 frames.], batch size: 18, lr: 2.99e-03
2022-05-03 11:48:55,922 INFO [train.py:715] (2/8) Epoch 0, batch 600, loss[loss=0.3103, simple_loss=0.6206, pruned_loss=6.748, over 4871.00 frames.], tot_loss[loss=0.336, simple_loss=0.672, pruned_loss=6.66, over 923491.79 frames.], batch size: 20, lr: 2.99e-03
2022-05-03 11:49:35,141 INFO [train.py:715] (2/8) Epoch 0, batch 650, loss[loss=0.2741, simple_loss=0.5482, pruned_loss=6.606, over 4846.00 frames.], tot_loss[loss=0.3259, simple_loss=0.6518, pruned_loss=6.68, over 934154.99 frames.], batch size: 30, lr: 2.99e-03
2022-05-03 11:50:14,489 INFO [train.py:715] (2/8) Epoch 0, batch 700, loss[loss=0.2357, simple_loss=0.4713, pruned_loss=6.79, over 4825.00 frames.], tot_loss[loss=0.314, simple_loss=0.6281, pruned_loss=6.7, over 942817.87 frames.], batch size: 12, lr: 2.99e-03
2022-05-03 11:50:52,994 INFO [train.py:715] (2/8) Epoch 0, batch 750, loss[loss=0.285, simple_loss=0.57, pruned_loss=6.884, over 4808.00 frames.], tot_loss[loss=0.3021, simple_loss=0.6042, pruned_loss=6.712, over 948719.76 frames.], batch size: 21, lr: 2.98e-03
2022-05-03 11:51:32,774 INFO [train.py:715] (2/8) Epoch 0, batch 800, loss[loss=0.2526, simple_loss=0.5051, pruned_loss=6.727, over 4828.00 frames.], tot_loss[loss=0.2911, simple_loss=0.5823, pruned_loss=6.717, over 953407.49 frames.], batch size: 15, lr: 2.98e-03
2022-05-03 11:52:12,738 INFO [train.py:715] (2/8) Epoch 0, batch 850, loss[loss=0.2572, simple_loss=0.5144, pruned_loss=6.75, over 4980.00 frames.], tot_loss[loss=0.2806, simple_loss=0.5613, pruned_loss=6.718, over 957757.50 frames.], batch size: 33, lr: 2.98e-03
2022-05-03 11:52:51,633 INFO [train.py:715] (2/8) Epoch 0, batch 900, loss[loss=0.2227, simple_loss=0.4453, pruned_loss=6.727, over 4901.00 frames.], tot_loss[loss=0.2702, simple_loss=0.5404, pruned_loss=6.716, over 960511.58 frames.], batch size: 19, lr: 2.98e-03
2022-05-03 11:53:30,223 INFO [train.py:715] (2/8) Epoch 0, batch 950, loss[loss=0.2278, simple_loss=0.4556, pruned_loss=6.652, over 4936.00 frames.], tot_loss[loss=0.259, simple_loss=0.518, pruned_loss=6.706, over 963057.46 frames.], batch size: 23, lr: 2.97e-03
2022-05-03 11:54:09,536 INFO [train.py:715] (2/8) Epoch 0, batch 1000, loss[loss=0.2642, simple_loss=0.5284, pruned_loss=6.779, over 4888.00 frames.], tot_loss[loss=0.2508, simple_loss=0.5016, pruned_loss=6.707, over 965657.64 frames.], batch size: 39, lr: 2.97e-03
2022-05-03 11:54:48,894 INFO [train.py:715] (2/8) Epoch 0, batch 1050, loss[loss=0.2157, simple_loss=0.4314, pruned_loss=6.746, over 4978.00 frames.], tot_loss[loss=0.2439, simple_loss=0.4879, pruned_loss=6.707, over 967667.17 frames.], batch size: 25, lr: 2.97e-03
2022-05-03 11:55:27,468 INFO [train.py:715] (2/8) Epoch 0, batch 1100, loss[loss=0.2105, simple_loss=0.4211, pruned_loss=6.773, over 4902.00 frames.], tot_loss[loss=0.2365, simple_loss=0.473, pruned_loss=6.705, over 968274.61 frames.], batch size: 19, lr: 2.96e-03
2022-05-03 11:56:07,476 INFO [train.py:715] (2/8) Epoch 0, batch 1150, loss[loss=0.2028, simple_loss=0.4055, pruned_loss=6.773, over 4898.00 frames.], tot_loss[loss=0.2307, simple_loss=0.4615, pruned_loss=6.71, over 969097.01 frames.], batch size: 17, lr: 2.96e-03
2022-05-03 11:56:47,809 INFO [train.py:715] (2/8) Epoch 0, batch 1200, loss[loss=0.2215, simple_loss=0.443, pruned_loss=6.796, over 4743.00 frames.], tot_loss[loss=0.2261, simple_loss=0.4521, pruned_loss=6.715, over 969420.07 frames.], batch size: 16, lr: 2.96e-03
2022-05-03 11:57:28,439 INFO [train.py:715] (2/8) Epoch 0, batch 1250, loss[loss=0.2013, simple_loss=0.4025, pruned_loss=6.678, over 4989.00 frames.], tot_loss[loss=0.2217, simple_loss=0.4434, pruned_loss=6.713, over 969538.44 frames.], batch size: 14, lr: 2.95e-03
2022-05-03 11:58:07,334 INFO [train.py:715] (2/8) Epoch 0, batch 1300, loss[loss=0.2049, simple_loss=0.4097, pruned_loss=6.79, over 4930.00 frames.], tot_loss[loss=0.2166, simple_loss=0.4332, pruned_loss=6.714, over 969638.09 frames.], batch size: 23, lr: 2.95e-03
2022-05-03 11:58:47,746 INFO [train.py:715] (2/8) Epoch 0, batch 1350, loss[loss=0.2168, simple_loss=0.4336, pruned_loss=6.727, over 4880.00 frames.], tot_loss[loss=0.2123, simple_loss=0.4246, pruned_loss=6.713, over 970806.84 frames.], batch size: 32, lr: 2.95e-03
2022-05-03 11:59:28,708 INFO [train.py:715] (2/8) Epoch 0, batch 1400, loss[loss=0.1683, simple_loss=0.3366, pruned_loss=6.653, over 4860.00 frames.], tot_loss[loss=0.2088, simple_loss=0.4177, pruned_loss=6.714, over 970583.96 frames.], batch size: 20, lr: 2.94e-03
2022-05-03 12:00:09,328 INFO [train.py:715] (2/8) Epoch 0, batch 1450, loss[loss=0.2012, simple_loss=0.4025, pruned_loss=6.807, over 4798.00 frames.], tot_loss[loss=0.2059, simple_loss=0.4117, pruned_loss=6.711, over 970033.91 frames.], batch size: 25, lr: 2.94e-03
2022-05-03 12:00:48,846 INFO [train.py:715] (2/8) Epoch 0, batch 1500, loss[loss=0.2086, simple_loss=0.4171, pruned_loss=6.785, over 4789.00 frames.], tot_loss[loss=0.2028, simple_loss=0.4056, pruned_loss=6.709, over 970291.70 frames.], batch size: 18, lr: 2.94e-03
2022-05-03 12:01:29,919 INFO [train.py:715] (2/8) Epoch 0, batch 1550, loss[loss=0.2293, simple_loss=0.4586, pruned_loss=6.804, over 4874.00 frames.], tot_loss[loss=0.2008, simple_loss=0.4016, pruned_loss=6.709, over 971827.93 frames.], batch size: 20, lr: 2.93e-03
2022-05-03 12:02:11,266 INFO [train.py:715] (2/8) Epoch 0, batch 1600, loss[loss=0.1944, simple_loss=0.3887, pruned_loss=6.711, over 4792.00 frames.], tot_loss[loss=0.1982, simple_loss=0.3965, pruned_loss=6.701, over 971252.57 frames.], batch size: 21, lr: 2.93e-03
2022-05-03 12:02:51,039 INFO [train.py:715] (2/8) Epoch 0, batch 1650, loss[loss=0.1858, simple_loss=0.3715, pruned_loss=6.536, over 4873.00 frames.], tot_loss[loss=0.1961, simple_loss=0.3922, pruned_loss=6.699, over 970747.85 frames.], batch size: 32, lr: 2.92e-03
2022-05-03 12:03:32,807 INFO [train.py:715] (2/8) Epoch 0, batch 1700, loss[loss=0.1976, simple_loss=0.3952, pruned_loss=6.644, over 4854.00 frames.], tot_loss[loss=0.1944, simple_loss=0.3888, pruned_loss=6.694, over 970993.52 frames.], batch size: 30, lr: 2.92e-03
2022-05-03 12:04:14,550 INFO [train.py:715] (2/8) Epoch 0, batch 1750, loss[loss=0.2138, simple_loss=0.4275, pruned_loss=6.659, over 4783.00 frames.], tot_loss[loss=0.193, simple_loss=0.386, pruned_loss=6.692, over 971910.30 frames.], batch size: 14, lr: 2.91e-03
2022-05-03 12:04:56,006 INFO [train.py:715] (2/8) Epoch 0, batch 1800, loss[loss=0.1915, simple_loss=0.3829, pruned_loss=6.687, over 4921.00 frames.], tot_loss[loss=0.1913, simple_loss=0.3826, pruned_loss=6.684, over 972424.07 frames.], batch size: 23, lr: 2.91e-03
2022-05-03 12:05:36,578 INFO [train.py:715] (2/8) Epoch 0, batch 1850, loss[loss=0.216, simple_loss=0.4321, pruned_loss=6.682, over 4801.00 frames.], tot_loss[loss=0.1901, simple_loss=0.3801, pruned_loss=6.678, over 971491.12 frames.], batch size: 21, lr: 2.91e-03
2022-05-03 12:06:18,613 INFO [train.py:715] (2/8) Epoch 0, batch 1900, loss[loss=0.1823, simple_loss=0.3646, pruned_loss=6.669, over 4865.00 frames.], tot_loss[loss=0.1884, simple_loss=0.3768, pruned_loss=6.674, over 971431.36 frames.], batch size: 32, lr: 2.90e-03
2022-05-03 12:07:00,149 INFO [train.py:715] (2/8) Epoch 0, batch 1950, loss[loss=0.1963, simple_loss=0.3926, pruned_loss=6.62, over 4961.00 frames.], tot_loss[loss=0.1869, simple_loss=0.3738, pruned_loss=6.671, over 972126.39 frames.], batch size: 39, lr: 2.90e-03
2022-05-03 12:07:38,873 INFO [train.py:715] (2/8) Epoch 0, batch 2000, loss[loss=0.1725, simple_loss=0.345, pruned_loss=6.619, over 4842.00 frames.], tot_loss[loss=0.1853, simple_loss=0.3705, pruned_loss=6.665, over 972763.39 frames.], batch size: 15, lr: 2.89e-03
2022-05-03 12:08:19,994 INFO [train.py:715] (2/8) Epoch 0, batch 2050, loss[loss=0.1968, simple_loss=0.3936, pruned_loss=6.816, over 4978.00 frames.], tot_loss[loss=0.185, simple_loss=0.3699, pruned_loss=6.661, over 972858.35 frames.], batch size: 14, lr: 2.89e-03
2022-05-03 12:09:00,592 INFO [train.py:715] (2/8) Epoch 0, batch 2100, loss[loss=0.18, simple_loss=0.3601, pruned_loss=6.642, over 4797.00 frames.], tot_loss[loss=0.1836, simple_loss=0.3672, pruned_loss=6.659, over 972022.85 frames.], batch size: 24, lr: 2.88e-03
2022-05-03 12:09:41,209 INFO [train.py:715] (2/8) Epoch 0, batch 2150, loss[loss=0.171, simple_loss=0.3421, pruned_loss=6.61, over 4992.00 frames.], tot_loss[loss=0.1824, simple_loss=0.3647, pruned_loss=6.661, over 973127.95 frames.], batch size: 16, lr: 2.88e-03
2022-05-03 12:10:20,505 INFO [train.py:715] (2/8) Epoch 0, batch 2200, loss[loss=0.2024, simple_loss=0.4048, pruned_loss=6.796, over 4819.00 frames.], tot_loss[loss=0.181, simple_loss=0.3621, pruned_loss=6.664, over 972818.91 frames.], batch size: 15, lr: 2.87e-03
2022-05-03 12:11:01,491 INFO [train.py:715] (2/8) Epoch 0, batch 2250, loss[loss=0.1765, simple_loss=0.3531, pruned_loss=6.67, over 4809.00 frames.], tot_loss[loss=0.1805, simple_loss=0.3611, pruned_loss=6.663, over 971865.07 frames.], batch size: 21, lr: 2.86e-03
2022-05-03 12:11:42,777 INFO [train.py:715] (2/8) Epoch 0, batch 2300, loss[loss=0.1856, simple_loss=0.3711, pruned_loss=6.655, over 4955.00 frames.], tot_loss[loss=0.1799, simple_loss=0.3598, pruned_loss=6.664, over 972376.62 frames.], batch size: 29, lr: 2.86e-03
2022-05-03 12:12:22,376 INFO [train.py:715] (2/8) Epoch 0, batch 2350, loss[loss=0.1722, simple_loss=0.3444, pruned_loss=6.69, over 4830.00 frames.], tot_loss[loss=0.1786, simple_loss=0.3572, pruned_loss=6.661, over 973239.69 frames.], batch size: 26, lr: 2.85e-03
2022-05-03 12:13:03,128 INFO [train.py:715] (2/8) Epoch 0, batch 2400, loss[loss=0.1812, simple_loss=0.3623, pruned_loss=6.639, over 4945.00 frames.], tot_loss[loss=0.1778, simple_loss=0.3556, pruned_loss=6.667, over 973446.93 frames.], batch size: 29, lr: 2.85e-03
2022-05-03 12:13:43,814 INFO [train.py:715] (2/8) Epoch 0, batch 2450, loss[loss=0.151, simple_loss=0.3019, pruned_loss=6.632, over 4807.00 frames.], tot_loss[loss=0.1766, simple_loss=0.3531, pruned_loss=6.668, over 972514.49 frames.], batch size: 24, lr: 2.84e-03
2022-05-03 12:14:24,678 INFO [train.py:715] (2/8) Epoch 0, batch 2500, loss[loss=0.1632, simple_loss=0.3264, pruned_loss=6.567, over 4860.00 frames.], tot_loss[loss=0.1759, simple_loss=0.3517, pruned_loss=6.664, over 972063.14 frames.], batch size: 32, lr: 2.84e-03
2022-05-03 12:15:03,908 INFO [train.py:715] (2/8) Epoch 0, batch 2550, loss[loss=0.1517, simple_loss=0.3034, pruned_loss=6.612, over 4769.00 frames.], tot_loss[loss=0.1752, simple_loss=0.3504, pruned_loss=6.663, over 971460.73 frames.], batch size: 14, lr: 2.83e-03
2022-05-03 12:15:44,622 INFO [train.py:715] (2/8) Epoch 0, batch 2600, loss[loss=0.1625, simple_loss=0.325, pruned_loss=6.612, over 4850.00 frames.], tot_loss[loss=0.1744, simple_loss=0.3487, pruned_loss=6.655, over 971623.63 frames.], batch size: 20, lr: 2.83e-03
2022-05-03 12:16:25,705 INFO [train.py:715] (2/8) Epoch 0, batch 2650, loss[loss=0.1494, simple_loss=0.2988, pruned_loss=6.623, over 4880.00 frames.], tot_loss[loss=0.173, simple_loss=0.346, pruned_loss=6.653, over 972569.24 frames.], batch size: 22, lr: 2.82e-03
2022-05-03 12:17:08,081 INFO [train.py:715] (2/8) Epoch 0, batch 2700, loss[loss=0.1613, simple_loss=0.3226, pruned_loss=6.658, over 4882.00 frames.], tot_loss[loss=0.1727, simple_loss=0.3454, pruned_loss=6.647, over 973084.31 frames.], batch size: 22, lr: 2.81e-03
2022-05-03 12:17:48,869 INFO [train.py:715] (2/8) Epoch 0, batch 2750, loss[loss=0.1513, simple_loss=0.3025, pruned_loss=6.567, over 4988.00 frames.], tot_loss[loss=0.1719, simple_loss=0.3438, pruned_loss=6.651, over 973195.44 frames.], batch size: 15, lr: 2.81e-03
2022-05-03 12:18:29,708 INFO [train.py:715] (2/8) Epoch 0, batch 2800, loss[loss=0.1687, simple_loss=0.3373, pruned_loss=6.644, over 4831.00 frames.], tot_loss[loss=0.171, simple_loss=0.3419, pruned_loss=6.646, over 973669.76 frames.], batch size: 15, lr: 2.80e-03
2022-05-03 12:19:10,273 INFO [train.py:715] (2/8) Epoch 0, batch 2850, loss[loss=0.1865, simple_loss=0.373, pruned_loss=6.633, over 4749.00 frames.], tot_loss[loss=0.171, simple_loss=0.3421, pruned_loss=6.646, over 973163.89 frames.], batch size: 19, lr: 2.80e-03
2022-05-03 12:19:49,117 INFO [train.py:715] (2/8) Epoch 0, batch 2900, loss[loss=0.1945, simple_loss=0.389, pruned_loss=6.794, over 4784.00 frames.], tot_loss[loss=0.1702, simple_loss=0.3403, pruned_loss=6.636, over 972054.65 frames.], batch size: 18, lr: 2.79e-03
2022-05-03 12:20:29,367 INFO [train.py:715] (2/8) Epoch 0, batch 2950, loss[loss=0.1776, simple_loss=0.3551, pruned_loss=6.686, over 4938.00 frames.], tot_loss[loss=0.17, simple_loss=0.34, pruned_loss=6.636, over 972194.67 frames.], batch size: 35, lr: 2.78e-03
2022-05-03 12:21:11,354 INFO [train.py:715] (2/8) Epoch 0, batch 3000, loss[loss=0.8432, simple_loss=0.3643, pruned_loss=6.611, over 4938.00 frames.], tot_loss[loss=0.2064, simple_loss=0.3398, pruned_loss=6.643, over 971797.62 frames.], batch size: 35, lr: 2.78e-03
2022-05-03 12:21:11,355 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 12:21:21,130 INFO [train.py:742] (2/8) Epoch 0, validation: loss=2.223, simple_loss=0.2788, pruned_loss=2.083, over 914524.00 frames.
2022-05-03 12:22:02,154 INFO [train.py:715] (2/8) Epoch 0, batch 3050, loss[loss=0.234, simple_loss=0.3358, pruned_loss=0.661, over 4991.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3412, pruned_loss=5.4, over 972664.25 frames.], batch size: 16, lr: 2.77e-03
2022-05-03 12:22:41,560 INFO [train.py:715] (2/8) Epoch 0, batch 3100, loss[loss=0.2003, simple_loss=0.328, pruned_loss=0.3628, over 4951.00 frames.], tot_loss[loss=0.2207, simple_loss=0.3392, pruned_loss=4.315, over 971466.99 frames.], batch size: 21, lr: 2.77e-03
2022-05-03 12:23:22,418 INFO [train.py:715] (2/8) Epoch 0, batch 3150, loss[loss=0.1712, simple_loss=0.2978, pruned_loss=0.2225, over 4844.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3378, pruned_loss=3.423, over 972393.91 frames.], batch size: 30, lr: 2.76e-03
2022-05-03 12:24:03,658 INFO [train.py:715] (2/8) Epoch 0, batch 3200, loss[loss=0.1839, simple_loss=0.327, pruned_loss=0.2033, over 4924.00 frames.], tot_loss[loss=0.2093, simple_loss=0.3354, pruned_loss=2.719, over 971879.11 frames.], batch size: 23, lr: 2.75e-03
2022-05-03 12:24:44,874 INFO [train.py:715] (2/8) Epoch 0, batch 3250, loss[loss=0.1941, simple_loss=0.3431, pruned_loss=0.2257, over 4766.00 frames.], tot_loss[loss=0.2055, simple_loss=0.3359, pruned_loss=2.169, over 971437.41 frames.], batch size: 14, lr: 2.75e-03
2022-05-03 12:25:24,108 INFO [train.py:715] (2/8) Epoch 0, batch 3300, loss[loss=0.2041, simple_loss=0.3604, pruned_loss=0.2389, over 4875.00 frames.], tot_loss[loss=0.2023, simple_loss=0.3365, pruned_loss=1.736, over 971352.38 frames.], batch size: 39, lr: 2.74e-03
2022-05-03 12:26:05,351 INFO [train.py:715] (2/8) Epoch 0, batch 3350, loss[loss=0.1842, simple_loss=0.3313, pruned_loss=0.1856, over 4984.00 frames.], tot_loss[loss=0.1976, simple_loss=0.3337, pruned_loss=1.393, over 972338.10 frames.], batch size: 15, lr: 2.73e-03
2022-05-03 12:26:46,184 INFO [train.py:715] (2/8) Epoch 0, batch 3400, loss[loss=0.2377, simple_loss=0.419, pruned_loss=0.2823, over 4902.00 frames.], tot_loss[loss=0.1947, simple_loss=0.3328, pruned_loss=1.128, over 972592.65 frames.], batch size: 19, lr: 2.73e-03
2022-05-03 12:27:25,306 INFO [train.py:715] (2/8) Epoch 0, batch 3450, loss[loss=0.17, simple_loss=0.3104, pruned_loss=0.1478, over 4938.00 frames.], tot_loss[loss=0.1934, simple_loss=0.3339, pruned_loss=0.9227, over 971951.94 frames.], batch size: 21, lr: 2.72e-03
2022-05-03 12:28:06,918 INFO [train.py:715] (2/8) Epoch 0, batch 3500, loss[loss=0.1803, simple_loss=0.3236, pruned_loss=0.1852, over 4771.00 frames.], tot_loss[loss=0.1906, simple_loss=0.3319, pruned_loss=0.7581, over 972549.94 frames.], batch size: 17, lr: 2.72e-03
2022-05-03 12:28:48,555 INFO [train.py:715] (2/8) Epoch 0, batch 3550, loss[loss=0.1954, simple_loss=0.3509, pruned_loss=0.1995, over 4964.00 frames.], tot_loss[loss=0.1894, simple_loss=0.3321, pruned_loss=0.632, over 972190.95 frames.], batch size: 24, lr: 2.71e-03
2022-05-03 12:29:29,798 INFO [train.py:715] (2/8) Epoch 0, batch 3600, loss[loss=0.188, simple_loss=0.338, pruned_loss=0.1901, over 4898.00 frames.], tot_loss[loss=0.188, simple_loss=0.3315, pruned_loss=0.5322, over 972546.00 frames.], batch size: 17, lr: 2.70e-03
2022-05-03 12:30:09,002 INFO [train.py:715] (2/8) Epoch 0, batch 3650, loss[loss=0.2051, simple_loss=0.3698, pruned_loss=0.2016, over 4959.00 frames.], tot_loss[loss=0.1865, simple_loss=0.3306, pruned_loss=0.4535, over 972631.21 frames.], batch size: 28, lr: 2.70e-03
2022-05-03 12:30:50,509 INFO [train.py:715] (2/8) Epoch 0, batch 3700, loss[loss=0.1732, simple_loss=0.313, pruned_loss=0.1666, over 4876.00 frames.], tot_loss[loss=0.1859, simple_loss=0.3307, pruned_loss=0.393, over 972462.20 frames.], batch size: 22, lr: 2.69e-03
2022-05-03 12:31:32,103 INFO [train.py:715] (2/8) Epoch 0, batch 3750, loss[loss=0.184, simple_loss=0.3289, pruned_loss=0.1957, over 4856.00 frames.], tot_loss[loss=0.1844, simple_loss=0.3293, pruned_loss=0.3436, over 972953.66 frames.], batch size: 32, lr: 2.68e-03
2022-05-03 12:32:11,305 INFO [train.py:715] (2/8) Epoch 0, batch 3800, loss[loss=0.1439, simple_loss=0.2675, pruned_loss=0.1012, over 4770.00 frames.], tot_loss[loss=0.1839, simple_loss=0.3292, pruned_loss=0.3062, over 973321.06 frames.], batch size: 19, lr: 2.68e-03
2022-05-03 12:33:05,628 INFO [train.py:715] (2/8) Epoch 0, batch 3850, loss[loss=0.1845, simple_loss=0.3332, pruned_loss=0.1795, over 4887.00 frames.], tot_loss[loss=0.1835, simple_loss=0.3293, pruned_loss=0.2772, over 973263.13 frames.], batch size: 22, lr: 2.67e-03
2022-05-03 12:33:46,696 INFO [train.py:715] (2/8) Epoch 0, batch 3900, loss[loss=0.1784, simple_loss=0.3239, pruned_loss=0.1641, over 4779.00 frames.], tot_loss[loss=0.1817, simple_loss=0.3269, pruned_loss=0.2517, over 973522.06 frames.], batch size: 18, lr: 2.66e-03
2022-05-03 12:34:26,856 INFO [train.py:715] (2/8) Epoch 0, batch 3950, loss[loss=0.1834, simple_loss=0.3316, pruned_loss=0.1762, over 4983.00 frames.], tot_loss[loss=0.1805, simple_loss=0.3255, pruned_loss=0.2314, over 974410.24 frames.], batch size: 25, lr: 2.66e-03
2022-05-03 12:35:06,662 INFO [train.py:715] (2/8) Epoch 0, batch 4000, loss[loss=0.2067, simple_loss=0.3727, pruned_loss=0.2037, over 4947.00 frames.], tot_loss[loss=0.1808, simple_loss=0.3264, pruned_loss=0.2174, over 973303.69 frames.], batch size: 24, lr: 2.65e-03
2022-05-03 12:35:47,590 INFO [train.py:715] (2/8) Epoch 0, batch 4050, loss[loss=0.1671, simple_loss=0.3054, pruned_loss=0.1442, over 4928.00 frames.], tot_loss[loss=0.1808, simple_loss=0.3266, pruned_loss=0.2068, over 973104.90 frames.], batch size: 18, lr: 2.64e-03
2022-05-03 12:36:28,807 INFO [train.py:715] (2/8) Epoch 0, batch 4100, loss[loss=0.1736, simple_loss=0.3176, pruned_loss=0.1484, over 4961.00 frames.], tot_loss[loss=0.1792, simple_loss=0.3244, pruned_loss=0.1956, over 972034.93 frames.], batch size: 29, lr: 2.64e-03
2022-05-03 12:37:07,953 INFO [train.py:715] (2/8) Epoch 0, batch 4150, loss[loss=0.1748, simple_loss=0.3175, pruned_loss=0.1603, over 4906.00 frames.], tot_loss[loss=0.1784, simple_loss=0.3232, pruned_loss=0.1876, over 972287.55 frames.], batch size: 19, lr: 2.63e-03
2022-05-03 12:37:49,186 INFO [train.py:715] (2/8) Epoch 0, batch 4200, loss[loss=0.1777, simple_loss=0.3243, pruned_loss=0.1554, over 4918.00 frames.], tot_loss[loss=0.1785, simple_loss=0.3236, pruned_loss=0.182, over 971749.62 frames.], batch size: 18, lr: 2.63e-03
2022-05-03 12:38:30,915 INFO [train.py:715] (2/8) Epoch 0, batch 4250, loss[loss=0.1696, simple_loss=0.3101, pruned_loss=0.1458, over 4804.00 frames.], tot_loss[loss=0.1779, simple_loss=0.3228, pruned_loss=0.1767, over 971744.56 frames.], batch size: 25, lr: 2.62e-03
2022-05-03 12:39:11,494 INFO [train.py:715] (2/8) Epoch 0, batch 4300, loss[loss=0.1762, simple_loss=0.3193, pruned_loss=0.1661, over 4784.00 frames.], tot_loss[loss=0.1761, simple_loss=0.3201, pruned_loss=0.17, over 973352.28 frames.], batch size: 17, lr: 2.61e-03
2022-05-03 12:39:51,571 INFO [train.py:715] (2/8) Epoch 0, batch 4350, loss[loss=0.1617, simple_loss=0.297, pruned_loss=0.1321, over 4931.00 frames.], tot_loss[loss=0.1762, simple_loss=0.3203, pruned_loss=0.1679, over 972880.62 frames.], batch size: 29, lr: 2.61e-03
2022-05-03 12:40:33,091 INFO [train.py:715] (2/8) Epoch 0, batch 4400, loss[loss=0.1799, simple_loss=0.3259, pruned_loss=0.1696, over 4849.00 frames.], tot_loss[loss=0.1752, simple_loss=0.3189, pruned_loss=0.1638, over 973149.84 frames.], batch size: 32, lr: 2.60e-03
2022-05-03 12:41:14,310 INFO [train.py:715] (2/8) Epoch 0, batch 4450, loss[loss=0.1561, simple_loss=0.2877, pruned_loss=0.1226, over 4913.00 frames.], tot_loss[loss=0.1743, simple_loss=0.3175, pruned_loss=0.1603, over 973763.52 frames.], batch size: 18, lr: 2.59e-03
2022-05-03 12:41:53,442 INFO [train.py:715] (2/8) Epoch 0, batch 4500, loss[loss=0.1689, simple_loss=0.3078, pruned_loss=0.1499, over 4818.00 frames.], tot_loss[loss=0.1747, simple_loss=0.3181, pruned_loss=0.1596, over 973950.71 frames.], batch size: 27, lr: 2.59e-03
2022-05-03 12:42:34,816 INFO [train.py:715] (2/8) Epoch 0, batch 4550, loss[loss=0.1695, simple_loss=0.3079, pruned_loss=0.1553, over 4912.00 frames.], tot_loss[loss=0.1749, simple_loss=0.3188, pruned_loss=0.1581, over 974212.12 frames.], batch size: 17, lr: 2.58e-03
2022-05-03 12:43:16,365 INFO [train.py:715] (2/8) Epoch 0, batch 4600, loss[loss=0.1498, simple_loss=0.2771, pruned_loss=0.1131, over 4777.00 frames.], tot_loss[loss=0.1743, simple_loss=0.3179, pruned_loss=0.1556, over 973437.77 frames.], batch size: 17, lr: 2.57e-03
2022-05-03 12:43:56,535 INFO [train.py:715] (2/8) Epoch 0, batch 4650, loss[loss=0.1976, simple_loss=0.3574, pruned_loss=0.1887, over 4786.00 frames.], tot_loss[loss=0.1739, simple_loss=0.3173, pruned_loss=0.1544, over 973179.61 frames.], batch size: 14, lr: 2.57e-03
2022-05-03 12:44:36,469 INFO [train.py:715] (2/8) Epoch 0, batch 4700, loss[loss=0.1685, simple_loss=0.3113, pruned_loss=0.1283, over 4974.00 frames.], tot_loss[loss=0.1732, simple_loss=0.3161, pruned_loss=0.1527, over 972997.20 frames.], batch size: 28, lr: 2.56e-03
2022-05-03 12:45:17,605 INFO [train.py:715] (2/8) Epoch 0, batch 4750, loss[loss=0.1589, simple_loss=0.2936, pruned_loss=0.1212, over 4851.00 frames.], tot_loss[loss=0.1721, simple_loss=0.3142, pruned_loss=0.1506, over 973536.20 frames.], batch size: 20, lr: 2.55e-03
2022-05-03 12:45:58,870 INFO [train.py:715] (2/8) Epoch 0, batch 4800, loss[loss=0.1617, simple_loss=0.2951, pruned_loss=0.1411, over 4951.00 frames.], tot_loss[loss=0.1722, simple_loss=0.3143, pruned_loss=0.1508, over 973830.03 frames.], batch size: 14, lr: 2.55e-03
2022-05-03 12:46:38,832 INFO [train.py:715] (2/8) Epoch 0, batch 4850, loss[loss=0.1661, simple_loss=0.3028, pruned_loss=0.1468, over 4969.00 frames.], tot_loss[loss=0.172, simple_loss=0.3142, pruned_loss=0.1496, over 973708.65 frames.], batch size: 35, lr: 2.54e-03
2022-05-03 12:47:19,637 INFO [train.py:715] (2/8) Epoch 0, batch 4900, loss[loss=0.1683, simple_loss=0.3048, pruned_loss=0.1589, over 4862.00 frames.], tot_loss[loss=0.1716, simple_loss=0.3137, pruned_loss=0.1485, over 973682.96 frames.], batch size: 16, lr: 2.54e-03
2022-05-03 12:48:01,139 INFO [train.py:715] (2/8) Epoch 0, batch 4950, loss[loss=0.1803, simple_loss=0.3287, pruned_loss=0.1596, over 4852.00 frames.], tot_loss[loss=0.1721, simple_loss=0.3145, pruned_loss=0.1489, over 973226.42 frames.], batch size: 12, lr: 2.53e-03
2022-05-03 12:48:41,419 INFO [train.py:715] (2/8) Epoch 0, batch 5000, loss[loss=0.1592, simple_loss=0.2965, pruned_loss=0.1098, over 4886.00 frames.], tot_loss[loss=0.1724, simple_loss=0.315, pruned_loss=0.1492, over 972470.36 frames.], batch size: 22, lr: 2.52e-03
2022-05-03 12:49:22,152 INFO [train.py:715] (2/8) Epoch 0, batch 5050, loss[loss=0.1574, simple_loss=0.2898, pruned_loss=0.1248, over 4975.00 frames.], tot_loss[loss=0.1722, simple_loss=0.3148, pruned_loss=0.1485, over 972839.18 frames.], batch size: 31, lr: 2.52e-03
2022-05-03 12:50:04,998 INFO [train.py:715] (2/8) Epoch 0, batch 5100, loss[loss=0.1556, simple_loss=0.2877, pruned_loss=0.118, over 4880.00 frames.], tot_loss[loss=0.1711, simple_loss=0.313, pruned_loss=0.1463, over 973022.75 frames.], batch size: 22, lr: 2.51e-03
2022-05-03 12:50:48,207 INFO [train.py:715] (2/8) Epoch 0, batch 5150, loss[loss=0.1634, simple_loss=0.301, pruned_loss=0.1288, over 4759.00 frames.], tot_loss[loss=0.1705, simple_loss=0.312, pruned_loss=0.1448, over 972641.60 frames.], batch size: 19, lr: 2.50e-03
2022-05-03 12:51:28,078 INFO [train.py:715] (2/8) Epoch 0, batch 5200, loss[loss=0.2175, simple_loss=0.395, pruned_loss=0.1996, over 4791.00 frames.], tot_loss[loss=0.1703, simple_loss=0.3119, pruned_loss=0.1437, over 972731.54 frames.], batch size: 17, lr: 2.50e-03
2022-05-03 12:52:08,694 INFO [train.py:715] (2/8) Epoch 0, batch 5250, loss[loss=0.1515, simple_loss=0.2802, pruned_loss=0.1143, over 4970.00 frames.], tot_loss[loss=0.1699, simple_loss=0.3112, pruned_loss=0.1428, over 973214.49 frames.], batch size: 28, lr: 2.49e-03
2022-05-03 12:52:49,810 INFO [train.py:715] (2/8) Epoch 0, batch 5300, loss[loss=0.1544, simple_loss=0.2857, pruned_loss=0.115, over 4936.00 frames.], tot_loss[loss=0.1701, simple_loss=0.3116, pruned_loss=0.143, over 973560.58 frames.], batch size: 21, lr: 2.49e-03
2022-05-03 12:53:30,336 INFO [train.py:715] (2/8) Epoch 0, batch 5350, loss[loss=0.1764, simple_loss=0.3222, pruned_loss=0.1525, over 4838.00 frames.], tot_loss[loss=0.1701, simple_loss=0.3116, pruned_loss=0.1427, over 973331.66 frames.], batch size: 30, lr: 2.48e-03
2022-05-03 12:54:10,017 INFO [train.py:715] (2/8) Epoch 0, batch 5400, loss[loss=0.1851, simple_loss=0.3346, pruned_loss=0.1783, over 4942.00 frames.], tot_loss[loss=0.1699, simple_loss=0.3114, pruned_loss=0.1422, over 973975.02 frames.], batch size: 21, lr: 2.47e-03
2022-05-03 12:54:50,451 INFO [train.py:715] (2/8) Epoch 0, batch 5450, loss[loss=0.1933, simple_loss=0.3476, pruned_loss=0.1946, over 4822.00 frames.], tot_loss[loss=0.1695, simple_loss=0.3107, pruned_loss=0.1418, over 974224.30 frames.], batch size: 15, lr: 2.47e-03
2022-05-03 12:55:31,406 INFO [train.py:715] (2/8) Epoch 0, batch 5500, loss[loss=0.1521, simple_loss=0.2796, pruned_loss=0.1234, over 4794.00 frames.], tot_loss[loss=0.1694, simple_loss=0.3107, pruned_loss=0.1411, over 973748.22 frames.], batch size: 21, lr: 2.46e-03
2022-05-03 12:56:11,125 INFO [train.py:715] (2/8) Epoch 0, batch 5550, loss[loss=0.1975, simple_loss=0.3571, pruned_loss=0.1898, over 4964.00 frames.], tot_loss[loss=0.1698, simple_loss=0.3113, pruned_loss=0.1415, over 973131.72 frames.], batch size: 25, lr: 2.45e-03
2022-05-03 12:56:51,159 INFO [train.py:715] (2/8) Epoch 0, batch 5600, loss[loss=0.1623, simple_loss=0.298, pruned_loss=0.1331, over 4708.00 frames.], tot_loss[loss=0.1695, simple_loss=0.3109, pruned_loss=0.1405, over 973315.68 frames.], batch size: 15, lr: 2.45e-03
2022-05-03 12:57:32,359 INFO [train.py:715] (2/8) Epoch 0, batch 5650, loss[loss=0.1709, simple_loss=0.3134, pruned_loss=0.1425, over 4897.00 frames.], tot_loss[loss=0.1695, simple_loss=0.3109, pruned_loss=0.1407, over 973206.87 frames.], batch size: 17, lr: 2.44e-03
2022-05-03 12:58:12,920 INFO [train.py:715] (2/8) Epoch 0, batch 5700, loss[loss=0.1611, simple_loss=0.298, pruned_loss=0.1209, over 4981.00 frames.], tot_loss[loss=0.169, simple_loss=0.31, pruned_loss=0.1394, over 973166.54 frames.], batch size: 24, lr: 2.44e-03
2022-05-03 12:58:52,121 INFO [train.py:715] (2/8) Epoch 0, batch 5750, loss[loss=0.1765, simple_loss=0.3221, pruned_loss=0.1544, over 4891.00 frames.], tot_loss[loss=0.1684, simple_loss=0.309, pruned_loss=0.1388, over 973264.67 frames.], batch size: 22, lr: 2.43e-03
2022-05-03 12:59:33,126 INFO [train.py:715] (2/8) Epoch 0, batch 5800, loss[loss=0.1774, simple_loss=0.3225, pruned_loss=0.1613, over 4737.00 frames.], tot_loss[loss=0.1671, simple_loss=0.307, pruned_loss=0.1363, over 973033.08 frames.], batch size: 16, lr: 2.42e-03
2022-05-03 13:00:14,317 INFO [train.py:715] (2/8) Epoch 0, batch 5850, loss[loss=0.1359, simple_loss=0.2537, pruned_loss=0.09039, over 4925.00 frames.], tot_loss[loss=0.1673, simple_loss=0.3073, pruned_loss=0.1367, over 972667.99 frames.], batch size: 29, lr: 2.42e-03
2022-05-03 13:00:54,230 INFO [train.py:715] (2/8) Epoch 0, batch 5900, loss[loss=0.1651, simple_loss=0.3024, pruned_loss=0.1385, over 4717.00 frames.], tot_loss[loss=0.1666, simple_loss=0.306, pruned_loss=0.1354, over 972556.29 frames.], batch size: 12, lr: 2.41e-03
2022-05-03 13:01:33,982 INFO [train.py:715] (2/8) Epoch 0, batch 5950, loss[loss=0.1737, simple_loss=0.3184, pruned_loss=0.1452, over 4788.00 frames.], tot_loss[loss=0.1668, simple_loss=0.3065, pruned_loss=0.1357, over 971495.27 frames.], batch size: 24, lr: 2.41e-03
2022-05-03 13:02:14,774 INFO [train.py:715] (2/8) Epoch 0, batch 6000, loss[loss=0.2962, simple_loss=0.3116, pruned_loss=0.1403, over 4962.00 frames.], tot_loss[loss=0.1674, simple_loss=0.3053, pruned_loss=0.1343, over 971165.67 frames.], batch size: 15, lr: 2.40e-03
2022-05-03 13:02:14,775 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 13:02:25,809 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.1779, simple_loss=0.2457, pruned_loss=0.05502, over 914524.00 frames.
2022-05-03 13:03:07,308 INFO [train.py:715] (2/8) Epoch 0, batch 6050, loss[loss=0.2871, simple_loss=0.2958, pruned_loss=0.1392, over 4754.00 frames.], tot_loss[loss=0.1993, simple_loss=0.3079, pruned_loss=0.1385, over 970368.78 frames.], batch size: 12, lr: 2.39e-03
2022-05-03 13:03:47,837 INFO [train.py:715] (2/8) Epoch 0, batch 6100, loss[loss=0.3013, simple_loss=0.3136, pruned_loss=0.1444, over 4772.00 frames.], tot_loss[loss=0.2184, simple_loss=0.3072, pruned_loss=0.1372, over 970807.72 frames.], batch size: 12, lr: 2.39e-03
2022-05-03 13:04:27,374 INFO [train.py:715] (2/8) Epoch 0, batch 6150, loss[loss=0.2653, simple_loss=0.2902, pruned_loss=0.1202, over 4949.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3096, pruned_loss=0.1377, over 971254.28 frames.], batch size: 15, lr: 2.38e-03
2022-05-03 13:05:08,105 INFO [train.py:715] (2/8) Epoch 0, batch 6200, loss[loss=0.2488, simple_loss=0.2836, pruned_loss=0.107, over 4858.00 frames.], tot_loss[loss=0.2477, simple_loss=0.3095, pruned_loss=0.1368, over 971276.46 frames.], batch size: 20, lr: 2.38e-03
2022-05-03 13:05:48,910 INFO [train.py:715] (2/8) Epoch 0, batch 6250, loss[loss=0.2461, simple_loss=0.2968, pruned_loss=0.09772, over 4983.00 frames.], tot_loss[loss=0.2542, simple_loss=0.308, pruned_loss=0.1344, over 971562.47 frames.], batch size: 28, lr: 2.37e-03
2022-05-03 13:06:29,108 INFO [train.py:715] (2/8) Epoch 0, batch 6300, loss[loss=0.2903, simple_loss=0.3025, pruned_loss=0.139, over 4956.00 frames.], tot_loss[loss=0.2616, simple_loss=0.3087, pruned_loss=0.1338, over 971973.21 frames.], batch size: 35, lr: 2.37e-03
2022-05-03 13:07:09,794 INFO [train.py:715] (2/8) Epoch 0, batch 6350, loss[loss=0.39, simple_loss=0.3782, pruned_loss=0.2009, over 4982.00 frames.], tot_loss[loss=0.2657, simple_loss=0.3079, pruned_loss=0.1325, over 971914.02 frames.], batch size: 24, lr: 2.36e-03
2022-05-03 13:07:50,716 INFO [train.py:715] (2/8) Epoch 0, batch 6400, loss[loss=0.2604, simple_loss=0.3045, pruned_loss=0.1081, over 4913.00 frames.], tot_loss[loss=0.2697, simple_loss=0.3081, pruned_loss=0.1317, over 972883.71 frames.], batch size: 18, lr: 2.35e-03
2022-05-03 13:08:30,730 INFO [train.py:715] (2/8) Epoch 0, batch 6450, loss[loss=0.2704, simple_loss=0.3035, pruned_loss=0.1186, over 4817.00 frames.], tot_loss[loss=0.273, simple_loss=0.3082, pruned_loss=0.1314, over 972661.89 frames.], batch size: 26, lr: 2.35e-03
2022-05-03 13:09:10,062 INFO [train.py:715] (2/8) Epoch 0, batch 6500, loss[loss=0.336, simple_loss=0.3369, pruned_loss=0.1676, over 4917.00 frames.], tot_loss[loss=0.2744, simple_loss=0.3078, pruned_loss=0.1303, over 972973.46 frames.], batch size: 18, lr: 2.34e-03
2022-05-03 13:09:50,932 INFO [train.py:715] (2/8) Epoch 0, batch 6550, loss[loss=0.2604, simple_loss=0.2999, pruned_loss=0.1104, over 4833.00 frames.], tot_loss[loss=0.2755, simple_loss=0.3072, pruned_loss=0.1295, over 972614.23 frames.], batch size: 26, lr: 2.34e-03
2022-05-03 13:10:31,731 INFO [train.py:715] (2/8) Epoch 0, batch 6600, loss[loss=0.2735, simple_loss=0.3016, pruned_loss=0.1227, over 4958.00 frames.], tot_loss[loss=0.2753, simple_loss=0.3064, pruned_loss=0.1279, over 972568.15 frames.], batch size: 35, lr: 2.33e-03
2022-05-03 13:11:11,208 INFO [train.py:715] (2/8) Epoch 0, batch 6650, loss[loss=0.2317, simple_loss=0.2658, pruned_loss=0.09884, over 4824.00 frames.], tot_loss[loss=0.277, simple_loss=0.3074, pruned_loss=0.1279, over 972402.54 frames.], batch size: 15, lr: 2.33e-03
2022-05-03 13:11:51,651 INFO [train.py:715] (2/8) Epoch 0, batch 6700, loss[loss=0.2773, simple_loss=0.3044, pruned_loss=0.1251, over 4837.00 frames.], tot_loss[loss=0.2774, simple_loss=0.3069, pruned_loss=0.1275, over 971930.38 frames.], batch size: 30, lr: 2.32e-03
2022-05-03 13:12:32,418 INFO [train.py:715] (2/8) Epoch 0, batch 6750, loss[loss=0.2424, simple_loss=0.2876, pruned_loss=0.09856, over 4765.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3073, pruned_loss=0.1274, over 971267.25 frames.], batch size: 19, lr: 2.31e-03
2022-05-03 13:13:12,495 INFO [train.py:715] (2/8) Epoch 0, batch 6800, loss[loss=0.2281, simple_loss=0.2564, pruned_loss=0.09989, over 4834.00 frames.], tot_loss[loss=0.2787, simple_loss=0.3076, pruned_loss=0.1271, over 972024.52 frames.], batch size: 13, lr: 2.31e-03
2022-05-03 13:13:52,211 INFO [train.py:715] (2/8) Epoch 0, batch 6850, loss[loss=0.3027, simple_loss=0.3256, pruned_loss=0.1399, over 4912.00 frames.], tot_loss[loss=0.28, simple_loss=0.3085, pruned_loss=0.1275, over 972060.90 frames.], batch size: 18, lr: 2.30e-03
2022-05-03 13:14:32,489 INFO [train.py:715] (2/8) Epoch 0, batch 6900, loss[loss=0.2825, simple_loss=0.307, pruned_loss=0.129, over 4938.00 frames.], tot_loss[loss=0.28, simple_loss=0.308, pruned_loss=0.1273, over 972216.95 frames.], batch size: 39, lr: 2.30e-03
2022-05-03 13:15:12,910 INFO [train.py:715] (2/8) Epoch 0, batch 6950, loss[loss=0.2197, simple_loss=0.2649, pruned_loss=0.08727, over 4884.00 frames.], tot_loss[loss=0.2788, simple_loss=0.3079, pruned_loss=0.1258, over 972311.61 frames.], batch size: 16, lr: 2.29e-03
2022-05-03 13:15:53,030 INFO [train.py:715] (2/8) Epoch 0, batch 7000, loss[loss=0.3298, simple_loss=0.3292, pruned_loss=0.1652, over 4816.00 frames.], tot_loss[loss=0.2777, simple_loss=0.3073, pruned_loss=0.1248, over 972823.68 frames.], batch size: 13, lr: 2.29e-03
2022-05-03 13:16:33,736 INFO [train.py:715] (2/8) Epoch 0, batch 7050, loss[loss=0.3702, simple_loss=0.3734, pruned_loss=0.1835, over 4891.00 frames.], tot_loss[loss=0.2769, simple_loss=0.307, pruned_loss=0.124, over 973630.17 frames.], batch size: 39, lr: 2.28e-03
2022-05-03 13:17:14,921 INFO [train.py:715] (2/8) Epoch 0, batch 7100, loss[loss=0.2618, simple_loss=0.2985, pruned_loss=0.1125, over 4908.00 frames.], tot_loss[loss=0.2776, simple_loss=0.3071, pruned_loss=0.1246, over 973068.91 frames.], batch size: 18, lr: 2.28e-03
2022-05-03 13:17:55,869 INFO [train.py:715] (2/8) Epoch 0, batch 7150, loss[loss=0.2814, simple_loss=0.3141, pruned_loss=0.1244, over 4906.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3064, pruned_loss=0.1235, over 972495.66 frames.], batch size: 17, lr: 2.27e-03
2022-05-03 13:18:35,503 INFO [train.py:715] (2/8) Epoch 0, batch 7200, loss[loss=0.2908, simple_loss=0.3163, pruned_loss=0.1326, over 4814.00 frames.], tot_loss[loss=0.276, simple_loss=0.3066, pruned_loss=0.123, over 971951.94 frames.], batch size: 26, lr: 2.27e-03
2022-05-03 13:19:16,088 INFO [train.py:715] (2/8) Epoch 0, batch 7250, loss[loss=0.2929, simple_loss=0.3112, pruned_loss=0.1373, over 4941.00 frames.], tot_loss[loss=0.2754, simple_loss=0.3061, pruned_loss=0.1226, over 971483.13 frames.], batch size: 23, lr: 2.26e-03
2022-05-03 13:19:55,966 INFO [train.py:715] (2/8) Epoch 0, batch 7300, loss[loss=0.3112, simple_loss=0.334, pruned_loss=0.1442, over 4808.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3074, pruned_loss=0.1228, over 973104.70 frames.], batch size: 25, lr: 2.26e-03
2022-05-03 13:20:36,058 INFO [train.py:715] (2/8) Epoch 0, batch 7350, loss[loss=0.2564, simple_loss=0.3089, pruned_loss=0.1019, over 4683.00 frames.], tot_loss[loss=0.2736, simple_loss=0.3051, pruned_loss=0.1213, over 973741.00 frames.], batch size: 15, lr: 2.25e-03
2022-05-03 13:21:16,435 INFO [train.py:715] (2/8) Epoch 0, batch 7400, loss[loss=0.2961, simple_loss=0.3253, pruned_loss=0.1334, over 4718.00 frames.], tot_loss[loss=0.2733, simple_loss=0.3048, pruned_loss=0.121, over 974105.89 frames.], batch size: 15, lr: 2.24e-03
2022-05-03 13:21:57,040 INFO [train.py:715] (2/8) Epoch 0, batch 7450, loss[loss=0.2889, simple_loss=0.3182, pruned_loss=0.1298, over 4781.00 frames.], tot_loss[loss=0.2727, simple_loss=0.3047, pruned_loss=0.1204, over 972237.34 frames.], batch size: 14, lr: 2.24e-03
2022-05-03 13:22:36,841 INFO [train.py:715] (2/8) Epoch 0, batch 7500, loss[loss=0.2482, simple_loss=0.2798, pruned_loss=0.1083, over 4976.00 frames.], tot_loss[loss=0.2713, simple_loss=0.3036, pruned_loss=0.1195, over 972209.33 frames.], batch size: 24, lr: 2.23e-03
2022-05-03 13:23:16,569 INFO [train.py:715] (2/8) Epoch 0, batch 7550, loss[loss=0.2533, simple_loss=0.291, pruned_loss=0.1078, over 4758.00 frames.], tot_loss[loss=0.2729, simple_loss=0.3046, pruned_loss=0.1207, over 971224.61 frames.], batch size: 19, lr: 2.23e-03
2022-05-03 13:23:57,048 INFO [train.py:715] (2/8) Epoch 0, batch 7600, loss[loss=0.3352, simple_loss=0.3356, pruned_loss=0.1674, over 4785.00 frames.], tot_loss[loss=0.2737, simple_loss=0.3054, pruned_loss=0.1211, over 971956.07 frames.], batch size: 17, lr: 2.22e-03
2022-05-03 13:24:37,497 INFO [train.py:715] (2/8) Epoch 0, batch 7650, loss[loss=0.3018, simple_loss=0.3247, pruned_loss=0.1395, over 4847.00 frames.], tot_loss[loss=0.2718, simple_loss=0.3041, pruned_loss=0.1197, over 971469.03 frames.], batch size: 15, lr: 2.22e-03
2022-05-03 13:25:16,992 INFO [train.py:715] (2/8) Epoch 0, batch 7700, loss[loss=0.2378, simple_loss=0.2817, pruned_loss=0.09697, over 4944.00 frames.], tot_loss[loss=0.271, simple_loss=0.3036, pruned_loss=0.1192, over 972177.62 frames.], batch size: 21, lr: 2.21e-03
2022-05-03 13:25:57,319 INFO [train.py:715] (2/8) Epoch 0, batch 7750, loss[loss=0.2441, simple_loss=0.2898, pruned_loss=0.09918, over 4940.00 frames.], tot_loss[loss=0.27, simple_loss=0.3037, pruned_loss=0.1182, over 972043.95 frames.], batch size: 29, lr: 2.21e-03
2022-05-03 13:26:38,370 INFO [train.py:715] (2/8) Epoch 0, batch 7800, loss[loss=0.2731, simple_loss=0.3039, pruned_loss=0.1212, over 4820.00 frames.], tot_loss[loss=0.2718, simple_loss=0.3053, pruned_loss=0.1192, over 971803.94 frames.], batch size: 13, lr: 2.20e-03
2022-05-03 13:27:18,715 INFO [train.py:715] (2/8) Epoch 0, batch 7850, loss[loss=0.2742, simple_loss=0.3008, pruned_loss=0.1238, over 4866.00 frames.], tot_loss[loss=0.2724, simple_loss=0.3055, pruned_loss=0.1197, over 972131.07 frames.], batch size: 32, lr: 2.20e-03
2022-05-03 13:27:58,877 INFO [train.py:715] (2/8) Epoch 0, batch 7900, loss[loss=0.2678, simple_loss=0.3094, pruned_loss=0.1132, over 4988.00 frames.], tot_loss[loss=0.2715, simple_loss=0.3049, pruned_loss=0.119, over 971871.63 frames.], batch size: 28, lr: 2.19e-03
2022-05-03 13:28:39,523 INFO [train.py:715] (2/8) Epoch 0, batch 7950, loss[loss=0.2063, simple_loss=0.2467, pruned_loss=0.08296, over 4790.00 frames.], tot_loss[loss=0.2687, simple_loss=0.3033, pruned_loss=0.1171, over 972189.25 frames.], batch size: 12, lr: 2.19e-03
2022-05-03 13:29:22,240 INFO [train.py:715] (2/8) Epoch 0, batch 8000, loss[loss=0.258, simple_loss=0.293, pruned_loss=0.1116, over 4835.00 frames.], tot_loss[loss=0.2697, simple_loss=0.3039, pruned_loss=0.1178, over 972660.10 frames.], batch size: 15, lr: 2.18e-03
2022-05-03 13:30:02,103 INFO [train.py:715] (2/8) Epoch 0, batch 8050, loss[loss=0.2799, simple_loss=0.31, pruned_loss=0.1249, over 4989.00 frames.], tot_loss[loss=0.2685, simple_loss=0.3034, pruned_loss=0.1168, over 973107.96 frames.], batch size: 25, lr: 2.18e-03
2022-05-03 13:30:41,973 INFO [train.py:715] (2/8) Epoch 0, batch 8100, loss[loss=0.3073, simple_loss=0.3214, pruned_loss=0.1466, over 4854.00 frames.], tot_loss[loss=0.2665, simple_loss=0.3021, pruned_loss=0.1154, over 972887.72 frames.], batch size: 32, lr: 2.17e-03
2022-05-03 13:31:22,993 INFO [train.py:715] (2/8) Epoch 0, batch 8150, loss[loss=0.2496, simple_loss=0.2918, pruned_loss=0.1037, over 4872.00 frames.], tot_loss[loss=0.2654, simple_loss=0.3016, pruned_loss=0.1146, over 973260.23 frames.], batch size: 22, lr: 2.17e-03
2022-05-03 13:32:02,637 INFO [train.py:715] (2/8) Epoch 0, batch 8200, loss[loss=0.3135, simple_loss=0.3248, pruned_loss=0.1511, over 4972.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3029, pruned_loss=0.1162, over 972666.90 frames.], batch size: 15, lr: 2.16e-03
2022-05-03 13:32:42,136 INFO [train.py:715] (2/8) Epoch 0, batch 8250, loss[loss=0.277, simple_loss=0.3135, pruned_loss=0.1203, over 4815.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3022, pruned_loss=0.1151, over 973130.46 frames.], batch size: 21, lr: 2.16e-03
2022-05-03 13:33:22,997 INFO [train.py:715] (2/8) Epoch 0, batch 8300, loss[loss=0.2264, simple_loss=0.2781, pruned_loss=0.08741, over 4898.00 frames.], tot_loss[loss=0.2635, simple_loss=0.2999, pruned_loss=0.1136, over 972641.47 frames.], batch size: 19, lr: 2.15e-03
2022-05-03 13:34:03,429 INFO [train.py:715] (2/8) Epoch 0, batch 8350, loss[loss=0.3031, simple_loss=0.3244, pruned_loss=0.1409, over 4959.00 frames.], tot_loss[loss=0.2645, simple_loss=0.301, pruned_loss=0.114, over 972346.39 frames.], batch size: 35, lr: 2.15e-03
2022-05-03 13:34:43,092 INFO [train.py:715] (2/8) Epoch 0, batch 8400, loss[loss=0.3318, simple_loss=0.3332, pruned_loss=0.1652, over 4828.00 frames.], tot_loss[loss=0.2622, simple_loss=0.2996, pruned_loss=0.1124, over 972206.87 frames.], batch size: 15, lr: 2.15e-03
2022-05-03 13:35:23,378 INFO [train.py:715] (2/8) Epoch 0, batch 8450, loss[loss=0.2818, simple_loss=0.307, pruned_loss=0.1283, over 4784.00 frames.], tot_loss[loss=0.2634, simple_loss=0.3006, pruned_loss=0.1131, over 972198.25 frames.], batch size: 18, lr: 2.14e-03
2022-05-03 13:36:04,639 INFO [train.py:715] (2/8) Epoch 0, batch 8500, loss[loss=0.3046, simple_loss=0.3285, pruned_loss=0.1403, over 4872.00 frames.], tot_loss[loss=0.264, simple_loss=0.3008, pruned_loss=0.1136, over 972114.10 frames.], batch size: 16, lr: 2.14e-03
2022-05-03 13:36:45,700 INFO [train.py:715] (2/8) Epoch 0, batch 8550, loss[loss=0.2509, simple_loss=0.285, pruned_loss=0.1084, over 4933.00 frames.], tot_loss[loss=0.2638, simple_loss=0.3006, pruned_loss=0.1135, over 972581.12 frames.], batch size: 23, lr: 2.13e-03
2022-05-03 13:37:25,351 INFO [train.py:715] (2/8) Epoch 0, batch 8600, loss[loss=0.2024, simple_loss=0.265, pruned_loss=0.06989, over 4944.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3, pruned_loss=0.1124, over 973292.15 frames.], batch size: 21, lr: 2.13e-03
2022-05-03 13:38:06,733 INFO [train.py:715] (2/8) Epoch 0, batch 8650, loss[loss=0.2344, simple_loss=0.271, pruned_loss=0.09894, over 4738.00 frames.], tot_loss[loss=0.2602, simple_loss=0.2981, pruned_loss=0.1112, over 971949.18 frames.], batch size: 16, lr: 2.12e-03
2022-05-03 13:38:47,675 INFO [train.py:715] (2/8) Epoch 0, batch 8700, loss[loss=0.2481, simple_loss=0.2899, pruned_loss=0.1032, over 4825.00 frames.], tot_loss[loss=0.263, simple_loss=0.3, pruned_loss=0.113, over 971988.27 frames.], batch size: 15, lr: 2.12e-03
2022-05-03 13:39:27,759 INFO [train.py:715] (2/8) Epoch 0, batch 8750, loss[loss=0.2264, simple_loss=0.2805, pruned_loss=0.08613, over 4920.00 frames.], tot_loss[loss=0.263, simple_loss=0.3002, pruned_loss=0.1129, over 971419.04 frames.], batch size: 29, lr: 2.11e-03
2022-05-03 13:40:08,238 INFO [train.py:715] (2/8) Epoch 0, batch 8800, loss[loss=0.2544, simple_loss=0.2784, pruned_loss=0.1152, over 4701.00 frames.], tot_loss[loss=0.2642, simple_loss=0.3013, pruned_loss=0.1136, over 972174.76 frames.], batch size: 15, lr: 2.11e-03
2022-05-03 13:40:48,804 INFO [train.py:715] (2/8) Epoch 0, batch 8850, loss[loss=0.2095, simple_loss=0.2701, pruned_loss=0.07441, over 4954.00 frames.], tot_loss[loss=0.2629, simple_loss=0.3004, pruned_loss=0.1127, over 972196.22 frames.], batch size: 24, lr: 2.10e-03
2022-05-03 13:41:29,535 INFO [train.py:715] (2/8) Epoch 0, batch 8900, loss[loss=0.2628, simple_loss=0.302, pruned_loss=0.1118, over 4953.00 frames.], tot_loss[loss=0.2617, simple_loss=0.2996, pruned_loss=0.112, over 972048.47 frames.], batch size: 39, lr: 2.10e-03
2022-05-03 13:42:09,370 INFO [train.py:715] (2/8) Epoch 0, batch 8950, loss[loss=0.2376, simple_loss=0.2749, pruned_loss=0.1002, over 4826.00 frames.], tot_loss[loss=0.2606, simple_loss=0.2984, pruned_loss=0.1114, over 971848.88 frames.], batch size: 13, lr: 2.10e-03
2022-05-03 13:42:49,915 INFO [train.py:715] (2/8) Epoch 0, batch 9000, loss[loss=0.2628, simple_loss=0.3051, pruned_loss=0.1102, over 4825.00 frames.], tot_loss[loss=0.2598, simple_loss=0.2987, pruned_loss=0.1105, over 971645.52 frames.], batch size: 15, lr: 2.09e-03
2022-05-03 13:42:49,916 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 13:43:03,385 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.1592, simple_loss=0.2426, pruned_loss=0.03794, over 914524.00 frames.
2022-05-03 13:43:44,293 INFO [train.py:715] (2/8) Epoch 0, batch 9050, loss[loss=0.2558, simple_loss=0.2889, pruned_loss=0.1113, over 4828.00 frames.], tot_loss[loss=0.2573, simple_loss=0.2967, pruned_loss=0.1089, over 972578.33 frames.], batch size: 15, lr: 2.09e-03
2022-05-03 13:44:24,659 INFO [train.py:715] (2/8) Epoch 0, batch 9100, loss[loss=0.2171, simple_loss=0.2624, pruned_loss=0.0859, over 4785.00 frames.], tot_loss[loss=0.2557, simple_loss=0.2957, pruned_loss=0.1079, over 972436.86 frames.], batch size: 14, lr: 2.08e-03
2022-05-03 13:45:04,783 INFO [train.py:715] (2/8) Epoch 0, batch 9150, loss[loss=0.2304, simple_loss=0.2802, pruned_loss=0.09033, over 4856.00 frames.], tot_loss[loss=0.2563, simple_loss=0.2962, pruned_loss=0.1082, over 971636.17 frames.], batch size: 32, lr: 2.08e-03
2022-05-03 13:45:44,984 INFO [train.py:715] (2/8) Epoch 0, batch 9200, loss[loss=0.2302, simple_loss=0.2794, pruned_loss=0.09049, over 4830.00 frames.], tot_loss[loss=0.2578, simple_loss=0.2972, pruned_loss=0.1092, over 971624.31 frames.], batch size: 13, lr: 2.07e-03
2022-05-03 13:46:26,067 INFO [train.py:715] (2/8) Epoch 0, batch 9250, loss[loss=0.2486, simple_loss=0.2765, pruned_loss=0.1104, over 4781.00 frames.], tot_loss[loss=0.2587, simple_loss=0.2982, pruned_loss=0.1096, over 971938.55 frames.], batch size: 12, lr: 2.07e-03
2022-05-03 13:47:06,381 INFO [train.py:715] (2/8) Epoch 0, batch 9300, loss[loss=0.2377, simple_loss=0.2797, pruned_loss=0.09785, over 4808.00 frames.], tot_loss[loss=0.2578, simple_loss=0.2978, pruned_loss=0.1089, over 971742.66 frames.], batch size: 26, lr: 2.06e-03
2022-05-03 13:47:45,664 INFO [train.py:715] (2/8) Epoch 0, batch 9350, loss[loss=0.2607, simple_loss=0.2907, pruned_loss=0.1154, over 4810.00 frames.], tot_loss[loss=0.2579, simple_loss=0.2973, pruned_loss=0.1093, over 971571.75 frames.], batch size: 21, lr: 2.06e-03
2022-05-03 13:48:27,104 INFO [train.py:715] (2/8) Epoch 0, batch 9400, loss[loss=0.3147, simple_loss=0.3372, pruned_loss=0.1461, over 4798.00 frames.], tot_loss[loss=0.2585, simple_loss=0.2975, pruned_loss=0.1097, over 971238.49 frames.], batch size: 14, lr: 2.06e-03
2022-05-03 13:49:07,593 INFO [train.py:715] (2/8) Epoch 0, batch 9450, loss[loss=0.2611, simple_loss=0.3028, pruned_loss=0.1097, over 4904.00 frames.], tot_loss[loss=0.2569, simple_loss=0.2967, pruned_loss=0.1086, over 971129.60 frames.], batch size: 17, lr: 2.05e-03
2022-05-03 13:49:47,921 INFO [train.py:715] (2/8) Epoch 0, batch 9500, loss[loss=0.2313, simple_loss=0.2877, pruned_loss=0.08746, over 4791.00 frames.], tot_loss[loss=0.2567, simple_loss=0.297, pruned_loss=0.1083, over 971572.84 frames.], batch size: 21, lr: 2.05e-03
2022-05-03 13:50:28,010 INFO [train.py:715] (2/8) Epoch 0, batch 9550, loss[loss=0.2368, simple_loss=0.2847, pruned_loss=0.09449, over 4802.00 frames.], tot_loss[loss=0.2581, simple_loss=0.298, pruned_loss=0.109, over 972034.86 frames.], batch size: 24, lr: 2.04e-03
2022-05-03 13:51:08,458 INFO [train.py:715] (2/8) Epoch 0, batch 9600, loss[loss=0.2298, simple_loss=0.2856, pruned_loss=0.08693, over 4799.00 frames.], tot_loss[loss=0.257, simple_loss=0.2976, pruned_loss=0.1082, over 971961.31 frames.], batch size: 24, lr: 2.04e-03
2022-05-03 13:51:48,906 INFO [train.py:715] (2/8) Epoch 0, batch 9650, loss[loss=0.275, simple_loss=0.3019, pruned_loss=0.124, over 4984.00 frames.], tot_loss[loss=0.2543, simple_loss=0.2958, pruned_loss=0.1064, over 971187.67 frames.], batch size: 28, lr: 2.03e-03
2022-05-03 13:52:27,664 INFO [train.py:715] (2/8) Epoch 0, batch 9700, loss[loss=0.2731, simple_loss=0.306, pruned_loss=0.1201, over 4877.00 frames.], tot_loss[loss=0.2553, simple_loss=0.2962, pruned_loss=0.1072, over 970902.44 frames.], batch size: 32, lr: 2.03e-03
2022-05-03 13:53:08,241 INFO [train.py:715] (2/8) Epoch 0, batch 9750, loss[loss=0.2449, simple_loss=0.2932, pruned_loss=0.09833, over 4812.00 frames.], tot_loss[loss=0.255, simple_loss=0.2962, pruned_loss=0.1069, over 971439.10 frames.], batch size: 25, lr: 2.03e-03
2022-05-03 13:53:47,969 INFO [train.py:715] (2/8) Epoch 0, batch 9800, loss[loss=0.2522, simple_loss=0.2985, pruned_loss=0.1029, over 4754.00 frames.], tot_loss[loss=0.2549, simple_loss=0.2963, pruned_loss=0.1067, over 971020.81 frames.], batch size: 19, lr: 2.02e-03
2022-05-03 13:54:27,872 INFO [train.py:715] (2/8) Epoch 0, batch 9850, loss[loss=0.3006, simple_loss=0.3136, pruned_loss=0.1438, over 4887.00 frames.], tot_loss[loss=0.2555, simple_loss=0.297, pruned_loss=0.107, over 970893.04 frames.], batch size: 16, lr: 2.02e-03
2022-05-03 13:55:07,635 INFO [train.py:715] (2/8) Epoch 0, batch 9900, loss[loss=0.2496, simple_loss=0.293, pruned_loss=0.103, over 4722.00 frames.], tot_loss[loss=0.2541, simple_loss=0.2964, pruned_loss=0.1059, over 971434.75 frames.], batch size: 16, lr: 2.01e-03
2022-05-03 13:55:47,704 INFO [train.py:715] (2/8) Epoch 0, batch 9950, loss[loss=0.2278, simple_loss=0.2773, pruned_loss=0.08919, over 4836.00 frames.], tot_loss[loss=0.2543, simple_loss=0.2965, pruned_loss=0.1061, over 971680.10 frames.], batch size: 12, lr: 2.01e-03
2022-05-03 13:56:27,931 INFO [train.py:715] (2/8) Epoch 0, batch 10000, loss[loss=0.2182, simple_loss=0.2681, pruned_loss=0.08413, over 4835.00 frames.], tot_loss[loss=0.2531, simple_loss=0.2951, pruned_loss=0.1056, over 971627.05 frames.], batch size: 15, lr: 2.01e-03
2022-05-03 13:57:07,307 INFO [train.py:715] (2/8) Epoch 0, batch 10050, loss[loss=0.2664, simple_loss=0.2949, pruned_loss=0.1189, over 4909.00 frames.], tot_loss[loss=0.2537, simple_loss=0.2951, pruned_loss=0.1061, over 971680.06 frames.], batch size: 17, lr: 2.00e-03
2022-05-03 13:57:47,858 INFO [train.py:715] (2/8) Epoch 0, batch 10100, loss[loss=0.2566, simple_loss=0.2939, pruned_loss=0.1097, over 4931.00 frames.], tot_loss[loss=0.2527, simple_loss=0.295, pruned_loss=0.1052, over 971716.09 frames.], batch size: 29, lr: 2.00e-03
2022-05-03 13:58:27,706 INFO [train.py:715] (2/8) Epoch 0, batch 10150, loss[loss=0.2453, simple_loss=0.2921, pruned_loss=0.09928, over 4868.00 frames.], tot_loss[loss=0.2518, simple_loss=0.2944, pruned_loss=0.1046, over 971439.70 frames.], batch size: 16, lr: 1.99e-03
2022-05-03 13:59:07,277 INFO [train.py:715] (2/8) Epoch 0, batch 10200, loss[loss=0.3099, simple_loss=0.3353, pruned_loss=0.1423, over 4848.00 frames.], tot_loss[loss=0.252, simple_loss=0.2947, pruned_loss=0.1046, over 971696.97 frames.], batch size: 20, lr: 1.99e-03
2022-05-03 13:59:47,207 INFO [train.py:715] (2/8) Epoch 0, batch 10250, loss[loss=0.2734, simple_loss=0.2974, pruned_loss=0.1247, over 4833.00 frames.], tot_loss[loss=0.2529, simple_loss=0.2954, pruned_loss=0.1052, over 972139.55 frames.], batch size: 13, lr: 1.99e-03
2022-05-03 14:00:28,078 INFO [train.py:715] (2/8) Epoch 0, batch 10300, loss[loss=0.2313, simple_loss=0.2814, pruned_loss=0.09066, over 4802.00 frames.], tot_loss[loss=0.2515, simple_loss=0.2941, pruned_loss=0.1045, over 972611.44 frames.], batch size: 25, lr: 1.98e-03
2022-05-03 14:01:08,328 INFO [train.py:715] (2/8) Epoch 0, batch 10350, loss[loss=0.2391, simple_loss=0.2836, pruned_loss=0.09728, over 4765.00 frames.], tot_loss[loss=0.2502, simple_loss=0.2932, pruned_loss=0.1036, over 972251.30 frames.], batch size: 12, lr: 1.98e-03
2022-05-03 14:01:47,785 INFO [train.py:715] (2/8) Epoch 0, batch 10400, loss[loss=0.2646, simple_loss=0.2879, pruned_loss=0.1207, over 4954.00 frames.], tot_loss[loss=0.251, simple_loss=0.2939, pruned_loss=0.1041, over 971283.94 frames.], batch size: 14, lr: 1.97e-03
2022-05-03 14:02:28,422 INFO [train.py:715] (2/8) Epoch 0, batch 10450, loss[loss=0.2693, simple_loss=0.3086, pruned_loss=0.115, over 4836.00 frames.], tot_loss[loss=0.2501, simple_loss=0.2935, pruned_loss=0.1033, over 971332.74 frames.], batch size: 30, lr: 1.97e-03
2022-05-03 14:03:09,167 INFO [train.py:715] (2/8) Epoch 0, batch 10500, loss[loss=0.2261, simple_loss=0.2686, pruned_loss=0.09185, over 4846.00 frames.], tot_loss[loss=0.2496, simple_loss=0.2931, pruned_loss=0.1031, over 971480.96 frames.], batch size: 13, lr: 1.97e-03
2022-05-03 14:03:48,863 INFO [train.py:715] (2/8) Epoch 0, batch 10550, loss[loss=0.232, simple_loss=0.2698, pruned_loss=0.0971, over 4959.00 frames.], tot_loss[loss=0.2501, simple_loss=0.2936, pruned_loss=0.1033, over 972177.08 frames.], batch size: 15, lr: 1.96e-03
2022-05-03 14:04:28,873 INFO [train.py:715] (2/8) Epoch 0, batch 10600, loss[loss=0.2634, simple_loss=0.2869, pruned_loss=0.12, over 4985.00 frames.], tot_loss[loss=0.249, simple_loss=0.293, pruned_loss=0.1025, over 972107.17 frames.], batch size: 33, lr: 1.96e-03
2022-05-03 14:05:09,745 INFO [train.py:715] (2/8) Epoch 0, batch 10650, loss[loss=0.227, simple_loss=0.2772, pruned_loss=0.08841, over 4877.00 frames.], tot_loss[loss=0.2494, simple_loss=0.2932, pruned_loss=0.1028, over 972818.67 frames.], batch size: 22, lr: 1.96e-03
2022-05-03 14:05:49,651 INFO [train.py:715] (2/8) Epoch 0, batch 10700, loss[loss=0.1913, simple_loss=0.2504, pruned_loss=0.06611, over 4776.00 frames.], tot_loss[loss=0.2498, simple_loss=0.2936, pruned_loss=0.103, over 973626.70 frames.], batch size: 12, lr: 1.95e-03
2022-05-03 14:06:29,541 INFO [train.py:715] (2/8) Epoch 0, batch 10750, loss[loss=0.2635, simple_loss=0.3004, pruned_loss=0.1132, over 4852.00 frames.], tot_loss[loss=0.2497, simple_loss=0.2936, pruned_loss=0.1029, over 973010.71 frames.], batch size: 34, lr: 1.95e-03
2022-05-03 14:07:09,717 INFO [train.py:715] (2/8) Epoch 0, batch 10800, loss[loss=0.2521, simple_loss=0.2903, pruned_loss=0.107, over 4817.00 frames.], tot_loss[loss=0.2492, simple_loss=0.2932, pruned_loss=0.1026, over 972097.51 frames.], batch size: 25, lr: 1.94e-03
2022-05-03 14:07:50,562 INFO [train.py:715] (2/8) Epoch 0, batch 10850, loss[loss=0.2671, simple_loss=0.3157, pruned_loss=0.1093, over 4857.00 frames.], tot_loss[loss=0.2484, simple_loss=0.2929, pruned_loss=0.102, over 972448.38 frames.], batch size: 32, lr: 1.94e-03
2022-05-03 14:08:30,097 INFO [train.py:715] (2/8) Epoch 0, batch 10900, loss[loss=0.2198, simple_loss=0.2817, pruned_loss=0.07891, over 4987.00 frames.], tot_loss[loss=0.2469, simple_loss=0.2918, pruned_loss=0.101, over 972655.63 frames.], batch size: 14, lr: 1.94e-03
2022-05-03 14:09:10,039 INFO [train.py:715] (2/8) Epoch 0, batch 10950, loss[loss=0.2612, simple_loss=0.3083, pruned_loss=0.1071, over 4839.00 frames.], tot_loss[loss=0.2468, simple_loss=0.2916, pruned_loss=0.101, over 972625.46 frames.], batch size: 15, lr: 1.93e-03
2022-05-03 14:09:50,815 INFO [train.py:715] (2/8) Epoch 0, batch 11000, loss[loss=0.2692, simple_loss=0.3078, pruned_loss=0.1152, over 4780.00 frames.], tot_loss[loss=0.2466, simple_loss=0.2911, pruned_loss=0.101, over 972412.05 frames.], batch size: 14, lr: 1.93e-03
2022-05-03 14:10:31,094 INFO [train.py:715] (2/8) Epoch 0, batch 11050, loss[loss=0.267, simple_loss=0.3091, pruned_loss=0.1125, over 4769.00 frames.], tot_loss[loss=0.2481, simple_loss=0.2921, pruned_loss=0.102, over 972561.70 frames.], batch size: 19, lr: 1.93e-03
2022-05-03 14:11:11,139 INFO [train.py:715] (2/8) Epoch 0, batch 11100, loss[loss=0.2723, simple_loss=0.3231, pruned_loss=0.1107, over 4882.00 frames.], tot_loss[loss=0.2478, simple_loss=0.2922, pruned_loss=0.1017, over 972286.56 frames.], batch size: 16, lr: 1.92e-03
2022-05-03 14:11:51,015 INFO [train.py:715] (2/8) Epoch 0, batch 11150, loss[loss=0.3106, simple_loss=0.3339, pruned_loss=0.1436, over 4799.00 frames.], tot_loss[loss=0.2471, simple_loss=0.2918, pruned_loss=0.1012, over 972329.45 frames.], batch size: 25, lr: 1.92e-03
2022-05-03 14:12:31,463 INFO [train.py:715] (2/8) Epoch 0, batch 11200, loss[loss=0.2873, simple_loss=0.3273, pruned_loss=0.1237, over 4941.00 frames.], tot_loss[loss=0.2472, simple_loss=0.2916, pruned_loss=0.1014, over 971972.66 frames.], batch size: 29, lr: 1.92e-03
2022-05-03 14:13:10,944 INFO [train.py:715] (2/8) Epoch 0, batch 11250, loss[loss=0.2168, simple_loss=0.2763, pruned_loss=0.07863, over 4774.00 frames.], tot_loss[loss=0.247, simple_loss=0.2913, pruned_loss=0.1013, over 971896.19 frames.], batch size: 17, lr: 1.91e-03
2022-05-03 14:13:51,034 INFO [train.py:715] (2/8) Epoch 0, batch 11300, loss[loss=0.2641, simple_loss=0.2989, pruned_loss=0.1147, over 4875.00 frames.], tot_loss[loss=0.247, simple_loss=0.291, pruned_loss=0.1016, over 971195.84 frames.], batch size: 32, lr: 1.91e-03
2022-05-03 14:14:31,678 INFO [train.py:715] (2/8) Epoch 0, batch 11350, loss[loss=0.2685, simple_loss=0.2997, pruned_loss=0.1186, over 4827.00 frames.], tot_loss[loss=0.2445, simple_loss=0.2889, pruned_loss=0.1001, over 972788.06 frames.], batch size: 25, lr: 1.90e-03
2022-05-03 14:15:12,110 INFO [train.py:715] (2/8) Epoch 0, batch 11400, loss[loss=0.207, simple_loss=0.2712, pruned_loss=0.07136, over 4870.00 frames.], tot_loss[loss=0.2455, simple_loss=0.29, pruned_loss=0.1005, over 973621.40 frames.], batch size: 20, lr: 1.90e-03
2022-05-03 14:15:51,350 INFO [train.py:715] (2/8) Epoch 0, batch 11450, loss[loss=0.2402, simple_loss=0.294, pruned_loss=0.09319, over 4943.00 frames.], tot_loss[loss=0.2445, simple_loss=0.2894, pruned_loss=0.09981, over 973872.22 frames.], batch size: 29, lr: 1.90e-03
2022-05-03 14:16:32,010 INFO [train.py:715] (2/8) Epoch 0, batch 11500, loss[loss=0.3357, simple_loss=0.3615, pruned_loss=0.1549, over 4947.00 frames.], tot_loss[loss=0.2436, simple_loss=0.2886, pruned_loss=0.0993, over 973377.58 frames.], batch size: 21, lr: 1.89e-03
2022-05-03 14:17:12,402 INFO [train.py:715] (2/8) Epoch 0, batch 11550, loss[loss=0.2673, simple_loss=0.2969, pruned_loss=0.1188, over 4903.00 frames.], tot_loss[loss=0.2428, simple_loss=0.2881, pruned_loss=0.09871, over 973346.48 frames.], batch size: 17, lr: 1.89e-03
2022-05-03 14:17:52,478 INFO [train.py:715] (2/8) Epoch 0, batch 11600, loss[loss=0.2856, simple_loss=0.3155, pruned_loss=0.1278, over 4971.00 frames.], tot_loss[loss=0.2409, simple_loss=0.2865, pruned_loss=0.09768, over 973386.68 frames.], batch size: 35, lr: 1.89e-03
2022-05-03 14:18:32,570 INFO [train.py:715] (2/8) Epoch 0, batch 11650, loss[loss=0.2244, simple_loss=0.2751, pruned_loss=0.08681, over 4842.00 frames.], tot_loss[loss=0.2425, simple_loss=0.2878, pruned_loss=0.09862, over 972804.89 frames.], batch size: 26, lr: 1.88e-03
2022-05-03 14:19:13,487 INFO [train.py:715] (2/8) Epoch 0, batch 11700, loss[loss=0.2503, simple_loss=0.2779, pruned_loss=0.1113, over 4959.00 frames.], tot_loss[loss=0.2422, simple_loss=0.2876, pruned_loss=0.09836, over 972449.90 frames.], batch size: 35, lr: 1.88e-03
2022-05-03 14:19:53,836 INFO [train.py:715] (2/8) Epoch 0, batch 11750, loss[loss=0.28, simple_loss=0.3146, pruned_loss=0.1227, over 4976.00 frames.], tot_loss[loss=0.243, simple_loss=0.2883, pruned_loss=0.09884, over 972983.52 frames.], batch size: 15, lr: 1.88e-03
2022-05-03 14:20:34,213 INFO [train.py:715] (2/8) Epoch 0, batch 11800, loss[loss=0.1824, simple_loss=0.25, pruned_loss=0.0574, over 4963.00 frames.], tot_loss[loss=0.2427, simple_loss=0.2881, pruned_loss=0.09872, over 973043.41 frames.], batch size: 24, lr: 1.87e-03
2022-05-03 14:21:14,569 INFO [train.py:715] (2/8) Epoch 0, batch 11850, loss[loss=0.2382, simple_loss=0.2926, pruned_loss=0.09186, over 4778.00 frames.], tot_loss[loss=0.2409, simple_loss=0.2867, pruned_loss=0.09753, over 973529.68 frames.], batch size: 18, lr: 1.87e-03
2022-05-03 14:21:55,672 INFO [train.py:715] (2/8) Epoch 0, batch 11900, loss[loss=0.2309, simple_loss=0.2794, pruned_loss=0.09116, over 4771.00 frames.], tot_loss[loss=0.2395, simple_loss=0.2855, pruned_loss=0.0968, over 972961.26 frames.], batch size: 18, lr: 1.87e-03
2022-05-03 14:22:35,855 INFO [train.py:715] (2/8) Epoch 0, batch 11950, loss[loss=0.262, simple_loss=0.297, pruned_loss=0.1135, over 4891.00 frames.], tot_loss[loss=0.2399, simple_loss=0.2862, pruned_loss=0.09687, over 972261.06 frames.], batch size: 19, lr: 1.86e-03
2022-05-03 14:23:15,969 INFO [train.py:715] (2/8) Epoch 0, batch 12000, loss[loss=0.1907, simple_loss=0.26, pruned_loss=0.06068, over 4886.00 frames.], tot_loss[loss=0.2409, simple_loss=0.2874, pruned_loss=0.09724, over 972599.12 frames.], batch size: 19, lr: 1.86e-03
2022-05-03 14:23:15,970 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 14:23:31,273 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.1516, simple_loss=0.2368, pruned_loss=0.03315, over 914524.00 frames.
2022-05-03 14:24:11,263 INFO [train.py:715] (2/8) Epoch 0, batch 12050, loss[loss=0.2781, simple_loss=0.3193, pruned_loss=0.1184, over 4810.00 frames.], tot_loss[loss=0.2412, simple_loss=0.2876, pruned_loss=0.09741, over 973046.46 frames.], batch size: 21, lr: 1.86e-03
2022-05-03 14:24:51,291 INFO [train.py:715] (2/8) Epoch 0, batch 12100, loss[loss=0.2379, simple_loss=0.2866, pruned_loss=0.09457, over 4917.00 frames.], tot_loss[loss=0.2409, simple_loss=0.2872, pruned_loss=0.09729, over 972221.66 frames.], batch size: 18, lr: 1.85e-03
2022-05-03 14:25:31,588 INFO [train.py:715] (2/8) Epoch 0, batch 12150, loss[loss=0.2485, simple_loss=0.2932, pruned_loss=0.1019, over 4952.00 frames.], tot_loss[loss=0.2392, simple_loss=0.2858, pruned_loss=0.09631, over 972166.51 frames.], batch size: 35, lr: 1.85e-03
2022-05-03 14:26:11,155 INFO [train.py:715] (2/8) Epoch 0, batch 12200, loss[loss=0.2204, simple_loss=0.2806, pruned_loss=0.08006, over 4786.00 frames.], tot_loss[loss=0.2407, simple_loss=0.2872, pruned_loss=0.09713, over 972617.57 frames.], batch size: 17, lr: 1.85e-03
2022-05-03 14:26:51,056 INFO [train.py:715] (2/8) Epoch 0, batch 12250, loss[loss=0.2011, simple_loss=0.246, pruned_loss=0.07815, over 4821.00 frames.], tot_loss[loss=0.242, simple_loss=0.2883, pruned_loss=0.09786, over 972714.50 frames.], batch size: 13, lr: 1.84e-03
2022-05-03 14:27:31,543 INFO [train.py:715] (2/8) Epoch 0, batch 12300, loss[loss=0.2488, simple_loss=0.2935, pruned_loss=0.102, over 4857.00 frames.], tot_loss[loss=0.2407, simple_loss=0.2877, pruned_loss=0.09681, over 971181.19 frames.], batch size: 30, lr: 1.84e-03
2022-05-03 14:28:10,853 INFO [train.py:715] (2/8) Epoch 0, batch 12350, loss[loss=0.1959, simple_loss=0.2606, pruned_loss=0.06554, over 4886.00 frames.], tot_loss[loss=0.241, simple_loss=0.2884, pruned_loss=0.09683, over 971501.63 frames.], batch size: 19, lr: 1.84e-03
2022-05-03 14:28:50,836 INFO [train.py:715] (2/8) Epoch 0, batch 12400, loss[loss=0.248, simple_loss=0.293, pruned_loss=0.1015, over 4888.00 frames.], tot_loss[loss=0.2415, simple_loss=0.2889, pruned_loss=0.09708, over 971185.37 frames.], batch size: 22, lr: 1.83e-03
2022-05-03 14:29:31,163 INFO [train.py:715] (2/8) Epoch 0, batch 12450, loss[loss=0.228, simple_loss=0.2733, pruned_loss=0.09134, over 4969.00 frames.], tot_loss[loss=0.2427, simple_loss=0.2893, pruned_loss=0.09801, over 971434.39 frames.], batch size: 15, lr: 1.83e-03
2022-05-03 14:30:11,381 INFO [train.py:715] (2/8) Epoch 0, batch 12500, loss[loss=0.2598, simple_loss=0.3022, pruned_loss=0.1087, over 4797.00 frames.], tot_loss[loss=0.2424, simple_loss=0.2893, pruned_loss=0.09774, over 971948.77 frames.], batch size: 17, lr: 1.83e-03
2022-05-03 14:30:50,301 INFO [train.py:715] (2/8) Epoch 0, batch 12550, loss[loss=0.2443, simple_loss=0.2948, pruned_loss=0.09691, over 4764.00 frames.], tot_loss[loss=0.2424, simple_loss=0.2895, pruned_loss=0.0977, over 972623.85 frames.], batch size: 19, lr: 1.83e-03
2022-05-03 14:31:30,330 INFO [train.py:715] (2/8) Epoch 0, batch 12600, loss[loss=0.2867, simple_loss=0.3254, pruned_loss=0.124, over 4962.00 frames.], tot_loss[loss=0.2419, simple_loss=0.289, pruned_loss=0.09742, over 972761.30 frames.], batch size: 21, lr: 1.82e-03
2022-05-03 14:32:11,360 INFO [train.py:715] (2/8) Epoch 0, batch 12650, loss[loss=0.2031, simple_loss=0.2596, pruned_loss=0.07329, over 4922.00 frames.], tot_loss[loss=0.24, simple_loss=0.2873, pruned_loss=0.09631, over 972160.85 frames.], batch size: 29, lr: 1.82e-03
2022-05-03 14:32:51,081 INFO [train.py:715] (2/8) Epoch 0, batch 12700, loss[loss=0.2261, simple_loss=0.2825, pruned_loss=0.08483, over 4940.00 frames.], tot_loss[loss=0.24, simple_loss=0.2874, pruned_loss=0.09634, over 972608.53 frames.], batch size: 39, lr: 1.82e-03
2022-05-03 14:33:30,726 INFO [train.py:715] (2/8) Epoch 0, batch 12750, loss[loss=0.2873, simple_loss=0.3219, pruned_loss=0.1263, over 4817.00 frames.], tot_loss[loss=0.2405, simple_loss=0.2876, pruned_loss=0.09672, over 972731.03 frames.], batch size: 26, lr: 1.81e-03
2022-05-03 14:34:11,166 INFO [train.py:715] (2/8) Epoch 0, batch 12800, loss[loss=0.2985, simple_loss=0.3182, pruned_loss=0.1394, over 4837.00 frames.], tot_loss[loss=0.2395, simple_loss=0.2869, pruned_loss=0.09609, over 972806.50 frames.], batch size: 30, lr: 1.81e-03
2022-05-03 14:34:51,657 INFO [train.py:715] (2/8) Epoch 0, batch 12850, loss[loss=0.2371, simple_loss=0.297, pruned_loss=0.0886, over 4820.00 frames.], tot_loss[loss=0.238, simple_loss=0.286, pruned_loss=0.09496, over 972484.44 frames.], batch size: 24, lr: 1.81e-03
2022-05-03 14:35:31,478 INFO [train.py:715] (2/8) Epoch 0, batch 12900, loss[loss=0.2283, simple_loss=0.2747, pruned_loss=0.09097, over 4935.00 frames.], tot_loss[loss=0.2377, simple_loss=0.2857, pruned_loss=0.09483, over 972199.80 frames.], batch size: 29, lr: 1.80e-03
2022-05-03 14:36:11,735 INFO [train.py:715] (2/8) Epoch 0, batch 12950, loss[loss=0.2408, simple_loss=0.2951, pruned_loss=0.09326, over 4937.00 frames.], tot_loss[loss=0.2377, simple_loss=0.2859, pruned_loss=0.09472, over 972860.17 frames.], batch size: 24, lr: 1.80e-03
2022-05-03 14:36:52,260 INFO [train.py:715] (2/8) Epoch 0, batch 13000, loss[loss=0.2133, simple_loss=0.2823, pruned_loss=0.07211, over 4983.00 frames.], tot_loss[loss=0.238, simple_loss=0.2859, pruned_loss=0.09502, over 972872.23 frames.], batch size: 14, lr: 1.80e-03
2022-05-03 14:37:32,732 INFO [train.py:715] (2/8) Epoch 0, batch 13050, loss[loss=0.1899, simple_loss=0.2506, pruned_loss=0.06457, over 4994.00 frames.], tot_loss[loss=0.2372, simple_loss=0.2851, pruned_loss=0.09471, over 972325.73 frames.], batch size: 14, lr: 1.79e-03
2022-05-03 14:38:12,061 INFO [train.py:715] (2/8) Epoch 0, batch 13100, loss[loss=0.2208, simple_loss=0.2707, pruned_loss=0.08547, over 4853.00 frames.], tot_loss[loss=0.2359, simple_loss=0.2839, pruned_loss=0.09396, over 972214.49 frames.], batch size: 32, lr: 1.79e-03
2022-05-03 14:38:52,501 INFO [train.py:715] (2/8) Epoch 0, batch 13150, loss[loss=0.1964, simple_loss=0.2506, pruned_loss=0.07108, over 4848.00 frames.], tot_loss[loss=0.2358, simple_loss=0.2838, pruned_loss=0.09387, over 972263.86 frames.], batch size: 26, lr: 1.79e-03
2022-05-03 14:39:32,994 INFO [train.py:715] (2/8) Epoch 0, batch 13200, loss[loss=0.2404, simple_loss=0.2721, pruned_loss=0.1043, over 4975.00 frames.], tot_loss[loss=0.2365, simple_loss=0.2851, pruned_loss=0.09396, over 972742.20 frames.], batch size: 14, lr: 1.79e-03
2022-05-03 14:40:12,559 INFO [train.py:715] (2/8) Epoch 0, batch 13250, loss[loss=0.2004, simple_loss=0.2584, pruned_loss=0.07117, over 4847.00 frames.], tot_loss[loss=0.2375, simple_loss=0.2854, pruned_loss=0.09482, over 973346.19 frames.], batch size: 30, lr: 1.78e-03
2022-05-03 14:40:52,436 INFO [train.py:715] (2/8) Epoch 0, batch 13300, loss[loss=0.2098, simple_loss=0.2759, pruned_loss=0.07182, over 4792.00 frames.], tot_loss[loss=0.2366, simple_loss=0.2847, pruned_loss=0.09431, over 973573.74 frames.], batch size: 21, lr: 1.78e-03
2022-05-03 14:41:32,814 INFO [train.py:715] (2/8) Epoch 0, batch 13350, loss[loss=0.2523, simple_loss=0.3001, pruned_loss=0.1023, over 4859.00 frames.], tot_loss[loss=0.2378, simple_loss=0.2856, pruned_loss=0.09503, over 973205.50 frames.], batch size: 32, lr: 1.78e-03
2022-05-03 14:42:13,144 INFO [train.py:715] (2/8) Epoch 0, batch 13400, loss[loss=0.2006, simple_loss=0.2552, pruned_loss=0.07297, over 4832.00 frames.], tot_loss[loss=0.2377, simple_loss=0.2856, pruned_loss=0.09489, over 973484.35 frames.], batch size: 12, lr: 1.77e-03
2022-05-03 14:42:52,939 INFO [train.py:715] (2/8) Epoch 0, batch 13450, loss[loss=0.2555, simple_loss=0.2996, pruned_loss=0.1057, over 4899.00 frames.], tot_loss[loss=0.2364, simple_loss=0.2846, pruned_loss=0.09414, over 973617.94 frames.], batch size: 18, lr: 1.77e-03
2022-05-03 14:43:33,171 INFO [train.py:715] (2/8) Epoch 0, batch 13500, loss[loss=0.2225, simple_loss=0.2769, pruned_loss=0.08406, over 4944.00 frames.], tot_loss[loss=0.2366, simple_loss=0.2847, pruned_loss=0.09425, over 974342.83 frames.], batch size: 29, lr: 1.77e-03
2022-05-03 14:44:13,347 INFO [train.py:715] (2/8) Epoch 0, batch 13550, loss[loss=0.2145, simple_loss=0.2801, pruned_loss=0.07441, over 4896.00 frames.], tot_loss[loss=0.2366, simple_loss=0.285, pruned_loss=0.09408, over 974003.63 frames.], batch size: 19, lr: 1.77e-03
2022-05-03 14:44:52,788 INFO [train.py:715] (2/8) Epoch 0, batch 13600, loss[loss=0.2141, simple_loss=0.2716, pruned_loss=0.07832, over 4792.00 frames.], tot_loss[loss=0.2356, simple_loss=0.2844, pruned_loss=0.09339, over 973585.09 frames.], batch size: 17, lr: 1.76e-03
2022-05-03 14:45:32,763 INFO [train.py:715] (2/8) Epoch 0, batch 13650, loss[loss=0.2843, simple_loss=0.3293, pruned_loss=0.1197, over 4978.00 frames.], tot_loss[loss=0.2363, simple_loss=0.2847, pruned_loss=0.09392, over 973757.50 frames.], batch size: 39, lr: 1.76e-03
2022-05-03 14:46:12,695 INFO [train.py:715] (2/8) Epoch 0, batch 13700, loss[loss=0.2714, simple_loss=0.3004, pruned_loss=0.1212, over 4787.00 frames.], tot_loss[loss=0.2373, simple_loss=0.2854, pruned_loss=0.09464, over 972998.00 frames.], batch size: 14, lr: 1.76e-03
2022-05-03 14:46:52,704 INFO [train.py:715] (2/8) Epoch 0, batch 13750, loss[loss=0.1825, simple_loss=0.2485, pruned_loss=0.05821, over 4968.00 frames.], tot_loss[loss=0.2366, simple_loss=0.2847, pruned_loss=0.09427, over 972931.66 frames.], batch size: 24, lr: 1.75e-03
2022-05-03 14:47:32,539 INFO [train.py:715] (2/8) Epoch 0, batch 13800, loss[loss=0.1907, simple_loss=0.2598, pruned_loss=0.06083, over 4975.00 frames.], tot_loss[loss=0.237, simple_loss=0.2851, pruned_loss=0.09443, over 972669.04 frames.], batch size: 14, lr: 1.75e-03
2022-05-03 14:48:12,868 INFO [train.py:715] (2/8) Epoch 0, batch 13850, loss[loss=0.2112, simple_loss=0.2607, pruned_loss=0.08081, over 4935.00 frames.], tot_loss[loss=0.2369, simple_loss=0.2848, pruned_loss=0.0945, over 972645.01 frames.], batch size: 18, lr: 1.75e-03
2022-05-03 14:48:53,728 INFO [train.py:715] (2/8) Epoch 0, batch 13900, loss[loss=0.1805, simple_loss=0.2417, pruned_loss=0.05965, over 4882.00 frames.], tot_loss[loss=0.2364, simple_loss=0.2848, pruned_loss=0.09401, over 972651.98 frames.], batch size: 32, lr: 1.75e-03
2022-05-03 14:49:33,776 INFO [train.py:715] (2/8) Epoch 0, batch 13950, loss[loss=0.2118, simple_loss=0.266, pruned_loss=0.07876, over 4815.00 frames.], tot_loss[loss=0.2351, simple_loss=0.2841, pruned_loss=0.09306, over 972544.84 frames.], batch size: 26, lr: 1.74e-03
2022-05-03 14:50:14,373 INFO [train.py:715] (2/8) Epoch 0, batch 14000, loss[loss=0.2913, simple_loss=0.3207, pruned_loss=0.131, over 4776.00 frames.], tot_loss[loss=0.236, simple_loss=0.2847, pruned_loss=0.09368, over 972290.33 frames.], batch size: 18, lr: 1.74e-03
2022-05-03 14:50:55,231 INFO [train.py:715] (2/8) Epoch 0, batch 14050, loss[loss=0.2437, simple_loss=0.2903, pruned_loss=0.09858, over 4934.00 frames.], tot_loss[loss=0.2359, simple_loss=0.2843, pruned_loss=0.09373, over 972132.51 frames.], batch size: 23, lr: 1.74e-03
2022-05-03 14:51:35,691 INFO [train.py:715] (2/8) Epoch 0, batch 14100, loss[loss=0.2814, simple_loss=0.3254, pruned_loss=0.1187, over 4837.00 frames.], tot_loss[loss=0.2384, simple_loss=0.2867, pruned_loss=0.09511, over 971899.41 frames.], batch size: 15, lr: 1.73e-03
2022-05-03 14:52:16,202 INFO [train.py:715] (2/8) Epoch 0, batch 14150, loss[loss=0.1966, simple_loss=0.2617, pruned_loss=0.06579, over 4949.00 frames.], tot_loss[loss=0.2378, simple_loss=0.2861, pruned_loss=0.09475, over 972521.57 frames.], batch size: 21, lr: 1.73e-03
2022-05-03 14:52:56,855 INFO [train.py:715] (2/8) Epoch 0, batch 14200, loss[loss=0.195, simple_loss=0.2514, pruned_loss=0.06929, over 4967.00 frames.], tot_loss[loss=0.2372, simple_loss=0.2855, pruned_loss=0.0944, over 970854.32 frames.], batch size: 35, lr: 1.73e-03
2022-05-03 14:53:37,706 INFO [train.py:715] (2/8) Epoch 0, batch 14250, loss[loss=0.2278, simple_loss=0.2872, pruned_loss=0.08415, over 4781.00 frames.], tot_loss[loss=0.2373, simple_loss=0.2855, pruned_loss=0.09462, over 970656.07 frames.], batch size: 17, lr: 1.73e-03
2022-05-03 14:54:18,409 INFO [train.py:715] (2/8) Epoch 0, batch 14300, loss[loss=0.2893, simple_loss=0.322, pruned_loss=0.1282, over 4856.00 frames.], tot_loss[loss=0.2365, simple_loss=0.2851, pruned_loss=0.09391, over 971371.43 frames.], batch size: 38, lr: 1.72e-03
2022-05-03 14:54:59,477 INFO [train.py:715] (2/8) Epoch 0, batch 14350, loss[loss=0.2208, simple_loss=0.2675, pruned_loss=0.08705, over 4750.00 frames.], tot_loss[loss=0.2354, simple_loss=0.2843, pruned_loss=0.09325, over 971633.29 frames.], batch size: 16, lr: 1.72e-03
2022-05-03 14:55:40,709 INFO [train.py:715] (2/8) Epoch 0, batch 14400, loss[loss=0.2273, simple_loss=0.2753, pruned_loss=0.08967, over 4919.00 frames.], tot_loss[loss=0.2339, simple_loss=0.2832, pruned_loss=0.09234, over 971506.59 frames.], batch size: 19, lr: 1.72e-03
2022-05-03 14:56:21,179 INFO [train.py:715] (2/8) Epoch 0, batch 14450, loss[loss=0.2161, simple_loss=0.2749, pruned_loss=0.07864, over 4911.00 frames.], tot_loss[loss=0.2326, simple_loss=0.2821, pruned_loss=0.09148, over 971052.13 frames.], batch size: 17, lr: 1.72e-03
2022-05-03 14:57:01,533 INFO [train.py:715] (2/8) Epoch 0, batch 14500, loss[loss=0.2165, simple_loss=0.2707, pruned_loss=0.08116, over 4713.00 frames.], tot_loss[loss=0.2337, simple_loss=0.2831, pruned_loss=0.0921, over 971404.15 frames.], batch size: 15, lr: 1.71e-03
2022-05-03 14:57:42,201 INFO [train.py:715] (2/8) Epoch 0, batch 14550, loss[loss=0.2195, simple_loss=0.2737, pruned_loss=0.08264, over 4854.00 frames.], tot_loss[loss=0.2327, simple_loss=0.2825, pruned_loss=0.09141, over 972137.08 frames.], batch size: 20, lr: 1.71e-03
2022-05-03 14:58:22,168 INFO [train.py:715] (2/8) Epoch 0, batch 14600, loss[loss=0.2104, simple_loss=0.2629, pruned_loss=0.07891, over 4859.00 frames.], tot_loss[loss=0.2337, simple_loss=0.2833, pruned_loss=0.09208, over 972228.13 frames.], batch size: 20, lr: 1.71e-03
2022-05-03 14:59:01,450 INFO [train.py:715] (2/8) Epoch 0, batch 14650, loss[loss=0.2461, simple_loss=0.2976, pruned_loss=0.0973, over 4821.00 frames.], tot_loss[loss=0.2333, simple_loss=0.283, pruned_loss=0.09184, over 971411.01 frames.], batch size: 15, lr: 1.70e-03
2022-05-03 14:59:41,806 INFO [train.py:715] (2/8) Epoch 0, batch 14700, loss[loss=0.2435, simple_loss=0.2953, pruned_loss=0.09588, over 4805.00 frames.], tot_loss[loss=0.2349, simple_loss=0.2839, pruned_loss=0.09296, over 971539.18 frames.], batch size: 25, lr: 1.70e-03
2022-05-03 15:00:22,079 INFO [train.py:715] (2/8) Epoch 0, batch 14750, loss[loss=0.2509, simple_loss=0.2985, pruned_loss=0.1016, over 4942.00 frames.], tot_loss[loss=0.2355, simple_loss=0.2842, pruned_loss=0.0934, over 972709.56 frames.], batch size: 29, lr: 1.70e-03
2022-05-03 15:01:02,112 INFO [train.py:715] (2/8) Epoch 0, batch 14800, loss[loss=0.2432, simple_loss=0.2899, pruned_loss=0.09828, over 4865.00 frames.], tot_loss[loss=0.2357, simple_loss=0.2846, pruned_loss=0.09338, over 973607.38 frames.], batch size: 20, lr: 1.70e-03
2022-05-03 15:01:41,991 INFO [train.py:715] (2/8) Epoch 0, batch 14850, loss[loss=0.221, simple_loss=0.2696, pruned_loss=0.08621, over 4833.00 frames.], tot_loss[loss=0.2343, simple_loss=0.2836, pruned_loss=0.0925, over 973110.15 frames.], batch size: 27, lr: 1.69e-03
2022-05-03 15:02:22,715 INFO [train.py:715] (2/8) Epoch 0, batch 14900, loss[loss=0.2071, simple_loss=0.2567, pruned_loss=0.07876, over 4900.00 frames.], tot_loss[loss=0.2338, simple_loss=0.283, pruned_loss=0.09228, over 972323.98 frames.], batch size: 32, lr: 1.69e-03
2022-05-03 15:03:02,599 INFO [train.py:715] (2/8) Epoch 0, batch 14950, loss[loss=0.2051, simple_loss=0.2605, pruned_loss=0.07486, over 4971.00 frames.], tot_loss[loss=0.2338, simple_loss=0.2832, pruned_loss=0.09225, over 972476.27 frames.], batch size: 14, lr: 1.69e-03
2022-05-03 15:03:42,032 INFO [train.py:715] (2/8) Epoch 0, batch 15000, loss[loss=0.2656, simple_loss=0.2929, pruned_loss=0.1191, over 4741.00 frames.], tot_loss[loss=0.2336, simple_loss=0.283, pruned_loss=0.09208, over 972549.06 frames.], batch size: 16, lr: 1.69e-03
2022-05-03 15:03:42,033 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 15:03:53,633 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.1454, simple_loss=0.2314, pruned_loss=0.02968, over 914524.00 frames.
2022-05-03 15:04:32,988 INFO [train.py:715] (2/8) Epoch 0, batch 15050, loss[loss=0.2185, simple_loss=0.278, pruned_loss=0.0795, over 4801.00 frames.], tot_loss[loss=0.2331, simple_loss=0.283, pruned_loss=0.09154, over 972457.67 frames.], batch size: 21, lr: 1.68e-03
2022-05-03 15:05:13,555 INFO [train.py:715] (2/8) Epoch 0, batch 15100, loss[loss=0.2361, simple_loss=0.288, pruned_loss=0.09207, over 4774.00 frames.], tot_loss[loss=0.2321, simple_loss=0.2821, pruned_loss=0.09102, over 972467.45 frames.], batch size: 14, lr: 1.68e-03
2022-05-03 15:05:53,891 INFO [train.py:715] (2/8) Epoch 0, batch 15150, loss[loss=0.2194, simple_loss=0.267, pruned_loss=0.08586, over 4701.00 frames.], tot_loss[loss=0.2301, simple_loss=0.2809, pruned_loss=0.08963, over 972083.52 frames.], batch size: 15, lr: 1.68e-03
2022-05-03 15:06:33,817 INFO [train.py:715] (2/8) Epoch 0, batch 15200, loss[loss=0.2012, simple_loss=0.2406, pruned_loss=0.08094, over 4808.00 frames.], tot_loss[loss=0.2298, simple_loss=0.2802, pruned_loss=0.08974, over 971485.55 frames.], batch size: 12, lr: 1.68e-03
2022-05-03 15:07:13,387 INFO [train.py:715] (2/8) Epoch 0, batch 15250, loss[loss=0.1879, simple_loss=0.2402, pruned_loss=0.06784, over 4921.00 frames.], tot_loss[loss=0.2301, simple_loss=0.2806, pruned_loss=0.08981, over 971970.99 frames.], batch size: 17, lr: 1.67e-03
2022-05-03 15:07:53,249 INFO [train.py:715] (2/8) Epoch 0, batch 15300, loss[loss=0.2059, simple_loss=0.2666, pruned_loss=0.07264, over 4639.00 frames.], tot_loss[loss=0.2284, simple_loss=0.2791, pruned_loss=0.08883, over 972294.13 frames.], batch size: 13, lr: 1.67e-03
2022-05-03 15:08:33,605 INFO [train.py:715] (2/8) Epoch 0, batch 15350, loss[loss=0.236, simple_loss=0.2818, pruned_loss=0.09507, over 4759.00 frames.], tot_loss[loss=0.2276, simple_loss=0.2786, pruned_loss=0.08831, over 972033.94 frames.], batch size: 19, lr: 1.67e-03
2022-05-03 15:09:13,454 INFO [train.py:715] (2/8) Epoch 0, batch 15400, loss[loss=0.2143, simple_loss=0.2783, pruned_loss=0.07517, over 4874.00 frames.], tot_loss[loss=0.2294, simple_loss=0.2801, pruned_loss=0.08939, over 972399.96 frames.], batch size: 22, lr: 1.67e-03
2022-05-03 15:09:53,906 INFO [train.py:715] (2/8) Epoch 0, batch 15450, loss[loss=0.2816, simple_loss=0.3181, pruned_loss=0.1225, over 4966.00 frames.], tot_loss[loss=0.2292, simple_loss=0.2797, pruned_loss=0.08934, over 971303.30 frames.], batch size: 35, lr: 1.66e-03
2022-05-03 15:10:33,367 INFO [train.py:715] (2/8) Epoch 0, batch 15500, loss[loss=0.2123, simple_loss=0.2731, pruned_loss=0.07574, over 4971.00 frames.], tot_loss[loss=0.2293, simple_loss=0.28, pruned_loss=0.08931, over 971330.35 frames.], batch size: 28, lr: 1.66e-03
2022-05-03 15:11:12,565 INFO [train.py:715] (2/8) Epoch 0, batch 15550, loss[loss=0.3029, simple_loss=0.3334, pruned_loss=0.1362, over 4831.00 frames.], tot_loss[loss=0.2287, simple_loss=0.2798, pruned_loss=0.08885, over 972696.68 frames.], batch size: 26, lr: 1.66e-03
2022-05-03 15:11:52,060 INFO [train.py:715] (2/8) Epoch 0, batch 15600, loss[loss=0.1854, simple_loss=0.2451, pruned_loss=0.06291, over 4783.00 frames.], tot_loss[loss=0.2283, simple_loss=0.2795, pruned_loss=0.0885, over 972319.11 frames.], batch size: 18, lr: 1.66e-03
2022-05-03 15:12:31,509 INFO [train.py:715] (2/8) Epoch 0, batch 15650, loss[loss=0.2388, simple_loss=0.2919, pruned_loss=0.09286, over 4967.00 frames.], tot_loss[loss=0.2294, simple_loss=0.2809, pruned_loss=0.08899, over 972904.04 frames.], batch size: 24, lr: 1.65e-03
2022-05-03 15:13:11,295 INFO [train.py:715] (2/8) Epoch 0, batch 15700, loss[loss=0.2615, simple_loss=0.3033, pruned_loss=0.1098, over 4756.00 frames.], tot_loss[loss=0.2283, simple_loss=0.2806, pruned_loss=0.08803, over 972326.87 frames.], batch size: 18, lr: 1.65e-03
2022-05-03 15:13:50,897 INFO [train.py:715] (2/8) Epoch 0, batch 15750, loss[loss=0.2584, simple_loss=0.2996, pruned_loss=0.1086, over 4981.00 frames.], tot_loss[loss=0.2291, simple_loss=0.2808, pruned_loss=0.08871, over 971934.32 frames.], batch size: 35, lr: 1.65e-03
2022-05-03 15:14:30,841 INFO [train.py:715] (2/8) Epoch 0, batch 15800, loss[loss=0.2541, simple_loss=0.285, pruned_loss=0.1116, over 4789.00 frames.], tot_loss[loss=0.2286, simple_loss=0.2803, pruned_loss=0.08844, over 972113.78 frames.], batch size: 18, lr: 1.65e-03
2022-05-03 15:15:10,658 INFO [train.py:715] (2/8) Epoch 0, batch 15850, loss[loss=0.2427, simple_loss=0.2921, pruned_loss=0.09661, over 4902.00 frames.], tot_loss[loss=0.2285, simple_loss=0.2799, pruned_loss=0.08856, over 972363.97 frames.], batch size: 19, lr: 1.65e-03
2022-05-03 15:15:50,235 INFO [train.py:715] (2/8) Epoch 0, batch 15900, loss[loss=0.1814, simple_loss=0.2438, pruned_loss=0.05947, over 4817.00 frames.], tot_loss[loss=0.229, simple_loss=0.2801, pruned_loss=0.08893, over 972083.65 frames.], batch size: 25, lr: 1.64e-03
2022-05-03 15:16:30,469 INFO [train.py:715] (2/8) Epoch 0, batch 15950, loss[loss=0.179, simple_loss=0.2433, pruned_loss=0.05738, over 4829.00 frames.], tot_loss[loss=0.228, simple_loss=0.2792, pruned_loss=0.08836, over 972272.87 frames.], batch size: 13, lr: 1.64e-03
2022-05-03 15:17:12,818 INFO [train.py:715] (2/8) Epoch 0, batch 16000, loss[loss=0.2192, simple_loss=0.2706, pruned_loss=0.08387, over 4745.00 frames.], tot_loss[loss=0.2273, simple_loss=0.2787, pruned_loss=0.08794, over 972163.40 frames.], batch size: 19, lr: 1.64e-03
2022-05-03 15:17:52,701 INFO [train.py:715] (2/8) Epoch 0, batch 16050, loss[loss=0.2715, simple_loss=0.3128, pruned_loss=0.1151, over 4825.00 frames.], tot_loss[loss=0.2275, simple_loss=0.2786, pruned_loss=0.08821, over 971639.83 frames.], batch size: 26, lr: 1.64e-03
2022-05-03 15:18:33,251 INFO [train.py:715] (2/8) Epoch 0, batch 16100, loss[loss=0.2516, simple_loss=0.2975, pruned_loss=0.1029, over 4900.00 frames.], tot_loss[loss=0.2268, simple_loss=0.2782, pruned_loss=0.0877, over 971266.82 frames.], batch size: 17, lr: 1.63e-03
2022-05-03 15:19:13,424 INFO [train.py:715] (2/8) Epoch 0, batch 16150, loss[loss=0.1972, simple_loss=0.2676, pruned_loss=0.06345, over 4939.00 frames.], tot_loss[loss=0.2268, simple_loss=0.2782, pruned_loss=0.08773, over 972003.04 frames.], batch size: 29, lr: 1.63e-03
2022-05-03 15:19:52,892 INFO [train.py:715] (2/8) Epoch 0, batch 16200, loss[loss=0.2508, simple_loss=0.2918, pruned_loss=0.1049, over 4817.00 frames.], tot_loss[loss=0.227, simple_loss=0.2786, pruned_loss=0.0877, over 971899.91 frames.], batch size: 27, lr: 1.63e-03
2022-05-03 15:20:32,314 INFO [train.py:715] (2/8) Epoch 0, batch 16250, loss[loss=0.2033, simple_loss=0.2735, pruned_loss=0.06656, over 4890.00 frames.], tot_loss[loss=0.2276, simple_loss=0.2788, pruned_loss=0.08814, over 971601.15 frames.], batch size: 22, lr: 1.63e-03
2022-05-03 15:21:12,240 INFO [train.py:715] (2/8) Epoch 0, batch 16300, loss[loss=0.1869, simple_loss=0.2502, pruned_loss=0.06181, over 4795.00 frames.], tot_loss[loss=0.2276, simple_loss=0.2789, pruned_loss=0.08811, over 971349.24 frames.], batch size: 24, lr: 1.62e-03
2022-05-03 15:21:51,668 INFO [train.py:715] (2/8) Epoch 0, batch 16350, loss[loss=0.2388, simple_loss=0.2983, pruned_loss=0.08965, over 4793.00 frames.], tot_loss[loss=0.2283, simple_loss=0.2796, pruned_loss=0.08852, over 972038.08 frames.], batch size: 14, lr: 1.62e-03
2022-05-03 15:22:31,094 INFO [train.py:715] (2/8) Epoch 0, batch 16400, loss[loss=0.2008, simple_loss=0.2566, pruned_loss=0.07249, over 4947.00 frames.], tot_loss[loss=0.2265, simple_loss=0.2782, pruned_loss=0.08745, over 972069.71 frames.], batch size: 35, lr: 1.62e-03
2022-05-03 15:23:11,041 INFO [train.py:715] (2/8) Epoch 0, batch 16450, loss[loss=0.2166, simple_loss=0.2712, pruned_loss=0.08104, over 4931.00 frames.], tot_loss[loss=0.2257, simple_loss=0.2775, pruned_loss=0.08694, over 972751.10 frames.], batch size: 23, lr: 1.62e-03
2022-05-03 15:23:51,577 INFO [train.py:715] (2/8) Epoch 0, batch 16500, loss[loss=0.2821, simple_loss=0.3152, pruned_loss=0.1245, over 4894.00 frames.], tot_loss[loss=0.2267, simple_loss=0.2783, pruned_loss=0.08759, over 972622.95 frames.], batch size: 22, lr: 1.62e-03
2022-05-03 15:24:31,532 INFO [train.py:715] (2/8) Epoch 0, batch 16550, loss[loss=0.2444, simple_loss=0.2878, pruned_loss=0.1005, over 4859.00 frames.], tot_loss[loss=0.2255, simple_loss=0.2776, pruned_loss=0.08667, over 972718.10 frames.], batch size: 20, lr: 1.61e-03
2022-05-03 15:25:11,221 INFO [train.py:715] (2/8) Epoch 0, batch 16600, loss[loss=0.2069, simple_loss=0.2647, pruned_loss=0.07455, over 4912.00 frames.], tot_loss[loss=0.224, simple_loss=0.2764, pruned_loss=0.0858, over 973252.34 frames.], batch size: 18, lr: 1.61e-03
2022-05-03 15:25:50,672 INFO [train.py:715] (2/8) Epoch 0, batch 16650, loss[loss=0.2205, simple_loss=0.2589, pruned_loss=0.09105, over 4846.00 frames.], tot_loss[loss=0.2246, simple_loss=0.2771, pruned_loss=0.08606, over 972536.33 frames.], batch size: 30, lr: 1.61e-03
2022-05-03 15:26:30,536 INFO [train.py:715] (2/8) Epoch 0, batch 16700, loss[loss=0.2037, simple_loss=0.2524, pruned_loss=0.07752, over 4836.00 frames.], tot_loss[loss=0.223, simple_loss=0.2757, pruned_loss=0.08518, over 971884.39 frames.], batch size: 13, lr: 1.61e-03
2022-05-03 15:27:09,628 INFO [train.py:715] (2/8) Epoch 0, batch 16750, loss[loss=0.2322, simple_loss=0.2816, pruned_loss=0.09142, over 4782.00 frames.], tot_loss[loss=0.2246, simple_loss=0.2766, pruned_loss=0.08632, over 971027.06 frames.], batch size: 17, lr: 1.60e-03
2022-05-03 15:27:48,775 INFO [train.py:715] (2/8) Epoch 0, batch 16800, loss[loss=0.2189, simple_loss=0.2828, pruned_loss=0.07746, over 4964.00 frames.], tot_loss[loss=0.2254, simple_loss=0.2776, pruned_loss=0.0866, over 971798.64 frames.], batch size: 24, lr: 1.60e-03
2022-05-03 15:28:28,410 INFO [train.py:715] (2/8) Epoch 0, batch 16850, loss[loss=0.1962, simple_loss=0.255, pruned_loss=0.0687, over 4772.00 frames.], tot_loss[loss=0.2264, simple_loss=0.2785, pruned_loss=0.08713, over 972395.07 frames.], batch size: 18, lr: 1.60e-03
2022-05-03 15:29:08,020 INFO [train.py:715] (2/8) Epoch 0, batch 16900, loss[loss=0.2456, simple_loss=0.2915, pruned_loss=0.09981, over 4977.00 frames.], tot_loss[loss=0.2266, simple_loss=0.2785, pruned_loss=0.08734, over 971859.27 frames.], batch size: 15, lr: 1.60e-03
2022-05-03 15:29:47,259 INFO [train.py:715] (2/8) Epoch 0, batch 16950, loss[loss=0.2373, simple_loss=0.2818, pruned_loss=0.0964, over 4839.00 frames.], tot_loss[loss=0.2266, simple_loss=0.2782, pruned_loss=0.08753, over 971928.67 frames.], batch size: 13, lr: 1.60e-03
2022-05-03 15:30:27,227 INFO [train.py:715] (2/8) Epoch 0, batch 17000, loss[loss=0.199, simple_loss=0.2519, pruned_loss=0.07304, over 4810.00 frames.], tot_loss[loss=0.2268, simple_loss=0.2783, pruned_loss=0.08767, over 971962.18 frames.], batch size: 26, lr: 1.59e-03
2022-05-03 15:31:07,725 INFO [train.py:715] (2/8) Epoch 0, batch 17050, loss[loss=0.2033, simple_loss=0.2571, pruned_loss=0.07474, over 4982.00 frames.], tot_loss[loss=0.2282, simple_loss=0.2792, pruned_loss=0.08857, over 972042.45 frames.], batch size: 24, lr: 1.59e-03
2022-05-03 15:31:47,479 INFO [train.py:715] (2/8) Epoch 0, batch 17100, loss[loss=0.2042, simple_loss=0.2618, pruned_loss=0.0733, over 4796.00 frames.], tot_loss[loss=0.2264, simple_loss=0.278, pruned_loss=0.0874, over 972700.70 frames.], batch size: 21, lr: 1.59e-03
2022-05-03 15:32:26,647 INFO [train.py:715] (2/8) Epoch 0, batch 17150, loss[loss=0.1833, simple_loss=0.2497, pruned_loss=0.0584, over 4982.00 frames.], tot_loss[loss=0.2275, simple_loss=0.2791, pruned_loss=0.08799, over 972140.86 frames.], batch size: 25, lr: 1.59e-03
2022-05-03 15:33:06,902 INFO [train.py:715] (2/8) Epoch 0, batch 17200, loss[loss=0.2316, simple_loss=0.284, pruned_loss=0.08959, over 4892.00 frames.], tot_loss[loss=0.2259, simple_loss=0.2776, pruned_loss=0.08705, over 972120.01 frames.], batch size: 39, lr: 1.58e-03
2022-05-03 15:33:46,675 INFO [train.py:715] (2/8) Epoch 0, batch 17250, loss[loss=0.2324, simple_loss=0.2705, pruned_loss=0.09714, over 4844.00 frames.], tot_loss[loss=0.2251, simple_loss=0.2769, pruned_loss=0.08669, over 972366.21 frames.], batch size: 13, lr: 1.58e-03
2022-05-03 15:34:26,231 INFO [train.py:715] (2/8) Epoch 0, batch 17300, loss[loss=0.2298, simple_loss=0.2736, pruned_loss=0.09303, over 4791.00 frames.], tot_loss[loss=0.226, simple_loss=0.2778, pruned_loss=0.08707, over 972288.32 frames.], batch size: 24, lr: 1.58e-03
2022-05-03 15:35:06,287 INFO [train.py:715] (2/8) Epoch 0, batch 17350, loss[loss=0.2766, simple_loss=0.3183, pruned_loss=0.1174, over 4822.00 frames.], tot_loss[loss=0.2265, simple_loss=0.2785, pruned_loss=0.08727, over 972217.46 frames.], batch size: 25, lr: 1.58e-03
2022-05-03 15:35:46,523 INFO [train.py:715] (2/8) Epoch 0, batch 17400, loss[loss=0.202, simple_loss=0.2546, pruned_loss=0.07473, over 4707.00 frames.], tot_loss[loss=0.2257, simple_loss=0.2778, pruned_loss=0.08685, over 971587.38 frames.], batch size: 15, lr: 1.58e-03
2022-05-03 15:36:26,415 INFO [train.py:715] (2/8) Epoch 0, batch 17450, loss[loss=0.1937, simple_loss=0.2493, pruned_loss=0.06904, over 4794.00 frames.], tot_loss[loss=0.2263, simple_loss=0.2782, pruned_loss=0.08722, over 972051.37 frames.], batch size: 24, lr: 1.57e-03
2022-05-03 15:37:07,027 INFO [train.py:715] (2/8) Epoch 0, batch 17500, loss[loss=0.1974, simple_loss=0.2527, pruned_loss=0.07105, over 4751.00 frames.], tot_loss[loss=0.2246, simple_loss=0.2766, pruned_loss=0.08633, over 972250.12 frames.], batch size: 19, lr: 1.57e-03
2022-05-03 15:37:47,456 INFO [train.py:715] (2/8) Epoch 0, batch 17550, loss[loss=0.2787, simple_loss=0.3209, pruned_loss=0.1182, over 4863.00 frames.], tot_loss[loss=0.2247, simple_loss=0.2767, pruned_loss=0.08636, over 972453.00 frames.], batch size: 20, lr: 1.57e-03
2022-05-03 15:38:27,015 INFO [train.py:715] (2/8) Epoch 0, batch 17600, loss[loss=0.2093, simple_loss=0.2572, pruned_loss=0.08076, over 4875.00 frames.], tot_loss[loss=0.2243, simple_loss=0.2765, pruned_loss=0.08609, over 972394.99 frames.], batch size: 32, lr: 1.57e-03
2022-05-03 15:39:06,938 INFO [train.py:715] (2/8) Epoch 0, batch 17650, loss[loss=0.185, simple_loss=0.2541, pruned_loss=0.05798, over 4882.00 frames.], tot_loss[loss=0.2233, simple_loss=0.2756, pruned_loss=0.08551, over 972818.19 frames.], batch size: 22, lr: 1.57e-03
2022-05-03 15:39:47,475 INFO [train.py:715] (2/8) Epoch 0, batch 17700, loss[loss=0.184, simple_loss=0.2342, pruned_loss=0.06693, over 4752.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2739, pruned_loss=0.0847, over 973398.72 frames.], batch size: 16, lr: 1.56e-03
2022-05-03 15:40:27,375 INFO [train.py:715] (2/8) Epoch 0, batch 17750, loss[loss=0.1721, simple_loss=0.2315, pruned_loss=0.05634, over 4959.00 frames.], tot_loss[loss=0.2226, simple_loss=0.275, pruned_loss=0.08507, over 973844.73 frames.], batch size: 24, lr: 1.56e-03
2022-05-03 15:41:07,050 INFO [train.py:715] (2/8) Epoch 0, batch 17800, loss[loss=0.2349, simple_loss=0.2889, pruned_loss=0.09045, over 4791.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2742, pruned_loss=0.08444, over 973441.70 frames.], batch size: 18, lr: 1.56e-03
2022-05-03 15:41:47,855 INFO [train.py:715] (2/8) Epoch 0, batch 17850, loss[loss=0.2351, simple_loss=0.2843, pruned_loss=0.09292, over 4879.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2735, pruned_loss=0.08377, over 973038.88 frames.], batch size: 38, lr: 1.56e-03
2022-05-03 15:42:28,477 INFO [train.py:715] (2/8) Epoch 0, batch 17900, loss[loss=0.2656, simple_loss=0.3062, pruned_loss=0.1125, over 4933.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2747, pruned_loss=0.08444, over 972706.96 frames.], batch size: 29, lr: 1.56e-03
2022-05-03 15:43:07,984 INFO [train.py:715] (2/8) Epoch 0, batch 17950, loss[loss=0.2657, simple_loss=0.2993, pruned_loss=0.1161, over 4840.00 frames.], tot_loss[loss=0.2227, simple_loss=0.2754, pruned_loss=0.08502, over 971777.39 frames.], batch size: 30, lr: 1.55e-03
2022-05-03 15:43:48,220 INFO [train.py:715] (2/8) Epoch 0, batch 18000, loss[loss=0.2648, simple_loss=0.3065, pruned_loss=0.1115, over 4949.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2756, pruned_loss=0.08534, over 971213.74 frames.], batch size: 35, lr: 1.55e-03
2022-05-03 15:43:48,220 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 15:43:57,827 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.141, simple_loss=0.228, pruned_loss=0.02706, over 914524.00 frames.
2022-05-03 15:44:38,085 INFO [train.py:715] (2/8) Epoch 0, batch 18050, loss[loss=0.1977, simple_loss=0.2543, pruned_loss=0.07058, over 4830.00 frames.], tot_loss[loss=0.224, simple_loss=0.276, pruned_loss=0.08601, over 971376.55 frames.], batch size: 25, lr: 1.55e-03
2022-05-03 15:45:18,342 INFO [train.py:715] (2/8) Epoch 0, batch 18100, loss[loss=0.1896, simple_loss=0.2388, pruned_loss=0.07022, over 4822.00 frames.], tot_loss[loss=0.2237, simple_loss=0.2764, pruned_loss=0.08555, over 971887.74 frames.], batch size: 12, lr: 1.55e-03
2022-05-03 15:45:58,155 INFO [train.py:715] (2/8) Epoch 0, batch 18150, loss[loss=0.2365, simple_loss=0.2875, pruned_loss=0.0927, over 4930.00 frames.], tot_loss[loss=0.2229, simple_loss=0.2757, pruned_loss=0.08507, over 972639.55 frames.], batch size: 29, lr: 1.55e-03
2022-05-03 15:46:37,566 INFO [train.py:715] (2/8) Epoch 0, batch 18200, loss[loss=0.2287, simple_loss=0.2676, pruned_loss=0.09484, over 4988.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2751, pruned_loss=0.08433, over 972480.96 frames.], batch size: 25, lr: 1.54e-03
2022-05-03 15:47:17,738 INFO [train.py:715] (2/8) Epoch 0, batch 18250, loss[loss=0.2391, simple_loss=0.2888, pruned_loss=0.09474, over 4843.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2749, pruned_loss=0.0838, over 972473.15 frames.], batch size: 20, lr: 1.54e-03
2022-05-03 15:47:59,024 INFO [train.py:715] (2/8) Epoch 0, batch 18300, loss[loss=0.2443, simple_loss=0.2788, pruned_loss=0.1049, over 4984.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2749, pruned_loss=0.0839, over 971649.76 frames.], batch size: 35, lr: 1.54e-03
2022-05-03 15:48:38,796 INFO [train.py:715] (2/8) Epoch 0, batch 18350, loss[loss=0.2308, simple_loss=0.2899, pruned_loss=0.08585, over 4945.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2757, pruned_loss=0.08403, over 972112.39 frames.], batch size: 29, lr: 1.54e-03
2022-05-03 15:49:19,072 INFO [train.py:715] (2/8) Epoch 0, batch 18400, loss[loss=0.2222, simple_loss=0.2699, pruned_loss=0.08723, over 4929.00 frames.], tot_loss[loss=0.2225, simple_loss=0.2755, pruned_loss=0.08475, over 972266.97 frames.], batch size: 23, lr: 1.54e-03
2022-05-03 15:49:59,572 INFO [train.py:715] (2/8) Epoch 0, batch 18450, loss[loss=0.2307, simple_loss=0.2841, pruned_loss=0.08865, over 4873.00 frames.], tot_loss[loss=0.221, simple_loss=0.2744, pruned_loss=0.0838, over 972375.30 frames.], batch size: 32, lr: 1.53e-03
2022-05-03 15:50:39,240 INFO [train.py:715] (2/8) Epoch 0, batch 18500, loss[loss=0.2459, simple_loss=0.2884, pruned_loss=0.1017, over 4782.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2751, pruned_loss=0.08418, over 971516.44 frames.], batch size: 18, lr: 1.53e-03
2022-05-03 15:51:19,770 INFO [train.py:715] (2/8) Epoch 0, batch 18550, loss[loss=0.2319, simple_loss=0.2816, pruned_loss=0.09109, over 4855.00 frames.], tot_loss[loss=0.223, simple_loss=0.276, pruned_loss=0.08503, over 971743.64 frames.], batch size: 32, lr: 1.53e-03
2022-05-03 15:52:00,078 INFO [train.py:715] (2/8) Epoch 0, batch 18600, loss[loss=0.2543, simple_loss=0.2886, pruned_loss=0.11, over 4800.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2755, pruned_loss=0.08472, over 972649.83 frames.], batch size: 12, lr: 1.53e-03
2022-05-03 15:52:40,193 INFO [train.py:715] (2/8) Epoch 0, batch 18650, loss[loss=0.2061, simple_loss=0.2696, pruned_loss=0.07129, over 4752.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2754, pruned_loss=0.08472, over 972174.67 frames.], batch size: 16, lr: 1.53e-03
2022-05-03 15:53:19,593 INFO [train.py:715] (2/8) Epoch 0, batch 18700, loss[loss=0.2491, simple_loss=0.2855, pruned_loss=0.1064, over 4692.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2757, pruned_loss=0.08457, over 972351.54 frames.], batch size: 15, lr: 1.52e-03
2022-05-03 15:53:59,901 INFO [train.py:715] (2/8) Epoch 0, batch 18750, loss[loss=0.1953, simple_loss=0.2555, pruned_loss=0.06757, over 4928.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2755, pruned_loss=0.08438, over 972528.36 frames.], batch size: 29, lr: 1.52e-03
2022-05-03 15:54:41,171 INFO [train.py:715] (2/8) Epoch 0, batch 18800, loss[loss=0.2515, simple_loss=0.2988, pruned_loss=0.1021, over 4767.00 frames.], tot_loss[loss=0.221, simple_loss=0.2742, pruned_loss=0.08388, over 973241.33 frames.], batch size: 18, lr: 1.52e-03
2022-05-03 15:55:20,396 INFO [train.py:715] (2/8) Epoch 0, batch 18850, loss[loss=0.1991, simple_loss=0.2595, pruned_loss=0.0694, over 4773.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2723, pruned_loss=0.08273, over 972614.71 frames.], batch size: 14, lr: 1.52e-03
2022-05-03 15:56:01,300 INFO [train.py:715] (2/8) Epoch 0, batch 18900, loss[loss=0.1992, simple_loss=0.2671, pruned_loss=0.06567, over 4800.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2732, pruned_loss=0.08291, over 973373.48 frames.], batch size: 21, lr: 1.52e-03
2022-05-03 15:56:41,735 INFO [train.py:715] (2/8) Epoch 0, batch 18950, loss[loss=0.2123, simple_loss=0.2693, pruned_loss=0.07766, over 4896.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2745, pruned_loss=0.08383, over 974115.66 frames.], batch size: 22, lr: 1.52e-03
2022-05-03 15:57:21,396 INFO [train.py:715] (2/8) Epoch 0, batch 19000, loss[loss=0.2304, simple_loss=0.2752, pruned_loss=0.09284, over 4988.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2748, pruned_loss=0.08381, over 973675.57 frames.], batch size: 28, lr: 1.51e-03
2022-05-03 15:58:01,842 INFO [train.py:715] (2/8) Epoch 0, batch 19050, loss[loss=0.2356, simple_loss=0.2989, pruned_loss=0.08615, over 4730.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2762, pruned_loss=0.08495, over 973397.36 frames.], batch size: 16, lr: 1.51e-03
2022-05-03 15:58:42,176 INFO [train.py:715] (2/8) Epoch 0, batch 19100, loss[loss=0.2253, simple_loss=0.2889, pruned_loss=0.08087, over 4893.00 frames.], tot_loss[loss=0.2239, simple_loss=0.2764, pruned_loss=0.08566, over 972389.75 frames.], batch size: 19, lr: 1.51e-03
2022-05-03 15:59:22,497 INFO [train.py:715] (2/8) Epoch 0, batch 19150, loss[loss=0.2247, simple_loss=0.268, pruned_loss=0.09072, over 4972.00 frames.], tot_loss[loss=0.2238, simple_loss=0.2761, pruned_loss=0.08579, over 971497.44 frames.], batch size: 15, lr: 1.51e-03
2022-05-03 16:00:01,712 INFO [train.py:715] (2/8) Epoch 0, batch 19200, loss[loss=0.1827, simple_loss=0.2409, pruned_loss=0.06219, over 4812.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2751, pruned_loss=0.08479, over 971343.57 frames.], batch size: 25, lr: 1.51e-03
2022-05-03 16:00:42,574 INFO [train.py:715] (2/8) Epoch 0, batch 19250, loss[loss=0.2689, simple_loss=0.309, pruned_loss=0.1144, over 4934.00 frames.], tot_loss[loss=0.2227, simple_loss=0.2756, pruned_loss=0.08494, over 971376.18 frames.], batch size: 23, lr: 1.50e-03
2022-05-03 16:01:23,353 INFO [train.py:715] (2/8) Epoch 0, batch 19300, loss[loss=0.2735, simple_loss=0.3118, pruned_loss=0.1176, over 4956.00 frames.], tot_loss[loss=0.2235, simple_loss=0.2762, pruned_loss=0.08543, over 971278.10 frames.], batch size: 24, lr: 1.50e-03
2022-05-03 16:02:03,049 INFO [train.py:715] (2/8) Epoch 0, batch 19350, loss[loss=0.2244, simple_loss=0.2686, pruned_loss=0.09006, over 4834.00 frames.], tot_loss[loss=0.2228, simple_loss=0.2755, pruned_loss=0.08503, over 970976.13 frames.], batch size: 30, lr: 1.50e-03
2022-05-03 16:02:43,214 INFO [train.py:715] (2/8) Epoch 0, batch 19400, loss[loss=0.1681, simple_loss=0.2289, pruned_loss=0.05364, over 4823.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2729, pruned_loss=0.08298, over 970894.02 frames.], batch size: 26, lr: 1.50e-03
2022-05-03 16:03:24,057 INFO [train.py:715] (2/8) Epoch 0, batch 19450, loss[loss=0.2229, simple_loss=0.2778, pruned_loss=0.08404, over 4806.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2725, pruned_loss=0.08283, over 970591.10 frames.], batch size: 21, lr: 1.50e-03
2022-05-03 16:04:03,570 INFO [train.py:715] (2/8) Epoch 0, batch 19500, loss[loss=0.2314, simple_loss=0.2864, pruned_loss=0.08818, over 4975.00 frames.], tot_loss[loss=0.22, simple_loss=0.2734, pruned_loss=0.08328, over 971319.12 frames.], batch size: 35, lr: 1.50e-03
2022-05-03 16:04:42,922 INFO [train.py:715] (2/8) Epoch 0, batch 19550, loss[loss=0.1846, simple_loss=0.2485, pruned_loss=0.06035, over 4968.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2733, pruned_loss=0.08363, over 972120.05 frames.], batch size: 24, lr: 1.49e-03
2022-05-03 16:05:23,271 INFO [train.py:715] (2/8) Epoch 0, batch 19600, loss[loss=0.2387, simple_loss=0.2821, pruned_loss=0.09769, over 4910.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2726, pruned_loss=0.08288, over 972750.73 frames.], batch size: 17, lr: 1.49e-03
2022-05-03 16:06:03,055 INFO [train.py:715] (2/8) Epoch 0, batch 19650, loss[loss=0.2815, simple_loss=0.3286, pruned_loss=0.1172, over 4784.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2715, pruned_loss=0.08185, over 972728.02 frames.], batch size: 18, lr: 1.49e-03
2022-05-03 16:06:42,545 INFO [train.py:715] (2/8) Epoch 0, batch 19700, loss[loss=0.2489, simple_loss=0.2993, pruned_loss=0.09931, over 4820.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2726, pruned_loss=0.08238, over 972417.14 frames.], batch size: 25, lr: 1.49e-03
2022-05-03 16:07:22,612 INFO [train.py:715] (2/8) Epoch 0, batch 19750, loss[loss=0.2892, simple_loss=0.3172, pruned_loss=0.1306, over 4984.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2732, pruned_loss=0.08296, over 972593.81 frames.], batch size: 15, lr: 1.49e-03
2022-05-03 16:08:02,292 INFO [train.py:715] (2/8) Epoch 0, batch 19800, loss[loss=0.2129, simple_loss=0.2635, pruned_loss=0.08112, over 4842.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2734, pruned_loss=0.08339, over 971868.04 frames.], batch size: 20, lr: 1.48e-03
2022-05-03 16:08:42,103 INFO [train.py:715] (2/8) Epoch 0, batch 19850, loss[loss=0.2081, simple_loss=0.2527, pruned_loss=0.08177, over 4798.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2729, pruned_loss=0.08251, over 971833.68 frames.], batch size: 24, lr: 1.48e-03
2022-05-03 16:09:21,337 INFO [train.py:715] (2/8) Epoch 0, batch 19900, loss[loss=0.1865, simple_loss=0.2394, pruned_loss=0.06678, over 4769.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2729, pruned_loss=0.08232, over 971199.19 frames.], batch size: 12, lr: 1.48e-03
2022-05-03 16:10:02,116 INFO [train.py:715] (2/8) Epoch 0, batch 19950, loss[loss=0.2117, simple_loss=0.2749, pruned_loss=0.07428, over 4690.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2732, pruned_loss=0.08255, over 971352.48 frames.], batch size: 15, lr: 1.48e-03
2022-05-03 16:10:42,164 INFO [train.py:715] (2/8) Epoch 0, batch 20000, loss[loss=0.2518, simple_loss=0.2981, pruned_loss=0.1027, over 4750.00 frames.], tot_loss[loss=0.218, simple_loss=0.2724, pruned_loss=0.08178, over 971237.39 frames.], batch size: 19, lr: 1.48e-03
2022-05-03 16:11:21,517 INFO [train.py:715] (2/8) Epoch 0, batch 20050, loss[loss=0.2, simple_loss=0.2542, pruned_loss=0.07292, over 4863.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2726, pruned_loss=0.08205, over 971240.55 frames.], batch size: 30, lr: 1.48e-03
2022-05-03 16:12:01,696 INFO [train.py:715] (2/8) Epoch 0, batch 20100, loss[loss=0.2123, simple_loss=0.2694, pruned_loss=0.07759, over 4772.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2723, pruned_loss=0.08205, over 971180.33 frames.], batch size: 18, lr: 1.47e-03
2022-05-03 16:12:41,688 INFO [train.py:715] (2/8) Epoch 0, batch 20150, loss[loss=0.2045, simple_loss=0.26, pruned_loss=0.07449, over 4903.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2722, pruned_loss=0.08147, over 971552.50 frames.], batch size: 19, lr: 1.47e-03
2022-05-03 16:13:21,721 INFO [train.py:715] (2/8) Epoch 0, batch 20200, loss[loss=0.2054, simple_loss=0.2454, pruned_loss=0.08272, over 4889.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2717, pruned_loss=0.08084, over 971651.76 frames.], batch size: 19, lr: 1.47e-03
2022-05-03 16:14:01,250 INFO [train.py:715] (2/8) Epoch 0, batch 20250, loss[loss=0.1711, simple_loss=0.2439, pruned_loss=0.04921, over 4737.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2738, pruned_loss=0.08242, over 972198.41 frames.], batch size: 16, lr: 1.47e-03
2022-05-03 16:14:42,000 INFO [train.py:715] (2/8) Epoch 0, batch 20300, loss[loss=0.23, simple_loss=0.2677, pruned_loss=0.09615, over 4847.00 frames.], tot_loss[loss=0.2191, simple_loss=0.274, pruned_loss=0.08209, over 971986.33 frames.], batch size: 30, lr: 1.47e-03
2022-05-03 16:15:21,886 INFO [train.py:715] (2/8) Epoch 0, batch 20350, loss[loss=0.2155, simple_loss=0.2708, pruned_loss=0.08008, over 4842.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2726, pruned_loss=0.0813, over 971126.09 frames.], batch size: 32, lr: 1.47e-03
2022-05-03 16:16:00,949 INFO [train.py:715] (2/8) Epoch 0, batch 20400, loss[loss=0.2465, simple_loss=0.2867, pruned_loss=0.1032, over 4740.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2737, pruned_loss=0.08248, over 971522.76 frames.], batch size: 16, lr: 1.46e-03
2022-05-03 16:16:40,893 INFO [train.py:715] (2/8) Epoch 0, batch 20450, loss[loss=0.2121, simple_loss=0.2615, pruned_loss=0.08135, over 4936.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2729, pruned_loss=0.08179, over 972049.38 frames.], batch size: 21, lr: 1.46e-03
2022-05-03 16:17:20,432 INFO [train.py:715] (2/8) Epoch 0, batch 20500, loss[loss=0.2423, simple_loss=0.2931, pruned_loss=0.09574, over 4945.00 frames.], tot_loss[loss=0.2185, simple_loss=0.273, pruned_loss=0.08202, over 971898.85 frames.], batch size: 29, lr: 1.46e-03
2022-05-03 16:18:00,496 INFO [train.py:715] (2/8) Epoch 0, batch 20550, loss[loss=0.2066, simple_loss=0.2577, pruned_loss=0.07773, over 4948.00 frames.], tot_loss[loss=0.2184, simple_loss=0.273, pruned_loss=0.08192, over 972001.21 frames.], batch size: 23, lr: 1.46e-03
2022-05-03 16:18:39,951 INFO [train.py:715] (2/8) Epoch 0, batch 20600, loss[loss=0.2238, simple_loss=0.2804, pruned_loss=0.08361, over 4984.00 frames.], tot_loss[loss=0.2185, simple_loss=0.2731, pruned_loss=0.08196, over 971726.35 frames.], batch size: 25, lr: 1.46e-03
2022-05-03 16:19:19,645 INFO [train.py:715] (2/8) Epoch 0, batch 20650, loss[loss=0.1793, simple_loss=0.2462, pruned_loss=0.05614, over 4981.00 frames.], tot_loss[loss=0.2185, simple_loss=0.2731, pruned_loss=0.08197, over 972759.13 frames.], batch size: 14, lr: 1.46e-03
2022-05-03 16:20:00,375 INFO [train.py:715] (2/8) Epoch 0, batch 20700, loss[loss=0.2023, simple_loss=0.2555, pruned_loss=0.07454, over 4871.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2731, pruned_loss=0.08157, over 972615.84 frames.], batch size: 22, lr: 1.45e-03
2022-05-03 16:20:39,699 INFO [train.py:715] (2/8) Epoch 0, batch 20750, loss[loss=0.2398, simple_loss=0.2844, pruned_loss=0.09764, over 4988.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2735, pruned_loss=0.08189, over 972315.44 frames.], batch size: 14, lr: 1.45e-03
2022-05-03 16:21:19,876 INFO [train.py:715] (2/8) Epoch 0, batch 20800, loss[loss=0.1506, simple_loss=0.2243, pruned_loss=0.03843, over 4862.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2718, pruned_loss=0.08035, over 972381.18 frames.], batch size: 22, lr: 1.45e-03
2022-05-03 16:21:59,636 INFO [train.py:715] (2/8) Epoch 0, batch 20850, loss[loss=0.2306, simple_loss=0.2747, pruned_loss=0.09321, over 4988.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2713, pruned_loss=0.08048, over 973045.12 frames.], batch size: 35, lr: 1.45e-03
2022-05-03 16:22:39,121 INFO [train.py:715] (2/8) Epoch 0, batch 20900, loss[loss=0.2306, simple_loss=0.2639, pruned_loss=0.09859, over 4781.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2708, pruned_loss=0.08046, over 972635.20 frames.], batch size: 18, lr: 1.45e-03
2022-05-03 16:23:19,648 INFO [train.py:715] (2/8) Epoch 0, batch 20950, loss[loss=0.2234, simple_loss=0.2715, pruned_loss=0.08765, over 4843.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2697, pruned_loss=0.07989, over 973087.39 frames.], batch size: 30, lr: 1.45e-03
2022-05-03 16:24:00,679 INFO [train.py:715] (2/8) Epoch 0, batch 21000, loss[loss=0.1923, simple_loss=0.2596, pruned_loss=0.06249, over 4808.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2699, pruned_loss=0.0802, over 972365.12 frames.], batch size: 21, lr: 1.44e-03
2022-05-03 16:24:00,680 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 16:24:16,219 INFO [train.py:742] (2/8) 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] (2/8) Epoch 0, batch 21050, loss[loss=0.2309, simple_loss=0.2884, pruned_loss=0.0867, over 4892.00 frames.], tot_loss[loss=0.2169, simple_loss=0.271, pruned_loss=0.08141, over 971580.05 frames.], batch size: 19, lr: 1.44e-03
2022-05-03 16:25:36,592 INFO [train.py:715] (2/8) Epoch 0, batch 21100, loss[loss=0.2021, simple_loss=0.2649, pruned_loss=0.06964, over 4783.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2703, pruned_loss=0.08031, over 971238.68 frames.], batch size: 17, lr: 1.44e-03
2022-05-03 16:26:16,946 INFO [train.py:715] (2/8) Epoch 0, batch 21150, loss[loss=0.1931, simple_loss=0.2576, pruned_loss=0.0643, over 4821.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2721, pruned_loss=0.08135, over 971468.56 frames.], batch size: 25, lr: 1.44e-03
2022-05-03 16:26:56,809 INFO [train.py:715] (2/8) Epoch 0, batch 21200, loss[loss=0.2041, simple_loss=0.2704, pruned_loss=0.06887, over 4862.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2725, pruned_loss=0.08213, over 971459.33 frames.], batch size: 20, lr: 1.44e-03
2022-05-03 16:27:37,349 INFO [train.py:715] (2/8) Epoch 0, batch 21250, loss[loss=0.2311, simple_loss=0.2846, pruned_loss=0.08873, over 4814.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2726, pruned_loss=0.08204, over 972009.14 frames.], batch size: 21, lr: 1.44e-03
2022-05-03 16:28:17,120 INFO [train.py:715] (2/8) Epoch 0, batch 21300, loss[loss=0.2345, simple_loss=0.2841, pruned_loss=0.09243, over 4985.00 frames.], tot_loss[loss=0.2176, simple_loss=0.272, pruned_loss=0.08157, over 972189.31 frames.], batch size: 35, lr: 1.43e-03
2022-05-03 16:28:57,540 INFO [train.py:715] (2/8) Epoch 0, batch 21350, loss[loss=0.2213, simple_loss=0.2812, pruned_loss=0.08068, over 4986.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2711, pruned_loss=0.08105, over 972717.94 frames.], batch size: 28, lr: 1.43e-03
2022-05-03 16:29:38,276 INFO [train.py:715] (2/8) Epoch 0, batch 21400, loss[loss=0.219, simple_loss=0.2849, pruned_loss=0.07659, over 4954.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2719, pruned_loss=0.08127, over 973039.86 frames.], batch size: 24, lr: 1.43e-03
2022-05-03 16:30:17,945 INFO [train.py:715] (2/8) Epoch 0, batch 21450, loss[loss=0.2246, simple_loss=0.276, pruned_loss=0.08663, over 4986.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2725, pruned_loss=0.08146, over 973511.82 frames.], batch size: 15, lr: 1.43e-03
2022-05-03 16:30:57,788 INFO [train.py:715] (2/8) Epoch 0, batch 21500, loss[loss=0.2099, simple_loss=0.2768, pruned_loss=0.07149, over 4774.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2709, pruned_loss=0.08005, over 973482.79 frames.], batch size: 18, lr: 1.43e-03
2022-05-03 16:31:38,004 INFO [train.py:715] (2/8) Epoch 0, batch 21550, loss[loss=0.1797, simple_loss=0.2281, pruned_loss=0.06565, over 4984.00 frames.], tot_loss[loss=0.2144, simple_loss=0.27, pruned_loss=0.07944, over 973452.74 frames.], batch size: 14, lr: 1.43e-03
2022-05-03 16:32:18,469 INFO [train.py:715] (2/8) Epoch 0, batch 21600, loss[loss=0.2333, simple_loss=0.2852, pruned_loss=0.09067, over 4924.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2704, pruned_loss=0.07955, over 972714.66 frames.], batch size: 23, lr: 1.42e-03
2022-05-03 16:32:58,231 INFO [train.py:715] (2/8) Epoch 0, batch 21650, loss[loss=0.1754, simple_loss=0.2412, pruned_loss=0.05482, over 4824.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2702, pruned_loss=0.07946, over 972517.48 frames.], batch size: 13, lr: 1.42e-03
2022-05-03 16:33:39,046 INFO [train.py:715] (2/8) Epoch 0, batch 21700, loss[loss=0.1936, simple_loss=0.247, pruned_loss=0.07015, over 4982.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2703, pruned_loss=0.07921, over 973464.95 frames.], batch size: 25, lr: 1.42e-03
2022-05-03 16:34:19,202 INFO [train.py:715] (2/8) Epoch 0, batch 21750, loss[loss=0.1545, simple_loss=0.2187, pruned_loss=0.04516, over 4884.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2702, pruned_loss=0.07915, over 972909.13 frames.], batch size: 22, lr: 1.42e-03
2022-05-03 16:34:58,785 INFO [train.py:715] (2/8) Epoch 0, batch 21800, loss[loss=0.1745, simple_loss=0.2443, pruned_loss=0.05236, over 4932.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2696, pruned_loss=0.07896, over 973377.26 frames.], batch size: 21, lr: 1.42e-03
2022-05-03 16:35:38,615 INFO [train.py:715] (2/8) Epoch 0, batch 21850, loss[loss=0.2573, simple_loss=0.3024, pruned_loss=0.1061, over 4892.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2697, pruned_loss=0.07941, over 973526.85 frames.], batch size: 22, lr: 1.42e-03
2022-05-03 16:36:19,091 INFO [train.py:715] (2/8) Epoch 0, batch 21900, loss[loss=0.2031, simple_loss=0.2647, pruned_loss=0.0707, over 4860.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2688, pruned_loss=0.07888, over 974150.11 frames.], batch size: 32, lr: 1.42e-03
2022-05-03 16:36:58,997 INFO [train.py:715] (2/8) Epoch 0, batch 21950, loss[loss=0.1744, simple_loss=0.2384, pruned_loss=0.05524, over 4917.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2682, pruned_loss=0.07877, over 974099.71 frames.], batch size: 19, lr: 1.41e-03
2022-05-03 16:37:38,282 INFO [train.py:715] (2/8) Epoch 0, batch 22000, loss[loss=0.2063, simple_loss=0.2823, pruned_loss=0.06509, over 4788.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2698, pruned_loss=0.07933, over 973752.20 frames.], batch size: 17, lr: 1.41e-03
2022-05-03 16:38:18,441 INFO [train.py:715] (2/8) Epoch 0, batch 22050, loss[loss=0.2264, simple_loss=0.2728, pruned_loss=0.09005, over 4786.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2706, pruned_loss=0.07946, over 974270.12 frames.], batch size: 18, lr: 1.41e-03
2022-05-03 16:38:58,599 INFO [train.py:715] (2/8) Epoch 0, batch 22100, loss[loss=0.2007, simple_loss=0.2621, pruned_loss=0.06962, over 4941.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2696, pruned_loss=0.07878, over 973495.31 frames.], batch size: 18, lr: 1.41e-03
2022-05-03 16:39:38,114 INFO [train.py:715] (2/8) Epoch 0, batch 22150, loss[loss=0.1775, simple_loss=0.2462, pruned_loss=0.05439, over 4788.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2704, pruned_loss=0.07967, over 973292.10 frames.], batch size: 17, lr: 1.41e-03
2022-05-03 16:40:17,920 INFO [train.py:715] (2/8) Epoch 0, batch 22200, loss[loss=0.1995, simple_loss=0.2629, pruned_loss=0.06803, over 4827.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2704, pruned_loss=0.07948, over 972807.95 frames.], batch size: 15, lr: 1.41e-03
2022-05-03 16:40:58,308 INFO [train.py:715] (2/8) Epoch 0, batch 22250, loss[loss=0.1941, simple_loss=0.2694, pruned_loss=0.05941, over 4893.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2698, pruned_loss=0.07867, over 973675.57 frames.], batch size: 17, lr: 1.40e-03
2022-05-03 16:41:38,373 INFO [train.py:715] (2/8) Epoch 0, batch 22300, loss[loss=0.21, simple_loss=0.2732, pruned_loss=0.07339, over 4806.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2695, pruned_loss=0.07833, over 973378.30 frames.], batch size: 21, lr: 1.40e-03
2022-05-03 16:42:18,076 INFO [train.py:715] (2/8) Epoch 0, batch 22350, loss[loss=0.2455, simple_loss=0.3015, pruned_loss=0.0947, over 4984.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2687, pruned_loss=0.07785, over 972553.59 frames.], batch size: 14, lr: 1.40e-03
2022-05-03 16:42:58,244 INFO [train.py:715] (2/8) Epoch 0, batch 22400, loss[loss=0.2372, simple_loss=0.2891, pruned_loss=0.09269, over 4847.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2698, pruned_loss=0.0789, over 972058.10 frames.], batch size: 20, lr: 1.40e-03
2022-05-03 16:43:38,078 INFO [train.py:715] (2/8) Epoch 0, batch 22450, loss[loss=0.2258, simple_loss=0.2747, pruned_loss=0.08842, over 4880.00 frames.], tot_loss[loss=0.214, simple_loss=0.2697, pruned_loss=0.07909, over 971303.36 frames.], batch size: 22, lr: 1.40e-03
2022-05-03 16:44:17,441 INFO [train.py:715] (2/8) Epoch 0, batch 22500, loss[loss=0.194, simple_loss=0.2458, pruned_loss=0.07109, over 4889.00 frames.], tot_loss[loss=0.2143, simple_loss=0.27, pruned_loss=0.07926, over 971466.01 frames.], batch size: 32, lr: 1.40e-03
2022-05-03 16:44:57,226 INFO [train.py:715] (2/8) Epoch 0, batch 22550, loss[loss=0.1937, simple_loss=0.2481, pruned_loss=0.06959, over 4992.00 frames.], tot_loss[loss=0.215, simple_loss=0.2702, pruned_loss=0.0799, over 972042.44 frames.], batch size: 14, lr: 1.40e-03
2022-05-03 16:45:37,435 INFO [train.py:715] (2/8) Epoch 0, batch 22600, loss[loss=0.2298, simple_loss=0.2942, pruned_loss=0.08264, over 4925.00 frames.], tot_loss[loss=0.215, simple_loss=0.2705, pruned_loss=0.07971, over 972344.04 frames.], batch size: 21, lr: 1.39e-03
2022-05-03 16:46:18,080 INFO [train.py:715] (2/8) Epoch 0, batch 22650, loss[loss=0.2089, simple_loss=0.2556, pruned_loss=0.08111, over 4872.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2701, pruned_loss=0.07932, over 971633.01 frames.], batch size: 16, lr: 1.39e-03
2022-05-03 16:46:57,295 INFO [train.py:715] (2/8) Epoch 0, batch 22700, loss[loss=0.2789, simple_loss=0.3205, pruned_loss=0.1186, over 4714.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2697, pruned_loss=0.07891, over 971812.57 frames.], batch size: 15, lr: 1.39e-03
2022-05-03 16:47:37,370 INFO [train.py:715] (2/8) Epoch 0, batch 22750, loss[loss=0.21, simple_loss=0.2787, pruned_loss=0.07064, over 4957.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2707, pruned_loss=0.0801, over 971896.93 frames.], batch size: 24, lr: 1.39e-03
2022-05-03 16:48:17,853 INFO [train.py:715] (2/8) Epoch 0, batch 22800, loss[loss=0.247, simple_loss=0.3, pruned_loss=0.09705, over 4903.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2709, pruned_loss=0.08088, over 971858.31 frames.], batch size: 17, lr: 1.39e-03
2022-05-03 16:48:57,450 INFO [train.py:715] (2/8) Epoch 0, batch 22850, loss[loss=0.2315, simple_loss=0.2908, pruned_loss=0.0861, over 4784.00 frames.], tot_loss[loss=0.2162, simple_loss=0.271, pruned_loss=0.08073, over 972046.68 frames.], batch size: 17, lr: 1.39e-03
2022-05-03 16:49:37,558 INFO [train.py:715] (2/8) Epoch 0, batch 22900, loss[loss=0.1692, simple_loss=0.2393, pruned_loss=0.04959, over 4806.00 frames.], tot_loss[loss=0.214, simple_loss=0.2695, pruned_loss=0.07927, over 971943.92 frames.], batch size: 13, lr: 1.39e-03
2022-05-03 16:50:17,826 INFO [train.py:715] (2/8) Epoch 0, batch 22950, loss[loss=0.2162, simple_loss=0.2713, pruned_loss=0.08055, over 4804.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2697, pruned_loss=0.07897, over 971477.57 frames.], batch size: 25, lr: 1.38e-03
2022-05-03 16:50:58,460 INFO [train.py:715] (2/8) Epoch 0, batch 23000, loss[loss=0.2149, simple_loss=0.2818, pruned_loss=0.07404, over 4754.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2698, pruned_loss=0.07936, over 971903.40 frames.], batch size: 19, lr: 1.38e-03
2022-05-03 16:51:37,478 INFO [train.py:715] (2/8) Epoch 0, batch 23050, loss[loss=0.2113, simple_loss=0.2614, pruned_loss=0.08063, over 4761.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2694, pruned_loss=0.07896, over 972234.39 frames.], batch size: 18, lr: 1.38e-03
2022-05-03 16:52:18,412 INFO [train.py:715] (2/8) Epoch 0, batch 23100, loss[loss=0.2144, simple_loss=0.2713, pruned_loss=0.07879, over 4808.00 frames.], tot_loss[loss=0.214, simple_loss=0.2696, pruned_loss=0.0792, over 971841.17 frames.], batch size: 25, lr: 1.38e-03
2022-05-03 16:52:59,438 INFO [train.py:715] (2/8) Epoch 0, batch 23150, loss[loss=0.219, simple_loss=0.2667, pruned_loss=0.08565, over 4760.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2698, pruned_loss=0.07892, over 972002.43 frames.], batch size: 12, lr: 1.38e-03
2022-05-03 16:53:39,181 INFO [train.py:715] (2/8) Epoch 0, batch 23200, loss[loss=0.2069, simple_loss=0.2605, pruned_loss=0.07669, over 4757.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2686, pruned_loss=0.07849, over 971341.74 frames.], batch size: 19, lr: 1.38e-03
2022-05-03 16:54:19,749 INFO [train.py:715] (2/8) Epoch 0, batch 23250, loss[loss=0.2548, simple_loss=0.2858, pruned_loss=0.1119, over 4928.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2673, pruned_loss=0.0776, over 971168.18 frames.], batch size: 35, lr: 1.38e-03
2022-05-03 16:55:00,171 INFO [train.py:715] (2/8) Epoch 0, batch 23300, loss[loss=0.2219, simple_loss=0.2674, pruned_loss=0.08817, over 4818.00 frames.], tot_loss[loss=0.211, simple_loss=0.2673, pruned_loss=0.07734, over 972013.59 frames.], batch size: 27, lr: 1.37e-03
2022-05-03 16:55:40,651 INFO [train.py:715] (2/8) Epoch 0, batch 23350, loss[loss=0.1904, simple_loss=0.2533, pruned_loss=0.06371, over 4869.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2671, pruned_loss=0.07717, over 972771.42 frames.], batch size: 30, lr: 1.37e-03
2022-05-03 16:56:21,250 INFO [train.py:715] (2/8) Epoch 0, batch 23400, loss[loss=0.2115, simple_loss=0.2618, pruned_loss=0.08059, over 4941.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2671, pruned_loss=0.07809, over 973427.36 frames.], batch size: 21, lr: 1.37e-03
2022-05-03 16:57:02,263 INFO [train.py:715] (2/8) Epoch 0, batch 23450, loss[loss=0.1963, simple_loss=0.2571, pruned_loss=0.06775, over 4866.00 frames.], tot_loss[loss=0.2111, simple_loss=0.267, pruned_loss=0.07762, over 973044.58 frames.], batch size: 16, lr: 1.37e-03
2022-05-03 16:57:43,365 INFO [train.py:715] (2/8) Epoch 0, batch 23500, loss[loss=0.2077, simple_loss=0.2725, pruned_loss=0.07148, over 4856.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2666, pruned_loss=0.07746, over 972598.13 frames.], batch size: 16, lr: 1.37e-03
2022-05-03 16:58:23,219 INFO [train.py:715] (2/8) Epoch 0, batch 23550, loss[loss=0.2303, simple_loss=0.2926, pruned_loss=0.08402, over 4923.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2671, pruned_loss=0.07771, over 971913.80 frames.], batch size: 23, lr: 1.37e-03
2022-05-03 16:59:04,082 INFO [train.py:715] (2/8) Epoch 0, batch 23600, loss[loss=0.2078, simple_loss=0.2661, pruned_loss=0.07469, over 4880.00 frames.], tot_loss[loss=0.211, simple_loss=0.2671, pruned_loss=0.07742, over 972243.03 frames.], batch size: 32, lr: 1.37e-03
2022-05-03 16:59:44,342 INFO [train.py:715] (2/8) Epoch 0, batch 23650, loss[loss=0.2199, simple_loss=0.2658, pruned_loss=0.08696, over 4917.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2658, pruned_loss=0.07658, over 971786.35 frames.], batch size: 23, lr: 1.36e-03
2022-05-03 17:00:24,473 INFO [train.py:715] (2/8) Epoch 0, batch 23700, loss[loss=0.189, simple_loss=0.2514, pruned_loss=0.06336, over 4899.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2661, pruned_loss=0.07664, over 972088.03 frames.], batch size: 19, lr: 1.36e-03
2022-05-03 17:01:03,658 INFO [train.py:715] (2/8) Epoch 0, batch 23750, loss[loss=0.1985, simple_loss=0.2438, pruned_loss=0.07663, over 4819.00 frames.], tot_loss[loss=0.211, simple_loss=0.267, pruned_loss=0.07749, over 971635.83 frames.], batch size: 13, lr: 1.36e-03
2022-05-03 17:01:43,656 INFO [train.py:715] (2/8) Epoch 0, batch 23800, loss[loss=0.1988, simple_loss=0.2493, pruned_loss=0.07414, over 4849.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2665, pruned_loss=0.0772, over 971384.75 frames.], batch size: 30, lr: 1.36e-03
2022-05-03 17:02:24,145 INFO [train.py:715] (2/8) Epoch 0, batch 23850, loss[loss=0.224, simple_loss=0.2776, pruned_loss=0.08515, over 4899.00 frames.], tot_loss[loss=0.213, simple_loss=0.2689, pruned_loss=0.07854, over 972363.60 frames.], batch size: 39, lr: 1.36e-03
2022-05-03 17:03:03,302 INFO [train.py:715] (2/8) Epoch 0, batch 23900, loss[loss=0.2591, simple_loss=0.3187, pruned_loss=0.09974, over 4971.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2683, pruned_loss=0.07811, over 972444.63 frames.], batch size: 15, lr: 1.36e-03
2022-05-03 17:03:43,449 INFO [train.py:715] (2/8) Epoch 0, batch 23950, loss[loss=0.184, simple_loss=0.23, pruned_loss=0.06897, over 4973.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2678, pruned_loss=0.0776, over 972602.35 frames.], batch size: 25, lr: 1.36e-03
2022-05-03 17:04:26,562 INFO [train.py:715] (2/8) Epoch 0, batch 24000, loss[loss=0.1971, simple_loss=0.2617, pruned_loss=0.06628, over 4943.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2667, pruned_loss=0.07711, over 972928.18 frames.], batch size: 21, lr: 1.35e-03
2022-05-03 17:04:26,562 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 17:04:40,850 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.1357, simple_loss=0.2226, pruned_loss=0.02435, over 914524.00 frames.
2022-05-03 17:05:21,163 INFO [train.py:715] (2/8) Epoch 0, batch 24050, loss[loss=0.2382, simple_loss=0.2805, pruned_loss=0.09794, over 4856.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2679, pruned_loss=0.0783, over 973042.77 frames.], batch size: 38, lr: 1.35e-03
2022-05-03 17:06:00,602 INFO [train.py:715] (2/8) Epoch 0, batch 24100, loss[loss=0.1914, simple_loss=0.2516, pruned_loss=0.06563, over 4802.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2676, pruned_loss=0.07775, over 973453.35 frames.], batch size: 13, lr: 1.35e-03
2022-05-03 17:06:40,576 INFO [train.py:715] (2/8) Epoch 0, batch 24150, loss[loss=0.2427, simple_loss=0.2932, pruned_loss=0.09604, over 4906.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2684, pruned_loss=0.078, over 973050.38 frames.], batch size: 39, lr: 1.35e-03
2022-05-03 17:07:20,603 INFO [train.py:715] (2/8) Epoch 0, batch 24200, loss[loss=0.1869, simple_loss=0.2414, pruned_loss=0.06615, over 4705.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2677, pruned_loss=0.07774, over 972506.95 frames.], batch size: 15, lr: 1.35e-03
2022-05-03 17:08:01,224 INFO [train.py:715] (2/8) Epoch 0, batch 24250, loss[loss=0.2086, simple_loss=0.2664, pruned_loss=0.07543, over 4964.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2676, pruned_loss=0.07746, over 972968.40 frames.], batch size: 15, lr: 1.35e-03
2022-05-03 17:08:40,827 INFO [train.py:715] (2/8) Epoch 0, batch 24300, loss[loss=0.1457, simple_loss=0.2103, pruned_loss=0.04055, over 4753.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2678, pruned_loss=0.07725, over 973196.52 frames.], batch size: 12, lr: 1.35e-03
2022-05-03 17:09:21,006 INFO [train.py:715] (2/8) Epoch 0, batch 24350, loss[loss=0.2422, simple_loss=0.2872, pruned_loss=0.09862, over 4971.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2669, pruned_loss=0.0768, over 973329.67 frames.], batch size: 15, lr: 1.35e-03
2022-05-03 17:10:01,412 INFO [train.py:715] (2/8) Epoch 0, batch 24400, loss[loss=0.2358, simple_loss=0.2924, pruned_loss=0.08959, over 4952.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2677, pruned_loss=0.07699, over 973695.18 frames.], batch size: 21, lr: 1.34e-03
2022-05-03 17:10:40,935 INFO [train.py:715] (2/8) Epoch 0, batch 24450, loss[loss=0.1728, simple_loss=0.2443, pruned_loss=0.05063, over 4833.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2686, pruned_loss=0.07729, over 972363.38 frames.], batch size: 15, lr: 1.34e-03
2022-05-03 17:11:21,044 INFO [train.py:715] (2/8) Epoch 0, batch 24500, loss[loss=0.1691, simple_loss=0.2398, pruned_loss=0.04918, over 4838.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2672, pruned_loss=0.07695, over 971755.02 frames.], batch size: 15, lr: 1.34e-03
2022-05-03 17:12:01,314 INFO [train.py:715] (2/8) Epoch 0, batch 24550, loss[loss=0.1641, simple_loss=0.2346, pruned_loss=0.04681, over 4764.00 frames.], tot_loss[loss=0.2101, simple_loss=0.267, pruned_loss=0.07655, over 972470.40 frames.], batch size: 14, lr: 1.34e-03
2022-05-03 17:12:41,509 INFO [train.py:715] (2/8) Epoch 0, batch 24600, loss[loss=0.2519, simple_loss=0.3002, pruned_loss=0.1018, over 4984.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2697, pruned_loss=0.07858, over 972666.90 frames.], batch size: 28, lr: 1.34e-03
2022-05-03 17:13:20,987 INFO [train.py:715] (2/8) Epoch 0, batch 24650, loss[loss=0.1813, simple_loss=0.2465, pruned_loss=0.05801, over 4898.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2681, pruned_loss=0.07715, over 972540.99 frames.], batch size: 19, lr: 1.34e-03
2022-05-03 17:14:01,414 INFO [train.py:715] (2/8) Epoch 0, batch 24700, loss[loss=0.1931, simple_loss=0.2436, pruned_loss=0.07135, over 4905.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2687, pruned_loss=0.0774, over 972489.56 frames.], batch size: 17, lr: 1.34e-03
2022-05-03 17:14:42,115 INFO [train.py:715] (2/8) Epoch 0, batch 24750, loss[loss=0.2087, simple_loss=0.2575, pruned_loss=0.07993, over 4735.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2683, pruned_loss=0.07718, over 972580.13 frames.], batch size: 16, lr: 1.33e-03
2022-05-03 17:15:21,170 INFO [train.py:715] (2/8) Epoch 0, batch 24800, loss[loss=0.1987, simple_loss=0.2573, pruned_loss=0.07009, over 4762.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2682, pruned_loss=0.0773, over 972245.19 frames.], batch size: 16, lr: 1.33e-03
2022-05-03 17:16:01,308 INFO [train.py:715] (2/8) Epoch 0, batch 24850, loss[loss=0.1822, simple_loss=0.2424, pruned_loss=0.06099, over 4883.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2673, pruned_loss=0.07698, over 971634.32 frames.], batch size: 22, lr: 1.33e-03
2022-05-03 17:16:41,582 INFO [train.py:715] (2/8) Epoch 0, batch 24900, loss[loss=0.2025, simple_loss=0.263, pruned_loss=0.07106, over 4953.00 frames.], tot_loss[loss=0.211, simple_loss=0.2673, pruned_loss=0.07739, over 972428.85 frames.], batch size: 21, lr: 1.33e-03
2022-05-03 17:17:21,624 INFO [train.py:715] (2/8) Epoch 0, batch 24950, loss[loss=0.2517, simple_loss=0.2838, pruned_loss=0.1098, over 4993.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2677, pruned_loss=0.07784, over 972323.96 frames.], batch size: 14, lr: 1.33e-03
2022-05-03 17:18:01,145 INFO [train.py:715] (2/8) Epoch 0, batch 25000, loss[loss=0.1586, simple_loss=0.2265, pruned_loss=0.0453, over 4937.00 frames.], tot_loss[loss=0.211, simple_loss=0.2672, pruned_loss=0.07736, over 972779.42 frames.], batch size: 29, lr: 1.33e-03
2022-05-03 17:18:41,401 INFO [train.py:715] (2/8) Epoch 0, batch 25050, loss[loss=0.2239, simple_loss=0.2725, pruned_loss=0.08766, over 4806.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2672, pruned_loss=0.07673, over 971261.22 frames.], batch size: 24, lr: 1.33e-03
2022-05-03 17:19:21,097 INFO [train.py:715] (2/8) Epoch 0, batch 25100, loss[loss=0.1867, simple_loss=0.2407, pruned_loss=0.06633, over 4818.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2668, pruned_loss=0.07631, over 972654.09 frames.], batch size: 13, lr: 1.33e-03
2022-05-03 17:20:00,593 INFO [train.py:715] (2/8) Epoch 0, batch 25150, loss[loss=0.2316, simple_loss=0.276, pruned_loss=0.09357, over 4953.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2665, pruned_loss=0.07618, over 973027.12 frames.], batch size: 21, lr: 1.32e-03
2022-05-03 17:20:41,127 INFO [train.py:715] (2/8) Epoch 0, batch 25200, loss[loss=0.217, simple_loss=0.2867, pruned_loss=0.07367, over 4687.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2664, pruned_loss=0.07569, over 972847.64 frames.], batch size: 15, lr: 1.32e-03
2022-05-03 17:21:21,694 INFO [train.py:715] (2/8) Epoch 0, batch 25250, loss[loss=0.1518, simple_loss=0.2246, pruned_loss=0.03953, over 4890.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2655, pruned_loss=0.07508, over 973112.72 frames.], batch size: 22, lr: 1.32e-03
2022-05-03 17:22:02,259 INFO [train.py:715] (2/8) Epoch 0, batch 25300, loss[loss=0.2144, simple_loss=0.268, pruned_loss=0.08036, over 4749.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2669, pruned_loss=0.07609, over 972329.26 frames.], batch size: 19, lr: 1.32e-03
2022-05-03 17:22:42,089 INFO [train.py:715] (2/8) Epoch 0, batch 25350, loss[loss=0.205, simple_loss=0.2578, pruned_loss=0.07614, over 4808.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2678, pruned_loss=0.07704, over 972717.11 frames.], batch size: 25, lr: 1.32e-03
2022-05-03 17:23:22,548 INFO [train.py:715] (2/8) Epoch 0, batch 25400, loss[loss=0.2168, simple_loss=0.264, pruned_loss=0.08473, over 4840.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2665, pruned_loss=0.07647, over 972996.17 frames.], batch size: 12, lr: 1.32e-03
2022-05-03 17:24:02,717 INFO [train.py:715] (2/8) Epoch 0, batch 25450, loss[loss=0.2052, simple_loss=0.2655, pruned_loss=0.0725, over 4822.00 frames.], tot_loss[loss=0.2101, simple_loss=0.267, pruned_loss=0.0766, over 973091.72 frames.], batch size: 26, lr: 1.32e-03
2022-05-03 17:24:41,707 INFO [train.py:715] (2/8) Epoch 0, batch 25500, loss[loss=0.1896, simple_loss=0.2515, pruned_loss=0.06384, over 4781.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2671, pruned_loss=0.07677, over 972358.77 frames.], batch size: 18, lr: 1.32e-03
2022-05-03 17:25:22,418 INFO [train.py:715] (2/8) Epoch 0, batch 25550, loss[loss=0.2767, simple_loss=0.3127, pruned_loss=0.1203, over 4919.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2669, pruned_loss=0.07651, over 972948.44 frames.], batch size: 39, lr: 1.31e-03
2022-05-03 17:26:02,022 INFO [train.py:715] (2/8) Epoch 0, batch 25600, loss[loss=0.2154, simple_loss=0.264, pruned_loss=0.08347, over 4703.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2667, pruned_loss=0.07631, over 972955.08 frames.], batch size: 15, lr: 1.31e-03
2022-05-03 17:26:41,731 INFO [train.py:715] (2/8) Epoch 0, batch 25650, loss[loss=0.2072, simple_loss=0.2669, pruned_loss=0.07371, over 4935.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2671, pruned_loss=0.0759, over 972837.23 frames.], batch size: 23, lr: 1.31e-03
2022-05-03 17:27:21,448 INFO [train.py:715] (2/8) Epoch 0, batch 25700, loss[loss=0.2543, simple_loss=0.3047, pruned_loss=0.1019, over 4980.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2656, pruned_loss=0.0748, over 973007.92 frames.], batch size: 14, lr: 1.31e-03
2022-05-03 17:28:01,728 INFO [train.py:715] (2/8) Epoch 0, batch 25750, loss[loss=0.1672, simple_loss=0.2331, pruned_loss=0.05062, over 4798.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2662, pruned_loss=0.07546, over 972869.55 frames.], batch size: 24, lr: 1.31e-03
2022-05-03 17:28:41,508 INFO [train.py:715] (2/8) Epoch 0, batch 25800, loss[loss=0.1661, simple_loss=0.2333, pruned_loss=0.0495, over 4921.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2655, pruned_loss=0.07496, over 973633.85 frames.], batch size: 29, lr: 1.31e-03
2022-05-03 17:29:20,750 INFO [train.py:715] (2/8) Epoch 0, batch 25850, loss[loss=0.1716, simple_loss=0.2292, pruned_loss=0.05703, over 4778.00 frames.], tot_loss[loss=0.207, simple_loss=0.2648, pruned_loss=0.07461, over 973523.45 frames.], batch size: 18, lr: 1.31e-03
2022-05-03 17:30:01,468 INFO [train.py:715] (2/8) Epoch 0, batch 25900, loss[loss=0.2086, simple_loss=0.2673, pruned_loss=0.07495, over 4875.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2646, pruned_loss=0.07492, over 973257.04 frames.], batch size: 16, lr: 1.31e-03
2022-05-03 17:30:41,212 INFO [train.py:715] (2/8) Epoch 0, batch 25950, loss[loss=0.2282, simple_loss=0.2781, pruned_loss=0.08913, over 4935.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2655, pruned_loss=0.07537, over 973905.84 frames.], batch size: 39, lr: 1.30e-03
2022-05-03 17:31:21,221 INFO [train.py:715] (2/8) Epoch 0, batch 26000, loss[loss=0.2197, simple_loss=0.2769, pruned_loss=0.08122, over 4878.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2654, pruned_loss=0.07542, over 973295.81 frames.], batch size: 16, lr: 1.30e-03
2022-05-03 17:32:01,166 INFO [train.py:715] (2/8) Epoch 0, batch 26050, loss[loss=0.2038, simple_loss=0.2627, pruned_loss=0.0724, over 4808.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2646, pruned_loss=0.07516, over 972654.56 frames.], batch size: 26, lr: 1.30e-03
2022-05-03 17:32:41,630 INFO [train.py:715] (2/8) Epoch 0, batch 26100, loss[loss=0.1987, simple_loss=0.2452, pruned_loss=0.07611, over 4978.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2651, pruned_loss=0.0756, over 972226.94 frames.], batch size: 28, lr: 1.30e-03
2022-05-03 17:33:21,953 INFO [train.py:715] (2/8) Epoch 0, batch 26150, loss[loss=0.2467, simple_loss=0.2967, pruned_loss=0.09835, over 4927.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2637, pruned_loss=0.07499, over 972118.75 frames.], batch size: 39, lr: 1.30e-03
2022-05-03 17:34:00,852 INFO [train.py:715] (2/8) Epoch 0, batch 26200, loss[loss=0.1804, simple_loss=0.2496, pruned_loss=0.05558, over 4969.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2635, pruned_loss=0.07453, over 971525.68 frames.], batch size: 28, lr: 1.30e-03
2022-05-03 17:34:41,482 INFO [train.py:715] (2/8) Epoch 0, batch 26250, loss[loss=0.1853, simple_loss=0.242, pruned_loss=0.06426, over 4782.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2628, pruned_loss=0.07406, over 971537.03 frames.], batch size: 18, lr: 1.30e-03
2022-05-03 17:35:21,432 INFO [train.py:715] (2/8) Epoch 0, batch 26300, loss[loss=0.1836, simple_loss=0.2511, pruned_loss=0.05804, over 4851.00 frames.], tot_loss[loss=0.205, simple_loss=0.2625, pruned_loss=0.07378, over 971322.93 frames.], batch size: 34, lr: 1.30e-03
2022-05-03 17:36:01,274 INFO [train.py:715] (2/8) Epoch 0, batch 26350, loss[loss=0.2113, simple_loss=0.2607, pruned_loss=0.08092, over 4752.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2621, pruned_loss=0.07338, over 971357.50 frames.], batch size: 14, lr: 1.30e-03
2022-05-03 17:36:41,213 INFO [train.py:715] (2/8) Epoch 0, batch 26400, loss[loss=0.2188, simple_loss=0.2757, pruned_loss=0.08095, over 4985.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2633, pruned_loss=0.07451, over 971335.27 frames.], batch size: 25, lr: 1.29e-03
2022-05-03 17:37:21,336 INFO [train.py:715] (2/8) Epoch 0, batch 26450, loss[loss=0.215, simple_loss=0.2732, pruned_loss=0.07838, over 4702.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2628, pruned_loss=0.07397, over 971884.70 frames.], batch size: 15, lr: 1.29e-03
2022-05-03 17:38:02,045 INFO [train.py:715] (2/8) Epoch 0, batch 26500, loss[loss=0.1553, simple_loss=0.2148, pruned_loss=0.04789, over 4794.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2637, pruned_loss=0.07454, over 971838.75 frames.], batch size: 12, lr: 1.29e-03
2022-05-03 17:38:41,406 INFO [train.py:715] (2/8) Epoch 0, batch 26550, loss[loss=0.1809, simple_loss=0.248, pruned_loss=0.05692, over 4766.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2638, pruned_loss=0.07472, over 971846.18 frames.], batch size: 17, lr: 1.29e-03
2022-05-03 17:39:21,078 INFO [train.py:715] (2/8) Epoch 0, batch 26600, loss[loss=0.1889, simple_loss=0.2459, pruned_loss=0.06596, over 4931.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2642, pruned_loss=0.07504, over 971238.53 frames.], batch size: 18, lr: 1.29e-03
2022-05-03 17:40:01,330 INFO [train.py:715] (2/8) Epoch 0, batch 26650, loss[loss=0.1647, simple_loss=0.231, pruned_loss=0.04921, over 4790.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2655, pruned_loss=0.07556, over 971077.25 frames.], batch size: 18, lr: 1.29e-03
2022-05-03 17:40:40,791 INFO [train.py:715] (2/8) Epoch 0, batch 26700, loss[loss=0.2744, simple_loss=0.3255, pruned_loss=0.1117, over 4806.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2663, pruned_loss=0.07643, over 972257.84 frames.], batch size: 24, lr: 1.29e-03
2022-05-03 17:41:20,815 INFO [train.py:715] (2/8) Epoch 0, batch 26750, loss[loss=0.247, simple_loss=0.2934, pruned_loss=0.1003, over 4796.00 frames.], tot_loss[loss=0.21, simple_loss=0.2666, pruned_loss=0.07666, over 972227.91 frames.], batch size: 14, lr: 1.29e-03
2022-05-03 17:42:01,244 INFO [train.py:715] (2/8) Epoch 0, batch 26800, loss[loss=0.23, simple_loss=0.2757, pruned_loss=0.09216, over 4816.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2666, pruned_loss=0.07629, over 972130.09 frames.], batch size: 26, lr: 1.28e-03
2022-05-03 17:42:41,664 INFO [train.py:715] (2/8) Epoch 0, batch 26850, loss[loss=0.1964, simple_loss=0.2421, pruned_loss=0.07539, over 4820.00 frames.], tot_loss[loss=0.2102, simple_loss=0.267, pruned_loss=0.07674, over 972156.45 frames.], batch size: 15, lr: 1.28e-03
2022-05-03 17:43:21,527 INFO [train.py:715] (2/8) Epoch 0, batch 26900, loss[loss=0.2169, simple_loss=0.2716, pruned_loss=0.08105, over 4788.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2665, pruned_loss=0.07612, over 972177.42 frames.], batch size: 24, lr: 1.28e-03
2022-05-03 17:44:02,259 INFO [train.py:715] (2/8) Epoch 0, batch 26950, loss[loss=0.2325, simple_loss=0.3025, pruned_loss=0.08126, over 4981.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2664, pruned_loss=0.07615, over 972105.97 frames.], batch size: 24, lr: 1.28e-03
2022-05-03 17:44:42,413 INFO [train.py:715] (2/8) Epoch 0, batch 27000, loss[loss=0.1754, simple_loss=0.2444, pruned_loss=0.05322, over 4802.00 frames.], tot_loss[loss=0.2086, simple_loss=0.266, pruned_loss=0.07562, over 972402.07 frames.], batch size: 25, lr: 1.28e-03
2022-05-03 17:44:42,414 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 17:44:51,201 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.1338, simple_loss=0.2208, pruned_loss=0.02337, over 914524.00 frames.
2022-05-03 17:45:31,269 INFO [train.py:715] (2/8) Epoch 0, batch 27050, loss[loss=0.1783, simple_loss=0.2353, pruned_loss=0.06067, over 4782.00 frames.], tot_loss[loss=0.208, simple_loss=0.2653, pruned_loss=0.07536, over 972535.48 frames.], batch size: 17, lr: 1.28e-03
2022-05-03 17:46:10,740 INFO [train.py:715] (2/8) Epoch 0, batch 27100, loss[loss=0.3044, simple_loss=0.3265, pruned_loss=0.1411, over 4848.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2656, pruned_loss=0.07574, over 972613.22 frames.], batch size: 30, lr: 1.28e-03
2022-05-03 17:46:51,326 INFO [train.py:715] (2/8) Epoch 0, batch 27150, loss[loss=0.2124, simple_loss=0.2769, pruned_loss=0.07398, over 4836.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2658, pruned_loss=0.07557, over 973093.92 frames.], batch size: 30, lr: 1.28e-03
2022-05-03 17:47:31,708 INFO [train.py:715] (2/8) Epoch 0, batch 27200, loss[loss=0.1756, simple_loss=0.2435, pruned_loss=0.05387, over 4787.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2658, pruned_loss=0.0753, over 972369.74 frames.], batch size: 18, lr: 1.28e-03
2022-05-03 17:48:11,808 INFO [train.py:715] (2/8) Epoch 0, batch 27250, loss[loss=0.1649, simple_loss=0.2387, pruned_loss=0.04557, over 4931.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2654, pruned_loss=0.07481, over 972588.53 frames.], batch size: 21, lr: 1.27e-03
2022-05-03 17:48:51,954 INFO [train.py:715] (2/8) Epoch 0, batch 27300, loss[loss=0.1727, simple_loss=0.2415, pruned_loss=0.05191, over 4987.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2651, pruned_loss=0.07451, over 971728.29 frames.], batch size: 28, lr: 1.27e-03
2022-05-03 17:49:31,858 INFO [train.py:715] (2/8) Epoch 0, batch 27350, loss[loss=0.1899, simple_loss=0.2636, pruned_loss=0.05807, over 4902.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2659, pruned_loss=0.07519, over 971664.21 frames.], batch size: 16, lr: 1.27e-03
2022-05-03 17:50:11,821 INFO [train.py:715] (2/8) Epoch 0, batch 27400, loss[loss=0.1829, simple_loss=0.2556, pruned_loss=0.05507, over 4779.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2659, pruned_loss=0.07535, over 971103.82 frames.], batch size: 17, lr: 1.27e-03
2022-05-03 17:50:51,090 INFO [train.py:715] (2/8) Epoch 0, batch 27450, loss[loss=0.2143, simple_loss=0.2664, pruned_loss=0.08108, over 4857.00 frames.], tot_loss[loss=0.2079, simple_loss=0.265, pruned_loss=0.07539, over 971534.68 frames.], batch size: 20, lr: 1.27e-03
2022-05-03 17:51:31,233 INFO [train.py:715] (2/8) Epoch 0, batch 27500, loss[loss=0.1633, simple_loss=0.2335, pruned_loss=0.04652, over 4819.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2645, pruned_loss=0.0749, over 972098.49 frames.], batch size: 13, lr: 1.27e-03
2022-05-03 17:52:11,046 INFO [train.py:715] (2/8) Epoch 0, batch 27550, loss[loss=0.1796, simple_loss=0.2501, pruned_loss=0.05457, over 4808.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2651, pruned_loss=0.07465, over 972873.74 frames.], batch size: 25, lr: 1.27e-03
2022-05-03 17:52:50,534 INFO [train.py:715] (2/8) Epoch 0, batch 27600, loss[loss=0.1525, simple_loss=0.2252, pruned_loss=0.03989, over 4864.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2643, pruned_loss=0.07394, over 972566.41 frames.], batch size: 32, lr: 1.27e-03
2022-05-03 17:53:29,964 INFO [train.py:715] (2/8) Epoch 0, batch 27650, loss[loss=0.2054, simple_loss=0.2563, pruned_loss=0.07728, over 4693.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2635, pruned_loss=0.07393, over 971600.86 frames.], batch size: 15, lr: 1.27e-03
2022-05-03 17:54:09,968 INFO [train.py:715] (2/8) Epoch 0, batch 27700, loss[loss=0.2136, simple_loss=0.2678, pruned_loss=0.07973, over 4795.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2642, pruned_loss=0.07473, over 972470.95 frames.], batch size: 21, lr: 1.26e-03
2022-05-03 17:54:50,337 INFO [train.py:715] (2/8) Epoch 0, batch 27750, loss[loss=0.1677, simple_loss=0.237, pruned_loss=0.04924, over 4897.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2625, pruned_loss=0.07351, over 971984.01 frames.], batch size: 17, lr: 1.26e-03
2022-05-03 17:55:30,112 INFO [train.py:715] (2/8) Epoch 0, batch 27800, loss[loss=0.2276, simple_loss=0.276, pruned_loss=0.08961, over 4847.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2621, pruned_loss=0.07333, over 972870.67 frames.], batch size: 32, lr: 1.26e-03
2022-05-03 17:56:10,379 INFO [train.py:715] (2/8) Epoch 0, batch 27850, loss[loss=0.2494, simple_loss=0.306, pruned_loss=0.09644, over 4941.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2637, pruned_loss=0.07433, over 973207.16 frames.], batch size: 21, lr: 1.26e-03
2022-05-03 17:56:49,937 INFO [train.py:715] (2/8) Epoch 0, batch 27900, loss[loss=0.1983, simple_loss=0.2602, pruned_loss=0.06819, over 4988.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2635, pruned_loss=0.0747, over 973293.11 frames.], batch size: 14, lr: 1.26e-03
2022-05-03 17:57:29,404 INFO [train.py:715] (2/8) Epoch 0, batch 27950, loss[loss=0.2081, simple_loss=0.2623, pruned_loss=0.07693, over 4815.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2634, pruned_loss=0.07453, over 973406.66 frames.], batch size: 25, lr: 1.26e-03
2022-05-03 17:58:09,427 INFO [train.py:715] (2/8) Epoch 0, batch 28000, loss[loss=0.2181, simple_loss=0.2616, pruned_loss=0.08726, over 4765.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2639, pruned_loss=0.07396, over 973214.29 frames.], batch size: 17, lr: 1.26e-03
2022-05-03 17:58:49,654 INFO [train.py:715] (2/8) Epoch 0, batch 28050, loss[loss=0.1728, simple_loss=0.2387, pruned_loss=0.05345, over 4795.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2641, pruned_loss=0.07428, over 972839.71 frames.], batch size: 24, lr: 1.26e-03
2022-05-03 17:59:29,704 INFO [train.py:715] (2/8) Epoch 0, batch 28100, loss[loss=0.2189, simple_loss=0.2857, pruned_loss=0.07611, over 4778.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2637, pruned_loss=0.074, over 973129.00 frames.], batch size: 18, lr: 1.26e-03
2022-05-03 18:00:08,959 INFO [train.py:715] (2/8) Epoch 0, batch 28150, loss[loss=0.1732, simple_loss=0.2387, pruned_loss=0.05379, over 4960.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2643, pruned_loss=0.07353, over 972619.14 frames.], batch size: 24, lr: 1.25e-03
2022-05-03 18:00:49,197 INFO [train.py:715] (2/8) Epoch 0, batch 28200, loss[loss=0.2163, simple_loss=0.2688, pruned_loss=0.0819, over 4836.00 frames.], tot_loss[loss=0.205, simple_loss=0.2637, pruned_loss=0.07314, over 973392.54 frames.], batch size: 15, lr: 1.25e-03
2022-05-03 18:01:28,904 INFO [train.py:715] (2/8) Epoch 0, batch 28250, loss[loss=0.1673, simple_loss=0.2389, pruned_loss=0.04781, over 4858.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2641, pruned_loss=0.07373, over 972962.13 frames.], batch size: 30, lr: 1.25e-03
2022-05-03 18:02:07,671 INFO [train.py:715] (2/8) Epoch 0, batch 28300, loss[loss=0.1794, simple_loss=0.2277, pruned_loss=0.06554, over 4984.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2635, pruned_loss=0.07369, over 972530.97 frames.], batch size: 14, lr: 1.25e-03
2022-05-03 18:02:48,210 INFO [train.py:715] (2/8) Epoch 0, batch 28350, loss[loss=0.171, simple_loss=0.224, pruned_loss=0.05904, over 4970.00 frames.], tot_loss[loss=0.206, simple_loss=0.264, pruned_loss=0.07396, over 973759.06 frames.], batch size: 15, lr: 1.25e-03
2022-05-03 18:03:27,708 INFO [train.py:715] (2/8) Epoch 0, batch 28400, loss[loss=0.2052, simple_loss=0.2517, pruned_loss=0.07934, over 4938.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2639, pruned_loss=0.07348, over 973263.76 frames.], batch size: 35, lr: 1.25e-03
2022-05-03 18:04:07,955 INFO [train.py:715] (2/8) Epoch 0, batch 28450, loss[loss=0.2284, simple_loss=0.2831, pruned_loss=0.08684, over 4976.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2639, pruned_loss=0.0742, over 973269.67 frames.], batch size: 24, lr: 1.25e-03
2022-05-03 18:04:47,628 INFO [train.py:715] (2/8) Epoch 0, batch 28500, loss[loss=0.1901, simple_loss=0.2414, pruned_loss=0.06939, over 4744.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2626, pruned_loss=0.07339, over 974175.07 frames.], batch size: 16, lr: 1.25e-03
2022-05-03 18:05:28,101 INFO [train.py:715] (2/8) Epoch 0, batch 28550, loss[loss=0.2116, simple_loss=0.2639, pruned_loss=0.0797, over 4912.00 frames.], tot_loss[loss=0.2046, simple_loss=0.263, pruned_loss=0.07311, over 973698.98 frames.], batch size: 19, lr: 1.25e-03
2022-05-03 18:06:07,730 INFO [train.py:715] (2/8) Epoch 0, batch 28600, loss[loss=0.1613, simple_loss=0.2274, pruned_loss=0.04758, over 4748.00 frames.], tot_loss[loss=0.2049, simple_loss=0.263, pruned_loss=0.07338, over 972821.61 frames.], batch size: 19, lr: 1.24e-03
2022-05-03 18:06:46,957 INFO [train.py:715] (2/8) Epoch 0, batch 28650, loss[loss=0.2088, simple_loss=0.2766, pruned_loss=0.07055, over 4903.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2636, pruned_loss=0.07358, over 972495.73 frames.], batch size: 17, lr: 1.24e-03
2022-05-03 18:07:26,839 INFO [train.py:715] (2/8) Epoch 0, batch 28700, loss[loss=0.1903, simple_loss=0.2573, pruned_loss=0.06162, over 4954.00 frames.], tot_loss[loss=0.2046, simple_loss=0.263, pruned_loss=0.07314, over 972677.13 frames.], batch size: 15, lr: 1.24e-03
2022-05-03 18:08:06,481 INFO [train.py:715] (2/8) Epoch 0, batch 28750, loss[loss=0.2261, simple_loss=0.2788, pruned_loss=0.08671, over 4868.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2635, pruned_loss=0.07355, over 972921.75 frames.], batch size: 22, lr: 1.24e-03
2022-05-03 18:08:46,803 INFO [train.py:715] (2/8) Epoch 0, batch 28800, loss[loss=0.1758, simple_loss=0.2305, pruned_loss=0.06052, over 4868.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2633, pruned_loss=0.07301, over 972728.50 frames.], batch size: 30, lr: 1.24e-03
2022-05-03 18:09:25,920 INFO [train.py:715] (2/8) Epoch 0, batch 28850, loss[loss=0.1707, simple_loss=0.2392, pruned_loss=0.05114, over 4739.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2618, pruned_loss=0.07194, over 972564.30 frames.], batch size: 16, lr: 1.24e-03
2022-05-03 18:10:05,946 INFO [train.py:715] (2/8) Epoch 0, batch 28900, loss[loss=0.2375, simple_loss=0.2935, pruned_loss=0.09079, over 4756.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2623, pruned_loss=0.0724, over 972882.45 frames.], batch size: 19, lr: 1.24e-03
2022-05-03 18:10:45,829 INFO [train.py:715] (2/8) Epoch 0, batch 28950, loss[loss=0.2313, simple_loss=0.2859, pruned_loss=0.08842, over 4692.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2617, pruned_loss=0.07144, over 971932.18 frames.], batch size: 15, lr: 1.24e-03
2022-05-03 18:11:24,702 INFO [train.py:715] (2/8) Epoch 0, batch 29000, loss[loss=0.2177, simple_loss=0.2634, pruned_loss=0.08596, over 4957.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2616, pruned_loss=0.07212, over 972364.72 frames.], batch size: 35, lr: 1.24e-03
2022-05-03 18:12:05,305 INFO [train.py:715] (2/8) Epoch 0, batch 29050, loss[loss=0.1968, simple_loss=0.2598, pruned_loss=0.06686, over 4754.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2625, pruned_loss=0.07209, over 972256.38 frames.], batch size: 19, lr: 1.24e-03
2022-05-03 18:12:45,437 INFO [train.py:715] (2/8) Epoch 0, batch 29100, loss[loss=0.2209, simple_loss=0.2834, pruned_loss=0.07924, over 4698.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2624, pruned_loss=0.07227, over 972337.98 frames.], batch size: 15, lr: 1.23e-03
2022-05-03 18:13:25,062 INFO [train.py:715] (2/8) Epoch 0, batch 29150, loss[loss=0.2246, simple_loss=0.2761, pruned_loss=0.08651, over 4891.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2622, pruned_loss=0.07206, over 971936.71 frames.], batch size: 19, lr: 1.23e-03
2022-05-03 18:14:04,263 INFO [train.py:715] (2/8) Epoch 0, batch 29200, loss[loss=0.1925, simple_loss=0.2454, pruned_loss=0.0698, over 4833.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2622, pruned_loss=0.0723, over 972967.55 frames.], batch size: 15, lr: 1.23e-03
2022-05-03 18:14:44,206 INFO [train.py:715] (2/8) Epoch 0, batch 29250, loss[loss=0.2248, simple_loss=0.2781, pruned_loss=0.08574, over 4800.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2629, pruned_loss=0.07282, over 972970.34 frames.], batch size: 17, lr: 1.23e-03
2022-05-03 18:15:24,223 INFO [train.py:715] (2/8) Epoch 0, batch 29300, loss[loss=0.177, simple_loss=0.2506, pruned_loss=0.05167, over 4816.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2643, pruned_loss=0.07413, over 971834.07 frames.], batch size: 25, lr: 1.23e-03
2022-05-03 18:16:04,635 INFO [train.py:715] (2/8) Epoch 0, batch 29350, loss[loss=0.2014, simple_loss=0.27, pruned_loss=0.06634, over 4945.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2641, pruned_loss=0.07407, over 972044.72 frames.], batch size: 29, lr: 1.23e-03
2022-05-03 18:16:44,078 INFO [train.py:715] (2/8) Epoch 0, batch 29400, loss[loss=0.2233, simple_loss=0.2751, pruned_loss=0.08577, over 4848.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2646, pruned_loss=0.07435, over 971653.87 frames.], batch size: 30, lr: 1.23e-03
2022-05-03 18:17:23,549 INFO [train.py:715] (2/8) Epoch 0, batch 29450, loss[loss=0.2175, simple_loss=0.2692, pruned_loss=0.08285, over 4887.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2638, pruned_loss=0.0737, over 971173.63 frames.], batch size: 19, lr: 1.23e-03
2022-05-03 18:18:03,743 INFO [train.py:715] (2/8) Epoch 0, batch 29500, loss[loss=0.1956, simple_loss=0.2563, pruned_loss=0.06751, over 4829.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2625, pruned_loss=0.07294, over 971873.44 frames.], batch size: 30, lr: 1.23e-03
2022-05-03 18:18:42,855 INFO [train.py:715] (2/8) Epoch 0, batch 29550, loss[loss=0.222, simple_loss=0.2844, pruned_loss=0.07975, over 4803.00 frames.], tot_loss[loss=0.205, simple_loss=0.263, pruned_loss=0.0735, over 972600.99 frames.], batch size: 25, lr: 1.23e-03
2022-05-03 18:19:23,014 INFO [train.py:715] (2/8) Epoch 0, batch 29600, loss[loss=0.1899, simple_loss=0.2553, pruned_loss=0.06229, over 4877.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2633, pruned_loss=0.07373, over 972453.07 frames.], batch size: 22, lr: 1.22e-03
2022-05-03 18:20:02,958 INFO [train.py:715] (2/8) Epoch 0, batch 29650, loss[loss=0.2017, simple_loss=0.2542, pruned_loss=0.0746, over 4796.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2634, pruned_loss=0.07401, over 972937.82 frames.], batch size: 12, lr: 1.22e-03
2022-05-03 18:20:42,825 INFO [train.py:715] (2/8) Epoch 0, batch 29700, loss[loss=0.187, simple_loss=0.2485, pruned_loss=0.06276, over 4918.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2625, pruned_loss=0.07338, over 972535.42 frames.], batch size: 23, lr: 1.22e-03
2022-05-03 18:21:23,321 INFO [train.py:715] (2/8) Epoch 0, batch 29750, loss[loss=0.228, simple_loss=0.2867, pruned_loss=0.08466, over 4979.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2631, pruned_loss=0.07326, over 972316.51 frames.], batch size: 35, lr: 1.22e-03
2022-05-03 18:22:03,146 INFO [train.py:715] (2/8) Epoch 0, batch 29800, loss[loss=0.1922, simple_loss=0.2487, pruned_loss=0.06788, over 4756.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2618, pruned_loss=0.07239, over 971618.93 frames.], batch size: 16, lr: 1.22e-03
2022-05-03 18:22:44,054 INFO [train.py:715] (2/8) Epoch 0, batch 29850, loss[loss=0.2236, simple_loss=0.29, pruned_loss=0.07862, over 4818.00 frames.], tot_loss[loss=0.204, simple_loss=0.262, pruned_loss=0.07295, over 971924.38 frames.], batch size: 26, lr: 1.22e-03
2022-05-03 18:23:23,984 INFO [train.py:715] (2/8) Epoch 0, batch 29900, loss[loss=0.1872, simple_loss=0.249, pruned_loss=0.06271, over 4772.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2619, pruned_loss=0.07267, over 972005.45 frames.], batch size: 18, lr: 1.22e-03
2022-05-03 18:24:03,882 INFO [train.py:715] (2/8) Epoch 0, batch 29950, loss[loss=0.1965, simple_loss=0.2574, pruned_loss=0.06783, over 4919.00 frames.], tot_loss[loss=0.2027, simple_loss=0.261, pruned_loss=0.07219, over 971925.45 frames.], batch size: 23, lr: 1.22e-03
2022-05-03 18:24:43,763 INFO [train.py:715] (2/8) Epoch 0, batch 30000, loss[loss=0.2003, simple_loss=0.2605, pruned_loss=0.07008, over 4899.00 frames.], tot_loss[loss=0.2038, simple_loss=0.262, pruned_loss=0.07279, over 972460.02 frames.], batch size: 19, lr: 1.22e-03
2022-05-03 18:24:43,764 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 18:25:00,379 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.1316, simple_loss=0.2189, pruned_loss=0.02213, over 914524.00 frames.
2022-05-03 18:25:40,679 INFO [train.py:715] (2/8) Epoch 0, batch 30050, loss[loss=0.169, simple_loss=0.2477, pruned_loss=0.04515, over 4947.00 frames.], tot_loss[loss=0.204, simple_loss=0.2623, pruned_loss=0.07282, over 972559.73 frames.], batch size: 21, lr: 1.22e-03
2022-05-03 18:26:21,232 INFO [train.py:715] (2/8) Epoch 0, batch 30100, loss[loss=0.1709, simple_loss=0.2387, pruned_loss=0.05157, over 4837.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2617, pruned_loss=0.07244, over 972205.03 frames.], batch size: 30, lr: 1.21e-03
2022-05-03 18:27:01,911 INFO [train.py:715] (2/8) Epoch 0, batch 30150, loss[loss=0.1978, simple_loss=0.2707, pruned_loss=0.06239, over 4902.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2623, pruned_loss=0.07297, over 973061.19 frames.], batch size: 17, lr: 1.21e-03
2022-05-03 18:27:42,048 INFO [train.py:715] (2/8) Epoch 0, batch 30200, loss[loss=0.2025, simple_loss=0.2588, pruned_loss=0.0731, over 4941.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2624, pruned_loss=0.0725, over 971880.95 frames.], batch size: 21, lr: 1.21e-03
2022-05-03 18:28:22,539 INFO [train.py:715] (2/8) Epoch 0, batch 30250, loss[loss=0.1919, simple_loss=0.2608, pruned_loss=0.06146, over 4782.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2627, pruned_loss=0.07233, over 971556.18 frames.], batch size: 18, lr: 1.21e-03
2022-05-03 18:29:02,639 INFO [train.py:715] (2/8) Epoch 0, batch 30300, loss[loss=0.1769, simple_loss=0.251, pruned_loss=0.0514, over 4799.00 frames.], tot_loss[loss=0.2024, simple_loss=0.262, pruned_loss=0.07138, over 971379.54 frames.], batch size: 21, lr: 1.21e-03
2022-05-03 18:29:43,070 INFO [train.py:715] (2/8) Epoch 0, batch 30350, loss[loss=0.2243, simple_loss=0.2879, pruned_loss=0.08034, over 4834.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2611, pruned_loss=0.07122, over 970204.45 frames.], batch size: 27, lr: 1.21e-03
2022-05-03 18:30:23,196 INFO [train.py:715] (2/8) Epoch 0, batch 30400, loss[loss=0.2075, simple_loss=0.2636, pruned_loss=0.07568, over 4978.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2612, pruned_loss=0.07196, over 970721.62 frames.], batch size: 35, lr: 1.21e-03
2022-05-03 18:31:02,964 INFO [train.py:715] (2/8) Epoch 0, batch 30450, loss[loss=0.217, simple_loss=0.2759, pruned_loss=0.07905, over 4792.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2606, pruned_loss=0.07164, over 970583.39 frames.], batch size: 17, lr: 1.21e-03
2022-05-03 18:31:42,718 INFO [train.py:715] (2/8) Epoch 0, batch 30500, loss[loss=0.2095, simple_loss=0.2683, pruned_loss=0.07534, over 4913.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2604, pruned_loss=0.07168, over 970493.17 frames.], batch size: 23, lr: 1.21e-03
2022-05-03 18:32:22,637 INFO [train.py:715] (2/8) Epoch 0, batch 30550, loss[loss=0.155, simple_loss=0.2292, pruned_loss=0.04037, over 4840.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2602, pruned_loss=0.07174, over 970798.35 frames.], batch size: 13, lr: 1.21e-03
2022-05-03 18:33:01,758 INFO [train.py:715] (2/8) Epoch 0, batch 30600, loss[loss=0.2032, simple_loss=0.2567, pruned_loss=0.07487, over 4748.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2607, pruned_loss=0.07187, over 971129.57 frames.], batch size: 16, lr: 1.20e-03
2022-05-03 18:33:41,699 INFO [train.py:715] (2/8) Epoch 0, batch 30650, loss[loss=0.2258, simple_loss=0.2905, pruned_loss=0.0806, over 4875.00 frames.], tot_loss[loss=0.2025, simple_loss=0.261, pruned_loss=0.07194, over 971066.62 frames.], batch size: 16, lr: 1.20e-03
2022-05-03 18:34:21,516 INFO [train.py:715] (2/8) Epoch 0, batch 30700, loss[loss=0.1894, simple_loss=0.2549, pruned_loss=0.062, over 4948.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2617, pruned_loss=0.0724, over 971450.53 frames.], batch size: 29, lr: 1.20e-03
2022-05-03 18:35:01,616 INFO [train.py:715] (2/8) Epoch 0, batch 30750, loss[loss=0.1745, simple_loss=0.2399, pruned_loss=0.0546, over 4883.00 frames.], tot_loss[loss=0.203, simple_loss=0.2618, pruned_loss=0.07211, over 972334.32 frames.], batch size: 22, lr: 1.20e-03
2022-05-03 18:35:40,966 INFO [train.py:715] (2/8) Epoch 0, batch 30800, loss[loss=0.1919, simple_loss=0.2581, pruned_loss=0.06281, over 4810.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2616, pruned_loss=0.07193, over 972031.55 frames.], batch size: 12, lr: 1.20e-03
2022-05-03 18:36:21,303 INFO [train.py:715] (2/8) Epoch 0, batch 30850, loss[loss=0.2316, simple_loss=0.2876, pruned_loss=0.08783, over 4952.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2625, pruned_loss=0.07264, over 972361.14 frames.], batch size: 21, lr: 1.20e-03
2022-05-03 18:37:01,148 INFO [train.py:715] (2/8) Epoch 0, batch 30900, loss[loss=0.212, simple_loss=0.2727, pruned_loss=0.07561, over 4920.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2615, pruned_loss=0.07217, over 972434.01 frames.], batch size: 23, lr: 1.20e-03
2022-05-03 18:37:40,859 INFO [train.py:715] (2/8) Epoch 0, batch 30950, loss[loss=0.2127, simple_loss=0.2795, pruned_loss=0.07294, over 4938.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2617, pruned_loss=0.07185, over 973111.79 frames.], batch size: 21, lr: 1.20e-03
2022-05-03 18:38:20,949 INFO [train.py:715] (2/8) Epoch 0, batch 31000, loss[loss=0.2215, simple_loss=0.2715, pruned_loss=0.08576, over 4816.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2624, pruned_loss=0.07232, over 972158.48 frames.], batch size: 26, lr: 1.20e-03
2022-05-03 18:39:00,963 INFO [train.py:715] (2/8) Epoch 0, batch 31050, loss[loss=0.2162, simple_loss=0.2749, pruned_loss=0.07879, over 4902.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2614, pruned_loss=0.07146, over 972580.25 frames.], batch size: 19, lr: 1.20e-03
2022-05-03 18:39:40,370 INFO [train.py:715] (2/8) Epoch 0, batch 31100, loss[loss=0.1969, simple_loss=0.2435, pruned_loss=0.07516, over 4918.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2605, pruned_loss=0.07064, over 972285.30 frames.], batch size: 18, lr: 1.20e-03
2022-05-03 18:40:19,538 INFO [train.py:715] (2/8) Epoch 0, batch 31150, loss[loss=0.2374, simple_loss=0.2878, pruned_loss=0.09347, over 4855.00 frames.], tot_loss[loss=0.2018, simple_loss=0.261, pruned_loss=0.07134, over 970982.48 frames.], batch size: 32, lr: 1.19e-03
2022-05-03 18:40:59,611 INFO [train.py:715] (2/8) Epoch 0, batch 31200, loss[loss=0.1721, simple_loss=0.2216, pruned_loss=0.06127, over 4793.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2605, pruned_loss=0.07125, over 970802.47 frames.], batch size: 12, lr: 1.19e-03
2022-05-03 18:41:39,404 INFO [train.py:715] (2/8) Epoch 0, batch 31250, loss[loss=0.2207, simple_loss=0.2699, pruned_loss=0.08573, over 4973.00 frames.], tot_loss[loss=0.2021, simple_loss=0.261, pruned_loss=0.07163, over 971915.92 frames.], batch size: 24, lr: 1.19e-03
2022-05-03 18:42:18,880 INFO [train.py:715] (2/8) Epoch 0, batch 31300, loss[loss=0.1635, simple_loss=0.2239, pruned_loss=0.05155, over 4866.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2595, pruned_loss=0.07067, over 971592.27 frames.], batch size: 20, lr: 1.19e-03
2022-05-03 18:42:59,216 INFO [train.py:715] (2/8) Epoch 0, batch 31350, loss[loss=0.2527, simple_loss=0.3104, pruned_loss=0.09747, over 4931.00 frames.], tot_loss[loss=0.2, simple_loss=0.2592, pruned_loss=0.07046, over 972688.79 frames.], batch size: 21, lr: 1.19e-03
2022-05-03 18:43:38,891 INFO [train.py:715] (2/8) Epoch 0, batch 31400, loss[loss=0.1999, simple_loss=0.2594, pruned_loss=0.07021, over 4925.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2598, pruned_loss=0.07093, over 972340.83 frames.], batch size: 23, lr: 1.19e-03
2022-05-03 18:44:18,167 INFO [train.py:715] (2/8) Epoch 0, batch 31450, loss[loss=0.185, simple_loss=0.2486, pruned_loss=0.06064, over 4765.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2607, pruned_loss=0.07126, over 971297.45 frames.], batch size: 14, lr: 1.19e-03
2022-05-03 18:44:57,271 INFO [train.py:715] (2/8) Epoch 0, batch 31500, loss[loss=0.2128, simple_loss=0.2585, pruned_loss=0.08349, over 4892.00 frames.], tot_loss[loss=0.2012, simple_loss=0.26, pruned_loss=0.07118, over 971810.22 frames.], batch size: 22, lr: 1.19e-03
2022-05-03 18:45:37,318 INFO [train.py:715] (2/8) Epoch 0, batch 31550, loss[loss=0.1848, simple_loss=0.249, pruned_loss=0.06026, over 4766.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2607, pruned_loss=0.07133, over 972944.94 frames.], batch size: 18, lr: 1.19e-03
2022-05-03 18:46:17,099 INFO [train.py:715] (2/8) Epoch 0, batch 31600, loss[loss=0.2056, simple_loss=0.2581, pruned_loss=0.07652, over 4869.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2603, pruned_loss=0.07046, over 971821.28 frames.], batch size: 16, lr: 1.19e-03
2022-05-03 18:46:56,331 INFO [train.py:715] (2/8) Epoch 0, batch 31650, loss[loss=0.204, simple_loss=0.255, pruned_loss=0.07653, over 4788.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2613, pruned_loss=0.0712, over 971855.73 frames.], batch size: 18, lr: 1.19e-03
2022-05-03 18:47:36,242 INFO [train.py:715] (2/8) Epoch 0, batch 31700, loss[loss=0.173, simple_loss=0.2333, pruned_loss=0.05639, over 4840.00 frames.], tot_loss[loss=0.2013, simple_loss=0.261, pruned_loss=0.07082, over 972067.75 frames.], batch size: 26, lr: 1.18e-03
2022-05-03 18:48:16,467 INFO [train.py:715] (2/8) Epoch 0, batch 31750, loss[loss=0.1775, simple_loss=0.2467, pruned_loss=0.05419, over 4785.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2603, pruned_loss=0.07069, over 971749.03 frames.], batch size: 18, lr: 1.18e-03
2022-05-03 18:48:56,197 INFO [train.py:715] (2/8) Epoch 0, batch 31800, loss[loss=0.1784, simple_loss=0.2382, pruned_loss=0.05937, over 4903.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2594, pruned_loss=0.07016, over 972236.34 frames.], batch size: 19, lr: 1.18e-03
2022-05-03 18:49:35,464 INFO [train.py:715] (2/8) Epoch 0, batch 31850, loss[loss=0.1862, simple_loss=0.2554, pruned_loss=0.05844, over 4881.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2588, pruned_loss=0.06965, over 972140.44 frames.], batch size: 22, lr: 1.18e-03
2022-05-03 18:50:15,962 INFO [train.py:715] (2/8) Epoch 0, batch 31900, loss[loss=0.1704, simple_loss=0.2374, pruned_loss=0.0517, over 4810.00 frames.], tot_loss[loss=0.198, simple_loss=0.2576, pruned_loss=0.06915, over 971766.38 frames.], batch size: 21, lr: 1.18e-03
2022-05-03 18:50:55,668 INFO [train.py:715] (2/8) Epoch 0, batch 31950, loss[loss=0.1714, simple_loss=0.2456, pruned_loss=0.0486, over 4822.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2592, pruned_loss=0.07012, over 971721.38 frames.], batch size: 25, lr: 1.18e-03
2022-05-03 18:51:37,231 INFO [train.py:715] (2/8) Epoch 0, batch 32000, loss[loss=0.1788, simple_loss=0.246, pruned_loss=0.05582, over 4916.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2599, pruned_loss=0.07074, over 972045.22 frames.], batch size: 23, lr: 1.18e-03
2022-05-03 18:52:17,382 INFO [train.py:715] (2/8) Epoch 0, batch 32050, loss[loss=0.1771, simple_loss=0.2398, pruned_loss=0.05723, over 4838.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2588, pruned_loss=0.06986, over 972136.50 frames.], batch size: 12, lr: 1.18e-03
2022-05-03 18:52:57,280 INFO [train.py:715] (2/8) Epoch 0, batch 32100, loss[loss=0.1547, simple_loss=0.2264, pruned_loss=0.04151, over 4884.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2579, pruned_loss=0.06937, over 972321.49 frames.], batch size: 19, lr: 1.18e-03
2022-05-03 18:53:36,622 INFO [train.py:715] (2/8) Epoch 0, batch 32150, loss[loss=0.2612, simple_loss=0.3026, pruned_loss=0.1099, over 4856.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2587, pruned_loss=0.07004, over 972900.87 frames.], batch size: 34, lr: 1.18e-03
2022-05-03 18:54:15,805 INFO [train.py:715] (2/8) Epoch 0, batch 32200, loss[loss=0.1839, simple_loss=0.2517, pruned_loss=0.05805, over 4777.00 frames.], tot_loss[loss=0.2009, simple_loss=0.26, pruned_loss=0.07092, over 972492.88 frames.], batch size: 12, lr: 1.18e-03
2022-05-03 18:54:55,959 INFO [train.py:715] (2/8) Epoch 0, batch 32250, loss[loss=0.2181, simple_loss=0.2671, pruned_loss=0.08455, over 4906.00 frames.], tot_loss[loss=0.2007, simple_loss=0.26, pruned_loss=0.07075, over 972974.27 frames.], batch size: 19, lr: 1.17e-03
2022-05-03 18:55:35,805 INFO [train.py:715] (2/8) Epoch 0, batch 32300, loss[loss=0.194, simple_loss=0.2641, pruned_loss=0.06198, over 4967.00 frames.], tot_loss[loss=0.2006, simple_loss=0.26, pruned_loss=0.07055, over 973611.79 frames.], batch size: 15, lr: 1.17e-03
2022-05-03 18:56:15,317 INFO [train.py:715] (2/8) Epoch 0, batch 32350, loss[loss=0.2049, simple_loss=0.2568, pruned_loss=0.07648, over 4941.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2596, pruned_loss=0.06997, over 973396.62 frames.], batch size: 21, lr: 1.17e-03
2022-05-03 18:56:55,309 INFO [train.py:715] (2/8) Epoch 0, batch 32400, loss[loss=0.2082, simple_loss=0.2592, pruned_loss=0.07861, over 4908.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2586, pruned_loss=0.06949, over 973519.73 frames.], batch size: 19, lr: 1.17e-03
2022-05-03 18:57:35,383 INFO [train.py:715] (2/8) Epoch 0, batch 32450, loss[loss=0.1886, simple_loss=0.2409, pruned_loss=0.06821, over 4870.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2605, pruned_loss=0.07096, over 973115.63 frames.], batch size: 22, lr: 1.17e-03
2022-05-03 18:58:15,179 INFO [train.py:715] (2/8) Epoch 0, batch 32500, loss[loss=0.1884, simple_loss=0.2497, pruned_loss=0.0635, over 4899.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2599, pruned_loss=0.07067, over 972343.08 frames.], batch size: 18, lr: 1.17e-03
2022-05-03 18:58:54,502 INFO [train.py:715] (2/8) Epoch 0, batch 32550, loss[loss=0.1873, simple_loss=0.2458, pruned_loss=0.06442, over 4982.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2603, pruned_loss=0.07095, over 972497.34 frames.], batch size: 14, lr: 1.17e-03
2022-05-03 18:59:34,019 INFO [train.py:715] (2/8) Epoch 0, batch 32600, loss[loss=0.2299, simple_loss=0.2866, pruned_loss=0.08659, over 4809.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2606, pruned_loss=0.07138, over 971750.84 frames.], batch size: 21, lr: 1.17e-03
2022-05-03 19:00:13,277 INFO [train.py:715] (2/8) Epoch 0, batch 32650, loss[loss=0.1949, simple_loss=0.2449, pruned_loss=0.0725, over 4966.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2606, pruned_loss=0.07118, over 973183.15 frames.], batch size: 14, lr: 1.17e-03
2022-05-03 19:00:52,615 INFO [train.py:715] (2/8) Epoch 0, batch 32700, loss[loss=0.1501, simple_loss=0.2194, pruned_loss=0.04043, over 4980.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2586, pruned_loss=0.06979, over 971803.10 frames.], batch size: 14, lr: 1.17e-03
2022-05-03 19:01:32,094 INFO [train.py:715] (2/8) Epoch 0, batch 32750, loss[loss=0.1685, simple_loss=0.2407, pruned_loss=0.0482, over 4875.00 frames.], tot_loss[loss=0.2, simple_loss=0.2596, pruned_loss=0.07021, over 971110.40 frames.], batch size: 22, lr: 1.17e-03
2022-05-03 19:02:12,123 INFO [train.py:715] (2/8) Epoch 0, batch 32800, loss[loss=0.2115, simple_loss=0.2748, pruned_loss=0.07413, over 4980.00 frames.], tot_loss[loss=0.2, simple_loss=0.2597, pruned_loss=0.07012, over 972058.92 frames.], batch size: 25, lr: 1.16e-03
2022-05-03 19:02:51,632 INFO [train.py:715] (2/8) Epoch 0, batch 32850, loss[loss=0.2054, simple_loss=0.2721, pruned_loss=0.06928, over 4854.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2591, pruned_loss=0.06965, over 972177.68 frames.], batch size: 20, lr: 1.16e-03
2022-05-03 19:03:31,117 INFO [train.py:715] (2/8) Epoch 0, batch 32900, loss[loss=0.2495, simple_loss=0.2942, pruned_loss=0.1024, over 4761.00 frames.], tot_loss[loss=0.1994, simple_loss=0.259, pruned_loss=0.06989, over 973143.39 frames.], batch size: 14, lr: 1.16e-03
2022-05-03 19:04:11,176 INFO [train.py:715] (2/8) Epoch 0, batch 32950, loss[loss=0.2128, simple_loss=0.2738, pruned_loss=0.07586, over 4884.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2592, pruned_loss=0.06949, over 973015.10 frames.], batch size: 19, lr: 1.16e-03
2022-05-03 19:04:50,680 INFO [train.py:715] (2/8) Epoch 0, batch 33000, loss[loss=0.1782, simple_loss=0.2275, pruned_loss=0.06442, over 4765.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2594, pruned_loss=0.07, over 972866.69 frames.], batch size: 19, lr: 1.16e-03
2022-05-03 19:04:50,681 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 19:05:00,797 INFO [train.py:742] (2/8) Epoch 0, validation: loss=0.1303, simple_loss=0.2174, pruned_loss=0.02158, over 914524.00 frames.
2022-05-03 19:05:40,737 INFO [train.py:715] (2/8) Epoch 0, batch 33050, loss[loss=0.233, simple_loss=0.2814, pruned_loss=0.09226, over 4942.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2595, pruned_loss=0.0697, over 972536.54 frames.], batch size: 39, lr: 1.16e-03
2022-05-03 19:06:20,342 INFO [train.py:715] (2/8) Epoch 0, batch 33100, loss[loss=0.1682, simple_loss=0.2345, pruned_loss=0.05098, over 4871.00 frames.], tot_loss[loss=0.2001, simple_loss=0.26, pruned_loss=0.07015, over 972171.00 frames.], batch size: 30, lr: 1.16e-03
2022-05-03 19:07:01,015 INFO [train.py:715] (2/8) Epoch 0, batch 33150, loss[loss=0.2013, simple_loss=0.251, pruned_loss=0.07581, over 4935.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2591, pruned_loss=0.06962, over 972191.06 frames.], batch size: 18, lr: 1.16e-03
2022-05-03 19:07:41,356 INFO [train.py:715] (2/8) Epoch 0, batch 33200, loss[loss=0.1633, simple_loss=0.2388, pruned_loss=0.04395, over 4817.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2594, pruned_loss=0.06978, over 971623.47 frames.], batch size: 27, lr: 1.16e-03
2022-05-03 19:08:21,592 INFO [train.py:715] (2/8) Epoch 0, batch 33250, loss[loss=0.195, simple_loss=0.2623, pruned_loss=0.06383, over 4957.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2594, pruned_loss=0.06982, over 972583.41 frames.], batch size: 14, lr: 1.16e-03
2022-05-03 19:09:01,803 INFO [train.py:715] (2/8) Epoch 0, batch 33300, loss[loss=0.2021, simple_loss=0.2619, pruned_loss=0.0711, over 4846.00 frames.], tot_loss[loss=0.1989, simple_loss=0.259, pruned_loss=0.06945, over 972167.53 frames.], batch size: 30, lr: 1.16e-03
2022-05-03 19:09:42,522 INFO [train.py:715] (2/8) Epoch 0, batch 33350, loss[loss=0.2399, simple_loss=0.2942, pruned_loss=0.09277, over 4829.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2601, pruned_loss=0.07019, over 972117.79 frames.], batch size: 27, lr: 1.16e-03
2022-05-03 19:10:22,672 INFO [train.py:715] (2/8) Epoch 0, batch 33400, loss[loss=0.1686, simple_loss=0.2424, pruned_loss=0.04736, over 4862.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2604, pruned_loss=0.07029, over 971722.58 frames.], batch size: 20, lr: 1.15e-03
2022-05-03 19:11:02,696 INFO [train.py:715] (2/8) Epoch 0, batch 33450, loss[loss=0.1944, simple_loss=0.2473, pruned_loss=0.07071, over 4777.00 frames.], tot_loss[loss=0.2, simple_loss=0.2603, pruned_loss=0.06985, over 971411.91 frames.], batch size: 18, lr: 1.15e-03
2022-05-03 19:11:43,355 INFO [train.py:715] (2/8) Epoch 0, batch 33500, loss[loss=0.1925, simple_loss=0.2575, pruned_loss=0.06379, over 4961.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2607, pruned_loss=0.07018, over 971197.73 frames.], batch size: 24, lr: 1.15e-03
2022-05-03 19:12:23,709 INFO [train.py:715] (2/8) Epoch 0, batch 33550, loss[loss=0.206, simple_loss=0.259, pruned_loss=0.07647, over 4933.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2599, pruned_loss=0.06978, over 971196.32 frames.], batch size: 21, lr: 1.15e-03
2022-05-03 19:13:02,891 INFO [train.py:715] (2/8) Epoch 0, batch 33600, loss[loss=0.2131, simple_loss=0.2716, pruned_loss=0.07731, over 4969.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2592, pruned_loss=0.069, over 970978.08 frames.], batch size: 15, lr: 1.15e-03
2022-05-03 19:13:43,469 INFO [train.py:715] (2/8) Epoch 0, batch 33650, loss[loss=0.1698, simple_loss=0.2406, pruned_loss=0.04948, over 4952.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2592, pruned_loss=0.06862, over 971378.92 frames.], batch size: 21, lr: 1.15e-03
2022-05-03 19:14:23,802 INFO [train.py:715] (2/8) Epoch 0, batch 33700, loss[loss=0.2254, simple_loss=0.2778, pruned_loss=0.08654, over 4818.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2584, pruned_loss=0.06858, over 971693.54 frames.], batch size: 25, lr: 1.15e-03
2022-05-03 19:15:03,026 INFO [train.py:715] (2/8) Epoch 0, batch 33750, loss[loss=0.2343, simple_loss=0.2913, pruned_loss=0.08865, over 4977.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2596, pruned_loss=0.06943, over 972808.59 frames.], batch size: 15, lr: 1.15e-03
2022-05-03 19:15:42,517 INFO [train.py:715] (2/8) Epoch 0, batch 33800, loss[loss=0.1955, simple_loss=0.26, pruned_loss=0.06552, over 4925.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2603, pruned_loss=0.0705, over 971789.97 frames.], batch size: 29, lr: 1.15e-03
2022-05-03 19:16:22,767 INFO [train.py:715] (2/8) Epoch 0, batch 33850, loss[loss=0.1415, simple_loss=0.2009, pruned_loss=0.04103, over 4783.00 frames.], tot_loss[loss=0.2002, simple_loss=0.26, pruned_loss=0.07021, over 972077.64 frames.], batch size: 14, lr: 1.15e-03
2022-05-03 19:17:02,054 INFO [train.py:715] (2/8) Epoch 0, batch 33900, loss[loss=0.2482, simple_loss=0.3107, pruned_loss=0.09282, over 4765.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2591, pruned_loss=0.06961, over 972249.10 frames.], batch size: 18, lr: 1.15e-03
2022-05-03 19:17:41,111 INFO [train.py:715] (2/8) Epoch 0, batch 33950, loss[loss=0.1867, simple_loss=0.2588, pruned_loss=0.05737, over 4963.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2596, pruned_loss=0.06945, over 972181.69 frames.], batch size: 15, lr: 1.15e-03
2022-05-03 19:18:21,083 INFO [train.py:715] (2/8) Epoch 0, batch 34000, loss[loss=0.1987, simple_loss=0.26, pruned_loss=0.06871, over 4817.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2581, pruned_loss=0.06855, over 972231.17 frames.], batch size: 15, lr: 1.14e-03
2022-05-03 19:19:00,959 INFO [train.py:715] (2/8) Epoch 0, batch 34050, loss[loss=0.154, simple_loss=0.2229, pruned_loss=0.04257, over 4954.00 frames.], tot_loss[loss=0.1977, simple_loss=0.258, pruned_loss=0.06875, over 971682.93 frames.], batch size: 21, lr: 1.14e-03
2022-05-03 19:19:40,626 INFO [train.py:715] (2/8) Epoch 0, batch 34100, loss[loss=0.1524, simple_loss=0.2119, pruned_loss=0.0465, over 4789.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2574, pruned_loss=0.06851, over 971189.10 frames.], batch size: 14, lr: 1.14e-03
2022-05-03 19:20:19,822 INFO [train.py:715] (2/8) Epoch 0, batch 34150, loss[loss=0.1871, simple_loss=0.2522, pruned_loss=0.06096, over 4767.00 frames.], tot_loss[loss=0.196, simple_loss=0.2563, pruned_loss=0.06782, over 971114.44 frames.], batch size: 19, lr: 1.14e-03
2022-05-03 19:20:59,750 INFO [train.py:715] (2/8) Epoch 0, batch 34200, loss[loss=0.2261, simple_loss=0.2871, pruned_loss=0.08257, over 4882.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2574, pruned_loss=0.06872, over 972561.39 frames.], batch size: 32, lr: 1.14e-03
2022-05-03 19:21:39,292 INFO [train.py:715] (2/8) Epoch 0, batch 34250, loss[loss=0.1938, simple_loss=0.247, pruned_loss=0.07031, over 4928.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2577, pruned_loss=0.06931, over 972379.61 frames.], batch size: 21, lr: 1.14e-03
2022-05-03 19:22:18,597 INFO [train.py:715] (2/8) Epoch 0, batch 34300, loss[loss=0.1657, simple_loss=0.2304, pruned_loss=0.05052, over 4691.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2571, pruned_loss=0.06892, over 973114.74 frames.], batch size: 15, lr: 1.14e-03
2022-05-03 19:22:58,851 INFO [train.py:715] (2/8) Epoch 0, batch 34350, loss[loss=0.2267, simple_loss=0.2777, pruned_loss=0.08785, over 4837.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2555, pruned_loss=0.06775, over 972105.17 frames.], batch size: 15, lr: 1.14e-03
2022-05-03 19:23:39,054 INFO [train.py:715] (2/8) Epoch 0, batch 34400, loss[loss=0.2211, simple_loss=0.2863, pruned_loss=0.07794, over 4807.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2573, pruned_loss=0.06868, over 971595.23 frames.], batch size: 25, lr: 1.14e-03
2022-05-03 19:24:18,626 INFO [train.py:715] (2/8) Epoch 0, batch 34450, loss[loss=0.1923, simple_loss=0.2576, pruned_loss=0.06355, over 4694.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2581, pruned_loss=0.06878, over 971790.64 frames.], batch size: 15, lr: 1.14e-03
2022-05-03 19:24:57,898 INFO [train.py:715] (2/8) Epoch 0, batch 34500, loss[loss=0.1837, simple_loss=0.2496, pruned_loss=0.05886, over 4752.00 frames.], tot_loss[loss=0.1977, simple_loss=0.258, pruned_loss=0.06864, over 971813.16 frames.], batch size: 19, lr: 1.14e-03
2022-05-03 19:25:38,241 INFO [train.py:715] (2/8) Epoch 0, batch 34550, loss[loss=0.1731, simple_loss=0.2353, pruned_loss=0.05544, over 4872.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2586, pruned_loss=0.06932, over 971332.67 frames.], batch size: 32, lr: 1.14e-03
2022-05-03 19:26:17,976 INFO [train.py:715] (2/8) Epoch 0, batch 34600, loss[loss=0.2197, simple_loss=0.29, pruned_loss=0.07471, over 4869.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2586, pruned_loss=0.06925, over 971529.32 frames.], batch size: 16, lr: 1.13e-03
2022-05-03 19:26:57,208 INFO [train.py:715] (2/8) Epoch 0, batch 34650, loss[loss=0.2059, simple_loss=0.2659, pruned_loss=0.07294, over 4936.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2602, pruned_loss=0.07011, over 971673.45 frames.], batch size: 23, lr: 1.13e-03
2022-05-03 19:27:37,734 INFO [train.py:715] (2/8) Epoch 0, batch 34700, loss[loss=0.218, simple_loss=0.2819, pruned_loss=0.07709, over 4805.00 frames.], tot_loss[loss=0.201, simple_loss=0.2608, pruned_loss=0.07058, over 971759.72 frames.], batch size: 21, lr: 1.13e-03
2022-05-03 19:28:15,918 INFO [train.py:715] (2/8) Epoch 0, batch 34750, loss[loss=0.165, simple_loss=0.2337, pruned_loss=0.04813, over 4876.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2593, pruned_loss=0.06963, over 971292.95 frames.], batch size: 22, lr: 1.13e-03
2022-05-03 19:28:53,208 INFO [train.py:715] (2/8) Epoch 0, batch 34800, loss[loss=0.2723, simple_loss=0.3282, pruned_loss=0.1082, over 4915.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2584, pruned_loss=0.06898, over 971327.76 frames.], batch size: 18, lr: 1.13e-03
2022-05-03 19:29:42,565 INFO [train.py:715] (2/8) Epoch 1, batch 0, loss[loss=0.2074, simple_loss=0.2688, pruned_loss=0.07296, over 4984.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2688, pruned_loss=0.07296, over 4984.00 frames.], batch size: 14, lr: 1.11e-03
2022-05-03 19:30:21,866 INFO [train.py:715] (2/8) Epoch 1, batch 50, loss[loss=0.2163, simple_loss=0.2745, pruned_loss=0.07908, over 4777.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2569, pruned_loss=0.06909, over 219350.78 frames.], batch size: 18, lr: 1.11e-03
2022-05-03 19:31:01,841 INFO [train.py:715] (2/8) Epoch 1, batch 100, loss[loss=0.1863, simple_loss=0.2548, pruned_loss=0.05887, over 4866.00 frames.], tot_loss[loss=0.201, simple_loss=0.2595, pruned_loss=0.07123, over 386014.62 frames.], batch size: 22, lr: 1.11e-03
2022-05-03 19:31:41,278 INFO [train.py:715] (2/8) Epoch 1, batch 150, loss[loss=0.1887, simple_loss=0.2473, pruned_loss=0.06507, over 4864.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2575, pruned_loss=0.06958, over 516279.27 frames.], batch size: 16, lr: 1.11e-03
2022-05-03 19:32:20,514 INFO [train.py:715] (2/8) Epoch 1, batch 200, loss[loss=0.1778, simple_loss=0.2413, pruned_loss=0.05715, over 4756.00 frames.], tot_loss[loss=0.196, simple_loss=0.2555, pruned_loss=0.06829, over 616731.62 frames.], batch size: 19, lr: 1.11e-03
2022-05-03 19:33:00,049 INFO [train.py:715] (2/8) Epoch 1, batch 250, loss[loss=0.1671, simple_loss=0.2341, pruned_loss=0.05008, over 4891.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2573, pruned_loss=0.06886, over 695564.07 frames.], batch size: 22, lr: 1.11e-03
2022-05-03 19:33:40,737 INFO [train.py:715] (2/8) Epoch 1, batch 300, loss[loss=0.1946, simple_loss=0.2578, pruned_loss=0.06567, over 4951.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2567, pruned_loss=0.06847, over 757110.66 frames.], batch size: 24, lr: 1.11e-03
2022-05-03 19:34:21,103 INFO [train.py:715] (2/8) Epoch 1, batch 350, loss[loss=0.2, simple_loss=0.2654, pruned_loss=0.06732, over 4774.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2579, pruned_loss=0.06937, over 805041.48 frames.], batch size: 18, lr: 1.11e-03
2022-05-03 19:35:01,375 INFO [train.py:715] (2/8) Epoch 1, batch 400, loss[loss=0.2098, simple_loss=0.2662, pruned_loss=0.07673, over 4851.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2583, pruned_loss=0.06974, over 842734.14 frames.], batch size: 30, lr: 1.11e-03
2022-05-03 19:35:42,054 INFO [train.py:715] (2/8) Epoch 1, batch 450, loss[loss=0.1774, simple_loss=0.238, pruned_loss=0.0584, over 4796.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2575, pruned_loss=0.06893, over 870872.66 frames.], batch size: 14, lr: 1.11e-03
2022-05-03 19:36:22,760 INFO [train.py:715] (2/8) Epoch 1, batch 500, loss[loss=0.1861, simple_loss=0.2407, pruned_loss=0.06579, over 4972.00 frames.], tot_loss[loss=0.1984, simple_loss=0.258, pruned_loss=0.06941, over 893837.38 frames.], batch size: 15, lr: 1.11e-03
2022-05-03 19:37:03,282 INFO [train.py:715] (2/8) Epoch 1, batch 550, loss[loss=0.2217, simple_loss=0.2687, pruned_loss=0.08736, over 4955.00 frames.], tot_loss[loss=0.1983, simple_loss=0.258, pruned_loss=0.06931, over 911816.74 frames.], batch size: 35, lr: 1.11e-03
2022-05-03 19:37:43,261 INFO [train.py:715] (2/8) Epoch 1, batch 600, loss[loss=0.1922, simple_loss=0.2486, pruned_loss=0.06787, over 4791.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2581, pruned_loss=0.06936, over 925630.24 frames.], batch size: 17, lr: 1.10e-03
2022-05-03 19:38:23,972 INFO [train.py:715] (2/8) Epoch 1, batch 650, loss[loss=0.1955, simple_loss=0.2618, pruned_loss=0.06458, over 4962.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2587, pruned_loss=0.06906, over 936361.86 frames.], batch size: 39, lr: 1.10e-03
2022-05-03 19:39:04,135 INFO [train.py:715] (2/8) Epoch 1, batch 700, loss[loss=0.2594, simple_loss=0.2901, pruned_loss=0.1143, over 4760.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2592, pruned_loss=0.06963, over 944429.49 frames.], batch size: 16, lr: 1.10e-03
2022-05-03 19:39:44,113 INFO [train.py:715] (2/8) Epoch 1, batch 750, loss[loss=0.2076, simple_loss=0.2622, pruned_loss=0.07653, over 4791.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2586, pruned_loss=0.06938, over 950748.51 frames.], batch size: 14, lr: 1.10e-03
2022-05-03 19:40:24,210 INFO [train.py:715] (2/8) Epoch 1, batch 800, loss[loss=0.172, simple_loss=0.2327, pruned_loss=0.05565, over 4927.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2578, pruned_loss=0.06847, over 955577.65 frames.], batch size: 35, lr: 1.10e-03
2022-05-03 19:41:04,453 INFO [train.py:715] (2/8) Epoch 1, batch 850, loss[loss=0.1954, simple_loss=0.2567, pruned_loss=0.06711, over 4851.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2571, pruned_loss=0.06794, over 959002.57 frames.], batch size: 32, lr: 1.10e-03
2022-05-03 19:41:43,684 INFO [train.py:715] (2/8) Epoch 1, batch 900, loss[loss=0.194, simple_loss=0.2552, pruned_loss=0.06639, over 4792.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2553, pruned_loss=0.06676, over 962028.39 frames.], batch size: 12, lr: 1.10e-03
2022-05-03 19:42:22,964 INFO [train.py:715] (2/8) Epoch 1, batch 950, loss[loss=0.2094, simple_loss=0.2676, pruned_loss=0.07559, over 4815.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2563, pruned_loss=0.06738, over 964703.11 frames.], batch size: 26, lr: 1.10e-03
2022-05-03 19:43:02,558 INFO [train.py:715] (2/8) Epoch 1, batch 1000, loss[loss=0.1732, simple_loss=0.2395, pruned_loss=0.05345, over 4784.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2572, pruned_loss=0.06861, over 966233.09 frames.], batch size: 18, lr: 1.10e-03
2022-05-03 19:43:41,896 INFO [train.py:715] (2/8) Epoch 1, batch 1050, loss[loss=0.1841, simple_loss=0.2481, pruned_loss=0.06006, over 4744.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2572, pruned_loss=0.06849, over 967261.46 frames.], batch size: 16, lr: 1.10e-03
2022-05-03 19:44:20,958 INFO [train.py:715] (2/8) Epoch 1, batch 1100, loss[loss=0.1729, simple_loss=0.2402, pruned_loss=0.05278, over 4838.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2572, pruned_loss=0.06851, over 968753.00 frames.], batch size: 26, lr: 1.10e-03
2022-05-03 19:45:00,269 INFO [train.py:715] (2/8) Epoch 1, batch 1150, loss[loss=0.1867, simple_loss=0.2457, pruned_loss=0.06384, over 4972.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2574, pruned_loss=0.06849, over 970355.61 frames.], batch size: 35, lr: 1.10e-03
2022-05-03 19:45:40,267 INFO [train.py:715] (2/8) Epoch 1, batch 1200, loss[loss=0.2188, simple_loss=0.2705, pruned_loss=0.08358, over 4809.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2575, pruned_loss=0.06859, over 970795.18 frames.], batch size: 14, lr: 1.10e-03
2022-05-03 19:46:19,421 INFO [train.py:715] (2/8) Epoch 1, batch 1250, loss[loss=0.2203, simple_loss=0.2817, pruned_loss=0.07949, over 4805.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2564, pruned_loss=0.06804, over 970032.86 frames.], batch size: 21, lr: 1.10e-03
2022-05-03 19:46:58,951 INFO [train.py:715] (2/8) Epoch 1, batch 1300, loss[loss=0.1722, simple_loss=0.2341, pruned_loss=0.05517, over 4756.00 frames.], tot_loss[loss=0.1948, simple_loss=0.255, pruned_loss=0.06729, over 970189.28 frames.], batch size: 19, lr: 1.09e-03
2022-05-03 19:47:39,263 INFO [train.py:715] (2/8) Epoch 1, batch 1350, loss[loss=0.2173, simple_loss=0.2696, pruned_loss=0.08256, over 4955.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2555, pruned_loss=0.06795, over 970243.46 frames.], batch size: 29, lr: 1.09e-03
2022-05-03 19:48:18,888 INFO [train.py:715] (2/8) Epoch 1, batch 1400, loss[loss=0.1993, simple_loss=0.2604, pruned_loss=0.06907, over 4902.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2561, pruned_loss=0.0683, over 970800.61 frames.], batch size: 17, lr: 1.09e-03
2022-05-03 19:48:58,739 INFO [train.py:715] (2/8) Epoch 1, batch 1450, loss[loss=0.1921, simple_loss=0.2546, pruned_loss=0.06478, over 4977.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2558, pruned_loss=0.06803, over 970063.43 frames.], batch size: 14, lr: 1.09e-03
2022-05-03 19:49:38,347 INFO [train.py:715] (2/8) Epoch 1, batch 1500, loss[loss=0.18, simple_loss=0.2341, pruned_loss=0.06299, over 4906.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2571, pruned_loss=0.06886, over 970840.34 frames.], batch size: 18, lr: 1.09e-03
2022-05-03 19:50:17,868 INFO [train.py:715] (2/8) Epoch 1, batch 1550, loss[loss=0.1915, simple_loss=0.2564, pruned_loss=0.06328, over 4804.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2572, pruned_loss=0.06877, over 969972.59 frames.], batch size: 24, lr: 1.09e-03
2022-05-03 19:50:57,097 INFO [train.py:715] (2/8) Epoch 1, batch 1600, loss[loss=0.2008, simple_loss=0.2632, pruned_loss=0.06922, over 4857.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2566, pruned_loss=0.06849, over 969628.09 frames.], batch size: 30, lr: 1.09e-03
2022-05-03 19:51:36,393 INFO [train.py:715] (2/8) Epoch 1, batch 1650, loss[loss=0.1834, simple_loss=0.2425, pruned_loss=0.06218, over 4969.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2556, pruned_loss=0.06744, over 970556.91 frames.], batch size: 35, lr: 1.09e-03
2022-05-03 19:52:16,976 INFO [train.py:715] (2/8) Epoch 1, batch 1700, loss[loss=0.2946, simple_loss=0.3217, pruned_loss=0.1338, over 4938.00 frames.], tot_loss[loss=0.194, simple_loss=0.2549, pruned_loss=0.06652, over 970943.56 frames.], batch size: 18, lr: 1.09e-03
2022-05-03 19:52:56,156 INFO [train.py:715] (2/8) Epoch 1, batch 1750, loss[loss=0.2119, simple_loss=0.263, pruned_loss=0.08043, over 4708.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2563, pruned_loss=0.06735, over 971383.18 frames.], batch size: 15, lr: 1.09e-03
2022-05-03 19:53:35,890 INFO [train.py:715] (2/8) Epoch 1, batch 1800, loss[loss=0.188, simple_loss=0.248, pruned_loss=0.06395, over 4826.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2566, pruned_loss=0.06707, over 972312.56 frames.], batch size: 26, lr: 1.09e-03
2022-05-03 19:54:15,251 INFO [train.py:715] (2/8) Epoch 1, batch 1850, loss[loss=0.1621, simple_loss=0.2382, pruned_loss=0.04304, over 4932.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2564, pruned_loss=0.06744, over 972554.74 frames.], batch size: 23, lr: 1.09e-03
2022-05-03 19:54:54,771 INFO [train.py:715] (2/8) Epoch 1, batch 1900, loss[loss=0.1529, simple_loss=0.2203, pruned_loss=0.04274, over 4823.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2554, pruned_loss=0.06653, over 971842.42 frames.], batch size: 27, lr: 1.09e-03
2022-05-03 19:55:34,081 INFO [train.py:715] (2/8) Epoch 1, batch 1950, loss[loss=0.2122, simple_loss=0.2613, pruned_loss=0.08161, over 4785.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2555, pruned_loss=0.06675, over 970972.76 frames.], batch size: 18, lr: 1.08e-03
2022-05-03 19:56:14,071 INFO [train.py:715] (2/8) Epoch 1, batch 2000, loss[loss=0.1763, simple_loss=0.246, pruned_loss=0.05334, over 4783.00 frames.], tot_loss[loss=0.1935, simple_loss=0.255, pruned_loss=0.06601, over 970415.11 frames.], batch size: 17, lr: 1.08e-03
2022-05-03 19:56:53,561 INFO [train.py:715] (2/8) Epoch 1, batch 2050, loss[loss=0.1873, simple_loss=0.2515, pruned_loss=0.06151, over 4965.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2556, pruned_loss=0.06633, over 971192.37 frames.], batch size: 24, lr: 1.08e-03
2022-05-03 19:57:33,034 INFO [train.py:715] (2/8) Epoch 1, batch 2100, loss[loss=0.1538, simple_loss=0.2264, pruned_loss=0.04055, over 4881.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2565, pruned_loss=0.06799, over 971228.22 frames.], batch size: 16, lr: 1.08e-03
2022-05-03 19:58:12,717 INFO [train.py:715] (2/8) Epoch 1, batch 2150, loss[loss=0.2228, simple_loss=0.2896, pruned_loss=0.078, over 4784.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2574, pruned_loss=0.06864, over 971727.81 frames.], batch size: 17, lr: 1.08e-03
2022-05-03 19:58:52,396 INFO [train.py:715] (2/8) Epoch 1, batch 2200, loss[loss=0.1845, simple_loss=0.2469, pruned_loss=0.06106, over 4751.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2579, pruned_loss=0.06865, over 970775.59 frames.], batch size: 19, lr: 1.08e-03
2022-05-03 19:59:32,128 INFO [train.py:715] (2/8) Epoch 1, batch 2250, loss[loss=0.1815, simple_loss=0.2451, pruned_loss=0.05898, over 4764.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2578, pruned_loss=0.06853, over 971286.90 frames.], batch size: 19, lr: 1.08e-03
2022-05-03 20:00:11,169 INFO [train.py:715] (2/8) Epoch 1, batch 2300, loss[loss=0.236, simple_loss=0.2881, pruned_loss=0.0919, over 4835.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2577, pruned_loss=0.06839, over 972285.16 frames.], batch size: 30, lr: 1.08e-03
2022-05-03 20:00:51,303 INFO [train.py:715] (2/8) Epoch 1, batch 2350, loss[loss=0.2023, simple_loss=0.2462, pruned_loss=0.07917, over 4791.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2564, pruned_loss=0.06744, over 972535.58 frames.], batch size: 18, lr: 1.08e-03
2022-05-03 20:01:30,580 INFO [train.py:715] (2/8) Epoch 1, batch 2400, loss[loss=0.2011, simple_loss=0.264, pruned_loss=0.06906, over 4758.00 frames.], tot_loss[loss=0.1951, simple_loss=0.256, pruned_loss=0.06708, over 971788.07 frames.], batch size: 16, lr: 1.08e-03
2022-05-03 20:02:09,725 INFO [train.py:715] (2/8) Epoch 1, batch 2450, loss[loss=0.1717, simple_loss=0.2374, pruned_loss=0.05302, over 4995.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2544, pruned_loss=0.06614, over 971962.51 frames.], batch size: 14, lr: 1.08e-03
2022-05-03 20:02:48,977 INFO [train.py:715] (2/8) Epoch 1, batch 2500, loss[loss=0.218, simple_loss=0.2542, pruned_loss=0.09094, over 4774.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2551, pruned_loss=0.0667, over 972654.06 frames.], batch size: 14, lr: 1.08e-03
2022-05-03 20:03:28,528 INFO [train.py:715] (2/8) Epoch 1, batch 2550, loss[loss=0.2092, simple_loss=0.2713, pruned_loss=0.07359, over 4900.00 frames.], tot_loss[loss=0.194, simple_loss=0.255, pruned_loss=0.06655, over 972537.70 frames.], batch size: 17, lr: 1.08e-03
2022-05-03 20:04:08,258 INFO [train.py:715] (2/8) Epoch 1, batch 2600, loss[loss=0.1658, simple_loss=0.2367, pruned_loss=0.04742, over 4764.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2557, pruned_loss=0.06664, over 971820.12 frames.], batch size: 19, lr: 1.08e-03
2022-05-03 20:04:47,467 INFO [train.py:715] (2/8) Epoch 1, batch 2650, loss[loss=0.1795, simple_loss=0.2418, pruned_loss=0.05856, over 4708.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2557, pruned_loss=0.06701, over 971485.84 frames.], batch size: 15, lr: 1.07e-03
2022-05-03 20:05:27,534 INFO [train.py:715] (2/8) Epoch 1, batch 2700, loss[loss=0.196, simple_loss=0.264, pruned_loss=0.06404, over 4810.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2556, pruned_loss=0.06683, over 971795.25 frames.], batch size: 15, lr: 1.07e-03
2022-05-03 20:06:06,949 INFO [train.py:715] (2/8) Epoch 1, batch 2750, loss[loss=0.192, simple_loss=0.2644, pruned_loss=0.05982, over 4751.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2564, pruned_loss=0.06743, over 971431.13 frames.], batch size: 19, lr: 1.07e-03
2022-05-03 20:06:45,683 INFO [train.py:715] (2/8) Epoch 1, batch 2800, loss[loss=0.2045, simple_loss=0.2505, pruned_loss=0.07929, over 4853.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2554, pruned_loss=0.06686, over 972325.62 frames.], batch size: 15, lr: 1.07e-03
2022-05-03 20:07:25,345 INFO [train.py:715] (2/8) Epoch 1, batch 2850, loss[loss=0.1574, simple_loss=0.2202, pruned_loss=0.04724, over 4970.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2552, pruned_loss=0.06694, over 971789.75 frames.], batch size: 24, lr: 1.07e-03
2022-05-03 20:08:05,003 INFO [train.py:715] (2/8) Epoch 1, batch 2900, loss[loss=0.2152, simple_loss=0.271, pruned_loss=0.07967, over 4930.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2551, pruned_loss=0.0673, over 971769.89 frames.], batch size: 17, lr: 1.07e-03
2022-05-03 20:08:44,119 INFO [train.py:715] (2/8) Epoch 1, batch 2950, loss[loss=0.1614, simple_loss=0.2119, pruned_loss=0.05545, over 4972.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2551, pruned_loss=0.06714, over 971857.78 frames.], batch size: 14, lr: 1.07e-03
2022-05-03 20:09:22,830 INFO [train.py:715] (2/8) Epoch 1, batch 3000, loss[loss=0.2664, simple_loss=0.2862, pruned_loss=0.1233, over 4761.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2551, pruned_loss=0.06734, over 971781.74 frames.], batch size: 17, lr: 1.07e-03
2022-05-03 20:09:22,831 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 20:09:34,567 INFO [train.py:742] (2/8) Epoch 1, validation: loss=0.1276, simple_loss=0.2149, pruned_loss=0.0201, over 914524.00 frames.
2022-05-03 20:10:13,435 INFO [train.py:715] (2/8) Epoch 1, batch 3050, loss[loss=0.1976, simple_loss=0.2626, pruned_loss=0.06628, over 4981.00 frames.], tot_loss[loss=0.195, simple_loss=0.2557, pruned_loss=0.06722, over 971714.87 frames.], batch size: 14, lr: 1.07e-03
2022-05-03 20:10:53,449 INFO [train.py:715] (2/8) Epoch 1, batch 3100, loss[loss=0.2106, simple_loss=0.2633, pruned_loss=0.07896, over 4978.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2547, pruned_loss=0.06607, over 972139.00 frames.], batch size: 15, lr: 1.07e-03
2022-05-03 20:11:32,596 INFO [train.py:715] (2/8) Epoch 1, batch 3150, loss[loss=0.1964, simple_loss=0.2749, pruned_loss=0.05893, over 4801.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2555, pruned_loss=0.06653, over 972816.62 frames.], batch size: 24, lr: 1.07e-03
2022-05-03 20:12:11,813 INFO [train.py:715] (2/8) Epoch 1, batch 3200, loss[loss=0.2205, simple_loss=0.2809, pruned_loss=0.08003, over 4977.00 frames.], tot_loss[loss=0.195, simple_loss=0.2563, pruned_loss=0.06687, over 972667.13 frames.], batch size: 14, lr: 1.07e-03
2022-05-03 20:12:51,451 INFO [train.py:715] (2/8) Epoch 1, batch 3250, loss[loss=0.1962, simple_loss=0.2699, pruned_loss=0.06126, over 4812.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2559, pruned_loss=0.06645, over 973206.47 frames.], batch size: 25, lr: 1.07e-03
2022-05-03 20:13:31,207 INFO [train.py:715] (2/8) Epoch 1, batch 3300, loss[loss=0.179, simple_loss=0.2393, pruned_loss=0.05932, over 4771.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2554, pruned_loss=0.06617, over 972003.55 frames.], batch size: 17, lr: 1.07e-03
2022-05-03 20:14:10,763 INFO [train.py:715] (2/8) Epoch 1, batch 3350, loss[loss=0.1924, simple_loss=0.2429, pruned_loss=0.07091, over 4884.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2546, pruned_loss=0.06578, over 972533.18 frames.], batch size: 32, lr: 1.07e-03
2022-05-03 20:14:50,044 INFO [train.py:715] (2/8) Epoch 1, batch 3400, loss[loss=0.1988, simple_loss=0.2627, pruned_loss=0.06744, over 4983.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2538, pruned_loss=0.06559, over 972576.66 frames.], batch size: 27, lr: 1.06e-03
2022-05-03 20:15:30,663 INFO [train.py:715] (2/8) Epoch 1, batch 3450, loss[loss=0.2086, simple_loss=0.2532, pruned_loss=0.08201, over 4774.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2541, pruned_loss=0.06581, over 972663.06 frames.], batch size: 17, lr: 1.06e-03
2022-05-03 20:16:09,587 INFO [train.py:715] (2/8) Epoch 1, batch 3500, loss[loss=0.1897, simple_loss=0.2463, pruned_loss=0.06657, over 4928.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2537, pruned_loss=0.06548, over 972214.81 frames.], batch size: 35, lr: 1.06e-03
2022-05-03 20:16:48,610 INFO [train.py:715] (2/8) Epoch 1, batch 3550, loss[loss=0.2364, simple_loss=0.2954, pruned_loss=0.08871, over 4848.00 frames.], tot_loss[loss=0.1941, simple_loss=0.255, pruned_loss=0.06658, over 972289.52 frames.], batch size: 34, lr: 1.06e-03
2022-05-03 20:17:28,368 INFO [train.py:715] (2/8) Epoch 1, batch 3600, loss[loss=0.1872, simple_loss=0.2504, pruned_loss=0.06197, over 4765.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2548, pruned_loss=0.06643, over 973457.83 frames.], batch size: 19, lr: 1.06e-03
2022-05-03 20:18:08,016 INFO [train.py:715] (2/8) Epoch 1, batch 3650, loss[loss=0.2163, simple_loss=0.2689, pruned_loss=0.08188, over 4846.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2549, pruned_loss=0.06678, over 973553.01 frames.], batch size: 12, lr: 1.06e-03
2022-05-03 20:18:46,979 INFO [train.py:715] (2/8) Epoch 1, batch 3700, loss[loss=0.203, simple_loss=0.2626, pruned_loss=0.07169, over 4950.00 frames.], tot_loss[loss=0.1933, simple_loss=0.254, pruned_loss=0.06631, over 972775.20 frames.], batch size: 29, lr: 1.06e-03
2022-05-03 20:19:25,654 INFO [train.py:715] (2/8) Epoch 1, batch 3750, loss[loss=0.215, simple_loss=0.2804, pruned_loss=0.07484, over 4931.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2553, pruned_loss=0.06664, over 972127.31 frames.], batch size: 18, lr: 1.06e-03
2022-05-03 20:20:05,927 INFO [train.py:715] (2/8) Epoch 1, batch 3800, loss[loss=0.1788, simple_loss=0.2538, pruned_loss=0.05194, over 4841.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2547, pruned_loss=0.06619, over 972138.39 frames.], batch size: 26, lr: 1.06e-03
2022-05-03 20:20:44,897 INFO [train.py:715] (2/8) Epoch 1, batch 3850, loss[loss=0.2259, simple_loss=0.277, pruned_loss=0.08741, over 4951.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2547, pruned_loss=0.0662, over 972986.56 frames.], batch size: 21, lr: 1.06e-03
2022-05-03 20:21:23,751 INFO [train.py:715] (2/8) Epoch 1, batch 3900, loss[loss=0.1958, simple_loss=0.2437, pruned_loss=0.07399, over 4783.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2542, pruned_loss=0.06626, over 973666.71 frames.], batch size: 17, lr: 1.06e-03
2022-05-03 20:22:03,277 INFO [train.py:715] (2/8) Epoch 1, batch 3950, loss[loss=0.2021, simple_loss=0.2571, pruned_loss=0.07352, over 4933.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2552, pruned_loss=0.06703, over 973458.61 frames.], batch size: 29, lr: 1.06e-03
2022-05-03 20:22:42,790 INFO [train.py:715] (2/8) Epoch 1, batch 4000, loss[loss=0.2184, simple_loss=0.287, pruned_loss=0.07486, over 4809.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2551, pruned_loss=0.06676, over 973327.12 frames.], batch size: 15, lr: 1.06e-03
2022-05-03 20:23:21,451 INFO [train.py:715] (2/8) Epoch 1, batch 4050, loss[loss=0.3259, simple_loss=0.3749, pruned_loss=0.1385, over 4911.00 frames.], tot_loss[loss=0.195, simple_loss=0.2558, pruned_loss=0.06708, over 974094.08 frames.], batch size: 18, lr: 1.06e-03
2022-05-03 20:24:00,883 INFO [train.py:715] (2/8) Epoch 1, batch 4100, loss[loss=0.1978, simple_loss=0.2698, pruned_loss=0.06293, over 4893.00 frames.], tot_loss[loss=0.1948, simple_loss=0.256, pruned_loss=0.06685, over 973458.48 frames.], batch size: 19, lr: 1.05e-03
2022-05-03 20:24:40,532 INFO [train.py:715] (2/8) Epoch 1, batch 4150, loss[loss=0.2045, simple_loss=0.2591, pruned_loss=0.07495, over 4854.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2566, pruned_loss=0.06751, over 972767.67 frames.], batch size: 30, lr: 1.05e-03
2022-05-03 20:25:19,579 INFO [train.py:715] (2/8) Epoch 1, batch 4200, loss[loss=0.1819, simple_loss=0.2572, pruned_loss=0.05329, over 4748.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2546, pruned_loss=0.06617, over 972480.42 frames.], batch size: 16, lr: 1.05e-03
2022-05-03 20:25:58,619 INFO [train.py:715] (2/8) Epoch 1, batch 4250, loss[loss=0.1792, simple_loss=0.246, pruned_loss=0.05622, over 4934.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2539, pruned_loss=0.06545, over 971724.71 frames.], batch size: 23, lr: 1.05e-03
2022-05-03 20:26:38,137 INFO [train.py:715] (2/8) Epoch 1, batch 4300, loss[loss=0.1891, simple_loss=0.2511, pruned_loss=0.06351, over 4854.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2542, pruned_loss=0.0655, over 971432.44 frames.], batch size: 20, lr: 1.05e-03
2022-05-03 20:27:17,799 INFO [train.py:715] (2/8) Epoch 1, batch 4350, loss[loss=0.1771, simple_loss=0.2545, pruned_loss=0.04989, over 4779.00 frames.], tot_loss[loss=0.1936, simple_loss=0.255, pruned_loss=0.06607, over 971586.13 frames.], batch size: 17, lr: 1.05e-03
2022-05-03 20:27:56,247 INFO [train.py:715] (2/8) Epoch 1, batch 4400, loss[loss=0.1824, simple_loss=0.2482, pruned_loss=0.05837, over 4862.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2547, pruned_loss=0.06601, over 972455.29 frames.], batch size: 32, lr: 1.05e-03
2022-05-03 20:28:35,839 INFO [train.py:715] (2/8) Epoch 1, batch 4450, loss[loss=0.2258, simple_loss=0.2808, pruned_loss=0.08534, over 4747.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2543, pruned_loss=0.06578, over 971430.41 frames.], batch size: 16, lr: 1.05e-03
2022-05-03 20:29:15,590 INFO [train.py:715] (2/8) Epoch 1, batch 4500, loss[loss=0.1768, simple_loss=0.243, pruned_loss=0.05529, over 4789.00 frames.], tot_loss[loss=0.194, simple_loss=0.255, pruned_loss=0.06646, over 971371.36 frames.], batch size: 14, lr: 1.05e-03
2022-05-03 20:29:54,813 INFO [train.py:715] (2/8) Epoch 1, batch 4550, loss[loss=0.2092, simple_loss=0.2654, pruned_loss=0.07652, over 4752.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2546, pruned_loss=0.066, over 972250.97 frames.], batch size: 19, lr: 1.05e-03
2022-05-03 20:30:33,515 INFO [train.py:715] (2/8) Epoch 1, batch 4600, loss[loss=0.2201, simple_loss=0.2759, pruned_loss=0.08212, over 4786.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2543, pruned_loss=0.06558, over 971934.70 frames.], batch size: 18, lr: 1.05e-03
2022-05-03 20:31:13,053 INFO [train.py:715] (2/8) Epoch 1, batch 4650, loss[loss=0.158, simple_loss=0.206, pruned_loss=0.055, over 4836.00 frames.], tot_loss[loss=0.1929, simple_loss=0.254, pruned_loss=0.06589, over 972371.94 frames.], batch size: 13, lr: 1.05e-03
2022-05-03 20:31:52,498 INFO [train.py:715] (2/8) Epoch 1, batch 4700, loss[loss=0.1498, simple_loss=0.2157, pruned_loss=0.04196, over 4829.00 frames.], tot_loss[loss=0.1907, simple_loss=0.252, pruned_loss=0.06469, over 971595.66 frames.], batch size: 13, lr: 1.05e-03
2022-05-03 20:32:31,317 INFO [train.py:715] (2/8) Epoch 1, batch 4750, loss[loss=0.1661, simple_loss=0.2265, pruned_loss=0.05287, over 4870.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2524, pruned_loss=0.06494, over 971611.04 frames.], batch size: 20, lr: 1.05e-03
2022-05-03 20:33:11,338 INFO [train.py:715] (2/8) Epoch 1, batch 4800, loss[loss=0.1666, simple_loss=0.24, pruned_loss=0.04657, over 4900.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2536, pruned_loss=0.06573, over 972579.67 frames.], batch size: 19, lr: 1.05e-03
2022-05-03 20:33:51,174 INFO [train.py:715] (2/8) Epoch 1, batch 4850, loss[loss=0.2371, simple_loss=0.2897, pruned_loss=0.09227, over 4829.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2529, pruned_loss=0.06535, over 972817.95 frames.], batch size: 15, lr: 1.05e-03
2022-05-03 20:34:30,461 INFO [train.py:715] (2/8) Epoch 1, batch 4900, loss[loss=0.1578, simple_loss=0.2225, pruned_loss=0.0466, over 4914.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2529, pruned_loss=0.06541, over 972348.45 frames.], batch size: 17, lr: 1.04e-03
2022-05-03 20:35:09,820 INFO [train.py:715] (2/8) Epoch 1, batch 4950, loss[loss=0.188, simple_loss=0.2505, pruned_loss=0.0628, over 4856.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2533, pruned_loss=0.06561, over 972098.28 frames.], batch size: 20, lr: 1.04e-03
2022-05-03 20:35:50,155 INFO [train.py:715] (2/8) Epoch 1, batch 5000, loss[loss=0.2003, simple_loss=0.2767, pruned_loss=0.06193, over 4819.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2521, pruned_loss=0.0647, over 971133.59 frames.], batch size: 27, lr: 1.04e-03
2022-05-03 20:36:29,713 INFO [train.py:715] (2/8) Epoch 1, batch 5050, loss[loss=0.1398, simple_loss=0.1996, pruned_loss=0.04004, over 4729.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2532, pruned_loss=0.06555, over 970876.94 frames.], batch size: 12, lr: 1.04e-03
2022-05-03 20:37:08,712 INFO [train.py:715] (2/8) Epoch 1, batch 5100, loss[loss=0.1635, simple_loss=0.2269, pruned_loss=0.05, over 4853.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2529, pruned_loss=0.06494, over 971206.18 frames.], batch size: 20, lr: 1.04e-03
2022-05-03 20:37:48,744 INFO [train.py:715] (2/8) Epoch 1, batch 5150, loss[loss=0.1719, simple_loss=0.2425, pruned_loss=0.05065, over 4813.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2533, pruned_loss=0.06484, over 971344.77 frames.], batch size: 27, lr: 1.04e-03
2022-05-03 20:38:30,124 INFO [train.py:715] (2/8) Epoch 1, batch 5200, loss[loss=0.1891, simple_loss=0.254, pruned_loss=0.0621, over 4949.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2538, pruned_loss=0.06524, over 971952.16 frames.], batch size: 21, lr: 1.04e-03
2022-05-03 20:39:09,100 INFO [train.py:715] (2/8) Epoch 1, batch 5250, loss[loss=0.1804, simple_loss=0.238, pruned_loss=0.0614, over 4889.00 frames.], tot_loss[loss=0.1923, simple_loss=0.254, pruned_loss=0.06525, over 971877.68 frames.], batch size: 16, lr: 1.04e-03
2022-05-03 20:39:48,463 INFO [train.py:715] (2/8) Epoch 1, batch 5300, loss[loss=0.1965, simple_loss=0.2549, pruned_loss=0.06904, over 4899.00 frames.], tot_loss[loss=0.191, simple_loss=0.2527, pruned_loss=0.06463, over 972548.99 frames.], batch size: 19, lr: 1.04e-03
2022-05-03 20:40:28,097 INFO [train.py:715] (2/8) Epoch 1, batch 5350, loss[loss=0.1633, simple_loss=0.233, pruned_loss=0.04678, over 4796.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2524, pruned_loss=0.06427, over 972744.91 frames.], batch size: 17, lr: 1.04e-03
2022-05-03 20:41:07,639 INFO [train.py:715] (2/8) Epoch 1, batch 5400, loss[loss=0.2186, simple_loss=0.2738, pruned_loss=0.08166, over 4764.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2537, pruned_loss=0.06549, over 972348.04 frames.], batch size: 18, lr: 1.04e-03
2022-05-03 20:41:46,689 INFO [train.py:715] (2/8) Epoch 1, batch 5450, loss[loss=0.1998, simple_loss=0.2671, pruned_loss=0.06621, over 4823.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2549, pruned_loss=0.06574, over 971820.84 frames.], batch size: 25, lr: 1.04e-03
2022-05-03 20:42:26,570 INFO [train.py:715] (2/8) Epoch 1, batch 5500, loss[loss=0.2246, simple_loss=0.2717, pruned_loss=0.08877, over 4827.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2549, pruned_loss=0.06622, over 971576.41 frames.], batch size: 26, lr: 1.04e-03
2022-05-03 20:43:06,476 INFO [train.py:715] (2/8) Epoch 1, batch 5550, loss[loss=0.2112, simple_loss=0.2713, pruned_loss=0.07559, over 4964.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2539, pruned_loss=0.06575, over 971068.20 frames.], batch size: 14, lr: 1.04e-03
2022-05-03 20:43:45,487 INFO [train.py:715] (2/8) Epoch 1, batch 5600, loss[loss=0.2249, simple_loss=0.2841, pruned_loss=0.0828, over 4944.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2544, pruned_loss=0.06601, over 971140.29 frames.], batch size: 35, lr: 1.04e-03
2022-05-03 20:44:24,779 INFO [train.py:715] (2/8) Epoch 1, batch 5650, loss[loss=0.1878, simple_loss=0.2603, pruned_loss=0.05765, over 4945.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2553, pruned_loss=0.06647, over 971047.29 frames.], batch size: 35, lr: 1.03e-03
2022-05-03 20:45:04,544 INFO [train.py:715] (2/8) Epoch 1, batch 5700, loss[loss=0.1946, simple_loss=0.2516, pruned_loss=0.06883, over 4800.00 frames.], tot_loss[loss=0.194, simple_loss=0.255, pruned_loss=0.06649, over 971486.92 frames.], batch size: 25, lr: 1.03e-03
2022-05-03 20:45:44,074 INFO [train.py:715] (2/8) Epoch 1, batch 5750, loss[loss=0.2122, simple_loss=0.2582, pruned_loss=0.08314, over 4906.00 frames.], tot_loss[loss=0.1941, simple_loss=0.255, pruned_loss=0.06661, over 971343.38 frames.], batch size: 17, lr: 1.03e-03
2022-05-03 20:46:23,086 INFO [train.py:715] (2/8) Epoch 1, batch 5800, loss[loss=0.2012, simple_loss=0.2503, pruned_loss=0.07604, over 4956.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2558, pruned_loss=0.06679, over 971677.33 frames.], batch size: 14, lr: 1.03e-03
2022-05-03 20:47:03,036 INFO [train.py:715] (2/8) Epoch 1, batch 5850, loss[loss=0.1933, simple_loss=0.2519, pruned_loss=0.06733, over 4800.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2561, pruned_loss=0.06707, over 971879.64 frames.], batch size: 21, lr: 1.03e-03
2022-05-03 20:47:42,846 INFO [train.py:715] (2/8) Epoch 1, batch 5900, loss[loss=0.1725, simple_loss=0.229, pruned_loss=0.058, over 4803.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2553, pruned_loss=0.06664, over 972062.38 frames.], batch size: 26, lr: 1.03e-03
2022-05-03 20:48:21,950 INFO [train.py:715] (2/8) Epoch 1, batch 5950, loss[loss=0.1982, simple_loss=0.2507, pruned_loss=0.07289, over 4980.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2553, pruned_loss=0.06682, over 972619.94 frames.], batch size: 16, lr: 1.03e-03
2022-05-03 20:49:01,781 INFO [train.py:715] (2/8) Epoch 1, batch 6000, loss[loss=0.1875, simple_loss=0.2478, pruned_loss=0.06364, over 4964.00 frames.], tot_loss[loss=0.1926, simple_loss=0.254, pruned_loss=0.06567, over 971863.68 frames.], batch size: 24, lr: 1.03e-03
2022-05-03 20:49:01,782 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 20:49:14,259 INFO [train.py:742] (2/8) Epoch 1, validation: loss=0.1267, simple_loss=0.2135, pruned_loss=0.01993, over 914524.00 frames.
2022-05-03 20:49:53,679 INFO [train.py:715] (2/8) Epoch 1, batch 6050, loss[loss=0.1613, simple_loss=0.211, pruned_loss=0.05574, over 4798.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2526, pruned_loss=0.06516, over 972277.98 frames.], batch size: 12, lr: 1.03e-03
2022-05-03 20:50:33,745 INFO [train.py:715] (2/8) Epoch 1, batch 6100, loss[loss=0.1799, simple_loss=0.2419, pruned_loss=0.05896, over 4833.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2524, pruned_loss=0.06468, over 972409.91 frames.], batch size: 13, lr: 1.03e-03
2022-05-03 20:51:13,270 INFO [train.py:715] (2/8) Epoch 1, batch 6150, loss[loss=0.1759, simple_loss=0.2538, pruned_loss=0.04899, over 4984.00 frames.], tot_loss[loss=0.1902, simple_loss=0.252, pruned_loss=0.06425, over 972990.25 frames.], batch size: 35, lr: 1.03e-03
2022-05-03 20:51:51,970 INFO [train.py:715] (2/8) Epoch 1, batch 6200, loss[loss=0.1863, simple_loss=0.2554, pruned_loss=0.05857, over 4873.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2517, pruned_loss=0.06406, over 972705.25 frames.], batch size: 22, lr: 1.03e-03
2022-05-03 20:52:32,158 INFO [train.py:715] (2/8) Epoch 1, batch 6250, loss[loss=0.1929, simple_loss=0.2568, pruned_loss=0.06451, over 4967.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2527, pruned_loss=0.06473, over 972710.18 frames.], batch size: 39, lr: 1.03e-03
2022-05-03 20:53:11,872 INFO [train.py:715] (2/8) Epoch 1, batch 6300, loss[loss=0.1895, simple_loss=0.2607, pruned_loss=0.05916, over 4924.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2517, pruned_loss=0.06394, over 973386.24 frames.], batch size: 18, lr: 1.03e-03
2022-05-03 20:53:51,070 INFO [train.py:715] (2/8) Epoch 1, batch 6350, loss[loss=0.1587, simple_loss=0.2269, pruned_loss=0.04527, over 4987.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2528, pruned_loss=0.06448, over 972979.69 frames.], batch size: 25, lr: 1.03e-03
2022-05-03 20:54:30,383 INFO [train.py:715] (2/8) Epoch 1, batch 6400, loss[loss=0.2132, simple_loss=0.2773, pruned_loss=0.07455, over 4748.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2535, pruned_loss=0.06489, over 973266.21 frames.], batch size: 19, lr: 1.03e-03
2022-05-03 20:55:09,936 INFO [train.py:715] (2/8) Epoch 1, batch 6450, loss[loss=0.2028, simple_loss=0.267, pruned_loss=0.06924, over 4756.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2541, pruned_loss=0.06567, over 972601.80 frames.], batch size: 16, lr: 1.02e-03
2022-05-03 20:55:49,576 INFO [train.py:715] (2/8) Epoch 1, batch 6500, loss[loss=0.2066, simple_loss=0.2727, pruned_loss=0.07022, over 4837.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2543, pruned_loss=0.06593, over 973477.31 frames.], batch size: 15, lr: 1.02e-03
2022-05-03 20:56:28,195 INFO [train.py:715] (2/8) Epoch 1, batch 6550, loss[loss=0.1915, simple_loss=0.2484, pruned_loss=0.06728, over 4785.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2537, pruned_loss=0.0655, over 972997.31 frames.], batch size: 17, lr: 1.02e-03
2022-05-03 20:57:08,072 INFO [train.py:715] (2/8) Epoch 1, batch 6600, loss[loss=0.209, simple_loss=0.257, pruned_loss=0.08049, over 4707.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2527, pruned_loss=0.06522, over 972225.68 frames.], batch size: 15, lr: 1.02e-03
2022-05-03 20:57:48,544 INFO [train.py:715] (2/8) Epoch 1, batch 6650, loss[loss=0.1826, simple_loss=0.2448, pruned_loss=0.06024, over 4927.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2536, pruned_loss=0.06599, over 972421.47 frames.], batch size: 18, lr: 1.02e-03
2022-05-03 20:58:28,001 INFO [train.py:715] (2/8) Epoch 1, batch 6700, loss[loss=0.2812, simple_loss=0.313, pruned_loss=0.1247, over 4694.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2529, pruned_loss=0.06568, over 972798.51 frames.], batch size: 15, lr: 1.02e-03
2022-05-03 20:59:07,317 INFO [train.py:715] (2/8) Epoch 1, batch 6750, loss[loss=0.1736, simple_loss=0.2285, pruned_loss=0.05933, over 4746.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2531, pruned_loss=0.06557, over 972914.94 frames.], batch size: 12, lr: 1.02e-03
2022-05-03 20:59:47,251 INFO [train.py:715] (2/8) Epoch 1, batch 6800, loss[loss=0.1691, simple_loss=0.2288, pruned_loss=0.05467, over 4986.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2519, pruned_loss=0.06439, over 973065.44 frames.], batch size: 28, lr: 1.02e-03
2022-05-03 21:00:26,794 INFO [train.py:715] (2/8) Epoch 1, batch 6850, loss[loss=0.2135, simple_loss=0.2746, pruned_loss=0.07623, over 4782.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2528, pruned_loss=0.06443, over 972897.85 frames.], batch size: 14, lr: 1.02e-03
2022-05-03 21:01:05,418 INFO [train.py:715] (2/8) Epoch 1, batch 6900, loss[loss=0.1982, simple_loss=0.2462, pruned_loss=0.07511, over 4757.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2533, pruned_loss=0.06469, over 973142.77 frames.], batch size: 16, lr: 1.02e-03
2022-05-03 21:01:44,709 INFO [train.py:715] (2/8) Epoch 1, batch 6950, loss[loss=0.1545, simple_loss=0.2339, pruned_loss=0.03757, over 4799.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2538, pruned_loss=0.06515, over 971996.25 frames.], batch size: 24, lr: 1.02e-03
2022-05-03 21:02:24,789 INFO [train.py:715] (2/8) Epoch 1, batch 7000, loss[loss=0.1932, simple_loss=0.2515, pruned_loss=0.06745, over 4880.00 frames.], tot_loss[loss=0.1908, simple_loss=0.253, pruned_loss=0.06428, over 972381.56 frames.], batch size: 39, lr: 1.02e-03
2022-05-03 21:03:03,635 INFO [train.py:715] (2/8) Epoch 1, batch 7050, loss[loss=0.1899, simple_loss=0.242, pruned_loss=0.06886, over 4891.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2541, pruned_loss=0.06551, over 972916.35 frames.], batch size: 19, lr: 1.02e-03
2022-05-03 21:03:42,602 INFO [train.py:715] (2/8) Epoch 1, batch 7100, loss[loss=0.183, simple_loss=0.2491, pruned_loss=0.05844, over 4924.00 frames.], tot_loss[loss=0.1924, simple_loss=0.254, pruned_loss=0.06542, over 972602.17 frames.], batch size: 29, lr: 1.02e-03
2022-05-03 21:04:22,589 INFO [train.py:715] (2/8) Epoch 1, batch 7150, loss[loss=0.1917, simple_loss=0.26, pruned_loss=0.06171, over 4760.00 frames.], tot_loss[loss=0.193, simple_loss=0.2544, pruned_loss=0.06582, over 972837.14 frames.], batch size: 18, lr: 1.02e-03
2022-05-03 21:05:02,509 INFO [train.py:715] (2/8) Epoch 1, batch 7200, loss[loss=0.2315, simple_loss=0.2978, pruned_loss=0.08262, over 4759.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2543, pruned_loss=0.06555, over 972687.48 frames.], batch size: 16, lr: 1.02e-03
2022-05-03 21:05:41,151 INFO [train.py:715] (2/8) Epoch 1, batch 7250, loss[loss=0.1809, simple_loss=0.2429, pruned_loss=0.05942, over 4819.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2535, pruned_loss=0.06505, over 972483.98 frames.], batch size: 25, lr: 1.02e-03
2022-05-03 21:06:21,081 INFO [train.py:715] (2/8) Epoch 1, batch 7300, loss[loss=0.1946, simple_loss=0.2605, pruned_loss=0.06438, over 4798.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2538, pruned_loss=0.0657, over 972208.32 frames.], batch size: 21, lr: 1.01e-03
2022-05-03 21:07:00,824 INFO [train.py:715] (2/8) Epoch 1, batch 7350, loss[loss=0.212, simple_loss=0.2707, pruned_loss=0.07665, over 4776.00 frames.], tot_loss[loss=0.1926, simple_loss=0.254, pruned_loss=0.06558, over 971345.16 frames.], batch size: 14, lr: 1.01e-03
2022-05-03 21:07:39,610 INFO [train.py:715] (2/8) Epoch 1, batch 7400, loss[loss=0.1926, simple_loss=0.2549, pruned_loss=0.06513, over 4803.00 frames.], tot_loss[loss=0.1914, simple_loss=0.253, pruned_loss=0.06495, over 972214.94 frames.], batch size: 24, lr: 1.01e-03
2022-05-03 21:08:18,525 INFO [train.py:715] (2/8) Epoch 1, batch 7450, loss[loss=0.2488, simple_loss=0.2874, pruned_loss=0.1051, over 4803.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2533, pruned_loss=0.06565, over 972431.74 frames.], batch size: 21, lr: 1.01e-03
2022-05-03 21:08:58,339 INFO [train.py:715] (2/8) Epoch 1, batch 7500, loss[loss=0.1742, simple_loss=0.2446, pruned_loss=0.05193, over 4969.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2536, pruned_loss=0.06556, over 972123.47 frames.], batch size: 15, lr: 1.01e-03
2022-05-03 21:09:38,018 INFO [train.py:715] (2/8) Epoch 1, batch 7550, loss[loss=0.1903, simple_loss=0.2579, pruned_loss=0.06134, over 4817.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2535, pruned_loss=0.06533, over 973021.27 frames.], batch size: 25, lr: 1.01e-03
2022-05-03 21:10:16,227 INFO [train.py:715] (2/8) Epoch 1, batch 7600, loss[loss=0.1698, simple_loss=0.235, pruned_loss=0.05228, over 4980.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2536, pruned_loss=0.06536, over 973081.08 frames.], batch size: 14, lr: 1.01e-03
2022-05-03 21:10:55,966 INFO [train.py:715] (2/8) Epoch 1, batch 7650, loss[loss=0.204, simple_loss=0.2664, pruned_loss=0.07075, over 4869.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2541, pruned_loss=0.06572, over 973016.86 frames.], batch size: 22, lr: 1.01e-03
2022-05-03 21:11:35,785 INFO [train.py:715] (2/8) Epoch 1, batch 7700, loss[loss=0.2077, simple_loss=0.2578, pruned_loss=0.07882, over 4990.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2531, pruned_loss=0.06517, over 972721.21 frames.], batch size: 15, lr: 1.01e-03
2022-05-03 21:12:14,128 INFO [train.py:715] (2/8) Epoch 1, batch 7750, loss[loss=0.1817, simple_loss=0.2521, pruned_loss=0.05565, over 4874.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2533, pruned_loss=0.06521, over 971979.39 frames.], batch size: 22, lr: 1.01e-03
2022-05-03 21:12:53,241 INFO [train.py:715] (2/8) Epoch 1, batch 7800, loss[loss=0.1725, simple_loss=0.2316, pruned_loss=0.05672, over 4872.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2544, pruned_loss=0.06641, over 971606.43 frames.], batch size: 32, lr: 1.01e-03
2022-05-03 21:13:33,307 INFO [train.py:715] (2/8) Epoch 1, batch 7850, loss[loss=0.2003, simple_loss=0.2585, pruned_loss=0.07109, over 4983.00 frames.], tot_loss[loss=0.192, simple_loss=0.2531, pruned_loss=0.06542, over 971828.86 frames.], batch size: 28, lr: 1.01e-03
2022-05-03 21:14:12,710 INFO [train.py:715] (2/8) Epoch 1, batch 7900, loss[loss=0.1786, simple_loss=0.2381, pruned_loss=0.05961, over 4772.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2538, pruned_loss=0.0659, over 971956.50 frames.], batch size: 12, lr: 1.01e-03
2022-05-03 21:14:51,149 INFO [train.py:715] (2/8) Epoch 1, batch 7950, loss[loss=0.1895, simple_loss=0.2623, pruned_loss=0.05835, over 4894.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2537, pruned_loss=0.0659, over 971855.56 frames.], batch size: 17, lr: 1.01e-03
2022-05-03 21:15:31,255 INFO [train.py:715] (2/8) Epoch 1, batch 8000, loss[loss=0.1637, simple_loss=0.2303, pruned_loss=0.0486, over 4802.00 frames.], tot_loss[loss=0.1928, simple_loss=0.254, pruned_loss=0.06579, over 972504.97 frames.], batch size: 13, lr: 1.01e-03
2022-05-03 21:16:11,045 INFO [train.py:715] (2/8) Epoch 1, batch 8050, loss[loss=0.1942, simple_loss=0.2485, pruned_loss=0.06994, over 4872.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2527, pruned_loss=0.06499, over 972295.16 frames.], batch size: 32, lr: 1.01e-03
2022-05-03 21:16:50,419 INFO [train.py:715] (2/8) Epoch 1, batch 8100, loss[loss=0.1457, simple_loss=0.2102, pruned_loss=0.04063, over 4989.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2533, pruned_loss=0.06501, over 972306.16 frames.], batch size: 25, lr: 1.01e-03
2022-05-03 21:17:28,620 INFO [train.py:715] (2/8) Epoch 1, batch 8150, loss[loss=0.163, simple_loss=0.2372, pruned_loss=0.04443, over 4910.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2522, pruned_loss=0.06455, over 971891.74 frames.], batch size: 22, lr: 1.00e-03
2022-05-03 21:18:08,540 INFO [train.py:715] (2/8) Epoch 1, batch 8200, loss[loss=0.1759, simple_loss=0.241, pruned_loss=0.05537, over 4923.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2541, pruned_loss=0.06575, over 971611.02 frames.], batch size: 18, lr: 1.00e-03
2022-05-03 21:18:48,015 INFO [train.py:715] (2/8) Epoch 1, batch 8250, loss[loss=0.1547, simple_loss=0.2292, pruned_loss=0.04011, over 4793.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2537, pruned_loss=0.06588, over 971072.33 frames.], batch size: 21, lr: 1.00e-03
2022-05-03 21:19:26,201 INFO [train.py:715] (2/8) Epoch 1, batch 8300, loss[loss=0.2244, simple_loss=0.2705, pruned_loss=0.08915, over 4974.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2543, pruned_loss=0.06601, over 971947.15 frames.], batch size: 15, lr: 1.00e-03
2022-05-03 21:20:06,140 INFO [train.py:715] (2/8) Epoch 1, batch 8350, loss[loss=0.1677, simple_loss=0.2345, pruned_loss=0.05043, over 4838.00 frames.], tot_loss[loss=0.1925, simple_loss=0.254, pruned_loss=0.06548, over 971813.21 frames.], batch size: 25, lr: 1.00e-03
2022-05-03 21:20:45,723 INFO [train.py:715] (2/8) Epoch 1, batch 8400, loss[loss=0.185, simple_loss=0.2524, pruned_loss=0.05879, over 4910.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2543, pruned_loss=0.0653, over 972089.03 frames.], batch size: 19, lr: 1.00e-03
2022-05-03 21:21:25,098 INFO [train.py:715] (2/8) Epoch 1, batch 8450, loss[loss=0.2422, simple_loss=0.2933, pruned_loss=0.09559, over 4970.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2551, pruned_loss=0.06619, over 972619.54 frames.], batch size: 24, lr: 1.00e-03
2022-05-03 21:22:03,492 INFO [train.py:715] (2/8) Epoch 1, batch 8500, loss[loss=0.1639, simple_loss=0.2183, pruned_loss=0.05473, over 4701.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2545, pruned_loss=0.06582, over 972395.48 frames.], batch size: 15, lr: 1.00e-03
2022-05-03 21:22:43,388 INFO [train.py:715] (2/8) Epoch 1, batch 8550, loss[loss=0.1932, simple_loss=0.2401, pruned_loss=0.07318, over 4690.00 frames.], tot_loss[loss=0.1928, simple_loss=0.254, pruned_loss=0.06583, over 971811.36 frames.], batch size: 15, lr: 1.00e-03
2022-05-03 21:23:22,897 INFO [train.py:715] (2/8) Epoch 1, batch 8600, loss[loss=0.2084, simple_loss=0.2837, pruned_loss=0.06655, over 4989.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2527, pruned_loss=0.06489, over 972394.60 frames.], batch size: 14, lr: 1.00e-03
2022-05-03 21:24:00,899 INFO [train.py:715] (2/8) Epoch 1, batch 8650, loss[loss=0.172, simple_loss=0.2395, pruned_loss=0.0523, over 4865.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2526, pruned_loss=0.06493, over 973279.68 frames.], batch size: 22, lr: 9.99e-04
2022-05-03 21:24:41,119 INFO [train.py:715] (2/8) Epoch 1, batch 8700, loss[loss=0.2128, simple_loss=0.2721, pruned_loss=0.07671, over 4819.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2528, pruned_loss=0.06524, over 972291.59 frames.], batch size: 26, lr: 9.98e-04
2022-05-03 21:25:21,112 INFO [train.py:715] (2/8) Epoch 1, batch 8750, loss[loss=0.1498, simple_loss=0.221, pruned_loss=0.03934, over 4946.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2536, pruned_loss=0.06607, over 971418.80 frames.], batch size: 29, lr: 9.98e-04
2022-05-03 21:26:00,205 INFO [train.py:715] (2/8) Epoch 1, batch 8800, loss[loss=0.1647, simple_loss=0.2235, pruned_loss=0.05297, over 4870.00 frames.], tot_loss[loss=0.1922, simple_loss=0.253, pruned_loss=0.06569, over 971428.60 frames.], batch size: 13, lr: 9.97e-04
2022-05-03 21:26:39,528 INFO [train.py:715] (2/8) Epoch 1, batch 8850, loss[loss=0.197, simple_loss=0.2602, pruned_loss=0.06692, over 4870.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2522, pruned_loss=0.06527, over 971692.78 frames.], batch size: 16, lr: 9.97e-04
2022-05-03 21:27:19,645 INFO [train.py:715] (2/8) Epoch 1, batch 8900, loss[loss=0.1742, simple_loss=0.2355, pruned_loss=0.05641, over 4745.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2529, pruned_loss=0.06519, over 972213.15 frames.], batch size: 16, lr: 9.96e-04
2022-05-03 21:27:59,345 INFO [train.py:715] (2/8) Epoch 1, batch 8950, loss[loss=0.2079, simple_loss=0.2796, pruned_loss=0.06809, over 4820.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2543, pruned_loss=0.06568, over 972469.18 frames.], batch size: 14, lr: 9.96e-04
2022-05-03 21:28:37,777 INFO [train.py:715] (2/8) Epoch 1, batch 9000, loss[loss=0.1751, simple_loss=0.2443, pruned_loss=0.05293, over 4937.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2536, pruned_loss=0.06545, over 972717.58 frames.], batch size: 21, lr: 9.95e-04
2022-05-03 21:28:37,778 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 21:28:47,501 INFO [train.py:742] (2/8) Epoch 1, validation: loss=0.1253, simple_loss=0.2125, pruned_loss=0.01906, over 914524.00 frames.
2022-05-03 21:29:25,992 INFO [train.py:715] (2/8) Epoch 1, batch 9050, loss[loss=0.2147, simple_loss=0.2708, pruned_loss=0.07931, over 4823.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2532, pruned_loss=0.06516, over 971807.09 frames.], batch size: 15, lr: 9.94e-04
2022-05-03 21:30:06,201 INFO [train.py:715] (2/8) Epoch 1, batch 9100, loss[loss=0.1871, simple_loss=0.2576, pruned_loss=0.05827, over 4963.00 frames.], tot_loss[loss=0.1912, simple_loss=0.253, pruned_loss=0.06466, over 971676.30 frames.], batch size: 24, lr: 9.94e-04
2022-05-03 21:30:45,838 INFO [train.py:715] (2/8) Epoch 1, batch 9150, loss[loss=0.1673, simple_loss=0.2292, pruned_loss=0.05266, over 4879.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2527, pruned_loss=0.06441, over 972035.82 frames.], batch size: 16, lr: 9.93e-04
2022-05-03 21:31:24,117 INFO [train.py:715] (2/8) Epoch 1, batch 9200, loss[loss=0.2021, simple_loss=0.2587, pruned_loss=0.07275, over 4985.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2534, pruned_loss=0.06473, over 972788.16 frames.], batch size: 25, lr: 9.93e-04
2022-05-03 21:32:03,937 INFO [train.py:715] (2/8) Epoch 1, batch 9250, loss[loss=0.1895, simple_loss=0.2552, pruned_loss=0.06187, over 4973.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2537, pruned_loss=0.06469, over 972399.97 frames.], batch size: 24, lr: 9.92e-04
2022-05-03 21:32:43,817 INFO [train.py:715] (2/8) Epoch 1, batch 9300, loss[loss=0.163, simple_loss=0.2308, pruned_loss=0.04756, over 4788.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2531, pruned_loss=0.06416, over 972324.53 frames.], batch size: 14, lr: 9.92e-04
2022-05-03 21:33:22,870 INFO [train.py:715] (2/8) Epoch 1, batch 9350, loss[loss=0.1759, simple_loss=0.2396, pruned_loss=0.05611, over 4826.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2528, pruned_loss=0.06442, over 972291.05 frames.], batch size: 25, lr: 9.91e-04
2022-05-03 21:34:02,356 INFO [train.py:715] (2/8) Epoch 1, batch 9400, loss[loss=0.1747, simple_loss=0.232, pruned_loss=0.05867, over 4846.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2523, pruned_loss=0.06422, over 972842.29 frames.], batch size: 13, lr: 9.91e-04
2022-05-03 21:34:42,528 INFO [train.py:715] (2/8) Epoch 1, batch 9450, loss[loss=0.1859, simple_loss=0.2454, pruned_loss=0.06325, over 4812.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2522, pruned_loss=0.0638, over 972629.92 frames.], batch size: 25, lr: 9.90e-04
2022-05-03 21:35:22,122 INFO [train.py:715] (2/8) Epoch 1, batch 9500, loss[loss=0.1971, simple_loss=0.2577, pruned_loss=0.06821, over 4859.00 frames.], tot_loss[loss=0.19, simple_loss=0.2521, pruned_loss=0.06395, over 972340.39 frames.], batch size: 30, lr: 9.89e-04
2022-05-03 21:36:00,392 INFO [train.py:715] (2/8) Epoch 1, batch 9550, loss[loss=0.1703, simple_loss=0.2426, pruned_loss=0.04905, over 4954.00 frames.], tot_loss[loss=0.1897, simple_loss=0.252, pruned_loss=0.06371, over 972619.87 frames.], batch size: 21, lr: 9.89e-04
2022-05-03 21:36:40,614 INFO [train.py:715] (2/8) Epoch 1, batch 9600, loss[loss=0.2332, simple_loss=0.2836, pruned_loss=0.09144, over 4873.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2518, pruned_loss=0.06355, over 972924.65 frames.], batch size: 16, lr: 9.88e-04
2022-05-03 21:37:20,351 INFO [train.py:715] (2/8) Epoch 1, batch 9650, loss[loss=0.1763, simple_loss=0.2323, pruned_loss=0.06012, over 4873.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2511, pruned_loss=0.06295, over 972950.13 frames.], batch size: 22, lr: 9.88e-04
2022-05-03 21:37:58,741 INFO [train.py:715] (2/8) Epoch 1, batch 9700, loss[loss=0.1934, simple_loss=0.2601, pruned_loss=0.06334, over 4949.00 frames.], tot_loss[loss=0.1885, simple_loss=0.251, pruned_loss=0.06304, over 972444.70 frames.], batch size: 35, lr: 9.87e-04
2022-05-03 21:38:38,637 INFO [train.py:715] (2/8) Epoch 1, batch 9750, loss[loss=0.1997, simple_loss=0.2664, pruned_loss=0.06649, over 4864.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2514, pruned_loss=0.06374, over 971859.84 frames.], batch size: 20, lr: 9.87e-04
2022-05-03 21:39:19,054 INFO [train.py:715] (2/8) Epoch 1, batch 9800, loss[loss=0.2572, simple_loss=0.3155, pruned_loss=0.0995, over 4978.00 frames.], tot_loss[loss=0.1898, simple_loss=0.252, pruned_loss=0.06384, over 971260.20 frames.], batch size: 28, lr: 9.86e-04
2022-05-03 21:39:58,292 INFO [train.py:715] (2/8) Epoch 1, batch 9850, loss[loss=0.1779, simple_loss=0.2433, pruned_loss=0.05624, over 4887.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2524, pruned_loss=0.06421, over 970925.61 frames.], batch size: 22, lr: 9.86e-04
2022-05-03 21:40:37,076 INFO [train.py:715] (2/8) Epoch 1, batch 9900, loss[loss=0.166, simple_loss=0.2371, pruned_loss=0.04743, over 4962.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2527, pruned_loss=0.06428, over 971125.78 frames.], batch size: 15, lr: 9.85e-04
2022-05-03 21:41:17,356 INFO [train.py:715] (2/8) Epoch 1, batch 9950, loss[loss=0.2659, simple_loss=0.3071, pruned_loss=0.1123, over 4745.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2526, pruned_loss=0.06464, over 971914.36 frames.], batch size: 16, lr: 9.85e-04
2022-05-03 21:41:57,259 INFO [train.py:715] (2/8) Epoch 1, batch 10000, loss[loss=0.1782, simple_loss=0.2423, pruned_loss=0.05702, over 4975.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2532, pruned_loss=0.06481, over 972262.86 frames.], batch size: 39, lr: 9.84e-04
2022-05-03 21:42:36,318 INFO [train.py:715] (2/8) Epoch 1, batch 10050, loss[loss=0.1644, simple_loss=0.2318, pruned_loss=0.04847, over 4965.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2529, pruned_loss=0.06482, over 972813.47 frames.], batch size: 24, lr: 9.83e-04
2022-05-03 21:43:15,948 INFO [train.py:715] (2/8) Epoch 1, batch 10100, loss[loss=0.1828, simple_loss=0.2469, pruned_loss=0.05931, over 4842.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2531, pruned_loss=0.06531, over 973280.30 frames.], batch size: 30, lr: 9.83e-04
2022-05-03 21:43:55,965 INFO [train.py:715] (2/8) Epoch 1, batch 10150, loss[loss=0.2262, simple_loss=0.288, pruned_loss=0.0822, over 4925.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2528, pruned_loss=0.06465, over 973477.36 frames.], batch size: 17, lr: 9.82e-04
2022-05-03 21:44:35,075 INFO [train.py:715] (2/8) Epoch 1, batch 10200, loss[loss=0.205, simple_loss=0.2657, pruned_loss=0.07216, over 4889.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2535, pruned_loss=0.06469, over 973000.59 frames.], batch size: 22, lr: 9.82e-04
2022-05-03 21:45:14,032 INFO [train.py:715] (2/8) Epoch 1, batch 10250, loss[loss=0.174, simple_loss=0.2429, pruned_loss=0.05254, over 4802.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2537, pruned_loss=0.06439, over 973880.52 frames.], batch size: 21, lr: 9.81e-04
2022-05-03 21:45:54,199 INFO [train.py:715] (2/8) Epoch 1, batch 10300, loss[loss=0.1801, simple_loss=0.2405, pruned_loss=0.05979, over 4758.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2543, pruned_loss=0.06497, over 973378.47 frames.], batch size: 16, lr: 9.81e-04
2022-05-03 21:46:34,443 INFO [train.py:715] (2/8) Epoch 1, batch 10350, loss[loss=0.1937, simple_loss=0.2587, pruned_loss=0.06438, over 4743.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2533, pruned_loss=0.06459, over 973652.16 frames.], batch size: 16, lr: 9.80e-04
2022-05-03 21:47:13,899 INFO [train.py:715] (2/8) Epoch 1, batch 10400, loss[loss=0.1867, simple_loss=0.2432, pruned_loss=0.06508, over 4856.00 frames.], tot_loss[loss=0.19, simple_loss=0.2519, pruned_loss=0.06407, over 973899.15 frames.], batch size: 32, lr: 9.80e-04
2022-05-03 21:47:53,939 INFO [train.py:715] (2/8) Epoch 1, batch 10450, loss[loss=0.2072, simple_loss=0.2701, pruned_loss=0.07211, over 4769.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2518, pruned_loss=0.06396, over 973154.94 frames.], batch size: 18, lr: 9.79e-04
2022-05-03 21:48:34,471 INFO [train.py:715] (2/8) Epoch 1, batch 10500, loss[loss=0.2081, simple_loss=0.2687, pruned_loss=0.07378, over 4936.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2505, pruned_loss=0.06398, over 972700.47 frames.], batch size: 29, lr: 9.79e-04
2022-05-03 21:49:13,756 INFO [train.py:715] (2/8) Epoch 1, batch 10550, loss[loss=0.1525, simple_loss=0.2215, pruned_loss=0.04176, over 4901.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2503, pruned_loss=0.06305, over 972024.70 frames.], batch size: 19, lr: 9.78e-04
2022-05-03 21:49:52,636 INFO [train.py:715] (2/8) Epoch 1, batch 10600, loss[loss=0.2051, simple_loss=0.2689, pruned_loss=0.07069, over 4987.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2505, pruned_loss=0.06312, over 971609.35 frames.], batch size: 26, lr: 9.78e-04
2022-05-03 21:50:33,171 INFO [train.py:715] (2/8) Epoch 1, batch 10650, loss[loss=0.1689, simple_loss=0.2469, pruned_loss=0.04549, over 4831.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2492, pruned_loss=0.06208, over 970847.21 frames.], batch size: 26, lr: 9.77e-04
2022-05-03 21:51:13,721 INFO [train.py:715] (2/8) Epoch 1, batch 10700, loss[loss=0.2416, simple_loss=0.3086, pruned_loss=0.08728, over 4778.00 frames.], tot_loss[loss=0.1886, simple_loss=0.251, pruned_loss=0.06314, over 971036.02 frames.], batch size: 17, lr: 9.76e-04
2022-05-03 21:51:52,986 INFO [train.py:715] (2/8) Epoch 1, batch 10750, loss[loss=0.1846, simple_loss=0.2587, pruned_loss=0.0553, over 4806.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2524, pruned_loss=0.06359, over 971254.07 frames.], batch size: 25, lr: 9.76e-04
2022-05-03 21:52:32,267 INFO [train.py:715] (2/8) Epoch 1, batch 10800, loss[loss=0.2061, simple_loss=0.2702, pruned_loss=0.07101, over 4884.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2505, pruned_loss=0.06262, over 970742.01 frames.], batch size: 22, lr: 9.75e-04
2022-05-03 21:53:12,725 INFO [train.py:715] (2/8) Epoch 1, batch 10850, loss[loss=0.1665, simple_loss=0.2287, pruned_loss=0.05216, over 4949.00 frames.], tot_loss[loss=0.189, simple_loss=0.2513, pruned_loss=0.06336, over 970677.11 frames.], batch size: 21, lr: 9.75e-04
2022-05-03 21:53:52,213 INFO [train.py:715] (2/8) Epoch 1, batch 10900, loss[loss=0.2126, simple_loss=0.2632, pruned_loss=0.08102, over 4847.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2518, pruned_loss=0.06302, over 970697.64 frames.], batch size: 32, lr: 9.74e-04
2022-05-03 21:54:30,702 INFO [train.py:715] (2/8) Epoch 1, batch 10950, loss[loss=0.1616, simple_loss=0.2275, pruned_loss=0.0478, over 4749.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2509, pruned_loss=0.0623, over 971260.62 frames.], batch size: 19, lr: 9.74e-04
2022-05-03 21:55:10,748 INFO [train.py:715] (2/8) Epoch 1, batch 11000, loss[loss=0.1657, simple_loss=0.237, pruned_loss=0.04716, over 4773.00 frames.], tot_loss[loss=0.1878, simple_loss=0.251, pruned_loss=0.06236, over 971269.09 frames.], batch size: 14, lr: 9.73e-04
2022-05-03 21:55:50,512 INFO [train.py:715] (2/8) Epoch 1, batch 11050, loss[loss=0.1809, simple_loss=0.2461, pruned_loss=0.05788, over 4815.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2516, pruned_loss=0.06299, over 971384.49 frames.], batch size: 26, lr: 9.73e-04
2022-05-03 21:56:29,265 INFO [train.py:715] (2/8) Epoch 1, batch 11100, loss[loss=0.1624, simple_loss=0.2302, pruned_loss=0.04729, over 4811.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2508, pruned_loss=0.06296, over 971604.74 frames.], batch size: 26, lr: 9.72e-04
2022-05-03 21:57:08,671 INFO [train.py:715] (2/8) Epoch 1, batch 11150, loss[loss=0.2022, simple_loss=0.2676, pruned_loss=0.06839, over 4807.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2515, pruned_loss=0.06335, over 971879.06 frames.], batch size: 26, lr: 9.72e-04
2022-05-03 21:57:48,796 INFO [train.py:715] (2/8) Epoch 1, batch 11200, loss[loss=0.1795, simple_loss=0.2295, pruned_loss=0.06478, over 4830.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2511, pruned_loss=0.0633, over 971247.72 frames.], batch size: 14, lr: 9.71e-04
2022-05-03 21:58:28,393 INFO [train.py:715] (2/8) Epoch 1, batch 11250, loss[loss=0.1732, simple_loss=0.234, pruned_loss=0.05624, over 4811.00 frames.], tot_loss[loss=0.19, simple_loss=0.2523, pruned_loss=0.06387, over 971561.58 frames.], batch size: 25, lr: 9.71e-04
2022-05-03 21:59:06,577 INFO [train.py:715] (2/8) Epoch 1, batch 11300, loss[loss=0.1502, simple_loss=0.2197, pruned_loss=0.0403, over 4962.00 frames.], tot_loss[loss=0.187, simple_loss=0.2499, pruned_loss=0.06209, over 972320.33 frames.], batch size: 15, lr: 9.70e-04
2022-05-03 21:59:46,981 INFO [train.py:715] (2/8) Epoch 1, batch 11350, loss[loss=0.2136, simple_loss=0.2643, pruned_loss=0.08148, over 4977.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2506, pruned_loss=0.06236, over 973100.10 frames.], batch size: 25, lr: 9.70e-04
2022-05-03 22:00:26,691 INFO [train.py:715] (2/8) Epoch 1, batch 11400, loss[loss=0.202, simple_loss=0.244, pruned_loss=0.08, over 4942.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2503, pruned_loss=0.06241, over 972627.90 frames.], batch size: 29, lr: 9.69e-04
2022-05-03 22:01:04,854 INFO [train.py:715] (2/8) Epoch 1, batch 11450, loss[loss=0.1594, simple_loss=0.2143, pruned_loss=0.05227, over 4736.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2495, pruned_loss=0.06216, over 972011.10 frames.], batch size: 12, lr: 9.69e-04
2022-05-03 22:01:44,064 INFO [train.py:715] (2/8) Epoch 1, batch 11500, loss[loss=0.1668, simple_loss=0.2327, pruned_loss=0.05042, over 4846.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2484, pruned_loss=0.06164, over 971386.94 frames.], batch size: 30, lr: 9.68e-04
2022-05-03 22:02:23,953 INFO [train.py:715] (2/8) Epoch 1, batch 11550, loss[loss=0.2052, simple_loss=0.2581, pruned_loss=0.07611, over 4957.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2491, pruned_loss=0.062, over 971777.78 frames.], batch size: 21, lr: 9.68e-04
2022-05-03 22:03:03,164 INFO [train.py:715] (2/8) Epoch 1, batch 11600, loss[loss=0.1852, simple_loss=0.2476, pruned_loss=0.06135, over 4778.00 frames.], tot_loss[loss=0.187, simple_loss=0.2499, pruned_loss=0.06198, over 971242.20 frames.], batch size: 14, lr: 9.67e-04
2022-05-03 22:03:41,489 INFO [train.py:715] (2/8) Epoch 1, batch 11650, loss[loss=0.1851, simple_loss=0.2508, pruned_loss=0.05971, over 4927.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2506, pruned_loss=0.0626, over 972082.07 frames.], batch size: 29, lr: 9.67e-04
2022-05-03 22:04:21,428 INFO [train.py:715] (2/8) Epoch 1, batch 11700, loss[loss=0.2079, simple_loss=0.2769, pruned_loss=0.06947, over 4782.00 frames.], tot_loss[loss=0.19, simple_loss=0.2522, pruned_loss=0.06397, over 972664.87 frames.], batch size: 17, lr: 9.66e-04
2022-05-03 22:05:01,244 INFO [train.py:715] (2/8) Epoch 1, batch 11750, loss[loss=0.2003, simple_loss=0.2589, pruned_loss=0.07091, over 4885.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2523, pruned_loss=0.06361, over 972258.51 frames.], batch size: 39, lr: 9.66e-04
2022-05-03 22:05:40,546 INFO [train.py:715] (2/8) Epoch 1, batch 11800, loss[loss=0.1808, simple_loss=0.2414, pruned_loss=0.06005, over 4961.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2529, pruned_loss=0.06429, over 972023.10 frames.], batch size: 24, lr: 9.65e-04
2022-05-03 22:06:19,250 INFO [train.py:715] (2/8) Epoch 1, batch 11850, loss[loss=0.1831, simple_loss=0.2447, pruned_loss=0.06073, over 4958.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2519, pruned_loss=0.06352, over 972517.90 frames.], batch size: 24, lr: 9.65e-04
2022-05-03 22:06:59,283 INFO [train.py:715] (2/8) Epoch 1, batch 11900, loss[loss=0.1933, simple_loss=0.2587, pruned_loss=0.06399, over 4964.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2514, pruned_loss=0.06301, over 972473.94 frames.], batch size: 35, lr: 9.64e-04
2022-05-03 22:07:38,630 INFO [train.py:715] (2/8) Epoch 1, batch 11950, loss[loss=0.1891, simple_loss=0.2599, pruned_loss=0.05917, over 4780.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2511, pruned_loss=0.06223, over 972750.90 frames.], batch size: 19, lr: 9.63e-04
2022-05-03 22:08:17,113 INFO [train.py:715] (2/8) Epoch 1, batch 12000, loss[loss=0.1944, simple_loss=0.2583, pruned_loss=0.06528, over 4804.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2509, pruned_loss=0.06218, over 973374.74 frames.], batch size: 14, lr: 9.63e-04
2022-05-03 22:08:17,114 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 22:08:27,631 INFO [train.py:742] (2/8) 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] (2/8) Epoch 1, batch 12050, loss[loss=0.2295, simple_loss=0.2854, pruned_loss=0.08678, over 4965.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2509, pruned_loss=0.06282, over 973208.98 frames.], batch size: 15, lr: 9.62e-04
2022-05-03 22:09:46,982 INFO [train.py:715] (2/8) Epoch 1, batch 12100, loss[loss=0.1806, simple_loss=0.2464, pruned_loss=0.05734, over 4889.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2512, pruned_loss=0.06308, over 973168.76 frames.], batch size: 19, lr: 9.62e-04
2022-05-03 22:10:27,667 INFO [train.py:715] (2/8) Epoch 1, batch 12150, loss[loss=0.1605, simple_loss=0.2256, pruned_loss=0.04767, over 4878.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2518, pruned_loss=0.06378, over 972687.57 frames.], batch size: 16, lr: 9.61e-04
2022-05-03 22:11:06,635 INFO [train.py:715] (2/8) Epoch 1, batch 12200, loss[loss=0.1444, simple_loss=0.2141, pruned_loss=0.03741, over 4760.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2522, pruned_loss=0.06395, over 972584.13 frames.], batch size: 19, lr: 9.61e-04
2022-05-03 22:11:46,540 INFO [train.py:715] (2/8) Epoch 1, batch 12250, loss[loss=0.195, simple_loss=0.2515, pruned_loss=0.06921, over 4832.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2539, pruned_loss=0.06497, over 971979.06 frames.], batch size: 15, lr: 9.60e-04
2022-05-03 22:12:27,152 INFO [train.py:715] (2/8) Epoch 1, batch 12300, loss[loss=0.1271, simple_loss=0.1978, pruned_loss=0.02824, over 4774.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2523, pruned_loss=0.06358, over 971499.88 frames.], batch size: 12, lr: 9.60e-04
2022-05-03 22:13:06,768 INFO [train.py:715] (2/8) Epoch 1, batch 12350, loss[loss=0.1811, simple_loss=0.2434, pruned_loss=0.05939, over 4785.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2518, pruned_loss=0.06301, over 971554.16 frames.], batch size: 12, lr: 9.59e-04
2022-05-03 22:13:45,538 INFO [train.py:715] (2/8) Epoch 1, batch 12400, loss[loss=0.189, simple_loss=0.2535, pruned_loss=0.06225, over 4885.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2518, pruned_loss=0.0634, over 972317.73 frames.], batch size: 32, lr: 9.59e-04
2022-05-03 22:14:25,682 INFO [train.py:715] (2/8) Epoch 1, batch 12450, loss[loss=0.1688, simple_loss=0.2432, pruned_loss=0.0472, over 4977.00 frames.], tot_loss[loss=0.189, simple_loss=0.2517, pruned_loss=0.06316, over 972029.40 frames.], batch size: 25, lr: 9.58e-04
2022-05-03 22:15:05,664 INFO [train.py:715] (2/8) Epoch 1, batch 12500, loss[loss=0.1555, simple_loss=0.2257, pruned_loss=0.04262, over 4783.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2519, pruned_loss=0.06395, over 972346.84 frames.], batch size: 14, lr: 9.58e-04
2022-05-03 22:15:44,872 INFO [train.py:715] (2/8) Epoch 1, batch 12550, loss[loss=0.19, simple_loss=0.2714, pruned_loss=0.05427, over 4789.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2517, pruned_loss=0.06404, over 972273.89 frames.], batch size: 18, lr: 9.57e-04
2022-05-03 22:16:24,268 INFO [train.py:715] (2/8) Epoch 1, batch 12600, loss[loss=0.1552, simple_loss=0.2168, pruned_loss=0.0468, over 4806.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2512, pruned_loss=0.06374, over 971765.10 frames.], batch size: 24, lr: 9.57e-04
2022-05-03 22:17:04,544 INFO [train.py:715] (2/8) Epoch 1, batch 12650, loss[loss=0.1297, simple_loss=0.2079, pruned_loss=0.02576, over 4915.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2507, pruned_loss=0.06314, over 972359.29 frames.], batch size: 18, lr: 9.56e-04
2022-05-03 22:17:43,550 INFO [train.py:715] (2/8) Epoch 1, batch 12700, loss[loss=0.1787, simple_loss=0.2502, pruned_loss=0.05357, over 4799.00 frames.], tot_loss[loss=0.1886, simple_loss=0.251, pruned_loss=0.06309, over 972405.49 frames.], batch size: 21, lr: 9.56e-04
2022-05-03 22:18:22,945 INFO [train.py:715] (2/8) Epoch 1, batch 12750, loss[loss=0.1759, simple_loss=0.2346, pruned_loss=0.05856, over 4864.00 frames.], tot_loss[loss=0.1876, simple_loss=0.25, pruned_loss=0.06255, over 972712.92 frames.], batch size: 20, lr: 9.55e-04
2022-05-03 22:19:03,047 INFO [train.py:715] (2/8) Epoch 1, batch 12800, loss[loss=0.1946, simple_loss=0.2601, pruned_loss=0.06452, over 4695.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2492, pruned_loss=0.062, over 973302.36 frames.], batch size: 15, lr: 9.55e-04
2022-05-03 22:19:42,866 INFO [train.py:715] (2/8) Epoch 1, batch 12850, loss[loss=0.1829, simple_loss=0.2495, pruned_loss=0.05818, over 4930.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2483, pruned_loss=0.06152, over 973927.00 frames.], batch size: 21, lr: 9.54e-04
2022-05-03 22:20:21,817 INFO [train.py:715] (2/8) Epoch 1, batch 12900, loss[loss=0.1829, simple_loss=0.2381, pruned_loss=0.06382, over 4877.00 frames.], tot_loss[loss=0.186, simple_loss=0.2485, pruned_loss=0.06176, over 973551.33 frames.], batch size: 16, lr: 9.54e-04
2022-05-03 22:21:01,111 INFO [train.py:715] (2/8) Epoch 1, batch 12950, loss[loss=0.2148, simple_loss=0.2706, pruned_loss=0.07952, over 4858.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2477, pruned_loss=0.06079, over 972508.23 frames.], batch size: 32, lr: 9.53e-04
2022-05-03 22:21:41,523 INFO [train.py:715] (2/8) Epoch 1, batch 13000, loss[loss=0.2046, simple_loss=0.2697, pruned_loss=0.0698, over 4784.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2484, pruned_loss=0.06134, over 972051.86 frames.], batch size: 18, lr: 9.53e-04
2022-05-03 22:22:21,095 INFO [train.py:715] (2/8) Epoch 1, batch 13050, loss[loss=0.1594, simple_loss=0.2215, pruned_loss=0.04858, over 4748.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2493, pruned_loss=0.06176, over 971889.20 frames.], batch size: 14, lr: 9.52e-04
2022-05-03 22:23:01,172 INFO [train.py:715] (2/8) Epoch 1, batch 13100, loss[loss=0.2197, simple_loss=0.2682, pruned_loss=0.08562, over 4741.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2501, pruned_loss=0.06256, over 972660.42 frames.], batch size: 16, lr: 9.52e-04
2022-05-03 22:23:41,357 INFO [train.py:715] (2/8) Epoch 1, batch 13150, loss[loss=0.2116, simple_loss=0.2653, pruned_loss=0.07893, over 4925.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2497, pruned_loss=0.06247, over 971987.45 frames.], batch size: 18, lr: 9.51e-04
2022-05-03 22:24:23,875 INFO [train.py:715] (2/8) Epoch 1, batch 13200, loss[loss=0.202, simple_loss=0.2612, pruned_loss=0.07136, over 4837.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2509, pruned_loss=0.06329, over 972307.91 frames.], batch size: 15, lr: 9.51e-04
2022-05-03 22:25:03,003 INFO [train.py:715] (2/8) Epoch 1, batch 13250, loss[loss=0.1683, simple_loss=0.2423, pruned_loss=0.04713, over 4781.00 frames.], tot_loss[loss=0.188, simple_loss=0.2503, pruned_loss=0.06284, over 972555.38 frames.], batch size: 18, lr: 9.51e-04
2022-05-03 22:25:41,748 INFO [train.py:715] (2/8) Epoch 1, batch 13300, loss[loss=0.2169, simple_loss=0.2679, pruned_loss=0.08293, over 4743.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2502, pruned_loss=0.0624, over 972503.63 frames.], batch size: 16, lr: 9.50e-04
2022-05-03 22:26:21,977 INFO [train.py:715] (2/8) Epoch 1, batch 13350, loss[loss=0.1855, simple_loss=0.2458, pruned_loss=0.06265, over 4789.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2502, pruned_loss=0.06239, over 972226.67 frames.], batch size: 14, lr: 9.50e-04
2022-05-03 22:27:01,382 INFO [train.py:715] (2/8) Epoch 1, batch 13400, loss[loss=0.1807, simple_loss=0.2509, pruned_loss=0.05522, over 4893.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2495, pruned_loss=0.06161, over 972666.41 frames.], batch size: 19, lr: 9.49e-04
2022-05-03 22:27:41,353 INFO [train.py:715] (2/8) Epoch 1, batch 13450, loss[loss=0.1597, simple_loss=0.2425, pruned_loss=0.03842, over 4954.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2501, pruned_loss=0.06177, over 972994.50 frames.], batch size: 24, lr: 9.49e-04
2022-05-03 22:28:21,065 INFO [train.py:715] (2/8) Epoch 1, batch 13500, loss[loss=0.2361, simple_loss=0.296, pruned_loss=0.08809, over 4911.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2506, pruned_loss=0.06206, over 973534.96 frames.], batch size: 19, lr: 9.48e-04
2022-05-03 22:29:01,034 INFO [train.py:715] (2/8) Epoch 1, batch 13550, loss[loss=0.2003, simple_loss=0.2617, pruned_loss=0.06949, over 4821.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2508, pruned_loss=0.06212, over 974625.06 frames.], batch size: 25, lr: 9.48e-04
2022-05-03 22:29:39,295 INFO [train.py:715] (2/8) Epoch 1, batch 13600, loss[loss=0.2267, simple_loss=0.2876, pruned_loss=0.08291, over 4943.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2514, pruned_loss=0.06277, over 974678.52 frames.], batch size: 35, lr: 9.47e-04
2022-05-03 22:30:18,503 INFO [train.py:715] (2/8) Epoch 1, batch 13650, loss[loss=0.1705, simple_loss=0.2487, pruned_loss=0.04619, over 4801.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2518, pruned_loss=0.06273, over 974289.26 frames.], batch size: 24, lr: 9.47e-04
2022-05-03 22:30:58,732 INFO [train.py:715] (2/8) Epoch 1, batch 13700, loss[loss=0.2008, simple_loss=0.2462, pruned_loss=0.0777, over 4817.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2513, pruned_loss=0.06267, over 973500.81 frames.], batch size: 12, lr: 9.46e-04
2022-05-03 22:31:38,129 INFO [train.py:715] (2/8) Epoch 1, batch 13750, loss[loss=0.1578, simple_loss=0.2161, pruned_loss=0.04975, over 4780.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2504, pruned_loss=0.06217, over 973396.93 frames.], batch size: 12, lr: 9.46e-04
2022-05-03 22:32:17,277 INFO [train.py:715] (2/8) Epoch 1, batch 13800, loss[loss=0.1728, simple_loss=0.2458, pruned_loss=0.04985, over 4983.00 frames.], tot_loss[loss=0.188, simple_loss=0.2505, pruned_loss=0.06273, over 972859.44 frames.], batch size: 24, lr: 9.45e-04
2022-05-03 22:32:56,964 INFO [train.py:715] (2/8) Epoch 1, batch 13850, loss[loss=0.2112, simple_loss=0.2718, pruned_loss=0.07531, over 4753.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2501, pruned_loss=0.0624, over 972488.60 frames.], batch size: 19, lr: 9.45e-04
2022-05-03 22:33:36,806 INFO [train.py:715] (2/8) Epoch 1, batch 13900, loss[loss=0.1463, simple_loss=0.2119, pruned_loss=0.04036, over 4796.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2492, pruned_loss=0.06183, over 972364.75 frames.], batch size: 25, lr: 9.44e-04
2022-05-03 22:34:15,302 INFO [train.py:715] (2/8) Epoch 1, batch 13950, loss[loss=0.1313, simple_loss=0.2025, pruned_loss=0.03006, over 4841.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2494, pruned_loss=0.06162, over 972297.54 frames.], batch size: 13, lr: 9.44e-04
2022-05-03 22:34:54,562 INFO [train.py:715] (2/8) Epoch 1, batch 14000, loss[loss=0.2153, simple_loss=0.263, pruned_loss=0.08377, over 4870.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2495, pruned_loss=0.06211, over 972509.04 frames.], batch size: 32, lr: 9.43e-04
2022-05-03 22:35:34,709 INFO [train.py:715] (2/8) Epoch 1, batch 14050, loss[loss=0.159, simple_loss=0.2321, pruned_loss=0.04298, over 4757.00 frames.], tot_loss[loss=0.1867, simple_loss=0.249, pruned_loss=0.06222, over 972470.57 frames.], batch size: 19, lr: 9.43e-04
2022-05-03 22:36:13,510 INFO [train.py:715] (2/8) Epoch 1, batch 14100, loss[loss=0.3012, simple_loss=0.3444, pruned_loss=0.129, over 4929.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2498, pruned_loss=0.06301, over 972394.67 frames.], batch size: 39, lr: 9.42e-04
2022-05-03 22:36:52,744 INFO [train.py:715] (2/8) Epoch 1, batch 14150, loss[loss=0.1546, simple_loss=0.2247, pruned_loss=0.04225, over 4951.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2505, pruned_loss=0.06345, over 972989.46 frames.], batch size: 21, lr: 9.42e-04
2022-05-03 22:37:31,977 INFO [train.py:715] (2/8) Epoch 1, batch 14200, loss[loss=0.1832, simple_loss=0.2426, pruned_loss=0.06195, over 4850.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2499, pruned_loss=0.06313, over 973314.20 frames.], batch size: 20, lr: 9.41e-04
2022-05-03 22:38:12,090 INFO [train.py:715] (2/8) Epoch 1, batch 14250, loss[loss=0.1883, simple_loss=0.2474, pruned_loss=0.06463, over 4982.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2489, pruned_loss=0.06205, over 972699.13 frames.], batch size: 15, lr: 9.41e-04
2022-05-03 22:38:50,567 INFO [train.py:715] (2/8) Epoch 1, batch 14300, loss[loss=0.1886, simple_loss=0.2517, pruned_loss=0.06279, over 4785.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2492, pruned_loss=0.06203, over 972876.36 frames.], batch size: 18, lr: 9.40e-04
2022-05-03 22:39:29,553 INFO [train.py:715] (2/8) Epoch 1, batch 14350, loss[loss=0.1616, simple_loss=0.2283, pruned_loss=0.04744, over 4872.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2492, pruned_loss=0.06228, over 972882.96 frames.], batch size: 16, lr: 9.40e-04
2022-05-03 22:40:09,903 INFO [train.py:715] (2/8) Epoch 1, batch 14400, loss[loss=0.2393, simple_loss=0.296, pruned_loss=0.09128, over 4778.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2487, pruned_loss=0.06199, over 972765.25 frames.], batch size: 18, lr: 9.39e-04
2022-05-03 22:40:48,724 INFO [train.py:715] (2/8) Epoch 1, batch 14450, loss[loss=0.1669, simple_loss=0.2352, pruned_loss=0.04933, over 4914.00 frames.], tot_loss[loss=0.1863, simple_loss=0.249, pruned_loss=0.06179, over 972232.79 frames.], batch size: 17, lr: 9.39e-04
2022-05-03 22:41:28,248 INFO [train.py:715] (2/8) Epoch 1, batch 14500, loss[loss=0.2011, simple_loss=0.2617, pruned_loss=0.07024, over 4891.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2488, pruned_loss=0.06166, over 972636.12 frames.], batch size: 19, lr: 9.39e-04
2022-05-03 22:42:08,351 INFO [train.py:715] (2/8) Epoch 1, batch 14550, loss[loss=0.1829, simple_loss=0.2464, pruned_loss=0.05972, over 4957.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2493, pruned_loss=0.06174, over 973619.90 frames.], batch size: 39, lr: 9.38e-04
2022-05-03 22:42:47,863 INFO [train.py:715] (2/8) Epoch 1, batch 14600, loss[loss=0.1844, simple_loss=0.2528, pruned_loss=0.05801, over 4983.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2506, pruned_loss=0.06279, over 973403.45 frames.], batch size: 25, lr: 9.38e-04
2022-05-03 22:43:26,823 INFO [train.py:715] (2/8) Epoch 1, batch 14650, loss[loss=0.2398, simple_loss=0.2955, pruned_loss=0.09203, over 4922.00 frames.], tot_loss[loss=0.189, simple_loss=0.2516, pruned_loss=0.06318, over 974178.89 frames.], batch size: 18, lr: 9.37e-04
2022-05-03 22:44:05,660 INFO [train.py:715] (2/8) Epoch 1, batch 14700, loss[loss=0.2132, simple_loss=0.2744, pruned_loss=0.07598, over 4829.00 frames.], tot_loss[loss=0.188, simple_loss=0.2503, pruned_loss=0.06279, over 974087.85 frames.], batch size: 15, lr: 9.37e-04
2022-05-03 22:44:45,788 INFO [train.py:715] (2/8) Epoch 1, batch 14750, loss[loss=0.2076, simple_loss=0.2701, pruned_loss=0.0726, over 4878.00 frames.], tot_loss[loss=0.1883, simple_loss=0.251, pruned_loss=0.06284, over 973343.05 frames.], batch size: 16, lr: 9.36e-04
2022-05-03 22:45:24,933 INFO [train.py:715] (2/8) Epoch 1, batch 14800, loss[loss=0.1802, simple_loss=0.2506, pruned_loss=0.05494, over 4760.00 frames.], tot_loss[loss=0.1869, simple_loss=0.25, pruned_loss=0.06192, over 972255.52 frames.], batch size: 19, lr: 9.36e-04
2022-05-03 22:46:04,488 INFO [train.py:715] (2/8) Epoch 1, batch 14850, loss[loss=0.1649, simple_loss=0.2332, pruned_loss=0.04829, over 4781.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2499, pruned_loss=0.06183, over 972521.86 frames.], batch size: 12, lr: 9.35e-04
2022-05-03 22:46:43,808 INFO [train.py:715] (2/8) Epoch 1, batch 14900, loss[loss=0.1943, simple_loss=0.2621, pruned_loss=0.0633, over 4948.00 frames.], tot_loss[loss=0.1853, simple_loss=0.249, pruned_loss=0.06078, over 972491.64 frames.], batch size: 21, lr: 9.35e-04
2022-05-03 22:47:22,413 INFO [train.py:715] (2/8) Epoch 1, batch 14950, loss[loss=0.1661, simple_loss=0.2381, pruned_loss=0.04703, over 4899.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2484, pruned_loss=0.06058, over 971879.40 frames.], batch size: 19, lr: 9.34e-04
2022-05-03 22:48:02,031 INFO [train.py:715] (2/8) Epoch 1, batch 15000, loss[loss=0.1691, simple_loss=0.2527, pruned_loss=0.04276, over 4987.00 frames.], tot_loss[loss=0.1868, simple_loss=0.25, pruned_loss=0.06174, over 973076.09 frames.], batch size: 25, lr: 9.34e-04
2022-05-03 22:48:02,032 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 22:48:17,509 INFO [train.py:742] (2/8) Epoch 1, validation: loss=0.1242, simple_loss=0.2115, pruned_loss=0.01842, over 914524.00 frames.
2022-05-03 22:48:57,641 INFO [train.py:715] (2/8) Epoch 1, batch 15050, loss[loss=0.2127, simple_loss=0.2763, pruned_loss=0.07461, over 4759.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2501, pruned_loss=0.06228, over 972961.84 frames.], batch size: 19, lr: 9.33e-04
2022-05-03 22:49:37,555 INFO [train.py:715] (2/8) Epoch 1, batch 15100, loss[loss=0.1392, simple_loss=0.2085, pruned_loss=0.0349, over 4834.00 frames.], tot_loss[loss=0.186, simple_loss=0.249, pruned_loss=0.06151, over 971744.03 frames.], batch size: 15, lr: 9.33e-04
2022-05-03 22:50:18,093 INFO [train.py:715] (2/8) Epoch 1, batch 15150, loss[loss=0.1711, simple_loss=0.2372, pruned_loss=0.05256, over 4936.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2495, pruned_loss=0.06203, over 971228.76 frames.], batch size: 18, lr: 9.32e-04
2022-05-03 22:50:57,471 INFO [train.py:715] (2/8) Epoch 1, batch 15200, loss[loss=0.1938, simple_loss=0.2523, pruned_loss=0.06768, over 4929.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2493, pruned_loss=0.06207, over 971487.25 frames.], batch size: 23, lr: 9.32e-04
2022-05-03 22:51:37,950 INFO [train.py:715] (2/8) Epoch 1, batch 15250, loss[loss=0.2268, simple_loss=0.2765, pruned_loss=0.08859, over 4943.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2511, pruned_loss=0.06293, over 971157.13 frames.], batch size: 35, lr: 9.32e-04
2022-05-03 22:52:17,870 INFO [train.py:715] (2/8) Epoch 1, batch 15300, loss[loss=0.1983, simple_loss=0.2597, pruned_loss=0.06849, over 4799.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2507, pruned_loss=0.06252, over 971228.81 frames.], batch size: 21, lr: 9.31e-04
2022-05-03 22:52:57,757 INFO [train.py:715] (2/8) Epoch 1, batch 15350, loss[loss=0.176, simple_loss=0.26, pruned_loss=0.046, over 4984.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2506, pruned_loss=0.06245, over 971917.87 frames.], batch size: 25, lr: 9.31e-04
2022-05-03 22:53:37,894 INFO [train.py:715] (2/8) Epoch 1, batch 15400, loss[loss=0.2225, simple_loss=0.2793, pruned_loss=0.08279, over 4936.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2509, pruned_loss=0.0627, over 972252.28 frames.], batch size: 18, lr: 9.30e-04
2022-05-03 22:54:18,164 INFO [train.py:715] (2/8) Epoch 1, batch 15450, loss[loss=0.2062, simple_loss=0.2719, pruned_loss=0.07024, over 4894.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2501, pruned_loss=0.06229, over 971392.86 frames.], batch size: 17, lr: 9.30e-04
2022-05-03 22:54:58,639 INFO [train.py:715] (2/8) Epoch 1, batch 15500, loss[loss=0.1747, simple_loss=0.2378, pruned_loss=0.05575, over 4866.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2504, pruned_loss=0.06257, over 972183.22 frames.], batch size: 20, lr: 9.29e-04
2022-05-03 22:55:37,732 INFO [train.py:715] (2/8) Epoch 1, batch 15550, loss[loss=0.1327, simple_loss=0.1924, pruned_loss=0.03655, over 4733.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2503, pruned_loss=0.06234, over 972015.31 frames.], batch size: 12, lr: 9.29e-04
2022-05-03 22:56:18,058 INFO [train.py:715] (2/8) Epoch 1, batch 15600, loss[loss=0.199, simple_loss=0.2584, pruned_loss=0.06984, over 4780.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2507, pruned_loss=0.0622, over 972636.58 frames.], batch size: 17, lr: 9.28e-04
2022-05-03 22:56:58,349 INFO [train.py:715] (2/8) Epoch 1, batch 15650, loss[loss=0.2114, simple_loss=0.2734, pruned_loss=0.07475, over 4981.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2494, pruned_loss=0.06176, over 973029.18 frames.], batch size: 25, lr: 9.28e-04
2022-05-03 22:57:38,268 INFO [train.py:715] (2/8) Epoch 1, batch 15700, loss[loss=0.2224, simple_loss=0.2775, pruned_loss=0.08364, over 4783.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2485, pruned_loss=0.06144, over 971736.33 frames.], batch size: 14, lr: 9.27e-04
2022-05-03 22:58:17,909 INFO [train.py:715] (2/8) Epoch 1, batch 15750, loss[loss=0.2216, simple_loss=0.2709, pruned_loss=0.08616, over 4912.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2487, pruned_loss=0.06146, over 971507.12 frames.], batch size: 39, lr: 9.27e-04
2022-05-03 22:58:58,190 INFO [train.py:715] (2/8) Epoch 1, batch 15800, loss[loss=0.1932, simple_loss=0.2561, pruned_loss=0.06512, over 4980.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2494, pruned_loss=0.06163, over 972411.07 frames.], batch size: 35, lr: 9.27e-04
2022-05-03 22:59:38,872 INFO [train.py:715] (2/8) Epoch 1, batch 15850, loss[loss=0.1856, simple_loss=0.2535, pruned_loss=0.0589, over 4986.00 frames.], tot_loss[loss=0.1872, simple_loss=0.25, pruned_loss=0.06219, over 972084.59 frames.], batch size: 25, lr: 9.26e-04
2022-05-03 23:00:18,428 INFO [train.py:715] (2/8) Epoch 1, batch 15900, loss[loss=0.2057, simple_loss=0.2536, pruned_loss=0.07892, over 4837.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2498, pruned_loss=0.06236, over 971400.97 frames.], batch size: 30, lr: 9.26e-04
2022-05-03 23:00:58,066 INFO [train.py:715] (2/8) Epoch 1, batch 15950, loss[loss=0.1517, simple_loss=0.2158, pruned_loss=0.04387, over 4796.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2478, pruned_loss=0.06105, over 971877.62 frames.], batch size: 21, lr: 9.25e-04
2022-05-03 23:01:37,497 INFO [train.py:715] (2/8) Epoch 1, batch 16000, loss[loss=0.208, simple_loss=0.2664, pruned_loss=0.0748, over 4909.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2483, pruned_loss=0.06175, over 972877.62 frames.], batch size: 17, lr: 9.25e-04
2022-05-03 23:02:16,255 INFO [train.py:715] (2/8) Epoch 1, batch 16050, loss[loss=0.1669, simple_loss=0.2426, pruned_loss=0.04561, over 4915.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2481, pruned_loss=0.06166, over 973149.61 frames.], batch size: 18, lr: 9.24e-04
2022-05-03 23:02:55,581 INFO [train.py:715] (2/8) Epoch 1, batch 16100, loss[loss=0.2073, simple_loss=0.2684, pruned_loss=0.07306, over 4898.00 frames.], tot_loss[loss=0.1858, simple_loss=0.248, pruned_loss=0.06175, over 973604.65 frames.], batch size: 17, lr: 9.24e-04
2022-05-03 23:03:35,228 INFO [train.py:715] (2/8) Epoch 1, batch 16150, loss[loss=0.1758, simple_loss=0.235, pruned_loss=0.05837, over 4959.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2483, pruned_loss=0.06199, over 974430.86 frames.], batch size: 15, lr: 9.23e-04
2022-05-03 23:04:15,416 INFO [train.py:715] (2/8) Epoch 1, batch 16200, loss[loss=0.1971, simple_loss=0.2488, pruned_loss=0.07269, over 4748.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2479, pruned_loss=0.06173, over 973901.49 frames.], batch size: 12, lr: 9.23e-04
2022-05-03 23:04:53,723 INFO [train.py:715] (2/8) Epoch 1, batch 16250, loss[loss=0.1956, simple_loss=0.2582, pruned_loss=0.06652, over 4811.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2495, pruned_loss=0.06179, over 973694.00 frames.], batch size: 27, lr: 9.22e-04
2022-05-03 23:05:33,189 INFO [train.py:715] (2/8) Epoch 1, batch 16300, loss[loss=0.2047, simple_loss=0.2712, pruned_loss=0.06905, over 4800.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2508, pruned_loss=0.06277, over 972754.58 frames.], batch size: 14, lr: 9.22e-04
2022-05-03 23:06:12,737 INFO [train.py:715] (2/8) Epoch 1, batch 16350, loss[loss=0.2011, simple_loss=0.2516, pruned_loss=0.07524, over 4925.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2503, pruned_loss=0.06254, over 972265.57 frames.], batch size: 29, lr: 9.22e-04
2022-05-03 23:06:51,395 INFO [train.py:715] (2/8) Epoch 1, batch 16400, loss[loss=0.2021, simple_loss=0.2678, pruned_loss=0.06816, over 4878.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2502, pruned_loss=0.06311, over 972961.35 frames.], batch size: 20, lr: 9.21e-04
2022-05-03 23:07:30,892 INFO [train.py:715] (2/8) Epoch 1, batch 16450, loss[loss=0.1995, simple_loss=0.2582, pruned_loss=0.07037, over 4964.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2491, pruned_loss=0.06202, over 973405.92 frames.], batch size: 35, lr: 9.21e-04
2022-05-03 23:08:10,537 INFO [train.py:715] (2/8) Epoch 1, batch 16500, loss[loss=0.1629, simple_loss=0.2289, pruned_loss=0.04847, over 4980.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2501, pruned_loss=0.0623, over 973155.00 frames.], batch size: 14, lr: 9.20e-04
2022-05-03 23:08:50,449 INFO [train.py:715] (2/8) Epoch 1, batch 16550, loss[loss=0.172, simple_loss=0.2367, pruned_loss=0.05364, over 4882.00 frames.], tot_loss[loss=0.188, simple_loss=0.2509, pruned_loss=0.06258, over 973658.27 frames.], batch size: 32, lr: 9.20e-04
2022-05-03 23:09:28,841 INFO [train.py:715] (2/8) Epoch 1, batch 16600, loss[loss=0.1793, simple_loss=0.2397, pruned_loss=0.05951, over 4815.00 frames.], tot_loss[loss=0.188, simple_loss=0.2508, pruned_loss=0.06261, over 973369.63 frames.], batch size: 26, lr: 9.19e-04
2022-05-03 23:10:08,999 INFO [train.py:715] (2/8) Epoch 1, batch 16650, loss[loss=0.2216, simple_loss=0.2752, pruned_loss=0.08403, over 4830.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2508, pruned_loss=0.06272, over 974170.55 frames.], batch size: 30, lr: 9.19e-04
2022-05-03 23:10:48,678 INFO [train.py:715] (2/8) Epoch 1, batch 16700, loss[loss=0.1885, simple_loss=0.258, pruned_loss=0.05944, over 4925.00 frames.], tot_loss[loss=0.1878, simple_loss=0.251, pruned_loss=0.06225, over 973556.30 frames.], batch size: 23, lr: 9.18e-04
2022-05-03 23:11:28,437 INFO [train.py:715] (2/8) Epoch 1, batch 16750, loss[loss=0.1842, simple_loss=0.2415, pruned_loss=0.06351, over 4735.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2493, pruned_loss=0.06128, over 973478.80 frames.], batch size: 16, lr: 9.18e-04
2022-05-03 23:12:08,275 INFO [train.py:715] (2/8) Epoch 1, batch 16800, loss[loss=0.2243, simple_loss=0.2851, pruned_loss=0.08172, over 4801.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2474, pruned_loss=0.06019, over 973479.37 frames.], batch size: 21, lr: 9.18e-04
2022-05-03 23:12:47,921 INFO [train.py:715] (2/8) Epoch 1, batch 16850, loss[loss=0.2045, simple_loss=0.279, pruned_loss=0.06495, over 4832.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2472, pruned_loss=0.05983, over 973544.92 frames.], batch size: 27, lr: 9.17e-04
2022-05-03 23:13:27,902 INFO [train.py:715] (2/8) Epoch 1, batch 16900, loss[loss=0.1996, simple_loss=0.2642, pruned_loss=0.06755, over 4776.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2476, pruned_loss=0.0603, over 973905.07 frames.], batch size: 17, lr: 9.17e-04
2022-05-03 23:14:06,926 INFO [train.py:715] (2/8) Epoch 1, batch 16950, loss[loss=0.1549, simple_loss=0.2285, pruned_loss=0.04064, over 4872.00 frames.], tot_loss[loss=0.1847, simple_loss=0.248, pruned_loss=0.06067, over 973786.06 frames.], batch size: 20, lr: 9.16e-04
2022-05-03 23:14:46,341 INFO [train.py:715] (2/8) Epoch 1, batch 17000, loss[loss=0.1647, simple_loss=0.2315, pruned_loss=0.04893, over 4940.00 frames.], tot_loss[loss=0.1861, simple_loss=0.249, pruned_loss=0.0616, over 972920.77 frames.], batch size: 29, lr: 9.16e-04
2022-05-03 23:15:26,353 INFO [train.py:715] (2/8) Epoch 1, batch 17050, loss[loss=0.1807, simple_loss=0.2422, pruned_loss=0.05962, over 4984.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2493, pruned_loss=0.06181, over 972776.54 frames.], batch size: 14, lr: 9.15e-04
2022-05-03 23:16:05,135 INFO [train.py:715] (2/8) Epoch 1, batch 17100, loss[loss=0.192, simple_loss=0.2424, pruned_loss=0.0708, over 4793.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2483, pruned_loss=0.06101, over 973227.00 frames.], batch size: 18, lr: 9.15e-04
2022-05-03 23:16:44,843 INFO [train.py:715] (2/8) Epoch 1, batch 17150, loss[loss=0.1937, simple_loss=0.2548, pruned_loss=0.06636, over 4957.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2489, pruned_loss=0.0608, over 973863.41 frames.], batch size: 24, lr: 9.15e-04
2022-05-03 23:17:25,474 INFO [train.py:715] (2/8) Epoch 1, batch 17200, loss[loss=0.1953, simple_loss=0.2509, pruned_loss=0.06984, over 4975.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2491, pruned_loss=0.06131, over 973470.34 frames.], batch size: 39, lr: 9.14e-04
2022-05-03 23:18:05,277 INFO [train.py:715] (2/8) Epoch 1, batch 17250, loss[loss=0.2284, simple_loss=0.2708, pruned_loss=0.09299, over 4943.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2498, pruned_loss=0.06161, over 972532.03 frames.], batch size: 21, lr: 9.14e-04
2022-05-03 23:18:43,783 INFO [train.py:715] (2/8) Epoch 1, batch 17300, loss[loss=0.1938, simple_loss=0.2585, pruned_loss=0.06461, over 4793.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2497, pruned_loss=0.06181, over 971614.98 frames.], batch size: 24, lr: 9.13e-04
2022-05-03 23:19:23,807 INFO [train.py:715] (2/8) Epoch 1, batch 17350, loss[loss=0.1913, simple_loss=0.2528, pruned_loss=0.0649, over 4882.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2494, pruned_loss=0.06156, over 971209.96 frames.], batch size: 16, lr: 9.13e-04
2022-05-03 23:20:03,639 INFO [train.py:715] (2/8) Epoch 1, batch 17400, loss[loss=0.1803, simple_loss=0.2456, pruned_loss=0.05747, over 4758.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2491, pruned_loss=0.06132, over 971771.03 frames.], batch size: 19, lr: 9.12e-04
2022-05-03 23:20:42,893 INFO [train.py:715] (2/8) Epoch 1, batch 17450, loss[loss=0.2204, simple_loss=0.2848, pruned_loss=0.07796, over 4965.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2485, pruned_loss=0.06105, over 971407.44 frames.], batch size: 39, lr: 9.12e-04
2022-05-03 23:21:23,292 INFO [train.py:715] (2/8) Epoch 1, batch 17500, loss[loss=0.243, simple_loss=0.2859, pruned_loss=0.1001, over 4979.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2487, pruned_loss=0.06115, over 971667.02 frames.], batch size: 26, lr: 9.11e-04
2022-05-03 23:22:03,720 INFO [train.py:715] (2/8) Epoch 1, batch 17550, loss[loss=0.1981, simple_loss=0.2677, pruned_loss=0.06421, over 4912.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2505, pruned_loss=0.06222, over 971403.31 frames.], batch size: 39, lr: 9.11e-04
2022-05-03 23:22:44,341 INFO [train.py:715] (2/8) Epoch 1, batch 17600, loss[loss=0.1792, simple_loss=0.2416, pruned_loss=0.05845, over 4965.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2496, pruned_loss=0.06167, over 971305.52 frames.], batch size: 35, lr: 9.11e-04
2022-05-03 23:23:24,041 INFO [train.py:715] (2/8) Epoch 1, batch 17650, loss[loss=0.2069, simple_loss=0.261, pruned_loss=0.07637, over 4872.00 frames.], tot_loss[loss=0.185, simple_loss=0.2482, pruned_loss=0.06087, over 971262.79 frames.], batch size: 30, lr: 9.10e-04
2022-05-03 23:24:04,734 INFO [train.py:715] (2/8) Epoch 1, batch 17700, loss[loss=0.1395, simple_loss=0.2043, pruned_loss=0.0374, over 4739.00 frames.], tot_loss[loss=0.185, simple_loss=0.248, pruned_loss=0.061, over 970962.96 frames.], batch size: 16, lr: 9.10e-04
2022-05-03 23:24:44,980 INFO [train.py:715] (2/8) Epoch 1, batch 17750, loss[loss=0.1632, simple_loss=0.234, pruned_loss=0.04621, over 4951.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2494, pruned_loss=0.06207, over 972158.76 frames.], batch size: 14, lr: 9.09e-04
2022-05-03 23:25:24,517 INFO [train.py:715] (2/8) Epoch 1, batch 17800, loss[loss=0.187, simple_loss=0.2519, pruned_loss=0.06102, over 4862.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2494, pruned_loss=0.06245, over 972129.14 frames.], batch size: 30, lr: 9.09e-04
2022-05-03 23:26:04,924 INFO [train.py:715] (2/8) Epoch 1, batch 17850, loss[loss=0.2003, simple_loss=0.2558, pruned_loss=0.07246, over 4920.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2491, pruned_loss=0.06209, over 973226.54 frames.], batch size: 18, lr: 9.08e-04
2022-05-03 23:26:44,319 INFO [train.py:715] (2/8) Epoch 1, batch 17900, loss[loss=0.2168, simple_loss=0.2662, pruned_loss=0.08369, over 4910.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2485, pruned_loss=0.06144, over 972749.05 frames.], batch size: 19, lr: 9.08e-04
2022-05-03 23:27:23,556 INFO [train.py:715] (2/8) Epoch 1, batch 17950, loss[loss=0.1429, simple_loss=0.2193, pruned_loss=0.03323, over 4984.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2498, pruned_loss=0.06245, over 972449.47 frames.], batch size: 28, lr: 9.08e-04
2022-05-03 23:28:02,855 INFO [train.py:715] (2/8) Epoch 1, batch 18000, loss[loss=0.1457, simple_loss=0.2207, pruned_loss=0.03534, over 4884.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2501, pruned_loss=0.06266, over 972118.74 frames.], batch size: 22, lr: 9.07e-04
2022-05-03 23:28:02,856 INFO [train.py:733] (2/8) Computing validation loss
2022-05-03 23:28:17,470 INFO [train.py:742] (2/8) Epoch 1, validation: loss=0.123, simple_loss=0.21, pruned_loss=0.01804, over 914524.00 frames.
2022-05-03 23:28:56,677 INFO [train.py:715] (2/8) Epoch 1, batch 18050, loss[loss=0.1535, simple_loss=0.2237, pruned_loss=0.04165, over 4753.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2499, pruned_loss=0.062, over 971439.42 frames.], batch size: 19, lr: 9.07e-04
2022-05-03 23:29:37,115 INFO [train.py:715] (2/8) Epoch 1, batch 18100, loss[loss=0.2079, simple_loss=0.2669, pruned_loss=0.07443, over 4940.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2495, pruned_loss=0.06176, over 971230.60 frames.], batch size: 29, lr: 9.06e-04
2022-05-03 23:30:16,928 INFO [train.py:715] (2/8) Epoch 1, batch 18150, loss[loss=0.2428, simple_loss=0.2976, pruned_loss=0.09401, over 4789.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2484, pruned_loss=0.06108, over 971638.63 frames.], batch size: 17, lr: 9.06e-04
2022-05-03 23:30:55,296 INFO [train.py:715] (2/8) Epoch 1, batch 18200, loss[loss=0.1609, simple_loss=0.2307, pruned_loss=0.0456, over 4764.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2477, pruned_loss=0.06035, over 972118.45 frames.], batch size: 19, lr: 9.05e-04
2022-05-03 23:31:34,984 INFO [train.py:715] (2/8) Epoch 1, batch 18250, loss[loss=0.148, simple_loss=0.2123, pruned_loss=0.04178, over 4783.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2483, pruned_loss=0.06075, over 972161.58 frames.], batch size: 18, lr: 9.05e-04
2022-05-03 23:32:14,610 INFO [train.py:715] (2/8) Epoch 1, batch 18300, loss[loss=0.1803, simple_loss=0.2458, pruned_loss=0.05743, over 4752.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2492, pruned_loss=0.06149, over 971242.19 frames.], batch size: 19, lr: 9.05e-04
2022-05-03 23:32:53,396 INFO [train.py:715] (2/8) Epoch 1, batch 18350, loss[loss=0.2089, simple_loss=0.2646, pruned_loss=0.0766, over 4964.00 frames.], tot_loss[loss=0.1858, simple_loss=0.249, pruned_loss=0.06126, over 972116.64 frames.], batch size: 15, lr: 9.04e-04
2022-05-03 23:33:33,129 INFO [train.py:715] (2/8) Epoch 1, batch 18400, loss[loss=0.1657, simple_loss=0.2348, pruned_loss=0.04827, over 4984.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2491, pruned_loss=0.061, over 972115.05 frames.], batch size: 28, lr: 9.04e-04
2022-05-03 23:34:13,405 INFO [train.py:715] (2/8) Epoch 1, batch 18450, loss[loss=0.2293, simple_loss=0.2944, pruned_loss=0.08216, over 4966.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2486, pruned_loss=0.06096, over 971278.13 frames.], batch size: 15, lr: 9.03e-04
2022-05-03 23:34:52,231 INFO [train.py:715] (2/8) Epoch 1, batch 18500, loss[loss=0.1732, simple_loss=0.2335, pruned_loss=0.05641, over 4927.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2492, pruned_loss=0.06126, over 972127.58 frames.], batch size: 29, lr: 9.03e-04
2022-05-03 23:35:31,268 INFO [train.py:715] (2/8) Epoch 1, batch 18550, loss[loss=0.1843, simple_loss=0.2445, pruned_loss=0.062, over 4905.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2492, pruned_loss=0.06146, over 972418.56 frames.], batch size: 17, lr: 9.03e-04
2022-05-03 23:36:11,448 INFO [train.py:715] (2/8) Epoch 1, batch 18600, loss[loss=0.2042, simple_loss=0.2609, pruned_loss=0.0738, over 4850.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2487, pruned_loss=0.06129, over 972260.13 frames.], batch size: 20, lr: 9.02e-04
2022-05-03 23:36:50,765 INFO [train.py:715] (2/8) Epoch 1, batch 18650, loss[loss=0.2068, simple_loss=0.2707, pruned_loss=0.07139, over 4895.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2479, pruned_loss=0.06065, over 972465.55 frames.], batch size: 19, lr: 9.02e-04
2022-05-03 23:37:29,511 INFO [train.py:715] (2/8) Epoch 1, batch 18700, loss[loss=0.1626, simple_loss=0.2254, pruned_loss=0.04991, over 4909.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2472, pruned_loss=0.06047, over 972621.07 frames.], batch size: 19, lr: 9.01e-04
2022-05-03 23:38:08,757 INFO [train.py:715] (2/8) Epoch 1, batch 18750, loss[loss=0.191, simple_loss=0.243, pruned_loss=0.06944, over 4874.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2479, pruned_loss=0.06033, over 972414.84 frames.], batch size: 16, lr: 9.01e-04
2022-05-03 23:38:48,683 INFO [train.py:715] (2/8) Epoch 1, batch 18800, loss[loss=0.1465, simple_loss=0.2055, pruned_loss=0.04381, over 4864.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2483, pruned_loss=0.06135, over 973291.45 frames.], batch size: 32, lr: 9.00e-04
2022-05-03 23:39:27,385 INFO [train.py:715] (2/8) Epoch 1, batch 18850, loss[loss=0.1587, simple_loss=0.2284, pruned_loss=0.04448, over 4897.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2473, pruned_loss=0.06051, over 973091.22 frames.], batch size: 22, lr: 9.00e-04
2022-05-03 23:40:06,870 INFO [train.py:715] (2/8) Epoch 1, batch 18900, loss[loss=0.2181, simple_loss=0.2666, pruned_loss=0.08485, over 4942.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2466, pruned_loss=0.06008, over 972384.02 frames.], batch size: 35, lr: 9.00e-04
2022-05-03 23:40:46,605 INFO [train.py:715] (2/8) Epoch 1, batch 18950, loss[loss=0.1494, simple_loss=0.2122, pruned_loss=0.04329, over 4984.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2465, pruned_loss=0.06002, over 972349.11 frames.], batch size: 14, lr: 8.99e-04
2022-05-03 23:41:25,990 INFO [train.py:715] (2/8) Epoch 1, batch 19000, loss[loss=0.1512, simple_loss=0.2247, pruned_loss=0.03887, over 4874.00 frames.], tot_loss[loss=0.1836, simple_loss=0.247, pruned_loss=0.06009, over 972208.07 frames.], batch size: 22, lr: 8.99e-04
2022-05-03 23:42:05,671 INFO [train.py:715] (2/8) Epoch 1, batch 19050, loss[loss=0.2325, simple_loss=0.2867, pruned_loss=0.08914, over 4912.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2472, pruned_loss=0.06054, over 972331.77 frames.], batch size: 17, lr: 8.98e-04
2022-05-03 23:42:44,843 INFO [train.py:715] (2/8) Epoch 1, batch 19100, loss[loss=0.1774, simple_loss=0.244, pruned_loss=0.05538, over 4903.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2476, pruned_loss=0.06041, over 972019.38 frames.], batch size: 19, lr: 8.98e-04
2022-05-03 23:43:24,770 INFO [train.py:715] (2/8) Epoch 1, batch 19150, loss[loss=0.1786, simple_loss=0.2457, pruned_loss=0.05578, over 4745.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2489, pruned_loss=0.06094, over 972332.87 frames.], batch size: 12, lr: 8.98e-04
2022-05-03 23:44:03,409 INFO [train.py:715] (2/8) Epoch 1, batch 19200, loss[loss=0.2047, simple_loss=0.2557, pruned_loss=0.07682, over 4962.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2476, pruned_loss=0.06034, over 972358.04 frames.], batch size: 24, lr: 8.97e-04
2022-05-03 23:44:42,693 INFO [train.py:715] (2/8) Epoch 1, batch 19250, loss[loss=0.1901, simple_loss=0.248, pruned_loss=0.06605, over 4840.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2476, pruned_loss=0.06076, over 971744.54 frames.], batch size: 26, lr: 8.97e-04
2022-05-03 23:45:23,319 INFO [train.py:715] (2/8) Epoch 1, batch 19300, loss[loss=0.1729, simple_loss=0.2454, pruned_loss=0.0502, over 4969.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2479, pruned_loss=0.06055, over 971442.76 frames.], batch size: 39, lr: 8.96e-04
2022-05-03 23:46:02,781 INFO [train.py:715] (2/8) Epoch 1, batch 19350, loss[loss=0.2097, simple_loss=0.2677, pruned_loss=0.07582, over 4954.00 frames.], tot_loss[loss=0.185, simple_loss=0.2483, pruned_loss=0.06086, over 971747.87 frames.], batch size: 35, lr: 8.96e-04
2022-05-03 23:46:41,165 INFO [train.py:715] (2/8) Epoch 1, batch 19400, loss[loss=0.1446, simple_loss=0.2218, pruned_loss=0.03367, over 4858.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2478, pruned_loss=0.06075, over 972073.73 frames.], batch size: 20, lr: 8.95e-04
2022-05-03 23:47:20,590 INFO [train.py:715] (2/8) Epoch 1, batch 19450, loss[loss=0.2413, simple_loss=0.2867, pruned_loss=0.09794, over 4805.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2484, pruned_loss=0.06133, over 972500.62 frames.], batch size: 15, lr: 8.95e-04
2022-05-03 23:48:00,484 INFO [train.py:715] (2/8) Epoch 1, batch 19500, loss[loss=0.2075, simple_loss=0.2647, pruned_loss=0.07513, over 4871.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2482, pruned_loss=0.0612, over 972388.49 frames.], batch size: 32, lr: 8.95e-04
2022-05-03 23:48:39,197 INFO [train.py:715] (2/8) Epoch 1, batch 19550, loss[loss=0.2203, simple_loss=0.278, pruned_loss=0.08128, over 4971.00 frames.], tot_loss[loss=0.185, simple_loss=0.248, pruned_loss=0.061, over 971921.26 frames.], batch size: 15, lr: 8.94e-04
2022-05-03 23:49:18,322 INFO [train.py:715] (2/8) Epoch 1, batch 19600, loss[loss=0.1757, simple_loss=0.2559, pruned_loss=0.0478, over 4953.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2484, pruned_loss=0.06141, over 970992.38 frames.], batch size: 21, lr: 8.94e-04
2022-05-03 23:49:58,541 INFO [train.py:715] (2/8) Epoch 1, batch 19650, loss[loss=0.1447, simple_loss=0.2157, pruned_loss=0.03683, over 4822.00 frames.], tot_loss[loss=0.185, simple_loss=0.248, pruned_loss=0.06099, over 971645.69 frames.], batch size: 13, lr: 8.93e-04
2022-05-03 23:50:37,442 INFO [train.py:715] (2/8) Epoch 1, batch 19700, loss[loss=0.2152, simple_loss=0.2675, pruned_loss=0.08144, over 4841.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2474, pruned_loss=0.06062, over 971477.12 frames.], batch size: 15, lr: 8.93e-04
2022-05-03 23:51:16,593 INFO [train.py:715] (2/8) Epoch 1, batch 19750, loss[loss=0.1922, simple_loss=0.251, pruned_loss=0.06671, over 4915.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2482, pruned_loss=0.06117, over 972489.63 frames.], batch size: 19, lr: 8.93e-04
2022-05-03 23:51:56,234 INFO [train.py:715] (2/8) Epoch 1, batch 19800, loss[loss=0.1758, simple_loss=0.2458, pruned_loss=0.05284, over 4904.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2491, pruned_loss=0.0617, over 972864.91 frames.], batch size: 17, lr: 8.92e-04
2022-05-03 23:52:36,502 INFO [train.py:715] (2/8) Epoch 1, batch 19850, loss[loss=0.1729, simple_loss=0.245, pruned_loss=0.05034, over 4797.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2488, pruned_loss=0.06134, over 972254.27 frames.], batch size: 25, lr: 8.92e-04
2022-05-03 23:53:15,886 INFO [train.py:715] (2/8) Epoch 1, batch 19900, loss[loss=0.1986, simple_loss=0.2584, pruned_loss=0.06936, over 4861.00 frames.], tot_loss[loss=0.185, simple_loss=0.2485, pruned_loss=0.06073, over 972031.16 frames.], batch size: 38, lr: 8.91e-04
2022-05-03 23:53:54,983 INFO [train.py:715] (2/8) Epoch 1, batch 19950, loss[loss=0.1665, simple_loss=0.2409, pruned_loss=0.04611, over 4987.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2479, pruned_loss=0.06025, over 972437.60 frames.], batch size: 27, lr: 8.91e-04
2022-05-03 23:54:35,244 INFO [train.py:715] (2/8) Epoch 1, batch 20000, loss[loss=0.2218, simple_loss=0.2834, pruned_loss=0.08012, over 4967.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2472, pruned_loss=0.05965, over 972239.52 frames.], batch size: 24, lr: 8.91e-04
2022-05-03 23:55:14,860 INFO [train.py:715] (2/8) Epoch 1, batch 20050, loss[loss=0.1795, simple_loss=0.2535, pruned_loss=0.05271, over 4830.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2473, pruned_loss=0.05962, over 972189.80 frames.], batch size: 25, lr: 8.90e-04
2022-05-03 23:55:54,260 INFO [train.py:715] (2/8) Epoch 1, batch 20100, loss[loss=0.1903, simple_loss=0.2592, pruned_loss=0.06067, over 4834.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2474, pruned_loss=0.05988, over 972495.30 frames.], batch size: 15, lr: 8.90e-04
2022-05-03 23:56:34,280 INFO [train.py:715] (2/8) Epoch 1, batch 20150, loss[loss=0.1672, simple_loss=0.2333, pruned_loss=0.05053, over 4816.00 frames.], tot_loss[loss=0.1842, simple_loss=0.248, pruned_loss=0.06023, over 972908.48 frames.], batch size: 25, lr: 8.89e-04
2022-05-03 23:57:15,153 INFO [train.py:715] (2/8) Epoch 1, batch 20200, loss[loss=0.2103, simple_loss=0.275, pruned_loss=0.07282, over 4913.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2472, pruned_loss=0.05984, over 972840.47 frames.], batch size: 23, lr: 8.89e-04
2022-05-03 23:57:53,967 INFO [train.py:715] (2/8) Epoch 1, batch 20250, loss[loss=0.154, simple_loss=0.2292, pruned_loss=0.03942, over 4910.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2472, pruned_loss=0.06, over 973739.88 frames.], batch size: 17, lr: 8.89e-04
2022-05-03 23:58:33,267 INFO [train.py:715] (2/8) Epoch 1, batch 20300, loss[loss=0.1647, simple_loss=0.2227, pruned_loss=0.05341, over 4791.00 frames.], tot_loss[loss=0.1839, simple_loss=0.247, pruned_loss=0.06035, over 974042.56 frames.], batch size: 14, lr: 8.88e-04
2022-05-03 23:59:13,195 INFO [train.py:715] (2/8) Epoch 1, batch 20350, loss[loss=0.1712, simple_loss=0.2376, pruned_loss=0.05237, over 4819.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2469, pruned_loss=0.06001, over 973945.71 frames.], batch size: 15, lr: 8.88e-04
2022-05-03 23:59:51,738 INFO [train.py:715] (2/8) Epoch 1, batch 20400, loss[loss=0.2014, simple_loss=0.2599, pruned_loss=0.07145, over 4756.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2469, pruned_loss=0.06036, over 972970.48 frames.], batch size: 14, lr: 8.87e-04
2022-05-04 00:00:31,294 INFO [train.py:715] (2/8) Epoch 1, batch 20450, loss[loss=0.159, simple_loss=0.2329, pruned_loss=0.04258, over 4868.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2466, pruned_loss=0.05999, over 973304.23 frames.], batch size: 22, lr: 8.87e-04
2022-05-04 00:01:10,340 INFO [train.py:715] (2/8) Epoch 1, batch 20500, loss[loss=0.2086, simple_loss=0.2789, pruned_loss=0.06919, over 4945.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2465, pruned_loss=0.05992, over 973762.39 frames.], batch size: 29, lr: 8.87e-04
2022-05-04 00:01:50,037 INFO [train.py:715] (2/8) Epoch 1, batch 20550, loss[loss=0.1643, simple_loss=0.2288, pruned_loss=0.04991, over 4777.00 frames.], tot_loss[loss=0.1827, simple_loss=0.246, pruned_loss=0.05971, over 973368.12 frames.], batch size: 18, lr: 8.86e-04
2022-05-04 00:02:28,905 INFO [train.py:715] (2/8) Epoch 1, batch 20600, loss[loss=0.1815, simple_loss=0.2465, pruned_loss=0.05829, over 4874.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2462, pruned_loss=0.0595, over 974109.25 frames.], batch size: 32, lr: 8.86e-04
2022-05-04 00:03:08,449 INFO [train.py:715] (2/8) Epoch 1, batch 20650, loss[loss=0.181, simple_loss=0.2485, pruned_loss=0.05678, over 4976.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2474, pruned_loss=0.06018, over 974413.26 frames.], batch size: 24, lr: 8.85e-04
2022-05-04 00:03:48,936 INFO [train.py:715] (2/8) Epoch 1, batch 20700, loss[loss=0.1904, simple_loss=0.2603, pruned_loss=0.06023, over 4744.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2464, pruned_loss=0.05943, over 973830.70 frames.], batch size: 16, lr: 8.85e-04
2022-05-04 00:04:28,573 INFO [train.py:715] (2/8) Epoch 1, batch 20750, loss[loss=0.1624, simple_loss=0.2363, pruned_loss=0.04428, over 4981.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2464, pruned_loss=0.05955, over 973758.73 frames.], batch size: 27, lr: 8.85e-04
2022-05-04 00:05:07,872 INFO [train.py:715] (2/8) Epoch 1, batch 20800, loss[loss=0.1944, simple_loss=0.2513, pruned_loss=0.0688, over 4796.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2456, pruned_loss=0.05893, over 973052.99 frames.], batch size: 17, lr: 8.84e-04
2022-05-04 00:05:47,724 INFO [train.py:715] (2/8) Epoch 1, batch 20850, loss[loss=0.1809, simple_loss=0.2503, pruned_loss=0.05575, over 4789.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2466, pruned_loss=0.05961, over 972958.51 frames.], batch size: 18, lr: 8.84e-04
2022-05-04 00:06:27,480 INFO [train.py:715] (2/8) Epoch 1, batch 20900, loss[loss=0.1922, simple_loss=0.2434, pruned_loss=0.07044, over 4948.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2468, pruned_loss=0.05987, over 974043.05 frames.], batch size: 39, lr: 8.83e-04
2022-05-04 00:07:06,271 INFO [train.py:715] (2/8) Epoch 1, batch 20950, loss[loss=0.1875, simple_loss=0.2481, pruned_loss=0.0635, over 4819.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2472, pruned_loss=0.06011, over 973640.29 frames.], batch size: 27, lr: 8.83e-04
2022-05-04 00:07:45,655 INFO [train.py:715] (2/8) Epoch 1, batch 21000, loss[loss=0.1767, simple_loss=0.2414, pruned_loss=0.056, over 4899.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2458, pruned_loss=0.05965, over 974126.74 frames.], batch size: 19, lr: 8.83e-04
2022-05-04 00:07:45,656 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 00:08:00,762 INFO [train.py:742] (2/8) 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] (2/8) Epoch 1, batch 21050, loss[loss=0.1883, simple_loss=0.2501, pruned_loss=0.06321, over 4919.00 frames.], tot_loss[loss=0.184, simple_loss=0.2473, pruned_loss=0.06036, over 973410.26 frames.], batch size: 23, lr: 8.82e-04
2022-05-04 00:09:19,944 INFO [train.py:715] (2/8) Epoch 1, batch 21100, loss[loss=0.1575, simple_loss=0.2165, pruned_loss=0.04925, over 4926.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2475, pruned_loss=0.06038, over 972503.64 frames.], batch size: 23, lr: 8.82e-04
2022-05-04 00:09:58,316 INFO [train.py:715] (2/8) Epoch 1, batch 21150, loss[loss=0.1801, simple_loss=0.245, pruned_loss=0.05755, over 4970.00 frames.], tot_loss[loss=0.1834, simple_loss=0.247, pruned_loss=0.05986, over 972665.82 frames.], batch size: 14, lr: 8.81e-04
2022-05-04 00:10:40,727 INFO [train.py:715] (2/8) Epoch 1, batch 21200, loss[loss=0.2096, simple_loss=0.2741, pruned_loss=0.07259, over 4899.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2471, pruned_loss=0.06003, over 972275.43 frames.], batch size: 18, lr: 8.81e-04
2022-05-04 00:11:20,086 INFO [train.py:715] (2/8) Epoch 1, batch 21250, loss[loss=0.1939, simple_loss=0.2603, pruned_loss=0.06378, over 4984.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2471, pruned_loss=0.06018, over 972484.19 frames.], batch size: 33, lr: 8.81e-04
2022-05-04 00:11:59,255 INFO [train.py:715] (2/8) Epoch 1, batch 21300, loss[loss=0.1686, simple_loss=0.2322, pruned_loss=0.05247, over 4846.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2471, pruned_loss=0.0603, over 971568.60 frames.], batch size: 15, lr: 8.80e-04
2022-05-04 00:12:38,142 INFO [train.py:715] (2/8) Epoch 1, batch 21350, loss[loss=0.1763, simple_loss=0.2388, pruned_loss=0.0569, over 4815.00 frames.], tot_loss[loss=0.1837, simple_loss=0.247, pruned_loss=0.06025, over 971024.53 frames.], batch size: 26, lr: 8.80e-04
2022-05-04 00:13:17,798 INFO [train.py:715] (2/8) Epoch 1, batch 21400, loss[loss=0.2249, simple_loss=0.2788, pruned_loss=0.08552, over 4824.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2469, pruned_loss=0.06022, over 970618.26 frames.], batch size: 26, lr: 8.80e-04
2022-05-04 00:13:57,964 INFO [train.py:715] (2/8) Epoch 1, batch 21450, loss[loss=0.165, simple_loss=0.2234, pruned_loss=0.0533, over 4856.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2468, pruned_loss=0.05989, over 970307.68 frames.], batch size: 13, lr: 8.79e-04
2022-05-04 00:14:36,213 INFO [train.py:715] (2/8) Epoch 1, batch 21500, loss[loss=0.201, simple_loss=0.2634, pruned_loss=0.06927, over 4975.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2471, pruned_loss=0.0602, over 970579.82 frames.], batch size: 15, lr: 8.79e-04
2022-05-04 00:15:15,305 INFO [train.py:715] (2/8) Epoch 1, batch 21550, loss[loss=0.165, simple_loss=0.2261, pruned_loss=0.05197, over 4838.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2469, pruned_loss=0.06022, over 970820.97 frames.], batch size: 15, lr: 8.78e-04
2022-05-04 00:15:54,607 INFO [train.py:715] (2/8) Epoch 1, batch 21600, loss[loss=0.2283, simple_loss=0.2682, pruned_loss=0.09415, over 4959.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2473, pruned_loss=0.06111, over 970368.55 frames.], batch size: 14, lr: 8.78e-04
2022-05-04 00:16:33,913 INFO [train.py:715] (2/8) Epoch 1, batch 21650, loss[loss=0.1946, simple_loss=0.261, pruned_loss=0.06405, over 4887.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2472, pruned_loss=0.06055, over 970857.63 frames.], batch size: 22, lr: 8.78e-04
2022-05-04 00:17:12,476 INFO [train.py:715] (2/8) Epoch 1, batch 21700, loss[loss=0.1566, simple_loss=0.2221, pruned_loss=0.04553, over 4894.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2472, pruned_loss=0.06071, over 970364.11 frames.], batch size: 22, lr: 8.77e-04
2022-05-04 00:17:52,130 INFO [train.py:715] (2/8) Epoch 1, batch 21750, loss[loss=0.1954, simple_loss=0.2504, pruned_loss=0.07018, over 4858.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2476, pruned_loss=0.06054, over 972182.84 frames.], batch size: 32, lr: 8.77e-04
2022-05-04 00:18:31,685 INFO [train.py:715] (2/8) Epoch 1, batch 21800, loss[loss=0.1586, simple_loss=0.2129, pruned_loss=0.05214, over 4831.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2475, pruned_loss=0.06093, over 971973.38 frames.], batch size: 30, lr: 8.76e-04
2022-05-04 00:19:10,438 INFO [train.py:715] (2/8) Epoch 1, batch 21850, loss[loss=0.2028, simple_loss=0.2742, pruned_loss=0.0657, over 4976.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2467, pruned_loss=0.05991, over 971475.51 frames.], batch size: 24, lr: 8.76e-04
2022-05-04 00:19:50,592 INFO [train.py:715] (2/8) Epoch 1, batch 21900, loss[loss=0.1817, simple_loss=0.2478, pruned_loss=0.05776, over 4702.00 frames.], tot_loss[loss=0.1836, simple_loss=0.247, pruned_loss=0.06009, over 971361.80 frames.], batch size: 15, lr: 8.76e-04
2022-05-04 00:20:30,148 INFO [train.py:715] (2/8) Epoch 1, batch 21950, loss[loss=0.1932, simple_loss=0.262, pruned_loss=0.06218, over 4924.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2473, pruned_loss=0.05989, over 972186.18 frames.], batch size: 21, lr: 8.75e-04
2022-05-04 00:21:09,934 INFO [train.py:715] (2/8) Epoch 1, batch 22000, loss[loss=0.1648, simple_loss=0.2283, pruned_loss=0.05064, over 4851.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2466, pruned_loss=0.05988, over 973149.83 frames.], batch size: 30, lr: 8.75e-04
2022-05-04 00:21:48,900 INFO [train.py:715] (2/8) Epoch 1, batch 22050, loss[loss=0.1852, simple_loss=0.2543, pruned_loss=0.058, over 4969.00 frames.], tot_loss[loss=0.183, simple_loss=0.2469, pruned_loss=0.05953, over 973257.62 frames.], batch size: 35, lr: 8.75e-04
2022-05-04 00:22:28,888 INFO [train.py:715] (2/8) Epoch 1, batch 22100, loss[loss=0.2055, simple_loss=0.2514, pruned_loss=0.07982, over 4789.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2457, pruned_loss=0.05892, over 972366.82 frames.], batch size: 18, lr: 8.74e-04
2022-05-04 00:23:08,219 INFO [train.py:715] (2/8) Epoch 1, batch 22150, loss[loss=0.1879, simple_loss=0.2551, pruned_loss=0.06032, over 4934.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2474, pruned_loss=0.05999, over 971932.12 frames.], batch size: 21, lr: 8.74e-04
2022-05-04 00:23:46,643 INFO [train.py:715] (2/8) Epoch 1, batch 22200, loss[loss=0.1708, simple_loss=0.2423, pruned_loss=0.04966, over 4987.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2477, pruned_loss=0.05998, over 972218.22 frames.], batch size: 28, lr: 8.73e-04
2022-05-04 00:24:25,881 INFO [train.py:715] (2/8) Epoch 1, batch 22250, loss[loss=0.1833, simple_loss=0.256, pruned_loss=0.05531, over 4933.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2483, pruned_loss=0.06018, over 973185.63 frames.], batch size: 21, lr: 8.73e-04
2022-05-04 00:25:05,558 INFO [train.py:715] (2/8) Epoch 1, batch 22300, loss[loss=0.1941, simple_loss=0.2757, pruned_loss=0.0563, over 4829.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2489, pruned_loss=0.0603, over 972763.24 frames.], batch size: 15, lr: 8.73e-04
2022-05-04 00:25:45,326 INFO [train.py:715] (2/8) Epoch 1, batch 22350, loss[loss=0.2105, simple_loss=0.2665, pruned_loss=0.07723, over 4940.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2488, pruned_loss=0.06054, over 971754.91 frames.], batch size: 21, lr: 8.72e-04
2022-05-04 00:26:24,284 INFO [train.py:715] (2/8) Epoch 1, batch 22400, loss[loss=0.1705, simple_loss=0.2313, pruned_loss=0.05487, over 4915.00 frames.], tot_loss[loss=0.1854, simple_loss=0.249, pruned_loss=0.06093, over 971411.27 frames.], batch size: 29, lr: 8.72e-04
2022-05-04 00:27:04,008 INFO [train.py:715] (2/8) Epoch 1, batch 22450, loss[loss=0.1772, simple_loss=0.2426, pruned_loss=0.0559, over 4929.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2478, pruned_loss=0.06051, over 972270.14 frames.], batch size: 23, lr: 8.72e-04
2022-05-04 00:27:43,645 INFO [train.py:715] (2/8) Epoch 1, batch 22500, loss[loss=0.1668, simple_loss=0.2292, pruned_loss=0.0522, over 4818.00 frames.], tot_loss[loss=0.186, simple_loss=0.2491, pruned_loss=0.06143, over 972323.65 frames.], batch size: 15, lr: 8.71e-04
2022-05-04 00:28:22,141 INFO [train.py:715] (2/8) Epoch 1, batch 22550, loss[loss=0.1576, simple_loss=0.2173, pruned_loss=0.04895, over 4747.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2497, pruned_loss=0.06195, over 972125.71 frames.], batch size: 16, lr: 8.71e-04
2022-05-04 00:29:02,208 INFO [train.py:715] (2/8) Epoch 1, batch 22600, loss[loss=0.1898, simple_loss=0.2477, pruned_loss=0.06598, over 4924.00 frames.], tot_loss[loss=0.186, simple_loss=0.2497, pruned_loss=0.06118, over 973042.65 frames.], batch size: 23, lr: 8.70e-04
2022-05-04 00:29:42,686 INFO [train.py:715] (2/8) Epoch 1, batch 22650, loss[loss=0.2082, simple_loss=0.2599, pruned_loss=0.07825, over 4778.00 frames.], tot_loss[loss=0.1855, simple_loss=0.249, pruned_loss=0.06101, over 972819.14 frames.], batch size: 18, lr: 8.70e-04
2022-05-04 00:30:22,582 INFO [train.py:715] (2/8) Epoch 1, batch 22700, loss[loss=0.1769, simple_loss=0.2441, pruned_loss=0.05488, over 4897.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2493, pruned_loss=0.06123, over 973516.64 frames.], batch size: 19, lr: 8.70e-04
2022-05-04 00:31:00,974 INFO [train.py:715] (2/8) Epoch 1, batch 22750, loss[loss=0.1614, simple_loss=0.2345, pruned_loss=0.04412, over 4933.00 frames.], tot_loss[loss=0.185, simple_loss=0.2481, pruned_loss=0.06088, over 973523.08 frames.], batch size: 18, lr: 8.69e-04
2022-05-04 00:31:41,159 INFO [train.py:715] (2/8) Epoch 1, batch 22800, loss[loss=0.2206, simple_loss=0.2732, pruned_loss=0.084, over 4877.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2477, pruned_loss=0.0608, over 973652.36 frames.], batch size: 22, lr: 8.69e-04
2022-05-04 00:32:20,883 INFO [train.py:715] (2/8) Epoch 1, batch 22850, loss[loss=0.1524, simple_loss=0.2304, pruned_loss=0.03715, over 4901.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2485, pruned_loss=0.06119, over 973301.16 frames.], batch size: 19, lr: 8.68e-04
2022-05-04 00:32:59,719 INFO [train.py:715] (2/8) Epoch 1, batch 22900, loss[loss=0.1888, simple_loss=0.2503, pruned_loss=0.0636, over 4805.00 frames.], tot_loss[loss=0.1857, simple_loss=0.249, pruned_loss=0.06117, over 973054.56 frames.], batch size: 26, lr: 8.68e-04
2022-05-04 00:33:39,269 INFO [train.py:715] (2/8) Epoch 1, batch 22950, loss[loss=0.1817, simple_loss=0.2511, pruned_loss=0.05614, over 4941.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2486, pruned_loss=0.06089, over 973309.23 frames.], batch size: 21, lr: 8.68e-04
2022-05-04 00:34:19,072 INFO [train.py:715] (2/8) Epoch 1, batch 23000, loss[loss=0.1883, simple_loss=0.2536, pruned_loss=0.06147, over 4865.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2487, pruned_loss=0.06099, over 973251.81 frames.], batch size: 32, lr: 8.67e-04
2022-05-04 00:34:57,977 INFO [train.py:715] (2/8) Epoch 1, batch 23050, loss[loss=0.2278, simple_loss=0.2899, pruned_loss=0.08285, over 4786.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2489, pruned_loss=0.06065, over 973758.22 frames.], batch size: 14, lr: 8.67e-04
2022-05-04 00:35:37,119 INFO [train.py:715] (2/8) Epoch 1, batch 23100, loss[loss=0.1379, simple_loss=0.216, pruned_loss=0.02993, over 4883.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2485, pruned_loss=0.06012, over 973535.54 frames.], batch size: 22, lr: 8.67e-04
2022-05-04 00:36:16,852 INFO [train.py:715] (2/8) Epoch 1, batch 23150, loss[loss=0.1817, simple_loss=0.2536, pruned_loss=0.05488, over 4824.00 frames.], tot_loss[loss=0.1841, simple_loss=0.248, pruned_loss=0.06006, over 973619.64 frames.], batch size: 26, lr: 8.66e-04
2022-05-04 00:36:56,379 INFO [train.py:715] (2/8) Epoch 1, batch 23200, loss[loss=0.1721, simple_loss=0.2354, pruned_loss=0.05439, over 4758.00 frames.], tot_loss[loss=0.184, simple_loss=0.248, pruned_loss=0.06005, over 972557.82 frames.], batch size: 19, lr: 8.66e-04
2022-05-04 00:37:34,613 INFO [train.py:715] (2/8) Epoch 1, batch 23250, loss[loss=0.1682, simple_loss=0.2171, pruned_loss=0.05959, over 4752.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2483, pruned_loss=0.06045, over 971852.84 frames.], batch size: 16, lr: 8.66e-04
2022-05-04 00:38:14,192 INFO [train.py:715] (2/8) Epoch 1, batch 23300, loss[loss=0.1539, simple_loss=0.2247, pruned_loss=0.04157, over 4700.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2473, pruned_loss=0.0599, over 972387.59 frames.], batch size: 15, lr: 8.65e-04
2022-05-04 00:38:53,768 INFO [train.py:715] (2/8) Epoch 1, batch 23350, loss[loss=0.1807, simple_loss=0.2486, pruned_loss=0.05638, over 4976.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2476, pruned_loss=0.05977, over 972235.73 frames.], batch size: 31, lr: 8.65e-04
2022-05-04 00:39:32,064 INFO [train.py:715] (2/8) Epoch 1, batch 23400, loss[loss=0.15, simple_loss=0.2298, pruned_loss=0.03509, over 4984.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2478, pruned_loss=0.05986, over 972830.52 frames.], batch size: 28, lr: 8.64e-04
2022-05-04 00:40:11,303 INFO [train.py:715] (2/8) Epoch 1, batch 23450, loss[loss=0.2011, simple_loss=0.2512, pruned_loss=0.0755, over 4895.00 frames.], tot_loss[loss=0.185, simple_loss=0.2487, pruned_loss=0.06061, over 972543.52 frames.], batch size: 19, lr: 8.64e-04
2022-05-04 00:40:50,689 INFO [train.py:715] (2/8) Epoch 1, batch 23500, loss[loss=0.1801, simple_loss=0.2393, pruned_loss=0.06042, over 4962.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2483, pruned_loss=0.06002, over 971650.39 frames.], batch size: 15, lr: 8.64e-04
2022-05-04 00:41:29,525 INFO [train.py:715] (2/8) Epoch 1, batch 23550, loss[loss=0.181, simple_loss=0.2531, pruned_loss=0.05445, over 4975.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2487, pruned_loss=0.06058, over 972006.10 frames.], batch size: 25, lr: 8.63e-04
2022-05-04 00:42:07,723 INFO [train.py:715] (2/8) Epoch 1, batch 23600, loss[loss=0.1853, simple_loss=0.2554, pruned_loss=0.0576, over 4807.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2467, pruned_loss=0.0593, over 972672.14 frames.], batch size: 24, lr: 8.63e-04
2022-05-04 00:42:47,232 INFO [train.py:715] (2/8) Epoch 1, batch 23650, loss[loss=0.1719, simple_loss=0.2405, pruned_loss=0.05165, over 4877.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2456, pruned_loss=0.0587, over 972769.96 frames.], batch size: 22, lr: 8.63e-04
2022-05-04 00:43:26,746 INFO [train.py:715] (2/8) Epoch 1, batch 23700, loss[loss=0.1903, simple_loss=0.2599, pruned_loss=0.06032, over 4976.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2456, pruned_loss=0.05831, over 973243.09 frames.], batch size: 15, lr: 8.62e-04
2022-05-04 00:44:05,086 INFO [train.py:715] (2/8) Epoch 1, batch 23750, loss[loss=0.1567, simple_loss=0.2196, pruned_loss=0.04689, over 4909.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2458, pruned_loss=0.05817, over 973607.12 frames.], batch size: 18, lr: 8.62e-04
2022-05-04 00:44:44,139 INFO [train.py:715] (2/8) Epoch 1, batch 23800, loss[loss=0.2, simple_loss=0.2662, pruned_loss=0.06691, over 4922.00 frames.], tot_loss[loss=0.1822, simple_loss=0.247, pruned_loss=0.0587, over 972840.39 frames.], batch size: 29, lr: 8.61e-04
2022-05-04 00:45:24,224 INFO [train.py:715] (2/8) Epoch 1, batch 23850, loss[loss=0.181, simple_loss=0.2382, pruned_loss=0.06187, over 4966.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2463, pruned_loss=0.05858, over 973498.01 frames.], batch size: 35, lr: 8.61e-04
2022-05-04 00:46:03,786 INFO [train.py:715] (2/8) Epoch 1, batch 23900, loss[loss=0.1488, simple_loss=0.221, pruned_loss=0.0383, over 4950.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2465, pruned_loss=0.05895, over 972258.14 frames.], batch size: 15, lr: 8.61e-04
2022-05-04 00:46:42,588 INFO [train.py:715] (2/8) Epoch 1, batch 23950, loss[loss=0.178, simple_loss=0.2381, pruned_loss=0.05892, over 4859.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2456, pruned_loss=0.05839, over 972319.65 frames.], batch size: 20, lr: 8.60e-04
2022-05-04 00:47:22,326 INFO [train.py:715] (2/8) Epoch 1, batch 24000, loss[loss=0.1825, simple_loss=0.2448, pruned_loss=0.06007, over 4880.00 frames.], tot_loss[loss=0.1813, simple_loss=0.246, pruned_loss=0.05835, over 972585.64 frames.], batch size: 32, lr: 8.60e-04
2022-05-04 00:47:22,327 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 00:47:34,529 INFO [train.py:742] (2/8) Epoch 1, validation: loss=0.1217, simple_loss=0.2087, pruned_loss=0.01736, over 914524.00 frames.
2022-05-04 00:48:14,352 INFO [train.py:715] (2/8) Epoch 1, batch 24050, loss[loss=0.1846, simple_loss=0.2511, pruned_loss=0.05911, over 4957.00 frames.], tot_loss[loss=0.1818, simple_loss=0.246, pruned_loss=0.05875, over 972644.78 frames.], batch size: 35, lr: 8.60e-04
2022-05-04 00:48:53,677 INFO [train.py:715] (2/8) Epoch 1, batch 24100, loss[loss=0.1884, simple_loss=0.2602, pruned_loss=0.05829, over 4974.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2456, pruned_loss=0.05843, over 973136.54 frames.], batch size: 28, lr: 8.59e-04
2022-05-04 00:49:32,274 INFO [train.py:715] (2/8) Epoch 1, batch 24150, loss[loss=0.1601, simple_loss=0.2373, pruned_loss=0.04145, over 4825.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2456, pruned_loss=0.05835, over 972592.43 frames.], batch size: 26, lr: 8.59e-04
2022-05-04 00:50:11,568 INFO [train.py:715] (2/8) Epoch 1, batch 24200, loss[loss=0.196, simple_loss=0.2664, pruned_loss=0.06275, over 4976.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2463, pruned_loss=0.05894, over 972474.24 frames.], batch size: 28, lr: 8.59e-04
2022-05-04 00:50:52,247 INFO [train.py:715] (2/8) Epoch 1, batch 24250, loss[loss=0.1603, simple_loss=0.2283, pruned_loss=0.04616, over 4848.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2465, pruned_loss=0.05908, over 972843.17 frames.], batch size: 20, lr: 8.58e-04
2022-05-04 00:51:31,675 INFO [train.py:715] (2/8) Epoch 1, batch 24300, loss[loss=0.1857, simple_loss=0.2439, pruned_loss=0.0638, over 4943.00 frames.], tot_loss[loss=0.182, simple_loss=0.2462, pruned_loss=0.05895, over 973114.56 frames.], batch size: 21, lr: 8.58e-04
2022-05-04 00:52:11,121 INFO [train.py:715] (2/8) Epoch 1, batch 24350, loss[loss=0.1662, simple_loss=0.2289, pruned_loss=0.05176, over 4833.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2459, pruned_loss=0.05866, over 973011.68 frames.], batch size: 15, lr: 8.57e-04
2022-05-04 00:52:51,494 INFO [train.py:715] (2/8) Epoch 1, batch 24400, loss[loss=0.1715, simple_loss=0.2427, pruned_loss=0.05011, over 4821.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2466, pruned_loss=0.05893, over 973827.73 frames.], batch size: 15, lr: 8.57e-04
2022-05-04 00:53:30,575 INFO [train.py:715] (2/8) Epoch 1, batch 24450, loss[loss=0.1661, simple_loss=0.2216, pruned_loss=0.05531, over 4889.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2472, pruned_loss=0.05943, over 973908.77 frames.], batch size: 32, lr: 8.57e-04
2022-05-04 00:54:09,296 INFO [train.py:715] (2/8) Epoch 1, batch 24500, loss[loss=0.1835, simple_loss=0.2588, pruned_loss=0.05414, over 4885.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2479, pruned_loss=0.05943, over 973487.25 frames.], batch size: 16, lr: 8.56e-04
2022-05-04 00:54:48,959 INFO [train.py:715] (2/8) Epoch 1, batch 24550, loss[loss=0.2028, simple_loss=0.2634, pruned_loss=0.07108, over 4963.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2472, pruned_loss=0.05907, over 973577.96 frames.], batch size: 28, lr: 8.56e-04
2022-05-04 00:55:29,261 INFO [train.py:715] (2/8) Epoch 1, batch 24600, loss[loss=0.2064, simple_loss=0.2731, pruned_loss=0.06988, over 4897.00 frames.], tot_loss[loss=0.1828, simple_loss=0.247, pruned_loss=0.0593, over 972555.07 frames.], batch size: 19, lr: 8.56e-04
2022-05-04 00:56:08,129 INFO [train.py:715] (2/8) Epoch 1, batch 24650, loss[loss=0.1714, simple_loss=0.2437, pruned_loss=0.04959, over 4866.00 frames.], tot_loss[loss=0.184, simple_loss=0.2477, pruned_loss=0.06018, over 972272.63 frames.], batch size: 20, lr: 8.55e-04
2022-05-04 00:56:47,162 INFO [train.py:715] (2/8) Epoch 1, batch 24700, loss[loss=0.2189, simple_loss=0.2852, pruned_loss=0.0763, over 4686.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2474, pruned_loss=0.05956, over 972112.82 frames.], batch size: 15, lr: 8.55e-04
2022-05-04 00:57:27,336 INFO [train.py:715] (2/8) Epoch 1, batch 24750, loss[loss=0.1831, simple_loss=0.2553, pruned_loss=0.05544, over 4803.00 frames.], tot_loss[loss=0.185, simple_loss=0.2487, pruned_loss=0.06062, over 972176.08 frames.], batch size: 21, lr: 8.55e-04
2022-05-04 00:58:06,475 INFO [train.py:715] (2/8) Epoch 1, batch 24800, loss[loss=0.1571, simple_loss=0.2261, pruned_loss=0.04401, over 4688.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2488, pruned_loss=0.06048, over 971586.43 frames.], batch size: 15, lr: 8.54e-04
2022-05-04 00:58:45,099 INFO [train.py:715] (2/8) Epoch 1, batch 24850, loss[loss=0.1563, simple_loss=0.2131, pruned_loss=0.04978, over 4957.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2479, pruned_loss=0.06032, over 972101.09 frames.], batch size: 35, lr: 8.54e-04
2022-05-04 00:59:25,584 INFO [train.py:715] (2/8) Epoch 1, batch 24900, loss[loss=0.2069, simple_loss=0.2717, pruned_loss=0.07108, over 4787.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2478, pruned_loss=0.06034, over 971501.43 frames.], batch size: 21, lr: 8.54e-04
2022-05-04 01:00:05,516 INFO [train.py:715] (2/8) Epoch 1, batch 24950, loss[loss=0.1496, simple_loss=0.2231, pruned_loss=0.03801, over 4927.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2474, pruned_loss=0.06025, over 971372.31 frames.], batch size: 29, lr: 8.53e-04
2022-05-04 01:00:44,288 INFO [train.py:715] (2/8) Epoch 1, batch 25000, loss[loss=0.1608, simple_loss=0.2212, pruned_loss=0.05025, over 4855.00 frames.], tot_loss[loss=0.183, simple_loss=0.2467, pruned_loss=0.05962, over 971340.74 frames.], batch size: 32, lr: 8.53e-04
2022-05-04 01:01:22,930 INFO [train.py:715] (2/8) Epoch 1, batch 25050, loss[loss=0.1801, simple_loss=0.2411, pruned_loss=0.05949, over 4772.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2458, pruned_loss=0.05925, over 972527.96 frames.], batch size: 19, lr: 8.53e-04
2022-05-04 01:02:02,851 INFO [train.py:715] (2/8) Epoch 1, batch 25100, loss[loss=0.194, simple_loss=0.2574, pruned_loss=0.06533, over 4943.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2459, pruned_loss=0.05928, over 972275.38 frames.], batch size: 21, lr: 8.52e-04
2022-05-04 01:02:42,028 INFO [train.py:715] (2/8) Epoch 1, batch 25150, loss[loss=0.1939, simple_loss=0.2614, pruned_loss=0.06316, over 4756.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2443, pruned_loss=0.05822, over 972730.99 frames.], batch size: 19, lr: 8.52e-04
2022-05-04 01:03:20,870 INFO [train.py:715] (2/8) Epoch 1, batch 25200, loss[loss=0.1311, simple_loss=0.2031, pruned_loss=0.02956, over 4764.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2446, pruned_loss=0.05841, over 973055.93 frames.], batch size: 12, lr: 8.51e-04
2022-05-04 01:04:00,094 INFO [train.py:715] (2/8) Epoch 1, batch 25250, loss[loss=0.201, simple_loss=0.2521, pruned_loss=0.07498, over 4957.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2442, pruned_loss=0.05778, over 972926.28 frames.], batch size: 35, lr: 8.51e-04
2022-05-04 01:04:40,219 INFO [train.py:715] (2/8) Epoch 1, batch 25300, loss[loss=0.2204, simple_loss=0.2841, pruned_loss=0.07834, over 4708.00 frames.], tot_loss[loss=0.18, simple_loss=0.2442, pruned_loss=0.05793, over 972835.23 frames.], batch size: 15, lr: 8.51e-04
2022-05-04 01:05:18,871 INFO [train.py:715] (2/8) Epoch 1, batch 25350, loss[loss=0.1732, simple_loss=0.2424, pruned_loss=0.05194, over 4879.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2449, pruned_loss=0.05816, over 973161.00 frames.], batch size: 16, lr: 8.50e-04
2022-05-04 01:05:58,212 INFO [train.py:715] (2/8) Epoch 1, batch 25400, loss[loss=0.191, simple_loss=0.258, pruned_loss=0.06198, over 4774.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2448, pruned_loss=0.05787, over 972778.21 frames.], batch size: 18, lr: 8.50e-04
2022-05-04 01:06:38,476 INFO [train.py:715] (2/8) Epoch 1, batch 25450, loss[loss=0.1516, simple_loss=0.2151, pruned_loss=0.04407, over 4714.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2458, pruned_loss=0.05842, over 972623.21 frames.], batch size: 12, lr: 8.50e-04
2022-05-04 01:07:18,417 INFO [train.py:715] (2/8) Epoch 1, batch 25500, loss[loss=0.1667, simple_loss=0.2358, pruned_loss=0.04877, over 4755.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2468, pruned_loss=0.05899, over 972744.51 frames.], batch size: 18, lr: 8.49e-04
2022-05-04 01:07:56,842 INFO [train.py:715] (2/8) Epoch 1, batch 25550, loss[loss=0.168, simple_loss=0.2345, pruned_loss=0.05078, over 4809.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2459, pruned_loss=0.0589, over 972158.30 frames.], batch size: 25, lr: 8.49e-04
2022-05-04 01:08:36,975 INFO [train.py:715] (2/8) Epoch 1, batch 25600, loss[loss=0.1848, simple_loss=0.2441, pruned_loss=0.06272, over 4826.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2452, pruned_loss=0.05853, over 971697.42 frames.], batch size: 25, lr: 8.49e-04
2022-05-04 01:09:17,497 INFO [train.py:715] (2/8) Epoch 1, batch 25650, loss[loss=0.1982, simple_loss=0.2592, pruned_loss=0.06854, over 4837.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2448, pruned_loss=0.05817, over 970950.08 frames.], batch size: 15, lr: 8.48e-04
2022-05-04 01:09:56,985 INFO [train.py:715] (2/8) Epoch 1, batch 25700, loss[loss=0.1253, simple_loss=0.1974, pruned_loss=0.02666, over 4826.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2456, pruned_loss=0.05879, over 970772.22 frames.], batch size: 26, lr: 8.48e-04
2022-05-04 01:10:36,896 INFO [train.py:715] (2/8) Epoch 1, batch 25750, loss[loss=0.159, simple_loss=0.2381, pruned_loss=0.03994, over 4788.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2446, pruned_loss=0.05816, over 971070.41 frames.], batch size: 24, lr: 8.48e-04
2022-05-04 01:11:17,391 INFO [train.py:715] (2/8) Epoch 1, batch 25800, loss[loss=0.166, simple_loss=0.2314, pruned_loss=0.05035, over 4749.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2435, pruned_loss=0.05754, over 971969.56 frames.], batch size: 19, lr: 8.47e-04
2022-05-04 01:11:56,812 INFO [train.py:715] (2/8) Epoch 1, batch 25850, loss[loss=0.2348, simple_loss=0.2853, pruned_loss=0.09212, over 4783.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2446, pruned_loss=0.05853, over 972484.77 frames.], batch size: 17, lr: 8.47e-04
2022-05-04 01:12:35,643 INFO [train.py:715] (2/8) Epoch 1, batch 25900, loss[loss=0.2027, simple_loss=0.2611, pruned_loss=0.07211, over 4948.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2439, pruned_loss=0.05778, over 972267.19 frames.], batch size: 29, lr: 8.47e-04
2022-05-04 01:13:15,319 INFO [train.py:715] (2/8) Epoch 1, batch 25950, loss[loss=0.1909, simple_loss=0.2525, pruned_loss=0.06463, over 4863.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2455, pruned_loss=0.05863, over 972109.89 frames.], batch size: 20, lr: 8.46e-04
2022-05-04 01:13:55,199 INFO [train.py:715] (2/8) Epoch 1, batch 26000, loss[loss=0.2086, simple_loss=0.2701, pruned_loss=0.07361, over 4867.00 frames.], tot_loss[loss=0.1819, simple_loss=0.246, pruned_loss=0.05891, over 972373.04 frames.], batch size: 39, lr: 8.46e-04
2022-05-04 01:14:34,087 INFO [train.py:715] (2/8) Epoch 1, batch 26050, loss[loss=0.1839, simple_loss=0.2489, pruned_loss=0.0595, over 4975.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2456, pruned_loss=0.05872, over 972797.50 frames.], batch size: 25, lr: 8.46e-04
2022-05-04 01:15:13,486 INFO [train.py:715] (2/8) Epoch 1, batch 26100, loss[loss=0.1439, simple_loss=0.2214, pruned_loss=0.03327, over 4926.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2457, pruned_loss=0.05932, over 972227.22 frames.], batch size: 29, lr: 8.45e-04
2022-05-04 01:15:53,618 INFO [train.py:715] (2/8) Epoch 1, batch 26150, loss[loss=0.2126, simple_loss=0.2606, pruned_loss=0.08229, over 4836.00 frames.], tot_loss[loss=0.181, simple_loss=0.2445, pruned_loss=0.05873, over 972201.10 frames.], batch size: 15, lr: 8.45e-04
2022-05-04 01:16:32,566 INFO [train.py:715] (2/8) Epoch 1, batch 26200, loss[loss=0.1453, simple_loss=0.2133, pruned_loss=0.03867, over 4853.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2449, pruned_loss=0.05833, over 971557.16 frames.], batch size: 13, lr: 8.44e-04
2022-05-04 01:17:11,430 INFO [train.py:715] (2/8) Epoch 1, batch 26250, loss[loss=0.1911, simple_loss=0.2518, pruned_loss=0.06516, over 4876.00 frames.], tot_loss[loss=0.18, simple_loss=0.2445, pruned_loss=0.05769, over 971637.51 frames.], batch size: 16, lr: 8.44e-04
2022-05-04 01:17:51,339 INFO [train.py:715] (2/8) Epoch 1, batch 26300, loss[loss=0.1285, simple_loss=0.2014, pruned_loss=0.02784, over 4916.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2453, pruned_loss=0.05824, over 971342.19 frames.], batch size: 19, lr: 8.44e-04
2022-05-04 01:18:31,196 INFO [train.py:715] (2/8) Epoch 1, batch 26350, loss[loss=0.2011, simple_loss=0.266, pruned_loss=0.0681, over 4835.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2459, pruned_loss=0.05827, over 971183.82 frames.], batch size: 15, lr: 8.43e-04
2022-05-04 01:19:09,964 INFO [train.py:715] (2/8) Epoch 1, batch 26400, loss[loss=0.2179, simple_loss=0.2683, pruned_loss=0.08375, over 4836.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2467, pruned_loss=0.05911, over 972049.03 frames.], batch size: 15, lr: 8.43e-04
2022-05-04 01:19:49,165 INFO [train.py:715] (2/8) Epoch 1, batch 26450, loss[loss=0.1725, simple_loss=0.2424, pruned_loss=0.05132, over 4986.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2466, pruned_loss=0.05926, over 972543.67 frames.], batch size: 28, lr: 8.43e-04
2022-05-04 01:20:28,901 INFO [train.py:715] (2/8) Epoch 1, batch 26500, loss[loss=0.226, simple_loss=0.2909, pruned_loss=0.08051, over 4817.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2475, pruned_loss=0.05918, over 972138.18 frames.], batch size: 26, lr: 8.42e-04
2022-05-04 01:21:08,257 INFO [train.py:715] (2/8) Epoch 1, batch 26550, loss[loss=0.1358, simple_loss=0.1905, pruned_loss=0.04057, over 4975.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2465, pruned_loss=0.0585, over 971028.87 frames.], batch size: 14, lr: 8.42e-04
2022-05-04 01:21:47,612 INFO [train.py:715] (2/8) Epoch 1, batch 26600, loss[loss=0.1317, simple_loss=0.1998, pruned_loss=0.0318, over 4977.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2467, pruned_loss=0.05879, over 971387.11 frames.], batch size: 14, lr: 8.42e-04
2022-05-04 01:22:27,656 INFO [train.py:715] (2/8) Epoch 1, batch 26650, loss[loss=0.1899, simple_loss=0.2585, pruned_loss=0.06059, over 4963.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2473, pruned_loss=0.05916, over 971444.91 frames.], batch size: 28, lr: 8.41e-04
2022-05-04 01:23:07,608 INFO [train.py:715] (2/8) Epoch 1, batch 26700, loss[loss=0.2056, simple_loss=0.2697, pruned_loss=0.07078, over 4978.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2472, pruned_loss=0.05976, over 971239.12 frames.], batch size: 25, lr: 8.41e-04
2022-05-04 01:23:46,579 INFO [train.py:715] (2/8) Epoch 1, batch 26750, loss[loss=0.1829, simple_loss=0.2417, pruned_loss=0.06207, over 4946.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2462, pruned_loss=0.05904, over 971372.61 frames.], batch size: 35, lr: 8.41e-04
2022-05-04 01:24:26,593 INFO [train.py:715] (2/8) Epoch 1, batch 26800, loss[loss=0.1904, simple_loss=0.2453, pruned_loss=0.06769, over 4862.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2469, pruned_loss=0.0592, over 972793.64 frames.], batch size: 32, lr: 8.40e-04
2022-05-04 01:25:06,134 INFO [train.py:715] (2/8) Epoch 1, batch 26850, loss[loss=0.1864, simple_loss=0.2422, pruned_loss=0.06529, over 4768.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2479, pruned_loss=0.05985, over 972334.12 frames.], batch size: 14, lr: 8.40e-04
2022-05-04 01:25:45,414 INFO [train.py:715] (2/8) Epoch 1, batch 26900, loss[loss=0.196, simple_loss=0.2566, pruned_loss=0.06774, over 4805.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2474, pruned_loss=0.05947, over 971843.62 frames.], batch size: 21, lr: 8.40e-04
2022-05-04 01:26:24,106 INFO [train.py:715] (2/8) Epoch 1, batch 26950, loss[loss=0.2529, simple_loss=0.2936, pruned_loss=0.1061, over 4864.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2476, pruned_loss=0.05966, over 971161.29 frames.], batch size: 34, lr: 8.39e-04
2022-05-04 01:27:04,119 INFO [train.py:715] (2/8) Epoch 1, batch 27000, loss[loss=0.1671, simple_loss=0.2352, pruned_loss=0.04953, over 4976.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2473, pruned_loss=0.05964, over 971398.57 frames.], batch size: 15, lr: 8.39e-04
2022-05-04 01:27:04,120 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 01:27:12,718 INFO [train.py:742] (2/8) 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] (2/8) Epoch 1, batch 27050, loss[loss=0.2331, simple_loss=0.2982, pruned_loss=0.08403, over 4824.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2468, pruned_loss=0.05956, over 971807.68 frames.], batch size: 12, lr: 8.39e-04
2022-05-04 01:28:33,368 INFO [train.py:715] (2/8) Epoch 1, batch 27100, loss[loss=0.1912, simple_loss=0.2422, pruned_loss=0.07012, over 4821.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2462, pruned_loss=0.05925, over 972062.35 frames.], batch size: 25, lr: 8.38e-04
2022-05-04 01:29:11,774 INFO [train.py:715] (2/8) Epoch 1, batch 27150, loss[loss=0.1995, simple_loss=0.2643, pruned_loss=0.06739, over 4827.00 frames.], tot_loss[loss=0.183, simple_loss=0.247, pruned_loss=0.05945, over 972223.03 frames.], batch size: 26, lr: 8.38e-04
2022-05-04 01:29:51,716 INFO [train.py:715] (2/8) Epoch 1, batch 27200, loss[loss=0.2063, simple_loss=0.2615, pruned_loss=0.07562, over 4865.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2467, pruned_loss=0.05922, over 972705.64 frames.], batch size: 32, lr: 8.38e-04
2022-05-04 01:30:32,008 INFO [train.py:715] (2/8) Epoch 1, batch 27250, loss[loss=0.1506, simple_loss=0.2243, pruned_loss=0.03847, over 4925.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2456, pruned_loss=0.05866, over 972727.65 frames.], batch size: 23, lr: 8.37e-04
2022-05-04 01:31:11,129 INFO [train.py:715] (2/8) Epoch 1, batch 27300, loss[loss=0.2103, simple_loss=0.2681, pruned_loss=0.07624, over 4842.00 frames.], tot_loss[loss=0.181, simple_loss=0.2451, pruned_loss=0.05844, over 973567.04 frames.], batch size: 15, lr: 8.37e-04
2022-05-04 01:31:49,668 INFO [train.py:715] (2/8) Epoch 1, batch 27350, loss[loss=0.1688, simple_loss=0.231, pruned_loss=0.05333, over 4828.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2452, pruned_loss=0.05755, over 973271.89 frames.], batch size: 26, lr: 8.37e-04
2022-05-04 01:32:29,596 INFO [train.py:715] (2/8) Epoch 1, batch 27400, loss[loss=0.1742, simple_loss=0.2437, pruned_loss=0.05234, over 4826.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2457, pruned_loss=0.05802, over 972032.68 frames.], batch size: 27, lr: 8.36e-04
2022-05-04 01:33:09,592 INFO [train.py:715] (2/8) Epoch 1, batch 27450, loss[loss=0.2033, simple_loss=0.2758, pruned_loss=0.06543, over 4946.00 frames.], tot_loss[loss=0.18, simple_loss=0.2452, pruned_loss=0.0574, over 972428.22 frames.], batch size: 23, lr: 8.36e-04
2022-05-04 01:33:48,100 INFO [train.py:715] (2/8) Epoch 1, batch 27500, loss[loss=0.1638, simple_loss=0.2214, pruned_loss=0.0531, over 4860.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2442, pruned_loss=0.05697, over 972869.53 frames.], batch size: 20, lr: 8.36e-04
2022-05-04 01:34:27,756 INFO [train.py:715] (2/8) Epoch 1, batch 27550, loss[loss=0.1773, simple_loss=0.2393, pruned_loss=0.05767, over 4983.00 frames.], tot_loss[loss=0.18, simple_loss=0.245, pruned_loss=0.05756, over 973173.02 frames.], batch size: 25, lr: 8.35e-04
2022-05-04 01:35:07,983 INFO [train.py:715] (2/8) Epoch 1, batch 27600, loss[loss=0.1604, simple_loss=0.2269, pruned_loss=0.04696, over 4870.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2448, pruned_loss=0.05752, over 973506.35 frames.], batch size: 22, lr: 8.35e-04
2022-05-04 01:35:47,291 INFO [train.py:715] (2/8) Epoch 1, batch 27650, loss[loss=0.1436, simple_loss=0.2123, pruned_loss=0.03745, over 4745.00 frames.], tot_loss[loss=0.1807, simple_loss=0.245, pruned_loss=0.05821, over 972888.81 frames.], batch size: 16, lr: 8.35e-04
2022-05-04 01:36:26,729 INFO [train.py:715] (2/8) Epoch 1, batch 27700, loss[loss=0.188, simple_loss=0.2432, pruned_loss=0.06643, over 4887.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2438, pruned_loss=0.05729, over 971677.84 frames.], batch size: 16, lr: 8.34e-04
2022-05-04 01:37:07,280 INFO [train.py:715] (2/8) Epoch 1, batch 27750, loss[loss=0.1868, simple_loss=0.2489, pruned_loss=0.06235, over 4960.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2434, pruned_loss=0.05746, over 970746.24 frames.], batch size: 15, lr: 8.34e-04
2022-05-04 01:37:47,066 INFO [train.py:715] (2/8) Epoch 1, batch 27800, loss[loss=0.17, simple_loss=0.2274, pruned_loss=0.0563, over 4982.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2441, pruned_loss=0.05807, over 971510.94 frames.], batch size: 24, lr: 8.34e-04
2022-05-04 01:38:26,353 INFO [train.py:715] (2/8) Epoch 1, batch 27850, loss[loss=0.1716, simple_loss=0.2517, pruned_loss=0.04581, over 4750.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2444, pruned_loss=0.05806, over 971410.61 frames.], batch size: 19, lr: 8.33e-04
2022-05-04 01:39:06,463 INFO [train.py:715] (2/8) Epoch 1, batch 27900, loss[loss=0.2057, simple_loss=0.277, pruned_loss=0.06721, over 4935.00 frames.], tot_loss[loss=0.1809, simple_loss=0.245, pruned_loss=0.05837, over 972018.78 frames.], batch size: 23, lr: 8.33e-04
2022-05-04 01:39:45,942 INFO [train.py:715] (2/8) Epoch 1, batch 27950, loss[loss=0.1598, simple_loss=0.2256, pruned_loss=0.047, over 4949.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2445, pruned_loss=0.05823, over 971639.63 frames.], batch size: 35, lr: 8.33e-04
2022-05-04 01:40:25,326 INFO [train.py:715] (2/8) Epoch 1, batch 28000, loss[loss=0.1488, simple_loss=0.214, pruned_loss=0.04176, over 4776.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2454, pruned_loss=0.05858, over 971307.74 frames.], batch size: 17, lr: 8.32e-04
2022-05-04 01:41:04,101 INFO [train.py:715] (2/8) Epoch 1, batch 28050, loss[loss=0.1519, simple_loss=0.222, pruned_loss=0.04088, over 4940.00 frames.], tot_loss[loss=0.181, simple_loss=0.2452, pruned_loss=0.05835, over 971352.66 frames.], batch size: 35, lr: 8.32e-04
2022-05-04 01:41:44,521 INFO [train.py:715] (2/8) Epoch 1, batch 28100, loss[loss=0.1879, simple_loss=0.249, pruned_loss=0.06338, over 4958.00 frames.], tot_loss[loss=0.182, simple_loss=0.246, pruned_loss=0.05905, over 971586.64 frames.], batch size: 15, lr: 8.32e-04
2022-05-04 01:42:23,895 INFO [train.py:715] (2/8) Epoch 1, batch 28150, loss[loss=0.1693, simple_loss=0.2399, pruned_loss=0.04935, over 4852.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2474, pruned_loss=0.05978, over 972820.18 frames.], batch size: 20, lr: 8.31e-04
2022-05-04 01:43:03,284 INFO [train.py:715] (2/8) Epoch 1, batch 28200, loss[loss=0.1936, simple_loss=0.2537, pruned_loss=0.06675, over 4981.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2478, pruned_loss=0.05994, over 972877.89 frames.], batch size: 35, lr: 8.31e-04
2022-05-04 01:43:43,968 INFO [train.py:715] (2/8) Epoch 1, batch 28250, loss[loss=0.1698, simple_loss=0.2457, pruned_loss=0.04694, over 4917.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2478, pruned_loss=0.05981, over 972467.69 frames.], batch size: 18, lr: 8.31e-04
2022-05-04 01:44:24,411 INFO [train.py:715] (2/8) Epoch 1, batch 28300, loss[loss=0.1882, simple_loss=0.2472, pruned_loss=0.06457, over 4746.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2464, pruned_loss=0.05856, over 972558.24 frames.], batch size: 19, lr: 8.30e-04
2022-05-04 01:45:03,745 INFO [train.py:715] (2/8) Epoch 1, batch 28350, loss[loss=0.1614, simple_loss=0.2288, pruned_loss=0.04705, over 4810.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2471, pruned_loss=0.05923, over 972577.05 frames.], batch size: 27, lr: 8.30e-04
2022-05-04 01:45:42,696 INFO [train.py:715] (2/8) Epoch 1, batch 28400, loss[loss=0.1649, simple_loss=0.2364, pruned_loss=0.04673, over 4919.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2461, pruned_loss=0.05902, over 973355.55 frames.], batch size: 17, lr: 8.30e-04
2022-05-04 01:46:23,125 INFO [train.py:715] (2/8) Epoch 1, batch 28450, loss[loss=0.2187, simple_loss=0.2745, pruned_loss=0.08142, over 4825.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2465, pruned_loss=0.05901, over 973835.87 frames.], batch size: 15, lr: 8.29e-04
2022-05-04 01:47:02,710 INFO [train.py:715] (2/8) Epoch 1, batch 28500, loss[loss=0.1624, simple_loss=0.2281, pruned_loss=0.04832, over 4885.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2462, pruned_loss=0.05901, over 973641.10 frames.], batch size: 32, lr: 8.29e-04
2022-05-04 01:47:41,711 INFO [train.py:715] (2/8) Epoch 1, batch 28550, loss[loss=0.1849, simple_loss=0.2601, pruned_loss=0.0549, over 4906.00 frames.], tot_loss[loss=0.182, simple_loss=0.2462, pruned_loss=0.05891, over 973249.67 frames.], batch size: 17, lr: 8.29e-04
2022-05-04 01:48:21,999 INFO [train.py:715] (2/8) Epoch 1, batch 28600, loss[loss=0.183, simple_loss=0.2468, pruned_loss=0.05961, over 4908.00 frames.], tot_loss[loss=0.1819, simple_loss=0.246, pruned_loss=0.05886, over 972559.76 frames.], batch size: 18, lr: 8.28e-04
2022-05-04 01:49:01,944 INFO [train.py:715] (2/8) Epoch 1, batch 28650, loss[loss=0.1367, simple_loss=0.2057, pruned_loss=0.0338, over 4969.00 frames.], tot_loss[loss=0.181, simple_loss=0.2451, pruned_loss=0.05841, over 972917.31 frames.], batch size: 14, lr: 8.28e-04
2022-05-04 01:49:41,097 INFO [train.py:715] (2/8) Epoch 1, batch 28700, loss[loss=0.164, simple_loss=0.2319, pruned_loss=0.04807, over 4825.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2451, pruned_loss=0.05832, over 973065.58 frames.], batch size: 27, lr: 8.28e-04
2022-05-04 01:50:20,237 INFO [train.py:715] (2/8) Epoch 1, batch 28750, loss[loss=0.2167, simple_loss=0.2824, pruned_loss=0.07544, over 4893.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2443, pruned_loss=0.05798, over 973300.25 frames.], batch size: 22, lr: 8.27e-04
2022-05-04 01:51:00,831 INFO [train.py:715] (2/8) Epoch 1, batch 28800, loss[loss=0.1849, simple_loss=0.2442, pruned_loss=0.06279, over 4925.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2443, pruned_loss=0.0576, over 972799.72 frames.], batch size: 23, lr: 8.27e-04
2022-05-04 01:51:40,140 INFO [train.py:715] (2/8) Epoch 1, batch 28850, loss[loss=0.162, simple_loss=0.2293, pruned_loss=0.04737, over 4842.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2437, pruned_loss=0.05747, over 971523.24 frames.], batch size: 20, lr: 8.27e-04
2022-05-04 01:52:19,903 INFO [train.py:715] (2/8) Epoch 1, batch 28900, loss[loss=0.2053, simple_loss=0.2687, pruned_loss=0.07099, over 4897.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2452, pruned_loss=0.05824, over 971392.21 frames.], batch size: 39, lr: 8.27e-04
2022-05-04 01:53:00,600 INFO [train.py:715] (2/8) Epoch 1, batch 28950, loss[loss=0.1805, simple_loss=0.2508, pruned_loss=0.05509, over 4984.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2441, pruned_loss=0.05761, over 970923.58 frames.], batch size: 26, lr: 8.26e-04
2022-05-04 01:53:40,733 INFO [train.py:715] (2/8) Epoch 1, batch 29000, loss[loss=0.1962, simple_loss=0.2599, pruned_loss=0.06626, over 4956.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2448, pruned_loss=0.05776, over 971126.68 frames.], batch size: 15, lr: 8.26e-04
2022-05-04 01:54:19,712 INFO [train.py:715] (2/8) Epoch 1, batch 29050, loss[loss=0.15, simple_loss=0.2249, pruned_loss=0.03757, over 4986.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2449, pruned_loss=0.05741, over 971483.00 frames.], batch size: 25, lr: 8.26e-04
2022-05-04 01:54:59,582 INFO [train.py:715] (2/8) Epoch 1, batch 29100, loss[loss=0.1206, simple_loss=0.1931, pruned_loss=0.02405, over 4814.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2436, pruned_loss=0.05632, over 971618.69 frames.], batch size: 25, lr: 8.25e-04
2022-05-04 01:55:40,262 INFO [train.py:715] (2/8) Epoch 1, batch 29150, loss[loss=0.1727, simple_loss=0.2404, pruned_loss=0.05252, over 4854.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2431, pruned_loss=0.05624, over 972253.18 frames.], batch size: 20, lr: 8.25e-04
2022-05-04 01:56:22,366 INFO [train.py:715] (2/8) Epoch 1, batch 29200, loss[loss=0.1805, simple_loss=0.2446, pruned_loss=0.0582, over 4784.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2428, pruned_loss=0.05677, over 972380.89 frames.], batch size: 18, lr: 8.25e-04
2022-05-04 01:57:01,391 INFO [train.py:715] (2/8) Epoch 1, batch 29250, loss[loss=0.2203, simple_loss=0.2763, pruned_loss=0.08214, over 4891.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2445, pruned_loss=0.05779, over 973113.32 frames.], batch size: 19, lr: 8.24e-04
2022-05-04 01:57:41,940 INFO [train.py:715] (2/8) Epoch 1, batch 29300, loss[loss=0.1691, simple_loss=0.2446, pruned_loss=0.04675, over 4820.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2442, pruned_loss=0.0572, over 973105.95 frames.], batch size: 25, lr: 8.24e-04
2022-05-04 01:58:22,145 INFO [train.py:715] (2/8) Epoch 1, batch 29350, loss[loss=0.1838, simple_loss=0.2496, pruned_loss=0.05898, over 4926.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2442, pruned_loss=0.05728, over 972216.79 frames.], batch size: 29, lr: 8.24e-04
2022-05-04 01:59:00,685 INFO [train.py:715] (2/8) Epoch 1, batch 29400, loss[loss=0.1492, simple_loss=0.2162, pruned_loss=0.04111, over 4756.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2438, pruned_loss=0.05732, over 972042.84 frames.], batch size: 19, lr: 8.23e-04
2022-05-04 01:59:40,301 INFO [train.py:715] (2/8) Epoch 1, batch 29450, loss[loss=0.1911, simple_loss=0.2589, pruned_loss=0.06167, over 4804.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2437, pruned_loss=0.05758, over 972905.52 frames.], batch size: 26, lr: 8.23e-04
2022-05-04 02:00:19,999 INFO [train.py:715] (2/8) Epoch 1, batch 29500, loss[loss=0.2121, simple_loss=0.2702, pruned_loss=0.07701, over 4961.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2431, pruned_loss=0.05712, over 972251.69 frames.], batch size: 35, lr: 8.23e-04
2022-05-04 02:00:59,404 INFO [train.py:715] (2/8) Epoch 1, batch 29550, loss[loss=0.1768, simple_loss=0.2434, pruned_loss=0.05511, over 4791.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2427, pruned_loss=0.05687, over 972214.83 frames.], batch size: 24, lr: 8.22e-04
2022-05-04 02:01:37,987 INFO [train.py:715] (2/8) Epoch 1, batch 29600, loss[loss=0.1523, simple_loss=0.2133, pruned_loss=0.04564, over 4978.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2436, pruned_loss=0.05761, over 972088.48 frames.], batch size: 14, lr: 8.22e-04
2022-05-04 02:02:18,234 INFO [train.py:715] (2/8) Epoch 1, batch 29650, loss[loss=0.1856, simple_loss=0.247, pruned_loss=0.06211, over 4981.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2447, pruned_loss=0.05821, over 972298.26 frames.], batch size: 25, lr: 8.22e-04
2022-05-04 02:02:58,328 INFO [train.py:715] (2/8) Epoch 1, batch 29700, loss[loss=0.2022, simple_loss=0.274, pruned_loss=0.06524, over 4830.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2445, pruned_loss=0.058, over 971622.99 frames.], batch size: 26, lr: 8.21e-04
2022-05-04 02:03:36,325 INFO [train.py:715] (2/8) Epoch 1, batch 29750, loss[loss=0.1939, simple_loss=0.2593, pruned_loss=0.06424, over 4947.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2456, pruned_loss=0.05847, over 971545.21 frames.], batch size: 35, lr: 8.21e-04
2022-05-04 02:04:15,634 INFO [train.py:715] (2/8) Epoch 1, batch 29800, loss[loss=0.1842, simple_loss=0.2369, pruned_loss=0.06576, over 4759.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2449, pruned_loss=0.05844, over 971495.46 frames.], batch size: 19, lr: 8.21e-04
2022-05-04 02:04:55,046 INFO [train.py:715] (2/8) Epoch 1, batch 29850, loss[loss=0.1777, simple_loss=0.2438, pruned_loss=0.05579, over 4845.00 frames.], tot_loss[loss=0.1825, simple_loss=0.246, pruned_loss=0.05951, over 971657.52 frames.], batch size: 30, lr: 8.20e-04
2022-05-04 02:05:34,423 INFO [train.py:715] (2/8) Epoch 1, batch 29900, loss[loss=0.1765, simple_loss=0.2374, pruned_loss=0.05783, over 4695.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2458, pruned_loss=0.05933, over 971032.85 frames.], batch size: 15, lr: 8.20e-04
2022-05-04 02:06:12,924 INFO [train.py:715] (2/8) Epoch 1, batch 29950, loss[loss=0.1642, simple_loss=0.2332, pruned_loss=0.04758, over 4792.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2453, pruned_loss=0.05864, over 970304.94 frames.], batch size: 17, lr: 8.20e-04
2022-05-04 02:06:52,732 INFO [train.py:715] (2/8) Epoch 1, batch 30000, loss[loss=0.2179, simple_loss=0.2834, pruned_loss=0.07617, over 4933.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2441, pruned_loss=0.05773, over 970655.22 frames.], batch size: 29, lr: 8.20e-04
2022-05-04 02:06:52,733 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 02:07:09,692 INFO [train.py:742] (2/8) Epoch 1, validation: loss=0.1207, simple_loss=0.2076, pruned_loss=0.01687, over 914524.00 frames.
2022-05-04 02:07:50,176 INFO [train.py:715] (2/8) Epoch 1, batch 30050, loss[loss=0.156, simple_loss=0.2332, pruned_loss=0.03939, over 4857.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2443, pruned_loss=0.05803, over 971629.09 frames.], batch size: 30, lr: 8.19e-04
2022-05-04 02:08:29,660 INFO [train.py:715] (2/8) Epoch 1, batch 30100, loss[loss=0.1491, simple_loss=0.2131, pruned_loss=0.04258, over 4824.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2445, pruned_loss=0.05829, over 971870.75 frames.], batch size: 27, lr: 8.19e-04
2022-05-04 02:09:09,055 INFO [train.py:715] (2/8) Epoch 1, batch 30150, loss[loss=0.1942, simple_loss=0.2641, pruned_loss=0.06215, over 4823.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2445, pruned_loss=0.0582, over 971539.27 frames.], batch size: 25, lr: 8.19e-04
2022-05-04 02:09:48,367 INFO [train.py:715] (2/8) Epoch 1, batch 30200, loss[loss=0.2289, simple_loss=0.2884, pruned_loss=0.08467, over 4878.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2445, pruned_loss=0.05797, over 972137.69 frames.], batch size: 19, lr: 8.18e-04
2022-05-04 02:10:28,814 INFO [train.py:715] (2/8) Epoch 1, batch 30250, loss[loss=0.1689, simple_loss=0.234, pruned_loss=0.0519, over 4821.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2441, pruned_loss=0.05719, over 972162.86 frames.], batch size: 21, lr: 8.18e-04
2022-05-04 02:11:08,793 INFO [train.py:715] (2/8) Epoch 1, batch 30300, loss[loss=0.1744, simple_loss=0.2503, pruned_loss=0.04931, over 4835.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2451, pruned_loss=0.05759, over 972327.25 frames.], batch size: 13, lr: 8.18e-04
2022-05-04 02:11:47,705 INFO [train.py:715] (2/8) Epoch 1, batch 30350, loss[loss=0.1556, simple_loss=0.2206, pruned_loss=0.04531, over 4809.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2442, pruned_loss=0.05717, over 972246.41 frames.], batch size: 12, lr: 8.17e-04
2022-05-04 02:12:27,770 INFO [train.py:715] (2/8) Epoch 1, batch 30400, loss[loss=0.1846, simple_loss=0.2481, pruned_loss=0.06056, over 4960.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2434, pruned_loss=0.05644, over 972371.03 frames.], batch size: 35, lr: 8.17e-04
2022-05-04 02:13:07,261 INFO [train.py:715] (2/8) Epoch 1, batch 30450, loss[loss=0.1762, simple_loss=0.2467, pruned_loss=0.05283, over 4887.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2437, pruned_loss=0.05662, over 972518.06 frames.], batch size: 22, lr: 8.17e-04
2022-05-04 02:13:46,435 INFO [train.py:715] (2/8) Epoch 1, batch 30500, loss[loss=0.1838, simple_loss=0.2462, pruned_loss=0.06068, over 4850.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2439, pruned_loss=0.05656, over 971966.80 frames.], batch size: 20, lr: 8.16e-04
2022-05-04 02:14:25,537 INFO [train.py:715] (2/8) Epoch 1, batch 30550, loss[loss=0.1859, simple_loss=0.2569, pruned_loss=0.0574, over 4808.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2441, pruned_loss=0.05667, over 971787.46 frames.], batch size: 26, lr: 8.16e-04
2022-05-04 02:15:05,335 INFO [train.py:715] (2/8) Epoch 1, batch 30600, loss[loss=0.1702, simple_loss=0.2478, pruned_loss=0.04626, over 4784.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2447, pruned_loss=0.05702, over 971850.34 frames.], batch size: 18, lr: 8.16e-04
2022-05-04 02:15:44,800 INFO [train.py:715] (2/8) Epoch 1, batch 30650, loss[loss=0.1289, simple_loss=0.1981, pruned_loss=0.0299, over 4779.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2442, pruned_loss=0.05681, over 972017.70 frames.], batch size: 12, lr: 8.15e-04
2022-05-04 02:16:23,382 INFO [train.py:715] (2/8) Epoch 1, batch 30700, loss[loss=0.178, simple_loss=0.2383, pruned_loss=0.0588, over 4951.00 frames.], tot_loss[loss=0.179, simple_loss=0.2439, pruned_loss=0.05707, over 972807.72 frames.], batch size: 23, lr: 8.15e-04
2022-05-04 02:17:03,632 INFO [train.py:715] (2/8) Epoch 1, batch 30750, loss[loss=0.2272, simple_loss=0.2756, pruned_loss=0.08942, over 4960.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2447, pruned_loss=0.05756, over 971727.88 frames.], batch size: 15, lr: 8.15e-04
2022-05-04 02:17:43,202 INFO [train.py:715] (2/8) Epoch 1, batch 30800, loss[loss=0.177, simple_loss=0.2461, pruned_loss=0.0539, over 4818.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2445, pruned_loss=0.05761, over 971230.21 frames.], batch size: 26, lr: 8.15e-04
2022-05-04 02:18:22,125 INFO [train.py:715] (2/8) Epoch 1, batch 30850, loss[loss=0.1479, simple_loss=0.2095, pruned_loss=0.04311, over 4776.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2438, pruned_loss=0.0573, over 971908.35 frames.], batch size: 14, lr: 8.14e-04
2022-05-04 02:19:01,711 INFO [train.py:715] (2/8) Epoch 1, batch 30900, loss[loss=0.171, simple_loss=0.2186, pruned_loss=0.06168, over 4791.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2434, pruned_loss=0.05712, over 971125.26 frames.], batch size: 12, lr: 8.14e-04
2022-05-04 02:19:41,338 INFO [train.py:715] (2/8) Epoch 1, batch 30950, loss[loss=0.1556, simple_loss=0.2339, pruned_loss=0.03865, over 4872.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2428, pruned_loss=0.05701, over 971657.47 frames.], batch size: 16, lr: 8.14e-04
2022-05-04 02:20:20,848 INFO [train.py:715] (2/8) Epoch 1, batch 31000, loss[loss=0.1763, simple_loss=0.2445, pruned_loss=0.05405, over 4905.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2431, pruned_loss=0.05712, over 971575.63 frames.], batch size: 19, lr: 8.13e-04
2022-05-04 02:21:00,352 INFO [train.py:715] (2/8) Epoch 1, batch 31050, loss[loss=0.1937, simple_loss=0.2454, pruned_loss=0.07102, over 4788.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2439, pruned_loss=0.05792, over 972065.01 frames.], batch size: 12, lr: 8.13e-04
2022-05-04 02:21:40,833 INFO [train.py:715] (2/8) Epoch 1, batch 31100, loss[loss=0.1817, simple_loss=0.2403, pruned_loss=0.0615, over 4697.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2436, pruned_loss=0.05796, over 971675.32 frames.], batch size: 15, lr: 8.13e-04
2022-05-04 02:22:20,577 INFO [train.py:715] (2/8) Epoch 1, batch 31150, loss[loss=0.1604, simple_loss=0.2176, pruned_loss=0.05162, over 4853.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2435, pruned_loss=0.05762, over 971548.38 frames.], batch size: 32, lr: 8.12e-04
2022-05-04 02:22:59,621 INFO [train.py:715] (2/8) Epoch 1, batch 31200, loss[loss=0.1662, simple_loss=0.2326, pruned_loss=0.04993, over 4919.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2428, pruned_loss=0.05719, over 972332.69 frames.], batch size: 17, lr: 8.12e-04
2022-05-04 02:23:39,855 INFO [train.py:715] (2/8) Epoch 1, batch 31250, loss[loss=0.1677, simple_loss=0.2352, pruned_loss=0.05009, over 4937.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2429, pruned_loss=0.0574, over 972575.22 frames.], batch size: 29, lr: 8.12e-04
2022-05-04 02:24:19,616 INFO [train.py:715] (2/8) Epoch 1, batch 31300, loss[loss=0.1953, simple_loss=0.2518, pruned_loss=0.06944, over 4853.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2433, pruned_loss=0.05752, over 973189.08 frames.], batch size: 20, lr: 8.11e-04
2022-05-04 02:24:59,058 INFO [train.py:715] (2/8) Epoch 1, batch 31350, loss[loss=0.1584, simple_loss=0.2185, pruned_loss=0.04916, over 4836.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2432, pruned_loss=0.05734, over 973189.48 frames.], batch size: 30, lr: 8.11e-04
2022-05-04 02:25:38,854 INFO [train.py:715] (2/8) Epoch 1, batch 31400, loss[loss=0.207, simple_loss=0.2613, pruned_loss=0.0763, over 4734.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2437, pruned_loss=0.05723, over 972590.04 frames.], batch size: 16, lr: 8.11e-04
2022-05-04 02:26:18,860 INFO [train.py:715] (2/8) Epoch 1, batch 31450, loss[loss=0.215, simple_loss=0.2636, pruned_loss=0.08326, over 4792.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2439, pruned_loss=0.0576, over 972707.97 frames.], batch size: 21, lr: 8.11e-04
2022-05-04 02:26:58,725 INFO [train.py:715] (2/8) Epoch 1, batch 31500, loss[loss=0.2024, simple_loss=0.2638, pruned_loss=0.07048, over 4982.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2447, pruned_loss=0.05784, over 973651.55 frames.], batch size: 33, lr: 8.10e-04
2022-05-04 02:27:37,225 INFO [train.py:715] (2/8) Epoch 1, batch 31550, loss[loss=0.1673, simple_loss=0.2439, pruned_loss=0.04532, over 4941.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2448, pruned_loss=0.05784, over 973285.84 frames.], batch size: 29, lr: 8.10e-04
2022-05-04 02:28:17,411 INFO [train.py:715] (2/8) Epoch 1, batch 31600, loss[loss=0.1734, simple_loss=0.2366, pruned_loss=0.05515, over 4884.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2444, pruned_loss=0.05756, over 973667.88 frames.], batch size: 16, lr: 8.10e-04
2022-05-04 02:28:57,085 INFO [train.py:715] (2/8) Epoch 1, batch 31650, loss[loss=0.2116, simple_loss=0.2702, pruned_loss=0.07649, over 4909.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2447, pruned_loss=0.05749, over 973644.48 frames.], batch size: 39, lr: 8.09e-04
2022-05-04 02:29:36,996 INFO [train.py:715] (2/8) Epoch 1, batch 31700, loss[loss=0.259, simple_loss=0.301, pruned_loss=0.1085, over 4943.00 frames.], tot_loss[loss=0.18, simple_loss=0.2448, pruned_loss=0.05763, over 974660.00 frames.], batch size: 29, lr: 8.09e-04
2022-05-04 02:30:16,359 INFO [train.py:715] (2/8) Epoch 1, batch 31750, loss[loss=0.1665, simple_loss=0.2299, pruned_loss=0.05149, over 4793.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2455, pruned_loss=0.05813, over 974661.01 frames.], batch size: 14, lr: 8.09e-04
2022-05-04 02:30:56,480 INFO [train.py:715] (2/8) Epoch 1, batch 31800, loss[loss=0.1841, simple_loss=0.2546, pruned_loss=0.05684, over 4881.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2454, pruned_loss=0.0582, over 973662.75 frames.], batch size: 22, lr: 8.08e-04
2022-05-04 02:31:36,269 INFO [train.py:715] (2/8) Epoch 1, batch 31850, loss[loss=0.1323, simple_loss=0.1832, pruned_loss=0.04066, over 4769.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2458, pruned_loss=0.05831, over 973839.21 frames.], batch size: 12, lr: 8.08e-04
2022-05-04 02:32:15,738 INFO [train.py:715] (2/8) Epoch 1, batch 31900, loss[loss=0.1763, simple_loss=0.2463, pruned_loss=0.05311, over 4958.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2439, pruned_loss=0.05754, over 973199.45 frames.], batch size: 21, lr: 8.08e-04
2022-05-04 02:32:55,102 INFO [train.py:715] (2/8) Epoch 1, batch 31950, loss[loss=0.2049, simple_loss=0.2672, pruned_loss=0.07136, over 4845.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2452, pruned_loss=0.05797, over 972462.16 frames.], batch size: 32, lr: 8.08e-04
2022-05-04 02:33:34,634 INFO [train.py:715] (2/8) Epoch 1, batch 32000, loss[loss=0.1641, simple_loss=0.2423, pruned_loss=0.04296, over 4709.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2446, pruned_loss=0.05758, over 973125.60 frames.], batch size: 15, lr: 8.07e-04
2022-05-04 02:34:14,062 INFO [train.py:715] (2/8) Epoch 1, batch 32050, loss[loss=0.164, simple_loss=0.237, pruned_loss=0.04548, over 4825.00 frames.], tot_loss[loss=0.179, simple_loss=0.2436, pruned_loss=0.05723, over 972497.84 frames.], batch size: 26, lr: 8.07e-04
2022-05-04 02:34:53,313 INFO [train.py:715] (2/8) Epoch 1, batch 32100, loss[loss=0.2138, simple_loss=0.2663, pruned_loss=0.08065, over 4801.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2438, pruned_loss=0.05697, over 971537.19 frames.], batch size: 21, lr: 8.07e-04
2022-05-04 02:35:32,935 INFO [train.py:715] (2/8) Epoch 1, batch 32150, loss[loss=0.1855, simple_loss=0.2448, pruned_loss=0.0631, over 4930.00 frames.], tot_loss[loss=0.1794, simple_loss=0.244, pruned_loss=0.05741, over 971700.51 frames.], batch size: 23, lr: 8.06e-04
2022-05-04 02:36:12,933 INFO [train.py:715] (2/8) Epoch 1, batch 32200, loss[loss=0.1786, simple_loss=0.2438, pruned_loss=0.05673, over 4820.00 frames.], tot_loss[loss=0.179, simple_loss=0.2438, pruned_loss=0.05713, over 972834.86 frames.], batch size: 26, lr: 8.06e-04
2022-05-04 02:36:51,834 INFO [train.py:715] (2/8) Epoch 1, batch 32250, loss[loss=0.1951, simple_loss=0.2643, pruned_loss=0.06291, over 4947.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2428, pruned_loss=0.05653, over 973274.43 frames.], batch size: 21, lr: 8.06e-04
2022-05-04 02:37:31,247 INFO [train.py:715] (2/8) Epoch 1, batch 32300, loss[loss=0.1594, simple_loss=0.2358, pruned_loss=0.04152, over 4749.00 frames.], tot_loss[loss=0.1776, simple_loss=0.243, pruned_loss=0.05604, over 972509.00 frames.], batch size: 19, lr: 8.05e-04
2022-05-04 02:38:10,682 INFO [train.py:715] (2/8) Epoch 1, batch 32350, loss[loss=0.1567, simple_loss=0.2271, pruned_loss=0.04309, over 4930.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2431, pruned_loss=0.05662, over 972211.96 frames.], batch size: 29, lr: 8.05e-04
2022-05-04 02:38:50,277 INFO [train.py:715] (2/8) Epoch 1, batch 32400, loss[loss=0.1789, simple_loss=0.226, pruned_loss=0.0659, over 4822.00 frames.], tot_loss[loss=0.1781, simple_loss=0.243, pruned_loss=0.05657, over 972460.28 frames.], batch size: 13, lr: 8.05e-04
2022-05-04 02:39:29,210 INFO [train.py:715] (2/8) Epoch 1, batch 32450, loss[loss=0.1602, simple_loss=0.2323, pruned_loss=0.04401, over 4783.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2439, pruned_loss=0.05725, over 972822.13 frames.], batch size: 14, lr: 8.05e-04
2022-05-04 02:40:08,854 INFO [train.py:715] (2/8) Epoch 1, batch 32500, loss[loss=0.1814, simple_loss=0.2449, pruned_loss=0.05891, over 4995.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2434, pruned_loss=0.05664, over 972739.81 frames.], batch size: 14, lr: 8.04e-04
2022-05-04 02:40:48,373 INFO [train.py:715] (2/8) Epoch 1, batch 32550, loss[loss=0.168, simple_loss=0.2318, pruned_loss=0.05205, over 4849.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2415, pruned_loss=0.05545, over 972383.95 frames.], batch size: 32, lr: 8.04e-04
2022-05-04 02:41:27,293 INFO [train.py:715] (2/8) Epoch 1, batch 32600, loss[loss=0.2096, simple_loss=0.269, pruned_loss=0.07513, over 4987.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2427, pruned_loss=0.05622, over 972803.36 frames.], batch size: 25, lr: 8.04e-04
2022-05-04 02:42:06,684 INFO [train.py:715] (2/8) Epoch 1, batch 32650, loss[loss=0.1569, simple_loss=0.2219, pruned_loss=0.04597, over 4834.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2435, pruned_loss=0.05738, over 971667.31 frames.], batch size: 15, lr: 8.03e-04
2022-05-04 02:42:46,233 INFO [train.py:715] (2/8) Epoch 1, batch 32700, loss[loss=0.1575, simple_loss=0.2201, pruned_loss=0.04741, over 4972.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2423, pruned_loss=0.05614, over 971988.81 frames.], batch size: 24, lr: 8.03e-04
2022-05-04 02:43:25,958 INFO [train.py:715] (2/8) Epoch 1, batch 32750, loss[loss=0.1574, simple_loss=0.2272, pruned_loss=0.04385, over 4838.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2423, pruned_loss=0.0563, over 972271.93 frames.], batch size: 15, lr: 8.03e-04
2022-05-04 02:44:05,917 INFO [train.py:715] (2/8) Epoch 1, batch 32800, loss[loss=0.1706, simple_loss=0.2379, pruned_loss=0.05161, over 4893.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2423, pruned_loss=0.05638, over 971874.42 frames.], batch size: 17, lr: 8.02e-04
2022-05-04 02:44:45,550 INFO [train.py:715] (2/8) Epoch 1, batch 32850, loss[loss=0.1652, simple_loss=0.2314, pruned_loss=0.0495, over 4801.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2422, pruned_loss=0.05574, over 972606.95 frames.], batch size: 14, lr: 8.02e-04
2022-05-04 02:45:24,928 INFO [train.py:715] (2/8) Epoch 1, batch 32900, loss[loss=0.1972, simple_loss=0.2644, pruned_loss=0.06501, over 4777.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2428, pruned_loss=0.0562, over 972010.43 frames.], batch size: 17, lr: 8.02e-04
2022-05-04 02:46:04,174 INFO [train.py:715] (2/8) Epoch 1, batch 32950, loss[loss=0.1714, simple_loss=0.2354, pruned_loss=0.05367, over 4954.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2432, pruned_loss=0.05676, over 972272.36 frames.], batch size: 14, lr: 8.02e-04
2022-05-04 02:46:43,638 INFO [train.py:715] (2/8) Epoch 1, batch 33000, loss[loss=0.1498, simple_loss=0.218, pruned_loss=0.0408, over 4947.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2421, pruned_loss=0.05609, over 971624.44 frames.], batch size: 21, lr: 8.01e-04
2022-05-04 02:46:43,639 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 02:46:52,425 INFO [train.py:742] (2/8) Epoch 1, validation: loss=0.1208, simple_loss=0.2074, pruned_loss=0.01714, over 914524.00 frames.
2022-05-04 02:47:32,099 INFO [train.py:715] (2/8) Epoch 1, batch 33050, loss[loss=0.1767, simple_loss=0.2457, pruned_loss=0.05389, over 4928.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2434, pruned_loss=0.05669, over 972612.80 frames.], batch size: 23, lr: 8.01e-04
2022-05-04 02:48:12,130 INFO [train.py:715] (2/8) Epoch 1, batch 33100, loss[loss=0.1769, simple_loss=0.2449, pruned_loss=0.05442, over 4971.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2428, pruned_loss=0.05678, over 973256.17 frames.], batch size: 21, lr: 8.01e-04
2022-05-04 02:48:51,996 INFO [train.py:715] (2/8) Epoch 1, batch 33150, loss[loss=0.1598, simple_loss=0.2355, pruned_loss=0.04204, over 4920.00 frames.], tot_loss[loss=0.1777, simple_loss=0.243, pruned_loss=0.05621, over 973165.67 frames.], batch size: 18, lr: 8.00e-04
2022-05-04 02:49:31,133 INFO [train.py:715] (2/8) Epoch 1, batch 33200, loss[loss=0.1791, simple_loss=0.2335, pruned_loss=0.06229, over 4847.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2434, pruned_loss=0.0566, over 973603.33 frames.], batch size: 30, lr: 8.00e-04
2022-05-04 02:50:11,553 INFO [train.py:715] (2/8) Epoch 1, batch 33250, loss[loss=0.1404, simple_loss=0.2058, pruned_loss=0.0375, over 4953.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2436, pruned_loss=0.05689, over 973393.64 frames.], batch size: 24, lr: 8.00e-04
2022-05-04 02:50:51,586 INFO [train.py:715] (2/8) Epoch 1, batch 33300, loss[loss=0.1433, simple_loss=0.2103, pruned_loss=0.03821, over 4835.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2431, pruned_loss=0.05659, over 973325.33 frames.], batch size: 30, lr: 8.00e-04
2022-05-04 02:51:31,057 INFO [train.py:715] (2/8) Epoch 1, batch 33350, loss[loss=0.1843, simple_loss=0.2551, pruned_loss=0.05681, over 4992.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2437, pruned_loss=0.05683, over 973146.47 frames.], batch size: 14, lr: 7.99e-04
2022-05-04 02:52:11,430 INFO [train.py:715] (2/8) Epoch 1, batch 33400, loss[loss=0.1397, simple_loss=0.2092, pruned_loss=0.03507, over 4804.00 frames.], tot_loss[loss=0.18, simple_loss=0.2446, pruned_loss=0.05768, over 971903.66 frames.], batch size: 21, lr: 7.99e-04
2022-05-04 02:52:51,297 INFO [train.py:715] (2/8) Epoch 1, batch 33450, loss[loss=0.1751, simple_loss=0.2441, pruned_loss=0.05304, over 4886.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2449, pruned_loss=0.05708, over 971364.17 frames.], batch size: 16, lr: 7.99e-04
2022-05-04 02:53:30,404 INFO [train.py:715] (2/8) Epoch 1, batch 33500, loss[loss=0.1952, simple_loss=0.2636, pruned_loss=0.0634, over 4994.00 frames.], tot_loss[loss=0.1797, simple_loss=0.245, pruned_loss=0.05716, over 972026.03 frames.], batch size: 16, lr: 7.98e-04
2022-05-04 02:54:10,334 INFO [train.py:715] (2/8) Epoch 1, batch 33550, loss[loss=0.212, simple_loss=0.2723, pruned_loss=0.07584, over 4774.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2454, pruned_loss=0.05782, over 971778.80 frames.], batch size: 14, lr: 7.98e-04
2022-05-04 02:54:50,178 INFO [train.py:715] (2/8) Epoch 1, batch 33600, loss[loss=0.1716, simple_loss=0.2381, pruned_loss=0.05258, over 4871.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2463, pruned_loss=0.05835, over 973644.91 frames.], batch size: 20, lr: 7.98e-04
2022-05-04 02:55:29,602 INFO [train.py:715] (2/8) Epoch 1, batch 33650, loss[loss=0.1578, simple_loss=0.2217, pruned_loss=0.04695, over 4956.00 frames.], tot_loss[loss=0.18, simple_loss=0.245, pruned_loss=0.05746, over 973450.77 frames.], batch size: 15, lr: 7.97e-04
2022-05-04 02:56:08,645 INFO [train.py:715] (2/8) Epoch 1, batch 33700, loss[loss=0.1527, simple_loss=0.2169, pruned_loss=0.04423, over 4773.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2441, pruned_loss=0.05704, over 972383.67 frames.], batch size: 18, lr: 7.97e-04
2022-05-04 02:56:47,802 INFO [train.py:715] (2/8) Epoch 1, batch 33750, loss[loss=0.1656, simple_loss=0.2341, pruned_loss=0.04855, over 4813.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2436, pruned_loss=0.05677, over 972616.31 frames.], batch size: 21, lr: 7.97e-04
2022-05-04 02:57:27,448 INFO [train.py:715] (2/8) Epoch 1, batch 33800, loss[loss=0.1636, simple_loss=0.2296, pruned_loss=0.04881, over 4769.00 frames.], tot_loss[loss=0.1778, simple_loss=0.243, pruned_loss=0.05627, over 972457.59 frames.], batch size: 18, lr: 7.97e-04
2022-05-04 02:58:06,279 INFO [train.py:715] (2/8) Epoch 1, batch 33850, loss[loss=0.1502, simple_loss=0.2201, pruned_loss=0.04016, over 4933.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2436, pruned_loss=0.05666, over 972773.62 frames.], batch size: 29, lr: 7.96e-04
2022-05-04 02:58:45,798 INFO [train.py:715] (2/8) Epoch 1, batch 33900, loss[loss=0.1873, simple_loss=0.2458, pruned_loss=0.0644, over 4708.00 frames.], tot_loss[loss=0.1788, simple_loss=0.244, pruned_loss=0.0568, over 972266.36 frames.], batch size: 15, lr: 7.96e-04
2022-05-04 02:59:25,364 INFO [train.py:715] (2/8) Epoch 1, batch 33950, loss[loss=0.1767, simple_loss=0.2413, pruned_loss=0.05601, over 4824.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2448, pruned_loss=0.05751, over 972033.84 frames.], batch size: 26, lr: 7.96e-04
2022-05-04 03:00:05,088 INFO [train.py:715] (2/8) Epoch 1, batch 34000, loss[loss=0.1646, simple_loss=0.2328, pruned_loss=0.04817, over 4961.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2447, pruned_loss=0.05716, over 971105.18 frames.], batch size: 24, lr: 7.95e-04
2022-05-04 03:00:44,409 INFO [train.py:715] (2/8) Epoch 1, batch 34050, loss[loss=0.1701, simple_loss=0.2433, pruned_loss=0.04846, over 4817.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2451, pruned_loss=0.05721, over 971279.58 frames.], batch size: 25, lr: 7.95e-04
2022-05-04 03:01:23,792 INFO [train.py:715] (2/8) Epoch 1, batch 34100, loss[loss=0.1453, simple_loss=0.2241, pruned_loss=0.03326, over 4806.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2453, pruned_loss=0.05694, over 971790.78 frames.], batch size: 21, lr: 7.95e-04
2022-05-04 03:02:03,176 INFO [train.py:715] (2/8) Epoch 1, batch 34150, loss[loss=0.1714, simple_loss=0.2429, pruned_loss=0.04998, over 4784.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2431, pruned_loss=0.05566, over 971884.47 frames.], batch size: 14, lr: 7.95e-04
2022-05-04 03:02:42,206 INFO [train.py:715] (2/8) Epoch 1, batch 34200, loss[loss=0.134, simple_loss=0.2172, pruned_loss=0.02541, over 4941.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2419, pruned_loss=0.05508, over 972755.98 frames.], batch size: 21, lr: 7.94e-04
2022-05-04 03:03:21,754 INFO [train.py:715] (2/8) Epoch 1, batch 34250, loss[loss=0.1692, simple_loss=0.238, pruned_loss=0.0502, over 4875.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2422, pruned_loss=0.05525, over 972593.33 frames.], batch size: 39, lr: 7.94e-04
2022-05-04 03:04:01,434 INFO [train.py:715] (2/8) Epoch 1, batch 34300, loss[loss=0.1815, simple_loss=0.2409, pruned_loss=0.06108, over 4856.00 frames.], tot_loss[loss=0.1781, simple_loss=0.243, pruned_loss=0.05659, over 972839.66 frames.], batch size: 20, lr: 7.94e-04
2022-05-04 03:04:40,843 INFO [train.py:715] (2/8) Epoch 1, batch 34350, loss[loss=0.1658, simple_loss=0.253, pruned_loss=0.03927, over 4921.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2429, pruned_loss=0.05608, over 972549.55 frames.], batch size: 18, lr: 7.93e-04
2022-05-04 03:05:19,748 INFO [train.py:715] (2/8) Epoch 1, batch 34400, loss[loss=0.1806, simple_loss=0.2437, pruned_loss=0.05872, over 4710.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2436, pruned_loss=0.05608, over 972294.83 frames.], batch size: 15, lr: 7.93e-04
2022-05-04 03:05:59,255 INFO [train.py:715] (2/8) Epoch 1, batch 34450, loss[loss=0.1827, simple_loss=0.2497, pruned_loss=0.05782, over 4992.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2439, pruned_loss=0.05648, over 972708.30 frames.], batch size: 14, lr: 7.93e-04
2022-05-04 03:06:38,475 INFO [train.py:715] (2/8) Epoch 1, batch 34500, loss[loss=0.1478, simple_loss=0.2312, pruned_loss=0.03219, over 4931.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2438, pruned_loss=0.05619, over 973171.04 frames.], batch size: 23, lr: 7.93e-04
2022-05-04 03:07:17,761 INFO [train.py:715] (2/8) Epoch 1, batch 34550, loss[loss=0.187, simple_loss=0.2552, pruned_loss=0.05943, over 4800.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2431, pruned_loss=0.05551, over 972178.57 frames.], batch size: 14, lr: 7.92e-04
2022-05-04 03:07:57,336 INFO [train.py:715] (2/8) Epoch 1, batch 34600, loss[loss=0.1941, simple_loss=0.2631, pruned_loss=0.06254, over 4816.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2437, pruned_loss=0.05577, over 972726.76 frames.], batch size: 26, lr: 7.92e-04
2022-05-04 03:08:37,223 INFO [train.py:715] (2/8) Epoch 1, batch 34650, loss[loss=0.1928, simple_loss=0.2492, pruned_loss=0.0682, over 4891.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2435, pruned_loss=0.056, over 973170.06 frames.], batch size: 19, lr: 7.92e-04
2022-05-04 03:09:17,426 INFO [train.py:715] (2/8) Epoch 1, batch 34700, loss[loss=0.1593, simple_loss=0.2395, pruned_loss=0.03953, over 4788.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2423, pruned_loss=0.05529, over 972696.05 frames.], batch size: 14, lr: 7.91e-04
2022-05-04 03:09:55,735 INFO [train.py:715] (2/8) Epoch 1, batch 34750, loss[loss=0.1621, simple_loss=0.2324, pruned_loss=0.04589, over 4819.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2423, pruned_loss=0.05558, over 972445.05 frames.], batch size: 21, lr: 7.91e-04
2022-05-04 03:10:32,244 INFO [train.py:715] (2/8) Epoch 1, batch 34800, loss[loss=0.1857, simple_loss=0.2351, pruned_loss=0.06818, over 4888.00 frames.], tot_loss[loss=0.1761, simple_loss=0.242, pruned_loss=0.05509, over 973455.62 frames.], batch size: 22, lr: 7.91e-04
2022-05-04 03:11:25,705 INFO [train.py:715] (2/8) Epoch 2, batch 0, loss[loss=0.2015, simple_loss=0.266, pruned_loss=0.06852, over 4955.00 frames.], tot_loss[loss=0.2015, simple_loss=0.266, pruned_loss=0.06852, over 4955.00 frames.], batch size: 35, lr: 7.59e-04
2022-05-04 03:12:05,762 INFO [train.py:715] (2/8) Epoch 2, batch 50, loss[loss=0.188, simple_loss=0.2504, pruned_loss=0.06279, over 4906.00 frames.], tot_loss[loss=0.1822, simple_loss=0.246, pruned_loss=0.05919, over 219427.28 frames.], batch size: 18, lr: 7.59e-04
2022-05-04 03:12:46,580 INFO [train.py:715] (2/8) Epoch 2, batch 100, loss[loss=0.216, simple_loss=0.2713, pruned_loss=0.08032, over 4964.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2434, pruned_loss=0.05713, over 386153.75 frames.], batch size: 15, lr: 7.59e-04
2022-05-04 03:13:27,198 INFO [train.py:715] (2/8) Epoch 2, batch 150, loss[loss=0.2153, simple_loss=0.278, pruned_loss=0.07634, over 4866.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2439, pruned_loss=0.05727, over 516435.03 frames.], batch size: 20, lr: 7.59e-04
2022-05-04 03:14:07,248 INFO [train.py:715] (2/8) Epoch 2, batch 200, loss[loss=0.1942, simple_loss=0.2509, pruned_loss=0.06874, over 4857.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2459, pruned_loss=0.05798, over 618681.75 frames.], batch size: 20, lr: 7.58e-04
2022-05-04 03:14:48,004 INFO [train.py:715] (2/8) Epoch 2, batch 250, loss[loss=0.1897, simple_loss=0.2456, pruned_loss=0.06694, over 4793.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2454, pruned_loss=0.05816, over 697322.16 frames.], batch size: 18, lr: 7.58e-04
2022-05-04 03:15:29,354 INFO [train.py:715] (2/8) Epoch 2, batch 300, loss[loss=0.1943, simple_loss=0.2553, pruned_loss=0.06668, over 4852.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2439, pruned_loss=0.05748, over 757634.52 frames.], batch size: 20, lr: 7.58e-04
2022-05-04 03:16:10,304 INFO [train.py:715] (2/8) Epoch 2, batch 350, loss[loss=0.169, simple_loss=0.234, pruned_loss=0.05194, over 4834.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2435, pruned_loss=0.05685, over 805978.08 frames.], batch size: 13, lr: 7.57e-04
2022-05-04 03:16:49,963 INFO [train.py:715] (2/8) Epoch 2, batch 400, loss[loss=0.1471, simple_loss=0.206, pruned_loss=0.04415, over 4844.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2426, pruned_loss=0.0566, over 842657.43 frames.], batch size: 13, lr: 7.57e-04
2022-05-04 03:17:30,470 INFO [train.py:715] (2/8) Epoch 2, batch 450, loss[loss=0.2119, simple_loss=0.2767, pruned_loss=0.07349, over 4691.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2442, pruned_loss=0.05765, over 871523.02 frames.], batch size: 15, lr: 7.57e-04
2022-05-04 03:18:11,619 INFO [train.py:715] (2/8) Epoch 2, batch 500, loss[loss=0.1823, simple_loss=0.2454, pruned_loss=0.05957, over 4951.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2432, pruned_loss=0.05716, over 894167.04 frames.], batch size: 21, lr: 7.57e-04
2022-05-04 03:18:51,550 INFO [train.py:715] (2/8) Epoch 2, batch 550, loss[loss=0.2136, simple_loss=0.2854, pruned_loss=0.07085, over 4785.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2432, pruned_loss=0.05677, over 911803.17 frames.], batch size: 14, lr: 7.56e-04
2022-05-04 03:19:31,919 INFO [train.py:715] (2/8) Epoch 2, batch 600, loss[loss=0.1935, simple_loss=0.2583, pruned_loss=0.06431, over 4902.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2451, pruned_loss=0.05819, over 924788.69 frames.], batch size: 17, lr: 7.56e-04
2022-05-04 03:20:12,753 INFO [train.py:715] (2/8) Epoch 2, batch 650, loss[loss=0.1459, simple_loss=0.2097, pruned_loss=0.04106, over 4826.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2434, pruned_loss=0.05672, over 935287.73 frames.], batch size: 30, lr: 7.56e-04
2022-05-04 03:20:53,346 INFO [train.py:715] (2/8) Epoch 2, batch 700, loss[loss=0.1649, simple_loss=0.231, pruned_loss=0.04935, over 4700.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2434, pruned_loss=0.05668, over 943223.27 frames.], batch size: 15, lr: 7.56e-04
2022-05-04 03:21:32,902 INFO [train.py:715] (2/8) Epoch 2, batch 750, loss[loss=0.1836, simple_loss=0.2461, pruned_loss=0.06059, over 4867.00 frames.], tot_loss[loss=0.1794, simple_loss=0.244, pruned_loss=0.0574, over 949816.67 frames.], batch size: 20, lr: 7.55e-04
2022-05-04 03:22:13,347 INFO [train.py:715] (2/8) Epoch 2, batch 800, loss[loss=0.2173, simple_loss=0.2799, pruned_loss=0.07739, over 4984.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2431, pruned_loss=0.05693, over 955155.80 frames.], batch size: 20, lr: 7.55e-04
2022-05-04 03:22:53,984 INFO [train.py:715] (2/8) Epoch 2, batch 850, loss[loss=0.1763, simple_loss=0.2299, pruned_loss=0.06142, over 4850.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2434, pruned_loss=0.05669, over 958234.26 frames.], batch size: 20, lr: 7.55e-04
2022-05-04 03:23:34,290 INFO [train.py:715] (2/8) Epoch 2, batch 900, loss[loss=0.1732, simple_loss=0.2305, pruned_loss=0.05794, over 4978.00 frames.], tot_loss[loss=0.179, simple_loss=0.2437, pruned_loss=0.05721, over 961958.39 frames.], batch size: 24, lr: 7.55e-04
2022-05-04 03:24:14,710 INFO [train.py:715] (2/8) Epoch 2, batch 950, loss[loss=0.1704, simple_loss=0.2344, pruned_loss=0.05317, over 4827.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2427, pruned_loss=0.05653, over 964540.56 frames.], batch size: 15, lr: 7.54e-04
2022-05-04 03:24:55,403 INFO [train.py:715] (2/8) Epoch 2, batch 1000, loss[loss=0.1831, simple_loss=0.2388, pruned_loss=0.0637, over 4986.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2431, pruned_loss=0.05697, over 966802.27 frames.], batch size: 14, lr: 7.54e-04
2022-05-04 03:25:36,197 INFO [train.py:715] (2/8) Epoch 2, batch 1050, loss[loss=0.1585, simple_loss=0.234, pruned_loss=0.0415, over 4906.00 frames.], tot_loss[loss=0.1785, simple_loss=0.243, pruned_loss=0.057, over 967910.73 frames.], batch size: 17, lr: 7.54e-04
2022-05-04 03:26:15,803 INFO [train.py:715] (2/8) Epoch 2, batch 1100, loss[loss=0.1896, simple_loss=0.2583, pruned_loss=0.06044, over 4807.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2424, pruned_loss=0.05648, over 967892.51 frames.], batch size: 21, lr: 7.53e-04
2022-05-04 03:26:56,302 INFO [train.py:715] (2/8) Epoch 2, batch 1150, loss[loss=0.1458, simple_loss=0.216, pruned_loss=0.03781, over 4818.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2413, pruned_loss=0.05578, over 968269.84 frames.], batch size: 26, lr: 7.53e-04
2022-05-04 03:27:37,635 INFO [train.py:715] (2/8) Epoch 2, batch 1200, loss[loss=0.1797, simple_loss=0.2477, pruned_loss=0.05583, over 4965.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2405, pruned_loss=0.05521, over 968049.67 frames.], batch size: 39, lr: 7.53e-04
2022-05-04 03:28:18,252 INFO [train.py:715] (2/8) Epoch 2, batch 1250, loss[loss=0.1351, simple_loss=0.2025, pruned_loss=0.03388, over 4812.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2415, pruned_loss=0.05531, over 969200.20 frames.], batch size: 25, lr: 7.53e-04
2022-05-04 03:28:57,933 INFO [train.py:715] (2/8) Epoch 2, batch 1300, loss[loss=0.1849, simple_loss=0.2488, pruned_loss=0.06047, over 4779.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2408, pruned_loss=0.0552, over 969577.26 frames.], batch size: 18, lr: 7.52e-04
2022-05-04 03:29:38,473 INFO [train.py:715] (2/8) Epoch 2, batch 1350, loss[loss=0.1576, simple_loss=0.2061, pruned_loss=0.05453, over 4775.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2407, pruned_loss=0.05539, over 970044.60 frames.], batch size: 18, lr: 7.52e-04
2022-05-04 03:30:19,106 INFO [train.py:715] (2/8) Epoch 2, batch 1400, loss[loss=0.1742, simple_loss=0.2415, pruned_loss=0.05349, over 4783.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2406, pruned_loss=0.05492, over 970759.09 frames.], batch size: 12, lr: 7.52e-04
2022-05-04 03:30:59,076 INFO [train.py:715] (2/8) Epoch 2, batch 1450, loss[loss=0.1937, simple_loss=0.2633, pruned_loss=0.06203, over 4803.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2397, pruned_loss=0.05384, over 970982.13 frames.], batch size: 21, lr: 7.52e-04
2022-05-04 03:31:39,480 INFO [train.py:715] (2/8) Epoch 2, batch 1500, loss[loss=0.2023, simple_loss=0.2667, pruned_loss=0.06898, over 4989.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2394, pruned_loss=0.05409, over 972323.88 frames.], batch size: 28, lr: 7.51e-04
2022-05-04 03:32:20,462 INFO [train.py:715] (2/8) Epoch 2, batch 1550, loss[loss=0.1689, simple_loss=0.2416, pruned_loss=0.04816, over 4781.00 frames.], tot_loss[loss=0.1743, simple_loss=0.24, pruned_loss=0.05431, over 972640.51 frames.], batch size: 18, lr: 7.51e-04
2022-05-04 03:33:00,540 INFO [train.py:715] (2/8) Epoch 2, batch 1600, loss[loss=0.1681, simple_loss=0.228, pruned_loss=0.05414, over 4962.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2405, pruned_loss=0.05446, over 972815.46 frames.], batch size: 35, lr: 7.51e-04
2022-05-04 03:33:40,360 INFO [train.py:715] (2/8) Epoch 2, batch 1650, loss[loss=0.1553, simple_loss=0.2269, pruned_loss=0.04183, over 4865.00 frames.], tot_loss[loss=0.1762, simple_loss=0.242, pruned_loss=0.0552, over 972978.70 frames.], batch size: 32, lr: 7.51e-04
2022-05-04 03:34:21,228 INFO [train.py:715] (2/8) Epoch 2, batch 1700, loss[loss=0.1622, simple_loss=0.2287, pruned_loss=0.0479, over 4969.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2411, pruned_loss=0.05483, over 972294.37 frames.], batch size: 35, lr: 7.50e-04
2022-05-04 03:35:02,276 INFO [train.py:715] (2/8) Epoch 2, batch 1750, loss[loss=0.2187, simple_loss=0.2657, pruned_loss=0.08592, over 4931.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2409, pruned_loss=0.05489, over 972225.61 frames.], batch size: 23, lr: 7.50e-04
2022-05-04 03:35:42,179 INFO [train.py:715] (2/8) Epoch 2, batch 1800, loss[loss=0.202, simple_loss=0.2614, pruned_loss=0.07128, over 4976.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2415, pruned_loss=0.0555, over 972188.29 frames.], batch size: 15, lr: 7.50e-04
2022-05-04 03:36:22,544 INFO [train.py:715] (2/8) Epoch 2, batch 1850, loss[loss=0.1525, simple_loss=0.2283, pruned_loss=0.0383, over 4888.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2419, pruned_loss=0.05611, over 972859.74 frames.], batch size: 22, lr: 7.50e-04
2022-05-04 03:37:03,509 INFO [train.py:715] (2/8) Epoch 2, batch 1900, loss[loss=0.1512, simple_loss=0.2204, pruned_loss=0.041, over 4985.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2412, pruned_loss=0.05524, over 973250.55 frames.], batch size: 15, lr: 7.49e-04
2022-05-04 03:37:44,302 INFO [train.py:715] (2/8) Epoch 2, batch 1950, loss[loss=0.1622, simple_loss=0.2351, pruned_loss=0.04462, over 4921.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2406, pruned_loss=0.05492, over 972780.12 frames.], batch size: 17, lr: 7.49e-04
2022-05-04 03:38:24,070 INFO [train.py:715] (2/8) Epoch 2, batch 2000, loss[loss=0.1751, simple_loss=0.2436, pruned_loss=0.05332, over 4701.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2411, pruned_loss=0.05504, over 971728.55 frames.], batch size: 15, lr: 7.49e-04
2022-05-04 03:39:04,260 INFO [train.py:715] (2/8) Epoch 2, batch 2050, loss[loss=0.2023, simple_loss=0.2645, pruned_loss=0.07004, over 4983.00 frames.], tot_loss[loss=0.177, simple_loss=0.2422, pruned_loss=0.05587, over 972439.16 frames.], batch size: 28, lr: 7.48e-04
2022-05-04 03:39:45,392 INFO [train.py:715] (2/8) Epoch 2, batch 2100, loss[loss=0.1529, simple_loss=0.233, pruned_loss=0.03639, over 4784.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2425, pruned_loss=0.05561, over 973178.00 frames.], batch size: 18, lr: 7.48e-04
2022-05-04 03:40:25,364 INFO [train.py:715] (2/8) Epoch 2, batch 2150, loss[loss=0.19, simple_loss=0.2525, pruned_loss=0.06372, over 4815.00 frames.], tot_loss[loss=0.176, simple_loss=0.2424, pruned_loss=0.05477, over 973474.17 frames.], batch size: 25, lr: 7.48e-04
2022-05-04 03:41:04,896 INFO [train.py:715] (2/8) Epoch 2, batch 2200, loss[loss=0.206, simple_loss=0.2545, pruned_loss=0.07875, over 4984.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2426, pruned_loss=0.05521, over 973217.00 frames.], batch size: 14, lr: 7.48e-04
2022-05-04 03:41:45,610 INFO [train.py:715] (2/8) Epoch 2, batch 2250, loss[loss=0.1906, simple_loss=0.2509, pruned_loss=0.06514, over 4902.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2428, pruned_loss=0.05547, over 973226.93 frames.], batch size: 19, lr: 7.47e-04
2022-05-04 03:42:26,409 INFO [train.py:715] (2/8) Epoch 2, batch 2300, loss[loss=0.1733, simple_loss=0.2397, pruned_loss=0.05351, over 4941.00 frames.], tot_loss[loss=0.176, simple_loss=0.2423, pruned_loss=0.0549, over 973075.89 frames.], batch size: 29, lr: 7.47e-04
2022-05-04 03:43:05,607 INFO [train.py:715] (2/8) Epoch 2, batch 2350, loss[loss=0.1661, simple_loss=0.2462, pruned_loss=0.04297, over 4796.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2414, pruned_loss=0.05456, over 971756.76 frames.], batch size: 21, lr: 7.47e-04
2022-05-04 03:43:48,330 INFO [train.py:715] (2/8) Epoch 2, batch 2400, loss[loss=0.1762, simple_loss=0.2302, pruned_loss=0.06107, over 4989.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2406, pruned_loss=0.05383, over 971464.24 frames.], batch size: 15, lr: 7.47e-04
2022-05-04 03:44:29,317 INFO [train.py:715] (2/8) Epoch 2, batch 2450, loss[loss=0.1723, simple_loss=0.2314, pruned_loss=0.05658, over 4922.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2405, pruned_loss=0.05398, over 971437.22 frames.], batch size: 23, lr: 7.46e-04
2022-05-04 03:45:09,458 INFO [train.py:715] (2/8) Epoch 2, batch 2500, loss[loss=0.1763, simple_loss=0.2495, pruned_loss=0.05149, over 4940.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2403, pruned_loss=0.05366, over 971941.82 frames.], batch size: 21, lr: 7.46e-04
2022-05-04 03:45:49,048 INFO [train.py:715] (2/8) Epoch 2, batch 2550, loss[loss=0.171, simple_loss=0.236, pruned_loss=0.05301, over 4976.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2408, pruned_loss=0.05378, over 972480.83 frames.], batch size: 35, lr: 7.46e-04
2022-05-04 03:46:29,884 INFO [train.py:715] (2/8) Epoch 2, batch 2600, loss[loss=0.1859, simple_loss=0.2494, pruned_loss=0.06115, over 4910.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2403, pruned_loss=0.05379, over 972647.90 frames.], batch size: 17, lr: 7.46e-04
2022-05-04 03:47:10,394 INFO [train.py:715] (2/8) Epoch 2, batch 2650, loss[loss=0.1727, simple_loss=0.2457, pruned_loss=0.04991, over 4977.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2416, pruned_loss=0.05439, over 974020.44 frames.], batch size: 14, lr: 7.45e-04
2022-05-04 03:47:49,286 INFO [train.py:715] (2/8) Epoch 2, batch 2700, loss[loss=0.1857, simple_loss=0.2467, pruned_loss=0.06239, over 4846.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2409, pruned_loss=0.05417, over 974061.07 frames.], batch size: 30, lr: 7.45e-04
2022-05-04 03:48:29,312 INFO [train.py:715] (2/8) Epoch 2, batch 2750, loss[loss=0.2352, simple_loss=0.2806, pruned_loss=0.09491, over 4779.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2421, pruned_loss=0.05476, over 973483.61 frames.], batch size: 18, lr: 7.45e-04
2022-05-04 03:49:10,350 INFO [train.py:715] (2/8) Epoch 2, batch 2800, loss[loss=0.2589, simple_loss=0.2971, pruned_loss=0.1104, over 4753.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2427, pruned_loss=0.05538, over 972691.46 frames.], batch size: 16, lr: 7.45e-04
2022-05-04 03:49:50,282 INFO [train.py:715] (2/8) Epoch 2, batch 2850, loss[loss=0.1867, simple_loss=0.2542, pruned_loss=0.05957, over 4888.00 frames.], tot_loss[loss=0.176, simple_loss=0.2416, pruned_loss=0.05515, over 973173.43 frames.], batch size: 22, lr: 7.44e-04
2022-05-04 03:50:29,538 INFO [train.py:715] (2/8) Epoch 2, batch 2900, loss[loss=0.1711, simple_loss=0.2396, pruned_loss=0.05126, over 4784.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2413, pruned_loss=0.05599, over 972137.44 frames.], batch size: 14, lr: 7.44e-04
2022-05-04 03:51:09,903 INFO [train.py:715] (2/8) Epoch 2, batch 2950, loss[loss=0.1423, simple_loss=0.2243, pruned_loss=0.03018, over 4982.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2407, pruned_loss=0.05532, over 973228.78 frames.], batch size: 28, lr: 7.44e-04
2022-05-04 03:51:50,607 INFO [train.py:715] (2/8) Epoch 2, batch 3000, loss[loss=0.1887, simple_loss=0.2616, pruned_loss=0.05791, over 4960.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2408, pruned_loss=0.0555, over 973414.93 frames.], batch size: 24, lr: 7.44e-04
2022-05-04 03:51:50,608 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 03:52:00,002 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1191, simple_loss=0.2058, pruned_loss=0.01615, over 914524.00 frames.
2022-05-04 03:52:40,627 INFO [train.py:715] (2/8) Epoch 2, batch 3050, loss[loss=0.1845, simple_loss=0.2536, pruned_loss=0.05771, over 4765.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2419, pruned_loss=0.05598, over 972424.78 frames.], batch size: 14, lr: 7.43e-04
2022-05-04 03:53:19,878 INFO [train.py:715] (2/8) Epoch 2, batch 3100, loss[loss=0.1923, simple_loss=0.2371, pruned_loss=0.07379, over 4738.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2414, pruned_loss=0.05569, over 970926.61 frames.], batch size: 16, lr: 7.43e-04
2022-05-04 03:53:59,885 INFO [train.py:715] (2/8) Epoch 2, batch 3150, loss[loss=0.1659, simple_loss=0.2274, pruned_loss=0.05219, over 4828.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2417, pruned_loss=0.05608, over 971264.52 frames.], batch size: 13, lr: 7.43e-04
2022-05-04 03:54:40,159 INFO [train.py:715] (2/8) Epoch 2, batch 3200, loss[loss=0.192, simple_loss=0.2404, pruned_loss=0.07175, over 4815.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2419, pruned_loss=0.05629, over 971323.94 frames.], batch size: 21, lr: 7.43e-04
2022-05-04 03:55:19,792 INFO [train.py:715] (2/8) Epoch 2, batch 3250, loss[loss=0.2214, simple_loss=0.2689, pruned_loss=0.08691, over 4951.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2418, pruned_loss=0.05619, over 971595.69 frames.], batch size: 24, lr: 7.42e-04
2022-05-04 03:55:59,353 INFO [train.py:715] (2/8) Epoch 2, batch 3300, loss[loss=0.1679, simple_loss=0.238, pruned_loss=0.04893, over 4815.00 frames.], tot_loss[loss=0.177, simple_loss=0.2416, pruned_loss=0.05618, over 971183.52 frames.], batch size: 26, lr: 7.42e-04
2022-05-04 03:56:39,596 INFO [train.py:715] (2/8) Epoch 2, batch 3350, loss[loss=0.1675, simple_loss=0.2269, pruned_loss=0.05411, over 4952.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2413, pruned_loss=0.056, over 972085.05 frames.], batch size: 35, lr: 7.42e-04
2022-05-04 03:57:20,088 INFO [train.py:715] (2/8) Epoch 2, batch 3400, loss[loss=0.1891, simple_loss=0.2581, pruned_loss=0.0601, over 4926.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2412, pruned_loss=0.05591, over 972416.58 frames.], batch size: 18, lr: 7.42e-04
2022-05-04 03:57:58,918 INFO [train.py:715] (2/8) Epoch 2, batch 3450, loss[loss=0.1966, simple_loss=0.2645, pruned_loss=0.06434, over 4906.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2416, pruned_loss=0.05573, over 972605.67 frames.], batch size: 23, lr: 7.41e-04
2022-05-04 03:58:38,938 INFO [train.py:715] (2/8) Epoch 2, batch 3500, loss[loss=0.1846, simple_loss=0.2503, pruned_loss=0.05948, over 4922.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2421, pruned_loss=0.0562, over 972376.53 frames.], batch size: 18, lr: 7.41e-04
2022-05-04 03:59:19,004 INFO [train.py:715] (2/8) Epoch 2, batch 3550, loss[loss=0.2002, simple_loss=0.2586, pruned_loss=0.07094, over 4965.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2424, pruned_loss=0.05627, over 972535.15 frames.], batch size: 24, lr: 7.41e-04
2022-05-04 03:59:58,779 INFO [train.py:715] (2/8) Epoch 2, batch 3600, loss[loss=0.2141, simple_loss=0.273, pruned_loss=0.07759, over 4844.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2424, pruned_loss=0.05642, over 972726.47 frames.], batch size: 30, lr: 7.41e-04
2022-05-04 04:00:37,768 INFO [train.py:715] (2/8) Epoch 2, batch 3650, loss[loss=0.1842, simple_loss=0.2465, pruned_loss=0.06098, over 4819.00 frames.], tot_loss[loss=0.1772, simple_loss=0.242, pruned_loss=0.05623, over 971485.50 frames.], batch size: 25, lr: 7.40e-04
2022-05-04 04:01:18,179 INFO [train.py:715] (2/8) Epoch 2, batch 3700, loss[loss=0.159, simple_loss=0.2228, pruned_loss=0.04756, over 4767.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2417, pruned_loss=0.05579, over 972238.51 frames.], batch size: 17, lr: 7.40e-04
2022-05-04 04:01:58,352 INFO [train.py:715] (2/8) Epoch 2, batch 3750, loss[loss=0.1521, simple_loss=0.2137, pruned_loss=0.04525, over 4983.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2404, pruned_loss=0.05542, over 972174.15 frames.], batch size: 25, lr: 7.40e-04
2022-05-04 04:02:37,086 INFO [train.py:715] (2/8) Epoch 2, batch 3800, loss[loss=0.1663, simple_loss=0.2386, pruned_loss=0.04698, over 4938.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2412, pruned_loss=0.05546, over 973443.47 frames.], batch size: 29, lr: 7.40e-04
2022-05-04 04:03:17,275 INFO [train.py:715] (2/8) Epoch 2, batch 3850, loss[loss=0.1753, simple_loss=0.2465, pruned_loss=0.05206, over 4840.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2404, pruned_loss=0.05454, over 973416.08 frames.], batch size: 15, lr: 7.39e-04
2022-05-04 04:03:57,609 INFO [train.py:715] (2/8) Epoch 2, batch 3900, loss[loss=0.2063, simple_loss=0.257, pruned_loss=0.07781, over 4795.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2413, pruned_loss=0.05522, over 972922.98 frames.], batch size: 24, lr: 7.39e-04
2022-05-04 04:04:36,836 INFO [train.py:715] (2/8) Epoch 2, batch 3950, loss[loss=0.1362, simple_loss=0.2094, pruned_loss=0.03151, over 4800.00 frames.], tot_loss[loss=0.176, simple_loss=0.2412, pruned_loss=0.05542, over 972847.74 frames.], batch size: 18, lr: 7.39e-04
2022-05-04 04:05:16,465 INFO [train.py:715] (2/8) Epoch 2, batch 4000, loss[loss=0.1925, simple_loss=0.2443, pruned_loss=0.07038, over 4831.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2406, pruned_loss=0.05506, over 972873.60 frames.], batch size: 30, lr: 7.39e-04
2022-05-04 04:05:57,026 INFO [train.py:715] (2/8) Epoch 2, batch 4050, loss[loss=0.1947, simple_loss=0.2399, pruned_loss=0.07473, over 4796.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2407, pruned_loss=0.0548, over 972494.68 frames.], batch size: 18, lr: 7.38e-04
2022-05-04 04:06:37,521 INFO [train.py:715] (2/8) Epoch 2, batch 4100, loss[loss=0.2085, simple_loss=0.2768, pruned_loss=0.07014, over 4802.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2406, pruned_loss=0.0549, over 971093.53 frames.], batch size: 18, lr: 7.38e-04
2022-05-04 04:07:16,030 INFO [train.py:715] (2/8) Epoch 2, batch 4150, loss[loss=0.1936, simple_loss=0.2642, pruned_loss=0.06153, over 4933.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2403, pruned_loss=0.05476, over 971225.79 frames.], batch size: 21, lr: 7.38e-04
2022-05-04 04:07:55,383 INFO [train.py:715] (2/8) Epoch 2, batch 4200, loss[loss=0.132, simple_loss=0.2076, pruned_loss=0.02823, over 4748.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2403, pruned_loss=0.0546, over 972271.83 frames.], batch size: 16, lr: 7.38e-04
2022-05-04 04:08:35,829 INFO [train.py:715] (2/8) Epoch 2, batch 4250, loss[loss=0.2173, simple_loss=0.278, pruned_loss=0.07833, over 4905.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2412, pruned_loss=0.05527, over 972810.18 frames.], batch size: 18, lr: 7.37e-04
2022-05-04 04:09:15,082 INFO [train.py:715] (2/8) Epoch 2, batch 4300, loss[loss=0.1501, simple_loss=0.2194, pruned_loss=0.04042, over 4837.00 frames.], tot_loss[loss=0.176, simple_loss=0.2418, pruned_loss=0.05509, over 973695.85 frames.], batch size: 15, lr: 7.37e-04
2022-05-04 04:09:54,865 INFO [train.py:715] (2/8) Epoch 2, batch 4350, loss[loss=0.1938, simple_loss=0.2647, pruned_loss=0.06141, over 4940.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2414, pruned_loss=0.0547, over 973648.16 frames.], batch size: 23, lr: 7.37e-04
2022-05-04 04:10:34,721 INFO [train.py:715] (2/8) Epoch 2, batch 4400, loss[loss=0.1936, simple_loss=0.2585, pruned_loss=0.06438, over 4835.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2406, pruned_loss=0.0544, over 973802.58 frames.], batch size: 26, lr: 7.37e-04
2022-05-04 04:11:14,728 INFO [train.py:715] (2/8) Epoch 2, batch 4450, loss[loss=0.1496, simple_loss=0.2172, pruned_loss=0.04104, over 4757.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2411, pruned_loss=0.05465, over 973665.39 frames.], batch size: 16, lr: 7.36e-04
2022-05-04 04:11:53,878 INFO [train.py:715] (2/8) Epoch 2, batch 4500, loss[loss=0.1811, simple_loss=0.2547, pruned_loss=0.05379, over 4779.00 frames.], tot_loss[loss=0.1748, simple_loss=0.241, pruned_loss=0.05433, over 972898.41 frames.], batch size: 17, lr: 7.36e-04
2022-05-04 04:12:33,899 INFO [train.py:715] (2/8) Epoch 2, batch 4550, loss[loss=0.1511, simple_loss=0.2236, pruned_loss=0.03926, over 4931.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2414, pruned_loss=0.05488, over 973424.15 frames.], batch size: 18, lr: 7.36e-04
2022-05-04 04:13:14,644 INFO [train.py:715] (2/8) Epoch 2, batch 4600, loss[loss=0.1511, simple_loss=0.2217, pruned_loss=0.04023, over 4902.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2412, pruned_loss=0.05475, over 973207.36 frames.], batch size: 19, lr: 7.36e-04
2022-05-04 04:13:53,698 INFO [train.py:715] (2/8) Epoch 2, batch 4650, loss[loss=0.1638, simple_loss=0.2265, pruned_loss=0.05058, over 4828.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2414, pruned_loss=0.05502, over 972611.02 frames.], batch size: 30, lr: 7.35e-04
2022-05-04 04:14:33,004 INFO [train.py:715] (2/8) Epoch 2, batch 4700, loss[loss=0.2255, simple_loss=0.2842, pruned_loss=0.08343, over 4698.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2413, pruned_loss=0.05543, over 972914.85 frames.], batch size: 15, lr: 7.35e-04
2022-05-04 04:15:13,196 INFO [train.py:715] (2/8) Epoch 2, batch 4750, loss[loss=0.1357, simple_loss=0.2074, pruned_loss=0.03194, over 4765.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2413, pruned_loss=0.05548, over 972605.44 frames.], batch size: 16, lr: 7.35e-04
2022-05-04 04:15:53,745 INFO [train.py:715] (2/8) Epoch 2, batch 4800, loss[loss=0.1478, simple_loss=0.2058, pruned_loss=0.0449, over 4991.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2418, pruned_loss=0.05586, over 972895.53 frames.], batch size: 20, lr: 7.35e-04
2022-05-04 04:16:33,015 INFO [train.py:715] (2/8) Epoch 2, batch 4850, loss[loss=0.2316, simple_loss=0.2962, pruned_loss=0.0835, over 4796.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2414, pruned_loss=0.05518, over 972952.17 frames.], batch size: 14, lr: 7.34e-04
2022-05-04 04:17:12,483 INFO [train.py:715] (2/8) Epoch 2, batch 4900, loss[loss=0.1532, simple_loss=0.2352, pruned_loss=0.03563, over 4796.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2418, pruned_loss=0.0549, over 973653.72 frames.], batch size: 24, lr: 7.34e-04
2022-05-04 04:17:52,926 INFO [train.py:715] (2/8) Epoch 2, batch 4950, loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.0318, over 4876.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2405, pruned_loss=0.05418, over 972991.55 frames.], batch size: 22, lr: 7.34e-04
2022-05-04 04:18:32,545 INFO [train.py:715] (2/8) Epoch 2, batch 5000, loss[loss=0.1645, simple_loss=0.2395, pruned_loss=0.0448, over 4887.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2402, pruned_loss=0.05397, over 972806.90 frames.], batch size: 22, lr: 7.34e-04
2022-05-04 04:19:12,100 INFO [train.py:715] (2/8) Epoch 2, batch 5050, loss[loss=0.1707, simple_loss=0.2288, pruned_loss=0.05631, over 4962.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2408, pruned_loss=0.05433, over 972931.56 frames.], batch size: 15, lr: 7.33e-04
2022-05-04 04:19:53,170 INFO [train.py:715] (2/8) Epoch 2, batch 5100, loss[loss=0.2024, simple_loss=0.2758, pruned_loss=0.06449, over 4893.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2415, pruned_loss=0.05476, over 972444.02 frames.], batch size: 19, lr: 7.33e-04
2022-05-04 04:20:34,122 INFO [train.py:715] (2/8) Epoch 2, batch 5150, loss[loss=0.1762, simple_loss=0.2446, pruned_loss=0.05388, over 4811.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2409, pruned_loss=0.05464, over 971991.80 frames.], batch size: 24, lr: 7.33e-04
2022-05-04 04:21:13,074 INFO [train.py:715] (2/8) Epoch 2, batch 5200, loss[loss=0.191, simple_loss=0.254, pruned_loss=0.064, over 4816.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2391, pruned_loss=0.05356, over 971955.31 frames.], batch size: 25, lr: 7.33e-04
2022-05-04 04:21:52,856 INFO [train.py:715] (2/8) Epoch 2, batch 5250, loss[loss=0.159, simple_loss=0.2366, pruned_loss=0.04071, over 4958.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2395, pruned_loss=0.05361, over 972306.48 frames.], batch size: 21, lr: 7.32e-04
2022-05-04 04:22:33,067 INFO [train.py:715] (2/8) Epoch 2, batch 5300, loss[loss=0.1917, simple_loss=0.2629, pruned_loss=0.06026, over 4776.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2404, pruned_loss=0.05409, over 972732.82 frames.], batch size: 18, lr: 7.32e-04
2022-05-04 04:23:12,245 INFO [train.py:715] (2/8) Epoch 2, batch 5350, loss[loss=0.1948, simple_loss=0.2535, pruned_loss=0.06807, over 4902.00 frames.], tot_loss[loss=0.1737, simple_loss=0.24, pruned_loss=0.05372, over 972985.88 frames.], batch size: 19, lr: 7.32e-04
2022-05-04 04:23:51,607 INFO [train.py:715] (2/8) Epoch 2, batch 5400, loss[loss=0.1546, simple_loss=0.2158, pruned_loss=0.04668, over 4821.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2393, pruned_loss=0.05322, over 973421.88 frames.], batch size: 25, lr: 7.32e-04
2022-05-04 04:24:32,280 INFO [train.py:715] (2/8) Epoch 2, batch 5450, loss[loss=0.1634, simple_loss=0.2208, pruned_loss=0.05301, over 4992.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2403, pruned_loss=0.05404, over 974044.35 frames.], batch size: 14, lr: 7.31e-04
2022-05-04 04:25:12,074 INFO [train.py:715] (2/8) Epoch 2, batch 5500, loss[loss=0.1967, simple_loss=0.2555, pruned_loss=0.06891, over 4789.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2401, pruned_loss=0.05431, over 973360.32 frames.], batch size: 17, lr: 7.31e-04
2022-05-04 04:25:51,713 INFO [train.py:715] (2/8) Epoch 2, batch 5550, loss[loss=0.1873, simple_loss=0.2466, pruned_loss=0.06396, over 4938.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2398, pruned_loss=0.05381, over 973691.24 frames.], batch size: 23, lr: 7.31e-04
2022-05-04 04:26:32,206 INFO [train.py:715] (2/8) Epoch 2, batch 5600, loss[loss=0.1758, simple_loss=0.2393, pruned_loss=0.05621, over 4805.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2406, pruned_loss=0.05424, over 973612.09 frames.], batch size: 15, lr: 7.31e-04
2022-05-04 04:27:13,262 INFO [train.py:715] (2/8) Epoch 2, batch 5650, loss[loss=0.179, simple_loss=0.2481, pruned_loss=0.05497, over 4906.00 frames.], tot_loss[loss=0.174, simple_loss=0.2403, pruned_loss=0.05389, over 973268.65 frames.], batch size: 19, lr: 7.30e-04
2022-05-04 04:27:53,186 INFO [train.py:715] (2/8) Epoch 2, batch 5700, loss[loss=0.1608, simple_loss=0.2233, pruned_loss=0.04915, over 4809.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2405, pruned_loss=0.05411, over 972889.40 frames.], batch size: 25, lr: 7.30e-04
2022-05-04 04:28:33,025 INFO [train.py:715] (2/8) Epoch 2, batch 5750, loss[loss=0.1679, simple_loss=0.2381, pruned_loss=0.04882, over 4929.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2415, pruned_loss=0.05502, over 972890.30 frames.], batch size: 21, lr: 7.30e-04
2022-05-04 04:29:13,948 INFO [train.py:715] (2/8) Epoch 2, batch 5800, loss[loss=0.1946, simple_loss=0.2562, pruned_loss=0.06646, over 4954.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2417, pruned_loss=0.0548, over 973369.79 frames.], batch size: 14, lr: 7.30e-04
2022-05-04 04:29:55,086 INFO [train.py:715] (2/8) Epoch 2, batch 5850, loss[loss=0.1629, simple_loss=0.2354, pruned_loss=0.04524, over 4823.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2416, pruned_loss=0.05489, over 973190.29 frames.], batch size: 26, lr: 7.29e-04
2022-05-04 04:30:34,557 INFO [train.py:715] (2/8) Epoch 2, batch 5900, loss[loss=0.1884, simple_loss=0.258, pruned_loss=0.05946, over 4945.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2427, pruned_loss=0.05558, over 972618.42 frames.], batch size: 29, lr: 7.29e-04
2022-05-04 04:31:15,148 INFO [train.py:715] (2/8) Epoch 2, batch 5950, loss[loss=0.1771, simple_loss=0.2303, pruned_loss=0.06196, over 4772.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2422, pruned_loss=0.05535, over 972435.88 frames.], batch size: 17, lr: 7.29e-04
2022-05-04 04:31:56,156 INFO [train.py:715] (2/8) Epoch 2, batch 6000, loss[loss=0.1509, simple_loss=0.2295, pruned_loss=0.03615, over 4978.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2415, pruned_loss=0.05467, over 972943.88 frames.], batch size: 15, lr: 7.29e-04
2022-05-04 04:31:56,157 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 04:32:04,807 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1188, simple_loss=0.2054, pruned_loss=0.01614, over 914524.00 frames.
2022-05-04 04:32:46,137 INFO [train.py:715] (2/8) Epoch 2, batch 6050, loss[loss=0.164, simple_loss=0.2332, pruned_loss=0.04737, over 4878.00 frames.], tot_loss[loss=0.174, simple_loss=0.2401, pruned_loss=0.05395, over 972896.07 frames.], batch size: 22, lr: 7.29e-04
2022-05-04 04:33:25,855 INFO [train.py:715] (2/8) Epoch 2, batch 6100, loss[loss=0.1785, simple_loss=0.2517, pruned_loss=0.05264, over 4799.00 frames.], tot_loss[loss=0.1744, simple_loss=0.24, pruned_loss=0.05441, over 972299.81 frames.], batch size: 21, lr: 7.28e-04
2022-05-04 04:34:05,822 INFO [train.py:715] (2/8) Epoch 2, batch 6150, loss[loss=0.1598, simple_loss=0.2141, pruned_loss=0.05275, over 4973.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2395, pruned_loss=0.05386, over 972510.79 frames.], batch size: 15, lr: 7.28e-04
2022-05-04 04:34:46,188 INFO [train.py:715] (2/8) Epoch 2, batch 6200, loss[loss=0.1708, simple_loss=0.2372, pruned_loss=0.0522, over 4697.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2392, pruned_loss=0.05398, over 972759.07 frames.], batch size: 15, lr: 7.28e-04
2022-05-04 04:35:26,605 INFO [train.py:715] (2/8) Epoch 2, batch 6250, loss[loss=0.1803, simple_loss=0.2288, pruned_loss=0.06586, over 4786.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2386, pruned_loss=0.05383, over 972524.99 frames.], batch size: 12, lr: 7.28e-04
2022-05-04 04:36:05,795 INFO [train.py:715] (2/8) Epoch 2, batch 6300, loss[loss=0.1927, simple_loss=0.2535, pruned_loss=0.06595, over 4802.00 frames.], tot_loss[loss=0.173, simple_loss=0.2389, pruned_loss=0.0536, over 972577.11 frames.], batch size: 14, lr: 7.27e-04
2022-05-04 04:36:46,020 INFO [train.py:715] (2/8) Epoch 2, batch 6350, loss[loss=0.1761, simple_loss=0.2255, pruned_loss=0.06333, over 4802.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2396, pruned_loss=0.05402, over 971902.73 frames.], batch size: 12, lr: 7.27e-04
2022-05-04 04:37:26,512 INFO [train.py:715] (2/8) Epoch 2, batch 6400, loss[loss=0.1486, simple_loss=0.2098, pruned_loss=0.04368, over 4778.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2408, pruned_loss=0.05491, over 972506.03 frames.], batch size: 12, lr: 7.27e-04
2022-05-04 04:38:05,323 INFO [train.py:715] (2/8) Epoch 2, batch 6450, loss[loss=0.1619, simple_loss=0.2168, pruned_loss=0.05356, over 4846.00 frames.], tot_loss[loss=0.175, simple_loss=0.2405, pruned_loss=0.05478, over 973097.31 frames.], batch size: 12, lr: 7.27e-04
2022-05-04 04:38:44,598 INFO [train.py:715] (2/8) Epoch 2, batch 6500, loss[loss=0.2058, simple_loss=0.2636, pruned_loss=0.07396, over 4914.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2405, pruned_loss=0.05483, over 973683.81 frames.], batch size: 17, lr: 7.26e-04
2022-05-04 04:39:24,829 INFO [train.py:715] (2/8) Epoch 2, batch 6550, loss[loss=0.1956, simple_loss=0.2519, pruned_loss=0.06964, over 4791.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2405, pruned_loss=0.0546, over 973544.57 frames.], batch size: 14, lr: 7.26e-04
2022-05-04 04:40:04,763 INFO [train.py:715] (2/8) Epoch 2, batch 6600, loss[loss=0.1739, simple_loss=0.2486, pruned_loss=0.04964, over 4973.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2399, pruned_loss=0.05444, over 973745.14 frames.], batch size: 15, lr: 7.26e-04
2022-05-04 04:40:43,854 INFO [train.py:715] (2/8) Epoch 2, batch 6650, loss[loss=0.1606, simple_loss=0.2284, pruned_loss=0.04636, over 4921.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2408, pruned_loss=0.05475, over 972694.08 frames.], batch size: 39, lr: 7.26e-04
2022-05-04 04:41:23,365 INFO [train.py:715] (2/8) Epoch 2, batch 6700, loss[loss=0.1464, simple_loss=0.2195, pruned_loss=0.03662, over 4804.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2411, pruned_loss=0.05516, over 972509.69 frames.], batch size: 21, lr: 7.25e-04
2022-05-04 04:42:03,548 INFO [train.py:715] (2/8) Epoch 2, batch 6750, loss[loss=0.1697, simple_loss=0.2348, pruned_loss=0.05233, over 4825.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2411, pruned_loss=0.05537, over 973124.63 frames.], batch size: 25, lr: 7.25e-04
2022-05-04 04:42:41,713 INFO [train.py:715] (2/8) Epoch 2, batch 6800, loss[loss=0.1325, simple_loss=0.2088, pruned_loss=0.02813, over 4733.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2418, pruned_loss=0.05526, over 972559.81 frames.], batch size: 16, lr: 7.25e-04
2022-05-04 04:43:20,935 INFO [train.py:715] (2/8) Epoch 2, batch 6850, loss[loss=0.1685, simple_loss=0.2345, pruned_loss=0.05123, over 4739.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2402, pruned_loss=0.05412, over 972457.10 frames.], batch size: 16, lr: 7.25e-04
2022-05-04 04:44:01,036 INFO [train.py:715] (2/8) Epoch 2, batch 6900, loss[loss=0.1312, simple_loss=0.209, pruned_loss=0.02669, over 4798.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2396, pruned_loss=0.05392, over 972574.30 frames.], batch size: 12, lr: 7.24e-04
2022-05-04 04:44:41,202 INFO [train.py:715] (2/8) Epoch 2, batch 6950, loss[loss=0.1774, simple_loss=0.2554, pruned_loss=0.04968, over 4886.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2395, pruned_loss=0.05392, over 972138.99 frames.], batch size: 19, lr: 7.24e-04
2022-05-04 04:45:19,402 INFO [train.py:715] (2/8) Epoch 2, batch 7000, loss[loss=0.171, simple_loss=0.2336, pruned_loss=0.05416, over 4769.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2403, pruned_loss=0.054, over 972304.95 frames.], batch size: 18, lr: 7.24e-04
2022-05-04 04:45:59,975 INFO [train.py:715] (2/8) Epoch 2, batch 7050, loss[loss=0.1668, simple_loss=0.2301, pruned_loss=0.05177, over 4870.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2402, pruned_loss=0.05395, over 971488.26 frames.], batch size: 20, lr: 7.24e-04
2022-05-04 04:46:40,399 INFO [train.py:715] (2/8) Epoch 2, batch 7100, loss[loss=0.1672, simple_loss=0.244, pruned_loss=0.04525, over 4869.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2406, pruned_loss=0.05382, over 971866.04 frames.], batch size: 16, lr: 7.24e-04
2022-05-04 04:47:19,787 INFO [train.py:715] (2/8) Epoch 2, batch 7150, loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03379, over 4909.00 frames.], tot_loss[loss=0.175, simple_loss=0.2411, pruned_loss=0.05449, over 971553.96 frames.], batch size: 19, lr: 7.23e-04
2022-05-04 04:48:00,084 INFO [train.py:715] (2/8) Epoch 2, batch 7200, loss[loss=0.1977, simple_loss=0.2603, pruned_loss=0.06759, over 4849.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2409, pruned_loss=0.05418, over 971611.55 frames.], batch size: 20, lr: 7.23e-04
2022-05-04 04:48:41,275 INFO [train.py:715] (2/8) Epoch 2, batch 7250, loss[loss=0.2248, simple_loss=0.288, pruned_loss=0.08082, over 4957.00 frames.], tot_loss[loss=0.1736, simple_loss=0.24, pruned_loss=0.05356, over 971552.26 frames.], batch size: 24, lr: 7.23e-04
2022-05-04 04:49:21,912 INFO [train.py:715] (2/8) Epoch 2, batch 7300, loss[loss=0.1796, simple_loss=0.2444, pruned_loss=0.05742, over 4888.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2409, pruned_loss=0.05407, over 971094.66 frames.], batch size: 19, lr: 7.23e-04
2022-05-04 04:50:01,596 INFO [train.py:715] (2/8) Epoch 2, batch 7350, loss[loss=0.1305, simple_loss=0.1896, pruned_loss=0.0357, over 4748.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2398, pruned_loss=0.05381, over 970854.83 frames.], batch size: 12, lr: 7.22e-04
2022-05-04 04:50:42,524 INFO [train.py:715] (2/8) Epoch 2, batch 7400, loss[loss=0.2016, simple_loss=0.2592, pruned_loss=0.07203, over 4848.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2398, pruned_loss=0.05354, over 970843.52 frames.], batch size: 20, lr: 7.22e-04
2022-05-04 04:51:24,316 INFO [train.py:715] (2/8) Epoch 2, batch 7450, loss[loss=0.1853, simple_loss=0.2507, pruned_loss=0.05995, over 4963.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2408, pruned_loss=0.05448, over 971189.59 frames.], batch size: 21, lr: 7.22e-04
2022-05-04 04:52:04,704 INFO [train.py:715] (2/8) Epoch 2, batch 7500, loss[loss=0.1868, simple_loss=0.2595, pruned_loss=0.05704, over 4798.00 frames.], tot_loss[loss=0.1748, simple_loss=0.241, pruned_loss=0.05425, over 971701.58 frames.], batch size: 21, lr: 7.22e-04
2022-05-04 04:52:45,154 INFO [train.py:715] (2/8) Epoch 2, batch 7550, loss[loss=0.1952, simple_loss=0.2667, pruned_loss=0.06184, over 4892.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2417, pruned_loss=0.05491, over 972220.51 frames.], batch size: 19, lr: 7.21e-04
2022-05-04 04:53:26,931 INFO [train.py:715] (2/8) Epoch 2, batch 7600, loss[loss=0.1621, simple_loss=0.2267, pruned_loss=0.0488, over 4936.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2415, pruned_loss=0.05451, over 972081.45 frames.], batch size: 21, lr: 7.21e-04
2022-05-04 04:54:08,309 INFO [train.py:715] (2/8) Epoch 2, batch 7650, loss[loss=0.1707, simple_loss=0.242, pruned_loss=0.04966, over 4792.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2414, pruned_loss=0.05438, over 971613.13 frames.], batch size: 24, lr: 7.21e-04
2022-05-04 04:54:48,376 INFO [train.py:715] (2/8) Epoch 2, batch 7700, loss[loss=0.1649, simple_loss=0.2295, pruned_loss=0.05014, over 4845.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2418, pruned_loss=0.055, over 971151.61 frames.], batch size: 30, lr: 7.21e-04
2022-05-04 04:55:29,827 INFO [train.py:715] (2/8) Epoch 2, batch 7750, loss[loss=0.1718, simple_loss=0.2389, pruned_loss=0.05236, over 4945.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2406, pruned_loss=0.05428, over 971369.80 frames.], batch size: 24, lr: 7.21e-04
2022-05-04 04:56:11,494 INFO [train.py:715] (2/8) Epoch 2, batch 7800, loss[loss=0.1442, simple_loss=0.2169, pruned_loss=0.03577, over 4731.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2411, pruned_loss=0.05489, over 971164.62 frames.], batch size: 16, lr: 7.20e-04
2022-05-04 04:56:51,999 INFO [train.py:715] (2/8) Epoch 2, batch 7850, loss[loss=0.1787, simple_loss=0.2329, pruned_loss=0.06226, over 4796.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2403, pruned_loss=0.0543, over 970964.74 frames.], batch size: 17, lr: 7.20e-04
2022-05-04 04:57:33,353 INFO [train.py:715] (2/8) Epoch 2, batch 7900, loss[loss=0.1269, simple_loss=0.2043, pruned_loss=0.02481, over 4822.00 frames.], tot_loss[loss=0.174, simple_loss=0.2399, pruned_loss=0.054, over 970265.50 frames.], batch size: 27, lr: 7.20e-04
2022-05-04 04:58:15,545 INFO [train.py:715] (2/8) Epoch 2, batch 7950, loss[loss=0.1718, simple_loss=0.2339, pruned_loss=0.05483, over 4982.00 frames.], tot_loss[loss=0.175, simple_loss=0.2409, pruned_loss=0.05454, over 971776.91 frames.], batch size: 28, lr: 7.20e-04
2022-05-04 04:58:57,040 INFO [train.py:715] (2/8) Epoch 2, batch 8000, loss[loss=0.1519, simple_loss=0.2264, pruned_loss=0.03873, over 4853.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2419, pruned_loss=0.05512, over 972363.71 frames.], batch size: 20, lr: 7.19e-04
2022-05-04 04:59:37,241 INFO [train.py:715] (2/8) Epoch 2, batch 8050, loss[loss=0.2007, simple_loss=0.2694, pruned_loss=0.06598, over 4921.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2422, pruned_loss=0.0552, over 972017.70 frames.], batch size: 17, lr: 7.19e-04
2022-05-04 05:00:18,965 INFO [train.py:715] (2/8) Epoch 2, batch 8100, loss[loss=0.157, simple_loss=0.234, pruned_loss=0.03996, over 4946.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2418, pruned_loss=0.05491, over 973002.45 frames.], batch size: 23, lr: 7.19e-04
2022-05-04 05:01:00,830 INFO [train.py:715] (2/8) Epoch 2, batch 8150, loss[loss=0.1519, simple_loss=0.2332, pruned_loss=0.03533, over 4773.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2416, pruned_loss=0.05492, over 972762.93 frames.], batch size: 14, lr: 7.19e-04
2022-05-04 05:01:41,269 INFO [train.py:715] (2/8) Epoch 2, batch 8200, loss[loss=0.1688, simple_loss=0.2435, pruned_loss=0.04706, over 4808.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2415, pruned_loss=0.05484, over 972593.32 frames.], batch size: 21, lr: 7.18e-04
2022-05-04 05:02:22,242 INFO [train.py:715] (2/8) Epoch 2, batch 8250, loss[loss=0.1986, simple_loss=0.2577, pruned_loss=0.06972, over 4862.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2421, pruned_loss=0.05563, over 972783.28 frames.], batch size: 20, lr: 7.18e-04
2022-05-04 05:03:04,360 INFO [train.py:715] (2/8) Epoch 2, batch 8300, loss[loss=0.1681, simple_loss=0.2352, pruned_loss=0.05056, over 4779.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2417, pruned_loss=0.05479, over 972782.16 frames.], batch size: 14, lr: 7.18e-04
2022-05-04 05:03:46,073 INFO [train.py:715] (2/8) Epoch 2, batch 8350, loss[loss=0.152, simple_loss=0.2139, pruned_loss=0.04506, over 4752.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2422, pruned_loss=0.05529, over 972993.89 frames.], batch size: 12, lr: 7.18e-04
2022-05-04 05:04:26,319 INFO [train.py:715] (2/8) Epoch 2, batch 8400, loss[loss=0.2097, simple_loss=0.2698, pruned_loss=0.07478, over 4781.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2416, pruned_loss=0.05505, over 972989.77 frames.], batch size: 17, lr: 7.18e-04
2022-05-04 05:05:07,468 INFO [train.py:715] (2/8) Epoch 2, batch 8450, loss[loss=0.1272, simple_loss=0.1963, pruned_loss=0.02907, over 4792.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2405, pruned_loss=0.05409, over 973626.15 frames.], batch size: 12, lr: 7.17e-04
2022-05-04 05:05:49,558 INFO [train.py:715] (2/8) Epoch 2, batch 8500, loss[loss=0.1547, simple_loss=0.2229, pruned_loss=0.04323, over 4900.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2407, pruned_loss=0.05419, over 973532.98 frames.], batch size: 17, lr: 7.17e-04
2022-05-04 05:06:29,757 INFO [train.py:715] (2/8) Epoch 2, batch 8550, loss[loss=0.1765, simple_loss=0.2488, pruned_loss=0.05208, over 4930.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2401, pruned_loss=0.0538, over 972741.57 frames.], batch size: 23, lr: 7.17e-04
2022-05-04 05:07:10,942 INFO [train.py:715] (2/8) Epoch 2, batch 8600, loss[loss=0.1879, simple_loss=0.2496, pruned_loss=0.06311, over 4769.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2396, pruned_loss=0.05373, over 972398.68 frames.], batch size: 17, lr: 7.17e-04
2022-05-04 05:07:52,990 INFO [train.py:715] (2/8) Epoch 2, batch 8650, loss[loss=0.1806, simple_loss=0.2452, pruned_loss=0.05803, over 4955.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2402, pruned_loss=0.05384, over 972671.09 frames.], batch size: 24, lr: 7.16e-04
2022-05-04 05:08:34,282 INFO [train.py:715] (2/8) Epoch 2, batch 8700, loss[loss=0.1762, simple_loss=0.2381, pruned_loss=0.05716, over 4768.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2398, pruned_loss=0.05386, over 972227.13 frames.], batch size: 18, lr: 7.16e-04
2022-05-04 05:09:14,824 INFO [train.py:715] (2/8) Epoch 2, batch 8750, loss[loss=0.1818, simple_loss=0.2397, pruned_loss=0.06198, over 4978.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2404, pruned_loss=0.05449, over 972596.56 frames.], batch size: 35, lr: 7.16e-04
2022-05-04 05:09:56,623 INFO [train.py:715] (2/8) Epoch 2, batch 8800, loss[loss=0.1846, simple_loss=0.2521, pruned_loss=0.0585, over 4885.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2415, pruned_loss=0.05537, over 972632.10 frames.], batch size: 19, lr: 7.16e-04
2022-05-04 05:10:38,728 INFO [train.py:715] (2/8) Epoch 2, batch 8850, loss[loss=0.1847, simple_loss=0.234, pruned_loss=0.06769, over 4917.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2417, pruned_loss=0.05537, over 972324.17 frames.], batch size: 18, lr: 7.15e-04
2022-05-04 05:11:18,688 INFO [train.py:715] (2/8) Epoch 2, batch 8900, loss[loss=0.1637, simple_loss=0.2329, pruned_loss=0.04723, over 4986.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2402, pruned_loss=0.05467, over 972880.90 frames.], batch size: 28, lr: 7.15e-04
2022-05-04 05:12:00,187 INFO [train.py:715] (2/8) Epoch 2, batch 8950, loss[loss=0.2152, simple_loss=0.2686, pruned_loss=0.08089, over 4817.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2412, pruned_loss=0.05497, over 973091.45 frames.], batch size: 25, lr: 7.15e-04
2022-05-04 05:12:42,400 INFO [train.py:715] (2/8) Epoch 2, batch 9000, loss[loss=0.1625, simple_loss=0.2343, pruned_loss=0.04541, over 4843.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2413, pruned_loss=0.05455, over 973140.37 frames.], batch size: 15, lr: 7.15e-04
2022-05-04 05:12:42,401 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 05:12:58,992 INFO [train.py:742] (2/8) 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] (2/8) Epoch 2, batch 9050, loss[loss=0.1589, simple_loss=0.2393, pruned_loss=0.03927, over 4813.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2403, pruned_loss=0.05436, over 971993.55 frames.], batch size: 27, lr: 7.15e-04
2022-05-04 05:14:21,238 INFO [train.py:715] (2/8) Epoch 2, batch 9100, loss[loss=0.1693, simple_loss=0.2358, pruned_loss=0.05145, over 4868.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2398, pruned_loss=0.05427, over 973137.24 frames.], batch size: 16, lr: 7.14e-04
2022-05-04 05:15:02,336 INFO [train.py:715] (2/8) Epoch 2, batch 9150, loss[loss=0.1514, simple_loss=0.2268, pruned_loss=0.03798, over 4910.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2398, pruned_loss=0.05441, over 973410.29 frames.], batch size: 19, lr: 7.14e-04
2022-05-04 05:15:43,579 INFO [train.py:715] (2/8) Epoch 2, batch 9200, loss[loss=0.1477, simple_loss=0.2095, pruned_loss=0.04293, over 4970.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2395, pruned_loss=0.05402, over 974398.31 frames.], batch size: 35, lr: 7.14e-04
2022-05-04 05:16:25,083 INFO [train.py:715] (2/8) Epoch 2, batch 9250, loss[loss=0.1547, simple_loss=0.223, pruned_loss=0.04324, over 4752.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2393, pruned_loss=0.0538, over 974737.05 frames.], batch size: 12, lr: 7.14e-04
2022-05-04 05:17:05,069 INFO [train.py:715] (2/8) Epoch 2, batch 9300, loss[loss=0.183, simple_loss=0.2381, pruned_loss=0.06399, over 4895.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2405, pruned_loss=0.05444, over 973856.15 frames.], batch size: 16, lr: 7.13e-04
2022-05-04 05:17:46,760 INFO [train.py:715] (2/8) Epoch 2, batch 9350, loss[loss=0.1995, simple_loss=0.261, pruned_loss=0.06904, over 4746.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2398, pruned_loss=0.05414, over 973552.32 frames.], batch size: 16, lr: 7.13e-04
2022-05-04 05:18:28,854 INFO [train.py:715] (2/8) Epoch 2, batch 9400, loss[loss=0.1199, simple_loss=0.1912, pruned_loss=0.02425, over 4831.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2387, pruned_loss=0.05314, over 973362.57 frames.], batch size: 12, lr: 7.13e-04
2022-05-04 05:19:08,503 INFO [train.py:715] (2/8) Epoch 2, batch 9450, loss[loss=0.183, simple_loss=0.2459, pruned_loss=0.06007, over 4974.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2388, pruned_loss=0.05332, over 973129.76 frames.], batch size: 28, lr: 7.13e-04
2022-05-04 05:19:48,357 INFO [train.py:715] (2/8) Epoch 2, batch 9500, loss[loss=0.1742, simple_loss=0.2369, pruned_loss=0.05576, over 4826.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2393, pruned_loss=0.0541, over 973644.58 frames.], batch size: 25, lr: 7.13e-04
2022-05-04 05:20:28,627 INFO [train.py:715] (2/8) Epoch 2, batch 9550, loss[loss=0.1511, simple_loss=0.2149, pruned_loss=0.04364, over 4886.00 frames.], tot_loss[loss=0.174, simple_loss=0.2395, pruned_loss=0.05421, over 973431.90 frames.], batch size: 32, lr: 7.12e-04
2022-05-04 05:21:08,634 INFO [train.py:715] (2/8) Epoch 2, batch 9600, loss[loss=0.161, simple_loss=0.2368, pruned_loss=0.04255, over 4807.00 frames.], tot_loss[loss=0.1739, simple_loss=0.24, pruned_loss=0.05395, over 973789.68 frames.], batch size: 25, lr: 7.12e-04
2022-05-04 05:21:47,531 INFO [train.py:715] (2/8) Epoch 2, batch 9650, loss[loss=0.206, simple_loss=0.2754, pruned_loss=0.06828, over 4905.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2404, pruned_loss=0.05461, over 974032.93 frames.], batch size: 17, lr: 7.12e-04
2022-05-04 05:22:27,775 INFO [train.py:715] (2/8) Epoch 2, batch 9700, loss[loss=0.1849, simple_loss=0.2571, pruned_loss=0.05635, over 4906.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2402, pruned_loss=0.05467, over 974282.82 frames.], batch size: 17, lr: 7.12e-04
2022-05-04 05:23:08,404 INFO [train.py:715] (2/8) Epoch 2, batch 9750, loss[loss=0.1959, simple_loss=0.2693, pruned_loss=0.0613, over 4751.00 frames.], tot_loss[loss=0.175, simple_loss=0.2407, pruned_loss=0.05463, over 974313.70 frames.], batch size: 19, lr: 7.11e-04
2022-05-04 05:23:47,694 INFO [train.py:715] (2/8) Epoch 2, batch 9800, loss[loss=0.1886, simple_loss=0.2481, pruned_loss=0.06448, over 4797.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2411, pruned_loss=0.05454, over 973771.92 frames.], batch size: 21, lr: 7.11e-04
2022-05-04 05:24:26,792 INFO [train.py:715] (2/8) Epoch 2, batch 9850, loss[loss=0.1749, simple_loss=0.2397, pruned_loss=0.05503, over 4987.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2415, pruned_loss=0.05487, over 973144.58 frames.], batch size: 14, lr: 7.11e-04
2022-05-04 05:25:06,813 INFO [train.py:715] (2/8) Epoch 2, batch 9900, loss[loss=0.1999, simple_loss=0.257, pruned_loss=0.07142, over 4759.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2411, pruned_loss=0.05473, over 972623.32 frames.], batch size: 17, lr: 7.11e-04
2022-05-04 05:25:46,406 INFO [train.py:715] (2/8) Epoch 2, batch 9950, loss[loss=0.1192, simple_loss=0.1848, pruned_loss=0.02684, over 4787.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2406, pruned_loss=0.05409, over 972972.60 frames.], batch size: 12, lr: 7.11e-04
2022-05-04 05:26:25,424 INFO [train.py:715] (2/8) Epoch 2, batch 10000, loss[loss=0.2071, simple_loss=0.2683, pruned_loss=0.073, over 4917.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2405, pruned_loss=0.05437, over 972810.06 frames.], batch size: 39, lr: 7.10e-04
2022-05-04 05:27:06,108 INFO [train.py:715] (2/8) Epoch 2, batch 10050, loss[loss=0.1448, simple_loss=0.196, pruned_loss=0.04679, over 4774.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2399, pruned_loss=0.05457, over 973256.36 frames.], batch size: 12, lr: 7.10e-04
2022-05-04 05:27:45,911 INFO [train.py:715] (2/8) Epoch 2, batch 10100, loss[loss=0.1506, simple_loss=0.2235, pruned_loss=0.03885, over 4890.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2387, pruned_loss=0.05404, over 972605.33 frames.], batch size: 19, lr: 7.10e-04
2022-05-04 05:28:25,915 INFO [train.py:715] (2/8) Epoch 2, batch 10150, loss[loss=0.2176, simple_loss=0.2753, pruned_loss=0.07997, over 4874.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2393, pruned_loss=0.05407, over 973450.58 frames.], batch size: 22, lr: 7.10e-04
2022-05-04 05:29:06,173 INFO [train.py:715] (2/8) Epoch 2, batch 10200, loss[loss=0.1662, simple_loss=0.2327, pruned_loss=0.04989, over 4814.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2392, pruned_loss=0.05381, over 972528.41 frames.], batch size: 27, lr: 7.09e-04
2022-05-04 05:29:47,599 INFO [train.py:715] (2/8) Epoch 2, batch 10250, loss[loss=0.2014, simple_loss=0.2584, pruned_loss=0.07218, over 4907.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2398, pruned_loss=0.05399, over 973797.95 frames.], batch size: 17, lr: 7.09e-04
2022-05-04 05:30:27,419 INFO [train.py:715] (2/8) Epoch 2, batch 10300, loss[loss=0.1939, simple_loss=0.2542, pruned_loss=0.06677, over 4868.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2405, pruned_loss=0.05435, over 973341.46 frames.], batch size: 39, lr: 7.09e-04
2022-05-04 05:31:07,039 INFO [train.py:715] (2/8) Epoch 2, batch 10350, loss[loss=0.1774, simple_loss=0.2397, pruned_loss=0.05754, over 4867.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2409, pruned_loss=0.05509, over 972367.05 frames.], batch size: 13, lr: 7.09e-04
2022-05-04 05:31:49,851 INFO [train.py:715] (2/8) Epoch 2, batch 10400, loss[loss=0.175, simple_loss=0.2446, pruned_loss=0.05265, over 4829.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2417, pruned_loss=0.05544, over 972831.92 frames.], batch size: 15, lr: 7.09e-04
2022-05-04 05:32:31,017 INFO [train.py:715] (2/8) Epoch 2, batch 10450, loss[loss=0.1903, simple_loss=0.2564, pruned_loss=0.06206, over 4756.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2414, pruned_loss=0.05516, over 971789.55 frames.], batch size: 19, lr: 7.08e-04
2022-05-04 05:33:11,270 INFO [train.py:715] (2/8) Epoch 2, batch 10500, loss[loss=0.1831, simple_loss=0.2525, pruned_loss=0.0569, over 4866.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2411, pruned_loss=0.05508, over 971566.33 frames.], batch size: 20, lr: 7.08e-04
2022-05-04 05:33:50,618 INFO [train.py:715] (2/8) Epoch 2, batch 10550, loss[loss=0.177, simple_loss=0.2394, pruned_loss=0.05727, over 4818.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2405, pruned_loss=0.05461, over 971298.30 frames.], batch size: 27, lr: 7.08e-04
2022-05-04 05:34:31,844 INFO [train.py:715] (2/8) Epoch 2, batch 10600, loss[loss=0.1607, simple_loss=0.2259, pruned_loss=0.04775, over 4826.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2403, pruned_loss=0.05432, over 971431.03 frames.], batch size: 26, lr: 7.08e-04
2022-05-04 05:35:12,035 INFO [train.py:715] (2/8) Epoch 2, batch 10650, loss[loss=0.2046, simple_loss=0.2631, pruned_loss=0.07299, over 4714.00 frames.], tot_loss[loss=0.174, simple_loss=0.2399, pruned_loss=0.05407, over 971555.18 frames.], batch size: 15, lr: 7.07e-04
2022-05-04 05:35:51,937 INFO [train.py:715] (2/8) Epoch 2, batch 10700, loss[loss=0.2282, simple_loss=0.2911, pruned_loss=0.08261, over 4826.00 frames.], tot_loss[loss=0.175, simple_loss=0.2409, pruned_loss=0.05458, over 971478.83 frames.], batch size: 26, lr: 7.07e-04
2022-05-04 05:36:32,501 INFO [train.py:715] (2/8) Epoch 2, batch 10750, loss[loss=0.1412, simple_loss=0.2109, pruned_loss=0.03576, over 4808.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2395, pruned_loss=0.05393, over 971706.13 frames.], batch size: 21, lr: 7.07e-04
2022-05-04 05:37:13,627 INFO [train.py:715] (2/8) Epoch 2, batch 10800, loss[loss=0.1586, simple_loss=0.2195, pruned_loss=0.04885, over 4879.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2385, pruned_loss=0.05327, over 972130.96 frames.], batch size: 16, lr: 7.07e-04
2022-05-04 05:37:53,806 INFO [train.py:715] (2/8) Epoch 2, batch 10850, loss[loss=0.1684, simple_loss=0.229, pruned_loss=0.05394, over 4757.00 frames.], tot_loss[loss=0.172, simple_loss=0.2381, pruned_loss=0.053, over 972254.35 frames.], batch size: 19, lr: 7.07e-04
2022-05-04 05:38:33,323 INFO [train.py:715] (2/8) Epoch 2, batch 10900, loss[loss=0.1459, simple_loss=0.2036, pruned_loss=0.04415, over 4753.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2375, pruned_loss=0.0527, over 971799.90 frames.], batch size: 12, lr: 7.06e-04
2022-05-04 05:39:14,354 INFO [train.py:715] (2/8) Epoch 2, batch 10950, loss[loss=0.1411, simple_loss=0.2076, pruned_loss=0.03734, over 4982.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2377, pruned_loss=0.05272, over 972559.98 frames.], batch size: 14, lr: 7.06e-04
2022-05-04 05:39:54,160 INFO [train.py:715] (2/8) Epoch 2, batch 11000, loss[loss=0.1539, simple_loss=0.2343, pruned_loss=0.03682, over 4817.00 frames.], tot_loss[loss=0.171, simple_loss=0.2376, pruned_loss=0.05223, over 972329.49 frames.], batch size: 25, lr: 7.06e-04
2022-05-04 05:40:33,757 INFO [train.py:715] (2/8) Epoch 2, batch 11050, loss[loss=0.1508, simple_loss=0.2143, pruned_loss=0.04369, over 4833.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2382, pruned_loss=0.05245, over 972134.34 frames.], batch size: 30, lr: 7.06e-04
2022-05-04 05:41:14,430 INFO [train.py:715] (2/8) Epoch 2, batch 11100, loss[loss=0.1798, simple_loss=0.2358, pruned_loss=0.06195, over 4875.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2381, pruned_loss=0.05258, over 972780.17 frames.], batch size: 32, lr: 7.05e-04
2022-05-04 05:41:54,862 INFO [train.py:715] (2/8) Epoch 2, batch 11150, loss[loss=0.1765, simple_loss=0.2592, pruned_loss=0.04688, over 4936.00 frames.], tot_loss[loss=0.172, simple_loss=0.2385, pruned_loss=0.05272, over 972338.36 frames.], batch size: 21, lr: 7.05e-04
2022-05-04 05:42:35,622 INFO [train.py:715] (2/8) Epoch 2, batch 11200, loss[loss=0.1759, simple_loss=0.2433, pruned_loss=0.05422, over 4878.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2397, pruned_loss=0.0537, over 971676.38 frames.], batch size: 32, lr: 7.05e-04
2022-05-04 05:43:15,652 INFO [train.py:715] (2/8) Epoch 2, batch 11250, loss[loss=0.1763, simple_loss=0.2366, pruned_loss=0.05799, over 4948.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2397, pruned_loss=0.05373, over 972594.49 frames.], batch size: 14, lr: 7.05e-04
2022-05-04 05:43:56,714 INFO [train.py:715] (2/8) Epoch 2, batch 11300, loss[loss=0.1589, simple_loss=0.2228, pruned_loss=0.04753, over 4802.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2389, pruned_loss=0.05375, over 972569.31 frames.], batch size: 24, lr: 7.05e-04
2022-05-04 05:44:37,055 INFO [train.py:715] (2/8) Epoch 2, batch 11350, loss[loss=0.1919, simple_loss=0.2525, pruned_loss=0.06558, over 4752.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2387, pruned_loss=0.05397, over 972101.17 frames.], batch size: 16, lr: 7.04e-04
2022-05-04 05:45:16,681 INFO [train.py:715] (2/8) Epoch 2, batch 11400, loss[loss=0.1662, simple_loss=0.2337, pruned_loss=0.04939, over 4914.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2375, pruned_loss=0.053, over 971414.25 frames.], batch size: 19, lr: 7.04e-04
2022-05-04 05:45:56,732 INFO [train.py:715] (2/8) Epoch 2, batch 11450, loss[loss=0.1724, simple_loss=0.2425, pruned_loss=0.05118, over 4964.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2376, pruned_loss=0.05312, over 971772.37 frames.], batch size: 35, lr: 7.04e-04
2022-05-04 05:46:37,324 INFO [train.py:715] (2/8) Epoch 2, batch 11500, loss[loss=0.1521, simple_loss=0.2137, pruned_loss=0.04523, over 4762.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2377, pruned_loss=0.05321, over 972638.40 frames.], batch size: 14, lr: 7.04e-04
2022-05-04 05:47:18,051 INFO [train.py:715] (2/8) Epoch 2, batch 11550, loss[loss=0.1674, simple_loss=0.2401, pruned_loss=0.04737, over 4888.00 frames.], tot_loss[loss=0.172, simple_loss=0.2376, pruned_loss=0.05323, over 972745.90 frames.], batch size: 22, lr: 7.04e-04
2022-05-04 05:47:58,019 INFO [train.py:715] (2/8) Epoch 2, batch 11600, loss[loss=0.1908, simple_loss=0.2511, pruned_loss=0.0652, over 4857.00 frames.], tot_loss[loss=0.171, simple_loss=0.2367, pruned_loss=0.05266, over 972693.12 frames.], batch size: 30, lr: 7.03e-04
2022-05-04 05:48:39,175 INFO [train.py:715] (2/8) Epoch 2, batch 11650, loss[loss=0.1337, simple_loss=0.1985, pruned_loss=0.03445, over 4980.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2366, pruned_loss=0.05255, over 972468.31 frames.], batch size: 31, lr: 7.03e-04
2022-05-04 05:49:19,422 INFO [train.py:715] (2/8) Epoch 2, batch 11700, loss[loss=0.1804, simple_loss=0.257, pruned_loss=0.05184, over 4782.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2371, pruned_loss=0.05285, over 971986.95 frames.], batch size: 17, lr: 7.03e-04
2022-05-04 05:49:59,620 INFO [train.py:715] (2/8) Epoch 2, batch 11750, loss[loss=0.2007, simple_loss=0.2795, pruned_loss=0.06098, over 4948.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2374, pruned_loss=0.05241, over 972114.02 frames.], batch size: 23, lr: 7.03e-04
2022-05-04 05:50:40,398 INFO [train.py:715] (2/8) Epoch 2, batch 11800, loss[loss=0.1927, simple_loss=0.2549, pruned_loss=0.06519, over 4821.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2378, pruned_loss=0.05275, over 971518.15 frames.], batch size: 15, lr: 7.02e-04
2022-05-04 05:51:20,985 INFO [train.py:715] (2/8) Epoch 2, batch 11850, loss[loss=0.1774, simple_loss=0.2582, pruned_loss=0.04828, over 4975.00 frames.], tot_loss[loss=0.1731, simple_loss=0.239, pruned_loss=0.05365, over 972287.33 frames.], batch size: 14, lr: 7.02e-04
2022-05-04 05:52:00,404 INFO [train.py:715] (2/8) Epoch 2, batch 11900, loss[loss=0.1512, simple_loss=0.2153, pruned_loss=0.04353, over 4833.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2391, pruned_loss=0.05405, over 972218.83 frames.], batch size: 15, lr: 7.02e-04
2022-05-04 05:52:40,330 INFO [train.py:715] (2/8) Epoch 2, batch 11950, loss[loss=0.1338, simple_loss=0.2055, pruned_loss=0.03111, over 4701.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2383, pruned_loss=0.05348, over 972744.62 frames.], batch size: 15, lr: 7.02e-04
2022-05-04 05:53:21,653 INFO [train.py:715] (2/8) Epoch 2, batch 12000, loss[loss=0.1685, simple_loss=0.2368, pruned_loss=0.05009, over 4779.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2385, pruned_loss=0.05394, over 972815.93 frames.], batch size: 17, lr: 7.02e-04
2022-05-04 05:53:21,654 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 05:53:45,623 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1181, simple_loss=0.2049, pruned_loss=0.01568, over 914524.00 frames.
2022-05-04 05:54:27,022 INFO [train.py:715] (2/8) Epoch 2, batch 12050, loss[loss=0.1869, simple_loss=0.2463, pruned_loss=0.06374, over 4779.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2381, pruned_loss=0.05322, over 972007.33 frames.], batch size: 14, lr: 7.01e-04
2022-05-04 05:55:07,114 INFO [train.py:715] (2/8) Epoch 2, batch 12100, loss[loss=0.1749, simple_loss=0.2217, pruned_loss=0.06405, over 4762.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2386, pruned_loss=0.05384, over 971051.21 frames.], batch size: 14, lr: 7.01e-04
2022-05-04 05:55:47,106 INFO [train.py:715] (2/8) Epoch 2, batch 12150, loss[loss=0.1737, simple_loss=0.2369, pruned_loss=0.05529, over 4763.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2391, pruned_loss=0.054, over 971026.96 frames.], batch size: 14, lr: 7.01e-04
2022-05-04 05:56:27,805 INFO [train.py:715] (2/8) Epoch 2, batch 12200, loss[loss=0.1454, simple_loss=0.2128, pruned_loss=0.03897, over 4812.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2391, pruned_loss=0.05405, over 971236.82 frames.], batch size: 21, lr: 7.01e-04
2022-05-04 05:57:07,982 INFO [train.py:715] (2/8) Epoch 2, batch 12250, loss[loss=0.1733, simple_loss=0.2423, pruned_loss=0.05214, over 4988.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2391, pruned_loss=0.05378, over 971094.55 frames.], batch size: 28, lr: 7.01e-04
2022-05-04 05:57:48,410 INFO [train.py:715] (2/8) Epoch 2, batch 12300, loss[loss=0.1816, simple_loss=0.2529, pruned_loss=0.05516, over 4777.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2386, pruned_loss=0.05324, over 971894.78 frames.], batch size: 17, lr: 7.00e-04
2022-05-04 05:58:28,535 INFO [train.py:715] (2/8) Epoch 2, batch 12350, loss[loss=0.2077, simple_loss=0.2664, pruned_loss=0.07448, over 4793.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2393, pruned_loss=0.05368, over 972338.55 frames.], batch size: 14, lr: 7.00e-04
2022-05-04 05:59:09,753 INFO [train.py:715] (2/8) Epoch 2, batch 12400, loss[loss=0.1571, simple_loss=0.2276, pruned_loss=0.04327, over 4801.00 frames.], tot_loss[loss=0.173, simple_loss=0.2388, pruned_loss=0.05359, over 972729.27 frames.], batch size: 14, lr: 7.00e-04
2022-05-04 05:59:50,011 INFO [train.py:715] (2/8) Epoch 2, batch 12450, loss[loss=0.162, simple_loss=0.2446, pruned_loss=0.03972, over 4898.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2381, pruned_loss=0.05312, over 972657.80 frames.], batch size: 39, lr: 7.00e-04
2022-05-04 06:00:29,867 INFO [train.py:715] (2/8) Epoch 2, batch 12500, loss[loss=0.1704, simple_loss=0.2429, pruned_loss=0.04893, over 4964.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2383, pruned_loss=0.05334, over 973512.51 frames.], batch size: 35, lr: 6.99e-04
2022-05-04 06:01:10,535 INFO [train.py:715] (2/8) Epoch 2, batch 12550, loss[loss=0.1858, simple_loss=0.2575, pruned_loss=0.05701, over 4707.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2387, pruned_loss=0.05345, over 973466.92 frames.], batch size: 15, lr: 6.99e-04
2022-05-04 06:01:50,873 INFO [train.py:715] (2/8) Epoch 2, batch 12600, loss[loss=0.2014, simple_loss=0.2566, pruned_loss=0.07304, over 4734.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2389, pruned_loss=0.05376, over 972718.26 frames.], batch size: 16, lr: 6.99e-04
2022-05-04 06:02:30,890 INFO [train.py:715] (2/8) Epoch 2, batch 12650, loss[loss=0.1917, simple_loss=0.2554, pruned_loss=0.06406, over 4808.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2404, pruned_loss=0.05432, over 972667.07 frames.], batch size: 21, lr: 6.99e-04
2022-05-04 06:03:11,021 INFO [train.py:715] (2/8) Epoch 2, batch 12700, loss[loss=0.1791, simple_loss=0.2419, pruned_loss=0.05812, over 4807.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2393, pruned_loss=0.05349, over 972092.20 frames.], batch size: 26, lr: 6.99e-04
2022-05-04 06:03:51,749 INFO [train.py:715] (2/8) Epoch 2, batch 12750, loss[loss=0.1966, simple_loss=0.2576, pruned_loss=0.06777, over 4898.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2398, pruned_loss=0.05394, over 972654.64 frames.], batch size: 39, lr: 6.98e-04
2022-05-04 06:04:31,913 INFO [train.py:715] (2/8) Epoch 2, batch 12800, loss[loss=0.2099, simple_loss=0.2728, pruned_loss=0.07355, over 4904.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2406, pruned_loss=0.05443, over 972243.55 frames.], batch size: 19, lr: 6.98e-04
2022-05-04 06:05:11,602 INFO [train.py:715] (2/8) Epoch 2, batch 12850, loss[loss=0.2157, simple_loss=0.2601, pruned_loss=0.08563, over 4838.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2414, pruned_loss=0.05465, over 973157.29 frames.], batch size: 30, lr: 6.98e-04
2022-05-04 06:05:52,431 INFO [train.py:715] (2/8) Epoch 2, batch 12900, loss[loss=0.1654, simple_loss=0.2374, pruned_loss=0.04672, over 4782.00 frames.], tot_loss[loss=0.174, simple_loss=0.2401, pruned_loss=0.054, over 973269.92 frames.], batch size: 17, lr: 6.98e-04
2022-05-04 06:06:32,856 INFO [train.py:715] (2/8) Epoch 2, batch 12950, loss[loss=0.1743, simple_loss=0.2389, pruned_loss=0.05489, over 4908.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2398, pruned_loss=0.05426, over 972759.58 frames.], batch size: 17, lr: 6.98e-04
2022-05-04 06:07:12,806 INFO [train.py:715] (2/8) Epoch 2, batch 13000, loss[loss=0.1264, simple_loss=0.1889, pruned_loss=0.03192, over 4836.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2402, pruned_loss=0.0548, over 971429.80 frames.], batch size: 13, lr: 6.97e-04
2022-05-04 06:07:53,248 INFO [train.py:715] (2/8) Epoch 2, batch 13050, loss[loss=0.2021, simple_loss=0.2659, pruned_loss=0.06919, over 4936.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2407, pruned_loss=0.05491, over 971997.83 frames.], batch size: 29, lr: 6.97e-04
2022-05-04 06:08:34,485 INFO [train.py:715] (2/8) Epoch 2, batch 13100, loss[loss=0.1963, simple_loss=0.2566, pruned_loss=0.06797, over 4838.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2409, pruned_loss=0.05495, over 972945.40 frames.], batch size: 13, lr: 6.97e-04
2022-05-04 06:09:14,669 INFO [train.py:715] (2/8) Epoch 2, batch 13150, loss[loss=0.2274, simple_loss=0.2748, pruned_loss=0.08995, over 4813.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2398, pruned_loss=0.05422, over 972580.74 frames.], batch size: 15, lr: 6.97e-04
2022-05-04 06:09:54,432 INFO [train.py:715] (2/8) Epoch 2, batch 13200, loss[loss=0.1896, simple_loss=0.2446, pruned_loss=0.06734, over 4960.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2401, pruned_loss=0.05448, over 973175.11 frames.], batch size: 15, lr: 6.96e-04
2022-05-04 06:10:35,326 INFO [train.py:715] (2/8) Epoch 2, batch 13250, loss[loss=0.17, simple_loss=0.2269, pruned_loss=0.05652, over 4888.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2392, pruned_loss=0.05391, over 972838.06 frames.], batch size: 16, lr: 6.96e-04
2022-05-04 06:11:15,863 INFO [train.py:715] (2/8) Epoch 2, batch 13300, loss[loss=0.1394, simple_loss=0.2066, pruned_loss=0.03612, over 4953.00 frames.], tot_loss[loss=0.1734, simple_loss=0.239, pruned_loss=0.05387, over 973842.02 frames.], batch size: 24, lr: 6.96e-04
2022-05-04 06:11:55,893 INFO [train.py:715] (2/8) Epoch 2, batch 13350, loss[loss=0.1472, simple_loss=0.2221, pruned_loss=0.03619, over 4796.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2395, pruned_loss=0.05394, over 973616.32 frames.], batch size: 21, lr: 6.96e-04
2022-05-04 06:12:36,491 INFO [train.py:715] (2/8) Epoch 2, batch 13400, loss[loss=0.1847, simple_loss=0.2488, pruned_loss=0.06031, over 4976.00 frames.], tot_loss[loss=0.1741, simple_loss=0.24, pruned_loss=0.05408, over 972715.82 frames.], batch size: 35, lr: 6.96e-04
2022-05-04 06:13:17,575 INFO [train.py:715] (2/8) Epoch 2, batch 13450, loss[loss=0.1702, simple_loss=0.246, pruned_loss=0.04726, over 4760.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2402, pruned_loss=0.05403, over 971751.06 frames.], batch size: 19, lr: 6.95e-04
2022-05-04 06:13:57,528 INFO [train.py:715] (2/8) Epoch 2, batch 13500, loss[loss=0.1991, simple_loss=0.2686, pruned_loss=0.06484, over 4947.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2389, pruned_loss=0.05347, over 972250.95 frames.], batch size: 21, lr: 6.95e-04
2022-05-04 06:14:37,534 INFO [train.py:715] (2/8) Epoch 2, batch 13550, loss[loss=0.1933, simple_loss=0.259, pruned_loss=0.06383, over 4823.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2398, pruned_loss=0.05385, over 972056.33 frames.], batch size: 25, lr: 6.95e-04
2022-05-04 06:15:18,680 INFO [train.py:715] (2/8) Epoch 2, batch 13600, loss[loss=0.1949, simple_loss=0.2615, pruned_loss=0.06413, over 4833.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2392, pruned_loss=0.05372, over 971870.38 frames.], batch size: 13, lr: 6.95e-04
2022-05-04 06:15:59,125 INFO [train.py:715] (2/8) Epoch 2, batch 13650, loss[loss=0.1541, simple_loss=0.2191, pruned_loss=0.04459, over 4859.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2389, pruned_loss=0.05322, over 971874.89 frames.], batch size: 20, lr: 6.95e-04
2022-05-04 06:16:38,693 INFO [train.py:715] (2/8) Epoch 2, batch 13700, loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03157, over 4987.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2389, pruned_loss=0.05333, over 971484.27 frames.], batch size: 28, lr: 6.94e-04
2022-05-04 06:17:19,953 INFO [train.py:715] (2/8) Epoch 2, batch 13750, loss[loss=0.1544, simple_loss=0.2209, pruned_loss=0.04397, over 4727.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2383, pruned_loss=0.05253, over 971593.00 frames.], batch size: 12, lr: 6.94e-04
2022-05-04 06:18:00,033 INFO [train.py:715] (2/8) Epoch 2, batch 13800, loss[loss=0.1789, simple_loss=0.2489, pruned_loss=0.05449, over 4805.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2377, pruned_loss=0.05205, over 971247.14 frames.], batch size: 21, lr: 6.94e-04
2022-05-04 06:18:39,726 INFO [train.py:715] (2/8) Epoch 2, batch 13850, loss[loss=0.1644, simple_loss=0.233, pruned_loss=0.04785, over 4819.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2373, pruned_loss=0.05191, over 972196.11 frames.], batch size: 25, lr: 6.94e-04
2022-05-04 06:19:19,315 INFO [train.py:715] (2/8) Epoch 2, batch 13900, loss[loss=0.1418, simple_loss=0.2205, pruned_loss=0.0315, over 4843.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2371, pruned_loss=0.05227, over 972184.11 frames.], batch size: 15, lr: 6.94e-04
2022-05-04 06:20:00,080 INFO [train.py:715] (2/8) Epoch 2, batch 13950, loss[loss=0.1781, simple_loss=0.2368, pruned_loss=0.05968, over 4849.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2371, pruned_loss=0.05231, over 972790.78 frames.], batch size: 30, lr: 6.93e-04
2022-05-04 06:20:40,294 INFO [train.py:715] (2/8) Epoch 2, batch 14000, loss[loss=0.136, simple_loss=0.2113, pruned_loss=0.03035, over 4762.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2369, pruned_loss=0.05182, over 972843.93 frames.], batch size: 19, lr: 6.93e-04
2022-05-04 06:21:19,542 INFO [train.py:715] (2/8) Epoch 2, batch 14050, loss[loss=0.1698, simple_loss=0.2406, pruned_loss=0.04944, over 4971.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2377, pruned_loss=0.05245, over 973325.26 frames.], batch size: 24, lr: 6.93e-04
2022-05-04 06:22:01,047 INFO [train.py:715] (2/8) Epoch 2, batch 14100, loss[loss=0.1549, simple_loss=0.2204, pruned_loss=0.04466, over 4974.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2381, pruned_loss=0.05214, over 972572.22 frames.], batch size: 14, lr: 6.93e-04
2022-05-04 06:22:41,692 INFO [train.py:715] (2/8) Epoch 2, batch 14150, loss[loss=0.2031, simple_loss=0.2682, pruned_loss=0.06897, over 4832.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2383, pruned_loss=0.05276, over 971945.65 frames.], batch size: 15, lr: 6.93e-04
2022-05-04 06:23:21,637 INFO [train.py:715] (2/8) Epoch 2, batch 14200, loss[loss=0.1732, simple_loss=0.2467, pruned_loss=0.04988, over 4914.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2386, pruned_loss=0.05318, over 972316.50 frames.], batch size: 39, lr: 6.92e-04
2022-05-04 06:24:01,479 INFO [train.py:715] (2/8) Epoch 2, batch 14250, loss[loss=0.1565, simple_loss=0.2348, pruned_loss=0.03908, over 4985.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2376, pruned_loss=0.05257, over 972268.01 frames.], batch size: 25, lr: 6.92e-04
2022-05-04 06:24:42,093 INFO [train.py:715] (2/8) Epoch 2, batch 14300, loss[loss=0.1774, simple_loss=0.2559, pruned_loss=0.04946, over 4934.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2376, pruned_loss=0.0524, over 971568.70 frames.], batch size: 21, lr: 6.92e-04
2022-05-04 06:25:21,656 INFO [train.py:715] (2/8) Epoch 2, batch 14350, loss[loss=0.2099, simple_loss=0.2644, pruned_loss=0.07768, over 4750.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2384, pruned_loss=0.05241, over 971531.58 frames.], batch size: 16, lr: 6.92e-04
2022-05-04 06:26:01,518 INFO [train.py:715] (2/8) Epoch 2, batch 14400, loss[loss=0.1487, simple_loss=0.2246, pruned_loss=0.03639, over 4784.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2382, pruned_loss=0.0528, over 970950.44 frames.], batch size: 18, lr: 6.92e-04
2022-05-04 06:26:41,857 INFO [train.py:715] (2/8) Epoch 2, batch 14450, loss[loss=0.154, simple_loss=0.2237, pruned_loss=0.04214, over 4835.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2381, pruned_loss=0.05255, over 971774.44 frames.], batch size: 30, lr: 6.91e-04
2022-05-04 06:27:22,095 INFO [train.py:715] (2/8) Epoch 2, batch 14500, loss[loss=0.1331, simple_loss=0.2048, pruned_loss=0.03068, over 4829.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2377, pruned_loss=0.05204, over 971094.46 frames.], batch size: 26, lr: 6.91e-04
2022-05-04 06:28:01,690 INFO [train.py:715] (2/8) Epoch 2, batch 14550, loss[loss=0.164, simple_loss=0.2371, pruned_loss=0.04547, over 4917.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2364, pruned_loss=0.05131, over 971998.60 frames.], batch size: 18, lr: 6.91e-04
2022-05-04 06:28:42,166 INFO [train.py:715] (2/8) Epoch 2, batch 14600, loss[loss=0.1636, simple_loss=0.2428, pruned_loss=0.04226, over 4901.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2372, pruned_loss=0.05225, over 971849.48 frames.], batch size: 22, lr: 6.91e-04
2022-05-04 06:29:22,664 INFO [train.py:715] (2/8) Epoch 2, batch 14650, loss[loss=0.1467, simple_loss=0.2233, pruned_loss=0.03502, over 4945.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2377, pruned_loss=0.05227, over 972042.25 frames.], batch size: 29, lr: 6.90e-04
2022-05-04 06:30:01,954 INFO [train.py:715] (2/8) Epoch 2, batch 14700, loss[loss=0.184, simple_loss=0.2476, pruned_loss=0.06024, over 4993.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2376, pruned_loss=0.05243, over 972113.81 frames.], batch size: 16, lr: 6.90e-04
2022-05-04 06:30:41,279 INFO [train.py:715] (2/8) Epoch 2, batch 14750, loss[loss=0.1596, simple_loss=0.2326, pruned_loss=0.04327, over 4844.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2384, pruned_loss=0.05256, over 971713.86 frames.], batch size: 15, lr: 6.90e-04
2022-05-04 06:31:21,766 INFO [train.py:715] (2/8) Epoch 2, batch 14800, loss[loss=0.1493, simple_loss=0.219, pruned_loss=0.03977, over 4947.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2376, pruned_loss=0.05212, over 971942.15 frames.], batch size: 21, lr: 6.90e-04
2022-05-04 06:32:01,267 INFO [train.py:715] (2/8) Epoch 2, batch 14850, loss[loss=0.1701, simple_loss=0.2474, pruned_loss=0.04639, over 4801.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2385, pruned_loss=0.0524, over 971915.88 frames.], batch size: 26, lr: 6.90e-04
2022-05-04 06:32:40,950 INFO [train.py:715] (2/8) Epoch 2, batch 14900, loss[loss=0.1584, simple_loss=0.2343, pruned_loss=0.04127, over 4839.00 frames.], tot_loss[loss=0.1737, simple_loss=0.24, pruned_loss=0.05368, over 972057.23 frames.], batch size: 26, lr: 6.89e-04
2022-05-04 06:33:21,117 INFO [train.py:715] (2/8) Epoch 2, batch 14950, loss[loss=0.2068, simple_loss=0.282, pruned_loss=0.0658, over 4903.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2387, pruned_loss=0.05293, over 972545.34 frames.], batch size: 17, lr: 6.89e-04
2022-05-04 06:34:01,755 INFO [train.py:715] (2/8) Epoch 2, batch 15000, loss[loss=0.1576, simple_loss=0.2255, pruned_loss=0.0449, over 4926.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2385, pruned_loss=0.0533, over 971235.70 frames.], batch size: 29, lr: 6.89e-04
2022-05-04 06:34:01,756 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 06:34:11,142 INFO [train.py:742] (2/8) 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] (2/8) Epoch 2, batch 15050, loss[loss=0.1846, simple_loss=0.248, pruned_loss=0.06063, over 4766.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2389, pruned_loss=0.05306, over 971194.01 frames.], batch size: 14, lr: 6.89e-04
2022-05-04 06:35:31,185 INFO [train.py:715] (2/8) Epoch 2, batch 15100, loss[loss=0.1578, simple_loss=0.2239, pruned_loss=0.04582, over 4824.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2391, pruned_loss=0.05319, over 970702.62 frames.], batch size: 15, lr: 6.89e-04
2022-05-04 06:36:11,668 INFO [train.py:715] (2/8) Epoch 2, batch 15150, loss[loss=0.1872, simple_loss=0.2638, pruned_loss=0.05529, over 4995.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2388, pruned_loss=0.0527, over 971199.67 frames.], batch size: 16, lr: 6.88e-04
2022-05-04 06:36:52,158 INFO [train.py:715] (2/8) Epoch 2, batch 15200, loss[loss=0.1683, simple_loss=0.2364, pruned_loss=0.05008, over 4819.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2392, pruned_loss=0.05279, over 971746.24 frames.], batch size: 13, lr: 6.88e-04
2022-05-04 06:37:31,882 INFO [train.py:715] (2/8) Epoch 2, batch 15250, loss[loss=0.1525, simple_loss=0.2138, pruned_loss=0.04561, over 4849.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2386, pruned_loss=0.05291, over 971700.76 frames.], batch size: 30, lr: 6.88e-04
2022-05-04 06:38:11,343 INFO [train.py:715] (2/8) Epoch 2, batch 15300, loss[loss=0.1772, simple_loss=0.2449, pruned_loss=0.05475, over 4835.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2384, pruned_loss=0.05294, over 972020.93 frames.], batch size: 13, lr: 6.88e-04
2022-05-04 06:38:51,798 INFO [train.py:715] (2/8) Epoch 2, batch 15350, loss[loss=0.1858, simple_loss=0.255, pruned_loss=0.05833, over 4811.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2395, pruned_loss=0.05359, over 971748.63 frames.], batch size: 26, lr: 6.88e-04
2022-05-04 06:39:32,692 INFO [train.py:715] (2/8) Epoch 2, batch 15400, loss[loss=0.1724, simple_loss=0.232, pruned_loss=0.05642, over 4925.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2387, pruned_loss=0.05335, over 971685.53 frames.], batch size: 23, lr: 6.87e-04
2022-05-04 06:40:11,866 INFO [train.py:715] (2/8) Epoch 2, batch 15450, loss[loss=0.1551, simple_loss=0.2227, pruned_loss=0.04373, over 4974.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2382, pruned_loss=0.05303, over 972210.03 frames.], batch size: 35, lr: 6.87e-04
2022-05-04 06:40:52,371 INFO [train.py:715] (2/8) Epoch 2, batch 15500, loss[loss=0.2189, simple_loss=0.2745, pruned_loss=0.08164, over 4869.00 frames.], tot_loss[loss=0.172, simple_loss=0.2386, pruned_loss=0.05265, over 972088.12 frames.], batch size: 38, lr: 6.87e-04
2022-05-04 06:41:32,619 INFO [train.py:715] (2/8) Epoch 2, batch 15550, loss[loss=0.1622, simple_loss=0.2306, pruned_loss=0.04691, over 4971.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2384, pruned_loss=0.05243, over 971753.70 frames.], batch size: 24, lr: 6.87e-04
2022-05-04 06:42:12,559 INFO [train.py:715] (2/8) Epoch 2, batch 15600, loss[loss=0.1279, simple_loss=0.2081, pruned_loss=0.02386, over 4913.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2374, pruned_loss=0.05168, over 971207.03 frames.], batch size: 18, lr: 6.87e-04
2022-05-04 06:42:52,371 INFO [train.py:715] (2/8) Epoch 2, batch 15650, loss[loss=0.1731, simple_loss=0.2477, pruned_loss=0.04928, over 4944.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2375, pruned_loss=0.05187, over 971186.50 frames.], batch size: 29, lr: 6.86e-04
2022-05-04 06:43:33,095 INFO [train.py:715] (2/8) Epoch 2, batch 15700, loss[loss=0.202, simple_loss=0.2677, pruned_loss=0.06819, over 4951.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2372, pruned_loss=0.05194, over 971149.85 frames.], batch size: 39, lr: 6.86e-04
2022-05-04 06:44:13,627 INFO [train.py:715] (2/8) Epoch 2, batch 15750, loss[loss=0.1732, simple_loss=0.2374, pruned_loss=0.05454, over 4758.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2376, pruned_loss=0.05245, over 971442.37 frames.], batch size: 19, lr: 6.86e-04
2022-05-04 06:44:52,970 INFO [train.py:715] (2/8) Epoch 2, batch 15800, loss[loss=0.176, simple_loss=0.2558, pruned_loss=0.04816, over 4938.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2365, pruned_loss=0.0523, over 971398.10 frames.], batch size: 39, lr: 6.86e-04
2022-05-04 06:45:33,629 INFO [train.py:715] (2/8) Epoch 2, batch 15850, loss[loss=0.1563, simple_loss=0.2272, pruned_loss=0.04268, over 4785.00 frames.], tot_loss[loss=0.1712, simple_loss=0.237, pruned_loss=0.05263, over 972000.21 frames.], batch size: 17, lr: 6.86e-04
2022-05-04 06:46:14,110 INFO [train.py:715] (2/8) Epoch 2, batch 15900, loss[loss=0.2048, simple_loss=0.2666, pruned_loss=0.07149, over 4701.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2373, pruned_loss=0.05289, over 971664.56 frames.], batch size: 15, lr: 6.85e-04
2022-05-04 06:46:53,876 INFO [train.py:715] (2/8) Epoch 2, batch 15950, loss[loss=0.169, simple_loss=0.2293, pruned_loss=0.05429, over 4822.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2374, pruned_loss=0.05254, over 972139.87 frames.], batch size: 15, lr: 6.85e-04
2022-05-04 06:47:34,106 INFO [train.py:715] (2/8) Epoch 2, batch 16000, loss[loss=0.18, simple_loss=0.2392, pruned_loss=0.06038, over 4933.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2382, pruned_loss=0.05304, over 972613.72 frames.], batch size: 29, lr: 6.85e-04
2022-05-04 06:48:14,438 INFO [train.py:715] (2/8) Epoch 2, batch 16050, loss[loss=0.1943, simple_loss=0.2542, pruned_loss=0.06717, over 4988.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2387, pruned_loss=0.05338, over 973018.05 frames.], batch size: 28, lr: 6.85e-04
2022-05-04 06:48:54,890 INFO [train.py:715] (2/8) Epoch 2, batch 16100, loss[loss=0.1887, simple_loss=0.2601, pruned_loss=0.05867, over 4912.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2386, pruned_loss=0.05283, over 973057.69 frames.], batch size: 29, lr: 6.85e-04
2022-05-04 06:49:34,153 INFO [train.py:715] (2/8) Epoch 2, batch 16150, loss[loss=0.1248, simple_loss=0.2074, pruned_loss=0.0211, over 4823.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2383, pruned_loss=0.05237, over 972784.52 frames.], batch size: 25, lr: 6.84e-04
2022-05-04 06:50:14,542 INFO [train.py:715] (2/8) Epoch 2, batch 16200, loss[loss=0.1728, simple_loss=0.2541, pruned_loss=0.04577, over 4971.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2382, pruned_loss=0.05256, over 972183.32 frames.], batch size: 15, lr: 6.84e-04
2022-05-04 06:50:54,949 INFO [train.py:715] (2/8) Epoch 2, batch 16250, loss[loss=0.1385, simple_loss=0.2101, pruned_loss=0.03344, over 4953.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2382, pruned_loss=0.05274, over 973141.09 frames.], batch size: 24, lr: 6.84e-04
2022-05-04 06:51:34,790 INFO [train.py:715] (2/8) Epoch 2, batch 16300, loss[loss=0.169, simple_loss=0.2361, pruned_loss=0.05095, over 4921.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2376, pruned_loss=0.05259, over 972705.21 frames.], batch size: 18, lr: 6.84e-04
2022-05-04 06:52:14,668 INFO [train.py:715] (2/8) Epoch 2, batch 16350, loss[loss=0.1989, simple_loss=0.2541, pruned_loss=0.07184, over 4741.00 frames.], tot_loss[loss=0.172, simple_loss=0.2384, pruned_loss=0.05282, over 972494.62 frames.], batch size: 16, lr: 6.84e-04
2022-05-04 06:52:55,174 INFO [train.py:715] (2/8) Epoch 2, batch 16400, loss[loss=0.1698, simple_loss=0.233, pruned_loss=0.05328, over 4694.00 frames.], tot_loss[loss=0.1723, simple_loss=0.239, pruned_loss=0.05278, over 971859.08 frames.], batch size: 15, lr: 6.83e-04
2022-05-04 06:53:35,560 INFO [train.py:715] (2/8) Epoch 2, batch 16450, loss[loss=0.2021, simple_loss=0.2562, pruned_loss=0.07395, over 4916.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2378, pruned_loss=0.05244, over 971196.22 frames.], batch size: 17, lr: 6.83e-04
2022-05-04 06:54:15,142 INFO [train.py:715] (2/8) Epoch 2, batch 16500, loss[loss=0.1673, simple_loss=0.234, pruned_loss=0.05027, over 4789.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2383, pruned_loss=0.05247, over 971561.94 frames.], batch size: 18, lr: 6.83e-04
2022-05-04 06:54:56,126 INFO [train.py:715] (2/8) Epoch 2, batch 16550, loss[loss=0.191, simple_loss=0.253, pruned_loss=0.06446, over 4948.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2386, pruned_loss=0.05255, over 970585.05 frames.], batch size: 29, lr: 6.83e-04
2022-05-04 06:55:36,885 INFO [train.py:715] (2/8) Epoch 2, batch 16600, loss[loss=0.184, simple_loss=0.2654, pruned_loss=0.05129, over 4936.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2384, pruned_loss=0.05239, over 972134.71 frames.], batch size: 29, lr: 6.83e-04
2022-05-04 06:56:16,714 INFO [train.py:715] (2/8) Epoch 2, batch 16650, loss[loss=0.2109, simple_loss=0.2679, pruned_loss=0.07697, over 4866.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2376, pruned_loss=0.05191, over 972451.86 frames.], batch size: 30, lr: 6.82e-04
2022-05-04 06:56:57,157 INFO [train.py:715] (2/8) Epoch 2, batch 16700, loss[loss=0.1795, simple_loss=0.2471, pruned_loss=0.05593, over 4780.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2381, pruned_loss=0.0525, over 972315.79 frames.], batch size: 14, lr: 6.82e-04
2022-05-04 06:57:37,913 INFO [train.py:715] (2/8) Epoch 2, batch 16750, loss[loss=0.1777, simple_loss=0.2434, pruned_loss=0.056, over 4747.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2378, pruned_loss=0.05248, over 971865.62 frames.], batch size: 19, lr: 6.82e-04
2022-05-04 06:58:18,621 INFO [train.py:715] (2/8) Epoch 2, batch 16800, loss[loss=0.1844, simple_loss=0.2636, pruned_loss=0.05258, over 4957.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2389, pruned_loss=0.05297, over 971264.31 frames.], batch size: 15, lr: 6.82e-04
2022-05-04 06:58:58,046 INFO [train.py:715] (2/8) Epoch 2, batch 16850, loss[loss=0.1636, simple_loss=0.2311, pruned_loss=0.048, over 4898.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2384, pruned_loss=0.05332, over 971456.96 frames.], batch size: 19, lr: 6.82e-04
2022-05-04 06:59:39,310 INFO [train.py:715] (2/8) Epoch 2, batch 16900, loss[loss=0.1616, simple_loss=0.2286, pruned_loss=0.04724, over 4826.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2375, pruned_loss=0.05252, over 971559.98 frames.], batch size: 25, lr: 6.81e-04
2022-05-04 07:00:20,135 INFO [train.py:715] (2/8) Epoch 2, batch 16950, loss[loss=0.2407, simple_loss=0.29, pruned_loss=0.09568, over 4757.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2378, pruned_loss=0.05235, over 971464.97 frames.], batch size: 19, lr: 6.81e-04
2022-05-04 07:00:59,939 INFO [train.py:715] (2/8) Epoch 2, batch 17000, loss[loss=0.1448, simple_loss=0.215, pruned_loss=0.0373, over 4980.00 frames.], tot_loss[loss=0.1715, simple_loss=0.238, pruned_loss=0.05254, over 972511.65 frames.], batch size: 28, lr: 6.81e-04
2022-05-04 07:01:40,369 INFO [train.py:715] (2/8) Epoch 2, batch 17050, loss[loss=0.1617, simple_loss=0.2249, pruned_loss=0.04926, over 4987.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2379, pruned_loss=0.05251, over 971969.93 frames.], batch size: 14, lr: 6.81e-04
2022-05-04 07:02:20,959 INFO [train.py:715] (2/8) Epoch 2, batch 17100, loss[loss=0.1468, simple_loss=0.2316, pruned_loss=0.03102, over 4775.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2386, pruned_loss=0.05284, over 971740.89 frames.], batch size: 17, lr: 6.81e-04
2022-05-04 07:03:01,188 INFO [train.py:715] (2/8) Epoch 2, batch 17150, loss[loss=0.1571, simple_loss=0.2216, pruned_loss=0.04627, over 4849.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2378, pruned_loss=0.05245, over 971844.36 frames.], batch size: 32, lr: 6.81e-04
2022-05-04 07:03:40,478 INFO [train.py:715] (2/8) Epoch 2, batch 17200, loss[loss=0.1684, simple_loss=0.2339, pruned_loss=0.05144, over 4993.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2373, pruned_loss=0.05201, over 972613.20 frames.], batch size: 16, lr: 6.80e-04
2022-05-04 07:04:20,882 INFO [train.py:715] (2/8) Epoch 2, batch 17250, loss[loss=0.1729, simple_loss=0.2279, pruned_loss=0.05898, over 4859.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2361, pruned_loss=0.05134, over 973479.43 frames.], batch size: 32, lr: 6.80e-04
2022-05-04 07:05:01,343 INFO [train.py:715] (2/8) Epoch 2, batch 17300, loss[loss=0.1566, simple_loss=0.2217, pruned_loss=0.0458, over 4796.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2369, pruned_loss=0.05207, over 973188.24 frames.], batch size: 24, lr: 6.80e-04
2022-05-04 07:05:40,920 INFO [train.py:715] (2/8) Epoch 2, batch 17350, loss[loss=0.147, simple_loss=0.2152, pruned_loss=0.03943, over 4863.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2363, pruned_loss=0.05161, over 972547.51 frames.], batch size: 20, lr: 6.80e-04
2022-05-04 07:06:20,381 INFO [train.py:715] (2/8) Epoch 2, batch 17400, loss[loss=0.1728, simple_loss=0.2344, pruned_loss=0.05563, over 4886.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2374, pruned_loss=0.0524, over 972100.18 frames.], batch size: 32, lr: 6.80e-04
2022-05-04 07:07:00,338 INFO [train.py:715] (2/8) Epoch 2, batch 17450, loss[loss=0.2129, simple_loss=0.2657, pruned_loss=0.08006, over 4937.00 frames.], tot_loss[loss=0.1704, simple_loss=0.237, pruned_loss=0.05189, over 972418.48 frames.], batch size: 39, lr: 6.79e-04
2022-05-04 07:07:40,087 INFO [train.py:715] (2/8) Epoch 2, batch 17500, loss[loss=0.1703, simple_loss=0.2383, pruned_loss=0.05113, over 4773.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2372, pruned_loss=0.05199, over 971555.65 frames.], batch size: 17, lr: 6.79e-04
2022-05-04 07:08:18,847 INFO [train.py:715] (2/8) Epoch 2, batch 17550, loss[loss=0.1579, simple_loss=0.2321, pruned_loss=0.04189, over 4764.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2385, pruned_loss=0.05246, over 970897.91 frames.], batch size: 19, lr: 6.79e-04
2022-05-04 07:08:58,966 INFO [train.py:715] (2/8) Epoch 2, batch 17600, loss[loss=0.1917, simple_loss=0.2527, pruned_loss=0.06535, over 4840.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2386, pruned_loss=0.05251, over 971367.41 frames.], batch size: 32, lr: 6.79e-04
2022-05-04 07:09:38,407 INFO [train.py:715] (2/8) Epoch 2, batch 17650, loss[loss=0.1797, simple_loss=0.25, pruned_loss=0.05467, over 4977.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2383, pruned_loss=0.05258, over 970712.76 frames.], batch size: 28, lr: 6.79e-04
2022-05-04 07:10:17,887 INFO [train.py:715] (2/8) Epoch 2, batch 17700, loss[loss=0.1636, simple_loss=0.2306, pruned_loss=0.04831, over 4961.00 frames.], tot_loss[loss=0.171, simple_loss=0.2377, pruned_loss=0.05218, over 970943.65 frames.], batch size: 14, lr: 6.78e-04
2022-05-04 07:10:57,822 INFO [train.py:715] (2/8) Epoch 2, batch 17750, loss[loss=0.1773, simple_loss=0.2493, pruned_loss=0.0527, over 4871.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2381, pruned_loss=0.05257, over 971503.18 frames.], batch size: 22, lr: 6.78e-04
2022-05-04 07:11:37,684 INFO [train.py:715] (2/8) Epoch 2, batch 17800, loss[loss=0.1679, simple_loss=0.2303, pruned_loss=0.05274, over 4990.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2368, pruned_loss=0.0517, over 971993.16 frames.], batch size: 14, lr: 6.78e-04
2022-05-04 07:12:17,970 INFO [train.py:715] (2/8) Epoch 2, batch 17850, loss[loss=0.1741, simple_loss=0.2321, pruned_loss=0.05808, over 4886.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2372, pruned_loss=0.05196, over 972478.03 frames.], batch size: 32, lr: 6.78e-04
2022-05-04 07:12:56,810 INFO [train.py:715] (2/8) Epoch 2, batch 17900, loss[loss=0.2246, simple_loss=0.2851, pruned_loss=0.08208, over 4987.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2379, pruned_loss=0.05221, over 972131.92 frames.], batch size: 28, lr: 6.78e-04
2022-05-04 07:13:36,729 INFO [train.py:715] (2/8) Epoch 2, batch 17950, loss[loss=0.1442, simple_loss=0.2057, pruned_loss=0.04137, over 4910.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2381, pruned_loss=0.05227, over 972474.93 frames.], batch size: 23, lr: 6.77e-04
2022-05-04 07:14:16,905 INFO [train.py:715] (2/8) Epoch 2, batch 18000, loss[loss=0.1614, simple_loss=0.2226, pruned_loss=0.05004, over 4772.00 frames.], tot_loss[loss=0.1714, simple_loss=0.238, pruned_loss=0.05239, over 971439.81 frames.], batch size: 12, lr: 6.77e-04
2022-05-04 07:14:16,906 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 07:14:26,627 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1173, simple_loss=0.2039, pruned_loss=0.01538, over 914524.00 frames.
2022-05-04 07:15:07,345 INFO [train.py:715] (2/8) Epoch 2, batch 18050, loss[loss=0.1681, simple_loss=0.2315, pruned_loss=0.05239, over 4920.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2381, pruned_loss=0.05221, over 971472.82 frames.], batch size: 23, lr: 6.77e-04
2022-05-04 07:15:46,520 INFO [train.py:715] (2/8) Epoch 2, batch 18100, loss[loss=0.1905, simple_loss=0.249, pruned_loss=0.06603, over 4966.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2384, pruned_loss=0.05264, over 972622.63 frames.], batch size: 15, lr: 6.77e-04
2022-05-04 07:16:27,416 INFO [train.py:715] (2/8) Epoch 2, batch 18150, loss[loss=0.1919, simple_loss=0.2607, pruned_loss=0.0616, over 4768.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2396, pruned_loss=0.05331, over 972448.53 frames.], batch size: 19, lr: 6.77e-04
2022-05-04 07:17:08,361 INFO [train.py:715] (2/8) Epoch 2, batch 18200, loss[loss=0.1459, simple_loss=0.2167, pruned_loss=0.03753, over 4827.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2394, pruned_loss=0.05315, over 972011.73 frames.], batch size: 15, lr: 6.76e-04
2022-05-04 07:17:49,836 INFO [train.py:715] (2/8) Epoch 2, batch 18250, loss[loss=0.1851, simple_loss=0.2496, pruned_loss=0.06032, over 4873.00 frames.], tot_loss[loss=0.174, simple_loss=0.2403, pruned_loss=0.05385, over 973549.88 frames.], batch size: 30, lr: 6.76e-04
2022-05-04 07:18:30,264 INFO [train.py:715] (2/8) Epoch 2, batch 18300, loss[loss=0.1398, simple_loss=0.2014, pruned_loss=0.03908, over 4814.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2395, pruned_loss=0.05328, over 973231.82 frames.], batch size: 13, lr: 6.76e-04
2022-05-04 07:19:12,139 INFO [train.py:715] (2/8) Epoch 2, batch 18350, loss[loss=0.163, simple_loss=0.2329, pruned_loss=0.04649, over 4924.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2393, pruned_loss=0.05313, over 972807.75 frames.], batch size: 23, lr: 6.76e-04
2022-05-04 07:19:56,490 INFO [train.py:715] (2/8) Epoch 2, batch 18400, loss[loss=0.1674, simple_loss=0.2339, pruned_loss=0.0504, over 4985.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2393, pruned_loss=0.05311, over 972342.34 frames.], batch size: 14, lr: 6.76e-04
2022-05-04 07:20:36,584 INFO [train.py:715] (2/8) Epoch 2, batch 18450, loss[loss=0.1514, simple_loss=0.2262, pruned_loss=0.03835, over 4988.00 frames.], tot_loss[loss=0.172, simple_loss=0.2387, pruned_loss=0.05265, over 973021.32 frames.], batch size: 25, lr: 6.75e-04
2022-05-04 07:21:18,111 INFO [train.py:715] (2/8) Epoch 2, batch 18500, loss[loss=0.2056, simple_loss=0.261, pruned_loss=0.07507, over 4938.00 frames.], tot_loss[loss=0.1698, simple_loss=0.237, pruned_loss=0.05125, over 973578.07 frames.], batch size: 39, lr: 6.75e-04
2022-05-04 07:21:59,804 INFO [train.py:715] (2/8) Epoch 2, batch 18550, loss[loss=0.1757, simple_loss=0.2412, pruned_loss=0.05507, over 4977.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2374, pruned_loss=0.05149, over 973085.18 frames.], batch size: 15, lr: 6.75e-04
2022-05-04 07:22:41,500 INFO [train.py:715] (2/8) Epoch 2, batch 18600, loss[loss=0.158, simple_loss=0.221, pruned_loss=0.04751, over 4760.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2387, pruned_loss=0.05251, over 972797.19 frames.], batch size: 18, lr: 6.75e-04
2022-05-04 07:23:21,824 INFO [train.py:715] (2/8) Epoch 2, batch 18650, loss[loss=0.1381, simple_loss=0.2057, pruned_loss=0.0353, over 4824.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2376, pruned_loss=0.05189, over 972651.24 frames.], batch size: 15, lr: 6.75e-04
2022-05-04 07:24:03,467 INFO [train.py:715] (2/8) Epoch 2, batch 18700, loss[loss=0.1591, simple_loss=0.2337, pruned_loss=0.04221, over 4758.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2381, pruned_loss=0.0522, over 971954.11 frames.], batch size: 16, lr: 6.75e-04
2022-05-04 07:24:45,158 INFO [train.py:715] (2/8) Epoch 2, batch 18750, loss[loss=0.1944, simple_loss=0.2474, pruned_loss=0.07065, over 4891.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2373, pruned_loss=0.05162, over 971541.25 frames.], batch size: 16, lr: 6.74e-04
2022-05-04 07:25:25,708 INFO [train.py:715] (2/8) Epoch 2, batch 18800, loss[loss=0.1686, simple_loss=0.2341, pruned_loss=0.05158, over 4870.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2371, pruned_loss=0.05116, over 972080.07 frames.], batch size: 16, lr: 6.74e-04
2022-05-04 07:26:06,653 INFO [train.py:715] (2/8) Epoch 2, batch 18850, loss[loss=0.1916, simple_loss=0.2615, pruned_loss=0.06088, over 4847.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2364, pruned_loss=0.05112, over 972135.17 frames.], batch size: 30, lr: 6.74e-04
2022-05-04 07:26:48,062 INFO [train.py:715] (2/8) Epoch 2, batch 18900, loss[loss=0.1664, simple_loss=0.24, pruned_loss=0.04637, over 4935.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2376, pruned_loss=0.05172, over 972780.47 frames.], batch size: 29, lr: 6.74e-04
2022-05-04 07:27:29,060 INFO [train.py:715] (2/8) Epoch 2, batch 18950, loss[loss=0.1542, simple_loss=0.2222, pruned_loss=0.04311, over 4922.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2385, pruned_loss=0.05221, over 973124.37 frames.], batch size: 29, lr: 6.74e-04
2022-05-04 07:28:09,456 INFO [train.py:715] (2/8) Epoch 2, batch 19000, loss[loss=0.1564, simple_loss=0.2314, pruned_loss=0.04069, over 4992.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2383, pruned_loss=0.05222, over 973280.58 frames.], batch size: 24, lr: 6.73e-04
2022-05-04 07:28:50,986 INFO [train.py:715] (2/8) Epoch 2, batch 19050, loss[loss=0.1654, simple_loss=0.2324, pruned_loss=0.04921, over 4991.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2382, pruned_loss=0.05265, over 973159.96 frames.], batch size: 14, lr: 6.73e-04
2022-05-04 07:29:32,575 INFO [train.py:715] (2/8) Epoch 2, batch 19100, loss[loss=0.1614, simple_loss=0.2467, pruned_loss=0.03804, over 4897.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2381, pruned_loss=0.05228, over 972584.67 frames.], batch size: 22, lr: 6.73e-04
2022-05-04 07:30:13,182 INFO [train.py:715] (2/8) Epoch 2, batch 19150, loss[loss=0.1804, simple_loss=0.2476, pruned_loss=0.05658, over 4899.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2385, pruned_loss=0.0526, over 972388.46 frames.], batch size: 17, lr: 6.73e-04
2022-05-04 07:30:53,890 INFO [train.py:715] (2/8) Epoch 2, batch 19200, loss[loss=0.178, simple_loss=0.24, pruned_loss=0.05795, over 4974.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2391, pruned_loss=0.05286, over 972155.32 frames.], batch size: 24, lr: 6.73e-04
2022-05-04 07:31:34,997 INFO [train.py:715] (2/8) Epoch 2, batch 19250, loss[loss=0.1853, simple_loss=0.245, pruned_loss=0.06277, over 4925.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2381, pruned_loss=0.05215, over 971640.76 frames.], batch size: 18, lr: 6.72e-04
2022-05-04 07:32:15,441 INFO [train.py:715] (2/8) Epoch 2, batch 19300, loss[loss=0.1727, simple_loss=0.2478, pruned_loss=0.04879, over 4813.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2381, pruned_loss=0.05216, over 971603.20 frames.], batch size: 21, lr: 6.72e-04
2022-05-04 07:32:55,595 INFO [train.py:715] (2/8) Epoch 2, batch 19350, loss[loss=0.1628, simple_loss=0.2335, pruned_loss=0.04608, over 4818.00 frames.], tot_loss[loss=0.1712, simple_loss=0.238, pruned_loss=0.05217, over 971076.95 frames.], batch size: 27, lr: 6.72e-04
2022-05-04 07:33:36,548 INFO [train.py:715] (2/8) Epoch 2, batch 19400, loss[loss=0.1844, simple_loss=0.252, pruned_loss=0.05843, over 4892.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2377, pruned_loss=0.05205, over 971061.07 frames.], batch size: 22, lr: 6.72e-04
2022-05-04 07:34:18,467 INFO [train.py:715] (2/8) Epoch 2, batch 19450, loss[loss=0.178, simple_loss=0.2486, pruned_loss=0.05369, over 4812.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2367, pruned_loss=0.0512, over 971911.59 frames.], batch size: 26, lr: 6.72e-04
2022-05-04 07:34:58,699 INFO [train.py:715] (2/8) Epoch 2, batch 19500, loss[loss=0.1737, simple_loss=0.247, pruned_loss=0.05022, over 4954.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2368, pruned_loss=0.05121, over 971472.48 frames.], batch size: 39, lr: 6.72e-04
2022-05-04 07:35:38,972 INFO [train.py:715] (2/8) Epoch 2, batch 19550, loss[loss=0.1422, simple_loss=0.2111, pruned_loss=0.03665, over 4910.00 frames.], tot_loss[loss=0.17, simple_loss=0.2369, pruned_loss=0.0515, over 972181.09 frames.], batch size: 23, lr: 6.71e-04
2022-05-04 07:36:20,464 INFO [train.py:715] (2/8) Epoch 2, batch 19600, loss[loss=0.1621, simple_loss=0.2284, pruned_loss=0.0479, over 4784.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2371, pruned_loss=0.05166, over 972770.26 frames.], batch size: 14, lr: 6.71e-04
2022-05-04 07:37:01,100 INFO [train.py:715] (2/8) Epoch 2, batch 19650, loss[loss=0.1542, simple_loss=0.2254, pruned_loss=0.04148, over 4686.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2372, pruned_loss=0.05214, over 972866.67 frames.], batch size: 15, lr: 6.71e-04
2022-05-04 07:37:40,938 INFO [train.py:715] (2/8) Epoch 2, batch 19700, loss[loss=0.1656, simple_loss=0.225, pruned_loss=0.05312, over 4812.00 frames.], tot_loss[loss=0.1704, simple_loss=0.237, pruned_loss=0.05192, over 973139.51 frames.], batch size: 26, lr: 6.71e-04
2022-05-04 07:38:21,825 INFO [train.py:715] (2/8) Epoch 2, batch 19750, loss[loss=0.1628, simple_loss=0.2438, pruned_loss=0.04085, over 4727.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2371, pruned_loss=0.05192, over 972880.72 frames.], batch size: 16, lr: 6.71e-04
2022-05-04 07:39:02,963 INFO [train.py:715] (2/8) Epoch 2, batch 19800, loss[loss=0.1889, simple_loss=0.2536, pruned_loss=0.06213, over 4965.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2378, pruned_loss=0.0524, over 973388.81 frames.], batch size: 24, lr: 6.70e-04
2022-05-04 07:39:42,758 INFO [train.py:715] (2/8) Epoch 2, batch 19850, loss[loss=0.1743, simple_loss=0.2399, pruned_loss=0.05436, over 4700.00 frames.], tot_loss[loss=0.171, simple_loss=0.2376, pruned_loss=0.05217, over 972977.63 frames.], batch size: 15, lr: 6.70e-04
2022-05-04 07:40:23,471 INFO [train.py:715] (2/8) Epoch 2, batch 19900, loss[loss=0.1432, simple_loss=0.2145, pruned_loss=0.03593, over 4910.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2379, pruned_loss=0.05226, over 972914.47 frames.], batch size: 18, lr: 6.70e-04
2022-05-04 07:41:04,449 INFO [train.py:715] (2/8) Epoch 2, batch 19950, loss[loss=0.1594, simple_loss=0.2161, pruned_loss=0.0513, over 4947.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2379, pruned_loss=0.05238, over 972780.14 frames.], batch size: 21, lr: 6.70e-04
2022-05-04 07:41:44,792 INFO [train.py:715] (2/8) Epoch 2, batch 20000, loss[loss=0.1724, simple_loss=0.2335, pruned_loss=0.05562, over 4975.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2371, pruned_loss=0.052, over 973560.65 frames.], batch size: 35, lr: 6.70e-04
2022-05-04 07:42:25,545 INFO [train.py:715] (2/8) Epoch 2, batch 20050, loss[loss=0.159, simple_loss=0.227, pruned_loss=0.04544, over 4844.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2365, pruned_loss=0.05133, over 974318.96 frames.], batch size: 20, lr: 6.69e-04
2022-05-04 07:43:06,877 INFO [train.py:715] (2/8) Epoch 2, batch 20100, loss[loss=0.1933, simple_loss=0.2657, pruned_loss=0.06048, over 4983.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2367, pruned_loss=0.05117, over 973724.62 frames.], batch size: 15, lr: 6.69e-04
2022-05-04 07:43:48,579 INFO [train.py:715] (2/8) Epoch 2, batch 20150, loss[loss=0.1816, simple_loss=0.2424, pruned_loss=0.06039, over 4917.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2375, pruned_loss=0.05175, over 973310.92 frames.], batch size: 18, lr: 6.69e-04
2022-05-04 07:44:28,874 INFO [train.py:715] (2/8) Epoch 2, batch 20200, loss[loss=0.1458, simple_loss=0.2091, pruned_loss=0.04122, over 4798.00 frames.], tot_loss[loss=0.1697, simple_loss=0.237, pruned_loss=0.05117, over 973524.75 frames.], batch size: 12, lr: 6.69e-04
2022-05-04 07:45:10,317 INFO [train.py:715] (2/8) Epoch 2, batch 20250, loss[loss=0.1754, simple_loss=0.2348, pruned_loss=0.05801, over 4887.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2365, pruned_loss=0.05069, over 973914.42 frames.], batch size: 16, lr: 6.69e-04
2022-05-04 07:45:52,264 INFO [train.py:715] (2/8) Epoch 2, batch 20300, loss[loss=0.1472, simple_loss=0.2111, pruned_loss=0.04161, over 4791.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2368, pruned_loss=0.05087, over 973360.38 frames.], batch size: 14, lr: 6.69e-04
2022-05-04 07:46:33,087 INFO [train.py:715] (2/8) Epoch 2, batch 20350, loss[loss=0.1777, simple_loss=0.2372, pruned_loss=0.05909, over 4780.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2367, pruned_loss=0.05105, over 972789.09 frames.], batch size: 18, lr: 6.68e-04
2022-05-04 07:47:14,065 INFO [train.py:715] (2/8) Epoch 2, batch 20400, loss[loss=0.2094, simple_loss=0.2694, pruned_loss=0.07473, over 4837.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2376, pruned_loss=0.05174, over 972846.09 frames.], batch size: 15, lr: 6.68e-04
2022-05-04 07:47:56,136 INFO [train.py:715] (2/8) Epoch 2, batch 20450, loss[loss=0.1818, simple_loss=0.2528, pruned_loss=0.05545, over 4875.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2372, pruned_loss=0.05182, over 972289.96 frames.], batch size: 16, lr: 6.68e-04
2022-05-04 07:48:37,698 INFO [train.py:715] (2/8) Epoch 2, batch 20500, loss[loss=0.1815, simple_loss=0.2422, pruned_loss=0.06042, over 4924.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2375, pruned_loss=0.05164, over 973285.89 frames.], batch size: 18, lr: 6.68e-04
2022-05-04 07:49:18,507 INFO [train.py:715] (2/8) Epoch 2, batch 20550, loss[loss=0.1623, simple_loss=0.238, pruned_loss=0.04329, over 4785.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2377, pruned_loss=0.05187, over 973840.24 frames.], batch size: 18, lr: 6.68e-04
2022-05-04 07:49:59,713 INFO [train.py:715] (2/8) Epoch 2, batch 20600, loss[loss=0.1911, simple_loss=0.2623, pruned_loss=0.05996, over 4962.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2367, pruned_loss=0.05087, over 974167.57 frames.], batch size: 24, lr: 6.67e-04
2022-05-04 07:50:41,270 INFO [train.py:715] (2/8) Epoch 2, batch 20650, loss[loss=0.1307, simple_loss=0.2037, pruned_loss=0.02884, over 4780.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2366, pruned_loss=0.05119, over 973184.36 frames.], batch size: 14, lr: 6.67e-04
2022-05-04 07:51:22,500 INFO [train.py:715] (2/8) Epoch 2, batch 20700, loss[loss=0.1525, simple_loss=0.2254, pruned_loss=0.03982, over 4907.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2358, pruned_loss=0.05085, over 973199.24 frames.], batch size: 19, lr: 6.67e-04
2022-05-04 07:52:03,024 INFO [train.py:715] (2/8) Epoch 2, batch 20750, loss[loss=0.1551, simple_loss=0.2269, pruned_loss=0.04161, over 4923.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2362, pruned_loss=0.05133, over 973559.12 frames.], batch size: 18, lr: 6.67e-04
2022-05-04 07:52:44,269 INFO [train.py:715] (2/8) Epoch 2, batch 20800, loss[loss=0.1903, simple_loss=0.2689, pruned_loss=0.0558, over 4990.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2363, pruned_loss=0.05119, over 973677.61 frames.], batch size: 28, lr: 6.67e-04
2022-05-04 07:53:25,477 INFO [train.py:715] (2/8) Epoch 2, batch 20850, loss[loss=0.175, simple_loss=0.2506, pruned_loss=0.04966, over 4881.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2358, pruned_loss=0.05134, over 973339.74 frames.], batch size: 16, lr: 6.66e-04
2022-05-04 07:54:06,134 INFO [train.py:715] (2/8) Epoch 2, batch 20900, loss[loss=0.1525, simple_loss=0.2277, pruned_loss=0.03864, over 4820.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2362, pruned_loss=0.05133, over 972275.53 frames.], batch size: 26, lr: 6.66e-04
2022-05-04 07:54:47,182 INFO [train.py:715] (2/8) Epoch 2, batch 20950, loss[loss=0.1555, simple_loss=0.2363, pruned_loss=0.03732, over 4958.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2361, pruned_loss=0.05124, over 972701.74 frames.], batch size: 39, lr: 6.66e-04
2022-05-04 07:55:28,391 INFO [train.py:715] (2/8) Epoch 2, batch 21000, loss[loss=0.1765, simple_loss=0.2425, pruned_loss=0.05525, over 4902.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2361, pruned_loss=0.0515, over 971613.78 frames.], batch size: 38, lr: 6.66e-04
2022-05-04 07:55:28,392 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 07:55:39,044 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1174, simple_loss=0.2036, pruned_loss=0.01562, over 914524.00 frames.
2022-05-04 07:56:20,520 INFO [train.py:715] (2/8) Epoch 2, batch 21050, loss[loss=0.1668, simple_loss=0.2232, pruned_loss=0.05523, over 4856.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2358, pruned_loss=0.05148, over 971628.56 frames.], batch size: 34, lr: 6.66e-04
2022-05-04 07:57:00,978 INFO [train.py:715] (2/8) Epoch 2, batch 21100, loss[loss=0.1834, simple_loss=0.2496, pruned_loss=0.05855, over 4901.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2367, pruned_loss=0.05227, over 971235.32 frames.], batch size: 18, lr: 6.66e-04
2022-05-04 07:57:41,521 INFO [train.py:715] (2/8) Epoch 2, batch 21150, loss[loss=0.1656, simple_loss=0.2367, pruned_loss=0.04726, over 4887.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2358, pruned_loss=0.05134, over 971410.04 frames.], batch size: 22, lr: 6.65e-04
2022-05-04 07:58:22,030 INFO [train.py:715] (2/8) Epoch 2, batch 21200, loss[loss=0.1326, simple_loss=0.2021, pruned_loss=0.03152, over 4835.00 frames.], tot_loss[loss=0.1693, simple_loss=0.236, pruned_loss=0.05131, over 971799.47 frames.], batch size: 13, lr: 6.65e-04
2022-05-04 07:59:02,140 INFO [train.py:715] (2/8) Epoch 2, batch 21250, loss[loss=0.1687, simple_loss=0.2425, pruned_loss=0.04747, over 4932.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2355, pruned_loss=0.05149, over 972486.62 frames.], batch size: 23, lr: 6.65e-04
2022-05-04 07:59:42,835 INFO [train.py:715] (2/8) Epoch 2, batch 21300, loss[loss=0.185, simple_loss=0.2633, pruned_loss=0.05335, over 4686.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2362, pruned_loss=0.05133, over 973361.08 frames.], batch size: 15, lr: 6.65e-04
2022-05-04 08:00:23,550 INFO [train.py:715] (2/8) Epoch 2, batch 21350, loss[loss=0.1449, simple_loss=0.2013, pruned_loss=0.04423, over 4802.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2376, pruned_loss=0.05206, over 973315.56 frames.], batch size: 13, lr: 6.65e-04
2022-05-04 08:01:04,854 INFO [train.py:715] (2/8) Epoch 2, batch 21400, loss[loss=0.1568, simple_loss=0.2224, pruned_loss=0.04557, over 4859.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2369, pruned_loss=0.05164, over 973508.81 frames.], batch size: 20, lr: 6.64e-04
2022-05-04 08:01:45,136 INFO [train.py:715] (2/8) Epoch 2, batch 21450, loss[loss=0.1409, simple_loss=0.2101, pruned_loss=0.03588, over 4835.00 frames.], tot_loss[loss=0.1714, simple_loss=0.238, pruned_loss=0.05241, over 973227.37 frames.], batch size: 30, lr: 6.64e-04
2022-05-04 08:02:26,052 INFO [train.py:715] (2/8) Epoch 2, batch 21500, loss[loss=0.1947, simple_loss=0.2545, pruned_loss=0.0674, over 4890.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2381, pruned_loss=0.05257, over 973539.53 frames.], batch size: 22, lr: 6.64e-04
2022-05-04 08:03:07,347 INFO [train.py:715] (2/8) Epoch 2, batch 21550, loss[loss=0.1705, simple_loss=0.239, pruned_loss=0.05099, over 4882.00 frames.], tot_loss[loss=0.172, simple_loss=0.2383, pruned_loss=0.05283, over 973515.41 frames.], batch size: 22, lr: 6.64e-04
2022-05-04 08:03:47,331 INFO [train.py:715] (2/8) Epoch 2, batch 21600, loss[loss=0.2101, simple_loss=0.2697, pruned_loss=0.07523, over 4749.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2383, pruned_loss=0.05307, over 974151.84 frames.], batch size: 16, lr: 6.64e-04
2022-05-04 08:04:28,560 INFO [train.py:715] (2/8) Epoch 2, batch 21650, loss[loss=0.1431, simple_loss=0.2146, pruned_loss=0.03584, over 4824.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2366, pruned_loss=0.05188, over 973782.77 frames.], batch size: 13, lr: 6.64e-04
2022-05-04 08:05:10,100 INFO [train.py:715] (2/8) Epoch 2, batch 21700, loss[loss=0.1836, simple_loss=0.233, pruned_loss=0.06712, over 4960.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2368, pruned_loss=0.05186, over 974249.55 frames.], batch size: 35, lr: 6.63e-04
2022-05-04 08:05:50,681 INFO [train.py:715] (2/8) Epoch 2, batch 21750, loss[loss=0.1504, simple_loss=0.2129, pruned_loss=0.04395, over 4823.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2361, pruned_loss=0.05121, over 974375.27 frames.], batch size: 12, lr: 6.63e-04
2022-05-04 08:06:31,752 INFO [train.py:715] (2/8) Epoch 2, batch 21800, loss[loss=0.1756, simple_loss=0.2455, pruned_loss=0.05283, over 4917.00 frames.], tot_loss[loss=0.17, simple_loss=0.2367, pruned_loss=0.05171, over 974738.39 frames.], batch size: 29, lr: 6.63e-04
2022-05-04 08:07:12,168 INFO [train.py:715] (2/8) Epoch 2, batch 21850, loss[loss=0.1557, simple_loss=0.2214, pruned_loss=0.04503, over 4885.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2353, pruned_loss=0.0509, over 975304.46 frames.], batch size: 22, lr: 6.63e-04
2022-05-04 08:07:53,261 INFO [train.py:715] (2/8) Epoch 2, batch 21900, loss[loss=0.1866, simple_loss=0.2634, pruned_loss=0.05488, over 4804.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2343, pruned_loss=0.05004, over 974128.90 frames.], batch size: 25, lr: 6.63e-04
2022-05-04 08:08:33,955 INFO [train.py:715] (2/8) Epoch 2, batch 21950, loss[loss=0.1805, simple_loss=0.2449, pruned_loss=0.05801, over 4968.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2346, pruned_loss=0.05063, over 973898.01 frames.], batch size: 35, lr: 6.62e-04
2022-05-04 08:09:15,709 INFO [train.py:715] (2/8) Epoch 2, batch 22000, loss[loss=0.1728, simple_loss=0.2346, pruned_loss=0.05545, over 4916.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2344, pruned_loss=0.05043, over 973033.80 frames.], batch size: 17, lr: 6.62e-04
2022-05-04 08:09:57,809 INFO [train.py:715] (2/8) Epoch 2, batch 22050, loss[loss=0.1612, simple_loss=0.2223, pruned_loss=0.05007, over 4810.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2353, pruned_loss=0.05118, over 972728.91 frames.], batch size: 25, lr: 6.62e-04
2022-05-04 08:10:38,621 INFO [train.py:715] (2/8) Epoch 2, batch 22100, loss[loss=0.1739, simple_loss=0.2441, pruned_loss=0.05185, over 4771.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2358, pruned_loss=0.0513, over 972864.13 frames.], batch size: 17, lr: 6.62e-04
2022-05-04 08:11:20,104 INFO [train.py:715] (2/8) Epoch 2, batch 22150, loss[loss=0.1959, simple_loss=0.2548, pruned_loss=0.06847, over 4970.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2359, pruned_loss=0.05143, over 972902.31 frames.], batch size: 28, lr: 6.62e-04
2022-05-04 08:12:01,860 INFO [train.py:715] (2/8) Epoch 2, batch 22200, loss[loss=0.1856, simple_loss=0.249, pruned_loss=0.06107, over 4866.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2349, pruned_loss=0.05067, over 971536.93 frames.], batch size: 16, lr: 6.62e-04
2022-05-04 08:12:43,321 INFO [train.py:715] (2/8) Epoch 2, batch 22250, loss[loss=0.2083, simple_loss=0.2778, pruned_loss=0.06945, over 4947.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2359, pruned_loss=0.05081, over 972295.95 frames.], batch size: 23, lr: 6.61e-04
2022-05-04 08:13:24,121 INFO [train.py:715] (2/8) Epoch 2, batch 22300, loss[loss=0.1817, simple_loss=0.2419, pruned_loss=0.06078, over 4983.00 frames.], tot_loss[loss=0.1693, simple_loss=0.236, pruned_loss=0.05128, over 972325.16 frames.], batch size: 15, lr: 6.61e-04
2022-05-04 08:14:05,205 INFO [train.py:715] (2/8) Epoch 2, batch 22350, loss[loss=0.1716, simple_loss=0.2401, pruned_loss=0.05152, over 4773.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2353, pruned_loss=0.05048, over 971500.53 frames.], batch size: 19, lr: 6.61e-04
2022-05-04 08:14:46,089 INFO [train.py:715] (2/8) Epoch 2, batch 22400, loss[loss=0.2164, simple_loss=0.2718, pruned_loss=0.08048, over 4827.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2345, pruned_loss=0.04991, over 971917.99 frames.], batch size: 15, lr: 6.61e-04
2022-05-04 08:15:26,439 INFO [train.py:715] (2/8) Epoch 2, batch 22450, loss[loss=0.212, simple_loss=0.2766, pruned_loss=0.07373, over 4798.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2356, pruned_loss=0.05114, over 971704.12 frames.], batch size: 14, lr: 6.61e-04
2022-05-04 08:16:07,658 INFO [train.py:715] (2/8) Epoch 2, batch 22500, loss[loss=0.1406, simple_loss=0.2111, pruned_loss=0.03505, over 4975.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2362, pruned_loss=0.05174, over 972426.80 frames.], batch size: 35, lr: 6.61e-04
2022-05-04 08:16:48,515 INFO [train.py:715] (2/8) Epoch 2, batch 22550, loss[loss=0.153, simple_loss=0.2177, pruned_loss=0.04413, over 4761.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2367, pruned_loss=0.05195, over 973160.25 frames.], batch size: 12, lr: 6.60e-04
2022-05-04 08:17:29,220 INFO [train.py:715] (2/8) Epoch 2, batch 22600, loss[loss=0.1648, simple_loss=0.2363, pruned_loss=0.04662, over 4788.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2374, pruned_loss=0.05255, over 972770.53 frames.], batch size: 21, lr: 6.60e-04
2022-05-04 08:18:09,988 INFO [train.py:715] (2/8) Epoch 2, batch 22650, loss[loss=0.1291, simple_loss=0.1944, pruned_loss=0.03194, over 4918.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2372, pruned_loss=0.0519, over 973080.11 frames.], batch size: 23, lr: 6.60e-04
2022-05-04 08:18:50,667 INFO [train.py:715] (2/8) Epoch 2, batch 22700, loss[loss=0.1974, simple_loss=0.2623, pruned_loss=0.06627, over 4798.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2374, pruned_loss=0.05201, over 972085.88 frames.], batch size: 13, lr: 6.60e-04
2022-05-04 08:19:31,394 INFO [train.py:715] (2/8) Epoch 2, batch 22750, loss[loss=0.1546, simple_loss=0.2256, pruned_loss=0.04183, over 4816.00 frames.], tot_loss[loss=0.17, simple_loss=0.237, pruned_loss=0.05144, over 972207.99 frames.], batch size: 12, lr: 6.60e-04
2022-05-04 08:20:12,244 INFO [train.py:715] (2/8) Epoch 2, batch 22800, loss[loss=0.1812, simple_loss=0.2488, pruned_loss=0.05677, over 4937.00 frames.], tot_loss[loss=0.1701, simple_loss=0.237, pruned_loss=0.05161, over 971335.25 frames.], batch size: 21, lr: 6.59e-04
2022-05-04 08:20:53,295 INFO [train.py:715] (2/8) Epoch 2, batch 22850, loss[loss=0.1653, simple_loss=0.2345, pruned_loss=0.04806, over 4825.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2383, pruned_loss=0.05244, over 970654.42 frames.], batch size: 26, lr: 6.59e-04
2022-05-04 08:21:34,648 INFO [train.py:715] (2/8) Epoch 2, batch 22900, loss[loss=0.1772, simple_loss=0.2386, pruned_loss=0.05786, over 4921.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2376, pruned_loss=0.05197, over 971044.81 frames.], batch size: 18, lr: 6.59e-04
2022-05-04 08:22:15,448 INFO [train.py:715] (2/8) Epoch 2, batch 22950, loss[loss=0.1479, simple_loss=0.2183, pruned_loss=0.03876, over 4771.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2379, pruned_loss=0.05192, over 972277.09 frames.], batch size: 12, lr: 6.59e-04
2022-05-04 08:22:56,045 INFO [train.py:715] (2/8) Epoch 2, batch 23000, loss[loss=0.1643, simple_loss=0.2335, pruned_loss=0.04758, over 4848.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2366, pruned_loss=0.05123, over 972102.41 frames.], batch size: 13, lr: 6.59e-04
2022-05-04 08:23:37,058 INFO [train.py:715] (2/8) Epoch 2, batch 23050, loss[loss=0.1646, simple_loss=0.2293, pruned_loss=0.04991, over 4785.00 frames.], tot_loss[loss=0.1697, simple_loss=0.237, pruned_loss=0.05118, over 971823.36 frames.], batch size: 14, lr: 6.59e-04
2022-05-04 08:24:17,892 INFO [train.py:715] (2/8) Epoch 2, batch 23100, loss[loss=0.1712, simple_loss=0.2379, pruned_loss=0.05229, over 4892.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2374, pruned_loss=0.05194, over 972461.61 frames.], batch size: 19, lr: 6.58e-04
2022-05-04 08:24:58,397 INFO [train.py:715] (2/8) Epoch 2, batch 23150, loss[loss=0.1802, simple_loss=0.2666, pruned_loss=0.0469, over 4807.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2377, pruned_loss=0.05225, over 972297.73 frames.], batch size: 25, lr: 6.58e-04
2022-05-04 08:25:39,716 INFO [train.py:715] (2/8) Epoch 2, batch 23200, loss[loss=0.1686, simple_loss=0.2372, pruned_loss=0.04996, over 4763.00 frames.], tot_loss[loss=0.1704, simple_loss=0.237, pruned_loss=0.05188, over 972207.57 frames.], batch size: 19, lr: 6.58e-04
2022-05-04 08:26:20,391 INFO [train.py:715] (2/8) Epoch 2, batch 23250, loss[loss=0.1447, simple_loss=0.207, pruned_loss=0.04115, over 4825.00 frames.], tot_loss[loss=0.1701, simple_loss=0.237, pruned_loss=0.05158, over 972337.49 frames.], batch size: 26, lr: 6.58e-04
2022-05-04 08:27:00,743 INFO [train.py:715] (2/8) Epoch 2, batch 23300, loss[loss=0.1441, simple_loss=0.2111, pruned_loss=0.03861, over 4784.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2366, pruned_loss=0.05113, over 972364.57 frames.], batch size: 18, lr: 6.58e-04
2022-05-04 08:27:41,447 INFO [train.py:715] (2/8) Epoch 2, batch 23350, loss[loss=0.1535, simple_loss=0.2153, pruned_loss=0.0458, over 4836.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2367, pruned_loss=0.05109, over 972037.34 frames.], batch size: 12, lr: 6.57e-04
2022-05-04 08:28:22,385 INFO [train.py:715] (2/8) Epoch 2, batch 23400, loss[loss=0.1644, simple_loss=0.2354, pruned_loss=0.04673, over 4960.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2356, pruned_loss=0.05064, over 971945.12 frames.], batch size: 28, lr: 6.57e-04
2022-05-04 08:29:03,322 INFO [train.py:715] (2/8) Epoch 2, batch 23450, loss[loss=0.149, simple_loss=0.2301, pruned_loss=0.0339, over 4969.00 frames.], tot_loss[loss=0.168, simple_loss=0.2349, pruned_loss=0.0505, over 972053.15 frames.], batch size: 24, lr: 6.57e-04
2022-05-04 08:29:43,614 INFO [train.py:715] (2/8) Epoch 2, batch 23500, loss[loss=0.1443, simple_loss=0.2202, pruned_loss=0.03421, over 4985.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2334, pruned_loss=0.04955, over 972021.05 frames.], batch size: 28, lr: 6.57e-04
2022-05-04 08:30:24,809 INFO [train.py:715] (2/8) Epoch 2, batch 23550, loss[loss=0.1955, simple_loss=0.2559, pruned_loss=0.06751, over 4886.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2345, pruned_loss=0.0501, over 971635.42 frames.], batch size: 16, lr: 6.57e-04
2022-05-04 08:31:05,699 INFO [train.py:715] (2/8) Epoch 2, batch 23600, loss[loss=0.1909, simple_loss=0.2575, pruned_loss=0.06215, over 4867.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2348, pruned_loss=0.05022, over 972214.09 frames.], batch size: 32, lr: 6.57e-04
2022-05-04 08:31:45,431 INFO [train.py:715] (2/8) Epoch 2, batch 23650, loss[loss=0.1856, simple_loss=0.2442, pruned_loss=0.06345, over 4768.00 frames.], tot_loss[loss=0.167, simple_loss=0.2341, pruned_loss=0.04994, over 971895.66 frames.], batch size: 14, lr: 6.56e-04
2022-05-04 08:32:27,502 INFO [train.py:715] (2/8) Epoch 2, batch 23700, loss[loss=0.1601, simple_loss=0.2351, pruned_loss=0.04249, over 4814.00 frames.], tot_loss[loss=0.1682, simple_loss=0.235, pruned_loss=0.05065, over 971390.48 frames.], batch size: 25, lr: 6.56e-04
2022-05-04 08:33:07,927 INFO [train.py:715] (2/8) Epoch 2, batch 23750, loss[loss=0.1536, simple_loss=0.225, pruned_loss=0.04111, over 4940.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2355, pruned_loss=0.05059, over 972553.09 frames.], batch size: 21, lr: 6.56e-04
2022-05-04 08:33:48,789 INFO [train.py:715] (2/8) Epoch 2, batch 23800, loss[loss=0.1837, simple_loss=0.2468, pruned_loss=0.06031, over 4856.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2359, pruned_loss=0.05121, over 972181.17 frames.], batch size: 32, lr: 6.56e-04
2022-05-04 08:34:29,258 INFO [train.py:715] (2/8) Epoch 2, batch 23850, loss[loss=0.1612, simple_loss=0.2424, pruned_loss=0.04002, over 4849.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2366, pruned_loss=0.05153, over 972735.89 frames.], batch size: 20, lr: 6.56e-04
2022-05-04 08:35:10,693 INFO [train.py:715] (2/8) Epoch 2, batch 23900, loss[loss=0.1357, simple_loss=0.2075, pruned_loss=0.0319, over 4804.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2356, pruned_loss=0.05106, over 971872.56 frames.], batch size: 12, lr: 6.56e-04
2022-05-04 08:35:51,713 INFO [train.py:715] (2/8) Epoch 2, batch 23950, loss[loss=0.1861, simple_loss=0.2464, pruned_loss=0.06289, over 4710.00 frames.], tot_loss[loss=0.1694, simple_loss=0.236, pruned_loss=0.05137, over 971585.65 frames.], batch size: 15, lr: 6.55e-04
2022-05-04 08:36:31,642 INFO [train.py:715] (2/8) Epoch 2, batch 24000, loss[loss=0.1367, simple_loss=0.2087, pruned_loss=0.03235, over 4969.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2362, pruned_loss=0.05136, over 972487.25 frames.], batch size: 24, lr: 6.55e-04
2022-05-04 08:36:31,643 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 08:36:40,334 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1168, simple_loss=0.2032, pruned_loss=0.01518, over 914524.00 frames.
2022-05-04 08:37:20,454 INFO [train.py:715] (2/8) Epoch 2, batch 24050, loss[loss=0.1781, simple_loss=0.2491, pruned_loss=0.05353, over 4918.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2369, pruned_loss=0.05182, over 972299.76 frames.], batch size: 18, lr: 6.55e-04
2022-05-04 08:38:01,989 INFO [train.py:715] (2/8) Epoch 2, batch 24100, loss[loss=0.1705, simple_loss=0.2381, pruned_loss=0.05147, over 4819.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2371, pruned_loss=0.05199, over 972555.87 frames.], batch size: 26, lr: 6.55e-04
2022-05-04 08:38:42,990 INFO [train.py:715] (2/8) Epoch 2, batch 24150, loss[loss=0.2172, simple_loss=0.267, pruned_loss=0.08374, over 4837.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2366, pruned_loss=0.05206, over 972762.90 frames.], batch size: 13, lr: 6.55e-04
2022-05-04 08:39:24,306 INFO [train.py:715] (2/8) Epoch 2, batch 24200, loss[loss=0.1677, simple_loss=0.2386, pruned_loss=0.0484, over 4867.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2368, pruned_loss=0.05238, over 972669.07 frames.], batch size: 20, lr: 6.55e-04
2022-05-04 08:40:05,190 INFO [train.py:715] (2/8) Epoch 2, batch 24250, loss[loss=0.1629, simple_loss=0.2287, pruned_loss=0.04851, over 4922.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2375, pruned_loss=0.0526, over 973228.10 frames.], batch size: 23, lr: 6.54e-04
2022-05-04 08:40:46,088 INFO [train.py:715] (2/8) Epoch 2, batch 24300, loss[loss=0.1402, simple_loss=0.2064, pruned_loss=0.03698, over 4948.00 frames.], tot_loss[loss=0.17, simple_loss=0.2367, pruned_loss=0.05168, over 972491.38 frames.], batch size: 29, lr: 6.54e-04
2022-05-04 08:41:26,657 INFO [train.py:715] (2/8) Epoch 2, batch 24350, loss[loss=0.1824, simple_loss=0.2558, pruned_loss=0.05451, over 4875.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2365, pruned_loss=0.0512, over 971682.56 frames.], batch size: 16, lr: 6.54e-04
2022-05-04 08:42:06,525 INFO [train.py:715] (2/8) Epoch 2, batch 24400, loss[loss=0.144, simple_loss=0.2142, pruned_loss=0.03696, over 4780.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2358, pruned_loss=0.05031, over 972070.87 frames.], batch size: 21, lr: 6.54e-04
2022-05-04 08:42:47,540 INFO [train.py:715] (2/8) Epoch 2, batch 24450, loss[loss=0.2342, simple_loss=0.3007, pruned_loss=0.0839, over 4814.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2368, pruned_loss=0.05088, over 972616.17 frames.], batch size: 21, lr: 6.54e-04
2022-05-04 08:43:27,486 INFO [train.py:715] (2/8) Epoch 2, batch 24500, loss[loss=0.1697, simple_loss=0.2311, pruned_loss=0.05413, over 4777.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2352, pruned_loss=0.05052, over 971822.63 frames.], batch size: 17, lr: 6.53e-04
2022-05-04 08:44:07,364 INFO [train.py:715] (2/8) Epoch 2, batch 24550, loss[loss=0.2208, simple_loss=0.2781, pruned_loss=0.08175, over 4975.00 frames.], tot_loss[loss=0.168, simple_loss=0.2351, pruned_loss=0.05039, over 972129.97 frames.], batch size: 35, lr: 6.53e-04
2022-05-04 08:44:46,870 INFO [train.py:715] (2/8) Epoch 2, batch 24600, loss[loss=0.1572, simple_loss=0.2174, pruned_loss=0.04848, over 4844.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2347, pruned_loss=0.05032, over 972475.24 frames.], batch size: 12, lr: 6.53e-04
2022-05-04 08:45:27,050 INFO [train.py:715] (2/8) Epoch 2, batch 24650, loss[loss=0.1526, simple_loss=0.2205, pruned_loss=0.04236, over 4768.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2346, pruned_loss=0.05039, over 973183.64 frames.], batch size: 19, lr: 6.53e-04
2022-05-04 08:46:06,406 INFO [train.py:715] (2/8) Epoch 2, batch 24700, loss[loss=0.1471, simple_loss=0.207, pruned_loss=0.04361, over 4841.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2359, pruned_loss=0.05117, over 974029.87 frames.], batch size: 34, lr: 6.53e-04
2022-05-04 08:46:45,147 INFO [train.py:715] (2/8) Epoch 2, batch 24750, loss[loss=0.1748, simple_loss=0.2441, pruned_loss=0.05278, over 4943.00 frames.], tot_loss[loss=0.168, simple_loss=0.2351, pruned_loss=0.05042, over 973718.32 frames.], batch size: 21, lr: 6.53e-04
2022-05-04 08:47:24,972 INFO [train.py:715] (2/8) Epoch 2, batch 24800, loss[loss=0.1663, simple_loss=0.2341, pruned_loss=0.04921, over 4939.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2363, pruned_loss=0.05142, over 973689.14 frames.], batch size: 29, lr: 6.52e-04
2022-05-04 08:48:04,566 INFO [train.py:715] (2/8) Epoch 2, batch 24850, loss[loss=0.1193, simple_loss=0.1915, pruned_loss=0.02352, over 4638.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2355, pruned_loss=0.05102, over 972857.10 frames.], batch size: 13, lr: 6.52e-04
2022-05-04 08:48:43,451 INFO [train.py:715] (2/8) Epoch 2, batch 24900, loss[loss=0.1812, simple_loss=0.2497, pruned_loss=0.05631, over 4975.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2358, pruned_loss=0.05101, over 973181.50 frames.], batch size: 15, lr: 6.52e-04
2022-05-04 08:49:22,920 INFO [train.py:715] (2/8) Epoch 2, batch 24950, loss[loss=0.1833, simple_loss=0.2461, pruned_loss=0.06023, over 4801.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2361, pruned_loss=0.0512, over 973297.40 frames.], batch size: 21, lr: 6.52e-04
2022-05-04 08:50:02,452 INFO [train.py:715] (2/8) Epoch 2, batch 25000, loss[loss=0.1595, simple_loss=0.2258, pruned_loss=0.04658, over 4969.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2364, pruned_loss=0.05117, over 973747.75 frames.], batch size: 35, lr: 6.52e-04
2022-05-04 08:50:41,249 INFO [train.py:715] (2/8) Epoch 2, batch 25050, loss[loss=0.1428, simple_loss=0.2181, pruned_loss=0.03373, over 4860.00 frames.], tot_loss[loss=0.17, simple_loss=0.2369, pruned_loss=0.05156, over 973292.73 frames.], batch size: 20, lr: 6.52e-04
2022-05-04 08:51:19,777 INFO [train.py:715] (2/8) Epoch 2, batch 25100, loss[loss=0.1447, simple_loss=0.2172, pruned_loss=0.03606, over 4825.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2368, pruned_loss=0.05137, over 973054.07 frames.], batch size: 15, lr: 6.51e-04
2022-05-04 08:51:59,028 INFO [train.py:715] (2/8) Epoch 2, batch 25150, loss[loss=0.2004, simple_loss=0.2462, pruned_loss=0.07732, over 4735.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2364, pruned_loss=0.05101, over 973236.45 frames.], batch size: 16, lr: 6.51e-04
2022-05-04 08:52:37,839 INFO [train.py:715] (2/8) Epoch 2, batch 25200, loss[loss=0.1648, simple_loss=0.2246, pruned_loss=0.05246, over 4831.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2364, pruned_loss=0.05104, over 973602.24 frames.], batch size: 30, lr: 6.51e-04
2022-05-04 08:53:16,872 INFO [train.py:715] (2/8) Epoch 2, batch 25250, loss[loss=0.1705, simple_loss=0.2365, pruned_loss=0.05223, over 4981.00 frames.], tot_loss[loss=0.1702, simple_loss=0.237, pruned_loss=0.05176, over 973687.86 frames.], batch size: 14, lr: 6.51e-04
2022-05-04 08:53:55,848 INFO [train.py:715] (2/8) Epoch 2, batch 25300, loss[loss=0.1594, simple_loss=0.2217, pruned_loss=0.04858, over 4658.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2365, pruned_loss=0.05132, over 972925.59 frames.], batch size: 13, lr: 6.51e-04
2022-05-04 08:54:35,066 INFO [train.py:715] (2/8) Epoch 2, batch 25350, loss[loss=0.2077, simple_loss=0.2598, pruned_loss=0.07783, over 4909.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2379, pruned_loss=0.05228, over 972667.87 frames.], batch size: 19, lr: 6.51e-04
2022-05-04 08:55:14,140 INFO [train.py:715] (2/8) Epoch 2, batch 25400, loss[loss=0.1708, simple_loss=0.2371, pruned_loss=0.05224, over 4977.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2371, pruned_loss=0.05171, over 971879.35 frames.], batch size: 25, lr: 6.50e-04
2022-05-04 08:55:52,990 INFO [train.py:715] (2/8) Epoch 2, batch 25450, loss[loss=0.1641, simple_loss=0.2231, pruned_loss=0.05252, over 4991.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2365, pruned_loss=0.05163, over 972521.05 frames.], batch size: 14, lr: 6.50e-04
2022-05-04 08:56:32,016 INFO [train.py:715] (2/8) Epoch 2, batch 25500, loss[loss=0.2108, simple_loss=0.2793, pruned_loss=0.07115, over 4696.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2362, pruned_loss=0.05156, over 973142.44 frames.], batch size: 15, lr: 6.50e-04
2022-05-04 08:57:11,293 INFO [train.py:715] (2/8) Epoch 2, batch 25550, loss[loss=0.1674, simple_loss=0.2269, pruned_loss=0.05397, over 4872.00 frames.], tot_loss[loss=0.17, simple_loss=0.2364, pruned_loss=0.05181, over 972284.64 frames.], batch size: 16, lr: 6.50e-04
2022-05-04 08:57:50,298 INFO [train.py:715] (2/8) Epoch 2, batch 25600, loss[loss=0.1912, simple_loss=0.2438, pruned_loss=0.06933, over 4861.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2361, pruned_loss=0.05164, over 972380.94 frames.], batch size: 30, lr: 6.50e-04
2022-05-04 08:58:29,639 INFO [train.py:715] (2/8) Epoch 2, batch 25650, loss[loss=0.1602, simple_loss=0.2264, pruned_loss=0.04699, over 4976.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2362, pruned_loss=0.05105, over 972717.00 frames.], batch size: 25, lr: 6.50e-04
2022-05-04 08:59:09,547 INFO [train.py:715] (2/8) Epoch 2, batch 25700, loss[loss=0.1806, simple_loss=0.2394, pruned_loss=0.06094, over 4986.00 frames.], tot_loss[loss=0.1704, simple_loss=0.237, pruned_loss=0.05189, over 972785.55 frames.], batch size: 31, lr: 6.49e-04
2022-05-04 08:59:48,683 INFO [train.py:715] (2/8) Epoch 2, batch 25750, loss[loss=0.1172, simple_loss=0.1808, pruned_loss=0.02679, over 4839.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2359, pruned_loss=0.05127, over 973102.01 frames.], batch size: 13, lr: 6.49e-04
2022-05-04 09:00:27,434 INFO [train.py:715] (2/8) Epoch 2, batch 25800, loss[loss=0.1517, simple_loss=0.2104, pruned_loss=0.04653, over 4792.00 frames.], tot_loss[loss=0.1693, simple_loss=0.236, pruned_loss=0.05132, over 973340.84 frames.], batch size: 21, lr: 6.49e-04
2022-05-04 09:01:06,415 INFO [train.py:715] (2/8) Epoch 2, batch 25850, loss[loss=0.1522, simple_loss=0.2239, pruned_loss=0.04029, over 4793.00 frames.], tot_loss[loss=0.17, simple_loss=0.2367, pruned_loss=0.05166, over 972396.50 frames.], batch size: 18, lr: 6.49e-04
2022-05-04 09:01:46,175 INFO [train.py:715] (2/8) Epoch 2, batch 25900, loss[loss=0.1478, simple_loss=0.219, pruned_loss=0.0383, over 4804.00 frames.], tot_loss[loss=0.17, simple_loss=0.2367, pruned_loss=0.05166, over 972392.81 frames.], batch size: 12, lr: 6.49e-04
2022-05-04 09:02:25,984 INFO [train.py:715] (2/8) Epoch 2, batch 25950, loss[loss=0.1694, simple_loss=0.2397, pruned_loss=0.04952, over 4867.00 frames.], tot_loss[loss=0.1692, simple_loss=0.236, pruned_loss=0.05117, over 971885.22 frames.], batch size: 16, lr: 6.49e-04
2022-05-04 09:03:05,060 INFO [train.py:715] (2/8) Epoch 2, batch 26000, loss[loss=0.1391, simple_loss=0.2212, pruned_loss=0.02856, over 4885.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2368, pruned_loss=0.05124, over 972444.95 frames.], batch size: 16, lr: 6.48e-04
2022-05-04 09:03:44,729 INFO [train.py:715] (2/8) Epoch 2, batch 26050, loss[loss=0.1661, simple_loss=0.2362, pruned_loss=0.048, over 4963.00 frames.], tot_loss[loss=0.17, simple_loss=0.2368, pruned_loss=0.05156, over 972034.48 frames.], batch size: 24, lr: 6.48e-04
2022-05-04 09:04:24,300 INFO [train.py:715] (2/8) Epoch 2, batch 26100, loss[loss=0.1837, simple_loss=0.2523, pruned_loss=0.05756, over 4862.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2363, pruned_loss=0.05122, over 972281.50 frames.], batch size: 39, lr: 6.48e-04
2022-05-04 09:05:03,474 INFO [train.py:715] (2/8) Epoch 2, batch 26150, loss[loss=0.1601, simple_loss=0.2304, pruned_loss=0.04492, over 4742.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2365, pruned_loss=0.05145, over 972163.53 frames.], batch size: 16, lr: 6.48e-04
2022-05-04 09:05:42,980 INFO [train.py:715] (2/8) Epoch 2, batch 26200, loss[loss=0.2116, simple_loss=0.2751, pruned_loss=0.07409, over 4692.00 frames.], tot_loss[loss=0.169, simple_loss=0.2354, pruned_loss=0.05136, over 971360.17 frames.], batch size: 15, lr: 6.48e-04
2022-05-04 09:06:22,728 INFO [train.py:715] (2/8) Epoch 2, batch 26250, loss[loss=0.1597, simple_loss=0.2276, pruned_loss=0.04592, over 4827.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2356, pruned_loss=0.05144, over 970680.09 frames.], batch size: 15, lr: 6.48e-04
2022-05-04 09:07:02,314 INFO [train.py:715] (2/8) Epoch 2, batch 26300, loss[loss=0.1565, simple_loss=0.2296, pruned_loss=0.04165, over 4860.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2369, pruned_loss=0.05129, over 972263.12 frames.], batch size: 32, lr: 6.47e-04
2022-05-04 09:07:40,819 INFO [train.py:715] (2/8) Epoch 2, batch 26350, loss[loss=0.138, simple_loss=0.2046, pruned_loss=0.03563, over 4979.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2368, pruned_loss=0.0512, over 972491.17 frames.], batch size: 28, lr: 6.47e-04
2022-05-04 09:08:23,924 INFO [train.py:715] (2/8) Epoch 2, batch 26400, loss[loss=0.1913, simple_loss=0.2603, pruned_loss=0.06115, over 4941.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2364, pruned_loss=0.05095, over 972300.28 frames.], batch size: 23, lr: 6.47e-04
2022-05-04 09:09:03,679 INFO [train.py:715] (2/8) Epoch 2, batch 26450, loss[loss=0.1892, simple_loss=0.2616, pruned_loss=0.0584, over 4903.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2364, pruned_loss=0.05098, over 971778.17 frames.], batch size: 18, lr: 6.47e-04
2022-05-04 09:09:42,576 INFO [train.py:715] (2/8) Epoch 2, batch 26500, loss[loss=0.1559, simple_loss=0.2273, pruned_loss=0.04223, over 4896.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2367, pruned_loss=0.05107, over 972456.50 frames.], batch size: 19, lr: 6.47e-04
2022-05-04 09:10:22,386 INFO [train.py:715] (2/8) Epoch 2, batch 26550, loss[loss=0.1864, simple_loss=0.2537, pruned_loss=0.05958, over 4778.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2349, pruned_loss=0.04971, over 972730.16 frames.], batch size: 18, lr: 6.46e-04
2022-05-04 09:11:02,371 INFO [train.py:715] (2/8) Epoch 2, batch 26600, loss[loss=0.152, simple_loss=0.2203, pruned_loss=0.04188, over 4751.00 frames.], tot_loss[loss=0.169, simple_loss=0.2364, pruned_loss=0.05084, over 972731.80 frames.], batch size: 19, lr: 6.46e-04
2022-05-04 09:11:41,990 INFO [train.py:715] (2/8) Epoch 2, batch 26650, loss[loss=0.1946, simple_loss=0.2612, pruned_loss=0.064, over 4838.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2367, pruned_loss=0.05132, over 973041.40 frames.], batch size: 15, lr: 6.46e-04
2022-05-04 09:12:20,998 INFO [train.py:715] (2/8) Epoch 2, batch 26700, loss[loss=0.1826, simple_loss=0.2523, pruned_loss=0.05642, over 4826.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2363, pruned_loss=0.05147, over 972376.53 frames.], batch size: 15, lr: 6.46e-04
2022-05-04 09:13:00,962 INFO [train.py:715] (2/8) Epoch 2, batch 26750, loss[loss=0.1754, simple_loss=0.2349, pruned_loss=0.05794, over 4764.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2361, pruned_loss=0.05105, over 972713.47 frames.], batch size: 14, lr: 6.46e-04
2022-05-04 09:13:40,191 INFO [train.py:715] (2/8) Epoch 2, batch 26800, loss[loss=0.1703, simple_loss=0.2417, pruned_loss=0.04947, over 4838.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2366, pruned_loss=0.051, over 973186.37 frames.], batch size: 30, lr: 6.46e-04
2022-05-04 09:14:19,178 INFO [train.py:715] (2/8) Epoch 2, batch 26850, loss[loss=0.1593, simple_loss=0.2255, pruned_loss=0.04657, over 4930.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2349, pruned_loss=0.05, over 972972.33 frames.], batch size: 23, lr: 6.45e-04
2022-05-04 09:14:58,113 INFO [train.py:715] (2/8) Epoch 2, batch 26900, loss[loss=0.1531, simple_loss=0.2221, pruned_loss=0.04208, over 4926.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2357, pruned_loss=0.05057, over 972910.45 frames.], batch size: 29, lr: 6.45e-04
2022-05-04 09:15:37,576 INFO [train.py:715] (2/8) Epoch 2, batch 26950, loss[loss=0.1657, simple_loss=0.2308, pruned_loss=0.05026, over 4933.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2353, pruned_loss=0.05001, over 973671.48 frames.], batch size: 21, lr: 6.45e-04
2022-05-04 09:16:16,461 INFO [train.py:715] (2/8) Epoch 2, batch 27000, loss[loss=0.145, simple_loss=0.2047, pruned_loss=0.04263, over 4824.00 frames.], tot_loss[loss=0.168, simple_loss=0.2355, pruned_loss=0.05023, over 973570.63 frames.], batch size: 13, lr: 6.45e-04
2022-05-04 09:16:16,462 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 09:16:25,253 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1164, simple_loss=0.2027, pruned_loss=0.01502, over 914524.00 frames.
2022-05-04 09:17:03,614 INFO [train.py:715] (2/8) Epoch 2, batch 27050, loss[loss=0.1514, simple_loss=0.2164, pruned_loss=0.04317, over 4866.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2359, pruned_loss=0.05049, over 973991.48 frames.], batch size: 32, lr: 6.45e-04
2022-05-04 09:17:42,879 INFO [train.py:715] (2/8) Epoch 2, batch 27100, loss[loss=0.1756, simple_loss=0.2429, pruned_loss=0.05422, over 4803.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2374, pruned_loss=0.05139, over 973309.21 frames.], batch size: 26, lr: 6.45e-04
2022-05-04 09:18:22,880 INFO [train.py:715] (2/8) Epoch 2, batch 27150, loss[loss=0.162, simple_loss=0.2295, pruned_loss=0.04729, over 4942.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2373, pruned_loss=0.05156, over 973103.80 frames.], batch size: 21, lr: 6.44e-04
2022-05-04 09:19:02,259 INFO [train.py:715] (2/8) Epoch 2, batch 27200, loss[loss=0.1661, simple_loss=0.2284, pruned_loss=0.05192, over 4824.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2373, pruned_loss=0.05161, over 973076.47 frames.], batch size: 14, lr: 6.44e-04
2022-05-04 09:19:41,115 INFO [train.py:715] (2/8) Epoch 2, batch 27250, loss[loss=0.2169, simple_loss=0.2774, pruned_loss=0.07816, over 4852.00 frames.], tot_loss[loss=0.1695, simple_loss=0.237, pruned_loss=0.05102, over 973493.76 frames.], batch size: 30, lr: 6.44e-04
2022-05-04 09:20:20,685 INFO [train.py:715] (2/8) Epoch 2, batch 27300, loss[loss=0.1209, simple_loss=0.183, pruned_loss=0.02939, over 4763.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2356, pruned_loss=0.05004, over 973989.37 frames.], batch size: 12, lr: 6.44e-04
2022-05-04 09:20:59,718 INFO [train.py:715] (2/8) Epoch 2, batch 27350, loss[loss=0.1589, simple_loss=0.2303, pruned_loss=0.04371, over 4636.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2368, pruned_loss=0.051, over 973700.11 frames.], batch size: 13, lr: 6.44e-04
2022-05-04 09:21:38,797 INFO [train.py:715] (2/8) Epoch 2, batch 27400, loss[loss=0.1851, simple_loss=0.244, pruned_loss=0.06303, over 4896.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2362, pruned_loss=0.05071, over 973826.60 frames.], batch size: 22, lr: 6.44e-04
2022-05-04 09:22:17,476 INFO [train.py:715] (2/8) Epoch 2, batch 27450, loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02813, over 4798.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2348, pruned_loss=0.0498, over 972657.58 frames.], batch size: 14, lr: 6.44e-04
2022-05-04 09:22:57,208 INFO [train.py:715] (2/8) Epoch 2, batch 27500, loss[loss=0.1613, simple_loss=0.2264, pruned_loss=0.04814, over 4803.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2345, pruned_loss=0.04964, over 972089.57 frames.], batch size: 12, lr: 6.43e-04
2022-05-04 09:23:37,089 INFO [train.py:715] (2/8) Epoch 2, batch 27550, loss[loss=0.1372, simple_loss=0.204, pruned_loss=0.03524, over 4739.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2349, pruned_loss=0.05012, over 972137.64 frames.], batch size: 12, lr: 6.43e-04
2022-05-04 09:24:16,416 INFO [train.py:715] (2/8) Epoch 2, batch 27600, loss[loss=0.1937, simple_loss=0.2638, pruned_loss=0.06174, over 4757.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2356, pruned_loss=0.05042, over 971856.80 frames.], batch size: 19, lr: 6.43e-04
2022-05-04 09:24:55,992 INFO [train.py:715] (2/8) Epoch 2, batch 27650, loss[loss=0.159, simple_loss=0.2296, pruned_loss=0.04419, over 4784.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2347, pruned_loss=0.05008, over 972475.81 frames.], batch size: 14, lr: 6.43e-04
2022-05-04 09:25:36,587 INFO [train.py:715] (2/8) Epoch 2, batch 27700, loss[loss=0.1639, simple_loss=0.2359, pruned_loss=0.046, over 4883.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2347, pruned_loss=0.05053, over 972438.31 frames.], batch size: 22, lr: 6.43e-04
2022-05-04 09:26:16,916 INFO [train.py:715] (2/8) Epoch 2, batch 27750, loss[loss=0.1571, simple_loss=0.2362, pruned_loss=0.03904, over 4795.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2358, pruned_loss=0.05116, over 972668.09 frames.], batch size: 21, lr: 6.43e-04
2022-05-04 09:26:56,299 INFO [train.py:715] (2/8) Epoch 2, batch 27800, loss[loss=0.1737, simple_loss=0.2341, pruned_loss=0.05658, over 4925.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2348, pruned_loss=0.05011, over 972509.73 frames.], batch size: 39, lr: 6.42e-04
2022-05-04 09:27:36,590 INFO [train.py:715] (2/8) Epoch 2, batch 27850, loss[loss=0.1499, simple_loss=0.221, pruned_loss=0.03938, over 4875.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2343, pruned_loss=0.05006, over 971271.30 frames.], batch size: 19, lr: 6.42e-04
2022-05-04 09:28:15,900 INFO [train.py:715] (2/8) Epoch 2, batch 27900, loss[loss=0.1994, simple_loss=0.2555, pruned_loss=0.07162, over 4899.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2346, pruned_loss=0.05025, over 971928.07 frames.], batch size: 18, lr: 6.42e-04
2022-05-04 09:28:55,084 INFO [train.py:715] (2/8) Epoch 2, batch 27950, loss[loss=0.1759, simple_loss=0.2448, pruned_loss=0.05349, over 4949.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2352, pruned_loss=0.05052, over 971412.54 frames.], batch size: 21, lr: 6.42e-04
2022-05-04 09:29:34,665 INFO [train.py:715] (2/8) Epoch 2, batch 28000, loss[loss=0.1699, simple_loss=0.2467, pruned_loss=0.04656, over 4882.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2358, pruned_loss=0.05099, over 971580.50 frames.], batch size: 22, lr: 6.42e-04
2022-05-04 09:30:15,040 INFO [train.py:715] (2/8) Epoch 2, batch 28050, loss[loss=0.1559, simple_loss=0.2265, pruned_loss=0.04261, over 4955.00 frames.], tot_loss[loss=0.169, simple_loss=0.2356, pruned_loss=0.05119, over 970790.88 frames.], batch size: 21, lr: 6.42e-04
2022-05-04 09:30:54,014 INFO [train.py:715] (2/8) Epoch 2, batch 28100, loss[loss=0.2075, simple_loss=0.2604, pruned_loss=0.07725, over 4688.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2357, pruned_loss=0.05125, over 971050.40 frames.], batch size: 15, lr: 6.41e-04
2022-05-04 09:31:33,554 INFO [train.py:715] (2/8) Epoch 2, batch 28150, loss[loss=0.1598, simple_loss=0.2249, pruned_loss=0.04733, over 4947.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2352, pruned_loss=0.0512, over 971517.98 frames.], batch size: 21, lr: 6.41e-04
2022-05-04 09:32:13,292 INFO [train.py:715] (2/8) Epoch 2, batch 28200, loss[loss=0.1582, simple_loss=0.2271, pruned_loss=0.0446, over 4814.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2352, pruned_loss=0.05115, over 972145.76 frames.], batch size: 26, lr: 6.41e-04
2022-05-04 09:32:52,896 INFO [train.py:715] (2/8) Epoch 2, batch 28250, loss[loss=0.1726, simple_loss=0.2416, pruned_loss=0.05184, over 4776.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2354, pruned_loss=0.05094, over 972496.02 frames.], batch size: 18, lr: 6.41e-04
2022-05-04 09:33:31,973 INFO [train.py:715] (2/8) Epoch 2, batch 28300, loss[loss=0.1633, simple_loss=0.2376, pruned_loss=0.04447, over 4801.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2356, pruned_loss=0.05095, over 972302.43 frames.], batch size: 21, lr: 6.41e-04
2022-05-04 09:34:11,314 INFO [train.py:715] (2/8) Epoch 2, batch 28350, loss[loss=0.1605, simple_loss=0.23, pruned_loss=0.04556, over 4967.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2356, pruned_loss=0.05083, over 971896.02 frames.], batch size: 15, lr: 6.41e-04
2022-05-04 09:34:51,507 INFO [train.py:715] (2/8) Epoch 2, batch 28400, loss[loss=0.1889, simple_loss=0.2543, pruned_loss=0.06178, over 4780.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2368, pruned_loss=0.05181, over 971964.00 frames.], batch size: 18, lr: 6.40e-04
2022-05-04 09:35:30,754 INFO [train.py:715] (2/8) Epoch 2, batch 28450, loss[loss=0.1494, simple_loss=0.2127, pruned_loss=0.04301, over 4814.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2362, pruned_loss=0.05114, over 972061.77 frames.], batch size: 15, lr: 6.40e-04
2022-05-04 09:36:10,154 INFO [train.py:715] (2/8) Epoch 2, batch 28500, loss[loss=0.1478, simple_loss=0.2156, pruned_loss=0.03995, over 4695.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2361, pruned_loss=0.05134, over 971895.04 frames.], batch size: 15, lr: 6.40e-04
2022-05-04 09:36:50,105 INFO [train.py:715] (2/8) Epoch 2, batch 28550, loss[loss=0.1961, simple_loss=0.2731, pruned_loss=0.05952, over 4777.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2359, pruned_loss=0.05121, over 971975.03 frames.], batch size: 18, lr: 6.40e-04
2022-05-04 09:37:30,232 INFO [train.py:715] (2/8) Epoch 2, batch 28600, loss[loss=0.1543, simple_loss=0.2333, pruned_loss=0.03764, over 4775.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2358, pruned_loss=0.05098, over 972071.57 frames.], batch size: 17, lr: 6.40e-04
2022-05-04 09:38:09,267 INFO [train.py:715] (2/8) Epoch 2, batch 28650, loss[loss=0.1786, simple_loss=0.2423, pruned_loss=0.05744, over 4849.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2358, pruned_loss=0.05089, over 972001.03 frames.], batch size: 32, lr: 6.40e-04
2022-05-04 09:38:49,120 INFO [train.py:715] (2/8) Epoch 2, batch 28700, loss[loss=0.2059, simple_loss=0.2558, pruned_loss=0.07801, over 4780.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2355, pruned_loss=0.05097, over 971654.87 frames.], batch size: 19, lr: 6.39e-04
2022-05-04 09:39:29,578 INFO [train.py:715] (2/8) Epoch 2, batch 28750, loss[loss=0.1478, simple_loss=0.2073, pruned_loss=0.04411, over 4781.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2349, pruned_loss=0.05044, over 971734.36 frames.], batch size: 14, lr: 6.39e-04
2022-05-04 09:40:08,509 INFO [train.py:715] (2/8) Epoch 2, batch 28800, loss[loss=0.1519, simple_loss=0.231, pruned_loss=0.03645, over 4786.00 frames.], tot_loss[loss=0.1694, simple_loss=0.236, pruned_loss=0.05142, over 970707.16 frames.], batch size: 17, lr: 6.39e-04
2022-05-04 09:40:48,098 INFO [train.py:715] (2/8) Epoch 2, batch 28850, loss[loss=0.1551, simple_loss=0.2255, pruned_loss=0.04239, over 4859.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2364, pruned_loss=0.05162, over 971997.81 frames.], batch size: 30, lr: 6.39e-04
2022-05-04 09:41:28,105 INFO [train.py:715] (2/8) Epoch 2, batch 28900, loss[loss=0.1519, simple_loss=0.2207, pruned_loss=0.04156, over 4970.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2364, pruned_loss=0.05147, over 971967.79 frames.], batch size: 15, lr: 6.39e-04
2022-05-04 09:42:07,480 INFO [train.py:715] (2/8) Epoch 2, batch 28950, loss[loss=0.1711, simple_loss=0.2373, pruned_loss=0.05243, over 4860.00 frames.], tot_loss[loss=0.1689, simple_loss=0.236, pruned_loss=0.05093, over 971356.90 frames.], batch size: 38, lr: 6.39e-04
2022-05-04 09:42:46,852 INFO [train.py:715] (2/8) Epoch 2, batch 29000, loss[loss=0.17, simple_loss=0.242, pruned_loss=0.04898, over 4990.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2372, pruned_loss=0.0513, over 971450.56 frames.], batch size: 28, lr: 6.38e-04
2022-05-04 09:43:26,609 INFO [train.py:715] (2/8) Epoch 2, batch 29050, loss[loss=0.1933, simple_loss=0.2509, pruned_loss=0.06781, over 4988.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2368, pruned_loss=0.05101, over 972287.58 frames.], batch size: 25, lr: 6.38e-04
2022-05-04 09:44:06,284 INFO [train.py:715] (2/8) Epoch 2, batch 29100, loss[loss=0.1814, simple_loss=0.235, pruned_loss=0.06396, over 4858.00 frames.], tot_loss[loss=0.169, simple_loss=0.2361, pruned_loss=0.05092, over 972934.16 frames.], batch size: 32, lr: 6.38e-04
2022-05-04 09:44:45,457 INFO [train.py:715] (2/8) Epoch 2, batch 29150, loss[loss=0.1766, simple_loss=0.2355, pruned_loss=0.05884, over 4768.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2366, pruned_loss=0.05124, over 972603.45 frames.], batch size: 17, lr: 6.38e-04
2022-05-04 09:45:24,940 INFO [train.py:715] (2/8) Epoch 2, batch 29200, loss[loss=0.1751, simple_loss=0.246, pruned_loss=0.0521, over 4842.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2361, pruned_loss=0.05103, over 973251.12 frames.], batch size: 15, lr: 6.38e-04
2022-05-04 09:46:05,370 INFO [train.py:715] (2/8) Epoch 2, batch 29250, loss[loss=0.1784, simple_loss=0.2407, pruned_loss=0.05807, over 4823.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2363, pruned_loss=0.05138, over 972277.90 frames.], batch size: 26, lr: 6.38e-04
2022-05-04 09:46:44,472 INFO [train.py:715] (2/8) Epoch 2, batch 29300, loss[loss=0.1962, simple_loss=0.2762, pruned_loss=0.05813, over 4874.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2363, pruned_loss=0.05106, over 971758.86 frames.], batch size: 16, lr: 6.37e-04
2022-05-04 09:47:23,243 INFO [train.py:715] (2/8) Epoch 2, batch 29350, loss[loss=0.1882, simple_loss=0.2538, pruned_loss=0.06128, over 4837.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2358, pruned_loss=0.05067, over 971352.51 frames.], batch size: 15, lr: 6.37e-04
2022-05-04 09:48:02,460 INFO [train.py:715] (2/8) Epoch 2, batch 29400, loss[loss=0.1765, simple_loss=0.244, pruned_loss=0.05446, over 4819.00 frames.], tot_loss[loss=0.1687, simple_loss=0.236, pruned_loss=0.05068, over 971843.16 frames.], batch size: 25, lr: 6.37e-04
2022-05-04 09:48:41,881 INFO [train.py:715] (2/8) Epoch 2, batch 29450, loss[loss=0.1732, simple_loss=0.221, pruned_loss=0.06277, over 4961.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2365, pruned_loss=0.05085, over 971728.37 frames.], batch size: 14, lr: 6.37e-04
2022-05-04 09:49:20,750 INFO [train.py:715] (2/8) Epoch 2, batch 29500, loss[loss=0.1709, simple_loss=0.2319, pruned_loss=0.05492, over 4810.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2368, pruned_loss=0.05105, over 972059.56 frames.], batch size: 21, lr: 6.37e-04
2022-05-04 09:49:59,763 INFO [train.py:715] (2/8) Epoch 2, batch 29550, loss[loss=0.1448, simple_loss=0.2051, pruned_loss=0.04222, over 4836.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2352, pruned_loss=0.05053, over 971716.99 frames.], batch size: 13, lr: 6.37e-04
2022-05-04 09:50:39,175 INFO [train.py:715] (2/8) Epoch 2, batch 29600, loss[loss=0.2016, simple_loss=0.2645, pruned_loss=0.06932, over 4754.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2354, pruned_loss=0.05058, over 971825.50 frames.], batch size: 19, lr: 6.37e-04
2022-05-04 09:51:18,360 INFO [train.py:715] (2/8) Epoch 2, batch 29650, loss[loss=0.1404, simple_loss=0.2065, pruned_loss=0.03713, over 4869.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2351, pruned_loss=0.05004, over 972074.34 frames.], batch size: 20, lr: 6.36e-04
2022-05-04 09:51:57,123 INFO [train.py:715] (2/8) Epoch 2, batch 29700, loss[loss=0.2083, simple_loss=0.2604, pruned_loss=0.07807, over 4786.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2354, pruned_loss=0.0506, over 971718.43 frames.], batch size: 18, lr: 6.36e-04
2022-05-04 09:52:36,247 INFO [train.py:715] (2/8) Epoch 2, batch 29750, loss[loss=0.1262, simple_loss=0.1927, pruned_loss=0.02984, over 4796.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2361, pruned_loss=0.05108, over 971791.54 frames.], batch size: 14, lr: 6.36e-04
2022-05-04 09:53:15,364 INFO [train.py:715] (2/8) Epoch 2, batch 29800, loss[loss=0.1536, simple_loss=0.2205, pruned_loss=0.0434, over 4929.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2359, pruned_loss=0.05084, over 971375.74 frames.], batch size: 35, lr: 6.36e-04
2022-05-04 09:53:53,996 INFO [train.py:715] (2/8) Epoch 2, batch 29850, loss[loss=0.1671, simple_loss=0.2402, pruned_loss=0.04701, over 4837.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2361, pruned_loss=0.05074, over 971411.97 frames.], batch size: 15, lr: 6.36e-04
2022-05-04 09:54:33,003 INFO [train.py:715] (2/8) Epoch 2, batch 29900, loss[loss=0.1735, simple_loss=0.2433, pruned_loss=0.05184, over 4857.00 frames.], tot_loss[loss=0.169, simple_loss=0.2365, pruned_loss=0.05075, over 972091.86 frames.], batch size: 30, lr: 6.36e-04
2022-05-04 09:55:12,823 INFO [train.py:715] (2/8) Epoch 2, batch 29950, loss[loss=0.1558, simple_loss=0.2204, pruned_loss=0.04556, over 4749.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2355, pruned_loss=0.0502, over 971727.09 frames.], batch size: 16, lr: 6.35e-04
2022-05-04 09:55:51,631 INFO [train.py:715] (2/8) Epoch 2, batch 30000, loss[loss=0.1711, simple_loss=0.2368, pruned_loss=0.0527, over 4768.00 frames.], tot_loss[loss=0.1697, simple_loss=0.237, pruned_loss=0.05121, over 972258.54 frames.], batch size: 17, lr: 6.35e-04
2022-05-04 09:55:51,632 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 09:56:00,453 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1166, simple_loss=0.2028, pruned_loss=0.01515, over 914524.00 frames.
2022-05-04 09:56:39,112 INFO [train.py:715] (2/8) Epoch 2, batch 30050, loss[loss=0.1615, simple_loss=0.2369, pruned_loss=0.04303, over 4944.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2375, pruned_loss=0.05164, over 972982.29 frames.], batch size: 23, lr: 6.35e-04
2022-05-04 09:57:18,472 INFO [train.py:715] (2/8) Epoch 2, batch 30100, loss[loss=0.159, simple_loss=0.2388, pruned_loss=0.03964, over 4827.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2378, pruned_loss=0.05164, over 973234.29 frames.], batch size: 13, lr: 6.35e-04
2022-05-04 09:57:57,545 INFO [train.py:715] (2/8) Epoch 2, batch 30150, loss[loss=0.1707, simple_loss=0.2294, pruned_loss=0.05599, over 4972.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2374, pruned_loss=0.05121, over 971925.66 frames.], batch size: 14, lr: 6.35e-04
2022-05-04 09:58:37,025 INFO [train.py:715] (2/8) Epoch 2, batch 30200, loss[loss=0.2062, simple_loss=0.2506, pruned_loss=0.08091, over 4852.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2367, pruned_loss=0.05103, over 971112.59 frames.], batch size: 13, lr: 6.35e-04
2022-05-04 09:59:15,769 INFO [train.py:715] (2/8) Epoch 2, batch 30250, loss[loss=0.141, simple_loss=0.2141, pruned_loss=0.03393, over 4686.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2375, pruned_loss=0.05135, over 971336.59 frames.], batch size: 15, lr: 6.34e-04
2022-05-04 09:59:55,021 INFO [train.py:715] (2/8) Epoch 2, batch 30300, loss[loss=0.1507, simple_loss=0.2403, pruned_loss=0.03052, over 4981.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2363, pruned_loss=0.05048, over 971719.45 frames.], batch size: 24, lr: 6.34e-04
2022-05-04 10:00:35,000 INFO [train.py:715] (2/8) Epoch 2, batch 30350, loss[loss=0.151, simple_loss=0.2263, pruned_loss=0.03787, over 4912.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2351, pruned_loss=0.04988, over 970873.78 frames.], batch size: 23, lr: 6.34e-04
2022-05-04 10:01:14,084 INFO [train.py:715] (2/8) Epoch 2, batch 30400, loss[loss=0.1569, simple_loss=0.2133, pruned_loss=0.05019, over 4978.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2349, pruned_loss=0.04985, over 971278.30 frames.], batch size: 14, lr: 6.34e-04
2022-05-04 10:01:53,188 INFO [train.py:715] (2/8) Epoch 2, batch 30450, loss[loss=0.2019, simple_loss=0.2607, pruned_loss=0.07153, over 4801.00 frames.], tot_loss[loss=0.1674, simple_loss=0.235, pruned_loss=0.0499, over 971282.44 frames.], batch size: 17, lr: 6.34e-04
2022-05-04 10:02:32,957 INFO [train.py:715] (2/8) Epoch 2, batch 30500, loss[loss=0.1667, simple_loss=0.2235, pruned_loss=0.05493, over 4788.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2368, pruned_loss=0.05102, over 971561.03 frames.], batch size: 12, lr: 6.34e-04
2022-05-04 10:03:12,632 INFO [train.py:715] (2/8) Epoch 2, batch 30550, loss[loss=0.2068, simple_loss=0.2695, pruned_loss=0.07206, over 4902.00 frames.], tot_loss[loss=0.169, simple_loss=0.2365, pruned_loss=0.05073, over 971159.05 frames.], batch size: 38, lr: 6.33e-04
2022-05-04 10:03:51,358 INFO [train.py:715] (2/8) Epoch 2, batch 30600, loss[loss=0.1932, simple_loss=0.2466, pruned_loss=0.0699, over 4931.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2357, pruned_loss=0.05077, over 972043.46 frames.], batch size: 23, lr: 6.33e-04
2022-05-04 10:04:31,212 INFO [train.py:715] (2/8) Epoch 2, batch 30650, loss[loss=0.1855, simple_loss=0.2505, pruned_loss=0.06024, over 4819.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2359, pruned_loss=0.05046, over 971834.29 frames.], batch size: 25, lr: 6.33e-04
2022-05-04 10:05:11,273 INFO [train.py:715] (2/8) Epoch 2, batch 30700, loss[loss=0.2149, simple_loss=0.2635, pruned_loss=0.08314, over 4708.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2364, pruned_loss=0.05126, over 972230.34 frames.], batch size: 15, lr: 6.33e-04
2022-05-04 10:05:51,101 INFO [train.py:715] (2/8) Epoch 2, batch 30750, loss[loss=0.1985, simple_loss=0.2694, pruned_loss=0.06373, over 4874.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2362, pruned_loss=0.05143, over 972629.27 frames.], batch size: 22, lr: 6.33e-04
2022-05-04 10:06:30,167 INFO [train.py:715] (2/8) Epoch 2, batch 30800, loss[loss=0.1564, simple_loss=0.2287, pruned_loss=0.04208, over 4888.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2361, pruned_loss=0.05101, over 972676.59 frames.], batch size: 16, lr: 6.33e-04
2022-05-04 10:07:09,678 INFO [train.py:715] (2/8) Epoch 2, batch 30850, loss[loss=0.158, simple_loss=0.2285, pruned_loss=0.04371, over 4924.00 frames.], tot_loss[loss=0.1685, simple_loss=0.236, pruned_loss=0.05053, over 972337.16 frames.], batch size: 18, lr: 6.33e-04
2022-05-04 10:07:49,323 INFO [train.py:715] (2/8) Epoch 2, batch 30900, loss[loss=0.1381, simple_loss=0.2078, pruned_loss=0.03427, over 4954.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2363, pruned_loss=0.05024, over 972121.73 frames.], batch size: 29, lr: 6.32e-04
2022-05-04 10:08:27,841 INFO [train.py:715] (2/8) Epoch 2, batch 30950, loss[loss=0.1637, simple_loss=0.2343, pruned_loss=0.04654, over 4804.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2363, pruned_loss=0.05064, over 971356.81 frames.], batch size: 25, lr: 6.32e-04
2022-05-04 10:09:07,769 INFO [train.py:715] (2/8) Epoch 2, batch 31000, loss[loss=0.1602, simple_loss=0.2358, pruned_loss=0.04227, over 4797.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2354, pruned_loss=0.05001, over 970961.25 frames.], batch size: 17, lr: 6.32e-04
2022-05-04 10:09:48,208 INFO [train.py:715] (2/8) Epoch 2, batch 31050, loss[loss=0.1482, simple_loss=0.2247, pruned_loss=0.03588, over 4978.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2349, pruned_loss=0.04977, over 970948.58 frames.], batch size: 24, lr: 6.32e-04
2022-05-04 10:10:27,685 INFO [train.py:715] (2/8) Epoch 2, batch 31100, loss[loss=0.174, simple_loss=0.2471, pruned_loss=0.05045, over 4809.00 frames.], tot_loss[loss=0.167, simple_loss=0.2348, pruned_loss=0.04957, over 970887.09 frames.], batch size: 25, lr: 6.32e-04
2022-05-04 10:11:07,476 INFO [train.py:715] (2/8) Epoch 2, batch 31150, loss[loss=0.1876, simple_loss=0.2496, pruned_loss=0.06278, over 4899.00 frames.], tot_loss[loss=0.168, simple_loss=0.2356, pruned_loss=0.05018, over 970597.97 frames.], batch size: 17, lr: 6.32e-04
2022-05-04 10:11:47,657 INFO [train.py:715] (2/8) Epoch 2, batch 31200, loss[loss=0.157, simple_loss=0.2237, pruned_loss=0.0452, over 4919.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2349, pruned_loss=0.04988, over 969792.89 frames.], batch size: 17, lr: 6.31e-04
2022-05-04 10:12:27,438 INFO [train.py:715] (2/8) Epoch 2, batch 31250, loss[loss=0.1531, simple_loss=0.2253, pruned_loss=0.0405, over 4809.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2356, pruned_loss=0.05055, over 969855.78 frames.], batch size: 21, lr: 6.31e-04
2022-05-04 10:13:06,638 INFO [train.py:715] (2/8) Epoch 2, batch 31300, loss[loss=0.1526, simple_loss=0.2286, pruned_loss=0.03835, over 4901.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2359, pruned_loss=0.05064, over 970275.59 frames.], batch size: 19, lr: 6.31e-04
2022-05-04 10:13:46,582 INFO [train.py:715] (2/8) Epoch 2, batch 31350, loss[loss=0.1567, simple_loss=0.2238, pruned_loss=0.04476, over 4826.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2361, pruned_loss=0.05053, over 970648.08 frames.], batch size: 15, lr: 6.31e-04
2022-05-04 10:14:26,947 INFO [train.py:715] (2/8) Epoch 2, batch 31400, loss[loss=0.1599, simple_loss=0.2304, pruned_loss=0.04473, over 4943.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2354, pruned_loss=0.05037, over 971757.40 frames.], batch size: 39, lr: 6.31e-04
2022-05-04 10:15:06,591 INFO [train.py:715] (2/8) Epoch 2, batch 31450, loss[loss=0.1684, simple_loss=0.2374, pruned_loss=0.04966, over 4879.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2345, pruned_loss=0.0496, over 971686.32 frames.], batch size: 22, lr: 6.31e-04
2022-05-04 10:15:46,233 INFO [train.py:715] (2/8) Epoch 2, batch 31500, loss[loss=0.1571, simple_loss=0.231, pruned_loss=0.04161, over 4798.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2354, pruned_loss=0.05009, over 972494.38 frames.], batch size: 25, lr: 6.31e-04
2022-05-04 10:16:26,027 INFO [train.py:715] (2/8) Epoch 2, batch 31550, loss[loss=0.1756, simple_loss=0.2515, pruned_loss=0.04984, over 4875.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2353, pruned_loss=0.04977, over 972311.96 frames.], batch size: 16, lr: 6.30e-04
2022-05-04 10:17:05,435 INFO [train.py:715] (2/8) Epoch 2, batch 31600, loss[loss=0.1708, simple_loss=0.2414, pruned_loss=0.05014, over 4908.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2352, pruned_loss=0.05028, over 972877.09 frames.], batch size: 19, lr: 6.30e-04
2022-05-04 10:17:44,218 INFO [train.py:715] (2/8) Epoch 2, batch 31650, loss[loss=0.1613, simple_loss=0.2367, pruned_loss=0.04299, over 4941.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2351, pruned_loss=0.05023, over 972585.68 frames.], batch size: 18, lr: 6.30e-04
2022-05-04 10:18:24,064 INFO [train.py:715] (2/8) Epoch 2, batch 31700, loss[loss=0.142, simple_loss=0.2038, pruned_loss=0.04008, over 4852.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2347, pruned_loss=0.04989, over 972693.04 frames.], batch size: 30, lr: 6.30e-04
2022-05-04 10:19:04,299 INFO [train.py:715] (2/8) Epoch 2, batch 31750, loss[loss=0.1616, simple_loss=0.2319, pruned_loss=0.04561, over 4957.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2353, pruned_loss=0.05008, over 973758.86 frames.], batch size: 21, lr: 6.30e-04
2022-05-04 10:19:44,135 INFO [train.py:715] (2/8) Epoch 2, batch 31800, loss[loss=0.1716, simple_loss=0.2513, pruned_loss=0.04598, over 4909.00 frames.], tot_loss[loss=0.1663, simple_loss=0.234, pruned_loss=0.04933, over 974080.56 frames.], batch size: 22, lr: 6.30e-04
2022-05-04 10:20:23,460 INFO [train.py:715] (2/8) Epoch 2, batch 31850, loss[loss=0.1621, simple_loss=0.2342, pruned_loss=0.04503, over 4902.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2345, pruned_loss=0.04983, over 974035.73 frames.], batch size: 19, lr: 6.29e-04
2022-05-04 10:21:02,949 INFO [train.py:715] (2/8) Epoch 2, batch 31900, loss[loss=0.1936, simple_loss=0.25, pruned_loss=0.06863, over 4774.00 frames.], tot_loss[loss=0.1685, simple_loss=0.236, pruned_loss=0.05049, over 973641.94 frames.], batch size: 12, lr: 6.29e-04
2022-05-04 10:21:42,547 INFO [train.py:715] (2/8) Epoch 2, batch 31950, loss[loss=0.1582, simple_loss=0.2216, pruned_loss=0.04737, over 4994.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2363, pruned_loss=0.05073, over 974205.76 frames.], batch size: 14, lr: 6.29e-04
2022-05-04 10:22:21,469 INFO [train.py:715] (2/8) Epoch 2, batch 32000, loss[loss=0.1742, simple_loss=0.2455, pruned_loss=0.05149, over 4878.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2359, pruned_loss=0.05064, over 974102.37 frames.], batch size: 20, lr: 6.29e-04
2022-05-04 10:23:01,102 INFO [train.py:715] (2/8) Epoch 2, batch 32050, loss[loss=0.1637, simple_loss=0.2345, pruned_loss=0.04641, over 4954.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2358, pruned_loss=0.05058, over 974149.58 frames.], batch size: 15, lr: 6.29e-04
2022-05-04 10:23:41,011 INFO [train.py:715] (2/8) Epoch 2, batch 32100, loss[loss=0.1296, simple_loss=0.1975, pruned_loss=0.0309, over 4920.00 frames.], tot_loss[loss=0.1677, simple_loss=0.235, pruned_loss=0.05025, over 975017.91 frames.], batch size: 29, lr: 6.29e-04
2022-05-04 10:24:20,298 INFO [train.py:715] (2/8) Epoch 2, batch 32150, loss[loss=0.1561, simple_loss=0.2323, pruned_loss=0.03993, over 4806.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2346, pruned_loss=0.05006, over 974276.56 frames.], batch size: 21, lr: 6.29e-04
2022-05-04 10:24:59,272 INFO [train.py:715] (2/8) Epoch 2, batch 32200, loss[loss=0.1432, simple_loss=0.2046, pruned_loss=0.04093, over 4843.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2355, pruned_loss=0.05065, over 974361.57 frames.], batch size: 32, lr: 6.28e-04
2022-05-04 10:25:39,133 INFO [train.py:715] (2/8) Epoch 2, batch 32250, loss[loss=0.1579, simple_loss=0.2315, pruned_loss=0.04212, over 4843.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2352, pruned_loss=0.05067, over 972836.06 frames.], batch size: 30, lr: 6.28e-04
2022-05-04 10:26:18,490 INFO [train.py:715] (2/8) Epoch 2, batch 32300, loss[loss=0.1981, simple_loss=0.264, pruned_loss=0.06611, over 4915.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2344, pruned_loss=0.05006, over 973664.86 frames.], batch size: 39, lr: 6.28e-04
2022-05-04 10:26:57,484 INFO [train.py:715] (2/8) Epoch 2, batch 32350, loss[loss=0.1484, simple_loss=0.2162, pruned_loss=0.04028, over 4893.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2352, pruned_loss=0.05024, over 973530.08 frames.], batch size: 16, lr: 6.28e-04
2022-05-04 10:27:37,320 INFO [train.py:715] (2/8) Epoch 2, batch 32400, loss[loss=0.1809, simple_loss=0.2269, pruned_loss=0.06746, over 4790.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2349, pruned_loss=0.05031, over 973546.68 frames.], batch size: 12, lr: 6.28e-04
2022-05-04 10:28:17,087 INFO [train.py:715] (2/8) Epoch 2, batch 32450, loss[loss=0.1398, simple_loss=0.2054, pruned_loss=0.03705, over 4755.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2347, pruned_loss=0.04986, over 972414.10 frames.], batch size: 12, lr: 6.28e-04
2022-05-04 10:28:56,073 INFO [train.py:715] (2/8) Epoch 2, batch 32500, loss[loss=0.1373, simple_loss=0.203, pruned_loss=0.03585, over 4971.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2342, pruned_loss=0.04977, over 972114.23 frames.], batch size: 15, lr: 6.27e-04
2022-05-04 10:29:35,588 INFO [train.py:715] (2/8) Epoch 2, batch 32550, loss[loss=0.1376, simple_loss=0.2042, pruned_loss=0.03547, over 4764.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2339, pruned_loss=0.04933, over 972124.91 frames.], batch size: 19, lr: 6.27e-04
2022-05-04 10:30:15,643 INFO [train.py:715] (2/8) Epoch 2, batch 32600, loss[loss=0.2143, simple_loss=0.2952, pruned_loss=0.06668, over 4904.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2354, pruned_loss=0.05011, over 971941.96 frames.], batch size: 17, lr: 6.27e-04
2022-05-04 10:30:54,900 INFO [train.py:715] (2/8) Epoch 2, batch 32650, loss[loss=0.1586, simple_loss=0.2248, pruned_loss=0.04618, over 4890.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2352, pruned_loss=0.04986, over 972763.02 frames.], batch size: 17, lr: 6.27e-04
2022-05-04 10:31:33,742 INFO [train.py:715] (2/8) Epoch 2, batch 32700, loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02892, over 4763.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2343, pruned_loss=0.04919, over 973118.15 frames.], batch size: 19, lr: 6.27e-04
2022-05-04 10:32:13,534 INFO [train.py:715] (2/8) Epoch 2, batch 32750, loss[loss=0.1725, simple_loss=0.2296, pruned_loss=0.05769, over 4877.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2349, pruned_loss=0.05015, over 971983.45 frames.], batch size: 32, lr: 6.27e-04
2022-05-04 10:32:53,517 INFO [train.py:715] (2/8) Epoch 2, batch 32800, loss[loss=0.1682, simple_loss=0.233, pruned_loss=0.05174, over 4879.00 frames.], tot_loss[loss=0.168, simple_loss=0.2353, pruned_loss=0.05036, over 973263.14 frames.], batch size: 32, lr: 6.27e-04
2022-05-04 10:33:32,245 INFO [train.py:715] (2/8) Epoch 2, batch 32850, loss[loss=0.144, simple_loss=0.2042, pruned_loss=0.04188, over 4784.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2339, pruned_loss=0.04941, over 972976.42 frames.], batch size: 17, lr: 6.26e-04
2022-05-04 10:34:11,590 INFO [train.py:715] (2/8) Epoch 2, batch 32900, loss[loss=0.1323, simple_loss=0.2036, pruned_loss=0.03051, over 4986.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2339, pruned_loss=0.04947, over 972922.33 frames.], batch size: 24, lr: 6.26e-04
2022-05-04 10:34:51,515 INFO [train.py:715] (2/8) Epoch 2, batch 32950, loss[loss=0.1582, simple_loss=0.2249, pruned_loss=0.04578, over 4919.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2335, pruned_loss=0.04936, over 972039.73 frames.], batch size: 18, lr: 6.26e-04
2022-05-04 10:35:30,088 INFO [train.py:715] (2/8) Epoch 2, batch 33000, loss[loss=0.1832, simple_loss=0.2531, pruned_loss=0.05663, over 4951.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2342, pruned_loss=0.04938, over 971899.41 frames.], batch size: 21, lr: 6.26e-04
2022-05-04 10:35:30,089 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 10:35:38,852 INFO [train.py:742] (2/8) Epoch 2, validation: loss=0.1163, simple_loss=0.2025, pruned_loss=0.01504, over 914524.00 frames.
2022-05-04 10:36:17,835 INFO [train.py:715] (2/8) Epoch 2, batch 33050, loss[loss=0.1564, simple_loss=0.2338, pruned_loss=0.0395, over 4906.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2347, pruned_loss=0.04951, over 971692.71 frames.], batch size: 39, lr: 6.26e-04
2022-05-04 10:36:57,375 INFO [train.py:715] (2/8) Epoch 2, batch 33100, loss[loss=0.118, simple_loss=0.1776, pruned_loss=0.02924, over 4640.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2342, pruned_loss=0.04936, over 972551.16 frames.], batch size: 13, lr: 6.26e-04
2022-05-04 10:37:37,171 INFO [train.py:715] (2/8) Epoch 2, batch 33150, loss[loss=0.1485, simple_loss=0.2327, pruned_loss=0.03217, over 4885.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2349, pruned_loss=0.04982, over 972289.59 frames.], batch size: 22, lr: 6.25e-04
2022-05-04 10:38:16,776 INFO [train.py:715] (2/8) Epoch 2, batch 33200, loss[loss=0.174, simple_loss=0.2375, pruned_loss=0.05518, over 4789.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2345, pruned_loss=0.04963, over 971306.45 frames.], batch size: 12, lr: 6.25e-04
2022-05-04 10:38:56,312 INFO [train.py:715] (2/8) Epoch 2, batch 33250, loss[loss=0.1574, simple_loss=0.2275, pruned_loss=0.04372, over 4916.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2348, pruned_loss=0.05001, over 970915.27 frames.], batch size: 17, lr: 6.25e-04
2022-05-04 10:39:35,517 INFO [train.py:715] (2/8) Epoch 2, batch 33300, loss[loss=0.1996, simple_loss=0.247, pruned_loss=0.07612, over 4941.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2347, pruned_loss=0.0502, over 971720.25 frames.], batch size: 21, lr: 6.25e-04
2022-05-04 10:40:14,688 INFO [train.py:715] (2/8) Epoch 2, batch 33350, loss[loss=0.1682, simple_loss=0.2405, pruned_loss=0.0479, over 4979.00 frames.], tot_loss[loss=0.1702, simple_loss=0.237, pruned_loss=0.05168, over 972326.23 frames.], batch size: 24, lr: 6.25e-04
2022-05-04 10:40:53,955 INFO [train.py:715] (2/8) Epoch 2, batch 33400, loss[loss=0.2008, simple_loss=0.2626, pruned_loss=0.06956, over 4696.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2373, pruned_loss=0.05207, over 972056.18 frames.], batch size: 15, lr: 6.25e-04
2022-05-04 10:41:33,177 INFO [train.py:715] (2/8) Epoch 2, batch 33450, loss[loss=0.1585, simple_loss=0.2319, pruned_loss=0.04256, over 4905.00 frames.], tot_loss[loss=0.17, simple_loss=0.2368, pruned_loss=0.05158, over 972228.42 frames.], batch size: 17, lr: 6.25e-04
2022-05-04 10:42:13,242 INFO [train.py:715] (2/8) Epoch 2, batch 33500, loss[loss=0.1573, simple_loss=0.2142, pruned_loss=0.0502, over 4969.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2373, pruned_loss=0.05217, over 971415.86 frames.], batch size: 14, lr: 6.24e-04
2022-05-04 10:42:52,004 INFO [train.py:715] (2/8) Epoch 2, batch 33550, loss[loss=0.1834, simple_loss=0.231, pruned_loss=0.06796, over 4973.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2361, pruned_loss=0.05136, over 972120.55 frames.], batch size: 14, lr: 6.24e-04
2022-05-04 10:43:31,498 INFO [train.py:715] (2/8) Epoch 2, batch 33600, loss[loss=0.1519, simple_loss=0.2299, pruned_loss=0.03696, over 4808.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2359, pruned_loss=0.0514, over 971961.76 frames.], batch size: 25, lr: 6.24e-04
2022-05-04 10:44:11,046 INFO [train.py:715] (2/8) Epoch 2, batch 33650, loss[loss=0.1809, simple_loss=0.2462, pruned_loss=0.05774, over 4957.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2355, pruned_loss=0.05081, over 970975.31 frames.], batch size: 24, lr: 6.24e-04
2022-05-04 10:44:50,482 INFO [train.py:715] (2/8) Epoch 2, batch 33700, loss[loss=0.156, simple_loss=0.236, pruned_loss=0.03801, over 4802.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2348, pruned_loss=0.04981, over 971381.24 frames.], batch size: 15, lr: 6.24e-04
2022-05-04 10:45:29,900 INFO [train.py:715] (2/8) Epoch 2, batch 33750, loss[loss=0.1575, simple_loss=0.2335, pruned_loss=0.04074, over 4810.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2358, pruned_loss=0.05047, over 972465.91 frames.], batch size: 21, lr: 6.24e-04
2022-05-04 10:46:09,304 INFO [train.py:715] (2/8) Epoch 2, batch 33800, loss[loss=0.1671, simple_loss=0.226, pruned_loss=0.05408, over 4781.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2346, pruned_loss=0.04997, over 971986.27 frames.], batch size: 14, lr: 6.23e-04
2022-05-04 10:46:49,487 INFO [train.py:715] (2/8) Epoch 2, batch 33850, loss[loss=0.1564, simple_loss=0.2252, pruned_loss=0.04382, over 4824.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2343, pruned_loss=0.0505, over 971920.95 frames.], batch size: 15, lr: 6.23e-04
2022-05-04 10:47:28,881 INFO [train.py:715] (2/8) Epoch 2, batch 33900, loss[loss=0.1433, simple_loss=0.2106, pruned_loss=0.03798, over 4938.00 frames.], tot_loss[loss=0.167, simple_loss=0.2337, pruned_loss=0.05014, over 971782.83 frames.], batch size: 23, lr: 6.23e-04
2022-05-04 10:48:08,023 INFO [train.py:715] (2/8) Epoch 2, batch 33950, loss[loss=0.1591, simple_loss=0.2226, pruned_loss=0.04783, over 4802.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2332, pruned_loss=0.04954, over 971276.92 frames.], batch size: 14, lr: 6.23e-04
2022-05-04 10:48:47,947 INFO [train.py:715] (2/8) Epoch 2, batch 34000, loss[loss=0.1945, simple_loss=0.246, pruned_loss=0.07146, over 4778.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2336, pruned_loss=0.04984, over 970689.66 frames.], batch size: 17, lr: 6.23e-04
2022-05-04 10:49:27,577 INFO [train.py:715] (2/8) Epoch 2, batch 34050, loss[loss=0.1251, simple_loss=0.1955, pruned_loss=0.02737, over 4784.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2333, pruned_loss=0.04965, over 969755.79 frames.], batch size: 12, lr: 6.23e-04
2022-05-04 10:50:07,042 INFO [train.py:715] (2/8) Epoch 2, batch 34100, loss[loss=0.1489, simple_loss=0.2153, pruned_loss=0.04131, over 4857.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2336, pruned_loss=0.04948, over 969557.58 frames.], batch size: 32, lr: 6.23e-04
2022-05-04 10:50:46,455 INFO [train.py:715] (2/8) Epoch 2, batch 34150, loss[loss=0.1592, simple_loss=0.2294, pruned_loss=0.04448, over 4954.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2329, pruned_loss=0.04945, over 970426.07 frames.], batch size: 35, lr: 6.22e-04
2022-05-04 10:51:26,743 INFO [train.py:715] (2/8) Epoch 2, batch 34200, loss[loss=0.158, simple_loss=0.2282, pruned_loss=0.04391, over 4909.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2337, pruned_loss=0.04988, over 970294.66 frames.], batch size: 19, lr: 6.22e-04
2022-05-04 10:52:06,314 INFO [train.py:715] (2/8) Epoch 2, batch 34250, loss[loss=0.2168, simple_loss=0.2773, pruned_loss=0.07809, over 4987.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2343, pruned_loss=0.0503, over 971276.44 frames.], batch size: 35, lr: 6.22e-04
2022-05-04 10:52:45,476 INFO [train.py:715] (2/8) Epoch 2, batch 34300, loss[loss=0.1902, simple_loss=0.2479, pruned_loss=0.06625, over 4980.00 frames.], tot_loss[loss=0.1681, simple_loss=0.235, pruned_loss=0.05061, over 971611.63 frames.], batch size: 28, lr: 6.22e-04
2022-05-04 10:53:25,362 INFO [train.py:715] (2/8) Epoch 2, batch 34350, loss[loss=0.1723, simple_loss=0.2329, pruned_loss=0.05588, over 4982.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2346, pruned_loss=0.05024, over 971324.57 frames.], batch size: 14, lr: 6.22e-04
2022-05-04 10:54:07,387 INFO [train.py:715] (2/8) Epoch 2, batch 34400, loss[loss=0.1498, simple_loss=0.2193, pruned_loss=0.04017, over 4878.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2346, pruned_loss=0.05006, over 971796.23 frames.], batch size: 22, lr: 6.22e-04
2022-05-04 10:54:46,510 INFO [train.py:715] (2/8) Epoch 2, batch 34450, loss[loss=0.1652, simple_loss=0.2377, pruned_loss=0.04635, over 4754.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2348, pruned_loss=0.05043, over 972251.77 frames.], batch size: 19, lr: 6.22e-04
2022-05-04 10:55:25,434 INFO [train.py:715] (2/8) Epoch 2, batch 34500, loss[loss=0.1949, simple_loss=0.2541, pruned_loss=0.06791, over 4980.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2348, pruned_loss=0.04984, over 973231.20 frames.], batch size: 15, lr: 6.21e-04
2022-05-04 10:56:05,349 INFO [train.py:715] (2/8) Epoch 2, batch 34550, loss[loss=0.1621, simple_loss=0.2313, pruned_loss=0.04642, over 4790.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2349, pruned_loss=0.05015, over 972675.13 frames.], batch size: 21, lr: 6.21e-04
2022-05-04 10:56:44,136 INFO [train.py:715] (2/8) Epoch 2, batch 34600, loss[loss=0.167, simple_loss=0.2385, pruned_loss=0.04778, over 4959.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2343, pruned_loss=0.05026, over 972823.70 frames.], batch size: 24, lr: 6.21e-04
2022-05-04 10:57:23,170 INFO [train.py:715] (2/8) Epoch 2, batch 34650, loss[loss=0.1555, simple_loss=0.2222, pruned_loss=0.04442, over 4982.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2345, pruned_loss=0.05027, over 973989.18 frames.], batch size: 27, lr: 6.21e-04
2022-05-04 10:58:02,534 INFO [train.py:715] (2/8) Epoch 2, batch 34700, loss[loss=0.1625, simple_loss=0.2368, pruned_loss=0.0441, over 4811.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2344, pruned_loss=0.05012, over 972894.30 frames.], batch size: 25, lr: 6.21e-04
2022-05-04 10:58:40,561 INFO [train.py:715] (2/8) Epoch 2, batch 34750, loss[loss=0.161, simple_loss=0.2159, pruned_loss=0.05306, over 4789.00 frames.], tot_loss[loss=0.1682, simple_loss=0.235, pruned_loss=0.05067, over 972389.96 frames.], batch size: 14, lr: 6.21e-04
2022-05-04 10:59:17,100 INFO [train.py:715] (2/8) Epoch 2, batch 34800, loss[loss=0.1279, simple_loss=0.1998, pruned_loss=0.02803, over 4804.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2346, pruned_loss=0.05045, over 971928.41 frames.], batch size: 12, lr: 6.20e-04
2022-05-04 11:00:07,063 INFO [train.py:715] (2/8) Epoch 3, batch 0, loss[loss=0.1868, simple_loss=0.2497, pruned_loss=0.06197, over 4774.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2497, pruned_loss=0.06197, over 4774.00 frames.], batch size: 18, lr: 5.87e-04
2022-05-04 11:00:45,735 INFO [train.py:715] (2/8) Epoch 3, batch 50, loss[loss=0.1377, simple_loss=0.2064, pruned_loss=0.0345, over 4955.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2353, pruned_loss=0.052, over 219717.21 frames.], batch size: 21, lr: 5.87e-04
2022-05-04 11:01:25,673 INFO [train.py:715] (2/8) Epoch 3, batch 100, loss[loss=0.1719, simple_loss=0.235, pruned_loss=0.05439, over 4686.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2333, pruned_loss=0.04975, over 386854.39 frames.], batch size: 15, lr: 5.87e-04
2022-05-04 11:02:05,233 INFO [train.py:715] (2/8) Epoch 3, batch 150, loss[loss=0.1697, simple_loss=0.2387, pruned_loss=0.05035, over 4924.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2332, pruned_loss=0.04956, over 516999.66 frames.], batch size: 18, lr: 5.86e-04
2022-05-04 11:02:44,381 INFO [train.py:715] (2/8) Epoch 3, batch 200, loss[loss=0.2266, simple_loss=0.2898, pruned_loss=0.08165, over 4807.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2338, pruned_loss=0.04996, over 617287.22 frames.], batch size: 15, lr: 5.86e-04
2022-05-04 11:03:23,621 INFO [train.py:715] (2/8) Epoch 3, batch 250, loss[loss=0.159, simple_loss=0.2402, pruned_loss=0.03893, over 4774.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2357, pruned_loss=0.05067, over 695472.35 frames.], batch size: 17, lr: 5.86e-04
2022-05-04 11:04:03,630 INFO [train.py:715] (2/8) Epoch 3, batch 300, loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02838, over 4926.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2352, pruned_loss=0.0503, over 756793.06 frames.], batch size: 18, lr: 5.86e-04
2022-05-04 11:04:42,639 INFO [train.py:715] (2/8) Epoch 3, batch 350, loss[loss=0.1885, simple_loss=0.2617, pruned_loss=0.0576, over 4760.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2359, pruned_loss=0.0504, over 804274.23 frames.], batch size: 19, lr: 5.86e-04
2022-05-04 11:05:21,840 INFO [train.py:715] (2/8) Epoch 3, batch 400, loss[loss=0.1589, simple_loss=0.2244, pruned_loss=0.04669, over 4766.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2334, pruned_loss=0.04914, over 840915.91 frames.], batch size: 18, lr: 5.86e-04
2022-05-04 11:06:01,614 INFO [train.py:715] (2/8) Epoch 3, batch 450, loss[loss=0.2041, simple_loss=0.2594, pruned_loss=0.07442, over 4984.00 frames.], tot_loss[loss=0.166, simple_loss=0.2335, pruned_loss=0.04925, over 868923.63 frames.], batch size: 28, lr: 5.86e-04
2022-05-04 11:06:41,118 INFO [train.py:715] (2/8) Epoch 3, batch 500, loss[loss=0.1906, simple_loss=0.2554, pruned_loss=0.06291, over 4889.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2339, pruned_loss=0.04932, over 892072.89 frames.], batch size: 22, lr: 5.85e-04
2022-05-04 11:07:20,463 INFO [train.py:715] (2/8) Epoch 3, batch 550, loss[loss=0.1469, simple_loss=0.2212, pruned_loss=0.03635, over 4702.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2339, pruned_loss=0.0497, over 909215.25 frames.], batch size: 15, lr: 5.85e-04
2022-05-04 11:07:59,334 INFO [train.py:715] (2/8) Epoch 3, batch 600, loss[loss=0.151, simple_loss=0.2156, pruned_loss=0.04322, over 4847.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2338, pruned_loss=0.04998, over 923241.17 frames.], batch size: 15, lr: 5.85e-04
2022-05-04 11:08:39,292 INFO [train.py:715] (2/8) Epoch 3, batch 650, loss[loss=0.1719, simple_loss=0.2452, pruned_loss=0.04927, over 4983.00 frames.], tot_loss[loss=0.1668, simple_loss=0.234, pruned_loss=0.04977, over 934260.88 frames.], batch size: 25, lr: 5.85e-04
2022-05-04 11:09:18,634 INFO [train.py:715] (2/8) Epoch 3, batch 700, loss[loss=0.1711, simple_loss=0.2383, pruned_loss=0.0519, over 4975.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2342, pruned_loss=0.04985, over 943247.18 frames.], batch size: 35, lr: 5.85e-04
2022-05-04 11:09:57,734 INFO [train.py:715] (2/8) Epoch 3, batch 750, loss[loss=0.1481, simple_loss=0.2159, pruned_loss=0.04009, over 4886.00 frames.], tot_loss[loss=0.1677, simple_loss=0.235, pruned_loss=0.05018, over 949602.84 frames.], batch size: 16, lr: 5.85e-04
2022-05-04 11:10:37,299 INFO [train.py:715] (2/8) Epoch 3, batch 800, loss[loss=0.1716, simple_loss=0.2427, pruned_loss=0.05024, over 4692.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2367, pruned_loss=0.05147, over 954968.84 frames.], batch size: 15, lr: 5.85e-04
2022-05-04 11:11:17,436 INFO [train.py:715] (2/8) Epoch 3, batch 850, loss[loss=0.1751, simple_loss=0.2442, pruned_loss=0.053, over 4917.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2364, pruned_loss=0.05116, over 958654.20 frames.], batch size: 21, lr: 5.84e-04
2022-05-04 11:11:56,825 INFO [train.py:715] (2/8) Epoch 3, batch 900, loss[loss=0.1633, simple_loss=0.2202, pruned_loss=0.05324, over 4976.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2349, pruned_loss=0.05041, over 961632.20 frames.], batch size: 35, lr: 5.84e-04
2022-05-04 11:12:35,437 INFO [train.py:715] (2/8) Epoch 3, batch 950, loss[loss=0.2044, simple_loss=0.2625, pruned_loss=0.07313, over 4649.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2346, pruned_loss=0.05035, over 963526.07 frames.], batch size: 13, lr: 5.84e-04
2022-05-04 11:13:15,421 INFO [train.py:715] (2/8) Epoch 3, batch 1000, loss[loss=0.1417, simple_loss=0.2122, pruned_loss=0.03558, over 4699.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2337, pruned_loss=0.04975, over 964746.21 frames.], batch size: 15, lr: 5.84e-04
2022-05-04 11:13:55,088 INFO [train.py:715] (2/8) Epoch 3, batch 1050, loss[loss=0.1329, simple_loss=0.2023, pruned_loss=0.03181, over 4737.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2333, pruned_loss=0.04922, over 967080.41 frames.], batch size: 16, lr: 5.84e-04
2022-05-04 11:14:33,999 INFO [train.py:715] (2/8) Epoch 3, batch 1100, loss[loss=0.1588, simple_loss=0.2273, pruned_loss=0.04518, over 4933.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2333, pruned_loss=0.04894, over 967631.18 frames.], batch size: 23, lr: 5.84e-04
2022-05-04 11:15:12,871 INFO [train.py:715] (2/8) Epoch 3, batch 1150, loss[loss=0.168, simple_loss=0.2437, pruned_loss=0.04612, over 4936.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2337, pruned_loss=0.04861, over 969114.13 frames.], batch size: 29, lr: 5.84e-04
2022-05-04 11:15:52,678 INFO [train.py:715] (2/8) Epoch 3, batch 1200, loss[loss=0.1827, simple_loss=0.2452, pruned_loss=0.06005, over 4767.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2337, pruned_loss=0.04881, over 970362.86 frames.], batch size: 18, lr: 5.83e-04
2022-05-04 11:16:31,654 INFO [train.py:715] (2/8) Epoch 3, batch 1250, loss[loss=0.2102, simple_loss=0.2769, pruned_loss=0.07174, over 4980.00 frames.], tot_loss[loss=0.168, simple_loss=0.2353, pruned_loss=0.05032, over 970254.19 frames.], batch size: 35, lr: 5.83e-04
2022-05-04 11:17:10,159 INFO [train.py:715] (2/8) Epoch 3, batch 1300, loss[loss=0.2088, simple_loss=0.2518, pruned_loss=0.08287, over 4692.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2351, pruned_loss=0.05028, over 970938.97 frames.], batch size: 15, lr: 5.83e-04
2022-05-04 11:17:49,718 INFO [train.py:715] (2/8) Epoch 3, batch 1350, loss[loss=0.1561, simple_loss=0.2204, pruned_loss=0.04589, over 4768.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2345, pruned_loss=0.04981, over 971595.14 frames.], batch size: 18, lr: 5.83e-04
2022-05-04 11:18:28,994 INFO [train.py:715] (2/8) Epoch 3, batch 1400, loss[loss=0.1902, simple_loss=0.2506, pruned_loss=0.06492, over 4807.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2349, pruned_loss=0.0499, over 971610.84 frames.], batch size: 26, lr: 5.83e-04
2022-05-04 11:19:07,860 INFO [train.py:715] (2/8) Epoch 3, batch 1450, loss[loss=0.1639, simple_loss=0.2402, pruned_loss=0.04373, over 4885.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2348, pruned_loss=0.04972, over 971836.36 frames.], batch size: 16, lr: 5.83e-04
2022-05-04 11:19:46,417 INFO [train.py:715] (2/8) Epoch 3, batch 1500, loss[loss=0.1596, simple_loss=0.2286, pruned_loss=0.04534, over 4953.00 frames.], tot_loss[loss=0.167, simple_loss=0.2345, pruned_loss=0.04974, over 972338.08 frames.], batch size: 21, lr: 5.83e-04
2022-05-04 11:20:26,145 INFO [train.py:715] (2/8) Epoch 3, batch 1550, loss[loss=0.1817, simple_loss=0.2491, pruned_loss=0.05719, over 4771.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2339, pruned_loss=0.04962, over 971680.74 frames.], batch size: 18, lr: 5.83e-04
2022-05-04 11:21:05,410 INFO [train.py:715] (2/8) Epoch 3, batch 1600, loss[loss=0.1725, simple_loss=0.2409, pruned_loss=0.05206, over 4916.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2334, pruned_loss=0.04966, over 972188.04 frames.], batch size: 18, lr: 5.82e-04
2022-05-04 11:21:43,526 INFO [train.py:715] (2/8) Epoch 3, batch 1650, loss[loss=0.146, simple_loss=0.2157, pruned_loss=0.03817, over 4947.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2325, pruned_loss=0.04888, over 972497.37 frames.], batch size: 21, lr: 5.82e-04
2022-05-04 11:22:22,776 INFO [train.py:715] (2/8) Epoch 3, batch 1700, loss[loss=0.2017, simple_loss=0.2848, pruned_loss=0.05936, over 4933.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2329, pruned_loss=0.04891, over 973560.52 frames.], batch size: 21, lr: 5.82e-04
2022-05-04 11:23:02,316 INFO [train.py:715] (2/8) Epoch 3, batch 1750, loss[loss=0.1379, simple_loss=0.2041, pruned_loss=0.03582, over 4742.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2331, pruned_loss=0.04874, over 973581.32 frames.], batch size: 12, lr: 5.82e-04
2022-05-04 11:23:41,616 INFO [train.py:715] (2/8) Epoch 3, batch 1800, loss[loss=0.1639, simple_loss=0.2205, pruned_loss=0.0537, over 4906.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2331, pruned_loss=0.04872, over 972482.53 frames.], batch size: 17, lr: 5.82e-04
2022-05-04 11:24:20,316 INFO [train.py:715] (2/8) Epoch 3, batch 1850, loss[loss=0.1801, simple_loss=0.2472, pruned_loss=0.05651, over 4847.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2335, pruned_loss=0.04916, over 970890.56 frames.], batch size: 30, lr: 5.82e-04
2022-05-04 11:25:00,290 INFO [train.py:715] (2/8) Epoch 3, batch 1900, loss[loss=0.1579, simple_loss=0.2285, pruned_loss=0.04369, over 4940.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2319, pruned_loss=0.04793, over 970683.13 frames.], batch size: 21, lr: 5.82e-04
2022-05-04 11:25:39,883 INFO [train.py:715] (2/8) Epoch 3, batch 1950, loss[loss=0.1438, simple_loss=0.2104, pruned_loss=0.03854, over 4778.00 frames.], tot_loss[loss=0.1644, simple_loss=0.232, pruned_loss=0.04843, over 971110.96 frames.], batch size: 19, lr: 5.81e-04
2022-05-04 11:26:18,801 INFO [train.py:715] (2/8) Epoch 3, batch 2000, loss[loss=0.1679, simple_loss=0.244, pruned_loss=0.04595, over 4961.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2319, pruned_loss=0.04821, over 971614.65 frames.], batch size: 29, lr: 5.81e-04
2022-05-04 11:26:58,007 INFO [train.py:715] (2/8) Epoch 3, batch 2050, loss[loss=0.1596, simple_loss=0.2223, pruned_loss=0.04841, over 4854.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2329, pruned_loss=0.04866, over 970811.28 frames.], batch size: 30, lr: 5.81e-04
2022-05-04 11:27:37,792 INFO [train.py:715] (2/8) Epoch 3, batch 2100, loss[loss=0.162, simple_loss=0.243, pruned_loss=0.04055, over 4917.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2324, pruned_loss=0.04856, over 971682.16 frames.], batch size: 18, lr: 5.81e-04
2022-05-04 11:28:17,046 INFO [train.py:715] (2/8) Epoch 3, batch 2150, loss[loss=0.1745, simple_loss=0.2399, pruned_loss=0.05451, over 4992.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2316, pruned_loss=0.04882, over 971213.91 frames.], batch size: 16, lr: 5.81e-04
2022-05-04 11:28:55,719 INFO [train.py:715] (2/8) Epoch 3, batch 2200, loss[loss=0.1557, simple_loss=0.2219, pruned_loss=0.04477, over 4970.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2319, pruned_loss=0.04858, over 971876.21 frames.], batch size: 33, lr: 5.81e-04
2022-05-04 11:29:35,098 INFO [train.py:715] (2/8) Epoch 3, batch 2250, loss[loss=0.144, simple_loss=0.2119, pruned_loss=0.03805, over 4980.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2322, pruned_loss=0.04863, over 971977.78 frames.], batch size: 39, lr: 5.81e-04
2022-05-04 11:30:14,517 INFO [train.py:715] (2/8) Epoch 3, batch 2300, loss[loss=0.1457, simple_loss=0.224, pruned_loss=0.03369, over 4939.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2319, pruned_loss=0.04817, over 972151.64 frames.], batch size: 21, lr: 5.80e-04
2022-05-04 11:30:53,575 INFO [train.py:715] (2/8) Epoch 3, batch 2350, loss[loss=0.1545, simple_loss=0.2218, pruned_loss=0.04356, over 4822.00 frames.], tot_loss[loss=0.164, simple_loss=0.2318, pruned_loss=0.04813, over 971566.57 frames.], batch size: 27, lr: 5.80e-04
2022-05-04 11:31:32,368 INFO [train.py:715] (2/8) Epoch 3, batch 2400, loss[loss=0.1782, simple_loss=0.2487, pruned_loss=0.05383, over 4900.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2327, pruned_loss=0.04837, over 972068.56 frames.], batch size: 39, lr: 5.80e-04
2022-05-04 11:32:12,608 INFO [train.py:715] (2/8) Epoch 3, batch 2450, loss[loss=0.156, simple_loss=0.2241, pruned_loss=0.04397, over 4925.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2331, pruned_loss=0.04887, over 972155.14 frames.], batch size: 23, lr: 5.80e-04
2022-05-04 11:32:51,961 INFO [train.py:715] (2/8) Epoch 3, batch 2500, loss[loss=0.1817, simple_loss=0.2444, pruned_loss=0.05954, over 4922.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2334, pruned_loss=0.04887, over 971865.59 frames.], batch size: 39, lr: 5.80e-04
2022-05-04 11:33:30,785 INFO [train.py:715] (2/8) Epoch 3, batch 2550, loss[loss=0.1585, simple_loss=0.2262, pruned_loss=0.04536, over 4927.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2328, pruned_loss=0.04844, over 971976.95 frames.], batch size: 18, lr: 5.80e-04
2022-05-04 11:34:11,442 INFO [train.py:715] (2/8) Epoch 3, batch 2600, loss[loss=0.1889, simple_loss=0.2547, pruned_loss=0.06153, over 4786.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2333, pruned_loss=0.0488, over 972399.56 frames.], batch size: 17, lr: 5.80e-04
2022-05-04 11:34:51,557 INFO [train.py:715] (2/8) Epoch 3, batch 2650, loss[loss=0.1976, simple_loss=0.2645, pruned_loss=0.06535, over 4971.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2335, pruned_loss=0.04903, over 971901.90 frames.], batch size: 15, lr: 5.80e-04
2022-05-04 11:35:30,752 INFO [train.py:715] (2/8) Epoch 3, batch 2700, loss[loss=0.1698, simple_loss=0.2433, pruned_loss=0.04811, over 4963.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2335, pruned_loss=0.04901, over 971265.79 frames.], batch size: 15, lr: 5.79e-04
2022-05-04 11:36:10,252 INFO [train.py:715] (2/8) Epoch 3, batch 2750, loss[loss=0.172, simple_loss=0.2421, pruned_loss=0.05099, over 4830.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2331, pruned_loss=0.04859, over 971410.61 frames.], batch size: 26, lr: 5.79e-04
2022-05-04 11:36:50,507 INFO [train.py:715] (2/8) Epoch 3, batch 2800, loss[loss=0.1724, simple_loss=0.2415, pruned_loss=0.05162, over 4784.00 frames.], tot_loss[loss=0.165, simple_loss=0.2331, pruned_loss=0.04849, over 971943.22 frames.], batch size: 14, lr: 5.79e-04
2022-05-04 11:37:29,788 INFO [train.py:715] (2/8) Epoch 3, batch 2850, loss[loss=0.1573, simple_loss=0.214, pruned_loss=0.05033, over 4822.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2329, pruned_loss=0.04827, over 971879.66 frames.], batch size: 13, lr: 5.79e-04
2022-05-04 11:38:08,465 INFO [train.py:715] (2/8) Epoch 3, batch 2900, loss[loss=0.172, simple_loss=0.2472, pruned_loss=0.04844, over 4957.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2325, pruned_loss=0.04811, over 972415.68 frames.], batch size: 21, lr: 5.79e-04
2022-05-04 11:38:48,420 INFO [train.py:715] (2/8) Epoch 3, batch 2950, loss[loss=0.1845, simple_loss=0.2404, pruned_loss=0.06432, over 4965.00 frames.], tot_loss[loss=0.1641, simple_loss=0.232, pruned_loss=0.0481, over 972735.28 frames.], batch size: 15, lr: 5.79e-04
2022-05-04 11:39:28,051 INFO [train.py:715] (2/8) Epoch 3, batch 3000, loss[loss=0.1733, simple_loss=0.2371, pruned_loss=0.05478, over 4961.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2324, pruned_loss=0.04849, over 972395.66 frames.], batch size: 14, lr: 5.79e-04
2022-05-04 11:39:28,052 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 11:39:36,790 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1153, simple_loss=0.2015, pruned_loss=0.0146, over 914524.00 frames.
2022-05-04 11:40:16,880 INFO [train.py:715] (2/8) Epoch 3, batch 3050, loss[loss=0.1541, simple_loss=0.2269, pruned_loss=0.04067, over 4879.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2329, pruned_loss=0.04863, over 972172.50 frames.], batch size: 16, lr: 5.78e-04
2022-05-04 11:40:55,666 INFO [train.py:715] (2/8) Epoch 3, batch 3100, loss[loss=0.167, simple_loss=0.234, pruned_loss=0.04998, over 4954.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2334, pruned_loss=0.04904, over 972266.78 frames.], batch size: 14, lr: 5.78e-04
2022-05-04 11:41:35,051 INFO [train.py:715] (2/8) Epoch 3, batch 3150, loss[loss=0.2081, simple_loss=0.2579, pruned_loss=0.07919, over 4885.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2334, pruned_loss=0.04919, over 972195.34 frames.], batch size: 22, lr: 5.78e-04
2022-05-04 11:42:14,850 INFO [train.py:715] (2/8) Epoch 3, batch 3200, loss[loss=0.1951, simple_loss=0.2601, pruned_loss=0.06507, over 4963.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2342, pruned_loss=0.04975, over 972312.69 frames.], batch size: 35, lr: 5.78e-04
2022-05-04 11:42:54,651 INFO [train.py:715] (2/8) Epoch 3, batch 3250, loss[loss=0.2075, simple_loss=0.2724, pruned_loss=0.07136, over 4886.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2336, pruned_loss=0.0493, over 972565.32 frames.], batch size: 17, lr: 5.78e-04
2022-05-04 11:43:33,191 INFO [train.py:715] (2/8) Epoch 3, batch 3300, loss[loss=0.1709, simple_loss=0.2507, pruned_loss=0.04556, over 4723.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2328, pruned_loss=0.04883, over 972210.18 frames.], batch size: 15, lr: 5.78e-04
2022-05-04 11:44:13,004 INFO [train.py:715] (2/8) Epoch 3, batch 3350, loss[loss=0.1564, simple_loss=0.22, pruned_loss=0.04643, over 4858.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2318, pruned_loss=0.04789, over 972185.01 frames.], batch size: 20, lr: 5.78e-04
2022-05-04 11:44:52,479 INFO [train.py:715] (2/8) Epoch 3, batch 3400, loss[loss=0.1978, simple_loss=0.2661, pruned_loss=0.06474, over 4885.00 frames.], tot_loss[loss=0.164, simple_loss=0.2318, pruned_loss=0.04808, over 972749.04 frames.], batch size: 16, lr: 5.77e-04
2022-05-04 11:45:31,166 INFO [train.py:715] (2/8) Epoch 3, batch 3450, loss[loss=0.1877, simple_loss=0.2529, pruned_loss=0.06123, over 4945.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2327, pruned_loss=0.04877, over 971893.99 frames.], batch size: 21, lr: 5.77e-04
2022-05-04 11:46:10,499 INFO [train.py:715] (2/8) Epoch 3, batch 3500, loss[loss=0.1427, simple_loss=0.2156, pruned_loss=0.03493, over 4946.00 frames.], tot_loss[loss=0.166, simple_loss=0.2339, pruned_loss=0.04908, over 972341.57 frames.], batch size: 23, lr: 5.77e-04
2022-05-04 11:46:50,805 INFO [train.py:715] (2/8) Epoch 3, batch 3550, loss[loss=0.1462, simple_loss=0.2238, pruned_loss=0.03431, over 4904.00 frames.], tot_loss[loss=0.166, simple_loss=0.2337, pruned_loss=0.0492, over 973203.62 frames.], batch size: 17, lr: 5.77e-04
2022-05-04 11:47:30,661 INFO [train.py:715] (2/8) Epoch 3, batch 3600, loss[loss=0.2163, simple_loss=0.2547, pruned_loss=0.08888, over 4645.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2328, pruned_loss=0.04882, over 972565.10 frames.], batch size: 13, lr: 5.77e-04
2022-05-04 11:48:09,895 INFO [train.py:715] (2/8) Epoch 3, batch 3650, loss[loss=0.1354, simple_loss=0.2001, pruned_loss=0.03535, over 4901.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2325, pruned_loss=0.04885, over 972823.77 frames.], batch size: 18, lr: 5.77e-04
2022-05-04 11:48:49,619 INFO [train.py:715] (2/8) Epoch 3, batch 3700, loss[loss=0.1521, simple_loss=0.2164, pruned_loss=0.04386, over 4849.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2323, pruned_loss=0.04909, over 972865.45 frames.], batch size: 34, lr: 5.77e-04
2022-05-04 11:49:29,639 INFO [train.py:715] (2/8) Epoch 3, batch 3750, loss[loss=0.1682, simple_loss=0.2516, pruned_loss=0.0424, over 4916.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2336, pruned_loss=0.04941, over 972830.70 frames.], batch size: 19, lr: 5.77e-04
2022-05-04 11:50:09,327 INFO [train.py:715] (2/8) Epoch 3, batch 3800, loss[loss=0.1503, simple_loss=0.2226, pruned_loss=0.03899, over 4777.00 frames.], tot_loss[loss=0.166, simple_loss=0.2334, pruned_loss=0.04932, over 973178.06 frames.], batch size: 17, lr: 5.76e-04
2022-05-04 11:50:48,716 INFO [train.py:715] (2/8) Epoch 3, batch 3850, loss[loss=0.1557, simple_loss=0.2253, pruned_loss=0.04304, over 4808.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2335, pruned_loss=0.04944, over 972685.89 frames.], batch size: 13, lr: 5.76e-04
2022-05-04 11:51:28,558 INFO [train.py:715] (2/8) Epoch 3, batch 3900, loss[loss=0.165, simple_loss=0.2316, pruned_loss=0.04923, over 4852.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2339, pruned_loss=0.04965, over 973100.65 frames.], batch size: 32, lr: 5.76e-04
2022-05-04 11:52:08,056 INFO [train.py:715] (2/8) Epoch 3, batch 3950, loss[loss=0.1802, simple_loss=0.2415, pruned_loss=0.05946, over 4776.00 frames.], tot_loss[loss=0.1668, simple_loss=0.234, pruned_loss=0.04985, over 972442.24 frames.], batch size: 14, lr: 5.76e-04
2022-05-04 11:52:47,076 INFO [train.py:715] (2/8) Epoch 3, batch 4000, loss[loss=0.2167, simple_loss=0.2728, pruned_loss=0.08035, over 4873.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2341, pruned_loss=0.05021, over 972416.81 frames.], batch size: 38, lr: 5.76e-04
2022-05-04 11:53:26,524 INFO [train.py:715] (2/8) Epoch 3, batch 4050, loss[loss=0.1698, simple_loss=0.2398, pruned_loss=0.04985, over 4951.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2354, pruned_loss=0.05068, over 972526.04 frames.], batch size: 35, lr: 5.76e-04
2022-05-04 11:54:06,698 INFO [train.py:715] (2/8) Epoch 3, batch 4100, loss[loss=0.1779, simple_loss=0.2376, pruned_loss=0.05907, over 4844.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2352, pruned_loss=0.05053, over 973031.32 frames.], batch size: 34, lr: 5.76e-04
2022-05-04 11:54:45,651 INFO [train.py:715] (2/8) Epoch 3, batch 4150, loss[loss=0.1794, simple_loss=0.2534, pruned_loss=0.0527, over 4860.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2346, pruned_loss=0.05038, over 972949.39 frames.], batch size: 32, lr: 5.76e-04
2022-05-04 11:55:24,488 INFO [train.py:715] (2/8) Epoch 3, batch 4200, loss[loss=0.1359, simple_loss=0.2121, pruned_loss=0.02988, over 4985.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2333, pruned_loss=0.05, over 973579.12 frames.], batch size: 15, lr: 5.75e-04
2022-05-04 11:56:04,942 INFO [train.py:715] (2/8) Epoch 3, batch 4250, loss[loss=0.1698, simple_loss=0.2316, pruned_loss=0.05403, over 4765.00 frames.], tot_loss[loss=0.1664, simple_loss=0.233, pruned_loss=0.04987, over 972975.22 frames.], batch size: 14, lr: 5.75e-04
2022-05-04 11:56:44,316 INFO [train.py:715] (2/8) Epoch 3, batch 4300, loss[loss=0.1635, simple_loss=0.2352, pruned_loss=0.04586, over 4939.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2323, pruned_loss=0.04914, over 972938.06 frames.], batch size: 29, lr: 5.75e-04
2022-05-04 11:57:23,794 INFO [train.py:715] (2/8) Epoch 3, batch 4350, loss[loss=0.1615, simple_loss=0.2246, pruned_loss=0.04921, over 4937.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2335, pruned_loss=0.04995, over 972594.33 frames.], batch size: 29, lr: 5.75e-04
2022-05-04 11:58:03,474 INFO [train.py:715] (2/8) Epoch 3, batch 4400, loss[loss=0.1533, simple_loss=0.229, pruned_loss=0.03883, over 4985.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2329, pruned_loss=0.04911, over 972152.60 frames.], batch size: 28, lr: 5.75e-04
2022-05-04 11:58:43,516 INFO [train.py:715] (2/8) Epoch 3, batch 4450, loss[loss=0.1766, simple_loss=0.2618, pruned_loss=0.04573, over 4920.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2327, pruned_loss=0.04893, over 971701.77 frames.], batch size: 17, lr: 5.75e-04
2022-05-04 11:59:22,561 INFO [train.py:715] (2/8) Epoch 3, batch 4500, loss[loss=0.1869, simple_loss=0.2515, pruned_loss=0.06117, over 4858.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2345, pruned_loss=0.04993, over 972014.79 frames.], batch size: 20, lr: 5.75e-04
2022-05-04 12:00:01,988 INFO [train.py:715] (2/8) Epoch 3, batch 4550, loss[loss=0.1624, simple_loss=0.2324, pruned_loss=0.0462, over 4878.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2347, pruned_loss=0.04986, over 971809.42 frames.], batch size: 22, lr: 5.74e-04
2022-05-04 12:00:41,741 INFO [train.py:715] (2/8) Epoch 3, batch 4600, loss[loss=0.1156, simple_loss=0.1886, pruned_loss=0.02132, over 4816.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2332, pruned_loss=0.04867, over 971987.50 frames.], batch size: 26, lr: 5.74e-04
2022-05-04 12:01:20,997 INFO [train.py:715] (2/8) Epoch 3, batch 4650, loss[loss=0.1419, simple_loss=0.2191, pruned_loss=0.03231, over 4957.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2332, pruned_loss=0.04911, over 971697.85 frames.], batch size: 24, lr: 5.74e-04
2022-05-04 12:01:59,928 INFO [train.py:715] (2/8) Epoch 3, batch 4700, loss[loss=0.1376, simple_loss=0.2081, pruned_loss=0.03351, over 4817.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2331, pruned_loss=0.04909, over 971813.31 frames.], batch size: 13, lr: 5.74e-04
2022-05-04 12:02:39,131 INFO [train.py:715] (2/8) Epoch 3, batch 4750, loss[loss=0.146, simple_loss=0.2229, pruned_loss=0.03452, over 4819.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2335, pruned_loss=0.04893, over 972044.61 frames.], batch size: 25, lr: 5.74e-04
2022-05-04 12:03:18,735 INFO [train.py:715] (2/8) Epoch 3, batch 4800, loss[loss=0.1657, simple_loss=0.2316, pruned_loss=0.04985, over 4802.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2332, pruned_loss=0.04879, over 972080.73 frames.], batch size: 14, lr: 5.74e-04
2022-05-04 12:03:58,119 INFO [train.py:715] (2/8) Epoch 3, batch 4850, loss[loss=0.145, simple_loss=0.2095, pruned_loss=0.04031, over 4982.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2332, pruned_loss=0.04922, over 972660.25 frames.], batch size: 14, lr: 5.74e-04
2022-05-04 12:04:36,948 INFO [train.py:715] (2/8) Epoch 3, batch 4900, loss[loss=0.1434, simple_loss=0.2183, pruned_loss=0.03421, over 4950.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2327, pruned_loss=0.04885, over 972281.09 frames.], batch size: 21, lr: 5.74e-04
2022-05-04 12:05:16,862 INFO [train.py:715] (2/8) Epoch 3, batch 4950, loss[loss=0.1703, simple_loss=0.2444, pruned_loss=0.04811, over 4792.00 frames.], tot_loss[loss=0.165, simple_loss=0.2328, pruned_loss=0.04859, over 972677.86 frames.], batch size: 18, lr: 5.73e-04
2022-05-04 12:05:56,317 INFO [train.py:715] (2/8) Epoch 3, batch 5000, loss[loss=0.1548, simple_loss=0.2253, pruned_loss=0.04213, over 4693.00 frames.], tot_loss[loss=0.165, simple_loss=0.2328, pruned_loss=0.04861, over 972946.10 frames.], batch size: 15, lr: 5.73e-04
2022-05-04 12:06:35,115 INFO [train.py:715] (2/8) Epoch 3, batch 5050, loss[loss=0.1313, simple_loss=0.2013, pruned_loss=0.03059, over 4822.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2332, pruned_loss=0.04884, over 972860.38 frames.], batch size: 27, lr: 5.73e-04
2022-05-04 12:07:14,483 INFO [train.py:715] (2/8) Epoch 3, batch 5100, loss[loss=0.1665, simple_loss=0.2305, pruned_loss=0.05122, over 4885.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2343, pruned_loss=0.04952, over 973235.01 frames.], batch size: 16, lr: 5.73e-04
2022-05-04 12:07:54,242 INFO [train.py:715] (2/8) Epoch 3, batch 5150, loss[loss=0.1531, simple_loss=0.2208, pruned_loss=0.04271, over 4883.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2331, pruned_loss=0.0488, over 973823.65 frames.], batch size: 13, lr: 5.73e-04
2022-05-04 12:08:32,987 INFO [train.py:715] (2/8) Epoch 3, batch 5200, loss[loss=0.1431, simple_loss=0.2136, pruned_loss=0.03628, over 4903.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2325, pruned_loss=0.04833, over 973850.62 frames.], batch size: 32, lr: 5.73e-04
2022-05-04 12:09:12,106 INFO [train.py:715] (2/8) Epoch 3, batch 5250, loss[loss=0.1606, simple_loss=0.2395, pruned_loss=0.04083, over 4750.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2318, pruned_loss=0.04775, over 973343.00 frames.], batch size: 16, lr: 5.73e-04
2022-05-04 12:09:52,192 INFO [train.py:715] (2/8) Epoch 3, batch 5300, loss[loss=0.1499, simple_loss=0.2172, pruned_loss=0.04129, over 4981.00 frames.], tot_loss[loss=0.1642, simple_loss=0.232, pruned_loss=0.04819, over 974616.64 frames.], batch size: 28, lr: 5.72e-04
2022-05-04 12:10:31,367 INFO [train.py:715] (2/8) Epoch 3, batch 5350, loss[loss=0.1732, simple_loss=0.2532, pruned_loss=0.04661, over 4830.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2324, pruned_loss=0.04832, over 974572.23 frames.], batch size: 15, lr: 5.72e-04
2022-05-04 12:11:10,299 INFO [train.py:715] (2/8) Epoch 3, batch 5400, loss[loss=0.1713, simple_loss=0.2451, pruned_loss=0.04876, over 4699.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2333, pruned_loss=0.0489, over 973902.77 frames.], batch size: 15, lr: 5.72e-04
2022-05-04 12:11:49,946 INFO [train.py:715] (2/8) Epoch 3, batch 5450, loss[loss=0.1574, simple_loss=0.2249, pruned_loss=0.04499, over 4852.00 frames.], tot_loss[loss=0.166, simple_loss=0.2341, pruned_loss=0.04891, over 974842.46 frames.], batch size: 34, lr: 5.72e-04
2022-05-04 12:12:30,201 INFO [train.py:715] (2/8) Epoch 3, batch 5500, loss[loss=0.1969, simple_loss=0.2646, pruned_loss=0.06459, over 4931.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2336, pruned_loss=0.04813, over 973902.61 frames.], batch size: 18, lr: 5.72e-04
2022-05-04 12:13:09,474 INFO [train.py:715] (2/8) Epoch 3, batch 5550, loss[loss=0.1786, simple_loss=0.2507, pruned_loss=0.05329, over 4989.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2336, pruned_loss=0.04854, over 973735.25 frames.], batch size: 25, lr: 5.72e-04
2022-05-04 12:13:49,873 INFO [train.py:715] (2/8) Epoch 3, batch 5600, loss[loss=0.2005, simple_loss=0.2777, pruned_loss=0.06159, over 4923.00 frames.], tot_loss[loss=0.1645, simple_loss=0.233, pruned_loss=0.04803, over 973779.60 frames.], batch size: 23, lr: 5.72e-04
2022-05-04 12:14:29,642 INFO [train.py:715] (2/8) Epoch 3, batch 5650, loss[loss=0.1752, simple_loss=0.2339, pruned_loss=0.0582, over 4954.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2329, pruned_loss=0.04794, over 973468.21 frames.], batch size: 35, lr: 5.72e-04
2022-05-04 12:15:08,730 INFO [train.py:715] (2/8) Epoch 3, batch 5700, loss[loss=0.1704, simple_loss=0.2385, pruned_loss=0.05111, over 4882.00 frames.], tot_loss[loss=0.1657, simple_loss=0.234, pruned_loss=0.04873, over 973086.68 frames.], batch size: 19, lr: 5.71e-04
2022-05-04 12:15:48,066 INFO [train.py:715] (2/8) Epoch 3, batch 5750, loss[loss=0.1529, simple_loss=0.2178, pruned_loss=0.04406, over 4820.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2336, pruned_loss=0.04936, over 972385.38 frames.], batch size: 15, lr: 5.71e-04
2022-05-04 12:16:27,883 INFO [train.py:715] (2/8) Epoch 3, batch 5800, loss[loss=0.1643, simple_loss=0.2339, pruned_loss=0.0473, over 4910.00 frames.], tot_loss[loss=0.1654, simple_loss=0.233, pruned_loss=0.04892, over 972569.36 frames.], batch size: 18, lr: 5.71e-04
2022-05-04 12:17:07,625 INFO [train.py:715] (2/8) Epoch 3, batch 5850, loss[loss=0.1747, simple_loss=0.2511, pruned_loss=0.04913, over 4826.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2324, pruned_loss=0.04827, over 972235.68 frames.], batch size: 15, lr: 5.71e-04
2022-05-04 12:17:46,983 INFO [train.py:715] (2/8) Epoch 3, batch 5900, loss[loss=0.1463, simple_loss=0.2148, pruned_loss=0.03886, over 4888.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2317, pruned_loss=0.04764, over 972346.13 frames.], batch size: 19, lr: 5.71e-04
2022-05-04 12:18:26,956 INFO [train.py:715] (2/8) Epoch 3, batch 5950, loss[loss=0.1707, simple_loss=0.2368, pruned_loss=0.05232, over 4936.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2316, pruned_loss=0.0476, over 972165.27 frames.], batch size: 29, lr: 5.71e-04
2022-05-04 12:19:06,641 INFO [train.py:715] (2/8) Epoch 3, batch 6000, loss[loss=0.1706, simple_loss=0.2295, pruned_loss=0.05585, over 4781.00 frames.], tot_loss[loss=0.1642, simple_loss=0.232, pruned_loss=0.04817, over 972475.21 frames.], batch size: 18, lr: 5.71e-04
2022-05-04 12:19:06,642 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 12:19:15,397 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1149, simple_loss=0.2013, pruned_loss=0.01424, over 914524.00 frames.
2022-05-04 12:19:55,204 INFO [train.py:715] (2/8) Epoch 3, batch 6050, loss[loss=0.1752, simple_loss=0.255, pruned_loss=0.04771, over 4780.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2334, pruned_loss=0.04855, over 972369.32 frames.], batch size: 17, lr: 5.71e-04
2022-05-04 12:20:34,635 INFO [train.py:715] (2/8) Epoch 3, batch 6100, loss[loss=0.1692, simple_loss=0.2387, pruned_loss=0.04982, over 4890.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2333, pruned_loss=0.04824, over 972641.05 frames.], batch size: 16, lr: 5.70e-04
2022-05-04 12:21:13,557 INFO [train.py:715] (2/8) Epoch 3, batch 6150, loss[loss=0.1932, simple_loss=0.2609, pruned_loss=0.06276, over 4807.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2331, pruned_loss=0.04812, over 972896.18 frames.], batch size: 26, lr: 5.70e-04
2022-05-04 12:21:53,155 INFO [train.py:715] (2/8) Epoch 3, batch 6200, loss[loss=0.1492, simple_loss=0.2235, pruned_loss=0.03749, over 4881.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2333, pruned_loss=0.04813, over 972779.87 frames.], batch size: 22, lr: 5.70e-04
2022-05-04 12:22:33,149 INFO [train.py:715] (2/8) Epoch 3, batch 6250, loss[loss=0.1842, simple_loss=0.2471, pruned_loss=0.06059, over 4809.00 frames.], tot_loss[loss=0.1635, simple_loss=0.232, pruned_loss=0.04754, over 972819.62 frames.], batch size: 26, lr: 5.70e-04
2022-05-04 12:23:12,499 INFO [train.py:715] (2/8) Epoch 3, batch 6300, loss[loss=0.1502, simple_loss=0.229, pruned_loss=0.03565, over 4958.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2317, pruned_loss=0.04763, over 973579.95 frames.], batch size: 24, lr: 5.70e-04
2022-05-04 12:23:51,734 INFO [train.py:715] (2/8) Epoch 3, batch 6350, loss[loss=0.151, simple_loss=0.224, pruned_loss=0.03896, over 4825.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2317, pruned_loss=0.0476, over 972950.09 frames.], batch size: 26, lr: 5.70e-04
2022-05-04 12:24:31,946 INFO [train.py:715] (2/8) Epoch 3, batch 6400, loss[loss=0.158, simple_loss=0.2392, pruned_loss=0.03834, over 4784.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2315, pruned_loss=0.04737, over 972950.71 frames.], batch size: 18, lr: 5.70e-04
2022-05-04 12:25:11,497 INFO [train.py:715] (2/8) Epoch 3, batch 6450, loss[loss=0.1504, simple_loss=0.2301, pruned_loss=0.03535, over 4957.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2329, pruned_loss=0.04786, over 973194.17 frames.], batch size: 15, lr: 5.70e-04
2022-05-04 12:25:50,477 INFO [train.py:715] (2/8) Epoch 3, batch 6500, loss[loss=0.1402, simple_loss=0.2107, pruned_loss=0.03485, over 4762.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2324, pruned_loss=0.04757, over 972989.83 frames.], batch size: 19, lr: 5.69e-04
2022-05-04 12:26:30,129 INFO [train.py:715] (2/8) Epoch 3, batch 6550, loss[loss=0.163, simple_loss=0.2224, pruned_loss=0.05182, over 4975.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2327, pruned_loss=0.04813, over 973270.48 frames.], batch size: 35, lr: 5.69e-04
2022-05-04 12:27:09,925 INFO [train.py:715] (2/8) Epoch 3, batch 6600, loss[loss=0.1412, simple_loss=0.211, pruned_loss=0.03571, over 4927.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2336, pruned_loss=0.04879, over 973261.64 frames.], batch size: 23, lr: 5.69e-04
2022-05-04 12:27:49,179 INFO [train.py:715] (2/8) Epoch 3, batch 6650, loss[loss=0.1864, simple_loss=0.2465, pruned_loss=0.06321, over 4976.00 frames.], tot_loss[loss=0.166, simple_loss=0.2338, pruned_loss=0.04906, over 974188.99 frames.], batch size: 35, lr: 5.69e-04
2022-05-04 12:28:28,356 INFO [train.py:715] (2/8) Epoch 3, batch 6700, loss[loss=0.1441, simple_loss=0.2022, pruned_loss=0.04301, over 4823.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2332, pruned_loss=0.04902, over 973863.27 frames.], batch size: 27, lr: 5.69e-04
2022-05-04 12:29:08,699 INFO [train.py:715] (2/8) Epoch 3, batch 6750, loss[loss=0.1736, simple_loss=0.2345, pruned_loss=0.05636, over 4958.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2326, pruned_loss=0.04834, over 974017.67 frames.], batch size: 24, lr: 5.69e-04
2022-05-04 12:29:47,737 INFO [train.py:715] (2/8) Epoch 3, batch 6800, loss[loss=0.1348, simple_loss=0.2016, pruned_loss=0.03403, over 4878.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2335, pruned_loss=0.04851, over 973171.07 frames.], batch size: 16, lr: 5.69e-04
2022-05-04 12:30:27,113 INFO [train.py:715] (2/8) Epoch 3, batch 6850, loss[loss=0.1979, simple_loss=0.2617, pruned_loss=0.06699, over 4924.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2321, pruned_loss=0.04784, over 972492.05 frames.], batch size: 39, lr: 5.68e-04
2022-05-04 12:31:06,814 INFO [train.py:715] (2/8) Epoch 3, batch 6900, loss[loss=0.1764, simple_loss=0.2514, pruned_loss=0.05068, over 4751.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2315, pruned_loss=0.04783, over 972689.20 frames.], batch size: 16, lr: 5.68e-04
2022-05-04 12:31:46,646 INFO [train.py:715] (2/8) Epoch 3, batch 6950, loss[loss=0.1727, simple_loss=0.2352, pruned_loss=0.05513, over 4894.00 frames.], tot_loss[loss=0.1637, simple_loss=0.232, pruned_loss=0.0477, over 972363.22 frames.], batch size: 19, lr: 5.68e-04
2022-05-04 12:32:25,802 INFO [train.py:715] (2/8) Epoch 3, batch 7000, loss[loss=0.1743, simple_loss=0.2441, pruned_loss=0.05229, over 4926.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2328, pruned_loss=0.04814, over 972755.09 frames.], batch size: 23, lr: 5.68e-04
2022-05-04 12:33:05,824 INFO [train.py:715] (2/8) Epoch 3, batch 7050, loss[loss=0.1621, simple_loss=0.2339, pruned_loss=0.04521, over 4739.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2332, pruned_loss=0.04836, over 973009.34 frames.], batch size: 16, lr: 5.68e-04
2022-05-04 12:33:45,715 INFO [train.py:715] (2/8) Epoch 3, batch 7100, loss[loss=0.1675, simple_loss=0.2407, pruned_loss=0.04717, over 4778.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2328, pruned_loss=0.04812, over 971859.05 frames.], batch size: 18, lr: 5.68e-04
2022-05-04 12:34:24,803 INFO [train.py:715] (2/8) Epoch 3, batch 7150, loss[loss=0.1577, simple_loss=0.2287, pruned_loss=0.04336, over 4894.00 frames.], tot_loss[loss=0.164, simple_loss=0.2322, pruned_loss=0.04788, over 972328.30 frames.], batch size: 19, lr: 5.68e-04
2022-05-04 12:35:04,371 INFO [train.py:715] (2/8) Epoch 3, batch 7200, loss[loss=0.1853, simple_loss=0.2559, pruned_loss=0.05735, over 4956.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2331, pruned_loss=0.0487, over 972136.26 frames.], batch size: 24, lr: 5.68e-04
2022-05-04 12:35:44,144 INFO [train.py:715] (2/8) Epoch 3, batch 7250, loss[loss=0.1115, simple_loss=0.1724, pruned_loss=0.02534, over 4779.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2322, pruned_loss=0.04844, over 971896.31 frames.], batch size: 12, lr: 5.67e-04
2022-05-04 12:36:23,541 INFO [train.py:715] (2/8) Epoch 3, batch 7300, loss[loss=0.1391, simple_loss=0.2059, pruned_loss=0.03617, over 4979.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2314, pruned_loss=0.04779, over 972338.74 frames.], batch size: 24, lr: 5.67e-04
2022-05-04 12:37:03,010 INFO [train.py:715] (2/8) Epoch 3, batch 7350, loss[loss=0.1713, simple_loss=0.2468, pruned_loss=0.04788, over 4915.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2318, pruned_loss=0.04821, over 971966.49 frames.], batch size: 23, lr: 5.67e-04
2022-05-04 12:37:42,371 INFO [train.py:715] (2/8) Epoch 3, batch 7400, loss[loss=0.1697, simple_loss=0.2406, pruned_loss=0.04937, over 4857.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2322, pruned_loss=0.04812, over 972770.02 frames.], batch size: 22, lr: 5.67e-04
2022-05-04 12:38:22,633 INFO [train.py:715] (2/8) Epoch 3, batch 7450, loss[loss=0.1558, simple_loss=0.2234, pruned_loss=0.04409, over 4915.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2326, pruned_loss=0.04859, over 971927.16 frames.], batch size: 23, lr: 5.67e-04
2022-05-04 12:39:01,773 INFO [train.py:715] (2/8) Epoch 3, batch 7500, loss[loss=0.1562, simple_loss=0.226, pruned_loss=0.04322, over 4926.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2326, pruned_loss=0.04866, over 970997.58 frames.], batch size: 23, lr: 5.67e-04
2022-05-04 12:39:41,038 INFO [train.py:715] (2/8) Epoch 3, batch 7550, loss[loss=0.1573, simple_loss=0.2126, pruned_loss=0.05098, over 4856.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2324, pruned_loss=0.04838, over 971635.83 frames.], batch size: 32, lr: 5.67e-04
2022-05-04 12:40:22,792 INFO [train.py:715] (2/8) Epoch 3, batch 7600, loss[loss=0.1861, simple_loss=0.2475, pruned_loss=0.06238, over 4979.00 frames.], tot_loss[loss=0.166, simple_loss=0.234, pruned_loss=0.04895, over 972263.87 frames.], batch size: 15, lr: 5.67e-04
2022-05-04 12:41:02,145 INFO [train.py:715] (2/8) Epoch 3, batch 7650, loss[loss=0.1404, simple_loss=0.2079, pruned_loss=0.03644, over 4857.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2338, pruned_loss=0.04884, over 972688.57 frames.], batch size: 30, lr: 5.66e-04
2022-05-04 12:41:41,411 INFO [train.py:715] (2/8) Epoch 3, batch 7700, loss[loss=0.1341, simple_loss=0.2043, pruned_loss=0.03199, over 4938.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2329, pruned_loss=0.04876, over 972439.26 frames.], batch size: 21, lr: 5.66e-04
2022-05-04 12:42:20,879 INFO [train.py:715] (2/8) Epoch 3, batch 7750, loss[loss=0.1486, simple_loss=0.2167, pruned_loss=0.04023, over 4775.00 frames.], tot_loss[loss=0.165, simple_loss=0.2331, pruned_loss=0.04842, over 972505.52 frames.], batch size: 12, lr: 5.66e-04
2022-05-04 12:43:00,210 INFO [train.py:715] (2/8) Epoch 3, batch 7800, loss[loss=0.1737, simple_loss=0.2332, pruned_loss=0.05708, over 4784.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2332, pruned_loss=0.04807, over 972084.71 frames.], batch size: 18, lr: 5.66e-04
2022-05-04 12:43:38,783 INFO [train.py:715] (2/8) Epoch 3, batch 7850, loss[loss=0.1512, simple_loss=0.2162, pruned_loss=0.04305, over 4758.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2331, pruned_loss=0.04813, over 971390.28 frames.], batch size: 19, lr: 5.66e-04
2022-05-04 12:44:18,370 INFO [train.py:715] (2/8) Epoch 3, batch 7900, loss[loss=0.1757, simple_loss=0.2473, pruned_loss=0.05208, over 4942.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2335, pruned_loss=0.0486, over 972257.86 frames.], batch size: 23, lr: 5.66e-04
2022-05-04 12:44:58,145 INFO [train.py:715] (2/8) Epoch 3, batch 7950, loss[loss=0.1815, simple_loss=0.251, pruned_loss=0.05601, over 4983.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2338, pruned_loss=0.04861, over 972233.81 frames.], batch size: 31, lr: 5.66e-04
2022-05-04 12:45:36,732 INFO [train.py:715] (2/8) Epoch 3, batch 8000, loss[loss=0.215, simple_loss=0.2774, pruned_loss=0.07628, over 4982.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2341, pruned_loss=0.04915, over 973420.42 frames.], batch size: 15, lr: 5.66e-04
2022-05-04 12:46:14,906 INFO [train.py:715] (2/8) Epoch 3, batch 8050, loss[loss=0.1651, simple_loss=0.2363, pruned_loss=0.04701, over 4932.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2342, pruned_loss=0.04942, over 973040.81 frames.], batch size: 23, lr: 5.65e-04
2022-05-04 12:46:53,637 INFO [train.py:715] (2/8) Epoch 3, batch 8100, loss[loss=0.1991, simple_loss=0.2567, pruned_loss=0.07077, over 4746.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2341, pruned_loss=0.04964, over 972450.91 frames.], batch size: 16, lr: 5.65e-04
2022-05-04 12:47:31,942 INFO [train.py:715] (2/8) Epoch 3, batch 8150, loss[loss=0.1555, simple_loss=0.2198, pruned_loss=0.04556, over 4978.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2322, pruned_loss=0.04873, over 972023.17 frames.], batch size: 14, lr: 5.65e-04
2022-05-04 12:48:10,086 INFO [train.py:715] (2/8) Epoch 3, batch 8200, loss[loss=0.1775, simple_loss=0.2474, pruned_loss=0.05378, over 4809.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2331, pruned_loss=0.04915, over 972466.10 frames.], batch size: 21, lr: 5.65e-04
2022-05-04 12:48:49,891 INFO [train.py:715] (2/8) Epoch 3, batch 8250, loss[loss=0.1805, simple_loss=0.2578, pruned_loss=0.05163, over 4699.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2329, pruned_loss=0.04877, over 972901.94 frames.], batch size: 15, lr: 5.65e-04
2022-05-04 12:49:30,614 INFO [train.py:715] (2/8) Epoch 3, batch 8300, loss[loss=0.1843, simple_loss=0.2483, pruned_loss=0.06014, over 4791.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2329, pruned_loss=0.04839, over 973367.50 frames.], batch size: 21, lr: 5.65e-04
2022-05-04 12:50:10,670 INFO [train.py:715] (2/8) Epoch 3, batch 8350, loss[loss=0.1278, simple_loss=0.1993, pruned_loss=0.02811, over 4975.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2322, pruned_loss=0.04785, over 972654.71 frames.], batch size: 25, lr: 5.65e-04
2022-05-04 12:50:50,666 INFO [train.py:715] (2/8) Epoch 3, batch 8400, loss[loss=0.1261, simple_loss=0.1967, pruned_loss=0.02778, over 4843.00 frames.], tot_loss[loss=0.164, simple_loss=0.232, pruned_loss=0.048, over 971951.38 frames.], batch size: 13, lr: 5.65e-04
2022-05-04 12:51:30,648 INFO [train.py:715] (2/8) Epoch 3, batch 8450, loss[loss=0.1537, simple_loss=0.2281, pruned_loss=0.03965, over 4916.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2311, pruned_loss=0.04733, over 973186.47 frames.], batch size: 23, lr: 5.64e-04
2022-05-04 12:52:10,873 INFO [train.py:715] (2/8) Epoch 3, batch 8500, loss[loss=0.1716, simple_loss=0.2388, pruned_loss=0.0522, over 4914.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2306, pruned_loss=0.04707, over 972503.24 frames.], batch size: 23, lr: 5.64e-04
2022-05-04 12:52:49,925 INFO [train.py:715] (2/8) Epoch 3, batch 8550, loss[loss=0.1406, simple_loss=0.2185, pruned_loss=0.0314, over 4820.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2313, pruned_loss=0.04753, over 973150.09 frames.], batch size: 26, lr: 5.64e-04
2022-05-04 12:53:31,545 INFO [train.py:715] (2/8) Epoch 3, batch 8600, loss[loss=0.169, simple_loss=0.2334, pruned_loss=0.05234, over 4831.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2299, pruned_loss=0.0466, over 973639.64 frames.], batch size: 15, lr: 5.64e-04
2022-05-04 12:54:13,122 INFO [train.py:715] (2/8) Epoch 3, batch 8650, loss[loss=0.1624, simple_loss=0.2283, pruned_loss=0.04822, over 4921.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2309, pruned_loss=0.04706, over 972469.30 frames.], batch size: 29, lr: 5.64e-04
2022-05-04 12:54:53,241 INFO [train.py:715] (2/8) Epoch 3, batch 8700, loss[loss=0.1886, simple_loss=0.2518, pruned_loss=0.06268, over 4762.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2313, pruned_loss=0.04755, over 972337.20 frames.], batch size: 18, lr: 5.64e-04
2022-05-04 12:55:34,484 INFO [train.py:715] (2/8) Epoch 3, batch 8750, loss[loss=0.2038, simple_loss=0.2533, pruned_loss=0.07716, over 4684.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2325, pruned_loss=0.04852, over 972168.54 frames.], batch size: 15, lr: 5.64e-04
2022-05-04 12:56:14,900 INFO [train.py:715] (2/8) Epoch 3, batch 8800, loss[loss=0.163, simple_loss=0.2395, pruned_loss=0.04321, over 4919.00 frames.], tot_loss[loss=0.1644, simple_loss=0.232, pruned_loss=0.04835, over 972715.74 frames.], batch size: 29, lr: 5.64e-04
2022-05-04 12:56:55,634 INFO [train.py:715] (2/8) Epoch 3, batch 8850, loss[loss=0.1855, simple_loss=0.2456, pruned_loss=0.0627, over 4770.00 frames.], tot_loss[loss=0.165, simple_loss=0.2327, pruned_loss=0.04868, over 973346.64 frames.], batch size: 14, lr: 5.63e-04
2022-05-04 12:57:35,616 INFO [train.py:715] (2/8) Epoch 3, batch 8900, loss[loss=0.1638, simple_loss=0.2363, pruned_loss=0.04567, over 4793.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2324, pruned_loss=0.0482, over 972828.03 frames.], batch size: 24, lr: 5.63e-04
2022-05-04 12:58:17,387 INFO [train.py:715] (2/8) Epoch 3, batch 8950, loss[loss=0.1322, simple_loss=0.1981, pruned_loss=0.03316, over 4766.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2322, pruned_loss=0.04814, over 972327.25 frames.], batch size: 12, lr: 5.63e-04
2022-05-04 12:58:59,335 INFO [train.py:715] (2/8) Epoch 3, batch 9000, loss[loss=0.1633, simple_loss=0.2271, pruned_loss=0.04972, over 4980.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2322, pruned_loss=0.04822, over 973206.26 frames.], batch size: 14, lr: 5.63e-04
2022-05-04 12:58:59,335 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 12:59:08,109 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1147, simple_loss=0.2006, pruned_loss=0.01442, over 914524.00 frames.
2022-05-04 12:59:49,674 INFO [train.py:715] (2/8) Epoch 3, batch 9050, loss[loss=0.1364, simple_loss=0.1951, pruned_loss=0.0388, over 4980.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2314, pruned_loss=0.04804, over 972924.29 frames.], batch size: 24, lr: 5.63e-04
2022-05-04 13:00:30,622 INFO [train.py:715] (2/8) Epoch 3, batch 9100, loss[loss=0.1664, simple_loss=0.2384, pruned_loss=0.04723, over 4879.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2328, pruned_loss=0.04827, over 972168.70 frames.], batch size: 16, lr: 5.63e-04
2022-05-04 13:01:11,924 INFO [train.py:715] (2/8) Epoch 3, batch 9150, loss[loss=0.1713, simple_loss=0.2424, pruned_loss=0.0501, over 4918.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2325, pruned_loss=0.04821, over 971798.85 frames.], batch size: 23, lr: 5.63e-04
2022-05-04 13:01:53,286 INFO [train.py:715] (2/8) Epoch 3, batch 9200, loss[loss=0.1338, simple_loss=0.209, pruned_loss=0.02931, over 4789.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2332, pruned_loss=0.0483, over 971777.81 frames.], batch size: 12, lr: 5.63e-04
2022-05-04 13:02:34,664 INFO [train.py:715] (2/8) Epoch 3, batch 9250, loss[loss=0.1267, simple_loss=0.2109, pruned_loss=0.02125, over 4986.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2342, pruned_loss=0.04884, over 971805.15 frames.], batch size: 25, lr: 5.62e-04
2022-05-04 13:03:15,390 INFO [train.py:715] (2/8) Epoch 3, batch 9300, loss[loss=0.1704, simple_loss=0.2426, pruned_loss=0.0491, over 4765.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2335, pruned_loss=0.04858, over 972360.10 frames.], batch size: 16, lr: 5.62e-04
2022-05-04 13:03:56,642 INFO [train.py:715] (2/8) Epoch 3, batch 9350, loss[loss=0.1673, simple_loss=0.2269, pruned_loss=0.05384, over 4832.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2339, pruned_loss=0.0489, over 972903.84 frames.], batch size: 30, lr: 5.62e-04
2022-05-04 13:04:38,912 INFO [train.py:715] (2/8) Epoch 3, batch 9400, loss[loss=0.1678, simple_loss=0.2372, pruned_loss=0.04919, over 4892.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2339, pruned_loss=0.04912, over 974108.83 frames.], batch size: 32, lr: 5.62e-04
2022-05-04 13:05:19,295 INFO [train.py:715] (2/8) Epoch 3, batch 9450, loss[loss=0.1657, simple_loss=0.2323, pruned_loss=0.04951, over 4919.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2338, pruned_loss=0.04902, over 973899.98 frames.], batch size: 18, lr: 5.62e-04
2022-05-04 13:06:00,821 INFO [train.py:715] (2/8) Epoch 3, batch 9500, loss[loss=0.1819, simple_loss=0.2465, pruned_loss=0.05866, over 4795.00 frames.], tot_loss[loss=0.1652, simple_loss=0.233, pruned_loss=0.0487, over 973691.13 frames.], batch size: 24, lr: 5.62e-04
2022-05-04 13:06:42,691 INFO [train.py:715] (2/8) Epoch 3, batch 9550, loss[loss=0.1539, simple_loss=0.223, pruned_loss=0.04247, over 4897.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2323, pruned_loss=0.04804, over 973430.88 frames.], batch size: 17, lr: 5.62e-04
2022-05-04 13:07:24,287 INFO [train.py:715] (2/8) Epoch 3, batch 9600, loss[loss=0.1327, simple_loss=0.215, pruned_loss=0.02518, over 4782.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2314, pruned_loss=0.04765, over 973072.83 frames.], batch size: 18, lr: 5.62e-04
2022-05-04 13:08:05,435 INFO [train.py:715] (2/8) Epoch 3, batch 9650, loss[loss=0.1522, simple_loss=0.2131, pruned_loss=0.0456, over 4834.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2314, pruned_loss=0.04804, over 972810.68 frames.], batch size: 12, lr: 5.61e-04
2022-05-04 13:08:46,927 INFO [train.py:715] (2/8) Epoch 3, batch 9700, loss[loss=0.1447, simple_loss=0.2151, pruned_loss=0.03719, over 4783.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2328, pruned_loss=0.04871, over 972768.27 frames.], batch size: 17, lr: 5.61e-04
2022-05-04 13:09:27,936 INFO [train.py:715] (2/8) Epoch 3, batch 9750, loss[loss=0.1536, simple_loss=0.208, pruned_loss=0.04965, over 4752.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2336, pruned_loss=0.04911, over 973046.04 frames.], batch size: 12, lr: 5.61e-04
2022-05-04 13:10:08,809 INFO [train.py:715] (2/8) Epoch 3, batch 9800, loss[loss=0.1805, simple_loss=0.2463, pruned_loss=0.05734, over 4794.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2332, pruned_loss=0.04899, over 972444.89 frames.], batch size: 21, lr: 5.61e-04
2022-05-04 13:10:50,543 INFO [train.py:715] (2/8) Epoch 3, batch 9850, loss[loss=0.1763, simple_loss=0.2504, pruned_loss=0.05112, over 4756.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2332, pruned_loss=0.04917, over 972114.42 frames.], batch size: 19, lr: 5.61e-04
2022-05-04 13:11:32,495 INFO [train.py:715] (2/8) Epoch 3, batch 9900, loss[loss=0.1832, simple_loss=0.2561, pruned_loss=0.05515, over 4784.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2334, pruned_loss=0.04914, over 972362.41 frames.], batch size: 14, lr: 5.61e-04
2022-05-04 13:12:12,996 INFO [train.py:715] (2/8) Epoch 3, batch 9950, loss[loss=0.1697, simple_loss=0.2229, pruned_loss=0.05821, over 4893.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2331, pruned_loss=0.04888, over 972373.22 frames.], batch size: 17, lr: 5.61e-04
2022-05-04 13:12:54,727 INFO [train.py:715] (2/8) Epoch 3, batch 10000, loss[loss=0.1314, simple_loss=0.1959, pruned_loss=0.0335, over 4805.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2316, pruned_loss=0.04771, over 972362.15 frames.], batch size: 13, lr: 5.61e-04
2022-05-04 13:13:36,176 INFO [train.py:715] (2/8) Epoch 3, batch 10050, loss[loss=0.1836, simple_loss=0.2459, pruned_loss=0.06063, over 4769.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2318, pruned_loss=0.04766, over 972149.48 frames.], batch size: 14, lr: 5.61e-04
2022-05-04 13:14:17,623 INFO [train.py:715] (2/8) Epoch 3, batch 10100, loss[loss=0.1657, simple_loss=0.2322, pruned_loss=0.04958, over 4857.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2309, pruned_loss=0.04709, over 971716.39 frames.], batch size: 32, lr: 5.60e-04
2022-05-04 13:14:58,621 INFO [train.py:715] (2/8) Epoch 3, batch 10150, loss[loss=0.1812, simple_loss=0.2405, pruned_loss=0.06097, over 4841.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2322, pruned_loss=0.04757, over 972106.41 frames.], batch size: 13, lr: 5.60e-04
2022-05-04 13:15:40,217 INFO [train.py:715] (2/8) Epoch 3, batch 10200, loss[loss=0.1629, simple_loss=0.2407, pruned_loss=0.0426, over 4867.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2311, pruned_loss=0.04699, over 972285.40 frames.], batch size: 30, lr: 5.60e-04
2022-05-04 13:16:21,939 INFO [train.py:715] (2/8) Epoch 3, batch 10250, loss[loss=0.1516, simple_loss=0.2157, pruned_loss=0.04374, over 4927.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2318, pruned_loss=0.0473, over 971284.06 frames.], batch size: 21, lr: 5.60e-04
2022-05-04 13:17:01,802 INFO [train.py:715] (2/8) Epoch 3, batch 10300, loss[loss=0.1463, simple_loss=0.2157, pruned_loss=0.03847, over 4925.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2316, pruned_loss=0.0473, over 972217.57 frames.], batch size: 23, lr: 5.60e-04
2022-05-04 13:17:42,038 INFO [train.py:715] (2/8) Epoch 3, batch 10350, loss[loss=0.1159, simple_loss=0.1805, pruned_loss=0.02563, over 4839.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2315, pruned_loss=0.04748, over 972163.54 frames.], batch size: 12, lr: 5.60e-04
2022-05-04 13:18:22,571 INFO [train.py:715] (2/8) Epoch 3, batch 10400, loss[loss=0.1714, simple_loss=0.2411, pruned_loss=0.05087, over 4937.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2329, pruned_loss=0.04807, over 972461.72 frames.], batch size: 21, lr: 5.60e-04
2022-05-04 13:19:03,198 INFO [train.py:715] (2/8) Epoch 3, batch 10450, loss[loss=0.1493, simple_loss=0.2123, pruned_loss=0.04315, over 4862.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2323, pruned_loss=0.0479, over 972795.23 frames.], batch size: 32, lr: 5.60e-04
2022-05-04 13:19:43,607 INFO [train.py:715] (2/8) Epoch 3, batch 10500, loss[loss=0.179, simple_loss=0.2493, pruned_loss=0.0544, over 4909.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2332, pruned_loss=0.04815, over 973733.17 frames.], batch size: 39, lr: 5.59e-04
2022-05-04 13:20:24,616 INFO [train.py:715] (2/8) Epoch 3, batch 10550, loss[loss=0.1566, simple_loss=0.2236, pruned_loss=0.04484, over 4866.00 frames.], tot_loss[loss=0.1645, simple_loss=0.233, pruned_loss=0.04804, over 973124.08 frames.], batch size: 20, lr: 5.59e-04
2022-05-04 13:21:07,129 INFO [train.py:715] (2/8) Epoch 3, batch 10600, loss[loss=0.1815, simple_loss=0.2443, pruned_loss=0.05931, over 4737.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2327, pruned_loss=0.04809, over 973348.30 frames.], batch size: 16, lr: 5.59e-04
2022-05-04 13:21:48,625 INFO [train.py:715] (2/8) Epoch 3, batch 10650, loss[loss=0.1485, simple_loss=0.222, pruned_loss=0.03746, over 4955.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2327, pruned_loss=0.04772, over 973414.79 frames.], batch size: 21, lr: 5.59e-04
2022-05-04 13:22:30,744 INFO [train.py:715] (2/8) Epoch 3, batch 10700, loss[loss=0.1923, simple_loss=0.251, pruned_loss=0.06682, over 4802.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2328, pruned_loss=0.04795, over 972484.62 frames.], batch size: 14, lr: 5.59e-04
2022-05-04 13:23:13,527 INFO [train.py:715] (2/8) Epoch 3, batch 10750, loss[loss=0.1709, simple_loss=0.235, pruned_loss=0.05341, over 4987.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2327, pruned_loss=0.04845, over 972006.74 frames.], batch size: 20, lr: 5.59e-04
2022-05-04 13:23:56,758 INFO [train.py:715] (2/8) Epoch 3, batch 10800, loss[loss=0.1353, simple_loss=0.2013, pruned_loss=0.03472, over 4797.00 frames.], tot_loss[loss=0.1638, simple_loss=0.232, pruned_loss=0.04783, over 972415.72 frames.], batch size: 14, lr: 5.59e-04
2022-05-04 13:24:38,547 INFO [train.py:715] (2/8) Epoch 3, batch 10850, loss[loss=0.1754, simple_loss=0.2486, pruned_loss=0.05114, over 4931.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2323, pruned_loss=0.04834, over 971911.29 frames.], batch size: 23, lr: 5.59e-04
2022-05-04 13:25:21,314 INFO [train.py:715] (2/8) Epoch 3, batch 10900, loss[loss=0.1772, simple_loss=0.2436, pruned_loss=0.05541, over 4829.00 frames.], tot_loss[loss=0.166, simple_loss=0.2332, pruned_loss=0.04937, over 972308.42 frames.], batch size: 15, lr: 5.58e-04
2022-05-04 13:26:04,564 INFO [train.py:715] (2/8) Epoch 3, batch 10950, loss[loss=0.183, simple_loss=0.2327, pruned_loss=0.06664, over 4955.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2316, pruned_loss=0.04844, over 972891.36 frames.], batch size: 35, lr: 5.58e-04
2022-05-04 13:26:46,516 INFO [train.py:715] (2/8) Epoch 3, batch 11000, loss[loss=0.1552, simple_loss=0.2312, pruned_loss=0.03963, over 4917.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2314, pruned_loss=0.04783, over 973285.15 frames.], batch size: 29, lr: 5.58e-04
2022-05-04 13:27:28,072 INFO [train.py:715] (2/8) Epoch 3, batch 11050, loss[loss=0.1553, simple_loss=0.2151, pruned_loss=0.04777, over 4815.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2316, pruned_loss=0.04806, over 972782.40 frames.], batch size: 15, lr: 5.58e-04
2022-05-04 13:28:11,600 INFO [train.py:715] (2/8) Epoch 3, batch 11100, loss[loss=0.1728, simple_loss=0.2367, pruned_loss=0.05447, over 4910.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2315, pruned_loss=0.04795, over 972928.45 frames.], batch size: 18, lr: 5.58e-04
2022-05-04 13:28:53,674 INFO [train.py:715] (2/8) Epoch 3, batch 11150, loss[loss=0.1643, simple_loss=0.2357, pruned_loss=0.04649, over 4825.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2312, pruned_loss=0.04772, over 972289.37 frames.], batch size: 27, lr: 5.58e-04
2022-05-04 13:29:35,735 INFO [train.py:715] (2/8) Epoch 3, batch 11200, loss[loss=0.1665, simple_loss=0.2455, pruned_loss=0.04372, over 4941.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2326, pruned_loss=0.04804, over 972548.85 frames.], batch size: 29, lr: 5.58e-04
2022-05-04 13:30:18,280 INFO [train.py:715] (2/8) Epoch 3, batch 11250, loss[loss=0.1873, simple_loss=0.2634, pruned_loss=0.05559, over 4793.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2315, pruned_loss=0.04744, over 972964.81 frames.], batch size: 24, lr: 5.58e-04
2022-05-04 13:31:01,510 INFO [train.py:715] (2/8) Epoch 3, batch 11300, loss[loss=0.1546, simple_loss=0.235, pruned_loss=0.03714, over 4853.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2307, pruned_loss=0.04697, over 972828.66 frames.], batch size: 20, lr: 5.57e-04
2022-05-04 13:31:42,773 INFO [train.py:715] (2/8) Epoch 3, batch 11350, loss[loss=0.151, simple_loss=0.2121, pruned_loss=0.04491, over 4985.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2305, pruned_loss=0.04703, over 972867.24 frames.], batch size: 15, lr: 5.57e-04
2022-05-04 13:32:25,115 INFO [train.py:715] (2/8) Epoch 3, batch 11400, loss[loss=0.2033, simple_loss=0.271, pruned_loss=0.06784, over 4932.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2312, pruned_loss=0.04751, over 972410.99 frames.], batch size: 21, lr: 5.57e-04
2022-05-04 13:33:08,054 INFO [train.py:715] (2/8) Epoch 3, batch 11450, loss[loss=0.1363, simple_loss=0.1995, pruned_loss=0.03652, over 4773.00 frames.], tot_loss[loss=0.164, simple_loss=0.2318, pruned_loss=0.04814, over 972687.15 frames.], batch size: 14, lr: 5.57e-04
2022-05-04 13:33:50,181 INFO [train.py:715] (2/8) Epoch 3, batch 11500, loss[loss=0.1542, simple_loss=0.2237, pruned_loss=0.04231, over 4840.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2319, pruned_loss=0.04831, over 971592.85 frames.], batch size: 12, lr: 5.57e-04
2022-05-04 13:34:32,226 INFO [train.py:715] (2/8) Epoch 3, batch 11550, loss[loss=0.1287, simple_loss=0.1947, pruned_loss=0.03133, over 4983.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2311, pruned_loss=0.04799, over 972557.92 frames.], batch size: 28, lr: 5.57e-04
2022-05-04 13:35:14,410 INFO [train.py:715] (2/8) Epoch 3, batch 11600, loss[loss=0.1601, simple_loss=0.2346, pruned_loss=0.04274, over 4935.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2306, pruned_loss=0.04763, over 972869.60 frames.], batch size: 23, lr: 5.57e-04
2022-05-04 13:35:57,184 INFO [train.py:715] (2/8) Epoch 3, batch 11650, loss[loss=0.1311, simple_loss=0.2005, pruned_loss=0.03084, over 4989.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2303, pruned_loss=0.04732, over 972970.33 frames.], batch size: 14, lr: 5.57e-04
2022-05-04 13:36:39,263 INFO [train.py:715] (2/8) Epoch 3, batch 11700, loss[loss=0.121, simple_loss=0.1901, pruned_loss=0.02597, over 4817.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2311, pruned_loss=0.04761, over 972514.48 frames.], batch size: 13, lr: 5.57e-04
2022-05-04 13:37:21,474 INFO [train.py:715] (2/8) Epoch 3, batch 11750, loss[loss=0.1615, simple_loss=0.2282, pruned_loss=0.04739, over 4897.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2296, pruned_loss=0.04697, over 972193.26 frames.], batch size: 18, lr: 5.56e-04
2022-05-04 13:38:05,286 INFO [train.py:715] (2/8) Epoch 3, batch 11800, loss[loss=0.1933, simple_loss=0.2619, pruned_loss=0.06236, over 4750.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2307, pruned_loss=0.04807, over 971460.49 frames.], batch size: 16, lr: 5.56e-04
2022-05-04 13:38:47,457 INFO [train.py:715] (2/8) Epoch 3, batch 11850, loss[loss=0.1839, simple_loss=0.2526, pruned_loss=0.05762, over 4890.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2316, pruned_loss=0.04829, over 971694.03 frames.], batch size: 19, lr: 5.56e-04
2022-05-04 13:39:29,602 INFO [train.py:715] (2/8) Epoch 3, batch 11900, loss[loss=0.1289, simple_loss=0.1905, pruned_loss=0.03365, over 4738.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2304, pruned_loss=0.04748, over 971217.77 frames.], batch size: 12, lr: 5.56e-04
2022-05-04 13:40:11,695 INFO [train.py:715] (2/8) Epoch 3, batch 11950, loss[loss=0.1485, simple_loss=0.2268, pruned_loss=0.0351, over 4811.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2316, pruned_loss=0.04807, over 972035.31 frames.], batch size: 21, lr: 5.56e-04
2022-05-04 13:40:54,204 INFO [train.py:715] (2/8) Epoch 3, batch 12000, loss[loss=0.1622, simple_loss=0.2316, pruned_loss=0.0464, over 4949.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2307, pruned_loss=0.04719, over 972061.31 frames.], batch size: 24, lr: 5.56e-04
2022-05-04 13:40:54,205 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 13:41:02,572 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1142, simple_loss=0.2003, pruned_loss=0.01401, over 914524.00 frames.
2022-05-04 13:41:44,690 INFO [train.py:715] (2/8) Epoch 3, batch 12050, loss[loss=0.1717, simple_loss=0.236, pruned_loss=0.05371, over 4801.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2318, pruned_loss=0.04798, over 971863.74 frames.], batch size: 25, lr: 5.56e-04
2022-05-04 13:42:26,378 INFO [train.py:715] (2/8) Epoch 3, batch 12100, loss[loss=0.1433, simple_loss=0.2163, pruned_loss=0.03512, over 4908.00 frames.], tot_loss[loss=0.164, simple_loss=0.2321, pruned_loss=0.04792, over 972173.31 frames.], batch size: 17, lr: 5.56e-04
2022-05-04 13:43:08,783 INFO [train.py:715] (2/8) Epoch 3, batch 12150, loss[loss=0.1961, simple_loss=0.2637, pruned_loss=0.06425, over 4695.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2323, pruned_loss=0.0481, over 972486.67 frames.], batch size: 15, lr: 5.55e-04
2022-05-04 13:43:52,031 INFO [train.py:715] (2/8) Epoch 3, batch 12200, loss[loss=0.1774, simple_loss=0.2451, pruned_loss=0.05489, over 4745.00 frames.], tot_loss[loss=0.164, simple_loss=0.2321, pruned_loss=0.04796, over 972843.99 frames.], batch size: 19, lr: 5.55e-04
2022-05-04 13:44:33,688 INFO [train.py:715] (2/8) Epoch 3, batch 12250, loss[loss=0.1678, simple_loss=0.2369, pruned_loss=0.04933, over 4920.00 frames.], tot_loss[loss=0.165, simple_loss=0.2332, pruned_loss=0.04841, over 972508.69 frames.], batch size: 23, lr: 5.55e-04
2022-05-04 13:45:15,593 INFO [train.py:715] (2/8) Epoch 3, batch 12300, loss[loss=0.1905, simple_loss=0.2545, pruned_loss=0.06326, over 4871.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2341, pruned_loss=0.04909, over 972568.52 frames.], batch size: 20, lr: 5.55e-04
2022-05-04 13:45:58,053 INFO [train.py:715] (2/8) Epoch 3, batch 12350, loss[loss=0.1881, simple_loss=0.2524, pruned_loss=0.06194, over 4781.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2339, pruned_loss=0.04933, over 972784.94 frames.], batch size: 17, lr: 5.55e-04
2022-05-04 13:46:41,403 INFO [train.py:715] (2/8) Epoch 3, batch 12400, loss[loss=0.1658, simple_loss=0.2267, pruned_loss=0.05247, over 4862.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2337, pruned_loss=0.04923, over 972355.94 frames.], batch size: 16, lr: 5.55e-04
2022-05-04 13:47:23,071 INFO [train.py:715] (2/8) Epoch 3, batch 12450, loss[loss=0.242, simple_loss=0.3111, pruned_loss=0.08642, over 4962.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2339, pruned_loss=0.04946, over 973561.74 frames.], batch size: 24, lr: 5.55e-04
2022-05-04 13:48:04,575 INFO [train.py:715] (2/8) Epoch 3, batch 12500, loss[loss=0.1483, simple_loss=0.2144, pruned_loss=0.04106, over 4914.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2338, pruned_loss=0.04959, over 973386.24 frames.], batch size: 18, lr: 5.55e-04
2022-05-04 13:48:47,320 INFO [train.py:715] (2/8) Epoch 3, batch 12550, loss[loss=0.133, simple_loss=0.2047, pruned_loss=0.03068, over 4773.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2337, pruned_loss=0.04941, over 972823.11 frames.], batch size: 12, lr: 5.54e-04
2022-05-04 13:49:29,603 INFO [train.py:715] (2/8) Epoch 3, batch 12600, loss[loss=0.1837, simple_loss=0.2463, pruned_loss=0.06061, over 4873.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2335, pruned_loss=0.04934, over 973267.02 frames.], batch size: 16, lr: 5.54e-04
2022-05-04 13:50:11,354 INFO [train.py:715] (2/8) Epoch 3, batch 12650, loss[loss=0.1327, simple_loss=0.2015, pruned_loss=0.03197, over 4749.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2333, pruned_loss=0.04913, over 971588.12 frames.], batch size: 19, lr: 5.54e-04
2022-05-04 13:50:53,055 INFO [train.py:715] (2/8) Epoch 3, batch 12700, loss[loss=0.1843, simple_loss=0.2572, pruned_loss=0.05571, over 4964.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2336, pruned_loss=0.04899, over 972050.31 frames.], batch size: 24, lr: 5.54e-04
2022-05-04 13:51:35,150 INFO [train.py:715] (2/8) Epoch 3, batch 12750, loss[loss=0.1631, simple_loss=0.221, pruned_loss=0.05262, over 4908.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2328, pruned_loss=0.0488, over 972018.83 frames.], batch size: 23, lr: 5.54e-04
2022-05-04 13:52:17,422 INFO [train.py:715] (2/8) Epoch 3, batch 12800, loss[loss=0.1423, simple_loss=0.2045, pruned_loss=0.04008, over 4860.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2321, pruned_loss=0.04829, over 972235.32 frames.], batch size: 16, lr: 5.54e-04
2022-05-04 13:52:58,262 INFO [train.py:715] (2/8) Epoch 3, batch 12850, loss[loss=0.1655, simple_loss=0.2355, pruned_loss=0.04775, over 4761.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2327, pruned_loss=0.04816, over 972655.38 frames.], batch size: 16, lr: 5.54e-04
2022-05-04 13:53:40,955 INFO [train.py:715] (2/8) Epoch 3, batch 12900, loss[loss=0.1618, simple_loss=0.2271, pruned_loss=0.04824, over 4905.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2323, pruned_loss=0.04774, over 971748.48 frames.], batch size: 17, lr: 5.54e-04
2022-05-04 13:54:23,554 INFO [train.py:715] (2/8) Epoch 3, batch 12950, loss[loss=0.1739, simple_loss=0.2407, pruned_loss=0.05352, over 4976.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2319, pruned_loss=0.04755, over 972076.93 frames.], batch size: 35, lr: 5.54e-04
2022-05-04 13:55:04,924 INFO [train.py:715] (2/8) Epoch 3, batch 13000, loss[loss=0.1728, simple_loss=0.248, pruned_loss=0.04876, over 4883.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2326, pruned_loss=0.04793, over 971506.06 frames.], batch size: 16, lr: 5.53e-04
2022-05-04 13:55:46,797 INFO [train.py:715] (2/8) Epoch 3, batch 13050, loss[loss=0.1372, simple_loss=0.213, pruned_loss=0.03072, over 4795.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2328, pruned_loss=0.04793, over 971362.75 frames.], batch size: 24, lr: 5.53e-04
2022-05-04 13:56:28,783 INFO [train.py:715] (2/8) Epoch 3, batch 13100, loss[loss=0.1653, simple_loss=0.2425, pruned_loss=0.04405, over 4915.00 frames.], tot_loss[loss=0.164, simple_loss=0.2326, pruned_loss=0.04773, over 971280.73 frames.], batch size: 23, lr: 5.53e-04
2022-05-04 13:57:10,551 INFO [train.py:715] (2/8) Epoch 3, batch 13150, loss[loss=0.1382, simple_loss=0.2176, pruned_loss=0.02938, over 4815.00 frames.], tot_loss[loss=0.1646, simple_loss=0.233, pruned_loss=0.04806, over 971255.97 frames.], batch size: 13, lr: 5.53e-04
2022-05-04 13:57:52,111 INFO [train.py:715] (2/8) Epoch 3, batch 13200, loss[loss=0.1591, simple_loss=0.2337, pruned_loss=0.04227, over 4919.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2324, pruned_loss=0.04798, over 972038.40 frames.], batch size: 17, lr: 5.53e-04
2022-05-04 13:58:34,746 INFO [train.py:715] (2/8) Epoch 3, batch 13250, loss[loss=0.1534, simple_loss=0.2187, pruned_loss=0.04403, over 4977.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2322, pruned_loss=0.04774, over 972416.57 frames.], batch size: 29, lr: 5.53e-04
2022-05-04 13:59:17,144 INFO [train.py:715] (2/8) Epoch 3, batch 13300, loss[loss=0.1848, simple_loss=0.25, pruned_loss=0.05979, over 4979.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2318, pruned_loss=0.04762, over 972270.78 frames.], batch size: 39, lr: 5.53e-04
2022-05-04 13:59:58,636 INFO [train.py:715] (2/8) Epoch 3, batch 13350, loss[loss=0.158, simple_loss=0.2197, pruned_loss=0.04812, over 4904.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2305, pruned_loss=0.04713, over 972271.34 frames.], batch size: 19, lr: 5.53e-04
2022-05-04 14:00:40,464 INFO [train.py:715] (2/8) Epoch 3, batch 13400, loss[loss=0.156, simple_loss=0.2279, pruned_loss=0.04208, over 4802.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2304, pruned_loss=0.04717, over 972986.48 frames.], batch size: 25, lr: 5.52e-04
2022-05-04 14:01:23,051 INFO [train.py:715] (2/8) Epoch 3, batch 13450, loss[loss=0.1825, simple_loss=0.2528, pruned_loss=0.05609, over 4909.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2306, pruned_loss=0.04759, over 972553.53 frames.], batch size: 38, lr: 5.52e-04
2022-05-04 14:02:04,525 INFO [train.py:715] (2/8) Epoch 3, batch 13500, loss[loss=0.1491, simple_loss=0.224, pruned_loss=0.03707, over 4981.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2314, pruned_loss=0.04797, over 972635.10 frames.], batch size: 28, lr: 5.52e-04
2022-05-04 14:02:46,047 INFO [train.py:715] (2/8) Epoch 3, batch 13550, loss[loss=0.1346, simple_loss=0.2043, pruned_loss=0.0324, over 4910.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2305, pruned_loss=0.04747, over 973186.59 frames.], batch size: 17, lr: 5.52e-04
2022-05-04 14:03:28,374 INFO [train.py:715] (2/8) Epoch 3, batch 13600, loss[loss=0.1532, simple_loss=0.2111, pruned_loss=0.0477, over 4874.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2309, pruned_loss=0.04769, over 972500.17 frames.], batch size: 32, lr: 5.52e-04
2022-05-04 14:04:10,278 INFO [train.py:715] (2/8) Epoch 3, batch 13650, loss[loss=0.1734, simple_loss=0.2432, pruned_loss=0.05184, over 4949.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2306, pruned_loss=0.04712, over 973169.63 frames.], batch size: 21, lr: 5.52e-04
2022-05-04 14:04:51,695 INFO [train.py:715] (2/8) Epoch 3, batch 13700, loss[loss=0.1524, simple_loss=0.2107, pruned_loss=0.04703, over 4989.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2307, pruned_loss=0.04709, over 973697.77 frames.], batch size: 14, lr: 5.52e-04
2022-05-04 14:05:34,460 INFO [train.py:715] (2/8) Epoch 3, batch 13750, loss[loss=0.1987, simple_loss=0.2713, pruned_loss=0.06304, over 4842.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2305, pruned_loss=0.04705, over 972971.40 frames.], batch size: 30, lr: 5.52e-04
2022-05-04 14:06:16,545 INFO [train.py:715] (2/8) Epoch 3, batch 13800, loss[loss=0.1509, simple_loss=0.2222, pruned_loss=0.03983, over 4804.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2304, pruned_loss=0.0469, over 972999.13 frames.], batch size: 21, lr: 5.52e-04
2022-05-04 14:06:58,038 INFO [train.py:715] (2/8) Epoch 3, batch 13850, loss[loss=0.1738, simple_loss=0.2447, pruned_loss=0.0515, over 4906.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2297, pruned_loss=0.04665, over 973270.28 frames.], batch size: 19, lr: 5.51e-04
2022-05-04 14:07:39,261 INFO [train.py:715] (2/8) Epoch 3, batch 13900, loss[loss=0.1533, simple_loss=0.2343, pruned_loss=0.03616, over 4823.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2305, pruned_loss=0.04739, over 972744.33 frames.], batch size: 25, lr: 5.51e-04
2022-05-04 14:08:21,702 INFO [train.py:715] (2/8) Epoch 3, batch 13950, loss[loss=0.1628, simple_loss=0.244, pruned_loss=0.0408, over 4946.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2307, pruned_loss=0.0475, over 972689.67 frames.], batch size: 21, lr: 5.51e-04
2022-05-04 14:09:04,163 INFO [train.py:715] (2/8) Epoch 3, batch 14000, loss[loss=0.1994, simple_loss=0.2642, pruned_loss=0.06728, over 4954.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2314, pruned_loss=0.04757, over 972814.27 frames.], batch size: 21, lr: 5.51e-04
2022-05-04 14:09:45,586 INFO [train.py:715] (2/8) Epoch 3, batch 14050, loss[loss=0.1386, simple_loss=0.2071, pruned_loss=0.03505, over 4969.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2312, pruned_loss=0.0473, over 973201.26 frames.], batch size: 35, lr: 5.51e-04
2022-05-04 14:10:28,385 INFO [train.py:715] (2/8) Epoch 3, batch 14100, loss[loss=0.1796, simple_loss=0.2489, pruned_loss=0.05512, over 4915.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2321, pruned_loss=0.04771, over 972857.06 frames.], batch size: 29, lr: 5.51e-04
2022-05-04 14:11:10,229 INFO [train.py:715] (2/8) Epoch 3, batch 14150, loss[loss=0.1456, simple_loss=0.228, pruned_loss=0.0316, over 4824.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2313, pruned_loss=0.0474, over 972881.95 frames.], batch size: 25, lr: 5.51e-04
2022-05-04 14:11:51,360 INFO [train.py:715] (2/8) Epoch 3, batch 14200, loss[loss=0.165, simple_loss=0.226, pruned_loss=0.052, over 4917.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2311, pruned_loss=0.04707, over 972583.01 frames.], batch size: 17, lr: 5.51e-04
2022-05-04 14:12:33,498 INFO [train.py:715] (2/8) Epoch 3, batch 14250, loss[loss=0.1645, simple_loss=0.2347, pruned_loss=0.0472, over 4787.00 frames.], tot_loss[loss=0.163, simple_loss=0.2314, pruned_loss=0.04731, over 972267.14 frames.], batch size: 18, lr: 5.51e-04
2022-05-04 14:13:15,872 INFO [train.py:715] (2/8) Epoch 3, batch 14300, loss[loss=0.1328, simple_loss=0.2023, pruned_loss=0.03169, over 4967.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2313, pruned_loss=0.0472, over 972718.92 frames.], batch size: 14, lr: 5.50e-04
2022-05-04 14:13:58,169 INFO [train.py:715] (2/8) Epoch 3, batch 14350, loss[loss=0.1997, simple_loss=0.2718, pruned_loss=0.0638, over 4750.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2325, pruned_loss=0.04808, over 972874.39 frames.], batch size: 16, lr: 5.50e-04
2022-05-04 14:14:38,947 INFO [train.py:715] (2/8) Epoch 3, batch 14400, loss[loss=0.189, simple_loss=0.2562, pruned_loss=0.0609, over 4855.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2326, pruned_loss=0.04836, over 972584.26 frames.], batch size: 32, lr: 5.50e-04
2022-05-04 14:15:21,402 INFO [train.py:715] (2/8) Epoch 3, batch 14450, loss[loss=0.1562, simple_loss=0.2333, pruned_loss=0.03956, over 4965.00 frames.], tot_loss[loss=0.1646, simple_loss=0.233, pruned_loss=0.04815, over 971795.70 frames.], batch size: 21, lr: 5.50e-04
2022-05-04 14:16:03,336 INFO [train.py:715] (2/8) Epoch 3, batch 14500, loss[loss=0.147, simple_loss=0.209, pruned_loss=0.0425, over 4946.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2324, pruned_loss=0.04799, over 972582.70 frames.], batch size: 29, lr: 5.50e-04
2022-05-04 14:16:44,541 INFO [train.py:715] (2/8) Epoch 3, batch 14550, loss[loss=0.1538, simple_loss=0.218, pruned_loss=0.04483, over 4990.00 frames.], tot_loss[loss=0.165, simple_loss=0.233, pruned_loss=0.04848, over 973176.02 frames.], batch size: 20, lr: 5.50e-04
2022-05-04 14:17:26,973 INFO [train.py:715] (2/8) Epoch 3, batch 14600, loss[loss=0.1305, simple_loss=0.2079, pruned_loss=0.02653, over 4969.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2326, pruned_loss=0.04802, over 973270.24 frames.], batch size: 28, lr: 5.50e-04
2022-05-04 14:18:08,860 INFO [train.py:715] (2/8) Epoch 3, batch 14650, loss[loss=0.1698, simple_loss=0.2322, pruned_loss=0.05376, over 4971.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2319, pruned_loss=0.04786, over 972965.55 frames.], batch size: 24, lr: 5.50e-04
2022-05-04 14:18:50,907 INFO [train.py:715] (2/8) Epoch 3, batch 14700, loss[loss=0.159, simple_loss=0.238, pruned_loss=0.03996, over 4859.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2318, pruned_loss=0.04764, over 972371.81 frames.], batch size: 20, lr: 5.49e-04
2022-05-04 14:19:32,214 INFO [train.py:715] (2/8) Epoch 3, batch 14750, loss[loss=0.1714, simple_loss=0.2515, pruned_loss=0.04569, over 4873.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2334, pruned_loss=0.04869, over 972332.31 frames.], batch size: 20, lr: 5.49e-04
2022-05-04 14:20:14,631 INFO [train.py:715] (2/8) Epoch 3, batch 14800, loss[loss=0.1534, simple_loss=0.224, pruned_loss=0.04136, over 4755.00 frames.], tot_loss[loss=0.166, simple_loss=0.2338, pruned_loss=0.04914, over 971854.80 frames.], batch size: 19, lr: 5.49e-04
2022-05-04 14:20:56,944 INFO [train.py:715] (2/8) Epoch 3, batch 14850, loss[loss=0.136, simple_loss=0.2108, pruned_loss=0.03055, over 4807.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2331, pruned_loss=0.04889, over 971933.02 frames.], batch size: 21, lr: 5.49e-04
2022-05-04 14:21:37,851 INFO [train.py:715] (2/8) Epoch 3, batch 14900, loss[loss=0.137, simple_loss=0.2132, pruned_loss=0.03041, over 4872.00 frames.], tot_loss[loss=0.165, simple_loss=0.2328, pruned_loss=0.04864, over 972179.69 frames.], batch size: 20, lr: 5.49e-04
2022-05-04 14:22:20,807 INFO [train.py:715] (2/8) Epoch 3, batch 14950, loss[loss=0.134, simple_loss=0.2101, pruned_loss=0.02895, over 4910.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2324, pruned_loss=0.04824, over 972816.05 frames.], batch size: 19, lr: 5.49e-04
2022-05-04 14:23:02,215 INFO [train.py:715] (2/8) Epoch 3, batch 15000, loss[loss=0.2024, simple_loss=0.257, pruned_loss=0.07392, over 4863.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2324, pruned_loss=0.04854, over 971259.70 frames.], batch size: 30, lr: 5.49e-04
2022-05-04 14:23:02,217 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 14:23:10,877 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1142, simple_loss=0.2003, pruned_loss=0.01402, over 914524.00 frames.
2022-05-04 14:23:52,703 INFO [train.py:715] (2/8) Epoch 3, batch 15050, loss[loss=0.1864, simple_loss=0.2432, pruned_loss=0.06482, over 4875.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2321, pruned_loss=0.04857, over 970954.11 frames.], batch size: 16, lr: 5.49e-04
2022-05-04 14:24:34,023 INFO [train.py:715] (2/8) Epoch 3, batch 15100, loss[loss=0.1917, simple_loss=0.2556, pruned_loss=0.06385, over 4952.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2321, pruned_loss=0.0483, over 971700.31 frames.], batch size: 15, lr: 5.49e-04
2022-05-04 14:25:16,190 INFO [train.py:715] (2/8) Epoch 3, batch 15150, loss[loss=0.1604, simple_loss=0.242, pruned_loss=0.03941, over 4755.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2324, pruned_loss=0.04813, over 971644.90 frames.], batch size: 16, lr: 5.48e-04
2022-05-04 14:25:57,815 INFO [train.py:715] (2/8) Epoch 3, batch 15200, loss[loss=0.1755, simple_loss=0.2363, pruned_loss=0.05733, over 4791.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2314, pruned_loss=0.04777, over 971301.51 frames.], batch size: 24, lr: 5.48e-04
2022-05-04 14:26:39,363 INFO [train.py:715] (2/8) Epoch 3, batch 15250, loss[loss=0.1743, simple_loss=0.2469, pruned_loss=0.05083, over 4901.00 frames.], tot_loss[loss=0.1648, simple_loss=0.233, pruned_loss=0.04833, over 971746.59 frames.], batch size: 17, lr: 5.48e-04
2022-05-04 14:27:20,702 INFO [train.py:715] (2/8) Epoch 3, batch 15300, loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.03088, over 4958.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2335, pruned_loss=0.0484, over 971923.42 frames.], batch size: 21, lr: 5.48e-04
2022-05-04 14:28:02,526 INFO [train.py:715] (2/8) Epoch 3, batch 15350, loss[loss=0.1537, simple_loss=0.2292, pruned_loss=0.03906, over 4637.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2326, pruned_loss=0.04799, over 971572.94 frames.], batch size: 13, lr: 5.48e-04
2022-05-04 14:28:44,647 INFO [train.py:715] (2/8) Epoch 3, batch 15400, loss[loss=0.1732, simple_loss=0.2445, pruned_loss=0.05095, over 4936.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2323, pruned_loss=0.04795, over 972150.85 frames.], batch size: 23, lr: 5.48e-04
2022-05-04 14:29:25,739 INFO [train.py:715] (2/8) Epoch 3, batch 15450, loss[loss=0.151, simple_loss=0.2306, pruned_loss=0.03574, over 4810.00 frames.], tot_loss[loss=0.163, simple_loss=0.2313, pruned_loss=0.04735, over 971920.86 frames.], batch size: 21, lr: 5.48e-04
2022-05-04 14:30:08,674 INFO [train.py:715] (2/8) Epoch 3, batch 15500, loss[loss=0.1632, simple_loss=0.2358, pruned_loss=0.04531, over 4870.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2324, pruned_loss=0.04794, over 972297.46 frames.], batch size: 22, lr: 5.48e-04
2022-05-04 14:30:50,506 INFO [train.py:715] (2/8) Epoch 3, batch 15550, loss[loss=0.1914, simple_loss=0.2711, pruned_loss=0.05589, over 4702.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2321, pruned_loss=0.04784, over 972141.28 frames.], batch size: 15, lr: 5.48e-04
2022-05-04 14:31:35,082 INFO [train.py:715] (2/8) Epoch 3, batch 15600, loss[loss=0.1698, simple_loss=0.2381, pruned_loss=0.05071, over 4846.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2321, pruned_loss=0.04774, over 972719.53 frames.], batch size: 20, lr: 5.47e-04
2022-05-04 14:32:16,099 INFO [train.py:715] (2/8) Epoch 3, batch 15650, loss[loss=0.1261, simple_loss=0.1973, pruned_loss=0.02748, over 4863.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2324, pruned_loss=0.04756, over 973259.14 frames.], batch size: 20, lr: 5.47e-04
2022-05-04 14:32:57,685 INFO [train.py:715] (2/8) Epoch 3, batch 15700, loss[loss=0.1402, simple_loss=0.2105, pruned_loss=0.03499, over 4774.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2317, pruned_loss=0.04733, over 972898.11 frames.], batch size: 17, lr: 5.47e-04
2022-05-04 14:33:40,520 INFO [train.py:715] (2/8) Epoch 3, batch 15750, loss[loss=0.129, simple_loss=0.1976, pruned_loss=0.03016, over 4768.00 frames.], tot_loss[loss=0.163, simple_loss=0.2315, pruned_loss=0.04727, over 972561.98 frames.], batch size: 12, lr: 5.47e-04
2022-05-04 14:34:22,321 INFO [train.py:715] (2/8) Epoch 3, batch 15800, loss[loss=0.1603, simple_loss=0.2217, pruned_loss=0.04943, over 4848.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2307, pruned_loss=0.04727, over 972056.69 frames.], batch size: 20, lr: 5.47e-04
2022-05-04 14:35:03,584 INFO [train.py:715] (2/8) Epoch 3, batch 15850, loss[loss=0.1769, simple_loss=0.2436, pruned_loss=0.05508, over 4753.00 frames.], tot_loss[loss=0.1619, simple_loss=0.23, pruned_loss=0.04687, over 972188.08 frames.], batch size: 16, lr: 5.47e-04
2022-05-04 14:35:45,967 INFO [train.py:715] (2/8) Epoch 3, batch 15900, loss[loss=0.186, simple_loss=0.2506, pruned_loss=0.06068, over 4868.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2303, pruned_loss=0.04643, over 971585.16 frames.], batch size: 30, lr: 5.47e-04
2022-05-04 14:36:28,598 INFO [train.py:715] (2/8) Epoch 3, batch 15950, loss[loss=0.1485, simple_loss=0.2103, pruned_loss=0.0433, over 4649.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2304, pruned_loss=0.04697, over 971798.02 frames.], batch size: 13, lr: 5.47e-04
2022-05-04 14:37:09,202 INFO [train.py:715] (2/8) Epoch 3, batch 16000, loss[loss=0.1545, simple_loss=0.2212, pruned_loss=0.04392, over 4972.00 frames.], tot_loss[loss=0.1631, simple_loss=0.231, pruned_loss=0.04758, over 971984.75 frames.], batch size: 24, lr: 5.47e-04
2022-05-04 14:37:50,847 INFO [train.py:715] (2/8) Epoch 3, batch 16050, loss[loss=0.1304, simple_loss=0.2011, pruned_loss=0.02984, over 4984.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2315, pruned_loss=0.04761, over 972346.70 frames.], batch size: 15, lr: 5.46e-04
2022-05-04 14:38:33,467 INFO [train.py:715] (2/8) Epoch 3, batch 16100, loss[loss=0.2006, simple_loss=0.2618, pruned_loss=0.06966, over 4750.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2306, pruned_loss=0.04692, over 972240.04 frames.], batch size: 19, lr: 5.46e-04
2022-05-04 14:39:15,442 INFO [train.py:715] (2/8) Epoch 3, batch 16150, loss[loss=0.1701, simple_loss=0.2413, pruned_loss=0.04941, over 4976.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2308, pruned_loss=0.04693, over 971741.95 frames.], batch size: 31, lr: 5.46e-04
2022-05-04 14:39:56,176 INFO [train.py:715] (2/8) Epoch 3, batch 16200, loss[loss=0.1568, simple_loss=0.2164, pruned_loss=0.0486, over 4871.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2305, pruned_loss=0.04636, over 972207.81 frames.], batch size: 32, lr: 5.46e-04
2022-05-04 14:40:38,484 INFO [train.py:715] (2/8) Epoch 3, batch 16250, loss[loss=0.1825, simple_loss=0.2501, pruned_loss=0.05739, over 4832.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2314, pruned_loss=0.04713, over 972112.03 frames.], batch size: 15, lr: 5.46e-04
2022-05-04 14:41:20,551 INFO [train.py:715] (2/8) Epoch 3, batch 16300, loss[loss=0.1929, simple_loss=0.2541, pruned_loss=0.06586, over 4986.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2326, pruned_loss=0.04747, over 971689.67 frames.], batch size: 33, lr: 5.46e-04
2022-05-04 14:42:01,222 INFO [train.py:715] (2/8) Epoch 3, batch 16350, loss[loss=0.1414, simple_loss=0.2166, pruned_loss=0.03308, over 4943.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2328, pruned_loss=0.04774, over 972567.01 frames.], batch size: 23, lr: 5.46e-04
2022-05-04 14:42:43,185 INFO [train.py:715] (2/8) Epoch 3, batch 16400, loss[loss=0.1496, simple_loss=0.2165, pruned_loss=0.04135, over 4779.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2323, pruned_loss=0.04752, over 972658.06 frames.], batch size: 18, lr: 5.46e-04
2022-05-04 14:43:25,719 INFO [train.py:715] (2/8) Epoch 3, batch 16450, loss[loss=0.1853, simple_loss=0.2474, pruned_loss=0.0616, over 4979.00 frames.], tot_loss[loss=0.164, simple_loss=0.2325, pruned_loss=0.04777, over 973059.38 frames.], batch size: 24, lr: 5.45e-04
2022-05-04 14:44:08,338 INFO [train.py:715] (2/8) Epoch 3, batch 16500, loss[loss=0.1673, simple_loss=0.2295, pruned_loss=0.05257, over 4757.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2316, pruned_loss=0.04698, over 972673.42 frames.], batch size: 19, lr: 5.45e-04
2022-05-04 14:44:49,041 INFO [train.py:715] (2/8) Epoch 3, batch 16550, loss[loss=0.2219, simple_loss=0.2909, pruned_loss=0.07641, over 4836.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2317, pruned_loss=0.04768, over 972899.59 frames.], batch size: 15, lr: 5.45e-04
2022-05-04 14:45:31,921 INFO [train.py:715] (2/8) Epoch 3, batch 16600, loss[loss=0.2051, simple_loss=0.2753, pruned_loss=0.06742, over 4920.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2318, pruned_loss=0.04727, over 972705.96 frames.], batch size: 18, lr: 5.45e-04
2022-05-04 14:46:14,678 INFO [train.py:715] (2/8) Epoch 3, batch 16650, loss[loss=0.1798, simple_loss=0.237, pruned_loss=0.06123, over 4744.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2312, pruned_loss=0.04691, over 972577.05 frames.], batch size: 12, lr: 5.45e-04
2022-05-04 14:46:55,372 INFO [train.py:715] (2/8) Epoch 3, batch 16700, loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02897, over 4927.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2307, pruned_loss=0.04677, over 972452.16 frames.], batch size: 21, lr: 5.45e-04
2022-05-04 14:47:37,397 INFO [train.py:715] (2/8) Epoch 3, batch 16750, loss[loss=0.1218, simple_loss=0.1977, pruned_loss=0.02296, over 4926.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2305, pruned_loss=0.04658, over 972631.35 frames.], batch size: 29, lr: 5.45e-04
2022-05-04 14:48:19,849 INFO [train.py:715] (2/8) Epoch 3, batch 16800, loss[loss=0.1804, simple_loss=0.2452, pruned_loss=0.05781, over 4992.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2301, pruned_loss=0.04622, over 972978.58 frames.], batch size: 14, lr: 5.45e-04
2022-05-04 14:49:01,322 INFO [train.py:715] (2/8) Epoch 3, batch 16850, loss[loss=0.1338, simple_loss=0.2031, pruned_loss=0.03228, over 4901.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2298, pruned_loss=0.04629, over 973023.12 frames.], batch size: 19, lr: 5.45e-04
2022-05-04 14:49:42,723 INFO [train.py:715] (2/8) Epoch 3, batch 16900, loss[loss=0.1457, simple_loss=0.2136, pruned_loss=0.03895, over 4856.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2296, pruned_loss=0.04612, over 973427.54 frames.], batch size: 15, lr: 5.44e-04
2022-05-04 14:50:24,687 INFO [train.py:715] (2/8) Epoch 3, batch 16950, loss[loss=0.1474, simple_loss=0.2176, pruned_loss=0.03863, over 4968.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2305, pruned_loss=0.04661, over 972806.07 frames.], batch size: 28, lr: 5.44e-04
2022-05-04 14:51:07,228 INFO [train.py:715] (2/8) Epoch 3, batch 17000, loss[loss=0.1406, simple_loss=0.2186, pruned_loss=0.03129, over 4746.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2302, pruned_loss=0.04679, over 972277.61 frames.], batch size: 19, lr: 5.44e-04
2022-05-04 14:51:47,550 INFO [train.py:715] (2/8) Epoch 3, batch 17050, loss[loss=0.173, simple_loss=0.2427, pruned_loss=0.05164, over 4908.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2302, pruned_loss=0.04666, over 973002.92 frames.], batch size: 39, lr: 5.44e-04
2022-05-04 14:52:29,473 INFO [train.py:715] (2/8) Epoch 3, batch 17100, loss[loss=0.1776, simple_loss=0.2398, pruned_loss=0.0577, over 4792.00 frames.], tot_loss[loss=0.162, simple_loss=0.2308, pruned_loss=0.04659, over 973632.22 frames.], batch size: 24, lr: 5.44e-04
2022-05-04 14:53:11,176 INFO [train.py:715] (2/8) Epoch 3, batch 17150, loss[loss=0.1715, simple_loss=0.2377, pruned_loss=0.05267, over 4738.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2316, pruned_loss=0.04734, over 972599.85 frames.], batch size: 16, lr: 5.44e-04
2022-05-04 14:53:52,352 INFO [train.py:715] (2/8) Epoch 3, batch 17200, loss[loss=0.1449, simple_loss=0.2142, pruned_loss=0.03784, over 4989.00 frames.], tot_loss[loss=0.1616, simple_loss=0.23, pruned_loss=0.04654, over 973271.58 frames.], batch size: 25, lr: 5.44e-04
2022-05-04 14:54:33,048 INFO [train.py:715] (2/8) Epoch 3, batch 17250, loss[loss=0.1701, simple_loss=0.2407, pruned_loss=0.0498, over 4961.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2296, pruned_loss=0.04636, over 972165.77 frames.], batch size: 35, lr: 5.44e-04
2022-05-04 14:55:14,504 INFO [train.py:715] (2/8) Epoch 3, batch 17300, loss[loss=0.166, simple_loss=0.2317, pruned_loss=0.05022, over 4790.00 frames.], tot_loss[loss=0.1601, simple_loss=0.229, pruned_loss=0.04566, over 972461.90 frames.], batch size: 18, lr: 5.44e-04
2022-05-04 14:55:56,122 INFO [train.py:715] (2/8) Epoch 3, batch 17350, loss[loss=0.1834, simple_loss=0.2402, pruned_loss=0.06335, over 4980.00 frames.], tot_loss[loss=0.1616, simple_loss=0.23, pruned_loss=0.04656, over 972180.77 frames.], batch size: 15, lr: 5.43e-04
2022-05-04 14:56:36,186 INFO [train.py:715] (2/8) Epoch 3, batch 17400, loss[loss=0.1364, simple_loss=0.2043, pruned_loss=0.03426, over 4921.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2304, pruned_loss=0.04635, over 972513.26 frames.], batch size: 23, lr: 5.43e-04
2022-05-04 14:57:18,250 INFO [train.py:715] (2/8) Epoch 3, batch 17450, loss[loss=0.2094, simple_loss=0.2798, pruned_loss=0.06953, over 4821.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2304, pruned_loss=0.0463, over 972498.67 frames.], batch size: 26, lr: 5.43e-04
2022-05-04 14:58:00,485 INFO [train.py:715] (2/8) Epoch 3, batch 17500, loss[loss=0.1488, simple_loss=0.2125, pruned_loss=0.04256, over 4780.00 frames.], tot_loss[loss=0.1622, simple_loss=0.231, pruned_loss=0.04673, over 973502.76 frames.], batch size: 17, lr: 5.43e-04
2022-05-04 14:58:41,505 INFO [train.py:715] (2/8) Epoch 3, batch 17550, loss[loss=0.1954, simple_loss=0.2649, pruned_loss=0.06291, over 4798.00 frames.], tot_loss[loss=0.162, simple_loss=0.2306, pruned_loss=0.04664, over 973442.67 frames.], batch size: 21, lr: 5.43e-04
2022-05-04 14:59:22,852 INFO [train.py:715] (2/8) Epoch 3, batch 17600, loss[loss=0.1926, simple_loss=0.2444, pruned_loss=0.07033, over 4746.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2313, pruned_loss=0.04719, over 973240.53 frames.], batch size: 12, lr: 5.43e-04
2022-05-04 15:00:04,532 INFO [train.py:715] (2/8) Epoch 3, batch 17650, loss[loss=0.1608, simple_loss=0.2421, pruned_loss=0.03976, over 4819.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2315, pruned_loss=0.04711, over 972334.31 frames.], batch size: 15, lr: 5.43e-04
2022-05-04 15:00:46,083 INFO [train.py:715] (2/8) Epoch 3, batch 17700, loss[loss=0.1311, simple_loss=0.201, pruned_loss=0.0306, over 4808.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2305, pruned_loss=0.04657, over 972526.34 frames.], batch size: 26, lr: 5.43e-04
2022-05-04 15:01:26,893 INFO [train.py:715] (2/8) Epoch 3, batch 17750, loss[loss=0.1706, simple_loss=0.2543, pruned_loss=0.04348, over 4769.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2307, pruned_loss=0.04657, over 972856.87 frames.], batch size: 19, lr: 5.43e-04
2022-05-04 15:02:08,926 INFO [train.py:715] (2/8) Epoch 3, batch 17800, loss[loss=0.1704, simple_loss=0.2369, pruned_loss=0.05193, over 4859.00 frames.], tot_loss[loss=0.163, simple_loss=0.2317, pruned_loss=0.04713, over 972556.72 frames.], batch size: 12, lr: 5.42e-04
2022-05-04 15:02:50,343 INFO [train.py:715] (2/8) Epoch 3, batch 17850, loss[loss=0.1135, simple_loss=0.1794, pruned_loss=0.0238, over 4757.00 frames.], tot_loss[loss=0.1636, simple_loss=0.232, pruned_loss=0.04754, over 972317.77 frames.], batch size: 14, lr: 5.42e-04
2022-05-04 15:03:30,306 INFO [train.py:715] (2/8) Epoch 3, batch 17900, loss[loss=0.1533, simple_loss=0.2213, pruned_loss=0.04262, over 4859.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2319, pruned_loss=0.04739, over 972857.53 frames.], batch size: 30, lr: 5.42e-04
2022-05-04 15:04:12,145 INFO [train.py:715] (2/8) Epoch 3, batch 17950, loss[loss=0.1458, simple_loss=0.2085, pruned_loss=0.04154, over 4975.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2313, pruned_loss=0.04717, over 972687.78 frames.], batch size: 14, lr: 5.42e-04
2022-05-04 15:04:53,402 INFO [train.py:715] (2/8) Epoch 3, batch 18000, loss[loss=0.1634, simple_loss=0.2337, pruned_loss=0.04656, over 4845.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2305, pruned_loss=0.04694, over 972184.19 frames.], batch size: 15, lr: 5.42e-04
2022-05-04 15:04:53,403 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 15:05:02,070 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1143, simple_loss=0.2002, pruned_loss=0.01414, over 914524.00 frames.
2022-05-04 15:05:43,867 INFO [train.py:715] (2/8) Epoch 3, batch 18050, loss[loss=0.1498, simple_loss=0.2197, pruned_loss=0.03998, over 4867.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2314, pruned_loss=0.04775, over 972607.36 frames.], batch size: 32, lr: 5.42e-04
2022-05-04 15:06:25,505 INFO [train.py:715] (2/8) Epoch 3, batch 18100, loss[loss=0.1968, simple_loss=0.2512, pruned_loss=0.07121, over 4966.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2318, pruned_loss=0.04819, over 972647.61 frames.], batch size: 15, lr: 5.42e-04
2022-05-04 15:07:06,170 INFO [train.py:715] (2/8) Epoch 3, batch 18150, loss[loss=0.1426, simple_loss=0.2227, pruned_loss=0.03125, over 4981.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2314, pruned_loss=0.04799, over 971849.66 frames.], batch size: 28, lr: 5.42e-04
2022-05-04 15:07:47,679 INFO [train.py:715] (2/8) Epoch 3, batch 18200, loss[loss=0.1331, simple_loss=0.2027, pruned_loss=0.03174, over 4818.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2314, pruned_loss=0.04787, over 970629.74 frames.], batch size: 21, lr: 5.42e-04
2022-05-04 15:08:29,472 INFO [train.py:715] (2/8) Epoch 3, batch 18250, loss[loss=0.186, simple_loss=0.2534, pruned_loss=0.05931, over 4917.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2319, pruned_loss=0.04859, over 970929.67 frames.], batch size: 18, lr: 5.41e-04
2022-05-04 15:09:10,294 INFO [train.py:715] (2/8) Epoch 3, batch 18300, loss[loss=0.1578, simple_loss=0.2257, pruned_loss=0.04493, over 4929.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2312, pruned_loss=0.04805, over 971875.75 frames.], batch size: 23, lr: 5.41e-04
2022-05-04 15:09:51,598 INFO [train.py:715] (2/8) Epoch 3, batch 18350, loss[loss=0.1515, simple_loss=0.2201, pruned_loss=0.04144, over 4736.00 frames.], tot_loss[loss=0.164, simple_loss=0.2318, pruned_loss=0.04816, over 972401.21 frames.], batch size: 12, lr: 5.41e-04
2022-05-04 15:10:33,027 INFO [train.py:715] (2/8) Epoch 3, batch 18400, loss[loss=0.1443, simple_loss=0.2064, pruned_loss=0.04113, over 4974.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2316, pruned_loss=0.0478, over 972344.23 frames.], batch size: 15, lr: 5.41e-04
2022-05-04 15:11:13,984 INFO [train.py:715] (2/8) Epoch 3, batch 18450, loss[loss=0.1465, simple_loss=0.2117, pruned_loss=0.04062, over 4786.00 frames.], tot_loss[loss=0.1647, simple_loss=0.233, pruned_loss=0.04819, over 972149.35 frames.], batch size: 14, lr: 5.41e-04
2022-05-04 15:11:55,036 INFO [train.py:715] (2/8) Epoch 3, batch 18500, loss[loss=0.1365, simple_loss=0.2153, pruned_loss=0.02882, over 4962.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2335, pruned_loss=0.04819, over 972542.15 frames.], batch size: 24, lr: 5.41e-04
2022-05-04 15:12:36,396 INFO [train.py:715] (2/8) Epoch 3, batch 18550, loss[loss=0.1388, simple_loss=0.2093, pruned_loss=0.03413, over 4800.00 frames.], tot_loss[loss=0.165, simple_loss=0.2336, pruned_loss=0.04818, over 972971.85 frames.], batch size: 17, lr: 5.41e-04
2022-05-04 15:13:18,628 INFO [train.py:715] (2/8) Epoch 3, batch 18600, loss[loss=0.1618, simple_loss=0.2369, pruned_loss=0.04337, over 4851.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2327, pruned_loss=0.04785, over 972665.34 frames.], batch size: 20, lr: 5.41e-04
2022-05-04 15:13:58,618 INFO [train.py:715] (2/8) Epoch 3, batch 18650, loss[loss=0.1491, simple_loss=0.2023, pruned_loss=0.04798, over 4867.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2318, pruned_loss=0.04749, over 972512.24 frames.], batch size: 30, lr: 5.41e-04
2022-05-04 15:14:39,317 INFO [train.py:715] (2/8) Epoch 3, batch 18700, loss[loss=0.1565, simple_loss=0.2398, pruned_loss=0.03665, over 4945.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2319, pruned_loss=0.04742, over 972046.58 frames.], batch size: 21, lr: 5.40e-04
2022-05-04 15:15:20,415 INFO [train.py:715] (2/8) Epoch 3, batch 18750, loss[loss=0.1765, simple_loss=0.2342, pruned_loss=0.05938, over 4985.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2315, pruned_loss=0.04714, over 972733.56 frames.], batch size: 16, lr: 5.40e-04
2022-05-04 15:16:00,292 INFO [train.py:715] (2/8) Epoch 3, batch 18800, loss[loss=0.1432, simple_loss=0.2076, pruned_loss=0.0394, over 4983.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2313, pruned_loss=0.04687, over 972298.68 frames.], batch size: 24, lr: 5.40e-04
2022-05-04 15:16:41,092 INFO [train.py:715] (2/8) Epoch 3, batch 18850, loss[loss=0.1662, simple_loss=0.2342, pruned_loss=0.04906, over 4778.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2319, pruned_loss=0.04717, over 972802.25 frames.], batch size: 17, lr: 5.40e-04
2022-05-04 15:17:21,057 INFO [train.py:715] (2/8) Epoch 3, batch 18900, loss[loss=0.1706, simple_loss=0.2389, pruned_loss=0.05112, over 4932.00 frames.], tot_loss[loss=0.1643, simple_loss=0.233, pruned_loss=0.04783, over 972295.36 frames.], batch size: 29, lr: 5.40e-04
2022-05-04 15:18:01,537 INFO [train.py:715] (2/8) Epoch 3, batch 18950, loss[loss=0.1527, simple_loss=0.224, pruned_loss=0.04076, over 4874.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2315, pruned_loss=0.04775, over 972956.59 frames.], batch size: 16, lr: 5.40e-04
2022-05-04 15:18:40,942 INFO [train.py:715] (2/8) Epoch 3, batch 19000, loss[loss=0.172, simple_loss=0.2233, pruned_loss=0.06036, over 4842.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2309, pruned_loss=0.04724, over 972906.19 frames.], batch size: 32, lr: 5.40e-04
2022-05-04 15:19:20,770 INFO [train.py:715] (2/8) Epoch 3, batch 19050, loss[loss=0.1461, simple_loss=0.2164, pruned_loss=0.03784, over 4847.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2309, pruned_loss=0.04699, over 971803.59 frames.], batch size: 15, lr: 5.40e-04
2022-05-04 15:20:01,078 INFO [train.py:715] (2/8) Epoch 3, batch 19100, loss[loss=0.1466, simple_loss=0.2173, pruned_loss=0.038, over 4889.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2307, pruned_loss=0.04681, over 973122.54 frames.], batch size: 16, lr: 5.40e-04
2022-05-04 15:20:40,498 INFO [train.py:715] (2/8) Epoch 3, batch 19150, loss[loss=0.1583, simple_loss=0.2289, pruned_loss=0.04385, over 4855.00 frames.], tot_loss[loss=0.162, simple_loss=0.2304, pruned_loss=0.04684, over 972871.93 frames.], batch size: 30, lr: 5.40e-04
2022-05-04 15:21:20,180 INFO [train.py:715] (2/8) Epoch 3, batch 19200, loss[loss=0.1479, simple_loss=0.2119, pruned_loss=0.04194, over 4795.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2306, pruned_loss=0.04702, over 972835.73 frames.], batch size: 24, lr: 5.39e-04
2022-05-04 15:21:59,821 INFO [train.py:715] (2/8) Epoch 3, batch 19250, loss[loss=0.1642, simple_loss=0.2286, pruned_loss=0.04984, over 4819.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2309, pruned_loss=0.04746, over 972608.00 frames.], batch size: 13, lr: 5.39e-04
2022-05-04 15:22:40,130 INFO [train.py:715] (2/8) Epoch 3, batch 19300, loss[loss=0.1725, simple_loss=0.2376, pruned_loss=0.05368, over 4844.00 frames.], tot_loss[loss=0.162, simple_loss=0.2302, pruned_loss=0.04691, over 971981.92 frames.], batch size: 30, lr: 5.39e-04
2022-05-04 15:23:19,471 INFO [train.py:715] (2/8) Epoch 3, batch 19350, loss[loss=0.1923, simple_loss=0.2708, pruned_loss=0.05685, over 4896.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2309, pruned_loss=0.04692, over 972831.06 frames.], batch size: 22, lr: 5.39e-04
2022-05-04 15:23:59,202 INFO [train.py:715] (2/8) Epoch 3, batch 19400, loss[loss=0.1623, simple_loss=0.2325, pruned_loss=0.04607, over 4775.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2304, pruned_loss=0.04663, over 972684.55 frames.], batch size: 17, lr: 5.39e-04
2022-05-04 15:24:39,295 INFO [train.py:715] (2/8) Epoch 3, batch 19450, loss[loss=0.1399, simple_loss=0.2187, pruned_loss=0.0305, over 4878.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2312, pruned_loss=0.04687, over 973310.98 frames.], batch size: 16, lr: 5.39e-04
2022-05-04 15:25:18,371 INFO [train.py:715] (2/8) Epoch 3, batch 19500, loss[loss=0.1964, simple_loss=0.2427, pruned_loss=0.07506, over 4638.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2309, pruned_loss=0.04697, over 972988.74 frames.], batch size: 13, lr: 5.39e-04
2022-05-04 15:25:58,127 INFO [train.py:715] (2/8) Epoch 3, batch 19550, loss[loss=0.1582, simple_loss=0.2274, pruned_loss=0.04456, over 4987.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2311, pruned_loss=0.04717, over 973041.81 frames.], batch size: 25, lr: 5.39e-04
2022-05-04 15:26:37,669 INFO [train.py:715] (2/8) Epoch 3, batch 19600, loss[loss=0.1755, simple_loss=0.2435, pruned_loss=0.05379, over 4920.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2315, pruned_loss=0.04765, over 973774.03 frames.], batch size: 18, lr: 5.39e-04
2022-05-04 15:27:17,575 INFO [train.py:715] (2/8) Epoch 3, batch 19650, loss[loss=0.1484, simple_loss=0.2157, pruned_loss=0.04054, over 4929.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2309, pruned_loss=0.04734, over 973402.21 frames.], batch size: 18, lr: 5.38e-04
2022-05-04 15:27:56,472 INFO [train.py:715] (2/8) Epoch 3, batch 19700, loss[loss=0.1744, simple_loss=0.2402, pruned_loss=0.05433, over 4805.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2311, pruned_loss=0.04771, over 973191.17 frames.], batch size: 14, lr: 5.38e-04
2022-05-04 15:28:36,070 INFO [train.py:715] (2/8) Epoch 3, batch 19750, loss[loss=0.1714, simple_loss=0.2359, pruned_loss=0.05347, over 4806.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2304, pruned_loss=0.04736, over 973104.01 frames.], batch size: 12, lr: 5.38e-04
2022-05-04 15:29:15,543 INFO [train.py:715] (2/8) Epoch 3, batch 19800, loss[loss=0.1364, simple_loss=0.2018, pruned_loss=0.03554, over 4962.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2311, pruned_loss=0.04788, over 972618.30 frames.], batch size: 21, lr: 5.38e-04
2022-05-04 15:29:55,118 INFO [train.py:715] (2/8) Epoch 3, batch 19850, loss[loss=0.1502, simple_loss=0.2283, pruned_loss=0.03611, over 4909.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2312, pruned_loss=0.04748, over 972302.21 frames.], batch size: 18, lr: 5.38e-04
2022-05-04 15:30:34,819 INFO [train.py:715] (2/8) Epoch 3, batch 19900, loss[loss=0.1577, simple_loss=0.238, pruned_loss=0.03865, over 4906.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2311, pruned_loss=0.04754, over 972849.73 frames.], batch size: 17, lr: 5.38e-04
2022-05-04 15:31:15,111 INFO [train.py:715] (2/8) Epoch 3, batch 19950, loss[loss=0.1483, simple_loss=0.2221, pruned_loss=0.03724, over 4963.00 frames.], tot_loss[loss=0.1628, simple_loss=0.231, pruned_loss=0.04732, over 972404.53 frames.], batch size: 24, lr: 5.38e-04
2022-05-04 15:31:54,891 INFO [train.py:715] (2/8) Epoch 3, batch 20000, loss[loss=0.1888, simple_loss=0.2482, pruned_loss=0.06473, over 4935.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2305, pruned_loss=0.04693, over 973301.87 frames.], batch size: 39, lr: 5.38e-04
2022-05-04 15:32:34,161 INFO [train.py:715] (2/8) Epoch 3, batch 20050, loss[loss=0.158, simple_loss=0.2324, pruned_loss=0.04177, over 4762.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2311, pruned_loss=0.04754, over 972893.54 frames.], batch size: 19, lr: 5.38e-04
2022-05-04 15:33:14,399 INFO [train.py:715] (2/8) Epoch 3, batch 20100, loss[loss=0.1428, simple_loss=0.2174, pruned_loss=0.03406, over 4839.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04775, over 972966.40 frames.], batch size: 13, lr: 5.37e-04
2022-05-04 15:33:54,296 INFO [train.py:715] (2/8) Epoch 3, batch 20150, loss[loss=0.1617, simple_loss=0.235, pruned_loss=0.04419, over 4982.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2322, pruned_loss=0.0478, over 972331.98 frames.], batch size: 39, lr: 5.37e-04
2022-05-04 15:34:33,628 INFO [train.py:715] (2/8) Epoch 3, batch 20200, loss[loss=0.1598, simple_loss=0.2417, pruned_loss=0.03895, over 4931.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2318, pruned_loss=0.04717, over 972305.33 frames.], batch size: 29, lr: 5.37e-04
2022-05-04 15:35:13,294 INFO [train.py:715] (2/8) Epoch 3, batch 20250, loss[loss=0.2164, simple_loss=0.2741, pruned_loss=0.07929, over 4799.00 frames.], tot_loss[loss=0.163, simple_loss=0.2315, pruned_loss=0.0472, over 972091.09 frames.], batch size: 24, lr: 5.37e-04
2022-05-04 15:35:53,118 INFO [train.py:715] (2/8) Epoch 3, batch 20300, loss[loss=0.1589, simple_loss=0.2358, pruned_loss=0.04104, over 4833.00 frames.], tot_loss[loss=0.1621, simple_loss=0.231, pruned_loss=0.04666, over 971576.46 frames.], batch size: 15, lr: 5.37e-04
2022-05-04 15:36:33,506 INFO [train.py:715] (2/8) Epoch 3, batch 20350, loss[loss=0.1689, simple_loss=0.2336, pruned_loss=0.05207, over 4781.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2306, pruned_loss=0.04652, over 971581.44 frames.], batch size: 17, lr: 5.37e-04
2022-05-04 15:37:12,086 INFO [train.py:715] (2/8) Epoch 3, batch 20400, loss[loss=0.1535, simple_loss=0.224, pruned_loss=0.04145, over 4784.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2301, pruned_loss=0.04664, over 970937.52 frames.], batch size: 17, lr: 5.37e-04
2022-05-04 15:37:51,789 INFO [train.py:715] (2/8) Epoch 3, batch 20450, loss[loss=0.1666, simple_loss=0.2505, pruned_loss=0.04133, over 4754.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2297, pruned_loss=0.04581, over 970541.71 frames.], batch size: 19, lr: 5.37e-04
2022-05-04 15:38:31,859 INFO [train.py:715] (2/8) Epoch 3, batch 20500, loss[loss=0.1614, simple_loss=0.2312, pruned_loss=0.04586, over 4983.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2296, pruned_loss=0.04598, over 970874.32 frames.], batch size: 35, lr: 5.37e-04
2022-05-04 15:39:10,980 INFO [train.py:715] (2/8) Epoch 3, batch 20550, loss[loss=0.1643, simple_loss=0.2286, pruned_loss=0.05, over 4808.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2297, pruned_loss=0.04577, over 971327.97 frames.], batch size: 26, lr: 5.36e-04
2022-05-04 15:39:50,437 INFO [train.py:715] (2/8) Epoch 3, batch 20600, loss[loss=0.1531, simple_loss=0.2248, pruned_loss=0.04066, over 4814.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2294, pruned_loss=0.04592, over 970454.25 frames.], batch size: 27, lr: 5.36e-04
2022-05-04 15:40:30,882 INFO [train.py:715] (2/8) Epoch 3, batch 20650, loss[loss=0.1861, simple_loss=0.2396, pruned_loss=0.06628, over 4761.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2296, pruned_loss=0.04601, over 970602.48 frames.], batch size: 14, lr: 5.36e-04
2022-05-04 15:41:10,736 INFO [train.py:715] (2/8) Epoch 3, batch 20700, loss[loss=0.1312, simple_loss=0.2027, pruned_loss=0.0299, over 4653.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2291, pruned_loss=0.04593, over 970810.40 frames.], batch size: 13, lr: 5.36e-04
2022-05-04 15:41:50,196 INFO [train.py:715] (2/8) Epoch 3, batch 20750, loss[loss=0.1724, simple_loss=0.2364, pruned_loss=0.0542, over 4925.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2286, pruned_loss=0.04603, over 970632.02 frames.], batch size: 29, lr: 5.36e-04
2022-05-04 15:42:30,289 INFO [train.py:715] (2/8) Epoch 3, batch 20800, loss[loss=0.1618, simple_loss=0.2371, pruned_loss=0.04328, over 4816.00 frames.], tot_loss[loss=0.16, simple_loss=0.2283, pruned_loss=0.04591, over 970977.86 frames.], batch size: 25, lr: 5.36e-04
2022-05-04 15:43:11,025 INFO [train.py:715] (2/8) Epoch 3, batch 20850, loss[loss=0.1472, simple_loss=0.2225, pruned_loss=0.03597, over 4953.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2291, pruned_loss=0.0465, over 971218.11 frames.], batch size: 29, lr: 5.36e-04
2022-05-04 15:43:50,800 INFO [train.py:715] (2/8) Epoch 3, batch 20900, loss[loss=0.1609, simple_loss=0.219, pruned_loss=0.05137, over 4689.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2299, pruned_loss=0.04699, over 971342.76 frames.], batch size: 15, lr: 5.36e-04
2022-05-04 15:44:31,206 INFO [train.py:715] (2/8) Epoch 3, batch 20950, loss[loss=0.1656, simple_loss=0.2464, pruned_loss=0.04236, over 4839.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2299, pruned_loss=0.04696, over 970758.92 frames.], batch size: 15, lr: 5.36e-04
2022-05-04 15:45:11,741 INFO [train.py:715] (2/8) Epoch 3, batch 21000, loss[loss=0.138, simple_loss=0.2041, pruned_loss=0.03595, over 4811.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2302, pruned_loss=0.04703, over 971317.59 frames.], batch size: 12, lr: 5.36e-04
2022-05-04 15:45:11,742 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 15:45:24,193 INFO [train.py:742] (2/8) 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] (2/8) Epoch 3, batch 21050, loss[loss=0.1653, simple_loss=0.2305, pruned_loss=0.05006, over 4925.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2311, pruned_loss=0.04789, over 971411.97 frames.], batch size: 21, lr: 5.35e-04
2022-05-04 15:46:45,380 INFO [train.py:715] (2/8) Epoch 3, batch 21100, loss[loss=0.1692, simple_loss=0.2282, pruned_loss=0.05509, over 4923.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2309, pruned_loss=0.04782, over 970772.66 frames.], batch size: 35, lr: 5.35e-04
2022-05-04 15:47:25,768 INFO [train.py:715] (2/8) Epoch 3, batch 21150, loss[loss=0.1629, simple_loss=0.235, pruned_loss=0.0454, over 4987.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2304, pruned_loss=0.04743, over 971909.60 frames.], batch size: 25, lr: 5.35e-04
2022-05-04 15:48:08,587 INFO [train.py:715] (2/8) Epoch 3, batch 21200, loss[loss=0.1777, simple_loss=0.2522, pruned_loss=0.05156, over 4943.00 frames.], tot_loss[loss=0.163, simple_loss=0.2312, pruned_loss=0.04742, over 971571.78 frames.], batch size: 21, lr: 5.35e-04
2022-05-04 15:48:49,627 INFO [train.py:715] (2/8) Epoch 3, batch 21250, loss[loss=0.1399, simple_loss=0.2111, pruned_loss=0.03441, over 4933.00 frames.], tot_loss[loss=0.162, simple_loss=0.2302, pruned_loss=0.04692, over 971483.97 frames.], batch size: 39, lr: 5.35e-04
2022-05-04 15:49:28,343 INFO [train.py:715] (2/8) Epoch 3, batch 21300, loss[loss=0.1422, simple_loss=0.2077, pruned_loss=0.0384, over 4868.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2297, pruned_loss=0.04671, over 972033.70 frames.], batch size: 20, lr: 5.35e-04
2022-05-04 15:50:10,529 INFO [train.py:715] (2/8) Epoch 3, batch 21350, loss[loss=0.1863, simple_loss=0.2512, pruned_loss=0.06072, over 4896.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2297, pruned_loss=0.04654, over 972816.49 frames.], batch size: 19, lr: 5.35e-04
2022-05-04 15:50:51,373 INFO [train.py:715] (2/8) Epoch 3, batch 21400, loss[loss=0.1762, simple_loss=0.2388, pruned_loss=0.0568, over 4869.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2298, pruned_loss=0.04643, over 972774.19 frames.], batch size: 38, lr: 5.35e-04
2022-05-04 15:51:30,338 INFO [train.py:715] (2/8) Epoch 3, batch 21450, loss[loss=0.1476, simple_loss=0.224, pruned_loss=0.0356, over 4985.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2304, pruned_loss=0.04641, over 973095.57 frames.], batch size: 28, lr: 5.35e-04
2022-05-04 15:52:08,621 INFO [train.py:715] (2/8) Epoch 3, batch 21500, loss[loss=0.1567, simple_loss=0.233, pruned_loss=0.04018, over 4890.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2304, pruned_loss=0.04625, over 972929.04 frames.], batch size: 22, lr: 5.34e-04
2022-05-04 15:52:47,661 INFO [train.py:715] (2/8) Epoch 3, batch 21550, loss[loss=0.1502, simple_loss=0.2277, pruned_loss=0.03631, over 4741.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04579, over 972763.06 frames.], batch size: 16, lr: 5.34e-04
2022-05-04 15:53:27,190 INFO [train.py:715] (2/8) Epoch 3, batch 21600, loss[loss=0.1525, simple_loss=0.2285, pruned_loss=0.03822, over 4944.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2295, pruned_loss=0.04602, over 972634.07 frames.], batch size: 21, lr: 5.34e-04
2022-05-04 15:54:06,105 INFO [train.py:715] (2/8) Epoch 3, batch 21650, loss[loss=0.1759, simple_loss=0.2483, pruned_loss=0.05174, over 4801.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2299, pruned_loss=0.04639, over 972660.48 frames.], batch size: 21, lr: 5.34e-04
2022-05-04 15:54:46,388 INFO [train.py:715] (2/8) Epoch 3, batch 21700, loss[loss=0.204, simple_loss=0.2642, pruned_loss=0.07186, over 4976.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2299, pruned_loss=0.04619, over 972618.53 frames.], batch size: 24, lr: 5.34e-04
2022-05-04 15:55:26,901 INFO [train.py:715] (2/8) Epoch 3, batch 21750, loss[loss=0.1604, simple_loss=0.2225, pruned_loss=0.0491, over 4844.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2299, pruned_loss=0.04642, over 972035.94 frames.], batch size: 30, lr: 5.34e-04
2022-05-04 15:56:06,027 INFO [train.py:715] (2/8) Epoch 3, batch 21800, loss[loss=0.1359, simple_loss=0.2083, pruned_loss=0.03172, over 4824.00 frames.], tot_loss[loss=0.161, simple_loss=0.2299, pruned_loss=0.04603, over 971661.79 frames.], batch size: 13, lr: 5.34e-04
2022-05-04 15:56:44,182 INFO [train.py:715] (2/8) Epoch 3, batch 21850, loss[loss=0.1789, simple_loss=0.2421, pruned_loss=0.05786, over 4801.00 frames.], tot_loss[loss=0.162, simple_loss=0.2305, pruned_loss=0.04674, over 972697.45 frames.], batch size: 21, lr: 5.34e-04
2022-05-04 15:57:22,932 INFO [train.py:715] (2/8) Epoch 3, batch 21900, loss[loss=0.1567, simple_loss=0.2359, pruned_loss=0.03878, over 4978.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2302, pruned_loss=0.04628, over 973222.80 frames.], batch size: 28, lr: 5.34e-04
2022-05-04 15:58:03,623 INFO [train.py:715] (2/8) Epoch 3, batch 21950, loss[loss=0.1315, simple_loss=0.2017, pruned_loss=0.03064, over 4805.00 frames.], tot_loss[loss=0.1611, simple_loss=0.23, pruned_loss=0.04608, over 973509.09 frames.], batch size: 12, lr: 5.34e-04
2022-05-04 15:58:43,258 INFO [train.py:715] (2/8) Epoch 3, batch 22000, loss[loss=0.1647, simple_loss=0.2279, pruned_loss=0.05075, over 4915.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2301, pruned_loss=0.0465, over 973599.54 frames.], batch size: 23, lr: 5.33e-04
2022-05-04 15:59:23,577 INFO [train.py:715] (2/8) Epoch 3, batch 22050, loss[loss=0.1699, simple_loss=0.2309, pruned_loss=0.05443, over 4859.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2312, pruned_loss=0.04714, over 972917.74 frames.], batch size: 30, lr: 5.33e-04
2022-05-04 16:00:04,303 INFO [train.py:715] (2/8) Epoch 3, batch 22100, loss[loss=0.1563, simple_loss=0.2255, pruned_loss=0.04356, over 4933.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2312, pruned_loss=0.04701, over 972131.92 frames.], batch size: 29, lr: 5.33e-04
2022-05-04 16:00:44,831 INFO [train.py:715] (2/8) Epoch 3, batch 22150, loss[loss=0.1828, simple_loss=0.2321, pruned_loss=0.06677, over 4975.00 frames.], tot_loss[loss=0.1626, simple_loss=0.231, pruned_loss=0.04708, over 972791.42 frames.], batch size: 31, lr: 5.33e-04
2022-05-04 16:01:24,046 INFO [train.py:715] (2/8) Epoch 3, batch 22200, loss[loss=0.1809, simple_loss=0.2646, pruned_loss=0.04864, over 4955.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2311, pruned_loss=0.04694, over 972641.62 frames.], batch size: 24, lr: 5.33e-04
2022-05-04 16:02:04,300 INFO [train.py:715] (2/8) Epoch 3, batch 22250, loss[loss=0.1681, simple_loss=0.2305, pruned_loss=0.05282, over 4969.00 frames.], tot_loss[loss=0.162, simple_loss=0.2305, pruned_loss=0.04671, over 972383.38 frames.], batch size: 15, lr: 5.33e-04
2022-05-04 16:02:45,556 INFO [train.py:715] (2/8) Epoch 3, batch 22300, loss[loss=0.1742, simple_loss=0.2429, pruned_loss=0.05269, over 4831.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2298, pruned_loss=0.04656, over 972335.22 frames.], batch size: 26, lr: 5.33e-04
2022-05-04 16:03:24,539 INFO [train.py:715] (2/8) Epoch 3, batch 22350, loss[loss=0.2025, simple_loss=0.2636, pruned_loss=0.07068, over 4971.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2302, pruned_loss=0.04662, over 972416.58 frames.], batch size: 35, lr: 5.33e-04
2022-05-04 16:04:04,619 INFO [train.py:715] (2/8) Epoch 3, batch 22400, loss[loss=0.187, simple_loss=0.242, pruned_loss=0.066, over 4807.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2299, pruned_loss=0.04675, over 972261.29 frames.], batch size: 14, lr: 5.33e-04
2022-05-04 16:04:45,522 INFO [train.py:715] (2/8) Epoch 3, batch 22450, loss[loss=0.1696, simple_loss=0.2406, pruned_loss=0.04924, over 4749.00 frames.], tot_loss[loss=0.1619, simple_loss=0.23, pruned_loss=0.04693, over 972199.78 frames.], batch size: 16, lr: 5.32e-04
2022-05-04 16:05:25,978 INFO [train.py:715] (2/8) Epoch 3, batch 22500, loss[loss=0.1803, simple_loss=0.2449, pruned_loss=0.05784, over 4910.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2301, pruned_loss=0.047, over 971619.89 frames.], batch size: 23, lr: 5.32e-04
2022-05-04 16:06:05,390 INFO [train.py:715] (2/8) Epoch 3, batch 22550, loss[loss=0.1793, simple_loss=0.2474, pruned_loss=0.05558, over 4957.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2303, pruned_loss=0.0471, over 970877.58 frames.], batch size: 39, lr: 5.32e-04
2022-05-04 16:06:45,637 INFO [train.py:715] (2/8) Epoch 3, batch 22600, loss[loss=0.1991, simple_loss=0.2656, pruned_loss=0.06626, over 4872.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2307, pruned_loss=0.04722, over 971392.96 frames.], batch size: 16, lr: 5.32e-04
2022-05-04 16:07:26,491 INFO [train.py:715] (2/8) Epoch 3, batch 22650, loss[loss=0.2326, simple_loss=0.2902, pruned_loss=0.0875, over 4760.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2305, pruned_loss=0.04711, over 971799.05 frames.], batch size: 17, lr: 5.32e-04
2022-05-04 16:08:06,300 INFO [train.py:715] (2/8) Epoch 3, batch 22700, loss[loss=0.1878, simple_loss=0.2519, pruned_loss=0.06186, over 4909.00 frames.], tot_loss[loss=0.1628, simple_loss=0.231, pruned_loss=0.04729, over 971242.16 frames.], batch size: 39, lr: 5.32e-04
2022-05-04 16:08:46,710 INFO [train.py:715] (2/8) Epoch 3, batch 22750, loss[loss=0.2011, simple_loss=0.2504, pruned_loss=0.07586, over 4907.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2316, pruned_loss=0.04786, over 971905.05 frames.], batch size: 17, lr: 5.32e-04
2022-05-04 16:09:27,109 INFO [train.py:715] (2/8) Epoch 3, batch 22800, loss[loss=0.1384, simple_loss=0.2113, pruned_loss=0.03274, over 4794.00 frames.], tot_loss[loss=0.163, simple_loss=0.2309, pruned_loss=0.04758, over 972008.90 frames.], batch size: 21, lr: 5.32e-04
2022-05-04 16:10:07,182 INFO [train.py:715] (2/8) Epoch 3, batch 22850, loss[loss=0.1557, simple_loss=0.2262, pruned_loss=0.04259, over 4701.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2306, pruned_loss=0.04746, over 971433.06 frames.], batch size: 15, lr: 5.32e-04
2022-05-04 16:10:46,905 INFO [train.py:715] (2/8) Epoch 3, batch 22900, loss[loss=0.1631, simple_loss=0.2215, pruned_loss=0.05231, over 4934.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2304, pruned_loss=0.04735, over 971013.98 frames.], batch size: 23, lr: 5.32e-04
2022-05-04 16:11:27,332 INFO [train.py:715] (2/8) Epoch 3, batch 22950, loss[loss=0.1597, simple_loss=0.2311, pruned_loss=0.04415, over 4834.00 frames.], tot_loss[loss=0.162, simple_loss=0.2296, pruned_loss=0.04722, over 971140.36 frames.], batch size: 13, lr: 5.31e-04
2022-05-04 16:12:08,437 INFO [train.py:715] (2/8) Epoch 3, batch 23000, loss[loss=0.1724, simple_loss=0.2319, pruned_loss=0.05648, over 4913.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2302, pruned_loss=0.04808, over 971593.15 frames.], batch size: 18, lr: 5.31e-04
2022-05-04 16:12:48,303 INFO [train.py:715] (2/8) Epoch 3, batch 23050, loss[loss=0.12, simple_loss=0.1966, pruned_loss=0.02175, over 4975.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2295, pruned_loss=0.04739, over 972074.87 frames.], batch size: 28, lr: 5.31e-04
2022-05-04 16:13:28,632 INFO [train.py:715] (2/8) Epoch 3, batch 23100, loss[loss=0.1382, simple_loss=0.2098, pruned_loss=0.03329, over 4828.00 frames.], tot_loss[loss=0.162, simple_loss=0.2297, pruned_loss=0.04709, over 972636.68 frames.], batch size: 13, lr: 5.31e-04
2022-05-04 16:14:09,398 INFO [train.py:715] (2/8) Epoch 3, batch 23150, loss[loss=0.143, simple_loss=0.2182, pruned_loss=0.03395, over 4985.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2293, pruned_loss=0.04662, over 972171.47 frames.], batch size: 35, lr: 5.31e-04
2022-05-04 16:14:49,971 INFO [train.py:715] (2/8) Epoch 3, batch 23200, loss[loss=0.1287, simple_loss=0.1967, pruned_loss=0.0304, over 4830.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2285, pruned_loss=0.04562, over 972364.67 frames.], batch size: 13, lr: 5.31e-04
2022-05-04 16:15:29,496 INFO [train.py:715] (2/8) Epoch 3, batch 23250, loss[loss=0.1508, simple_loss=0.2204, pruned_loss=0.04059, over 4896.00 frames.], tot_loss[loss=0.161, simple_loss=0.2296, pruned_loss=0.04614, over 971925.72 frames.], batch size: 19, lr: 5.31e-04
2022-05-04 16:16:10,268 INFO [train.py:715] (2/8) Epoch 3, batch 23300, loss[loss=0.2167, simple_loss=0.273, pruned_loss=0.08019, over 4869.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2305, pruned_loss=0.04681, over 971599.74 frames.], batch size: 32, lr: 5.31e-04
2022-05-04 16:16:49,872 INFO [train.py:715] (2/8) Epoch 3, batch 23350, loss[loss=0.1438, simple_loss=0.2186, pruned_loss=0.03448, over 4981.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2302, pruned_loss=0.04634, over 971650.66 frames.], batch size: 28, lr: 5.31e-04
2022-05-04 16:17:27,677 INFO [train.py:715] (2/8) Epoch 3, batch 23400, loss[loss=0.2086, simple_loss=0.2649, pruned_loss=0.0761, over 4851.00 frames.], tot_loss[loss=0.1614, simple_loss=0.23, pruned_loss=0.04634, over 971990.94 frames.], batch size: 30, lr: 5.30e-04
2022-05-04 16:18:06,223 INFO [train.py:715] (2/8) Epoch 3, batch 23450, loss[loss=0.1615, simple_loss=0.2322, pruned_loss=0.04536, over 4823.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2297, pruned_loss=0.04631, over 971684.89 frames.], batch size: 25, lr: 5.30e-04
2022-05-04 16:18:44,912 INFO [train.py:715] (2/8) Epoch 3, batch 23500, loss[loss=0.1476, simple_loss=0.212, pruned_loss=0.04158, over 4819.00 frames.], tot_loss[loss=0.161, simple_loss=0.2298, pruned_loss=0.04613, over 971858.56 frames.], batch size: 13, lr: 5.30e-04
2022-05-04 16:19:24,105 INFO [train.py:715] (2/8) Epoch 3, batch 23550, loss[loss=0.1511, simple_loss=0.2168, pruned_loss=0.0427, over 4766.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2301, pruned_loss=0.04653, over 970856.07 frames.], batch size: 17, lr: 5.30e-04
2022-05-04 16:20:05,335 INFO [train.py:715] (2/8) Epoch 3, batch 23600, loss[loss=0.1512, simple_loss=0.2228, pruned_loss=0.03978, over 4799.00 frames.], tot_loss[loss=0.162, simple_loss=0.2304, pruned_loss=0.04675, over 970995.99 frames.], batch size: 18, lr: 5.30e-04
2022-05-04 16:20:44,862 INFO [train.py:715] (2/8) Epoch 3, batch 23650, loss[loss=0.1422, simple_loss=0.222, pruned_loss=0.03115, over 4905.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2301, pruned_loss=0.04673, over 971715.31 frames.], batch size: 19, lr: 5.30e-04
2022-05-04 16:21:24,818 INFO [train.py:715] (2/8) Epoch 3, batch 23700, loss[loss=0.1405, simple_loss=0.2004, pruned_loss=0.0403, over 4789.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2302, pruned_loss=0.04658, over 972900.90 frames.], batch size: 17, lr: 5.30e-04
2022-05-04 16:22:03,569 INFO [train.py:715] (2/8) Epoch 3, batch 23750, loss[loss=0.1382, simple_loss=0.2197, pruned_loss=0.02836, over 4785.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2308, pruned_loss=0.0469, over 973061.22 frames.], batch size: 14, lr: 5.30e-04
2022-05-04 16:22:43,184 INFO [train.py:715] (2/8) Epoch 3, batch 23800, loss[loss=0.1757, simple_loss=0.2436, pruned_loss=0.05394, over 4762.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2305, pruned_loss=0.04699, over 972320.00 frames.], batch size: 16, lr: 5.30e-04
2022-05-04 16:23:22,781 INFO [train.py:715] (2/8) Epoch 3, batch 23850, loss[loss=0.1845, simple_loss=0.2484, pruned_loss=0.06029, over 4961.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2317, pruned_loss=0.04779, over 971890.42 frames.], batch size: 39, lr: 5.30e-04
2022-05-04 16:24:02,497 INFO [train.py:715] (2/8) Epoch 3, batch 23900, loss[loss=0.1678, simple_loss=0.2362, pruned_loss=0.04976, over 4982.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2309, pruned_loss=0.04699, over 971638.46 frames.], batch size: 35, lr: 5.29e-04
2022-05-04 16:24:41,551 INFO [train.py:715] (2/8) Epoch 3, batch 23950, loss[loss=0.1488, simple_loss=0.2136, pruned_loss=0.04198, over 4745.00 frames.], tot_loss[loss=0.1616, simple_loss=0.23, pruned_loss=0.04658, over 971734.84 frames.], batch size: 19, lr: 5.29e-04
2022-05-04 16:25:20,399 INFO [train.py:715] (2/8) Epoch 3, batch 24000, loss[loss=0.1753, simple_loss=0.2538, pruned_loss=0.04842, over 4945.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2294, pruned_loss=0.0465, over 972146.83 frames.], batch size: 29, lr: 5.29e-04
2022-05-04 16:25:20,399 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 16:25:32,861 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1132, simple_loss=0.1992, pruned_loss=0.0136, over 914524.00 frames.
2022-05-04 16:26:12,213 INFO [train.py:715] (2/8) Epoch 3, batch 24050, loss[loss=0.1409, simple_loss=0.2093, pruned_loss=0.03626, over 4942.00 frames.], tot_loss[loss=0.161, simple_loss=0.2292, pruned_loss=0.04636, over 971511.84 frames.], batch size: 29, lr: 5.29e-04
2022-05-04 16:26:52,062 INFO [train.py:715] (2/8) Epoch 3, batch 24100, loss[loss=0.1411, simple_loss=0.207, pruned_loss=0.0376, over 4849.00 frames.], tot_loss[loss=0.161, simple_loss=0.2293, pruned_loss=0.0464, over 971120.73 frames.], batch size: 32, lr: 5.29e-04
2022-05-04 16:27:30,861 INFO [train.py:715] (2/8) Epoch 3, batch 24150, loss[loss=0.1473, simple_loss=0.2158, pruned_loss=0.03938, over 4759.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2289, pruned_loss=0.0461, over 970792.88 frames.], batch size: 19, lr: 5.29e-04
2022-05-04 16:28:10,106 INFO [train.py:715] (2/8) Epoch 3, batch 24200, loss[loss=0.158, simple_loss=0.2344, pruned_loss=0.04077, over 4773.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2292, pruned_loss=0.04628, over 971133.29 frames.], batch size: 14, lr: 5.29e-04
2022-05-04 16:28:50,502 INFO [train.py:715] (2/8) Epoch 3, batch 24250, loss[loss=0.1896, simple_loss=0.2536, pruned_loss=0.06274, over 4990.00 frames.], tot_loss[loss=0.1606, simple_loss=0.229, pruned_loss=0.04606, over 971673.43 frames.], batch size: 14, lr: 5.29e-04
2022-05-04 16:29:30,765 INFO [train.py:715] (2/8) Epoch 3, batch 24300, loss[loss=0.1673, simple_loss=0.2299, pruned_loss=0.05237, over 4806.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2287, pruned_loss=0.04554, over 972077.62 frames.], batch size: 21, lr: 5.29e-04
2022-05-04 16:30:10,088 INFO [train.py:715] (2/8) Epoch 3, batch 24350, loss[loss=0.1493, simple_loss=0.2214, pruned_loss=0.03856, over 4798.00 frames.], tot_loss[loss=0.1592, simple_loss=0.228, pruned_loss=0.04518, over 971725.07 frames.], batch size: 24, lr: 5.29e-04
2022-05-04 16:30:49,733 INFO [train.py:715] (2/8) Epoch 3, batch 24400, loss[loss=0.168, simple_loss=0.2342, pruned_loss=0.05092, over 4823.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2283, pruned_loss=0.04524, over 972223.53 frames.], batch size: 15, lr: 5.28e-04
2022-05-04 16:31:29,802 INFO [train.py:715] (2/8) Epoch 3, batch 24450, loss[loss=0.1443, simple_loss=0.2127, pruned_loss=0.03797, over 4924.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2276, pruned_loss=0.04484, over 972467.36 frames.], batch size: 23, lr: 5.28e-04
2022-05-04 16:32:09,116 INFO [train.py:715] (2/8) Epoch 3, batch 24500, loss[loss=0.1494, simple_loss=0.2174, pruned_loss=0.04073, over 4876.00 frames.], tot_loss[loss=0.159, simple_loss=0.2279, pruned_loss=0.04506, over 973124.16 frames.], batch size: 16, lr: 5.28e-04
2022-05-04 16:32:48,514 INFO [train.py:715] (2/8) Epoch 3, batch 24550, loss[loss=0.1527, simple_loss=0.2211, pruned_loss=0.04218, over 4800.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2282, pruned_loss=0.04506, over 974141.59 frames.], batch size: 24, lr: 5.28e-04
2022-05-04 16:33:28,753 INFO [train.py:715] (2/8) Epoch 3, batch 24600, loss[loss=0.1565, simple_loss=0.2198, pruned_loss=0.04665, over 4985.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2283, pruned_loss=0.04517, over 974340.04 frames.], batch size: 14, lr: 5.28e-04
2022-05-04 16:34:08,288 INFO [train.py:715] (2/8) Epoch 3, batch 24650, loss[loss=0.1605, simple_loss=0.2298, pruned_loss=0.04559, over 4894.00 frames.], tot_loss[loss=0.16, simple_loss=0.2287, pruned_loss=0.0457, over 973603.53 frames.], batch size: 19, lr: 5.28e-04
2022-05-04 16:34:47,791 INFO [train.py:715] (2/8) Epoch 3, batch 24700, loss[loss=0.1424, simple_loss=0.222, pruned_loss=0.03145, over 4963.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2298, pruned_loss=0.04598, over 973740.77 frames.], batch size: 24, lr: 5.28e-04
2022-05-04 16:35:26,410 INFO [train.py:715] (2/8) Epoch 3, batch 24750, loss[loss=0.1797, simple_loss=0.241, pruned_loss=0.05914, over 4803.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2309, pruned_loss=0.04685, over 973455.37 frames.], batch size: 25, lr: 5.28e-04
2022-05-04 16:36:07,061 INFO [train.py:715] (2/8) Epoch 3, batch 24800, loss[loss=0.1452, simple_loss=0.2246, pruned_loss=0.03289, over 4805.00 frames.], tot_loss[loss=0.1624, simple_loss=0.231, pruned_loss=0.04688, over 972656.06 frames.], batch size: 25, lr: 5.28e-04
2022-05-04 16:36:46,784 INFO [train.py:715] (2/8) Epoch 3, batch 24850, loss[loss=0.1802, simple_loss=0.2498, pruned_loss=0.05529, over 4932.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2301, pruned_loss=0.04621, over 973513.80 frames.], batch size: 23, lr: 5.28e-04
2022-05-04 16:37:25,565 INFO [train.py:715] (2/8) Epoch 3, batch 24900, loss[loss=0.1707, simple_loss=0.232, pruned_loss=0.05473, over 4894.00 frames.], tot_loss[loss=0.1622, simple_loss=0.231, pruned_loss=0.04672, over 973110.26 frames.], batch size: 16, lr: 5.27e-04
2022-05-04 16:38:05,478 INFO [train.py:715] (2/8) Epoch 3, batch 24950, loss[loss=0.1533, simple_loss=0.2312, pruned_loss=0.03769, over 4928.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2317, pruned_loss=0.04693, over 973567.71 frames.], batch size: 29, lr: 5.27e-04
2022-05-04 16:38:45,653 INFO [train.py:715] (2/8) Epoch 3, batch 25000, loss[loss=0.1466, simple_loss=0.2104, pruned_loss=0.04142, over 4751.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2315, pruned_loss=0.04669, over 972935.42 frames.], batch size: 12, lr: 5.27e-04
2022-05-04 16:39:25,199 INFO [train.py:715] (2/8) Epoch 3, batch 25050, loss[loss=0.1581, simple_loss=0.2302, pruned_loss=0.04304, over 4844.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2308, pruned_loss=0.04611, over 972229.11 frames.], batch size: 15, lr: 5.27e-04
2022-05-04 16:40:04,367 INFO [train.py:715] (2/8) Epoch 3, batch 25100, loss[loss=0.1545, simple_loss=0.2283, pruned_loss=0.04041, over 4938.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2309, pruned_loss=0.04623, over 972533.60 frames.], batch size: 29, lr: 5.27e-04
2022-05-04 16:40:44,394 INFO [train.py:715] (2/8) Epoch 3, batch 25150, loss[loss=0.147, simple_loss=0.2157, pruned_loss=0.03911, over 4804.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2314, pruned_loss=0.04622, over 972135.11 frames.], batch size: 24, lr: 5.27e-04
2022-05-04 16:41:23,892 INFO [train.py:715] (2/8) Epoch 3, batch 25200, loss[loss=0.1654, simple_loss=0.2331, pruned_loss=0.0489, over 4955.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2301, pruned_loss=0.04537, over 973218.14 frames.], batch size: 39, lr: 5.27e-04
2022-05-04 16:42:03,024 INFO [train.py:715] (2/8) Epoch 3, batch 25250, loss[loss=0.1383, simple_loss=0.1956, pruned_loss=0.04055, over 4758.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2295, pruned_loss=0.04514, over 971972.98 frames.], batch size: 12, lr: 5.27e-04
2022-05-04 16:42:43,126 INFO [train.py:715] (2/8) Epoch 3, batch 25300, loss[loss=0.1568, simple_loss=0.2209, pruned_loss=0.04637, over 4820.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2298, pruned_loss=0.04565, over 971823.50 frames.], batch size: 25, lr: 5.27e-04
2022-05-04 16:43:22,954 INFO [train.py:715] (2/8) Epoch 3, batch 25350, loss[loss=0.155, simple_loss=0.2093, pruned_loss=0.05032, over 4989.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2306, pruned_loss=0.04633, over 972374.82 frames.], batch size: 14, lr: 5.26e-04
2022-05-04 16:44:02,967 INFO [train.py:715] (2/8) Epoch 3, batch 25400, loss[loss=0.1305, simple_loss=0.2012, pruned_loss=0.02986, over 4980.00 frames.], tot_loss[loss=0.162, simple_loss=0.2306, pruned_loss=0.04672, over 973401.28 frames.], batch size: 28, lr: 5.26e-04
2022-05-04 16:44:42,163 INFO [train.py:715] (2/8) Epoch 3, batch 25450, loss[loss=0.1584, simple_loss=0.2297, pruned_loss=0.04356, over 4959.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2303, pruned_loss=0.04689, over 972704.85 frames.], batch size: 15, lr: 5.26e-04
2022-05-04 16:45:22,356 INFO [train.py:715] (2/8) Epoch 3, batch 25500, loss[loss=0.1858, simple_loss=0.2476, pruned_loss=0.06198, over 4700.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2304, pruned_loss=0.04709, over 972671.90 frames.], batch size: 15, lr: 5.26e-04
2022-05-04 16:46:02,185 INFO [train.py:715] (2/8) Epoch 3, batch 25550, loss[loss=0.1301, simple_loss=0.2096, pruned_loss=0.02524, over 4959.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2296, pruned_loss=0.04655, over 971900.84 frames.], batch size: 24, lr: 5.26e-04
2022-05-04 16:46:41,643 INFO [train.py:715] (2/8) Epoch 3, batch 25600, loss[loss=0.1856, simple_loss=0.2381, pruned_loss=0.06648, over 4827.00 frames.], tot_loss[loss=0.1616, simple_loss=0.23, pruned_loss=0.04662, over 971585.10 frames.], batch size: 15, lr: 5.26e-04
2022-05-04 16:47:22,025 INFO [train.py:715] (2/8) Epoch 3, batch 25650, loss[loss=0.156, simple_loss=0.2237, pruned_loss=0.04412, over 4982.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2301, pruned_loss=0.04674, over 972245.90 frames.], batch size: 28, lr: 5.26e-04
2022-05-04 16:48:02,222 INFO [train.py:715] (2/8) Epoch 3, batch 25700, loss[loss=0.1455, simple_loss=0.2138, pruned_loss=0.03859, over 4913.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2301, pruned_loss=0.04649, over 972683.34 frames.], batch size: 19, lr: 5.26e-04
2022-05-04 16:48:41,538 INFO [train.py:715] (2/8) Epoch 3, batch 25750, loss[loss=0.1434, simple_loss=0.2202, pruned_loss=0.03326, over 4977.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2297, pruned_loss=0.04585, over 972705.67 frames.], batch size: 28, lr: 5.26e-04
2022-05-04 16:49:21,105 INFO [train.py:715] (2/8) Epoch 3, batch 25800, loss[loss=0.1441, simple_loss=0.2146, pruned_loss=0.03679, over 4767.00 frames.], tot_loss[loss=0.1613, simple_loss=0.23, pruned_loss=0.0463, over 972622.52 frames.], batch size: 14, lr: 5.26e-04
2022-05-04 16:50:01,077 INFO [train.py:715] (2/8) Epoch 3, batch 25850, loss[loss=0.182, simple_loss=0.246, pruned_loss=0.05899, over 4746.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2293, pruned_loss=0.04589, over 973021.49 frames.], batch size: 16, lr: 5.25e-04
2022-05-04 16:50:39,398 INFO [train.py:715] (2/8) Epoch 3, batch 25900, loss[loss=0.1527, simple_loss=0.2233, pruned_loss=0.04111, over 4977.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2302, pruned_loss=0.04634, over 973263.45 frames.], batch size: 25, lr: 5.25e-04
2022-05-04 16:51:18,328 INFO [train.py:715] (2/8) Epoch 3, batch 25950, loss[loss=0.1758, simple_loss=0.2404, pruned_loss=0.05553, over 4887.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2293, pruned_loss=0.04588, over 972189.46 frames.], batch size: 16, lr: 5.25e-04
2022-05-04 16:51:58,432 INFO [train.py:715] (2/8) Epoch 3, batch 26000, loss[loss=0.1754, simple_loss=0.2444, pruned_loss=0.05323, over 4747.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2298, pruned_loss=0.04587, over 971395.38 frames.], batch size: 19, lr: 5.25e-04
2022-05-04 16:52:37,677 INFO [train.py:715] (2/8) Epoch 3, batch 26050, loss[loss=0.1629, simple_loss=0.2267, pruned_loss=0.04955, over 4977.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2284, pruned_loss=0.04534, over 970774.67 frames.], batch size: 28, lr: 5.25e-04
2022-05-04 16:53:16,013 INFO [train.py:715] (2/8) Epoch 3, batch 26100, loss[loss=0.1403, simple_loss=0.2123, pruned_loss=0.03412, over 4930.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2288, pruned_loss=0.04568, over 971528.23 frames.], batch size: 18, lr: 5.25e-04
2022-05-04 16:53:55,502 INFO [train.py:715] (2/8) Epoch 3, batch 26150, loss[loss=0.1641, simple_loss=0.2387, pruned_loss=0.04468, over 4817.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2285, pruned_loss=0.0455, over 971063.24 frames.], batch size: 25, lr: 5.25e-04
2022-05-04 16:54:35,542 INFO [train.py:715] (2/8) Epoch 3, batch 26200, loss[loss=0.1734, simple_loss=0.2475, pruned_loss=0.0496, over 4918.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2285, pruned_loss=0.04511, over 971634.65 frames.], batch size: 29, lr: 5.25e-04
2022-05-04 16:55:13,647 INFO [train.py:715] (2/8) Epoch 3, batch 26250, loss[loss=0.1557, simple_loss=0.2252, pruned_loss=0.0431, over 4934.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2287, pruned_loss=0.04502, over 971312.87 frames.], batch size: 23, lr: 5.25e-04
2022-05-04 16:55:52,856 INFO [train.py:715] (2/8) Epoch 3, batch 26300, loss[loss=0.1583, simple_loss=0.2274, pruned_loss=0.04461, over 4966.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2293, pruned_loss=0.04547, over 971949.39 frames.], batch size: 35, lr: 5.25e-04
2022-05-04 16:56:32,818 INFO [train.py:715] (2/8) Epoch 3, batch 26350, loss[loss=0.1807, simple_loss=0.2473, pruned_loss=0.05703, over 4905.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2298, pruned_loss=0.04595, over 972570.17 frames.], batch size: 19, lr: 5.24e-04
2022-05-04 16:57:12,182 INFO [train.py:715] (2/8) Epoch 3, batch 26400, loss[loss=0.1327, simple_loss=0.201, pruned_loss=0.03215, over 4898.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2304, pruned_loss=0.04666, over 972770.56 frames.], batch size: 19, lr: 5.24e-04
2022-05-04 16:57:51,174 INFO [train.py:715] (2/8) Epoch 3, batch 26450, loss[loss=0.1851, simple_loss=0.2458, pruned_loss=0.06225, over 4946.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2302, pruned_loss=0.04652, over 972951.00 frames.], batch size: 21, lr: 5.24e-04
2022-05-04 16:58:30,425 INFO [train.py:715] (2/8) Epoch 3, batch 26500, loss[loss=0.1649, simple_loss=0.237, pruned_loss=0.04636, over 4931.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2299, pruned_loss=0.04579, over 972741.13 frames.], batch size: 23, lr: 5.24e-04
2022-05-04 16:59:09,909 INFO [train.py:715] (2/8) Epoch 3, batch 26550, loss[loss=0.2009, simple_loss=0.2578, pruned_loss=0.07206, over 4955.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2305, pruned_loss=0.04661, over 973705.46 frames.], batch size: 35, lr: 5.24e-04
2022-05-04 16:59:48,114 INFO [train.py:715] (2/8) Epoch 3, batch 26600, loss[loss=0.1516, simple_loss=0.222, pruned_loss=0.04062, over 4797.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2296, pruned_loss=0.04573, over 973788.88 frames.], batch size: 12, lr: 5.24e-04
2022-05-04 17:00:27,331 INFO [train.py:715] (2/8) Epoch 3, batch 26650, loss[loss=0.1583, simple_loss=0.2213, pruned_loss=0.04767, over 4767.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2289, pruned_loss=0.04564, over 972083.38 frames.], batch size: 18, lr: 5.24e-04
2022-05-04 17:01:07,872 INFO [train.py:715] (2/8) Epoch 3, batch 26700, loss[loss=0.152, simple_loss=0.2192, pruned_loss=0.04244, over 4990.00 frames.], tot_loss[loss=0.16, simple_loss=0.2286, pruned_loss=0.04574, over 972495.16 frames.], batch size: 28, lr: 5.24e-04
2022-05-04 17:01:47,353 INFO [train.py:715] (2/8) Epoch 3, batch 26750, loss[loss=0.1411, simple_loss=0.2085, pruned_loss=0.03681, over 4925.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2291, pruned_loss=0.04617, over 972983.91 frames.], batch size: 17, lr: 5.24e-04
2022-05-04 17:02:26,599 INFO [train.py:715] (2/8) Epoch 3, batch 26800, loss[loss=0.1432, simple_loss=0.2122, pruned_loss=0.03708, over 4911.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2305, pruned_loss=0.04628, over 973444.35 frames.], batch size: 17, lr: 5.24e-04
2022-05-04 17:03:06,721 INFO [train.py:715] (2/8) Epoch 3, batch 26850, loss[loss=0.1561, simple_loss=0.2229, pruned_loss=0.04461, over 4760.00 frames.], tot_loss[loss=0.1601, simple_loss=0.229, pruned_loss=0.04562, over 973045.60 frames.], batch size: 18, lr: 5.23e-04
2022-05-04 17:03:47,106 INFO [train.py:715] (2/8) Epoch 3, batch 26900, loss[loss=0.1512, simple_loss=0.205, pruned_loss=0.04871, over 4927.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2282, pruned_loss=0.04542, over 973064.25 frames.], batch size: 17, lr: 5.23e-04
2022-05-04 17:04:26,663 INFO [train.py:715] (2/8) Epoch 3, batch 26950, loss[loss=0.1908, simple_loss=0.2762, pruned_loss=0.05272, over 4872.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2289, pruned_loss=0.0459, over 973172.29 frames.], batch size: 16, lr: 5.23e-04
2022-05-04 17:05:05,427 INFO [train.py:715] (2/8) Epoch 3, batch 27000, loss[loss=0.1762, simple_loss=0.2475, pruned_loss=0.05248, over 4862.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2295, pruned_loss=0.0459, over 973181.38 frames.], batch size: 20, lr: 5.23e-04
2022-05-04 17:05:05,428 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 17:05:14,908 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1134, simple_loss=0.1995, pruned_loss=0.01366, over 914524.00 frames.
2022-05-04 17:05:54,549 INFO [train.py:715] (2/8) Epoch 3, batch 27050, loss[loss=0.1636, simple_loss=0.2522, pruned_loss=0.03747, over 4927.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2289, pruned_loss=0.04602, over 972743.91 frames.], batch size: 29, lr: 5.23e-04
2022-05-04 17:06:34,873 INFO [train.py:715] (2/8) Epoch 3, batch 27100, loss[loss=0.1777, simple_loss=0.2346, pruned_loss=0.06038, over 4806.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2292, pruned_loss=0.04605, over 971773.39 frames.], batch size: 25, lr: 5.23e-04
2022-05-04 17:07:14,169 INFO [train.py:715] (2/8) Epoch 3, batch 27150, loss[loss=0.1283, simple_loss=0.2037, pruned_loss=0.02646, over 4825.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2299, pruned_loss=0.04611, over 972753.83 frames.], batch size: 27, lr: 5.23e-04
2022-05-04 17:07:52,930 INFO [train.py:715] (2/8) Epoch 3, batch 27200, loss[loss=0.1679, simple_loss=0.2345, pruned_loss=0.05064, over 4928.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2292, pruned_loss=0.04577, over 973734.58 frames.], batch size: 23, lr: 5.23e-04
2022-05-04 17:08:32,669 INFO [train.py:715] (2/8) Epoch 3, batch 27250, loss[loss=0.1329, simple_loss=0.2087, pruned_loss=0.0285, over 4792.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2287, pruned_loss=0.04557, over 973422.60 frames.], batch size: 24, lr: 5.23e-04
2022-05-04 17:09:12,366 INFO [train.py:715] (2/8) Epoch 3, batch 27300, loss[loss=0.1827, simple_loss=0.2487, pruned_loss=0.05834, over 4916.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2272, pruned_loss=0.04453, over 973001.82 frames.], batch size: 23, lr: 5.23e-04
2022-05-04 17:09:51,022 INFO [train.py:715] (2/8) Epoch 3, batch 27350, loss[loss=0.174, simple_loss=0.2504, pruned_loss=0.04876, over 4978.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2274, pruned_loss=0.0448, over 973174.13 frames.], batch size: 15, lr: 5.22e-04
2022-05-04 17:10:30,270 INFO [train.py:715] (2/8) Epoch 3, batch 27400, loss[loss=0.1728, simple_loss=0.248, pruned_loss=0.04885, over 4883.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2282, pruned_loss=0.04566, over 972832.66 frames.], batch size: 22, lr: 5.22e-04
2022-05-04 17:11:10,417 INFO [train.py:715] (2/8) Epoch 3, batch 27450, loss[loss=0.1606, simple_loss=0.2335, pruned_loss=0.04383, over 4771.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2275, pruned_loss=0.04535, over 972813.53 frames.], batch size: 19, lr: 5.22e-04
2022-05-04 17:11:49,743 INFO [train.py:715] (2/8) Epoch 3, batch 27500, loss[loss=0.1781, simple_loss=0.2343, pruned_loss=0.06092, over 4864.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2286, pruned_loss=0.04556, over 973286.05 frames.], batch size: 32, lr: 5.22e-04
2022-05-04 17:12:28,641 INFO [train.py:715] (2/8) Epoch 3, batch 27550, loss[loss=0.1988, simple_loss=0.2682, pruned_loss=0.06467, over 4893.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2292, pruned_loss=0.04608, over 972547.38 frames.], batch size: 19, lr: 5.22e-04
2022-05-04 17:13:08,354 INFO [train.py:715] (2/8) Epoch 3, batch 27600, loss[loss=0.1715, simple_loss=0.2395, pruned_loss=0.05171, over 4799.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2287, pruned_loss=0.04554, over 972383.72 frames.], batch size: 21, lr: 5.22e-04
2022-05-04 17:13:48,000 INFO [train.py:715] (2/8) Epoch 3, batch 27650, loss[loss=0.1636, simple_loss=0.23, pruned_loss=0.04863, over 4956.00 frames.], tot_loss[loss=0.1606, simple_loss=0.229, pruned_loss=0.04613, over 972192.52 frames.], batch size: 15, lr: 5.22e-04
2022-05-04 17:14:26,623 INFO [train.py:715] (2/8) Epoch 3, batch 27700, loss[loss=0.1635, simple_loss=0.2251, pruned_loss=0.05094, over 4857.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2291, pruned_loss=0.04613, over 973192.00 frames.], batch size: 30, lr: 5.22e-04
2022-05-04 17:15:06,396 INFO [train.py:715] (2/8) Epoch 3, batch 27750, loss[loss=0.139, simple_loss=0.2066, pruned_loss=0.03573, over 4932.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2292, pruned_loss=0.04598, over 973318.85 frames.], batch size: 29, lr: 5.22e-04
2022-05-04 17:15:46,351 INFO [train.py:715] (2/8) Epoch 3, batch 27800, loss[loss=0.1781, simple_loss=0.2473, pruned_loss=0.05448, over 4807.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2294, pruned_loss=0.04608, over 972948.97 frames.], batch size: 25, lr: 5.22e-04
2022-05-04 17:16:25,745 INFO [train.py:715] (2/8) Epoch 3, batch 27850, loss[loss=0.1855, simple_loss=0.26, pruned_loss=0.05553, over 4694.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2289, pruned_loss=0.0458, over 973720.63 frames.], batch size: 15, lr: 5.21e-04
2022-05-04 17:17:04,210 INFO [train.py:715] (2/8) Epoch 3, batch 27900, loss[loss=0.1696, simple_loss=0.2358, pruned_loss=0.05171, over 4814.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2296, pruned_loss=0.04636, over 972858.20 frames.], batch size: 24, lr: 5.21e-04
2022-05-04 17:17:43,816 INFO [train.py:715] (2/8) Epoch 3, batch 27950, loss[loss=0.1475, simple_loss=0.2238, pruned_loss=0.0356, over 4777.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2293, pruned_loss=0.04625, over 973125.88 frames.], batch size: 18, lr: 5.21e-04
2022-05-04 17:18:23,715 INFO [train.py:715] (2/8) Epoch 3, batch 28000, loss[loss=0.1708, simple_loss=0.2432, pruned_loss=0.04922, over 4898.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2293, pruned_loss=0.04611, over 972635.52 frames.], batch size: 39, lr: 5.21e-04
2022-05-04 17:19:02,277 INFO [train.py:715] (2/8) Epoch 3, batch 28050, loss[loss=0.1457, simple_loss=0.2206, pruned_loss=0.03541, over 4689.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2279, pruned_loss=0.04533, over 972005.78 frames.], batch size: 15, lr: 5.21e-04
2022-05-04 17:19:41,710 INFO [train.py:715] (2/8) Epoch 3, batch 28100, loss[loss=0.1699, simple_loss=0.237, pruned_loss=0.05139, over 4809.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2291, pruned_loss=0.04627, over 972357.47 frames.], batch size: 25, lr: 5.21e-04
2022-05-04 17:20:21,589 INFO [train.py:715] (2/8) Epoch 3, batch 28150, loss[loss=0.1594, simple_loss=0.2279, pruned_loss=0.04548, over 4939.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2296, pruned_loss=0.04683, over 972684.90 frames.], batch size: 29, lr: 5.21e-04
2022-05-04 17:21:00,811 INFO [train.py:715] (2/8) Epoch 3, batch 28200, loss[loss=0.1268, simple_loss=0.1953, pruned_loss=0.02911, over 4796.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2291, pruned_loss=0.04624, over 972090.12 frames.], batch size: 24, lr: 5.21e-04
2022-05-04 17:21:39,659 INFO [train.py:715] (2/8) Epoch 3, batch 28250, loss[loss=0.1627, simple_loss=0.2257, pruned_loss=0.04985, over 4968.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2295, pruned_loss=0.04669, over 971964.96 frames.], batch size: 35, lr: 5.21e-04
2022-05-04 17:22:18,999 INFO [train.py:715] (2/8) Epoch 3, batch 28300, loss[loss=0.1521, simple_loss=0.2356, pruned_loss=0.03425, over 4954.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2297, pruned_loss=0.04664, over 972767.84 frames.], batch size: 24, lr: 5.21e-04
2022-05-04 17:22:58,003 INFO [train.py:715] (2/8) Epoch 3, batch 28350, loss[loss=0.1902, simple_loss=0.2528, pruned_loss=0.06384, over 4838.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2286, pruned_loss=0.04614, over 972635.51 frames.], batch size: 32, lr: 5.21e-04
2022-05-04 17:23:37,194 INFO [train.py:715] (2/8) Epoch 3, batch 28400, loss[loss=0.139, simple_loss=0.2088, pruned_loss=0.03464, over 4781.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2295, pruned_loss=0.04662, over 971928.64 frames.], batch size: 17, lr: 5.20e-04
2022-05-04 17:24:15,827 INFO [train.py:715] (2/8) Epoch 3, batch 28450, loss[loss=0.1808, simple_loss=0.2395, pruned_loss=0.06099, over 4847.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2295, pruned_loss=0.04643, over 972046.74 frames.], batch size: 32, lr: 5.20e-04
2022-05-04 17:24:55,565 INFO [train.py:715] (2/8) Epoch 3, batch 28500, loss[loss=0.1436, simple_loss=0.2088, pruned_loss=0.03917, over 4964.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2293, pruned_loss=0.04624, over 972212.48 frames.], batch size: 14, lr: 5.20e-04
2022-05-04 17:25:34,507 INFO [train.py:715] (2/8) Epoch 3, batch 28550, loss[loss=0.1643, simple_loss=0.2402, pruned_loss=0.04418, over 4973.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2287, pruned_loss=0.0459, over 972060.96 frames.], batch size: 28, lr: 5.20e-04
2022-05-04 17:26:13,421 INFO [train.py:715] (2/8) Epoch 3, batch 28600, loss[loss=0.188, simple_loss=0.2559, pruned_loss=0.06007, over 4780.00 frames.], tot_loss[loss=0.16, simple_loss=0.2284, pruned_loss=0.04584, over 971314.04 frames.], batch size: 18, lr: 5.20e-04
2022-05-04 17:26:53,129 INFO [train.py:715] (2/8) Epoch 3, batch 28650, loss[loss=0.141, simple_loss=0.2131, pruned_loss=0.03443, over 4782.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2283, pruned_loss=0.04557, over 971798.22 frames.], batch size: 18, lr: 5.20e-04
2022-05-04 17:27:33,006 INFO [train.py:715] (2/8) Epoch 3, batch 28700, loss[loss=0.1372, simple_loss=0.2074, pruned_loss=0.03343, over 4783.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2288, pruned_loss=0.04568, over 972502.86 frames.], batch size: 18, lr: 5.20e-04
2022-05-04 17:28:12,161 INFO [train.py:715] (2/8) Epoch 3, batch 28750, loss[loss=0.171, simple_loss=0.2394, pruned_loss=0.05124, over 4800.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2286, pruned_loss=0.04538, over 972177.89 frames.], batch size: 21, lr: 5.20e-04
2022-05-04 17:28:52,004 INFO [train.py:715] (2/8) Epoch 3, batch 28800, loss[loss=0.1711, simple_loss=0.2447, pruned_loss=0.04875, over 4949.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2295, pruned_loss=0.046, over 971581.76 frames.], batch size: 21, lr: 5.20e-04
2022-05-04 17:29:32,019 INFO [train.py:715] (2/8) Epoch 3, batch 28850, loss[loss=0.1753, simple_loss=0.251, pruned_loss=0.0498, over 4861.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2294, pruned_loss=0.04562, over 971394.37 frames.], batch size: 32, lr: 5.20e-04
2022-05-04 17:30:11,199 INFO [train.py:715] (2/8) Epoch 3, batch 28900, loss[loss=0.1147, simple_loss=0.1855, pruned_loss=0.02192, over 4815.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2295, pruned_loss=0.0456, over 971355.38 frames.], batch size: 27, lr: 5.19e-04
2022-05-04 17:30:50,082 INFO [train.py:715] (2/8) Epoch 3, batch 28950, loss[loss=0.178, simple_loss=0.2502, pruned_loss=0.05289, over 4937.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2295, pruned_loss=0.04585, over 972117.73 frames.], batch size: 18, lr: 5.19e-04
2022-05-04 17:31:29,815 INFO [train.py:715] (2/8) Epoch 3, batch 29000, loss[loss=0.154, simple_loss=0.2279, pruned_loss=0.04008, over 4839.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2298, pruned_loss=0.04588, over 972187.96 frames.], batch size: 13, lr: 5.19e-04
2022-05-04 17:32:10,062 INFO [train.py:715] (2/8) Epoch 3, batch 29050, loss[loss=0.1443, simple_loss=0.2227, pruned_loss=0.03297, over 4907.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2292, pruned_loss=0.04571, over 971826.09 frames.], batch size: 17, lr: 5.19e-04
2022-05-04 17:32:48,615 INFO [train.py:715] (2/8) Epoch 3, batch 29100, loss[loss=0.1425, simple_loss=0.2093, pruned_loss=0.03787, over 4969.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2291, pruned_loss=0.04558, over 972677.53 frames.], batch size: 15, lr: 5.19e-04
2022-05-04 17:33:28,199 INFO [train.py:715] (2/8) Epoch 3, batch 29150, loss[loss=0.1396, simple_loss=0.2026, pruned_loss=0.03828, over 4806.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2291, pruned_loss=0.04608, over 973387.71 frames.], batch size: 21, lr: 5.19e-04
2022-05-04 17:34:08,091 INFO [train.py:715] (2/8) Epoch 3, batch 29200, loss[loss=0.1456, simple_loss=0.2208, pruned_loss=0.0352, over 4824.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2282, pruned_loss=0.04576, over 973695.87 frames.], batch size: 15, lr: 5.19e-04
2022-05-04 17:34:47,191 INFO [train.py:715] (2/8) Epoch 3, batch 29250, loss[loss=0.1734, simple_loss=0.2365, pruned_loss=0.05514, over 4897.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2295, pruned_loss=0.04604, over 974472.66 frames.], batch size: 17, lr: 5.19e-04
2022-05-04 17:35:26,069 INFO [train.py:715] (2/8) Epoch 3, batch 29300, loss[loss=0.2105, simple_loss=0.2637, pruned_loss=0.07859, over 4850.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2286, pruned_loss=0.04555, over 973018.29 frames.], batch size: 30, lr: 5.19e-04
2022-05-04 17:36:06,261 INFO [train.py:715] (2/8) Epoch 3, batch 29350, loss[loss=0.1637, simple_loss=0.2345, pruned_loss=0.04649, over 4817.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2283, pruned_loss=0.0452, over 972697.80 frames.], batch size: 27, lr: 5.19e-04
2022-05-04 17:36:45,935 INFO [train.py:715] (2/8) Epoch 3, batch 29400, loss[loss=0.1856, simple_loss=0.241, pruned_loss=0.06509, over 4801.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2282, pruned_loss=0.04548, over 972343.00 frames.], batch size: 21, lr: 5.18e-04
2022-05-04 17:37:24,686 INFO [train.py:715] (2/8) Epoch 3, batch 29450, loss[loss=0.1754, simple_loss=0.2442, pruned_loss=0.05333, over 4887.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2285, pruned_loss=0.04565, over 972528.85 frames.], batch size: 16, lr: 5.18e-04
2022-05-04 17:38:03,871 INFO [train.py:715] (2/8) Epoch 3, batch 29500, loss[loss=0.1298, simple_loss=0.2054, pruned_loss=0.02709, over 4819.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2292, pruned_loss=0.04554, over 972947.96 frames.], batch size: 26, lr: 5.18e-04
2022-05-04 17:38:43,452 INFO [train.py:715] (2/8) Epoch 3, batch 29550, loss[loss=0.1789, simple_loss=0.2302, pruned_loss=0.06376, over 4766.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2291, pruned_loss=0.04571, over 972784.44 frames.], batch size: 19, lr: 5.18e-04
2022-05-04 17:39:22,771 INFO [train.py:715] (2/8) Epoch 3, batch 29600, loss[loss=0.1585, simple_loss=0.2181, pruned_loss=0.0494, over 4801.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2302, pruned_loss=0.04606, over 973516.11 frames.], batch size: 14, lr: 5.18e-04
2022-05-04 17:40:01,837 INFO [train.py:715] (2/8) Epoch 3, batch 29650, loss[loss=0.1797, simple_loss=0.2411, pruned_loss=0.0592, over 4846.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2299, pruned_loss=0.04581, over 973774.92 frames.], batch size: 15, lr: 5.18e-04
2022-05-04 17:40:41,989 INFO [train.py:715] (2/8) Epoch 3, batch 29700, loss[loss=0.1221, simple_loss=0.192, pruned_loss=0.02609, over 4849.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2305, pruned_loss=0.04602, over 973794.21 frames.], batch size: 12, lr: 5.18e-04
2022-05-04 17:41:22,015 INFO [train.py:715] (2/8) Epoch 3, batch 29750, loss[loss=0.1572, simple_loss=0.2327, pruned_loss=0.04089, over 4921.00 frames.], tot_loss[loss=0.1618, simple_loss=0.231, pruned_loss=0.04628, over 973609.12 frames.], batch size: 29, lr: 5.18e-04
2022-05-04 17:42:00,524 INFO [train.py:715] (2/8) Epoch 3, batch 29800, loss[loss=0.1417, simple_loss=0.2086, pruned_loss=0.03745, over 4927.00 frames.], tot_loss[loss=0.161, simple_loss=0.2301, pruned_loss=0.04593, over 973039.15 frames.], batch size: 29, lr: 5.18e-04
2022-05-04 17:42:40,509 INFO [train.py:715] (2/8) Epoch 3, batch 29850, loss[loss=0.1771, simple_loss=0.2369, pruned_loss=0.05863, over 4919.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2297, pruned_loss=0.04575, over 972509.64 frames.], batch size: 19, lr: 5.18e-04
2022-05-04 17:43:20,046 INFO [train.py:715] (2/8) Epoch 3, batch 29900, loss[loss=0.1583, simple_loss=0.227, pruned_loss=0.04477, over 4774.00 frames.], tot_loss[loss=0.1613, simple_loss=0.23, pruned_loss=0.04623, over 972824.05 frames.], batch size: 18, lr: 5.18e-04
2022-05-04 17:43:58,719 INFO [train.py:715] (2/8) Epoch 3, batch 29950, loss[loss=0.139, simple_loss=0.2095, pruned_loss=0.03424, over 4987.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2291, pruned_loss=0.04565, over 972680.26 frames.], batch size: 25, lr: 5.17e-04
2022-05-04 17:44:37,450 INFO [train.py:715] (2/8) Epoch 3, batch 30000, loss[loss=0.1706, simple_loss=0.2458, pruned_loss=0.04773, over 4983.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2284, pruned_loss=0.04454, over 972335.11 frames.], batch size: 31, lr: 5.17e-04
2022-05-04 17:44:37,451 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 17:44:47,857 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1135, simple_loss=0.1993, pruned_loss=0.01381, over 914524.00 frames.
2022-05-04 17:45:26,668 INFO [train.py:715] (2/8) Epoch 3, batch 30050, loss[loss=0.1615, simple_loss=0.2406, pruned_loss=0.04122, over 4819.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2287, pruned_loss=0.04497, over 973051.82 frames.], batch size: 21, lr: 5.17e-04
2022-05-04 17:46:06,304 INFO [train.py:715] (2/8) Epoch 3, batch 30100, loss[loss=0.1485, simple_loss=0.2274, pruned_loss=0.03485, over 4870.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2288, pruned_loss=0.04483, over 972840.94 frames.], batch size: 16, lr: 5.17e-04
2022-05-04 17:46:46,370 INFO [train.py:715] (2/8) Epoch 3, batch 30150, loss[loss=0.1662, simple_loss=0.2407, pruned_loss=0.0459, over 4922.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2294, pruned_loss=0.04537, over 971881.65 frames.], batch size: 23, lr: 5.17e-04
2022-05-04 17:47:24,501 INFO [train.py:715] (2/8) Epoch 3, batch 30200, loss[loss=0.1618, simple_loss=0.2365, pruned_loss=0.04355, over 4636.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2293, pruned_loss=0.0454, over 971693.03 frames.], batch size: 13, lr: 5.17e-04
2022-05-04 17:48:04,129 INFO [train.py:715] (2/8) Epoch 3, batch 30250, loss[loss=0.1503, simple_loss=0.2208, pruned_loss=0.03988, over 4804.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2283, pruned_loss=0.04469, over 972343.65 frames.], batch size: 25, lr: 5.17e-04
2022-05-04 17:48:44,310 INFO [train.py:715] (2/8) Epoch 3, batch 30300, loss[loss=0.1847, simple_loss=0.2582, pruned_loss=0.05554, over 4979.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2281, pruned_loss=0.04465, over 972654.52 frames.], batch size: 15, lr: 5.17e-04
2022-05-04 17:49:23,079 INFO [train.py:715] (2/8) Epoch 3, batch 30350, loss[loss=0.1901, simple_loss=0.2574, pruned_loss=0.06147, over 4904.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2289, pruned_loss=0.04484, over 972444.13 frames.], batch size: 29, lr: 5.17e-04
2022-05-04 17:50:02,736 INFO [train.py:715] (2/8) Epoch 3, batch 30400, loss[loss=0.176, simple_loss=0.253, pruned_loss=0.04956, over 4782.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2288, pruned_loss=0.0453, over 972750.50 frames.], batch size: 17, lr: 5.17e-04
2022-05-04 17:50:42,520 INFO [train.py:715] (2/8) Epoch 3, batch 30450, loss[loss=0.1701, simple_loss=0.2397, pruned_loss=0.05028, over 4967.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2288, pruned_loss=0.04521, over 974267.16 frames.], batch size: 24, lr: 5.16e-04
2022-05-04 17:51:22,932 INFO [train.py:715] (2/8) Epoch 3, batch 30500, loss[loss=0.1091, simple_loss=0.1786, pruned_loss=0.01976, over 4843.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2288, pruned_loss=0.04494, over 973853.79 frames.], batch size: 12, lr: 5.16e-04
2022-05-04 17:52:02,153 INFO [train.py:715] (2/8) Epoch 3, batch 30550, loss[loss=0.2111, simple_loss=0.2633, pruned_loss=0.07941, over 4758.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2286, pruned_loss=0.04554, over 973103.74 frames.], batch size: 16, lr: 5.16e-04
2022-05-04 17:52:41,686 INFO [train.py:715] (2/8) Epoch 3, batch 30600, loss[loss=0.1769, simple_loss=0.2403, pruned_loss=0.05675, over 4842.00 frames.], tot_loss[loss=0.1594, simple_loss=0.228, pruned_loss=0.04538, over 972699.80 frames.], batch size: 15, lr: 5.16e-04
2022-05-04 17:53:21,641 INFO [train.py:715] (2/8) Epoch 3, batch 30650, loss[loss=0.142, simple_loss=0.2094, pruned_loss=0.03732, over 4765.00 frames.], tot_loss[loss=0.1584, simple_loss=0.227, pruned_loss=0.04486, over 971511.16 frames.], batch size: 19, lr: 5.16e-04
2022-05-04 17:54:00,304 INFO [train.py:715] (2/8) Epoch 3, batch 30700, loss[loss=0.1421, simple_loss=0.2156, pruned_loss=0.03432, over 4844.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2278, pruned_loss=0.04505, over 971624.31 frames.], batch size: 34, lr: 5.16e-04
2022-05-04 17:54:39,863 INFO [train.py:715] (2/8) Epoch 3, batch 30750, loss[loss=0.1376, simple_loss=0.213, pruned_loss=0.0311, over 4884.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2278, pruned_loss=0.04493, over 971109.20 frames.], batch size: 22, lr: 5.16e-04
2022-05-04 17:55:19,269 INFO [train.py:715] (2/8) Epoch 3, batch 30800, loss[loss=0.1743, simple_loss=0.2502, pruned_loss=0.04922, over 4774.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2283, pruned_loss=0.0453, over 971503.23 frames.], batch size: 17, lr: 5.16e-04
2022-05-04 17:55:59,085 INFO [train.py:715] (2/8) Epoch 3, batch 30850, loss[loss=0.1252, simple_loss=0.1986, pruned_loss=0.02593, over 4829.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2293, pruned_loss=0.04559, over 972949.80 frames.], batch size: 25, lr: 5.16e-04
2022-05-04 17:56:37,366 INFO [train.py:715] (2/8) Epoch 3, batch 30900, loss[loss=0.1277, simple_loss=0.1989, pruned_loss=0.02824, over 4769.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2287, pruned_loss=0.04539, over 972580.44 frames.], batch size: 19, lr: 5.16e-04
2022-05-04 17:57:16,439 INFO [train.py:715] (2/8) Epoch 3, batch 30950, loss[loss=0.1844, simple_loss=0.2509, pruned_loss=0.05895, over 4783.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04586, over 973155.48 frames.], batch size: 18, lr: 5.15e-04
2022-05-04 17:57:55,758 INFO [train.py:715] (2/8) Epoch 3, batch 31000, loss[loss=0.1541, simple_loss=0.2224, pruned_loss=0.04287, over 4868.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2296, pruned_loss=0.04568, over 973579.67 frames.], batch size: 20, lr: 5.15e-04
2022-05-04 17:58:35,032 INFO [train.py:715] (2/8) Epoch 3, batch 31050, loss[loss=0.1586, simple_loss=0.2202, pruned_loss=0.04847, over 4959.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2296, pruned_loss=0.04591, over 973639.24 frames.], batch size: 39, lr: 5.15e-04
2022-05-04 17:59:13,607 INFO [train.py:715] (2/8) Epoch 3, batch 31100, loss[loss=0.1526, simple_loss=0.2333, pruned_loss=0.03592, over 4988.00 frames.], tot_loss[loss=0.1603, simple_loss=0.229, pruned_loss=0.04581, over 972888.04 frames.], batch size: 28, lr: 5.15e-04
2022-05-04 17:59:53,184 INFO [train.py:715] (2/8) Epoch 3, batch 31150, loss[loss=0.1883, simple_loss=0.2482, pruned_loss=0.06419, over 4984.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2292, pruned_loss=0.04615, over 972521.45 frames.], batch size: 15, lr: 5.15e-04
2022-05-04 18:00:32,419 INFO [train.py:715] (2/8) Epoch 3, batch 31200, loss[loss=0.2026, simple_loss=0.2703, pruned_loss=0.06748, over 4789.00 frames.], tot_loss[loss=0.161, simple_loss=0.2295, pruned_loss=0.04623, over 972567.66 frames.], batch size: 18, lr: 5.15e-04
2022-05-04 18:01:11,062 INFO [train.py:715] (2/8) Epoch 3, batch 31250, loss[loss=0.1408, simple_loss=0.2127, pruned_loss=0.03449, over 4868.00 frames.], tot_loss[loss=0.16, simple_loss=0.2289, pruned_loss=0.0456, over 972552.85 frames.], batch size: 20, lr: 5.15e-04
2022-05-04 18:01:50,134 INFO [train.py:715] (2/8) Epoch 3, batch 31300, loss[loss=0.149, simple_loss=0.2237, pruned_loss=0.03721, over 4784.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2297, pruned_loss=0.04593, over 971656.51 frames.], batch size: 14, lr: 5.15e-04
2022-05-04 18:02:29,482 INFO [train.py:715] (2/8) Epoch 3, batch 31350, loss[loss=0.1483, simple_loss=0.2311, pruned_loss=0.03281, over 4987.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2298, pruned_loss=0.04604, over 972308.08 frames.], batch size: 25, lr: 5.15e-04
2022-05-04 18:03:08,645 INFO [train.py:715] (2/8) Epoch 3, batch 31400, loss[loss=0.1562, simple_loss=0.2417, pruned_loss=0.03536, over 4889.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2294, pruned_loss=0.0459, over 972917.54 frames.], batch size: 22, lr: 5.15e-04
2022-05-04 18:03:47,229 INFO [train.py:715] (2/8) Epoch 3, batch 31450, loss[loss=0.1715, simple_loss=0.2329, pruned_loss=0.05503, over 4823.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2295, pruned_loss=0.04615, over 973122.85 frames.], batch size: 25, lr: 5.15e-04
2022-05-04 18:04:26,975 INFO [train.py:715] (2/8) Epoch 3, batch 31500, loss[loss=0.1632, simple_loss=0.2422, pruned_loss=0.04207, over 4919.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04583, over 972784.92 frames.], batch size: 23, lr: 5.14e-04
2022-05-04 18:05:06,851 INFO [train.py:715] (2/8) Epoch 3, batch 31550, loss[loss=0.1411, simple_loss=0.2149, pruned_loss=0.03361, over 4829.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2287, pruned_loss=0.04548, over 972951.62 frames.], batch size: 13, lr: 5.14e-04
2022-05-04 18:05:47,989 INFO [train.py:715] (2/8) Epoch 3, batch 31600, loss[loss=0.1566, simple_loss=0.2211, pruned_loss=0.04607, over 4902.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2302, pruned_loss=0.04631, over 973083.60 frames.], batch size: 19, lr: 5.14e-04
2022-05-04 18:06:26,994 INFO [train.py:715] (2/8) Epoch 3, batch 31650, loss[loss=0.1692, simple_loss=0.2379, pruned_loss=0.05031, over 4690.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2304, pruned_loss=0.04621, over 972888.80 frames.], batch size: 15, lr: 5.14e-04
2022-05-04 18:07:07,177 INFO [train.py:715] (2/8) Epoch 3, batch 31700, loss[loss=0.2026, simple_loss=0.2597, pruned_loss=0.07281, over 4775.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2295, pruned_loss=0.04564, over 972151.25 frames.], batch size: 18, lr: 5.14e-04
2022-05-04 18:07:46,357 INFO [train.py:715] (2/8) Epoch 3, batch 31750, loss[loss=0.1589, simple_loss=0.2223, pruned_loss=0.04778, over 4968.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2298, pruned_loss=0.04567, over 971397.10 frames.], batch size: 14, lr: 5.14e-04
2022-05-04 18:08:24,495 INFO [train.py:715] (2/8) Epoch 3, batch 31800, loss[loss=0.1293, simple_loss=0.2013, pruned_loss=0.02865, over 4828.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2299, pruned_loss=0.04555, over 972120.74 frames.], batch size: 26, lr: 5.14e-04
2022-05-04 18:09:04,270 INFO [train.py:715] (2/8) Epoch 3, batch 31850, loss[loss=0.1691, simple_loss=0.2381, pruned_loss=0.05003, over 4784.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2294, pruned_loss=0.04518, over 971906.74 frames.], batch size: 17, lr: 5.14e-04
2022-05-04 18:09:43,772 INFO [train.py:715] (2/8) Epoch 3, batch 31900, loss[loss=0.1775, simple_loss=0.2461, pruned_loss=0.0545, over 4783.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2295, pruned_loss=0.04579, over 971454.37 frames.], batch size: 17, lr: 5.14e-04
2022-05-04 18:10:22,482 INFO [train.py:715] (2/8) Epoch 3, batch 31950, loss[loss=0.1548, simple_loss=0.2279, pruned_loss=0.04086, over 4940.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2297, pruned_loss=0.04553, over 972809.87 frames.], batch size: 29, lr: 5.14e-04
2022-05-04 18:11:01,412 INFO [train.py:715] (2/8) Epoch 3, batch 32000, loss[loss=0.1778, simple_loss=0.2472, pruned_loss=0.05418, over 4977.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2299, pruned_loss=0.04568, over 972452.63 frames.], batch size: 15, lr: 5.14e-04
2022-05-04 18:11:41,009 INFO [train.py:715] (2/8) Epoch 3, batch 32050, loss[loss=0.1813, simple_loss=0.2427, pruned_loss=0.05992, over 4783.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2301, pruned_loss=0.04563, over 971441.97 frames.], batch size: 17, lr: 5.13e-04
2022-05-04 18:12:19,204 INFO [train.py:715] (2/8) Epoch 3, batch 32100, loss[loss=0.1698, simple_loss=0.2416, pruned_loss=0.04897, over 4858.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2306, pruned_loss=0.04588, over 970679.42 frames.], batch size: 30, lr: 5.13e-04
2022-05-04 18:12:58,310 INFO [train.py:715] (2/8) Epoch 3, batch 32150, loss[loss=0.1676, simple_loss=0.2418, pruned_loss=0.04673, over 4746.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2298, pruned_loss=0.04597, over 971636.62 frames.], batch size: 16, lr: 5.13e-04
2022-05-04 18:13:37,852 INFO [train.py:715] (2/8) Epoch 3, batch 32200, loss[loss=0.1863, simple_loss=0.2589, pruned_loss=0.05685, over 4965.00 frames.], tot_loss[loss=0.161, simple_loss=0.2293, pruned_loss=0.04638, over 971244.54 frames.], batch size: 24, lr: 5.13e-04
2022-05-04 18:14:16,673 INFO [train.py:715] (2/8) Epoch 3, batch 32250, loss[loss=0.1548, simple_loss=0.2287, pruned_loss=0.04042, over 4788.00 frames.], tot_loss[loss=0.1604, simple_loss=0.229, pruned_loss=0.04593, over 971054.94 frames.], batch size: 14, lr: 5.13e-04
2022-05-04 18:14:55,235 INFO [train.py:715] (2/8) Epoch 3, batch 32300, loss[loss=0.191, simple_loss=0.253, pruned_loss=0.06454, over 4957.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2287, pruned_loss=0.04545, over 971121.90 frames.], batch size: 21, lr: 5.13e-04
2022-05-04 18:15:34,899 INFO [train.py:715] (2/8) Epoch 3, batch 32350, loss[loss=0.1861, simple_loss=0.2503, pruned_loss=0.06099, over 4942.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2302, pruned_loss=0.04611, over 971343.10 frames.], batch size: 21, lr: 5.13e-04
2022-05-04 18:16:14,618 INFO [train.py:715] (2/8) Epoch 3, batch 32400, loss[loss=0.1465, simple_loss=0.221, pruned_loss=0.03595, over 4873.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2295, pruned_loss=0.04598, over 971754.31 frames.], batch size: 20, lr: 5.13e-04
2022-05-04 18:16:52,599 INFO [train.py:715] (2/8) Epoch 3, batch 32450, loss[loss=0.1388, simple_loss=0.2071, pruned_loss=0.03522, over 4953.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2304, pruned_loss=0.0462, over 972898.22 frames.], batch size: 24, lr: 5.13e-04
2022-05-04 18:17:32,077 INFO [train.py:715] (2/8) Epoch 3, batch 32500, loss[loss=0.1645, simple_loss=0.2239, pruned_loss=0.05259, over 4868.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2302, pruned_loss=0.04598, over 972451.12 frames.], batch size: 20, lr: 5.13e-04
2022-05-04 18:18:11,714 INFO [train.py:715] (2/8) Epoch 3, batch 32550, loss[loss=0.1614, simple_loss=0.2194, pruned_loss=0.05168, over 4954.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2302, pruned_loss=0.04634, over 972687.91 frames.], batch size: 35, lr: 5.12e-04
2022-05-04 18:18:50,229 INFO [train.py:715] (2/8) Epoch 3, batch 32600, loss[loss=0.1669, simple_loss=0.2348, pruned_loss=0.04947, over 4879.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2309, pruned_loss=0.04645, over 972553.95 frames.], batch size: 22, lr: 5.12e-04
2022-05-04 18:19:29,061 INFO [train.py:715] (2/8) Epoch 3, batch 32650, loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03216, over 4833.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2298, pruned_loss=0.04586, over 972404.53 frames.], batch size: 26, lr: 5.12e-04
2022-05-04 18:20:08,687 INFO [train.py:715] (2/8) Epoch 3, batch 32700, loss[loss=0.1636, simple_loss=0.2334, pruned_loss=0.04691, over 4774.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2296, pruned_loss=0.046, over 972284.84 frames.], batch size: 18, lr: 5.12e-04
2022-05-04 18:20:47,703 INFO [train.py:715] (2/8) Epoch 3, batch 32750, loss[loss=0.1617, simple_loss=0.2232, pruned_loss=0.05008, over 4868.00 frames.], tot_loss[loss=0.16, simple_loss=0.229, pruned_loss=0.04551, over 972672.06 frames.], batch size: 38, lr: 5.12e-04
2022-05-04 18:21:26,287 INFO [train.py:715] (2/8) Epoch 3, batch 32800, loss[loss=0.1415, simple_loss=0.2099, pruned_loss=0.03656, over 4859.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2281, pruned_loss=0.04482, over 972884.38 frames.], batch size: 20, lr: 5.12e-04
2022-05-04 18:22:05,409 INFO [train.py:715] (2/8) Epoch 3, batch 32850, loss[loss=0.1564, simple_loss=0.2262, pruned_loss=0.04331, over 4844.00 frames.], tot_loss[loss=0.1602, simple_loss=0.229, pruned_loss=0.04564, over 973544.99 frames.], batch size: 30, lr: 5.12e-04
2022-05-04 18:22:44,589 INFO [train.py:715] (2/8) Epoch 3, batch 32900, loss[loss=0.1436, simple_loss=0.2138, pruned_loss=0.03672, over 4981.00 frames.], tot_loss[loss=0.1614, simple_loss=0.23, pruned_loss=0.04637, over 973879.53 frames.], batch size: 25, lr: 5.12e-04
2022-05-04 18:23:23,655 INFO [train.py:715] (2/8) Epoch 3, batch 32950, loss[loss=0.1498, simple_loss=0.2247, pruned_loss=0.03747, over 4775.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2295, pruned_loss=0.0457, over 974001.35 frames.], batch size: 17, lr: 5.12e-04
2022-05-04 18:24:02,387 INFO [train.py:715] (2/8) Epoch 3, batch 33000, loss[loss=0.1527, simple_loss=0.2328, pruned_loss=0.03634, over 4744.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04581, over 972846.16 frames.], batch size: 16, lr: 5.12e-04
2022-05-04 18:24:02,388 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 18:24:11,704 INFO [train.py:742] (2/8) Epoch 3, validation: loss=0.1131, simple_loss=0.199, pruned_loss=0.01363, over 914524.00 frames.
2022-05-04 18:24:50,804 INFO [train.py:715] (2/8) Epoch 3, batch 33050, loss[loss=0.2074, simple_loss=0.2654, pruned_loss=0.07465, over 4708.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2293, pruned_loss=0.04616, over 973086.32 frames.], batch size: 15, lr: 5.12e-04
2022-05-04 18:25:30,711 INFO [train.py:715] (2/8) Epoch 3, batch 33100, loss[loss=0.1455, simple_loss=0.2201, pruned_loss=0.03541, over 4790.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2303, pruned_loss=0.04698, over 973421.77 frames.], batch size: 21, lr: 5.11e-04
2022-05-04 18:26:09,586 INFO [train.py:715] (2/8) Epoch 3, batch 33150, loss[loss=0.1676, simple_loss=0.2264, pruned_loss=0.05442, over 4857.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2305, pruned_loss=0.04696, over 972995.77 frames.], batch size: 32, lr: 5.11e-04
2022-05-04 18:26:48,264 INFO [train.py:715] (2/8) Epoch 3, batch 33200, loss[loss=0.1914, simple_loss=0.2553, pruned_loss=0.06375, over 4703.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2303, pruned_loss=0.04665, over 973034.35 frames.], batch size: 15, lr: 5.11e-04
2022-05-04 18:27:28,161 INFO [train.py:715] (2/8) Epoch 3, batch 33250, loss[loss=0.1545, simple_loss=0.2206, pruned_loss=0.04418, over 4978.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2302, pruned_loss=0.04634, over 972525.38 frames.], batch size: 25, lr: 5.11e-04
2022-05-04 18:28:07,721 INFO [train.py:715] (2/8) Epoch 3, batch 33300, loss[loss=0.1463, simple_loss=0.2155, pruned_loss=0.03852, over 4947.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2292, pruned_loss=0.04574, over 973594.57 frames.], batch size: 29, lr: 5.11e-04
2022-05-04 18:28:46,234 INFO [train.py:715] (2/8) Epoch 3, batch 33350, loss[loss=0.1496, simple_loss=0.2221, pruned_loss=0.03857, over 4946.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2287, pruned_loss=0.04545, over 973562.65 frames.], batch size: 29, lr: 5.11e-04
2022-05-04 18:29:25,534 INFO [train.py:715] (2/8) Epoch 3, batch 33400, loss[loss=0.1605, simple_loss=0.2301, pruned_loss=0.0455, over 4922.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2304, pruned_loss=0.0464, over 973680.14 frames.], batch size: 39, lr: 5.11e-04
2022-05-04 18:30:05,188 INFO [train.py:715] (2/8) Epoch 3, batch 33450, loss[loss=0.1893, simple_loss=0.2551, pruned_loss=0.06178, over 4688.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2302, pruned_loss=0.04636, over 973350.58 frames.], batch size: 15, lr: 5.11e-04
2022-05-04 18:30:44,206 INFO [train.py:715] (2/8) Epoch 3, batch 33500, loss[loss=0.1731, simple_loss=0.2466, pruned_loss=0.0498, over 4994.00 frames.], tot_loss[loss=0.161, simple_loss=0.2301, pruned_loss=0.04602, over 973017.50 frames.], batch size: 16, lr: 5.11e-04
2022-05-04 18:31:23,292 INFO [train.py:715] (2/8) Epoch 3, batch 33550, loss[loss=0.1492, simple_loss=0.2245, pruned_loss=0.03691, over 4788.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2295, pruned_loss=0.04589, over 972665.40 frames.], batch size: 21, lr: 5.11e-04
2022-05-04 18:32:03,654 INFO [train.py:715] (2/8) Epoch 3, batch 33600, loss[loss=0.1728, simple_loss=0.2416, pruned_loss=0.05197, over 4816.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2291, pruned_loss=0.04558, over 972300.45 frames.], batch size: 27, lr: 5.11e-04
2022-05-04 18:32:43,011 INFO [train.py:715] (2/8) Epoch 3, batch 33650, loss[loss=0.1588, simple_loss=0.232, pruned_loss=0.04283, over 4907.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2301, pruned_loss=0.04591, over 971330.78 frames.], batch size: 29, lr: 5.10e-04
2022-05-04 18:33:21,655 INFO [train.py:715] (2/8) Epoch 3, batch 33700, loss[loss=0.1509, simple_loss=0.2214, pruned_loss=0.04023, over 4779.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2302, pruned_loss=0.04618, over 971407.67 frames.], batch size: 17, lr: 5.10e-04
2022-05-04 18:34:01,450 INFO [train.py:715] (2/8) Epoch 3, batch 33750, loss[loss=0.1443, simple_loss=0.2195, pruned_loss=0.03457, over 4945.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2287, pruned_loss=0.04499, over 971762.01 frames.], batch size: 21, lr: 5.10e-04
2022-05-04 18:34:40,933 INFO [train.py:715] (2/8) Epoch 3, batch 33800, loss[loss=0.1554, simple_loss=0.2306, pruned_loss=0.04009, over 4783.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2295, pruned_loss=0.04558, over 971833.31 frames.], batch size: 17, lr: 5.10e-04
2022-05-04 18:35:19,312 INFO [train.py:715] (2/8) Epoch 3, batch 33850, loss[loss=0.1849, simple_loss=0.26, pruned_loss=0.05487, over 4738.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2288, pruned_loss=0.04547, over 971047.94 frames.], batch size: 16, lr: 5.10e-04
2022-05-04 18:35:58,145 INFO [train.py:715] (2/8) Epoch 3, batch 33900, loss[loss=0.1708, simple_loss=0.2344, pruned_loss=0.05363, over 4763.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2287, pruned_loss=0.04513, over 971500.62 frames.], batch size: 19, lr: 5.10e-04
2022-05-04 18:36:38,299 INFO [train.py:715] (2/8) Epoch 3, batch 33950, loss[loss=0.1588, simple_loss=0.2274, pruned_loss=0.04513, over 4691.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2283, pruned_loss=0.04505, over 971916.93 frames.], batch size: 15, lr: 5.10e-04
2022-05-04 18:37:17,238 INFO [train.py:715] (2/8) Epoch 3, batch 34000, loss[loss=0.1763, simple_loss=0.2494, pruned_loss=0.05165, over 4812.00 frames.], tot_loss[loss=0.159, simple_loss=0.2279, pruned_loss=0.04502, over 973164.49 frames.], batch size: 21, lr: 5.10e-04
2022-05-04 18:37:55,980 INFO [train.py:715] (2/8) Epoch 3, batch 34050, loss[loss=0.1639, simple_loss=0.2354, pruned_loss=0.0462, over 4786.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2278, pruned_loss=0.04477, over 973516.41 frames.], batch size: 18, lr: 5.10e-04
2022-05-04 18:38:35,311 INFO [train.py:715] (2/8) Epoch 3, batch 34100, loss[loss=0.162, simple_loss=0.2289, pruned_loss=0.0476, over 4880.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2282, pruned_loss=0.04502, over 973968.94 frames.], batch size: 22, lr: 5.10e-04
2022-05-04 18:39:15,279 INFO [train.py:715] (2/8) Epoch 3, batch 34150, loss[loss=0.1478, simple_loss=0.2201, pruned_loss=0.03778, over 4837.00 frames.], tot_loss[loss=0.159, simple_loss=0.2279, pruned_loss=0.04502, over 973766.84 frames.], batch size: 15, lr: 5.10e-04
2022-05-04 18:39:53,557 INFO [train.py:715] (2/8) Epoch 3, batch 34200, loss[loss=0.1869, simple_loss=0.2572, pruned_loss=0.05833, over 4907.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2284, pruned_loss=0.04522, over 973595.28 frames.], batch size: 17, lr: 5.09e-04
2022-05-04 18:40:33,004 INFO [train.py:715] (2/8) Epoch 3, batch 34250, loss[loss=0.1368, simple_loss=0.2086, pruned_loss=0.03243, over 4833.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2277, pruned_loss=0.04485, over 972646.76 frames.], batch size: 27, lr: 5.09e-04
2022-05-04 18:41:13,064 INFO [train.py:715] (2/8) Epoch 3, batch 34300, loss[loss=0.1481, simple_loss=0.2232, pruned_loss=0.03647, over 4781.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2282, pruned_loss=0.04515, over 972169.00 frames.], batch size: 18, lr: 5.09e-04
2022-05-04 18:41:52,484 INFO [train.py:715] (2/8) Epoch 3, batch 34350, loss[loss=0.1537, simple_loss=0.2314, pruned_loss=0.03801, over 4930.00 frames.], tot_loss[loss=0.159, simple_loss=0.2278, pruned_loss=0.04508, over 972185.92 frames.], batch size: 21, lr: 5.09e-04
2022-05-04 18:42:31,604 INFO [train.py:715] (2/8) Epoch 3, batch 34400, loss[loss=0.2306, simple_loss=0.2696, pruned_loss=0.09582, over 4882.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2293, pruned_loss=0.04613, over 971898.20 frames.], batch size: 16, lr: 5.09e-04
2022-05-04 18:43:11,183 INFO [train.py:715] (2/8) Epoch 3, batch 34450, loss[loss=0.1638, simple_loss=0.2308, pruned_loss=0.0484, over 4956.00 frames.], tot_loss[loss=0.162, simple_loss=0.2303, pruned_loss=0.04683, over 971579.05 frames.], batch size: 24, lr: 5.09e-04
2022-05-04 18:43:51,339 INFO [train.py:715] (2/8) Epoch 3, batch 34500, loss[loss=0.1511, simple_loss=0.2132, pruned_loss=0.04453, over 4927.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2299, pruned_loss=0.04648, over 972623.37 frames.], batch size: 23, lr: 5.09e-04
2022-05-04 18:44:29,766 INFO [train.py:715] (2/8) Epoch 3, batch 34550, loss[loss=0.1678, simple_loss=0.2289, pruned_loss=0.0533, over 4975.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2303, pruned_loss=0.04667, over 972582.48 frames.], batch size: 14, lr: 5.09e-04
2022-05-04 18:45:08,809 INFO [train.py:715] (2/8) Epoch 3, batch 34600, loss[loss=0.1683, simple_loss=0.2385, pruned_loss=0.04903, over 4794.00 frames.], tot_loss[loss=0.1629, simple_loss=0.231, pruned_loss=0.04736, over 972823.98 frames.], batch size: 17, lr: 5.09e-04
2022-05-04 18:45:49,188 INFO [train.py:715] (2/8) Epoch 3, batch 34650, loss[loss=0.164, simple_loss=0.2266, pruned_loss=0.05074, over 4774.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2307, pruned_loss=0.04749, over 972477.59 frames.], batch size: 14, lr: 5.09e-04
2022-05-04 18:46:28,783 INFO [train.py:715] (2/8) Epoch 3, batch 34700, loss[loss=0.1314, simple_loss=0.208, pruned_loss=0.02735, over 4797.00 frames.], tot_loss[loss=0.162, simple_loss=0.23, pruned_loss=0.04704, over 972175.43 frames.], batch size: 21, lr: 5.09e-04
2022-05-04 18:47:07,060 INFO [train.py:715] (2/8) Epoch 3, batch 34750, loss[loss=0.1257, simple_loss=0.2008, pruned_loss=0.02528, over 4934.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2297, pruned_loss=0.04671, over 971829.91 frames.], batch size: 21, lr: 5.08e-04
2022-05-04 18:47:44,761 INFO [train.py:715] (2/8) Epoch 3, batch 34800, loss[loss=0.1471, simple_loss=0.2176, pruned_loss=0.03831, over 4927.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2283, pruned_loss=0.04598, over 971676.37 frames.], batch size: 23, lr: 5.08e-04
2022-05-04 18:48:35,150 INFO [train.py:715] (2/8) Epoch 4, batch 0, loss[loss=0.1545, simple_loss=0.2146, pruned_loss=0.04723, over 4839.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2146, pruned_loss=0.04723, over 4839.00 frames.], batch size: 30, lr: 4.78e-04
2022-05-04 18:49:16,504 INFO [train.py:715] (2/8) Epoch 4, batch 50, loss[loss=0.1608, simple_loss=0.2216, pruned_loss=0.04995, over 4854.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2266, pruned_loss=0.04446, over 219967.70 frames.], batch size: 30, lr: 4.78e-04
2022-05-04 18:49:57,160 INFO [train.py:715] (2/8) Epoch 4, batch 100, loss[loss=0.1397, simple_loss=0.21, pruned_loss=0.03475, over 4804.00 frames.], tot_loss[loss=0.1581, simple_loss=0.227, pruned_loss=0.04459, over 387389.33 frames.], batch size: 17, lr: 4.78e-04
2022-05-04 18:50:37,994 INFO [train.py:715] (2/8) Epoch 4, batch 150, loss[loss=0.1865, simple_loss=0.262, pruned_loss=0.05555, over 4891.00 frames.], tot_loss[loss=0.1612, simple_loss=0.23, pruned_loss=0.0462, over 516382.68 frames.], batch size: 19, lr: 4.78e-04
2022-05-04 18:51:19,046 INFO [train.py:715] (2/8) Epoch 4, batch 200, loss[loss=0.1513, simple_loss=0.2177, pruned_loss=0.04242, over 4804.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2298, pruned_loss=0.04585, over 617020.18 frames.], batch size: 21, lr: 4.78e-04
2022-05-04 18:52:00,249 INFO [train.py:715] (2/8) Epoch 4, batch 250, loss[loss=0.1864, simple_loss=0.2526, pruned_loss=0.06008, over 4878.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2297, pruned_loss=0.04555, over 695917.36 frames.], batch size: 19, lr: 4.77e-04
2022-05-04 18:52:41,171 INFO [train.py:715] (2/8) Epoch 4, batch 300, loss[loss=0.1914, simple_loss=0.2539, pruned_loss=0.06448, over 4771.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2301, pruned_loss=0.0458, over 757344.39 frames.], batch size: 18, lr: 4.77e-04
2022-05-04 18:53:22,423 INFO [train.py:715] (2/8) Epoch 4, batch 350, loss[loss=0.1929, simple_loss=0.2717, pruned_loss=0.05709, over 4924.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2293, pruned_loss=0.04577, over 804072.77 frames.], batch size: 23, lr: 4.77e-04
2022-05-04 18:54:04,548 INFO [train.py:715] (2/8) Epoch 4, batch 400, loss[loss=0.1582, simple_loss=0.2345, pruned_loss=0.04093, over 4920.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04582, over 842095.36 frames.], batch size: 29, lr: 4.77e-04
2022-05-04 18:54:45,178 INFO [train.py:715] (2/8) Epoch 4, batch 450, loss[loss=0.1674, simple_loss=0.2279, pruned_loss=0.0535, over 4860.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2289, pruned_loss=0.04561, over 871876.56 frames.], batch size: 32, lr: 4.77e-04
2022-05-04 18:55:26,260 INFO [train.py:715] (2/8) Epoch 4, batch 500, loss[loss=0.1828, simple_loss=0.2539, pruned_loss=0.05583, over 4845.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2293, pruned_loss=0.04561, over 894378.43 frames.], batch size: 30, lr: 4.77e-04
2022-05-04 18:56:07,506 INFO [train.py:715] (2/8) Epoch 4, batch 550, loss[loss=0.1585, simple_loss=0.222, pruned_loss=0.04752, over 4915.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2283, pruned_loss=0.04526, over 911652.98 frames.], batch size: 17, lr: 4.77e-04
2022-05-04 18:56:48,414 INFO [train.py:715] (2/8) Epoch 4, batch 600, loss[loss=0.1414, simple_loss=0.2211, pruned_loss=0.03085, over 4956.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2293, pruned_loss=0.04583, over 925495.80 frames.], batch size: 24, lr: 4.77e-04
2022-05-04 18:57:28,918 INFO [train.py:715] (2/8) Epoch 4, batch 650, loss[loss=0.1633, simple_loss=0.2296, pruned_loss=0.04851, over 4950.00 frames.], tot_loss[loss=0.16, simple_loss=0.2287, pruned_loss=0.04564, over 934998.08 frames.], batch size: 39, lr: 4.77e-04
2022-05-04 18:58:09,996 INFO [train.py:715] (2/8) Epoch 4, batch 700, loss[loss=0.1373, simple_loss=0.2139, pruned_loss=0.0303, over 4787.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2292, pruned_loss=0.04574, over 943668.02 frames.], batch size: 13, lr: 4.77e-04
2022-05-04 18:58:51,943 INFO [train.py:715] (2/8) Epoch 4, batch 750, loss[loss=0.1655, simple_loss=0.226, pruned_loss=0.05251, over 4988.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2283, pruned_loss=0.04541, over 950427.35 frames.], batch size: 14, lr: 4.77e-04
2022-05-04 18:59:33,004 INFO [train.py:715] (2/8) Epoch 4, batch 800, loss[loss=0.2046, simple_loss=0.2782, pruned_loss=0.06549, over 4925.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2286, pruned_loss=0.04539, over 955360.82 frames.], batch size: 39, lr: 4.77e-04
2022-05-04 19:00:13,430 INFO [train.py:715] (2/8) Epoch 4, batch 850, loss[loss=0.131, simple_loss=0.2062, pruned_loss=0.02791, over 4971.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2281, pruned_loss=0.04529, over 958905.04 frames.], batch size: 14, lr: 4.76e-04
2022-05-04 19:00:54,505 INFO [train.py:715] (2/8) Epoch 4, batch 900, loss[loss=0.1381, simple_loss=0.2191, pruned_loss=0.02851, over 4821.00 frames.], tot_loss[loss=0.16, simple_loss=0.2286, pruned_loss=0.04567, over 962293.40 frames.], batch size: 21, lr: 4.76e-04
2022-05-04 19:01:35,343 INFO [train.py:715] (2/8) Epoch 4, batch 950, loss[loss=0.1802, simple_loss=0.2424, pruned_loss=0.05899, over 4965.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2286, pruned_loss=0.04541, over 964761.46 frames.], batch size: 35, lr: 4.76e-04
2022-05-04 19:02:16,225 INFO [train.py:715] (2/8) Epoch 4, batch 1000, loss[loss=0.1552, simple_loss=0.232, pruned_loss=0.03918, over 4804.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2286, pruned_loss=0.04543, over 966896.65 frames.], batch size: 21, lr: 4.76e-04
2022-05-04 19:02:56,936 INFO [train.py:715] (2/8) Epoch 4, batch 1050, loss[loss=0.1883, simple_loss=0.2595, pruned_loss=0.05854, over 4935.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2296, pruned_loss=0.0463, over 967878.28 frames.], batch size: 21, lr: 4.76e-04
2022-05-04 19:03:38,126 INFO [train.py:715] (2/8) Epoch 4, batch 1100, loss[loss=0.1568, simple_loss=0.2226, pruned_loss=0.04551, over 4768.00 frames.], tot_loss[loss=0.16, simple_loss=0.2287, pruned_loss=0.04563, over 969203.98 frames.], batch size: 18, lr: 4.76e-04
2022-05-04 19:04:18,526 INFO [train.py:715] (2/8) Epoch 4, batch 1150, loss[loss=0.1377, simple_loss=0.2019, pruned_loss=0.03674, over 4863.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2289, pruned_loss=0.04587, over 969567.78 frames.], batch size: 20, lr: 4.76e-04
2022-05-04 19:04:58,024 INFO [train.py:715] (2/8) Epoch 4, batch 1200, loss[loss=0.1736, simple_loss=0.2253, pruned_loss=0.06091, over 4865.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2296, pruned_loss=0.04611, over 970354.10 frames.], batch size: 30, lr: 4.76e-04
2022-05-04 19:05:38,581 INFO [train.py:715] (2/8) Epoch 4, batch 1250, loss[loss=0.15, simple_loss=0.2219, pruned_loss=0.03903, over 4911.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2289, pruned_loss=0.04579, over 970451.36 frames.], batch size: 17, lr: 4.76e-04
2022-05-04 19:06:19,646 INFO [train.py:715] (2/8) Epoch 4, batch 1300, loss[loss=0.1691, simple_loss=0.2344, pruned_loss=0.05188, over 4858.00 frames.], tot_loss[loss=0.159, simple_loss=0.228, pruned_loss=0.04504, over 971567.63 frames.], batch size: 32, lr: 4.76e-04
2022-05-04 19:06:59,661 INFO [train.py:715] (2/8) Epoch 4, batch 1350, loss[loss=0.1628, simple_loss=0.2368, pruned_loss=0.04435, over 4804.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2282, pruned_loss=0.04557, over 972369.81 frames.], batch size: 24, lr: 4.76e-04
2022-05-04 19:07:40,375 INFO [train.py:715] (2/8) Epoch 4, batch 1400, loss[loss=0.1493, simple_loss=0.2231, pruned_loss=0.03779, over 4907.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2282, pruned_loss=0.04558, over 972146.45 frames.], batch size: 18, lr: 4.76e-04
2022-05-04 19:08:21,350 INFO [train.py:715] (2/8) Epoch 4, batch 1450, loss[loss=0.1527, simple_loss=0.2103, pruned_loss=0.04762, over 4912.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2277, pruned_loss=0.0454, over 971870.27 frames.], batch size: 18, lr: 4.75e-04
2022-05-04 19:09:02,418 INFO [train.py:715] (2/8) Epoch 4, batch 1500, loss[loss=0.2, simple_loss=0.2649, pruned_loss=0.06756, over 4974.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2275, pruned_loss=0.04517, over 972793.28 frames.], batch size: 15, lr: 4.75e-04
2022-05-04 19:09:42,045 INFO [train.py:715] (2/8) Epoch 4, batch 1550, loss[loss=0.1622, simple_loss=0.2284, pruned_loss=0.04806, over 4890.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2278, pruned_loss=0.04542, over 973184.22 frames.], batch size: 22, lr: 4.75e-04
2022-05-04 19:10:23,007 INFO [train.py:715] (2/8) Epoch 4, batch 1600, loss[loss=0.1471, simple_loss=0.2196, pruned_loss=0.0373, over 4776.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2277, pruned_loss=0.04572, over 973037.27 frames.], batch size: 14, lr: 4.75e-04
2022-05-04 19:11:04,736 INFO [train.py:715] (2/8) Epoch 4, batch 1650, loss[loss=0.1539, simple_loss=0.2216, pruned_loss=0.04315, over 4902.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2268, pruned_loss=0.04504, over 972751.07 frames.], batch size: 17, lr: 4.75e-04
2022-05-04 19:11:45,100 INFO [train.py:715] (2/8) Epoch 4, batch 1700, loss[loss=0.1432, simple_loss=0.2233, pruned_loss=0.03152, over 4981.00 frames.], tot_loss[loss=0.158, simple_loss=0.2267, pruned_loss=0.0447, over 972598.02 frames.], batch size: 28, lr: 4.75e-04
2022-05-04 19:12:25,109 INFO [train.py:715] (2/8) Epoch 4, batch 1750, loss[loss=0.1412, simple_loss=0.2187, pruned_loss=0.03183, over 4868.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2266, pruned_loss=0.04456, over 972458.47 frames.], batch size: 22, lr: 4.75e-04
2022-05-04 19:13:06,310 INFO [train.py:715] (2/8) Epoch 4, batch 1800, loss[loss=0.1541, simple_loss=0.2175, pruned_loss=0.04528, over 4842.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2272, pruned_loss=0.04489, over 972348.71 frames.], batch size: 32, lr: 4.75e-04
2022-05-04 19:13:47,661 INFO [train.py:715] (2/8) Epoch 4, batch 1850, loss[loss=0.2014, simple_loss=0.2509, pruned_loss=0.07596, over 4842.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2276, pruned_loss=0.04488, over 973223.79 frames.], batch size: 15, lr: 4.75e-04
2022-05-04 19:14:27,699 INFO [train.py:715] (2/8) Epoch 4, batch 1900, loss[loss=0.1281, simple_loss=0.2007, pruned_loss=0.02778, over 4771.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2273, pruned_loss=0.04485, over 972114.49 frames.], batch size: 14, lr: 4.75e-04
2022-05-04 19:15:08,449 INFO [train.py:715] (2/8) Epoch 4, batch 1950, loss[loss=0.1648, simple_loss=0.2258, pruned_loss=0.05195, over 4831.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2278, pruned_loss=0.04489, over 972504.99 frames.], batch size: 30, lr: 4.75e-04
2022-05-04 19:15:48,967 INFO [train.py:715] (2/8) Epoch 4, batch 2000, loss[loss=0.139, simple_loss=0.21, pruned_loss=0.03403, over 4987.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2279, pruned_loss=0.04478, over 972410.86 frames.], batch size: 25, lr: 4.74e-04
2022-05-04 19:16:28,965 INFO [train.py:715] (2/8) Epoch 4, batch 2050, loss[loss=0.1491, simple_loss=0.2222, pruned_loss=0.03798, over 4901.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2282, pruned_loss=0.04484, over 972730.79 frames.], batch size: 17, lr: 4.74e-04
2022-05-04 19:17:08,514 INFO [train.py:715] (2/8) Epoch 4, batch 2100, loss[loss=0.1396, simple_loss=0.208, pruned_loss=0.0356, over 4867.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2284, pruned_loss=0.04508, over 972864.85 frames.], batch size: 20, lr: 4.74e-04
2022-05-04 19:17:48,261 INFO [train.py:715] (2/8) Epoch 4, batch 2150, loss[loss=0.1392, simple_loss=0.2117, pruned_loss=0.03336, over 4787.00 frames.], tot_loss[loss=0.1585, simple_loss=0.228, pruned_loss=0.04449, over 972782.62 frames.], batch size: 18, lr: 4.74e-04
2022-05-04 19:18:29,063 INFO [train.py:715] (2/8) Epoch 4, batch 2200, loss[loss=0.1537, simple_loss=0.2178, pruned_loss=0.04478, over 4989.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2286, pruned_loss=0.04477, over 972451.48 frames.], batch size: 14, lr: 4.74e-04
2022-05-04 19:19:09,440 INFO [train.py:715] (2/8) Epoch 4, batch 2250, loss[loss=0.1559, simple_loss=0.2268, pruned_loss=0.04249, over 4989.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2287, pruned_loss=0.04512, over 972861.07 frames.], batch size: 25, lr: 4.74e-04
2022-05-04 19:19:48,813 INFO [train.py:715] (2/8) Epoch 4, batch 2300, loss[loss=0.145, simple_loss=0.2246, pruned_loss=0.03271, over 4817.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2286, pruned_loss=0.04493, over 972021.22 frames.], batch size: 27, lr: 4.74e-04
2022-05-04 19:20:28,745 INFO [train.py:715] (2/8) Epoch 4, batch 2350, loss[loss=0.1667, simple_loss=0.2401, pruned_loss=0.04663, over 4971.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2289, pruned_loss=0.04526, over 972363.75 frames.], batch size: 35, lr: 4.74e-04
2022-05-04 19:21:08,833 INFO [train.py:715] (2/8) Epoch 4, batch 2400, loss[loss=0.1324, simple_loss=0.1992, pruned_loss=0.03283, over 4797.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2294, pruned_loss=0.0456, over 971338.65 frames.], batch size: 24, lr: 4.74e-04
2022-05-04 19:21:48,318 INFO [train.py:715] (2/8) Epoch 4, batch 2450, loss[loss=0.1651, simple_loss=0.221, pruned_loss=0.05464, over 4859.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2293, pruned_loss=0.04547, over 971028.25 frames.], batch size: 30, lr: 4.74e-04
2022-05-04 19:22:28,660 INFO [train.py:715] (2/8) Epoch 4, batch 2500, loss[loss=0.1359, simple_loss=0.2039, pruned_loss=0.03393, over 4906.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2288, pruned_loss=0.04539, over 971309.95 frames.], batch size: 18, lr: 4.74e-04
2022-05-04 19:23:09,576 INFO [train.py:715] (2/8) Epoch 4, batch 2550, loss[loss=0.1663, simple_loss=0.2321, pruned_loss=0.05028, over 4926.00 frames.], tot_loss[loss=0.16, simple_loss=0.2289, pruned_loss=0.04549, over 971657.99 frames.], batch size: 29, lr: 4.74e-04
2022-05-04 19:23:49,884 INFO [train.py:715] (2/8) Epoch 4, batch 2600, loss[loss=0.168, simple_loss=0.2271, pruned_loss=0.05441, over 4890.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2293, pruned_loss=0.04605, over 972461.25 frames.], batch size: 19, lr: 4.73e-04
2022-05-04 19:24:29,151 INFO [train.py:715] (2/8) Epoch 4, batch 2650, loss[loss=0.1802, simple_loss=0.2594, pruned_loss=0.05048, over 4934.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04582, over 972622.75 frames.], batch size: 39, lr: 4.73e-04
2022-05-04 19:25:09,500 INFO [train.py:715] (2/8) Epoch 4, batch 2700, loss[loss=0.1477, simple_loss=0.2253, pruned_loss=0.03503, over 4759.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2289, pruned_loss=0.04474, over 973081.04 frames.], batch size: 16, lr: 4.73e-04
2022-05-04 19:25:49,764 INFO [train.py:715] (2/8) Epoch 4, batch 2750, loss[loss=0.1406, simple_loss=0.2172, pruned_loss=0.032, over 4935.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2293, pruned_loss=0.04474, over 973748.62 frames.], batch size: 23, lr: 4.73e-04
2022-05-04 19:26:29,539 INFO [train.py:715] (2/8) Epoch 4, batch 2800, loss[loss=0.1537, simple_loss=0.2395, pruned_loss=0.03391, over 4946.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2275, pruned_loss=0.04371, over 973350.22 frames.], batch size: 23, lr: 4.73e-04
2022-05-04 19:27:08,933 INFO [train.py:715] (2/8) Epoch 4, batch 2850, loss[loss=0.1733, simple_loss=0.2408, pruned_loss=0.05285, over 4977.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2266, pruned_loss=0.04333, over 973667.07 frames.], batch size: 15, lr: 4.73e-04
2022-05-04 19:27:49,244 INFO [train.py:715] (2/8) Epoch 4, batch 2900, loss[loss=0.1543, simple_loss=0.2182, pruned_loss=0.04514, over 4830.00 frames.], tot_loss[loss=0.157, simple_loss=0.2269, pruned_loss=0.04359, over 974305.88 frames.], batch size: 26, lr: 4.73e-04
2022-05-04 19:28:29,132 INFO [train.py:715] (2/8) Epoch 4, batch 2950, loss[loss=0.1803, simple_loss=0.252, pruned_loss=0.05435, over 4981.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2267, pruned_loss=0.04356, over 974176.10 frames.], batch size: 40, lr: 4.73e-04
2022-05-04 19:29:08,448 INFO [train.py:715] (2/8) Epoch 4, batch 3000, loss[loss=0.162, simple_loss=0.2248, pruned_loss=0.04965, over 4942.00 frames.], tot_loss[loss=0.1574, simple_loss=0.227, pruned_loss=0.04387, over 973882.47 frames.], batch size: 39, lr: 4.73e-04
2022-05-04 19:29:08,449 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 19:29:17,943 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1127, simple_loss=0.1984, pruned_loss=0.01346, over 914524.00 frames.
2022-05-04 19:29:57,094 INFO [train.py:715] (2/8) Epoch 4, batch 3050, loss[loss=0.1969, simple_loss=0.2648, pruned_loss=0.06452, over 4919.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2275, pruned_loss=0.04409, over 973573.66 frames.], batch size: 29, lr: 4.73e-04
2022-05-04 19:30:37,134 INFO [train.py:715] (2/8) Epoch 4, batch 3100, loss[loss=0.1647, simple_loss=0.235, pruned_loss=0.0472, over 4844.00 frames.], tot_loss[loss=0.1575, simple_loss=0.227, pruned_loss=0.04403, over 973246.85 frames.], batch size: 30, lr: 4.73e-04
2022-05-04 19:31:17,411 INFO [train.py:715] (2/8) Epoch 4, batch 3150, loss[loss=0.2058, simple_loss=0.2703, pruned_loss=0.07066, over 4808.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2273, pruned_loss=0.04456, over 972217.77 frames.], batch size: 21, lr: 4.73e-04
2022-05-04 19:31:57,024 INFO [train.py:715] (2/8) Epoch 4, batch 3200, loss[loss=0.2185, simple_loss=0.2598, pruned_loss=0.08858, over 4868.00 frames.], tot_loss[loss=0.159, simple_loss=0.2277, pruned_loss=0.04511, over 971654.58 frames.], batch size: 32, lr: 4.72e-04
2022-05-04 19:32:36,975 INFO [train.py:715] (2/8) Epoch 4, batch 3250, loss[loss=0.13, simple_loss=0.1963, pruned_loss=0.0319, over 4771.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2274, pruned_loss=0.04481, over 972527.07 frames.], batch size: 18, lr: 4.72e-04
2022-05-04 19:33:16,912 INFO [train.py:715] (2/8) Epoch 4, batch 3300, loss[loss=0.1915, simple_loss=0.2562, pruned_loss=0.06337, over 4961.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2274, pruned_loss=0.04462, over 972653.33 frames.], batch size: 14, lr: 4.72e-04
2022-05-04 19:33:56,289 INFO [train.py:715] (2/8) Epoch 4, batch 3350, loss[loss=0.1591, simple_loss=0.2314, pruned_loss=0.04336, over 4934.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2268, pruned_loss=0.04415, over 972151.31 frames.], batch size: 39, lr: 4.72e-04
2022-05-04 19:34:35,329 INFO [train.py:715] (2/8) Epoch 4, batch 3400, loss[loss=0.1353, simple_loss=0.2044, pruned_loss=0.03307, over 4884.00 frames.], tot_loss[loss=0.158, simple_loss=0.2274, pruned_loss=0.04435, over 972127.72 frames.], batch size: 32, lr: 4.72e-04
2022-05-04 19:35:15,776 INFO [train.py:715] (2/8) Epoch 4, batch 3450, loss[loss=0.156, simple_loss=0.2262, pruned_loss=0.04294, over 4753.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2276, pruned_loss=0.04431, over 971425.63 frames.], batch size: 16, lr: 4.72e-04
2022-05-04 19:35:55,190 INFO [train.py:715] (2/8) Epoch 4, batch 3500, loss[loss=0.1226, simple_loss=0.1979, pruned_loss=0.02363, over 4829.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2273, pruned_loss=0.04444, over 971275.77 frames.], batch size: 26, lr: 4.72e-04
2022-05-04 19:36:34,857 INFO [train.py:715] (2/8) Epoch 4, batch 3550, loss[loss=0.1628, simple_loss=0.2313, pruned_loss=0.04713, over 4974.00 frames.], tot_loss[loss=0.1578, simple_loss=0.227, pruned_loss=0.0443, over 972204.34 frames.], batch size: 15, lr: 4.72e-04
2022-05-04 19:37:14,696 INFO [train.py:715] (2/8) Epoch 4, batch 3600, loss[loss=0.1555, simple_loss=0.2265, pruned_loss=0.04229, over 4914.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2268, pruned_loss=0.04436, over 972000.26 frames.], batch size: 23, lr: 4.72e-04
2022-05-04 19:37:54,697 INFO [train.py:715] (2/8) Epoch 4, batch 3650, loss[loss=0.1551, simple_loss=0.2179, pruned_loss=0.04612, over 4894.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2266, pruned_loss=0.04429, over 971576.94 frames.], batch size: 19, lr: 4.72e-04
2022-05-04 19:38:34,068 INFO [train.py:715] (2/8) Epoch 4, batch 3700, loss[loss=0.1455, simple_loss=0.2211, pruned_loss=0.03501, over 4832.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2265, pruned_loss=0.04416, over 971383.63 frames.], batch size: 30, lr: 4.72e-04
2022-05-04 19:39:13,349 INFO [train.py:715] (2/8) Epoch 4, batch 3750, loss[loss=0.1684, simple_loss=0.252, pruned_loss=0.04245, over 4845.00 frames.], tot_loss[loss=0.1578, simple_loss=0.227, pruned_loss=0.04433, over 971975.70 frames.], batch size: 32, lr: 4.72e-04
2022-05-04 19:39:53,217 INFO [train.py:715] (2/8) Epoch 4, batch 3800, loss[loss=0.1708, simple_loss=0.236, pruned_loss=0.05286, over 4955.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2275, pruned_loss=0.04471, over 971752.33 frames.], batch size: 21, lr: 4.72e-04
2022-05-04 19:40:32,934 INFO [train.py:715] (2/8) Epoch 4, batch 3850, loss[loss=0.1706, simple_loss=0.2391, pruned_loss=0.05106, over 4880.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2277, pruned_loss=0.04461, over 972087.30 frames.], batch size: 22, lr: 4.71e-04
2022-05-04 19:41:13,116 INFO [train.py:715] (2/8) Epoch 4, batch 3900, loss[loss=0.1408, simple_loss=0.2166, pruned_loss=0.03255, over 4759.00 frames.], tot_loss[loss=0.1587, simple_loss=0.228, pruned_loss=0.04471, over 971423.87 frames.], batch size: 19, lr: 4.71e-04
2022-05-04 19:41:53,261 INFO [train.py:715] (2/8) Epoch 4, batch 3950, loss[loss=0.185, simple_loss=0.2568, pruned_loss=0.05664, over 4903.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2279, pruned_loss=0.04458, over 971979.88 frames.], batch size: 17, lr: 4.71e-04
2022-05-04 19:42:33,630 INFO [train.py:715] (2/8) Epoch 4, batch 4000, loss[loss=0.1419, simple_loss=0.2107, pruned_loss=0.03653, over 4828.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2271, pruned_loss=0.04409, over 971839.77 frames.], batch size: 26, lr: 4.71e-04
2022-05-04 19:43:13,665 INFO [train.py:715] (2/8) Epoch 4, batch 4050, loss[loss=0.1187, simple_loss=0.1889, pruned_loss=0.02427, over 4922.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2268, pruned_loss=0.04429, over 971807.30 frames.], batch size: 29, lr: 4.71e-04
2022-05-04 19:43:53,245 INFO [train.py:715] (2/8) Epoch 4, batch 4100, loss[loss=0.1896, simple_loss=0.2651, pruned_loss=0.057, over 4860.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2273, pruned_loss=0.04448, over 972361.64 frames.], batch size: 20, lr: 4.71e-04
2022-05-04 19:44:33,947 INFO [train.py:715] (2/8) Epoch 4, batch 4150, loss[loss=0.1391, simple_loss=0.2034, pruned_loss=0.03741, over 4687.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2277, pruned_loss=0.04456, over 972161.81 frames.], batch size: 15, lr: 4.71e-04
2022-05-04 19:45:13,437 INFO [train.py:715] (2/8) Epoch 4, batch 4200, loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.02804, over 4940.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2283, pruned_loss=0.045, over 971541.76 frames.], batch size: 29, lr: 4.71e-04
2022-05-04 19:45:52,908 INFO [train.py:715] (2/8) Epoch 4, batch 4250, loss[loss=0.1918, simple_loss=0.252, pruned_loss=0.06576, over 4963.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2281, pruned_loss=0.04516, over 970696.11 frames.], batch size: 15, lr: 4.71e-04
2022-05-04 19:46:33,011 INFO [train.py:715] (2/8) Epoch 4, batch 4300, loss[loss=0.129, simple_loss=0.2128, pruned_loss=0.02263, over 4901.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2279, pruned_loss=0.04514, over 971426.55 frames.], batch size: 17, lr: 4.71e-04
2022-05-04 19:47:13,033 INFO [train.py:715] (2/8) Epoch 4, batch 4350, loss[loss=0.1554, simple_loss=0.2204, pruned_loss=0.04525, over 4982.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2277, pruned_loss=0.04471, over 971414.65 frames.], batch size: 15, lr: 4.71e-04
2022-05-04 19:47:52,119 INFO [train.py:715] (2/8) Epoch 4, batch 4400, loss[loss=0.1343, simple_loss=0.2015, pruned_loss=0.03353, over 4852.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2279, pruned_loss=0.04437, over 971892.77 frames.], batch size: 32, lr: 4.71e-04
2022-05-04 19:48:31,827 INFO [train.py:715] (2/8) Epoch 4, batch 4450, loss[loss=0.16, simple_loss=0.2252, pruned_loss=0.0474, over 4958.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2275, pruned_loss=0.04439, over 971667.83 frames.], batch size: 24, lr: 4.70e-04
2022-05-04 19:49:12,001 INFO [train.py:715] (2/8) Epoch 4, batch 4500, loss[loss=0.1484, simple_loss=0.2192, pruned_loss=0.03878, over 4845.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2275, pruned_loss=0.04439, over 972100.33 frames.], batch size: 34, lr: 4.70e-04
2022-05-04 19:49:51,274 INFO [train.py:715] (2/8) Epoch 4, batch 4550, loss[loss=0.1443, simple_loss=0.2087, pruned_loss=0.03998, over 4993.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2277, pruned_loss=0.04458, over 971759.55 frames.], batch size: 20, lr: 4.70e-04
2022-05-04 19:50:30,674 INFO [train.py:715] (2/8) Epoch 4, batch 4600, loss[loss=0.1772, simple_loss=0.2513, pruned_loss=0.05155, over 4968.00 frames.], tot_loss[loss=0.158, simple_loss=0.2275, pruned_loss=0.04422, over 972713.26 frames.], batch size: 39, lr: 4.70e-04
2022-05-04 19:51:10,986 INFO [train.py:715] (2/8) Epoch 4, batch 4650, loss[loss=0.1311, simple_loss=0.1917, pruned_loss=0.03527, over 4890.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2261, pruned_loss=0.04339, over 972401.76 frames.], batch size: 22, lr: 4.70e-04
2022-05-04 19:51:51,341 INFO [train.py:715] (2/8) Epoch 4, batch 4700, loss[loss=0.1621, simple_loss=0.2195, pruned_loss=0.05238, over 4914.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04371, over 972485.27 frames.], batch size: 18, lr: 4.70e-04
2022-05-04 19:52:31,248 INFO [train.py:715] (2/8) Epoch 4, batch 4750, loss[loss=0.1613, simple_loss=0.2406, pruned_loss=0.04099, over 4844.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2267, pruned_loss=0.04392, over 973539.38 frames.], batch size: 15, lr: 4.70e-04
2022-05-04 19:53:13,035 INFO [train.py:715] (2/8) Epoch 4, batch 4800, loss[loss=0.1474, simple_loss=0.2081, pruned_loss=0.04337, over 4752.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04386, over 973471.89 frames.], batch size: 19, lr: 4.70e-04
2022-05-04 19:53:53,557 INFO [train.py:715] (2/8) Epoch 4, batch 4850, loss[loss=0.1948, simple_loss=0.2584, pruned_loss=0.06558, over 4765.00 frames.], tot_loss[loss=0.1569, simple_loss=0.226, pruned_loss=0.04388, over 972644.92 frames.], batch size: 18, lr: 4.70e-04
2022-05-04 19:54:32,963 INFO [train.py:715] (2/8) Epoch 4, batch 4900, loss[loss=0.1489, simple_loss=0.2287, pruned_loss=0.03454, over 4862.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2259, pruned_loss=0.04386, over 973277.40 frames.], batch size: 20, lr: 4.70e-04
2022-05-04 19:55:12,348 INFO [train.py:715] (2/8) Epoch 4, batch 4950, loss[loss=0.1626, simple_loss=0.239, pruned_loss=0.04312, over 4981.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2275, pruned_loss=0.04452, over 973431.71 frames.], batch size: 15, lr: 4.70e-04
2022-05-04 19:55:52,410 INFO [train.py:715] (2/8) Epoch 4, batch 5000, loss[loss=0.1514, simple_loss=0.2219, pruned_loss=0.04039, over 4961.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2278, pruned_loss=0.04491, over 973301.95 frames.], batch size: 21, lr: 4.70e-04
2022-05-04 19:56:32,441 INFO [train.py:715] (2/8) Epoch 4, batch 5050, loss[loss=0.1706, simple_loss=0.2396, pruned_loss=0.05081, over 4987.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2282, pruned_loss=0.04519, over 973561.69 frames.], batch size: 14, lr: 4.69e-04
2022-05-04 19:57:12,349 INFO [train.py:715] (2/8) Epoch 4, batch 5100, loss[loss=0.1571, simple_loss=0.23, pruned_loss=0.0421, over 4888.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2285, pruned_loss=0.04491, over 973355.19 frames.], batch size: 17, lr: 4.69e-04
2022-05-04 19:57:51,519 INFO [train.py:715] (2/8) Epoch 4, batch 5150, loss[loss=0.1492, simple_loss=0.2243, pruned_loss=0.0371, over 4755.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2284, pruned_loss=0.04485, over 973130.58 frames.], batch size: 19, lr: 4.69e-04
2022-05-04 19:58:31,723 INFO [train.py:715] (2/8) Epoch 4, batch 5200, loss[loss=0.1214, simple_loss=0.1827, pruned_loss=0.03006, over 4819.00 frames.], tot_loss[loss=0.158, simple_loss=0.2269, pruned_loss=0.04452, over 973818.56 frames.], batch size: 13, lr: 4.69e-04
2022-05-04 19:59:11,083 INFO [train.py:715] (2/8) Epoch 4, batch 5250, loss[loss=0.1926, simple_loss=0.259, pruned_loss=0.06315, over 4973.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2275, pruned_loss=0.04444, over 974011.82 frames.], batch size: 15, lr: 4.69e-04
2022-05-04 19:59:50,712 INFO [train.py:715] (2/8) Epoch 4, batch 5300, loss[loss=0.1422, simple_loss=0.2126, pruned_loss=0.03592, over 4927.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2277, pruned_loss=0.04446, over 973884.06 frames.], batch size: 29, lr: 4.69e-04
2022-05-04 20:00:30,977 INFO [train.py:715] (2/8) Epoch 4, batch 5350, loss[loss=0.1598, simple_loss=0.2406, pruned_loss=0.03946, over 4823.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2275, pruned_loss=0.04429, over 973551.82 frames.], batch size: 27, lr: 4.69e-04
2022-05-04 20:01:11,128 INFO [train.py:715] (2/8) Epoch 4, batch 5400, loss[loss=0.1412, simple_loss=0.2103, pruned_loss=0.036, over 4816.00 frames.], tot_loss[loss=0.1574, simple_loss=0.227, pruned_loss=0.04392, over 973308.46 frames.], batch size: 25, lr: 4.69e-04
2022-05-04 20:01:51,428 INFO [train.py:715] (2/8) Epoch 4, batch 5450, loss[loss=0.1883, simple_loss=0.2517, pruned_loss=0.06244, over 4905.00 frames.], tot_loss[loss=0.158, simple_loss=0.2274, pruned_loss=0.04426, over 973334.46 frames.], batch size: 17, lr: 4.69e-04
2022-05-04 20:02:30,840 INFO [train.py:715] (2/8) Epoch 4, batch 5500, loss[loss=0.1408, simple_loss=0.2132, pruned_loss=0.03417, over 4887.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2268, pruned_loss=0.04379, over 972988.90 frames.], batch size: 22, lr: 4.69e-04
2022-05-04 20:03:11,383 INFO [train.py:715] (2/8) Epoch 4, batch 5550, loss[loss=0.1381, simple_loss=0.212, pruned_loss=0.03209, over 4766.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2262, pruned_loss=0.04323, over 972690.68 frames.], batch size: 19, lr: 4.69e-04
2022-05-04 20:03:51,126 INFO [train.py:715] (2/8) Epoch 4, batch 5600, loss[loss=0.156, simple_loss=0.2322, pruned_loss=0.0399, over 4762.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2267, pruned_loss=0.04351, over 973124.88 frames.], batch size: 18, lr: 4.69e-04
2022-05-04 20:04:31,008 INFO [train.py:715] (2/8) Epoch 4, batch 5650, loss[loss=0.1567, simple_loss=0.2227, pruned_loss=0.04535, over 4672.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2265, pruned_loss=0.04339, over 972816.96 frames.], batch size: 13, lr: 4.68e-04
2022-05-04 20:05:10,990 INFO [train.py:715] (2/8) Epoch 4, batch 5700, loss[loss=0.1427, simple_loss=0.224, pruned_loss=0.03072, over 4824.00 frames.], tot_loss[loss=0.1574, simple_loss=0.227, pruned_loss=0.04395, over 972465.43 frames.], batch size: 25, lr: 4.68e-04
2022-05-04 20:05:51,206 INFO [train.py:715] (2/8) Epoch 4, batch 5750, loss[loss=0.1407, simple_loss=0.2202, pruned_loss=0.03055, over 4750.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2271, pruned_loss=0.04375, over 973034.30 frames.], batch size: 16, lr: 4.68e-04
2022-05-04 20:06:31,313 INFO [train.py:715] (2/8) Epoch 4, batch 5800, loss[loss=0.1772, simple_loss=0.2372, pruned_loss=0.05863, over 4954.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2271, pruned_loss=0.04357, over 974181.47 frames.], batch size: 39, lr: 4.68e-04
2022-05-04 20:07:10,964 INFO [train.py:715] (2/8) Epoch 4, batch 5850, loss[loss=0.1857, simple_loss=0.2635, pruned_loss=0.05395, over 4916.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2267, pruned_loss=0.04333, over 974571.14 frames.], batch size: 17, lr: 4.68e-04
2022-05-04 20:07:51,257 INFO [train.py:715] (2/8) Epoch 4, batch 5900, loss[loss=0.137, simple_loss=0.214, pruned_loss=0.02997, over 4772.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2268, pruned_loss=0.04371, over 973925.02 frames.], batch size: 14, lr: 4.68e-04
2022-05-04 20:08:30,935 INFO [train.py:715] (2/8) Epoch 4, batch 5950, loss[loss=0.1457, simple_loss=0.2206, pruned_loss=0.03542, over 4751.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2271, pruned_loss=0.04366, over 972606.23 frames.], batch size: 16, lr: 4.68e-04
2022-05-04 20:09:10,572 INFO [train.py:715] (2/8) Epoch 4, batch 6000, loss[loss=0.1614, simple_loss=0.238, pruned_loss=0.04237, over 4914.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2281, pruned_loss=0.04405, over 972032.41 frames.], batch size: 18, lr: 4.68e-04
2022-05-04 20:09:10,572 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 20:09:20,451 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1124, simple_loss=0.1981, pruned_loss=0.01337, over 914524.00 frames.
2022-05-04 20:10:00,569 INFO [train.py:715] (2/8) Epoch 4, batch 6050, loss[loss=0.1935, simple_loss=0.2612, pruned_loss=0.06292, over 4950.00 frames.], tot_loss[loss=0.158, simple_loss=0.2279, pruned_loss=0.04402, over 972402.50 frames.], batch size: 35, lr: 4.68e-04
2022-05-04 20:10:40,767 INFO [train.py:715] (2/8) Epoch 4, batch 6100, loss[loss=0.1807, simple_loss=0.2532, pruned_loss=0.05414, over 4890.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2275, pruned_loss=0.04362, over 972713.80 frames.], batch size: 16, lr: 4.68e-04
2022-05-04 20:11:21,160 INFO [train.py:715] (2/8) Epoch 4, batch 6150, loss[loss=0.1557, simple_loss=0.2205, pruned_loss=0.04546, over 4637.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2271, pruned_loss=0.04371, over 972288.70 frames.], batch size: 13, lr: 4.68e-04
2022-05-04 20:12:01,192 INFO [train.py:715] (2/8) Epoch 4, batch 6200, loss[loss=0.1344, simple_loss=0.2059, pruned_loss=0.03147, over 4849.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2267, pruned_loss=0.0434, over 972674.96 frames.], batch size: 12, lr: 4.68e-04
2022-05-04 20:12:40,827 INFO [train.py:715] (2/8) Epoch 4, batch 6250, loss[loss=0.1412, simple_loss=0.2102, pruned_loss=0.03614, over 4822.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2267, pruned_loss=0.0434, over 972798.08 frames.], batch size: 26, lr: 4.68e-04
2022-05-04 20:13:21,465 INFO [train.py:715] (2/8) Epoch 4, batch 6300, loss[loss=0.1639, simple_loss=0.2266, pruned_loss=0.05062, over 4756.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2268, pruned_loss=0.04346, over 972454.55 frames.], batch size: 12, lr: 4.67e-04
2022-05-04 20:14:00,895 INFO [train.py:715] (2/8) Epoch 4, batch 6350, loss[loss=0.1214, simple_loss=0.1985, pruned_loss=0.02222, over 4768.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2272, pruned_loss=0.04374, over 972479.34 frames.], batch size: 14, lr: 4.67e-04
2022-05-04 20:14:41,821 INFO [train.py:715] (2/8) Epoch 4, batch 6400, loss[loss=0.1502, simple_loss=0.2201, pruned_loss=0.04013, over 4904.00 frames.], tot_loss[loss=0.158, simple_loss=0.2277, pruned_loss=0.04412, over 972945.37 frames.], batch size: 22, lr: 4.67e-04
2022-05-04 20:15:21,558 INFO [train.py:715] (2/8) Epoch 4, batch 6450, loss[loss=0.1807, simple_loss=0.2522, pruned_loss=0.05465, over 4923.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2286, pruned_loss=0.04451, over 972749.77 frames.], batch size: 29, lr: 4.67e-04
2022-05-04 20:16:01,664 INFO [train.py:715] (2/8) Epoch 4, batch 6500, loss[loss=0.1616, simple_loss=0.2437, pruned_loss=0.03972, over 4876.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2292, pruned_loss=0.04508, over 972804.34 frames.], batch size: 20, lr: 4.67e-04
2022-05-04 20:16:41,334 INFO [train.py:715] (2/8) Epoch 4, batch 6550, loss[loss=0.1455, simple_loss=0.2186, pruned_loss=0.03617, over 4847.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2292, pruned_loss=0.04526, over 972383.01 frames.], batch size: 13, lr: 4.67e-04
2022-05-04 20:17:20,645 INFO [train.py:715] (2/8) Epoch 4, batch 6600, loss[loss=0.1534, simple_loss=0.2201, pruned_loss=0.04339, over 4749.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2292, pruned_loss=0.04506, over 972302.27 frames.], batch size: 16, lr: 4.67e-04
2022-05-04 20:18:01,338 INFO [train.py:715] (2/8) Epoch 4, batch 6650, loss[loss=0.1894, simple_loss=0.2505, pruned_loss=0.0641, over 4946.00 frames.], tot_loss[loss=0.1595, simple_loss=0.229, pruned_loss=0.04498, over 973554.03 frames.], batch size: 39, lr: 4.67e-04
2022-05-04 20:18:40,887 INFO [train.py:715] (2/8) Epoch 4, batch 6700, loss[loss=0.1453, simple_loss=0.2236, pruned_loss=0.03346, over 4788.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2282, pruned_loss=0.04458, over 973366.06 frames.], batch size: 21, lr: 4.67e-04
2022-05-04 20:19:21,000 INFO [train.py:715] (2/8) Epoch 4, batch 6750, loss[loss=0.1712, simple_loss=0.24, pruned_loss=0.05118, over 4936.00 frames.], tot_loss[loss=0.1579, simple_loss=0.228, pruned_loss=0.04383, over 973041.53 frames.], batch size: 21, lr: 4.67e-04
2022-05-04 20:20:00,757 INFO [train.py:715] (2/8) Epoch 4, batch 6800, loss[loss=0.1137, simple_loss=0.1883, pruned_loss=0.01958, over 4760.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2289, pruned_loss=0.04446, over 972802.23 frames.], batch size: 12, lr: 4.67e-04
2022-05-04 20:20:40,796 INFO [train.py:715] (2/8) Epoch 4, batch 6850, loss[loss=0.1645, simple_loss=0.2271, pruned_loss=0.05094, over 4977.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2285, pruned_loss=0.04409, over 972801.92 frames.], batch size: 15, lr: 4.67e-04
2022-05-04 20:21:20,097 INFO [train.py:715] (2/8) Epoch 4, batch 6900, loss[loss=0.1932, simple_loss=0.2531, pruned_loss=0.06661, over 4890.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2271, pruned_loss=0.04368, over 973011.26 frames.], batch size: 16, lr: 4.66e-04
2022-05-04 20:21:59,578 INFO [train.py:715] (2/8) Epoch 4, batch 6950, loss[loss=0.1388, simple_loss=0.2134, pruned_loss=0.03209, over 4857.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2272, pruned_loss=0.04367, over 972665.72 frames.], batch size: 32, lr: 4.66e-04
2022-05-04 20:22:39,323 INFO [train.py:715] (2/8) Epoch 4, batch 7000, loss[loss=0.1406, simple_loss=0.2136, pruned_loss=0.03382, over 4937.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2279, pruned_loss=0.04385, over 972596.42 frames.], batch size: 24, lr: 4.66e-04
2022-05-04 20:23:19,195 INFO [train.py:715] (2/8) Epoch 4, batch 7050, loss[loss=0.1585, simple_loss=0.2329, pruned_loss=0.04204, over 4827.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2281, pruned_loss=0.04419, over 972681.01 frames.], batch size: 13, lr: 4.66e-04
2022-05-04 20:23:58,924 INFO [train.py:715] (2/8) Epoch 4, batch 7100, loss[loss=0.1565, simple_loss=0.2312, pruned_loss=0.04086, over 4964.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2274, pruned_loss=0.04404, over 972542.36 frames.], batch size: 24, lr: 4.66e-04
2022-05-04 20:24:39,016 INFO [train.py:715] (2/8) Epoch 4, batch 7150, loss[loss=0.1625, simple_loss=0.238, pruned_loss=0.0435, over 4896.00 frames.], tot_loss[loss=0.158, simple_loss=0.2277, pruned_loss=0.04418, over 972102.85 frames.], batch size: 19, lr: 4.66e-04
2022-05-04 20:25:18,944 INFO [train.py:715] (2/8) Epoch 4, batch 7200, loss[loss=0.1289, simple_loss=0.2001, pruned_loss=0.02886, over 4774.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2277, pruned_loss=0.04404, over 972396.55 frames.], batch size: 16, lr: 4.66e-04
2022-05-04 20:25:59,098 INFO [train.py:715] (2/8) Epoch 4, batch 7250, loss[loss=0.1517, simple_loss=0.2265, pruned_loss=0.03847, over 4975.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2273, pruned_loss=0.04399, over 972635.67 frames.], batch size: 28, lr: 4.66e-04
2022-05-04 20:26:38,420 INFO [train.py:715] (2/8) Epoch 4, batch 7300, loss[loss=0.1341, simple_loss=0.2151, pruned_loss=0.02649, over 4848.00 frames.], tot_loss[loss=0.1584, simple_loss=0.228, pruned_loss=0.0444, over 972133.90 frames.], batch size: 15, lr: 4.66e-04
2022-05-04 20:27:18,105 INFO [train.py:715] (2/8) Epoch 4, batch 7350, loss[loss=0.1501, simple_loss=0.2185, pruned_loss=0.04089, over 4964.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2271, pruned_loss=0.04406, over 973020.06 frames.], batch size: 15, lr: 4.66e-04
2022-05-04 20:27:58,075 INFO [train.py:715] (2/8) Epoch 4, batch 7400, loss[loss=0.1217, simple_loss=0.1877, pruned_loss=0.02787, over 4914.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2279, pruned_loss=0.04438, over 973298.17 frames.], batch size: 17, lr: 4.66e-04
2022-05-04 20:28:38,813 INFO [train.py:715] (2/8) Epoch 4, batch 7450, loss[loss=0.1611, simple_loss=0.2306, pruned_loss=0.04581, over 4798.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2281, pruned_loss=0.04476, over 972149.51 frames.], batch size: 24, lr: 4.66e-04
2022-05-04 20:29:18,222 INFO [train.py:715] (2/8) Epoch 4, batch 7500, loss[loss=0.1518, simple_loss=0.2267, pruned_loss=0.03846, over 4775.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2286, pruned_loss=0.04506, over 972452.11 frames.], batch size: 18, lr: 4.66e-04
2022-05-04 20:29:58,241 INFO [train.py:715] (2/8) Epoch 4, batch 7550, loss[loss=0.157, simple_loss=0.2379, pruned_loss=0.03804, over 4989.00 frames.], tot_loss[loss=0.16, simple_loss=0.2289, pruned_loss=0.04556, over 972059.68 frames.], batch size: 26, lr: 4.65e-04
2022-05-04 20:30:38,894 INFO [train.py:715] (2/8) Epoch 4, batch 7600, loss[loss=0.1538, simple_loss=0.2269, pruned_loss=0.04035, over 4760.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2282, pruned_loss=0.04516, over 971180.55 frames.], batch size: 19, lr: 4.65e-04
2022-05-04 20:31:18,408 INFO [train.py:715] (2/8) Epoch 4, batch 7650, loss[loss=0.1352, simple_loss=0.2038, pruned_loss=0.03331, over 4764.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2285, pruned_loss=0.04534, over 971333.82 frames.], batch size: 16, lr: 4.65e-04
2022-05-04 20:31:58,070 INFO [train.py:715] (2/8) Epoch 4, batch 7700, loss[loss=0.1571, simple_loss=0.22, pruned_loss=0.04709, over 4785.00 frames.], tot_loss[loss=0.159, simple_loss=0.2281, pruned_loss=0.04495, over 971050.69 frames.], batch size: 14, lr: 4.65e-04
2022-05-04 20:32:38,170 INFO [train.py:715] (2/8) Epoch 4, batch 7750, loss[loss=0.1581, simple_loss=0.231, pruned_loss=0.04255, over 4758.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2283, pruned_loss=0.0446, over 971319.35 frames.], batch size: 16, lr: 4.65e-04
2022-05-04 20:33:18,308 INFO [train.py:715] (2/8) Epoch 4, batch 7800, loss[loss=0.134, simple_loss=0.2126, pruned_loss=0.02769, over 4827.00 frames.], tot_loss[loss=0.1581, simple_loss=0.228, pruned_loss=0.04414, over 971261.51 frames.], batch size: 26, lr: 4.65e-04
2022-05-04 20:33:57,312 INFO [train.py:715] (2/8) Epoch 4, batch 7850, loss[loss=0.1801, simple_loss=0.2474, pruned_loss=0.05642, over 4930.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2279, pruned_loss=0.04432, over 971729.88 frames.], batch size: 39, lr: 4.65e-04
2022-05-04 20:34:36,908 INFO [train.py:715] (2/8) Epoch 4, batch 7900, loss[loss=0.1311, simple_loss=0.2083, pruned_loss=0.02697, over 4892.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2279, pruned_loss=0.04431, over 972017.62 frames.], batch size: 22, lr: 4.65e-04
2022-05-04 20:35:16,766 INFO [train.py:715] (2/8) Epoch 4, batch 7950, loss[loss=0.1549, simple_loss=0.2242, pruned_loss=0.04277, over 4813.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2277, pruned_loss=0.0444, over 972123.38 frames.], batch size: 12, lr: 4.65e-04
2022-05-04 20:35:56,347 INFO [train.py:715] (2/8) Epoch 4, batch 8000, loss[loss=0.1469, simple_loss=0.2208, pruned_loss=0.03649, over 4899.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2279, pruned_loss=0.04426, over 972271.59 frames.], batch size: 19, lr: 4.65e-04
2022-05-04 20:36:36,312 INFO [train.py:715] (2/8) Epoch 4, batch 8050, loss[loss=0.135, simple_loss=0.1989, pruned_loss=0.03559, over 4773.00 frames.], tot_loss[loss=0.1585, simple_loss=0.228, pruned_loss=0.04446, over 972278.10 frames.], batch size: 17, lr: 4.65e-04
2022-05-04 20:37:16,268 INFO [train.py:715] (2/8) Epoch 4, batch 8100, loss[loss=0.1634, simple_loss=0.2363, pruned_loss=0.04519, over 4985.00 frames.], tot_loss[loss=0.159, simple_loss=0.2279, pruned_loss=0.04505, over 972536.14 frames.], batch size: 28, lr: 4.65e-04
2022-05-04 20:37:56,523 INFO [train.py:715] (2/8) Epoch 4, batch 8150, loss[loss=0.1354, simple_loss=0.2054, pruned_loss=0.03265, over 4930.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2281, pruned_loss=0.04501, over 972192.53 frames.], batch size: 23, lr: 4.65e-04
2022-05-04 20:38:35,991 INFO [train.py:715] (2/8) Epoch 4, batch 8200, loss[loss=0.1428, simple_loss=0.2185, pruned_loss=0.0336, over 4772.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2271, pruned_loss=0.04474, over 973000.79 frames.], batch size: 17, lr: 4.64e-04
2022-05-04 20:39:15,728 INFO [train.py:715] (2/8) Epoch 4, batch 8250, loss[loss=0.1846, simple_loss=0.2504, pruned_loss=0.05942, over 4816.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2267, pruned_loss=0.04434, over 973195.39 frames.], batch size: 26, lr: 4.64e-04
2022-05-04 20:39:55,877 INFO [train.py:715] (2/8) Epoch 4, batch 8300, loss[loss=0.1651, simple_loss=0.2291, pruned_loss=0.05052, over 4899.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2281, pruned_loss=0.04483, over 973015.42 frames.], batch size: 22, lr: 4.64e-04
2022-05-04 20:40:35,314 INFO [train.py:715] (2/8) Epoch 4, batch 8350, loss[loss=0.1516, simple_loss=0.2221, pruned_loss=0.04053, over 4924.00 frames.], tot_loss[loss=0.1599, simple_loss=0.229, pruned_loss=0.04536, over 973306.18 frames.], batch size: 18, lr: 4.64e-04
2022-05-04 20:41:15,401 INFO [train.py:715] (2/8) Epoch 4, batch 8400, loss[loss=0.1385, simple_loss=0.2089, pruned_loss=0.03403, over 4762.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2287, pruned_loss=0.0454, over 972897.22 frames.], batch size: 16, lr: 4.64e-04
2022-05-04 20:41:55,744 INFO [train.py:715] (2/8) Epoch 4, batch 8450, loss[loss=0.1777, simple_loss=0.247, pruned_loss=0.05422, over 4778.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2296, pruned_loss=0.04585, over 972512.02 frames.], batch size: 18, lr: 4.64e-04
2022-05-04 20:42:35,852 INFO [train.py:715] (2/8) Epoch 4, batch 8500, loss[loss=0.1393, simple_loss=0.2053, pruned_loss=0.03668, over 4940.00 frames.], tot_loss[loss=0.159, simple_loss=0.2281, pruned_loss=0.04494, over 972732.86 frames.], batch size: 21, lr: 4.64e-04
2022-05-04 20:43:15,265 INFO [train.py:715] (2/8) Epoch 4, batch 8550, loss[loss=0.1641, simple_loss=0.2326, pruned_loss=0.0478, over 4718.00 frames.], tot_loss[loss=0.1587, simple_loss=0.228, pruned_loss=0.0447, over 973313.13 frames.], batch size: 12, lr: 4.64e-04
2022-05-04 20:43:55,081 INFO [train.py:715] (2/8) Epoch 4, batch 8600, loss[loss=0.1531, simple_loss=0.2295, pruned_loss=0.03834, over 4934.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2286, pruned_loss=0.04519, over 973280.83 frames.], batch size: 23, lr: 4.64e-04
2022-05-04 20:44:35,239 INFO [train.py:715] (2/8) Epoch 4, batch 8650, loss[loss=0.1544, simple_loss=0.217, pruned_loss=0.04587, over 4858.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2287, pruned_loss=0.04505, over 972861.02 frames.], batch size: 32, lr: 4.64e-04
2022-05-04 20:45:14,887 INFO [train.py:715] (2/8) Epoch 4, batch 8700, loss[loss=0.1542, simple_loss=0.2245, pruned_loss=0.04201, over 4897.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2293, pruned_loss=0.04571, over 973505.43 frames.], batch size: 17, lr: 4.64e-04
2022-05-04 20:45:55,167 INFO [train.py:715] (2/8) Epoch 4, batch 8750, loss[loss=0.1363, simple_loss=0.2084, pruned_loss=0.03212, over 4901.00 frames.], tot_loss[loss=0.1588, simple_loss=0.228, pruned_loss=0.04481, over 973297.41 frames.], batch size: 17, lr: 4.64e-04
2022-05-04 20:46:35,396 INFO [train.py:715] (2/8) Epoch 4, batch 8800, loss[loss=0.178, simple_loss=0.2451, pruned_loss=0.05548, over 4924.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2276, pruned_loss=0.04489, over 973463.72 frames.], batch size: 18, lr: 4.63e-04
2022-05-04 20:47:15,432 INFO [train.py:715] (2/8) Epoch 4, batch 8850, loss[loss=0.1829, simple_loss=0.247, pruned_loss=0.05945, over 4752.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2288, pruned_loss=0.04549, over 972501.64 frames.], batch size: 19, lr: 4.63e-04
2022-05-04 20:47:55,129 INFO [train.py:715] (2/8) Epoch 4, batch 8900, loss[loss=0.1631, simple_loss=0.2354, pruned_loss=0.04543, over 4865.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2277, pruned_loss=0.04493, over 973430.55 frames.], batch size: 20, lr: 4.63e-04
2022-05-04 20:48:34,760 INFO [train.py:715] (2/8) Epoch 4, batch 8950, loss[loss=0.1696, simple_loss=0.2414, pruned_loss=0.04889, over 4802.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2267, pruned_loss=0.04431, over 973273.51 frames.], batch size: 24, lr: 4.63e-04
2022-05-04 20:49:15,024 INFO [train.py:715] (2/8) Epoch 4, batch 9000, loss[loss=0.1645, simple_loss=0.2282, pruned_loss=0.05042, over 4906.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2268, pruned_loss=0.04441, over 973820.78 frames.], batch size: 29, lr: 4.63e-04
2022-05-04 20:49:15,025 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 20:49:24,976 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1123, simple_loss=0.1979, pruned_loss=0.01336, over 914524.00 frames.
2022-05-04 20:50:05,302 INFO [train.py:715] (2/8) Epoch 4, batch 9050, loss[loss=0.1362, simple_loss=0.2112, pruned_loss=0.03062, over 4960.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2271, pruned_loss=0.04411, over 973897.12 frames.], batch size: 24, lr: 4.63e-04
2022-05-04 20:50:45,312 INFO [train.py:715] (2/8) Epoch 4, batch 9100, loss[loss=0.1598, simple_loss=0.2313, pruned_loss=0.04416, over 4928.00 frames.], tot_loss[loss=0.158, simple_loss=0.2273, pruned_loss=0.04432, over 973218.67 frames.], batch size: 18, lr: 4.63e-04
2022-05-04 20:51:24,731 INFO [train.py:715] (2/8) Epoch 4, batch 9150, loss[loss=0.1403, simple_loss=0.2041, pruned_loss=0.03825, over 4840.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2263, pruned_loss=0.04379, over 972861.49 frames.], batch size: 30, lr: 4.63e-04
2022-05-04 20:52:04,887 INFO [train.py:715] (2/8) Epoch 4, batch 9200, loss[loss=0.1512, simple_loss=0.226, pruned_loss=0.03815, over 4765.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2263, pruned_loss=0.04362, over 973063.53 frames.], batch size: 16, lr: 4.63e-04
2022-05-04 20:52:45,292 INFO [train.py:715] (2/8) Epoch 4, batch 9250, loss[loss=0.1473, simple_loss=0.2138, pruned_loss=0.04039, over 4904.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2273, pruned_loss=0.0439, over 972756.34 frames.], batch size: 32, lr: 4.63e-04
2022-05-04 20:53:24,537 INFO [train.py:715] (2/8) Epoch 4, batch 9300, loss[loss=0.1504, simple_loss=0.227, pruned_loss=0.03692, over 4755.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2282, pruned_loss=0.04446, over 970769.10 frames.], batch size: 19, lr: 4.63e-04
2022-05-04 20:54:04,528 INFO [train.py:715] (2/8) Epoch 4, batch 9350, loss[loss=0.1587, simple_loss=0.2298, pruned_loss=0.04387, over 4872.00 frames.], tot_loss[loss=0.1583, simple_loss=0.228, pruned_loss=0.04429, over 971013.11 frames.], batch size: 22, lr: 4.63e-04
2022-05-04 20:54:44,471 INFO [train.py:715] (2/8) Epoch 4, batch 9400, loss[loss=0.1845, simple_loss=0.2544, pruned_loss=0.05733, over 4986.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2275, pruned_loss=0.04417, over 970756.39 frames.], batch size: 28, lr: 4.63e-04
2022-05-04 20:55:24,019 INFO [train.py:715] (2/8) Epoch 4, batch 9450, loss[loss=0.1296, simple_loss=0.1953, pruned_loss=0.03195, over 4898.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2264, pruned_loss=0.04395, over 971708.96 frames.], batch size: 17, lr: 4.62e-04
2022-05-04 20:56:04,092 INFO [train.py:715] (2/8) Epoch 4, batch 9500, loss[loss=0.2067, simple_loss=0.2633, pruned_loss=0.07509, over 4872.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2261, pruned_loss=0.04401, over 972138.82 frames.], batch size: 22, lr: 4.62e-04
2022-05-04 20:56:44,150 INFO [train.py:715] (2/8) Epoch 4, batch 9550, loss[loss=0.1561, simple_loss=0.2203, pruned_loss=0.04593, over 4813.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2272, pruned_loss=0.04432, over 971678.93 frames.], batch size: 27, lr: 4.62e-04
2022-05-04 20:57:24,668 INFO [train.py:715] (2/8) Epoch 4, batch 9600, loss[loss=0.1717, simple_loss=0.2432, pruned_loss=0.05013, over 4837.00 frames.], tot_loss[loss=0.158, simple_loss=0.2272, pruned_loss=0.04435, over 971456.63 frames.], batch size: 30, lr: 4.62e-04
2022-05-04 20:58:04,094 INFO [train.py:715] (2/8) Epoch 4, batch 9650, loss[loss=0.1516, simple_loss=0.2173, pruned_loss=0.043, over 4747.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2269, pruned_loss=0.04428, over 972115.80 frames.], batch size: 16, lr: 4.62e-04
2022-05-04 20:58:44,657 INFO [train.py:715] (2/8) Epoch 4, batch 9700, loss[loss=0.1617, simple_loss=0.2266, pruned_loss=0.04841, over 4732.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2274, pruned_loss=0.04458, over 971662.33 frames.], batch size: 16, lr: 4.62e-04
2022-05-04 20:59:25,200 INFO [train.py:715] (2/8) Epoch 4, batch 9750, loss[loss=0.1435, simple_loss=0.2188, pruned_loss=0.03408, over 4777.00 frames.], tot_loss[loss=0.159, simple_loss=0.228, pruned_loss=0.04504, over 971805.41 frames.], batch size: 18, lr: 4.62e-04
2022-05-04 21:00:04,727 INFO [train.py:715] (2/8) Epoch 4, batch 9800, loss[loss=0.185, simple_loss=0.2354, pruned_loss=0.06727, over 4982.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2275, pruned_loss=0.04448, over 972126.89 frames.], batch size: 15, lr: 4.62e-04
2022-05-04 21:00:43,863 INFO [train.py:715] (2/8) Epoch 4, batch 9850, loss[loss=0.1421, simple_loss=0.2063, pruned_loss=0.03893, over 4641.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2268, pruned_loss=0.04381, over 971667.64 frames.], batch size: 13, lr: 4.62e-04
2022-05-04 21:01:23,905 INFO [train.py:715] (2/8) Epoch 4, batch 9900, loss[loss=0.1324, simple_loss=0.2098, pruned_loss=0.02751, over 4945.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2262, pruned_loss=0.04343, over 972064.34 frames.], batch size: 39, lr: 4.62e-04
2022-05-04 21:02:03,379 INFO [train.py:715] (2/8) Epoch 4, batch 9950, loss[loss=0.1661, simple_loss=0.2342, pruned_loss=0.04902, over 4813.00 frames.], tot_loss[loss=0.1563, simple_loss=0.226, pruned_loss=0.04333, over 972541.14 frames.], batch size: 27, lr: 4.62e-04
2022-05-04 21:02:42,755 INFO [train.py:715] (2/8) Epoch 4, batch 10000, loss[loss=0.1876, simple_loss=0.2541, pruned_loss=0.06052, over 4946.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2264, pruned_loss=0.04361, over 972989.56 frames.], batch size: 39, lr: 4.62e-04
2022-05-04 21:03:22,515 INFO [train.py:715] (2/8) Epoch 4, batch 10050, loss[loss=0.1938, simple_loss=0.2721, pruned_loss=0.0578, over 4835.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2271, pruned_loss=0.04388, over 972295.52 frames.], batch size: 15, lr: 4.62e-04
2022-05-04 21:04:02,314 INFO [train.py:715] (2/8) Epoch 4, batch 10100, loss[loss=0.1496, simple_loss=0.2292, pruned_loss=0.03496, over 4934.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2274, pruned_loss=0.04394, over 971631.17 frames.], batch size: 23, lr: 4.61e-04
2022-05-04 21:04:41,561 INFO [train.py:715] (2/8) Epoch 4, batch 10150, loss[loss=0.1782, simple_loss=0.2472, pruned_loss=0.05464, over 4884.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2271, pruned_loss=0.04377, over 972039.27 frames.], batch size: 17, lr: 4.61e-04
2022-05-04 21:05:21,477 INFO [train.py:715] (2/8) Epoch 4, batch 10200, loss[loss=0.1412, simple_loss=0.2066, pruned_loss=0.03785, over 4823.00 frames.], tot_loss[loss=0.157, simple_loss=0.227, pruned_loss=0.0435, over 971235.02 frames.], batch size: 13, lr: 4.61e-04
2022-05-04 21:06:02,062 INFO [train.py:715] (2/8) Epoch 4, batch 10250, loss[loss=0.1428, simple_loss=0.2067, pruned_loss=0.03942, over 4945.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2275, pruned_loss=0.04399, over 970959.33 frames.], batch size: 23, lr: 4.61e-04
2022-05-04 21:06:41,846 INFO [train.py:715] (2/8) Epoch 4, batch 10300, loss[loss=0.1648, simple_loss=0.2405, pruned_loss=0.04456, over 4891.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2284, pruned_loss=0.04468, over 971655.51 frames.], batch size: 19, lr: 4.61e-04
2022-05-04 21:07:21,505 INFO [train.py:715] (2/8) Epoch 4, batch 10350, loss[loss=0.1484, simple_loss=0.2199, pruned_loss=0.03845, over 4844.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2279, pruned_loss=0.04444, over 972006.72 frames.], batch size: 20, lr: 4.61e-04
2022-05-04 21:08:01,704 INFO [train.py:715] (2/8) Epoch 4, batch 10400, loss[loss=0.1365, simple_loss=0.2092, pruned_loss=0.0319, over 4764.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2279, pruned_loss=0.04415, over 971622.95 frames.], batch size: 16, lr: 4.61e-04
2022-05-04 21:08:42,281 INFO [train.py:715] (2/8) Epoch 4, batch 10450, loss[loss=0.16, simple_loss=0.2335, pruned_loss=0.04321, over 4986.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2293, pruned_loss=0.04461, over 972196.58 frames.], batch size: 28, lr: 4.61e-04
2022-05-04 21:09:21,890 INFO [train.py:715] (2/8) Epoch 4, batch 10500, loss[loss=0.1511, simple_loss=0.22, pruned_loss=0.04104, over 4959.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2283, pruned_loss=0.04425, over 972139.27 frames.], batch size: 24, lr: 4.61e-04
2022-05-04 21:10:02,143 INFO [train.py:715] (2/8) Epoch 4, batch 10550, loss[loss=0.1578, simple_loss=0.2271, pruned_loss=0.04423, over 4969.00 frames.], tot_loss[loss=0.1577, simple_loss=0.228, pruned_loss=0.04368, over 971998.59 frames.], batch size: 39, lr: 4.61e-04
2022-05-04 21:10:42,492 INFO [train.py:715] (2/8) Epoch 4, batch 10600, loss[loss=0.1205, simple_loss=0.1963, pruned_loss=0.02228, over 4802.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2266, pruned_loss=0.04287, over 971900.38 frames.], batch size: 25, lr: 4.61e-04
2022-05-04 21:11:22,295 INFO [train.py:715] (2/8) Epoch 4, batch 10650, loss[loss=0.1536, simple_loss=0.2308, pruned_loss=0.03822, over 4807.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2263, pruned_loss=0.04309, over 972348.93 frames.], batch size: 24, lr: 4.61e-04
2022-05-04 21:12:02,341 INFO [train.py:715] (2/8) Epoch 4, batch 10700, loss[loss=0.1507, simple_loss=0.2073, pruned_loss=0.04701, over 4685.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2274, pruned_loss=0.04366, over 972969.68 frames.], batch size: 15, lr: 4.61e-04
2022-05-04 21:12:42,038 INFO [train.py:715] (2/8) Epoch 4, batch 10750, loss[loss=0.1616, simple_loss=0.2403, pruned_loss=0.04142, over 4824.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2264, pruned_loss=0.04301, over 971796.75 frames.], batch size: 26, lr: 4.60e-04
2022-05-04 21:13:22,454 INFO [train.py:715] (2/8) Epoch 4, batch 10800, loss[loss=0.1553, simple_loss=0.2218, pruned_loss=0.04438, over 4853.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2269, pruned_loss=0.0431, over 972329.18 frames.], batch size: 20, lr: 4.60e-04
2022-05-04 21:14:01,772 INFO [train.py:715] (2/8) Epoch 4, batch 10850, loss[loss=0.1669, simple_loss=0.2258, pruned_loss=0.05403, over 4899.00 frames.], tot_loss[loss=0.1565, simple_loss=0.227, pruned_loss=0.04301, over 971877.47 frames.], batch size: 16, lr: 4.60e-04
2022-05-04 21:14:41,704 INFO [train.py:715] (2/8) Epoch 4, batch 10900, loss[loss=0.1673, simple_loss=0.2309, pruned_loss=0.05189, over 4764.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2276, pruned_loss=0.04358, over 971331.80 frames.], batch size: 18, lr: 4.60e-04
2022-05-04 21:15:22,019 INFO [train.py:715] (2/8) Epoch 4, batch 10950, loss[loss=0.1662, simple_loss=0.2317, pruned_loss=0.05038, over 4873.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2269, pruned_loss=0.04332, over 971778.39 frames.], batch size: 32, lr: 4.60e-04
2022-05-04 21:16:01,656 INFO [train.py:715] (2/8) Epoch 4, batch 11000, loss[loss=0.1673, simple_loss=0.234, pruned_loss=0.05028, over 4654.00 frames.], tot_loss[loss=0.157, simple_loss=0.2269, pruned_loss=0.04353, over 971823.51 frames.], batch size: 13, lr: 4.60e-04
2022-05-04 21:16:44,069 INFO [train.py:715] (2/8) Epoch 4, batch 11050, loss[loss=0.1818, simple_loss=0.2485, pruned_loss=0.05758, over 4931.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2274, pruned_loss=0.04399, over 973257.17 frames.], batch size: 23, lr: 4.60e-04
2022-05-04 21:17:24,552 INFO [train.py:715] (2/8) Epoch 4, batch 11100, loss[loss=0.1279, simple_loss=0.1937, pruned_loss=0.03108, over 4967.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2261, pruned_loss=0.04363, over 973430.61 frames.], batch size: 35, lr: 4.60e-04
2022-05-04 21:18:07,350 INFO [train.py:715] (2/8) Epoch 4, batch 11150, loss[loss=0.1546, simple_loss=0.2197, pruned_loss=0.04471, over 4779.00 frames.], tot_loss[loss=0.157, simple_loss=0.2262, pruned_loss=0.04389, over 972162.11 frames.], batch size: 17, lr: 4.60e-04
2022-05-04 21:18:49,570 INFO [train.py:715] (2/8) Epoch 4, batch 11200, loss[loss=0.1775, simple_loss=0.2422, pruned_loss=0.05638, over 4880.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04384, over 972432.91 frames.], batch size: 32, lr: 4.60e-04
2022-05-04 21:19:29,976 INFO [train.py:715] (2/8) Epoch 4, batch 11250, loss[loss=0.176, simple_loss=0.2455, pruned_loss=0.05327, over 4747.00 frames.], tot_loss[loss=0.1574, simple_loss=0.227, pruned_loss=0.0439, over 972042.21 frames.], batch size: 16, lr: 4.60e-04
2022-05-04 21:20:12,900 INFO [train.py:715] (2/8) Epoch 4, batch 11300, loss[loss=0.1364, simple_loss=0.2114, pruned_loss=0.03065, over 4940.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04377, over 972099.01 frames.], batch size: 21, lr: 4.60e-04
2022-05-04 21:20:52,358 INFO [train.py:715] (2/8) Epoch 4, batch 11350, loss[loss=0.1565, simple_loss=0.222, pruned_loss=0.0455, over 4655.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2265, pruned_loss=0.04341, over 971321.92 frames.], batch size: 13, lr: 4.60e-04
2022-05-04 21:21:31,874 INFO [train.py:715] (2/8) Epoch 4, batch 11400, loss[loss=0.1395, simple_loss=0.2095, pruned_loss=0.0348, over 4861.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2254, pruned_loss=0.04315, over 971598.70 frames.], batch size: 16, lr: 4.59e-04
2022-05-04 21:22:11,698 INFO [train.py:715] (2/8) Epoch 4, batch 11450, loss[loss=0.135, simple_loss=0.2097, pruned_loss=0.03017, over 4797.00 frames.], tot_loss[loss=0.1553, simple_loss=0.225, pruned_loss=0.04276, over 972096.28 frames.], batch size: 21, lr: 4.59e-04
2022-05-04 21:22:51,364 INFO [train.py:715] (2/8) Epoch 4, batch 11500, loss[loss=0.1459, simple_loss=0.2264, pruned_loss=0.03275, over 4927.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2255, pruned_loss=0.04271, over 972149.72 frames.], batch size: 23, lr: 4.59e-04
2022-05-04 21:23:30,602 INFO [train.py:715] (2/8) Epoch 4, batch 11550, loss[loss=0.1267, simple_loss=0.1992, pruned_loss=0.02714, over 4819.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2263, pruned_loss=0.04304, over 972445.99 frames.], batch size: 26, lr: 4.59e-04
2022-05-04 21:24:09,868 INFO [train.py:715] (2/8) Epoch 4, batch 11600, loss[loss=0.1502, simple_loss=0.2113, pruned_loss=0.04454, over 4960.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2268, pruned_loss=0.04383, over 972400.73 frames.], batch size: 35, lr: 4.59e-04
2022-05-04 21:24:50,383 INFO [train.py:715] (2/8) Epoch 4, batch 11650, loss[loss=0.1438, simple_loss=0.2114, pruned_loss=0.03816, over 4904.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2268, pruned_loss=0.04394, over 973163.79 frames.], batch size: 29, lr: 4.59e-04
2022-05-04 21:25:30,282 INFO [train.py:715] (2/8) Epoch 4, batch 11700, loss[loss=0.1614, simple_loss=0.2235, pruned_loss=0.04968, over 4810.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2281, pruned_loss=0.04447, over 972269.02 frames.], batch size: 14, lr: 4.59e-04
2022-05-04 21:26:10,253 INFO [train.py:715] (2/8) Epoch 4, batch 11750, loss[loss=0.1458, simple_loss=0.2095, pruned_loss=0.04099, over 4838.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2274, pruned_loss=0.04391, over 972386.95 frames.], batch size: 13, lr: 4.59e-04
2022-05-04 21:26:50,002 INFO [train.py:715] (2/8) Epoch 4, batch 11800, loss[loss=0.1858, simple_loss=0.2499, pruned_loss=0.06084, over 4800.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2274, pruned_loss=0.04406, over 973023.05 frames.], batch size: 21, lr: 4.59e-04
2022-05-04 21:27:30,266 INFO [train.py:715] (2/8) Epoch 4, batch 11850, loss[loss=0.1547, simple_loss=0.2292, pruned_loss=0.04012, over 4751.00 frames.], tot_loss[loss=0.1566, simple_loss=0.226, pruned_loss=0.04363, over 972596.99 frames.], batch size: 16, lr: 4.59e-04
2022-05-04 21:28:09,529 INFO [train.py:715] (2/8) Epoch 4, batch 11900, loss[loss=0.1371, simple_loss=0.2213, pruned_loss=0.02644, over 4860.00 frames.], tot_loss[loss=0.157, simple_loss=0.2262, pruned_loss=0.04391, over 972986.11 frames.], batch size: 20, lr: 4.59e-04
2022-05-04 21:28:49,304 INFO [train.py:715] (2/8) Epoch 4, batch 11950, loss[loss=0.1408, simple_loss=0.2072, pruned_loss=0.03716, over 4755.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2257, pruned_loss=0.04342, over 972774.59 frames.], batch size: 19, lr: 4.59e-04
2022-05-04 21:29:29,759 INFO [train.py:715] (2/8) Epoch 4, batch 12000, loss[loss=0.1431, simple_loss=0.2239, pruned_loss=0.03118, over 4968.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2267, pruned_loss=0.04347, over 973236.20 frames.], batch size: 28, lr: 4.59e-04
2022-05-04 21:29:29,761 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 21:29:49,525 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1122, simple_loss=0.198, pruned_loss=0.01324, over 914524.00 frames.
2022-05-04 21:30:30,081 INFO [train.py:715] (2/8) Epoch 4, batch 12050, loss[loss=0.1804, simple_loss=0.2375, pruned_loss=0.06165, over 4874.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2267, pruned_loss=0.0439, over 972064.73 frames.], batch size: 22, lr: 4.58e-04
2022-05-04 21:31:09,895 INFO [train.py:715] (2/8) Epoch 4, batch 12100, loss[loss=0.1651, simple_loss=0.2386, pruned_loss=0.04583, over 4794.00 frames.], tot_loss[loss=0.1576, simple_loss=0.227, pruned_loss=0.04415, over 972184.82 frames.], batch size: 18, lr: 4.58e-04
2022-05-04 21:31:50,049 INFO [train.py:715] (2/8) Epoch 4, batch 12150, loss[loss=0.1589, simple_loss=0.2375, pruned_loss=0.04011, over 4933.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2269, pruned_loss=0.04411, over 971819.13 frames.], batch size: 23, lr: 4.58e-04
2022-05-04 21:32:30,094 INFO [train.py:715] (2/8) Epoch 4, batch 12200, loss[loss=0.1848, simple_loss=0.2526, pruned_loss=0.0585, over 4821.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2269, pruned_loss=0.04382, over 971218.15 frames.], batch size: 25, lr: 4.58e-04
2022-05-04 21:33:10,432 INFO [train.py:715] (2/8) Epoch 4, batch 12250, loss[loss=0.1414, simple_loss=0.2107, pruned_loss=0.03608, over 4904.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2263, pruned_loss=0.04356, over 971672.35 frames.], batch size: 18, lr: 4.58e-04
2022-05-04 21:33:49,414 INFO [train.py:715] (2/8) Epoch 4, batch 12300, loss[loss=0.2031, simple_loss=0.2722, pruned_loss=0.06695, over 4789.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2264, pruned_loss=0.04375, over 972805.84 frames.], batch size: 18, lr: 4.58e-04
2022-05-04 21:34:29,432 INFO [train.py:715] (2/8) Epoch 4, batch 12350, loss[loss=0.147, simple_loss=0.2197, pruned_loss=0.0371, over 4754.00 frames.], tot_loss[loss=0.158, simple_loss=0.2275, pruned_loss=0.04422, over 972545.03 frames.], batch size: 19, lr: 4.58e-04
2022-05-04 21:35:10,020 INFO [train.py:715] (2/8) Epoch 4, batch 12400, loss[loss=0.194, simple_loss=0.2606, pruned_loss=0.06377, over 4954.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2279, pruned_loss=0.04438, over 972197.00 frames.], batch size: 24, lr: 4.58e-04
2022-05-04 21:35:49,230 INFO [train.py:715] (2/8) Epoch 4, batch 12450, loss[loss=0.1774, simple_loss=0.2417, pruned_loss=0.05659, over 4953.00 frames.], tot_loss[loss=0.158, simple_loss=0.2276, pruned_loss=0.04423, over 971790.80 frames.], batch size: 39, lr: 4.58e-04
2022-05-04 21:36:29,195 INFO [train.py:715] (2/8) Epoch 4, batch 12500, loss[loss=0.202, simple_loss=0.2599, pruned_loss=0.07204, over 4934.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2273, pruned_loss=0.04403, over 972346.74 frames.], batch size: 21, lr: 4.58e-04
2022-05-04 21:37:08,752 INFO [train.py:715] (2/8) Epoch 4, batch 12550, loss[loss=0.144, simple_loss=0.2095, pruned_loss=0.03925, over 4994.00 frames.], tot_loss[loss=0.1572, simple_loss=0.227, pruned_loss=0.04367, over 972138.73 frames.], batch size: 14, lr: 4.58e-04
2022-05-04 21:37:48,536 INFO [train.py:715] (2/8) Epoch 4, batch 12600, loss[loss=0.1428, simple_loss=0.214, pruned_loss=0.03583, over 4939.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2278, pruned_loss=0.04433, over 972029.15 frames.], batch size: 23, lr: 4.58e-04
2022-05-04 21:38:27,426 INFO [train.py:715] (2/8) Epoch 4, batch 12650, loss[loss=0.1365, simple_loss=0.1986, pruned_loss=0.03721, over 4888.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2277, pruned_loss=0.04461, over 972152.73 frames.], batch size: 19, lr: 4.58e-04
2022-05-04 21:39:07,269 INFO [train.py:715] (2/8) Epoch 4, batch 12700, loss[loss=0.1662, simple_loss=0.233, pruned_loss=0.04968, over 4752.00 frames.], tot_loss[loss=0.1587, simple_loss=0.228, pruned_loss=0.04471, over 973057.85 frames.], batch size: 16, lr: 4.58e-04
2022-05-04 21:39:47,343 INFO [train.py:715] (2/8) Epoch 4, batch 12750, loss[loss=0.1335, simple_loss=0.2037, pruned_loss=0.03164, over 4990.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2284, pruned_loss=0.04526, over 972750.29 frames.], batch size: 14, lr: 4.57e-04
2022-05-04 21:40:29,594 INFO [train.py:715] (2/8) Epoch 4, batch 12800, loss[loss=0.1567, simple_loss=0.2275, pruned_loss=0.04296, over 4902.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2267, pruned_loss=0.04427, over 973173.22 frames.], batch size: 19, lr: 4.57e-04
2022-05-04 21:41:08,986 INFO [train.py:715] (2/8) Epoch 4, batch 12850, loss[loss=0.1425, simple_loss=0.2071, pruned_loss=0.03899, over 4841.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2267, pruned_loss=0.04374, over 973056.69 frames.], batch size: 13, lr: 4.57e-04
2022-05-04 21:41:49,118 INFO [train.py:715] (2/8) Epoch 4, batch 12900, loss[loss=0.1691, simple_loss=0.2372, pruned_loss=0.0505, over 4927.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2273, pruned_loss=0.04418, over 972625.81 frames.], batch size: 39, lr: 4.57e-04
2022-05-04 21:42:29,045 INFO [train.py:715] (2/8) Epoch 4, batch 12950, loss[loss=0.1527, simple_loss=0.2219, pruned_loss=0.04171, over 4924.00 frames.], tot_loss[loss=0.1573, simple_loss=0.227, pruned_loss=0.04381, over 973021.25 frames.], batch size: 18, lr: 4.57e-04
2022-05-04 21:43:07,909 INFO [train.py:715] (2/8) Epoch 4, batch 13000, loss[loss=0.1449, simple_loss=0.2183, pruned_loss=0.03571, over 4819.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2278, pruned_loss=0.04443, over 972566.66 frames.], batch size: 15, lr: 4.57e-04
2022-05-04 21:43:47,495 INFO [train.py:715] (2/8) Epoch 4, batch 13050, loss[loss=0.14, simple_loss=0.215, pruned_loss=0.03249, over 4820.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2275, pruned_loss=0.04453, over 971920.97 frames.], batch size: 27, lr: 4.57e-04
2022-05-04 21:44:27,455 INFO [train.py:715] (2/8) Epoch 4, batch 13100, loss[loss=0.1567, simple_loss=0.2323, pruned_loss=0.0405, over 4772.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2272, pruned_loss=0.04413, over 971522.33 frames.], batch size: 18, lr: 4.57e-04
2022-05-04 21:45:06,500 INFO [train.py:715] (2/8) Epoch 4, batch 13150, loss[loss=0.1505, simple_loss=0.2249, pruned_loss=0.03801, over 4908.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2268, pruned_loss=0.04392, over 972340.37 frames.], batch size: 19, lr: 4.57e-04
2022-05-04 21:45:46,237 INFO [train.py:715] (2/8) Epoch 4, batch 13200, loss[loss=0.182, simple_loss=0.2468, pruned_loss=0.05867, over 4783.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2271, pruned_loss=0.04406, over 973039.17 frames.], batch size: 18, lr: 4.57e-04
2022-05-04 21:46:26,560 INFO [train.py:715] (2/8) Epoch 4, batch 13250, loss[loss=0.14, simple_loss=0.2094, pruned_loss=0.03531, over 4882.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2268, pruned_loss=0.04402, over 972087.82 frames.], batch size: 32, lr: 4.57e-04
2022-05-04 21:47:06,165 INFO [train.py:715] (2/8) Epoch 4, batch 13300, loss[loss=0.1374, simple_loss=0.21, pruned_loss=0.0324, over 4921.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04369, over 972248.08 frames.], batch size: 23, lr: 4.57e-04
2022-05-04 21:47:45,755 INFO [train.py:715] (2/8) Epoch 4, batch 13350, loss[loss=0.1467, simple_loss=0.2218, pruned_loss=0.03577, over 4898.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2263, pruned_loss=0.04364, over 972350.86 frames.], batch size: 19, lr: 4.57e-04
2022-05-04 21:48:25,394 INFO [train.py:715] (2/8) Epoch 4, batch 13400, loss[loss=0.1469, simple_loss=0.2213, pruned_loss=0.03625, over 4975.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2265, pruned_loss=0.04394, over 972226.09 frames.], batch size: 26, lr: 4.56e-04
2022-05-04 21:49:05,421 INFO [train.py:715] (2/8) Epoch 4, batch 13450, loss[loss=0.1575, simple_loss=0.2423, pruned_loss=0.03631, over 4753.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04378, over 972785.70 frames.], batch size: 16, lr: 4.56e-04
2022-05-04 21:49:45,241 INFO [train.py:715] (2/8) Epoch 4, batch 13500, loss[loss=0.1427, simple_loss=0.2057, pruned_loss=0.03985, over 4855.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2262, pruned_loss=0.04363, over 972048.16 frames.], batch size: 32, lr: 4.56e-04
2022-05-04 21:50:27,087 INFO [train.py:715] (2/8) Epoch 4, batch 13550, loss[loss=0.1832, simple_loss=0.2493, pruned_loss=0.05857, over 4770.00 frames.], tot_loss[loss=0.1568, simple_loss=0.226, pruned_loss=0.04382, over 972189.84 frames.], batch size: 18, lr: 4.56e-04
2022-05-04 21:51:07,674 INFO [train.py:715] (2/8) Epoch 4, batch 13600, loss[loss=0.1204, simple_loss=0.1941, pruned_loss=0.02332, over 4815.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04384, over 972096.52 frames.], batch size: 13, lr: 4.56e-04
2022-05-04 21:51:47,209 INFO [train.py:715] (2/8) Epoch 4, batch 13650, loss[loss=0.1395, simple_loss=0.2144, pruned_loss=0.03228, over 4988.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04384, over 972207.03 frames.], batch size: 27, lr: 4.56e-04
2022-05-04 21:52:26,522 INFO [train.py:715] (2/8) Epoch 4, batch 13700, loss[loss=0.136, simple_loss=0.2152, pruned_loss=0.02833, over 4926.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2255, pruned_loss=0.04317, over 972818.08 frames.], batch size: 29, lr: 4.56e-04
2022-05-04 21:53:06,450 INFO [train.py:715] (2/8) Epoch 4, batch 13750, loss[loss=0.1504, simple_loss=0.2145, pruned_loss=0.04316, over 4835.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2251, pruned_loss=0.04312, over 971767.86 frames.], batch size: 15, lr: 4.56e-04
2022-05-04 21:53:48,113 INFO [train.py:715] (2/8) Epoch 4, batch 13800, loss[loss=0.1622, simple_loss=0.2267, pruned_loss=0.04889, over 4907.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2256, pruned_loss=0.04341, over 972472.33 frames.], batch size: 17, lr: 4.56e-04
2022-05-04 21:54:29,032 INFO [train.py:715] (2/8) Epoch 4, batch 13850, loss[loss=0.1357, simple_loss=0.2107, pruned_loss=0.03035, over 4826.00 frames.], tot_loss[loss=0.1555, simple_loss=0.225, pruned_loss=0.04298, over 972774.66 frames.], batch size: 26, lr: 4.56e-04
2022-05-04 21:55:10,918 INFO [train.py:715] (2/8) Epoch 4, batch 13900, loss[loss=0.1614, simple_loss=0.2322, pruned_loss=0.04526, over 4781.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2262, pruned_loss=0.04382, over 973045.06 frames.], batch size: 18, lr: 4.56e-04
2022-05-04 21:55:52,329 INFO [train.py:715] (2/8) Epoch 4, batch 13950, loss[loss=0.1601, simple_loss=0.2325, pruned_loss=0.04384, over 4974.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.04352, over 972878.77 frames.], batch size: 35, lr: 4.56e-04
2022-05-04 21:56:31,849 INFO [train.py:715] (2/8) Epoch 4, batch 14000, loss[loss=0.1736, simple_loss=0.2401, pruned_loss=0.05362, over 4837.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2269, pruned_loss=0.04386, over 973498.49 frames.], batch size: 30, lr: 4.56e-04
2022-05-04 21:57:12,897 INFO [train.py:715] (2/8) Epoch 4, batch 14050, loss[loss=0.1493, simple_loss=0.2222, pruned_loss=0.03821, over 4737.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2257, pruned_loss=0.0435, over 972666.68 frames.], batch size: 16, lr: 4.55e-04
2022-05-04 21:57:52,560 INFO [train.py:715] (2/8) Epoch 4, batch 14100, loss[loss=0.1715, simple_loss=0.2468, pruned_loss=0.04808, over 4923.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2269, pruned_loss=0.04425, over 971927.00 frames.], batch size: 18, lr: 4.55e-04
2022-05-04 21:58:32,926 INFO [train.py:715] (2/8) Epoch 4, batch 14150, loss[loss=0.1247, simple_loss=0.1886, pruned_loss=0.03035, over 4836.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2266, pruned_loss=0.04405, over 972376.56 frames.], batch size: 13, lr: 4.55e-04
2022-05-04 21:59:12,280 INFO [train.py:715] (2/8) Epoch 4, batch 14200, loss[loss=0.1572, simple_loss=0.2349, pruned_loss=0.03976, over 4963.00 frames.], tot_loss[loss=0.1576, simple_loss=0.227, pruned_loss=0.04414, over 972119.55 frames.], batch size: 24, lr: 4.55e-04
2022-05-04 21:59:51,971 INFO [train.py:715] (2/8) Epoch 4, batch 14250, loss[loss=0.1591, simple_loss=0.2372, pruned_loss=0.04054, over 4870.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2267, pruned_loss=0.04382, over 972487.78 frames.], batch size: 20, lr: 4.55e-04
2022-05-04 22:00:32,130 INFO [train.py:715] (2/8) Epoch 4, batch 14300, loss[loss=0.1498, simple_loss=0.2272, pruned_loss=0.03626, over 4826.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2265, pruned_loss=0.04358, over 972234.95 frames.], batch size: 26, lr: 4.55e-04
2022-05-04 22:01:10,595 INFO [train.py:715] (2/8) Epoch 4, batch 14350, loss[loss=0.1773, simple_loss=0.2452, pruned_loss=0.05471, over 4815.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2277, pruned_loss=0.04441, over 972252.47 frames.], batch size: 25, lr: 4.55e-04
2022-05-04 22:01:50,875 INFO [train.py:715] (2/8) Epoch 4, batch 14400, loss[loss=0.1806, simple_loss=0.2464, pruned_loss=0.0574, over 4864.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2281, pruned_loss=0.04462, over 972401.02 frames.], batch size: 38, lr: 4.55e-04
2022-05-04 22:02:30,288 INFO [train.py:715] (2/8) Epoch 4, batch 14450, loss[loss=0.1738, simple_loss=0.2331, pruned_loss=0.05722, over 4749.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2275, pruned_loss=0.04454, over 971941.49 frames.], batch size: 16, lr: 4.55e-04
2022-05-04 22:03:09,286 INFO [train.py:715] (2/8) Epoch 4, batch 14500, loss[loss=0.1536, simple_loss=0.2296, pruned_loss=0.03883, over 4891.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2274, pruned_loss=0.04441, over 972066.25 frames.], batch size: 17, lr: 4.55e-04
2022-05-04 22:03:48,139 INFO [train.py:715] (2/8) Epoch 4, batch 14550, loss[loss=0.1607, simple_loss=0.2261, pruned_loss=0.04765, over 4877.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2272, pruned_loss=0.0437, over 971842.25 frames.], batch size: 13, lr: 4.55e-04
2022-05-04 22:04:27,639 INFO [train.py:715] (2/8) Epoch 4, batch 14600, loss[loss=0.183, simple_loss=0.2486, pruned_loss=0.05874, over 4791.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2264, pruned_loss=0.04358, over 972727.55 frames.], batch size: 17, lr: 4.55e-04
2022-05-04 22:05:07,542 INFO [train.py:715] (2/8) Epoch 4, batch 14650, loss[loss=0.1401, simple_loss=0.217, pruned_loss=0.03164, over 4902.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2271, pruned_loss=0.04399, over 973072.63 frames.], batch size: 19, lr: 4.55e-04
2022-05-04 22:05:46,287 INFO [train.py:715] (2/8) Epoch 4, batch 14700, loss[loss=0.1633, simple_loss=0.2143, pruned_loss=0.05611, over 4836.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2257, pruned_loss=0.04342, over 973488.61 frames.], batch size: 15, lr: 4.55e-04
2022-05-04 22:06:26,139 INFO [train.py:715] (2/8) Epoch 4, batch 14750, loss[loss=0.1646, simple_loss=0.2337, pruned_loss=0.0478, over 4706.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2254, pruned_loss=0.0434, over 973342.41 frames.], batch size: 15, lr: 4.54e-04
2022-05-04 22:07:06,149 INFO [train.py:715] (2/8) Epoch 4, batch 14800, loss[loss=0.2139, simple_loss=0.2728, pruned_loss=0.07756, over 4758.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2255, pruned_loss=0.04329, over 972174.35 frames.], batch size: 16, lr: 4.54e-04
2022-05-04 22:07:51,041 INFO [train.py:715] (2/8) Epoch 4, batch 14850, loss[loss=0.1631, simple_loss=0.238, pruned_loss=0.04412, over 4876.00 frames.], tot_loss[loss=0.1555, simple_loss=0.225, pruned_loss=0.04303, over 972525.58 frames.], batch size: 22, lr: 4.54e-04
2022-05-04 22:08:31,238 INFO [train.py:715] (2/8) Epoch 4, batch 14900, loss[loss=0.1437, simple_loss=0.192, pruned_loss=0.04776, over 4795.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2261, pruned_loss=0.04402, over 971834.52 frames.], batch size: 12, lr: 4.54e-04
2022-05-04 22:09:11,325 INFO [train.py:715] (2/8) Epoch 4, batch 14950, loss[loss=0.1862, simple_loss=0.2495, pruned_loss=0.06141, over 4988.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2257, pruned_loss=0.04409, over 972216.58 frames.], batch size: 25, lr: 4.54e-04
2022-05-04 22:09:51,663 INFO [train.py:715] (2/8) Epoch 4, batch 15000, loss[loss=0.1642, simple_loss=0.2335, pruned_loss=0.0475, over 4700.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2258, pruned_loss=0.04391, over 972191.29 frames.], batch size: 15, lr: 4.54e-04
2022-05-04 22:09:51,664 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 22:10:32,003 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1122, simple_loss=0.1978, pruned_loss=0.01336, over 914524.00 frames.
2022-05-04 22:11:12,734 INFO [train.py:715] (2/8) Epoch 4, batch 15050, loss[loss=0.1531, simple_loss=0.2146, pruned_loss=0.04585, over 4826.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2269, pruned_loss=0.04438, over 972376.94 frames.], batch size: 13, lr: 4.54e-04
2022-05-04 22:11:52,177 INFO [train.py:715] (2/8) Epoch 4, batch 15100, loss[loss=0.1636, simple_loss=0.2311, pruned_loss=0.04807, over 4870.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2277, pruned_loss=0.04458, over 972198.84 frames.], batch size: 16, lr: 4.54e-04
2022-05-04 22:12:32,069 INFO [train.py:715] (2/8) Epoch 4, batch 15150, loss[loss=0.1818, simple_loss=0.2484, pruned_loss=0.0576, over 4859.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2281, pruned_loss=0.04476, over 972244.63 frames.], batch size: 20, lr: 4.54e-04
2022-05-04 22:13:12,044 INFO [train.py:715] (2/8) Epoch 4, batch 15200, loss[loss=0.1461, simple_loss=0.2272, pruned_loss=0.03246, over 4893.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2277, pruned_loss=0.0442, over 972966.98 frames.], batch size: 19, lr: 4.54e-04
2022-05-04 22:13:51,750 INFO [train.py:715] (2/8) Epoch 4, batch 15250, loss[loss=0.1396, simple_loss=0.2199, pruned_loss=0.02968, over 4871.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2268, pruned_loss=0.04375, over 972402.69 frames.], batch size: 20, lr: 4.54e-04
2022-05-04 22:14:31,962 INFO [train.py:715] (2/8) Epoch 4, batch 15300, loss[loss=0.1672, simple_loss=0.2375, pruned_loss=0.04851, over 4924.00 frames.], tot_loss[loss=0.1583, simple_loss=0.228, pruned_loss=0.04424, over 972346.91 frames.], batch size: 23, lr: 4.54e-04
2022-05-04 22:15:12,429 INFO [train.py:715] (2/8) Epoch 4, batch 15350, loss[loss=0.1919, simple_loss=0.2467, pruned_loss=0.06852, over 4745.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2275, pruned_loss=0.04385, over 971455.64 frames.], batch size: 19, lr: 4.54e-04
2022-05-04 22:15:52,262 INFO [train.py:715] (2/8) Epoch 4, batch 15400, loss[loss=0.1345, simple_loss=0.2044, pruned_loss=0.03225, over 4694.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2274, pruned_loss=0.04411, over 971887.41 frames.], batch size: 15, lr: 4.53e-04
2022-05-04 22:16:32,477 INFO [train.py:715] (2/8) Epoch 4, batch 15450, loss[loss=0.1817, simple_loss=0.2374, pruned_loss=0.06302, over 4829.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2276, pruned_loss=0.0447, over 972022.81 frames.], batch size: 15, lr: 4.53e-04
2022-05-04 22:17:12,955 INFO [train.py:715] (2/8) Epoch 4, batch 15500, loss[loss=0.1653, simple_loss=0.2354, pruned_loss=0.04758, over 4877.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2273, pruned_loss=0.04442, over 971998.52 frames.], batch size: 20, lr: 4.53e-04
2022-05-04 22:17:53,289 INFO [train.py:715] (2/8) Epoch 4, batch 15550, loss[loss=0.1361, simple_loss=0.206, pruned_loss=0.0331, over 4814.00 frames.], tot_loss[loss=0.158, simple_loss=0.2273, pruned_loss=0.04433, over 972683.80 frames.], batch size: 15, lr: 4.53e-04
2022-05-04 22:18:32,668 INFO [train.py:715] (2/8) Epoch 4, batch 15600, loss[loss=0.152, simple_loss=0.2155, pruned_loss=0.04426, over 4747.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2265, pruned_loss=0.0435, over 972986.05 frames.], batch size: 19, lr: 4.53e-04
2022-05-04 22:19:13,500 INFO [train.py:715] (2/8) Epoch 4, batch 15650, loss[loss=0.1601, simple_loss=0.2332, pruned_loss=0.04349, over 4942.00 frames.], tot_loss[loss=0.156, simple_loss=0.2257, pruned_loss=0.04322, over 972768.84 frames.], batch size: 21, lr: 4.53e-04
2022-05-04 22:19:53,092 INFO [train.py:715] (2/8) Epoch 4, batch 15700, loss[loss=0.1492, simple_loss=0.204, pruned_loss=0.04719, over 4869.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2263, pruned_loss=0.0435, over 972431.52 frames.], batch size: 32, lr: 4.53e-04
2022-05-04 22:20:33,264 INFO [train.py:715] (2/8) Epoch 4, batch 15750, loss[loss=0.1353, simple_loss=0.2125, pruned_loss=0.02905, over 4973.00 frames.], tot_loss[loss=0.1562, simple_loss=0.226, pruned_loss=0.04317, over 972061.01 frames.], batch size: 24, lr: 4.53e-04
2022-05-04 22:21:12,815 INFO [train.py:715] (2/8) Epoch 4, batch 15800, loss[loss=0.1662, simple_loss=0.237, pruned_loss=0.04766, over 4858.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2257, pruned_loss=0.04289, over 971788.22 frames.], batch size: 20, lr: 4.53e-04
2022-05-04 22:21:53,776 INFO [train.py:715] (2/8) Epoch 4, batch 15850, loss[loss=0.1649, simple_loss=0.2384, pruned_loss=0.04572, over 4886.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2264, pruned_loss=0.04329, over 971928.72 frames.], batch size: 16, lr: 4.53e-04
2022-05-04 22:22:34,973 INFO [train.py:715] (2/8) Epoch 4, batch 15900, loss[loss=0.1937, simple_loss=0.2549, pruned_loss=0.06619, over 4869.00 frames.], tot_loss[loss=0.157, simple_loss=0.2268, pruned_loss=0.04358, over 971165.00 frames.], batch size: 38, lr: 4.53e-04
2022-05-04 22:23:14,300 INFO [train.py:715] (2/8) Epoch 4, batch 15950, loss[loss=0.1982, simple_loss=0.2489, pruned_loss=0.0737, over 4800.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2268, pruned_loss=0.04387, over 971218.71 frames.], batch size: 17, lr: 4.53e-04
2022-05-04 22:23:54,446 INFO [train.py:715] (2/8) Epoch 4, batch 16000, loss[loss=0.1741, simple_loss=0.2477, pruned_loss=0.05023, over 4740.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2271, pruned_loss=0.04407, over 971110.29 frames.], batch size: 16, lr: 4.53e-04
2022-05-04 22:24:34,935 INFO [train.py:715] (2/8) Epoch 4, batch 16050, loss[loss=0.1879, simple_loss=0.2548, pruned_loss=0.06047, over 4929.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2282, pruned_loss=0.04455, over 973027.94 frames.], batch size: 39, lr: 4.53e-04
2022-05-04 22:25:14,767 INFO [train.py:715] (2/8) Epoch 4, batch 16100, loss[loss=0.1353, simple_loss=0.2106, pruned_loss=0.03002, over 4882.00 frames.], tot_loss[loss=0.159, simple_loss=0.2288, pruned_loss=0.04454, over 972444.86 frames.], batch size: 22, lr: 4.52e-04
2022-05-04 22:25:54,148 INFO [train.py:715] (2/8) Epoch 4, batch 16150, loss[loss=0.1332, simple_loss=0.2052, pruned_loss=0.0306, over 4952.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2281, pruned_loss=0.04453, over 972018.40 frames.], batch size: 15, lr: 4.52e-04
2022-05-04 22:26:34,756 INFO [train.py:715] (2/8) Epoch 4, batch 16200, loss[loss=0.1556, simple_loss=0.2307, pruned_loss=0.04022, over 4780.00 frames.], tot_loss[loss=0.159, simple_loss=0.2285, pruned_loss=0.04473, over 972768.78 frames.], batch size: 18, lr: 4.52e-04
2022-05-04 22:27:15,081 INFO [train.py:715] (2/8) Epoch 4, batch 16250, loss[loss=0.1486, simple_loss=0.2163, pruned_loss=0.04051, over 4976.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2283, pruned_loss=0.04465, over 972268.03 frames.], batch size: 35, lr: 4.52e-04
2022-05-04 22:27:54,413 INFO [train.py:715] (2/8) Epoch 4, batch 16300, loss[loss=0.1376, simple_loss=0.22, pruned_loss=0.0276, over 4982.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2279, pruned_loss=0.04421, over 971221.15 frames.], batch size: 28, lr: 4.52e-04
2022-05-04 22:28:35,010 INFO [train.py:715] (2/8) Epoch 4, batch 16350, loss[loss=0.1381, simple_loss=0.2216, pruned_loss=0.02728, over 4882.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2288, pruned_loss=0.04485, over 971418.55 frames.], batch size: 22, lr: 4.52e-04
2022-05-04 22:29:15,663 INFO [train.py:715] (2/8) Epoch 4, batch 16400, loss[loss=0.1646, simple_loss=0.2225, pruned_loss=0.05339, over 4812.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2278, pruned_loss=0.0444, over 971930.94 frames.], batch size: 14, lr: 4.52e-04
2022-05-04 22:29:56,025 INFO [train.py:715] (2/8) Epoch 4, batch 16450, loss[loss=0.1227, simple_loss=0.1865, pruned_loss=0.0295, over 4790.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2273, pruned_loss=0.04414, over 972459.59 frames.], batch size: 12, lr: 4.52e-04
2022-05-04 22:30:35,459 INFO [train.py:715] (2/8) Epoch 4, batch 16500, loss[loss=0.1499, simple_loss=0.2153, pruned_loss=0.04224, over 4770.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2266, pruned_loss=0.04394, over 972893.96 frames.], batch size: 19, lr: 4.52e-04
2022-05-04 22:31:15,348 INFO [train.py:715] (2/8) Epoch 4, batch 16550, loss[loss=0.1298, simple_loss=0.2047, pruned_loss=0.02743, over 4807.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2259, pruned_loss=0.04366, over 973656.23 frames.], batch size: 14, lr: 4.52e-04
2022-05-04 22:31:55,169 INFO [train.py:715] (2/8) Epoch 4, batch 16600, loss[loss=0.1486, simple_loss=0.2225, pruned_loss=0.03738, over 4784.00 frames.], tot_loss[loss=0.156, simple_loss=0.2255, pruned_loss=0.04327, over 973927.50 frames.], batch size: 17, lr: 4.52e-04
2022-05-04 22:32:33,986 INFO [train.py:715] (2/8) Epoch 4, batch 16650, loss[loss=0.1344, simple_loss=0.2091, pruned_loss=0.02988, over 4921.00 frames.], tot_loss[loss=0.1563, simple_loss=0.226, pruned_loss=0.04332, over 973885.08 frames.], batch size: 17, lr: 4.52e-04
2022-05-04 22:33:12,846 INFO [train.py:715] (2/8) Epoch 4, batch 16700, loss[loss=0.177, simple_loss=0.2495, pruned_loss=0.05222, over 4959.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2268, pruned_loss=0.04348, over 974416.65 frames.], batch size: 24, lr: 4.52e-04
2022-05-04 22:33:52,191 INFO [train.py:715] (2/8) Epoch 4, batch 16750, loss[loss=0.1607, simple_loss=0.2298, pruned_loss=0.04586, over 4884.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2267, pruned_loss=0.04377, over 973924.88 frames.], batch size: 22, lr: 4.52e-04
2022-05-04 22:34:31,640 INFO [train.py:715] (2/8) Epoch 4, batch 16800, loss[loss=0.1466, simple_loss=0.2269, pruned_loss=0.0332, over 4803.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2264, pruned_loss=0.04373, over 974532.61 frames.], batch size: 24, lr: 4.51e-04
2022-05-04 22:35:10,392 INFO [train.py:715] (2/8) Epoch 4, batch 16850, loss[loss=0.1277, simple_loss=0.1951, pruned_loss=0.0301, over 4804.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2262, pruned_loss=0.044, over 973945.65 frames.], batch size: 21, lr: 4.51e-04
2022-05-04 22:35:50,749 INFO [train.py:715] (2/8) Epoch 4, batch 16900, loss[loss=0.1321, simple_loss=0.2042, pruned_loss=0.02997, over 4838.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2262, pruned_loss=0.04374, over 972662.93 frames.], batch size: 13, lr: 4.51e-04
2022-05-04 22:36:31,078 INFO [train.py:715] (2/8) Epoch 4, batch 16950, loss[loss=0.1661, simple_loss=0.2381, pruned_loss=0.04701, over 4801.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2255, pruned_loss=0.04338, over 972297.01 frames.], batch size: 25, lr: 4.51e-04
2022-05-04 22:37:10,621 INFO [train.py:715] (2/8) Epoch 4, batch 17000, loss[loss=0.181, simple_loss=0.2387, pruned_loss=0.06166, over 4808.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2264, pruned_loss=0.04393, over 972099.98 frames.], batch size: 14, lr: 4.51e-04
2022-05-04 22:37:50,447 INFO [train.py:715] (2/8) Epoch 4, batch 17050, loss[loss=0.1738, simple_loss=0.24, pruned_loss=0.05378, over 4767.00 frames.], tot_loss[loss=0.157, simple_loss=0.2261, pruned_loss=0.04391, over 971987.13 frames.], batch size: 16, lr: 4.51e-04
2022-05-04 22:38:30,852 INFO [train.py:715] (2/8) Epoch 4, batch 17100, loss[loss=0.1568, simple_loss=0.2243, pruned_loss=0.04472, over 4834.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2265, pruned_loss=0.04435, over 972829.73 frames.], batch size: 30, lr: 4.51e-04
2022-05-04 22:39:10,956 INFO [train.py:715] (2/8) Epoch 4, batch 17150, loss[loss=0.1447, simple_loss=0.2133, pruned_loss=0.03805, over 4917.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2274, pruned_loss=0.0446, over 972021.25 frames.], batch size: 23, lr: 4.51e-04
2022-05-04 22:39:50,097 INFO [train.py:715] (2/8) Epoch 4, batch 17200, loss[loss=0.1259, simple_loss=0.2001, pruned_loss=0.02585, over 4808.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2269, pruned_loss=0.04409, over 971679.57 frames.], batch size: 12, lr: 4.51e-04
2022-05-04 22:40:30,247 INFO [train.py:715] (2/8) Epoch 4, batch 17250, loss[loss=0.1459, simple_loss=0.2078, pruned_loss=0.04195, over 4867.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2271, pruned_loss=0.04388, over 971520.62 frames.], batch size: 16, lr: 4.51e-04
2022-05-04 22:41:10,188 INFO [train.py:715] (2/8) Epoch 4, batch 17300, loss[loss=0.153, simple_loss=0.2369, pruned_loss=0.03455, over 4831.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2276, pruned_loss=0.04401, over 971650.92 frames.], batch size: 25, lr: 4.51e-04
2022-05-04 22:41:49,921 INFO [train.py:715] (2/8) Epoch 4, batch 17350, loss[loss=0.1798, simple_loss=0.2307, pruned_loss=0.06442, over 4798.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2274, pruned_loss=0.04421, over 971116.27 frames.], batch size: 14, lr: 4.51e-04
2022-05-04 22:42:29,440 INFO [train.py:715] (2/8) Epoch 4, batch 17400, loss[loss=0.1188, simple_loss=0.1901, pruned_loss=0.02369, over 4901.00 frames.], tot_loss[loss=0.158, simple_loss=0.2272, pruned_loss=0.04439, over 971497.41 frames.], batch size: 19, lr: 4.51e-04
2022-05-04 22:43:09,751 INFO [train.py:715] (2/8) Epoch 4, batch 17450, loss[loss=0.162, simple_loss=0.2291, pruned_loss=0.04748, over 4971.00 frames.], tot_loss[loss=0.1566, simple_loss=0.226, pruned_loss=0.04364, over 972614.74 frames.], batch size: 24, lr: 4.51e-04
2022-05-04 22:43:50,023 INFO [train.py:715] (2/8) Epoch 4, batch 17500, loss[loss=0.1648, simple_loss=0.2298, pruned_loss=0.04995, over 4927.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2265, pruned_loss=0.04358, over 972364.08 frames.], batch size: 23, lr: 4.50e-04
2022-05-04 22:44:29,242 INFO [train.py:715] (2/8) Epoch 4, batch 17550, loss[loss=0.1407, simple_loss=0.214, pruned_loss=0.03369, over 4871.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2266, pruned_loss=0.04388, over 972220.90 frames.], batch size: 22, lr: 4.50e-04
2022-05-04 22:45:09,113 INFO [train.py:715] (2/8) Epoch 4, batch 17600, loss[loss=0.126, simple_loss=0.1933, pruned_loss=0.02939, over 4930.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2265, pruned_loss=0.04355, over 972014.81 frames.], batch size: 29, lr: 4.50e-04
2022-05-04 22:45:49,507 INFO [train.py:715] (2/8) Epoch 4, batch 17650, loss[loss=0.1436, simple_loss=0.213, pruned_loss=0.03713, over 4947.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2256, pruned_loss=0.043, over 972620.61 frames.], batch size: 21, lr: 4.50e-04
2022-05-04 22:46:29,567 INFO [train.py:715] (2/8) Epoch 4, batch 17700, loss[loss=0.155, simple_loss=0.2298, pruned_loss=0.04007, over 4936.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2255, pruned_loss=0.04335, over 971990.74 frames.], batch size: 29, lr: 4.50e-04
2022-05-04 22:47:09,155 INFO [train.py:715] (2/8) Epoch 4, batch 17750, loss[loss=0.2067, simple_loss=0.2655, pruned_loss=0.07394, over 4802.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2255, pruned_loss=0.04376, over 971825.65 frames.], batch size: 21, lr: 4.50e-04
2022-05-04 22:47:49,251 INFO [train.py:715] (2/8) Epoch 4, batch 17800, loss[loss=0.1797, simple_loss=0.2589, pruned_loss=0.0502, over 4760.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2261, pruned_loss=0.04376, over 972842.78 frames.], batch size: 16, lr: 4.50e-04
2022-05-04 22:48:29,923 INFO [train.py:715] (2/8) Epoch 4, batch 17850, loss[loss=0.1614, simple_loss=0.2293, pruned_loss=0.04677, over 4823.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2254, pruned_loss=0.04343, over 972945.24 frames.], batch size: 13, lr: 4.50e-04
2022-05-04 22:49:09,018 INFO [train.py:715] (2/8) Epoch 4, batch 17900, loss[loss=0.1479, simple_loss=0.2294, pruned_loss=0.03324, over 4705.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.04351, over 972399.74 frames.], batch size: 15, lr: 4.50e-04
2022-05-04 22:49:49,020 INFO [train.py:715] (2/8) Epoch 4, batch 17950, loss[loss=0.1511, simple_loss=0.2146, pruned_loss=0.04386, over 4925.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2255, pruned_loss=0.04305, over 972518.53 frames.], batch size: 18, lr: 4.50e-04
2022-05-04 22:50:29,178 INFO [train.py:715] (2/8) Epoch 4, batch 18000, loss[loss=0.1497, simple_loss=0.2246, pruned_loss=0.03744, over 4806.00 frames.], tot_loss[loss=0.157, simple_loss=0.2269, pruned_loss=0.04356, over 971742.56 frames.], batch size: 21, lr: 4.50e-04
2022-05-04 22:50:29,178 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 22:50:38,824 INFO [train.py:742] (2/8) 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] (2/8) Epoch 4, batch 18050, loss[loss=0.1598, simple_loss=0.2189, pruned_loss=0.05031, over 4792.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2267, pruned_loss=0.04355, over 972534.91 frames.], batch size: 24, lr: 4.50e-04
2022-05-04 22:51:59,528 INFO [train.py:715] (2/8) Epoch 4, batch 18100, loss[loss=0.1251, simple_loss=0.1963, pruned_loss=0.02692, over 4811.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2267, pruned_loss=0.04357, over 973078.19 frames.], batch size: 25, lr: 4.50e-04
2022-05-04 22:52:39,088 INFO [train.py:715] (2/8) Epoch 4, batch 18150, loss[loss=0.1487, simple_loss=0.2266, pruned_loss=0.03544, over 4908.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2263, pruned_loss=0.04338, over 973101.71 frames.], batch size: 23, lr: 4.50e-04
2022-05-04 22:53:19,400 INFO [train.py:715] (2/8) Epoch 4, batch 18200, loss[loss=0.1662, simple_loss=0.2217, pruned_loss=0.05535, over 4859.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2268, pruned_loss=0.044, over 973194.56 frames.], batch size: 32, lr: 4.49e-04
2022-05-04 22:53:59,864 INFO [train.py:715] (2/8) Epoch 4, batch 18250, loss[loss=0.1469, simple_loss=0.2134, pruned_loss=0.04017, over 4851.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2267, pruned_loss=0.04415, over 972367.39 frames.], batch size: 20, lr: 4.49e-04
2022-05-04 22:54:39,564 INFO [train.py:715] (2/8) Epoch 4, batch 18300, loss[loss=0.153, simple_loss=0.2132, pruned_loss=0.04633, over 4948.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.0438, over 972812.04 frames.], batch size: 21, lr: 4.49e-04
2022-05-04 22:55:19,274 INFO [train.py:715] (2/8) Epoch 4, batch 18350, loss[loss=0.149, simple_loss=0.2153, pruned_loss=0.0414, over 4739.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2272, pruned_loss=0.04395, over 971766.57 frames.], batch size: 16, lr: 4.49e-04
2022-05-04 22:56:00,391 INFO [train.py:715] (2/8) Epoch 4, batch 18400, loss[loss=0.1393, simple_loss=0.2209, pruned_loss=0.02888, over 4965.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2273, pruned_loss=0.04403, over 973084.39 frames.], batch size: 21, lr: 4.49e-04
2022-05-04 22:56:40,804 INFO [train.py:715] (2/8) Epoch 4, batch 18450, loss[loss=0.1421, simple_loss=0.2188, pruned_loss=0.03271, over 4916.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2269, pruned_loss=0.04379, over 972864.24 frames.], batch size: 23, lr: 4.49e-04
2022-05-04 22:57:20,901 INFO [train.py:715] (2/8) Epoch 4, batch 18500, loss[loss=0.19, simple_loss=0.2602, pruned_loss=0.05993, over 4782.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2273, pruned_loss=0.04399, over 972525.98 frames.], batch size: 14, lr: 4.49e-04
2022-05-04 22:58:01,182 INFO [train.py:715] (2/8) Epoch 4, batch 18550, loss[loss=0.158, simple_loss=0.2376, pruned_loss=0.03918, over 4805.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2275, pruned_loss=0.04404, over 973770.04 frames.], batch size: 21, lr: 4.49e-04
2022-05-04 22:58:41,893 INFO [train.py:715] (2/8) Epoch 4, batch 18600, loss[loss=0.1846, simple_loss=0.2506, pruned_loss=0.05929, over 4761.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2278, pruned_loss=0.04374, over 973606.64 frames.], batch size: 16, lr: 4.49e-04
2022-05-04 22:59:21,450 INFO [train.py:715] (2/8) Epoch 4, batch 18650, loss[loss=0.1319, simple_loss=0.1993, pruned_loss=0.03226, over 4911.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2278, pruned_loss=0.04398, over 972812.90 frames.], batch size: 18, lr: 4.49e-04
2022-05-04 23:00:01,608 INFO [train.py:715] (2/8) Epoch 4, batch 18700, loss[loss=0.1822, simple_loss=0.2605, pruned_loss=0.05192, over 4913.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2273, pruned_loss=0.04323, over 971906.49 frames.], batch size: 18, lr: 4.49e-04
2022-05-04 23:00:42,462 INFO [train.py:715] (2/8) Epoch 4, batch 18750, loss[loss=0.1727, simple_loss=0.2361, pruned_loss=0.05466, over 4803.00 frames.], tot_loss[loss=0.1555, simple_loss=0.226, pruned_loss=0.04247, over 972662.11 frames.], batch size: 21, lr: 4.49e-04
2022-05-04 23:01:21,936 INFO [train.py:715] (2/8) Epoch 4, batch 18800, loss[loss=0.1194, simple_loss=0.195, pruned_loss=0.02189, over 4756.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2252, pruned_loss=0.04225, over 972700.39 frames.], batch size: 16, lr: 4.49e-04
2022-05-04 23:02:02,020 INFO [train.py:715] (2/8) Epoch 4, batch 18850, loss[loss=0.1419, simple_loss=0.2185, pruned_loss=0.03268, over 4939.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2246, pruned_loss=0.04218, over 972087.54 frames.], batch size: 29, lr: 4.49e-04
2022-05-04 23:02:42,418 INFO [train.py:715] (2/8) Epoch 4, batch 18900, loss[loss=0.1713, simple_loss=0.2343, pruned_loss=0.05411, over 4848.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2245, pruned_loss=0.04288, over 971568.64 frames.], batch size: 32, lr: 4.48e-04
2022-05-04 23:03:22,739 INFO [train.py:715] (2/8) Epoch 4, batch 18950, loss[loss=0.1438, simple_loss=0.2074, pruned_loss=0.04004, over 4782.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2251, pruned_loss=0.04319, over 972594.08 frames.], batch size: 17, lr: 4.48e-04
2022-05-04 23:04:01,991 INFO [train.py:715] (2/8) Epoch 4, batch 19000, loss[loss=0.1634, simple_loss=0.2212, pruned_loss=0.05286, over 4792.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2247, pruned_loss=0.04286, over 973001.51 frames.], batch size: 24, lr: 4.48e-04
2022-05-04 23:04:42,495 INFO [train.py:715] (2/8) Epoch 4, batch 19050, loss[loss=0.1557, simple_loss=0.2296, pruned_loss=0.04091, over 4984.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2253, pruned_loss=0.04307, over 972236.35 frames.], batch size: 31, lr: 4.48e-04
2022-05-04 23:05:23,217 INFO [train.py:715] (2/8) Epoch 4, batch 19100, loss[loss=0.162, simple_loss=0.2275, pruned_loss=0.04828, over 4991.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2257, pruned_loss=0.04307, over 972121.88 frames.], batch size: 20, lr: 4.48e-04
2022-05-04 23:06:03,170 INFO [train.py:715] (2/8) Epoch 4, batch 19150, loss[loss=0.1582, simple_loss=0.2284, pruned_loss=0.04404, over 4822.00 frames.], tot_loss[loss=0.1562, simple_loss=0.226, pruned_loss=0.04322, over 972897.72 frames.], batch size: 27, lr: 4.48e-04
2022-05-04 23:06:43,533 INFO [train.py:715] (2/8) Epoch 4, batch 19200, loss[loss=0.1508, simple_loss=0.2239, pruned_loss=0.03887, over 4865.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2258, pruned_loss=0.04329, over 972809.83 frames.], batch size: 30, lr: 4.48e-04
2022-05-04 23:07:24,300 INFO [train.py:715] (2/8) Epoch 4, batch 19250, loss[loss=0.1589, simple_loss=0.2227, pruned_loss=0.04758, over 4925.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2259, pruned_loss=0.04323, over 973097.71 frames.], batch size: 39, lr: 4.48e-04
2022-05-04 23:08:04,902 INFO [train.py:715] (2/8) Epoch 4, batch 19300, loss[loss=0.1821, simple_loss=0.2369, pruned_loss=0.06365, over 4776.00 frames.], tot_loss[loss=0.158, simple_loss=0.2271, pruned_loss=0.04443, over 972842.74 frames.], batch size: 14, lr: 4.48e-04
2022-05-04 23:08:44,078 INFO [train.py:715] (2/8) Epoch 4, batch 19350, loss[loss=0.1674, simple_loss=0.2306, pruned_loss=0.05211, over 4992.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04384, over 972756.25 frames.], batch size: 16, lr: 4.48e-04
2022-05-04 23:09:24,768 INFO [train.py:715] (2/8) Epoch 4, batch 19400, loss[loss=0.1552, simple_loss=0.2232, pruned_loss=0.04354, over 4909.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2268, pruned_loss=0.04405, over 972732.40 frames.], batch size: 18, lr: 4.48e-04
2022-05-04 23:10:06,273 INFO [train.py:715] (2/8) Epoch 4, batch 19450, loss[loss=0.1355, simple_loss=0.2052, pruned_loss=0.03294, over 4899.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2255, pruned_loss=0.04316, over 973339.40 frames.], batch size: 17, lr: 4.48e-04
2022-05-04 23:10:47,426 INFO [train.py:715] (2/8) Epoch 4, batch 19500, loss[loss=0.1432, simple_loss=0.2229, pruned_loss=0.03174, over 4890.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2254, pruned_loss=0.04301, over 973771.79 frames.], batch size: 16, lr: 4.48e-04
2022-05-04 23:11:27,083 INFO [train.py:715] (2/8) Epoch 4, batch 19550, loss[loss=0.1643, simple_loss=0.2232, pruned_loss=0.05263, over 4796.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2255, pruned_loss=0.04319, over 973242.00 frames.], batch size: 14, lr: 4.48e-04
2022-05-04 23:12:07,472 INFO [train.py:715] (2/8) Epoch 4, batch 19600, loss[loss=0.1512, simple_loss=0.2241, pruned_loss=0.03918, over 4932.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2256, pruned_loss=0.04338, over 972989.48 frames.], batch size: 29, lr: 4.47e-04
2022-05-04 23:12:47,696 INFO [train.py:715] (2/8) Epoch 4, batch 19650, loss[loss=0.1404, simple_loss=0.2052, pruned_loss=0.03782, over 4994.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2257, pruned_loss=0.04358, over 971876.66 frames.], batch size: 14, lr: 4.47e-04
2022-05-04 23:13:26,462 INFO [train.py:715] (2/8) Epoch 4, batch 19700, loss[loss=0.1452, simple_loss=0.2107, pruned_loss=0.03987, over 4949.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2258, pruned_loss=0.04362, over 972571.02 frames.], batch size: 24, lr: 4.47e-04
2022-05-04 23:14:07,137 INFO [train.py:715] (2/8) Epoch 4, batch 19750, loss[loss=0.1683, simple_loss=0.2519, pruned_loss=0.04236, over 4968.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2264, pruned_loss=0.04367, over 972530.82 frames.], batch size: 24, lr: 4.47e-04
2022-05-04 23:14:47,959 INFO [train.py:715] (2/8) Epoch 4, batch 19800, loss[loss=0.1675, simple_loss=0.2203, pruned_loss=0.05733, over 4765.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04382, over 971715.98 frames.], batch size: 19, lr: 4.47e-04
2022-05-04 23:15:27,701 INFO [train.py:715] (2/8) Epoch 4, batch 19850, loss[loss=0.1547, simple_loss=0.2231, pruned_loss=0.0431, over 4907.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2266, pruned_loss=0.04415, over 971551.55 frames.], batch size: 19, lr: 4.47e-04
2022-05-04 23:16:07,780 INFO [train.py:715] (2/8) Epoch 4, batch 19900, loss[loss=0.147, simple_loss=0.2203, pruned_loss=0.03684, over 4757.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2265, pruned_loss=0.04414, over 970587.45 frames.], batch size: 19, lr: 4.47e-04
2022-05-04 23:16:47,892 INFO [train.py:715] (2/8) Epoch 4, batch 19950, loss[loss=0.1525, simple_loss=0.2286, pruned_loss=0.03824, over 4757.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2266, pruned_loss=0.04379, over 971384.06 frames.], batch size: 16, lr: 4.47e-04
2022-05-04 23:17:28,062 INFO [train.py:715] (2/8) Epoch 4, batch 20000, loss[loss=0.1555, simple_loss=0.2294, pruned_loss=0.04076, over 4938.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2264, pruned_loss=0.04363, over 971929.00 frames.], batch size: 23, lr: 4.47e-04
2022-05-04 23:18:06,763 INFO [train.py:715] (2/8) Epoch 4, batch 20050, loss[loss=0.1516, simple_loss=0.2198, pruned_loss=0.04169, over 4967.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2262, pruned_loss=0.04383, over 971935.20 frames.], batch size: 15, lr: 4.47e-04
2022-05-04 23:18:46,555 INFO [train.py:715] (2/8) Epoch 4, batch 20100, loss[loss=0.1823, simple_loss=0.2535, pruned_loss=0.05552, over 4805.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2267, pruned_loss=0.04376, over 971628.77 frames.], batch size: 21, lr: 4.47e-04
2022-05-04 23:19:26,623 INFO [train.py:715] (2/8) Epoch 4, batch 20150, loss[loss=0.1809, simple_loss=0.2524, pruned_loss=0.05471, over 4869.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2267, pruned_loss=0.04385, over 972032.74 frames.], batch size: 20, lr: 4.47e-04
2022-05-04 23:20:06,048 INFO [train.py:715] (2/8) Epoch 4, batch 20200, loss[loss=0.1362, simple_loss=0.2091, pruned_loss=0.03159, over 4804.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2252, pruned_loss=0.04315, over 972151.14 frames.], batch size: 25, lr: 4.47e-04
2022-05-04 23:20:45,790 INFO [train.py:715] (2/8) Epoch 4, batch 20250, loss[loss=0.1496, simple_loss=0.2143, pruned_loss=0.04244, over 4891.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2262, pruned_loss=0.04358, over 972082.63 frames.], batch size: 16, lr: 4.47e-04
2022-05-04 23:21:26,108 INFO [train.py:715] (2/8) Epoch 4, batch 20300, loss[loss=0.1399, simple_loss=0.1991, pruned_loss=0.04034, over 4784.00 frames.], tot_loss[loss=0.156, simple_loss=0.2259, pruned_loss=0.0431, over 972005.80 frames.], batch size: 17, lr: 4.46e-04
2022-05-04 23:22:06,209 INFO [train.py:715] (2/8) Epoch 4, batch 20350, loss[loss=0.1619, simple_loss=0.2184, pruned_loss=0.05273, over 4886.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2259, pruned_loss=0.04311, over 971661.99 frames.], batch size: 19, lr: 4.46e-04
2022-05-04 23:22:45,045 INFO [train.py:715] (2/8) Epoch 4, batch 20400, loss[loss=0.1842, simple_loss=0.2584, pruned_loss=0.05499, over 4963.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2249, pruned_loss=0.04274, over 971499.79 frames.], batch size: 35, lr: 4.46e-04
2022-05-04 23:23:25,032 INFO [train.py:715] (2/8) Epoch 4, batch 20450, loss[loss=0.1308, simple_loss=0.204, pruned_loss=0.02884, over 4847.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2258, pruned_loss=0.04316, over 971829.86 frames.], batch size: 13, lr: 4.46e-04
2022-05-04 23:24:04,955 INFO [train.py:715] (2/8) Epoch 4, batch 20500, loss[loss=0.1935, simple_loss=0.2535, pruned_loss=0.06676, over 4770.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.04346, over 971885.32 frames.], batch size: 17, lr: 4.46e-04
2022-05-04 23:24:44,747 INFO [train.py:715] (2/8) Epoch 4, batch 20550, loss[loss=0.1409, simple_loss=0.2206, pruned_loss=0.03053, over 4871.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2256, pruned_loss=0.04288, over 972637.39 frames.], batch size: 16, lr: 4.46e-04
2022-05-04 23:25:23,715 INFO [train.py:715] (2/8) Epoch 4, batch 20600, loss[loss=0.1421, simple_loss=0.2129, pruned_loss=0.03571, over 4966.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2253, pruned_loss=0.04278, over 972478.48 frames.], batch size: 35, lr: 4.46e-04
2022-05-04 23:26:03,652 INFO [train.py:715] (2/8) Epoch 4, batch 20650, loss[loss=0.1158, simple_loss=0.1892, pruned_loss=0.02122, over 4780.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2258, pruned_loss=0.04302, over 971628.06 frames.], batch size: 14, lr: 4.46e-04
2022-05-04 23:26:44,152 INFO [train.py:715] (2/8) Epoch 4, batch 20700, loss[loss=0.151, simple_loss=0.2228, pruned_loss=0.03955, over 4893.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2247, pruned_loss=0.04245, over 971772.06 frames.], batch size: 32, lr: 4.46e-04
2022-05-04 23:27:22,802 INFO [train.py:715] (2/8) Epoch 4, batch 20750, loss[loss=0.1638, simple_loss=0.2366, pruned_loss=0.04551, over 4824.00 frames.], tot_loss[loss=0.155, simple_loss=0.2252, pruned_loss=0.04237, over 970978.20 frames.], batch size: 15, lr: 4.46e-04
2022-05-04 23:28:04,810 INFO [train.py:715] (2/8) Epoch 4, batch 20800, loss[loss=0.1338, simple_loss=0.2066, pruned_loss=0.03046, over 4833.00 frames.], tot_loss[loss=0.155, simple_loss=0.2253, pruned_loss=0.04237, over 971678.65 frames.], batch size: 30, lr: 4.46e-04
2022-05-04 23:28:44,591 INFO [train.py:715] (2/8) Epoch 4, batch 20850, loss[loss=0.1712, simple_loss=0.2357, pruned_loss=0.05332, over 4754.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2252, pruned_loss=0.04264, over 972385.98 frames.], batch size: 16, lr: 4.46e-04
2022-05-04 23:29:24,434 INFO [train.py:715] (2/8) Epoch 4, batch 20900, loss[loss=0.1332, simple_loss=0.2042, pruned_loss=0.03109, over 4797.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2252, pruned_loss=0.04268, over 972511.82 frames.], batch size: 21, lr: 4.46e-04
2022-05-04 23:30:03,465 INFO [train.py:715] (2/8) Epoch 4, batch 20950, loss[loss=0.1798, simple_loss=0.2417, pruned_loss=0.05895, over 4811.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2258, pruned_loss=0.04296, over 972625.52 frames.], batch size: 26, lr: 4.46e-04
2022-05-04 23:30:43,441 INFO [train.py:715] (2/8) Epoch 4, batch 21000, loss[loss=0.1274, simple_loss=0.1995, pruned_loss=0.02763, over 4944.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2256, pruned_loss=0.04289, over 972918.08 frames.], batch size: 18, lr: 4.46e-04
2022-05-04 23:30:43,441 INFO [train.py:733] (2/8) Computing validation loss
2022-05-04 23:30:52,894 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1116, simple_loss=0.1973, pruned_loss=0.01293, over 914524.00 frames.
2022-05-04 23:31:33,186 INFO [train.py:715] (2/8) Epoch 4, batch 21050, loss[loss=0.1376, simple_loss=0.2166, pruned_loss=0.02936, over 4748.00 frames.], tot_loss[loss=0.1572, simple_loss=0.227, pruned_loss=0.04373, over 972970.39 frames.], batch size: 19, lr: 4.45e-04
2022-05-04 23:32:12,976 INFO [train.py:715] (2/8) Epoch 4, batch 21100, loss[loss=0.1347, simple_loss=0.2053, pruned_loss=0.03201, over 4787.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2279, pruned_loss=0.04437, over 972798.31 frames.], batch size: 14, lr: 4.45e-04
2022-05-04 23:32:52,568 INFO [train.py:715] (2/8) Epoch 4, batch 21150, loss[loss=0.17, simple_loss=0.237, pruned_loss=0.0515, over 4796.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2279, pruned_loss=0.04453, over 972511.38 frames.], batch size: 24, lr: 4.45e-04
2022-05-04 23:33:32,143 INFO [train.py:715] (2/8) Epoch 4, batch 21200, loss[loss=0.1436, simple_loss=0.2122, pruned_loss=0.03749, over 4773.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2274, pruned_loss=0.04411, over 972793.47 frames.], batch size: 17, lr: 4.45e-04
2022-05-04 23:34:12,357 INFO [train.py:715] (2/8) Epoch 4, batch 21250, loss[loss=0.1819, simple_loss=0.25, pruned_loss=0.05693, over 4825.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2268, pruned_loss=0.04393, over 972286.84 frames.], batch size: 21, lr: 4.45e-04
2022-05-04 23:34:51,183 INFO [train.py:715] (2/8) Epoch 4, batch 21300, loss[loss=0.1554, simple_loss=0.2295, pruned_loss=0.04064, over 4912.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2273, pruned_loss=0.04424, over 972613.62 frames.], batch size: 17, lr: 4.45e-04
2022-05-04 23:35:30,237 INFO [train.py:715] (2/8) Epoch 4, batch 21350, loss[loss=0.179, simple_loss=0.2449, pruned_loss=0.05653, over 4865.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2267, pruned_loss=0.04388, over 972676.77 frames.], batch size: 20, lr: 4.45e-04
2022-05-04 23:36:09,889 INFO [train.py:715] (2/8) Epoch 4, batch 21400, loss[loss=0.165, simple_loss=0.2409, pruned_loss=0.04457, over 4897.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2262, pruned_loss=0.04352, over 972232.56 frames.], batch size: 19, lr: 4.45e-04
2022-05-04 23:36:49,453 INFO [train.py:715] (2/8) Epoch 4, batch 21450, loss[loss=0.1622, simple_loss=0.2411, pruned_loss=0.04169, over 4942.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2263, pruned_loss=0.04356, over 972799.33 frames.], batch size: 29, lr: 4.45e-04
2022-05-04 23:37:28,640 INFO [train.py:715] (2/8) Epoch 4, batch 21500, loss[loss=0.1237, simple_loss=0.1904, pruned_loss=0.02852, over 4783.00 frames.], tot_loss[loss=0.157, simple_loss=0.2266, pruned_loss=0.04369, over 971967.42 frames.], batch size: 18, lr: 4.45e-04
2022-05-04 23:38:08,472 INFO [train.py:715] (2/8) Epoch 4, batch 21550, loss[loss=0.1731, simple_loss=0.2365, pruned_loss=0.05485, over 4748.00 frames.], tot_loss[loss=0.158, simple_loss=0.2268, pruned_loss=0.04459, over 972115.43 frames.], batch size: 19, lr: 4.45e-04
2022-05-04 23:38:48,843 INFO [train.py:715] (2/8) Epoch 4, batch 21600, loss[loss=0.1689, simple_loss=0.2399, pruned_loss=0.04892, over 4698.00 frames.], tot_loss[loss=0.158, simple_loss=0.227, pruned_loss=0.04453, over 971920.34 frames.], batch size: 15, lr: 4.45e-04
2022-05-04 23:39:28,092 INFO [train.py:715] (2/8) Epoch 4, batch 21650, loss[loss=0.1699, simple_loss=0.2449, pruned_loss=0.0474, over 4819.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2266, pruned_loss=0.04441, over 971759.52 frames.], batch size: 26, lr: 4.45e-04
2022-05-04 23:40:08,365 INFO [train.py:715] (2/8) Epoch 4, batch 21700, loss[loss=0.138, simple_loss=0.2098, pruned_loss=0.03312, over 4988.00 frames.], tot_loss[loss=0.158, simple_loss=0.2271, pruned_loss=0.04445, over 972386.98 frames.], batch size: 33, lr: 4.45e-04
2022-05-04 23:40:49,360 INFO [train.py:715] (2/8) Epoch 4, batch 21750, loss[loss=0.1362, simple_loss=0.204, pruned_loss=0.03424, over 4934.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2254, pruned_loss=0.0431, over 973148.39 frames.], batch size: 23, lr: 4.44e-04
2022-05-04 23:41:29,008 INFO [train.py:715] (2/8) Epoch 4, batch 21800, loss[loss=0.1696, simple_loss=0.2433, pruned_loss=0.04791, over 4801.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2253, pruned_loss=0.04325, over 973416.13 frames.], batch size: 25, lr: 4.44e-04
2022-05-04 23:42:08,601 INFO [train.py:715] (2/8) Epoch 4, batch 21850, loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.03341, over 4971.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2259, pruned_loss=0.0437, over 973777.01 frames.], batch size: 25, lr: 4.44e-04
2022-05-04 23:42:48,643 INFO [train.py:715] (2/8) Epoch 4, batch 21900, loss[loss=0.1743, simple_loss=0.2537, pruned_loss=0.04743, over 4975.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.04345, over 973367.70 frames.], batch size: 39, lr: 4.44e-04
2022-05-04 23:43:29,088 INFO [train.py:715] (2/8) Epoch 4, batch 21950, loss[loss=0.1342, simple_loss=0.2096, pruned_loss=0.02937, over 4751.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2259, pruned_loss=0.04332, over 973395.22 frames.], batch size: 14, lr: 4.44e-04
2022-05-04 23:44:08,289 INFO [train.py:715] (2/8) Epoch 4, batch 22000, loss[loss=0.1714, simple_loss=0.2217, pruned_loss=0.06059, over 4905.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2251, pruned_loss=0.04267, over 973622.67 frames.], batch size: 17, lr: 4.44e-04
2022-05-04 23:44:48,072 INFO [train.py:715] (2/8) Epoch 4, batch 22050, loss[loss=0.1505, simple_loss=0.2226, pruned_loss=0.0392, over 4887.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2254, pruned_loss=0.04277, over 973324.49 frames.], batch size: 16, lr: 4.44e-04
2022-05-04 23:45:28,539 INFO [train.py:715] (2/8) Epoch 4, batch 22100, loss[loss=0.139, simple_loss=0.2178, pruned_loss=0.03007, over 4939.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2269, pruned_loss=0.04371, over 973257.60 frames.], batch size: 23, lr: 4.44e-04
2022-05-04 23:46:08,384 INFO [train.py:715] (2/8) Epoch 4, batch 22150, loss[loss=0.1469, simple_loss=0.2219, pruned_loss=0.03596, over 4934.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2261, pruned_loss=0.04326, over 973259.73 frames.], batch size: 29, lr: 4.44e-04
2022-05-04 23:46:47,293 INFO [train.py:715] (2/8) Epoch 4, batch 22200, loss[loss=0.159, simple_loss=0.2305, pruned_loss=0.04372, over 4782.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2258, pruned_loss=0.04294, over 973133.11 frames.], batch size: 14, lr: 4.44e-04
2022-05-04 23:47:27,358 INFO [train.py:715] (2/8) Epoch 4, batch 22250, loss[loss=0.2014, simple_loss=0.257, pruned_loss=0.07288, over 4836.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2261, pruned_loss=0.04339, over 973571.41 frames.], batch size: 15, lr: 4.44e-04
2022-05-04 23:48:07,759 INFO [train.py:715] (2/8) Epoch 4, batch 22300, loss[loss=0.1259, simple_loss=0.2013, pruned_loss=0.02522, over 4828.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2258, pruned_loss=0.04294, over 972980.71 frames.], batch size: 26, lr: 4.44e-04
2022-05-04 23:48:46,511 INFO [train.py:715] (2/8) Epoch 4, batch 22350, loss[loss=0.1468, simple_loss=0.2196, pruned_loss=0.03697, over 4799.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2249, pruned_loss=0.04242, over 972671.64 frames.], batch size: 21, lr: 4.44e-04
2022-05-04 23:49:25,541 INFO [train.py:715] (2/8) Epoch 4, batch 22400, loss[loss=0.1446, simple_loss=0.2206, pruned_loss=0.03433, over 4818.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2249, pruned_loss=0.04248, over 971844.99 frames.], batch size: 25, lr: 4.44e-04
2022-05-04 23:50:06,128 INFO [train.py:715] (2/8) Epoch 4, batch 22450, loss[loss=0.1759, simple_loss=0.2452, pruned_loss=0.05329, over 4751.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2244, pruned_loss=0.04221, over 972059.60 frames.], batch size: 19, lr: 4.44e-04
2022-05-04 23:50:45,307 INFO [train.py:715] (2/8) Epoch 4, batch 22500, loss[loss=0.1782, simple_loss=0.2528, pruned_loss=0.0518, over 4865.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2242, pruned_loss=0.04217, over 972209.92 frames.], batch size: 20, lr: 4.43e-04
2022-05-04 23:51:24,252 INFO [train.py:715] (2/8) Epoch 4, batch 22550, loss[loss=0.1388, simple_loss=0.2054, pruned_loss=0.03611, over 4897.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2244, pruned_loss=0.04255, over 972425.09 frames.], batch size: 22, lr: 4.43e-04
2022-05-04 23:52:04,196 INFO [train.py:715] (2/8) Epoch 4, batch 22600, loss[loss=0.148, simple_loss=0.2165, pruned_loss=0.0398, over 4839.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2243, pruned_loss=0.04233, over 971993.59 frames.], batch size: 30, lr: 4.43e-04
2022-05-04 23:52:44,017 INFO [train.py:715] (2/8) Epoch 4, batch 22650, loss[loss=0.2032, simple_loss=0.2633, pruned_loss=0.07153, over 4739.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2243, pruned_loss=0.04197, over 971768.03 frames.], batch size: 16, lr: 4.43e-04
2022-05-04 23:53:22,941 INFO [train.py:715] (2/8) Epoch 4, batch 22700, loss[loss=0.1617, simple_loss=0.2359, pruned_loss=0.04377, over 4862.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2254, pruned_loss=0.04278, over 972110.70 frames.], batch size: 22, lr: 4.43e-04
2022-05-04 23:54:02,342 INFO [train.py:715] (2/8) Epoch 4, batch 22750, loss[loss=0.1492, simple_loss=0.2221, pruned_loss=0.03819, over 4981.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2257, pruned_loss=0.04279, over 972425.85 frames.], batch size: 28, lr: 4.43e-04
2022-05-04 23:54:42,047 INFO [train.py:715] (2/8) Epoch 4, batch 22800, loss[loss=0.1704, simple_loss=0.2325, pruned_loss=0.05416, over 4961.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2256, pruned_loss=0.04279, over 973053.91 frames.], batch size: 21, lr: 4.43e-04
2022-05-04 23:55:21,166 INFO [train.py:715] (2/8) Epoch 4, batch 22850, loss[loss=0.1349, simple_loss=0.206, pruned_loss=0.03191, over 4963.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2265, pruned_loss=0.04326, over 972813.30 frames.], batch size: 24, lr: 4.43e-04
2022-05-04 23:55:59,896 INFO [train.py:715] (2/8) Epoch 4, batch 22900, loss[loss=0.1379, simple_loss=0.2042, pruned_loss=0.03578, over 4823.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2261, pruned_loss=0.04325, over 973334.14 frames.], batch size: 26, lr: 4.43e-04
2022-05-04 23:56:39,564 INFO [train.py:715] (2/8) Epoch 4, batch 22950, loss[loss=0.1815, simple_loss=0.2297, pruned_loss=0.06668, over 4791.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04367, over 972958.55 frames.], batch size: 14, lr: 4.43e-04
2022-05-04 23:57:19,675 INFO [train.py:715] (2/8) Epoch 4, batch 23000, loss[loss=0.1606, simple_loss=0.2321, pruned_loss=0.04455, over 4801.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2263, pruned_loss=0.04333, over 973316.44 frames.], batch size: 25, lr: 4.43e-04
2022-05-04 23:57:58,010 INFO [train.py:715] (2/8) Epoch 4, batch 23050, loss[loss=0.1861, simple_loss=0.2475, pruned_loss=0.06237, over 4970.00 frames.], tot_loss[loss=0.1572, simple_loss=0.227, pruned_loss=0.04368, over 973589.66 frames.], batch size: 15, lr: 4.43e-04
2022-05-04 23:58:37,637 INFO [train.py:715] (2/8) Epoch 4, batch 23100, loss[loss=0.1824, simple_loss=0.2422, pruned_loss=0.06128, over 4645.00 frames.], tot_loss[loss=0.1571, simple_loss=0.227, pruned_loss=0.04361, over 972621.48 frames.], batch size: 13, lr: 4.43e-04
2022-05-04 23:59:18,003 INFO [train.py:715] (2/8) Epoch 4, batch 23150, loss[loss=0.1487, simple_loss=0.2252, pruned_loss=0.03613, over 4985.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2272, pruned_loss=0.04387, over 972665.86 frames.], batch size: 25, lr: 4.43e-04
2022-05-04 23:59:57,817 INFO [train.py:715] (2/8) Epoch 4, batch 23200, loss[loss=0.173, simple_loss=0.2491, pruned_loss=0.04846, over 4966.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2271, pruned_loss=0.04398, over 972223.48 frames.], batch size: 15, lr: 4.42e-04
2022-05-05 00:00:36,519 INFO [train.py:715] (2/8) Epoch 4, batch 23250, loss[loss=0.1311, simple_loss=0.2165, pruned_loss=0.02281, over 4811.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2273, pruned_loss=0.0441, over 971705.22 frames.], batch size: 27, lr: 4.42e-04
2022-05-05 00:01:16,397 INFO [train.py:715] (2/8) Epoch 4, batch 23300, loss[loss=0.174, simple_loss=0.2542, pruned_loss=0.04684, over 4787.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2265, pruned_loss=0.04343, over 971975.81 frames.], batch size: 14, lr: 4.42e-04
2022-05-05 00:01:56,690 INFO [train.py:715] (2/8) Epoch 4, batch 23350, loss[loss=0.1735, simple_loss=0.2273, pruned_loss=0.05982, over 4904.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2256, pruned_loss=0.04288, over 972572.66 frames.], batch size: 18, lr: 4.42e-04
2022-05-05 00:02:35,076 INFO [train.py:715] (2/8) Epoch 4, batch 23400, loss[loss=0.1181, simple_loss=0.1824, pruned_loss=0.02688, over 4700.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2254, pruned_loss=0.04314, over 971625.33 frames.], batch size: 15, lr: 4.42e-04
2022-05-05 00:03:14,411 INFO [train.py:715] (2/8) Epoch 4, batch 23450, loss[loss=0.1409, simple_loss=0.2103, pruned_loss=0.03572, over 4892.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2256, pruned_loss=0.04311, over 971516.18 frames.], batch size: 19, lr: 4.42e-04
2022-05-05 00:03:55,002 INFO [train.py:715] (2/8) Epoch 4, batch 23500, loss[loss=0.1565, simple_loss=0.2403, pruned_loss=0.03634, over 4827.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2258, pruned_loss=0.04273, over 972739.74 frames.], batch size: 15, lr: 4.42e-04
2022-05-05 00:04:33,369 INFO [train.py:715] (2/8) Epoch 4, batch 23550, loss[loss=0.1705, simple_loss=0.2387, pruned_loss=0.05114, over 4775.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2266, pruned_loss=0.04337, over 972733.73 frames.], batch size: 17, lr: 4.42e-04
2022-05-05 00:05:12,655 INFO [train.py:715] (2/8) Epoch 4, batch 23600, loss[loss=0.154, simple_loss=0.226, pruned_loss=0.04104, over 4950.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2277, pruned_loss=0.04387, over 972981.90 frames.], batch size: 29, lr: 4.42e-04
2022-05-05 00:05:53,466 INFO [train.py:715] (2/8) Epoch 4, batch 23650, loss[loss=0.144, simple_loss=0.2237, pruned_loss=0.03211, over 4753.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2294, pruned_loss=0.04468, over 973380.10 frames.], batch size: 19, lr: 4.42e-04
2022-05-05 00:06:34,857 INFO [train.py:715] (2/8) Epoch 4, batch 23700, loss[loss=0.1344, simple_loss=0.2053, pruned_loss=0.03169, over 4795.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2282, pruned_loss=0.04418, over 972736.32 frames.], batch size: 24, lr: 4.42e-04
2022-05-05 00:07:14,369 INFO [train.py:715] (2/8) Epoch 4, batch 23750, loss[loss=0.1265, simple_loss=0.2025, pruned_loss=0.02525, over 4878.00 frames.], tot_loss[loss=0.158, simple_loss=0.2281, pruned_loss=0.04402, over 972789.97 frames.], batch size: 22, lr: 4.42e-04
2022-05-05 00:07:53,777 INFO [train.py:715] (2/8) Epoch 4, batch 23800, loss[loss=0.1855, simple_loss=0.2578, pruned_loss=0.05657, over 4813.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2275, pruned_loss=0.04353, over 972717.58 frames.], batch size: 26, lr: 4.42e-04
2022-05-05 00:08:34,378 INFO [train.py:715] (2/8) Epoch 4, batch 23850, loss[loss=0.1381, simple_loss=0.2082, pruned_loss=0.03397, over 4862.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2269, pruned_loss=0.04328, over 973090.76 frames.], batch size: 20, lr: 4.42e-04
2022-05-05 00:09:13,910 INFO [train.py:715] (2/8) Epoch 4, batch 23900, loss[loss=0.1415, simple_loss=0.1965, pruned_loss=0.04321, over 4781.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2267, pruned_loss=0.04348, over 972896.10 frames.], batch size: 17, lr: 4.42e-04
2022-05-05 00:09:53,727 INFO [train.py:715] (2/8) Epoch 4, batch 23950, loss[loss=0.1426, simple_loss=0.2238, pruned_loss=0.0307, over 4971.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2267, pruned_loss=0.0432, over 972599.49 frames.], batch size: 15, lr: 4.41e-04
2022-05-05 00:10:34,500 INFO [train.py:715] (2/8) Epoch 4, batch 24000, loss[loss=0.1568, simple_loss=0.2268, pruned_loss=0.04339, over 4916.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2265, pruned_loss=0.04284, over 972329.41 frames.], batch size: 18, lr: 4.41e-04
2022-05-05 00:10:34,501 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 00:10:44,332 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1115, simple_loss=0.1974, pruned_loss=0.01276, over 914524.00 frames.
2022-05-05 00:11:25,498 INFO [train.py:715] (2/8) Epoch 4, batch 24050, loss[loss=0.1555, simple_loss=0.2247, pruned_loss=0.04309, over 4881.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2264, pruned_loss=0.04309, over 972825.72 frames.], batch size: 22, lr: 4.41e-04
2022-05-05 00:12:06,055 INFO [train.py:715] (2/8) Epoch 4, batch 24100, loss[loss=0.1429, simple_loss=0.2208, pruned_loss=0.03252, over 4784.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2276, pruned_loss=0.04347, over 972280.36 frames.], batch size: 17, lr: 4.41e-04
2022-05-05 00:12:45,925 INFO [train.py:715] (2/8) Epoch 4, batch 24150, loss[loss=0.1841, simple_loss=0.2479, pruned_loss=0.06016, over 4968.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2275, pruned_loss=0.04345, over 972612.73 frames.], batch size: 15, lr: 4.41e-04
2022-05-05 00:13:25,916 INFO [train.py:715] (2/8) Epoch 4, batch 24200, loss[loss=0.1515, simple_loss=0.2212, pruned_loss=0.04096, over 4760.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2274, pruned_loss=0.04366, over 972486.68 frames.], batch size: 14, lr: 4.41e-04
2022-05-05 00:14:07,339 INFO [train.py:715] (2/8) Epoch 4, batch 24250, loss[loss=0.1602, simple_loss=0.2252, pruned_loss=0.04761, over 4826.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2273, pruned_loss=0.04368, over 972148.03 frames.], batch size: 25, lr: 4.41e-04
2022-05-05 00:14:46,255 INFO [train.py:715] (2/8) Epoch 4, batch 24300, loss[loss=0.1414, simple_loss=0.2118, pruned_loss=0.03553, over 4957.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2271, pruned_loss=0.0437, over 972187.15 frames.], batch size: 29, lr: 4.41e-04
2022-05-05 00:15:26,712 INFO [train.py:715] (2/8) Epoch 4, batch 24350, loss[loss=0.1604, simple_loss=0.2392, pruned_loss=0.04085, over 4741.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2261, pruned_loss=0.04356, over 972169.25 frames.], batch size: 16, lr: 4.41e-04
2022-05-05 00:16:07,661 INFO [train.py:715] (2/8) Epoch 4, batch 24400, loss[loss=0.1495, simple_loss=0.2071, pruned_loss=0.04594, over 4785.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2253, pruned_loss=0.04312, over 971794.93 frames.], batch size: 14, lr: 4.41e-04
2022-05-05 00:16:47,242 INFO [train.py:715] (2/8) Epoch 4, batch 24450, loss[loss=0.1309, simple_loss=0.2084, pruned_loss=0.02672, over 4697.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2246, pruned_loss=0.0429, over 971604.74 frames.], batch size: 15, lr: 4.41e-04
2022-05-05 00:17:27,006 INFO [train.py:715] (2/8) Epoch 4, batch 24500, loss[loss=0.1166, simple_loss=0.1829, pruned_loss=0.02512, over 4857.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2236, pruned_loss=0.04242, over 972377.94 frames.], batch size: 20, lr: 4.41e-04
2022-05-05 00:18:06,875 INFO [train.py:715] (2/8) Epoch 4, batch 24550, loss[loss=0.1423, simple_loss=0.2177, pruned_loss=0.03343, over 4818.00 frames.], tot_loss[loss=0.155, simple_loss=0.2246, pruned_loss=0.04269, over 972164.73 frames.], batch size: 26, lr: 4.41e-04
2022-05-05 00:18:48,115 INFO [train.py:715] (2/8) Epoch 4, batch 24600, loss[loss=0.1866, simple_loss=0.2396, pruned_loss=0.06684, over 4698.00 frames.], tot_loss[loss=0.156, simple_loss=0.2256, pruned_loss=0.04321, over 972275.34 frames.], batch size: 15, lr: 4.41e-04
2022-05-05 00:19:27,477 INFO [train.py:715] (2/8) Epoch 4, batch 24650, loss[loss=0.1797, simple_loss=0.2366, pruned_loss=0.06134, over 4786.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2267, pruned_loss=0.04418, over 972254.96 frames.], batch size: 18, lr: 4.41e-04
2022-05-05 00:20:08,194 INFO [train.py:715] (2/8) Epoch 4, batch 24700, loss[loss=0.1666, simple_loss=0.2234, pruned_loss=0.0549, over 4954.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2271, pruned_loss=0.04472, over 973025.85 frames.], batch size: 35, lr: 4.40e-04
2022-05-05 00:20:49,273 INFO [train.py:715] (2/8) Epoch 4, batch 24750, loss[loss=0.1635, simple_loss=0.2311, pruned_loss=0.04791, over 4918.00 frames.], tot_loss[loss=0.1566, simple_loss=0.226, pruned_loss=0.04359, over 973216.49 frames.], batch size: 17, lr: 4.40e-04
2022-05-05 00:21:28,792 INFO [train.py:715] (2/8) Epoch 4, batch 24800, loss[loss=0.1262, simple_loss=0.1955, pruned_loss=0.02845, over 4759.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2259, pruned_loss=0.04328, over 973378.35 frames.], batch size: 19, lr: 4.40e-04
2022-05-05 00:22:08,801 INFO [train.py:715] (2/8) Epoch 4, batch 24850, loss[loss=0.1376, simple_loss=0.2036, pruned_loss=0.03577, over 4840.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2257, pruned_loss=0.04302, over 973981.49 frames.], batch size: 12, lr: 4.40e-04
2022-05-05 00:22:49,033 INFO [train.py:715] (2/8) Epoch 4, batch 24900, loss[loss=0.1513, simple_loss=0.23, pruned_loss=0.03631, over 4911.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2261, pruned_loss=0.04307, over 973774.63 frames.], batch size: 18, lr: 4.40e-04
2022-05-05 00:23:30,185 INFO [train.py:715] (2/8) Epoch 4, batch 24950, loss[loss=0.1466, simple_loss=0.2141, pruned_loss=0.03961, over 4767.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2265, pruned_loss=0.04299, over 974035.96 frames.], batch size: 18, lr: 4.40e-04
2022-05-05 00:24:09,091 INFO [train.py:715] (2/8) Epoch 4, batch 25000, loss[loss=0.1174, simple_loss=0.1959, pruned_loss=0.0194, over 4913.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2269, pruned_loss=0.04294, over 974853.84 frames.], batch size: 17, lr: 4.40e-04
2022-05-05 00:24:49,346 INFO [train.py:715] (2/8) Epoch 4, batch 25050, loss[loss=0.1323, simple_loss=0.2037, pruned_loss=0.03048, over 4960.00 frames.], tot_loss[loss=0.156, simple_loss=0.2266, pruned_loss=0.04268, over 974476.47 frames.], batch size: 35, lr: 4.40e-04
2022-05-05 00:25:30,443 INFO [train.py:715] (2/8) Epoch 4, batch 25100, loss[loss=0.1341, simple_loss=0.1979, pruned_loss=0.03514, over 4814.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2256, pruned_loss=0.04229, over 974261.06 frames.], batch size: 21, lr: 4.40e-04
2022-05-05 00:26:10,368 INFO [train.py:715] (2/8) Epoch 4, batch 25150, loss[loss=0.1728, simple_loss=0.242, pruned_loss=0.05185, over 4849.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2258, pruned_loss=0.04264, over 974360.76 frames.], batch size: 20, lr: 4.40e-04
2022-05-05 00:26:49,788 INFO [train.py:715] (2/8) Epoch 4, batch 25200, loss[loss=0.1679, simple_loss=0.2438, pruned_loss=0.04601, over 4842.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2255, pruned_loss=0.04231, over 973303.99 frames.], batch size: 15, lr: 4.40e-04
2022-05-05 00:27:30,056 INFO [train.py:715] (2/8) Epoch 4, batch 25250, loss[loss=0.1587, simple_loss=0.2328, pruned_loss=0.04228, over 4776.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2257, pruned_loss=0.04254, over 972472.26 frames.], batch size: 17, lr: 4.40e-04
2022-05-05 00:28:10,081 INFO [train.py:715] (2/8) Epoch 4, batch 25300, loss[loss=0.1675, simple_loss=0.2362, pruned_loss=0.04939, over 4771.00 frames.], tot_loss[loss=0.155, simple_loss=0.2247, pruned_loss=0.04265, over 972419.50 frames.], batch size: 19, lr: 4.40e-04
2022-05-05 00:28:47,883 INFO [train.py:715] (2/8) Epoch 4, batch 25350, loss[loss=0.1566, simple_loss=0.2233, pruned_loss=0.0449, over 4991.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2258, pruned_loss=0.04327, over 972616.66 frames.], batch size: 28, lr: 4.40e-04
2022-05-05 00:29:26,725 INFO [train.py:715] (2/8) Epoch 4, batch 25400, loss[loss=0.1171, simple_loss=0.1951, pruned_loss=0.01956, over 4811.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2243, pruned_loss=0.04268, over 972664.95 frames.], batch size: 26, lr: 4.40e-04
2022-05-05 00:30:06,396 INFO [train.py:715] (2/8) Epoch 4, batch 25450, loss[loss=0.176, simple_loss=0.2461, pruned_loss=0.05294, over 4948.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2262, pruned_loss=0.04358, over 973309.72 frames.], batch size: 21, lr: 4.39e-04
2022-05-05 00:30:45,457 INFO [train.py:715] (2/8) Epoch 4, batch 25500, loss[loss=0.1625, simple_loss=0.2322, pruned_loss=0.04641, over 4918.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2253, pruned_loss=0.043, over 972929.81 frames.], batch size: 17, lr: 4.39e-04
2022-05-05 00:31:25,317 INFO [train.py:715] (2/8) Epoch 4, batch 25550, loss[loss=0.1541, simple_loss=0.2241, pruned_loss=0.04209, over 4791.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.0435, over 972754.95 frames.], batch size: 18, lr: 4.39e-04
2022-05-05 00:32:05,293 INFO [train.py:715] (2/8) Epoch 4, batch 25600, loss[loss=0.1699, simple_loss=0.2534, pruned_loss=0.0432, over 4773.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2264, pruned_loss=0.0436, over 971970.55 frames.], batch size: 17, lr: 4.39e-04
2022-05-05 00:32:45,562 INFO [train.py:715] (2/8) Epoch 4, batch 25650, loss[loss=0.1409, simple_loss=0.2023, pruned_loss=0.03973, over 4817.00 frames.], tot_loss[loss=0.157, simple_loss=0.2265, pruned_loss=0.04377, over 971431.30 frames.], batch size: 15, lr: 4.39e-04
2022-05-05 00:33:24,690 INFO [train.py:715] (2/8) Epoch 4, batch 25700, loss[loss=0.1327, simple_loss=0.2134, pruned_loss=0.02604, over 4789.00 frames.], tot_loss[loss=0.1572, simple_loss=0.227, pruned_loss=0.04369, over 971067.40 frames.], batch size: 14, lr: 4.39e-04
2022-05-05 00:34:04,657 INFO [train.py:715] (2/8) Epoch 4, batch 25750, loss[loss=0.1409, simple_loss=0.2117, pruned_loss=0.03502, over 4852.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04368, over 971666.90 frames.], batch size: 20, lr: 4.39e-04
2022-05-05 00:34:45,102 INFO [train.py:715] (2/8) Epoch 4, batch 25800, loss[loss=0.1589, simple_loss=0.2306, pruned_loss=0.04357, over 4964.00 frames.], tot_loss[loss=0.1574, simple_loss=0.227, pruned_loss=0.04389, over 972066.89 frames.], batch size: 21, lr: 4.39e-04
2022-05-05 00:35:24,454 INFO [train.py:715] (2/8) Epoch 4, batch 25850, loss[loss=0.1289, simple_loss=0.2002, pruned_loss=0.02879, over 4860.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2262, pruned_loss=0.04345, over 972049.51 frames.], batch size: 13, lr: 4.39e-04
2022-05-05 00:36:03,600 INFO [train.py:715] (2/8) Epoch 4, batch 25900, loss[loss=0.1685, simple_loss=0.2277, pruned_loss=0.05462, over 4994.00 frames.], tot_loss[loss=0.156, simple_loss=0.2256, pruned_loss=0.04322, over 971751.44 frames.], batch size: 14, lr: 4.39e-04
2022-05-05 00:36:43,843 INFO [train.py:715] (2/8) Epoch 4, batch 25950, loss[loss=0.1444, simple_loss=0.2175, pruned_loss=0.03562, over 4851.00 frames.], tot_loss[loss=0.1557, simple_loss=0.225, pruned_loss=0.0432, over 971547.01 frames.], batch size: 13, lr: 4.39e-04
2022-05-05 00:37:24,111 INFO [train.py:715] (2/8) Epoch 4, batch 26000, loss[loss=0.1737, simple_loss=0.2516, pruned_loss=0.04792, over 4873.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2261, pruned_loss=0.04322, over 971821.86 frames.], batch size: 22, lr: 4.39e-04
2022-05-05 00:38:02,818 INFO [train.py:715] (2/8) Epoch 4, batch 26050, loss[loss=0.1454, simple_loss=0.2205, pruned_loss=0.0351, over 4990.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2262, pruned_loss=0.04316, over 971712.19 frames.], batch size: 15, lr: 4.39e-04
2022-05-05 00:38:42,225 INFO [train.py:715] (2/8) Epoch 4, batch 26100, loss[loss=0.1601, simple_loss=0.2365, pruned_loss=0.04191, over 4802.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2254, pruned_loss=0.04283, over 971124.69 frames.], batch size: 25, lr: 4.39e-04
2022-05-05 00:39:22,680 INFO [train.py:715] (2/8) Epoch 4, batch 26150, loss[loss=0.146, simple_loss=0.2168, pruned_loss=0.03764, over 4944.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2259, pruned_loss=0.04344, over 971648.39 frames.], batch size: 35, lr: 4.39e-04
2022-05-05 00:40:01,758 INFO [train.py:715] (2/8) Epoch 4, batch 26200, loss[loss=0.1601, simple_loss=0.2356, pruned_loss=0.04225, over 4910.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2261, pruned_loss=0.04325, over 971336.63 frames.], batch size: 19, lr: 4.38e-04
2022-05-05 00:40:41,522 INFO [train.py:715] (2/8) Epoch 4, batch 26250, loss[loss=0.1724, simple_loss=0.234, pruned_loss=0.05537, over 4971.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2256, pruned_loss=0.04328, over 971843.67 frames.], batch size: 35, lr: 4.38e-04
2022-05-05 00:41:21,386 INFO [train.py:715] (2/8) Epoch 4, batch 26300, loss[loss=0.1294, simple_loss=0.1963, pruned_loss=0.03122, over 4919.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2249, pruned_loss=0.04262, over 971298.75 frames.], batch size: 21, lr: 4.38e-04
2022-05-05 00:42:01,535 INFO [train.py:715] (2/8) Epoch 4, batch 26350, loss[loss=0.1499, simple_loss=0.2253, pruned_loss=0.03726, over 4893.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2252, pruned_loss=0.04265, over 971074.41 frames.], batch size: 17, lr: 4.38e-04
2022-05-05 00:42:40,877 INFO [train.py:715] (2/8) Epoch 4, batch 26400, loss[loss=0.1293, simple_loss=0.1993, pruned_loss=0.02963, over 4956.00 frames.], tot_loss[loss=0.1549, simple_loss=0.225, pruned_loss=0.04237, over 971539.72 frames.], batch size: 24, lr: 4.38e-04
2022-05-05 00:43:20,962 INFO [train.py:715] (2/8) Epoch 4, batch 26450, loss[loss=0.1661, simple_loss=0.2297, pruned_loss=0.0512, over 4779.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2246, pruned_loss=0.04247, over 971960.37 frames.], batch size: 19, lr: 4.38e-04
2022-05-05 00:44:01,485 INFO [train.py:715] (2/8) Epoch 4, batch 26500, loss[loss=0.1345, simple_loss=0.2022, pruned_loss=0.0334, over 4856.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2233, pruned_loss=0.04171, over 972149.03 frames.], batch size: 32, lr: 4.38e-04
2022-05-05 00:44:40,390 INFO [train.py:715] (2/8) Epoch 4, batch 26550, loss[loss=0.1533, simple_loss=0.2171, pruned_loss=0.04482, over 4837.00 frames.], tot_loss[loss=0.1545, simple_loss=0.224, pruned_loss=0.0425, over 972621.77 frames.], batch size: 13, lr: 4.38e-04
2022-05-05 00:45:20,029 INFO [train.py:715] (2/8) Epoch 4, batch 26600, loss[loss=0.1728, simple_loss=0.2381, pruned_loss=0.05376, over 4929.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2243, pruned_loss=0.04263, over 972475.47 frames.], batch size: 18, lr: 4.38e-04
2022-05-05 00:46:00,418 INFO [train.py:715] (2/8) Epoch 4, batch 26650, loss[loss=0.1441, simple_loss=0.208, pruned_loss=0.04009, over 4846.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2241, pruned_loss=0.04237, over 971881.93 frames.], batch size: 12, lr: 4.38e-04
2022-05-05 00:46:41,231 INFO [train.py:715] (2/8) Epoch 4, batch 26700, loss[loss=0.1235, simple_loss=0.1881, pruned_loss=0.02942, over 4975.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2238, pruned_loss=0.04218, over 972327.27 frames.], batch size: 15, lr: 4.38e-04
2022-05-05 00:47:20,021 INFO [train.py:715] (2/8) Epoch 4, batch 26750, loss[loss=0.1484, simple_loss=0.2178, pruned_loss=0.03951, over 4746.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2248, pruned_loss=0.04233, over 973158.88 frames.], batch size: 16, lr: 4.38e-04
2022-05-05 00:47:59,595 INFO [train.py:715] (2/8) Epoch 4, batch 26800, loss[loss=0.1601, simple_loss=0.2363, pruned_loss=0.04189, over 4792.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2263, pruned_loss=0.04345, over 972805.72 frames.], batch size: 24, lr: 4.38e-04
2022-05-05 00:48:39,807 INFO [train.py:715] (2/8) Epoch 4, batch 26850, loss[loss=0.1339, simple_loss=0.213, pruned_loss=0.02746, over 4771.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2263, pruned_loss=0.04323, over 972873.38 frames.], batch size: 17, lr: 4.38e-04
2022-05-05 00:49:18,739 INFO [train.py:715] (2/8) Epoch 4, batch 26900, loss[loss=0.1696, simple_loss=0.2387, pruned_loss=0.05028, over 4970.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2256, pruned_loss=0.04289, over 973463.82 frames.], batch size: 15, lr: 4.38e-04
2022-05-05 00:49:58,559 INFO [train.py:715] (2/8) Epoch 4, batch 26950, loss[loss=0.1484, simple_loss=0.2118, pruned_loss=0.04252, over 4928.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2262, pruned_loss=0.04328, over 973199.24 frames.], batch size: 23, lr: 4.37e-04
2022-05-05 00:50:38,532 INFO [train.py:715] (2/8) Epoch 4, batch 27000, loss[loss=0.1483, simple_loss=0.2165, pruned_loss=0.03999, over 4761.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2269, pruned_loss=0.04384, over 972388.84 frames.], batch size: 19, lr: 4.37e-04
2022-05-05 00:50:38,532 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 00:50:48,691 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1114, simple_loss=0.197, pruned_loss=0.01284, over 914524.00 frames.
2022-05-05 00:51:28,850 INFO [train.py:715] (2/8) Epoch 4, batch 27050, loss[loss=0.1972, simple_loss=0.2691, pruned_loss=0.06269, over 4954.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2268, pruned_loss=0.04325, over 972716.57 frames.], batch size: 21, lr: 4.37e-04
2022-05-05 00:52:08,423 INFO [train.py:715] (2/8) Epoch 4, batch 27100, loss[loss=0.1419, simple_loss=0.2218, pruned_loss=0.03102, over 4978.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2265, pruned_loss=0.0435, over 973210.92 frames.], batch size: 25, lr: 4.37e-04
2022-05-05 00:52:47,727 INFO [train.py:715] (2/8) Epoch 4, batch 27150, loss[loss=0.17, simple_loss=0.2435, pruned_loss=0.0482, over 4931.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2257, pruned_loss=0.04304, over 972297.14 frames.], batch size: 29, lr: 4.37e-04
2022-05-05 00:53:27,407 INFO [train.py:715] (2/8) Epoch 4, batch 27200, loss[loss=0.1709, simple_loss=0.2299, pruned_loss=0.05597, over 4877.00 frames.], tot_loss[loss=0.1562, simple_loss=0.226, pruned_loss=0.04324, over 972288.83 frames.], batch size: 30, lr: 4.37e-04
2022-05-05 00:54:07,870 INFO [train.py:715] (2/8) Epoch 4, batch 27250, loss[loss=0.1424, simple_loss=0.2252, pruned_loss=0.02978, over 4746.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2259, pruned_loss=0.04326, over 971926.61 frames.], batch size: 16, lr: 4.37e-04
2022-05-05 00:54:46,634 INFO [train.py:715] (2/8) Epoch 4, batch 27300, loss[loss=0.151, simple_loss=0.2128, pruned_loss=0.04455, over 4801.00 frames.], tot_loss[loss=0.156, simple_loss=0.2256, pruned_loss=0.0432, over 972617.08 frames.], batch size: 21, lr: 4.37e-04
2022-05-05 00:55:26,631 INFO [train.py:715] (2/8) Epoch 4, batch 27350, loss[loss=0.1456, simple_loss=0.2131, pruned_loss=0.03911, over 4856.00 frames.], tot_loss[loss=0.1564, simple_loss=0.226, pruned_loss=0.04336, over 971579.07 frames.], batch size: 38, lr: 4.37e-04
2022-05-05 00:56:06,585 INFO [train.py:715] (2/8) Epoch 4, batch 27400, loss[loss=0.1789, simple_loss=0.2396, pruned_loss=0.0591, over 4840.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2258, pruned_loss=0.0432, over 970955.58 frames.], batch size: 30, lr: 4.37e-04
2022-05-05 00:56:45,009 INFO [train.py:715] (2/8) Epoch 4, batch 27450, loss[loss=0.1255, simple_loss=0.1891, pruned_loss=0.03093, over 4822.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2258, pruned_loss=0.04337, over 971570.39 frames.], batch size: 13, lr: 4.37e-04
2022-05-05 00:57:24,954 INFO [train.py:715] (2/8) Epoch 4, batch 27500, loss[loss=0.1393, simple_loss=0.2115, pruned_loss=0.03356, over 4802.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2253, pruned_loss=0.04342, over 971730.48 frames.], batch size: 14, lr: 4.37e-04
2022-05-05 00:58:03,978 INFO [train.py:715] (2/8) Epoch 4, batch 27550, loss[loss=0.1438, simple_loss=0.2088, pruned_loss=0.03939, over 4782.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2268, pruned_loss=0.04453, over 971548.36 frames.], batch size: 17, lr: 4.37e-04
2022-05-05 00:58:43,891 INFO [train.py:715] (2/8) Epoch 4, batch 27600, loss[loss=0.1679, simple_loss=0.2373, pruned_loss=0.04931, over 4923.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2266, pruned_loss=0.04446, over 970799.09 frames.], batch size: 23, lr: 4.37e-04
2022-05-05 00:59:22,448 INFO [train.py:715] (2/8) Epoch 4, batch 27650, loss[loss=0.1702, simple_loss=0.2423, pruned_loss=0.0491, over 4790.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2273, pruned_loss=0.04438, over 972154.59 frames.], batch size: 14, lr: 4.37e-04
2022-05-05 01:00:01,782 INFO [train.py:715] (2/8) Epoch 4, batch 27700, loss[loss=0.1247, simple_loss=0.1986, pruned_loss=0.02545, over 4825.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2266, pruned_loss=0.04345, over 972261.01 frames.], batch size: 13, lr: 4.36e-04
2022-05-05 01:00:41,403 INFO [train.py:715] (2/8) Epoch 4, batch 27750, loss[loss=0.1573, simple_loss=0.2279, pruned_loss=0.04329, over 4936.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2259, pruned_loss=0.04318, over 971798.66 frames.], batch size: 23, lr: 4.36e-04
2022-05-05 01:01:20,720 INFO [train.py:715] (2/8) Epoch 4, batch 27800, loss[loss=0.1288, simple_loss=0.2059, pruned_loss=0.02583, over 4841.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2258, pruned_loss=0.04332, over 971452.48 frames.], batch size: 15, lr: 4.36e-04
2022-05-05 01:01:59,771 INFO [train.py:715] (2/8) Epoch 4, batch 27850, loss[loss=0.1219, simple_loss=0.1933, pruned_loss=0.02529, over 4801.00 frames.], tot_loss[loss=0.155, simple_loss=0.2245, pruned_loss=0.04271, over 970535.48 frames.], batch size: 13, lr: 4.36e-04
2022-05-05 01:02:38,863 INFO [train.py:715] (2/8) Epoch 4, batch 27900, loss[loss=0.1739, simple_loss=0.2339, pruned_loss=0.05696, over 4845.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2244, pruned_loss=0.04264, over 970980.24 frames.], batch size: 30, lr: 4.36e-04
2022-05-05 01:03:18,311 INFO [train.py:715] (2/8) Epoch 4, batch 27950, loss[loss=0.1595, simple_loss=0.2362, pruned_loss=0.04139, over 4933.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2253, pruned_loss=0.04271, over 971303.21 frames.], batch size: 21, lr: 4.36e-04
2022-05-05 01:03:57,878 INFO [train.py:715] (2/8) Epoch 4, batch 28000, loss[loss=0.1503, simple_loss=0.2179, pruned_loss=0.04133, over 4765.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2251, pruned_loss=0.04277, over 971493.26 frames.], batch size: 17, lr: 4.36e-04
2022-05-05 01:04:37,837 INFO [train.py:715] (2/8) Epoch 4, batch 28050, loss[loss=0.1984, simple_loss=0.2771, pruned_loss=0.05986, over 4777.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2258, pruned_loss=0.04277, over 971784.10 frames.], batch size: 17, lr: 4.36e-04
2022-05-05 01:05:17,719 INFO [train.py:715] (2/8) Epoch 4, batch 28100, loss[loss=0.1545, simple_loss=0.2192, pruned_loss=0.04495, over 4928.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2254, pruned_loss=0.04258, over 972019.81 frames.], batch size: 23, lr: 4.36e-04
2022-05-05 01:05:57,313 INFO [train.py:715] (2/8) Epoch 4, batch 28150, loss[loss=0.1843, simple_loss=0.257, pruned_loss=0.05574, over 4938.00 frames.], tot_loss[loss=0.156, simple_loss=0.226, pruned_loss=0.04305, over 972668.15 frames.], batch size: 39, lr: 4.36e-04
2022-05-05 01:06:36,804 INFO [train.py:715] (2/8) Epoch 4, batch 28200, loss[loss=0.1756, simple_loss=0.2361, pruned_loss=0.05759, over 4785.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2261, pruned_loss=0.04341, over 972076.49 frames.], batch size: 14, lr: 4.36e-04
2022-05-05 01:07:15,870 INFO [train.py:715] (2/8) Epoch 4, batch 28250, loss[loss=0.1548, simple_loss=0.2156, pruned_loss=0.04699, over 4732.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2265, pruned_loss=0.04336, over 971609.27 frames.], batch size: 16, lr: 4.36e-04
2022-05-05 01:07:55,444 INFO [train.py:715] (2/8) Epoch 4, batch 28300, loss[loss=0.184, simple_loss=0.2553, pruned_loss=0.05631, over 4820.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2271, pruned_loss=0.04386, over 972091.28 frames.], batch size: 15, lr: 4.36e-04
2022-05-05 01:08:34,755 INFO [train.py:715] (2/8) Epoch 4, batch 28350, loss[loss=0.1506, simple_loss=0.2163, pruned_loss=0.04247, over 4950.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2263, pruned_loss=0.04358, over 972101.81 frames.], batch size: 21, lr: 4.36e-04
2022-05-05 01:09:14,656 INFO [train.py:715] (2/8) Epoch 4, batch 28400, loss[loss=0.148, simple_loss=0.2058, pruned_loss=0.04512, over 4777.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.0435, over 972623.95 frames.], batch size: 14, lr: 4.36e-04
2022-05-05 01:09:53,871 INFO [train.py:715] (2/8) Epoch 4, batch 28450, loss[loss=0.165, simple_loss=0.2333, pruned_loss=0.04838, over 4795.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2264, pruned_loss=0.04369, over 973216.38 frames.], batch size: 21, lr: 4.36e-04
2022-05-05 01:10:32,529 INFO [train.py:715] (2/8) Epoch 4, batch 28500, loss[loss=0.1433, simple_loss=0.2204, pruned_loss=0.03309, over 4813.00 frames.], tot_loss[loss=0.1563, simple_loss=0.226, pruned_loss=0.04328, over 972631.65 frames.], batch size: 21, lr: 4.35e-04
2022-05-05 01:11:12,039 INFO [train.py:715] (2/8) Epoch 4, batch 28550, loss[loss=0.1484, simple_loss=0.22, pruned_loss=0.03843, over 4922.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2262, pruned_loss=0.04305, over 972209.95 frames.], batch size: 18, lr: 4.35e-04
2022-05-05 01:11:51,206 INFO [train.py:715] (2/8) Epoch 4, batch 28600, loss[loss=0.1186, simple_loss=0.1904, pruned_loss=0.02342, over 4979.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2262, pruned_loss=0.04331, over 972551.17 frames.], batch size: 14, lr: 4.35e-04
2022-05-05 01:12:30,864 INFO [train.py:715] (2/8) Epoch 4, batch 28650, loss[loss=0.163, simple_loss=0.2245, pruned_loss=0.05076, over 4931.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2268, pruned_loss=0.04342, over 972695.23 frames.], batch size: 18, lr: 4.35e-04
2022-05-05 01:13:10,039 INFO [train.py:715] (2/8) Epoch 4, batch 28700, loss[loss=0.1537, simple_loss=0.224, pruned_loss=0.04167, over 4983.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2261, pruned_loss=0.04324, over 972355.26 frames.], batch size: 25, lr: 4.35e-04
2022-05-05 01:13:49,557 INFO [train.py:715] (2/8) Epoch 4, batch 28750, loss[loss=0.1617, simple_loss=0.2205, pruned_loss=0.05143, over 4957.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2252, pruned_loss=0.04283, over 971917.58 frames.], batch size: 15, lr: 4.35e-04
2022-05-05 01:14:31,713 INFO [train.py:715] (2/8) Epoch 4, batch 28800, loss[loss=0.1549, simple_loss=0.2285, pruned_loss=0.04061, over 4873.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2256, pruned_loss=0.04268, over 971171.44 frames.], batch size: 16, lr: 4.35e-04
2022-05-05 01:15:10,515 INFO [train.py:715] (2/8) Epoch 4, batch 28850, loss[loss=0.1899, simple_loss=0.2469, pruned_loss=0.06642, over 4832.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2255, pruned_loss=0.04284, over 971594.80 frames.], batch size: 30, lr: 4.35e-04
2022-05-05 01:15:50,213 INFO [train.py:715] (2/8) Epoch 4, batch 28900, loss[loss=0.1777, simple_loss=0.2406, pruned_loss=0.05737, over 4786.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2262, pruned_loss=0.04337, over 971239.48 frames.], batch size: 17, lr: 4.35e-04
2022-05-05 01:16:29,309 INFO [train.py:715] (2/8) Epoch 4, batch 28950, loss[loss=0.2125, simple_loss=0.2558, pruned_loss=0.08463, over 4868.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2267, pruned_loss=0.0438, over 970926.05 frames.], batch size: 22, lr: 4.35e-04
2022-05-05 01:17:08,542 INFO [train.py:715] (2/8) Epoch 4, batch 29000, loss[loss=0.1642, simple_loss=0.2346, pruned_loss=0.04687, over 4941.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2262, pruned_loss=0.04323, over 971474.25 frames.], batch size: 24, lr: 4.35e-04
2022-05-05 01:17:48,159 INFO [train.py:715] (2/8) Epoch 4, batch 29050, loss[loss=0.1655, simple_loss=0.2349, pruned_loss=0.04802, over 4949.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2267, pruned_loss=0.0433, over 972177.92 frames.], batch size: 39, lr: 4.35e-04
2022-05-05 01:18:28,175 INFO [train.py:715] (2/8) Epoch 4, batch 29100, loss[loss=0.1491, simple_loss=0.2123, pruned_loss=0.04294, over 4782.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2264, pruned_loss=0.04309, over 971434.95 frames.], batch size: 18, lr: 4.35e-04
2022-05-05 01:19:07,856 INFO [train.py:715] (2/8) Epoch 4, batch 29150, loss[loss=0.1532, simple_loss=0.2214, pruned_loss=0.04252, over 4890.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2257, pruned_loss=0.04296, over 971460.71 frames.], batch size: 32, lr: 4.35e-04
2022-05-05 01:19:46,741 INFO [train.py:715] (2/8) Epoch 4, batch 29200, loss[loss=0.1389, simple_loss=0.2088, pruned_loss=0.03457, over 4985.00 frames.], tot_loss[loss=0.1562, simple_loss=0.226, pruned_loss=0.04319, over 971973.79 frames.], batch size: 28, lr: 4.35e-04
2022-05-05 01:20:26,106 INFO [train.py:715] (2/8) Epoch 4, batch 29250, loss[loss=0.1778, simple_loss=0.2444, pruned_loss=0.05558, over 4945.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2252, pruned_loss=0.04289, over 972569.58 frames.], batch size: 23, lr: 4.34e-04
2022-05-05 01:21:05,000 INFO [train.py:715] (2/8) Epoch 4, batch 29300, loss[loss=0.1659, simple_loss=0.2441, pruned_loss=0.0439, over 4888.00 frames.], tot_loss[loss=0.1555, simple_loss=0.225, pruned_loss=0.04298, over 972246.97 frames.], batch size: 17, lr: 4.34e-04
2022-05-05 01:21:43,984 INFO [train.py:715] (2/8) Epoch 4, batch 29350, loss[loss=0.1339, simple_loss=0.201, pruned_loss=0.03339, over 4931.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2247, pruned_loss=0.04294, over 972687.06 frames.], batch size: 18, lr: 4.34e-04
2022-05-05 01:22:22,966 INFO [train.py:715] (2/8) Epoch 4, batch 29400, loss[loss=0.2059, simple_loss=0.2516, pruned_loss=0.08011, over 4855.00 frames.], tot_loss[loss=0.155, simple_loss=0.2244, pruned_loss=0.04279, over 973158.85 frames.], batch size: 30, lr: 4.34e-04
2022-05-05 01:23:02,045 INFO [train.py:715] (2/8) Epoch 4, batch 29450, loss[loss=0.1164, simple_loss=0.1962, pruned_loss=0.01826, over 4941.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2238, pruned_loss=0.04224, over 973079.73 frames.], batch size: 21, lr: 4.34e-04
2022-05-05 01:23:41,623 INFO [train.py:715] (2/8) Epoch 4, batch 29500, loss[loss=0.111, simple_loss=0.1814, pruned_loss=0.02028, over 4983.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2233, pruned_loss=0.04199, over 972853.66 frames.], batch size: 14, lr: 4.34e-04
2022-05-05 01:24:20,880 INFO [train.py:715] (2/8) Epoch 4, batch 29550, loss[loss=0.154, simple_loss=0.22, pruned_loss=0.04394, over 4981.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2239, pruned_loss=0.04246, over 973284.78 frames.], batch size: 24, lr: 4.34e-04
2022-05-05 01:25:00,163 INFO [train.py:715] (2/8) Epoch 4, batch 29600, loss[loss=0.1632, simple_loss=0.2302, pruned_loss=0.04812, over 4774.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2248, pruned_loss=0.04316, over 974425.62 frames.], batch size: 18, lr: 4.34e-04
2022-05-05 01:25:39,285 INFO [train.py:715] (2/8) Epoch 4, batch 29650, loss[loss=0.1592, simple_loss=0.2439, pruned_loss=0.03728, over 4734.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2249, pruned_loss=0.0427, over 973709.17 frames.], batch size: 16, lr: 4.34e-04
2022-05-05 01:26:18,055 INFO [train.py:715] (2/8) Epoch 4, batch 29700, loss[loss=0.1318, simple_loss=0.1899, pruned_loss=0.03691, over 4862.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2252, pruned_loss=0.04275, over 973942.39 frames.], batch size: 13, lr: 4.34e-04
2022-05-05 01:26:57,624 INFO [train.py:715] (2/8) Epoch 4, batch 29750, loss[loss=0.163, simple_loss=0.2182, pruned_loss=0.05387, over 4701.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2249, pruned_loss=0.0423, over 973613.37 frames.], batch size: 15, lr: 4.34e-04
2022-05-05 01:27:36,801 INFO [train.py:715] (2/8) Epoch 4, batch 29800, loss[loss=0.1413, simple_loss=0.2088, pruned_loss=0.03693, over 4855.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2257, pruned_loss=0.04294, over 974261.31 frames.], batch size: 20, lr: 4.34e-04
2022-05-05 01:28:16,331 INFO [train.py:715] (2/8) Epoch 4, batch 29850, loss[loss=0.1702, simple_loss=0.2438, pruned_loss=0.04826, over 4956.00 frames.], tot_loss[loss=0.156, simple_loss=0.2257, pruned_loss=0.04309, over 973926.19 frames.], batch size: 35, lr: 4.34e-04
2022-05-05 01:28:55,199 INFO [train.py:715] (2/8) Epoch 4, batch 29900, loss[loss=0.1364, simple_loss=0.2068, pruned_loss=0.03298, over 4897.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2249, pruned_loss=0.043, over 973942.00 frames.], batch size: 19, lr: 4.34e-04
2022-05-05 01:29:34,840 INFO [train.py:715] (2/8) Epoch 4, batch 29950, loss[loss=0.1664, simple_loss=0.2287, pruned_loss=0.05207, over 4789.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2257, pruned_loss=0.04295, over 974101.35 frames.], batch size: 24, lr: 4.34e-04
2022-05-05 01:30:13,995 INFO [train.py:715] (2/8) Epoch 4, batch 30000, loss[loss=0.1665, simple_loss=0.2438, pruned_loss=0.04456, over 4939.00 frames.], tot_loss[loss=0.1562, simple_loss=0.226, pruned_loss=0.04322, over 973866.98 frames.], batch size: 35, lr: 4.34e-04
2022-05-05 01:30:13,996 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 01:30:23,829 INFO [train.py:742] (2/8) Epoch 4, validation: loss=0.1113, simple_loss=0.1968, pruned_loss=0.01286, over 914524.00 frames.
2022-05-05 01:31:03,994 INFO [train.py:715] (2/8) Epoch 4, batch 30050, loss[loss=0.1622, simple_loss=0.2423, pruned_loss=0.04099, over 4793.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2251, pruned_loss=0.04305, over 973718.07 frames.], batch size: 21, lr: 4.33e-04
2022-05-05 01:31:43,424 INFO [train.py:715] (2/8) Epoch 4, batch 30100, loss[loss=0.162, simple_loss=0.2307, pruned_loss=0.0466, over 4935.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2252, pruned_loss=0.04334, over 974488.95 frames.], batch size: 29, lr: 4.33e-04
2022-05-05 01:32:23,322 INFO [train.py:715] (2/8) Epoch 4, batch 30150, loss[loss=0.1685, simple_loss=0.23, pruned_loss=0.05348, over 4915.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2238, pruned_loss=0.04222, over 973269.18 frames.], batch size: 17, lr: 4.33e-04
2022-05-05 01:33:02,790 INFO [train.py:715] (2/8) Epoch 4, batch 30200, loss[loss=0.1549, simple_loss=0.2164, pruned_loss=0.04665, over 4829.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2245, pruned_loss=0.04263, over 972474.69 frames.], batch size: 13, lr: 4.33e-04
2022-05-05 01:33:42,426 INFO [train.py:715] (2/8) Epoch 4, batch 30250, loss[loss=0.1462, simple_loss=0.2161, pruned_loss=0.03817, over 4881.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2248, pruned_loss=0.04277, over 972268.46 frames.], batch size: 22, lr: 4.33e-04
2022-05-05 01:34:21,598 INFO [train.py:715] (2/8) Epoch 4, batch 30300, loss[loss=0.1678, simple_loss=0.245, pruned_loss=0.0453, over 4854.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2255, pruned_loss=0.04353, over 972615.45 frames.], batch size: 13, lr: 4.33e-04
2022-05-05 01:35:01,076 INFO [train.py:715] (2/8) Epoch 4, batch 30350, loss[loss=0.1546, simple_loss=0.2322, pruned_loss=0.03845, over 4698.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2251, pruned_loss=0.04336, over 971627.60 frames.], batch size: 15, lr: 4.33e-04
2022-05-05 01:35:41,055 INFO [train.py:715] (2/8) Epoch 4, batch 30400, loss[loss=0.1667, simple_loss=0.2309, pruned_loss=0.05122, over 4969.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2245, pruned_loss=0.04333, over 971652.76 frames.], batch size: 35, lr: 4.33e-04
2022-05-05 01:36:20,215 INFO [train.py:715] (2/8) Epoch 4, batch 30450, loss[loss=0.1596, simple_loss=0.2372, pruned_loss=0.04106, over 4764.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2246, pruned_loss=0.04283, over 972397.68 frames.], batch size: 12, lr: 4.33e-04
2022-05-05 01:36:59,979 INFO [train.py:715] (2/8) Epoch 4, batch 30500, loss[loss=0.1396, simple_loss=0.2298, pruned_loss=0.02467, over 4829.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2258, pruned_loss=0.04323, over 972955.92 frames.], batch size: 26, lr: 4.33e-04
2022-05-05 01:37:40,026 INFO [train.py:715] (2/8) Epoch 4, batch 30550, loss[loss=0.1545, simple_loss=0.2212, pruned_loss=0.04387, over 4753.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2262, pruned_loss=0.0436, over 972579.65 frames.], batch size: 19, lr: 4.33e-04
2022-05-05 01:38:19,334 INFO [train.py:715] (2/8) Epoch 4, batch 30600, loss[loss=0.1375, simple_loss=0.2084, pruned_loss=0.03327, over 4821.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2259, pruned_loss=0.04393, over 972586.60 frames.], batch size: 13, lr: 4.33e-04
2022-05-05 01:38:58,941 INFO [train.py:715] (2/8) Epoch 4, batch 30650, loss[loss=0.1087, simple_loss=0.1893, pruned_loss=0.01405, over 4816.00 frames.], tot_loss[loss=0.156, simple_loss=0.2255, pruned_loss=0.04324, over 973656.77 frames.], batch size: 26, lr: 4.33e-04
2022-05-05 01:39:38,414 INFO [train.py:715] (2/8) Epoch 4, batch 30700, loss[loss=0.1804, simple_loss=0.2334, pruned_loss=0.06373, over 4832.00 frames.], tot_loss[loss=0.156, simple_loss=0.2257, pruned_loss=0.04317, over 972917.56 frames.], batch size: 30, lr: 4.33e-04
2022-05-05 01:40:18,147 INFO [train.py:715] (2/8) Epoch 4, batch 30750, loss[loss=0.1776, simple_loss=0.2464, pruned_loss=0.05438, over 4954.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2258, pruned_loss=0.04324, over 972796.07 frames.], batch size: 39, lr: 4.33e-04
2022-05-05 01:40:57,685 INFO [train.py:715] (2/8) Epoch 4, batch 30800, loss[loss=0.157, simple_loss=0.228, pruned_loss=0.043, over 4790.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2253, pruned_loss=0.04304, over 972754.93 frames.], batch size: 17, lr: 4.32e-04
2022-05-05 01:41:37,510 INFO [train.py:715] (2/8) Epoch 4, batch 30850, loss[loss=0.2029, simple_loss=0.2422, pruned_loss=0.08177, over 4701.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2248, pruned_loss=0.04241, over 972027.66 frames.], batch size: 15, lr: 4.32e-04
2022-05-05 01:42:17,791 INFO [train.py:715] (2/8) Epoch 4, batch 30900, loss[loss=0.125, simple_loss=0.1923, pruned_loss=0.02881, over 4782.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2256, pruned_loss=0.04267, over 972169.09 frames.], batch size: 14, lr: 4.32e-04
2022-05-05 01:42:57,263 INFO [train.py:715] (2/8) Epoch 4, batch 30950, loss[loss=0.1782, simple_loss=0.2546, pruned_loss=0.05086, over 4872.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2256, pruned_loss=0.04292, over 971824.32 frames.], batch size: 16, lr: 4.32e-04
2022-05-05 01:43:36,635 INFO [train.py:715] (2/8) Epoch 4, batch 31000, loss[loss=0.1783, simple_loss=0.2453, pruned_loss=0.05566, over 4959.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2256, pruned_loss=0.04327, over 971792.48 frames.], batch size: 39, lr: 4.32e-04
2022-05-05 01:44:16,108 INFO [train.py:715] (2/8) Epoch 4, batch 31050, loss[loss=0.1734, simple_loss=0.2466, pruned_loss=0.05012, over 4791.00 frames.], tot_loss[loss=0.1565, simple_loss=0.226, pruned_loss=0.04356, over 971614.71 frames.], batch size: 17, lr: 4.32e-04
2022-05-05 01:44:55,519 INFO [train.py:715] (2/8) Epoch 4, batch 31100, loss[loss=0.18, simple_loss=0.2492, pruned_loss=0.05543, over 4979.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2258, pruned_loss=0.04372, over 972040.82 frames.], batch size: 15, lr: 4.32e-04
2022-05-05 01:45:35,034 INFO [train.py:715] (2/8) Epoch 4, batch 31150, loss[loss=0.1279, simple_loss=0.207, pruned_loss=0.02439, over 4983.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2262, pruned_loss=0.04357, over 972108.97 frames.], batch size: 28, lr: 4.32e-04
2022-05-05 01:46:13,902 INFO [train.py:715] (2/8) Epoch 4, batch 31200, loss[loss=0.1474, simple_loss=0.2245, pruned_loss=0.0352, over 4788.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2262, pruned_loss=0.04307, over 971251.17 frames.], batch size: 12, lr: 4.32e-04
2022-05-05 01:46:53,972 INFO [train.py:715] (2/8) Epoch 4, batch 31250, loss[loss=0.1413, simple_loss=0.2212, pruned_loss=0.03067, over 4836.00 frames.], tot_loss[loss=0.157, simple_loss=0.2267, pruned_loss=0.04363, over 970552.39 frames.], batch size: 15, lr: 4.32e-04
2022-05-05 01:47:33,180 INFO [train.py:715] (2/8) Epoch 4, batch 31300, loss[loss=0.1723, simple_loss=0.2491, pruned_loss=0.04776, over 4874.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2262, pruned_loss=0.04322, over 970827.59 frames.], batch size: 20, lr: 4.32e-04
2022-05-05 01:48:12,188 INFO [train.py:715] (2/8) Epoch 4, batch 31350, loss[loss=0.1449, simple_loss=0.2173, pruned_loss=0.03624, over 4869.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2255, pruned_loss=0.04271, over 971150.01 frames.], batch size: 22, lr: 4.32e-04
2022-05-05 01:48:52,071 INFO [train.py:715] (2/8) Epoch 4, batch 31400, loss[loss=0.1705, simple_loss=0.2358, pruned_loss=0.0526, over 4776.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2259, pruned_loss=0.04316, over 971539.68 frames.], batch size: 18, lr: 4.32e-04
2022-05-05 01:49:31,805 INFO [train.py:715] (2/8) Epoch 4, batch 31450, loss[loss=0.1613, simple_loss=0.2313, pruned_loss=0.04569, over 4812.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2262, pruned_loss=0.04303, over 972151.10 frames.], batch size: 25, lr: 4.32e-04
2022-05-05 01:50:11,368 INFO [train.py:715] (2/8) Epoch 4, batch 31500, loss[loss=0.1726, simple_loss=0.237, pruned_loss=0.05408, over 4879.00 frames.], tot_loss[loss=0.156, simple_loss=0.2258, pruned_loss=0.04316, over 971962.52 frames.], batch size: 16, lr: 4.32e-04
2022-05-05 01:50:51,737 INFO [train.py:715] (2/8) Epoch 4, batch 31550, loss[loss=0.1478, simple_loss=0.2102, pruned_loss=0.04271, over 4859.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2261, pruned_loss=0.04309, over 972438.61 frames.], batch size: 20, lr: 4.32e-04
2022-05-05 01:51:32,266 INFO [train.py:715] (2/8) Epoch 4, batch 31600, loss[loss=0.1375, simple_loss=0.2073, pruned_loss=0.03383, over 4743.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2249, pruned_loss=0.04233, over 972924.42 frames.], batch size: 16, lr: 4.31e-04
2022-05-05 01:52:11,915 INFO [train.py:715] (2/8) Epoch 4, batch 31650, loss[loss=0.1497, simple_loss=0.2154, pruned_loss=0.04199, over 4804.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2252, pruned_loss=0.0421, over 972711.88 frames.], batch size: 13, lr: 4.31e-04
2022-05-05 01:52:51,500 INFO [train.py:715] (2/8) Epoch 4, batch 31700, loss[loss=0.1565, simple_loss=0.2295, pruned_loss=0.04173, over 4844.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2251, pruned_loss=0.04209, over 973497.62 frames.], batch size: 15, lr: 4.31e-04
2022-05-05 01:53:31,555 INFO [train.py:715] (2/8) Epoch 4, batch 31750, loss[loss=0.1363, simple_loss=0.2109, pruned_loss=0.03082, over 4968.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2262, pruned_loss=0.04262, over 973691.59 frames.], batch size: 24, lr: 4.31e-04
2022-05-05 01:54:11,604 INFO [train.py:715] (2/8) Epoch 4, batch 31800, loss[loss=0.1372, simple_loss=0.2066, pruned_loss=0.0339, over 4806.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2255, pruned_loss=0.04229, over 973613.63 frames.], batch size: 25, lr: 4.31e-04
2022-05-05 01:54:51,197 INFO [train.py:715] (2/8) Epoch 4, batch 31850, loss[loss=0.1608, simple_loss=0.2213, pruned_loss=0.05016, over 4821.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2245, pruned_loss=0.04204, over 973778.30 frames.], batch size: 26, lr: 4.31e-04
2022-05-05 01:55:30,807 INFO [train.py:715] (2/8) Epoch 4, batch 31900, loss[loss=0.1742, simple_loss=0.2283, pruned_loss=0.06006, over 4949.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2246, pruned_loss=0.04235, over 974077.61 frames.], batch size: 18, lr: 4.31e-04
2022-05-05 01:56:11,028 INFO [train.py:715] (2/8) Epoch 4, batch 31950, loss[loss=0.1791, simple_loss=0.2504, pruned_loss=0.05391, over 4948.00 frames.], tot_loss[loss=0.155, simple_loss=0.2246, pruned_loss=0.04265, over 973729.75 frames.], batch size: 31, lr: 4.31e-04
2022-05-05 01:56:50,985 INFO [train.py:715] (2/8) Epoch 4, batch 32000, loss[loss=0.1536, simple_loss=0.223, pruned_loss=0.04205, over 4835.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2244, pruned_loss=0.04249, over 973169.95 frames.], batch size: 30, lr: 4.31e-04
2022-05-05 01:57:30,375 INFO [train.py:715] (2/8) Epoch 4, batch 32050, loss[loss=0.1588, simple_loss=0.233, pruned_loss=0.04229, over 4976.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2235, pruned_loss=0.04151, over 972046.19 frames.], batch size: 28, lr: 4.31e-04
2022-05-05 01:58:10,942 INFO [train.py:715] (2/8) Epoch 4, batch 32100, loss[loss=0.1268, simple_loss=0.196, pruned_loss=0.02881, over 4794.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2239, pruned_loss=0.04198, over 972238.09 frames.], batch size: 24, lr: 4.31e-04
2022-05-05 01:58:50,867 INFO [train.py:715] (2/8) Epoch 4, batch 32150, loss[loss=0.12, simple_loss=0.1846, pruned_loss=0.02773, over 4779.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2242, pruned_loss=0.04214, over 971330.24 frames.], batch size: 14, lr: 4.31e-04
2022-05-05 01:59:30,405 INFO [train.py:715] (2/8) Epoch 4, batch 32200, loss[loss=0.1525, simple_loss=0.2229, pruned_loss=0.04111, over 4835.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2242, pruned_loss=0.04211, over 971580.67 frames.], batch size: 30, lr: 4.31e-04
2022-05-05 02:00:10,360 INFO [train.py:715] (2/8) Epoch 4, batch 32250, loss[loss=0.1506, simple_loss=0.228, pruned_loss=0.03665, over 4945.00 frames.], tot_loss[loss=0.1541, simple_loss=0.224, pruned_loss=0.04212, over 972796.83 frames.], batch size: 21, lr: 4.31e-04
2022-05-05 02:00:51,157 INFO [train.py:715] (2/8) Epoch 4, batch 32300, loss[loss=0.1697, simple_loss=0.2278, pruned_loss=0.05584, over 4866.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2235, pruned_loss=0.0421, over 972157.66 frames.], batch size: 16, lr: 4.31e-04
2022-05-05 02:01:31,942 INFO [train.py:715] (2/8) Epoch 4, batch 32350, loss[loss=0.149, simple_loss=0.2223, pruned_loss=0.03782, over 4837.00 frames.], tot_loss[loss=0.154, simple_loss=0.224, pruned_loss=0.042, over 971943.76 frames.], batch size: 12, lr: 4.31e-04
2022-05-05 02:02:12,274 INFO [train.py:715] (2/8) Epoch 4, batch 32400, loss[loss=0.1717, simple_loss=0.2494, pruned_loss=0.04705, over 4795.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2242, pruned_loss=0.04208, over 972032.62 frames.], batch size: 21, lr: 4.30e-04
2022-05-05 02:02:52,622 INFO [train.py:715] (2/8) Epoch 4, batch 32450, loss[loss=0.126, simple_loss=0.1912, pruned_loss=0.0304, over 4788.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2245, pruned_loss=0.04247, over 972384.62 frames.], batch size: 14, lr: 4.30e-04
2022-05-05 02:03:31,863 INFO [train.py:715] (2/8) Epoch 4, batch 32500, loss[loss=0.1457, simple_loss=0.2164, pruned_loss=0.03748, over 4855.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2234, pruned_loss=0.04201, over 972403.86 frames.], batch size: 32, lr: 4.30e-04
2022-05-05 02:04:11,771 INFO [train.py:715] (2/8) Epoch 4, batch 32550, loss[loss=0.1352, simple_loss=0.2064, pruned_loss=0.03202, over 4851.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2235, pruned_loss=0.04218, over 972308.51 frames.], batch size: 30, lr: 4.30e-04
2022-05-05 02:04:50,740 INFO [train.py:715] (2/8) Epoch 4, batch 32600, loss[loss=0.1646, simple_loss=0.2291, pruned_loss=0.05009, over 4755.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2234, pruned_loss=0.04251, over 972105.48 frames.], batch size: 16, lr: 4.30e-04
2022-05-05 02:05:30,803 INFO [train.py:715] (2/8) Epoch 4, batch 32650, loss[loss=0.1513, simple_loss=0.2271, pruned_loss=0.03779, over 4764.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2243, pruned_loss=0.043, over 971474.93 frames.], batch size: 19, lr: 4.30e-04
2022-05-05 02:06:09,913 INFO [train.py:715] (2/8) Epoch 4, batch 32700, loss[loss=0.1342, simple_loss=0.2018, pruned_loss=0.03331, over 4882.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2235, pruned_loss=0.0425, over 971784.55 frames.], batch size: 32, lr: 4.30e-04
2022-05-05 02:06:49,543 INFO [train.py:715] (2/8) Epoch 4, batch 32750, loss[loss=0.1393, simple_loss=0.2024, pruned_loss=0.0381, over 4820.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2233, pruned_loss=0.04259, over 971568.14 frames.], batch size: 15, lr: 4.30e-04
2022-05-05 02:07:29,259 INFO [train.py:715] (2/8) Epoch 4, batch 32800, loss[loss=0.1579, simple_loss=0.23, pruned_loss=0.04294, over 4871.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2243, pruned_loss=0.04297, over 972437.30 frames.], batch size: 22, lr: 4.30e-04
2022-05-05 02:08:09,337 INFO [train.py:715] (2/8) Epoch 4, batch 32850, loss[loss=0.189, simple_loss=0.2569, pruned_loss=0.06053, over 4806.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2249, pruned_loss=0.0432, over 972283.19 frames.], batch size: 21, lr: 4.30e-04
2022-05-05 02:08:49,846 INFO [train.py:715] (2/8) Epoch 4, batch 32900, loss[loss=0.1889, simple_loss=0.2503, pruned_loss=0.06372, over 4800.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2248, pruned_loss=0.04299, over 972706.16 frames.], batch size: 24, lr: 4.30e-04
2022-05-05 02:09:30,076 INFO [train.py:715] (2/8) Epoch 4, batch 32950, loss[loss=0.1591, simple_loss=0.2202, pruned_loss=0.04898, over 4801.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2257, pruned_loss=0.04369, over 973268.42 frames.], batch size: 12, lr: 4.30e-04
2022-05-05 02:10:10,302 INFO [train.py:715] (2/8) Epoch 4, batch 33000, loss[loss=0.1749, simple_loss=0.2321, pruned_loss=0.05887, over 4989.00 frames.], tot_loss[loss=0.1566, simple_loss=0.226, pruned_loss=0.04359, over 972305.20 frames.], batch size: 16, lr: 4.30e-04
2022-05-05 02:10:10,303 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 02:10:20,091 INFO [train.py:742] (2/8) 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] (2/8) Epoch 4, batch 33050, loss[loss=0.1531, simple_loss=0.2151, pruned_loss=0.0455, over 4983.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2259, pruned_loss=0.04343, over 972029.91 frames.], batch size: 35, lr: 4.30e-04
2022-05-05 02:11:40,005 INFO [train.py:715] (2/8) Epoch 4, batch 33100, loss[loss=0.1867, simple_loss=0.2561, pruned_loss=0.05863, over 4877.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2265, pruned_loss=0.0433, over 972417.08 frames.], batch size: 20, lr: 4.30e-04
2022-05-05 02:12:20,026 INFO [train.py:715] (2/8) Epoch 4, batch 33150, loss[loss=0.1919, simple_loss=0.2496, pruned_loss=0.06706, over 4857.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2267, pruned_loss=0.04307, over 973198.22 frames.], batch size: 30, lr: 4.30e-04
2022-05-05 02:13:00,225 INFO [train.py:715] (2/8) Epoch 4, batch 33200, loss[loss=0.1543, simple_loss=0.2116, pruned_loss=0.04851, over 4785.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2252, pruned_loss=0.04222, over 973288.05 frames.], batch size: 12, lr: 4.29e-04
2022-05-05 02:13:40,202 INFO [train.py:715] (2/8) Epoch 4, batch 33250, loss[loss=0.1292, simple_loss=0.2071, pruned_loss=0.02564, over 4869.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2262, pruned_loss=0.04297, over 972977.72 frames.], batch size: 20, lr: 4.29e-04
2022-05-05 02:14:20,214 INFO [train.py:715] (2/8) Epoch 4, batch 33300, loss[loss=0.1438, simple_loss=0.213, pruned_loss=0.03731, over 4922.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2269, pruned_loss=0.04331, over 972883.26 frames.], batch size: 18, lr: 4.29e-04
2022-05-05 02:14:59,208 INFO [train.py:715] (2/8) Epoch 4, batch 33350, loss[loss=0.1483, simple_loss=0.2078, pruned_loss=0.04442, over 4825.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2263, pruned_loss=0.04307, over 973075.64 frames.], batch size: 26, lr: 4.29e-04
2022-05-05 02:15:38,981 INFO [train.py:715] (2/8) Epoch 4, batch 33400, loss[loss=0.1441, simple_loss=0.2127, pruned_loss=0.03773, over 4788.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2255, pruned_loss=0.04236, over 972482.74 frames.], batch size: 12, lr: 4.29e-04
2022-05-05 02:16:18,843 INFO [train.py:715] (2/8) Epoch 4, batch 33450, loss[loss=0.1412, simple_loss=0.2221, pruned_loss=0.03019, over 4834.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2251, pruned_loss=0.0421, over 972315.49 frames.], batch size: 27, lr: 4.29e-04
2022-05-05 02:16:58,393 INFO [train.py:715] (2/8) Epoch 4, batch 33500, loss[loss=0.1266, simple_loss=0.2029, pruned_loss=0.02512, over 4751.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2246, pruned_loss=0.0421, over 972660.20 frames.], batch size: 16, lr: 4.29e-04
2022-05-05 02:17:38,199 INFO [train.py:715] (2/8) Epoch 4, batch 33550, loss[loss=0.1556, simple_loss=0.2251, pruned_loss=0.04307, over 4821.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2247, pruned_loss=0.04217, over 971715.79 frames.], batch size: 25, lr: 4.29e-04
2022-05-05 02:18:17,698 INFO [train.py:715] (2/8) Epoch 4, batch 33600, loss[loss=0.1688, simple_loss=0.2381, pruned_loss=0.04974, over 4807.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2242, pruned_loss=0.04175, over 971521.38 frames.], batch size: 27, lr: 4.29e-04
2022-05-05 02:18:57,442 INFO [train.py:715] (2/8) Epoch 4, batch 33650, loss[loss=0.1641, simple_loss=0.2319, pruned_loss=0.04818, over 4825.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2232, pruned_loss=0.04111, over 971401.01 frames.], batch size: 13, lr: 4.29e-04
2022-05-05 02:19:36,829 INFO [train.py:715] (2/8) Epoch 4, batch 33700, loss[loss=0.1408, simple_loss=0.2155, pruned_loss=0.03311, over 4814.00 frames.], tot_loss[loss=0.154, simple_loss=0.2242, pruned_loss=0.0419, over 971708.35 frames.], batch size: 26, lr: 4.29e-04
2022-05-05 02:20:16,627 INFO [train.py:715] (2/8) Epoch 4, batch 33750, loss[loss=0.1938, simple_loss=0.2573, pruned_loss=0.06516, over 4887.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2241, pruned_loss=0.04209, over 971545.13 frames.], batch size: 19, lr: 4.29e-04
2022-05-05 02:20:56,487 INFO [train.py:715] (2/8) Epoch 4, batch 33800, loss[loss=0.1678, simple_loss=0.2381, pruned_loss=0.04869, over 4893.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2243, pruned_loss=0.04217, over 971914.56 frames.], batch size: 22, lr: 4.29e-04
2022-05-05 02:21:35,971 INFO [train.py:715] (2/8) Epoch 4, batch 33850, loss[loss=0.1346, simple_loss=0.204, pruned_loss=0.03261, over 4662.00 frames.], tot_loss[loss=0.155, simple_loss=0.2247, pruned_loss=0.0427, over 971824.23 frames.], batch size: 13, lr: 4.29e-04
2022-05-05 02:22:15,607 INFO [train.py:715] (2/8) Epoch 4, batch 33900, loss[loss=0.1366, simple_loss=0.2002, pruned_loss=0.03647, over 4741.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2246, pruned_loss=0.04234, over 972380.99 frames.], batch size: 16, lr: 4.29e-04
2022-05-05 02:22:55,360 INFO [train.py:715] (2/8) Epoch 4, batch 33950, loss[loss=0.1457, simple_loss=0.209, pruned_loss=0.0412, over 4976.00 frames.], tot_loss[loss=0.155, simple_loss=0.2253, pruned_loss=0.04239, over 972276.13 frames.], batch size: 14, lr: 4.29e-04
2022-05-05 02:23:35,325 INFO [train.py:715] (2/8) Epoch 4, batch 34000, loss[loss=0.1407, simple_loss=0.2115, pruned_loss=0.03496, over 4851.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2252, pruned_loss=0.04251, over 972360.17 frames.], batch size: 30, lr: 4.28e-04
2022-05-05 02:24:14,850 INFO [train.py:715] (2/8) Epoch 4, batch 34050, loss[loss=0.1526, simple_loss=0.228, pruned_loss=0.03858, over 4914.00 frames.], tot_loss[loss=0.1557, simple_loss=0.226, pruned_loss=0.04266, over 972475.64 frames.], batch size: 19, lr: 4.28e-04
2022-05-05 02:24:54,569 INFO [train.py:715] (2/8) Epoch 4, batch 34100, loss[loss=0.173, simple_loss=0.2391, pruned_loss=0.05347, over 4907.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2265, pruned_loss=0.04303, over 972017.40 frames.], batch size: 17, lr: 4.28e-04
2022-05-05 02:25:34,631 INFO [train.py:715] (2/8) Epoch 4, batch 34150, loss[loss=0.1623, simple_loss=0.2283, pruned_loss=0.04812, over 4940.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2258, pruned_loss=0.04289, over 971383.29 frames.], batch size: 29, lr: 4.28e-04
2022-05-05 02:26:13,484 INFO [train.py:715] (2/8) Epoch 4, batch 34200, loss[loss=0.1515, simple_loss=0.2234, pruned_loss=0.0398, over 4904.00 frames.], tot_loss[loss=0.1551, simple_loss=0.225, pruned_loss=0.04256, over 970861.64 frames.], batch size: 22, lr: 4.28e-04
2022-05-05 02:26:54,316 INFO [train.py:715] (2/8) Epoch 4, batch 34250, loss[loss=0.1478, simple_loss=0.2274, pruned_loss=0.0341, over 4844.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2249, pruned_loss=0.04243, over 971612.18 frames.], batch size: 15, lr: 4.28e-04
2022-05-05 02:27:34,189 INFO [train.py:715] (2/8) Epoch 4, batch 34300, loss[loss=0.1264, simple_loss=0.2059, pruned_loss=0.02347, over 4774.00 frames.], tot_loss[loss=0.1549, simple_loss=0.225, pruned_loss=0.0424, over 970871.06 frames.], batch size: 14, lr: 4.28e-04
2022-05-05 02:28:13,944 INFO [train.py:715] (2/8) Epoch 4, batch 34350, loss[loss=0.1592, simple_loss=0.2249, pruned_loss=0.0467, over 4910.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2248, pruned_loss=0.04271, over 971043.29 frames.], batch size: 18, lr: 4.28e-04
2022-05-05 02:28:53,975 INFO [train.py:715] (2/8) Epoch 4, batch 34400, loss[loss=0.1517, simple_loss=0.2311, pruned_loss=0.03619, over 4856.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2253, pruned_loss=0.04284, over 971149.93 frames.], batch size: 15, lr: 4.28e-04
2022-05-05 02:29:33,806 INFO [train.py:715] (2/8) Epoch 4, batch 34450, loss[loss=0.1333, simple_loss=0.2013, pruned_loss=0.03267, over 4778.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2256, pruned_loss=0.04333, over 971306.30 frames.], batch size: 18, lr: 4.28e-04
2022-05-05 02:30:14,468 INFO [train.py:715] (2/8) Epoch 4, batch 34500, loss[loss=0.1547, simple_loss=0.232, pruned_loss=0.03872, over 4876.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2255, pruned_loss=0.04309, over 970705.94 frames.], batch size: 20, lr: 4.28e-04
2022-05-05 02:30:53,315 INFO [train.py:715] (2/8) Epoch 4, batch 34550, loss[loss=0.1425, simple_loss=0.2096, pruned_loss=0.03772, over 4900.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2258, pruned_loss=0.04343, over 970494.29 frames.], batch size: 22, lr: 4.28e-04
2022-05-05 02:31:33,260 INFO [train.py:715] (2/8) Epoch 4, batch 34600, loss[loss=0.1437, simple_loss=0.2163, pruned_loss=0.03556, over 4903.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2257, pruned_loss=0.04308, over 970654.34 frames.], batch size: 18, lr: 4.28e-04
2022-05-05 02:32:13,235 INFO [train.py:715] (2/8) Epoch 4, batch 34650, loss[loss=0.163, simple_loss=0.2296, pruned_loss=0.04818, over 4853.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2249, pruned_loss=0.04263, over 971800.20 frames.], batch size: 32, lr: 4.28e-04
2022-05-05 02:32:52,590 INFO [train.py:715] (2/8) Epoch 4, batch 34700, loss[loss=0.1939, simple_loss=0.2543, pruned_loss=0.06669, over 4693.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2245, pruned_loss=0.04245, over 970861.82 frames.], batch size: 15, lr: 4.28e-04
2022-05-05 02:33:30,871 INFO [train.py:715] (2/8) Epoch 4, batch 34750, loss[loss=0.1661, simple_loss=0.2343, pruned_loss=0.049, over 4944.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2244, pruned_loss=0.04248, over 971356.63 frames.], batch size: 21, lr: 4.28e-04
2022-05-05 02:34:07,932 INFO [train.py:715] (2/8) Epoch 4, batch 34800, loss[loss=0.2066, simple_loss=0.2809, pruned_loss=0.06616, over 4909.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2247, pruned_loss=0.04218, over 972422.14 frames.], batch size: 18, lr: 4.27e-04
2022-05-05 02:34:57,761 INFO [train.py:715] (2/8) Epoch 5, batch 0, loss[loss=0.1652, simple_loss=0.2383, pruned_loss=0.04602, over 4693.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2383, pruned_loss=0.04602, over 4693.00 frames.], batch size: 15, lr: 4.02e-04
2022-05-05 02:35:38,097 INFO [train.py:715] (2/8) Epoch 5, batch 50, loss[loss=0.1691, simple_loss=0.2328, pruned_loss=0.05272, over 4948.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2208, pruned_loss=0.04279, over 219869.29 frames.], batch size: 21, lr: 4.02e-04
2022-05-05 02:36:17,798 INFO [train.py:715] (2/8) Epoch 5, batch 100, loss[loss=0.1281, simple_loss=0.2012, pruned_loss=0.02751, over 4983.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2215, pruned_loss=0.04199, over 386520.33 frames.], batch size: 35, lr: 4.02e-04
2022-05-05 02:36:57,764 INFO [train.py:715] (2/8) Epoch 5, batch 150, loss[loss=0.1662, simple_loss=0.2449, pruned_loss=0.0437, over 4763.00 frames.], tot_loss[loss=0.153, simple_loss=0.2223, pruned_loss=0.04184, over 515833.41 frames.], batch size: 19, lr: 4.02e-04
2022-05-05 02:37:38,286 INFO [train.py:715] (2/8) Epoch 5, batch 200, loss[loss=0.1708, simple_loss=0.2383, pruned_loss=0.05163, over 4780.00 frames.], tot_loss[loss=0.152, simple_loss=0.2221, pruned_loss=0.04091, over 617179.42 frames.], batch size: 17, lr: 4.02e-04
2022-05-05 02:38:17,740 INFO [train.py:715] (2/8) Epoch 5, batch 250, loss[loss=0.1476, simple_loss=0.2173, pruned_loss=0.03901, over 4702.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2215, pruned_loss=0.04067, over 695788.05 frames.], batch size: 15, lr: 4.02e-04
2022-05-05 02:38:57,161 INFO [train.py:715] (2/8) Epoch 5, batch 300, loss[loss=0.1417, simple_loss=0.2085, pruned_loss=0.0375, over 4801.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2227, pruned_loss=0.04104, over 756019.29 frames.], batch size: 14, lr: 4.01e-04
2022-05-05 02:39:36,891 INFO [train.py:715] (2/8) Epoch 5, batch 350, loss[loss=0.1722, simple_loss=0.24, pruned_loss=0.05225, over 4885.00 frames.], tot_loss[loss=0.152, simple_loss=0.2222, pruned_loss=0.04096, over 804496.16 frames.], batch size: 22, lr: 4.01e-04
2022-05-05 02:40:16,659 INFO [train.py:715] (2/8) Epoch 5, batch 400, loss[loss=0.1534, simple_loss=0.2209, pruned_loss=0.04301, over 4902.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2233, pruned_loss=0.04114, over 842188.98 frames.], batch size: 17, lr: 4.01e-04
2022-05-05 02:40:56,046 INFO [train.py:715] (2/8) Epoch 5, batch 450, loss[loss=0.1487, simple_loss=0.2323, pruned_loss=0.03258, over 4942.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.04051, over 870996.09 frames.], batch size: 21, lr: 4.01e-04
2022-05-05 02:41:35,798 INFO [train.py:715] (2/8) Epoch 5, batch 500, loss[loss=0.169, simple_loss=0.2435, pruned_loss=0.04723, over 4778.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2225, pruned_loss=0.04057, over 893027.64 frames.], batch size: 18, lr: 4.01e-04
2022-05-05 02:42:15,654 INFO [train.py:715] (2/8) Epoch 5, batch 550, loss[loss=0.1698, simple_loss=0.2337, pruned_loss=0.05291, over 4987.00 frames.], tot_loss[loss=0.152, simple_loss=0.2228, pruned_loss=0.04062, over 910941.40 frames.], batch size: 28, lr: 4.01e-04
2022-05-05 02:42:54,759 INFO [train.py:715] (2/8) Epoch 5, batch 600, loss[loss=0.1384, simple_loss=0.2055, pruned_loss=0.03563, over 4796.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2233, pruned_loss=0.04099, over 924158.37 frames.], batch size: 14, lr: 4.01e-04
2022-05-05 02:43:34,143 INFO [train.py:715] (2/8) Epoch 5, batch 650, loss[loss=0.1725, simple_loss=0.2337, pruned_loss=0.05565, over 4874.00 frames.], tot_loss[loss=0.1536, simple_loss=0.224, pruned_loss=0.04166, over 934386.99 frames.], batch size: 32, lr: 4.01e-04
2022-05-05 02:44:13,846 INFO [train.py:715] (2/8) Epoch 5, batch 700, loss[loss=0.1906, simple_loss=0.2604, pruned_loss=0.06042, over 4880.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2246, pruned_loss=0.04205, over 942555.47 frames.], batch size: 22, lr: 4.01e-04
2022-05-05 02:44:53,913 INFO [train.py:715] (2/8) Epoch 5, batch 750, loss[loss=0.1621, simple_loss=0.2301, pruned_loss=0.04704, over 4850.00 frames.], tot_loss[loss=0.1536, simple_loss=0.224, pruned_loss=0.04161, over 949764.93 frames.], batch size: 30, lr: 4.01e-04
2022-05-05 02:45:33,283 INFO [train.py:715] (2/8) Epoch 5, batch 800, loss[loss=0.1534, simple_loss=0.2212, pruned_loss=0.04279, over 4879.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2233, pruned_loss=0.04149, over 954825.26 frames.], batch size: 32, lr: 4.01e-04
2022-05-05 02:46:12,788 INFO [train.py:715] (2/8) Epoch 5, batch 850, loss[loss=0.178, simple_loss=0.2401, pruned_loss=0.05799, over 4761.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2242, pruned_loss=0.04221, over 958601.43 frames.], batch size: 19, lr: 4.01e-04
2022-05-05 02:46:52,356 INFO [train.py:715] (2/8) Epoch 5, batch 900, loss[loss=0.1473, simple_loss=0.2174, pruned_loss=0.03859, over 4827.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2235, pruned_loss=0.04196, over 961562.99 frames.], batch size: 26, lr: 4.01e-04
2022-05-05 02:47:31,844 INFO [train.py:715] (2/8) Epoch 5, batch 950, loss[loss=0.1637, simple_loss=0.2285, pruned_loss=0.04948, over 4842.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2236, pruned_loss=0.04186, over 963572.76 frames.], batch size: 30, lr: 4.01e-04
2022-05-05 02:48:11,355 INFO [train.py:715] (2/8) Epoch 5, batch 1000, loss[loss=0.1173, simple_loss=0.1876, pruned_loss=0.02345, over 4765.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2232, pruned_loss=0.04182, over 966013.39 frames.], batch size: 19, lr: 4.01e-04
2022-05-05 02:48:50,618 INFO [train.py:715] (2/8) Epoch 5, batch 1050, loss[loss=0.1484, simple_loss=0.2148, pruned_loss=0.041, over 4699.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2236, pruned_loss=0.0417, over 967321.31 frames.], batch size: 15, lr: 4.01e-04
2022-05-05 02:49:30,325 INFO [train.py:715] (2/8) Epoch 5, batch 1100, loss[loss=0.1373, simple_loss=0.2012, pruned_loss=0.03667, over 4740.00 frames.], tot_loss[loss=0.153, simple_loss=0.2229, pruned_loss=0.04158, over 968195.44 frames.], batch size: 12, lr: 4.01e-04
2022-05-05 02:50:09,330 INFO [train.py:715] (2/8) Epoch 5, batch 1150, loss[loss=0.1356, simple_loss=0.2195, pruned_loss=0.02584, over 4778.00 frames.], tot_loss[loss=0.1532, simple_loss=0.223, pruned_loss=0.04173, over 969350.70 frames.], batch size: 17, lr: 4.00e-04
2022-05-05 02:50:49,093 INFO [train.py:715] (2/8) Epoch 5, batch 1200, loss[loss=0.136, simple_loss=0.2134, pruned_loss=0.02934, over 4891.00 frames.], tot_loss[loss=0.1534, simple_loss=0.223, pruned_loss=0.04187, over 968799.14 frames.], batch size: 19, lr: 4.00e-04
2022-05-05 02:51:29,242 INFO [train.py:715] (2/8) Epoch 5, batch 1250, loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03084, over 4933.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2236, pruned_loss=0.04225, over 969266.85 frames.], batch size: 23, lr: 4.00e-04
2022-05-05 02:52:08,411 INFO [train.py:715] (2/8) Epoch 5, batch 1300, loss[loss=0.1591, simple_loss=0.2356, pruned_loss=0.04133, over 4975.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2235, pruned_loss=0.04175, over 969927.70 frames.], batch size: 28, lr: 4.00e-04
2022-05-05 02:52:48,192 INFO [train.py:715] (2/8) Epoch 5, batch 1350, loss[loss=0.1906, simple_loss=0.2439, pruned_loss=0.0687, over 4842.00 frames.], tot_loss[loss=0.1527, simple_loss=0.223, pruned_loss=0.04124, over 970581.28 frames.], batch size: 32, lr: 4.00e-04
2022-05-05 02:53:27,487 INFO [train.py:715] (2/8) Epoch 5, batch 1400, loss[loss=0.171, simple_loss=0.2357, pruned_loss=0.05317, over 4848.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2237, pruned_loss=0.04177, over 971702.21 frames.], batch size: 15, lr: 4.00e-04
2022-05-05 02:54:07,302 INFO [train.py:715] (2/8) Epoch 5, batch 1450, loss[loss=0.1248, simple_loss=0.2008, pruned_loss=0.02442, over 4851.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2239, pruned_loss=0.04159, over 970944.15 frames.], batch size: 20, lr: 4.00e-04
2022-05-05 02:54:46,730 INFO [train.py:715] (2/8) Epoch 5, batch 1500, loss[loss=0.1572, simple_loss=0.2225, pruned_loss=0.04596, over 4756.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2236, pruned_loss=0.0413, over 971170.23 frames.], batch size: 19, lr: 4.00e-04
2022-05-05 02:55:25,726 INFO [train.py:715] (2/8) Epoch 5, batch 1550, loss[loss=0.1406, simple_loss=0.211, pruned_loss=0.03508, over 4909.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2237, pruned_loss=0.04129, over 971606.62 frames.], batch size: 19, lr: 4.00e-04
2022-05-05 02:56:05,366 INFO [train.py:715] (2/8) Epoch 5, batch 1600, loss[loss=0.1511, simple_loss=0.2292, pruned_loss=0.03652, over 4986.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2238, pruned_loss=0.04131, over 972052.09 frames.], batch size: 39, lr: 4.00e-04
2022-05-05 02:56:45,703 INFO [train.py:715] (2/8) Epoch 5, batch 1650, loss[loss=0.1687, simple_loss=0.2309, pruned_loss=0.05321, over 4981.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2237, pruned_loss=0.04131, over 973546.97 frames.], batch size: 25, lr: 4.00e-04
2022-05-05 02:57:24,643 INFO [train.py:715] (2/8) Epoch 5, batch 1700, loss[loss=0.1304, simple_loss=0.2069, pruned_loss=0.02696, over 4814.00 frames.], tot_loss[loss=0.1536, simple_loss=0.224, pruned_loss=0.04157, over 973614.25 frames.], batch size: 25, lr: 4.00e-04
2022-05-05 02:58:05,304 INFO [train.py:715] (2/8) Epoch 5, batch 1750, loss[loss=0.1351, simple_loss=0.203, pruned_loss=0.03359, over 4831.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2235, pruned_loss=0.04169, over 973318.70 frames.], batch size: 12, lr: 4.00e-04
2022-05-05 02:58:45,458 INFO [train.py:715] (2/8) Epoch 5, batch 1800, loss[loss=0.1441, simple_loss=0.2127, pruned_loss=0.03775, over 4951.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2238, pruned_loss=0.04183, over 972952.60 frames.], batch size: 15, lr: 4.00e-04
2022-05-05 02:59:25,900 INFO [train.py:715] (2/8) Epoch 5, batch 1850, loss[loss=0.1394, simple_loss=0.2149, pruned_loss=0.03188, over 4863.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2238, pruned_loss=0.04168, over 972844.60 frames.], batch size: 20, lr: 4.00e-04
2022-05-05 03:00:06,295 INFO [train.py:715] (2/8) Epoch 5, batch 1900, loss[loss=0.1508, simple_loss=0.2189, pruned_loss=0.0414, over 4814.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2246, pruned_loss=0.04213, over 972301.99 frames.], batch size: 21, lr: 4.00e-04
2022-05-05 03:00:46,053 INFO [train.py:715] (2/8) Epoch 5, batch 1950, loss[loss=0.1779, simple_loss=0.2378, pruned_loss=0.05895, over 4742.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2246, pruned_loss=0.04212, over 972196.16 frames.], batch size: 16, lr: 4.00e-04
2022-05-05 03:01:29,140 INFO [train.py:715] (2/8) Epoch 5, batch 2000, loss[loss=0.1265, simple_loss=0.2003, pruned_loss=0.02631, over 4778.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2237, pruned_loss=0.04171, over 972407.62 frames.], batch size: 14, lr: 4.00e-04
2022-05-05 03:02:09,158 INFO [train.py:715] (2/8) Epoch 5, batch 2050, loss[loss=0.1647, simple_loss=0.2394, pruned_loss=0.04493, over 4641.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2247, pruned_loss=0.04209, over 972842.13 frames.], batch size: 13, lr: 3.99e-04
2022-05-05 03:02:49,515 INFO [train.py:715] (2/8) Epoch 5, batch 2100, loss[loss=0.177, simple_loss=0.2454, pruned_loss=0.05435, over 4971.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2239, pruned_loss=0.04159, over 973050.30 frames.], batch size: 24, lr: 3.99e-04
2022-05-05 03:03:30,095 INFO [train.py:715] (2/8) Epoch 5, batch 2150, loss[loss=0.1479, simple_loss=0.2205, pruned_loss=0.03766, over 4818.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2236, pruned_loss=0.04163, over 973465.23 frames.], batch size: 26, lr: 3.99e-04
2022-05-05 03:04:09,665 INFO [train.py:715] (2/8) Epoch 5, batch 2200, loss[loss=0.1373, simple_loss=0.2088, pruned_loss=0.03288, over 4852.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2241, pruned_loss=0.0417, over 973617.76 frames.], batch size: 20, lr: 3.99e-04
2022-05-05 03:04:50,060 INFO [train.py:715] (2/8) Epoch 5, batch 2250, loss[loss=0.1684, simple_loss=0.2268, pruned_loss=0.05494, over 4824.00 frames.], tot_loss[loss=0.1536, simple_loss=0.224, pruned_loss=0.04162, over 973193.43 frames.], batch size: 26, lr: 3.99e-04
2022-05-05 03:05:30,782 INFO [train.py:715] (2/8) Epoch 5, batch 2300, loss[loss=0.1459, simple_loss=0.2194, pruned_loss=0.03618, over 4769.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2233, pruned_loss=0.04115, over 973537.18 frames.], batch size: 18, lr: 3.99e-04
2022-05-05 03:06:10,990 INFO [train.py:715] (2/8) Epoch 5, batch 2350, loss[loss=0.1322, simple_loss=0.2079, pruned_loss=0.02829, over 4753.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2235, pruned_loss=0.04133, over 973443.94 frames.], batch size: 19, lr: 3.99e-04
2022-05-05 03:06:51,196 INFO [train.py:715] (2/8) Epoch 5, batch 2400, loss[loss=0.1601, simple_loss=0.2353, pruned_loss=0.04241, over 4922.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2237, pruned_loss=0.04136, over 973020.00 frames.], batch size: 29, lr: 3.99e-04
2022-05-05 03:07:31,703 INFO [train.py:715] (2/8) Epoch 5, batch 2450, loss[loss=0.1682, simple_loss=0.2397, pruned_loss=0.0484, over 4926.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2235, pruned_loss=0.0412, over 972532.51 frames.], batch size: 18, lr: 3.99e-04
2022-05-05 03:08:12,405 INFO [train.py:715] (2/8) Epoch 5, batch 2500, loss[loss=0.1556, simple_loss=0.2205, pruned_loss=0.04539, over 4987.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2238, pruned_loss=0.04143, over 971849.41 frames.], batch size: 31, lr: 3.99e-04
2022-05-05 03:08:52,450 INFO [train.py:715] (2/8) Epoch 5, batch 2550, loss[loss=0.131, simple_loss=0.2076, pruned_loss=0.02719, over 4814.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2237, pruned_loss=0.04138, over 972549.55 frames.], batch size: 27, lr: 3.99e-04
2022-05-05 03:09:33,371 INFO [train.py:715] (2/8) Epoch 5, batch 2600, loss[loss=0.1844, simple_loss=0.2514, pruned_loss=0.05869, over 4981.00 frames.], tot_loss[loss=0.1545, simple_loss=0.225, pruned_loss=0.04198, over 973223.38 frames.], batch size: 25, lr: 3.99e-04
2022-05-05 03:10:13,572 INFO [train.py:715] (2/8) Epoch 5, batch 2650, loss[loss=0.146, simple_loss=0.2242, pruned_loss=0.03388, over 4928.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2242, pruned_loss=0.04126, over 973444.50 frames.], batch size: 29, lr: 3.99e-04
2022-05-05 03:10:54,131 INFO [train.py:715] (2/8) Epoch 5, batch 2700, loss[loss=0.1723, simple_loss=0.2352, pruned_loss=0.05473, over 4838.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2242, pruned_loss=0.04152, over 973027.81 frames.], batch size: 15, lr: 3.99e-04
2022-05-05 03:11:34,323 INFO [train.py:715] (2/8) Epoch 5, batch 2750, loss[loss=0.1518, simple_loss=0.2256, pruned_loss=0.03901, over 4783.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2231, pruned_loss=0.04114, over 973158.71 frames.], batch size: 14, lr: 3.99e-04
2022-05-05 03:12:14,292 INFO [train.py:715] (2/8) Epoch 5, batch 2800, loss[loss=0.1742, simple_loss=0.2239, pruned_loss=0.06224, over 4962.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2236, pruned_loss=0.04142, over 974174.62 frames.], batch size: 35, lr: 3.99e-04
2022-05-05 03:12:54,882 INFO [train.py:715] (2/8) Epoch 5, batch 2850, loss[loss=0.1524, simple_loss=0.2172, pruned_loss=0.04387, over 4946.00 frames.], tot_loss[loss=0.152, simple_loss=0.2225, pruned_loss=0.04079, over 973814.72 frames.], batch size: 35, lr: 3.99e-04
2022-05-05 03:13:35,028 INFO [train.py:715] (2/8) Epoch 5, batch 2900, loss[loss=0.1627, simple_loss=0.232, pruned_loss=0.04672, over 4972.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2226, pruned_loss=0.04105, over 973184.34 frames.], batch size: 25, lr: 3.99e-04
2022-05-05 03:14:15,391 INFO [train.py:715] (2/8) Epoch 5, batch 2950, loss[loss=0.1265, simple_loss=0.2016, pruned_loss=0.02572, over 4647.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2224, pruned_loss=0.04057, over 973393.66 frames.], batch size: 13, lr: 3.98e-04
2022-05-05 03:14:54,471 INFO [train.py:715] (2/8) Epoch 5, batch 3000, loss[loss=0.1557, simple_loss=0.2256, pruned_loss=0.04286, over 4883.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2227, pruned_loss=0.04058, over 972542.03 frames.], batch size: 22, lr: 3.98e-04
2022-05-05 03:14:54,472 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 03:15:03,919 INFO [train.py:742] (2/8) Epoch 5, validation: loss=0.1108, simple_loss=0.1962, pruned_loss=0.01274, over 914524.00 frames.
2022-05-05 03:15:42,396 INFO [train.py:715] (2/8) Epoch 5, batch 3050, loss[loss=0.159, simple_loss=0.2327, pruned_loss=0.04262, over 4773.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2233, pruned_loss=0.04056, over 972484.58 frames.], batch size: 19, lr: 3.98e-04
2022-05-05 03:16:21,554 INFO [train.py:715] (2/8) Epoch 5, batch 3100, loss[loss=0.2116, simple_loss=0.2482, pruned_loss=0.08749, over 4703.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2242, pruned_loss=0.04119, over 972130.81 frames.], batch size: 15, lr: 3.98e-04
2022-05-05 03:17:00,520 INFO [train.py:715] (2/8) Epoch 5, batch 3150, loss[loss=0.1926, simple_loss=0.259, pruned_loss=0.06306, over 4870.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2243, pruned_loss=0.04148, over 972149.00 frames.], batch size: 22, lr: 3.98e-04
2022-05-05 03:17:40,036 INFO [train.py:715] (2/8) Epoch 5, batch 3200, loss[loss=0.1617, simple_loss=0.2356, pruned_loss=0.04394, over 4987.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2254, pruned_loss=0.04214, over 972549.86 frames.], batch size: 25, lr: 3.98e-04
2022-05-05 03:18:19,744 INFO [train.py:715] (2/8) Epoch 5, batch 3250, loss[loss=0.1588, simple_loss=0.2334, pruned_loss=0.04216, over 4902.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2258, pruned_loss=0.0424, over 972131.45 frames.], batch size: 19, lr: 3.98e-04
2022-05-05 03:18:58,957 INFO [train.py:715] (2/8) Epoch 5, batch 3300, loss[loss=0.1517, simple_loss=0.2308, pruned_loss=0.0363, over 4754.00 frames.], tot_loss[loss=0.1541, simple_loss=0.225, pruned_loss=0.04163, over 971755.94 frames.], batch size: 19, lr: 3.98e-04
2022-05-05 03:19:38,239 INFO [train.py:715] (2/8) Epoch 5, batch 3350, loss[loss=0.1314, simple_loss=0.213, pruned_loss=0.02495, over 4883.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2246, pruned_loss=0.04143, over 972831.20 frames.], batch size: 16, lr: 3.98e-04
2022-05-05 03:20:17,970 INFO [train.py:715] (2/8) Epoch 5, batch 3400, loss[loss=0.1699, simple_loss=0.243, pruned_loss=0.04839, over 4939.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2249, pruned_loss=0.04179, over 972489.70 frames.], batch size: 29, lr: 3.98e-04
2022-05-05 03:20:57,513 INFO [train.py:715] (2/8) Epoch 5, batch 3450, loss[loss=0.1788, simple_loss=0.2444, pruned_loss=0.05657, over 4888.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2253, pruned_loss=0.04187, over 972350.86 frames.], batch size: 17, lr: 3.98e-04
2022-05-05 03:21:36,806 INFO [train.py:715] (2/8) Epoch 5, batch 3500, loss[loss=0.1558, simple_loss=0.2242, pruned_loss=0.04372, over 4777.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2255, pruned_loss=0.042, over 972584.40 frames.], batch size: 18, lr: 3.98e-04
2022-05-05 03:22:16,030 INFO [train.py:715] (2/8) Epoch 5, batch 3550, loss[loss=0.1663, simple_loss=0.238, pruned_loss=0.04724, over 4978.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2248, pruned_loss=0.04193, over 973236.70 frames.], batch size: 39, lr: 3.98e-04
2022-05-05 03:22:55,530 INFO [train.py:715] (2/8) Epoch 5, batch 3600, loss[loss=0.151, simple_loss=0.2186, pruned_loss=0.04173, over 4844.00 frames.], tot_loss[loss=0.1549, simple_loss=0.225, pruned_loss=0.04243, over 972810.34 frames.], batch size: 34, lr: 3.98e-04
2022-05-05 03:23:34,520 INFO [train.py:715] (2/8) Epoch 5, batch 3650, loss[loss=0.1365, simple_loss=0.2059, pruned_loss=0.03349, over 4900.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2243, pruned_loss=0.04225, over 971806.50 frames.], batch size: 17, lr: 3.98e-04
2022-05-05 03:24:13,761 INFO [train.py:715] (2/8) Epoch 5, batch 3700, loss[loss=0.1611, simple_loss=0.233, pruned_loss=0.04466, over 4864.00 frames.], tot_loss[loss=0.154, simple_loss=0.224, pruned_loss=0.04202, over 972559.86 frames.], batch size: 20, lr: 3.98e-04
2022-05-05 03:24:53,921 INFO [train.py:715] (2/8) Epoch 5, batch 3750, loss[loss=0.1497, simple_loss=0.2281, pruned_loss=0.03568, over 4773.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2239, pruned_loss=0.0417, over 972026.56 frames.], batch size: 17, lr: 3.98e-04
2022-05-05 03:25:33,698 INFO [train.py:715] (2/8) Epoch 5, batch 3800, loss[loss=0.1426, simple_loss=0.2111, pruned_loss=0.03708, over 4907.00 frames.], tot_loss[loss=0.1538, simple_loss=0.224, pruned_loss=0.04182, over 972638.50 frames.], batch size: 22, lr: 3.97e-04
2022-05-05 03:26:13,109 INFO [train.py:715] (2/8) Epoch 5, batch 3850, loss[loss=0.1358, simple_loss=0.1946, pruned_loss=0.03845, over 4870.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2244, pruned_loss=0.04193, over 973198.99 frames.], batch size: 16, lr: 3.97e-04
2022-05-05 03:26:52,975 INFO [train.py:715] (2/8) Epoch 5, batch 3900, loss[loss=0.1422, simple_loss=0.2282, pruned_loss=0.02808, over 4885.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2247, pruned_loss=0.04208, over 973217.39 frames.], batch size: 16, lr: 3.97e-04
2022-05-05 03:27:33,013 INFO [train.py:715] (2/8) Epoch 5, batch 3950, loss[loss=0.1422, simple_loss=0.2136, pruned_loss=0.03545, over 4877.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2237, pruned_loss=0.0414, over 972887.28 frames.], batch size: 38, lr: 3.97e-04
2022-05-05 03:28:13,099 INFO [train.py:715] (2/8) Epoch 5, batch 4000, loss[loss=0.1589, simple_loss=0.2277, pruned_loss=0.04505, over 4862.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2225, pruned_loss=0.04066, over 973068.20 frames.], batch size: 20, lr: 3.97e-04
2022-05-05 03:28:53,754 INFO [train.py:715] (2/8) Epoch 5, batch 4050, loss[loss=0.1388, simple_loss=0.2111, pruned_loss=0.03322, over 4791.00 frames.], tot_loss[loss=0.1524, simple_loss=0.223, pruned_loss=0.04088, over 972535.10 frames.], batch size: 21, lr: 3.97e-04
2022-05-05 03:29:33,862 INFO [train.py:715] (2/8) Epoch 5, batch 4100, loss[loss=0.1681, simple_loss=0.2327, pruned_loss=0.05175, over 4828.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2239, pruned_loss=0.04133, over 973243.37 frames.], batch size: 30, lr: 3.97e-04
2022-05-05 03:30:14,066 INFO [train.py:715] (2/8) Epoch 5, batch 4150, loss[loss=0.1387, simple_loss=0.2073, pruned_loss=0.03502, over 4885.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2237, pruned_loss=0.04126, over 972035.67 frames.], batch size: 22, lr: 3.97e-04
2022-05-05 03:30:53,451 INFO [train.py:715] (2/8) Epoch 5, batch 4200, loss[loss=0.1352, simple_loss=0.1978, pruned_loss=0.03628, over 4857.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2232, pruned_loss=0.04104, over 972261.93 frames.], batch size: 16, lr: 3.97e-04
2022-05-05 03:31:32,793 INFO [train.py:715] (2/8) Epoch 5, batch 4250, loss[loss=0.1508, simple_loss=0.2231, pruned_loss=0.03928, over 4840.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2242, pruned_loss=0.0413, over 971758.49 frames.], batch size: 13, lr: 3.97e-04
2022-05-05 03:32:12,488 INFO [train.py:715] (2/8) Epoch 5, batch 4300, loss[loss=0.1375, simple_loss=0.2138, pruned_loss=0.0306, over 4888.00 frames.], tot_loss[loss=0.154, simple_loss=0.2243, pruned_loss=0.04183, over 972393.51 frames.], batch size: 19, lr: 3.97e-04
2022-05-05 03:32:52,100 INFO [train.py:715] (2/8) Epoch 5, batch 4350, loss[loss=0.1508, simple_loss=0.2279, pruned_loss=0.03686, over 4967.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2247, pruned_loss=0.04182, over 972725.11 frames.], batch size: 24, lr: 3.97e-04
2022-05-05 03:33:32,069 INFO [train.py:715] (2/8) Epoch 5, batch 4400, loss[loss=0.1467, simple_loss=0.208, pruned_loss=0.04268, over 4849.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2244, pruned_loss=0.04144, over 972427.86 frames.], batch size: 32, lr: 3.97e-04
2022-05-05 03:34:10,947 INFO [train.py:715] (2/8) Epoch 5, batch 4450, loss[loss=0.167, simple_loss=0.2351, pruned_loss=0.04944, over 4956.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2241, pruned_loss=0.04147, over 972624.30 frames.], batch size: 21, lr: 3.97e-04
2022-05-05 03:34:50,792 INFO [train.py:715] (2/8) Epoch 5, batch 4500, loss[loss=0.1213, simple_loss=0.1952, pruned_loss=0.02369, over 4807.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2242, pruned_loss=0.04174, over 971729.77 frames.], batch size: 25, lr: 3.97e-04
2022-05-05 03:35:30,124 INFO [train.py:715] (2/8) Epoch 5, batch 4550, loss[loss=0.1569, simple_loss=0.23, pruned_loss=0.04186, over 4741.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2235, pruned_loss=0.04152, over 973013.95 frames.], batch size: 16, lr: 3.97e-04
2022-05-05 03:36:09,759 INFO [train.py:715] (2/8) Epoch 5, batch 4600, loss[loss=0.1456, simple_loss=0.2213, pruned_loss=0.035, over 4811.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2236, pruned_loss=0.04172, over 973133.64 frames.], batch size: 27, lr: 3.97e-04
2022-05-05 03:36:50,116 INFO [train.py:715] (2/8) Epoch 5, batch 4650, loss[loss=0.1637, simple_loss=0.2344, pruned_loss=0.04654, over 4986.00 frames.], tot_loss[loss=0.154, simple_loss=0.224, pruned_loss=0.04198, over 972666.54 frames.], batch size: 31, lr: 3.97e-04
2022-05-05 03:37:30,449 INFO [train.py:715] (2/8) Epoch 5, batch 4700, loss[loss=0.1674, simple_loss=0.2471, pruned_loss=0.04383, over 4913.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2227, pruned_loss=0.04127, over 973284.38 frames.], batch size: 17, lr: 3.96e-04
2022-05-05 03:38:10,949 INFO [train.py:715] (2/8) Epoch 5, batch 4750, loss[loss=0.1807, simple_loss=0.2419, pruned_loss=0.05978, over 4948.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2227, pruned_loss=0.0414, over 973008.93 frames.], batch size: 35, lr: 3.96e-04
2022-05-05 03:38:50,714 INFO [train.py:715] (2/8) Epoch 5, batch 4800, loss[loss=0.1172, simple_loss=0.193, pruned_loss=0.02063, over 4801.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2225, pruned_loss=0.04128, over 972814.33 frames.], batch size: 18, lr: 3.96e-04
2022-05-05 03:39:31,201 INFO [train.py:715] (2/8) Epoch 5, batch 4850, loss[loss=0.1559, simple_loss=0.2294, pruned_loss=0.0412, over 4971.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2224, pruned_loss=0.04103, over 972603.83 frames.], batch size: 35, lr: 3.96e-04
2022-05-05 03:40:11,805 INFO [train.py:715] (2/8) Epoch 5, batch 4900, loss[loss=0.1508, simple_loss=0.2166, pruned_loss=0.0425, over 4913.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2226, pruned_loss=0.04156, over 973055.90 frames.], batch size: 17, lr: 3.96e-04
2022-05-05 03:40:51,935 INFO [train.py:715] (2/8) Epoch 5, batch 4950, loss[loss=0.1511, simple_loss=0.2076, pruned_loss=0.04727, over 4835.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2223, pruned_loss=0.04125, over 972051.93 frames.], batch size: 13, lr: 3.96e-04
2022-05-05 03:41:32,258 INFO [train.py:715] (2/8) Epoch 5, batch 5000, loss[loss=0.1358, simple_loss=0.2184, pruned_loss=0.02659, over 4755.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2232, pruned_loss=0.04155, over 972265.58 frames.], batch size: 19, lr: 3.96e-04
2022-05-05 03:42:13,229 INFO [train.py:715] (2/8) Epoch 5, batch 5050, loss[loss=0.1652, simple_loss=0.2277, pruned_loss=0.05139, over 4791.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2228, pruned_loss=0.04138, over 972377.65 frames.], batch size: 18, lr: 3.96e-04
2022-05-05 03:42:52,852 INFO [train.py:715] (2/8) Epoch 5, batch 5100, loss[loss=0.1523, simple_loss=0.227, pruned_loss=0.03878, over 4954.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2236, pruned_loss=0.04164, over 972407.94 frames.], batch size: 39, lr: 3.96e-04
2022-05-05 03:43:32,133 INFO [train.py:715] (2/8) Epoch 5, batch 5150, loss[loss=0.1399, simple_loss=0.2163, pruned_loss=0.03181, over 4762.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2236, pruned_loss=0.04149, over 972437.14 frames.], batch size: 19, lr: 3.96e-04
2022-05-05 03:44:11,859 INFO [train.py:715] (2/8) Epoch 5, batch 5200, loss[loss=0.1354, simple_loss=0.206, pruned_loss=0.03238, over 4955.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2237, pruned_loss=0.04142, over 973444.71 frames.], batch size: 29, lr: 3.96e-04
2022-05-05 03:44:51,640 INFO [train.py:715] (2/8) Epoch 5, batch 5250, loss[loss=0.1586, simple_loss=0.2333, pruned_loss=0.04196, over 4795.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2243, pruned_loss=0.04142, over 972375.76 frames.], batch size: 24, lr: 3.96e-04
2022-05-05 03:45:32,232 INFO [train.py:715] (2/8) Epoch 5, batch 5300, loss[loss=0.1592, simple_loss=0.2275, pruned_loss=0.04547, over 4817.00 frames.], tot_loss[loss=0.1534, simple_loss=0.224, pruned_loss=0.04137, over 971946.88 frames.], batch size: 25, lr: 3.96e-04
2022-05-05 03:46:12,544 INFO [train.py:715] (2/8) Epoch 5, batch 5350, loss[loss=0.1298, simple_loss=0.2086, pruned_loss=0.02556, over 4946.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2235, pruned_loss=0.04093, over 973201.13 frames.], batch size: 21, lr: 3.96e-04
2022-05-05 03:46:52,866 INFO [train.py:715] (2/8) Epoch 5, batch 5400, loss[loss=0.1645, simple_loss=0.2284, pruned_loss=0.05027, over 4965.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2248, pruned_loss=0.04183, over 972446.44 frames.], batch size: 15, lr: 3.96e-04
2022-05-05 03:47:32,581 INFO [train.py:715] (2/8) Epoch 5, batch 5450, loss[loss=0.1467, simple_loss=0.2156, pruned_loss=0.03886, over 4987.00 frames.], tot_loss[loss=0.154, simple_loss=0.2246, pruned_loss=0.04174, over 972882.12 frames.], batch size: 33, lr: 3.96e-04
2022-05-05 03:48:12,697 INFO [train.py:715] (2/8) Epoch 5, batch 5500, loss[loss=0.1279, simple_loss=0.2037, pruned_loss=0.02607, over 4776.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2251, pruned_loss=0.04191, over 972648.72 frames.], batch size: 17, lr: 3.96e-04
2022-05-05 03:48:53,029 INFO [train.py:715] (2/8) Epoch 5, batch 5550, loss[loss=0.1415, simple_loss=0.2184, pruned_loss=0.03231, over 4912.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2253, pruned_loss=0.04181, over 971618.35 frames.], batch size: 19, lr: 3.96e-04
2022-05-05 03:49:33,408 INFO [train.py:715] (2/8) Epoch 5, batch 5600, loss[loss=0.116, simple_loss=0.1956, pruned_loss=0.01825, over 4974.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2244, pruned_loss=0.04121, over 972130.48 frames.], batch size: 25, lr: 3.95e-04
2022-05-05 03:50:13,547 INFO [train.py:715] (2/8) Epoch 5, batch 5650, loss[loss=0.1533, simple_loss=0.2212, pruned_loss=0.04272, over 4757.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2252, pruned_loss=0.04179, over 971498.58 frames.], batch size: 19, lr: 3.95e-04
2022-05-05 03:50:52,899 INFO [train.py:715] (2/8) Epoch 5, batch 5700, loss[loss=0.1581, simple_loss=0.2225, pruned_loss=0.04687, over 4765.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2247, pruned_loss=0.04154, over 972179.72 frames.], batch size: 19, lr: 3.95e-04
2022-05-05 03:51:33,318 INFO [train.py:715] (2/8) Epoch 5, batch 5750, loss[loss=0.1939, simple_loss=0.2386, pruned_loss=0.07456, over 4831.00 frames.], tot_loss[loss=0.1529, simple_loss=0.224, pruned_loss=0.04096, over 973476.53 frames.], batch size: 13, lr: 3.95e-04
2022-05-05 03:52:13,227 INFO [train.py:715] (2/8) Epoch 5, batch 5800, loss[loss=0.1425, simple_loss=0.2197, pruned_loss=0.03268, over 4882.00 frames.], tot_loss[loss=0.152, simple_loss=0.2236, pruned_loss=0.04018, over 972782.05 frames.], batch size: 19, lr: 3.95e-04
2022-05-05 03:52:53,762 INFO [train.py:715] (2/8) Epoch 5, batch 5850, loss[loss=0.1499, simple_loss=0.2198, pruned_loss=0.03998, over 4829.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2241, pruned_loss=0.04087, over 972650.96 frames.], batch size: 13, lr: 3.95e-04
2022-05-05 03:53:33,395 INFO [train.py:715] (2/8) Epoch 5, batch 5900, loss[loss=0.177, simple_loss=0.2487, pruned_loss=0.0527, over 4789.00 frames.], tot_loss[loss=0.153, simple_loss=0.2241, pruned_loss=0.04096, over 972438.97 frames.], batch size: 17, lr: 3.95e-04
2022-05-05 03:54:13,785 INFO [train.py:715] (2/8) Epoch 5, batch 5950, loss[loss=0.1683, simple_loss=0.2438, pruned_loss=0.04639, over 4957.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2243, pruned_loss=0.0412, over 972655.68 frames.], batch size: 35, lr: 3.95e-04
2022-05-05 03:54:53,621 INFO [train.py:715] (2/8) Epoch 5, batch 6000, loss[loss=0.1546, simple_loss=0.2249, pruned_loss=0.04213, over 4951.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2237, pruned_loss=0.04079, over 973110.55 frames.], batch size: 23, lr: 3.95e-04
2022-05-05 03:54:53,621 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 03:55:03,071 INFO [train.py:742] (2/8) Epoch 5, validation: loss=0.1106, simple_loss=0.1959, pruned_loss=0.01263, over 914524.00 frames.
2022-05-05 03:55:42,937 INFO [train.py:715] (2/8) Epoch 5, batch 6050, loss[loss=0.1446, simple_loss=0.2208, pruned_loss=0.03418, over 4918.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2235, pruned_loss=0.04064, over 972585.36 frames.], batch size: 18, lr: 3.95e-04
2022-05-05 03:56:22,016 INFO [train.py:715] (2/8) Epoch 5, batch 6100, loss[loss=0.1817, simple_loss=0.2497, pruned_loss=0.0569, over 4924.00 frames.], tot_loss[loss=0.151, simple_loss=0.222, pruned_loss=0.04007, over 972643.30 frames.], batch size: 23, lr: 3.95e-04
2022-05-05 03:57:01,849 INFO [train.py:715] (2/8) Epoch 5, batch 6150, loss[loss=0.1509, simple_loss=0.2278, pruned_loss=0.03698, over 4976.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2226, pruned_loss=0.04086, over 972746.43 frames.], batch size: 14, lr: 3.95e-04
2022-05-05 03:57:40,839 INFO [train.py:715] (2/8) Epoch 5, batch 6200, loss[loss=0.1694, simple_loss=0.2388, pruned_loss=0.04995, over 4752.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2234, pruned_loss=0.04156, over 973192.63 frames.], batch size: 19, lr: 3.95e-04
2022-05-05 03:58:21,087 INFO [train.py:715] (2/8) Epoch 5, batch 6250, loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03223, over 4865.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2238, pruned_loss=0.04158, over 972917.15 frames.], batch size: 32, lr: 3.95e-04
2022-05-05 03:58:59,727 INFO [train.py:715] (2/8) Epoch 5, batch 6300, loss[loss=0.1653, simple_loss=0.2168, pruned_loss=0.05688, over 4771.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2251, pruned_loss=0.04227, over 972604.73 frames.], batch size: 12, lr: 3.95e-04
2022-05-05 03:59:39,538 INFO [train.py:715] (2/8) Epoch 5, batch 6350, loss[loss=0.1411, simple_loss=0.2166, pruned_loss=0.0328, over 4761.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2253, pruned_loss=0.04219, over 971914.43 frames.], batch size: 19, lr: 3.95e-04
2022-05-05 04:00:18,901 INFO [train.py:715] (2/8) Epoch 5, batch 6400, loss[loss=0.1704, simple_loss=0.2442, pruned_loss=0.04826, over 4890.00 frames.], tot_loss[loss=0.1544, simple_loss=0.225, pruned_loss=0.04192, over 972006.90 frames.], batch size: 22, lr: 3.95e-04
2022-05-05 04:00:57,770 INFO [train.py:715] (2/8) Epoch 5, batch 6450, loss[loss=0.1556, simple_loss=0.2275, pruned_loss=0.0419, over 4829.00 frames.], tot_loss[loss=0.1547, simple_loss=0.225, pruned_loss=0.04219, over 971655.58 frames.], batch size: 30, lr: 3.95e-04
2022-05-05 04:01:37,236 INFO [train.py:715] (2/8) Epoch 5, batch 6500, loss[loss=0.1813, simple_loss=0.255, pruned_loss=0.05385, over 4986.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2254, pruned_loss=0.04246, over 970942.11 frames.], batch size: 28, lr: 3.95e-04
2022-05-05 04:02:16,582 INFO [train.py:715] (2/8) Epoch 5, batch 6550, loss[loss=0.1521, simple_loss=0.2205, pruned_loss=0.0419, over 4819.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2255, pruned_loss=0.04231, over 971389.37 frames.], batch size: 15, lr: 3.94e-04
2022-05-05 04:02:55,731 INFO [train.py:715] (2/8) Epoch 5, batch 6600, loss[loss=0.1591, simple_loss=0.2326, pruned_loss=0.04283, over 4785.00 frames.], tot_loss[loss=0.155, simple_loss=0.2254, pruned_loss=0.04232, over 972164.50 frames.], batch size: 17, lr: 3.94e-04
2022-05-05 04:03:35,251 INFO [train.py:715] (2/8) Epoch 5, batch 6650, loss[loss=0.1355, simple_loss=0.207, pruned_loss=0.03196, over 4891.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2249, pruned_loss=0.04212, over 971997.55 frames.], batch size: 19, lr: 3.94e-04
2022-05-05 04:04:15,787 INFO [train.py:715] (2/8) Epoch 5, batch 6700, loss[loss=0.1585, simple_loss=0.2341, pruned_loss=0.04142, over 4906.00 frames.], tot_loss[loss=0.155, simple_loss=0.225, pruned_loss=0.04251, over 972567.96 frames.], batch size: 39, lr: 3.94e-04
2022-05-05 04:04:56,121 INFO [train.py:715] (2/8) Epoch 5, batch 6750, loss[loss=0.1559, simple_loss=0.2173, pruned_loss=0.04726, over 4879.00 frames.], tot_loss[loss=0.1553, simple_loss=0.225, pruned_loss=0.04278, over 972124.22 frames.], batch size: 16, lr: 3.94e-04
2022-05-05 04:05:36,107 INFO [train.py:715] (2/8) Epoch 5, batch 6800, loss[loss=0.1417, simple_loss=0.2176, pruned_loss=0.03287, over 4808.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2243, pruned_loss=0.04207, over 972364.39 frames.], batch size: 21, lr: 3.94e-04
2022-05-05 04:06:16,592 INFO [train.py:715] (2/8) Epoch 5, batch 6850, loss[loss=0.1497, simple_loss=0.2278, pruned_loss=0.03578, over 4956.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2254, pruned_loss=0.04265, over 972224.47 frames.], batch size: 35, lr: 3.94e-04
2022-05-05 04:06:56,551 INFO [train.py:715] (2/8) Epoch 5, batch 6900, loss[loss=0.1431, simple_loss=0.2058, pruned_loss=0.04019, over 4751.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2247, pruned_loss=0.04236, over 971744.88 frames.], batch size: 16, lr: 3.94e-04
2022-05-05 04:07:37,126 INFO [train.py:715] (2/8) Epoch 5, batch 6950, loss[loss=0.1358, simple_loss=0.2211, pruned_loss=0.02521, over 4815.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2243, pruned_loss=0.04154, over 972128.67 frames.], batch size: 26, lr: 3.94e-04
2022-05-05 04:08:16,566 INFO [train.py:715] (2/8) Epoch 5, batch 7000, loss[loss=0.132, simple_loss=0.2106, pruned_loss=0.02666, over 4820.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2243, pruned_loss=0.04164, over 971536.35 frames.], batch size: 25, lr: 3.94e-04
2022-05-05 04:08:56,463 INFO [train.py:715] (2/8) Epoch 5, batch 7050, loss[loss=0.1497, simple_loss=0.2273, pruned_loss=0.03607, over 4972.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2236, pruned_loss=0.04145, over 971794.94 frames.], batch size: 15, lr: 3.94e-04
2022-05-05 04:09:36,250 INFO [train.py:715] (2/8) Epoch 5, batch 7100, loss[loss=0.1363, simple_loss=0.1976, pruned_loss=0.03748, over 4858.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2241, pruned_loss=0.0422, over 971202.61 frames.], batch size: 13, lr: 3.94e-04
2022-05-05 04:10:15,691 INFO [train.py:715] (2/8) Epoch 5, batch 7150, loss[loss=0.1755, simple_loss=0.2466, pruned_loss=0.0522, over 4906.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2248, pruned_loss=0.04295, over 971891.47 frames.], batch size: 17, lr: 3.94e-04
2022-05-05 04:10:55,642 INFO [train.py:715] (2/8) Epoch 5, batch 7200, loss[loss=0.1649, simple_loss=0.2266, pruned_loss=0.0516, over 4921.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2237, pruned_loss=0.04252, over 971578.33 frames.], batch size: 39, lr: 3.94e-04
2022-05-05 04:11:35,255 INFO [train.py:715] (2/8) Epoch 5, batch 7250, loss[loss=0.1384, simple_loss=0.2099, pruned_loss=0.03346, over 4925.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2228, pruned_loss=0.04164, over 972335.71 frames.], batch size: 29, lr: 3.94e-04
2022-05-05 04:12:15,755 INFO [train.py:715] (2/8) Epoch 5, batch 7300, loss[loss=0.1457, simple_loss=0.2283, pruned_loss=0.03153, over 4974.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2235, pruned_loss=0.04186, over 972584.28 frames.], batch size: 15, lr: 3.94e-04
2022-05-05 04:12:55,317 INFO [train.py:715] (2/8) Epoch 5, batch 7350, loss[loss=0.1657, simple_loss=0.2307, pruned_loss=0.05032, over 4774.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2232, pruned_loss=0.0417, over 972854.52 frames.], batch size: 14, lr: 3.94e-04
2022-05-05 04:13:34,915 INFO [train.py:715] (2/8) Epoch 5, batch 7400, loss[loss=0.1604, simple_loss=0.2296, pruned_loss=0.04561, over 4969.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2237, pruned_loss=0.04184, over 973096.00 frames.], batch size: 24, lr: 3.94e-04
2022-05-05 04:14:14,461 INFO [train.py:715] (2/8) Epoch 5, batch 7450, loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03319, over 4903.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2236, pruned_loss=0.04161, over 972667.14 frames.], batch size: 19, lr: 3.93e-04
2022-05-05 04:14:53,550 INFO [train.py:715] (2/8) Epoch 5, batch 7500, loss[loss=0.1827, simple_loss=0.2559, pruned_loss=0.05471, over 4788.00 frames.], tot_loss[loss=0.1528, simple_loss=0.223, pruned_loss=0.04132, over 972289.45 frames.], batch size: 17, lr: 3.93e-04
2022-05-05 04:15:33,686 INFO [train.py:715] (2/8) Epoch 5, batch 7550, loss[loss=0.134, simple_loss=0.2041, pruned_loss=0.03193, over 4820.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2236, pruned_loss=0.04141, over 972578.85 frames.], batch size: 14, lr: 3.93e-04
2022-05-05 04:16:13,351 INFO [train.py:715] (2/8) Epoch 5, batch 7600, loss[loss=0.1529, simple_loss=0.2167, pruned_loss=0.04461, over 4872.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2244, pruned_loss=0.04189, over 973054.77 frames.], batch size: 20, lr: 3.93e-04
2022-05-05 04:16:53,610 INFO [train.py:715] (2/8) Epoch 5, batch 7650, loss[loss=0.1684, simple_loss=0.2283, pruned_loss=0.05432, over 4825.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2244, pruned_loss=0.04201, over 973099.21 frames.], batch size: 13, lr: 3.93e-04
2022-05-05 04:17:33,266 INFO [train.py:715] (2/8) Epoch 5, batch 7700, loss[loss=0.1314, simple_loss=0.1942, pruned_loss=0.03433, over 4987.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2239, pruned_loss=0.0415, over 972651.35 frames.], batch size: 14, lr: 3.93e-04
2022-05-05 04:18:12,777 INFO [train.py:715] (2/8) Epoch 5, batch 7750, loss[loss=0.1881, simple_loss=0.2605, pruned_loss=0.05782, over 4981.00 frames.], tot_loss[loss=0.153, simple_loss=0.2235, pruned_loss=0.04124, over 972146.35 frames.], batch size: 35, lr: 3.93e-04
2022-05-05 04:18:52,926 INFO [train.py:715] (2/8) Epoch 5, batch 7800, loss[loss=0.168, simple_loss=0.2466, pruned_loss=0.04476, over 4844.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2233, pruned_loss=0.0414, over 972025.42 frames.], batch size: 30, lr: 3.93e-04
2022-05-05 04:19:32,130 INFO [train.py:715] (2/8) Epoch 5, batch 7850, loss[loss=0.1265, simple_loss=0.203, pruned_loss=0.02494, over 4812.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2239, pruned_loss=0.04157, over 971825.54 frames.], batch size: 12, lr: 3.93e-04
2022-05-05 04:20:12,357 INFO [train.py:715] (2/8) Epoch 5, batch 7900, loss[loss=0.1438, simple_loss=0.2141, pruned_loss=0.03671, over 4957.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2242, pruned_loss=0.04178, over 972795.41 frames.], batch size: 35, lr: 3.93e-04
2022-05-05 04:20:51,912 INFO [train.py:715] (2/8) Epoch 5, batch 7950, loss[loss=0.1537, simple_loss=0.2224, pruned_loss=0.04248, over 4929.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2239, pruned_loss=0.04145, over 973123.82 frames.], batch size: 29, lr: 3.93e-04
2022-05-05 04:21:32,117 INFO [train.py:715] (2/8) Epoch 5, batch 8000, loss[loss=0.1289, simple_loss=0.198, pruned_loss=0.02989, over 4882.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2238, pruned_loss=0.04123, over 973334.16 frames.], batch size: 20, lr: 3.93e-04
2022-05-05 04:22:11,571 INFO [train.py:715] (2/8) Epoch 5, batch 8050, loss[loss=0.1245, simple_loss=0.2016, pruned_loss=0.02371, over 4979.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2246, pruned_loss=0.0416, over 972441.17 frames.], batch size: 24, lr: 3.93e-04
2022-05-05 04:22:51,023 INFO [train.py:715] (2/8) Epoch 5, batch 8100, loss[loss=0.1234, simple_loss=0.1905, pruned_loss=0.02813, over 4782.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2241, pruned_loss=0.04164, over 972057.35 frames.], batch size: 17, lr: 3.93e-04
2022-05-05 04:23:30,811 INFO [train.py:715] (2/8) Epoch 5, batch 8150, loss[loss=0.1599, simple_loss=0.2331, pruned_loss=0.04333, over 4819.00 frames.], tot_loss[loss=0.154, simple_loss=0.2244, pruned_loss=0.04186, over 972563.21 frames.], batch size: 25, lr: 3.93e-04
2022-05-05 04:24:09,995 INFO [train.py:715] (2/8) Epoch 5, batch 8200, loss[loss=0.1781, simple_loss=0.2372, pruned_loss=0.05945, over 4831.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2237, pruned_loss=0.04152, over 973264.79 frames.], batch size: 30, lr: 3.93e-04
2022-05-05 04:24:50,011 INFO [train.py:715] (2/8) Epoch 5, batch 8250, loss[loss=0.1587, simple_loss=0.2284, pruned_loss=0.04449, over 4803.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2237, pruned_loss=0.04132, over 973680.94 frames.], batch size: 21, lr: 3.93e-04
2022-05-05 04:25:29,483 INFO [train.py:715] (2/8) Epoch 5, batch 8300, loss[loss=0.1709, simple_loss=0.2395, pruned_loss=0.05114, over 4965.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2247, pruned_loss=0.04174, over 973952.78 frames.], batch size: 39, lr: 3.93e-04
2022-05-05 04:26:09,424 INFO [train.py:715] (2/8) Epoch 5, batch 8350, loss[loss=0.1301, simple_loss=0.2096, pruned_loss=0.0253, over 4832.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2239, pruned_loss=0.04117, over 973087.87 frames.], batch size: 27, lr: 3.93e-04
2022-05-05 04:26:48,501 INFO [train.py:715] (2/8) Epoch 5, batch 8400, loss[loss=0.1555, simple_loss=0.2216, pruned_loss=0.04467, over 4915.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2239, pruned_loss=0.04175, over 973015.56 frames.], batch size: 19, lr: 3.92e-04
2022-05-05 04:27:27,551 INFO [train.py:715] (2/8) Epoch 5, batch 8450, loss[loss=0.1736, simple_loss=0.2435, pruned_loss=0.05186, over 4759.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2235, pruned_loss=0.04185, over 972764.25 frames.], batch size: 16, lr: 3.92e-04
2022-05-05 04:28:06,815 INFO [train.py:715] (2/8) Epoch 5, batch 8500, loss[loss=0.1584, simple_loss=0.223, pruned_loss=0.0469, over 4802.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2245, pruned_loss=0.04258, over 972843.19 frames.], batch size: 14, lr: 3.92e-04
2022-05-05 04:28:45,803 INFO [train.py:715] (2/8) Epoch 5, batch 8550, loss[loss=0.1387, simple_loss=0.2053, pruned_loss=0.03607, over 4911.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2235, pruned_loss=0.04182, over 972316.22 frames.], batch size: 17, lr: 3.92e-04
2022-05-05 04:29:25,248 INFO [train.py:715] (2/8) Epoch 5, batch 8600, loss[loss=0.1427, simple_loss=0.2004, pruned_loss=0.04248, over 4692.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2228, pruned_loss=0.04148, over 972499.11 frames.], batch size: 15, lr: 3.92e-04
2022-05-05 04:30:04,407 INFO [train.py:715] (2/8) Epoch 5, batch 8650, loss[loss=0.1213, simple_loss=0.1915, pruned_loss=0.02553, over 4941.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2226, pruned_loss=0.04135, over 972520.86 frames.], batch size: 29, lr: 3.92e-04
2022-05-05 04:30:43,884 INFO [train.py:715] (2/8) Epoch 5, batch 8700, loss[loss=0.1371, simple_loss=0.2027, pruned_loss=0.03569, over 4790.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2226, pruned_loss=0.04144, over 971810.57 frames.], batch size: 12, lr: 3.92e-04
2022-05-05 04:31:23,273 INFO [train.py:715] (2/8) Epoch 5, batch 8750, loss[loss=0.173, simple_loss=0.2276, pruned_loss=0.05917, over 4688.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2223, pruned_loss=0.04161, over 972011.33 frames.], batch size: 15, lr: 3.92e-04
2022-05-05 04:32:02,280 INFO [train.py:715] (2/8) Epoch 5, batch 8800, loss[loss=0.1156, simple_loss=0.1937, pruned_loss=0.0188, over 4979.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2223, pruned_loss=0.04138, over 972384.42 frames.], batch size: 24, lr: 3.92e-04
2022-05-05 04:32:42,164 INFO [train.py:715] (2/8) Epoch 5, batch 8850, loss[loss=0.1271, simple_loss=0.1994, pruned_loss=0.02743, over 4662.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2228, pruned_loss=0.04109, over 972425.01 frames.], batch size: 13, lr: 3.92e-04
2022-05-05 04:33:20,884 INFO [train.py:715] (2/8) Epoch 5, batch 8900, loss[loss=0.146, simple_loss=0.2242, pruned_loss=0.03395, over 4976.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2219, pruned_loss=0.04082, over 972673.88 frames.], batch size: 15, lr: 3.92e-04
2022-05-05 04:33:59,745 INFO [train.py:715] (2/8) Epoch 5, batch 8950, loss[loss=0.1608, simple_loss=0.2351, pruned_loss=0.04323, over 4780.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2222, pruned_loss=0.04109, over 971440.69 frames.], batch size: 17, lr: 3.92e-04
2022-05-05 04:34:39,030 INFO [train.py:715] (2/8) Epoch 5, batch 9000, loss[loss=0.135, simple_loss=0.2141, pruned_loss=0.028, over 4835.00 frames.], tot_loss[loss=0.1526, simple_loss=0.223, pruned_loss=0.0411, over 971791.65 frames.], batch size: 26, lr: 3.92e-04
2022-05-05 04:34:39,031 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 04:34:48,553 INFO [train.py:742] (2/8) Epoch 5, validation: loss=0.1105, simple_loss=0.196, pruned_loss=0.01252, over 914524.00 frames.
2022-05-05 04:35:28,194 INFO [train.py:715] (2/8) Epoch 5, batch 9050, loss[loss=0.1583, simple_loss=0.2309, pruned_loss=0.04288, over 4990.00 frames.], tot_loss[loss=0.153, simple_loss=0.2231, pruned_loss=0.04142, over 972223.80 frames.], batch size: 20, lr: 3.92e-04
2022-05-05 04:36:07,671 INFO [train.py:715] (2/8) Epoch 5, batch 9100, loss[loss=0.1233, simple_loss=0.2022, pruned_loss=0.02218, over 4792.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2232, pruned_loss=0.04171, over 971991.96 frames.], batch size: 14, lr: 3.92e-04
2022-05-05 04:36:46,711 INFO [train.py:715] (2/8) Epoch 5, batch 9150, loss[loss=0.1373, simple_loss=0.2146, pruned_loss=0.02999, over 4967.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2231, pruned_loss=0.04133, over 971605.71 frames.], batch size: 24, lr: 3.92e-04
2022-05-05 04:37:26,220 INFO [train.py:715] (2/8) Epoch 5, batch 9200, loss[loss=0.1741, simple_loss=0.2423, pruned_loss=0.05296, over 4844.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2227, pruned_loss=0.04138, over 971776.93 frames.], batch size: 32, lr: 3.92e-04
2022-05-05 04:38:06,418 INFO [train.py:715] (2/8) Epoch 5, batch 9250, loss[loss=0.154, simple_loss=0.2188, pruned_loss=0.04458, over 4972.00 frames.], tot_loss[loss=0.154, simple_loss=0.224, pruned_loss=0.042, over 972061.55 frames.], batch size: 24, lr: 3.92e-04
2022-05-05 04:38:45,290 INFO [train.py:715] (2/8) Epoch 5, batch 9300, loss[loss=0.1661, simple_loss=0.2395, pruned_loss=0.04639, over 4968.00 frames.], tot_loss[loss=0.1538, simple_loss=0.224, pruned_loss=0.04176, over 972323.74 frames.], batch size: 39, lr: 3.91e-04
2022-05-05 04:39:24,929 INFO [train.py:715] (2/8) Epoch 5, batch 9350, loss[loss=0.1531, simple_loss=0.2304, pruned_loss=0.03794, over 4775.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2239, pruned_loss=0.04131, over 972949.03 frames.], batch size: 19, lr: 3.91e-04
2022-05-05 04:40:04,421 INFO [train.py:715] (2/8) Epoch 5, batch 9400, loss[loss=0.1476, simple_loss=0.2208, pruned_loss=0.03722, over 4890.00 frames.], tot_loss[loss=0.1546, simple_loss=0.225, pruned_loss=0.04203, over 972459.02 frames.], batch size: 39, lr: 3.91e-04
2022-05-05 04:40:43,712 INFO [train.py:715] (2/8) Epoch 5, batch 9450, loss[loss=0.1583, simple_loss=0.2271, pruned_loss=0.04472, over 4883.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2241, pruned_loss=0.04186, over 972745.27 frames.], batch size: 22, lr: 3.91e-04
2022-05-05 04:41:22,596 INFO [train.py:715] (2/8) Epoch 5, batch 9500, loss[loss=0.1443, simple_loss=0.2091, pruned_loss=0.03971, over 4681.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2228, pruned_loss=0.04103, over 973142.64 frames.], batch size: 15, lr: 3.91e-04
2022-05-05 04:42:02,153 INFO [train.py:715] (2/8) Epoch 5, batch 9550, loss[loss=0.1548, simple_loss=0.2255, pruned_loss=0.0421, over 4846.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2228, pruned_loss=0.04083, over 972245.08 frames.], batch size: 30, lr: 3.91e-04
2022-05-05 04:42:41,919 INFO [train.py:715] (2/8) Epoch 5, batch 9600, loss[loss=0.1383, simple_loss=0.2081, pruned_loss=0.03432, over 4830.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2233, pruned_loss=0.04107, over 971568.72 frames.], batch size: 26, lr: 3.91e-04
2022-05-05 04:43:21,155 INFO [train.py:715] (2/8) Epoch 5, batch 9650, loss[loss=0.1171, simple_loss=0.1852, pruned_loss=0.02446, over 4888.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2222, pruned_loss=0.04073, over 971484.61 frames.], batch size: 19, lr: 3.91e-04
2022-05-05 04:44:00,807 INFO [train.py:715] (2/8) Epoch 5, batch 9700, loss[loss=0.1698, simple_loss=0.2369, pruned_loss=0.05137, over 4697.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2221, pruned_loss=0.04077, over 970839.49 frames.], batch size: 15, lr: 3.91e-04
2022-05-05 04:44:40,235 INFO [train.py:715] (2/8) Epoch 5, batch 9750, loss[loss=0.1697, simple_loss=0.2342, pruned_loss=0.05259, over 4969.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2227, pruned_loss=0.04081, over 971227.01 frames.], batch size: 35, lr: 3.91e-04
2022-05-05 04:45:19,135 INFO [train.py:715] (2/8) Epoch 5, batch 9800, loss[loss=0.1202, simple_loss=0.1977, pruned_loss=0.02138, over 4790.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2237, pruned_loss=0.04088, over 971662.32 frames.], batch size: 17, lr: 3.91e-04
2022-05-05 04:45:58,977 INFO [train.py:715] (2/8) Epoch 5, batch 9850, loss[loss=0.1374, simple_loss=0.2145, pruned_loss=0.03016, over 4961.00 frames.], tot_loss[loss=0.153, simple_loss=0.224, pruned_loss=0.04099, over 971437.41 frames.], batch size: 24, lr: 3.91e-04
2022-05-05 04:46:38,175 INFO [train.py:715] (2/8) Epoch 5, batch 9900, loss[loss=0.1198, simple_loss=0.1937, pruned_loss=0.02297, over 4815.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2236, pruned_loss=0.04112, over 970797.29 frames.], batch size: 27, lr: 3.91e-04
2022-05-05 04:47:17,942 INFO [train.py:715] (2/8) Epoch 5, batch 9950, loss[loss=0.1651, simple_loss=0.2289, pruned_loss=0.05061, over 4882.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2237, pruned_loss=0.04124, over 971234.15 frames.], batch size: 20, lr: 3.91e-04
2022-05-05 04:47:59,850 INFO [train.py:715] (2/8) Epoch 5, batch 10000, loss[loss=0.1769, simple_loss=0.2391, pruned_loss=0.05739, over 4860.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2233, pruned_loss=0.04128, over 970807.44 frames.], batch size: 32, lr: 3.91e-04
2022-05-05 04:48:39,808 INFO [train.py:715] (2/8) Epoch 5, batch 10050, loss[loss=0.1817, simple_loss=0.2386, pruned_loss=0.06244, over 4990.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2237, pruned_loss=0.04139, over 970846.03 frames.], batch size: 15, lr: 3.91e-04
2022-05-05 04:49:19,415 INFO [train.py:715] (2/8) Epoch 5, batch 10100, loss[loss=0.1624, simple_loss=0.2222, pruned_loss=0.05128, over 4852.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2237, pruned_loss=0.04119, over 972063.37 frames.], batch size: 32, lr: 3.91e-04
2022-05-05 04:49:58,584 INFO [train.py:715] (2/8) Epoch 5, batch 10150, loss[loss=0.1355, simple_loss=0.2064, pruned_loss=0.03234, over 4799.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2242, pruned_loss=0.04146, over 972619.95 frames.], batch size: 25, lr: 3.91e-04
2022-05-05 04:50:38,452 INFO [train.py:715] (2/8) Epoch 5, batch 10200, loss[loss=0.2141, simple_loss=0.2761, pruned_loss=0.076, over 4748.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2249, pruned_loss=0.04186, over 972732.25 frames.], batch size: 19, lr: 3.91e-04
2022-05-05 04:51:17,795 INFO [train.py:715] (2/8) Epoch 5, batch 10250, loss[loss=0.1484, simple_loss=0.2253, pruned_loss=0.03576, over 4908.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2244, pruned_loss=0.04151, over 972682.75 frames.], batch size: 17, lr: 3.90e-04
2022-05-05 04:51:56,801 INFO [train.py:715] (2/8) Epoch 5, batch 10300, loss[loss=0.1728, simple_loss=0.2454, pruned_loss=0.05016, over 4703.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2243, pruned_loss=0.04158, over 972040.55 frames.], batch size: 15, lr: 3.90e-04
2022-05-05 04:52:36,625 INFO [train.py:715] (2/8) Epoch 5, batch 10350, loss[loss=0.129, simple_loss=0.211, pruned_loss=0.02347, over 4876.00 frames.], tot_loss[loss=0.154, simple_loss=0.2245, pruned_loss=0.04172, over 971648.02 frames.], batch size: 22, lr: 3.90e-04
2022-05-05 04:53:15,664 INFO [train.py:715] (2/8) Epoch 5, batch 10400, loss[loss=0.1548, simple_loss=0.2276, pruned_loss=0.04096, over 4929.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2243, pruned_loss=0.04166, over 971059.77 frames.], batch size: 21, lr: 3.90e-04
2022-05-05 04:53:55,618 INFO [train.py:715] (2/8) Epoch 5, batch 10450, loss[loss=0.1562, simple_loss=0.2167, pruned_loss=0.04792, over 4836.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2238, pruned_loss=0.04157, over 971910.04 frames.], batch size: 12, lr: 3.90e-04
2022-05-05 04:54:35,515 INFO [train.py:715] (2/8) Epoch 5, batch 10500, loss[loss=0.1482, simple_loss=0.2237, pruned_loss=0.03637, over 4926.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2231, pruned_loss=0.04136, over 972697.94 frames.], batch size: 29, lr: 3.90e-04
2022-05-05 04:55:15,981 INFO [train.py:715] (2/8) Epoch 5, batch 10550, loss[loss=0.144, simple_loss=0.2223, pruned_loss=0.03279, over 4867.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2225, pruned_loss=0.04092, over 973427.15 frames.], batch size: 16, lr: 3.90e-04
2022-05-05 04:55:55,072 INFO [train.py:715] (2/8) Epoch 5, batch 10600, loss[loss=0.1714, simple_loss=0.248, pruned_loss=0.04738, over 4961.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2226, pruned_loss=0.04058, over 973061.91 frames.], batch size: 15, lr: 3.90e-04
2022-05-05 04:56:34,539 INFO [train.py:715] (2/8) Epoch 5, batch 10650, loss[loss=0.1331, simple_loss=0.2141, pruned_loss=0.02604, over 4800.00 frames.], tot_loss[loss=0.152, simple_loss=0.223, pruned_loss=0.04051, over 973398.16 frames.], batch size: 25, lr: 3.90e-04
2022-05-05 04:57:14,069 INFO [train.py:715] (2/8) Epoch 5, batch 10700, loss[loss=0.1495, simple_loss=0.2293, pruned_loss=0.03482, over 4778.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2232, pruned_loss=0.04082, over 972621.82 frames.], batch size: 17, lr: 3.90e-04
2022-05-05 04:57:53,026 INFO [train.py:715] (2/8) Epoch 5, batch 10750, loss[loss=0.1694, simple_loss=0.2467, pruned_loss=0.04605, over 4881.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2221, pruned_loss=0.04006, over 972487.30 frames.], batch size: 22, lr: 3.90e-04
2022-05-05 04:58:32,277 INFO [train.py:715] (2/8) Epoch 5, batch 10800, loss[loss=0.1656, simple_loss=0.2379, pruned_loss=0.04664, over 4892.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2213, pruned_loss=0.04003, over 973006.96 frames.], batch size: 19, lr: 3.90e-04
2022-05-05 04:59:11,503 INFO [train.py:715] (2/8) Epoch 5, batch 10850, loss[loss=0.1487, simple_loss=0.224, pruned_loss=0.0367, over 4742.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2219, pruned_loss=0.04037, over 973020.06 frames.], batch size: 16, lr: 3.90e-04
2022-05-05 04:59:51,499 INFO [train.py:715] (2/8) Epoch 5, batch 10900, loss[loss=0.2139, simple_loss=0.2709, pruned_loss=0.07844, over 4924.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2219, pruned_loss=0.04042, over 973198.38 frames.], batch size: 35, lr: 3.90e-04
2022-05-05 05:00:30,698 INFO [train.py:715] (2/8) Epoch 5, batch 10950, loss[loss=0.1901, simple_loss=0.247, pruned_loss=0.06657, over 4916.00 frames.], tot_loss[loss=0.1523, simple_loss=0.223, pruned_loss=0.04081, over 973661.91 frames.], batch size: 18, lr: 3.90e-04
2022-05-05 05:01:10,469 INFO [train.py:715] (2/8) Epoch 5, batch 11000, loss[loss=0.1354, simple_loss=0.2082, pruned_loss=0.03128, over 4924.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2232, pruned_loss=0.04084, over 973690.96 frames.], batch size: 18, lr: 3.90e-04
2022-05-05 05:01:49,964 INFO [train.py:715] (2/8) Epoch 5, batch 11050, loss[loss=0.167, simple_loss=0.235, pruned_loss=0.0495, over 4828.00 frames.], tot_loss[loss=0.152, simple_loss=0.2225, pruned_loss=0.04076, over 971846.25 frames.], batch size: 25, lr: 3.90e-04
2022-05-05 05:02:29,386 INFO [train.py:715] (2/8) Epoch 5, batch 11100, loss[loss=0.1799, simple_loss=0.244, pruned_loss=0.05793, over 4979.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2226, pruned_loss=0.04086, over 972603.46 frames.], batch size: 15, lr: 3.90e-04
2022-05-05 05:03:08,928 INFO [train.py:715] (2/8) Epoch 5, batch 11150, loss[loss=0.1469, simple_loss=0.2209, pruned_loss=0.03642, over 4895.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2229, pruned_loss=0.04123, over 972040.65 frames.], batch size: 19, lr: 3.90e-04
2022-05-05 05:03:48,022 INFO [train.py:715] (2/8) Epoch 5, batch 11200, loss[loss=0.1592, simple_loss=0.2353, pruned_loss=0.04157, over 4951.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2229, pruned_loss=0.04108, over 972538.92 frames.], batch size: 23, lr: 3.89e-04
2022-05-05 05:04:27,937 INFO [train.py:715] (2/8) Epoch 5, batch 11250, loss[loss=0.1556, simple_loss=0.231, pruned_loss=0.04016, over 4934.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2228, pruned_loss=0.04101, over 972201.69 frames.], batch size: 23, lr: 3.89e-04
2022-05-05 05:05:07,258 INFO [train.py:715] (2/8) Epoch 5, batch 11300, loss[loss=0.1561, simple_loss=0.2099, pruned_loss=0.05119, over 4935.00 frames.], tot_loss[loss=0.1526, simple_loss=0.223, pruned_loss=0.04109, over 972550.27 frames.], batch size: 23, lr: 3.89e-04
2022-05-05 05:05:46,392 INFO [train.py:715] (2/8) Epoch 5, batch 11350, loss[loss=0.144, simple_loss=0.2203, pruned_loss=0.03383, over 4793.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2232, pruned_loss=0.04128, over 973372.70 frames.], batch size: 18, lr: 3.89e-04
2022-05-05 05:06:27,197 INFO [train.py:715] (2/8) Epoch 5, batch 11400, loss[loss=0.1475, simple_loss=0.2079, pruned_loss=0.04353, over 4896.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2226, pruned_loss=0.04133, over 973687.88 frames.], batch size: 22, lr: 3.89e-04
2022-05-05 05:07:07,371 INFO [train.py:715] (2/8) Epoch 5, batch 11450, loss[loss=0.1282, simple_loss=0.2062, pruned_loss=0.02514, over 4971.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2228, pruned_loss=0.04128, over 973461.52 frames.], batch size: 15, lr: 3.89e-04
2022-05-05 05:07:47,409 INFO [train.py:715] (2/8) Epoch 5, batch 11500, loss[loss=0.1308, simple_loss=0.1925, pruned_loss=0.03456, over 4928.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2225, pruned_loss=0.04113, over 973276.21 frames.], batch size: 18, lr: 3.89e-04
2022-05-05 05:08:27,434 INFO [train.py:715] (2/8) Epoch 5, batch 11550, loss[loss=0.1233, simple_loss=0.1974, pruned_loss=0.02464, over 4875.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2215, pruned_loss=0.04092, over 973490.27 frames.], batch size: 22, lr: 3.89e-04
2022-05-05 05:09:07,617 INFO [train.py:715] (2/8) Epoch 5, batch 11600, loss[loss=0.1357, simple_loss=0.2143, pruned_loss=0.02862, over 4927.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2215, pruned_loss=0.04042, over 973406.96 frames.], batch size: 29, lr: 3.89e-04
2022-05-05 05:09:48,322 INFO [train.py:715] (2/8) Epoch 5, batch 11650, loss[loss=0.1427, simple_loss=0.2165, pruned_loss=0.03449, over 4783.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2227, pruned_loss=0.04091, over 973233.52 frames.], batch size: 17, lr: 3.89e-04
2022-05-05 05:10:28,074 INFO [train.py:715] (2/8) Epoch 5, batch 11700, loss[loss=0.1327, simple_loss=0.2094, pruned_loss=0.02801, over 4784.00 frames.], tot_loss[loss=0.1519, simple_loss=0.222, pruned_loss=0.0409, over 973662.76 frames.], batch size: 17, lr: 3.89e-04
2022-05-05 05:11:08,790 INFO [train.py:715] (2/8) Epoch 5, batch 11750, loss[loss=0.1435, simple_loss=0.2207, pruned_loss=0.03316, over 4986.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2217, pruned_loss=0.04073, over 973813.87 frames.], batch size: 27, lr: 3.89e-04
2022-05-05 05:11:48,934 INFO [train.py:715] (2/8) Epoch 5, batch 11800, loss[loss=0.1297, simple_loss=0.1973, pruned_loss=0.03103, over 4757.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2216, pruned_loss=0.04046, over 973208.97 frames.], batch size: 19, lr: 3.89e-04
2022-05-05 05:12:29,043 INFO [train.py:715] (2/8) Epoch 5, batch 11850, loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03715, over 4935.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2218, pruned_loss=0.04078, over 972619.82 frames.], batch size: 23, lr: 3.89e-04
2022-05-05 05:13:08,179 INFO [train.py:715] (2/8) Epoch 5, batch 11900, loss[loss=0.1901, simple_loss=0.2646, pruned_loss=0.0578, over 4752.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2218, pruned_loss=0.04075, over 972935.84 frames.], batch size: 16, lr: 3.89e-04
2022-05-05 05:13:47,505 INFO [train.py:715] (2/8) Epoch 5, batch 11950, loss[loss=0.1839, simple_loss=0.254, pruned_loss=0.05689, over 4933.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2231, pruned_loss=0.04132, over 972778.72 frames.], batch size: 21, lr: 3.89e-04
2022-05-05 05:14:27,514 INFO [train.py:715] (2/8) Epoch 5, batch 12000, loss[loss=0.1331, simple_loss=0.1975, pruned_loss=0.03431, over 4824.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2235, pruned_loss=0.04131, over 972478.84 frames.], batch size: 13, lr: 3.89e-04
2022-05-05 05:14:27,514 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 05:14:37,327 INFO [train.py:742] (2/8) Epoch 5, validation: loss=0.1103, simple_loss=0.1957, pruned_loss=0.01243, over 914524.00 frames.
2022-05-05 05:15:17,602 INFO [train.py:715] (2/8) Epoch 5, batch 12050, loss[loss=0.1868, simple_loss=0.2566, pruned_loss=0.05846, over 4714.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.0411, over 972741.47 frames.], batch size: 15, lr: 3.89e-04
2022-05-05 05:15:57,246 INFO [train.py:715] (2/8) Epoch 5, batch 12100, loss[loss=0.1401, simple_loss=0.2154, pruned_loss=0.03244, over 4905.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2227, pruned_loss=0.04109, over 972825.58 frames.], batch size: 17, lr: 3.89e-04
2022-05-05 05:16:36,758 INFO [train.py:715] (2/8) Epoch 5, batch 12150, loss[loss=0.1613, simple_loss=0.2341, pruned_loss=0.04426, over 4832.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2232, pruned_loss=0.04166, over 973233.49 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:17:16,021 INFO [train.py:715] (2/8) Epoch 5, batch 12200, loss[loss=0.1477, simple_loss=0.2152, pruned_loss=0.04007, over 4965.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2223, pruned_loss=0.04102, over 972589.54 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:17:56,096 INFO [train.py:715] (2/8) Epoch 5, batch 12250, loss[loss=0.1782, simple_loss=0.233, pruned_loss=0.06173, over 4908.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2205, pruned_loss=0.04022, over 972410.04 frames.], batch size: 39, lr: 3.88e-04
2022-05-05 05:18:35,376 INFO [train.py:715] (2/8) Epoch 5, batch 12300, loss[loss=0.1304, simple_loss=0.202, pruned_loss=0.02938, over 4740.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2206, pruned_loss=0.03961, over 972330.49 frames.], batch size: 16, lr: 3.88e-04
2022-05-05 05:19:14,278 INFO [train.py:715] (2/8) Epoch 5, batch 12350, loss[loss=0.1451, simple_loss=0.217, pruned_loss=0.03656, over 4976.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2223, pruned_loss=0.04038, over 971904.01 frames.], batch size: 28, lr: 3.88e-04
2022-05-05 05:19:53,842 INFO [train.py:715] (2/8) Epoch 5, batch 12400, loss[loss=0.1421, simple_loss=0.2175, pruned_loss=0.03339, over 4696.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2227, pruned_loss=0.04049, over 971721.48 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:20:33,447 INFO [train.py:715] (2/8) Epoch 5, batch 12450, loss[loss=0.1256, simple_loss=0.2009, pruned_loss=0.02517, over 4752.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2229, pruned_loss=0.04082, over 972696.57 frames.], batch size: 19, lr: 3.88e-04
2022-05-05 05:21:12,659 INFO [train.py:715] (2/8) Epoch 5, batch 12500, loss[loss=0.1355, simple_loss=0.214, pruned_loss=0.02854, over 4888.00 frames.], tot_loss[loss=0.152, simple_loss=0.2228, pruned_loss=0.04056, over 972513.74 frames.], batch size: 22, lr: 3.88e-04
2022-05-05 05:21:51,879 INFO [train.py:715] (2/8) Epoch 5, batch 12550, loss[loss=0.1282, simple_loss=0.2095, pruned_loss=0.02342, over 4852.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2228, pruned_loss=0.04032, over 972442.75 frames.], batch size: 20, lr: 3.88e-04
2022-05-05 05:22:30,629 INFO [train.py:715] (2/8) Epoch 5, batch 12600, loss[loss=0.1496, simple_loss=0.2177, pruned_loss=0.04069, over 4971.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.04051, over 972459.91 frames.], batch size: 25, lr: 3.88e-04
2022-05-05 05:23:08,929 INFO [train.py:715] (2/8) Epoch 5, batch 12650, loss[loss=0.1729, simple_loss=0.2444, pruned_loss=0.05072, over 4860.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2231, pruned_loss=0.04071, over 971989.46 frames.], batch size: 20, lr: 3.88e-04
2022-05-05 05:23:47,148 INFO [train.py:715] (2/8) Epoch 5, batch 12700, loss[loss=0.1618, simple_loss=0.2388, pruned_loss=0.04246, over 4837.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2239, pruned_loss=0.04113, over 972899.30 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:24:27,031 INFO [train.py:715] (2/8) Epoch 5, batch 12750, loss[loss=0.1488, simple_loss=0.2221, pruned_loss=0.03776, over 4773.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2234, pruned_loss=0.04124, over 973960.14 frames.], batch size: 17, lr: 3.88e-04
2022-05-05 05:25:06,592 INFO [train.py:715] (2/8) Epoch 5, batch 12800, loss[loss=0.16, simple_loss=0.2309, pruned_loss=0.04462, over 4709.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2234, pruned_loss=0.0407, over 974074.57 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:25:46,754 INFO [train.py:715] (2/8) Epoch 5, batch 12850, loss[loss=0.1634, simple_loss=0.2441, pruned_loss=0.04131, over 4878.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2235, pruned_loss=0.04097, over 973721.19 frames.], batch size: 22, lr: 3.88e-04
2022-05-05 05:26:26,311 INFO [train.py:715] (2/8) Epoch 5, batch 12900, loss[loss=0.127, simple_loss=0.2077, pruned_loss=0.02318, over 4903.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2232, pruned_loss=0.04114, over 973473.46 frames.], batch size: 19, lr: 3.88e-04
2022-05-05 05:27:06,310 INFO [train.py:715] (2/8) Epoch 5, batch 12950, loss[loss=0.1828, simple_loss=0.2504, pruned_loss=0.05757, over 4955.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2234, pruned_loss=0.04161, over 973016.14 frames.], batch size: 15, lr: 3.88e-04
2022-05-05 05:27:45,748 INFO [train.py:715] (2/8) Epoch 5, batch 13000, loss[loss=0.1379, simple_loss=0.2087, pruned_loss=0.03351, over 4777.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2229, pruned_loss=0.04141, over 972306.19 frames.], batch size: 18, lr: 3.88e-04
2022-05-05 05:28:25,603 INFO [train.py:715] (2/8) Epoch 5, batch 13050, loss[loss=0.1499, simple_loss=0.2144, pruned_loss=0.04268, over 4981.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2226, pruned_loss=0.04124, over 972075.78 frames.], batch size: 35, lr: 3.88e-04
2022-05-05 05:29:03,807 INFO [train.py:715] (2/8) Epoch 5, batch 13100, loss[loss=0.1497, simple_loss=0.2285, pruned_loss=0.03545, over 4862.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2229, pruned_loss=0.04162, over 972036.40 frames.], batch size: 16, lr: 3.87e-04
2022-05-05 05:29:42,389 INFO [train.py:715] (2/8) Epoch 5, batch 13150, loss[loss=0.1287, simple_loss=0.2048, pruned_loss=0.02632, over 4939.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2224, pruned_loss=0.04133, over 971773.66 frames.], batch size: 39, lr: 3.87e-04
2022-05-05 05:30:20,477 INFO [train.py:715] (2/8) Epoch 5, batch 13200, loss[loss=0.1595, simple_loss=0.2313, pruned_loss=0.04385, over 4832.00 frames.], tot_loss[loss=0.1534, simple_loss=0.223, pruned_loss=0.04191, over 971736.47 frames.], batch size: 15, lr: 3.87e-04
2022-05-05 05:30:58,488 INFO [train.py:715] (2/8) Epoch 5, batch 13250, loss[loss=0.1571, simple_loss=0.2312, pruned_loss=0.04154, over 4988.00 frames.], tot_loss[loss=0.1521, simple_loss=0.222, pruned_loss=0.0411, over 972397.63 frames.], batch size: 33, lr: 3.87e-04
2022-05-05 05:31:37,093 INFO [train.py:715] (2/8) Epoch 5, batch 13300, loss[loss=0.1682, simple_loss=0.233, pruned_loss=0.05166, over 4792.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2222, pruned_loss=0.04084, over 971740.02 frames.], batch size: 21, lr: 3.87e-04
2022-05-05 05:32:14,953 INFO [train.py:715] (2/8) Epoch 5, batch 13350, loss[loss=0.1728, simple_loss=0.2432, pruned_loss=0.05115, over 4814.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2219, pruned_loss=0.04089, over 971420.38 frames.], batch size: 27, lr: 3.87e-04
2022-05-05 05:32:53,085 INFO [train.py:715] (2/8) Epoch 5, batch 13400, loss[loss=0.1779, simple_loss=0.2426, pruned_loss=0.05658, over 4879.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2227, pruned_loss=0.04144, over 971912.52 frames.], batch size: 16, lr: 3.87e-04
2022-05-05 05:33:30,831 INFO [train.py:715] (2/8) Epoch 5, batch 13450, loss[loss=0.1438, simple_loss=0.2195, pruned_loss=0.03405, over 4938.00 frames.], tot_loss[loss=0.153, simple_loss=0.2233, pruned_loss=0.04139, over 972946.20 frames.], batch size: 21, lr: 3.87e-04
2022-05-05 05:34:09,167 INFO [train.py:715] (2/8) Epoch 5, batch 13500, loss[loss=0.1316, simple_loss=0.2136, pruned_loss=0.02475, over 4774.00 frames.], tot_loss[loss=0.153, simple_loss=0.2238, pruned_loss=0.04106, over 974101.88 frames.], batch size: 17, lr: 3.87e-04
2022-05-05 05:34:47,072 INFO [train.py:715] (2/8) Epoch 5, batch 13550, loss[loss=0.1671, simple_loss=0.2366, pruned_loss=0.0488, over 4749.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2235, pruned_loss=0.04083, over 973703.19 frames.], batch size: 16, lr: 3.87e-04
2022-05-05 05:35:24,567 INFO [train.py:715] (2/8) Epoch 5, batch 13600, loss[loss=0.1406, simple_loss=0.2247, pruned_loss=0.02821, over 4884.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2234, pruned_loss=0.04091, over 973623.87 frames.], batch size: 22, lr: 3.87e-04
2022-05-05 05:36:03,223 INFO [train.py:715] (2/8) Epoch 5, batch 13650, loss[loss=0.1636, simple_loss=0.228, pruned_loss=0.04965, over 4840.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2244, pruned_loss=0.04148, over 973970.33 frames.], batch size: 15, lr: 3.87e-04
2022-05-05 05:36:41,017 INFO [train.py:715] (2/8) Epoch 5, batch 13700, loss[loss=0.1427, simple_loss=0.2135, pruned_loss=0.03593, over 4969.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2233, pruned_loss=0.04101, over 973305.57 frames.], batch size: 14, lr: 3.87e-04
2022-05-05 05:37:19,074 INFO [train.py:715] (2/8) Epoch 5, batch 13750, loss[loss=0.1399, simple_loss=0.2116, pruned_loss=0.03404, over 4786.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2236, pruned_loss=0.04139, over 973130.73 frames.], batch size: 17, lr: 3.87e-04
2022-05-05 05:37:56,881 INFO [train.py:715] (2/8) Epoch 5, batch 13800, loss[loss=0.1543, simple_loss=0.2179, pruned_loss=0.04535, over 4840.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2238, pruned_loss=0.04162, over 973630.43 frames.], batch size: 20, lr: 3.87e-04
2022-05-05 05:38:35,345 INFO [train.py:715] (2/8) Epoch 5, batch 13850, loss[loss=0.1614, simple_loss=0.2281, pruned_loss=0.04742, over 4975.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2234, pruned_loss=0.04151, over 973263.18 frames.], batch size: 35, lr: 3.87e-04
2022-05-05 05:39:13,571 INFO [train.py:715] (2/8) Epoch 5, batch 13900, loss[loss=0.1599, simple_loss=0.2203, pruned_loss=0.04974, over 4801.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2232, pruned_loss=0.04131, over 972455.44 frames.], batch size: 25, lr: 3.87e-04
2022-05-05 05:39:51,056 INFO [train.py:715] (2/8) Epoch 5, batch 13950, loss[loss=0.178, simple_loss=0.2497, pruned_loss=0.0531, over 4897.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2227, pruned_loss=0.04046, over 972444.11 frames.], batch size: 22, lr: 3.87e-04
2022-05-05 05:40:29,788 INFO [train.py:715] (2/8) Epoch 5, batch 14000, loss[loss=0.1335, simple_loss=0.209, pruned_loss=0.029, over 4751.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.04099, over 972662.73 frames.], batch size: 19, lr: 3.87e-04
2022-05-05 05:41:07,816 INFO [train.py:715] (2/8) Epoch 5, batch 14050, loss[loss=0.1545, simple_loss=0.2301, pruned_loss=0.0395, over 4912.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2232, pruned_loss=0.04097, over 972408.84 frames.], batch size: 18, lr: 3.87e-04
2022-05-05 05:41:45,577 INFO [train.py:715] (2/8) Epoch 5, batch 14100, loss[loss=0.14, simple_loss=0.2212, pruned_loss=0.02934, over 4992.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2235, pruned_loss=0.04073, over 972923.32 frames.], batch size: 14, lr: 3.86e-04
2022-05-05 05:42:23,455 INFO [train.py:715] (2/8) Epoch 5, batch 14150, loss[loss=0.1513, simple_loss=0.2088, pruned_loss=0.04692, over 4973.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2233, pruned_loss=0.04102, over 973078.13 frames.], batch size: 15, lr: 3.86e-04
2022-05-05 05:43:01,802 INFO [train.py:715] (2/8) Epoch 5, batch 14200, loss[loss=0.1428, simple_loss=0.2117, pruned_loss=0.03698, over 4767.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2235, pruned_loss=0.04117, over 972474.85 frames.], batch size: 17, lr: 3.86e-04
2022-05-05 05:43:40,051 INFO [train.py:715] (2/8) Epoch 5, batch 14250, loss[loss=0.1759, simple_loss=0.2437, pruned_loss=0.054, over 4694.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2245, pruned_loss=0.04152, over 972216.03 frames.], batch size: 15, lr: 3.86e-04
2022-05-05 05:44:18,051 INFO [train.py:715] (2/8) Epoch 5, batch 14300, loss[loss=0.1612, simple_loss=0.227, pruned_loss=0.04773, over 4976.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2244, pruned_loss=0.04142, over 972533.74 frames.], batch size: 39, lr: 3.86e-04
2022-05-05 05:44:56,439 INFO [train.py:715] (2/8) Epoch 5, batch 14350, loss[loss=0.1485, simple_loss=0.2332, pruned_loss=0.03191, over 4932.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2256, pruned_loss=0.04194, over 972584.67 frames.], batch size: 29, lr: 3.86e-04
2022-05-05 05:45:34,231 INFO [train.py:715] (2/8) Epoch 5, batch 14400, loss[loss=0.1259, simple_loss=0.2027, pruned_loss=0.02459, over 4882.00 frames.], tot_loss[loss=0.1544, simple_loss=0.225, pruned_loss=0.04187, over 972541.93 frames.], batch size: 16, lr: 3.86e-04
2022-05-05 05:46:11,864 INFO [train.py:715] (2/8) Epoch 5, batch 14450, loss[loss=0.1733, simple_loss=0.2495, pruned_loss=0.04857, over 4984.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2254, pruned_loss=0.04206, over 972528.71 frames.], batch size: 28, lr: 3.86e-04
2022-05-05 05:46:49,661 INFO [train.py:715] (2/8) Epoch 5, batch 14500, loss[loss=0.1655, simple_loss=0.2296, pruned_loss=0.05073, over 4964.00 frames.], tot_loss[loss=0.155, simple_loss=0.2254, pruned_loss=0.04233, over 973002.69 frames.], batch size: 15, lr: 3.86e-04
2022-05-05 05:47:28,000 INFO [train.py:715] (2/8) Epoch 5, batch 14550, loss[loss=0.1782, simple_loss=0.257, pruned_loss=0.04969, over 4768.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2251, pruned_loss=0.04214, over 973197.34 frames.], batch size: 17, lr: 3.86e-04
2022-05-05 05:48:06,093 INFO [train.py:715] (2/8) Epoch 5, batch 14600, loss[loss=0.1853, simple_loss=0.2344, pruned_loss=0.06816, over 4691.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2258, pruned_loss=0.04271, over 972656.24 frames.], batch size: 15, lr: 3.86e-04
2022-05-05 05:48:44,026 INFO [train.py:715] (2/8) Epoch 5, batch 14650, loss[loss=0.1412, simple_loss=0.2227, pruned_loss=0.02984, over 4975.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2246, pruned_loss=0.04192, over 972909.80 frames.], batch size: 28, lr: 3.86e-04
2022-05-05 05:49:22,273 INFO [train.py:715] (2/8) Epoch 5, batch 14700, loss[loss=0.1533, simple_loss=0.2181, pruned_loss=0.04421, over 4737.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2244, pruned_loss=0.04197, over 972331.96 frames.], batch size: 16, lr: 3.86e-04
2022-05-05 05:49:59,646 INFO [train.py:715] (2/8) Epoch 5, batch 14750, loss[loss=0.1396, simple_loss=0.2066, pruned_loss=0.0363, over 4944.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2236, pruned_loss=0.04156, over 972681.68 frames.], batch size: 35, lr: 3.86e-04
2022-05-05 05:50:37,677 INFO [train.py:715] (2/8) Epoch 5, batch 14800, loss[loss=0.1345, simple_loss=0.2102, pruned_loss=0.02947, over 4920.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2229, pruned_loss=0.04102, over 972150.97 frames.], batch size: 18, lr: 3.86e-04
2022-05-05 05:51:15,493 INFO [train.py:715] (2/8) Epoch 5, batch 14850, loss[loss=0.1584, simple_loss=0.2251, pruned_loss=0.04581, over 4958.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2228, pruned_loss=0.04103, over 972106.81 frames.], batch size: 21, lr: 3.86e-04
2022-05-05 05:51:54,090 INFO [train.py:715] (2/8) Epoch 5, batch 14900, loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.0322, over 4897.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.04106, over 971298.47 frames.], batch size: 19, lr: 3.86e-04
2022-05-05 05:52:32,750 INFO [train.py:715] (2/8) Epoch 5, batch 14950, loss[loss=0.1275, simple_loss=0.2018, pruned_loss=0.02663, over 4929.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2228, pruned_loss=0.04125, over 971872.49 frames.], batch size: 23, lr: 3.86e-04
2022-05-05 05:53:10,810 INFO [train.py:715] (2/8) Epoch 5, batch 15000, loss[loss=0.1424, simple_loss=0.2174, pruned_loss=0.03366, over 4812.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2231, pruned_loss=0.04118, over 971739.28 frames.], batch size: 13, lr: 3.86e-04
2022-05-05 05:53:10,811 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 05:53:21,083 INFO [train.py:742] (2/8) 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] (2/8) Epoch 5, batch 15050, loss[loss=0.1708, simple_loss=0.2504, pruned_loss=0.04558, over 4872.00 frames.], tot_loss[loss=0.1526, simple_loss=0.223, pruned_loss=0.04112, over 971488.58 frames.], batch size: 22, lr: 3.85e-04
2022-05-05 05:54:37,214 INFO [train.py:715] (2/8) Epoch 5, batch 15100, loss[loss=0.1412, simple_loss=0.2089, pruned_loss=0.03677, over 4869.00 frames.], tot_loss[loss=0.153, simple_loss=0.2233, pruned_loss=0.04138, over 971604.90 frames.], batch size: 16, lr: 3.85e-04
2022-05-05 05:55:15,136 INFO [train.py:715] (2/8) Epoch 5, batch 15150, loss[loss=0.1187, simple_loss=0.194, pruned_loss=0.02171, over 4771.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2235, pruned_loss=0.0415, over 971552.90 frames.], batch size: 17, lr: 3.85e-04
2022-05-05 05:55:53,271 INFO [train.py:715] (2/8) Epoch 5, batch 15200, loss[loss=0.1678, simple_loss=0.2314, pruned_loss=0.05207, over 4916.00 frames.], tot_loss[loss=0.154, simple_loss=0.2237, pruned_loss=0.04219, over 972919.60 frames.], batch size: 18, lr: 3.85e-04
2022-05-05 05:56:32,188 INFO [train.py:715] (2/8) Epoch 5, batch 15250, loss[loss=0.1509, simple_loss=0.2241, pruned_loss=0.03886, over 4823.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2238, pruned_loss=0.04174, over 973021.41 frames.], batch size: 26, lr: 3.85e-04
2022-05-05 05:57:10,899 INFO [train.py:715] (2/8) Epoch 5, batch 15300, loss[loss=0.1575, simple_loss=0.2279, pruned_loss=0.04355, over 4896.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2239, pruned_loss=0.04199, over 972786.58 frames.], batch size: 19, lr: 3.85e-04
2022-05-05 05:57:50,140 INFO [train.py:715] (2/8) Epoch 5, batch 15350, loss[loss=0.1687, simple_loss=0.2423, pruned_loss=0.04753, over 4829.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2239, pruned_loss=0.04131, over 972852.67 frames.], batch size: 15, lr: 3.85e-04
2022-05-05 05:58:28,475 INFO [train.py:715] (2/8) Epoch 5, batch 15400, loss[loss=0.1783, simple_loss=0.2512, pruned_loss=0.05275, over 4863.00 frames.], tot_loss[loss=0.1533, simple_loss=0.224, pruned_loss=0.04128, over 973101.33 frames.], batch size: 16, lr: 3.85e-04
2022-05-05 05:59:07,520 INFO [train.py:715] (2/8) Epoch 5, batch 15450, loss[loss=0.1553, simple_loss=0.2248, pruned_loss=0.04288, over 4763.00 frames.], tot_loss[loss=0.1543, simple_loss=0.225, pruned_loss=0.04179, over 973218.05 frames.], batch size: 16, lr: 3.85e-04
2022-05-05 05:59:46,049 INFO [train.py:715] (2/8) Epoch 5, batch 15500, loss[loss=0.1381, simple_loss=0.2122, pruned_loss=0.03196, over 4949.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2249, pruned_loss=0.042, over 973073.47 frames.], batch size: 35, lr: 3.85e-04
2022-05-05 06:00:25,317 INFO [train.py:715] (2/8) Epoch 5, batch 15550, loss[loss=0.153, simple_loss=0.2249, pruned_loss=0.0406, over 4889.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2243, pruned_loss=0.04137, over 973326.76 frames.], batch size: 19, lr: 3.85e-04
2022-05-05 06:01:03,325 INFO [train.py:715] (2/8) Epoch 5, batch 15600, loss[loss=0.1983, simple_loss=0.2616, pruned_loss=0.0675, over 4805.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2244, pruned_loss=0.04138, over 973248.19 frames.], batch size: 14, lr: 3.85e-04
2022-05-05 06:01:40,924 INFO [train.py:715] (2/8) Epoch 5, batch 15650, loss[loss=0.1454, simple_loss=0.2133, pruned_loss=0.03869, over 4825.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2236, pruned_loss=0.04074, over 972743.95 frames.], batch size: 15, lr: 3.85e-04
2022-05-05 06:02:18,447 INFO [train.py:715] (2/8) Epoch 5, batch 15700, loss[loss=0.1748, simple_loss=0.2374, pruned_loss=0.05614, over 4846.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2239, pruned_loss=0.04118, over 972452.56 frames.], batch size: 32, lr: 3.85e-04
2022-05-05 06:02:56,464 INFO [train.py:715] (2/8) Epoch 5, batch 15750, loss[loss=0.182, simple_loss=0.2359, pruned_loss=0.06408, over 4810.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2239, pruned_loss=0.04154, over 971739.96 frames.], batch size: 21, lr: 3.85e-04
2022-05-05 06:03:34,888 INFO [train.py:715] (2/8) Epoch 5, batch 15800, loss[loss=0.1525, simple_loss=0.2209, pruned_loss=0.04207, over 4972.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2247, pruned_loss=0.0421, over 971597.76 frames.], batch size: 15, lr: 3.85e-04
2022-05-05 06:04:12,956 INFO [train.py:715] (2/8) Epoch 5, batch 15850, loss[loss=0.137, simple_loss=0.21, pruned_loss=0.03196, over 4908.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2236, pruned_loss=0.04153, over 972100.51 frames.], batch size: 19, lr: 3.85e-04
2022-05-05 06:04:50,530 INFO [train.py:715] (2/8) Epoch 5, batch 15900, loss[loss=0.1547, simple_loss=0.2258, pruned_loss=0.04179, over 4889.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2247, pruned_loss=0.04183, over 971775.53 frames.], batch size: 22, lr: 3.85e-04
2022-05-05 06:05:28,344 INFO [train.py:715] (2/8) Epoch 5, batch 15950, loss[loss=0.1596, simple_loss=0.2462, pruned_loss=0.03645, over 4852.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2242, pruned_loss=0.04131, over 971979.22 frames.], batch size: 20, lr: 3.85e-04
2022-05-05 06:06:05,813 INFO [train.py:715] (2/8) Epoch 5, batch 16000, loss[loss=0.1439, simple_loss=0.2079, pruned_loss=0.03991, over 4690.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2237, pruned_loss=0.04128, over 971962.92 frames.], batch size: 15, lr: 3.85e-04
2022-05-05 06:06:43,535 INFO [train.py:715] (2/8) Epoch 5, batch 16050, loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02812, over 4940.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2233, pruned_loss=0.04107, over 972228.59 frames.], batch size: 29, lr: 3.84e-04
2022-05-05 06:07:21,618 INFO [train.py:715] (2/8) Epoch 5, batch 16100, loss[loss=0.1426, simple_loss=0.2051, pruned_loss=0.04004, over 4701.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2232, pruned_loss=0.04102, over 972586.03 frames.], batch size: 15, lr: 3.84e-04
2022-05-05 06:08:00,791 INFO [train.py:715] (2/8) Epoch 5, batch 16150, loss[loss=0.1276, simple_loss=0.189, pruned_loss=0.03312, over 4975.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2228, pruned_loss=0.0412, over 972744.62 frames.], batch size: 15, lr: 3.84e-04
2022-05-05 06:08:39,746 INFO [train.py:715] (2/8) Epoch 5, batch 16200, loss[loss=0.1567, simple_loss=0.2251, pruned_loss=0.04414, over 4834.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2221, pruned_loss=0.04066, over 971768.04 frames.], batch size: 15, lr: 3.84e-04
2022-05-05 06:09:18,292 INFO [train.py:715] (2/8) Epoch 5, batch 16250, loss[loss=0.1464, simple_loss=0.2298, pruned_loss=0.03151, over 4926.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2231, pruned_loss=0.04098, over 971674.33 frames.], batch size: 23, lr: 3.84e-04
2022-05-05 06:09:56,100 INFO [train.py:715] (2/8) Epoch 5, batch 16300, loss[loss=0.1318, simple_loss=0.2117, pruned_loss=0.02593, over 4825.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2228, pruned_loss=0.04075, over 972398.24 frames.], batch size: 26, lr: 3.84e-04
2022-05-05 06:10:34,112 INFO [train.py:715] (2/8) Epoch 5, batch 16350, loss[loss=0.1241, simple_loss=0.193, pruned_loss=0.02762, over 4786.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2238, pruned_loss=0.04082, over 973068.45 frames.], batch size: 12, lr: 3.84e-04
2022-05-05 06:11:12,511 INFO [train.py:715] (2/8) Epoch 5, batch 16400, loss[loss=0.1373, simple_loss=0.2235, pruned_loss=0.02552, over 4743.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2223, pruned_loss=0.03982, over 972435.50 frames.], batch size: 19, lr: 3.84e-04
2022-05-05 06:11:50,966 INFO [train.py:715] (2/8) Epoch 5, batch 16450, loss[loss=0.1278, simple_loss=0.202, pruned_loss=0.02681, over 4777.00 frames.], tot_loss[loss=0.1512, simple_loss=0.222, pruned_loss=0.04024, over 972172.45 frames.], batch size: 18, lr: 3.84e-04
2022-05-05 06:12:30,318 INFO [train.py:715] (2/8) Epoch 5, batch 16500, loss[loss=0.1591, simple_loss=0.2379, pruned_loss=0.04012, over 4789.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2219, pruned_loss=0.0401, over 971510.60 frames.], batch size: 14, lr: 3.84e-04
2022-05-05 06:13:08,237 INFO [train.py:715] (2/8) Epoch 5, batch 16550, loss[loss=0.1679, simple_loss=0.2323, pruned_loss=0.05171, over 4944.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2227, pruned_loss=0.04029, over 970775.37 frames.], batch size: 23, lr: 3.84e-04
2022-05-05 06:13:46,922 INFO [train.py:715] (2/8) Epoch 5, batch 16600, loss[loss=0.1309, simple_loss=0.2102, pruned_loss=0.02575, over 4958.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2229, pruned_loss=0.04033, over 970855.44 frames.], batch size: 21, lr: 3.84e-04
2022-05-05 06:14:25,637 INFO [train.py:715] (2/8) Epoch 5, batch 16650, loss[loss=0.1915, simple_loss=0.2556, pruned_loss=0.06374, over 4858.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2237, pruned_loss=0.04054, over 970818.29 frames.], batch size: 34, lr: 3.84e-04
2022-05-05 06:15:04,313 INFO [train.py:715] (2/8) Epoch 5, batch 16700, loss[loss=0.1377, simple_loss=0.2151, pruned_loss=0.0301, over 4813.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2231, pruned_loss=0.04059, over 970821.64 frames.], batch size: 25, lr: 3.84e-04
2022-05-05 06:15:42,502 INFO [train.py:715] (2/8) Epoch 5, batch 16750, loss[loss=0.1331, simple_loss=0.2027, pruned_loss=0.03175, over 4827.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2227, pruned_loss=0.04036, over 971625.25 frames.], batch size: 13, lr: 3.84e-04
2022-05-05 06:16:20,952 INFO [train.py:715] (2/8) Epoch 5, batch 16800, loss[loss=0.1325, simple_loss=0.2062, pruned_loss=0.02936, over 4826.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.04055, over 970451.64 frames.], batch size: 25, lr: 3.84e-04
2022-05-05 06:17:00,086 INFO [train.py:715] (2/8) Epoch 5, batch 16850, loss[loss=0.1434, simple_loss=0.2177, pruned_loss=0.03459, over 4884.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2234, pruned_loss=0.04075, over 970811.92 frames.], batch size: 22, lr: 3.84e-04
2022-05-05 06:17:37,946 INFO [train.py:715] (2/8) Epoch 5, batch 16900, loss[loss=0.1648, simple_loss=0.2234, pruned_loss=0.05316, over 4979.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2243, pruned_loss=0.04122, over 972138.15 frames.], batch size: 14, lr: 3.84e-04
2022-05-05 06:18:16,774 INFO [train.py:715] (2/8) Epoch 5, batch 16950, loss[loss=0.1552, simple_loss=0.2172, pruned_loss=0.04665, over 4750.00 frames.], tot_loss[loss=0.153, simple_loss=0.2239, pruned_loss=0.04105, over 971646.89 frames.], batch size: 16, lr: 3.84e-04
2022-05-05 06:18:55,177 INFO [train.py:715] (2/8) Epoch 5, batch 17000, loss[loss=0.1282, simple_loss=0.2071, pruned_loss=0.02466, over 4844.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2241, pruned_loss=0.04101, over 971659.31 frames.], batch size: 15, lr: 3.84e-04
2022-05-05 06:19:33,566 INFO [train.py:715] (2/8) Epoch 5, batch 17050, loss[loss=0.1482, simple_loss=0.2143, pruned_loss=0.04102, over 4851.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2234, pruned_loss=0.0412, over 972207.65 frames.], batch size: 20, lr: 3.83e-04
2022-05-05 06:20:11,961 INFO [train.py:715] (2/8) Epoch 5, batch 17100, loss[loss=0.1835, simple_loss=0.2568, pruned_loss=0.05504, over 4819.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2234, pruned_loss=0.04124, over 972326.82 frames.], batch size: 15, lr: 3.83e-04
2022-05-05 06:20:49,750 INFO [train.py:715] (2/8) Epoch 5, batch 17150, loss[loss=0.1351, simple_loss=0.2175, pruned_loss=0.02637, over 4765.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2234, pruned_loss=0.04143, over 972666.95 frames.], batch size: 19, lr: 3.83e-04
2022-05-05 06:21:27,632 INFO [train.py:715] (2/8) Epoch 5, batch 17200, loss[loss=0.1541, simple_loss=0.2298, pruned_loss=0.03921, over 4904.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2244, pruned_loss=0.04219, over 972957.42 frames.], batch size: 18, lr: 3.83e-04
2022-05-05 06:22:04,737 INFO [train.py:715] (2/8) Epoch 5, batch 17250, loss[loss=0.1423, simple_loss=0.2166, pruned_loss=0.034, over 4966.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2238, pruned_loss=0.0414, over 971641.96 frames.], batch size: 28, lr: 3.83e-04
2022-05-05 06:22:42,971 INFO [train.py:715] (2/8) Epoch 5, batch 17300, loss[loss=0.1456, simple_loss=0.223, pruned_loss=0.03412, over 4981.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2245, pruned_loss=0.04146, over 972174.54 frames.], batch size: 25, lr: 3.83e-04
2022-05-05 06:23:22,499 INFO [train.py:715] (2/8) Epoch 5, batch 17350, loss[loss=0.1556, simple_loss=0.2172, pruned_loss=0.04705, over 4853.00 frames.], tot_loss[loss=0.153, simple_loss=0.2239, pruned_loss=0.04107, over 972410.65 frames.], batch size: 13, lr: 3.83e-04
2022-05-05 06:24:00,883 INFO [train.py:715] (2/8) Epoch 5, batch 17400, loss[loss=0.1751, simple_loss=0.2342, pruned_loss=0.05802, over 4779.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2239, pruned_loss=0.04131, over 972457.31 frames.], batch size: 14, lr: 3.83e-04
2022-05-05 06:24:39,482 INFO [train.py:715] (2/8) Epoch 5, batch 17450, loss[loss=0.1498, simple_loss=0.2052, pruned_loss=0.04723, over 4916.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2235, pruned_loss=0.04111, over 972796.41 frames.], batch size: 18, lr: 3.83e-04
2022-05-05 06:25:17,956 INFO [train.py:715] (2/8) Epoch 5, batch 17500, loss[loss=0.165, simple_loss=0.2454, pruned_loss=0.04226, over 4781.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2234, pruned_loss=0.04099, over 972713.52 frames.], batch size: 18, lr: 3.83e-04
2022-05-05 06:25:56,807 INFO [train.py:715] (2/8) Epoch 5, batch 17550, loss[loss=0.1334, simple_loss=0.1995, pruned_loss=0.03363, over 4968.00 frames.], tot_loss[loss=0.1523, simple_loss=0.223, pruned_loss=0.04079, over 971902.02 frames.], batch size: 15, lr: 3.83e-04
2022-05-05 06:26:35,442 INFO [train.py:715] (2/8) Epoch 5, batch 17600, loss[loss=0.1538, simple_loss=0.2401, pruned_loss=0.03371, over 4978.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2228, pruned_loss=0.04069, over 971736.18 frames.], batch size: 24, lr: 3.83e-04
2022-05-05 06:27:14,153 INFO [train.py:715] (2/8) Epoch 5, batch 17650, loss[loss=0.1576, simple_loss=0.2275, pruned_loss=0.04391, over 4920.00 frames.], tot_loss[loss=0.1525, simple_loss=0.223, pruned_loss=0.04103, over 971531.10 frames.], batch size: 29, lr: 3.83e-04
2022-05-05 06:27:52,810 INFO [train.py:715] (2/8) Epoch 5, batch 17700, loss[loss=0.196, simple_loss=0.2753, pruned_loss=0.05836, over 4840.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2235, pruned_loss=0.041, over 971196.96 frames.], batch size: 20, lr: 3.83e-04
2022-05-05 06:28:31,729 INFO [train.py:715] (2/8) Epoch 5, batch 17750, loss[loss=0.1601, simple_loss=0.2304, pruned_loss=0.04487, over 4952.00 frames.], tot_loss[loss=0.153, simple_loss=0.2236, pruned_loss=0.04123, over 972699.81 frames.], batch size: 14, lr: 3.83e-04
2022-05-05 06:29:09,754 INFO [train.py:715] (2/8) Epoch 5, batch 17800, loss[loss=0.1139, simple_loss=0.1859, pruned_loss=0.02091, over 4961.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2232, pruned_loss=0.04126, over 972444.56 frames.], batch size: 15, lr: 3.83e-04
2022-05-05 06:29:48,586 INFO [train.py:715] (2/8) Epoch 5, batch 17850, loss[loss=0.1719, simple_loss=0.2566, pruned_loss=0.0436, over 4968.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2234, pruned_loss=0.04145, over 972218.11 frames.], batch size: 24, lr: 3.83e-04
2022-05-05 06:30:27,680 INFO [train.py:715] (2/8) Epoch 5, batch 17900, loss[loss=0.2099, simple_loss=0.2875, pruned_loss=0.06616, over 4898.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2233, pruned_loss=0.04102, over 972553.05 frames.], batch size: 17, lr: 3.83e-04
2022-05-05 06:31:06,329 INFO [train.py:715] (2/8) Epoch 5, batch 17950, loss[loss=0.1772, simple_loss=0.2384, pruned_loss=0.05805, over 4868.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2233, pruned_loss=0.04148, over 973198.97 frames.], batch size: 32, lr: 3.83e-04
2022-05-05 06:31:47,056 INFO [train.py:715] (2/8) Epoch 5, batch 18000, loss[loss=0.1452, simple_loss=0.2127, pruned_loss=0.03887, over 4952.00 frames.], tot_loss[loss=0.1526, simple_loss=0.223, pruned_loss=0.04114, over 972826.77 frames.], batch size: 35, lr: 3.83e-04
2022-05-05 06:31:47,056 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 06:31:59,753 INFO [train.py:742] (2/8) Epoch 5, validation: loss=0.1102, simple_loss=0.1955, pruned_loss=0.01245, over 914524.00 frames.
2022-05-05 06:32:38,373 INFO [train.py:715] (2/8) Epoch 5, batch 18050, loss[loss=0.1348, simple_loss=0.1997, pruned_loss=0.03498, over 4965.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2234, pruned_loss=0.04139, over 972250.20 frames.], batch size: 14, lr: 3.82e-04
2022-05-05 06:33:17,598 INFO [train.py:715] (2/8) Epoch 5, batch 18100, loss[loss=0.1585, simple_loss=0.2389, pruned_loss=0.03904, over 4834.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2241, pruned_loss=0.04148, over 972135.60 frames.], batch size: 26, lr: 3.82e-04
2022-05-05 06:33:56,334 INFO [train.py:715] (2/8) Epoch 5, batch 18150, loss[loss=0.1228, simple_loss=0.2073, pruned_loss=0.0191, over 4832.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2235, pruned_loss=0.04098, over 972563.65 frames.], batch size: 12, lr: 3.82e-04
2022-05-05 06:34:34,858 INFO [train.py:715] (2/8) Epoch 5, batch 18200, loss[loss=0.1483, simple_loss=0.2202, pruned_loss=0.0382, over 4919.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2234, pruned_loss=0.04121, over 972209.22 frames.], batch size: 23, lr: 3.82e-04
2022-05-05 06:35:14,236 INFO [train.py:715] (2/8) Epoch 5, batch 18250, loss[loss=0.2255, simple_loss=0.2765, pruned_loss=0.08723, over 4912.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2237, pruned_loss=0.04124, over 972205.56 frames.], batch size: 18, lr: 3.82e-04
2022-05-05 06:35:53,136 INFO [train.py:715] (2/8) Epoch 5, batch 18300, loss[loss=0.1324, simple_loss=0.2127, pruned_loss=0.0261, over 4875.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2249, pruned_loss=0.04187, over 972252.09 frames.], batch size: 16, lr: 3.82e-04
2022-05-05 06:36:31,710 INFO [train.py:715] (2/8) Epoch 5, batch 18350, loss[loss=0.1659, simple_loss=0.2495, pruned_loss=0.04116, over 4927.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2247, pruned_loss=0.04197, over 971907.74 frames.], batch size: 29, lr: 3.82e-04
2022-05-05 06:37:09,999 INFO [train.py:715] (2/8) Epoch 5, batch 18400, loss[loss=0.1296, simple_loss=0.1984, pruned_loss=0.03043, over 4929.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2236, pruned_loss=0.04124, over 972177.79 frames.], batch size: 29, lr: 3.82e-04
2022-05-05 06:37:49,155 INFO [train.py:715] (2/8) Epoch 5, batch 18450, loss[loss=0.13, simple_loss=0.2043, pruned_loss=0.02784, over 4920.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2223, pruned_loss=0.04051, over 973011.41 frames.], batch size: 29, lr: 3.82e-04
2022-05-05 06:38:27,816 INFO [train.py:715] (2/8) Epoch 5, batch 18500, loss[loss=0.1312, simple_loss=0.2018, pruned_loss=0.03026, over 4942.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2226, pruned_loss=0.04061, over 972942.04 frames.], batch size: 23, lr: 3.82e-04
2022-05-05 06:39:06,127 INFO [train.py:715] (2/8) Epoch 5, batch 18550, loss[loss=0.1267, simple_loss=0.1901, pruned_loss=0.03167, over 4647.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2225, pruned_loss=0.04096, over 972644.99 frames.], batch size: 13, lr: 3.82e-04
2022-05-05 06:39:45,171 INFO [train.py:715] (2/8) Epoch 5, batch 18600, loss[loss=0.1321, simple_loss=0.2127, pruned_loss=0.02575, over 4840.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2216, pruned_loss=0.04058, over 972390.35 frames.], batch size: 27, lr: 3.82e-04
2022-05-05 06:40:23,780 INFO [train.py:715] (2/8) Epoch 5, batch 18650, loss[loss=0.133, simple_loss=0.2111, pruned_loss=0.0275, over 4767.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2216, pruned_loss=0.04045, over 972380.81 frames.], batch size: 19, lr: 3.82e-04
2022-05-05 06:41:01,938 INFO [train.py:715] (2/8) Epoch 5, batch 18700, loss[loss=0.1506, simple_loss=0.2302, pruned_loss=0.03551, over 4882.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2222, pruned_loss=0.0408, over 972425.93 frames.], batch size: 32, lr: 3.82e-04
2022-05-05 06:41:40,678 INFO [train.py:715] (2/8) Epoch 5, batch 18750, loss[loss=0.1446, simple_loss=0.2183, pruned_loss=0.03552, over 4965.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2225, pruned_loss=0.04084, over 972676.22 frames.], batch size: 21, lr: 3.82e-04
2022-05-05 06:42:19,956 INFO [train.py:715] (2/8) Epoch 5, batch 18800, loss[loss=0.1667, simple_loss=0.238, pruned_loss=0.04774, over 4828.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2228, pruned_loss=0.04109, over 972705.32 frames.], batch size: 15, lr: 3.82e-04
2022-05-05 06:42:59,659 INFO [train.py:715] (2/8) Epoch 5, batch 18850, loss[loss=0.1742, simple_loss=0.2331, pruned_loss=0.05761, over 4841.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2234, pruned_loss=0.04155, over 972732.21 frames.], batch size: 32, lr: 3.82e-04
2022-05-05 06:43:38,448 INFO [train.py:715] (2/8) Epoch 5, batch 18900, loss[loss=0.135, simple_loss=0.206, pruned_loss=0.03203, over 4875.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2231, pruned_loss=0.04126, over 972692.98 frames.], batch size: 32, lr: 3.82e-04
2022-05-05 06:44:16,645 INFO [train.py:715] (2/8) Epoch 5, batch 18950, loss[loss=0.1903, simple_loss=0.2528, pruned_loss=0.06393, over 4871.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2229, pruned_loss=0.0413, over 972593.53 frames.], batch size: 16, lr: 3.82e-04
2022-05-05 06:44:56,115 INFO [train.py:715] (2/8) Epoch 5, batch 19000, loss[loss=0.1196, simple_loss=0.1912, pruned_loss=0.02407, over 4789.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.04102, over 972232.23 frames.], batch size: 13, lr: 3.82e-04
2022-05-05 06:45:34,090 INFO [train.py:715] (2/8) Epoch 5, batch 19050, loss[loss=0.1552, simple_loss=0.2205, pruned_loss=0.04492, over 4801.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2223, pruned_loss=0.04091, over 973330.68 frames.], batch size: 21, lr: 3.81e-04
2022-05-05 06:46:13,049 INFO [train.py:715] (2/8) Epoch 5, batch 19100, loss[loss=0.1397, simple_loss=0.2097, pruned_loss=0.0349, over 4897.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2223, pruned_loss=0.04127, over 973076.28 frames.], batch size: 19, lr: 3.81e-04
2022-05-05 06:46:52,736 INFO [train.py:715] (2/8) Epoch 5, batch 19150, loss[loss=0.1461, simple_loss=0.226, pruned_loss=0.03314, over 4890.00 frames.], tot_loss[loss=0.1519, simple_loss=0.222, pruned_loss=0.04087, over 973405.46 frames.], batch size: 19, lr: 3.81e-04
2022-05-05 06:47:31,316 INFO [train.py:715] (2/8) Epoch 5, batch 19200, loss[loss=0.1479, simple_loss=0.2196, pruned_loss=0.03808, over 4919.00 frames.], tot_loss[loss=0.1518, simple_loss=0.222, pruned_loss=0.04084, over 974086.51 frames.], batch size: 18, lr: 3.81e-04
2022-05-05 06:48:10,846 INFO [train.py:715] (2/8) Epoch 5, batch 19250, loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03243, over 4778.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2226, pruned_loss=0.04083, over 973388.40 frames.], batch size: 17, lr: 3.81e-04
2022-05-05 06:48:48,907 INFO [train.py:715] (2/8) Epoch 5, batch 19300, loss[loss=0.1792, simple_loss=0.2657, pruned_loss=0.04633, over 4960.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2224, pruned_loss=0.04057, over 972889.67 frames.], batch size: 24, lr: 3.81e-04
2022-05-05 06:49:28,000 INFO [train.py:715] (2/8) Epoch 5, batch 19350, loss[loss=0.1579, simple_loss=0.2236, pruned_loss=0.04612, over 4958.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2223, pruned_loss=0.04032, over 972765.32 frames.], batch size: 35, lr: 3.81e-04
2022-05-05 06:50:06,760 INFO [train.py:715] (2/8) Epoch 5, batch 19400, loss[loss=0.1756, simple_loss=0.243, pruned_loss=0.05412, over 4933.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2229, pruned_loss=0.04081, over 972085.20 frames.], batch size: 21, lr: 3.81e-04
2022-05-05 06:50:45,415 INFO [train.py:715] (2/8) Epoch 5, batch 19450, loss[loss=0.1427, simple_loss=0.2114, pruned_loss=0.03701, over 4798.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2226, pruned_loss=0.04106, over 972069.10 frames.], batch size: 12, lr: 3.81e-04
2022-05-05 06:51:25,050 INFO [train.py:715] (2/8) Epoch 5, batch 19500, loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03021, over 4778.00 frames.], tot_loss[loss=0.1515, simple_loss=0.222, pruned_loss=0.04051, over 972549.94 frames.], batch size: 18, lr: 3.81e-04
2022-05-05 06:52:03,851 INFO [train.py:715] (2/8) Epoch 5, batch 19550, loss[loss=0.1495, simple_loss=0.2332, pruned_loss=0.0329, over 4763.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2236, pruned_loss=0.04133, over 972859.55 frames.], batch size: 14, lr: 3.81e-04
2022-05-05 06:52:42,737 INFO [train.py:715] (2/8) Epoch 5, batch 19600, loss[loss=0.1629, simple_loss=0.2206, pruned_loss=0.05259, over 4947.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2243, pruned_loss=0.04167, over 972527.60 frames.], batch size: 35, lr: 3.81e-04
2022-05-05 06:53:21,192 INFO [train.py:715] (2/8) Epoch 5, batch 19650, loss[loss=0.1429, simple_loss=0.2101, pruned_loss=0.03783, over 4807.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2244, pruned_loss=0.04143, over 973263.81 frames.], batch size: 21, lr: 3.81e-04
2022-05-05 06:54:00,717 INFO [train.py:715] (2/8) Epoch 5, batch 19700, loss[loss=0.1454, simple_loss=0.2256, pruned_loss=0.03259, over 4820.00 frames.], tot_loss[loss=0.153, simple_loss=0.2237, pruned_loss=0.04115, over 972309.21 frames.], batch size: 25, lr: 3.81e-04
2022-05-05 06:54:39,905 INFO [train.py:715] (2/8) Epoch 5, batch 19750, loss[loss=0.1657, simple_loss=0.2268, pruned_loss=0.05227, over 4980.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2233, pruned_loss=0.04075, over 973048.20 frames.], batch size: 39, lr: 3.81e-04
2022-05-05 06:55:17,846 INFO [train.py:715] (2/8) Epoch 5, batch 19800, loss[loss=0.1583, simple_loss=0.2196, pruned_loss=0.04847, over 4858.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2238, pruned_loss=0.0412, over 973007.92 frames.], batch size: 20, lr: 3.81e-04
2022-05-05 06:55:56,846 INFO [train.py:715] (2/8) Epoch 5, batch 19850, loss[loss=0.1299, simple_loss=0.2038, pruned_loss=0.02799, over 4797.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2239, pruned_loss=0.04132, over 971794.87 frames.], batch size: 21, lr: 3.81e-04
2022-05-05 06:56:35,744 INFO [train.py:715] (2/8) Epoch 5, batch 19900, loss[loss=0.1568, simple_loss=0.2329, pruned_loss=0.04033, over 4919.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.04099, over 971760.21 frames.], batch size: 18, lr: 3.81e-04
2022-05-05 06:57:14,679 INFO [train.py:715] (2/8) Epoch 5, batch 19950, loss[loss=0.1636, simple_loss=0.2393, pruned_loss=0.0439, over 4958.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.04105, over 971692.86 frames.], batch size: 39, lr: 3.81e-04
2022-05-05 06:57:53,093 INFO [train.py:715] (2/8) Epoch 5, batch 20000, loss[loss=0.1591, simple_loss=0.2267, pruned_loss=0.04579, over 4890.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2221, pruned_loss=0.0406, over 972356.94 frames.], batch size: 17, lr: 3.81e-04
2022-05-05 06:58:32,599 INFO [train.py:715] (2/8) Epoch 5, batch 20050, loss[loss=0.1202, simple_loss=0.1929, pruned_loss=0.02379, over 4706.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2223, pruned_loss=0.04091, over 972244.16 frames.], batch size: 15, lr: 3.81e-04
2022-05-05 06:59:12,132 INFO [train.py:715] (2/8) Epoch 5, batch 20100, loss[loss=0.1727, simple_loss=0.247, pruned_loss=0.04917, over 4983.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2231, pruned_loss=0.0413, over 972050.14 frames.], batch size: 28, lr: 3.80e-04
2022-05-05 06:59:50,437 INFO [train.py:715] (2/8) Epoch 5, batch 20150, loss[loss=0.1675, simple_loss=0.2361, pruned_loss=0.04942, over 4897.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.04101, over 972531.17 frames.], batch size: 38, lr: 3.80e-04
2022-05-05 07:00:30,258 INFO [train.py:715] (2/8) Epoch 5, batch 20200, loss[loss=0.1527, simple_loss=0.2273, pruned_loss=0.03903, over 4796.00 frames.], tot_loss[loss=0.151, simple_loss=0.2218, pruned_loss=0.04012, over 972821.74 frames.], batch size: 24, lr: 3.80e-04
2022-05-05 07:01:09,278 INFO [train.py:715] (2/8) Epoch 5, batch 20250, loss[loss=0.1876, simple_loss=0.2502, pruned_loss=0.06244, over 4984.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2214, pruned_loss=0.03993, over 973940.44 frames.], batch size: 25, lr: 3.80e-04
2022-05-05 07:01:47,790 INFO [train.py:715] (2/8) Epoch 5, batch 20300, loss[loss=0.1403, simple_loss=0.2156, pruned_loss=0.03247, over 4819.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2223, pruned_loss=0.0409, over 973112.78 frames.], batch size: 26, lr: 3.80e-04
2022-05-05 07:02:25,751 INFO [train.py:715] (2/8) Epoch 5, batch 20350, loss[loss=0.1558, simple_loss=0.2206, pruned_loss=0.04545, over 4963.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2222, pruned_loss=0.04097, over 972784.34 frames.], batch size: 35, lr: 3.80e-04
2022-05-05 07:03:04,302 INFO [train.py:715] (2/8) Epoch 5, batch 20400, loss[loss=0.1428, simple_loss=0.207, pruned_loss=0.03927, over 4811.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2237, pruned_loss=0.04192, over 972330.67 frames.], batch size: 24, lr: 3.80e-04
2022-05-05 07:03:43,174 INFO [train.py:715] (2/8) Epoch 5, batch 20450, loss[loss=0.1467, simple_loss=0.219, pruned_loss=0.03722, over 4831.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2234, pruned_loss=0.0416, over 972563.36 frames.], batch size: 15, lr: 3.80e-04
2022-05-05 07:04:21,315 INFO [train.py:715] (2/8) Epoch 5, batch 20500, loss[loss=0.1418, simple_loss=0.2176, pruned_loss=0.03301, over 4830.00 frames.], tot_loss[loss=0.153, simple_loss=0.2231, pruned_loss=0.04146, over 972488.52 frames.], batch size: 25, lr: 3.80e-04
2022-05-05 07:05:00,717 INFO [train.py:715] (2/8) Epoch 5, batch 20550, loss[loss=0.1327, simple_loss=0.2106, pruned_loss=0.02742, over 4812.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2226, pruned_loss=0.04087, over 973099.94 frames.], batch size: 25, lr: 3.80e-04
2022-05-05 07:05:39,987 INFO [train.py:715] (2/8) Epoch 5, batch 20600, loss[loss=0.146, simple_loss=0.2157, pruned_loss=0.03812, over 4990.00 frames.], tot_loss[loss=0.1518, simple_loss=0.222, pruned_loss=0.0408, over 972596.73 frames.], batch size: 14, lr: 3.80e-04
2022-05-05 07:06:18,975 INFO [train.py:715] (2/8) Epoch 5, batch 20650, loss[loss=0.1865, simple_loss=0.2365, pruned_loss=0.06831, over 4693.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2223, pruned_loss=0.04062, over 971984.73 frames.], batch size: 15, lr: 3.80e-04
2022-05-05 07:06:58,195 INFO [train.py:715] (2/8) Epoch 5, batch 20700, loss[loss=0.1486, simple_loss=0.2241, pruned_loss=0.03661, over 4862.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2214, pruned_loss=0.03978, over 971967.51 frames.], batch size: 22, lr: 3.80e-04
2022-05-05 07:07:36,951 INFO [train.py:715] (2/8) Epoch 5, batch 20750, loss[loss=0.1557, simple_loss=0.2192, pruned_loss=0.04607, over 4963.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2211, pruned_loss=0.03992, over 971182.05 frames.], batch size: 35, lr: 3.80e-04
2022-05-05 07:08:16,400 INFO [train.py:715] (2/8) Epoch 5, batch 20800, loss[loss=0.169, simple_loss=0.2361, pruned_loss=0.05091, over 4824.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2227, pruned_loss=0.04096, over 971497.85 frames.], batch size: 27, lr: 3.80e-04
2022-05-05 07:08:55,024 INFO [train.py:715] (2/8) Epoch 5, batch 20850, loss[loss=0.1396, simple_loss=0.218, pruned_loss=0.03054, over 4953.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2228, pruned_loss=0.04096, over 971434.63 frames.], batch size: 29, lr: 3.80e-04
2022-05-05 07:09:34,328 INFO [train.py:715] (2/8) Epoch 5, batch 20900, loss[loss=0.1214, simple_loss=0.1842, pruned_loss=0.02932, over 4812.00 frames.], tot_loss[loss=0.1516, simple_loss=0.222, pruned_loss=0.04057, over 972250.25 frames.], batch size: 12, lr: 3.80e-04
2022-05-05 07:10:12,904 INFO [train.py:715] (2/8) Epoch 5, batch 20950, loss[loss=0.148, simple_loss=0.2265, pruned_loss=0.03476, over 4977.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2219, pruned_loss=0.03995, over 972166.53 frames.], batch size: 24, lr: 3.80e-04
2022-05-05 07:10:51,487 INFO [train.py:715] (2/8) Epoch 5, batch 21000, loss[loss=0.1521, simple_loss=0.2199, pruned_loss=0.04212, over 4951.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2215, pruned_loss=0.04045, over 971846.31 frames.], batch size: 35, lr: 3.80e-04
2022-05-05 07:10:51,487 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 07:11:01,470 INFO [train.py:742] (2/8) 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] (2/8) Epoch 5, batch 21050, loss[loss=0.1552, simple_loss=0.2241, pruned_loss=0.04313, over 4826.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2228, pruned_loss=0.04103, over 972514.79 frames.], batch size: 26, lr: 3.80e-04
2022-05-05 07:12:19,700 INFO [train.py:715] (2/8) Epoch 5, batch 21100, loss[loss=0.1447, simple_loss=0.2131, pruned_loss=0.03818, over 4967.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2228, pruned_loss=0.04068, over 972612.79 frames.], batch size: 35, lr: 3.79e-04
2022-05-05 07:12:58,338 INFO [train.py:715] (2/8) Epoch 5, batch 21150, loss[loss=0.1638, simple_loss=0.2211, pruned_loss=0.05322, over 4976.00 frames.], tot_loss[loss=0.152, simple_loss=0.2224, pruned_loss=0.04083, over 973123.26 frames.], batch size: 35, lr: 3.79e-04
2022-05-05 07:13:37,167 INFO [train.py:715] (2/8) Epoch 5, batch 21200, loss[loss=0.1394, simple_loss=0.2161, pruned_loss=0.03138, over 4969.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2222, pruned_loss=0.04063, over 973300.25 frames.], batch size: 15, lr: 3.79e-04
2022-05-05 07:14:15,843 INFO [train.py:715] (2/8) Epoch 5, batch 21250, loss[loss=0.1528, simple_loss=0.2247, pruned_loss=0.04044, over 4966.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2219, pruned_loss=0.04041, over 972932.39 frames.], batch size: 35, lr: 3.79e-04
2022-05-05 07:14:54,663 INFO [train.py:715] (2/8) Epoch 5, batch 21300, loss[loss=0.1653, simple_loss=0.2345, pruned_loss=0.04803, over 4796.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2232, pruned_loss=0.04095, over 972858.05 frames.], batch size: 17, lr: 3.79e-04
2022-05-05 07:15:33,358 INFO [train.py:715] (2/8) Epoch 5, batch 21350, loss[loss=0.1214, simple_loss=0.1972, pruned_loss=0.02286, over 4935.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2221, pruned_loss=0.04033, over 973522.88 frames.], batch size: 29, lr: 3.79e-04
2022-05-05 07:16:11,916 INFO [train.py:715] (2/8) Epoch 5, batch 21400, loss[loss=0.1705, simple_loss=0.239, pruned_loss=0.05098, over 4956.00 frames.], tot_loss[loss=0.152, simple_loss=0.2228, pruned_loss=0.04064, over 973241.08 frames.], batch size: 14, lr: 3.79e-04
2022-05-05 07:16:50,973 INFO [train.py:715] (2/8) Epoch 5, batch 21450, loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02921, over 4943.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.04047, over 973477.97 frames.], batch size: 29, lr: 3.79e-04
2022-05-05 07:17:29,100 INFO [train.py:715] (2/8) Epoch 5, batch 21500, loss[loss=0.1572, simple_loss=0.2244, pruned_loss=0.04496, over 4825.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2226, pruned_loss=0.04057, over 973201.49 frames.], batch size: 25, lr: 3.79e-04
2022-05-05 07:18:08,224 INFO [train.py:715] (2/8) Epoch 5, batch 21550, loss[loss=0.1459, simple_loss=0.2217, pruned_loss=0.03504, over 4981.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2232, pruned_loss=0.04092, over 973097.95 frames.], batch size: 31, lr: 3.79e-04
2022-05-05 07:18:46,744 INFO [train.py:715] (2/8) Epoch 5, batch 21600, loss[loss=0.1493, simple_loss=0.2223, pruned_loss=0.03811, over 4873.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2236, pruned_loss=0.04107, over 972956.15 frames.], batch size: 20, lr: 3.79e-04
2022-05-05 07:19:25,823 INFO [train.py:715] (2/8) Epoch 5, batch 21650, loss[loss=0.1371, simple_loss=0.2124, pruned_loss=0.03094, over 4941.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2224, pruned_loss=0.04074, over 971873.00 frames.], batch size: 29, lr: 3.79e-04
2022-05-05 07:20:04,070 INFO [train.py:715] (2/8) Epoch 5, batch 21700, loss[loss=0.1326, simple_loss=0.2083, pruned_loss=0.02844, over 4968.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2222, pruned_loss=0.04037, over 972200.11 frames.], batch size: 35, lr: 3.79e-04
2022-05-05 07:20:42,465 INFO [train.py:715] (2/8) Epoch 5, batch 21750, loss[loss=0.1507, simple_loss=0.2217, pruned_loss=0.03987, over 4805.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2224, pruned_loss=0.04049, over 972098.50 frames.], batch size: 25, lr: 3.79e-04
2022-05-05 07:21:20,818 INFO [train.py:715] (2/8) Epoch 5, batch 21800, loss[loss=0.1317, simple_loss=0.1993, pruned_loss=0.03205, over 4778.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2233, pruned_loss=0.04101, over 972845.89 frames.], batch size: 13, lr: 3.79e-04
2022-05-05 07:22:00,030 INFO [train.py:715] (2/8) Epoch 5, batch 21850, loss[loss=0.1578, simple_loss=0.2309, pruned_loss=0.04237, over 4784.00 frames.], tot_loss[loss=0.1529, simple_loss=0.224, pruned_loss=0.04086, over 973408.26 frames.], batch size: 18, lr: 3.79e-04
2022-05-05 07:22:38,259 INFO [train.py:715] (2/8) Epoch 5, batch 21900, loss[loss=0.1547, simple_loss=0.2214, pruned_loss=0.04403, over 4881.00 frames.], tot_loss[loss=0.1545, simple_loss=0.225, pruned_loss=0.04196, over 972660.34 frames.], batch size: 22, lr: 3.79e-04
2022-05-05 07:23:16,806 INFO [train.py:715] (2/8) Epoch 5, batch 21950, loss[loss=0.1711, simple_loss=0.2412, pruned_loss=0.05053, over 4984.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2241, pruned_loss=0.04149, over 973202.45 frames.], batch size: 25, lr: 3.79e-04
2022-05-05 07:23:55,217 INFO [train.py:715] (2/8) Epoch 5, batch 22000, loss[loss=0.1317, simple_loss=0.2045, pruned_loss=0.02948, over 4928.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2236, pruned_loss=0.04147, over 973345.18 frames.], batch size: 18, lr: 3.79e-04
2022-05-05 07:24:34,725 INFO [train.py:715] (2/8) Epoch 5, batch 22050, loss[loss=0.1586, simple_loss=0.235, pruned_loss=0.04113, over 4819.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2235, pruned_loss=0.04155, over 971822.03 frames.], batch size: 15, lr: 3.79e-04
2022-05-05 07:25:13,188 INFO [train.py:715] (2/8) Epoch 5, batch 22100, loss[loss=0.1765, simple_loss=0.2498, pruned_loss=0.05158, over 4834.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2233, pruned_loss=0.04156, over 971360.69 frames.], batch size: 15, lr: 3.79e-04
2022-05-05 07:25:52,415 INFO [train.py:715] (2/8) Epoch 5, batch 22150, loss[loss=0.1729, simple_loss=0.2417, pruned_loss=0.05208, over 4910.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2234, pruned_loss=0.04111, over 972277.13 frames.], batch size: 19, lr: 3.78e-04
2022-05-05 07:26:31,447 INFO [train.py:715] (2/8) Epoch 5, batch 22200, loss[loss=0.1584, simple_loss=0.2219, pruned_loss=0.04743, over 4970.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2231, pruned_loss=0.04082, over 972634.83 frames.], batch size: 28, lr: 3.78e-04
2022-05-05 07:27:11,167 INFO [train.py:715] (2/8) Epoch 5, batch 22250, loss[loss=0.2153, simple_loss=0.2576, pruned_loss=0.0865, over 4775.00 frames.], tot_loss[loss=0.152, simple_loss=0.2226, pruned_loss=0.04066, over 973155.94 frames.], batch size: 17, lr: 3.78e-04
2022-05-05 07:27:50,341 INFO [train.py:715] (2/8) Epoch 5, batch 22300, loss[loss=0.1503, simple_loss=0.213, pruned_loss=0.04381, over 4848.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2218, pruned_loss=0.04003, over 973207.09 frames.], batch size: 15, lr: 3.78e-04
2022-05-05 07:28:28,461 INFO [train.py:715] (2/8) Epoch 5, batch 22350, loss[loss=0.1326, simple_loss=0.2172, pruned_loss=0.02402, over 4940.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2211, pruned_loss=0.0399, over 973300.64 frames.], batch size: 23, lr: 3.78e-04
2022-05-05 07:29:06,835 INFO [train.py:715] (2/8) Epoch 5, batch 22400, loss[loss=0.1499, simple_loss=0.2238, pruned_loss=0.03802, over 4957.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2218, pruned_loss=0.04006, over 972900.88 frames.], batch size: 15, lr: 3.78e-04
2022-05-05 07:29:45,745 INFO [train.py:715] (2/8) Epoch 5, batch 22450, loss[loss=0.1294, simple_loss=0.2038, pruned_loss=0.02746, over 4768.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2223, pruned_loss=0.04021, over 972186.98 frames.], batch size: 18, lr: 3.78e-04
2022-05-05 07:30:25,211 INFO [train.py:715] (2/8) Epoch 5, batch 22500, loss[loss=0.1428, simple_loss=0.2229, pruned_loss=0.03132, over 4820.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2222, pruned_loss=0.04035, over 972350.95 frames.], batch size: 25, lr: 3.78e-04
2022-05-05 07:31:03,490 INFO [train.py:715] (2/8) Epoch 5, batch 22550, loss[loss=0.1696, simple_loss=0.2383, pruned_loss=0.05048, over 4749.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2225, pruned_loss=0.04065, over 972079.47 frames.], batch size: 16, lr: 3.78e-04
2022-05-05 07:31:42,561 INFO [train.py:715] (2/8) Epoch 5, batch 22600, loss[loss=0.1637, simple_loss=0.2424, pruned_loss=0.04251, over 4798.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2223, pruned_loss=0.04014, over 972132.39 frames.], batch size: 21, lr: 3.78e-04
2022-05-05 07:32:21,688 INFO [train.py:715] (2/8) Epoch 5, batch 22650, loss[loss=0.1382, simple_loss=0.2028, pruned_loss=0.0368, over 4806.00 frames.], tot_loss[loss=0.151, simple_loss=0.2217, pruned_loss=0.04012, over 972367.66 frames.], batch size: 25, lr: 3.78e-04
2022-05-05 07:33:00,846 INFO [train.py:715] (2/8) Epoch 5, batch 22700, loss[loss=0.149, simple_loss=0.2255, pruned_loss=0.03621, over 4826.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2225, pruned_loss=0.04028, over 972327.34 frames.], batch size: 27, lr: 3.78e-04
2022-05-05 07:33:39,169 INFO [train.py:715] (2/8) Epoch 5, batch 22750, loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03084, over 4933.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2222, pruned_loss=0.04013, over 972047.21 frames.], batch size: 23, lr: 3.78e-04
2022-05-05 07:34:18,369 INFO [train.py:715] (2/8) Epoch 5, batch 22800, loss[loss=0.1456, simple_loss=0.2248, pruned_loss=0.03321, over 4878.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2228, pruned_loss=0.04037, over 972315.20 frames.], batch size: 22, lr: 3.78e-04
2022-05-05 07:34:57,944 INFO [train.py:715] (2/8) Epoch 5, batch 22850, loss[loss=0.1252, simple_loss=0.2003, pruned_loss=0.02506, over 4824.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2231, pruned_loss=0.04069, over 972218.76 frames.], batch size: 26, lr: 3.78e-04
2022-05-05 07:35:36,340 INFO [train.py:715] (2/8) Epoch 5, batch 22900, loss[loss=0.1267, simple_loss=0.1976, pruned_loss=0.02787, over 4857.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2233, pruned_loss=0.04103, over 971722.55 frames.], batch size: 13, lr: 3.78e-04
2022-05-05 07:36:15,065 INFO [train.py:715] (2/8) Epoch 5, batch 22950, loss[loss=0.1525, simple_loss=0.2234, pruned_loss=0.04083, over 4837.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2233, pruned_loss=0.04092, over 971910.70 frames.], batch size: 30, lr: 3.78e-04
2022-05-05 07:36:54,428 INFO [train.py:715] (2/8) Epoch 5, batch 23000, loss[loss=0.207, simple_loss=0.2675, pruned_loss=0.07322, over 4985.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2229, pruned_loss=0.04074, over 971807.98 frames.], batch size: 14, lr: 3.78e-04
2022-05-05 07:37:33,568 INFO [train.py:715] (2/8) Epoch 5, batch 23050, loss[loss=0.1591, simple_loss=0.2286, pruned_loss=0.04474, over 4776.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2234, pruned_loss=0.04089, over 971866.28 frames.], batch size: 14, lr: 3.78e-04
2022-05-05 07:38:12,018 INFO [train.py:715] (2/8) Epoch 5, batch 23100, loss[loss=0.1533, simple_loss=0.2262, pruned_loss=0.04017, over 4985.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2241, pruned_loss=0.0415, over 971491.18 frames.], batch size: 28, lr: 3.78e-04
2022-05-05 07:38:51,179 INFO [train.py:715] (2/8) Epoch 5, batch 23150, loss[loss=0.1691, simple_loss=0.229, pruned_loss=0.05462, over 4898.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2227, pruned_loss=0.04074, over 971524.00 frames.], batch size: 19, lr: 3.78e-04
2022-05-05 07:39:30,786 INFO [train.py:715] (2/8) Epoch 5, batch 23200, loss[loss=0.156, simple_loss=0.2323, pruned_loss=0.03989, over 4646.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2228, pruned_loss=0.04079, over 971169.38 frames.], batch size: 13, lr: 3.77e-04
2022-05-05 07:40:09,161 INFO [train.py:715] (2/8) Epoch 5, batch 23250, loss[loss=0.1562, simple_loss=0.2282, pruned_loss=0.04207, over 4841.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2222, pruned_loss=0.04046, over 971586.92 frames.], batch size: 32, lr: 3.77e-04
2022-05-05 07:40:47,785 INFO [train.py:715] (2/8) Epoch 5, batch 23300, loss[loss=0.1606, simple_loss=0.2399, pruned_loss=0.04065, over 4881.00 frames.], tot_loss[loss=0.152, simple_loss=0.2226, pruned_loss=0.0407, over 971835.13 frames.], batch size: 22, lr: 3.77e-04
2022-05-05 07:41:27,179 INFO [train.py:715] (2/8) Epoch 5, batch 23350, loss[loss=0.153, simple_loss=0.224, pruned_loss=0.04099, over 4816.00 frames.], tot_loss[loss=0.1512, simple_loss=0.222, pruned_loss=0.0402, over 972577.59 frames.], batch size: 27, lr: 3.77e-04
2022-05-05 07:42:05,802 INFO [train.py:715] (2/8) Epoch 5, batch 23400, loss[loss=0.1508, simple_loss=0.2189, pruned_loss=0.04134, over 4830.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2224, pruned_loss=0.04068, over 972612.73 frames.], batch size: 30, lr: 3.77e-04
2022-05-05 07:42:44,246 INFO [train.py:715] (2/8) Epoch 5, batch 23450, loss[loss=0.1583, simple_loss=0.2252, pruned_loss=0.04568, over 4787.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2228, pruned_loss=0.04122, over 972446.87 frames.], batch size: 17, lr: 3.77e-04
2022-05-05 07:43:22,951 INFO [train.py:715] (2/8) Epoch 5, batch 23500, loss[loss=0.1423, simple_loss=0.2162, pruned_loss=0.03418, over 4866.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2239, pruned_loss=0.0417, over 971972.67 frames.], batch size: 16, lr: 3.77e-04
2022-05-05 07:44:02,013 INFO [train.py:715] (2/8) Epoch 5, batch 23550, loss[loss=0.1613, simple_loss=0.2305, pruned_loss=0.04601, over 4763.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2238, pruned_loss=0.04173, over 972532.67 frames.], batch size: 16, lr: 3.77e-04
2022-05-05 07:44:40,888 INFO [train.py:715] (2/8) Epoch 5, batch 23600, loss[loss=0.1822, simple_loss=0.2435, pruned_loss=0.06048, over 4923.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2244, pruned_loss=0.04222, over 972340.17 frames.], batch size: 23, lr: 3.77e-04
2022-05-05 07:45:19,393 INFO [train.py:715] (2/8) Epoch 5, batch 23650, loss[loss=0.1688, simple_loss=0.2549, pruned_loss=0.04136, over 4890.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2237, pruned_loss=0.0419, over 972861.34 frames.], batch size: 22, lr: 3.77e-04
2022-05-05 07:45:58,897 INFO [train.py:715] (2/8) Epoch 5, batch 23700, loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03908, over 4968.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2238, pruned_loss=0.04203, over 972886.67 frames.], batch size: 35, lr: 3.77e-04
2022-05-05 07:46:37,475 INFO [train.py:715] (2/8) Epoch 5, batch 23750, loss[loss=0.1859, simple_loss=0.2394, pruned_loss=0.06613, over 4794.00 frames.], tot_loss[loss=0.153, simple_loss=0.2229, pruned_loss=0.04156, over 972582.23 frames.], batch size: 12, lr: 3.77e-04
2022-05-05 07:47:16,505 INFO [train.py:715] (2/8) Epoch 5, batch 23800, loss[loss=0.1528, simple_loss=0.2238, pruned_loss=0.04091, over 4980.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2228, pruned_loss=0.04102, over 972331.64 frames.], batch size: 39, lr: 3.77e-04
2022-05-05 07:47:55,211 INFO [train.py:715] (2/8) Epoch 5, batch 23850, loss[loss=0.1674, simple_loss=0.2404, pruned_loss=0.0472, over 4900.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2233, pruned_loss=0.04127, over 972048.23 frames.], batch size: 19, lr: 3.77e-04
2022-05-05 07:48:34,419 INFO [train.py:715] (2/8) Epoch 5, batch 23900, loss[loss=0.1305, simple_loss=0.2006, pruned_loss=0.03022, over 4908.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2228, pruned_loss=0.04086, over 972535.60 frames.], batch size: 19, lr: 3.77e-04
2022-05-05 07:49:13,372 INFO [train.py:715] (2/8) Epoch 5, batch 23950, loss[loss=0.1691, simple_loss=0.236, pruned_loss=0.0511, over 4884.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.04107, over 972670.89 frames.], batch size: 16, lr: 3.77e-04
2022-05-05 07:49:51,754 INFO [train.py:715] (2/8) Epoch 5, batch 24000, loss[loss=0.138, simple_loss=0.2127, pruned_loss=0.03159, over 4781.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2229, pruned_loss=0.04094, over 973044.37 frames.], batch size: 14, lr: 3.77e-04
2022-05-05 07:49:51,755 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 07:50:02,183 INFO [train.py:742] (2/8) Epoch 5, validation: loss=0.11, simple_loss=0.1955, pruned_loss=0.0123, over 914524.00 frames.
2022-05-05 07:50:40,726 INFO [train.py:715] (2/8) Epoch 5, batch 24050, loss[loss=0.1201, simple_loss=0.1931, pruned_loss=0.02354, over 4822.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2232, pruned_loss=0.04096, over 972892.89 frames.], batch size: 26, lr: 3.77e-04
2022-05-05 07:51:20,435 INFO [train.py:715] (2/8) Epoch 5, batch 24100, loss[loss=0.1179, simple_loss=0.1879, pruned_loss=0.02395, over 4817.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2227, pruned_loss=0.04051, over 972311.49 frames.], batch size: 13, lr: 3.77e-04
2022-05-05 07:51:59,183 INFO [train.py:715] (2/8) Epoch 5, batch 24150, loss[loss=0.1522, simple_loss=0.2129, pruned_loss=0.04574, over 4881.00 frames.], tot_loss[loss=0.1515, simple_loss=0.222, pruned_loss=0.04053, over 972171.47 frames.], batch size: 16, lr: 3.77e-04
2022-05-05 07:52:37,493 INFO [train.py:715] (2/8) Epoch 5, batch 24200, loss[loss=0.1666, simple_loss=0.2291, pruned_loss=0.05203, over 4812.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2211, pruned_loss=0.04034, over 972104.71 frames.], batch size: 15, lr: 3.77e-04
2022-05-05 07:53:16,811 INFO [train.py:715] (2/8) Epoch 5, batch 24250, loss[loss=0.1569, simple_loss=0.2376, pruned_loss=0.03804, over 4804.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2213, pruned_loss=0.0401, over 972964.23 frames.], batch size: 21, lr: 3.76e-04
2022-05-05 07:53:55,924 INFO [train.py:715] (2/8) Epoch 5, batch 24300, loss[loss=0.1442, simple_loss=0.2127, pruned_loss=0.0378, over 4981.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2212, pruned_loss=0.04004, over 972665.45 frames.], batch size: 35, lr: 3.76e-04
2022-05-05 07:54:34,804 INFO [train.py:715] (2/8) Epoch 5, batch 24350, loss[loss=0.1242, simple_loss=0.2028, pruned_loss=0.02286, over 4821.00 frames.], tot_loss[loss=0.151, simple_loss=0.2217, pruned_loss=0.04011, over 972279.06 frames.], batch size: 26, lr: 3.76e-04
2022-05-05 07:55:13,057 INFO [train.py:715] (2/8) Epoch 5, batch 24400, loss[loss=0.1407, simple_loss=0.2033, pruned_loss=0.03903, over 4709.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2224, pruned_loss=0.04046, over 972109.68 frames.], batch size: 15, lr: 3.76e-04
2022-05-05 07:55:52,740 INFO [train.py:715] (2/8) Epoch 5, batch 24450, loss[loss=0.1558, simple_loss=0.224, pruned_loss=0.04385, over 4924.00 frames.], tot_loss[loss=0.151, simple_loss=0.2217, pruned_loss=0.04009, over 971111.21 frames.], batch size: 23, lr: 3.76e-04
2022-05-05 07:56:30,708 INFO [train.py:715] (2/8) Epoch 5, batch 24500, loss[loss=0.1285, simple_loss=0.2026, pruned_loss=0.02719, over 4818.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2219, pruned_loss=0.03968, over 971581.12 frames.], batch size: 25, lr: 3.76e-04
2022-05-05 07:57:09,367 INFO [train.py:715] (2/8) Epoch 5, batch 24550, loss[loss=0.1468, simple_loss=0.2097, pruned_loss=0.04196, over 4824.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2219, pruned_loss=0.04033, over 972690.25 frames.], batch size: 25, lr: 3.76e-04
2022-05-05 07:57:48,724 INFO [train.py:715] (2/8) Epoch 5, batch 24600, loss[loss=0.1735, simple_loss=0.2356, pruned_loss=0.05565, over 4962.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2224, pruned_loss=0.04041, over 973318.91 frames.], batch size: 39, lr: 3.76e-04
2022-05-05 07:58:27,793 INFO [train.py:715] (2/8) Epoch 5, batch 24650, loss[loss=0.1534, simple_loss=0.2256, pruned_loss=0.04058, over 4894.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2215, pruned_loss=0.0398, over 972187.04 frames.], batch size: 19, lr: 3.76e-04
2022-05-05 07:59:06,983 INFO [train.py:715] (2/8) Epoch 5, batch 24700, loss[loss=0.1369, simple_loss=0.2079, pruned_loss=0.03299, over 4983.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2213, pruned_loss=0.03987, over 972104.83 frames.], batch size: 24, lr: 3.76e-04
2022-05-05 07:59:45,118 INFO [train.py:715] (2/8) Epoch 5, batch 24750, loss[loss=0.1497, simple_loss=0.2251, pruned_loss=0.03719, over 4964.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2212, pruned_loss=0.03989, over 971538.12 frames.], batch size: 24, lr: 3.76e-04
2022-05-05 08:00:24,685 INFO [train.py:715] (2/8) Epoch 5, batch 24800, loss[loss=0.1359, simple_loss=0.2151, pruned_loss=0.02835, over 4856.00 frames.], tot_loss[loss=0.15, simple_loss=0.2207, pruned_loss=0.03959, over 971283.44 frames.], batch size: 20, lr: 3.76e-04
2022-05-05 08:01:03,113 INFO [train.py:715] (2/8) Epoch 5, batch 24850, loss[loss=0.1504, simple_loss=0.2158, pruned_loss=0.04255, over 4808.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2212, pruned_loss=0.03971, over 972226.31 frames.], batch size: 14, lr: 3.76e-04
2022-05-05 08:01:41,876 INFO [train.py:715] (2/8) Epoch 5, batch 24900, loss[loss=0.2048, simple_loss=0.2727, pruned_loss=0.0685, over 4806.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2214, pruned_loss=0.03972, over 972507.01 frames.], batch size: 21, lr: 3.76e-04
2022-05-05 08:02:21,425 INFO [train.py:715] (2/8) Epoch 5, batch 24950, loss[loss=0.1208, simple_loss=0.1963, pruned_loss=0.0226, over 4725.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2213, pruned_loss=0.03962, over 972725.20 frames.], batch size: 16, lr: 3.76e-04
2022-05-05 08:03:00,473 INFO [train.py:715] (2/8) Epoch 5, batch 25000, loss[loss=0.1512, simple_loss=0.2212, pruned_loss=0.04062, over 4836.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2209, pruned_loss=0.03988, over 972552.76 frames.], batch size: 15, lr: 3.76e-04
2022-05-05 08:03:39,039 INFO [train.py:715] (2/8) Epoch 5, batch 25050, loss[loss=0.1412, simple_loss=0.207, pruned_loss=0.03775, over 4984.00 frames.], tot_loss[loss=0.1502, simple_loss=0.221, pruned_loss=0.03971, over 972658.36 frames.], batch size: 14, lr: 3.76e-04
2022-05-05 08:04:17,285 INFO [train.py:715] (2/8) Epoch 5, batch 25100, loss[loss=0.1605, simple_loss=0.2364, pruned_loss=0.04225, over 4782.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2215, pruned_loss=0.04002, over 972480.55 frames.], batch size: 18, lr: 3.76e-04
2022-05-05 08:04:57,544 INFO [train.py:715] (2/8) Epoch 5, batch 25150, loss[loss=0.1348, simple_loss=0.2017, pruned_loss=0.03391, over 4905.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2214, pruned_loss=0.04041, over 972437.66 frames.], batch size: 17, lr: 3.76e-04
2022-05-05 08:05:35,727 INFO [train.py:715] (2/8) Epoch 5, batch 25200, loss[loss=0.1498, simple_loss=0.2293, pruned_loss=0.03519, over 4829.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2231, pruned_loss=0.04078, over 972736.61 frames.], batch size: 26, lr: 3.76e-04
2022-05-05 08:06:14,579 INFO [train.py:715] (2/8) Epoch 5, batch 25250, loss[loss=0.1665, simple_loss=0.2317, pruned_loss=0.05068, over 4892.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2232, pruned_loss=0.04116, over 972715.16 frames.], batch size: 32, lr: 3.76e-04
2022-05-05 08:06:53,409 INFO [train.py:715] (2/8) Epoch 5, batch 25300, loss[loss=0.1624, simple_loss=0.2264, pruned_loss=0.0492, over 4960.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2223, pruned_loss=0.04074, over 972292.25 frames.], batch size: 35, lr: 3.75e-04
2022-05-05 08:07:31,748 INFO [train.py:715] (2/8) Epoch 5, batch 25350, loss[loss=0.137, simple_loss=0.2146, pruned_loss=0.02968, over 4967.00 frames.], tot_loss[loss=0.1513, simple_loss=0.222, pruned_loss=0.04035, over 971245.32 frames.], batch size: 21, lr: 3.75e-04
2022-05-05 08:08:10,247 INFO [train.py:715] (2/8) Epoch 5, batch 25400, loss[loss=0.1252, simple_loss=0.1896, pruned_loss=0.03047, over 4795.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2219, pruned_loss=0.04051, over 971044.44 frames.], batch size: 12, lr: 3.75e-04
2022-05-05 08:08:49,165 INFO [train.py:715] (2/8) Epoch 5, batch 25450, loss[loss=0.12, simple_loss=0.1933, pruned_loss=0.02331, over 4990.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2225, pruned_loss=0.04034, over 971750.41 frames.], batch size: 14, lr: 3.75e-04
2022-05-05 08:09:28,379 INFO [train.py:715] (2/8) Epoch 5, batch 25500, loss[loss=0.1233, simple_loss=0.2058, pruned_loss=0.02045, over 4828.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2234, pruned_loss=0.04064, over 971971.43 frames.], batch size: 26, lr: 3.75e-04
2022-05-05 08:10:07,143 INFO [train.py:715] (2/8) Epoch 5, batch 25550, loss[loss=0.1802, simple_loss=0.2445, pruned_loss=0.058, over 4809.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2233, pruned_loss=0.04086, over 971933.93 frames.], batch size: 21, lr: 3.75e-04
2022-05-05 08:10:45,633 INFO [train.py:715] (2/8) Epoch 5, batch 25600, loss[loss=0.1492, simple_loss=0.2163, pruned_loss=0.04106, over 4798.00 frames.], tot_loss[loss=0.1523, simple_loss=0.223, pruned_loss=0.04074, over 971592.55 frames.], batch size: 24, lr: 3.75e-04
2022-05-05 08:11:24,704 INFO [train.py:715] (2/8) Epoch 5, batch 25650, loss[loss=0.148, simple_loss=0.2183, pruned_loss=0.03885, over 4853.00 frames.], tot_loss[loss=0.153, simple_loss=0.2236, pruned_loss=0.04122, over 972083.34 frames.], batch size: 32, lr: 3.75e-04
2022-05-05 08:12:03,095 INFO [train.py:715] (2/8) Epoch 5, batch 25700, loss[loss=0.1561, simple_loss=0.2221, pruned_loss=0.04505, over 4811.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2218, pruned_loss=0.04032, over 971869.33 frames.], batch size: 21, lr: 3.75e-04
2022-05-05 08:12:41,257 INFO [train.py:715] (2/8) Epoch 5, batch 25750, loss[loss=0.1468, simple_loss=0.2193, pruned_loss=0.0371, over 4929.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2216, pruned_loss=0.04012, over 973136.99 frames.], batch size: 23, lr: 3.75e-04
2022-05-05 08:13:20,755 INFO [train.py:715] (2/8) Epoch 5, batch 25800, loss[loss=0.1317, simple_loss=0.211, pruned_loss=0.0262, over 4872.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2211, pruned_loss=0.04035, over 972849.85 frames.], batch size: 16, lr: 3.75e-04
2022-05-05 08:13:59,838 INFO [train.py:715] (2/8) Epoch 5, batch 25850, loss[loss=0.1711, simple_loss=0.2304, pruned_loss=0.05592, over 4899.00 frames.], tot_loss[loss=0.1521, simple_loss=0.222, pruned_loss=0.04109, over 973425.91 frames.], batch size: 22, lr: 3.75e-04
2022-05-05 08:14:38,584 INFO [train.py:715] (2/8) Epoch 5, batch 25900, loss[loss=0.1432, simple_loss=0.2212, pruned_loss=0.03264, over 4948.00 frames.], tot_loss[loss=0.1516, simple_loss=0.222, pruned_loss=0.04064, over 972331.51 frames.], batch size: 29, lr: 3.75e-04
2022-05-05 08:15:17,128 INFO [train.py:715] (2/8) Epoch 5, batch 25950, loss[loss=0.1812, simple_loss=0.2511, pruned_loss=0.05562, over 4902.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2229, pruned_loss=0.04106, over 972370.84 frames.], batch size: 17, lr: 3.75e-04
2022-05-05 08:15:58,603 INFO [train.py:715] (2/8) Epoch 5, batch 26000, loss[loss=0.1318, simple_loss=0.1966, pruned_loss=0.03349, over 4921.00 frames.], tot_loss[loss=0.152, simple_loss=0.2221, pruned_loss=0.04099, over 972107.77 frames.], batch size: 18, lr: 3.75e-04
2022-05-05 08:16:37,295 INFO [train.py:715] (2/8) Epoch 5, batch 26050, loss[loss=0.1349, simple_loss=0.201, pruned_loss=0.03442, over 4924.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2228, pruned_loss=0.04116, over 972092.13 frames.], batch size: 23, lr: 3.75e-04
2022-05-05 08:17:15,756 INFO [train.py:715] (2/8) Epoch 5, batch 26100, loss[loss=0.1318, simple_loss=0.2011, pruned_loss=0.03118, over 4963.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2225, pruned_loss=0.04115, over 972498.19 frames.], batch size: 35, lr: 3.75e-04
2022-05-05 08:17:54,716 INFO [train.py:715] (2/8) Epoch 5, batch 26150, loss[loss=0.1298, simple_loss=0.2036, pruned_loss=0.02797, over 4793.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2219, pruned_loss=0.04094, over 972073.00 frames.], batch size: 14, lr: 3.75e-04
2022-05-05 08:18:33,050 INFO [train.py:715] (2/8) Epoch 5, batch 26200, loss[loss=0.1292, simple_loss=0.2026, pruned_loss=0.02787, over 4803.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2215, pruned_loss=0.04071, over 972631.39 frames.], batch size: 25, lr: 3.75e-04
2022-05-05 08:19:12,107 INFO [train.py:715] (2/8) Epoch 5, batch 26250, loss[loss=0.1395, simple_loss=0.2136, pruned_loss=0.03273, over 4876.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2203, pruned_loss=0.04005, over 972691.23 frames.], batch size: 16, lr: 3.75e-04
2022-05-05 08:19:51,345 INFO [train.py:715] (2/8) Epoch 5, batch 26300, loss[loss=0.1606, simple_loss=0.2283, pruned_loss=0.04644, over 4956.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2206, pruned_loss=0.03994, over 973048.70 frames.], batch size: 15, lr: 3.75e-04
2022-05-05 08:20:30,627 INFO [train.py:715] (2/8) Epoch 5, batch 26350, loss[loss=0.1406, simple_loss=0.2145, pruned_loss=0.03333, over 4984.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2208, pruned_loss=0.0399, over 973056.81 frames.], batch size: 27, lr: 3.74e-04
2022-05-05 08:21:09,425 INFO [train.py:715] (2/8) Epoch 5, batch 26400, loss[loss=0.1816, simple_loss=0.2619, pruned_loss=0.0506, over 4748.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2222, pruned_loss=0.04061, over 972650.72 frames.], batch size: 16, lr: 3.74e-04
2022-05-05 08:21:48,026 INFO [train.py:715] (2/8) Epoch 5, batch 26450, loss[loss=0.1455, simple_loss=0.2094, pruned_loss=0.04082, over 4812.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2221, pruned_loss=0.04076, over 972707.82 frames.], batch size: 27, lr: 3.74e-04
2022-05-05 08:22:26,947 INFO [train.py:715] (2/8) Epoch 5, batch 26500, loss[loss=0.1589, simple_loss=0.2394, pruned_loss=0.03923, over 4838.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2221, pruned_loss=0.04071, over 973007.53 frames.], batch size: 15, lr: 3.74e-04
2022-05-05 08:23:06,040 INFO [train.py:715] (2/8) Epoch 5, batch 26550, loss[loss=0.1465, simple_loss=0.2147, pruned_loss=0.03914, over 4830.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2216, pruned_loss=0.04059, over 971932.05 frames.], batch size: 13, lr: 3.74e-04
2022-05-05 08:23:44,738 INFO [train.py:715] (2/8) Epoch 5, batch 26600, loss[loss=0.1215, simple_loss=0.1894, pruned_loss=0.02679, over 4749.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2211, pruned_loss=0.04029, over 971323.23 frames.], batch size: 19, lr: 3.74e-04
2022-05-05 08:24:24,177 INFO [train.py:715] (2/8) Epoch 5, batch 26650, loss[loss=0.1325, simple_loss=0.212, pruned_loss=0.02646, over 4782.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2206, pruned_loss=0.04006, over 971699.12 frames.], batch size: 14, lr: 3.74e-04
2022-05-05 08:25:02,987 INFO [train.py:715] (2/8) Epoch 5, batch 26700, loss[loss=0.157, simple_loss=0.2298, pruned_loss=0.0421, over 4933.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2206, pruned_loss=0.04002, over 971134.12 frames.], batch size: 18, lr: 3.74e-04
2022-05-05 08:25:41,812 INFO [train.py:715] (2/8) Epoch 5, batch 26750, loss[loss=0.1348, simple_loss=0.2046, pruned_loss=0.03248, over 4956.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2217, pruned_loss=0.04049, over 972038.30 frames.], batch size: 24, lr: 3.74e-04
2022-05-05 08:26:20,196 INFO [train.py:715] (2/8) Epoch 5, batch 26800, loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03416, over 4990.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2222, pruned_loss=0.04087, over 971918.63 frames.], batch size: 20, lr: 3.74e-04
2022-05-05 08:26:59,364 INFO [train.py:715] (2/8) Epoch 5, batch 26850, loss[loss=0.1429, simple_loss=0.2178, pruned_loss=0.03399, over 4876.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2222, pruned_loss=0.04109, over 971820.75 frames.], batch size: 22, lr: 3.74e-04
2022-05-05 08:27:38,336 INFO [train.py:715] (2/8) Epoch 5, batch 26900, loss[loss=0.1627, simple_loss=0.2366, pruned_loss=0.0444, over 4883.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2217, pruned_loss=0.04075, over 971591.56 frames.], batch size: 19, lr: 3.74e-04
2022-05-05 08:28:17,271 INFO [train.py:715] (2/8) Epoch 5, batch 26950, loss[loss=0.141, simple_loss=0.2227, pruned_loss=0.0297, over 4932.00 frames.], tot_loss[loss=0.1508, simple_loss=0.221, pruned_loss=0.04031, over 971380.61 frames.], batch size: 23, lr: 3.74e-04
2022-05-05 08:28:55,974 INFO [train.py:715] (2/8) Epoch 5, batch 27000, loss[loss=0.1892, simple_loss=0.2586, pruned_loss=0.05985, over 4911.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2213, pruned_loss=0.04029, over 972024.36 frames.], batch size: 17, lr: 3.74e-04
2022-05-05 08:28:55,975 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 08:29:05,775 INFO [train.py:742] (2/8) Epoch 5, validation: loss=0.1098, simple_loss=0.195, pruned_loss=0.01232, over 914524.00 frames.
2022-05-05 08:29:45,279 INFO [train.py:715] (2/8) Epoch 5, batch 27050, loss[loss=0.1548, simple_loss=0.2239, pruned_loss=0.04283, over 4950.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2203, pruned_loss=0.03969, over 971291.48 frames.], batch size: 21, lr: 3.74e-04
2022-05-05 08:30:24,755 INFO [train.py:715] (2/8) Epoch 5, batch 27100, loss[loss=0.1546, simple_loss=0.2191, pruned_loss=0.04501, over 4837.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2205, pruned_loss=0.04005, over 971112.47 frames.], batch size: 15, lr: 3.74e-04
2022-05-05 08:31:04,147 INFO [train.py:715] (2/8) Epoch 5, batch 27150, loss[loss=0.1397, simple_loss=0.2057, pruned_loss=0.03688, over 4787.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2201, pruned_loss=0.03974, over 971941.48 frames.], batch size: 17, lr: 3.74e-04
2022-05-05 08:31:42,963 INFO [train.py:715] (2/8) Epoch 5, batch 27200, loss[loss=0.2218, simple_loss=0.2844, pruned_loss=0.0796, over 4881.00 frames.], tot_loss[loss=0.1514, simple_loss=0.222, pruned_loss=0.04041, over 972438.50 frames.], batch size: 16, lr: 3.74e-04
2022-05-05 08:32:22,590 INFO [train.py:715] (2/8) Epoch 5, batch 27250, loss[loss=0.1198, simple_loss=0.1891, pruned_loss=0.02524, over 4818.00 frames.], tot_loss[loss=0.1505, simple_loss=0.221, pruned_loss=0.03994, over 971877.09 frames.], batch size: 13, lr: 3.74e-04
2022-05-05 08:33:01,564 INFO [train.py:715] (2/8) Epoch 5, batch 27300, loss[loss=0.119, simple_loss=0.1952, pruned_loss=0.02139, over 4970.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2225, pruned_loss=0.04058, over 972246.43 frames.], batch size: 14, lr: 3.74e-04
2022-05-05 08:33:40,119 INFO [train.py:715] (2/8) Epoch 5, batch 27350, loss[loss=0.1871, simple_loss=0.247, pruned_loss=0.06358, over 4772.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2228, pruned_loss=0.04091, over 972184.42 frames.], batch size: 14, lr: 3.74e-04
2022-05-05 08:34:18,998 INFO [train.py:715] (2/8) Epoch 5, batch 27400, loss[loss=0.1532, simple_loss=0.2194, pruned_loss=0.04355, over 4809.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2228, pruned_loss=0.04064, over 971083.93 frames.], batch size: 12, lr: 3.74e-04
2022-05-05 08:34:58,265 INFO [train.py:715] (2/8) Epoch 5, batch 27450, loss[loss=0.1279, simple_loss=0.1996, pruned_loss=0.02809, over 4791.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2232, pruned_loss=0.04077, over 971389.62 frames.], batch size: 12, lr: 3.73e-04
2022-05-05 08:35:38,042 INFO [train.py:715] (2/8) Epoch 5, batch 27500, loss[loss=0.1319, simple_loss=0.2156, pruned_loss=0.0241, over 4847.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2219, pruned_loss=0.03993, over 972139.39 frames.], batch size: 15, lr: 3.73e-04
2022-05-05 08:36:16,527 INFO [train.py:715] (2/8) Epoch 5, batch 27550, loss[loss=0.1434, simple_loss=0.2194, pruned_loss=0.03373, over 4791.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2218, pruned_loss=0.03981, over 971305.88 frames.], batch size: 24, lr: 3.73e-04
2022-05-05 08:36:55,894 INFO [train.py:715] (2/8) Epoch 5, batch 27600, loss[loss=0.1188, simple_loss=0.1994, pruned_loss=0.01905, over 4937.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2223, pruned_loss=0.04029, over 971943.24 frames.], batch size: 29, lr: 3.73e-04
2022-05-05 08:37:34,977 INFO [train.py:715] (2/8) Epoch 5, batch 27650, loss[loss=0.143, simple_loss=0.2212, pruned_loss=0.03243, over 4820.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2225, pruned_loss=0.0406, over 972479.29 frames.], batch size: 12, lr: 3.73e-04
2022-05-05 08:38:13,251 INFO [train.py:715] (2/8) Epoch 5, batch 27700, loss[loss=0.1562, simple_loss=0.2248, pruned_loss=0.04384, over 4859.00 frames.], tot_loss[loss=0.152, simple_loss=0.2225, pruned_loss=0.04077, over 972392.97 frames.], batch size: 20, lr: 3.73e-04
2022-05-05 08:38:52,830 INFO [train.py:715] (2/8) Epoch 5, batch 27750, loss[loss=0.1904, simple_loss=0.2406, pruned_loss=0.07016, over 4800.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2221, pruned_loss=0.04081, over 973095.47 frames.], batch size: 12, lr: 3.73e-04
2022-05-05 08:39:32,593 INFO [train.py:715] (2/8) Epoch 5, batch 27800, loss[loss=0.1329, simple_loss=0.21, pruned_loss=0.02786, over 4872.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2224, pruned_loss=0.04111, over 972620.88 frames.], batch size: 22, lr: 3.73e-04
2022-05-05 08:40:11,945 INFO [train.py:715] (2/8) Epoch 5, batch 27850, loss[loss=0.1377, simple_loss=0.2133, pruned_loss=0.03107, over 4778.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2225, pruned_loss=0.04088, over 972296.73 frames.], batch size: 18, lr: 3.73e-04
2022-05-05 08:40:50,650 INFO [train.py:715] (2/8) Epoch 5, batch 27900, loss[loss=0.1279, simple_loss=0.2057, pruned_loss=0.02507, over 4822.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2227, pruned_loss=0.04081, over 972840.46 frames.], batch size: 26, lr: 3.73e-04
2022-05-05 08:41:29,602 INFO [train.py:715] (2/8) Epoch 5, batch 27950, loss[loss=0.1617, simple_loss=0.2324, pruned_loss=0.04547, over 4912.00 frames.], tot_loss[loss=0.1516, simple_loss=0.222, pruned_loss=0.04059, over 972804.37 frames.], batch size: 39, lr: 3.73e-04
2022-05-05 08:42:09,042 INFO [train.py:715] (2/8) Epoch 5, batch 28000, loss[loss=0.127, simple_loss=0.2008, pruned_loss=0.02661, over 4988.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2217, pruned_loss=0.04039, over 973079.51 frames.], batch size: 25, lr: 3.73e-04
2022-05-05 08:42:47,127 INFO [train.py:715] (2/8) Epoch 5, batch 28050, loss[loss=0.1654, simple_loss=0.2379, pruned_loss=0.04644, over 4894.00 frames.], tot_loss[loss=0.152, simple_loss=0.2225, pruned_loss=0.04071, over 973148.26 frames.], batch size: 22, lr: 3.73e-04
2022-05-05 08:43:25,856 INFO [train.py:715] (2/8) Epoch 5, batch 28100, loss[loss=0.1395, simple_loss=0.2098, pruned_loss=0.03464, over 4979.00 frames.], tot_loss[loss=0.153, simple_loss=0.2234, pruned_loss=0.04127, over 973680.89 frames.], batch size: 28, lr: 3.73e-04
2022-05-05 08:44:04,996 INFO [train.py:715] (2/8) Epoch 5, batch 28150, loss[loss=0.1723, simple_loss=0.2505, pruned_loss=0.04703, over 4937.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2238, pruned_loss=0.04142, over 974205.31 frames.], batch size: 21, lr: 3.73e-04
2022-05-05 08:44:43,937 INFO [train.py:715] (2/8) Epoch 5, batch 28200, loss[loss=0.1527, simple_loss=0.2228, pruned_loss=0.04134, over 4979.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2232, pruned_loss=0.04059, over 973682.67 frames.], batch size: 31, lr: 3.73e-04
2022-05-05 08:45:22,618 INFO [train.py:715] (2/8) Epoch 5, batch 28250, loss[loss=0.1569, simple_loss=0.2274, pruned_loss=0.04327, over 4818.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2227, pruned_loss=0.04041, over 972968.87 frames.], batch size: 13, lr: 3.73e-04
2022-05-05 08:46:01,489 INFO [train.py:715] (2/8) Epoch 5, batch 28300, loss[loss=0.1415, simple_loss=0.2193, pruned_loss=0.03191, over 4864.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2238, pruned_loss=0.04081, over 972534.84 frames.], batch size: 20, lr: 3.73e-04
2022-05-05 08:46:39,905 INFO [train.py:715] (2/8) Epoch 5, batch 28350, loss[loss=0.1752, simple_loss=0.2345, pruned_loss=0.05792, over 4967.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2246, pruned_loss=0.0414, over 973318.27 frames.], batch size: 15, lr: 3.73e-04
2022-05-05 08:47:18,560 INFO [train.py:715] (2/8) Epoch 5, batch 28400, loss[loss=0.1623, simple_loss=0.2397, pruned_loss=0.04247, over 4860.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2235, pruned_loss=0.04091, over 973519.66 frames.], batch size: 32, lr: 3.73e-04
2022-05-05 08:47:57,680 INFO [train.py:715] (2/8) Epoch 5, batch 28450, loss[loss=0.1625, simple_loss=0.2277, pruned_loss=0.04862, over 4840.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2234, pruned_loss=0.04114, over 972866.45 frames.], batch size: 15, lr: 3.73e-04
2022-05-05 08:48:36,727 INFO [train.py:715] (2/8) Epoch 5, batch 28500, loss[loss=0.1422, simple_loss=0.2108, pruned_loss=0.03686, over 4781.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2235, pruned_loss=0.04135, over 972380.74 frames.], batch size: 18, lr: 3.72e-04
2022-05-05 08:49:15,939 INFO [train.py:715] (2/8) Epoch 5, batch 28550, loss[loss=0.1585, simple_loss=0.2203, pruned_loss=0.04831, over 4843.00 frames.], tot_loss[loss=0.152, simple_loss=0.2229, pruned_loss=0.04056, over 972564.04 frames.], batch size: 15, lr: 3.72e-04
2022-05-05 08:49:54,645 INFO [train.py:715] (2/8) Epoch 5, batch 28600, loss[loss=0.1679, simple_loss=0.2353, pruned_loss=0.05025, over 4876.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.04053, over 972503.69 frames.], batch size: 32, lr: 3.72e-04
2022-05-05 08:50:34,062 INFO [train.py:715] (2/8) Epoch 5, batch 28650, loss[loss=0.1435, simple_loss=0.2217, pruned_loss=0.0327, over 4973.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2218, pruned_loss=0.03993, over 971997.19 frames.], batch size: 35, lr: 3.72e-04
2022-05-05 08:51:12,503 INFO [train.py:715] (2/8) Epoch 5, batch 28700, loss[loss=0.2039, simple_loss=0.2692, pruned_loss=0.06933, over 4742.00 frames.], tot_loss[loss=0.1509, simple_loss=0.222, pruned_loss=0.0399, over 971110.01 frames.], batch size: 16, lr: 3.72e-04
2022-05-05 08:51:51,353 INFO [train.py:715] (2/8) Epoch 5, batch 28750, loss[loss=0.2019, simple_loss=0.2629, pruned_loss=0.07044, over 4862.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2225, pruned_loss=0.04028, over 970894.57 frames.], batch size: 20, lr: 3.72e-04
2022-05-05 08:52:30,122 INFO [train.py:715] (2/8) Epoch 5, batch 28800, loss[loss=0.1566, simple_loss=0.2294, pruned_loss=0.04188, over 4916.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.04052, over 971468.98 frames.], batch size: 23, lr: 3.72e-04
2022-05-05 08:53:09,039 INFO [train.py:715] (2/8) Epoch 5, batch 28850, loss[loss=0.1851, simple_loss=0.2456, pruned_loss=0.0623, over 4963.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2224, pruned_loss=0.04002, over 971600.41 frames.], batch size: 24, lr: 3.72e-04
2022-05-05 08:53:47,808 INFO [train.py:715] (2/8) Epoch 5, batch 28900, loss[loss=0.1473, simple_loss=0.2036, pruned_loss=0.04549, over 4829.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2224, pruned_loss=0.04024, over 972226.46 frames.], batch size: 15, lr: 3.72e-04
2022-05-05 08:54:26,496 INFO [train.py:715] (2/8) Epoch 5, batch 28950, loss[loss=0.1465, simple_loss=0.2271, pruned_loss=0.03298, over 4856.00 frames.], tot_loss[loss=0.1521, simple_loss=0.223, pruned_loss=0.04058, over 972101.05 frames.], batch size: 38, lr: 3.72e-04
2022-05-05 08:55:05,612 INFO [train.py:715] (2/8) Epoch 5, batch 29000, loss[loss=0.1335, simple_loss=0.2095, pruned_loss=0.0287, over 4685.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2229, pruned_loss=0.04078, over 971183.35 frames.], batch size: 15, lr: 3.72e-04
2022-05-05 08:55:43,854 INFO [train.py:715] (2/8) Epoch 5, batch 29050, loss[loss=0.1601, simple_loss=0.2205, pruned_loss=0.04983, over 4776.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2222, pruned_loss=0.04067, over 971786.70 frames.], batch size: 17, lr: 3.72e-04
2022-05-05 08:56:22,924 INFO [train.py:715] (2/8) Epoch 5, batch 29100, loss[loss=0.125, simple_loss=0.1984, pruned_loss=0.02577, over 4978.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2215, pruned_loss=0.04012, over 971828.56 frames.], batch size: 24, lr: 3.72e-04
2022-05-05 08:57:01,740 INFO [train.py:715] (2/8) Epoch 5, batch 29150, loss[loss=0.1368, simple_loss=0.2191, pruned_loss=0.02723, over 4811.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2222, pruned_loss=0.04049, over 971922.75 frames.], batch size: 26, lr: 3.72e-04
2022-05-05 08:57:40,490 INFO [train.py:715] (2/8) Epoch 5, batch 29200, loss[loss=0.1617, simple_loss=0.2269, pruned_loss=0.04826, over 4849.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2216, pruned_loss=0.04012, over 972038.49 frames.], batch size: 20, lr: 3.72e-04
2022-05-05 08:58:19,234 INFO [train.py:715] (2/8) Epoch 5, batch 29250, loss[loss=0.165, simple_loss=0.2364, pruned_loss=0.04683, over 4751.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.0395, over 972247.41 frames.], batch size: 16, lr: 3.72e-04
2022-05-05 08:58:57,802 INFO [train.py:715] (2/8) Epoch 5, batch 29300, loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03502, over 4848.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2204, pruned_loss=0.03946, over 970802.73 frames.], batch size: 20, lr: 3.72e-04
2022-05-05 08:59:37,059 INFO [train.py:715] (2/8) Epoch 5, batch 29350, loss[loss=0.1818, simple_loss=0.2687, pruned_loss=0.04749, over 4854.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2223, pruned_loss=0.04053, over 970913.48 frames.], batch size: 15, lr: 3.72e-04
2022-05-05 09:00:15,739 INFO [train.py:715] (2/8) Epoch 5, batch 29400, loss[loss=0.1984, simple_loss=0.2679, pruned_loss=0.06444, over 4951.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03976, over 970982.04 frames.], batch size: 24, lr: 3.72e-04
2022-05-05 09:00:54,488 INFO [train.py:715] (2/8) Epoch 5, batch 29450, loss[loss=0.1532, simple_loss=0.2254, pruned_loss=0.04053, over 4857.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2201, pruned_loss=0.03903, over 971463.61 frames.], batch size: 20, lr: 3.72e-04
2022-05-05 09:01:34,122 INFO [train.py:715] (2/8) Epoch 5, batch 29500, loss[loss=0.1508, simple_loss=0.2193, pruned_loss=0.04112, over 4852.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2212, pruned_loss=0.03954, over 972289.79 frames.], batch size: 30, lr: 3.72e-04
2022-05-05 09:02:13,206 INFO [train.py:715] (2/8) Epoch 5, batch 29550, loss[loss=0.1784, simple_loss=0.2546, pruned_loss=0.05105, over 4926.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2212, pruned_loss=0.03972, over 972503.07 frames.], batch size: 23, lr: 3.72e-04
2022-05-05 09:02:52,390 INFO [train.py:715] (2/8) Epoch 5, batch 29600, loss[loss=0.152, simple_loss=0.2336, pruned_loss=0.03516, over 4882.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2219, pruned_loss=0.03946, over 972795.26 frames.], batch size: 16, lr: 3.71e-04
2022-05-05 09:03:31,060 INFO [train.py:715] (2/8) Epoch 5, batch 29650, loss[loss=0.1421, simple_loss=0.2115, pruned_loss=0.03636, over 4821.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2215, pruned_loss=0.03938, over 974381.63 frames.], batch size: 26, lr: 3.71e-04
2022-05-05 09:04:09,889 INFO [train.py:715] (2/8) Epoch 5, batch 29700, loss[loss=0.117, simple_loss=0.1789, pruned_loss=0.0275, over 4770.00 frames.], tot_loss[loss=0.1499, simple_loss=0.221, pruned_loss=0.03938, over 973759.11 frames.], batch size: 12, lr: 3.71e-04
2022-05-05 09:04:48,812 INFO [train.py:715] (2/8) Epoch 5, batch 29750, loss[loss=0.1706, simple_loss=0.2322, pruned_loss=0.05452, over 4953.00 frames.], tot_loss[loss=0.15, simple_loss=0.2213, pruned_loss=0.03932, over 973858.22 frames.], batch size: 21, lr: 3.71e-04
2022-05-05 09:05:27,384 INFO [train.py:715] (2/8) Epoch 5, batch 29800, loss[loss=0.1299, simple_loss=0.2098, pruned_loss=0.02499, over 4988.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2221, pruned_loss=0.03961, over 974334.72 frames.], batch size: 27, lr: 3.71e-04
2022-05-05 09:06:05,623 INFO [train.py:715] (2/8) Epoch 5, batch 29850, loss[loss=0.1638, simple_loss=0.2337, pruned_loss=0.04697, over 4962.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2217, pruned_loss=0.03958, over 974124.14 frames.], batch size: 39, lr: 3.71e-04
2022-05-05 09:06:44,670 INFO [train.py:715] (2/8) Epoch 5, batch 29900, loss[loss=0.146, simple_loss=0.2125, pruned_loss=0.03981, over 4824.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2224, pruned_loss=0.04013, over 973909.87 frames.], batch size: 15, lr: 3.71e-04
2022-05-05 09:07:24,016 INFO [train.py:715] (2/8) Epoch 5, batch 29950, loss[loss=0.1496, simple_loss=0.2216, pruned_loss=0.03882, over 4892.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2217, pruned_loss=0.03991, over 974068.56 frames.], batch size: 17, lr: 3.71e-04
2022-05-05 09:08:02,567 INFO [train.py:715] (2/8) Epoch 5, batch 30000, loss[loss=0.1467, simple_loss=0.2187, pruned_loss=0.03732, over 4949.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.03951, over 973234.72 frames.], batch size: 15, lr: 3.71e-04
2022-05-05 09:08:02,568 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 09:08:12,296 INFO [train.py:742] (2/8) Epoch 5, validation: loss=0.11, simple_loss=0.1953, pruned_loss=0.01241, over 914524.00 frames.
2022-05-05 09:08:51,344 INFO [train.py:715] (2/8) Epoch 5, batch 30050, loss[loss=0.1649, simple_loss=0.2351, pruned_loss=0.04737, over 4817.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2216, pruned_loss=0.0399, over 972743.88 frames.], batch size: 25, lr: 3.71e-04
2022-05-05 09:09:31,493 INFO [train.py:715] (2/8) Epoch 5, batch 30100, loss[loss=0.1422, simple_loss=0.2079, pruned_loss=0.03829, over 4855.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2214, pruned_loss=0.03997, over 972008.63 frames.], batch size: 32, lr: 3.71e-04
2022-05-05 09:10:10,294 INFO [train.py:715] (2/8) Epoch 5, batch 30150, loss[loss=0.1663, simple_loss=0.2471, pruned_loss=0.04274, over 4864.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2215, pruned_loss=0.04007, over 971317.91 frames.], batch size: 20, lr: 3.71e-04
2022-05-05 09:10:48,827 INFO [train.py:715] (2/8) Epoch 5, batch 30200, loss[loss=0.122, simple_loss=0.1897, pruned_loss=0.02717, over 4888.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2219, pruned_loss=0.04023, over 970613.63 frames.], batch size: 19, lr: 3.71e-04
2022-05-05 09:11:27,807 INFO [train.py:715] (2/8) Epoch 5, batch 30250, loss[loss=0.1512, simple_loss=0.2213, pruned_loss=0.0405, over 4908.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2215, pruned_loss=0.03982, over 970994.61 frames.], batch size: 23, lr: 3.71e-04
2022-05-05 09:12:06,782 INFO [train.py:715] (2/8) Epoch 5, batch 30300, loss[loss=0.1581, simple_loss=0.2264, pruned_loss=0.04492, over 4913.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2214, pruned_loss=0.03983, over 971893.35 frames.], batch size: 18, lr: 3.71e-04
2022-05-05 09:12:45,785 INFO [train.py:715] (2/8) Epoch 5, batch 30350, loss[loss=0.1606, simple_loss=0.2257, pruned_loss=0.04778, over 4970.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2218, pruned_loss=0.04027, over 972272.14 frames.], batch size: 15, lr: 3.71e-04
2022-05-05 09:13:24,287 INFO [train.py:715] (2/8) Epoch 5, batch 30400, loss[loss=0.1395, simple_loss=0.2147, pruned_loss=0.03216, over 4975.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2219, pruned_loss=0.04042, over 972338.39 frames.], batch size: 25, lr: 3.71e-04
2022-05-05 09:14:03,374 INFO [train.py:715] (2/8) Epoch 5, batch 30450, loss[loss=0.1544, simple_loss=0.2285, pruned_loss=0.04012, over 4799.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2217, pruned_loss=0.04028, over 972135.03 frames.], batch size: 17, lr: 3.71e-04
2022-05-05 09:14:42,252 INFO [train.py:715] (2/8) Epoch 5, batch 30500, loss[loss=0.1823, simple_loss=0.2542, pruned_loss=0.05516, over 4796.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2215, pruned_loss=0.04004, over 972299.31 frames.], batch size: 21, lr: 3.71e-04
2022-05-05 09:15:20,923 INFO [train.py:715] (2/8) Epoch 5, batch 30550, loss[loss=0.1187, simple_loss=0.1962, pruned_loss=0.02059, over 4812.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2204, pruned_loss=0.0394, over 972120.76 frames.], batch size: 25, lr: 3.71e-04
2022-05-05 09:15:58,936 INFO [train.py:715] (2/8) Epoch 5, batch 30600, loss[loss=0.1477, simple_loss=0.2136, pruned_loss=0.04092, over 4782.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2208, pruned_loss=0.03944, over 971568.16 frames.], batch size: 17, lr: 3.71e-04
2022-05-05 09:16:37,781 INFO [train.py:715] (2/8) Epoch 5, batch 30650, loss[loss=0.1483, simple_loss=0.2173, pruned_loss=0.03964, over 4772.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2212, pruned_loss=0.03998, over 971618.74 frames.], batch size: 18, lr: 3.71e-04
2022-05-05 09:17:16,920 INFO [train.py:715] (2/8) Epoch 5, batch 30700, loss[loss=0.1519, simple_loss=0.226, pruned_loss=0.03891, over 4817.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2214, pruned_loss=0.04018, over 972024.74 frames.], batch size: 27, lr: 3.70e-04
2022-05-05 09:17:55,175 INFO [train.py:715] (2/8) Epoch 5, batch 30750, loss[loss=0.1659, simple_loss=0.2391, pruned_loss=0.04637, over 4900.00 frames.], tot_loss[loss=0.151, simple_loss=0.2215, pruned_loss=0.0403, over 972564.40 frames.], batch size: 17, lr: 3.70e-04
2022-05-05 09:18:33,968 INFO [train.py:715] (2/8) Epoch 5, batch 30800, loss[loss=0.1552, simple_loss=0.2254, pruned_loss=0.04248, over 4981.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2216, pruned_loss=0.04072, over 972331.06 frames.], batch size: 24, lr: 3.70e-04
2022-05-05 09:19:12,987 INFO [train.py:715] (2/8) Epoch 5, batch 30850, loss[loss=0.1422, simple_loss=0.2152, pruned_loss=0.03458, over 4846.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2219, pruned_loss=0.04114, over 973227.23 frames.], batch size: 32, lr: 3.70e-04
2022-05-05 09:19:51,001 INFO [train.py:715] (2/8) Epoch 5, batch 30900, loss[loss=0.1343, simple_loss=0.2138, pruned_loss=0.02733, over 4872.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2229, pruned_loss=0.04123, over 972571.72 frames.], batch size: 22, lr: 3.70e-04
2022-05-05 09:20:29,874 INFO [train.py:715] (2/8) Epoch 5, batch 30950, loss[loss=0.1365, simple_loss=0.1942, pruned_loss=0.03938, over 4973.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2227, pruned_loss=0.04113, over 972731.80 frames.], batch size: 15, lr: 3.70e-04
2022-05-05 09:21:09,512 INFO [train.py:715] (2/8) Epoch 5, batch 31000, loss[loss=0.1651, simple_loss=0.2335, pruned_loss=0.0484, over 4947.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2231, pruned_loss=0.04096, over 973149.52 frames.], batch size: 24, lr: 3.70e-04
2022-05-05 09:21:48,979 INFO [train.py:715] (2/8) Epoch 5, batch 31050, loss[loss=0.1601, simple_loss=0.2278, pruned_loss=0.04621, over 4907.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2229, pruned_loss=0.0411, over 972787.23 frames.], batch size: 17, lr: 3.70e-04
2022-05-05 09:22:27,593 INFO [train.py:715] (2/8) Epoch 5, batch 31100, loss[loss=0.1604, simple_loss=0.235, pruned_loss=0.04295, over 4911.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2225, pruned_loss=0.04086, over 972017.63 frames.], batch size: 17, lr: 3.70e-04
2022-05-05 09:23:06,677 INFO [train.py:715] (2/8) Epoch 5, batch 31150, loss[loss=0.1495, simple_loss=0.2295, pruned_loss=0.03475, over 4787.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2222, pruned_loss=0.04058, over 971751.88 frames.], batch size: 18, lr: 3.70e-04
2022-05-05 09:23:45,588 INFO [train.py:715] (2/8) Epoch 5, batch 31200, loss[loss=0.1168, simple_loss=0.1852, pruned_loss=0.02417, over 4822.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2214, pruned_loss=0.03993, over 971208.86 frames.], batch size: 12, lr: 3.70e-04
2022-05-05 09:24:24,057 INFO [train.py:715] (2/8) Epoch 5, batch 31250, loss[loss=0.143, simple_loss=0.2149, pruned_loss=0.03557, over 4944.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2218, pruned_loss=0.04037, over 971207.90 frames.], batch size: 21, lr: 3.70e-04
2022-05-05 09:25:02,648 INFO [train.py:715] (2/8) Epoch 5, batch 31300, loss[loss=0.1356, simple_loss=0.2029, pruned_loss=0.03414, over 4755.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2214, pruned_loss=0.04006, over 971189.64 frames.], batch size: 16, lr: 3.70e-04
2022-05-05 09:25:41,534 INFO [train.py:715] (2/8) Epoch 5, batch 31350, loss[loss=0.1723, simple_loss=0.2394, pruned_loss=0.05262, over 4950.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2213, pruned_loss=0.03993, over 972584.07 frames.], batch size: 35, lr: 3.70e-04
2022-05-05 09:26:20,319 INFO [train.py:715] (2/8) Epoch 5, batch 31400, loss[loss=0.1398, simple_loss=0.2089, pruned_loss=0.03535, over 4918.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2199, pruned_loss=0.03934, over 973000.22 frames.], batch size: 29, lr: 3.70e-04
2022-05-05 09:26:59,041 INFO [train.py:715] (2/8) Epoch 5, batch 31450, loss[loss=0.1625, simple_loss=0.2222, pruned_loss=0.05145, over 4770.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2203, pruned_loss=0.03934, over 972782.70 frames.], batch size: 17, lr: 3.70e-04
2022-05-05 09:27:37,868 INFO [train.py:715] (2/8) Epoch 5, batch 31500, loss[loss=0.1232, simple_loss=0.1987, pruned_loss=0.02384, over 4779.00 frames.], tot_loss[loss=0.1502, simple_loss=0.221, pruned_loss=0.0397, over 973504.71 frames.], batch size: 17, lr: 3.70e-04
2022-05-05 09:28:16,784 INFO [train.py:715] (2/8) Epoch 5, batch 31550, loss[loss=0.125, simple_loss=0.1848, pruned_loss=0.03262, over 4792.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2205, pruned_loss=0.03956, over 972034.46 frames.], batch size: 12, lr: 3.70e-04
2022-05-05 09:28:55,563 INFO [train.py:715] (2/8) Epoch 5, batch 31600, loss[loss=0.1657, simple_loss=0.2269, pruned_loss=0.05223, over 4787.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2212, pruned_loss=0.03995, over 972399.74 frames.], batch size: 14, lr: 3.70e-04
2022-05-05 09:29:34,424 INFO [train.py:715] (2/8) Epoch 5, batch 31650, loss[loss=0.125, simple_loss=0.204, pruned_loss=0.02302, over 4795.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2206, pruned_loss=0.03993, over 972389.00 frames.], batch size: 24, lr: 3.70e-04
2022-05-05 09:30:13,324 INFO [train.py:715] (2/8) Epoch 5, batch 31700, loss[loss=0.1237, simple_loss=0.1911, pruned_loss=0.02818, over 4927.00 frames.], tot_loss[loss=0.15, simple_loss=0.2204, pruned_loss=0.03981, over 972268.21 frames.], batch size: 23, lr: 3.70e-04
2022-05-05 09:30:52,076 INFO [train.py:715] (2/8) Epoch 5, batch 31750, loss[loss=0.1643, simple_loss=0.2265, pruned_loss=0.05103, over 4863.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2213, pruned_loss=0.04048, over 971637.38 frames.], batch size: 30, lr: 3.70e-04
2022-05-05 09:31:31,168 INFO [train.py:715] (2/8) Epoch 5, batch 31800, loss[loss=0.1522, simple_loss=0.2247, pruned_loss=0.03988, over 4762.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2208, pruned_loss=0.04001, over 971956.20 frames.], batch size: 19, lr: 3.69e-04
2022-05-05 09:32:09,899 INFO [train.py:715] (2/8) Epoch 5, batch 31850, loss[loss=0.1374, simple_loss=0.208, pruned_loss=0.03344, over 4746.00 frames.], tot_loss[loss=0.151, simple_loss=0.2215, pruned_loss=0.0402, over 971377.21 frames.], batch size: 12, lr: 3.69e-04
2022-05-05 09:32:49,449 INFO [train.py:715] (2/8) Epoch 5, batch 31900, loss[loss=0.1581, simple_loss=0.229, pruned_loss=0.04358, over 4754.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2217, pruned_loss=0.0396, over 972123.27 frames.], batch size: 19, lr: 3.69e-04
2022-05-05 09:33:28,132 INFO [train.py:715] (2/8) Epoch 5, batch 31950, loss[loss=0.1403, simple_loss=0.2179, pruned_loss=0.0314, over 4875.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2225, pruned_loss=0.04002, over 972634.45 frames.], batch size: 20, lr: 3.69e-04
2022-05-05 09:34:06,676 INFO [train.py:715] (2/8) Epoch 5, batch 32000, loss[loss=0.1884, simple_loss=0.2438, pruned_loss=0.06649, over 4947.00 frames.], tot_loss[loss=0.153, simple_loss=0.2239, pruned_loss=0.04105, over 973335.62 frames.], batch size: 24, lr: 3.69e-04
2022-05-05 09:34:45,047 INFO [train.py:715] (2/8) Epoch 5, batch 32050, loss[loss=0.1411, simple_loss=0.2067, pruned_loss=0.03779, over 4791.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2235, pruned_loss=0.04105, over 973209.25 frames.], batch size: 18, lr: 3.69e-04
2022-05-05 09:35:24,097 INFO [train.py:715] (2/8) Epoch 5, batch 32100, loss[loss=0.1729, simple_loss=0.2424, pruned_loss=0.05172, over 4858.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2235, pruned_loss=0.04134, over 972304.76 frames.], batch size: 30, lr: 3.69e-04
2022-05-05 09:36:02,962 INFO [train.py:715] (2/8) Epoch 5, batch 32150, loss[loss=0.1291, simple_loss=0.2071, pruned_loss=0.02551, over 4807.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2233, pruned_loss=0.04121, over 973000.00 frames.], batch size: 21, lr: 3.69e-04
2022-05-05 09:36:41,524 INFO [train.py:715] (2/8) Epoch 5, batch 32200, loss[loss=0.1515, simple_loss=0.2149, pruned_loss=0.04406, over 4798.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.04108, over 972558.97 frames.], batch size: 14, lr: 3.69e-04
2022-05-05 09:37:20,073 INFO [train.py:715] (2/8) Epoch 5, batch 32250, loss[loss=0.1448, simple_loss=0.2189, pruned_loss=0.03537, over 4938.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2224, pruned_loss=0.04069, over 972908.89 frames.], batch size: 29, lr: 3.69e-04
2022-05-05 09:37:59,210 INFO [train.py:715] (2/8) Epoch 5, batch 32300, loss[loss=0.1695, simple_loss=0.2332, pruned_loss=0.05292, over 4867.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2215, pruned_loss=0.04005, over 972016.42 frames.], batch size: 30, lr: 3.69e-04
2022-05-05 09:38:37,805 INFO [train.py:715] (2/8) Epoch 5, batch 32350, loss[loss=0.1337, simple_loss=0.2137, pruned_loss=0.02687, over 4860.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2212, pruned_loss=0.03955, over 971177.71 frames.], batch size: 20, lr: 3.69e-04
2022-05-05 09:39:16,504 INFO [train.py:715] (2/8) Epoch 5, batch 32400, loss[loss=0.1535, simple_loss=0.2171, pruned_loss=0.04497, over 4836.00 frames.], tot_loss[loss=0.15, simple_loss=0.2211, pruned_loss=0.03951, over 972215.54 frames.], batch size: 32, lr: 3.69e-04
2022-05-05 09:39:55,118 INFO [train.py:715] (2/8) Epoch 5, batch 32450, loss[loss=0.155, simple_loss=0.2243, pruned_loss=0.04284, over 4958.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2223, pruned_loss=0.04009, over 971372.01 frames.], batch size: 35, lr: 3.69e-04
2022-05-05 09:40:33,915 INFO [train.py:715] (2/8) Epoch 5, batch 32500, loss[loss=0.1345, simple_loss=0.2068, pruned_loss=0.03112, over 4770.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2218, pruned_loss=0.04, over 971156.48 frames.], batch size: 14, lr: 3.69e-04
2022-05-05 09:41:13,474 INFO [train.py:715] (2/8) Epoch 5, batch 32550, loss[loss=0.149, simple_loss=0.2105, pruned_loss=0.04374, over 4895.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2217, pruned_loss=0.04045, over 972048.85 frames.], batch size: 22, lr: 3.69e-04
2022-05-05 09:41:51,933 INFO [train.py:715] (2/8) Epoch 5, batch 32600, loss[loss=0.1551, simple_loss=0.2281, pruned_loss=0.04108, over 4927.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2222, pruned_loss=0.04078, over 971732.45 frames.], batch size: 23, lr: 3.69e-04
2022-05-05 09:42:30,727 INFO [train.py:715] (2/8) Epoch 5, batch 32650, loss[loss=0.1337, simple_loss=0.2098, pruned_loss=0.02882, over 4970.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2227, pruned_loss=0.041, over 971947.90 frames.], batch size: 24, lr: 3.69e-04
2022-05-05 09:43:09,272 INFO [train.py:715] (2/8) Epoch 5, batch 32700, loss[loss=0.1285, simple_loss=0.1968, pruned_loss=0.0301, over 4812.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2221, pruned_loss=0.04042, over 972101.81 frames.], batch size: 25, lr: 3.69e-04
2022-05-05 09:43:47,572 INFO [train.py:715] (2/8) Epoch 5, batch 32750, loss[loss=0.1719, simple_loss=0.2409, pruned_loss=0.05144, over 4984.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2214, pruned_loss=0.04001, over 972075.36 frames.], batch size: 15, lr: 3.69e-04
2022-05-05 09:44:26,278 INFO [train.py:715] (2/8) Epoch 5, batch 32800, loss[loss=0.1464, simple_loss=0.221, pruned_loss=0.03589, over 4802.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2211, pruned_loss=0.04007, over 971674.45 frames.], batch size: 25, lr: 3.69e-04
2022-05-05 09:45:05,106 INFO [train.py:715] (2/8) Epoch 5, batch 32850, loss[loss=0.1795, simple_loss=0.2616, pruned_loss=0.04871, over 4771.00 frames.], tot_loss[loss=0.1506, simple_loss=0.221, pruned_loss=0.04006, over 971498.20 frames.], batch size: 18, lr: 3.69e-04
2022-05-05 09:45:44,050 INFO [train.py:715] (2/8) Epoch 5, batch 32900, loss[loss=0.133, simple_loss=0.202, pruned_loss=0.03205, over 4821.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2214, pruned_loss=0.04074, over 971559.65 frames.], batch size: 26, lr: 3.69e-04
2022-05-05 09:46:22,919 INFO [train.py:715] (2/8) Epoch 5, batch 32950, loss[loss=0.1333, simple_loss=0.2158, pruned_loss=0.02542, over 4892.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2207, pruned_loss=0.0404, over 971405.24 frames.], batch size: 17, lr: 3.68e-04
2022-05-05 09:47:01,975 INFO [train.py:715] (2/8) Epoch 5, batch 33000, loss[loss=0.132, simple_loss=0.2081, pruned_loss=0.02798, over 4983.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2206, pruned_loss=0.04031, over 971076.56 frames.], batch size: 25, lr: 3.68e-04
2022-05-05 09:47:01,975 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 09:47:11,684 INFO [train.py:742] (2/8) 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] (2/8) Epoch 5, batch 33050, loss[loss=0.1295, simple_loss=0.2046, pruned_loss=0.02719, over 4888.00 frames.], tot_loss[loss=0.151, simple_loss=0.2215, pruned_loss=0.0402, over 970953.73 frames.], batch size: 22, lr: 3.68e-04
2022-05-05 09:48:29,615 INFO [train.py:715] (2/8) Epoch 5, batch 33100, loss[loss=0.1376, simple_loss=0.2179, pruned_loss=0.02866, over 4813.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2219, pruned_loss=0.04022, over 971475.85 frames.], batch size: 27, lr: 3.68e-04
2022-05-05 09:49:07,624 INFO [train.py:715] (2/8) Epoch 5, batch 33150, loss[loss=0.1356, simple_loss=0.2143, pruned_loss=0.0284, over 4979.00 frames.], tot_loss[loss=0.15, simple_loss=0.2207, pruned_loss=0.03962, over 973029.43 frames.], batch size: 28, lr: 3.68e-04
2022-05-05 09:49:46,217 INFO [train.py:715] (2/8) Epoch 5, batch 33200, loss[loss=0.1519, simple_loss=0.2294, pruned_loss=0.03721, over 4763.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2207, pruned_loss=0.0397, over 973940.60 frames.], batch size: 19, lr: 3.68e-04
2022-05-05 09:50:25,072 INFO [train.py:715] (2/8) Epoch 5, batch 33250, loss[loss=0.1376, simple_loss=0.2097, pruned_loss=0.03276, over 4903.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2209, pruned_loss=0.03969, over 974693.01 frames.], batch size: 19, lr: 3.68e-04
2022-05-05 09:51:03,572 INFO [train.py:715] (2/8) Epoch 5, batch 33300, loss[loss=0.1358, simple_loss=0.2055, pruned_loss=0.03303, over 4765.00 frames.], tot_loss[loss=0.15, simple_loss=0.2211, pruned_loss=0.0394, over 974664.41 frames.], batch size: 12, lr: 3.68e-04
2022-05-05 09:51:41,938 INFO [train.py:715] (2/8) Epoch 5, batch 33350, loss[loss=0.1503, simple_loss=0.225, pruned_loss=0.03775, over 4730.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2217, pruned_loss=0.04002, over 974349.95 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 09:52:21,211 INFO [train.py:715] (2/8) Epoch 5, batch 33400, loss[loss=0.1478, simple_loss=0.2192, pruned_loss=0.03817, over 4964.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2224, pruned_loss=0.0404, over 973862.54 frames.], batch size: 28, lr: 3.68e-04
2022-05-05 09:52:59,900 INFO [train.py:715] (2/8) Epoch 5, batch 33450, loss[loss=0.1299, simple_loss=0.2084, pruned_loss=0.02568, over 4646.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2231, pruned_loss=0.0411, over 973596.97 frames.], batch size: 13, lr: 3.68e-04
2022-05-05 09:53:38,246 INFO [train.py:715] (2/8) Epoch 5, batch 33500, loss[loss=0.1972, simple_loss=0.2612, pruned_loss=0.06658, over 4792.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2242, pruned_loss=0.04124, over 972946.10 frames.], batch size: 24, lr: 3.68e-04
2022-05-05 09:54:16,985 INFO [train.py:715] (2/8) Epoch 5, batch 33550, loss[loss=0.09575, simple_loss=0.162, pruned_loss=0.01475, over 4758.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2224, pruned_loss=0.04059, over 972813.34 frames.], batch size: 12, lr: 3.68e-04
2022-05-05 09:54:55,688 INFO [train.py:715] (2/8) Epoch 5, batch 33600, loss[loss=0.1422, simple_loss=0.2091, pruned_loss=0.03767, over 4983.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2225, pruned_loss=0.04053, over 972976.38 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 09:55:34,355 INFO [train.py:715] (2/8) Epoch 5, batch 33650, loss[loss=0.1578, simple_loss=0.2248, pruned_loss=0.04538, over 4885.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2222, pruned_loss=0.04063, over 971448.70 frames.], batch size: 22, lr: 3.68e-04
2022-05-05 09:56:12,632 INFO [train.py:715] (2/8) Epoch 5, batch 33700, loss[loss=0.142, simple_loss=0.2203, pruned_loss=0.03181, over 4743.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2225, pruned_loss=0.04106, over 970753.53 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 09:56:51,515 INFO [train.py:715] (2/8) Epoch 5, batch 33750, loss[loss=0.1721, simple_loss=0.2374, pruned_loss=0.0534, over 4790.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2214, pruned_loss=0.04048, over 971449.57 frames.], batch size: 14, lr: 3.68e-04
2022-05-05 09:57:30,146 INFO [train.py:715] (2/8) Epoch 5, batch 33800, loss[loss=0.1465, simple_loss=0.224, pruned_loss=0.0345, over 4963.00 frames.], tot_loss[loss=0.151, simple_loss=0.2214, pruned_loss=0.04025, over 971600.71 frames.], batch size: 24, lr: 3.68e-04
2022-05-05 09:58:09,141 INFO [train.py:715] (2/8) Epoch 5, batch 33850, loss[loss=0.1393, simple_loss=0.2213, pruned_loss=0.02868, over 4876.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2211, pruned_loss=0.03972, over 971825.92 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 09:58:47,622 INFO [train.py:715] (2/8) Epoch 5, batch 33900, loss[loss=0.1394, simple_loss=0.2134, pruned_loss=0.03274, over 4822.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2216, pruned_loss=0.04008, over 971456.20 frames.], batch size: 25, lr: 3.68e-04
2022-05-05 09:59:25,968 INFO [train.py:715] (2/8) Epoch 5, batch 33950, loss[loss=0.1734, simple_loss=0.233, pruned_loss=0.05688, over 4835.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2215, pruned_loss=0.04004, over 972443.88 frames.], batch size: 30, lr: 3.68e-04
2022-05-05 10:00:06,982 INFO [train.py:715] (2/8) Epoch 5, batch 34000, loss[loss=0.1348, simple_loss=0.2095, pruned_loss=0.02999, over 4751.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2212, pruned_loss=0.03954, over 972467.69 frames.], batch size: 16, lr: 3.68e-04
2022-05-05 10:00:45,230 INFO [train.py:715] (2/8) Epoch 5, batch 34050, loss[loss=0.1416, simple_loss=0.2142, pruned_loss=0.03451, over 4947.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2209, pruned_loss=0.03991, over 972945.77 frames.], batch size: 23, lr: 3.67e-04
2022-05-05 10:01:23,934 INFO [train.py:715] (2/8) Epoch 5, batch 34100, loss[loss=0.1547, simple_loss=0.2326, pruned_loss=0.03841, over 4843.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2214, pruned_loss=0.04023, over 972693.06 frames.], batch size: 15, lr: 3.67e-04
2022-05-05 10:02:02,747 INFO [train.py:715] (2/8) Epoch 5, batch 34150, loss[loss=0.1309, simple_loss=0.2005, pruned_loss=0.03067, over 4776.00 frames.], tot_loss[loss=0.151, simple_loss=0.221, pruned_loss=0.04052, over 972144.75 frames.], batch size: 17, lr: 3.67e-04
2022-05-05 10:02:41,106 INFO [train.py:715] (2/8) Epoch 5, batch 34200, loss[loss=0.1453, simple_loss=0.2182, pruned_loss=0.03614, over 4781.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2207, pruned_loss=0.04012, over 972655.90 frames.], batch size: 18, lr: 3.67e-04
2022-05-05 10:03:20,096 INFO [train.py:715] (2/8) Epoch 5, batch 34250, loss[loss=0.1424, simple_loss=0.2116, pruned_loss=0.0366, over 4955.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2204, pruned_loss=0.04019, over 973580.48 frames.], batch size: 24, lr: 3.67e-04
2022-05-05 10:03:58,248 INFO [train.py:715] (2/8) Epoch 5, batch 34300, loss[loss=0.1323, simple_loss=0.2, pruned_loss=0.03228, over 4784.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2211, pruned_loss=0.04028, over 973252.34 frames.], batch size: 12, lr: 3.67e-04
2022-05-05 10:04:36,912 INFO [train.py:715] (2/8) Epoch 5, batch 34350, loss[loss=0.1333, simple_loss=0.2125, pruned_loss=0.02708, over 4804.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2208, pruned_loss=0.03981, over 973198.53 frames.], batch size: 12, lr: 3.67e-04
2022-05-05 10:05:14,795 INFO [train.py:715] (2/8) Epoch 5, batch 34400, loss[loss=0.1585, simple_loss=0.2301, pruned_loss=0.04344, over 4842.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2213, pruned_loss=0.03968, over 972980.55 frames.], batch size: 32, lr: 3.67e-04
2022-05-05 10:05:53,764 INFO [train.py:715] (2/8) Epoch 5, batch 34450, loss[loss=0.1467, simple_loss=0.2253, pruned_loss=0.0341, over 4964.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2215, pruned_loss=0.03983, over 972200.68 frames.], batch size: 35, lr: 3.67e-04
2022-05-05 10:06:32,732 INFO [train.py:715] (2/8) Epoch 5, batch 34500, loss[loss=0.1384, simple_loss=0.21, pruned_loss=0.03337, over 4858.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2215, pruned_loss=0.03988, over 972849.40 frames.], batch size: 20, lr: 3.67e-04
2022-05-05 10:07:11,201 INFO [train.py:715] (2/8) Epoch 5, batch 34550, loss[loss=0.1593, simple_loss=0.2325, pruned_loss=0.04304, over 4744.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2219, pruned_loss=0.04038, over 972729.12 frames.], batch size: 16, lr: 3.67e-04
2022-05-05 10:07:49,950 INFO [train.py:715] (2/8) Epoch 5, batch 34600, loss[loss=0.1289, simple_loss=0.2067, pruned_loss=0.02554, over 4660.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2233, pruned_loss=0.04075, over 972766.42 frames.], batch size: 13, lr: 3.67e-04
2022-05-05 10:08:28,654 INFO [train.py:715] (2/8) Epoch 5, batch 34650, loss[loss=0.1801, simple_loss=0.2451, pruned_loss=0.05753, over 4913.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2226, pruned_loss=0.0405, over 973138.74 frames.], batch size: 32, lr: 3.67e-04
2022-05-05 10:09:07,579 INFO [train.py:715] (2/8) Epoch 5, batch 34700, loss[loss=0.1461, simple_loss=0.2098, pruned_loss=0.04119, over 4914.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2226, pruned_loss=0.04042, over 973014.89 frames.], batch size: 23, lr: 3.67e-04
2022-05-05 10:09:44,907 INFO [train.py:715] (2/8) Epoch 5, batch 34750, loss[loss=0.1373, simple_loss=0.2036, pruned_loss=0.03548, over 4825.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2232, pruned_loss=0.04088, over 972434.41 frames.], batch size: 12, lr: 3.67e-04
2022-05-05 10:10:21,600 INFO [train.py:715] (2/8) Epoch 5, batch 34800, loss[loss=0.1134, simple_loss=0.1875, pruned_loss=0.01963, over 4744.00 frames.], tot_loss[loss=0.1518, simple_loss=0.222, pruned_loss=0.04081, over 970175.86 frames.], batch size: 12, lr: 3.67e-04
2022-05-05 10:11:11,225 INFO [train.py:715] (2/8) Epoch 6, batch 0, loss[loss=0.1528, simple_loss=0.2165, pruned_loss=0.04456, over 4778.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2165, pruned_loss=0.04456, over 4778.00 frames.], batch size: 14, lr: 3.46e-04
2022-05-05 10:11:50,184 INFO [train.py:715] (2/8) Epoch 6, batch 50, loss[loss=0.1616, simple_loss=0.2332, pruned_loss=0.04503, over 4897.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2187, pruned_loss=0.03701, over 218739.23 frames.], batch size: 19, lr: 3.46e-04
2022-05-05 10:12:29,111 INFO [train.py:715] (2/8) Epoch 6, batch 100, loss[loss=0.1475, simple_loss=0.2264, pruned_loss=0.03432, over 4802.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2196, pruned_loss=0.03899, over 386084.54 frames.], batch size: 24, lr: 3.46e-04
2022-05-05 10:13:08,348 INFO [train.py:715] (2/8) Epoch 6, batch 150, loss[loss=0.1307, simple_loss=0.2093, pruned_loss=0.02605, over 4808.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2209, pruned_loss=0.03983, over 515879.01 frames.], batch size: 25, lr: 3.46e-04
2022-05-05 10:13:47,628 INFO [train.py:715] (2/8) Epoch 6, batch 200, loss[loss=0.1559, simple_loss=0.2242, pruned_loss=0.04377, over 4984.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2204, pruned_loss=0.03922, over 616815.43 frames.], batch size: 35, lr: 3.45e-04
2022-05-05 10:14:26,641 INFO [train.py:715] (2/8) Epoch 6, batch 250, loss[loss=0.1668, simple_loss=0.2342, pruned_loss=0.04968, over 4986.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2199, pruned_loss=0.03882, over 695210.41 frames.], batch size: 15, lr: 3.45e-04
2022-05-05 10:15:05,464 INFO [train.py:715] (2/8) Epoch 6, batch 300, loss[loss=0.1277, simple_loss=0.2017, pruned_loss=0.02683, over 4893.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2205, pruned_loss=0.03902, over 757174.01 frames.], batch size: 19, lr: 3.45e-04
2022-05-05 10:15:44,448 INFO [train.py:715] (2/8) Epoch 6, batch 350, loss[loss=0.1767, simple_loss=0.245, pruned_loss=0.05427, over 4834.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2212, pruned_loss=0.03968, over 804109.94 frames.], batch size: 26, lr: 3.45e-04
2022-05-05 10:16:23,654 INFO [train.py:715] (2/8) Epoch 6, batch 400, loss[loss=0.1569, simple_loss=0.2272, pruned_loss=0.04331, over 4696.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2217, pruned_loss=0.03993, over 841676.03 frames.], batch size: 15, lr: 3.45e-04
2022-05-05 10:17:02,410 INFO [train.py:715] (2/8) Epoch 6, batch 450, loss[loss=0.1077, simple_loss=0.1849, pruned_loss=0.01526, over 4935.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2209, pruned_loss=0.03933, over 870852.53 frames.], batch size: 21, lr: 3.45e-04
2022-05-05 10:17:41,009 INFO [train.py:715] (2/8) Epoch 6, batch 500, loss[loss=0.1521, simple_loss=0.2288, pruned_loss=0.03767, over 4792.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2218, pruned_loss=0.03979, over 893195.71 frames.], batch size: 24, lr: 3.45e-04
2022-05-05 10:18:20,499 INFO [train.py:715] (2/8) Epoch 6, batch 550, loss[loss=0.1477, simple_loss=0.221, pruned_loss=0.03723, over 4810.00 frames.], tot_loss[loss=0.1499, simple_loss=0.221, pruned_loss=0.03944, over 910734.44 frames.], batch size: 24, lr: 3.45e-04
2022-05-05 10:18:59,385 INFO [train.py:715] (2/8) Epoch 6, batch 600, loss[loss=0.1317, simple_loss=0.2014, pruned_loss=0.03094, over 4793.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2209, pruned_loss=0.03936, over 924717.91 frames.], batch size: 12, lr: 3.45e-04
2022-05-05 10:19:38,401 INFO [train.py:715] (2/8) Epoch 6, batch 650, loss[loss=0.1319, simple_loss=0.2136, pruned_loss=0.02512, over 4980.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2219, pruned_loss=0.03986, over 934941.38 frames.], batch size: 15, lr: 3.45e-04
2022-05-05 10:20:17,487 INFO [train.py:715] (2/8) Epoch 6, batch 700, loss[loss=0.1768, simple_loss=0.236, pruned_loss=0.0588, over 4782.00 frames.], tot_loss[loss=0.1508, simple_loss=0.222, pruned_loss=0.0398, over 943280.14 frames.], batch size: 17, lr: 3.45e-04
2022-05-05 10:20:57,080 INFO [train.py:715] (2/8) Epoch 6, batch 750, loss[loss=0.1569, simple_loss=0.2396, pruned_loss=0.0371, over 4789.00 frames.], tot_loss[loss=0.1509, simple_loss=0.222, pruned_loss=0.03985, over 950535.18 frames.], batch size: 21, lr: 3.45e-04
2022-05-05 10:21:35,855 INFO [train.py:715] (2/8) Epoch 6, batch 800, loss[loss=0.1791, simple_loss=0.2477, pruned_loss=0.05528, over 4747.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2227, pruned_loss=0.04049, over 955418.59 frames.], batch size: 16, lr: 3.45e-04
2022-05-05 10:22:14,570 INFO [train.py:715] (2/8) Epoch 6, batch 850, loss[loss=0.167, simple_loss=0.2314, pruned_loss=0.0513, over 4895.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2229, pruned_loss=0.04034, over 959390.37 frames.], batch size: 22, lr: 3.45e-04
2022-05-05 10:22:54,100 INFO [train.py:715] (2/8) Epoch 6, batch 900, loss[loss=0.1116, simple_loss=0.1856, pruned_loss=0.01874, over 4955.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2214, pruned_loss=0.03956, over 962482.73 frames.], batch size: 21, lr: 3.45e-04
2022-05-05 10:23:33,401 INFO [train.py:715] (2/8) Epoch 6, batch 950, loss[loss=0.1287, simple_loss=0.2053, pruned_loss=0.02601, over 4764.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2205, pruned_loss=0.03932, over 964556.31 frames.], batch size: 19, lr: 3.45e-04
2022-05-05 10:24:12,117 INFO [train.py:715] (2/8) Epoch 6, batch 1000, loss[loss=0.1127, simple_loss=0.1867, pruned_loss=0.0193, over 4656.00 frames.], tot_loss[loss=0.15, simple_loss=0.2208, pruned_loss=0.03964, over 966590.29 frames.], batch size: 13, lr: 3.45e-04
2022-05-05 10:24:51,182 INFO [train.py:715] (2/8) Epoch 6, batch 1050, loss[loss=0.1604, simple_loss=0.2372, pruned_loss=0.04179, over 4880.00 frames.], tot_loss[loss=0.1503, simple_loss=0.221, pruned_loss=0.03977, over 967026.13 frames.], batch size: 16, lr: 3.45e-04
2022-05-05 10:25:30,705 INFO [train.py:715] (2/8) Epoch 6, batch 1100, loss[loss=0.1556, simple_loss=0.2322, pruned_loss=0.03947, over 4844.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2207, pruned_loss=0.03922, over 968505.11 frames.], batch size: 15, lr: 3.45e-04
2022-05-05 10:26:09,924 INFO [train.py:715] (2/8) Epoch 6, batch 1150, loss[loss=0.1643, simple_loss=0.2207, pruned_loss=0.05398, over 4874.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2204, pruned_loss=0.03891, over 968559.50 frames.], batch size: 16, lr: 3.45e-04
2022-05-05 10:26:48,494 INFO [train.py:715] (2/8) Epoch 6, batch 1200, loss[loss=0.1407, simple_loss=0.2217, pruned_loss=0.02986, over 4789.00 frames.], tot_loss[loss=0.1487, simple_loss=0.22, pruned_loss=0.03868, over 968794.97 frames.], batch size: 14, lr: 3.45e-04
2022-05-05 10:27:28,195 INFO [train.py:715] (2/8) Epoch 6, batch 1250, loss[loss=0.1428, simple_loss=0.2209, pruned_loss=0.03238, over 4818.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2194, pruned_loss=0.03816, over 970262.76 frames.], batch size: 27, lr: 3.45e-04
2022-05-05 10:28:07,472 INFO [train.py:715] (2/8) Epoch 6, batch 1300, loss[loss=0.1507, simple_loss=0.2205, pruned_loss=0.0404, over 4895.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2197, pruned_loss=0.03855, over 970481.04 frames.], batch size: 19, lr: 3.45e-04
2022-05-05 10:28:46,067 INFO [train.py:715] (2/8) Epoch 6, batch 1350, loss[loss=0.1487, simple_loss=0.2143, pruned_loss=0.04158, over 4852.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2196, pruned_loss=0.03864, over 971616.66 frames.], batch size: 13, lr: 3.45e-04
2022-05-05 10:29:24,987 INFO [train.py:715] (2/8) Epoch 6, batch 1400, loss[loss=0.1457, simple_loss=0.2206, pruned_loss=0.03536, over 4973.00 frames.], tot_loss[loss=0.1478, simple_loss=0.219, pruned_loss=0.03829, over 972109.89 frames.], batch size: 28, lr: 3.45e-04
2022-05-05 10:30:04,137 INFO [train.py:715] (2/8) Epoch 6, batch 1450, loss[loss=0.161, simple_loss=0.2463, pruned_loss=0.03786, over 4889.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2202, pruned_loss=0.03907, over 972372.23 frames.], batch size: 16, lr: 3.44e-04
2022-05-05 10:30:42,812 INFO [train.py:715] (2/8) Epoch 6, batch 1500, loss[loss=0.1566, simple_loss=0.2264, pruned_loss=0.04345, over 4812.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2202, pruned_loss=0.03901, over 972160.13 frames.], batch size: 21, lr: 3.44e-04
2022-05-05 10:31:21,207 INFO [train.py:715] (2/8) Epoch 6, batch 1550, loss[loss=0.1394, simple_loss=0.2188, pruned_loss=0.03001, over 4907.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2208, pruned_loss=0.03886, over 971456.12 frames.], batch size: 19, lr: 3.44e-04
2022-05-05 10:32:00,468 INFO [train.py:715] (2/8) Epoch 6, batch 1600, loss[loss=0.1746, simple_loss=0.2484, pruned_loss=0.05037, over 4907.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2212, pruned_loss=0.03918, over 971715.76 frames.], batch size: 18, lr: 3.44e-04
2022-05-05 10:32:40,016 INFO [train.py:715] (2/8) Epoch 6, batch 1650, loss[loss=0.1435, simple_loss=0.2105, pruned_loss=0.03823, over 4954.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2222, pruned_loss=0.04, over 972490.01 frames.], batch size: 14, lr: 3.44e-04
2022-05-05 10:33:18,413 INFO [train.py:715] (2/8) Epoch 6, batch 1700, loss[loss=0.153, simple_loss=0.2281, pruned_loss=0.03899, over 4756.00 frames.], tot_loss[loss=0.151, simple_loss=0.2221, pruned_loss=0.03996, over 971907.10 frames.], batch size: 19, lr: 3.44e-04
2022-05-05 10:33:57,729 INFO [train.py:715] (2/8) Epoch 6, batch 1750, loss[loss=0.1568, simple_loss=0.2186, pruned_loss=0.04748, over 4952.00 frames.], tot_loss[loss=0.1506, simple_loss=0.222, pruned_loss=0.03957, over 971883.90 frames.], batch size: 21, lr: 3.44e-04
2022-05-05 10:34:37,315 INFO [train.py:715] (2/8) Epoch 6, batch 1800, loss[loss=0.1489, simple_loss=0.2067, pruned_loss=0.04558, over 4775.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2212, pruned_loss=0.03925, over 971743.28 frames.], batch size: 18, lr: 3.44e-04
2022-05-05 10:35:16,403 INFO [train.py:715] (2/8) Epoch 6, batch 1850, loss[loss=0.138, simple_loss=0.2082, pruned_loss=0.03387, over 4848.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2221, pruned_loss=0.04007, over 971411.82 frames.], batch size: 30, lr: 3.44e-04
2022-05-05 10:35:54,732 INFO [train.py:715] (2/8) Epoch 6, batch 1900, loss[loss=0.1354, simple_loss=0.2169, pruned_loss=0.02697, over 4758.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2199, pruned_loss=0.03918, over 971406.19 frames.], batch size: 19, lr: 3.44e-04
2022-05-05 10:36:34,277 INFO [train.py:715] (2/8) Epoch 6, batch 1950, loss[loss=0.1166, simple_loss=0.1893, pruned_loss=0.02197, over 4831.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2195, pruned_loss=0.03943, over 971719.84 frames.], batch size: 25, lr: 3.44e-04
2022-05-05 10:37:13,037 INFO [train.py:715] (2/8) Epoch 6, batch 2000, loss[loss=0.156, simple_loss=0.224, pruned_loss=0.04401, over 4881.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2201, pruned_loss=0.03953, over 971222.37 frames.], batch size: 16, lr: 3.44e-04
2022-05-05 10:37:52,080 INFO [train.py:715] (2/8) Epoch 6, batch 2050, loss[loss=0.1321, simple_loss=0.1995, pruned_loss=0.03236, over 4982.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2207, pruned_loss=0.03995, over 971705.98 frames.], batch size: 16, lr: 3.44e-04
2022-05-05 10:38:30,929 INFO [train.py:715] (2/8) Epoch 6, batch 2100, loss[loss=0.1611, simple_loss=0.2252, pruned_loss=0.04854, over 4856.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2212, pruned_loss=0.04011, over 972424.23 frames.], batch size: 30, lr: 3.44e-04
2022-05-05 10:39:10,112 INFO [train.py:715] (2/8) Epoch 6, batch 2150, loss[loss=0.1631, simple_loss=0.227, pruned_loss=0.04956, over 4955.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2217, pruned_loss=0.04032, over 973148.55 frames.], batch size: 24, lr: 3.44e-04
2022-05-05 10:39:49,071 INFO [train.py:715] (2/8) Epoch 6, batch 2200, loss[loss=0.1292, simple_loss=0.2077, pruned_loss=0.02532, over 4985.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2219, pruned_loss=0.04013, over 972204.52 frames.], batch size: 28, lr: 3.44e-04
2022-05-05 10:40:27,528 INFO [train.py:715] (2/8) Epoch 6, batch 2250, loss[loss=0.1282, simple_loss=0.1947, pruned_loss=0.0308, over 4985.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2217, pruned_loss=0.04004, over 971733.37 frames.], batch size: 24, lr: 3.44e-04
2022-05-05 10:41:06,874 INFO [train.py:715] (2/8) Epoch 6, batch 2300, loss[loss=0.1629, simple_loss=0.2214, pruned_loss=0.05221, over 4894.00 frames.], tot_loss[loss=0.151, simple_loss=0.2217, pruned_loss=0.04011, over 971229.94 frames.], batch size: 17, lr: 3.44e-04
2022-05-05 10:41:45,980 INFO [train.py:715] (2/8) Epoch 6, batch 2350, loss[loss=0.1539, simple_loss=0.2302, pruned_loss=0.03883, over 4921.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2211, pruned_loss=0.03979, over 970257.93 frames.], batch size: 29, lr: 3.44e-04
2022-05-05 10:42:24,702 INFO [train.py:715] (2/8) Epoch 6, batch 2400, loss[loss=0.1328, simple_loss=0.2089, pruned_loss=0.02833, over 4929.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2205, pruned_loss=0.03937, over 970527.15 frames.], batch size: 18, lr: 3.44e-04
2022-05-05 10:43:03,444 INFO [train.py:715] (2/8) Epoch 6, batch 2450, loss[loss=0.126, simple_loss=0.1929, pruned_loss=0.02954, over 4906.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2199, pruned_loss=0.03921, over 970944.37 frames.], batch size: 18, lr: 3.44e-04
2022-05-05 10:43:42,679 INFO [train.py:715] (2/8) Epoch 6, batch 2500, loss[loss=0.1492, simple_loss=0.2291, pruned_loss=0.03472, over 4924.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2197, pruned_loss=0.03903, over 971074.10 frames.], batch size: 39, lr: 3.44e-04
2022-05-05 10:44:21,860 INFO [train.py:715] (2/8) Epoch 6, batch 2550, loss[loss=0.1647, simple_loss=0.2324, pruned_loss=0.04854, over 4893.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2204, pruned_loss=0.03953, over 972088.31 frames.], batch size: 22, lr: 3.44e-04
2022-05-05 10:45:00,759 INFO [train.py:715] (2/8) Epoch 6, batch 2600, loss[loss=0.1471, simple_loss=0.2124, pruned_loss=0.04093, over 4884.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2203, pruned_loss=0.03947, over 971627.58 frames.], batch size: 16, lr: 3.44e-04
2022-05-05 10:45:40,391 INFO [train.py:715] (2/8) Epoch 6, batch 2650, loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03489, over 4888.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2204, pruned_loss=0.03923, over 971818.45 frames.], batch size: 16, lr: 3.43e-04
2022-05-05 10:46:19,966 INFO [train.py:715] (2/8) Epoch 6, batch 2700, loss[loss=0.1543, simple_loss=0.2307, pruned_loss=0.03896, over 4831.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2212, pruned_loss=0.03995, over 972258.93 frames.], batch size: 15, lr: 3.43e-04
2022-05-05 10:46:58,104 INFO [train.py:715] (2/8) Epoch 6, batch 2750, loss[loss=0.139, simple_loss=0.2144, pruned_loss=0.03177, over 4681.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2218, pruned_loss=0.04019, over 971618.52 frames.], batch size: 15, lr: 3.43e-04
2022-05-05 10:47:37,113 INFO [train.py:715] (2/8) Epoch 6, batch 2800, loss[loss=0.1492, simple_loss=0.2216, pruned_loss=0.03842, over 4692.00 frames.], tot_loss[loss=0.15, simple_loss=0.2206, pruned_loss=0.03974, over 971915.25 frames.], batch size: 15, lr: 3.43e-04
2022-05-05 10:48:16,469 INFO [train.py:715] (2/8) Epoch 6, batch 2850, loss[loss=0.1652, simple_loss=0.2341, pruned_loss=0.04815, over 4904.00 frames.], tot_loss[loss=0.15, simple_loss=0.2208, pruned_loss=0.03966, over 971966.63 frames.], batch size: 19, lr: 3.43e-04
2022-05-05 10:48:55,295 INFO [train.py:715] (2/8) Epoch 6, batch 2900, loss[loss=0.1622, simple_loss=0.2262, pruned_loss=0.04907, over 4694.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2201, pruned_loss=0.03908, over 972296.78 frames.], batch size: 15, lr: 3.43e-04
2022-05-05 10:49:33,640 INFO [train.py:715] (2/8) Epoch 6, batch 2950, loss[loss=0.1349, simple_loss=0.2113, pruned_loss=0.02922, over 4952.00 frames.], tot_loss[loss=0.15, simple_loss=0.2211, pruned_loss=0.03941, over 971786.90 frames.], batch size: 29, lr: 3.43e-04
2022-05-05 10:50:12,862 INFO [train.py:715] (2/8) Epoch 6, batch 3000, loss[loss=0.1511, simple_loss=0.2239, pruned_loss=0.03911, over 4943.00 frames.], tot_loss[loss=0.15, simple_loss=0.2207, pruned_loss=0.03959, over 971971.44 frames.], batch size: 35, lr: 3.43e-04
2022-05-05 10:50:12,862 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 10:50:22,539 INFO [train.py:742] (2/8) 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] (2/8) Epoch 6, batch 3050, loss[loss=0.1431, simple_loss=0.2202, pruned_loss=0.03304, over 4893.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2213, pruned_loss=0.03983, over 972511.13 frames.], batch size: 16, lr: 3.43e-04
2022-05-05 10:51:41,565 INFO [train.py:715] (2/8) Epoch 6, batch 3100, loss[loss=0.1578, simple_loss=0.2269, pruned_loss=0.04438, over 4903.00 frames.], tot_loss[loss=0.1503, simple_loss=0.221, pruned_loss=0.03981, over 972805.44 frames.], batch size: 17, lr: 3.43e-04
2022-05-05 10:52:20,136 INFO [train.py:715] (2/8) Epoch 6, batch 3150, loss[loss=0.1786, simple_loss=0.2552, pruned_loss=0.05097, over 4781.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2223, pruned_loss=0.04013, over 972838.44 frames.], batch size: 18, lr: 3.43e-04
2022-05-05 10:52:58,782 INFO [train.py:715] (2/8) Epoch 6, batch 3200, loss[loss=0.1668, simple_loss=0.2249, pruned_loss=0.05438, over 4764.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2216, pruned_loss=0.03991, over 972845.07 frames.], batch size: 18, lr: 3.43e-04
2022-05-05 10:53:38,608 INFO [train.py:715] (2/8) Epoch 6, batch 3250, loss[loss=0.1645, simple_loss=0.2417, pruned_loss=0.04365, over 4894.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2214, pruned_loss=0.03976, over 971799.29 frames.], batch size: 22, lr: 3.43e-04
2022-05-05 10:54:17,328 INFO [train.py:715] (2/8) Epoch 6, batch 3300, loss[loss=0.1755, simple_loss=0.2331, pruned_loss=0.05898, over 4917.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2215, pruned_loss=0.04, over 972109.53 frames.], batch size: 17, lr: 3.43e-04
2022-05-05 10:54:55,864 INFO [train.py:715] (2/8) Epoch 6, batch 3350, loss[loss=0.1644, simple_loss=0.2496, pruned_loss=0.03962, over 4955.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.03961, over 971323.06 frames.], batch size: 21, lr: 3.43e-04
2022-05-05 10:55:35,256 INFO [train.py:715] (2/8) Epoch 6, batch 3400, loss[loss=0.1467, simple_loss=0.2237, pruned_loss=0.03486, over 4929.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2223, pruned_loss=0.04026, over 971253.09 frames.], batch size: 23, lr: 3.43e-04
2022-05-05 10:56:14,434 INFO [train.py:715] (2/8) Epoch 6, batch 3450, loss[loss=0.1681, simple_loss=0.2425, pruned_loss=0.04687, over 4943.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2217, pruned_loss=0.03979, over 971681.54 frames.], batch size: 23, lr: 3.43e-04
2022-05-05 10:56:52,541 INFO [train.py:715] (2/8) Epoch 6, batch 3500, loss[loss=0.1631, simple_loss=0.234, pruned_loss=0.0461, over 4910.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2223, pruned_loss=0.04022, over 971107.68 frames.], batch size: 18, lr: 3.43e-04
2022-05-05 10:57:31,372 INFO [train.py:715] (2/8) Epoch 6, batch 3550, loss[loss=0.1967, simple_loss=0.2574, pruned_loss=0.06805, over 4922.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2221, pruned_loss=0.04013, over 972062.94 frames.], batch size: 18, lr: 3.43e-04
2022-05-05 10:58:10,830 INFO [train.py:715] (2/8) Epoch 6, batch 3600, loss[loss=0.1271, simple_loss=0.197, pruned_loss=0.02858, over 4836.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2214, pruned_loss=0.03969, over 972203.74 frames.], batch size: 30, lr: 3.43e-04
2022-05-05 10:58:49,772 INFO [train.py:715] (2/8) Epoch 6, batch 3650, loss[loss=0.15, simple_loss=0.2165, pruned_loss=0.04179, over 4776.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2218, pruned_loss=0.03984, over 972366.58 frames.], batch size: 17, lr: 3.43e-04
2022-05-05 10:59:27,964 INFO [train.py:715] (2/8) Epoch 6, batch 3700, loss[loss=0.1318, simple_loss=0.2014, pruned_loss=0.03107, over 4775.00 frames.], tot_loss[loss=0.1496, simple_loss=0.221, pruned_loss=0.03915, over 972791.34 frames.], batch size: 14, lr: 3.43e-04
2022-05-05 11:00:07,229 INFO [train.py:715] (2/8) Epoch 6, batch 3750, loss[loss=0.1624, simple_loss=0.2269, pruned_loss=0.04896, over 4950.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.03951, over 972855.38 frames.], batch size: 39, lr: 3.43e-04
2022-05-05 11:00:46,317 INFO [train.py:715] (2/8) Epoch 6, batch 3800, loss[loss=0.1465, simple_loss=0.2202, pruned_loss=0.03642, over 4838.00 frames.], tot_loss[loss=0.15, simple_loss=0.2212, pruned_loss=0.03945, over 972183.21 frames.], batch size: 25, lr: 3.43e-04
2022-05-05 11:01:24,434 INFO [train.py:715] (2/8) Epoch 6, batch 3850, loss[loss=0.1396, simple_loss=0.201, pruned_loss=0.03913, over 4972.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2213, pruned_loss=0.03941, over 971710.78 frames.], batch size: 35, lr: 3.43e-04
2022-05-05 11:02:03,349 INFO [train.py:715] (2/8) Epoch 6, batch 3900, loss[loss=0.1227, simple_loss=0.1816, pruned_loss=0.03188, over 4830.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2215, pruned_loss=0.03988, over 972676.88 frames.], batch size: 13, lr: 3.42e-04
2022-05-05 11:02:42,646 INFO [train.py:715] (2/8) Epoch 6, batch 3950, loss[loss=0.1619, simple_loss=0.2161, pruned_loss=0.05385, over 4800.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2217, pruned_loss=0.0398, over 972534.17 frames.], batch size: 13, lr: 3.42e-04
2022-05-05 11:03:21,702 INFO [train.py:715] (2/8) Epoch 6, batch 4000, loss[loss=0.1542, simple_loss=0.2258, pruned_loss=0.04129, over 4933.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2219, pruned_loss=0.04032, over 972059.24 frames.], batch size: 23, lr: 3.42e-04
2022-05-05 11:04:00,015 INFO [train.py:715] (2/8) Epoch 6, batch 4050, loss[loss=0.1666, simple_loss=0.2403, pruned_loss=0.04641, over 4912.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2214, pruned_loss=0.03969, over 972684.73 frames.], batch size: 17, lr: 3.42e-04
2022-05-05 11:04:39,115 INFO [train.py:715] (2/8) Epoch 6, batch 4100, loss[loss=0.1478, simple_loss=0.2081, pruned_loss=0.04371, over 4962.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2211, pruned_loss=0.03964, over 973018.08 frames.], batch size: 15, lr: 3.42e-04
2022-05-05 11:05:17,849 INFO [train.py:715] (2/8) Epoch 6, batch 4150, loss[loss=0.1328, simple_loss=0.209, pruned_loss=0.0283, over 4947.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2215, pruned_loss=0.03958, over 972563.49 frames.], batch size: 29, lr: 3.42e-04
2022-05-05 11:05:56,005 INFO [train.py:715] (2/8) Epoch 6, batch 4200, loss[loss=0.1804, simple_loss=0.2508, pruned_loss=0.05498, over 4934.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2216, pruned_loss=0.03957, over 972895.18 frames.], batch size: 23, lr: 3.42e-04
2022-05-05 11:06:34,726 INFO [train.py:715] (2/8) Epoch 6, batch 4250, loss[loss=0.1844, simple_loss=0.2446, pruned_loss=0.06207, over 4963.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2213, pruned_loss=0.03951, over 971808.65 frames.], batch size: 15, lr: 3.42e-04
2022-05-05 11:07:13,788 INFO [train.py:715] (2/8) Epoch 6, batch 4300, loss[loss=0.1676, simple_loss=0.2506, pruned_loss=0.04229, over 4765.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2216, pruned_loss=0.0394, over 972280.98 frames.], batch size: 18, lr: 3.42e-04
2022-05-05 11:07:52,579 INFO [train.py:715] (2/8) Epoch 6, batch 4350, loss[loss=0.1564, simple_loss=0.2363, pruned_loss=0.03826, over 4914.00 frames.], tot_loss[loss=0.1497, simple_loss=0.221, pruned_loss=0.03921, over 972691.88 frames.], batch size: 23, lr: 3.42e-04
2022-05-05 11:08:30,488 INFO [train.py:715] (2/8) Epoch 6, batch 4400, loss[loss=0.1477, simple_loss=0.2257, pruned_loss=0.03487, over 4808.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2212, pruned_loss=0.03893, over 972982.68 frames.], batch size: 25, lr: 3.42e-04
2022-05-05 11:09:08,935 INFO [train.py:715] (2/8) Epoch 6, batch 4450, loss[loss=0.1321, simple_loss=0.1972, pruned_loss=0.03352, over 4759.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2207, pruned_loss=0.03905, over 972739.93 frames.], batch size: 19, lr: 3.42e-04
2022-05-05 11:09:48,072 INFO [train.py:715] (2/8) Epoch 6, batch 4500, loss[loss=0.1417, simple_loss=0.2121, pruned_loss=0.03566, over 4988.00 frames.], tot_loss[loss=0.15, simple_loss=0.2213, pruned_loss=0.03935, over 973258.82 frames.], batch size: 31, lr: 3.42e-04
2022-05-05 11:10:26,353 INFO [train.py:715] (2/8) Epoch 6, batch 4550, loss[loss=0.1273, simple_loss=0.2039, pruned_loss=0.02534, over 4922.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2216, pruned_loss=0.03967, over 972776.28 frames.], batch size: 29, lr: 3.42e-04
2022-05-05 11:11:04,821 INFO [train.py:715] (2/8) Epoch 6, batch 4600, loss[loss=0.1499, simple_loss=0.2131, pruned_loss=0.04339, over 4945.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2223, pruned_loss=0.03992, over 972617.90 frames.], batch size: 35, lr: 3.42e-04
2022-05-05 11:11:44,243 INFO [train.py:715] (2/8) Epoch 6, batch 4650, loss[loss=0.1816, simple_loss=0.2456, pruned_loss=0.05883, over 4833.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2221, pruned_loss=0.04012, over 972336.55 frames.], batch size: 26, lr: 3.42e-04
2022-05-05 11:12:23,350 INFO [train.py:715] (2/8) Epoch 6, batch 4700, loss[loss=0.1341, simple_loss=0.2105, pruned_loss=0.02891, over 4940.00 frames.], tot_loss[loss=0.151, simple_loss=0.2221, pruned_loss=0.03993, over 972159.45 frames.], batch size: 21, lr: 3.42e-04
2022-05-05 11:13:01,631 INFO [train.py:715] (2/8) Epoch 6, batch 4750, loss[loss=0.1576, simple_loss=0.2261, pruned_loss=0.04459, over 4837.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03974, over 972420.60 frames.], batch size: 30, lr: 3.42e-04
2022-05-05 11:13:40,646 INFO [train.py:715] (2/8) Epoch 6, batch 4800, loss[loss=0.1414, simple_loss=0.2021, pruned_loss=0.04039, over 4934.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2205, pruned_loss=0.03912, over 972960.11 frames.], batch size: 23, lr: 3.42e-04
2022-05-05 11:14:19,739 INFO [train.py:715] (2/8) Epoch 6, batch 4850, loss[loss=0.134, simple_loss=0.2108, pruned_loss=0.02859, over 4699.00 frames.], tot_loss[loss=0.1509, simple_loss=0.222, pruned_loss=0.03986, over 973220.08 frames.], batch size: 15, lr: 3.42e-04
2022-05-05 11:14:58,280 INFO [train.py:715] (2/8) Epoch 6, batch 4900, loss[loss=0.1512, simple_loss=0.2286, pruned_loss=0.0369, over 4907.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2216, pruned_loss=0.03948, over 972803.44 frames.], batch size: 18, lr: 3.42e-04
2022-05-05 11:15:37,166 INFO [train.py:715] (2/8) Epoch 6, batch 4950, loss[loss=0.1451, simple_loss=0.2182, pruned_loss=0.03604, over 4785.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2204, pruned_loss=0.03896, over 973162.45 frames.], batch size: 18, lr: 3.42e-04
2022-05-05 11:16:16,922 INFO [train.py:715] (2/8) Epoch 6, batch 5000, loss[loss=0.1761, simple_loss=0.2452, pruned_loss=0.05356, over 4944.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2201, pruned_loss=0.03872, over 972760.84 frames.], batch size: 21, lr: 3.42e-04
2022-05-05 11:16:55,993 INFO [train.py:715] (2/8) Epoch 6, batch 5050, loss[loss=0.1543, simple_loss=0.2298, pruned_loss=0.03946, over 4880.00 frames.], tot_loss[loss=0.15, simple_loss=0.2212, pruned_loss=0.03935, over 972748.63 frames.], batch size: 16, lr: 3.42e-04
2022-05-05 11:17:34,329 INFO [train.py:715] (2/8) Epoch 6, batch 5100, loss[loss=0.2208, simple_loss=0.2678, pruned_loss=0.08692, over 4763.00 frames.], tot_loss[loss=0.1501, simple_loss=0.221, pruned_loss=0.03958, over 972402.12 frames.], batch size: 14, lr: 3.42e-04
2022-05-05 11:18:13,258 INFO [train.py:715] (2/8) Epoch 6, batch 5150, loss[loss=0.1584, simple_loss=0.2011, pruned_loss=0.0579, over 4973.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2208, pruned_loss=0.03952, over 972820.82 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:18:52,359 INFO [train.py:715] (2/8) Epoch 6, batch 5200, loss[loss=0.1489, simple_loss=0.2199, pruned_loss=0.03901, over 4644.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2207, pruned_loss=0.03946, over 972691.03 frames.], batch size: 13, lr: 3.41e-04
2022-05-05 11:19:30,493 INFO [train.py:715] (2/8) Epoch 6, batch 5250, loss[loss=0.1531, simple_loss=0.2196, pruned_loss=0.04326, over 4804.00 frames.], tot_loss[loss=0.15, simple_loss=0.2205, pruned_loss=0.03978, over 972625.13 frames.], batch size: 21, lr: 3.41e-04
2022-05-05 11:20:09,575 INFO [train.py:715] (2/8) Epoch 6, batch 5300, loss[loss=0.147, simple_loss=0.2233, pruned_loss=0.03531, over 4687.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2205, pruned_loss=0.0397, over 973006.19 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:20:48,896 INFO [train.py:715] (2/8) Epoch 6, batch 5350, loss[loss=0.1661, simple_loss=0.2447, pruned_loss=0.0437, over 4988.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2201, pruned_loss=0.03912, over 973478.48 frames.], batch size: 28, lr: 3.41e-04
2022-05-05 11:21:27,941 INFO [train.py:715] (2/8) Epoch 6, batch 5400, loss[loss=0.1462, simple_loss=0.2244, pruned_loss=0.03402, over 4953.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2215, pruned_loss=0.03998, over 973345.26 frames.], batch size: 29, lr: 3.41e-04
2022-05-05 11:22:06,520 INFO [train.py:715] (2/8) Epoch 6, batch 5450, loss[loss=0.1806, simple_loss=0.2524, pruned_loss=0.05443, over 4891.00 frames.], tot_loss[loss=0.151, simple_loss=0.2218, pruned_loss=0.04008, over 973248.12 frames.], batch size: 16, lr: 3.41e-04
2022-05-05 11:22:45,328 INFO [train.py:715] (2/8) Epoch 6, batch 5500, loss[loss=0.1641, simple_loss=0.23, pruned_loss=0.04916, over 4984.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2214, pruned_loss=0.04014, over 973679.97 frames.], batch size: 14, lr: 3.41e-04
2022-05-05 11:23:24,193 INFO [train.py:715] (2/8) Epoch 6, batch 5550, loss[loss=0.1523, simple_loss=0.2132, pruned_loss=0.04575, over 4842.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2211, pruned_loss=0.03981, over 973696.49 frames.], batch size: 13, lr: 3.41e-04
2022-05-05 11:24:02,781 INFO [train.py:715] (2/8) Epoch 6, batch 5600, loss[loss=0.1587, simple_loss=0.2233, pruned_loss=0.04704, over 4780.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2214, pruned_loss=0.0402, over 973210.03 frames.], batch size: 14, lr: 3.41e-04
2022-05-05 11:24:42,275 INFO [train.py:715] (2/8) Epoch 6, batch 5650, loss[loss=0.134, simple_loss=0.2056, pruned_loss=0.03118, over 4859.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2205, pruned_loss=0.03985, over 972427.69 frames.], batch size: 12, lr: 3.41e-04
2022-05-05 11:25:21,628 INFO [train.py:715] (2/8) Epoch 6, batch 5700, loss[loss=0.1342, simple_loss=0.2028, pruned_loss=0.03282, over 4809.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2208, pruned_loss=0.03983, over 972567.17 frames.], batch size: 12, lr: 3.41e-04
2022-05-05 11:26:00,234 INFO [train.py:715] (2/8) Epoch 6, batch 5750, loss[loss=0.1428, simple_loss=0.2033, pruned_loss=0.04111, over 4830.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2209, pruned_loss=0.03985, over 972606.04 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:26:38,646 INFO [train.py:715] (2/8) Epoch 6, batch 5800, loss[loss=0.1539, simple_loss=0.2257, pruned_loss=0.04105, over 4895.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03918, over 972198.42 frames.], batch size: 19, lr: 3.41e-04
2022-05-05 11:27:17,532 INFO [train.py:715] (2/8) Epoch 6, batch 5850, loss[loss=0.1379, simple_loss=0.2124, pruned_loss=0.03168, over 4753.00 frames.], tot_loss[loss=0.15, simple_loss=0.221, pruned_loss=0.03945, over 973363.75 frames.], batch size: 19, lr: 3.41e-04
2022-05-05 11:27:56,995 INFO [train.py:715] (2/8) Epoch 6, batch 5900, loss[loss=0.1403, simple_loss=0.2127, pruned_loss=0.0339, over 4705.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2211, pruned_loss=0.03938, over 973299.10 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:28:34,910 INFO [train.py:715] (2/8) Epoch 6, batch 5950, loss[loss=0.1476, simple_loss=0.2193, pruned_loss=0.03798, over 4847.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.03959, over 973578.29 frames.], batch size: 30, lr: 3.41e-04
2022-05-05 11:29:14,285 INFO [train.py:715] (2/8) Epoch 6, batch 6000, loss[loss=0.1419, simple_loss=0.2085, pruned_loss=0.03764, over 4815.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2203, pruned_loss=0.03946, over 973730.94 frames.], batch size: 13, lr: 3.41e-04
2022-05-05 11:29:14,286 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 11:29:24,854 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1095, simple_loss=0.1945, pruned_loss=0.01229, over 914524.00 frames.
2022-05-05 11:30:04,469 INFO [train.py:715] (2/8) Epoch 6, batch 6050, loss[loss=0.1371, simple_loss=0.2029, pruned_loss=0.03566, over 4979.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2195, pruned_loss=0.03903, over 973918.27 frames.], batch size: 28, lr: 3.41e-04
2022-05-05 11:30:43,725 INFO [train.py:715] (2/8) Epoch 6, batch 6100, loss[loss=0.1254, simple_loss=0.1955, pruned_loss=0.02763, over 4983.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2208, pruned_loss=0.03978, over 973464.00 frames.], batch size: 35, lr: 3.41e-04
2022-05-05 11:31:23,121 INFO [train.py:715] (2/8) Epoch 6, batch 6150, loss[loss=0.1419, simple_loss=0.2233, pruned_loss=0.03023, over 4979.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2202, pruned_loss=0.03947, over 973767.14 frames.], batch size: 35, lr: 3.41e-04
2022-05-05 11:32:01,615 INFO [train.py:715] (2/8) Epoch 6, batch 6200, loss[loss=0.1448, simple_loss=0.2148, pruned_loss=0.0374, over 4850.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2217, pruned_loss=0.04031, over 972930.94 frames.], batch size: 20, lr: 3.41e-04
2022-05-05 11:32:40,952 INFO [train.py:715] (2/8) Epoch 6, batch 6250, loss[loss=0.1402, simple_loss=0.2145, pruned_loss=0.03293, over 4868.00 frames.], tot_loss[loss=0.1511, simple_loss=0.222, pruned_loss=0.0401, over 972838.92 frames.], batch size: 22, lr: 3.41e-04
2022-05-05 11:33:20,238 INFO [train.py:715] (2/8) Epoch 6, batch 6300, loss[loss=0.1357, simple_loss=0.2144, pruned_loss=0.02851, over 4694.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.03953, over 972928.13 frames.], batch size: 15, lr: 3.41e-04
2022-05-05 11:33:58,707 INFO [train.py:715] (2/8) Epoch 6, batch 6350, loss[loss=0.1502, simple_loss=0.2031, pruned_loss=0.04871, over 4760.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2203, pruned_loss=0.03934, over 972508.81 frames.], batch size: 12, lr: 3.41e-04
2022-05-05 11:34:37,340 INFO [train.py:715] (2/8) Epoch 6, batch 6400, loss[loss=0.143, simple_loss=0.2212, pruned_loss=0.03246, over 4933.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2205, pruned_loss=0.03938, over 972217.35 frames.], batch size: 21, lr: 3.40e-04
2022-05-05 11:35:16,565 INFO [train.py:715] (2/8) Epoch 6, batch 6450, loss[loss=0.1282, simple_loss=0.2034, pruned_loss=0.02647, over 4685.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2203, pruned_loss=0.03949, over 971435.18 frames.], batch size: 15, lr: 3.40e-04
2022-05-05 11:35:55,387 INFO [train.py:715] (2/8) Epoch 6, batch 6500, loss[loss=0.1501, simple_loss=0.2089, pruned_loss=0.04566, over 4822.00 frames.], tot_loss[loss=0.1495, simple_loss=0.22, pruned_loss=0.03948, over 972252.66 frames.], batch size: 13, lr: 3.40e-04
2022-05-05 11:36:33,973 INFO [train.py:715] (2/8) Epoch 6, batch 6550, loss[loss=0.1618, simple_loss=0.2162, pruned_loss=0.05368, over 4956.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03909, over 972338.70 frames.], batch size: 35, lr: 3.40e-04
2022-05-05 11:37:12,777 INFO [train.py:715] (2/8) Epoch 6, batch 6600, loss[loss=0.1455, simple_loss=0.2204, pruned_loss=0.03533, over 4916.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03873, over 972942.11 frames.], batch size: 18, lr: 3.40e-04
2022-05-05 11:37:52,973 INFO [train.py:715] (2/8) Epoch 6, batch 6650, loss[loss=0.1249, simple_loss=0.2018, pruned_loss=0.02394, over 4790.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2194, pruned_loss=0.03852, over 972403.22 frames.], batch size: 14, lr: 3.40e-04
2022-05-05 11:38:31,783 INFO [train.py:715] (2/8) Epoch 6, batch 6700, loss[loss=0.1537, simple_loss=0.234, pruned_loss=0.03665, over 4987.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2205, pruned_loss=0.03909, over 972378.59 frames.], batch size: 14, lr: 3.40e-04
2022-05-05 11:39:10,522 INFO [train.py:715] (2/8) Epoch 6, batch 6750, loss[loss=0.1484, simple_loss=0.225, pruned_loss=0.03593, over 4755.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2213, pruned_loss=0.03998, over 972649.93 frames.], batch size: 16, lr: 3.40e-04
2022-05-05 11:39:49,800 INFO [train.py:715] (2/8) Epoch 6, batch 6800, loss[loss=0.2168, simple_loss=0.2664, pruned_loss=0.08359, over 4860.00 frames.], tot_loss[loss=0.1518, simple_loss=0.222, pruned_loss=0.04081, over 971886.86 frames.], batch size: 32, lr: 3.40e-04
2022-05-05 11:40:28,791 INFO [train.py:715] (2/8) Epoch 6, batch 6850, loss[loss=0.1387, simple_loss=0.2174, pruned_loss=0.03004, over 4774.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2218, pruned_loss=0.04079, over 971640.47 frames.], batch size: 18, lr: 3.40e-04
2022-05-05 11:41:06,842 INFO [train.py:715] (2/8) Epoch 6, batch 6900, loss[loss=0.1345, simple_loss=0.2152, pruned_loss=0.02688, over 4704.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2215, pruned_loss=0.04034, over 971861.26 frames.], batch size: 15, lr: 3.40e-04
2022-05-05 11:41:45,909 INFO [train.py:715] (2/8) Epoch 6, batch 6950, loss[loss=0.152, simple_loss=0.2358, pruned_loss=0.03412, over 4886.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2217, pruned_loss=0.04024, over 971137.60 frames.], batch size: 16, lr: 3.40e-04
2022-05-05 11:42:25,621 INFO [train.py:715] (2/8) Epoch 6, batch 7000, loss[loss=0.1613, simple_loss=0.2311, pruned_loss=0.04577, over 4882.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2215, pruned_loss=0.04001, over 970857.07 frames.], batch size: 16, lr: 3.40e-04
2022-05-05 11:43:04,217 INFO [train.py:715] (2/8) Epoch 6, batch 7050, loss[loss=0.1608, simple_loss=0.2348, pruned_loss=0.04336, over 4845.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2203, pruned_loss=0.03949, over 971135.93 frames.], batch size: 30, lr: 3.40e-04
2022-05-05 11:43:42,733 INFO [train.py:715] (2/8) Epoch 6, batch 7100, loss[loss=0.1252, simple_loss=0.2042, pruned_loss=0.02308, over 4965.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2208, pruned_loss=0.03987, over 970637.24 frames.], batch size: 24, lr: 3.40e-04
2022-05-05 11:44:25,532 INFO [train.py:715] (2/8) Epoch 6, batch 7150, loss[loss=0.1416, simple_loss=0.2027, pruned_loss=0.04021, over 4801.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2209, pruned_loss=0.03994, over 970871.15 frames.], batch size: 12, lr: 3.40e-04
2022-05-05 11:45:04,231 INFO [train.py:715] (2/8) Epoch 6, batch 7200, loss[loss=0.1482, simple_loss=0.2225, pruned_loss=0.03694, over 4948.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2207, pruned_loss=0.03994, over 971728.61 frames.], batch size: 24, lr: 3.40e-04
2022-05-05 11:45:42,695 INFO [train.py:715] (2/8) Epoch 6, batch 7250, loss[loss=0.1492, simple_loss=0.2213, pruned_loss=0.03859, over 4966.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2215, pruned_loss=0.03994, over 972568.11 frames.], batch size: 39, lr: 3.40e-04
2022-05-05 11:46:21,451 INFO [train.py:715] (2/8) Epoch 6, batch 7300, loss[loss=0.1429, simple_loss=0.2182, pruned_loss=0.03383, over 4823.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2203, pruned_loss=0.03928, over 972834.30 frames.], batch size: 26, lr: 3.40e-04
2022-05-05 11:47:01,049 INFO [train.py:715] (2/8) Epoch 6, batch 7350, loss[loss=0.1785, simple_loss=0.2388, pruned_loss=0.05914, over 4979.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2208, pruned_loss=0.03924, over 972967.43 frames.], batch size: 35, lr: 3.40e-04
2022-05-05 11:47:38,867 INFO [train.py:715] (2/8) Epoch 6, batch 7400, loss[loss=0.156, simple_loss=0.2205, pruned_loss=0.04578, over 4917.00 frames.], tot_loss[loss=0.1487, simple_loss=0.22, pruned_loss=0.03874, over 972325.45 frames.], batch size: 17, lr: 3.40e-04
2022-05-05 11:48:18,379 INFO [train.py:715] (2/8) Epoch 6, batch 7450, loss[loss=0.1428, simple_loss=0.2187, pruned_loss=0.03351, over 4973.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2206, pruned_loss=0.03926, over 972482.68 frames.], batch size: 15, lr: 3.40e-04
2022-05-05 11:48:56,996 INFO [train.py:715] (2/8) Epoch 6, batch 7500, loss[loss=0.1322, simple_loss=0.2082, pruned_loss=0.02809, over 4842.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2207, pruned_loss=0.03955, over 972751.86 frames.], batch size: 30, lr: 3.40e-04
2022-05-05 11:49:35,691 INFO [train.py:715] (2/8) Epoch 6, batch 7550, loss[loss=0.1511, simple_loss=0.2156, pruned_loss=0.04331, over 4909.00 frames.], tot_loss[loss=0.1492, simple_loss=0.22, pruned_loss=0.03915, over 973051.15 frames.], batch size: 18, lr: 3.40e-04
2022-05-05 11:50:14,634 INFO [train.py:715] (2/8) Epoch 6, batch 7600, loss[loss=0.1442, simple_loss=0.2127, pruned_loss=0.03788, over 4883.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2205, pruned_loss=0.039, over 973068.43 frames.], batch size: 19, lr: 3.40e-04
2022-05-05 11:50:53,762 INFO [train.py:715] (2/8) Epoch 6, batch 7650, loss[loss=0.1436, simple_loss=0.2094, pruned_loss=0.0389, over 4823.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03806, over 973114.08 frames.], batch size: 26, lr: 3.40e-04
2022-05-05 11:51:33,383 INFO [train.py:715] (2/8) Epoch 6, batch 7700, loss[loss=0.1265, simple_loss=0.202, pruned_loss=0.02548, over 4889.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2191, pruned_loss=0.03809, over 972837.19 frames.], batch size: 22, lr: 3.39e-04
2022-05-05 11:52:11,584 INFO [train.py:715] (2/8) Epoch 6, batch 7750, loss[loss=0.114, simple_loss=0.1898, pruned_loss=0.01913, over 4788.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2189, pruned_loss=0.03769, over 972892.79 frames.], batch size: 12, lr: 3.39e-04
2022-05-05 11:52:51,084 INFO [train.py:715] (2/8) Epoch 6, batch 7800, loss[loss=0.1602, simple_loss=0.2237, pruned_loss=0.04835, over 4858.00 frames.], tot_loss[loss=0.1482, simple_loss=0.22, pruned_loss=0.03816, over 972756.09 frames.], batch size: 32, lr: 3.39e-04
2022-05-05 11:53:30,019 INFO [train.py:715] (2/8) Epoch 6, batch 7850, loss[loss=0.1534, simple_loss=0.2218, pruned_loss=0.04254, over 4948.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2209, pruned_loss=0.03875, over 972947.29 frames.], batch size: 29, lr: 3.39e-04
2022-05-05 11:54:08,583 INFO [train.py:715] (2/8) Epoch 6, batch 7900, loss[loss=0.1317, simple_loss=0.2096, pruned_loss=0.02692, over 4953.00 frames.], tot_loss[loss=0.149, simple_loss=0.2205, pruned_loss=0.03871, over 973117.76 frames.], batch size: 21, lr: 3.39e-04
2022-05-05 11:54:47,343 INFO [train.py:715] (2/8) Epoch 6, batch 7950, loss[loss=0.1555, simple_loss=0.223, pruned_loss=0.04401, over 4916.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2204, pruned_loss=0.03896, over 972576.59 frames.], batch size: 18, lr: 3.39e-04
2022-05-05 11:55:26,518 INFO [train.py:715] (2/8) Epoch 6, batch 8000, loss[loss=0.1429, simple_loss=0.2105, pruned_loss=0.03766, over 4903.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2196, pruned_loss=0.03873, over 972720.80 frames.], batch size: 19, lr: 3.39e-04
2022-05-05 11:56:05,893 INFO [train.py:715] (2/8) Epoch 6, batch 8050, loss[loss=0.1445, simple_loss=0.2049, pruned_loss=0.04205, over 4806.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2203, pruned_loss=0.03924, over 971896.64 frames.], batch size: 14, lr: 3.39e-04
2022-05-05 11:56:43,893 INFO [train.py:715] (2/8) Epoch 6, batch 8100, loss[loss=0.1513, simple_loss=0.2399, pruned_loss=0.03138, over 4795.00 frames.], tot_loss[loss=0.1493, simple_loss=0.22, pruned_loss=0.0393, over 971574.62 frames.], batch size: 21, lr: 3.39e-04
2022-05-05 11:57:22,883 INFO [train.py:715] (2/8) Epoch 6, batch 8150, loss[loss=0.1647, simple_loss=0.2411, pruned_loss=0.04409, over 4835.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2197, pruned_loss=0.03933, over 971625.50 frames.], batch size: 15, lr: 3.39e-04
2022-05-05 11:58:01,957 INFO [train.py:715] (2/8) Epoch 6, batch 8200, loss[loss=0.1443, simple_loss=0.2129, pruned_loss=0.03788, over 4863.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2192, pruned_loss=0.03884, over 972042.95 frames.], batch size: 16, lr: 3.39e-04
2022-05-05 11:58:41,279 INFO [train.py:715] (2/8) Epoch 6, batch 8250, loss[loss=0.1241, simple_loss=0.2021, pruned_loss=0.02299, over 4922.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2192, pruned_loss=0.03899, over 971855.85 frames.], batch size: 21, lr: 3.39e-04
2022-05-05 11:59:19,581 INFO [train.py:715] (2/8) Epoch 6, batch 8300, loss[loss=0.1276, simple_loss=0.1972, pruned_loss=0.02902, over 4970.00 frames.], tot_loss[loss=0.148, simple_loss=0.2188, pruned_loss=0.03859, over 972144.51 frames.], batch size: 14, lr: 3.39e-04
2022-05-05 11:59:58,759 INFO [train.py:715] (2/8) Epoch 6, batch 8350, loss[loss=0.1233, simple_loss=0.1885, pruned_loss=0.029, over 4745.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2194, pruned_loss=0.03903, over 972144.82 frames.], batch size: 19, lr: 3.39e-04
2022-05-05 12:00:37,621 INFO [train.py:715] (2/8) Epoch 6, batch 8400, loss[loss=0.1759, simple_loss=0.2562, pruned_loss=0.04783, over 4806.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2185, pruned_loss=0.0388, over 972379.42 frames.], batch size: 21, lr: 3.39e-04
2022-05-05 12:01:15,842 INFO [train.py:715] (2/8) Epoch 6, batch 8450, loss[loss=0.1346, simple_loss=0.2006, pruned_loss=0.03429, over 4791.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2184, pruned_loss=0.03898, over 972907.09 frames.], batch size: 13, lr: 3.39e-04
2022-05-05 12:01:54,987 INFO [train.py:715] (2/8) Epoch 6, batch 8500, loss[loss=0.129, simple_loss=0.2003, pruned_loss=0.02883, over 4814.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2182, pruned_loss=0.03856, over 973005.85 frames.], batch size: 26, lr: 3.39e-04
2022-05-05 12:02:33,548 INFO [train.py:715] (2/8) Epoch 6, batch 8550, loss[loss=0.1783, simple_loss=0.2511, pruned_loss=0.05277, over 4896.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2195, pruned_loss=0.03913, over 973737.85 frames.], batch size: 22, lr: 3.39e-04
2022-05-05 12:03:12,438 INFO [train.py:715] (2/8) Epoch 6, batch 8600, loss[loss=0.1441, simple_loss=0.2147, pruned_loss=0.03674, over 4988.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2197, pruned_loss=0.0391, over 973386.60 frames.], batch size: 31, lr: 3.39e-04
2022-05-05 12:03:50,310 INFO [train.py:715] (2/8) Epoch 6, batch 8650, loss[loss=0.1457, simple_loss=0.2121, pruned_loss=0.03962, over 4842.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2196, pruned_loss=0.03948, over 972958.55 frames.], batch size: 32, lr: 3.39e-04
2022-05-05 12:04:29,732 INFO [train.py:715] (2/8) Epoch 6, batch 8700, loss[loss=0.1569, simple_loss=0.2382, pruned_loss=0.03785, over 4906.00 frames.], tot_loss[loss=0.148, simple_loss=0.2189, pruned_loss=0.03862, over 971820.61 frames.], batch size: 29, lr: 3.39e-04
2022-05-05 12:05:08,432 INFO [train.py:715] (2/8) Epoch 6, batch 8750, loss[loss=0.1688, simple_loss=0.2432, pruned_loss=0.04723, over 4809.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2197, pruned_loss=0.03883, over 971831.39 frames.], batch size: 25, lr: 3.39e-04
2022-05-05 12:05:46,858 INFO [train.py:715] (2/8) Epoch 6, batch 8800, loss[loss=0.1263, simple_loss=0.2021, pruned_loss=0.02527, over 4821.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2201, pruned_loss=0.03956, over 972285.47 frames.], batch size: 26, lr: 3.39e-04
2022-05-05 12:06:25,686 INFO [train.py:715] (2/8) Epoch 6, batch 8850, loss[loss=0.1782, simple_loss=0.2428, pruned_loss=0.05684, over 4985.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2204, pruned_loss=0.03955, over 972634.12 frames.], batch size: 14, lr: 3.39e-04
2022-05-05 12:07:04,757 INFO [train.py:715] (2/8) Epoch 6, batch 8900, loss[loss=0.135, simple_loss=0.2069, pruned_loss=0.03156, over 4726.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2202, pruned_loss=0.03922, over 971917.42 frames.], batch size: 16, lr: 3.39e-04
2022-05-05 12:07:43,994 INFO [train.py:715] (2/8) Epoch 6, batch 8950, loss[loss=0.1769, simple_loss=0.2461, pruned_loss=0.05388, over 4922.00 frames.], tot_loss[loss=0.1503, simple_loss=0.221, pruned_loss=0.03974, over 972366.84 frames.], batch size: 18, lr: 3.38e-04
2022-05-05 12:08:22,492 INFO [train.py:715] (2/8) Epoch 6, batch 9000, loss[loss=0.1401, simple_loss=0.2082, pruned_loss=0.03599, over 4792.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2213, pruned_loss=0.03957, over 972801.69 frames.], batch size: 14, lr: 3.38e-04
2022-05-05 12:08:22,492 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 12:08:35,890 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1094, simple_loss=0.1946, pruned_loss=0.01213, over 914524.00 frames.
2022-05-05 12:09:14,899 INFO [train.py:715] (2/8) Epoch 6, batch 9050, loss[loss=0.1313, simple_loss=0.2076, pruned_loss=0.02745, over 4889.00 frames.], tot_loss[loss=0.1499, simple_loss=0.221, pruned_loss=0.03937, over 972055.55 frames.], batch size: 17, lr: 3.38e-04
2022-05-05 12:09:53,934 INFO [train.py:715] (2/8) Epoch 6, batch 9100, loss[loss=0.135, simple_loss=0.2072, pruned_loss=0.03142, over 4754.00 frames.], tot_loss[loss=0.15, simple_loss=0.2209, pruned_loss=0.03951, over 971721.76 frames.], batch size: 16, lr: 3.38e-04
2022-05-05 12:10:33,369 INFO [train.py:715] (2/8) Epoch 6, batch 9150, loss[loss=0.1262, simple_loss=0.1884, pruned_loss=0.03205, over 4983.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2212, pruned_loss=0.03976, over 971538.26 frames.], batch size: 14, lr: 3.38e-04
2022-05-05 12:11:11,395 INFO [train.py:715] (2/8) Epoch 6, batch 9200, loss[loss=0.1432, simple_loss=0.2168, pruned_loss=0.03477, over 4937.00 frames.], tot_loss[loss=0.1509, simple_loss=0.222, pruned_loss=0.03997, over 971726.50 frames.], batch size: 21, lr: 3.38e-04
2022-05-05 12:11:50,797 INFO [train.py:715] (2/8) Epoch 6, batch 9250, loss[loss=0.1568, simple_loss=0.2316, pruned_loss=0.04098, over 4980.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2214, pruned_loss=0.03967, over 971452.85 frames.], batch size: 35, lr: 3.38e-04
2022-05-05 12:12:29,890 INFO [train.py:715] (2/8) Epoch 6, batch 9300, loss[loss=0.1292, simple_loss=0.2072, pruned_loss=0.0256, over 4774.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2218, pruned_loss=0.03986, over 971481.49 frames.], batch size: 18, lr: 3.38e-04
2022-05-05 12:13:08,402 INFO [train.py:715] (2/8) Epoch 6, batch 9350, loss[loss=0.1301, simple_loss=0.2082, pruned_loss=0.02598, over 4787.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2206, pruned_loss=0.0392, over 971389.53 frames.], batch size: 14, lr: 3.38e-04
2022-05-05 12:13:47,630 INFO [train.py:715] (2/8) Epoch 6, batch 9400, loss[loss=0.1434, simple_loss=0.2193, pruned_loss=0.0337, over 4831.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2208, pruned_loss=0.03941, over 970987.69 frames.], batch size: 26, lr: 3.38e-04
2022-05-05 12:14:26,437 INFO [train.py:715] (2/8) Epoch 6, batch 9450, loss[loss=0.1462, simple_loss=0.2276, pruned_loss=0.03244, over 4927.00 frames.], tot_loss[loss=0.1489, simple_loss=0.22, pruned_loss=0.03891, over 970626.56 frames.], batch size: 18, lr: 3.38e-04
2022-05-05 12:15:05,764 INFO [train.py:715] (2/8) Epoch 6, batch 9500, loss[loss=0.1682, simple_loss=0.2244, pruned_loss=0.05597, over 4928.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2202, pruned_loss=0.03942, over 971252.29 frames.], batch size: 29, lr: 3.38e-04
2022-05-05 12:15:44,436 INFO [train.py:715] (2/8) Epoch 6, batch 9550, loss[loss=0.1515, simple_loss=0.2301, pruned_loss=0.03646, over 4838.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2208, pruned_loss=0.03986, over 971679.93 frames.], batch size: 26, lr: 3.38e-04
2022-05-05 12:16:23,398 INFO [train.py:715] (2/8) Epoch 6, batch 9600, loss[loss=0.1432, simple_loss=0.2146, pruned_loss=0.03592, over 4969.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2212, pruned_loss=0.04062, over 971900.44 frames.], batch size: 28, lr: 3.38e-04
2022-05-05 12:17:02,131 INFO [train.py:715] (2/8) Epoch 6, batch 9650, loss[loss=0.164, simple_loss=0.2266, pruned_loss=0.0507, over 4969.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2202, pruned_loss=0.03978, over 971677.21 frames.], batch size: 35, lr: 3.38e-04
2022-05-05 12:17:40,469 INFO [train.py:715] (2/8) Epoch 6, batch 9700, loss[loss=0.1324, simple_loss=0.2135, pruned_loss=0.02563, over 4938.00 frames.], tot_loss[loss=0.1507, simple_loss=0.221, pruned_loss=0.04016, over 972399.37 frames.], batch size: 24, lr: 3.38e-04
2022-05-05 12:18:19,758 INFO [train.py:715] (2/8) Epoch 6, batch 9750, loss[loss=0.1436, simple_loss=0.2141, pruned_loss=0.03661, over 4925.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2207, pruned_loss=0.04014, over 972419.13 frames.], batch size: 17, lr: 3.38e-04
2022-05-05 12:18:59,509 INFO [train.py:715] (2/8) Epoch 6, batch 9800, loss[loss=0.1464, simple_loss=0.2298, pruned_loss=0.03145, over 4822.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2216, pruned_loss=0.04051, over 972257.57 frames.], batch size: 27, lr: 3.38e-04
2022-05-05 12:19:39,866 INFO [train.py:715] (2/8) Epoch 6, batch 9850, loss[loss=0.1423, simple_loss=0.2185, pruned_loss=0.03306, over 4902.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2219, pruned_loss=0.04059, over 972915.74 frames.], batch size: 19, lr: 3.38e-04
2022-05-05 12:20:19,020 INFO [train.py:715] (2/8) Epoch 6, batch 9900, loss[loss=0.1707, simple_loss=0.2392, pruned_loss=0.05105, over 4924.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2215, pruned_loss=0.04033, over 973879.93 frames.], batch size: 18, lr: 3.38e-04
2022-05-05 12:20:59,152 INFO [train.py:715] (2/8) Epoch 6, batch 9950, loss[loss=0.1739, simple_loss=0.2422, pruned_loss=0.05276, over 4862.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2226, pruned_loss=0.04056, over 973834.06 frames.], batch size: 16, lr: 3.38e-04
2022-05-05 12:21:39,152 INFO [train.py:715] (2/8) Epoch 6, batch 10000, loss[loss=0.1132, simple_loss=0.1812, pruned_loss=0.02254, over 4794.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2224, pruned_loss=0.04087, over 973281.06 frames.], batch size: 18, lr: 3.38e-04
2022-05-05 12:22:17,403 INFO [train.py:715] (2/8) Epoch 6, batch 10050, loss[loss=0.1454, simple_loss=0.2114, pruned_loss=0.0397, over 4985.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2211, pruned_loss=0.04028, over 973066.92 frames.], batch size: 14, lr: 3.38e-04
2022-05-05 12:22:56,770 INFO [train.py:715] (2/8) Epoch 6, batch 10100, loss[loss=0.136, simple_loss=0.2022, pruned_loss=0.03492, over 4792.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2209, pruned_loss=0.03972, over 973625.23 frames.], batch size: 24, lr: 3.38e-04
2022-05-05 12:23:34,993 INFO [train.py:715] (2/8) Epoch 6, batch 10150, loss[loss=0.1255, simple_loss=0.2009, pruned_loss=0.0251, over 4939.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2209, pruned_loss=0.03938, over 973098.42 frames.], batch size: 29, lr: 3.38e-04
2022-05-05 12:24:14,024 INFO [train.py:715] (2/8) Epoch 6, batch 10200, loss[loss=0.128, simple_loss=0.1907, pruned_loss=0.03261, over 4896.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2208, pruned_loss=0.03905, over 973053.16 frames.], batch size: 17, lr: 3.38e-04
2022-05-05 12:24:52,554 INFO [train.py:715] (2/8) Epoch 6, batch 10250, loss[loss=0.1657, simple_loss=0.2329, pruned_loss=0.04925, over 4754.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2204, pruned_loss=0.03865, over 972566.96 frames.], batch size: 14, lr: 3.37e-04
2022-05-05 12:25:31,644 INFO [train.py:715] (2/8) Epoch 6, batch 10300, loss[loss=0.1259, simple_loss=0.2026, pruned_loss=0.02462, over 4941.00 frames.], tot_loss[loss=0.148, simple_loss=0.2196, pruned_loss=0.0382, over 971560.78 frames.], batch size: 29, lr: 3.37e-04
2022-05-05 12:26:10,145 INFO [train.py:715] (2/8) Epoch 6, batch 10350, loss[loss=0.1654, simple_loss=0.2454, pruned_loss=0.04266, over 4924.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2202, pruned_loss=0.03819, over 972122.43 frames.], batch size: 23, lr: 3.37e-04
2022-05-05 12:26:49,279 INFO [train.py:715] (2/8) Epoch 6, batch 10400, loss[loss=0.122, simple_loss=0.2031, pruned_loss=0.02049, over 4952.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2196, pruned_loss=0.03803, over 971135.52 frames.], batch size: 21, lr: 3.37e-04
2022-05-05 12:27:27,711 INFO [train.py:715] (2/8) Epoch 6, batch 10450, loss[loss=0.1582, simple_loss=0.223, pruned_loss=0.04674, over 4729.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2201, pruned_loss=0.03837, over 971310.33 frames.], batch size: 16, lr: 3.37e-04
2022-05-05 12:28:06,364 INFO [train.py:715] (2/8) Epoch 6, batch 10500, loss[loss=0.1329, simple_loss=0.2138, pruned_loss=0.02599, over 4960.00 frames.], tot_loss[loss=0.1494, simple_loss=0.221, pruned_loss=0.03883, over 971658.18 frames.], batch size: 35, lr: 3.37e-04
2022-05-05 12:28:45,432 INFO [train.py:715] (2/8) Epoch 6, batch 10550, loss[loss=0.1356, simple_loss=0.2022, pruned_loss=0.03451, over 4862.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2203, pruned_loss=0.03852, over 971070.61 frames.], batch size: 32, lr: 3.37e-04
2022-05-05 12:29:23,700 INFO [train.py:715] (2/8) Epoch 6, batch 10600, loss[loss=0.1388, simple_loss=0.2187, pruned_loss=0.02944, over 4981.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2201, pruned_loss=0.03846, over 970681.81 frames.], batch size: 14, lr: 3.37e-04
2022-05-05 12:30:02,902 INFO [train.py:715] (2/8) Epoch 6, batch 10650, loss[loss=0.1461, simple_loss=0.2114, pruned_loss=0.04035, over 4879.00 frames.], tot_loss[loss=0.1488, simple_loss=0.22, pruned_loss=0.03874, over 971448.28 frames.], batch size: 22, lr: 3.37e-04
2022-05-05 12:30:41,619 INFO [train.py:715] (2/8) Epoch 6, batch 10700, loss[loss=0.1404, simple_loss=0.2144, pruned_loss=0.03323, over 4690.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2203, pruned_loss=0.03838, over 972119.39 frames.], batch size: 15, lr: 3.37e-04
2022-05-05 12:31:20,571 INFO [train.py:715] (2/8) Epoch 6, batch 10750, loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03657, over 4637.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2196, pruned_loss=0.03839, over 971063.60 frames.], batch size: 13, lr: 3.37e-04
2022-05-05 12:31:59,032 INFO [train.py:715] (2/8) Epoch 6, batch 10800, loss[loss=0.1382, simple_loss=0.1979, pruned_loss=0.03923, over 4903.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2201, pruned_loss=0.03889, over 971394.99 frames.], batch size: 22, lr: 3.37e-04
2022-05-05 12:32:37,569 INFO [train.py:715] (2/8) Epoch 6, batch 10850, loss[loss=0.1545, simple_loss=0.2243, pruned_loss=0.04234, over 4839.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2201, pruned_loss=0.03883, over 971440.27 frames.], batch size: 20, lr: 3.37e-04
2022-05-05 12:33:15,995 INFO [train.py:715] (2/8) Epoch 6, batch 10900, loss[loss=0.1305, simple_loss=0.198, pruned_loss=0.0315, over 4635.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2196, pruned_loss=0.03857, over 970994.40 frames.], batch size: 13, lr: 3.37e-04
2022-05-05 12:33:54,115 INFO [train.py:715] (2/8) Epoch 6, batch 10950, loss[loss=0.1428, simple_loss=0.211, pruned_loss=0.03729, over 4884.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2204, pruned_loss=0.03913, over 971577.40 frames.], batch size: 22, lr: 3.37e-04
2022-05-05 12:34:33,261 INFO [train.py:715] (2/8) Epoch 6, batch 11000, loss[loss=0.1583, simple_loss=0.2251, pruned_loss=0.04574, over 4747.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2197, pruned_loss=0.03862, over 971842.80 frames.], batch size: 19, lr: 3.37e-04
2022-05-05 12:35:11,625 INFO [train.py:715] (2/8) Epoch 6, batch 11050, loss[loss=0.1283, simple_loss=0.1956, pruned_loss=0.03051, over 4856.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03796, over 972558.81 frames.], batch size: 15, lr: 3.37e-04
2022-05-05 12:35:50,647 INFO [train.py:715] (2/8) Epoch 6, batch 11100, loss[loss=0.1371, simple_loss=0.2037, pruned_loss=0.03522, over 4843.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03806, over 972394.69 frames.], batch size: 20, lr: 3.37e-04
2022-05-05 12:36:29,029 INFO [train.py:715] (2/8) Epoch 6, batch 11150, loss[loss=0.1376, simple_loss=0.2068, pruned_loss=0.03419, over 4799.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2183, pruned_loss=0.03757, over 971565.33 frames.], batch size: 21, lr: 3.37e-04
2022-05-05 12:37:07,407 INFO [train.py:715] (2/8) Epoch 6, batch 11200, loss[loss=0.174, simple_loss=0.2386, pruned_loss=0.0547, over 4842.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2194, pruned_loss=0.0378, over 971816.58 frames.], batch size: 13, lr: 3.37e-04
2022-05-05 12:37:45,844 INFO [train.py:715] (2/8) Epoch 6, batch 11250, loss[loss=0.1242, simple_loss=0.1942, pruned_loss=0.02713, over 4963.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2204, pruned_loss=0.0386, over 971437.83 frames.], batch size: 24, lr: 3.37e-04
2022-05-05 12:38:24,406 INFO [train.py:715] (2/8) Epoch 6, batch 11300, loss[loss=0.1965, simple_loss=0.2486, pruned_loss=0.07223, over 4964.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2194, pruned_loss=0.03833, over 971483.80 frames.], batch size: 15, lr: 3.37e-04
2022-05-05 12:39:03,681 INFO [train.py:715] (2/8) Epoch 6, batch 11350, loss[loss=0.2034, simple_loss=0.2793, pruned_loss=0.06372, over 4917.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2195, pruned_loss=0.03811, over 972624.67 frames.], batch size: 29, lr: 3.37e-04
2022-05-05 12:39:42,622 INFO [train.py:715] (2/8) Epoch 6, batch 11400, loss[loss=0.1698, simple_loss=0.2369, pruned_loss=0.05134, over 4752.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2186, pruned_loss=0.03788, over 971306.75 frames.], batch size: 19, lr: 3.37e-04
2022-05-05 12:40:21,681 INFO [train.py:715] (2/8) Epoch 6, batch 11450, loss[loss=0.1424, simple_loss=0.2194, pruned_loss=0.03269, over 4928.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2194, pruned_loss=0.03821, over 972251.91 frames.], batch size: 18, lr: 3.37e-04
2022-05-05 12:40:59,950 INFO [train.py:715] (2/8) Epoch 6, batch 11500, loss[loss=0.1271, simple_loss=0.1983, pruned_loss=0.02793, over 4834.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03841, over 971318.42 frames.], batch size: 30, lr: 3.37e-04
2022-05-05 12:41:38,301 INFO [train.py:715] (2/8) Epoch 6, batch 11550, loss[loss=0.1792, simple_loss=0.2377, pruned_loss=0.06041, over 4978.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2188, pruned_loss=0.03801, over 971430.40 frames.], batch size: 33, lr: 3.36e-04
2022-05-05 12:42:17,677 INFO [train.py:715] (2/8) Epoch 6, batch 11600, loss[loss=0.1566, simple_loss=0.2409, pruned_loss=0.03611, over 4895.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2181, pruned_loss=0.03777, over 971379.74 frames.], batch size: 17, lr: 3.36e-04
2022-05-05 12:42:56,131 INFO [train.py:715] (2/8) Epoch 6, batch 11650, loss[loss=0.1282, simple_loss=0.2001, pruned_loss=0.02816, over 4771.00 frames.], tot_loss[loss=0.1478, simple_loss=0.219, pruned_loss=0.03833, over 972302.02 frames.], batch size: 12, lr: 3.36e-04
2022-05-05 12:43:34,997 INFO [train.py:715] (2/8) Epoch 6, batch 11700, loss[loss=0.1595, simple_loss=0.2452, pruned_loss=0.03688, over 4839.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2197, pruned_loss=0.03865, over 972696.90 frames.], batch size: 15, lr: 3.36e-04
2022-05-05 12:44:13,937 INFO [train.py:715] (2/8) Epoch 6, batch 11750, loss[loss=0.1655, simple_loss=0.2298, pruned_loss=0.05054, over 4866.00 frames.], tot_loss[loss=0.149, simple_loss=0.2201, pruned_loss=0.03902, over 972380.12 frames.], batch size: 38, lr: 3.36e-04
2022-05-05 12:44:53,166 INFO [train.py:715] (2/8) Epoch 6, batch 11800, loss[loss=0.1584, simple_loss=0.2218, pruned_loss=0.04751, over 4772.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2202, pruned_loss=0.03903, over 972949.47 frames.], batch size: 14, lr: 3.36e-04
2022-05-05 12:45:31,842 INFO [train.py:715] (2/8) Epoch 6, batch 11850, loss[loss=0.1443, simple_loss=0.2213, pruned_loss=0.0336, over 4864.00 frames.], tot_loss[loss=0.149, simple_loss=0.2201, pruned_loss=0.03889, over 972519.85 frames.], batch size: 20, lr: 3.36e-04
2022-05-05 12:46:10,414 INFO [train.py:715] (2/8) Epoch 6, batch 11900, loss[loss=0.134, simple_loss=0.2037, pruned_loss=0.0321, over 4755.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03819, over 973190.96 frames.], batch size: 19, lr: 3.36e-04
2022-05-05 12:46:49,725 INFO [train.py:715] (2/8) Epoch 6, batch 11950, loss[loss=0.1284, simple_loss=0.2011, pruned_loss=0.02783, over 4803.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03805, over 972273.61 frames.], batch size: 13, lr: 3.36e-04
2022-05-05 12:47:28,221 INFO [train.py:715] (2/8) Epoch 6, batch 12000, loss[loss=0.1435, simple_loss=0.2104, pruned_loss=0.03835, over 4857.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2186, pruned_loss=0.03784, over 972156.28 frames.], batch size: 13, lr: 3.36e-04
2022-05-05 12:47:28,222 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 12:47:37,946 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1091, simple_loss=0.1942, pruned_loss=0.01199, over 914524.00 frames.
2022-05-05 12:48:16,713 INFO [train.py:715] (2/8) Epoch 6, batch 12050, loss[loss=0.131, simple_loss=0.2082, pruned_loss=0.02689, over 4922.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2185, pruned_loss=0.03784, over 971907.90 frames.], batch size: 29, lr: 3.36e-04
2022-05-05 12:48:56,376 INFO [train.py:715] (2/8) Epoch 6, batch 12100, loss[loss=0.1287, simple_loss=0.2061, pruned_loss=0.0256, over 4911.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2197, pruned_loss=0.03824, over 973198.79 frames.], batch size: 18, lr: 3.36e-04
2022-05-05 12:49:35,322 INFO [train.py:715] (2/8) Epoch 6, batch 12150, loss[loss=0.1448, simple_loss=0.2146, pruned_loss=0.03748, over 4781.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03891, over 972986.72 frames.], batch size: 18, lr: 3.36e-04
2022-05-05 12:50:14,104 INFO [train.py:715] (2/8) Epoch 6, batch 12200, loss[loss=0.1648, simple_loss=0.2353, pruned_loss=0.04722, over 4832.00 frames.], tot_loss[loss=0.149, simple_loss=0.2208, pruned_loss=0.03864, over 972888.10 frames.], batch size: 12, lr: 3.36e-04
2022-05-05 12:50:53,317 INFO [train.py:715] (2/8) Epoch 6, batch 12250, loss[loss=0.146, simple_loss=0.2162, pruned_loss=0.03789, over 4987.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2208, pruned_loss=0.0392, over 972478.13 frames.], batch size: 25, lr: 3.36e-04
2022-05-05 12:51:32,109 INFO [train.py:715] (2/8) Epoch 6, batch 12300, loss[loss=0.1563, simple_loss=0.2326, pruned_loss=0.04002, over 4987.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2207, pruned_loss=0.03917, over 973308.89 frames.], batch size: 31, lr: 3.36e-04
2022-05-05 12:52:11,906 INFO [train.py:715] (2/8) Epoch 6, batch 12350, loss[loss=0.1721, simple_loss=0.246, pruned_loss=0.04912, over 4761.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2215, pruned_loss=0.03945, over 974233.70 frames.], batch size: 19, lr: 3.36e-04
2022-05-05 12:52:50,505 INFO [train.py:715] (2/8) Epoch 6, batch 12400, loss[loss=0.1231, simple_loss=0.2, pruned_loss=0.02307, over 4975.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2211, pruned_loss=0.03931, over 972676.74 frames.], batch size: 25, lr: 3.36e-04
2022-05-05 12:53:29,633 INFO [train.py:715] (2/8) Epoch 6, batch 12450, loss[loss=0.1669, simple_loss=0.2385, pruned_loss=0.04767, over 4852.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2212, pruned_loss=0.03954, over 973059.75 frames.], batch size: 34, lr: 3.36e-04
2022-05-05 12:54:08,747 INFO [train.py:715] (2/8) Epoch 6, batch 12500, loss[loss=0.1345, simple_loss=0.2101, pruned_loss=0.02939, over 4850.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2208, pruned_loss=0.03922, over 973748.89 frames.], batch size: 20, lr: 3.36e-04
2022-05-05 12:54:47,051 INFO [train.py:715] (2/8) Epoch 6, batch 12550, loss[loss=0.1542, simple_loss=0.221, pruned_loss=0.04364, over 4753.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2215, pruned_loss=0.03974, over 972587.71 frames.], batch size: 14, lr: 3.36e-04
2022-05-05 12:55:26,405 INFO [train.py:715] (2/8) Epoch 6, batch 12600, loss[loss=0.1342, simple_loss=0.2151, pruned_loss=0.02665, over 4853.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2218, pruned_loss=0.04004, over 971946.66 frames.], batch size: 13, lr: 3.36e-04
2022-05-05 12:56:05,094 INFO [train.py:715] (2/8) Epoch 6, batch 12650, loss[loss=0.1531, simple_loss=0.2265, pruned_loss=0.03982, over 4850.00 frames.], tot_loss[loss=0.15, simple_loss=0.221, pruned_loss=0.03949, over 972419.76 frames.], batch size: 15, lr: 3.36e-04
2022-05-05 12:56:43,910 INFO [train.py:715] (2/8) Epoch 6, batch 12700, loss[loss=0.1304, simple_loss=0.2066, pruned_loss=0.02707, over 4948.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2205, pruned_loss=0.03896, over 973054.25 frames.], batch size: 24, lr: 3.36e-04
2022-05-05 12:57:22,047 INFO [train.py:715] (2/8) Epoch 6, batch 12750, loss[loss=0.1266, simple_loss=0.1957, pruned_loss=0.02875, over 4858.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2195, pruned_loss=0.03851, over 972998.25 frames.], batch size: 32, lr: 3.36e-04
2022-05-05 12:58:01,007 INFO [train.py:715] (2/8) Epoch 6, batch 12800, loss[loss=0.1684, simple_loss=0.2328, pruned_loss=0.05205, over 4889.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2194, pruned_loss=0.0386, over 973917.68 frames.], batch size: 16, lr: 3.36e-04
2022-05-05 12:58:39,732 INFO [train.py:715] (2/8) Epoch 6, batch 12850, loss[loss=0.1424, simple_loss=0.2061, pruned_loss=0.03932, over 4828.00 frames.], tot_loss[loss=0.1478, simple_loss=0.219, pruned_loss=0.03829, over 973547.97 frames.], batch size: 13, lr: 3.35e-04
2022-05-05 12:59:18,385 INFO [train.py:715] (2/8) Epoch 6, batch 12900, loss[loss=0.1384, simple_loss=0.2007, pruned_loss=0.03809, over 4859.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.03769, over 973286.11 frames.], batch size: 15, lr: 3.35e-04
2022-05-05 12:59:58,351 INFO [train.py:715] (2/8) Epoch 6, batch 12950, loss[loss=0.1813, simple_loss=0.2438, pruned_loss=0.05935, over 4913.00 frames.], tot_loss[loss=0.147, simple_loss=0.2183, pruned_loss=0.03783, over 972917.10 frames.], batch size: 17, lr: 3.35e-04
2022-05-05 13:00:37,483 INFO [train.py:715] (2/8) Epoch 6, batch 13000, loss[loss=0.1098, simple_loss=0.1837, pruned_loss=0.01792, over 4968.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2178, pruned_loss=0.03751, over 972474.64 frames.], batch size: 24, lr: 3.35e-04
2022-05-05 13:01:16,479 INFO [train.py:715] (2/8) Epoch 6, batch 13050, loss[loss=0.1254, simple_loss=0.1979, pruned_loss=0.02647, over 4704.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2186, pruned_loss=0.0384, over 971659.59 frames.], batch size: 15, lr: 3.35e-04
2022-05-05 13:01:54,767 INFO [train.py:715] (2/8) Epoch 6, batch 13100, loss[loss=0.1289, simple_loss=0.1899, pruned_loss=0.03393, over 4881.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2189, pruned_loss=0.03835, over 971746.35 frames.], batch size: 22, lr: 3.35e-04
2022-05-05 13:02:34,347 INFO [train.py:715] (2/8) Epoch 6, batch 13150, loss[loss=0.1515, simple_loss=0.2195, pruned_loss=0.04172, over 4800.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2198, pruned_loss=0.03866, over 971407.01 frames.], batch size: 21, lr: 3.35e-04
2022-05-05 13:03:12,924 INFO [train.py:715] (2/8) Epoch 6, batch 13200, loss[loss=0.171, simple_loss=0.2297, pruned_loss=0.05618, over 4891.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2212, pruned_loss=0.03964, over 971876.09 frames.], batch size: 17, lr: 3.35e-04
2022-05-05 13:03:51,788 INFO [train.py:715] (2/8) Epoch 6, batch 13250, loss[loss=0.1556, simple_loss=0.2247, pruned_loss=0.04326, over 4904.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2212, pruned_loss=0.03959, over 971170.82 frames.], batch size: 17, lr: 3.35e-04
2022-05-05 13:04:30,644 INFO [train.py:715] (2/8) Epoch 6, batch 13300, loss[loss=0.1425, simple_loss=0.2219, pruned_loss=0.03155, over 4789.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2204, pruned_loss=0.03936, over 971224.60 frames.], batch size: 24, lr: 3.35e-04
2022-05-05 13:05:09,759 INFO [train.py:715] (2/8) Epoch 6, batch 13350, loss[loss=0.1463, simple_loss=0.2256, pruned_loss=0.03347, over 4967.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2211, pruned_loss=0.03982, over 971000.55 frames.], batch size: 28, lr: 3.35e-04
2022-05-05 13:05:48,894 INFO [train.py:715] (2/8) Epoch 6, batch 13400, loss[loss=0.1375, simple_loss=0.2238, pruned_loss=0.02566, over 4868.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2206, pruned_loss=0.03962, over 970702.84 frames.], batch size: 20, lr: 3.35e-04
2022-05-05 13:06:27,485 INFO [train.py:715] (2/8) Epoch 6, batch 13450, loss[loss=0.1473, simple_loss=0.2258, pruned_loss=0.03443, over 4941.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2204, pruned_loss=0.03949, over 971159.14 frames.], batch size: 29, lr: 3.35e-04
2022-05-05 13:07:07,013 INFO [train.py:715] (2/8) Epoch 6, batch 13500, loss[loss=0.1449, simple_loss=0.2218, pruned_loss=0.03398, over 4743.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2205, pruned_loss=0.03948, over 971871.43 frames.], batch size: 16, lr: 3.35e-04
2022-05-05 13:07:45,024 INFO [train.py:715] (2/8) Epoch 6, batch 13550, loss[loss=0.1298, simple_loss=0.2002, pruned_loss=0.02975, over 4893.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2205, pruned_loss=0.03925, over 972303.70 frames.], batch size: 19, lr: 3.35e-04
2022-05-05 13:08:23,969 INFO [train.py:715] (2/8) Epoch 6, batch 13600, loss[loss=0.1306, simple_loss=0.1938, pruned_loss=0.03375, over 4788.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2193, pruned_loss=0.03847, over 972583.25 frames.], batch size: 12, lr: 3.35e-04
2022-05-05 13:09:03,111 INFO [train.py:715] (2/8) Epoch 6, batch 13650, loss[loss=0.1425, simple_loss=0.2114, pruned_loss=0.03676, over 4871.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2199, pruned_loss=0.03878, over 971842.10 frames.], batch size: 38, lr: 3.35e-04
2022-05-05 13:09:42,437 INFO [train.py:715] (2/8) Epoch 6, batch 13700, loss[loss=0.1277, simple_loss=0.2007, pruned_loss=0.02734, over 4755.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2196, pruned_loss=0.03875, over 972467.75 frames.], batch size: 12, lr: 3.35e-04
2022-05-05 13:10:21,547 INFO [train.py:715] (2/8) Epoch 6, batch 13750, loss[loss=0.1389, simple_loss=0.2035, pruned_loss=0.0372, over 4807.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2184, pruned_loss=0.03842, over 972635.14 frames.], batch size: 21, lr: 3.35e-04
2022-05-05 13:11:00,146 INFO [train.py:715] (2/8) Epoch 6, batch 13800, loss[loss=0.1464, simple_loss=0.2159, pruned_loss=0.03842, over 4778.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2182, pruned_loss=0.03852, over 972976.33 frames.], batch size: 18, lr: 3.35e-04
2022-05-05 13:11:40,118 INFO [train.py:715] (2/8) Epoch 6, batch 13850, loss[loss=0.1429, simple_loss=0.2104, pruned_loss=0.03766, over 4847.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2178, pruned_loss=0.03822, over 971656.03 frames.], batch size: 30, lr: 3.35e-04
2022-05-05 13:12:18,448 INFO [train.py:715] (2/8) Epoch 6, batch 13900, loss[loss=0.1019, simple_loss=0.1751, pruned_loss=0.01431, over 4924.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2178, pruned_loss=0.03859, over 971978.85 frames.], batch size: 23, lr: 3.35e-04
2022-05-05 13:12:57,457 INFO [train.py:715] (2/8) Epoch 6, batch 13950, loss[loss=0.1546, simple_loss=0.2298, pruned_loss=0.03973, over 4798.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2176, pruned_loss=0.03832, over 971790.62 frames.], batch size: 24, lr: 3.35e-04
2022-05-05 13:13:36,064 INFO [train.py:715] (2/8) Epoch 6, batch 14000, loss[loss=0.1161, simple_loss=0.1939, pruned_loss=0.0192, over 4800.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2188, pruned_loss=0.03829, over 971592.43 frames.], batch size: 25, lr: 3.35e-04
2022-05-05 13:14:15,112 INFO [train.py:715] (2/8) Epoch 6, batch 14050, loss[loss=0.1884, simple_loss=0.2493, pruned_loss=0.06379, over 4894.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2185, pruned_loss=0.03789, over 972369.94 frames.], batch size: 22, lr: 3.35e-04
2022-05-05 13:14:53,530 INFO [train.py:715] (2/8) Epoch 6, batch 14100, loss[loss=0.1399, simple_loss=0.2198, pruned_loss=0.02998, over 4924.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2196, pruned_loss=0.03845, over 972450.88 frames.], batch size: 23, lr: 3.35e-04
2022-05-05 13:15:32,013 INFO [train.py:715] (2/8) Epoch 6, batch 14150, loss[loss=0.1613, simple_loss=0.233, pruned_loss=0.04477, over 4870.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2188, pruned_loss=0.03805, over 973322.35 frames.], batch size: 38, lr: 3.35e-04
2022-05-05 13:16:11,450 INFO [train.py:715] (2/8) Epoch 6, batch 14200, loss[loss=0.1426, simple_loss=0.2187, pruned_loss=0.03325, over 4797.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2187, pruned_loss=0.03821, over 972733.52 frames.], batch size: 25, lr: 3.34e-04
2022-05-05 13:16:50,090 INFO [train.py:715] (2/8) Epoch 6, batch 14250, loss[loss=0.1314, simple_loss=0.1998, pruned_loss=0.03148, over 4756.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2178, pruned_loss=0.03781, over 971680.28 frames.], batch size: 12, lr: 3.34e-04
2022-05-05 13:17:29,124 INFO [train.py:715] (2/8) Epoch 6, batch 14300, loss[loss=0.19, simple_loss=0.2565, pruned_loss=0.06176, over 4744.00 frames.], tot_loss[loss=0.1469, simple_loss=0.218, pruned_loss=0.03794, over 971660.47 frames.], batch size: 16, lr: 3.34e-04
2022-05-05 13:18:07,581 INFO [train.py:715] (2/8) Epoch 6, batch 14350, loss[loss=0.1344, simple_loss=0.2042, pruned_loss=0.03231, over 4893.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2179, pruned_loss=0.03795, over 971730.84 frames.], batch size: 16, lr: 3.34e-04
2022-05-05 13:18:47,508 INFO [train.py:715] (2/8) Epoch 6, batch 14400, loss[loss=0.1834, simple_loss=0.254, pruned_loss=0.0564, over 4870.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2186, pruned_loss=0.03842, over 972734.35 frames.], batch size: 20, lr: 3.34e-04
2022-05-05 13:19:25,858 INFO [train.py:715] (2/8) Epoch 6, batch 14450, loss[loss=0.1542, simple_loss=0.2197, pruned_loss=0.04429, over 4941.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2185, pruned_loss=0.03818, over 972910.24 frames.], batch size: 24, lr: 3.34e-04
2022-05-05 13:20:04,247 INFO [train.py:715] (2/8) Epoch 6, batch 14500, loss[loss=0.1929, simple_loss=0.2618, pruned_loss=0.06195, over 4698.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2184, pruned_loss=0.03826, over 972372.06 frames.], batch size: 15, lr: 3.34e-04
2022-05-05 13:20:43,929 INFO [train.py:715] (2/8) Epoch 6, batch 14550, loss[loss=0.1626, simple_loss=0.2418, pruned_loss=0.04175, over 4888.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2191, pruned_loss=0.03854, over 973643.83 frames.], batch size: 39, lr: 3.34e-04
2022-05-05 13:21:22,652 INFO [train.py:715] (2/8) Epoch 6, batch 14600, loss[loss=0.1379, simple_loss=0.2028, pruned_loss=0.03655, over 4819.00 frames.], tot_loss[loss=0.148, simple_loss=0.2191, pruned_loss=0.03839, over 973310.31 frames.], batch size: 25, lr: 3.34e-04
2022-05-05 13:22:01,119 INFO [train.py:715] (2/8) Epoch 6, batch 14650, loss[loss=0.1029, simple_loss=0.1717, pruned_loss=0.01703, over 4755.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2197, pruned_loss=0.03869, over 973118.33 frames.], batch size: 12, lr: 3.34e-04
2022-05-05 13:22:40,130 INFO [train.py:715] (2/8) Epoch 6, batch 14700, loss[loss=0.1477, simple_loss=0.2287, pruned_loss=0.03332, over 4892.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2191, pruned_loss=0.03825, over 971897.64 frames.], batch size: 19, lr: 3.34e-04
2022-05-05 13:23:19,674 INFO [train.py:715] (2/8) Epoch 6, batch 14750, loss[loss=0.1356, simple_loss=0.2151, pruned_loss=0.02809, over 4784.00 frames.], tot_loss[loss=0.148, simple_loss=0.2196, pruned_loss=0.03824, over 971865.78 frames.], batch size: 17, lr: 3.34e-04
2022-05-05 13:23:57,829 INFO [train.py:715] (2/8) Epoch 6, batch 14800, loss[loss=0.1517, simple_loss=0.2111, pruned_loss=0.04613, over 4968.00 frames.], tot_loss[loss=0.149, simple_loss=0.2205, pruned_loss=0.0388, over 971970.87 frames.], batch size: 35, lr: 3.34e-04
2022-05-05 13:24:35,997 INFO [train.py:715] (2/8) Epoch 6, batch 14850, loss[loss=0.1514, simple_loss=0.2317, pruned_loss=0.0355, over 4992.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2212, pruned_loss=0.03947, over 972753.50 frames.], batch size: 14, lr: 3.34e-04
2022-05-05 13:25:15,102 INFO [train.py:715] (2/8) Epoch 6, batch 14900, loss[loss=0.1513, simple_loss=0.2222, pruned_loss=0.04016, over 4985.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2214, pruned_loss=0.03956, over 972391.33 frames.], batch size: 25, lr: 3.34e-04
2022-05-05 13:25:53,365 INFO [train.py:715] (2/8) Epoch 6, batch 14950, loss[loss=0.1355, simple_loss=0.226, pruned_loss=0.02255, over 4942.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2206, pruned_loss=0.03885, over 972352.35 frames.], batch size: 21, lr: 3.34e-04
2022-05-05 13:26:32,022 INFO [train.py:715] (2/8) Epoch 6, batch 15000, loss[loss=0.1317, simple_loss=0.2085, pruned_loss=0.02751, over 4770.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2208, pruned_loss=0.03916, over 972519.64 frames.], batch size: 17, lr: 3.34e-04
2022-05-05 13:26:32,023 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 13:26:41,818 INFO [train.py:742] (2/8) 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] (2/8) Epoch 6, batch 15050, loss[loss=0.1626, simple_loss=0.2465, pruned_loss=0.03933, over 4969.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2214, pruned_loss=0.03968, over 971917.38 frames.], batch size: 25, lr: 3.34e-04
2022-05-05 13:27:59,351 INFO [train.py:715] (2/8) Epoch 6, batch 15100, loss[loss=0.127, simple_loss=0.198, pruned_loss=0.02802, over 4956.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2205, pruned_loss=0.0392, over 971588.99 frames.], batch size: 21, lr: 3.34e-04
2022-05-05 13:28:41,261 INFO [train.py:715] (2/8) Epoch 6, batch 15150, loss[loss=0.1608, simple_loss=0.2436, pruned_loss=0.03897, over 4820.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2203, pruned_loss=0.03926, over 971045.21 frames.], batch size: 26, lr: 3.34e-04
2022-05-05 13:29:19,849 INFO [train.py:715] (2/8) Epoch 6, batch 15200, loss[loss=0.1521, simple_loss=0.2197, pruned_loss=0.04227, over 4914.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2206, pruned_loss=0.03927, over 971248.54 frames.], batch size: 17, lr: 3.34e-04
2022-05-05 13:29:58,375 INFO [train.py:715] (2/8) Epoch 6, batch 15250, loss[loss=0.1649, simple_loss=0.2345, pruned_loss=0.04766, over 4922.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2218, pruned_loss=0.0403, over 972124.03 frames.], batch size: 29, lr: 3.34e-04
2022-05-05 13:30:37,907 INFO [train.py:715] (2/8) Epoch 6, batch 15300, loss[loss=0.1577, simple_loss=0.2282, pruned_loss=0.04357, over 4799.00 frames.], tot_loss[loss=0.15, simple_loss=0.2209, pruned_loss=0.03955, over 971659.79 frames.], batch size: 18, lr: 3.34e-04
2022-05-05 13:31:15,933 INFO [train.py:715] (2/8) Epoch 6, batch 15350, loss[loss=0.1598, simple_loss=0.2274, pruned_loss=0.04608, over 4870.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2208, pruned_loss=0.03954, over 972218.09 frames.], batch size: 20, lr: 3.34e-04
2022-05-05 13:31:54,940 INFO [train.py:715] (2/8) Epoch 6, batch 15400, loss[loss=0.159, simple_loss=0.2204, pruned_loss=0.04883, over 4967.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2208, pruned_loss=0.03955, over 972346.81 frames.], batch size: 35, lr: 3.34e-04
2022-05-05 13:32:33,865 INFO [train.py:715] (2/8) Epoch 6, batch 15450, loss[loss=0.1273, simple_loss=0.1868, pruned_loss=0.0339, over 4770.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2199, pruned_loss=0.03927, over 972631.11 frames.], batch size: 12, lr: 3.34e-04
2022-05-05 13:33:13,326 INFO [train.py:715] (2/8) Epoch 6, batch 15500, loss[loss=0.1762, simple_loss=0.2388, pruned_loss=0.05674, over 4982.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2205, pruned_loss=0.03966, over 973045.27 frames.], batch size: 16, lr: 3.34e-04
2022-05-05 13:33:51,504 INFO [train.py:715] (2/8) Epoch 6, batch 15550, loss[loss=0.1717, simple_loss=0.2301, pruned_loss=0.05661, over 4848.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2199, pruned_loss=0.03918, over 972851.79 frames.], batch size: 32, lr: 3.33e-04
2022-05-05 13:34:30,395 INFO [train.py:715] (2/8) Epoch 6, batch 15600, loss[loss=0.1567, simple_loss=0.2339, pruned_loss=0.03975, over 4973.00 frames.], tot_loss[loss=0.149, simple_loss=0.2199, pruned_loss=0.03909, over 973099.55 frames.], batch size: 25, lr: 3.33e-04
2022-05-05 13:35:09,327 INFO [train.py:715] (2/8) Epoch 6, batch 15650, loss[loss=0.1587, simple_loss=0.2203, pruned_loss=0.04857, over 4884.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2202, pruned_loss=0.0394, over 972641.74 frames.], batch size: 19, lr: 3.33e-04
2022-05-05 13:35:47,371 INFO [train.py:715] (2/8) Epoch 6, batch 15700, loss[loss=0.1473, simple_loss=0.2226, pruned_loss=0.03599, over 4982.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03871, over 972058.57 frames.], batch size: 28, lr: 3.33e-04
2022-05-05 13:36:26,052 INFO [train.py:715] (2/8) Epoch 6, batch 15750, loss[loss=0.1453, simple_loss=0.2094, pruned_loss=0.04061, over 4963.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2204, pruned_loss=0.0392, over 971941.00 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:37:04,796 INFO [train.py:715] (2/8) Epoch 6, batch 15800, loss[loss=0.1471, simple_loss=0.2157, pruned_loss=0.03927, over 4988.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2204, pruned_loss=0.03925, over 971382.16 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:37:43,840 INFO [train.py:715] (2/8) Epoch 6, batch 15850, loss[loss=0.1484, simple_loss=0.2156, pruned_loss=0.04055, over 4903.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2196, pruned_loss=0.03878, over 973536.29 frames.], batch size: 19, lr: 3.33e-04
2022-05-05 13:38:22,283 INFO [train.py:715] (2/8) Epoch 6, batch 15900, loss[loss=0.1344, simple_loss=0.2128, pruned_loss=0.02794, over 4985.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2195, pruned_loss=0.03844, over 973833.17 frames.], batch size: 15, lr: 3.33e-04
2022-05-05 13:39:00,648 INFO [train.py:715] (2/8) Epoch 6, batch 15950, loss[loss=0.1445, simple_loss=0.219, pruned_loss=0.03504, over 4896.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2195, pruned_loss=0.03847, over 973553.55 frames.], batch size: 22, lr: 3.33e-04
2022-05-05 13:39:39,975 INFO [train.py:715] (2/8) Epoch 6, batch 16000, loss[loss=0.1559, simple_loss=0.2261, pruned_loss=0.0429, over 4865.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2197, pruned_loss=0.03852, over 972542.59 frames.], batch size: 32, lr: 3.33e-04
2022-05-05 13:40:18,432 INFO [train.py:715] (2/8) Epoch 6, batch 16050, loss[loss=0.1487, simple_loss=0.2175, pruned_loss=0.03993, over 4830.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2202, pruned_loss=0.03884, over 972958.60 frames.], batch size: 15, lr: 3.33e-04
2022-05-05 13:40:56,900 INFO [train.py:715] (2/8) Epoch 6, batch 16100, loss[loss=0.1714, simple_loss=0.2396, pruned_loss=0.05159, over 4884.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2205, pruned_loss=0.03935, over 973265.44 frames.], batch size: 16, lr: 3.33e-04
2022-05-05 13:41:35,295 INFO [train.py:715] (2/8) Epoch 6, batch 16150, loss[loss=0.1579, simple_loss=0.2145, pruned_loss=0.05064, over 4853.00 frames.], tot_loss[loss=0.1491, simple_loss=0.22, pruned_loss=0.03911, over 973607.74 frames.], batch size: 32, lr: 3.33e-04
2022-05-05 13:42:14,793 INFO [train.py:715] (2/8) Epoch 6, batch 16200, loss[loss=0.1878, simple_loss=0.2488, pruned_loss=0.06338, over 4896.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2196, pruned_loss=0.03902, over 974110.45 frames.], batch size: 17, lr: 3.33e-04
2022-05-05 13:42:53,111 INFO [train.py:715] (2/8) Epoch 6, batch 16250, loss[loss=0.2116, simple_loss=0.2871, pruned_loss=0.06802, over 4894.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2205, pruned_loss=0.03964, over 974151.77 frames.], batch size: 17, lr: 3.33e-04
2022-05-05 13:43:31,726 INFO [train.py:715] (2/8) Epoch 6, batch 16300, loss[loss=0.1435, simple_loss=0.2149, pruned_loss=0.03602, over 4773.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2197, pruned_loss=0.03927, over 972834.85 frames.], batch size: 16, lr: 3.33e-04
2022-05-05 13:44:11,200 INFO [train.py:715] (2/8) Epoch 6, batch 16350, loss[loss=0.1807, simple_loss=0.2435, pruned_loss=0.05893, over 4970.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2202, pruned_loss=0.03926, over 972872.48 frames.], batch size: 35, lr: 3.33e-04
2022-05-05 13:44:49,508 INFO [train.py:715] (2/8) Epoch 6, batch 16400, loss[loss=0.1406, simple_loss=0.2167, pruned_loss=0.03224, over 4990.00 frames.], tot_loss[loss=0.148, simple_loss=0.2191, pruned_loss=0.03847, over 973128.77 frames.], batch size: 25, lr: 3.33e-04
2022-05-05 13:45:28,823 INFO [train.py:715] (2/8) Epoch 6, batch 16450, loss[loss=0.134, simple_loss=0.206, pruned_loss=0.03098, over 4832.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2189, pruned_loss=0.03836, over 973491.31 frames.], batch size: 13, lr: 3.33e-04
2022-05-05 13:46:07,627 INFO [train.py:715] (2/8) Epoch 6, batch 16500, loss[loss=0.1534, simple_loss=0.2314, pruned_loss=0.03771, over 4782.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2185, pruned_loss=0.03835, over 972321.35 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:46:46,603 INFO [train.py:715] (2/8) Epoch 6, batch 16550, loss[loss=0.1311, simple_loss=0.203, pruned_loss=0.02959, over 4847.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2182, pruned_loss=0.03829, over 971932.43 frames.], batch size: 32, lr: 3.33e-04
2022-05-05 13:47:24,408 INFO [train.py:715] (2/8) Epoch 6, batch 16600, loss[loss=0.1506, simple_loss=0.23, pruned_loss=0.0356, over 4880.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2189, pruned_loss=0.03877, over 972018.82 frames.], batch size: 32, lr: 3.33e-04
2022-05-05 13:48:03,149 INFO [train.py:715] (2/8) Epoch 6, batch 16650, loss[loss=0.1429, simple_loss=0.2219, pruned_loss=0.03198, over 4919.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2196, pruned_loss=0.03907, over 972116.80 frames.], batch size: 18, lr: 3.33e-04
2022-05-05 13:48:42,813 INFO [train.py:715] (2/8) Epoch 6, batch 16700, loss[loss=0.1892, simple_loss=0.257, pruned_loss=0.06075, over 4831.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2195, pruned_loss=0.03895, over 972855.73 frames.], batch size: 15, lr: 3.33e-04
2022-05-05 13:49:21,221 INFO [train.py:715] (2/8) Epoch 6, batch 16750, loss[loss=0.129, simple_loss=0.1999, pruned_loss=0.02908, over 4976.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2196, pruned_loss=0.03893, over 973120.98 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:50:00,119 INFO [train.py:715] (2/8) Epoch 6, batch 16800, loss[loss=0.1481, simple_loss=0.218, pruned_loss=0.03904, over 4855.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2192, pruned_loss=0.03822, over 973729.44 frames.], batch size: 30, lr: 3.33e-04
2022-05-05 13:50:39,327 INFO [train.py:715] (2/8) Epoch 6, batch 16850, loss[loss=0.1443, simple_loss=0.2152, pruned_loss=0.03668, over 4765.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2191, pruned_loss=0.0384, over 973756.48 frames.], batch size: 14, lr: 3.33e-04
2022-05-05 13:51:19,121 INFO [train.py:715] (2/8) Epoch 6, batch 16900, loss[loss=0.1591, simple_loss=0.2317, pruned_loss=0.04327, over 4966.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2188, pruned_loss=0.03848, over 974078.81 frames.], batch size: 35, lr: 3.32e-04
2022-05-05 13:51:57,173 INFO [train.py:715] (2/8) Epoch 6, batch 16950, loss[loss=0.1525, simple_loss=0.2277, pruned_loss=0.03871, over 4687.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2181, pruned_loss=0.03839, over 973157.94 frames.], batch size: 15, lr: 3.32e-04
2022-05-05 13:52:36,225 INFO [train.py:715] (2/8) Epoch 6, batch 17000, loss[loss=0.1565, simple_loss=0.2325, pruned_loss=0.04023, over 4839.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2188, pruned_loss=0.03868, over 972701.44 frames.], batch size: 26, lr: 3.32e-04
2022-05-05 13:53:15,744 INFO [train.py:715] (2/8) Epoch 6, batch 17050, loss[loss=0.1391, simple_loss=0.2145, pruned_loss=0.0318, over 4962.00 frames.], tot_loss[loss=0.149, simple_loss=0.2198, pruned_loss=0.03915, over 972605.53 frames.], batch size: 24, lr: 3.32e-04
2022-05-05 13:53:53,897 INFO [train.py:715] (2/8) Epoch 6, batch 17100, loss[loss=0.1632, simple_loss=0.2296, pruned_loss=0.04845, over 4779.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2188, pruned_loss=0.03819, over 972337.45 frames.], batch size: 18, lr: 3.32e-04
2022-05-05 13:54:32,775 INFO [train.py:715] (2/8) Epoch 6, batch 17150, loss[loss=0.1608, simple_loss=0.2234, pruned_loss=0.04905, over 4987.00 frames.], tot_loss[loss=0.148, simple_loss=0.2194, pruned_loss=0.03835, over 972604.08 frames.], batch size: 35, lr: 3.32e-04
2022-05-05 13:55:11,750 INFO [train.py:715] (2/8) Epoch 6, batch 17200, loss[loss=0.1347, simple_loss=0.2053, pruned_loss=0.03203, over 4905.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2192, pruned_loss=0.03851, over 973103.60 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 13:55:51,109 INFO [train.py:715] (2/8) Epoch 6, batch 17250, loss[loss=0.1277, simple_loss=0.1957, pruned_loss=0.02981, over 4774.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03836, over 973615.90 frames.], batch size: 12, lr: 3.32e-04
2022-05-05 13:56:29,076 INFO [train.py:715] (2/8) Epoch 6, batch 17300, loss[loss=0.1463, simple_loss=0.2241, pruned_loss=0.03423, over 4840.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2193, pruned_loss=0.03851, over 973948.44 frames.], batch size: 20, lr: 3.32e-04
2022-05-05 13:57:07,893 INFO [train.py:715] (2/8) Epoch 6, batch 17350, loss[loss=0.1503, simple_loss=0.2219, pruned_loss=0.03938, over 4854.00 frames.], tot_loss[loss=0.1488, simple_loss=0.22, pruned_loss=0.03875, over 973291.90 frames.], batch size: 32, lr: 3.32e-04
2022-05-05 13:57:47,274 INFO [train.py:715] (2/8) Epoch 6, batch 17400, loss[loss=0.1659, simple_loss=0.2391, pruned_loss=0.04635, over 4908.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2198, pruned_loss=0.03853, over 973106.34 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 13:58:26,213 INFO [train.py:715] (2/8) Epoch 6, batch 17450, loss[loss=0.1431, simple_loss=0.2181, pruned_loss=0.03403, over 4860.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2198, pruned_loss=0.03849, over 972710.99 frames.], batch size: 20, lr: 3.32e-04
2022-05-05 13:59:04,830 INFO [train.py:715] (2/8) Epoch 6, batch 17500, loss[loss=0.1276, simple_loss=0.2037, pruned_loss=0.02571, over 4866.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2191, pruned_loss=0.03831, over 972817.57 frames.], batch size: 20, lr: 3.32e-04
2022-05-05 13:59:43,983 INFO [train.py:715] (2/8) Epoch 6, batch 17550, loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03242, over 4895.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2193, pruned_loss=0.03858, over 972941.65 frames.], batch size: 22, lr: 3.32e-04
2022-05-05 14:00:23,863 INFO [train.py:715] (2/8) Epoch 6, batch 17600, loss[loss=0.1513, simple_loss=0.2088, pruned_loss=0.04691, over 4803.00 frames.], tot_loss[loss=0.1479, simple_loss=0.219, pruned_loss=0.03842, over 972140.67 frames.], batch size: 14, lr: 3.32e-04
2022-05-05 14:01:01,425 INFO [train.py:715] (2/8) Epoch 6, batch 17650, loss[loss=0.16, simple_loss=0.2402, pruned_loss=0.03987, over 4747.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2191, pruned_loss=0.03805, over 972855.78 frames.], batch size: 19, lr: 3.32e-04
2022-05-05 14:01:40,864 INFO [train.py:715] (2/8) Epoch 6, batch 17700, loss[loss=0.1297, simple_loss=0.2006, pruned_loss=0.02945, over 4788.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2186, pruned_loss=0.03836, over 970856.84 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 14:02:20,253 INFO [train.py:715] (2/8) Epoch 6, batch 17750, loss[loss=0.1527, simple_loss=0.2277, pruned_loss=0.03885, over 4911.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2196, pruned_loss=0.03867, over 971216.79 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 14:02:58,608 INFO [train.py:715] (2/8) Epoch 6, batch 17800, loss[loss=0.1853, simple_loss=0.2433, pruned_loss=0.06367, over 4920.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2207, pruned_loss=0.03939, over 972255.80 frames.], batch size: 18, lr: 3.32e-04
2022-05-05 14:03:37,542 INFO [train.py:715] (2/8) Epoch 6, batch 17850, loss[loss=0.1775, simple_loss=0.2365, pruned_loss=0.05921, over 4825.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2204, pruned_loss=0.03908, over 972906.86 frames.], batch size: 15, lr: 3.32e-04
2022-05-05 14:04:16,749 INFO [train.py:715] (2/8) Epoch 6, batch 17900, loss[loss=0.1695, simple_loss=0.2341, pruned_loss=0.05251, over 4959.00 frames.], tot_loss[loss=0.1497, simple_loss=0.221, pruned_loss=0.03921, over 972091.54 frames.], batch size: 24, lr: 3.32e-04
2022-05-05 14:04:56,312 INFO [train.py:715] (2/8) Epoch 6, batch 17950, loss[loss=0.1274, simple_loss=0.2027, pruned_loss=0.0261, over 4812.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.03869, over 971715.19 frames.], batch size: 26, lr: 3.32e-04
2022-05-05 14:05:34,137 INFO [train.py:715] (2/8) Epoch 6, batch 18000, loss[loss=0.1821, simple_loss=0.247, pruned_loss=0.05859, over 4714.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03886, over 971454.54 frames.], batch size: 15, lr: 3.32e-04
2022-05-05 14:05:34,138 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 14:05:43,884 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1087, simple_loss=0.1939, pruned_loss=0.0118, over 914524.00 frames.
2022-05-05 14:06:22,339 INFO [train.py:715] (2/8) Epoch 6, batch 18050, loss[loss=0.1243, simple_loss=0.1947, pruned_loss=0.027, over 4765.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2201, pruned_loss=0.03863, over 971320.00 frames.], batch size: 19, lr: 3.32e-04
2022-05-05 14:07:01,820 INFO [train.py:715] (2/8) Epoch 6, batch 18100, loss[loss=0.1496, simple_loss=0.2197, pruned_loss=0.03974, over 4785.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03878, over 971799.61 frames.], batch size: 17, lr: 3.32e-04
2022-05-05 14:07:41,267 INFO [train.py:715] (2/8) Epoch 6, batch 18150, loss[loss=0.1428, simple_loss=0.2191, pruned_loss=0.03322, over 4642.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2211, pruned_loss=0.03955, over 971264.69 frames.], batch size: 13, lr: 3.32e-04
2022-05-05 14:08:19,365 INFO [train.py:715] (2/8) Epoch 6, batch 18200, loss[loss=0.1511, simple_loss=0.2241, pruned_loss=0.03899, over 4974.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2204, pruned_loss=0.03913, over 971820.30 frames.], batch size: 24, lr: 3.32e-04
2022-05-05 14:08:58,863 INFO [train.py:715] (2/8) Epoch 6, batch 18250, loss[loss=0.1452, simple_loss=0.2187, pruned_loss=0.03588, over 4973.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2207, pruned_loss=0.03918, over 971757.70 frames.], batch size: 15, lr: 3.31e-04
2022-05-05 14:09:38,213 INFO [train.py:715] (2/8) Epoch 6, batch 18300, loss[loss=0.1416, simple_loss=0.2212, pruned_loss=0.03102, over 4921.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2208, pruned_loss=0.03943, over 972459.55 frames.], batch size: 23, lr: 3.31e-04
2022-05-05 14:10:17,261 INFO [train.py:715] (2/8) Epoch 6, batch 18350, loss[loss=0.1777, simple_loss=0.243, pruned_loss=0.05621, over 4824.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2206, pruned_loss=0.03911, over 972946.99 frames.], batch size: 15, lr: 3.31e-04
2022-05-05 14:10:55,594 INFO [train.py:715] (2/8) Epoch 6, batch 18400, loss[loss=0.1602, simple_loss=0.219, pruned_loss=0.05068, over 4743.00 frames.], tot_loss[loss=0.1484, simple_loss=0.22, pruned_loss=0.03841, over 972896.56 frames.], batch size: 16, lr: 3.31e-04
2022-05-05 14:11:34,886 INFO [train.py:715] (2/8) Epoch 6, batch 18450, loss[loss=0.1098, simple_loss=0.1805, pruned_loss=0.01958, over 4961.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03838, over 972642.20 frames.], batch size: 35, lr: 3.31e-04
2022-05-05 14:12:14,311 INFO [train.py:715] (2/8) Epoch 6, batch 18500, loss[loss=0.1377, simple_loss=0.2094, pruned_loss=0.033, over 4787.00 frames.], tot_loss[loss=0.148, simple_loss=0.2191, pruned_loss=0.03844, over 971798.95 frames.], batch size: 17, lr: 3.31e-04
2022-05-05 14:12:52,317 INFO [train.py:715] (2/8) Epoch 6, batch 18550, loss[loss=0.1466, simple_loss=0.2142, pruned_loss=0.03949, over 4933.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2201, pruned_loss=0.03901, over 971702.20 frames.], batch size: 21, lr: 3.31e-04
2022-05-05 14:13:31,753 INFO [train.py:715] (2/8) Epoch 6, batch 18600, loss[loss=0.1752, simple_loss=0.2328, pruned_loss=0.05878, over 4904.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2196, pruned_loss=0.03829, over 972243.90 frames.], batch size: 19, lr: 3.31e-04
2022-05-05 14:14:10,849 INFO [train.py:715] (2/8) Epoch 6, batch 18650, loss[loss=0.1284, simple_loss=0.2053, pruned_loss=0.0257, over 4814.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2193, pruned_loss=0.03846, over 971655.48 frames.], batch size: 21, lr: 3.31e-04
2022-05-05 14:14:50,390 INFO [train.py:715] (2/8) Epoch 6, batch 18700, loss[loss=0.1454, simple_loss=0.2344, pruned_loss=0.02822, over 4910.00 frames.], tot_loss[loss=0.1484, simple_loss=0.22, pruned_loss=0.03842, over 972989.28 frames.], batch size: 19, lr: 3.31e-04
2022-05-05 14:15:28,529 INFO [train.py:715] (2/8) Epoch 6, batch 18750, loss[loss=0.1413, simple_loss=0.2204, pruned_loss=0.03112, over 4984.00 frames.], tot_loss[loss=0.148, simple_loss=0.2197, pruned_loss=0.03818, over 973534.46 frames.], batch size: 25, lr: 3.31e-04
2022-05-05 14:16:07,703 INFO [train.py:715] (2/8) Epoch 6, batch 18800, loss[loss=0.1672, simple_loss=0.2439, pruned_loss=0.04526, over 4924.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2203, pruned_loss=0.0382, over 973278.59 frames.], batch size: 19, lr: 3.31e-04
2022-05-05 14:16:47,210 INFO [train.py:715] (2/8) Epoch 6, batch 18850, loss[loss=0.1538, simple_loss=0.2258, pruned_loss=0.04092, over 4784.00 frames.], tot_loss[loss=0.1481, simple_loss=0.22, pruned_loss=0.03806, over 972707.79 frames.], batch size: 18, lr: 3.31e-04
2022-05-05 14:17:25,255 INFO [train.py:715] (2/8) Epoch 6, batch 18900, loss[loss=0.1275, simple_loss=0.2053, pruned_loss=0.02487, over 4930.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2196, pruned_loss=0.03779, over 972865.37 frames.], batch size: 23, lr: 3.31e-04
2022-05-05 14:18:04,840 INFO [train.py:715] (2/8) Epoch 6, batch 18950, loss[loss=0.1517, simple_loss=0.2258, pruned_loss=0.0388, over 4873.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2199, pruned_loss=0.03812, over 973564.22 frames.], batch size: 16, lr: 3.31e-04
2022-05-05 14:18:43,967 INFO [train.py:715] (2/8) Epoch 6, batch 19000, loss[loss=0.1123, simple_loss=0.1908, pruned_loss=0.01693, over 4928.00 frames.], tot_loss[loss=0.148, simple_loss=0.2198, pruned_loss=0.03808, over 973307.29 frames.], batch size: 29, lr: 3.31e-04
2022-05-05 14:19:23,157 INFO [train.py:715] (2/8) Epoch 6, batch 19050, loss[loss=0.1877, simple_loss=0.2505, pruned_loss=0.06248, over 4698.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2187, pruned_loss=0.03793, over 972971.91 frames.], batch size: 15, lr: 3.31e-04
2022-05-05 14:20:01,545 INFO [train.py:715] (2/8) Epoch 6, batch 19100, loss[loss=0.1591, simple_loss=0.2227, pruned_loss=0.04773, over 4813.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2195, pruned_loss=0.03848, over 973194.87 frames.], batch size: 25, lr: 3.31e-04
2022-05-05 14:20:40,516 INFO [train.py:715] (2/8) Epoch 6, batch 19150, loss[loss=0.1175, simple_loss=0.1941, pruned_loss=0.02048, over 4936.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2189, pruned_loss=0.03851, over 973078.01 frames.], batch size: 23, lr: 3.31e-04
2022-05-05 14:21:20,172 INFO [train.py:715] (2/8) Epoch 6, batch 19200, loss[loss=0.1675, simple_loss=0.2455, pruned_loss=0.04474, over 4898.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2196, pruned_loss=0.03907, over 973617.15 frames.], batch size: 22, lr: 3.31e-04
2022-05-05 14:21:58,238 INFO [train.py:715] (2/8) Epoch 6, batch 19250, loss[loss=0.1719, simple_loss=0.2328, pruned_loss=0.05544, over 4951.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2189, pruned_loss=0.03872, over 973970.12 frames.], batch size: 21, lr: 3.31e-04
2022-05-05 14:22:37,141 INFO [train.py:715] (2/8) Epoch 6, batch 19300, loss[loss=0.1556, simple_loss=0.2299, pruned_loss=0.04068, over 4902.00 frames.], tot_loss[loss=0.1481, simple_loss=0.219, pruned_loss=0.0386, over 973955.16 frames.], batch size: 23, lr: 3.31e-04
2022-05-05 14:23:16,401 INFO [train.py:715] (2/8) Epoch 6, batch 19350, loss[loss=0.156, simple_loss=0.2282, pruned_loss=0.04187, over 4821.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2197, pruned_loss=0.03887, over 972616.51 frames.], batch size: 26, lr: 3.31e-04
2022-05-05 14:23:54,985 INFO [train.py:715] (2/8) Epoch 6, batch 19400, loss[loss=0.1398, simple_loss=0.2131, pruned_loss=0.03327, over 4893.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2193, pruned_loss=0.03874, over 972396.69 frames.], batch size: 22, lr: 3.31e-04
2022-05-05 14:24:33,671 INFO [train.py:715] (2/8) Epoch 6, batch 19450, loss[loss=0.1584, simple_loss=0.2245, pruned_loss=0.0462, over 4930.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2198, pruned_loss=0.03847, over 972288.23 frames.], batch size: 39, lr: 3.31e-04
2022-05-05 14:25:13,065 INFO [train.py:715] (2/8) Epoch 6, batch 19500, loss[loss=0.164, simple_loss=0.2314, pruned_loss=0.04834, over 4876.00 frames.], tot_loss[loss=0.147, simple_loss=0.2182, pruned_loss=0.03792, over 972202.00 frames.], batch size: 22, lr: 3.31e-04
2022-05-05 14:25:51,974 INFO [train.py:715] (2/8) Epoch 6, batch 19550, loss[loss=0.1297, simple_loss=0.2039, pruned_loss=0.02775, over 4752.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2179, pruned_loss=0.03797, over 973215.36 frames.], batch size: 19, lr: 3.31e-04
2022-05-05 14:26:30,329 INFO [train.py:715] (2/8) Epoch 6, batch 19600, loss[loss=0.137, simple_loss=0.2127, pruned_loss=0.03063, over 4807.00 frames.], tot_loss[loss=0.1481, simple_loss=0.219, pruned_loss=0.03862, over 972701.07 frames.], batch size: 12, lr: 3.31e-04
2022-05-05 14:27:09,234 INFO [train.py:715] (2/8) Epoch 6, batch 19650, loss[loss=0.1487, simple_loss=0.2172, pruned_loss=0.04011, over 4740.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2194, pruned_loss=0.03857, over 972277.55 frames.], batch size: 16, lr: 3.30e-04
2022-05-05 14:27:48,352 INFO [train.py:715] (2/8) Epoch 6, batch 19700, loss[loss=0.1255, simple_loss=0.19, pruned_loss=0.03052, over 4738.00 frames.], tot_loss[loss=0.148, simple_loss=0.219, pruned_loss=0.03849, over 972464.69 frames.], batch size: 16, lr: 3.30e-04
2022-05-05 14:28:27,135 INFO [train.py:715] (2/8) Epoch 6, batch 19750, loss[loss=0.1554, simple_loss=0.2235, pruned_loss=0.04361, over 4978.00 frames.], tot_loss[loss=0.148, simple_loss=0.219, pruned_loss=0.03847, over 972418.02 frames.], batch size: 15, lr: 3.30e-04
2022-05-05 14:29:05,245 INFO [train.py:715] (2/8) Epoch 6, batch 19800, loss[loss=0.1392, simple_loss=0.2002, pruned_loss=0.03915, over 4839.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2197, pruned_loss=0.03867, over 972157.17 frames.], batch size: 30, lr: 3.30e-04
2022-05-05 14:29:44,606 INFO [train.py:715] (2/8) Epoch 6, batch 19850, loss[loss=0.1683, simple_loss=0.24, pruned_loss=0.04835, over 4800.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2215, pruned_loss=0.03922, over 971729.56 frames.], batch size: 24, lr: 3.30e-04
2022-05-05 14:30:24,343 INFO [train.py:715] (2/8) Epoch 6, batch 19900, loss[loss=0.1739, simple_loss=0.2297, pruned_loss=0.05907, over 4922.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2212, pruned_loss=0.03951, over 971604.24 frames.], batch size: 29, lr: 3.30e-04
2022-05-05 14:31:02,424 INFO [train.py:715] (2/8) Epoch 6, batch 19950, loss[loss=0.1248, simple_loss=0.1922, pruned_loss=0.02869, over 4795.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2209, pruned_loss=0.039, over 972300.97 frames.], batch size: 17, lr: 3.30e-04
2022-05-05 14:31:41,548 INFO [train.py:715] (2/8) Epoch 6, batch 20000, loss[loss=0.1541, simple_loss=0.2277, pruned_loss=0.04031, over 4986.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2204, pruned_loss=0.03886, over 972984.13 frames.], batch size: 25, lr: 3.30e-04
2022-05-05 14:32:21,020 INFO [train.py:715] (2/8) Epoch 6, batch 20050, loss[loss=0.1311, simple_loss=0.2039, pruned_loss=0.0292, over 4906.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2195, pruned_loss=0.03847, over 973031.69 frames.], batch size: 17, lr: 3.30e-04
2022-05-05 14:32:59,452 INFO [train.py:715] (2/8) Epoch 6, batch 20100, loss[loss=0.176, simple_loss=0.2507, pruned_loss=0.05062, over 4805.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2197, pruned_loss=0.03846, over 973066.97 frames.], batch size: 21, lr: 3.30e-04
2022-05-05 14:33:38,528 INFO [train.py:715] (2/8) Epoch 6, batch 20150, loss[loss=0.1391, simple_loss=0.2104, pruned_loss=0.0339, over 4957.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2198, pruned_loss=0.0384, over 972278.29 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:34:17,810 INFO [train.py:715] (2/8) Epoch 6, batch 20200, loss[loss=0.1438, simple_loss=0.2221, pruned_loss=0.03279, over 4813.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2186, pruned_loss=0.03809, over 972420.34 frames.], batch size: 13, lr: 3.30e-04
2022-05-05 14:34:56,737 INFO [train.py:715] (2/8) Epoch 6, batch 20250, loss[loss=0.1634, simple_loss=0.2254, pruned_loss=0.0507, over 4901.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2188, pruned_loss=0.03809, over 971736.08 frames.], batch size: 17, lr: 3.30e-04
2022-05-05 14:35:35,499 INFO [train.py:715] (2/8) Epoch 6, batch 20300, loss[loss=0.1452, simple_loss=0.2111, pruned_loss=0.03958, over 4938.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2182, pruned_loss=0.03827, over 970893.59 frames.], batch size: 23, lr: 3.30e-04
2022-05-05 14:36:14,862 INFO [train.py:715] (2/8) Epoch 6, batch 20350, loss[loss=0.1407, simple_loss=0.2124, pruned_loss=0.03452, over 4981.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2185, pruned_loss=0.0383, over 971185.59 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:36:54,324 INFO [train.py:715] (2/8) Epoch 6, batch 20400, loss[loss=0.1644, simple_loss=0.2405, pruned_loss=0.0441, over 4832.00 frames.], tot_loss[loss=0.146, simple_loss=0.2175, pruned_loss=0.03721, over 972030.46 frames.], batch size: 27, lr: 3.30e-04
2022-05-05 14:37:32,665 INFO [train.py:715] (2/8) Epoch 6, batch 20450, loss[loss=0.1306, simple_loss=0.2033, pruned_loss=0.02889, over 4818.00 frames.], tot_loss[loss=0.1468, simple_loss=0.218, pruned_loss=0.03781, over 972129.69 frames.], batch size: 13, lr: 3.30e-04
2022-05-05 14:38:11,469 INFO [train.py:715] (2/8) Epoch 6, batch 20500, loss[loss=0.1524, simple_loss=0.2293, pruned_loss=0.03777, over 4816.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2187, pruned_loss=0.03803, over 971840.27 frames.], batch size: 26, lr: 3.30e-04
2022-05-05 14:38:50,521 INFO [train.py:715] (2/8) Epoch 6, batch 20550, loss[loss=0.181, simple_loss=0.2677, pruned_loss=0.04714, over 4698.00 frames.], tot_loss[loss=0.1474, simple_loss=0.219, pruned_loss=0.03788, over 971668.00 frames.], batch size: 15, lr: 3.30e-04
2022-05-05 14:39:29,698 INFO [train.py:715] (2/8) Epoch 6, batch 20600, loss[loss=0.1911, simple_loss=0.2687, pruned_loss=0.05677, over 4793.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03822, over 971140.97 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:40:07,964 INFO [train.py:715] (2/8) Epoch 6, batch 20650, loss[loss=0.1416, simple_loss=0.2119, pruned_loss=0.03568, over 4697.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2187, pruned_loss=0.03783, over 970214.90 frames.], batch size: 15, lr: 3.30e-04
2022-05-05 14:40:46,654 INFO [train.py:715] (2/8) Epoch 6, batch 20700, loss[loss=0.1388, simple_loss=0.2094, pruned_loss=0.03411, over 4901.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2194, pruned_loss=0.03843, over 971542.25 frames.], batch size: 19, lr: 3.30e-04
2022-05-05 14:41:25,991 INFO [train.py:715] (2/8) Epoch 6, batch 20750, loss[loss=0.1267, simple_loss=0.2072, pruned_loss=0.02306, over 4975.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03841, over 971916.25 frames.], batch size: 26, lr: 3.30e-04
2022-05-05 14:42:04,388 INFO [train.py:715] (2/8) Epoch 6, batch 20800, loss[loss=0.1462, simple_loss=0.2137, pruned_loss=0.0393, over 4878.00 frames.], tot_loss[loss=0.148, simple_loss=0.2194, pruned_loss=0.03834, over 972286.19 frames.], batch size: 32, lr: 3.30e-04
2022-05-05 14:42:43,609 INFO [train.py:715] (2/8) Epoch 6, batch 20850, loss[loss=0.1802, simple_loss=0.2347, pruned_loss=0.0628, over 4780.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2178, pruned_loss=0.0377, over 972291.09 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:43:22,882 INFO [train.py:715] (2/8) Epoch 6, batch 20900, loss[loss=0.1222, simple_loss=0.19, pruned_loss=0.02719, over 4969.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2181, pruned_loss=0.03762, over 972217.15 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:44:02,109 INFO [train.py:715] (2/8) Epoch 6, batch 20950, loss[loss=0.1402, simple_loss=0.2127, pruned_loss=0.03384, over 4764.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.03771, over 971704.63 frames.], batch size: 14, lr: 3.30e-04
2022-05-05 14:44:40,092 INFO [train.py:715] (2/8) Epoch 6, batch 21000, loss[loss=0.1717, simple_loss=0.2434, pruned_loss=0.04998, over 4840.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2186, pruned_loss=0.0376, over 972105.17 frames.], batch size: 30, lr: 3.29e-04
2022-05-05 14:44:40,093 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 14:44:51,876 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1089, simple_loss=0.1939, pruned_loss=0.01192, over 914524.00 frames.
2022-05-05 14:45:30,120 INFO [train.py:715] (2/8) Epoch 6, batch 21050, loss[loss=0.1406, simple_loss=0.2195, pruned_loss=0.03084, over 4815.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2189, pruned_loss=0.03797, over 972789.34 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 14:46:09,486 INFO [train.py:715] (2/8) Epoch 6, batch 21100, loss[loss=0.1398, simple_loss=0.206, pruned_loss=0.03682, over 4856.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2199, pruned_loss=0.03913, over 972840.93 frames.], batch size: 12, lr: 3.29e-04
2022-05-05 14:46:48,884 INFO [train.py:715] (2/8) Epoch 6, batch 21150, loss[loss=0.1678, simple_loss=0.2225, pruned_loss=0.05652, over 4983.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2198, pruned_loss=0.03893, over 973284.33 frames.], batch size: 14, lr: 3.29e-04
2022-05-05 14:47:27,346 INFO [train.py:715] (2/8) Epoch 6, batch 21200, loss[loss=0.1572, simple_loss=0.2231, pruned_loss=0.04563, over 4702.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2204, pruned_loss=0.0389, over 972318.37 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 14:48:06,354 INFO [train.py:715] (2/8) Epoch 6, batch 21250, loss[loss=0.1295, simple_loss=0.2118, pruned_loss=0.02361, over 4825.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2197, pruned_loss=0.03874, over 971569.77 frames.], batch size: 21, lr: 3.29e-04
2022-05-05 14:48:45,978 INFO [train.py:715] (2/8) Epoch 6, batch 21300, loss[loss=0.228, simple_loss=0.2761, pruned_loss=0.09001, over 4830.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03918, over 970763.74 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 14:49:24,955 INFO [train.py:715] (2/8) Epoch 6, batch 21350, loss[loss=0.1382, simple_loss=0.2181, pruned_loss=0.02922, over 4786.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2205, pruned_loss=0.03921, over 971192.28 frames.], batch size: 17, lr: 3.29e-04
2022-05-05 14:50:03,789 INFO [train.py:715] (2/8) Epoch 6, batch 21400, loss[loss=0.1334, simple_loss=0.2023, pruned_loss=0.03223, over 4854.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2206, pruned_loss=0.03926, over 971837.98 frames.], batch size: 20, lr: 3.29e-04
2022-05-05 14:50:42,549 INFO [train.py:715] (2/8) Epoch 6, batch 21450, loss[loss=0.1416, simple_loss=0.2116, pruned_loss=0.03578, over 4800.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2206, pruned_loss=0.03942, over 972612.39 frames.], batch size: 21, lr: 3.29e-04
2022-05-05 14:51:21,821 INFO [train.py:715] (2/8) Epoch 6, batch 21500, loss[loss=0.1542, simple_loss=0.2215, pruned_loss=0.04339, over 4760.00 frames.], tot_loss[loss=0.1493, simple_loss=0.22, pruned_loss=0.03925, over 972028.23 frames.], batch size: 14, lr: 3.29e-04
2022-05-05 14:52:00,288 INFO [train.py:715] (2/8) Epoch 6, batch 21550, loss[loss=0.1191, simple_loss=0.1946, pruned_loss=0.02185, over 4987.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2201, pruned_loss=0.03905, over 971748.69 frames.], batch size: 28, lr: 3.29e-04
2022-05-05 14:52:39,315 INFO [train.py:715] (2/8) Epoch 6, batch 21600, loss[loss=0.146, simple_loss=0.2158, pruned_loss=0.03816, over 4803.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2195, pruned_loss=0.03885, over 972537.49 frames.], batch size: 12, lr: 3.29e-04
2022-05-05 14:53:18,464 INFO [train.py:715] (2/8) Epoch 6, batch 21650, loss[loss=0.15, simple_loss=0.218, pruned_loss=0.04101, over 4819.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2192, pruned_loss=0.03847, over 972516.84 frames.], batch size: 26, lr: 3.29e-04
2022-05-05 14:53:57,746 INFO [train.py:715] (2/8) Epoch 6, batch 21700, loss[loss=0.1746, simple_loss=0.2513, pruned_loss=0.049, over 4775.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2206, pruned_loss=0.03922, over 972903.94 frames.], batch size: 17, lr: 3.29e-04
2022-05-05 14:54:36,455 INFO [train.py:715] (2/8) Epoch 6, batch 21750, loss[loss=0.1329, simple_loss=0.2008, pruned_loss=0.03248, over 4869.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03841, over 972629.96 frames.], batch size: 16, lr: 3.29e-04
2022-05-05 14:55:15,315 INFO [train.py:715] (2/8) Epoch 6, batch 21800, loss[loss=0.1465, simple_loss=0.2189, pruned_loss=0.03698, over 4841.00 frames.], tot_loss[loss=0.149, simple_loss=0.2197, pruned_loss=0.03914, over 972937.32 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 14:55:54,108 INFO [train.py:715] (2/8) Epoch 6, batch 21850, loss[loss=0.1436, simple_loss=0.214, pruned_loss=0.03656, over 4699.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2205, pruned_loss=0.03948, over 972153.93 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 14:56:32,648 INFO [train.py:715] (2/8) Epoch 6, batch 21900, loss[loss=0.1174, simple_loss=0.1857, pruned_loss=0.02453, over 4801.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2193, pruned_loss=0.03896, over 972479.92 frames.], batch size: 12, lr: 3.29e-04
2022-05-05 14:57:11,519 INFO [train.py:715] (2/8) Epoch 6, batch 21950, loss[loss=0.142, simple_loss=0.208, pruned_loss=0.03801, over 4930.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2187, pruned_loss=0.0385, over 972881.69 frames.], batch size: 23, lr: 3.29e-04
2022-05-05 14:57:50,235 INFO [train.py:715] (2/8) Epoch 6, batch 22000, loss[loss=0.1592, simple_loss=0.243, pruned_loss=0.0377, over 4773.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2184, pruned_loss=0.03856, over 972144.05 frames.], batch size: 18, lr: 3.29e-04
2022-05-05 14:58:29,939 INFO [train.py:715] (2/8) Epoch 6, batch 22050, loss[loss=0.1733, simple_loss=0.2385, pruned_loss=0.05398, over 4979.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2192, pruned_loss=0.03852, over 972665.54 frames.], batch size: 31, lr: 3.29e-04
2022-05-05 14:59:08,264 INFO [train.py:715] (2/8) Epoch 6, batch 22100, loss[loss=0.1361, simple_loss=0.2106, pruned_loss=0.03083, over 4899.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2191, pruned_loss=0.03835, over 971609.39 frames.], batch size: 17, lr: 3.29e-04
2022-05-05 14:59:47,063 INFO [train.py:715] (2/8) Epoch 6, batch 22150, loss[loss=0.1265, simple_loss=0.2004, pruned_loss=0.02632, over 4770.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2184, pruned_loss=0.03848, over 970754.75 frames.], batch size: 19, lr: 3.29e-04
2022-05-05 15:00:26,256 INFO [train.py:715] (2/8) Epoch 6, batch 22200, loss[loss=0.1883, simple_loss=0.2532, pruned_loss=0.0617, over 4920.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2193, pruned_loss=0.03894, over 971502.00 frames.], batch size: 17, lr: 3.29e-04
2022-05-05 15:01:04,919 INFO [train.py:715] (2/8) Epoch 6, batch 22250, loss[loss=0.1305, simple_loss=0.2035, pruned_loss=0.02873, over 4921.00 frames.], tot_loss[loss=0.149, simple_loss=0.2198, pruned_loss=0.03907, over 971634.32 frames.], batch size: 29, lr: 3.29e-04
2022-05-05 15:01:43,599 INFO [train.py:715] (2/8) Epoch 6, batch 22300, loss[loss=0.1598, simple_loss=0.2245, pruned_loss=0.0475, over 4978.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2196, pruned_loss=0.03911, over 971408.84 frames.], batch size: 14, lr: 3.29e-04
2022-05-05 15:02:22,658 INFO [train.py:715] (2/8) Epoch 6, batch 22350, loss[loss=0.1451, simple_loss=0.2157, pruned_loss=0.03726, over 4867.00 frames.], tot_loss[loss=0.1481, simple_loss=0.219, pruned_loss=0.03862, over 972178.70 frames.], batch size: 16, lr: 3.29e-04
2022-05-05 15:03:02,004 INFO [train.py:715] (2/8) Epoch 6, batch 22400, loss[loss=0.142, simple_loss=0.2075, pruned_loss=0.0382, over 4957.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2195, pruned_loss=0.03871, over 972795.54 frames.], batch size: 15, lr: 3.29e-04
2022-05-05 15:03:40,492 INFO [train.py:715] (2/8) Epoch 6, batch 22450, loss[loss=0.129, simple_loss=0.2034, pruned_loss=0.02736, over 4828.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2186, pruned_loss=0.03804, over 972631.54 frames.], batch size: 26, lr: 3.28e-04
2022-05-05 15:04:19,441 INFO [train.py:715] (2/8) Epoch 6, batch 22500, loss[loss=0.1481, simple_loss=0.2239, pruned_loss=0.03611, over 4911.00 frames.], tot_loss[loss=0.147, simple_loss=0.218, pruned_loss=0.03796, over 972913.64 frames.], batch size: 18, lr: 3.28e-04
2022-05-05 15:04:58,760 INFO [train.py:715] (2/8) Epoch 6, batch 22550, loss[loss=0.1319, simple_loss=0.1994, pruned_loss=0.03223, over 4695.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2183, pruned_loss=0.03863, over 972761.55 frames.], batch size: 15, lr: 3.28e-04
2022-05-05 15:05:37,171 INFO [train.py:715] (2/8) Epoch 6, batch 22600, loss[loss=0.1403, simple_loss=0.2158, pruned_loss=0.03239, over 4834.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2187, pruned_loss=0.03823, over 971671.76 frames.], batch size: 25, lr: 3.28e-04
2022-05-05 15:06:16,008 INFO [train.py:715] (2/8) Epoch 6, batch 22650, loss[loss=0.1336, simple_loss=0.2013, pruned_loss=0.03294, over 4968.00 frames.], tot_loss[loss=0.148, simple_loss=0.2194, pruned_loss=0.03829, over 972085.06 frames.], batch size: 15, lr: 3.28e-04
2022-05-05 15:06:54,605 INFO [train.py:715] (2/8) Epoch 6, batch 22700, loss[loss=0.1227, simple_loss=0.1967, pruned_loss=0.02435, over 4822.00 frames.], tot_loss[loss=0.149, simple_loss=0.2205, pruned_loss=0.03881, over 972711.18 frames.], batch size: 26, lr: 3.28e-04
2022-05-05 15:07:33,407 INFO [train.py:715] (2/8) Epoch 6, batch 22750, loss[loss=0.1624, simple_loss=0.2379, pruned_loss=0.04349, over 4815.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2211, pruned_loss=0.03873, over 972979.94 frames.], batch size: 25, lr: 3.28e-04
2022-05-05 15:08:11,866 INFO [train.py:715] (2/8) Epoch 6, batch 22800, loss[loss=0.159, simple_loss=0.2214, pruned_loss=0.04828, over 4697.00 frames.], tot_loss[loss=0.15, simple_loss=0.2215, pruned_loss=0.03925, over 972917.02 frames.], batch size: 15, lr: 3.28e-04
2022-05-05 15:08:50,371 INFO [train.py:715] (2/8) Epoch 6, batch 22850, loss[loss=0.1747, simple_loss=0.235, pruned_loss=0.05717, over 4788.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2209, pruned_loss=0.03895, over 971874.02 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:09:29,030 INFO [train.py:715] (2/8) Epoch 6, batch 22900, loss[loss=0.1325, simple_loss=0.2056, pruned_loss=0.02972, over 4803.00 frames.], tot_loss[loss=0.1489, simple_loss=0.22, pruned_loss=0.03883, over 971540.76 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:10:08,162 INFO [train.py:715] (2/8) Epoch 6, batch 22950, loss[loss=0.1648, simple_loss=0.2253, pruned_loss=0.05219, over 4703.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2196, pruned_loss=0.03866, over 970630.32 frames.], batch size: 15, lr: 3.28e-04
2022-05-05 15:10:46,572 INFO [train.py:715] (2/8) Epoch 6, batch 23000, loss[loss=0.1522, simple_loss=0.222, pruned_loss=0.04122, over 4800.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2184, pruned_loss=0.03807, over 970550.62 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:11:25,821 INFO [train.py:715] (2/8) Epoch 6, batch 23050, loss[loss=0.1205, simple_loss=0.1933, pruned_loss=0.02378, over 4813.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.0381, over 972001.98 frames.], batch size: 27, lr: 3.28e-04
2022-05-05 15:12:05,301 INFO [train.py:715] (2/8) Epoch 6, batch 23100, loss[loss=0.1696, simple_loss=0.2421, pruned_loss=0.04856, over 4635.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2183, pruned_loss=0.03804, over 971977.60 frames.], batch size: 13, lr: 3.28e-04
2022-05-05 15:12:46,121 INFO [train.py:715] (2/8) Epoch 6, batch 23150, loss[loss=0.1269, simple_loss=0.2051, pruned_loss=0.02433, over 4768.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2173, pruned_loss=0.03715, over 972049.24 frames.], batch size: 19, lr: 3.28e-04
2022-05-05 15:13:25,467 INFO [train.py:715] (2/8) Epoch 6, batch 23200, loss[loss=0.1484, simple_loss=0.2078, pruned_loss=0.04454, over 4749.00 frames.], tot_loss[loss=0.1459, simple_loss=0.217, pruned_loss=0.03737, over 971738.91 frames.], batch size: 12, lr: 3.28e-04
2022-05-05 15:14:04,867 INFO [train.py:715] (2/8) Epoch 6, batch 23250, loss[loss=0.1625, simple_loss=0.2313, pruned_loss=0.04688, over 4810.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2168, pruned_loss=0.03731, over 972304.05 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:14:43,531 INFO [train.py:715] (2/8) Epoch 6, batch 23300, loss[loss=0.1904, simple_loss=0.2652, pruned_loss=0.05786, over 4992.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2184, pruned_loss=0.03799, over 971877.60 frames.], batch size: 16, lr: 3.28e-04
2022-05-05 15:15:21,520 INFO [train.py:715] (2/8) Epoch 6, batch 23350, loss[loss=0.1337, simple_loss=0.216, pruned_loss=0.02572, over 4950.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2184, pruned_loss=0.03811, over 972286.11 frames.], batch size: 24, lr: 3.28e-04
2022-05-05 15:16:00,567 INFO [train.py:715] (2/8) Epoch 6, batch 23400, loss[loss=0.1266, simple_loss=0.1979, pruned_loss=0.02764, over 4856.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2179, pruned_loss=0.03776, over 972543.68 frames.], batch size: 22, lr: 3.28e-04
2022-05-05 15:16:40,151 INFO [train.py:715] (2/8) Epoch 6, batch 23450, loss[loss=0.1301, simple_loss=0.2037, pruned_loss=0.02823, over 4851.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2177, pruned_loss=0.03748, over 972982.66 frames.], batch size: 20, lr: 3.28e-04
2022-05-05 15:17:19,122 INFO [train.py:715] (2/8) Epoch 6, batch 23500, loss[loss=0.1347, simple_loss=0.211, pruned_loss=0.02915, over 4902.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2175, pruned_loss=0.03758, over 973358.05 frames.], batch size: 19, lr: 3.28e-04
2022-05-05 15:17:58,301 INFO [train.py:715] (2/8) Epoch 6, batch 23550, loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02838, over 4937.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2188, pruned_loss=0.03826, over 973449.47 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:18:37,515 INFO [train.py:715] (2/8) Epoch 6, batch 23600, loss[loss=0.1437, simple_loss=0.2144, pruned_loss=0.03648, over 4961.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03867, over 973615.14 frames.], batch size: 15, lr: 3.28e-04
2022-05-05 15:19:16,260 INFO [train.py:715] (2/8) Epoch 6, batch 23650, loss[loss=0.1928, simple_loss=0.2463, pruned_loss=0.0697, over 4881.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2205, pruned_loss=0.03901, over 973262.84 frames.], batch size: 16, lr: 3.28e-04
2022-05-05 15:19:54,393 INFO [train.py:715] (2/8) Epoch 6, batch 23700, loss[loss=0.1624, simple_loss=0.2311, pruned_loss=0.04684, over 4961.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2203, pruned_loss=0.03933, over 972510.22 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:20:33,415 INFO [train.py:715] (2/8) Epoch 6, batch 23750, loss[loss=0.1292, simple_loss=0.205, pruned_loss=0.02673, over 4816.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2208, pruned_loss=0.0392, over 972418.27 frames.], batch size: 27, lr: 3.28e-04
2022-05-05 15:21:12,834 INFO [train.py:715] (2/8) Epoch 6, batch 23800, loss[loss=0.1619, simple_loss=0.2274, pruned_loss=0.04816, over 4803.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2201, pruned_loss=0.03882, over 972420.40 frames.], batch size: 21, lr: 3.28e-04
2022-05-05 15:21:51,203 INFO [train.py:715] (2/8) Epoch 6, batch 23850, loss[loss=0.1587, simple_loss=0.2327, pruned_loss=0.04234, over 4924.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2199, pruned_loss=0.03886, over 972416.21 frames.], batch size: 21, lr: 3.27e-04
2022-05-05 15:22:29,815 INFO [train.py:715] (2/8) Epoch 6, batch 23900, loss[loss=0.1216, simple_loss=0.1973, pruned_loss=0.02302, over 4810.00 frames.], tot_loss[loss=0.148, simple_loss=0.2191, pruned_loss=0.03845, over 971840.30 frames.], batch size: 25, lr: 3.27e-04
2022-05-05 15:23:08,546 INFO [train.py:715] (2/8) Epoch 6, batch 23950, loss[loss=0.1854, simple_loss=0.2616, pruned_loss=0.05459, over 4828.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2183, pruned_loss=0.0382, over 972090.79 frames.], batch size: 25, lr: 3.27e-04
2022-05-05 15:23:47,222 INFO [train.py:715] (2/8) Epoch 6, batch 24000, loss[loss=0.1486, simple_loss=0.2153, pruned_loss=0.04091, over 4799.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2187, pruned_loss=0.03831, over 972347.38 frames.], batch size: 13, lr: 3.27e-04
2022-05-05 15:23:47,223 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 15:23:58,203 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1089, simple_loss=0.1939, pruned_loss=0.01195, over 914524.00 frames.
2022-05-05 15:24:36,966 INFO [train.py:715] (2/8) Epoch 6, batch 24050, loss[loss=0.1806, simple_loss=0.2409, pruned_loss=0.06017, over 4955.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2192, pruned_loss=0.03885, over 972273.52 frames.], batch size: 35, lr: 3.27e-04
2022-05-05 15:25:15,032 INFO [train.py:715] (2/8) Epoch 6, batch 24100, loss[loss=0.1484, simple_loss=0.2312, pruned_loss=0.03282, over 4911.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2185, pruned_loss=0.03825, over 972620.24 frames.], batch size: 23, lr: 3.27e-04
2022-05-05 15:25:53,706 INFO [train.py:715] (2/8) Epoch 6, batch 24150, loss[loss=0.1529, simple_loss=0.2308, pruned_loss=0.03746, over 4768.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2185, pruned_loss=0.03825, over 972181.43 frames.], batch size: 17, lr: 3.27e-04
2022-05-05 15:26:32,799 INFO [train.py:715] (2/8) Epoch 6, batch 24200, loss[loss=0.1513, simple_loss=0.225, pruned_loss=0.03882, over 4799.00 frames.], tot_loss[loss=0.147, simple_loss=0.2181, pruned_loss=0.03794, over 972298.34 frames.], batch size: 21, lr: 3.27e-04
2022-05-05 15:27:10,720 INFO [train.py:715] (2/8) Epoch 6, batch 24250, loss[loss=0.1493, simple_loss=0.223, pruned_loss=0.03776, over 4786.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2182, pruned_loss=0.03779, over 972210.82 frames.], batch size: 12, lr: 3.27e-04
2022-05-05 15:27:49,115 INFO [train.py:715] (2/8) Epoch 6, batch 24300, loss[loss=0.1418, simple_loss=0.2178, pruned_loss=0.03287, over 4924.00 frames.], tot_loss[loss=0.147, simple_loss=0.2181, pruned_loss=0.03796, over 971290.34 frames.], batch size: 23, lr: 3.27e-04
2022-05-05 15:28:28,043 INFO [train.py:715] (2/8) Epoch 6, batch 24350, loss[loss=0.1339, simple_loss=0.2124, pruned_loss=0.02764, over 4941.00 frames.], tot_loss[loss=0.1476, simple_loss=0.219, pruned_loss=0.03807, over 971658.04 frames.], batch size: 23, lr: 3.27e-04
2022-05-05 15:29:07,161 INFO [train.py:715] (2/8) Epoch 6, batch 24400, loss[loss=0.14, simple_loss=0.2118, pruned_loss=0.03408, over 4946.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2182, pruned_loss=0.03759, over 970698.61 frames.], batch size: 35, lr: 3.27e-04
2022-05-05 15:29:45,509 INFO [train.py:715] (2/8) Epoch 6, batch 24450, loss[loss=0.1599, simple_loss=0.233, pruned_loss=0.04338, over 4831.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2184, pruned_loss=0.03764, over 970290.69 frames.], batch size: 26, lr: 3.27e-04
2022-05-05 15:30:24,124 INFO [train.py:715] (2/8) Epoch 6, batch 24500, loss[loss=0.1572, simple_loss=0.234, pruned_loss=0.04013, over 4887.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2182, pruned_loss=0.03798, over 969851.77 frames.], batch size: 22, lr: 3.27e-04
2022-05-05 15:31:03,940 INFO [train.py:715] (2/8) Epoch 6, batch 24550, loss[loss=0.1477, simple_loss=0.2247, pruned_loss=0.0353, over 4835.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2177, pruned_loss=0.03782, over 970355.10 frames.], batch size: 30, lr: 3.27e-04
2022-05-05 15:31:42,161 INFO [train.py:715] (2/8) Epoch 6, batch 24600, loss[loss=0.1381, simple_loss=0.2068, pruned_loss=0.03465, over 4968.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2181, pruned_loss=0.0384, over 970461.17 frames.], batch size: 35, lr: 3.27e-04
2022-05-05 15:32:21,362 INFO [train.py:715] (2/8) Epoch 6, batch 24650, loss[loss=0.1583, simple_loss=0.2229, pruned_loss=0.04684, over 4823.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2184, pruned_loss=0.0383, over 970913.08 frames.], batch size: 26, lr: 3.27e-04
2022-05-05 15:33:00,613 INFO [train.py:715] (2/8) Epoch 6, batch 24700, loss[loss=0.1615, simple_loss=0.2307, pruned_loss=0.04615, over 4970.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2193, pruned_loss=0.03868, over 971189.75 frames.], batch size: 15, lr: 3.27e-04
2022-05-05 15:33:39,472 INFO [train.py:715] (2/8) Epoch 6, batch 24750, loss[loss=0.1675, simple_loss=0.2381, pruned_loss=0.04844, over 4686.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2192, pruned_loss=0.03826, over 971257.88 frames.], batch size: 15, lr: 3.27e-04
2022-05-05 15:34:17,834 INFO [train.py:715] (2/8) Epoch 6, batch 24800, loss[loss=0.1394, simple_loss=0.2088, pruned_loss=0.03501, over 4781.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2193, pruned_loss=0.03862, over 971548.56 frames.], batch size: 14, lr: 3.27e-04
2022-05-05 15:34:56,838 INFO [train.py:715] (2/8) Epoch 6, batch 24850, loss[loss=0.1752, simple_loss=0.2445, pruned_loss=0.05293, over 4861.00 frames.], tot_loss[loss=0.148, simple_loss=0.2191, pruned_loss=0.03847, over 971466.91 frames.], batch size: 38, lr: 3.27e-04
2022-05-05 15:35:36,648 INFO [train.py:715] (2/8) Epoch 6, batch 24900, loss[loss=0.1758, simple_loss=0.2314, pruned_loss=0.06016, over 4896.00 frames.], tot_loss[loss=0.148, simple_loss=0.219, pruned_loss=0.03844, over 972125.27 frames.], batch size: 17, lr: 3.27e-04
2022-05-05 15:36:14,921 INFO [train.py:715] (2/8) Epoch 6, batch 24950, loss[loss=0.1171, simple_loss=0.1847, pruned_loss=0.02479, over 4779.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2181, pruned_loss=0.03774, over 971974.53 frames.], batch size: 12, lr: 3.27e-04
2022-05-05 15:36:53,552 INFO [train.py:715] (2/8) Epoch 6, batch 25000, loss[loss=0.142, simple_loss=0.2134, pruned_loss=0.03532, over 4832.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2187, pruned_loss=0.03818, over 972100.55 frames.], batch size: 25, lr: 3.27e-04
2022-05-05 15:37:32,635 INFO [train.py:715] (2/8) Epoch 6, batch 25050, loss[loss=0.1338, simple_loss=0.2046, pruned_loss=0.03152, over 4728.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.03809, over 971952.37 frames.], batch size: 16, lr: 3.27e-04
2022-05-05 15:38:11,564 INFO [train.py:715] (2/8) Epoch 6, batch 25100, loss[loss=0.1625, simple_loss=0.2251, pruned_loss=0.05001, over 4960.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03795, over 972440.44 frames.], batch size: 35, lr: 3.27e-04
2022-05-05 15:38:50,094 INFO [train.py:715] (2/8) Epoch 6, batch 25150, loss[loss=0.141, simple_loss=0.2065, pruned_loss=0.03773, over 4755.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2203, pruned_loss=0.03845, over 972117.17 frames.], batch size: 19, lr: 3.27e-04
2022-05-05 15:39:28,930 INFO [train.py:715] (2/8) Epoch 6, batch 25200, loss[loss=0.1618, simple_loss=0.2296, pruned_loss=0.04702, over 4792.00 frames.], tot_loss[loss=0.149, simple_loss=0.2211, pruned_loss=0.03848, over 971303.53 frames.], batch size: 18, lr: 3.27e-04
2022-05-05 15:40:07,779 INFO [train.py:715] (2/8) Epoch 6, batch 25250, loss[loss=0.1396, simple_loss=0.2072, pruned_loss=0.03594, over 4932.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2204, pruned_loss=0.03816, over 971111.30 frames.], batch size: 29, lr: 3.26e-04
2022-05-05 15:40:46,084 INFO [train.py:715] (2/8) Epoch 6, batch 25300, loss[loss=0.1743, simple_loss=0.2388, pruned_loss=0.05491, over 4971.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2202, pruned_loss=0.03877, over 971902.08 frames.], batch size: 35, lr: 3.26e-04
2022-05-05 15:41:24,369 INFO [train.py:715] (2/8) Epoch 6, batch 25350, loss[loss=0.1227, simple_loss=0.1979, pruned_loss=0.02379, over 4781.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2198, pruned_loss=0.03861, over 971504.62 frames.], batch size: 18, lr: 3.26e-04
2022-05-05 15:42:03,176 INFO [train.py:715] (2/8) Epoch 6, batch 25400, loss[loss=0.1498, simple_loss=0.2221, pruned_loss=0.03878, over 4969.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03891, over 970981.08 frames.], batch size: 24, lr: 3.26e-04
2022-05-05 15:42:41,991 INFO [train.py:715] (2/8) Epoch 6, batch 25450, loss[loss=0.1068, simple_loss=0.1779, pruned_loss=0.01787, over 4825.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2193, pruned_loss=0.03848, over 971167.28 frames.], batch size: 13, lr: 3.26e-04
2022-05-05 15:43:20,085 INFO [train.py:715] (2/8) Epoch 6, batch 25500, loss[loss=0.1787, simple_loss=0.2533, pruned_loss=0.05209, over 4963.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2205, pruned_loss=0.03905, over 971209.99 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:43:58,583 INFO [train.py:715] (2/8) Epoch 6, batch 25550, loss[loss=0.1396, simple_loss=0.2249, pruned_loss=0.02717, over 4820.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2209, pruned_loss=0.039, over 971663.68 frames.], batch size: 27, lr: 3.26e-04
2022-05-05 15:44:37,704 INFO [train.py:715] (2/8) Epoch 6, batch 25600, loss[loss=0.1281, simple_loss=0.1956, pruned_loss=0.03028, over 4963.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2204, pruned_loss=0.03854, over 972898.71 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:45:15,936 INFO [train.py:715] (2/8) Epoch 6, batch 25650, loss[loss=0.1453, simple_loss=0.2162, pruned_loss=0.03725, over 4858.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2197, pruned_loss=0.03827, over 972490.68 frames.], batch size: 20, lr: 3.26e-04
2022-05-05 15:45:54,740 INFO [train.py:715] (2/8) Epoch 6, batch 25700, loss[loss=0.153, simple_loss=0.2274, pruned_loss=0.03933, over 4686.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2198, pruned_loss=0.03883, over 972153.71 frames.], batch size: 15, lr: 3.26e-04
2022-05-05 15:46:34,047 INFO [train.py:715] (2/8) Epoch 6, batch 25750, loss[loss=0.1444, simple_loss=0.2121, pruned_loss=0.03838, over 4922.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03884, over 972148.66 frames.], batch size: 23, lr: 3.26e-04
2022-05-05 15:47:12,320 INFO [train.py:715] (2/8) Epoch 6, batch 25800, loss[loss=0.1453, simple_loss=0.2132, pruned_loss=0.03875, over 4783.00 frames.], tot_loss[loss=0.1495, simple_loss=0.221, pruned_loss=0.03905, over 972850.62 frames.], batch size: 12, lr: 3.26e-04
2022-05-05 15:47:50,577 INFO [train.py:715] (2/8) Epoch 6, batch 25850, loss[loss=0.1302, simple_loss=0.2046, pruned_loss=0.02785, over 4862.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2203, pruned_loss=0.03909, over 972245.42 frames.], batch size: 20, lr: 3.26e-04
2022-05-05 15:48:29,220 INFO [train.py:715] (2/8) Epoch 6, batch 25900, loss[loss=0.1601, simple_loss=0.2274, pruned_loss=0.04645, over 4963.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2198, pruned_loss=0.03882, over 971928.16 frames.], batch size: 24, lr: 3.26e-04
2022-05-05 15:49:08,367 INFO [train.py:715] (2/8) Epoch 6, batch 25950, loss[loss=0.1448, simple_loss=0.2156, pruned_loss=0.03698, over 4820.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2203, pruned_loss=0.03867, over 972417.73 frames.], batch size: 21, lr: 3.26e-04
2022-05-05 15:49:46,044 INFO [train.py:715] (2/8) Epoch 6, batch 26000, loss[loss=0.1487, simple_loss=0.2102, pruned_loss=0.04357, over 4928.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2206, pruned_loss=0.03903, over 971396.56 frames.], batch size: 23, lr: 3.26e-04
2022-05-05 15:50:24,229 INFO [train.py:715] (2/8) Epoch 6, batch 26050, loss[loss=0.1232, simple_loss=0.1996, pruned_loss=0.0234, over 4746.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2196, pruned_loss=0.03852, over 971077.30 frames.], batch size: 16, lr: 3.26e-04
2022-05-05 15:51:03,213 INFO [train.py:715] (2/8) Epoch 6, batch 26100, loss[loss=0.1292, simple_loss=0.2084, pruned_loss=0.02497, over 4798.00 frames.], tot_loss[loss=0.1488, simple_loss=0.22, pruned_loss=0.03875, over 970658.47 frames.], batch size: 25, lr: 3.26e-04
2022-05-05 15:51:41,623 INFO [train.py:715] (2/8) Epoch 6, batch 26150, loss[loss=0.1476, simple_loss=0.2198, pruned_loss=0.03766, over 4900.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2204, pruned_loss=0.03908, over 971546.69 frames.], batch size: 17, lr: 3.26e-04
2022-05-05 15:52:20,120 INFO [train.py:715] (2/8) Epoch 6, batch 26200, loss[loss=0.1455, simple_loss=0.2197, pruned_loss=0.03564, over 4787.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2197, pruned_loss=0.03861, over 971817.99 frames.], batch size: 17, lr: 3.26e-04
2022-05-05 15:52:58,597 INFO [train.py:715] (2/8) Epoch 6, batch 26250, loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03502, over 4929.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2201, pruned_loss=0.03889, over 972367.51 frames.], batch size: 35, lr: 3.26e-04
2022-05-05 15:53:37,252 INFO [train.py:715] (2/8) Epoch 6, batch 26300, loss[loss=0.143, simple_loss=0.2015, pruned_loss=0.04229, over 4767.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2196, pruned_loss=0.03899, over 972291.36 frames.], batch size: 14, lr: 3.26e-04
2022-05-05 15:54:15,318 INFO [train.py:715] (2/8) Epoch 6, batch 26350, loss[loss=0.1362, simple_loss=0.2076, pruned_loss=0.03239, over 4745.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2189, pruned_loss=0.03884, over 971924.87 frames.], batch size: 19, lr: 3.26e-04
2022-05-05 15:54:53,793 INFO [train.py:715] (2/8) Epoch 6, batch 26400, loss[loss=0.158, simple_loss=0.2267, pruned_loss=0.04465, over 4865.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2199, pruned_loss=0.03892, over 971407.66 frames.], batch size: 20, lr: 3.26e-04
2022-05-05 15:55:33,106 INFO [train.py:715] (2/8) Epoch 6, batch 26450, loss[loss=0.1561, simple_loss=0.2236, pruned_loss=0.04427, over 4865.00 frames.], tot_loss[loss=0.149, simple_loss=0.22, pruned_loss=0.03901, over 971235.14 frames.], batch size: 20, lr: 3.26e-04
2022-05-05 15:56:11,693 INFO [train.py:715] (2/8) Epoch 6, batch 26500, loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03074, over 4929.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2203, pruned_loss=0.03912, over 971052.15 frames.], batch size: 21, lr: 3.26e-04
2022-05-05 15:56:50,073 INFO [train.py:715] (2/8) Epoch 6, batch 26550, loss[loss=0.1421, simple_loss=0.2038, pruned_loss=0.04016, over 4849.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2192, pruned_loss=0.03848, over 970984.23 frames.], batch size: 30, lr: 3.26e-04
2022-05-05 15:57:28,902 INFO [train.py:715] (2/8) Epoch 6, batch 26600, loss[loss=0.1685, simple_loss=0.2316, pruned_loss=0.05268, over 4946.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03881, over 971095.00 frames.], batch size: 21, lr: 3.26e-04
2022-05-05 15:58:07,541 INFO [train.py:715] (2/8) Epoch 6, batch 26650, loss[loss=0.1854, simple_loss=0.2692, pruned_loss=0.05078, over 4883.00 frames.], tot_loss[loss=0.149, simple_loss=0.2202, pruned_loss=0.03889, over 971216.08 frames.], batch size: 20, lr: 3.26e-04
2022-05-05 15:58:46,267 INFO [train.py:715] (2/8) Epoch 6, batch 26700, loss[loss=0.1493, simple_loss=0.2163, pruned_loss=0.04111, over 4862.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03884, over 970878.12 frames.], batch size: 13, lr: 3.25e-04
2022-05-05 15:59:24,555 INFO [train.py:715] (2/8) Epoch 6, batch 26750, loss[loss=0.1762, simple_loss=0.2324, pruned_loss=0.06003, over 4834.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.03862, over 970617.03 frames.], batch size: 30, lr: 3.25e-04
2022-05-05 16:00:03,786 INFO [train.py:715] (2/8) Epoch 6, batch 26800, loss[loss=0.1276, simple_loss=0.2003, pruned_loss=0.02747, over 4742.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2198, pruned_loss=0.03862, over 971010.13 frames.], batch size: 19, lr: 3.25e-04
2022-05-05 16:00:41,900 INFO [train.py:715] (2/8) Epoch 6, batch 26850, loss[loss=0.1696, simple_loss=0.2334, pruned_loss=0.0529, over 4804.00 frames.], tot_loss[loss=0.149, simple_loss=0.2203, pruned_loss=0.03882, over 971224.77 frames.], batch size: 15, lr: 3.25e-04
2022-05-05 16:01:20,552 INFO [train.py:715] (2/8) Epoch 6, batch 26900, loss[loss=0.1689, simple_loss=0.2425, pruned_loss=0.04764, over 4972.00 frames.], tot_loss[loss=0.148, simple_loss=0.2196, pruned_loss=0.03819, over 972495.82 frames.], batch size: 39, lr: 3.25e-04
2022-05-05 16:01:59,792 INFO [train.py:715] (2/8) Epoch 6, batch 26950, loss[loss=0.1516, simple_loss=0.2306, pruned_loss=0.03625, over 4874.00 frames.], tot_loss[loss=0.149, simple_loss=0.2202, pruned_loss=0.0389, over 972496.95 frames.], batch size: 22, lr: 3.25e-04
2022-05-05 16:02:39,038 INFO [train.py:715] (2/8) Epoch 6, batch 27000, loss[loss=0.1636, simple_loss=0.2346, pruned_loss=0.04627, over 4816.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2195, pruned_loss=0.03855, over 972720.66 frames.], batch size: 27, lr: 3.25e-04
2022-05-05 16:02:39,039 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 16:02:48,795 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1088, simple_loss=0.1938, pruned_loss=0.01188, over 914524.00 frames.
2022-05-05 16:03:28,074 INFO [train.py:715] (2/8) Epoch 6, batch 27050, loss[loss=0.171, simple_loss=0.2314, pruned_loss=0.05532, over 4841.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2196, pruned_loss=0.03879, over 973537.32 frames.], batch size: 15, lr: 3.25e-04
2022-05-05 16:04:06,806 INFO [train.py:715] (2/8) Epoch 6, batch 27100, loss[loss=0.1204, simple_loss=0.189, pruned_loss=0.02591, over 4812.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2192, pruned_loss=0.03869, over 972616.75 frames.], batch size: 13, lr: 3.25e-04
2022-05-05 16:04:45,453 INFO [train.py:715] (2/8) Epoch 6, batch 27150, loss[loss=0.1406, simple_loss=0.2092, pruned_loss=0.03601, over 4908.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2202, pruned_loss=0.03919, over 972136.76 frames.], batch size: 17, lr: 3.25e-04
2022-05-05 16:05:25,174 INFO [train.py:715] (2/8) Epoch 6, batch 27200, loss[loss=0.1332, simple_loss=0.2049, pruned_loss=0.03073, over 4714.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2193, pruned_loss=0.03873, over 971710.25 frames.], batch size: 15, lr: 3.25e-04
2022-05-05 16:06:03,413 INFO [train.py:715] (2/8) Epoch 6, batch 27250, loss[loss=0.1404, simple_loss=0.2194, pruned_loss=0.03067, over 4964.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2202, pruned_loss=0.03918, over 971141.27 frames.], batch size: 15, lr: 3.25e-04
2022-05-05 16:06:43,063 INFO [train.py:715] (2/8) Epoch 6, batch 27300, loss[loss=0.1389, simple_loss=0.2208, pruned_loss=0.02852, over 4864.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2192, pruned_loss=0.03862, over 971879.83 frames.], batch size: 20, lr: 3.25e-04
2022-05-05 16:07:22,057 INFO [train.py:715] (2/8) Epoch 6, batch 27350, loss[loss=0.1294, simple_loss=0.2151, pruned_loss=0.0218, over 4740.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2193, pruned_loss=0.03862, over 971587.67 frames.], batch size: 16, lr: 3.25e-04
2022-05-05 16:08:01,183 INFO [train.py:715] (2/8) Epoch 6, batch 27400, loss[loss=0.1704, simple_loss=0.2467, pruned_loss=0.047, over 4783.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2197, pruned_loss=0.03903, over 971642.76 frames.], batch size: 17, lr: 3.25e-04
2022-05-05 16:08:39,770 INFO [train.py:715] (2/8) Epoch 6, batch 27450, loss[loss=0.1418, simple_loss=0.2136, pruned_loss=0.03504, over 4966.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2192, pruned_loss=0.03907, over 972004.52 frames.], batch size: 24, lr: 3.25e-04
2022-05-05 16:09:18,813 INFO [train.py:715] (2/8) Epoch 6, batch 27500, loss[loss=0.154, simple_loss=0.2303, pruned_loss=0.03882, over 4832.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2194, pruned_loss=0.0392, over 971642.60 frames.], batch size: 30, lr: 3.25e-04
2022-05-05 16:09:58,189 INFO [train.py:715] (2/8) Epoch 6, batch 27550, loss[loss=0.1551, simple_loss=0.2307, pruned_loss=0.0398, over 4881.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2192, pruned_loss=0.03872, over 972397.34 frames.], batch size: 22, lr: 3.25e-04
2022-05-05 16:10:36,913 INFO [train.py:715] (2/8) Epoch 6, batch 27600, loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02939, over 4937.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2194, pruned_loss=0.03845, over 972425.86 frames.], batch size: 29, lr: 3.25e-04
2022-05-05 16:11:15,426 INFO [train.py:715] (2/8) Epoch 6, batch 27650, loss[loss=0.1718, simple_loss=0.2347, pruned_loss=0.05442, over 4833.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2191, pruned_loss=0.03857, over 972145.46 frames.], batch size: 15, lr: 3.25e-04
2022-05-05 16:11:54,438 INFO [train.py:715] (2/8) Epoch 6, batch 27700, loss[loss=0.1702, simple_loss=0.2378, pruned_loss=0.05133, over 4914.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2187, pruned_loss=0.03851, over 971635.96 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:12:32,979 INFO [train.py:715] (2/8) Epoch 6, batch 27750, loss[loss=0.1408, simple_loss=0.2154, pruned_loss=0.03312, over 4818.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2192, pruned_loss=0.0391, over 971534.43 frames.], batch size: 26, lr: 3.25e-04
2022-05-05 16:13:12,194 INFO [train.py:715] (2/8) Epoch 6, batch 27800, loss[loss=0.1345, simple_loss=0.21, pruned_loss=0.02954, over 4771.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2195, pruned_loss=0.03885, over 971595.08 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:13:51,230 INFO [train.py:715] (2/8) Epoch 6, batch 27850, loss[loss=0.1619, simple_loss=0.2262, pruned_loss=0.04878, over 4904.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2191, pruned_loss=0.03884, over 971777.96 frames.], batch size: 17, lr: 3.25e-04
2022-05-05 16:14:30,898 INFO [train.py:715] (2/8) Epoch 6, batch 27900, loss[loss=0.1764, simple_loss=0.2433, pruned_loss=0.05469, over 4913.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2196, pruned_loss=0.03908, over 972312.83 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:15:09,373 INFO [train.py:715] (2/8) Epoch 6, batch 27950, loss[loss=0.138, simple_loss=0.2169, pruned_loss=0.02951, over 4911.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2196, pruned_loss=0.039, over 972477.93 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:15:48,254 INFO [train.py:715] (2/8) Epoch 6, batch 28000, loss[loss=0.13, simple_loss=0.2015, pruned_loss=0.02926, over 4769.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2197, pruned_loss=0.03891, over 972650.09 frames.], batch size: 18, lr: 3.25e-04
2022-05-05 16:16:27,386 INFO [train.py:715] (2/8) Epoch 6, batch 28050, loss[loss=0.1525, simple_loss=0.2308, pruned_loss=0.03708, over 4812.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2187, pruned_loss=0.03829, over 972418.03 frames.], batch size: 27, lr: 3.25e-04
2022-05-05 16:17:06,023 INFO [train.py:715] (2/8) Epoch 6, batch 28100, loss[loss=0.1442, simple_loss=0.2231, pruned_loss=0.0327, over 4988.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2196, pruned_loss=0.03875, over 972670.63 frames.], batch size: 25, lr: 3.25e-04
2022-05-05 16:17:44,944 INFO [train.py:715] (2/8) Epoch 6, batch 28150, loss[loss=0.1954, simple_loss=0.2638, pruned_loss=0.06353, over 4803.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2193, pruned_loss=0.03849, over 973263.70 frames.], batch size: 21, lr: 3.24e-04
2022-05-05 16:18:24,090 INFO [train.py:715] (2/8) Epoch 6, batch 28200, loss[loss=0.1229, simple_loss=0.1939, pruned_loss=0.02594, over 4797.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2189, pruned_loss=0.0383, over 972673.79 frames.], batch size: 24, lr: 3.24e-04
2022-05-05 16:19:03,412 INFO [train.py:715] (2/8) Epoch 6, batch 28250, loss[loss=0.1329, simple_loss=0.2178, pruned_loss=0.02399, over 4952.00 frames.], tot_loss[loss=0.147, simple_loss=0.2182, pruned_loss=0.0379, over 972612.18 frames.], batch size: 21, lr: 3.24e-04
2022-05-05 16:19:41,792 INFO [train.py:715] (2/8) Epoch 6, batch 28300, loss[loss=0.1422, simple_loss=0.2174, pruned_loss=0.03344, over 4877.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03826, over 972022.26 frames.], batch size: 16, lr: 3.24e-04
2022-05-05 16:20:20,025 INFO [train.py:715] (2/8) Epoch 6, batch 28350, loss[loss=0.1284, simple_loss=0.2108, pruned_loss=0.02297, over 4791.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2193, pruned_loss=0.03806, over 970699.83 frames.], batch size: 18, lr: 3.24e-04
2022-05-05 16:20:59,873 INFO [train.py:715] (2/8) Epoch 6, batch 28400, loss[loss=0.1619, simple_loss=0.2186, pruned_loss=0.05257, over 4841.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2195, pruned_loss=0.0383, over 970922.33 frames.], batch size: 30, lr: 3.24e-04
2022-05-05 16:21:38,667 INFO [train.py:715] (2/8) Epoch 6, batch 28450, loss[loss=0.1421, simple_loss=0.2038, pruned_loss=0.04016, over 4789.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2187, pruned_loss=0.0379, over 971568.75 frames.], batch size: 14, lr: 3.24e-04
2022-05-05 16:22:17,504 INFO [train.py:715] (2/8) Epoch 6, batch 28500, loss[loss=0.1533, simple_loss=0.2169, pruned_loss=0.04489, over 4839.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2183, pruned_loss=0.03815, over 971760.19 frames.], batch size: 30, lr: 3.24e-04
2022-05-05 16:22:56,651 INFO [train.py:715] (2/8) Epoch 6, batch 28550, loss[loss=0.1636, simple_loss=0.2348, pruned_loss=0.04621, over 4814.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2194, pruned_loss=0.0385, over 971874.76 frames.], batch size: 25, lr: 3.24e-04
2022-05-05 16:23:36,091 INFO [train.py:715] (2/8) Epoch 6, batch 28600, loss[loss=0.1546, simple_loss=0.2333, pruned_loss=0.03797, over 4756.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2195, pruned_loss=0.03874, over 971607.34 frames.], batch size: 16, lr: 3.24e-04
2022-05-05 16:24:14,191 INFO [train.py:715] (2/8) Epoch 6, batch 28650, loss[loss=0.1289, simple_loss=0.2004, pruned_loss=0.02867, over 4921.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2186, pruned_loss=0.03807, over 972313.78 frames.], batch size: 35, lr: 3.24e-04
2022-05-05 16:24:52,990 INFO [train.py:715] (2/8) Epoch 6, batch 28700, loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02876, over 4915.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2197, pruned_loss=0.03859, over 972765.66 frames.], batch size: 23, lr: 3.24e-04
2022-05-05 16:25:32,177 INFO [train.py:715] (2/8) Epoch 6, batch 28750, loss[loss=0.1483, simple_loss=0.2037, pruned_loss=0.04643, over 4985.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03837, over 973143.62 frames.], batch size: 26, lr: 3.24e-04
2022-05-05 16:26:10,900 INFO [train.py:715] (2/8) Epoch 6, batch 28800, loss[loss=0.1669, simple_loss=0.2437, pruned_loss=0.04502, over 4964.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2194, pruned_loss=0.03819, over 973215.26 frames.], batch size: 24, lr: 3.24e-04
2022-05-05 16:26:49,768 INFO [train.py:715] (2/8) Epoch 6, batch 28850, loss[loss=0.1591, simple_loss=0.22, pruned_loss=0.04907, over 4768.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2198, pruned_loss=0.03865, over 972443.08 frames.], batch size: 17, lr: 3.24e-04
2022-05-05 16:27:28,068 INFO [train.py:715] (2/8) Epoch 6, batch 28900, loss[loss=0.1531, simple_loss=0.2111, pruned_loss=0.04753, over 4776.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03815, over 972784.09 frames.], batch size: 17, lr: 3.24e-04
2022-05-05 16:28:07,515 INFO [train.py:715] (2/8) Epoch 6, batch 28950, loss[loss=0.1727, simple_loss=0.2423, pruned_loss=0.05152, over 4884.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2188, pruned_loss=0.03776, over 973023.24 frames.], batch size: 16, lr: 3.24e-04
2022-05-05 16:28:45,750 INFO [train.py:715] (2/8) Epoch 6, batch 29000, loss[loss=0.1682, simple_loss=0.2263, pruned_loss=0.05501, over 4696.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2183, pruned_loss=0.03761, over 973103.46 frames.], batch size: 15, lr: 3.24e-04
2022-05-05 16:29:23,907 INFO [train.py:715] (2/8) Epoch 6, batch 29050, loss[loss=0.1414, simple_loss=0.2174, pruned_loss=0.03268, over 4821.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2198, pruned_loss=0.03853, over 973437.84 frames.], batch size: 27, lr: 3.24e-04
2022-05-05 16:30:02,946 INFO [train.py:715] (2/8) Epoch 6, batch 29100, loss[loss=0.1372, simple_loss=0.2125, pruned_loss=0.03098, over 4932.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2189, pruned_loss=0.03797, over 972935.85 frames.], batch size: 21, lr: 3.24e-04
2022-05-05 16:30:41,838 INFO [train.py:715] (2/8) Epoch 6, batch 29150, loss[loss=0.1375, simple_loss=0.2129, pruned_loss=0.03104, over 4939.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2189, pruned_loss=0.03814, over 972968.41 frames.], batch size: 21, lr: 3.24e-04
2022-05-05 16:31:20,670 INFO [train.py:715] (2/8) Epoch 6, batch 29200, loss[loss=0.1638, simple_loss=0.2245, pruned_loss=0.05155, over 4953.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2188, pruned_loss=0.03827, over 972161.59 frames.], batch size: 21, lr: 3.24e-04
2022-05-05 16:31:59,883 INFO [train.py:715] (2/8) Epoch 6, batch 29250, loss[loss=0.1394, simple_loss=0.2036, pruned_loss=0.03755, over 4853.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2184, pruned_loss=0.03793, over 972642.42 frames.], batch size: 30, lr: 3.24e-04
2022-05-05 16:32:39,922 INFO [train.py:715] (2/8) Epoch 6, batch 29300, loss[loss=0.1542, simple_loss=0.2145, pruned_loss=0.04692, over 4846.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2185, pruned_loss=0.03787, over 972377.77 frames.], batch size: 12, lr: 3.24e-04
2022-05-05 16:33:18,207 INFO [train.py:715] (2/8) Epoch 6, batch 29350, loss[loss=0.1473, simple_loss=0.218, pruned_loss=0.03832, over 4777.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2187, pruned_loss=0.03777, over 972092.70 frames.], batch size: 14, lr: 3.24e-04
2022-05-05 16:33:57,193 INFO [train.py:715] (2/8) Epoch 6, batch 29400, loss[loss=0.1264, simple_loss=0.206, pruned_loss=0.02338, over 4825.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2177, pruned_loss=0.03742, over 972491.00 frames.], batch size: 13, lr: 3.24e-04
2022-05-05 16:34:36,597 INFO [train.py:715] (2/8) Epoch 6, batch 29450, loss[loss=0.126, simple_loss=0.2023, pruned_loss=0.02482, over 4787.00 frames.], tot_loss[loss=0.147, simple_loss=0.2183, pruned_loss=0.0379, over 972499.97 frames.], batch size: 17, lr: 3.24e-04
2022-05-05 16:35:15,803 INFO [train.py:715] (2/8) Epoch 6, batch 29500, loss[loss=0.1575, simple_loss=0.2225, pruned_loss=0.04623, over 4964.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2189, pruned_loss=0.03842, over 972050.30 frames.], batch size: 35, lr: 3.24e-04
2022-05-05 16:35:53,793 INFO [train.py:715] (2/8) Epoch 6, batch 29550, loss[loss=0.1472, simple_loss=0.2217, pruned_loss=0.03629, over 4789.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2195, pruned_loss=0.03874, over 972480.20 frames.], batch size: 14, lr: 3.24e-04
2022-05-05 16:36:33,141 INFO [train.py:715] (2/8) Epoch 6, batch 29600, loss[loss=0.1386, simple_loss=0.2107, pruned_loss=0.03322, over 4780.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2202, pruned_loss=0.03932, over 972077.70 frames.], batch size: 18, lr: 3.24e-04
2022-05-05 16:37:12,532 INFO [train.py:715] (2/8) Epoch 6, batch 29650, loss[loss=0.1311, simple_loss=0.2004, pruned_loss=0.03093, over 4932.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2195, pruned_loss=0.03877, over 972231.65 frames.], batch size: 21, lr: 3.23e-04
2022-05-05 16:37:51,062 INFO [train.py:715] (2/8) Epoch 6, batch 29700, loss[loss=0.1565, simple_loss=0.2287, pruned_loss=0.04212, over 4980.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2201, pruned_loss=0.03902, over 972743.74 frames.], batch size: 28, lr: 3.23e-04
2022-05-05 16:38:29,762 INFO [train.py:715] (2/8) Epoch 6, batch 29750, loss[loss=0.1367, simple_loss=0.2114, pruned_loss=0.03103, over 4972.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2193, pruned_loss=0.03848, over 972792.54 frames.], batch size: 24, lr: 3.23e-04
2022-05-05 16:39:08,776 INFO [train.py:715] (2/8) Epoch 6, batch 29800, loss[loss=0.1345, simple_loss=0.2037, pruned_loss=0.03266, over 4752.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2202, pruned_loss=0.03849, over 972743.46 frames.], batch size: 19, lr: 3.23e-04
2022-05-05 16:39:48,203 INFO [train.py:715] (2/8) Epoch 6, batch 29850, loss[loss=0.1576, simple_loss=0.2158, pruned_loss=0.04967, over 4870.00 frames.], tot_loss[loss=0.148, simple_loss=0.2193, pruned_loss=0.03836, over 973130.15 frames.], batch size: 32, lr: 3.23e-04
2022-05-05 16:40:26,713 INFO [train.py:715] (2/8) Epoch 6, batch 29900, loss[loss=0.1359, simple_loss=0.2101, pruned_loss=0.03088, over 4864.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2198, pruned_loss=0.03863, over 972586.17 frames.], batch size: 30, lr: 3.23e-04
2022-05-05 16:41:05,702 INFO [train.py:715] (2/8) Epoch 6, batch 29950, loss[loss=0.1573, simple_loss=0.2403, pruned_loss=0.0372, over 4950.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2195, pruned_loss=0.03862, over 973116.36 frames.], batch size: 35, lr: 3.23e-04
2022-05-05 16:41:45,055 INFO [train.py:715] (2/8) Epoch 6, batch 30000, loss[loss=0.1794, simple_loss=0.2514, pruned_loss=0.05373, over 4925.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2198, pruned_loss=0.0383, over 972944.04 frames.], batch size: 29, lr: 3.23e-04
2022-05-05 16:41:45,056 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 16:41:54,714 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1088, simple_loss=0.1938, pruned_loss=0.0119, over 914524.00 frames.
2022-05-05 16:42:34,428 INFO [train.py:715] (2/8) Epoch 6, batch 30050, loss[loss=0.1214, simple_loss=0.2028, pruned_loss=0.02003, over 4770.00 frames.], tot_loss[loss=0.147, simple_loss=0.2188, pruned_loss=0.03758, over 972875.85 frames.], batch size: 19, lr: 3.23e-04
2022-05-05 16:43:12,815 INFO [train.py:715] (2/8) Epoch 6, batch 30100, loss[loss=0.1378, simple_loss=0.213, pruned_loss=0.03132, over 4818.00 frames.], tot_loss[loss=0.147, simple_loss=0.2187, pruned_loss=0.03769, over 972931.24 frames.], batch size: 21, lr: 3.23e-04
2022-05-05 16:43:51,560 INFO [train.py:715] (2/8) Epoch 6, batch 30150, loss[loss=0.1499, simple_loss=0.2148, pruned_loss=0.04253, over 4935.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2186, pruned_loss=0.03745, over 973143.20 frames.], batch size: 39, lr: 3.23e-04
2022-05-05 16:44:30,968 INFO [train.py:715] (2/8) Epoch 6, batch 30200, loss[loss=0.1423, simple_loss=0.2013, pruned_loss=0.04161, over 4828.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.03762, over 972772.67 frames.], batch size: 13, lr: 3.23e-04
2022-05-05 16:45:10,341 INFO [train.py:715] (2/8) Epoch 6, batch 30250, loss[loss=0.161, simple_loss=0.2283, pruned_loss=0.04686, over 4945.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2189, pruned_loss=0.03786, over 972359.78 frames.], batch size: 39, lr: 3.23e-04
2022-05-05 16:45:48,512 INFO [train.py:715] (2/8) Epoch 6, batch 30300, loss[loss=0.1271, simple_loss=0.1998, pruned_loss=0.02719, over 4830.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2183, pruned_loss=0.03756, over 972220.27 frames.], batch size: 26, lr: 3.23e-04
2022-05-05 16:46:27,517 INFO [train.py:715] (2/8) Epoch 6, batch 30350, loss[loss=0.1656, simple_loss=0.23, pruned_loss=0.05053, over 4829.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2192, pruned_loss=0.03833, over 972646.84 frames.], batch size: 15, lr: 3.23e-04
2022-05-05 16:47:06,587 INFO [train.py:715] (2/8) Epoch 6, batch 30400, loss[loss=0.1615, simple_loss=0.2235, pruned_loss=0.04973, over 4700.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03837, over 972356.96 frames.], batch size: 15, lr: 3.23e-04
2022-05-05 16:47:45,263 INFO [train.py:715] (2/8) Epoch 6, batch 30450, loss[loss=0.1435, simple_loss=0.2083, pruned_loss=0.03929, over 4851.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.03803, over 972422.54 frames.], batch size: 15, lr: 3.23e-04
2022-05-05 16:48:23,948 INFO [train.py:715] (2/8) Epoch 6, batch 30500, loss[loss=0.1232, simple_loss=0.2002, pruned_loss=0.02314, over 4904.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2192, pruned_loss=0.03776, over 972988.05 frames.], batch size: 19, lr: 3.23e-04
2022-05-05 16:49:02,696 INFO [train.py:715] (2/8) Epoch 6, batch 30550, loss[loss=0.1593, simple_loss=0.2327, pruned_loss=0.04293, over 4875.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03731, over 972418.29 frames.], batch size: 16, lr: 3.23e-04
2022-05-05 16:49:41,870 INFO [train.py:715] (2/8) Epoch 6, batch 30600, loss[loss=0.1202, simple_loss=0.1909, pruned_loss=0.02476, over 4875.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2183, pruned_loss=0.03769, over 971578.58 frames.], batch size: 16, lr: 3.23e-04
2022-05-05 16:50:20,374 INFO [train.py:715] (2/8) Epoch 6, batch 30650, loss[loss=0.1938, simple_loss=0.2721, pruned_loss=0.05773, over 4973.00 frames.], tot_loss[loss=0.148, simple_loss=0.2191, pruned_loss=0.03839, over 971980.55 frames.], batch size: 40, lr: 3.23e-04
2022-05-05 16:50:59,250 INFO [train.py:715] (2/8) Epoch 6, batch 30700, loss[loss=0.1667, simple_loss=0.2347, pruned_loss=0.04938, over 4802.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03806, over 973500.24 frames.], batch size: 21, lr: 3.23e-04
2022-05-05 16:51:38,195 INFO [train.py:715] (2/8) Epoch 6, batch 30750, loss[loss=0.1438, simple_loss=0.2031, pruned_loss=0.04224, over 4981.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2175, pruned_loss=0.03777, over 972495.93 frames.], batch size: 14, lr: 3.23e-04
2022-05-05 16:52:17,036 INFO [train.py:715] (2/8) Epoch 6, batch 30800, loss[loss=0.1295, simple_loss=0.2105, pruned_loss=0.02431, over 4900.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2177, pruned_loss=0.03785, over 972747.61 frames.], batch size: 19, lr: 3.23e-04
2022-05-05 16:52:55,436 INFO [train.py:715] (2/8) Epoch 6, batch 30850, loss[loss=0.1357, simple_loss=0.2022, pruned_loss=0.03459, over 4856.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2176, pruned_loss=0.03794, over 973129.93 frames.], batch size: 20, lr: 3.23e-04
2022-05-05 16:53:34,166 INFO [train.py:715] (2/8) Epoch 6, batch 30900, loss[loss=0.1385, simple_loss=0.22, pruned_loss=0.0285, over 4980.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2183, pruned_loss=0.03831, over 973690.58 frames.], batch size: 25, lr: 3.23e-04
2022-05-05 16:54:13,772 INFO [train.py:715] (2/8) Epoch 6, batch 30950, loss[loss=0.15, simple_loss=0.2247, pruned_loss=0.03761, over 4844.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2178, pruned_loss=0.03791, over 973440.13 frames.], batch size: 30, lr: 3.23e-04
2022-05-05 16:54:51,910 INFO [train.py:715] (2/8) Epoch 6, batch 31000, loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.0347, over 4880.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2188, pruned_loss=0.03802, over 973168.24 frames.], batch size: 22, lr: 3.23e-04
2022-05-05 16:55:30,913 INFO [train.py:715] (2/8) Epoch 6, batch 31050, loss[loss=0.1492, simple_loss=0.2123, pruned_loss=0.04301, over 4965.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2192, pruned_loss=0.03825, over 972989.05 frames.], batch size: 15, lr: 3.23e-04
2022-05-05 16:56:10,160 INFO [train.py:715] (2/8) Epoch 6, batch 31100, loss[loss=0.132, simple_loss=0.2087, pruned_loss=0.02768, over 4876.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2186, pruned_loss=0.03763, over 973742.57 frames.], batch size: 22, lr: 3.22e-04
2022-05-05 16:56:51,385 INFO [train.py:715] (2/8) Epoch 6, batch 31150, loss[loss=0.1578, simple_loss=0.2332, pruned_loss=0.0412, over 4787.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2184, pruned_loss=0.03754, over 973317.99 frames.], batch size: 18, lr: 3.22e-04
2022-05-05 16:57:30,158 INFO [train.py:715] (2/8) Epoch 6, batch 31200, loss[loss=0.1357, simple_loss=0.2074, pruned_loss=0.03204, over 4931.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2191, pruned_loss=0.03777, over 973344.35 frames.], batch size: 23, lr: 3.22e-04
2022-05-05 16:58:09,408 INFO [train.py:715] (2/8) Epoch 6, batch 31250, loss[loss=0.1416, simple_loss=0.2128, pruned_loss=0.03524, over 4871.00 frames.], tot_loss[loss=0.1473, simple_loss=0.219, pruned_loss=0.03781, over 972241.76 frames.], batch size: 22, lr: 3.22e-04
2022-05-05 16:58:48,247 INFO [train.py:715] (2/8) Epoch 6, batch 31300, loss[loss=0.1401, simple_loss=0.2109, pruned_loss=0.03465, over 4928.00 frames.], tot_loss[loss=0.147, simple_loss=0.2187, pruned_loss=0.03764, over 973108.76 frames.], batch size: 18, lr: 3.22e-04
2022-05-05 16:59:27,127 INFO [train.py:715] (2/8) Epoch 6, batch 31350, loss[loss=0.142, simple_loss=0.2258, pruned_loss=0.02909, over 4828.00 frames.], tot_loss[loss=0.148, simple_loss=0.2194, pruned_loss=0.0383, over 972443.91 frames.], batch size: 25, lr: 3.22e-04
2022-05-05 17:00:06,354 INFO [train.py:715] (2/8) Epoch 6, batch 31400, loss[loss=0.1584, simple_loss=0.231, pruned_loss=0.04289, over 4898.00 frames.], tot_loss[loss=0.147, simple_loss=0.219, pruned_loss=0.03753, over 972622.21 frames.], batch size: 18, lr: 3.22e-04
2022-05-05 17:00:45,705 INFO [train.py:715] (2/8) Epoch 6, batch 31450, loss[loss=0.1427, simple_loss=0.2197, pruned_loss=0.03284, over 4759.00 frames.], tot_loss[loss=0.147, simple_loss=0.219, pruned_loss=0.03751, over 972889.11 frames.], batch size: 19, lr: 3.22e-04
2022-05-05 17:01:23,999 INFO [train.py:715] (2/8) Epoch 6, batch 31500, loss[loss=0.1196, simple_loss=0.1946, pruned_loss=0.02233, over 4762.00 frames.], tot_loss[loss=0.1481, simple_loss=0.22, pruned_loss=0.03811, over 971922.82 frames.], batch size: 19, lr: 3.22e-04
2022-05-05 17:02:02,412 INFO [train.py:715] (2/8) Epoch 6, batch 31550, loss[loss=0.1301, simple_loss=0.216, pruned_loss=0.02203, over 4838.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2202, pruned_loss=0.03843, over 971584.01 frames.], batch size: 15, lr: 3.22e-04
2022-05-05 17:02:41,957 INFO [train.py:715] (2/8) Epoch 6, batch 31600, loss[loss=0.1494, simple_loss=0.223, pruned_loss=0.03787, over 4851.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2202, pruned_loss=0.0383, over 972399.29 frames.], batch size: 30, lr: 3.22e-04
2022-05-05 17:03:21,198 INFO [train.py:715] (2/8) Epoch 6, batch 31650, loss[loss=0.1563, simple_loss=0.237, pruned_loss=0.03781, over 4772.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2203, pruned_loss=0.03879, over 972586.94 frames.], batch size: 18, lr: 3.22e-04
2022-05-05 17:03:59,734 INFO [train.py:715] (2/8) Epoch 6, batch 31700, loss[loss=0.1452, simple_loss=0.2389, pruned_loss=0.0258, over 4865.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2216, pruned_loss=0.03906, over 972198.44 frames.], batch size: 20, lr: 3.22e-04
2022-05-05 17:04:38,253 INFO [train.py:715] (2/8) Epoch 6, batch 31750, loss[loss=0.1225, simple_loss=0.1954, pruned_loss=0.0248, over 4696.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2213, pruned_loss=0.03899, over 971893.88 frames.], batch size: 15, lr: 3.22e-04
2022-05-05 17:05:17,758 INFO [train.py:715] (2/8) Epoch 6, batch 31800, loss[loss=0.1726, simple_loss=0.2534, pruned_loss=0.04592, over 4969.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2206, pruned_loss=0.03895, over 972799.93 frames.], batch size: 28, lr: 3.22e-04
2022-05-05 17:05:56,239 INFO [train.py:715] (2/8) Epoch 6, batch 31850, loss[loss=0.132, simple_loss=0.1902, pruned_loss=0.03689, over 4952.00 frames.], tot_loss[loss=0.149, simple_loss=0.2205, pruned_loss=0.03876, over 972698.87 frames.], batch size: 21, lr: 3.22e-04
2022-05-05 17:06:34,779 INFO [train.py:715] (2/8) Epoch 6, batch 31900, loss[loss=0.1298, simple_loss=0.2084, pruned_loss=0.02557, over 4939.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2187, pruned_loss=0.03805, over 972695.44 frames.], batch size: 21, lr: 3.22e-04
2022-05-05 17:07:13,871 INFO [train.py:715] (2/8) Epoch 6, batch 31950, loss[loss=0.1609, simple_loss=0.2433, pruned_loss=0.03922, over 4838.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2186, pruned_loss=0.03789, over 972169.56 frames.], batch size: 26, lr: 3.22e-04
2022-05-05 17:07:52,487 INFO [train.py:715] (2/8) Epoch 6, batch 32000, loss[loss=0.1639, simple_loss=0.2385, pruned_loss=0.0446, over 4951.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2194, pruned_loss=0.03835, over 972951.09 frames.], batch size: 35, lr: 3.22e-04
2022-05-05 17:08:31,940 INFO [train.py:715] (2/8) Epoch 6, batch 32050, loss[loss=0.1606, simple_loss=0.2372, pruned_loss=0.04202, over 4826.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2189, pruned_loss=0.03839, over 972303.15 frames.], batch size: 26, lr: 3.22e-04
2022-05-05 17:09:11,463 INFO [train.py:715] (2/8) Epoch 6, batch 32100, loss[loss=0.1637, simple_loss=0.2363, pruned_loss=0.04557, over 4936.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2196, pruned_loss=0.03869, over 972281.60 frames.], batch size: 21, lr: 3.22e-04
2022-05-05 17:09:50,453 INFO [train.py:715] (2/8) Epoch 6, batch 32150, loss[loss=0.1499, simple_loss=0.224, pruned_loss=0.03792, over 4780.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2192, pruned_loss=0.03878, over 971721.23 frames.], batch size: 17, lr: 3.22e-04
2022-05-05 17:10:28,949 INFO [train.py:715] (2/8) Epoch 6, batch 32200, loss[loss=0.1698, simple_loss=0.2419, pruned_loss=0.0488, over 4945.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2197, pruned_loss=0.03903, over 972406.07 frames.], batch size: 21, lr: 3.22e-04
2022-05-05 17:11:08,025 INFO [train.py:715] (2/8) Epoch 6, batch 32250, loss[loss=0.1338, simple_loss=0.2139, pruned_loss=0.0269, over 4809.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2203, pruned_loss=0.03904, over 971465.16 frames.], batch size: 25, lr: 3.22e-04
2022-05-05 17:11:46,853 INFO [train.py:715] (2/8) Epoch 6, batch 32300, loss[loss=0.1404, simple_loss=0.2176, pruned_loss=0.03164, over 4766.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2199, pruned_loss=0.03836, over 972215.42 frames.], batch size: 14, lr: 3.22e-04
2022-05-05 17:12:26,141 INFO [train.py:715] (2/8) Epoch 6, batch 32350, loss[loss=0.1648, simple_loss=0.2389, pruned_loss=0.04536, over 4967.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.03861, over 972653.30 frames.], batch size: 24, lr: 3.22e-04
2022-05-05 17:13:04,503 INFO [train.py:715] (2/8) Epoch 6, batch 32400, loss[loss=0.1552, simple_loss=0.2235, pruned_loss=0.04339, over 4847.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03846, over 972214.83 frames.], batch size: 32, lr: 3.22e-04
2022-05-05 17:13:43,923 INFO [train.py:715] (2/8) Epoch 6, batch 32450, loss[loss=0.1412, simple_loss=0.2111, pruned_loss=0.03561, over 4862.00 frames.], tot_loss[loss=0.1488, simple_loss=0.22, pruned_loss=0.0388, over 972593.77 frames.], batch size: 16, lr: 3.22e-04
2022-05-05 17:14:23,268 INFO [train.py:715] (2/8) Epoch 6, batch 32500, loss[loss=0.1726, simple_loss=0.2434, pruned_loss=0.05086, over 4758.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2196, pruned_loss=0.03887, over 972673.77 frames.], batch size: 16, lr: 3.22e-04
2022-05-05 17:15:01,983 INFO [train.py:715] (2/8) Epoch 6, batch 32550, loss[loss=0.1355, simple_loss=0.2114, pruned_loss=0.02981, over 4828.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2204, pruned_loss=0.03944, over 971794.47 frames.], batch size: 25, lr: 3.22e-04
2022-05-05 17:15:40,776 INFO [train.py:715] (2/8) Epoch 6, batch 32600, loss[loss=0.1489, simple_loss=0.2184, pruned_loss=0.03972, over 4982.00 frames.], tot_loss[loss=0.15, simple_loss=0.221, pruned_loss=0.03955, over 971675.69 frames.], batch size: 31, lr: 3.21e-04
2022-05-05 17:16:19,204 INFO [train.py:715] (2/8) Epoch 6, batch 32650, loss[loss=0.1323, simple_loss=0.2005, pruned_loss=0.03209, over 4788.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2208, pruned_loss=0.03969, over 971916.47 frames.], batch size: 14, lr: 3.21e-04
2022-05-05 17:16:57,840 INFO [train.py:715] (2/8) Epoch 6, batch 32700, loss[loss=0.1459, simple_loss=0.2185, pruned_loss=0.03668, over 4933.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2207, pruned_loss=0.03992, over 972273.78 frames.], batch size: 21, lr: 3.21e-04
2022-05-05 17:17:35,888 INFO [train.py:715] (2/8) Epoch 6, batch 32750, loss[loss=0.1525, simple_loss=0.2253, pruned_loss=0.03979, over 4925.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2211, pruned_loss=0.03984, over 972028.85 frames.], batch size: 18, lr: 3.21e-04
2022-05-05 17:18:14,605 INFO [train.py:715] (2/8) Epoch 6, batch 32800, loss[loss=0.1286, simple_loss=0.2056, pruned_loss=0.0258, over 4760.00 frames.], tot_loss[loss=0.149, simple_loss=0.2198, pruned_loss=0.0391, over 972177.04 frames.], batch size: 19, lr: 3.21e-04
2022-05-05 17:18:53,200 INFO [train.py:715] (2/8) Epoch 6, batch 32850, loss[loss=0.1164, simple_loss=0.1835, pruned_loss=0.02468, over 4875.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2194, pruned_loss=0.03879, over 972741.91 frames.], batch size: 22, lr: 3.21e-04
2022-05-05 17:19:31,605 INFO [train.py:715] (2/8) Epoch 6, batch 32900, loss[loss=0.1451, simple_loss=0.219, pruned_loss=0.03565, over 4875.00 frames.], tot_loss[loss=0.148, simple_loss=0.2189, pruned_loss=0.03851, over 972650.17 frames.], batch size: 16, lr: 3.21e-04
2022-05-05 17:20:09,700 INFO [train.py:715] (2/8) Epoch 6, batch 32950, loss[loss=0.1875, simple_loss=0.2328, pruned_loss=0.07107, over 4990.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2188, pruned_loss=0.03831, over 972675.86 frames.], batch size: 14, lr: 3.21e-04
2022-05-05 17:20:48,508 INFO [train.py:715] (2/8) Epoch 6, batch 33000, loss[loss=0.177, simple_loss=0.2405, pruned_loss=0.05674, over 4887.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2196, pruned_loss=0.03882, over 972321.41 frames.], batch size: 32, lr: 3.21e-04
2022-05-05 17:20:48,509 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 17:20:58,110 INFO [train.py:742] (2/8) Epoch 6, validation: loss=0.1087, simple_loss=0.1938, pruned_loss=0.01183, over 914524.00 frames.
2022-05-05 17:21:36,675 INFO [train.py:715] (2/8) Epoch 6, batch 33050, loss[loss=0.1454, simple_loss=0.212, pruned_loss=0.03942, over 4792.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2195, pruned_loss=0.03884, over 972823.31 frames.], batch size: 14, lr: 3.21e-04
2022-05-05 17:22:15,263 INFO [train.py:715] (2/8) Epoch 6, batch 33100, loss[loss=0.1651, simple_loss=0.2369, pruned_loss=0.04667, over 4754.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2185, pruned_loss=0.0382, over 971679.11 frames.], batch size: 16, lr: 3.21e-04
2022-05-05 17:22:53,011 INFO [train.py:715] (2/8) Epoch 6, batch 33150, loss[loss=0.1547, simple_loss=0.2332, pruned_loss=0.03809, over 4798.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2186, pruned_loss=0.03807, over 971794.77 frames.], batch size: 21, lr: 3.21e-04
2022-05-05 17:23:31,915 INFO [train.py:715] (2/8) Epoch 6, batch 33200, loss[loss=0.2009, simple_loss=0.2717, pruned_loss=0.06508, over 4746.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2197, pruned_loss=0.03886, over 971793.44 frames.], batch size: 16, lr: 3.21e-04
2022-05-05 17:24:10,786 INFO [train.py:715] (2/8) Epoch 6, batch 33250, loss[loss=0.1662, simple_loss=0.2317, pruned_loss=0.0503, over 4865.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2195, pruned_loss=0.03864, over 972787.92 frames.], batch size: 16, lr: 3.21e-04
2022-05-05 17:24:49,864 INFO [train.py:715] (2/8) Epoch 6, batch 33300, loss[loss=0.1573, simple_loss=0.2253, pruned_loss=0.04467, over 4961.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2214, pruned_loss=0.0394, over 973817.68 frames.], batch size: 14, lr: 3.21e-04
2022-05-05 17:25:28,469 INFO [train.py:715] (2/8) Epoch 6, batch 33350, loss[loss=0.1687, simple_loss=0.24, pruned_loss=0.04871, over 4968.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2199, pruned_loss=0.03838, over 974067.25 frames.], batch size: 15, lr: 3.21e-04
2022-05-05 17:26:07,936 INFO [train.py:715] (2/8) Epoch 6, batch 33400, loss[loss=0.1526, simple_loss=0.2254, pruned_loss=0.03992, over 4911.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2195, pruned_loss=0.03802, over 974206.27 frames.], batch size: 19, lr: 3.21e-04
2022-05-05 17:26:47,035 INFO [train.py:715] (2/8) Epoch 6, batch 33450, loss[loss=0.1492, simple_loss=0.213, pruned_loss=0.0427, over 4984.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2187, pruned_loss=0.03749, over 972393.11 frames.], batch size: 14, lr: 3.21e-04
2022-05-05 17:27:25,292 INFO [train.py:715] (2/8) Epoch 6, batch 33500, loss[loss=0.1381, simple_loss=0.2134, pruned_loss=0.03139, over 4972.00 frames.], tot_loss[loss=0.147, simple_loss=0.2189, pruned_loss=0.03756, over 972768.71 frames.], batch size: 15, lr: 3.21e-04
2022-05-05 17:28:04,317 INFO [train.py:715] (2/8) Epoch 6, batch 33550, loss[loss=0.1348, simple_loss=0.2015, pruned_loss=0.03409, over 4684.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03727, over 973143.96 frames.], batch size: 15, lr: 3.21e-04
2022-05-05 17:28:43,724 INFO [train.py:715] (2/8) Epoch 6, batch 33600, loss[loss=0.1269, simple_loss=0.1941, pruned_loss=0.02984, over 4830.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2183, pruned_loss=0.03725, over 972355.16 frames.], batch size: 13, lr: 3.21e-04
2022-05-05 17:29:22,677 INFO [train.py:715] (2/8) Epoch 6, batch 33650, loss[loss=0.1396, simple_loss=0.2243, pruned_loss=0.02751, over 4883.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2177, pruned_loss=0.0371, over 972246.21 frames.], batch size: 16, lr: 3.21e-04
2022-05-05 17:30:01,276 INFO [train.py:715] (2/8) Epoch 6, batch 33700, loss[loss=0.1877, simple_loss=0.2659, pruned_loss=0.05476, over 4926.00 frames.], tot_loss[loss=0.146, simple_loss=0.2179, pruned_loss=0.03702, over 972180.09 frames.], batch size: 18, lr: 3.21e-04
2022-05-05 17:30:39,886 INFO [train.py:715] (2/8) Epoch 6, batch 33750, loss[loss=0.1501, simple_loss=0.2272, pruned_loss=0.03652, over 4840.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2182, pruned_loss=0.03728, over 972112.42 frames.], batch size: 20, lr: 3.21e-04
2022-05-05 17:31:19,207 INFO [train.py:715] (2/8) Epoch 6, batch 33800, loss[loss=0.131, simple_loss=0.1951, pruned_loss=0.03344, over 4775.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2181, pruned_loss=0.03722, over 970692.03 frames.], batch size: 14, lr: 3.21e-04
2022-05-05 17:31:58,018 INFO [train.py:715] (2/8) Epoch 6, batch 33850, loss[loss=0.1429, simple_loss=0.2191, pruned_loss=0.03334, over 4882.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2185, pruned_loss=0.03758, over 971047.06 frames.], batch size: 16, lr: 3.21e-04
2022-05-05 17:32:36,707 INFO [train.py:715] (2/8) Epoch 6, batch 33900, loss[loss=0.1554, simple_loss=0.222, pruned_loss=0.04441, over 4817.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2182, pruned_loss=0.03722, over 969677.79 frames.], batch size: 26, lr: 3.21e-04
2022-05-05 17:33:16,067 INFO [train.py:715] (2/8) Epoch 6, batch 33950, loss[loss=0.1226, simple_loss=0.184, pruned_loss=0.03062, over 4792.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2195, pruned_loss=0.03803, over 969763.28 frames.], batch size: 12, lr: 3.21e-04
2022-05-05 17:33:55,031 INFO [train.py:715] (2/8) Epoch 6, batch 34000, loss[loss=0.151, simple_loss=0.2218, pruned_loss=0.04004, over 4851.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2203, pruned_loss=0.0386, over 970113.66 frames.], batch size: 20, lr: 3.21e-04
2022-05-05 17:34:33,701 INFO [train.py:715] (2/8) Epoch 6, batch 34050, loss[loss=0.1521, simple_loss=0.2272, pruned_loss=0.03847, over 4868.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2203, pruned_loss=0.03859, over 970117.61 frames.], batch size: 20, lr: 3.21e-04
2022-05-05 17:35:12,976 INFO [train.py:715] (2/8) Epoch 6, batch 34100, loss[loss=0.16, simple_loss=0.227, pruned_loss=0.04652, over 4852.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2199, pruned_loss=0.0382, over 969524.08 frames.], batch size: 30, lr: 3.20e-04
2022-05-05 17:35:51,935 INFO [train.py:715] (2/8) Epoch 6, batch 34150, loss[loss=0.1287, simple_loss=0.1983, pruned_loss=0.02952, over 4799.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2193, pruned_loss=0.03772, over 969909.52 frames.], batch size: 25, lr: 3.20e-04
2022-05-05 17:36:30,537 INFO [train.py:715] (2/8) Epoch 6, batch 34200, loss[loss=0.1352, simple_loss=0.2149, pruned_loss=0.02777, over 4815.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2187, pruned_loss=0.03732, over 971342.03 frames.], batch size: 21, lr: 3.20e-04
2022-05-05 17:37:09,179 INFO [train.py:715] (2/8) Epoch 6, batch 34250, loss[loss=0.1626, simple_loss=0.2326, pruned_loss=0.04635, over 4689.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2177, pruned_loss=0.0372, over 971825.60 frames.], batch size: 15, lr: 3.20e-04
2022-05-05 17:37:48,389 INFO [train.py:715] (2/8) Epoch 6, batch 34300, loss[loss=0.1321, simple_loss=0.209, pruned_loss=0.0276, over 4773.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2179, pruned_loss=0.03727, over 972987.02 frames.], batch size: 18, lr: 3.20e-04
2022-05-05 17:38:26,982 INFO [train.py:715] (2/8) Epoch 6, batch 34350, loss[loss=0.1471, simple_loss=0.2255, pruned_loss=0.03435, over 4881.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2175, pruned_loss=0.03709, over 973854.11 frames.], batch size: 22, lr: 3.20e-04
2022-05-05 17:39:05,618 INFO [train.py:715] (2/8) Epoch 6, batch 34400, loss[loss=0.1649, simple_loss=0.2416, pruned_loss=0.04412, over 4938.00 frames.], tot_loss[loss=0.1463, simple_loss=0.218, pruned_loss=0.03731, over 973120.60 frames.], batch size: 21, lr: 3.20e-04
2022-05-05 17:39:45,300 INFO [train.py:715] (2/8) Epoch 6, batch 34450, loss[loss=0.173, simple_loss=0.2348, pruned_loss=0.05563, over 4822.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2189, pruned_loss=0.03778, over 973015.11 frames.], batch size: 14, lr: 3.20e-04
2022-05-05 17:40:24,041 INFO [train.py:715] (2/8) Epoch 6, batch 34500, loss[loss=0.1854, simple_loss=0.2412, pruned_loss=0.06479, over 4889.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2192, pruned_loss=0.03828, over 973447.92 frames.], batch size: 19, lr: 3.20e-04
2022-05-05 17:41:02,894 INFO [train.py:715] (2/8) Epoch 6, batch 34550, loss[loss=0.1547, simple_loss=0.2192, pruned_loss=0.04509, over 4911.00 frames.], tot_loss[loss=0.148, simple_loss=0.2195, pruned_loss=0.03822, over 973771.10 frames.], batch size: 18, lr: 3.20e-04
2022-05-05 17:41:41,800 INFO [train.py:715] (2/8) Epoch 6, batch 34600, loss[loss=0.1589, simple_loss=0.2306, pruned_loss=0.04359, over 4789.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2201, pruned_loss=0.03848, over 973569.09 frames.], batch size: 17, lr: 3.20e-04
2022-05-05 17:42:20,616 INFO [train.py:715] (2/8) Epoch 6, batch 34650, loss[loss=0.1328, simple_loss=0.2038, pruned_loss=0.03087, over 4911.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2192, pruned_loss=0.03801, over 973490.31 frames.], batch size: 19, lr: 3.20e-04
2022-05-05 17:42:59,316 INFO [train.py:715] (2/8) Epoch 6, batch 34700, loss[loss=0.1313, simple_loss=0.193, pruned_loss=0.03483, over 4988.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2188, pruned_loss=0.03795, over 973455.60 frames.], batch size: 14, lr: 3.20e-04
2022-05-05 17:43:37,140 INFO [train.py:715] (2/8) Epoch 6, batch 34750, loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03253, over 4927.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2184, pruned_loss=0.03794, over 972605.96 frames.], batch size: 21, lr: 3.20e-04
2022-05-05 17:44:13,984 INFO [train.py:715] (2/8) Epoch 6, batch 34800, loss[loss=0.1146, simple_loss=0.184, pruned_loss=0.02258, over 4799.00 frames.], tot_loss[loss=0.1466, simple_loss=0.218, pruned_loss=0.03765, over 972292.99 frames.], batch size: 12, lr: 3.20e-04
2022-05-05 17:45:04,005 INFO [train.py:715] (2/8) Epoch 7, batch 0, loss[loss=0.1211, simple_loss=0.1867, pruned_loss=0.02779, over 4795.00 frames.], tot_loss[loss=0.1211, simple_loss=0.1867, pruned_loss=0.02779, over 4795.00 frames.], batch size: 21, lr: 3.03e-04
2022-05-05 17:45:42,574 INFO [train.py:715] (2/8) Epoch 7, batch 50, loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03313, over 4924.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2199, pruned_loss=0.03987, over 219809.60 frames.], batch size: 18, lr: 3.03e-04
2022-05-05 17:46:21,355 INFO [train.py:715] (2/8) Epoch 7, batch 100, loss[loss=0.1612, simple_loss=0.2257, pruned_loss=0.0484, over 4949.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2186, pruned_loss=0.03865, over 386755.85 frames.], batch size: 39, lr: 3.03e-04
2022-05-05 17:47:00,260 INFO [train.py:715] (2/8) Epoch 7, batch 150, loss[loss=0.1458, simple_loss=0.2152, pruned_loss=0.03824, over 4760.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2187, pruned_loss=0.03836, over 516052.38 frames.], batch size: 19, lr: 3.03e-04
2022-05-05 17:47:39,938 INFO [train.py:715] (2/8) Epoch 7, batch 200, loss[loss=0.1601, simple_loss=0.2353, pruned_loss=0.04244, over 4901.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2196, pruned_loss=0.03852, over 617736.01 frames.], batch size: 17, lr: 3.03e-04
2022-05-05 17:48:18,730 INFO [train.py:715] (2/8) Epoch 7, batch 250, loss[loss=0.1593, simple_loss=0.2343, pruned_loss=0.04212, over 4895.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03737, over 696821.22 frames.], batch size: 39, lr: 3.03e-04
2022-05-05 17:48:58,165 INFO [train.py:715] (2/8) Epoch 7, batch 300, loss[loss=0.1496, simple_loss=0.242, pruned_loss=0.02855, over 4694.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.0371, over 756229.18 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 17:49:36,844 INFO [train.py:715] (2/8) Epoch 7, batch 350, loss[loss=0.1414, simple_loss=0.2176, pruned_loss=0.0326, over 4899.00 frames.], tot_loss[loss=0.147, simple_loss=0.2188, pruned_loss=0.03754, over 804303.47 frames.], batch size: 23, lr: 3.02e-04
2022-05-05 17:50:16,224 INFO [train.py:715] (2/8) Epoch 7, batch 400, loss[loss=0.1615, simple_loss=0.2364, pruned_loss=0.04326, over 4986.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2198, pruned_loss=0.03823, over 841656.44 frames.], batch size: 25, lr: 3.02e-04
2022-05-05 17:50:54,886 INFO [train.py:715] (2/8) Epoch 7, batch 450, loss[loss=0.1691, simple_loss=0.2289, pruned_loss=0.05461, over 4880.00 frames.], tot_loss[loss=0.149, simple_loss=0.2204, pruned_loss=0.03878, over 870351.80 frames.], batch size: 16, lr: 3.02e-04
2022-05-05 17:51:33,737 INFO [train.py:715] (2/8) Epoch 7, batch 500, loss[loss=0.1412, simple_loss=0.2172, pruned_loss=0.03259, over 4825.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2198, pruned_loss=0.03839, over 893406.56 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 17:52:12,472 INFO [train.py:715] (2/8) Epoch 7, batch 550, loss[loss=0.1536, simple_loss=0.2235, pruned_loss=0.04186, over 4992.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.03806, over 911281.69 frames.], batch size: 28, lr: 3.02e-04
2022-05-05 17:52:51,635 INFO [train.py:715] (2/8) Epoch 7, batch 600, loss[loss=0.1621, simple_loss=0.2323, pruned_loss=0.04597, over 4987.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2198, pruned_loss=0.03857, over 925351.19 frames.], batch size: 31, lr: 3.02e-04
2022-05-05 17:53:29,946 INFO [train.py:715] (2/8) Epoch 7, batch 650, loss[loss=0.1712, simple_loss=0.2358, pruned_loss=0.05324, over 4985.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2198, pruned_loss=0.03862, over 935970.97 frames.], batch size: 28, lr: 3.02e-04
2022-05-05 17:54:08,328 INFO [train.py:715] (2/8) Epoch 7, batch 700, loss[loss=0.1522, simple_loss=0.2281, pruned_loss=0.0382, over 4975.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2187, pruned_loss=0.03809, over 944377.23 frames.], batch size: 24, lr: 3.02e-04
2022-05-05 17:54:47,593 INFO [train.py:715] (2/8) Epoch 7, batch 750, loss[loss=0.1348, simple_loss=0.2138, pruned_loss=0.02785, over 4823.00 frames.], tot_loss[loss=0.1468, simple_loss=0.218, pruned_loss=0.03776, over 950147.85 frames.], batch size: 26, lr: 3.02e-04
2022-05-05 17:55:26,297 INFO [train.py:715] (2/8) Epoch 7, batch 800, loss[loss=0.1792, simple_loss=0.2448, pruned_loss=0.05682, over 4778.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2188, pruned_loss=0.03839, over 954840.04 frames.], batch size: 18, lr: 3.02e-04
2022-05-05 17:56:04,983 INFO [train.py:715] (2/8) Epoch 7, batch 850, loss[loss=0.1252, simple_loss=0.2015, pruned_loss=0.02445, over 4979.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2186, pruned_loss=0.0382, over 957687.94 frames.], batch size: 28, lr: 3.02e-04
2022-05-05 17:56:44,242 INFO [train.py:715] (2/8) Epoch 7, batch 900, loss[loss=0.127, simple_loss=0.1974, pruned_loss=0.02832, over 4964.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2197, pruned_loss=0.03866, over 961053.88 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 17:57:23,222 INFO [train.py:715] (2/8) Epoch 7, batch 950, loss[loss=0.164, simple_loss=0.2211, pruned_loss=0.05342, over 4646.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2191, pruned_loss=0.0381, over 964213.83 frames.], batch size: 13, lr: 3.02e-04
2022-05-05 17:58:01,723 INFO [train.py:715] (2/8) Epoch 7, batch 1000, loss[loss=0.1535, simple_loss=0.2286, pruned_loss=0.03922, over 4906.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2197, pruned_loss=0.03828, over 965661.12 frames.], batch size: 19, lr: 3.02e-04
2022-05-05 17:58:40,407 INFO [train.py:715] (2/8) Epoch 7, batch 1050, loss[loss=0.1554, simple_loss=0.227, pruned_loss=0.04184, over 4831.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2198, pruned_loss=0.03872, over 966618.45 frames.], batch size: 30, lr: 3.02e-04
2022-05-05 17:59:19,623 INFO [train.py:715] (2/8) Epoch 7, batch 1100, loss[loss=0.1353, simple_loss=0.1937, pruned_loss=0.03848, over 4806.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2195, pruned_loss=0.03838, over 968322.83 frames.], batch size: 12, lr: 3.02e-04
2022-05-05 17:59:57,784 INFO [train.py:715] (2/8) Epoch 7, batch 1150, loss[loss=0.1277, simple_loss=0.1966, pruned_loss=0.0294, over 4978.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2191, pruned_loss=0.03798, over 968912.98 frames.], batch size: 14, lr: 3.02e-04
2022-05-05 18:00:36,963 INFO [train.py:715] (2/8) Epoch 7, batch 1200, loss[loss=0.1624, simple_loss=0.2263, pruned_loss=0.04921, over 4788.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03803, over 969817.79 frames.], batch size: 17, lr: 3.02e-04
2022-05-05 18:01:16,052 INFO [train.py:715] (2/8) Epoch 7, batch 1250, loss[loss=0.1189, simple_loss=0.1782, pruned_loss=0.02983, over 4776.00 frames.], tot_loss[loss=0.147, simple_loss=0.2184, pruned_loss=0.0378, over 969841.21 frames.], batch size: 12, lr: 3.02e-04
2022-05-05 18:01:55,175 INFO [train.py:715] (2/8) Epoch 7, batch 1300, loss[loss=0.124, simple_loss=0.2007, pruned_loss=0.0237, over 4722.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2184, pruned_loss=0.03786, over 971007.73 frames.], batch size: 12, lr: 3.02e-04
2022-05-05 18:02:33,765 INFO [train.py:715] (2/8) Epoch 7, batch 1350, loss[loss=0.1908, simple_loss=0.2573, pruned_loss=0.06217, over 4784.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2186, pruned_loss=0.03793, over 970721.26 frames.], batch size: 18, lr: 3.02e-04
2022-05-05 18:03:12,552 INFO [train.py:715] (2/8) Epoch 7, batch 1400, loss[loss=0.1334, simple_loss=0.2059, pruned_loss=0.03051, over 4967.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03825, over 971349.49 frames.], batch size: 24, lr: 3.02e-04
2022-05-05 18:03:51,643 INFO [train.py:715] (2/8) Epoch 7, batch 1450, loss[loss=0.1528, simple_loss=0.2203, pruned_loss=0.04266, over 4935.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2179, pruned_loss=0.0376, over 972496.95 frames.], batch size: 29, lr: 3.02e-04
2022-05-05 18:04:29,771 INFO [train.py:715] (2/8) Epoch 7, batch 1500, loss[loss=0.1568, simple_loss=0.217, pruned_loss=0.04831, over 4943.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2168, pruned_loss=0.03701, over 972435.66 frames.], batch size: 39, lr: 3.02e-04
2022-05-05 18:05:08,980 INFO [train.py:715] (2/8) Epoch 7, batch 1550, loss[loss=0.1567, simple_loss=0.2246, pruned_loss=0.04438, over 4786.00 frames.], tot_loss[loss=0.146, simple_loss=0.2173, pruned_loss=0.0373, over 971499.80 frames.], batch size: 21, lr: 3.02e-04
2022-05-05 18:05:47,789 INFO [train.py:715] (2/8) Epoch 7, batch 1600, loss[loss=0.1212, simple_loss=0.201, pruned_loss=0.02074, over 4963.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2181, pruned_loss=0.03743, over 971315.18 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 18:06:26,680 INFO [train.py:715] (2/8) Epoch 7, batch 1650, loss[loss=0.1411, simple_loss=0.2023, pruned_loss=0.04001, over 4788.00 frames.], tot_loss[loss=0.147, simple_loss=0.2185, pruned_loss=0.03777, over 971844.92 frames.], batch size: 17, lr: 3.02e-04
2022-05-05 18:07:05,257 INFO [train.py:715] (2/8) Epoch 7, batch 1700, loss[loss=0.1866, simple_loss=0.2507, pruned_loss=0.06129, over 4906.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2186, pruned_loss=0.03797, over 971587.45 frames.], batch size: 39, lr: 3.02e-04
2022-05-05 18:07:44,164 INFO [train.py:715] (2/8) Epoch 7, batch 1750, loss[loss=0.1326, simple_loss=0.1937, pruned_loss=0.03578, over 4903.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2187, pruned_loss=0.03787, over 972340.28 frames.], batch size: 19, lr: 3.02e-04
2022-05-05 18:08:24,139 INFO [train.py:715] (2/8) Epoch 7, batch 1800, loss[loss=0.1432, simple_loss=0.2296, pruned_loss=0.02835, over 4838.00 frames.], tot_loss[loss=0.1478, simple_loss=0.219, pruned_loss=0.03831, over 971762.07 frames.], batch size: 15, lr: 3.02e-04
2022-05-05 18:09:03,071 INFO [train.py:715] (2/8) Epoch 7, batch 1850, loss[loss=0.1194, simple_loss=0.1877, pruned_loss=0.02551, over 4753.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2187, pruned_loss=0.03811, over 972432.59 frames.], batch size: 12, lr: 3.02e-04
2022-05-05 18:09:41,925 INFO [train.py:715] (2/8) Epoch 7, batch 1900, loss[loss=0.1334, simple_loss=0.196, pruned_loss=0.03537, over 4749.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2199, pruned_loss=0.03852, over 972882.34 frames.], batch size: 12, lr: 3.01e-04
2022-05-05 18:10:20,113 INFO [train.py:715] (2/8) Epoch 7, batch 1950, loss[loss=0.1242, simple_loss=0.2035, pruned_loss=0.02247, over 4919.00 frames.], tot_loss[loss=0.1478, simple_loss=0.219, pruned_loss=0.03832, over 973752.80 frames.], batch size: 23, lr: 3.01e-04
2022-05-05 18:10:59,293 INFO [train.py:715] (2/8) Epoch 7, batch 2000, loss[loss=0.1271, simple_loss=0.1872, pruned_loss=0.03352, over 4802.00 frames.], tot_loss[loss=0.148, simple_loss=0.2191, pruned_loss=0.0384, over 974050.59 frames.], batch size: 12, lr: 3.01e-04
2022-05-05 18:11:37,489 INFO [train.py:715] (2/8) Epoch 7, batch 2050, loss[loss=0.1959, simple_loss=0.2711, pruned_loss=0.06038, over 4847.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2197, pruned_loss=0.03868, over 973570.16 frames.], batch size: 26, lr: 3.01e-04
2022-05-05 18:12:16,137 INFO [train.py:715] (2/8) Epoch 7, batch 2100, loss[loss=0.1439, simple_loss=0.2118, pruned_loss=0.03801, over 4782.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2192, pruned_loss=0.03847, over 972245.30 frames.], batch size: 18, lr: 3.01e-04
2022-05-05 18:12:54,591 INFO [train.py:715] (2/8) Epoch 7, batch 2150, loss[loss=0.164, simple_loss=0.2377, pruned_loss=0.04513, over 4959.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2195, pruned_loss=0.0385, over 972007.00 frames.], batch size: 24, lr: 3.01e-04
2022-05-05 18:13:32,800 INFO [train.py:715] (2/8) Epoch 7, batch 2200, loss[loss=0.1438, simple_loss=0.211, pruned_loss=0.03827, over 4776.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.03865, over 971501.93 frames.], batch size: 17, lr: 3.01e-04
2022-05-05 18:14:11,046 INFO [train.py:715] (2/8) Epoch 7, batch 2250, loss[loss=0.1205, simple_loss=0.1963, pruned_loss=0.02237, over 4657.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2192, pruned_loss=0.03804, over 970953.50 frames.], batch size: 13, lr: 3.01e-04
2022-05-05 18:14:50,078 INFO [train.py:715] (2/8) Epoch 7, batch 2300, loss[loss=0.1274, simple_loss=0.2095, pruned_loss=0.02262, over 4776.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2178, pruned_loss=0.03738, over 971141.13 frames.], batch size: 18, lr: 3.01e-04
2022-05-05 18:15:29,543 INFO [train.py:715] (2/8) Epoch 7, batch 2350, loss[loss=0.1303, simple_loss=0.2031, pruned_loss=0.02874, over 4893.00 frames.], tot_loss[loss=0.1464, simple_loss=0.218, pruned_loss=0.03745, over 971506.04 frames.], batch size: 22, lr: 3.01e-04
2022-05-05 18:16:08,313 INFO [train.py:715] (2/8) Epoch 7, batch 2400, loss[loss=0.1532, simple_loss=0.2113, pruned_loss=0.04755, over 4844.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2181, pruned_loss=0.03749, over 971913.64 frames.], batch size: 30, lr: 3.01e-04
2022-05-05 18:16:46,790 INFO [train.py:715] (2/8) Epoch 7, batch 2450, loss[loss=0.1513, simple_loss=0.2311, pruned_loss=0.03577, over 4881.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2189, pruned_loss=0.0378, over 972299.17 frames.], batch size: 16, lr: 3.01e-04
2022-05-05 18:17:25,559 INFO [train.py:715] (2/8) Epoch 7, batch 2500, loss[loss=0.1322, simple_loss=0.2185, pruned_loss=0.02292, over 4879.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.03719, over 972152.24 frames.], batch size: 38, lr: 3.01e-04
2022-05-05 18:18:03,861 INFO [train.py:715] (2/8) Epoch 7, batch 2550, loss[loss=0.1467, simple_loss=0.2095, pruned_loss=0.04194, over 4752.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2187, pruned_loss=0.03732, over 972225.23 frames.], batch size: 16, lr: 3.01e-04
2022-05-05 18:18:42,384 INFO [train.py:715] (2/8) Epoch 7, batch 2600, loss[loss=0.1404, simple_loss=0.2079, pruned_loss=0.03644, over 4993.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2184, pruned_loss=0.03692, over 972240.82 frames.], batch size: 15, lr: 3.01e-04
2022-05-05 18:19:21,121 INFO [train.py:715] (2/8) Epoch 7, batch 2650, loss[loss=0.1669, simple_loss=0.2379, pruned_loss=0.04794, over 4974.00 frames.], tot_loss[loss=0.146, simple_loss=0.2179, pruned_loss=0.03705, over 972374.78 frames.], batch size: 15, lr: 3.01e-04
2022-05-05 18:19:59,711 INFO [train.py:715] (2/8) Epoch 7, batch 2700, loss[loss=0.1429, simple_loss=0.214, pruned_loss=0.03591, over 4750.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.037, over 972278.18 frames.], batch size: 19, lr: 3.01e-04
2022-05-05 18:20:37,585 INFO [train.py:715] (2/8) Epoch 7, batch 2750, loss[loss=0.142, simple_loss=0.2113, pruned_loss=0.03631, over 4961.00 frames.], tot_loss[loss=0.146, simple_loss=0.2177, pruned_loss=0.03709, over 971827.01 frames.], batch size: 14, lr: 3.01e-04
2022-05-05 18:21:16,373 INFO [train.py:715] (2/8) Epoch 7, batch 2800, loss[loss=0.1287, simple_loss=0.2015, pruned_loss=0.0279, over 4809.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2176, pruned_loss=0.03701, over 972213.42 frames.], batch size: 25, lr: 3.01e-04
2022-05-05 18:21:55,734 INFO [train.py:715] (2/8) Epoch 7, batch 2850, loss[loss=0.1406, simple_loss=0.2154, pruned_loss=0.03283, over 4952.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2169, pruned_loss=0.03646, over 971352.21 frames.], batch size: 23, lr: 3.01e-04
2022-05-05 18:22:35,311 INFO [train.py:715] (2/8) Epoch 7, batch 2900, loss[loss=0.1441, simple_loss=0.2174, pruned_loss=0.03536, over 4957.00 frames.], tot_loss[loss=0.145, simple_loss=0.2168, pruned_loss=0.03662, over 972101.05 frames.], batch size: 24, lr: 3.01e-04
2022-05-05 18:23:14,210 INFO [train.py:715] (2/8) Epoch 7, batch 2950, loss[loss=0.1436, simple_loss=0.2237, pruned_loss=0.0317, over 4880.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2166, pruned_loss=0.03644, over 972922.73 frames.], batch size: 22, lr: 3.01e-04
2022-05-05 18:23:53,379 INFO [train.py:715] (2/8) Epoch 7, batch 3000, loss[loss=0.1504, simple_loss=0.2221, pruned_loss=0.03939, over 4935.00 frames.], tot_loss[loss=0.1455, simple_loss=0.217, pruned_loss=0.03705, over 973500.66 frames.], batch size: 29, lr: 3.01e-04
2022-05-05 18:23:53,380 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 18:24:04,766 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.1084, simple_loss=0.1933, pruned_loss=0.01171, over 914524.00 frames.
2022-05-05 18:24:44,252 INFO [train.py:715] (2/8) Epoch 7, batch 3050, loss[loss=0.1269, simple_loss=0.2051, pruned_loss=0.02441, over 4785.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2169, pruned_loss=0.03691, over 972810.33 frames.], batch size: 21, lr: 3.01e-04
2022-05-05 18:25:23,055 INFO [train.py:715] (2/8) Epoch 7, batch 3100, loss[loss=0.1553, simple_loss=0.2263, pruned_loss=0.04212, over 4989.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2171, pruned_loss=0.03704, over 973535.67 frames.], batch size: 14, lr: 3.01e-04
2022-05-05 18:26:01,760 INFO [train.py:715] (2/8) Epoch 7, batch 3150, loss[loss=0.1317, simple_loss=0.201, pruned_loss=0.03119, over 4768.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2175, pruned_loss=0.03691, over 972971.94 frames.], batch size: 18, lr: 3.01e-04
2022-05-05 18:26:39,663 INFO [train.py:715] (2/8) Epoch 7, batch 3200, loss[loss=0.1409, simple_loss=0.2164, pruned_loss=0.03267, over 4841.00 frames.], tot_loss[loss=0.146, simple_loss=0.2179, pruned_loss=0.03711, over 973321.30 frames.], batch size: 15, lr: 3.01e-04
2022-05-05 18:27:17,885 INFO [train.py:715] (2/8) Epoch 7, batch 3250, loss[loss=0.1576, simple_loss=0.2284, pruned_loss=0.04338, over 4899.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.03763, over 972214.35 frames.], batch size: 39, lr: 3.01e-04
2022-05-05 18:27:56,441 INFO [train.py:715] (2/8) Epoch 7, batch 3300, loss[loss=0.1604, simple_loss=0.2373, pruned_loss=0.04176, over 4800.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2181, pruned_loss=0.03747, over 972075.03 frames.], batch size: 25, lr: 3.01e-04
2022-05-05 18:28:35,033 INFO [train.py:715] (2/8) Epoch 7, batch 3350, loss[loss=0.1333, simple_loss=0.2067, pruned_loss=0.02997, over 4931.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2177, pruned_loss=0.03722, over 972861.62 frames.], batch size: 29, lr: 3.01e-04
2022-05-05 18:29:13,824 INFO [train.py:715] (2/8) Epoch 7, batch 3400, loss[loss=0.133, simple_loss=0.2163, pruned_loss=0.02486, over 4829.00 frames.], tot_loss[loss=0.1466, simple_loss=0.218, pruned_loss=0.03762, over 972865.90 frames.], batch size: 15, lr: 3.01e-04
2022-05-05 18:29:52,251 INFO [train.py:715] (2/8) Epoch 7, batch 3450, loss[loss=0.1288, simple_loss=0.2114, pruned_loss=0.02309, over 4805.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2184, pruned_loss=0.03788, over 972404.07 frames.], batch size: 21, lr: 3.01e-04
2022-05-05 18:30:31,305 INFO [train.py:715] (2/8) Epoch 7, batch 3500, loss[loss=0.1569, simple_loss=0.2176, pruned_loss=0.04806, over 4640.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2189, pruned_loss=0.03808, over 971985.19 frames.], batch size: 13, lr: 3.01e-04
2022-05-05 18:31:09,924 INFO [train.py:715] (2/8) Epoch 7, batch 3550, loss[loss=0.1522, simple_loss=0.2314, pruned_loss=0.03652, over 4911.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2188, pruned_loss=0.03808, over 971899.43 frames.], batch size: 23, lr: 3.00e-04
2022-05-05 18:31:48,699 INFO [train.py:715] (2/8) Epoch 7, batch 3600, loss[loss=0.128, simple_loss=0.2086, pruned_loss=0.02367, over 4979.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2183, pruned_loss=0.03769, over 972115.27 frames.], batch size: 15, lr: 3.00e-04
2022-05-05 18:32:27,426 INFO [train.py:715] (2/8) Epoch 7, batch 3650, loss[loss=0.1406, simple_loss=0.2119, pruned_loss=0.0346, over 4929.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2184, pruned_loss=0.03749, over 972404.32 frames.], batch size: 35, lr: 3.00e-04
2022-05-05 18:33:06,461 INFO [train.py:715] (2/8) Epoch 7, batch 3700, loss[loss=0.1354, simple_loss=0.2117, pruned_loss=0.02955, over 4752.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2181, pruned_loss=0.03755, over 972728.68 frames.], batch size: 19, lr: 3.00e-04
2022-05-05 18:33:45,235 INFO [train.py:715] (2/8) Epoch 7, batch 3750, loss[loss=0.1532, simple_loss=0.2235, pruned_loss=0.04145, over 4898.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2176, pruned_loss=0.03761, over 972398.43 frames.], batch size: 19, lr: 3.00e-04
2022-05-05 18:34:23,491 INFO [train.py:715] (2/8) Epoch 7, batch 3800, loss[loss=0.1572, simple_loss=0.2112, pruned_loss=0.0516, over 4849.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2179, pruned_loss=0.0378, over 972435.69 frames.], batch size: 30, lr: 3.00e-04
2022-05-05 18:35:01,656 INFO [train.py:715] (2/8) Epoch 7, batch 3850, loss[loss=0.1274, simple_loss=0.2019, pruned_loss=0.02646, over 4909.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2176, pruned_loss=0.03759, over 971836.36 frames.], batch size: 29, lr: 3.00e-04
2022-05-05 18:35:39,928 INFO [train.py:715] (2/8) Epoch 7, batch 3900, loss[loss=0.1414, simple_loss=0.2122, pruned_loss=0.03525, over 4929.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2174, pruned_loss=0.03738, over 971398.32 frames.], batch size: 29, lr: 3.00e-04
2022-05-05 18:36:18,412 INFO [train.py:715] (2/8) Epoch 7, batch 3950, loss[loss=0.1333, simple_loss=0.2008, pruned_loss=0.0329, over 4697.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2174, pruned_loss=0.03738, over 971118.48 frames.], batch size: 15, lr: 3.00e-04
2022-05-05 18:36:57,039 INFO [train.py:715] (2/8) Epoch 7, batch 4000, loss[loss=0.1525, simple_loss=0.2243, pruned_loss=0.04031, over 4755.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2176, pruned_loss=0.03738, over 970374.67 frames.], batch size: 19, lr: 3.00e-04
2022-05-05 18:37:35,132 INFO [train.py:715] (2/8) Epoch 7, batch 4050, loss[loss=0.1323, simple_loss=0.2015, pruned_loss=0.03149, over 4971.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2184, pruned_loss=0.03798, over 970155.55 frames.], batch size: 15, lr: 3.00e-04
2022-05-05 18:38:14,043 INFO [train.py:715] (2/8) Epoch 7, batch 4100, loss[loss=0.2054, simple_loss=0.2676, pruned_loss=0.07154, over 4932.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2191, pruned_loss=0.0382, over 970248.43 frames.], batch size: 21, lr: 3.00e-04
2022-05-05 18:38:52,562 INFO [train.py:715] (2/8) Epoch 7, batch 4150, loss[loss=0.1319, simple_loss=0.2001, pruned_loss=0.03188, over 4925.00 frames.], tot_loss[loss=0.1477, simple_loss=0.219, pruned_loss=0.03823, over 970382.44 frames.], batch size: 23, lr: 3.00e-04
2022-05-05 18:39:31,260 INFO [train.py:715] (2/8) Epoch 7, batch 4200, loss[loss=0.1423, simple_loss=0.2197, pruned_loss=0.03245, over 4814.00 frames.], tot_loss[loss=0.147, simple_loss=0.2185, pruned_loss=0.03778, over 971133.92 frames.], batch size: 26, lr: 3.00e-04
2022-05-05 18:40:09,114 INFO [train.py:715] (2/8) Epoch 7, batch 4250, loss[loss=0.1673, simple_loss=0.255, pruned_loss=0.0398, over 4789.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2183, pruned_loss=0.03728, over 971228.70 frames.], batch size: 24, lr: 3.00e-04
2022-05-05 18:40:47,960 INFO [train.py:715] (2/8) Epoch 7, batch 4300, loss[loss=0.1412, simple_loss=0.2067, pruned_loss=0.03784, over 4972.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2183, pruned_loss=0.03729, over 970763.02 frames.], batch size: 14, lr: 3.00e-04
2022-05-05 18:41:28,783 INFO [train.py:715] (2/8) Epoch 7, batch 4350, loss[loss=0.1247, simple_loss=0.2029, pruned_loss=0.02323, over 4748.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2188, pruned_loss=0.03727, over 971567.64 frames.], batch size: 16, lr: 3.00e-04
2022-05-05 18:42:07,269 INFO [train.py:715] (2/8) Epoch 7, batch 4400, loss[loss=0.1309, simple_loss=0.2066, pruned_loss=0.02757, over 4783.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2176, pruned_loss=0.03702, over 970947.28 frames.], batch size: 18, lr: 3.00e-04
2022-05-05 18:42:46,326 INFO [train.py:715] (2/8) Epoch 7, batch 4450, loss[loss=0.1824, simple_loss=0.251, pruned_loss=0.05691, over 4833.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2188, pruned_loss=0.03779, over 970960.97 frames.], batch size: 15, lr: 3.00e-04
2022-05-05 18:43:25,201 INFO [train.py:715] (2/8) Epoch 7, batch 4500, loss[loss=0.132, simple_loss=0.2082, pruned_loss=0.02788, over 4761.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2179, pruned_loss=0.03756, over 971062.85 frames.], batch size: 16, lr: 3.00e-04
2022-05-05 18:44:03,953 INFO [train.py:715] (2/8) Epoch 7, batch 4550, loss[loss=0.1477, simple_loss=0.2055, pruned_loss=0.04496, over 4782.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2169, pruned_loss=0.03682, over 971230.90 frames.], batch size: 12, lr: 3.00e-04
2022-05-05 18:44:42,556 INFO [train.py:715] (2/8) Epoch 7, batch 4600, loss[loss=0.1756, simple_loss=0.2462, pruned_loss=0.05252, over 4870.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2171, pruned_loss=0.03699, over 972382.66 frames.], batch size: 16, lr: 3.00e-04
2022-05-05 18:45:21,318 INFO [train.py:715] (2/8) Epoch 7, batch 4650, loss[loss=0.1702, simple_loss=0.2365, pruned_loss=0.05192, over 4845.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.0367, over 972176.67 frames.], batch size: 30, lr: 3.00e-04
2022-05-05 18:45:59,788 INFO [train.py:715] (2/8) Epoch 7, batch 4700, loss[loss=0.1395, simple_loss=0.2096, pruned_loss=0.0347, over 4959.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2169, pruned_loss=0.03671, over 971437.27 frames.], batch size: 24, lr: 3.00e-04
2022-05-05 18:46:37,973 INFO [train.py:715] (2/8) Epoch 7, batch 4750, loss[loss=0.1372, simple_loss=0.201, pruned_loss=0.0367, over 4857.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2171, pruned_loss=0.03664, over 971938.29 frames.], batch size: 20, lr: 3.00e-04
2022-05-05 18:47:17,155 INFO [train.py:715] (2/8) Epoch 7, batch 4800, loss[loss=0.1538, simple_loss=0.2197, pruned_loss=0.04398, over 4818.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2185, pruned_loss=0.03751, over 972322.81 frames.], batch size: 27, lr: 3.00e-04
2022-05-05 18:47:55,561 INFO [train.py:715] (2/8) Epoch 7, batch 4850, loss[loss=0.1273, simple_loss=0.2093, pruned_loss=0.02266, over 4800.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2181, pruned_loss=0.03739, over 973510.54 frames.], batch size: 21, lr: 3.00e-04
2022-05-05 18:48:34,304 INFO [train.py:715] (2/8) Epoch 7, batch 4900, loss[loss=0.127, simple_loss=0.2059, pruned_loss=0.02402, over 4771.00 frames.], tot_loss[loss=0.146, simple_loss=0.218, pruned_loss=0.03705, over 972873.56 frames.], batch size: 16, lr: 3.00e-04
2022-05-05 18:49:12,733 INFO [train.py:715] (2/8) Epoch 7, batch 4950, loss[loss=0.1615, simple_loss=0.2343, pruned_loss=0.04439, over 4915.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2177, pruned_loss=0.03706, over 972582.05 frames.], batch size: 39, lr: 3.00e-04
2022-05-05 18:49:51,779 INFO [train.py:715] (2/8) Epoch 7, batch 5000, loss[loss=0.1303, simple_loss=0.1971, pruned_loss=0.03175, over 4898.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2168, pruned_loss=0.03703, over 972190.38 frames.], batch size: 19, lr: 3.00e-04
2022-05-05 18:50:30,774 INFO [train.py:715] (2/8) Epoch 7, batch 5050, loss[loss=0.181, simple_loss=0.2374, pruned_loss=0.06234, over 4777.00 frames.], tot_loss[loss=0.146, simple_loss=0.2173, pruned_loss=0.03738, over 972543.83 frames.], batch size: 17, lr: 3.00e-04
2022-05-05 18:51:09,362 INFO [train.py:715] (2/8) Epoch 7, batch 5100, loss[loss=0.1207, simple_loss=0.1945, pruned_loss=0.02345, over 4758.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2174, pruned_loss=0.03785, over 972536.05 frames.], batch size: 19, lr: 3.00e-04
2022-05-05 18:51:48,427 INFO [train.py:715] (2/8) Epoch 7, batch 5150, loss[loss=0.1356, simple_loss=0.2001, pruned_loss=0.0355, over 4946.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2182, pruned_loss=0.03802, over 972020.87 frames.], batch size: 21, lr: 3.00e-04
2022-05-05 18:52:27,137 INFO [train.py:715] (2/8) Epoch 7, batch 5200, loss[loss=0.1439, simple_loss=0.2059, pruned_loss=0.04095, over 4925.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2178, pruned_loss=0.0379, over 972749.63 frames.], batch size: 17, lr: 2.99e-04
2022-05-05 18:53:06,160 INFO [train.py:715] (2/8) Epoch 7, batch 5250, loss[loss=0.1651, simple_loss=0.2348, pruned_loss=0.04773, over 4771.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2185, pruned_loss=0.03806, over 972307.73 frames.], batch size: 17, lr: 2.99e-04
2022-05-05 18:53:44,794 INFO [train.py:715] (2/8) Epoch 7, batch 5300, loss[loss=0.1521, simple_loss=0.2231, pruned_loss=0.04058, over 4803.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2187, pruned_loss=0.03774, over 972250.14 frames.], batch size: 21, lr: 2.99e-04
2022-05-05 18:54:24,160 INFO [train.py:715] (2/8) Epoch 7, batch 5350, loss[loss=0.1392, simple_loss=0.2229, pruned_loss=0.02777, over 4966.00 frames.], tot_loss[loss=0.147, simple_loss=0.2189, pruned_loss=0.03755, over 972691.67 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 18:55:02,367 INFO [train.py:715] (2/8) Epoch 7, batch 5400, loss[loss=0.1742, simple_loss=0.2489, pruned_loss=0.04975, over 4647.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.0376, over 971698.68 frames.], batch size: 13, lr: 2.99e-04
2022-05-05 18:55:41,207 INFO [train.py:715] (2/8) Epoch 7, batch 5450, loss[loss=0.1494, simple_loss=0.2116, pruned_loss=0.04361, over 4830.00 frames.], tot_loss[loss=0.1466, simple_loss=0.218, pruned_loss=0.03759, over 972235.91 frames.], batch size: 13, lr: 2.99e-04
2022-05-05 18:56:20,341 INFO [train.py:715] (2/8) Epoch 7, batch 5500, loss[loss=0.1416, simple_loss=0.2116, pruned_loss=0.03578, over 4829.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2186, pruned_loss=0.03759, over 972116.29 frames.], batch size: 13, lr: 2.99e-04
2022-05-05 18:56:59,141 INFO [train.py:715] (2/8) Epoch 7, batch 5550, loss[loss=0.1826, simple_loss=0.2532, pruned_loss=0.05606, over 4683.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03703, over 972821.54 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 18:57:38,239 INFO [train.py:715] (2/8) Epoch 7, batch 5600, loss[loss=0.1731, simple_loss=0.2544, pruned_loss=0.04586, over 4801.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2187, pruned_loss=0.03735, over 972394.63 frames.], batch size: 21, lr: 2.99e-04
2022-05-05 18:58:17,275 INFO [train.py:715] (2/8) Epoch 7, batch 5650, loss[loss=0.1167, simple_loss=0.1889, pruned_loss=0.02227, over 4985.00 frames.], tot_loss[loss=0.1457, simple_loss=0.218, pruned_loss=0.0367, over 972118.09 frames.], batch size: 14, lr: 2.99e-04
2022-05-05 18:58:56,368 INFO [train.py:715] (2/8) Epoch 7, batch 5700, loss[loss=0.1893, simple_loss=0.2444, pruned_loss=0.06707, over 4958.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2183, pruned_loss=0.03714, over 972449.56 frames.], batch size: 35, lr: 2.99e-04
2022-05-05 18:59:34,743 INFO [train.py:715] (2/8) Epoch 7, batch 5750, loss[loss=0.1567, simple_loss=0.2184, pruned_loss=0.04743, over 4852.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2183, pruned_loss=0.03718, over 971965.46 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:00:12,900 INFO [train.py:715] (2/8) Epoch 7, batch 5800, loss[loss=0.1585, simple_loss=0.2229, pruned_loss=0.04708, over 4874.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2166, pruned_loss=0.03652, over 972868.47 frames.], batch size: 16, lr: 2.99e-04
2022-05-05 19:00:52,630 INFO [train.py:715] (2/8) Epoch 7, batch 5850, loss[loss=0.146, simple_loss=0.2207, pruned_loss=0.03565, over 4779.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03695, over 973146.73 frames.], batch size: 14, lr: 2.99e-04
2022-05-05 19:01:30,924 INFO [train.py:715] (2/8) Epoch 7, batch 5900, loss[loss=0.1333, simple_loss=0.2058, pruned_loss=0.03041, over 4809.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03694, over 972923.46 frames.], batch size: 21, lr: 2.99e-04
2022-05-05 19:02:09,960 INFO [train.py:715] (2/8) Epoch 7, batch 5950, loss[loss=0.1667, simple_loss=0.2309, pruned_loss=0.05129, over 4896.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2173, pruned_loss=0.0368, over 973071.13 frames.], batch size: 17, lr: 2.99e-04
2022-05-05 19:02:48,383 INFO [train.py:715] (2/8) Epoch 7, batch 6000, loss[loss=0.1642, simple_loss=0.2302, pruned_loss=0.0491, over 4700.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2175, pruned_loss=0.03716, over 972561.19 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:02:48,384 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 19:02:58,047 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.1085, simple_loss=0.1933, pruned_loss=0.0119, over 914524.00 frames.
2022-05-05 19:03:36,917 INFO [train.py:715] (2/8) Epoch 7, batch 6050, loss[loss=0.1261, simple_loss=0.2042, pruned_loss=0.02403, over 4975.00 frames.], tot_loss[loss=0.146, simple_loss=0.2175, pruned_loss=0.03723, over 972143.57 frames.], batch size: 14, lr: 2.99e-04
2022-05-05 19:04:16,084 INFO [train.py:715] (2/8) Epoch 7, batch 6100, loss[loss=0.163, simple_loss=0.2359, pruned_loss=0.045, over 4774.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2174, pruned_loss=0.0377, over 972190.18 frames.], batch size: 19, lr: 2.99e-04
2022-05-05 19:04:55,379 INFO [train.py:715] (2/8) Epoch 7, batch 6150, loss[loss=0.1354, simple_loss=0.2098, pruned_loss=0.0305, over 4823.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2175, pruned_loss=0.03722, over 972065.75 frames.], batch size: 26, lr: 2.99e-04
2022-05-05 19:05:33,829 INFO [train.py:715] (2/8) Epoch 7, batch 6200, loss[loss=0.1485, simple_loss=0.2101, pruned_loss=0.04345, over 4773.00 frames.], tot_loss[loss=0.146, simple_loss=0.2174, pruned_loss=0.03734, over 971030.41 frames.], batch size: 12, lr: 2.99e-04
2022-05-05 19:06:13,680 INFO [train.py:715] (2/8) Epoch 7, batch 6250, loss[loss=0.1919, simple_loss=0.2599, pruned_loss=0.06191, over 4932.00 frames.], tot_loss[loss=0.146, simple_loss=0.2173, pruned_loss=0.03733, over 971691.27 frames.], batch size: 23, lr: 2.99e-04
2022-05-05 19:06:52,572 INFO [train.py:715] (2/8) Epoch 7, batch 6300, loss[loss=0.1316, simple_loss=0.2084, pruned_loss=0.02741, over 4925.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2163, pruned_loss=0.03672, over 972250.30 frames.], batch size: 29, lr: 2.99e-04
2022-05-05 19:07:30,975 INFO [train.py:715] (2/8) Epoch 7, batch 6350, loss[loss=0.1244, simple_loss=0.1968, pruned_loss=0.026, over 4830.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2168, pruned_loss=0.03691, over 971639.89 frames.], batch size: 13, lr: 2.99e-04
2022-05-05 19:08:10,036 INFO [train.py:715] (2/8) Epoch 7, batch 6400, loss[loss=0.1697, simple_loss=0.2349, pruned_loss=0.05218, over 4785.00 frames.], tot_loss[loss=0.146, simple_loss=0.2173, pruned_loss=0.03738, over 971861.81 frames.], batch size: 14, lr: 2.99e-04
2022-05-05 19:08:49,046 INFO [train.py:715] (2/8) Epoch 7, batch 6450, loss[loss=0.1648, simple_loss=0.2358, pruned_loss=0.04693, over 4829.00 frames.], tot_loss[loss=0.1459, simple_loss=0.217, pruned_loss=0.03739, over 971524.97 frames.], batch size: 25, lr: 2.99e-04
2022-05-05 19:09:27,585 INFO [train.py:715] (2/8) Epoch 7, batch 6500, loss[loss=0.144, simple_loss=0.2118, pruned_loss=0.03809, over 4879.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2173, pruned_loss=0.03742, over 972438.34 frames.], batch size: 22, lr: 2.99e-04
2022-05-05 19:10:06,573 INFO [train.py:715] (2/8) Epoch 7, batch 6550, loss[loss=0.1618, simple_loss=0.222, pruned_loss=0.05079, over 4707.00 frames.], tot_loss[loss=0.147, simple_loss=0.2181, pruned_loss=0.03794, over 972216.16 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:10:46,394 INFO [train.py:715] (2/8) Epoch 7, batch 6600, loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.032, over 4802.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2183, pruned_loss=0.03814, over 971878.82 frames.], batch size: 25, lr: 2.99e-04
2022-05-05 19:11:25,246 INFO [train.py:715] (2/8) Epoch 7, batch 6650, loss[loss=0.1514, simple_loss=0.2303, pruned_loss=0.03626, over 4952.00 frames.], tot_loss[loss=0.148, simple_loss=0.2192, pruned_loss=0.03839, over 971590.16 frames.], batch size: 39, lr: 2.99e-04
2022-05-05 19:12:04,477 INFO [train.py:715] (2/8) Epoch 7, batch 6700, loss[loss=0.144, simple_loss=0.2146, pruned_loss=0.03664, over 4926.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2199, pruned_loss=0.03872, over 971577.96 frames.], batch size: 29, lr: 2.99e-04
2022-05-05 19:12:43,222 INFO [train.py:715] (2/8) Epoch 7, batch 6750, loss[loss=0.1462, simple_loss=0.221, pruned_loss=0.03573, over 4956.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2192, pruned_loss=0.03808, over 971741.25 frames.], batch size: 24, lr: 2.99e-04
2022-05-05 19:13:22,217 INFO [train.py:715] (2/8) Epoch 7, batch 6800, loss[loss=0.1519, simple_loss=0.2329, pruned_loss=0.03544, over 4815.00 frames.], tot_loss[loss=0.148, simple_loss=0.2195, pruned_loss=0.03826, over 971395.99 frames.], batch size: 26, lr: 2.99e-04
2022-05-05 19:14:00,596 INFO [train.py:715] (2/8) Epoch 7, batch 6850, loss[loss=0.1389, simple_loss=0.2161, pruned_loss=0.03084, over 4835.00 frames.], tot_loss[loss=0.1486, simple_loss=0.22, pruned_loss=0.03858, over 971730.97 frames.], batch size: 15, lr: 2.99e-04
2022-05-05 19:14:39,179 INFO [train.py:715] (2/8) Epoch 7, batch 6900, loss[loss=0.1477, simple_loss=0.2217, pruned_loss=0.03685, over 4873.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2184, pruned_loss=0.03767, over 971435.80 frames.], batch size: 16, lr: 2.98e-04
2022-05-05 19:15:18,694 INFO [train.py:715] (2/8) Epoch 7, batch 6950, loss[loss=0.1515, simple_loss=0.2294, pruned_loss=0.03683, over 4787.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2185, pruned_loss=0.0373, over 971827.64 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:15:56,859 INFO [train.py:715] (2/8) Epoch 7, batch 7000, loss[loss=0.1555, simple_loss=0.2351, pruned_loss=0.03798, over 4984.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2189, pruned_loss=0.0377, over 972211.27 frames.], batch size: 24, lr: 2.98e-04
2022-05-05 19:16:35,554 INFO [train.py:715] (2/8) Epoch 7, batch 7050, loss[loss=0.1406, simple_loss=0.2094, pruned_loss=0.0359, over 4754.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2196, pruned_loss=0.0381, over 971883.27 frames.], batch size: 16, lr: 2.98e-04
2022-05-05 19:17:14,122 INFO [train.py:715] (2/8) Epoch 7, batch 7100, loss[loss=0.1306, simple_loss=0.1913, pruned_loss=0.03492, over 4839.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2189, pruned_loss=0.03785, over 972541.51 frames.], batch size: 30, lr: 2.98e-04
2022-05-05 19:17:52,400 INFO [train.py:715] (2/8) Epoch 7, batch 7150, loss[loss=0.1558, simple_loss=0.2352, pruned_loss=0.03819, over 4957.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2191, pruned_loss=0.0378, over 972107.24 frames.], batch size: 39, lr: 2.98e-04
2022-05-05 19:18:31,020 INFO [train.py:715] (2/8) Epoch 7, batch 7200, loss[loss=0.157, simple_loss=0.2292, pruned_loss=0.04247, over 4980.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03739, over 972280.37 frames.], batch size: 25, lr: 2.98e-04
2022-05-05 19:19:10,026 INFO [train.py:715] (2/8) Epoch 7, batch 7250, loss[loss=0.1567, simple_loss=0.2366, pruned_loss=0.03841, over 4920.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2186, pruned_loss=0.0378, over 972285.47 frames.], batch size: 17, lr: 2.98e-04
2022-05-05 19:19:49,674 INFO [train.py:715] (2/8) Epoch 7, batch 7300, loss[loss=0.1313, simple_loss=0.2069, pruned_loss=0.02786, over 4757.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03713, over 971976.03 frames.], batch size: 19, lr: 2.98e-04
2022-05-05 19:20:28,207 INFO [train.py:715] (2/8) Epoch 7, batch 7350, loss[loss=0.128, simple_loss=0.2077, pruned_loss=0.02413, over 4832.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03728, over 972454.22 frames.], batch size: 26, lr: 2.98e-04
2022-05-05 19:21:06,665 INFO [train.py:715] (2/8) Epoch 7, batch 7400, loss[loss=0.1555, simple_loss=0.2259, pruned_loss=0.04257, over 4685.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03691, over 973135.81 frames.], batch size: 15, lr: 2.98e-04
2022-05-05 19:21:45,793 INFO [train.py:715] (2/8) Epoch 7, batch 7450, loss[loss=0.1285, simple_loss=0.1997, pruned_loss=0.0286, over 4788.00 frames.], tot_loss[loss=0.146, simple_loss=0.2182, pruned_loss=0.03693, over 972840.34 frames.], batch size: 14, lr: 2.98e-04
2022-05-05 19:22:24,000 INFO [train.py:715] (2/8) Epoch 7, batch 7500, loss[loss=0.1346, simple_loss=0.2135, pruned_loss=0.02782, over 4743.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2187, pruned_loss=0.03734, over 972170.25 frames.], batch size: 16, lr: 2.98e-04
2022-05-05 19:23:02,794 INFO [train.py:715] (2/8) Epoch 7, batch 7550, loss[loss=0.1819, simple_loss=0.2514, pruned_loss=0.05624, over 4644.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2182, pruned_loss=0.03698, over 972530.06 frames.], batch size: 13, lr: 2.98e-04
2022-05-05 19:23:41,663 INFO [train.py:715] (2/8) Epoch 7, batch 7600, loss[loss=0.1427, simple_loss=0.2251, pruned_loss=0.03014, over 4972.00 frames.], tot_loss[loss=0.146, simple_loss=0.2185, pruned_loss=0.03676, over 973102.55 frames.], batch size: 24, lr: 2.98e-04
2022-05-05 19:24:20,789 INFO [train.py:715] (2/8) Epoch 7, batch 7650, loss[loss=0.1696, simple_loss=0.2367, pruned_loss=0.05125, over 4960.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.03718, over 973831.86 frames.], batch size: 35, lr: 2.98e-04
2022-05-05 19:24:59,081 INFO [train.py:715] (2/8) Epoch 7, batch 7700, loss[loss=0.1408, simple_loss=0.2048, pruned_loss=0.03843, over 4833.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2187, pruned_loss=0.03751, over 972651.46 frames.], batch size: 15, lr: 2.98e-04
2022-05-05 19:25:38,047 INFO [train.py:715] (2/8) Epoch 7, batch 7750, loss[loss=0.1223, simple_loss=0.1936, pruned_loss=0.02548, over 4744.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2193, pruned_loss=0.03768, over 971927.58 frames.], batch size: 16, lr: 2.98e-04
2022-05-05 19:26:17,067 INFO [train.py:715] (2/8) Epoch 7, batch 7800, loss[loss=0.1269, simple_loss=0.1978, pruned_loss=0.02793, over 4825.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2194, pruned_loss=0.03792, over 972216.98 frames.], batch size: 25, lr: 2.98e-04
2022-05-05 19:26:55,230 INFO [train.py:715] (2/8) Epoch 7, batch 7850, loss[loss=0.1411, simple_loss=0.2263, pruned_loss=0.02793, over 4955.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2198, pruned_loss=0.03794, over 971489.37 frames.], batch size: 21, lr: 2.98e-04
2022-05-05 19:27:34,427 INFO [train.py:715] (2/8) Epoch 7, batch 7900, loss[loss=0.1336, simple_loss=0.2098, pruned_loss=0.02867, over 4941.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2196, pruned_loss=0.03788, over 972266.41 frames.], batch size: 21, lr: 2.98e-04
2022-05-05 19:28:13,174 INFO [train.py:715] (2/8) Epoch 7, batch 7950, loss[loss=0.1227, simple_loss=0.1895, pruned_loss=0.02799, over 4939.00 frames.], tot_loss[loss=0.147, simple_loss=0.219, pruned_loss=0.03755, over 972149.04 frames.], batch size: 23, lr: 2.98e-04
2022-05-05 19:28:52,649 INFO [train.py:715] (2/8) Epoch 7, batch 8000, loss[loss=0.1498, simple_loss=0.2187, pruned_loss=0.04049, over 4961.00 frames.], tot_loss[loss=0.1481, simple_loss=0.22, pruned_loss=0.03816, over 972852.65 frames.], batch size: 39, lr: 2.98e-04
2022-05-05 19:29:30,738 INFO [train.py:715] (2/8) Epoch 7, batch 8050, loss[loss=0.1506, simple_loss=0.2223, pruned_loss=0.03948, over 4782.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2194, pruned_loss=0.03783, over 972277.07 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:30:09,297 INFO [train.py:715] (2/8) Epoch 7, batch 8100, loss[loss=0.1562, simple_loss=0.2209, pruned_loss=0.0457, over 4899.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2192, pruned_loss=0.0375, over 972293.73 frames.], batch size: 22, lr: 2.98e-04
2022-05-05 19:30:48,380 INFO [train.py:715] (2/8) Epoch 7, batch 8150, loss[loss=0.1492, simple_loss=0.2244, pruned_loss=0.03698, over 4819.00 frames.], tot_loss[loss=0.146, simple_loss=0.2183, pruned_loss=0.03688, over 971993.94 frames.], batch size: 25, lr: 2.98e-04
2022-05-05 19:31:26,679 INFO [train.py:715] (2/8) Epoch 7, batch 8200, loss[loss=0.1299, simple_loss=0.1994, pruned_loss=0.03017, over 4897.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2177, pruned_loss=0.0366, over 972264.58 frames.], batch size: 39, lr: 2.98e-04
2022-05-05 19:32:05,127 INFO [train.py:715] (2/8) Epoch 7, batch 8250, loss[loss=0.1545, simple_loss=0.22, pruned_loss=0.0445, over 4927.00 frames.], tot_loss[loss=0.146, simple_loss=0.2183, pruned_loss=0.03686, over 972737.87 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:32:43,780 INFO [train.py:715] (2/8) Epoch 7, batch 8300, loss[loss=0.1422, simple_loss=0.217, pruned_loss=0.03373, over 4795.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.03718, over 973378.11 frames.], batch size: 18, lr: 2.98e-04
2022-05-05 19:33:22,690 INFO [train.py:715] (2/8) Epoch 7, batch 8350, loss[loss=0.1362, simple_loss=0.2025, pruned_loss=0.03499, over 4760.00 frames.], tot_loss[loss=0.147, simple_loss=0.2188, pruned_loss=0.03754, over 972802.18 frames.], batch size: 12, lr: 2.98e-04
2022-05-05 19:34:00,663 INFO [train.py:715] (2/8) Epoch 7, batch 8400, loss[loss=0.1558, simple_loss=0.2268, pruned_loss=0.04237, over 4787.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03736, over 972668.17 frames.], batch size: 12, lr: 2.98e-04
2022-05-05 19:34:39,719 INFO [train.py:715] (2/8) Epoch 7, batch 8450, loss[loss=0.1859, simple_loss=0.2492, pruned_loss=0.06132, over 4830.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03716, over 972120.76 frames.], batch size: 30, lr: 2.98e-04
2022-05-05 19:35:18,879 INFO [train.py:715] (2/8) Epoch 7, batch 8500, loss[loss=0.1692, simple_loss=0.2397, pruned_loss=0.04932, over 4954.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2186, pruned_loss=0.03707, over 972206.47 frames.], batch size: 24, lr: 2.98e-04
2022-05-05 19:35:58,080 INFO [train.py:715] (2/8) Epoch 7, batch 8550, loss[loss=0.16, simple_loss=0.2267, pruned_loss=0.04659, over 4848.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2189, pruned_loss=0.03736, over 972597.15 frames.], batch size: 34, lr: 2.97e-04
2022-05-05 19:36:36,299 INFO [train.py:715] (2/8) Epoch 7, batch 8600, loss[loss=0.1559, simple_loss=0.2252, pruned_loss=0.0433, over 4885.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2191, pruned_loss=0.03764, over 971844.42 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:37:14,962 INFO [train.py:715] (2/8) Epoch 7, batch 8650, loss[loss=0.1439, simple_loss=0.2175, pruned_loss=0.03517, over 4956.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2182, pruned_loss=0.0374, over 970343.07 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:37:54,310 INFO [train.py:715] (2/8) Epoch 7, batch 8700, loss[loss=0.1492, simple_loss=0.2222, pruned_loss=0.0381, over 4986.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2184, pruned_loss=0.03748, over 971685.90 frames.], batch size: 15, lr: 2.97e-04
2022-05-05 19:38:32,516 INFO [train.py:715] (2/8) Epoch 7, batch 8750, loss[loss=0.1209, simple_loss=0.1903, pruned_loss=0.02573, over 4740.00 frames.], tot_loss[loss=0.146, simple_loss=0.2177, pruned_loss=0.03719, over 971562.07 frames.], batch size: 12, lr: 2.97e-04
2022-05-05 19:39:11,386 INFO [train.py:715] (2/8) Epoch 7, batch 8800, loss[loss=0.1325, simple_loss=0.2043, pruned_loss=0.03033, over 4762.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2177, pruned_loss=0.03707, over 972158.53 frames.], batch size: 18, lr: 2.97e-04
2022-05-05 19:39:50,319 INFO [train.py:715] (2/8) Epoch 7, batch 8850, loss[loss=0.132, simple_loss=0.2053, pruned_loss=0.02932, over 4786.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2191, pruned_loss=0.03833, over 972463.31 frames.], batch size: 17, lr: 2.97e-04
2022-05-05 19:40:30,008 INFO [train.py:715] (2/8) Epoch 7, batch 8900, loss[loss=0.1281, simple_loss=0.2089, pruned_loss=0.02365, over 4948.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2182, pruned_loss=0.03769, over 972544.41 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:41:08,238 INFO [train.py:715] (2/8) Epoch 7, batch 8950, loss[loss=0.1605, simple_loss=0.2417, pruned_loss=0.03966, over 4821.00 frames.], tot_loss[loss=0.1459, simple_loss=0.217, pruned_loss=0.03735, over 972553.25 frames.], batch size: 26, lr: 2.97e-04
2022-05-05 19:41:46,836 INFO [train.py:715] (2/8) Epoch 7, batch 9000, loss[loss=0.1391, simple_loss=0.2169, pruned_loss=0.03068, over 4821.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2169, pruned_loss=0.03692, over 972482.11 frames.], batch size: 26, lr: 2.97e-04
2022-05-05 19:41:46,837 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 19:41:56,559 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.1085, simple_loss=0.1932, pruned_loss=0.01192, over 914524.00 frames.
2022-05-05 19:42:35,335 INFO [train.py:715] (2/8) Epoch 7, batch 9050, loss[loss=0.1533, simple_loss=0.2111, pruned_loss=0.04779, over 4753.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2178, pruned_loss=0.03729, over 972995.84 frames.], batch size: 16, lr: 2.97e-04
2022-05-05 19:43:15,395 INFO [train.py:715] (2/8) Epoch 7, batch 9100, loss[loss=0.1713, simple_loss=0.2322, pruned_loss=0.05514, over 4986.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2185, pruned_loss=0.0372, over 972752.52 frames.], batch size: 15, lr: 2.97e-04
2022-05-05 19:43:54,073 INFO [train.py:715] (2/8) Epoch 7, batch 9150, loss[loss=0.1745, simple_loss=0.2631, pruned_loss=0.04295, over 4874.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2182, pruned_loss=0.03675, over 973838.20 frames.], batch size: 39, lr: 2.97e-04
2022-05-05 19:44:32,872 INFO [train.py:715] (2/8) Epoch 7, batch 9200, loss[loss=0.1453, simple_loss=0.2067, pruned_loss=0.04195, over 4836.00 frames.], tot_loss[loss=0.1445, simple_loss=0.217, pruned_loss=0.03601, over 973187.55 frames.], batch size: 13, lr: 2.97e-04
2022-05-05 19:45:12,204 INFO [train.py:715] (2/8) Epoch 7, batch 9250, loss[loss=0.1481, simple_loss=0.2191, pruned_loss=0.03855, over 4953.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2181, pruned_loss=0.0365, over 973187.58 frames.], batch size: 14, lr: 2.97e-04
2022-05-05 19:45:51,292 INFO [train.py:715] (2/8) Epoch 7, batch 9300, loss[loss=0.1209, simple_loss=0.1836, pruned_loss=0.02909, over 4754.00 frames.], tot_loss[loss=0.1455, simple_loss=0.218, pruned_loss=0.03653, over 972775.07 frames.], batch size: 12, lr: 2.97e-04
2022-05-05 19:46:30,345 INFO [train.py:715] (2/8) Epoch 7, batch 9350, loss[loss=0.1326, simple_loss=0.2045, pruned_loss=0.03032, over 4872.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03624, over 971856.84 frames.], batch size: 20, lr: 2.97e-04
2022-05-05 19:47:08,479 INFO [train.py:715] (2/8) Epoch 7, batch 9400, loss[loss=0.1279, simple_loss=0.1866, pruned_loss=0.03462, over 4836.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2169, pruned_loss=0.03668, over 972467.38 frames.], batch size: 13, lr: 2.97e-04
2022-05-05 19:47:48,272 INFO [train.py:715] (2/8) Epoch 7, batch 9450, loss[loss=0.1378, simple_loss=0.2168, pruned_loss=0.02945, over 4812.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2167, pruned_loss=0.0365, over 972893.21 frames.], batch size: 21, lr: 2.97e-04
2022-05-05 19:48:27,274 INFO [train.py:715] (2/8) Epoch 7, batch 9500, loss[loss=0.1253, simple_loss=0.1993, pruned_loss=0.02564, over 4798.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2168, pruned_loss=0.03642, over 972150.76 frames.], batch size: 24, lr: 2.97e-04
2022-05-05 19:49:05,879 INFO [train.py:715] (2/8) Epoch 7, batch 9550, loss[loss=0.1295, simple_loss=0.2201, pruned_loss=0.01951, over 4927.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2169, pruned_loss=0.03665, over 972834.52 frames.], batch size: 18, lr: 2.97e-04
2022-05-05 19:49:44,836 INFO [train.py:715] (2/8) Epoch 7, batch 9600, loss[loss=0.145, simple_loss=0.2152, pruned_loss=0.03738, over 4899.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03722, over 972808.72 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:50:23,439 INFO [train.py:715] (2/8) Epoch 7, batch 9650, loss[loss=0.143, simple_loss=0.2084, pruned_loss=0.03881, over 4829.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03676, over 972483.25 frames.], batch size: 27, lr: 2.97e-04
2022-05-05 19:51:02,959 INFO [train.py:715] (2/8) Epoch 7, batch 9700, loss[loss=0.1708, simple_loss=0.2419, pruned_loss=0.04985, over 4753.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03647, over 972467.86 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:51:41,572 INFO [train.py:715] (2/8) Epoch 7, batch 9750, loss[loss=0.1745, simple_loss=0.2493, pruned_loss=0.04979, over 4906.00 frames.], tot_loss[loss=0.1458, simple_loss=0.218, pruned_loss=0.03674, over 972435.10 frames.], batch size: 19, lr: 2.97e-04
2022-05-05 19:52:20,962 INFO [train.py:715] (2/8) Epoch 7, batch 9800, loss[loss=0.132, simple_loss=0.2021, pruned_loss=0.031, over 4806.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2194, pruned_loss=0.03756, over 973137.22 frames.], batch size: 14, lr: 2.97e-04
2022-05-05 19:52:59,042 INFO [train.py:715] (2/8) Epoch 7, batch 9850, loss[loss=0.1524, simple_loss=0.2249, pruned_loss=0.03992, over 4907.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2198, pruned_loss=0.03822, over 973223.63 frames.], batch size: 17, lr: 2.97e-04
2022-05-05 19:53:37,272 INFO [train.py:715] (2/8) Epoch 7, batch 9900, loss[loss=0.1598, simple_loss=0.2187, pruned_loss=0.05045, over 4843.00 frames.], tot_loss[loss=0.1484, simple_loss=0.22, pruned_loss=0.03841, over 973299.08 frames.], batch size: 32, lr: 2.97e-04
2022-05-05 19:54:16,174 INFO [train.py:715] (2/8) Epoch 7, batch 9950, loss[loss=0.1525, simple_loss=0.2284, pruned_loss=0.03829, over 4972.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03822, over 972644.19 frames.], batch size: 24, lr: 2.97e-04
2022-05-05 19:54:55,287 INFO [train.py:715] (2/8) Epoch 7, batch 10000, loss[loss=0.1315, simple_loss=0.2099, pruned_loss=0.02659, over 4913.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2198, pruned_loss=0.03848, over 973096.56 frames.], batch size: 29, lr: 2.97e-04
2022-05-05 19:55:33,942 INFO [train.py:715] (2/8) Epoch 7, batch 10050, loss[loss=0.1456, simple_loss=0.214, pruned_loss=0.0386, over 4847.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2197, pruned_loss=0.0383, over 973746.64 frames.], batch size: 30, lr: 2.97e-04
2022-05-05 19:56:12,505 INFO [train.py:715] (2/8) Epoch 7, batch 10100, loss[loss=0.1364, simple_loss=0.2137, pruned_loss=0.02954, over 4966.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2192, pruned_loss=0.0377, over 973259.48 frames.], batch size: 14, lr: 2.97e-04
2022-05-05 19:56:51,793 INFO [train.py:715] (2/8) Epoch 7, batch 10150, loss[loss=0.1367, simple_loss=0.2135, pruned_loss=0.02999, over 4940.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2184, pruned_loss=0.03741, over 972913.30 frames.], batch size: 39, lr: 2.97e-04
2022-05-05 19:57:30,414 INFO [train.py:715] (2/8) Epoch 7, batch 10200, loss[loss=0.1414, simple_loss=0.2159, pruned_loss=0.03347, over 4789.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2183, pruned_loss=0.03728, over 972447.12 frames.], batch size: 18, lr: 2.97e-04
2022-05-05 19:58:09,059 INFO [train.py:715] (2/8) Epoch 7, batch 10250, loss[loss=0.1381, simple_loss=0.2042, pruned_loss=0.03598, over 4930.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2184, pruned_loss=0.03745, over 972648.31 frames.], batch size: 29, lr: 2.96e-04
2022-05-05 19:58:48,250 INFO [train.py:715] (2/8) Epoch 7, batch 10300, loss[loss=0.1518, simple_loss=0.2221, pruned_loss=0.04073, over 4711.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2187, pruned_loss=0.03748, over 972609.46 frames.], batch size: 15, lr: 2.96e-04
2022-05-05 19:59:26,902 INFO [train.py:715] (2/8) Epoch 7, batch 10350, loss[loss=0.1267, simple_loss=0.2051, pruned_loss=0.0242, over 4971.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2187, pruned_loss=0.03736, over 973587.73 frames.], batch size: 24, lr: 2.96e-04
2022-05-05 20:00:05,913 INFO [train.py:715] (2/8) Epoch 7, batch 10400, loss[loss=0.158, simple_loss=0.2245, pruned_loss=0.04569, over 4838.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2187, pruned_loss=0.03751, over 973047.48 frames.], batch size: 32, lr: 2.96e-04
2022-05-05 20:00:44,696 INFO [train.py:715] (2/8) Epoch 7, batch 10450, loss[loss=0.1561, simple_loss=0.2299, pruned_loss=0.04114, over 4933.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2175, pruned_loss=0.0368, over 972578.81 frames.], batch size: 35, lr: 2.96e-04
2022-05-05 20:01:24,298 INFO [train.py:715] (2/8) Epoch 7, batch 10500, loss[loss=0.1565, simple_loss=0.241, pruned_loss=0.03593, over 4754.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2172, pruned_loss=0.03674, over 971960.78 frames.], batch size: 14, lr: 2.96e-04
2022-05-05 20:02:03,023 INFO [train.py:715] (2/8) Epoch 7, batch 10550, loss[loss=0.1868, simple_loss=0.2431, pruned_loss=0.06523, over 4859.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2174, pruned_loss=0.03683, over 973197.43 frames.], batch size: 32, lr: 2.96e-04
2022-05-05 20:02:41,166 INFO [train.py:715] (2/8) Epoch 7, batch 10600, loss[loss=0.1415, simple_loss=0.2132, pruned_loss=0.03487, over 4988.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2174, pruned_loss=0.03683, over 972724.02 frames.], batch size: 14, lr: 2.96e-04
2022-05-05 20:03:20,355 INFO [train.py:715] (2/8) Epoch 7, batch 10650, loss[loss=0.1483, simple_loss=0.2197, pruned_loss=0.03845, over 4847.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03678, over 972494.91 frames.], batch size: 20, lr: 2.96e-04
2022-05-05 20:03:59,394 INFO [train.py:715] (2/8) Epoch 7, batch 10700, loss[loss=0.1436, simple_loss=0.2154, pruned_loss=0.03589, over 4792.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2163, pruned_loss=0.03647, over 972710.02 frames.], batch size: 18, lr: 2.96e-04
2022-05-05 20:04:38,883 INFO [train.py:715] (2/8) Epoch 7, batch 10750, loss[loss=0.1209, simple_loss=0.1985, pruned_loss=0.02166, over 4882.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.0364, over 971901.21 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:05:17,667 INFO [train.py:715] (2/8) Epoch 7, batch 10800, loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.03527, over 4916.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.03644, over 972581.42 frames.], batch size: 18, lr: 2.96e-04
2022-05-05 20:05:57,416 INFO [train.py:715] (2/8) Epoch 7, batch 10850, loss[loss=0.1502, simple_loss=0.214, pruned_loss=0.04322, over 4796.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2159, pruned_loss=0.0365, over 971769.36 frames.], batch size: 21, lr: 2.96e-04
2022-05-05 20:06:35,666 INFO [train.py:715] (2/8) Epoch 7, batch 10900, loss[loss=0.1633, simple_loss=0.2311, pruned_loss=0.04772, over 4917.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.0367, over 971957.80 frames.], batch size: 39, lr: 2.96e-04
2022-05-05 20:07:14,749 INFO [train.py:715] (2/8) Epoch 7, batch 10950, loss[loss=0.1828, simple_loss=0.254, pruned_loss=0.0558, over 4769.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.03674, over 972273.64 frames.], batch size: 17, lr: 2.96e-04
2022-05-05 20:07:53,907 INFO [train.py:715] (2/8) Epoch 7, batch 11000, loss[loss=0.1416, simple_loss=0.2187, pruned_loss=0.03223, over 4922.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2162, pruned_loss=0.03676, over 972783.59 frames.], batch size: 18, lr: 2.96e-04
2022-05-05 20:08:32,746 INFO [train.py:715] (2/8) Epoch 7, batch 11050, loss[loss=0.1709, simple_loss=0.2332, pruned_loss=0.05429, over 4701.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2155, pruned_loss=0.03616, over 972209.79 frames.], batch size: 15, lr: 2.96e-04
2022-05-05 20:09:11,471 INFO [train.py:715] (2/8) Epoch 7, batch 11100, loss[loss=0.1396, simple_loss=0.2154, pruned_loss=0.03196, over 4832.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2152, pruned_loss=0.03607, over 971404.56 frames.], batch size: 26, lr: 2.96e-04
2022-05-05 20:09:50,083 INFO [train.py:715] (2/8) Epoch 7, batch 11150, loss[loss=0.1355, simple_loss=0.2145, pruned_loss=0.02827, over 4915.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03638, over 971274.39 frames.], batch size: 18, lr: 2.96e-04
2022-05-05 20:10:29,710 INFO [train.py:715] (2/8) Epoch 7, batch 11200, loss[loss=0.1221, simple_loss=0.203, pruned_loss=0.02063, over 4783.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2164, pruned_loss=0.03666, over 970785.80 frames.], batch size: 14, lr: 2.96e-04
2022-05-05 20:11:08,077 INFO [train.py:715] (2/8) Epoch 7, batch 11250, loss[loss=0.1524, simple_loss=0.2233, pruned_loss=0.04071, over 4861.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2172, pruned_loss=0.03691, over 971225.49 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:11:46,233 INFO [train.py:715] (2/8) Epoch 7, batch 11300, loss[loss=0.1246, simple_loss=0.1933, pruned_loss=0.028, over 4972.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2176, pruned_loss=0.03754, over 970804.19 frames.], batch size: 24, lr: 2.96e-04
2022-05-05 20:12:25,979 INFO [train.py:715] (2/8) Epoch 7, batch 11350, loss[loss=0.1401, simple_loss=0.2174, pruned_loss=0.03141, over 4984.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2172, pruned_loss=0.03735, over 971597.29 frames.], batch size: 28, lr: 2.96e-04
2022-05-05 20:13:04,518 INFO [train.py:715] (2/8) Epoch 7, batch 11400, loss[loss=0.129, simple_loss=0.1863, pruned_loss=0.03582, over 4789.00 frames.], tot_loss[loss=0.146, simple_loss=0.2169, pruned_loss=0.03761, over 972565.22 frames.], batch size: 12, lr: 2.96e-04
2022-05-05 20:13:43,554 INFO [train.py:715] (2/8) Epoch 7, batch 11450, loss[loss=0.1335, simple_loss=0.2066, pruned_loss=0.03021, over 4815.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2175, pruned_loss=0.03787, over 972730.15 frames.], batch size: 21, lr: 2.96e-04
2022-05-05 20:14:22,143 INFO [train.py:715] (2/8) Epoch 7, batch 11500, loss[loss=0.1559, simple_loss=0.2389, pruned_loss=0.03651, over 4939.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2178, pruned_loss=0.03798, over 972787.29 frames.], batch size: 29, lr: 2.96e-04
2022-05-05 20:15:01,731 INFO [train.py:715] (2/8) Epoch 7, batch 11550, loss[loss=0.1433, simple_loss=0.2312, pruned_loss=0.0277, over 4827.00 frames.], tot_loss[loss=0.147, simple_loss=0.2184, pruned_loss=0.03783, over 972873.83 frames.], batch size: 15, lr: 2.96e-04
2022-05-05 20:15:39,999 INFO [train.py:715] (2/8) Epoch 7, batch 11600, loss[loss=0.1359, simple_loss=0.2045, pruned_loss=0.03369, over 4867.00 frames.], tot_loss[loss=0.147, simple_loss=0.2183, pruned_loss=0.03779, over 972379.85 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:16:18,810 INFO [train.py:715] (2/8) Epoch 7, batch 11650, loss[loss=0.1475, simple_loss=0.2313, pruned_loss=0.0318, over 4948.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2182, pruned_loss=0.03779, over 972400.11 frames.], batch size: 29, lr: 2.96e-04
2022-05-05 20:16:58,204 INFO [train.py:715] (2/8) Epoch 7, batch 11700, loss[loss=0.1462, simple_loss=0.2204, pruned_loss=0.03605, over 4875.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2171, pruned_loss=0.03706, over 971428.39 frames.], batch size: 16, lr: 2.96e-04
2022-05-05 20:17:36,281 INFO [train.py:715] (2/8) Epoch 7, batch 11750, loss[loss=0.13, simple_loss=0.1973, pruned_loss=0.03138, over 4818.00 frames.], tot_loss[loss=0.1456, simple_loss=0.217, pruned_loss=0.03712, over 971913.42 frames.], batch size: 15, lr: 2.96e-04
2022-05-05 20:18:15,077 INFO [train.py:715] (2/8) Epoch 7, batch 11800, loss[loss=0.131, simple_loss=0.2094, pruned_loss=0.02627, over 4837.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2164, pruned_loss=0.03715, over 971115.50 frames.], batch size: 26, lr: 2.96e-04
2022-05-05 20:18:54,268 INFO [train.py:715] (2/8) Epoch 7, batch 11850, loss[loss=0.1168, simple_loss=0.193, pruned_loss=0.02027, over 4804.00 frames.], tot_loss[loss=0.1457, simple_loss=0.217, pruned_loss=0.03721, over 971295.84 frames.], batch size: 24, lr: 2.96e-04
2022-05-05 20:19:32,628 INFO [train.py:715] (2/8) Epoch 7, batch 11900, loss[loss=0.1418, simple_loss=0.2305, pruned_loss=0.02661, over 4937.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2165, pruned_loss=0.03669, over 971058.80 frames.], batch size: 21, lr: 2.96e-04
2022-05-05 20:20:11,939 INFO [train.py:715] (2/8) Epoch 7, batch 11950, loss[loss=0.1704, simple_loss=0.2336, pruned_loss=0.0536, over 4861.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2156, pruned_loss=0.03632, over 971498.70 frames.], batch size: 32, lr: 2.96e-04
2022-05-05 20:20:50,616 INFO [train.py:715] (2/8) Epoch 7, batch 12000, loss[loss=0.145, simple_loss=0.2111, pruned_loss=0.03947, over 4786.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2154, pruned_loss=0.03619, over 971891.66 frames.], batch size: 17, lr: 2.95e-04
2022-05-05 20:20:50,616 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 20:21:00,227 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.108, simple_loss=0.193, pruned_loss=0.01154, over 914524.00 frames.
2022-05-05 20:21:38,893 INFO [train.py:715] (2/8) Epoch 7, batch 12050, loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.0355, over 4865.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2162, pruned_loss=0.03646, over 971805.61 frames.], batch size: 32, lr: 2.95e-04
2022-05-05 20:22:18,264 INFO [train.py:715] (2/8) Epoch 7, batch 12100, loss[loss=0.1292, simple_loss=0.2018, pruned_loss=0.02834, over 4824.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2163, pruned_loss=0.03655, over 972191.31 frames.], batch size: 12, lr: 2.95e-04
2022-05-05 20:22:56,860 INFO [train.py:715] (2/8) Epoch 7, batch 12150, loss[loss=0.1661, simple_loss=0.237, pruned_loss=0.04761, over 4745.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03657, over 972807.71 frames.], batch size: 19, lr: 2.95e-04
2022-05-05 20:23:35,619 INFO [train.py:715] (2/8) Epoch 7, batch 12200, loss[loss=0.1303, simple_loss=0.2071, pruned_loss=0.02677, over 4769.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2165, pruned_loss=0.03643, over 971434.62 frames.], batch size: 17, lr: 2.95e-04
2022-05-05 20:24:14,744 INFO [train.py:715] (2/8) Epoch 7, batch 12250, loss[loss=0.148, simple_loss=0.2214, pruned_loss=0.0373, over 4816.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2169, pruned_loss=0.03642, over 971743.69 frames.], batch size: 25, lr: 2.95e-04
2022-05-05 20:24:53,366 INFO [train.py:715] (2/8) Epoch 7, batch 12300, loss[loss=0.1199, simple_loss=0.1962, pruned_loss=0.02186, over 4825.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2174, pruned_loss=0.03675, over 971719.71 frames.], batch size: 26, lr: 2.95e-04
2022-05-05 20:25:35,090 INFO [train.py:715] (2/8) Epoch 7, batch 12350, loss[loss=0.1529, simple_loss=0.2229, pruned_loss=0.04143, over 4734.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2172, pruned_loss=0.03713, over 970867.47 frames.], batch size: 16, lr: 2.95e-04
2022-05-05 20:26:13,787 INFO [train.py:715] (2/8) Epoch 7, batch 12400, loss[loss=0.1345, simple_loss=0.2129, pruned_loss=0.02802, over 4937.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2182, pruned_loss=0.03778, over 971382.03 frames.], batch size: 29, lr: 2.95e-04
2022-05-05 20:26:53,002 INFO [train.py:715] (2/8) Epoch 7, batch 12450, loss[loss=0.1341, simple_loss=0.2091, pruned_loss=0.02953, over 4910.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2186, pruned_loss=0.03796, over 972251.73 frames.], batch size: 18, lr: 2.95e-04
2022-05-05 20:27:31,401 INFO [train.py:715] (2/8) Epoch 7, batch 12500, loss[loss=0.1547, simple_loss=0.2164, pruned_loss=0.04651, over 4797.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2193, pruned_loss=0.03817, over 972937.81 frames.], batch size: 14, lr: 2.95e-04
2022-05-05 20:28:10,098 INFO [train.py:715] (2/8) Epoch 7, batch 12550, loss[loss=0.1637, simple_loss=0.2357, pruned_loss=0.04587, over 4775.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2197, pruned_loss=0.03847, over 972203.19 frames.], batch size: 19, lr: 2.95e-04
2022-05-05 20:28:49,193 INFO [train.py:715] (2/8) Epoch 7, batch 12600, loss[loss=0.1272, simple_loss=0.2028, pruned_loss=0.02585, over 4877.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2204, pruned_loss=0.03859, over 972346.82 frames.], batch size: 22, lr: 2.95e-04
2022-05-05 20:29:27,377 INFO [train.py:715] (2/8) Epoch 7, batch 12650, loss[loss=0.1385, simple_loss=0.2157, pruned_loss=0.03068, over 4753.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2203, pruned_loss=0.0381, over 972536.38 frames.], batch size: 14, lr: 2.95e-04
2022-05-05 20:30:06,576 INFO [train.py:715] (2/8) Epoch 7, batch 12700, loss[loss=0.1583, simple_loss=0.23, pruned_loss=0.04332, over 4945.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2198, pruned_loss=0.03773, over 972640.56 frames.], batch size: 29, lr: 2.95e-04
2022-05-05 20:30:44,741 INFO [train.py:715] (2/8) Epoch 7, batch 12750, loss[loss=0.1624, simple_loss=0.2223, pruned_loss=0.05122, over 4908.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2198, pruned_loss=0.03753, over 972577.05 frames.], batch size: 18, lr: 2.95e-04
2022-05-05 20:31:23,969 INFO [train.py:715] (2/8) Epoch 7, batch 12800, loss[loss=0.1086, simple_loss=0.1914, pruned_loss=0.01286, over 4986.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2175, pruned_loss=0.03668, over 973150.10 frames.], batch size: 25, lr: 2.95e-04
2022-05-05 20:32:02,921 INFO [train.py:715] (2/8) Epoch 7, batch 12850, loss[loss=0.1411, simple_loss=0.213, pruned_loss=0.03459, over 4900.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.03674, over 972792.29 frames.], batch size: 18, lr: 2.95e-04
2022-05-05 20:32:41,510 INFO [train.py:715] (2/8) Epoch 7, batch 12900, loss[loss=0.1356, simple_loss=0.2056, pruned_loss=0.03282, over 4848.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2169, pruned_loss=0.03647, over 972767.45 frames.], batch size: 12, lr: 2.95e-04
2022-05-05 20:33:20,985 INFO [train.py:715] (2/8) Epoch 7, batch 12950, loss[loss=0.1296, simple_loss=0.1956, pruned_loss=0.03173, over 4829.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03688, over 972902.56 frames.], batch size: 15, lr: 2.95e-04
2022-05-05 20:33:59,928 INFO [train.py:715] (2/8) Epoch 7, batch 13000, loss[loss=0.1487, simple_loss=0.2221, pruned_loss=0.03762, over 4943.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03689, over 972275.04 frames.], batch size: 23, lr: 2.95e-04
2022-05-05 20:34:38,877 INFO [train.py:715] (2/8) Epoch 7, batch 13050, loss[loss=0.1406, simple_loss=0.2099, pruned_loss=0.03563, over 4799.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2177, pruned_loss=0.03705, over 972311.46 frames.], batch size: 24, lr: 2.95e-04
2022-05-05 20:35:17,656 INFO [train.py:715] (2/8) Epoch 7, batch 13100, loss[loss=0.151, simple_loss=0.2222, pruned_loss=0.03994, over 4851.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2185, pruned_loss=0.03721, over 972960.83 frames.], batch size: 15, lr: 2.95e-04
2022-05-05 20:35:57,327 INFO [train.py:715] (2/8) Epoch 7, batch 13150, loss[loss=0.1596, simple_loss=0.232, pruned_loss=0.04358, over 4761.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03736, over 972778.02 frames.], batch size: 19, lr: 2.95e-04
2022-05-05 20:36:35,854 INFO [train.py:715] (2/8) Epoch 7, batch 13200, loss[loss=0.1615, simple_loss=0.2376, pruned_loss=0.04267, over 4941.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.0372, over 972855.33 frames.], batch size: 18, lr: 2.95e-04
2022-05-05 20:37:15,484 INFO [train.py:715] (2/8) Epoch 7, batch 13250, loss[loss=0.161, simple_loss=0.2355, pruned_loss=0.04327, over 4789.00 frames.], tot_loss[loss=0.1471, simple_loss=0.219, pruned_loss=0.03755, over 972014.93 frames.], batch size: 14, lr: 2.95e-04
2022-05-05 20:37:54,872 INFO [train.py:715] (2/8) Epoch 7, batch 13300, loss[loss=0.1338, simple_loss=0.2026, pruned_loss=0.03249, over 4837.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2192, pruned_loss=0.03811, over 972827.82 frames.], batch size: 13, lr: 2.95e-04
2022-05-05 20:38:33,800 INFO [train.py:715] (2/8) Epoch 7, batch 13350, loss[loss=0.1599, simple_loss=0.2278, pruned_loss=0.04605, over 4761.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2191, pruned_loss=0.03789, over 972655.93 frames.], batch size: 16, lr: 2.95e-04
2022-05-05 20:39:12,814 INFO [train.py:715] (2/8) Epoch 7, batch 13400, loss[loss=0.1346, simple_loss=0.2021, pruned_loss=0.03354, over 4841.00 frames.], tot_loss[loss=0.147, simple_loss=0.2185, pruned_loss=0.03775, over 973805.79 frames.], batch size: 13, lr: 2.95e-04
2022-05-05 20:39:51,471 INFO [train.py:715] (2/8) Epoch 7, batch 13450, loss[loss=0.1579, simple_loss=0.247, pruned_loss=0.03443, over 4926.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2192, pruned_loss=0.03818, over 972834.23 frames.], batch size: 18, lr: 2.95e-04
2022-05-05 20:40:30,905 INFO [train.py:715] (2/8) Epoch 7, batch 13500, loss[loss=0.1521, simple_loss=0.2142, pruned_loss=0.04497, over 4869.00 frames.], tot_loss[loss=0.148, simple_loss=0.219, pruned_loss=0.03844, over 973210.19 frames.], batch size: 30, lr: 2.95e-04
2022-05-05 20:41:09,549 INFO [train.py:715] (2/8) Epoch 7, batch 13550, loss[loss=0.1462, simple_loss=0.2125, pruned_loss=0.03989, over 4830.00 frames.], tot_loss[loss=0.1479, simple_loss=0.219, pruned_loss=0.03841, over 972391.08 frames.], batch size: 15, lr: 2.95e-04
2022-05-05 20:41:48,025 INFO [train.py:715] (2/8) Epoch 7, batch 13600, loss[loss=0.1364, simple_loss=0.2089, pruned_loss=0.032, over 4836.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2197, pruned_loss=0.03886, over 972268.19 frames.], batch size: 13, lr: 2.95e-04
2022-05-05 20:42:26,964 INFO [train.py:715] (2/8) Epoch 7, batch 13650, loss[loss=0.1388, simple_loss=0.2139, pruned_loss=0.03183, over 4963.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2189, pruned_loss=0.03802, over 972946.73 frames.], batch size: 24, lr: 2.95e-04
2022-05-05 20:43:05,966 INFO [train.py:715] (2/8) Epoch 7, batch 13700, loss[loss=0.1234, simple_loss=0.1992, pruned_loss=0.02383, over 4828.00 frames.], tot_loss[loss=0.147, simple_loss=0.2186, pruned_loss=0.03765, over 973622.14 frames.], batch size: 26, lr: 2.95e-04
2022-05-05 20:43:44,944 INFO [train.py:715] (2/8) Epoch 7, batch 13750, loss[loss=0.1536, simple_loss=0.2268, pruned_loss=0.04026, over 4982.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2188, pruned_loss=0.03728, over 973803.16 frames.], batch size: 25, lr: 2.94e-04
2022-05-05 20:44:23,924 INFO [train.py:715] (2/8) Epoch 7, batch 13800, loss[loss=0.1589, simple_loss=0.2347, pruned_loss=0.04152, over 4804.00 frames.], tot_loss[loss=0.1468, simple_loss=0.219, pruned_loss=0.03734, over 973708.45 frames.], batch size: 21, lr: 2.94e-04
2022-05-05 20:45:03,236 INFO [train.py:715] (2/8) Epoch 7, batch 13850, loss[loss=0.1545, simple_loss=0.223, pruned_loss=0.04301, over 4925.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2197, pruned_loss=0.03777, over 974058.71 frames.], batch size: 35, lr: 2.94e-04
2022-05-05 20:45:41,497 INFO [train.py:715] (2/8) Epoch 7, batch 13900, loss[loss=0.1542, simple_loss=0.2253, pruned_loss=0.04158, over 4741.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2201, pruned_loss=0.03771, over 974273.55 frames.], batch size: 16, lr: 2.94e-04
2022-05-05 20:46:20,518 INFO [train.py:715] (2/8) Epoch 7, batch 13950, loss[loss=0.1633, simple_loss=0.2322, pruned_loss=0.04718, over 4799.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2191, pruned_loss=0.0376, over 973167.51 frames.], batch size: 21, lr: 2.94e-04
2022-05-05 20:46:59,563 INFO [train.py:715] (2/8) Epoch 7, batch 14000, loss[loss=0.133, simple_loss=0.2081, pruned_loss=0.02898, over 4838.00 frames.], tot_loss[loss=0.148, simple_loss=0.2203, pruned_loss=0.03784, over 972938.24 frames.], batch size: 26, lr: 2.94e-04
2022-05-05 20:47:38,925 INFO [train.py:715] (2/8) Epoch 7, batch 14050, loss[loss=0.1574, simple_loss=0.2288, pruned_loss=0.04296, over 4849.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2199, pruned_loss=0.03784, over 972950.18 frames.], batch size: 32, lr: 2.94e-04
2022-05-05 20:48:18,052 INFO [train.py:715] (2/8) Epoch 7, batch 14100, loss[loss=0.1523, simple_loss=0.231, pruned_loss=0.03679, over 4761.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2193, pruned_loss=0.03762, over 972309.61 frames.], batch size: 17, lr: 2.94e-04
2022-05-05 20:48:56,863 INFO [train.py:715] (2/8) Epoch 7, batch 14150, loss[loss=0.1895, simple_loss=0.2478, pruned_loss=0.06562, over 4920.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2193, pruned_loss=0.0379, over 971896.33 frames.], batch size: 39, lr: 2.94e-04
2022-05-05 20:49:36,152 INFO [train.py:715] (2/8) Epoch 7, batch 14200, loss[loss=0.1502, simple_loss=0.2085, pruned_loss=0.04588, over 4775.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2184, pruned_loss=0.0374, over 971952.34 frames.], batch size: 19, lr: 2.94e-04
2022-05-05 20:50:14,409 INFO [train.py:715] (2/8) Epoch 7, batch 14250, loss[loss=0.1522, simple_loss=0.2286, pruned_loss=0.03785, over 4846.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2184, pruned_loss=0.03745, over 972185.91 frames.], batch size: 20, lr: 2.94e-04
2022-05-05 20:50:53,730 INFO [train.py:715] (2/8) Epoch 7, batch 14300, loss[loss=0.1604, simple_loss=0.2366, pruned_loss=0.04215, over 4923.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2188, pruned_loss=0.03779, over 972431.67 frames.], batch size: 23, lr: 2.94e-04
2022-05-05 20:51:33,016 INFO [train.py:715] (2/8) Epoch 7, batch 14350, loss[loss=0.171, simple_loss=0.2393, pruned_loss=0.05132, over 4930.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2187, pruned_loss=0.03778, over 973011.24 frames.], batch size: 18, lr: 2.94e-04
2022-05-05 20:52:12,027 INFO [train.py:715] (2/8) Epoch 7, batch 14400, loss[loss=0.1594, simple_loss=0.2249, pruned_loss=0.04698, over 4898.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2193, pruned_loss=0.03798, over 972529.14 frames.], batch size: 17, lr: 2.94e-04
2022-05-05 20:52:50,741 INFO [train.py:715] (2/8) Epoch 7, batch 14450, loss[loss=0.188, simple_loss=0.2459, pruned_loss=0.06507, over 4956.00 frames.], tot_loss[loss=0.1487, simple_loss=0.22, pruned_loss=0.03875, over 972603.72 frames.], batch size: 35, lr: 2.94e-04
2022-05-05 20:53:29,523 INFO [train.py:715] (2/8) Epoch 7, batch 14500, loss[loss=0.1333, simple_loss=0.2088, pruned_loss=0.0289, over 4853.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2203, pruned_loss=0.03923, over 971671.69 frames.], batch size: 13, lr: 2.94e-04
2022-05-05 20:54:09,102 INFO [train.py:715] (2/8) Epoch 7, batch 14550, loss[loss=0.1393, simple_loss=0.2095, pruned_loss=0.03454, over 4763.00 frames.], tot_loss[loss=0.1481, simple_loss=0.219, pruned_loss=0.0386, over 971366.05 frames.], batch size: 14, lr: 2.94e-04
2022-05-05 20:54:47,910 INFO [train.py:715] (2/8) Epoch 7, batch 14600, loss[loss=0.1385, simple_loss=0.2059, pruned_loss=0.03553, over 4968.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2189, pruned_loss=0.03827, over 970953.58 frames.], batch size: 35, lr: 2.94e-04
2022-05-05 20:55:26,849 INFO [train.py:715] (2/8) Epoch 7, batch 14650, loss[loss=0.1512, simple_loss=0.2366, pruned_loss=0.03292, over 4761.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2177, pruned_loss=0.03738, over 970258.15 frames.], batch size: 19, lr: 2.94e-04
2022-05-05 20:56:05,813 INFO [train.py:715] (2/8) Epoch 7, batch 14700, loss[loss=0.142, simple_loss=0.2184, pruned_loss=0.0328, over 4774.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2173, pruned_loss=0.03694, over 970899.98 frames.], batch size: 17, lr: 2.94e-04
2022-05-05 20:56:44,941 INFO [train.py:715] (2/8) Epoch 7, batch 14750, loss[loss=0.1277, simple_loss=0.2036, pruned_loss=0.02591, over 4835.00 frames.], tot_loss[loss=0.1456, simple_loss=0.217, pruned_loss=0.03713, over 970694.61 frames.], batch size: 26, lr: 2.94e-04
2022-05-05 20:57:23,494 INFO [train.py:715] (2/8) Epoch 7, batch 14800, loss[loss=0.1274, simple_loss=0.1972, pruned_loss=0.02882, over 4749.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2169, pruned_loss=0.03641, over 970102.63 frames.], batch size: 16, lr: 2.94e-04
2022-05-05 20:58:03,002 INFO [train.py:715] (2/8) Epoch 7, batch 14850, loss[loss=0.1655, simple_loss=0.2405, pruned_loss=0.04521, over 4909.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2169, pruned_loss=0.03676, over 971077.42 frames.], batch size: 23, lr: 2.94e-04
2022-05-05 20:58:41,952 INFO [train.py:715] (2/8) Epoch 7, batch 14900, loss[loss=0.148, simple_loss=0.2035, pruned_loss=0.04625, over 4819.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2167, pruned_loss=0.03646, over 971229.33 frames.], batch size: 13, lr: 2.94e-04
2022-05-05 20:59:20,317 INFO [train.py:715] (2/8) Epoch 7, batch 14950, loss[loss=0.1561, simple_loss=0.223, pruned_loss=0.04453, over 4910.00 frames.], tot_loss[loss=0.1464, simple_loss=0.218, pruned_loss=0.03747, over 972413.49 frames.], batch size: 17, lr: 2.94e-04
2022-05-05 20:59:59,927 INFO [train.py:715] (2/8) Epoch 7, batch 15000, loss[loss=0.1498, simple_loss=0.2177, pruned_loss=0.04094, over 4853.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2182, pruned_loss=0.03737, over 972752.03 frames.], batch size: 30, lr: 2.94e-04
2022-05-05 20:59:59,928 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 21:00:14,354 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.1083, simple_loss=0.1931, pruned_loss=0.01175, over 914524.00 frames.
2022-05-05 21:00:53,498 INFO [train.py:715] (2/8) Epoch 7, batch 15050, loss[loss=0.1365, simple_loss=0.2049, pruned_loss=0.03408, over 4759.00 frames.], tot_loss[loss=0.1472, simple_loss=0.219, pruned_loss=0.03774, over 972551.61 frames.], batch size: 14, lr: 2.94e-04
2022-05-05 21:01:32,728 INFO [train.py:715] (2/8) Epoch 7, batch 15100, loss[loss=0.1349, simple_loss=0.2116, pruned_loss=0.02916, over 4952.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2189, pruned_loss=0.0378, over 972492.12 frames.], batch size: 35, lr: 2.94e-04
2022-05-05 21:02:11,969 INFO [train.py:715] (2/8) Epoch 7, batch 15150, loss[loss=0.1374, simple_loss=0.2141, pruned_loss=0.03034, over 4815.00 frames.], tot_loss[loss=0.1474, simple_loss=0.219, pruned_loss=0.03791, over 972749.93 frames.], batch size: 21, lr: 2.94e-04
2022-05-05 21:02:50,725 INFO [train.py:715] (2/8) Epoch 7, batch 15200, loss[loss=0.1201, simple_loss=0.2001, pruned_loss=0.02002, over 4948.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2179, pruned_loss=0.03744, over 972894.11 frames.], batch size: 21, lr: 2.94e-04
2022-05-05 21:03:30,198 INFO [train.py:715] (2/8) Epoch 7, batch 15250, loss[loss=0.1574, simple_loss=0.2265, pruned_loss=0.04416, over 4907.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2169, pruned_loss=0.03702, over 973063.04 frames.], batch size: 19, lr: 2.94e-04
2022-05-05 21:04:09,410 INFO [train.py:715] (2/8) Epoch 7, batch 15300, loss[loss=0.1372, simple_loss=0.2093, pruned_loss=0.03254, over 4751.00 frames.], tot_loss[loss=0.146, simple_loss=0.2179, pruned_loss=0.0371, over 973269.89 frames.], batch size: 12, lr: 2.94e-04
2022-05-05 21:04:48,397 INFO [train.py:715] (2/8) Epoch 7, batch 15350, loss[loss=0.1776, simple_loss=0.2566, pruned_loss=0.04931, over 4970.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2182, pruned_loss=0.03733, over 973000.90 frames.], batch size: 15, lr: 2.94e-04
2022-05-05 21:05:27,505 INFO [train.py:715] (2/8) Epoch 7, batch 15400, loss[loss=0.1439, simple_loss=0.2173, pruned_loss=0.03522, over 4792.00 frames.], tot_loss[loss=0.1457, simple_loss=0.218, pruned_loss=0.03671, over 973017.93 frames.], batch size: 17, lr: 2.94e-04
2022-05-05 21:06:05,998 INFO [train.py:715] (2/8) Epoch 7, batch 15450, loss[loss=0.1326, simple_loss=0.2171, pruned_loss=0.02405, over 4968.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03684, over 972787.68 frames.], batch size: 15, lr: 2.94e-04
2022-05-05 21:06:45,045 INFO [train.py:715] (2/8) Epoch 7, batch 15500, loss[loss=0.143, simple_loss=0.2222, pruned_loss=0.0319, over 4976.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2184, pruned_loss=0.0369, over 972783.01 frames.], batch size: 28, lr: 2.93e-04
2022-05-05 21:07:23,166 INFO [train.py:715] (2/8) Epoch 7, batch 15550, loss[loss=0.1607, simple_loss=0.2401, pruned_loss=0.04065, over 4991.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2188, pruned_loss=0.0368, over 972643.29 frames.], batch size: 14, lr: 2.93e-04
2022-05-05 21:08:02,568 INFO [train.py:715] (2/8) Epoch 7, batch 15600, loss[loss=0.1385, simple_loss=0.2074, pruned_loss=0.03484, over 4961.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2183, pruned_loss=0.03669, over 973021.30 frames.], batch size: 35, lr: 2.93e-04
2022-05-05 21:08:42,088 INFO [train.py:715] (2/8) Epoch 7, batch 15650, loss[loss=0.1237, simple_loss=0.2033, pruned_loss=0.02205, over 4778.00 frames.], tot_loss[loss=0.146, simple_loss=0.2184, pruned_loss=0.03683, over 972533.81 frames.], batch size: 18, lr: 2.93e-04
2022-05-05 21:09:20,365 INFO [train.py:715] (2/8) Epoch 7, batch 15700, loss[loss=0.1363, simple_loss=0.2219, pruned_loss=0.02529, over 4915.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2192, pruned_loss=0.03706, over 973058.57 frames.], batch size: 29, lr: 2.93e-04
2022-05-05 21:09:59,353 INFO [train.py:715] (2/8) Epoch 7, batch 15750, loss[loss=0.1508, simple_loss=0.2132, pruned_loss=0.04423, over 4751.00 frames.], tot_loss[loss=0.146, simple_loss=0.2183, pruned_loss=0.0369, over 972846.18 frames.], batch size: 16, lr: 2.93e-04
2022-05-05 21:10:39,022 INFO [train.py:715] (2/8) Epoch 7, batch 15800, loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03286, over 4993.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2178, pruned_loss=0.03677, over 973452.90 frames.], batch size: 20, lr: 2.93e-04
2022-05-05 21:11:18,134 INFO [train.py:715] (2/8) Epoch 7, batch 15850, loss[loss=0.1382, simple_loss=0.214, pruned_loss=0.03119, over 4794.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2174, pruned_loss=0.03661, over 973196.14 frames.], batch size: 13, lr: 2.93e-04
2022-05-05 21:11:57,176 INFO [train.py:715] (2/8) Epoch 7, batch 15900, loss[loss=0.1266, simple_loss=0.2033, pruned_loss=0.02497, over 4804.00 frames.], tot_loss[loss=0.145, simple_loss=0.2172, pruned_loss=0.03642, over 972499.30 frames.], batch size: 21, lr: 2.93e-04
2022-05-05 21:12:36,479 INFO [train.py:715] (2/8) Epoch 7, batch 15950, loss[loss=0.1116, simple_loss=0.1863, pruned_loss=0.01845, over 4841.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2176, pruned_loss=0.0366, over 972161.90 frames.], batch size: 13, lr: 2.93e-04
2022-05-05 21:13:15,928 INFO [train.py:715] (2/8) Epoch 7, batch 16000, loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03326, over 4911.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2169, pruned_loss=0.03618, over 972212.82 frames.], batch size: 19, lr: 2.93e-04
2022-05-05 21:13:54,028 INFO [train.py:715] (2/8) Epoch 7, batch 16050, loss[loss=0.1485, simple_loss=0.2205, pruned_loss=0.03827, over 4644.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2166, pruned_loss=0.03598, over 972103.91 frames.], batch size: 13, lr: 2.93e-04
2022-05-05 21:14:33,357 INFO [train.py:715] (2/8) Epoch 7, batch 16100, loss[loss=0.1164, simple_loss=0.1828, pruned_loss=0.025, over 4647.00 frames.], tot_loss[loss=0.1452, simple_loss=0.217, pruned_loss=0.03664, over 971974.39 frames.], batch size: 13, lr: 2.93e-04
2022-05-05 21:15:12,281 INFO [train.py:715] (2/8) Epoch 7, batch 16150, loss[loss=0.1307, simple_loss=0.1949, pruned_loss=0.03331, over 4743.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2171, pruned_loss=0.03627, over 970827.56 frames.], batch size: 16, lr: 2.93e-04
2022-05-05 21:15:50,930 INFO [train.py:715] (2/8) Epoch 7, batch 16200, loss[loss=0.1554, simple_loss=0.2252, pruned_loss=0.04276, over 4764.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2181, pruned_loss=0.03684, over 971932.24 frames.], batch size: 18, lr: 2.93e-04
2022-05-05 21:16:30,082 INFO [train.py:715] (2/8) Epoch 7, batch 16250, loss[loss=0.153, simple_loss=0.2106, pruned_loss=0.04766, over 4810.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2182, pruned_loss=0.03673, over 971161.04 frames.], batch size: 27, lr: 2.93e-04
2022-05-05 21:17:08,725 INFO [train.py:715] (2/8) Epoch 7, batch 16300, loss[loss=0.1225, simple_loss=0.1997, pruned_loss=0.02267, over 4794.00 frames.], tot_loss[loss=0.146, simple_loss=0.2181, pruned_loss=0.03693, over 970889.55 frames.], batch size: 24, lr: 2.93e-04
2022-05-05 21:17:48,273 INFO [train.py:715] (2/8) Epoch 7, batch 16350, loss[loss=0.1556, simple_loss=0.2335, pruned_loss=0.03887, over 4869.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2176, pruned_loss=0.03677, over 971226.83 frames.], batch size: 38, lr: 2.93e-04
2022-05-05 21:18:26,609 INFO [train.py:715] (2/8) Epoch 7, batch 16400, loss[loss=0.1415, simple_loss=0.2114, pruned_loss=0.0358, over 4972.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03693, over 971856.52 frames.], batch size: 15, lr: 2.93e-04
2022-05-05 21:19:05,501 INFO [train.py:715] (2/8) Epoch 7, batch 16450, loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03057, over 4916.00 frames.], tot_loss[loss=0.1461, simple_loss=0.218, pruned_loss=0.03711, over 971953.10 frames.], batch size: 23, lr: 2.93e-04
2022-05-05 21:19:44,557 INFO [train.py:715] (2/8) Epoch 7, batch 16500, loss[loss=0.1434, simple_loss=0.2239, pruned_loss=0.03141, over 4834.00 frames.], tot_loss[loss=0.147, simple_loss=0.219, pruned_loss=0.03747, over 972679.84 frames.], batch size: 27, lr: 2.93e-04
2022-05-05 21:20:22,828 INFO [train.py:715] (2/8) Epoch 7, batch 16550, loss[loss=0.1702, simple_loss=0.2347, pruned_loss=0.05283, over 4986.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2194, pruned_loss=0.03761, over 972691.93 frames.], batch size: 15, lr: 2.93e-04
2022-05-05 21:21:02,225 INFO [train.py:715] (2/8) Epoch 7, batch 16600, loss[loss=0.1459, simple_loss=0.2198, pruned_loss=0.03602, over 4986.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2189, pruned_loss=0.03722, over 972437.32 frames.], batch size: 26, lr: 2.93e-04
2022-05-05 21:21:41,397 INFO [train.py:715] (2/8) Epoch 7, batch 16650, loss[loss=0.1438, simple_loss=0.2119, pruned_loss=0.03787, over 4806.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2189, pruned_loss=0.03725, over 973224.56 frames.], batch size: 21, lr: 2.93e-04
2022-05-05 21:22:20,543 INFO [train.py:715] (2/8) Epoch 7, batch 16700, loss[loss=0.1537, simple_loss=0.2256, pruned_loss=0.04094, over 4793.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2188, pruned_loss=0.03717, over 973536.80 frames.], batch size: 24, lr: 2.93e-04
2022-05-05 21:22:59,811 INFO [train.py:715] (2/8) Epoch 7, batch 16750, loss[loss=0.139, simple_loss=0.2087, pruned_loss=0.0346, over 4803.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2194, pruned_loss=0.03762, over 973304.83 frames.], batch size: 25, lr: 2.93e-04
2022-05-05 21:23:38,669 INFO [train.py:715] (2/8) Epoch 7, batch 16800, loss[loss=0.1311, simple_loss=0.2145, pruned_loss=0.02391, over 4769.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03738, over 973108.65 frames.], batch size: 17, lr: 2.93e-04
2022-05-05 21:24:17,714 INFO [train.py:715] (2/8) Epoch 7, batch 16850, loss[loss=0.1297, simple_loss=0.2046, pruned_loss=0.0274, over 4937.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2176, pruned_loss=0.03746, over 973545.56 frames.], batch size: 29, lr: 2.93e-04
2022-05-05 21:24:56,999 INFO [train.py:715] (2/8) Epoch 7, batch 16900, loss[loss=0.164, simple_loss=0.2435, pruned_loss=0.0422, over 4956.00 frames.], tot_loss[loss=0.147, simple_loss=0.2184, pruned_loss=0.03781, over 973793.67 frames.], batch size: 39, lr: 2.93e-04
2022-05-05 21:25:36,249 INFO [train.py:715] (2/8) Epoch 7, batch 16950, loss[loss=0.1729, simple_loss=0.2473, pruned_loss=0.04929, over 4747.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2183, pruned_loss=0.03772, over 974171.96 frames.], batch size: 16, lr: 2.93e-04
2022-05-05 21:26:14,897 INFO [train.py:715] (2/8) Epoch 7, batch 17000, loss[loss=0.1313, simple_loss=0.2159, pruned_loss=0.02336, over 4981.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2191, pruned_loss=0.03814, over 973505.18 frames.], batch size: 20, lr: 2.93e-04
2022-05-05 21:26:54,054 INFO [train.py:715] (2/8) Epoch 7, batch 17050, loss[loss=0.1257, simple_loss=0.2075, pruned_loss=0.02188, over 4916.00 frames.], tot_loss[loss=0.1475, simple_loss=0.219, pruned_loss=0.03799, over 972556.03 frames.], batch size: 23, lr: 2.93e-04
2022-05-05 21:27:32,507 INFO [train.py:715] (2/8) Epoch 7, batch 17100, loss[loss=0.1608, simple_loss=0.2262, pruned_loss=0.04767, over 4908.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2196, pruned_loss=0.03785, over 972287.64 frames.], batch size: 18, lr: 2.93e-04
2022-05-05 21:28:11,645 INFO [train.py:715] (2/8) Epoch 7, batch 17150, loss[loss=0.1175, simple_loss=0.1983, pruned_loss=0.01831, over 4773.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2196, pruned_loss=0.03766, over 971814.25 frames.], batch size: 18, lr: 2.93e-04
2022-05-05 21:28:50,899 INFO [train.py:715] (2/8) Epoch 7, batch 17200, loss[loss=0.1367, simple_loss=0.2109, pruned_loss=0.03127, over 4773.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2198, pruned_loss=0.03758, over 970698.15 frames.], batch size: 18, lr: 2.93e-04
2022-05-05 21:29:29,222 INFO [train.py:715] (2/8) Epoch 7, batch 17250, loss[loss=0.1416, simple_loss=0.2221, pruned_loss=0.03054, over 4771.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2193, pruned_loss=0.03759, over 970688.08 frames.], batch size: 18, lr: 2.92e-04
2022-05-05 21:30:08,294 INFO [train.py:715] (2/8) Epoch 7, batch 17300, loss[loss=0.1417, simple_loss=0.2054, pruned_loss=0.03906, over 4848.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2184, pruned_loss=0.03714, over 971182.27 frames.], batch size: 30, lr: 2.92e-04
2022-05-05 21:30:46,576 INFO [train.py:715] (2/8) Epoch 7, batch 17350, loss[loss=0.1593, simple_loss=0.2221, pruned_loss=0.04823, over 4932.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2186, pruned_loss=0.03718, over 971274.65 frames.], batch size: 29, lr: 2.92e-04
2022-05-05 21:31:25,650 INFO [train.py:715] (2/8) Epoch 7, batch 17400, loss[loss=0.1736, simple_loss=0.2411, pruned_loss=0.05312, over 4833.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2195, pruned_loss=0.03752, over 971050.66 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:32:04,440 INFO [train.py:715] (2/8) Epoch 7, batch 17450, loss[loss=0.1489, simple_loss=0.2247, pruned_loss=0.03654, over 4932.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2194, pruned_loss=0.03756, over 971809.87 frames.], batch size: 29, lr: 2.92e-04
2022-05-05 21:32:43,221 INFO [train.py:715] (2/8) Epoch 7, batch 17500, loss[loss=0.129, simple_loss=0.2028, pruned_loss=0.02761, over 4989.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2189, pruned_loss=0.03723, over 971893.63 frames.], batch size: 28, lr: 2.92e-04
2022-05-05 21:33:22,414 INFO [train.py:715] (2/8) Epoch 7, batch 17550, loss[loss=0.1481, simple_loss=0.2261, pruned_loss=0.03504, over 4881.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2183, pruned_loss=0.03707, over 972073.08 frames.], batch size: 16, lr: 2.92e-04
2022-05-05 21:34:00,737 INFO [train.py:715] (2/8) Epoch 7, batch 17600, loss[loss=0.1435, simple_loss=0.2114, pruned_loss=0.03782, over 4734.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2184, pruned_loss=0.03719, over 972678.27 frames.], batch size: 16, lr: 2.92e-04
2022-05-05 21:34:39,810 INFO [train.py:715] (2/8) Epoch 7, batch 17650, loss[loss=0.1498, simple_loss=0.2217, pruned_loss=0.03896, over 4932.00 frames.], tot_loss[loss=0.146, simple_loss=0.218, pruned_loss=0.03697, over 973204.82 frames.], batch size: 29, lr: 2.92e-04
2022-05-05 21:35:19,110 INFO [train.py:715] (2/8) Epoch 7, batch 17700, loss[loss=0.1656, simple_loss=0.2341, pruned_loss=0.04854, over 4923.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03657, over 972544.74 frames.], batch size: 23, lr: 2.92e-04
2022-05-05 21:35:58,225 INFO [train.py:715] (2/8) Epoch 7, batch 17750, loss[loss=0.1549, simple_loss=0.232, pruned_loss=0.03895, over 4955.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2165, pruned_loss=0.03664, over 972733.21 frames.], batch size: 24, lr: 2.92e-04
2022-05-05 21:36:37,515 INFO [train.py:715] (2/8) Epoch 7, batch 17800, loss[loss=0.141, simple_loss=0.2136, pruned_loss=0.03426, over 4908.00 frames.], tot_loss[loss=0.144, simple_loss=0.2159, pruned_loss=0.03606, over 972373.91 frames.], batch size: 17, lr: 2.92e-04
2022-05-05 21:37:16,002 INFO [train.py:715] (2/8) Epoch 7, batch 17850, loss[loss=0.161, simple_loss=0.2231, pruned_loss=0.0494, over 4799.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.03615, over 971926.01 frames.], batch size: 17, lr: 2.92e-04
2022-05-05 21:37:55,627 INFO [train.py:715] (2/8) Epoch 7, batch 17900, loss[loss=0.1445, simple_loss=0.2188, pruned_loss=0.03514, over 4990.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03651, over 972301.46 frames.], batch size: 20, lr: 2.92e-04
2022-05-05 21:38:34,075 INFO [train.py:715] (2/8) Epoch 7, batch 17950, loss[loss=0.1551, simple_loss=0.2412, pruned_loss=0.03448, over 4815.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03648, over 972498.26 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:39:13,126 INFO [train.py:715] (2/8) Epoch 7, batch 18000, loss[loss=0.1275, simple_loss=0.1954, pruned_loss=0.02985, over 4762.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2172, pruned_loss=0.03696, over 971702.39 frames.], batch size: 14, lr: 2.92e-04
2022-05-05 21:39:13,127 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 21:39:22,793 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.1081, simple_loss=0.193, pruned_loss=0.01158, over 914524.00 frames.
2022-05-05 21:40:01,808 INFO [train.py:715] (2/8) Epoch 7, batch 18050, loss[loss=0.1525, simple_loss=0.2174, pruned_loss=0.04377, over 4860.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2167, pruned_loss=0.03659, over 971499.88 frames.], batch size: 32, lr: 2.92e-04
2022-05-05 21:40:41,011 INFO [train.py:715] (2/8) Epoch 7, batch 18100, loss[loss=0.1464, simple_loss=0.2132, pruned_loss=0.03976, over 4900.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2164, pruned_loss=0.0363, over 971939.72 frames.], batch size: 19, lr: 2.92e-04
2022-05-05 21:41:19,569 INFO [train.py:715] (2/8) Epoch 7, batch 18150, loss[loss=0.1371, simple_loss=0.2103, pruned_loss=0.03193, over 4909.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2166, pruned_loss=0.03627, over 971812.28 frames.], batch size: 19, lr: 2.92e-04
2022-05-05 21:41:57,882 INFO [train.py:715] (2/8) Epoch 7, batch 18200, loss[loss=0.1525, simple_loss=0.2236, pruned_loss=0.04073, over 4761.00 frames.], tot_loss[loss=0.1448, simple_loss=0.217, pruned_loss=0.03633, over 972159.67 frames.], batch size: 17, lr: 2.92e-04
2022-05-05 21:42:36,256 INFO [train.py:715] (2/8) Epoch 7, batch 18250, loss[loss=0.1412, simple_loss=0.211, pruned_loss=0.03572, over 4778.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03737, over 972657.68 frames.], batch size: 18, lr: 2.92e-04
2022-05-05 21:43:15,545 INFO [train.py:715] (2/8) Epoch 7, batch 18300, loss[loss=0.1574, simple_loss=0.2306, pruned_loss=0.04206, over 4692.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2186, pruned_loss=0.03758, over 971781.01 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:43:53,558 INFO [train.py:715] (2/8) Epoch 7, batch 18350, loss[loss=0.1438, simple_loss=0.2165, pruned_loss=0.03551, over 4844.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2186, pruned_loss=0.0375, over 971708.25 frames.], batch size: 27, lr: 2.92e-04
2022-05-05 21:44:31,935 INFO [train.py:715] (2/8) Epoch 7, batch 18400, loss[loss=0.1352, simple_loss=0.1965, pruned_loss=0.03688, over 4889.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2192, pruned_loss=0.03768, over 971558.13 frames.], batch size: 19, lr: 2.92e-04
2022-05-05 21:45:11,790 INFO [train.py:715] (2/8) Epoch 7, batch 18450, loss[loss=0.1461, simple_loss=0.2139, pruned_loss=0.03913, over 4863.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2194, pruned_loss=0.03772, over 972684.63 frames.], batch size: 20, lr: 2.92e-04
2022-05-05 21:45:50,716 INFO [train.py:715] (2/8) Epoch 7, batch 18500, loss[loss=0.1857, simple_loss=0.2434, pruned_loss=0.06398, over 4898.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2192, pruned_loss=0.03792, over 973787.04 frames.], batch size: 19, lr: 2.92e-04
2022-05-05 21:46:29,380 INFO [train.py:715] (2/8) Epoch 7, batch 18550, loss[loss=0.133, simple_loss=0.2036, pruned_loss=0.03115, over 4780.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2188, pruned_loss=0.03736, over 972749.00 frames.], batch size: 14, lr: 2.92e-04
2022-05-05 21:47:08,452 INFO [train.py:715] (2/8) Epoch 7, batch 18600, loss[loss=0.1422, simple_loss=0.2219, pruned_loss=0.03125, over 4692.00 frames.], tot_loss[loss=0.147, simple_loss=0.2187, pruned_loss=0.0377, over 972959.08 frames.], batch size: 15, lr: 2.92e-04
2022-05-05 21:47:47,273 INFO [train.py:715] (2/8) Epoch 7, batch 18650, loss[loss=0.1958, simple_loss=0.2624, pruned_loss=0.06466, over 4843.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2186, pruned_loss=0.03748, over 972862.53 frames.], batch size: 30, lr: 2.92e-04
2022-05-05 21:48:25,130 INFO [train.py:715] (2/8) Epoch 7, batch 18700, loss[loss=0.1403, simple_loss=0.211, pruned_loss=0.03474, over 4923.00 frames.], tot_loss[loss=0.146, simple_loss=0.2181, pruned_loss=0.03692, over 972974.94 frames.], batch size: 21, lr: 2.92e-04
2022-05-05 21:49:03,390 INFO [train.py:715] (2/8) Epoch 7, batch 18750, loss[loss=0.1388, simple_loss=0.2127, pruned_loss=0.03248, over 4751.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03698, over 973042.59 frames.], batch size: 16, lr: 2.92e-04
2022-05-05 21:49:42,762 INFO [train.py:715] (2/8) Epoch 7, batch 18800, loss[loss=0.1158, simple_loss=0.1816, pruned_loss=0.02503, over 4821.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2169, pruned_loss=0.03613, over 973807.58 frames.], batch size: 13, lr: 2.92e-04
2022-05-05 21:50:21,362 INFO [train.py:715] (2/8) Epoch 7, batch 18850, loss[loss=0.1426, simple_loss=0.2214, pruned_loss=0.03188, over 4932.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2163, pruned_loss=0.03566, over 973957.15 frames.], batch size: 39, lr: 2.92e-04
2022-05-05 21:50:59,416 INFO [train.py:715] (2/8) Epoch 7, batch 18900, loss[loss=0.1583, simple_loss=0.2238, pruned_loss=0.04636, over 4921.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2166, pruned_loss=0.03615, over 973469.16 frames.], batch size: 18, lr: 2.92e-04
2022-05-05 21:51:36,461 INFO [train.py:715] (2/8) Epoch 7, batch 18950, loss[loss=0.1903, simple_loss=0.2555, pruned_loss=0.06254, over 4968.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03654, over 974034.33 frames.], batch size: 31, lr: 2.92e-04
2022-05-05 21:52:14,913 INFO [train.py:715] (2/8) Epoch 7, batch 19000, loss[loss=0.158, simple_loss=0.2242, pruned_loss=0.04587, over 4985.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2175, pruned_loss=0.03657, over 973188.80 frames.], batch size: 31, lr: 2.92e-04
2022-05-05 21:52:52,514 INFO [train.py:715] (2/8) Epoch 7, batch 19050, loss[loss=0.157, simple_loss=0.219, pruned_loss=0.04748, over 4800.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03711, over 973018.93 frames.], batch size: 13, lr: 2.91e-04
2022-05-05 21:53:30,743 INFO [train.py:715] (2/8) Epoch 7, batch 19100, loss[loss=0.1151, simple_loss=0.193, pruned_loss=0.01856, over 4920.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2178, pruned_loss=0.03704, over 972845.05 frames.], batch size: 29, lr: 2.91e-04
2022-05-05 21:54:09,413 INFO [train.py:715] (2/8) Epoch 7, batch 19150, loss[loss=0.1472, simple_loss=0.2096, pruned_loss=0.04236, over 4979.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2188, pruned_loss=0.03767, over 973157.44 frames.], batch size: 35, lr: 2.91e-04
2022-05-05 21:54:47,125 INFO [train.py:715] (2/8) Epoch 7, batch 19200, loss[loss=0.1527, simple_loss=0.2259, pruned_loss=0.03973, over 4800.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03731, over 972805.91 frames.], batch size: 21, lr: 2.91e-04
2022-05-05 21:55:24,840 INFO [train.py:715] (2/8) Epoch 7, batch 19250, loss[loss=0.1461, simple_loss=0.222, pruned_loss=0.03514, over 4967.00 frames.], tot_loss[loss=0.147, simple_loss=0.2188, pruned_loss=0.03758, over 971517.98 frames.], batch size: 35, lr: 2.91e-04
2022-05-05 21:56:02,878 INFO [train.py:715] (2/8) Epoch 7, batch 19300, loss[loss=0.1434, simple_loss=0.2193, pruned_loss=0.03372, over 4856.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2189, pruned_loss=0.03745, over 971279.76 frames.], batch size: 32, lr: 2.91e-04
2022-05-05 21:56:41,357 INFO [train.py:715] (2/8) Epoch 7, batch 19350, loss[loss=0.1561, simple_loss=0.2271, pruned_loss=0.04252, over 4945.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2188, pruned_loss=0.03748, over 971500.87 frames.], batch size: 29, lr: 2.91e-04
2022-05-05 21:57:18,830 INFO [train.py:715] (2/8) Epoch 7, batch 19400, loss[loss=0.1304, simple_loss=0.1987, pruned_loss=0.03105, over 4822.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03735, over 970780.28 frames.], batch size: 13, lr: 2.91e-04
2022-05-05 21:57:56,263 INFO [train.py:715] (2/8) Epoch 7, batch 19450, loss[loss=0.1434, simple_loss=0.2132, pruned_loss=0.03675, over 4816.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2189, pruned_loss=0.03774, over 971218.15 frames.], batch size: 15, lr: 2.91e-04
2022-05-05 21:58:34,321 INFO [train.py:715] (2/8) Epoch 7, batch 19500, loss[loss=0.1432, simple_loss=0.2227, pruned_loss=0.03186, over 4923.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2186, pruned_loss=0.03762, over 970984.13 frames.], batch size: 23, lr: 2.91e-04
2022-05-05 21:59:11,843 INFO [train.py:715] (2/8) Epoch 7, batch 19550, loss[loss=0.1867, simple_loss=0.253, pruned_loss=0.0602, over 4843.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2187, pruned_loss=0.03784, over 970708.08 frames.], batch size: 15, lr: 2.91e-04
2022-05-05 21:59:49,564 INFO [train.py:715] (2/8) Epoch 7, batch 19600, loss[loss=0.1615, simple_loss=0.23, pruned_loss=0.04654, over 4962.00 frames.], tot_loss[loss=0.147, simple_loss=0.2184, pruned_loss=0.03776, over 970521.26 frames.], batch size: 24, lr: 2.91e-04
2022-05-05 22:00:27,123 INFO [train.py:715] (2/8) Epoch 7, batch 19650, loss[loss=0.1497, simple_loss=0.2231, pruned_loss=0.03814, over 4807.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2174, pruned_loss=0.03736, over 970093.30 frames.], batch size: 26, lr: 2.91e-04
2022-05-05 22:01:05,539 INFO [train.py:715] (2/8) Epoch 7, batch 19700, loss[loss=0.1786, simple_loss=0.2382, pruned_loss=0.05951, over 4869.00 frames.], tot_loss[loss=0.146, simple_loss=0.2171, pruned_loss=0.03745, over 969746.90 frames.], batch size: 32, lr: 2.91e-04
2022-05-05 22:01:42,747 INFO [train.py:715] (2/8) Epoch 7, batch 19750, loss[loss=0.1554, simple_loss=0.2237, pruned_loss=0.04357, over 4968.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2183, pruned_loss=0.03815, over 969827.04 frames.], batch size: 15, lr: 2.91e-04
2022-05-05 22:02:20,222 INFO [train.py:715] (2/8) Epoch 7, batch 19800, loss[loss=0.1661, simple_loss=0.2308, pruned_loss=0.05066, over 4786.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2184, pruned_loss=0.03843, over 970169.33 frames.], batch size: 17, lr: 2.91e-04
2022-05-05 22:02:58,033 INFO [train.py:715] (2/8) Epoch 7, batch 19850, loss[loss=0.1763, simple_loss=0.2329, pruned_loss=0.05987, over 4885.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2176, pruned_loss=0.03789, over 970054.96 frames.], batch size: 16, lr: 2.91e-04
2022-05-05 22:03:35,857 INFO [train.py:715] (2/8) Epoch 7, batch 19900, loss[loss=0.1624, simple_loss=0.2285, pruned_loss=0.04816, over 4843.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2183, pruned_loss=0.03793, over 970595.12 frames.], batch size: 30, lr: 2.91e-04
2022-05-05 22:04:12,821 INFO [train.py:715] (2/8) Epoch 7, batch 19950, loss[loss=0.1478, simple_loss=0.2143, pruned_loss=0.0406, over 4865.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2181, pruned_loss=0.0381, over 970678.84 frames.], batch size: 30, lr: 2.91e-04
2022-05-05 22:04:50,679 INFO [train.py:715] (2/8) Epoch 7, batch 20000, loss[loss=0.153, simple_loss=0.222, pruned_loss=0.04204, over 4859.00 frames.], tot_loss[loss=0.147, simple_loss=0.2181, pruned_loss=0.03795, over 971345.56 frames.], batch size: 39, lr: 2.91e-04
2022-05-05 22:05:28,967 INFO [train.py:715] (2/8) Epoch 7, batch 20050, loss[loss=0.1578, simple_loss=0.2241, pruned_loss=0.04576, over 4975.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2178, pruned_loss=0.03775, over 971429.51 frames.], batch size: 15, lr: 2.91e-04
2022-05-05 22:06:06,297 INFO [train.py:715] (2/8) Epoch 7, batch 20100, loss[loss=0.148, simple_loss=0.2212, pruned_loss=0.03739, over 4844.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2177, pruned_loss=0.03775, over 972257.38 frames.], batch size: 27, lr: 2.91e-04
2022-05-05 22:06:43,749 INFO [train.py:715] (2/8) Epoch 7, batch 20150, loss[loss=0.1365, simple_loss=0.2198, pruned_loss=0.02662, over 4901.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2183, pruned_loss=0.03781, over 973363.80 frames.], batch size: 19, lr: 2.91e-04
2022-05-05 22:07:21,915 INFO [train.py:715] (2/8) Epoch 7, batch 20200, loss[loss=0.1233, simple_loss=0.199, pruned_loss=0.02379, over 4947.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2185, pruned_loss=0.03749, over 974025.41 frames.], batch size: 21, lr: 2.91e-04
2022-05-05 22:08:00,054 INFO [train.py:715] (2/8) Epoch 7, batch 20250, loss[loss=0.15, simple_loss=0.2303, pruned_loss=0.03488, over 4987.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2179, pruned_loss=0.03746, over 974951.77 frames.], batch size: 25, lr: 2.91e-04
2022-05-05 22:08:37,464 INFO [train.py:715] (2/8) Epoch 7, batch 20300, loss[loss=0.141, simple_loss=0.2203, pruned_loss=0.03083, over 4974.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2177, pruned_loss=0.03724, over 974479.81 frames.], batch size: 39, lr: 2.91e-04
2022-05-05 22:09:17,216 INFO [train.py:715] (2/8) Epoch 7, batch 20350, loss[loss=0.1717, simple_loss=0.2319, pruned_loss=0.05577, over 4887.00 frames.], tot_loss[loss=0.1469, simple_loss=0.218, pruned_loss=0.03784, over 973797.59 frames.], batch size: 19, lr: 2.91e-04
2022-05-05 22:09:55,131 INFO [train.py:715] (2/8) Epoch 7, batch 20400, loss[loss=0.1268, simple_loss=0.2043, pruned_loss=0.02466, over 4947.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2183, pruned_loss=0.03778, over 973789.04 frames.], batch size: 21, lr: 2.91e-04
2022-05-05 22:10:33,045 INFO [train.py:715] (2/8) Epoch 7, batch 20450, loss[loss=0.1384, simple_loss=0.2334, pruned_loss=0.02165, over 4944.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2175, pruned_loss=0.0374, over 973206.88 frames.], batch size: 24, lr: 2.91e-04
2022-05-05 22:11:10,605 INFO [train.py:715] (2/8) Epoch 7, batch 20500, loss[loss=0.145, simple_loss=0.225, pruned_loss=0.03249, over 4816.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03719, over 973044.91 frames.], batch size: 26, lr: 2.91e-04
2022-05-05 22:11:48,693 INFO [train.py:715] (2/8) Epoch 7, batch 20550, loss[loss=0.1476, simple_loss=0.221, pruned_loss=0.03715, over 4966.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2189, pruned_loss=0.03749, over 973136.64 frames.], batch size: 35, lr: 2.91e-04
2022-05-05 22:12:26,842 INFO [train.py:715] (2/8) Epoch 7, batch 20600, loss[loss=0.1364, simple_loss=0.2095, pruned_loss=0.03162, over 4861.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2192, pruned_loss=0.03709, over 973009.91 frames.], batch size: 32, lr: 2.91e-04
2022-05-05 22:13:04,071 INFO [train.py:715] (2/8) Epoch 7, batch 20650, loss[loss=0.1018, simple_loss=0.1701, pruned_loss=0.01678, over 4975.00 frames.], tot_loss[loss=0.146, simple_loss=0.2185, pruned_loss=0.03671, over 973137.34 frames.], batch size: 15, lr: 2.91e-04
2022-05-05 22:13:41,771 INFO [train.py:715] (2/8) Epoch 7, batch 20700, loss[loss=0.1534, simple_loss=0.2245, pruned_loss=0.04116, over 4904.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2174, pruned_loss=0.03597, over 973252.28 frames.], batch size: 19, lr: 2.91e-04
2022-05-05 22:14:19,742 INFO [train.py:715] (2/8) Epoch 7, batch 20750, loss[loss=0.1455, simple_loss=0.2168, pruned_loss=0.03708, over 4982.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2175, pruned_loss=0.03609, over 972777.55 frames.], batch size: 39, lr: 2.91e-04
2022-05-05 22:14:57,388 INFO [train.py:715] (2/8) Epoch 7, batch 20800, loss[loss=0.1564, simple_loss=0.2263, pruned_loss=0.0433, over 4808.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2169, pruned_loss=0.03608, over 971851.87 frames.], batch size: 21, lr: 2.91e-04
2022-05-05 22:15:34,692 INFO [train.py:715] (2/8) Epoch 7, batch 20850, loss[loss=0.1756, simple_loss=0.2408, pruned_loss=0.05521, over 4842.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03555, over 971890.08 frames.], batch size: 32, lr: 2.90e-04
2022-05-05 22:16:13,018 INFO [train.py:715] (2/8) Epoch 7, batch 20900, loss[loss=0.1237, simple_loss=0.208, pruned_loss=0.01975, over 4965.00 frames.], tot_loss[loss=0.143, simple_loss=0.2159, pruned_loss=0.03508, over 972634.49 frames.], batch size: 24, lr: 2.90e-04
2022-05-05 22:16:50,909 INFO [train.py:715] (2/8) Epoch 7, batch 20950, loss[loss=0.1179, simple_loss=0.1923, pruned_loss=0.02178, over 4800.00 frames.], tot_loss[loss=0.1432, simple_loss=0.216, pruned_loss=0.0352, over 972197.53 frames.], batch size: 25, lr: 2.90e-04
2022-05-05 22:17:29,167 INFO [train.py:715] (2/8) Epoch 7, batch 21000, loss[loss=0.1546, simple_loss=0.2323, pruned_loss=0.03848, over 4753.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2169, pruned_loss=0.03575, over 972391.56 frames.], batch size: 19, lr: 2.90e-04
2022-05-05 22:17:29,168 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 22:17:39,072 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.1082, simple_loss=0.193, pruned_loss=0.01169, over 914524.00 frames.
2022-05-05 22:18:17,068 INFO [train.py:715] (2/8) Epoch 7, batch 21050, loss[loss=0.1406, simple_loss=0.2108, pruned_loss=0.03526, over 4965.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2181, pruned_loss=0.03639, over 974027.29 frames.], batch size: 24, lr: 2.90e-04
2022-05-05 22:18:54,965 INFO [train.py:715] (2/8) Epoch 7, batch 21100, loss[loss=0.1255, simple_loss=0.1944, pruned_loss=0.0283, over 4960.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2177, pruned_loss=0.03654, over 974217.14 frames.], batch size: 28, lr: 2.90e-04
2022-05-05 22:19:32,990 INFO [train.py:715] (2/8) Epoch 7, batch 21150, loss[loss=0.1536, simple_loss=0.226, pruned_loss=0.04066, over 4731.00 frames.], tot_loss[loss=0.1459, simple_loss=0.218, pruned_loss=0.03691, over 973708.71 frames.], batch size: 16, lr: 2.90e-04
2022-05-05 22:20:10,794 INFO [train.py:715] (2/8) Epoch 7, batch 21200, loss[loss=0.1736, simple_loss=0.2465, pruned_loss=0.05037, over 4844.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2181, pruned_loss=0.03688, over 973977.67 frames.], batch size: 30, lr: 2.90e-04
2022-05-05 22:20:49,001 INFO [train.py:715] (2/8) Epoch 7, batch 21250, loss[loss=0.1447, simple_loss=0.2135, pruned_loss=0.03792, over 4968.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03678, over 973622.60 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:21:27,133 INFO [train.py:715] (2/8) Epoch 7, batch 21300, loss[loss=0.1466, simple_loss=0.2252, pruned_loss=0.034, over 4821.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2175, pruned_loss=0.03688, over 972484.21 frames.], batch size: 13, lr: 2.90e-04
2022-05-05 22:22:04,502 INFO [train.py:715] (2/8) Epoch 7, batch 21350, loss[loss=0.1293, simple_loss=0.2044, pruned_loss=0.02711, over 4850.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03676, over 972621.83 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:22:42,287 INFO [train.py:715] (2/8) Epoch 7, batch 21400, loss[loss=0.1294, simple_loss=0.2074, pruned_loss=0.02572, over 4765.00 frames.], tot_loss[loss=0.145, simple_loss=0.2169, pruned_loss=0.03656, over 972184.95 frames.], batch size: 14, lr: 2.90e-04
2022-05-05 22:23:20,549 INFO [train.py:715] (2/8) Epoch 7, batch 21450, loss[loss=0.1648, simple_loss=0.2347, pruned_loss=0.04745, over 4800.00 frames.], tot_loss[loss=0.1447, simple_loss=0.217, pruned_loss=0.03624, over 971425.74 frames.], batch size: 21, lr: 2.90e-04
2022-05-05 22:23:58,722 INFO [train.py:715] (2/8) Epoch 7, batch 21500, loss[loss=0.1459, simple_loss=0.2238, pruned_loss=0.03395, over 4848.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2175, pruned_loss=0.03673, over 971933.55 frames.], batch size: 26, lr: 2.90e-04
2022-05-05 22:24:36,575 INFO [train.py:715] (2/8) Epoch 7, batch 21550, loss[loss=0.1841, simple_loss=0.2635, pruned_loss=0.05237, over 4901.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2178, pruned_loss=0.0368, over 971814.57 frames.], batch size: 19, lr: 2.90e-04
2022-05-05 22:25:14,831 INFO [train.py:715] (2/8) Epoch 7, batch 21600, loss[loss=0.1302, simple_loss=0.1939, pruned_loss=0.03325, over 4777.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2179, pruned_loss=0.03725, over 972365.50 frames.], batch size: 14, lr: 2.90e-04
2022-05-05 22:25:53,302 INFO [train.py:715] (2/8) Epoch 7, batch 21650, loss[loss=0.1292, simple_loss=0.2032, pruned_loss=0.02763, over 4846.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2179, pruned_loss=0.03688, over 972568.16 frames.], batch size: 30, lr: 2.90e-04
2022-05-05 22:26:30,668 INFO [train.py:715] (2/8) Epoch 7, batch 21700, loss[loss=0.1799, simple_loss=0.2348, pruned_loss=0.06246, over 4968.00 frames.], tot_loss[loss=0.146, simple_loss=0.2177, pruned_loss=0.03714, over 973655.07 frames.], batch size: 35, lr: 2.90e-04
2022-05-05 22:27:08,759 INFO [train.py:715] (2/8) Epoch 7, batch 21750, loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.03211, over 4868.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03703, over 973919.72 frames.], batch size: 20, lr: 2.90e-04
2022-05-05 22:27:46,872 INFO [train.py:715] (2/8) Epoch 7, batch 21800, loss[loss=0.1407, simple_loss=0.207, pruned_loss=0.03719, over 4811.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2173, pruned_loss=0.03741, over 973095.75 frames.], batch size: 25, lr: 2.90e-04
2022-05-05 22:28:24,962 INFO [train.py:715] (2/8) Epoch 7, batch 21850, loss[loss=0.1609, simple_loss=0.237, pruned_loss=0.04243, over 4861.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2166, pruned_loss=0.03693, over 972528.50 frames.], batch size: 22, lr: 2.90e-04
2022-05-05 22:29:02,873 INFO [train.py:715] (2/8) Epoch 7, batch 21900, loss[loss=0.126, simple_loss=0.2037, pruned_loss=0.02417, over 4783.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2174, pruned_loss=0.03667, over 972071.85 frames.], batch size: 17, lr: 2.90e-04
2022-05-05 22:29:40,816 INFO [train.py:715] (2/8) Epoch 7, batch 21950, loss[loss=0.1415, simple_loss=0.2123, pruned_loss=0.03533, over 4868.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03697, over 971963.97 frames.], batch size: 20, lr: 2.90e-04
2022-05-05 22:30:19,541 INFO [train.py:715] (2/8) Epoch 7, batch 22000, loss[loss=0.1659, simple_loss=0.2331, pruned_loss=0.0494, over 4909.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.0367, over 971908.78 frames.], batch size: 17, lr: 2.90e-04
2022-05-05 22:30:57,078 INFO [train.py:715] (2/8) Epoch 7, batch 22050, loss[loss=0.1665, simple_loss=0.2496, pruned_loss=0.04171, over 4956.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2183, pruned_loss=0.03701, over 972749.36 frames.], batch size: 21, lr: 2.90e-04
2022-05-05 22:31:35,218 INFO [train.py:715] (2/8) Epoch 7, batch 22100, loss[loss=0.1209, simple_loss=0.191, pruned_loss=0.02543, over 4851.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2178, pruned_loss=0.03749, over 972270.77 frames.], batch size: 20, lr: 2.90e-04
2022-05-05 22:32:13,474 INFO [train.py:715] (2/8) Epoch 7, batch 22150, loss[loss=0.1347, simple_loss=0.2127, pruned_loss=0.02831, over 4738.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2181, pruned_loss=0.03756, over 972459.95 frames.], batch size: 16, lr: 2.90e-04
2022-05-05 22:32:51,988 INFO [train.py:715] (2/8) Epoch 7, batch 22200, loss[loss=0.1806, simple_loss=0.2512, pruned_loss=0.05503, over 4977.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2182, pruned_loss=0.03756, over 971924.07 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:33:29,482 INFO [train.py:715] (2/8) Epoch 7, batch 22250, loss[loss=0.1674, simple_loss=0.2525, pruned_loss=0.04113, over 4706.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2187, pruned_loss=0.03777, over 972424.30 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:34:07,239 INFO [train.py:715] (2/8) Epoch 7, batch 22300, loss[loss=0.1227, simple_loss=0.1883, pruned_loss=0.02855, over 4973.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2182, pruned_loss=0.03765, over 972542.55 frames.], batch size: 24, lr: 2.90e-04
2022-05-05 22:34:45,536 INFO [train.py:715] (2/8) Epoch 7, batch 22350, loss[loss=0.1289, simple_loss=0.2064, pruned_loss=0.02572, over 4905.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2187, pruned_loss=0.03742, over 972214.61 frames.], batch size: 19, lr: 2.90e-04
2022-05-05 22:35:22,814 INFO [train.py:715] (2/8) Epoch 7, batch 22400, loss[loss=0.1094, simple_loss=0.182, pruned_loss=0.01844, over 4940.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2184, pruned_loss=0.03714, over 972496.57 frames.], batch size: 21, lr: 2.90e-04
2022-05-05 22:36:00,505 INFO [train.py:715] (2/8) Epoch 7, batch 22450, loss[loss=0.1471, simple_loss=0.213, pruned_loss=0.04061, over 4769.00 frames.], tot_loss[loss=0.147, simple_loss=0.2186, pruned_loss=0.03774, over 971325.00 frames.], batch size: 16, lr: 2.90e-04
2022-05-05 22:36:38,653 INFO [train.py:715] (2/8) Epoch 7, batch 22500, loss[loss=0.1637, simple_loss=0.2348, pruned_loss=0.04628, over 4918.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2186, pruned_loss=0.0375, over 972272.97 frames.], batch size: 18, lr: 2.90e-04
2022-05-05 22:37:16,690 INFO [train.py:715] (2/8) Epoch 7, batch 22550, loss[loss=0.143, simple_loss=0.2108, pruned_loss=0.0376, over 4958.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2191, pruned_loss=0.038, over 973090.72 frames.], batch size: 15, lr: 2.90e-04
2022-05-05 22:37:54,356 INFO [train.py:715] (2/8) Epoch 7, batch 22600, loss[loss=0.1576, simple_loss=0.2274, pruned_loss=0.04394, over 4863.00 frames.], tot_loss[loss=0.147, simple_loss=0.2185, pruned_loss=0.03774, over 972143.23 frames.], batch size: 32, lr: 2.90e-04
2022-05-05 22:38:32,389 INFO [train.py:715] (2/8) Epoch 7, batch 22650, loss[loss=0.1257, simple_loss=0.1949, pruned_loss=0.02828, over 4955.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2185, pruned_loss=0.03769, over 972888.57 frames.], batch size: 21, lr: 2.90e-04
2022-05-05 22:39:10,752 INFO [train.py:715] (2/8) Epoch 7, batch 22700, loss[loss=0.1548, simple_loss=0.2305, pruned_loss=0.03959, over 4789.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2198, pruned_loss=0.03833, over 972938.53 frames.], batch size: 24, lr: 2.89e-04
2022-05-05 22:39:48,097 INFO [train.py:715] (2/8) Epoch 7, batch 22750, loss[loss=0.1405, simple_loss=0.2169, pruned_loss=0.03209, over 4860.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2191, pruned_loss=0.03755, over 973472.61 frames.], batch size: 16, lr: 2.89e-04
2022-05-05 22:40:25,727 INFO [train.py:715] (2/8) Epoch 7, batch 22800, loss[loss=0.1407, simple_loss=0.2051, pruned_loss=0.03816, over 4783.00 frames.], tot_loss[loss=0.1484, simple_loss=0.22, pruned_loss=0.03835, over 973413.05 frames.], batch size: 17, lr: 2.89e-04
2022-05-05 22:41:03,918 INFO [train.py:715] (2/8) Epoch 7, batch 22850, loss[loss=0.1529, simple_loss=0.2327, pruned_loss=0.03653, over 4967.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2191, pruned_loss=0.03784, over 972643.13 frames.], batch size: 24, lr: 2.89e-04
2022-05-05 22:41:41,491 INFO [train.py:715] (2/8) Epoch 7, batch 22900, loss[loss=0.129, simple_loss=0.2023, pruned_loss=0.02783, over 4956.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2188, pruned_loss=0.03749, over 972506.45 frames.], batch size: 24, lr: 2.89e-04
2022-05-05 22:42:19,139 INFO [train.py:715] (2/8) Epoch 7, batch 22950, loss[loss=0.166, simple_loss=0.2271, pruned_loss=0.05247, over 4698.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03686, over 971917.61 frames.], batch size: 15, lr: 2.89e-04
2022-05-05 22:42:57,045 INFO [train.py:715] (2/8) Epoch 7, batch 23000, loss[loss=0.1461, simple_loss=0.225, pruned_loss=0.03358, over 4838.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2178, pruned_loss=0.03684, over 972391.05 frames.], batch size: 25, lr: 2.89e-04
2022-05-05 22:43:35,193 INFO [train.py:715] (2/8) Epoch 7, batch 23050, loss[loss=0.1332, simple_loss=0.209, pruned_loss=0.02868, over 4941.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.03697, over 972038.67 frames.], batch size: 35, lr: 2.89e-04
2022-05-05 22:44:12,640 INFO [train.py:715] (2/8) Epoch 7, batch 23100, loss[loss=0.1355, simple_loss=0.2038, pruned_loss=0.03355, over 4808.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03653, over 970957.53 frames.], batch size: 26, lr: 2.89e-04
2022-05-05 22:44:49,937 INFO [train.py:715] (2/8) Epoch 7, batch 23150, loss[loss=0.1467, simple_loss=0.2213, pruned_loss=0.03605, over 4819.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03653, over 971111.67 frames.], batch size: 27, lr: 2.89e-04
2022-05-05 22:45:28,255 INFO [train.py:715] (2/8) Epoch 7, batch 23200, loss[loss=0.1623, simple_loss=0.2392, pruned_loss=0.04268, over 4940.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03696, over 972179.82 frames.], batch size: 23, lr: 2.89e-04
2022-05-05 22:46:06,318 INFO [train.py:715] (2/8) Epoch 7, batch 23250, loss[loss=0.143, simple_loss=0.219, pruned_loss=0.03348, over 4958.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2185, pruned_loss=0.03722, over 972042.04 frames.], batch size: 39, lr: 2.89e-04
2022-05-05 22:46:43,802 INFO [train.py:715] (2/8) Epoch 7, batch 23300, loss[loss=0.1506, simple_loss=0.233, pruned_loss=0.03412, over 4875.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2183, pruned_loss=0.03693, over 972051.04 frames.], batch size: 22, lr: 2.89e-04
2022-05-05 22:47:22,579 INFO [train.py:715] (2/8) Epoch 7, batch 23350, loss[loss=0.154, simple_loss=0.2168, pruned_loss=0.04557, over 4751.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2183, pruned_loss=0.03729, over 973127.07 frames.], batch size: 16, lr: 2.89e-04
2022-05-05 22:48:01,691 INFO [train.py:715] (2/8) Epoch 7, batch 23400, loss[loss=0.1425, simple_loss=0.2191, pruned_loss=0.03296, over 4982.00 frames.], tot_loss[loss=0.146, simple_loss=0.2178, pruned_loss=0.03712, over 972460.51 frames.], batch size: 28, lr: 2.89e-04
2022-05-05 22:48:40,125 INFO [train.py:715] (2/8) Epoch 7, batch 23450, loss[loss=0.1627, simple_loss=0.2342, pruned_loss=0.04557, over 4835.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2173, pruned_loss=0.03724, over 971763.22 frames.], batch size: 26, lr: 2.89e-04
2022-05-05 22:49:18,249 INFO [train.py:715] (2/8) Epoch 7, batch 23500, loss[loss=0.1167, simple_loss=0.1816, pruned_loss=0.02586, over 4745.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2176, pruned_loss=0.03734, over 971825.94 frames.], batch size: 12, lr: 2.89e-04
2022-05-05 22:49:56,230 INFO [train.py:715] (2/8) Epoch 7, batch 23550, loss[loss=0.1344, simple_loss=0.2142, pruned_loss=0.02729, over 4932.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2173, pruned_loss=0.03719, over 971359.69 frames.], batch size: 23, lr: 2.89e-04
2022-05-05 22:50:34,439 INFO [train.py:715] (2/8) Epoch 7, batch 23600, loss[loss=0.1399, simple_loss=0.2157, pruned_loss=0.03205, over 4748.00 frames.], tot_loss[loss=0.146, simple_loss=0.2176, pruned_loss=0.03722, over 972017.91 frames.], batch size: 16, lr: 2.89e-04
2022-05-05 22:51:11,416 INFO [train.py:715] (2/8) Epoch 7, batch 23650, loss[loss=0.1054, simple_loss=0.1794, pruned_loss=0.01572, over 4861.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2176, pruned_loss=0.03749, over 972596.22 frames.], batch size: 20, lr: 2.89e-04
2022-05-05 22:51:49,264 INFO [train.py:715] (2/8) Epoch 7, batch 23700, loss[loss=0.1225, simple_loss=0.2073, pruned_loss=0.01879, over 4876.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2177, pruned_loss=0.03756, over 972061.27 frames.], batch size: 22, lr: 2.89e-04
2022-05-05 22:52:27,396 INFO [train.py:715] (2/8) Epoch 7, batch 23750, loss[loss=0.1519, simple_loss=0.2207, pruned_loss=0.04158, over 4920.00 frames.], tot_loss[loss=0.146, simple_loss=0.2174, pruned_loss=0.03729, over 972480.71 frames.], batch size: 29, lr: 2.89e-04
2022-05-05 22:53:04,577 INFO [train.py:715] (2/8) Epoch 7, batch 23800, loss[loss=0.1382, simple_loss=0.2054, pruned_loss=0.03552, over 4862.00 frames.], tot_loss[loss=0.146, simple_loss=0.2173, pruned_loss=0.03732, over 972751.49 frames.], batch size: 32, lr: 2.89e-04
2022-05-05 22:53:42,352 INFO [train.py:715] (2/8) Epoch 7, batch 23850, loss[loss=0.1697, simple_loss=0.2301, pruned_loss=0.05466, over 4779.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2192, pruned_loss=0.03829, over 972886.16 frames.], batch size: 14, lr: 2.89e-04
2022-05-05 22:54:21,020 INFO [train.py:715] (2/8) Epoch 7, batch 23900, loss[loss=0.1261, simple_loss=0.1976, pruned_loss=0.02726, over 4955.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2183, pruned_loss=0.03741, over 972383.16 frames.], batch size: 29, lr: 2.89e-04
2022-05-05 22:54:59,165 INFO [train.py:715] (2/8) Epoch 7, batch 23950, loss[loss=0.1287, simple_loss=0.2054, pruned_loss=0.02598, over 4937.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03734, over 972437.87 frames.], batch size: 23, lr: 2.89e-04
2022-05-05 22:55:36,641 INFO [train.py:715] (2/8) Epoch 7, batch 24000, loss[loss=0.1437, simple_loss=0.2163, pruned_loss=0.03558, over 4782.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2189, pruned_loss=0.03784, over 972531.88 frames.], batch size: 17, lr: 2.89e-04
2022-05-05 22:55:36,641 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 22:55:46,187 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.108, simple_loss=0.1929, pruned_loss=0.01156, over 914524.00 frames.
2022-05-05 22:56:23,729 INFO [train.py:715] (2/8) Epoch 7, batch 24050, loss[loss=0.1478, simple_loss=0.2212, pruned_loss=0.03718, over 4751.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2187, pruned_loss=0.03752, over 972213.77 frames.], batch size: 16, lr: 2.89e-04
2022-05-05 22:57:02,033 INFO [train.py:715] (2/8) Epoch 7, batch 24100, loss[loss=0.1228, simple_loss=0.1997, pruned_loss=0.02297, over 4965.00 frames.], tot_loss[loss=0.146, simple_loss=0.2178, pruned_loss=0.03707, over 971634.86 frames.], batch size: 25, lr: 2.89e-04
2022-05-05 22:57:40,437 INFO [train.py:715] (2/8) Epoch 7, batch 24150, loss[loss=0.1554, simple_loss=0.2296, pruned_loss=0.04057, over 4840.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2175, pruned_loss=0.03735, over 971105.61 frames.], batch size: 26, lr: 2.89e-04
2022-05-05 22:58:18,172 INFO [train.py:715] (2/8) Epoch 7, batch 24200, loss[loss=0.1248, simple_loss=0.1961, pruned_loss=0.02671, over 4847.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03713, over 971298.34 frames.], batch size: 15, lr: 2.89e-04
2022-05-05 22:58:55,939 INFO [train.py:715] (2/8) Epoch 7, batch 24250, loss[loss=0.1455, simple_loss=0.219, pruned_loss=0.03599, over 4957.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2183, pruned_loss=0.03741, over 971482.41 frames.], batch size: 39, lr: 2.89e-04
2022-05-05 22:59:34,585 INFO [train.py:715] (2/8) Epoch 7, batch 24300, loss[loss=0.1422, simple_loss=0.2132, pruned_loss=0.03558, over 4687.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.03696, over 971528.68 frames.], batch size: 15, lr: 2.89e-04
2022-05-05 23:00:12,423 INFO [train.py:715] (2/8) Epoch 7, batch 24350, loss[loss=0.1114, simple_loss=0.1748, pruned_loss=0.024, over 4987.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03706, over 971968.15 frames.], batch size: 14, lr: 2.89e-04
2022-05-05 23:00:50,090 INFO [train.py:715] (2/8) Epoch 7, batch 24400, loss[loss=0.1309, simple_loss=0.2042, pruned_loss=0.02879, over 4933.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2171, pruned_loss=0.03662, over 972493.03 frames.], batch size: 29, lr: 2.89e-04
2022-05-05 23:01:28,246 INFO [train.py:715] (2/8) Epoch 7, batch 24450, loss[loss=0.1393, simple_loss=0.2168, pruned_loss=0.03089, over 4927.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2167, pruned_loss=0.03645, over 972232.80 frames.], batch size: 29, lr: 2.89e-04
2022-05-05 23:02:06,218 INFO [train.py:715] (2/8) Epoch 7, batch 24500, loss[loss=0.1297, simple_loss=0.1965, pruned_loss=0.03145, over 4763.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03576, over 971957.75 frames.], batch size: 19, lr: 2.89e-04
2022-05-05 23:02:43,834 INFO [train.py:715] (2/8) Epoch 7, batch 24550, loss[loss=0.1612, simple_loss=0.232, pruned_loss=0.04515, over 4857.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03551, over 971836.07 frames.], batch size: 32, lr: 2.88e-04
2022-05-05 23:03:22,003 INFO [train.py:715] (2/8) Epoch 7, batch 24600, loss[loss=0.1254, simple_loss=0.1975, pruned_loss=0.0267, over 4902.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.03577, over 973218.71 frames.], batch size: 19, lr: 2.88e-04
2022-05-05 23:04:01,118 INFO [train.py:715] (2/8) Epoch 7, batch 24650, loss[loss=0.1225, simple_loss=0.1949, pruned_loss=0.02504, over 4831.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.03604, over 972635.01 frames.], batch size: 13, lr: 2.88e-04
2022-05-05 23:04:39,574 INFO [train.py:715] (2/8) Epoch 7, batch 24700, loss[loss=0.1543, simple_loss=0.2226, pruned_loss=0.04298, over 4821.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03628, over 972987.07 frames.], batch size: 26, lr: 2.88e-04
2022-05-05 23:05:17,696 INFO [train.py:715] (2/8) Epoch 7, batch 24750, loss[loss=0.1431, simple_loss=0.2127, pruned_loss=0.0367, over 4938.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03657, over 972773.26 frames.], batch size: 21, lr: 2.88e-04
2022-05-05 23:05:56,160 INFO [train.py:715] (2/8) Epoch 7, batch 24800, loss[loss=0.1517, simple_loss=0.2348, pruned_loss=0.03429, over 4882.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2179, pruned_loss=0.03721, over 972562.48 frames.], batch size: 16, lr: 2.88e-04
2022-05-05 23:06:35,231 INFO [train.py:715] (2/8) Epoch 7, batch 24850, loss[loss=0.1496, simple_loss=0.2223, pruned_loss=0.03842, over 4980.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2177, pruned_loss=0.03705, over 972163.68 frames.], batch size: 35, lr: 2.88e-04
2022-05-05 23:07:13,822 INFO [train.py:715] (2/8) Epoch 7, batch 24900, loss[loss=0.1685, simple_loss=0.2326, pruned_loss=0.05222, over 4956.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03682, over 972837.06 frames.], batch size: 35, lr: 2.88e-04
2022-05-05 23:07:53,090 INFO [train.py:715] (2/8) Epoch 7, batch 24950, loss[loss=0.1286, simple_loss=0.2042, pruned_loss=0.02647, over 4889.00 frames.], tot_loss[loss=0.1462, simple_loss=0.218, pruned_loss=0.03715, over 972390.88 frames.], batch size: 16, lr: 2.88e-04
2022-05-05 23:08:32,941 INFO [train.py:715] (2/8) Epoch 7, batch 25000, loss[loss=0.1701, simple_loss=0.2368, pruned_loss=0.05169, over 4956.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2185, pruned_loss=0.03738, over 973007.85 frames.], batch size: 21, lr: 2.88e-04
2022-05-05 23:09:12,213 INFO [train.py:715] (2/8) Epoch 7, batch 25050, loss[loss=0.1426, simple_loss=0.2173, pruned_loss=0.03399, over 4740.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2184, pruned_loss=0.03694, over 973095.31 frames.], batch size: 16, lr: 2.88e-04
2022-05-05 23:09:51,233 INFO [train.py:715] (2/8) Epoch 7, batch 25100, loss[loss=0.1808, simple_loss=0.2407, pruned_loss=0.06048, over 4850.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2182, pruned_loss=0.037, over 973430.83 frames.], batch size: 32, lr: 2.88e-04
2022-05-05 23:10:31,401 INFO [train.py:715] (2/8) Epoch 7, batch 25150, loss[loss=0.1677, simple_loss=0.2272, pruned_loss=0.05411, over 4977.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03699, over 973104.77 frames.], batch size: 28, lr: 2.88e-04
2022-05-05 23:11:11,710 INFO [train.py:715] (2/8) Epoch 7, batch 25200, loss[loss=0.1244, simple_loss=0.1934, pruned_loss=0.02776, over 4809.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2185, pruned_loss=0.03727, over 973225.48 frames.], batch size: 13, lr: 2.88e-04
2022-05-05 23:11:51,362 INFO [train.py:715] (2/8) Epoch 7, batch 25250, loss[loss=0.1352, simple_loss=0.2116, pruned_loss=0.02939, over 4830.00 frames.], tot_loss[loss=0.1464, simple_loss=0.218, pruned_loss=0.0374, over 972642.56 frames.], batch size: 26, lr: 2.88e-04
2022-05-05 23:12:31,930 INFO [train.py:715] (2/8) Epoch 7, batch 25300, loss[loss=0.1523, simple_loss=0.2292, pruned_loss=0.03771, over 4859.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2175, pruned_loss=0.03703, over 971339.70 frames.], batch size: 22, lr: 2.88e-04
2022-05-05 23:13:13,661 INFO [train.py:715] (2/8) Epoch 7, batch 25350, loss[loss=0.1471, simple_loss=0.2187, pruned_loss=0.03776, over 4819.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2184, pruned_loss=0.03734, over 971323.71 frames.], batch size: 25, lr: 2.88e-04
2022-05-05 23:13:55,229 INFO [train.py:715] (2/8) Epoch 7, batch 25400, loss[loss=0.1212, simple_loss=0.1874, pruned_loss=0.02746, over 4898.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2188, pruned_loss=0.03745, over 971270.80 frames.], batch size: 19, lr: 2.88e-04
2022-05-05 23:14:36,163 INFO [train.py:715] (2/8) Epoch 7, batch 25450, loss[loss=0.1684, simple_loss=0.2408, pruned_loss=0.048, over 4768.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2191, pruned_loss=0.03738, over 971318.87 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:15:18,362 INFO [train.py:715] (2/8) Epoch 7, batch 25500, loss[loss=0.1446, simple_loss=0.216, pruned_loss=0.03657, over 4972.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2176, pruned_loss=0.03704, over 972150.77 frames.], batch size: 35, lr: 2.88e-04
2022-05-05 23:16:00,246 INFO [train.py:715] (2/8) Epoch 7, batch 25550, loss[loss=0.1341, simple_loss=0.2119, pruned_loss=0.0282, over 4655.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2182, pruned_loss=0.03684, over 971571.38 frames.], batch size: 13, lr: 2.88e-04
2022-05-05 23:16:41,005 INFO [train.py:715] (2/8) Epoch 7, batch 25600, loss[loss=0.1572, simple_loss=0.2301, pruned_loss=0.04218, over 4746.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2177, pruned_loss=0.03681, over 972146.96 frames.], batch size: 16, lr: 2.88e-04
2022-05-05 23:17:22,268 INFO [train.py:715] (2/8) Epoch 7, batch 25650, loss[loss=0.1473, simple_loss=0.2141, pruned_loss=0.04021, over 4982.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2173, pruned_loss=0.03646, over 973205.94 frames.], batch size: 35, lr: 2.88e-04
2022-05-05 23:18:03,669 INFO [train.py:715] (2/8) Epoch 7, batch 25700, loss[loss=0.1595, simple_loss=0.2229, pruned_loss=0.0481, over 4815.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2166, pruned_loss=0.03598, over 973269.99 frames.], batch size: 27, lr: 2.88e-04
2022-05-05 23:18:45,499 INFO [train.py:715] (2/8) Epoch 7, batch 25750, loss[loss=0.1555, simple_loss=0.2379, pruned_loss=0.03648, over 4928.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2168, pruned_loss=0.03603, over 972793.28 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:19:26,140 INFO [train.py:715] (2/8) Epoch 7, batch 25800, loss[loss=0.1596, simple_loss=0.2267, pruned_loss=0.04625, over 4975.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2176, pruned_loss=0.03632, over 972517.56 frames.], batch size: 35, lr: 2.88e-04
2022-05-05 23:20:08,460 INFO [train.py:715] (2/8) Epoch 7, batch 25850, loss[loss=0.1713, simple_loss=0.2402, pruned_loss=0.05123, over 4822.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2171, pruned_loss=0.03612, over 972171.12 frames.], batch size: 15, lr: 2.88e-04
2022-05-05 23:20:50,388 INFO [train.py:715] (2/8) Epoch 7, batch 25900, loss[loss=0.2366, simple_loss=0.2896, pruned_loss=0.09181, over 4820.00 frames.], tot_loss[loss=0.1447, simple_loss=0.217, pruned_loss=0.03619, over 971946.55 frames.], batch size: 15, lr: 2.88e-04
2022-05-05 23:21:31,299 INFO [train.py:715] (2/8) Epoch 7, batch 25950, loss[loss=0.1566, simple_loss=0.2246, pruned_loss=0.04426, over 4871.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2174, pruned_loss=0.03635, over 971789.70 frames.], batch size: 22, lr: 2.88e-04
2022-05-05 23:22:12,742 INFO [train.py:715] (2/8) Epoch 7, batch 26000, loss[loss=0.1434, simple_loss=0.2233, pruned_loss=0.03173, over 4794.00 frames.], tot_loss[loss=0.1455, simple_loss=0.218, pruned_loss=0.03653, over 972038.48 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:22:54,187 INFO [train.py:715] (2/8) Epoch 7, batch 26050, loss[loss=0.1643, simple_loss=0.2475, pruned_loss=0.04061, over 4771.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2174, pruned_loss=0.03641, over 972916.65 frames.], batch size: 18, lr: 2.88e-04
2022-05-05 23:23:36,133 INFO [train.py:715] (2/8) Epoch 7, batch 26100, loss[loss=0.135, simple_loss=0.2206, pruned_loss=0.02469, over 4747.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2166, pruned_loss=0.03611, over 971777.35 frames.], batch size: 16, lr: 2.88e-04
2022-05-05 23:24:16,469 INFO [train.py:715] (2/8) Epoch 7, batch 26150, loss[loss=0.144, simple_loss=0.2094, pruned_loss=0.03934, over 4789.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.03566, over 972124.37 frames.], batch size: 17, lr: 2.88e-04
2022-05-05 23:24:57,984 INFO [train.py:715] (2/8) Epoch 7, batch 26200, loss[loss=0.126, simple_loss=0.2077, pruned_loss=0.02215, over 4882.00 frames.], tot_loss[loss=0.144, simple_loss=0.2163, pruned_loss=0.03587, over 971794.56 frames.], batch size: 22, lr: 2.88e-04
2022-05-05 23:25:39,231 INFO [train.py:715] (2/8) Epoch 7, batch 26250, loss[loss=0.1473, simple_loss=0.2277, pruned_loss=0.03348, over 4943.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2161, pruned_loss=0.03564, over 971673.82 frames.], batch size: 29, lr: 2.88e-04
2022-05-05 23:26:19,595 INFO [train.py:715] (2/8) Epoch 7, batch 26300, loss[loss=0.145, simple_loss=0.2093, pruned_loss=0.04034, over 4952.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.036, over 971972.18 frames.], batch size: 21, lr: 2.88e-04
2022-05-05 23:26:59,770 INFO [train.py:715] (2/8) Epoch 7, batch 26350, loss[loss=0.1454, simple_loss=0.2276, pruned_loss=0.03159, over 4956.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2178, pruned_loss=0.03648, over 971772.39 frames.], batch size: 21, lr: 2.88e-04
2022-05-05 23:27:40,224 INFO [train.py:715] (2/8) Epoch 7, batch 26400, loss[loss=0.1438, simple_loss=0.2144, pruned_loss=0.03666, over 4890.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2179, pruned_loss=0.03686, over 971283.25 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:28:20,880 INFO [train.py:715] (2/8) Epoch 7, batch 26450, loss[loss=0.126, simple_loss=0.2021, pruned_loss=0.02499, over 4975.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2186, pruned_loss=0.03726, over 971849.69 frames.], batch size: 40, lr: 2.87e-04
2022-05-05 23:29:00,630 INFO [train.py:715] (2/8) Epoch 7, batch 26500, loss[loss=0.137, simple_loss=0.2133, pruned_loss=0.03037, over 4724.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2186, pruned_loss=0.03712, over 971519.80 frames.], batch size: 16, lr: 2.87e-04
2022-05-05 23:29:40,312 INFO [train.py:715] (2/8) Epoch 7, batch 26550, loss[loss=0.1332, simple_loss=0.2114, pruned_loss=0.02744, over 4913.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2182, pruned_loss=0.03698, over 971455.64 frames.], batch size: 18, lr: 2.87e-04
2022-05-05 23:30:20,787 INFO [train.py:715] (2/8) Epoch 7, batch 26600, loss[loss=0.1495, simple_loss=0.2205, pruned_loss=0.03927, over 4807.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2176, pruned_loss=0.03653, over 972174.34 frames.], batch size: 25, lr: 2.87e-04
2022-05-05 23:31:00,456 INFO [train.py:715] (2/8) Epoch 7, batch 26650, loss[loss=0.1752, simple_loss=0.2396, pruned_loss=0.05541, over 4913.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2186, pruned_loss=0.03712, over 971791.90 frames.], batch size: 18, lr: 2.87e-04
2022-05-05 23:31:40,552 INFO [train.py:715] (2/8) Epoch 7, batch 26700, loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03145, over 4971.00 frames.], tot_loss[loss=0.145, simple_loss=0.2171, pruned_loss=0.03646, over 971393.03 frames.], batch size: 28, lr: 2.87e-04
2022-05-05 23:32:21,229 INFO [train.py:715] (2/8) Epoch 7, batch 26750, loss[loss=0.1496, simple_loss=0.2266, pruned_loss=0.03635, over 4914.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2169, pruned_loss=0.0362, over 972338.31 frames.], batch size: 18, lr: 2.87e-04
2022-05-05 23:33:01,188 INFO [train.py:715] (2/8) Epoch 7, batch 26800, loss[loss=0.139, simple_loss=0.2082, pruned_loss=0.03492, over 4879.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2176, pruned_loss=0.03665, over 973150.93 frames.], batch size: 16, lr: 2.87e-04
2022-05-05 23:33:40,951 INFO [train.py:715] (2/8) Epoch 7, batch 26850, loss[loss=0.1434, simple_loss=0.2183, pruned_loss=0.03426, over 4892.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03652, over 972900.71 frames.], batch size: 17, lr: 2.87e-04
2022-05-05 23:34:21,591 INFO [train.py:715] (2/8) Epoch 7, batch 26900, loss[loss=0.1365, simple_loss=0.2087, pruned_loss=0.0321, over 4807.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03635, over 972781.72 frames.], batch size: 27, lr: 2.87e-04
2022-05-05 23:35:02,617 INFO [train.py:715] (2/8) Epoch 7, batch 26950, loss[loss=0.1314, simple_loss=0.2064, pruned_loss=0.02823, over 4876.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2171, pruned_loss=0.03666, over 972363.84 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:35:42,967 INFO [train.py:715] (2/8) Epoch 7, batch 27000, loss[loss=0.1684, simple_loss=0.2431, pruned_loss=0.04681, over 4790.00 frames.], tot_loss[loss=0.145, simple_loss=0.2166, pruned_loss=0.03665, over 972446.87 frames.], batch size: 24, lr: 2.87e-04
2022-05-05 23:35:42,967 INFO [train.py:733] (2/8) Computing validation loss
2022-05-05 23:35:52,669 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.108, simple_loss=0.1928, pruned_loss=0.01156, over 914524.00 frames.
2022-05-05 23:36:33,214 INFO [train.py:715] (2/8) Epoch 7, batch 27050, loss[loss=0.1901, simple_loss=0.255, pruned_loss=0.06255, over 4960.00 frames.], tot_loss[loss=0.1448, simple_loss=0.216, pruned_loss=0.03682, over 971734.54 frames.], batch size: 24, lr: 2.87e-04
2022-05-05 23:37:14,409 INFO [train.py:715] (2/8) Epoch 7, batch 27100, loss[loss=0.1443, simple_loss=0.2123, pruned_loss=0.03813, over 4879.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2171, pruned_loss=0.03719, over 972484.10 frames.], batch size: 16, lr: 2.87e-04
2022-05-05 23:37:56,258 INFO [train.py:715] (2/8) Epoch 7, batch 27150, loss[loss=0.1284, simple_loss=0.2036, pruned_loss=0.0266, over 4876.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2182, pruned_loss=0.0377, over 972063.24 frames.], batch size: 13, lr: 2.87e-04
2022-05-05 23:38:37,509 INFO [train.py:715] (2/8) Epoch 7, batch 27200, loss[loss=0.1296, simple_loss=0.1947, pruned_loss=0.03218, over 4818.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2175, pruned_loss=0.03747, over 972459.70 frames.], batch size: 15, lr: 2.87e-04
2022-05-05 23:39:18,986 INFO [train.py:715] (2/8) Epoch 7, batch 27250, loss[loss=0.1359, simple_loss=0.2124, pruned_loss=0.02967, over 4976.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2183, pruned_loss=0.0377, over 972991.26 frames.], batch size: 28, lr: 2.87e-04
2022-05-05 23:40:00,802 INFO [train.py:715] (2/8) Epoch 7, batch 27300, loss[loss=0.1757, simple_loss=0.2444, pruned_loss=0.05347, over 4793.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2179, pruned_loss=0.03743, over 972949.18 frames.], batch size: 24, lr: 2.87e-04
2022-05-05 23:40:41,768 INFO [train.py:715] (2/8) Epoch 7, batch 27350, loss[loss=0.1539, simple_loss=0.2284, pruned_loss=0.03973, over 4837.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2174, pruned_loss=0.03722, over 972328.83 frames.], batch size: 30, lr: 2.87e-04
2022-05-05 23:41:23,080 INFO [train.py:715] (2/8) Epoch 7, batch 27400, loss[loss=0.1502, simple_loss=0.2149, pruned_loss=0.04281, over 4859.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2171, pruned_loss=0.0369, over 972913.84 frames.], batch size: 32, lr: 2.87e-04
2022-05-05 23:42:04,085 INFO [train.py:715] (2/8) Epoch 7, batch 27450, loss[loss=0.1655, simple_loss=0.2402, pruned_loss=0.04539, over 4880.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2169, pruned_loss=0.03699, over 973377.75 frames.], batch size: 20, lr: 2.87e-04
2022-05-05 23:42:45,307 INFO [train.py:715] (2/8) Epoch 7, batch 27500, loss[loss=0.117, simple_loss=0.1879, pruned_loss=0.02309, over 4752.00 frames.], tot_loss[loss=0.146, simple_loss=0.217, pruned_loss=0.03747, over 972668.00 frames.], batch size: 16, lr: 2.87e-04
2022-05-05 23:43:25,878 INFO [train.py:715] (2/8) Epoch 7, batch 27550, loss[loss=0.1271, simple_loss=0.1924, pruned_loss=0.03091, over 4898.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2174, pruned_loss=0.03718, over 973532.49 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:44:06,398 INFO [train.py:715] (2/8) Epoch 7, batch 27600, loss[loss=0.1412, simple_loss=0.2119, pruned_loss=0.03521, over 4772.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2171, pruned_loss=0.03639, over 972900.41 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:44:47,790 INFO [train.py:715] (2/8) Epoch 7, batch 27650, loss[loss=0.1164, simple_loss=0.1955, pruned_loss=0.01865, over 4809.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2172, pruned_loss=0.03611, over 973003.12 frames.], batch size: 27, lr: 2.87e-04
2022-05-05 23:45:28,507 INFO [train.py:715] (2/8) Epoch 7, batch 27700, loss[loss=0.1504, simple_loss=0.2142, pruned_loss=0.04329, over 4833.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03655, over 973411.85 frames.], batch size: 30, lr: 2.87e-04
2022-05-05 23:46:09,246 INFO [train.py:715] (2/8) Epoch 7, batch 27750, loss[loss=0.1104, simple_loss=0.1876, pruned_loss=0.01662, over 4850.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2178, pruned_loss=0.03692, over 973326.53 frames.], batch size: 20, lr: 2.87e-04
2022-05-05 23:46:50,127 INFO [train.py:715] (2/8) Epoch 7, batch 27800, loss[loss=0.1303, simple_loss=0.2053, pruned_loss=0.02768, over 4933.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03619, over 973186.95 frames.], batch size: 29, lr: 2.87e-04
2022-05-05 23:47:31,341 INFO [train.py:715] (2/8) Epoch 7, batch 27850, loss[loss=0.1293, simple_loss=0.2057, pruned_loss=0.02644, over 4807.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.03599, over 972215.50 frames.], batch size: 26, lr: 2.87e-04
2022-05-05 23:48:11,405 INFO [train.py:715] (2/8) Epoch 7, batch 27900, loss[loss=0.1429, simple_loss=0.2292, pruned_loss=0.02833, over 4930.00 frames.], tot_loss[loss=0.1448, simple_loss=0.217, pruned_loss=0.0363, over 971846.60 frames.], batch size: 23, lr: 2.87e-04
2022-05-05 23:48:52,367 INFO [train.py:715] (2/8) Epoch 7, batch 27950, loss[loss=0.1606, simple_loss=0.2349, pruned_loss=0.04318, over 4794.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2176, pruned_loss=0.03657, over 970652.97 frames.], batch size: 24, lr: 2.87e-04
2022-05-05 23:49:33,558 INFO [train.py:715] (2/8) Epoch 7, batch 28000, loss[loss=0.132, simple_loss=0.2026, pruned_loss=0.03064, over 4776.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.03608, over 970643.34 frames.], batch size: 18, lr: 2.87e-04
2022-05-05 23:50:14,245 INFO [train.py:715] (2/8) Epoch 7, batch 28050, loss[loss=0.128, simple_loss=0.2037, pruned_loss=0.02612, over 4750.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2171, pruned_loss=0.0367, over 971652.77 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:50:54,408 INFO [train.py:715] (2/8) Epoch 7, batch 28100, loss[loss=0.1606, simple_loss=0.2356, pruned_loss=0.04277, over 4888.00 frames.], tot_loss[loss=0.145, simple_loss=0.2168, pruned_loss=0.0366, over 971205.68 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:51:35,206 INFO [train.py:715] (2/8) Epoch 7, batch 28150, loss[loss=0.1486, simple_loss=0.2143, pruned_loss=0.04147, over 4827.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2178, pruned_loss=0.037, over 971128.65 frames.], batch size: 25, lr: 2.87e-04
2022-05-05 23:52:16,646 INFO [train.py:715] (2/8) Epoch 7, batch 28200, loss[loss=0.1408, simple_loss=0.2046, pruned_loss=0.03845, over 4775.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2178, pruned_loss=0.03699, over 971645.44 frames.], batch size: 17, lr: 2.87e-04
2022-05-05 23:52:56,850 INFO [train.py:715] (2/8) Epoch 7, batch 28250, loss[loss=0.153, simple_loss=0.225, pruned_loss=0.04045, over 4760.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2181, pruned_loss=0.0372, over 971672.01 frames.], batch size: 19, lr: 2.87e-04
2022-05-05 23:53:38,369 INFO [train.py:715] (2/8) Epoch 7, batch 28300, loss[loss=0.1622, simple_loss=0.2402, pruned_loss=0.04208, over 4803.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2179, pruned_loss=0.03677, over 971620.89 frames.], batch size: 25, lr: 2.86e-04
2022-05-05 23:54:21,508 INFO [train.py:715] (2/8) Epoch 7, batch 28350, loss[loss=0.1546, simple_loss=0.2236, pruned_loss=0.04283, over 4764.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2175, pruned_loss=0.03649, over 971796.25 frames.], batch size: 19, lr: 2.86e-04
2022-05-05 23:55:01,298 INFO [train.py:715] (2/8) Epoch 7, batch 28400, loss[loss=0.1515, simple_loss=0.2268, pruned_loss=0.03807, over 4878.00 frames.], tot_loss[loss=0.146, simple_loss=0.2178, pruned_loss=0.0371, over 971873.06 frames.], batch size: 16, lr: 2.86e-04
2022-05-05 23:55:40,831 INFO [train.py:715] (2/8) Epoch 7, batch 28450, loss[loss=0.1377, simple_loss=0.2187, pruned_loss=0.02832, over 4768.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2169, pruned_loss=0.03684, over 971872.23 frames.], batch size: 18, lr: 2.86e-04
2022-05-05 23:56:20,935 INFO [train.py:715] (2/8) Epoch 7, batch 28500, loss[loss=0.1734, simple_loss=0.2402, pruned_loss=0.05328, over 4921.00 frames.], tot_loss[loss=0.1441, simple_loss=0.216, pruned_loss=0.03616, over 971446.35 frames.], batch size: 29, lr: 2.86e-04
2022-05-05 23:57:01,444 INFO [train.py:715] (2/8) Epoch 7, batch 28550, loss[loss=0.1275, simple_loss=0.2006, pruned_loss=0.02718, over 4768.00 frames.], tot_loss[loss=0.1443, simple_loss=0.216, pruned_loss=0.03636, over 971286.61 frames.], batch size: 12, lr: 2.86e-04
2022-05-05 23:57:41,436 INFO [train.py:715] (2/8) Epoch 7, batch 28600, loss[loss=0.1467, simple_loss=0.2112, pruned_loss=0.04108, over 4816.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2162, pruned_loss=0.03645, over 971770.34 frames.], batch size: 15, lr: 2.86e-04
2022-05-05 23:58:21,654 INFO [train.py:715] (2/8) Epoch 7, batch 28650, loss[loss=0.1544, simple_loss=0.2186, pruned_loss=0.04513, over 4945.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.037, over 971673.82 frames.], batch size: 35, lr: 2.86e-04
2022-05-05 23:59:03,090 INFO [train.py:715] (2/8) Epoch 7, batch 28700, loss[loss=0.1316, simple_loss=0.2153, pruned_loss=0.02398, over 4748.00 frames.], tot_loss[loss=0.146, simple_loss=0.2177, pruned_loss=0.03718, over 972811.43 frames.], batch size: 19, lr: 2.86e-04
2022-05-05 23:59:43,973 INFO [train.py:715] (2/8) Epoch 7, batch 28750, loss[loss=0.1379, simple_loss=0.2221, pruned_loss=0.02684, over 4864.00 frames.], tot_loss[loss=0.146, simple_loss=0.2178, pruned_loss=0.03711, over 972632.72 frames.], batch size: 20, lr: 2.86e-04
2022-05-06 00:00:24,207 INFO [train.py:715] (2/8) Epoch 7, batch 28800, loss[loss=0.1506, simple_loss=0.2274, pruned_loss=0.03694, over 4782.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2179, pruned_loss=0.03679, over 972629.01 frames.], batch size: 17, lr: 2.86e-04
2022-05-06 00:01:04,823 INFO [train.py:715] (2/8) Epoch 7, batch 28850, loss[loss=0.1273, simple_loss=0.2056, pruned_loss=0.02448, over 4934.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2178, pruned_loss=0.03657, over 971844.22 frames.], batch size: 18, lr: 2.86e-04
2022-05-06 00:01:45,177 INFO [train.py:715] (2/8) Epoch 7, batch 28900, loss[loss=0.1472, simple_loss=0.2141, pruned_loss=0.04014, over 4897.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2174, pruned_loss=0.03676, over 971639.15 frames.], batch size: 19, lr: 2.86e-04
2022-05-06 00:02:24,696 INFO [train.py:715] (2/8) Epoch 7, batch 28950, loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03634, over 4974.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2165, pruned_loss=0.03638, over 971584.95 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:03:04,251 INFO [train.py:715] (2/8) Epoch 7, batch 29000, loss[loss=0.1407, simple_loss=0.2208, pruned_loss=0.03034, over 4946.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03582, over 971941.65 frames.], batch size: 35, lr: 2.86e-04
2022-05-06 00:03:44,913 INFO [train.py:715] (2/8) Epoch 7, batch 29050, loss[loss=0.1493, simple_loss=0.2182, pruned_loss=0.04016, over 4986.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2163, pruned_loss=0.03621, over 972245.14 frames.], batch size: 25, lr: 2.86e-04
2022-05-06 00:04:24,479 INFO [train.py:715] (2/8) Epoch 7, batch 29100, loss[loss=0.16, simple_loss=0.2393, pruned_loss=0.04035, over 4810.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03581, over 972449.96 frames.], batch size: 26, lr: 2.86e-04
2022-05-06 00:05:04,255 INFO [train.py:715] (2/8) Epoch 7, batch 29150, loss[loss=0.1583, simple_loss=0.2257, pruned_loss=0.04539, over 4983.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2159, pruned_loss=0.03621, over 973220.17 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:05:44,147 INFO [train.py:715] (2/8) Epoch 7, batch 29200, loss[loss=0.16, simple_loss=0.2324, pruned_loss=0.0438, over 4869.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2158, pruned_loss=0.0362, over 972715.01 frames.], batch size: 30, lr: 2.86e-04
2022-05-06 00:06:24,420 INFO [train.py:715] (2/8) Epoch 7, batch 29250, loss[loss=0.1517, simple_loss=0.216, pruned_loss=0.04371, over 4960.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03584, over 973414.11 frames.], batch size: 35, lr: 2.86e-04
2022-05-06 00:07:04,331 INFO [train.py:715] (2/8) Epoch 7, batch 29300, loss[loss=0.1348, simple_loss=0.2114, pruned_loss=0.02911, over 4979.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2167, pruned_loss=0.03612, over 973856.41 frames.], batch size: 25, lr: 2.86e-04
2022-05-06 00:07:44,018 INFO [train.py:715] (2/8) Epoch 7, batch 29350, loss[loss=0.1607, simple_loss=0.2344, pruned_loss=0.04349, over 4901.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03627, over 972866.64 frames.], batch size: 19, lr: 2.86e-04
2022-05-06 00:08:24,289 INFO [train.py:715] (2/8) Epoch 7, batch 29400, loss[loss=0.1344, simple_loss=0.2119, pruned_loss=0.0284, over 4823.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2164, pruned_loss=0.03611, over 971776.34 frames.], batch size: 26, lr: 2.86e-04
2022-05-06 00:09:03,566 INFO [train.py:715] (2/8) Epoch 7, batch 29450, loss[loss=0.1324, simple_loss=0.2126, pruned_loss=0.02613, over 4928.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03626, over 971295.13 frames.], batch size: 29, lr: 2.86e-04
2022-05-06 00:09:43,847 INFO [train.py:715] (2/8) Epoch 7, batch 29500, loss[loss=0.1192, simple_loss=0.1918, pruned_loss=0.02331, over 4807.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2174, pruned_loss=0.03642, over 971208.57 frames.], batch size: 21, lr: 2.86e-04
2022-05-06 00:10:23,571 INFO [train.py:715] (2/8) Epoch 7, batch 29550, loss[loss=0.151, simple_loss=0.2266, pruned_loss=0.03773, over 4808.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2175, pruned_loss=0.03661, over 972211.51 frames.], batch size: 25, lr: 2.86e-04
2022-05-06 00:11:03,254 INFO [train.py:715] (2/8) Epoch 7, batch 29600, loss[loss=0.1522, simple_loss=0.2225, pruned_loss=0.04098, over 4799.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2174, pruned_loss=0.03621, over 972369.24 frames.], batch size: 21, lr: 2.86e-04
2022-05-06 00:11:43,210 INFO [train.py:715] (2/8) Epoch 7, batch 29650, loss[loss=0.1702, simple_loss=0.2532, pruned_loss=0.04357, over 4952.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2181, pruned_loss=0.03638, over 972277.07 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:12:23,007 INFO [train.py:715] (2/8) Epoch 7, batch 29700, loss[loss=0.141, simple_loss=0.2029, pruned_loss=0.03957, over 4792.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2185, pruned_loss=0.03697, over 971706.11 frames.], batch size: 12, lr: 2.86e-04
2022-05-06 00:13:02,663 INFO [train.py:715] (2/8) Epoch 7, batch 29750, loss[loss=0.2038, simple_loss=0.2564, pruned_loss=0.07559, over 4855.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2181, pruned_loss=0.03674, over 971841.13 frames.], batch size: 30, lr: 2.86e-04
2022-05-06 00:13:42,296 INFO [train.py:715] (2/8) Epoch 7, batch 29800, loss[loss=0.1376, simple_loss=0.2128, pruned_loss=0.03123, over 4953.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03707, over 971802.02 frames.], batch size: 35, lr: 2.86e-04
2022-05-06 00:14:22,413 INFO [train.py:715] (2/8) Epoch 7, batch 29850, loss[loss=0.1607, simple_loss=0.2282, pruned_loss=0.04656, over 4909.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03707, over 971703.62 frames.], batch size: 19, lr: 2.86e-04
2022-05-06 00:15:02,281 INFO [train.py:715] (2/8) Epoch 7, batch 29900, loss[loss=0.1246, simple_loss=0.1998, pruned_loss=0.02468, over 4809.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03691, over 972046.06 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:15:41,859 INFO [train.py:715] (2/8) Epoch 7, batch 29950, loss[loss=0.1572, simple_loss=0.2188, pruned_loss=0.04782, over 4802.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.03668, over 971888.70 frames.], batch size: 21, lr: 2.86e-04
2022-05-06 00:16:21,224 INFO [train.py:715] (2/8) Epoch 7, batch 30000, loss[loss=0.1815, simple_loss=0.2559, pruned_loss=0.05353, over 4769.00 frames.], tot_loss[loss=0.145, simple_loss=0.2169, pruned_loss=0.03658, over 973156.72 frames.], batch size: 19, lr: 2.86e-04
2022-05-06 00:16:21,224 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 00:16:41,747 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.1081, simple_loss=0.1929, pruned_loss=0.01164, over 914524.00 frames.
2022-05-06 00:17:21,553 INFO [train.py:715] (2/8) Epoch 7, batch 30050, loss[loss=0.1811, simple_loss=0.2434, pruned_loss=0.05939, over 4973.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03663, over 972876.49 frames.], batch size: 35, lr: 2.86e-04
2022-05-06 00:18:00,838 INFO [train.py:715] (2/8) Epoch 7, batch 30100, loss[loss=0.1436, simple_loss=0.221, pruned_loss=0.03313, over 4958.00 frames.], tot_loss[loss=0.1461, simple_loss=0.218, pruned_loss=0.03707, over 972448.43 frames.], batch size: 24, lr: 2.86e-04
2022-05-06 00:18:40,792 INFO [train.py:715] (2/8) Epoch 7, batch 30150, loss[loss=0.128, simple_loss=0.185, pruned_loss=0.03556, over 4832.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2174, pruned_loss=0.03693, over 972606.12 frames.], batch size: 15, lr: 2.86e-04
2022-05-06 00:19:20,432 INFO [train.py:715] (2/8) Epoch 7, batch 30200, loss[loss=0.151, simple_loss=0.2168, pruned_loss=0.04261, over 4985.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2172, pruned_loss=0.03648, over 972786.79 frames.], batch size: 15, lr: 2.85e-04
2022-05-06 00:20:00,688 INFO [train.py:715] (2/8) Epoch 7, batch 30250, loss[loss=0.1561, simple_loss=0.2117, pruned_loss=0.05023, over 4833.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2172, pruned_loss=0.0367, over 972264.48 frames.], batch size: 12, lr: 2.85e-04
2022-05-06 00:20:39,867 INFO [train.py:715] (2/8) Epoch 7, batch 30300, loss[loss=0.157, simple_loss=0.2253, pruned_loss=0.04434, over 4882.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2175, pruned_loss=0.03686, over 972137.35 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:21:19,493 INFO [train.py:715] (2/8) Epoch 7, batch 30350, loss[loss=0.1258, simple_loss=0.1942, pruned_loss=0.02871, over 4848.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.0368, over 971965.17 frames.], batch size: 13, lr: 2.85e-04
2022-05-06 00:21:58,986 INFO [train.py:715] (2/8) Epoch 7, batch 30400, loss[loss=0.1806, simple_loss=0.2558, pruned_loss=0.05275, over 4993.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2172, pruned_loss=0.03696, over 972317.73 frames.], batch size: 15, lr: 2.85e-04
2022-05-06 00:22:38,984 INFO [train.py:715] (2/8) Epoch 7, batch 30450, loss[loss=0.1681, simple_loss=0.239, pruned_loss=0.04861, over 4988.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03684, over 972413.54 frames.], batch size: 28, lr: 2.85e-04
2022-05-06 00:23:18,898 INFO [train.py:715] (2/8) Epoch 7, batch 30500, loss[loss=0.1563, simple_loss=0.2324, pruned_loss=0.04009, over 4755.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2171, pruned_loss=0.03667, over 972487.09 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:23:58,827 INFO [train.py:715] (2/8) Epoch 7, batch 30550, loss[loss=0.1238, simple_loss=0.1981, pruned_loss=0.02476, over 4756.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2174, pruned_loss=0.0367, over 971755.16 frames.], batch size: 19, lr: 2.85e-04
2022-05-06 00:24:38,529 INFO [train.py:715] (2/8) Epoch 7, batch 30600, loss[loss=0.148, simple_loss=0.2213, pruned_loss=0.03733, over 4991.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2171, pruned_loss=0.03672, over 971865.79 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:25:18,167 INFO [train.py:715] (2/8) Epoch 7, batch 30650, loss[loss=0.1458, simple_loss=0.2147, pruned_loss=0.03845, over 4953.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03658, over 971395.33 frames.], batch size: 24, lr: 2.85e-04
2022-05-06 00:25:57,787 INFO [train.py:715] (2/8) Epoch 7, batch 30700, loss[loss=0.1278, simple_loss=0.2059, pruned_loss=0.0248, over 4876.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.03601, over 971919.43 frames.], batch size: 22, lr: 2.85e-04
2022-05-06 00:26:36,842 INFO [train.py:715] (2/8) Epoch 7, batch 30750, loss[loss=0.1115, simple_loss=0.1879, pruned_loss=0.01755, over 4812.00 frames.], tot_loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.03595, over 971161.36 frames.], batch size: 25, lr: 2.85e-04
2022-05-06 00:27:15,905 INFO [train.py:715] (2/8) Epoch 7, batch 30800, loss[loss=0.129, simple_loss=0.2098, pruned_loss=0.02413, over 4919.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.03549, over 971171.62 frames.], batch size: 23, lr: 2.85e-04
2022-05-06 00:27:55,687 INFO [train.py:715] (2/8) Epoch 7, batch 30850, loss[loss=0.145, simple_loss=0.2181, pruned_loss=0.03593, over 4971.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03546, over 971223.54 frames.], batch size: 15, lr: 2.85e-04
2022-05-06 00:28:35,193 INFO [train.py:715] (2/8) Epoch 7, batch 30900, loss[loss=0.1379, simple_loss=0.2141, pruned_loss=0.0309, over 4869.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03657, over 971713.02 frames.], batch size: 13, lr: 2.85e-04
2022-05-06 00:29:15,593 INFO [train.py:715] (2/8) Epoch 7, batch 30950, loss[loss=0.1117, simple_loss=0.187, pruned_loss=0.01817, over 4808.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03625, over 972211.67 frames.], batch size: 25, lr: 2.85e-04
2022-05-06 00:29:54,980 INFO [train.py:715] (2/8) Epoch 7, batch 31000, loss[loss=0.129, simple_loss=0.1886, pruned_loss=0.03466, over 4992.00 frames.], tot_loss[loss=0.1441, simple_loss=0.216, pruned_loss=0.0361, over 972418.74 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:30:34,538 INFO [train.py:715] (2/8) Epoch 7, batch 31050, loss[loss=0.1575, simple_loss=0.2205, pruned_loss=0.04726, over 4944.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.0363, over 972395.22 frames.], batch size: 29, lr: 2.85e-04
2022-05-06 00:31:14,390 INFO [train.py:715] (2/8) Epoch 7, batch 31100, loss[loss=0.1334, simple_loss=0.2002, pruned_loss=0.03331, over 4835.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03609, over 971849.08 frames.], batch size: 13, lr: 2.85e-04
2022-05-06 00:31:54,495 INFO [train.py:715] (2/8) Epoch 7, batch 31150, loss[loss=0.1407, simple_loss=0.208, pruned_loss=0.03667, over 4925.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2169, pruned_loss=0.03612, over 971650.54 frames.], batch size: 18, lr: 2.85e-04
2022-05-06 00:32:33,850 INFO [train.py:715] (2/8) Epoch 7, batch 31200, loss[loss=0.1759, simple_loss=0.2459, pruned_loss=0.05293, over 4967.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2178, pruned_loss=0.03678, over 972515.02 frames.], batch size: 15, lr: 2.85e-04
2022-05-06 00:33:13,813 INFO [train.py:715] (2/8) Epoch 7, batch 31250, loss[loss=0.1271, simple_loss=0.2008, pruned_loss=0.02669, over 4928.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2178, pruned_loss=0.03688, over 971690.44 frames.], batch size: 23, lr: 2.85e-04
2022-05-06 00:33:54,543 INFO [train.py:715] (2/8) Epoch 7, batch 31300, loss[loss=0.1482, simple_loss=0.2217, pruned_loss=0.0374, over 4881.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2175, pruned_loss=0.03677, over 970897.87 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:34:34,118 INFO [train.py:715] (2/8) Epoch 7, batch 31350, loss[loss=0.1401, simple_loss=0.2192, pruned_loss=0.03056, over 4892.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2178, pruned_loss=0.03668, over 970692.04 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:35:14,070 INFO [train.py:715] (2/8) Epoch 7, batch 31400, loss[loss=0.1341, simple_loss=0.2131, pruned_loss=0.02758, over 4844.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2181, pruned_loss=0.03702, over 970324.76 frames.], batch size: 20, lr: 2.85e-04
2022-05-06 00:35:53,416 INFO [train.py:715] (2/8) Epoch 7, batch 31450, loss[loss=0.1209, simple_loss=0.193, pruned_loss=0.02435, over 4827.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2177, pruned_loss=0.03673, over 970813.59 frames.], batch size: 25, lr: 2.85e-04
2022-05-06 00:36:33,191 INFO [train.py:715] (2/8) Epoch 7, batch 31500, loss[loss=0.1547, simple_loss=0.2253, pruned_loss=0.04209, over 4735.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2175, pruned_loss=0.03686, over 971384.88 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:37:12,316 INFO [train.py:715] (2/8) Epoch 7, batch 31550, loss[loss=0.1405, simple_loss=0.2063, pruned_loss=0.03733, over 4805.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2167, pruned_loss=0.03654, over 971200.64 frames.], batch size: 12, lr: 2.85e-04
2022-05-06 00:37:52,278 INFO [train.py:715] (2/8) Epoch 7, batch 31600, loss[loss=0.1355, simple_loss=0.2076, pruned_loss=0.03176, over 4930.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2164, pruned_loss=0.03617, over 971859.20 frames.], batch size: 18, lr: 2.85e-04
2022-05-06 00:38:32,105 INFO [train.py:715] (2/8) Epoch 7, batch 31650, loss[loss=0.1403, simple_loss=0.2127, pruned_loss=0.03397, over 4837.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03659, over 972354.46 frames.], batch size: 30, lr: 2.85e-04
2022-05-06 00:39:11,527 INFO [train.py:715] (2/8) Epoch 7, batch 31700, loss[loss=0.1415, simple_loss=0.2088, pruned_loss=0.03709, over 4929.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2179, pruned_loss=0.03681, over 972519.19 frames.], batch size: 23, lr: 2.85e-04
2022-05-06 00:39:51,223 INFO [train.py:715] (2/8) Epoch 7, batch 31750, loss[loss=0.1399, simple_loss=0.2136, pruned_loss=0.03307, over 4794.00 frames.], tot_loss[loss=0.1449, simple_loss=0.217, pruned_loss=0.03647, over 972265.20 frames.], batch size: 24, lr: 2.85e-04
2022-05-06 00:40:30,493 INFO [train.py:715] (2/8) Epoch 7, batch 31800, loss[loss=0.1456, simple_loss=0.2183, pruned_loss=0.03642, over 4889.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2155, pruned_loss=0.03569, over 972588.26 frames.], batch size: 16, lr: 2.85e-04
2022-05-06 00:41:09,608 INFO [train.py:715] (2/8) Epoch 7, batch 31850, loss[loss=0.1314, simple_loss=0.1996, pruned_loss=0.03163, over 4981.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03583, over 973031.12 frames.], batch size: 25, lr: 2.85e-04
2022-05-06 00:41:49,868 INFO [train.py:715] (2/8) Epoch 7, batch 31900, loss[loss=0.119, simple_loss=0.1936, pruned_loss=0.02224, over 4750.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03602, over 972308.62 frames.], batch size: 12, lr: 2.85e-04
2022-05-06 00:42:30,606 INFO [train.py:715] (2/8) Epoch 7, batch 31950, loss[loss=0.143, simple_loss=0.2181, pruned_loss=0.03399, over 4644.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2159, pruned_loss=0.03617, over 971999.28 frames.], batch size: 13, lr: 2.85e-04
2022-05-06 00:43:11,069 INFO [train.py:715] (2/8) Epoch 7, batch 32000, loss[loss=0.1771, simple_loss=0.2448, pruned_loss=0.05464, over 4869.00 frames.], tot_loss[loss=0.145, simple_loss=0.2169, pruned_loss=0.03652, over 972089.34 frames.], batch size: 32, lr: 2.85e-04
2022-05-06 00:43:50,734 INFO [train.py:715] (2/8) Epoch 7, batch 32050, loss[loss=0.1674, simple_loss=0.2419, pruned_loss=0.04644, over 4921.00 frames.], tot_loss[loss=0.1446, simple_loss=0.217, pruned_loss=0.03614, over 971374.32 frames.], batch size: 23, lr: 2.85e-04
2022-05-06 00:44:30,665 INFO [train.py:715] (2/8) Epoch 7, batch 32100, loss[loss=0.135, simple_loss=0.2155, pruned_loss=0.02727, over 4923.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2171, pruned_loss=0.0363, over 971820.00 frames.], batch size: 23, lr: 2.85e-04
2022-05-06 00:45:10,479 INFO [train.py:715] (2/8) Epoch 7, batch 32150, loss[loss=0.1349, simple_loss=0.2142, pruned_loss=0.02779, over 4797.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2166, pruned_loss=0.03646, over 970676.50 frames.], batch size: 24, lr: 2.84e-04
2022-05-06 00:45:50,033 INFO [train.py:715] (2/8) Epoch 7, batch 32200, loss[loss=0.1287, simple_loss=0.1948, pruned_loss=0.03129, over 4918.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2168, pruned_loss=0.03611, over 969792.94 frames.], batch size: 19, lr: 2.84e-04
2022-05-06 00:46:29,885 INFO [train.py:715] (2/8) Epoch 7, batch 32250, loss[loss=0.135, simple_loss=0.2156, pruned_loss=0.02717, over 4783.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2167, pruned_loss=0.03638, over 969950.47 frames.], batch size: 12, lr: 2.84e-04
2022-05-06 00:47:09,674 INFO [train.py:715] (2/8) Epoch 7, batch 32300, loss[loss=0.1297, simple_loss=0.2031, pruned_loss=0.02816, over 4797.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2169, pruned_loss=0.03603, over 970597.52 frames.], batch size: 24, lr: 2.84e-04
2022-05-06 00:47:50,013 INFO [train.py:715] (2/8) Epoch 7, batch 32350, loss[loss=0.1317, simple_loss=0.2014, pruned_loss=0.031, over 4780.00 frames.], tot_loss[loss=0.145, simple_loss=0.2174, pruned_loss=0.03634, over 971419.87 frames.], batch size: 17, lr: 2.84e-04
2022-05-06 00:48:29,372 INFO [train.py:715] (2/8) Epoch 7, batch 32400, loss[loss=0.1571, simple_loss=0.2345, pruned_loss=0.03986, over 4977.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2169, pruned_loss=0.03616, over 971209.57 frames.], batch size: 15, lr: 2.84e-04
2022-05-06 00:49:09,265 INFO [train.py:715] (2/8) Epoch 7, batch 32450, loss[loss=0.1596, simple_loss=0.2505, pruned_loss=0.03441, over 4907.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2175, pruned_loss=0.0367, over 971630.88 frames.], batch size: 18, lr: 2.84e-04
2022-05-06 00:49:48,737 INFO [train.py:715] (2/8) Epoch 7, batch 32500, loss[loss=0.1602, simple_loss=0.2279, pruned_loss=0.04622, over 4796.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03656, over 971376.62 frames.], batch size: 14, lr: 2.84e-04
2022-05-06 00:50:28,300 INFO [train.py:715] (2/8) Epoch 7, batch 32550, loss[loss=0.1403, simple_loss=0.2091, pruned_loss=0.0358, over 4783.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2168, pruned_loss=0.03683, over 971516.84 frames.], batch size: 17, lr: 2.84e-04
2022-05-06 00:51:08,056 INFO [train.py:715] (2/8) Epoch 7, batch 32600, loss[loss=0.1753, simple_loss=0.2469, pruned_loss=0.05186, over 4926.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2178, pruned_loss=0.03754, over 972615.12 frames.], batch size: 23, lr: 2.84e-04
2022-05-06 00:51:47,566 INFO [train.py:715] (2/8) Epoch 7, batch 32650, loss[loss=0.1089, simple_loss=0.1862, pruned_loss=0.01579, over 4814.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2174, pruned_loss=0.03705, over 971904.58 frames.], batch size: 26, lr: 2.84e-04
2022-05-06 00:52:27,385 INFO [train.py:715] (2/8) Epoch 7, batch 32700, loss[loss=0.1397, simple_loss=0.2083, pruned_loss=0.03554, over 4868.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2178, pruned_loss=0.03723, over 972274.41 frames.], batch size: 20, lr: 2.84e-04
2022-05-06 00:53:06,819 INFO [train.py:715] (2/8) Epoch 7, batch 32750, loss[loss=0.1703, simple_loss=0.246, pruned_loss=0.04729, over 4966.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2175, pruned_loss=0.03694, over 972393.47 frames.], batch size: 15, lr: 2.84e-04
2022-05-06 00:53:47,305 INFO [train.py:715] (2/8) Epoch 7, batch 32800, loss[loss=0.1876, simple_loss=0.2522, pruned_loss=0.0615, over 4980.00 frames.], tot_loss[loss=0.1461, simple_loss=0.218, pruned_loss=0.03713, over 972597.98 frames.], batch size: 25, lr: 2.84e-04
2022-05-06 00:54:27,992 INFO [train.py:715] (2/8) Epoch 7, batch 32850, loss[loss=0.1637, simple_loss=0.2334, pruned_loss=0.04695, over 4928.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.037, over 972394.83 frames.], batch size: 23, lr: 2.84e-04
2022-05-06 00:55:08,133 INFO [train.py:715] (2/8) Epoch 7, batch 32900, loss[loss=0.1476, simple_loss=0.2216, pruned_loss=0.03674, over 4863.00 frames.], tot_loss[loss=0.146, simple_loss=0.2177, pruned_loss=0.03719, over 973211.31 frames.], batch size: 16, lr: 2.84e-04
2022-05-06 00:55:48,471 INFO [train.py:715] (2/8) Epoch 7, batch 32950, loss[loss=0.1341, simple_loss=0.1998, pruned_loss=0.03413, over 4849.00 frames.], tot_loss[loss=0.146, simple_loss=0.2176, pruned_loss=0.03724, over 972778.18 frames.], batch size: 32, lr: 2.84e-04
2022-05-06 00:56:28,434 INFO [train.py:715] (2/8) Epoch 7, batch 33000, loss[loss=0.1053, simple_loss=0.1781, pruned_loss=0.0162, over 4934.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2175, pruned_loss=0.03687, over 972603.69 frames.], batch size: 29, lr: 2.84e-04
2022-05-06 00:56:28,435 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 00:56:38,008 INFO [train.py:742] (2/8) Epoch 7, validation: loss=0.108, simple_loss=0.1927, pruned_loss=0.01164, over 914524.00 frames.
2022-05-06 00:57:17,523 INFO [train.py:715] (2/8) Epoch 7, batch 33050, loss[loss=0.1974, simple_loss=0.274, pruned_loss=0.06035, over 4911.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2186, pruned_loss=0.03732, over 972851.48 frames.], batch size: 18, lr: 2.84e-04
2022-05-06 00:57:57,501 INFO [train.py:715] (2/8) Epoch 7, batch 33100, loss[loss=0.1445, simple_loss=0.2133, pruned_loss=0.03786, over 4787.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2189, pruned_loss=0.03743, over 972526.33 frames.], batch size: 17, lr: 2.84e-04
2022-05-06 00:58:36,954 INFO [train.py:715] (2/8) Epoch 7, batch 33150, loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02905, over 4873.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2191, pruned_loss=0.03732, over 972510.29 frames.], batch size: 16, lr: 2.84e-04
2022-05-06 00:59:16,724 INFO [train.py:715] (2/8) Epoch 7, batch 33200, loss[loss=0.1469, simple_loss=0.2132, pruned_loss=0.04027, over 4831.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03709, over 971770.70 frames.], batch size: 15, lr: 2.84e-04
2022-05-06 00:59:56,298 INFO [train.py:715] (2/8) Epoch 7, batch 33250, loss[loss=0.1585, simple_loss=0.2353, pruned_loss=0.0409, over 4892.00 frames.], tot_loss[loss=0.1458, simple_loss=0.218, pruned_loss=0.0368, over 972534.12 frames.], batch size: 19, lr: 2.84e-04
2022-05-06 01:00:35,762 INFO [train.py:715] (2/8) Epoch 7, batch 33300, loss[loss=0.1323, simple_loss=0.2074, pruned_loss=0.02859, over 4935.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2179, pruned_loss=0.03722, over 972990.21 frames.], batch size: 29, lr: 2.84e-04
2022-05-06 01:01:15,278 INFO [train.py:715] (2/8) Epoch 7, batch 33350, loss[loss=0.1518, simple_loss=0.2222, pruned_loss=0.04071, over 4819.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.03689, over 972571.78 frames.], batch size: 13, lr: 2.84e-04
2022-05-06 01:01:55,577 INFO [train.py:715] (2/8) Epoch 7, batch 33400, loss[loss=0.1266, simple_loss=0.1926, pruned_loss=0.03025, over 4780.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2179, pruned_loss=0.03692, over 972140.84 frames.], batch size: 17, lr: 2.84e-04
2022-05-06 01:02:35,672 INFO [train.py:715] (2/8) Epoch 7, batch 33450, loss[loss=0.1281, simple_loss=0.2047, pruned_loss=0.02581, over 4911.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2174, pruned_loss=0.03659, over 972930.32 frames.], batch size: 17, lr: 2.84e-04
2022-05-06 01:03:16,259 INFO [train.py:715] (2/8) Epoch 7, batch 33500, loss[loss=0.1176, simple_loss=0.1927, pruned_loss=0.02125, over 4912.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2169, pruned_loss=0.03645, over 972822.22 frames.], batch size: 17, lr: 2.84e-04
2022-05-06 01:03:56,835 INFO [train.py:715] (2/8) Epoch 7, batch 33550, loss[loss=0.1352, simple_loss=0.2045, pruned_loss=0.03291, over 4797.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2166, pruned_loss=0.03637, over 971734.50 frames.], batch size: 25, lr: 2.84e-04
2022-05-06 01:04:37,439 INFO [train.py:715] (2/8) Epoch 7, batch 33600, loss[loss=0.1612, simple_loss=0.2264, pruned_loss=0.048, over 4876.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2171, pruned_loss=0.03661, over 972365.90 frames.], batch size: 16, lr: 2.84e-04
2022-05-06 01:05:17,937 INFO [train.py:715] (2/8) Epoch 7, batch 33650, loss[loss=0.1643, simple_loss=0.2416, pruned_loss=0.04349, over 4986.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2177, pruned_loss=0.03674, over 972786.13 frames.], batch size: 15, lr: 2.84e-04
2022-05-06 01:05:57,813 INFO [train.py:715] (2/8) Epoch 7, batch 33700, loss[loss=0.1397, simple_loss=0.209, pruned_loss=0.03518, over 4864.00 frames.], tot_loss[loss=0.1449, simple_loss=0.217, pruned_loss=0.03639, over 973005.18 frames.], batch size: 30, lr: 2.84e-04
2022-05-06 01:06:37,963 INFO [train.py:715] (2/8) Epoch 7, batch 33750, loss[loss=0.1731, simple_loss=0.2367, pruned_loss=0.05475, over 4839.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03663, over 973432.10 frames.], batch size: 15, lr: 2.84e-04
2022-05-06 01:07:17,451 INFO [train.py:715] (2/8) Epoch 7, batch 33800, loss[loss=0.1455, simple_loss=0.2156, pruned_loss=0.03769, over 4808.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2177, pruned_loss=0.03657, over 973389.99 frames.], batch size: 21, lr: 2.84e-04
2022-05-06 01:07:58,047 INFO [train.py:715] (2/8) Epoch 7, batch 33850, loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02926, over 4874.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2168, pruned_loss=0.03598, over 974140.11 frames.], batch size: 16, lr: 2.84e-04
2022-05-06 01:08:37,727 INFO [train.py:715] (2/8) Epoch 7, batch 33900, loss[loss=0.1296, simple_loss=0.2022, pruned_loss=0.02846, over 4802.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.0356, over 973284.68 frames.], batch size: 14, lr: 2.84e-04
2022-05-06 01:09:17,827 INFO [train.py:715] (2/8) Epoch 7, batch 33950, loss[loss=0.1317, simple_loss=0.2086, pruned_loss=0.0274, over 4954.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2168, pruned_loss=0.03595, over 974007.69 frames.], batch size: 21, lr: 2.84e-04
2022-05-06 01:09:57,288 INFO [train.py:715] (2/8) Epoch 7, batch 34000, loss[loss=0.1548, simple_loss=0.219, pruned_loss=0.04536, over 4876.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2172, pruned_loss=0.03591, over 973340.64 frames.], batch size: 22, lr: 2.84e-04
2022-05-06 01:10:37,478 INFO [train.py:715] (2/8) Epoch 7, batch 34050, loss[loss=0.1878, simple_loss=0.2606, pruned_loss=0.05751, over 4965.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2177, pruned_loss=0.03628, over 972408.90 frames.], batch size: 28, lr: 2.84e-04
2022-05-06 01:11:17,480 INFO [train.py:715] (2/8) Epoch 7, batch 34100, loss[loss=0.1836, simple_loss=0.248, pruned_loss=0.05959, over 4856.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2182, pruned_loss=0.03685, over 972222.09 frames.], batch size: 15, lr: 2.83e-04
2022-05-06 01:11:56,985 INFO [train.py:715] (2/8) Epoch 7, batch 34150, loss[loss=0.1489, simple_loss=0.229, pruned_loss=0.0344, over 4787.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2178, pruned_loss=0.03681, over 972794.42 frames.], batch size: 18, lr: 2.83e-04
2022-05-06 01:12:37,407 INFO [train.py:715] (2/8) Epoch 7, batch 34200, loss[loss=0.1588, simple_loss=0.2179, pruned_loss=0.04984, over 4858.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2171, pruned_loss=0.03665, over 972879.75 frames.], batch size: 13, lr: 2.83e-04
2022-05-06 01:13:17,642 INFO [train.py:715] (2/8) Epoch 7, batch 34250, loss[loss=0.1793, simple_loss=0.2362, pruned_loss=0.06119, over 4889.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2172, pruned_loss=0.03654, over 973348.51 frames.], batch size: 19, lr: 2.83e-04
2022-05-06 01:13:58,298 INFO [train.py:715] (2/8) Epoch 7, batch 34300, loss[loss=0.1092, simple_loss=0.1878, pruned_loss=0.01533, over 4740.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2166, pruned_loss=0.03625, over 974001.99 frames.], batch size: 16, lr: 2.83e-04
2022-05-06 01:14:38,118 INFO [train.py:715] (2/8) Epoch 7, batch 34350, loss[loss=0.1296, simple_loss=0.2019, pruned_loss=0.02866, over 4832.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03576, over 973514.82 frames.], batch size: 30, lr: 2.83e-04
2022-05-06 01:15:18,248 INFO [train.py:715] (2/8) Epoch 7, batch 34400, loss[loss=0.1547, simple_loss=0.2287, pruned_loss=0.04031, over 4917.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2158, pruned_loss=0.03587, over 973312.92 frames.], batch size: 19, lr: 2.83e-04
2022-05-06 01:15:58,918 INFO [train.py:715] (2/8) Epoch 7, batch 34450, loss[loss=0.1786, simple_loss=0.2442, pruned_loss=0.05654, over 4951.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03615, over 973660.82 frames.], batch size: 39, lr: 2.83e-04
2022-05-06 01:16:38,146 INFO [train.py:715] (2/8) Epoch 7, batch 34500, loss[loss=0.1595, simple_loss=0.236, pruned_loss=0.0415, over 4935.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2169, pruned_loss=0.03668, over 973330.64 frames.], batch size: 23, lr: 2.83e-04
2022-05-06 01:17:18,210 INFO [train.py:715] (2/8) Epoch 7, batch 34550, loss[loss=0.1268, simple_loss=0.1953, pruned_loss=0.02917, over 4862.00 frames.], tot_loss[loss=0.1451, simple_loss=0.217, pruned_loss=0.03663, over 973362.16 frames.], batch size: 32, lr: 2.83e-04
2022-05-06 01:17:58,848 INFO [train.py:715] (2/8) Epoch 7, batch 34600, loss[loss=0.1246, simple_loss=0.196, pruned_loss=0.02657, over 4855.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2163, pruned_loss=0.03646, over 973364.49 frames.], batch size: 13, lr: 2.83e-04
2022-05-06 01:18:38,815 INFO [train.py:715] (2/8) Epoch 7, batch 34650, loss[loss=0.1642, simple_loss=0.2189, pruned_loss=0.05478, over 4830.00 frames.], tot_loss[loss=0.145, simple_loss=0.2165, pruned_loss=0.03672, over 972964.23 frames.], batch size: 15, lr: 2.83e-04
2022-05-06 01:19:19,028 INFO [train.py:715] (2/8) Epoch 7, batch 34700, loss[loss=0.1614, simple_loss=0.2423, pruned_loss=0.04021, over 4883.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2175, pruned_loss=0.03657, over 972461.57 frames.], batch size: 22, lr: 2.83e-04
2022-05-06 01:19:57,502 INFO [train.py:715] (2/8) Epoch 7, batch 34750, loss[loss=0.1472, simple_loss=0.2127, pruned_loss=0.04085, over 4851.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03669, over 972315.21 frames.], batch size: 30, lr: 2.83e-04
2022-05-06 01:20:35,932 INFO [train.py:715] (2/8) Epoch 7, batch 34800, loss[loss=0.1707, simple_loss=0.2503, pruned_loss=0.04558, over 4918.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2165, pruned_loss=0.03653, over 972131.02 frames.], batch size: 18, lr: 2.83e-04
2022-05-06 01:21:27,031 INFO [train.py:715] (2/8) Epoch 8, batch 0, loss[loss=0.1322, simple_loss=0.2025, pruned_loss=0.03095, over 4795.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2025, pruned_loss=0.03095, over 4795.00 frames.], batch size: 21, lr: 2.69e-04
2022-05-06 01:22:06,301 INFO [train.py:715] (2/8) Epoch 8, batch 50, loss[loss=0.1343, simple_loss=0.2065, pruned_loss=0.03101, over 4885.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03477, over 219877.86 frames.], batch size: 22, lr: 2.69e-04
2022-05-06 01:22:47,067 INFO [train.py:715] (2/8) Epoch 8, batch 100, loss[loss=0.1526, simple_loss=0.2267, pruned_loss=0.03929, over 4884.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2156, pruned_loss=0.03478, over 387623.79 frames.], batch size: 16, lr: 2.69e-04
2022-05-06 01:23:26,803 INFO [train.py:715] (2/8) Epoch 8, batch 150, loss[loss=0.1694, simple_loss=0.2403, pruned_loss=0.04924, over 4870.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03567, over 517743.96 frames.], batch size: 38, lr: 2.69e-04
2022-05-06 01:24:07,304 INFO [train.py:715] (2/8) Epoch 8, batch 200, loss[loss=0.1256, simple_loss=0.1955, pruned_loss=0.02782, over 4987.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2169, pruned_loss=0.03617, over 619376.88 frames.], batch size: 25, lr: 2.69e-04
2022-05-06 01:24:47,119 INFO [train.py:715] (2/8) Epoch 8, batch 250, loss[loss=0.1692, simple_loss=0.2407, pruned_loss=0.04883, over 4990.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2164, pruned_loss=0.03568, over 698274.42 frames.], batch size: 28, lr: 2.69e-04
2022-05-06 01:25:27,381 INFO [train.py:715] (2/8) Epoch 8, batch 300, loss[loss=0.1197, simple_loss=0.1939, pruned_loss=0.02273, over 4778.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2168, pruned_loss=0.03644, over 760158.40 frames.], batch size: 17, lr: 2.69e-04
2022-05-06 01:26:07,155 INFO [train.py:715] (2/8) Epoch 8, batch 350, loss[loss=0.1282, simple_loss=0.1886, pruned_loss=0.03396, over 4788.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2166, pruned_loss=0.03638, over 807827.44 frames.], batch size: 14, lr: 2.69e-04
2022-05-06 01:26:46,035 INFO [train.py:715] (2/8) Epoch 8, batch 400, loss[loss=0.1274, simple_loss=0.2179, pruned_loss=0.01844, over 4881.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2171, pruned_loss=0.03679, over 843815.43 frames.], batch size: 22, lr: 2.69e-04
2022-05-06 01:27:26,635 INFO [train.py:715] (2/8) Epoch 8, batch 450, loss[loss=0.1608, simple_loss=0.2281, pruned_loss=0.04674, over 4865.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03684, over 872181.35 frames.], batch size: 20, lr: 2.69e-04
2022-05-06 01:28:06,610 INFO [train.py:715] (2/8) Epoch 8, batch 500, loss[loss=0.125, simple_loss=0.2018, pruned_loss=0.0241, over 4975.00 frames.], tot_loss[loss=0.146, simple_loss=0.218, pruned_loss=0.03701, over 894448.43 frames.], batch size: 24, lr: 2.69e-04
2022-05-06 01:28:47,244 INFO [train.py:715] (2/8) Epoch 8, batch 550, loss[loss=0.1448, simple_loss=0.222, pruned_loss=0.0338, over 4788.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2178, pruned_loss=0.03699, over 911390.07 frames.], batch size: 17, lr: 2.69e-04
2022-05-06 01:29:26,916 INFO [train.py:715] (2/8) Epoch 8, batch 600, loss[loss=0.1541, simple_loss=0.2287, pruned_loss=0.03974, over 4956.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2177, pruned_loss=0.03743, over 924437.60 frames.], batch size: 24, lr: 2.69e-04
2022-05-06 01:30:07,134 INFO [train.py:715] (2/8) Epoch 8, batch 650, loss[loss=0.1349, simple_loss=0.2008, pruned_loss=0.03446, over 4833.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2166, pruned_loss=0.03687, over 934962.61 frames.], batch size: 30, lr: 2.68e-04
2022-05-06 01:30:47,387 INFO [train.py:715] (2/8) Epoch 8, batch 700, loss[loss=0.1616, simple_loss=0.2151, pruned_loss=0.05404, over 4989.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2165, pruned_loss=0.03662, over 944347.54 frames.], batch size: 14, lr: 2.68e-04
2022-05-06 01:31:27,081 INFO [train.py:715] (2/8) Epoch 8, batch 750, loss[loss=0.116, simple_loss=0.1892, pruned_loss=0.02142, over 4938.00 frames.], tot_loss[loss=0.146, simple_loss=0.2176, pruned_loss=0.03724, over 950661.05 frames.], batch size: 21, lr: 2.68e-04
2022-05-06 01:32:07,140 INFO [train.py:715] (2/8) Epoch 8, batch 800, loss[loss=0.1602, simple_loss=0.2249, pruned_loss=0.04773, over 4842.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2175, pruned_loss=0.03702, over 954756.73 frames.], batch size: 13, lr: 2.68e-04
2022-05-06 01:32:47,128 INFO [train.py:715] (2/8) Epoch 8, batch 850, loss[loss=0.1283, simple_loss=0.2066, pruned_loss=0.02503, over 4943.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2172, pruned_loss=0.03681, over 958955.93 frames.], batch size: 29, lr: 2.68e-04
2022-05-06 01:33:28,539 INFO [train.py:715] (2/8) Epoch 8, batch 900, loss[loss=0.1476, simple_loss=0.2203, pruned_loss=0.03749, over 4742.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2169, pruned_loss=0.03693, over 963054.04 frames.], batch size: 16, lr: 2.68e-04
2022-05-06 01:34:08,663 INFO [train.py:715] (2/8) Epoch 8, batch 950, loss[loss=0.1524, simple_loss=0.2317, pruned_loss=0.03649, over 4789.00 frames.], tot_loss[loss=0.146, simple_loss=0.2175, pruned_loss=0.03727, over 964048.18 frames.], batch size: 17, lr: 2.68e-04
2022-05-06 01:34:49,695 INFO [train.py:715] (2/8) Epoch 8, batch 1000, loss[loss=0.1456, simple_loss=0.2224, pruned_loss=0.03436, over 4904.00 frames.], tot_loss[loss=0.1455, simple_loss=0.217, pruned_loss=0.03699, over 966371.11 frames.], batch size: 19, lr: 2.68e-04
2022-05-06 01:35:30,785 INFO [train.py:715] (2/8) Epoch 8, batch 1050, loss[loss=0.1292, simple_loss=0.2073, pruned_loss=0.02557, over 4896.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2168, pruned_loss=0.0368, over 968391.69 frames.], batch size: 22, lr: 2.68e-04
2022-05-06 01:36:11,898 INFO [train.py:715] (2/8) Epoch 8, batch 1100, loss[loss=0.1554, simple_loss=0.2318, pruned_loss=0.03948, over 4758.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2167, pruned_loss=0.03685, over 968491.23 frames.], batch size: 16, lr: 2.68e-04
2022-05-06 01:36:52,402 INFO [train.py:715] (2/8) Epoch 8, batch 1150, loss[loss=0.1518, simple_loss=0.2327, pruned_loss=0.03543, over 4977.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2173, pruned_loss=0.03682, over 969981.68 frames.], batch size: 28, lr: 2.68e-04
2022-05-06 01:37:33,430 INFO [train.py:715] (2/8) Epoch 8, batch 1200, loss[loss=0.1791, simple_loss=0.2381, pruned_loss=0.06007, over 4862.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2183, pruned_loss=0.03738, over 970751.83 frames.], batch size: 32, lr: 2.68e-04
2022-05-06 01:38:14,751 INFO [train.py:715] (2/8) Epoch 8, batch 1250, loss[loss=0.1127, simple_loss=0.1835, pruned_loss=0.02092, over 4812.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2171, pruned_loss=0.0363, over 972045.38 frames.], batch size: 25, lr: 2.68e-04
2022-05-06 01:38:55,091 INFO [train.py:715] (2/8) Epoch 8, batch 1300, loss[loss=0.16, simple_loss=0.2335, pruned_loss=0.04324, over 4694.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2183, pruned_loss=0.03704, over 970581.82 frames.], batch size: 15, lr: 2.68e-04
2022-05-06 01:39:36,450 INFO [train.py:715] (2/8) Epoch 8, batch 1350, loss[loss=0.1652, simple_loss=0.2256, pruned_loss=0.05239, over 4698.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.03696, over 970982.87 frames.], batch size: 15, lr: 2.68e-04
2022-05-06 01:40:17,092 INFO [train.py:715] (2/8) Epoch 8, batch 1400, loss[loss=0.1494, simple_loss=0.2132, pruned_loss=0.04281, over 4892.00 frames.], tot_loss[loss=0.1458, simple_loss=0.218, pruned_loss=0.0368, over 971528.37 frames.], batch size: 19, lr: 2.68e-04
2022-05-06 01:40:57,926 INFO [train.py:715] (2/8) Epoch 8, batch 1450, loss[loss=0.1301, simple_loss=0.1945, pruned_loss=0.03287, over 4930.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2177, pruned_loss=0.03639, over 971748.62 frames.], batch size: 21, lr: 2.68e-04
2022-05-06 01:41:37,792 INFO [train.py:715] (2/8) Epoch 8, batch 1500, loss[loss=0.1426, simple_loss=0.2193, pruned_loss=0.03297, over 4929.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2172, pruned_loss=0.03618, over 971856.43 frames.], batch size: 29, lr: 2.68e-04
2022-05-06 01:42:20,414 INFO [train.py:715] (2/8) Epoch 8, batch 1550, loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03121, over 4983.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2174, pruned_loss=0.03625, over 972225.27 frames.], batch size: 28, lr: 2.68e-04
2022-05-06 01:43:00,550 INFO [train.py:715] (2/8) Epoch 8, batch 1600, loss[loss=0.1539, simple_loss=0.2117, pruned_loss=0.04807, over 4692.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2176, pruned_loss=0.03661, over 971176.50 frames.], batch size: 15, lr: 2.68e-04
2022-05-06 01:43:39,991 INFO [train.py:715] (2/8) Epoch 8, batch 1650, loss[loss=0.1664, simple_loss=0.2276, pruned_loss=0.05257, over 4776.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2167, pruned_loss=0.03622, over 971484.83 frames.], batch size: 14, lr: 2.68e-04
2022-05-06 01:44:20,213 INFO [train.py:715] (2/8) Epoch 8, batch 1700, loss[loss=0.146, simple_loss=0.2083, pruned_loss=0.04191, over 4855.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.0357, over 972319.15 frames.], batch size: 30, lr: 2.68e-04
2022-05-06 01:44:59,608 INFO [train.py:715] (2/8) Epoch 8, batch 1750, loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02881, over 4822.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03538, over 971844.88 frames.], batch size: 15, lr: 2.68e-04
2022-05-06 01:45:39,057 INFO [train.py:715] (2/8) Epoch 8, batch 1800, loss[loss=0.1428, simple_loss=0.2223, pruned_loss=0.03161, over 4821.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.03558, over 972467.25 frames.], batch size: 15, lr: 2.68e-04
2022-05-06 01:46:18,114 INFO [train.py:715] (2/8) Epoch 8, batch 1850, loss[loss=0.1576, simple_loss=0.2214, pruned_loss=0.04693, over 4892.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2162, pruned_loss=0.03632, over 972141.72 frames.], batch size: 17, lr: 2.68e-04
2022-05-06 01:46:57,513 INFO [train.py:715] (2/8) Epoch 8, batch 1900, loss[loss=0.1508, simple_loss=0.2322, pruned_loss=0.03467, over 4955.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2166, pruned_loss=0.03612, over 973227.39 frames.], batch size: 21, lr: 2.68e-04
2022-05-06 01:47:37,011 INFO [train.py:715] (2/8) Epoch 8, batch 1950, loss[loss=0.1746, simple_loss=0.2509, pruned_loss=0.04915, over 4912.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03576, over 973088.21 frames.], batch size: 18, lr: 2.68e-04
2022-05-06 01:48:16,131 INFO [train.py:715] (2/8) Epoch 8, batch 2000, loss[loss=0.1325, simple_loss=0.2023, pruned_loss=0.03137, over 4977.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2165, pruned_loss=0.03591, over 972871.31 frames.], batch size: 14, lr: 2.68e-04
2022-05-06 01:48:56,144 INFO [train.py:715] (2/8) Epoch 8, batch 2050, loss[loss=0.1482, simple_loss=0.2217, pruned_loss=0.03736, over 4761.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2168, pruned_loss=0.03611, over 972326.17 frames.], batch size: 14, lr: 2.68e-04
2022-05-06 01:49:35,102 INFO [train.py:715] (2/8) Epoch 8, batch 2100, loss[loss=0.183, simple_loss=0.2477, pruned_loss=0.05913, over 4896.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03596, over 972357.90 frames.], batch size: 16, lr: 2.68e-04
2022-05-06 01:50:14,047 INFO [train.py:715] (2/8) Epoch 8, batch 2150, loss[loss=0.1664, simple_loss=0.2315, pruned_loss=0.05063, over 4846.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03599, over 972088.00 frames.], batch size: 34, lr: 2.68e-04
2022-05-06 01:50:53,034 INFO [train.py:715] (2/8) Epoch 8, batch 2200, loss[loss=0.1442, simple_loss=0.219, pruned_loss=0.03464, over 4982.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2156, pruned_loss=0.03596, over 972336.61 frames.], batch size: 25, lr: 2.68e-04
2022-05-06 01:51:32,660 INFO [train.py:715] (2/8) Epoch 8, batch 2250, loss[loss=0.1547, simple_loss=0.225, pruned_loss=0.04224, over 4982.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.03591, over 973474.30 frames.], batch size: 25, lr: 2.68e-04
2022-05-06 01:52:12,077 INFO [train.py:715] (2/8) Epoch 8, batch 2300, loss[loss=0.1259, simple_loss=0.2145, pruned_loss=0.01859, over 4796.00 frames.], tot_loss[loss=0.143, simple_loss=0.215, pruned_loss=0.03546, over 972560.22 frames.], batch size: 24, lr: 2.68e-04
2022-05-06 01:52:50,787 INFO [train.py:715] (2/8) Epoch 8, batch 2350, loss[loss=0.1352, simple_loss=0.2109, pruned_loss=0.02981, over 4970.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.0354, over 972720.24 frames.], batch size: 31, lr: 2.68e-04
2022-05-06 01:53:30,841 INFO [train.py:715] (2/8) Epoch 8, batch 2400, loss[loss=0.1233, simple_loss=0.1973, pruned_loss=0.02469, over 4937.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2152, pruned_loss=0.03591, over 973405.62 frames.], batch size: 24, lr: 2.68e-04
2022-05-06 01:54:10,338 INFO [train.py:715] (2/8) Epoch 8, batch 2450, loss[loss=0.1492, simple_loss=0.2151, pruned_loss=0.04159, over 4866.00 frames.], tot_loss[loss=0.143, simple_loss=0.2145, pruned_loss=0.0358, over 973640.55 frames.], batch size: 16, lr: 2.68e-04
2022-05-06 01:54:49,892 INFO [train.py:715] (2/8) Epoch 8, batch 2500, loss[loss=0.1339, simple_loss=0.2051, pruned_loss=0.03142, over 4746.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2152, pruned_loss=0.03596, over 974112.63 frames.], batch size: 12, lr: 2.68e-04
2022-05-06 01:55:28,673 INFO [train.py:715] (2/8) Epoch 8, batch 2550, loss[loss=0.1021, simple_loss=0.177, pruned_loss=0.0136, over 4928.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2144, pruned_loss=0.03522, over 973876.06 frames.], batch size: 29, lr: 2.68e-04
2022-05-06 01:56:08,301 INFO [train.py:715] (2/8) Epoch 8, batch 2600, loss[loss=0.1474, simple_loss=0.2251, pruned_loss=0.03479, over 4924.00 frames.], tot_loss[loss=0.1431, simple_loss=0.215, pruned_loss=0.03556, over 974105.23 frames.], batch size: 18, lr: 2.68e-04
2022-05-06 01:56:47,550 INFO [train.py:715] (2/8) Epoch 8, batch 2650, loss[loss=0.1055, simple_loss=0.1717, pruned_loss=0.01961, over 4839.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2145, pruned_loss=0.03523, over 973599.47 frames.], batch size: 13, lr: 2.68e-04
2022-05-06 01:57:27,029 INFO [train.py:715] (2/8) Epoch 8, batch 2700, loss[loss=0.1398, simple_loss=0.2117, pruned_loss=0.03399, over 4833.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2155, pruned_loss=0.03543, over 973967.86 frames.], batch size: 13, lr: 2.68e-04
2022-05-06 01:58:06,372 INFO [train.py:715] (2/8) Epoch 8, batch 2750, loss[loss=0.1607, simple_loss=0.2317, pruned_loss=0.04486, over 4975.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.03594, over 973860.72 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 01:58:45,750 INFO [train.py:715] (2/8) Epoch 8, batch 2800, loss[loss=0.1491, simple_loss=0.2081, pruned_loss=0.04507, over 4785.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2163, pruned_loss=0.03607, over 972774.60 frames.], batch size: 17, lr: 2.67e-04
2022-05-06 01:59:24,995 INFO [train.py:715] (2/8) Epoch 8, batch 2850, loss[loss=0.1315, simple_loss=0.2089, pruned_loss=0.02711, over 4821.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.03599, over 972405.55 frames.], batch size: 27, lr: 2.67e-04
2022-05-06 02:00:03,843 INFO [train.py:715] (2/8) Epoch 8, batch 2900, loss[loss=0.1518, simple_loss=0.2132, pruned_loss=0.04517, over 4944.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2165, pruned_loss=0.03621, over 973086.91 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 02:00:43,806 INFO [train.py:715] (2/8) Epoch 8, batch 2950, loss[loss=0.1363, simple_loss=0.2106, pruned_loss=0.03101, over 4948.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03558, over 972859.58 frames.], batch size: 21, lr: 2.67e-04
2022-05-06 02:01:22,467 INFO [train.py:715] (2/8) Epoch 8, batch 3000, loss[loss=0.1824, simple_loss=0.2546, pruned_loss=0.05508, over 4692.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03502, over 973268.68 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:01:22,467 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 02:01:32,130 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1076, simple_loss=0.1923, pruned_loss=0.0115, over 914524.00 frames.
2022-05-06 02:02:11,365 INFO [train.py:715] (2/8) Epoch 8, batch 3050, loss[loss=0.1394, simple_loss=0.216, pruned_loss=0.03139, over 4920.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03575, over 973154.05 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 02:02:50,368 INFO [train.py:715] (2/8) Epoch 8, batch 3100, loss[loss=0.151, simple_loss=0.2257, pruned_loss=0.03815, over 4926.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.0366, over 973149.55 frames.], batch size: 29, lr: 2.67e-04
2022-05-06 02:03:29,321 INFO [train.py:715] (2/8) Epoch 8, batch 3150, loss[loss=0.1348, simple_loss=0.2097, pruned_loss=0.02993, over 4827.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2177, pruned_loss=0.0365, over 972774.12 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:04:09,014 INFO [train.py:715] (2/8) Epoch 8, batch 3200, loss[loss=0.1523, simple_loss=0.2252, pruned_loss=0.03976, over 4990.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2182, pruned_loss=0.03683, over 972535.31 frames.], batch size: 24, lr: 2.67e-04
2022-05-06 02:04:48,446 INFO [train.py:715] (2/8) Epoch 8, batch 3250, loss[loss=0.1589, simple_loss=0.2243, pruned_loss=0.04672, over 4815.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2184, pruned_loss=0.03647, over 972484.92 frames.], batch size: 26, lr: 2.67e-04
2022-05-06 02:05:28,477 INFO [train.py:715] (2/8) Epoch 8, batch 3300, loss[loss=0.175, simple_loss=0.2295, pruned_loss=0.0602, over 4861.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2183, pruned_loss=0.03669, over 973132.09 frames.], batch size: 16, lr: 2.67e-04
2022-05-06 02:06:08,838 INFO [train.py:715] (2/8) Epoch 8, batch 3350, loss[loss=0.1243, simple_loss=0.1904, pruned_loss=0.02908, over 4960.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2169, pruned_loss=0.0362, over 973640.14 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:06:49,942 INFO [train.py:715] (2/8) Epoch 8, batch 3400, loss[loss=0.1255, simple_loss=0.1997, pruned_loss=0.02564, over 4966.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2162, pruned_loss=0.03544, over 973274.11 frames.], batch size: 14, lr: 2.67e-04
2022-05-06 02:07:30,801 INFO [train.py:715] (2/8) Epoch 8, batch 3450, loss[loss=0.1392, simple_loss=0.2136, pruned_loss=0.03241, over 4940.00 frames.], tot_loss[loss=0.144, simple_loss=0.2165, pruned_loss=0.03572, over 973512.43 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 02:08:11,004 INFO [train.py:715] (2/8) Epoch 8, batch 3500, loss[loss=0.1214, simple_loss=0.1906, pruned_loss=0.0261, over 4796.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2166, pruned_loss=0.03617, over 973022.80 frames.], batch size: 12, lr: 2.67e-04
2022-05-06 02:08:52,346 INFO [train.py:715] (2/8) Epoch 8, batch 3550, loss[loss=0.1274, simple_loss=0.2018, pruned_loss=0.02653, over 4894.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2169, pruned_loss=0.03584, over 972382.29 frames.], batch size: 22, lr: 2.67e-04
2022-05-06 02:09:33,212 INFO [train.py:715] (2/8) Epoch 8, batch 3600, loss[loss=0.1338, simple_loss=0.2097, pruned_loss=0.029, over 4785.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2166, pruned_loss=0.0355, over 972935.35 frames.], batch size: 17, lr: 2.67e-04
2022-05-06 02:10:13,454 INFO [train.py:715] (2/8) Epoch 8, batch 3650, loss[loss=0.11, simple_loss=0.1858, pruned_loss=0.01705, over 4784.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2167, pruned_loss=0.03591, over 971605.89 frames.], batch size: 12, lr: 2.67e-04
2022-05-06 02:10:53,932 INFO [train.py:715] (2/8) Epoch 8, batch 3700, loss[loss=0.1858, simple_loss=0.2426, pruned_loss=0.06451, over 4865.00 frames.], tot_loss[loss=0.144, simple_loss=0.2165, pruned_loss=0.03578, over 972214.46 frames.], batch size: 32, lr: 2.67e-04
2022-05-06 02:11:34,282 INFO [train.py:715] (2/8) Epoch 8, batch 3750, loss[loss=0.1249, simple_loss=0.1982, pruned_loss=0.02581, over 4928.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03562, over 972837.63 frames.], batch size: 29, lr: 2.67e-04
2022-05-06 02:12:13,643 INFO [train.py:715] (2/8) Epoch 8, batch 3800, loss[loss=0.1225, simple_loss=0.2003, pruned_loss=0.02238, over 4858.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03563, over 972905.68 frames.], batch size: 34, lr: 2.67e-04
2022-05-06 02:12:54,028 INFO [train.py:715] (2/8) Epoch 8, batch 3850, loss[loss=0.1445, simple_loss=0.2248, pruned_loss=0.03206, over 4791.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2161, pruned_loss=0.03569, over 972806.07 frames.], batch size: 24, lr: 2.67e-04
2022-05-06 02:13:34,219 INFO [train.py:715] (2/8) Epoch 8, batch 3900, loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.0314, over 4814.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.0353, over 972822.61 frames.], batch size: 15, lr: 2.67e-04
2022-05-06 02:14:14,988 INFO [train.py:715] (2/8) Epoch 8, batch 3950, loss[loss=0.1167, simple_loss=0.1941, pruned_loss=0.01964, over 4981.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03522, over 972614.07 frames.], batch size: 25, lr: 2.67e-04
2022-05-06 02:14:54,920 INFO [train.py:715] (2/8) Epoch 8, batch 4000, loss[loss=0.148, simple_loss=0.2115, pruned_loss=0.04225, over 4944.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.0351, over 972801.79 frames.], batch size: 21, lr: 2.67e-04
2022-05-06 02:15:35,358 INFO [train.py:715] (2/8) Epoch 8, batch 4050, loss[loss=0.1403, simple_loss=0.2325, pruned_loss=0.02402, over 4808.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2151, pruned_loss=0.03538, over 973189.44 frames.], batch size: 27, lr: 2.67e-04
2022-05-06 02:16:16,174 INFO [train.py:715] (2/8) Epoch 8, batch 4100, loss[loss=0.1732, simple_loss=0.2389, pruned_loss=0.05376, over 4753.00 frames.], tot_loss[loss=0.1429, simple_loss=0.215, pruned_loss=0.03545, over 972271.48 frames.], batch size: 16, lr: 2.67e-04
2022-05-06 02:16:55,926 INFO [train.py:715] (2/8) Epoch 8, batch 4150, loss[loss=0.1784, simple_loss=0.243, pruned_loss=0.05695, over 4884.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2141, pruned_loss=0.03507, over 972258.87 frames.], batch size: 19, lr: 2.67e-04
2022-05-06 02:17:35,661 INFO [train.py:715] (2/8) Epoch 8, batch 4200, loss[loss=0.1494, simple_loss=0.2336, pruned_loss=0.03267, over 4785.00 frames.], tot_loss[loss=0.142, simple_loss=0.214, pruned_loss=0.03504, over 972640.56 frames.], batch size: 18, lr: 2.67e-04
2022-05-06 02:18:15,256 INFO [train.py:715] (2/8) Epoch 8, batch 4250, loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03059, over 4970.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2145, pruned_loss=0.03518, over 971735.09 frames.], batch size: 25, lr: 2.67e-04
2022-05-06 02:18:54,989 INFO [train.py:715] (2/8) Epoch 8, batch 4300, loss[loss=0.1562, simple_loss=0.2372, pruned_loss=0.03759, over 4960.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03502, over 972034.78 frames.], batch size: 39, lr: 2.67e-04
2022-05-06 02:19:34,153 INFO [train.py:715] (2/8) Epoch 8, batch 4350, loss[loss=0.1605, simple_loss=0.2163, pruned_loss=0.05237, over 4872.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03545, over 972103.14 frames.], batch size: 16, lr: 2.67e-04
2022-05-06 02:20:13,561 INFO [train.py:715] (2/8) Epoch 8, batch 4400, loss[loss=0.1298, simple_loss=0.1965, pruned_loss=0.0315, over 4817.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03569, over 972606.92 frames.], batch size: 13, lr: 2.67e-04
2022-05-06 02:20:53,465 INFO [train.py:715] (2/8) Epoch 8, batch 4450, loss[loss=0.1628, simple_loss=0.2313, pruned_loss=0.04716, over 4957.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03575, over 973414.84 frames.], batch size: 35, lr: 2.67e-04
2022-05-06 02:21:33,240 INFO [train.py:715] (2/8) Epoch 8, batch 4500, loss[loss=0.1456, simple_loss=0.2116, pruned_loss=0.03983, over 4830.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2168, pruned_loss=0.03632, over 972658.18 frames.], batch size: 30, lr: 2.67e-04
2022-05-06 02:22:12,202 INFO [train.py:715] (2/8) Epoch 8, batch 4550, loss[loss=0.1363, simple_loss=0.2123, pruned_loss=0.03016, over 4766.00 frames.], tot_loss[loss=0.144, simple_loss=0.216, pruned_loss=0.03599, over 972679.35 frames.], batch size: 19, lr: 2.67e-04
2022-05-06 02:22:52,188 INFO [train.py:715] (2/8) Epoch 8, batch 4600, loss[loss=0.1466, simple_loss=0.2212, pruned_loss=0.03602, over 4795.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2158, pruned_loss=0.03587, over 972281.49 frames.], batch size: 18, lr: 2.67e-04
2022-05-06 02:23:31,721 INFO [train.py:715] (2/8) Epoch 8, batch 4650, loss[loss=0.161, simple_loss=0.2455, pruned_loss=0.03824, over 4975.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2168, pruned_loss=0.03613, over 972971.74 frames.], batch size: 24, lr: 2.67e-04
2022-05-06 02:24:11,300 INFO [train.py:715] (2/8) Epoch 8, batch 4700, loss[loss=0.1305, simple_loss=0.1966, pruned_loss=0.0322, over 4747.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03544, over 972809.36 frames.], batch size: 16, lr: 2.67e-04
2022-05-06 02:24:50,831 INFO [train.py:715] (2/8) Epoch 8, batch 4750, loss[loss=0.1535, simple_loss=0.2096, pruned_loss=0.04874, over 4735.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03559, over 973143.38 frames.], batch size: 16, lr: 2.67e-04
2022-05-06 02:25:30,504 INFO [train.py:715] (2/8) Epoch 8, batch 4800, loss[loss=0.1325, simple_loss=0.2143, pruned_loss=0.02534, over 4931.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2155, pruned_loss=0.03579, over 972845.21 frames.], batch size: 23, lr: 2.67e-04
2022-05-06 02:26:10,388 INFO [train.py:715] (2/8) Epoch 8, batch 4850, loss[loss=0.1237, simple_loss=0.1996, pruned_loss=0.02391, over 4891.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03542, over 973348.36 frames.], batch size: 19, lr: 2.66e-04
2022-05-06 02:26:49,517 INFO [train.py:715] (2/8) Epoch 8, batch 4900, loss[loss=0.1219, simple_loss=0.1966, pruned_loss=0.02358, over 4983.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.03606, over 973459.68 frames.], batch size: 25, lr: 2.66e-04
2022-05-06 02:27:29,277 INFO [train.py:715] (2/8) Epoch 8, batch 4950, loss[loss=0.1375, simple_loss=0.2081, pruned_loss=0.03349, over 4764.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03676, over 973128.16 frames.], batch size: 12, lr: 2.66e-04
2022-05-06 02:28:08,943 INFO [train.py:715] (2/8) Epoch 8, batch 5000, loss[loss=0.1338, simple_loss=0.2102, pruned_loss=0.02873, over 4914.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03652, over 973053.21 frames.], batch size: 18, lr: 2.66e-04
2022-05-06 02:28:47,816 INFO [train.py:715] (2/8) Epoch 8, batch 5050, loss[loss=0.1126, simple_loss=0.192, pruned_loss=0.01656, over 4990.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03655, over 973257.11 frames.], batch size: 14, lr: 2.66e-04
2022-05-06 02:29:26,961 INFO [train.py:715] (2/8) Epoch 8, batch 5100, loss[loss=0.1614, simple_loss=0.2244, pruned_loss=0.04922, over 4892.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03666, over 973034.71 frames.], batch size: 22, lr: 2.66e-04
2022-05-06 02:30:06,430 INFO [train.py:715] (2/8) Epoch 8, batch 5150, loss[loss=0.1436, simple_loss=0.2082, pruned_loss=0.0395, over 4934.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2175, pruned_loss=0.03661, over 973007.47 frames.], batch size: 21, lr: 2.66e-04
2022-05-06 02:30:45,334 INFO [train.py:715] (2/8) Epoch 8, batch 5200, loss[loss=0.1792, simple_loss=0.2408, pruned_loss=0.05884, over 4957.00 frames.], tot_loss[loss=0.1446, simple_loss=0.217, pruned_loss=0.03612, over 973587.19 frames.], batch size: 35, lr: 2.66e-04
2022-05-06 02:31:24,027 INFO [train.py:715] (2/8) Epoch 8, batch 5250, loss[loss=0.1184, simple_loss=0.183, pruned_loss=0.02685, over 4842.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2161, pruned_loss=0.03555, over 972879.34 frames.], batch size: 13, lr: 2.66e-04
2022-05-06 02:32:04,135 INFO [train.py:715] (2/8) Epoch 8, batch 5300, loss[loss=0.1202, simple_loss=0.1972, pruned_loss=0.02162, over 4868.00 frames.], tot_loss[loss=0.144, simple_loss=0.2166, pruned_loss=0.0357, over 973243.87 frames.], batch size: 20, lr: 2.66e-04
2022-05-06 02:32:43,758 INFO [train.py:715] (2/8) Epoch 8, batch 5350, loss[loss=0.1511, simple_loss=0.2219, pruned_loss=0.0402, over 4767.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03518, over 973048.93 frames.], batch size: 18, lr: 2.66e-04
2022-05-06 02:33:23,694 INFO [train.py:715] (2/8) Epoch 8, batch 5400, loss[loss=0.1462, simple_loss=0.223, pruned_loss=0.03468, over 4953.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2153, pruned_loss=0.0355, over 972574.29 frames.], batch size: 21, lr: 2.66e-04
2022-05-06 02:34:04,181 INFO [train.py:715] (2/8) Epoch 8, batch 5450, loss[loss=0.1497, simple_loss=0.224, pruned_loss=0.03772, over 4753.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03587, over 972479.21 frames.], batch size: 19, lr: 2.66e-04
2022-05-06 02:34:44,676 INFO [train.py:715] (2/8) Epoch 8, batch 5500, loss[loss=0.1543, simple_loss=0.2204, pruned_loss=0.04409, over 4803.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03565, over 972514.94 frames.], batch size: 14, lr: 2.66e-04
2022-05-06 02:35:24,971 INFO [train.py:715] (2/8) Epoch 8, batch 5550, loss[loss=0.1243, simple_loss=0.1973, pruned_loss=0.02562, over 4893.00 frames.], tot_loss[loss=0.145, simple_loss=0.2169, pruned_loss=0.03654, over 972451.60 frames.], batch size: 22, lr: 2.66e-04
2022-05-06 02:36:04,811 INFO [train.py:715] (2/8) Epoch 8, batch 5600, loss[loss=0.1565, simple_loss=0.2245, pruned_loss=0.04425, over 4735.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03639, over 972532.92 frames.], batch size: 16, lr: 2.66e-04
2022-05-06 02:36:44,876 INFO [train.py:715] (2/8) Epoch 8, batch 5650, loss[loss=0.1411, simple_loss=0.2167, pruned_loss=0.03276, over 4823.00 frames.], tot_loss[loss=0.144, simple_loss=0.2155, pruned_loss=0.0362, over 971985.62 frames.], batch size: 21, lr: 2.66e-04
2022-05-06 02:37:24,006 INFO [train.py:715] (2/8) Epoch 8, batch 5700, loss[loss=0.1264, simple_loss=0.1979, pruned_loss=0.0274, over 4775.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2166, pruned_loss=0.03693, over 972378.01 frames.], batch size: 12, lr: 2.66e-04
2022-05-06 02:38:03,515 INFO [train.py:715] (2/8) Epoch 8, batch 5750, loss[loss=0.1417, simple_loss=0.2114, pruned_loss=0.03597, over 4866.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2167, pruned_loss=0.03673, over 973583.37 frames.], batch size: 39, lr: 2.66e-04
2022-05-06 02:38:42,303 INFO [train.py:715] (2/8) Epoch 8, batch 5800, loss[loss=0.1606, simple_loss=0.2217, pruned_loss=0.04975, over 4884.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2164, pruned_loss=0.03622, over 973581.53 frames.], batch size: 32, lr: 2.66e-04
2022-05-06 02:39:21,799 INFO [train.py:715] (2/8) Epoch 8, batch 5850, loss[loss=0.1383, simple_loss=0.2185, pruned_loss=0.0291, over 4947.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2158, pruned_loss=0.03569, over 972797.43 frames.], batch size: 21, lr: 2.66e-04
2022-05-06 02:40:00,571 INFO [train.py:715] (2/8) Epoch 8, batch 5900, loss[loss=0.1688, simple_loss=0.2413, pruned_loss=0.04812, over 4810.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03532, over 972248.50 frames.], batch size: 21, lr: 2.66e-04
2022-05-06 02:40:40,149 INFO [train.py:715] (2/8) Epoch 8, batch 5950, loss[loss=0.1398, simple_loss=0.2162, pruned_loss=0.03174, over 4872.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2162, pruned_loss=0.03536, over 971997.78 frames.], batch size: 16, lr: 2.66e-04
2022-05-06 02:41:20,033 INFO [train.py:715] (2/8) Epoch 8, batch 6000, loss[loss=0.1592, simple_loss=0.2336, pruned_loss=0.04243, over 4907.00 frames.], tot_loss[loss=0.144, simple_loss=0.2168, pruned_loss=0.03565, over 972008.25 frames.], batch size: 19, lr: 2.66e-04
2022-05-06 02:41:20,033 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 02:41:29,608 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1075, simple_loss=0.1921, pruned_loss=0.01146, over 914524.00 frames.
2022-05-06 02:42:09,069 INFO [train.py:715] (2/8) Epoch 8, batch 6050, loss[loss=0.1412, simple_loss=0.2078, pruned_loss=0.03731, over 4934.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2177, pruned_loss=0.03622, over 971825.77 frames.], batch size: 39, lr: 2.66e-04
2022-05-06 02:42:48,789 INFO [train.py:715] (2/8) Epoch 8, batch 6100, loss[loss=0.1507, simple_loss=0.2214, pruned_loss=0.03998, over 4973.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2179, pruned_loss=0.03612, over 972399.46 frames.], batch size: 28, lr: 2.66e-04
2022-05-06 02:43:28,439 INFO [train.py:715] (2/8) Epoch 8, batch 6150, loss[loss=0.1396, simple_loss=0.2196, pruned_loss=0.02977, over 4877.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2185, pruned_loss=0.03654, over 973103.12 frames.], batch size: 16, lr: 2.66e-04
2022-05-06 02:44:08,983 INFO [train.py:715] (2/8) Epoch 8, batch 6200, loss[loss=0.1114, simple_loss=0.1872, pruned_loss=0.01784, over 4741.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2181, pruned_loss=0.03683, over 971889.09 frames.], batch size: 12, lr: 2.66e-04
2022-05-06 02:44:49,472 INFO [train.py:715] (2/8) Epoch 8, batch 6250, loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.0353, over 4983.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2166, pruned_loss=0.03581, over 972471.26 frames.], batch size: 25, lr: 2.66e-04
2022-05-06 02:45:29,140 INFO [train.py:715] (2/8) Epoch 8, batch 6300, loss[loss=0.148, simple_loss=0.2176, pruned_loss=0.03924, over 4801.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2169, pruned_loss=0.03595, over 973036.38 frames.], batch size: 24, lr: 2.66e-04
2022-05-06 02:46:08,065 INFO [train.py:715] (2/8) Epoch 8, batch 6350, loss[loss=0.1152, simple_loss=0.1795, pruned_loss=0.02538, over 4825.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2161, pruned_loss=0.03554, over 973386.28 frames.], batch size: 30, lr: 2.66e-04
2022-05-06 02:46:47,829 INFO [train.py:715] (2/8) Epoch 8, batch 6400, loss[loss=0.1424, simple_loss=0.2125, pruned_loss=0.03617, over 4832.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2161, pruned_loss=0.03533, over 972629.83 frames.], batch size: 26, lr: 2.66e-04
2022-05-06 02:47:27,069 INFO [train.py:715] (2/8) Epoch 8, batch 6450, loss[loss=0.1308, simple_loss=0.2015, pruned_loss=0.03003, over 4957.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2164, pruned_loss=0.03619, over 973198.89 frames.], batch size: 39, lr: 2.66e-04
2022-05-06 02:48:06,521 INFO [train.py:715] (2/8) Epoch 8, batch 6500, loss[loss=0.1688, simple_loss=0.2456, pruned_loss=0.04598, over 4846.00 frames.], tot_loss[loss=0.144, simple_loss=0.2162, pruned_loss=0.03589, over 973263.35 frames.], batch size: 30, lr: 2.66e-04
2022-05-06 02:48:45,639 INFO [train.py:715] (2/8) Epoch 8, batch 6550, loss[loss=0.1409, simple_loss=0.2141, pruned_loss=0.03382, over 4964.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03534, over 972915.98 frames.], batch size: 15, lr: 2.66e-04
2022-05-06 02:49:25,301 INFO [train.py:715] (2/8) Epoch 8, batch 6600, loss[loss=0.1504, simple_loss=0.229, pruned_loss=0.03591, over 4855.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03538, over 972592.30 frames.], batch size: 30, lr: 2.66e-04
2022-05-06 02:50:04,626 INFO [train.py:715] (2/8) Epoch 8, batch 6650, loss[loss=0.1357, simple_loss=0.2043, pruned_loss=0.03355, over 4764.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.03556, over 972044.66 frames.], batch size: 19, lr: 2.66e-04
2022-05-06 02:50:43,403 INFO [train.py:715] (2/8) Epoch 8, batch 6700, loss[loss=0.1247, simple_loss=0.1988, pruned_loss=0.02526, over 4808.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03569, over 971407.94 frames.], batch size: 21, lr: 2.66e-04
2022-05-06 02:51:23,636 INFO [train.py:715] (2/8) Epoch 8, batch 6750, loss[loss=0.1601, simple_loss=0.2337, pruned_loss=0.04324, over 4969.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2167, pruned_loss=0.03596, over 972294.36 frames.], batch size: 24, lr: 2.66e-04
2022-05-06 02:52:03,057 INFO [train.py:715] (2/8) Epoch 8, batch 6800, loss[loss=0.1583, simple_loss=0.2303, pruned_loss=0.04314, over 4850.00 frames.], tot_loss[loss=0.145, simple_loss=0.2178, pruned_loss=0.03613, over 971822.89 frames.], batch size: 30, lr: 2.66e-04
2022-05-06 02:52:42,035 INFO [train.py:715] (2/8) Epoch 8, batch 6850, loss[loss=0.1161, simple_loss=0.2009, pruned_loss=0.0157, over 4953.00 frames.], tot_loss[loss=0.1451, simple_loss=0.218, pruned_loss=0.03607, over 972111.66 frames.], batch size: 29, lr: 2.66e-04
2022-05-06 02:53:21,963 INFO [train.py:715] (2/8) Epoch 8, batch 6900, loss[loss=0.1384, simple_loss=0.2098, pruned_loss=0.03354, over 4855.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2173, pruned_loss=0.0362, over 972492.74 frames.], batch size: 39, lr: 2.66e-04
2022-05-06 02:54:02,377 INFO [train.py:715] (2/8) Epoch 8, batch 6950, loss[loss=0.1253, simple_loss=0.2053, pruned_loss=0.02259, over 4863.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2172, pruned_loss=0.03624, over 972181.47 frames.], batch size: 20, lr: 2.66e-04
2022-05-06 02:54:42,193 INFO [train.py:715] (2/8) Epoch 8, batch 7000, loss[loss=0.1475, simple_loss=0.2198, pruned_loss=0.03755, over 4844.00 frames.], tot_loss[loss=0.1448, simple_loss=0.217, pruned_loss=0.03626, over 971515.64 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 02:55:21,786 INFO [train.py:715] (2/8) Epoch 8, batch 7050, loss[loss=0.1462, simple_loss=0.2169, pruned_loss=0.03773, over 4908.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2162, pruned_loss=0.03633, over 971788.46 frames.], batch size: 17, lr: 2.65e-04
2022-05-06 02:56:01,481 INFO [train.py:715] (2/8) Epoch 8, batch 7100, loss[loss=0.1764, simple_loss=0.2478, pruned_loss=0.0525, over 4924.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03589, over 972391.98 frames.], batch size: 39, lr: 2.65e-04
2022-05-06 02:56:41,145 INFO [train.py:715] (2/8) Epoch 8, batch 7150, loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03098, over 4978.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03583, over 972508.02 frames.], batch size: 28, lr: 2.65e-04
2022-05-06 02:57:20,446 INFO [train.py:715] (2/8) Epoch 8, batch 7200, loss[loss=0.1967, simple_loss=0.2628, pruned_loss=0.06528, over 4913.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.03574, over 972283.34 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 02:57:59,469 INFO [train.py:715] (2/8) Epoch 8, batch 7250, loss[loss=0.14, simple_loss=0.2054, pruned_loss=0.0373, over 4959.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03562, over 971637.84 frames.], batch size: 35, lr: 2.65e-04
2022-05-06 02:58:39,557 INFO [train.py:715] (2/8) Epoch 8, batch 7300, loss[loss=0.1456, simple_loss=0.2228, pruned_loss=0.03425, over 4742.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03578, over 972344.55 frames.], batch size: 16, lr: 2.65e-04
2022-05-06 02:59:18,930 INFO [train.py:715] (2/8) Epoch 8, batch 7350, loss[loss=0.1481, simple_loss=0.2195, pruned_loss=0.03837, over 4922.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03565, over 971965.08 frames.], batch size: 23, lr: 2.65e-04
2022-05-06 02:59:58,523 INFO [train.py:715] (2/8) Epoch 8, batch 7400, loss[loss=0.1494, simple_loss=0.2379, pruned_loss=0.03043, over 4795.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.0355, over 972637.42 frames.], batch size: 21, lr: 2.65e-04
2022-05-06 03:00:38,460 INFO [train.py:715] (2/8) Epoch 8, batch 7450, loss[loss=0.1224, simple_loss=0.1932, pruned_loss=0.02585, over 4990.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2163, pruned_loss=0.03608, over 972275.50 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:01:18,182 INFO [train.py:715] (2/8) Epoch 8, batch 7500, loss[loss=0.1429, simple_loss=0.203, pruned_loss=0.04137, over 4929.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2165, pruned_loss=0.03655, over 972189.25 frames.], batch size: 39, lr: 2.65e-04
2022-05-06 03:01:57,873 INFO [train.py:715] (2/8) Epoch 8, batch 7550, loss[loss=0.1495, simple_loss=0.2242, pruned_loss=0.03741, over 4804.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2168, pruned_loss=0.03686, over 971683.91 frames.], batch size: 21, lr: 2.65e-04
2022-05-06 03:02:37,820 INFO [train.py:715] (2/8) Epoch 8, batch 7600, loss[loss=0.1408, simple_loss=0.2301, pruned_loss=0.02573, over 4985.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2163, pruned_loss=0.03636, over 971587.04 frames.], batch size: 28, lr: 2.65e-04
2022-05-06 03:03:17,992 INFO [train.py:715] (2/8) Epoch 8, batch 7650, loss[loss=0.1687, simple_loss=0.2385, pruned_loss=0.04944, over 4819.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2167, pruned_loss=0.03633, over 972183.61 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:03:57,438 INFO [train.py:715] (2/8) Epoch 8, batch 7700, loss[loss=0.1794, simple_loss=0.2429, pruned_loss=0.05794, over 4842.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2173, pruned_loss=0.03681, over 972025.96 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:04:36,609 INFO [train.py:715] (2/8) Epoch 8, batch 7750, loss[loss=0.151, simple_loss=0.2217, pruned_loss=0.04014, over 4767.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.03674, over 972145.07 frames.], batch size: 18, lr: 2.65e-04
2022-05-06 03:05:16,799 INFO [train.py:715] (2/8) Epoch 8, batch 7800, loss[loss=0.1315, simple_loss=0.2016, pruned_loss=0.03071, over 4850.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03681, over 971857.41 frames.], batch size: 30, lr: 2.65e-04
2022-05-06 03:05:56,861 INFO [train.py:715] (2/8) Epoch 8, batch 7850, loss[loss=0.1426, simple_loss=0.207, pruned_loss=0.03906, over 4756.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2164, pruned_loss=0.03633, over 971655.84 frames.], batch size: 12, lr: 2.65e-04
2022-05-06 03:06:35,515 INFO [train.py:715] (2/8) Epoch 8, batch 7900, loss[loss=0.1579, simple_loss=0.2395, pruned_loss=0.03816, over 4822.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.03604, over 972141.78 frames.], batch size: 25, lr: 2.65e-04
2022-05-06 03:07:15,006 INFO [train.py:715] (2/8) Epoch 8, batch 7950, loss[loss=0.1269, simple_loss=0.2059, pruned_loss=0.024, over 4819.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2154, pruned_loss=0.03569, over 972285.15 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:07:54,691 INFO [train.py:715] (2/8) Epoch 8, batch 8000, loss[loss=0.1411, simple_loss=0.2186, pruned_loss=0.03184, over 4770.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2154, pruned_loss=0.03583, over 972059.83 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:08:33,646 INFO [train.py:715] (2/8) Epoch 8, batch 8050, loss[loss=0.1369, simple_loss=0.2043, pruned_loss=0.03471, over 4932.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2151, pruned_loss=0.03594, over 972698.82 frames.], batch size: 29, lr: 2.65e-04
2022-05-06 03:09:12,021 INFO [train.py:715] (2/8) Epoch 8, batch 8100, loss[loss=0.1337, simple_loss=0.2142, pruned_loss=0.0266, over 4794.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2151, pruned_loss=0.03574, over 972144.86 frames.], batch size: 24, lr: 2.65e-04
2022-05-06 03:09:51,245 INFO [train.py:715] (2/8) Epoch 8, batch 8150, loss[loss=0.1352, simple_loss=0.2131, pruned_loss=0.02864, over 4739.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2145, pruned_loss=0.03566, over 971682.69 frames.], batch size: 16, lr: 2.65e-04
2022-05-06 03:10:31,279 INFO [train.py:715] (2/8) Epoch 8, batch 8200, loss[loss=0.1294, simple_loss=0.2083, pruned_loss=0.02521, over 4817.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2151, pruned_loss=0.03535, over 972745.32 frames.], batch size: 21, lr: 2.65e-04
2022-05-06 03:11:09,919 INFO [train.py:715] (2/8) Epoch 8, batch 8250, loss[loss=0.1265, simple_loss=0.1914, pruned_loss=0.03082, over 4971.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2146, pruned_loss=0.03536, over 973006.57 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:11:48,870 INFO [train.py:715] (2/8) Epoch 8, batch 8300, loss[loss=0.1248, simple_loss=0.2001, pruned_loss=0.02471, over 4718.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03509, over 971905.53 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:12:28,296 INFO [train.py:715] (2/8) Epoch 8, batch 8350, loss[loss=0.1265, simple_loss=0.1983, pruned_loss=0.0274, over 4906.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2147, pruned_loss=0.03538, over 971840.09 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 03:13:07,311 INFO [train.py:715] (2/8) Epoch 8, batch 8400, loss[loss=0.1531, simple_loss=0.2286, pruned_loss=0.03883, over 4903.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03507, over 972226.04 frames.], batch size: 17, lr: 2.65e-04
2022-05-06 03:13:45,967 INFO [train.py:715] (2/8) Epoch 8, batch 8450, loss[loss=0.1279, simple_loss=0.2051, pruned_loss=0.02536, over 4984.00 frames.], tot_loss[loss=0.1434, simple_loss=0.216, pruned_loss=0.03542, over 973230.06 frames.], batch size: 20, lr: 2.65e-04
2022-05-06 03:14:25,534 INFO [train.py:715] (2/8) Epoch 8, batch 8500, loss[loss=0.194, simple_loss=0.2464, pruned_loss=0.0708, over 4918.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2164, pruned_loss=0.03589, over 973708.10 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 03:15:05,499 INFO [train.py:715] (2/8) Epoch 8, batch 8550, loss[loss=0.1288, simple_loss=0.2068, pruned_loss=0.02543, over 4970.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.03599, over 973539.25 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:15:44,164 INFO [train.py:715] (2/8) Epoch 8, batch 8600, loss[loss=0.1522, simple_loss=0.2278, pruned_loss=0.03831, over 4778.00 frames.], tot_loss[loss=0.144, simple_loss=0.2167, pruned_loss=0.03565, over 974534.30 frames.], batch size: 18, lr: 2.65e-04
2022-05-06 03:16:23,283 INFO [train.py:715] (2/8) Epoch 8, batch 8650, loss[loss=0.1298, simple_loss=0.208, pruned_loss=0.02584, over 4699.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2166, pruned_loss=0.03593, over 974307.77 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:17:02,902 INFO [train.py:715] (2/8) Epoch 8, batch 8700, loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03084, over 4909.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2158, pruned_loss=0.03564, over 973428.17 frames.], batch size: 18, lr: 2.65e-04
2022-05-06 03:17:41,700 INFO [train.py:715] (2/8) Epoch 8, batch 8750, loss[loss=0.1986, simple_loss=0.2676, pruned_loss=0.0648, over 4698.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.0358, over 973088.60 frames.], batch size: 15, lr: 2.65e-04
2022-05-06 03:18:20,673 INFO [train.py:715] (2/8) Epoch 8, batch 8800, loss[loss=0.1448, simple_loss=0.2023, pruned_loss=0.04361, over 4967.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.03615, over 972881.12 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:19:00,217 INFO [train.py:715] (2/8) Epoch 8, batch 8850, loss[loss=0.1684, simple_loss=0.2359, pruned_loss=0.05038, over 4935.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2171, pruned_loss=0.03621, over 972404.48 frames.], batch size: 18, lr: 2.65e-04
2022-05-06 03:19:39,728 INFO [train.py:715] (2/8) Epoch 8, batch 8900, loss[loss=0.1576, simple_loss=0.236, pruned_loss=0.03965, over 4910.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2172, pruned_loss=0.03625, over 971687.24 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 03:20:18,229 INFO [train.py:715] (2/8) Epoch 8, batch 8950, loss[loss=0.1356, simple_loss=0.1981, pruned_loss=0.03659, over 4753.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2164, pruned_loss=0.03599, over 972198.05 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 03:20:57,338 INFO [train.py:715] (2/8) Epoch 8, batch 9000, loss[loss=0.1237, simple_loss=0.2009, pruned_loss=0.02323, over 4882.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03589, over 972196.34 frames.], batch size: 22, lr: 2.65e-04
2022-05-06 03:20:57,339 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 03:21:06,881 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1075, simple_loss=0.1922, pruned_loss=0.01144, over 914524.00 frames.
2022-05-06 03:21:46,744 INFO [train.py:715] (2/8) Epoch 8, batch 9050, loss[loss=0.1059, simple_loss=0.1854, pruned_loss=0.01319, over 4755.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03524, over 972236.08 frames.], batch size: 19, lr: 2.65e-04
2022-05-06 03:22:26,224 INFO [train.py:715] (2/8) Epoch 8, batch 9100, loss[loss=0.1416, simple_loss=0.2112, pruned_loss=0.03604, over 4980.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.0355, over 973140.21 frames.], batch size: 14, lr: 2.65e-04
2022-05-06 03:23:05,922 INFO [train.py:715] (2/8) Epoch 8, batch 9150, loss[loss=0.1515, simple_loss=0.2242, pruned_loss=0.03942, over 4907.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2163, pruned_loss=0.03557, over 972417.37 frames.], batch size: 39, lr: 2.64e-04
2022-05-06 03:23:44,124 INFO [train.py:715] (2/8) Epoch 8, batch 9200, loss[loss=0.1343, simple_loss=0.2038, pruned_loss=0.03233, over 4832.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2165, pruned_loss=0.0359, over 971774.67 frames.], batch size: 30, lr: 2.64e-04
2022-05-06 03:24:23,667 INFO [train.py:715] (2/8) Epoch 8, batch 9250, loss[loss=0.1482, simple_loss=0.2274, pruned_loss=0.0345, over 4812.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2166, pruned_loss=0.03623, over 972150.21 frames.], batch size: 26, lr: 2.64e-04
2022-05-06 03:25:03,200 INFO [train.py:715] (2/8) Epoch 8, batch 9300, loss[loss=0.1524, simple_loss=0.2276, pruned_loss=0.03861, over 4850.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2168, pruned_loss=0.03615, over 973197.46 frames.], batch size: 32, lr: 2.64e-04
2022-05-06 03:25:42,062 INFO [train.py:715] (2/8) Epoch 8, batch 9350, loss[loss=0.1151, simple_loss=0.1936, pruned_loss=0.01833, over 4897.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.03548, over 972555.62 frames.], batch size: 19, lr: 2.64e-04
2022-05-06 03:26:20,917 INFO [train.py:715] (2/8) Epoch 8, batch 9400, loss[loss=0.1566, simple_loss=0.235, pruned_loss=0.03906, over 4986.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03538, over 972367.45 frames.], batch size: 28, lr: 2.64e-04
2022-05-06 03:27:00,378 INFO [train.py:715] (2/8) Epoch 8, batch 9450, loss[loss=0.1549, simple_loss=0.2187, pruned_loss=0.04557, over 4960.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03493, over 972569.61 frames.], batch size: 15, lr: 2.64e-04
2022-05-06 03:27:40,556 INFO [train.py:715] (2/8) Epoch 8, batch 9500, loss[loss=0.1377, simple_loss=0.1984, pruned_loss=0.03854, over 4828.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.03523, over 971823.18 frames.], batch size: 13, lr: 2.64e-04
2022-05-06 03:28:21,698 INFO [train.py:715] (2/8) Epoch 8, batch 9550, loss[loss=0.1509, simple_loss=0.2233, pruned_loss=0.03923, over 4988.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2151, pruned_loss=0.03564, over 971993.83 frames.], batch size: 20, lr: 2.64e-04
2022-05-06 03:29:01,752 INFO [train.py:715] (2/8) Epoch 8, batch 9600, loss[loss=0.155, simple_loss=0.222, pruned_loss=0.04401, over 4980.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2147, pruned_loss=0.03556, over 972163.59 frames.], batch size: 35, lr: 2.64e-04
2022-05-06 03:29:41,773 INFO [train.py:715] (2/8) Epoch 8, batch 9650, loss[loss=0.1199, simple_loss=0.1933, pruned_loss=0.02328, over 4771.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2141, pruned_loss=0.03519, over 972424.69 frames.], batch size: 18, lr: 2.64e-04
2022-05-06 03:30:21,100 INFO [train.py:715] (2/8) Epoch 8, batch 9700, loss[loss=0.1376, simple_loss=0.2135, pruned_loss=0.03088, over 4758.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2147, pruned_loss=0.03541, over 971937.26 frames.], batch size: 16, lr: 2.64e-04
2022-05-06 03:30:59,881 INFO [train.py:715] (2/8) Epoch 8, batch 9750, loss[loss=0.1708, simple_loss=0.2421, pruned_loss=0.04972, over 4835.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2145, pruned_loss=0.03508, over 971804.02 frames.], batch size: 30, lr: 2.64e-04
2022-05-06 03:31:39,480 INFO [train.py:715] (2/8) Epoch 8, batch 9800, loss[loss=0.1316, simple_loss=0.195, pruned_loss=0.03405, over 4839.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2154, pruned_loss=0.03547, over 971963.84 frames.], batch size: 15, lr: 2.64e-04
2022-05-06 03:32:18,976 INFO [train.py:715] (2/8) Epoch 8, batch 9850, loss[loss=0.1404, simple_loss=0.214, pruned_loss=0.03342, over 4961.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2157, pruned_loss=0.03526, over 972021.68 frames.], batch size: 24, lr: 2.64e-04
2022-05-06 03:32:58,276 INFO [train.py:715] (2/8) Epoch 8, batch 9900, loss[loss=0.1542, simple_loss=0.2349, pruned_loss=0.03671, over 4956.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2165, pruned_loss=0.0354, over 971897.15 frames.], batch size: 24, lr: 2.64e-04
2022-05-06 03:33:37,621 INFO [train.py:715] (2/8) Epoch 8, batch 9950, loss[loss=0.1533, simple_loss=0.2383, pruned_loss=0.03414, over 4992.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2154, pruned_loss=0.03501, over 971818.17 frames.], batch size: 20, lr: 2.64e-04
2022-05-06 03:34:17,534 INFO [train.py:715] (2/8) Epoch 8, batch 10000, loss[loss=0.126, simple_loss=0.2037, pruned_loss=0.02412, over 4785.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2154, pruned_loss=0.03482, over 972370.24 frames.], batch size: 18, lr: 2.64e-04
2022-05-06 03:34:56,512 INFO [train.py:715] (2/8) Epoch 8, batch 10050, loss[loss=0.2004, simple_loss=0.2678, pruned_loss=0.06647, over 4971.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2168, pruned_loss=0.03607, over 971589.12 frames.], batch size: 15, lr: 2.64e-04
2022-05-06 03:35:35,079 INFO [train.py:715] (2/8) Epoch 8, batch 10100, loss[loss=0.1487, simple_loss=0.2361, pruned_loss=0.03068, over 4856.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2163, pruned_loss=0.03599, over 971601.20 frames.], batch size: 20, lr: 2.64e-04
2022-05-06 03:36:15,140 INFO [train.py:715] (2/8) Epoch 8, batch 10150, loss[loss=0.1767, simple_loss=0.2336, pruned_loss=0.05995, over 4835.00 frames.], tot_loss[loss=0.144, simple_loss=0.2163, pruned_loss=0.03584, over 971277.01 frames.], batch size: 15, lr: 2.64e-04
2022-05-06 03:36:55,127 INFO [train.py:715] (2/8) Epoch 8, batch 10200, loss[loss=0.1253, simple_loss=0.1959, pruned_loss=0.02737, over 4758.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03564, over 971538.83 frames.], batch size: 19, lr: 2.64e-04
2022-05-06 03:37:34,624 INFO [train.py:715] (2/8) Epoch 8, batch 10250, loss[loss=0.1266, simple_loss=0.2045, pruned_loss=0.02437, over 4950.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03607, over 971802.29 frames.], batch size: 21, lr: 2.64e-04
2022-05-06 03:38:14,430 INFO [train.py:715] (2/8) Epoch 8, batch 10300, loss[loss=0.1728, simple_loss=0.2406, pruned_loss=0.05253, over 4894.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03569, over 972175.52 frames.], batch size: 16, lr: 2.64e-04
2022-05-06 03:38:53,951 INFO [train.py:715] (2/8) Epoch 8, batch 10350, loss[loss=0.1525, simple_loss=0.2185, pruned_loss=0.04318, over 4875.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2168, pruned_loss=0.03643, over 971034.19 frames.], batch size: 22, lr: 2.64e-04
2022-05-06 03:39:32,637 INFO [train.py:715] (2/8) Epoch 8, batch 10400, loss[loss=0.153, simple_loss=0.2289, pruned_loss=0.03852, over 4976.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2155, pruned_loss=0.03589, over 971423.75 frames.], batch size: 28, lr: 2.64e-04
2022-05-06 03:40:12,242 INFO [train.py:715] (2/8) Epoch 8, batch 10450, loss[loss=0.1353, simple_loss=0.2183, pruned_loss=0.02613, over 4986.00 frames.], tot_loss[loss=0.143, simple_loss=0.2148, pruned_loss=0.03554, over 971456.40 frames.], batch size: 28, lr: 2.64e-04
2022-05-06 03:40:51,305 INFO [train.py:715] (2/8) Epoch 8, batch 10500, loss[loss=0.1323, simple_loss=0.1886, pruned_loss=0.03801, over 4845.00 frames.], tot_loss[loss=0.1432, simple_loss=0.215, pruned_loss=0.03568, over 970752.74 frames.], batch size: 30, lr: 2.64e-04
2022-05-06 03:41:30,155 INFO [train.py:715] (2/8) Epoch 8, batch 10550, loss[loss=0.111, simple_loss=0.1771, pruned_loss=0.02246, over 4873.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2141, pruned_loss=0.03513, over 971550.70 frames.], batch size: 12, lr: 2.64e-04
2022-05-06 03:42:08,775 INFO [train.py:715] (2/8) Epoch 8, batch 10600, loss[loss=0.1684, simple_loss=0.2307, pruned_loss=0.05306, over 4950.00 frames.], tot_loss[loss=0.143, simple_loss=0.2151, pruned_loss=0.03549, over 971236.43 frames.], batch size: 35, lr: 2.64e-04
2022-05-06 03:42:48,075 INFO [train.py:715] (2/8) Epoch 8, batch 10650, loss[loss=0.1382, simple_loss=0.2135, pruned_loss=0.03146, over 4707.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.03569, over 971862.45 frames.], batch size: 15, lr: 2.64e-04
2022-05-06 03:43:27,255 INFO [train.py:715] (2/8) Epoch 8, batch 10700, loss[loss=0.1194, simple_loss=0.195, pruned_loss=0.02189, over 4932.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.03549, over 972602.12 frames.], batch size: 29, lr: 2.64e-04
2022-05-06 03:44:06,352 INFO [train.py:715] (2/8) Epoch 8, batch 10750, loss[loss=0.1248, simple_loss=0.1953, pruned_loss=0.02718, over 4889.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03581, over 972432.74 frames.], batch size: 22, lr: 2.64e-04
2022-05-06 03:44:46,313 INFO [train.py:715] (2/8) Epoch 8, batch 10800, loss[loss=0.1582, simple_loss=0.2272, pruned_loss=0.0446, over 4923.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2165, pruned_loss=0.03591, over 972144.23 frames.], batch size: 29, lr: 2.64e-04
2022-05-06 03:45:26,103 INFO [train.py:715] (2/8) Epoch 8, batch 10850, loss[loss=0.1597, simple_loss=0.2227, pruned_loss=0.04838, over 4859.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.03603, over 972173.82 frames.], batch size: 32, lr: 2.64e-04
2022-05-06 03:46:05,371 INFO [train.py:715] (2/8) Epoch 8, batch 10900, loss[loss=0.1808, simple_loss=0.2607, pruned_loss=0.05045, over 4951.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03633, over 972874.47 frames.], batch size: 35, lr: 2.64e-04
2022-05-06 03:46:44,374 INFO [train.py:715] (2/8) Epoch 8, batch 10950, loss[loss=0.1295, simple_loss=0.2009, pruned_loss=0.02905, over 4951.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2168, pruned_loss=0.03614, over 972980.10 frames.], batch size: 29, lr: 2.64e-04
2022-05-06 03:47:24,375 INFO [train.py:715] (2/8) Epoch 8, batch 11000, loss[loss=0.1474, simple_loss=0.2218, pruned_loss=0.03647, over 4933.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2174, pruned_loss=0.03667, over 972559.89 frames.], batch size: 18, lr: 2.64e-04
2022-05-06 03:48:03,912 INFO [train.py:715] (2/8) Epoch 8, batch 11050, loss[loss=0.1504, simple_loss=0.2223, pruned_loss=0.03922, over 4884.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2176, pruned_loss=0.03658, over 973324.92 frames.], batch size: 22, lr: 2.64e-04
2022-05-06 03:48:42,671 INFO [train.py:715] (2/8) Epoch 8, batch 11100, loss[loss=0.1313, simple_loss=0.2104, pruned_loss=0.02613, over 4865.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2169, pruned_loss=0.03601, over 973217.16 frames.], batch size: 20, lr: 2.64e-04
2022-05-06 03:49:22,146 INFO [train.py:715] (2/8) Epoch 8, batch 11150, loss[loss=0.1474, simple_loss=0.2255, pruned_loss=0.03471, over 4964.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2171, pruned_loss=0.03609, over 973135.72 frames.], batch size: 23, lr: 2.64e-04
2022-05-06 03:50:01,940 INFO [train.py:715] (2/8) Epoch 8, batch 11200, loss[loss=0.1528, simple_loss=0.2172, pruned_loss=0.04419, over 4698.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2176, pruned_loss=0.03663, over 973419.80 frames.], batch size: 15, lr: 2.64e-04
2022-05-06 03:50:40,567 INFO [train.py:715] (2/8) Epoch 8, batch 11250, loss[loss=0.1393, simple_loss=0.2131, pruned_loss=0.03278, over 4902.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2167, pruned_loss=0.03595, over 973300.39 frames.], batch size: 19, lr: 2.64e-04
2022-05-06 03:51:19,592 INFO [train.py:715] (2/8) Epoch 8, batch 11300, loss[loss=0.1316, simple_loss=0.2024, pruned_loss=0.0304, over 4775.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2169, pruned_loss=0.03607, over 972462.72 frames.], batch size: 12, lr: 2.64e-04
2022-05-06 03:51:58,928 INFO [train.py:715] (2/8) Epoch 8, batch 11350, loss[loss=0.2018, simple_loss=0.2629, pruned_loss=0.0703, over 4892.00 frames.], tot_loss[loss=0.145, simple_loss=0.2175, pruned_loss=0.03628, over 973312.04 frames.], batch size: 19, lr: 2.63e-04
2022-05-06 03:52:37,405 INFO [train.py:715] (2/8) Epoch 8, batch 11400, loss[loss=0.1228, simple_loss=0.1977, pruned_loss=0.02399, over 4833.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2173, pruned_loss=0.03668, over 972917.68 frames.], batch size: 13, lr: 2.63e-04
2022-05-06 03:53:16,049 INFO [train.py:715] (2/8) Epoch 8, batch 11450, loss[loss=0.1384, simple_loss=0.2137, pruned_loss=0.0316, over 4837.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2166, pruned_loss=0.03644, over 972463.02 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 03:53:55,355 INFO [train.py:715] (2/8) Epoch 8, batch 11500, loss[loss=0.1101, simple_loss=0.19, pruned_loss=0.01511, over 4954.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2159, pruned_loss=0.0359, over 972406.94 frames.], batch size: 21, lr: 2.63e-04
2022-05-06 03:54:34,457 INFO [train.py:715] (2/8) Epoch 8, batch 11550, loss[loss=0.1273, simple_loss=0.1981, pruned_loss=0.02822, over 4791.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2163, pruned_loss=0.03598, over 971605.08 frames.], batch size: 14, lr: 2.63e-04
2022-05-06 03:55:13,514 INFO [train.py:715] (2/8) Epoch 8, batch 11600, loss[loss=0.1355, simple_loss=0.208, pruned_loss=0.03146, over 4794.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2168, pruned_loss=0.03621, over 972234.68 frames.], batch size: 24, lr: 2.63e-04
2022-05-06 03:55:53,444 INFO [train.py:715] (2/8) Epoch 8, batch 11650, loss[loss=0.122, simple_loss=0.2108, pruned_loss=0.01657, over 4818.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2168, pruned_loss=0.03622, over 972942.29 frames.], batch size: 25, lr: 2.63e-04
2022-05-06 03:56:33,839 INFO [train.py:715] (2/8) Epoch 8, batch 11700, loss[loss=0.1616, simple_loss=0.2286, pruned_loss=0.04735, over 4899.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2169, pruned_loss=0.03649, over 972141.49 frames.], batch size: 19, lr: 2.63e-04
2022-05-06 03:57:13,266 INFO [train.py:715] (2/8) Epoch 8, batch 11750, loss[loss=0.1151, simple_loss=0.1831, pruned_loss=0.02353, over 4834.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2154, pruned_loss=0.03589, over 972632.12 frames.], batch size: 13, lr: 2.63e-04
2022-05-06 03:57:52,304 INFO [train.py:715] (2/8) Epoch 8, batch 11800, loss[loss=0.1462, simple_loss=0.2184, pruned_loss=0.03695, over 4909.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2161, pruned_loss=0.03639, over 972937.80 frames.], batch size: 23, lr: 2.63e-04
2022-05-06 03:58:32,066 INFO [train.py:715] (2/8) Epoch 8, batch 11850, loss[loss=0.1315, simple_loss=0.2078, pruned_loss=0.02764, over 4901.00 frames.], tot_loss[loss=0.144, simple_loss=0.2158, pruned_loss=0.03613, over 973169.23 frames.], batch size: 17, lr: 2.63e-04
2022-05-06 03:59:11,761 INFO [train.py:715] (2/8) Epoch 8, batch 11900, loss[loss=0.1483, simple_loss=0.2231, pruned_loss=0.03675, over 4713.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03574, over 971952.26 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 03:59:51,348 INFO [train.py:715] (2/8) Epoch 8, batch 11950, loss[loss=0.1471, simple_loss=0.2172, pruned_loss=0.03844, over 4702.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03572, over 972199.05 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 04:00:30,528 INFO [train.py:715] (2/8) Epoch 8, batch 12000, loss[loss=0.1453, simple_loss=0.2092, pruned_loss=0.04068, over 4845.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.0356, over 972323.74 frames.], batch size: 32, lr: 2.63e-04
2022-05-06 04:00:30,529 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 04:00:40,090 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1076, simple_loss=0.1923, pruned_loss=0.0115, over 914524.00 frames.
2022-05-06 04:01:19,839 INFO [train.py:715] (2/8) Epoch 8, batch 12050, loss[loss=0.1396, simple_loss=0.2188, pruned_loss=0.0302, over 4992.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2153, pruned_loss=0.03546, over 972361.03 frames.], batch size: 28, lr: 2.63e-04
2022-05-06 04:01:59,445 INFO [train.py:715] (2/8) Epoch 8, batch 12100, loss[loss=0.1451, simple_loss=0.2205, pruned_loss=0.03485, over 4928.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.03517, over 972973.97 frames.], batch size: 23, lr: 2.63e-04
2022-05-06 04:02:38,518 INFO [train.py:715] (2/8) Epoch 8, batch 12150, loss[loss=0.1408, simple_loss=0.2096, pruned_loss=0.03595, over 4889.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03504, over 973063.58 frames.], batch size: 22, lr: 2.63e-04
2022-05-06 04:03:17,590 INFO [train.py:715] (2/8) Epoch 8, batch 12200, loss[loss=0.1219, simple_loss=0.1935, pruned_loss=0.02514, over 4819.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.03527, over 972694.76 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 04:03:57,161 INFO [train.py:715] (2/8) Epoch 8, batch 12250, loss[loss=0.1477, simple_loss=0.2083, pruned_loss=0.04361, over 4938.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03489, over 972502.20 frames.], batch size: 18, lr: 2.63e-04
2022-05-06 04:04:36,394 INFO [train.py:715] (2/8) Epoch 8, batch 12300, loss[loss=0.1311, simple_loss=0.2109, pruned_loss=0.02568, over 4915.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03509, over 972620.95 frames.], batch size: 17, lr: 2.63e-04
2022-05-06 04:05:15,232 INFO [train.py:715] (2/8) Epoch 8, batch 12350, loss[loss=0.1387, simple_loss=0.2107, pruned_loss=0.03333, over 4750.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03533, over 972101.64 frames.], batch size: 16, lr: 2.63e-04
2022-05-06 04:05:54,658 INFO [train.py:715] (2/8) Epoch 8, batch 12400, loss[loss=0.1256, simple_loss=0.2026, pruned_loss=0.02436, over 4912.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2153, pruned_loss=0.03572, over 972398.32 frames.], batch size: 18, lr: 2.63e-04
2022-05-06 04:06:34,253 INFO [train.py:715] (2/8) Epoch 8, batch 12450, loss[loss=0.1146, simple_loss=0.1902, pruned_loss=0.01955, over 4994.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2149, pruned_loss=0.03544, over 972114.99 frames.], batch size: 14, lr: 2.63e-04
2022-05-06 04:07:13,257 INFO [train.py:715] (2/8) Epoch 8, batch 12500, loss[loss=0.1849, simple_loss=0.2625, pruned_loss=0.05368, over 4765.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2151, pruned_loss=0.03559, over 971653.32 frames.], batch size: 18, lr: 2.63e-04
2022-05-06 04:07:52,124 INFO [train.py:715] (2/8) Epoch 8, batch 12550, loss[loss=0.1273, simple_loss=0.203, pruned_loss=0.02578, over 4730.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2147, pruned_loss=0.03545, over 971887.65 frames.], batch size: 16, lr: 2.63e-04
2022-05-06 04:08:31,832 INFO [train.py:715] (2/8) Epoch 8, batch 12600, loss[loss=0.1234, simple_loss=0.2018, pruned_loss=0.02257, over 4966.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2148, pruned_loss=0.03527, over 972163.09 frames.], batch size: 24, lr: 2.63e-04
2022-05-06 04:09:10,878 INFO [train.py:715] (2/8) Epoch 8, batch 12650, loss[loss=0.1187, simple_loss=0.1959, pruned_loss=0.0208, over 4773.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03508, over 972745.18 frames.], batch size: 18, lr: 2.63e-04
2022-05-06 04:09:50,737 INFO [train.py:715] (2/8) Epoch 8, batch 12700, loss[loss=0.1575, simple_loss=0.2191, pruned_loss=0.04793, over 4784.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03555, over 972277.62 frames.], batch size: 17, lr: 2.63e-04
2022-05-06 04:10:30,140 INFO [train.py:715] (2/8) Epoch 8, batch 12750, loss[loss=0.1947, simple_loss=0.2677, pruned_loss=0.06087, over 4967.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03579, over 972193.76 frames.], batch size: 35, lr: 2.63e-04
2022-05-06 04:11:10,322 INFO [train.py:715] (2/8) Epoch 8, batch 12800, loss[loss=0.1305, simple_loss=0.2126, pruned_loss=0.0242, over 4923.00 frames.], tot_loss[loss=0.143, simple_loss=0.2151, pruned_loss=0.03548, over 972921.85 frames.], batch size: 23, lr: 2.63e-04
2022-05-06 04:11:48,982 INFO [train.py:715] (2/8) Epoch 8, batch 12850, loss[loss=0.12, simple_loss=0.2023, pruned_loss=0.01886, over 4977.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2151, pruned_loss=0.03551, over 972016.74 frames.], batch size: 35, lr: 2.63e-04
2022-05-06 04:12:28,015 INFO [train.py:715] (2/8) Epoch 8, batch 12900, loss[loss=0.1688, simple_loss=0.2459, pruned_loss=0.04584, over 4684.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03612, over 972417.48 frames.], batch size: 15, lr: 2.63e-04
2022-05-06 04:13:07,523 INFO [train.py:715] (2/8) Epoch 8, batch 12950, loss[loss=0.2007, simple_loss=0.2617, pruned_loss=0.06987, over 4918.00 frames.], tot_loss[loss=0.144, simple_loss=0.2158, pruned_loss=0.03611, over 973195.10 frames.], batch size: 18, lr: 2.63e-04
2022-05-06 04:13:46,911 INFO [train.py:715] (2/8) Epoch 8, batch 13000, loss[loss=0.1556, simple_loss=0.2229, pruned_loss=0.04415, over 4780.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2154, pruned_loss=0.03598, over 972456.23 frames.], batch size: 14, lr: 2.63e-04
2022-05-06 04:14:26,218 INFO [train.py:715] (2/8) Epoch 8, batch 13050, loss[loss=0.1389, simple_loss=0.2129, pruned_loss=0.0324, over 4897.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2153, pruned_loss=0.03563, over 972065.93 frames.], batch size: 19, lr: 2.63e-04
2022-05-06 04:15:05,641 INFO [train.py:715] (2/8) Epoch 8, batch 13100, loss[loss=0.2146, simple_loss=0.2904, pruned_loss=0.06943, over 4818.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03589, over 972063.60 frames.], batch size: 26, lr: 2.63e-04
2022-05-06 04:15:45,373 INFO [train.py:715] (2/8) Epoch 8, batch 13150, loss[loss=0.1333, simple_loss=0.2056, pruned_loss=0.03056, over 4963.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2161, pruned_loss=0.0365, over 972337.19 frames.], batch size: 21, lr: 2.63e-04
2022-05-06 04:16:24,329 INFO [train.py:715] (2/8) Epoch 8, batch 13200, loss[loss=0.134, simple_loss=0.2144, pruned_loss=0.02677, over 4888.00 frames.], tot_loss[loss=0.1446, simple_loss=0.216, pruned_loss=0.0366, over 973136.06 frames.], batch size: 19, lr: 2.63e-04
2022-05-06 04:17:03,716 INFO [train.py:715] (2/8) Epoch 8, batch 13250, loss[loss=0.1458, simple_loss=0.2246, pruned_loss=0.0335, over 4905.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03606, over 972767.76 frames.], batch size: 19, lr: 2.63e-04
2022-05-06 04:17:43,334 INFO [train.py:715] (2/8) Epoch 8, batch 13300, loss[loss=0.1481, simple_loss=0.2177, pruned_loss=0.03926, over 4800.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2154, pruned_loss=0.03602, over 972838.32 frames.], batch size: 12, lr: 2.63e-04
2022-05-06 04:18:22,354 INFO [train.py:715] (2/8) Epoch 8, batch 13350, loss[loss=0.1437, simple_loss=0.22, pruned_loss=0.03366, over 4758.00 frames.], tot_loss[loss=0.1434, simple_loss=0.215, pruned_loss=0.03588, over 972821.95 frames.], batch size: 16, lr: 2.63e-04
2022-05-06 04:19:01,001 INFO [train.py:715] (2/8) Epoch 8, batch 13400, loss[loss=0.155, simple_loss=0.2448, pruned_loss=0.03256, over 4980.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2144, pruned_loss=0.03522, over 972115.86 frames.], batch size: 28, lr: 2.63e-04
2022-05-06 04:19:39,798 INFO [train.py:715] (2/8) Epoch 8, batch 13450, loss[loss=0.1482, simple_loss=0.2238, pruned_loss=0.03631, over 4852.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2142, pruned_loss=0.03504, over 972845.33 frames.], batch size: 20, lr: 2.63e-04
2022-05-06 04:20:19,872 INFO [train.py:715] (2/8) Epoch 8, batch 13500, loss[loss=0.1272, simple_loss=0.203, pruned_loss=0.02574, over 4798.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03494, over 972792.47 frames.], batch size: 12, lr: 2.63e-04
2022-05-06 04:20:58,644 INFO [train.py:715] (2/8) Epoch 8, batch 13550, loss[loss=0.1523, simple_loss=0.2155, pruned_loss=0.0446, over 4822.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03465, over 972909.92 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:21:37,838 INFO [train.py:715] (2/8) Epoch 8, batch 13600, loss[loss=0.1585, simple_loss=0.2327, pruned_loss=0.04211, over 4767.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2143, pruned_loss=0.03478, over 972148.93 frames.], batch size: 16, lr: 2.62e-04
2022-05-06 04:22:16,977 INFO [train.py:715] (2/8) Epoch 8, batch 13650, loss[loss=0.128, simple_loss=0.1927, pruned_loss=0.03167, over 4874.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03507, over 972195.56 frames.], batch size: 32, lr: 2.62e-04
2022-05-06 04:22:56,127 INFO [train.py:715] (2/8) Epoch 8, batch 13700, loss[loss=0.1584, simple_loss=0.2292, pruned_loss=0.0438, over 4750.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2156, pruned_loss=0.03563, over 973051.11 frames.], batch size: 16, lr: 2.62e-04
2022-05-06 04:23:34,770 INFO [train.py:715] (2/8) Epoch 8, batch 13750, loss[loss=0.1105, simple_loss=0.1889, pruned_loss=0.01599, over 4806.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2147, pruned_loss=0.03525, over 973240.16 frames.], batch size: 24, lr: 2.62e-04
2022-05-06 04:24:13,492 INFO [train.py:715] (2/8) Epoch 8, batch 13800, loss[loss=0.1195, simple_loss=0.1983, pruned_loss=0.02034, over 4921.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2141, pruned_loss=0.03544, over 972521.66 frames.], batch size: 17, lr: 2.62e-04
2022-05-06 04:24:52,949 INFO [train.py:715] (2/8) Epoch 8, batch 13850, loss[loss=0.1283, simple_loss=0.2053, pruned_loss=0.02563, over 4757.00 frames.], tot_loss[loss=0.1423, simple_loss=0.214, pruned_loss=0.03532, over 972483.71 frames.], batch size: 16, lr: 2.62e-04
2022-05-06 04:25:31,240 INFO [train.py:715] (2/8) Epoch 8, batch 13900, loss[loss=0.1463, simple_loss=0.2231, pruned_loss=0.03477, over 4797.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2145, pruned_loss=0.0352, over 973154.92 frames.], batch size: 24, lr: 2.62e-04
2022-05-06 04:26:10,332 INFO [train.py:715] (2/8) Epoch 8, batch 13950, loss[loss=0.1202, simple_loss=0.1955, pruned_loss=0.02243, over 4689.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03574, over 971985.49 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:26:49,431 INFO [train.py:715] (2/8) Epoch 8, batch 14000, loss[loss=0.1529, simple_loss=0.2311, pruned_loss=0.0373, over 4757.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.036, over 972158.92 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:27:28,487 INFO [train.py:715] (2/8) Epoch 8, batch 14050, loss[loss=0.1493, simple_loss=0.222, pruned_loss=0.03834, over 4863.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2166, pruned_loss=0.03665, over 972131.80 frames.], batch size: 32, lr: 2.62e-04
2022-05-06 04:28:06,679 INFO [train.py:715] (2/8) Epoch 8, batch 14100, loss[loss=0.1453, simple_loss=0.2175, pruned_loss=0.03652, over 4899.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2163, pruned_loss=0.03602, over 972319.10 frames.], batch size: 17, lr: 2.62e-04
2022-05-06 04:28:45,330 INFO [train.py:715] (2/8) Epoch 8, batch 14150, loss[loss=0.1782, simple_loss=0.2433, pruned_loss=0.05658, over 4881.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2166, pruned_loss=0.03624, over 971030.49 frames.], batch size: 16, lr: 2.62e-04
2022-05-06 04:29:25,592 INFO [train.py:715] (2/8) Epoch 8, batch 14200, loss[loss=0.1165, simple_loss=0.1886, pruned_loss=0.02215, over 4971.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2154, pruned_loss=0.03578, over 971405.72 frames.], batch size: 25, lr: 2.62e-04
2022-05-06 04:30:04,163 INFO [train.py:715] (2/8) Epoch 8, batch 14250, loss[loss=0.1553, simple_loss=0.2085, pruned_loss=0.05108, over 4803.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2158, pruned_loss=0.03603, over 971003.51 frames.], batch size: 21, lr: 2.62e-04
2022-05-06 04:30:44,068 INFO [train.py:715] (2/8) Epoch 8, batch 14300, loss[loss=0.1344, simple_loss=0.216, pruned_loss=0.02646, over 4896.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.03613, over 971594.64 frames.], batch size: 17, lr: 2.62e-04
2022-05-06 04:31:23,530 INFO [train.py:715] (2/8) Epoch 8, batch 14350, loss[loss=0.1723, simple_loss=0.2455, pruned_loss=0.04957, over 4785.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2165, pruned_loss=0.03634, over 972467.22 frames.], batch size: 17, lr: 2.62e-04
2022-05-06 04:32:02,825 INFO [train.py:715] (2/8) Epoch 8, batch 14400, loss[loss=0.1375, simple_loss=0.2066, pruned_loss=0.03426, over 4848.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2171, pruned_loss=0.03611, over 972005.85 frames.], batch size: 20, lr: 2.62e-04
2022-05-06 04:32:41,517 INFO [train.py:715] (2/8) Epoch 8, batch 14450, loss[loss=0.1708, simple_loss=0.2493, pruned_loss=0.04617, over 4918.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2166, pruned_loss=0.03605, over 972539.16 frames.], batch size: 23, lr: 2.62e-04
2022-05-06 04:33:20,778 INFO [train.py:715] (2/8) Epoch 8, batch 14500, loss[loss=0.1246, simple_loss=0.2064, pruned_loss=0.02143, over 4776.00 frames.], tot_loss[loss=0.1447, simple_loss=0.217, pruned_loss=0.0362, over 971783.31 frames.], batch size: 17, lr: 2.62e-04
2022-05-06 04:34:00,256 INFO [train.py:715] (2/8) Epoch 8, batch 14550, loss[loss=0.1481, simple_loss=0.2165, pruned_loss=0.03988, over 4956.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.03635, over 972803.88 frames.], batch size: 21, lr: 2.62e-04
2022-05-06 04:34:38,289 INFO [train.py:715] (2/8) Epoch 8, batch 14600, loss[loss=0.1295, simple_loss=0.2069, pruned_loss=0.02611, over 4862.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03533, over 972069.10 frames.], batch size: 30, lr: 2.62e-04
2022-05-06 04:35:17,877 INFO [train.py:715] (2/8) Epoch 8, batch 14650, loss[loss=0.1658, simple_loss=0.2313, pruned_loss=0.05016, over 4943.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2155, pruned_loss=0.03584, over 971158.01 frames.], batch size: 39, lr: 2.62e-04
2022-05-06 04:35:57,139 INFO [train.py:715] (2/8) Epoch 8, batch 14700, loss[loss=0.1154, simple_loss=0.198, pruned_loss=0.01645, over 4830.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.03555, over 972030.41 frames.], batch size: 26, lr: 2.62e-04
2022-05-06 04:36:35,954 INFO [train.py:715] (2/8) Epoch 8, batch 14750, loss[loss=0.1216, simple_loss=0.2007, pruned_loss=0.0213, over 4785.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2165, pruned_loss=0.03595, over 971984.84 frames.], batch size: 17, lr: 2.62e-04
2022-05-06 04:37:14,352 INFO [train.py:715] (2/8) Epoch 8, batch 14800, loss[loss=0.146, simple_loss=0.2182, pruned_loss=0.03689, over 4820.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03614, over 971641.52 frames.], batch size: 27, lr: 2.62e-04
2022-05-06 04:37:54,164 INFO [train.py:715] (2/8) Epoch 8, batch 14850, loss[loss=0.1474, simple_loss=0.22, pruned_loss=0.03745, over 4923.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03577, over 971063.13 frames.], batch size: 18, lr: 2.62e-04
2022-05-06 04:38:33,086 INFO [train.py:715] (2/8) Epoch 8, batch 14900, loss[loss=0.131, simple_loss=0.2188, pruned_loss=0.02164, over 4775.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03591, over 971900.81 frames.], batch size: 14, lr: 2.62e-04
2022-05-06 04:39:11,871 INFO [train.py:715] (2/8) Epoch 8, batch 14950, loss[loss=0.1681, simple_loss=0.227, pruned_loss=0.05465, over 4928.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2153, pruned_loss=0.03575, over 972557.72 frames.], batch size: 35, lr: 2.62e-04
2022-05-06 04:39:51,071 INFO [train.py:715] (2/8) Epoch 8, batch 15000, loss[loss=0.157, simple_loss=0.2204, pruned_loss=0.04685, over 4919.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2151, pruned_loss=0.03565, over 972158.86 frames.], batch size: 18, lr: 2.62e-04
2022-05-06 04:39:51,071 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 04:40:00,792 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1076, simple_loss=0.1921, pruned_loss=0.01153, over 914524.00 frames.
2022-05-06 04:40:40,573 INFO [train.py:715] (2/8) Epoch 8, batch 15050, loss[loss=0.1429, simple_loss=0.2225, pruned_loss=0.03169, over 4827.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2149, pruned_loss=0.03563, over 973487.98 frames.], batch size: 27, lr: 2.62e-04
2022-05-06 04:41:19,877 INFO [train.py:715] (2/8) Epoch 8, batch 15100, loss[loss=0.1477, simple_loss=0.2112, pruned_loss=0.04212, over 4870.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2159, pruned_loss=0.03632, over 973278.65 frames.], batch size: 32, lr: 2.62e-04
2022-05-06 04:41:59,415 INFO [train.py:715] (2/8) Epoch 8, batch 15150, loss[loss=0.1776, simple_loss=0.2585, pruned_loss=0.04834, over 4966.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2161, pruned_loss=0.03603, over 973313.62 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:42:38,834 INFO [train.py:715] (2/8) Epoch 8, batch 15200, loss[loss=0.1206, simple_loss=0.2026, pruned_loss=0.01934, over 4693.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.03553, over 973115.31 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:43:18,560 INFO [train.py:715] (2/8) Epoch 8, batch 15250, loss[loss=0.1485, simple_loss=0.2246, pruned_loss=0.03618, over 4908.00 frames.], tot_loss[loss=0.1435, simple_loss=0.216, pruned_loss=0.03548, over 972915.96 frames.], batch size: 19, lr: 2.62e-04
2022-05-06 04:43:58,533 INFO [train.py:715] (2/8) Epoch 8, batch 15300, loss[loss=0.1242, simple_loss=0.1939, pruned_loss=0.0272, over 4861.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.03574, over 973349.88 frames.], batch size: 30, lr: 2.62e-04
2022-05-06 04:44:37,107 INFO [train.py:715] (2/8) Epoch 8, batch 15350, loss[loss=0.1658, simple_loss=0.2295, pruned_loss=0.05107, over 4971.00 frames.], tot_loss[loss=0.144, simple_loss=0.2164, pruned_loss=0.03587, over 972825.30 frames.], batch size: 39, lr: 2.62e-04
2022-05-06 04:45:16,995 INFO [train.py:715] (2/8) Epoch 8, batch 15400, loss[loss=0.1393, simple_loss=0.2132, pruned_loss=0.03269, over 4921.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2164, pruned_loss=0.03612, over 972383.51 frames.], batch size: 23, lr: 2.62e-04
2022-05-06 04:45:55,985 INFO [train.py:715] (2/8) Epoch 8, batch 15450, loss[loss=0.1478, simple_loss=0.2274, pruned_loss=0.03412, over 4824.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03619, over 972805.32 frames.], batch size: 26, lr: 2.62e-04
2022-05-06 04:46:34,942 INFO [train.py:715] (2/8) Epoch 8, batch 15500, loss[loss=0.1504, simple_loss=0.2242, pruned_loss=0.03834, over 4919.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03605, over 972838.29 frames.], batch size: 39, lr: 2.62e-04
2022-05-06 04:47:13,678 INFO [train.py:715] (2/8) Epoch 8, batch 15550, loss[loss=0.1358, simple_loss=0.2088, pruned_loss=0.03144, over 4918.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2167, pruned_loss=0.03622, over 972620.26 frames.], batch size: 18, lr: 2.62e-04
2022-05-06 04:47:52,418 INFO [train.py:715] (2/8) Epoch 8, batch 15600, loss[loss=0.1281, simple_loss=0.2089, pruned_loss=0.02364, over 4905.00 frames.], tot_loss[loss=0.144, simple_loss=0.2163, pruned_loss=0.03582, over 972564.51 frames.], batch size: 19, lr: 2.62e-04
2022-05-06 04:48:32,583 INFO [train.py:715] (2/8) Epoch 8, batch 15650, loss[loss=0.1134, simple_loss=0.1927, pruned_loss=0.01708, over 4800.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03576, over 972207.38 frames.], batch size: 21, lr: 2.62e-04
2022-05-06 04:49:11,089 INFO [train.py:715] (2/8) Epoch 8, batch 15700, loss[loss=0.1682, simple_loss=0.2395, pruned_loss=0.04844, over 4817.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.03524, over 972480.46 frames.], batch size: 15, lr: 2.62e-04
2022-05-06 04:49:50,912 INFO [train.py:715] (2/8) Epoch 8, batch 15750, loss[loss=0.142, simple_loss=0.2166, pruned_loss=0.03372, over 4920.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03528, over 973024.41 frames.], batch size: 29, lr: 2.62e-04
2022-05-06 04:50:30,391 INFO [train.py:715] (2/8) Epoch 8, batch 15800, loss[loss=0.1185, simple_loss=0.193, pruned_loss=0.02197, over 4925.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2158, pruned_loss=0.03533, over 973269.27 frames.], batch size: 29, lr: 2.61e-04
2022-05-06 04:51:09,452 INFO [train.py:715] (2/8) Epoch 8, batch 15850, loss[loss=0.137, simple_loss=0.2148, pruned_loss=0.0296, over 4857.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03577, over 973347.54 frames.], batch size: 20, lr: 2.61e-04
2022-05-06 04:51:48,553 INFO [train.py:715] (2/8) Epoch 8, batch 15900, loss[loss=0.1796, simple_loss=0.2493, pruned_loss=0.05493, over 4907.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03573, over 973618.23 frames.], batch size: 18, lr: 2.61e-04
2022-05-06 04:52:27,775 INFO [train.py:715] (2/8) Epoch 8, batch 15950, loss[loss=0.1442, simple_loss=0.206, pruned_loss=0.04121, over 4755.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2148, pruned_loss=0.03548, over 973883.54 frames.], batch size: 12, lr: 2.61e-04
2022-05-06 04:53:07,058 INFO [train.py:715] (2/8) Epoch 8, batch 16000, loss[loss=0.1438, simple_loss=0.2196, pruned_loss=0.03395, over 4805.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2159, pruned_loss=0.03588, over 972852.54 frames.], batch size: 26, lr: 2.61e-04
2022-05-06 04:53:45,655 INFO [train.py:715] (2/8) Epoch 8, batch 16050, loss[loss=0.1986, simple_loss=0.2921, pruned_loss=0.05258, over 4898.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03605, over 973454.45 frames.], batch size: 19, lr: 2.61e-04
2022-05-06 04:54:25,523 INFO [train.py:715] (2/8) Epoch 8, batch 16100, loss[loss=0.1538, simple_loss=0.218, pruned_loss=0.04482, over 4788.00 frames.], tot_loss[loss=0.144, simple_loss=0.2163, pruned_loss=0.03583, over 973388.98 frames.], batch size: 24, lr: 2.61e-04
2022-05-06 04:55:04,003 INFO [train.py:715] (2/8) Epoch 8, batch 16150, loss[loss=0.1678, simple_loss=0.2219, pruned_loss=0.05683, over 4831.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2164, pruned_loss=0.03609, over 972521.43 frames.], batch size: 15, lr: 2.61e-04
2022-05-06 04:55:43,546 INFO [train.py:715] (2/8) Epoch 8, batch 16200, loss[loss=0.1272, simple_loss=0.206, pruned_loss=0.02417, over 4899.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2167, pruned_loss=0.03616, over 972558.89 frames.], batch size: 32, lr: 2.61e-04
2022-05-06 04:56:21,929 INFO [train.py:715] (2/8) Epoch 8, batch 16250, loss[loss=0.1098, simple_loss=0.1818, pruned_loss=0.01887, over 4811.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2164, pruned_loss=0.0359, over 972735.58 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 04:57:01,392 INFO [train.py:715] (2/8) Epoch 8, batch 16300, loss[loss=0.156, simple_loss=0.2254, pruned_loss=0.04328, over 4844.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.03602, over 972755.18 frames.], batch size: 34, lr: 2.61e-04
2022-05-06 04:57:40,825 INFO [train.py:715] (2/8) Epoch 8, batch 16350, loss[loss=0.1254, simple_loss=0.2025, pruned_loss=0.02414, over 4757.00 frames.], tot_loss[loss=0.144, simple_loss=0.2163, pruned_loss=0.03588, over 972507.73 frames.], batch size: 19, lr: 2.61e-04
2022-05-06 04:58:19,597 INFO [train.py:715] (2/8) Epoch 8, batch 16400, loss[loss=0.1622, simple_loss=0.2391, pruned_loss=0.04263, over 4787.00 frames.], tot_loss[loss=0.1437, simple_loss=0.216, pruned_loss=0.03565, over 972267.35 frames.], batch size: 18, lr: 2.61e-04
2022-05-06 04:58:58,711 INFO [train.py:715] (2/8) Epoch 8, batch 16450, loss[loss=0.1418, simple_loss=0.2225, pruned_loss=0.03059, over 4939.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2172, pruned_loss=0.03654, over 972371.17 frames.], batch size: 18, lr: 2.61e-04
2022-05-06 04:59:37,557 INFO [train.py:715] (2/8) Epoch 8, batch 16500, loss[loss=0.1519, simple_loss=0.2274, pruned_loss=0.03819, over 4850.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03657, over 972074.23 frames.], batch size: 20, lr: 2.61e-04
2022-05-06 05:00:17,262 INFO [train.py:715] (2/8) Epoch 8, batch 16550, loss[loss=0.1211, simple_loss=0.1996, pruned_loss=0.02132, over 4984.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2173, pruned_loss=0.03656, over 971969.84 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 05:00:56,282 INFO [train.py:715] (2/8) Epoch 8, batch 16600, loss[loss=0.1649, simple_loss=0.229, pruned_loss=0.05035, over 4989.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2169, pruned_loss=0.0363, over 972224.28 frames.], batch size: 14, lr: 2.61e-04
2022-05-06 05:01:35,314 INFO [train.py:715] (2/8) Epoch 8, batch 16650, loss[loss=0.1569, simple_loss=0.2311, pruned_loss=0.04133, over 4989.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2173, pruned_loss=0.03641, over 973031.36 frames.], batch size: 28, lr: 2.61e-04
2022-05-06 05:02:14,554 INFO [train.py:715] (2/8) Epoch 8, batch 16700, loss[loss=0.1808, simple_loss=0.2581, pruned_loss=0.05176, over 4898.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2173, pruned_loss=0.03602, over 972867.48 frames.], batch size: 19, lr: 2.61e-04
2022-05-06 05:02:53,474 INFO [train.py:715] (2/8) Epoch 8, batch 16750, loss[loss=0.1623, simple_loss=0.2288, pruned_loss=0.04793, over 4760.00 frames.], tot_loss[loss=0.1445, simple_loss=0.217, pruned_loss=0.03602, over 972804.72 frames.], batch size: 19, lr: 2.61e-04
2022-05-06 05:03:33,068 INFO [train.py:715] (2/8) Epoch 8, batch 16800, loss[loss=0.136, simple_loss=0.2111, pruned_loss=0.03042, over 4819.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2158, pruned_loss=0.03521, over 971600.05 frames.], batch size: 27, lr: 2.61e-04
2022-05-06 05:04:12,045 INFO [train.py:715] (2/8) Epoch 8, batch 16850, loss[loss=0.1388, simple_loss=0.2122, pruned_loss=0.03266, over 4943.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.03548, over 972226.66 frames.], batch size: 39, lr: 2.61e-04
2022-05-06 05:04:51,956 INFO [train.py:715] (2/8) Epoch 8, batch 16900, loss[loss=0.1638, simple_loss=0.2298, pruned_loss=0.04887, over 4804.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03577, over 972225.67 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 05:05:30,452 INFO [train.py:715] (2/8) Epoch 8, batch 16950, loss[loss=0.1898, simple_loss=0.2712, pruned_loss=0.0542, over 4943.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03577, over 972106.63 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:06:10,150 INFO [train.py:715] (2/8) Epoch 8, batch 17000, loss[loss=0.151, simple_loss=0.2196, pruned_loss=0.04125, over 4835.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03545, over 971953.56 frames.], batch size: 15, lr: 2.61e-04
2022-05-06 05:06:49,664 INFO [train.py:715] (2/8) Epoch 8, batch 17050, loss[loss=0.1579, simple_loss=0.2315, pruned_loss=0.04211, over 4819.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03567, over 972603.91 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 05:07:28,338 INFO [train.py:715] (2/8) Epoch 8, batch 17100, loss[loss=0.1473, simple_loss=0.2277, pruned_loss=0.03344, over 4803.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03577, over 972793.14 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:08:08,034 INFO [train.py:715] (2/8) Epoch 8, batch 17150, loss[loss=0.1565, simple_loss=0.2177, pruned_loss=0.04762, over 4750.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03579, over 972831.30 frames.], batch size: 19, lr: 2.61e-04
2022-05-06 05:08:47,205 INFO [train.py:715] (2/8) Epoch 8, batch 17200, loss[loss=0.129, simple_loss=0.2049, pruned_loss=0.02648, over 4820.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03517, over 972668.40 frames.], batch size: 26, lr: 2.61e-04
2022-05-06 05:09:26,329 INFO [train.py:715] (2/8) Epoch 8, batch 17250, loss[loss=0.1674, simple_loss=0.2403, pruned_loss=0.04723, over 4865.00 frames.], tot_loss[loss=0.143, simple_loss=0.2157, pruned_loss=0.03511, over 973214.58 frames.], batch size: 32, lr: 2.61e-04
2022-05-06 05:10:04,656 INFO [train.py:715] (2/8) Epoch 8, batch 17300, loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.0356, over 4922.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2152, pruned_loss=0.03485, over 972956.21 frames.], batch size: 29, lr: 2.61e-04
2022-05-06 05:10:44,495 INFO [train.py:715] (2/8) Epoch 8, batch 17350, loss[loss=0.1474, simple_loss=0.2075, pruned_loss=0.04368, over 4842.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03525, over 972217.30 frames.], batch size: 30, lr: 2.61e-04
2022-05-06 05:11:23,595 INFO [train.py:715] (2/8) Epoch 8, batch 17400, loss[loss=0.1372, simple_loss=0.219, pruned_loss=0.02772, over 4797.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2158, pruned_loss=0.03539, over 972023.67 frames.], batch size: 25, lr: 2.61e-04
2022-05-06 05:12:02,708 INFO [train.py:715] (2/8) Epoch 8, batch 17450, loss[loss=0.1389, simple_loss=0.2107, pruned_loss=0.0335, over 4875.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.03558, over 971926.13 frames.], batch size: 32, lr: 2.61e-04
2022-05-06 05:12:42,126 INFO [train.py:715] (2/8) Epoch 8, batch 17500, loss[loss=0.1624, simple_loss=0.2457, pruned_loss=0.03955, over 4790.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.0351, over 972501.33 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:13:23,165 INFO [train.py:715] (2/8) Epoch 8, batch 17550, loss[loss=0.1486, simple_loss=0.2148, pruned_loss=0.04117, over 4790.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03526, over 971868.55 frames.], batch size: 14, lr: 2.61e-04
2022-05-06 05:14:02,977 INFO [train.py:715] (2/8) Epoch 8, batch 17600, loss[loss=0.1308, simple_loss=0.2013, pruned_loss=0.03015, over 4827.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2152, pruned_loss=0.03573, over 971366.02 frames.], batch size: 15, lr: 2.61e-04
2022-05-06 05:14:41,722 INFO [train.py:715] (2/8) Epoch 8, batch 17650, loss[loss=0.1156, simple_loss=0.1915, pruned_loss=0.01985, over 4774.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2144, pruned_loss=0.03509, over 970196.61 frames.], batch size: 18, lr: 2.61e-04
2022-05-06 05:15:22,841 INFO [train.py:715] (2/8) Epoch 8, batch 17700, loss[loss=0.1503, simple_loss=0.2472, pruned_loss=0.02666, over 4886.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.0351, over 970973.13 frames.], batch size: 22, lr: 2.61e-04
2022-05-06 05:16:02,814 INFO [train.py:715] (2/8) Epoch 8, batch 17750, loss[loss=0.1171, simple_loss=0.1864, pruned_loss=0.02392, over 4791.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2138, pruned_loss=0.03497, over 971626.68 frames.], batch size: 12, lr: 2.61e-04
2022-05-06 05:16:43,293 INFO [train.py:715] (2/8) Epoch 8, batch 17800, loss[loss=0.1439, simple_loss=0.2218, pruned_loss=0.03298, over 4840.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2156, pruned_loss=0.03498, over 971656.71 frames.], batch size: 26, lr: 2.61e-04
2022-05-06 05:17:23,943 INFO [train.py:715] (2/8) Epoch 8, batch 17850, loss[loss=0.1166, simple_loss=0.1867, pruned_loss=0.02321, over 4775.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03489, over 972194.49 frames.], batch size: 17, lr: 2.61e-04
2022-05-06 05:18:04,814 INFO [train.py:715] (2/8) Epoch 8, batch 17900, loss[loss=0.1442, simple_loss=0.2235, pruned_loss=0.03245, over 4929.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03475, over 972141.07 frames.], batch size: 39, lr: 2.61e-04
2022-05-06 05:18:46,223 INFO [train.py:715] (2/8) Epoch 8, batch 17950, loss[loss=0.1304, simple_loss=0.2053, pruned_loss=0.02781, over 4886.00 frames.], tot_loss[loss=0.1436, simple_loss=0.216, pruned_loss=0.03559, over 972260.55 frames.], batch size: 39, lr: 2.61e-04
2022-05-06 05:19:26,618 INFO [train.py:715] (2/8) Epoch 8, batch 18000, loss[loss=0.1495, simple_loss=0.2276, pruned_loss=0.03575, over 4943.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2153, pruned_loss=0.03508, over 971842.34 frames.], batch size: 21, lr: 2.61e-04
2022-05-06 05:19:26,619 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 05:19:36,397 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1073, simple_loss=0.1919, pruned_loss=0.01138, over 914524.00 frames.
2022-05-06 05:20:17,017 INFO [train.py:715] (2/8) Epoch 8, batch 18050, loss[loss=0.1479, simple_loss=0.215, pruned_loss=0.04042, over 4877.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2157, pruned_loss=0.03504, over 972535.12 frames.], batch size: 38, lr: 2.60e-04
2022-05-06 05:20:59,053 INFO [train.py:715] (2/8) Epoch 8, batch 18100, loss[loss=0.1574, simple_loss=0.2365, pruned_loss=0.03914, over 4910.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2162, pruned_loss=0.03521, over 973340.22 frames.], batch size: 19, lr: 2.60e-04
2022-05-06 05:21:40,100 INFO [train.py:715] (2/8) Epoch 8, batch 18150, loss[loss=0.1362, simple_loss=0.2083, pruned_loss=0.03205, over 4832.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2164, pruned_loss=0.03524, over 974019.98 frames.], batch size: 26, lr: 2.60e-04
2022-05-06 05:22:21,016 INFO [train.py:715] (2/8) Epoch 8, batch 18200, loss[loss=0.1496, simple_loss=0.2114, pruned_loss=0.04394, over 4827.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2161, pruned_loss=0.03556, over 973597.57 frames.], batch size: 15, lr: 2.60e-04
2022-05-06 05:23:02,791 INFO [train.py:715] (2/8) Epoch 8, batch 18250, loss[loss=0.156, simple_loss=0.2388, pruned_loss=0.03659, over 4779.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03586, over 972957.46 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:23:43,815 INFO [train.py:715] (2/8) Epoch 8, batch 18300, loss[loss=0.1303, simple_loss=0.2018, pruned_loss=0.0294, over 4785.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03609, over 972993.88 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:24:25,286 INFO [train.py:715] (2/8) Epoch 8, batch 18350, loss[loss=0.1232, simple_loss=0.1988, pruned_loss=0.02373, over 4952.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2157, pruned_loss=0.03595, over 973932.68 frames.], batch size: 39, lr: 2.60e-04
2022-05-06 05:25:06,140 INFO [train.py:715] (2/8) Epoch 8, batch 18400, loss[loss=0.1258, simple_loss=0.1935, pruned_loss=0.02911, over 4840.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2164, pruned_loss=0.03637, over 973325.74 frames.], batch size: 13, lr: 2.60e-04
2022-05-06 05:25:47,828 INFO [train.py:715] (2/8) Epoch 8, batch 18450, loss[loss=0.148, simple_loss=0.2228, pruned_loss=0.03655, over 4869.00 frames.], tot_loss[loss=0.145, simple_loss=0.2171, pruned_loss=0.03643, over 973544.81 frames.], batch size: 20, lr: 2.60e-04
2022-05-06 05:26:28,558 INFO [train.py:715] (2/8) Epoch 8, batch 18500, loss[loss=0.1472, simple_loss=0.2187, pruned_loss=0.03788, over 4847.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.03552, over 973114.87 frames.], batch size: 15, lr: 2.60e-04
2022-05-06 05:27:08,967 INFO [train.py:715] (2/8) Epoch 8, batch 18550, loss[loss=0.157, simple_loss=0.227, pruned_loss=0.04352, over 4868.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03576, over 972485.00 frames.], batch size: 30, lr: 2.60e-04
2022-05-06 05:27:50,215 INFO [train.py:715] (2/8) Epoch 8, batch 18600, loss[loss=0.1574, simple_loss=0.2287, pruned_loss=0.04304, over 4751.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2158, pruned_loss=0.03537, over 972367.34 frames.], batch size: 16, lr: 2.60e-04
2022-05-06 05:28:30,416 INFO [train.py:715] (2/8) Epoch 8, batch 18650, loss[loss=0.2123, simple_loss=0.2869, pruned_loss=0.0688, over 4867.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2157, pruned_loss=0.03521, over 972072.67 frames.], batch size: 20, lr: 2.60e-04
2022-05-06 05:29:09,924 INFO [train.py:715] (2/8) Epoch 8, batch 18700, loss[loss=0.1827, simple_loss=0.2505, pruned_loss=0.05738, over 4779.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03515, over 972283.09 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:29:49,899 INFO [train.py:715] (2/8) Epoch 8, batch 18750, loss[loss=0.1288, simple_loss=0.1999, pruned_loss=0.02889, over 4846.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03488, over 972639.41 frames.], batch size: 30, lr: 2.60e-04
2022-05-06 05:30:30,989 INFO [train.py:715] (2/8) Epoch 8, batch 18800, loss[loss=0.1803, simple_loss=0.2556, pruned_loss=0.0525, over 4904.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2154, pruned_loss=0.03506, over 972935.90 frames.], batch size: 19, lr: 2.60e-04
2022-05-06 05:31:10,612 INFO [train.py:715] (2/8) Epoch 8, batch 18850, loss[loss=0.1743, simple_loss=0.2298, pruned_loss=0.05943, over 4704.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2153, pruned_loss=0.03499, over 972603.75 frames.], batch size: 15, lr: 2.60e-04
2022-05-06 05:31:50,014 INFO [train.py:715] (2/8) Epoch 8, batch 18900, loss[loss=0.148, simple_loss=0.2041, pruned_loss=0.04596, over 4847.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.0357, over 972258.16 frames.], batch size: 30, lr: 2.60e-04
2022-05-06 05:32:30,306 INFO [train.py:715] (2/8) Epoch 8, batch 18950, loss[loss=0.1251, simple_loss=0.2009, pruned_loss=0.02465, over 4941.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03576, over 972383.82 frames.], batch size: 23, lr: 2.60e-04
2022-05-06 05:33:10,175 INFO [train.py:715] (2/8) Epoch 8, batch 19000, loss[loss=0.1484, simple_loss=0.2248, pruned_loss=0.036, over 4835.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2156, pruned_loss=0.03546, over 971962.83 frames.], batch size: 26, lr: 2.60e-04
2022-05-06 05:33:50,109 INFO [train.py:715] (2/8) Epoch 8, batch 19050, loss[loss=0.1734, simple_loss=0.2349, pruned_loss=0.05597, over 4982.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03558, over 972373.50 frames.], batch size: 31, lr: 2.60e-04
2022-05-06 05:34:31,416 INFO [train.py:715] (2/8) Epoch 8, batch 19100, loss[loss=0.1299, simple_loss=0.1997, pruned_loss=0.03007, over 4862.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2155, pruned_loss=0.03547, over 972011.97 frames.], batch size: 16, lr: 2.60e-04
2022-05-06 05:35:13,336 INFO [train.py:715] (2/8) Epoch 8, batch 19150, loss[loss=0.1701, simple_loss=0.2371, pruned_loss=0.05153, over 4835.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03576, over 971587.07 frames.], batch size: 30, lr: 2.60e-04
2022-05-06 05:35:54,999 INFO [train.py:715] (2/8) Epoch 8, batch 19200, loss[loss=0.1548, simple_loss=0.2172, pruned_loss=0.04616, over 4786.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2149, pruned_loss=0.03541, over 971429.65 frames.], batch size: 14, lr: 2.60e-04
2022-05-06 05:36:35,259 INFO [train.py:715] (2/8) Epoch 8, batch 19250, loss[loss=0.1718, simple_loss=0.2457, pruned_loss=0.04897, over 4922.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2142, pruned_loss=0.03536, over 971615.56 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:37:17,447 INFO [train.py:715] (2/8) Epoch 8, batch 19300, loss[loss=0.1387, simple_loss=0.2251, pruned_loss=0.02617, over 4898.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2136, pruned_loss=0.03504, over 970934.16 frames.], batch size: 22, lr: 2.60e-04
2022-05-06 05:37:58,605 INFO [train.py:715] (2/8) Epoch 8, batch 19350, loss[loss=0.1467, simple_loss=0.2276, pruned_loss=0.03286, over 4862.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2135, pruned_loss=0.03503, over 971392.39 frames.], batch size: 34, lr: 2.60e-04
2022-05-06 05:38:39,846 INFO [train.py:715] (2/8) Epoch 8, batch 19400, loss[loss=0.1754, simple_loss=0.2511, pruned_loss=0.04982, over 4933.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2142, pruned_loss=0.03518, over 971468.99 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:39:21,785 INFO [train.py:715] (2/8) Epoch 8, batch 19450, loss[loss=0.1644, simple_loss=0.2297, pruned_loss=0.04958, over 4834.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2141, pruned_loss=0.03508, over 972050.91 frames.], batch size: 30, lr: 2.60e-04
2022-05-06 05:40:03,273 INFO [train.py:715] (2/8) Epoch 8, batch 19500, loss[loss=0.1443, simple_loss=0.2154, pruned_loss=0.03659, over 4892.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2154, pruned_loss=0.03581, over 972240.11 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:40:44,558 INFO [train.py:715] (2/8) Epoch 8, batch 19550, loss[loss=0.1437, simple_loss=0.219, pruned_loss=0.03417, over 4781.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2161, pruned_loss=0.03626, over 972229.64 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:41:25,033 INFO [train.py:715] (2/8) Epoch 8, batch 19600, loss[loss=0.1531, simple_loss=0.2198, pruned_loss=0.04326, over 4682.00 frames.], tot_loss[loss=0.1452, simple_loss=0.217, pruned_loss=0.03677, over 972614.30 frames.], batch size: 15, lr: 2.60e-04
2022-05-06 05:42:06,543 INFO [train.py:715] (2/8) Epoch 8, batch 19650, loss[loss=0.1538, simple_loss=0.2241, pruned_loss=0.04173, over 4781.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2164, pruned_loss=0.03629, over 973113.54 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:42:47,225 INFO [train.py:715] (2/8) Epoch 8, batch 19700, loss[loss=0.1512, simple_loss=0.2241, pruned_loss=0.03916, over 4875.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2156, pruned_loss=0.03581, over 973999.68 frames.], batch size: 32, lr: 2.60e-04
2022-05-06 05:43:28,186 INFO [train.py:715] (2/8) Epoch 8, batch 19750, loss[loss=0.1413, simple_loss=0.2075, pruned_loss=0.03761, over 4807.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03571, over 974579.53 frames.], batch size: 12, lr: 2.60e-04
2022-05-06 05:44:09,856 INFO [train.py:715] (2/8) Epoch 8, batch 19800, loss[loss=0.1537, simple_loss=0.2265, pruned_loss=0.04047, over 4778.00 frames.], tot_loss[loss=0.1451, simple_loss=0.217, pruned_loss=0.03663, over 974009.77 frames.], batch size: 14, lr: 2.60e-04
2022-05-06 05:44:50,901 INFO [train.py:715] (2/8) Epoch 8, batch 19850, loss[loss=0.1527, simple_loss=0.2181, pruned_loss=0.0436, over 4884.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2161, pruned_loss=0.03624, over 973820.74 frames.], batch size: 22, lr: 2.60e-04
2022-05-06 05:45:31,216 INFO [train.py:715] (2/8) Epoch 8, batch 19900, loss[loss=0.1267, simple_loss=0.1972, pruned_loss=0.02808, over 4825.00 frames.], tot_loss[loss=0.144, simple_loss=0.2159, pruned_loss=0.03603, over 973620.98 frames.], batch size: 13, lr: 2.60e-04
2022-05-06 05:46:10,973 INFO [train.py:715] (2/8) Epoch 8, batch 19950, loss[loss=0.1445, simple_loss=0.2181, pruned_loss=0.03548, over 4783.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.036, over 973239.98 frames.], batch size: 18, lr: 2.60e-04
2022-05-06 05:46:51,595 INFO [train.py:715] (2/8) Epoch 8, batch 20000, loss[loss=0.1608, simple_loss=0.2319, pruned_loss=0.04486, over 4794.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.03603, over 972453.64 frames.], batch size: 17, lr: 2.60e-04
2022-05-06 05:47:32,108 INFO [train.py:715] (2/8) Epoch 8, batch 20050, loss[loss=0.1595, simple_loss=0.2123, pruned_loss=0.05339, over 4704.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2156, pruned_loss=0.03583, over 972614.33 frames.], batch size: 15, lr: 2.60e-04
2022-05-06 05:48:12,622 INFO [train.py:715] (2/8) Epoch 8, batch 20100, loss[loss=0.1346, simple_loss=0.2103, pruned_loss=0.02951, over 4977.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03572, over 973055.27 frames.], batch size: 39, lr: 2.60e-04
2022-05-06 05:48:53,758 INFO [train.py:715] (2/8) Epoch 8, batch 20150, loss[loss=0.1456, simple_loss=0.2179, pruned_loss=0.0366, over 4796.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2151, pruned_loss=0.03564, over 972272.45 frames.], batch size: 21, lr: 2.60e-04
2022-05-06 05:49:34,570 INFO [train.py:715] (2/8) Epoch 8, batch 20200, loss[loss=0.1258, simple_loss=0.2062, pruned_loss=0.02265, over 4868.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2151, pruned_loss=0.03565, over 972639.88 frames.], batch size: 16, lr: 2.60e-04
2022-05-06 05:50:15,439 INFO [train.py:715] (2/8) Epoch 8, batch 20250, loss[loss=0.119, simple_loss=0.1991, pruned_loss=0.01946, over 4829.00 frames.], tot_loss[loss=0.1429, simple_loss=0.215, pruned_loss=0.03537, over 971947.41 frames.], batch size: 26, lr: 2.60e-04
2022-05-06 05:50:56,707 INFO [train.py:715] (2/8) Epoch 8, batch 20300, loss[loss=0.1568, simple_loss=0.2317, pruned_loss=0.041, over 4857.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.03567, over 971849.79 frames.], batch size: 32, lr: 2.60e-04
2022-05-06 05:51:37,706 INFO [train.py:715] (2/8) Epoch 8, batch 20350, loss[loss=0.1811, simple_loss=0.2496, pruned_loss=0.05626, over 4862.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2157, pruned_loss=0.0359, over 972012.25 frames.], batch size: 20, lr: 2.59e-04
2022-05-06 05:52:18,272 INFO [train.py:715] (2/8) Epoch 8, batch 20400, loss[loss=0.1449, simple_loss=0.214, pruned_loss=0.0379, over 4967.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2154, pruned_loss=0.03589, over 972061.86 frames.], batch size: 33, lr: 2.59e-04
2022-05-06 05:52:58,520 INFO [train.py:715] (2/8) Epoch 8, batch 20450, loss[loss=0.149, simple_loss=0.2213, pruned_loss=0.0384, over 4902.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2158, pruned_loss=0.03586, over 971956.43 frames.], batch size: 17, lr: 2.59e-04
2022-05-06 05:53:39,600 INFO [train.py:715] (2/8) Epoch 8, batch 20500, loss[loss=0.1595, simple_loss=0.2193, pruned_loss=0.04988, over 4872.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2163, pruned_loss=0.03647, over 972141.83 frames.], batch size: 22, lr: 2.59e-04
2022-05-06 05:54:20,086 INFO [train.py:715] (2/8) Epoch 8, batch 20550, loss[loss=0.1205, simple_loss=0.2022, pruned_loss=0.01945, over 4930.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2166, pruned_loss=0.03652, over 972338.62 frames.], batch size: 18, lr: 2.59e-04
2022-05-06 05:55:00,452 INFO [train.py:715] (2/8) Epoch 8, batch 20600, loss[loss=0.1212, simple_loss=0.1909, pruned_loss=0.0258, over 4808.00 frames.], tot_loss[loss=0.145, simple_loss=0.2165, pruned_loss=0.03668, over 972284.99 frames.], batch size: 12, lr: 2.59e-04
2022-05-06 05:55:41,408 INFO [train.py:715] (2/8) Epoch 8, batch 20650, loss[loss=0.1411, simple_loss=0.2106, pruned_loss=0.03583, over 4785.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03599, over 972115.80 frames.], batch size: 17, lr: 2.59e-04
2022-05-06 05:56:22,561 INFO [train.py:715] (2/8) Epoch 8, batch 20700, loss[loss=0.135, simple_loss=0.2096, pruned_loss=0.03026, over 4977.00 frames.], tot_loss[loss=0.144, simple_loss=0.2159, pruned_loss=0.03606, over 972503.73 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 05:57:02,754 INFO [train.py:715] (2/8) Epoch 8, batch 20750, loss[loss=0.1199, simple_loss=0.1898, pruned_loss=0.02499, over 4851.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2154, pruned_loss=0.03594, over 971807.85 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 05:57:42,965 INFO [train.py:715] (2/8) Epoch 8, batch 20800, loss[loss=0.1275, simple_loss=0.1955, pruned_loss=0.02975, over 4842.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2143, pruned_loss=0.03529, over 971771.10 frames.], batch size: 13, lr: 2.59e-04
2022-05-06 05:58:24,025 INFO [train.py:715] (2/8) Epoch 8, batch 20850, loss[loss=0.1532, simple_loss=0.2197, pruned_loss=0.04339, over 4988.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2144, pruned_loss=0.03514, over 972377.77 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 05:59:04,455 INFO [train.py:715] (2/8) Epoch 8, batch 20900, loss[loss=0.1671, simple_loss=0.2252, pruned_loss=0.05455, over 4838.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2143, pruned_loss=0.03474, over 971947.85 frames.], batch size: 30, lr: 2.59e-04
2022-05-06 05:59:43,023 INFO [train.py:715] (2/8) Epoch 8, batch 20950, loss[loss=0.1411, simple_loss=0.2047, pruned_loss=0.03875, over 4822.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03512, over 971540.69 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 06:00:22,707 INFO [train.py:715] (2/8) Epoch 8, batch 21000, loss[loss=0.1578, simple_loss=0.221, pruned_loss=0.04736, over 4747.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2151, pruned_loss=0.035, over 971431.15 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 06:00:22,708 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 06:00:32,255 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1072, simple_loss=0.1919, pruned_loss=0.01129, over 914524.00 frames.
2022-05-06 06:01:12,648 INFO [train.py:715] (2/8) Epoch 8, batch 21050, loss[loss=0.1486, simple_loss=0.2119, pruned_loss=0.04268, over 4962.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03496, over 972806.57 frames.], batch size: 21, lr: 2.59e-04
2022-05-06 06:01:52,990 INFO [train.py:715] (2/8) Epoch 8, batch 21100, loss[loss=0.14, simple_loss=0.2248, pruned_loss=0.02756, over 4889.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2144, pruned_loss=0.03436, over 972779.22 frames.], batch size: 17, lr: 2.59e-04
2022-05-06 06:02:31,462 INFO [train.py:715] (2/8) Epoch 8, batch 21150, loss[loss=0.1432, simple_loss=0.2216, pruned_loss=0.03238, over 4975.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2138, pruned_loss=0.03444, over 972768.44 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 06:03:10,264 INFO [train.py:715] (2/8) Epoch 8, batch 21200, loss[loss=0.1375, simple_loss=0.2008, pruned_loss=0.03703, over 4771.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2147, pruned_loss=0.03435, over 972847.27 frames.], batch size: 19, lr: 2.59e-04
2022-05-06 06:03:49,970 INFO [train.py:715] (2/8) Epoch 8, batch 21250, loss[loss=0.1321, simple_loss=0.2033, pruned_loss=0.03044, over 4940.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.0342, over 972323.31 frames.], batch size: 24, lr: 2.59e-04
2022-05-06 06:04:29,228 INFO [train.py:715] (2/8) Epoch 8, batch 21300, loss[loss=0.1445, simple_loss=0.2074, pruned_loss=0.04077, over 4859.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2149, pruned_loss=0.03464, over 972668.87 frames.], batch size: 32, lr: 2.59e-04
2022-05-06 06:05:07,766 INFO [train.py:715] (2/8) Epoch 8, batch 21350, loss[loss=0.1688, simple_loss=0.222, pruned_loss=0.05775, over 4925.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03464, over 972315.95 frames.], batch size: 23, lr: 2.59e-04
2022-05-06 06:05:47,407 INFO [train.py:715] (2/8) Epoch 8, batch 21400, loss[loss=0.1565, simple_loss=0.2357, pruned_loss=0.03859, over 4965.00 frames.], tot_loss[loss=0.1421, simple_loss=0.215, pruned_loss=0.03463, over 972358.07 frames.], batch size: 14, lr: 2.59e-04
2022-05-06 06:06:27,497 INFO [train.py:715] (2/8) Epoch 8, batch 21450, loss[loss=0.1971, simple_loss=0.2595, pruned_loss=0.06732, over 4909.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03484, over 972560.10 frames.], batch size: 19, lr: 2.59e-04
2022-05-06 06:07:06,788 INFO [train.py:715] (2/8) Epoch 8, batch 21500, loss[loss=0.1387, simple_loss=0.2154, pruned_loss=0.03097, over 4830.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2144, pruned_loss=0.03446, over 972417.01 frames.], batch size: 27, lr: 2.59e-04
2022-05-06 06:07:45,791 INFO [train.py:715] (2/8) Epoch 8, batch 21550, loss[loss=0.143, simple_loss=0.2132, pruned_loss=0.03645, over 4785.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03461, over 972391.79 frames.], batch size: 17, lr: 2.59e-04
2022-05-06 06:08:25,814 INFO [train.py:715] (2/8) Epoch 8, batch 21600, loss[loss=0.1227, simple_loss=0.2053, pruned_loss=0.02001, over 4835.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.0346, over 972767.82 frames.], batch size: 30, lr: 2.59e-04
2022-05-06 06:09:04,795 INFO [train.py:715] (2/8) Epoch 8, batch 21650, loss[loss=0.1471, simple_loss=0.2223, pruned_loss=0.03598, over 4905.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03521, over 972686.63 frames.], batch size: 17, lr: 2.59e-04
2022-05-06 06:09:43,496 INFO [train.py:715] (2/8) Epoch 8, batch 21700, loss[loss=0.1349, simple_loss=0.2009, pruned_loss=0.03448, over 4762.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2164, pruned_loss=0.03548, over 972360.23 frames.], batch size: 14, lr: 2.59e-04
2022-05-06 06:10:23,865 INFO [train.py:715] (2/8) Epoch 8, batch 21750, loss[loss=0.1483, simple_loss=0.2186, pruned_loss=0.03898, over 4869.00 frames.], tot_loss[loss=0.143, simple_loss=0.2151, pruned_loss=0.0354, over 971677.81 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 06:11:03,699 INFO [train.py:715] (2/8) Epoch 8, batch 21800, loss[loss=0.1343, simple_loss=0.2035, pruned_loss=0.03258, over 4877.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2155, pruned_loss=0.03567, over 972036.37 frames.], batch size: 32, lr: 2.59e-04
2022-05-06 06:11:42,817 INFO [train.py:715] (2/8) Epoch 8, batch 21850, loss[loss=0.1367, simple_loss=0.2107, pruned_loss=0.0313, over 4866.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03591, over 972759.81 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 06:12:21,179 INFO [train.py:715] (2/8) Epoch 8, batch 21900, loss[loss=0.1705, simple_loss=0.2361, pruned_loss=0.05249, over 4992.00 frames.], tot_loss[loss=0.1453, simple_loss=0.217, pruned_loss=0.03685, over 972290.27 frames.], batch size: 14, lr: 2.59e-04
2022-05-06 06:13:00,618 INFO [train.py:715] (2/8) Epoch 8, batch 21950, loss[loss=0.1353, simple_loss=0.2046, pruned_loss=0.03302, over 4989.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03655, over 972087.99 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 06:13:39,700 INFO [train.py:715] (2/8) Epoch 8, batch 22000, loss[loss=0.1588, simple_loss=0.2372, pruned_loss=0.04017, over 4754.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2153, pruned_loss=0.03573, over 970953.12 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 06:14:18,328 INFO [train.py:715] (2/8) Epoch 8, batch 22050, loss[loss=0.148, simple_loss=0.2276, pruned_loss=0.03422, over 4861.00 frames.], tot_loss[loss=0.1428, simple_loss=0.215, pruned_loss=0.03531, over 971412.83 frames.], batch size: 22, lr: 2.59e-04
2022-05-06 06:14:58,049 INFO [train.py:715] (2/8) Epoch 8, batch 22100, loss[loss=0.1503, simple_loss=0.2206, pruned_loss=0.04004, over 4829.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2154, pruned_loss=0.03549, over 970773.97 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 06:15:37,423 INFO [train.py:715] (2/8) Epoch 8, batch 22150, loss[loss=0.1618, simple_loss=0.2368, pruned_loss=0.04344, over 4866.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2147, pruned_loss=0.03513, over 971277.63 frames.], batch size: 20, lr: 2.59e-04
2022-05-06 06:16:16,519 INFO [train.py:715] (2/8) Epoch 8, batch 22200, loss[loss=0.1323, simple_loss=0.201, pruned_loss=0.03181, over 4881.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2155, pruned_loss=0.03567, over 971615.09 frames.], batch size: 16, lr: 2.59e-04
2022-05-06 06:16:55,350 INFO [train.py:715] (2/8) Epoch 8, batch 22250, loss[loss=0.1614, simple_loss=0.2333, pruned_loss=0.04474, over 4830.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2166, pruned_loss=0.03581, over 972000.04 frames.], batch size: 15, lr: 2.59e-04
2022-05-06 06:17:34,571 INFO [train.py:715] (2/8) Epoch 8, batch 22300, loss[loss=0.1446, simple_loss=0.211, pruned_loss=0.03912, over 4811.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2174, pruned_loss=0.03613, over 971617.22 frames.], batch size: 25, lr: 2.59e-04
2022-05-06 06:18:13,310 INFO [train.py:715] (2/8) Epoch 8, batch 22350, loss[loss=0.1494, simple_loss=0.2192, pruned_loss=0.03983, over 4972.00 frames.], tot_loss[loss=0.1444, simple_loss=0.217, pruned_loss=0.03587, over 972057.00 frames.], batch size: 28, lr: 2.59e-04
2022-05-06 06:18:51,907 INFO [train.py:715] (2/8) Epoch 8, batch 22400, loss[loss=0.1633, simple_loss=0.2306, pruned_loss=0.04795, over 4905.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2173, pruned_loss=0.03591, over 970752.82 frames.], batch size: 19, lr: 2.59e-04
2022-05-06 06:19:31,240 INFO [train.py:715] (2/8) Epoch 8, batch 22450, loss[loss=0.1417, simple_loss=0.2173, pruned_loss=0.03303, over 4986.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2167, pruned_loss=0.03609, over 971471.72 frames.], batch size: 25, lr: 2.59e-04
2022-05-06 06:20:10,736 INFO [train.py:715] (2/8) Epoch 8, batch 22500, loss[loss=0.1372, simple_loss=0.2097, pruned_loss=0.03232, over 4951.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2156, pruned_loss=0.03603, over 971179.62 frames.], batch size: 39, lr: 2.59e-04
2022-05-06 06:20:49,348 INFO [train.py:715] (2/8) Epoch 8, batch 22550, loss[loss=0.1325, simple_loss=0.2039, pruned_loss=0.0305, over 4957.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03471, over 971635.23 frames.], batch size: 39, lr: 2.59e-04
2022-05-06 06:21:28,255 INFO [train.py:715] (2/8) Epoch 8, batch 22600, loss[loss=0.1481, simple_loss=0.2124, pruned_loss=0.0419, over 4771.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03502, over 972023.62 frames.], batch size: 12, lr: 2.59e-04
2022-05-06 06:22:07,737 INFO [train.py:715] (2/8) Epoch 8, batch 22650, loss[loss=0.1289, simple_loss=0.2045, pruned_loss=0.02658, over 4851.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2155, pruned_loss=0.03493, over 972044.71 frames.], batch size: 20, lr: 2.58e-04
2022-05-06 06:22:46,457 INFO [train.py:715] (2/8) Epoch 8, batch 22700, loss[loss=0.1421, simple_loss=0.2062, pruned_loss=0.03904, over 4750.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2158, pruned_loss=0.03539, over 972037.94 frames.], batch size: 19, lr: 2.58e-04
2022-05-06 06:23:24,777 INFO [train.py:715] (2/8) Epoch 8, batch 22750, loss[loss=0.1323, simple_loss=0.205, pruned_loss=0.02975, over 4866.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03576, over 972278.64 frames.], batch size: 20, lr: 2.58e-04
2022-05-06 06:24:04,596 INFO [train.py:715] (2/8) Epoch 8, batch 22800, loss[loss=0.1468, simple_loss=0.2149, pruned_loss=0.03931, over 4739.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2162, pruned_loss=0.03536, over 972224.98 frames.], batch size: 16, lr: 2.58e-04
2022-05-06 06:24:43,770 INFO [train.py:715] (2/8) Epoch 8, batch 22850, loss[loss=0.1806, simple_loss=0.2416, pruned_loss=0.05979, over 4690.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03574, over 971240.16 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:25:22,843 INFO [train.py:715] (2/8) Epoch 8, batch 22900, loss[loss=0.1211, simple_loss=0.1959, pruned_loss=0.02313, over 4954.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2158, pruned_loss=0.03575, over 971853.80 frames.], batch size: 24, lr: 2.58e-04
2022-05-06 06:26:01,960 INFO [train.py:715] (2/8) Epoch 8, batch 22950, loss[loss=0.1519, simple_loss=0.221, pruned_loss=0.04142, over 4905.00 frames.], tot_loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.03598, over 972138.06 frames.], batch size: 18, lr: 2.58e-04
2022-05-06 06:26:41,733 INFO [train.py:715] (2/8) Epoch 8, batch 23000, loss[loss=0.1083, simple_loss=0.1799, pruned_loss=0.01833, over 4794.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2158, pruned_loss=0.03571, over 972163.69 frames.], batch size: 13, lr: 2.58e-04
2022-05-06 06:27:20,528 INFO [train.py:715] (2/8) Epoch 8, batch 23050, loss[loss=0.1179, simple_loss=0.196, pruned_loss=0.01995, over 4978.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2149, pruned_loss=0.03534, over 972356.60 frames.], batch size: 35, lr: 2.58e-04
2022-05-06 06:27:59,220 INFO [train.py:715] (2/8) Epoch 8, batch 23100, loss[loss=0.1656, simple_loss=0.236, pruned_loss=0.04764, over 4770.00 frames.], tot_loss[loss=0.143, simple_loss=0.2151, pruned_loss=0.03542, over 972903.33 frames.], batch size: 18, lr: 2.58e-04
2022-05-06 06:28:39,376 INFO [train.py:715] (2/8) Epoch 8, batch 23150, loss[loss=0.1283, simple_loss=0.2038, pruned_loss=0.02643, over 4819.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2148, pruned_loss=0.03548, over 972383.00 frames.], batch size: 27, lr: 2.58e-04
2022-05-06 06:29:18,753 INFO [train.py:715] (2/8) Epoch 8, batch 23200, loss[loss=0.1478, simple_loss=0.2113, pruned_loss=0.04211, over 4813.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03556, over 972381.62 frames.], batch size: 21, lr: 2.58e-04
2022-05-06 06:29:57,395 INFO [train.py:715] (2/8) Epoch 8, batch 23250, loss[loss=0.1268, simple_loss=0.198, pruned_loss=0.02778, over 4803.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2157, pruned_loss=0.03596, over 972889.36 frames.], batch size: 24, lr: 2.58e-04
2022-05-06 06:30:36,515 INFO [train.py:715] (2/8) Epoch 8, batch 23300, loss[loss=0.1577, simple_loss=0.235, pruned_loss=0.04017, over 4822.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2156, pruned_loss=0.03582, over 972261.29 frames.], batch size: 26, lr: 2.58e-04
2022-05-06 06:31:16,260 INFO [train.py:715] (2/8) Epoch 8, batch 23350, loss[loss=0.1178, simple_loss=0.2, pruned_loss=0.01787, over 4811.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2151, pruned_loss=0.03532, over 972357.96 frames.], batch size: 26, lr: 2.58e-04
2022-05-06 06:31:55,029 INFO [train.py:715] (2/8) Epoch 8, batch 23400, loss[loss=0.1263, simple_loss=0.2042, pruned_loss=0.02426, over 4992.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2147, pruned_loss=0.03519, over 972892.51 frames.], batch size: 14, lr: 2.58e-04
2022-05-06 06:32:33,888 INFO [train.py:715] (2/8) Epoch 8, batch 23450, loss[loss=0.1128, simple_loss=0.1852, pruned_loss=0.02023, over 4803.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2141, pruned_loss=0.03464, over 972850.42 frames.], batch size: 13, lr: 2.58e-04
2022-05-06 06:33:13,365 INFO [train.py:715] (2/8) Epoch 8, batch 23500, loss[loss=0.1305, simple_loss=0.1989, pruned_loss=0.031, over 4980.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03503, over 971898.66 frames.], batch size: 14, lr: 2.58e-04
2022-05-06 06:33:52,530 INFO [train.py:715] (2/8) Epoch 8, batch 23550, loss[loss=0.1584, simple_loss=0.2281, pruned_loss=0.04437, over 4696.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03516, over 971614.72 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:34:31,318 INFO [train.py:715] (2/8) Epoch 8, batch 23600, loss[loss=0.12, simple_loss=0.183, pruned_loss=0.0285, over 4833.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03502, over 971729.53 frames.], batch size: 30, lr: 2.58e-04
2022-05-06 06:35:10,240 INFO [train.py:715] (2/8) Epoch 8, batch 23650, loss[loss=0.1523, simple_loss=0.2199, pruned_loss=0.04239, over 4813.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2155, pruned_loss=0.0358, over 971040.02 frames.], batch size: 25, lr: 2.58e-04
2022-05-06 06:35:50,047 INFO [train.py:715] (2/8) Epoch 8, batch 23700, loss[loss=0.137, simple_loss=0.2071, pruned_loss=0.03346, over 4803.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.0355, over 971992.05 frames.], batch size: 14, lr: 2.58e-04
2022-05-06 06:36:28,664 INFO [train.py:715] (2/8) Epoch 8, batch 23750, loss[loss=0.1269, simple_loss=0.2026, pruned_loss=0.02558, over 4929.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03557, over 971695.86 frames.], batch size: 29, lr: 2.58e-04
2022-05-06 06:37:07,513 INFO [train.py:715] (2/8) Epoch 8, batch 23800, loss[loss=0.1282, simple_loss=0.1996, pruned_loss=0.02838, over 4768.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2164, pruned_loss=0.03576, over 970873.99 frames.], batch size: 12, lr: 2.58e-04
2022-05-06 06:37:46,982 INFO [train.py:715] (2/8) Epoch 8, batch 23850, loss[loss=0.1573, simple_loss=0.2221, pruned_loss=0.04625, over 4815.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2164, pruned_loss=0.03566, over 971021.99 frames.], batch size: 21, lr: 2.58e-04
2022-05-06 06:38:26,657 INFO [train.py:715] (2/8) Epoch 8, batch 23900, loss[loss=0.1689, simple_loss=0.2237, pruned_loss=0.05702, over 4887.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03571, over 971821.12 frames.], batch size: 17, lr: 2.58e-04
2022-05-06 06:39:05,505 INFO [train.py:715] (2/8) Epoch 8, batch 23950, loss[loss=0.1305, simple_loss=0.2045, pruned_loss=0.0283, over 4888.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.03559, over 971477.97 frames.], batch size: 32, lr: 2.58e-04
2022-05-06 06:39:44,888 INFO [train.py:715] (2/8) Epoch 8, batch 24000, loss[loss=0.1389, simple_loss=0.1997, pruned_loss=0.03904, over 4840.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2165, pruned_loss=0.03553, over 971327.47 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:39:44,888 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 06:39:54,530 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1075, simple_loss=0.192, pruned_loss=0.01146, over 914524.00 frames.
2022-05-06 06:40:33,719 INFO [train.py:715] (2/8) Epoch 8, batch 24050, loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03611, over 4784.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2159, pruned_loss=0.03563, over 972053.47 frames.], batch size: 14, lr: 2.58e-04
2022-05-06 06:41:13,152 INFO [train.py:715] (2/8) Epoch 8, batch 24100, loss[loss=0.1411, simple_loss=0.2248, pruned_loss=0.02871, over 4811.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2158, pruned_loss=0.03542, over 971872.88 frames.], batch size: 26, lr: 2.58e-04
2022-05-06 06:41:52,116 INFO [train.py:715] (2/8) Epoch 8, batch 24150, loss[loss=0.1489, simple_loss=0.2233, pruned_loss=0.03723, over 4884.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03535, over 972339.76 frames.], batch size: 16, lr: 2.58e-04
2022-05-06 06:42:31,050 INFO [train.py:715] (2/8) Epoch 8, batch 24200, loss[loss=0.1704, simple_loss=0.2323, pruned_loss=0.05422, over 4845.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03575, over 973790.79 frames.], batch size: 30, lr: 2.58e-04
2022-05-06 06:43:11,239 INFO [train.py:715] (2/8) Epoch 8, batch 24250, loss[loss=0.1885, simple_loss=0.2559, pruned_loss=0.06062, over 4862.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03528, over 973207.82 frames.], batch size: 30, lr: 2.58e-04
2022-05-06 06:43:50,603 INFO [train.py:715] (2/8) Epoch 8, batch 24300, loss[loss=0.1362, simple_loss=0.2187, pruned_loss=0.02681, over 4931.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03547, over 973335.59 frames.], batch size: 18, lr: 2.58e-04
2022-05-06 06:44:29,316 INFO [train.py:715] (2/8) Epoch 8, batch 24350, loss[loss=0.1442, simple_loss=0.218, pruned_loss=0.0352, over 4822.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2158, pruned_loss=0.03521, over 972928.12 frames.], batch size: 25, lr: 2.58e-04
2022-05-06 06:45:08,117 INFO [train.py:715] (2/8) Epoch 8, batch 24400, loss[loss=0.1854, simple_loss=0.2262, pruned_loss=0.07229, over 4855.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2157, pruned_loss=0.03529, over 973268.43 frames.], batch size: 22, lr: 2.58e-04
2022-05-06 06:45:47,153 INFO [train.py:715] (2/8) Epoch 8, batch 24450, loss[loss=0.1516, simple_loss=0.2139, pruned_loss=0.04463, over 4703.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03459, over 971915.55 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:46:26,138 INFO [train.py:715] (2/8) Epoch 8, batch 24500, loss[loss=0.1574, simple_loss=0.2321, pruned_loss=0.04137, over 4845.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.03449, over 971333.83 frames.], batch size: 26, lr: 2.58e-04
2022-05-06 06:47:04,985 INFO [train.py:715] (2/8) Epoch 8, batch 24550, loss[loss=0.1593, simple_loss=0.235, pruned_loss=0.04182, over 4985.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03472, over 972207.91 frames.], batch size: 20, lr: 2.58e-04
2022-05-06 06:47:44,928 INFO [train.py:715] (2/8) Epoch 8, batch 24600, loss[loss=0.163, simple_loss=0.23, pruned_loss=0.04802, over 4845.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03539, over 972504.72 frames.], batch size: 30, lr: 2.58e-04
2022-05-06 06:48:24,235 INFO [train.py:715] (2/8) Epoch 8, batch 24650, loss[loss=0.1751, simple_loss=0.245, pruned_loss=0.0526, over 4764.00 frames.], tot_loss[loss=0.1435, simple_loss=0.216, pruned_loss=0.03551, over 971987.44 frames.], batch size: 17, lr: 2.58e-04
2022-05-06 06:49:02,874 INFO [train.py:715] (2/8) Epoch 8, batch 24700, loss[loss=0.1256, simple_loss=0.2007, pruned_loss=0.02522, over 4940.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2158, pruned_loss=0.03525, over 972177.20 frames.], batch size: 23, lr: 2.58e-04
2022-05-06 06:49:42,050 INFO [train.py:715] (2/8) Epoch 8, batch 24750, loss[loss=0.1443, simple_loss=0.2231, pruned_loss=0.03279, over 4851.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.03525, over 971111.75 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:50:21,621 INFO [train.py:715] (2/8) Epoch 8, batch 24800, loss[loss=0.1503, simple_loss=0.2146, pruned_loss=0.04301, over 4681.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03571, over 970760.51 frames.], batch size: 15, lr: 2.58e-04
2022-05-06 06:51:00,473 INFO [train.py:715] (2/8) Epoch 8, batch 24850, loss[loss=0.1354, simple_loss=0.2138, pruned_loss=0.02854, over 4970.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2162, pruned_loss=0.03609, over 970519.40 frames.], batch size: 28, lr: 2.58e-04
2022-05-06 06:51:39,142 INFO [train.py:715] (2/8) Epoch 8, batch 24900, loss[loss=0.1496, simple_loss=0.2234, pruned_loss=0.03785, over 4917.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2159, pruned_loss=0.03616, over 971286.24 frames.], batch size: 18, lr: 2.58e-04
2022-05-06 06:52:19,144 INFO [train.py:715] (2/8) Epoch 8, batch 24950, loss[loss=0.1217, simple_loss=0.205, pruned_loss=0.01918, over 4798.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2159, pruned_loss=0.03617, over 971483.40 frames.], batch size: 24, lr: 2.58e-04
2022-05-06 06:52:58,631 INFO [train.py:715] (2/8) Epoch 8, batch 25000, loss[loss=0.1539, simple_loss=0.228, pruned_loss=0.03987, over 4990.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2158, pruned_loss=0.03584, over 971851.80 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 06:53:37,566 INFO [train.py:715] (2/8) Epoch 8, batch 25050, loss[loss=0.1401, simple_loss=0.2186, pruned_loss=0.03082, over 4842.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2158, pruned_loss=0.03588, over 971933.75 frames.], batch size: 30, lr: 2.57e-04
2022-05-06 06:54:16,391 INFO [train.py:715] (2/8) Epoch 8, batch 25100, loss[loss=0.1475, simple_loss=0.2123, pruned_loss=0.04137, over 4643.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2153, pruned_loss=0.0356, over 971903.01 frames.], batch size: 13, lr: 2.57e-04
2022-05-06 06:54:55,809 INFO [train.py:715] (2/8) Epoch 8, batch 25150, loss[loss=0.1215, simple_loss=0.1948, pruned_loss=0.02408, over 4831.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03526, over 972526.26 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 06:55:34,831 INFO [train.py:715] (2/8) Epoch 8, batch 25200, loss[loss=0.1378, simple_loss=0.218, pruned_loss=0.02878, over 4761.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.0356, over 972551.72 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 06:56:13,819 INFO [train.py:715] (2/8) Epoch 8, batch 25250, loss[loss=0.1299, simple_loss=0.2017, pruned_loss=0.029, over 4899.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2158, pruned_loss=0.03548, over 972171.13 frames.], batch size: 19, lr: 2.57e-04
2022-05-06 06:56:53,390 INFO [train.py:715] (2/8) Epoch 8, batch 25300, loss[loss=0.137, simple_loss=0.2073, pruned_loss=0.03336, over 4783.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03557, over 972481.62 frames.], batch size: 17, lr: 2.57e-04
2022-05-06 06:57:32,353 INFO [train.py:715] (2/8) Epoch 8, batch 25350, loss[loss=0.155, simple_loss=0.2333, pruned_loss=0.03829, over 4895.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03562, over 972267.12 frames.], batch size: 17, lr: 2.57e-04
2022-05-06 06:58:11,169 INFO [train.py:715] (2/8) Epoch 8, batch 25400, loss[loss=0.1426, simple_loss=0.211, pruned_loss=0.03706, over 4978.00 frames.], tot_loss[loss=0.144, simple_loss=0.2161, pruned_loss=0.03594, over 972951.43 frames.], batch size: 28, lr: 2.57e-04
2022-05-06 06:58:50,229 INFO [train.py:715] (2/8) Epoch 8, batch 25450, loss[loss=0.128, simple_loss=0.2035, pruned_loss=0.02626, over 4808.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03592, over 972188.06 frames.], batch size: 25, lr: 2.57e-04
2022-05-06 06:59:30,374 INFO [train.py:715] (2/8) Epoch 8, batch 25500, loss[loss=0.1354, simple_loss=0.2124, pruned_loss=0.02917, over 4820.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2159, pruned_loss=0.03588, over 972935.57 frames.], batch size: 27, lr: 2.57e-04
2022-05-06 07:00:12,379 INFO [train.py:715] (2/8) Epoch 8, batch 25550, loss[loss=0.1429, simple_loss=0.2189, pruned_loss=0.03351, over 4938.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2161, pruned_loss=0.03584, over 973004.58 frames.], batch size: 23, lr: 2.57e-04
2022-05-06 07:00:51,653 INFO [train.py:715] (2/8) Epoch 8, batch 25600, loss[loss=0.1309, simple_loss=0.2104, pruned_loss=0.02571, over 4905.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2164, pruned_loss=0.03569, over 973177.84 frames.], batch size: 19, lr: 2.57e-04
2022-05-06 07:01:30,734 INFO [train.py:715] (2/8) Epoch 8, batch 25650, loss[loss=0.1367, simple_loss=0.2059, pruned_loss=0.03369, over 4834.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2152, pruned_loss=0.03486, over 972391.24 frames.], batch size: 30, lr: 2.57e-04
2022-05-06 07:02:09,697 INFO [train.py:715] (2/8) Epoch 8, batch 25700, loss[loss=0.1635, simple_loss=0.2348, pruned_loss=0.04607, over 4816.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03501, over 971012.00 frames.], batch size: 26, lr: 2.57e-04
2022-05-06 07:02:48,863 INFO [train.py:715] (2/8) Epoch 8, batch 25750, loss[loss=0.1269, simple_loss=0.1993, pruned_loss=0.02724, over 4937.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2143, pruned_loss=0.03514, over 971162.26 frames.], batch size: 29, lr: 2.57e-04
2022-05-06 07:03:27,688 INFO [train.py:715] (2/8) Epoch 8, batch 25800, loss[loss=0.1521, simple_loss=0.215, pruned_loss=0.04455, over 4804.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03518, over 970771.94 frames.], batch size: 14, lr: 2.57e-04
2022-05-06 07:04:06,654 INFO [train.py:715] (2/8) Epoch 8, batch 25850, loss[loss=0.1231, simple_loss=0.1894, pruned_loss=0.02843, over 4951.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2149, pruned_loss=0.03538, over 971172.29 frames.], batch size: 21, lr: 2.57e-04
2022-05-06 07:04:45,940 INFO [train.py:715] (2/8) Epoch 8, batch 25900, loss[loss=0.1601, simple_loss=0.2195, pruned_loss=0.05035, over 4753.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2149, pruned_loss=0.03549, over 969923.05 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 07:05:24,607 INFO [train.py:715] (2/8) Epoch 8, batch 25950, loss[loss=0.1498, simple_loss=0.2151, pruned_loss=0.04226, over 4991.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2154, pruned_loss=0.03575, over 970288.40 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 07:06:03,740 INFO [train.py:715] (2/8) Epoch 8, batch 26000, loss[loss=0.1694, simple_loss=0.2347, pruned_loss=0.05202, over 4949.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2152, pruned_loss=0.03604, over 970642.22 frames.], batch size: 35, lr: 2.57e-04
2022-05-06 07:06:42,907 INFO [train.py:715] (2/8) Epoch 8, batch 26050, loss[loss=0.1309, simple_loss=0.2011, pruned_loss=0.03032, over 4769.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2154, pruned_loss=0.036, over 969471.06 frames.], batch size: 14, lr: 2.57e-04
2022-05-06 07:07:21,669 INFO [train.py:715] (2/8) Epoch 8, batch 26100, loss[loss=0.1449, simple_loss=0.2151, pruned_loss=0.03729, over 4696.00 frames.], tot_loss[loss=0.144, simple_loss=0.2159, pruned_loss=0.03598, over 969214.31 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:08:01,300 INFO [train.py:715] (2/8) Epoch 8, batch 26150, loss[loss=0.1344, simple_loss=0.2103, pruned_loss=0.02927, over 4835.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2163, pruned_loss=0.03622, over 969639.16 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:08:40,495 INFO [train.py:715] (2/8) Epoch 8, batch 26200, loss[loss=0.116, simple_loss=0.1884, pruned_loss=0.02176, over 4693.00 frames.], tot_loss[loss=0.1439, simple_loss=0.216, pruned_loss=0.03592, over 969917.08 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:09:19,622 INFO [train.py:715] (2/8) Epoch 8, batch 26250, loss[loss=0.1491, simple_loss=0.2214, pruned_loss=0.03833, over 4863.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2155, pruned_loss=0.03561, over 969286.81 frames.], batch size: 30, lr: 2.57e-04
2022-05-06 07:09:57,936 INFO [train.py:715] (2/8) Epoch 8, batch 26300, loss[loss=0.1695, simple_loss=0.2338, pruned_loss=0.05258, over 4914.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03572, over 969966.90 frames.], batch size: 19, lr: 2.57e-04
2022-05-06 07:10:37,575 INFO [train.py:715] (2/8) Epoch 8, batch 26350, loss[loss=0.1523, simple_loss=0.221, pruned_loss=0.04182, over 4813.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.03564, over 970710.10 frames.], batch size: 25, lr: 2.57e-04
2022-05-06 07:11:16,887 INFO [train.py:715] (2/8) Epoch 8, batch 26400, loss[loss=0.1263, simple_loss=0.1999, pruned_loss=0.02636, over 4984.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03572, over 971690.67 frames.], batch size: 24, lr: 2.57e-04
2022-05-06 07:11:55,837 INFO [train.py:715] (2/8) Epoch 8, batch 26450, loss[loss=0.1349, simple_loss=0.1904, pruned_loss=0.03974, over 4868.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2159, pruned_loss=0.03574, over 971581.89 frames.], batch size: 20, lr: 2.57e-04
2022-05-06 07:12:34,670 INFO [train.py:715] (2/8) Epoch 8, batch 26500, loss[loss=0.1459, simple_loss=0.2183, pruned_loss=0.03679, over 4839.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.0355, over 970091.47 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:13:13,273 INFO [train.py:715] (2/8) Epoch 8, batch 26550, loss[loss=0.1447, simple_loss=0.2157, pruned_loss=0.03686, over 4978.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2162, pruned_loss=0.03543, over 970860.83 frames.], batch size: 14, lr: 2.57e-04
2022-05-06 07:13:52,655 INFO [train.py:715] (2/8) Epoch 8, batch 26600, loss[loss=0.1571, simple_loss=0.2265, pruned_loss=0.04381, over 4946.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2176, pruned_loss=0.036, over 971656.21 frames.], batch size: 35, lr: 2.57e-04
2022-05-06 07:14:30,715 INFO [train.py:715] (2/8) Epoch 8, batch 26650, loss[loss=0.1553, simple_loss=0.2316, pruned_loss=0.03944, over 4906.00 frames.], tot_loss[loss=0.144, simple_loss=0.217, pruned_loss=0.03551, over 972452.51 frames.], batch size: 19, lr: 2.57e-04
2022-05-06 07:15:10,079 INFO [train.py:715] (2/8) Epoch 8, batch 26700, loss[loss=0.1263, simple_loss=0.2067, pruned_loss=0.023, over 4795.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2173, pruned_loss=0.03579, over 972566.65 frames.], batch size: 21, lr: 2.57e-04
2022-05-06 07:15:49,153 INFO [train.py:715] (2/8) Epoch 8, batch 26750, loss[loss=0.1715, simple_loss=0.2522, pruned_loss=0.04539, over 4839.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2166, pruned_loss=0.03565, over 972202.87 frames.], batch size: 26, lr: 2.57e-04
2022-05-06 07:16:27,935 INFO [train.py:715] (2/8) Epoch 8, batch 26800, loss[loss=0.1519, simple_loss=0.2229, pruned_loss=0.04048, over 4764.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2164, pruned_loss=0.03561, over 972351.41 frames.], batch size: 17, lr: 2.57e-04
2022-05-06 07:17:07,166 INFO [train.py:715] (2/8) Epoch 8, batch 26850, loss[loss=0.133, simple_loss=0.2108, pruned_loss=0.02758, over 4988.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03559, over 972780.08 frames.], batch size: 26, lr: 2.57e-04
2022-05-06 07:17:46,414 INFO [train.py:715] (2/8) Epoch 8, batch 26900, loss[loss=0.1485, simple_loss=0.2208, pruned_loss=0.03815, over 4868.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03538, over 973180.86 frames.], batch size: 22, lr: 2.57e-04
2022-05-06 07:18:25,464 INFO [train.py:715] (2/8) Epoch 8, batch 26950, loss[loss=0.1013, simple_loss=0.1631, pruned_loss=0.01972, over 4749.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2149, pruned_loss=0.03541, over 972852.80 frames.], batch size: 12, lr: 2.57e-04
2022-05-06 07:19:04,351 INFO [train.py:715] (2/8) Epoch 8, batch 27000, loss[loss=0.1526, simple_loss=0.2371, pruned_loss=0.03402, over 4698.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03564, over 972183.02 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:19:04,352 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 07:19:13,680 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1072, simple_loss=0.1919, pruned_loss=0.01129, over 914524.00 frames.
2022-05-06 07:19:52,525 INFO [train.py:715] (2/8) Epoch 8, batch 27050, loss[loss=0.1385, simple_loss=0.203, pruned_loss=0.03695, over 4934.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2155, pruned_loss=0.03583, over 972537.80 frames.], batch size: 35, lr: 2.57e-04
2022-05-06 07:20:31,870 INFO [train.py:715] (2/8) Epoch 8, batch 27100, loss[loss=0.1553, simple_loss=0.2202, pruned_loss=0.04522, over 4853.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03526, over 971731.16 frames.], batch size: 15, lr: 2.57e-04
2022-05-06 07:21:10,969 INFO [train.py:715] (2/8) Epoch 8, batch 27150, loss[loss=0.1355, simple_loss=0.2041, pruned_loss=0.03344, over 4935.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03518, over 971947.67 frames.], batch size: 35, lr: 2.57e-04
2022-05-06 07:21:49,184 INFO [train.py:715] (2/8) Epoch 8, batch 27200, loss[loss=0.1672, simple_loss=0.2334, pruned_loss=0.0505, over 4750.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2163, pruned_loss=0.03562, over 971305.34 frames.], batch size: 16, lr: 2.57e-04
2022-05-06 07:22:28,511 INFO [train.py:715] (2/8) Epoch 8, batch 27250, loss[loss=0.1288, simple_loss=0.1957, pruned_loss=0.03095, over 4821.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2164, pruned_loss=0.0357, over 971947.45 frames.], batch size: 25, lr: 2.57e-04
2022-05-06 07:23:07,825 INFO [train.py:715] (2/8) Epoch 8, batch 27300, loss[loss=0.1727, simple_loss=0.2528, pruned_loss=0.0463, over 4936.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2165, pruned_loss=0.03546, over 971664.03 frames.], batch size: 35, lr: 2.57e-04
2022-05-06 07:23:46,494 INFO [train.py:715] (2/8) Epoch 8, batch 27350, loss[loss=0.1242, simple_loss=0.1955, pruned_loss=0.02646, over 4916.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2159, pruned_loss=0.03519, over 972060.24 frames.], batch size: 18, lr: 2.57e-04
2022-05-06 07:24:25,184 INFO [train.py:715] (2/8) Epoch 8, batch 27400, loss[loss=0.1532, simple_loss=0.2288, pruned_loss=0.03878, over 4911.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2159, pruned_loss=0.03495, over 972489.12 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:25:04,323 INFO [train.py:715] (2/8) Epoch 8, batch 27450, loss[loss=0.1388, simple_loss=0.2113, pruned_loss=0.03316, over 4934.00 frames.], tot_loss[loss=0.143, simple_loss=0.2159, pruned_loss=0.03502, over 972310.88 frames.], batch size: 23, lr: 2.56e-04
2022-05-06 07:25:43,017 INFO [train.py:715] (2/8) Epoch 8, batch 27500, loss[loss=0.1407, simple_loss=0.2075, pruned_loss=0.03693, over 4850.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03489, over 972249.40 frames.], batch size: 32, lr: 2.56e-04
2022-05-06 07:26:21,672 INFO [train.py:715] (2/8) Epoch 8, batch 27550, loss[loss=0.1794, simple_loss=0.2483, pruned_loss=0.05521, over 4835.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2158, pruned_loss=0.03519, over 972610.37 frames.], batch size: 13, lr: 2.56e-04
2022-05-06 07:27:01,334 INFO [train.py:715] (2/8) Epoch 8, batch 27600, loss[loss=0.1356, simple_loss=0.2136, pruned_loss=0.02879, over 4921.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2159, pruned_loss=0.0354, over 971835.32 frames.], batch size: 29, lr: 2.56e-04
2022-05-06 07:27:40,426 INFO [train.py:715] (2/8) Epoch 8, batch 27650, loss[loss=0.1267, simple_loss=0.2063, pruned_loss=0.02359, over 4771.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2163, pruned_loss=0.03572, over 971315.36 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:28:19,092 INFO [train.py:715] (2/8) Epoch 8, batch 27700, loss[loss=0.1307, simple_loss=0.1987, pruned_loss=0.03132, over 4826.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.0353, over 971350.87 frames.], batch size: 13, lr: 2.56e-04
2022-05-06 07:28:58,324 INFO [train.py:715] (2/8) Epoch 8, batch 27750, loss[loss=0.1484, simple_loss=0.2174, pruned_loss=0.03966, over 4917.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2159, pruned_loss=0.03586, over 971804.99 frames.], batch size: 19, lr: 2.56e-04
2022-05-06 07:29:38,023 INFO [train.py:715] (2/8) Epoch 8, batch 27800, loss[loss=0.1422, simple_loss=0.2231, pruned_loss=0.03068, over 4916.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2154, pruned_loss=0.03541, over 971692.39 frames.], batch size: 18, lr: 2.56e-04
2022-05-06 07:30:16,791 INFO [train.py:715] (2/8) Epoch 8, batch 27850, loss[loss=0.1315, simple_loss=0.2042, pruned_loss=0.02941, over 4950.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2164, pruned_loss=0.03591, over 973098.29 frames.], batch size: 21, lr: 2.56e-04
2022-05-06 07:30:54,917 INFO [train.py:715] (2/8) Epoch 8, batch 27900, loss[loss=0.1542, simple_loss=0.2283, pruned_loss=0.04009, over 4736.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03575, over 973041.02 frames.], batch size: 16, lr: 2.56e-04
2022-05-06 07:31:34,148 INFO [train.py:715] (2/8) Epoch 8, batch 27950, loss[loss=0.1136, simple_loss=0.1918, pruned_loss=0.01769, over 4785.00 frames.], tot_loss[loss=0.1436, simple_loss=0.216, pruned_loss=0.03566, over 972180.51 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:32:13,475 INFO [train.py:715] (2/8) Epoch 8, batch 28000, loss[loss=0.1263, simple_loss=0.2015, pruned_loss=0.0256, over 4762.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2165, pruned_loss=0.03603, over 972076.67 frames.], batch size: 19, lr: 2.56e-04
2022-05-06 07:32:51,689 INFO [train.py:715] (2/8) Epoch 8, batch 28050, loss[loss=0.1657, simple_loss=0.2395, pruned_loss=0.04601, over 4774.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2167, pruned_loss=0.03633, over 972219.67 frames.], batch size: 14, lr: 2.56e-04
2022-05-06 07:33:31,448 INFO [train.py:715] (2/8) Epoch 8, batch 28100, loss[loss=0.1236, simple_loss=0.1925, pruned_loss=0.02732, over 4787.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2162, pruned_loss=0.036, over 972183.69 frames.], batch size: 12, lr: 2.56e-04
2022-05-06 07:34:10,515 INFO [train.py:715] (2/8) Epoch 8, batch 28150, loss[loss=0.1254, simple_loss=0.1808, pruned_loss=0.03498, over 4645.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.03577, over 972812.95 frames.], batch size: 13, lr: 2.56e-04
2022-05-06 07:34:49,971 INFO [train.py:715] (2/8) Epoch 8, batch 28200, loss[loss=0.1364, simple_loss=0.2091, pruned_loss=0.03189, over 4752.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03613, over 973260.65 frames.], batch size: 16, lr: 2.56e-04
2022-05-06 07:35:29,418 INFO [train.py:715] (2/8) Epoch 8, batch 28250, loss[loss=0.1271, simple_loss=0.2123, pruned_loss=0.02098, over 4783.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2173, pruned_loss=0.03638, over 973144.29 frames.], batch size: 18, lr: 2.56e-04
2022-05-06 07:36:09,692 INFO [train.py:715] (2/8) Epoch 8, batch 28300, loss[loss=0.1247, simple_loss=0.1947, pruned_loss=0.02734, over 4783.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2169, pruned_loss=0.03601, over 972663.07 frames.], batch size: 12, lr: 2.56e-04
2022-05-06 07:36:49,606 INFO [train.py:715] (2/8) Epoch 8, batch 28350, loss[loss=0.1228, simple_loss=0.1882, pruned_loss=0.02865, over 4986.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03571, over 972572.97 frames.], batch size: 14, lr: 2.56e-04
2022-05-06 07:37:28,954 INFO [train.py:715] (2/8) Epoch 8, batch 28400, loss[loss=0.1597, simple_loss=0.234, pruned_loss=0.04271, over 4953.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03562, over 973124.93 frames.], batch size: 39, lr: 2.56e-04
2022-05-06 07:38:08,995 INFO [train.py:715] (2/8) Epoch 8, batch 28450, loss[loss=0.1296, simple_loss=0.1945, pruned_loss=0.03235, over 4768.00 frames.], tot_loss[loss=0.1429, simple_loss=0.215, pruned_loss=0.03541, over 972228.59 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:38:48,157 INFO [train.py:715] (2/8) Epoch 8, batch 28500, loss[loss=0.1302, simple_loss=0.2095, pruned_loss=0.02547, over 4749.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2156, pruned_loss=0.03594, over 971236.31 frames.], batch size: 19, lr: 2.56e-04
2022-05-06 07:39:26,865 INFO [train.py:715] (2/8) Epoch 8, batch 28550, loss[loss=0.1565, simple_loss=0.2219, pruned_loss=0.04556, over 4955.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2152, pruned_loss=0.03553, over 971621.40 frames.], batch size: 35, lr: 2.56e-04
2022-05-06 07:40:05,723 INFO [train.py:715] (2/8) Epoch 8, batch 28600, loss[loss=0.1236, simple_loss=0.2018, pruned_loss=0.02264, over 4907.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2157, pruned_loss=0.03564, over 971616.42 frames.], batch size: 19, lr: 2.56e-04
2022-05-06 07:40:45,401 INFO [train.py:715] (2/8) Epoch 8, batch 28650, loss[loss=0.1412, simple_loss=0.2096, pruned_loss=0.0364, over 4789.00 frames.], tot_loss[loss=0.1433, simple_loss=0.216, pruned_loss=0.03532, over 971695.42 frames.], batch size: 18, lr: 2.56e-04
2022-05-06 07:41:24,252 INFO [train.py:715] (2/8) Epoch 8, batch 28700, loss[loss=0.127, simple_loss=0.1988, pruned_loss=0.0276, over 4721.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2156, pruned_loss=0.03513, over 972200.29 frames.], batch size: 16, lr: 2.56e-04
2022-05-06 07:42:02,601 INFO [train.py:715] (2/8) Epoch 8, batch 28750, loss[loss=0.1258, simple_loss=0.1935, pruned_loss=0.02904, over 4806.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03504, over 972656.37 frames.], batch size: 13, lr: 2.56e-04
2022-05-06 07:42:42,148 INFO [train.py:715] (2/8) Epoch 8, batch 28800, loss[loss=0.1304, simple_loss=0.202, pruned_loss=0.02939, over 4882.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2148, pruned_loss=0.03454, over 972503.87 frames.], batch size: 22, lr: 2.56e-04
2022-05-06 07:43:21,537 INFO [train.py:715] (2/8) Epoch 8, batch 28850, loss[loss=0.1487, simple_loss=0.2225, pruned_loss=0.03745, over 4916.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03486, over 972245.77 frames.], batch size: 29, lr: 2.56e-04
2022-05-06 07:44:00,546 INFO [train.py:715] (2/8) Epoch 8, batch 28900, loss[loss=0.1356, simple_loss=0.2081, pruned_loss=0.03158, over 4961.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2155, pruned_loss=0.03482, over 972244.16 frames.], batch size: 25, lr: 2.56e-04
2022-05-06 07:44:39,170 INFO [train.py:715] (2/8) Epoch 8, batch 28950, loss[loss=0.1403, simple_loss=0.2126, pruned_loss=0.03396, over 4922.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03468, over 971460.28 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:45:18,517 INFO [train.py:715] (2/8) Epoch 8, batch 29000, loss[loss=0.1288, simple_loss=0.1953, pruned_loss=0.03115, over 4918.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2154, pruned_loss=0.03557, over 971071.89 frames.], batch size: 29, lr: 2.56e-04
2022-05-06 07:45:57,178 INFO [train.py:715] (2/8) Epoch 8, batch 29050, loss[loss=0.1371, simple_loss=0.22, pruned_loss=0.02706, over 4896.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2153, pruned_loss=0.03553, over 971246.65 frames.], batch size: 19, lr: 2.56e-04
2022-05-06 07:46:36,419 INFO [train.py:715] (2/8) Epoch 8, batch 29100, loss[loss=0.109, simple_loss=0.1836, pruned_loss=0.01726, over 4846.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03535, over 971492.85 frames.], batch size: 12, lr: 2.56e-04
2022-05-06 07:47:14,942 INFO [train.py:715] (2/8) Epoch 8, batch 29150, loss[loss=0.1259, simple_loss=0.1985, pruned_loss=0.02662, over 4903.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2154, pruned_loss=0.03549, over 971779.64 frames.], batch size: 19, lr: 2.56e-04
2022-05-06 07:47:54,241 INFO [train.py:715] (2/8) Epoch 8, batch 29200, loss[loss=0.1154, simple_loss=0.186, pruned_loss=0.02241, over 4757.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.03547, over 971555.67 frames.], batch size: 19, lr: 2.56e-04
2022-05-06 07:48:32,866 INFO [train.py:715] (2/8) Epoch 8, batch 29250, loss[loss=0.1713, simple_loss=0.2387, pruned_loss=0.05197, over 4901.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2161, pruned_loss=0.03556, over 970890.33 frames.], batch size: 19, lr: 2.56e-04
2022-05-06 07:49:11,138 INFO [train.py:715] (2/8) Epoch 8, batch 29300, loss[loss=0.1918, simple_loss=0.2705, pruned_loss=0.05653, over 4901.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03582, over 971633.42 frames.], batch size: 17, lr: 2.56e-04
2022-05-06 07:49:50,319 INFO [train.py:715] (2/8) Epoch 8, batch 29350, loss[loss=0.1587, simple_loss=0.2355, pruned_loss=0.04096, over 4979.00 frames.], tot_loss[loss=0.1445, simple_loss=0.217, pruned_loss=0.03594, over 971980.18 frames.], batch size: 24, lr: 2.56e-04
2022-05-06 07:50:29,149 INFO [train.py:715] (2/8) Epoch 8, batch 29400, loss[loss=0.1497, simple_loss=0.2187, pruned_loss=0.0403, over 4791.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2169, pruned_loss=0.03621, over 971551.06 frames.], batch size: 12, lr: 2.56e-04
2022-05-06 07:51:08,795 INFO [train.py:715] (2/8) Epoch 8, batch 29450, loss[loss=0.1269, simple_loss=0.1993, pruned_loss=0.02724, over 4818.00 frames.], tot_loss[loss=0.144, simple_loss=0.2162, pruned_loss=0.03591, over 971397.57 frames.], batch size: 25, lr: 2.56e-04
2022-05-06 07:51:48,080 INFO [train.py:715] (2/8) Epoch 8, batch 29500, loss[loss=0.1334, simple_loss=0.2055, pruned_loss=0.03059, over 4974.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2156, pruned_loss=0.03532, over 971468.72 frames.], batch size: 25, lr: 2.56e-04
2022-05-06 07:52:27,549 INFO [train.py:715] (2/8) Epoch 8, batch 29550, loss[loss=0.1319, simple_loss=0.2147, pruned_loss=0.02451, over 4924.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03527, over 970853.91 frames.], batch size: 23, lr: 2.56e-04
2022-05-06 07:53:06,113 INFO [train.py:715] (2/8) Epoch 8, batch 29600, loss[loss=0.1555, simple_loss=0.234, pruned_loss=0.03852, over 4749.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03547, over 970628.00 frames.], batch size: 16, lr: 2.56e-04
2022-05-06 07:53:45,381 INFO [train.py:715] (2/8) Epoch 8, batch 29650, loss[loss=0.1571, simple_loss=0.2276, pruned_loss=0.0433, over 4954.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2154, pruned_loss=0.03549, over 970960.26 frames.], batch size: 29, lr: 2.56e-04
2022-05-06 07:54:24,985 INFO [train.py:715] (2/8) Epoch 8, batch 29700, loss[loss=0.1335, simple_loss=0.205, pruned_loss=0.031, over 4805.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03503, over 971008.77 frames.], batch size: 25, lr: 2.56e-04
2022-05-06 07:55:03,540 INFO [train.py:715] (2/8) Epoch 8, batch 29750, loss[loss=0.1238, simple_loss=0.1922, pruned_loss=0.02767, over 4974.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03534, over 970810.44 frames.], batch size: 14, lr: 2.56e-04
2022-05-06 07:55:42,376 INFO [train.py:715] (2/8) Epoch 8, batch 29800, loss[loss=0.1171, simple_loss=0.1921, pruned_loss=0.02107, over 4815.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.03514, over 971146.02 frames.], batch size: 27, lr: 2.55e-04
2022-05-06 07:56:21,282 INFO [train.py:715] (2/8) Epoch 8, batch 29850, loss[loss=0.1879, simple_loss=0.2512, pruned_loss=0.06236, over 4945.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.03521, over 972052.97 frames.], batch size: 39, lr: 2.55e-04
2022-05-06 07:57:00,650 INFO [train.py:715] (2/8) Epoch 8, batch 29900, loss[loss=0.1675, simple_loss=0.2452, pruned_loss=0.04496, over 4741.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.0353, over 972065.26 frames.], batch size: 16, lr: 2.55e-04
2022-05-06 07:57:39,541 INFO [train.py:715] (2/8) Epoch 8, batch 29950, loss[loss=0.1277, simple_loss=0.1974, pruned_loss=0.02898, over 4742.00 frames.], tot_loss[loss=0.1426, simple_loss=0.215, pruned_loss=0.03514, over 972489.45 frames.], batch size: 16, lr: 2.55e-04
2022-05-06 07:58:18,655 INFO [train.py:715] (2/8) Epoch 8, batch 30000, loss[loss=0.1051, simple_loss=0.1863, pruned_loss=0.01201, over 4966.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03503, over 972097.97 frames.], batch size: 24, lr: 2.55e-04
2022-05-06 07:58:18,656 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 07:58:28,240 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1073, simple_loss=0.1918, pruned_loss=0.01141, over 914524.00 frames.
2022-05-06 07:59:07,026 INFO [train.py:715] (2/8) Epoch 8, batch 30050, loss[loss=0.1146, simple_loss=0.1844, pruned_loss=0.0224, over 4806.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03519, over 971937.97 frames.], batch size: 12, lr: 2.55e-04
2022-05-06 07:59:46,357 INFO [train.py:715] (2/8) Epoch 8, batch 30100, loss[loss=0.1792, simple_loss=0.2586, pruned_loss=0.04994, over 4942.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2154, pruned_loss=0.03516, over 972809.98 frames.], batch size: 39, lr: 2.55e-04
2022-05-06 08:00:25,655 INFO [train.py:715] (2/8) Epoch 8, batch 30150, loss[loss=0.1424, simple_loss=0.2123, pruned_loss=0.0363, over 4933.00 frames.], tot_loss[loss=0.1438, simple_loss=0.216, pruned_loss=0.03581, over 972547.54 frames.], batch size: 17, lr: 2.55e-04
2022-05-06 08:01:04,255 INFO [train.py:715] (2/8) Epoch 8, batch 30200, loss[loss=0.1249, simple_loss=0.1965, pruned_loss=0.02666, over 4908.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2156, pruned_loss=0.0355, over 972431.45 frames.], batch size: 23, lr: 2.55e-04
2022-05-06 08:01:43,184 INFO [train.py:715] (2/8) Epoch 8, batch 30250, loss[loss=0.1608, simple_loss=0.2413, pruned_loss=0.04012, over 4884.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03547, over 972018.36 frames.], batch size: 19, lr: 2.55e-04
2022-05-06 08:02:22,870 INFO [train.py:715] (2/8) Epoch 8, batch 30300, loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.03383, over 4759.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2153, pruned_loss=0.03507, over 972594.62 frames.], batch size: 14, lr: 2.55e-04
2022-05-06 08:03:01,869 INFO [train.py:715] (2/8) Epoch 8, batch 30350, loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02883, over 4774.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.0348, over 972154.05 frames.], batch size: 17, lr: 2.55e-04
2022-05-06 08:03:40,562 INFO [train.py:715] (2/8) Epoch 8, batch 30400, loss[loss=0.1619, simple_loss=0.2308, pruned_loss=0.04652, over 4736.00 frames.], tot_loss[loss=0.143, simple_loss=0.2156, pruned_loss=0.03521, over 972204.67 frames.], batch size: 16, lr: 2.55e-04
2022-05-06 08:04:19,871 INFO [train.py:715] (2/8) Epoch 8, batch 30450, loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03563, over 4803.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2165, pruned_loss=0.03559, over 972859.52 frames.], batch size: 26, lr: 2.55e-04
2022-05-06 08:04:58,851 INFO [train.py:715] (2/8) Epoch 8, batch 30500, loss[loss=0.1313, simple_loss=0.2101, pruned_loss=0.02621, over 4990.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2169, pruned_loss=0.03573, over 972489.39 frames.], batch size: 25, lr: 2.55e-04
2022-05-06 08:05:37,496 INFO [train.py:715] (2/8) Epoch 8, batch 30550, loss[loss=0.1044, simple_loss=0.1676, pruned_loss=0.02056, over 4827.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2156, pruned_loss=0.03504, over 971865.41 frames.], batch size: 12, lr: 2.55e-04
2022-05-06 08:06:16,535 INFO [train.py:715] (2/8) Epoch 8, batch 30600, loss[loss=0.1757, simple_loss=0.2443, pruned_loss=0.05355, over 4953.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03485, over 972211.04 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 08:06:56,250 INFO [train.py:715] (2/8) Epoch 8, batch 30650, loss[loss=0.1423, simple_loss=0.2006, pruned_loss=0.04194, over 4819.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2151, pruned_loss=0.03471, over 971728.02 frames.], batch size: 13, lr: 2.55e-04
2022-05-06 08:07:35,433 INFO [train.py:715] (2/8) Epoch 8, batch 30700, loss[loss=0.1509, simple_loss=0.2086, pruned_loss=0.04662, over 4838.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2143, pruned_loss=0.03429, over 971890.32 frames.], batch size: 13, lr: 2.55e-04
2022-05-06 08:08:15,309 INFO [train.py:715] (2/8) Epoch 8, batch 30750, loss[loss=0.1277, simple_loss=0.201, pruned_loss=0.02723, over 4806.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2151, pruned_loss=0.03507, over 972420.32 frames.], batch size: 26, lr: 2.55e-04
2022-05-06 08:08:55,429 INFO [train.py:715] (2/8) Epoch 8, batch 30800, loss[loss=0.1684, simple_loss=0.2346, pruned_loss=0.05111, over 4697.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03487, over 972465.15 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 08:09:33,881 INFO [train.py:715] (2/8) Epoch 8, batch 30850, loss[loss=0.1287, simple_loss=0.2058, pruned_loss=0.02577, over 4747.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2153, pruned_loss=0.03541, over 972696.87 frames.], batch size: 19, lr: 2.55e-04
2022-05-06 08:10:12,783 INFO [train.py:715] (2/8) Epoch 8, batch 30900, loss[loss=0.118, simple_loss=0.2005, pruned_loss=0.01774, over 4882.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03516, over 973043.01 frames.], batch size: 16, lr: 2.55e-04
2022-05-06 08:10:52,540 INFO [train.py:715] (2/8) Epoch 8, batch 30950, loss[loss=0.1646, simple_loss=0.2361, pruned_loss=0.04659, over 4959.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03499, over 972106.57 frames.], batch size: 14, lr: 2.55e-04
2022-05-06 08:11:32,558 INFO [train.py:715] (2/8) Epoch 8, batch 31000, loss[loss=0.1356, simple_loss=0.207, pruned_loss=0.03213, over 4765.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2151, pruned_loss=0.03479, over 971929.94 frames.], batch size: 19, lr: 2.55e-04
2022-05-06 08:12:11,803 INFO [train.py:715] (2/8) Epoch 8, batch 31050, loss[loss=0.1727, simple_loss=0.2229, pruned_loss=0.06126, over 4864.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03536, over 972080.45 frames.], batch size: 32, lr: 2.55e-04
2022-05-06 08:12:51,422 INFO [train.py:715] (2/8) Epoch 8, batch 31100, loss[loss=0.1354, simple_loss=0.207, pruned_loss=0.03188, over 4847.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2165, pruned_loss=0.0358, over 971931.64 frames.], batch size: 30, lr: 2.55e-04
2022-05-06 08:13:30,939 INFO [train.py:715] (2/8) Epoch 8, batch 31150, loss[loss=0.1606, simple_loss=0.2322, pruned_loss=0.04452, over 4867.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2168, pruned_loss=0.03637, over 972562.01 frames.], batch size: 32, lr: 2.55e-04
2022-05-06 08:14:09,968 INFO [train.py:715] (2/8) Epoch 8, batch 31200, loss[loss=0.142, simple_loss=0.2045, pruned_loss=0.03969, over 4840.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2168, pruned_loss=0.03649, over 972039.60 frames.], batch size: 30, lr: 2.55e-04
2022-05-06 08:14:48,714 INFO [train.py:715] (2/8) Epoch 8, batch 31250, loss[loss=0.1305, simple_loss=0.2075, pruned_loss=0.02679, over 4746.00 frames.], tot_loss[loss=0.144, simple_loss=0.216, pruned_loss=0.03602, over 971485.61 frames.], batch size: 16, lr: 2.55e-04
2022-05-06 08:15:28,181 INFO [train.py:715] (2/8) Epoch 8, batch 31300, loss[loss=0.1431, simple_loss=0.214, pruned_loss=0.03613, over 4991.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03557, over 972707.80 frames.], batch size: 14, lr: 2.55e-04
2022-05-06 08:16:07,667 INFO [train.py:715] (2/8) Epoch 8, batch 31350, loss[loss=0.1192, simple_loss=0.1843, pruned_loss=0.02706, over 4792.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03617, over 974023.72 frames.], batch size: 12, lr: 2.55e-04
2022-05-06 08:16:46,298 INFO [train.py:715] (2/8) Epoch 8, batch 31400, loss[loss=0.1269, simple_loss=0.1982, pruned_loss=0.02775, over 4745.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2162, pruned_loss=0.03616, over 973953.82 frames.], batch size: 16, lr: 2.55e-04
2022-05-06 08:17:25,751 INFO [train.py:715] (2/8) Epoch 8, batch 31450, loss[loss=0.1462, simple_loss=0.2182, pruned_loss=0.03711, over 4781.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2162, pruned_loss=0.03554, over 973266.88 frames.], batch size: 14, lr: 2.55e-04
2022-05-06 08:18:05,873 INFO [train.py:715] (2/8) Epoch 8, batch 31500, loss[loss=0.11, simple_loss=0.1913, pruned_loss=0.01435, over 4850.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2159, pruned_loss=0.03553, over 973538.07 frames.], batch size: 13, lr: 2.55e-04
2022-05-06 08:18:45,121 INFO [train.py:715] (2/8) Epoch 8, batch 31550, loss[loss=0.1632, simple_loss=0.2259, pruned_loss=0.05027, over 4990.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03519, over 972713.37 frames.], batch size: 14, lr: 2.55e-04
2022-05-06 08:19:24,105 INFO [train.py:715] (2/8) Epoch 8, batch 31600, loss[loss=0.1322, simple_loss=0.2196, pruned_loss=0.02238, over 4801.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03502, over 972749.80 frames.], batch size: 21, lr: 2.55e-04
2022-05-06 08:20:03,754 INFO [train.py:715] (2/8) Epoch 8, batch 31650, loss[loss=0.1409, simple_loss=0.2123, pruned_loss=0.03471, over 4697.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2161, pruned_loss=0.03546, over 971689.38 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 08:20:43,091 INFO [train.py:715] (2/8) Epoch 8, batch 31700, loss[loss=0.1278, simple_loss=0.2089, pruned_loss=0.0233, over 4905.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03539, over 971905.12 frames.], batch size: 19, lr: 2.55e-04
2022-05-06 08:21:22,753 INFO [train.py:715] (2/8) Epoch 8, batch 31750, loss[loss=0.1917, simple_loss=0.2596, pruned_loss=0.06185, over 4821.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2154, pruned_loss=0.03496, over 971138.09 frames.], batch size: 26, lr: 2.55e-04
2022-05-06 08:22:01,966 INFO [train.py:715] (2/8) Epoch 8, batch 31800, loss[loss=0.1515, simple_loss=0.2215, pruned_loss=0.04075, over 4785.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2163, pruned_loss=0.03528, over 972036.35 frames.], batch size: 18, lr: 2.55e-04
2022-05-06 08:22:41,010 INFO [train.py:715] (2/8) Epoch 8, batch 31850, loss[loss=0.1415, simple_loss=0.219, pruned_loss=0.03203, over 4823.00 frames.], tot_loss[loss=0.143, simple_loss=0.2159, pruned_loss=0.03506, over 972194.60 frames.], batch size: 25, lr: 2.55e-04
2022-05-06 08:23:19,919 INFO [train.py:715] (2/8) Epoch 8, batch 31900, loss[loss=0.1496, simple_loss=0.2238, pruned_loss=0.03768, over 4890.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03472, over 972252.33 frames.], batch size: 22, lr: 2.55e-04
2022-05-06 08:23:58,317 INFO [train.py:715] (2/8) Epoch 8, batch 31950, loss[loss=0.1462, simple_loss=0.2197, pruned_loss=0.03634, over 4797.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.03444, over 972804.66 frames.], batch size: 24, lr: 2.55e-04
2022-05-06 08:24:37,605 INFO [train.py:715] (2/8) Epoch 8, batch 32000, loss[loss=0.1445, simple_loss=0.2151, pruned_loss=0.03694, over 4696.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03459, over 972654.99 frames.], batch size: 15, lr: 2.55e-04
2022-05-06 08:25:17,173 INFO [train.py:715] (2/8) Epoch 8, batch 32050, loss[loss=0.1437, simple_loss=0.2209, pruned_loss=0.03324, over 4811.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03464, over 972501.46 frames.], batch size: 21, lr: 2.55e-04
2022-05-06 08:25:55,736 INFO [train.py:715] (2/8) Epoch 8, batch 32100, loss[loss=0.1174, simple_loss=0.1946, pruned_loss=0.02005, over 4831.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2152, pruned_loss=0.03491, over 972594.26 frames.], batch size: 12, lr: 2.55e-04
2022-05-06 08:26:34,467 INFO [train.py:715] (2/8) Epoch 8, batch 32150, loss[loss=0.1506, simple_loss=0.2143, pruned_loss=0.04344, over 4850.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03512, over 972589.90 frames.], batch size: 32, lr: 2.55e-04
2022-05-06 08:27:14,061 INFO [train.py:715] (2/8) Epoch 8, batch 32200, loss[loss=0.1364, simple_loss=0.2113, pruned_loss=0.03071, over 4766.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2158, pruned_loss=0.03537, over 971679.85 frames.], batch size: 14, lr: 2.54e-04
2022-05-06 08:27:52,861 INFO [train.py:715] (2/8) Epoch 8, batch 32250, loss[loss=0.1553, simple_loss=0.2215, pruned_loss=0.0445, over 4833.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03527, over 972239.87 frames.], batch size: 30, lr: 2.54e-04
2022-05-06 08:28:32,349 INFO [train.py:715] (2/8) Epoch 8, batch 32300, loss[loss=0.143, simple_loss=0.2118, pruned_loss=0.03713, over 4649.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2151, pruned_loss=0.03534, over 971377.39 frames.], batch size: 13, lr: 2.54e-04
2022-05-06 08:29:11,543 INFO [train.py:715] (2/8) Epoch 8, batch 32350, loss[loss=0.1383, simple_loss=0.2173, pruned_loss=0.02967, over 4813.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2161, pruned_loss=0.0357, over 971448.15 frames.], batch size: 25, lr: 2.54e-04
2022-05-06 08:29:51,474 INFO [train.py:715] (2/8) Epoch 8, batch 32400, loss[loss=0.1268, simple_loss=0.2003, pruned_loss=0.02661, over 4981.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03543, over 972283.59 frames.], batch size: 24, lr: 2.54e-04
2022-05-06 08:30:30,398 INFO [train.py:715] (2/8) Epoch 8, batch 32450, loss[loss=0.119, simple_loss=0.1914, pruned_loss=0.02334, over 4928.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03551, over 972247.13 frames.], batch size: 18, lr: 2.54e-04
2022-05-06 08:31:09,405 INFO [train.py:715] (2/8) Epoch 8, batch 32500, loss[loss=0.1464, simple_loss=0.222, pruned_loss=0.03537, over 4788.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.03554, over 972148.72 frames.], batch size: 17, lr: 2.54e-04
2022-05-06 08:31:48,952 INFO [train.py:715] (2/8) Epoch 8, batch 32550, loss[loss=0.1282, simple_loss=0.2056, pruned_loss=0.02538, over 4798.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03481, over 972069.79 frames.], batch size: 24, lr: 2.54e-04
2022-05-06 08:32:27,501 INFO [train.py:715] (2/8) Epoch 8, batch 32600, loss[loss=0.1359, simple_loss=0.2023, pruned_loss=0.03473, over 4959.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2152, pruned_loss=0.03506, over 972608.14 frames.], batch size: 24, lr: 2.54e-04
2022-05-06 08:33:06,744 INFO [train.py:715] (2/8) Epoch 8, batch 32650, loss[loss=0.1378, simple_loss=0.2094, pruned_loss=0.03308, over 4878.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03489, over 973154.76 frames.], batch size: 22, lr: 2.54e-04
2022-05-06 08:33:45,982 INFO [train.py:715] (2/8) Epoch 8, batch 32700, loss[loss=0.1477, simple_loss=0.2146, pruned_loss=0.04037, over 4772.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2154, pruned_loss=0.03552, over 973439.66 frames.], batch size: 14, lr: 2.54e-04
2022-05-06 08:34:26,179 INFO [train.py:715] (2/8) Epoch 8, batch 32750, loss[loss=0.1456, simple_loss=0.2164, pruned_loss=0.03743, over 4949.00 frames.], tot_loss[loss=0.1426, simple_loss=0.215, pruned_loss=0.03508, over 973665.12 frames.], batch size: 29, lr: 2.54e-04
2022-05-06 08:35:04,666 INFO [train.py:715] (2/8) Epoch 8, batch 32800, loss[loss=0.1486, simple_loss=0.2132, pruned_loss=0.042, over 4947.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03523, over 973401.97 frames.], batch size: 21, lr: 2.54e-04
2022-05-06 08:35:43,310 INFO [train.py:715] (2/8) Epoch 8, batch 32850, loss[loss=0.1657, simple_loss=0.2276, pruned_loss=0.05187, over 4856.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03463, over 973042.84 frames.], batch size: 32, lr: 2.54e-04
2022-05-06 08:36:22,461 INFO [train.py:715] (2/8) Epoch 8, batch 32900, loss[loss=0.1393, simple_loss=0.2131, pruned_loss=0.03271, over 4692.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2151, pruned_loss=0.03475, over 972524.49 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:37:00,743 INFO [train.py:715] (2/8) Epoch 8, batch 32950, loss[loss=0.0987, simple_loss=0.1646, pruned_loss=0.01638, over 4757.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03502, over 973063.14 frames.], batch size: 12, lr: 2.54e-04
2022-05-06 08:37:39,628 INFO [train.py:715] (2/8) Epoch 8, batch 33000, loss[loss=0.153, simple_loss=0.2229, pruned_loss=0.0415, over 4882.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03456, over 972756.69 frames.], batch size: 19, lr: 2.54e-04
2022-05-06 08:37:39,629 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 08:37:52,640 INFO [train.py:742] (2/8) Epoch 8, validation: loss=0.1071, simple_loss=0.1917, pruned_loss=0.01126, over 914524.00 frames.
2022-05-06 08:38:31,977 INFO [train.py:715] (2/8) Epoch 8, batch 33050, loss[loss=0.1442, simple_loss=0.2223, pruned_loss=0.03306, over 4855.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03492, over 972662.87 frames.], batch size: 20, lr: 2.54e-04
2022-05-06 08:39:10,829 INFO [train.py:715] (2/8) Epoch 8, batch 33100, loss[loss=0.1812, simple_loss=0.2532, pruned_loss=0.05464, over 4942.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2152, pruned_loss=0.03474, over 973281.54 frames.], batch size: 29, lr: 2.54e-04
2022-05-06 08:39:50,127 INFO [train.py:715] (2/8) Epoch 8, batch 33150, loss[loss=0.1722, simple_loss=0.2385, pruned_loss=0.053, over 4857.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2159, pruned_loss=0.03523, over 973196.86 frames.], batch size: 20, lr: 2.54e-04
2022-05-06 08:40:28,832 INFO [train.py:715] (2/8) Epoch 8, batch 33200, loss[loss=0.1356, simple_loss=0.2204, pruned_loss=0.0254, over 4776.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2152, pruned_loss=0.03503, over 973545.36 frames.], batch size: 14, lr: 2.54e-04
2022-05-06 08:41:08,504 INFO [train.py:715] (2/8) Epoch 8, batch 33250, loss[loss=0.1462, simple_loss=0.2181, pruned_loss=0.03714, over 4845.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2155, pruned_loss=0.03516, over 973417.94 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:41:48,119 INFO [train.py:715] (2/8) Epoch 8, batch 33300, loss[loss=0.1933, simple_loss=0.2745, pruned_loss=0.05609, over 4705.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03496, over 972817.65 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:42:26,899 INFO [train.py:715] (2/8) Epoch 8, batch 33350, loss[loss=0.1187, simple_loss=0.1869, pruned_loss=0.0253, over 4644.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03524, over 972700.40 frames.], batch size: 13, lr: 2.54e-04
2022-05-06 08:43:06,262 INFO [train.py:715] (2/8) Epoch 8, batch 33400, loss[loss=0.1125, simple_loss=0.1846, pruned_loss=0.02023, over 4780.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03528, over 973208.12 frames.], batch size: 17, lr: 2.54e-04
2022-05-06 08:43:45,177 INFO [train.py:715] (2/8) Epoch 8, batch 33450, loss[loss=0.1327, simple_loss=0.2112, pruned_loss=0.02712, over 4994.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03495, over 973383.83 frames.], batch size: 14, lr: 2.54e-04
2022-05-06 08:44:24,009 INFO [train.py:715] (2/8) Epoch 8, batch 33500, loss[loss=0.1372, simple_loss=0.2139, pruned_loss=0.03028, over 4788.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2155, pruned_loss=0.03486, over 973210.09 frames.], batch size: 13, lr: 2.54e-04
2022-05-06 08:45:05,007 INFO [train.py:715] (2/8) Epoch 8, batch 33550, loss[loss=0.167, simple_loss=0.2347, pruned_loss=0.04967, over 4976.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2155, pruned_loss=0.03485, over 974623.14 frames.], batch size: 31, lr: 2.54e-04
2022-05-06 08:45:44,463 INFO [train.py:715] (2/8) Epoch 8, batch 33600, loss[loss=0.195, simple_loss=0.2629, pruned_loss=0.06355, over 4905.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2166, pruned_loss=0.0353, over 974637.08 frames.], batch size: 17, lr: 2.54e-04
2022-05-06 08:46:23,909 INFO [train.py:715] (2/8) Epoch 8, batch 33650, loss[loss=0.2164, simple_loss=0.2949, pruned_loss=0.06893, over 4841.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2167, pruned_loss=0.03557, over 973609.49 frames.], batch size: 20, lr: 2.54e-04
2022-05-06 08:47:02,973 INFO [train.py:715] (2/8) Epoch 8, batch 33700, loss[loss=0.1197, simple_loss=0.2027, pruned_loss=0.01832, over 4834.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2166, pruned_loss=0.0356, over 973383.96 frames.], batch size: 25, lr: 2.54e-04
2022-05-06 08:47:41,967 INFO [train.py:715] (2/8) Epoch 8, batch 33750, loss[loss=0.137, simple_loss=0.2072, pruned_loss=0.03346, over 4961.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2162, pruned_loss=0.03555, over 973197.32 frames.], batch size: 14, lr: 2.54e-04
2022-05-06 08:48:20,686 INFO [train.py:715] (2/8) Epoch 8, batch 33800, loss[loss=0.1482, simple_loss=0.2234, pruned_loss=0.03654, over 4840.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2164, pruned_loss=0.03568, over 972883.59 frames.], batch size: 26, lr: 2.54e-04
2022-05-06 08:48:59,304 INFO [train.py:715] (2/8) Epoch 8, batch 33850, loss[loss=0.1355, simple_loss=0.2165, pruned_loss=0.02721, over 4805.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03571, over 972233.06 frames.], batch size: 25, lr: 2.54e-04
2022-05-06 08:49:38,116 INFO [train.py:715] (2/8) Epoch 8, batch 33900, loss[loss=0.1331, simple_loss=0.196, pruned_loss=0.0351, over 4825.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2153, pruned_loss=0.03559, over 972225.89 frames.], batch size: 13, lr: 2.54e-04
2022-05-06 08:50:17,037 INFO [train.py:715] (2/8) Epoch 8, batch 33950, loss[loss=0.1839, simple_loss=0.2515, pruned_loss=0.05819, over 4798.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2151, pruned_loss=0.03556, over 972019.46 frames.], batch size: 24, lr: 2.54e-04
2022-05-06 08:50:56,633 INFO [train.py:715] (2/8) Epoch 8, batch 34000, loss[loss=0.1383, simple_loss=0.2001, pruned_loss=0.0383, over 4744.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03518, over 970820.91 frames.], batch size: 16, lr: 2.54e-04
2022-05-06 08:51:35,549 INFO [train.py:715] (2/8) Epoch 8, batch 34050, loss[loss=0.1607, simple_loss=0.2296, pruned_loss=0.04584, over 4843.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2145, pruned_loss=0.0351, over 970461.81 frames.], batch size: 32, lr: 2.54e-04
2022-05-06 08:52:14,818 INFO [train.py:715] (2/8) Epoch 8, batch 34100, loss[loss=0.1506, simple_loss=0.2207, pruned_loss=0.04024, over 4867.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.03534, over 970439.07 frames.], batch size: 30, lr: 2.54e-04
2022-05-06 08:52:53,781 INFO [train.py:715] (2/8) Epoch 8, batch 34150, loss[loss=0.1384, simple_loss=0.1994, pruned_loss=0.03871, over 4823.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2156, pruned_loss=0.03594, over 970782.22 frames.], batch size: 26, lr: 2.54e-04
2022-05-06 08:53:32,398 INFO [train.py:715] (2/8) Epoch 8, batch 34200, loss[loss=0.1684, simple_loss=0.2374, pruned_loss=0.04972, over 4907.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2146, pruned_loss=0.03528, over 971217.99 frames.], batch size: 17, lr: 2.54e-04
2022-05-06 08:54:11,302 INFO [train.py:715] (2/8) Epoch 8, batch 34250, loss[loss=0.1529, simple_loss=0.2261, pruned_loss=0.03983, over 4933.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.03512, over 972368.24 frames.], batch size: 18, lr: 2.54e-04
2022-05-06 08:54:50,276 INFO [train.py:715] (2/8) Epoch 8, batch 34300, loss[loss=0.1212, simple_loss=0.1975, pruned_loss=0.02248, over 4849.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2152, pruned_loss=0.03562, over 971780.69 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:55:29,026 INFO [train.py:715] (2/8) Epoch 8, batch 34350, loss[loss=0.1291, simple_loss=0.2005, pruned_loss=0.02882, over 4756.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2141, pruned_loss=0.03523, over 971626.07 frames.], batch size: 16, lr: 2.54e-04
2022-05-06 08:56:07,454 INFO [train.py:715] (2/8) Epoch 8, batch 34400, loss[loss=0.1231, simple_loss=0.19, pruned_loss=0.02813, over 4928.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2144, pruned_loss=0.03488, over 971449.49 frames.], batch size: 18, lr: 2.54e-04
2022-05-06 08:56:46,677 INFO [train.py:715] (2/8) Epoch 8, batch 34450, loss[loss=0.1818, simple_loss=0.236, pruned_loss=0.0638, over 4835.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.0353, over 971573.67 frames.], batch size: 30, lr: 2.54e-04
2022-05-06 08:57:26,048 INFO [train.py:715] (2/8) Epoch 8, batch 34500, loss[loss=0.14, simple_loss=0.2167, pruned_loss=0.03163, over 4962.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03581, over 971307.84 frames.], batch size: 24, lr: 2.54e-04
2022-05-06 08:58:04,290 INFO [train.py:715] (2/8) Epoch 8, batch 34550, loss[loss=0.1524, simple_loss=0.224, pruned_loss=0.04039, over 4697.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2169, pruned_loss=0.03595, over 971777.84 frames.], batch size: 15, lr: 2.54e-04
2022-05-06 08:58:42,924 INFO [train.py:715] (2/8) Epoch 8, batch 34600, loss[loss=0.136, simple_loss=0.2099, pruned_loss=0.03108, over 4944.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2166, pruned_loss=0.03603, over 971498.84 frames.], batch size: 35, lr: 2.54e-04
2022-05-06 08:59:21,844 INFO [train.py:715] (2/8) Epoch 8, batch 34650, loss[loss=0.1407, simple_loss=0.2126, pruned_loss=0.03437, over 4799.00 frames.], tot_loss[loss=0.144, simple_loss=0.2164, pruned_loss=0.03584, over 970554.45 frames.], batch size: 25, lr: 2.53e-04
2022-05-06 09:00:01,503 INFO [train.py:715] (2/8) Epoch 8, batch 34700, loss[loss=0.135, simple_loss=0.2048, pruned_loss=0.03258, over 4785.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2163, pruned_loss=0.03607, over 969723.67 frames.], batch size: 18, lr: 2.53e-04
2022-05-06 09:00:38,663 INFO [train.py:715] (2/8) Epoch 8, batch 34750, loss[loss=0.1189, simple_loss=0.1991, pruned_loss=0.01937, over 4749.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2165, pruned_loss=0.03616, over 969589.31 frames.], batch size: 12, lr: 2.53e-04
2022-05-06 09:01:15,265 INFO [train.py:715] (2/8) Epoch 8, batch 34800, loss[loss=0.1495, simple_loss=0.2028, pruned_loss=0.04814, over 4802.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2154, pruned_loss=0.03618, over 969689.33 frames.], batch size: 12, lr: 2.53e-04
2022-05-06 09:02:04,642 INFO [train.py:715] (2/8) Epoch 9, batch 0, loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02994, over 4883.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02994, over 4883.00 frames.], batch size: 22, lr: 2.42e-04
2022-05-06 09:02:43,975 INFO [train.py:715] (2/8) Epoch 9, batch 50, loss[loss=0.1396, simple_loss=0.208, pruned_loss=0.0356, over 4769.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2127, pruned_loss=0.03479, over 219106.71 frames.], batch size: 19, lr: 2.41e-04
2022-05-06 09:03:23,609 INFO [train.py:715] (2/8) Epoch 9, batch 100, loss[loss=0.1472, simple_loss=0.22, pruned_loss=0.03719, over 4704.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2133, pruned_loss=0.03502, over 386243.22 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:04:02,104 INFO [train.py:715] (2/8) Epoch 9, batch 150, loss[loss=0.1546, simple_loss=0.2162, pruned_loss=0.04652, over 4798.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2136, pruned_loss=0.03503, over 515980.23 frames.], batch size: 14, lr: 2.41e-04
2022-05-06 09:04:42,539 INFO [train.py:715] (2/8) Epoch 9, batch 200, loss[loss=0.1633, simple_loss=0.2366, pruned_loss=0.04501, over 4802.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2149, pruned_loss=0.03572, over 616746.73 frames.], batch size: 21, lr: 2.41e-04
2022-05-06 09:05:21,806 INFO [train.py:715] (2/8) Epoch 9, batch 250, loss[loss=0.1107, simple_loss=0.1822, pruned_loss=0.01958, over 4810.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2147, pruned_loss=0.03517, over 694629.90 frames.], batch size: 21, lr: 2.41e-04
2022-05-06 09:06:01,093 INFO [train.py:715] (2/8) Epoch 9, batch 300, loss[loss=0.1439, simple_loss=0.2183, pruned_loss=0.03471, over 4761.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03484, over 756580.22 frames.], batch size: 19, lr: 2.41e-04
2022-05-06 09:06:40,657 INFO [train.py:715] (2/8) Epoch 9, batch 350, loss[loss=0.138, simple_loss=0.2037, pruned_loss=0.03615, over 4749.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2138, pruned_loss=0.03463, over 803846.79 frames.], batch size: 19, lr: 2.41e-04
2022-05-06 09:07:20,402 INFO [train.py:715] (2/8) Epoch 9, batch 400, loss[loss=0.1434, simple_loss=0.2216, pruned_loss=0.0326, over 4894.00 frames.], tot_loss[loss=0.142, simple_loss=0.2142, pruned_loss=0.03491, over 842650.47 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:07:59,736 INFO [train.py:715] (2/8) Epoch 9, batch 450, loss[loss=0.1227, simple_loss=0.2117, pruned_loss=0.01688, over 4767.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03509, over 870730.92 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:08:38,887 INFO [train.py:715] (2/8) Epoch 9, batch 500, loss[loss=0.1521, simple_loss=0.2326, pruned_loss=0.03583, over 4808.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2133, pruned_loss=0.03476, over 892789.17 frames.], batch size: 25, lr: 2.41e-04
2022-05-06 09:09:19,203 INFO [train.py:715] (2/8) Epoch 9, batch 550, loss[loss=0.1432, simple_loss=0.2173, pruned_loss=0.03457, over 4937.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2136, pruned_loss=0.03508, over 910624.88 frames.], batch size: 35, lr: 2.41e-04
2022-05-06 09:09:58,810 INFO [train.py:715] (2/8) Epoch 9, batch 600, loss[loss=0.1094, simple_loss=0.1752, pruned_loss=0.02179, over 4775.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2136, pruned_loss=0.03502, over 924350.67 frames.], batch size: 14, lr: 2.41e-04
2022-05-06 09:10:37,830 INFO [train.py:715] (2/8) Epoch 9, batch 650, loss[loss=0.1387, simple_loss=0.2134, pruned_loss=0.03201, over 4853.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2141, pruned_loss=0.03512, over 935044.37 frames.], batch size: 30, lr: 2.41e-04
2022-05-06 09:11:16,916 INFO [train.py:715] (2/8) Epoch 9, batch 700, loss[loss=0.165, simple_loss=0.2316, pruned_loss=0.04921, over 4750.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2141, pruned_loss=0.03487, over 943604.05 frames.], batch size: 19, lr: 2.41e-04
2022-05-06 09:11:56,397 INFO [train.py:715] (2/8) Epoch 9, batch 750, loss[loss=0.1275, simple_loss=0.1971, pruned_loss=0.02897, over 4767.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2141, pruned_loss=0.0347, over 949757.45 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:12:35,539 INFO [train.py:715] (2/8) Epoch 9, batch 800, loss[loss=0.1288, simple_loss=0.1988, pruned_loss=0.0294, over 4983.00 frames.], tot_loss[loss=0.142, simple_loss=0.2141, pruned_loss=0.03494, over 955635.64 frames.], batch size: 35, lr: 2.41e-04
2022-05-06 09:13:14,319 INFO [train.py:715] (2/8) Epoch 9, batch 850, loss[loss=0.144, simple_loss=0.2181, pruned_loss=0.03498, over 4885.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2145, pruned_loss=0.03524, over 959352.37 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:13:53,321 INFO [train.py:715] (2/8) Epoch 9, batch 900, loss[loss=0.1409, simple_loss=0.2134, pruned_loss=0.03421, over 4924.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2145, pruned_loss=0.03497, over 962400.65 frames.], batch size: 23, lr: 2.41e-04
2022-05-06 09:14:32,596 INFO [train.py:715] (2/8) Epoch 9, batch 950, loss[loss=0.1808, simple_loss=0.256, pruned_loss=0.0528, over 4858.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2152, pruned_loss=0.0353, over 964649.88 frames.], batch size: 20, lr: 2.41e-04
2022-05-06 09:15:12,212 INFO [train.py:715] (2/8) Epoch 9, batch 1000, loss[loss=0.1534, simple_loss=0.2259, pruned_loss=0.04046, over 4923.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.03511, over 966214.39 frames.], batch size: 23, lr: 2.41e-04
2022-05-06 09:15:50,366 INFO [train.py:715] (2/8) Epoch 9, batch 1050, loss[loss=0.1187, simple_loss=0.1939, pruned_loss=0.02172, over 4877.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03459, over 967628.84 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:16:30,509 INFO [train.py:715] (2/8) Epoch 9, batch 1100, loss[loss=0.1375, simple_loss=0.2127, pruned_loss=0.03119, over 4905.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2142, pruned_loss=0.0345, over 968402.62 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:17:10,364 INFO [train.py:715] (2/8) Epoch 9, batch 1150, loss[loss=0.163, simple_loss=0.2463, pruned_loss=0.03979, over 4813.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2142, pruned_loss=0.03431, over 969204.35 frames.], batch size: 21, lr: 2.41e-04
2022-05-06 09:17:49,484 INFO [train.py:715] (2/8) Epoch 9, batch 1200, loss[loss=0.1318, simple_loss=0.2068, pruned_loss=0.02842, over 4890.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03474, over 969832.54 frames.], batch size: 22, lr: 2.41e-04
2022-05-06 09:18:28,819 INFO [train.py:715] (2/8) Epoch 9, batch 1250, loss[loss=0.1171, simple_loss=0.1891, pruned_loss=0.02261, over 4933.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2155, pruned_loss=0.03503, over 970187.60 frames.], batch size: 29, lr: 2.41e-04
2022-05-06 09:19:08,559 INFO [train.py:715] (2/8) Epoch 9, batch 1300, loss[loss=0.1135, simple_loss=0.1837, pruned_loss=0.02166, over 4824.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2152, pruned_loss=0.03497, over 970797.67 frames.], batch size: 13, lr: 2.41e-04
2022-05-06 09:19:48,098 INFO [train.py:715] (2/8) Epoch 9, batch 1350, loss[loss=0.1159, simple_loss=0.1901, pruned_loss=0.02081, over 4925.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2144, pruned_loss=0.03466, over 970504.91 frames.], batch size: 29, lr: 2.41e-04
2022-05-06 09:20:26,897 INFO [train.py:715] (2/8) Epoch 9, batch 1400, loss[loss=0.1609, simple_loss=0.2303, pruned_loss=0.04572, over 4865.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2137, pruned_loss=0.03469, over 970365.23 frames.], batch size: 20, lr: 2.41e-04
2022-05-06 09:21:06,500 INFO [train.py:715] (2/8) Epoch 9, batch 1450, loss[loss=0.1268, simple_loss=0.2041, pruned_loss=0.02482, over 4980.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03511, over 970581.45 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:21:45,311 INFO [train.py:715] (2/8) Epoch 9, batch 1500, loss[loss=0.149, simple_loss=0.2231, pruned_loss=0.03741, over 4835.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03483, over 971188.53 frames.], batch size: 27, lr: 2.41e-04
2022-05-06 09:22:24,145 INFO [train.py:715] (2/8) Epoch 9, batch 1550, loss[loss=0.1456, simple_loss=0.2227, pruned_loss=0.03425, over 4905.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03506, over 970840.63 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:23:03,176 INFO [train.py:715] (2/8) Epoch 9, batch 1600, loss[loss=0.1551, simple_loss=0.2256, pruned_loss=0.04225, over 4976.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2164, pruned_loss=0.03568, over 970933.37 frames.], batch size: 25, lr: 2.41e-04
2022-05-06 09:23:42,084 INFO [train.py:715] (2/8) Epoch 9, batch 1650, loss[loss=0.1408, simple_loss=0.2237, pruned_loss=0.02898, over 4972.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2154, pruned_loss=0.03511, over 971986.02 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:24:21,074 INFO [train.py:715] (2/8) Epoch 9, batch 1700, loss[loss=0.1485, simple_loss=0.2129, pruned_loss=0.04203, over 4839.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03522, over 972446.34 frames.], batch size: 30, lr: 2.41e-04
2022-05-06 09:25:00,145 INFO [train.py:715] (2/8) Epoch 9, batch 1750, loss[loss=0.1513, simple_loss=0.2252, pruned_loss=0.0387, over 4854.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2154, pruned_loss=0.03548, over 971448.59 frames.], batch size: 30, lr: 2.41e-04
2022-05-06 09:25:39,672 INFO [train.py:715] (2/8) Epoch 9, batch 1800, loss[loss=0.1583, simple_loss=0.2286, pruned_loss=0.04402, over 4976.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2155, pruned_loss=0.03574, over 971979.91 frames.], batch size: 39, lr: 2.41e-04
2022-05-06 09:26:18,855 INFO [train.py:715] (2/8) Epoch 9, batch 1850, loss[loss=0.1461, simple_loss=0.2112, pruned_loss=0.04052, over 4879.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2158, pruned_loss=0.03622, over 971625.74 frames.], batch size: 38, lr: 2.41e-04
2022-05-06 09:26:57,983 INFO [train.py:715] (2/8) Epoch 9, batch 1900, loss[loss=0.1243, simple_loss=0.1975, pruned_loss=0.02554, over 4816.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2148, pruned_loss=0.03537, over 972165.04 frames.], batch size: 26, lr: 2.41e-04
2022-05-06 09:27:37,989 INFO [train.py:715] (2/8) Epoch 9, batch 1950, loss[loss=0.1391, simple_loss=0.2168, pruned_loss=0.03074, over 4689.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03509, over 972327.54 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:28:17,648 INFO [train.py:715] (2/8) Epoch 9, batch 2000, loss[loss=0.1341, simple_loss=0.1993, pruned_loss=0.03445, over 4819.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03507, over 973147.80 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:28:56,802 INFO [train.py:715] (2/8) Epoch 9, batch 2050, loss[loss=0.1564, simple_loss=0.2089, pruned_loss=0.05197, over 4799.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2154, pruned_loss=0.03547, over 972160.12 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:29:35,326 INFO [train.py:715] (2/8) Epoch 9, batch 2100, loss[loss=0.1496, simple_loss=0.2275, pruned_loss=0.03587, over 4767.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03481, over 971985.39 frames.], batch size: 17, lr: 2.41e-04
2022-05-06 09:30:14,644 INFO [train.py:715] (2/8) Epoch 9, batch 2150, loss[loss=0.1242, simple_loss=0.1961, pruned_loss=0.02613, over 4807.00 frames.], tot_loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03436, over 971264.13 frames.], batch size: 25, lr: 2.41e-04
2022-05-06 09:30:53,734 INFO [train.py:715] (2/8) Epoch 9, batch 2200, loss[loss=0.1354, simple_loss=0.2122, pruned_loss=0.02926, over 4752.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2134, pruned_loss=0.03501, over 970617.41 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:31:32,490 INFO [train.py:715] (2/8) Epoch 9, batch 2250, loss[loss=0.1192, simple_loss=0.1883, pruned_loss=0.02504, over 4967.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2141, pruned_loss=0.0352, over 970602.21 frames.], batch size: 35, lr: 2.41e-04
2022-05-06 09:32:11,658 INFO [train.py:715] (2/8) Epoch 9, batch 2300, loss[loss=0.1243, simple_loss=0.1985, pruned_loss=0.02507, over 4819.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2142, pruned_loss=0.0354, over 970146.99 frames.], batch size: 26, lr: 2.41e-04
2022-05-06 09:32:50,737 INFO [train.py:715] (2/8) Epoch 9, batch 2350, loss[loss=0.1604, simple_loss=0.2291, pruned_loss=0.04587, over 4830.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2144, pruned_loss=0.03514, over 970041.47 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:33:30,102 INFO [train.py:715] (2/8) Epoch 9, batch 2400, loss[loss=0.1395, simple_loss=0.2092, pruned_loss=0.03491, over 4948.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2139, pruned_loss=0.0349, over 971747.27 frames.], batch size: 39, lr: 2.41e-04
2022-05-06 09:34:08,887 INFO [train.py:715] (2/8) Epoch 9, batch 2450, loss[loss=0.1387, simple_loss=0.2172, pruned_loss=0.03011, over 4859.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2127, pruned_loss=0.03421, over 972064.32 frames.], batch size: 20, lr: 2.41e-04
2022-05-06 09:34:48,501 INFO [train.py:715] (2/8) Epoch 9, batch 2500, loss[loss=0.127, simple_loss=0.2068, pruned_loss=0.02355, over 4780.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2134, pruned_loss=0.03461, over 971908.48 frames.], batch size: 18, lr: 2.41e-04
2022-05-06 09:35:27,020 INFO [train.py:715] (2/8) Epoch 9, batch 2550, loss[loss=0.1548, simple_loss=0.2403, pruned_loss=0.0347, over 4958.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03498, over 971998.21 frames.], batch size: 21, lr: 2.41e-04
2022-05-06 09:36:06,040 INFO [train.py:715] (2/8) Epoch 9, batch 2600, loss[loss=0.1428, simple_loss=0.2138, pruned_loss=0.03593, over 4698.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03518, over 971646.23 frames.], batch size: 15, lr: 2.41e-04
2022-05-06 09:36:45,109 INFO [train.py:715] (2/8) Epoch 9, batch 2650, loss[loss=0.1335, simple_loss=0.2068, pruned_loss=0.03006, over 4738.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03507, over 972486.40 frames.], batch size: 16, lr: 2.41e-04
2022-05-06 09:37:24,473 INFO [train.py:715] (2/8) Epoch 9, batch 2700, loss[loss=0.1323, simple_loss=0.2113, pruned_loss=0.02666, over 4766.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03481, over 972108.53 frames.], batch size: 17, lr: 2.40e-04
2022-05-06 09:38:03,294 INFO [train.py:715] (2/8) Epoch 9, batch 2750, loss[loss=0.1334, simple_loss=0.2171, pruned_loss=0.02483, over 4879.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03481, over 971291.63 frames.], batch size: 16, lr: 2.40e-04
2022-05-06 09:38:42,264 INFO [train.py:715] (2/8) Epoch 9, batch 2800, loss[loss=0.1544, simple_loss=0.225, pruned_loss=0.04191, over 4888.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03501, over 972319.66 frames.], batch size: 16, lr: 2.40e-04
2022-05-06 09:39:21,839 INFO [train.py:715] (2/8) Epoch 9, batch 2850, loss[loss=0.1562, simple_loss=0.2292, pruned_loss=0.04163, over 4808.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03469, over 972469.60 frames.], batch size: 21, lr: 2.40e-04
2022-05-06 09:40:00,914 INFO [train.py:715] (2/8) Epoch 9, batch 2900, loss[loss=0.1635, simple_loss=0.2337, pruned_loss=0.04664, over 4896.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2145, pruned_loss=0.035, over 971858.73 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 09:40:39,677 INFO [train.py:715] (2/8) Epoch 9, batch 2950, loss[loss=0.1259, simple_loss=0.2074, pruned_loss=0.02226, over 4794.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2145, pruned_loss=0.03498, over 971829.79 frames.], batch size: 24, lr: 2.40e-04
2022-05-06 09:41:18,903 INFO [train.py:715] (2/8) Epoch 9, batch 3000, loss[loss=0.1643, simple_loss=0.2266, pruned_loss=0.05097, over 4888.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03521, over 973117.57 frames.], batch size: 32, lr: 2.40e-04
2022-05-06 09:41:18,904 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 09:41:28,535 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.1069, simple_loss=0.1915, pruned_loss=0.01118, over 914524.00 frames.
2022-05-06 09:42:08,254 INFO [train.py:715] (2/8) Epoch 9, batch 3050, loss[loss=0.1273, simple_loss=0.207, pruned_loss=0.02384, over 4963.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03494, over 973497.67 frames.], batch size: 24, lr: 2.40e-04
2022-05-06 09:42:47,738 INFO [train.py:715] (2/8) Epoch 9, batch 3100, loss[loss=0.1519, simple_loss=0.222, pruned_loss=0.04087, over 4780.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03483, over 972748.00 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 09:43:27,212 INFO [train.py:715] (2/8) Epoch 9, batch 3150, loss[loss=0.141, simple_loss=0.22, pruned_loss=0.031, over 4863.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03495, over 973270.56 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 09:44:06,424 INFO [train.py:715] (2/8) Epoch 9, batch 3200, loss[loss=0.1745, simple_loss=0.2379, pruned_loss=0.05559, over 4886.00 frames.], tot_loss[loss=0.1434, simple_loss=0.216, pruned_loss=0.03542, over 972973.33 frames.], batch size: 39, lr: 2.40e-04
2022-05-06 09:44:45,580 INFO [train.py:715] (2/8) Epoch 9, batch 3250, loss[loss=0.1518, simple_loss=0.2305, pruned_loss=0.03654, over 4882.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.03526, over 973161.42 frames.], batch size: 22, lr: 2.40e-04
2022-05-06 09:45:24,840 INFO [train.py:715] (2/8) Epoch 9, batch 3300, loss[loss=0.1277, simple_loss=0.203, pruned_loss=0.0262, over 4783.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2155, pruned_loss=0.03513, over 972935.27 frames.], batch size: 17, lr: 2.40e-04
2022-05-06 09:46:03,662 INFO [train.py:715] (2/8) Epoch 9, batch 3350, loss[loss=0.1476, simple_loss=0.2209, pruned_loss=0.03716, over 4970.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2156, pruned_loss=0.03501, over 973565.64 frames.], batch size: 24, lr: 2.40e-04
2022-05-06 09:46:42,967 INFO [train.py:715] (2/8) Epoch 9, batch 3400, loss[loss=0.1521, simple_loss=0.223, pruned_loss=0.0406, over 4905.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03519, over 973117.78 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 09:47:22,073 INFO [train.py:715] (2/8) Epoch 9, batch 3450, loss[loss=0.1504, simple_loss=0.2181, pruned_loss=0.04136, over 4951.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03497, over 973450.85 frames.], batch size: 21, lr: 2.40e-04
2022-05-06 09:48:00,726 INFO [train.py:715] (2/8) Epoch 9, batch 3500, loss[loss=0.1412, simple_loss=0.2023, pruned_loss=0.04002, over 4988.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.0349, over 973416.15 frames.], batch size: 25, lr: 2.40e-04
2022-05-06 09:48:40,284 INFO [train.py:715] (2/8) Epoch 9, batch 3550, loss[loss=0.1446, simple_loss=0.2179, pruned_loss=0.03559, over 4859.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2155, pruned_loss=0.03553, over 972814.95 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 09:49:19,724 INFO [train.py:715] (2/8) Epoch 9, batch 3600, loss[loss=0.1348, simple_loss=0.2072, pruned_loss=0.03119, over 4760.00 frames.], tot_loss[loss=0.1428, simple_loss=0.215, pruned_loss=0.0353, over 972485.31 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 09:49:59,015 INFO [train.py:715] (2/8) Epoch 9, batch 3650, loss[loss=0.12, simple_loss=0.2011, pruned_loss=0.01944, over 4842.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2152, pruned_loss=0.03558, over 972959.30 frames.], batch size: 13, lr: 2.40e-04
2022-05-06 09:50:37,659 INFO [train.py:715] (2/8) Epoch 9, batch 3700, loss[loss=0.1125, simple_loss=0.1873, pruned_loss=0.01888, over 4794.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03506, over 973266.88 frames.], batch size: 12, lr: 2.40e-04
2022-05-06 09:51:17,146 INFO [train.py:715] (2/8) Epoch 9, batch 3750, loss[loss=0.1361, simple_loss=0.2089, pruned_loss=0.0317, over 4771.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03519, over 972967.11 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 09:51:56,936 INFO [train.py:715] (2/8) Epoch 9, batch 3800, loss[loss=0.1306, simple_loss=0.2052, pruned_loss=0.02803, over 4854.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03444, over 973033.62 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 09:52:35,335 INFO [train.py:715] (2/8) Epoch 9, batch 3850, loss[loss=0.1601, simple_loss=0.2443, pruned_loss=0.03796, over 4882.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2138, pruned_loss=0.03461, over 972660.70 frames.], batch size: 22, lr: 2.40e-04
2022-05-06 09:53:14,340 INFO [train.py:715] (2/8) Epoch 9, batch 3900, loss[loss=0.1371, simple_loss=0.2191, pruned_loss=0.02748, over 4785.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.0345, over 972847.26 frames.], batch size: 18, lr: 2.40e-04
2022-05-06 09:53:53,825 INFO [train.py:715] (2/8) Epoch 9, batch 3950, loss[loss=0.1606, simple_loss=0.2269, pruned_loss=0.04716, over 4847.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.03397, over 972392.08 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 09:54:33,400 INFO [train.py:715] (2/8) Epoch 9, batch 4000, loss[loss=0.1421, simple_loss=0.2086, pruned_loss=0.03779, over 4974.00 frames.], tot_loss[loss=0.1411, simple_loss=0.214, pruned_loss=0.03408, over 972739.64 frames.], batch size: 35, lr: 2.40e-04
2022-05-06 09:55:12,124 INFO [train.py:715] (2/8) Epoch 9, batch 4050, loss[loss=0.1194, simple_loss=0.1976, pruned_loss=0.02059, over 4785.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03424, over 972459.20 frames.], batch size: 12, lr: 2.40e-04
2022-05-06 09:55:52,106 INFO [train.py:715] (2/8) Epoch 9, batch 4100, loss[loss=0.1502, simple_loss=0.2176, pruned_loss=0.04138, over 4818.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03427, over 971888.98 frames.], batch size: 24, lr: 2.40e-04
2022-05-06 09:56:30,805 INFO [train.py:715] (2/8) Epoch 9, batch 4150, loss[loss=0.1273, simple_loss=0.1987, pruned_loss=0.02791, over 4780.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2148, pruned_loss=0.03451, over 971389.44 frames.], batch size: 14, lr: 2.40e-04
2022-05-06 09:57:10,176 INFO [train.py:715] (2/8) Epoch 9, batch 4200, loss[loss=0.1783, simple_loss=0.241, pruned_loss=0.05787, over 4871.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2145, pruned_loss=0.03443, over 971656.98 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 09:57:49,737 INFO [train.py:715] (2/8) Epoch 9, batch 4250, loss[loss=0.1449, simple_loss=0.2173, pruned_loss=0.03621, over 4979.00 frames.], tot_loss[loss=0.1419, simple_loss=0.215, pruned_loss=0.03439, over 972070.05 frames.], batch size: 39, lr: 2.40e-04
2022-05-06 09:58:29,634 INFO [train.py:715] (2/8) Epoch 9, batch 4300, loss[loss=0.1862, simple_loss=0.2477, pruned_loss=0.06234, over 4902.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03479, over 972180.99 frames.], batch size: 39, lr: 2.40e-04
2022-05-06 09:59:09,597 INFO [train.py:715] (2/8) Epoch 9, batch 4350, loss[loss=0.1543, simple_loss=0.2212, pruned_loss=0.04364, over 4880.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2158, pruned_loss=0.03496, over 972472.43 frames.], batch size: 32, lr: 2.40e-04
2022-05-06 09:59:48,191 INFO [train.py:715] (2/8) Epoch 9, batch 4400, loss[loss=0.1666, simple_loss=0.2468, pruned_loss=0.04318, over 4970.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2159, pruned_loss=0.03456, over 972532.65 frames.], batch size: 24, lr: 2.40e-04
2022-05-06 10:00:27,689 INFO [train.py:715] (2/8) Epoch 9, batch 4450, loss[loss=0.1362, simple_loss=0.2157, pruned_loss=0.02829, over 4986.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2161, pruned_loss=0.03483, over 972467.32 frames.], batch size: 20, lr: 2.40e-04
2022-05-06 10:01:06,478 INFO [train.py:715] (2/8) Epoch 9, batch 4500, loss[loss=0.122, simple_loss=0.2015, pruned_loss=0.02123, over 4893.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2153, pruned_loss=0.03419, over 972095.00 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 10:01:45,467 INFO [train.py:715] (2/8) Epoch 9, batch 4550, loss[loss=0.1461, simple_loss=0.2187, pruned_loss=0.03673, over 4766.00 frames.], tot_loss[loss=0.1423, simple_loss=0.216, pruned_loss=0.03433, over 971917.35 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 10:02:24,743 INFO [train.py:715] (2/8) Epoch 9, batch 4600, loss[loss=0.1443, simple_loss=0.2067, pruned_loss=0.04091, over 4812.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2159, pruned_loss=0.0343, over 971693.05 frames.], batch size: 13, lr: 2.40e-04
2022-05-06 10:03:04,309 INFO [train.py:715] (2/8) Epoch 9, batch 4650, loss[loss=0.1311, simple_loss=0.2092, pruned_loss=0.02648, over 4794.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2153, pruned_loss=0.03428, over 972223.91 frames.], batch size: 14, lr: 2.40e-04
2022-05-06 10:03:43,918 INFO [train.py:715] (2/8) Epoch 9, batch 4700, loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02977, over 4986.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2155, pruned_loss=0.03471, over 971919.39 frames.], batch size: 28, lr: 2.40e-04
2022-05-06 10:04:22,848 INFO [train.py:715] (2/8) Epoch 9, batch 4750, loss[loss=0.1376, simple_loss=0.2094, pruned_loss=0.03285, over 4911.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2154, pruned_loss=0.03522, over 972243.62 frames.], batch size: 17, lr: 2.40e-04
2022-05-06 10:05:02,422 INFO [train.py:715] (2/8) Epoch 9, batch 4800, loss[loss=0.1197, simple_loss=0.2046, pruned_loss=0.01743, over 4828.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03528, over 972488.18 frames.], batch size: 25, lr: 2.40e-04
2022-05-06 10:05:41,421 INFO [train.py:715] (2/8) Epoch 9, batch 4850, loss[loss=0.1451, simple_loss=0.2227, pruned_loss=0.0337, over 4971.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03501, over 972710.58 frames.], batch size: 35, lr: 2.40e-04
2022-05-06 10:06:20,852 INFO [train.py:715] (2/8) Epoch 9, batch 4900, loss[loss=0.1106, simple_loss=0.1812, pruned_loss=0.02002, over 4742.00 frames.], tot_loss[loss=0.143, simple_loss=0.2151, pruned_loss=0.03545, over 972616.06 frames.], batch size: 16, lr: 2.40e-04
2022-05-06 10:06:59,738 INFO [train.py:715] (2/8) Epoch 9, batch 4950, loss[loss=0.1052, simple_loss=0.1759, pruned_loss=0.01722, over 4780.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2157, pruned_loss=0.03607, over 972249.01 frames.], batch size: 14, lr: 2.40e-04
2022-05-06 10:07:39,116 INFO [train.py:715] (2/8) Epoch 9, batch 5000, loss[loss=0.1507, simple_loss=0.218, pruned_loss=0.0417, over 4785.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.0358, over 972479.62 frames.], batch size: 17, lr: 2.40e-04
2022-05-06 10:08:18,416 INFO [train.py:715] (2/8) Epoch 9, batch 5050, loss[loss=0.1453, simple_loss=0.2149, pruned_loss=0.0379, over 4864.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2156, pruned_loss=0.03499, over 971888.40 frames.], batch size: 32, lr: 2.40e-04
2022-05-06 10:08:57,171 INFO [train.py:715] (2/8) Epoch 9, batch 5100, loss[loss=0.15, simple_loss=0.2229, pruned_loss=0.03855, over 4930.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03484, over 972491.54 frames.], batch size: 23, lr: 2.40e-04
2022-05-06 10:09:36,560 INFO [train.py:715] (2/8) Epoch 9, batch 5150, loss[loss=0.1309, simple_loss=0.1962, pruned_loss=0.03281, over 4802.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03491, over 972419.19 frames.], batch size: 21, lr: 2.40e-04
2022-05-06 10:10:15,464 INFO [train.py:715] (2/8) Epoch 9, batch 5200, loss[loss=0.1415, simple_loss=0.2137, pruned_loss=0.0346, over 4815.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03485, over 972916.79 frames.], batch size: 26, lr: 2.40e-04
2022-05-06 10:10:54,752 INFO [train.py:715] (2/8) Epoch 9, batch 5250, loss[loss=0.141, simple_loss=0.214, pruned_loss=0.03394, over 4894.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03436, over 972151.37 frames.], batch size: 19, lr: 2.40e-04
2022-05-06 10:11:33,956 INFO [train.py:715] (2/8) Epoch 9, batch 5300, loss[loss=0.1452, simple_loss=0.219, pruned_loss=0.03573, over 4951.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2141, pruned_loss=0.0347, over 971875.91 frames.], batch size: 35, lr: 2.39e-04
2022-05-06 10:12:13,444 INFO [train.py:715] (2/8) Epoch 9, batch 5350, loss[loss=0.1235, simple_loss=0.1953, pruned_loss=0.02586, over 4814.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03459, over 972445.83 frames.], batch size: 13, lr: 2.39e-04
2022-05-06 10:12:52,102 INFO [train.py:715] (2/8) Epoch 9, batch 5400, loss[loss=0.1514, simple_loss=0.2331, pruned_loss=0.03483, over 4847.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03482, over 973307.77 frames.], batch size: 32, lr: 2.39e-04
2022-05-06 10:13:30,899 INFO [train.py:715] (2/8) Epoch 9, batch 5450, loss[loss=0.1367, simple_loss=0.2182, pruned_loss=0.02756, over 4882.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03487, over 973329.64 frames.], batch size: 22, lr: 2.39e-04
2022-05-06 10:14:10,210 INFO [train.py:715] (2/8) Epoch 9, batch 5500, loss[loss=0.168, simple_loss=0.2257, pruned_loss=0.0551, over 4785.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2154, pruned_loss=0.03512, over 973077.30 frames.], batch size: 18, lr: 2.39e-04
2022-05-06 10:14:49,297 INFO [train.py:715] (2/8) Epoch 9, batch 5550, loss[loss=0.1467, simple_loss=0.2102, pruned_loss=0.04164, over 4987.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2155, pruned_loss=0.03512, over 972933.69 frames.], batch size: 31, lr: 2.39e-04
2022-05-06 10:15:28,466 INFO [train.py:715] (2/8) Epoch 9, batch 5600, loss[loss=0.1179, simple_loss=0.1867, pruned_loss=0.02455, over 4712.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2151, pruned_loss=0.03485, over 972472.50 frames.], batch size: 12, lr: 2.39e-04
2022-05-06 10:16:07,455 INFO [train.py:715] (2/8) Epoch 9, batch 5650, loss[loss=0.1431, simple_loss=0.2132, pruned_loss=0.03646, over 4774.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2154, pruned_loss=0.03511, over 971906.03 frames.], batch size: 19, lr: 2.39e-04
2022-05-06 10:16:47,097 INFO [train.py:715] (2/8) Epoch 9, batch 5700, loss[loss=0.137, simple_loss=0.2038, pruned_loss=0.0351, over 4949.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2144, pruned_loss=0.03442, over 972210.39 frames.], batch size: 23, lr: 2.39e-04
2022-05-06 10:17:26,140 INFO [train.py:715] (2/8) Epoch 9, batch 5750, loss[loss=0.1335, simple_loss=0.2014, pruned_loss=0.03282, over 4775.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2139, pruned_loss=0.03445, over 972116.41 frames.], batch size: 12, lr: 2.39e-04
2022-05-06 10:18:04,786 INFO [train.py:715] (2/8) Epoch 9, batch 5800, loss[loss=0.1437, simple_loss=0.2083, pruned_loss=0.0395, over 4842.00 frames.], tot_loss[loss=0.1426, simple_loss=0.215, pruned_loss=0.03509, over 971779.22 frames.], batch size: 13, lr: 2.39e-04
2022-05-06 10:18:44,316 INFO [train.py:715] (2/8) Epoch 9, batch 5850, loss[loss=0.1739, simple_loss=0.2317, pruned_loss=0.05806, over 4781.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03522, over 972171.97 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:19:23,131 INFO [train.py:715] (2/8) Epoch 9, batch 5900, loss[loss=0.1581, simple_loss=0.2243, pruned_loss=0.04595, over 4704.00 frames.], tot_loss[loss=0.1425, simple_loss=0.215, pruned_loss=0.03496, over 972576.50 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:20:02,779 INFO [train.py:715] (2/8) Epoch 9, batch 5950, loss[loss=0.1274, simple_loss=0.1957, pruned_loss=0.02956, over 4970.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2149, pruned_loss=0.0349, over 971940.08 frames.], batch size: 14, lr: 2.39e-04
2022-05-06 10:20:41,541 INFO [train.py:715] (2/8) Epoch 9, batch 6000, loss[loss=0.1219, simple_loss=0.1964, pruned_loss=0.02366, over 4950.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2152, pruned_loss=0.03472, over 972502.17 frames.], batch size: 23, lr: 2.39e-04
2022-05-06 10:20:41,542 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 10:20:51,194 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.107, simple_loss=0.1914, pruned_loss=0.0113, over 914524.00 frames.
2022-05-06 10:21:30,883 INFO [train.py:715] (2/8) Epoch 9, batch 6050, loss[loss=0.1705, simple_loss=0.2323, pruned_loss=0.05435, over 4872.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2153, pruned_loss=0.0347, over 972366.10 frames.], batch size: 20, lr: 2.39e-04
2022-05-06 10:22:10,752 INFO [train.py:715] (2/8) Epoch 9, batch 6100, loss[loss=0.1477, simple_loss=0.223, pruned_loss=0.03617, over 4799.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03492, over 973366.84 frames.], batch size: 12, lr: 2.39e-04
2022-05-06 10:22:49,971 INFO [train.py:715] (2/8) Epoch 9, batch 6150, loss[loss=0.1254, simple_loss=0.1971, pruned_loss=0.02691, over 4845.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2144, pruned_loss=0.0347, over 973767.50 frames.], batch size: 20, lr: 2.39e-04
2022-05-06 10:23:28,786 INFO [train.py:715] (2/8) Epoch 9, batch 6200, loss[loss=0.1493, simple_loss=0.2164, pruned_loss=0.0411, over 4979.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03517, over 974351.00 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:24:08,422 INFO [train.py:715] (2/8) Epoch 9, batch 6250, loss[loss=0.1643, simple_loss=0.2428, pruned_loss=0.04294, over 4886.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03557, over 974018.48 frames.], batch size: 19, lr: 2.39e-04
2022-05-06 10:24:47,201 INFO [train.py:715] (2/8) Epoch 9, batch 6300, loss[loss=0.1451, simple_loss=0.2072, pruned_loss=0.0415, over 4825.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2157, pruned_loss=0.03553, over 973845.93 frames.], batch size: 25, lr: 2.39e-04
2022-05-06 10:25:26,320 INFO [train.py:715] (2/8) Epoch 9, batch 6350, loss[loss=0.1388, simple_loss=0.2155, pruned_loss=0.03104, over 4957.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.0356, over 973487.57 frames.], batch size: 35, lr: 2.39e-04
2022-05-06 10:26:05,972 INFO [train.py:715] (2/8) Epoch 9, batch 6400, loss[loss=0.13, simple_loss=0.2059, pruned_loss=0.02705, over 4818.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2156, pruned_loss=0.03568, over 972737.65 frames.], batch size: 25, lr: 2.39e-04
2022-05-06 10:26:46,117 INFO [train.py:715] (2/8) Epoch 9, batch 6450, loss[loss=0.1528, simple_loss=0.228, pruned_loss=0.03885, over 4806.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.03519, over 972352.67 frames.], batch size: 25, lr: 2.39e-04
2022-05-06 10:27:25,423 INFO [train.py:715] (2/8) Epoch 9, batch 6500, loss[loss=0.1407, simple_loss=0.2013, pruned_loss=0.04004, over 4779.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2149, pruned_loss=0.03495, over 972782.43 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:28:04,257 INFO [train.py:715] (2/8) Epoch 9, batch 6550, loss[loss=0.1089, simple_loss=0.1666, pruned_loss=0.02559, over 4801.00 frames.], tot_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03518, over 973350.49 frames.], batch size: 12, lr: 2.39e-04
2022-05-06 10:28:44,037 INFO [train.py:715] (2/8) Epoch 9, batch 6600, loss[loss=0.1556, simple_loss=0.2208, pruned_loss=0.04521, over 4905.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03468, over 973240.86 frames.], batch size: 19, lr: 2.39e-04
2022-05-06 10:29:23,595 INFO [train.py:715] (2/8) Epoch 9, batch 6650, loss[loss=0.1352, simple_loss=0.2121, pruned_loss=0.02908, over 4945.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03465, over 973227.77 frames.], batch size: 35, lr: 2.39e-04
2022-05-06 10:30:02,749 INFO [train.py:715] (2/8) Epoch 9, batch 6700, loss[loss=0.1428, simple_loss=0.2048, pruned_loss=0.04036, over 4860.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03506, over 973090.83 frames.], batch size: 16, lr: 2.39e-04
2022-05-06 10:30:44,171 INFO [train.py:715] (2/8) Epoch 9, batch 6750, loss[loss=0.1784, simple_loss=0.2408, pruned_loss=0.05798, over 4863.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03521, over 972702.26 frames.], batch size: 20, lr: 2.39e-04
2022-05-06 10:31:23,603 INFO [train.py:715] (2/8) Epoch 9, batch 6800, loss[loss=0.1478, simple_loss=0.2165, pruned_loss=0.03948, over 4854.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2148, pruned_loss=0.03498, over 972292.02 frames.], batch size: 22, lr: 2.39e-04
2022-05-06 10:32:02,558 INFO [train.py:715] (2/8) Epoch 9, batch 6850, loss[loss=0.1201, simple_loss=0.1924, pruned_loss=0.02389, over 4875.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2155, pruned_loss=0.03512, over 973351.59 frames.], batch size: 20, lr: 2.39e-04
2022-05-06 10:32:40,757 INFO [train.py:715] (2/8) Epoch 9, batch 6900, loss[loss=0.1415, simple_loss=0.2191, pruned_loss=0.03201, over 4890.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03465, over 973129.30 frames.], batch size: 16, lr: 2.39e-04
2022-05-06 10:33:20,059 INFO [train.py:715] (2/8) Epoch 9, batch 6950, loss[loss=0.1176, simple_loss=0.1943, pruned_loss=0.02041, over 4814.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2143, pruned_loss=0.03412, over 973581.10 frames.], batch size: 27, lr: 2.39e-04
2022-05-06 10:33:59,864 INFO [train.py:715] (2/8) Epoch 9, batch 7000, loss[loss=0.1325, simple_loss=0.1982, pruned_loss=0.03335, over 4973.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2142, pruned_loss=0.03426, over 973899.81 frames.], batch size: 35, lr: 2.39e-04
2022-05-06 10:34:38,728 INFO [train.py:715] (2/8) Epoch 9, batch 7050, loss[loss=0.1429, simple_loss=0.2086, pruned_loss=0.03858, over 4905.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2142, pruned_loss=0.03428, over 973763.75 frames.], batch size: 17, lr: 2.39e-04
2022-05-06 10:35:17,347 INFO [train.py:715] (2/8) Epoch 9, batch 7100, loss[loss=0.1412, simple_loss=0.2159, pruned_loss=0.03323, over 4968.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03473, over 973031.88 frames.], batch size: 14, lr: 2.39e-04
2022-05-06 10:35:56,812 INFO [train.py:715] (2/8) Epoch 9, batch 7150, loss[loss=0.1396, simple_loss=0.2248, pruned_loss=0.02722, over 4951.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03481, over 972540.86 frames.], batch size: 24, lr: 2.39e-04
2022-05-06 10:36:35,506 INFO [train.py:715] (2/8) Epoch 9, batch 7200, loss[loss=0.1215, simple_loss=0.1943, pruned_loss=0.02439, over 4771.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03486, over 972842.93 frames.], batch size: 12, lr: 2.39e-04
2022-05-06 10:37:14,247 INFO [train.py:715] (2/8) Epoch 9, batch 7250, loss[loss=0.1436, simple_loss=0.2178, pruned_loss=0.03471, over 4990.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2154, pruned_loss=0.03474, over 972880.23 frames.], batch size: 14, lr: 2.39e-04
2022-05-06 10:37:53,513 INFO [train.py:715] (2/8) Epoch 9, batch 7300, loss[loss=0.1645, simple_loss=0.2404, pruned_loss=0.0443, over 4877.00 frames.], tot_loss[loss=0.143, simple_loss=0.2158, pruned_loss=0.03509, over 972473.15 frames.], batch size: 20, lr: 2.39e-04
2022-05-06 10:38:32,801 INFO [train.py:715] (2/8) Epoch 9, batch 7350, loss[loss=0.1438, simple_loss=0.2136, pruned_loss=0.03701, over 4989.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2161, pruned_loss=0.03539, over 971884.31 frames.], batch size: 25, lr: 2.39e-04
2022-05-06 10:39:11,300 INFO [train.py:715] (2/8) Epoch 9, batch 7400, loss[loss=0.1728, simple_loss=0.2361, pruned_loss=0.05475, over 4844.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2162, pruned_loss=0.03578, over 972852.94 frames.], batch size: 30, lr: 2.39e-04
2022-05-06 10:39:50,258 INFO [train.py:715] (2/8) Epoch 9, batch 7450, loss[loss=0.1426, simple_loss=0.206, pruned_loss=0.03963, over 4971.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2164, pruned_loss=0.03612, over 972399.52 frames.], batch size: 35, lr: 2.39e-04
2022-05-06 10:40:30,203 INFO [train.py:715] (2/8) Epoch 9, batch 7500, loss[loss=0.1511, simple_loss=0.2313, pruned_loss=0.03543, over 4950.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2161, pruned_loss=0.03601, over 972176.61 frames.], batch size: 21, lr: 2.39e-04
2022-05-06 10:41:09,248 INFO [train.py:715] (2/8) Epoch 9, batch 7550, loss[loss=0.1742, simple_loss=0.2431, pruned_loss=0.05266, over 4948.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2171, pruned_loss=0.03627, over 972818.03 frames.], batch size: 35, lr: 2.39e-04
2022-05-06 10:41:48,089 INFO [train.py:715] (2/8) Epoch 9, batch 7600, loss[loss=0.1502, simple_loss=0.2195, pruned_loss=0.04051, over 4986.00 frames.], tot_loss[loss=0.145, simple_loss=0.2173, pruned_loss=0.03634, over 972915.59 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:42:27,543 INFO [train.py:715] (2/8) Epoch 9, batch 7650, loss[loss=0.1397, simple_loss=0.2077, pruned_loss=0.03583, over 4915.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2181, pruned_loss=0.03675, over 972310.55 frames.], batch size: 23, lr: 2.39e-04
2022-05-06 10:43:06,740 INFO [train.py:715] (2/8) Epoch 9, batch 7700, loss[loss=0.1162, simple_loss=0.1891, pruned_loss=0.02163, over 4922.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2168, pruned_loss=0.03622, over 972550.86 frames.], batch size: 18, lr: 2.39e-04
2022-05-06 10:43:45,563 INFO [train.py:715] (2/8) Epoch 9, batch 7750, loss[loss=0.1657, simple_loss=0.2552, pruned_loss=0.03808, over 4789.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2161, pruned_loss=0.03582, over 972519.96 frames.], batch size: 18, lr: 2.39e-04
2022-05-06 10:44:24,376 INFO [train.py:715] (2/8) Epoch 9, batch 7800, loss[loss=0.1373, simple_loss=0.2121, pruned_loss=0.03126, over 4805.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03523, over 973034.28 frames.], batch size: 21, lr: 2.39e-04
2022-05-06 10:45:04,416 INFO [train.py:715] (2/8) Epoch 9, batch 7850, loss[loss=0.1419, simple_loss=0.2084, pruned_loss=0.03773, over 4832.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2139, pruned_loss=0.03497, over 973259.42 frames.], batch size: 15, lr: 2.39e-04
2022-05-06 10:45:43,411 INFO [train.py:715] (2/8) Epoch 9, batch 7900, loss[loss=0.1394, simple_loss=0.2271, pruned_loss=0.02585, over 4922.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03479, over 973039.07 frames.], batch size: 23, lr: 2.39e-04
2022-05-06 10:46:21,528 INFO [train.py:715] (2/8) Epoch 9, batch 7950, loss[loss=0.1279, simple_loss=0.21, pruned_loss=0.02294, over 4988.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2139, pruned_loss=0.03459, over 972706.65 frames.], batch size: 28, lr: 2.39e-04
2022-05-06 10:47:00,914 INFO [train.py:715] (2/8) Epoch 9, batch 8000, loss[loss=0.1231, simple_loss=0.1965, pruned_loss=0.02479, over 4914.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2139, pruned_loss=0.0348, over 972366.89 frames.], batch size: 23, lr: 2.38e-04
2022-05-06 10:47:39,937 INFO [train.py:715] (2/8) Epoch 9, batch 8050, loss[loss=0.1448, simple_loss=0.2199, pruned_loss=0.03491, over 4864.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03418, over 972380.60 frames.], batch size: 16, lr: 2.38e-04
2022-05-06 10:48:18,557 INFO [train.py:715] (2/8) Epoch 9, batch 8100, loss[loss=0.1289, simple_loss=0.2072, pruned_loss=0.0253, over 4985.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03445, over 972753.99 frames.], batch size: 28, lr: 2.38e-04
2022-05-06 10:48:57,111 INFO [train.py:715] (2/8) Epoch 9, batch 8150, loss[loss=0.1237, simple_loss=0.1969, pruned_loss=0.02529, over 4741.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.03443, over 971948.58 frames.], batch size: 12, lr: 2.38e-04
2022-05-06 10:49:36,459 INFO [train.py:715] (2/8) Epoch 9, batch 8200, loss[loss=0.1209, simple_loss=0.1841, pruned_loss=0.0288, over 4818.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2133, pruned_loss=0.03414, over 972388.35 frames.], batch size: 12, lr: 2.38e-04
2022-05-06 10:50:15,125 INFO [train.py:715] (2/8) Epoch 9, batch 8250, loss[loss=0.1407, simple_loss=0.221, pruned_loss=0.03016, over 4959.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2137, pruned_loss=0.03406, over 972266.77 frames.], batch size: 24, lr: 2.38e-04
2022-05-06 10:50:53,696 INFO [train.py:715] (2/8) Epoch 9, batch 8300, loss[loss=0.1561, simple_loss=0.2445, pruned_loss=0.03382, over 4891.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2133, pruned_loss=0.0341, over 972333.89 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 10:51:32,740 INFO [train.py:715] (2/8) Epoch 9, batch 8350, loss[loss=0.1543, simple_loss=0.2285, pruned_loss=0.04006, over 4906.00 frames.], tot_loss[loss=0.141, simple_loss=0.2134, pruned_loss=0.03431, over 972969.11 frames.], batch size: 23, lr: 2.38e-04
2022-05-06 10:52:12,415 INFO [train.py:715] (2/8) Epoch 9, batch 8400, loss[loss=0.1622, simple_loss=0.2349, pruned_loss=0.04478, over 4835.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03452, over 972873.18 frames.], batch size: 30, lr: 2.38e-04
2022-05-06 10:52:50,772 INFO [train.py:715] (2/8) Epoch 9, batch 8450, loss[loss=0.1204, simple_loss=0.1915, pruned_loss=0.02468, over 4784.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03463, over 972721.59 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 10:53:29,412 INFO [train.py:715] (2/8) Epoch 9, batch 8500, loss[loss=0.1161, simple_loss=0.1932, pruned_loss=0.0195, over 4970.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2149, pruned_loss=0.03474, over 973474.59 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 10:54:08,959 INFO [train.py:715] (2/8) Epoch 9, batch 8550, loss[loss=0.1527, simple_loss=0.2318, pruned_loss=0.03687, over 4805.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03458, over 973179.17 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 10:54:48,127 INFO [train.py:715] (2/8) Epoch 9, batch 8600, loss[loss=0.1545, simple_loss=0.2245, pruned_loss=0.04225, over 4770.00 frames.], tot_loss[loss=0.143, simple_loss=0.2154, pruned_loss=0.03525, over 972732.66 frames.], batch size: 12, lr: 2.38e-04
2022-05-06 10:55:26,987 INFO [train.py:715] (2/8) Epoch 9, batch 8650, loss[loss=0.1186, simple_loss=0.1929, pruned_loss=0.02217, over 4961.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2153, pruned_loss=0.03516, over 972761.07 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 10:56:06,800 INFO [train.py:715] (2/8) Epoch 9, batch 8700, loss[loss=0.1627, simple_loss=0.24, pruned_loss=0.0427, over 4973.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.0352, over 973906.50 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 10:56:46,701 INFO [train.py:715] (2/8) Epoch 9, batch 8750, loss[loss=0.1379, simple_loss=0.2098, pruned_loss=0.033, over 4977.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.03495, over 973435.60 frames.], batch size: 24, lr: 2.38e-04
2022-05-06 10:57:25,009 INFO [train.py:715] (2/8) Epoch 9, batch 8800, loss[loss=0.1401, simple_loss=0.2098, pruned_loss=0.03513, over 4874.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2146, pruned_loss=0.03515, over 973209.08 frames.], batch size: 16, lr: 2.38e-04
2022-05-06 10:58:04,390 INFO [train.py:715] (2/8) Epoch 9, batch 8850, loss[loss=0.1471, simple_loss=0.2214, pruned_loss=0.03636, over 4898.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03499, over 973352.90 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 10:58:43,839 INFO [train.py:715] (2/8) Epoch 9, batch 8900, loss[loss=0.1469, simple_loss=0.2153, pruned_loss=0.03923, over 4734.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03452, over 972788.87 frames.], batch size: 16, lr: 2.38e-04
2022-05-06 10:59:22,969 INFO [train.py:715] (2/8) Epoch 9, batch 8950, loss[loss=0.1244, simple_loss=0.1879, pruned_loss=0.03045, over 4868.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03405, over 972499.15 frames.], batch size: 32, lr: 2.38e-04
2022-05-06 11:00:01,616 INFO [train.py:715] (2/8) Epoch 9, batch 9000, loss[loss=0.1404, simple_loss=0.2028, pruned_loss=0.03898, over 4960.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03403, over 973215.45 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 11:00:01,617 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 11:00:11,232 INFO [train.py:742] (2/8) 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] (2/8) Epoch 9, batch 9050, loss[loss=0.1308, simple_loss=0.2048, pruned_loss=0.02835, over 4860.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03405, over 972930.34 frames.], batch size: 20, lr: 2.38e-04
2022-05-06 11:01:30,081 INFO [train.py:715] (2/8) Epoch 9, batch 9100, loss[loss=0.1534, simple_loss=0.2224, pruned_loss=0.04217, over 4892.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.0345, over 972815.38 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:02:09,668 INFO [train.py:715] (2/8) Epoch 9, batch 9150, loss[loss=0.1699, simple_loss=0.2379, pruned_loss=0.05092, over 4696.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2144, pruned_loss=0.03488, over 971986.33 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:02:48,630 INFO [train.py:715] (2/8) Epoch 9, batch 9200, loss[loss=0.1427, simple_loss=0.218, pruned_loss=0.03364, over 4856.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2147, pruned_loss=0.03482, over 971535.65 frames.], batch size: 20, lr: 2.38e-04
2022-05-06 11:03:28,188 INFO [train.py:715] (2/8) Epoch 9, batch 9250, loss[loss=0.1515, simple_loss=0.2255, pruned_loss=0.03874, over 4800.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03515, over 971351.96 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:04:07,599 INFO [train.py:715] (2/8) Epoch 9, batch 9300, loss[loss=0.1209, simple_loss=0.1956, pruned_loss=0.0231, over 4952.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03475, over 971758.80 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:04:46,768 INFO [train.py:715] (2/8) Epoch 9, batch 9350, loss[loss=0.1459, simple_loss=0.2165, pruned_loss=0.03767, over 4962.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2144, pruned_loss=0.03441, over 971616.07 frames.], batch size: 35, lr: 2.38e-04
2022-05-06 11:05:25,230 INFO [train.py:715] (2/8) Epoch 9, batch 9400, loss[loss=0.1369, simple_loss=0.2012, pruned_loss=0.03626, over 4839.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2143, pruned_loss=0.03437, over 971856.01 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:06:05,138 INFO [train.py:715] (2/8) Epoch 9, batch 9450, loss[loss=0.1267, simple_loss=0.1984, pruned_loss=0.02755, over 4806.00 frames.], tot_loss[loss=0.1411, simple_loss=0.214, pruned_loss=0.03409, over 971273.75 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:06:44,277 INFO [train.py:715] (2/8) Epoch 9, batch 9500, loss[loss=0.1301, simple_loss=0.2079, pruned_loss=0.0262, over 4762.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.0346, over 970412.81 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:07:22,929 INFO [train.py:715] (2/8) Epoch 9, batch 9550, loss[loss=0.1326, simple_loss=0.2056, pruned_loss=0.02984, over 4797.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2142, pruned_loss=0.03415, over 971317.54 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:08:02,128 INFO [train.py:715] (2/8) Epoch 9, batch 9600, loss[loss=0.1345, simple_loss=0.2048, pruned_loss=0.03205, over 4942.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.0342, over 972234.55 frames.], batch size: 29, lr: 2.38e-04
2022-05-06 11:08:41,398 INFO [train.py:715] (2/8) Epoch 9, batch 9650, loss[loss=0.12, simple_loss=0.1894, pruned_loss=0.02525, over 4965.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2143, pruned_loss=0.03418, over 972838.44 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:09:20,426 INFO [train.py:715] (2/8) Epoch 9, batch 9700, loss[loss=0.1316, simple_loss=0.2077, pruned_loss=0.02782, over 4815.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03483, over 973342.61 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:09:58,456 INFO [train.py:715] (2/8) Epoch 9, batch 9750, loss[loss=0.145, simple_loss=0.2148, pruned_loss=0.03761, over 4866.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03512, over 972351.71 frames.], batch size: 32, lr: 2.38e-04
2022-05-06 11:10:38,591 INFO [train.py:715] (2/8) Epoch 9, batch 9800, loss[loss=0.1218, simple_loss=0.2035, pruned_loss=0.02006, over 4935.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2158, pruned_loss=0.03556, over 972685.45 frames.], batch size: 39, lr: 2.38e-04
2022-05-06 11:11:18,277 INFO [train.py:715] (2/8) Epoch 9, batch 9850, loss[loss=0.1628, simple_loss=0.2278, pruned_loss=0.04897, over 4875.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2162, pruned_loss=0.03564, over 972753.19 frames.], batch size: 22, lr: 2.38e-04
2022-05-06 11:11:56,608 INFO [train.py:715] (2/8) Epoch 9, batch 9900, loss[loss=0.1306, simple_loss=0.2073, pruned_loss=0.02693, over 4756.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2151, pruned_loss=0.03469, over 973076.20 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:12:35,818 INFO [train.py:715] (2/8) Epoch 9, batch 9950, loss[loss=0.1603, simple_loss=0.2379, pruned_loss=0.04136, over 4904.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03453, over 973056.17 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:13:15,753 INFO [train.py:715] (2/8) Epoch 9, batch 10000, loss[loss=0.1381, simple_loss=0.2047, pruned_loss=0.03569, over 4941.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2139, pruned_loss=0.03462, over 973004.40 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:13:55,091 INFO [train.py:715] (2/8) Epoch 9, batch 10050, loss[loss=0.1553, simple_loss=0.2411, pruned_loss=0.03471, over 4863.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03443, over 972766.83 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:14:33,375 INFO [train.py:715] (2/8) Epoch 9, batch 10100, loss[loss=0.1247, simple_loss=0.1982, pruned_loss=0.02561, over 4796.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2134, pruned_loss=0.03424, over 972198.46 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 11:15:12,911 INFO [train.py:715] (2/8) Epoch 9, batch 10150, loss[loss=0.1689, simple_loss=0.2414, pruned_loss=0.04822, over 4768.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2139, pruned_loss=0.03476, over 972145.05 frames.], batch size: 14, lr: 2.38e-04
2022-05-06 11:15:52,571 INFO [train.py:715] (2/8) Epoch 9, batch 10200, loss[loss=0.1549, simple_loss=0.2354, pruned_loss=0.0372, over 4762.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2135, pruned_loss=0.03439, over 972095.91 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:16:31,362 INFO [train.py:715] (2/8) Epoch 9, batch 10250, loss[loss=0.1471, simple_loss=0.2263, pruned_loss=0.03389, over 4952.00 frames.], tot_loss[loss=0.1421, simple_loss=0.214, pruned_loss=0.03505, over 971620.46 frames.], batch size: 24, lr: 2.38e-04
2022-05-06 11:17:10,103 INFO [train.py:715] (2/8) Epoch 9, batch 10300, loss[loss=0.1511, simple_loss=0.2192, pruned_loss=0.04152, over 4690.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03449, over 972329.97 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:17:49,728 INFO [train.py:715] (2/8) Epoch 9, batch 10350, loss[loss=0.1436, simple_loss=0.2204, pruned_loss=0.03343, over 4980.00 frames.], tot_loss[loss=0.1408, simple_loss=0.213, pruned_loss=0.03424, over 971444.45 frames.], batch size: 24, lr: 2.38e-04
2022-05-06 11:18:28,423 INFO [train.py:715] (2/8) Epoch 9, batch 10400, loss[loss=0.142, simple_loss=0.2267, pruned_loss=0.02866, over 4898.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2136, pruned_loss=0.03458, over 971587.94 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:19:06,742 INFO [train.py:715] (2/8) Epoch 9, batch 10450, loss[loss=0.1338, simple_loss=0.2121, pruned_loss=0.02779, over 4947.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2136, pruned_loss=0.03473, over 971126.99 frames.], batch size: 21, lr: 2.38e-04
2022-05-06 11:19:45,852 INFO [train.py:715] (2/8) Epoch 9, batch 10500, loss[loss=0.1367, simple_loss=0.2094, pruned_loss=0.03204, over 4892.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03499, over 971102.04 frames.], batch size: 19, lr: 2.38e-04
2022-05-06 11:20:25,283 INFO [train.py:715] (2/8) Epoch 9, batch 10550, loss[loss=0.1133, simple_loss=0.1935, pruned_loss=0.01654, over 4778.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03456, over 971847.50 frames.], batch size: 18, lr: 2.38e-04
2022-05-06 11:21:04,104 INFO [train.py:715] (2/8) Epoch 9, batch 10600, loss[loss=0.1469, simple_loss=0.2249, pruned_loss=0.0344, over 4951.00 frames.], tot_loss[loss=0.142, simple_loss=0.2144, pruned_loss=0.03478, over 972314.37 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:21:42,611 INFO [train.py:715] (2/8) Epoch 9, batch 10650, loss[loss=0.1613, simple_loss=0.2401, pruned_loss=0.04129, over 4832.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03463, over 972547.94 frames.], batch size: 15, lr: 2.38e-04
2022-05-06 11:22:21,911 INFO [train.py:715] (2/8) Epoch 9, batch 10700, loss[loss=0.1636, simple_loss=0.2393, pruned_loss=0.04395, over 4914.00 frames.], tot_loss[loss=0.143, simple_loss=0.2159, pruned_loss=0.03507, over 972522.90 frames.], batch size: 18, lr: 2.37e-04
2022-05-06 11:23:01,944 INFO [train.py:715] (2/8) Epoch 9, batch 10750, loss[loss=0.1597, simple_loss=0.2304, pruned_loss=0.0445, over 4923.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2159, pruned_loss=0.03489, over 972723.54 frames.], batch size: 29, lr: 2.37e-04
2022-05-06 11:23:40,535 INFO [train.py:715] (2/8) Epoch 9, batch 10800, loss[loss=0.1329, simple_loss=0.2012, pruned_loss=0.03229, over 4830.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03499, over 972533.20 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:24:20,016 INFO [train.py:715] (2/8) Epoch 9, batch 10850, loss[loss=0.1778, simple_loss=0.2577, pruned_loss=0.0489, over 4754.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2148, pruned_loss=0.0347, over 971724.97 frames.], batch size: 16, lr: 2.37e-04
2022-05-06 11:24:59,858 INFO [train.py:715] (2/8) Epoch 9, batch 10900, loss[loss=0.1453, simple_loss=0.2241, pruned_loss=0.03323, over 4754.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2146, pruned_loss=0.03452, over 972042.48 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:25:40,136 INFO [train.py:715] (2/8) Epoch 9, batch 10950, loss[loss=0.1624, simple_loss=0.2304, pruned_loss=0.0472, over 4837.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03464, over 972217.33 frames.], batch size: 26, lr: 2.37e-04
2022-05-06 11:26:20,025 INFO [train.py:715] (2/8) Epoch 9, batch 11000, loss[loss=0.1642, simple_loss=0.2316, pruned_loss=0.04839, over 4962.00 frames.], tot_loss[loss=0.1419, simple_loss=0.215, pruned_loss=0.0344, over 972455.93 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:27:00,855 INFO [train.py:715] (2/8) Epoch 9, batch 11050, loss[loss=0.159, simple_loss=0.2358, pruned_loss=0.04107, over 4936.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2155, pruned_loss=0.03472, over 972690.47 frames.], batch size: 29, lr: 2.37e-04
2022-05-06 11:27:42,116 INFO [train.py:715] (2/8) Epoch 9, batch 11100, loss[loss=0.1746, simple_loss=0.2487, pruned_loss=0.05019, over 4882.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2149, pruned_loss=0.03445, over 972313.79 frames.], batch size: 16, lr: 2.37e-04
2022-05-06 11:28:22,779 INFO [train.py:715] (2/8) Epoch 9, batch 11150, loss[loss=0.1395, simple_loss=0.2148, pruned_loss=0.03216, over 4913.00 frames.], tot_loss[loss=0.1411, simple_loss=0.214, pruned_loss=0.03412, over 972496.85 frames.], batch size: 17, lr: 2.37e-04
2022-05-06 11:29:03,598 INFO [train.py:715] (2/8) Epoch 9, batch 11200, loss[loss=0.1284, simple_loss=0.197, pruned_loss=0.02986, over 4804.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2148, pruned_loss=0.03407, over 972348.77 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:29:45,081 INFO [train.py:715] (2/8) Epoch 9, batch 11250, loss[loss=0.1218, simple_loss=0.2083, pruned_loss=0.01766, over 4743.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2148, pruned_loss=0.03447, over 972081.22 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:30:26,201 INFO [train.py:715] (2/8) Epoch 9, batch 11300, loss[loss=0.1163, simple_loss=0.2004, pruned_loss=0.01613, over 4960.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03447, over 972526.66 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:31:06,649 INFO [train.py:715] (2/8) Epoch 9, batch 11350, loss[loss=0.1589, simple_loss=0.2307, pruned_loss=0.04351, over 4916.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2143, pruned_loss=0.03425, over 972398.27 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:31:47,931 INFO [train.py:715] (2/8) Epoch 9, batch 11400, loss[loss=0.1207, simple_loss=0.1967, pruned_loss=0.02238, over 4981.00 frames.], tot_loss[loss=0.1411, simple_loss=0.214, pruned_loss=0.03415, over 973456.98 frames.], batch size: 31, lr: 2.37e-04
2022-05-06 11:32:29,494 INFO [train.py:715] (2/8) Epoch 9, batch 11450, loss[loss=0.1493, simple_loss=0.2233, pruned_loss=0.03764, over 4878.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2144, pruned_loss=0.03472, over 972912.00 frames.], batch size: 16, lr: 2.37e-04
2022-05-06 11:33:10,075 INFO [train.py:715] (2/8) Epoch 9, batch 11500, loss[loss=0.1363, simple_loss=0.2121, pruned_loss=0.03028, over 4961.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2139, pruned_loss=0.03467, over 972803.10 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:33:50,771 INFO [train.py:715] (2/8) Epoch 9, batch 11550, loss[loss=0.1452, simple_loss=0.2137, pruned_loss=0.03835, over 4979.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2127, pruned_loss=0.0339, over 973343.25 frames.], batch size: 24, lr: 2.37e-04
2022-05-06 11:34:32,080 INFO [train.py:715] (2/8) Epoch 9, batch 11600, loss[loss=0.1293, simple_loss=0.2036, pruned_loss=0.02747, over 4846.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2127, pruned_loss=0.03408, over 973403.46 frames.], batch size: 20, lr: 2.37e-04
2022-05-06 11:35:13,602 INFO [train.py:715] (2/8) Epoch 9, batch 11650, loss[loss=0.1634, simple_loss=0.2368, pruned_loss=0.045, over 4839.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2134, pruned_loss=0.03442, over 972427.74 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:35:53,520 INFO [train.py:715] (2/8) Epoch 9, batch 11700, loss[loss=0.1763, simple_loss=0.2324, pruned_loss=0.0601, over 4773.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2138, pruned_loss=0.03473, over 971345.19 frames.], batch size: 12, lr: 2.37e-04
2022-05-06 11:36:34,968 INFO [train.py:715] (2/8) Epoch 9, batch 11750, loss[loss=0.1428, simple_loss=0.216, pruned_loss=0.03481, over 4752.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2132, pruned_loss=0.0347, over 971570.72 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:37:16,487 INFO [train.py:715] (2/8) Epoch 9, batch 11800, loss[loss=0.1693, simple_loss=0.2327, pruned_loss=0.05296, over 4945.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2136, pruned_loss=0.03451, over 972013.30 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:37:56,814 INFO [train.py:715] (2/8) Epoch 9, batch 11850, loss[loss=0.1547, simple_loss=0.2232, pruned_loss=0.04316, over 4923.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2133, pruned_loss=0.03444, over 973005.87 frames.], batch size: 17, lr: 2.37e-04
2022-05-06 11:38:37,228 INFO [train.py:715] (2/8) Epoch 9, batch 11900, loss[loss=0.1068, simple_loss=0.1711, pruned_loss=0.02128, over 4760.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03458, over 973006.94 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:39:18,263 INFO [train.py:715] (2/8) Epoch 9, batch 11950, loss[loss=0.144, simple_loss=0.2132, pruned_loss=0.03742, over 4777.00 frames.], tot_loss[loss=0.142, simple_loss=0.2144, pruned_loss=0.03479, over 972728.09 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:39:59,369 INFO [train.py:715] (2/8) Epoch 9, batch 12000, loss[loss=0.1454, simple_loss=0.2091, pruned_loss=0.04083, over 4958.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03478, over 972972.87 frames.], batch size: 35, lr: 2.37e-04
2022-05-06 11:39:59,370 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 11:40:09,082 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.107, simple_loss=0.1913, pruned_loss=0.01136, over 914524.00 frames.
2022-05-06 11:40:50,122 INFO [train.py:715] (2/8) Epoch 9, batch 12050, loss[loss=0.1343, simple_loss=0.211, pruned_loss=0.02875, over 4987.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2135, pruned_loss=0.03434, over 973386.94 frames.], batch size: 31, lr: 2.37e-04
2022-05-06 11:41:29,621 INFO [train.py:715] (2/8) Epoch 9, batch 12100, loss[loss=0.1491, simple_loss=0.223, pruned_loss=0.03761, over 4805.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2137, pruned_loss=0.03433, over 973162.42 frames.], batch size: 13, lr: 2.37e-04
2022-05-06 11:42:10,008 INFO [train.py:715] (2/8) Epoch 9, batch 12150, loss[loss=0.134, simple_loss=0.2072, pruned_loss=0.0304, over 4928.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2141, pruned_loss=0.03462, over 973202.86 frames.], batch size: 23, lr: 2.37e-04
2022-05-06 11:42:50,005 INFO [train.py:715] (2/8) Epoch 9, batch 12200, loss[loss=0.1678, simple_loss=0.2389, pruned_loss=0.04837, over 4864.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03486, over 973035.80 frames.], batch size: 38, lr: 2.37e-04
2022-05-06 11:43:29,263 INFO [train.py:715] (2/8) Epoch 9, batch 12250, loss[loss=0.1292, simple_loss=0.2055, pruned_loss=0.02647, over 4973.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2156, pruned_loss=0.03514, over 973264.85 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:44:08,224 INFO [train.py:715] (2/8) Epoch 9, batch 12300, loss[loss=0.1503, simple_loss=0.2225, pruned_loss=0.03909, over 4863.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03511, over 973709.37 frames.], batch size: 32, lr: 2.37e-04
2022-05-06 11:44:47,990 INFO [train.py:715] (2/8) Epoch 9, batch 12350, loss[loss=0.126, simple_loss=0.1935, pruned_loss=0.02928, over 4800.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03487, over 973300.22 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:45:28,036 INFO [train.py:715] (2/8) Epoch 9, batch 12400, loss[loss=0.1395, simple_loss=0.225, pruned_loss=0.02702, over 4912.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.03515, over 972703.82 frames.], batch size: 18, lr: 2.37e-04
2022-05-06 11:46:07,546 INFO [train.py:715] (2/8) Epoch 9, batch 12450, loss[loss=0.1308, simple_loss=0.2112, pruned_loss=0.02521, over 4964.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2148, pruned_loss=0.03513, over 973348.99 frames.], batch size: 24, lr: 2.37e-04
2022-05-06 11:46:47,598 INFO [train.py:715] (2/8) Epoch 9, batch 12500, loss[loss=0.1449, simple_loss=0.2243, pruned_loss=0.03272, over 4918.00 frames.], tot_loss[loss=0.143, simple_loss=0.215, pruned_loss=0.03555, over 973079.99 frames.], batch size: 18, lr: 2.37e-04
2022-05-06 11:47:27,731 INFO [train.py:715] (2/8) Epoch 9, batch 12550, loss[loss=0.1368, simple_loss=0.2126, pruned_loss=0.03055, over 4852.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03527, over 973385.50 frames.], batch size: 20, lr: 2.37e-04
2022-05-06 11:48:07,693 INFO [train.py:715] (2/8) Epoch 9, batch 12600, loss[loss=0.1668, simple_loss=0.2376, pruned_loss=0.04802, over 4762.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2149, pruned_loss=0.0353, over 973129.41 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:48:46,467 INFO [train.py:715] (2/8) Epoch 9, batch 12650, loss[loss=0.1515, simple_loss=0.2227, pruned_loss=0.04013, over 4856.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2143, pruned_loss=0.03505, over 972289.67 frames.], batch size: 13, lr: 2.37e-04
2022-05-06 11:49:26,600 INFO [train.py:715] (2/8) Epoch 9, batch 12700, loss[loss=0.1525, simple_loss=0.2222, pruned_loss=0.04135, over 4825.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.03495, over 972640.46 frames.], batch size: 27, lr: 2.37e-04
2022-05-06 11:50:06,591 INFO [train.py:715] (2/8) Epoch 9, batch 12750, loss[loss=0.1484, simple_loss=0.218, pruned_loss=0.03946, over 4791.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2139, pruned_loss=0.0348, over 972486.69 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:50:45,759 INFO [train.py:715] (2/8) Epoch 9, batch 12800, loss[loss=0.1211, simple_loss=0.2021, pruned_loss=0.02003, over 4751.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2133, pruned_loss=0.0348, over 971917.91 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:51:25,604 INFO [train.py:715] (2/8) Epoch 9, batch 12850, loss[loss=0.1214, simple_loss=0.1872, pruned_loss=0.02783, over 4968.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2136, pruned_loss=0.03475, over 972258.06 frames.], batch size: 15, lr: 2.37e-04
2022-05-06 11:52:05,501 INFO [train.py:715] (2/8) Epoch 9, batch 12900, loss[loss=0.1057, simple_loss=0.1821, pruned_loss=0.01468, over 4939.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03442, over 972723.64 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:52:45,478 INFO [train.py:715] (2/8) Epoch 9, batch 12950, loss[loss=0.1264, simple_loss=0.2088, pruned_loss=0.02201, over 4986.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2131, pruned_loss=0.03423, over 972896.89 frames.], batch size: 26, lr: 2.37e-04
2022-05-06 11:53:24,506 INFO [train.py:715] (2/8) Epoch 9, batch 13000, loss[loss=0.1049, simple_loss=0.172, pruned_loss=0.01894, over 4836.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03479, over 972647.44 frames.], batch size: 13, lr: 2.37e-04
2022-05-06 11:54:04,861 INFO [train.py:715] (2/8) Epoch 9, batch 13050, loss[loss=0.1178, simple_loss=0.1921, pruned_loss=0.02173, over 4813.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2143, pruned_loss=0.03512, over 973161.90 frames.], batch size: 21, lr: 2.37e-04
2022-05-06 11:54:44,623 INFO [train.py:715] (2/8) Epoch 9, batch 13100, loss[loss=0.1322, simple_loss=0.2148, pruned_loss=0.02485, over 4911.00 frames.], tot_loss[loss=0.142, simple_loss=0.2141, pruned_loss=0.0349, over 972541.95 frames.], batch size: 17, lr: 2.37e-04
2022-05-06 11:55:23,867 INFO [train.py:715] (2/8) Epoch 9, batch 13150, loss[loss=0.1203, simple_loss=0.1915, pruned_loss=0.02457, over 4925.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2146, pruned_loss=0.03533, over 972030.21 frames.], batch size: 17, lr: 2.37e-04
2022-05-06 11:56:03,853 INFO [train.py:715] (2/8) Epoch 9, batch 13200, loss[loss=0.1478, simple_loss=0.2191, pruned_loss=0.03823, over 4969.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.03499, over 972160.79 frames.], batch size: 35, lr: 2.37e-04
2022-05-06 11:56:44,166 INFO [train.py:715] (2/8) Epoch 9, batch 13250, loss[loss=0.1609, simple_loss=0.2307, pruned_loss=0.04554, over 4758.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03493, over 972511.10 frames.], batch size: 19, lr: 2.37e-04
2022-05-06 11:57:23,745 INFO [train.py:715] (2/8) Epoch 9, batch 13300, loss[loss=0.1263, simple_loss=0.1933, pruned_loss=0.02969, over 4895.00 frames.], tot_loss[loss=0.142, simple_loss=0.2142, pruned_loss=0.03485, over 972979.76 frames.], batch size: 32, lr: 2.37e-04
2022-05-06 11:58:03,447 INFO [train.py:715] (2/8) Epoch 9, batch 13350, loss[loss=0.1373, simple_loss=0.2053, pruned_loss=0.03465, over 4978.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03508, over 972585.33 frames.], batch size: 14, lr: 2.37e-04
2022-05-06 11:58:43,523 INFO [train.py:715] (2/8) Epoch 9, batch 13400, loss[loss=0.1435, simple_loss=0.2121, pruned_loss=0.03743, over 4863.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03535, over 971834.01 frames.], batch size: 30, lr: 2.37e-04
2022-05-06 11:59:23,794 INFO [train.py:715] (2/8) Epoch 9, batch 13450, loss[loss=0.1392, simple_loss=0.228, pruned_loss=0.02526, over 4753.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03531, over 971513.23 frames.], batch size: 16, lr: 2.36e-04
2022-05-06 12:00:02,968 INFO [train.py:715] (2/8) Epoch 9, batch 13500, loss[loss=0.1444, simple_loss=0.2039, pruned_loss=0.04244, over 4922.00 frames.], tot_loss[loss=0.143, simple_loss=0.2155, pruned_loss=0.0352, over 971942.46 frames.], batch size: 18, lr: 2.36e-04
2022-05-06 12:00:42,984 INFO [train.py:715] (2/8) Epoch 9, batch 13550, loss[loss=0.1352, simple_loss=0.2094, pruned_loss=0.03051, over 4872.00 frames.], tot_loss[loss=0.1431, simple_loss=0.216, pruned_loss=0.03504, over 972477.52 frames.], batch size: 16, lr: 2.36e-04
2022-05-06 12:01:22,498 INFO [train.py:715] (2/8) Epoch 9, batch 13600, loss[loss=0.1441, simple_loss=0.2139, pruned_loss=0.03717, over 4937.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2151, pruned_loss=0.03486, over 973176.59 frames.], batch size: 29, lr: 2.36e-04
2022-05-06 12:02:01,622 INFO [train.py:715] (2/8) Epoch 9, batch 13650, loss[loss=0.125, simple_loss=0.2083, pruned_loss=0.02079, over 4801.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2154, pruned_loss=0.03495, over 972501.02 frames.], batch size: 24, lr: 2.36e-04
2022-05-06 12:02:40,852 INFO [train.py:715] (2/8) Epoch 9, batch 13700, loss[loss=0.1282, simple_loss=0.2042, pruned_loss=0.02616, over 4745.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2152, pruned_loss=0.03511, over 972675.70 frames.], batch size: 12, lr: 2.36e-04
2022-05-06 12:03:20,736 INFO [train.py:715] (2/8) Epoch 9, batch 13750, loss[loss=0.1313, simple_loss=0.209, pruned_loss=0.02675, over 4822.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03487, over 973237.34 frames.], batch size: 27, lr: 2.36e-04
2022-05-06 12:03:59,887 INFO [train.py:715] (2/8) Epoch 9, batch 13800, loss[loss=0.1435, simple_loss=0.2264, pruned_loss=0.03027, over 4804.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2155, pruned_loss=0.03522, over 973538.24 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:04:38,383 INFO [train.py:715] (2/8) Epoch 9, batch 13850, loss[loss=0.1452, simple_loss=0.2021, pruned_loss=0.04414, over 4834.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2152, pruned_loss=0.03481, over 973269.29 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:05:17,813 INFO [train.py:715] (2/8) Epoch 9, batch 13900, loss[loss=0.1563, simple_loss=0.2244, pruned_loss=0.04409, over 4833.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03469, over 973010.74 frames.], batch size: 30, lr: 2.36e-04
2022-05-06 12:05:57,958 INFO [train.py:715] (2/8) Epoch 9, batch 13950, loss[loss=0.1312, simple_loss=0.2111, pruned_loss=0.02567, over 4934.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03462, over 973145.54 frames.], batch size: 29, lr: 2.36e-04
2022-05-06 12:06:36,917 INFO [train.py:715] (2/8) Epoch 9, batch 14000, loss[loss=0.1616, simple_loss=0.2248, pruned_loss=0.04914, over 4922.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03494, over 973383.33 frames.], batch size: 17, lr: 2.36e-04
2022-05-06 12:07:16,028 INFO [train.py:715] (2/8) Epoch 9, batch 14050, loss[loss=0.1503, simple_loss=0.2194, pruned_loss=0.04053, over 4973.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03471, over 973479.68 frames.], batch size: 35, lr: 2.36e-04
2022-05-06 12:07:55,563 INFO [train.py:715] (2/8) Epoch 9, batch 14100, loss[loss=0.1564, simple_loss=0.2199, pruned_loss=0.04649, over 4892.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03478, over 973364.55 frames.], batch size: 16, lr: 2.36e-04
2022-05-06 12:08:35,132 INFO [train.py:715] (2/8) Epoch 9, batch 14150, loss[loss=0.129, simple_loss=0.209, pruned_loss=0.02449, over 4891.00 frames.], tot_loss[loss=0.1421, simple_loss=0.214, pruned_loss=0.03505, over 973531.99 frames.], batch size: 16, lr: 2.36e-04
2022-05-06 12:09:14,478 INFO [train.py:715] (2/8) Epoch 9, batch 14200, loss[loss=0.146, simple_loss=0.2323, pruned_loss=0.02984, over 4893.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2157, pruned_loss=0.03548, over 972641.22 frames.], batch size: 19, lr: 2.36e-04
2022-05-06 12:09:53,803 INFO [train.py:715] (2/8) Epoch 9, batch 14250, loss[loss=0.1421, simple_loss=0.2211, pruned_loss=0.03161, over 4912.00 frames.], tot_loss[loss=0.144, simple_loss=0.2165, pruned_loss=0.03571, over 972424.41 frames.], batch size: 39, lr: 2.36e-04
2022-05-06 12:10:33,296 INFO [train.py:715] (2/8) Epoch 9, batch 14300, loss[loss=0.1537, simple_loss=0.225, pruned_loss=0.04117, over 4805.00 frames.], tot_loss[loss=0.1433, simple_loss=0.216, pruned_loss=0.03526, over 972394.47 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:11:11,972 INFO [train.py:715] (2/8) Epoch 9, batch 14350, loss[loss=0.1527, simple_loss=0.2218, pruned_loss=0.04183, over 4775.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2163, pruned_loss=0.03502, over 971818.50 frames.], batch size: 14, lr: 2.36e-04
2022-05-06 12:11:50,598 INFO [train.py:715] (2/8) Epoch 9, batch 14400, loss[loss=0.1709, simple_loss=0.2172, pruned_loss=0.06227, over 4774.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2157, pruned_loss=0.0348, over 972281.65 frames.], batch size: 12, lr: 2.36e-04
2022-05-06 12:12:30,353 INFO [train.py:715] (2/8) Epoch 9, batch 14450, loss[loss=0.1359, simple_loss=0.2082, pruned_loss=0.03182, over 4743.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2156, pruned_loss=0.03491, over 972256.66 frames.], batch size: 19, lr: 2.36e-04
2022-05-06 12:13:09,686 INFO [train.py:715] (2/8) Epoch 9, batch 14500, loss[loss=0.1147, simple_loss=0.1786, pruned_loss=0.02534, over 4947.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2156, pruned_loss=0.03507, over 971967.79 frames.], batch size: 35, lr: 2.36e-04
2022-05-06 12:13:48,634 INFO [train.py:715] (2/8) Epoch 9, batch 14550, loss[loss=0.1371, simple_loss=0.1986, pruned_loss=0.03782, over 4865.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2162, pruned_loss=0.03548, over 971935.92 frames.], batch size: 13, lr: 2.36e-04
2022-05-06 12:14:27,683 INFO [train.py:715] (2/8) Epoch 9, batch 14600, loss[loss=0.1055, simple_loss=0.1813, pruned_loss=0.01489, over 4795.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2158, pruned_loss=0.03522, over 971978.37 frames.], batch size: 24, lr: 2.36e-04
2022-05-06 12:15:07,386 INFO [train.py:715] (2/8) Epoch 9, batch 14650, loss[loss=0.1282, simple_loss=0.1988, pruned_loss=0.0288, over 4931.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2152, pruned_loss=0.03463, over 971905.96 frames.], batch size: 23, lr: 2.36e-04
2022-05-06 12:15:45,917 INFO [train.py:715] (2/8) Epoch 9, batch 14700, loss[loss=0.1423, simple_loss=0.2109, pruned_loss=0.03683, over 4941.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2162, pruned_loss=0.03526, over 972583.05 frames.], batch size: 29, lr: 2.36e-04
2022-05-06 12:16:27,519 INFO [train.py:715] (2/8) Epoch 9, batch 14750, loss[loss=0.1148, simple_loss=0.194, pruned_loss=0.01776, over 4958.00 frames.], tot_loss[loss=0.1426, simple_loss=0.215, pruned_loss=0.0351, over 971554.25 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:17:06,569 INFO [train.py:715] (2/8) Epoch 9, batch 14800, loss[loss=0.1607, simple_loss=0.229, pruned_loss=0.04617, over 4865.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2147, pruned_loss=0.03493, over 971560.15 frames.], batch size: 20, lr: 2.36e-04
2022-05-06 12:17:45,496 INFO [train.py:715] (2/8) Epoch 9, batch 14850, loss[loss=0.1451, simple_loss=0.2169, pruned_loss=0.03665, over 4947.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.0346, over 972112.13 frames.], batch size: 39, lr: 2.36e-04
2022-05-06 12:18:24,548 INFO [train.py:715] (2/8) Epoch 9, batch 14900, loss[loss=0.1533, simple_loss=0.2235, pruned_loss=0.04158, over 4970.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2145, pruned_loss=0.03481, over 972381.04 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:19:03,082 INFO [train.py:715] (2/8) Epoch 9, batch 14950, loss[loss=0.1439, simple_loss=0.2106, pruned_loss=0.0386, over 4919.00 frames.], tot_loss[loss=0.143, simple_loss=0.2156, pruned_loss=0.03519, over 972071.04 frames.], batch size: 39, lr: 2.36e-04
2022-05-06 12:19:42,676 INFO [train.py:715] (2/8) Epoch 9, batch 15000, loss[loss=0.2183, simple_loss=0.2968, pruned_loss=0.06987, over 4915.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2156, pruned_loss=0.03469, over 972030.30 frames.], batch size: 17, lr: 2.36e-04
2022-05-06 12:19:42,677 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 12:19:52,343 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.1071, simple_loss=0.1915, pruned_loss=0.01139, over 914524.00 frames.
2022-05-06 12:20:32,096 INFO [train.py:715] (2/8) Epoch 9, batch 15050, loss[loss=0.1388, simple_loss=0.2201, pruned_loss=0.02871, over 4800.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2146, pruned_loss=0.03416, over 971411.14 frames.], batch size: 24, lr: 2.36e-04
2022-05-06 12:21:11,097 INFO [train.py:715] (2/8) Epoch 9, batch 15100, loss[loss=0.1368, simple_loss=0.2076, pruned_loss=0.03304, over 4977.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03368, over 971326.66 frames.], batch size: 35, lr: 2.36e-04
2022-05-06 12:21:50,202 INFO [train.py:715] (2/8) Epoch 9, batch 15150, loss[loss=0.143, simple_loss=0.2203, pruned_loss=0.03285, over 4933.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03338, over 970862.40 frames.], batch size: 23, lr: 2.36e-04
2022-05-06 12:22:30,014 INFO [train.py:715] (2/8) Epoch 9, batch 15200, loss[loss=0.1538, simple_loss=0.2262, pruned_loss=0.04072, over 4756.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2132, pruned_loss=0.03332, over 971095.46 frames.], batch size: 16, lr: 2.36e-04
2022-05-06 12:23:09,315 INFO [train.py:715] (2/8) Epoch 9, batch 15250, loss[loss=0.126, simple_loss=0.1981, pruned_loss=0.02699, over 4701.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2129, pruned_loss=0.03316, over 970177.82 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:23:48,031 INFO [train.py:715] (2/8) Epoch 9, batch 15300, loss[loss=0.1402, simple_loss=0.1969, pruned_loss=0.0417, over 4852.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03372, over 970588.34 frames.], batch size: 32, lr: 2.36e-04
2022-05-06 12:24:27,150 INFO [train.py:715] (2/8) Epoch 9, batch 15350, loss[loss=0.1417, simple_loss=0.2136, pruned_loss=0.03488, over 4920.00 frames.], tot_loss[loss=0.142, simple_loss=0.2149, pruned_loss=0.03453, over 970730.10 frames.], batch size: 39, lr: 2.36e-04
2022-05-06 12:25:06,183 INFO [train.py:715] (2/8) Epoch 9, batch 15400, loss[loss=0.1243, simple_loss=0.1859, pruned_loss=0.03136, over 4834.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2132, pruned_loss=0.03385, over 970832.57 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:25:44,960 INFO [train.py:715] (2/8) Epoch 9, batch 15450, loss[loss=0.1411, simple_loss=0.2146, pruned_loss=0.0338, over 4828.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2126, pruned_loss=0.03376, over 971879.37 frames.], batch size: 26, lr: 2.36e-04
2022-05-06 12:26:23,389 INFO [train.py:715] (2/8) Epoch 9, batch 15500, loss[loss=0.1325, simple_loss=0.2083, pruned_loss=0.02842, over 4680.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2137, pruned_loss=0.03402, over 972556.43 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:27:03,116 INFO [train.py:715] (2/8) Epoch 9, batch 15550, loss[loss=0.1254, simple_loss=0.2034, pruned_loss=0.02366, over 4944.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03456, over 973393.88 frames.], batch size: 29, lr: 2.36e-04
2022-05-06 12:27:41,878 INFO [train.py:715] (2/8) Epoch 9, batch 15600, loss[loss=0.1451, simple_loss=0.2127, pruned_loss=0.03868, over 4873.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2145, pruned_loss=0.03443, over 973589.07 frames.], batch size: 22, lr: 2.36e-04
2022-05-06 12:28:20,225 INFO [train.py:715] (2/8) Epoch 9, batch 15650, loss[loss=0.1526, simple_loss=0.2228, pruned_loss=0.0412, over 4760.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03468, over 973246.33 frames.], batch size: 14, lr: 2.36e-04
2022-05-06 12:28:59,316 INFO [train.py:715] (2/8) Epoch 9, batch 15700, loss[loss=0.14, simple_loss=0.2085, pruned_loss=0.03574, over 4825.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2136, pruned_loss=0.03429, over 973660.86 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:29:39,072 INFO [train.py:715] (2/8) Epoch 9, batch 15750, loss[loss=0.1103, simple_loss=0.1792, pruned_loss=0.02064, over 4800.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2129, pruned_loss=0.03408, over 972378.02 frames.], batch size: 12, lr: 2.36e-04
2022-05-06 12:30:17,866 INFO [train.py:715] (2/8) Epoch 9, batch 15800, loss[loss=0.1369, simple_loss=0.2032, pruned_loss=0.03532, over 4786.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2127, pruned_loss=0.03407, over 972218.05 frames.], batch size: 18, lr: 2.36e-04
2022-05-06 12:30:56,797 INFO [train.py:715] (2/8) Epoch 9, batch 15850, loss[loss=0.1584, simple_loss=0.2227, pruned_loss=0.0471, over 4948.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2127, pruned_loss=0.03405, over 973220.78 frames.], batch size: 21, lr: 2.36e-04
2022-05-06 12:31:36,407 INFO [train.py:715] (2/8) Epoch 9, batch 15900, loss[loss=0.1407, simple_loss=0.223, pruned_loss=0.02926, over 4814.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.0344, over 973293.26 frames.], batch size: 27, lr: 2.36e-04
2022-05-06 12:32:15,978 INFO [train.py:715] (2/8) Epoch 9, batch 15950, loss[loss=0.145, simple_loss=0.2206, pruned_loss=0.03471, over 4830.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2138, pruned_loss=0.03449, over 973056.12 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:32:54,619 INFO [train.py:715] (2/8) Epoch 9, batch 16000, loss[loss=0.1617, simple_loss=0.2232, pruned_loss=0.05014, over 4866.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.034, over 972725.67 frames.], batch size: 32, lr: 2.36e-04
2022-05-06 12:33:33,295 INFO [train.py:715] (2/8) Epoch 9, batch 16050, loss[loss=0.1529, simple_loss=0.236, pruned_loss=0.03494, over 4904.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03471, over 972779.58 frames.], batch size: 17, lr: 2.36e-04
2022-05-06 12:34:12,505 INFO [train.py:715] (2/8) Epoch 9, batch 16100, loss[loss=0.1331, simple_loss=0.2011, pruned_loss=0.03257, over 4769.00 frames.], tot_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03535, over 972371.31 frames.], batch size: 14, lr: 2.36e-04
2022-05-06 12:34:51,595 INFO [train.py:715] (2/8) Epoch 9, batch 16150, loss[loss=0.1576, simple_loss=0.2305, pruned_loss=0.04236, over 4734.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03499, over 972452.89 frames.], batch size: 16, lr: 2.36e-04
2022-05-06 12:35:30,768 INFO [train.py:715] (2/8) Epoch 9, batch 16200, loss[loss=0.115, simple_loss=0.1762, pruned_loss=0.02695, over 4970.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2143, pruned_loss=0.03472, over 972574.99 frames.], batch size: 15, lr: 2.36e-04
2022-05-06 12:36:10,109 INFO [train.py:715] (2/8) Epoch 9, batch 16250, loss[loss=0.1365, simple_loss=0.2076, pruned_loss=0.0327, over 4703.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03469, over 972642.74 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 12:36:49,784 INFO [train.py:715] (2/8) Epoch 9, batch 16300, loss[loss=0.1506, simple_loss=0.2251, pruned_loss=0.0381, over 4777.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2152, pruned_loss=0.0349, over 973233.31 frames.], batch size: 17, lr: 2.35e-04
2022-05-06 12:37:27,728 INFO [train.py:715] (2/8) Epoch 9, batch 16350, loss[loss=0.1756, simple_loss=0.2373, pruned_loss=0.05691, over 4844.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03531, over 972908.69 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 12:38:07,161 INFO [train.py:715] (2/8) Epoch 9, batch 16400, loss[loss=0.132, simple_loss=0.2013, pruned_loss=0.03135, over 4749.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03487, over 973780.87 frames.], batch size: 19, lr: 2.35e-04
2022-05-06 12:38:47,054 INFO [train.py:715] (2/8) Epoch 9, batch 16450, loss[loss=0.1238, simple_loss=0.2051, pruned_loss=0.02126, over 4808.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2146, pruned_loss=0.03452, over 972947.41 frames.], batch size: 25, lr: 2.35e-04
2022-05-06 12:39:25,806 INFO [train.py:715] (2/8) Epoch 9, batch 16500, loss[loss=0.1641, simple_loss=0.2322, pruned_loss=0.04807, over 4876.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.03443, over 972524.44 frames.], batch size: 16, lr: 2.35e-04
2022-05-06 12:40:04,384 INFO [train.py:715] (2/8) Epoch 9, batch 16550, loss[loss=0.1303, simple_loss=0.1999, pruned_loss=0.03034, over 4966.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.03453, over 972395.18 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 12:40:43,846 INFO [train.py:715] (2/8) Epoch 9, batch 16600, loss[loss=0.1281, simple_loss=0.2023, pruned_loss=0.02696, over 4941.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.03434, over 972940.10 frames.], batch size: 23, lr: 2.35e-04
2022-05-06 12:41:23,435 INFO [train.py:715] (2/8) Epoch 9, batch 16650, loss[loss=0.14, simple_loss=0.2145, pruned_loss=0.03277, over 4953.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03411, over 973476.19 frames.], batch size: 14, lr: 2.35e-04
2022-05-06 12:42:02,351 INFO [train.py:715] (2/8) Epoch 9, batch 16700, loss[loss=0.1293, simple_loss=0.1944, pruned_loss=0.03206, over 4969.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03384, over 973809.32 frames.], batch size: 35, lr: 2.35e-04
2022-05-06 12:42:41,615 INFO [train.py:715] (2/8) Epoch 9, batch 16750, loss[loss=0.149, simple_loss=0.2274, pruned_loss=0.03533, over 4932.00 frames.], tot_loss[loss=0.141, simple_loss=0.2141, pruned_loss=0.03401, over 974107.20 frames.], batch size: 21, lr: 2.35e-04
2022-05-06 12:43:21,409 INFO [train.py:715] (2/8) Epoch 9, batch 16800, loss[loss=0.1393, simple_loss=0.2152, pruned_loss=0.03171, over 4931.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2145, pruned_loss=0.03427, over 973989.55 frames.], batch size: 39, lr: 2.35e-04
2022-05-06 12:44:01,034 INFO [train.py:715] (2/8) Epoch 9, batch 16850, loss[loss=0.1289, simple_loss=0.2045, pruned_loss=0.02667, over 4925.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03369, over 974261.73 frames.], batch size: 23, lr: 2.35e-04
2022-05-06 12:44:40,454 INFO [train.py:715] (2/8) Epoch 9, batch 16900, loss[loss=0.14, simple_loss=0.2147, pruned_loss=0.03267, over 4773.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03346, over 974335.45 frames.], batch size: 14, lr: 2.35e-04
2022-05-06 12:45:20,535 INFO [train.py:715] (2/8) Epoch 9, batch 16950, loss[loss=0.1179, simple_loss=0.1887, pruned_loss=0.02349, over 4982.00 frames.], tot_loss[loss=0.1393, simple_loss=0.212, pruned_loss=0.03328, over 973714.38 frames.], batch size: 24, lr: 2.35e-04
2022-05-06 12:46:00,233 INFO [train.py:715] (2/8) Epoch 9, batch 17000, loss[loss=0.1211, simple_loss=0.1913, pruned_loss=0.02548, over 4877.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2129, pruned_loss=0.03397, over 973565.26 frames.], batch size: 32, lr: 2.35e-04
2022-05-06 12:46:38,806 INFO [train.py:715] (2/8) Epoch 9, batch 17050, loss[loss=0.1953, simple_loss=0.2554, pruned_loss=0.06757, over 4900.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2136, pruned_loss=0.0346, over 973111.27 frames.], batch size: 16, lr: 2.35e-04
2022-05-06 12:47:18,388 INFO [train.py:715] (2/8) Epoch 9, batch 17100, loss[loss=0.1355, simple_loss=0.2111, pruned_loss=0.02992, over 4990.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2145, pruned_loss=0.03496, over 973262.34 frames.], batch size: 25, lr: 2.35e-04
2022-05-06 12:47:58,064 INFO [train.py:715] (2/8) Epoch 9, batch 17150, loss[loss=0.1356, simple_loss=0.1999, pruned_loss=0.03572, over 4784.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2139, pruned_loss=0.03464, over 972401.61 frames.], batch size: 14, lr: 2.35e-04
2022-05-06 12:48:37,321 INFO [train.py:715] (2/8) Epoch 9, batch 17200, loss[loss=0.1783, simple_loss=0.2501, pruned_loss=0.05323, over 4932.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03467, over 972451.71 frames.], batch size: 39, lr: 2.35e-04
2022-05-06 12:49:15,995 INFO [train.py:715] (2/8) Epoch 9, batch 17250, loss[loss=0.132, simple_loss=0.2141, pruned_loss=0.02501, over 4901.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2136, pruned_loss=0.03462, over 971737.06 frames.], batch size: 23, lr: 2.35e-04
2022-05-06 12:49:54,885 INFO [train.py:715] (2/8) Epoch 9, batch 17300, loss[loss=0.1419, simple_loss=0.2137, pruned_loss=0.03503, over 4878.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2142, pruned_loss=0.03498, over 971141.62 frames.], batch size: 20, lr: 2.35e-04
2022-05-06 12:50:33,977 INFO [train.py:715] (2/8) Epoch 9, batch 17350, loss[loss=0.1693, simple_loss=0.2465, pruned_loss=0.04608, over 4858.00 frames.], tot_loss[loss=0.1421, simple_loss=0.214, pruned_loss=0.03508, over 971396.50 frames.], batch size: 30, lr: 2.35e-04
2022-05-06 12:51:13,078 INFO [train.py:715] (2/8) Epoch 9, batch 17400, loss[loss=0.1448, simple_loss=0.2242, pruned_loss=0.03267, over 4785.00 frames.], tot_loss[loss=0.1416, simple_loss=0.214, pruned_loss=0.03464, over 971271.30 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 12:51:52,391 INFO [train.py:715] (2/8) Epoch 9, batch 17450, loss[loss=0.121, simple_loss=0.2036, pruned_loss=0.01927, over 4782.00 frames.], tot_loss[loss=0.1416, simple_loss=0.214, pruned_loss=0.03463, over 970402.81 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 12:52:31,597 INFO [train.py:715] (2/8) Epoch 9, batch 17500, loss[loss=0.1918, simple_loss=0.2595, pruned_loss=0.062, over 4741.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2142, pruned_loss=0.03451, over 971183.09 frames.], batch size: 16, lr: 2.35e-04
2022-05-06 12:53:10,813 INFO [train.py:715] (2/8) Epoch 9, batch 17550, loss[loss=0.153, simple_loss=0.2224, pruned_loss=0.0418, over 4848.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03457, over 971112.34 frames.], batch size: 20, lr: 2.35e-04
2022-05-06 12:53:49,891 INFO [train.py:715] (2/8) Epoch 9, batch 17600, loss[loss=0.1397, simple_loss=0.2159, pruned_loss=0.03176, over 4982.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2149, pruned_loss=0.03502, over 971118.46 frames.], batch size: 27, lr: 2.35e-04
2022-05-06 12:54:29,584 INFO [train.py:715] (2/8) Epoch 9, batch 17650, loss[loss=0.1256, simple_loss=0.2043, pruned_loss=0.02352, over 4834.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2132, pruned_loss=0.03432, over 970880.53 frames.], batch size: 26, lr: 2.35e-04
2022-05-06 12:55:08,480 INFO [train.py:715] (2/8) Epoch 9, batch 17700, loss[loss=0.1357, simple_loss=0.2063, pruned_loss=0.03254, over 4784.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03443, over 971826.68 frames.], batch size: 17, lr: 2.35e-04
2022-05-06 12:55:47,744 INFO [train.py:715] (2/8) Epoch 9, batch 17750, loss[loss=0.1562, simple_loss=0.2178, pruned_loss=0.04732, over 4948.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03507, over 971642.34 frames.], batch size: 35, lr: 2.35e-04
2022-05-06 12:56:27,548 INFO [train.py:715] (2/8) Epoch 9, batch 17800, loss[loss=0.1452, simple_loss=0.2092, pruned_loss=0.04057, over 4886.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2143, pruned_loss=0.03503, over 972132.24 frames.], batch size: 32, lr: 2.35e-04
2022-05-06 12:57:06,524 INFO [train.py:715] (2/8) Epoch 9, batch 17850, loss[loss=0.1365, simple_loss=0.206, pruned_loss=0.03348, over 4890.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2138, pruned_loss=0.03478, over 971716.23 frames.], batch size: 22, lr: 2.35e-04
2022-05-06 12:57:45,750 INFO [train.py:715] (2/8) Epoch 9, batch 17900, loss[loss=0.1996, simple_loss=0.2487, pruned_loss=0.07522, over 4636.00 frames.], tot_loss[loss=0.141, simple_loss=0.2134, pruned_loss=0.03431, over 971535.57 frames.], batch size: 13, lr: 2.35e-04
2022-05-06 12:58:25,610 INFO [train.py:715] (2/8) Epoch 9, batch 17950, loss[loss=0.1528, simple_loss=0.222, pruned_loss=0.04184, over 4861.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03448, over 972101.21 frames.], batch size: 30, lr: 2.35e-04
2022-05-06 12:59:04,972 INFO [train.py:715] (2/8) Epoch 9, batch 18000, loss[loss=0.1567, simple_loss=0.2308, pruned_loss=0.04133, over 4922.00 frames.], tot_loss[loss=0.1407, simple_loss=0.213, pruned_loss=0.03418, over 972258.77 frames.], batch size: 23, lr: 2.35e-04
2022-05-06 12:59:04,973 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 12:59:14,501 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.1068, simple_loss=0.1912, pruned_loss=0.01121, over 914524.00 frames.
2022-05-06 12:59:53,954 INFO [train.py:715] (2/8) Epoch 9, batch 18050, loss[loss=0.1644, simple_loss=0.2274, pruned_loss=0.05075, over 4843.00 frames.], tot_loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03443, over 972508.58 frames.], batch size: 13, lr: 2.35e-04
2022-05-06 13:00:33,776 INFO [train.py:715] (2/8) Epoch 9, batch 18100, loss[loss=0.1559, simple_loss=0.2151, pruned_loss=0.04838, over 4857.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2138, pruned_loss=0.03474, over 972382.95 frames.], batch size: 32, lr: 2.35e-04
2022-05-06 13:01:13,063 INFO [train.py:715] (2/8) Epoch 9, batch 18150, loss[loss=0.127, simple_loss=0.2005, pruned_loss=0.02677, over 4890.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03462, over 972005.71 frames.], batch size: 19, lr: 2.35e-04
2022-05-06 13:01:52,674 INFO [train.py:715] (2/8) Epoch 9, batch 18200, loss[loss=0.1147, simple_loss=0.2028, pruned_loss=0.01331, over 4975.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.0343, over 972844.95 frames.], batch size: 28, lr: 2.35e-04
2022-05-06 13:02:31,901 INFO [train.py:715] (2/8) Epoch 9, batch 18250, loss[loss=0.1816, simple_loss=0.2588, pruned_loss=0.05216, over 4754.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2142, pruned_loss=0.03428, over 972327.16 frames.], batch size: 19, lr: 2.35e-04
2022-05-06 13:03:11,075 INFO [train.py:715] (2/8) Epoch 9, batch 18300, loss[loss=0.1496, simple_loss=0.2145, pruned_loss=0.04238, over 4792.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2148, pruned_loss=0.03478, over 972237.12 frames.], batch size: 24, lr: 2.35e-04
2022-05-06 13:03:50,427 INFO [train.py:715] (2/8) Epoch 9, batch 18350, loss[loss=0.1264, simple_loss=0.2056, pruned_loss=0.02359, over 4951.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03449, over 971764.00 frames.], batch size: 21, lr: 2.35e-04
2022-05-06 13:04:29,596 INFO [train.py:715] (2/8) Epoch 9, batch 18400, loss[loss=0.1249, simple_loss=0.1877, pruned_loss=0.03106, over 4948.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03397, over 971681.67 frames.], batch size: 18, lr: 2.35e-04
2022-05-06 13:05:08,637 INFO [train.py:715] (2/8) Epoch 9, batch 18450, loss[loss=0.1237, simple_loss=0.1935, pruned_loss=0.02692, over 4978.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2131, pruned_loss=0.03408, over 971746.44 frames.], batch size: 14, lr: 2.35e-04
2022-05-06 13:05:47,601 INFO [train.py:715] (2/8) Epoch 9, batch 18500, loss[loss=0.1163, simple_loss=0.1918, pruned_loss=0.02037, over 4742.00 frames.], tot_loss[loss=0.14, simple_loss=0.2126, pruned_loss=0.03371, over 972096.24 frames.], batch size: 16, lr: 2.35e-04
2022-05-06 13:06:26,996 INFO [train.py:715] (2/8) Epoch 9, batch 18550, loss[loss=0.1091, simple_loss=0.1732, pruned_loss=0.02252, over 4788.00 frames.], tot_loss[loss=0.139, simple_loss=0.2115, pruned_loss=0.03322, over 971903.53 frames.], batch size: 12, lr: 2.35e-04
2022-05-06 13:07:06,066 INFO [train.py:715] (2/8) Epoch 9, batch 18600, loss[loss=0.134, simple_loss=0.2016, pruned_loss=0.03325, over 4826.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2114, pruned_loss=0.03318, over 971410.86 frames.], batch size: 15, lr: 2.35e-04
2022-05-06 13:07:44,915 INFO [train.py:715] (2/8) Epoch 9, batch 18650, loss[loss=0.1344, simple_loss=0.213, pruned_loss=0.02789, over 4816.00 frames.], tot_loss[loss=0.139, simple_loss=0.2116, pruned_loss=0.03321, over 972120.55 frames.], batch size: 21, lr: 2.35e-04
2022-05-06 13:08:24,473 INFO [train.py:715] (2/8) Epoch 9, batch 18700, loss[loss=0.117, simple_loss=0.1907, pruned_loss=0.02165, over 4785.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2125, pruned_loss=0.03354, over 972902.37 frames.], batch size: 21, lr: 2.35e-04
2022-05-06 13:09:03,186 INFO [train.py:715] (2/8) Epoch 9, batch 18750, loss[loss=0.1752, simple_loss=0.2486, pruned_loss=0.05088, over 4808.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2122, pruned_loss=0.03336, over 972917.35 frames.], batch size: 25, lr: 2.35e-04
2022-05-06 13:09:42,758 INFO [train.py:715] (2/8) Epoch 9, batch 18800, loss[loss=0.1466, simple_loss=0.2218, pruned_loss=0.03576, over 4845.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03302, over 973052.49 frames.], batch size: 13, lr: 2.35e-04
2022-05-06 13:10:21,586 INFO [train.py:715] (2/8) Epoch 9, batch 18850, loss[loss=0.1358, simple_loss=0.2193, pruned_loss=0.02612, over 4742.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03344, over 972890.40 frames.], batch size: 19, lr: 2.35e-04
2022-05-06 13:11:00,819 INFO [train.py:715] (2/8) Epoch 9, batch 18900, loss[loss=0.159, simple_loss=0.2258, pruned_loss=0.04609, over 4857.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.034, over 972922.73 frames.], batch size: 30, lr: 2.35e-04
2022-05-06 13:11:40,161 INFO [train.py:715] (2/8) Epoch 9, batch 18950, loss[loss=0.1344, simple_loss=0.2031, pruned_loss=0.03287, over 4830.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03409, over 972736.29 frames.], batch size: 27, lr: 2.35e-04
2022-05-06 13:12:18,867 INFO [train.py:715] (2/8) Epoch 9, batch 19000, loss[loss=0.1833, simple_loss=0.2391, pruned_loss=0.06374, over 4982.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2146, pruned_loss=0.03439, over 973031.65 frames.], batch size: 31, lr: 2.35e-04
2022-05-06 13:12:58,960 INFO [train.py:715] (2/8) Epoch 9, batch 19050, loss[loss=0.1621, simple_loss=0.243, pruned_loss=0.04056, over 4774.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2144, pruned_loss=0.03423, over 973211.71 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:13:38,428 INFO [train.py:715] (2/8) Epoch 9, batch 19100, loss[loss=0.1536, simple_loss=0.2258, pruned_loss=0.0407, over 4848.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03366, over 973918.56 frames.], batch size: 13, lr: 2.34e-04
2022-05-06 13:14:17,273 INFO [train.py:715] (2/8) Epoch 9, batch 19150, loss[loss=0.1593, simple_loss=0.2384, pruned_loss=0.04009, over 4919.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.03391, over 973359.22 frames.], batch size: 29, lr: 2.34e-04
2022-05-06 13:14:57,088 INFO [train.py:715] (2/8) Epoch 9, batch 19200, loss[loss=0.1794, simple_loss=0.2418, pruned_loss=0.05848, over 4747.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.03434, over 972943.86 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:15:36,594 INFO [train.py:715] (2/8) Epoch 9, batch 19250, loss[loss=0.1874, simple_loss=0.2511, pruned_loss=0.06181, over 4946.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03451, over 973325.14 frames.], batch size: 35, lr: 2.34e-04
2022-05-06 13:16:15,484 INFO [train.py:715] (2/8) Epoch 9, batch 19300, loss[loss=0.1537, simple_loss=0.2365, pruned_loss=0.03546, over 4848.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.03385, over 972112.36 frames.], batch size: 30, lr: 2.34e-04
2022-05-06 13:16:54,062 INFO [train.py:715] (2/8) Epoch 9, batch 19350, loss[loss=0.1591, simple_loss=0.2284, pruned_loss=0.04486, over 4907.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2121, pruned_loss=0.03343, over 971847.90 frames.], batch size: 39, lr: 2.34e-04
2022-05-06 13:17:34,089 INFO [train.py:715] (2/8) Epoch 9, batch 19400, loss[loss=0.1836, simple_loss=0.249, pruned_loss=0.05906, over 4796.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2123, pruned_loss=0.03363, over 971602.30 frames.], batch size: 21, lr: 2.34e-04
2022-05-06 13:18:13,120 INFO [train.py:715] (2/8) Epoch 9, batch 19450, loss[loss=0.1451, simple_loss=0.2186, pruned_loss=0.03582, over 4807.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2131, pruned_loss=0.03399, over 972125.81 frames.], batch size: 21, lr: 2.34e-04
2022-05-06 13:18:51,813 INFO [train.py:715] (2/8) Epoch 9, batch 19500, loss[loss=0.1254, simple_loss=0.2046, pruned_loss=0.02309, over 4960.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03385, over 972597.82 frames.], batch size: 24, lr: 2.34e-04
2022-05-06 13:19:30,941 INFO [train.py:715] (2/8) Epoch 9, batch 19550, loss[loss=0.1537, simple_loss=0.2141, pruned_loss=0.04665, over 4908.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2133, pruned_loss=0.03427, over 972479.36 frames.], batch size: 17, lr: 2.34e-04
2022-05-06 13:20:10,203 INFO [train.py:715] (2/8) Epoch 9, batch 19600, loss[loss=0.1471, simple_loss=0.2185, pruned_loss=0.03791, over 4870.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.0351, over 972987.19 frames.], batch size: 22, lr: 2.34e-04
2022-05-06 13:20:48,781 INFO [train.py:715] (2/8) Epoch 9, batch 19650, loss[loss=0.1494, simple_loss=0.2038, pruned_loss=0.0475, over 4847.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2137, pruned_loss=0.03472, over 973338.17 frames.], batch size: 13, lr: 2.34e-04
2022-05-06 13:21:27,272 INFO [train.py:715] (2/8) Epoch 9, batch 19700, loss[loss=0.1413, simple_loss=0.2215, pruned_loss=0.0306, over 4691.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2134, pruned_loss=0.03457, over 973497.85 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:22:07,200 INFO [train.py:715] (2/8) Epoch 9, batch 19750, loss[loss=0.1364, simple_loss=0.2153, pruned_loss=0.02877, over 4852.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2149, pruned_loss=0.0348, over 973587.41 frames.], batch size: 30, lr: 2.34e-04
2022-05-06 13:22:46,869 INFO [train.py:715] (2/8) Epoch 9, batch 19800, loss[loss=0.1365, simple_loss=0.2119, pruned_loss=0.03054, over 4898.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2154, pruned_loss=0.03501, over 973318.90 frames.], batch size: 17, lr: 2.34e-04
2022-05-06 13:23:26,648 INFO [train.py:715] (2/8) Epoch 9, batch 19850, loss[loss=0.1264, simple_loss=0.1904, pruned_loss=0.03121, over 4778.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2144, pruned_loss=0.03464, over 973527.58 frames.], batch size: 12, lr: 2.34e-04
2022-05-06 13:24:06,291 INFO [train.py:715] (2/8) Epoch 9, batch 19900, loss[loss=0.1641, simple_loss=0.2338, pruned_loss=0.04715, over 4816.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2138, pruned_loss=0.0347, over 972454.83 frames.], batch size: 26, lr: 2.34e-04
2022-05-06 13:24:45,454 INFO [train.py:715] (2/8) Epoch 9, batch 19950, loss[loss=0.1397, simple_loss=0.2023, pruned_loss=0.03857, over 4905.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2137, pruned_loss=0.03497, over 972736.00 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:25:24,506 INFO [train.py:715] (2/8) Epoch 9, batch 20000, loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02964, over 4890.00 frames.], tot_loss[loss=0.141, simple_loss=0.2129, pruned_loss=0.03459, over 972794.15 frames.], batch size: 22, lr: 2.34e-04
2022-05-06 13:26:02,954 INFO [train.py:715] (2/8) Epoch 9, batch 20050, loss[loss=0.1666, simple_loss=0.2284, pruned_loss=0.05238, over 4834.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2128, pruned_loss=0.03441, over 972364.84 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:26:42,422 INFO [train.py:715] (2/8) Epoch 9, batch 20100, loss[loss=0.1386, simple_loss=0.2097, pruned_loss=0.03378, over 4914.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2135, pruned_loss=0.03445, over 972639.83 frames.], batch size: 17, lr: 2.34e-04
2022-05-06 13:27:21,488 INFO [train.py:715] (2/8) Epoch 9, batch 20150, loss[loss=0.1557, simple_loss=0.2355, pruned_loss=0.03798, over 4753.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2141, pruned_loss=0.03465, over 972627.06 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:27:59,968 INFO [train.py:715] (2/8) Epoch 9, batch 20200, loss[loss=0.1457, simple_loss=0.2178, pruned_loss=0.0368, over 4978.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2142, pruned_loss=0.03477, over 972906.25 frames.], batch size: 14, lr: 2.34e-04
2022-05-06 13:28:39,475 INFO [train.py:715] (2/8) Epoch 9, batch 20250, loss[loss=0.1306, simple_loss=0.2039, pruned_loss=0.02865, over 4757.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2143, pruned_loss=0.03477, over 973660.34 frames.], batch size: 16, lr: 2.34e-04
2022-05-06 13:29:18,328 INFO [train.py:715] (2/8) Epoch 9, batch 20300, loss[loss=0.1433, simple_loss=0.2266, pruned_loss=0.02999, over 4768.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.0346, over 974446.75 frames.], batch size: 14, lr: 2.34e-04
2022-05-06 13:29:57,719 INFO [train.py:715] (2/8) Epoch 9, batch 20350, loss[loss=0.1412, simple_loss=0.2034, pruned_loss=0.03949, over 4915.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03458, over 973638.15 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:30:37,201 INFO [train.py:715] (2/8) Epoch 9, batch 20400, loss[loss=0.1383, simple_loss=0.2111, pruned_loss=0.03277, over 4885.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2137, pruned_loss=0.0344, over 973617.79 frames.], batch size: 32, lr: 2.34e-04
2022-05-06 13:31:17,090 INFO [train.py:715] (2/8) Epoch 9, batch 20450, loss[loss=0.1184, simple_loss=0.1909, pruned_loss=0.02297, over 4939.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03425, over 973835.61 frames.], batch size: 29, lr: 2.34e-04
2022-05-06 13:31:56,597 INFO [train.py:715] (2/8) Epoch 9, batch 20500, loss[loss=0.1343, simple_loss=0.2055, pruned_loss=0.03156, over 4963.00 frames.], tot_loss[loss=0.141, simple_loss=0.2141, pruned_loss=0.03397, over 973101.24 frames.], batch size: 24, lr: 2.34e-04
2022-05-06 13:32:35,667 INFO [train.py:715] (2/8) Epoch 9, batch 20550, loss[loss=0.1335, simple_loss=0.2013, pruned_loss=0.03284, over 4767.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2147, pruned_loss=0.03441, over 973219.32 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:33:14,861 INFO [train.py:715] (2/8) Epoch 9, batch 20600, loss[loss=0.1255, simple_loss=0.2032, pruned_loss=0.02391, over 4904.00 frames.], tot_loss[loss=0.1411, simple_loss=0.214, pruned_loss=0.03405, over 972516.23 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:33:53,310 INFO [train.py:715] (2/8) Epoch 9, batch 20650, loss[loss=0.1403, simple_loss=0.2148, pruned_loss=0.03285, over 4788.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03418, over 972562.35 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:34:32,417 INFO [train.py:715] (2/8) Epoch 9, batch 20700, loss[loss=0.1497, simple_loss=0.2234, pruned_loss=0.03802, over 4805.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03433, over 972338.43 frames.], batch size: 25, lr: 2.34e-04
2022-05-06 13:35:11,250 INFO [train.py:715] (2/8) Epoch 9, batch 20750, loss[loss=0.1833, simple_loss=0.2515, pruned_loss=0.05754, over 4768.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2148, pruned_loss=0.03442, over 972914.27 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:35:50,839 INFO [train.py:715] (2/8) Epoch 9, batch 20800, loss[loss=0.1607, simple_loss=0.2334, pruned_loss=0.04404, over 4954.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2152, pruned_loss=0.03451, over 973206.62 frames.], batch size: 35, lr: 2.34e-04
2022-05-06 13:36:30,209 INFO [train.py:715] (2/8) Epoch 9, batch 20850, loss[loss=0.124, simple_loss=0.1957, pruned_loss=0.02617, over 4767.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2154, pruned_loss=0.0347, over 973319.55 frames.], batch size: 19, lr: 2.34e-04
2022-05-06 13:37:09,649 INFO [train.py:715] (2/8) Epoch 9, batch 20900, loss[loss=0.138, simple_loss=0.209, pruned_loss=0.03353, over 4940.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2153, pruned_loss=0.03458, over 973362.02 frames.], batch size: 23, lr: 2.34e-04
2022-05-06 13:37:49,145 INFO [train.py:715] (2/8) Epoch 9, batch 20950, loss[loss=0.1314, simple_loss=0.2077, pruned_loss=0.02752, over 4920.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2158, pruned_loss=0.03486, over 972576.28 frames.], batch size: 23, lr: 2.34e-04
2022-05-06 13:38:28,444 INFO [train.py:715] (2/8) Epoch 9, batch 21000, loss[loss=0.131, simple_loss=0.2002, pruned_loss=0.03084, over 4913.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2147, pruned_loss=0.03422, over 972816.91 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:38:28,445 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 13:38:38,083 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.1069, simple_loss=0.1912, pruned_loss=0.01129, over 914524.00 frames.
2022-05-06 13:39:17,245 INFO [train.py:715] (2/8) Epoch 9, batch 21050, loss[loss=0.1481, simple_loss=0.2131, pruned_loss=0.04153, over 4881.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2142, pruned_loss=0.03378, over 972682.43 frames.], batch size: 32, lr: 2.34e-04
2022-05-06 13:39:56,157 INFO [train.py:715] (2/8) Epoch 9, batch 21100, loss[loss=0.1318, simple_loss=0.2018, pruned_loss=0.03088, over 4919.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2135, pruned_loss=0.0333, over 973003.79 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:40:35,519 INFO [train.py:715] (2/8) Epoch 9, batch 21150, loss[loss=0.138, simple_loss=0.2153, pruned_loss=0.03033, over 4809.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2141, pruned_loss=0.03358, over 972948.71 frames.], batch size: 25, lr: 2.34e-04
2022-05-06 13:41:14,528 INFO [train.py:715] (2/8) Epoch 9, batch 21200, loss[loss=0.1266, simple_loss=0.2058, pruned_loss=0.02374, over 4788.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2147, pruned_loss=0.03405, over 972723.24 frames.], batch size: 24, lr: 2.34e-04
2022-05-06 13:41:54,099 INFO [train.py:715] (2/8) Epoch 9, batch 21250, loss[loss=0.1551, simple_loss=0.2285, pruned_loss=0.04082, over 4995.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2145, pruned_loss=0.0341, over 972750.28 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:42:32,488 INFO [train.py:715] (2/8) Epoch 9, batch 21300, loss[loss=0.1513, simple_loss=0.218, pruned_loss=0.04227, over 4882.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2151, pruned_loss=0.03462, over 972346.40 frames.], batch size: 16, lr: 2.34e-04
2022-05-06 13:43:11,099 INFO [train.py:715] (2/8) Epoch 9, batch 21350, loss[loss=0.129, simple_loss=0.2035, pruned_loss=0.02721, over 4797.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2155, pruned_loss=0.03501, over 973619.69 frames.], batch size: 13, lr: 2.34e-04
2022-05-06 13:43:50,033 INFO [train.py:715] (2/8) Epoch 9, batch 21400, loss[loss=0.1383, simple_loss=0.2124, pruned_loss=0.03208, over 4858.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2143, pruned_loss=0.03435, over 973332.01 frames.], batch size: 30, lr: 2.34e-04
2022-05-06 13:44:28,775 INFO [train.py:715] (2/8) Epoch 9, batch 21450, loss[loss=0.1282, simple_loss=0.1991, pruned_loss=0.02866, over 4786.00 frames.], tot_loss[loss=0.141, simple_loss=0.214, pruned_loss=0.03404, over 972225.69 frames.], batch size: 17, lr: 2.34e-04
2022-05-06 13:45:07,166 INFO [train.py:715] (2/8) Epoch 9, batch 21500, loss[loss=0.144, simple_loss=0.2154, pruned_loss=0.03629, over 4942.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2147, pruned_loss=0.03393, over 971895.41 frames.], batch size: 23, lr: 2.34e-04
2022-05-06 13:45:46,282 INFO [train.py:715] (2/8) Epoch 9, batch 21550, loss[loss=0.1057, simple_loss=0.1781, pruned_loss=0.01669, over 4909.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2134, pruned_loss=0.03351, over 972501.87 frames.], batch size: 18, lr: 2.34e-04
2022-05-06 13:46:24,997 INFO [train.py:715] (2/8) Epoch 9, batch 21600, loss[loss=0.1549, simple_loss=0.2364, pruned_loss=0.03669, over 4869.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2132, pruned_loss=0.03315, over 973298.84 frames.], batch size: 20, lr: 2.34e-04
2022-05-06 13:47:04,089 INFO [train.py:715] (2/8) Epoch 9, batch 21650, loss[loss=0.1996, simple_loss=0.2735, pruned_loss=0.06288, over 4854.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2143, pruned_loss=0.03411, over 972522.30 frames.], batch size: 32, lr: 2.34e-04
2022-05-06 13:47:43,368 INFO [train.py:715] (2/8) Epoch 9, batch 21700, loss[loss=0.1575, simple_loss=0.233, pruned_loss=0.04098, over 4770.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.03453, over 973030.35 frames.], batch size: 17, lr: 2.34e-04
2022-05-06 13:48:22,455 INFO [train.py:715] (2/8) Epoch 9, batch 21750, loss[loss=0.1441, simple_loss=0.2172, pruned_loss=0.03552, over 4841.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2139, pruned_loss=0.03416, over 972969.85 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:49:01,563 INFO [train.py:715] (2/8) Epoch 9, batch 21800, loss[loss=0.1342, simple_loss=0.2162, pruned_loss=0.02615, over 4840.00 frames.], tot_loss[loss=0.142, simple_loss=0.2149, pruned_loss=0.03455, over 973625.69 frames.], batch size: 26, lr: 2.34e-04
2022-05-06 13:49:41,088 INFO [train.py:715] (2/8) Epoch 9, batch 21850, loss[loss=0.1257, simple_loss=0.192, pruned_loss=0.02972, over 4846.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2144, pruned_loss=0.03432, over 972603.34 frames.], batch size: 15, lr: 2.34e-04
2022-05-06 13:50:20,438 INFO [train.py:715] (2/8) Epoch 9, batch 21900, loss[loss=0.1698, simple_loss=0.2401, pruned_loss=0.04975, over 4933.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2145, pruned_loss=0.03496, over 973235.17 frames.], batch size: 23, lr: 2.33e-04
2022-05-06 13:50:59,008 INFO [train.py:715] (2/8) Epoch 9, batch 21950, loss[loss=0.1257, simple_loss=0.1981, pruned_loss=0.02667, over 4788.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2129, pruned_loss=0.03442, over 973289.27 frames.], batch size: 17, lr: 2.33e-04
2022-05-06 13:51:37,911 INFO [train.py:715] (2/8) Epoch 9, batch 22000, loss[loss=0.1445, simple_loss=0.2088, pruned_loss=0.04014, over 4755.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2134, pruned_loss=0.03488, over 973176.71 frames.], batch size: 19, lr: 2.33e-04
2022-05-06 13:52:16,827 INFO [train.py:715] (2/8) Epoch 9, batch 22050, loss[loss=0.114, simple_loss=0.1857, pruned_loss=0.02109, over 4857.00 frames.], tot_loss[loss=0.141, simple_loss=0.2131, pruned_loss=0.03446, over 973139.67 frames.], batch size: 13, lr: 2.33e-04
2022-05-06 13:52:56,532 INFO [train.py:715] (2/8) Epoch 9, batch 22100, loss[loss=0.1367, simple_loss=0.2089, pruned_loss=0.0322, over 4801.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2133, pruned_loss=0.03442, over 973119.07 frames.], batch size: 21, lr: 2.33e-04
2022-05-06 13:53:35,798 INFO [train.py:715] (2/8) Epoch 9, batch 22150, loss[loss=0.157, simple_loss=0.2131, pruned_loss=0.05048, over 4769.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2138, pruned_loss=0.03448, over 973280.08 frames.], batch size: 14, lr: 2.33e-04
2022-05-06 13:54:14,967 INFO [train.py:715] (2/8) Epoch 9, batch 22200, loss[loss=0.1361, simple_loss=0.2062, pruned_loss=0.03301, over 4862.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2139, pruned_loss=0.03484, over 972789.24 frames.], batch size: 38, lr: 2.33e-04
2022-05-06 13:54:54,445 INFO [train.py:715] (2/8) Epoch 9, batch 22250, loss[loss=0.1286, simple_loss=0.1978, pruned_loss=0.02966, over 4931.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2139, pruned_loss=0.03479, over 973520.09 frames.], batch size: 23, lr: 2.33e-04
2022-05-06 13:55:33,231 INFO [train.py:715] (2/8) Epoch 9, batch 22300, loss[loss=0.1198, simple_loss=0.1882, pruned_loss=0.02565, over 4763.00 frames.], tot_loss[loss=0.141, simple_loss=0.2133, pruned_loss=0.03431, over 973791.42 frames.], batch size: 12, lr: 2.33e-04
2022-05-06 13:56:11,832 INFO [train.py:715] (2/8) Epoch 9, batch 22350, loss[loss=0.141, simple_loss=0.2173, pruned_loss=0.03241, over 4842.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03415, over 973252.21 frames.], batch size: 20, lr: 2.33e-04
2022-05-06 13:56:50,721 INFO [train.py:715] (2/8) Epoch 9, batch 22400, loss[loss=0.1605, simple_loss=0.2333, pruned_loss=0.04386, over 4922.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03454, over 973189.46 frames.], batch size: 39, lr: 2.33e-04
2022-05-06 13:57:29,424 INFO [train.py:715] (2/8) Epoch 9, batch 22450, loss[loss=0.1854, simple_loss=0.2573, pruned_loss=0.05676, over 4781.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.0347, over 972752.99 frames.], batch size: 17, lr: 2.33e-04
2022-05-06 13:58:08,128 INFO [train.py:715] (2/8) Epoch 9, batch 22500, loss[loss=0.1164, simple_loss=0.1923, pruned_loss=0.02025, over 4973.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2135, pruned_loss=0.03431, over 973509.96 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 13:58:47,020 INFO [train.py:715] (2/8) Epoch 9, batch 22550, loss[loss=0.1702, simple_loss=0.23, pruned_loss=0.0552, over 4791.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2144, pruned_loss=0.03534, over 973521.27 frames.], batch size: 12, lr: 2.33e-04
2022-05-06 13:59:26,037 INFO [train.py:715] (2/8) Epoch 9, batch 22600, loss[loss=0.1135, simple_loss=0.1952, pruned_loss=0.0159, over 4932.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2144, pruned_loss=0.03488, over 974541.32 frames.], batch size: 29, lr: 2.33e-04
2022-05-06 14:00:05,202 INFO [train.py:715] (2/8) Epoch 9, batch 22650, loss[loss=0.1563, simple_loss=0.2283, pruned_loss=0.0421, over 4885.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.03461, over 973968.07 frames.], batch size: 32, lr: 2.33e-04
2022-05-06 14:00:44,244 INFO [train.py:715] (2/8) Epoch 9, batch 22700, loss[loss=0.1267, simple_loss=0.2071, pruned_loss=0.02316, over 4983.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03416, over 974314.25 frames.], batch size: 28, lr: 2.33e-04
2022-05-06 14:01:26,075 INFO [train.py:715] (2/8) Epoch 9, batch 22750, loss[loss=0.1377, simple_loss=0.2145, pruned_loss=0.03044, over 4834.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2145, pruned_loss=0.03447, over 973704.71 frames.], batch size: 30, lr: 2.33e-04
2022-05-06 14:02:04,857 INFO [train.py:715] (2/8) Epoch 9, batch 22800, loss[loss=0.147, simple_loss=0.2221, pruned_loss=0.03601, over 4844.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2143, pruned_loss=0.0342, over 973559.82 frames.], batch size: 30, lr: 2.33e-04
2022-05-06 14:02:44,151 INFO [train.py:715] (2/8) Epoch 9, batch 22850, loss[loss=0.1559, simple_loss=0.2219, pruned_loss=0.04494, over 4701.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2154, pruned_loss=0.03477, over 972926.45 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 14:03:22,720 INFO [train.py:715] (2/8) Epoch 9, batch 22900, loss[loss=0.1658, simple_loss=0.2308, pruned_loss=0.05042, over 4984.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2155, pruned_loss=0.03512, over 973123.51 frames.], batch size: 28, lr: 2.33e-04
2022-05-06 14:04:01,803 INFO [train.py:715] (2/8) Epoch 9, batch 22950, loss[loss=0.1229, simple_loss=0.1858, pruned_loss=0.02999, over 4962.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03504, over 973318.44 frames.], batch size: 14, lr: 2.33e-04
2022-05-06 14:04:40,858 INFO [train.py:715] (2/8) Epoch 9, batch 23000, loss[loss=0.1207, simple_loss=0.1968, pruned_loss=0.02226, over 4744.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2138, pruned_loss=0.03477, over 972650.69 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:05:20,250 INFO [train.py:715] (2/8) Epoch 9, batch 23050, loss[loss=0.1252, simple_loss=0.2033, pruned_loss=0.02354, over 4971.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2143, pruned_loss=0.03501, over 973813.65 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 14:05:59,523 INFO [train.py:715] (2/8) Epoch 9, batch 23100, loss[loss=0.1158, simple_loss=0.2, pruned_loss=0.01575, over 4814.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03445, over 973712.36 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 14:06:38,547 INFO [train.py:715] (2/8) Epoch 9, batch 23150, loss[loss=0.1534, simple_loss=0.2221, pruned_loss=0.04235, over 4827.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2131, pruned_loss=0.03422, over 972721.03 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 14:07:18,171 INFO [train.py:715] (2/8) Epoch 9, batch 23200, loss[loss=0.1429, simple_loss=0.2115, pruned_loss=0.03715, over 4845.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2131, pruned_loss=0.03398, over 972506.26 frames.], batch size: 32, lr: 2.33e-04
2022-05-06 14:07:57,915 INFO [train.py:715] (2/8) Epoch 9, batch 23250, loss[loss=0.1459, simple_loss=0.2209, pruned_loss=0.03544, over 4975.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03419, over 972132.47 frames.], batch size: 31, lr: 2.33e-04
2022-05-06 14:08:37,684 INFO [train.py:715] (2/8) Epoch 9, batch 23300, loss[loss=0.1626, simple_loss=0.2228, pruned_loss=0.05116, over 4740.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2146, pruned_loss=0.03446, over 972339.59 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:09:17,442 INFO [train.py:715] (2/8) Epoch 9, batch 23350, loss[loss=0.1468, simple_loss=0.2158, pruned_loss=0.03888, over 4769.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2144, pruned_loss=0.03468, over 971933.57 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:09:56,735 INFO [train.py:715] (2/8) Epoch 9, batch 23400, loss[loss=0.1402, simple_loss=0.2174, pruned_loss=0.03148, over 4696.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03449, over 971051.88 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 14:10:35,594 INFO [train.py:715] (2/8) Epoch 9, batch 23450, loss[loss=0.1161, simple_loss=0.1895, pruned_loss=0.02133, over 4927.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03388, over 970702.19 frames.], batch size: 18, lr: 2.33e-04
2022-05-06 14:11:14,355 INFO [train.py:715] (2/8) Epoch 9, batch 23500, loss[loss=0.1364, simple_loss=0.2144, pruned_loss=0.02925, over 4914.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03378, over 970256.89 frames.], batch size: 23, lr: 2.33e-04
2022-05-06 14:11:52,878 INFO [train.py:715] (2/8) Epoch 9, batch 23550, loss[loss=0.129, simple_loss=0.2085, pruned_loss=0.02473, over 4971.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03385, over 970069.04 frames.], batch size: 25, lr: 2.33e-04
2022-05-06 14:12:32,346 INFO [train.py:715] (2/8) Epoch 9, batch 23600, loss[loss=0.1299, simple_loss=0.2097, pruned_loss=0.0251, over 4981.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03378, over 970361.42 frames.], batch size: 28, lr: 2.33e-04
2022-05-06 14:13:11,497 INFO [train.py:715] (2/8) Epoch 9, batch 23650, loss[loss=0.1448, simple_loss=0.2112, pruned_loss=0.03922, over 4820.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2147, pruned_loss=0.03449, over 970057.17 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 14:13:50,877 INFO [train.py:715] (2/8) Epoch 9, batch 23700, loss[loss=0.1533, simple_loss=0.2251, pruned_loss=0.04076, over 4836.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.03425, over 970282.59 frames.], batch size: 20, lr: 2.33e-04
2022-05-06 14:14:30,052 INFO [train.py:715] (2/8) Epoch 9, batch 23750, loss[loss=0.1351, simple_loss=0.2013, pruned_loss=0.0344, over 4971.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2141, pruned_loss=0.03408, over 969783.04 frames.], batch size: 35, lr: 2.33e-04
2022-05-06 14:15:09,283 INFO [train.py:715] (2/8) Epoch 9, batch 23800, loss[loss=0.1343, simple_loss=0.211, pruned_loss=0.02879, over 4963.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03413, over 971119.79 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 14:15:48,392 INFO [train.py:715] (2/8) Epoch 9, batch 23850, loss[loss=0.1367, simple_loss=0.2134, pruned_loss=0.02999, over 4983.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2133, pruned_loss=0.03344, over 971972.71 frames.], batch size: 26, lr: 2.33e-04
2022-05-06 14:16:27,644 INFO [train.py:715] (2/8) Epoch 9, batch 23900, loss[loss=0.1251, simple_loss=0.1986, pruned_loss=0.02584, over 4817.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03347, over 971657.22 frames.], batch size: 26, lr: 2.33e-04
2022-05-06 14:17:06,535 INFO [train.py:715] (2/8) Epoch 9, batch 23950, loss[loss=0.1473, simple_loss=0.2163, pruned_loss=0.0392, over 4873.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03372, over 972385.68 frames.], batch size: 32, lr: 2.33e-04
2022-05-06 14:17:45,503 INFO [train.py:715] (2/8) Epoch 9, batch 24000, loss[loss=0.1457, simple_loss=0.2275, pruned_loss=0.03201, over 4971.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.03391, over 972247.35 frames.], batch size: 39, lr: 2.33e-04
2022-05-06 14:17:45,503 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 14:17:55,355 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.1069, simple_loss=0.1913, pruned_loss=0.01128, over 914524.00 frames.
2022-05-06 14:18:34,691 INFO [train.py:715] (2/8) Epoch 9, batch 24050, loss[loss=0.1496, simple_loss=0.2194, pruned_loss=0.03993, over 4931.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2127, pruned_loss=0.03386, over 972267.21 frames.], batch size: 23, lr: 2.33e-04
2022-05-06 14:19:14,962 INFO [train.py:715] (2/8) Epoch 9, batch 24100, loss[loss=0.1512, simple_loss=0.2277, pruned_loss=0.03731, over 4980.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03386, over 972074.12 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 14:19:54,486 INFO [train.py:715] (2/8) Epoch 9, batch 24150, loss[loss=0.1376, simple_loss=0.2153, pruned_loss=0.0299, over 4824.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2131, pruned_loss=0.03394, over 971454.68 frames.], batch size: 15, lr: 2.33e-04
2022-05-06 14:20:33,564 INFO [train.py:715] (2/8) Epoch 9, batch 24200, loss[loss=0.1251, simple_loss=0.207, pruned_loss=0.02157, over 4830.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03352, over 971670.39 frames.], batch size: 26, lr: 2.33e-04
2022-05-06 14:21:12,482 INFO [train.py:715] (2/8) Epoch 9, batch 24250, loss[loss=0.1364, simple_loss=0.2023, pruned_loss=0.03525, over 4757.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03337, over 971795.71 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:21:52,134 INFO [train.py:715] (2/8) Epoch 9, batch 24300, loss[loss=0.1224, simple_loss=0.1986, pruned_loss=0.02304, over 4752.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2127, pruned_loss=0.03389, over 970655.45 frames.], batch size: 19, lr: 2.33e-04
2022-05-06 14:22:31,315 INFO [train.py:715] (2/8) Epoch 9, batch 24350, loss[loss=0.1339, simple_loss=0.2176, pruned_loss=0.02509, over 4974.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03362, over 970677.82 frames.], batch size: 24, lr: 2.33e-04
2022-05-06 14:23:10,724 INFO [train.py:715] (2/8) Epoch 9, batch 24400, loss[loss=0.1591, simple_loss=0.2236, pruned_loss=0.04732, over 4908.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03424, over 971575.50 frames.], batch size: 17, lr: 2.33e-04
2022-05-06 14:23:50,633 INFO [train.py:715] (2/8) Epoch 9, batch 24450, loss[loss=0.1558, simple_loss=0.2286, pruned_loss=0.04146, over 4954.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2135, pruned_loss=0.03377, over 971358.42 frames.], batch size: 21, lr: 2.33e-04
2022-05-06 14:24:30,657 INFO [train.py:715] (2/8) Epoch 9, batch 24500, loss[loss=0.1224, simple_loss=0.1965, pruned_loss=0.02415, over 4993.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2129, pruned_loss=0.03374, over 971278.27 frames.], batch size: 16, lr: 2.33e-04
2022-05-06 14:25:11,014 INFO [train.py:715] (2/8) Epoch 9, batch 24550, loss[loss=0.1382, simple_loss=0.2123, pruned_loss=0.03202, over 4803.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2124, pruned_loss=0.03367, over 971683.40 frames.], batch size: 25, lr: 2.33e-04
2022-05-06 14:25:50,760 INFO [train.py:715] (2/8) Epoch 9, batch 24600, loss[loss=0.1612, simple_loss=0.2304, pruned_loss=0.046, over 4925.00 frames.], tot_loss[loss=0.14, simple_loss=0.2127, pruned_loss=0.03368, over 972103.90 frames.], batch size: 18, lr: 2.33e-04
2022-05-06 14:26:30,715 INFO [train.py:715] (2/8) Epoch 9, batch 24650, loss[loss=0.1153, simple_loss=0.201, pruned_loss=0.01483, over 4942.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2128, pruned_loss=0.0338, over 972337.06 frames.], batch size: 29, lr: 2.33e-04
2022-05-06 14:27:09,793 INFO [train.py:715] (2/8) Epoch 9, batch 24700, loss[loss=0.1656, simple_loss=0.2291, pruned_loss=0.05101, over 4870.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03397, over 972143.31 frames.], batch size: 32, lr: 2.33e-04
2022-05-06 14:27:48,505 INFO [train.py:715] (2/8) Epoch 9, batch 24750, loss[loss=0.1342, simple_loss=0.2072, pruned_loss=0.03054, over 4983.00 frames.], tot_loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03435, over 972215.22 frames.], batch size: 25, lr: 2.33e-04
2022-05-06 14:28:28,024 INFO [train.py:715] (2/8) Epoch 9, batch 24800, loss[loss=0.1164, simple_loss=0.1905, pruned_loss=0.02113, over 4777.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2131, pruned_loss=0.03473, over 970618.75 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 14:29:07,571 INFO [train.py:715] (2/8) Epoch 9, batch 24850, loss[loss=0.1292, simple_loss=0.196, pruned_loss=0.03115, over 4983.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2131, pruned_loss=0.03466, over 970859.74 frames.], batch size: 14, lr: 2.32e-04
2022-05-06 14:29:46,970 INFO [train.py:715] (2/8) Epoch 9, batch 24900, loss[loss=0.1235, simple_loss=0.2054, pruned_loss=0.02083, over 4933.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2131, pruned_loss=0.03473, over 972090.74 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:30:26,386 INFO [train.py:715] (2/8) Epoch 9, batch 24950, loss[loss=0.1392, simple_loss=0.2201, pruned_loss=0.02914, over 4689.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2134, pruned_loss=0.0348, over 971281.58 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 14:31:06,085 INFO [train.py:715] (2/8) Epoch 9, batch 25000, loss[loss=0.1236, simple_loss=0.2003, pruned_loss=0.02347, over 4903.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2129, pruned_loss=0.03437, over 972391.95 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 14:31:44,921 INFO [train.py:715] (2/8) Epoch 9, batch 25050, loss[loss=0.1342, simple_loss=0.205, pruned_loss=0.03171, over 4939.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03399, over 972586.32 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:32:24,416 INFO [train.py:715] (2/8) Epoch 9, batch 25100, loss[loss=0.1369, simple_loss=0.2138, pruned_loss=0.03001, over 4958.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2132, pruned_loss=0.03409, over 972761.47 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 14:33:03,524 INFO [train.py:715] (2/8) Epoch 9, batch 25150, loss[loss=0.1386, simple_loss=0.2135, pruned_loss=0.03178, over 4737.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03382, over 972687.06 frames.], batch size: 16, lr: 2.32e-04
2022-05-06 14:33:42,581 INFO [train.py:715] (2/8) Epoch 9, batch 25200, loss[loss=0.1358, simple_loss=0.2072, pruned_loss=0.03224, over 4694.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2132, pruned_loss=0.03402, over 972944.97 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 14:34:21,838 INFO [train.py:715] (2/8) Epoch 9, batch 25250, loss[loss=0.1741, simple_loss=0.2471, pruned_loss=0.05053, over 4797.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03456, over 972875.10 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:35:00,582 INFO [train.py:715] (2/8) Epoch 9, batch 25300, loss[loss=0.1372, simple_loss=0.2124, pruned_loss=0.03102, over 4789.00 frames.], tot_loss[loss=0.142, simple_loss=0.2143, pruned_loss=0.03487, over 973366.47 frames.], batch size: 14, lr: 2.32e-04
2022-05-06 14:35:40,266 INFO [train.py:715] (2/8) Epoch 9, batch 25350, loss[loss=0.1586, simple_loss=0.2217, pruned_loss=0.0477, over 4815.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2147, pruned_loss=0.03507, over 971848.72 frames.], batch size: 26, lr: 2.32e-04
2022-05-06 14:36:20,108 INFO [train.py:715] (2/8) Epoch 9, batch 25400, loss[loss=0.1366, simple_loss=0.2199, pruned_loss=0.02661, over 4980.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03485, over 972775.11 frames.], batch size: 24, lr: 2.32e-04
2022-05-06 14:37:00,344 INFO [train.py:715] (2/8) Epoch 9, batch 25450, loss[loss=0.1707, simple_loss=0.2376, pruned_loss=0.05191, over 4984.00 frames.], tot_loss[loss=0.1424, simple_loss=0.215, pruned_loss=0.03489, over 973010.91 frames.], batch size: 14, lr: 2.32e-04
2022-05-06 14:37:38,912 INFO [train.py:715] (2/8) Epoch 9, batch 25500, loss[loss=0.1627, simple_loss=0.2337, pruned_loss=0.04584, over 4773.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2145, pruned_loss=0.03452, over 972584.27 frames.], batch size: 14, lr: 2.32e-04
2022-05-06 14:38:18,074 INFO [train.py:715] (2/8) Epoch 9, batch 25550, loss[loss=0.1396, simple_loss=0.1976, pruned_loss=0.0408, over 4771.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2146, pruned_loss=0.03461, over 971513.73 frames.], batch size: 18, lr: 2.32e-04
2022-05-06 14:38:57,226 INFO [train.py:715] (2/8) Epoch 9, batch 25600, loss[loss=0.1479, simple_loss=0.2193, pruned_loss=0.03826, over 4812.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2132, pruned_loss=0.03391, over 972337.60 frames.], batch size: 27, lr: 2.32e-04
2022-05-06 14:39:36,162 INFO [train.py:715] (2/8) Epoch 9, batch 25650, loss[loss=0.1136, simple_loss=0.1864, pruned_loss=0.02044, over 4714.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03354, over 971980.91 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 14:40:15,297 INFO [train.py:715] (2/8) Epoch 9, batch 25700, loss[loss=0.1362, simple_loss=0.2049, pruned_loss=0.03372, over 4864.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03327, over 971642.50 frames.], batch size: 30, lr: 2.32e-04
2022-05-06 14:40:54,416 INFO [train.py:715] (2/8) Epoch 9, batch 25750, loss[loss=0.1415, simple_loss=0.2213, pruned_loss=0.03084, over 4782.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2131, pruned_loss=0.03377, over 971707.30 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 14:41:33,410 INFO [train.py:715] (2/8) Epoch 9, batch 25800, loss[loss=0.1303, simple_loss=0.2073, pruned_loss=0.0267, over 4960.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03329, over 972767.76 frames.], batch size: 24, lr: 2.32e-04
2022-05-06 14:42:13,628 INFO [train.py:715] (2/8) Epoch 9, batch 25850, loss[loss=0.1799, simple_loss=0.253, pruned_loss=0.05344, over 4810.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03382, over 972533.83 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:42:53,071 INFO [train.py:715] (2/8) Epoch 9, batch 25900, loss[loss=0.1266, simple_loss=0.1908, pruned_loss=0.03124, over 4792.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03464, over 972087.63 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 14:43:32,745 INFO [train.py:715] (2/8) Epoch 9, batch 25950, loss[loss=0.1445, simple_loss=0.2158, pruned_loss=0.03659, over 4964.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2152, pruned_loss=0.03495, over 972723.94 frames.], batch size: 25, lr: 2.32e-04
2022-05-06 14:44:11,977 INFO [train.py:715] (2/8) Epoch 9, batch 26000, loss[loss=0.1326, simple_loss=0.2226, pruned_loss=0.02124, over 4825.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2153, pruned_loss=0.03484, over 972684.82 frames.], batch size: 26, lr: 2.32e-04
2022-05-06 14:44:51,308 INFO [train.py:715] (2/8) Epoch 9, batch 26050, loss[loss=0.1404, simple_loss=0.22, pruned_loss=0.03041, over 4966.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2158, pruned_loss=0.03504, over 973223.09 frames.], batch size: 28, lr: 2.32e-04
2022-05-06 14:45:30,101 INFO [train.py:715] (2/8) Epoch 9, batch 26100, loss[loss=0.1507, simple_loss=0.2159, pruned_loss=0.04274, over 4818.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2152, pruned_loss=0.03482, over 972211.54 frames.], batch size: 27, lr: 2.32e-04
2022-05-06 14:46:09,803 INFO [train.py:715] (2/8) Epoch 9, batch 26150, loss[loss=0.1437, simple_loss=0.2212, pruned_loss=0.03305, over 4833.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2154, pruned_loss=0.03495, over 971310.62 frames.], batch size: 26, lr: 2.32e-04
2022-05-06 14:46:50,051 INFO [train.py:715] (2/8) Epoch 9, batch 26200, loss[loss=0.151, simple_loss=0.2348, pruned_loss=0.0336, over 4921.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03476, over 972197.20 frames.], batch size: 18, lr: 2.32e-04
2022-05-06 14:47:29,916 INFO [train.py:715] (2/8) Epoch 9, batch 26250, loss[loss=0.1531, simple_loss=0.2145, pruned_loss=0.04586, over 4954.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.03434, over 971689.38 frames.], batch size: 35, lr: 2.32e-04
2022-05-06 14:48:09,839 INFO [train.py:715] (2/8) Epoch 9, batch 26300, loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03025, over 4965.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2134, pruned_loss=0.03437, over 971492.42 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 14:48:49,365 INFO [train.py:715] (2/8) Epoch 9, batch 26350, loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03524, over 4865.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2134, pruned_loss=0.03415, over 970931.28 frames.], batch size: 20, lr: 2.32e-04
2022-05-06 14:49:28,727 INFO [train.py:715] (2/8) Epoch 9, batch 26400, loss[loss=0.1382, simple_loss=0.2087, pruned_loss=0.0339, over 4929.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.03441, over 971090.25 frames.], batch size: 23, lr: 2.32e-04
2022-05-06 14:50:07,638 INFO [train.py:715] (2/8) Epoch 9, batch 26450, loss[loss=0.1603, simple_loss=0.2378, pruned_loss=0.04137, over 4691.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03487, over 971276.07 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 14:50:46,957 INFO [train.py:715] (2/8) Epoch 9, batch 26500, loss[loss=0.1344, simple_loss=0.199, pruned_loss=0.03496, over 4750.00 frames.], tot_loss[loss=0.1435, simple_loss=0.216, pruned_loss=0.03547, over 971145.06 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 14:51:26,797 INFO [train.py:715] (2/8) Epoch 9, batch 26550, loss[loss=0.1597, simple_loss=0.2301, pruned_loss=0.04465, over 4884.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2149, pruned_loss=0.0347, over 971489.48 frames.], batch size: 19, lr: 2.32e-04
2022-05-06 14:52:06,134 INFO [train.py:715] (2/8) Epoch 9, batch 26600, loss[loss=0.1799, simple_loss=0.2498, pruned_loss=0.05498, over 4954.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03449, over 971977.90 frames.], batch size: 39, lr: 2.32e-04
2022-05-06 14:52:46,087 INFO [train.py:715] (2/8) Epoch 9, batch 26650, loss[loss=0.1441, simple_loss=0.2148, pruned_loss=0.03671, over 4813.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03421, over 972167.98 frames.], batch size: 13, lr: 2.32e-04
2022-05-06 14:53:25,379 INFO [train.py:715] (2/8) Epoch 9, batch 26700, loss[loss=0.1484, simple_loss=0.2346, pruned_loss=0.03113, over 4795.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2136, pruned_loss=0.03424, over 971669.75 frames.], batch size: 18, lr: 2.32e-04
2022-05-06 14:54:04,745 INFO [train.py:715] (2/8) Epoch 9, batch 26750, loss[loss=0.1832, simple_loss=0.2416, pruned_loss=0.06239, over 4859.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.03438, over 971343.48 frames.], batch size: 32, lr: 2.32e-04
2022-05-06 14:54:43,924 INFO [train.py:715] (2/8) Epoch 9, batch 26800, loss[loss=0.1342, simple_loss=0.2113, pruned_loss=0.02858, over 4847.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.0344, over 971442.82 frames.], batch size: 20, lr: 2.32e-04
2022-05-06 14:55:22,921 INFO [train.py:715] (2/8) Epoch 9, batch 26850, loss[loss=0.1461, simple_loss=0.2302, pruned_loss=0.03103, over 4796.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03461, over 971175.24 frames.], batch size: 18, lr: 2.32e-04
2022-05-06 14:56:02,399 INFO [train.py:715] (2/8) Epoch 9, batch 26900, loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.0305, over 4968.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2143, pruned_loss=0.03432, over 971859.67 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:56:42,229 INFO [train.py:715] (2/8) Epoch 9, batch 26950, loss[loss=0.1398, simple_loss=0.2194, pruned_loss=0.03011, over 4809.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2147, pruned_loss=0.03454, over 972255.54 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 14:57:21,399 INFO [train.py:715] (2/8) Epoch 9, batch 27000, loss[loss=0.1515, simple_loss=0.2258, pruned_loss=0.03863, over 4977.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2157, pruned_loss=0.03541, over 973391.10 frames.], batch size: 28, lr: 2.32e-04
2022-05-06 14:57:21,399 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 14:57:30,963 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.1068, simple_loss=0.1912, pruned_loss=0.01121, over 914524.00 frames.
2022-05-06 14:58:10,508 INFO [train.py:715] (2/8) Epoch 9, batch 27050, loss[loss=0.1805, simple_loss=0.239, pruned_loss=0.06102, over 4916.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03523, over 972639.42 frames.], batch size: 18, lr: 2.32e-04
2022-05-06 14:58:50,066 INFO [train.py:715] (2/8) Epoch 9, batch 27100, loss[loss=0.1448, simple_loss=0.2106, pruned_loss=0.03946, over 4844.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03493, over 972089.38 frames.], batch size: 30, lr: 2.32e-04
2022-05-06 14:59:30,122 INFO [train.py:715] (2/8) Epoch 9, batch 27150, loss[loss=0.1514, simple_loss=0.2302, pruned_loss=0.03627, over 4694.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2144, pruned_loss=0.03462, over 972076.06 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 15:00:09,248 INFO [train.py:715] (2/8) Epoch 9, batch 27200, loss[loss=0.1353, simple_loss=0.2084, pruned_loss=0.03112, over 4890.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.0343, over 971832.30 frames.], batch size: 22, lr: 2.32e-04
2022-05-06 15:00:48,163 INFO [train.py:715] (2/8) Epoch 9, batch 27250, loss[loss=0.1498, simple_loss=0.2211, pruned_loss=0.03922, over 4964.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.03433, over 971705.20 frames.], batch size: 29, lr: 2.32e-04
2022-05-06 15:01:27,382 INFO [train.py:715] (2/8) Epoch 9, batch 27300, loss[loss=0.1704, simple_loss=0.2477, pruned_loss=0.04652, over 4787.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.0339, over 971395.79 frames.], batch size: 17, lr: 2.32e-04
2022-05-06 15:02:06,269 INFO [train.py:715] (2/8) Epoch 9, batch 27350, loss[loss=0.1488, simple_loss=0.2164, pruned_loss=0.04064, over 4981.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03389, over 971695.84 frames.], batch size: 28, lr: 2.32e-04
2022-05-06 15:02:45,336 INFO [train.py:715] (2/8) Epoch 9, batch 27400, loss[loss=0.1835, simple_loss=0.2466, pruned_loss=0.06023, over 4965.00 frames.], tot_loss[loss=0.1407, simple_loss=0.213, pruned_loss=0.0342, over 972169.29 frames.], batch size: 15, lr: 2.32e-04
2022-05-06 15:03:24,467 INFO [train.py:715] (2/8) Epoch 9, batch 27450, loss[loss=0.1659, simple_loss=0.2425, pruned_loss=0.04465, over 4800.00 frames.], tot_loss[loss=0.1419, simple_loss=0.214, pruned_loss=0.03491, over 972023.91 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 15:04:03,431 INFO [train.py:715] (2/8) Epoch 9, batch 27500, loss[loss=0.1636, simple_loss=0.2352, pruned_loss=0.04604, over 4804.00 frames.], tot_loss[loss=0.142, simple_loss=0.2141, pruned_loss=0.03497, over 971926.13 frames.], batch size: 26, lr: 2.32e-04
2022-05-06 15:04:42,461 INFO [train.py:715] (2/8) Epoch 9, batch 27550, loss[loss=0.1581, simple_loss=0.231, pruned_loss=0.04258, over 4941.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2146, pruned_loss=0.03533, over 972251.54 frames.], batch size: 21, lr: 2.32e-04
2022-05-06 15:05:21,387 INFO [train.py:715] (2/8) Epoch 9, batch 27600, loss[loss=0.122, simple_loss=0.1989, pruned_loss=0.02249, over 4753.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2149, pruned_loss=0.0351, over 971435.92 frames.], batch size: 19, lr: 2.32e-04
2022-05-06 15:06:00,165 INFO [train.py:715] (2/8) Epoch 9, batch 27650, loss[loss=0.1225, simple_loss=0.1787, pruned_loss=0.03314, over 4849.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2137, pruned_loss=0.03476, over 972099.95 frames.], batch size: 12, lr: 2.32e-04
2022-05-06 15:06:39,017 INFO [train.py:715] (2/8) Epoch 9, batch 27700, loss[loss=0.1457, simple_loss=0.2211, pruned_loss=0.03517, over 4935.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2138, pruned_loss=0.03495, over 971991.75 frames.], batch size: 23, lr: 2.32e-04
2022-05-06 15:07:18,265 INFO [train.py:715] (2/8) Epoch 9, batch 27750, loss[loss=0.1503, simple_loss=0.2288, pruned_loss=0.03591, over 4945.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03521, over 973440.61 frames.], batch size: 21, lr: 2.31e-04
2022-05-06 15:07:57,617 INFO [train.py:715] (2/8) Epoch 9, batch 27800, loss[loss=0.1497, simple_loss=0.2127, pruned_loss=0.04339, over 4935.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03495, over 973297.62 frames.], batch size: 39, lr: 2.31e-04
2022-05-06 15:08:36,551 INFO [train.py:715] (2/8) Epoch 9, batch 27850, loss[loss=0.1212, simple_loss=0.1952, pruned_loss=0.0236, over 4783.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2148, pruned_loss=0.03519, over 972276.74 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:09:16,415 INFO [train.py:715] (2/8) Epoch 9, batch 27900, loss[loss=0.1457, simple_loss=0.2068, pruned_loss=0.04226, over 4965.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03463, over 972250.33 frames.], batch size: 35, lr: 2.31e-04
2022-05-06 15:09:54,912 INFO [train.py:715] (2/8) Epoch 9, batch 27950, loss[loss=0.1339, simple_loss=0.2111, pruned_loss=0.02837, over 4781.00 frames.], tot_loss[loss=0.1424, simple_loss=0.215, pruned_loss=0.03486, over 971614.69 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:10:34,267 INFO [train.py:715] (2/8) Epoch 9, batch 28000, loss[loss=0.1236, simple_loss=0.2041, pruned_loss=0.02154, over 4893.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2147, pruned_loss=0.0345, over 972073.93 frames.], batch size: 22, lr: 2.31e-04
2022-05-06 15:11:13,573 INFO [train.py:715] (2/8) Epoch 9, batch 28050, loss[loss=0.1365, simple_loss=0.2034, pruned_loss=0.03481, over 4843.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2146, pruned_loss=0.0345, over 972583.30 frames.], batch size: 30, lr: 2.31e-04
2022-05-06 15:11:52,643 INFO [train.py:715] (2/8) Epoch 9, batch 28100, loss[loss=0.1544, simple_loss=0.2298, pruned_loss=0.0395, over 4745.00 frames.], tot_loss[loss=0.1422, simple_loss=0.215, pruned_loss=0.03468, over 971829.79 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:12:31,906 INFO [train.py:715] (2/8) Epoch 9, batch 28150, loss[loss=0.1568, simple_loss=0.2233, pruned_loss=0.04513, over 4868.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2158, pruned_loss=0.0352, over 972953.24 frames.], batch size: 32, lr: 2.31e-04
2022-05-06 15:13:10,823 INFO [train.py:715] (2/8) Epoch 9, batch 28200, loss[loss=0.1256, simple_loss=0.2049, pruned_loss=0.02317, over 4818.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2151, pruned_loss=0.03493, over 972597.93 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:13:50,249 INFO [train.py:715] (2/8) Epoch 9, batch 28250, loss[loss=0.1341, simple_loss=0.2089, pruned_loss=0.02969, over 4787.00 frames.], tot_loss[loss=0.1424, simple_loss=0.215, pruned_loss=0.03496, over 971706.25 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:14:28,527 INFO [train.py:715] (2/8) Epoch 9, batch 28300, loss[loss=0.1341, simple_loss=0.2036, pruned_loss=0.03231, over 4895.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2146, pruned_loss=0.03499, over 971264.71 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:15:07,477 INFO [train.py:715] (2/8) Epoch 9, batch 28350, loss[loss=0.1498, simple_loss=0.2238, pruned_loss=0.03785, over 4781.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2147, pruned_loss=0.0351, over 971109.88 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:15:46,872 INFO [train.py:715] (2/8) Epoch 9, batch 28400, loss[loss=0.1573, simple_loss=0.2199, pruned_loss=0.04732, over 4823.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.03526, over 971536.47 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:16:25,950 INFO [train.py:715] (2/8) Epoch 9, batch 28450, loss[loss=0.1253, simple_loss=0.2023, pruned_loss=0.02417, over 4871.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2146, pruned_loss=0.03477, over 971652.66 frames.], batch size: 22, lr: 2.31e-04
2022-05-06 15:17:04,387 INFO [train.py:715] (2/8) Epoch 9, batch 28500, loss[loss=0.1277, simple_loss=0.2069, pruned_loss=0.02432, over 4878.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03494, over 972005.39 frames.], batch size: 16, lr: 2.31e-04
2022-05-06 15:17:43,522 INFO [train.py:715] (2/8) Epoch 9, batch 28550, loss[loss=0.1309, simple_loss=0.2014, pruned_loss=0.03025, over 4860.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2153, pruned_loss=0.03494, over 971381.77 frames.], batch size: 30, lr: 2.31e-04
2022-05-06 15:18:22,912 INFO [train.py:715] (2/8) Epoch 9, batch 28600, loss[loss=0.1291, simple_loss=0.2054, pruned_loss=0.02637, over 4812.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.03433, over 971590.45 frames.], batch size: 25, lr: 2.31e-04
2022-05-06 15:19:01,329 INFO [train.py:715] (2/8) Epoch 9, batch 28650, loss[loss=0.1443, simple_loss=0.2228, pruned_loss=0.03292, over 4918.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03452, over 972056.24 frames.], batch size: 39, lr: 2.31e-04
2022-05-06 15:19:40,173 INFO [train.py:715] (2/8) Epoch 9, batch 28700, loss[loss=0.1533, simple_loss=0.2229, pruned_loss=0.04184, over 4779.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03465, over 972364.58 frames.], batch size: 17, lr: 2.31e-04
2022-05-06 15:20:19,621 INFO [train.py:715] (2/8) Epoch 9, batch 28750, loss[loss=0.1298, simple_loss=0.2136, pruned_loss=0.02293, over 4885.00 frames.], tot_loss[loss=0.142, simple_loss=0.2151, pruned_loss=0.03444, over 972471.64 frames.], batch size: 16, lr: 2.31e-04
2022-05-06 15:20:58,322 INFO [train.py:715] (2/8) Epoch 9, batch 28800, loss[loss=0.1414, simple_loss=0.2108, pruned_loss=0.03599, over 4776.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2143, pruned_loss=0.03414, over 971984.24 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:21:36,722 INFO [train.py:715] (2/8) Epoch 9, batch 28850, loss[loss=0.1284, simple_loss=0.205, pruned_loss=0.02584, over 4798.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2145, pruned_loss=0.03419, over 972041.02 frames.], batch size: 24, lr: 2.31e-04
2022-05-06 15:22:16,102 INFO [train.py:715] (2/8) Epoch 9, batch 28900, loss[loss=0.1517, simple_loss=0.2261, pruned_loss=0.0387, over 4923.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2152, pruned_loss=0.03469, over 972068.50 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:22:55,366 INFO [train.py:715] (2/8) Epoch 9, batch 28950, loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03172, over 4942.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03406, over 972495.86 frames.], batch size: 21, lr: 2.31e-04
2022-05-06 15:23:33,684 INFO [train.py:715] (2/8) Epoch 9, batch 29000, loss[loss=0.1213, simple_loss=0.1993, pruned_loss=0.02165, over 4769.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.03408, over 972239.39 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:24:12,157 INFO [train.py:715] (2/8) Epoch 9, batch 29050, loss[loss=0.1217, simple_loss=0.1929, pruned_loss=0.02518, over 4889.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03409, over 972039.92 frames.], batch size: 22, lr: 2.31e-04
2022-05-06 15:24:51,099 INFO [train.py:715] (2/8) Epoch 9, batch 29100, loss[loss=0.1258, simple_loss=0.2053, pruned_loss=0.02309, over 4853.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03412, over 971077.15 frames.], batch size: 30, lr: 2.31e-04
2022-05-06 15:25:30,248 INFO [train.py:715] (2/8) Epoch 9, batch 29150, loss[loss=0.1465, simple_loss=0.2202, pruned_loss=0.03644, over 4751.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2134, pruned_loss=0.03362, over 972310.59 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:26:09,095 INFO [train.py:715] (2/8) Epoch 9, batch 29200, loss[loss=0.1325, simple_loss=0.2088, pruned_loss=0.02811, over 4969.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2144, pruned_loss=0.03435, over 971934.08 frames.], batch size: 24, lr: 2.31e-04
2022-05-06 15:26:48,460 INFO [train.py:715] (2/8) Epoch 9, batch 29250, loss[loss=0.122, simple_loss=0.2058, pruned_loss=0.01903, over 4759.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2133, pruned_loss=0.03417, over 971720.73 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:27:27,199 INFO [train.py:715] (2/8) Epoch 9, batch 29300, loss[loss=0.1444, simple_loss=0.2197, pruned_loss=0.03455, over 4889.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2125, pruned_loss=0.03366, over 971413.95 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:28:06,268 INFO [train.py:715] (2/8) Epoch 9, batch 29350, loss[loss=0.1426, simple_loss=0.2164, pruned_loss=0.03437, over 4968.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2131, pruned_loss=0.03409, over 972144.42 frames.], batch size: 24, lr: 2.31e-04
2022-05-06 15:28:45,211 INFO [train.py:715] (2/8) Epoch 9, batch 29400, loss[loss=0.1457, simple_loss=0.2107, pruned_loss=0.04038, over 4821.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2136, pruned_loss=0.03432, over 972068.10 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:29:23,948 INFO [train.py:715] (2/8) Epoch 9, batch 29450, loss[loss=0.1638, simple_loss=0.2325, pruned_loss=0.04757, over 4783.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2137, pruned_loss=0.03458, over 972560.54 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:30:02,405 INFO [train.py:715] (2/8) Epoch 9, batch 29500, loss[loss=0.1109, simple_loss=0.1822, pruned_loss=0.01983, over 4831.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2138, pruned_loss=0.03415, over 972896.78 frames.], batch size: 13, lr: 2.31e-04
2022-05-06 15:30:41,338 INFO [train.py:715] (2/8) Epoch 9, batch 29550, loss[loss=0.1506, simple_loss=0.2311, pruned_loss=0.03502, over 4910.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03461, over 972499.38 frames.], batch size: 29, lr: 2.31e-04
2022-05-06 15:31:20,279 INFO [train.py:715] (2/8) Epoch 9, batch 29600, loss[loss=0.1404, simple_loss=0.222, pruned_loss=0.02939, over 4929.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03459, over 972115.35 frames.], batch size: 29, lr: 2.31e-04
2022-05-06 15:31:59,540 INFO [train.py:715] (2/8) Epoch 9, batch 29650, loss[loss=0.1515, simple_loss=0.2291, pruned_loss=0.03698, over 4893.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2142, pruned_loss=0.0346, over 972977.54 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:32:39,151 INFO [train.py:715] (2/8) Epoch 9, batch 29700, loss[loss=0.1364, simple_loss=0.2205, pruned_loss=0.02613, over 4950.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03415, over 972549.75 frames.], batch size: 24, lr: 2.31e-04
2022-05-06 15:33:17,089 INFO [train.py:715] (2/8) Epoch 9, batch 29750, loss[loss=0.1284, simple_loss=0.1926, pruned_loss=0.03205, over 4922.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2137, pruned_loss=0.03408, over 971830.05 frames.], batch size: 17, lr: 2.31e-04
2022-05-06 15:33:55,772 INFO [train.py:715] (2/8) Epoch 9, batch 29800, loss[loss=0.1642, simple_loss=0.2229, pruned_loss=0.0528, over 4780.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2147, pruned_loss=0.03416, over 972235.12 frames.], batch size: 12, lr: 2.31e-04
2022-05-06 15:34:34,893 INFO [train.py:715] (2/8) Epoch 9, batch 29850, loss[loss=0.1493, simple_loss=0.2238, pruned_loss=0.03733, over 4962.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2157, pruned_loss=0.0349, over 971427.85 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:35:13,059 INFO [train.py:715] (2/8) Epoch 9, batch 29900, loss[loss=0.1418, simple_loss=0.2158, pruned_loss=0.03394, over 4941.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2151, pruned_loss=0.03471, over 971999.06 frames.], batch size: 35, lr: 2.31e-04
2022-05-06 15:35:52,535 INFO [train.py:715] (2/8) Epoch 9, batch 29950, loss[loss=0.1372, simple_loss=0.2085, pruned_loss=0.03299, over 4755.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2154, pruned_loss=0.03466, over 971353.05 frames.], batch size: 19, lr: 2.31e-04
2022-05-06 15:36:31,406 INFO [train.py:715] (2/8) Epoch 9, batch 30000, loss[loss=0.1713, simple_loss=0.2492, pruned_loss=0.04664, over 4698.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2152, pruned_loss=0.03482, over 971859.50 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:36:31,407 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 15:36:40,919 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.1068, simple_loss=0.1911, pruned_loss=0.01124, over 914524.00 frames.
2022-05-06 15:37:20,161 INFO [train.py:715] (2/8) Epoch 9, batch 30050, loss[loss=0.1404, simple_loss=0.2103, pruned_loss=0.03524, over 4929.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2162, pruned_loss=0.03514, over 971354.31 frames.], batch size: 23, lr: 2.31e-04
2022-05-06 15:37:58,805 INFO [train.py:715] (2/8) Epoch 9, batch 30100, loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03371, over 4934.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2149, pruned_loss=0.03474, over 972094.19 frames.], batch size: 23, lr: 2.31e-04
2022-05-06 15:38:38,127 INFO [train.py:715] (2/8) Epoch 9, batch 30150, loss[loss=0.1786, simple_loss=0.258, pruned_loss=0.04964, over 4983.00 frames.], tot_loss[loss=0.142, simple_loss=0.2149, pruned_loss=0.03461, over 973216.54 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:39:17,504 INFO [train.py:715] (2/8) Epoch 9, batch 30200, loss[loss=0.1622, simple_loss=0.2415, pruned_loss=0.04151, over 4836.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03483, over 972570.84 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:39:56,689 INFO [train.py:715] (2/8) Epoch 9, batch 30250, loss[loss=0.1139, simple_loss=0.1931, pruned_loss=0.01739, over 4841.00 frames.], tot_loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.0347, over 972176.32 frames.], batch size: 30, lr: 2.31e-04
2022-05-06 15:40:35,248 INFO [train.py:715] (2/8) Epoch 9, batch 30300, loss[loss=0.1391, simple_loss=0.2144, pruned_loss=0.03189, over 4762.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.03464, over 973015.84 frames.], batch size: 16, lr: 2.31e-04
2022-05-06 15:41:14,057 INFO [train.py:715] (2/8) Epoch 9, batch 30350, loss[loss=0.1448, simple_loss=0.2222, pruned_loss=0.03372, over 4763.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2137, pruned_loss=0.03425, over 972786.91 frames.], batch size: 17, lr: 2.31e-04
2022-05-06 15:41:53,486 INFO [train.py:715] (2/8) Epoch 9, batch 30400, loss[loss=0.1403, simple_loss=0.2104, pruned_loss=0.03512, over 4772.00 frames.], tot_loss[loss=0.1415, simple_loss=0.214, pruned_loss=0.03447, over 972822.35 frames.], batch size: 14, lr: 2.31e-04
2022-05-06 15:42:32,294 INFO [train.py:715] (2/8) Epoch 9, batch 30450, loss[loss=0.1635, simple_loss=0.2224, pruned_loss=0.05235, over 4869.00 frames.], tot_loss[loss=0.1409, simple_loss=0.214, pruned_loss=0.03392, over 971860.76 frames.], batch size: 32, lr: 2.31e-04
2022-05-06 15:43:10,917 INFO [train.py:715] (2/8) Epoch 9, batch 30500, loss[loss=0.1125, simple_loss=0.1863, pruned_loss=0.01937, over 4860.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03311, over 972264.56 frames.], batch size: 13, lr: 2.31e-04
2022-05-06 15:43:49,986 INFO [train.py:715] (2/8) Epoch 9, batch 30550, loss[loss=0.1365, simple_loss=0.2071, pruned_loss=0.03297, over 4974.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03382, over 971903.30 frames.], batch size: 15, lr: 2.31e-04
2022-05-06 15:44:28,846 INFO [train.py:715] (2/8) Epoch 9, batch 30600, loss[loss=0.1187, simple_loss=0.1876, pruned_loss=0.02489, over 4774.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03313, over 971130.52 frames.], batch size: 18, lr: 2.31e-04
2022-05-06 15:45:06,882 INFO [train.py:715] (2/8) Epoch 9, batch 30650, loss[loss=0.1286, simple_loss=0.1977, pruned_loss=0.02973, over 4781.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03363, over 970826.75 frames.], batch size: 17, lr: 2.31e-04
2022-05-06 15:45:45,884 INFO [train.py:715] (2/8) Epoch 9, batch 30700, loss[loss=0.1441, simple_loss=0.2182, pruned_loss=0.03495, over 4889.00 frames.], tot_loss[loss=0.1402, simple_loss=0.213, pruned_loss=0.03373, over 970623.25 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 15:46:27,572 INFO [train.py:715] (2/8) Epoch 9, batch 30750, loss[loss=0.1332, simple_loss=0.2067, pruned_loss=0.02985, over 4956.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2125, pruned_loss=0.03385, over 971508.01 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 15:47:06,257 INFO [train.py:715] (2/8) Epoch 9, batch 30800, loss[loss=0.1302, simple_loss=0.2119, pruned_loss=0.02424, over 4951.00 frames.], tot_loss[loss=0.14, simple_loss=0.2125, pruned_loss=0.03374, over 972039.66 frames.], batch size: 21, lr: 2.30e-04
2022-05-06 15:47:44,604 INFO [train.py:715] (2/8) Epoch 9, batch 30850, loss[loss=0.1369, simple_loss=0.2035, pruned_loss=0.03516, over 4818.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2126, pruned_loss=0.03383, over 972052.05 frames.], batch size: 27, lr: 2.30e-04
2022-05-06 15:48:23,857 INFO [train.py:715] (2/8) Epoch 9, batch 30900, loss[loss=0.1307, simple_loss=0.203, pruned_loss=0.02927, over 4799.00 frames.], tot_loss[loss=0.141, simple_loss=0.2134, pruned_loss=0.03426, over 971583.34 frames.], batch size: 21, lr: 2.30e-04
2022-05-06 15:49:03,045 INFO [train.py:715] (2/8) Epoch 9, batch 30950, loss[loss=0.1471, simple_loss=0.2194, pruned_loss=0.03744, over 4753.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.03438, over 971694.26 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 15:49:41,534 INFO [train.py:715] (2/8) Epoch 9, batch 31000, loss[loss=0.1317, simple_loss=0.1975, pruned_loss=0.03295, over 4774.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2129, pruned_loss=0.03418, over 971919.45 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 15:50:20,507 INFO [train.py:715] (2/8) Epoch 9, batch 31050, loss[loss=0.149, simple_loss=0.2265, pruned_loss=0.03579, over 4762.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2127, pruned_loss=0.03437, over 971504.46 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 15:50:59,766 INFO [train.py:715] (2/8) Epoch 9, batch 31100, loss[loss=0.1511, simple_loss=0.2218, pruned_loss=0.04019, over 4741.00 frames.], tot_loss[loss=0.141, simple_loss=0.2133, pruned_loss=0.03428, over 971568.30 frames.], batch size: 12, lr: 2.30e-04
2022-05-06 15:51:38,436 INFO [train.py:715] (2/8) Epoch 9, batch 31150, loss[loss=0.1233, simple_loss=0.1986, pruned_loss=0.02403, over 4983.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03444, over 973276.39 frames.], batch size: 25, lr: 2.30e-04
2022-05-06 15:52:17,019 INFO [train.py:715] (2/8) Epoch 9, batch 31200, loss[loss=0.1272, simple_loss=0.201, pruned_loss=0.0267, over 4740.00 frames.], tot_loss[loss=0.142, simple_loss=0.2143, pruned_loss=0.03483, over 973630.72 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 15:52:56,548 INFO [train.py:715] (2/8) Epoch 9, batch 31250, loss[loss=0.1259, simple_loss=0.1972, pruned_loss=0.02728, over 4734.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2155, pruned_loss=0.03538, over 973139.38 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 15:53:36,000 INFO [train.py:715] (2/8) Epoch 9, batch 31300, loss[loss=0.1325, simple_loss=0.2096, pruned_loss=0.02766, over 4829.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2154, pruned_loss=0.03554, over 973502.42 frames.], batch size: 27, lr: 2.30e-04
2022-05-06 15:54:14,967 INFO [train.py:715] (2/8) Epoch 9, batch 31350, loss[loss=0.1422, simple_loss=0.2168, pruned_loss=0.03377, over 4817.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2148, pruned_loss=0.03528, over 973575.42 frames.], batch size: 25, lr: 2.30e-04
2022-05-06 15:54:53,758 INFO [train.py:715] (2/8) Epoch 9, batch 31400, loss[loss=0.1446, simple_loss=0.2222, pruned_loss=0.03351, over 4980.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2147, pruned_loss=0.03533, over 973480.40 frames.], batch size: 26, lr: 2.30e-04
2022-05-06 15:55:32,700 INFO [train.py:715] (2/8) Epoch 9, batch 31450, loss[loss=0.1494, simple_loss=0.2143, pruned_loss=0.04227, over 4817.00 frames.], tot_loss[loss=0.1428, simple_loss=0.215, pruned_loss=0.03532, over 973667.16 frames.], batch size: 27, lr: 2.30e-04
2022-05-06 15:56:11,771 INFO [train.py:715] (2/8) Epoch 9, batch 31500, loss[loss=0.1374, simple_loss=0.2084, pruned_loss=0.03317, over 4918.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2149, pruned_loss=0.03485, over 973607.86 frames.], batch size: 29, lr: 2.30e-04
2022-05-06 15:56:50,182 INFO [train.py:715] (2/8) Epoch 9, batch 31550, loss[loss=0.1361, simple_loss=0.2081, pruned_loss=0.0321, over 4780.00 frames.], tot_loss[loss=0.1423, simple_loss=0.215, pruned_loss=0.03483, over 973780.08 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 15:57:29,718 INFO [train.py:715] (2/8) Epoch 9, batch 31600, loss[loss=0.126, simple_loss=0.1935, pruned_loss=0.02924, over 4967.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2145, pruned_loss=0.03462, over 973338.66 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 15:58:09,738 INFO [train.py:715] (2/8) Epoch 9, batch 31650, loss[loss=0.1237, simple_loss=0.1971, pruned_loss=0.02508, over 4744.00 frames.], tot_loss[loss=0.1417, simple_loss=0.214, pruned_loss=0.03468, over 972557.67 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 15:58:48,440 INFO [train.py:715] (2/8) Epoch 9, batch 31700, loss[loss=0.1152, simple_loss=0.1929, pruned_loss=0.01878, over 4822.00 frames.], tot_loss[loss=0.142, simple_loss=0.2145, pruned_loss=0.03478, over 972574.98 frames.], batch size: 25, lr: 2.30e-04
2022-05-06 15:59:27,454 INFO [train.py:715] (2/8) Epoch 9, batch 31750, loss[loss=0.142, simple_loss=0.2125, pruned_loss=0.03578, over 4782.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2147, pruned_loss=0.03483, over 973310.85 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 16:00:06,076 INFO [train.py:715] (2/8) Epoch 9, batch 31800, loss[loss=0.1409, simple_loss=0.2077, pruned_loss=0.03704, over 4966.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03488, over 972454.96 frames.], batch size: 39, lr: 2.30e-04
2022-05-06 16:00:45,144 INFO [train.py:715] (2/8) Epoch 9, batch 31850, loss[loss=0.1683, simple_loss=0.2297, pruned_loss=0.05346, over 4967.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2139, pruned_loss=0.03458, over 973164.32 frames.], batch size: 39, lr: 2.30e-04
2022-05-06 16:01:23,639 INFO [train.py:715] (2/8) Epoch 9, batch 31900, loss[loss=0.1461, simple_loss=0.2186, pruned_loss=0.03678, over 4913.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2144, pruned_loss=0.03476, over 973562.52 frames.], batch size: 29, lr: 2.30e-04
2022-05-06 16:02:02,945 INFO [train.py:715] (2/8) Epoch 9, batch 31950, loss[loss=0.1328, simple_loss=0.2056, pruned_loss=0.02996, over 4968.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03401, over 973148.72 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:02:42,216 INFO [train.py:715] (2/8) Epoch 9, batch 32000, loss[loss=0.1465, simple_loss=0.2253, pruned_loss=0.03383, over 4976.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.03367, over 973414.70 frames.], batch size: 35, lr: 2.30e-04
2022-05-06 16:03:20,780 INFO [train.py:715] (2/8) Epoch 9, batch 32050, loss[loss=0.1342, simple_loss=0.2048, pruned_loss=0.03179, over 4987.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2152, pruned_loss=0.03422, over 973339.54 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:03:59,265 INFO [train.py:715] (2/8) Epoch 9, batch 32100, loss[loss=0.1378, simple_loss=0.2021, pruned_loss=0.03675, over 4830.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2156, pruned_loss=0.03473, over 972436.63 frames.], batch size: 30, lr: 2.30e-04
2022-05-06 16:04:38,261 INFO [train.py:715] (2/8) Epoch 9, batch 32150, loss[loss=0.1152, simple_loss=0.19, pruned_loss=0.02026, over 4793.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2146, pruned_loss=0.03434, over 972553.09 frames.], batch size: 24, lr: 2.30e-04
2022-05-06 16:05:17,703 INFO [train.py:715] (2/8) Epoch 9, batch 32200, loss[loss=0.1268, simple_loss=0.2045, pruned_loss=0.02452, over 4883.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2138, pruned_loss=0.034, over 972858.35 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:05:55,463 INFO [train.py:715] (2/8) Epoch 9, batch 32250, loss[loss=0.1366, simple_loss=0.2011, pruned_loss=0.03604, over 4837.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.03468, over 972121.18 frames.], batch size: 30, lr: 2.30e-04
2022-05-06 16:06:34,666 INFO [train.py:715] (2/8) Epoch 9, batch 32300, loss[loss=0.1483, simple_loss=0.2183, pruned_loss=0.03911, over 4704.00 frames.], tot_loss[loss=0.141, simple_loss=0.2134, pruned_loss=0.03434, over 972864.27 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:07:13,841 INFO [train.py:715] (2/8) Epoch 9, batch 32350, loss[loss=0.1503, simple_loss=0.2277, pruned_loss=0.03642, over 4907.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2138, pruned_loss=0.03423, over 973172.72 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:07:52,333 INFO [train.py:715] (2/8) Epoch 9, batch 32400, loss[loss=0.1351, simple_loss=0.2117, pruned_loss=0.02925, over 4870.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2152, pruned_loss=0.03499, over 973283.31 frames.], batch size: 32, lr: 2.30e-04
2022-05-06 16:08:31,416 INFO [train.py:715] (2/8) Epoch 9, batch 32450, loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02844, over 4966.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2152, pruned_loss=0.03516, over 973336.59 frames.], batch size: 24, lr: 2.30e-04
2022-05-06 16:09:10,515 INFO [train.py:715] (2/8) Epoch 9, batch 32500, loss[loss=0.1306, simple_loss=0.2032, pruned_loss=0.02899, over 4764.00 frames.], tot_loss[loss=0.142, simple_loss=0.2147, pruned_loss=0.03468, over 972718.20 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 16:09:49,360 INFO [train.py:715] (2/8) Epoch 9, batch 32550, loss[loss=0.1375, simple_loss=0.2044, pruned_loss=0.03536, over 4866.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2137, pruned_loss=0.03446, over 973382.06 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 16:10:27,862 INFO [train.py:715] (2/8) Epoch 9, batch 32600, loss[loss=0.1242, simple_loss=0.1991, pruned_loss=0.02464, over 4859.00 frames.], tot_loss[loss=0.141, simple_loss=0.2134, pruned_loss=0.03432, over 972723.27 frames.], batch size: 30, lr: 2.30e-04
2022-05-06 16:11:06,893 INFO [train.py:715] (2/8) Epoch 9, batch 32650, loss[loss=0.1496, simple_loss=0.2212, pruned_loss=0.03903, over 4796.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2128, pruned_loss=0.03376, over 972244.09 frames.], batch size: 24, lr: 2.30e-04
2022-05-06 16:11:45,870 INFO [train.py:715] (2/8) Epoch 9, batch 32700, loss[loss=0.133, simple_loss=0.2085, pruned_loss=0.02875, over 4972.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2132, pruned_loss=0.03418, over 972121.51 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:12:24,795 INFO [train.py:715] (2/8) Epoch 9, batch 32750, loss[loss=0.144, simple_loss=0.2097, pruned_loss=0.03914, over 4794.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2127, pruned_loss=0.0338, over 972094.61 frames.], batch size: 21, lr: 2.30e-04
2022-05-06 16:13:03,522 INFO [train.py:715] (2/8) Epoch 9, batch 32800, loss[loss=0.1406, simple_loss=0.2089, pruned_loss=0.03618, over 4976.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2127, pruned_loss=0.03388, over 972806.68 frames.], batch size: 28, lr: 2.30e-04
2022-05-06 16:13:42,566 INFO [train.py:715] (2/8) Epoch 9, batch 32850, loss[loss=0.1369, simple_loss=0.2073, pruned_loss=0.0332, over 4874.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2122, pruned_loss=0.03395, over 973184.47 frames.], batch size: 22, lr: 2.30e-04
2022-05-06 16:14:21,302 INFO [train.py:715] (2/8) Epoch 9, batch 32900, loss[loss=0.1567, simple_loss=0.2298, pruned_loss=0.04183, over 4815.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2133, pruned_loss=0.03458, over 973004.79 frames.], batch size: 17, lr: 2.30e-04
2022-05-06 16:14:59,682 INFO [train.py:715] (2/8) Epoch 9, batch 32950, loss[loss=0.1324, simple_loss=0.2019, pruned_loss=0.03142, over 4762.00 frames.], tot_loss[loss=0.1413, simple_loss=0.213, pruned_loss=0.03479, over 971956.20 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 16:15:38,639 INFO [train.py:715] (2/8) Epoch 9, batch 33000, loss[loss=0.1572, simple_loss=0.2296, pruned_loss=0.04237, over 4759.00 frames.], tot_loss[loss=0.142, simple_loss=0.214, pruned_loss=0.03498, over 971360.88 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:15:38,640 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 16:15:48,000 INFO [train.py:742] (2/8) Epoch 9, validation: loss=0.1068, simple_loss=0.1913, pruned_loss=0.01119, over 914524.00 frames.
2022-05-06 16:16:27,263 INFO [train.py:715] (2/8) Epoch 9, batch 33050, loss[loss=0.136, simple_loss=0.2069, pruned_loss=0.03258, over 4971.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2131, pruned_loss=0.03457, over 972206.89 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 16:17:06,455 INFO [train.py:715] (2/8) Epoch 9, batch 33100, loss[loss=0.1594, simple_loss=0.2284, pruned_loss=0.04524, over 4789.00 frames.], tot_loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03435, over 972730.81 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 16:17:45,626 INFO [train.py:715] (2/8) Epoch 9, batch 33150, loss[loss=0.1411, simple_loss=0.2107, pruned_loss=0.03572, over 4789.00 frames.], tot_loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03435, over 972572.31 frames.], batch size: 14, lr: 2.30e-04
2022-05-06 16:18:25,451 INFO [train.py:715] (2/8) Epoch 9, batch 33200, loss[loss=0.1597, simple_loss=0.2245, pruned_loss=0.04743, over 4876.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2129, pruned_loss=0.03415, over 972597.41 frames.], batch size: 39, lr: 2.30e-04
2022-05-06 16:19:04,995 INFO [train.py:715] (2/8) Epoch 9, batch 33250, loss[loss=0.1344, simple_loss=0.2113, pruned_loss=0.02874, over 4874.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03384, over 972359.89 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:19:44,052 INFO [train.py:715] (2/8) Epoch 9, batch 33300, loss[loss=0.1323, simple_loss=0.2042, pruned_loss=0.03021, over 4690.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03354, over 972169.26 frames.], batch size: 15, lr: 2.30e-04
2022-05-06 16:20:23,551 INFO [train.py:715] (2/8) Epoch 9, batch 33350, loss[loss=0.117, simple_loss=0.1935, pruned_loss=0.02029, over 4849.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03378, over 972407.81 frames.], batch size: 30, lr: 2.30e-04
2022-05-06 16:21:03,298 INFO [train.py:715] (2/8) Epoch 9, batch 33400, loss[loss=0.1266, simple_loss=0.1965, pruned_loss=0.02831, over 4802.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03367, over 972655.79 frames.], batch size: 12, lr: 2.30e-04
2022-05-06 16:21:43,053 INFO [train.py:715] (2/8) Epoch 9, batch 33450, loss[loss=0.1112, simple_loss=0.1841, pruned_loss=0.01914, over 4816.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2134, pruned_loss=0.03359, over 972381.16 frames.], batch size: 27, lr: 2.30e-04
2022-05-06 16:22:22,076 INFO [train.py:715] (2/8) Epoch 9, batch 33500, loss[loss=0.1529, simple_loss=0.2236, pruned_loss=0.04108, over 4887.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2141, pruned_loss=0.03375, over 971645.34 frames.], batch size: 19, lr: 2.30e-04
2022-05-06 16:23:00,825 INFO [train.py:715] (2/8) Epoch 9, batch 33550, loss[loss=0.1631, simple_loss=0.2433, pruned_loss=0.04144, over 4930.00 frames.], tot_loss[loss=0.1407, simple_loss=0.214, pruned_loss=0.03372, over 970903.43 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 16:23:40,547 INFO [train.py:715] (2/8) Epoch 9, batch 33600, loss[loss=0.1316, simple_loss=0.2132, pruned_loss=0.025, over 4786.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2141, pruned_loss=0.0339, over 971731.74 frames.], batch size: 18, lr: 2.30e-04
2022-05-06 16:24:19,321 INFO [train.py:715] (2/8) Epoch 9, batch 33650, loss[loss=0.1342, simple_loss=0.2054, pruned_loss=0.0315, over 4889.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03415, over 973058.18 frames.], batch size: 16, lr: 2.30e-04
2022-05-06 16:24:58,233 INFO [train.py:715] (2/8) Epoch 9, batch 33700, loss[loss=0.1383, simple_loss=0.2141, pruned_loss=0.03125, over 4841.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2129, pruned_loss=0.03339, over 972917.52 frames.], batch size: 34, lr: 2.29e-04
2022-05-06 16:25:37,409 INFO [train.py:715] (2/8) Epoch 9, batch 33750, loss[loss=0.1438, simple_loss=0.2163, pruned_loss=0.03569, over 4867.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2133, pruned_loss=0.03329, over 973594.41 frames.], batch size: 20, lr: 2.29e-04
2022-05-06 16:26:16,198 INFO [train.py:715] (2/8) Epoch 9, batch 33800, loss[loss=0.1433, simple_loss=0.2201, pruned_loss=0.03325, over 4976.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03345, over 972975.00 frames.], batch size: 28, lr: 2.29e-04
2022-05-06 16:26:54,913 INFO [train.py:715] (2/8) Epoch 9, batch 33850, loss[loss=0.1193, simple_loss=0.194, pruned_loss=0.02228, over 4807.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03323, over 972536.64 frames.], batch size: 21, lr: 2.29e-04
2022-05-06 16:27:33,755 INFO [train.py:715] (2/8) Epoch 9, batch 33900, loss[loss=0.1282, simple_loss=0.2047, pruned_loss=0.02582, over 4884.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03319, over 973179.80 frames.], batch size: 16, lr: 2.29e-04
2022-05-06 16:28:13,483 INFO [train.py:715] (2/8) Epoch 9, batch 33950, loss[loss=0.103, simple_loss=0.1801, pruned_loss=0.01292, over 4947.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.03291, over 972312.96 frames.], batch size: 24, lr: 2.29e-04
2022-05-06 16:28:52,281 INFO [train.py:715] (2/8) Epoch 9, batch 34000, loss[loss=0.1479, simple_loss=0.2182, pruned_loss=0.03879, over 4772.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03321, over 972528.85 frames.], batch size: 14, lr: 2.29e-04
2022-05-06 16:29:31,512 INFO [train.py:715] (2/8) Epoch 9, batch 34050, loss[loss=0.1449, simple_loss=0.2186, pruned_loss=0.03565, over 4981.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03344, over 972138.98 frames.], batch size: 40, lr: 2.29e-04
2022-05-06 16:30:09,976 INFO [train.py:715] (2/8) Epoch 9, batch 34100, loss[loss=0.1433, simple_loss=0.2049, pruned_loss=0.0409, over 4832.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2113, pruned_loss=0.03301, over 971791.84 frames.], batch size: 13, lr: 2.29e-04
2022-05-06 16:30:49,072 INFO [train.py:715] (2/8) Epoch 9, batch 34150, loss[loss=0.1347, simple_loss=0.205, pruned_loss=0.03226, over 4846.00 frames.], tot_loss[loss=0.1394, simple_loss=0.212, pruned_loss=0.03336, over 971652.99 frames.], batch size: 30, lr: 2.29e-04
2022-05-06 16:31:27,541 INFO [train.py:715] (2/8) Epoch 9, batch 34200, loss[loss=0.1394, simple_loss=0.2084, pruned_loss=0.03522, over 4945.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03374, over 972049.72 frames.], batch size: 15, lr: 2.29e-04
2022-05-06 16:32:05,773 INFO [train.py:715] (2/8) Epoch 9, batch 34250, loss[loss=0.133, simple_loss=0.2134, pruned_loss=0.02625, over 4941.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03372, over 972383.70 frames.], batch size: 24, lr: 2.29e-04
2022-05-06 16:32:45,092 INFO [train.py:715] (2/8) Epoch 9, batch 34300, loss[loss=0.1303, simple_loss=0.2161, pruned_loss=0.02229, over 4829.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03428, over 972668.75 frames.], batch size: 26, lr: 2.29e-04
2022-05-06 16:33:23,851 INFO [train.py:715] (2/8) Epoch 9, batch 34350, loss[loss=0.1094, simple_loss=0.1896, pruned_loss=0.01457, over 4848.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03416, over 971986.44 frames.], batch size: 13, lr: 2.29e-04
2022-05-06 16:34:02,524 INFO [train.py:715] (2/8) Epoch 9, batch 34400, loss[loss=0.1384, simple_loss=0.2096, pruned_loss=0.03365, over 4988.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03413, over 971946.83 frames.], batch size: 14, lr: 2.29e-04
2022-05-06 16:34:41,411 INFO [train.py:715] (2/8) Epoch 9, batch 34450, loss[loss=0.1192, simple_loss=0.1901, pruned_loss=0.02415, over 4932.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03386, over 971699.50 frames.], batch size: 23, lr: 2.29e-04
2022-05-06 16:35:20,341 INFO [train.py:715] (2/8) Epoch 9, batch 34500, loss[loss=0.144, simple_loss=0.2198, pruned_loss=0.03414, over 4953.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2144, pruned_loss=0.034, over 971873.87 frames.], batch size: 14, lr: 2.29e-04
2022-05-06 16:35:59,376 INFO [train.py:715] (2/8) Epoch 9, batch 34550, loss[loss=0.1784, simple_loss=0.2377, pruned_loss=0.05952, over 4972.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2154, pruned_loss=0.03411, over 972683.75 frames.], batch size: 15, lr: 2.29e-04
2022-05-06 16:36:38,008 INFO [train.py:715] (2/8) Epoch 9, batch 34600, loss[loss=0.1231, simple_loss=0.1903, pruned_loss=0.02794, over 4973.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2143, pruned_loss=0.03397, over 972616.42 frames.], batch size: 15, lr: 2.29e-04
2022-05-06 16:37:17,110 INFO [train.py:715] (2/8) Epoch 9, batch 34650, loss[loss=0.1171, simple_loss=0.1966, pruned_loss=0.01877, over 4857.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2131, pruned_loss=0.03359, over 972687.79 frames.], batch size: 20, lr: 2.29e-04
2022-05-06 16:37:56,493 INFO [train.py:715] (2/8) Epoch 9, batch 34700, loss[loss=0.1307, simple_loss=0.2087, pruned_loss=0.02637, over 4891.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.03392, over 972533.11 frames.], batch size: 19, lr: 2.29e-04
2022-05-06 16:38:34,783 INFO [train.py:715] (2/8) Epoch 9, batch 34750, loss[loss=0.1278, simple_loss=0.2043, pruned_loss=0.02568, over 4764.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2142, pruned_loss=0.03439, over 972123.80 frames.], batch size: 18, lr: 2.29e-04
2022-05-06 16:39:12,248 INFO [train.py:715] (2/8) Epoch 9, batch 34800, loss[loss=0.1644, simple_loss=0.2302, pruned_loss=0.04933, over 4925.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2132, pruned_loss=0.03409, over 971984.28 frames.], batch size: 18, lr: 2.29e-04
2022-05-06 16:40:01,154 INFO [train.py:715] (2/8) Epoch 10, batch 0, loss[loss=0.1592, simple_loss=0.2369, pruned_loss=0.04076, over 4935.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2369, pruned_loss=0.04076, over 4935.00 frames.], batch size: 21, lr: 2.19e-04
2022-05-06 16:40:41,025 INFO [train.py:715] (2/8) Epoch 10, batch 50, loss[loss=0.1129, simple_loss=0.1888, pruned_loss=0.01848, over 4816.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2144, pruned_loss=0.03497, over 220922.58 frames.], batch size: 12, lr: 2.19e-04
2022-05-06 16:41:20,752 INFO [train.py:715] (2/8) Epoch 10, batch 100, loss[loss=0.1254, simple_loss=0.2042, pruned_loss=0.02329, over 4870.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2133, pruned_loss=0.03344, over 388115.91 frames.], batch size: 20, lr: 2.19e-04
2022-05-06 16:42:00,751 INFO [train.py:715] (2/8) Epoch 10, batch 150, loss[loss=0.1178, simple_loss=0.1792, pruned_loss=0.02817, over 4919.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03318, over 516923.40 frames.], batch size: 23, lr: 2.19e-04
2022-05-06 16:42:41,342 INFO [train.py:715] (2/8) Epoch 10, batch 200, loss[loss=0.139, simple_loss=0.2091, pruned_loss=0.03443, over 4932.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2128, pruned_loss=0.033, over 618024.65 frames.], batch size: 23, lr: 2.19e-04
2022-05-06 16:43:22,394 INFO [train.py:715] (2/8) Epoch 10, batch 250, loss[loss=0.1591, simple_loss=0.2283, pruned_loss=0.04501, over 4846.00 frames.], tot_loss[loss=0.1392, simple_loss=0.213, pruned_loss=0.03274, over 697055.29 frames.], batch size: 34, lr: 2.19e-04
2022-05-06 16:44:03,217 INFO [train.py:715] (2/8) Epoch 10, batch 300, loss[loss=0.1245, simple_loss=0.2053, pruned_loss=0.02178, over 4801.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2127, pruned_loss=0.03278, over 758407.90 frames.], batch size: 21, lr: 2.19e-04
2022-05-06 16:44:43,668 INFO [train.py:715] (2/8) Epoch 10, batch 350, loss[loss=0.1551, simple_loss=0.2307, pruned_loss=0.0397, over 4797.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.033, over 805286.48 frames.], batch size: 17, lr: 2.19e-04
2022-05-06 16:45:25,021 INFO [train.py:715] (2/8) Epoch 10, batch 400, loss[loss=0.1363, simple_loss=0.2169, pruned_loss=0.02786, over 4834.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.03359, over 842277.67 frames.], batch size: 26, lr: 2.19e-04
2022-05-06 16:46:06,714 INFO [train.py:715] (2/8) Epoch 10, batch 450, loss[loss=0.1408, simple_loss=0.2202, pruned_loss=0.03075, over 4919.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03352, over 870904.11 frames.], batch size: 18, lr: 2.19e-04
2022-05-06 16:46:47,443 INFO [train.py:715] (2/8) Epoch 10, batch 500, loss[loss=0.1503, simple_loss=0.2238, pruned_loss=0.03845, over 4890.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.0335, over 893253.55 frames.], batch size: 19, lr: 2.19e-04
2022-05-06 16:47:28,881 INFO [train.py:715] (2/8) Epoch 10, batch 550, loss[loss=0.1519, simple_loss=0.2189, pruned_loss=0.04244, over 4746.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03389, over 911112.03 frames.], batch size: 16, lr: 2.19e-04
2022-05-06 16:48:10,017 INFO [train.py:715] (2/8) Epoch 10, batch 600, loss[loss=0.148, simple_loss=0.2185, pruned_loss=0.03872, over 4983.00 frames.], tot_loss[loss=0.14, simple_loss=0.2127, pruned_loss=0.03369, over 924966.00 frames.], batch size: 33, lr: 2.19e-04
2022-05-06 16:48:50,534 INFO [train.py:715] (2/8) Epoch 10, batch 650, loss[loss=0.1663, simple_loss=0.2474, pruned_loss=0.04256, over 4688.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03351, over 936103.30 frames.], batch size: 15, lr: 2.19e-04
2022-05-06 16:49:31,183 INFO [train.py:715] (2/8) Epoch 10, batch 700, loss[loss=0.1642, simple_loss=0.2516, pruned_loss=0.03838, over 4803.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2133, pruned_loss=0.03346, over 944102.09 frames.], batch size: 21, lr: 2.19e-04
2022-05-06 16:50:12,725 INFO [train.py:715] (2/8) Epoch 10, batch 750, loss[loss=0.1319, simple_loss=0.2073, pruned_loss=0.02822, over 4890.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.03312, over 950777.15 frames.], batch size: 19, lr: 2.19e-04
2022-05-06 16:50:54,001 INFO [train.py:715] (2/8) Epoch 10, batch 800, loss[loss=0.1385, simple_loss=0.2098, pruned_loss=0.03365, over 4991.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2127, pruned_loss=0.03292, over 956052.43 frames.], batch size: 25, lr: 2.19e-04
2022-05-06 16:51:34,421 INFO [train.py:715] (2/8) Epoch 10, batch 850, loss[loss=0.1407, simple_loss=0.2205, pruned_loss=0.03045, over 4879.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2136, pruned_loss=0.03339, over 959372.68 frames.], batch size: 22, lr: 2.19e-04
2022-05-06 16:52:15,217 INFO [train.py:715] (2/8) Epoch 10, batch 900, loss[loss=0.1305, simple_loss=0.2111, pruned_loss=0.02495, over 4692.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2133, pruned_loss=0.03312, over 961754.08 frames.], batch size: 15, lr: 2.19e-04
2022-05-06 16:52:55,737 INFO [train.py:715] (2/8) Epoch 10, batch 950, loss[loss=0.1572, simple_loss=0.2279, pruned_loss=0.04326, over 4910.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2137, pruned_loss=0.03374, over 964535.02 frames.], batch size: 19, lr: 2.19e-04
2022-05-06 16:53:35,736 INFO [train.py:715] (2/8) Epoch 10, batch 1000, loss[loss=0.1669, simple_loss=0.2272, pruned_loss=0.05334, over 4858.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.03388, over 965640.77 frames.], batch size: 30, lr: 2.19e-04
2022-05-06 16:54:14,958 INFO [train.py:715] (2/8) Epoch 10, batch 1050, loss[loss=0.1151, simple_loss=0.1941, pruned_loss=0.01807, over 4821.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2137, pruned_loss=0.03402, over 967081.27 frames.], batch size: 26, lr: 2.19e-04
2022-05-06 16:54:55,331 INFO [train.py:715] (2/8) Epoch 10, batch 1100, loss[loss=0.1471, simple_loss=0.2105, pruned_loss=0.04183, over 4964.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2136, pruned_loss=0.0337, over 968044.80 frames.], batch size: 14, lr: 2.19e-04
2022-05-06 16:55:34,627 INFO [train.py:715] (2/8) Epoch 10, batch 1150, loss[loss=0.1318, simple_loss=0.2104, pruned_loss=0.02657, over 4758.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2135, pruned_loss=0.03368, over 968786.12 frames.], batch size: 19, lr: 2.19e-04
2022-05-06 16:56:13,829 INFO [train.py:715] (2/8) Epoch 10, batch 1200, loss[loss=0.1455, simple_loss=0.2163, pruned_loss=0.03731, over 4809.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03383, over 970219.66 frames.], batch size: 24, lr: 2.19e-04
2022-05-06 16:56:53,601 INFO [train.py:715] (2/8) Epoch 10, batch 1250, loss[loss=0.1618, simple_loss=0.2381, pruned_loss=0.04268, over 4944.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.03397, over 971346.96 frames.], batch size: 29, lr: 2.19e-04
2022-05-06 16:57:32,225 INFO [train.py:715] (2/8) Epoch 10, batch 1300, loss[loss=0.1235, simple_loss=0.2007, pruned_loss=0.02315, over 4759.00 frames.], tot_loss[loss=0.1405, simple_loss=0.213, pruned_loss=0.03398, over 971240.40 frames.], batch size: 19, lr: 2.19e-04
2022-05-06 16:58:11,022 INFO [train.py:715] (2/8) Epoch 10, batch 1350, loss[loss=0.1284, simple_loss=0.2134, pruned_loss=0.02173, over 4772.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03354, over 971179.74 frames.], batch size: 12, lr: 2.19e-04
2022-05-06 16:58:49,195 INFO [train.py:715] (2/8) Epoch 10, batch 1400, loss[loss=0.152, simple_loss=0.2237, pruned_loss=0.04013, over 4821.00 frames.], tot_loss[loss=0.1393, simple_loss=0.212, pruned_loss=0.03327, over 970701.32 frames.], batch size: 15, lr: 2.19e-04
2022-05-06 16:59:28,747 INFO [train.py:715] (2/8) Epoch 10, batch 1450, loss[loss=0.1613, simple_loss=0.2177, pruned_loss=0.05249, over 4797.00 frames.], tot_loss[loss=0.14, simple_loss=0.2124, pruned_loss=0.03379, over 970878.68 frames.], batch size: 13, lr: 2.19e-04
2022-05-06 17:00:07,716 INFO [train.py:715] (2/8) Epoch 10, batch 1500, loss[loss=0.1285, simple_loss=0.2074, pruned_loss=0.02484, over 4894.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.0337, over 971350.23 frames.], batch size: 16, lr: 2.19e-04
2022-05-06 17:00:46,474 INFO [train.py:715] (2/8) Epoch 10, batch 1550, loss[loss=0.1299, simple_loss=0.2147, pruned_loss=0.02256, over 4861.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.03376, over 970751.81 frames.], batch size: 22, lr: 2.19e-04
2022-05-06 17:01:25,571 INFO [train.py:715] (2/8) Epoch 10, batch 1600, loss[loss=0.1238, simple_loss=0.1989, pruned_loss=0.02434, over 4845.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.03341, over 971175.20 frames.], batch size: 13, lr: 2.19e-04
2022-05-06 17:02:04,987 INFO [train.py:715] (2/8) Epoch 10, batch 1650, loss[loss=0.1402, simple_loss=0.2163, pruned_loss=0.03203, over 4892.00 frames.], tot_loss[loss=0.1393, simple_loss=0.212, pruned_loss=0.03332, over 971350.74 frames.], batch size: 19, lr: 2.19e-04
2022-05-06 17:02:43,709 INFO [train.py:715] (2/8) Epoch 10, batch 1700, loss[loss=0.1565, simple_loss=0.2333, pruned_loss=0.03987, over 4960.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03329, over 971868.30 frames.], batch size: 15, lr: 2.19e-04
2022-05-06 17:03:22,054 INFO [train.py:715] (2/8) Epoch 10, batch 1750, loss[loss=0.1289, simple_loss=0.1959, pruned_loss=0.03091, over 4772.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03374, over 972274.07 frames.], batch size: 16, lr: 2.19e-04
2022-05-06 17:04:02,177 INFO [train.py:715] (2/8) Epoch 10, batch 1800, loss[loss=0.1047, simple_loss=0.177, pruned_loss=0.01624, over 4781.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2132, pruned_loss=0.03395, over 972262.91 frames.], batch size: 14, lr: 2.19e-04
2022-05-06 17:04:41,814 INFO [train.py:715] (2/8) Epoch 10, batch 1850, loss[loss=0.118, simple_loss=0.2018, pruned_loss=0.01706, over 4813.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.0339, over 972676.51 frames.], batch size: 25, lr: 2.19e-04
2022-05-06 17:05:20,555 INFO [train.py:715] (2/8) Epoch 10, batch 1900, loss[loss=0.1331, simple_loss=0.2091, pruned_loss=0.02857, over 4834.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2132, pruned_loss=0.03386, over 973729.86 frames.], batch size: 30, lr: 2.19e-04
2022-05-06 17:05:59,511 INFO [train.py:715] (2/8) Epoch 10, batch 1950, loss[loss=0.1423, simple_loss=0.2159, pruned_loss=0.03439, over 4928.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03382, over 973497.82 frames.], batch size: 23, lr: 2.18e-04
2022-05-06 17:06:39,855 INFO [train.py:715] (2/8) Epoch 10, batch 2000, loss[loss=0.1259, simple_loss=0.2024, pruned_loss=0.02469, over 4856.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2123, pruned_loss=0.03341, over 972611.80 frames.], batch size: 20, lr: 2.18e-04
2022-05-06 17:07:19,134 INFO [train.py:715] (2/8) Epoch 10, batch 2050, loss[loss=0.1823, simple_loss=0.2495, pruned_loss=0.05758, over 4826.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2131, pruned_loss=0.03357, over 971985.13 frames.], batch size: 25, lr: 2.18e-04
2022-05-06 17:07:57,716 INFO [train.py:715] (2/8) Epoch 10, batch 2100, loss[loss=0.1149, simple_loss=0.193, pruned_loss=0.01838, over 4935.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03442, over 972525.53 frames.], batch size: 29, lr: 2.18e-04
2022-05-06 17:08:37,349 INFO [train.py:715] (2/8) Epoch 10, batch 2150, loss[loss=0.1802, simple_loss=0.2544, pruned_loss=0.05304, over 4838.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2143, pruned_loss=0.03406, over 972838.91 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:09:16,485 INFO [train.py:715] (2/8) Epoch 10, batch 2200, loss[loss=0.1379, simple_loss=0.2159, pruned_loss=0.02995, over 4838.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2139, pruned_loss=0.03382, over 972437.39 frames.], batch size: 26, lr: 2.18e-04
2022-05-06 17:09:55,193 INFO [train.py:715] (2/8) Epoch 10, batch 2250, loss[loss=0.1416, simple_loss=0.2196, pruned_loss=0.03182, over 4704.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2146, pruned_loss=0.0343, over 972852.02 frames.], batch size: 15, lr: 2.18e-04
2022-05-06 17:10:33,967 INFO [train.py:715] (2/8) Epoch 10, batch 2300, loss[loss=0.115, simple_loss=0.1919, pruned_loss=0.01907, over 4777.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03409, over 973229.20 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:11:13,695 INFO [train.py:715] (2/8) Epoch 10, batch 2350, loss[loss=0.1306, simple_loss=0.209, pruned_loss=0.02615, over 4988.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2135, pruned_loss=0.03371, over 973413.10 frames.], batch size: 16, lr: 2.18e-04
2022-05-06 17:11:52,499 INFO [train.py:715] (2/8) Epoch 10, batch 2400, loss[loss=0.1227, simple_loss=0.2013, pruned_loss=0.02201, over 4826.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03336, over 971997.57 frames.], batch size: 26, lr: 2.18e-04
2022-05-06 17:12:31,235 INFO [train.py:715] (2/8) Epoch 10, batch 2450, loss[loss=0.1341, simple_loss=0.2071, pruned_loss=0.03056, over 4848.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.03344, over 972477.13 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:13:10,537 INFO [train.py:715] (2/8) Epoch 10, batch 2500, loss[loss=0.1347, simple_loss=0.2008, pruned_loss=0.03431, over 4860.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03349, over 972880.37 frames.], batch size: 32, lr: 2.18e-04
2022-05-06 17:13:49,921 INFO [train.py:715] (2/8) Epoch 10, batch 2550, loss[loss=0.1202, simple_loss=0.1909, pruned_loss=0.02476, over 4776.00 frames.], tot_loss[loss=0.1396, simple_loss=0.212, pruned_loss=0.0336, over 972416.96 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:14:29,341 INFO [train.py:715] (2/8) Epoch 10, batch 2600, loss[loss=0.1517, simple_loss=0.2321, pruned_loss=0.03567, over 4854.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2131, pruned_loss=0.03403, over 972598.14 frames.], batch size: 20, lr: 2.18e-04
2022-05-06 17:15:08,461 INFO [train.py:715] (2/8) Epoch 10, batch 2650, loss[loss=0.1599, simple_loss=0.2364, pruned_loss=0.04167, over 4988.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03414, over 973548.67 frames.], batch size: 28, lr: 2.18e-04
2022-05-06 17:15:47,657 INFO [train.py:715] (2/8) Epoch 10, batch 2700, loss[loss=0.1521, simple_loss=0.22, pruned_loss=0.0421, over 4910.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03418, over 973326.43 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:16:26,376 INFO [train.py:715] (2/8) Epoch 10, batch 2750, loss[loss=0.1348, simple_loss=0.2056, pruned_loss=0.03203, over 4957.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03437, over 972572.13 frames.], batch size: 39, lr: 2.18e-04
2022-05-06 17:17:05,078 INFO [train.py:715] (2/8) Epoch 10, batch 2800, loss[loss=0.1361, simple_loss=0.2084, pruned_loss=0.03193, over 4848.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.03397, over 972631.13 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:17:43,820 INFO [train.py:715] (2/8) Epoch 10, batch 2850, loss[loss=0.1042, simple_loss=0.1842, pruned_loss=0.01206, over 4861.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03364, over 973246.58 frames.], batch size: 20, lr: 2.18e-04
2022-05-06 17:18:23,065 INFO [train.py:715] (2/8) Epoch 10, batch 2900, loss[loss=0.1182, simple_loss=0.183, pruned_loss=0.02672, over 4667.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2135, pruned_loss=0.03418, over 973272.45 frames.], batch size: 13, lr: 2.18e-04
2022-05-06 17:19:02,252 INFO [train.py:715] (2/8) Epoch 10, batch 2950, loss[loss=0.1632, simple_loss=0.2386, pruned_loss=0.0439, over 4799.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2142, pruned_loss=0.03474, over 974012.71 frames.], batch size: 24, lr: 2.18e-04
2022-05-06 17:19:40,636 INFO [train.py:715] (2/8) Epoch 10, batch 3000, loss[loss=0.1079, simple_loss=0.1782, pruned_loss=0.01882, over 4762.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2143, pruned_loss=0.0347, over 973955.51 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:19:40,636 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 17:19:50,100 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1065, simple_loss=0.1908, pruned_loss=0.01113, over 914524.00 frames.
2022-05-06 17:20:28,628 INFO [train.py:715] (2/8) Epoch 10, batch 3050, loss[loss=0.1563, simple_loss=0.2362, pruned_loss=0.03819, over 4752.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2143, pruned_loss=0.03427, over 973563.14 frames.], batch size: 16, lr: 2.18e-04
2022-05-06 17:21:07,569 INFO [train.py:715] (2/8) Epoch 10, batch 3100, loss[loss=0.1344, simple_loss=0.2016, pruned_loss=0.03361, over 4860.00 frames.], tot_loss[loss=0.141, simple_loss=0.2142, pruned_loss=0.03394, over 973495.75 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:21:46,722 INFO [train.py:715] (2/8) Epoch 10, batch 3150, loss[loss=0.153, simple_loss=0.2267, pruned_loss=0.03963, over 4862.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2134, pruned_loss=0.03362, over 973071.58 frames.], batch size: 32, lr: 2.18e-04
2022-05-06 17:22:25,557 INFO [train.py:715] (2/8) Epoch 10, batch 3200, loss[loss=0.1592, simple_loss=0.2248, pruned_loss=0.04676, over 4850.00 frames.], tot_loss[loss=0.1411, simple_loss=0.214, pruned_loss=0.03411, over 973015.12 frames.], batch size: 15, lr: 2.18e-04
2022-05-06 17:23:03,966 INFO [train.py:715] (2/8) Epoch 10, batch 3250, loss[loss=0.1655, simple_loss=0.2397, pruned_loss=0.04562, over 4856.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2144, pruned_loss=0.0344, over 972095.50 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:23:44,487 INFO [train.py:715] (2/8) Epoch 10, batch 3300, loss[loss=0.125, simple_loss=0.19, pruned_loss=0.02999, over 4774.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2137, pruned_loss=0.03405, over 972306.63 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:24:24,205 INFO [train.py:715] (2/8) Epoch 10, batch 3350, loss[loss=0.1482, simple_loss=0.2176, pruned_loss=0.03944, over 4804.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2136, pruned_loss=0.03352, over 971824.36 frames.], batch size: 25, lr: 2.18e-04
2022-05-06 17:25:04,068 INFO [train.py:715] (2/8) Epoch 10, batch 3400, loss[loss=0.1298, simple_loss=0.2042, pruned_loss=0.02772, over 4954.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2139, pruned_loss=0.03357, over 972032.36 frames.], batch size: 24, lr: 2.18e-04
2022-05-06 17:25:44,882 INFO [train.py:715] (2/8) Epoch 10, batch 3450, loss[loss=0.146, simple_loss=0.2292, pruned_loss=0.03135, over 4922.00 frames.], tot_loss[loss=0.1406, simple_loss=0.214, pruned_loss=0.03366, over 972528.53 frames.], batch size: 23, lr: 2.18e-04
2022-05-06 17:26:26,600 INFO [train.py:715] (2/8) Epoch 10, batch 3500, loss[loss=0.1117, simple_loss=0.193, pruned_loss=0.01513, over 4753.00 frames.], tot_loss[loss=0.1407, simple_loss=0.214, pruned_loss=0.03368, over 972278.97 frames.], batch size: 12, lr: 2.18e-04
2022-05-06 17:27:07,260 INFO [train.py:715] (2/8) Epoch 10, batch 3550, loss[loss=0.1974, simple_loss=0.2614, pruned_loss=0.06667, over 4928.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2144, pruned_loss=0.03396, over 972032.81 frames.], batch size: 39, lr: 2.18e-04
2022-05-06 17:27:48,540 INFO [train.py:715] (2/8) Epoch 10, batch 3600, loss[loss=0.1388, simple_loss=0.2082, pruned_loss=0.03463, over 4844.00 frames.], tot_loss[loss=0.1409, simple_loss=0.214, pruned_loss=0.03391, over 972922.88 frames.], batch size: 34, lr: 2.18e-04
2022-05-06 17:28:29,168 INFO [train.py:715] (2/8) Epoch 10, batch 3650, loss[loss=0.1393, simple_loss=0.2075, pruned_loss=0.03555, over 4984.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2135, pruned_loss=0.03376, over 973574.73 frames.], batch size: 31, lr: 2.18e-04
2022-05-06 17:29:10,567 INFO [train.py:715] (2/8) Epoch 10, batch 3700, loss[loss=0.1516, simple_loss=0.233, pruned_loss=0.03511, over 4959.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03392, over 972673.35 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:29:51,154 INFO [train.py:715] (2/8) Epoch 10, batch 3750, loss[loss=0.1192, simple_loss=0.1916, pruned_loss=0.02335, over 4699.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2129, pruned_loss=0.0338, over 973057.79 frames.], batch size: 15, lr: 2.18e-04
2022-05-06 17:30:32,378 INFO [train.py:715] (2/8) Epoch 10, batch 3800, loss[loss=0.1247, simple_loss=0.1803, pruned_loss=0.03452, over 4733.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2119, pruned_loss=0.03359, over 972810.43 frames.], batch size: 16, lr: 2.18e-04
2022-05-06 17:31:13,742 INFO [train.py:715] (2/8) Epoch 10, batch 3850, loss[loss=0.1502, simple_loss=0.2193, pruned_loss=0.04057, over 4848.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2119, pruned_loss=0.0337, over 973099.39 frames.], batch size: 30, lr: 2.18e-04
2022-05-06 17:31:54,694 INFO [train.py:715] (2/8) Epoch 10, batch 3900, loss[loss=0.1448, simple_loss=0.2196, pruned_loss=0.035, over 4643.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2126, pruned_loss=0.0343, over 972610.92 frames.], batch size: 13, lr: 2.18e-04
2022-05-06 17:32:36,893 INFO [train.py:715] (2/8) Epoch 10, batch 3950, loss[loss=0.1546, simple_loss=0.2114, pruned_loss=0.04892, over 4865.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2127, pruned_loss=0.03427, over 973133.92 frames.], batch size: 13, lr: 2.18e-04
2022-05-06 17:33:16,172 INFO [train.py:715] (2/8) Epoch 10, batch 4000, loss[loss=0.1388, simple_loss=0.2083, pruned_loss=0.03465, over 4911.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2118, pruned_loss=0.03382, over 973381.78 frames.], batch size: 23, lr: 2.18e-04
2022-05-06 17:33:55,835 INFO [train.py:715] (2/8) Epoch 10, batch 4050, loss[loss=0.1257, simple_loss=0.1944, pruned_loss=0.02854, over 4861.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2119, pruned_loss=0.03378, over 972929.40 frames.], batch size: 32, lr: 2.18e-04
2022-05-06 17:34:34,556 INFO [train.py:715] (2/8) Epoch 10, batch 4100, loss[loss=0.1608, simple_loss=0.2322, pruned_loss=0.04474, over 4888.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2121, pruned_loss=0.03368, over 972701.63 frames.], batch size: 22, lr: 2.18e-04
2022-05-06 17:35:13,433 INFO [train.py:715] (2/8) Epoch 10, batch 4150, loss[loss=0.1317, simple_loss=0.2068, pruned_loss=0.02828, over 4937.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03334, over 972501.46 frames.], batch size: 29, lr: 2.18e-04
2022-05-06 17:35:52,990 INFO [train.py:715] (2/8) Epoch 10, batch 4200, loss[loss=0.1423, simple_loss=0.2185, pruned_loss=0.03304, over 4869.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2124, pruned_loss=0.03305, over 973058.26 frames.], batch size: 32, lr: 2.18e-04
2022-05-06 17:36:31,674 INFO [train.py:715] (2/8) Epoch 10, batch 4250, loss[loss=0.1366, simple_loss=0.2049, pruned_loss=0.03418, over 4774.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2138, pruned_loss=0.03364, over 972362.62 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:37:10,487 INFO [train.py:715] (2/8) Epoch 10, batch 4300, loss[loss=0.1328, simple_loss=0.2176, pruned_loss=0.02401, over 4789.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2132, pruned_loss=0.03346, over 972568.98 frames.], batch size: 18, lr: 2.18e-04
2022-05-06 17:37:49,694 INFO [train.py:715] (2/8) Epoch 10, batch 4350, loss[loss=0.1391, simple_loss=0.2096, pruned_loss=0.03431, over 4970.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.0338, over 972696.34 frames.], batch size: 35, lr: 2.18e-04
2022-05-06 17:38:28,648 INFO [train.py:715] (2/8) Epoch 10, batch 4400, loss[loss=0.16, simple_loss=0.2183, pruned_loss=0.05085, over 4859.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.03419, over 972136.68 frames.], batch size: 32, lr: 2.18e-04
2022-05-06 17:39:07,612 INFO [train.py:715] (2/8) Epoch 10, batch 4450, loss[loss=0.1558, simple_loss=0.2202, pruned_loss=0.04569, over 4903.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2144, pruned_loss=0.03452, over 972080.60 frames.], batch size: 17, lr: 2.18e-04
2022-05-06 17:39:46,322 INFO [train.py:715] (2/8) Epoch 10, batch 4500, loss[loss=0.1361, simple_loss=0.2139, pruned_loss=0.02913, over 4898.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.03432, over 972007.38 frames.], batch size: 17, lr: 2.18e-04
2022-05-06 17:40:25,796 INFO [train.py:715] (2/8) Epoch 10, batch 4550, loss[loss=0.1328, simple_loss=0.2107, pruned_loss=0.02744, over 4798.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2136, pruned_loss=0.03409, over 972085.06 frames.], batch size: 25, lr: 2.18e-04
2022-05-06 17:41:04,678 INFO [train.py:715] (2/8) Epoch 10, batch 4600, loss[loss=0.1522, simple_loss=0.219, pruned_loss=0.04267, over 4652.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2133, pruned_loss=0.03403, over 971838.26 frames.], batch size: 13, lr: 2.18e-04
2022-05-06 17:41:43,576 INFO [train.py:715] (2/8) Epoch 10, batch 4650, loss[loss=0.1547, simple_loss=0.2234, pruned_loss=0.04302, over 4791.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2149, pruned_loss=0.03443, over 972268.83 frames.], batch size: 14, lr: 2.18e-04
2022-05-06 17:42:23,829 INFO [train.py:715] (2/8) Epoch 10, batch 4700, loss[loss=0.1432, simple_loss=0.2118, pruned_loss=0.03734, over 4969.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2148, pruned_loss=0.03432, over 972178.07 frames.], batch size: 24, lr: 2.18e-04
2022-05-06 17:43:03,974 INFO [train.py:715] (2/8) Epoch 10, batch 4750, loss[loss=0.1311, simple_loss=0.2144, pruned_loss=0.02386, over 4946.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2142, pruned_loss=0.03413, over 971685.30 frames.], batch size: 21, lr: 2.18e-04
2022-05-06 17:43:43,161 INFO [train.py:715] (2/8) Epoch 10, batch 4800, loss[loss=0.1303, simple_loss=0.2067, pruned_loss=0.02691, over 4761.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.0341, over 971694.63 frames.], batch size: 19, lr: 2.18e-04
2022-05-06 17:44:22,998 INFO [train.py:715] (2/8) Epoch 10, batch 4850, loss[loss=0.1418, simple_loss=0.2218, pruned_loss=0.03091, over 4878.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.03372, over 972900.80 frames.], batch size: 16, lr: 2.18e-04
2022-05-06 17:45:02,946 INFO [train.py:715] (2/8) Epoch 10, batch 4900, loss[loss=0.1519, simple_loss=0.2114, pruned_loss=0.04619, over 4840.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.03443, over 972615.41 frames.], batch size: 32, lr: 2.18e-04
2022-05-06 17:45:42,396 INFO [train.py:715] (2/8) Epoch 10, batch 4950, loss[loss=0.1245, simple_loss=0.1955, pruned_loss=0.02675, over 4932.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2148, pruned_loss=0.03487, over 972848.59 frames.], batch size: 29, lr: 2.18e-04
2022-05-06 17:46:21,437 INFO [train.py:715] (2/8) Epoch 10, batch 5000, loss[loss=0.15, simple_loss=0.2256, pruned_loss=0.03723, over 4917.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2149, pruned_loss=0.03465, over 973057.92 frames.], batch size: 39, lr: 2.18e-04
2022-05-06 17:47:00,598 INFO [train.py:715] (2/8) Epoch 10, batch 5050, loss[loss=0.1515, simple_loss=0.2223, pruned_loss=0.04031, over 4938.00 frames.], tot_loss[loss=0.1414, simple_loss=0.214, pruned_loss=0.03442, over 973076.41 frames.], batch size: 39, lr: 2.18e-04
2022-05-06 17:47:39,528 INFO [train.py:715] (2/8) Epoch 10, batch 5100, loss[loss=0.126, simple_loss=0.2072, pruned_loss=0.02239, over 4986.00 frames.], tot_loss[loss=0.1429, simple_loss=0.215, pruned_loss=0.03541, over 972874.45 frames.], batch size: 25, lr: 2.18e-04
2022-05-06 17:48:18,800 INFO [train.py:715] (2/8) Epoch 10, batch 5150, loss[loss=0.1436, simple_loss=0.2151, pruned_loss=0.03608, over 4653.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2142, pruned_loss=0.03495, over 972786.62 frames.], batch size: 13, lr: 2.18e-04
2022-05-06 17:48:58,634 INFO [train.py:715] (2/8) Epoch 10, batch 5200, loss[loss=0.1429, simple_loss=0.2156, pruned_loss=0.03506, over 4961.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2142, pruned_loss=0.03505, over 973174.41 frames.], batch size: 35, lr: 2.17e-04
2022-05-06 17:49:38,472 INFO [train.py:715] (2/8) Epoch 10, batch 5250, loss[loss=0.1604, simple_loss=0.2438, pruned_loss=0.03846, over 4887.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2136, pruned_loss=0.0345, over 974052.57 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 17:50:17,853 INFO [train.py:715] (2/8) Epoch 10, batch 5300, loss[loss=0.1039, simple_loss=0.1725, pruned_loss=0.01765, over 4736.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03386, over 973834.78 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 17:50:57,191 INFO [train.py:715] (2/8) Epoch 10, batch 5350, loss[loss=0.1221, simple_loss=0.1886, pruned_loss=0.02777, over 4839.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.0339, over 973270.97 frames.], batch size: 32, lr: 2.17e-04
2022-05-06 17:51:37,023 INFO [train.py:715] (2/8) Epoch 10, batch 5400, loss[loss=0.1427, simple_loss=0.2211, pruned_loss=0.03209, over 4801.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2144, pruned_loss=0.03442, over 973841.15 frames.], batch size: 21, lr: 2.17e-04
2022-05-06 17:52:16,939 INFO [train.py:715] (2/8) Epoch 10, batch 5450, loss[loss=0.1689, simple_loss=0.2507, pruned_loss=0.04357, over 4787.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2146, pruned_loss=0.0342, over 974589.35 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 17:52:56,346 INFO [train.py:715] (2/8) Epoch 10, batch 5500, loss[loss=0.1528, simple_loss=0.2254, pruned_loss=0.04004, over 4838.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2148, pruned_loss=0.0341, over 974919.25 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 17:53:36,102 INFO [train.py:715] (2/8) Epoch 10, batch 5550, loss[loss=0.1554, simple_loss=0.2364, pruned_loss=0.03715, over 4858.00 frames.], tot_loss[loss=0.142, simple_loss=0.2148, pruned_loss=0.03459, over 974009.97 frames.], batch size: 20, lr: 2.17e-04
2022-05-06 17:54:16,053 INFO [train.py:715] (2/8) Epoch 10, batch 5600, loss[loss=0.1587, simple_loss=0.2242, pruned_loss=0.04659, over 4970.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2142, pruned_loss=0.03412, over 973827.96 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 17:54:55,812 INFO [train.py:715] (2/8) Epoch 10, batch 5650, loss[loss=0.1422, simple_loss=0.2197, pruned_loss=0.03233, over 4879.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2139, pruned_loss=0.03399, over 973685.46 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 17:55:34,979 INFO [train.py:715] (2/8) Epoch 10, batch 5700, loss[loss=0.1249, simple_loss=0.1994, pruned_loss=0.02522, over 4818.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.03384, over 973966.30 frames.], batch size: 26, lr: 2.17e-04
2022-05-06 17:56:15,023 INFO [train.py:715] (2/8) Epoch 10, batch 5750, loss[loss=0.1164, simple_loss=0.1909, pruned_loss=0.02095, over 4872.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.0339, over 973154.55 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 17:56:54,687 INFO [train.py:715] (2/8) Epoch 10, batch 5800, loss[loss=0.1521, simple_loss=0.2206, pruned_loss=0.04178, over 4855.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03348, over 973366.28 frames.], batch size: 20, lr: 2.17e-04
2022-05-06 17:57:34,209 INFO [train.py:715] (2/8) Epoch 10, batch 5850, loss[loss=0.148, simple_loss=0.2165, pruned_loss=0.0398, over 4854.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03439, over 972773.38 frames.], batch size: 32, lr: 2.17e-04
2022-05-06 17:58:14,028 INFO [train.py:715] (2/8) Epoch 10, batch 5900, loss[loss=0.1229, simple_loss=0.2038, pruned_loss=0.02098, over 4926.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2141, pruned_loss=0.03409, over 972894.68 frames.], batch size: 23, lr: 2.17e-04
2022-05-06 17:58:53,766 INFO [train.py:715] (2/8) Epoch 10, batch 5950, loss[loss=0.1854, simple_loss=0.2706, pruned_loss=0.05016, over 4885.00 frames.], tot_loss[loss=0.141, simple_loss=0.2141, pruned_loss=0.03396, over 973695.60 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 17:59:33,429 INFO [train.py:715] (2/8) Epoch 10, batch 6000, loss[loss=0.1667, simple_loss=0.2328, pruned_loss=0.05025, over 4685.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2137, pruned_loss=0.03395, over 973101.38 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 17:59:33,430 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 17:59:42,753 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1067, simple_loss=0.1909, pruned_loss=0.01126, over 914524.00 frames.
2022-05-06 18:00:22,326 INFO [train.py:715] (2/8) Epoch 10, batch 6050, loss[loss=0.115, simple_loss=0.1969, pruned_loss=0.0166, over 4984.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.0337, over 973216.92 frames.], batch size: 25, lr: 2.17e-04
2022-05-06 18:01:00,748 INFO [train.py:715] (2/8) Epoch 10, batch 6100, loss[loss=0.1275, simple_loss=0.1978, pruned_loss=0.02857, over 4766.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03407, over 972734.73 frames.], batch size: 12, lr: 2.17e-04
2022-05-06 18:01:40,208 INFO [train.py:715] (2/8) Epoch 10, batch 6150, loss[loss=0.1453, simple_loss=0.2202, pruned_loss=0.03515, over 4935.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.03385, over 973092.06 frames.], batch size: 21, lr: 2.17e-04
2022-05-06 18:02:20,067 INFO [train.py:715] (2/8) Epoch 10, batch 6200, loss[loss=0.1343, simple_loss=0.2107, pruned_loss=0.02894, over 4906.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2136, pruned_loss=0.03385, over 973615.61 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 18:02:59,955 INFO [train.py:715] (2/8) Epoch 10, batch 6250, loss[loss=0.1184, simple_loss=0.2003, pruned_loss=0.0182, over 4981.00 frames.], tot_loss[loss=0.1398, simple_loss=0.213, pruned_loss=0.03328, over 972908.29 frames.], batch size: 28, lr: 2.17e-04
2022-05-06 18:03:39,470 INFO [train.py:715] (2/8) Epoch 10, batch 6300, loss[loss=0.1331, simple_loss=0.2149, pruned_loss=0.02572, over 4799.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2127, pruned_loss=0.03299, over 972652.60 frames.], batch size: 24, lr: 2.17e-04
2022-05-06 18:04:19,283 INFO [train.py:715] (2/8) Epoch 10, batch 6350, loss[loss=0.1319, simple_loss=0.2008, pruned_loss=0.03144, over 4837.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03295, over 972808.57 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:04:58,325 INFO [train.py:715] (2/8) Epoch 10, batch 6400, loss[loss=0.1529, simple_loss=0.2168, pruned_loss=0.04454, over 4791.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2132, pruned_loss=0.03326, over 972718.92 frames.], batch size: 18, lr: 2.17e-04
2022-05-06 18:05:36,734 INFO [train.py:715] (2/8) Epoch 10, batch 6450, loss[loss=0.135, simple_loss=0.2043, pruned_loss=0.03286, over 4888.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2131, pruned_loss=0.03313, over 972536.12 frames.], batch size: 22, lr: 2.17e-04
2022-05-06 18:06:15,660 INFO [train.py:715] (2/8) Epoch 10, batch 6500, loss[loss=0.1435, simple_loss=0.2143, pruned_loss=0.03636, over 4882.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03347, over 973149.08 frames.], batch size: 22, lr: 2.17e-04
2022-05-06 18:06:54,773 INFO [train.py:715] (2/8) Epoch 10, batch 6550, loss[loss=0.1324, simple_loss=0.199, pruned_loss=0.0329, over 4992.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03372, over 973658.19 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 18:07:33,917 INFO [train.py:715] (2/8) Epoch 10, batch 6600, loss[loss=0.1459, simple_loss=0.2199, pruned_loss=0.03598, over 4769.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2134, pruned_loss=0.03359, over 972952.62 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 18:08:12,469 INFO [train.py:715] (2/8) Epoch 10, batch 6650, loss[loss=0.1385, simple_loss=0.2178, pruned_loss=0.02965, over 4983.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.03378, over 972803.17 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 18:08:52,601 INFO [train.py:715] (2/8) Epoch 10, batch 6700, loss[loss=0.11, simple_loss=0.1847, pruned_loss=0.0177, over 4786.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2137, pruned_loss=0.0339, over 972625.40 frames.], batch size: 12, lr: 2.17e-04
2022-05-06 18:09:31,854 INFO [train.py:715] (2/8) Epoch 10, batch 6750, loss[loss=0.1719, simple_loss=0.2317, pruned_loss=0.05601, over 4834.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03372, over 972594.42 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:10:10,542 INFO [train.py:715] (2/8) Epoch 10, batch 6800, loss[loss=0.1287, simple_loss=0.1914, pruned_loss=0.033, over 4795.00 frames.], tot_loss[loss=0.141, simple_loss=0.2145, pruned_loss=0.03375, over 972759.24 frames.], batch size: 12, lr: 2.17e-04
2022-05-06 18:10:50,412 INFO [train.py:715] (2/8) Epoch 10, batch 6850, loss[loss=0.1143, simple_loss=0.1914, pruned_loss=0.0186, over 4834.00 frames.], tot_loss[loss=0.1406, simple_loss=0.214, pruned_loss=0.03366, over 972064.61 frames.], batch size: 13, lr: 2.17e-04
2022-05-06 18:11:29,656 INFO [train.py:715] (2/8) Epoch 10, batch 6900, loss[loss=0.1494, simple_loss=0.2256, pruned_loss=0.03664, over 4981.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03338, over 972039.65 frames.], batch size: 25, lr: 2.17e-04
2022-05-06 18:12:08,733 INFO [train.py:715] (2/8) Epoch 10, batch 6950, loss[loss=0.1701, simple_loss=0.2411, pruned_loss=0.04949, over 4953.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2136, pruned_loss=0.03363, over 972422.75 frames.], batch size: 39, lr: 2.17e-04
2022-05-06 18:12:48,643 INFO [train.py:715] (2/8) Epoch 10, batch 7000, loss[loss=0.1549, simple_loss=0.2134, pruned_loss=0.04821, over 4821.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.03382, over 972353.34 frames.], batch size: 25, lr: 2.17e-04
2022-05-06 18:13:28,549 INFO [train.py:715] (2/8) Epoch 10, batch 7050, loss[loss=0.09485, simple_loss=0.1663, pruned_loss=0.01169, over 4727.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03356, over 971706.85 frames.], batch size: 12, lr: 2.17e-04
2022-05-06 18:14:07,742 INFO [train.py:715] (2/8) Epoch 10, batch 7100, loss[loss=0.1627, simple_loss=0.2542, pruned_loss=0.03564, over 4967.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03391, over 971159.56 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:14:46,900 INFO [train.py:715] (2/8) Epoch 10, batch 7150, loss[loss=0.1575, simple_loss=0.2256, pruned_loss=0.04471, over 4740.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03345, over 971696.64 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 18:15:26,296 INFO [train.py:715] (2/8) Epoch 10, batch 7200, loss[loss=0.1207, simple_loss=0.1923, pruned_loss=0.0246, over 4843.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03324, over 971714.83 frames.], batch size: 30, lr: 2.17e-04
2022-05-06 18:16:05,421 INFO [train.py:715] (2/8) Epoch 10, batch 7250, loss[loss=0.1343, simple_loss=0.2023, pruned_loss=0.03314, over 4782.00 frames.], tot_loss[loss=0.1393, simple_loss=0.212, pruned_loss=0.03326, over 971139.48 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:16:44,410 INFO [train.py:715] (2/8) Epoch 10, batch 7300, loss[loss=0.1334, simple_loss=0.2126, pruned_loss=0.02715, over 4751.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2113, pruned_loss=0.03273, over 971576.98 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 18:17:23,331 INFO [train.py:715] (2/8) Epoch 10, batch 7350, loss[loss=0.1584, simple_loss=0.2264, pruned_loss=0.04515, over 4867.00 frames.], tot_loss[loss=0.139, simple_loss=0.2115, pruned_loss=0.03322, over 971094.02 frames.], batch size: 32, lr: 2.17e-04
2022-05-06 18:18:02,743 INFO [train.py:715] (2/8) Epoch 10, batch 7400, loss[loss=0.153, simple_loss=0.2302, pruned_loss=0.03788, over 4865.00 frames.], tot_loss[loss=0.1385, simple_loss=0.211, pruned_loss=0.03301, over 971824.02 frames.], batch size: 20, lr: 2.17e-04
2022-05-06 18:18:41,886 INFO [train.py:715] (2/8) Epoch 10, batch 7450, loss[loss=0.1616, simple_loss=0.2319, pruned_loss=0.04567, over 4883.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2107, pruned_loss=0.03256, over 971842.91 frames.], batch size: 22, lr: 2.17e-04
2022-05-06 18:19:20,015 INFO [train.py:715] (2/8) Epoch 10, batch 7500, loss[loss=0.1396, simple_loss=0.2168, pruned_loss=0.03118, over 4892.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03247, over 972914.13 frames.], batch size: 22, lr: 2.17e-04
2022-05-06 18:19:59,645 INFO [train.py:715] (2/8) Epoch 10, batch 7550, loss[loss=0.09973, simple_loss=0.1678, pruned_loss=0.01582, over 4780.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03218, over 972227.33 frames.], batch size: 12, lr: 2.17e-04
2022-05-06 18:20:38,459 INFO [train.py:715] (2/8) Epoch 10, batch 7600, loss[loss=0.1179, simple_loss=0.1968, pruned_loss=0.0195, over 4836.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.03261, over 972276.72 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:21:17,037 INFO [train.py:715] (2/8) Epoch 10, batch 7650, loss[loss=0.1233, simple_loss=0.193, pruned_loss=0.02684, over 4920.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03256, over 973200.01 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:21:56,436 INFO [train.py:715] (2/8) Epoch 10, batch 7700, loss[loss=0.1438, simple_loss=0.2235, pruned_loss=0.03198, over 4915.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03265, over 972170.26 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:22:35,792 INFO [train.py:715] (2/8) Epoch 10, batch 7750, loss[loss=0.1333, simple_loss=0.2061, pruned_loss=0.03022, over 4892.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2126, pruned_loss=0.03298, over 972365.73 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 18:23:15,171 INFO [train.py:715] (2/8) Epoch 10, batch 7800, loss[loss=0.1236, simple_loss=0.204, pruned_loss=0.02163, over 4902.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2129, pruned_loss=0.03331, over 972418.06 frames.], batch size: 29, lr: 2.17e-04
2022-05-06 18:23:53,545 INFO [train.py:715] (2/8) Epoch 10, batch 7850, loss[loss=0.1335, simple_loss=0.2096, pruned_loss=0.02866, over 4801.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.0332, over 973392.69 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 18:24:33,023 INFO [train.py:715] (2/8) Epoch 10, batch 7900, loss[loss=0.1324, simple_loss=0.2011, pruned_loss=0.03185, over 4962.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2127, pruned_loss=0.03328, over 973608.84 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 18:25:12,544 INFO [train.py:715] (2/8) Epoch 10, batch 7950, loss[loss=0.1488, simple_loss=0.2166, pruned_loss=0.04052, over 4746.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03313, over 973703.89 frames.], batch size: 19, lr: 2.17e-04
2022-05-06 18:25:51,361 INFO [train.py:715] (2/8) Epoch 10, batch 8000, loss[loss=0.1457, simple_loss=0.2299, pruned_loss=0.03079, over 4903.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03316, over 973206.60 frames.], batch size: 17, lr: 2.17e-04
2022-05-06 18:26:30,785 INFO [train.py:715] (2/8) Epoch 10, batch 8050, loss[loss=0.1511, simple_loss=0.2297, pruned_loss=0.03625, over 4818.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03328, over 973014.20 frames.], batch size: 25, lr: 2.17e-04
2022-05-06 18:27:10,411 INFO [train.py:715] (2/8) Epoch 10, batch 8100, loss[loss=0.1451, simple_loss=0.2193, pruned_loss=0.03548, over 4774.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03386, over 973487.22 frames.], batch size: 18, lr: 2.17e-04
2022-05-06 18:27:49,303 INFO [train.py:715] (2/8) Epoch 10, batch 8150, loss[loss=0.1341, simple_loss=0.1957, pruned_loss=0.03621, over 4843.00 frames.], tot_loss[loss=0.141, simple_loss=0.2135, pruned_loss=0.03426, over 973284.17 frames.], batch size: 13, lr: 2.17e-04
2022-05-06 18:28:27,915 INFO [train.py:715] (2/8) Epoch 10, batch 8200, loss[loss=0.1148, simple_loss=0.1871, pruned_loss=0.02124, over 4821.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2139, pruned_loss=0.0343, over 972892.07 frames.], batch size: 15, lr: 2.17e-04
2022-05-06 18:29:07,590 INFO [train.py:715] (2/8) Epoch 10, batch 8250, loss[loss=0.1365, simple_loss=0.2136, pruned_loss=0.02969, over 4867.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2143, pruned_loss=0.0344, over 972290.46 frames.], batch size: 16, lr: 2.17e-04
2022-05-06 18:29:46,989 INFO [train.py:715] (2/8) Epoch 10, batch 8300, loss[loss=0.1561, simple_loss=0.2249, pruned_loss=0.04365, over 4876.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2135, pruned_loss=0.0337, over 972683.83 frames.], batch size: 22, lr: 2.17e-04
2022-05-06 18:30:25,734 INFO [train.py:715] (2/8) Epoch 10, batch 8350, loss[loss=0.1367, simple_loss=0.2074, pruned_loss=0.03299, over 4993.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03369, over 973714.08 frames.], batch size: 14, lr: 2.17e-04
2022-05-06 18:31:05,470 INFO [train.py:715] (2/8) Epoch 10, batch 8400, loss[loss=0.1453, simple_loss=0.2205, pruned_loss=0.03501, over 4855.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2142, pruned_loss=0.03425, over 973956.06 frames.], batch size: 20, lr: 2.17e-04
2022-05-06 18:31:44,984 INFO [train.py:715] (2/8) Epoch 10, batch 8450, loss[loss=0.1157, simple_loss=0.2041, pruned_loss=0.01367, over 4775.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2146, pruned_loss=0.03423, over 973432.93 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 18:32:23,260 INFO [train.py:715] (2/8) Epoch 10, batch 8500, loss[loss=0.1446, simple_loss=0.209, pruned_loss=0.04006, over 4980.00 frames.], tot_loss[loss=0.1409, simple_loss=0.214, pruned_loss=0.03395, over 973659.82 frames.], batch size: 25, lr: 2.16e-04
2022-05-06 18:33:02,053 INFO [train.py:715] (2/8) Epoch 10, batch 8550, loss[loss=0.1292, simple_loss=0.2062, pruned_loss=0.02606, over 4788.00 frames.], tot_loss[loss=0.141, simple_loss=0.2142, pruned_loss=0.03387, over 972856.06 frames.], batch size: 14, lr: 2.16e-04
2022-05-06 18:33:41,305 INFO [train.py:715] (2/8) Epoch 10, batch 8600, loss[loss=0.1224, simple_loss=0.1997, pruned_loss=0.02256, over 4765.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03358, over 974151.59 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:34:19,986 INFO [train.py:715] (2/8) Epoch 10, batch 8650, loss[loss=0.1316, simple_loss=0.2096, pruned_loss=0.0268, over 4763.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03339, over 974559.94 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:34:58,633 INFO [train.py:715] (2/8) Epoch 10, batch 8700, loss[loss=0.1282, simple_loss=0.1925, pruned_loss=0.03201, over 4977.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03331, over 974738.23 frames.], batch size: 14, lr: 2.16e-04
2022-05-06 18:35:37,459 INFO [train.py:715] (2/8) Epoch 10, batch 8750, loss[loss=0.1081, simple_loss=0.1853, pruned_loss=0.01543, over 4805.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03311, over 974096.74 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 18:36:15,827 INFO [train.py:715] (2/8) Epoch 10, batch 8800, loss[loss=0.17, simple_loss=0.2369, pruned_loss=0.05153, over 4985.00 frames.], tot_loss[loss=0.1397, simple_loss=0.213, pruned_loss=0.03315, over 974327.61 frames.], batch size: 35, lr: 2.16e-04
2022-05-06 18:36:54,706 INFO [train.py:715] (2/8) Epoch 10, batch 8850, loss[loss=0.1239, simple_loss=0.205, pruned_loss=0.02145, over 4936.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.03345, over 973783.76 frames.], batch size: 29, lr: 2.16e-04
2022-05-06 18:37:34,276 INFO [train.py:715] (2/8) Epoch 10, batch 8900, loss[loss=0.1225, simple_loss=0.1974, pruned_loss=0.02383, over 4988.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.0333, over 973645.77 frames.], batch size: 25, lr: 2.16e-04
2022-05-06 18:38:13,789 INFO [train.py:715] (2/8) Epoch 10, batch 8950, loss[loss=0.1536, simple_loss=0.2242, pruned_loss=0.04152, over 4878.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.0331, over 973100.61 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:38:53,304 INFO [train.py:715] (2/8) Epoch 10, batch 9000, loss[loss=0.1468, simple_loss=0.2308, pruned_loss=0.03142, over 4918.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03328, over 973000.43 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:38:53,305 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 18:39:02,858 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1064, simple_loss=0.1907, pruned_loss=0.01106, over 914524.00 frames.
2022-05-06 18:39:42,085 INFO [train.py:715] (2/8) Epoch 10, batch 9050, loss[loss=0.1239, simple_loss=0.2026, pruned_loss=0.02264, over 4883.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03304, over 973145.76 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 18:40:21,152 INFO [train.py:715] (2/8) Epoch 10, batch 9100, loss[loss=0.1408, simple_loss=0.2128, pruned_loss=0.0344, over 4690.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03339, over 972609.17 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:41:01,480 INFO [train.py:715] (2/8) Epoch 10, batch 9150, loss[loss=0.1659, simple_loss=0.2377, pruned_loss=0.0471, over 4948.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03372, over 972089.84 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:41:40,999 INFO [train.py:715] (2/8) Epoch 10, batch 9200, loss[loss=0.1407, simple_loss=0.2206, pruned_loss=0.0304, over 4696.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.034, over 971762.14 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:42:20,445 INFO [train.py:715] (2/8) Epoch 10, batch 9250, loss[loss=0.1194, simple_loss=0.1898, pruned_loss=0.0245, over 4702.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2139, pruned_loss=0.03445, over 972523.86 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:43:00,266 INFO [train.py:715] (2/8) Epoch 10, batch 9300, loss[loss=0.1111, simple_loss=0.1919, pruned_loss=0.01511, over 4945.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03379, over 972391.77 frames.], batch size: 23, lr: 2.16e-04
2022-05-06 18:43:39,884 INFO [train.py:715] (2/8) Epoch 10, batch 9350, loss[loss=0.149, simple_loss=0.2274, pruned_loss=0.03531, over 4946.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.03383, over 972488.62 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:44:19,394 INFO [train.py:715] (2/8) Epoch 10, batch 9400, loss[loss=0.1489, simple_loss=0.2288, pruned_loss=0.03445, over 4968.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2149, pruned_loss=0.03425, over 973549.29 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:44:58,980 INFO [train.py:715] (2/8) Epoch 10, batch 9450, loss[loss=0.1743, simple_loss=0.2372, pruned_loss=0.05571, over 4850.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2153, pruned_loss=0.0346, over 973741.72 frames.], batch size: 30, lr: 2.16e-04
2022-05-06 18:45:38,378 INFO [train.py:715] (2/8) Epoch 10, batch 9500, loss[loss=0.1609, simple_loss=0.2305, pruned_loss=0.0456, over 4865.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03435, over 972758.78 frames.], batch size: 20, lr: 2.16e-04
2022-05-06 18:46:17,353 INFO [train.py:715] (2/8) Epoch 10, batch 9550, loss[loss=0.1362, simple_loss=0.2083, pruned_loss=0.03199, over 4956.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.034, over 972519.85 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:46:55,765 INFO [train.py:715] (2/8) Epoch 10, batch 9600, loss[loss=0.1464, simple_loss=0.2156, pruned_loss=0.0386, over 4884.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03295, over 971899.80 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 18:47:34,907 INFO [train.py:715] (2/8) Epoch 10, batch 9650, loss[loss=0.1401, simple_loss=0.2139, pruned_loss=0.03315, over 4948.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03351, over 972081.56 frames.], batch size: 35, lr: 2.16e-04
2022-05-06 18:48:14,558 INFO [train.py:715] (2/8) Epoch 10, batch 9700, loss[loss=0.1553, simple_loss=0.224, pruned_loss=0.04328, over 4748.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2119, pruned_loss=0.03358, over 972237.46 frames.], batch size: 16, lr: 2.16e-04
2022-05-06 18:48:52,980 INFO [train.py:715] (2/8) Epoch 10, batch 9750, loss[loss=0.133, simple_loss=0.2214, pruned_loss=0.02226, over 4751.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03321, over 971934.44 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:49:32,214 INFO [train.py:715] (2/8) Epoch 10, batch 9800, loss[loss=0.1499, simple_loss=0.218, pruned_loss=0.04095, over 4835.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2128, pruned_loss=0.03415, over 971995.59 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 18:50:11,748 INFO [train.py:715] (2/8) Epoch 10, batch 9850, loss[loss=0.1238, simple_loss=0.1956, pruned_loss=0.02595, over 4877.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2128, pruned_loss=0.0338, over 972270.34 frames.], batch size: 22, lr: 2.16e-04
2022-05-06 18:50:51,054 INFO [train.py:715] (2/8) Epoch 10, batch 9900, loss[loss=0.1472, simple_loss=0.2118, pruned_loss=0.04126, over 4870.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03348, over 973044.37 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:51:30,048 INFO [train.py:715] (2/8) Epoch 10, batch 9950, loss[loss=0.1379, simple_loss=0.2148, pruned_loss=0.03045, over 4935.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2127, pruned_loss=0.03329, over 971489.07 frames.], batch size: 23, lr: 2.16e-04
2022-05-06 18:52:10,242 INFO [train.py:715] (2/8) Epoch 10, batch 10000, loss[loss=0.1193, simple_loss=0.1866, pruned_loss=0.02603, over 4730.00 frames.], tot_loss[loss=0.1398, simple_loss=0.213, pruned_loss=0.03333, over 971491.42 frames.], batch size: 16, lr: 2.16e-04
2022-05-06 18:52:49,844 INFO [train.py:715] (2/8) Epoch 10, batch 10050, loss[loss=0.1684, simple_loss=0.2341, pruned_loss=0.05132, over 4974.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.03314, over 972345.39 frames.], batch size: 35, lr: 2.16e-04
2022-05-06 18:53:27,870 INFO [train.py:715] (2/8) Epoch 10, batch 10100, loss[loss=0.2097, simple_loss=0.2562, pruned_loss=0.08161, over 4933.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03369, over 972538.19 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:54:06,624 INFO [train.py:715] (2/8) Epoch 10, batch 10150, loss[loss=0.1454, simple_loss=0.2267, pruned_loss=0.03198, over 4777.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03343, over 972036.57 frames.], batch size: 18, lr: 2.16e-04
2022-05-06 18:54:46,537 INFO [train.py:715] (2/8) Epoch 10, batch 10200, loss[loss=0.1419, simple_loss=0.2027, pruned_loss=0.04049, over 4758.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.0334, over 972034.82 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:55:25,657 INFO [train.py:715] (2/8) Epoch 10, batch 10250, loss[loss=0.1317, simple_loss=0.2005, pruned_loss=0.03145, over 4852.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2119, pruned_loss=0.03332, over 972275.23 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 18:56:04,515 INFO [train.py:715] (2/8) Epoch 10, batch 10300, loss[loss=0.1258, simple_loss=0.2093, pruned_loss=0.02118, over 4974.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03352, over 972957.05 frames.], batch size: 24, lr: 2.16e-04
2022-05-06 18:56:44,440 INFO [train.py:715] (2/8) Epoch 10, batch 10350, loss[loss=0.1374, simple_loss=0.2175, pruned_loss=0.02872, over 4917.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2133, pruned_loss=0.03402, over 973194.32 frames.], batch size: 18, lr: 2.16e-04
2022-05-06 18:57:24,438 INFO [train.py:715] (2/8) Epoch 10, batch 10400, loss[loss=0.1455, simple_loss=0.2262, pruned_loss=0.03239, over 4895.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03354, over 972687.06 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 18:58:02,843 INFO [train.py:715] (2/8) Epoch 10, batch 10450, loss[loss=0.1497, simple_loss=0.2156, pruned_loss=0.04194, over 4888.00 frames.], tot_loss[loss=0.14, simple_loss=0.2126, pruned_loss=0.03371, over 973285.88 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 18:58:41,114 INFO [train.py:715] (2/8) Epoch 10, batch 10500, loss[loss=0.1386, simple_loss=0.2187, pruned_loss=0.02919, over 4807.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2117, pruned_loss=0.03324, over 973138.70 frames.], batch size: 24, lr: 2.16e-04
2022-05-06 18:59:20,243 INFO [train.py:715] (2/8) Epoch 10, batch 10550, loss[loss=0.1388, simple_loss=0.2134, pruned_loss=0.03214, over 4907.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2121, pruned_loss=0.03345, over 973792.40 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 18:59:59,205 INFO [train.py:715] (2/8) Epoch 10, batch 10600, loss[loss=0.1442, simple_loss=0.2038, pruned_loss=0.04231, over 4969.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2122, pruned_loss=0.03331, over 973357.95 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 19:00:37,419 INFO [train.py:715] (2/8) Epoch 10, batch 10650, loss[loss=0.131, simple_loss=0.2092, pruned_loss=0.02638, over 4954.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03303, over 972273.63 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 19:01:16,839 INFO [train.py:715] (2/8) Epoch 10, batch 10700, loss[loss=0.1579, simple_loss=0.2348, pruned_loss=0.04049, over 4791.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.03338, over 972391.04 frames.], batch size: 24, lr: 2.16e-04
2022-05-06 19:01:56,164 INFO [train.py:715] (2/8) Epoch 10, batch 10750, loss[loss=0.1416, simple_loss=0.2111, pruned_loss=0.0361, over 4780.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03372, over 971920.94 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 19:02:34,992 INFO [train.py:715] (2/8) Epoch 10, batch 10800, loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02939, over 4782.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2125, pruned_loss=0.0336, over 971387.96 frames.], batch size: 14, lr: 2.16e-04
2022-05-06 19:03:13,439 INFO [train.py:715] (2/8) Epoch 10, batch 10850, loss[loss=0.1341, simple_loss=0.2032, pruned_loss=0.03248, over 4967.00 frames.], tot_loss[loss=0.139, simple_loss=0.2117, pruned_loss=0.03319, over 971264.73 frames.], batch size: 24, lr: 2.16e-04
2022-05-06 19:03:52,882 INFO [train.py:715] (2/8) Epoch 10, batch 10900, loss[loss=0.1709, simple_loss=0.2543, pruned_loss=0.04369, over 4921.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.03378, over 970981.30 frames.], batch size: 18, lr: 2.16e-04
2022-05-06 19:04:31,792 INFO [train.py:715] (2/8) Epoch 10, batch 10950, loss[loss=0.1483, simple_loss=0.2248, pruned_loss=0.03593, over 4905.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2146, pruned_loss=0.0344, over 970996.76 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 19:05:10,348 INFO [train.py:715] (2/8) Epoch 10, batch 11000, loss[loss=0.1454, simple_loss=0.2162, pruned_loss=0.03727, over 4919.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03431, over 971180.01 frames.], batch size: 19, lr: 2.16e-04
2022-05-06 19:05:49,499 INFO [train.py:715] (2/8) Epoch 10, batch 11050, loss[loss=0.1307, simple_loss=0.2091, pruned_loss=0.02615, over 4924.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2144, pruned_loss=0.03415, over 971946.70 frames.], batch size: 39, lr: 2.16e-04
2022-05-06 19:06:29,282 INFO [train.py:715] (2/8) Epoch 10, batch 11100, loss[loss=0.1365, simple_loss=0.2087, pruned_loss=0.03216, over 4833.00 frames.], tot_loss[loss=0.1409, simple_loss=0.214, pruned_loss=0.03393, over 971472.54 frames.], batch size: 13, lr: 2.16e-04
2022-05-06 19:07:07,074 INFO [train.py:715] (2/8) Epoch 10, batch 11150, loss[loss=0.1607, simple_loss=0.2259, pruned_loss=0.04778, over 4913.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2137, pruned_loss=0.03392, over 970300.81 frames.], batch size: 17, lr: 2.16e-04
2022-05-06 19:07:46,334 INFO [train.py:715] (2/8) Epoch 10, batch 11200, loss[loss=0.1397, simple_loss=0.2134, pruned_loss=0.03298, over 4817.00 frames.], tot_loss[loss=0.14, simple_loss=0.2126, pruned_loss=0.03367, over 970264.32 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 19:08:25,402 INFO [train.py:715] (2/8) Epoch 10, batch 11250, loss[loss=0.1499, simple_loss=0.2183, pruned_loss=0.04079, over 4832.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.03344, over 970066.64 frames.], batch size: 12, lr: 2.16e-04
2022-05-06 19:09:03,755 INFO [train.py:715] (2/8) Epoch 10, batch 11300, loss[loss=0.1284, simple_loss=0.2102, pruned_loss=0.02334, over 4811.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03332, over 970384.62 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 19:09:42,490 INFO [train.py:715] (2/8) Epoch 10, batch 11350, loss[loss=0.1608, simple_loss=0.224, pruned_loss=0.04883, over 4864.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03353, over 970761.22 frames.], batch size: 32, lr: 2.16e-04
2022-05-06 19:10:21,472 INFO [train.py:715] (2/8) Epoch 10, batch 11400, loss[loss=0.1608, simple_loss=0.2371, pruned_loss=0.04226, over 4696.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2129, pruned_loss=0.0338, over 970750.81 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 19:11:00,938 INFO [train.py:715] (2/8) Epoch 10, batch 11450, loss[loss=0.1529, simple_loss=0.2194, pruned_loss=0.04323, over 4985.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03379, over 971720.92 frames.], batch size: 28, lr: 2.16e-04
2022-05-06 19:11:38,822 INFO [train.py:715] (2/8) Epoch 10, batch 11500, loss[loss=0.1328, simple_loss=0.2142, pruned_loss=0.02572, over 4785.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03347, over 971607.94 frames.], batch size: 21, lr: 2.16e-04
2022-05-06 19:12:17,874 INFO [train.py:715] (2/8) Epoch 10, batch 11550, loss[loss=0.1435, simple_loss=0.2212, pruned_loss=0.03292, over 4932.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2129, pruned_loss=0.03332, over 972022.93 frames.], batch size: 29, lr: 2.16e-04
2022-05-06 19:12:57,425 INFO [train.py:715] (2/8) Epoch 10, batch 11600, loss[loss=0.161, simple_loss=0.2344, pruned_loss=0.04379, over 4987.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03299, over 971821.90 frames.], batch size: 28, lr: 2.16e-04
2022-05-06 19:13:35,826 INFO [train.py:715] (2/8) Epoch 10, batch 11650, loss[loss=0.156, simple_loss=0.2248, pruned_loss=0.04365, over 4832.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03293, over 971318.49 frames.], batch size: 30, lr: 2.16e-04
2022-05-06 19:14:14,881 INFO [train.py:715] (2/8) Epoch 10, batch 11700, loss[loss=0.1469, simple_loss=0.216, pruned_loss=0.03886, over 4965.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03298, over 971894.53 frames.], batch size: 15, lr: 2.16e-04
2022-05-06 19:14:53,450 INFO [train.py:715] (2/8) Epoch 10, batch 11750, loss[loss=0.1242, simple_loss=0.2035, pruned_loss=0.02244, over 4950.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03304, over 971652.53 frames.], batch size: 29, lr: 2.15e-04
2022-05-06 19:15:32,370 INFO [train.py:715] (2/8) Epoch 10, batch 11800, loss[loss=0.151, simple_loss=0.2172, pruned_loss=0.0424, over 4975.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03274, over 972158.91 frames.], batch size: 24, lr: 2.15e-04
2022-05-06 19:16:10,394 INFO [train.py:715] (2/8) Epoch 10, batch 11850, loss[loss=0.1327, simple_loss=0.2027, pruned_loss=0.03138, over 4817.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2116, pruned_loss=0.03277, over 972848.00 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:16:49,163 INFO [train.py:715] (2/8) Epoch 10, batch 11900, loss[loss=0.1396, simple_loss=0.2079, pruned_loss=0.03568, over 4860.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03298, over 972539.59 frames.], batch size: 30, lr: 2.15e-04
2022-05-06 19:17:30,483 INFO [train.py:715] (2/8) Epoch 10, batch 11950, loss[loss=0.1375, simple_loss=0.2135, pruned_loss=0.03076, over 4754.00 frames.], tot_loss[loss=0.1389, simple_loss=0.212, pruned_loss=0.03293, over 971155.84 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:18:09,367 INFO [train.py:715] (2/8) Epoch 10, batch 12000, loss[loss=0.1232, simple_loss=0.2107, pruned_loss=0.0178, over 4806.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03263, over 971218.44 frames.], batch size: 25, lr: 2.15e-04
2022-05-06 19:18:09,368 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 19:18:19,016 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1065, simple_loss=0.1908, pruned_loss=0.01105, over 914524.00 frames.
2022-05-06 19:18:57,893 INFO [train.py:715] (2/8) Epoch 10, batch 12050, loss[loss=0.142, simple_loss=0.2197, pruned_loss=0.03215, over 4821.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2114, pruned_loss=0.03308, over 972187.54 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:19:37,115 INFO [train.py:715] (2/8) Epoch 10, batch 12100, loss[loss=0.1449, simple_loss=0.2094, pruned_loss=0.04019, over 4959.00 frames.], tot_loss[loss=0.1406, simple_loss=0.213, pruned_loss=0.03413, over 971857.17 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:20:16,373 INFO [train.py:715] (2/8) Epoch 10, batch 12150, loss[loss=0.1213, simple_loss=0.1953, pruned_loss=0.02366, over 4979.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2125, pruned_loss=0.03366, over 971872.24 frames.], batch size: 24, lr: 2.15e-04
2022-05-06 19:20:55,545 INFO [train.py:715] (2/8) Epoch 10, batch 12200, loss[loss=0.1363, simple_loss=0.2084, pruned_loss=0.03207, over 4955.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03353, over 972304.78 frames.], batch size: 35, lr: 2.15e-04
2022-05-06 19:21:34,085 INFO [train.py:715] (2/8) Epoch 10, batch 12250, loss[loss=0.1519, simple_loss=0.2133, pruned_loss=0.04528, over 4983.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03355, over 973213.47 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:22:13,028 INFO [train.py:715] (2/8) Epoch 10, batch 12300, loss[loss=0.1301, simple_loss=0.2075, pruned_loss=0.02638, over 4954.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2134, pruned_loss=0.03361, over 973208.94 frames.], batch size: 21, lr: 2.15e-04
2022-05-06 19:22:51,959 INFO [train.py:715] (2/8) Epoch 10, batch 12350, loss[loss=0.1601, simple_loss=0.2292, pruned_loss=0.0455, over 4968.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2141, pruned_loss=0.0337, over 972219.98 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:23:30,791 INFO [train.py:715] (2/8) Epoch 10, batch 12400, loss[loss=0.1388, simple_loss=0.2259, pruned_loss=0.02583, over 4906.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03337, over 972858.44 frames.], batch size: 29, lr: 2.15e-04
2022-05-06 19:24:09,217 INFO [train.py:715] (2/8) Epoch 10, batch 12450, loss[loss=0.1869, simple_loss=0.2501, pruned_loss=0.0618, over 4748.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03346, over 972475.73 frames.], batch size: 16, lr: 2.15e-04
2022-05-06 19:24:48,250 INFO [train.py:715] (2/8) Epoch 10, batch 12500, loss[loss=0.1288, simple_loss=0.2061, pruned_loss=0.02578, over 4827.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03343, over 971463.45 frames.], batch size: 30, lr: 2.15e-04
2022-05-06 19:25:27,027 INFO [train.py:715] (2/8) Epoch 10, batch 12550, loss[loss=0.1019, simple_loss=0.1727, pruned_loss=0.01555, over 4970.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.0333, over 972212.42 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:26:05,181 INFO [train.py:715] (2/8) Epoch 10, batch 12600, loss[loss=0.1394, simple_loss=0.2119, pruned_loss=0.03345, over 4839.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03278, over 972637.86 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:26:43,472 INFO [train.py:715] (2/8) Epoch 10, batch 12650, loss[loss=0.1375, simple_loss=0.2146, pruned_loss=0.03017, over 4907.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03275, over 972548.38 frames.], batch size: 17, lr: 2.15e-04
2022-05-06 19:27:22,409 INFO [train.py:715] (2/8) Epoch 10, batch 12700, loss[loss=0.1263, simple_loss=0.1958, pruned_loss=0.02842, over 4779.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2122, pruned_loss=0.03271, over 971876.83 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:28:00,754 INFO [train.py:715] (2/8) Epoch 10, batch 12750, loss[loss=0.1588, simple_loss=0.238, pruned_loss=0.03983, over 4941.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.0324, over 971923.36 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:28:39,215 INFO [train.py:715] (2/8) Epoch 10, batch 12800, loss[loss=0.1533, simple_loss=0.2243, pruned_loss=0.04118, over 4929.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2108, pruned_loss=0.03211, over 971843.54 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:29:18,644 INFO [train.py:715] (2/8) Epoch 10, batch 12850, loss[loss=0.1419, simple_loss=0.2129, pruned_loss=0.03543, over 4844.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03275, over 971615.48 frames.], batch size: 20, lr: 2.15e-04
2022-05-06 19:29:57,809 INFO [train.py:715] (2/8) Epoch 10, batch 12900, loss[loss=0.155, simple_loss=0.2199, pruned_loss=0.04509, over 4817.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03255, over 971690.69 frames.], batch size: 26, lr: 2.15e-04
2022-05-06 19:30:36,229 INFO [train.py:715] (2/8) Epoch 10, batch 12950, loss[loss=0.1591, simple_loss=0.2296, pruned_loss=0.04433, over 4747.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03289, over 972529.65 frames.], batch size: 16, lr: 2.15e-04
2022-05-06 19:31:14,798 INFO [train.py:715] (2/8) Epoch 10, batch 13000, loss[loss=0.1198, simple_loss=0.191, pruned_loss=0.02429, over 4903.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2125, pruned_loss=0.0328, over 971576.55 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:31:54,375 INFO [train.py:715] (2/8) Epoch 10, batch 13050, loss[loss=0.1359, simple_loss=0.2032, pruned_loss=0.03429, over 4798.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03324, over 971039.98 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:32:32,861 INFO [train.py:715] (2/8) Epoch 10, batch 13100, loss[loss=0.1347, simple_loss=0.212, pruned_loss=0.0287, over 4824.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.0331, over 971417.91 frames.], batch size: 26, lr: 2.15e-04
2022-05-06 19:33:11,984 INFO [train.py:715] (2/8) Epoch 10, batch 13150, loss[loss=0.1388, simple_loss=0.2018, pruned_loss=0.03794, over 4848.00 frames.], tot_loss[loss=0.14, simple_loss=0.2133, pruned_loss=0.03336, over 971789.55 frames.], batch size: 26, lr: 2.15e-04
2022-05-06 19:33:51,012 INFO [train.py:715] (2/8) Epoch 10, batch 13200, loss[loss=0.1522, simple_loss=0.2338, pruned_loss=0.03528, over 4791.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03286, over 971554.62 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:34:30,052 INFO [train.py:715] (2/8) Epoch 10, batch 13250, loss[loss=0.1318, simple_loss=0.2108, pruned_loss=0.02635, over 4881.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.0329, over 972343.03 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:35:08,700 INFO [train.py:715] (2/8) Epoch 10, batch 13300, loss[loss=0.1484, simple_loss=0.2137, pruned_loss=0.04153, over 4967.00 frames.], tot_loss[loss=0.139, simple_loss=0.2117, pruned_loss=0.03313, over 972468.09 frames.], batch size: 35, lr: 2.15e-04
2022-05-06 19:35:47,101 INFO [train.py:715] (2/8) Epoch 10, batch 13350, loss[loss=0.1451, simple_loss=0.2178, pruned_loss=0.03622, over 4953.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03328, over 972819.49 frames.], batch size: 24, lr: 2.15e-04
2022-05-06 19:36:26,375 INFO [train.py:715] (2/8) Epoch 10, batch 13400, loss[loss=0.1457, simple_loss=0.2095, pruned_loss=0.04094, over 4864.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03318, over 973263.28 frames.], batch size: 32, lr: 2.15e-04
2022-05-06 19:37:04,723 INFO [train.py:715] (2/8) Epoch 10, batch 13450, loss[loss=0.1538, simple_loss=0.2267, pruned_loss=0.0405, over 4820.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.03344, over 972443.40 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:37:42,965 INFO [train.py:715] (2/8) Epoch 10, batch 13500, loss[loss=0.1425, simple_loss=0.211, pruned_loss=0.03694, over 4900.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.03381, over 973106.73 frames.], batch size: 17, lr: 2.15e-04
2022-05-06 19:38:22,036 INFO [train.py:715] (2/8) Epoch 10, batch 13550, loss[loss=0.1461, simple_loss=0.2167, pruned_loss=0.03775, over 4984.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2135, pruned_loss=0.03364, over 972853.53 frames.], batch size: 25, lr: 2.15e-04
2022-05-06 19:39:00,611 INFO [train.py:715] (2/8) Epoch 10, batch 13600, loss[loss=0.1177, simple_loss=0.1901, pruned_loss=0.02262, over 4769.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03326, over 972870.47 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:39:39,009 INFO [train.py:715] (2/8) Epoch 10, batch 13650, loss[loss=0.1405, simple_loss=0.2102, pruned_loss=0.03537, over 4892.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2112, pruned_loss=0.03268, over 972706.52 frames.], batch size: 17, lr: 2.15e-04
2022-05-06 19:40:17,579 INFO [train.py:715] (2/8) Epoch 10, batch 13700, loss[loss=0.128, simple_loss=0.1913, pruned_loss=0.03235, over 4887.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.03253, over 973291.02 frames.], batch size: 22, lr: 2.15e-04
2022-05-06 19:40:57,641 INFO [train.py:715] (2/8) Epoch 10, batch 13750, loss[loss=0.1563, simple_loss=0.2314, pruned_loss=0.0406, over 4864.00 frames.], tot_loss[loss=0.1393, simple_loss=0.212, pruned_loss=0.03324, over 972836.64 frames.], batch size: 20, lr: 2.15e-04
2022-05-06 19:41:37,005 INFO [train.py:715] (2/8) Epoch 10, batch 13800, loss[loss=0.1713, simple_loss=0.2363, pruned_loss=0.05318, over 4729.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2111, pruned_loss=0.03294, over 972528.77 frames.], batch size: 16, lr: 2.15e-04
2022-05-06 19:42:15,514 INFO [train.py:715] (2/8) Epoch 10, batch 13850, loss[loss=0.1331, simple_loss=0.2102, pruned_loss=0.02801, over 4773.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2122, pruned_loss=0.03331, over 972418.38 frames.], batch size: 16, lr: 2.15e-04
2022-05-06 19:42:55,152 INFO [train.py:715] (2/8) Epoch 10, batch 13900, loss[loss=0.1281, simple_loss=0.1988, pruned_loss=0.0287, over 4851.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2113, pruned_loss=0.03312, over 972836.26 frames.], batch size: 30, lr: 2.15e-04
2022-05-06 19:43:33,823 INFO [train.py:715] (2/8) Epoch 10, batch 13950, loss[loss=0.1256, simple_loss=0.1993, pruned_loss=0.02596, over 4863.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03291, over 973052.76 frames.], batch size: 20, lr: 2.15e-04
2022-05-06 19:44:12,830 INFO [train.py:715] (2/8) Epoch 10, batch 14000, loss[loss=0.1232, simple_loss=0.1908, pruned_loss=0.02786, over 4952.00 frames.], tot_loss[loss=0.1395, simple_loss=0.212, pruned_loss=0.03349, over 972222.81 frames.], batch size: 35, lr: 2.15e-04
2022-05-06 19:44:51,236 INFO [train.py:715] (2/8) Epoch 10, batch 14050, loss[loss=0.1258, simple_loss=0.2084, pruned_loss=0.02162, over 4784.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2118, pruned_loss=0.03345, over 972808.95 frames.], batch size: 17, lr: 2.15e-04
2022-05-06 19:45:30,766 INFO [train.py:715] (2/8) Epoch 10, batch 14100, loss[loss=0.1253, simple_loss=0.2052, pruned_loss=0.02269, over 4828.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03332, over 972504.38 frames.], batch size: 26, lr: 2.15e-04
2022-05-06 19:46:09,125 INFO [train.py:715] (2/8) Epoch 10, batch 14150, loss[loss=0.1242, simple_loss=0.2048, pruned_loss=0.02182, over 4808.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03404, over 972740.88 frames.], batch size: 25, lr: 2.15e-04
2022-05-06 19:46:47,034 INFO [train.py:715] (2/8) Epoch 10, batch 14200, loss[loss=0.1417, simple_loss=0.2215, pruned_loss=0.03092, over 4901.00 frames.], tot_loss[loss=0.14, simple_loss=0.2126, pruned_loss=0.0337, over 972467.62 frames.], batch size: 19, lr: 2.15e-04
2022-05-06 19:47:26,633 INFO [train.py:715] (2/8) Epoch 10, batch 14250, loss[loss=0.1144, simple_loss=0.1847, pruned_loss=0.02208, over 4788.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03365, over 972658.31 frames.], batch size: 17, lr: 2.15e-04
2022-05-06 19:48:05,009 INFO [train.py:715] (2/8) Epoch 10, batch 14300, loss[loss=0.1691, simple_loss=0.2323, pruned_loss=0.05292, over 4755.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03352, over 972848.43 frames.], batch size: 14, lr: 2.15e-04
2022-05-06 19:48:43,128 INFO [train.py:715] (2/8) Epoch 10, batch 14350, loss[loss=0.1222, simple_loss=0.2052, pruned_loss=0.01964, over 4709.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03318, over 972296.29 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:49:21,567 INFO [train.py:715] (2/8) Epoch 10, batch 14400, loss[loss=0.1275, simple_loss=0.2071, pruned_loss=0.02393, over 4988.00 frames.], tot_loss[loss=0.14, simple_loss=0.2133, pruned_loss=0.03338, over 973028.62 frames.], batch size: 28, lr: 2.15e-04
2022-05-06 19:50:01,194 INFO [train.py:715] (2/8) Epoch 10, batch 14450, loss[loss=0.1511, simple_loss=0.2279, pruned_loss=0.03717, over 4802.00 frames.], tot_loss[loss=0.1394, simple_loss=0.213, pruned_loss=0.03296, over 972711.48 frames.], batch size: 24, lr: 2.15e-04
2022-05-06 19:50:39,562 INFO [train.py:715] (2/8) Epoch 10, batch 14500, loss[loss=0.1271, simple_loss=0.202, pruned_loss=0.02611, over 4923.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2133, pruned_loss=0.03288, over 972403.25 frames.], batch size: 17, lr: 2.15e-04
2022-05-06 19:51:17,697 INFO [train.py:715] (2/8) Epoch 10, batch 14550, loss[loss=0.1301, simple_loss=0.2116, pruned_loss=0.02429, over 4782.00 frames.], tot_loss[loss=0.14, simple_loss=0.2133, pruned_loss=0.03334, over 972016.51 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:51:57,347 INFO [train.py:715] (2/8) Epoch 10, batch 14600, loss[loss=0.1287, simple_loss=0.2021, pruned_loss=0.0276, over 4924.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03363, over 972540.73 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:52:35,981 INFO [train.py:715] (2/8) Epoch 10, batch 14650, loss[loss=0.1642, simple_loss=0.2355, pruned_loss=0.04642, over 4938.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.0336, over 971895.19 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:53:14,371 INFO [train.py:715] (2/8) Epoch 10, batch 14700, loss[loss=0.1661, simple_loss=0.2366, pruned_loss=0.04782, over 4844.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2137, pruned_loss=0.03426, over 970665.24 frames.], batch size: 30, lr: 2.15e-04
2022-05-06 19:53:53,343 INFO [train.py:715] (2/8) Epoch 10, batch 14750, loss[loss=0.1393, simple_loss=0.2233, pruned_loss=0.02762, over 4935.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2148, pruned_loss=0.03451, over 970823.07 frames.], batch size: 29, lr: 2.15e-04
2022-05-06 19:54:33,138 INFO [train.py:715] (2/8) Epoch 10, batch 14800, loss[loss=0.1271, simple_loss=0.1994, pruned_loss=0.02736, over 4726.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2143, pruned_loss=0.03453, over 971005.01 frames.], batch size: 16, lr: 2.15e-04
2022-05-06 19:55:12,161 INFO [train.py:715] (2/8) Epoch 10, batch 14850, loss[loss=0.1413, simple_loss=0.2217, pruned_loss=0.0305, over 4830.00 frames.], tot_loss[loss=0.1415, simple_loss=0.214, pruned_loss=0.03444, over 970600.73 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:55:50,175 INFO [train.py:715] (2/8) Epoch 10, batch 14900, loss[loss=0.1297, simple_loss=0.2086, pruned_loss=0.02537, over 4863.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2142, pruned_loss=0.03429, over 969772.37 frames.], batch size: 20, lr: 2.15e-04
2022-05-06 19:56:30,295 INFO [train.py:715] (2/8) Epoch 10, batch 14950, loss[loss=0.1538, simple_loss=0.2221, pruned_loss=0.04275, over 4946.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2141, pruned_loss=0.03443, over 970634.00 frames.], batch size: 24, lr: 2.15e-04
2022-05-06 19:57:09,817 INFO [train.py:715] (2/8) Epoch 10, batch 15000, loss[loss=0.1532, simple_loss=0.2345, pruned_loss=0.036, over 4925.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.03368, over 970604.93 frames.], batch size: 23, lr: 2.15e-04
2022-05-06 19:57:09,818 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 19:57:19,461 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1065, simple_loss=0.1909, pruned_loss=0.01111, over 914524.00 frames.
2022-05-06 19:57:59,087 INFO [train.py:715] (2/8) Epoch 10, batch 15050, loss[loss=0.1355, simple_loss=0.2062, pruned_loss=0.0324, over 4824.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2139, pruned_loss=0.03393, over 971693.41 frames.], batch size: 15, lr: 2.15e-04
2022-05-06 19:58:38,145 INFO [train.py:715] (2/8) Epoch 10, batch 15100, loss[loss=0.1307, simple_loss=0.2024, pruned_loss=0.02953, over 4778.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03318, over 970745.37 frames.], batch size: 18, lr: 2.15e-04
2022-05-06 19:59:17,366 INFO [train.py:715] (2/8) Epoch 10, batch 15150, loss[loss=0.1894, simple_loss=0.2541, pruned_loss=0.06238, over 4880.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03359, over 971208.92 frames.], batch size: 39, lr: 2.14e-04
2022-05-06 19:59:56,362 INFO [train.py:715] (2/8) Epoch 10, batch 15200, loss[loss=0.1352, simple_loss=0.2161, pruned_loss=0.02711, over 4862.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2122, pruned_loss=0.03354, over 971815.89 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:00:35,742 INFO [train.py:715] (2/8) Epoch 10, batch 15250, loss[loss=0.1233, simple_loss=0.189, pruned_loss=0.02884, over 4964.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2129, pruned_loss=0.0339, over 971507.37 frames.], batch size: 35, lr: 2.14e-04
2022-05-06 20:01:14,785 INFO [train.py:715] (2/8) Epoch 10, batch 15300, loss[loss=0.1108, simple_loss=0.1891, pruned_loss=0.01626, over 4832.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03377, over 971239.33 frames.], batch size: 26, lr: 2.14e-04
2022-05-06 20:01:54,058 INFO [train.py:715] (2/8) Epoch 10, batch 15350, loss[loss=0.1419, simple_loss=0.225, pruned_loss=0.02943, over 4980.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03364, over 972197.70 frames.], batch size: 25, lr: 2.14e-04
2022-05-06 20:02:34,123 INFO [train.py:715] (2/8) Epoch 10, batch 15400, loss[loss=0.103, simple_loss=0.1699, pruned_loss=0.01808, over 4793.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2134, pruned_loss=0.03356, over 971992.52 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:03:13,392 INFO [train.py:715] (2/8) Epoch 10, batch 15450, loss[loss=0.1067, simple_loss=0.1898, pruned_loss=0.01179, over 4801.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.03363, over 971506.75 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 20:03:53,465 INFO [train.py:715] (2/8) Epoch 10, batch 15500, loss[loss=0.1534, simple_loss=0.2237, pruned_loss=0.04151, over 4760.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2136, pruned_loss=0.03344, over 971724.88 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:04:32,471 INFO [train.py:715] (2/8) Epoch 10, batch 15550, loss[loss=0.1458, simple_loss=0.2223, pruned_loss=0.03466, over 4932.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.0334, over 971334.08 frames.], batch size: 39, lr: 2.14e-04
2022-05-06 20:05:11,887 INFO [train.py:715] (2/8) Epoch 10, batch 15600, loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02975, over 4772.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2126, pruned_loss=0.03286, over 971793.92 frames.], batch size: 17, lr: 2.14e-04
2022-05-06 20:05:50,244 INFO [train.py:715] (2/8) Epoch 10, batch 15650, loss[loss=0.1142, simple_loss=0.1949, pruned_loss=0.01676, over 4819.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03262, over 971952.55 frames.], batch size: 25, lr: 2.14e-04
2022-05-06 20:06:28,933 INFO [train.py:715] (2/8) Epoch 10, batch 15700, loss[loss=0.156, simple_loss=0.2363, pruned_loss=0.03782, over 4953.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2132, pruned_loss=0.03297, over 972328.64 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:07:08,405 INFO [train.py:715] (2/8) Epoch 10, batch 15750, loss[loss=0.1524, simple_loss=0.2282, pruned_loss=0.03833, over 4869.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2133, pruned_loss=0.03321, over 971884.02 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:07:46,972 INFO [train.py:715] (2/8) Epoch 10, batch 15800, loss[loss=0.1299, simple_loss=0.2037, pruned_loss=0.02798, over 4703.00 frames.], tot_loss[loss=0.14, simple_loss=0.2135, pruned_loss=0.03331, over 972753.88 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:08:26,774 INFO [train.py:715] (2/8) Epoch 10, batch 15850, loss[loss=0.1342, simple_loss=0.2077, pruned_loss=0.03037, over 4798.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2137, pruned_loss=0.03326, over 972733.42 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:09:05,641 INFO [train.py:715] (2/8) Epoch 10, batch 15900, loss[loss=0.1166, simple_loss=0.1906, pruned_loss=0.02127, over 4821.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2128, pruned_loss=0.03299, over 972377.55 frames.], batch size: 26, lr: 2.14e-04
2022-05-06 20:09:44,836 INFO [train.py:715] (2/8) Epoch 10, batch 15950, loss[loss=0.1443, simple_loss=0.2179, pruned_loss=0.03537, over 4968.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2133, pruned_loss=0.03356, over 971759.39 frames.], batch size: 28, lr: 2.14e-04
2022-05-06 20:10:23,753 INFO [train.py:715] (2/8) Epoch 10, batch 16000, loss[loss=0.1325, simple_loss=0.2058, pruned_loss=0.02957, over 4926.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03374, over 971985.87 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:11:02,643 INFO [train.py:715] (2/8) Epoch 10, batch 16050, loss[loss=0.1223, simple_loss=0.1989, pruned_loss=0.02287, over 4933.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2133, pruned_loss=0.03361, over 971153.62 frames.], batch size: 23, lr: 2.14e-04
2022-05-06 20:11:41,916 INFO [train.py:715] (2/8) Epoch 10, batch 16100, loss[loss=0.1314, simple_loss=0.2073, pruned_loss=0.02781, over 4983.00 frames.], tot_loss[loss=0.1399, simple_loss=0.213, pruned_loss=0.0334, over 971093.87 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 20:12:21,126 INFO [train.py:715] (2/8) Epoch 10, batch 16150, loss[loss=0.1307, simple_loss=0.2077, pruned_loss=0.02686, over 4935.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03307, over 971730.68 frames.], batch size: 29, lr: 2.14e-04
2022-05-06 20:13:01,096 INFO [train.py:715] (2/8) Epoch 10, batch 16200, loss[loss=0.1361, simple_loss=0.2145, pruned_loss=0.02883, over 4814.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03253, over 972056.38 frames.], batch size: 26, lr: 2.14e-04
2022-05-06 20:13:40,633 INFO [train.py:715] (2/8) Epoch 10, batch 16250, loss[loss=0.1221, simple_loss=0.1932, pruned_loss=0.02556, over 4768.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2128, pruned_loss=0.03315, over 971843.67 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:14:19,847 INFO [train.py:715] (2/8) Epoch 10, batch 16300, loss[loss=0.1185, simple_loss=0.1914, pruned_loss=0.02284, over 4793.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2138, pruned_loss=0.03367, over 972315.81 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 20:14:59,849 INFO [train.py:715] (2/8) Epoch 10, batch 16350, loss[loss=0.1401, simple_loss=0.2123, pruned_loss=0.03402, over 4781.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2134, pruned_loss=0.03342, over 972303.02 frames.], batch size: 17, lr: 2.14e-04
2022-05-06 20:15:39,244 INFO [train.py:715] (2/8) Epoch 10, batch 16400, loss[loss=0.1263, simple_loss=0.193, pruned_loss=0.02985, over 4838.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2135, pruned_loss=0.03342, over 971816.30 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:16:18,980 INFO [train.py:715] (2/8) Epoch 10, batch 16450, loss[loss=0.1478, simple_loss=0.2024, pruned_loss=0.04656, over 4700.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.0331, over 971921.72 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:16:57,468 INFO [train.py:715] (2/8) Epoch 10, batch 16500, loss[loss=0.1516, simple_loss=0.2233, pruned_loss=0.03996, over 4830.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03304, over 971911.06 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:17:36,175 INFO [train.py:715] (2/8) Epoch 10, batch 16550, loss[loss=0.1296, simple_loss=0.2099, pruned_loss=0.02462, over 4952.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03287, over 972855.59 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 20:18:15,836 INFO [train.py:715] (2/8) Epoch 10, batch 16600, loss[loss=0.106, simple_loss=0.1725, pruned_loss=0.01974, over 4775.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03324, over 973303.61 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:18:54,011 INFO [train.py:715] (2/8) Epoch 10, batch 16650, loss[loss=0.127, simple_loss=0.2021, pruned_loss=0.02593, over 4690.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03332, over 973393.28 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:19:33,369 INFO [train.py:715] (2/8) Epoch 10, batch 16700, loss[loss=0.1456, simple_loss=0.222, pruned_loss=0.03466, over 4688.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2132, pruned_loss=0.0333, over 972931.43 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:20:12,354 INFO [train.py:715] (2/8) Epoch 10, batch 16750, loss[loss=0.1545, simple_loss=0.2192, pruned_loss=0.04493, over 4989.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.0334, over 973427.47 frames.], batch size: 20, lr: 2.14e-04
2022-05-06 20:20:52,510 INFO [train.py:715] (2/8) Epoch 10, batch 16800, loss[loss=0.1508, simple_loss=0.218, pruned_loss=0.04175, over 4907.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03352, over 974502.85 frames.], batch size: 39, lr: 2.14e-04
2022-05-06 20:21:31,830 INFO [train.py:715] (2/8) Epoch 10, batch 16850, loss[loss=0.1391, simple_loss=0.2183, pruned_loss=0.02998, over 4763.00 frames.], tot_loss[loss=0.1413, simple_loss=0.214, pruned_loss=0.03427, over 973461.29 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:22:11,632 INFO [train.py:715] (2/8) Epoch 10, batch 16900, loss[loss=0.1442, simple_loss=0.2137, pruned_loss=0.03739, over 4774.00 frames.], tot_loss[loss=0.141, simple_loss=0.214, pruned_loss=0.034, over 973546.42 frames.], batch size: 18, lr: 2.14e-04
2022-05-06 20:22:51,673 INFO [train.py:715] (2/8) Epoch 10, batch 16950, loss[loss=0.1801, simple_loss=0.2528, pruned_loss=0.05374, over 4875.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2141, pruned_loss=0.03407, over 973520.67 frames.], batch size: 30, lr: 2.14e-04
2022-05-06 20:23:29,923 INFO [train.py:715] (2/8) Epoch 10, batch 17000, loss[loss=0.1133, simple_loss=0.1882, pruned_loss=0.01921, over 4886.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2138, pruned_loss=0.03392, over 973101.84 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:24:09,513 INFO [train.py:715] (2/8) Epoch 10, batch 17050, loss[loss=0.1604, simple_loss=0.2275, pruned_loss=0.04663, over 4840.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03417, over 972898.56 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:24:48,215 INFO [train.py:715] (2/8) Epoch 10, batch 17100, loss[loss=0.1169, simple_loss=0.1961, pruned_loss=0.01889, over 4993.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03414, over 972435.41 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:25:27,435 INFO [train.py:715] (2/8) Epoch 10, batch 17150, loss[loss=0.125, simple_loss=0.2047, pruned_loss=0.02263, over 4963.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03359, over 972918.56 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:26:07,400 INFO [train.py:715] (2/8) Epoch 10, batch 17200, loss[loss=0.1159, simple_loss=0.1808, pruned_loss=0.02547, over 4830.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.03348, over 972755.10 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:26:47,014 INFO [train.py:715] (2/8) Epoch 10, batch 17250, loss[loss=0.149, simple_loss=0.2175, pruned_loss=0.04022, over 4989.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2143, pruned_loss=0.03398, over 972792.29 frames.], batch size: 25, lr: 2.14e-04
2022-05-06 20:27:26,663 INFO [train.py:715] (2/8) Epoch 10, batch 17300, loss[loss=0.1218, simple_loss=0.1814, pruned_loss=0.03104, over 4802.00 frames.], tot_loss[loss=0.1421, simple_loss=0.215, pruned_loss=0.03458, over 973019.15 frames.], batch size: 12, lr: 2.14e-04
2022-05-06 20:28:05,424 INFO [train.py:715] (2/8) Epoch 10, batch 17350, loss[loss=0.1327, simple_loss=0.2026, pruned_loss=0.03142, over 4815.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2141, pruned_loss=0.03428, over 972260.29 frames.], batch size: 13, lr: 2.14e-04
2022-05-06 20:28:44,831 INFO [train.py:715] (2/8) Epoch 10, batch 17400, loss[loss=0.1347, simple_loss=0.2024, pruned_loss=0.0335, over 4863.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2137, pruned_loss=0.03385, over 971683.14 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:29:24,008 INFO [train.py:715] (2/8) Epoch 10, batch 17450, loss[loss=0.1276, simple_loss=0.2051, pruned_loss=0.02503, over 4881.00 frames.], tot_loss[loss=0.14, simple_loss=0.2127, pruned_loss=0.03369, over 971624.96 frames.], batch size: 16, lr: 2.14e-04
2022-05-06 20:30:02,985 INFO [train.py:715] (2/8) Epoch 10, batch 17500, loss[loss=0.1225, simple_loss=0.1967, pruned_loss=0.02418, over 4943.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2131, pruned_loss=0.03378, over 971901.62 frames.], batch size: 23, lr: 2.14e-04
2022-05-06 20:30:42,973 INFO [train.py:715] (2/8) Epoch 10, batch 17550, loss[loss=0.1425, simple_loss=0.2204, pruned_loss=0.03226, over 4900.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03382, over 971513.46 frames.], batch size: 18, lr: 2.14e-04
2022-05-06 20:31:21,945 INFO [train.py:715] (2/8) Epoch 10, batch 17600, loss[loss=0.1268, simple_loss=0.1907, pruned_loss=0.03143, over 4863.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.0332, over 971667.61 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:32:01,507 INFO [train.py:715] (2/8) Epoch 10, batch 17650, loss[loss=0.1416, simple_loss=0.2163, pruned_loss=0.03345, over 4895.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03297, over 971774.84 frames.], batch size: 32, lr: 2.14e-04
2022-05-06 20:32:40,268 INFO [train.py:715] (2/8) Epoch 10, batch 17700, loss[loss=0.1466, simple_loss=0.2171, pruned_loss=0.03808, over 4938.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2122, pruned_loss=0.03332, over 972020.40 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:33:20,044 INFO [train.py:715] (2/8) Epoch 10, batch 17750, loss[loss=0.1247, simple_loss=0.199, pruned_loss=0.0252, over 4800.00 frames.], tot_loss[loss=0.14, simple_loss=0.2125, pruned_loss=0.03379, over 972525.88 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:33:59,768 INFO [train.py:715] (2/8) Epoch 10, batch 17800, loss[loss=0.1229, simple_loss=0.1978, pruned_loss=0.02401, over 4932.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2123, pruned_loss=0.03379, over 972826.81 frames.], batch size: 21, lr: 2.14e-04
2022-05-06 20:34:38,714 INFO [train.py:715] (2/8) Epoch 10, batch 17850, loss[loss=0.1122, simple_loss=0.191, pruned_loss=0.01673, over 4811.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2123, pruned_loss=0.03363, over 972354.69 frames.], batch size: 27, lr: 2.14e-04
2022-05-06 20:35:18,469 INFO [train.py:715] (2/8) Epoch 10, batch 17900, loss[loss=0.1719, simple_loss=0.2409, pruned_loss=0.05149, over 4877.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03298, over 972268.08 frames.], batch size: 34, lr: 2.14e-04
2022-05-06 20:35:57,404 INFO [train.py:715] (2/8) Epoch 10, batch 17950, loss[loss=0.1663, simple_loss=0.2484, pruned_loss=0.04213, over 4888.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.03278, over 971867.56 frames.], batch size: 22, lr: 2.14e-04
2022-05-06 20:36:36,022 INFO [train.py:715] (2/8) Epoch 10, batch 18000, loss[loss=0.1264, simple_loss=0.2025, pruned_loss=0.02513, over 4909.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03326, over 972357.32 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:36:36,022 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 20:36:45,528 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1064, simple_loss=0.1906, pruned_loss=0.01104, over 914524.00 frames.
2022-05-06 20:37:24,881 INFO [train.py:715] (2/8) Epoch 10, batch 18050, loss[loss=0.1336, simple_loss=0.207, pruned_loss=0.03012, over 4825.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03384, over 972320.17 frames.], batch size: 13, lr: 2.14e-04
2022-05-06 20:38:03,974 INFO [train.py:715] (2/8) Epoch 10, batch 18100, loss[loss=0.1141, simple_loss=0.1934, pruned_loss=0.01735, over 4799.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03387, over 971762.01 frames.], batch size: 24, lr: 2.14e-04
2022-05-06 20:38:43,263 INFO [train.py:715] (2/8) Epoch 10, batch 18150, loss[loss=0.1515, simple_loss=0.2364, pruned_loss=0.03334, over 4890.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.0336, over 970725.56 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:39:21,944 INFO [train.py:715] (2/8) Epoch 10, batch 18200, loss[loss=0.1266, simple_loss=0.1934, pruned_loss=0.02992, over 4795.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03384, over 971618.74 frames.], batch size: 18, lr: 2.14e-04
2022-05-06 20:40:00,620 INFO [train.py:715] (2/8) Epoch 10, batch 18250, loss[loss=0.125, simple_loss=0.205, pruned_loss=0.02248, over 4853.00 frames.], tot_loss[loss=0.14, simple_loss=0.213, pruned_loss=0.0335, over 971848.16 frames.], batch size: 20, lr: 2.14e-04
2022-05-06 20:40:40,107 INFO [train.py:715] (2/8) Epoch 10, batch 18300, loss[loss=0.1489, simple_loss=0.2217, pruned_loss=0.0381, over 4772.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2134, pruned_loss=0.03349, over 971853.73 frames.], batch size: 19, lr: 2.14e-04
2022-05-06 20:41:19,472 INFO [train.py:715] (2/8) Epoch 10, batch 18350, loss[loss=0.1634, simple_loss=0.2359, pruned_loss=0.04548, over 4924.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.0331, over 971631.71 frames.], batch size: 23, lr: 2.14e-04
2022-05-06 20:41:57,961 INFO [train.py:715] (2/8) Epoch 10, batch 18400, loss[loss=0.1325, simple_loss=0.2084, pruned_loss=0.02832, over 4959.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2138, pruned_loss=0.03351, over 972777.32 frames.], batch size: 15, lr: 2.14e-04
2022-05-06 20:42:37,148 INFO [train.py:715] (2/8) Epoch 10, batch 18450, loss[loss=0.1035, simple_loss=0.1836, pruned_loss=0.01175, over 4978.00 frames.], tot_loss[loss=0.139, simple_loss=0.2128, pruned_loss=0.03265, over 973397.82 frames.], batch size: 14, lr: 2.14e-04
2022-05-06 20:43:16,003 INFO [train.py:715] (2/8) Epoch 10, batch 18500, loss[loss=0.1147, simple_loss=0.181, pruned_loss=0.02421, over 4817.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2128, pruned_loss=0.03286, over 973976.46 frames.], batch size: 25, lr: 2.14e-04
2022-05-06 20:43:55,528 INFO [train.py:715] (2/8) Epoch 10, batch 18550, loss[loss=0.1386, simple_loss=0.2249, pruned_loss=0.02616, over 4756.00 frames.], tot_loss[loss=0.139, simple_loss=0.2123, pruned_loss=0.0329, over 972539.37 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 20:44:33,848 INFO [train.py:715] (2/8) Epoch 10, batch 18600, loss[loss=0.1309, simple_loss=0.2097, pruned_loss=0.02605, over 4745.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03347, over 972563.56 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 20:45:13,257 INFO [train.py:715] (2/8) Epoch 10, batch 18650, loss[loss=0.1758, simple_loss=0.2286, pruned_loss=0.06144, over 4778.00 frames.], tot_loss[loss=0.1404, simple_loss=0.213, pruned_loss=0.03394, over 971876.77 frames.], batch size: 14, lr: 2.13e-04
2022-05-06 20:45:52,991 INFO [train.py:715] (2/8) Epoch 10, batch 18700, loss[loss=0.1408, simple_loss=0.2215, pruned_loss=0.03006, over 4977.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2135, pruned_loss=0.03467, over 972396.18 frames.], batch size: 25, lr: 2.13e-04
2022-05-06 20:46:31,253 INFO [train.py:715] (2/8) Epoch 10, batch 18750, loss[loss=0.1293, simple_loss=0.2076, pruned_loss=0.02552, over 4863.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03501, over 972048.22 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 20:47:10,633 INFO [train.py:715] (2/8) Epoch 10, batch 18800, loss[loss=0.1468, simple_loss=0.2125, pruned_loss=0.04054, over 4690.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2147, pruned_loss=0.03532, over 971361.49 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 20:47:50,112 INFO [train.py:715] (2/8) Epoch 10, batch 18850, loss[loss=0.137, simple_loss=0.2078, pruned_loss=0.03311, over 4866.00 frames.], tot_loss[loss=0.142, simple_loss=0.2144, pruned_loss=0.03482, over 972212.55 frames.], batch size: 20, lr: 2.13e-04
2022-05-06 20:48:29,013 INFO [train.py:715] (2/8) Epoch 10, batch 18900, loss[loss=0.136, simple_loss=0.2121, pruned_loss=0.02991, over 4814.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2146, pruned_loss=0.03495, over 972214.90 frames.], batch size: 25, lr: 2.13e-04
2022-05-06 20:49:08,064 INFO [train.py:715] (2/8) Epoch 10, batch 18950, loss[loss=0.1467, simple_loss=0.2183, pruned_loss=0.03756, over 4884.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2143, pruned_loss=0.03489, over 972971.33 frames.], batch size: 22, lr: 2.13e-04
2022-05-06 20:49:48,334 INFO [train.py:715] (2/8) Epoch 10, batch 19000, loss[loss=0.1583, simple_loss=0.2341, pruned_loss=0.04125, over 4917.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2134, pruned_loss=0.03436, over 972697.79 frames.], batch size: 17, lr: 2.13e-04
2022-05-06 20:50:27,641 INFO [train.py:715] (2/8) Epoch 10, batch 19050, loss[loss=0.1439, simple_loss=0.2054, pruned_loss=0.04116, over 4869.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2131, pruned_loss=0.03401, over 972952.97 frames.], batch size: 32, lr: 2.13e-04
2022-05-06 20:51:06,451 INFO [train.py:715] (2/8) Epoch 10, batch 19100, loss[loss=0.1853, simple_loss=0.2703, pruned_loss=0.05015, over 4798.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03355, over 973676.47 frames.], batch size: 14, lr: 2.13e-04
2022-05-06 20:51:46,325 INFO [train.py:715] (2/8) Epoch 10, batch 19150, loss[loss=0.1179, simple_loss=0.1901, pruned_loss=0.02282, over 4770.00 frames.], tot_loss[loss=0.1404, simple_loss=0.213, pruned_loss=0.0339, over 974029.51 frames.], batch size: 12, lr: 2.13e-04
2022-05-06 20:52:26,498 INFO [train.py:715] (2/8) Epoch 10, batch 19200, loss[loss=0.1317, simple_loss=0.2132, pruned_loss=0.02503, over 4944.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.0335, over 973720.62 frames.], batch size: 23, lr: 2.13e-04
2022-05-06 20:53:06,171 INFO [train.py:715] (2/8) Epoch 10, batch 19250, loss[loss=0.1222, simple_loss=0.187, pruned_loss=0.02874, over 4913.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03296, over 973462.49 frames.], batch size: 17, lr: 2.13e-04
2022-05-06 20:53:46,070 INFO [train.py:715] (2/8) Epoch 10, batch 19300, loss[loss=0.1064, simple_loss=0.1828, pruned_loss=0.01495, over 4921.00 frames.], tot_loss[loss=0.139, simple_loss=0.2118, pruned_loss=0.03311, over 972439.19 frames.], batch size: 29, lr: 2.13e-04
2022-05-06 20:54:26,473 INFO [train.py:715] (2/8) Epoch 10, batch 19350, loss[loss=0.1499, simple_loss=0.2302, pruned_loss=0.03481, over 4826.00 frames.], tot_loss[loss=0.1389, simple_loss=0.212, pruned_loss=0.03289, over 972922.59 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 20:55:06,650 INFO [train.py:715] (2/8) Epoch 10, batch 19400, loss[loss=0.152, simple_loss=0.2239, pruned_loss=0.04001, over 4862.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2114, pruned_loss=0.03296, over 972854.11 frames.], batch size: 20, lr: 2.13e-04
2022-05-06 20:55:45,796 INFO [train.py:715] (2/8) Epoch 10, batch 19450, loss[loss=0.1392, simple_loss=0.2161, pruned_loss=0.03118, over 4929.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2111, pruned_loss=0.03285, over 972774.44 frames.], batch size: 23, lr: 2.13e-04
2022-05-06 20:56:25,408 INFO [train.py:715] (2/8) Epoch 10, batch 19500, loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02951, over 4896.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2115, pruned_loss=0.03305, over 973083.98 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 20:57:04,627 INFO [train.py:715] (2/8) Epoch 10, batch 19550, loss[loss=0.12, simple_loss=0.1867, pruned_loss=0.02667, over 4895.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03294, over 972297.61 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 20:57:43,330 INFO [train.py:715] (2/8) Epoch 10, batch 19600, loss[loss=0.1272, simple_loss=0.2065, pruned_loss=0.02396, over 4897.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2129, pruned_loss=0.03295, over 972522.18 frames.], batch size: 22, lr: 2.13e-04
2022-05-06 20:58:22,307 INFO [train.py:715] (2/8) Epoch 10, batch 19650, loss[loss=0.1173, simple_loss=0.1943, pruned_loss=0.02016, over 4746.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2121, pruned_loss=0.0325, over 972191.04 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 20:59:01,941 INFO [train.py:715] (2/8) Epoch 10, batch 19700, loss[loss=0.132, simple_loss=0.2087, pruned_loss=0.02767, over 4693.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2123, pruned_loss=0.03246, over 972539.09 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 20:59:41,296 INFO [train.py:715] (2/8) Epoch 10, batch 19750, loss[loss=0.142, simple_loss=0.2105, pruned_loss=0.03677, over 4915.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2131, pruned_loss=0.03264, over 971928.06 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:00:19,604 INFO [train.py:715] (2/8) Epoch 10, batch 19800, loss[loss=0.1287, simple_loss=0.1974, pruned_loss=0.02997, over 4778.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2127, pruned_loss=0.03319, over 972338.52 frames.], batch size: 12, lr: 2.13e-04
2022-05-06 21:00:59,242 INFO [train.py:715] (2/8) Epoch 10, batch 19850, loss[loss=0.1356, simple_loss=0.206, pruned_loss=0.03255, over 4854.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03329, over 971643.84 frames.], batch size: 32, lr: 2.13e-04
2022-05-06 21:01:38,758 INFO [train.py:715] (2/8) Epoch 10, batch 19900, loss[loss=0.1264, simple_loss=0.2019, pruned_loss=0.02542, over 4714.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.03361, over 971955.62 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:02:19,874 INFO [train.py:715] (2/8) Epoch 10, batch 19950, loss[loss=0.1302, simple_loss=0.2093, pruned_loss=0.02552, over 4965.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2134, pruned_loss=0.0338, over 972283.80 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 21:02:58,932 INFO [train.py:715] (2/8) Epoch 10, batch 20000, loss[loss=0.1801, simple_loss=0.2585, pruned_loss=0.05087, over 4758.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2138, pruned_loss=0.03385, over 971569.99 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 21:03:37,943 INFO [train.py:715] (2/8) Epoch 10, batch 20050, loss[loss=0.1408, simple_loss=0.2071, pruned_loss=0.03721, over 4841.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03332, over 971451.49 frames.], batch size: 13, lr: 2.13e-04
2022-05-06 21:04:17,425 INFO [train.py:715] (2/8) Epoch 10, batch 20100, loss[loss=0.1542, simple_loss=0.2217, pruned_loss=0.04342, over 4911.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03335, over 972599.41 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 21:04:55,527 INFO [train.py:715] (2/8) Epoch 10, batch 20150, loss[loss=0.133, simple_loss=0.2047, pruned_loss=0.03064, over 4816.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03329, over 973245.07 frames.], batch size: 25, lr: 2.13e-04
2022-05-06 21:05:34,942 INFO [train.py:715] (2/8) Epoch 10, batch 20200, loss[loss=0.1444, simple_loss=0.2299, pruned_loss=0.02941, over 4898.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03313, over 973287.16 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 21:06:13,959 INFO [train.py:715] (2/8) Epoch 10, batch 20250, loss[loss=0.1079, simple_loss=0.1817, pruned_loss=0.01709, over 4836.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03321, over 973175.96 frames.], batch size: 13, lr: 2.13e-04
2022-05-06 21:06:52,617 INFO [train.py:715] (2/8) Epoch 10, batch 20300, loss[loss=0.1479, simple_loss=0.2153, pruned_loss=0.04027, over 4903.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.033, over 973409.45 frames.], batch size: 19, lr: 2.13e-04
2022-05-06 21:07:31,401 INFO [train.py:715] (2/8) Epoch 10, batch 20350, loss[loss=0.1457, simple_loss=0.219, pruned_loss=0.0362, over 4814.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.03299, over 972693.68 frames.], batch size: 25, lr: 2.13e-04
2022-05-06 21:08:10,506 INFO [train.py:715] (2/8) Epoch 10, batch 20400, loss[loss=0.1463, simple_loss=0.2263, pruned_loss=0.03313, over 4943.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03267, over 972885.33 frames.], batch size: 21, lr: 2.13e-04
2022-05-06 21:08:49,426 INFO [train.py:715] (2/8) Epoch 10, batch 20450, loss[loss=0.1283, simple_loss=0.197, pruned_loss=0.02977, over 4924.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2129, pruned_loss=0.03325, over 972731.39 frames.], batch size: 29, lr: 2.13e-04
2022-05-06 21:09:27,883 INFO [train.py:715] (2/8) Epoch 10, batch 20500, loss[loss=0.1279, simple_loss=0.2003, pruned_loss=0.02773, over 4860.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2131, pruned_loss=0.03329, over 973000.02 frames.], batch size: 20, lr: 2.13e-04
2022-05-06 21:10:06,955 INFO [train.py:715] (2/8) Epoch 10, batch 20550, loss[loss=0.1164, simple_loss=0.1927, pruned_loss=0.02006, over 4769.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2131, pruned_loss=0.03306, over 973114.44 frames.], batch size: 12, lr: 2.13e-04
2022-05-06 21:10:46,033 INFO [train.py:715] (2/8) Epoch 10, batch 20600, loss[loss=0.134, simple_loss=0.2048, pruned_loss=0.03162, over 4636.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03293, over 972654.31 frames.], batch size: 13, lr: 2.13e-04
2022-05-06 21:11:25,464 INFO [train.py:715] (2/8) Epoch 10, batch 20650, loss[loss=0.1405, simple_loss=0.2142, pruned_loss=0.03339, over 4870.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03315, over 972651.21 frames.], batch size: 32, lr: 2.13e-04
2022-05-06 21:12:04,254 INFO [train.py:715] (2/8) Epoch 10, batch 20700, loss[loss=0.1286, simple_loss=0.2, pruned_loss=0.02859, over 4923.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03351, over 971994.31 frames.], batch size: 23, lr: 2.13e-04
2022-05-06 21:12:44,589 INFO [train.py:715] (2/8) Epoch 10, batch 20750, loss[loss=0.1285, simple_loss=0.2031, pruned_loss=0.02691, over 4785.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03349, over 971889.61 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 21:13:24,571 INFO [train.py:715] (2/8) Epoch 10, batch 20800, loss[loss=0.1291, simple_loss=0.2003, pruned_loss=0.02891, over 4839.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03295, over 970992.64 frames.], batch size: 20, lr: 2.13e-04
2022-05-06 21:14:03,345 INFO [train.py:715] (2/8) Epoch 10, batch 20850, loss[loss=0.1457, simple_loss=0.2222, pruned_loss=0.03456, over 4772.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03267, over 970440.61 frames.], batch size: 17, lr: 2.13e-04
2022-05-06 21:14:43,292 INFO [train.py:715] (2/8) Epoch 10, batch 20900, loss[loss=0.1373, simple_loss=0.2096, pruned_loss=0.03247, over 4862.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.03238, over 971155.43 frames.], batch size: 32, lr: 2.13e-04
2022-05-06 21:15:23,754 INFO [train.py:715] (2/8) Epoch 10, batch 20950, loss[loss=0.1414, simple_loss=0.2156, pruned_loss=0.03353, over 4814.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03258, over 970360.85 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 21:16:02,699 INFO [train.py:715] (2/8) Epoch 10, batch 21000, loss[loss=0.1128, simple_loss=0.1831, pruned_loss=0.02125, over 4799.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.03256, over 971209.76 frames.], batch size: 12, lr: 2.13e-04
2022-05-06 21:16:02,700 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 21:16:12,202 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1065, simple_loss=0.1909, pruned_loss=0.01111, over 914524.00 frames.
2022-05-06 21:16:51,726 INFO [train.py:715] (2/8) Epoch 10, batch 21050, loss[loss=0.1422, simple_loss=0.2166, pruned_loss=0.0339, over 4786.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03253, over 970899.77 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:17:32,556 INFO [train.py:715] (2/8) Epoch 10, batch 21100, loss[loss=0.1103, simple_loss=0.1885, pruned_loss=0.01603, over 4981.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03255, over 971322.14 frames.], batch size: 24, lr: 2.13e-04
2022-05-06 21:18:14,016 INFO [train.py:715] (2/8) Epoch 10, batch 21150, loss[loss=0.1409, simple_loss=0.2149, pruned_loss=0.03338, over 4946.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2108, pruned_loss=0.03242, over 971995.97 frames.], batch size: 21, lr: 2.13e-04
2022-05-06 21:18:55,108 INFO [train.py:715] (2/8) Epoch 10, batch 21200, loss[loss=0.1587, simple_loss=0.2289, pruned_loss=0.0443, over 4975.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2107, pruned_loss=0.03259, over 971954.58 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:19:35,763 INFO [train.py:715] (2/8) Epoch 10, batch 21250, loss[loss=0.1654, simple_loss=0.2465, pruned_loss=0.04216, over 4737.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2102, pruned_loss=0.03242, over 972991.25 frames.], batch size: 16, lr: 2.13e-04
2022-05-06 21:20:17,428 INFO [train.py:715] (2/8) Epoch 10, batch 21300, loss[loss=0.1439, simple_loss=0.2165, pruned_loss=0.03558, over 4835.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2097, pruned_loss=0.03222, over 972600.81 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 21:20:58,698 INFO [train.py:715] (2/8) Epoch 10, batch 21350, loss[loss=0.1368, simple_loss=0.2131, pruned_loss=0.03022, over 4916.00 frames.], tot_loss[loss=0.139, simple_loss=0.2116, pruned_loss=0.03317, over 973190.07 frames.], batch size: 23, lr: 2.13e-04
2022-05-06 21:21:39,111 INFO [train.py:715] (2/8) Epoch 10, batch 21400, loss[loss=0.1125, simple_loss=0.1849, pruned_loss=0.02003, over 4750.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03283, over 972665.05 frames.], batch size: 14, lr: 2.13e-04
2022-05-06 21:22:20,532 INFO [train.py:715] (2/8) Epoch 10, batch 21450, loss[loss=0.1196, simple_loss=0.1927, pruned_loss=0.0232, over 4838.00 frames.], tot_loss[loss=0.14, simple_loss=0.2131, pruned_loss=0.03348, over 972056.34 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:23:02,353 INFO [train.py:715] (2/8) Epoch 10, batch 21500, loss[loss=0.1323, simple_loss=0.2038, pruned_loss=0.03045, over 4869.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2128, pruned_loss=0.03335, over 972231.99 frames.], batch size: 22, lr: 2.13e-04
2022-05-06 21:23:43,371 INFO [train.py:715] (2/8) Epoch 10, batch 21550, loss[loss=0.1468, simple_loss=0.223, pruned_loss=0.03533, over 4930.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03363, over 971954.02 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:24:24,260 INFO [train.py:715] (2/8) Epoch 10, batch 21600, loss[loss=0.1204, simple_loss=0.1969, pruned_loss=0.02197, over 4821.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03367, over 971605.22 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:25:06,206 INFO [train.py:715] (2/8) Epoch 10, batch 21650, loss[loss=0.1585, simple_loss=0.2312, pruned_loss=0.04295, over 4887.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2132, pruned_loss=0.03382, over 971647.74 frames.], batch size: 39, lr: 2.13e-04
2022-05-06 21:25:47,747 INFO [train.py:715] (2/8) Epoch 10, batch 21700, loss[loss=0.1606, simple_loss=0.2359, pruned_loss=0.04261, over 4686.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2133, pruned_loss=0.03386, over 972153.69 frames.], batch size: 15, lr: 2.13e-04
2022-05-06 21:26:28,006 INFO [train.py:715] (2/8) Epoch 10, batch 21750, loss[loss=0.1615, simple_loss=0.2377, pruned_loss=0.04266, over 4823.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2133, pruned_loss=0.03391, over 972316.77 frames.], batch size: 26, lr: 2.13e-04
2022-05-06 21:27:08,993 INFO [train.py:715] (2/8) Epoch 10, batch 21800, loss[loss=0.1387, simple_loss=0.2093, pruned_loss=0.03405, over 4781.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2131, pruned_loss=0.03378, over 971886.65 frames.], batch size: 18, lr: 2.13e-04
2022-05-06 21:27:50,705 INFO [train.py:715] (2/8) Epoch 10, batch 21850, loss[loss=0.1187, simple_loss=0.189, pruned_loss=0.02422, over 4982.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.03373, over 972081.45 frames.], batch size: 14, lr: 2.13e-04
2022-05-06 21:28:31,164 INFO [train.py:715] (2/8) Epoch 10, batch 21900, loss[loss=0.1305, simple_loss=0.2125, pruned_loss=0.0243, over 4832.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2127, pruned_loss=0.03398, over 971490.51 frames.], batch size: 30, lr: 2.13e-04
2022-05-06 21:29:11,913 INFO [train.py:715] (2/8) Epoch 10, batch 21950, loss[loss=0.1487, simple_loss=0.217, pruned_loss=0.04024, over 4810.00 frames.], tot_loss[loss=0.139, simple_loss=0.2115, pruned_loss=0.0333, over 971538.72 frames.], batch size: 25, lr: 2.13e-04
2022-05-06 21:29:53,134 INFO [train.py:715] (2/8) Epoch 10, batch 22000, loss[loss=0.1662, simple_loss=0.245, pruned_loss=0.04366, over 4927.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2118, pruned_loss=0.03325, over 972470.80 frames.], batch size: 23, lr: 2.12e-04
2022-05-06 21:30:33,462 INFO [train.py:715] (2/8) Epoch 10, batch 22050, loss[loss=0.1305, simple_loss=0.1999, pruned_loss=0.03055, over 4785.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03316, over 971595.73 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:31:14,078 INFO [train.py:715] (2/8) Epoch 10, batch 22100, loss[loss=0.1657, simple_loss=0.2437, pruned_loss=0.04384, over 4889.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03301, over 971912.70 frames.], batch size: 39, lr: 2.12e-04
2022-05-06 21:31:54,931 INFO [train.py:715] (2/8) Epoch 10, batch 22150, loss[loss=0.1428, simple_loss=0.2102, pruned_loss=0.03771, over 4783.00 frames.], tot_loss[loss=0.1398, simple_loss=0.213, pruned_loss=0.03334, over 971705.55 frames.], batch size: 14, lr: 2.12e-04
2022-05-06 21:32:35,990 INFO [train.py:715] (2/8) Epoch 10, batch 22200, loss[loss=0.1278, simple_loss=0.1976, pruned_loss=0.02904, over 4847.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2133, pruned_loss=0.03378, over 971694.32 frames.], batch size: 32, lr: 2.12e-04
2022-05-06 21:33:16,099 INFO [train.py:715] (2/8) Epoch 10, batch 22250, loss[loss=0.1322, simple_loss=0.2026, pruned_loss=0.03092, over 4784.00 frames.], tot_loss[loss=0.1402, simple_loss=0.213, pruned_loss=0.03365, over 971800.04 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:33:56,746 INFO [train.py:715] (2/8) Epoch 10, batch 22300, loss[loss=0.1698, simple_loss=0.2531, pruned_loss=0.04327, over 4793.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2131, pruned_loss=0.03355, over 972189.73 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:34:37,767 INFO [train.py:715] (2/8) Epoch 10, batch 22350, loss[loss=0.1278, simple_loss=0.2048, pruned_loss=0.02537, over 4799.00 frames.], tot_loss[loss=0.1399, simple_loss=0.213, pruned_loss=0.03337, over 972924.67 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 21:35:17,619 INFO [train.py:715] (2/8) Epoch 10, batch 22400, loss[loss=0.1698, simple_loss=0.2371, pruned_loss=0.0513, over 4851.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2139, pruned_loss=0.03376, over 972687.60 frames.], batch size: 32, lr: 2.12e-04
2022-05-06 21:35:56,796 INFO [train.py:715] (2/8) Epoch 10, batch 22450, loss[loss=0.1674, simple_loss=0.2427, pruned_loss=0.04604, over 4798.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2138, pruned_loss=0.03338, over 972068.42 frames.], batch size: 14, lr: 2.12e-04
2022-05-06 21:36:36,731 INFO [train.py:715] (2/8) Epoch 10, batch 22500, loss[loss=0.1313, simple_loss=0.2067, pruned_loss=0.02797, over 4928.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2137, pruned_loss=0.03326, over 972213.30 frames.], batch size: 23, lr: 2.12e-04
2022-05-06 21:37:17,615 INFO [train.py:715] (2/8) Epoch 10, batch 22550, loss[loss=0.1991, simple_loss=0.2645, pruned_loss=0.06681, over 4856.00 frames.], tot_loss[loss=0.1407, simple_loss=0.214, pruned_loss=0.03366, over 972323.07 frames.], batch size: 30, lr: 2.12e-04
2022-05-06 21:37:56,431 INFO [train.py:715] (2/8) Epoch 10, batch 22600, loss[loss=0.1585, simple_loss=0.2211, pruned_loss=0.04795, over 4773.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2141, pruned_loss=0.03358, over 972085.44 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:38:37,513 INFO [train.py:715] (2/8) Epoch 10, batch 22650, loss[loss=0.1328, simple_loss=0.2137, pruned_loss=0.02593, over 4904.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2144, pruned_loss=0.0337, over 972736.02 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 21:39:19,367 INFO [train.py:715] (2/8) Epoch 10, batch 22700, loss[loss=0.1179, simple_loss=0.1888, pruned_loss=0.02346, over 4763.00 frames.], tot_loss[loss=0.1404, simple_loss=0.214, pruned_loss=0.03341, over 972507.98 frames.], batch size: 12, lr: 2.12e-04
2022-05-06 21:40:00,103 INFO [train.py:715] (2/8) Epoch 10, batch 22750, loss[loss=0.1134, simple_loss=0.1815, pruned_loss=0.02266, over 4993.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2139, pruned_loss=0.03326, over 972658.58 frames.], batch size: 14, lr: 2.12e-04
2022-05-06 21:40:41,324 INFO [train.py:715] (2/8) Epoch 10, batch 22800, loss[loss=0.1387, simple_loss=0.2148, pruned_loss=0.0313, over 4793.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2136, pruned_loss=0.03311, over 971967.97 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 21:41:22,877 INFO [train.py:715] (2/8) Epoch 10, batch 22850, loss[loss=0.1137, simple_loss=0.1943, pruned_loss=0.0166, over 4803.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2136, pruned_loss=0.03328, over 972468.78 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 21:42:04,581 INFO [train.py:715] (2/8) Epoch 10, batch 22900, loss[loss=0.1226, simple_loss=0.2068, pruned_loss=0.01922, over 4948.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2141, pruned_loss=0.03328, over 972407.95 frames.], batch size: 29, lr: 2.12e-04
2022-05-06 21:42:45,055 INFO [train.py:715] (2/8) Epoch 10, batch 22950, loss[loss=0.15, simple_loss=0.2214, pruned_loss=0.03926, over 4876.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2134, pruned_loss=0.03301, over 972727.79 frames.], batch size: 16, lr: 2.12e-04
2022-05-06 21:43:27,080 INFO [train.py:715] (2/8) Epoch 10, batch 23000, loss[loss=0.1354, simple_loss=0.2193, pruned_loss=0.02574, over 4868.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2131, pruned_loss=0.03279, over 972214.52 frames.], batch size: 20, lr: 2.12e-04
2022-05-06 21:44:09,143 INFO [train.py:715] (2/8) Epoch 10, batch 23050, loss[loss=0.1296, simple_loss=0.2102, pruned_loss=0.0245, over 4778.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2126, pruned_loss=0.03263, over 972447.88 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:44:49,659 INFO [train.py:715] (2/8) Epoch 10, batch 23100, loss[loss=0.1666, simple_loss=0.2331, pruned_loss=0.05008, over 4911.00 frames.], tot_loss[loss=0.139, simple_loss=0.2126, pruned_loss=0.03272, over 972744.04 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 21:45:30,867 INFO [train.py:715] (2/8) Epoch 10, batch 23150, loss[loss=0.1333, simple_loss=0.2066, pruned_loss=0.03003, over 4925.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2126, pruned_loss=0.03282, over 972284.12 frames.], batch size: 23, lr: 2.12e-04
2022-05-06 21:46:12,873 INFO [train.py:715] (2/8) Epoch 10, batch 23200, loss[loss=0.1633, simple_loss=0.2393, pruned_loss=0.04367, over 4770.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03282, over 971391.84 frames.], batch size: 14, lr: 2.12e-04
2022-05-06 21:46:54,162 INFO [train.py:715] (2/8) Epoch 10, batch 23250, loss[loss=0.1537, simple_loss=0.2337, pruned_loss=0.0369, over 4798.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03316, over 971175.58 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 21:47:34,832 INFO [train.py:715] (2/8) Epoch 10, batch 23300, loss[loss=0.151, simple_loss=0.2153, pruned_loss=0.0434, over 4849.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03346, over 971648.44 frames.], batch size: 13, lr: 2.12e-04
2022-05-06 21:48:16,731 INFO [train.py:715] (2/8) Epoch 10, batch 23350, loss[loss=0.1315, simple_loss=0.2086, pruned_loss=0.02722, over 4988.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2128, pruned_loss=0.03375, over 972139.05 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 21:48:58,867 INFO [train.py:715] (2/8) Epoch 10, batch 23400, loss[loss=0.1626, simple_loss=0.2339, pruned_loss=0.04566, over 4982.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2124, pruned_loss=0.03375, over 971226.77 frames.], batch size: 28, lr: 2.12e-04
2022-05-06 21:49:39,773 INFO [train.py:715] (2/8) Epoch 10, batch 23450, loss[loss=0.1089, simple_loss=0.1836, pruned_loss=0.01713, over 4957.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03383, over 971097.16 frames.], batch size: 35, lr: 2.12e-04
2022-05-06 21:50:20,134 INFO [train.py:715] (2/8) Epoch 10, batch 23500, loss[loss=0.1244, simple_loss=0.2124, pruned_loss=0.0182, over 4959.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2135, pruned_loss=0.03383, over 971489.33 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 21:51:02,210 INFO [train.py:715] (2/8) Epoch 10, batch 23550, loss[loss=0.1507, simple_loss=0.2199, pruned_loss=0.04078, over 4974.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03324, over 971970.03 frames.], batch size: 25, lr: 2.12e-04
2022-05-06 21:51:43,364 INFO [train.py:715] (2/8) Epoch 10, batch 23600, loss[loss=0.1396, simple_loss=0.2107, pruned_loss=0.03424, over 4986.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2129, pruned_loss=0.03339, over 971904.23 frames.], batch size: 31, lr: 2.12e-04
2022-05-06 21:52:23,127 INFO [train.py:715] (2/8) Epoch 10, batch 23650, loss[loss=0.1447, simple_loss=0.2363, pruned_loss=0.02654, over 4742.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03275, over 971310.73 frames.], batch size: 16, lr: 2.12e-04
2022-05-06 21:53:03,643 INFO [train.py:715] (2/8) Epoch 10, batch 23700, loss[loss=0.1285, simple_loss=0.201, pruned_loss=0.02804, over 4814.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03263, over 971712.66 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 21:53:44,219 INFO [train.py:715] (2/8) Epoch 10, batch 23750, loss[loss=0.1793, simple_loss=0.2364, pruned_loss=0.06114, over 4706.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2113, pruned_loss=0.03308, over 972200.67 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 21:54:24,357 INFO [train.py:715] (2/8) Epoch 10, batch 23800, loss[loss=0.1475, simple_loss=0.2215, pruned_loss=0.0367, over 4824.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2112, pruned_loss=0.03312, over 971537.95 frames.], batch size: 27, lr: 2.12e-04
2022-05-06 21:55:04,957 INFO [train.py:715] (2/8) Epoch 10, batch 23850, loss[loss=0.1244, simple_loss=0.2067, pruned_loss=0.02102, over 4781.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2113, pruned_loss=0.03312, over 971593.55 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 21:55:46,229 INFO [train.py:715] (2/8) Epoch 10, batch 23900, loss[loss=0.1287, simple_loss=0.1988, pruned_loss=0.02928, over 4930.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2119, pruned_loss=0.03372, over 971304.36 frames.], batch size: 23, lr: 2.12e-04
2022-05-06 21:56:25,834 INFO [train.py:715] (2/8) Epoch 10, batch 23950, loss[loss=0.1503, simple_loss=0.2268, pruned_loss=0.03686, over 4757.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2124, pruned_loss=0.03369, over 971163.55 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 21:57:06,224 INFO [train.py:715] (2/8) Epoch 10, batch 24000, loss[loss=0.1213, simple_loss=0.1868, pruned_loss=0.02792, over 4779.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2119, pruned_loss=0.03344, over 971182.19 frames.], batch size: 12, lr: 2.12e-04
2022-05-06 21:57:06,225 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 21:57:15,895 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1061, simple_loss=0.1905, pruned_loss=0.01087, over 914524.00 frames.
2022-05-06 21:57:55,801 INFO [train.py:715] (2/8) Epoch 10, batch 24050, loss[loss=0.1575, simple_loss=0.2462, pruned_loss=0.03443, over 4920.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.03362, over 971247.56 frames.], batch size: 39, lr: 2.12e-04
2022-05-06 21:58:36,845 INFO [train.py:715] (2/8) Epoch 10, batch 24100, loss[loss=0.1717, simple_loss=0.2386, pruned_loss=0.05244, over 4829.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.0338, over 970695.82 frames.], batch size: 30, lr: 2.12e-04
2022-05-06 21:59:18,107 INFO [train.py:715] (2/8) Epoch 10, batch 24150, loss[loss=0.1287, simple_loss=0.1863, pruned_loss=0.03558, over 4644.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2126, pruned_loss=0.03364, over 970756.94 frames.], batch size: 13, lr: 2.12e-04
2022-05-06 21:59:57,434 INFO [train.py:715] (2/8) Epoch 10, batch 24200, loss[loss=0.1456, simple_loss=0.2236, pruned_loss=0.03386, over 4980.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2121, pruned_loss=0.03362, over 972242.14 frames.], batch size: 25, lr: 2.12e-04
2022-05-06 22:00:38,180 INFO [train.py:715] (2/8) Epoch 10, batch 24250, loss[loss=0.1457, simple_loss=0.2152, pruned_loss=0.03811, over 4841.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2129, pruned_loss=0.03415, over 972217.19 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:01:19,308 INFO [train.py:715] (2/8) Epoch 10, batch 24300, loss[loss=0.1373, simple_loss=0.2163, pruned_loss=0.02915, over 4817.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03348, over 971652.38 frames.], batch size: 27, lr: 2.12e-04
2022-05-06 22:01:59,409 INFO [train.py:715] (2/8) Epoch 10, batch 24350, loss[loss=0.1017, simple_loss=0.1726, pruned_loss=0.01545, over 4821.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03328, over 971571.87 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:02:39,457 INFO [train.py:715] (2/8) Epoch 10, batch 24400, loss[loss=0.1281, simple_loss=0.2079, pruned_loss=0.02412, over 4960.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03368, over 971707.89 frames.], batch size: 24, lr: 2.12e-04
2022-05-06 22:03:20,173 INFO [train.py:715] (2/8) Epoch 10, batch 24450, loss[loss=0.1851, simple_loss=0.2358, pruned_loss=0.06723, over 4976.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.0342, over 971796.37 frames.], batch size: 31, lr: 2.12e-04
2022-05-06 22:04:01,130 INFO [train.py:715] (2/8) Epoch 10, batch 24500, loss[loss=0.1247, simple_loss=0.2024, pruned_loss=0.0235, over 4897.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03311, over 971238.67 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 22:04:40,218 INFO [train.py:715] (2/8) Epoch 10, batch 24550, loss[loss=0.1253, simple_loss=0.1983, pruned_loss=0.02616, over 4764.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03313, over 971505.39 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 22:05:20,203 INFO [train.py:715] (2/8) Epoch 10, batch 24600, loss[loss=0.1496, simple_loss=0.2256, pruned_loss=0.03675, over 4792.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2131, pruned_loss=0.03396, over 972068.71 frames.], batch size: 14, lr: 2.12e-04
2022-05-06 22:06:00,570 INFO [train.py:715] (2/8) Epoch 10, batch 24650, loss[loss=0.1443, simple_loss=0.2156, pruned_loss=0.0365, over 4960.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2132, pruned_loss=0.03391, over 971516.12 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 22:06:39,585 INFO [train.py:715] (2/8) Epoch 10, batch 24700, loss[loss=0.138, simple_loss=0.2176, pruned_loss=0.02921, over 4898.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2139, pruned_loss=0.03466, over 972379.82 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 22:07:18,184 INFO [train.py:715] (2/8) Epoch 10, batch 24750, loss[loss=0.1433, simple_loss=0.2261, pruned_loss=0.03023, over 4934.00 frames.], tot_loss[loss=0.141, simple_loss=0.2136, pruned_loss=0.03426, over 972025.12 frames.], batch size: 29, lr: 2.12e-04
2022-05-06 22:07:57,674 INFO [train.py:715] (2/8) Epoch 10, batch 24800, loss[loss=0.1497, simple_loss=0.2149, pruned_loss=0.04222, over 4867.00 frames.], tot_loss[loss=0.141, simple_loss=0.2137, pruned_loss=0.03415, over 972091.97 frames.], batch size: 34, lr: 2.12e-04
2022-05-06 22:08:36,822 INFO [train.py:715] (2/8) Epoch 10, batch 24850, loss[loss=0.1204, simple_loss=0.1989, pruned_loss=0.02096, over 4919.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2141, pruned_loss=0.03407, over 972308.17 frames.], batch size: 29, lr: 2.12e-04
2022-05-06 22:09:14,896 INFO [train.py:715] (2/8) Epoch 10, batch 24900, loss[loss=0.1416, simple_loss=0.2266, pruned_loss=0.02833, over 4832.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2138, pruned_loss=0.034, over 971330.45 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:09:54,524 INFO [train.py:715] (2/8) Epoch 10, batch 24950, loss[loss=0.1136, simple_loss=0.19, pruned_loss=0.01861, over 4753.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.0337, over 971199.76 frames.], batch size: 19, lr: 2.12e-04
2022-05-06 22:10:34,374 INFO [train.py:715] (2/8) Epoch 10, batch 25000, loss[loss=0.1292, simple_loss=0.2053, pruned_loss=0.02652, over 4707.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03291, over 971503.85 frames.], batch size: 15, lr: 2.12e-04
2022-05-06 22:11:13,226 INFO [train.py:715] (2/8) Epoch 10, batch 25050, loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02853, over 4794.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.03252, over 970606.74 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 22:11:52,697 INFO [train.py:715] (2/8) Epoch 10, batch 25100, loss[loss=0.1716, simple_loss=0.2349, pruned_loss=0.05418, over 4850.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2109, pruned_loss=0.03241, over 971110.76 frames.], batch size: 32, lr: 2.12e-04
2022-05-06 22:12:32,719 INFO [train.py:715] (2/8) Epoch 10, batch 25150, loss[loss=0.1624, simple_loss=0.2327, pruned_loss=0.04604, over 4948.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2104, pruned_loss=0.03226, over 970713.45 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 22:13:12,209 INFO [train.py:715] (2/8) Epoch 10, batch 25200, loss[loss=0.1674, simple_loss=0.2425, pruned_loss=0.0462, over 4901.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.03266, over 969838.12 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 22:13:50,342 INFO [train.py:715] (2/8) Epoch 10, batch 25250, loss[loss=0.1349, simple_loss=0.2005, pruned_loss=0.03465, over 4796.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03299, over 970187.48 frames.], batch size: 13, lr: 2.12e-04
2022-05-06 22:14:29,218 INFO [train.py:715] (2/8) Epoch 10, batch 25300, loss[loss=0.1837, simple_loss=0.248, pruned_loss=0.0597, over 4909.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2107, pruned_loss=0.03257, over 971732.58 frames.], batch size: 17, lr: 2.12e-04
2022-05-06 22:15:08,863 INFO [train.py:715] (2/8) Epoch 10, batch 25350, loss[loss=0.1369, simple_loss=0.2194, pruned_loss=0.02723, over 4938.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.03255, over 971307.97 frames.], batch size: 29, lr: 2.12e-04
2022-05-06 22:15:47,381 INFO [train.py:715] (2/8) Epoch 10, batch 25400, loss[loss=0.1246, simple_loss=0.1955, pruned_loss=0.02685, over 4941.00 frames.], tot_loss[loss=0.137, simple_loss=0.2099, pruned_loss=0.03201, over 971772.29 frames.], batch size: 21, lr: 2.12e-04
2022-05-06 22:16:26,236 INFO [train.py:715] (2/8) Epoch 10, batch 25450, loss[loss=0.1578, simple_loss=0.2203, pruned_loss=0.04769, over 4921.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2112, pruned_loss=0.03266, over 972578.59 frames.], batch size: 18, lr: 2.12e-04
2022-05-06 22:17:06,158 INFO [train.py:715] (2/8) Epoch 10, batch 25500, loss[loss=0.1308, simple_loss=0.204, pruned_loss=0.02879, over 4904.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03249, over 972988.73 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:17:45,978 INFO [train.py:715] (2/8) Epoch 10, batch 25550, loss[loss=0.1184, simple_loss=0.1802, pruned_loss=0.02828, over 4798.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03222, over 972864.21 frames.], batch size: 12, lr: 2.11e-04
2022-05-06 22:18:24,960 INFO [train.py:715] (2/8) Epoch 10, batch 25600, loss[loss=0.1617, simple_loss=0.2319, pruned_loss=0.04571, over 4917.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.0324, over 972590.61 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:19:05,112 INFO [train.py:715] (2/8) Epoch 10, batch 25650, loss[loss=0.1411, simple_loss=0.2167, pruned_loss=0.03269, over 4799.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2127, pruned_loss=0.03291, over 972822.98 frames.], batch size: 13, lr: 2.11e-04
2022-05-06 22:19:45,487 INFO [train.py:715] (2/8) Epoch 10, batch 25700, loss[loss=0.1766, simple_loss=0.2499, pruned_loss=0.05164, over 4914.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2125, pruned_loss=0.03285, over 973309.48 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 22:20:25,350 INFO [train.py:715] (2/8) Epoch 10, batch 25750, loss[loss=0.1624, simple_loss=0.2381, pruned_loss=0.0433, over 4803.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2122, pruned_loss=0.03268, over 973365.79 frames.], batch size: 24, lr: 2.11e-04
2022-05-06 22:21:04,754 INFO [train.py:715] (2/8) Epoch 10, batch 25800, loss[loss=0.1377, simple_loss=0.213, pruned_loss=0.03116, over 4821.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03234, over 973353.43 frames.], batch size: 27, lr: 2.11e-04
2022-05-06 22:21:45,292 INFO [train.py:715] (2/8) Epoch 10, batch 25850, loss[loss=0.1413, simple_loss=0.2187, pruned_loss=0.03201, over 4894.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2123, pruned_loss=0.03263, over 972974.87 frames.], batch size: 16, lr: 2.11e-04
2022-05-06 22:22:25,224 INFO [train.py:715] (2/8) Epoch 10, batch 25900, loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02906, over 4978.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03273, over 972322.96 frames.], batch size: 25, lr: 2.11e-04
2022-05-06 22:23:03,944 INFO [train.py:715] (2/8) Epoch 10, batch 25950, loss[loss=0.1487, simple_loss=0.2118, pruned_loss=0.0428, over 4787.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.03343, over 972901.46 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:23:42,717 INFO [train.py:715] (2/8) Epoch 10, batch 26000, loss[loss=0.1473, simple_loss=0.2112, pruned_loss=0.04169, over 4903.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2132, pruned_loss=0.03361, over 973305.12 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 22:24:21,989 INFO [train.py:715] (2/8) Epoch 10, batch 26050, loss[loss=0.1413, simple_loss=0.2163, pruned_loss=0.03316, over 4808.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03304, over 972876.16 frames.], batch size: 26, lr: 2.11e-04
2022-05-06 22:25:00,975 INFO [train.py:715] (2/8) Epoch 10, batch 26100, loss[loss=0.1017, simple_loss=0.1709, pruned_loss=0.01626, over 4809.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.033, over 972281.16 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 22:25:40,354 INFO [train.py:715] (2/8) Epoch 10, batch 26150, loss[loss=0.16, simple_loss=0.2253, pruned_loss=0.04737, over 4911.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03364, over 972148.47 frames.], batch size: 39, lr: 2.11e-04
2022-05-06 22:26:21,115 INFO [train.py:715] (2/8) Epoch 10, batch 26200, loss[loss=0.1128, simple_loss=0.1854, pruned_loss=0.02017, over 4776.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.03328, over 971987.01 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:27:00,367 INFO [train.py:715] (2/8) Epoch 10, batch 26250, loss[loss=0.1565, simple_loss=0.228, pruned_loss=0.04252, over 4779.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.03338, over 972573.92 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:27:40,000 INFO [train.py:715] (2/8) Epoch 10, batch 26300, loss[loss=0.1347, simple_loss=0.2046, pruned_loss=0.03237, over 4853.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03397, over 972611.86 frames.], batch size: 32, lr: 2.11e-04
2022-05-06 22:28:19,566 INFO [train.py:715] (2/8) Epoch 10, batch 26350, loss[loss=0.1421, simple_loss=0.216, pruned_loss=0.03415, over 4920.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2141, pruned_loss=0.03418, over 972224.04 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:28:59,183 INFO [train.py:715] (2/8) Epoch 10, batch 26400, loss[loss=0.1325, simple_loss=0.2017, pruned_loss=0.03169, over 4805.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03422, over 971925.04 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 22:29:38,871 INFO [train.py:715] (2/8) Epoch 10, batch 26450, loss[loss=0.1251, simple_loss=0.1994, pruned_loss=0.02538, over 4683.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2145, pruned_loss=0.0342, over 972356.33 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:30:18,701 INFO [train.py:715] (2/8) Epoch 10, batch 26500, loss[loss=0.135, simple_loss=0.2124, pruned_loss=0.02876, over 4966.00 frames.], tot_loss[loss=0.141, simple_loss=0.2139, pruned_loss=0.03408, over 972416.23 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:30:59,098 INFO [train.py:715] (2/8) Epoch 10, batch 26550, loss[loss=0.1277, simple_loss=0.2034, pruned_loss=0.026, over 4777.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2135, pruned_loss=0.03401, over 972566.72 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:31:37,640 INFO [train.py:715] (2/8) Epoch 10, batch 26600, loss[loss=0.1454, simple_loss=0.2117, pruned_loss=0.0395, over 4831.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2131, pruned_loss=0.03377, over 972929.57 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:32:17,162 INFO [train.py:715] (2/8) Epoch 10, batch 26650, loss[loss=0.1323, simple_loss=0.2057, pruned_loss=0.02944, over 4777.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2126, pruned_loss=0.03389, over 972741.76 frames.], batch size: 12, lr: 2.11e-04
2022-05-06 22:32:56,673 INFO [train.py:715] (2/8) Epoch 10, batch 26700, loss[loss=0.1456, simple_loss=0.2147, pruned_loss=0.03829, over 4775.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2117, pruned_loss=0.03364, over 971734.31 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:33:36,177 INFO [train.py:715] (2/8) Epoch 10, batch 26750, loss[loss=0.111, simple_loss=0.1874, pruned_loss=0.01727, over 4803.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03322, over 971583.89 frames.], batch size: 24, lr: 2.11e-04
2022-05-06 22:34:14,863 INFO [train.py:715] (2/8) Epoch 10, batch 26800, loss[loss=0.1108, simple_loss=0.1883, pruned_loss=0.01668, over 4950.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2113, pruned_loss=0.03312, over 971394.52 frames.], batch size: 24, lr: 2.11e-04
2022-05-06 22:34:54,619 INFO [train.py:715] (2/8) Epoch 10, batch 26850, loss[loss=0.1203, simple_loss=0.2036, pruned_loss=0.01853, over 4944.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03317, over 971611.18 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 22:35:34,127 INFO [train.py:715] (2/8) Epoch 10, batch 26900, loss[loss=0.1635, simple_loss=0.2282, pruned_loss=0.04944, over 4748.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2125, pruned_loss=0.03368, over 971186.24 frames.], batch size: 16, lr: 2.11e-04
2022-05-06 22:36:12,943 INFO [train.py:715] (2/8) Epoch 10, batch 26950, loss[loss=0.1473, simple_loss=0.2274, pruned_loss=0.03356, over 4937.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2118, pruned_loss=0.03363, over 971385.93 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:36:51,895 INFO [train.py:715] (2/8) Epoch 10, batch 27000, loss[loss=0.1526, simple_loss=0.2354, pruned_loss=0.03489, over 4694.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2125, pruned_loss=0.03409, over 971183.45 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:36:51,896 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 22:37:01,643 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1063, simple_loss=0.1906, pruned_loss=0.01104, over 914524.00 frames.
2022-05-06 22:37:41,041 INFO [train.py:715] (2/8) Epoch 10, batch 27050, loss[loss=0.1287, simple_loss=0.2047, pruned_loss=0.02633, over 4861.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2128, pruned_loss=0.03405, over 971605.22 frames.], batch size: 20, lr: 2.11e-04
2022-05-06 22:38:21,000 INFO [train.py:715] (2/8) Epoch 10, batch 27100, loss[loss=0.1454, simple_loss=0.2098, pruned_loss=0.04047, over 4933.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2129, pruned_loss=0.03397, over 971682.73 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:38:59,620 INFO [train.py:715] (2/8) Epoch 10, batch 27150, loss[loss=0.1658, simple_loss=0.2413, pruned_loss=0.04515, over 4916.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2125, pruned_loss=0.03361, over 971735.93 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:39:38,786 INFO [train.py:715] (2/8) Epoch 10, batch 27200, loss[loss=0.1326, simple_loss=0.2122, pruned_loss=0.02647, over 4814.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.03364, over 971624.04 frames.], batch size: 26, lr: 2.11e-04
2022-05-06 22:40:18,816 INFO [train.py:715] (2/8) Epoch 10, batch 27250, loss[loss=0.1263, simple_loss=0.2044, pruned_loss=0.02408, over 4964.00 frames.], tot_loss[loss=0.14, simple_loss=0.2133, pruned_loss=0.03339, over 972386.92 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:40:58,233 INFO [train.py:715] (2/8) Epoch 10, batch 27300, loss[loss=0.1324, simple_loss=0.2187, pruned_loss=0.02305, over 4805.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03299, over 972718.18 frames.], batch size: 24, lr: 2.11e-04
2022-05-06 22:41:36,436 INFO [train.py:715] (2/8) Epoch 10, batch 27350, loss[loss=0.1371, simple_loss=0.2148, pruned_loss=0.02969, over 4973.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.03305, over 972898.88 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:42:15,731 INFO [train.py:715] (2/8) Epoch 10, batch 27400, loss[loss=0.148, simple_loss=0.2318, pruned_loss=0.03209, over 4840.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03293, over 972206.17 frames.], batch size: 20, lr: 2.11e-04
2022-05-06 22:42:55,895 INFO [train.py:715] (2/8) Epoch 10, batch 27450, loss[loss=0.1284, simple_loss=0.1959, pruned_loss=0.03042, over 4889.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03312, over 972039.44 frames.], batch size: 16, lr: 2.11e-04
2022-05-06 22:43:34,160 INFO [train.py:715] (2/8) Epoch 10, batch 27500, loss[loss=0.1585, simple_loss=0.2232, pruned_loss=0.04693, over 4778.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.03396, over 972362.68 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:44:13,414 INFO [train.py:715] (2/8) Epoch 10, batch 27550, loss[loss=0.1273, simple_loss=0.1964, pruned_loss=0.02912, over 4740.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2138, pruned_loss=0.03396, over 972521.58 frames.], batch size: 12, lr: 2.11e-04
2022-05-06 22:44:52,783 INFO [train.py:715] (2/8) Epoch 10, batch 27600, loss[loss=0.1017, simple_loss=0.1771, pruned_loss=0.01311, over 4793.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.03334, over 971894.73 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:45:32,114 INFO [train.py:715] (2/8) Epoch 10, batch 27650, loss[loss=0.1413, simple_loss=0.213, pruned_loss=0.0348, over 4892.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03361, over 972226.26 frames.], batch size: 22, lr: 2.11e-04
2022-05-06 22:46:11,031 INFO [train.py:715] (2/8) Epoch 10, batch 27700, loss[loss=0.1299, simple_loss=0.202, pruned_loss=0.02895, over 4965.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.0339, over 972292.60 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:46:51,028 INFO [train.py:715] (2/8) Epoch 10, batch 27750, loss[loss=0.1407, simple_loss=0.2158, pruned_loss=0.03282, over 4988.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2126, pruned_loss=0.03335, over 973208.07 frames.], batch size: 25, lr: 2.11e-04
2022-05-06 22:47:31,101 INFO [train.py:715] (2/8) Epoch 10, batch 27800, loss[loss=0.1595, simple_loss=0.2254, pruned_loss=0.04686, over 4884.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03324, over 971994.23 frames.], batch size: 32, lr: 2.11e-04
2022-05-06 22:48:10,301 INFO [train.py:715] (2/8) Epoch 10, batch 27850, loss[loss=0.145, simple_loss=0.2289, pruned_loss=0.03055, over 4778.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03279, over 972642.01 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:48:50,676 INFO [train.py:715] (2/8) Epoch 10, batch 27900, loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02854, over 4979.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03284, over 973182.73 frames.], batch size: 28, lr: 2.11e-04
2022-05-06 22:49:34,037 INFO [train.py:715] (2/8) Epoch 10, batch 27950, loss[loss=0.1154, simple_loss=0.1959, pruned_loss=0.01744, over 4912.00 frames.], tot_loss[loss=0.139, simple_loss=0.2118, pruned_loss=0.0331, over 972685.69 frames.], batch size: 39, lr: 2.11e-04
2022-05-06 22:50:13,531 INFO [train.py:715] (2/8) Epoch 10, batch 28000, loss[loss=0.1358, simple_loss=0.1965, pruned_loss=0.03757, over 4796.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03292, over 972999.08 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:50:53,592 INFO [train.py:715] (2/8) Epoch 10, batch 28050, loss[loss=0.1531, simple_loss=0.2246, pruned_loss=0.04081, over 4815.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2126, pruned_loss=0.03358, over 972575.18 frames.], batch size: 26, lr: 2.11e-04
2022-05-06 22:51:34,459 INFO [train.py:715] (2/8) Epoch 10, batch 28100, loss[loss=0.1277, simple_loss=0.2058, pruned_loss=0.02475, over 4867.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03366, over 971985.32 frames.], batch size: 16, lr: 2.11e-04
2022-05-06 22:52:15,135 INFO [train.py:715] (2/8) Epoch 10, batch 28150, loss[loss=0.164, simple_loss=0.2367, pruned_loss=0.04564, over 4919.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2136, pruned_loss=0.03391, over 972679.31 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:52:54,874 INFO [train.py:715] (2/8) Epoch 10, batch 28200, loss[loss=0.1249, simple_loss=0.2113, pruned_loss=0.01919, over 4909.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03396, over 973017.51 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 22:53:35,212 INFO [train.py:715] (2/8) Epoch 10, batch 28250, loss[loss=0.1406, simple_loss=0.2191, pruned_loss=0.031, over 4776.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.03428, over 972346.85 frames.], batch size: 18, lr: 2.11e-04
2022-05-06 22:54:16,800 INFO [train.py:715] (2/8) Epoch 10, batch 28300, loss[loss=0.1414, simple_loss=0.2164, pruned_loss=0.03326, over 4837.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.03398, over 972448.11 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:54:56,895 INFO [train.py:715] (2/8) Epoch 10, batch 28350, loss[loss=0.1268, simple_loss=0.2098, pruned_loss=0.02196, over 4844.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2134, pruned_loss=0.03398, over 972580.08 frames.], batch size: 20, lr: 2.11e-04
2022-05-06 22:55:37,470 INFO [train.py:715] (2/8) Epoch 10, batch 28400, loss[loss=0.1269, simple_loss=0.1961, pruned_loss=0.02882, over 4898.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2129, pruned_loss=0.0342, over 972369.76 frames.], batch size: 17, lr: 2.11e-04
2022-05-06 22:56:19,128 INFO [train.py:715] (2/8) Epoch 10, batch 28450, loss[loss=0.1872, simple_loss=0.2521, pruned_loss=0.0612, over 4875.00 frames.], tot_loss[loss=0.1407, simple_loss=0.213, pruned_loss=0.03422, over 972797.68 frames.], batch size: 16, lr: 2.11e-04
2022-05-06 22:57:00,148 INFO [train.py:715] (2/8) Epoch 10, batch 28500, loss[loss=0.1221, simple_loss=0.1957, pruned_loss=0.02431, over 4783.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2126, pruned_loss=0.03393, over 972461.70 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 22:57:40,548 INFO [train.py:715] (2/8) Epoch 10, batch 28550, loss[loss=0.1517, simple_loss=0.2166, pruned_loss=0.04334, over 4781.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.03394, over 972198.04 frames.], batch size: 14, lr: 2.11e-04
2022-05-06 22:58:21,443 INFO [train.py:715] (2/8) Epoch 10, batch 28600, loss[loss=0.1491, simple_loss=0.2191, pruned_loss=0.03956, over 4854.00 frames.], tot_loss[loss=0.1403, simple_loss=0.213, pruned_loss=0.03383, over 972148.84 frames.], batch size: 30, lr: 2.11e-04
2022-05-06 22:59:03,592 INFO [train.py:715] (2/8) Epoch 10, batch 28650, loss[loss=0.1212, simple_loss=0.2033, pruned_loss=0.0195, over 4816.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03349, over 972114.50 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 22:59:43,751 INFO [train.py:715] (2/8) Epoch 10, batch 28700, loss[loss=0.1322, simple_loss=0.2086, pruned_loss=0.02792, over 4871.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03316, over 972535.00 frames.], batch size: 20, lr: 2.11e-04
2022-05-06 23:00:24,813 INFO [train.py:715] (2/8) Epoch 10, batch 28750, loss[loss=0.13, simple_loss=0.2041, pruned_loss=0.02794, over 4943.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2131, pruned_loss=0.03331, over 973104.19 frames.], batch size: 23, lr: 2.11e-04
2022-05-06 23:01:05,935 INFO [train.py:715] (2/8) Epoch 10, batch 28800, loss[loss=0.1127, simple_loss=0.1858, pruned_loss=0.0198, over 4747.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2133, pruned_loss=0.0331, over 972905.97 frames.], batch size: 19, lr: 2.11e-04
2022-05-06 23:01:46,831 INFO [train.py:715] (2/8) Epoch 10, batch 28850, loss[loss=0.1319, simple_loss=0.2071, pruned_loss=0.02837, over 4955.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2132, pruned_loss=0.03321, over 973271.57 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 23:02:27,341 INFO [train.py:715] (2/8) Epoch 10, batch 28900, loss[loss=0.139, simple_loss=0.2098, pruned_loss=0.03409, over 4715.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2138, pruned_loss=0.03375, over 973088.48 frames.], batch size: 15, lr: 2.11e-04
2022-05-06 23:03:08,207 INFO [train.py:715] (2/8) Epoch 10, batch 28950, loss[loss=0.1297, simple_loss=0.2068, pruned_loss=0.02634, over 4806.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.03311, over 972518.10 frames.], batch size: 21, lr: 2.11e-04
2022-05-06 23:03:49,286 INFO [train.py:715] (2/8) Epoch 10, batch 29000, loss[loss=0.1651, simple_loss=0.2338, pruned_loss=0.04824, over 4957.00 frames.], tot_loss[loss=0.1397, simple_loss=0.213, pruned_loss=0.0332, over 972271.96 frames.], batch size: 39, lr: 2.11e-04
2022-05-06 23:04:28,431 INFO [train.py:715] (2/8) Epoch 10, batch 29050, loss[loss=0.1468, simple_loss=0.214, pruned_loss=0.03977, over 4955.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2133, pruned_loss=0.03321, over 972745.60 frames.], batch size: 21, lr: 2.10e-04
2022-05-06 23:05:07,299 INFO [train.py:715] (2/8) Epoch 10, batch 29100, loss[loss=0.1257, simple_loss=0.201, pruned_loss=0.0252, over 4825.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2122, pruned_loss=0.03229, over 971626.70 frames.], batch size: 25, lr: 2.10e-04
2022-05-06 23:05:47,480 INFO [train.py:715] (2/8) Epoch 10, batch 29150, loss[loss=0.1416, simple_loss=0.2049, pruned_loss=0.03913, over 4690.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2123, pruned_loss=0.03229, over 971035.97 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:06:27,775 INFO [train.py:715] (2/8) Epoch 10, batch 29200, loss[loss=0.1717, simple_loss=0.2374, pruned_loss=0.05298, over 4837.00 frames.], tot_loss[loss=0.139, simple_loss=0.2128, pruned_loss=0.03261, over 971246.99 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:07:06,691 INFO [train.py:715] (2/8) Epoch 10, batch 29250, loss[loss=0.1308, simple_loss=0.2114, pruned_loss=0.02504, over 4824.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2132, pruned_loss=0.03284, over 971884.94 frames.], batch size: 27, lr: 2.10e-04
2022-05-06 23:07:46,938 INFO [train.py:715] (2/8) Epoch 10, batch 29300, loss[loss=0.1697, simple_loss=0.2402, pruned_loss=0.04965, over 4820.00 frames.], tot_loss[loss=0.1395, simple_loss=0.213, pruned_loss=0.03299, over 971899.91 frames.], batch size: 25, lr: 2.10e-04
2022-05-06 23:08:27,032 INFO [train.py:715] (2/8) Epoch 10, batch 29350, loss[loss=0.1829, simple_loss=0.2594, pruned_loss=0.05321, over 4965.00 frames.], tot_loss[loss=0.14, simple_loss=0.2135, pruned_loss=0.03322, over 971888.82 frames.], batch size: 39, lr: 2.10e-04
2022-05-06 23:09:06,038 INFO [train.py:715] (2/8) Epoch 10, batch 29400, loss[loss=0.1277, simple_loss=0.2047, pruned_loss=0.0254, over 4694.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2131, pruned_loss=0.03335, over 971982.97 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:09:45,820 INFO [train.py:715] (2/8) Epoch 10, batch 29450, loss[loss=0.1139, simple_loss=0.1925, pruned_loss=0.01768, over 4811.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2127, pruned_loss=0.03328, over 971694.32 frames.], batch size: 13, lr: 2.10e-04
2022-05-06 23:10:26,018 INFO [train.py:715] (2/8) Epoch 10, batch 29500, loss[loss=0.1313, simple_loss=0.203, pruned_loss=0.02982, over 4944.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03297, over 971586.44 frames.], batch size: 29, lr: 2.10e-04
2022-05-06 23:11:05,721 INFO [train.py:715] (2/8) Epoch 10, batch 29550, loss[loss=0.1587, simple_loss=0.2214, pruned_loss=0.04798, over 4853.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03319, over 971662.71 frames.], batch size: 32, lr: 2.10e-04
2022-05-06 23:11:44,344 INFO [train.py:715] (2/8) Epoch 10, batch 29600, loss[loss=0.1058, simple_loss=0.182, pruned_loss=0.01474, over 4921.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2122, pruned_loss=0.03341, over 972001.75 frames.], batch size: 23, lr: 2.10e-04
2022-05-06 23:12:23,997 INFO [train.py:715] (2/8) Epoch 10, batch 29650, loss[loss=0.1391, simple_loss=0.2054, pruned_loss=0.03637, over 4961.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2113, pruned_loss=0.03303, over 971278.39 frames.], batch size: 35, lr: 2.10e-04
2022-05-06 23:13:03,437 INFO [train.py:715] (2/8) Epoch 10, batch 29700, loss[loss=0.1464, simple_loss=0.2189, pruned_loss=0.03695, over 4949.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2112, pruned_loss=0.03295, over 971203.24 frames.], batch size: 35, lr: 2.10e-04
2022-05-06 23:13:42,105 INFO [train.py:715] (2/8) Epoch 10, batch 29750, loss[loss=0.1504, simple_loss=0.2225, pruned_loss=0.03913, over 4838.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03306, over 970839.21 frames.], batch size: 32, lr: 2.10e-04
2022-05-06 23:14:21,082 INFO [train.py:715] (2/8) Epoch 10, batch 29800, loss[loss=0.1388, simple_loss=0.2231, pruned_loss=0.02719, over 4923.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2125, pruned_loss=0.03289, over 972061.37 frames.], batch size: 18, lr: 2.10e-04
2022-05-06 23:15:00,555 INFO [train.py:715] (2/8) Epoch 10, batch 29850, loss[loss=0.1444, simple_loss=0.2142, pruned_loss=0.03727, over 4880.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.0331, over 972366.31 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:15:39,439 INFO [train.py:715] (2/8) Epoch 10, batch 29900, loss[loss=0.1166, simple_loss=0.1904, pruned_loss=0.02137, over 4789.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03326, over 973006.55 frames.], batch size: 17, lr: 2.10e-04
2022-05-06 23:16:17,895 INFO [train.py:715] (2/8) Epoch 10, batch 29950, loss[loss=0.1158, simple_loss=0.1736, pruned_loss=0.029, over 4860.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.03336, over 973384.88 frames.], batch size: 12, lr: 2.10e-04
2022-05-06 23:16:57,117 INFO [train.py:715] (2/8) Epoch 10, batch 30000, loss[loss=0.1296, simple_loss=0.1967, pruned_loss=0.03128, over 4638.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03294, over 973169.06 frames.], batch size: 13, lr: 2.10e-04
2022-05-06 23:16:57,117 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 23:17:06,541 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1063, simple_loss=0.1906, pruned_loss=0.01106, over 914524.00 frames.
2022-05-06 23:17:46,309 INFO [train.py:715] (2/8) Epoch 10, batch 30050, loss[loss=0.1409, simple_loss=0.2056, pruned_loss=0.03812, over 4777.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03276, over 972760.62 frames.], batch size: 17, lr: 2.10e-04
2022-05-06 23:18:25,803 INFO [train.py:715] (2/8) Epoch 10, batch 30100, loss[loss=0.1587, simple_loss=0.2424, pruned_loss=0.0375, over 4708.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2132, pruned_loss=0.03345, over 972436.02 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:19:04,215 INFO [train.py:715] (2/8) Epoch 10, batch 30150, loss[loss=0.1423, simple_loss=0.2084, pruned_loss=0.03815, over 4861.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2136, pruned_loss=0.03402, over 972664.83 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:19:44,566 INFO [train.py:715] (2/8) Epoch 10, batch 30200, loss[loss=0.1652, simple_loss=0.2403, pruned_loss=0.04503, over 4866.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03419, over 972023.07 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:20:24,572 INFO [train.py:715] (2/8) Epoch 10, batch 30250, loss[loss=0.1241, simple_loss=0.1982, pruned_loss=0.02501, over 4987.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2139, pruned_loss=0.03427, over 972419.50 frames.], batch size: 25, lr: 2.10e-04
2022-05-06 23:21:02,963 INFO [train.py:715] (2/8) Epoch 10, batch 30300, loss[loss=0.1368, simple_loss=0.2174, pruned_loss=0.02813, over 4817.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2138, pruned_loss=0.03418, over 972956.51 frames.], batch size: 21, lr: 2.10e-04
2022-05-06 23:21:41,379 INFO [train.py:715] (2/8) Epoch 10, batch 30350, loss[loss=0.1431, simple_loss=0.2119, pruned_loss=0.03715, over 4971.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.03342, over 972189.58 frames.], batch size: 14, lr: 2.10e-04
2022-05-06 23:22:21,182 INFO [train.py:715] (2/8) Epoch 10, batch 30400, loss[loss=0.1141, simple_loss=0.1994, pruned_loss=0.01443, over 4777.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.033, over 971270.13 frames.], batch size: 18, lr: 2.10e-04
2022-05-06 23:23:00,547 INFO [train.py:715] (2/8) Epoch 10, batch 30450, loss[loss=0.1332, simple_loss=0.215, pruned_loss=0.02565, over 4911.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03328, over 972235.00 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:23:38,706 INFO [train.py:715] (2/8) Epoch 10, batch 30500, loss[loss=0.1444, simple_loss=0.2084, pruned_loss=0.0402, over 4862.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2128, pruned_loss=0.03372, over 972726.80 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:24:18,303 INFO [train.py:715] (2/8) Epoch 10, batch 30550, loss[loss=0.1601, simple_loss=0.2263, pruned_loss=0.04693, over 4896.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03352, over 972986.75 frames.], batch size: 39, lr: 2.10e-04
2022-05-06 23:24:57,944 INFO [train.py:715] (2/8) Epoch 10, batch 30600, loss[loss=0.1617, simple_loss=0.2343, pruned_loss=0.04449, over 4935.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.03348, over 973131.38 frames.], batch size: 21, lr: 2.10e-04
2022-05-06 23:25:36,406 INFO [train.py:715] (2/8) Epoch 10, batch 30650, loss[loss=0.1566, simple_loss=0.2304, pruned_loss=0.0414, over 4795.00 frames.], tot_loss[loss=0.139, simple_loss=0.2117, pruned_loss=0.03317, over 972501.53 frames.], batch size: 21, lr: 2.10e-04
2022-05-06 23:26:15,885 INFO [train.py:715] (2/8) Epoch 10, batch 30700, loss[loss=0.1619, simple_loss=0.2332, pruned_loss=0.0453, over 4908.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03291, over 971924.28 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:26:55,011 INFO [train.py:715] (2/8) Epoch 10, batch 30750, loss[loss=0.1101, simple_loss=0.18, pruned_loss=0.02005, over 4791.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03266, over 971767.63 frames.], batch size: 12, lr: 2.10e-04
2022-05-06 23:27:33,902 INFO [train.py:715] (2/8) Epoch 10, batch 30800, loss[loss=0.1592, simple_loss=0.2396, pruned_loss=0.0394, over 4859.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.03244, over 972071.74 frames.], batch size: 38, lr: 2.10e-04
2022-05-06 23:28:12,408 INFO [train.py:715] (2/8) Epoch 10, batch 30850, loss[loss=0.1254, simple_loss=0.1972, pruned_loss=0.02682, over 4877.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03316, over 972239.38 frames.], batch size: 32, lr: 2.10e-04
2022-05-06 23:28:52,165 INFO [train.py:715] (2/8) Epoch 10, batch 30900, loss[loss=0.1254, simple_loss=0.196, pruned_loss=0.0274, over 4981.00 frames.], tot_loss[loss=0.1402, simple_loss=0.213, pruned_loss=0.03369, over 972722.73 frames.], batch size: 14, lr: 2.10e-04
2022-05-06 23:29:32,114 INFO [train.py:715] (2/8) Epoch 10, batch 30950, loss[loss=0.1521, simple_loss=0.2231, pruned_loss=0.04049, over 4863.00 frames.], tot_loss[loss=0.1404, simple_loss=0.213, pruned_loss=0.03384, over 972696.23 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:30:11,643 INFO [train.py:715] (2/8) Epoch 10, batch 31000, loss[loss=0.1628, simple_loss=0.2365, pruned_loss=0.0446, over 4981.00 frames.], tot_loss[loss=0.1406, simple_loss=0.213, pruned_loss=0.03407, over 973007.45 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:30:50,320 INFO [train.py:715] (2/8) Epoch 10, batch 31050, loss[loss=0.1443, simple_loss=0.2121, pruned_loss=0.03818, over 4933.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2129, pruned_loss=0.03384, over 973516.62 frames.], batch size: 29, lr: 2.10e-04
2022-05-06 23:31:29,593 INFO [train.py:715] (2/8) Epoch 10, batch 31100, loss[loss=0.1397, simple_loss=0.2137, pruned_loss=0.03278, over 4923.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2124, pruned_loss=0.0337, over 973269.16 frames.], batch size: 23, lr: 2.10e-04
2022-05-06 23:32:09,327 INFO [train.py:715] (2/8) Epoch 10, batch 31150, loss[loss=0.1377, simple_loss=0.2076, pruned_loss=0.03393, over 4967.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.03374, over 974197.34 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:32:47,335 INFO [train.py:715] (2/8) Epoch 10, batch 31200, loss[loss=0.1707, simple_loss=0.2368, pruned_loss=0.05225, over 4883.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2137, pruned_loss=0.03367, over 974244.10 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:33:26,827 INFO [train.py:715] (2/8) Epoch 10, batch 31250, loss[loss=0.1239, simple_loss=0.1943, pruned_loss=0.02673, over 4855.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2136, pruned_loss=0.03362, over 973470.69 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:34:06,251 INFO [train.py:715] (2/8) Epoch 10, batch 31300, loss[loss=0.1387, simple_loss=0.2017, pruned_loss=0.03786, over 4834.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2134, pruned_loss=0.03357, over 973477.98 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:34:45,238 INFO [train.py:715] (2/8) Epoch 10, batch 31350, loss[loss=0.1174, simple_loss=0.1973, pruned_loss=0.01879, over 4973.00 frames.], tot_loss[loss=0.14, simple_loss=0.2133, pruned_loss=0.03337, over 973322.02 frames.], batch size: 24, lr: 2.10e-04
2022-05-06 23:35:23,742 INFO [train.py:715] (2/8) Epoch 10, batch 31400, loss[loss=0.1369, simple_loss=0.2086, pruned_loss=0.03264, over 4912.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2129, pruned_loss=0.03296, over 973347.78 frames.], batch size: 29, lr: 2.10e-04
2022-05-06 23:36:02,746 INFO [train.py:715] (2/8) Epoch 10, batch 31450, loss[loss=0.1488, simple_loss=0.2243, pruned_loss=0.03669, over 4957.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2132, pruned_loss=0.03329, over 973488.17 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:36:42,178 INFO [train.py:715] (2/8) Epoch 10, batch 31500, loss[loss=0.1252, simple_loss=0.1868, pruned_loss=0.03182, over 4828.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03347, over 973851.95 frames.], batch size: 26, lr: 2.10e-04
2022-05-06 23:37:19,854 INFO [train.py:715] (2/8) Epoch 10, batch 31550, loss[loss=0.1242, simple_loss=0.2002, pruned_loss=0.02409, over 4988.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03291, over 974958.57 frames.], batch size: 25, lr: 2.10e-04
2022-05-06 23:37:58,954 INFO [train.py:715] (2/8) Epoch 10, batch 31600, loss[loss=0.15, simple_loss=0.2118, pruned_loss=0.04408, over 4832.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.0329, over 975068.51 frames.], batch size: 30, lr: 2.10e-04
2022-05-06 23:38:38,094 INFO [train.py:715] (2/8) Epoch 10, batch 31650, loss[loss=0.1251, simple_loss=0.1997, pruned_loss=0.02532, over 4950.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2129, pruned_loss=0.03326, over 974896.65 frames.], batch size: 29, lr: 2.10e-04
2022-05-06 23:39:17,241 INFO [train.py:715] (2/8) Epoch 10, batch 31700, loss[loss=0.1167, simple_loss=0.1898, pruned_loss=0.02177, over 4808.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03341, over 973903.34 frames.], batch size: 26, lr: 2.10e-04
2022-05-06 23:39:55,911 INFO [train.py:715] (2/8) Epoch 10, batch 31750, loss[loss=0.1893, simple_loss=0.2628, pruned_loss=0.05788, over 4860.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.03438, over 973541.33 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:40:34,951 INFO [train.py:715] (2/8) Epoch 10, batch 31800, loss[loss=0.1127, simple_loss=0.1863, pruned_loss=0.01951, over 4981.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03457, over 973177.04 frames.], batch size: 25, lr: 2.10e-04
2022-05-06 23:41:14,307 INFO [train.py:715] (2/8) Epoch 10, batch 31850, loss[loss=0.1411, simple_loss=0.2229, pruned_loss=0.02961, over 4778.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2127, pruned_loss=0.03369, over 973003.57 frames.], batch size: 18, lr: 2.10e-04
2022-05-06 23:41:52,373 INFO [train.py:715] (2/8) Epoch 10, batch 31900, loss[loss=0.1414, simple_loss=0.2132, pruned_loss=0.03479, over 4952.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03353, over 973683.64 frames.], batch size: 35, lr: 2.10e-04
2022-05-06 23:42:31,527 INFO [train.py:715] (2/8) Epoch 10, batch 31950, loss[loss=0.1356, simple_loss=0.2055, pruned_loss=0.0328, over 4847.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2132, pruned_loss=0.03425, over 973132.38 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:43:10,930 INFO [train.py:715] (2/8) Epoch 10, batch 32000, loss[loss=0.1217, simple_loss=0.1945, pruned_loss=0.02448, over 4963.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2135, pruned_loss=0.03436, over 972918.85 frames.], batch size: 35, lr: 2.10e-04
2022-05-06 23:43:49,599 INFO [train.py:715] (2/8) Epoch 10, batch 32050, loss[loss=0.1706, simple_loss=0.24, pruned_loss=0.05056, over 4763.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2136, pruned_loss=0.03435, over 971866.66 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:44:27,917 INFO [train.py:715] (2/8) Epoch 10, batch 32100, loss[loss=0.1467, simple_loss=0.2215, pruned_loss=0.03601, over 4840.00 frames.], tot_loss[loss=0.1412, simple_loss=0.214, pruned_loss=0.03416, over 972223.20 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:45:06,914 INFO [train.py:715] (2/8) Epoch 10, batch 32150, loss[loss=0.1245, simple_loss=0.1995, pruned_loss=0.02476, over 4933.00 frames.], tot_loss[loss=0.1401, simple_loss=0.213, pruned_loss=0.03366, over 973160.59 frames.], batch size: 23, lr: 2.10e-04
2022-05-06 23:45:45,856 INFO [train.py:715] (2/8) Epoch 10, batch 32200, loss[loss=0.1408, simple_loss=0.222, pruned_loss=0.02984, over 4960.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.0336, over 972733.14 frames.], batch size: 24, lr: 2.10e-04
2022-05-06 23:46:23,728 INFO [train.py:715] (2/8) Epoch 10, batch 32250, loss[loss=0.1447, simple_loss=0.2143, pruned_loss=0.0375, over 4708.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2136, pruned_loss=0.03383, over 972573.87 frames.], batch size: 15, lr: 2.10e-04
2022-05-06 23:47:02,888 INFO [train.py:715] (2/8) Epoch 10, batch 32300, loss[loss=0.1351, simple_loss=0.2127, pruned_loss=0.02876, over 4939.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2141, pruned_loss=0.03383, over 972510.41 frames.], batch size: 23, lr: 2.10e-04
2022-05-06 23:47:42,101 INFO [train.py:715] (2/8) Epoch 10, batch 32350, loss[loss=0.1428, simple_loss=0.219, pruned_loss=0.0333, over 4863.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2136, pruned_loss=0.03368, over 972666.63 frames.], batch size: 20, lr: 2.10e-04
2022-05-06 23:48:20,901 INFO [train.py:715] (2/8) Epoch 10, batch 32400, loss[loss=0.1203, simple_loss=0.1996, pruned_loss=0.0205, over 4969.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2138, pruned_loss=0.03394, over 972309.49 frames.], batch size: 24, lr: 2.10e-04
2022-05-06 23:48:59,312 INFO [train.py:715] (2/8) Epoch 10, batch 32450, loss[loss=0.1121, simple_loss=0.1873, pruned_loss=0.01845, over 4762.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2131, pruned_loss=0.03368, over 973051.11 frames.], batch size: 16, lr: 2.10e-04
2022-05-06 23:49:38,631 INFO [train.py:715] (2/8) Epoch 10, batch 32500, loss[loss=0.1319, simple_loss=0.2106, pruned_loss=0.02664, over 4794.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.03359, over 971649.91 frames.], batch size: 17, lr: 2.10e-04
2022-05-06 23:50:18,347 INFO [train.py:715] (2/8) Epoch 10, batch 32550, loss[loss=0.1303, simple_loss=0.2004, pruned_loss=0.03013, over 4836.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2121, pruned_loss=0.03356, over 971886.98 frames.], batch size: 12, lr: 2.10e-04
2022-05-06 23:50:56,262 INFO [train.py:715] (2/8) Epoch 10, batch 32600, loss[loss=0.1731, simple_loss=0.2432, pruned_loss=0.05149, over 4769.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03337, over 971447.88 frames.], batch size: 14, lr: 2.10e-04
2022-05-06 23:51:35,797 INFO [train.py:715] (2/8) Epoch 10, batch 32650, loss[loss=0.1155, simple_loss=0.1969, pruned_loss=0.01703, over 4767.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03299, over 971731.55 frames.], batch size: 19, lr: 2.10e-04
2022-05-06 23:52:15,569 INFO [train.py:715] (2/8) Epoch 10, batch 32700, loss[loss=0.1319, simple_loss=0.2051, pruned_loss=0.02933, over 4822.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03328, over 971636.09 frames.], batch size: 15, lr: 2.09e-04
2022-05-06 23:52:53,820 INFO [train.py:715] (2/8) Epoch 10, batch 32750, loss[loss=0.1267, simple_loss=0.1987, pruned_loss=0.02733, over 4795.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2132, pruned_loss=0.03354, over 971609.20 frames.], batch size: 12, lr: 2.09e-04
2022-05-06 23:53:34,507 INFO [train.py:715] (2/8) Epoch 10, batch 32800, loss[loss=0.1291, simple_loss=0.2135, pruned_loss=0.02234, over 4845.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2141, pruned_loss=0.03403, over 972075.87 frames.], batch size: 20, lr: 2.09e-04
2022-05-06 23:54:14,773 INFO [train.py:715] (2/8) Epoch 10, batch 32850, loss[loss=0.1219, simple_loss=0.1973, pruned_loss=0.02324, over 4791.00 frames.], tot_loss[loss=0.141, simple_loss=0.2138, pruned_loss=0.03412, over 971653.22 frames.], batch size: 14, lr: 2.09e-04
2022-05-06 23:54:54,885 INFO [train.py:715] (2/8) Epoch 10, batch 32900, loss[loss=0.1049, simple_loss=0.1765, pruned_loss=0.0167, over 4846.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2143, pruned_loss=0.03434, over 971051.60 frames.], batch size: 12, lr: 2.09e-04
2022-05-06 23:55:34,227 INFO [train.py:715] (2/8) Epoch 10, batch 32950, loss[loss=0.1369, simple_loss=0.2055, pruned_loss=0.03415, over 4921.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2134, pruned_loss=0.0339, over 971871.07 frames.], batch size: 18, lr: 2.09e-04
2022-05-06 23:56:14,908 INFO [train.py:715] (2/8) Epoch 10, batch 33000, loss[loss=0.1184, simple_loss=0.1952, pruned_loss=0.02078, over 4936.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2129, pruned_loss=0.03291, over 971315.07 frames.], batch size: 23, lr: 2.09e-04
2022-05-06 23:56:14,909 INFO [train.py:733] (2/8) Computing validation loss
2022-05-06 23:56:24,575 INFO [train.py:742] (2/8) Epoch 10, validation: loss=0.1063, simple_loss=0.1905, pruned_loss=0.01103, over 914524.00 frames.
2022-05-06 23:57:03,963 INFO [train.py:715] (2/8) Epoch 10, batch 33050, loss[loss=0.1533, simple_loss=0.2269, pruned_loss=0.03991, over 4922.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2133, pruned_loss=0.03308, over 970773.01 frames.], batch size: 18, lr: 2.09e-04
2022-05-06 23:57:43,741 INFO [train.py:715] (2/8) Epoch 10, batch 33100, loss[loss=0.1458, simple_loss=0.2196, pruned_loss=0.03597, over 4789.00 frames.], tot_loss[loss=0.14, simple_loss=0.2138, pruned_loss=0.03309, over 971257.95 frames.], batch size: 14, lr: 2.09e-04
2022-05-06 23:58:21,690 INFO [train.py:715] (2/8) Epoch 10, batch 33150, loss[loss=0.1493, simple_loss=0.2233, pruned_loss=0.03762, over 4799.00 frames.], tot_loss[loss=0.14, simple_loss=0.2138, pruned_loss=0.03312, over 971386.42 frames.], batch size: 21, lr: 2.09e-04
2022-05-06 23:59:00,823 INFO [train.py:715] (2/8) Epoch 10, batch 33200, loss[loss=0.1142, simple_loss=0.1834, pruned_loss=0.02246, over 4762.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2129, pruned_loss=0.03277, over 970627.97 frames.], batch size: 18, lr: 2.09e-04
2022-05-06 23:59:40,446 INFO [train.py:715] (2/8) Epoch 10, batch 33250, loss[loss=0.1512, simple_loss=0.2274, pruned_loss=0.03745, over 4969.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2131, pruned_loss=0.03294, over 971446.12 frames.], batch size: 28, lr: 2.09e-04
2022-05-07 00:00:18,362 INFO [train.py:715] (2/8) Epoch 10, batch 33300, loss[loss=0.1477, simple_loss=0.2132, pruned_loss=0.04115, over 4930.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2133, pruned_loss=0.03298, over 971387.22 frames.], batch size: 18, lr: 2.09e-04
2022-05-07 00:00:57,772 INFO [train.py:715] (2/8) Epoch 10, batch 33350, loss[loss=0.1441, simple_loss=0.2156, pruned_loss=0.03628, over 4772.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2134, pruned_loss=0.0331, over 971521.52 frames.], batch size: 19, lr: 2.09e-04
2022-05-07 00:01:37,017 INFO [train.py:715] (2/8) Epoch 10, batch 33400, loss[loss=0.1286, simple_loss=0.2114, pruned_loss=0.02291, over 4929.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2134, pruned_loss=0.03297, over 971836.17 frames.], batch size: 23, lr: 2.09e-04
2022-05-07 00:02:16,547 INFO [train.py:715] (2/8) Epoch 10, batch 33450, loss[loss=0.1346, simple_loss=0.2044, pruned_loss=0.03246, over 4849.00 frames.], tot_loss[loss=0.14, simple_loss=0.2136, pruned_loss=0.03317, over 971288.68 frames.], batch size: 30, lr: 2.09e-04
2022-05-07 00:02:54,364 INFO [train.py:715] (2/8) Epoch 10, batch 33500, loss[loss=0.1294, simple_loss=0.2035, pruned_loss=0.02766, over 4965.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2134, pruned_loss=0.03302, over 971636.82 frames.], batch size: 15, lr: 2.09e-04
2022-05-07 00:03:33,959 INFO [train.py:715] (2/8) Epoch 10, batch 33550, loss[loss=0.1126, simple_loss=0.191, pruned_loss=0.01714, over 4800.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2131, pruned_loss=0.03287, over 971075.15 frames.], batch size: 25, lr: 2.09e-04
2022-05-07 00:04:13,563 INFO [train.py:715] (2/8) Epoch 10, batch 33600, loss[loss=0.1377, simple_loss=0.2178, pruned_loss=0.02879, over 4805.00 frames.], tot_loss[loss=0.1394, simple_loss=0.213, pruned_loss=0.03293, over 971779.38 frames.], batch size: 25, lr: 2.09e-04
2022-05-07 00:04:52,115 INFO [train.py:715] (2/8) Epoch 10, batch 33650, loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.0281, over 4972.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2125, pruned_loss=0.03283, over 971530.25 frames.], batch size: 14, lr: 2.09e-04
2022-05-07 00:05:30,843 INFO [train.py:715] (2/8) Epoch 10, batch 33700, loss[loss=0.13, simple_loss=0.2058, pruned_loss=0.0271, over 4952.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03286, over 971985.58 frames.], batch size: 24, lr: 2.09e-04
2022-05-07 00:06:10,498 INFO [train.py:715] (2/8) Epoch 10, batch 33750, loss[loss=0.1702, simple_loss=0.2511, pruned_loss=0.0447, over 4799.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03328, over 972558.00 frames.], batch size: 24, lr: 2.09e-04
2022-05-07 00:06:50,168 INFO [train.py:715] (2/8) Epoch 10, batch 33800, loss[loss=0.153, simple_loss=0.2232, pruned_loss=0.04137, over 4949.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2129, pruned_loss=0.0331, over 972583.95 frames.], batch size: 21, lr: 2.09e-04
2022-05-07 00:07:29,176 INFO [train.py:715] (2/8) Epoch 10, batch 33850, loss[loss=0.16, simple_loss=0.2249, pruned_loss=0.04757, over 4909.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2132, pruned_loss=0.03308, over 973407.33 frames.], batch size: 17, lr: 2.09e-04
2022-05-07 00:08:08,840 INFO [train.py:715] (2/8) Epoch 10, batch 33900, loss[loss=0.1445, simple_loss=0.2216, pruned_loss=0.0337, over 4829.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2133, pruned_loss=0.0332, over 972426.10 frames.], batch size: 30, lr: 2.09e-04
2022-05-07 00:08:48,749 INFO [train.py:715] (2/8) Epoch 10, batch 33950, loss[loss=0.1281, simple_loss=0.2014, pruned_loss=0.02743, over 4781.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03314, over 972263.47 frames.], batch size: 18, lr: 2.09e-04
2022-05-07 00:09:27,305 INFO [train.py:715] (2/8) Epoch 10, batch 34000, loss[loss=0.1556, simple_loss=0.2259, pruned_loss=0.04265, over 4984.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2127, pruned_loss=0.03321, over 972780.32 frames.], batch size: 31, lr: 2.09e-04
2022-05-07 00:10:06,615 INFO [train.py:715] (2/8) Epoch 10, batch 34050, loss[loss=0.1289, simple_loss=0.1982, pruned_loss=0.02982, over 4916.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.0328, over 972333.73 frames.], batch size: 18, lr: 2.09e-04
2022-05-07 00:10:45,875 INFO [train.py:715] (2/8) Epoch 10, batch 34100, loss[loss=0.165, simple_loss=0.2272, pruned_loss=0.05144, over 4782.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03262, over 971976.29 frames.], batch size: 14, lr: 2.09e-04
2022-05-07 00:11:25,360 INFO [train.py:715] (2/8) Epoch 10, batch 34150, loss[loss=0.1389, simple_loss=0.2144, pruned_loss=0.03168, over 4858.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03248, over 971724.53 frames.], batch size: 32, lr: 2.09e-04
2022-05-07 00:12:04,926 INFO [train.py:715] (2/8) Epoch 10, batch 34200, loss[loss=0.1447, simple_loss=0.2192, pruned_loss=0.0351, over 4765.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03312, over 972234.04 frames.], batch size: 14, lr: 2.09e-04
2022-05-07 00:12:44,145 INFO [train.py:715] (2/8) Epoch 10, batch 34250, loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03059, over 4842.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2114, pruned_loss=0.03297, over 973167.20 frames.], batch size: 20, lr: 2.09e-04
2022-05-07 00:13:23,644 INFO [train.py:715] (2/8) Epoch 10, batch 34300, loss[loss=0.1432, simple_loss=0.215, pruned_loss=0.03565, over 4879.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03297, over 973397.05 frames.], batch size: 32, lr: 2.09e-04
2022-05-07 00:14:03,553 INFO [train.py:715] (2/8) Epoch 10, batch 34350, loss[loss=0.1337, simple_loss=0.2023, pruned_loss=0.03255, over 4784.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03291, over 973531.07 frames.], batch size: 17, lr: 2.09e-04
2022-05-07 00:14:43,433 INFO [train.py:715] (2/8) Epoch 10, batch 34400, loss[loss=0.1391, simple_loss=0.2142, pruned_loss=0.03202, over 4768.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03244, over 973322.08 frames.], batch size: 19, lr: 2.09e-04
2022-05-07 00:15:23,586 INFO [train.py:715] (2/8) Epoch 10, batch 34450, loss[loss=0.1445, simple_loss=0.2215, pruned_loss=0.0338, over 4954.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2125, pruned_loss=0.03279, over 973548.25 frames.], batch size: 29, lr: 2.09e-04
2022-05-07 00:16:03,652 INFO [train.py:715] (2/8) Epoch 10, batch 34500, loss[loss=0.1202, simple_loss=0.1909, pruned_loss=0.02478, over 4866.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2135, pruned_loss=0.03294, over 973756.77 frames.], batch size: 16, lr: 2.09e-04
2022-05-07 00:16:42,850 INFO [train.py:715] (2/8) Epoch 10, batch 34550, loss[loss=0.1283, simple_loss=0.2045, pruned_loss=0.02608, over 4966.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2133, pruned_loss=0.03279, over 973737.25 frames.], batch size: 24, lr: 2.09e-04
2022-05-07 00:17:23,152 INFO [train.py:715] (2/8) Epoch 10, batch 34600, loss[loss=0.1442, simple_loss=0.2148, pruned_loss=0.03679, over 4752.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2141, pruned_loss=0.03351, over 972849.51 frames.], batch size: 16, lr: 2.09e-04
2022-05-07 00:18:03,612 INFO [train.py:715] (2/8) Epoch 10, batch 34650, loss[loss=0.1104, simple_loss=0.1929, pruned_loss=0.01398, over 4783.00 frames.], tot_loss[loss=0.1397, simple_loss=0.213, pruned_loss=0.03313, over 972593.15 frames.], batch size: 12, lr: 2.09e-04
2022-05-07 00:18:42,654 INFO [train.py:715] (2/8) Epoch 10, batch 34700, loss[loss=0.1338, simple_loss=0.2065, pruned_loss=0.03056, over 4890.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2135, pruned_loss=0.03355, over 972901.47 frames.], batch size: 17, lr: 2.09e-04
2022-05-07 00:19:21,232 INFO [train.py:715] (2/8) Epoch 10, batch 34750, loss[loss=0.1454, simple_loss=0.2114, pruned_loss=0.03971, over 4836.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.0334, over 972504.41 frames.], batch size: 30, lr: 2.09e-04
2022-05-07 00:19:57,689 INFO [train.py:715] (2/8) Epoch 10, batch 34800, loss[loss=0.126, simple_loss=0.1946, pruned_loss=0.02867, over 4762.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.033, over 971542.26 frames.], batch size: 12, lr: 2.09e-04
2022-05-07 00:20:47,594 INFO [train.py:715] (2/8) Epoch 11, batch 0, loss[loss=0.1744, simple_loss=0.2408, pruned_loss=0.05404, over 4774.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2408, pruned_loss=0.05404, over 4774.00 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:21:26,501 INFO [train.py:715] (2/8) Epoch 11, batch 50, loss[loss=0.161, simple_loss=0.241, pruned_loss=0.0405, over 4789.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2151, pruned_loss=0.03514, over 219652.04 frames.], batch size: 24, lr: 2.00e-04
2022-05-07 00:22:06,400 INFO [train.py:715] (2/8) Epoch 11, batch 100, loss[loss=0.1543, simple_loss=0.2244, pruned_loss=0.04209, over 4796.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2128, pruned_loss=0.03322, over 386820.60 frames.], batch size: 21, lr: 2.00e-04
2022-05-07 00:22:46,276 INFO [train.py:715] (2/8) Epoch 11, batch 150, loss[loss=0.1445, simple_loss=0.2186, pruned_loss=0.03524, over 4935.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2135, pruned_loss=0.03337, over 516576.41 frames.], batch size: 21, lr: 2.00e-04
2022-05-07 00:23:26,826 INFO [train.py:715] (2/8) Epoch 11, batch 200, loss[loss=0.1395, simple_loss=0.2178, pruned_loss=0.03064, over 4759.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2136, pruned_loss=0.03362, over 617461.59 frames.], batch size: 19, lr: 2.00e-04
2022-05-07 00:24:06,701 INFO [train.py:715] (2/8) Epoch 11, batch 250, loss[loss=0.1328, simple_loss=0.21, pruned_loss=0.02782, over 4924.00 frames.], tot_loss[loss=0.1396, simple_loss=0.213, pruned_loss=0.03312, over 695862.08 frames.], batch size: 23, lr: 2.00e-04
2022-05-07 00:24:45,519 INFO [train.py:715] (2/8) Epoch 11, batch 300, loss[loss=0.1446, simple_loss=0.2142, pruned_loss=0.03748, over 4981.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2114, pruned_loss=0.03308, over 757376.89 frames.], batch size: 35, lr: 2.00e-04
2022-05-07 00:25:26,106 INFO [train.py:715] (2/8) Epoch 11, batch 350, loss[loss=0.1314, simple_loss=0.2106, pruned_loss=0.02609, over 4926.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03241, over 805173.11 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:26:05,773 INFO [train.py:715] (2/8) Epoch 11, batch 400, loss[loss=0.1563, simple_loss=0.2277, pruned_loss=0.04242, over 4691.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03194, over 841947.24 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:26:46,463 INFO [train.py:715] (2/8) Epoch 11, batch 450, loss[loss=0.1492, simple_loss=0.2143, pruned_loss=0.04203, over 4892.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03188, over 870931.87 frames.], batch size: 32, lr: 2.00e-04
2022-05-07 00:27:27,785 INFO [train.py:715] (2/8) Epoch 11, batch 500, loss[loss=0.1034, simple_loss=0.1693, pruned_loss=0.01873, over 4858.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03179, over 892781.28 frames.], batch size: 12, lr: 2.00e-04
2022-05-07 00:28:09,386 INFO [train.py:715] (2/8) Epoch 11, batch 550, loss[loss=0.1352, simple_loss=0.205, pruned_loss=0.0327, over 4818.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.03233, over 910649.71 frames.], batch size: 26, lr: 2.00e-04
2022-05-07 00:28:50,702 INFO [train.py:715] (2/8) Epoch 11, batch 600, loss[loss=0.1386, simple_loss=0.2029, pruned_loss=0.03719, over 4712.00 frames.], tot_loss[loss=0.1381, simple_loss=0.211, pruned_loss=0.03255, over 923640.58 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:29:32,036 INFO [train.py:715] (2/8) Epoch 11, batch 650, loss[loss=0.1278, simple_loss=0.2028, pruned_loss=0.02638, over 4756.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.03286, over 934295.17 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:30:13,299 INFO [train.py:715] (2/8) Epoch 11, batch 700, loss[loss=0.1389, simple_loss=0.216, pruned_loss=0.03095, over 4934.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03275, over 942718.16 frames.], batch size: 23, lr: 2.00e-04
2022-05-07 00:30:54,880 INFO [train.py:715] (2/8) Epoch 11, batch 750, loss[loss=0.1553, simple_loss=0.2267, pruned_loss=0.04195, over 4976.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03244, over 949504.07 frames.], batch size: 28, lr: 2.00e-04
2022-05-07 00:31:36,032 INFO [train.py:715] (2/8) Epoch 11, batch 800, loss[loss=0.1521, simple_loss=0.2161, pruned_loss=0.04401, over 4893.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03224, over 954172.26 frames.], batch size: 20, lr: 2.00e-04
2022-05-07 00:32:16,760 INFO [train.py:715] (2/8) Epoch 11, batch 850, loss[loss=0.142, simple_loss=0.2212, pruned_loss=0.03144, over 4838.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03259, over 958143.97 frames.], batch size: 30, lr: 2.00e-04
2022-05-07 00:32:58,359 INFO [train.py:715] (2/8) Epoch 11, batch 900, loss[loss=0.1445, simple_loss=0.2208, pruned_loss=0.03414, over 4885.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03271, over 960882.42 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:33:38,986 INFO [train.py:715] (2/8) Epoch 11, batch 950, loss[loss=0.1239, simple_loss=0.1918, pruned_loss=0.02797, over 4931.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2118, pruned_loss=0.03248, over 963135.81 frames.], batch size: 23, lr: 2.00e-04
2022-05-07 00:34:19,479 INFO [train.py:715] (2/8) Epoch 11, batch 1000, loss[loss=0.1252, simple_loss=0.1806, pruned_loss=0.03495, over 4819.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03297, over 964993.69 frames.], batch size: 13, lr: 2.00e-04
2022-05-07 00:34:58,894 INFO [train.py:715] (2/8) Epoch 11, batch 1050, loss[loss=0.1833, simple_loss=0.2559, pruned_loss=0.05535, over 4771.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2126, pruned_loss=0.03348, over 966094.85 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:35:41,051 INFO [train.py:715] (2/8) Epoch 11, batch 1100, loss[loss=0.1444, simple_loss=0.224, pruned_loss=0.03239, over 4702.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2123, pruned_loss=0.03347, over 967326.39 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:36:20,736 INFO [train.py:715] (2/8) Epoch 11, batch 1150, loss[loss=0.1233, simple_loss=0.1971, pruned_loss=0.02472, over 4868.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2122, pruned_loss=0.03341, over 968901.17 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:37:00,330 INFO [train.py:715] (2/8) Epoch 11, batch 1200, loss[loss=0.1474, simple_loss=0.2241, pruned_loss=0.03534, over 4840.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2131, pruned_loss=0.03384, over 970165.17 frames.], batch size: 34, lr: 2.00e-04
2022-05-07 00:37:39,164 INFO [train.py:715] (2/8) Epoch 11, batch 1250, loss[loss=0.1195, simple_loss=0.1938, pruned_loss=0.02262, over 4666.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2123, pruned_loss=0.03357, over 970227.30 frames.], batch size: 14, lr: 2.00e-04
2022-05-07 00:38:18,011 INFO [train.py:715] (2/8) Epoch 11, batch 1300, loss[loss=0.1089, simple_loss=0.1743, pruned_loss=0.0218, over 4777.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03336, over 970645.51 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:38:56,862 INFO [train.py:715] (2/8) Epoch 11, batch 1350, loss[loss=0.1278, simple_loss=0.2124, pruned_loss=0.02161, over 4987.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03298, over 970420.80 frames.], batch size: 26, lr: 2.00e-04
2022-05-07 00:39:35,881 INFO [train.py:715] (2/8) Epoch 11, batch 1400, loss[loss=0.1364, simple_loss=0.2135, pruned_loss=0.02962, over 4882.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03276, over 970954.82 frames.], batch size: 22, lr: 2.00e-04
2022-05-07 00:40:14,714 INFO [train.py:715] (2/8) Epoch 11, batch 1450, loss[loss=0.1417, simple_loss=0.2123, pruned_loss=0.03561, over 4854.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03286, over 971297.57 frames.], batch size: 30, lr: 2.00e-04
2022-05-07 00:40:53,347 INFO [train.py:715] (2/8) Epoch 11, batch 1500, loss[loss=0.1196, simple_loss=0.1957, pruned_loss=0.02176, over 4758.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03273, over 971380.74 frames.], batch size: 19, lr: 2.00e-04
2022-05-07 00:41:31,714 INFO [train.py:715] (2/8) Epoch 11, batch 1550, loss[loss=0.1262, simple_loss=0.2031, pruned_loss=0.02467, over 4757.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03243, over 970671.94 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:42:10,771 INFO [train.py:715] (2/8) Epoch 11, batch 1600, loss[loss=0.1054, simple_loss=0.1771, pruned_loss=0.01686, over 4768.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03253, over 971545.18 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:42:49,742 INFO [train.py:715] (2/8) Epoch 11, batch 1650, loss[loss=0.1417, simple_loss=0.2184, pruned_loss=0.03252, over 4772.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03262, over 971939.16 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:43:28,109 INFO [train.py:715] (2/8) Epoch 11, batch 1700, loss[loss=0.1645, simple_loss=0.2375, pruned_loss=0.04576, over 4752.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03221, over 971716.62 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:44:07,378 INFO [train.py:715] (2/8) Epoch 11, batch 1750, loss[loss=0.1401, simple_loss=0.219, pruned_loss=0.03061, over 4802.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03233, over 971823.30 frames.], batch size: 24, lr: 2.00e-04
2022-05-07 00:44:46,270 INFO [train.py:715] (2/8) Epoch 11, batch 1800, loss[loss=0.1275, simple_loss=0.1996, pruned_loss=0.02767, over 4920.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03237, over 972461.47 frames.], batch size: 23, lr: 2.00e-04
2022-05-07 00:45:25,302 INFO [train.py:715] (2/8) Epoch 11, batch 1850, loss[loss=0.1623, simple_loss=0.2322, pruned_loss=0.04616, over 4933.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03299, over 972079.29 frames.], batch size: 29, lr: 2.00e-04
2022-05-07 00:46:04,485 INFO [train.py:715] (2/8) Epoch 11, batch 1900, loss[loss=0.1268, simple_loss=0.1934, pruned_loss=0.03012, over 4802.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2125, pruned_loss=0.03341, over 972626.84 frames.], batch size: 12, lr: 2.00e-04
2022-05-07 00:46:43,764 INFO [train.py:715] (2/8) Epoch 11, batch 1950, loss[loss=0.1216, simple_loss=0.1941, pruned_loss=0.02455, over 4779.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.03329, over 972531.02 frames.], batch size: 19, lr: 2.00e-04
2022-05-07 00:47:23,300 INFO [train.py:715] (2/8) Epoch 11, batch 2000, loss[loss=0.1296, simple_loss=0.197, pruned_loss=0.03109, over 4910.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2127, pruned_loss=0.03362, over 972705.54 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:48:01,930 INFO [train.py:715] (2/8) Epoch 11, batch 2050, loss[loss=0.1183, simple_loss=0.1794, pruned_loss=0.02862, over 4809.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2125, pruned_loss=0.03353, over 972686.06 frames.], batch size: 13, lr: 2.00e-04
2022-05-07 00:48:41,073 INFO [train.py:715] (2/8) Epoch 11, batch 2100, loss[loss=0.1182, simple_loss=0.1868, pruned_loss=0.02483, over 4794.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03298, over 973186.50 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 00:49:20,362 INFO [train.py:715] (2/8) Epoch 11, batch 2150, loss[loss=0.1745, simple_loss=0.2316, pruned_loss=0.05876, over 4826.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03318, over 972766.47 frames.], batch size: 30, lr: 2.00e-04
2022-05-07 00:49:59,563 INFO [train.py:715] (2/8) Epoch 11, batch 2200, loss[loss=0.1356, simple_loss=0.2103, pruned_loss=0.03051, over 4899.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2129, pruned_loss=0.03334, over 972787.23 frames.], batch size: 19, lr: 2.00e-04
2022-05-07 00:50:38,223 INFO [train.py:715] (2/8) Epoch 11, batch 2250, loss[loss=0.1669, simple_loss=0.2335, pruned_loss=0.05019, over 4972.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2124, pruned_loss=0.03349, over 972922.76 frames.], batch size: 35, lr: 2.00e-04
2022-05-07 00:51:17,281 INFO [train.py:715] (2/8) Epoch 11, batch 2300, loss[loss=0.1362, simple_loss=0.2154, pruned_loss=0.02845, over 4817.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03277, over 972811.46 frames.], batch size: 27, lr: 2.00e-04
2022-05-07 00:51:56,680 INFO [train.py:715] (2/8) Epoch 11, batch 2350, loss[loss=0.1332, simple_loss=0.2126, pruned_loss=0.02693, over 4907.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2107, pruned_loss=0.03254, over 972965.96 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:52:35,083 INFO [train.py:715] (2/8) Epoch 11, batch 2400, loss[loss=0.1453, simple_loss=0.2168, pruned_loss=0.03692, over 4817.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.03259, over 973661.89 frames.], batch size: 25, lr: 2.00e-04
2022-05-07 00:53:14,458 INFO [train.py:715] (2/8) Epoch 11, batch 2450, loss[loss=0.1613, simple_loss=0.2252, pruned_loss=0.0487, over 4834.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03246, over 973193.19 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:53:54,049 INFO [train.py:715] (2/8) Epoch 11, batch 2500, loss[loss=0.1552, simple_loss=0.2278, pruned_loss=0.04125, over 4830.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03208, over 972818.70 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:54:33,180 INFO [train.py:715] (2/8) Epoch 11, batch 2550, loss[loss=0.1198, simple_loss=0.1954, pruned_loss=0.02204, over 4987.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03192, over 972110.30 frames.], batch size: 28, lr: 2.00e-04
2022-05-07 00:55:12,421 INFO [train.py:715] (2/8) Epoch 11, batch 2600, loss[loss=0.1895, simple_loss=0.2711, pruned_loss=0.05391, over 4881.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2112, pruned_loss=0.03216, over 972515.25 frames.], batch size: 16, lr: 2.00e-04
2022-05-07 00:55:51,264 INFO [train.py:715] (2/8) Epoch 11, batch 2650, loss[loss=0.1526, simple_loss=0.2253, pruned_loss=0.03999, over 4823.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03217, over 972367.07 frames.], batch size: 26, lr: 2.00e-04
2022-05-07 00:56:30,347 INFO [train.py:715] (2/8) Epoch 11, batch 2700, loss[loss=0.1349, simple_loss=0.2094, pruned_loss=0.03019, over 4814.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03214, over 973273.26 frames.], batch size: 26, lr: 2.00e-04
2022-05-07 00:57:09,095 INFO [train.py:715] (2/8) Epoch 11, batch 2750, loss[loss=0.1782, simple_loss=0.2273, pruned_loss=0.06455, over 4783.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.0325, over 973253.57 frames.], batch size: 14, lr: 2.00e-04
2022-05-07 00:57:48,074 INFO [train.py:715] (2/8) Epoch 11, batch 2800, loss[loss=0.1715, simple_loss=0.241, pruned_loss=0.05104, over 4951.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03315, over 973065.79 frames.], batch size: 21, lr: 2.00e-04
2022-05-07 00:58:27,252 INFO [train.py:715] (2/8) Epoch 11, batch 2850, loss[loss=0.1497, simple_loss=0.2224, pruned_loss=0.03846, over 4691.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03299, over 973250.15 frames.], batch size: 15, lr: 2.00e-04
2022-05-07 00:59:05,709 INFO [train.py:715] (2/8) Epoch 11, batch 2900, loss[loss=0.1235, simple_loss=0.1937, pruned_loss=0.02669, over 4768.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03283, over 973403.89 frames.], batch size: 17, lr: 2.00e-04
2022-05-07 00:59:45,167 INFO [train.py:715] (2/8) Epoch 11, batch 2950, loss[loss=0.1369, simple_loss=0.2158, pruned_loss=0.029, over 4915.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.03251, over 972924.50 frames.], batch size: 19, lr: 2.00e-04
2022-05-07 01:00:25,030 INFO [train.py:715] (2/8) Epoch 11, batch 3000, loss[loss=0.1152, simple_loss=0.1907, pruned_loss=0.01985, over 4869.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03216, over 973021.51 frames.], batch size: 22, lr: 2.00e-04
2022-05-07 01:00:25,031 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 01:00:34,771 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.1061, simple_loss=0.1902, pruned_loss=0.01097, over 914524.00 frames.
2022-05-07 01:01:14,748 INFO [train.py:715] (2/8) Epoch 11, batch 3050, loss[loss=0.1378, simple_loss=0.2044, pruned_loss=0.0356, over 4918.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.0326, over 972765.25 frames.], batch size: 18, lr: 2.00e-04
2022-05-07 01:01:54,016 INFO [train.py:715] (2/8) Epoch 11, batch 3100, loss[loss=0.1464, simple_loss=0.2163, pruned_loss=0.03823, over 4750.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2109, pruned_loss=0.0325, over 972650.88 frames.], batch size: 19, lr: 2.00e-04
2022-05-07 01:02:34,091 INFO [train.py:715] (2/8) Epoch 11, batch 3150, loss[loss=0.131, simple_loss=0.2162, pruned_loss=0.02288, over 4807.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2106, pruned_loss=0.03259, over 972611.18 frames.], batch size: 25, lr: 2.00e-04
2022-05-07 01:03:13,131 INFO [train.py:715] (2/8) Epoch 11, batch 3200, loss[loss=0.1494, simple_loss=0.2299, pruned_loss=0.03451, over 4872.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03311, over 970715.44 frames.], batch size: 20, lr: 2.00e-04
2022-05-07 01:03:52,801 INFO [train.py:715] (2/8) Epoch 11, batch 3250, loss[loss=0.1498, simple_loss=0.2302, pruned_loss=0.03467, over 4779.00 frames.], tot_loss[loss=0.14, simple_loss=0.2127, pruned_loss=0.03361, over 970986.19 frames.], batch size: 14, lr: 2.00e-04
2022-05-07 01:04:31,531 INFO [train.py:715] (2/8) Epoch 11, batch 3300, loss[loss=0.1309, simple_loss=0.1996, pruned_loss=0.03109, over 4976.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2112, pruned_loss=0.03295, over 971475.56 frames.], batch size: 35, lr: 2.00e-04
2022-05-07 01:05:10,792 INFO [train.py:715] (2/8) Epoch 11, batch 3350, loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.03443, over 4818.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2119, pruned_loss=0.03347, over 972347.91 frames.], batch size: 25, lr: 2.00e-04
2022-05-07 01:05:50,460 INFO [train.py:715] (2/8) Epoch 11, batch 3400, loss[loss=0.1332, simple_loss=0.1997, pruned_loss=0.03331, over 4976.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2112, pruned_loss=0.03286, over 972576.10 frames.], batch size: 24, lr: 2.00e-04
2022-05-07 01:06:29,436 INFO [train.py:715] (2/8) Epoch 11, batch 3450, loss[loss=0.1456, simple_loss=0.2222, pruned_loss=0.03446, over 4834.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03231, over 972275.65 frames.], batch size: 30, lr: 2.00e-04
2022-05-07 01:07:08,298 INFO [train.py:715] (2/8) Epoch 11, batch 3500, loss[loss=0.1295, simple_loss=0.2046, pruned_loss=0.02722, over 4802.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03272, over 971781.19 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:07:47,574 INFO [train.py:715] (2/8) Epoch 11, batch 3550, loss[loss=0.1072, simple_loss=0.1727, pruned_loss=0.02085, over 4736.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2108, pruned_loss=0.0324, over 971388.91 frames.], batch size: 12, lr: 1.99e-04
2022-05-07 01:08:27,196 INFO [train.py:715] (2/8) Epoch 11, batch 3600, loss[loss=0.1188, simple_loss=0.1877, pruned_loss=0.02492, over 4856.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03266, over 972436.88 frames.], batch size: 32, lr: 1.99e-04
2022-05-07 01:09:05,515 INFO [train.py:715] (2/8) Epoch 11, batch 3650, loss[loss=0.1376, simple_loss=0.2004, pruned_loss=0.03742, over 4773.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.0331, over 972914.63 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:09:45,167 INFO [train.py:715] (2/8) Epoch 11, batch 3700, loss[loss=0.1171, simple_loss=0.1873, pruned_loss=0.02341, over 4972.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03259, over 972092.81 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:10:24,603 INFO [train.py:715] (2/8) Epoch 11, batch 3750, loss[loss=0.1472, simple_loss=0.2198, pruned_loss=0.03727, over 4836.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03275, over 972072.13 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:11:03,049 INFO [train.py:715] (2/8) Epoch 11, batch 3800, loss[loss=0.1379, simple_loss=0.2067, pruned_loss=0.03457, over 4633.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03255, over 972038.00 frames.], batch size: 13, lr: 1.99e-04
2022-05-07 01:11:42,113 INFO [train.py:715] (2/8) Epoch 11, batch 3850, loss[loss=0.1237, simple_loss=0.2017, pruned_loss=0.02287, over 4894.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03264, over 972505.20 frames.], batch size: 22, lr: 1.99e-04
2022-05-07 01:12:21,421 INFO [train.py:715] (2/8) Epoch 11, batch 3900, loss[loss=0.1343, simple_loss=0.1947, pruned_loss=0.03697, over 4785.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03307, over 972527.19 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:13:01,148 INFO [train.py:715] (2/8) Epoch 11, batch 3950, loss[loss=0.1176, simple_loss=0.1949, pruned_loss=0.02021, over 4809.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03289, over 972136.77 frames.], batch size: 26, lr: 1.99e-04
2022-05-07 01:13:39,998 INFO [train.py:715] (2/8) Epoch 11, batch 4000, loss[loss=0.1456, simple_loss=0.2341, pruned_loss=0.02858, over 4701.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2123, pruned_loss=0.03333, over 971832.94 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:14:19,836 INFO [train.py:715] (2/8) Epoch 11, batch 4050, loss[loss=0.1424, simple_loss=0.2201, pruned_loss=0.03231, over 4975.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03258, over 971829.02 frames.], batch size: 24, lr: 1.99e-04
2022-05-07 01:14:59,479 INFO [train.py:715] (2/8) Epoch 11, batch 4100, loss[loss=0.1319, simple_loss=0.194, pruned_loss=0.03494, over 4962.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2124, pruned_loss=0.03285, over 972009.62 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:15:38,034 INFO [train.py:715] (2/8) Epoch 11, batch 4150, loss[loss=0.1528, simple_loss=0.2226, pruned_loss=0.04156, over 4951.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03282, over 972317.97 frames.], batch size: 35, lr: 1.99e-04
2022-05-07 01:16:16,419 INFO [train.py:715] (2/8) Epoch 11, batch 4200, loss[loss=0.1436, simple_loss=0.2191, pruned_loss=0.03409, over 4943.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03279, over 972964.50 frames.], batch size: 23, lr: 1.99e-04
2022-05-07 01:16:56,661 INFO [train.py:715] (2/8) Epoch 11, batch 4250, loss[loss=0.1477, simple_loss=0.23, pruned_loss=0.03273, over 4913.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2127, pruned_loss=0.03297, over 973008.73 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:17:36,659 INFO [train.py:715] (2/8) Epoch 11, batch 4300, loss[loss=0.1149, simple_loss=0.188, pruned_loss=0.02096, over 4766.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2124, pruned_loss=0.03296, over 972047.66 frames.], batch size: 12, lr: 1.99e-04
2022-05-07 01:18:15,823 INFO [train.py:715] (2/8) Epoch 11, batch 4350, loss[loss=0.1271, simple_loss=0.2135, pruned_loss=0.02039, over 4768.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.03315, over 973058.57 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:18:56,185 INFO [train.py:715] (2/8) Epoch 11, batch 4400, loss[loss=0.1332, simple_loss=0.1998, pruned_loss=0.03327, over 4819.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03306, over 972554.73 frames.], batch size: 12, lr: 1.99e-04
2022-05-07 01:19:36,296 INFO [train.py:715] (2/8) Epoch 11, batch 4450, loss[loss=0.1843, simple_loss=0.2592, pruned_loss=0.05472, over 4966.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03287, over 972570.43 frames.], batch size: 39, lr: 1.99e-04
2022-05-07 01:20:15,924 INFO [train.py:715] (2/8) Epoch 11, batch 4500, loss[loss=0.1305, simple_loss=0.2065, pruned_loss=0.02729, over 4927.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03261, over 972658.53 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:20:55,941 INFO [train.py:715] (2/8) Epoch 11, batch 4550, loss[loss=0.1726, simple_loss=0.2325, pruned_loss=0.05629, over 4875.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.03233, over 972385.74 frames.], batch size: 32, lr: 1.99e-04
2022-05-07 01:21:35,996 INFO [train.py:715] (2/8) Epoch 11, batch 4600, loss[loss=0.1365, simple_loss=0.2056, pruned_loss=0.03373, over 4733.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2123, pruned_loss=0.03264, over 972040.01 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:22:15,463 INFO [train.py:715] (2/8) Epoch 11, batch 4650, loss[loss=0.114, simple_loss=0.1916, pruned_loss=0.01817, over 4778.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03311, over 971831.63 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:22:55,179 INFO [train.py:715] (2/8) Epoch 11, batch 4700, loss[loss=0.1604, simple_loss=0.2201, pruned_loss=0.05036, over 4743.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03286, over 972274.15 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:23:35,351 INFO [train.py:715] (2/8) Epoch 11, batch 4750, loss[loss=0.1244, simple_loss=0.1994, pruned_loss=0.02468, over 4750.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.03247, over 972256.14 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:24:15,520 INFO [train.py:715] (2/8) Epoch 11, batch 4800, loss[loss=0.1438, simple_loss=0.2185, pruned_loss=0.03452, over 4860.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03268, over 973163.95 frames.], batch size: 34, lr: 1.99e-04
2022-05-07 01:24:55,133 INFO [train.py:715] (2/8) Epoch 11, batch 4850, loss[loss=0.1341, simple_loss=0.2062, pruned_loss=0.03107, over 4778.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2118, pruned_loss=0.03275, over 973319.15 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:25:34,922 INFO [train.py:715] (2/8) Epoch 11, batch 4900, loss[loss=0.1564, simple_loss=0.2267, pruned_loss=0.043, over 4933.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03301, over 972935.54 frames.], batch size: 39, lr: 1.99e-04
2022-05-07 01:26:14,638 INFO [train.py:715] (2/8) Epoch 11, batch 4950, loss[loss=0.1325, simple_loss=0.2045, pruned_loss=0.03031, over 4905.00 frames.], tot_loss[loss=0.139, simple_loss=0.2117, pruned_loss=0.03318, over 972979.23 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:26:53,440 INFO [train.py:715] (2/8) Epoch 11, batch 5000, loss[loss=0.146, simple_loss=0.2275, pruned_loss=0.03227, over 4941.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03329, over 973061.66 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:27:31,884 INFO [train.py:715] (2/8) Epoch 11, batch 5050, loss[loss=0.1523, simple_loss=0.2229, pruned_loss=0.04085, over 4776.00 frames.], tot_loss[loss=0.139, simple_loss=0.2123, pruned_loss=0.03291, over 972798.84 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:28:11,142 INFO [train.py:715] (2/8) Epoch 11, batch 5100, loss[loss=0.1538, simple_loss=0.2202, pruned_loss=0.04368, over 4748.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2118, pruned_loss=0.03255, over 972884.92 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:28:50,274 INFO [train.py:715] (2/8) Epoch 11, batch 5150, loss[loss=0.1667, simple_loss=0.2407, pruned_loss=0.04631, over 4923.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2121, pruned_loss=0.03253, over 972725.14 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:29:29,205 INFO [train.py:715] (2/8) Epoch 11, batch 5200, loss[loss=0.1182, simple_loss=0.2037, pruned_loss=0.01633, over 4797.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2124, pruned_loss=0.03272, over 973276.36 frames.], batch size: 21, lr: 1.99e-04
2022-05-07 01:30:08,613 INFO [train.py:715] (2/8) Epoch 11, batch 5250, loss[loss=0.1424, simple_loss=0.2116, pruned_loss=0.03659, over 4766.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03265, over 973208.28 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:30:48,294 INFO [train.py:715] (2/8) Epoch 11, batch 5300, loss[loss=0.1503, simple_loss=0.2072, pruned_loss=0.04671, over 4872.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03297, over 973362.63 frames.], batch size: 32, lr: 1.99e-04
2022-05-07 01:31:27,447 INFO [train.py:715] (2/8) Epoch 11, batch 5350, loss[loss=0.1355, simple_loss=0.2036, pruned_loss=0.03371, over 4974.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03345, over 973458.00 frames.], batch size: 24, lr: 1.99e-04
2022-05-07 01:32:06,514 INFO [train.py:715] (2/8) Epoch 11, batch 5400, loss[loss=0.1174, simple_loss=0.1929, pruned_loss=0.02098, over 4985.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03285, over 974202.83 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:32:45,902 INFO [train.py:715] (2/8) Epoch 11, batch 5450, loss[loss=0.1318, simple_loss=0.2079, pruned_loss=0.02785, over 4986.00 frames.], tot_loss[loss=0.1381, simple_loss=0.211, pruned_loss=0.03258, over 974003.78 frames.], batch size: 26, lr: 1.99e-04
2022-05-07 01:33:25,400 INFO [train.py:715] (2/8) Epoch 11, batch 5500, loss[loss=0.1371, simple_loss=0.2128, pruned_loss=0.03071, over 4899.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03302, over 973675.53 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:34:04,256 INFO [train.py:715] (2/8) Epoch 11, batch 5550, loss[loss=0.1715, simple_loss=0.2372, pruned_loss=0.05285, over 4890.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.03303, over 974497.96 frames.], batch size: 38, lr: 1.99e-04
2022-05-07 01:34:42,708 INFO [train.py:715] (2/8) Epoch 11, batch 5600, loss[loss=0.1413, simple_loss=0.2127, pruned_loss=0.03496, over 4858.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03289, over 973948.54 frames.], batch size: 20, lr: 1.99e-04
2022-05-07 01:35:22,175 INFO [train.py:715] (2/8) Epoch 11, batch 5650, loss[loss=0.1471, simple_loss=0.2244, pruned_loss=0.03489, over 4881.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2121, pruned_loss=0.03277, over 972569.61 frames.], batch size: 22, lr: 1.99e-04
2022-05-07 01:36:01,616 INFO [train.py:715] (2/8) Epoch 11, batch 5700, loss[loss=0.1547, simple_loss=0.2361, pruned_loss=0.03662, over 4865.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03257, over 972723.17 frames.], batch size: 22, lr: 1.99e-04
2022-05-07 01:36:40,399 INFO [train.py:715] (2/8) Epoch 11, batch 5750, loss[loss=0.1577, simple_loss=0.2145, pruned_loss=0.05043, over 4906.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03269, over 973528.29 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:37:19,376 INFO [train.py:715] (2/8) Epoch 11, batch 5800, loss[loss=0.1145, simple_loss=0.179, pruned_loss=0.02498, over 4793.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2108, pruned_loss=0.03242, over 972744.03 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:37:58,487 INFO [train.py:715] (2/8) Epoch 11, batch 5850, loss[loss=0.1364, simple_loss=0.2057, pruned_loss=0.03359, over 4835.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.0322, over 973008.96 frames.], batch size: 32, lr: 1.99e-04
2022-05-07 01:38:37,497 INFO [train.py:715] (2/8) Epoch 11, batch 5900, loss[loss=0.1492, simple_loss=0.2215, pruned_loss=0.03847, over 4750.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03189, over 972920.38 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:39:16,674 INFO [train.py:715] (2/8) Epoch 11, batch 5950, loss[loss=0.1259, simple_loss=0.1999, pruned_loss=0.02596, over 4987.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03293, over 972991.65 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:39:56,445 INFO [train.py:715] (2/8) Epoch 11, batch 6000, loss[loss=0.2079, simple_loss=0.2709, pruned_loss=0.07244, over 4890.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.03302, over 973796.82 frames.], batch size: 39, lr: 1.99e-04
2022-05-07 01:39:56,446 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 01:40:06,014 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.1059, simple_loss=0.1901, pruned_loss=0.01082, over 914524.00 frames.
2022-05-07 01:40:45,575 INFO [train.py:715] (2/8) Epoch 11, batch 6050, loss[loss=0.1061, simple_loss=0.1824, pruned_loss=0.01492, over 4986.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03266, over 973615.54 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:41:24,988 INFO [train.py:715] (2/8) Epoch 11, batch 6100, loss[loss=0.1493, simple_loss=0.2239, pruned_loss=0.03732, over 4993.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03271, over 973573.06 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:42:03,738 INFO [train.py:715] (2/8) Epoch 11, batch 6150, loss[loss=0.135, simple_loss=0.2077, pruned_loss=0.03116, over 4969.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03303, over 974147.36 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:42:43,200 INFO [train.py:715] (2/8) Epoch 11, batch 6200, loss[loss=0.1567, simple_loss=0.2468, pruned_loss=0.03327, over 4939.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2121, pruned_loss=0.03303, over 973693.04 frames.], batch size: 21, lr: 1.99e-04
2022-05-07 01:43:22,227 INFO [train.py:715] (2/8) Epoch 11, batch 6250, loss[loss=0.1308, simple_loss=0.196, pruned_loss=0.03281, over 4974.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2114, pruned_loss=0.03292, over 973929.80 frames.], batch size: 31, lr: 1.99e-04
2022-05-07 01:44:01,017 INFO [train.py:715] (2/8) Epoch 11, batch 6300, loss[loss=0.1507, simple_loss=0.2448, pruned_loss=0.02834, over 4973.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2121, pruned_loss=0.03332, over 973486.07 frames.], batch size: 24, lr: 1.99e-04
2022-05-07 01:44:39,690 INFO [train.py:715] (2/8) Epoch 11, batch 6350, loss[loss=0.1427, simple_loss=0.2139, pruned_loss=0.03574, over 4883.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.03309, over 973048.72 frames.], batch size: 39, lr: 1.99e-04
2022-05-07 01:45:20,273 INFO [train.py:715] (2/8) Epoch 11, batch 6400, loss[loss=0.1238, simple_loss=0.1957, pruned_loss=0.02601, over 4915.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03309, over 973010.61 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:45:59,616 INFO [train.py:715] (2/8) Epoch 11, batch 6450, loss[loss=0.1501, simple_loss=0.2286, pruned_loss=0.03577, over 4956.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03292, over 972843.65 frames.], batch size: 29, lr: 1.99e-04
2022-05-07 01:46:38,690 INFO [train.py:715] (2/8) Epoch 11, batch 6500, loss[loss=0.1496, simple_loss=0.2229, pruned_loss=0.03811, over 4928.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03219, over 972531.23 frames.], batch size: 18, lr: 1.99e-04
2022-05-07 01:47:18,054 INFO [train.py:715] (2/8) Epoch 11, batch 6550, loss[loss=0.1421, simple_loss=0.2165, pruned_loss=0.03388, over 4802.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2109, pruned_loss=0.03261, over 972907.83 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:47:58,235 INFO [train.py:715] (2/8) Epoch 11, batch 6600, loss[loss=0.1537, simple_loss=0.2255, pruned_loss=0.04097, over 4868.00 frames.], tot_loss[loss=0.139, simple_loss=0.2118, pruned_loss=0.03312, over 972749.13 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:48:38,359 INFO [train.py:715] (2/8) Epoch 11, batch 6650, loss[loss=0.1459, simple_loss=0.2219, pruned_loss=0.03496, over 4873.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2113, pruned_loss=0.0331, over 971090.34 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:49:17,570 INFO [train.py:715] (2/8) Epoch 11, batch 6700, loss[loss=0.1186, simple_loss=0.1867, pruned_loss=0.02523, over 4962.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03299, over 971258.05 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:49:57,824 INFO [train.py:715] (2/8) Epoch 11, batch 6750, loss[loss=0.1371, simple_loss=0.1987, pruned_loss=0.03772, over 4843.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2122, pruned_loss=0.0331, over 971373.71 frames.], batch size: 15, lr: 1.99e-04
2022-05-07 01:50:37,625 INFO [train.py:715] (2/8) Epoch 11, batch 6800, loss[loss=0.1132, simple_loss=0.1776, pruned_loss=0.02437, over 4788.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03305, over 971946.35 frames.], batch size: 12, lr: 1.99e-04
2022-05-07 01:51:16,479 INFO [train.py:715] (2/8) Epoch 11, batch 6850, loss[loss=0.1178, simple_loss=0.1854, pruned_loss=0.02508, over 4900.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03286, over 972747.55 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:51:55,544 INFO [train.py:715] (2/8) Epoch 11, batch 6900, loss[loss=0.1228, simple_loss=0.2053, pruned_loss=0.02017, over 4792.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03271, over 972845.19 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:52:34,236 INFO [train.py:715] (2/8) Epoch 11, batch 6950, loss[loss=0.1107, simple_loss=0.1909, pruned_loss=0.01522, over 4765.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2116, pruned_loss=0.03277, over 971978.98 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:53:13,691 INFO [train.py:715] (2/8) Epoch 11, batch 7000, loss[loss=0.1294, simple_loss=0.2064, pruned_loss=0.02624, over 4941.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03215, over 971353.72 frames.], batch size: 23, lr: 1.99e-04
2022-05-07 01:53:52,255 INFO [train.py:715] (2/8) Epoch 11, batch 7050, loss[loss=0.1368, simple_loss=0.204, pruned_loss=0.03478, over 4889.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03223, over 971786.39 frames.], batch size: 22, lr: 1.99e-04
2022-05-07 01:54:31,697 INFO [train.py:715] (2/8) Epoch 11, batch 7100, loss[loss=0.1464, simple_loss=0.221, pruned_loss=0.03588, over 4917.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03246, over 971330.81 frames.], batch size: 19, lr: 1.99e-04
2022-05-07 01:55:10,748 INFO [train.py:715] (2/8) Epoch 11, batch 7150, loss[loss=0.1176, simple_loss=0.1867, pruned_loss=0.02427, over 4905.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03162, over 972231.51 frames.], batch size: 17, lr: 1.99e-04
2022-05-07 01:55:49,509 INFO [train.py:715] (2/8) Epoch 11, batch 7200, loss[loss=0.1669, simple_loss=0.2382, pruned_loss=0.04775, over 4826.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03154, over 972928.98 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:56:28,453 INFO [train.py:715] (2/8) Epoch 11, batch 7250, loss[loss=0.1379, simple_loss=0.2183, pruned_loss=0.02881, over 4988.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03201, over 972365.85 frames.], batch size: 16, lr: 1.99e-04
2022-05-07 01:57:07,430 INFO [train.py:715] (2/8) Epoch 11, batch 7300, loss[loss=0.131, simple_loss=0.2047, pruned_loss=0.02866, over 4810.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03186, over 971593.97 frames.], batch size: 25, lr: 1.99e-04
2022-05-07 01:57:46,540 INFO [train.py:715] (2/8) Epoch 11, batch 7350, loss[loss=0.1269, simple_loss=0.2107, pruned_loss=0.02159, over 4798.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03147, over 971552.69 frames.], batch size: 14, lr: 1.99e-04
2022-05-07 01:58:25,305 INFO [train.py:715] (2/8) Epoch 11, batch 7400, loss[loss=0.1292, simple_loss=0.1996, pruned_loss=0.02943, over 4976.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03162, over 971351.54 frames.], batch size: 35, lr: 1.98e-04
2022-05-07 01:59:04,703 INFO [train.py:715] (2/8) Epoch 11, batch 7450, loss[loss=0.1293, simple_loss=0.2077, pruned_loss=0.02549, over 4769.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.0312, over 971661.04 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 01:59:43,841 INFO [train.py:715] (2/8) Epoch 11, batch 7500, loss[loss=0.13, simple_loss=0.2037, pruned_loss=0.02816, over 4915.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03133, over 971907.00 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:00:23,092 INFO [train.py:715] (2/8) Epoch 11, batch 7550, loss[loss=0.1372, simple_loss=0.2143, pruned_loss=0.03004, over 4813.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03177, over 971992.99 frames.], batch size: 25, lr: 1.98e-04
2022-05-07 02:01:02,845 INFO [train.py:715] (2/8) Epoch 11, batch 7600, loss[loss=0.1245, simple_loss=0.1965, pruned_loss=0.0262, over 4884.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03175, over 972694.68 frames.], batch size: 22, lr: 1.98e-04
2022-05-07 02:01:42,513 INFO [train.py:715] (2/8) Epoch 11, batch 7650, loss[loss=0.1466, simple_loss=0.2105, pruned_loss=0.0413, over 4841.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2095, pruned_loss=0.03166, over 971923.54 frames.], batch size: 34, lr: 1.98e-04
2022-05-07 02:02:22,054 INFO [train.py:715] (2/8) Epoch 11, batch 7700, loss[loss=0.1288, simple_loss=0.2087, pruned_loss=0.02446, over 4755.00 frames.], tot_loss[loss=0.136, simple_loss=0.209, pruned_loss=0.03147, over 970917.86 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:03:01,234 INFO [train.py:715] (2/8) Epoch 11, batch 7750, loss[loss=0.151, simple_loss=0.2231, pruned_loss=0.03943, over 4766.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2098, pruned_loss=0.03181, over 971343.90 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:03:40,568 INFO [train.py:715] (2/8) Epoch 11, batch 7800, loss[loss=0.1501, simple_loss=0.2397, pruned_loss=0.03027, over 4883.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03217, over 971551.78 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:04:19,854 INFO [train.py:715] (2/8) Epoch 11, batch 7850, loss[loss=0.1506, simple_loss=0.2198, pruned_loss=0.04071, over 4766.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03265, over 971124.52 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:04:58,993 INFO [train.py:715] (2/8) Epoch 11, batch 7900, loss[loss=0.1471, simple_loss=0.2198, pruned_loss=0.03724, over 4783.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2118, pruned_loss=0.03267, over 971460.31 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:05:37,729 INFO [train.py:715] (2/8) Epoch 11, batch 7950, loss[loss=0.1445, simple_loss=0.2303, pruned_loss=0.02937, over 4755.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.03225, over 971567.58 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:06:18,359 INFO [train.py:715] (2/8) Epoch 11, batch 8000, loss[loss=0.1224, simple_loss=0.2031, pruned_loss=0.02079, over 4807.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.0323, over 971362.64 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:06:57,624 INFO [train.py:715] (2/8) Epoch 11, batch 8050, loss[loss=0.1483, simple_loss=0.2133, pruned_loss=0.04165, over 4882.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2118, pruned_loss=0.03272, over 970607.96 frames.], batch size: 30, lr: 1.98e-04
2022-05-07 02:07:37,872 INFO [train.py:715] (2/8) Epoch 11, batch 8100, loss[loss=0.1537, simple_loss=0.2211, pruned_loss=0.04316, over 4871.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2129, pruned_loss=0.03345, over 970462.38 frames.], batch size: 30, lr: 1.98e-04
2022-05-07 02:08:17,869 INFO [train.py:715] (2/8) Epoch 11, batch 8150, loss[loss=0.1616, simple_loss=0.2262, pruned_loss=0.04852, over 4852.00 frames.], tot_loss[loss=0.14, simple_loss=0.2129, pruned_loss=0.0336, over 970185.54 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:08:57,397 INFO [train.py:715] (2/8) Epoch 11, batch 8200, loss[loss=0.189, simple_loss=0.2499, pruned_loss=0.06409, over 4860.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03319, over 970172.05 frames.], batch size: 30, lr: 1.98e-04
2022-05-07 02:09:36,723 INFO [train.py:715] (2/8) Epoch 11, batch 8250, loss[loss=0.1718, simple_loss=0.2255, pruned_loss=0.05901, over 4928.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2122, pruned_loss=0.03324, over 970994.88 frames.], batch size: 39, lr: 1.98e-04
2022-05-07 02:10:15,060 INFO [train.py:715] (2/8) Epoch 11, batch 8300, loss[loss=0.1193, simple_loss=0.1907, pruned_loss=0.02396, over 4850.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03271, over 971377.02 frames.], batch size: 20, lr: 1.98e-04
2022-05-07 02:10:54,960 INFO [train.py:715] (2/8) Epoch 11, batch 8350, loss[loss=0.1552, simple_loss=0.2317, pruned_loss=0.03934, over 4762.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.0328, over 970791.77 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:11:34,527 INFO [train.py:715] (2/8) Epoch 11, batch 8400, loss[loss=0.1511, simple_loss=0.2225, pruned_loss=0.03979, over 4915.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2119, pruned_loss=0.03337, over 971155.33 frames.], batch size: 23, lr: 1.98e-04
2022-05-07 02:12:13,502 INFO [train.py:715] (2/8) Epoch 11, batch 8450, loss[loss=0.1295, simple_loss=0.2103, pruned_loss=0.02437, over 4777.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2115, pruned_loss=0.0332, over 971476.07 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:12:52,196 INFO [train.py:715] (2/8) Epoch 11, batch 8500, loss[loss=0.1359, simple_loss=0.2123, pruned_loss=0.02976, over 4778.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2112, pruned_loss=0.03289, over 971863.19 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:13:32,005 INFO [train.py:715] (2/8) Epoch 11, batch 8550, loss[loss=0.1269, simple_loss=0.1985, pruned_loss=0.0277, over 4861.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2109, pruned_loss=0.03289, over 971945.37 frames.], batch size: 20, lr: 1.98e-04
2022-05-07 02:14:11,213 INFO [train.py:715] (2/8) Epoch 11, batch 8600, loss[loss=0.1261, simple_loss=0.2037, pruned_loss=0.02422, over 4763.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2104, pruned_loss=0.03237, over 972187.85 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:14:49,544 INFO [train.py:715] (2/8) Epoch 11, batch 8650, loss[loss=0.162, simple_loss=0.2287, pruned_loss=0.04767, over 4926.00 frames.], tot_loss[loss=0.139, simple_loss=0.2115, pruned_loss=0.03324, over 971955.82 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:15:29,402 INFO [train.py:715] (2/8) Epoch 11, batch 8700, loss[loss=0.1269, simple_loss=0.1995, pruned_loss=0.02716, over 4822.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2109, pruned_loss=0.03261, over 972118.35 frames.], batch size: 26, lr: 1.98e-04
2022-05-07 02:16:08,722 INFO [train.py:715] (2/8) Epoch 11, batch 8750, loss[loss=0.1338, simple_loss=0.2116, pruned_loss=0.02794, over 4768.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2114, pruned_loss=0.03296, over 972831.87 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:16:47,706 INFO [train.py:715] (2/8) Epoch 11, batch 8800, loss[loss=0.1272, simple_loss=0.1956, pruned_loss=0.02943, over 4961.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2119, pruned_loss=0.03337, over 973095.30 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:17:26,838 INFO [train.py:715] (2/8) Epoch 11, batch 8850, loss[loss=0.119, simple_loss=0.1925, pruned_loss=0.02271, over 4638.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2124, pruned_loss=0.03329, over 972655.36 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:18:06,536 INFO [train.py:715] (2/8) Epoch 11, batch 8900, loss[loss=0.1328, simple_loss=0.2014, pruned_loss=0.03204, over 4906.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03299, over 973514.18 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:18:46,168 INFO [train.py:715] (2/8) Epoch 11, batch 8950, loss[loss=0.1473, simple_loss=0.2203, pruned_loss=0.03714, over 4868.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2123, pruned_loss=0.03311, over 973182.05 frames.], batch size: 22, lr: 1.98e-04
2022-05-07 02:19:25,294 INFO [train.py:715] (2/8) Epoch 11, batch 9000, loss[loss=0.1385, simple_loss=0.2135, pruned_loss=0.03175, over 4802.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03347, over 973574.32 frames.], batch size: 21, lr: 1.98e-04
2022-05-07 02:19:25,294 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 02:19:34,856 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.1061, simple_loss=0.1903, pruned_loss=0.011, over 914524.00 frames.
2022-05-07 02:20:13,750 INFO [train.py:715] (2/8) Epoch 11, batch 9050, loss[loss=0.1855, simple_loss=0.2495, pruned_loss=0.06074, over 4789.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2124, pruned_loss=0.03346, over 973847.00 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:20:55,920 INFO [train.py:715] (2/8) Epoch 11, batch 9100, loss[loss=0.1699, simple_loss=0.2467, pruned_loss=0.0466, over 4915.00 frames.], tot_loss[loss=0.14, simple_loss=0.2128, pruned_loss=0.03357, over 974589.81 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:21:35,561 INFO [train.py:715] (2/8) Epoch 11, batch 9150, loss[loss=0.1312, simple_loss=0.2086, pruned_loss=0.02693, over 4914.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03318, over 973731.83 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:22:15,056 INFO [train.py:715] (2/8) Epoch 11, batch 9200, loss[loss=0.1551, simple_loss=0.2234, pruned_loss=0.0434, over 4853.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.0331, over 973863.42 frames.], batch size: 32, lr: 1.98e-04
2022-05-07 02:22:54,637 INFO [train.py:715] (2/8) Epoch 11, batch 9250, loss[loss=0.1704, simple_loss=0.2356, pruned_loss=0.0526, over 4867.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.0329, over 973873.81 frames.], batch size: 32, lr: 1.98e-04
2022-05-07 02:23:33,873 INFO [train.py:715] (2/8) Epoch 11, batch 9300, loss[loss=0.177, simple_loss=0.2393, pruned_loss=0.05739, over 4803.00 frames.], tot_loss[loss=0.139, simple_loss=0.2118, pruned_loss=0.03309, over 973484.00 frames.], batch size: 21, lr: 1.98e-04
2022-05-07 02:24:12,710 INFO [train.py:715] (2/8) Epoch 11, batch 9350, loss[loss=0.1231, simple_loss=0.1969, pruned_loss=0.02469, over 4755.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03297, over 973224.11 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:24:51,486 INFO [train.py:715] (2/8) Epoch 11, batch 9400, loss[loss=0.1471, simple_loss=0.2278, pruned_loss=0.03324, over 4917.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03288, over 973307.67 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:25:31,002 INFO [train.py:715] (2/8) Epoch 11, batch 9450, loss[loss=0.1408, simple_loss=0.2193, pruned_loss=0.03116, over 4989.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2113, pruned_loss=0.03283, over 973598.88 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:26:10,040 INFO [train.py:715] (2/8) Epoch 11, batch 9500, loss[loss=0.1388, simple_loss=0.2072, pruned_loss=0.03523, over 4973.00 frames.], tot_loss[loss=0.138, simple_loss=0.2109, pruned_loss=0.0325, over 973085.80 frames.], batch size: 35, lr: 1.98e-04
2022-05-07 02:26:48,576 INFO [train.py:715] (2/8) Epoch 11, batch 9550, loss[loss=0.1747, simple_loss=0.2457, pruned_loss=0.05182, over 4879.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.03253, over 973551.96 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:27:28,236 INFO [train.py:715] (2/8) Epoch 11, batch 9600, loss[loss=0.1477, simple_loss=0.2163, pruned_loss=0.03948, over 4852.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2108, pruned_loss=0.03229, over 972644.72 frames.], batch size: 34, lr: 1.98e-04
2022-05-07 02:28:07,059 INFO [train.py:715] (2/8) Epoch 11, batch 9650, loss[loss=0.1283, simple_loss=0.2041, pruned_loss=0.02624, over 4683.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03236, over 972218.91 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:28:45,588 INFO [train.py:715] (2/8) Epoch 11, batch 9700, loss[loss=0.1553, simple_loss=0.2249, pruned_loss=0.04286, over 4865.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03287, over 971701.45 frames.], batch size: 32, lr: 1.98e-04
2022-05-07 02:29:24,591 INFO [train.py:715] (2/8) Epoch 11, batch 9750, loss[loss=0.1294, simple_loss=0.2024, pruned_loss=0.02823, over 4837.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03276, over 971806.29 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:30:03,697 INFO [train.py:715] (2/8) Epoch 11, batch 9800, loss[loss=0.1543, simple_loss=0.2248, pruned_loss=0.04188, over 4955.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03258, over 972433.19 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:30:43,327 INFO [train.py:715] (2/8) Epoch 11, batch 9850, loss[loss=0.1257, simple_loss=0.1954, pruned_loss=0.02798, over 4660.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03253, over 972188.38 frames.], batch size: 13, lr: 1.98e-04
2022-05-07 02:31:22,284 INFO [train.py:715] (2/8) Epoch 11, batch 9900, loss[loss=0.1469, simple_loss=0.2208, pruned_loss=0.03653, over 4937.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03258, over 971980.93 frames.], batch size: 29, lr: 1.98e-04
2022-05-07 02:32:02,526 INFO [train.py:715] (2/8) Epoch 11, batch 9950, loss[loss=0.145, simple_loss=0.2066, pruned_loss=0.04166, over 4866.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2108, pruned_loss=0.03231, over 972164.52 frames.], batch size: 32, lr: 1.98e-04
2022-05-07 02:32:41,854 INFO [train.py:715] (2/8) Epoch 11, batch 10000, loss[loss=0.1264, simple_loss=0.2059, pruned_loss=0.0234, over 4803.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03189, over 972117.48 frames.], batch size: 21, lr: 1.98e-04
2022-05-07 02:33:21,580 INFO [train.py:715] (2/8) Epoch 11, batch 10050, loss[loss=0.1279, simple_loss=0.2028, pruned_loss=0.0265, over 4925.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2108, pruned_loss=0.03202, over 972583.20 frames.], batch size: 23, lr: 1.98e-04
2022-05-07 02:33:59,721 INFO [train.py:715] (2/8) Epoch 11, batch 10100, loss[loss=0.1251, simple_loss=0.197, pruned_loss=0.0266, over 4745.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2108, pruned_loss=0.03201, over 972122.06 frames.], batch size: 19, lr: 1.98e-04
2022-05-07 02:34:38,756 INFO [train.py:715] (2/8) Epoch 11, batch 10150, loss[loss=0.152, simple_loss=0.2193, pruned_loss=0.04231, over 4980.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2115, pruned_loss=0.0324, over 972422.42 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:35:17,188 INFO [train.py:715] (2/8) Epoch 11, batch 10200, loss[loss=0.154, simple_loss=0.2145, pruned_loss=0.04673, over 4839.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2121, pruned_loss=0.03257, over 972516.91 frames.], batch size: 30, lr: 1.98e-04
2022-05-07 02:35:55,358 INFO [train.py:715] (2/8) Epoch 11, batch 10250, loss[loss=0.16, simple_loss=0.2303, pruned_loss=0.04489, over 4973.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2144, pruned_loss=0.03374, over 972830.09 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:36:34,757 INFO [train.py:715] (2/8) Epoch 11, batch 10300, loss[loss=0.135, simple_loss=0.2072, pruned_loss=0.03147, over 4865.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2136, pruned_loss=0.03341, over 972342.52 frames.], batch size: 20, lr: 1.98e-04
2022-05-07 02:37:13,485 INFO [train.py:715] (2/8) Epoch 11, batch 10350, loss[loss=0.1297, simple_loss=0.2101, pruned_loss=0.02469, over 4786.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.03343, over 973157.67 frames.], batch size: 14, lr: 1.98e-04
2022-05-07 02:37:52,308 INFO [train.py:715] (2/8) Epoch 11, batch 10400, loss[loss=0.1713, simple_loss=0.2436, pruned_loss=0.04953, over 4949.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2128, pruned_loss=0.03324, over 972575.34 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:38:30,787 INFO [train.py:715] (2/8) Epoch 11, batch 10450, loss[loss=0.1522, simple_loss=0.2275, pruned_loss=0.03848, over 4943.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2132, pruned_loss=0.0337, over 972160.15 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:39:09,430 INFO [train.py:715] (2/8) Epoch 11, batch 10500, loss[loss=0.1526, simple_loss=0.2187, pruned_loss=0.04328, over 4882.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2134, pruned_loss=0.03376, over 972925.05 frames.], batch size: 34, lr: 1.98e-04
2022-05-07 02:39:48,502 INFO [train.py:715] (2/8) Epoch 11, batch 10550, loss[loss=0.1076, simple_loss=0.1847, pruned_loss=0.01527, over 4797.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03301, over 972980.98 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:40:27,834 INFO [train.py:715] (2/8) Epoch 11, batch 10600, loss[loss=0.1202, simple_loss=0.1949, pruned_loss=0.02274, over 4935.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03286, over 972827.05 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:41:06,624 INFO [train.py:715] (2/8) Epoch 11, batch 10650, loss[loss=0.1273, simple_loss=0.201, pruned_loss=0.02676, over 4816.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03273, over 973584.58 frames.], batch size: 26, lr: 1.98e-04
2022-05-07 02:41:45,851 INFO [train.py:715] (2/8) Epoch 11, batch 10700, loss[loss=0.1559, simple_loss=0.2379, pruned_loss=0.03695, over 4883.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03324, over 973074.43 frames.], batch size: 32, lr: 1.98e-04
2022-05-07 02:42:25,051 INFO [train.py:715] (2/8) Epoch 11, batch 10750, loss[loss=0.145, simple_loss=0.235, pruned_loss=0.02752, over 4799.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03301, over 973284.63 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:43:03,974 INFO [train.py:715] (2/8) Epoch 11, batch 10800, loss[loss=0.1448, simple_loss=0.222, pruned_loss=0.03382, over 4691.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2121, pruned_loss=0.03263, over 972750.86 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:43:43,679 INFO [train.py:715] (2/8) Epoch 11, batch 10850, loss[loss=0.1437, simple_loss=0.2232, pruned_loss=0.03209, over 4857.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2119, pruned_loss=0.03244, over 973081.67 frames.], batch size: 20, lr: 1.98e-04
2022-05-07 02:44:23,472 INFO [train.py:715] (2/8) Epoch 11, batch 10900, loss[loss=0.1307, simple_loss=0.2015, pruned_loss=0.02991, over 4913.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.0319, over 972573.17 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:45:02,831 INFO [train.py:715] (2/8) Epoch 11, batch 10950, loss[loss=0.1342, simple_loss=0.2181, pruned_loss=0.02515, over 4905.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03136, over 972585.05 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:45:42,051 INFO [train.py:715] (2/8) Epoch 11, batch 11000, loss[loss=0.1659, simple_loss=0.2351, pruned_loss=0.04837, over 4974.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2106, pruned_loss=0.03188, over 972825.97 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:46:21,452 INFO [train.py:715] (2/8) Epoch 11, batch 11050, loss[loss=0.1245, simple_loss=0.2051, pruned_loss=0.02193, over 4991.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.03197, over 972984.87 frames.], batch size: 28, lr: 1.98e-04
2022-05-07 02:47:00,459 INFO [train.py:715] (2/8) Epoch 11, batch 11100, loss[loss=0.1277, simple_loss=0.1923, pruned_loss=0.03159, over 4914.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.0323, over 973412.97 frames.], batch size: 18, lr: 1.98e-04
2022-05-07 02:47:39,067 INFO [train.py:715] (2/8) Epoch 11, batch 11150, loss[loss=0.137, simple_loss=0.212, pruned_loss=0.03102, over 4792.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03259, over 971636.96 frames.], batch size: 24, lr: 1.98e-04
2022-05-07 02:48:18,474 INFO [train.py:715] (2/8) Epoch 11, batch 11200, loss[loss=0.1611, simple_loss=0.2236, pruned_loss=0.04936, over 4918.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2111, pruned_loss=0.03275, over 971703.37 frames.], batch size: 17, lr: 1.98e-04
2022-05-07 02:48:57,589 INFO [train.py:715] (2/8) Epoch 11, batch 11250, loss[loss=0.124, simple_loss=0.1996, pruned_loss=0.0242, over 4694.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03237, over 972256.40 frames.], batch size: 15, lr: 1.98e-04
2022-05-07 02:49:35,930 INFO [train.py:715] (2/8) Epoch 11, batch 11300, loss[loss=0.1508, simple_loss=0.2297, pruned_loss=0.03598, over 4748.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03248, over 972281.63 frames.], batch size: 16, lr: 1.98e-04
2022-05-07 02:50:14,826 INFO [train.py:715] (2/8) Epoch 11, batch 11350, loss[loss=0.1201, simple_loss=0.2008, pruned_loss=0.01971, over 4877.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03195, over 971154.95 frames.], batch size: 20, lr: 1.97e-04
2022-05-07 02:50:54,372 INFO [train.py:715] (2/8) Epoch 11, batch 11400, loss[loss=0.1457, simple_loss=0.2207, pruned_loss=0.03542, over 4794.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03222, over 970627.57 frames.], batch size: 24, lr: 1.97e-04
2022-05-07 02:51:32,953 INFO [train.py:715] (2/8) Epoch 11, batch 11450, loss[loss=0.1488, simple_loss=0.2214, pruned_loss=0.03806, over 4941.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03282, over 971776.99 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 02:52:11,283 INFO [train.py:715] (2/8) Epoch 11, batch 11500, loss[loss=0.1211, simple_loss=0.1992, pruned_loss=0.02155, over 4974.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03239, over 972378.42 frames.], batch size: 35, lr: 1.97e-04
2022-05-07 02:52:50,111 INFO [train.py:715] (2/8) Epoch 11, batch 11550, loss[loss=0.1574, simple_loss=0.2323, pruned_loss=0.04127, over 4868.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03263, over 971887.44 frames.], batch size: 39, lr: 1.97e-04
2022-05-07 02:53:29,305 INFO [train.py:715] (2/8) Epoch 11, batch 11600, loss[loss=0.1573, simple_loss=0.2239, pruned_loss=0.04534, over 4971.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03317, over 972780.23 frames.], batch size: 35, lr: 1.97e-04
2022-05-07 02:54:08,233 INFO [train.py:715] (2/8) Epoch 11, batch 11650, loss[loss=0.1448, simple_loss=0.22, pruned_loss=0.03482, over 4776.00 frames.], tot_loss[loss=0.1392, simple_loss=0.212, pruned_loss=0.03317, over 973460.12 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 02:54:46,494 INFO [train.py:715] (2/8) Epoch 11, batch 11700, loss[loss=0.1408, simple_loss=0.2215, pruned_loss=0.0301, over 4864.00 frames.], tot_loss[loss=0.1387, simple_loss=0.212, pruned_loss=0.03277, over 973490.47 frames.], batch size: 20, lr: 1.97e-04
2022-05-07 02:55:25,413 INFO [train.py:715] (2/8) Epoch 11, batch 11750, loss[loss=0.1327, simple_loss=0.2033, pruned_loss=0.03099, over 4785.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03258, over 972519.49 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 02:56:04,628 INFO [train.py:715] (2/8) Epoch 11, batch 11800, loss[loss=0.1331, simple_loss=0.1947, pruned_loss=0.03574, over 4968.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2128, pruned_loss=0.03305, over 972942.69 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 02:56:43,716 INFO [train.py:715] (2/8) Epoch 11, batch 11850, loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02952, over 4865.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03279, over 972830.90 frames.], batch size: 20, lr: 1.97e-04
2022-05-07 02:57:23,413 INFO [train.py:715] (2/8) Epoch 11, batch 11900, loss[loss=0.1641, simple_loss=0.2431, pruned_loss=0.04257, over 4925.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03227, over 972065.45 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 02:58:03,753 INFO [train.py:715] (2/8) Epoch 11, batch 11950, loss[loss=0.1319, simple_loss=0.2064, pruned_loss=0.02863, over 4816.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.03233, over 971860.17 frames.], batch size: 26, lr: 1.97e-04
2022-05-07 02:58:43,547 INFO [train.py:715] (2/8) Epoch 11, batch 12000, loss[loss=0.1145, simple_loss=0.1782, pruned_loss=0.02535, over 4942.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03239, over 972051.29 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 02:58:43,548 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 02:58:53,276 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.1061, simple_loss=0.1902, pruned_loss=0.01096, over 914524.00 frames.
2022-05-07 02:59:33,215 INFO [train.py:715] (2/8) Epoch 11, batch 12050, loss[loss=0.1407, simple_loss=0.2149, pruned_loss=0.0333, over 4971.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03221, over 971445.23 frames.], batch size: 24, lr: 1.97e-04
2022-05-07 03:00:12,650 INFO [train.py:715] (2/8) Epoch 11, batch 12100, loss[loss=0.1396, simple_loss=0.2115, pruned_loss=0.0338, over 4753.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03276, over 970734.92 frames.], batch size: 19, lr: 1.97e-04
2022-05-07 03:00:51,874 INFO [train.py:715] (2/8) Epoch 11, batch 12150, loss[loss=0.145, simple_loss=0.2169, pruned_loss=0.03661, over 4782.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.0331, over 971596.96 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 03:01:31,402 INFO [train.py:715] (2/8) Epoch 11, batch 12200, loss[loss=0.1208, simple_loss=0.1957, pruned_loss=0.02297, over 4815.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2127, pruned_loss=0.03289, over 971168.68 frames.], batch size: 13, lr: 1.97e-04
2022-05-07 03:02:09,903 INFO [train.py:715] (2/8) Epoch 11, batch 12250, loss[loss=0.1425, simple_loss=0.217, pruned_loss=0.03396, over 4923.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2121, pruned_loss=0.03249, over 971464.41 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 03:02:49,517 INFO [train.py:715] (2/8) Epoch 11, batch 12300, loss[loss=0.1498, simple_loss=0.2415, pruned_loss=0.02905, over 4789.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2119, pruned_loss=0.03248, over 971404.35 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 03:03:29,338 INFO [train.py:715] (2/8) Epoch 11, batch 12350, loss[loss=0.1457, simple_loss=0.22, pruned_loss=0.0357, over 4777.00 frames.], tot_loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.03231, over 971349.39 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:04:08,694 INFO [train.py:715] (2/8) Epoch 11, batch 12400, loss[loss=0.1121, simple_loss=0.1964, pruned_loss=0.01388, over 4954.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03261, over 972074.06 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:04:46,932 INFO [train.py:715] (2/8) Epoch 11, batch 12450, loss[loss=0.1301, simple_loss=0.2037, pruned_loss=0.02831, over 4809.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03265, over 972499.59 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:05:26,166 INFO [train.py:715] (2/8) Epoch 11, batch 12500, loss[loss=0.1493, simple_loss=0.217, pruned_loss=0.04082, over 4717.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.0326, over 972461.92 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:06:05,435 INFO [train.py:715] (2/8) Epoch 11, batch 12550, loss[loss=0.1432, simple_loss=0.2167, pruned_loss=0.03485, over 4813.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2122, pruned_loss=0.0327, over 972531.25 frames.], batch size: 12, lr: 1.97e-04
2022-05-07 03:06:44,093 INFO [train.py:715] (2/8) Epoch 11, batch 12600, loss[loss=0.154, simple_loss=0.2202, pruned_loss=0.04393, over 4786.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03278, over 971736.55 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 03:07:23,081 INFO [train.py:715] (2/8) Epoch 11, batch 12650, loss[loss=0.1411, simple_loss=0.2155, pruned_loss=0.03337, over 4981.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2122, pruned_loss=0.03235, over 972049.40 frames.], batch size: 35, lr: 1.97e-04
2022-05-07 03:08:02,195 INFO [train.py:715] (2/8) Epoch 11, batch 12700, loss[loss=0.123, simple_loss=0.1891, pruned_loss=0.02842, over 4906.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2119, pruned_loss=0.03212, over 971923.68 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:08:40,889 INFO [train.py:715] (2/8) Epoch 11, batch 12750, loss[loss=0.1173, simple_loss=0.1915, pruned_loss=0.02151, over 4820.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2124, pruned_loss=0.03249, over 972181.75 frames.], batch size: 26, lr: 1.97e-04
2022-05-07 03:09:19,303 INFO [train.py:715] (2/8) Epoch 11, batch 12800, loss[loss=0.1416, simple_loss=0.2177, pruned_loss=0.03279, over 4854.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2127, pruned_loss=0.03249, over 972165.99 frames.], batch size: 30, lr: 1.97e-04
2022-05-07 03:09:58,875 INFO [train.py:715] (2/8) Epoch 11, batch 12850, loss[loss=0.1292, simple_loss=0.1985, pruned_loss=0.02997, over 4813.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2121, pruned_loss=0.03227, over 971876.26 frames.], batch size: 27, lr: 1.97e-04
2022-05-07 03:10:38,306 INFO [train.py:715] (2/8) Epoch 11, batch 12900, loss[loss=0.1381, simple_loss=0.2027, pruned_loss=0.03675, over 4965.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2113, pruned_loss=0.03187, over 970863.88 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:11:17,931 INFO [train.py:715] (2/8) Epoch 11, batch 12950, loss[loss=0.1293, simple_loss=0.215, pruned_loss=0.02178, over 4980.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2113, pruned_loss=0.0318, over 972000.27 frames.], batch size: 24, lr: 1.97e-04
2022-05-07 03:11:56,710 INFO [train.py:715] (2/8) Epoch 11, batch 13000, loss[loss=0.1635, simple_loss=0.2393, pruned_loss=0.04389, over 4926.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.03218, over 971644.12 frames.], batch size: 29, lr: 1.97e-04
2022-05-07 03:12:36,379 INFO [train.py:715] (2/8) Epoch 11, batch 13050, loss[loss=0.1564, simple_loss=0.2248, pruned_loss=0.04394, over 4731.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03264, over 971622.82 frames.], batch size: 16, lr: 1.97e-04
2022-05-07 03:13:15,474 INFO [train.py:715] (2/8) Epoch 11, batch 13100, loss[loss=0.1281, simple_loss=0.1963, pruned_loss=0.02997, over 4807.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03224, over 971130.71 frames.], batch size: 25, lr: 1.97e-04
2022-05-07 03:13:53,587 INFO [train.py:715] (2/8) Epoch 11, batch 13150, loss[loss=0.1152, simple_loss=0.1911, pruned_loss=0.0197, over 4818.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03273, over 971670.81 frames.], batch size: 26, lr: 1.97e-04
2022-05-07 03:14:32,700 INFO [train.py:715] (2/8) Epoch 11, batch 13200, loss[loss=0.112, simple_loss=0.1908, pruned_loss=0.01659, over 4708.00 frames.], tot_loss[loss=0.138, simple_loss=0.2109, pruned_loss=0.03253, over 971333.00 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:15:11,059 INFO [train.py:715] (2/8) Epoch 11, batch 13250, loss[loss=0.1551, simple_loss=0.2265, pruned_loss=0.04189, over 4860.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2107, pruned_loss=0.0324, over 972256.27 frames.], batch size: 20, lr: 1.97e-04
2022-05-07 03:15:50,453 INFO [train.py:715] (2/8) Epoch 11, batch 13300, loss[loss=0.1227, simple_loss=0.1911, pruned_loss=0.02717, over 4826.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03204, over 973187.01 frames.], batch size: 13, lr: 1.97e-04
2022-05-07 03:16:29,351 INFO [train.py:715] (2/8) Epoch 11, batch 13350, loss[loss=0.1614, simple_loss=0.2398, pruned_loss=0.04146, over 4836.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03263, over 972627.96 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:17:08,599 INFO [train.py:715] (2/8) Epoch 11, batch 13400, loss[loss=0.1265, simple_loss=0.2, pruned_loss=0.02649, over 4649.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2105, pruned_loss=0.0324, over 972068.01 frames.], batch size: 13, lr: 1.97e-04
2022-05-07 03:17:47,309 INFO [train.py:715] (2/8) Epoch 11, batch 13450, loss[loss=0.1229, simple_loss=0.1999, pruned_loss=0.02294, over 4949.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2109, pruned_loss=0.03241, over 972624.49 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:18:26,312 INFO [train.py:715] (2/8) Epoch 11, batch 13500, loss[loss=0.1391, simple_loss=0.2049, pruned_loss=0.03665, over 4836.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03266, over 972980.99 frames.], batch size: 12, lr: 1.97e-04
2022-05-07 03:19:05,024 INFO [train.py:715] (2/8) Epoch 11, batch 13550, loss[loss=0.112, simple_loss=0.1862, pruned_loss=0.01886, over 4856.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.03283, over 973083.57 frames.], batch size: 34, lr: 1.97e-04
2022-05-07 03:19:44,150 INFO [train.py:715] (2/8) Epoch 11, batch 13600, loss[loss=0.1219, simple_loss=0.2035, pruned_loss=0.02019, over 4978.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2114, pruned_loss=0.03283, over 973300.89 frames.], batch size: 25, lr: 1.97e-04
2022-05-07 03:20:22,540 INFO [train.py:715] (2/8) Epoch 11, batch 13650, loss[loss=0.1803, simple_loss=0.2386, pruned_loss=0.06103, over 4876.00 frames.], tot_loss[loss=0.139, simple_loss=0.2118, pruned_loss=0.03311, over 972893.39 frames.], batch size: 32, lr: 1.97e-04
2022-05-07 03:21:00,720 INFO [train.py:715] (2/8) Epoch 11, batch 13700, loss[loss=0.1306, simple_loss=0.2043, pruned_loss=0.02849, over 4922.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03314, over 972892.59 frames.], batch size: 23, lr: 1.97e-04
2022-05-07 03:21:39,796 INFO [train.py:715] (2/8) Epoch 11, batch 13750, loss[loss=0.1524, simple_loss=0.2089, pruned_loss=0.04788, over 4834.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2121, pruned_loss=0.03363, over 972868.60 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:22:19,174 INFO [train.py:715] (2/8) Epoch 11, batch 13800, loss[loss=0.1169, simple_loss=0.2079, pruned_loss=0.01291, over 4943.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2118, pruned_loss=0.0334, over 972579.42 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:22:57,642 INFO [train.py:715] (2/8) Epoch 11, batch 13850, loss[loss=0.1637, simple_loss=0.232, pruned_loss=0.04766, over 4983.00 frames.], tot_loss[loss=0.1398, simple_loss=0.212, pruned_loss=0.03383, over 972560.90 frames.], batch size: 31, lr: 1.97e-04
2022-05-07 03:23:37,054 INFO [train.py:715] (2/8) Epoch 11, batch 13900, loss[loss=0.1304, simple_loss=0.1984, pruned_loss=0.03124, over 4812.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2125, pruned_loss=0.03386, over 972904.84 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:24:15,993 INFO [train.py:715] (2/8) Epoch 11, batch 13950, loss[loss=0.138, simple_loss=0.2162, pruned_loss=0.02994, over 4935.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2128, pruned_loss=0.03387, over 973134.00 frames.], batch size: 18, lr: 1.97e-04
2022-05-07 03:24:55,163 INFO [train.py:715] (2/8) Epoch 11, batch 14000, loss[loss=0.1429, simple_loss=0.2165, pruned_loss=0.03468, over 4944.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03312, over 972574.60 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:25:34,611 INFO [train.py:715] (2/8) Epoch 11, batch 14050, loss[loss=0.1588, simple_loss=0.2299, pruned_loss=0.04387, over 4983.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2119, pruned_loss=0.03321, over 972489.31 frames.], batch size: 28, lr: 1.97e-04
2022-05-07 03:26:14,333 INFO [train.py:715] (2/8) Epoch 11, batch 14100, loss[loss=0.1228, simple_loss=0.196, pruned_loss=0.02485, over 4986.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.03312, over 972843.37 frames.], batch size: 26, lr: 1.97e-04
2022-05-07 03:26:53,600 INFO [train.py:715] (2/8) Epoch 11, batch 14150, loss[loss=0.1619, simple_loss=0.2342, pruned_loss=0.04479, over 4978.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2127, pruned_loss=0.03343, over 972559.53 frames.], batch size: 25, lr: 1.97e-04
2022-05-07 03:27:32,868 INFO [train.py:715] (2/8) Epoch 11, batch 14200, loss[loss=0.1328, simple_loss=0.208, pruned_loss=0.02879, over 4874.00 frames.], tot_loss[loss=0.1393, simple_loss=0.212, pruned_loss=0.03328, over 972298.43 frames.], batch size: 16, lr: 1.97e-04
2022-05-07 03:28:13,019 INFO [train.py:715] (2/8) Epoch 11, batch 14250, loss[loss=0.1464, simple_loss=0.2159, pruned_loss=0.03842, over 4902.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2108, pruned_loss=0.03276, over 972504.55 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:28:53,023 INFO [train.py:715] (2/8) Epoch 11, batch 14300, loss[loss=0.1693, simple_loss=0.2353, pruned_loss=0.05162, over 4845.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2116, pruned_loss=0.03311, over 972298.45 frames.], batch size: 30, lr: 1.97e-04
2022-05-07 03:29:32,290 INFO [train.py:715] (2/8) Epoch 11, batch 14350, loss[loss=0.136, simple_loss=0.2122, pruned_loss=0.02988, over 4930.00 frames.], tot_loss[loss=0.1393, simple_loss=0.212, pruned_loss=0.03325, over 972339.87 frames.], batch size: 29, lr: 1.97e-04
2022-05-07 03:30:12,238 INFO [train.py:715] (2/8) Epoch 11, batch 14400, loss[loss=0.1342, simple_loss=0.1965, pruned_loss=0.03589, over 4825.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2107, pruned_loss=0.03297, over 973398.10 frames.], batch size: 13, lr: 1.97e-04
2022-05-07 03:30:52,511 INFO [train.py:715] (2/8) Epoch 11, batch 14450, loss[loss=0.1474, simple_loss=0.2169, pruned_loss=0.03897, over 4895.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2102, pruned_loss=0.03241, over 974012.31 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:31:31,924 INFO [train.py:715] (2/8) Epoch 11, batch 14500, loss[loss=0.1287, simple_loss=0.2007, pruned_loss=0.02839, over 4955.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2105, pruned_loss=0.03257, over 974535.34 frames.], batch size: 21, lr: 1.97e-04
2022-05-07 03:32:11,423 INFO [train.py:715] (2/8) Epoch 11, batch 14550, loss[loss=0.1414, simple_loss=0.2114, pruned_loss=0.03569, over 4706.00 frames.], tot_loss[loss=0.1374, simple_loss=0.21, pruned_loss=0.03236, over 974561.69 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:32:51,263 INFO [train.py:715] (2/8) Epoch 11, batch 14600, loss[loss=0.138, simple_loss=0.2129, pruned_loss=0.03152, over 4916.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2102, pruned_loss=0.03231, over 974502.07 frames.], batch size: 23, lr: 1.97e-04
2022-05-07 03:33:30,637 INFO [train.py:715] (2/8) Epoch 11, batch 14650, loss[loss=0.1564, simple_loss=0.2274, pruned_loss=0.04272, over 4837.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2096, pruned_loss=0.03197, over 974096.50 frames.], batch size: 30, lr: 1.97e-04
2022-05-07 03:34:09,053 INFO [train.py:715] (2/8) Epoch 11, batch 14700, loss[loss=0.1456, simple_loss=0.233, pruned_loss=0.02915, over 4885.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2095, pruned_loss=0.03179, over 972825.10 frames.], batch size: 16, lr: 1.97e-04
2022-05-07 03:34:48,552 INFO [train.py:715] (2/8) Epoch 11, batch 14750, loss[loss=0.1778, simple_loss=0.2452, pruned_loss=0.0552, over 4815.00 frames.], tot_loss[loss=0.1364, simple_loss=0.209, pruned_loss=0.03194, over 972339.16 frames.], batch size: 27, lr: 1.97e-04
2022-05-07 03:35:27,680 INFO [train.py:715] (2/8) Epoch 11, batch 14800, loss[loss=0.1461, simple_loss=0.2228, pruned_loss=0.03467, over 4807.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2092, pruned_loss=0.03252, over 971637.18 frames.], batch size: 26, lr: 1.97e-04
2022-05-07 03:36:06,361 INFO [train.py:715] (2/8) Epoch 11, batch 14850, loss[loss=0.1204, simple_loss=0.2019, pruned_loss=0.01941, over 4793.00 frames.], tot_loss[loss=0.1376, simple_loss=0.21, pruned_loss=0.03257, over 972129.64 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 03:36:45,863 INFO [train.py:715] (2/8) Epoch 11, batch 14900, loss[loss=0.1177, simple_loss=0.1944, pruned_loss=0.02046, over 4701.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2113, pruned_loss=0.03293, over 972085.16 frames.], batch size: 15, lr: 1.97e-04
2022-05-07 03:37:25,088 INFO [train.py:715] (2/8) Epoch 11, batch 14950, loss[loss=0.1479, simple_loss=0.2158, pruned_loss=0.03999, over 4879.00 frames.], tot_loss[loss=0.14, simple_loss=0.2122, pruned_loss=0.03388, over 971625.89 frames.], batch size: 22, lr: 1.97e-04
2022-05-07 03:38:03,589 INFO [train.py:715] (2/8) Epoch 11, batch 15000, loss[loss=0.117, simple_loss=0.1905, pruned_loss=0.02179, over 4793.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2123, pruned_loss=0.03367, over 971364.83 frames.], batch size: 12, lr: 1.97e-04
2022-05-07 03:38:03,589 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 03:38:13,229 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.106, simple_loss=0.1901, pruned_loss=0.01091, over 914524.00 frames.
2022-05-07 03:38:52,001 INFO [train.py:715] (2/8) Epoch 11, batch 15050, loss[loss=0.1442, simple_loss=0.2193, pruned_loss=0.03453, over 4753.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2121, pruned_loss=0.03339, over 972167.85 frames.], batch size: 19, lr: 1.97e-04
2022-05-07 03:39:30,959 INFO [train.py:715] (2/8) Epoch 11, batch 15100, loss[loss=0.1252, simple_loss=0.1937, pruned_loss=0.02831, over 4973.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2119, pruned_loss=0.03292, over 971936.11 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 03:40:10,675 INFO [train.py:715] (2/8) Epoch 11, batch 15150, loss[loss=0.1627, simple_loss=0.224, pruned_loss=0.05076, over 4797.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2124, pruned_loss=0.03317, over 971768.50 frames.], batch size: 14, lr: 1.97e-04
2022-05-07 03:40:49,842 INFO [train.py:715] (2/8) Epoch 11, batch 15200, loss[loss=0.1137, simple_loss=0.1907, pruned_loss=0.01839, over 4972.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03264, over 971416.66 frames.], batch size: 35, lr: 1.97e-04
2022-05-07 03:41:28,410 INFO [train.py:715] (2/8) Epoch 11, batch 15250, loss[loss=0.1234, simple_loss=0.1871, pruned_loss=0.02987, over 4781.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.0331, over 971777.68 frames.], batch size: 17, lr: 1.97e-04
2022-05-07 03:42:07,669 INFO [train.py:715] (2/8) Epoch 11, batch 15300, loss[loss=0.1348, simple_loss=0.2116, pruned_loss=0.02897, over 4763.00 frames.], tot_loss[loss=0.1387, simple_loss=0.212, pruned_loss=0.03266, over 971936.26 frames.], batch size: 19, lr: 1.97e-04
2022-05-07 03:42:46,993 INFO [train.py:715] (2/8) Epoch 11, batch 15350, loss[loss=0.1509, simple_loss=0.23, pruned_loss=0.03587, over 4934.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2122, pruned_loss=0.03272, over 972440.06 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 03:43:25,865 INFO [train.py:715] (2/8) Epoch 11, batch 15400, loss[loss=0.1665, simple_loss=0.232, pruned_loss=0.05054, over 4699.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03235, over 972152.83 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 03:44:04,608 INFO [train.py:715] (2/8) Epoch 11, batch 15450, loss[loss=0.1262, simple_loss=0.2089, pruned_loss=0.02173, over 4736.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03262, over 972345.43 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 03:44:44,030 INFO [train.py:715] (2/8) Epoch 11, batch 15500, loss[loss=0.1521, simple_loss=0.2279, pruned_loss=0.03811, over 4797.00 frames.], tot_loss[loss=0.1387, simple_loss=0.212, pruned_loss=0.03275, over 972506.27 frames.], batch size: 13, lr: 1.96e-04
2022-05-07 03:45:23,174 INFO [train.py:715] (2/8) Epoch 11, batch 15550, loss[loss=0.1403, simple_loss=0.2096, pruned_loss=0.0355, over 4910.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03304, over 971579.18 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 03:46:01,709 INFO [train.py:715] (2/8) Epoch 11, batch 15600, loss[loss=0.1535, simple_loss=0.2278, pruned_loss=0.03956, over 4858.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2129, pruned_loss=0.03312, over 971853.37 frames.], batch size: 38, lr: 1.96e-04
2022-05-07 03:46:40,882 INFO [train.py:715] (2/8) Epoch 11, batch 15650, loss[loss=0.1688, simple_loss=0.234, pruned_loss=0.0518, over 4965.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03322, over 971638.23 frames.], batch size: 35, lr: 1.96e-04
2022-05-07 03:47:19,842 INFO [train.py:715] (2/8) Epoch 11, batch 15700, loss[loss=0.119, simple_loss=0.1775, pruned_loss=0.03031, over 4921.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.03306, over 971940.87 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 03:47:58,645 INFO [train.py:715] (2/8) Epoch 11, batch 15750, loss[loss=0.1149, simple_loss=0.1857, pruned_loss=0.02204, over 4771.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2115, pruned_loss=0.03283, over 971825.41 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 03:48:37,392 INFO [train.py:715] (2/8) Epoch 11, batch 15800, loss[loss=0.1426, simple_loss=0.2078, pruned_loss=0.03869, over 4995.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2118, pruned_loss=0.03332, over 972047.48 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 03:49:16,755 INFO [train.py:715] (2/8) Epoch 11, batch 15850, loss[loss=0.1523, simple_loss=0.2267, pruned_loss=0.039, over 4768.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03325, over 971980.01 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 03:49:55,695 INFO [train.py:715] (2/8) Epoch 11, batch 15900, loss[loss=0.1265, simple_loss=0.2027, pruned_loss=0.02519, over 4917.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2128, pruned_loss=0.03353, over 971672.33 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 03:50:34,611 INFO [train.py:715] (2/8) Epoch 11, batch 15950, loss[loss=0.1192, simple_loss=0.1932, pruned_loss=0.02262, over 4874.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03259, over 971661.70 frames.], batch size: 20, lr: 1.96e-04
2022-05-07 03:51:13,825 INFO [train.py:715] (2/8) Epoch 11, batch 16000, loss[loss=0.1794, simple_loss=0.2694, pruned_loss=0.04473, over 4922.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03255, over 972596.76 frames.], batch size: 29, lr: 1.96e-04
2022-05-07 03:51:53,250 INFO [train.py:715] (2/8) Epoch 11, batch 16050, loss[loss=0.1295, simple_loss=0.2077, pruned_loss=0.02559, over 4934.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03234, over 972465.74 frames.], batch size: 23, lr: 1.96e-04
2022-05-07 03:52:31,939 INFO [train.py:715] (2/8) Epoch 11, batch 16100, loss[loss=0.1484, simple_loss=0.2194, pruned_loss=0.03872, over 4959.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2122, pruned_loss=0.03239, over 971882.61 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 03:53:10,817 INFO [train.py:715] (2/8) Epoch 11, batch 16150, loss[loss=0.16, simple_loss=0.2228, pruned_loss=0.04857, over 4884.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2127, pruned_loss=0.03271, over 971891.74 frames.], batch size: 32, lr: 1.96e-04
2022-05-07 03:53:50,407 INFO [train.py:715] (2/8) Epoch 11, batch 16200, loss[loss=0.1429, simple_loss=0.206, pruned_loss=0.03989, over 4770.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2131, pruned_loss=0.03327, over 970749.71 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 03:54:29,887 INFO [train.py:715] (2/8) Epoch 11, batch 16250, loss[loss=0.1472, simple_loss=0.2106, pruned_loss=0.04196, over 4955.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2135, pruned_loss=0.03358, over 971486.77 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 03:55:08,235 INFO [train.py:715] (2/8) Epoch 11, batch 16300, loss[loss=0.1262, simple_loss=0.1988, pruned_loss=0.0268, over 4898.00 frames.], tot_loss[loss=0.14, simple_loss=0.2132, pruned_loss=0.0334, over 972077.33 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 03:55:47,433 INFO [train.py:715] (2/8) Epoch 11, batch 16350, loss[loss=0.1253, simple_loss=0.1986, pruned_loss=0.02596, over 4765.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2126, pruned_loss=0.03282, over 971218.60 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 03:56:26,683 INFO [train.py:715] (2/8) Epoch 11, batch 16400, loss[loss=0.1122, simple_loss=0.1871, pruned_loss=0.01864, over 4841.00 frames.], tot_loss[loss=0.139, simple_loss=0.2126, pruned_loss=0.03265, over 971427.67 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 03:57:05,179 INFO [train.py:715] (2/8) Epoch 11, batch 16450, loss[loss=0.124, simple_loss=0.1975, pruned_loss=0.02529, over 4906.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03263, over 971989.10 frames.], batch size: 17, lr: 1.96e-04
2022-05-07 03:57:44,149 INFO [train.py:715] (2/8) Epoch 11, batch 16500, loss[loss=0.134, simple_loss=0.1897, pruned_loss=0.03911, over 4693.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03238, over 971551.50 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 03:58:23,674 INFO [train.py:715] (2/8) Epoch 11, batch 16550, loss[loss=0.1466, simple_loss=0.2164, pruned_loss=0.0384, over 4971.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2121, pruned_loss=0.03269, over 971391.85 frames.], batch size: 35, lr: 1.96e-04
2022-05-07 03:59:02,826 INFO [train.py:715] (2/8) Epoch 11, batch 16600, loss[loss=0.1445, simple_loss=0.2145, pruned_loss=0.03719, over 4930.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2121, pruned_loss=0.03261, over 971571.19 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 03:59:41,211 INFO [train.py:715] (2/8) Epoch 11, batch 16650, loss[loss=0.1328, simple_loss=0.2113, pruned_loss=0.02711, over 4971.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2109, pruned_loss=0.03236, over 971847.75 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:00:20,434 INFO [train.py:715] (2/8) Epoch 11, batch 16700, loss[loss=0.1479, simple_loss=0.2179, pruned_loss=0.03888, over 4810.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03241, over 971859.93 frames.], batch size: 26, lr: 1.96e-04
2022-05-07 04:00:59,401 INFO [train.py:715] (2/8) Epoch 11, batch 16750, loss[loss=0.1178, simple_loss=0.1973, pruned_loss=0.01916, over 4768.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2116, pruned_loss=0.03196, over 972420.52 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:01:38,343 INFO [train.py:715] (2/8) Epoch 11, batch 16800, loss[loss=0.1438, simple_loss=0.2153, pruned_loss=0.03614, over 4833.00 frames.], tot_loss[loss=0.138, simple_loss=0.2117, pruned_loss=0.03216, over 972302.96 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:02:17,996 INFO [train.py:715] (2/8) Epoch 11, batch 16850, loss[loss=0.1552, simple_loss=0.2238, pruned_loss=0.04329, over 4931.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2111, pruned_loss=0.03176, over 972298.49 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 04:02:57,560 INFO [train.py:715] (2/8) Epoch 11, batch 16900, loss[loss=0.1284, simple_loss=0.2016, pruned_loss=0.02761, over 4781.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2116, pruned_loss=0.03175, over 973041.45 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:03:37,029 INFO [train.py:715] (2/8) Epoch 11, batch 16950, loss[loss=0.126, simple_loss=0.1983, pruned_loss=0.0269, over 4876.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2115, pruned_loss=0.03181, over 972468.70 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 04:04:15,766 INFO [train.py:715] (2/8) Epoch 11, batch 17000, loss[loss=0.1303, simple_loss=0.1913, pruned_loss=0.03467, over 4992.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03257, over 972263.79 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:04:55,495 INFO [train.py:715] (2/8) Epoch 11, batch 17050, loss[loss=0.1208, simple_loss=0.1948, pruned_loss=0.02335, over 4990.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03248, over 972096.89 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:05:38,134 INFO [train.py:715] (2/8) Epoch 11, batch 17100, loss[loss=0.1668, simple_loss=0.2489, pruned_loss=0.04239, over 4859.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2109, pruned_loss=0.03237, over 973227.58 frames.], batch size: 20, lr: 1.96e-04
2022-05-07 04:06:17,133 INFO [train.py:715] (2/8) Epoch 11, batch 17150, loss[loss=0.15, simple_loss=0.2184, pruned_loss=0.04082, over 4950.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.0323, over 974251.09 frames.], batch size: 35, lr: 1.96e-04
2022-05-07 04:06:56,397 INFO [train.py:715] (2/8) Epoch 11, batch 17200, loss[loss=0.1376, simple_loss=0.2145, pruned_loss=0.03031, over 4899.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03256, over 973303.38 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:07:35,864 INFO [train.py:715] (2/8) Epoch 11, batch 17250, loss[loss=0.1872, simple_loss=0.2529, pruned_loss=0.06072, over 4762.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.03237, over 972431.34 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:08:14,915 INFO [train.py:715] (2/8) Epoch 11, batch 17300, loss[loss=0.1241, simple_loss=0.203, pruned_loss=0.02257, over 4844.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.03241, over 972160.20 frames.], batch size: 20, lr: 1.96e-04
2022-05-07 04:08:53,648 INFO [train.py:715] (2/8) Epoch 11, batch 17350, loss[loss=0.1637, simple_loss=0.2454, pruned_loss=0.04102, over 4749.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2112, pruned_loss=0.03216, over 971792.31 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 04:09:33,976 INFO [train.py:715] (2/8) Epoch 11, batch 17400, loss[loss=0.1411, simple_loss=0.2117, pruned_loss=0.03528, over 4789.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03263, over 971873.65 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:10:14,471 INFO [train.py:715] (2/8) Epoch 11, batch 17450, loss[loss=0.1568, simple_loss=0.2257, pruned_loss=0.04399, over 4696.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03274, over 972644.14 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:10:53,785 INFO [train.py:715] (2/8) Epoch 11, batch 17500, loss[loss=0.1365, simple_loss=0.2131, pruned_loss=0.02995, over 4981.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03253, over 972472.08 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:11:33,221 INFO [train.py:715] (2/8) Epoch 11, batch 17550, loss[loss=0.1114, simple_loss=0.1865, pruned_loss=0.01819, over 4989.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2126, pruned_loss=0.03279, over 971603.94 frames.], batch size: 28, lr: 1.96e-04
2022-05-07 04:12:12,577 INFO [train.py:715] (2/8) Epoch 11, batch 17600, loss[loss=0.1228, simple_loss=0.1987, pruned_loss=0.02343, over 4982.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03278, over 971995.36 frames.], batch size: 27, lr: 1.96e-04
2022-05-07 04:12:51,731 INFO [train.py:715] (2/8) Epoch 11, batch 17650, loss[loss=0.1328, simple_loss=0.2016, pruned_loss=0.03197, over 4820.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03219, over 971348.26 frames.], batch size: 26, lr: 1.96e-04
2022-05-07 04:13:29,969 INFO [train.py:715] (2/8) Epoch 11, batch 17700, loss[loss=0.1392, simple_loss=0.2266, pruned_loss=0.02593, over 4748.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03265, over 971277.29 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 04:14:09,453 INFO [train.py:715] (2/8) Epoch 11, batch 17750, loss[loss=0.111, simple_loss=0.1872, pruned_loss=0.01742, over 4866.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2098, pruned_loss=0.03193, over 971484.85 frames.], batch size: 20, lr: 1.96e-04
2022-05-07 04:14:49,016 INFO [train.py:715] (2/8) Epoch 11, batch 17800, loss[loss=0.1561, simple_loss=0.2281, pruned_loss=0.04208, over 4822.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03227, over 970798.25 frames.], batch size: 15, lr: 1.96e-04
2022-05-07 04:15:27,265 INFO [train.py:715] (2/8) Epoch 11, batch 17850, loss[loss=0.1309, simple_loss=0.2101, pruned_loss=0.02584, over 4811.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03217, over 971753.08 frames.], batch size: 27, lr: 1.96e-04
2022-05-07 04:16:06,256 INFO [train.py:715] (2/8) Epoch 11, batch 17900, loss[loss=0.1222, simple_loss=0.191, pruned_loss=0.02672, over 4773.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.0326, over 971673.82 frames.], batch size: 12, lr: 1.96e-04
2022-05-07 04:16:45,883 INFO [train.py:715] (2/8) Epoch 11, batch 17950, loss[loss=0.13, simple_loss=0.2035, pruned_loss=0.02823, over 4918.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.0326, over 971365.84 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:17:24,871 INFO [train.py:715] (2/8) Epoch 11, batch 18000, loss[loss=0.136, simple_loss=0.2081, pruned_loss=0.03192, over 4988.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2115, pruned_loss=0.03248, over 971780.47 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:17:24,872 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 04:17:34,462 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.1061, simple_loss=0.1903, pruned_loss=0.01092, over 914524.00 frames.
2022-05-07 04:18:14,137 INFO [train.py:715] (2/8) Epoch 11, batch 18050, loss[loss=0.1693, simple_loss=0.2372, pruned_loss=0.05071, over 4985.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03195, over 972010.20 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:18:53,410 INFO [train.py:715] (2/8) Epoch 11, batch 18100, loss[loss=0.1229, simple_loss=0.1857, pruned_loss=0.03012, over 4822.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2109, pruned_loss=0.03173, over 971194.50 frames.], batch size: 12, lr: 1.96e-04
2022-05-07 04:19:32,615 INFO [train.py:715] (2/8) Epoch 11, batch 18150, loss[loss=0.1475, simple_loss=0.2119, pruned_loss=0.04158, over 4891.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.0322, over 970698.97 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 04:20:12,191 INFO [train.py:715] (2/8) Epoch 11, batch 18200, loss[loss=0.1858, simple_loss=0.2519, pruned_loss=0.05984, over 4922.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.0321, over 971249.60 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:20:50,626 INFO [train.py:715] (2/8) Epoch 11, batch 18250, loss[loss=0.1181, simple_loss=0.1816, pruned_loss=0.02732, over 4991.00 frames.], tot_loss[loss=0.1373, simple_loss=0.21, pruned_loss=0.03231, over 971151.47 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:21:29,929 INFO [train.py:715] (2/8) Epoch 11, batch 18300, loss[loss=0.1361, simple_loss=0.2113, pruned_loss=0.03046, over 4915.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2104, pruned_loss=0.03244, over 971957.38 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:22:09,173 INFO [train.py:715] (2/8) Epoch 11, batch 18350, loss[loss=0.1298, simple_loss=0.1965, pruned_loss=0.0315, over 4956.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2099, pruned_loss=0.03196, over 972191.40 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:22:47,572 INFO [train.py:715] (2/8) Epoch 11, batch 18400, loss[loss=0.1436, simple_loss=0.2047, pruned_loss=0.04125, over 4929.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03215, over 971499.25 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:23:25,988 INFO [train.py:715] (2/8) Epoch 11, batch 18450, loss[loss=0.1313, simple_loss=0.2014, pruned_loss=0.03058, over 4960.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2114, pruned_loss=0.03186, over 971853.80 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 04:24:05,022 INFO [train.py:715] (2/8) Epoch 11, batch 18500, loss[loss=0.1313, simple_loss=0.1996, pruned_loss=0.03152, over 4961.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03188, over 971132.35 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 04:24:44,461 INFO [train.py:715] (2/8) Epoch 11, batch 18550, loss[loss=0.1462, simple_loss=0.208, pruned_loss=0.04221, over 4791.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2108, pruned_loss=0.03201, over 971223.16 frames.], batch size: 24, lr: 1.96e-04
2022-05-07 04:25:22,564 INFO [train.py:715] (2/8) Epoch 11, batch 18600, loss[loss=0.1271, simple_loss=0.2015, pruned_loss=0.02637, over 4931.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03244, over 972020.29 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:26:01,409 INFO [train.py:715] (2/8) Epoch 11, batch 18650, loss[loss=0.1744, simple_loss=0.2353, pruned_loss=0.0568, over 4934.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03256, over 972579.29 frames.], batch size: 39, lr: 1.96e-04
2022-05-07 04:26:40,665 INFO [train.py:715] (2/8) Epoch 11, batch 18700, loss[loss=0.136, simple_loss=0.2084, pruned_loss=0.03175, over 4781.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03261, over 971740.01 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:27:18,909 INFO [train.py:715] (2/8) Epoch 11, batch 18750, loss[loss=0.1547, simple_loss=0.2287, pruned_loss=0.04036, over 4786.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2125, pruned_loss=0.03291, over 971119.58 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:27:57,976 INFO [train.py:715] (2/8) Epoch 11, batch 18800, loss[loss=0.1202, simple_loss=0.1907, pruned_loss=0.0249, over 4800.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03258, over 971099.87 frames.], batch size: 12, lr: 1.96e-04
2022-05-07 04:28:36,590 INFO [train.py:715] (2/8) Epoch 11, batch 18850, loss[loss=0.1512, simple_loss=0.2212, pruned_loss=0.04058, over 4887.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03294, over 972687.67 frames.], batch size: 22, lr: 1.96e-04
2022-05-07 04:29:16,483 INFO [train.py:715] (2/8) Epoch 11, batch 18900, loss[loss=0.1151, simple_loss=0.1844, pruned_loss=0.02286, over 4995.00 frames.], tot_loss[loss=0.1387, simple_loss=0.212, pruned_loss=0.03264, over 972421.81 frames.], batch size: 14, lr: 1.96e-04
2022-05-07 04:29:55,266 INFO [train.py:715] (2/8) Epoch 11, batch 18950, loss[loss=0.1376, simple_loss=0.2175, pruned_loss=0.02883, over 4986.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2126, pruned_loss=0.03296, over 974149.55 frames.], batch size: 35, lr: 1.96e-04
2022-05-07 04:30:34,362 INFO [train.py:715] (2/8) Epoch 11, batch 19000, loss[loss=0.1363, simple_loss=0.2065, pruned_loss=0.03307, over 4764.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03266, over 973725.53 frames.], batch size: 19, lr: 1.96e-04
2022-05-07 04:31:13,457 INFO [train.py:715] (2/8) Epoch 11, batch 19050, loss[loss=0.134, simple_loss=0.2098, pruned_loss=0.02912, over 4923.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03254, over 973195.08 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:31:52,053 INFO [train.py:715] (2/8) Epoch 11, batch 19100, loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03181, over 4916.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03232, over 973191.88 frames.], batch size: 18, lr: 1.96e-04
2022-05-07 04:32:31,178 INFO [train.py:715] (2/8) Epoch 11, batch 19150, loss[loss=0.1278, simple_loss=0.1974, pruned_loss=0.02903, over 4896.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2119, pruned_loss=0.03222, over 973512.42 frames.], batch size: 22, lr: 1.96e-04
2022-05-07 04:33:10,078 INFO [train.py:715] (2/8) Epoch 11, batch 19200, loss[loss=0.1129, simple_loss=0.1842, pruned_loss=0.02077, over 4757.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2124, pruned_loss=0.03258, over 973546.23 frames.], batch size: 16, lr: 1.96e-04
2022-05-07 04:33:49,485 INFO [train.py:715] (2/8) Epoch 11, batch 19250, loss[loss=0.1326, simple_loss=0.2116, pruned_loss=0.02674, over 4923.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03255, over 972886.84 frames.], batch size: 23, lr: 1.96e-04
2022-05-07 04:34:27,829 INFO [train.py:715] (2/8) Epoch 11, batch 19300, loss[loss=0.1249, simple_loss=0.1905, pruned_loss=0.0297, over 4969.00 frames.], tot_loss[loss=0.1394, simple_loss=0.213, pruned_loss=0.03291, over 972865.29 frames.], batch size: 35, lr: 1.96e-04
2022-05-07 04:35:06,982 INFO [train.py:715] (2/8) Epoch 11, batch 19350, loss[loss=0.145, simple_loss=0.2203, pruned_loss=0.03482, over 4843.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2128, pruned_loss=0.03283, over 972525.48 frames.], batch size: 30, lr: 1.96e-04
2022-05-07 04:35:46,160 INFO [train.py:715] (2/8) Epoch 11, batch 19400, loss[loss=0.1395, simple_loss=0.2256, pruned_loss=0.02668, over 4967.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2126, pruned_loss=0.03293, over 972841.20 frames.], batch size: 21, lr: 1.96e-04
2022-05-07 04:36:24,108 INFO [train.py:715] (2/8) Epoch 11, batch 19450, loss[loss=0.1125, simple_loss=0.1878, pruned_loss=0.01861, over 4934.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2118, pruned_loss=0.03236, over 972501.97 frames.], batch size: 23, lr: 1.95e-04
2022-05-07 04:37:03,252 INFO [train.py:715] (2/8) Epoch 11, batch 19500, loss[loss=0.1251, simple_loss=0.1902, pruned_loss=0.02999, over 4921.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03177, over 972555.88 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 04:37:42,220 INFO [train.py:715] (2/8) Epoch 11, batch 19550, loss[loss=0.1174, simple_loss=0.1799, pruned_loss=0.0274, over 4813.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03177, over 972834.46 frames.], batch size: 12, lr: 1.95e-04
2022-05-07 04:38:20,965 INFO [train.py:715] (2/8) Epoch 11, batch 19600, loss[loss=0.173, simple_loss=0.2432, pruned_loss=0.05142, over 4737.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2103, pruned_loss=0.03198, over 972759.89 frames.], batch size: 16, lr: 1.95e-04
2022-05-07 04:38:59,545 INFO [train.py:715] (2/8) Epoch 11, batch 19650, loss[loss=0.1205, simple_loss=0.1911, pruned_loss=0.02498, over 4912.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.0318, over 974167.29 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 04:39:38,338 INFO [train.py:715] (2/8) Epoch 11, batch 19700, loss[loss=0.1235, simple_loss=0.207, pruned_loss=0.02003, over 4929.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03158, over 973960.03 frames.], batch size: 29, lr: 1.95e-04
2022-05-07 04:40:17,421 INFO [train.py:715] (2/8) Epoch 11, batch 19750, loss[loss=0.1213, simple_loss=0.1885, pruned_loss=0.02708, over 4822.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03076, over 973977.24 frames.], batch size: 26, lr: 1.95e-04
2022-05-07 04:40:55,509 INFO [train.py:715] (2/8) Epoch 11, batch 19800, loss[loss=0.1536, simple_loss=0.2273, pruned_loss=0.03998, over 4960.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03122, over 973146.13 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 04:41:35,006 INFO [train.py:715] (2/8) Epoch 11, batch 19850, loss[loss=0.1492, simple_loss=0.2224, pruned_loss=0.03804, over 4648.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03168, over 971623.19 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 04:42:14,373 INFO [train.py:715] (2/8) Epoch 11, batch 19900, loss[loss=0.143, simple_loss=0.2125, pruned_loss=0.03681, over 4747.00 frames.], tot_loss[loss=0.1369, simple_loss=0.21, pruned_loss=0.03186, over 972095.07 frames.], batch size: 16, lr: 1.95e-04
2022-05-07 04:42:53,621 INFO [train.py:715] (2/8) Epoch 11, batch 19950, loss[loss=0.1378, simple_loss=0.2016, pruned_loss=0.037, over 4908.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2096, pruned_loss=0.03187, over 971698.54 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 04:43:32,803 INFO [train.py:715] (2/8) Epoch 11, batch 20000, loss[loss=0.1698, simple_loss=0.2285, pruned_loss=0.05556, over 4844.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2099, pruned_loss=0.03197, over 971858.13 frames.], batch size: 30, lr: 1.95e-04
2022-05-07 04:44:11,786 INFO [train.py:715] (2/8) Epoch 11, batch 20050, loss[loss=0.1249, simple_loss=0.2074, pruned_loss=0.02116, over 4932.00 frames.], tot_loss[loss=0.137, simple_loss=0.2099, pruned_loss=0.03201, over 972114.68 frames.], batch size: 29, lr: 1.95e-04
2022-05-07 04:44:51,033 INFO [train.py:715] (2/8) Epoch 11, batch 20100, loss[loss=0.1679, simple_loss=0.2365, pruned_loss=0.0496, over 4784.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2099, pruned_loss=0.03232, over 972035.29 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 04:45:29,362 INFO [train.py:715] (2/8) Epoch 11, batch 20150, loss[loss=0.1628, simple_loss=0.2493, pruned_loss=0.0381, over 4869.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2106, pruned_loss=0.03262, over 971485.81 frames.], batch size: 20, lr: 1.95e-04
2022-05-07 04:46:08,146 INFO [train.py:715] (2/8) Epoch 11, batch 20200, loss[loss=0.121, simple_loss=0.1963, pruned_loss=0.02285, over 4927.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03273, over 971448.40 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 04:46:46,986 INFO [train.py:715] (2/8) Epoch 11, batch 20250, loss[loss=0.1463, simple_loss=0.2281, pruned_loss=0.03222, over 4897.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2111, pruned_loss=0.03283, over 972608.99 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 04:47:25,725 INFO [train.py:715] (2/8) Epoch 11, batch 20300, loss[loss=0.1451, simple_loss=0.2115, pruned_loss=0.0394, over 4824.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2121, pruned_loss=0.03334, over 971578.14 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 04:48:04,824 INFO [train.py:715] (2/8) Epoch 11, batch 20350, loss[loss=0.1247, simple_loss=0.2102, pruned_loss=0.01957, over 4751.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.03297, over 972421.56 frames.], batch size: 16, lr: 1.95e-04
2022-05-07 04:48:43,797 INFO [train.py:715] (2/8) Epoch 11, batch 20400, loss[loss=0.1081, simple_loss=0.1809, pruned_loss=0.01763, over 4817.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2112, pruned_loss=0.03295, over 972828.64 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 04:49:23,225 INFO [train.py:715] (2/8) Epoch 11, batch 20450, loss[loss=0.1443, simple_loss=0.2128, pruned_loss=0.03795, over 4967.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03318, over 972122.37 frames.], batch size: 33, lr: 1.95e-04
2022-05-07 04:50:01,759 INFO [train.py:715] (2/8) Epoch 11, batch 20500, loss[loss=0.1447, simple_loss=0.2169, pruned_loss=0.03621, over 4941.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.03293, over 972920.12 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 04:50:41,079 INFO [train.py:715] (2/8) Epoch 11, batch 20550, loss[loss=0.1371, simple_loss=0.2066, pruned_loss=0.03379, over 4797.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.033, over 972680.54 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 04:51:19,711 INFO [train.py:715] (2/8) Epoch 11, batch 20600, loss[loss=0.1614, simple_loss=0.2403, pruned_loss=0.04126, over 4774.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2128, pruned_loss=0.03316, over 973140.78 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 04:51:57,490 INFO [train.py:715] (2/8) Epoch 11, batch 20650, loss[loss=0.1694, simple_loss=0.2363, pruned_loss=0.05127, over 4817.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2131, pruned_loss=0.03323, over 972780.33 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 04:52:36,867 INFO [train.py:715] (2/8) Epoch 11, batch 20700, loss[loss=0.1264, simple_loss=0.2086, pruned_loss=0.02211, over 4821.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03277, over 973032.61 frames.], batch size: 25, lr: 1.95e-04
2022-05-07 04:53:16,100 INFO [train.py:715] (2/8) Epoch 11, batch 20750, loss[loss=0.1499, simple_loss=0.217, pruned_loss=0.04135, over 4968.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.03214, over 974363.86 frames.], batch size: 35, lr: 1.95e-04
2022-05-07 04:53:54,799 INFO [train.py:715] (2/8) Epoch 11, batch 20800, loss[loss=0.1446, simple_loss=0.2084, pruned_loss=0.04045, over 4823.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.0329, over 973764.88 frames.], batch size: 12, lr: 1.95e-04
2022-05-07 04:54:33,171 INFO [train.py:715] (2/8) Epoch 11, batch 20850, loss[loss=0.158, simple_loss=0.233, pruned_loss=0.04152, over 4932.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.03293, over 973627.97 frames.], batch size: 23, lr: 1.95e-04
2022-05-07 04:55:12,418 INFO [train.py:715] (2/8) Epoch 11, batch 20900, loss[loss=0.1251, simple_loss=0.2001, pruned_loss=0.02508, over 4822.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2127, pruned_loss=0.03288, over 973944.28 frames.], batch size: 27, lr: 1.95e-04
2022-05-07 04:55:52,031 INFO [train.py:715] (2/8) Epoch 11, batch 20950, loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02903, over 4802.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03264, over 973607.22 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 04:56:30,995 INFO [train.py:715] (2/8) Epoch 11, batch 21000, loss[loss=0.1314, simple_loss=0.1886, pruned_loss=0.03706, over 4944.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03267, over 974346.73 frames.], batch size: 29, lr: 1.95e-04
2022-05-07 04:56:30,995 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 04:56:40,629 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.106, simple_loss=0.19, pruned_loss=0.01097, over 914524.00 frames.
2022-05-07 04:57:20,095 INFO [train.py:715] (2/8) Epoch 11, batch 21050, loss[loss=0.1461, simple_loss=0.2147, pruned_loss=0.03876, over 4941.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03246, over 973425.24 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 04:57:59,828 INFO [train.py:715] (2/8) Epoch 11, batch 21100, loss[loss=0.125, simple_loss=0.2021, pruned_loss=0.02397, over 4882.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.0325, over 973175.30 frames.], batch size: 22, lr: 1.95e-04
2022-05-07 04:58:38,863 INFO [train.py:715] (2/8) Epoch 11, batch 21150, loss[loss=0.1262, simple_loss=0.2071, pruned_loss=0.02259, over 4771.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03264, over 972569.80 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 04:59:18,201 INFO [train.py:715] (2/8) Epoch 11, batch 21200, loss[loss=0.138, simple_loss=0.2137, pruned_loss=0.0312, over 4787.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03197, over 972921.69 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 04:59:56,327 INFO [train.py:715] (2/8) Epoch 11, batch 21250, loss[loss=0.1567, simple_loss=0.2466, pruned_loss=0.03335, over 4924.00 frames.], tot_loss[loss=0.138, simple_loss=0.2117, pruned_loss=0.03212, over 973637.96 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 05:00:35,640 INFO [train.py:715] (2/8) Epoch 11, batch 21300, loss[loss=0.1154, simple_loss=0.1854, pruned_loss=0.02274, over 4781.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03168, over 973360.03 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:01:15,026 INFO [train.py:715] (2/8) Epoch 11, batch 21350, loss[loss=0.1354, simple_loss=0.2148, pruned_loss=0.02799, over 4814.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03095, over 972966.50 frames.], batch size: 27, lr: 1.95e-04
2022-05-07 05:01:53,535 INFO [train.py:715] (2/8) Epoch 11, batch 21400, loss[loss=0.1602, simple_loss=0.2143, pruned_loss=0.05304, over 4719.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.0313, over 973146.79 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 05:02:32,174 INFO [train.py:715] (2/8) Epoch 11, batch 21450, loss[loss=0.1377, simple_loss=0.212, pruned_loss=0.03175, over 4801.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03085, over 973155.13 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 05:03:11,026 INFO [train.py:715] (2/8) Epoch 11, batch 21500, loss[loss=0.1629, simple_loss=0.2335, pruned_loss=0.04618, over 4988.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03104, over 973241.65 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 05:03:50,389 INFO [train.py:715] (2/8) Epoch 11, batch 21550, loss[loss=0.1633, simple_loss=0.2366, pruned_loss=0.04501, over 4776.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.0308, over 972831.95 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:04:28,680 INFO [train.py:715] (2/8) Epoch 11, batch 21600, loss[loss=0.1562, simple_loss=0.2197, pruned_loss=0.04638, over 4948.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03096, over 972150.81 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 05:05:07,530 INFO [train.py:715] (2/8) Epoch 11, batch 21650, loss[loss=0.1407, simple_loss=0.2176, pruned_loss=0.03186, over 4788.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03114, over 972490.12 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:05:47,578 INFO [train.py:715] (2/8) Epoch 11, batch 21700, loss[loss=0.1226, simple_loss=0.205, pruned_loss=0.0201, over 4773.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2116, pruned_loss=0.03164, over 972108.94 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:06:26,870 INFO [train.py:715] (2/8) Epoch 11, batch 21750, loss[loss=0.1561, simple_loss=0.2373, pruned_loss=0.03742, over 4909.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2111, pruned_loss=0.03154, over 972222.81 frames.], batch size: 19, lr: 1.95e-04
2022-05-07 05:07:07,061 INFO [train.py:715] (2/8) Epoch 11, batch 21800, loss[loss=0.1231, simple_loss=0.198, pruned_loss=0.02413, over 4929.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2112, pruned_loss=0.03211, over 972566.70 frames.], batch size: 23, lr: 1.95e-04
2022-05-07 05:07:46,732 INFO [train.py:715] (2/8) Epoch 11, batch 21850, loss[loss=0.1355, simple_loss=0.2123, pruned_loss=0.02937, over 4801.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03261, over 973636.46 frames.], batch size: 21, lr: 1.95e-04
2022-05-07 05:08:27,224 INFO [train.py:715] (2/8) Epoch 11, batch 21900, loss[loss=0.124, simple_loss=0.205, pruned_loss=0.02146, over 4948.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2108, pruned_loss=0.03265, over 973903.27 frames.], batch size: 24, lr: 1.95e-04
2022-05-07 05:09:06,445 INFO [train.py:715] (2/8) Epoch 11, batch 21950, loss[loss=0.1251, simple_loss=0.1991, pruned_loss=0.02556, over 4821.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2099, pruned_loss=0.03219, over 973725.35 frames.], batch size: 13, lr: 1.95e-04
2022-05-07 05:09:46,764 INFO [train.py:715] (2/8) Epoch 11, batch 22000, loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02942, over 4818.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03226, over 973677.30 frames.], batch size: 27, lr: 1.95e-04
2022-05-07 05:10:27,231 INFO [train.py:715] (2/8) Epoch 11, batch 22050, loss[loss=0.1243, simple_loss=0.1928, pruned_loss=0.02787, over 4882.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2099, pruned_loss=0.03212, over 973428.62 frames.], batch size: 22, lr: 1.95e-04
2022-05-07 05:11:05,500 INFO [train.py:715] (2/8) Epoch 11, batch 22100, loss[loss=0.1276, simple_loss=0.2002, pruned_loss=0.02753, over 4784.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03267, over 972860.36 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 05:11:45,100 INFO [train.py:715] (2/8) Epoch 11, batch 22150, loss[loss=0.2042, simple_loss=0.2591, pruned_loss=0.07462, over 4896.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2121, pruned_loss=0.0333, over 972429.33 frames.], batch size: 39, lr: 1.95e-04
2022-05-07 05:12:24,690 INFO [train.py:715] (2/8) Epoch 11, batch 22200, loss[loss=0.1459, simple_loss=0.2114, pruned_loss=0.04019, over 4876.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03317, over 973154.04 frames.], batch size: 32, lr: 1.95e-04
2022-05-07 05:13:03,454 INFO [train.py:715] (2/8) Epoch 11, batch 22250, loss[loss=0.1365, simple_loss=0.2148, pruned_loss=0.02908, over 4930.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03264, over 972119.65 frames.], batch size: 18, lr: 1.95e-04
2022-05-07 05:13:41,886 INFO [train.py:715] (2/8) Epoch 11, batch 22300, loss[loss=0.1589, simple_loss=0.2241, pruned_loss=0.04686, over 4985.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2125, pruned_loss=0.03296, over 971780.47 frames.], batch size: 35, lr: 1.95e-04
2022-05-07 05:14:21,103 INFO [train.py:715] (2/8) Epoch 11, batch 22350, loss[loss=0.1334, simple_loss=0.2175, pruned_loss=0.0247, over 4839.00 frames.], tot_loss[loss=0.1385, simple_loss=0.212, pruned_loss=0.03252, over 972717.93 frames.], batch size: 30, lr: 1.95e-04
2022-05-07 05:15:00,566 INFO [train.py:715] (2/8) Epoch 11, batch 22400, loss[loss=0.1288, simple_loss=0.2038, pruned_loss=0.02684, over 4873.00 frames.], tot_loss[loss=0.138, simple_loss=0.2119, pruned_loss=0.03209, over 973161.13 frames.], batch size: 22, lr: 1.95e-04
2022-05-07 05:15:38,491 INFO [train.py:715] (2/8) Epoch 11, batch 22450, loss[loss=0.1623, simple_loss=0.2329, pruned_loss=0.0458, over 4943.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2116, pruned_loss=0.03202, over 972235.28 frames.], batch size: 29, lr: 1.95e-04
2022-05-07 05:16:18,410 INFO [train.py:715] (2/8) Epoch 11, batch 22500, loss[loss=0.1345, simple_loss=0.2035, pruned_loss=0.03271, over 4873.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03202, over 971863.54 frames.], batch size: 22, lr: 1.95e-04
2022-05-07 05:16:57,486 INFO [train.py:715] (2/8) Epoch 11, batch 22550, loss[loss=0.1228, simple_loss=0.1921, pruned_loss=0.02675, over 4898.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.03221, over 972802.12 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:17:36,657 INFO [train.py:715] (2/8) Epoch 11, batch 22600, loss[loss=0.1315, simple_loss=0.1954, pruned_loss=0.03376, over 4771.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2118, pruned_loss=0.03245, over 973320.46 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 05:18:15,029 INFO [train.py:715] (2/8) Epoch 11, batch 22650, loss[loss=0.1763, simple_loss=0.2445, pruned_loss=0.05408, over 4681.00 frames.], tot_loss[loss=0.1395, simple_loss=0.213, pruned_loss=0.03303, over 973728.70 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 05:18:54,216 INFO [train.py:715] (2/8) Epoch 11, batch 22700, loss[loss=0.127, simple_loss=0.1978, pruned_loss=0.0281, over 4980.00 frames.], tot_loss[loss=0.1393, simple_loss=0.213, pruned_loss=0.03283, over 973934.69 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 05:19:34,075 INFO [train.py:715] (2/8) Epoch 11, batch 22750, loss[loss=0.1238, simple_loss=0.1968, pruned_loss=0.02545, over 4796.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2133, pruned_loss=0.03295, over 972742.09 frames.], batch size: 24, lr: 1.95e-04
2022-05-07 05:20:12,496 INFO [train.py:715] (2/8) Epoch 11, batch 22800, loss[loss=0.1275, simple_loss=0.2028, pruned_loss=0.0261, over 4889.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2138, pruned_loss=0.03298, over 972648.48 frames.], batch size: 32, lr: 1.95e-04
2022-05-07 05:20:52,296 INFO [train.py:715] (2/8) Epoch 11, batch 22850, loss[loss=0.1281, simple_loss=0.2025, pruned_loss=0.02682, over 4747.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2132, pruned_loss=0.03297, over 972988.59 frames.], batch size: 16, lr: 1.95e-04
2022-05-07 05:21:31,221 INFO [train.py:715] (2/8) Epoch 11, batch 22900, loss[loss=0.1244, simple_loss=0.1994, pruned_loss=0.02465, over 4757.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2137, pruned_loss=0.03337, over 972595.84 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 05:22:10,211 INFO [train.py:715] (2/8) Epoch 11, batch 22950, loss[loss=0.1353, simple_loss=0.2039, pruned_loss=0.03338, over 4961.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03254, over 972310.21 frames.], batch size: 35, lr: 1.95e-04
2022-05-07 05:22:48,360 INFO [train.py:715] (2/8) Epoch 11, batch 23000, loss[loss=0.1219, simple_loss=0.1859, pruned_loss=0.02892, over 4844.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.0331, over 972389.52 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 05:23:27,329 INFO [train.py:715] (2/8) Epoch 11, batch 23050, loss[loss=0.1323, simple_loss=0.2129, pruned_loss=0.02587, over 4903.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03249, over 972037.09 frames.], batch size: 22, lr: 1.95e-04
2022-05-07 05:24:06,661 INFO [train.py:715] (2/8) Epoch 11, batch 23100, loss[loss=0.1231, simple_loss=0.2042, pruned_loss=0.02102, over 4920.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2121, pruned_loss=0.03269, over 971848.24 frames.], batch size: 29, lr: 1.95e-04
2022-05-07 05:24:44,407 INFO [train.py:715] (2/8) Epoch 11, batch 23150, loss[loss=0.1454, simple_loss=0.219, pruned_loss=0.03588, over 4862.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.03339, over 971545.43 frames.], batch size: 20, lr: 1.95e-04
2022-05-07 05:25:23,978 INFO [train.py:715] (2/8) Epoch 11, batch 23200, loss[loss=0.1616, simple_loss=0.2442, pruned_loss=0.03952, over 4849.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.03313, over 971912.78 frames.], batch size: 15, lr: 1.95e-04
2022-05-07 05:26:02,909 INFO [train.py:715] (2/8) Epoch 11, batch 23250, loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03211, over 4937.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03258, over 971865.16 frames.], batch size: 23, lr: 1.95e-04
2022-05-07 05:26:41,982 INFO [train.py:715] (2/8) Epoch 11, batch 23300, loss[loss=0.1583, simple_loss=0.2363, pruned_loss=0.04013, over 4856.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03269, over 972628.61 frames.], batch size: 32, lr: 1.95e-04
2022-05-07 05:27:20,072 INFO [train.py:715] (2/8) Epoch 11, batch 23350, loss[loss=0.1242, simple_loss=0.1978, pruned_loss=0.02523, over 4876.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03266, over 971520.44 frames.], batch size: 20, lr: 1.95e-04
2022-05-07 05:27:59,139 INFO [train.py:715] (2/8) Epoch 11, batch 23400, loss[loss=0.1339, simple_loss=0.216, pruned_loss=0.02587, over 4781.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03267, over 972080.73 frames.], batch size: 14, lr: 1.95e-04
2022-05-07 05:28:38,744 INFO [train.py:715] (2/8) Epoch 11, batch 23450, loss[loss=0.1285, simple_loss=0.2071, pruned_loss=0.02494, over 4905.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2111, pruned_loss=0.03258, over 971888.99 frames.], batch size: 17, lr: 1.95e-04
2022-05-07 05:29:16,869 INFO [train.py:715] (2/8) Epoch 11, batch 23500, loss[loss=0.1507, simple_loss=0.2186, pruned_loss=0.04138, over 4884.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03197, over 971708.81 frames.], batch size: 39, lr: 1.95e-04
2022-05-07 05:29:55,783 INFO [train.py:715] (2/8) Epoch 11, batch 23550, loss[loss=0.1525, simple_loss=0.2088, pruned_loss=0.0481, over 4967.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03213, over 972208.98 frames.], batch size: 35, lr: 1.95e-04
2022-05-07 05:30:34,767 INFO [train.py:715] (2/8) Epoch 11, batch 23600, loss[loss=0.1263, simple_loss=0.2035, pruned_loss=0.02462, over 4905.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03233, over 972377.79 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 05:31:14,120 INFO [train.py:715] (2/8) Epoch 11, batch 23650, loss[loss=0.1574, simple_loss=0.2329, pruned_loss=0.041, over 4937.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03239, over 972162.92 frames.], batch size: 39, lr: 1.94e-04
2022-05-07 05:31:51,831 INFO [train.py:715] (2/8) Epoch 11, batch 23700, loss[loss=0.1456, simple_loss=0.2145, pruned_loss=0.0383, over 4967.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03205, over 972227.91 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:32:30,813 INFO [train.py:715] (2/8) Epoch 11, batch 23750, loss[loss=0.1272, simple_loss=0.2042, pruned_loss=0.02505, over 4935.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2109, pruned_loss=0.03265, over 972239.77 frames.], batch size: 23, lr: 1.94e-04
2022-05-07 05:33:09,309 INFO [train.py:715] (2/8) Epoch 11, batch 23800, loss[loss=0.1236, simple_loss=0.1955, pruned_loss=0.02583, over 4967.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03288, over 972928.15 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:33:46,739 INFO [train.py:715] (2/8) Epoch 11, batch 23850, loss[loss=0.1427, simple_loss=0.2112, pruned_loss=0.03703, over 4905.00 frames.], tot_loss[loss=0.1392, simple_loss=0.212, pruned_loss=0.03323, over 972563.66 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 05:34:24,313 INFO [train.py:715] (2/8) Epoch 11, batch 23900, loss[loss=0.1509, simple_loss=0.2244, pruned_loss=0.03874, over 4804.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03315, over 973226.90 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:35:01,656 INFO [train.py:715] (2/8) Epoch 11, batch 23950, loss[loss=0.1502, simple_loss=0.229, pruned_loss=0.03571, over 4771.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.03302, over 973037.68 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 05:35:39,342 INFO [train.py:715] (2/8) Epoch 11, batch 24000, loss[loss=0.1432, simple_loss=0.2086, pruned_loss=0.03892, over 4826.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03318, over 972339.40 frames.], batch size: 13, lr: 1.94e-04
2022-05-07 05:35:39,343 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 05:35:48,813 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.1059, simple_loss=0.19, pruned_loss=0.01092, over 914524.00 frames.
2022-05-07 05:36:27,137 INFO [train.py:715] (2/8) Epoch 11, batch 24050, loss[loss=0.1457, simple_loss=0.2187, pruned_loss=0.03633, over 4974.00 frames.], tot_loss[loss=0.139, simple_loss=0.2118, pruned_loss=0.03315, over 972401.64 frames.], batch size: 39, lr: 1.94e-04
2022-05-07 05:37:04,268 INFO [train.py:715] (2/8) Epoch 11, batch 24100, loss[loss=0.1467, simple_loss=0.2214, pruned_loss=0.036, over 4982.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2111, pruned_loss=0.0327, over 972630.36 frames.], batch size: 28, lr: 1.94e-04
2022-05-07 05:37:42,096 INFO [train.py:715] (2/8) Epoch 11, batch 24150, loss[loss=0.1715, simple_loss=0.2358, pruned_loss=0.05363, over 4854.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2109, pruned_loss=0.03235, over 973045.66 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 05:38:20,367 INFO [train.py:715] (2/8) Epoch 11, batch 24200, loss[loss=0.1271, simple_loss=0.2003, pruned_loss=0.02696, over 4817.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03231, over 972895.51 frames.], batch size: 27, lr: 1.94e-04
2022-05-07 05:38:57,456 INFO [train.py:715] (2/8) Epoch 11, batch 24250, loss[loss=0.1736, simple_loss=0.2478, pruned_loss=0.04966, over 4870.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03207, over 972144.31 frames.], batch size: 32, lr: 1.94e-04
2022-05-07 05:39:35,484 INFO [train.py:715] (2/8) Epoch 11, batch 24300, loss[loss=0.1126, simple_loss=0.1817, pruned_loss=0.0217, over 4796.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03269, over 971967.24 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:40:13,072 INFO [train.py:715] (2/8) Epoch 11, batch 24350, loss[loss=0.1612, simple_loss=0.2318, pruned_loss=0.04536, over 4769.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2122, pruned_loss=0.03328, over 972015.60 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 05:40:50,677 INFO [train.py:715] (2/8) Epoch 11, batch 24400, loss[loss=0.1503, simple_loss=0.2179, pruned_loss=0.0413, over 4783.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03318, over 970936.51 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:41:28,272 INFO [train.py:715] (2/8) Epoch 11, batch 24450, loss[loss=0.1657, simple_loss=0.2458, pruned_loss=0.04279, over 4811.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03281, over 970355.09 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 05:42:06,386 INFO [train.py:715] (2/8) Epoch 11, batch 24500, loss[loss=0.1289, simple_loss=0.2159, pruned_loss=0.02093, over 4800.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2129, pruned_loss=0.03299, over 971245.31 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:42:45,026 INFO [train.py:715] (2/8) Epoch 11, batch 24550, loss[loss=0.145, simple_loss=0.2372, pruned_loss=0.02639, over 4849.00 frames.], tot_loss[loss=0.1399, simple_loss=0.213, pruned_loss=0.03337, over 971429.34 frames.], batch size: 20, lr: 1.94e-04
2022-05-07 05:43:23,050 INFO [train.py:715] (2/8) Epoch 11, batch 24600, loss[loss=0.1232, simple_loss=0.2026, pruned_loss=0.0219, over 4922.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2129, pruned_loss=0.03312, over 971032.61 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 05:44:01,527 INFO [train.py:715] (2/8) Epoch 11, batch 24650, loss[loss=0.1233, simple_loss=0.2028, pruned_loss=0.02195, over 4886.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2128, pruned_loss=0.03317, over 971436.68 frames.], batch size: 22, lr: 1.94e-04
2022-05-07 05:44:39,861 INFO [train.py:715] (2/8) Epoch 11, batch 24700, loss[loss=0.1276, simple_loss=0.1975, pruned_loss=0.02882, over 4866.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03252, over 971525.70 frames.], batch size: 20, lr: 1.94e-04
2022-05-07 05:45:18,488 INFO [train.py:715] (2/8) Epoch 11, batch 24750, loss[loss=0.135, simple_loss=0.2109, pruned_loss=0.02961, over 4791.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.0328, over 971656.74 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 05:45:56,383 INFO [train.py:715] (2/8) Epoch 11, batch 24800, loss[loss=0.1688, simple_loss=0.2372, pruned_loss=0.05023, over 4748.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03253, over 972591.20 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 05:46:34,703 INFO [train.py:715] (2/8) Epoch 11, batch 24850, loss[loss=0.1381, simple_loss=0.2118, pruned_loss=0.03225, over 4773.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2133, pruned_loss=0.03325, over 973260.62 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:47:13,630 INFO [train.py:715] (2/8) Epoch 11, batch 24900, loss[loss=0.09323, simple_loss=0.1732, pruned_loss=0.006622, over 4864.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2129, pruned_loss=0.0333, over 973204.30 frames.], batch size: 12, lr: 1.94e-04
2022-05-07 05:47:51,694 INFO [train.py:715] (2/8) Epoch 11, batch 24950, loss[loss=0.1519, simple_loss=0.2331, pruned_loss=0.03534, over 4792.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2129, pruned_loss=0.03323, over 972650.29 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:48:30,024 INFO [train.py:715] (2/8) Epoch 11, batch 25000, loss[loss=0.1421, simple_loss=0.2247, pruned_loss=0.02981, over 4937.00 frames.], tot_loss[loss=0.139, simple_loss=0.2127, pruned_loss=0.03271, over 972585.63 frames.], batch size: 23, lr: 1.94e-04
2022-05-07 05:49:08,319 INFO [train.py:715] (2/8) Epoch 11, batch 25050, loss[loss=0.1305, simple_loss=0.207, pruned_loss=0.02701, over 4800.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2134, pruned_loss=0.0331, over 972358.47 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:49:49,680 INFO [train.py:715] (2/8) Epoch 11, batch 25100, loss[loss=0.1249, simple_loss=0.1971, pruned_loss=0.02642, over 4740.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2117, pruned_loss=0.03251, over 972129.79 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 05:50:27,839 INFO [train.py:715] (2/8) Epoch 11, batch 25150, loss[loss=0.1203, simple_loss=0.19, pruned_loss=0.02537, over 4920.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2117, pruned_loss=0.03249, over 972668.19 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 05:51:06,431 INFO [train.py:715] (2/8) Epoch 11, batch 25200, loss[loss=0.1609, simple_loss=0.237, pruned_loss=0.04239, over 4935.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03268, over 973084.71 frames.], batch size: 39, lr: 1.94e-04
2022-05-07 05:51:45,284 INFO [train.py:715] (2/8) Epoch 11, batch 25250, loss[loss=0.1277, simple_loss=0.2117, pruned_loss=0.02183, over 4956.00 frames.], tot_loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.03225, over 971870.06 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:52:23,562 INFO [train.py:715] (2/8) Epoch 11, batch 25300, loss[loss=0.1534, simple_loss=0.2277, pruned_loss=0.03956, over 4859.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03265, over 971626.30 frames.], batch size: 20, lr: 1.94e-04
2022-05-07 05:53:01,962 INFO [train.py:715] (2/8) Epoch 11, batch 25350, loss[loss=0.1497, simple_loss=0.218, pruned_loss=0.04069, over 4837.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.03279, over 971438.19 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 05:53:40,601 INFO [train.py:715] (2/8) Epoch 11, batch 25400, loss[loss=0.1369, simple_loss=0.2173, pruned_loss=0.02829, over 4795.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03301, over 971712.00 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 05:54:19,418 INFO [train.py:715] (2/8) Epoch 11, batch 25450, loss[loss=0.1145, simple_loss=0.197, pruned_loss=0.01604, over 4984.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03303, over 971410.82 frames.], batch size: 20, lr: 1.94e-04
2022-05-07 05:54:57,470 INFO [train.py:715] (2/8) Epoch 11, batch 25500, loss[loss=0.1438, simple_loss=0.222, pruned_loss=0.03277, over 4774.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03279, over 971511.25 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 05:55:36,081 INFO [train.py:715] (2/8) Epoch 11, batch 25550, loss[loss=0.1229, simple_loss=0.1934, pruned_loss=0.02617, over 4869.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2113, pruned_loss=0.03245, over 972094.67 frames.], batch size: 32, lr: 1.94e-04
2022-05-07 05:56:15,315 INFO [train.py:715] (2/8) Epoch 11, batch 25600, loss[loss=0.1203, simple_loss=0.1914, pruned_loss=0.02457, over 4839.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2105, pruned_loss=0.03234, over 971942.84 frames.], batch size: 26, lr: 1.94e-04
2022-05-07 05:56:53,595 INFO [train.py:715] (2/8) Epoch 11, batch 25650, loss[loss=0.1308, simple_loss=0.2085, pruned_loss=0.0265, over 4921.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.0325, over 971913.15 frames.], batch size: 23, lr: 1.94e-04
2022-05-07 05:57:31,751 INFO [train.py:715] (2/8) Epoch 11, batch 25700, loss[loss=0.1527, simple_loss=0.2142, pruned_loss=0.04558, over 4842.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03297, over 972097.89 frames.], batch size: 32, lr: 1.94e-04
2022-05-07 05:58:10,581 INFO [train.py:715] (2/8) Epoch 11, batch 25750, loss[loss=0.1401, simple_loss=0.2034, pruned_loss=0.0384, over 4777.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.03246, over 971230.58 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 05:58:48,900 INFO [train.py:715] (2/8) Epoch 11, batch 25800, loss[loss=0.1592, simple_loss=0.2348, pruned_loss=0.04182, over 4905.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03213, over 971377.74 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 05:59:26,904 INFO [train.py:715] (2/8) Epoch 11, batch 25850, loss[loss=0.1817, simple_loss=0.2431, pruned_loss=0.06011, over 4962.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2112, pruned_loss=0.03268, over 971145.15 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:00:05,563 INFO [train.py:715] (2/8) Epoch 11, batch 25900, loss[loss=0.1555, simple_loss=0.2175, pruned_loss=0.04669, over 4925.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2119, pruned_loss=0.03323, over 971221.69 frames.], batch size: 23, lr: 1.94e-04
2022-05-07 06:00:44,270 INFO [train.py:715] (2/8) Epoch 11, batch 25950, loss[loss=0.1257, simple_loss=0.2052, pruned_loss=0.02314, over 4804.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2121, pruned_loss=0.03339, over 971682.80 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 06:01:22,319 INFO [train.py:715] (2/8) Epoch 11, batch 26000, loss[loss=0.1383, simple_loss=0.2188, pruned_loss=0.02883, over 4980.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.03323, over 971682.76 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:02:00,400 INFO [train.py:715] (2/8) Epoch 11, batch 26050, loss[loss=0.1704, simple_loss=0.2253, pruned_loss=0.05777, over 4801.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03317, over 971830.01 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 06:02:38,954 INFO [train.py:715] (2/8) Epoch 11, batch 26100, loss[loss=0.1413, simple_loss=0.2146, pruned_loss=0.03393, over 4828.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2114, pruned_loss=0.03319, over 971938.53 frames.], batch size: 30, lr: 1.94e-04
2022-05-07 06:03:17,346 INFO [train.py:715] (2/8) Epoch 11, batch 26150, loss[loss=0.1372, simple_loss=0.2065, pruned_loss=0.03392, over 4863.00 frames.], tot_loss[loss=0.138, simple_loss=0.2107, pruned_loss=0.03263, over 971735.06 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 06:03:55,314 INFO [train.py:715] (2/8) Epoch 11, batch 26200, loss[loss=0.1258, simple_loss=0.2028, pruned_loss=0.02437, over 4771.00 frames.], tot_loss[loss=0.1384, simple_loss=0.211, pruned_loss=0.0329, over 970648.23 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 06:04:32,932 INFO [train.py:715] (2/8) Epoch 11, batch 26250, loss[loss=0.1483, simple_loss=0.2184, pruned_loss=0.03904, over 4776.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2116, pruned_loss=0.033, over 971627.96 frames.], batch size: 14, lr: 1.94e-04
2022-05-07 06:05:10,976 INFO [train.py:715] (2/8) Epoch 11, batch 26300, loss[loss=0.1291, simple_loss=0.1951, pruned_loss=0.03157, over 4777.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03284, over 972713.08 frames.], batch size: 17, lr: 1.94e-04
2022-05-07 06:05:48,409 INFO [train.py:715] (2/8) Epoch 11, batch 26350, loss[loss=0.1591, simple_loss=0.2223, pruned_loss=0.04797, over 4802.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03305, over 972305.83 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 06:06:25,429 INFO [train.py:715] (2/8) Epoch 11, batch 26400, loss[loss=0.1774, simple_loss=0.25, pruned_loss=0.05234, over 4865.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2121, pruned_loss=0.03319, over 972277.01 frames.], batch size: 30, lr: 1.94e-04
2022-05-07 06:07:03,857 INFO [train.py:715] (2/8) Epoch 11, batch 26450, loss[loss=0.145, simple_loss=0.2192, pruned_loss=0.03547, over 4962.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2112, pruned_loss=0.03279, over 972627.33 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 06:07:41,344 INFO [train.py:715] (2/8) Epoch 11, batch 26500, loss[loss=0.1252, simple_loss=0.2082, pruned_loss=0.02106, over 4974.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03243, over 973054.40 frames.], batch size: 25, lr: 1.94e-04
2022-05-07 06:08:19,084 INFO [train.py:715] (2/8) Epoch 11, batch 26550, loss[loss=0.1407, simple_loss=0.2135, pruned_loss=0.03393, over 4811.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2114, pruned_loss=0.03294, over 973125.45 frames.], batch size: 27, lr: 1.94e-04
2022-05-07 06:08:56,822 INFO [train.py:715] (2/8) Epoch 11, batch 26600, loss[loss=0.1374, simple_loss=0.2056, pruned_loss=0.03463, over 4769.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03246, over 972712.18 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:09:34,847 INFO [train.py:715] (2/8) Epoch 11, batch 26650, loss[loss=0.1435, simple_loss=0.2162, pruned_loss=0.03544, over 4883.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2109, pruned_loss=0.03272, over 971932.47 frames.], batch size: 32, lr: 1.94e-04
2022-05-07 06:10:12,912 INFO [train.py:715] (2/8) Epoch 11, batch 26700, loss[loss=0.1337, simple_loss=0.213, pruned_loss=0.02717, over 4978.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03253, over 971644.76 frames.], batch size: 25, lr: 1.94e-04
2022-05-07 06:10:49,900 INFO [train.py:715] (2/8) Epoch 11, batch 26750, loss[loss=0.1295, simple_loss=0.2001, pruned_loss=0.0294, over 4918.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.03266, over 972226.59 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:11:28,542 INFO [train.py:715] (2/8) Epoch 11, batch 26800, loss[loss=0.1341, simple_loss=0.2098, pruned_loss=0.02923, over 4756.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03287, over 972385.26 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:12:06,146 INFO [train.py:715] (2/8) Epoch 11, batch 26850, loss[loss=0.1333, simple_loss=0.1944, pruned_loss=0.03611, over 4757.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03228, over 972344.48 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:12:43,633 INFO [train.py:715] (2/8) Epoch 11, batch 26900, loss[loss=0.1236, simple_loss=0.1989, pruned_loss=0.02421, over 4772.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03228, over 972255.51 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 06:13:21,273 INFO [train.py:715] (2/8) Epoch 11, batch 26950, loss[loss=0.1528, simple_loss=0.222, pruned_loss=0.04178, over 4958.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03272, over 972455.72 frames.], batch size: 21, lr: 1.94e-04
2022-05-07 06:13:59,655 INFO [train.py:715] (2/8) Epoch 11, batch 27000, loss[loss=0.1151, simple_loss=0.1925, pruned_loss=0.01879, over 4937.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03295, over 972995.81 frames.], batch size: 23, lr: 1.94e-04
2022-05-07 06:13:59,655 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 06:14:09,118 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.1059, simple_loss=0.19, pruned_loss=0.01084, over 914524.00 frames.
2022-05-07 06:14:47,550 INFO [train.py:715] (2/8) Epoch 11, batch 27050, loss[loss=0.1334, simple_loss=0.2132, pruned_loss=0.0268, over 4959.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03269, over 972598.54 frames.], batch size: 29, lr: 1.94e-04
2022-05-07 06:15:25,151 INFO [train.py:715] (2/8) Epoch 11, batch 27100, loss[loss=0.134, simple_loss=0.1905, pruned_loss=0.0387, over 4959.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.03233, over 972527.13 frames.], batch size: 35, lr: 1.94e-04
2022-05-07 06:16:02,383 INFO [train.py:715] (2/8) Epoch 11, batch 27150, loss[loss=0.126, simple_loss=0.2062, pruned_loss=0.02289, over 4887.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03248, over 972426.72 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 06:16:41,021 INFO [train.py:715] (2/8) Epoch 11, batch 27200, loss[loss=0.1428, simple_loss=0.2065, pruned_loss=0.03951, over 4976.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.03222, over 972945.88 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:17:18,730 INFO [train.py:715] (2/8) Epoch 11, batch 27250, loss[loss=0.1363, simple_loss=0.2108, pruned_loss=0.03094, over 4830.00 frames.], tot_loss[loss=0.138, simple_loss=0.2118, pruned_loss=0.03214, over 972186.35 frames.], batch size: 26, lr: 1.94e-04
2022-05-07 06:17:56,619 INFO [train.py:715] (2/8) Epoch 11, batch 27300, loss[loss=0.1524, simple_loss=0.2327, pruned_loss=0.03608, over 4739.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2122, pruned_loss=0.03263, over 972909.68 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 06:18:34,289 INFO [train.py:715] (2/8) Epoch 11, batch 27350, loss[loss=0.1364, simple_loss=0.2079, pruned_loss=0.0325, over 4935.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03269, over 973444.27 frames.], batch size: 18, lr: 1.94e-04
2022-05-07 06:19:13,065 INFO [train.py:715] (2/8) Epoch 11, batch 27400, loss[loss=0.1347, simple_loss=0.2206, pruned_loss=0.02441, over 4884.00 frames.], tot_loss[loss=0.139, simple_loss=0.2119, pruned_loss=0.03307, over 973161.69 frames.], batch size: 22, lr: 1.94e-04
2022-05-07 06:19:50,856 INFO [train.py:715] (2/8) Epoch 11, batch 27450, loss[loss=0.15, simple_loss=0.2191, pruned_loss=0.04042, over 4902.00 frames.], tot_loss[loss=0.139, simple_loss=0.212, pruned_loss=0.03303, over 973425.97 frames.], batch size: 19, lr: 1.94e-04
2022-05-07 06:20:28,122 INFO [train.py:715] (2/8) Epoch 11, batch 27500, loss[loss=0.1553, simple_loss=0.2241, pruned_loss=0.04328, over 4845.00 frames.], tot_loss[loss=0.139, simple_loss=0.2122, pruned_loss=0.0329, over 973276.17 frames.], batch size: 32, lr: 1.94e-04
2022-05-07 06:21:07,286 INFO [train.py:715] (2/8) Epoch 11, batch 27550, loss[loss=0.1431, simple_loss=0.2247, pruned_loss=0.03079, over 4982.00 frames.], tot_loss[loss=0.141, simple_loss=0.2142, pruned_loss=0.03387, over 973337.69 frames.], batch size: 25, lr: 1.94e-04
2022-05-07 06:21:45,753 INFO [train.py:715] (2/8) Epoch 11, batch 27600, loss[loss=0.1208, simple_loss=0.2019, pruned_loss=0.01985, over 4971.00 frames.], tot_loss[loss=0.14, simple_loss=0.2134, pruned_loss=0.03331, over 971742.79 frames.], batch size: 24, lr: 1.94e-04
2022-05-07 06:22:23,478 INFO [train.py:715] (2/8) Epoch 11, batch 27650, loss[loss=0.1368, simple_loss=0.2152, pruned_loss=0.02915, over 4985.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2124, pruned_loss=0.03309, over 972427.42 frames.], batch size: 35, lr: 1.94e-04
2022-05-07 06:23:01,301 INFO [train.py:715] (2/8) Epoch 11, batch 27700, loss[loss=0.1319, simple_loss=0.213, pruned_loss=0.02539, over 4979.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2117, pruned_loss=0.03291, over 973420.51 frames.], batch size: 15, lr: 1.94e-04
2022-05-07 06:23:39,626 INFO [train.py:715] (2/8) Epoch 11, batch 27750, loss[loss=0.1472, simple_loss=0.2181, pruned_loss=0.03813, over 4867.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03278, over 974356.10 frames.], batch size: 16, lr: 1.94e-04
2022-05-07 06:24:17,572 INFO [train.py:715] (2/8) Epoch 11, batch 27800, loss[loss=0.177, simple_loss=0.2378, pruned_loss=0.05808, over 4929.00 frames.], tot_loss[loss=0.1388, simple_loss=0.212, pruned_loss=0.03277, over 974737.31 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 06:24:54,557 INFO [train.py:715] (2/8) Epoch 11, batch 27850, loss[loss=0.1115, simple_loss=0.1881, pruned_loss=0.01746, over 4811.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.0329, over 973708.04 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 06:25:32,918 INFO [train.py:715] (2/8) Epoch 11, batch 27900, loss[loss=0.134, simple_loss=0.2224, pruned_loss=0.02279, over 4910.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2116, pruned_loss=0.03278, over 972841.29 frames.], batch size: 22, lr: 1.93e-04
2022-05-07 06:26:10,974 INFO [train.py:715] (2/8) Epoch 11, batch 27950, loss[loss=0.1289, simple_loss=0.1966, pruned_loss=0.03062, over 4963.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2107, pruned_loss=0.03238, over 973424.66 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 06:26:48,583 INFO [train.py:715] (2/8) Epoch 11, batch 28000, loss[loss=0.1188, simple_loss=0.1797, pruned_loss=0.02895, over 4828.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2101, pruned_loss=0.03232, over 972873.59 frames.], batch size: 13, lr: 1.93e-04
2022-05-07 06:27:26,139 INFO [train.py:715] (2/8) Epoch 11, batch 28050, loss[loss=0.1596, simple_loss=0.2199, pruned_loss=0.04966, over 4804.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2094, pruned_loss=0.03183, over 972725.21 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 06:28:04,143 INFO [train.py:715] (2/8) Epoch 11, batch 28100, loss[loss=0.1143, simple_loss=0.2006, pruned_loss=0.01406, over 4930.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2092, pruned_loss=0.03164, over 973138.64 frames.], batch size: 29, lr: 1.93e-04
2022-05-07 06:28:41,422 INFO [train.py:715] (2/8) Epoch 11, batch 28150, loss[loss=0.154, simple_loss=0.2157, pruned_loss=0.04617, over 4899.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2089, pruned_loss=0.03169, over 972916.68 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 06:29:18,870 INFO [train.py:715] (2/8) Epoch 11, batch 28200, loss[loss=0.1377, simple_loss=0.1994, pruned_loss=0.03796, over 4809.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2099, pruned_loss=0.03214, over 972933.24 frames.], batch size: 13, lr: 1.93e-04
2022-05-07 06:29:57,424 INFO [train.py:715] (2/8) Epoch 11, batch 28250, loss[loss=0.1524, simple_loss=0.2263, pruned_loss=0.03921, over 4732.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2097, pruned_loss=0.03188, over 972022.08 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:30:34,926 INFO [train.py:715] (2/8) Epoch 11, batch 28300, loss[loss=0.1229, simple_loss=0.1898, pruned_loss=0.02795, over 4887.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2104, pruned_loss=0.03232, over 971989.69 frames.], batch size: 19, lr: 1.93e-04
2022-05-07 06:31:12,832 INFO [train.py:715] (2/8) Epoch 11, batch 28350, loss[loss=0.163, simple_loss=0.2349, pruned_loss=0.04555, over 4764.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2112, pruned_loss=0.0329, over 971582.49 frames.], batch size: 19, lr: 1.93e-04
2022-05-07 06:31:50,528 INFO [train.py:715] (2/8) Epoch 11, batch 28400, loss[loss=0.1383, simple_loss=0.2083, pruned_loss=0.03416, over 4944.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2119, pruned_loss=0.03331, over 971642.96 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 06:32:28,902 INFO [train.py:715] (2/8) Epoch 11, batch 28450, loss[loss=0.1604, simple_loss=0.2402, pruned_loss=0.04029, over 4952.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03312, over 971981.57 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 06:33:06,940 INFO [train.py:715] (2/8) Epoch 11, batch 28500, loss[loss=0.1397, simple_loss=0.2241, pruned_loss=0.02766, over 4921.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2119, pruned_loss=0.03312, over 972097.67 frames.], batch size: 29, lr: 1.93e-04
2022-05-07 06:33:44,632 INFO [train.py:715] (2/8) Epoch 11, batch 28550, loss[loss=0.1301, simple_loss=0.2083, pruned_loss=0.02595, over 4850.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2118, pruned_loss=0.03316, over 971453.93 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:34:23,472 INFO [train.py:715] (2/8) Epoch 11, batch 28600, loss[loss=0.1447, simple_loss=0.2201, pruned_loss=0.03467, over 4899.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03306, over 972796.56 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 06:35:01,437 INFO [train.py:715] (2/8) Epoch 11, batch 28650, loss[loss=0.1255, simple_loss=0.2005, pruned_loss=0.02527, over 4981.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03216, over 972865.86 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 06:35:39,424 INFO [train.py:715] (2/8) Epoch 11, batch 28700, loss[loss=0.1232, simple_loss=0.2019, pruned_loss=0.02227, over 4796.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03165, over 972922.82 frames.], batch size: 13, lr: 1.93e-04
2022-05-07 06:36:17,176 INFO [train.py:715] (2/8) Epoch 11, batch 28750, loss[loss=0.1378, simple_loss=0.2164, pruned_loss=0.02962, over 4851.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03153, over 972399.39 frames.], batch size: 20, lr: 1.93e-04
2022-05-07 06:36:55,929 INFO [train.py:715] (2/8) Epoch 11, batch 28800, loss[loss=0.1549, simple_loss=0.2193, pruned_loss=0.04532, over 4831.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.032, over 972509.56 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:37:33,395 INFO [train.py:715] (2/8) Epoch 11, batch 28850, loss[loss=0.1216, simple_loss=0.1907, pruned_loss=0.02623, over 4872.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03245, over 971813.73 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:38:10,808 INFO [train.py:715] (2/8) Epoch 11, batch 28900, loss[loss=0.1659, simple_loss=0.2497, pruned_loss=0.04105, over 4820.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2132, pruned_loss=0.03323, over 971699.26 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:38:49,571 INFO [train.py:715] (2/8) Epoch 11, batch 28950, loss[loss=0.1257, simple_loss=0.1999, pruned_loss=0.02573, over 4741.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2128, pruned_loss=0.03283, over 971808.57 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:39:27,041 INFO [train.py:715] (2/8) Epoch 11, batch 29000, loss[loss=0.1465, simple_loss=0.2251, pruned_loss=0.03395, over 4798.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2127, pruned_loss=0.03279, over 972714.14 frames.], batch size: 24, lr: 1.93e-04
2022-05-07 06:40:04,959 INFO [train.py:715] (2/8) Epoch 11, batch 29050, loss[loss=0.1463, simple_loss=0.2145, pruned_loss=0.039, over 4835.00 frames.], tot_loss[loss=0.139, simple_loss=0.2128, pruned_loss=0.03259, over 972784.56 frames.], batch size: 30, lr: 1.93e-04
2022-05-07 06:40:42,754 INFO [train.py:715] (2/8) Epoch 11, batch 29100, loss[loss=0.1652, simple_loss=0.2371, pruned_loss=0.04668, over 4773.00 frames.], tot_loss[loss=0.139, simple_loss=0.2128, pruned_loss=0.03257, over 972478.57 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:41:21,085 INFO [train.py:715] (2/8) Epoch 11, batch 29150, loss[loss=0.1622, simple_loss=0.2292, pruned_loss=0.04759, over 4957.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2124, pruned_loss=0.03228, over 972528.37 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:41:58,822 INFO [train.py:715] (2/8) Epoch 11, batch 29200, loss[loss=0.1576, simple_loss=0.235, pruned_loss=0.04014, over 4761.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2127, pruned_loss=0.03256, over 972285.25 frames.], batch size: 19, lr: 1.93e-04
2022-05-07 06:42:36,371 INFO [train.py:715] (2/8) Epoch 11, batch 29250, loss[loss=0.1368, simple_loss=0.2073, pruned_loss=0.0331, over 4788.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2125, pruned_loss=0.03266, over 972315.04 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 06:43:15,065 INFO [train.py:715] (2/8) Epoch 11, batch 29300, loss[loss=0.1786, simple_loss=0.2425, pruned_loss=0.05732, over 4692.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2121, pruned_loss=0.03251, over 971163.91 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 06:43:53,137 INFO [train.py:715] (2/8) Epoch 11, batch 29350, loss[loss=0.1205, simple_loss=0.1906, pruned_loss=0.02522, over 4861.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03289, over 971081.14 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:44:30,904 INFO [train.py:715] (2/8) Epoch 11, batch 29400, loss[loss=0.1394, simple_loss=0.215, pruned_loss=0.03195, over 4749.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03274, over 971619.20 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:45:08,812 INFO [train.py:715] (2/8) Epoch 11, batch 29450, loss[loss=0.142, simple_loss=0.2167, pruned_loss=0.03363, over 4749.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2115, pruned_loss=0.03292, over 972407.97 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:45:46,712 INFO [train.py:715] (2/8) Epoch 11, batch 29500, loss[loss=0.1429, simple_loss=0.2192, pruned_loss=0.03331, over 4942.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2121, pruned_loss=0.03277, over 971170.73 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 06:46:25,303 INFO [train.py:715] (2/8) Epoch 11, batch 29550, loss[loss=0.1339, simple_loss=0.2054, pruned_loss=0.03121, over 4866.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03272, over 970035.46 frames.], batch size: 22, lr: 1.93e-04
2022-05-07 06:47:02,907 INFO [train.py:715] (2/8) Epoch 11, batch 29600, loss[loss=0.1425, simple_loss=0.2183, pruned_loss=0.03332, over 4940.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.03217, over 970816.33 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 06:47:41,476 INFO [train.py:715] (2/8) Epoch 11, batch 29650, loss[loss=0.1765, simple_loss=0.2405, pruned_loss=0.05623, over 4981.00 frames.], tot_loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.03227, over 970869.67 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 06:48:19,468 INFO [train.py:715] (2/8) Epoch 11, batch 29700, loss[loss=0.1461, simple_loss=0.2164, pruned_loss=0.03791, over 4962.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03244, over 971180.91 frames.], batch size: 24, lr: 1.93e-04
2022-05-07 06:48:57,628 INFO [train.py:715] (2/8) Epoch 11, batch 29750, loss[loss=0.1226, simple_loss=0.2022, pruned_loss=0.0215, over 4735.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03257, over 971752.99 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:49:35,439 INFO [train.py:715] (2/8) Epoch 11, batch 29800, loss[loss=0.1381, simple_loss=0.2162, pruned_loss=0.02996, over 4761.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03255, over 971838.90 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:50:13,831 INFO [train.py:715] (2/8) Epoch 11, batch 29850, loss[loss=0.1163, simple_loss=0.1868, pruned_loss=0.02288, over 4846.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03238, over 972445.22 frames.], batch size: 13, lr: 1.93e-04
2022-05-07 06:50:52,366 INFO [train.py:715] (2/8) Epoch 11, batch 29900, loss[loss=0.1478, simple_loss=0.2232, pruned_loss=0.03622, over 4980.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2116, pruned_loss=0.03256, over 972260.49 frames.], batch size: 26, lr: 1.93e-04
2022-05-07 06:51:29,992 INFO [train.py:715] (2/8) Epoch 11, batch 29950, loss[loss=0.1468, simple_loss=0.2265, pruned_loss=0.03362, over 4887.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03252, over 972811.91 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:52:08,181 INFO [train.py:715] (2/8) Epoch 11, batch 30000, loss[loss=0.162, simple_loss=0.2277, pruned_loss=0.0481, over 4965.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2126, pruned_loss=0.03326, over 972018.24 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 06:52:08,181 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 06:52:17,626 INFO [train.py:742] (2/8) 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] (2/8) Epoch 11, batch 30050, loss[loss=0.1299, simple_loss=0.2039, pruned_loss=0.02791, over 4971.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2128, pruned_loss=0.03337, over 973062.08 frames.], batch size: 35, lr: 1.93e-04
2022-05-07 06:53:34,391 INFO [train.py:715] (2/8) Epoch 11, batch 30100, loss[loss=0.1396, simple_loss=0.2121, pruned_loss=0.03354, over 4855.00 frames.], tot_loss[loss=0.1387, simple_loss=0.212, pruned_loss=0.03269, over 972789.85 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 06:54:13,052 INFO [train.py:715] (2/8) Epoch 11, batch 30150, loss[loss=0.1797, simple_loss=0.2456, pruned_loss=0.05692, over 4873.00 frames.], tot_loss[loss=0.1391, simple_loss=0.212, pruned_loss=0.03311, over 972346.57 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:54:50,401 INFO [train.py:715] (2/8) Epoch 11, batch 30200, loss[loss=0.1397, simple_loss=0.2127, pruned_loss=0.03338, over 4859.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03274, over 971664.87 frames.], batch size: 20, lr: 1.93e-04
2022-05-07 06:55:29,246 INFO [train.py:715] (2/8) Epoch 11, batch 30250, loss[loss=0.1448, simple_loss=0.2152, pruned_loss=0.03723, over 4905.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03292, over 971893.69 frames.], batch size: 17, lr: 1.93e-04
2022-05-07 06:56:07,229 INFO [train.py:715] (2/8) Epoch 11, batch 30300, loss[loss=0.1284, simple_loss=0.2113, pruned_loss=0.02271, over 4986.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2122, pruned_loss=0.0327, over 971857.80 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:56:45,179 INFO [train.py:715] (2/8) Epoch 11, batch 30350, loss[loss=0.1399, simple_loss=0.2042, pruned_loss=0.0378, over 4773.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03238, over 971668.29 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 06:57:23,262 INFO [train.py:715] (2/8) Epoch 11, batch 30400, loss[loss=0.1687, simple_loss=0.2377, pruned_loss=0.04986, over 4745.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03254, over 970846.44 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 06:58:01,502 INFO [train.py:715] (2/8) Epoch 11, batch 30450, loss[loss=0.1424, simple_loss=0.2158, pruned_loss=0.03452, over 4879.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.03269, over 972439.84 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 06:58:39,333 INFO [train.py:715] (2/8) Epoch 11, batch 30500, loss[loss=0.121, simple_loss=0.2049, pruned_loss=0.01854, over 4850.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2119, pruned_loss=0.03241, over 972652.59 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 06:59:17,143 INFO [train.py:715] (2/8) Epoch 11, batch 30550, loss[loss=0.134, simple_loss=0.2139, pruned_loss=0.02709, over 4806.00 frames.], tot_loss[loss=0.139, simple_loss=0.2127, pruned_loss=0.03265, over 972404.10 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 06:59:56,405 INFO [train.py:715] (2/8) Epoch 11, batch 30600, loss[loss=0.11, simple_loss=0.1892, pruned_loss=0.01543, over 4812.00 frames.], tot_loss[loss=0.139, simple_loss=0.2126, pruned_loss=0.03266, over 972605.77 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 07:00:35,034 INFO [train.py:715] (2/8) Epoch 11, batch 30650, loss[loss=0.1229, simple_loss=0.1992, pruned_loss=0.02328, over 4924.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.03263, over 972838.59 frames.], batch size: 23, lr: 1.93e-04
2022-05-07 07:01:13,829 INFO [train.py:715] (2/8) Epoch 11, batch 30700, loss[loss=0.1218, simple_loss=0.2047, pruned_loss=0.01948, over 4869.00 frames.], tot_loss[loss=0.138, simple_loss=0.2115, pruned_loss=0.0323, over 972350.80 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 07:01:52,335 INFO [train.py:715] (2/8) Epoch 11, batch 30750, loss[loss=0.1526, simple_loss=0.2314, pruned_loss=0.03692, over 4943.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03249, over 971974.71 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 07:02:30,948 INFO [train.py:715] (2/8) Epoch 11, batch 30800, loss[loss=0.1125, simple_loss=0.1904, pruned_loss=0.01729, over 4950.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03224, over 972418.92 frames.], batch size: 21, lr: 1.93e-04
2022-05-07 07:03:09,708 INFO [train.py:715] (2/8) Epoch 11, batch 30850, loss[loss=0.128, simple_loss=0.1926, pruned_loss=0.0317, over 4830.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.0318, over 971868.17 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 07:03:48,267 INFO [train.py:715] (2/8) Epoch 11, batch 30900, loss[loss=0.1167, simple_loss=0.1868, pruned_loss=0.02327, over 4816.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03164, over 971787.71 frames.], batch size: 13, lr: 1.93e-04
2022-05-07 07:04:27,075 INFO [train.py:715] (2/8) Epoch 11, batch 30950, loss[loss=0.1662, simple_loss=0.239, pruned_loss=0.0467, over 4818.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03181, over 972441.40 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 07:05:06,025 INFO [train.py:715] (2/8) Epoch 11, batch 31000, loss[loss=0.1163, simple_loss=0.1889, pruned_loss=0.02186, over 4976.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03202, over 972510.51 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 07:05:44,539 INFO [train.py:715] (2/8) Epoch 11, batch 31050, loss[loss=0.1509, simple_loss=0.2283, pruned_loss=0.03676, over 4967.00 frames.], tot_loss[loss=0.1383, simple_loss=0.212, pruned_loss=0.03229, over 971301.10 frames.], batch size: 24, lr: 1.93e-04
2022-05-07 07:06:23,362 INFO [train.py:715] (2/8) Epoch 11, batch 31100, loss[loss=0.1436, simple_loss=0.2316, pruned_loss=0.0278, over 4872.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2117, pruned_loss=0.03192, over 971705.44 frames.], batch size: 16, lr: 1.93e-04
2022-05-07 07:07:01,743 INFO [train.py:715] (2/8) Epoch 11, batch 31150, loss[loss=0.1558, simple_loss=0.2292, pruned_loss=0.04124, over 4884.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2114, pruned_loss=0.03197, over 972063.91 frames.], batch size: 22, lr: 1.93e-04
2022-05-07 07:07:39,379 INFO [train.py:715] (2/8) Epoch 11, batch 31200, loss[loss=0.1503, simple_loss=0.2179, pruned_loss=0.04131, over 4696.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2118, pruned_loss=0.03221, over 971777.82 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 07:08:17,483 INFO [train.py:715] (2/8) Epoch 11, batch 31250, loss[loss=0.1525, simple_loss=0.2269, pruned_loss=0.03906, over 4827.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03262, over 971493.81 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 07:08:55,765 INFO [train.py:715] (2/8) Epoch 11, batch 31300, loss[loss=0.1235, simple_loss=0.196, pruned_loss=0.02552, over 4816.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2118, pruned_loss=0.03296, over 971736.39 frames.], batch size: 26, lr: 1.93e-04
2022-05-07 07:09:33,561 INFO [train.py:715] (2/8) Epoch 11, batch 31350, loss[loss=0.152, simple_loss=0.2228, pruned_loss=0.04062, over 4932.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03227, over 971571.82 frames.], batch size: 29, lr: 1.93e-04
2022-05-07 07:10:10,910 INFO [train.py:715] (2/8) Epoch 11, batch 31400, loss[loss=0.1348, simple_loss=0.2099, pruned_loss=0.02985, over 4953.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03204, over 972088.67 frames.], batch size: 24, lr: 1.93e-04
2022-05-07 07:10:48,407 INFO [train.py:715] (2/8) Epoch 11, batch 31450, loss[loss=0.1449, simple_loss=0.2164, pruned_loss=0.03677, over 4855.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03202, over 971714.91 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 07:11:26,018 INFO [train.py:715] (2/8) Epoch 11, batch 31500, loss[loss=0.1431, simple_loss=0.2172, pruned_loss=0.03453, over 4982.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2117, pruned_loss=0.03223, over 971311.34 frames.], batch size: 25, lr: 1.93e-04
2022-05-07 07:12:03,666 INFO [train.py:715] (2/8) Epoch 11, batch 31550, loss[loss=0.1466, simple_loss=0.226, pruned_loss=0.03358, over 4869.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2116, pruned_loss=0.03194, over 972478.63 frames.], batch size: 20, lr: 1.93e-04
2022-05-07 07:12:41,671 INFO [train.py:715] (2/8) Epoch 11, batch 31600, loss[loss=0.1287, simple_loss=0.2108, pruned_loss=0.02334, over 4957.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.03219, over 972397.90 frames.], batch size: 24, lr: 1.93e-04
2022-05-07 07:13:19,756 INFO [train.py:715] (2/8) Epoch 11, batch 31650, loss[loss=0.1816, simple_loss=0.2432, pruned_loss=0.05999, over 4851.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.0325, over 971742.39 frames.], batch size: 32, lr: 1.93e-04
2022-05-07 07:13:57,688 INFO [train.py:715] (2/8) Epoch 11, batch 31700, loss[loss=0.121, simple_loss=0.1901, pruned_loss=0.02597, over 4834.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03232, over 972596.58 frames.], batch size: 15, lr: 1.93e-04
2022-05-07 07:14:35,211 INFO [train.py:715] (2/8) Epoch 11, batch 31750, loss[loss=0.1737, simple_loss=0.2474, pruned_loss=0.04996, over 4808.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2116, pruned_loss=0.03236, over 972313.76 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 07:15:14,061 INFO [train.py:715] (2/8) Epoch 11, batch 31800, loss[loss=0.1782, simple_loss=0.2399, pruned_loss=0.05827, over 4870.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2118, pruned_loss=0.03244, over 972291.29 frames.], batch size: 30, lr: 1.93e-04
2022-05-07 07:15:52,637 INFO [train.py:715] (2/8) Epoch 11, batch 31850, loss[loss=0.1535, simple_loss=0.2269, pruned_loss=0.0401, over 4785.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03279, over 972653.50 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 07:16:30,870 INFO [train.py:715] (2/8) Epoch 11, batch 31900, loss[loss=0.1383, simple_loss=0.2123, pruned_loss=0.03216, over 4899.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2117, pruned_loss=0.03246, over 972492.95 frames.], batch size: 39, lr: 1.93e-04
2022-05-07 07:17:09,161 INFO [train.py:715] (2/8) Epoch 11, batch 31950, loss[loss=0.1406, simple_loss=0.2148, pruned_loss=0.03325, over 4772.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03201, over 972154.74 frames.], batch size: 18, lr: 1.93e-04
2022-05-07 07:17:47,944 INFO [train.py:715] (2/8) Epoch 11, batch 32000, loss[loss=0.1612, simple_loss=0.2286, pruned_loss=0.04689, over 4910.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03216, over 971803.67 frames.], batch size: 19, lr: 1.93e-04
2022-05-07 07:18:26,169 INFO [train.py:715] (2/8) Epoch 11, batch 32050, loss[loss=0.139, simple_loss=0.2085, pruned_loss=0.03478, over 4977.00 frames.], tot_loss[loss=0.137, simple_loss=0.21, pruned_loss=0.03199, over 971861.45 frames.], batch size: 14, lr: 1.93e-04
2022-05-07 07:19:04,554 INFO [train.py:715] (2/8) Epoch 11, batch 32100, loss[loss=0.1498, simple_loss=0.2208, pruned_loss=0.0394, over 4952.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03133, over 972084.74 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:19:42,572 INFO [train.py:715] (2/8) Epoch 11, batch 32150, loss[loss=0.144, simple_loss=0.218, pruned_loss=0.03504, over 4941.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03096, over 972085.94 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:20:19,991 INFO [train.py:715] (2/8) Epoch 11, batch 32200, loss[loss=0.1548, simple_loss=0.2297, pruned_loss=0.03998, over 4875.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03105, over 971917.58 frames.], batch size: 20, lr: 1.92e-04
2022-05-07 07:20:57,516 INFO [train.py:715] (2/8) Epoch 11, batch 32250, loss[loss=0.1612, simple_loss=0.2315, pruned_loss=0.04545, over 4978.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2082, pruned_loss=0.03083, over 973762.91 frames.], batch size: 15, lr: 1.92e-04
2022-05-07 07:21:35,349 INFO [train.py:715] (2/8) Epoch 11, batch 32300, loss[loss=0.1102, simple_loss=0.1807, pruned_loss=0.01984, over 4974.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.0307, over 974387.33 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:22:13,998 INFO [train.py:715] (2/8) Epoch 11, batch 32350, loss[loss=0.1261, simple_loss=0.1991, pruned_loss=0.02648, over 4811.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03111, over 973227.76 frames.], batch size: 26, lr: 1.92e-04
2022-05-07 07:22:51,417 INFO [train.py:715] (2/8) Epoch 11, batch 32400, loss[loss=0.1583, simple_loss=0.2283, pruned_loss=0.04417, over 4976.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03163, over 972980.51 frames.], batch size: 39, lr: 1.92e-04
2022-05-07 07:23:29,418 INFO [train.py:715] (2/8) Epoch 11, batch 32450, loss[loss=0.1205, simple_loss=0.199, pruned_loss=0.02095, over 4792.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03223, over 973052.10 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:24:07,458 INFO [train.py:715] (2/8) Epoch 11, batch 32500, loss[loss=0.1424, simple_loss=0.2069, pruned_loss=0.039, over 4854.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03273, over 972176.72 frames.], batch size: 32, lr: 1.92e-04
2022-05-07 07:24:45,518 INFO [train.py:715] (2/8) Epoch 11, batch 32550, loss[loss=0.1533, simple_loss=0.2263, pruned_loss=0.04013, over 4990.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2115, pruned_loss=0.03274, over 972211.55 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:25:23,170 INFO [train.py:715] (2/8) Epoch 11, batch 32600, loss[loss=0.1452, simple_loss=0.2184, pruned_loss=0.03602, over 4812.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.03297, over 971514.51 frames.], batch size: 15, lr: 1.92e-04
2022-05-07 07:26:01,257 INFO [train.py:715] (2/8) Epoch 11, batch 32650, loss[loss=0.158, simple_loss=0.2164, pruned_loss=0.04985, over 4965.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03253, over 971791.76 frames.], batch size: 35, lr: 1.92e-04
2022-05-07 07:26:39,442 INFO [train.py:715] (2/8) Epoch 11, batch 32700, loss[loss=0.1286, simple_loss=0.2101, pruned_loss=0.02355, over 4817.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2112, pruned_loss=0.03277, over 971937.08 frames.], batch size: 25, lr: 1.92e-04
2022-05-07 07:27:16,890 INFO [train.py:715] (2/8) Epoch 11, batch 32750, loss[loss=0.1255, simple_loss=0.1877, pruned_loss=0.03167, over 4752.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2113, pruned_loss=0.03285, over 971937.45 frames.], batch size: 16, lr: 1.92e-04
2022-05-07 07:27:55,675 INFO [train.py:715] (2/8) Epoch 11, batch 32800, loss[loss=0.1332, simple_loss=0.1994, pruned_loss=0.03348, over 4830.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2113, pruned_loss=0.03307, over 971844.40 frames.], batch size: 13, lr: 1.92e-04
2022-05-07 07:28:35,384 INFO [train.py:715] (2/8) Epoch 11, batch 32850, loss[loss=0.1343, simple_loss=0.2042, pruned_loss=0.03224, over 4826.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2103, pruned_loss=0.03247, over 971918.13 frames.], batch size: 12, lr: 1.92e-04
2022-05-07 07:29:13,936 INFO [train.py:715] (2/8) Epoch 11, batch 32900, loss[loss=0.1396, simple_loss=0.215, pruned_loss=0.03212, over 4974.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2108, pruned_loss=0.03274, over 971431.23 frames.], batch size: 28, lr: 1.92e-04
2022-05-07 07:29:52,150 INFO [train.py:715] (2/8) Epoch 11, batch 32950, loss[loss=0.1425, simple_loss=0.2019, pruned_loss=0.04151, over 4988.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2109, pruned_loss=0.03282, over 972288.46 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:30:31,066 INFO [train.py:715] (2/8) Epoch 11, batch 33000, loss[loss=0.1456, simple_loss=0.2066, pruned_loss=0.04232, over 4752.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2107, pruned_loss=0.03274, over 972084.87 frames.], batch size: 16, lr: 1.92e-04
2022-05-07 07:30:31,067 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 07:30:40,493 INFO [train.py:742] (2/8) Epoch 11, validation: loss=0.1059, simple_loss=0.1899, pruned_loss=0.0109, over 914524.00 frames.
2022-05-07 07:31:19,415 INFO [train.py:715] (2/8) Epoch 11, batch 33050, loss[loss=0.1354, simple_loss=0.2152, pruned_loss=0.02784, over 4976.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2109, pruned_loss=0.0328, over 973097.24 frames.], batch size: 28, lr: 1.92e-04
2022-05-07 07:32:00,914 INFO [train.py:715] (2/8) Epoch 11, batch 33100, loss[loss=0.1739, simple_loss=0.2358, pruned_loss=0.05599, over 4976.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2125, pruned_loss=0.03312, over 973677.97 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:32:38,877 INFO [train.py:715] (2/8) Epoch 11, batch 33150, loss[loss=0.1153, simple_loss=0.1891, pruned_loss=0.02077, over 4894.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2122, pruned_loss=0.03315, over 973790.02 frames.], batch size: 19, lr: 1.92e-04
2022-05-07 07:33:17,492 INFO [train.py:715] (2/8) Epoch 11, batch 33200, loss[loss=0.1252, simple_loss=0.2068, pruned_loss=0.02183, over 4903.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2126, pruned_loss=0.03308, over 973386.64 frames.], batch size: 19, lr: 1.92e-04
2022-05-07 07:33:56,618 INFO [train.py:715] (2/8) Epoch 11, batch 33250, loss[loss=0.1457, simple_loss=0.2228, pruned_loss=0.0343, over 4697.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03296, over 973135.76 frames.], batch size: 15, lr: 1.92e-04
2022-05-07 07:34:35,411 INFO [train.py:715] (2/8) Epoch 11, batch 33300, loss[loss=0.1583, simple_loss=0.242, pruned_loss=0.03733, over 4916.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2131, pruned_loss=0.03352, over 973101.70 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:35:13,284 INFO [train.py:715] (2/8) Epoch 11, batch 33350, loss[loss=0.166, simple_loss=0.2291, pruned_loss=0.05147, over 4787.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2126, pruned_loss=0.03335, over 972864.62 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:35:51,706 INFO [train.py:715] (2/8) Epoch 11, batch 33400, loss[loss=0.1332, simple_loss=0.2097, pruned_loss=0.02837, over 4742.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2122, pruned_loss=0.03298, over 971595.72 frames.], batch size: 12, lr: 1.92e-04
2022-05-07 07:36:30,393 INFO [train.py:715] (2/8) Epoch 11, batch 33450, loss[loss=0.121, simple_loss=0.1968, pruned_loss=0.02262, over 4801.00 frames.], tot_loss[loss=0.1389, simple_loss=0.212, pruned_loss=0.03285, over 971393.16 frames.], batch size: 21, lr: 1.92e-04
2022-05-07 07:37:08,731 INFO [train.py:715] (2/8) Epoch 11, batch 33500, loss[loss=0.1254, simple_loss=0.2069, pruned_loss=0.02199, over 4813.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03274, over 971285.87 frames.], batch size: 25, lr: 1.92e-04
2022-05-07 07:37:47,175 INFO [train.py:715] (2/8) Epoch 11, batch 33550, loss[loss=0.1378, simple_loss=0.2095, pruned_loss=0.03301, over 4942.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03201, over 970986.15 frames.], batch size: 23, lr: 1.92e-04
2022-05-07 07:38:25,762 INFO [train.py:715] (2/8) Epoch 11, batch 33600, loss[loss=0.1416, simple_loss=0.2131, pruned_loss=0.0351, over 4788.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2123, pruned_loss=0.03234, over 970938.61 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:39:04,165 INFO [train.py:715] (2/8) Epoch 11, batch 33650, loss[loss=0.1371, simple_loss=0.2283, pruned_loss=0.02297, over 4898.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2125, pruned_loss=0.03224, over 972055.02 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:39:42,291 INFO [train.py:715] (2/8) Epoch 11, batch 33700, loss[loss=0.1455, simple_loss=0.2091, pruned_loss=0.041, over 4899.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.03218, over 971240.90 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:40:20,578 INFO [train.py:715] (2/8) Epoch 11, batch 33750, loss[loss=0.1219, simple_loss=0.1942, pruned_loss=0.02478, over 4800.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03145, over 972072.17 frames.], batch size: 25, lr: 1.92e-04
2022-05-07 07:40:59,160 INFO [train.py:715] (2/8) Epoch 11, batch 33800, loss[loss=0.1758, simple_loss=0.2436, pruned_loss=0.05396, over 4979.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03136, over 971837.96 frames.], batch size: 31, lr: 1.92e-04
2022-05-07 07:41:37,147 INFO [train.py:715] (2/8) Epoch 11, batch 33850, loss[loss=0.1405, simple_loss=0.2068, pruned_loss=0.0371, over 4846.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03176, over 971729.19 frames.], batch size: 13, lr: 1.92e-04
2022-05-07 07:42:15,180 INFO [train.py:715] (2/8) Epoch 11, batch 33900, loss[loss=0.1392, simple_loss=0.2076, pruned_loss=0.03542, over 4785.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03191, over 971019.66 frames.], batch size: 14, lr: 1.92e-04
2022-05-07 07:42:53,935 INFO [train.py:715] (2/8) Epoch 11, batch 33950, loss[loss=0.1471, simple_loss=0.2132, pruned_loss=0.04049, over 4866.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03213, over 971562.01 frames.], batch size: 32, lr: 1.92e-04
2022-05-07 07:43:32,252 INFO [train.py:715] (2/8) Epoch 11, batch 34000, loss[loss=0.1277, simple_loss=0.1998, pruned_loss=0.02782, over 4801.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03206, over 971605.83 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:44:10,351 INFO [train.py:715] (2/8) Epoch 11, batch 34050, loss[loss=0.176, simple_loss=0.2259, pruned_loss=0.06308, over 4794.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03213, over 971667.26 frames.], batch size: 24, lr: 1.92e-04
2022-05-07 07:44:48,894 INFO [train.py:715] (2/8) Epoch 11, batch 34100, loss[loss=0.1711, simple_loss=0.2339, pruned_loss=0.05413, over 4926.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03253, over 972184.66 frames.], batch size: 35, lr: 1.92e-04
2022-05-07 07:45:27,631 INFO [train.py:715] (2/8) Epoch 11, batch 34150, loss[loss=0.1077, simple_loss=0.1869, pruned_loss=0.01424, over 4930.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2104, pruned_loss=0.03241, over 972428.33 frames.], batch size: 23, lr: 1.92e-04
2022-05-07 07:46:05,702 INFO [train.py:715] (2/8) Epoch 11, batch 34200, loss[loss=0.1187, simple_loss=0.2012, pruned_loss=0.01811, over 4959.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2111, pruned_loss=0.03277, over 972634.35 frames.], batch size: 15, lr: 1.92e-04
2022-05-07 07:46:44,130 INFO [train.py:715] (2/8) Epoch 11, batch 34250, loss[loss=0.133, simple_loss=0.2088, pruned_loss=0.02862, over 4807.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.0327, over 972994.25 frames.], batch size: 12, lr: 1.92e-04
2022-05-07 07:47:23,290 INFO [train.py:715] (2/8) Epoch 11, batch 34300, loss[loss=0.1282, simple_loss=0.199, pruned_loss=0.0287, over 4879.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2109, pruned_loss=0.03266, over 972705.47 frames.], batch size: 30, lr: 1.92e-04
2022-05-07 07:48:01,582 INFO [train.py:715] (2/8) Epoch 11, batch 34350, loss[loss=0.1424, simple_loss=0.2098, pruned_loss=0.03748, over 4822.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2111, pruned_loss=0.0327, over 972400.47 frames.], batch size: 26, lr: 1.92e-04
2022-05-07 07:48:40,027 INFO [train.py:715] (2/8) Epoch 11, batch 34400, loss[loss=0.1664, simple_loss=0.2336, pruned_loss=0.04965, over 4864.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03189, over 973055.76 frames.], batch size: 38, lr: 1.92e-04
2022-05-07 07:49:18,676 INFO [train.py:715] (2/8) Epoch 11, batch 34450, loss[loss=0.1325, simple_loss=0.2032, pruned_loss=0.0309, over 4941.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03203, over 972708.40 frames.], batch size: 24, lr: 1.92e-04
2022-05-07 07:49:57,851 INFO [train.py:715] (2/8) Epoch 11, batch 34500, loss[loss=0.1258, simple_loss=0.202, pruned_loss=0.02478, over 4808.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03217, over 973236.08 frames.], batch size: 17, lr: 1.92e-04
2022-05-07 07:50:35,958 INFO [train.py:715] (2/8) Epoch 11, batch 34550, loss[loss=0.1249, simple_loss=0.2045, pruned_loss=0.02269, over 4778.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.0319, over 972666.65 frames.], batch size: 18, lr: 1.92e-04
2022-05-07 07:51:12,743 INFO [train.py:715] (2/8) Epoch 11, batch 34600, loss[loss=0.1501, simple_loss=0.2165, pruned_loss=0.04185, over 4871.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03158, over 971825.84 frames.], batch size: 32, lr: 1.92e-04
2022-05-07 07:51:50,533 INFO [train.py:715] (2/8) Epoch 11, batch 34650, loss[loss=0.1535, simple_loss=0.2412, pruned_loss=0.03288, over 4829.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03187, over 972227.94 frames.], batch size: 26, lr: 1.92e-04
2022-05-07 07:52:27,799 INFO [train.py:715] (2/8) Epoch 11, batch 34700, loss[loss=0.1223, simple_loss=0.1838, pruned_loss=0.03038, over 4986.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03176, over 971855.18 frames.], batch size: 16, lr: 1.92e-04
2022-05-07 07:53:04,319 INFO [train.py:715] (2/8) Epoch 11, batch 34750, loss[loss=0.141, simple_loss=0.2126, pruned_loss=0.03468, over 4764.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03196, over 971728.02 frames.], batch size: 18, lr: 1.92e-04
2022-05-07 07:53:39,315 INFO [train.py:715] (2/8) Epoch 11, batch 34800, loss[loss=0.131, simple_loss=0.2172, pruned_loss=0.02237, over 4919.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03192, over 972293.21 frames.], batch size: 18, lr: 1.92e-04
2022-05-07 07:54:26,266 INFO [train.py:715] (2/8) Epoch 12, batch 0, loss[loss=0.1399, simple_loss=0.212, pruned_loss=0.03389, over 4747.00 frames.], tot_loss[loss=0.1399, simple_loss=0.212, pruned_loss=0.03389, over 4747.00 frames.], batch size: 16, lr: 1.85e-04
2022-05-07 07:55:04,629 INFO [train.py:715] (2/8) Epoch 12, batch 50, loss[loss=0.1543, simple_loss=0.2302, pruned_loss=0.0392, over 4889.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2145, pruned_loss=0.03436, over 219519.23 frames.], batch size: 22, lr: 1.85e-04
2022-05-07 07:55:42,694 INFO [train.py:715] (2/8) Epoch 12, batch 100, loss[loss=0.1277, simple_loss=0.2059, pruned_loss=0.02476, over 4747.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2147, pruned_loss=0.03409, over 386659.93 frames.], batch size: 16, lr: 1.85e-04
2022-05-07 07:56:21,321 INFO [train.py:715] (2/8) Epoch 12, batch 150, loss[loss=0.1693, simple_loss=0.2365, pruned_loss=0.05102, over 4812.00 frames.], tot_loss[loss=0.14, simple_loss=0.2134, pruned_loss=0.03325, over 516215.89 frames.], batch size: 21, lr: 1.85e-04
2022-05-07 07:56:59,070 INFO [train.py:715] (2/8) Epoch 12, batch 200, loss[loss=0.1515, simple_loss=0.2108, pruned_loss=0.04606, over 4829.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2128, pruned_loss=0.03291, over 617141.17 frames.], batch size: 15, lr: 1.85e-04
2022-05-07 07:57:38,280 INFO [train.py:715] (2/8) Epoch 12, batch 250, loss[loss=0.1495, simple_loss=0.221, pruned_loss=0.03903, over 4775.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2132, pruned_loss=0.03324, over 695951.50 frames.], batch size: 18, lr: 1.85e-04
2022-05-07 07:58:16,545 INFO [train.py:715] (2/8) Epoch 12, batch 300, loss[loss=0.1257, simple_loss=0.1999, pruned_loss=0.02576, over 4898.00 frames.], tot_loss[loss=0.1385, simple_loss=0.212, pruned_loss=0.03246, over 756424.48 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 07:58:54,474 INFO [train.py:715] (2/8) Epoch 12, batch 350, loss[loss=0.1356, simple_loss=0.2199, pruned_loss=0.02562, over 4697.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2117, pruned_loss=0.03226, over 803731.61 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 07:59:32,945 INFO [train.py:715] (2/8) Epoch 12, batch 400, loss[loss=0.173, simple_loss=0.2489, pruned_loss=0.04851, over 4767.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2132, pruned_loss=0.03278, over 840172.20 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:00:10,577 INFO [train.py:715] (2/8) Epoch 12, batch 450, loss[loss=0.1415, simple_loss=0.2109, pruned_loss=0.03607, over 4791.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03227, over 869184.46 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:00:48,788 INFO [train.py:715] (2/8) Epoch 12, batch 500, loss[loss=0.1665, simple_loss=0.2431, pruned_loss=0.04497, over 4892.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2118, pruned_loss=0.03272, over 892022.12 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:01:26,235 INFO [train.py:715] (2/8) Epoch 12, batch 550, loss[loss=0.115, simple_loss=0.1854, pruned_loss=0.02236, over 4959.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03233, over 910161.78 frames.], batch size: 24, lr: 1.84e-04
2022-05-07 08:02:04,574 INFO [train.py:715] (2/8) Epoch 12, batch 600, loss[loss=0.1616, simple_loss=0.2226, pruned_loss=0.05029, over 4900.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03203, over 923324.85 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:02:41,618 INFO [train.py:715] (2/8) Epoch 12, batch 650, loss[loss=0.1266, simple_loss=0.2063, pruned_loss=0.02346, over 4812.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2098, pruned_loss=0.03196, over 933430.54 frames.], batch size: 26, lr: 1.84e-04
2022-05-07 08:03:20,203 INFO [train.py:715] (2/8) Epoch 12, batch 700, loss[loss=0.1258, simple_loss=0.1933, pruned_loss=0.02921, over 4682.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03218, over 942418.91 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:03:58,808 INFO [train.py:715] (2/8) Epoch 12, batch 750, loss[loss=0.1764, simple_loss=0.2569, pruned_loss=0.04797, over 4936.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03199, over 948837.06 frames.], batch size: 39, lr: 1.84e-04
2022-05-07 08:04:37,570 INFO [train.py:715] (2/8) Epoch 12, batch 800, loss[loss=0.1298, simple_loss=0.1961, pruned_loss=0.03174, over 4769.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03264, over 954679.82 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:05:16,053 INFO [train.py:715] (2/8) Epoch 12, batch 850, loss[loss=0.1748, simple_loss=0.2369, pruned_loss=0.0564, over 4875.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2109, pruned_loss=0.03264, over 958620.14 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:05:54,152 INFO [train.py:715] (2/8) Epoch 12, batch 900, loss[loss=0.1177, simple_loss=0.1859, pruned_loss=0.02475, over 4785.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2113, pruned_loss=0.03283, over 961456.34 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:06:32,488 INFO [train.py:715] (2/8) Epoch 12, batch 950, loss[loss=0.1445, simple_loss=0.2134, pruned_loss=0.03778, over 4975.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2103, pruned_loss=0.03249, over 963825.43 frames.], batch size: 35, lr: 1.84e-04
2022-05-07 08:07:09,852 INFO [train.py:715] (2/8) Epoch 12, batch 1000, loss[loss=0.1236, simple_loss=0.1939, pruned_loss=0.02662, over 4777.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2104, pruned_loss=0.03288, over 965293.67 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:07:47,349 INFO [train.py:715] (2/8) Epoch 12, batch 1050, loss[loss=0.1238, simple_loss=0.1898, pruned_loss=0.02895, over 4642.00 frames.], tot_loss[loss=0.1385, simple_loss=0.211, pruned_loss=0.03304, over 965625.36 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:08:25,195 INFO [train.py:715] (2/8) Epoch 12, batch 1100, loss[loss=0.1464, simple_loss=0.2181, pruned_loss=0.03732, over 4830.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2116, pruned_loss=0.03295, over 967458.46 frames.], batch size: 27, lr: 1.84e-04
2022-05-07 08:09:03,089 INFO [train.py:715] (2/8) Epoch 12, batch 1150, loss[loss=0.1407, simple_loss=0.2138, pruned_loss=0.03386, over 4901.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03248, over 969250.94 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:09:41,432 INFO [train.py:715] (2/8) Epoch 12, batch 1200, loss[loss=0.1429, simple_loss=0.2163, pruned_loss=0.03476, over 4748.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.0324, over 968691.28 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:10:18,738 INFO [train.py:715] (2/8) Epoch 12, batch 1250, loss[loss=0.1267, simple_loss=0.1961, pruned_loss=0.0286, over 4887.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03245, over 969983.08 frames.], batch size: 22, lr: 1.84e-04
2022-05-07 08:10:56,844 INFO [train.py:715] (2/8) Epoch 12, batch 1300, loss[loss=0.1559, simple_loss=0.2302, pruned_loss=0.04076, over 4896.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03215, over 970895.84 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:11:33,987 INFO [train.py:715] (2/8) Epoch 12, batch 1350, loss[loss=0.1521, simple_loss=0.2217, pruned_loss=0.04131, over 4845.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.03201, over 971035.71 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:12:12,112 INFO [train.py:715] (2/8) Epoch 12, batch 1400, loss[loss=0.1168, simple_loss=0.1909, pruned_loss=0.02129, over 4814.00 frames.], tot_loss[loss=0.1368, simple_loss=0.21, pruned_loss=0.03176, over 970906.38 frames.], batch size: 27, lr: 1.84e-04
2022-05-07 08:12:49,762 INFO [train.py:715] (2/8) Epoch 12, batch 1450, loss[loss=0.1267, simple_loss=0.2036, pruned_loss=0.0249, over 4900.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03198, over 970991.55 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:13:27,680 INFO [train.py:715] (2/8) Epoch 12, batch 1500, loss[loss=0.1663, simple_loss=0.2314, pruned_loss=0.05062, over 4844.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03205, over 972110.03 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:14:05,277 INFO [train.py:715] (2/8) Epoch 12, batch 1550, loss[loss=0.1203, simple_loss=0.1955, pruned_loss=0.0226, over 4840.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03216, over 971754.73 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:14:42,470 INFO [train.py:715] (2/8) Epoch 12, batch 1600, loss[loss=0.1482, simple_loss=0.2158, pruned_loss=0.04031, over 4746.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03212, over 972268.97 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:15:20,489 INFO [train.py:715] (2/8) Epoch 12, batch 1650, loss[loss=0.1286, simple_loss=0.1959, pruned_loss=0.03069, over 4954.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03188, over 972233.87 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:15:57,868 INFO [train.py:715] (2/8) Epoch 12, batch 1700, loss[loss=0.1166, simple_loss=0.1884, pruned_loss=0.02236, over 4887.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03179, over 972576.47 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:16:35,312 INFO [train.py:715] (2/8) Epoch 12, batch 1750, loss[loss=0.123, simple_loss=0.1901, pruned_loss=0.02793, over 4762.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03156, over 971569.05 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:17:12,472 INFO [train.py:715] (2/8) Epoch 12, batch 1800, loss[loss=0.1399, simple_loss=0.2177, pruned_loss=0.03103, over 4801.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03139, over 971543.32 frames.], batch size: 24, lr: 1.84e-04
2022-05-07 08:17:50,169 INFO [train.py:715] (2/8) Epoch 12, batch 1850, loss[loss=0.1394, simple_loss=0.2105, pruned_loss=0.03416, over 4802.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03168, over 971673.63 frames.], batch size: 26, lr: 1.84e-04
2022-05-07 08:18:27,660 INFO [train.py:715] (2/8) Epoch 12, batch 1900, loss[loss=0.1413, simple_loss=0.2047, pruned_loss=0.03901, over 4858.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.0317, over 972381.50 frames.], batch size: 20, lr: 1.84e-04
2022-05-07 08:19:05,285 INFO [train.py:715] (2/8) Epoch 12, batch 1950, loss[loss=0.1323, simple_loss=0.2092, pruned_loss=0.02767, over 4837.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2091, pruned_loss=0.03132, over 972343.79 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:19:43,104 INFO [train.py:715] (2/8) Epoch 12, batch 2000, loss[loss=0.0992, simple_loss=0.1681, pruned_loss=0.01513, over 4753.00 frames.], tot_loss[loss=0.136, simple_loss=0.2091, pruned_loss=0.03138, over 972520.74 frames.], batch size: 12, lr: 1.84e-04
2022-05-07 08:20:21,264 INFO [train.py:715] (2/8) Epoch 12, batch 2050, loss[loss=0.1217, simple_loss=0.1947, pruned_loss=0.02438, over 4877.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03142, over 973432.17 frames.], batch size: 32, lr: 1.84e-04
2022-05-07 08:20:59,323 INFO [train.py:715] (2/8) Epoch 12, batch 2100, loss[loss=0.1626, simple_loss=0.2303, pruned_loss=0.04743, over 4929.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03124, over 973257.02 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:21:36,624 INFO [train.py:715] (2/8) Epoch 12, batch 2150, loss[loss=0.149, simple_loss=0.2264, pruned_loss=0.03585, over 4763.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03108, over 973450.78 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:22:14,597 INFO [train.py:715] (2/8) Epoch 12, batch 2200, loss[loss=0.1305, simple_loss=0.2114, pruned_loss=0.02481, over 4797.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03183, over 973095.16 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:22:52,523 INFO [train.py:715] (2/8) Epoch 12, batch 2250, loss[loss=0.1139, simple_loss=0.1827, pruned_loss=0.02252, over 4830.00 frames.], tot_loss[loss=0.137, simple_loss=0.2099, pruned_loss=0.03209, over 972931.14 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:23:30,600 INFO [train.py:715] (2/8) Epoch 12, batch 2300, loss[loss=0.127, simple_loss=0.206, pruned_loss=0.02403, over 4858.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2109, pruned_loss=0.03246, over 973084.01 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:24:07,788 INFO [train.py:715] (2/8) Epoch 12, batch 2350, loss[loss=0.1327, simple_loss=0.2175, pruned_loss=0.02397, over 4989.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03193, over 972889.23 frames.], batch size: 25, lr: 1.84e-04
2022-05-07 08:24:45,332 INFO [train.py:715] (2/8) Epoch 12, batch 2400, loss[loss=0.1258, simple_loss=0.2078, pruned_loss=0.02191, over 4827.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03203, over 972949.99 frames.], batch size: 26, lr: 1.84e-04
2022-05-07 08:25:23,252 INFO [train.py:715] (2/8) Epoch 12, batch 2450, loss[loss=0.1465, simple_loss=0.2158, pruned_loss=0.03856, over 4804.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2114, pruned_loss=0.03262, over 973093.58 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:26:00,044 INFO [train.py:715] (2/8) Epoch 12, batch 2500, loss[loss=0.1718, simple_loss=0.2456, pruned_loss=0.04901, over 4966.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.0327, over 973283.81 frames.], batch size: 24, lr: 1.84e-04
2022-05-07 08:26:38,143 INFO [train.py:715] (2/8) Epoch 12, batch 2550, loss[loss=0.1431, simple_loss=0.2187, pruned_loss=0.03372, over 4881.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.03241, over 972452.84 frames.], batch size: 22, lr: 1.84e-04
2022-05-07 08:27:15,554 INFO [train.py:715] (2/8) Epoch 12, batch 2600, loss[loss=0.1468, simple_loss=0.2227, pruned_loss=0.03545, over 4938.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03253, over 972543.00 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:27:54,373 INFO [train.py:715] (2/8) Epoch 12, batch 2650, loss[loss=0.1193, simple_loss=0.188, pruned_loss=0.02529, over 4953.00 frames.], tot_loss[loss=0.1378, simple_loss=0.211, pruned_loss=0.03232, over 973256.04 frames.], batch size: 29, lr: 1.84e-04
2022-05-07 08:28:32,744 INFO [train.py:715] (2/8) Epoch 12, batch 2700, loss[loss=0.1344, simple_loss=0.2143, pruned_loss=0.02727, over 4805.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2121, pruned_loss=0.03244, over 972994.67 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:29:11,534 INFO [train.py:715] (2/8) Epoch 12, batch 2750, loss[loss=0.1455, simple_loss=0.2157, pruned_loss=0.03764, over 4791.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2121, pruned_loss=0.03225, over 972842.45 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:29:50,411 INFO [train.py:715] (2/8) Epoch 12, batch 2800, loss[loss=0.1478, simple_loss=0.2331, pruned_loss=0.03131, over 4842.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2126, pruned_loss=0.03242, over 972024.43 frames.], batch size: 13, lr: 1.84e-04
2022-05-07 08:30:28,422 INFO [train.py:715] (2/8) Epoch 12, batch 2850, loss[loss=0.1387, simple_loss=0.205, pruned_loss=0.03618, over 4979.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2129, pruned_loss=0.03269, over 971253.18 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:31:07,090 INFO [train.py:715] (2/8) Epoch 12, batch 2900, loss[loss=0.1483, simple_loss=0.2207, pruned_loss=0.03796, over 4986.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2121, pruned_loss=0.03241, over 971852.60 frames.], batch size: 33, lr: 1.84e-04
2022-05-07 08:31:45,563 INFO [train.py:715] (2/8) Epoch 12, batch 2950, loss[loss=0.1378, simple_loss=0.2174, pruned_loss=0.02912, over 4764.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2117, pruned_loss=0.03238, over 972545.87 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:32:24,276 INFO [train.py:715] (2/8) Epoch 12, batch 3000, loss[loss=0.1287, simple_loss=0.1986, pruned_loss=0.02936, over 4955.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03226, over 971431.71 frames.], batch size: 24, lr: 1.84e-04
2022-05-07 08:32:24,277 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 08:32:33,757 INFO [train.py:742] (2/8) 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] (2/8) Epoch 12, batch 3050, loss[loss=0.1628, simple_loss=0.2285, pruned_loss=0.04857, over 4963.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03213, over 971444.57 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:33:49,493 INFO [train.py:715] (2/8) Epoch 12, batch 3100, loss[loss=0.1083, simple_loss=0.1884, pruned_loss=0.01414, over 4862.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03223, over 970426.60 frames.], batch size: 22, lr: 1.84e-04
2022-05-07 08:34:27,407 INFO [train.py:715] (2/8) Epoch 12, batch 3150, loss[loss=0.143, simple_loss=0.2252, pruned_loss=0.03042, over 4749.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2111, pruned_loss=0.03173, over 970301.76 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:35:05,545 INFO [train.py:715] (2/8) Epoch 12, batch 3200, loss[loss=0.156, simple_loss=0.2351, pruned_loss=0.03848, over 4917.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.03215, over 970037.49 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:35:43,248 INFO [train.py:715] (2/8) Epoch 12, batch 3250, loss[loss=0.1427, simple_loss=0.2135, pruned_loss=0.03593, over 4848.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.0327, over 970744.27 frames.], batch size: 30, lr: 1.84e-04
2022-05-07 08:36:21,485 INFO [train.py:715] (2/8) Epoch 12, batch 3300, loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03014, over 4731.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03227, over 971138.74 frames.], batch size: 12, lr: 1.84e-04
2022-05-07 08:36:59,236 INFO [train.py:715] (2/8) Epoch 12, batch 3350, loss[loss=0.1284, simple_loss=0.2085, pruned_loss=0.02414, over 4889.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03234, over 972395.20 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:37:37,376 INFO [train.py:715] (2/8) Epoch 12, batch 3400, loss[loss=0.1345, simple_loss=0.2092, pruned_loss=0.02996, over 4759.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03253, over 971871.32 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:38:14,960 INFO [train.py:715] (2/8) Epoch 12, batch 3450, loss[loss=0.1709, simple_loss=0.256, pruned_loss=0.04288, over 4827.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03245, over 971774.14 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:38:52,884 INFO [train.py:715] (2/8) Epoch 12, batch 3500, loss[loss=0.1532, simple_loss=0.2224, pruned_loss=0.04198, over 4979.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03254, over 971839.72 frames.], batch size: 35, lr: 1.84e-04
2022-05-07 08:39:31,088 INFO [train.py:715] (2/8) Epoch 12, batch 3550, loss[loss=0.1302, simple_loss=0.2088, pruned_loss=0.02587, over 4733.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2118, pruned_loss=0.03263, over 972853.98 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:40:08,795 INFO [train.py:715] (2/8) Epoch 12, batch 3600, loss[loss=0.1348, simple_loss=0.2132, pruned_loss=0.02821, over 4950.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03266, over 972400.82 frames.], batch size: 23, lr: 1.84e-04
2022-05-07 08:40:46,533 INFO [train.py:715] (2/8) Epoch 12, batch 3650, loss[loss=0.1179, simple_loss=0.1855, pruned_loss=0.02517, over 4732.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.0327, over 971771.98 frames.], batch size: 12, lr: 1.84e-04
2022-05-07 08:41:24,469 INFO [train.py:715] (2/8) Epoch 12, batch 3700, loss[loss=0.1404, simple_loss=0.202, pruned_loss=0.03937, over 4699.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03218, over 971635.41 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:42:02,373 INFO [train.py:715] (2/8) Epoch 12, batch 3750, loss[loss=0.1157, simple_loss=0.1908, pruned_loss=0.02025, over 4964.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03188, over 972118.17 frames.], batch size: 35, lr: 1.84e-04
2022-05-07 08:42:40,471 INFO [train.py:715] (2/8) Epoch 12, batch 3800, loss[loss=0.1663, simple_loss=0.2318, pruned_loss=0.05039, over 4865.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2118, pruned_loss=0.0323, over 972756.24 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:43:18,089 INFO [train.py:715] (2/8) Epoch 12, batch 3850, loss[loss=0.1321, simple_loss=0.2133, pruned_loss=0.02542, over 4776.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2114, pruned_loss=0.03222, over 972361.99 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:43:55,565 INFO [train.py:715] (2/8) Epoch 12, batch 3900, loss[loss=0.1386, simple_loss=0.2177, pruned_loss=0.0297, over 4695.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.0317, over 972946.58 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:44:33,441 INFO [train.py:715] (2/8) Epoch 12, batch 3950, loss[loss=0.157, simple_loss=0.2212, pruned_loss=0.04639, over 4989.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03156, over 972988.54 frames.], batch size: 14, lr: 1.84e-04
2022-05-07 08:45:11,213 INFO [train.py:715] (2/8) Epoch 12, batch 4000, loss[loss=0.149, simple_loss=0.2149, pruned_loss=0.0415, over 4832.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03207, over 972135.43 frames.], batch size: 15, lr: 1.84e-04
2022-05-07 08:45:49,155 INFO [train.py:715] (2/8) Epoch 12, batch 4050, loss[loss=0.1443, simple_loss=0.2209, pruned_loss=0.03378, over 4789.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03236, over 971822.24 frames.], batch size: 18, lr: 1.84e-04
2022-05-07 08:46:27,046 INFO [train.py:715] (2/8) Epoch 12, batch 4100, loss[loss=0.1357, simple_loss=0.2133, pruned_loss=0.02911, over 4949.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03232, over 971995.34 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:47:05,071 INFO [train.py:715] (2/8) Epoch 12, batch 4150, loss[loss=0.1237, simple_loss=0.2019, pruned_loss=0.02275, over 4902.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03205, over 972721.86 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:47:43,033 INFO [train.py:715] (2/8) Epoch 12, batch 4200, loss[loss=0.138, simple_loss=0.2138, pruned_loss=0.03114, over 4948.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03208, over 972747.38 frames.], batch size: 21, lr: 1.84e-04
2022-05-07 08:48:20,658 INFO [train.py:715] (2/8) Epoch 12, batch 4250, loss[loss=0.1284, simple_loss=0.1931, pruned_loss=0.03185, over 4763.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.03224, over 972753.36 frames.], batch size: 12, lr: 1.84e-04
2022-05-07 08:48:58,346 INFO [train.py:715] (2/8) Epoch 12, batch 4300, loss[loss=0.1228, simple_loss=0.2023, pruned_loss=0.02167, over 4876.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2108, pruned_loss=0.03244, over 971950.55 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:49:37,532 INFO [train.py:715] (2/8) Epoch 12, batch 4350, loss[loss=0.1293, simple_loss=0.2007, pruned_loss=0.029, over 4787.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03235, over 972096.68 frames.], batch size: 12, lr: 1.84e-04
2022-05-07 08:50:16,280 INFO [train.py:715] (2/8) Epoch 12, batch 4400, loss[loss=0.1135, simple_loss=0.1981, pruned_loss=0.01446, over 4925.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03201, over 971761.83 frames.], batch size: 23, lr: 1.84e-04
2022-05-07 08:50:54,781 INFO [train.py:715] (2/8) Epoch 12, batch 4450, loss[loss=0.1437, simple_loss=0.218, pruned_loss=0.03468, over 4841.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2103, pruned_loss=0.03209, over 972345.40 frames.], batch size: 20, lr: 1.84e-04
2022-05-07 08:51:33,219 INFO [train.py:715] (2/8) Epoch 12, batch 4500, loss[loss=0.1318, simple_loss=0.203, pruned_loss=0.03028, over 4814.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03202, over 972728.50 frames.], batch size: 24, lr: 1.84e-04
2022-05-07 08:52:12,297 INFO [train.py:715] (2/8) Epoch 12, batch 4550, loss[loss=0.121, simple_loss=0.1914, pruned_loss=0.02525, over 4977.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03248, over 973335.97 frames.], batch size: 28, lr: 1.84e-04
2022-05-07 08:52:50,506 INFO [train.py:715] (2/8) Epoch 12, batch 4600, loss[loss=0.1618, simple_loss=0.2277, pruned_loss=0.0479, over 4751.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03261, over 974144.94 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:53:29,055 INFO [train.py:715] (2/8) Epoch 12, batch 4650, loss[loss=0.1408, simple_loss=0.2045, pruned_loss=0.03852, over 4757.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2115, pruned_loss=0.03238, over 973107.95 frames.], batch size: 16, lr: 1.84e-04
2022-05-07 08:54:07,746 INFO [train.py:715] (2/8) Epoch 12, batch 4700, loss[loss=0.1206, simple_loss=0.1908, pruned_loss=0.0252, over 4924.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03227, over 972644.60 frames.], batch size: 29, lr: 1.84e-04
2022-05-07 08:54:46,314 INFO [train.py:715] (2/8) Epoch 12, batch 4750, loss[loss=0.1587, simple_loss=0.2325, pruned_loss=0.04242, over 4808.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03224, over 972480.08 frames.], batch size: 26, lr: 1.84e-04
2022-05-07 08:55:25,015 INFO [train.py:715] (2/8) Epoch 12, batch 4800, loss[loss=0.127, simple_loss=0.2099, pruned_loss=0.022, over 4758.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03214, over 972355.09 frames.], batch size: 19, lr: 1.84e-04
2022-05-07 08:56:03,578 INFO [train.py:715] (2/8) Epoch 12, batch 4850, loss[loss=0.1387, simple_loss=0.2092, pruned_loss=0.03414, over 4898.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03273, over 973123.29 frames.], batch size: 17, lr: 1.84e-04
2022-05-07 08:56:42,595 INFO [train.py:715] (2/8) Epoch 12, batch 4900, loss[loss=0.1281, simple_loss=0.2074, pruned_loss=0.02439, over 4960.00 frames.], tot_loss[loss=0.138, simple_loss=0.2106, pruned_loss=0.03264, over 971963.51 frames.], batch size: 21, lr: 1.83e-04
2022-05-07 08:57:20,603 INFO [train.py:715] (2/8) Epoch 12, batch 4950, loss[loss=0.1543, simple_loss=0.2401, pruned_loss=0.03423, over 4925.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.03237, over 972455.46 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 08:57:58,208 INFO [train.py:715] (2/8) Epoch 12, batch 5000, loss[loss=0.1091, simple_loss=0.1795, pruned_loss=0.0193, over 4838.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03199, over 972433.81 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 08:58:36,393 INFO [train.py:715] (2/8) Epoch 12, batch 5050, loss[loss=0.129, simple_loss=0.1937, pruned_loss=0.03215, over 4978.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2111, pruned_loss=0.03254, over 972369.21 frames.], batch size: 28, lr: 1.83e-04
2022-05-07 08:59:13,985 INFO [train.py:715] (2/8) Epoch 12, batch 5100, loss[loss=0.1244, simple_loss=0.203, pruned_loss=0.02293, over 4967.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03197, over 972966.42 frames.], batch size: 24, lr: 1.83e-04
2022-05-07 08:59:52,114 INFO [train.py:715] (2/8) Epoch 12, batch 5150, loss[loss=0.1448, simple_loss=0.2158, pruned_loss=0.03686, over 4967.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03168, over 973096.82 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:00:30,013 INFO [train.py:715] (2/8) Epoch 12, batch 5200, loss[loss=0.1403, simple_loss=0.216, pruned_loss=0.03228, over 4972.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03094, over 973440.36 frames.], batch size: 24, lr: 1.83e-04
2022-05-07 09:01:08,126 INFO [train.py:715] (2/8) Epoch 12, batch 5250, loss[loss=0.1439, simple_loss=0.2132, pruned_loss=0.03735, over 4919.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03071, over 973476.67 frames.], batch size: 39, lr: 1.83e-04
2022-05-07 09:01:45,994 INFO [train.py:715] (2/8) Epoch 12, batch 5300, loss[loss=0.1161, simple_loss=0.1835, pruned_loss=0.02435, over 4936.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03092, over 972616.12 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:02:24,111 INFO [train.py:715] (2/8) Epoch 12, batch 5350, loss[loss=0.1303, simple_loss=0.2064, pruned_loss=0.02708, over 4868.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03155, over 973628.20 frames.], batch size: 32, lr: 1.83e-04
2022-05-07 09:03:02,672 INFO [train.py:715] (2/8) Epoch 12, batch 5400, loss[loss=0.1315, simple_loss=0.2108, pruned_loss=0.02609, over 4987.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03161, over 973280.97 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:03:40,515 INFO [train.py:715] (2/8) Epoch 12, batch 5450, loss[loss=0.1486, simple_loss=0.2137, pruned_loss=0.04176, over 4983.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03132, over 973669.79 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:04:18,719 INFO [train.py:715] (2/8) Epoch 12, batch 5500, loss[loss=0.1235, simple_loss=0.1993, pruned_loss=0.0239, over 4960.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03157, over 973850.35 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:04:56,509 INFO [train.py:715] (2/8) Epoch 12, batch 5550, loss[loss=0.1375, simple_loss=0.2072, pruned_loss=0.03389, over 4889.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03155, over 973563.94 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:05:35,155 INFO [train.py:715] (2/8) Epoch 12, batch 5600, loss[loss=0.1525, simple_loss=0.2251, pruned_loss=0.04, over 4889.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03138, over 973451.09 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:06:12,950 INFO [train.py:715] (2/8) Epoch 12, batch 5650, loss[loss=0.1606, simple_loss=0.2298, pruned_loss=0.04566, over 4852.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03172, over 973625.73 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:06:50,902 INFO [train.py:715] (2/8) Epoch 12, batch 5700, loss[loss=0.1436, simple_loss=0.2098, pruned_loss=0.03872, over 4867.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03146, over 973595.44 frames.], batch size: 32, lr: 1.83e-04
2022-05-07 09:07:29,808 INFO [train.py:715] (2/8) Epoch 12, batch 5750, loss[loss=0.1259, simple_loss=0.2001, pruned_loss=0.02587, over 4819.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03165, over 972674.66 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:08:07,981 INFO [train.py:715] (2/8) Epoch 12, batch 5800, loss[loss=0.1509, simple_loss=0.2169, pruned_loss=0.04245, over 4712.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03159, over 972649.35 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:08:46,180 INFO [train.py:715] (2/8) Epoch 12, batch 5850, loss[loss=0.1166, simple_loss=0.1877, pruned_loss=0.02274, over 4936.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03163, over 972031.58 frames.], batch size: 29, lr: 1.83e-04
2022-05-07 09:09:24,396 INFO [train.py:715] (2/8) Epoch 12, batch 5900, loss[loss=0.1792, simple_loss=0.2446, pruned_loss=0.05687, over 4745.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.03178, over 971908.11 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:10:02,493 INFO [train.py:715] (2/8) Epoch 12, batch 5950, loss[loss=0.1558, simple_loss=0.2341, pruned_loss=0.03878, over 4985.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03153, over 972003.20 frames.], batch size: 40, lr: 1.83e-04
2022-05-07 09:10:40,375 INFO [train.py:715] (2/8) Epoch 12, batch 6000, loss[loss=0.1801, simple_loss=0.2396, pruned_loss=0.06035, over 4740.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2098, pruned_loss=0.03186, over 972056.94 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:10:40,375 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 09:10:49,853 INFO [train.py:742] (2/8) Epoch 12, validation: loss=0.1057, simple_loss=0.1897, pruned_loss=0.01086, over 914524.00 frames.
2022-05-07 09:11:28,466 INFO [train.py:715] (2/8) Epoch 12, batch 6050, loss[loss=0.1179, simple_loss=0.194, pruned_loss=0.02093, over 4860.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03165, over 972837.53 frames.], batch size: 20, lr: 1.83e-04
2022-05-07 09:12:07,170 INFO [train.py:715] (2/8) Epoch 12, batch 6100, loss[loss=0.1437, simple_loss=0.2241, pruned_loss=0.03159, over 4684.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03126, over 972176.47 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:12:46,247 INFO [train.py:715] (2/8) Epoch 12, batch 6150, loss[loss=0.1403, simple_loss=0.202, pruned_loss=0.03935, over 4771.00 frames.], tot_loss[loss=0.1361, simple_loss=0.209, pruned_loss=0.03157, over 972213.65 frames.], batch size: 12, lr: 1.83e-04
2022-05-07 09:13:24,048 INFO [train.py:715] (2/8) Epoch 12, batch 6200, loss[loss=0.1046, simple_loss=0.1824, pruned_loss=0.01343, over 4828.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2092, pruned_loss=0.03161, over 972194.51 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:14:02,110 INFO [train.py:715] (2/8) Epoch 12, batch 6250, loss[loss=0.1196, simple_loss=0.1867, pruned_loss=0.02619, over 4702.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2098, pruned_loss=0.03203, over 972780.90 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:14:42,630 INFO [train.py:715] (2/8) Epoch 12, batch 6300, loss[loss=0.1499, simple_loss=0.2123, pruned_loss=0.0438, over 4805.00 frames.], tot_loss[loss=0.1363, simple_loss=0.209, pruned_loss=0.03186, over 972990.12 frames.], batch size: 24, lr: 1.83e-04
2022-05-07 09:15:20,411 INFO [train.py:715] (2/8) Epoch 12, batch 6350, loss[loss=0.1397, simple_loss=0.2261, pruned_loss=0.02668, over 4913.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2093, pruned_loss=0.03176, over 972271.60 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:15:58,260 INFO [train.py:715] (2/8) Epoch 12, batch 6400, loss[loss=0.126, simple_loss=0.208, pruned_loss=0.02201, over 4788.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2089, pruned_loss=0.03172, over 972487.37 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:16:36,192 INFO [train.py:715] (2/8) Epoch 12, batch 6450, loss[loss=0.1719, simple_loss=0.2312, pruned_loss=0.05633, over 4971.00 frames.], tot_loss[loss=0.137, simple_loss=0.2099, pruned_loss=0.03202, over 972693.38 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:17:14,183 INFO [train.py:715] (2/8) Epoch 12, batch 6500, loss[loss=0.14, simple_loss=0.2147, pruned_loss=0.03259, over 4918.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03224, over 972030.67 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:17:51,825 INFO [train.py:715] (2/8) Epoch 12, batch 6550, loss[loss=0.1266, simple_loss=0.2021, pruned_loss=0.02554, over 4904.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03241, over 972744.79 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:18:29,933 INFO [train.py:715] (2/8) Epoch 12, batch 6600, loss[loss=0.1466, simple_loss=0.2045, pruned_loss=0.04439, over 4908.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2109, pruned_loss=0.0323, over 972432.76 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:19:08,080 INFO [train.py:715] (2/8) Epoch 12, batch 6650, loss[loss=0.179, simple_loss=0.24, pruned_loss=0.05893, over 4915.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.03229, over 971991.30 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:19:46,571 INFO [train.py:715] (2/8) Epoch 12, batch 6700, loss[loss=0.1353, simple_loss=0.2182, pruned_loss=0.02617, over 4981.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2115, pruned_loss=0.03231, over 972006.82 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:20:24,046 INFO [train.py:715] (2/8) Epoch 12, batch 6750, loss[loss=0.1271, simple_loss=0.2015, pruned_loss=0.02633, over 4741.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03215, over 972632.55 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:21:02,174 INFO [train.py:715] (2/8) Epoch 12, batch 6800, loss[loss=0.1462, simple_loss=0.2337, pruned_loss=0.02937, over 4827.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03218, over 972717.59 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:21:40,239 INFO [train.py:715] (2/8) Epoch 12, batch 6850, loss[loss=0.1571, simple_loss=0.2123, pruned_loss=0.05098, over 4831.00 frames.], tot_loss[loss=0.1368, simple_loss=0.21, pruned_loss=0.03179, over 971922.87 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:22:18,035 INFO [train.py:715] (2/8) Epoch 12, batch 6900, loss[loss=0.1471, simple_loss=0.2272, pruned_loss=0.0335, over 4938.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03152, over 972487.56 frames.], batch size: 39, lr: 1.83e-04
2022-05-07 09:22:56,143 INFO [train.py:715] (2/8) Epoch 12, batch 6950, loss[loss=0.1484, simple_loss=0.2319, pruned_loss=0.03247, over 4893.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03151, over 972255.74 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:23:34,138 INFO [train.py:715] (2/8) Epoch 12, batch 7000, loss[loss=0.1273, simple_loss=0.2047, pruned_loss=0.02499, over 4905.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2113, pruned_loss=0.03177, over 971972.79 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:24:12,558 INFO [train.py:715] (2/8) Epoch 12, batch 7050, loss[loss=0.1235, simple_loss=0.2024, pruned_loss=0.02228, over 4880.00 frames.], tot_loss[loss=0.138, simple_loss=0.2118, pruned_loss=0.03213, over 971894.82 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:24:50,037 INFO [train.py:715] (2/8) Epoch 12, batch 7100, loss[loss=0.1298, simple_loss=0.2026, pruned_loss=0.02851, over 4935.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2117, pruned_loss=0.03223, over 972841.60 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:25:28,612 INFO [train.py:715] (2/8) Epoch 12, batch 7150, loss[loss=0.1503, simple_loss=0.217, pruned_loss=0.04177, over 4902.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03174, over 972115.40 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:26:06,441 INFO [train.py:715] (2/8) Epoch 12, batch 7200, loss[loss=0.1397, simple_loss=0.2261, pruned_loss=0.02667, over 4955.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2111, pruned_loss=0.0325, over 972462.29 frames.], batch size: 21, lr: 1.83e-04
2022-05-07 09:26:44,292 INFO [train.py:715] (2/8) Epoch 12, batch 7250, loss[loss=0.1358, simple_loss=0.2145, pruned_loss=0.02855, over 4962.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03191, over 973341.42 frames.], batch size: 15, lr: 1.83e-04
2022-05-07 09:27:22,554 INFO [train.py:715] (2/8) Epoch 12, batch 7300, loss[loss=0.1318, simple_loss=0.2127, pruned_loss=0.02545, over 4775.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03136, over 972414.32 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:28:00,292 INFO [train.py:715] (2/8) Epoch 12, batch 7350, loss[loss=0.1643, simple_loss=0.2537, pruned_loss=0.03748, over 4896.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03146, over 972083.02 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:28:38,318 INFO [train.py:715] (2/8) Epoch 12, batch 7400, loss[loss=0.1213, simple_loss=0.1875, pruned_loss=0.02751, over 4947.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03122, over 972706.81 frames.], batch size: 21, lr: 1.83e-04
2022-05-07 09:29:16,071 INFO [train.py:715] (2/8) Epoch 12, batch 7450, loss[loss=0.1153, simple_loss=0.1942, pruned_loss=0.01819, over 4816.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03116, over 972198.79 frames.], batch size: 26, lr: 1.83e-04
2022-05-07 09:29:54,163 INFO [train.py:715] (2/8) Epoch 12, batch 7500, loss[loss=0.1214, simple_loss=0.1909, pruned_loss=0.02594, over 4855.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2091, pruned_loss=0.03127, over 972220.72 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:30:32,181 INFO [train.py:715] (2/8) Epoch 12, batch 7550, loss[loss=0.1242, simple_loss=0.2008, pruned_loss=0.02384, over 4936.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03134, over 972349.76 frames.], batch size: 29, lr: 1.83e-04
2022-05-07 09:31:10,032 INFO [train.py:715] (2/8) Epoch 12, batch 7600, loss[loss=0.1457, simple_loss=0.2187, pruned_loss=0.03632, over 4870.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03165, over 972658.06 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:31:48,262 INFO [train.py:715] (2/8) Epoch 12, batch 7650, loss[loss=0.1302, simple_loss=0.1967, pruned_loss=0.03182, over 4923.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03213, over 973631.64 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:32:26,440 INFO [train.py:715] (2/8) Epoch 12, batch 7700, loss[loss=0.1742, simple_loss=0.2398, pruned_loss=0.0543, over 4883.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03202, over 973605.56 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:33:04,637 INFO [train.py:715] (2/8) Epoch 12, batch 7750, loss[loss=0.1199, simple_loss=0.1937, pruned_loss=0.0231, over 4823.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03164, over 973617.71 frames.], batch size: 12, lr: 1.83e-04
2022-05-07 09:33:42,404 INFO [train.py:715] (2/8) Epoch 12, batch 7800, loss[loss=0.1434, simple_loss=0.2067, pruned_loss=0.04, over 4959.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03141, over 973888.63 frames.], batch size: 35, lr: 1.83e-04
2022-05-07 09:34:20,596 INFO [train.py:715] (2/8) Epoch 12, batch 7850, loss[loss=0.1839, simple_loss=0.245, pruned_loss=0.06145, over 4780.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.0325, over 974179.05 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:34:58,403 INFO [train.py:715] (2/8) Epoch 12, batch 7900, loss[loss=0.1525, simple_loss=0.2223, pruned_loss=0.04137, over 4911.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03228, over 974198.58 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:35:36,649 INFO [train.py:715] (2/8) Epoch 12, batch 7950, loss[loss=0.1625, simple_loss=0.2376, pruned_loss=0.04364, over 4757.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03232, over 973267.62 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:36:14,623 INFO [train.py:715] (2/8) Epoch 12, batch 8000, loss[loss=0.1539, simple_loss=0.2313, pruned_loss=0.03829, over 4887.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.0323, over 973182.62 frames.], batch size: 39, lr: 1.83e-04
2022-05-07 09:36:53,083 INFO [train.py:715] (2/8) Epoch 12, batch 8050, loss[loss=0.1657, simple_loss=0.2257, pruned_loss=0.0529, over 4861.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03205, over 972304.25 frames.], batch size: 32, lr: 1.83e-04
2022-05-07 09:37:31,433 INFO [train.py:715] (2/8) Epoch 12, batch 8100, loss[loss=0.1082, simple_loss=0.1738, pruned_loss=0.02127, over 4818.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03193, over 971978.46 frames.], batch size: 12, lr: 1.83e-04
2022-05-07 09:38:09,022 INFO [train.py:715] (2/8) Epoch 12, batch 8150, loss[loss=0.1648, simple_loss=0.2281, pruned_loss=0.05073, over 4918.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03209, over 971358.92 frames.], batch size: 39, lr: 1.83e-04
2022-05-07 09:38:47,286 INFO [train.py:715] (2/8) Epoch 12, batch 8200, loss[loss=0.1406, simple_loss=0.2126, pruned_loss=0.03434, over 4916.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03162, over 971371.09 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:39:25,281 INFO [train.py:715] (2/8) Epoch 12, batch 8250, loss[loss=0.1556, simple_loss=0.2293, pruned_loss=0.04094, over 4925.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2118, pruned_loss=0.03199, over 971994.40 frames.], batch size: 23, lr: 1.83e-04
2022-05-07 09:40:03,013 INFO [train.py:715] (2/8) Epoch 12, batch 8300, loss[loss=0.1385, simple_loss=0.2021, pruned_loss=0.03741, over 4878.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03211, over 971373.17 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:40:41,114 INFO [train.py:715] (2/8) Epoch 12, batch 8350, loss[loss=0.1313, simple_loss=0.2052, pruned_loss=0.02867, over 4763.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03204, over 972016.10 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:41:19,301 INFO [train.py:715] (2/8) Epoch 12, batch 8400, loss[loss=0.1328, simple_loss=0.2061, pruned_loss=0.02976, over 4816.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03134, over 972440.85 frames.], batch size: 27, lr: 1.83e-04
2022-05-07 09:41:57,374 INFO [train.py:715] (2/8) Epoch 12, batch 8450, loss[loss=0.1286, simple_loss=0.1876, pruned_loss=0.03485, over 4975.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.0318, over 972600.20 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:42:34,902 INFO [train.py:715] (2/8) Epoch 12, batch 8500, loss[loss=0.1186, simple_loss=0.1848, pruned_loss=0.02616, over 4838.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03164, over 972446.37 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:43:13,188 INFO [train.py:715] (2/8) Epoch 12, batch 8550, loss[loss=0.1593, simple_loss=0.2269, pruned_loss=0.0459, over 4932.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.03187, over 972386.66 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:43:51,189 INFO [train.py:715] (2/8) Epoch 12, batch 8600, loss[loss=0.09476, simple_loss=0.1594, pruned_loss=0.01505, over 4649.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03168, over 972270.26 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:44:28,881 INFO [train.py:715] (2/8) Epoch 12, batch 8650, loss[loss=0.1766, simple_loss=0.2526, pruned_loss=0.05026, over 4929.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03164, over 972353.36 frames.], batch size: 23, lr: 1.83e-04
2022-05-07 09:45:07,105 INFO [train.py:715] (2/8) Epoch 12, batch 8700, loss[loss=0.1254, simple_loss=0.2024, pruned_loss=0.02421, over 4907.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03177, over 973143.84 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:45:45,272 INFO [train.py:715] (2/8) Epoch 12, batch 8750, loss[loss=0.1229, simple_loss=0.2004, pruned_loss=0.02275, over 4777.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03177, over 972135.83 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:46:23,701 INFO [train.py:715] (2/8) Epoch 12, batch 8800, loss[loss=0.1367, simple_loss=0.2154, pruned_loss=0.02901, over 4900.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.0315, over 972394.59 frames.], batch size: 17, lr: 1.83e-04
2022-05-07 09:47:01,615 INFO [train.py:715] (2/8) Epoch 12, batch 8850, loss[loss=0.1654, simple_loss=0.2378, pruned_loss=0.04653, over 4968.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03149, over 972000.70 frames.], batch size: 14, lr: 1.83e-04
2022-05-07 09:47:40,606 INFO [train.py:715] (2/8) Epoch 12, batch 8900, loss[loss=0.1142, simple_loss=0.1905, pruned_loss=0.01892, over 4842.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03112, over 972059.57 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:48:20,144 INFO [train.py:715] (2/8) Epoch 12, batch 8950, loss[loss=0.1482, simple_loss=0.2297, pruned_loss=0.03335, over 4802.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03108, over 972047.78 frames.], batch size: 25, lr: 1.83e-04
2022-05-07 09:48:58,104 INFO [train.py:715] (2/8) Epoch 12, batch 9000, loss[loss=0.1191, simple_loss=0.1896, pruned_loss=0.02433, over 4883.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.0315, over 972422.47 frames.], batch size: 22, lr: 1.83e-04
2022-05-07 09:48:58,105 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 09:49:07,571 INFO [train.py:742] (2/8) 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] (2/8) Epoch 12, batch 9050, loss[loss=0.1276, simple_loss=0.1998, pruned_loss=0.02768, over 4779.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.03173, over 971890.21 frames.], batch size: 18, lr: 1.83e-04
2022-05-07 09:50:23,565 INFO [train.py:715] (2/8) Epoch 12, batch 9100, loss[loss=0.1362, simple_loss=0.2171, pruned_loss=0.02767, over 4791.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.0319, over 971832.13 frames.], batch size: 24, lr: 1.83e-04
2022-05-07 09:51:01,823 INFO [train.py:715] (2/8) Epoch 12, batch 9150, loss[loss=0.1408, simple_loss=0.2068, pruned_loss=0.03744, over 4872.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03209, over 970888.65 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:51:39,541 INFO [train.py:715] (2/8) Epoch 12, batch 9200, loss[loss=0.1339, simple_loss=0.2042, pruned_loss=0.03174, over 4872.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03212, over 971102.24 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:52:17,395 INFO [train.py:715] (2/8) Epoch 12, batch 9250, loss[loss=0.1403, simple_loss=0.2005, pruned_loss=0.04009, over 4758.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03187, over 971961.22 frames.], batch size: 19, lr: 1.83e-04
2022-05-07 09:52:55,473 INFO [train.py:715] (2/8) Epoch 12, batch 9300, loss[loss=0.1162, simple_loss=0.1745, pruned_loss=0.02898, over 4764.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.0318, over 972354.53 frames.], batch size: 12, lr: 1.83e-04
2022-05-07 09:53:33,065 INFO [train.py:715] (2/8) Epoch 12, batch 9350, loss[loss=0.1057, simple_loss=0.1788, pruned_loss=0.01633, over 4825.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03202, over 972269.88 frames.], batch size: 13, lr: 1.83e-04
2022-05-07 09:54:10,844 INFO [train.py:715] (2/8) Epoch 12, batch 9400, loss[loss=0.1253, simple_loss=0.1897, pruned_loss=0.03048, over 4839.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2109, pruned_loss=0.03243, over 972901.79 frames.], batch size: 26, lr: 1.83e-04
2022-05-07 09:54:48,556 INFO [train.py:715] (2/8) Epoch 12, batch 9450, loss[loss=0.1505, simple_loss=0.2154, pruned_loss=0.04283, over 4754.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03219, over 972841.78 frames.], batch size: 16, lr: 1.83e-04
2022-05-07 09:55:26,597 INFO [train.py:715] (2/8) Epoch 12, batch 9500, loss[loss=0.1222, simple_loss=0.1906, pruned_loss=0.02696, over 4928.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2108, pruned_loss=0.03198, over 973205.99 frames.], batch size: 23, lr: 1.83e-04
2022-05-07 09:56:04,151 INFO [train.py:715] (2/8) Epoch 12, batch 9550, loss[loss=0.1472, simple_loss=0.2171, pruned_loss=0.03865, over 4838.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2116, pruned_loss=0.03239, over 973612.46 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 09:56:41,641 INFO [train.py:715] (2/8) Epoch 12, batch 9600, loss[loss=0.113, simple_loss=0.1853, pruned_loss=0.02039, over 4821.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03214, over 973929.28 frames.], batch size: 13, lr: 1.82e-04
2022-05-07 09:57:19,885 INFO [train.py:715] (2/8) Epoch 12, batch 9650, loss[loss=0.1322, simple_loss=0.2106, pruned_loss=0.02689, over 4757.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03225, over 972846.98 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 09:57:57,757 INFO [train.py:715] (2/8) Epoch 12, batch 9700, loss[loss=0.1146, simple_loss=0.1901, pruned_loss=0.01955, over 4961.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03214, over 973339.47 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 09:58:35,536 INFO [train.py:715] (2/8) Epoch 12, batch 9750, loss[loss=0.1519, simple_loss=0.2279, pruned_loss=0.03802, over 4794.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.03169, over 973404.71 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 09:59:13,484 INFO [train.py:715] (2/8) Epoch 12, batch 9800, loss[loss=0.1288, simple_loss=0.19, pruned_loss=0.03382, over 4909.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2102, pruned_loss=0.03229, over 973554.69 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 09:59:52,002 INFO [train.py:715] (2/8) Epoch 12, batch 9850, loss[loss=0.2014, simple_loss=0.2687, pruned_loss=0.067, over 4847.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.03203, over 973630.54 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:00:29,633 INFO [train.py:715] (2/8) Epoch 12, batch 9900, loss[loss=0.1313, simple_loss=0.2038, pruned_loss=0.02939, over 4696.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03232, over 973380.70 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:01:07,864 INFO [train.py:715] (2/8) Epoch 12, batch 9950, loss[loss=0.1145, simple_loss=0.19, pruned_loss=0.01953, over 4778.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.03252, over 973475.38 frames.], batch size: 12, lr: 1.82e-04
2022-05-07 10:01:46,618 INFO [train.py:715] (2/8) Epoch 12, batch 10000, loss[loss=0.1405, simple_loss=0.2085, pruned_loss=0.03622, over 4783.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2103, pruned_loss=0.03265, over 974143.36 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:02:25,150 INFO [train.py:715] (2/8) Epoch 12, batch 10050, loss[loss=0.1422, simple_loss=0.2235, pruned_loss=0.03043, over 4956.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2101, pruned_loss=0.03261, over 973577.02 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:03:03,488 INFO [train.py:715] (2/8) Epoch 12, batch 10100, loss[loss=0.138, simple_loss=0.2087, pruned_loss=0.03366, over 4928.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2101, pruned_loss=0.03286, over 972946.80 frames.], batch size: 23, lr: 1.82e-04
2022-05-07 10:03:41,897 INFO [train.py:715] (2/8) Epoch 12, batch 10150, loss[loss=0.1285, simple_loss=0.2012, pruned_loss=0.0279, over 4792.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2099, pruned_loss=0.03247, over 972552.37 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:04:20,551 INFO [train.py:715] (2/8) Epoch 12, batch 10200, loss[loss=0.1288, simple_loss=0.2024, pruned_loss=0.02763, over 4770.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2101, pruned_loss=0.03228, over 972812.57 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:04:57,864 INFO [train.py:715] (2/8) Epoch 12, batch 10250, loss[loss=0.1257, simple_loss=0.1933, pruned_loss=0.02905, over 4916.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2101, pruned_loss=0.03223, over 972220.41 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:05:36,038 INFO [train.py:715] (2/8) Epoch 12, batch 10300, loss[loss=0.1554, simple_loss=0.2352, pruned_loss=0.03779, over 4926.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2102, pruned_loss=0.03222, over 971876.08 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:06:14,198 INFO [train.py:715] (2/8) Epoch 12, batch 10350, loss[loss=0.1239, simple_loss=0.1961, pruned_loss=0.0258, over 4795.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03215, over 972881.17 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:06:52,243 INFO [train.py:715] (2/8) Epoch 12, batch 10400, loss[loss=0.1267, simple_loss=0.2016, pruned_loss=0.02588, over 4913.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.0316, over 973106.29 frames.], batch size: 29, lr: 1.82e-04
2022-05-07 10:07:29,800 INFO [train.py:715] (2/8) Epoch 12, batch 10450, loss[loss=0.1269, simple_loss=0.2071, pruned_loss=0.0234, over 4798.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03158, over 971734.17 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:08:07,729 INFO [train.py:715] (2/8) Epoch 12, batch 10500, loss[loss=0.1258, simple_loss=0.1992, pruned_loss=0.02622, over 4893.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03161, over 971789.60 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:08:46,137 INFO [train.py:715] (2/8) Epoch 12, batch 10550, loss[loss=0.1411, simple_loss=0.2148, pruned_loss=0.03365, over 4759.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03184, over 971189.93 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:09:23,510 INFO [train.py:715] (2/8) Epoch 12, batch 10600, loss[loss=0.1824, simple_loss=0.2489, pruned_loss=0.058, over 4804.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03168, over 970785.52 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:10:01,494 INFO [train.py:715] (2/8) Epoch 12, batch 10650, loss[loss=0.1403, simple_loss=0.2152, pruned_loss=0.03264, over 4696.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03158, over 970755.00 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:10:39,356 INFO [train.py:715] (2/8) Epoch 12, batch 10700, loss[loss=0.1233, simple_loss=0.1964, pruned_loss=0.02511, over 4805.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.0317, over 971312.83 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:11:16,858 INFO [train.py:715] (2/8) Epoch 12, batch 10750, loss[loss=0.1202, simple_loss=0.2023, pruned_loss=0.01908, over 4923.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2103, pruned_loss=0.03195, over 971987.09 frames.], batch size: 29, lr: 1.82e-04
2022-05-07 10:11:54,746 INFO [train.py:715] (2/8) Epoch 12, batch 10800, loss[loss=0.1271, simple_loss=0.2001, pruned_loss=0.02708, over 4817.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.03241, over 971498.94 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:12:32,735 INFO [train.py:715] (2/8) Epoch 12, batch 10850, loss[loss=0.1366, simple_loss=0.2013, pruned_loss=0.03594, over 4983.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.0325, over 972307.17 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:13:11,527 INFO [train.py:715] (2/8) Epoch 12, batch 10900, loss[loss=0.142, simple_loss=0.2237, pruned_loss=0.03019, over 4772.00 frames.], tot_loss[loss=0.1379, simple_loss=0.211, pruned_loss=0.0324, over 972232.74 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:13:48,733 INFO [train.py:715] (2/8) Epoch 12, batch 10950, loss[loss=0.1263, simple_loss=0.2013, pruned_loss=0.02566, over 4900.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03274, over 972870.34 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:14:26,876 INFO [train.py:715] (2/8) Epoch 12, batch 11000, loss[loss=0.1488, simple_loss=0.2198, pruned_loss=0.03893, over 4902.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03257, over 973761.26 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:15:05,148 INFO [train.py:715] (2/8) Epoch 12, batch 11050, loss[loss=0.1304, simple_loss=0.2098, pruned_loss=0.02546, over 4807.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03204, over 973704.68 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:15:42,772 INFO [train.py:715] (2/8) Epoch 12, batch 11100, loss[loss=0.1112, simple_loss=0.1933, pruned_loss=0.01457, over 4946.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.0318, over 972913.64 frames.], batch size: 29, lr: 1.82e-04
2022-05-07 10:16:21,274 INFO [train.py:715] (2/8) Epoch 12, batch 11150, loss[loss=0.1514, simple_loss=0.2236, pruned_loss=0.03956, over 4865.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.032, over 972313.23 frames.], batch size: 30, lr: 1.82e-04
2022-05-07 10:16:58,891 INFO [train.py:715] (2/8) Epoch 12, batch 11200, loss[loss=0.1136, simple_loss=0.1895, pruned_loss=0.01884, over 4750.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.03195, over 971589.53 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:17:36,989 INFO [train.py:715] (2/8) Epoch 12, batch 11250, loss[loss=0.1411, simple_loss=0.21, pruned_loss=0.03613, over 4932.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2111, pruned_loss=0.03195, over 971829.52 frames.], batch size: 29, lr: 1.82e-04
2022-05-07 10:18:14,709 INFO [train.py:715] (2/8) Epoch 12, batch 11300, loss[loss=0.1617, simple_loss=0.2307, pruned_loss=0.04633, over 4900.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.0316, over 972125.70 frames.], batch size: 39, lr: 1.82e-04
2022-05-07 10:18:51,981 INFO [train.py:715] (2/8) Epoch 12, batch 11350, loss[loss=0.1457, simple_loss=0.2184, pruned_loss=0.03652, over 4815.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03134, over 973184.07 frames.], batch size: 26, lr: 1.82e-04
2022-05-07 10:19:30,201 INFO [train.py:715] (2/8) Epoch 12, batch 11400, loss[loss=0.1535, simple_loss=0.2196, pruned_loss=0.04372, over 4841.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2111, pruned_loss=0.03181, over 973548.50 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:20:07,742 INFO [train.py:715] (2/8) Epoch 12, batch 11450, loss[loss=0.1067, simple_loss=0.1737, pruned_loss=0.01985, over 4763.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03203, over 972678.07 frames.], batch size: 12, lr: 1.82e-04
2022-05-07 10:20:45,258 INFO [train.py:715] (2/8) Epoch 12, batch 11500, loss[loss=0.1558, simple_loss=0.2229, pruned_loss=0.04439, over 4844.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2118, pruned_loss=0.03175, over 973055.72 frames.], batch size: 13, lr: 1.82e-04
2022-05-07 10:21:23,004 INFO [train.py:715] (2/8) Epoch 12, batch 11550, loss[loss=0.1321, simple_loss=0.1979, pruned_loss=0.03313, over 4843.00 frames.], tot_loss[loss=0.137, simple_loss=0.2109, pruned_loss=0.03154, over 972754.80 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:22:01,395 INFO [train.py:715] (2/8) Epoch 12, batch 11600, loss[loss=0.1256, simple_loss=0.1947, pruned_loss=0.02827, over 4914.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03208, over 973371.93 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:22:38,880 INFO [train.py:715] (2/8) Epoch 12, batch 11650, loss[loss=0.1256, simple_loss=0.1828, pruned_loss=0.03415, over 4986.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03221, over 973706.55 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:23:16,093 INFO [train.py:715] (2/8) Epoch 12, batch 11700, loss[loss=0.1182, simple_loss=0.1951, pruned_loss=0.02067, over 4821.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03238, over 972612.33 frames.], batch size: 27, lr: 1.82e-04
2022-05-07 10:23:53,750 INFO [train.py:715] (2/8) Epoch 12, batch 11750, loss[loss=0.1527, simple_loss=0.2126, pruned_loss=0.04641, over 4983.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.0327, over 971594.02 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:24:31,085 INFO [train.py:715] (2/8) Epoch 12, batch 11800, loss[loss=0.1856, simple_loss=0.2509, pruned_loss=0.06018, over 4746.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03192, over 970261.09 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:25:08,778 INFO [train.py:715] (2/8) Epoch 12, batch 11850, loss[loss=0.129, simple_loss=0.2079, pruned_loss=0.02504, over 4937.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2092, pruned_loss=0.03159, over 970279.85 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:25:46,625 INFO [train.py:715] (2/8) Epoch 12, batch 11900, loss[loss=0.1455, simple_loss=0.212, pruned_loss=0.03951, over 4968.00 frames.], tot_loss[loss=0.136, simple_loss=0.209, pruned_loss=0.03149, over 971571.73 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:26:24,516 INFO [train.py:715] (2/8) Epoch 12, batch 11950, loss[loss=0.1348, simple_loss=0.2066, pruned_loss=0.03153, over 4913.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03132, over 971753.03 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:27:01,978 INFO [train.py:715] (2/8) Epoch 12, batch 12000, loss[loss=0.1197, simple_loss=0.1979, pruned_loss=0.02073, over 4851.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03164, over 972546.00 frames.], batch size: 20, lr: 1.82e-04
2022-05-07 10:27:01,979 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 10:27:11,324 INFO [train.py:742] (2/8) Epoch 12, validation: loss=0.1058, simple_loss=0.1897, pruned_loss=0.01095, over 914524.00 frames.
2022-05-07 10:27:50,014 INFO [train.py:715] (2/8) Epoch 12, batch 12050, loss[loss=0.1178, simple_loss=0.1908, pruned_loss=0.02241, over 4963.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03194, over 972574.82 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:28:29,092 INFO [train.py:715] (2/8) Epoch 12, batch 12100, loss[loss=0.1385, simple_loss=0.2134, pruned_loss=0.03182, over 4764.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03194, over 972605.56 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:29:08,848 INFO [train.py:715] (2/8) Epoch 12, batch 12150, loss[loss=0.1129, simple_loss=0.1827, pruned_loss=0.0215, over 4727.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03206, over 972258.56 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:29:47,129 INFO [train.py:715] (2/8) Epoch 12, batch 12200, loss[loss=0.1554, simple_loss=0.2317, pruned_loss=0.03949, over 4865.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03149, over 972149.18 frames.], batch size: 38, lr: 1.82e-04
2022-05-07 10:30:25,386 INFO [train.py:715] (2/8) Epoch 12, batch 12250, loss[loss=0.1237, simple_loss=0.1998, pruned_loss=0.02376, over 4800.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03169, over 970942.38 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:31:04,238 INFO [train.py:715] (2/8) Epoch 12, batch 12300, loss[loss=0.1505, simple_loss=0.2133, pruned_loss=0.04392, over 4746.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.0317, over 971205.59 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:31:42,816 INFO [train.py:715] (2/8) Epoch 12, batch 12350, loss[loss=0.1355, simple_loss=0.2101, pruned_loss=0.03049, over 4885.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.03182, over 971218.78 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:32:20,261 INFO [train.py:715] (2/8) Epoch 12, batch 12400, loss[loss=0.138, simple_loss=0.2093, pruned_loss=0.03337, over 4837.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03175, over 971226.93 frames.], batch size: 32, lr: 1.82e-04
2022-05-07 10:32:57,988 INFO [train.py:715] (2/8) Epoch 12, batch 12450, loss[loss=0.1134, simple_loss=0.1854, pruned_loss=0.02065, over 4927.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03235, over 971669.58 frames.], batch size: 29, lr: 1.82e-04
2022-05-07 10:33:36,210 INFO [train.py:715] (2/8) Epoch 12, batch 12500, loss[loss=0.1543, simple_loss=0.2287, pruned_loss=0.03992, over 4800.00 frames.], tot_loss[loss=0.137, simple_loss=0.2101, pruned_loss=0.03189, over 971505.39 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:34:13,319 INFO [train.py:715] (2/8) Epoch 12, batch 12550, loss[loss=0.1337, simple_loss=0.2128, pruned_loss=0.02723, over 4709.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.03237, over 971009.37 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:34:51,155 INFO [train.py:715] (2/8) Epoch 12, batch 12600, loss[loss=0.1543, simple_loss=0.2167, pruned_loss=0.04592, over 4928.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2101, pruned_loss=0.03203, over 971836.52 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:35:28,920 INFO [train.py:715] (2/8) Epoch 12, batch 12650, loss[loss=0.1291, simple_loss=0.2107, pruned_loss=0.0238, over 4913.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2096, pruned_loss=0.0318, over 971658.18 frames.], batch size: 17, lr: 1.82e-04
2022-05-07 10:36:06,673 INFO [train.py:715] (2/8) Epoch 12, batch 12700, loss[loss=0.1566, simple_loss=0.22, pruned_loss=0.04661, over 4789.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2108, pruned_loss=0.03242, over 971439.44 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:36:44,125 INFO [train.py:715] (2/8) Epoch 12, batch 12750, loss[loss=0.1406, simple_loss=0.2154, pruned_loss=0.03291, over 4983.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2098, pruned_loss=0.03196, over 971741.84 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:37:22,153 INFO [train.py:715] (2/8) Epoch 12, batch 12800, loss[loss=0.1463, simple_loss=0.23, pruned_loss=0.03136, over 4813.00 frames.], tot_loss[loss=0.1372, simple_loss=0.21, pruned_loss=0.03223, over 971293.18 frames.], batch size: 25, lr: 1.82e-04
2022-05-07 10:38:00,582 INFO [train.py:715] (2/8) Epoch 12, batch 12850, loss[loss=0.1429, simple_loss=0.2238, pruned_loss=0.03101, over 4961.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2105, pruned_loss=0.0326, over 970961.01 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:38:37,910 INFO [train.py:715] (2/8) Epoch 12, batch 12900, loss[loss=0.135, simple_loss=0.2127, pruned_loss=0.02861, over 4774.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2103, pruned_loss=0.0322, over 971275.57 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:39:15,002 INFO [train.py:715] (2/8) Epoch 12, batch 12950, loss[loss=0.1537, simple_loss=0.221, pruned_loss=0.04319, over 4748.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03247, over 971964.07 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:39:52,998 INFO [train.py:715] (2/8) Epoch 12, batch 13000, loss[loss=0.179, simple_loss=0.2457, pruned_loss=0.05617, over 4922.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2111, pruned_loss=0.0325, over 971817.55 frames.], batch size: 18, lr: 1.82e-04
2022-05-07 10:40:30,781 INFO [train.py:715] (2/8) Epoch 12, batch 13050, loss[loss=0.1366, simple_loss=0.2172, pruned_loss=0.02798, over 4820.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2113, pruned_loss=0.03204, over 972475.95 frames.], batch size: 13, lr: 1.82e-04
2022-05-07 10:41:08,532 INFO [train.py:715] (2/8) Epoch 12, batch 13100, loss[loss=0.1453, simple_loss=0.2193, pruned_loss=0.03559, over 4861.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03187, over 972437.65 frames.], batch size: 38, lr: 1.82e-04
2022-05-07 10:41:46,122 INFO [train.py:715] (2/8) Epoch 12, batch 13150, loss[loss=0.1431, simple_loss=0.2209, pruned_loss=0.0326, over 4886.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2113, pruned_loss=0.0324, over 973661.22 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:42:23,790 INFO [train.py:715] (2/8) Epoch 12, batch 13200, loss[loss=0.1098, simple_loss=0.1812, pruned_loss=0.01926, over 4982.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2112, pruned_loss=0.03274, over 974079.14 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:43:01,013 INFO [train.py:715] (2/8) Epoch 12, batch 13250, loss[loss=0.1321, simple_loss=0.2025, pruned_loss=0.03087, over 4780.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03206, over 974054.35 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:43:38,187 INFO [train.py:715] (2/8) Epoch 12, batch 13300, loss[loss=0.1474, simple_loss=0.2198, pruned_loss=0.03752, over 4938.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03231, over 973369.39 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:44:16,077 INFO [train.py:715] (2/8) Epoch 12, batch 13350, loss[loss=0.1178, simple_loss=0.1927, pruned_loss=0.02141, over 4730.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.03245, over 972762.12 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:44:54,316 INFO [train.py:715] (2/8) Epoch 12, batch 13400, loss[loss=0.1455, simple_loss=0.2167, pruned_loss=0.03711, over 4760.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03249, over 971718.38 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:45:31,691 INFO [train.py:715] (2/8) Epoch 12, batch 13450, loss[loss=0.1119, simple_loss=0.1925, pruned_loss=0.0156, over 4943.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2117, pruned_loss=0.03264, over 972180.10 frames.], batch size: 29, lr: 1.82e-04
2022-05-07 10:46:09,030 INFO [train.py:715] (2/8) Epoch 12, batch 13500, loss[loss=0.1589, simple_loss=0.2242, pruned_loss=0.04677, over 4896.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2111, pruned_loss=0.03254, over 972432.06 frames.], batch size: 19, lr: 1.82e-04
2022-05-07 10:46:47,475 INFO [train.py:715] (2/8) Epoch 12, batch 13550, loss[loss=0.1403, simple_loss=0.2047, pruned_loss=0.038, over 4972.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03284, over 971578.42 frames.], batch size: 35, lr: 1.82e-04
2022-05-07 10:47:24,686 INFO [train.py:715] (2/8) Epoch 12, batch 13600, loss[loss=0.127, simple_loss=0.2066, pruned_loss=0.02365, over 4976.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03258, over 971355.64 frames.], batch size: 28, lr: 1.82e-04
2022-05-07 10:48:02,573 INFO [train.py:715] (2/8) Epoch 12, batch 13650, loss[loss=0.1156, simple_loss=0.1872, pruned_loss=0.02201, over 4783.00 frames.], tot_loss[loss=0.137, simple_loss=0.21, pruned_loss=0.03195, over 971106.96 frames.], batch size: 14, lr: 1.82e-04
2022-05-07 10:48:40,710 INFO [train.py:715] (2/8) Epoch 12, batch 13700, loss[loss=0.1474, simple_loss=0.2194, pruned_loss=0.03771, over 4850.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03202, over 972130.13 frames.], batch size: 32, lr: 1.82e-04
2022-05-07 10:49:18,440 INFO [train.py:715] (2/8) Epoch 12, batch 13750, loss[loss=0.1571, simple_loss=0.2289, pruned_loss=0.04265, over 4802.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03204, over 972001.33 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:49:56,509 INFO [train.py:715] (2/8) Epoch 12, batch 13800, loss[loss=0.1437, simple_loss=0.2182, pruned_loss=0.03459, over 4948.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03224, over 973346.51 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:50:34,448 INFO [train.py:715] (2/8) Epoch 12, batch 13850, loss[loss=0.1424, simple_loss=0.2037, pruned_loss=0.04051, over 4773.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03228, over 973409.91 frames.], batch size: 12, lr: 1.82e-04
2022-05-07 10:51:12,971 INFO [train.py:715] (2/8) Epoch 12, batch 13900, loss[loss=0.1346, simple_loss=0.213, pruned_loss=0.02805, over 4805.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03161, over 972586.49 frames.], batch size: 21, lr: 1.82e-04
2022-05-07 10:51:50,185 INFO [train.py:715] (2/8) Epoch 12, batch 13950, loss[loss=0.1382, simple_loss=0.21, pruned_loss=0.03322, over 4712.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03163, over 972112.17 frames.], batch size: 15, lr: 1.82e-04
2022-05-07 10:52:28,377 INFO [train.py:715] (2/8) Epoch 12, batch 14000, loss[loss=0.1121, simple_loss=0.1864, pruned_loss=0.01893, over 4887.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03182, over 971552.36 frames.], batch size: 22, lr: 1.82e-04
2022-05-07 10:53:06,889 INFO [train.py:715] (2/8) Epoch 12, batch 14050, loss[loss=0.1208, simple_loss=0.1963, pruned_loss=0.02262, over 4865.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2119, pruned_loss=0.03229, over 972887.61 frames.], batch size: 32, lr: 1.82e-04
2022-05-07 10:53:44,260 INFO [train.py:715] (2/8) Epoch 12, batch 14100, loss[loss=0.1551, simple_loss=0.234, pruned_loss=0.03809, over 4977.00 frames.], tot_loss[loss=0.1395, simple_loss=0.213, pruned_loss=0.03301, over 972765.38 frames.], batch size: 24, lr: 1.82e-04
2022-05-07 10:54:21,696 INFO [train.py:715] (2/8) Epoch 12, batch 14150, loss[loss=0.1784, simple_loss=0.25, pruned_loss=0.05341, over 4749.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2123, pruned_loss=0.03292, over 972080.26 frames.], batch size: 16, lr: 1.82e-04
2022-05-07 10:55:00,103 INFO [train.py:715] (2/8) Epoch 12, batch 14200, loss[loss=0.1375, simple_loss=0.2166, pruned_loss=0.02919, over 4978.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2125, pruned_loss=0.03333, over 972260.31 frames.], batch size: 28, lr: 1.82e-04
2022-05-07 10:55:38,424 INFO [train.py:715] (2/8) Epoch 12, batch 14250, loss[loss=0.146, simple_loss=0.2283, pruned_loss=0.03183, over 4820.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03327, over 972157.47 frames.], batch size: 26, lr: 1.81e-04
2022-05-07 10:56:18,082 INFO [train.py:715] (2/8) Epoch 12, batch 14300, loss[loss=0.1221, simple_loss=0.1879, pruned_loss=0.02815, over 4922.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2116, pruned_loss=0.03275, over 971823.39 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 10:56:56,581 INFO [train.py:715] (2/8) Epoch 12, batch 14350, loss[loss=0.148, simple_loss=0.2136, pruned_loss=0.04118, over 4929.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2119, pruned_loss=0.03287, over 972396.80 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 10:57:35,966 INFO [train.py:715] (2/8) Epoch 12, batch 14400, loss[loss=0.1658, simple_loss=0.2444, pruned_loss=0.0436, over 4792.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2123, pruned_loss=0.03324, over 972614.19 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 10:58:14,112 INFO [train.py:715] (2/8) Epoch 12, batch 14450, loss[loss=0.1335, simple_loss=0.2105, pruned_loss=0.02818, over 4874.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2125, pruned_loss=0.03322, over 972307.28 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 10:58:53,033 INFO [train.py:715] (2/8) Epoch 12, batch 14500, loss[loss=0.1378, simple_loss=0.2165, pruned_loss=0.02958, over 4849.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2123, pruned_loss=0.03329, over 973155.75 frames.], batch size: 20, lr: 1.81e-04
2022-05-07 10:59:32,146 INFO [train.py:715] (2/8) Epoch 12, batch 14550, loss[loss=0.1472, simple_loss=0.2196, pruned_loss=0.03742, over 4846.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03269, over 973404.84 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:00:11,028 INFO [train.py:715] (2/8) Epoch 12, batch 14600, loss[loss=0.1223, simple_loss=0.1875, pruned_loss=0.02854, over 4815.00 frames.], tot_loss[loss=0.1382, simple_loss=0.211, pruned_loss=0.03272, over 973026.78 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:00:49,646 INFO [train.py:715] (2/8) Epoch 12, batch 14650, loss[loss=0.1488, simple_loss=0.2242, pruned_loss=0.03672, over 4874.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03216, over 973295.86 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:01:27,544 INFO [train.py:715] (2/8) Epoch 12, batch 14700, loss[loss=0.1395, simple_loss=0.2123, pruned_loss=0.0333, over 4931.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03204, over 972930.17 frames.], batch size: 23, lr: 1.81e-04
2022-05-07 11:02:06,066 INFO [train.py:715] (2/8) Epoch 12, batch 14750, loss[loss=0.1279, simple_loss=0.2128, pruned_loss=0.0215, over 4811.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03183, over 972816.55 frames.], batch size: 25, lr: 1.81e-04
2022-05-07 11:02:43,581 INFO [train.py:715] (2/8) Epoch 12, batch 14800, loss[loss=0.1282, simple_loss=0.2085, pruned_loss=0.02398, over 4787.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03222, over 972801.27 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:03:21,326 INFO [train.py:715] (2/8) Epoch 12, batch 14850, loss[loss=0.1251, simple_loss=0.1961, pruned_loss=0.02701, over 4769.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03261, over 972692.24 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:03:59,687 INFO [train.py:715] (2/8) Epoch 12, batch 14900, loss[loss=0.1241, simple_loss=0.1981, pruned_loss=0.02506, over 4805.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03273, over 973055.66 frames.], batch size: 26, lr: 1.81e-04
2022-05-07 11:04:38,248 INFO [train.py:715] (2/8) Epoch 12, batch 14950, loss[loss=0.1221, simple_loss=0.1977, pruned_loss=0.02328, over 4828.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2125, pruned_loss=0.03304, over 972551.81 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:05:15,440 INFO [train.py:715] (2/8) Epoch 12, batch 15000, loss[loss=0.14, simple_loss=0.2094, pruned_loss=0.03529, over 4970.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2125, pruned_loss=0.03299, over 972098.05 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:05:15,441 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 11:05:25,070 INFO [train.py:742] (2/8) Epoch 12, validation: loss=0.1057, simple_loss=0.1897, pruned_loss=0.01083, over 914524.00 frames.
2022-05-07 11:06:02,918 INFO [train.py:715] (2/8) Epoch 12, batch 15050, loss[loss=0.1427, simple_loss=0.2101, pruned_loss=0.03765, over 4846.00 frames.], tot_loss[loss=0.139, simple_loss=0.2121, pruned_loss=0.0329, over 971668.92 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:06:41,208 INFO [train.py:715] (2/8) Epoch 12, batch 15100, loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.02973, over 4961.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2119, pruned_loss=0.03247, over 972594.79 frames.], batch size: 21, lr: 1.81e-04
2022-05-07 11:07:20,387 INFO [train.py:715] (2/8) Epoch 12, batch 15150, loss[loss=0.1526, simple_loss=0.2239, pruned_loss=0.04069, over 4820.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2127, pruned_loss=0.0329, over 972537.88 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:07:58,862 INFO [train.py:715] (2/8) Epoch 12, batch 15200, loss[loss=0.1252, simple_loss=0.1965, pruned_loss=0.02691, over 4870.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2128, pruned_loss=0.03301, over 972572.44 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 11:08:37,648 INFO [train.py:715] (2/8) Epoch 12, batch 15250, loss[loss=0.1339, simple_loss=0.2059, pruned_loss=0.03098, over 4896.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2117, pruned_loss=0.03272, over 973594.27 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:09:16,357 INFO [train.py:715] (2/8) Epoch 12, batch 15300, loss[loss=0.12, simple_loss=0.1887, pruned_loss=0.02568, over 4768.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2118, pruned_loss=0.03265, over 973665.54 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:09:54,568 INFO [train.py:715] (2/8) Epoch 12, batch 15350, loss[loss=0.1798, simple_loss=0.2439, pruned_loss=0.05788, over 4907.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03261, over 973651.02 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:10:31,952 INFO [train.py:715] (2/8) Epoch 12, batch 15400, loss[loss=0.1333, simple_loss=0.199, pruned_loss=0.03379, over 4840.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03199, over 974355.44 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:11:09,689 INFO [train.py:715] (2/8) Epoch 12, batch 15450, loss[loss=0.1274, simple_loss=0.2036, pruned_loss=0.02563, over 4894.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03222, over 973937.53 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:11:48,439 INFO [train.py:715] (2/8) Epoch 12, batch 15500, loss[loss=0.1419, simple_loss=0.2124, pruned_loss=0.03568, over 4678.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03173, over 973001.99 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:12:26,566 INFO [train.py:715] (2/8) Epoch 12, batch 15550, loss[loss=0.131, simple_loss=0.2059, pruned_loss=0.02805, over 4882.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03179, over 973111.24 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:13:04,459 INFO [train.py:715] (2/8) Epoch 12, batch 15600, loss[loss=0.166, simple_loss=0.2393, pruned_loss=0.04634, over 4800.00 frames.], tot_loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03122, over 973553.11 frames.], batch size: 25, lr: 1.81e-04
2022-05-07 11:13:42,239 INFO [train.py:715] (2/8) Epoch 12, batch 15650, loss[loss=0.1326, simple_loss=0.1984, pruned_loss=0.03338, over 4873.00 frames.], tot_loss[loss=0.137, simple_loss=0.2109, pruned_loss=0.03158, over 972948.96 frames.], batch size: 32, lr: 1.81e-04
2022-05-07 11:14:20,675 INFO [train.py:715] (2/8) Epoch 12, batch 15700, loss[loss=0.1298, simple_loss=0.2061, pruned_loss=0.02675, over 4889.00 frames.], tot_loss[loss=0.1371, simple_loss=0.211, pruned_loss=0.0316, over 972744.06 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:14:58,370 INFO [train.py:715] (2/8) Epoch 12, batch 15750, loss[loss=0.1677, simple_loss=0.2211, pruned_loss=0.05712, over 4967.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03167, over 972550.99 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:15:36,107 INFO [train.py:715] (2/8) Epoch 12, batch 15800, loss[loss=0.1264, simple_loss=0.196, pruned_loss=0.02837, over 4755.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2103, pruned_loss=0.03128, over 972716.42 frames.], batch size: 12, lr: 1.81e-04
2022-05-07 11:16:14,197 INFO [train.py:715] (2/8) Epoch 12, batch 15850, loss[loss=0.1769, simple_loss=0.2506, pruned_loss=0.05158, over 4887.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2111, pruned_loss=0.03186, over 972165.67 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:16:51,696 INFO [train.py:715] (2/8) Epoch 12, batch 15900, loss[loss=0.1365, simple_loss=0.2125, pruned_loss=0.03019, over 4808.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2111, pruned_loss=0.03183, over 971492.43 frames.], batch size: 25, lr: 1.81e-04
2022-05-07 11:17:29,508 INFO [train.py:715] (2/8) Epoch 12, batch 15950, loss[loss=0.1141, simple_loss=0.1921, pruned_loss=0.0181, over 4818.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03184, over 972549.46 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:18:07,571 INFO [train.py:715] (2/8) Epoch 12, batch 16000, loss[loss=0.1257, simple_loss=0.2037, pruned_loss=0.02386, over 4897.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03167, over 972265.54 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 11:18:47,328 INFO [train.py:715] (2/8) Epoch 12, batch 16050, loss[loss=0.1791, simple_loss=0.2444, pruned_loss=0.05692, over 4898.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03172, over 971943.71 frames.], batch size: 39, lr: 1.81e-04
2022-05-07 11:19:25,279 INFO [train.py:715] (2/8) Epoch 12, batch 16100, loss[loss=0.1353, simple_loss=0.2142, pruned_loss=0.0282, over 4864.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03215, over 972027.00 frames.], batch size: 20, lr: 1.81e-04
2022-05-07 11:20:04,192 INFO [train.py:715] (2/8) Epoch 12, batch 16150, loss[loss=0.1297, simple_loss=0.2037, pruned_loss=0.02785, over 4829.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03181, over 971346.90 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:20:43,069 INFO [train.py:715] (2/8) Epoch 12, batch 16200, loss[loss=0.1345, simple_loss=0.2091, pruned_loss=0.02999, over 4745.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03141, over 970982.47 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:21:21,843 INFO [train.py:715] (2/8) Epoch 12, batch 16250, loss[loss=0.1259, simple_loss=0.2001, pruned_loss=0.02591, over 4790.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2114, pruned_loss=0.03192, over 970604.09 frames.], batch size: 12, lr: 1.81e-04
2022-05-07 11:21:59,697 INFO [train.py:715] (2/8) Epoch 12, batch 16300, loss[loss=0.1266, simple_loss=0.1962, pruned_loss=0.02849, over 4637.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.0318, over 971169.21 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:22:37,475 INFO [train.py:715] (2/8) Epoch 12, batch 16350, loss[loss=0.1535, simple_loss=0.2237, pruned_loss=0.04164, over 4778.00 frames.], tot_loss[loss=0.1383, simple_loss=0.212, pruned_loss=0.03229, over 971389.37 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:23:16,253 INFO [train.py:715] (2/8) Epoch 12, batch 16400, loss[loss=0.133, simple_loss=0.2057, pruned_loss=0.03015, over 4785.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2119, pruned_loss=0.03251, over 971373.45 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:23:54,212 INFO [train.py:715] (2/8) Epoch 12, batch 16450, loss[loss=0.1357, simple_loss=0.2175, pruned_loss=0.02699, over 4871.00 frames.], tot_loss[loss=0.1385, simple_loss=0.212, pruned_loss=0.03255, over 972478.44 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 11:24:33,098 INFO [train.py:715] (2/8) Epoch 12, batch 16500, loss[loss=0.1213, simple_loss=0.1887, pruned_loss=0.027, over 4887.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03217, over 972452.48 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:25:12,171 INFO [train.py:715] (2/8) Epoch 12, batch 16550, loss[loss=0.175, simple_loss=0.2401, pruned_loss=0.05499, over 4844.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03219, over 972674.24 frames.], batch size: 30, lr: 1.81e-04
2022-05-07 11:25:51,307 INFO [train.py:715] (2/8) Epoch 12, batch 16600, loss[loss=0.1512, simple_loss=0.2253, pruned_loss=0.03855, over 4953.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2117, pruned_loss=0.0324, over 972832.40 frames.], batch size: 21, lr: 1.81e-04
2022-05-07 11:26:29,847 INFO [train.py:715] (2/8) Epoch 12, batch 16650, loss[loss=0.1449, simple_loss=0.2165, pruned_loss=0.03662, over 4930.00 frames.], tot_loss[loss=0.139, simple_loss=0.2124, pruned_loss=0.03278, over 973162.29 frames.], batch size: 39, lr: 1.81e-04
2022-05-07 11:27:08,907 INFO [train.py:715] (2/8) Epoch 12, batch 16700, loss[loss=0.134, simple_loss=0.2088, pruned_loss=0.02955, over 4763.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03225, over 972722.48 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:27:48,111 INFO [train.py:715] (2/8) Epoch 12, batch 16750, loss[loss=0.144, simple_loss=0.2179, pruned_loss=0.03504, over 4922.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2118, pruned_loss=0.0323, over 973012.83 frames.], batch size: 39, lr: 1.81e-04
2022-05-07 11:28:26,501 INFO [train.py:715] (2/8) Epoch 12, batch 16800, loss[loss=0.1206, simple_loss=0.1952, pruned_loss=0.02303, over 4834.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2113, pruned_loss=0.03206, over 972513.88 frames.], batch size: 30, lr: 1.81e-04
2022-05-07 11:29:05,272 INFO [train.py:715] (2/8) Epoch 12, batch 16850, loss[loss=0.1287, simple_loss=0.2032, pruned_loss=0.02713, over 4747.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03194, over 972150.69 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:29:44,425 INFO [train.py:715] (2/8) Epoch 12, batch 16900, loss[loss=0.1386, simple_loss=0.2146, pruned_loss=0.03131, over 4871.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.03169, over 972642.90 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:30:24,181 INFO [train.py:715] (2/8) Epoch 12, batch 16950, loss[loss=0.1228, simple_loss=0.1941, pruned_loss=0.02574, over 4945.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03189, over 973085.68 frames.], batch size: 21, lr: 1.81e-04
2022-05-07 11:31:02,693 INFO [train.py:715] (2/8) Epoch 12, batch 17000, loss[loss=0.1397, simple_loss=0.2105, pruned_loss=0.03448, over 4862.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2112, pruned_loss=0.03208, over 973514.37 frames.], batch size: 20, lr: 1.81e-04
2022-05-07 11:31:40,880 INFO [train.py:715] (2/8) Epoch 12, batch 17050, loss[loss=0.1717, simple_loss=0.2412, pruned_loss=0.05113, over 4954.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2116, pruned_loss=0.03211, over 972746.60 frames.], batch size: 24, lr: 1.81e-04
2022-05-07 11:32:19,765 INFO [train.py:715] (2/8) Epoch 12, batch 17100, loss[loss=0.1131, simple_loss=0.1834, pruned_loss=0.02134, over 4693.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03208, over 972170.95 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:32:58,568 INFO [train.py:715] (2/8) Epoch 12, batch 17150, loss[loss=0.1169, simple_loss=0.2014, pruned_loss=0.01618, over 4803.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2112, pruned_loss=0.03206, over 972257.54 frames.], batch size: 12, lr: 1.81e-04
2022-05-07 11:33:37,596 INFO [train.py:715] (2/8) Epoch 12, batch 17200, loss[loss=0.1436, simple_loss=0.2131, pruned_loss=0.03705, over 4774.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03199, over 971509.78 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:34:16,027 INFO [train.py:715] (2/8) Epoch 12, batch 17250, loss[loss=0.1323, simple_loss=0.2079, pruned_loss=0.02834, over 4709.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03205, over 970843.23 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:34:54,494 INFO [train.py:715] (2/8) Epoch 12, batch 17300, loss[loss=0.1339, simple_loss=0.212, pruned_loss=0.02796, over 4817.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2113, pruned_loss=0.0327, over 970689.42 frames.], batch size: 26, lr: 1.81e-04
2022-05-07 11:35:32,126 INFO [train.py:715] (2/8) Epoch 12, batch 17350, loss[loss=0.1034, simple_loss=0.1746, pruned_loss=0.01609, over 4819.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03196, over 971227.53 frames.], batch size: 26, lr: 1.81e-04
2022-05-07 11:36:10,077 INFO [train.py:715] (2/8) Epoch 12, batch 17400, loss[loss=0.1725, simple_loss=0.2385, pruned_loss=0.05323, over 4949.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03227, over 971782.33 frames.], batch size: 39, lr: 1.81e-04
2022-05-07 11:36:47,846 INFO [train.py:715] (2/8) Epoch 12, batch 17450, loss[loss=0.1658, simple_loss=0.2351, pruned_loss=0.04826, over 4826.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03203, over 971972.30 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:37:26,178 INFO [train.py:715] (2/8) Epoch 12, batch 17500, loss[loss=0.124, simple_loss=0.2022, pruned_loss=0.02294, over 4986.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03221, over 971985.26 frames.], batch size: 39, lr: 1.81e-04
2022-05-07 11:38:04,046 INFO [train.py:715] (2/8) Epoch 12, batch 17550, loss[loss=0.1337, simple_loss=0.2045, pruned_loss=0.03139, over 4903.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03215, over 971797.00 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:38:42,239 INFO [train.py:715] (2/8) Epoch 12, batch 17600, loss[loss=0.1243, simple_loss=0.197, pruned_loss=0.02579, over 4797.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03169, over 972256.69 frames.], batch size: 25, lr: 1.81e-04
2022-05-07 11:39:19,902 INFO [train.py:715] (2/8) Epoch 12, batch 17650, loss[loss=0.1108, simple_loss=0.1956, pruned_loss=0.01306, over 4880.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03186, over 971675.20 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 11:39:58,008 INFO [train.py:715] (2/8) Epoch 12, batch 17700, loss[loss=0.14, simple_loss=0.2106, pruned_loss=0.03471, over 4843.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03186, over 971198.73 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:40:36,866 INFO [train.py:715] (2/8) Epoch 12, batch 17750, loss[loss=0.1469, simple_loss=0.2154, pruned_loss=0.03925, over 4830.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2121, pruned_loss=0.03267, over 971698.44 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:41:15,706 INFO [train.py:715] (2/8) Epoch 12, batch 17800, loss[loss=0.1368, simple_loss=0.2168, pruned_loss=0.02846, over 4874.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03267, over 971376.64 frames.], batch size: 22, lr: 1.81e-04
2022-05-07 11:41:54,206 INFO [train.py:715] (2/8) Epoch 12, batch 17850, loss[loss=0.1204, simple_loss=0.2007, pruned_loss=0.02003, over 4982.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2119, pruned_loss=0.03246, over 971847.40 frames.], batch size: 28, lr: 1.81e-04
2022-05-07 11:42:32,958 INFO [train.py:715] (2/8) Epoch 12, batch 17900, loss[loss=0.1493, simple_loss=0.2379, pruned_loss=0.03037, over 4937.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2128, pruned_loss=0.03281, over 971670.17 frames.], batch size: 39, lr: 1.81e-04
2022-05-07 11:43:10,438 INFO [train.py:715] (2/8) Epoch 12, batch 17950, loss[loss=0.1524, simple_loss=0.2175, pruned_loss=0.04369, over 4802.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2123, pruned_loss=0.03306, over 971478.66 frames.], batch size: 14, lr: 1.81e-04
2022-05-07 11:43:48,629 INFO [train.py:715] (2/8) Epoch 12, batch 18000, loss[loss=0.1365, simple_loss=0.216, pruned_loss=0.02849, over 4935.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2116, pruned_loss=0.03275, over 972745.61 frames.], batch size: 29, lr: 1.81e-04
2022-05-07 11:43:48,630 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 11:43:58,181 INFO [train.py:742] (2/8) Epoch 12, validation: loss=0.106, simple_loss=0.19, pruned_loss=0.011, over 914524.00 frames.
2022-05-07 11:44:36,607 INFO [train.py:715] (2/8) Epoch 12, batch 18050, loss[loss=0.1329, simple_loss=0.2105, pruned_loss=0.02763, over 4912.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03194, over 973311.90 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:45:14,476 INFO [train.py:715] (2/8) Epoch 12, batch 18100, loss[loss=0.1373, simple_loss=0.2221, pruned_loss=0.02621, over 4910.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03215, over 973291.14 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:45:52,625 INFO [train.py:715] (2/8) Epoch 12, batch 18150, loss[loss=0.1193, simple_loss=0.1933, pruned_loss=0.02261, over 4754.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2105, pruned_loss=0.03238, over 972835.65 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:46:30,450 INFO [train.py:715] (2/8) Epoch 12, batch 18200, loss[loss=0.1403, simple_loss=0.216, pruned_loss=0.03231, over 4747.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03219, over 972235.18 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:47:08,252 INFO [train.py:715] (2/8) Epoch 12, batch 18250, loss[loss=0.115, simple_loss=0.1953, pruned_loss=0.01741, over 4838.00 frames.], tot_loss[loss=0.138, simple_loss=0.2109, pruned_loss=0.0325, over 972785.39 frames.], batch size: 13, lr: 1.81e-04
2022-05-07 11:47:46,408 INFO [train.py:715] (2/8) Epoch 12, batch 18300, loss[loss=0.124, simple_loss=0.1982, pruned_loss=0.02497, over 4890.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2113, pruned_loss=0.03261, over 972847.16 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:48:24,293 INFO [train.py:715] (2/8) Epoch 12, batch 18350, loss[loss=0.1112, simple_loss=0.1857, pruned_loss=0.01835, over 4773.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03195, over 973227.63 frames.], batch size: 17, lr: 1.81e-04
2022-05-07 11:49:02,249 INFO [train.py:715] (2/8) Epoch 12, batch 18400, loss[loss=0.1331, simple_loss=0.2016, pruned_loss=0.03227, over 4764.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2109, pruned_loss=0.03195, over 973556.48 frames.], batch size: 12, lr: 1.81e-04
2022-05-07 11:49:39,750 INFO [train.py:715] (2/8) Epoch 12, batch 18450, loss[loss=0.1399, simple_loss=0.2138, pruned_loss=0.03297, over 4846.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03196, over 972882.22 frames.], batch size: 34, lr: 1.81e-04
2022-05-07 11:50:17,838 INFO [train.py:715] (2/8) Epoch 12, batch 18500, loss[loss=0.1088, simple_loss=0.186, pruned_loss=0.0158, over 4819.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2111, pruned_loss=0.03178, over 972183.22 frames.], batch size: 25, lr: 1.81e-04
2022-05-07 11:50:55,666 INFO [train.py:715] (2/8) Epoch 12, batch 18550, loss[loss=0.1321, simple_loss=0.2104, pruned_loss=0.02692, over 4928.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2106, pruned_loss=0.03142, over 971952.66 frames.], batch size: 18, lr: 1.81e-04
2022-05-07 11:51:33,501 INFO [train.py:715] (2/8) Epoch 12, batch 18600, loss[loss=0.1487, simple_loss=0.2171, pruned_loss=0.04018, over 4845.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.0315, over 972599.76 frames.], batch size: 15, lr: 1.81e-04
2022-05-07 11:52:11,113 INFO [train.py:715] (2/8) Epoch 12, batch 18650, loss[loss=0.129, simple_loss=0.1996, pruned_loss=0.02919, over 4742.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2113, pruned_loss=0.03222, over 971661.69 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:52:48,674 INFO [train.py:715] (2/8) Epoch 12, batch 18700, loss[loss=0.1273, simple_loss=0.2015, pruned_loss=0.02654, over 4971.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03215, over 971295.02 frames.], batch size: 24, lr: 1.81e-04
2022-05-07 11:53:26,073 INFO [train.py:715] (2/8) Epoch 12, batch 18750, loss[loss=0.1322, simple_loss=0.2191, pruned_loss=0.0227, over 4892.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.03215, over 970928.48 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:54:04,015 INFO [train.py:715] (2/8) Epoch 12, batch 18800, loss[loss=0.1148, simple_loss=0.194, pruned_loss=0.01778, over 4939.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03151, over 970945.02 frames.], batch size: 21, lr: 1.81e-04
2022-05-07 11:54:41,890 INFO [train.py:715] (2/8) Epoch 12, batch 18850, loss[loss=0.1624, simple_loss=0.2324, pruned_loss=0.04616, over 4933.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2114, pruned_loss=0.0318, over 971085.02 frames.], batch size: 23, lr: 1.81e-04
2022-05-07 11:55:19,706 INFO [train.py:715] (2/8) Epoch 12, batch 18900, loss[loss=0.1558, simple_loss=0.2294, pruned_loss=0.04108, over 4751.00 frames.], tot_loss[loss=0.138, simple_loss=0.212, pruned_loss=0.03206, over 970836.46 frames.], batch size: 16, lr: 1.81e-04
2022-05-07 11:55:57,998 INFO [train.py:715] (2/8) Epoch 12, batch 18950, loss[loss=0.1304, simple_loss=0.2101, pruned_loss=0.0253, over 4757.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2107, pruned_loss=0.03125, over 970717.12 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:56:35,809 INFO [train.py:715] (2/8) Epoch 12, batch 19000, loss[loss=0.1365, simple_loss=0.2063, pruned_loss=0.03336, over 4759.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2108, pruned_loss=0.03126, over 970713.04 frames.], batch size: 19, lr: 1.81e-04
2022-05-07 11:57:13,290 INFO [train.py:715] (2/8) Epoch 12, batch 19050, loss[loss=0.1401, simple_loss=0.2129, pruned_loss=0.03363, over 4877.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2105, pruned_loss=0.03093, over 971103.40 frames.], batch size: 32, lr: 1.80e-04
2022-05-07 11:57:50,569 INFO [train.py:715] (2/8) Epoch 12, batch 19100, loss[loss=0.1416, simple_loss=0.2141, pruned_loss=0.03453, over 4869.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2104, pruned_loss=0.03125, over 970743.19 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 11:58:28,557 INFO [train.py:715] (2/8) Epoch 12, batch 19150, loss[loss=0.1281, simple_loss=0.203, pruned_loss=0.0266, over 4900.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03171, over 971459.91 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 11:59:07,195 INFO [train.py:715] (2/8) Epoch 12, batch 19200, loss[loss=0.1493, simple_loss=0.2197, pruned_loss=0.03948, over 4928.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2111, pruned_loss=0.03185, over 971836.32 frames.], batch size: 29, lr: 1.80e-04
2022-05-07 11:59:45,245 INFO [train.py:715] (2/8) Epoch 12, batch 19250, loss[loss=0.1199, simple_loss=0.1887, pruned_loss=0.02551, over 4689.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03153, over 970832.83 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:00:23,723 INFO [train.py:715] (2/8) Epoch 12, batch 19300, loss[loss=0.1433, simple_loss=0.2193, pruned_loss=0.03366, over 4686.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2105, pruned_loss=0.0315, over 971399.95 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:01:01,908 INFO [train.py:715] (2/8) Epoch 12, batch 19350, loss[loss=0.1564, simple_loss=0.2302, pruned_loss=0.04133, over 4816.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2113, pruned_loss=0.03176, over 971805.02 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:01:39,913 INFO [train.py:715] (2/8) Epoch 12, batch 19400, loss[loss=0.1178, simple_loss=0.2051, pruned_loss=0.01529, over 4978.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03145, over 971984.18 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:02:17,953 INFO [train.py:715] (2/8) Epoch 12, batch 19450, loss[loss=0.106, simple_loss=0.1735, pruned_loss=0.01929, over 4843.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03147, over 972490.45 frames.], batch size: 13, lr: 1.80e-04
2022-05-07 12:02:56,772 INFO [train.py:715] (2/8) Epoch 12, batch 19500, loss[loss=0.1665, simple_loss=0.2406, pruned_loss=0.04618, over 4763.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03155, over 972379.44 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:03:35,595 INFO [train.py:715] (2/8) Epoch 12, batch 19550, loss[loss=0.1317, simple_loss=0.2095, pruned_loss=0.02693, over 4903.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03111, over 971907.45 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:04:14,320 INFO [train.py:715] (2/8) Epoch 12, batch 19600, loss[loss=0.1109, simple_loss=0.1823, pruned_loss=0.01971, over 4797.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03174, over 971917.44 frames.], batch size: 12, lr: 1.80e-04
2022-05-07 12:04:53,465 INFO [train.py:715] (2/8) Epoch 12, batch 19650, loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.02813, over 4926.00 frames.], tot_loss[loss=0.136, simple_loss=0.2091, pruned_loss=0.03148, over 971302.69 frames.], batch size: 29, lr: 1.80e-04
2022-05-07 12:05:32,633 INFO [train.py:715] (2/8) Epoch 12, batch 19700, loss[loss=0.1513, simple_loss=0.2217, pruned_loss=0.0405, over 4883.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.03172, over 971181.92 frames.], batch size: 16, lr: 1.80e-04
2022-05-07 12:06:12,015 INFO [train.py:715] (2/8) Epoch 12, batch 19750, loss[loss=0.1763, simple_loss=0.2416, pruned_loss=0.05548, over 4770.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2108, pruned_loss=0.03244, over 971299.83 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:06:52,638 INFO [train.py:715] (2/8) Epoch 12, batch 19800, loss[loss=0.151, simple_loss=0.2321, pruned_loss=0.03498, over 4878.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.03183, over 972148.31 frames.], batch size: 16, lr: 1.80e-04
2022-05-07 12:07:33,083 INFO [train.py:715] (2/8) Epoch 12, batch 19850, loss[loss=0.1687, simple_loss=0.2313, pruned_loss=0.05309, over 4922.00 frames.], tot_loss[loss=0.1372, simple_loss=0.211, pruned_loss=0.03176, over 972194.72 frames.], batch size: 39, lr: 1.80e-04
2022-05-07 12:08:14,254 INFO [train.py:715] (2/8) Epoch 12, batch 19900, loss[loss=0.1394, simple_loss=0.2133, pruned_loss=0.03276, over 4778.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2111, pruned_loss=0.03223, over 972268.33 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:08:54,597 INFO [train.py:715] (2/8) Epoch 12, batch 19950, loss[loss=0.1065, simple_loss=0.1829, pruned_loss=0.01511, over 4927.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03203, over 972756.94 frames.], batch size: 23, lr: 1.80e-04
2022-05-07 12:09:35,206 INFO [train.py:715] (2/8) Epoch 12, batch 20000, loss[loss=0.1183, simple_loss=0.1954, pruned_loss=0.02064, over 4855.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03184, over 973098.60 frames.], batch size: 34, lr: 1.80e-04
2022-05-07 12:10:15,438 INFO [train.py:715] (2/8) Epoch 12, batch 20050, loss[loss=0.1322, simple_loss=0.2121, pruned_loss=0.02617, over 4828.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03165, over 972981.62 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:10:55,693 INFO [train.py:715] (2/8) Epoch 12, batch 20100, loss[loss=0.1271, simple_loss=0.2017, pruned_loss=0.02628, over 4922.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03159, over 972952.64 frames.], batch size: 29, lr: 1.80e-04
2022-05-07 12:11:35,649 INFO [train.py:715] (2/8) Epoch 12, batch 20150, loss[loss=0.1439, simple_loss=0.2219, pruned_loss=0.03295, over 4718.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2092, pruned_loss=0.03154, over 972480.88 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:12:16,050 INFO [train.py:715] (2/8) Epoch 12, batch 20200, loss[loss=0.1073, simple_loss=0.1898, pruned_loss=0.01245, over 4937.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03148, over 973418.11 frames.], batch size: 23, lr: 1.80e-04
2022-05-07 12:12:56,154 INFO [train.py:715] (2/8) Epoch 12, batch 20250, loss[loss=0.115, simple_loss=0.194, pruned_loss=0.01801, over 4905.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03119, over 973101.88 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:13:36,209 INFO [train.py:715] (2/8) Epoch 12, batch 20300, loss[loss=0.1245, simple_loss=0.2015, pruned_loss=0.02374, over 4927.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03199, over 973360.50 frames.], batch size: 29, lr: 1.80e-04
2022-05-07 12:14:16,790 INFO [train.py:715] (2/8) Epoch 12, batch 20350, loss[loss=0.1307, simple_loss=0.1991, pruned_loss=0.03114, over 4792.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03217, over 971667.34 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:14:56,464 INFO [train.py:715] (2/8) Epoch 12, batch 20400, loss[loss=0.1457, simple_loss=0.2164, pruned_loss=0.03754, over 4791.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03185, over 971860.91 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:15:36,246 INFO [train.py:715] (2/8) Epoch 12, batch 20450, loss[loss=0.1351, simple_loss=0.2069, pruned_loss=0.03158, over 4902.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2109, pruned_loss=0.03172, over 972274.84 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:16:15,876 INFO [train.py:715] (2/8) Epoch 12, batch 20500, loss[loss=0.161, simple_loss=0.2345, pruned_loss=0.04377, over 4984.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2105, pruned_loss=0.03152, over 972564.10 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:16:56,336 INFO [train.py:715] (2/8) Epoch 12, batch 20550, loss[loss=0.1566, simple_loss=0.2377, pruned_loss=0.03776, over 4794.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03151, over 972049.39 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:17:36,233 INFO [train.py:715] (2/8) Epoch 12, batch 20600, loss[loss=0.13, simple_loss=0.2035, pruned_loss=0.02826, over 4853.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2109, pruned_loss=0.03151, over 972190.11 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:18:15,187 INFO [train.py:715] (2/8) Epoch 12, batch 20650, loss[loss=0.1508, simple_loss=0.2324, pruned_loss=0.03454, over 4881.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2099, pruned_loss=0.03082, over 970721.59 frames.], batch size: 16, lr: 1.80e-04
2022-05-07 12:18:54,297 INFO [train.py:715] (2/8) Epoch 12, batch 20700, loss[loss=0.1108, simple_loss=0.186, pruned_loss=0.01784, over 4830.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2096, pruned_loss=0.0305, over 971772.42 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:19:32,273 INFO [train.py:715] (2/8) Epoch 12, batch 20750, loss[loss=0.1162, simple_loss=0.1878, pruned_loss=0.0223, over 4767.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2096, pruned_loss=0.0305, over 972125.71 frames.], batch size: 12, lr: 1.80e-04
2022-05-07 12:20:10,577 INFO [train.py:715] (2/8) Epoch 12, batch 20800, loss[loss=0.1252, simple_loss=0.1979, pruned_loss=0.02622, over 4820.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03087, over 972907.06 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:20:48,329 INFO [train.py:715] (2/8) Epoch 12, batch 20850, loss[loss=0.1519, simple_loss=0.2279, pruned_loss=0.03793, over 4817.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03107, over 972788.05 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:21:26,468 INFO [train.py:715] (2/8) Epoch 12, batch 20900, loss[loss=0.1305, simple_loss=0.2096, pruned_loss=0.02567, over 4815.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.0316, over 973500.71 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:22:04,011 INFO [train.py:715] (2/8) Epoch 12, batch 20950, loss[loss=0.1356, simple_loss=0.2146, pruned_loss=0.0283, over 4787.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03206, over 973513.60 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:22:41,375 INFO [train.py:715] (2/8) Epoch 12, batch 21000, loss[loss=0.136, simple_loss=0.2065, pruned_loss=0.0328, over 4776.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03221, over 973031.77 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:22:41,375 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 12:22:50,905 INFO [train.py:742] (2/8) Epoch 12, validation: loss=0.1056, simple_loss=0.1896, pruned_loss=0.01081, over 914524.00 frames.
2022-05-07 12:23:28,726 INFO [train.py:715] (2/8) Epoch 12, batch 21050, loss[loss=0.1298, simple_loss=0.2059, pruned_loss=0.02683, over 4849.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03219, over 974008.65 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:24:06,828 INFO [train.py:715] (2/8) Epoch 12, batch 21100, loss[loss=0.1329, simple_loss=0.2031, pruned_loss=0.03133, over 4916.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2116, pruned_loss=0.03208, over 973681.44 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:24:44,629 INFO [train.py:715] (2/8) Epoch 12, batch 21150, loss[loss=0.1173, simple_loss=0.2007, pruned_loss=0.01697, over 4933.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2114, pruned_loss=0.03204, over 973103.68 frames.], batch size: 23, lr: 1.80e-04
2022-05-07 12:25:22,420 INFO [train.py:715] (2/8) Epoch 12, batch 21200, loss[loss=0.148, simple_loss=0.2193, pruned_loss=0.03837, over 4710.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03166, over 972344.79 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:26:00,700 INFO [train.py:715] (2/8) Epoch 12, batch 21250, loss[loss=0.1203, simple_loss=0.1917, pruned_loss=0.02446, over 4819.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03169, over 972164.60 frames.], batch size: 12, lr: 1.80e-04
2022-05-07 12:26:39,506 INFO [train.py:715] (2/8) Epoch 12, batch 21300, loss[loss=0.1149, simple_loss=0.192, pruned_loss=0.01893, over 4760.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03108, over 972754.86 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:27:17,266 INFO [train.py:715] (2/8) Epoch 12, batch 21350, loss[loss=0.1524, simple_loss=0.2274, pruned_loss=0.03873, over 4893.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03101, over 973327.69 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:27:56,360 INFO [train.py:715] (2/8) Epoch 12, batch 21400, loss[loss=0.1373, simple_loss=0.2102, pruned_loss=0.0322, over 4695.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03044, over 973052.09 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:28:35,914 INFO [train.py:715] (2/8) Epoch 12, batch 21450, loss[loss=0.1133, simple_loss=0.1833, pruned_loss=0.02164, over 4979.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03046, over 973097.31 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:29:14,511 INFO [train.py:715] (2/8) Epoch 12, batch 21500, loss[loss=0.1468, simple_loss=0.2229, pruned_loss=0.0353, over 4898.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03097, over 973036.56 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:29:53,100 INFO [train.py:715] (2/8) Epoch 12, batch 21550, loss[loss=0.1896, simple_loss=0.2743, pruned_loss=0.05239, over 4778.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.03168, over 972336.11 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:30:31,274 INFO [train.py:715] (2/8) Epoch 12, batch 21600, loss[loss=0.1454, simple_loss=0.2224, pruned_loss=0.03419, over 4961.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03146, over 972323.22 frames.], batch size: 39, lr: 1.80e-04
2022-05-07 12:31:09,733 INFO [train.py:715] (2/8) Epoch 12, batch 21650, loss[loss=0.1402, simple_loss=0.218, pruned_loss=0.03123, over 4700.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03184, over 972352.07 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:31:46,936 INFO [train.py:715] (2/8) Epoch 12, batch 21700, loss[loss=0.1206, simple_loss=0.2019, pruned_loss=0.01959, over 4864.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2114, pruned_loss=0.03199, over 971983.32 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:32:25,498 INFO [train.py:715] (2/8) Epoch 12, batch 21750, loss[loss=0.133, simple_loss=0.2117, pruned_loss=0.02716, over 4907.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2113, pruned_loss=0.03199, over 972553.33 frames.], batch size: 19, lr: 1.80e-04
2022-05-07 12:33:04,220 INFO [train.py:715] (2/8) Epoch 12, batch 21800, loss[loss=0.1373, simple_loss=0.2173, pruned_loss=0.0286, over 4823.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03213, over 972785.88 frames.], batch size: 13, lr: 1.80e-04
2022-05-07 12:33:42,112 INFO [train.py:715] (2/8) Epoch 12, batch 21850, loss[loss=0.1204, simple_loss=0.1911, pruned_loss=0.02491, over 4982.00 frames.], tot_loss[loss=0.138, simple_loss=0.2119, pruned_loss=0.03207, over 972849.11 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:34:19,728 INFO [train.py:715] (2/8) Epoch 12, batch 21900, loss[loss=0.1505, simple_loss=0.2204, pruned_loss=0.04029, over 4779.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03182, over 973338.28 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:34:58,476 INFO [train.py:715] (2/8) Epoch 12, batch 21950, loss[loss=0.1554, simple_loss=0.2324, pruned_loss=0.0392, over 4853.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03223, over 972360.94 frames.], batch size: 34, lr: 1.80e-04
2022-05-07 12:35:37,481 INFO [train.py:715] (2/8) Epoch 12, batch 22000, loss[loss=0.1428, simple_loss=0.2086, pruned_loss=0.03855, over 4758.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03198, over 972855.00 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:36:15,712 INFO [train.py:715] (2/8) Epoch 12, batch 22050, loss[loss=0.147, simple_loss=0.2161, pruned_loss=0.039, over 4960.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03136, over 972753.47 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:36:54,701 INFO [train.py:715] (2/8) Epoch 12, batch 22100, loss[loss=0.1123, simple_loss=0.1912, pruned_loss=0.01668, over 4916.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03118, over 971739.32 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:37:33,660 INFO [train.py:715] (2/8) Epoch 12, batch 22150, loss[loss=0.1352, simple_loss=0.2052, pruned_loss=0.03263, over 4934.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03118, over 971521.89 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:38:11,935 INFO [train.py:715] (2/8) Epoch 12, batch 22200, loss[loss=0.1383, simple_loss=0.2043, pruned_loss=0.0362, over 4985.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03138, over 972028.42 frames.], batch size: 31, lr: 1.80e-04
2022-05-07 12:38:49,700 INFO [train.py:715] (2/8) Epoch 12, batch 22250, loss[loss=0.1353, simple_loss=0.2051, pruned_loss=0.0328, over 4760.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03112, over 971941.05 frames.], batch size: 16, lr: 1.80e-04
2022-05-07 12:39:30,401 INFO [train.py:715] (2/8) Epoch 12, batch 22300, loss[loss=0.1495, simple_loss=0.2194, pruned_loss=0.03979, over 4802.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03174, over 972229.62 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:40:08,653 INFO [train.py:715] (2/8) Epoch 12, batch 22350, loss[loss=0.1263, simple_loss=0.2045, pruned_loss=0.02402, over 4978.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03173, over 971803.84 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:40:46,766 INFO [train.py:715] (2/8) Epoch 12, batch 22400, loss[loss=0.1236, simple_loss=0.2089, pruned_loss=0.01914, over 4704.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2116, pruned_loss=0.03198, over 971730.38 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:41:25,344 INFO [train.py:715] (2/8) Epoch 12, batch 22450, loss[loss=0.1389, simple_loss=0.2227, pruned_loss=0.02761, over 4954.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2122, pruned_loss=0.03225, over 971395.61 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:42:03,785 INFO [train.py:715] (2/8) Epoch 12, batch 22500, loss[loss=0.126, simple_loss=0.2093, pruned_loss=0.0214, over 4858.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2113, pruned_loss=0.0315, over 971198.41 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:42:42,488 INFO [train.py:715] (2/8) Epoch 12, batch 22550, loss[loss=0.1281, simple_loss=0.1977, pruned_loss=0.02924, over 4934.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03146, over 971573.50 frames.], batch size: 23, lr: 1.80e-04
2022-05-07 12:43:20,634 INFO [train.py:715] (2/8) Epoch 12, batch 22600, loss[loss=0.1364, simple_loss=0.2039, pruned_loss=0.03446, over 4937.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2111, pruned_loss=0.0318, over 971509.93 frames.], batch size: 21, lr: 1.80e-04
2022-05-07 12:43:58,689 INFO [train.py:715] (2/8) Epoch 12, batch 22650, loss[loss=0.1164, simple_loss=0.1925, pruned_loss=0.02014, over 4849.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.03161, over 971077.83 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:44:36,596 INFO [train.py:715] (2/8) Epoch 12, batch 22700, loss[loss=0.1384, simple_loss=0.2045, pruned_loss=0.03616, over 4968.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2108, pruned_loss=0.03209, over 971029.75 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:45:14,815 INFO [train.py:715] (2/8) Epoch 12, batch 22750, loss[loss=0.1289, simple_loss=0.2061, pruned_loss=0.02588, over 4896.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.0317, over 971564.86 frames.], batch size: 17, lr: 1.80e-04
2022-05-07 12:45:53,321 INFO [train.py:715] (2/8) Epoch 12, batch 22800, loss[loss=0.1405, simple_loss=0.2089, pruned_loss=0.03605, over 4792.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03159, over 971998.80 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:46:32,315 INFO [train.py:715] (2/8) Epoch 12, batch 22850, loss[loss=0.1168, simple_loss=0.1823, pruned_loss=0.02559, over 4793.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03173, over 972688.91 frames.], batch size: 14, lr: 1.80e-04
2022-05-07 12:47:10,525 INFO [train.py:715] (2/8) Epoch 12, batch 22900, loss[loss=0.1572, simple_loss=0.2352, pruned_loss=0.03961, over 4801.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2118, pruned_loss=0.03246, over 972082.04 frames.], batch size: 26, lr: 1.80e-04
2022-05-07 12:47:48,407 INFO [train.py:715] (2/8) Epoch 12, batch 22950, loss[loss=0.1518, simple_loss=0.2314, pruned_loss=0.03606, over 4785.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2104, pruned_loss=0.03216, over 971723.80 frames.], batch size: 18, lr: 1.80e-04
2022-05-07 12:48:26,679 INFO [train.py:715] (2/8) Epoch 12, batch 23000, loss[loss=0.1208, simple_loss=0.2022, pruned_loss=0.01975, over 4941.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.0317, over 972082.05 frames.], batch size: 29, lr: 1.80e-04
2022-05-07 12:49:04,951 INFO [train.py:715] (2/8) Epoch 12, batch 23050, loss[loss=0.1299, simple_loss=0.2052, pruned_loss=0.02734, over 4855.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03186, over 972469.42 frames.], batch size: 20, lr: 1.80e-04
2022-05-07 12:49:43,053 INFO [train.py:715] (2/8) Epoch 12, batch 23100, loss[loss=0.1342, simple_loss=0.2128, pruned_loss=0.02782, over 4793.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03115, over 972212.90 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:50:21,955 INFO [train.py:715] (2/8) Epoch 12, batch 23150, loss[loss=0.1439, simple_loss=0.2107, pruned_loss=0.03857, over 4699.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2094, pruned_loss=0.0315, over 972510.80 frames.], batch size: 15, lr: 1.80e-04
2022-05-07 12:51:01,024 INFO [train.py:715] (2/8) Epoch 12, batch 23200, loss[loss=0.1501, simple_loss=0.2228, pruned_loss=0.03866, over 4833.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03173, over 972588.27 frames.], batch size: 13, lr: 1.80e-04
2022-05-07 12:51:39,418 INFO [train.py:715] (2/8) Epoch 12, batch 23250, loss[loss=0.119, simple_loss=0.1945, pruned_loss=0.02179, over 4981.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03208, over 973076.11 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:52:17,110 INFO [train.py:715] (2/8) Epoch 12, batch 23300, loss[loss=0.1523, simple_loss=0.2261, pruned_loss=0.03924, over 4949.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03206, over 972971.65 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:52:55,811 INFO [train.py:715] (2/8) Epoch 12, batch 23350, loss[loss=0.1389, simple_loss=0.2135, pruned_loss=0.03215, over 4970.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03205, over 972310.29 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:53:33,838 INFO [train.py:715] (2/8) Epoch 12, batch 23400, loss[loss=0.1085, simple_loss=0.1801, pruned_loss=0.01851, over 4860.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03227, over 972037.74 frames.], batch size: 12, lr: 1.80e-04
2022-05-07 12:54:11,389 INFO [train.py:715] (2/8) Epoch 12, batch 23450, loss[loss=0.1238, simple_loss=0.1907, pruned_loss=0.02852, over 4660.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2108, pruned_loss=0.03212, over 971808.05 frames.], batch size: 13, lr: 1.80e-04
2022-05-07 12:54:49,572 INFO [train.py:715] (2/8) Epoch 12, batch 23500, loss[loss=0.1593, simple_loss=0.2446, pruned_loss=0.03698, over 4789.00 frames.], tot_loss[loss=0.1369, simple_loss=0.21, pruned_loss=0.0319, over 971292.59 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:55:28,413 INFO [train.py:715] (2/8) Epoch 12, batch 23550, loss[loss=0.142, simple_loss=0.2204, pruned_loss=0.03183, over 4910.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03146, over 971578.15 frames.], batch size: 39, lr: 1.80e-04
2022-05-07 12:56:07,102 INFO [train.py:715] (2/8) Epoch 12, batch 23600, loss[loss=0.1416, simple_loss=0.2072, pruned_loss=0.03805, over 4941.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2089, pruned_loss=0.03138, over 971824.67 frames.], batch size: 35, lr: 1.80e-04
2022-05-07 12:56:45,800 INFO [train.py:715] (2/8) Epoch 12, batch 23650, loss[loss=0.1523, simple_loss=0.2345, pruned_loss=0.03501, over 4924.00 frames.], tot_loss[loss=0.137, simple_loss=0.2099, pruned_loss=0.03203, over 971603.33 frames.], batch size: 23, lr: 1.80e-04
2022-05-07 12:57:24,209 INFO [train.py:715] (2/8) Epoch 12, batch 23700, loss[loss=0.1363, simple_loss=0.216, pruned_loss=0.02828, over 4833.00 frames.], tot_loss[loss=0.1371, simple_loss=0.21, pruned_loss=0.03208, over 971313.28 frames.], batch size: 25, lr: 1.80e-04
2022-05-07 12:58:02,491 INFO [train.py:715] (2/8) Epoch 12, batch 23750, loss[loss=0.1483, simple_loss=0.2247, pruned_loss=0.03598, over 4928.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2096, pruned_loss=0.03174, over 972165.83 frames.], batch size: 23, lr: 1.80e-04
2022-05-07 12:58:41,204 INFO [train.py:715] (2/8) Epoch 12, batch 23800, loss[loss=0.1477, simple_loss=0.2313, pruned_loss=0.03202, over 4860.00 frames.], tot_loss[loss=0.1372, simple_loss=0.21, pruned_loss=0.03222, over 972572.50 frames.], batch size: 32, lr: 1.80e-04
2022-05-07 12:59:20,122 INFO [train.py:715] (2/8) Epoch 12, batch 23850, loss[loss=0.1227, simple_loss=0.2011, pruned_loss=0.02217, over 4984.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2112, pruned_loss=0.03258, over 973300.92 frames.], batch size: 24, lr: 1.80e-04
2022-05-07 12:59:59,699 INFO [train.py:715] (2/8) Epoch 12, batch 23900, loss[loss=0.1493, simple_loss=0.2101, pruned_loss=0.04427, over 4853.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2113, pruned_loss=0.03273, over 973032.41 frames.], batch size: 30, lr: 1.80e-04
2022-05-07 13:00:39,414 INFO [train.py:715] (2/8) Epoch 12, batch 23950, loss[loss=0.1253, simple_loss=0.2021, pruned_loss=0.02423, over 4986.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.03235, over 972891.08 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:01:18,252 INFO [train.py:715] (2/8) Epoch 12, batch 24000, loss[loss=0.1572, simple_loss=0.2187, pruned_loss=0.04786, over 4841.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.03231, over 971879.03 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:01:18,254 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 13:01:27,806 INFO [train.py:742] (2/8) Epoch 12, validation: loss=0.1054, simple_loss=0.1895, pruned_loss=0.01071, over 914524.00 frames.
2022-05-07 13:02:06,830 INFO [train.py:715] (2/8) Epoch 12, batch 24050, loss[loss=0.1239, simple_loss=0.1965, pruned_loss=0.02563, over 4816.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03194, over 971623.66 frames.], batch size: 25, lr: 1.79e-04
2022-05-07 13:02:47,352 INFO [train.py:715] (2/8) Epoch 12, batch 24100, loss[loss=0.159, simple_loss=0.2372, pruned_loss=0.04044, over 4980.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2112, pruned_loss=0.03196, over 972279.05 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:03:27,830 INFO [train.py:715] (2/8) Epoch 12, batch 24150, loss[loss=0.138, simple_loss=0.2125, pruned_loss=0.03173, over 4945.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03212, over 971813.49 frames.], batch size: 23, lr: 1.79e-04
2022-05-07 13:04:07,865 INFO [train.py:715] (2/8) Epoch 12, batch 24200, loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03172, over 4775.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.03251, over 970926.73 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:04:47,987 INFO [train.py:715] (2/8) Epoch 12, batch 24250, loss[loss=0.1544, simple_loss=0.2298, pruned_loss=0.03952, over 4902.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03199, over 971536.50 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:05:28,028 INFO [train.py:715] (2/8) Epoch 12, batch 24300, loss[loss=0.1281, simple_loss=0.214, pruned_loss=0.02109, over 4889.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2107, pruned_loss=0.03226, over 971322.01 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 13:06:07,758 INFO [train.py:715] (2/8) Epoch 12, batch 24350, loss[loss=0.1335, simple_loss=0.2051, pruned_loss=0.03092, over 4795.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03213, over 971050.90 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:06:47,580 INFO [train.py:715] (2/8) Epoch 12, batch 24400, loss[loss=0.1076, simple_loss=0.1765, pruned_loss=0.01937, over 4755.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03163, over 972272.05 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:07:27,543 INFO [train.py:715] (2/8) Epoch 12, batch 24450, loss[loss=0.1225, simple_loss=0.2106, pruned_loss=0.01716, over 4793.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03188, over 971478.23 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:08:07,314 INFO [train.py:715] (2/8) Epoch 12, batch 24500, loss[loss=0.1354, simple_loss=0.2027, pruned_loss=0.03405, over 4821.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03204, over 971129.61 frames.], batch size: 27, lr: 1.79e-04
2022-05-07 13:08:46,553 INFO [train.py:715] (2/8) Epoch 12, batch 24550, loss[loss=0.1435, simple_loss=0.2135, pruned_loss=0.03675, over 4832.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03178, over 971650.41 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:09:26,201 INFO [train.py:715] (2/8) Epoch 12, batch 24600, loss[loss=0.1203, simple_loss=0.1933, pruned_loss=0.02358, over 4969.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.03216, over 971380.97 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:10:05,928 INFO [train.py:715] (2/8) Epoch 12, batch 24650, loss[loss=0.1388, simple_loss=0.1975, pruned_loss=0.04009, over 4844.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.03201, over 971002.35 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:10:45,626 INFO [train.py:715] (2/8) Epoch 12, batch 24700, loss[loss=0.1445, simple_loss=0.2224, pruned_loss=0.03326, over 4701.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.0321, over 971098.20 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:11:24,792 INFO [train.py:715] (2/8) Epoch 12, batch 24750, loss[loss=0.155, simple_loss=0.2296, pruned_loss=0.04024, over 4788.00 frames.], tot_loss[loss=0.138, simple_loss=0.211, pruned_loss=0.03249, over 971610.14 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:12:04,999 INFO [train.py:715] (2/8) Epoch 12, batch 24800, loss[loss=0.169, simple_loss=0.2558, pruned_loss=0.04114, over 4805.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03203, over 971126.91 frames.], batch size: 25, lr: 1.79e-04
2022-05-07 13:12:44,882 INFO [train.py:715] (2/8) Epoch 12, batch 24850, loss[loss=0.1115, simple_loss=0.1936, pruned_loss=0.01473, over 4770.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2114, pruned_loss=0.03205, over 971185.42 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:13:24,109 INFO [train.py:715] (2/8) Epoch 12, batch 24900, loss[loss=0.1217, simple_loss=0.2038, pruned_loss=0.01977, over 4768.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.0313, over 971085.83 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:14:03,446 INFO [train.py:715] (2/8) Epoch 12, batch 24950, loss[loss=0.1291, simple_loss=0.1998, pruned_loss=0.0292, over 4836.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03121, over 971106.43 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:14:42,358 INFO [train.py:715] (2/8) Epoch 12, batch 25000, loss[loss=0.1051, simple_loss=0.171, pruned_loss=0.01961, over 4784.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2096, pruned_loss=0.03075, over 971317.67 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:15:20,413 INFO [train.py:715] (2/8) Epoch 12, batch 25050, loss[loss=0.1711, simple_loss=0.2338, pruned_loss=0.05418, over 4807.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03093, over 971984.06 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:15:58,462 INFO [train.py:715] (2/8) Epoch 12, batch 25100, loss[loss=0.1572, simple_loss=0.2264, pruned_loss=0.04399, over 4745.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03089, over 971821.23 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:16:36,854 INFO [train.py:715] (2/8) Epoch 12, batch 25150, loss[loss=0.1327, simple_loss=0.2049, pruned_loss=0.03024, over 4756.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03071, over 970932.60 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:17:15,107 INFO [train.py:715] (2/8) Epoch 12, batch 25200, loss[loss=0.1365, simple_loss=0.2083, pruned_loss=0.03234, over 4805.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03189, over 972492.51 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:17:52,727 INFO [train.py:715] (2/8) Epoch 12, batch 25250, loss[loss=0.1155, simple_loss=0.1886, pruned_loss=0.0212, over 4757.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.03167, over 972361.36 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:18:30,735 INFO [train.py:715] (2/8) Epoch 12, batch 25300, loss[loss=0.1185, simple_loss=0.191, pruned_loss=0.023, over 4830.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03176, over 972158.93 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:19:08,814 INFO [train.py:715] (2/8) Epoch 12, batch 25350, loss[loss=0.1164, simple_loss=0.1952, pruned_loss=0.01886, over 4943.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2091, pruned_loss=0.03131, over 972589.11 frames.], batch size: 23, lr: 1.79e-04
2022-05-07 13:19:47,795 INFO [train.py:715] (2/8) Epoch 12, batch 25400, loss[loss=0.1055, simple_loss=0.1846, pruned_loss=0.01318, over 4962.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.03132, over 973327.61 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:20:26,699 INFO [train.py:715] (2/8) Epoch 12, batch 25450, loss[loss=0.1725, simple_loss=0.2389, pruned_loss=0.05304, over 4836.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03171, over 973055.58 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:21:06,583 INFO [train.py:715] (2/8) Epoch 12, batch 25500, loss[loss=0.1399, simple_loss=0.2042, pruned_loss=0.03779, over 4770.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03199, over 972928.09 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:21:45,648 INFO [train.py:715] (2/8) Epoch 12, batch 25550, loss[loss=0.1476, simple_loss=0.2277, pruned_loss=0.03371, over 4879.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03146, over 972307.16 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 13:22:23,736 INFO [train.py:715] (2/8) Epoch 12, batch 25600, loss[loss=0.1409, simple_loss=0.2139, pruned_loss=0.03402, over 4878.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03149, over 971852.88 frames.], batch size: 38, lr: 1.79e-04
2022-05-07 13:23:02,066 INFO [train.py:715] (2/8) Epoch 12, batch 25650, loss[loss=0.1426, simple_loss=0.2101, pruned_loss=0.0375, over 4953.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03192, over 972043.60 frames.], batch size: 35, lr: 1.79e-04
2022-05-07 13:23:40,758 INFO [train.py:715] (2/8) Epoch 12, batch 25700, loss[loss=0.1365, simple_loss=0.22, pruned_loss=0.02651, over 4760.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03208, over 971881.62 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:24:19,544 INFO [train.py:715] (2/8) Epoch 12, batch 25750, loss[loss=0.1499, simple_loss=0.21, pruned_loss=0.04488, over 4841.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2107, pruned_loss=0.03247, over 972747.54 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:24:58,013 INFO [train.py:715] (2/8) Epoch 12, batch 25800, loss[loss=0.1354, simple_loss=0.2042, pruned_loss=0.03331, over 4823.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03224, over 971921.19 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:25:36,922 INFO [train.py:715] (2/8) Epoch 12, batch 25850, loss[loss=0.1482, simple_loss=0.2276, pruned_loss=0.03437, over 4793.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.03208, over 972186.66 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:26:15,480 INFO [train.py:715] (2/8) Epoch 12, batch 25900, loss[loss=0.1419, simple_loss=0.2037, pruned_loss=0.04003, over 4835.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2094, pruned_loss=0.03179, over 973190.80 frames.], batch size: 32, lr: 1.79e-04
2022-05-07 13:26:53,773 INFO [train.py:715] (2/8) Epoch 12, batch 25950, loss[loss=0.1405, simple_loss=0.2095, pruned_loss=0.03572, over 4875.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2088, pruned_loss=0.03165, over 972235.05 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 13:27:31,254 INFO [train.py:715] (2/8) Epoch 12, batch 26000, loss[loss=0.1385, simple_loss=0.2143, pruned_loss=0.0313, over 4837.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2095, pruned_loss=0.03179, over 972393.17 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:28:09,533 INFO [train.py:715] (2/8) Epoch 12, batch 26050, loss[loss=0.1311, simple_loss=0.2021, pruned_loss=0.03004, over 4828.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03139, over 971986.31 frames.], batch size: 27, lr: 1.79e-04
2022-05-07 13:28:48,388 INFO [train.py:715] (2/8) Epoch 12, batch 26100, loss[loss=0.1393, simple_loss=0.2182, pruned_loss=0.03019, over 4976.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03136, over 972429.20 frames.], batch size: 24, lr: 1.79e-04
2022-05-07 13:29:27,204 INFO [train.py:715] (2/8) Epoch 12, batch 26150, loss[loss=0.158, simple_loss=0.224, pruned_loss=0.04602, over 4787.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2088, pruned_loss=0.03118, over 971327.78 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:30:06,155 INFO [train.py:715] (2/8) Epoch 12, batch 26200, loss[loss=0.126, simple_loss=0.1937, pruned_loss=0.0291, over 4931.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03127, over 972065.64 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:30:44,508 INFO [train.py:715] (2/8) Epoch 12, batch 26250, loss[loss=0.1217, simple_loss=0.1892, pruned_loss=0.02703, over 4905.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03127, over 972719.57 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:31:23,014 INFO [train.py:715] (2/8) Epoch 12, batch 26300, loss[loss=0.1757, simple_loss=0.2433, pruned_loss=0.05407, over 4848.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03137, over 972880.59 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:32:02,155 INFO [train.py:715] (2/8) Epoch 12, batch 26350, loss[loss=0.1535, simple_loss=0.2224, pruned_loss=0.04232, over 4856.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03209, over 972352.42 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:32:40,224 INFO [train.py:715] (2/8) Epoch 12, batch 26400, loss[loss=0.1249, simple_loss=0.1949, pruned_loss=0.02747, over 4918.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03201, over 972765.17 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:33:18,355 INFO [train.py:715] (2/8) Epoch 12, batch 26450, loss[loss=0.1512, simple_loss=0.2227, pruned_loss=0.03987, over 4811.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2108, pruned_loss=0.03227, over 972184.00 frames.], batch size: 26, lr: 1.79e-04
2022-05-07 13:33:56,294 INFO [train.py:715] (2/8) Epoch 12, batch 26500, loss[loss=0.1245, simple_loss=0.1941, pruned_loss=0.02744, over 4943.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2109, pruned_loss=0.03222, over 971844.15 frames.], batch size: 35, lr: 1.79e-04
2022-05-07 13:34:34,593 INFO [train.py:715] (2/8) Epoch 12, batch 26550, loss[loss=0.1259, simple_loss=0.1998, pruned_loss=0.02604, over 4808.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2107, pruned_loss=0.03217, over 971688.97 frames.], batch size: 25, lr: 1.79e-04
2022-05-07 13:35:12,933 INFO [train.py:715] (2/8) Epoch 12, batch 26600, loss[loss=0.1142, simple_loss=0.1921, pruned_loss=0.01815, over 4937.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2112, pruned_loss=0.03219, over 971484.11 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 13:35:51,456 INFO [train.py:715] (2/8) Epoch 12, batch 26650, loss[loss=0.1362, simple_loss=0.2055, pruned_loss=0.03345, over 4847.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03217, over 972443.56 frames.], batch size: 32, lr: 1.79e-04
2022-05-07 13:36:30,041 INFO [train.py:715] (2/8) Epoch 12, batch 26700, loss[loss=0.1668, simple_loss=0.242, pruned_loss=0.04574, over 4809.00 frames.], tot_loss[loss=0.138, simple_loss=0.2117, pruned_loss=0.03215, over 972842.93 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 13:37:08,438 INFO [train.py:715] (2/8) Epoch 12, batch 26750, loss[loss=0.1662, simple_loss=0.2282, pruned_loss=0.05211, over 4892.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03205, over 972327.38 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:37:47,891 INFO [train.py:715] (2/8) Epoch 12, batch 26800, loss[loss=0.1297, simple_loss=0.2023, pruned_loss=0.02857, over 4816.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03206, over 972593.86 frames.], batch size: 26, lr: 1.79e-04
2022-05-07 13:38:27,720 INFO [train.py:715] (2/8) Epoch 12, batch 26850, loss[loss=0.1232, simple_loss=0.1981, pruned_loss=0.02417, over 4990.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03198, over 972690.49 frames.], batch size: 25, lr: 1.79e-04
2022-05-07 13:39:07,109 INFO [train.py:715] (2/8) Epoch 12, batch 26900, loss[loss=0.1182, simple_loss=0.187, pruned_loss=0.02467, over 4786.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2098, pruned_loss=0.03198, over 971968.63 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:39:45,937 INFO [train.py:715] (2/8) Epoch 12, batch 26950, loss[loss=0.1226, simple_loss=0.1931, pruned_loss=0.02601, over 4911.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2094, pruned_loss=0.03166, over 972408.83 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:40:25,468 INFO [train.py:715] (2/8) Epoch 12, batch 27000, loss[loss=0.1425, simple_loss=0.2263, pruned_loss=0.02937, over 4813.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03145, over 972472.45 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:40:25,469 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 13:40:37,912 INFO [train.py:742] (2/8) Epoch 12, validation: loss=0.1054, simple_loss=0.1894, pruned_loss=0.01072, over 914524.00 frames.
2022-05-07 13:41:17,253 INFO [train.py:715] (2/8) Epoch 12, batch 27050, loss[loss=0.1206, simple_loss=0.1878, pruned_loss=0.02664, over 4852.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2091, pruned_loss=0.03164, over 971842.26 frames.], batch size: 32, lr: 1.79e-04
2022-05-07 13:41:55,480 INFO [train.py:715] (2/8) Epoch 12, batch 27100, loss[loss=0.1343, simple_loss=0.21, pruned_loss=0.02927, over 4760.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2095, pruned_loss=0.03183, over 971860.62 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:42:33,836 INFO [train.py:715] (2/8) Epoch 12, batch 27150, loss[loss=0.1319, simple_loss=0.2009, pruned_loss=0.03138, over 4857.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2106, pruned_loss=0.03218, over 972178.09 frames.], batch size: 20, lr: 1.79e-04
2022-05-07 13:43:12,694 INFO [train.py:715] (2/8) Epoch 12, batch 27200, loss[loss=0.1341, simple_loss=0.2076, pruned_loss=0.03028, over 4941.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03214, over 972616.78 frames.], batch size: 39, lr: 1.79e-04
2022-05-07 13:43:50,996 INFO [train.py:715] (2/8) Epoch 12, batch 27250, loss[loss=0.111, simple_loss=0.1823, pruned_loss=0.0199, over 4789.00 frames.], tot_loss[loss=0.1385, simple_loss=0.212, pruned_loss=0.03245, over 972784.26 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:44:29,618 INFO [train.py:715] (2/8) Epoch 12, batch 27300, loss[loss=0.137, simple_loss=0.2067, pruned_loss=0.03366, over 4969.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.03222, over 973730.18 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:45:08,201 INFO [train.py:715] (2/8) Epoch 12, batch 27350, loss[loss=0.1432, simple_loss=0.2169, pruned_loss=0.03471, over 4767.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.0326, over 973569.61 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:45:47,188 INFO [train.py:715] (2/8) Epoch 12, batch 27400, loss[loss=0.1248, simple_loss=0.2001, pruned_loss=0.02476, over 4878.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2117, pruned_loss=0.0318, over 972441.43 frames.], batch size: 34, lr: 1.79e-04
2022-05-07 13:46:25,864 INFO [train.py:715] (2/8) Epoch 12, batch 27450, loss[loss=0.1515, simple_loss=0.2306, pruned_loss=0.03619, over 4866.00 frames.], tot_loss[loss=0.1368, simple_loss=0.211, pruned_loss=0.03132, over 972751.92 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 13:47:04,361 INFO [train.py:715] (2/8) Epoch 12, batch 27500, loss[loss=0.1404, simple_loss=0.2034, pruned_loss=0.03869, over 4688.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2108, pruned_loss=0.0313, over 973315.09 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:47:43,135 INFO [train.py:715] (2/8) Epoch 12, batch 27550, loss[loss=0.1262, simple_loss=0.2037, pruned_loss=0.02439, over 4992.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03139, over 972340.88 frames.], batch size: 16, lr: 1.79e-04
2022-05-07 13:48:21,815 INFO [train.py:715] (2/8) Epoch 12, batch 27600, loss[loss=0.1225, simple_loss=0.192, pruned_loss=0.02648, over 4986.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.0314, over 972474.90 frames.], batch size: 27, lr: 1.79e-04
2022-05-07 13:49:00,965 INFO [train.py:715] (2/8) Epoch 12, batch 27650, loss[loss=0.151, simple_loss=0.2287, pruned_loss=0.0367, over 4923.00 frames.], tot_loss[loss=0.138, simple_loss=0.2117, pruned_loss=0.03213, over 972310.15 frames.], batch size: 18, lr: 1.79e-04
2022-05-07 13:49:39,577 INFO [train.py:715] (2/8) Epoch 12, batch 27700, loss[loss=0.1429, simple_loss=0.2156, pruned_loss=0.03505, over 4924.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2119, pruned_loss=0.03266, over 971921.78 frames.], batch size: 23, lr: 1.79e-04
2022-05-07 13:50:18,424 INFO [train.py:715] (2/8) Epoch 12, batch 27750, loss[loss=0.1397, simple_loss=0.2122, pruned_loss=0.03356, over 4937.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2121, pruned_loss=0.03231, over 972005.20 frames.], batch size: 23, lr: 1.79e-04
2022-05-07 13:50:56,329 INFO [train.py:715] (2/8) Epoch 12, batch 27800, loss[loss=0.1457, simple_loss=0.2306, pruned_loss=0.03035, over 4844.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2123, pruned_loss=0.0323, over 970954.27 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 13:51:34,005 INFO [train.py:715] (2/8) Epoch 12, batch 27850, loss[loss=0.1211, simple_loss=0.1877, pruned_loss=0.02722, over 4834.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2121, pruned_loss=0.03234, over 970895.42 frames.], batch size: 13, lr: 1.79e-04
2022-05-07 13:52:12,422 INFO [train.py:715] (2/8) Epoch 12, batch 27900, loss[loss=0.1673, simple_loss=0.2299, pruned_loss=0.05232, over 4982.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2121, pruned_loss=0.03277, over 971385.91 frames.], batch size: 31, lr: 1.79e-04
2022-05-07 13:52:50,385 INFO [train.py:715] (2/8) Epoch 12, batch 27950, loss[loss=0.1248, simple_loss=0.2068, pruned_loss=0.02138, over 4818.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2119, pruned_loss=0.03227, over 971245.99 frames.], batch size: 27, lr: 1.79e-04
2022-05-07 13:53:28,677 INFO [train.py:715] (2/8) Epoch 12, batch 28000, loss[loss=0.1433, simple_loss=0.2099, pruned_loss=0.03832, over 4765.00 frames.], tot_loss[loss=0.138, simple_loss=0.2118, pruned_loss=0.03212, over 971570.70 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:54:06,332 INFO [train.py:715] (2/8) Epoch 12, batch 28050, loss[loss=0.1071, simple_loss=0.1724, pruned_loss=0.02091, over 4773.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2124, pruned_loss=0.0326, over 972049.60 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 13:54:44,516 INFO [train.py:715] (2/8) Epoch 12, batch 28100, loss[loss=0.1329, simple_loss=0.2023, pruned_loss=0.03176, over 4798.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.0328, over 972144.86 frames.], batch size: 25, lr: 1.79e-04
2022-05-07 13:55:22,260 INFO [train.py:715] (2/8) Epoch 12, batch 28150, loss[loss=0.1231, simple_loss=0.1939, pruned_loss=0.02611, over 4759.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2123, pruned_loss=0.03269, over 972345.49 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:56:00,666 INFO [train.py:715] (2/8) Epoch 12, batch 28200, loss[loss=0.1111, simple_loss=0.1894, pruned_loss=0.01645, over 4929.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2116, pruned_loss=0.03252, over 972665.69 frames.], batch size: 23, lr: 1.79e-04
2022-05-07 13:56:39,078 INFO [train.py:715] (2/8) Epoch 12, batch 28250, loss[loss=0.1238, simple_loss=0.1962, pruned_loss=0.02565, over 4754.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03211, over 971957.74 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 13:57:17,045 INFO [train.py:715] (2/8) Epoch 12, batch 28300, loss[loss=0.1354, simple_loss=0.204, pruned_loss=0.03342, over 4914.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03206, over 972531.25 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 13:57:55,848 INFO [train.py:715] (2/8) Epoch 12, batch 28350, loss[loss=0.1669, simple_loss=0.2394, pruned_loss=0.04724, over 4909.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2114, pruned_loss=0.03246, over 972195.55 frames.], batch size: 39, lr: 1.79e-04
2022-05-07 13:58:33,887 INFO [train.py:715] (2/8) Epoch 12, batch 28400, loss[loss=0.1395, simple_loss=0.2049, pruned_loss=0.03706, over 4783.00 frames.], tot_loss[loss=0.138, simple_loss=0.2112, pruned_loss=0.03244, over 972291.87 frames.], batch size: 14, lr: 1.79e-04
2022-05-07 13:59:12,073 INFO [train.py:715] (2/8) Epoch 12, batch 28450, loss[loss=0.1636, simple_loss=0.2187, pruned_loss=0.05424, over 4867.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2111, pruned_loss=0.03281, over 971746.31 frames.], batch size: 30, lr: 1.79e-04
2022-05-07 13:59:49,934 INFO [train.py:715] (2/8) Epoch 12, batch 28500, loss[loss=0.1105, simple_loss=0.1919, pruned_loss=0.01454, over 4790.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2114, pruned_loss=0.03276, over 972080.53 frames.], batch size: 17, lr: 1.79e-04
2022-05-07 14:00:27,902 INFO [train.py:715] (2/8) Epoch 12, batch 28550, loss[loss=0.1303, simple_loss=0.2003, pruned_loss=0.03017, over 4791.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03203, over 972614.16 frames.], batch size: 12, lr: 1.79e-04
2022-05-07 14:01:06,320 INFO [train.py:715] (2/8) Epoch 12, batch 28600, loss[loss=0.1194, simple_loss=0.1875, pruned_loss=0.02567, over 4873.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03192, over 972829.24 frames.], batch size: 22, lr: 1.79e-04
2022-05-07 14:01:44,218 INFO [train.py:715] (2/8) Epoch 12, batch 28650, loss[loss=0.1261, simple_loss=0.197, pruned_loss=0.02761, over 4816.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03144, over 972221.33 frames.], batch size: 15, lr: 1.79e-04
2022-05-07 14:02:23,316 INFO [train.py:715] (2/8) Epoch 12, batch 28700, loss[loss=0.1409, simple_loss=0.2221, pruned_loss=0.0299, over 4756.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2109, pruned_loss=0.03137, over 972637.82 frames.], batch size: 19, lr: 1.79e-04
2022-05-07 14:03:01,821 INFO [train.py:715] (2/8) Epoch 12, batch 28750, loss[loss=0.125, simple_loss=0.2022, pruned_loss=0.02389, over 4829.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03084, over 972224.66 frames.], batch size: 26, lr: 1.79e-04
2022-05-07 14:03:40,788 INFO [train.py:715] (2/8) Epoch 12, batch 28800, loss[loss=0.1268, simple_loss=0.1989, pruned_loss=0.02731, over 4841.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03082, over 973021.83 frames.], batch size: 34, lr: 1.79e-04
2022-05-07 14:04:18,678 INFO [train.py:715] (2/8) Epoch 12, batch 28850, loss[loss=0.1218, simple_loss=0.1956, pruned_loss=0.02393, over 4810.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2094, pruned_loss=0.0302, over 972424.57 frames.], batch size: 21, lr: 1.79e-04
2022-05-07 14:04:57,033 INFO [train.py:715] (2/8) Epoch 12, batch 28900, loss[loss=0.1309, simple_loss=0.2098, pruned_loss=0.02596, over 4841.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03024, over 972035.73 frames.], batch size: 20, lr: 1.78e-04
2022-05-07 14:05:35,791 INFO [train.py:715] (2/8) Epoch 12, batch 28950, loss[loss=0.1177, simple_loss=0.1942, pruned_loss=0.02058, over 4751.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03022, over 972091.79 frames.], batch size: 16, lr: 1.78e-04
2022-05-07 14:06:14,145 INFO [train.py:715] (2/8) Epoch 12, batch 29000, loss[loss=0.1703, simple_loss=0.234, pruned_loss=0.05327, over 4692.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.0303, over 971468.47 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:06:53,417 INFO [train.py:715] (2/8) Epoch 12, batch 29050, loss[loss=0.1319, simple_loss=0.2031, pruned_loss=0.0304, over 4776.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03068, over 971748.52 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:07:31,884 INFO [train.py:715] (2/8) Epoch 12, batch 29100, loss[loss=0.122, simple_loss=0.1959, pruned_loss=0.02407, over 4695.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03059, over 971562.39 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:08:10,554 INFO [train.py:715] (2/8) Epoch 12, batch 29150, loss[loss=0.139, simple_loss=0.2032, pruned_loss=0.03737, over 4808.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03104, over 972107.39 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:08:48,981 INFO [train.py:715] (2/8) Epoch 12, batch 29200, loss[loss=0.1365, simple_loss=0.214, pruned_loss=0.02956, over 4839.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03126, over 972286.19 frames.], batch size: 20, lr: 1.78e-04
2022-05-07 14:09:27,675 INFO [train.py:715] (2/8) Epoch 12, batch 29250, loss[loss=0.1163, simple_loss=0.1926, pruned_loss=0.02, over 4799.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03093, over 972090.98 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:10:05,810 INFO [train.py:715] (2/8) Epoch 12, batch 29300, loss[loss=0.1178, simple_loss=0.1965, pruned_loss=0.01961, over 4762.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03115, over 973266.94 frames.], batch size: 16, lr: 1.78e-04
2022-05-07 14:10:43,235 INFO [train.py:715] (2/8) Epoch 12, batch 29350, loss[loss=0.1443, simple_loss=0.2233, pruned_loss=0.03265, over 4822.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.03113, over 972513.61 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:11:22,340 INFO [train.py:715] (2/8) Epoch 12, batch 29400, loss[loss=0.1324, simple_loss=0.2041, pruned_loss=0.03032, over 4740.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2104, pruned_loss=0.0312, over 972361.93 frames.], batch size: 16, lr: 1.78e-04
2022-05-07 14:12:00,595 INFO [train.py:715] (2/8) Epoch 12, batch 29450, loss[loss=0.1718, simple_loss=0.2572, pruned_loss=0.04319, over 4945.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03161, over 972350.79 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 14:12:38,754 INFO [train.py:715] (2/8) Epoch 12, batch 29500, loss[loss=0.1406, simple_loss=0.2161, pruned_loss=0.03253, over 4963.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03171, over 972849.66 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:13:16,879 INFO [train.py:715] (2/8) Epoch 12, batch 29550, loss[loss=0.141, simple_loss=0.2165, pruned_loss=0.03277, over 4835.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03167, over 973291.10 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:13:55,809 INFO [train.py:715] (2/8) Epoch 12, batch 29600, loss[loss=0.1398, simple_loss=0.2063, pruned_loss=0.03661, over 4753.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03218, over 973036.82 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:14:34,034 INFO [train.py:715] (2/8) Epoch 12, batch 29650, loss[loss=0.1487, simple_loss=0.2195, pruned_loss=0.03891, over 4788.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03235, over 972454.88 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:15:11,742 INFO [train.py:715] (2/8) Epoch 12, batch 29700, loss[loss=0.1241, simple_loss=0.2023, pruned_loss=0.02292, over 4820.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2106, pruned_loss=0.03192, over 971863.66 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:15:51,280 INFO [train.py:715] (2/8) Epoch 12, batch 29750, loss[loss=0.1542, simple_loss=0.2296, pruned_loss=0.03939, over 4903.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03185, over 972613.45 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 14:16:30,393 INFO [train.py:715] (2/8) Epoch 12, batch 29800, loss[loss=0.1257, simple_loss=0.1966, pruned_loss=0.02736, over 4945.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03194, over 971847.36 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:17:09,204 INFO [train.py:715] (2/8) Epoch 12, batch 29850, loss[loss=0.149, simple_loss=0.2289, pruned_loss=0.03461, over 4931.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2116, pruned_loss=0.03212, over 972338.88 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:17:47,535 INFO [train.py:715] (2/8) Epoch 12, batch 29900, loss[loss=0.1243, simple_loss=0.199, pruned_loss=0.02482, over 4885.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2113, pruned_loss=0.03177, over 972617.45 frames.], batch size: 22, lr: 1.78e-04
2022-05-07 14:18:26,386 INFO [train.py:715] (2/8) Epoch 12, batch 29950, loss[loss=0.1218, simple_loss=0.1963, pruned_loss=0.02363, over 4922.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2107, pruned_loss=0.03133, over 972410.02 frames.], batch size: 23, lr: 1.78e-04
2022-05-07 14:19:04,509 INFO [train.py:715] (2/8) Epoch 12, batch 30000, loss[loss=0.1572, simple_loss=0.2232, pruned_loss=0.04557, over 4789.00 frames.], tot_loss[loss=0.1369, simple_loss=0.211, pruned_loss=0.03139, over 971099.10 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:19:04,509 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 14:19:14,012 INFO [train.py:742] (2/8) 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] (2/8) Epoch 12, batch 30050, loss[loss=0.1349, simple_loss=0.2055, pruned_loss=0.03214, over 4920.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2114, pruned_loss=0.03142, over 972143.19 frames.], batch size: 23, lr: 1.78e-04
2022-05-07 14:20:31,330 INFO [train.py:715] (2/8) Epoch 12, batch 30100, loss[loss=0.1683, simple_loss=0.2401, pruned_loss=0.04822, over 4937.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2117, pruned_loss=0.03178, over 972326.19 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:21:10,497 INFO [train.py:715] (2/8) Epoch 12, batch 30150, loss[loss=0.1136, simple_loss=0.1901, pruned_loss=0.01853, over 4792.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03182, over 971807.53 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:21:48,959 INFO [train.py:715] (2/8) Epoch 12, batch 30200, loss[loss=0.1298, simple_loss=0.1986, pruned_loss=0.03053, over 4774.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03141, over 971811.26 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:22:28,440 INFO [train.py:715] (2/8) Epoch 12, batch 30250, loss[loss=0.1267, simple_loss=0.1897, pruned_loss=0.03188, over 4773.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03127, over 971443.77 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 14:23:07,603 INFO [train.py:715] (2/8) Epoch 12, batch 30300, loss[loss=0.1227, simple_loss=0.1981, pruned_loss=0.02361, over 4879.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03154, over 971813.25 frames.], batch size: 22, lr: 1.78e-04
2022-05-07 14:23:45,573 INFO [train.py:715] (2/8) Epoch 12, batch 30350, loss[loss=0.1161, simple_loss=0.1912, pruned_loss=0.02055, over 4828.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03141, over 972009.54 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 14:24:23,564 INFO [train.py:715] (2/8) Epoch 12, batch 30400, loss[loss=0.1423, simple_loss=0.2178, pruned_loss=0.03339, over 4891.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03146, over 971551.85 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:25:01,298 INFO [train.py:715] (2/8) Epoch 12, batch 30450, loss[loss=0.1566, simple_loss=0.2222, pruned_loss=0.04546, over 4975.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.03123, over 972803.08 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:25:39,284 INFO [train.py:715] (2/8) Epoch 12, batch 30500, loss[loss=0.1278, simple_loss=0.1934, pruned_loss=0.0311, over 4773.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2109, pruned_loss=0.03167, over 973203.02 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 14:26:17,290 INFO [train.py:715] (2/8) Epoch 12, batch 30550, loss[loss=0.1644, simple_loss=0.2328, pruned_loss=0.04802, over 4934.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2108, pruned_loss=0.03135, over 972801.47 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:26:55,238 INFO [train.py:715] (2/8) Epoch 12, batch 30600, loss[loss=0.09908, simple_loss=0.1722, pruned_loss=0.01296, over 4880.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03116, over 971648.56 frames.], batch size: 20, lr: 1.78e-04
2022-05-07 14:27:32,208 INFO [train.py:715] (2/8) Epoch 12, batch 30650, loss[loss=0.141, simple_loss=0.209, pruned_loss=0.03653, over 4687.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03129, over 971460.99 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:28:10,735 INFO [train.py:715] (2/8) Epoch 12, batch 30700, loss[loss=0.1352, simple_loss=0.1985, pruned_loss=0.03589, over 4828.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03146, over 971211.28 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:28:48,656 INFO [train.py:715] (2/8) Epoch 12, batch 30750, loss[loss=0.1157, simple_loss=0.1937, pruned_loss=0.01883, over 4804.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2098, pruned_loss=0.03186, over 971059.98 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:29:27,185 INFO [train.py:715] (2/8) Epoch 12, batch 30800, loss[loss=0.1159, simple_loss=0.179, pruned_loss=0.02646, over 4969.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2087, pruned_loss=0.03158, over 971499.19 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:30:05,833 INFO [train.py:715] (2/8) Epoch 12, batch 30850, loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.0299, over 4971.00 frames.], tot_loss[loss=0.1363, simple_loss=0.209, pruned_loss=0.03176, over 971988.79 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 14:30:45,036 INFO [train.py:715] (2/8) Epoch 12, batch 30900, loss[loss=0.1395, simple_loss=0.2147, pruned_loss=0.03219, over 4851.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2095, pruned_loss=0.03167, over 971661.67 frames.], batch size: 32, lr: 1.78e-04
2022-05-07 14:31:23,377 INFO [train.py:715] (2/8) Epoch 12, batch 30950, loss[loss=0.1302, simple_loss=0.207, pruned_loss=0.02668, over 4804.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03164, over 971951.11 frames.], batch size: 25, lr: 1.78e-04
2022-05-07 14:32:02,082 INFO [train.py:715] (2/8) Epoch 12, batch 31000, loss[loss=0.1318, simple_loss=0.2134, pruned_loss=0.02509, over 4947.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03148, over 972375.26 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:32:41,210 INFO [train.py:715] (2/8) Epoch 12, batch 31050, loss[loss=0.1435, simple_loss=0.2172, pruned_loss=0.03493, over 4804.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03164, over 972946.46 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:33:19,691 INFO [train.py:715] (2/8) Epoch 12, batch 31100, loss[loss=0.1253, simple_loss=0.1969, pruned_loss=0.0269, over 4860.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03169, over 972761.68 frames.], batch size: 20, lr: 1.78e-04
2022-05-07 14:33:57,507 INFO [train.py:715] (2/8) Epoch 12, batch 31150, loss[loss=0.1451, simple_loss=0.215, pruned_loss=0.0376, over 4950.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03194, over 973594.92 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:34:36,504 INFO [train.py:715] (2/8) Epoch 12, batch 31200, loss[loss=0.1424, simple_loss=0.2176, pruned_loss=0.03362, over 4942.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03207, over 973486.20 frames.], batch size: 29, lr: 1.78e-04
2022-05-07 14:35:15,344 INFO [train.py:715] (2/8) Epoch 12, batch 31250, loss[loss=0.1787, simple_loss=0.2411, pruned_loss=0.05812, over 4769.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2113, pruned_loss=0.032, over 973550.66 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:35:54,048 INFO [train.py:715] (2/8) Epoch 12, batch 31300, loss[loss=0.1237, simple_loss=0.2006, pruned_loss=0.02338, over 4910.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03216, over 974030.48 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:36:32,569 INFO [train.py:715] (2/8) Epoch 12, batch 31350, loss[loss=0.1095, simple_loss=0.1909, pruned_loss=0.01412, over 4814.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03242, over 972703.66 frames.], batch size: 27, lr: 1.78e-04
2022-05-07 14:37:11,736 INFO [train.py:715] (2/8) Epoch 12, batch 31400, loss[loss=0.1659, simple_loss=0.2363, pruned_loss=0.04775, over 4973.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2114, pruned_loss=0.03242, over 972415.70 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 14:37:50,139 INFO [train.py:715] (2/8) Epoch 12, batch 31450, loss[loss=0.1272, simple_loss=0.2115, pruned_loss=0.02144, over 4799.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03221, over 972085.45 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:38:28,381 INFO [train.py:715] (2/8) Epoch 12, batch 31500, loss[loss=0.1357, simple_loss=0.2201, pruned_loss=0.02565, over 4778.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2105, pruned_loss=0.03216, over 970539.29 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:39:06,659 INFO [train.py:715] (2/8) Epoch 12, batch 31550, loss[loss=0.1081, simple_loss=0.1781, pruned_loss=0.01909, over 4763.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03253, over 970556.35 frames.], batch size: 12, lr: 1.78e-04
2022-05-07 14:39:45,222 INFO [train.py:715] (2/8) Epoch 12, batch 31600, loss[loss=0.1168, simple_loss=0.1948, pruned_loss=0.01941, over 4919.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03194, over 970288.57 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 14:40:22,892 INFO [train.py:715] (2/8) Epoch 12, batch 31650, loss[loss=0.1384, simple_loss=0.2181, pruned_loss=0.02934, over 4868.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03186, over 971048.41 frames.], batch size: 20, lr: 1.78e-04
2022-05-07 14:41:00,519 INFO [train.py:715] (2/8) Epoch 12, batch 31700, loss[loss=0.1228, simple_loss=0.1931, pruned_loss=0.02626, over 4808.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03175, over 972056.28 frames.], batch size: 26, lr: 1.78e-04
2022-05-07 14:41:38,634 INFO [train.py:715] (2/8) Epoch 12, batch 31750, loss[loss=0.1154, simple_loss=0.1996, pruned_loss=0.01558, over 4948.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2099, pruned_loss=0.03215, over 971037.10 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:42:16,749 INFO [train.py:715] (2/8) Epoch 12, batch 31800, loss[loss=0.1215, simple_loss=0.1966, pruned_loss=0.02323, over 4916.00 frames.], tot_loss[loss=0.1371, simple_loss=0.21, pruned_loss=0.03203, over 971654.20 frames.], batch size: 29, lr: 1.78e-04
2022-05-07 14:42:54,700 INFO [train.py:715] (2/8) Epoch 12, batch 31850, loss[loss=0.1369, simple_loss=0.2074, pruned_loss=0.03317, over 4845.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03212, over 972340.52 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:43:32,405 INFO [train.py:715] (2/8) Epoch 12, batch 31900, loss[loss=0.1263, simple_loss=0.1884, pruned_loss=0.03209, over 4834.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03224, over 971950.88 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:44:10,698 INFO [train.py:715] (2/8) Epoch 12, batch 31950, loss[loss=0.1325, simple_loss=0.2044, pruned_loss=0.03032, over 4985.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2108, pruned_loss=0.0322, over 971558.47 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 14:44:48,297 INFO [train.py:715] (2/8) Epoch 12, batch 32000, loss[loss=0.1359, simple_loss=0.2058, pruned_loss=0.03297, over 4815.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03212, over 972188.94 frames.], batch size: 26, lr: 1.78e-04
2022-05-07 14:45:26,163 INFO [train.py:715] (2/8) Epoch 12, batch 32050, loss[loss=0.1251, simple_loss=0.1905, pruned_loss=0.02992, over 4634.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03208, over 970970.21 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:46:04,020 INFO [train.py:715] (2/8) Epoch 12, batch 32100, loss[loss=0.1486, simple_loss=0.2243, pruned_loss=0.03644, over 4972.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.03179, over 971124.98 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:46:42,429 INFO [train.py:715] (2/8) Epoch 12, batch 32150, loss[loss=0.1258, simple_loss=0.1976, pruned_loss=0.02697, over 4910.00 frames.], tot_loss[loss=0.137, simple_loss=0.2111, pruned_loss=0.03151, over 971473.04 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:47:20,024 INFO [train.py:715] (2/8) Epoch 12, batch 32200, loss[loss=0.1255, simple_loss=0.1986, pruned_loss=0.02624, over 4981.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2116, pruned_loss=0.03175, over 971659.06 frames.], batch size: 31, lr: 1.78e-04
2022-05-07 14:47:58,174 INFO [train.py:715] (2/8) Epoch 12, batch 32250, loss[loss=0.1328, simple_loss=0.2125, pruned_loss=0.02657, over 4760.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2115, pruned_loss=0.03165, over 972309.04 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:48:36,826 INFO [train.py:715] (2/8) Epoch 12, batch 32300, loss[loss=0.1576, simple_loss=0.233, pruned_loss=0.0411, over 4852.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2109, pruned_loss=0.03137, over 972096.34 frames.], batch size: 34, lr: 1.78e-04
2022-05-07 14:49:14,383 INFO [train.py:715] (2/8) Epoch 12, batch 32350, loss[loss=0.1309, simple_loss=0.1986, pruned_loss=0.03158, over 4809.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2113, pruned_loss=0.03169, over 972772.85 frames.], batch size: 14, lr: 1.78e-04
2022-05-07 14:49:52,727 INFO [train.py:715] (2/8) Epoch 12, batch 32400, loss[loss=0.1104, simple_loss=0.1851, pruned_loss=0.01781, over 4980.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03173, over 972205.69 frames.], batch size: 25, lr: 1.78e-04
2022-05-07 14:50:30,828 INFO [train.py:715] (2/8) Epoch 12, batch 32450, loss[loss=0.1391, simple_loss=0.2125, pruned_loss=0.03279, over 4818.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03233, over 970904.60 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:51:09,333 INFO [train.py:715] (2/8) Epoch 12, batch 32500, loss[loss=0.1233, simple_loss=0.197, pruned_loss=0.0248, over 4941.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2106, pruned_loss=0.03217, over 970491.12 frames.], batch size: 29, lr: 1.78e-04
2022-05-07 14:51:46,831 INFO [train.py:715] (2/8) Epoch 12, batch 32550, loss[loss=0.1592, simple_loss=0.2382, pruned_loss=0.04006, over 4961.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2104, pruned_loss=0.03206, over 970658.63 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:52:25,071 INFO [train.py:715] (2/8) Epoch 12, batch 32600, loss[loss=0.1324, simple_loss=0.2083, pruned_loss=0.02827, over 4807.00 frames.], tot_loss[loss=0.1371, simple_loss=0.21, pruned_loss=0.03207, over 970371.82 frames.], batch size: 25, lr: 1.78e-04
2022-05-07 14:53:03,208 INFO [train.py:715] (2/8) Epoch 12, batch 32650, loss[loss=0.1559, simple_loss=0.2279, pruned_loss=0.04192, over 4694.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2116, pruned_loss=0.03304, over 971165.76 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 14:53:40,737 INFO [train.py:715] (2/8) Epoch 12, batch 32700, loss[loss=0.153, simple_loss=0.2276, pruned_loss=0.03923, over 4904.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2113, pruned_loss=0.033, over 971675.08 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:54:18,461 INFO [train.py:715] (2/8) Epoch 12, batch 32750, loss[loss=0.117, simple_loss=0.1851, pruned_loss=0.02439, over 4936.00 frames.], tot_loss[loss=0.138, simple_loss=0.2108, pruned_loss=0.03262, over 971075.31 frames.], batch size: 21, lr: 1.78e-04
2022-05-07 14:54:56,866 INFO [train.py:715] (2/8) Epoch 12, batch 32800, loss[loss=0.1168, simple_loss=0.1967, pruned_loss=0.0185, over 4863.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03188, over 971342.31 frames.], batch size: 20, lr: 1.78e-04
2022-05-07 14:55:35,235 INFO [train.py:715] (2/8) Epoch 12, batch 32850, loss[loss=0.1334, simple_loss=0.2161, pruned_loss=0.02536, over 4744.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03171, over 972785.99 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 14:56:12,923 INFO [train.py:715] (2/8) Epoch 12, batch 32900, loss[loss=0.1727, simple_loss=0.2407, pruned_loss=0.05239, over 4927.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2115, pruned_loss=0.03266, over 972463.14 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 14:56:51,023 INFO [train.py:715] (2/8) Epoch 12, batch 32950, loss[loss=0.1771, simple_loss=0.2397, pruned_loss=0.05724, over 4817.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2112, pruned_loss=0.03227, over 971857.41 frames.], batch size: 25, lr: 1.78e-04
2022-05-07 14:57:29,167 INFO [train.py:715] (2/8) Epoch 12, batch 33000, loss[loss=0.1269, simple_loss=0.1998, pruned_loss=0.02696, over 4892.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03163, over 972939.70 frames.], batch size: 22, lr: 1.78e-04
2022-05-07 14:57:29,167 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 14:57:38,689 INFO [train.py:742] (2/8) Epoch 12, validation: loss=0.1057, simple_loss=0.1896, pruned_loss=0.01085, over 914524.00 frames.
2022-05-07 14:58:18,189 INFO [train.py:715] (2/8) Epoch 12, batch 33050, loss[loss=0.1543, simple_loss=0.2353, pruned_loss=0.03665, over 4798.00 frames.], tot_loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03185, over 971572.14 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 14:58:56,556 INFO [train.py:715] (2/8) Epoch 12, batch 33100, loss[loss=0.1159, simple_loss=0.1741, pruned_loss=0.02886, over 4820.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2106, pruned_loss=0.0322, over 972479.43 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 14:59:34,844 INFO [train.py:715] (2/8) Epoch 12, batch 33150, loss[loss=0.1164, simple_loss=0.2006, pruned_loss=0.01611, over 4704.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03188, over 972507.80 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 15:00:12,868 INFO [train.py:715] (2/8) Epoch 12, batch 33200, loss[loss=0.1276, simple_loss=0.1897, pruned_loss=0.03278, over 4686.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03215, over 972131.53 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 15:00:51,450 INFO [train.py:715] (2/8) Epoch 12, batch 33250, loss[loss=0.174, simple_loss=0.2344, pruned_loss=0.05681, over 4915.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.03195, over 973486.46 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 15:01:29,591 INFO [train.py:715] (2/8) Epoch 12, batch 33300, loss[loss=0.1508, simple_loss=0.2297, pruned_loss=0.03594, over 4935.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03185, over 973035.95 frames.], batch size: 18, lr: 1.78e-04
2022-05-07 15:02:07,722 INFO [train.py:715] (2/8) Epoch 12, batch 33350, loss[loss=0.1437, simple_loss=0.2144, pruned_loss=0.03646, over 4759.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03148, over 972662.88 frames.], batch size: 19, lr: 1.78e-04
2022-05-07 15:02:46,382 INFO [train.py:715] (2/8) Epoch 12, batch 33400, loss[loss=0.1337, simple_loss=0.207, pruned_loss=0.03026, over 4922.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03159, over 973014.30 frames.], batch size: 39, lr: 1.78e-04
2022-05-07 15:03:25,036 INFO [train.py:715] (2/8) Epoch 12, batch 33450, loss[loss=0.1269, simple_loss=0.2032, pruned_loss=0.02529, over 4903.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03139, over 972298.46 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 15:04:03,394 INFO [train.py:715] (2/8) Epoch 12, batch 33500, loss[loss=0.1345, simple_loss=0.2159, pruned_loss=0.0265, over 4984.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03141, over 973178.24 frames.], batch size: 28, lr: 1.78e-04
2022-05-07 15:04:42,493 INFO [train.py:715] (2/8) Epoch 12, batch 33550, loss[loss=0.1577, simple_loss=0.2252, pruned_loss=0.04513, over 4819.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.0314, over 973114.51 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 15:05:21,127 INFO [train.py:715] (2/8) Epoch 12, batch 33600, loss[loss=0.1437, simple_loss=0.2156, pruned_loss=0.03589, over 4965.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03135, over 973171.62 frames.], batch size: 24, lr: 1.78e-04
2022-05-07 15:05:59,984 INFO [train.py:715] (2/8) Epoch 12, batch 33650, loss[loss=0.1306, simple_loss=0.2022, pruned_loss=0.02952, over 4855.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03155, over 972995.46 frames.], batch size: 30, lr: 1.78e-04
2022-05-07 15:06:38,063 INFO [train.py:715] (2/8) Epoch 12, batch 33700, loss[loss=0.1703, simple_loss=0.2421, pruned_loss=0.04922, over 4697.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03149, over 972104.75 frames.], batch size: 15, lr: 1.78e-04
2022-05-07 15:07:16,845 INFO [train.py:715] (2/8) Epoch 12, batch 33750, loss[loss=0.1319, simple_loss=0.199, pruned_loss=0.03243, over 4838.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2089, pruned_loss=0.03125, over 972247.43 frames.], batch size: 13, lr: 1.78e-04
2022-05-07 15:07:55,113 INFO [train.py:715] (2/8) Epoch 12, batch 33800, loss[loss=0.135, simple_loss=0.2004, pruned_loss=0.0348, over 4852.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2088, pruned_loss=0.03108, over 972940.24 frames.], batch size: 30, lr: 1.78e-04
2022-05-07 15:08:32,477 INFO [train.py:715] (2/8) Epoch 12, batch 33850, loss[loss=0.1208, simple_loss=0.2023, pruned_loss=0.01965, over 4909.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03061, over 972934.44 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 15:09:10,656 INFO [train.py:715] (2/8) Epoch 12, batch 33900, loss[loss=0.1186, simple_loss=0.1929, pruned_loss=0.02211, over 4918.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03058, over 972743.40 frames.], batch size: 17, lr: 1.78e-04
2022-05-07 15:09:47,908 INFO [train.py:715] (2/8) Epoch 12, batch 33950, loss[loss=0.1275, simple_loss=0.2072, pruned_loss=0.02392, over 4940.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03074, over 973584.53 frames.], batch size: 23, lr: 1.77e-04
2022-05-07 15:10:26,070 INFO [train.py:715] (2/8) Epoch 12, batch 34000, loss[loss=0.1146, simple_loss=0.1769, pruned_loss=0.02614, over 4799.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03123, over 973320.59 frames.], batch size: 12, lr: 1.77e-04
2022-05-07 15:11:03,706 INFO [train.py:715] (2/8) Epoch 12, batch 34050, loss[loss=0.1359, simple_loss=0.198, pruned_loss=0.03693, over 4977.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03166, over 972708.71 frames.], batch size: 15, lr: 1.77e-04
2022-05-07 15:11:41,637 INFO [train.py:715] (2/8) Epoch 12, batch 34100, loss[loss=0.1507, simple_loss=0.2151, pruned_loss=0.04311, over 4645.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03193, over 971922.73 frames.], batch size: 13, lr: 1.77e-04
2022-05-07 15:12:19,673 INFO [train.py:715] (2/8) Epoch 12, batch 34150, loss[loss=0.1109, simple_loss=0.1875, pruned_loss=0.01718, over 4860.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2095, pruned_loss=0.0316, over 971492.57 frames.], batch size: 32, lr: 1.77e-04
2022-05-07 15:12:57,180 INFO [train.py:715] (2/8) Epoch 12, batch 34200, loss[loss=0.1159, simple_loss=0.1925, pruned_loss=0.01968, over 4817.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03141, over 971839.48 frames.], batch size: 26, lr: 1.77e-04
2022-05-07 15:13:35,450 INFO [train.py:715] (2/8) Epoch 12, batch 34250, loss[loss=0.1255, simple_loss=0.2061, pruned_loss=0.02251, over 4870.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03105, over 971744.64 frames.], batch size: 39, lr: 1.77e-04
2022-05-07 15:14:12,818 INFO [train.py:715] (2/8) Epoch 12, batch 34300, loss[loss=0.1325, simple_loss=0.2022, pruned_loss=0.03142, over 4823.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.0306, over 972203.94 frames.], batch size: 13, lr: 1.77e-04
2022-05-07 15:14:51,107 INFO [train.py:715] (2/8) Epoch 12, batch 34350, loss[loss=0.1248, simple_loss=0.1955, pruned_loss=0.02701, over 4784.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03053, over 972226.40 frames.], batch size: 17, lr: 1.77e-04
2022-05-07 15:15:28,881 INFO [train.py:715] (2/8) Epoch 12, batch 34400, loss[loss=0.1192, simple_loss=0.1925, pruned_loss=0.02297, over 4984.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03052, over 972802.93 frames.], batch size: 28, lr: 1.77e-04
2022-05-07 15:16:07,247 INFO [train.py:715] (2/8) Epoch 12, batch 34450, loss[loss=0.1432, simple_loss=0.22, pruned_loss=0.03319, over 4794.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03073, over 971931.41 frames.], batch size: 24, lr: 1.77e-04
2022-05-07 15:16:45,350 INFO [train.py:715] (2/8) Epoch 12, batch 34500, loss[loss=0.1536, simple_loss=0.2196, pruned_loss=0.04384, over 4754.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03089, over 972813.97 frames.], batch size: 19, lr: 1.77e-04
2022-05-07 15:17:23,595 INFO [train.py:715] (2/8) Epoch 12, batch 34550, loss[loss=0.1565, simple_loss=0.2249, pruned_loss=0.04406, over 4839.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03099, over 971607.43 frames.], batch size: 15, lr: 1.77e-04
2022-05-07 15:18:02,279 INFO [train.py:715] (2/8) Epoch 12, batch 34600, loss[loss=0.1386, simple_loss=0.2142, pruned_loss=0.0315, over 4957.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03066, over 972728.23 frames.], batch size: 24, lr: 1.77e-04
2022-05-07 15:18:41,657 INFO [train.py:715] (2/8) Epoch 12, batch 34650, loss[loss=0.1455, simple_loss=0.2308, pruned_loss=0.03006, over 4737.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2077, pruned_loss=0.03045, over 972548.36 frames.], batch size: 16, lr: 1.77e-04
2022-05-07 15:19:21,043 INFO [train.py:715] (2/8) Epoch 12, batch 34700, loss[loss=0.1245, simple_loss=0.201, pruned_loss=0.02401, over 4778.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03096, over 972448.86 frames.], batch size: 18, lr: 1.77e-04
2022-05-07 15:19:58,682 INFO [train.py:715] (2/8) Epoch 12, batch 34750, loss[loss=0.1327, simple_loss=0.2094, pruned_loss=0.02799, over 4748.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03116, over 971959.18 frames.], batch size: 16, lr: 1.77e-04
2022-05-07 15:20:34,681 INFO [train.py:715] (2/8) Epoch 12, batch 34800, loss[loss=0.1667, simple_loss=0.2256, pruned_loss=0.05397, over 4747.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2103, pruned_loss=0.03133, over 972114.91 frames.], batch size: 12, lr: 1.77e-04
2022-05-07 15:21:23,129 INFO [train.py:715] (2/8) Epoch 13, batch 0, loss[loss=0.1276, simple_loss=0.2045, pruned_loss=0.02532, over 4747.00 frames.], tot_loss[loss=0.1276, simple_loss=0.2045, pruned_loss=0.02532, over 4747.00 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:22:01,158 INFO [train.py:715] (2/8) Epoch 13, batch 50, loss[loss=0.1364, simple_loss=0.2085, pruned_loss=0.03218, over 4969.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2071, pruned_loss=0.03031, over 219258.97 frames.], batch size: 24, lr: 1.71e-04
2022-05-07 15:22:39,469 INFO [train.py:715] (2/8) Epoch 13, batch 100, loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02936, over 4959.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2092, pruned_loss=0.03177, over 385989.65 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:23:17,857 INFO [train.py:715] (2/8) Epoch 13, batch 150, loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03017, over 4835.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2102, pruned_loss=0.03266, over 515775.15 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:23:57,343 INFO [train.py:715] (2/8) Epoch 13, batch 200, loss[loss=0.1112, simple_loss=0.1942, pruned_loss=0.01409, over 4742.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2091, pruned_loss=0.03171, over 617095.48 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:24:35,736 INFO [train.py:715] (2/8) Epoch 13, batch 250, loss[loss=0.1389, simple_loss=0.2074, pruned_loss=0.0352, over 4971.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2089, pruned_loss=0.03172, over 697020.68 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:25:15,234 INFO [train.py:715] (2/8) Epoch 13, batch 300, loss[loss=0.1341, simple_loss=0.2163, pruned_loss=0.026, over 4891.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03153, over 757289.73 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:25:53,991 INFO [train.py:715] (2/8) Epoch 13, batch 350, loss[loss=0.1394, simple_loss=0.2138, pruned_loss=0.03248, over 4988.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03062, over 804263.33 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:26:33,534 INFO [train.py:715] (2/8) Epoch 13, batch 400, loss[loss=0.141, simple_loss=0.2085, pruned_loss=0.03677, over 4984.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03065, over 842108.04 frames.], batch size: 35, lr: 1.71e-04
2022-05-07 15:27:13,019 INFO [train.py:715] (2/8) Epoch 13, batch 450, loss[loss=0.1927, simple_loss=0.2485, pruned_loss=0.06849, over 4978.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03072, over 870544.48 frames.], batch size: 14, lr: 1.71e-04
2022-05-07 15:27:53,171 INFO [train.py:715] (2/8) Epoch 13, batch 500, loss[loss=0.1291, simple_loss=0.206, pruned_loss=0.02607, over 4775.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2104, pruned_loss=0.03123, over 892435.45 frames.], batch size: 19, lr: 1.71e-04
2022-05-07 15:28:33,642 INFO [train.py:715] (2/8) Epoch 13, batch 550, loss[loss=0.1527, simple_loss=0.2228, pruned_loss=0.04134, over 4745.00 frames.], tot_loss[loss=0.137, simple_loss=0.2108, pruned_loss=0.0316, over 910440.18 frames.], batch size: 14, lr: 1.71e-04
2022-05-07 15:29:12,935 INFO [train.py:715] (2/8) Epoch 13, batch 600, loss[loss=0.1348, simple_loss=0.204, pruned_loss=0.03281, over 4834.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03134, over 924347.63 frames.], batch size: 30, lr: 1.71e-04
2022-05-07 15:29:53,386 INFO [train.py:715] (2/8) Epoch 13, batch 650, loss[loss=0.1561, simple_loss=0.2301, pruned_loss=0.04103, over 4983.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03105, over 934797.39 frames.], batch size: 24, lr: 1.71e-04
2022-05-07 15:30:33,365 INFO [train.py:715] (2/8) Epoch 13, batch 700, loss[loss=0.1404, simple_loss=0.2116, pruned_loss=0.03453, over 4893.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03128, over 943395.52 frames.], batch size: 19, lr: 1.71e-04
2022-05-07 15:31:13,995 INFO [train.py:715] (2/8) Epoch 13, batch 750, loss[loss=0.1519, simple_loss=0.2156, pruned_loss=0.04404, over 4947.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03152, over 949692.39 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:31:53,297 INFO [train.py:715] (2/8) Epoch 13, batch 800, loss[loss=0.1184, simple_loss=0.1907, pruned_loss=0.02302, over 4807.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03178, over 955099.73 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:32:32,567 INFO [train.py:715] (2/8) Epoch 13, batch 850, loss[loss=0.1329, simple_loss=0.2053, pruned_loss=0.03025, over 4800.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03167, over 959088.49 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:33:12,798 INFO [train.py:715] (2/8) Epoch 13, batch 900, loss[loss=0.1446, simple_loss=0.2207, pruned_loss=0.03421, over 4849.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03181, over 961720.74 frames.], batch size: 20, lr: 1.71e-04
2022-05-07 15:33:52,196 INFO [train.py:715] (2/8) Epoch 13, batch 950, loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03013, over 4928.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2106, pruned_loss=0.03187, over 965010.50 frames.], batch size: 39, lr: 1.71e-04
2022-05-07 15:34:32,778 INFO [train.py:715] (2/8) Epoch 13, batch 1000, loss[loss=0.1474, simple_loss=0.227, pruned_loss=0.03392, over 4816.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03204, over 966411.64 frames.], batch size: 13, lr: 1.71e-04
2022-05-07 15:35:12,238 INFO [train.py:715] (2/8) Epoch 13, batch 1050, loss[loss=0.1251, simple_loss=0.2098, pruned_loss=0.02015, over 4945.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03224, over 967339.67 frames.], batch size: 23, lr: 1.71e-04
2022-05-07 15:35:52,553 INFO [train.py:715] (2/8) Epoch 13, batch 1100, loss[loss=0.154, simple_loss=0.2244, pruned_loss=0.04182, over 4905.00 frames.], tot_loss[loss=0.138, simple_loss=0.2111, pruned_loss=0.03242, over 969119.27 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:36:32,013 INFO [train.py:715] (2/8) Epoch 13, batch 1150, loss[loss=0.1181, simple_loss=0.1943, pruned_loss=0.02099, over 4825.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2118, pruned_loss=0.03267, over 969791.30 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:37:11,794 INFO [train.py:715] (2/8) Epoch 13, batch 1200, loss[loss=0.1373, simple_loss=0.2115, pruned_loss=0.03151, over 4758.00 frames.], tot_loss[loss=0.1386, simple_loss=0.212, pruned_loss=0.0326, over 970577.08 frames.], batch size: 19, lr: 1.71e-04
2022-05-07 15:37:52,146 INFO [train.py:715] (2/8) Epoch 13, batch 1250, loss[loss=0.151, simple_loss=0.2197, pruned_loss=0.04113, over 4876.00 frames.], tot_loss[loss=0.138, simple_loss=0.2114, pruned_loss=0.03228, over 971325.68 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:38:31,100 INFO [train.py:715] (2/8) Epoch 13, batch 1300, loss[loss=0.1297, simple_loss=0.2102, pruned_loss=0.02465, over 4980.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2115, pruned_loss=0.03232, over 971467.07 frames.], batch size: 28, lr: 1.71e-04
2022-05-07 15:39:11,007 INFO [train.py:715] (2/8) Epoch 13, batch 1350, loss[loss=0.1605, simple_loss=0.2186, pruned_loss=0.05118, over 4860.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2119, pruned_loss=0.03247, over 972013.64 frames.], batch size: 30, lr: 1.71e-04
2022-05-07 15:39:49,792 INFO [train.py:715] (2/8) Epoch 13, batch 1400, loss[loss=0.1338, simple_loss=0.21, pruned_loss=0.02878, over 4904.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03211, over 971942.09 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:40:28,860 INFO [train.py:715] (2/8) Epoch 13, batch 1450, loss[loss=0.1349, simple_loss=0.2141, pruned_loss=0.02784, over 4979.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03166, over 971569.95 frames.], batch size: 28, lr: 1.71e-04
2022-05-07 15:41:06,534 INFO [train.py:715] (2/8) Epoch 13, batch 1500, loss[loss=0.1489, simple_loss=0.2269, pruned_loss=0.03546, over 4771.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03185, over 972085.92 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:41:44,152 INFO [train.py:715] (2/8) Epoch 13, batch 1550, loss[loss=0.1002, simple_loss=0.1756, pruned_loss=0.0124, over 4831.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2111, pruned_loss=0.03174, over 972492.44 frames.], batch size: 26, lr: 1.71e-04
2022-05-07 15:42:22,724 INFO [train.py:715] (2/8) Epoch 13, batch 1600, loss[loss=0.1691, simple_loss=0.2428, pruned_loss=0.04769, over 4929.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2113, pruned_loss=0.03213, over 972484.56 frames.], batch size: 23, lr: 1.71e-04
2022-05-07 15:43:00,640 INFO [train.py:715] (2/8) Epoch 13, batch 1650, loss[loss=0.1467, simple_loss=0.2287, pruned_loss=0.03241, over 4881.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2107, pruned_loss=0.03201, over 971862.00 frames.], batch size: 16, lr: 1.71e-04
2022-05-07 15:43:39,378 INFO [train.py:715] (2/8) Epoch 13, batch 1700, loss[loss=0.147, simple_loss=0.216, pruned_loss=0.03899, over 4897.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03192, over 971378.52 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:44:17,662 INFO [train.py:715] (2/8) Epoch 13, batch 1750, loss[loss=0.1243, simple_loss=0.201, pruned_loss=0.02382, over 4934.00 frames.], tot_loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.0322, over 970905.74 frames.], batch size: 23, lr: 1.71e-04
2022-05-07 15:44:57,089 INFO [train.py:715] (2/8) Epoch 13, batch 1800, loss[loss=0.1376, simple_loss=0.2083, pruned_loss=0.03343, over 4975.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.03179, over 972353.83 frames.], batch size: 25, lr: 1.71e-04
2022-05-07 15:45:35,169 INFO [train.py:715] (2/8) Epoch 13, batch 1850, loss[loss=0.1296, simple_loss=0.2042, pruned_loss=0.02754, over 4940.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03188, over 972392.17 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:46:13,430 INFO [train.py:715] (2/8) Epoch 13, batch 1900, loss[loss=0.1283, simple_loss=0.2124, pruned_loss=0.02215, over 4792.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03209, over 972265.46 frames.], batch size: 24, lr: 1.71e-04
2022-05-07 15:46:52,089 INFO [train.py:715] (2/8) Epoch 13, batch 1950, loss[loss=0.1273, simple_loss=0.2048, pruned_loss=0.02487, over 4885.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03153, over 972804.02 frames.], batch size: 22, lr: 1.71e-04
2022-05-07 15:47:30,464 INFO [train.py:715] (2/8) Epoch 13, batch 2000, loss[loss=0.1332, simple_loss=0.2108, pruned_loss=0.02776, over 4933.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03104, over 971878.11 frames.], batch size: 21, lr: 1.71e-04
2022-05-07 15:48:09,019 INFO [train.py:715] (2/8) Epoch 13, batch 2050, loss[loss=0.1799, simple_loss=0.2484, pruned_loss=0.05566, over 4922.00 frames.], tot_loss[loss=0.1368, simple_loss=0.21, pruned_loss=0.03174, over 971997.43 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:48:47,022 INFO [train.py:715] (2/8) Epoch 13, batch 2100, loss[loss=0.1183, simple_loss=0.195, pruned_loss=0.0208, over 4882.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03159, over 972331.55 frames.], batch size: 19, lr: 1.71e-04
2022-05-07 15:49:26,193 INFO [train.py:715] (2/8) Epoch 13, batch 2150, loss[loss=0.1172, simple_loss=0.1923, pruned_loss=0.021, over 4866.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03155, over 972670.61 frames.], batch size: 20, lr: 1.71e-04
2022-05-07 15:50:04,033 INFO [train.py:715] (2/8) Epoch 13, batch 2200, loss[loss=0.1539, simple_loss=0.2262, pruned_loss=0.04077, over 4860.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03147, over 973208.63 frames.], batch size: 38, lr: 1.71e-04
2022-05-07 15:50:42,243 INFO [train.py:715] (2/8) Epoch 13, batch 2250, loss[loss=0.1378, simple_loss=0.2187, pruned_loss=0.02849, over 4906.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2115, pruned_loss=0.03199, over 974214.02 frames.], batch size: 17, lr: 1.71e-04
2022-05-07 15:51:20,493 INFO [train.py:715] (2/8) Epoch 13, batch 2300, loss[loss=0.1487, simple_loss=0.2197, pruned_loss=0.03884, over 4824.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03156, over 973802.62 frames.], batch size: 26, lr: 1.71e-04
2022-05-07 15:51:59,648 INFO [train.py:715] (2/8) Epoch 13, batch 2350, loss[loss=0.121, simple_loss=0.1947, pruned_loss=0.02366, over 4813.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03157, over 973258.75 frames.], batch size: 27, lr: 1.71e-04
2022-05-07 15:52:38,011 INFO [train.py:715] (2/8) Epoch 13, batch 2400, loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03047, over 4762.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03152, over 973274.08 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:53:16,747 INFO [train.py:715] (2/8) Epoch 13, batch 2450, loss[loss=0.1415, simple_loss=0.2082, pruned_loss=0.03737, over 4691.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03113, over 972489.05 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:53:55,654 INFO [train.py:715] (2/8) Epoch 13, batch 2500, loss[loss=0.1462, simple_loss=0.2149, pruned_loss=0.03872, over 4980.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03107, over 971552.78 frames.], batch size: 15, lr: 1.71e-04
2022-05-07 15:54:34,063 INFO [train.py:715] (2/8) Epoch 13, batch 2550, loss[loss=0.1246, simple_loss=0.19, pruned_loss=0.02966, over 4928.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03183, over 971033.97 frames.], batch size: 29, lr: 1.71e-04
2022-05-07 15:55:12,161 INFO [train.py:715] (2/8) Epoch 13, batch 2600, loss[loss=0.1296, simple_loss=0.1988, pruned_loss=0.03018, over 4927.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03206, over 971840.44 frames.], batch size: 18, lr: 1.71e-04
2022-05-07 15:55:50,583 INFO [train.py:715] (2/8) Epoch 13, batch 2650, loss[loss=0.1415, simple_loss=0.2253, pruned_loss=0.02889, over 4933.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03203, over 971376.20 frames.], batch size: 23, lr: 1.71e-04
2022-05-07 15:56:28,664 INFO [train.py:715] (2/8) Epoch 13, batch 2700, loss[loss=0.138, simple_loss=0.2106, pruned_loss=0.03274, over 4883.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2119, pruned_loss=0.03228, over 971452.93 frames.], batch size: 22, lr: 1.70e-04
2022-05-07 15:57:06,440 INFO [train.py:715] (2/8) Epoch 13, batch 2750, loss[loss=0.122, simple_loss=0.1894, pruned_loss=0.02724, over 4835.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2119, pruned_loss=0.03232, over 971770.83 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 15:57:43,968 INFO [train.py:715] (2/8) Epoch 13, batch 2800, loss[loss=0.1519, simple_loss=0.2156, pruned_loss=0.04411, over 4972.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.03179, over 972681.40 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 15:58:22,563 INFO [train.py:715] (2/8) Epoch 13, batch 2850, loss[loss=0.1292, simple_loss=0.2048, pruned_loss=0.02679, over 4806.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2112, pruned_loss=0.03179, over 972804.46 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 15:59:00,070 INFO [train.py:715] (2/8) Epoch 13, batch 2900, loss[loss=0.1667, simple_loss=0.2474, pruned_loss=0.04299, over 4926.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03153, over 972806.01 frames.], batch size: 23, lr: 1.70e-04
2022-05-07 15:59:37,968 INFO [train.py:715] (2/8) Epoch 13, batch 2950, loss[loss=0.1712, simple_loss=0.2267, pruned_loss=0.05782, over 4691.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.0316, over 972254.15 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:00:15,988 INFO [train.py:715] (2/8) Epoch 13, batch 3000, loss[loss=0.1422, simple_loss=0.2159, pruned_loss=0.03419, over 4960.00 frames.], tot_loss[loss=0.137, simple_loss=0.2106, pruned_loss=0.03167, over 973622.51 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:00:15,988 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 16:00:25,446 INFO [train.py:742] (2/8) Epoch 13, validation: loss=0.1052, simple_loss=0.1893, pruned_loss=0.01058, over 914524.00 frames.
2022-05-07 16:01:03,674 INFO [train.py:715] (2/8) Epoch 13, batch 3050, loss[loss=0.1329, simple_loss=0.2114, pruned_loss=0.02721, over 4904.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03158, over 973905.87 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:01:42,202 INFO [train.py:715] (2/8) Epoch 13, batch 3100, loss[loss=0.1372, simple_loss=0.2181, pruned_loss=0.02816, over 4935.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2103, pruned_loss=0.0314, over 973348.32 frames.], batch size: 23, lr: 1.70e-04
2022-05-07 16:02:19,747 INFO [train.py:715] (2/8) Epoch 13, batch 3150, loss[loss=0.1455, simple_loss=0.208, pruned_loss=0.04152, over 4992.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03189, over 973082.12 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:02:57,076 INFO [train.py:715] (2/8) Epoch 13, batch 3200, loss[loss=0.1089, simple_loss=0.1882, pruned_loss=0.01479, over 4946.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2111, pruned_loss=0.03182, over 972901.70 frames.], batch size: 29, lr: 1.70e-04
2022-05-07 16:03:35,541 INFO [train.py:715] (2/8) Epoch 13, batch 3250, loss[loss=0.1258, simple_loss=0.2023, pruned_loss=0.02466, over 4941.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2105, pruned_loss=0.03168, over 973215.23 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:04:13,565 INFO [train.py:715] (2/8) Epoch 13, batch 3300, loss[loss=0.124, simple_loss=0.208, pruned_loss=0.01998, over 4955.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03166, over 972855.94 frames.], batch size: 39, lr: 1.70e-04
2022-05-07 16:04:51,382 INFO [train.py:715] (2/8) Epoch 13, batch 3350, loss[loss=0.1304, simple_loss=0.2097, pruned_loss=0.02548, over 4976.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2107, pruned_loss=0.03174, over 972533.10 frames.], batch size: 28, lr: 1.70e-04
2022-05-07 16:05:29,077 INFO [train.py:715] (2/8) Epoch 13, batch 3400, loss[loss=0.1322, simple_loss=0.2, pruned_loss=0.03223, over 4766.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03194, over 972459.02 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:06:07,373 INFO [train.py:715] (2/8) Epoch 13, batch 3450, loss[loss=0.1299, simple_loss=0.2032, pruned_loss=0.0283, over 4861.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03104, over 972981.67 frames.], batch size: 34, lr: 1.70e-04
2022-05-07 16:06:47,671 INFO [train.py:715] (2/8) Epoch 13, batch 3500, loss[loss=0.1574, simple_loss=0.2375, pruned_loss=0.03862, over 4848.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.03158, over 974101.00 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:07:25,033 INFO [train.py:715] (2/8) Epoch 13, batch 3550, loss[loss=0.1219, simple_loss=0.1918, pruned_loss=0.02595, over 4819.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2094, pruned_loss=0.03168, over 973587.45 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:08:03,490 INFO [train.py:715] (2/8) Epoch 13, batch 3600, loss[loss=0.1187, simple_loss=0.2032, pruned_loss=0.01711, over 4690.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2087, pruned_loss=0.03141, over 973013.55 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:08:41,280 INFO [train.py:715] (2/8) Epoch 13, batch 3650, loss[loss=0.1669, simple_loss=0.2445, pruned_loss=0.04466, over 4939.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2088, pruned_loss=0.03141, over 972160.65 frames.], batch size: 39, lr: 1.70e-04
2022-05-07 16:09:18,852 INFO [train.py:715] (2/8) Epoch 13, batch 3700, loss[loss=0.1511, simple_loss=0.2365, pruned_loss=0.03279, over 4903.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.0316, over 972228.78 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:09:56,570 INFO [train.py:715] (2/8) Epoch 13, batch 3750, loss[loss=0.1412, simple_loss=0.2125, pruned_loss=0.03498, over 4904.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2092, pruned_loss=0.0317, over 971708.55 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:10:34,803 INFO [train.py:715] (2/8) Epoch 13, batch 3800, loss[loss=0.1363, simple_loss=0.2022, pruned_loss=0.03524, over 4868.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2094, pruned_loss=0.03153, over 971548.65 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:11:11,947 INFO [train.py:715] (2/8) Epoch 13, batch 3850, loss[loss=0.1311, simple_loss=0.2098, pruned_loss=0.02619, over 4971.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2091, pruned_loss=0.03124, over 971566.79 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:11:49,245 INFO [train.py:715] (2/8) Epoch 13, batch 3900, loss[loss=0.1536, simple_loss=0.2274, pruned_loss=0.03995, over 4906.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2093, pruned_loss=0.03158, over 971427.00 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 16:12:27,132 INFO [train.py:715] (2/8) Epoch 13, batch 3950, loss[loss=0.1461, simple_loss=0.2216, pruned_loss=0.03532, over 4780.00 frames.], tot_loss[loss=0.1368, simple_loss=0.21, pruned_loss=0.03176, over 971361.12 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 16:13:05,300 INFO [train.py:715] (2/8) Epoch 13, batch 4000, loss[loss=0.1281, simple_loss=0.2136, pruned_loss=0.0213, over 4778.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03188, over 971549.73 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:13:42,998 INFO [train.py:715] (2/8) Epoch 13, batch 4050, loss[loss=0.1706, simple_loss=0.2518, pruned_loss=0.04467, over 4778.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.03204, over 971522.56 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:14:20,644 INFO [train.py:715] (2/8) Epoch 13, batch 4100, loss[loss=0.1418, simple_loss=0.2095, pruned_loss=0.03706, over 4757.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.03197, over 970756.84 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:14:59,187 INFO [train.py:715] (2/8) Epoch 13, batch 4150, loss[loss=0.1402, simple_loss=0.2016, pruned_loss=0.0394, over 4889.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03182, over 971345.02 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:15:36,530 INFO [train.py:715] (2/8) Epoch 13, batch 4200, loss[loss=0.1336, simple_loss=0.2125, pruned_loss=0.02737, over 4915.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03187, over 970229.26 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:16:14,502 INFO [train.py:715] (2/8) Epoch 13, batch 4250, loss[loss=0.1187, simple_loss=0.1874, pruned_loss=0.02497, over 4660.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03145, over 970844.26 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 16:16:52,600 INFO [train.py:715] (2/8) Epoch 13, batch 4300, loss[loss=0.1311, simple_loss=0.2175, pruned_loss=0.02238, over 4801.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03136, over 971327.74 frames.], batch size: 26, lr: 1.70e-04
2022-05-07 16:17:30,606 INFO [train.py:715] (2/8) Epoch 13, batch 4350, loss[loss=0.15, simple_loss=0.2152, pruned_loss=0.0424, over 4841.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03191, over 972027.01 frames.], batch size: 34, lr: 1.70e-04
2022-05-07 16:18:08,279 INFO [train.py:715] (2/8) Epoch 13, batch 4400, loss[loss=0.1149, simple_loss=0.1917, pruned_loss=0.01905, over 4811.00 frames.], tot_loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03182, over 971624.99 frames.], batch size: 25, lr: 1.70e-04
2022-05-07 16:18:46,449 INFO [train.py:715] (2/8) Epoch 13, batch 4450, loss[loss=0.1461, simple_loss=0.2088, pruned_loss=0.04172, over 4802.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2111, pruned_loss=0.03181, over 971884.06 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:19:25,668 INFO [train.py:715] (2/8) Epoch 13, batch 4500, loss[loss=0.1405, simple_loss=0.2092, pruned_loss=0.0359, over 4966.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2115, pruned_loss=0.03191, over 972811.05 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:20:03,832 INFO [train.py:715] (2/8) Epoch 13, batch 4550, loss[loss=0.1204, simple_loss=0.1996, pruned_loss=0.02064, over 4745.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2116, pruned_loss=0.03166, over 971857.94 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:20:40,818 INFO [train.py:715] (2/8) Epoch 13, batch 4600, loss[loss=0.1388, simple_loss=0.2172, pruned_loss=0.03016, over 4919.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2113, pruned_loss=0.03159, over 972049.88 frames.], batch size: 29, lr: 1.70e-04
2022-05-07 16:21:19,534 INFO [train.py:715] (2/8) Epoch 13, batch 4650, loss[loss=0.1269, simple_loss=0.2038, pruned_loss=0.02499, over 4780.00 frames.], tot_loss[loss=0.1372, simple_loss=0.211, pruned_loss=0.03163, over 972899.53 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:21:57,441 INFO [train.py:715] (2/8) Epoch 13, batch 4700, loss[loss=0.1175, simple_loss=0.1885, pruned_loss=0.02323, over 4924.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03147, over 973586.47 frames.], batch size: 29, lr: 1.70e-04
2022-05-07 16:22:35,614 INFO [train.py:715] (2/8) Epoch 13, batch 4750, loss[loss=0.1142, simple_loss=0.1901, pruned_loss=0.01916, over 4790.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03078, over 973682.33 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:23:13,887 INFO [train.py:715] (2/8) Epoch 13, batch 4800, loss[loss=0.1453, simple_loss=0.2207, pruned_loss=0.03496, over 4862.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2099, pruned_loss=0.03084, over 973990.95 frames.], batch size: 32, lr: 1.70e-04
2022-05-07 16:23:53,168 INFO [train.py:715] (2/8) Epoch 13, batch 4850, loss[loss=0.1607, simple_loss=0.2349, pruned_loss=0.04328, over 4843.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03082, over 974044.55 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:24:31,292 INFO [train.py:715] (2/8) Epoch 13, batch 4900, loss[loss=0.1415, simple_loss=0.2207, pruned_loss=0.03116, over 4892.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03091, over 974450.26 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:25:10,151 INFO [train.py:715] (2/8) Epoch 13, batch 4950, loss[loss=0.1453, simple_loss=0.221, pruned_loss=0.03485, over 4643.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03159, over 974578.39 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 16:25:49,563 INFO [train.py:715] (2/8) Epoch 13, batch 5000, loss[loss=0.1162, simple_loss=0.2059, pruned_loss=0.01324, over 4966.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03145, over 975208.14 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:26:28,895 INFO [train.py:715] (2/8) Epoch 13, batch 5050, loss[loss=0.1394, simple_loss=0.2084, pruned_loss=0.0352, over 4981.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.0311, over 974248.48 frames.], batch size: 35, lr: 1.70e-04
2022-05-07 16:27:07,534 INFO [train.py:715] (2/8) Epoch 13, batch 5100, loss[loss=0.1293, simple_loss=0.2051, pruned_loss=0.0268, over 4701.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.0311, over 973822.78 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:27:46,964 INFO [train.py:715] (2/8) Epoch 13, batch 5150, loss[loss=0.1375, simple_loss=0.2127, pruned_loss=0.03118, over 4989.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03116, over 973916.63 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:28:26,717 INFO [train.py:715] (2/8) Epoch 13, batch 5200, loss[loss=0.1175, simple_loss=0.1892, pruned_loss=0.02295, over 4854.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03153, over 972920.02 frames.], batch size: 30, lr: 1.70e-04
2022-05-07 16:29:06,549 INFO [train.py:715] (2/8) Epoch 13, batch 5250, loss[loss=0.1319, simple_loss=0.2026, pruned_loss=0.0306, over 4914.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.0313, over 973562.85 frames.], batch size: 23, lr: 1.70e-04
2022-05-07 16:29:45,229 INFO [train.py:715] (2/8) Epoch 13, batch 5300, loss[loss=0.163, simple_loss=0.2282, pruned_loss=0.04896, over 4743.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03134, over 973669.49 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:30:25,373 INFO [train.py:715] (2/8) Epoch 13, batch 5350, loss[loss=0.1497, simple_loss=0.2259, pruned_loss=0.03673, over 4915.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03086, over 974193.77 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:31:05,487 INFO [train.py:715] (2/8) Epoch 13, batch 5400, loss[loss=0.152, simple_loss=0.2324, pruned_loss=0.03578, over 4993.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03093, over 973319.45 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:31:45,406 INFO [train.py:715] (2/8) Epoch 13, batch 5450, loss[loss=0.1534, simple_loss=0.2267, pruned_loss=0.04007, over 4881.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03101, over 973197.95 frames.], batch size: 22, lr: 1.70e-04
2022-05-07 16:32:24,986 INFO [train.py:715] (2/8) Epoch 13, batch 5500, loss[loss=0.121, simple_loss=0.1966, pruned_loss=0.02268, over 4858.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03116, over 973492.56 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 16:33:04,812 INFO [train.py:715] (2/8) Epoch 13, batch 5550, loss[loss=0.1475, simple_loss=0.2158, pruned_loss=0.03959, over 4745.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03079, over 971843.98 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 16:33:44,067 INFO [train.py:715] (2/8) Epoch 13, batch 5600, loss[loss=0.1217, simple_loss=0.2004, pruned_loss=0.02149, over 4849.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03094, over 971590.51 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:34:23,516 INFO [train.py:715] (2/8) Epoch 13, batch 5650, loss[loss=0.1656, simple_loss=0.2289, pruned_loss=0.05118, over 4960.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.03116, over 972992.39 frames.], batch size: 39, lr: 1.70e-04
2022-05-07 16:35:03,785 INFO [train.py:715] (2/8) Epoch 13, batch 5700, loss[loss=0.1258, simple_loss=0.1884, pruned_loss=0.03156, over 4975.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03113, over 973016.55 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:35:43,896 INFO [train.py:715] (2/8) Epoch 13, batch 5750, loss[loss=0.195, simple_loss=0.2479, pruned_loss=0.07106, over 4851.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03124, over 973138.40 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 16:36:22,752 INFO [train.py:715] (2/8) Epoch 13, batch 5800, loss[loss=0.1547, simple_loss=0.2371, pruned_loss=0.03613, over 4873.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03119, over 973269.69 frames.], batch size: 38, lr: 1.70e-04
2022-05-07 16:37:02,234 INFO [train.py:715] (2/8) Epoch 13, batch 5850, loss[loss=0.164, simple_loss=0.2306, pruned_loss=0.04868, over 4991.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2089, pruned_loss=0.03127, over 973812.59 frames.], batch size: 20, lr: 1.70e-04
2022-05-07 16:37:42,374 INFO [train.py:715] (2/8) Epoch 13, batch 5900, loss[loss=0.1703, simple_loss=0.255, pruned_loss=0.04283, over 4992.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03114, over 972992.66 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:38:21,741 INFO [train.py:715] (2/8) Epoch 13, batch 5950, loss[loss=0.1487, simple_loss=0.2188, pruned_loss=0.03926, over 4814.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03118, over 972689.24 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:39:01,208 INFO [train.py:715] (2/8) Epoch 13, batch 6000, loss[loss=0.1222, simple_loss=0.1918, pruned_loss=0.02633, over 4831.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03095, over 972981.63 frames.], batch size: 25, lr: 1.70e-04
2022-05-07 16:39:01,208 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 16:39:10,779 INFO [train.py:742] (2/8) Epoch 13, validation: loss=0.1054, simple_loss=0.1893, pruned_loss=0.01078, over 914524.00 frames.
2022-05-07 16:39:50,260 INFO [train.py:715] (2/8) Epoch 13, batch 6050, loss[loss=0.1608, simple_loss=0.2292, pruned_loss=0.04624, over 4874.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03101, over 973797.47 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:40:29,777 INFO [train.py:715] (2/8) Epoch 13, batch 6100, loss[loss=0.1513, simple_loss=0.2217, pruned_loss=0.04042, over 4896.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03133, over 973587.99 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:41:09,343 INFO [train.py:715] (2/8) Epoch 13, batch 6150, loss[loss=0.108, simple_loss=0.1793, pruned_loss=0.01835, over 4895.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2105, pruned_loss=0.03139, over 973568.07 frames.], batch size: 23, lr: 1.70e-04
2022-05-07 16:41:47,242 INFO [train.py:715] (2/8) Epoch 13, batch 6200, loss[loss=0.1281, simple_loss=0.2029, pruned_loss=0.02664, over 4767.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2107, pruned_loss=0.03141, over 973089.58 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:42:26,293 INFO [train.py:715] (2/8) Epoch 13, batch 6250, loss[loss=0.1288, simple_loss=0.1962, pruned_loss=0.03072, over 4741.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03131, over 973343.31 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:43:05,824 INFO [train.py:715] (2/8) Epoch 13, batch 6300, loss[loss=0.1591, simple_loss=0.2367, pruned_loss=0.04079, over 4942.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03132, over 973848.38 frames.], batch size: 39, lr: 1.70e-04
2022-05-07 16:43:44,415 INFO [train.py:715] (2/8) Epoch 13, batch 6350, loss[loss=0.1461, simple_loss=0.222, pruned_loss=0.03505, over 4954.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03107, over 973397.00 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:44:24,230 INFO [train.py:715] (2/8) Epoch 13, batch 6400, loss[loss=0.1206, simple_loss=0.1912, pruned_loss=0.02503, over 4893.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03115, over 972609.29 frames.], batch size: 22, lr: 1.70e-04
2022-05-07 16:45:04,048 INFO [train.py:715] (2/8) Epoch 13, batch 6450, loss[loss=0.1494, simple_loss=0.2246, pruned_loss=0.0371, over 4923.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.03171, over 971800.69 frames.], batch size: 29, lr: 1.70e-04
2022-05-07 16:45:44,143 INFO [train.py:715] (2/8) Epoch 13, batch 6500, loss[loss=0.1093, simple_loss=0.1753, pruned_loss=0.02167, over 4803.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03165, over 972582.93 frames.], batch size: 25, lr: 1.70e-04
2022-05-07 16:46:23,313 INFO [train.py:715] (2/8) Epoch 13, batch 6550, loss[loss=0.1436, simple_loss=0.2093, pruned_loss=0.03899, over 4939.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03155, over 972926.06 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:47:02,650 INFO [train.py:715] (2/8) Epoch 13, batch 6600, loss[loss=0.1183, simple_loss=0.1875, pruned_loss=0.02452, over 4693.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2113, pruned_loss=0.0321, over 972910.99 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:47:42,042 INFO [train.py:715] (2/8) Epoch 13, batch 6650, loss[loss=0.1333, simple_loss=0.2177, pruned_loss=0.02444, over 4903.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2114, pruned_loss=0.03215, over 972811.62 frames.], batch size: 22, lr: 1.70e-04
2022-05-07 16:48:20,278 INFO [train.py:715] (2/8) Epoch 13, batch 6700, loss[loss=0.1355, simple_loss=0.2068, pruned_loss=0.03209, over 4866.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03148, over 973075.67 frames.], batch size: 38, lr: 1.70e-04
2022-05-07 16:48:58,717 INFO [train.py:715] (2/8) Epoch 13, batch 6750, loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03051, over 4717.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03182, over 972669.85 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 16:49:37,983 INFO [train.py:715] (2/8) Epoch 13, batch 6800, loss[loss=0.1444, simple_loss=0.2102, pruned_loss=0.03931, over 4767.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03179, over 971466.10 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:50:17,432 INFO [train.py:715] (2/8) Epoch 13, batch 6850, loss[loss=0.1373, simple_loss=0.2127, pruned_loss=0.03095, over 4773.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03166, over 971741.37 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 16:50:55,366 INFO [train.py:715] (2/8) Epoch 13, batch 6900, loss[loss=0.1502, simple_loss=0.2251, pruned_loss=0.03766, over 4931.00 frames.], tot_loss[loss=0.137, simple_loss=0.2108, pruned_loss=0.03157, over 971521.99 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:51:33,404 INFO [train.py:715] (2/8) Epoch 13, batch 6950, loss[loss=0.1524, simple_loss=0.2211, pruned_loss=0.04187, over 4889.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2107, pruned_loss=0.03119, over 971392.52 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 16:52:12,641 INFO [train.py:715] (2/8) Epoch 13, batch 7000, loss[loss=0.1114, simple_loss=0.1728, pruned_loss=0.02503, over 4847.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03083, over 971500.45 frames.], batch size: 32, lr: 1.70e-04
2022-05-07 16:52:51,282 INFO [train.py:715] (2/8) Epoch 13, batch 7050, loss[loss=0.1102, simple_loss=0.1852, pruned_loss=0.01753, over 4793.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03103, over 971681.08 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:53:30,247 INFO [train.py:715] (2/8) Epoch 13, batch 7100, loss[loss=0.14, simple_loss=0.2172, pruned_loss=0.03134, over 4908.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03145, over 971999.71 frames.], batch size: 18, lr: 1.70e-04
2022-05-07 16:54:09,706 INFO [train.py:715] (2/8) Epoch 13, batch 7150, loss[loss=0.1675, simple_loss=0.2404, pruned_loss=0.0473, over 4891.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03127, over 972580.51 frames.], batch size: 39, lr: 1.70e-04
2022-05-07 16:54:49,407 INFO [train.py:715] (2/8) Epoch 13, batch 7200, loss[loss=0.1294, simple_loss=0.1963, pruned_loss=0.03128, over 4845.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2088, pruned_loss=0.03129, over 972512.71 frames.], batch size: 13, lr: 1.70e-04
2022-05-07 16:55:27,555 INFO [train.py:715] (2/8) Epoch 13, batch 7250, loss[loss=0.128, simple_loss=0.1985, pruned_loss=0.02874, over 4967.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03103, over 973173.70 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:56:05,825 INFO [train.py:715] (2/8) Epoch 13, batch 7300, loss[loss=0.1389, simple_loss=0.2166, pruned_loss=0.03061, over 4941.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2083, pruned_loss=0.03125, over 974077.41 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 16:56:45,076 INFO [train.py:715] (2/8) Epoch 13, batch 7350, loss[loss=0.1089, simple_loss=0.178, pruned_loss=0.01996, over 4793.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03105, over 973076.65 frames.], batch size: 12, lr: 1.70e-04
2022-05-07 16:57:23,718 INFO [train.py:715] (2/8) Epoch 13, batch 7400, loss[loss=0.135, simple_loss=0.2071, pruned_loss=0.03143, over 4915.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03179, over 973501.37 frames.], batch size: 29, lr: 1.70e-04
2022-05-07 16:58:01,544 INFO [train.py:715] (2/8) Epoch 13, batch 7450, loss[loss=0.1453, simple_loss=0.2096, pruned_loss=0.04047, over 4973.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.03163, over 973753.63 frames.], batch size: 14, lr: 1.70e-04
2022-05-07 16:58:40,995 INFO [train.py:715] (2/8) Epoch 13, batch 7500, loss[loss=0.1208, simple_loss=0.1895, pruned_loss=0.02605, over 4985.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2087, pruned_loss=0.03116, over 973554.13 frames.], batch size: 25, lr: 1.70e-04
2022-05-07 16:59:20,236 INFO [train.py:715] (2/8) Epoch 13, batch 7550, loss[loss=0.1525, simple_loss=0.2279, pruned_loss=0.03858, over 4965.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2085, pruned_loss=0.0311, over 974050.74 frames.], batch size: 24, lr: 1.70e-04
2022-05-07 16:59:57,839 INFO [train.py:715] (2/8) Epoch 13, batch 7600, loss[loss=0.175, simple_loss=0.2496, pruned_loss=0.05024, over 4883.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2088, pruned_loss=0.03119, over 973053.41 frames.], batch size: 38, lr: 1.70e-04
2022-05-07 17:00:36,716 INFO [train.py:715] (2/8) Epoch 13, batch 7650, loss[loss=0.162, simple_loss=0.2388, pruned_loss=0.04258, over 4758.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2082, pruned_loss=0.03079, over 973325.42 frames.], batch size: 19, lr: 1.70e-04
2022-05-07 17:01:15,687 INFO [train.py:715] (2/8) Epoch 13, batch 7700, loss[loss=0.1211, simple_loss=0.1965, pruned_loss=0.02291, over 4749.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.0309, over 972522.16 frames.], batch size: 16, lr: 1.70e-04
2022-05-07 17:01:54,652 INFO [train.py:715] (2/8) Epoch 13, batch 7750, loss[loss=0.1316, simple_loss=0.2092, pruned_loss=0.02702, over 4703.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03151, over 972062.63 frames.], batch size: 15, lr: 1.70e-04
2022-05-07 17:02:32,578 INFO [train.py:715] (2/8) Epoch 13, batch 7800, loss[loss=0.1223, simple_loss=0.1975, pruned_loss=0.02358, over 4809.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2092, pruned_loss=0.03157, over 971642.34 frames.], batch size: 21, lr: 1.70e-04
2022-05-07 17:03:11,064 INFO [train.py:715] (2/8) Epoch 13, batch 7850, loss[loss=0.138, simple_loss=0.2055, pruned_loss=0.03518, over 4932.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.03165, over 972131.30 frames.], batch size: 35, lr: 1.70e-04
2022-05-07 17:03:50,704 INFO [train.py:715] (2/8) Epoch 13, batch 7900, loss[loss=0.1298, simple_loss=0.2121, pruned_loss=0.02375, over 4766.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03156, over 971633.14 frames.], batch size: 17, lr: 1.70e-04
2022-05-07 17:04:28,754 INFO [train.py:715] (2/8) Epoch 13, batch 7950, loss[loss=0.1681, simple_loss=0.2454, pruned_loss=0.04536, over 4982.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.03156, over 971586.21 frames.], batch size: 39, lr: 1.70e-04
2022-05-07 17:05:07,216 INFO [train.py:715] (2/8) Epoch 13, batch 8000, loss[loss=0.1139, simple_loss=0.1839, pruned_loss=0.02196, over 4833.00 frames.], tot_loss[loss=0.136, simple_loss=0.2099, pruned_loss=0.03109, over 971903.46 frames.], batch size: 12, lr: 1.70e-04
2022-05-07 17:05:45,984 INFO [train.py:715] (2/8) Epoch 13, batch 8050, loss[loss=0.1384, simple_loss=0.2158, pruned_loss=0.03045, over 4932.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03135, over 971673.15 frames.], batch size: 29, lr: 1.70e-04
2022-05-07 17:06:24,532 INFO [train.py:715] (2/8) Epoch 13, batch 8100, loss[loss=0.142, simple_loss=0.1998, pruned_loss=0.04214, over 4811.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03112, over 971187.53 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:07:02,517 INFO [train.py:715] (2/8) Epoch 13, batch 8150, loss[loss=0.1079, simple_loss=0.1871, pruned_loss=0.01432, over 4931.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2088, pruned_loss=0.03113, over 970843.66 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:07:40,996 INFO [train.py:715] (2/8) Epoch 13, batch 8200, loss[loss=0.1151, simple_loss=0.1903, pruned_loss=0.01998, over 4957.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03077, over 971264.19 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:08:20,204 INFO [train.py:715] (2/8) Epoch 13, batch 8250, loss[loss=0.1461, simple_loss=0.222, pruned_loss=0.03514, over 4941.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03076, over 971352.89 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:08:58,111 INFO [train.py:715] (2/8) Epoch 13, batch 8300, loss[loss=0.1496, simple_loss=0.2203, pruned_loss=0.03945, over 4881.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03101, over 971427.19 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:09:36,543 INFO [train.py:715] (2/8) Epoch 13, batch 8350, loss[loss=0.126, simple_loss=0.2037, pruned_loss=0.02417, over 4931.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2092, pruned_loss=0.03127, over 971222.49 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:10:15,703 INFO [train.py:715] (2/8) Epoch 13, batch 8400, loss[loss=0.1292, simple_loss=0.1975, pruned_loss=0.03047, over 4913.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2091, pruned_loss=0.03155, over 971367.24 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:10:54,563 INFO [train.py:715] (2/8) Epoch 13, batch 8450, loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02939, over 4890.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2088, pruned_loss=0.03166, over 972309.99 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:11:32,555 INFO [train.py:715] (2/8) Epoch 13, batch 8500, loss[loss=0.1287, simple_loss=0.2005, pruned_loss=0.02846, over 4969.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2086, pruned_loss=0.03181, over 972439.31 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:12:11,696 INFO [train.py:715] (2/8) Epoch 13, batch 8550, loss[loss=0.1257, simple_loss=0.1985, pruned_loss=0.02647, over 4838.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2094, pruned_loss=0.03199, over 972550.92 frames.], batch size: 32, lr: 1.69e-04
2022-05-07 17:12:50,648 INFO [train.py:715] (2/8) Epoch 13, batch 8600, loss[loss=0.145, simple_loss=0.2281, pruned_loss=0.03092, over 4873.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2097, pruned_loss=0.03195, over 973298.31 frames.], batch size: 32, lr: 1.69e-04
2022-05-07 17:13:28,884 INFO [train.py:715] (2/8) Epoch 13, batch 8650, loss[loss=0.1107, simple_loss=0.1749, pruned_loss=0.02319, over 4963.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2091, pruned_loss=0.03167, over 973381.92 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:14:07,226 INFO [train.py:715] (2/8) Epoch 13, batch 8700, loss[loss=0.1086, simple_loss=0.1734, pruned_loss=0.02189, over 4774.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2094, pruned_loss=0.03168, over 972743.13 frames.], batch size: 12, lr: 1.69e-04
2022-05-07 17:14:45,868 INFO [train.py:715] (2/8) Epoch 13, batch 8750, loss[loss=0.1254, simple_loss=0.1981, pruned_loss=0.02635, over 4863.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.03169, over 973095.15 frames.], batch size: 38, lr: 1.69e-04
2022-05-07 17:15:24,585 INFO [train.py:715] (2/8) Epoch 13, batch 8800, loss[loss=0.1317, simple_loss=0.214, pruned_loss=0.02469, over 4784.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03171, over 972328.73 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:16:02,889 INFO [train.py:715] (2/8) Epoch 13, batch 8850, loss[loss=0.1383, simple_loss=0.211, pruned_loss=0.03286, over 4988.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03164, over 972644.67 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:16:40,976 INFO [train.py:715] (2/8) Epoch 13, batch 8900, loss[loss=0.142, simple_loss=0.2198, pruned_loss=0.03208, over 4930.00 frames.], tot_loss[loss=0.137, simple_loss=0.2103, pruned_loss=0.03184, over 972957.49 frames.], batch size: 23, lr: 1.69e-04
2022-05-07 17:17:19,700 INFO [train.py:715] (2/8) Epoch 13, batch 8950, loss[loss=0.1735, simple_loss=0.2387, pruned_loss=0.05417, over 4861.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03168, over 973971.62 frames.], batch size: 32, lr: 1.69e-04
2022-05-07 17:17:57,830 INFO [train.py:715] (2/8) Epoch 13, batch 9000, loss[loss=0.1584, simple_loss=0.2247, pruned_loss=0.04607, over 4986.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03181, over 973542.03 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:17:57,831 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 17:18:07,452 INFO [train.py:742] (2/8) Epoch 13, validation: loss=0.1055, simple_loss=0.1893, pruned_loss=0.01084, over 914524.00 frames.
2022-05-07 17:18:45,504 INFO [train.py:715] (2/8) Epoch 13, batch 9050, loss[loss=0.151, simple_loss=0.2386, pruned_loss=0.03167, over 4943.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2096, pruned_loss=0.03182, over 973340.12 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:19:23,914 INFO [train.py:715] (2/8) Epoch 13, batch 9100, loss[loss=0.1568, simple_loss=0.2224, pruned_loss=0.04563, over 4914.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2089, pruned_loss=0.0317, over 973195.91 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:20:03,102 INFO [train.py:715] (2/8) Epoch 13, batch 9150, loss[loss=0.1625, simple_loss=0.2381, pruned_loss=0.04348, over 4782.00 frames.], tot_loss[loss=0.1359, simple_loss=0.209, pruned_loss=0.0314, over 973773.06 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:20:42,098 INFO [train.py:715] (2/8) Epoch 13, batch 9200, loss[loss=0.117, simple_loss=0.1979, pruned_loss=0.01802, over 4942.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03104, over 973261.43 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:21:20,017 INFO [train.py:715] (2/8) Epoch 13, batch 9250, loss[loss=0.1397, simple_loss=0.2139, pruned_loss=0.03272, over 4969.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03098, over 972879.87 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:21:58,916 INFO [train.py:715] (2/8) Epoch 13, batch 9300, loss[loss=0.1355, simple_loss=0.2052, pruned_loss=0.03295, over 4984.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03115, over 973596.31 frames.], batch size: 31, lr: 1.69e-04
2022-05-07 17:22:37,768 INFO [train.py:715] (2/8) Epoch 13, batch 9350, loss[loss=0.1391, simple_loss=0.217, pruned_loss=0.0306, over 4851.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03103, over 974172.83 frames.], batch size: 30, lr: 1.69e-04
2022-05-07 17:23:15,582 INFO [train.py:715] (2/8) Epoch 13, batch 9400, loss[loss=0.1531, simple_loss=0.2252, pruned_loss=0.04055, over 4934.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03102, over 973410.61 frames.], batch size: 23, lr: 1.69e-04
2022-05-07 17:23:54,035 INFO [train.py:715] (2/8) Epoch 13, batch 9450, loss[loss=0.1583, simple_loss=0.2283, pruned_loss=0.04419, over 4842.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03111, over 973502.90 frames.], batch size: 32, lr: 1.69e-04
2022-05-07 17:24:32,855 INFO [train.py:715] (2/8) Epoch 13, batch 9500, loss[loss=0.1296, simple_loss=0.1978, pruned_loss=0.0307, over 4785.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03136, over 973539.99 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:25:11,109 INFO [train.py:715] (2/8) Epoch 13, batch 9550, loss[loss=0.1334, simple_loss=0.2149, pruned_loss=0.02599, over 4863.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03102, over 973087.17 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:25:49,078 INFO [train.py:715] (2/8) Epoch 13, batch 9600, loss[loss=0.1231, simple_loss=0.1873, pruned_loss=0.02939, over 4829.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2088, pruned_loss=0.03114, over 972684.03 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:26:28,026 INFO [train.py:715] (2/8) Epoch 13, batch 9650, loss[loss=0.1347, simple_loss=0.205, pruned_loss=0.0322, over 4898.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03079, over 973072.99 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:27:06,437 INFO [train.py:715] (2/8) Epoch 13, batch 9700, loss[loss=0.1088, simple_loss=0.1796, pruned_loss=0.01899, over 4755.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03073, over 972794.89 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:27:44,981 INFO [train.py:715] (2/8) Epoch 13, batch 9750, loss[loss=0.1303, simple_loss=0.1978, pruned_loss=0.03139, over 4899.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03103, over 971890.35 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 17:28:23,844 INFO [train.py:715] (2/8) Epoch 13, batch 9800, loss[loss=0.1146, simple_loss=0.1869, pruned_loss=0.02111, over 4755.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03111, over 971860.00 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:29:03,035 INFO [train.py:715] (2/8) Epoch 13, batch 9850, loss[loss=0.176, simple_loss=0.2388, pruned_loss=0.05653, over 4765.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.0316, over 971161.11 frames.], batch size: 12, lr: 1.69e-04
2022-05-07 17:29:41,586 INFO [train.py:715] (2/8) Epoch 13, batch 9900, loss[loss=0.1363, simple_loss=0.2054, pruned_loss=0.03358, over 4763.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03195, over 971584.00 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:30:19,823 INFO [train.py:715] (2/8) Epoch 13, batch 9950, loss[loss=0.1353, simple_loss=0.2112, pruned_loss=0.02971, over 4829.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.03151, over 971831.16 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 17:30:58,617 INFO [train.py:715] (2/8) Epoch 13, batch 10000, loss[loss=0.129, simple_loss=0.2067, pruned_loss=0.02568, over 4845.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2104, pruned_loss=0.03117, over 972192.45 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 17:31:37,788 INFO [train.py:715] (2/8) Epoch 13, batch 10050, loss[loss=0.1372, simple_loss=0.1922, pruned_loss=0.04114, over 4762.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2108, pruned_loss=0.03142, over 972034.98 frames.], batch size: 12, lr: 1.69e-04
2022-05-07 17:32:16,723 INFO [train.py:715] (2/8) Epoch 13, batch 10100, loss[loss=0.1488, simple_loss=0.2244, pruned_loss=0.03664, over 4863.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03163, over 971821.70 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 17:32:54,967 INFO [train.py:715] (2/8) Epoch 13, batch 10150, loss[loss=0.1375, simple_loss=0.2055, pruned_loss=0.03471, over 4974.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03148, over 972563.36 frames.], batch size: 39, lr: 1.69e-04
2022-05-07 17:33:34,001 INFO [train.py:715] (2/8) Epoch 13, batch 10200, loss[loss=0.1433, simple_loss=0.2223, pruned_loss=0.03212, over 4988.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03139, over 972164.68 frames.], batch size: 28, lr: 1.69e-04
2022-05-07 17:34:13,397 INFO [train.py:715] (2/8) Epoch 13, batch 10250, loss[loss=0.1213, simple_loss=0.1997, pruned_loss=0.02144, over 4850.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03159, over 973136.57 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 17:34:52,085 INFO [train.py:715] (2/8) Epoch 13, batch 10300, loss[loss=0.1424, simple_loss=0.2185, pruned_loss=0.03316, over 4776.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2106, pruned_loss=0.03209, over 972778.94 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:35:31,129 INFO [train.py:715] (2/8) Epoch 13, batch 10350, loss[loss=0.1106, simple_loss=0.1855, pruned_loss=0.01781, over 4887.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2101, pruned_loss=0.03212, over 972446.22 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:36:10,307 INFO [train.py:715] (2/8) Epoch 13, batch 10400, loss[loss=0.1362, simple_loss=0.2054, pruned_loss=0.03355, over 4923.00 frames.], tot_loss[loss=0.1371, simple_loss=0.21, pruned_loss=0.03212, over 973678.10 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:36:49,253 INFO [train.py:715] (2/8) Epoch 13, batch 10450, loss[loss=0.1292, simple_loss=0.2069, pruned_loss=0.02577, over 4901.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2089, pruned_loss=0.03142, over 973775.74 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:37:26,680 INFO [train.py:715] (2/8) Epoch 13, batch 10500, loss[loss=0.1178, simple_loss=0.1973, pruned_loss=0.01912, over 4697.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2089, pruned_loss=0.03142, over 972847.83 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:38:05,568 INFO [train.py:715] (2/8) Epoch 13, batch 10550, loss[loss=0.1256, simple_loss=0.186, pruned_loss=0.03257, over 4777.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.03083, over 972999.95 frames.], batch size: 12, lr: 1.69e-04
2022-05-07 17:38:44,489 INFO [train.py:715] (2/8) Epoch 13, batch 10600, loss[loss=0.1338, simple_loss=0.2085, pruned_loss=0.02957, over 4833.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03101, over 973031.01 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 17:39:22,595 INFO [train.py:715] (2/8) Epoch 13, batch 10650, loss[loss=0.1442, simple_loss=0.2227, pruned_loss=0.03289, over 4909.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2107, pruned_loss=0.03182, over 972893.60 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:40:01,764 INFO [train.py:715] (2/8) Epoch 13, batch 10700, loss[loss=0.1209, simple_loss=0.1846, pruned_loss=0.02854, over 4770.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2109, pruned_loss=0.03175, over 972884.13 frames.], batch size: 12, lr: 1.69e-04
2022-05-07 17:40:41,062 INFO [train.py:715] (2/8) Epoch 13, batch 10750, loss[loss=0.1301, simple_loss=0.2057, pruned_loss=0.02731, over 4867.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03141, over 973083.20 frames.], batch size: 39, lr: 1.69e-04
2022-05-07 17:41:19,859 INFO [train.py:715] (2/8) Epoch 13, batch 10800, loss[loss=0.1504, simple_loss=0.2215, pruned_loss=0.03962, over 4829.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03149, over 973137.68 frames.], batch size: 25, lr: 1.69e-04
2022-05-07 17:41:57,880 INFO [train.py:715] (2/8) Epoch 13, batch 10850, loss[loss=0.1487, simple_loss=0.2243, pruned_loss=0.03656, over 4916.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2103, pruned_loss=0.03166, over 973077.82 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:42:37,035 INFO [train.py:715] (2/8) Epoch 13, batch 10900, loss[loss=0.1324, simple_loss=0.2103, pruned_loss=0.02726, over 4983.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2108, pruned_loss=0.03193, over 974225.05 frames.], batch size: 24, lr: 1.69e-04
2022-05-07 17:43:16,894 INFO [train.py:715] (2/8) Epoch 13, batch 10950, loss[loss=0.1235, simple_loss=0.2071, pruned_loss=0.01999, over 4965.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2107, pruned_loss=0.03198, over 973984.48 frames.], batch size: 24, lr: 1.69e-04
2022-05-07 17:43:56,320 INFO [train.py:715] (2/8) Epoch 13, batch 11000, loss[loss=0.1502, simple_loss=0.2133, pruned_loss=0.04359, over 4736.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03206, over 972730.96 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:44:34,955 INFO [train.py:715] (2/8) Epoch 13, batch 11050, loss[loss=0.1208, simple_loss=0.1951, pruned_loss=0.02327, over 4771.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2108, pruned_loss=0.03171, over 972609.46 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 17:45:14,252 INFO [train.py:715] (2/8) Epoch 13, batch 11100, loss[loss=0.1359, simple_loss=0.209, pruned_loss=0.03139, over 4913.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03169, over 972705.30 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:45:53,237 INFO [train.py:715] (2/8) Epoch 13, batch 11150, loss[loss=0.1257, simple_loss=0.1955, pruned_loss=0.02796, over 4911.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03152, over 973584.22 frames.], batch size: 17, lr: 1.69e-04
2022-05-07 17:46:30,992 INFO [train.py:715] (2/8) Epoch 13, batch 11200, loss[loss=0.123, simple_loss=0.1972, pruned_loss=0.02438, over 4831.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03151, over 973243.95 frames.], batch size: 26, lr: 1.69e-04
2022-05-07 17:47:09,194 INFO [train.py:715] (2/8) Epoch 13, batch 11250, loss[loss=0.1347, simple_loss=0.2043, pruned_loss=0.0326, over 4793.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.0311, over 973121.40 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:47:48,140 INFO [train.py:715] (2/8) Epoch 13, batch 11300, loss[loss=0.1403, simple_loss=0.217, pruned_loss=0.03177, over 4916.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03128, over 973139.26 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:48:27,088 INFO [train.py:715] (2/8) Epoch 13, batch 11350, loss[loss=0.145, simple_loss=0.2241, pruned_loss=0.03295, over 4825.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2108, pruned_loss=0.03142, over 973172.58 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:49:05,292 INFO [train.py:715] (2/8) Epoch 13, batch 11400, loss[loss=0.1471, simple_loss=0.2285, pruned_loss=0.03284, over 4932.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03113, over 972644.08 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:49:44,158 INFO [train.py:715] (2/8) Epoch 13, batch 11450, loss[loss=0.1194, simple_loss=0.1964, pruned_loss=0.0212, over 4651.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.03055, over 971455.94 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 17:50:25,718 INFO [train.py:715] (2/8) Epoch 13, batch 11500, loss[loss=0.1496, simple_loss=0.2251, pruned_loss=0.03709, over 4908.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2103, pruned_loss=0.03091, over 972128.43 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 17:51:03,650 INFO [train.py:715] (2/8) Epoch 13, batch 11550, loss[loss=0.09672, simple_loss=0.1608, pruned_loss=0.01633, over 4748.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.0304, over 972084.83 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 17:51:42,315 INFO [train.py:715] (2/8) Epoch 13, batch 11600, loss[loss=0.1696, simple_loss=0.2264, pruned_loss=0.05645, over 4794.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03116, over 972186.22 frames.], batch size: 24, lr: 1.69e-04
2022-05-07 17:52:21,600 INFO [train.py:715] (2/8) Epoch 13, batch 11650, loss[loss=0.1443, simple_loss=0.2116, pruned_loss=0.03847, over 4892.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03133, over 972976.74 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 17:53:00,310 INFO [train.py:715] (2/8) Epoch 13, batch 11700, loss[loss=0.1445, simple_loss=0.2125, pruned_loss=0.03828, over 4865.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03111, over 972396.95 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 17:53:38,278 INFO [train.py:715] (2/8) Epoch 13, batch 11750, loss[loss=0.1519, simple_loss=0.2198, pruned_loss=0.04202, over 4866.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.0311, over 972292.60 frames.], batch size: 38, lr: 1.69e-04
2022-05-07 17:54:16,755 INFO [train.py:715] (2/8) Epoch 13, batch 11800, loss[loss=0.1262, simple_loss=0.2006, pruned_loss=0.02587, over 4826.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03151, over 972890.21 frames.], batch size: 26, lr: 1.69e-04
2022-05-07 17:54:55,480 INFO [train.py:715] (2/8) Epoch 13, batch 11850, loss[loss=0.1224, simple_loss=0.1965, pruned_loss=0.02422, over 4810.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03164, over 971935.35 frames.], batch size: 26, lr: 1.69e-04
2022-05-07 17:55:32,889 INFO [train.py:715] (2/8) Epoch 13, batch 11900, loss[loss=0.1359, simple_loss=0.2032, pruned_loss=0.03433, over 4838.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03169, over 971630.83 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:56:11,546 INFO [train.py:715] (2/8) Epoch 13, batch 11950, loss[loss=0.1692, simple_loss=0.2419, pruned_loss=0.04829, over 4848.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03156, over 971700.52 frames.], batch size: 30, lr: 1.69e-04
2022-05-07 17:56:50,611 INFO [train.py:715] (2/8) Epoch 13, batch 12000, loss[loss=0.1339, simple_loss=0.2007, pruned_loss=0.03358, over 4857.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03137, over 971204.10 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 17:56:50,612 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 17:57:00,357 INFO [train.py:742] (2/8) Epoch 13, validation: loss=0.1055, simple_loss=0.1893, pruned_loss=0.01081, over 914524.00 frames.
2022-05-07 17:57:40,027 INFO [train.py:715] (2/8) Epoch 13, batch 12050, loss[loss=0.1659, simple_loss=0.2358, pruned_loss=0.04798, over 4857.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03147, over 971277.89 frames.], batch size: 34, lr: 1.69e-04
2022-05-07 17:58:18,319 INFO [train.py:715] (2/8) Epoch 13, batch 12100, loss[loss=0.1344, simple_loss=0.2108, pruned_loss=0.02903, over 4810.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03113, over 971558.12 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 17:58:56,073 INFO [train.py:715] (2/8) Epoch 13, batch 12150, loss[loss=0.119, simple_loss=0.1962, pruned_loss=0.02087, over 4828.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.0311, over 972223.05 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 17:59:34,974 INFO [train.py:715] (2/8) Epoch 13, batch 12200, loss[loss=0.1023, simple_loss=0.1794, pruned_loss=0.01265, over 4865.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03141, over 972116.21 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 18:00:13,891 INFO [train.py:715] (2/8) Epoch 13, batch 12250, loss[loss=0.1382, simple_loss=0.214, pruned_loss=0.03124, over 4744.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03136, over 971517.01 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 18:00:52,461 INFO [train.py:715] (2/8) Epoch 13, batch 12300, loss[loss=0.124, simple_loss=0.1928, pruned_loss=0.02759, over 4876.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2092, pruned_loss=0.03159, over 971520.45 frames.], batch size: 32, lr: 1.69e-04
2022-05-07 18:01:30,137 INFO [train.py:715] (2/8) Epoch 13, batch 12350, loss[loss=0.1453, simple_loss=0.2214, pruned_loss=0.03455, over 4817.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03086, over 972099.30 frames.], batch size: 25, lr: 1.69e-04
2022-05-07 18:02:09,069 INFO [train.py:715] (2/8) Epoch 13, batch 12400, loss[loss=0.1838, simple_loss=0.2611, pruned_loss=0.05327, over 4809.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03049, over 973375.72 frames.], batch size: 13, lr: 1.69e-04
2022-05-07 18:02:47,459 INFO [train.py:715] (2/8) Epoch 13, batch 12450, loss[loss=0.1174, simple_loss=0.1853, pruned_loss=0.02477, over 4782.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03023, over 972711.43 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 18:03:24,475 INFO [train.py:715] (2/8) Epoch 13, batch 12500, loss[loss=0.1428, simple_loss=0.2114, pruned_loss=0.0371, over 4958.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2091, pruned_loss=0.03008, over 973039.32 frames.], batch size: 35, lr: 1.69e-04
2022-05-07 18:04:03,261 INFO [train.py:715] (2/8) Epoch 13, batch 12550, loss[loss=0.1285, simple_loss=0.2032, pruned_loss=0.0269, over 4879.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2099, pruned_loss=0.0306, over 972856.74 frames.], batch size: 22, lr: 1.69e-04
2022-05-07 18:04:41,883 INFO [train.py:715] (2/8) Epoch 13, batch 12600, loss[loss=0.1495, simple_loss=0.222, pruned_loss=0.03848, over 4782.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2102, pruned_loss=0.03075, over 972025.23 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 18:05:20,412 INFO [train.py:715] (2/8) Epoch 13, batch 12650, loss[loss=0.1518, simple_loss=0.2362, pruned_loss=0.03366, over 4864.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2094, pruned_loss=0.03044, over 972632.85 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 18:05:58,208 INFO [train.py:715] (2/8) Epoch 13, batch 12700, loss[loss=0.1325, simple_loss=0.1989, pruned_loss=0.03308, over 4882.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.0302, over 972079.75 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 18:06:37,495 INFO [train.py:715] (2/8) Epoch 13, batch 12750, loss[loss=0.1262, simple_loss=0.1957, pruned_loss=0.02835, over 4915.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03014, over 973093.73 frames.], batch size: 19, lr: 1.69e-04
2022-05-07 18:07:16,118 INFO [train.py:715] (2/8) Epoch 13, batch 12800, loss[loss=0.1435, simple_loss=0.2097, pruned_loss=0.03865, over 4901.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03028, over 973855.05 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 18:07:53,802 INFO [train.py:715] (2/8) Epoch 13, batch 12850, loss[loss=0.1278, simple_loss=0.2019, pruned_loss=0.02686, over 4781.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03034, over 973736.46 frames.], batch size: 12, lr: 1.69e-04
2022-05-07 18:08:32,304 INFO [train.py:715] (2/8) Epoch 13, batch 12900, loss[loss=0.1661, simple_loss=0.229, pruned_loss=0.05162, over 4753.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03063, over 973647.25 frames.], batch size: 14, lr: 1.69e-04
2022-05-07 18:09:10,903 INFO [train.py:715] (2/8) Epoch 13, batch 12950, loss[loss=0.1353, simple_loss=0.2036, pruned_loss=0.03344, over 4921.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.03063, over 973520.24 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 18:09:48,857 INFO [train.py:715] (2/8) Epoch 13, batch 13000, loss[loss=0.1419, simple_loss=0.2118, pruned_loss=0.03595, over 4740.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.03091, over 972668.35 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 18:10:26,257 INFO [train.py:715] (2/8) Epoch 13, batch 13050, loss[loss=0.1313, simple_loss=0.2013, pruned_loss=0.03071, over 4903.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03073, over 972333.31 frames.], batch size: 18, lr: 1.69e-04
2022-05-07 18:11:05,302 INFO [train.py:715] (2/8) Epoch 13, batch 13100, loss[loss=0.1136, simple_loss=0.1852, pruned_loss=0.02101, over 4921.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.03117, over 972526.97 frames.], batch size: 23, lr: 1.69e-04
2022-05-07 18:11:43,997 INFO [train.py:715] (2/8) Epoch 13, batch 13150, loss[loss=0.1292, simple_loss=0.2128, pruned_loss=0.02282, over 4951.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03061, over 972233.49 frames.], batch size: 21, lr: 1.69e-04
2022-05-07 18:12:21,745 INFO [train.py:715] (2/8) Epoch 13, batch 13200, loss[loss=0.1254, simple_loss=0.1988, pruned_loss=0.02597, over 4988.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03039, over 972280.28 frames.], batch size: 25, lr: 1.69e-04
2022-05-07 18:13:00,179 INFO [train.py:715] (2/8) Epoch 13, batch 13250, loss[loss=0.1288, simple_loss=0.207, pruned_loss=0.02525, over 4969.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03144, over 972732.63 frames.], batch size: 24, lr: 1.69e-04
2022-05-07 18:13:38,868 INFO [train.py:715] (2/8) Epoch 13, batch 13300, loss[loss=0.143, simple_loss=0.2128, pruned_loss=0.03658, over 4985.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03116, over 972182.28 frames.], batch size: 24, lr: 1.69e-04
2022-05-07 18:14:17,605 INFO [train.py:715] (2/8) Epoch 13, batch 13350, loss[loss=0.1627, simple_loss=0.2436, pruned_loss=0.04094, over 4966.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2113, pruned_loss=0.03162, over 972268.88 frames.], batch size: 28, lr: 1.69e-04
2022-05-07 18:14:55,893 INFO [train.py:715] (2/8) Epoch 13, batch 13400, loss[loss=0.1288, simple_loss=0.2065, pruned_loss=0.02552, over 4849.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2109, pruned_loss=0.03142, over 971788.10 frames.], batch size: 20, lr: 1.69e-04
2022-05-07 18:15:35,683 INFO [train.py:715] (2/8) Epoch 13, batch 13450, loss[loss=0.1235, simple_loss=0.1871, pruned_loss=0.02999, over 4990.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2102, pruned_loss=0.03113, over 972949.15 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 18:16:14,409 INFO [train.py:715] (2/8) Epoch 13, batch 13500, loss[loss=0.1456, simple_loss=0.2226, pruned_loss=0.03431, over 4836.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2103, pruned_loss=0.03089, over 972747.48 frames.], batch size: 15, lr: 1.69e-04
2022-05-07 18:16:52,061 INFO [train.py:715] (2/8) Epoch 13, batch 13550, loss[loss=0.1289, simple_loss=0.2075, pruned_loss=0.02515, over 4752.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2102, pruned_loss=0.03068, over 971913.17 frames.], batch size: 16, lr: 1.69e-04
2022-05-07 18:17:29,851 INFO [train.py:715] (2/8) Epoch 13, batch 13600, loss[loss=0.1458, simple_loss=0.2177, pruned_loss=0.03699, over 4815.00 frames.], tot_loss[loss=0.136, simple_loss=0.2106, pruned_loss=0.0307, over 972288.00 frames.], batch size: 26, lr: 1.68e-04
2022-05-07 18:18:08,972 INFO [train.py:715] (2/8) Epoch 13, batch 13650, loss[loss=0.1248, simple_loss=0.2015, pruned_loss=0.02398, over 4810.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2109, pruned_loss=0.03086, over 971763.80 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 18:18:47,087 INFO [train.py:715] (2/8) Epoch 13, batch 13700, loss[loss=0.157, simple_loss=0.2288, pruned_loss=0.04256, over 4988.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2106, pruned_loss=0.03119, over 972410.70 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:19:24,726 INFO [train.py:715] (2/8) Epoch 13, batch 13750, loss[loss=0.1283, simple_loss=0.2091, pruned_loss=0.02377, over 4875.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.03085, over 972833.01 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 18:20:03,322 INFO [train.py:715] (2/8) Epoch 13, batch 13800, loss[loss=0.128, simple_loss=0.1972, pruned_loss=0.02939, over 4639.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03139, over 972738.09 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:20:41,462 INFO [train.py:715] (2/8) Epoch 13, batch 13850, loss[loss=0.1508, simple_loss=0.2249, pruned_loss=0.03835, over 4809.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03125, over 972684.29 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:21:19,873 INFO [train.py:715] (2/8) Epoch 13, batch 13900, loss[loss=0.1347, simple_loss=0.2033, pruned_loss=0.03301, over 4841.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.03062, over 971938.14 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:21:58,635 INFO [train.py:715] (2/8) Epoch 13, batch 13950, loss[loss=0.1337, simple_loss=0.2017, pruned_loss=0.03286, over 4749.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03058, over 971696.14 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:22:37,442 INFO [train.py:715] (2/8) Epoch 13, batch 14000, loss[loss=0.1353, simple_loss=0.2161, pruned_loss=0.0272, over 4864.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03031, over 972162.23 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 18:23:15,662 INFO [train.py:715] (2/8) Epoch 13, batch 14050, loss[loss=0.111, simple_loss=0.1795, pruned_loss=0.02131, over 4922.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03034, over 972843.30 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:23:53,255 INFO [train.py:715] (2/8) Epoch 13, batch 14100, loss[loss=0.1086, simple_loss=0.178, pruned_loss=0.01958, over 4808.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03048, over 974033.62 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:24:32,483 INFO [train.py:715] (2/8) Epoch 13, batch 14150, loss[loss=0.1381, simple_loss=0.2094, pruned_loss=0.03337, over 4773.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.0307, over 973399.43 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:25:10,631 INFO [train.py:715] (2/8) Epoch 13, batch 14200, loss[loss=0.1321, simple_loss=0.1954, pruned_loss=0.03443, over 4839.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03084, over 973959.78 frames.], batch size: 30, lr: 1.68e-04
2022-05-07 18:25:48,512 INFO [train.py:715] (2/8) Epoch 13, batch 14250, loss[loss=0.1292, simple_loss=0.1934, pruned_loss=0.03255, over 4847.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.03089, over 973888.98 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:26:26,775 INFO [train.py:715] (2/8) Epoch 13, batch 14300, loss[loss=0.1539, simple_loss=0.2323, pruned_loss=0.03776, over 4927.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2082, pruned_loss=0.03099, over 973143.32 frames.], batch size: 23, lr: 1.68e-04
2022-05-07 18:27:06,171 INFO [train.py:715] (2/8) Epoch 13, batch 14350, loss[loss=0.1567, simple_loss=0.2408, pruned_loss=0.03635, over 4690.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.0313, over 972158.49 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:27:44,510 INFO [train.py:715] (2/8) Epoch 13, batch 14400, loss[loss=0.1241, simple_loss=0.1937, pruned_loss=0.02727, over 4761.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03134, over 972291.79 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:28:22,437 INFO [train.py:715] (2/8) Epoch 13, batch 14450, loss[loss=0.1287, simple_loss=0.2128, pruned_loss=0.0223, over 4977.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03151, over 972424.02 frames.], batch size: 28, lr: 1.68e-04
2022-05-07 18:29:01,541 INFO [train.py:715] (2/8) Epoch 13, batch 14500, loss[loss=0.1598, simple_loss=0.2355, pruned_loss=0.04202, over 4872.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03183, over 972802.70 frames.], batch size: 20, lr: 1.68e-04
2022-05-07 18:29:40,347 INFO [train.py:715] (2/8) Epoch 13, batch 14550, loss[loss=0.1183, simple_loss=0.1888, pruned_loss=0.02391, over 4790.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03161, over 972279.51 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:30:18,694 INFO [train.py:715] (2/8) Epoch 13, batch 14600, loss[loss=0.1432, simple_loss=0.2181, pruned_loss=0.03417, over 4899.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2111, pruned_loss=0.03214, over 972620.36 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:30:57,061 INFO [train.py:715] (2/8) Epoch 13, batch 14650, loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02868, over 4954.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2106, pruned_loss=0.03233, over 972437.74 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 18:31:35,709 INFO [train.py:715] (2/8) Epoch 13, batch 14700, loss[loss=0.136, simple_loss=0.2155, pruned_loss=0.02826, over 4906.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2101, pruned_loss=0.03204, over 972952.28 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:32:13,645 INFO [train.py:715] (2/8) Epoch 13, batch 14750, loss[loss=0.1313, simple_loss=0.1984, pruned_loss=0.03204, over 4969.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2097, pruned_loss=0.0319, over 972601.92 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:32:50,801 INFO [train.py:715] (2/8) Epoch 13, batch 14800, loss[loss=0.1572, simple_loss=0.2251, pruned_loss=0.0446, over 4880.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03147, over 973201.60 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:33:29,891 INFO [train.py:715] (2/8) Epoch 13, batch 14850, loss[loss=0.1349, simple_loss=0.2206, pruned_loss=0.02467, over 4942.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03102, over 972482.33 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:34:08,571 INFO [train.py:715] (2/8) Epoch 13, batch 14900, loss[loss=0.1404, simple_loss=0.2026, pruned_loss=0.03911, over 4862.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2088, pruned_loss=0.03109, over 971864.56 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:34:46,495 INFO [train.py:715] (2/8) Epoch 13, batch 14950, loss[loss=0.1366, simple_loss=0.2141, pruned_loss=0.02955, over 4770.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03107, over 971178.15 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:35:24,995 INFO [train.py:715] (2/8) Epoch 13, batch 15000, loss[loss=0.1421, simple_loss=0.217, pruned_loss=0.03358, over 4837.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.0307, over 971682.95 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:35:24,996 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 18:35:34,567 INFO [train.py:742] (2/8) Epoch 13, validation: loss=0.1052, simple_loss=0.189, pruned_loss=0.01074, over 914524.00 frames.
2022-05-07 18:36:13,161 INFO [train.py:715] (2/8) Epoch 13, batch 15050, loss[loss=0.1321, simple_loss=0.2081, pruned_loss=0.02801, over 4918.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03059, over 972109.60 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 18:36:52,714 INFO [train.py:715] (2/8) Epoch 13, batch 15100, loss[loss=0.1183, simple_loss=0.1888, pruned_loss=0.02391, over 4793.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2078, pruned_loss=0.03046, over 972121.09 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 18:37:31,194 INFO [train.py:715] (2/8) Epoch 13, batch 15150, loss[loss=0.1294, simple_loss=0.1896, pruned_loss=0.03464, over 4853.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03089, over 972118.99 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:38:09,447 INFO [train.py:715] (2/8) Epoch 13, batch 15200, loss[loss=0.1377, simple_loss=0.2135, pruned_loss=0.031, over 4929.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2087, pruned_loss=0.03106, over 972260.02 frames.], batch size: 29, lr: 1.68e-04
2022-05-07 18:38:49,231 INFO [train.py:715] (2/8) Epoch 13, batch 15250, loss[loss=0.1531, simple_loss=0.2193, pruned_loss=0.04342, over 4856.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03129, over 971831.53 frames.], batch size: 32, lr: 1.68e-04
2022-05-07 18:39:27,975 INFO [train.py:715] (2/8) Epoch 13, batch 15300, loss[loss=0.1635, simple_loss=0.2444, pruned_loss=0.04128, over 4830.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03081, over 971486.14 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:40:06,015 INFO [train.py:715] (2/8) Epoch 13, batch 15350, loss[loss=0.1234, simple_loss=0.2013, pruned_loss=0.02277, over 4893.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03086, over 971275.14 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:40:45,013 INFO [train.py:715] (2/8) Epoch 13, batch 15400, loss[loss=0.1298, simple_loss=0.2064, pruned_loss=0.02662, over 4810.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03103, over 971870.48 frames.], batch size: 27, lr: 1.68e-04
2022-05-07 18:41:23,909 INFO [train.py:715] (2/8) Epoch 13, batch 15450, loss[loss=0.1328, simple_loss=0.2229, pruned_loss=0.02131, over 4881.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2103, pruned_loss=0.03118, over 972037.41 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:42:03,715 INFO [train.py:715] (2/8) Epoch 13, batch 15500, loss[loss=0.1468, simple_loss=0.2242, pruned_loss=0.03471, over 4851.00 frames.], tot_loss[loss=0.136, simple_loss=0.2101, pruned_loss=0.03094, over 972824.23 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:42:41,963 INFO [train.py:715] (2/8) Epoch 13, batch 15550, loss[loss=0.1859, simple_loss=0.2364, pruned_loss=0.06769, over 4849.00 frames.], tot_loss[loss=0.1369, simple_loss=0.211, pruned_loss=0.03139, over 972726.52 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:43:21,700 INFO [train.py:715] (2/8) Epoch 13, batch 15600, loss[loss=0.1661, simple_loss=0.2296, pruned_loss=0.05129, over 4884.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2103, pruned_loss=0.03115, over 972456.35 frames.], batch size: 38, lr: 1.68e-04
2022-05-07 18:44:01,140 INFO [train.py:715] (2/8) Epoch 13, batch 15650, loss[loss=0.1038, simple_loss=0.1696, pruned_loss=0.01898, over 4737.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03117, over 972776.05 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 18:44:39,624 INFO [train.py:715] (2/8) Epoch 13, batch 15700, loss[loss=0.1201, simple_loss=0.192, pruned_loss=0.02408, over 4849.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03117, over 973321.57 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:45:18,632 INFO [train.py:715] (2/8) Epoch 13, batch 15750, loss[loss=0.1382, simple_loss=0.2192, pruned_loss=0.02858, over 4907.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2104, pruned_loss=0.03187, over 973237.10 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 18:45:57,411 INFO [train.py:715] (2/8) Epoch 13, batch 15800, loss[loss=0.1339, simple_loss=0.2095, pruned_loss=0.02913, over 4962.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03143, over 973789.84 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 18:46:35,695 INFO [train.py:715] (2/8) Epoch 13, batch 15850, loss[loss=0.132, simple_loss=0.2014, pruned_loss=0.03129, over 4829.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2105, pruned_loss=0.03208, over 973643.74 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:47:13,600 INFO [train.py:715] (2/8) Epoch 13, batch 15900, loss[loss=0.1482, simple_loss=0.2226, pruned_loss=0.03687, over 4700.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2098, pruned_loss=0.03195, over 972375.81 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:47:52,837 INFO [train.py:715] (2/8) Epoch 13, batch 15950, loss[loss=0.132, simple_loss=0.2162, pruned_loss=0.02387, over 4736.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2102, pruned_loss=0.03208, over 971859.21 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 18:48:31,350 INFO [train.py:715] (2/8) Epoch 13, batch 16000, loss[loss=0.1518, simple_loss=0.2241, pruned_loss=0.03973, over 4986.00 frames.], tot_loss[loss=0.1367, simple_loss=0.21, pruned_loss=0.03169, over 972243.22 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 18:49:09,602 INFO [train.py:715] (2/8) Epoch 13, batch 16050, loss[loss=0.169, simple_loss=0.2487, pruned_loss=0.0446, over 4844.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2102, pruned_loss=0.03175, over 971818.85 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:49:48,080 INFO [train.py:715] (2/8) Epoch 13, batch 16100, loss[loss=0.1478, simple_loss=0.2275, pruned_loss=0.03405, over 4978.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03168, over 971934.26 frames.], batch size: 31, lr: 1.68e-04
2022-05-07 18:50:27,336 INFO [train.py:715] (2/8) Epoch 13, batch 16150, loss[loss=0.1285, simple_loss=0.202, pruned_loss=0.0275, over 4823.00 frames.], tot_loss[loss=0.1366, simple_loss=0.21, pruned_loss=0.03159, over 971586.52 frames.], batch size: 26, lr: 1.68e-04
2022-05-07 18:51:05,994 INFO [train.py:715] (2/8) Epoch 13, batch 16200, loss[loss=0.09061, simple_loss=0.1646, pruned_loss=0.008327, over 4787.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03119, over 971952.19 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 18:51:42,925 INFO [train.py:715] (2/8) Epoch 13, batch 16250, loss[loss=0.1205, simple_loss=0.2089, pruned_loss=0.01606, over 4822.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.03152, over 972154.17 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 18:52:22,102 INFO [train.py:715] (2/8) Epoch 13, batch 16300, loss[loss=0.1149, simple_loss=0.1964, pruned_loss=0.01667, over 4869.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03095, over 972848.65 frames.], batch size: 20, lr: 1.68e-04
2022-05-07 18:53:00,700 INFO [train.py:715] (2/8) Epoch 13, batch 16350, loss[loss=0.1297, simple_loss=0.2129, pruned_loss=0.02321, over 4839.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03134, over 973102.71 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:53:39,045 INFO [train.py:715] (2/8) Epoch 13, batch 16400, loss[loss=0.1216, simple_loss=0.1889, pruned_loss=0.02719, over 4823.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03087, over 971897.62 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:54:18,189 INFO [train.py:715] (2/8) Epoch 13, batch 16450, loss[loss=0.1473, simple_loss=0.2249, pruned_loss=0.03481, over 4843.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03069, over 972712.64 frames.], batch size: 20, lr: 1.68e-04
2022-05-07 18:54:57,407 INFO [train.py:715] (2/8) Epoch 13, batch 16500, loss[loss=0.1363, simple_loss=0.2136, pruned_loss=0.02956, over 4878.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03106, over 972545.46 frames.], batch size: 20, lr: 1.68e-04
2022-05-07 18:55:36,543 INFO [train.py:715] (2/8) Epoch 13, batch 16550, loss[loss=0.1406, simple_loss=0.2232, pruned_loss=0.02897, over 4968.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03107, over 972636.95 frames.], batch size: 28, lr: 1.68e-04
2022-05-07 18:56:13,926 INFO [train.py:715] (2/8) Epoch 13, batch 16600, loss[loss=0.1291, simple_loss=0.1961, pruned_loss=0.03107, over 4683.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03128, over 973030.15 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 18:56:53,166 INFO [train.py:715] (2/8) Epoch 13, batch 16650, loss[loss=0.1463, simple_loss=0.2065, pruned_loss=0.043, over 4855.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03102, over 973015.00 frames.], batch size: 32, lr: 1.68e-04
2022-05-07 18:57:31,701 INFO [train.py:715] (2/8) Epoch 13, batch 16700, loss[loss=0.1426, simple_loss=0.1988, pruned_loss=0.04321, over 4780.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03114, over 973803.47 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 18:58:09,691 INFO [train.py:715] (2/8) Epoch 13, batch 16750, loss[loss=0.1398, simple_loss=0.2157, pruned_loss=0.03196, over 4860.00 frames.], tot_loss[loss=0.136, simple_loss=0.2099, pruned_loss=0.03103, over 972950.32 frames.], batch size: 30, lr: 1.68e-04
2022-05-07 18:58:48,289 INFO [train.py:715] (2/8) Epoch 13, batch 16800, loss[loss=0.116, simple_loss=0.1943, pruned_loss=0.01884, over 4835.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2097, pruned_loss=0.03053, over 972378.86 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 18:59:27,922 INFO [train.py:715] (2/8) Epoch 13, batch 16850, loss[loss=0.1506, simple_loss=0.2232, pruned_loss=0.03901, over 4704.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2096, pruned_loss=0.03034, over 971636.29 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:00:06,298 INFO [train.py:715] (2/8) Epoch 13, batch 16900, loss[loss=0.1072, simple_loss=0.1833, pruned_loss=0.01552, over 4799.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2097, pruned_loss=0.03024, over 971561.85 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 19:00:44,804 INFO [train.py:715] (2/8) Epoch 13, batch 16950, loss[loss=0.1395, simple_loss=0.2149, pruned_loss=0.03202, over 4903.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2103, pruned_loss=0.03094, over 971714.55 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 19:01:23,717 INFO [train.py:715] (2/8) Epoch 13, batch 17000, loss[loss=0.183, simple_loss=0.2528, pruned_loss=0.0566, over 4815.00 frames.], tot_loss[loss=0.137, simple_loss=0.2111, pruned_loss=0.03149, over 971487.55 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 19:02:02,417 INFO [train.py:715] (2/8) Epoch 13, batch 17050, loss[loss=0.1508, simple_loss=0.2235, pruned_loss=0.03908, over 4963.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2107, pruned_loss=0.03133, over 971842.80 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:02:40,533 INFO [train.py:715] (2/8) Epoch 13, batch 17100, loss[loss=0.1199, simple_loss=0.1927, pruned_loss=0.02351, over 4808.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2102, pruned_loss=0.03108, over 971256.62 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 19:03:19,262 INFO [train.py:715] (2/8) Epoch 13, batch 17150, loss[loss=0.1203, simple_loss=0.1909, pruned_loss=0.02481, over 4966.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2101, pruned_loss=0.03108, over 972575.25 frames.], batch size: 28, lr: 1.68e-04
2022-05-07 19:03:58,102 INFO [train.py:715] (2/8) Epoch 13, batch 17200, loss[loss=0.1188, simple_loss=0.1888, pruned_loss=0.02433, over 4810.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03076, over 972499.17 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 19:04:36,808 INFO [train.py:715] (2/8) Epoch 13, batch 17250, loss[loss=0.125, simple_loss=0.2071, pruned_loss=0.02148, over 4816.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03068, over 973223.51 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 19:05:14,781 INFO [train.py:715] (2/8) Epoch 13, batch 17300, loss[loss=0.1507, simple_loss=0.2185, pruned_loss=0.04139, over 4940.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03097, over 973091.39 frames.], batch size: 39, lr: 1.68e-04
2022-05-07 19:05:53,539 INFO [train.py:715] (2/8) Epoch 13, batch 17350, loss[loss=0.1124, simple_loss=0.1878, pruned_loss=0.01855, over 4947.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03136, over 973448.45 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 19:06:32,453 INFO [train.py:715] (2/8) Epoch 13, batch 17400, loss[loss=0.1249, simple_loss=0.2098, pruned_loss=0.02007, over 4768.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2105, pruned_loss=0.03132, over 973541.68 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 19:07:10,068 INFO [train.py:715] (2/8) Epoch 13, batch 17450, loss[loss=0.1446, simple_loss=0.2115, pruned_loss=0.03883, over 4773.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03078, over 973504.90 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 19:07:48,570 INFO [train.py:715] (2/8) Epoch 13, batch 17500, loss[loss=0.1442, simple_loss=0.2214, pruned_loss=0.03349, over 4750.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.0304, over 973637.01 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 19:08:27,656 INFO [train.py:715] (2/8) Epoch 13, batch 17550, loss[loss=0.1228, simple_loss=0.1877, pruned_loss=0.02898, over 4986.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03043, over 973251.79 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:09:06,326 INFO [train.py:715] (2/8) Epoch 13, batch 17600, loss[loss=0.1472, simple_loss=0.2149, pruned_loss=0.03975, over 4912.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03092, over 974803.02 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 19:09:43,948 INFO [train.py:715] (2/8) Epoch 13, batch 17650, loss[loss=0.1181, simple_loss=0.1899, pruned_loss=0.02312, over 4886.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03088, over 974545.23 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:10:23,206 INFO [train.py:715] (2/8) Epoch 13, batch 17700, loss[loss=0.1353, simple_loss=0.2071, pruned_loss=0.03176, over 4838.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03076, over 973321.27 frames.], batch size: 26, lr: 1.68e-04
2022-05-07 19:11:02,062 INFO [train.py:715] (2/8) Epoch 13, batch 17750, loss[loss=0.1213, simple_loss=0.196, pruned_loss=0.02333, over 4808.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.0306, over 973603.88 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 19:11:39,681 INFO [train.py:715] (2/8) Epoch 13, batch 17800, loss[loss=0.1187, simple_loss=0.1913, pruned_loss=0.02306, over 4689.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03085, over 973558.19 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:12:18,455 INFO [train.py:715] (2/8) Epoch 13, batch 17850, loss[loss=0.1484, simple_loss=0.2077, pruned_loss=0.04461, over 4985.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03058, over 972866.97 frames.], batch size: 14, lr: 1.68e-04
2022-05-07 19:12:57,285 INFO [train.py:715] (2/8) Epoch 13, batch 17900, loss[loss=0.1248, simple_loss=0.1976, pruned_loss=0.02597, over 4766.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03091, over 972315.60 frames.], batch size: 18, lr: 1.68e-04
2022-05-07 19:13:35,478 INFO [train.py:715] (2/8) Epoch 13, batch 17950, loss[loss=0.1451, simple_loss=0.2217, pruned_loss=0.03429, over 4815.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03137, over 971629.05 frames.], batch size: 25, lr: 1.68e-04
2022-05-07 19:14:13,540 INFO [train.py:715] (2/8) Epoch 13, batch 18000, loss[loss=0.1238, simple_loss=0.1923, pruned_loss=0.02769, over 4852.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.031, over 970973.64 frames.], batch size: 20, lr: 1.68e-04
2022-05-07 19:14:13,540 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 19:14:23,028 INFO [train.py:742] (2/8) 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] (2/8) Epoch 13, batch 18050, loss[loss=0.1242, simple_loss=0.1922, pruned_loss=0.02809, over 4832.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03114, over 971750.98 frames.], batch size: 15, lr: 1.68e-04
2022-05-07 19:15:39,774 INFO [train.py:715] (2/8) Epoch 13, batch 18100, loss[loss=0.1247, simple_loss=0.2028, pruned_loss=0.02327, over 4834.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03183, over 970980.65 frames.], batch size: 12, lr: 1.68e-04
2022-05-07 19:16:18,124 INFO [train.py:715] (2/8) Epoch 13, batch 18150, loss[loss=0.1342, simple_loss=0.199, pruned_loss=0.0347, over 4788.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03142, over 971314.03 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 19:16:55,371 INFO [train.py:715] (2/8) Epoch 13, batch 18200, loss[loss=0.1234, simple_loss=0.204, pruned_loss=0.02142, over 4927.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2106, pruned_loss=0.03136, over 972931.34 frames.], batch size: 23, lr: 1.68e-04
2022-05-07 19:17:33,693 INFO [train.py:715] (2/8) Epoch 13, batch 18250, loss[loss=0.1201, simple_loss=0.1933, pruned_loss=0.0234, over 4972.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2103, pruned_loss=0.03118, over 972821.45 frames.], batch size: 28, lr: 1.68e-04
2022-05-07 19:18:12,483 INFO [train.py:715] (2/8) Epoch 13, batch 18300, loss[loss=0.171, simple_loss=0.2351, pruned_loss=0.05345, over 4820.00 frames.], tot_loss[loss=0.1369, simple_loss=0.211, pruned_loss=0.03136, over 971920.04 frames.], batch size: 13, lr: 1.68e-04
2022-05-07 19:18:51,122 INFO [train.py:715] (2/8) Epoch 13, batch 18350, loss[loss=0.157, simple_loss=0.2253, pruned_loss=0.04433, over 4916.00 frames.], tot_loss[loss=0.1369, simple_loss=0.211, pruned_loss=0.03139, over 972513.25 frames.], batch size: 23, lr: 1.68e-04
2022-05-07 19:19:29,016 INFO [train.py:715] (2/8) Epoch 13, batch 18400, loss[loss=0.1401, simple_loss=0.207, pruned_loss=0.03665, over 4844.00 frames.], tot_loss[loss=0.1367, simple_loss=0.211, pruned_loss=0.03117, over 972202.56 frames.], batch size: 30, lr: 1.68e-04
2022-05-07 19:20:07,826 INFO [train.py:715] (2/8) Epoch 13, batch 18450, loss[loss=0.1395, simple_loss=0.2122, pruned_loss=0.03334, over 4903.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2104, pruned_loss=0.03059, over 971589.35 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:20:46,497 INFO [train.py:715] (2/8) Epoch 13, batch 18500, loss[loss=0.1367, simple_loss=0.2081, pruned_loss=0.03264, over 4952.00 frames.], tot_loss[loss=0.1356, simple_loss=0.21, pruned_loss=0.03064, over 971099.66 frames.], batch size: 35, lr: 1.68e-04
2022-05-07 19:21:23,938 INFO [train.py:715] (2/8) Epoch 13, batch 18550, loss[loss=0.1437, simple_loss=0.2228, pruned_loss=0.0323, over 4895.00 frames.], tot_loss[loss=0.136, simple_loss=0.2103, pruned_loss=0.03089, over 971208.74 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:22:01,961 INFO [train.py:715] (2/8) Epoch 13, batch 18600, loss[loss=0.1141, simple_loss=0.1911, pruned_loss=0.01857, over 4962.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03082, over 971134.07 frames.], batch size: 24, lr: 1.68e-04
2022-05-07 19:22:40,557 INFO [train.py:715] (2/8) Epoch 13, batch 18650, loss[loss=0.1551, simple_loss=0.2335, pruned_loss=0.03837, over 4825.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.0309, over 971192.49 frames.], batch size: 26, lr: 1.68e-04
2022-05-07 19:23:18,503 INFO [train.py:715] (2/8) Epoch 13, batch 18700, loss[loss=0.1376, simple_loss=0.2119, pruned_loss=0.03161, over 4893.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03098, over 971909.79 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:23:56,288 INFO [train.py:715] (2/8) Epoch 13, batch 18750, loss[loss=0.1229, simple_loss=0.1988, pruned_loss=0.02346, over 4781.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.03163, over 972168.64 frames.], batch size: 17, lr: 1.68e-04
2022-05-07 19:24:35,597 INFO [train.py:715] (2/8) Epoch 13, batch 18800, loss[loss=0.114, simple_loss=0.1902, pruned_loss=0.0189, over 4874.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03141, over 972239.68 frames.], batch size: 22, lr: 1.68e-04
2022-05-07 19:25:14,016 INFO [train.py:715] (2/8) Epoch 13, batch 18850, loss[loss=0.1483, simple_loss=0.2243, pruned_loss=0.03616, over 4909.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03105, over 972592.85 frames.], batch size: 19, lr: 1.68e-04
2022-05-07 19:25:52,021 INFO [train.py:715] (2/8) Epoch 13, batch 18900, loss[loss=0.1198, simple_loss=0.2015, pruned_loss=0.01907, over 4867.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2096, pruned_loss=0.03076, over 972022.89 frames.], batch size: 16, lr: 1.68e-04
2022-05-07 19:26:30,884 INFO [train.py:715] (2/8) Epoch 13, batch 18950, loss[loss=0.1375, simple_loss=0.2124, pruned_loss=0.03129, over 4839.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03061, over 972798.12 frames.], batch size: 20, lr: 1.68e-04
2022-05-07 19:27:09,769 INFO [train.py:715] (2/8) Epoch 13, batch 19000, loss[loss=0.152, simple_loss=0.2251, pruned_loss=0.03942, over 4954.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.03082, over 973209.83 frames.], batch size: 39, lr: 1.68e-04
2022-05-07 19:27:48,114 INFO [train.py:715] (2/8) Epoch 13, batch 19050, loss[loss=0.1358, simple_loss=0.2213, pruned_loss=0.02518, over 4950.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.031, over 973316.37 frames.], batch size: 21, lr: 1.68e-04
2022-05-07 19:28:26,438 INFO [train.py:715] (2/8) Epoch 13, batch 19100, loss[loss=0.1278, simple_loss=0.2003, pruned_loss=0.02769, over 4982.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03117, over 973089.61 frames.], batch size: 35, lr: 1.68e-04
2022-05-07 19:29:05,441 INFO [train.py:715] (2/8) Epoch 13, batch 19150, loss[loss=0.1224, simple_loss=0.2025, pruned_loss=0.0212, over 4822.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03142, over 973118.93 frames.], batch size: 27, lr: 1.67e-04
2022-05-07 19:29:44,106 INFO [train.py:715] (2/8) Epoch 13, batch 19200, loss[loss=0.1457, simple_loss=0.2186, pruned_loss=0.03635, over 4979.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2084, pruned_loss=0.03101, over 972923.74 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:30:21,502 INFO [train.py:715] (2/8) Epoch 13, batch 19250, loss[loss=0.1634, simple_loss=0.2353, pruned_loss=0.04577, over 4946.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03099, over 973783.39 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 19:31:00,078 INFO [train.py:715] (2/8) Epoch 13, batch 19300, loss[loss=0.1417, simple_loss=0.2173, pruned_loss=0.03306, over 4784.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2081, pruned_loss=0.03076, over 973627.86 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 19:31:39,542 INFO [train.py:715] (2/8) Epoch 13, batch 19350, loss[loss=0.1152, simple_loss=0.1896, pruned_loss=0.02037, over 4904.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.03087, over 973814.93 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 19:32:18,082 INFO [train.py:715] (2/8) Epoch 13, batch 19400, loss[loss=0.1532, simple_loss=0.2139, pruned_loss=0.04624, over 4942.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2083, pruned_loss=0.03108, over 973491.85 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 19:32:56,516 INFO [train.py:715] (2/8) Epoch 13, batch 19450, loss[loss=0.1309, simple_loss=0.1982, pruned_loss=0.03183, over 4873.00 frames.], tot_loss[loss=0.135, simple_loss=0.2081, pruned_loss=0.03092, over 972563.61 frames.], batch size: 22, lr: 1.67e-04
2022-05-07 19:33:37,810 INFO [train.py:715] (2/8) Epoch 13, batch 19500, loss[loss=0.1508, simple_loss=0.2323, pruned_loss=0.03467, over 4982.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2084, pruned_loss=0.03099, over 972545.68 frames.], batch size: 28, lr: 1.67e-04
2022-05-07 19:34:16,750 INFO [train.py:715] (2/8) Epoch 13, batch 19550, loss[loss=0.1215, simple_loss=0.1936, pruned_loss=0.02469, over 4984.00 frames.], tot_loss[loss=0.136, simple_loss=0.2093, pruned_loss=0.03138, over 972874.09 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 19:34:54,322 INFO [train.py:715] (2/8) Epoch 13, batch 19600, loss[loss=0.1209, simple_loss=0.2035, pruned_loss=0.01916, over 4930.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2088, pruned_loss=0.03123, over 972927.66 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 19:35:32,449 INFO [train.py:715] (2/8) Epoch 13, batch 19650, loss[loss=0.133, simple_loss=0.2004, pruned_loss=0.03284, over 4989.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03105, over 972873.71 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 19:36:11,256 INFO [train.py:715] (2/8) Epoch 13, batch 19700, loss[loss=0.1284, simple_loss=0.1996, pruned_loss=0.02863, over 4785.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03127, over 973814.05 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 19:36:49,084 INFO [train.py:715] (2/8) Epoch 13, batch 19750, loss[loss=0.1173, simple_loss=0.1877, pruned_loss=0.02349, over 4831.00 frames.], tot_loss[loss=0.137, simple_loss=0.211, pruned_loss=0.03156, over 972979.68 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:37:26,940 INFO [train.py:715] (2/8) Epoch 13, batch 19800, loss[loss=0.1676, simple_loss=0.2375, pruned_loss=0.04891, over 4953.00 frames.], tot_loss[loss=0.1359, simple_loss=0.21, pruned_loss=0.0309, over 973352.70 frames.], batch size: 39, lr: 1.67e-04
2022-05-07 19:38:05,612 INFO [train.py:715] (2/8) Epoch 13, batch 19850, loss[loss=0.1524, simple_loss=0.2303, pruned_loss=0.03722, over 4872.00 frames.], tot_loss[loss=0.136, simple_loss=0.2101, pruned_loss=0.03094, over 972621.75 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 19:38:44,225 INFO [train.py:715] (2/8) Epoch 13, batch 19900, loss[loss=0.1434, simple_loss=0.2253, pruned_loss=0.03074, over 4866.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2101, pruned_loss=0.03106, over 973408.51 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 19:39:22,425 INFO [train.py:715] (2/8) Epoch 13, batch 19950, loss[loss=0.1497, simple_loss=0.2201, pruned_loss=0.03966, over 4807.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03138, over 973451.50 frames.], batch size: 24, lr: 1.67e-04
2022-05-07 19:40:01,314 INFO [train.py:715] (2/8) Epoch 13, batch 20000, loss[loss=0.1487, simple_loss=0.2123, pruned_loss=0.04252, over 4849.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2107, pruned_loss=0.03129, over 973545.66 frames.], batch size: 30, lr: 1.67e-04
2022-05-07 19:40:39,758 INFO [train.py:715] (2/8) Epoch 13, batch 20050, loss[loss=0.113, simple_loss=0.1921, pruned_loss=0.01701, over 4977.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2108, pruned_loss=0.03124, over 973777.92 frames.], batch size: 28, lr: 1.67e-04
2022-05-07 19:41:16,931 INFO [train.py:715] (2/8) Epoch 13, batch 20100, loss[loss=0.126, simple_loss=0.2145, pruned_loss=0.0187, over 4953.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03144, over 974283.64 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 19:41:54,382 INFO [train.py:715] (2/8) Epoch 13, batch 20150, loss[loss=0.1304, simple_loss=0.19, pruned_loss=0.03545, over 4843.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2105, pruned_loss=0.03158, over 973697.99 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:42:33,108 INFO [train.py:715] (2/8) Epoch 13, batch 20200, loss[loss=0.1471, simple_loss=0.2132, pruned_loss=0.04046, over 4826.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03104, over 972985.16 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:43:11,186 INFO [train.py:715] (2/8) Epoch 13, batch 20250, loss[loss=0.1188, simple_loss=0.1969, pruned_loss=0.02036, over 4990.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.031, over 973517.90 frames.], batch size: 28, lr: 1.67e-04
2022-05-07 19:43:48,892 INFO [train.py:715] (2/8) Epoch 13, batch 20300, loss[loss=0.1277, simple_loss=0.194, pruned_loss=0.0307, over 4769.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03101, over 972815.21 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 19:44:26,995 INFO [train.py:715] (2/8) Epoch 13, batch 20350, loss[loss=0.1495, simple_loss=0.2235, pruned_loss=0.03772, over 4801.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03175, over 972567.87 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:45:05,764 INFO [train.py:715] (2/8) Epoch 13, batch 20400, loss[loss=0.1309, simple_loss=0.2011, pruned_loss=0.03034, over 4865.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03172, over 972086.24 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 19:45:43,496 INFO [train.py:715] (2/8) Epoch 13, batch 20450, loss[loss=0.1231, simple_loss=0.2067, pruned_loss=0.01976, over 4781.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03146, over 971469.20 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 19:46:21,266 INFO [train.py:715] (2/8) Epoch 13, batch 20500, loss[loss=0.1276, simple_loss=0.202, pruned_loss=0.02661, over 4903.00 frames.], tot_loss[loss=0.1356, simple_loss=0.209, pruned_loss=0.0311, over 971252.66 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 19:46:59,825 INFO [train.py:715] (2/8) Epoch 13, batch 20550, loss[loss=0.1375, simple_loss=0.2103, pruned_loss=0.03235, over 4939.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03211, over 972033.07 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 19:47:37,481 INFO [train.py:715] (2/8) Epoch 13, batch 20600, loss[loss=0.149, simple_loss=0.2261, pruned_loss=0.03593, over 4954.00 frames.], tot_loss[loss=0.138, simple_loss=0.2113, pruned_loss=0.03234, over 971579.64 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 19:48:15,108 INFO [train.py:715] (2/8) Epoch 13, batch 20650, loss[loss=0.1105, simple_loss=0.1848, pruned_loss=0.01811, over 4949.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2106, pruned_loss=0.03228, over 970889.74 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 19:48:52,915 INFO [train.py:715] (2/8) Epoch 13, batch 20700, loss[loss=0.132, simple_loss=0.2015, pruned_loss=0.03124, over 4909.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2096, pruned_loss=0.03157, over 970543.80 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 19:49:31,352 INFO [train.py:715] (2/8) Epoch 13, batch 20750, loss[loss=0.1147, simple_loss=0.188, pruned_loss=0.02073, over 4989.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2093, pruned_loss=0.03166, over 970919.75 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 19:50:08,698 INFO [train.py:715] (2/8) Epoch 13, batch 20800, loss[loss=0.1148, simple_loss=0.1987, pruned_loss=0.01543, over 4927.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2089, pruned_loss=0.03125, over 971408.62 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 19:50:46,282 INFO [train.py:715] (2/8) Epoch 13, batch 20850, loss[loss=0.1384, simple_loss=0.2143, pruned_loss=0.0312, over 4753.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03142, over 971869.91 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 19:51:24,971 INFO [train.py:715] (2/8) Epoch 13, batch 20900, loss[loss=0.1257, simple_loss=0.2076, pruned_loss=0.02193, over 4700.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2091, pruned_loss=0.03133, over 971006.56 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:52:03,242 INFO [train.py:715] (2/8) Epoch 13, batch 20950, loss[loss=0.1348, simple_loss=0.2081, pruned_loss=0.03075, over 4778.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03095, over 970309.91 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 19:52:40,748 INFO [train.py:715] (2/8) Epoch 13, batch 21000, loss[loss=0.1276, simple_loss=0.2065, pruned_loss=0.02439, over 4799.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2091, pruned_loss=0.03136, over 970334.49 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 19:52:40,748 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 19:52:50,263 INFO [train.py:742] (2/8) 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] (2/8) Epoch 13, batch 21050, loss[loss=0.1363, simple_loss=0.1995, pruned_loss=0.03653, over 4681.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2093, pruned_loss=0.03146, over 970994.56 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:54:06,970 INFO [train.py:715] (2/8) Epoch 13, batch 21100, loss[loss=0.1349, simple_loss=0.2099, pruned_loss=0.02991, over 4832.00 frames.], tot_loss[loss=0.136, simple_loss=0.2091, pruned_loss=0.03142, over 971149.04 frames.], batch size: 30, lr: 1.67e-04
2022-05-07 19:54:46,057 INFO [train.py:715] (2/8) Epoch 13, batch 21150, loss[loss=0.1345, simple_loss=0.2003, pruned_loss=0.03438, over 4977.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03089, over 971514.12 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 19:55:23,879 INFO [train.py:715] (2/8) Epoch 13, batch 21200, loss[loss=0.1272, simple_loss=0.2044, pruned_loss=0.02495, over 4988.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03119, over 972094.07 frames.], batch size: 28, lr: 1.67e-04
2022-05-07 19:56:02,466 INFO [train.py:715] (2/8) Epoch 13, batch 21250, loss[loss=0.1381, simple_loss=0.2119, pruned_loss=0.03217, over 4958.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03137, over 972358.64 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 19:56:41,278 INFO [train.py:715] (2/8) Epoch 13, batch 21300, loss[loss=0.1634, simple_loss=0.2293, pruned_loss=0.0487, over 4872.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2103, pruned_loss=0.03207, over 972070.51 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 19:57:19,155 INFO [train.py:715] (2/8) Epoch 13, batch 21350, loss[loss=0.143, simple_loss=0.2149, pruned_loss=0.03551, over 4962.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2099, pruned_loss=0.03157, over 972393.86 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 19:57:57,084 INFO [train.py:715] (2/8) Epoch 13, batch 21400, loss[loss=0.1868, simple_loss=0.2597, pruned_loss=0.05697, over 4933.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03141, over 972934.20 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 19:58:35,344 INFO [train.py:715] (2/8) Epoch 13, batch 21450, loss[loss=0.1165, simple_loss=0.1915, pruned_loss=0.02075, over 4892.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03138, over 973446.97 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 19:59:14,501 INFO [train.py:715] (2/8) Epoch 13, batch 21500, loss[loss=0.1353, simple_loss=0.2177, pruned_loss=0.02649, over 4829.00 frames.], tot_loss[loss=0.1365, simple_loss=0.21, pruned_loss=0.0315, over 972529.28 frames.], batch size: 13, lr: 1.67e-04
2022-05-07 19:59:52,265 INFO [train.py:715] (2/8) Epoch 13, batch 21550, loss[loss=0.136, simple_loss=0.2136, pruned_loss=0.02919, over 4846.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03146, over 972984.43 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:00:30,899 INFO [train.py:715] (2/8) Epoch 13, batch 21600, loss[loss=0.1461, simple_loss=0.2169, pruned_loss=0.03768, over 4801.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03138, over 972037.41 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 20:01:09,867 INFO [train.py:715] (2/8) Epoch 13, batch 21650, loss[loss=0.1577, simple_loss=0.2338, pruned_loss=0.0408, over 4976.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2106, pruned_loss=0.03174, over 972159.59 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:01:48,639 INFO [train.py:715] (2/8) Epoch 13, batch 21700, loss[loss=0.1395, simple_loss=0.215, pruned_loss=0.03202, over 4907.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03151, over 972401.93 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 20:02:27,482 INFO [train.py:715] (2/8) Epoch 13, batch 21750, loss[loss=0.21, simple_loss=0.273, pruned_loss=0.07355, over 4863.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03173, over 972270.46 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:03:06,130 INFO [train.py:715] (2/8) Epoch 13, batch 21800, loss[loss=0.1311, simple_loss=0.1978, pruned_loss=0.03216, over 4985.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03188, over 972744.98 frames.], batch size: 31, lr: 1.67e-04
2022-05-07 20:03:45,431 INFO [train.py:715] (2/8) Epoch 13, batch 21850, loss[loss=0.1386, simple_loss=0.2071, pruned_loss=0.03504, over 4734.00 frames.], tot_loss[loss=0.137, simple_loss=0.21, pruned_loss=0.03195, over 972297.46 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:04:23,538 INFO [train.py:715] (2/8) Epoch 13, batch 21900, loss[loss=0.1268, simple_loss=0.202, pruned_loss=0.02581, over 4865.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03159, over 972603.92 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:05:01,703 INFO [train.py:715] (2/8) Epoch 13, batch 21950, loss[loss=0.1337, simple_loss=0.2164, pruned_loss=0.02549, over 4748.00 frames.], tot_loss[loss=0.1376, simple_loss=0.211, pruned_loss=0.03207, over 972348.90 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:05:40,176 INFO [train.py:715] (2/8) Epoch 13, batch 22000, loss[loss=0.1247, simple_loss=0.2, pruned_loss=0.02468, over 4986.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2098, pruned_loss=0.0316, over 973129.78 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 20:06:17,894 INFO [train.py:715] (2/8) Epoch 13, batch 22050, loss[loss=0.1435, simple_loss=0.2182, pruned_loss=0.0344, over 4962.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03136, over 972939.80 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 20:06:55,942 INFO [train.py:715] (2/8) Epoch 13, batch 22100, loss[loss=0.1238, simple_loss=0.1984, pruned_loss=0.0246, over 4931.00 frames.], tot_loss[loss=0.1369, simple_loss=0.21, pruned_loss=0.03191, over 972243.26 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 20:07:33,696 INFO [train.py:715] (2/8) Epoch 13, batch 22150, loss[loss=0.1317, simple_loss=0.2029, pruned_loss=0.03028, over 4817.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2097, pruned_loss=0.03177, over 972193.13 frames.], batch size: 27, lr: 1.67e-04
2022-05-07 20:08:12,649 INFO [train.py:715] (2/8) Epoch 13, batch 22200, loss[loss=0.1188, simple_loss=0.1923, pruned_loss=0.02268, over 4786.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.0314, over 972750.46 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 20:08:50,196 INFO [train.py:715] (2/8) Epoch 13, batch 22250, loss[loss=0.1418, simple_loss=0.2111, pruned_loss=0.03624, over 4974.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03104, over 973347.73 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:09:28,958 INFO [train.py:715] (2/8) Epoch 13, batch 22300, loss[loss=0.1484, simple_loss=0.2147, pruned_loss=0.04105, over 4639.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03144, over 972821.72 frames.], batch size: 13, lr: 1.67e-04
2022-05-07 20:10:07,702 INFO [train.py:715] (2/8) Epoch 13, batch 22350, loss[loss=0.155, simple_loss=0.2191, pruned_loss=0.04549, over 4735.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03151, over 972657.42 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:10:45,730 INFO [train.py:715] (2/8) Epoch 13, batch 22400, loss[loss=0.1435, simple_loss=0.2136, pruned_loss=0.03668, over 4941.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03092, over 972077.97 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 20:11:23,411 INFO [train.py:715] (2/8) Epoch 13, batch 22450, loss[loss=0.1409, simple_loss=0.2044, pruned_loss=0.03872, over 4776.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03137, over 971848.03 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 20:12:01,254 INFO [train.py:715] (2/8) Epoch 13, batch 22500, loss[loss=0.1411, simple_loss=0.2237, pruned_loss=0.0293, over 4863.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03158, over 972872.21 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:12:39,613 INFO [train.py:715] (2/8) Epoch 13, batch 22550, loss[loss=0.1315, simple_loss=0.2152, pruned_loss=0.0239, over 4899.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03087, over 973492.04 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 20:13:16,806 INFO [train.py:715] (2/8) Epoch 13, batch 22600, loss[loss=0.1402, simple_loss=0.2268, pruned_loss=0.02677, over 4818.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03103, over 974133.02 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:13:54,711 INFO [train.py:715] (2/8) Epoch 13, batch 22650, loss[loss=0.1222, simple_loss=0.189, pruned_loss=0.0277, over 4766.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.0312, over 973167.30 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 20:14:32,804 INFO [train.py:715] (2/8) Epoch 13, batch 22700, loss[loss=0.1411, simple_loss=0.2183, pruned_loss=0.03199, over 4801.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2116, pruned_loss=0.03179, over 972985.99 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 20:15:11,035 INFO [train.py:715] (2/8) Epoch 13, batch 22750, loss[loss=0.1398, simple_loss=0.2085, pruned_loss=0.03558, over 4896.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2108, pruned_loss=0.03146, over 971437.69 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:15:49,014 INFO [train.py:715] (2/8) Epoch 13, batch 22800, loss[loss=0.1482, simple_loss=0.2259, pruned_loss=0.03526, over 4760.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2103, pruned_loss=0.03108, over 971684.41 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:16:27,580 INFO [train.py:715] (2/8) Epoch 13, batch 22850, loss[loss=0.1882, simple_loss=0.2485, pruned_loss=0.06395, over 4846.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2104, pruned_loss=0.03106, over 971767.06 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:17:06,829 INFO [train.py:715] (2/8) Epoch 13, batch 22900, loss[loss=0.1251, simple_loss=0.2052, pruned_loss=0.02247, over 4702.00 frames.], tot_loss[loss=0.136, simple_loss=0.2103, pruned_loss=0.03091, over 971837.72 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:17:44,515 INFO [train.py:715] (2/8) Epoch 13, batch 22950, loss[loss=0.1361, simple_loss=0.2058, pruned_loss=0.03319, over 4977.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.03143, over 972520.62 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:18:23,096 INFO [train.py:715] (2/8) Epoch 13, batch 23000, loss[loss=0.1352, simple_loss=0.2172, pruned_loss=0.02666, over 4969.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2108, pruned_loss=0.03134, over 972782.59 frames.], batch size: 28, lr: 1.67e-04
2022-05-07 20:19:01,744 INFO [train.py:715] (2/8) Epoch 13, batch 23050, loss[loss=0.1214, simple_loss=0.2084, pruned_loss=0.01716, over 4920.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2115, pruned_loss=0.03133, over 972792.50 frames.], batch size: 23, lr: 1.67e-04
2022-05-07 20:19:40,070 INFO [train.py:715] (2/8) Epoch 13, batch 23100, loss[loss=0.1525, simple_loss=0.2193, pruned_loss=0.04284, over 4793.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2103, pruned_loss=0.03109, over 973162.11 frames.], batch size: 24, lr: 1.67e-04
2022-05-07 20:20:17,987 INFO [train.py:715] (2/8) Epoch 13, batch 23150, loss[loss=0.1165, simple_loss=0.196, pruned_loss=0.01848, over 4803.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.0311, over 972362.87 frames.], batch size: 14, lr: 1.67e-04
2022-05-07 20:20:56,165 INFO [train.py:715] (2/8) Epoch 13, batch 23200, loss[loss=0.1346, simple_loss=0.2121, pruned_loss=0.02857, over 4806.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03125, over 972250.65 frames.], batch size: 21, lr: 1.67e-04
2022-05-07 20:21:34,317 INFO [train.py:715] (2/8) Epoch 13, batch 23250, loss[loss=0.1319, simple_loss=0.1919, pruned_loss=0.03593, over 4963.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.0315, over 972463.42 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:22:11,787 INFO [train.py:715] (2/8) Epoch 13, batch 23300, loss[loss=0.1178, simple_loss=0.1969, pruned_loss=0.01936, over 4941.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03159, over 972632.85 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 20:22:50,103 INFO [train.py:715] (2/8) Epoch 13, batch 23350, loss[loss=0.1262, simple_loss=0.1976, pruned_loss=0.0274, over 4799.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03131, over 972355.06 frames.], batch size: 12, lr: 1.67e-04
2022-05-07 20:23:28,677 INFO [train.py:715] (2/8) Epoch 13, batch 23400, loss[loss=0.1243, simple_loss=0.1975, pruned_loss=0.02559, over 4816.00 frames.], tot_loss[loss=0.137, simple_loss=0.2101, pruned_loss=0.03191, over 972656.70 frames.], batch size: 26, lr: 1.67e-04
2022-05-07 20:24:06,994 INFO [train.py:715] (2/8) Epoch 13, batch 23450, loss[loss=0.1056, simple_loss=0.1795, pruned_loss=0.01582, over 4803.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2095, pruned_loss=0.0318, over 972327.58 frames.], batch size: 13, lr: 1.67e-04
2022-05-07 20:24:45,013 INFO [train.py:715] (2/8) Epoch 13, batch 23500, loss[loss=0.1328, simple_loss=0.2059, pruned_loss=0.02989, over 4925.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2099, pruned_loss=0.03195, over 972050.48 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 20:25:23,769 INFO [train.py:715] (2/8) Epoch 13, batch 23550, loss[loss=0.1215, simple_loss=0.2048, pruned_loss=0.01909, over 4822.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2098, pruned_loss=0.03193, over 971115.01 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 20:26:02,269 INFO [train.py:715] (2/8) Epoch 13, batch 23600, loss[loss=0.1442, simple_loss=0.2202, pruned_loss=0.03413, over 4901.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2091, pruned_loss=0.03163, over 970966.33 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:26:39,841 INFO [train.py:715] (2/8) Epoch 13, batch 23650, loss[loss=0.1556, simple_loss=0.2262, pruned_loss=0.04245, over 4971.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2097, pruned_loss=0.03191, over 971450.56 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:27:18,103 INFO [train.py:715] (2/8) Epoch 13, batch 23700, loss[loss=0.1481, simple_loss=0.2238, pruned_loss=0.0362, over 4770.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2086, pruned_loss=0.0311, over 971152.93 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 20:27:56,590 INFO [train.py:715] (2/8) Epoch 13, batch 23750, loss[loss=0.1373, simple_loss=0.22, pruned_loss=0.02736, over 4822.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03096, over 971664.53 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:28:34,761 INFO [train.py:715] (2/8) Epoch 13, batch 23800, loss[loss=0.1343, simple_loss=0.2054, pruned_loss=0.03164, over 4867.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2101, pruned_loss=0.03159, over 971914.27 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:29:12,136 INFO [train.py:715] (2/8) Epoch 13, batch 23850, loss[loss=0.1732, simple_loss=0.2483, pruned_loss=0.04907, over 4914.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03154, over 971467.86 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:29:51,249 INFO [train.py:715] (2/8) Epoch 13, batch 23900, loss[loss=0.1661, simple_loss=0.2473, pruned_loss=0.04244, over 4922.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2106, pruned_loss=0.03135, over 971711.53 frames.], batch size: 29, lr: 1.67e-04
2022-05-07 20:30:29,201 INFO [train.py:715] (2/8) Epoch 13, batch 23950, loss[loss=0.1193, simple_loss=0.1903, pruned_loss=0.02412, over 4766.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2104, pruned_loss=0.03123, over 971319.89 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:31:06,578 INFO [train.py:715] (2/8) Epoch 13, batch 24000, loss[loss=0.1696, simple_loss=0.2373, pruned_loss=0.05091, over 4691.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2108, pruned_loss=0.03137, over 971087.75 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:31:06,579 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 20:31:16,110 INFO [train.py:742] (2/8) 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] (2/8) Epoch 13, batch 24050, loss[loss=0.1159, simple_loss=0.195, pruned_loss=0.01841, over 4871.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03155, over 971153.42 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:32:31,543 INFO [train.py:715] (2/8) Epoch 13, batch 24100, loss[loss=0.1578, simple_loss=0.2196, pruned_loss=0.04801, over 4971.00 frames.], tot_loss[loss=0.136, simple_loss=0.2099, pruned_loss=0.03105, over 971371.02 frames.], batch size: 35, lr: 1.67e-04
2022-05-07 20:33:10,934 INFO [train.py:715] (2/8) Epoch 13, batch 24150, loss[loss=0.1372, simple_loss=0.2142, pruned_loss=0.03004, over 4833.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03093, over 971883.87 frames.], batch size: 15, lr: 1.67e-04
2022-05-07 20:33:49,902 INFO [train.py:715] (2/8) Epoch 13, batch 24200, loss[loss=0.1576, simple_loss=0.2215, pruned_loss=0.04688, over 4937.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2097, pruned_loss=0.03089, over 972089.99 frames.], batch size: 18, lr: 1.67e-04
2022-05-07 20:34:28,104 INFO [train.py:715] (2/8) Epoch 13, batch 24250, loss[loss=0.1178, simple_loss=0.1835, pruned_loss=0.02604, over 4885.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03091, over 972729.64 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:35:06,953 INFO [train.py:715] (2/8) Epoch 13, batch 24300, loss[loss=0.1397, simple_loss=0.2094, pruned_loss=0.03497, over 4817.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03108, over 972108.33 frames.], batch size: 27, lr: 1.67e-04
2022-05-07 20:35:45,650 INFO [train.py:715] (2/8) Epoch 13, batch 24350, loss[loss=0.1323, simple_loss=0.2007, pruned_loss=0.03193, over 4873.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03104, over 971415.10 frames.], batch size: 16, lr: 1.67e-04
2022-05-07 20:36:23,176 INFO [train.py:715] (2/8) Epoch 13, batch 24400, loss[loss=0.1362, simple_loss=0.2038, pruned_loss=0.03435, over 4844.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03117, over 970512.89 frames.], batch size: 12, lr: 1.67e-04
2022-05-07 20:37:01,583 INFO [train.py:715] (2/8) Epoch 13, batch 24450, loss[loss=0.2082, simple_loss=0.2626, pruned_loss=0.0769, over 4890.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2087, pruned_loss=0.03127, over 970080.00 frames.], batch size: 22, lr: 1.67e-04
2022-05-07 20:37:40,239 INFO [train.py:715] (2/8) Epoch 13, batch 24500, loss[loss=0.1198, simple_loss=0.2014, pruned_loss=0.01911, over 4904.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03134, over 970633.51 frames.], batch size: 22, lr: 1.67e-04
2022-05-07 20:38:18,539 INFO [train.py:715] (2/8) Epoch 13, batch 24550, loss[loss=0.1273, simple_loss=0.2046, pruned_loss=0.02495, over 4903.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03139, over 971407.99 frames.], batch size: 17, lr: 1.67e-04
2022-05-07 20:38:56,900 INFO [train.py:715] (2/8) Epoch 13, batch 24600, loss[loss=0.1306, simple_loss=0.2135, pruned_loss=0.02379, over 4989.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03156, over 972859.81 frames.], batch size: 25, lr: 1.67e-04
2022-05-07 20:39:36,090 INFO [train.py:715] (2/8) Epoch 13, batch 24650, loss[loss=0.1158, simple_loss=0.1939, pruned_loss=0.01884, over 4898.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03151, over 973391.51 frames.], batch size: 19, lr: 1.67e-04
2022-05-07 20:40:14,989 INFO [train.py:715] (2/8) Epoch 13, batch 24700, loss[loss=0.1378, simple_loss=0.2166, pruned_loss=0.02945, over 4968.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03142, over 973366.84 frames.], batch size: 31, lr: 1.67e-04
2022-05-07 20:40:52,894 INFO [train.py:715] (2/8) Epoch 13, batch 24750, loss[loss=0.1276, simple_loss=0.1971, pruned_loss=0.02904, over 4846.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03137, over 973860.14 frames.], batch size: 30, lr: 1.67e-04
2022-05-07 20:41:31,288 INFO [train.py:715] (2/8) Epoch 13, batch 24800, loss[loss=0.1055, simple_loss=0.1814, pruned_loss=0.01476, over 4988.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03203, over 973364.61 frames.], batch size: 20, lr: 1.67e-04
2022-05-07 20:42:10,093 INFO [train.py:715] (2/8) Epoch 13, batch 24850, loss[loss=0.1275, simple_loss=0.2011, pruned_loss=0.02698, over 4892.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03175, over 972582.46 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 20:42:48,217 INFO [train.py:715] (2/8) Epoch 13, batch 24900, loss[loss=0.1719, simple_loss=0.2364, pruned_loss=0.05372, over 4862.00 frames.], tot_loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03184, over 972268.32 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 20:43:26,336 INFO [train.py:715] (2/8) Epoch 13, batch 24950, loss[loss=0.1423, simple_loss=0.2294, pruned_loss=0.0276, over 4925.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2105, pruned_loss=0.03184, over 972968.31 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 20:44:04,943 INFO [train.py:715] (2/8) Epoch 13, batch 25000, loss[loss=0.1341, simple_loss=0.2103, pruned_loss=0.02899, over 4983.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.0318, over 973326.27 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 20:44:43,239 INFO [train.py:715] (2/8) Epoch 13, batch 25050, loss[loss=0.1348, simple_loss=0.2174, pruned_loss=0.02609, over 4758.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03205, over 973374.14 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 20:45:20,925 INFO [train.py:715] (2/8) Epoch 13, batch 25100, loss[loss=0.1321, simple_loss=0.1975, pruned_loss=0.0333, over 4866.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03142, over 974027.29 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 20:46:00,042 INFO [train.py:715] (2/8) Epoch 13, batch 25150, loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02885, over 4926.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03117, over 973753.47 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 20:46:38,604 INFO [train.py:715] (2/8) Epoch 13, batch 25200, loss[loss=0.142, simple_loss=0.2195, pruned_loss=0.03227, over 4805.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2101, pruned_loss=0.03138, over 973242.12 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 20:47:17,720 INFO [train.py:715] (2/8) Epoch 13, batch 25250, loss[loss=0.1255, simple_loss=0.1995, pruned_loss=0.02581, over 4886.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03146, over 973060.98 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 20:47:55,930 INFO [train.py:715] (2/8) Epoch 13, batch 25300, loss[loss=0.1357, simple_loss=0.2014, pruned_loss=0.03504, over 4966.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2089, pruned_loss=0.03127, over 972909.22 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 20:48:34,494 INFO [train.py:715] (2/8) Epoch 13, batch 25350, loss[loss=0.1647, simple_loss=0.235, pruned_loss=0.04715, over 4834.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03132, over 973062.24 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 20:49:13,704 INFO [train.py:715] (2/8) Epoch 13, batch 25400, loss[loss=0.1378, simple_loss=0.2165, pruned_loss=0.02954, over 4914.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03144, over 972588.60 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 20:49:51,567 INFO [train.py:715] (2/8) Epoch 13, batch 25450, loss[loss=0.1339, simple_loss=0.2085, pruned_loss=0.02971, over 4873.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2111, pruned_loss=0.03191, over 972867.28 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 20:50:30,631 INFO [train.py:715] (2/8) Epoch 13, batch 25500, loss[loss=0.1467, simple_loss=0.2152, pruned_loss=0.03909, over 4756.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03157, over 972323.37 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 20:51:09,206 INFO [train.py:715] (2/8) Epoch 13, batch 25550, loss[loss=0.1608, simple_loss=0.2372, pruned_loss=0.04215, over 4878.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03143, over 971353.80 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 20:51:47,750 INFO [train.py:715] (2/8) Epoch 13, batch 25600, loss[loss=0.1194, simple_loss=0.2036, pruned_loss=0.01757, over 4989.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2109, pruned_loss=0.03202, over 972127.23 frames.], batch size: 27, lr: 1.66e-04
2022-05-07 20:52:25,801 INFO [train.py:715] (2/8) Epoch 13, batch 25650, loss[loss=0.1203, simple_loss=0.204, pruned_loss=0.01834, over 4939.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.0317, over 971722.47 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 20:53:05,136 INFO [train.py:715] (2/8) Epoch 13, batch 25700, loss[loss=0.1629, simple_loss=0.2334, pruned_loss=0.04617, over 4935.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03169, over 971742.48 frames.], batch size: 39, lr: 1.66e-04
2022-05-07 20:53:43,489 INFO [train.py:715] (2/8) Epoch 13, batch 25750, loss[loss=0.1347, simple_loss=0.1957, pruned_loss=0.03686, over 4859.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2105, pruned_loss=0.0322, over 972181.90 frames.], batch size: 34, lr: 1.66e-04
2022-05-07 20:54:21,674 INFO [train.py:715] (2/8) Epoch 13, batch 25800, loss[loss=0.1507, simple_loss=0.2224, pruned_loss=0.03953, over 4854.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2101, pruned_loss=0.03221, over 972058.89 frames.], batch size: 30, lr: 1.66e-04
2022-05-07 20:55:00,567 INFO [train.py:715] (2/8) Epoch 13, batch 25850, loss[loss=0.1572, simple_loss=0.2454, pruned_loss=0.03454, over 4907.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2105, pruned_loss=0.03244, over 971456.04 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 20:55:39,360 INFO [train.py:715] (2/8) Epoch 13, batch 25900, loss[loss=0.1497, simple_loss=0.2304, pruned_loss=0.03448, over 4935.00 frames.], tot_loss[loss=0.1372, simple_loss=0.21, pruned_loss=0.03224, over 971936.90 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 20:56:18,180 INFO [train.py:715] (2/8) Epoch 13, batch 25950, loss[loss=0.1201, simple_loss=0.1966, pruned_loss=0.02176, over 4783.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2099, pruned_loss=0.03172, over 972964.24 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 20:56:57,182 INFO [train.py:715] (2/8) Epoch 13, batch 26000, loss[loss=0.1498, simple_loss=0.2236, pruned_loss=0.038, over 4774.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03189, over 973390.69 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 20:57:36,544 INFO [train.py:715] (2/8) Epoch 13, batch 26050, loss[loss=0.1515, simple_loss=0.2222, pruned_loss=0.04043, over 4944.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03146, over 972980.62 frames.], batch size: 35, lr: 1.66e-04
2022-05-07 20:58:15,740 INFO [train.py:715] (2/8) Epoch 13, batch 26100, loss[loss=0.1306, simple_loss=0.2113, pruned_loss=0.02495, over 4940.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.03135, over 973976.19 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 20:58:54,123 INFO [train.py:715] (2/8) Epoch 13, batch 26150, loss[loss=0.1505, simple_loss=0.2207, pruned_loss=0.04013, over 4772.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2094, pruned_loss=0.0314, over 974338.03 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 20:59:33,347 INFO [train.py:715] (2/8) Epoch 13, batch 26200, loss[loss=0.1331, simple_loss=0.1966, pruned_loss=0.0348, over 4878.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2097, pruned_loss=0.03171, over 973371.81 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:00:12,171 INFO [train.py:715] (2/8) Epoch 13, batch 26250, loss[loss=0.1229, simple_loss=0.202, pruned_loss=0.02186, over 4824.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2098, pruned_loss=0.03168, over 972910.27 frames.], batch size: 26, lr: 1.66e-04
2022-05-07 21:00:50,345 INFO [train.py:715] (2/8) Epoch 13, batch 26300, loss[loss=0.1281, simple_loss=0.2041, pruned_loss=0.02603, over 4828.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03146, over 973400.53 frames.], batch size: 12, lr: 1.66e-04
2022-05-07 21:01:28,298 INFO [train.py:715] (2/8) Epoch 13, batch 26350, loss[loss=0.1266, simple_loss=0.2037, pruned_loss=0.0248, over 4964.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03099, over 972666.67 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 21:02:07,166 INFO [train.py:715] (2/8) Epoch 13, batch 26400, loss[loss=0.1463, simple_loss=0.2162, pruned_loss=0.03825, over 4812.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03086, over 972824.34 frames.], batch size: 26, lr: 1.66e-04
2022-05-07 21:02:46,106 INFO [train.py:715] (2/8) Epoch 13, batch 26450, loss[loss=0.114, simple_loss=0.1876, pruned_loss=0.02026, over 4821.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.031, over 972157.39 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 21:03:24,278 INFO [train.py:715] (2/8) Epoch 13, batch 26500, loss[loss=0.1422, simple_loss=0.2153, pruned_loss=0.03453, over 4853.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2096, pruned_loss=0.0318, over 972241.22 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:04:03,400 INFO [train.py:715] (2/8) Epoch 13, batch 26550, loss[loss=0.1422, simple_loss=0.2112, pruned_loss=0.03659, over 4888.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2099, pruned_loss=0.03164, over 972778.86 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:04:41,839 INFO [train.py:715] (2/8) Epoch 13, batch 26600, loss[loss=0.1373, simple_loss=0.2197, pruned_loss=0.02745, over 4867.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03147, over 973226.00 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:05:20,070 INFO [train.py:715] (2/8) Epoch 13, batch 26650, loss[loss=0.1451, simple_loss=0.2164, pruned_loss=0.0369, over 4904.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03176, over 973323.56 frames.], batch size: 39, lr: 1.66e-04
2022-05-07 21:05:58,317 INFO [train.py:715] (2/8) Epoch 13, batch 26700, loss[loss=0.1154, simple_loss=0.204, pruned_loss=0.01341, over 4882.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03111, over 973545.86 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:06:37,482 INFO [train.py:715] (2/8) Epoch 13, batch 26750, loss[loss=0.1166, simple_loss=0.1863, pruned_loss=0.0235, over 4816.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.03087, over 973239.04 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:07:15,991 INFO [train.py:715] (2/8) Epoch 13, batch 26800, loss[loss=0.1421, simple_loss=0.2155, pruned_loss=0.03432, over 4804.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2099, pruned_loss=0.03085, over 973333.36 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:07:54,603 INFO [train.py:715] (2/8) Epoch 13, batch 26850, loss[loss=0.1458, simple_loss=0.2265, pruned_loss=0.03254, over 4876.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.03106, over 973230.30 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:08:33,348 INFO [train.py:715] (2/8) Epoch 13, batch 26900, loss[loss=0.147, simple_loss=0.2165, pruned_loss=0.03876, over 4856.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03096, over 973003.20 frames.], batch size: 30, lr: 1.66e-04
2022-05-07 21:09:11,797 INFO [train.py:715] (2/8) Epoch 13, batch 26950, loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.03031, over 4966.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.0309, over 973992.60 frames.], batch size: 28, lr: 1.66e-04
2022-05-07 21:09:50,394 INFO [train.py:715] (2/8) Epoch 13, batch 27000, loss[loss=0.1305, simple_loss=0.207, pruned_loss=0.02704, over 4787.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2102, pruned_loss=0.03081, over 972980.23 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:09:50,395 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 21:09:59,936 INFO [train.py:742] (2/8) 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] (2/8) Epoch 13, batch 27050, loss[loss=0.152, simple_loss=0.2288, pruned_loss=0.03754, over 4959.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2107, pruned_loss=0.03114, over 973899.56 frames.], batch size: 39, lr: 1.66e-04
2022-05-07 21:11:17,914 INFO [train.py:715] (2/8) Epoch 13, batch 27100, loss[loss=0.14, simple_loss=0.2162, pruned_loss=0.03193, over 4800.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2103, pruned_loss=0.03106, over 973466.03 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:11:57,149 INFO [train.py:715] (2/8) Epoch 13, batch 27150, loss[loss=0.1313, simple_loss=0.2039, pruned_loss=0.02936, over 4933.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2105, pruned_loss=0.0311, over 973653.07 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:12:36,111 INFO [train.py:715] (2/8) Epoch 13, batch 27200, loss[loss=0.1235, simple_loss=0.1978, pruned_loss=0.02464, over 4904.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.03063, over 973186.62 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 21:13:14,912 INFO [train.py:715] (2/8) Epoch 13, batch 27250, loss[loss=0.1072, simple_loss=0.1776, pruned_loss=0.01841, over 4815.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03091, over 973944.30 frames.], batch size: 13, lr: 1.66e-04
2022-05-07 21:13:54,909 INFO [train.py:715] (2/8) Epoch 13, batch 27300, loss[loss=0.1336, simple_loss=0.21, pruned_loss=0.02864, over 4942.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03136, over 973811.64 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:14:33,865 INFO [train.py:715] (2/8) Epoch 13, batch 27350, loss[loss=0.1343, simple_loss=0.2044, pruned_loss=0.03205, over 4958.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03132, over 974169.57 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 21:15:11,633 INFO [train.py:715] (2/8) Epoch 13, batch 27400, loss[loss=0.1628, simple_loss=0.2157, pruned_loss=0.05493, over 4825.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.03133, over 973642.76 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:15:49,744 INFO [train.py:715] (2/8) Epoch 13, batch 27450, loss[loss=0.1407, simple_loss=0.2092, pruned_loss=0.03608, over 4968.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03156, over 972890.01 frames.], batch size: 39, lr: 1.66e-04
2022-05-07 21:16:30,590 INFO [train.py:715] (2/8) Epoch 13, batch 27500, loss[loss=0.138, simple_loss=0.2079, pruned_loss=0.03407, over 4872.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03187, over 972873.82 frames.], batch size: 32, lr: 1.66e-04
2022-05-07 21:17:08,832 INFO [train.py:715] (2/8) Epoch 13, batch 27550, loss[loss=0.1298, simple_loss=0.2027, pruned_loss=0.02852, over 4842.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2097, pruned_loss=0.03188, over 972225.10 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:17:46,779 INFO [train.py:715] (2/8) Epoch 13, batch 27600, loss[loss=0.1569, simple_loss=0.2376, pruned_loss=0.03803, over 4884.00 frames.], tot_loss[loss=0.136, simple_loss=0.2092, pruned_loss=0.03141, over 972542.37 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:18:25,958 INFO [train.py:715] (2/8) Epoch 13, batch 27650, loss[loss=0.1358, simple_loss=0.2072, pruned_loss=0.03215, over 4798.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03133, over 973272.38 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:19:03,878 INFO [train.py:715] (2/8) Epoch 13, batch 27700, loss[loss=0.1248, simple_loss=0.2025, pruned_loss=0.02359, over 4929.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03109, over 974148.32 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 21:19:42,882 INFO [train.py:715] (2/8) Epoch 13, batch 27750, loss[loss=0.142, simple_loss=0.2099, pruned_loss=0.03705, over 4744.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03068, over 973489.95 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:20:21,395 INFO [train.py:715] (2/8) Epoch 13, batch 27800, loss[loss=0.1272, simple_loss=0.2043, pruned_loss=0.02505, over 4859.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03092, over 972504.47 frames.], batch size: 32, lr: 1.66e-04
2022-05-07 21:21:00,120 INFO [train.py:715] (2/8) Epoch 13, batch 27850, loss[loss=0.1166, simple_loss=0.1882, pruned_loss=0.02249, over 4911.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2096, pruned_loss=0.0317, over 972347.28 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 21:21:38,315 INFO [train.py:715] (2/8) Epoch 13, batch 27900, loss[loss=0.1748, simple_loss=0.234, pruned_loss=0.05777, over 4862.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2104, pruned_loss=0.03201, over 972853.91 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:22:16,098 INFO [train.py:715] (2/8) Epoch 13, batch 27950, loss[loss=0.1367, simple_loss=0.2077, pruned_loss=0.03285, over 4864.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03185, over 972424.96 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:22:55,056 INFO [train.py:715] (2/8) Epoch 13, batch 28000, loss[loss=0.1169, simple_loss=0.1877, pruned_loss=0.02305, over 4862.00 frames.], tot_loss[loss=0.137, simple_loss=0.2101, pruned_loss=0.03192, over 972521.44 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:23:33,525 INFO [train.py:715] (2/8) Epoch 13, batch 28050, loss[loss=0.1062, simple_loss=0.1814, pruned_loss=0.01554, over 4840.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03161, over 971845.01 frames.], batch size: 13, lr: 1.66e-04
2022-05-07 21:24:11,555 INFO [train.py:715] (2/8) Epoch 13, batch 28100, loss[loss=0.109, simple_loss=0.1848, pruned_loss=0.01664, over 4974.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03156, over 972600.08 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:24:49,599 INFO [train.py:715] (2/8) Epoch 13, batch 28150, loss[loss=0.1255, simple_loss=0.1956, pruned_loss=0.02767, over 4985.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03205, over 973176.44 frames.], batch size: 26, lr: 1.66e-04
2022-05-07 21:25:28,818 INFO [train.py:715] (2/8) Epoch 13, batch 28200, loss[loss=0.1175, simple_loss=0.1929, pruned_loss=0.02104, over 4859.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03147, over 973241.39 frames.], batch size: 30, lr: 1.66e-04
2022-05-07 21:26:06,612 INFO [train.py:715] (2/8) Epoch 13, batch 28250, loss[loss=0.1421, simple_loss=0.2056, pruned_loss=0.03926, over 4845.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2109, pruned_loss=0.03149, over 972995.61 frames.], batch size: 32, lr: 1.66e-04
2022-05-07 21:26:44,756 INFO [train.py:715] (2/8) Epoch 13, batch 28300, loss[loss=0.1181, simple_loss=0.1827, pruned_loss=0.02673, over 4830.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03123, over 972921.83 frames.], batch size: 13, lr: 1.66e-04
2022-05-07 21:27:23,465 INFO [train.py:715] (2/8) Epoch 13, batch 28350, loss[loss=0.1254, simple_loss=0.1939, pruned_loss=0.02849, over 4855.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2108, pruned_loss=0.03152, over 971960.49 frames.], batch size: 20, lr: 1.66e-04
2022-05-07 21:28:01,610 INFO [train.py:715] (2/8) Epoch 13, batch 28400, loss[loss=0.135, simple_loss=0.2138, pruned_loss=0.02813, over 4785.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2111, pruned_loss=0.03151, over 972208.21 frames.], batch size: 18, lr: 1.66e-04
2022-05-07 21:28:40,047 INFO [train.py:715] (2/8) Epoch 13, batch 28450, loss[loss=0.1373, simple_loss=0.2192, pruned_loss=0.02768, over 4890.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03116, over 972899.58 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 21:29:18,390 INFO [train.py:715] (2/8) Epoch 13, batch 28500, loss[loss=0.1443, simple_loss=0.2206, pruned_loss=0.03404, over 4784.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03115, over 972415.84 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 21:29:57,064 INFO [train.py:715] (2/8) Epoch 13, batch 28550, loss[loss=0.18, simple_loss=0.2507, pruned_loss=0.05467, over 4795.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03116, over 972770.71 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:30:35,262 INFO [train.py:715] (2/8) Epoch 13, batch 28600, loss[loss=0.1678, simple_loss=0.2577, pruned_loss=0.0389, over 4700.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03109, over 972457.75 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:31:13,619 INFO [train.py:715] (2/8) Epoch 13, batch 28650, loss[loss=0.1207, simple_loss=0.1969, pruned_loss=0.02223, over 4827.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03087, over 971474.52 frames.], batch size: 26, lr: 1.66e-04
2022-05-07 21:31:52,266 INFO [train.py:715] (2/8) Epoch 13, batch 28700, loss[loss=0.1165, simple_loss=0.1962, pruned_loss=0.01836, over 4814.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2087, pruned_loss=0.03102, over 971085.45 frames.], batch size: 26, lr: 1.66e-04
2022-05-07 21:32:30,335 INFO [train.py:715] (2/8) Epoch 13, batch 28750, loss[loss=0.1188, simple_loss=0.1989, pruned_loss=0.01931, over 4886.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03064, over 971337.15 frames.], batch size: 19, lr: 1.66e-04
2022-05-07 21:33:08,638 INFO [train.py:715] (2/8) Epoch 13, batch 28800, loss[loss=0.1464, simple_loss=0.2163, pruned_loss=0.03828, over 4745.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03078, over 971326.18 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:33:47,846 INFO [train.py:715] (2/8) Epoch 13, batch 28850, loss[loss=0.1281, simple_loss=0.1863, pruned_loss=0.0349, over 4813.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03123, over 970907.62 frames.], batch size: 13, lr: 1.66e-04
2022-05-07 21:34:26,367 INFO [train.py:715] (2/8) Epoch 13, batch 28900, loss[loss=0.14, simple_loss=0.2146, pruned_loss=0.03266, over 4796.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.03112, over 971645.01 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 21:35:04,280 INFO [train.py:715] (2/8) Epoch 13, batch 28950, loss[loss=0.1407, simple_loss=0.2192, pruned_loss=0.03106, over 4831.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03074, over 972060.79 frames.], batch size: 26, lr: 1.66e-04
2022-05-07 21:35:42,444 INFO [train.py:715] (2/8) Epoch 13, batch 29000, loss[loss=0.1554, simple_loss=0.2214, pruned_loss=0.04471, over 4863.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.03093, over 973216.12 frames.], batch size: 15, lr: 1.66e-04
2022-05-07 21:36:21,625 INFO [train.py:715] (2/8) Epoch 13, batch 29050, loss[loss=0.1378, simple_loss=0.2014, pruned_loss=0.03706, over 4829.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.03124, over 973302.67 frames.], batch size: 13, lr: 1.66e-04
2022-05-07 21:37:00,160 INFO [train.py:715] (2/8) Epoch 13, batch 29100, loss[loss=0.1471, simple_loss=0.2115, pruned_loss=0.04133, over 4845.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03148, over 973872.62 frames.], batch size: 32, lr: 1.66e-04
2022-05-07 21:37:38,206 INFO [train.py:715] (2/8) Epoch 13, batch 29150, loss[loss=0.1402, simple_loss=0.2189, pruned_loss=0.03069, over 4784.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.03148, over 973239.45 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:38:16,963 INFO [train.py:715] (2/8) Epoch 13, batch 29200, loss[loss=0.1093, simple_loss=0.1923, pruned_loss=0.01311, over 4790.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2104, pruned_loss=0.03152, over 972232.17 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:38:55,212 INFO [train.py:715] (2/8) Epoch 13, batch 29250, loss[loss=0.1246, simple_loss=0.1982, pruned_loss=0.02554, over 4817.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2104, pruned_loss=0.03225, over 971315.03 frames.], batch size: 27, lr: 1.66e-04
2022-05-07 21:39:34,054 INFO [train.py:715] (2/8) Epoch 13, batch 29300, loss[loss=0.1558, simple_loss=0.2417, pruned_loss=0.03489, over 4826.00 frames.], tot_loss[loss=0.1381, simple_loss=0.211, pruned_loss=0.03253, over 970918.60 frames.], batch size: 25, lr: 1.66e-04
2022-05-07 21:40:12,802 INFO [train.py:715] (2/8) Epoch 13, batch 29350, loss[loss=0.1536, simple_loss=0.2245, pruned_loss=0.04137, over 4888.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2112, pruned_loss=0.03252, over 970793.74 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:40:51,677 INFO [train.py:715] (2/8) Epoch 13, batch 29400, loss[loss=0.1096, simple_loss=0.183, pruned_loss=0.01815, over 4945.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2118, pruned_loss=0.03289, over 971075.10 frames.], batch size: 29, lr: 1.66e-04
2022-05-07 21:41:29,700 INFO [train.py:715] (2/8) Epoch 13, batch 29450, loss[loss=0.1427, simple_loss=0.2108, pruned_loss=0.03732, over 4790.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2109, pruned_loss=0.03235, over 970976.12 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 21:42:08,739 INFO [train.py:715] (2/8) Epoch 13, batch 29500, loss[loss=0.1366, simple_loss=0.2094, pruned_loss=0.03185, over 4856.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2098, pruned_loss=0.03177, over 971314.51 frames.], batch size: 34, lr: 1.66e-04
2022-05-07 21:42:47,372 INFO [train.py:715] (2/8) Epoch 13, batch 29550, loss[loss=0.135, simple_loss=0.2125, pruned_loss=0.02881, over 4812.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03145, over 971686.44 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:43:25,737 INFO [train.py:715] (2/8) Epoch 13, batch 29600, loss[loss=0.1424, simple_loss=0.2133, pruned_loss=0.03574, over 4967.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03165, over 972335.65 frames.], batch size: 35, lr: 1.66e-04
2022-05-07 21:44:03,484 INFO [train.py:715] (2/8) Epoch 13, batch 29650, loss[loss=0.1321, simple_loss=0.2121, pruned_loss=0.02606, over 4929.00 frames.], tot_loss[loss=0.138, simple_loss=0.2116, pruned_loss=0.0322, over 972109.05 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:44:41,768 INFO [train.py:715] (2/8) Epoch 13, batch 29700, loss[loss=0.1178, simple_loss=0.1916, pruned_loss=0.02201, over 4926.00 frames.], tot_loss[loss=0.1375, simple_loss=0.211, pruned_loss=0.03201, over 972579.62 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:45:20,121 INFO [train.py:715] (2/8) Epoch 13, batch 29750, loss[loss=0.1066, simple_loss=0.1839, pruned_loss=0.01461, over 4735.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2102, pruned_loss=0.03165, over 972328.24 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:45:59,491 INFO [train.py:715] (2/8) Epoch 13, batch 29800, loss[loss=0.1227, simple_loss=0.203, pruned_loss=0.02126, over 4776.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2101, pruned_loss=0.03177, over 972097.80 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:46:38,720 INFO [train.py:715] (2/8) Epoch 13, batch 29850, loss[loss=0.1314, simple_loss=0.2104, pruned_loss=0.02622, over 4972.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2109, pruned_loss=0.03209, over 972547.55 frames.], batch size: 24, lr: 1.66e-04
2022-05-07 21:47:18,328 INFO [train.py:715] (2/8) Epoch 13, batch 29900, loss[loss=0.1307, simple_loss=0.206, pruned_loss=0.02771, over 4780.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2105, pruned_loss=0.03192, over 973141.65 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 21:47:57,739 INFO [train.py:715] (2/8) Epoch 13, batch 29950, loss[loss=0.1099, simple_loss=0.1885, pruned_loss=0.01567, over 4882.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2102, pruned_loss=0.0317, over 972757.87 frames.], batch size: 22, lr: 1.66e-04
2022-05-07 21:48:36,359 INFO [train.py:715] (2/8) Epoch 13, batch 30000, loss[loss=0.1548, simple_loss=0.2308, pruned_loss=0.03942, over 4900.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03102, over 972755.02 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 21:48:36,360 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 21:48:45,862 INFO [train.py:742] (2/8) Epoch 13, validation: loss=0.1054, simple_loss=0.1891, pruned_loss=0.01083, over 914524.00 frames.
2022-05-07 21:49:25,289 INFO [train.py:715] (2/8) Epoch 13, batch 30050, loss[loss=0.1595, simple_loss=0.2251, pruned_loss=0.04699, over 4863.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03084, over 972484.85 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:50:05,102 INFO [train.py:715] (2/8) Epoch 13, batch 30100, loss[loss=0.1237, simple_loss=0.1993, pruned_loss=0.02405, over 4938.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03112, over 972884.83 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:50:44,576 INFO [train.py:715] (2/8) Epoch 13, batch 30150, loss[loss=0.1233, simple_loss=0.1983, pruned_loss=0.02411, over 4940.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03078, over 971892.78 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:51:23,146 INFO [train.py:715] (2/8) Epoch 13, batch 30200, loss[loss=0.1193, simple_loss=0.1894, pruned_loss=0.02462, over 4941.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03133, over 972138.68 frames.], batch size: 21, lr: 1.66e-04
2022-05-07 21:52:02,985 INFO [train.py:715] (2/8) Epoch 13, batch 30250, loss[loss=0.147, simple_loss=0.2174, pruned_loss=0.03833, over 4953.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2104, pruned_loss=0.03156, over 973244.90 frames.], batch size: 35, lr: 1.66e-04
2022-05-07 21:52:42,786 INFO [train.py:715] (2/8) Epoch 13, batch 30300, loss[loss=0.1435, simple_loss=0.2141, pruned_loss=0.0365, over 4992.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2111, pruned_loss=0.03232, over 973249.52 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:53:22,297 INFO [train.py:715] (2/8) Epoch 13, batch 30350, loss[loss=0.1134, simple_loss=0.1905, pruned_loss=0.01811, over 4934.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03153, over 972593.27 frames.], batch size: 23, lr: 1.66e-04
2022-05-07 21:54:01,877 INFO [train.py:715] (2/8) Epoch 13, batch 30400, loss[loss=0.1478, simple_loss=0.217, pruned_loss=0.0393, over 4782.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03114, over 972830.27 frames.], batch size: 17, lr: 1.66e-04
2022-05-07 21:54:42,501 INFO [train.py:715] (2/8) Epoch 13, batch 30450, loss[loss=0.134, simple_loss=0.202, pruned_loss=0.03296, over 4837.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03127, over 972571.19 frames.], batch size: 13, lr: 1.66e-04
2022-05-07 21:55:22,603 INFO [train.py:715] (2/8) Epoch 13, batch 30500, loss[loss=0.1201, simple_loss=0.1964, pruned_loss=0.02188, over 4869.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2102, pruned_loss=0.03149, over 971332.29 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:56:02,393 INFO [train.py:715] (2/8) Epoch 13, batch 30550, loss[loss=0.1218, simple_loss=0.1898, pruned_loss=0.02685, over 4984.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03094, over 972669.77 frames.], batch size: 14, lr: 1.66e-04
2022-05-07 21:56:43,829 INFO [train.py:715] (2/8) Epoch 13, batch 30600, loss[loss=0.1437, simple_loss=0.2062, pruned_loss=0.04063, over 4885.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03141, over 972111.82 frames.], batch size: 16, lr: 1.66e-04
2022-05-07 21:57:24,952 INFO [train.py:715] (2/8) Epoch 13, batch 30650, loss[loss=0.107, simple_loss=0.1791, pruned_loss=0.01748, over 4856.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2099, pruned_loss=0.03128, over 972165.61 frames.], batch size: 30, lr: 1.65e-04
2022-05-07 21:58:05,358 INFO [train.py:715] (2/8) Epoch 13, batch 30700, loss[loss=0.1665, simple_loss=0.237, pruned_loss=0.04798, over 4778.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03112, over 971641.97 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 21:58:45,821 INFO [train.py:715] (2/8) Epoch 13, batch 30750, loss[loss=0.1265, simple_loss=0.2062, pruned_loss=0.02343, over 4920.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.0309, over 972344.77 frames.], batch size: 39, lr: 1.65e-04
2022-05-07 21:59:26,819 INFO [train.py:715] (2/8) Epoch 13, batch 30800, loss[loss=0.1508, simple_loss=0.2356, pruned_loss=0.03297, over 4931.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2093, pruned_loss=0.0305, over 971606.04 frames.], batch size: 29, lr: 1.65e-04
2022-05-07 22:00:07,589 INFO [train.py:715] (2/8) Epoch 13, batch 30850, loss[loss=0.1071, simple_loss=0.1864, pruned_loss=0.01396, over 4867.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.0304, over 971565.82 frames.], batch size: 22, lr: 1.65e-04
2022-05-07 22:00:48,208 INFO [train.py:715] (2/8) Epoch 13, batch 30900, loss[loss=0.1356, simple_loss=0.2032, pruned_loss=0.03405, over 4869.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2093, pruned_loss=0.03029, over 971019.77 frames.], batch size: 16, lr: 1.65e-04
2022-05-07 22:01:29,251 INFO [train.py:715] (2/8) Epoch 13, batch 30950, loss[loss=0.1299, simple_loss=0.199, pruned_loss=0.03036, over 4869.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03077, over 971988.61 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:02:09,959 INFO [train.py:715] (2/8) Epoch 13, batch 31000, loss[loss=0.1521, simple_loss=0.2248, pruned_loss=0.03965, over 4793.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03073, over 972400.58 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:02:50,142 INFO [train.py:715] (2/8) Epoch 13, batch 31050, loss[loss=0.1777, simple_loss=0.2435, pruned_loss=0.05598, over 4914.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.03065, over 973007.45 frames.], batch size: 39, lr: 1.65e-04
2022-05-07 22:03:30,715 INFO [train.py:715] (2/8) Epoch 13, batch 31100, loss[loss=0.1276, simple_loss=0.2032, pruned_loss=0.02598, over 4874.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.0309, over 972544.56 frames.], batch size: 22, lr: 1.65e-04
2022-05-07 22:04:11,682 INFO [train.py:715] (2/8) Epoch 13, batch 31150, loss[loss=0.1357, simple_loss=0.2268, pruned_loss=0.02229, over 4974.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03128, over 973020.62 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:04:52,783 INFO [train.py:715] (2/8) Epoch 13, batch 31200, loss[loss=0.1146, simple_loss=0.1788, pruned_loss=0.02518, over 4983.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03112, over 972846.43 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:05:32,915 INFO [train.py:715] (2/8) Epoch 13, batch 31250, loss[loss=0.1135, simple_loss=0.1867, pruned_loss=0.0201, over 4809.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03146, over 972661.31 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:06:13,242 INFO [train.py:715] (2/8) Epoch 13, batch 31300, loss[loss=0.1413, simple_loss=0.2111, pruned_loss=0.03572, over 4971.00 frames.], tot_loss[loss=0.137, simple_loss=0.2099, pruned_loss=0.03208, over 971611.25 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:06:53,512 INFO [train.py:715] (2/8) Epoch 13, batch 31350, loss[loss=0.106, simple_loss=0.1886, pruned_loss=0.01164, over 4958.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03138, over 972413.81 frames.], batch size: 24, lr: 1.65e-04
2022-05-07 22:07:33,258 INFO [train.py:715] (2/8) Epoch 13, batch 31400, loss[loss=0.1243, simple_loss=0.1951, pruned_loss=0.0267, over 4941.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.0308, over 972066.51 frames.], batch size: 21, lr: 1.65e-04
2022-05-07 22:08:13,749 INFO [train.py:715] (2/8) Epoch 13, batch 31450, loss[loss=0.1309, simple_loss=0.2068, pruned_loss=0.02745, over 4826.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03108, over 972924.63 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:08:54,096 INFO [train.py:715] (2/8) Epoch 13, batch 31500, loss[loss=0.1312, simple_loss=0.2134, pruned_loss=0.0245, over 4941.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03086, over 973240.09 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:09:33,926 INFO [train.py:715] (2/8) Epoch 13, batch 31550, loss[loss=0.1163, simple_loss=0.194, pruned_loss=0.01931, over 4817.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03048, over 972506.52 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:10:14,442 INFO [train.py:715] (2/8) Epoch 13, batch 31600, loss[loss=0.1568, simple_loss=0.2359, pruned_loss=0.03884, over 4983.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03023, over 972926.74 frames.], batch size: 35, lr: 1.65e-04
2022-05-07 22:10:55,016 INFO [train.py:715] (2/8) Epoch 13, batch 31650, loss[loss=0.1279, simple_loss=0.2101, pruned_loss=0.02289, over 4926.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03016, over 973283.21 frames.], batch size: 29, lr: 1.65e-04
2022-05-07 22:11:35,402 INFO [train.py:715] (2/8) Epoch 13, batch 31700, loss[loss=0.1711, simple_loss=0.2467, pruned_loss=0.04774, over 4968.00 frames.], tot_loss[loss=0.1347, simple_loss=0.209, pruned_loss=0.03023, over 973014.77 frames.], batch size: 24, lr: 1.65e-04
2022-05-07 22:12:15,836 INFO [train.py:715] (2/8) Epoch 13, batch 31750, loss[loss=0.1286, simple_loss=0.2123, pruned_loss=0.02244, over 4796.00 frames.], tot_loss[loss=0.135, simple_loss=0.2094, pruned_loss=0.0303, over 974271.19 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:12:56,364 INFO [train.py:715] (2/8) Epoch 13, batch 31800, loss[loss=0.1392, simple_loss=0.2074, pruned_loss=0.03557, over 4832.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2094, pruned_loss=0.03046, over 973059.46 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:13:37,274 INFO [train.py:715] (2/8) Epoch 13, batch 31850, loss[loss=0.1385, simple_loss=0.2226, pruned_loss=0.02721, over 4935.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2096, pruned_loss=0.03064, over 973025.88 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:14:18,141 INFO [train.py:715] (2/8) Epoch 13, batch 31900, loss[loss=0.1094, simple_loss=0.1867, pruned_loss=0.01609, over 4900.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03106, over 973672.60 frames.], batch size: 22, lr: 1.65e-04
2022-05-07 22:14:59,152 INFO [train.py:715] (2/8) Epoch 13, batch 31950, loss[loss=0.1323, simple_loss=0.2113, pruned_loss=0.02665, over 4824.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03128, over 972991.93 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:15:39,538 INFO [train.py:715] (2/8) Epoch 13, batch 32000, loss[loss=0.1402, simple_loss=0.2013, pruned_loss=0.03958, over 4815.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03145, over 972849.33 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:16:20,149 INFO [train.py:715] (2/8) Epoch 13, batch 32050, loss[loss=0.1096, simple_loss=0.1835, pruned_loss=0.01781, over 4634.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03154, over 972613.48 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:17:00,689 INFO [train.py:715] (2/8) Epoch 13, batch 32100, loss[loss=0.1436, simple_loss=0.22, pruned_loss=0.03355, over 4885.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03145, over 972409.66 frames.], batch size: 22, lr: 1.65e-04
2022-05-07 22:17:41,701 INFO [train.py:715] (2/8) Epoch 13, batch 32150, loss[loss=0.1406, simple_loss=0.2104, pruned_loss=0.03544, over 4763.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03109, over 972076.54 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:18:22,392 INFO [train.py:715] (2/8) Epoch 13, batch 32200, loss[loss=0.1226, simple_loss=0.1963, pruned_loss=0.02445, over 4902.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03113, over 971695.50 frames.], batch size: 19, lr: 1.65e-04
2022-05-07 22:19:03,057 INFO [train.py:715] (2/8) Epoch 13, batch 32250, loss[loss=0.1076, simple_loss=0.1914, pruned_loss=0.01195, over 4991.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03075, over 971199.95 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:19:43,877 INFO [train.py:715] (2/8) Epoch 13, batch 32300, loss[loss=0.1277, simple_loss=0.2042, pruned_loss=0.02566, over 4807.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03003, over 971541.36 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:20:24,941 INFO [train.py:715] (2/8) Epoch 13, batch 32350, loss[loss=0.1375, simple_loss=0.2186, pruned_loss=0.02814, over 4818.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03007, over 972308.89 frames.], batch size: 26, lr: 1.65e-04
2022-05-07 22:21:06,364 INFO [train.py:715] (2/8) Epoch 13, batch 32400, loss[loss=0.1542, simple_loss=0.2456, pruned_loss=0.03138, over 4777.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2091, pruned_loss=0.0301, over 972316.73 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:21:47,422 INFO [train.py:715] (2/8) Epoch 13, batch 32450, loss[loss=0.1288, simple_loss=0.1969, pruned_loss=0.03036, over 4971.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2092, pruned_loss=0.03003, over 972443.64 frames.], batch size: 31, lr: 1.65e-04
2022-05-07 22:22:28,211 INFO [train.py:715] (2/8) Epoch 13, batch 32500, loss[loss=0.1366, simple_loss=0.209, pruned_loss=0.03212, over 4958.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02984, over 972341.33 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:23:09,255 INFO [train.py:715] (2/8) Epoch 13, batch 32550, loss[loss=0.1112, simple_loss=0.1826, pruned_loss=0.01993, over 4937.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02976, over 972424.34 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:23:49,652 INFO [train.py:715] (2/8) Epoch 13, batch 32600, loss[loss=0.1678, simple_loss=0.2464, pruned_loss=0.04456, over 4641.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03036, over 972780.60 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:24:30,017 INFO [train.py:715] (2/8) Epoch 13, batch 32650, loss[loss=0.1781, simple_loss=0.2433, pruned_loss=0.05643, over 4986.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.0307, over 973021.23 frames.], batch size: 31, lr: 1.65e-04
2022-05-07 22:25:10,589 INFO [train.py:715] (2/8) Epoch 13, batch 32700, loss[loss=0.1359, simple_loss=0.211, pruned_loss=0.03036, over 4749.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03091, over 973055.08 frames.], batch size: 19, lr: 1.65e-04
2022-05-07 22:25:50,907 INFO [train.py:715] (2/8) Epoch 13, batch 32750, loss[loss=0.1284, simple_loss=0.1965, pruned_loss=0.03019, over 4740.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03071, over 972611.29 frames.], batch size: 12, lr: 1.65e-04
2022-05-07 22:26:31,933 INFO [train.py:715] (2/8) Epoch 13, batch 32800, loss[loss=0.1072, simple_loss=0.1834, pruned_loss=0.01555, over 4830.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03118, over 973000.42 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:27:12,665 INFO [train.py:715] (2/8) Epoch 13, batch 32850, loss[loss=0.1485, simple_loss=0.235, pruned_loss=0.03097, over 4941.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03079, over 973884.15 frames.], batch size: 29, lr: 1.65e-04
2022-05-07 22:27:53,748 INFO [train.py:715] (2/8) Epoch 13, batch 32900, loss[loss=0.1447, simple_loss=0.2179, pruned_loss=0.03576, over 4974.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03086, over 974067.43 frames.], batch size: 28, lr: 1.65e-04
2022-05-07 22:28:33,951 INFO [train.py:715] (2/8) Epoch 13, batch 32950, loss[loss=0.1352, simple_loss=0.2219, pruned_loss=0.02425, over 4821.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03056, over 973457.52 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:29:14,622 INFO [train.py:715] (2/8) Epoch 13, batch 33000, loss[loss=0.1306, simple_loss=0.2063, pruned_loss=0.02749, over 4774.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03068, over 973646.29 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:29:14,623 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 22:29:24,504 INFO [train.py:742] (2/8) Epoch 13, validation: loss=0.1054, simple_loss=0.1892, pruned_loss=0.01081, over 914524.00 frames.
2022-05-07 22:30:05,559 INFO [train.py:715] (2/8) Epoch 13, batch 33050, loss[loss=0.1331, simple_loss=0.2163, pruned_loss=0.0249, over 4831.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2081, pruned_loss=0.0307, over 973989.89 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:30:45,205 INFO [train.py:715] (2/8) Epoch 13, batch 33100, loss[loss=0.1551, simple_loss=0.2172, pruned_loss=0.0465, over 4874.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03096, over 973341.70 frames.], batch size: 32, lr: 1.65e-04
2022-05-07 22:31:25,145 INFO [train.py:715] (2/8) Epoch 13, batch 33150, loss[loss=0.1371, simple_loss=0.2031, pruned_loss=0.0355, over 4831.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2098, pruned_loss=0.03148, over 973264.99 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:32:05,567 INFO [train.py:715] (2/8) Epoch 13, batch 33200, loss[loss=0.1476, simple_loss=0.2207, pruned_loss=0.03726, over 4908.00 frames.], tot_loss[loss=0.137, simple_loss=0.2105, pruned_loss=0.03172, over 972575.43 frames.], batch size: 17, lr: 1.65e-04
2022-05-07 22:32:46,034 INFO [train.py:715] (2/8) Epoch 13, batch 33250, loss[loss=0.1431, simple_loss=0.2057, pruned_loss=0.04023, over 4965.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03179, over 971632.81 frames.], batch size: 35, lr: 1.65e-04
2022-05-07 22:33:26,588 INFO [train.py:715] (2/8) Epoch 13, batch 33300, loss[loss=0.127, simple_loss=0.2067, pruned_loss=0.02369, over 4768.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03153, over 971657.61 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:34:07,013 INFO [train.py:715] (2/8) Epoch 13, batch 33350, loss[loss=0.1336, simple_loss=0.2009, pruned_loss=0.03317, over 4935.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2106, pruned_loss=0.03162, over 972308.42 frames.], batch size: 24, lr: 1.65e-04
2022-05-07 22:34:47,631 INFO [train.py:715] (2/8) Epoch 13, batch 33400, loss[loss=0.1544, simple_loss=0.2194, pruned_loss=0.04472, over 4985.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03183, over 971672.66 frames.], batch size: 31, lr: 1.65e-04
2022-05-07 22:35:28,233 INFO [train.py:715] (2/8) Epoch 13, batch 33450, loss[loss=0.1289, simple_loss=0.2097, pruned_loss=0.02404, over 4946.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2115, pruned_loss=0.03175, over 971834.62 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:36:08,954 INFO [train.py:715] (2/8) Epoch 13, batch 33500, loss[loss=0.127, simple_loss=0.1977, pruned_loss=0.02812, over 4957.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2115, pruned_loss=0.0324, over 971665.88 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:36:49,605 INFO [train.py:715] (2/8) Epoch 13, batch 33550, loss[loss=0.08568, simple_loss=0.1538, pruned_loss=0.008769, over 4867.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03169, over 971850.25 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:37:30,304 INFO [train.py:715] (2/8) Epoch 13, batch 33600, loss[loss=0.1319, simple_loss=0.2102, pruned_loss=0.02676, over 4693.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2099, pruned_loss=0.03126, over 971914.96 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:38:10,828 INFO [train.py:715] (2/8) Epoch 13, batch 33650, loss[loss=0.1379, simple_loss=0.2019, pruned_loss=0.03695, over 4849.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03081, over 972282.98 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:38:51,062 INFO [train.py:715] (2/8) Epoch 13, batch 33700, loss[loss=0.1209, simple_loss=0.2004, pruned_loss=0.02068, over 4964.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03066, over 972141.79 frames.], batch size: 24, lr: 1.65e-04
2022-05-07 22:39:32,031 INFO [train.py:715] (2/8) Epoch 13, batch 33750, loss[loss=0.1314, simple_loss=0.2074, pruned_loss=0.02766, over 4891.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03104, over 972014.70 frames.], batch size: 22, lr: 1.65e-04
2022-05-07 22:40:12,825 INFO [train.py:715] (2/8) Epoch 13, batch 33800, loss[loss=0.1063, simple_loss=0.1824, pruned_loss=0.01509, over 4914.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2085, pruned_loss=0.03104, over 972794.18 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:40:53,572 INFO [train.py:715] (2/8) Epoch 13, batch 33850, loss[loss=0.1168, simple_loss=0.1944, pruned_loss=0.0196, over 4934.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2089, pruned_loss=0.03115, over 972862.51 frames.], batch size: 29, lr: 1.65e-04
2022-05-07 22:41:34,021 INFO [train.py:715] (2/8) Epoch 13, batch 33900, loss[loss=0.1087, simple_loss=0.1822, pruned_loss=0.01757, over 4736.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2079, pruned_loss=0.03041, over 971980.01 frames.], batch size: 16, lr: 1.65e-04
2022-05-07 22:42:15,280 INFO [train.py:715] (2/8) Epoch 13, batch 33950, loss[loss=0.1145, simple_loss=0.1896, pruned_loss=0.01972, over 4935.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03017, over 972267.90 frames.], batch size: 29, lr: 1.65e-04
2022-05-07 22:42:56,285 INFO [train.py:715] (2/8) Epoch 13, batch 34000, loss[loss=0.1221, simple_loss=0.2049, pruned_loss=0.01969, over 4887.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03069, over 971240.65 frames.], batch size: 22, lr: 1.65e-04
2022-05-07 22:43:36,855 INFO [train.py:715] (2/8) Epoch 13, batch 34050, loss[loss=0.1464, simple_loss=0.2295, pruned_loss=0.03165, over 4840.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.03059, over 971953.78 frames.], batch size: 27, lr: 1.65e-04
2022-05-07 22:44:17,679 INFO [train.py:715] (2/8) Epoch 13, batch 34100, loss[loss=0.144, simple_loss=0.2346, pruned_loss=0.02664, over 4888.00 frames.], tot_loss[loss=0.1357, simple_loss=0.21, pruned_loss=0.03073, over 972480.00 frames.], batch size: 22, lr: 1.65e-04
2022-05-07 22:44:57,550 INFO [train.py:715] (2/8) Epoch 13, batch 34150, loss[loss=0.151, simple_loss=0.225, pruned_loss=0.03843, over 4967.00 frames.], tot_loss[loss=0.1357, simple_loss=0.21, pruned_loss=0.03073, over 972072.28 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:45:38,241 INFO [train.py:715] (2/8) Epoch 13, batch 34200, loss[loss=0.1437, simple_loss=0.2122, pruned_loss=0.03761, over 4868.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03128, over 972533.96 frames.], batch size: 34, lr: 1.65e-04
2022-05-07 22:46:18,601 INFO [train.py:715] (2/8) Epoch 13, batch 34250, loss[loss=0.1629, simple_loss=0.228, pruned_loss=0.04887, over 4817.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03099, over 972023.27 frames.], batch size: 13, lr: 1.65e-04
2022-05-07 22:46:59,523 INFO [train.py:715] (2/8) Epoch 13, batch 34300, loss[loss=0.1212, simple_loss=0.1954, pruned_loss=0.02354, over 4811.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03122, over 972201.26 frames.], batch size: 26, lr: 1.65e-04
2022-05-07 22:47:39,593 INFO [train.py:715] (2/8) Epoch 13, batch 34350, loss[loss=0.1433, simple_loss=0.2142, pruned_loss=0.03618, over 4915.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2096, pruned_loss=0.03149, over 971961.49 frames.], batch size: 18, lr: 1.65e-04
2022-05-07 22:48:20,186 INFO [train.py:715] (2/8) Epoch 13, batch 34400, loss[loss=0.1374, simple_loss=0.2136, pruned_loss=0.0306, over 4856.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2089, pruned_loss=0.03115, over 971904.50 frames.], batch size: 32, lr: 1.65e-04
2022-05-07 22:49:01,286 INFO [train.py:715] (2/8) Epoch 13, batch 34450, loss[loss=0.1441, simple_loss=0.2139, pruned_loss=0.03709, over 4973.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.0312, over 971087.71 frames.], batch size: 15, lr: 1.65e-04
2022-05-07 22:49:41,684 INFO [train.py:715] (2/8) Epoch 13, batch 34500, loss[loss=0.1372, simple_loss=0.2108, pruned_loss=0.03183, over 4865.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03115, over 972150.11 frames.], batch size: 16, lr: 1.65e-04
2022-05-07 22:50:21,498 INFO [train.py:715] (2/8) Epoch 13, batch 34550, loss[loss=0.1037, simple_loss=0.1807, pruned_loss=0.0133, over 4750.00 frames.], tot_loss[loss=0.137, simple_loss=0.2107, pruned_loss=0.03163, over 971873.30 frames.], batch size: 19, lr: 1.65e-04
2022-05-07 22:51:01,502 INFO [train.py:715] (2/8) Epoch 13, batch 34600, loss[loss=0.108, simple_loss=0.184, pruned_loss=0.01595, over 4828.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2109, pruned_loss=0.03136, over 972216.52 frames.], batch size: 25, lr: 1.65e-04
2022-05-07 22:51:40,866 INFO [train.py:715] (2/8) Epoch 13, batch 34650, loss[loss=0.1206, simple_loss=0.1925, pruned_loss=0.02434, over 4977.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2105, pruned_loss=0.0315, over 972046.98 frames.], batch size: 35, lr: 1.65e-04
2022-05-07 22:52:20,412 INFO [train.py:715] (2/8) Epoch 13, batch 34700, loss[loss=0.1167, simple_loss=0.1897, pruned_loss=0.02183, over 4966.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03147, over 972198.08 frames.], batch size: 14, lr: 1.65e-04
2022-05-07 22:52:59,363 INFO [train.py:715] (2/8) Epoch 13, batch 34750, loss[loss=0.1494, simple_loss=0.225, pruned_loss=0.03689, over 4943.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2106, pruned_loss=0.03117, over 971957.33 frames.], batch size: 35, lr: 1.65e-04
2022-05-07 22:53:36,112 INFO [train.py:715] (2/8) Epoch 13, batch 34800, loss[loss=0.159, simple_loss=0.2296, pruned_loss=0.04422, over 4949.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.0309, over 972839.19 frames.], batch size: 23, lr: 1.65e-04
2022-05-07 22:54:25,051 INFO [train.py:715] (2/8) Epoch 14, batch 0, loss[loss=0.1385, simple_loss=0.2011, pruned_loss=0.03799, over 4868.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2011, pruned_loss=0.03799, over 4868.00 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 22:55:04,007 INFO [train.py:715] (2/8) Epoch 14, batch 50, loss[loss=0.1199, simple_loss=0.1982, pruned_loss=0.02086, over 4791.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2067, pruned_loss=0.03082, over 219914.73 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 22:55:42,423 INFO [train.py:715] (2/8) Epoch 14, batch 100, loss[loss=0.1442, simple_loss=0.2149, pruned_loss=0.03674, over 4796.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2059, pruned_loss=0.03088, over 386803.97 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 22:56:21,310 INFO [train.py:715] (2/8) Epoch 14, batch 150, loss[loss=0.1288, simple_loss=0.2009, pruned_loss=0.02831, over 4638.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2075, pruned_loss=0.03091, over 517340.82 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 22:56:59,878 INFO [train.py:715] (2/8) Epoch 14, batch 200, loss[loss=0.1684, simple_loss=0.2178, pruned_loss=0.0595, over 4961.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2083, pruned_loss=0.03126, over 618114.17 frames.], batch size: 35, lr: 1.59e-04
2022-05-07 22:57:38,471 INFO [train.py:715] (2/8) Epoch 14, batch 250, loss[loss=0.1723, simple_loss=0.2336, pruned_loss=0.05554, over 4992.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2099, pruned_loss=0.03184, over 696457.04 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 22:58:17,255 INFO [train.py:715] (2/8) Epoch 14, batch 300, loss[loss=0.1784, simple_loss=0.2598, pruned_loss=0.04845, over 4960.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2102, pruned_loss=0.03133, over 757884.13 frames.], batch size: 35, lr: 1.59e-04
2022-05-07 22:58:56,807 INFO [train.py:715] (2/8) Epoch 14, batch 350, loss[loss=0.1257, simple_loss=0.2009, pruned_loss=0.02529, over 4872.00 frames.], tot_loss[loss=0.1358, simple_loss=0.21, pruned_loss=0.03083, over 805341.80 frames.], batch size: 22, lr: 1.59e-04
2022-05-07 22:59:35,351 INFO [train.py:715] (2/8) Epoch 14, batch 400, loss[loss=0.1232, simple_loss=0.1891, pruned_loss=0.02859, over 4821.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.031, over 842102.77 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:00:14,793 INFO [train.py:715] (2/8) Epoch 14, batch 450, loss[loss=0.1389, simple_loss=0.214, pruned_loss=0.03192, over 4650.00 frames.], tot_loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03117, over 871730.88 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:00:54,074 INFO [train.py:715] (2/8) Epoch 14, batch 500, loss[loss=0.1397, simple_loss=0.2131, pruned_loss=0.03313, over 4815.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.0312, over 894467.34 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:01:33,695 INFO [train.py:715] (2/8) Epoch 14, batch 550, loss[loss=0.1149, simple_loss=0.1838, pruned_loss=0.02304, over 4824.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03118, over 910656.81 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:02:12,475 INFO [train.py:715] (2/8) Epoch 14, batch 600, loss[loss=0.1214, simple_loss=0.1885, pruned_loss=0.0271, over 4784.00 frames.], tot_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03139, over 923895.00 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:02:51,126 INFO [train.py:715] (2/8) Epoch 14, batch 650, loss[loss=0.1517, simple_loss=0.2147, pruned_loss=0.04437, over 4869.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03144, over 935309.41 frames.], batch size: 32, lr: 1.59e-04
2022-05-07 23:03:32,631 INFO [train.py:715] (2/8) Epoch 14, batch 700, loss[loss=0.1278, simple_loss=0.2069, pruned_loss=0.02434, over 4967.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2107, pruned_loss=0.03138, over 943886.33 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:04:11,049 INFO [train.py:715] (2/8) Epoch 14, batch 750, loss[loss=0.1448, simple_loss=0.2094, pruned_loss=0.04005, over 4873.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03073, over 949969.43 frames.], batch size: 32, lr: 1.59e-04
2022-05-07 23:04:51,158 INFO [train.py:715] (2/8) Epoch 14, batch 800, loss[loss=0.1385, simple_loss=0.2151, pruned_loss=0.03092, over 4947.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03026, over 954661.44 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:05:30,247 INFO [train.py:715] (2/8) Epoch 14, batch 850, loss[loss=0.1283, simple_loss=0.198, pruned_loss=0.0293, over 4823.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03065, over 958226.12 frames.], batch size: 26, lr: 1.59e-04
2022-05-07 23:06:09,707 INFO [train.py:715] (2/8) Epoch 14, batch 900, loss[loss=0.1475, simple_loss=0.2301, pruned_loss=0.0325, over 4952.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03039, over 960905.51 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:06:48,331 INFO [train.py:715] (2/8) Epoch 14, batch 950, loss[loss=0.1427, simple_loss=0.2283, pruned_loss=0.02855, over 4761.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03077, over 963427.09 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:07:27,882 INFO [train.py:715] (2/8) Epoch 14, batch 1000, loss[loss=0.1299, simple_loss=0.2087, pruned_loss=0.02556, over 4875.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03094, over 966225.28 frames.], batch size: 20, lr: 1.59e-04
2022-05-07 23:08:07,943 INFO [train.py:715] (2/8) Epoch 14, batch 1050, loss[loss=0.1443, simple_loss=0.2259, pruned_loss=0.03142, over 4908.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.0309, over 967344.98 frames.], batch size: 19, lr: 1.59e-04
2022-05-07 23:08:47,255 INFO [train.py:715] (2/8) Epoch 14, batch 1100, loss[loss=0.1293, simple_loss=0.214, pruned_loss=0.02228, over 4807.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03052, over 967523.82 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:09:26,960 INFO [train.py:715] (2/8) Epoch 14, batch 1150, loss[loss=0.1289, simple_loss=0.1987, pruned_loss=0.02956, over 4981.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2076, pruned_loss=0.03041, over 968960.44 frames.], batch size: 35, lr: 1.59e-04
2022-05-07 23:10:07,015 INFO [train.py:715] (2/8) Epoch 14, batch 1200, loss[loss=0.1402, simple_loss=0.2145, pruned_loss=0.03299, over 4873.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03036, over 969702.66 frames.], batch size: 20, lr: 1.59e-04
2022-05-07 23:10:47,172 INFO [train.py:715] (2/8) Epoch 14, batch 1250, loss[loss=0.1352, simple_loss=0.2051, pruned_loss=0.03265, over 4795.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03031, over 969493.54 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:11:26,192 INFO [train.py:715] (2/8) Epoch 14, batch 1300, loss[loss=0.112, simple_loss=0.1864, pruned_loss=0.01886, over 4818.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03028, over 969820.01 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:12:05,713 INFO [train.py:715] (2/8) Epoch 14, batch 1350, loss[loss=0.1468, simple_loss=0.2264, pruned_loss=0.03357, over 4809.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.0304, over 969750.03 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:12:45,083 INFO [train.py:715] (2/8) Epoch 14, batch 1400, loss[loss=0.1602, simple_loss=0.2343, pruned_loss=0.04306, over 4910.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02999, over 970389.61 frames.], batch size: 19, lr: 1.59e-04
2022-05-07 23:13:24,651 INFO [train.py:715] (2/8) Epoch 14, batch 1450, loss[loss=0.1365, simple_loss=0.2059, pruned_loss=0.03361, over 4926.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03055, over 970008.81 frames.], batch size: 23, lr: 1.59e-04
2022-05-07 23:14:04,619 INFO [train.py:715] (2/8) Epoch 14, batch 1500, loss[loss=0.1145, simple_loss=0.1914, pruned_loss=0.01877, over 4881.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03035, over 971149.26 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:14:44,294 INFO [train.py:715] (2/8) Epoch 14, batch 1550, loss[loss=0.1343, simple_loss=0.2137, pruned_loss=0.02743, over 4702.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2094, pruned_loss=0.03042, over 971595.79 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:15:24,191 INFO [train.py:715] (2/8) Epoch 14, batch 1600, loss[loss=0.1144, simple_loss=0.1884, pruned_loss=0.02019, over 4804.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02989, over 971471.72 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:16:03,425 INFO [train.py:715] (2/8) Epoch 14, batch 1650, loss[loss=0.1506, simple_loss=0.2276, pruned_loss=0.03675, over 4901.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2093, pruned_loss=0.03019, over 972639.50 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:16:43,079 INFO [train.py:715] (2/8) Epoch 14, batch 1700, loss[loss=0.1114, simple_loss=0.1901, pruned_loss=0.01639, over 4807.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03024, over 973982.62 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:17:22,565 INFO [train.py:715] (2/8) Epoch 14, batch 1750, loss[loss=0.1258, simple_loss=0.2006, pruned_loss=0.02556, over 4790.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03045, over 973940.39 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:18:02,281 INFO [train.py:715] (2/8) Epoch 14, batch 1800, loss[loss=0.09698, simple_loss=0.1657, pruned_loss=0.01413, over 4802.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03055, over 973207.42 frames.], batch size: 12, lr: 1.59e-04
2022-05-07 23:18:40,641 INFO [train.py:715] (2/8) Epoch 14, batch 1850, loss[loss=0.1019, simple_loss=0.1807, pruned_loss=0.01155, over 4984.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03042, over 973383.75 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:19:19,861 INFO [train.py:715] (2/8) Epoch 14, batch 1900, loss[loss=0.1298, simple_loss=0.1935, pruned_loss=0.03311, over 4711.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03064, over 973376.04 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:19:59,661 INFO [train.py:715] (2/8) Epoch 14, batch 1950, loss[loss=0.1224, simple_loss=0.1979, pruned_loss=0.02344, over 4809.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03033, over 973327.19 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:20:39,807 INFO [train.py:715] (2/8) Epoch 14, batch 2000, loss[loss=0.138, simple_loss=0.2176, pruned_loss=0.0292, over 4897.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03029, over 973111.52 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:21:19,092 INFO [train.py:715] (2/8) Epoch 14, batch 2050, loss[loss=0.1249, simple_loss=0.1966, pruned_loss=0.02664, over 4975.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03041, over 973104.32 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:21:58,532 INFO [train.py:715] (2/8) Epoch 14, batch 2100, loss[loss=0.1137, simple_loss=0.2023, pruned_loss=0.01251, over 4782.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03012, over 973564.31 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:22:38,243 INFO [train.py:715] (2/8) Epoch 14, batch 2150, loss[loss=0.1371, simple_loss=0.2102, pruned_loss=0.03203, over 4961.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03013, over 973283.82 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:23:16,935 INFO [train.py:715] (2/8) Epoch 14, batch 2200, loss[loss=0.1491, simple_loss=0.2189, pruned_loss=0.03968, over 4937.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02974, over 973206.33 frames.], batch size: 18, lr: 1.59e-04
2022-05-07 23:23:55,886 INFO [train.py:715] (2/8) Epoch 14, batch 2250, loss[loss=0.1341, simple_loss=0.2057, pruned_loss=0.03124, over 4801.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03063, over 972319.92 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:24:34,962 INFO [train.py:715] (2/8) Epoch 14, batch 2300, loss[loss=0.1032, simple_loss=0.1725, pruned_loss=0.01691, over 4799.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03043, over 972153.10 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:25:14,125 INFO [train.py:715] (2/8) Epoch 14, batch 2350, loss[loss=0.1476, simple_loss=0.2163, pruned_loss=0.03943, over 4800.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2071, pruned_loss=0.03001, over 972469.95 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:25:53,237 INFO [train.py:715] (2/8) Epoch 14, batch 2400, loss[loss=0.1403, simple_loss=0.2061, pruned_loss=0.03721, over 4843.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03044, over 972405.74 frames.], batch size: 32, lr: 1.59e-04
2022-05-07 23:26:32,283 INFO [train.py:715] (2/8) Epoch 14, batch 2450, loss[loss=0.1315, simple_loss=0.199, pruned_loss=0.03196, over 4695.00 frames.], tot_loss[loss=0.1346, simple_loss=0.208, pruned_loss=0.03065, over 972104.19 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:27:11,605 INFO [train.py:715] (2/8) Epoch 14, batch 2500, loss[loss=0.1138, simple_loss=0.1904, pruned_loss=0.01866, over 4826.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2071, pruned_loss=0.03069, over 971476.41 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:27:50,105 INFO [train.py:715] (2/8) Epoch 14, batch 2550, loss[loss=0.1574, simple_loss=0.2298, pruned_loss=0.04246, over 4914.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2072, pruned_loss=0.03108, over 971500.08 frames.], batch size: 19, lr: 1.59e-04
2022-05-07 23:28:29,684 INFO [train.py:715] (2/8) Epoch 14, batch 2600, loss[loss=0.1474, simple_loss=0.2267, pruned_loss=0.03408, over 4842.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2082, pruned_loss=0.03104, over 970919.33 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:29:09,139 INFO [train.py:715] (2/8) Epoch 14, batch 2650, loss[loss=0.1234, simple_loss=0.1999, pruned_loss=0.02349, over 4946.00 frames.], tot_loss[loss=0.135, simple_loss=0.2082, pruned_loss=0.03092, over 971987.30 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:29:48,493 INFO [train.py:715] (2/8) Epoch 14, batch 2700, loss[loss=0.1211, simple_loss=0.2044, pruned_loss=0.01889, over 4935.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03026, over 972491.10 frames.], batch size: 29, lr: 1.59e-04
2022-05-07 23:30:27,052 INFO [train.py:715] (2/8) Epoch 14, batch 2750, loss[loss=0.1152, simple_loss=0.184, pruned_loss=0.02317, over 4750.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02975, over 973250.92 frames.], batch size: 12, lr: 1.59e-04
2022-05-07 23:31:06,232 INFO [train.py:715] (2/8) Epoch 14, batch 2800, loss[loss=0.1241, simple_loss=0.2039, pruned_loss=0.02212, over 4885.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02965, over 973378.17 frames.], batch size: 38, lr: 1.59e-04
2022-05-07 23:31:45,883 INFO [train.py:715] (2/8) Epoch 14, batch 2850, loss[loss=0.1348, simple_loss=0.2058, pruned_loss=0.03194, over 4758.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02979, over 972749.94 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:32:24,331 INFO [train.py:715] (2/8) Epoch 14, batch 2900, loss[loss=0.1252, simple_loss=0.2038, pruned_loss=0.02327, over 4977.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02996, over 972626.45 frames.], batch size: 39, lr: 1.59e-04
2022-05-07 23:33:06,133 INFO [train.py:715] (2/8) Epoch 14, batch 2950, loss[loss=0.1675, simple_loss=0.2446, pruned_loss=0.04518, over 4824.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2069, pruned_loss=0.03005, over 972755.06 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:33:45,667 INFO [train.py:715] (2/8) Epoch 14, batch 3000, loss[loss=0.1599, simple_loss=0.2313, pruned_loss=0.0443, over 4988.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2072, pruned_loss=0.0302, over 972532.73 frames.], batch size: 31, lr: 1.59e-04
2022-05-07 23:33:45,668 INFO [train.py:733] (2/8) Computing validation loss
2022-05-07 23:33:55,240 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.1052, simple_loss=0.1891, pruned_loss=0.01067, over 914524.00 frames.
2022-05-07 23:34:34,252 INFO [train.py:715] (2/8) Epoch 14, batch 3050, loss[loss=0.1174, simple_loss=0.1969, pruned_loss=0.01893, over 4784.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03067, over 971598.31 frames.], batch size: 12, lr: 1.59e-04
2022-05-07 23:35:14,223 INFO [train.py:715] (2/8) Epoch 14, batch 3100, loss[loss=0.1176, simple_loss=0.1962, pruned_loss=0.01948, over 4904.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.0305, over 971462.63 frames.], batch size: 19, lr: 1.59e-04
2022-05-07 23:35:53,771 INFO [train.py:715] (2/8) Epoch 14, batch 3150, loss[loss=0.1392, simple_loss=0.2146, pruned_loss=0.03193, over 4967.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03081, over 972326.37 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:36:33,461 INFO [train.py:715] (2/8) Epoch 14, batch 3200, loss[loss=0.1317, simple_loss=0.2076, pruned_loss=0.02789, over 4967.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.0311, over 972811.13 frames.], batch size: 39, lr: 1.59e-04
2022-05-07 23:37:14,485 INFO [train.py:715] (2/8) Epoch 14, batch 3250, loss[loss=0.1283, simple_loss=0.2023, pruned_loss=0.02708, over 4798.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.0312, over 971936.95 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:37:54,310 INFO [train.py:715] (2/8) Epoch 14, batch 3300, loss[loss=0.1475, simple_loss=0.224, pruned_loss=0.03547, over 4874.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.0311, over 971658.37 frames.], batch size: 20, lr: 1.59e-04
2022-05-07 23:38:34,437 INFO [train.py:715] (2/8) Epoch 14, batch 3350, loss[loss=0.1345, simple_loss=0.2026, pruned_loss=0.03325, over 4969.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2087, pruned_loss=0.03128, over 971521.76 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:39:15,378 INFO [train.py:715] (2/8) Epoch 14, batch 3400, loss[loss=0.1373, simple_loss=0.2161, pruned_loss=0.02923, over 4889.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2079, pruned_loss=0.03075, over 972437.88 frames.], batch size: 19, lr: 1.59e-04
2022-05-07 23:39:56,032 INFO [train.py:715] (2/8) Epoch 14, batch 3450, loss[loss=0.1245, simple_loss=0.1991, pruned_loss=0.02493, over 4804.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2082, pruned_loss=0.03066, over 972132.47 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:40:35,908 INFO [train.py:715] (2/8) Epoch 14, batch 3500, loss[loss=0.1448, simple_loss=0.2183, pruned_loss=0.03558, over 4818.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03086, over 971535.85 frames.], batch size: 25, lr: 1.59e-04
2022-05-07 23:41:15,987 INFO [train.py:715] (2/8) Epoch 14, batch 3550, loss[loss=0.1392, simple_loss=0.2121, pruned_loss=0.03319, over 4964.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03075, over 971542.01 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:41:56,123 INFO [train.py:715] (2/8) Epoch 14, batch 3600, loss[loss=0.113, simple_loss=0.188, pruned_loss=0.01901, over 4818.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03019, over 971295.92 frames.], batch size: 27, lr: 1.59e-04
2022-05-07 23:42:36,129 INFO [train.py:715] (2/8) Epoch 14, batch 3650, loss[loss=0.1441, simple_loss=0.2259, pruned_loss=0.03112, over 4843.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2075, pruned_loss=0.03017, over 972076.43 frames.], batch size: 20, lr: 1.59e-04
2022-05-07 23:43:16,032 INFO [train.py:715] (2/8) Epoch 14, batch 3700, loss[loss=0.14, simple_loss=0.219, pruned_loss=0.03046, over 4972.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2076, pruned_loss=0.03043, over 972780.46 frames.], batch size: 35, lr: 1.59e-04
2022-05-07 23:43:56,761 INFO [train.py:715] (2/8) Epoch 14, batch 3750, loss[loss=0.1318, simple_loss=0.2026, pruned_loss=0.03046, over 4794.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03036, over 972612.52 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:44:36,924 INFO [train.py:715] (2/8) Epoch 14, batch 3800, loss[loss=0.166, simple_loss=0.2428, pruned_loss=0.04458, over 4912.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03051, over 972124.47 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:45:16,164 INFO [train.py:715] (2/8) Epoch 14, batch 3850, loss[loss=0.1793, simple_loss=0.2449, pruned_loss=0.05683, over 4976.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2081, pruned_loss=0.03081, over 972537.22 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:45:56,644 INFO [train.py:715] (2/8) Epoch 14, batch 3900, loss[loss=0.1238, simple_loss=0.1976, pruned_loss=0.02495, over 4967.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03105, over 972144.57 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:46:37,882 INFO [train.py:715] (2/8) Epoch 14, batch 3950, loss[loss=0.1428, simple_loss=0.2146, pruned_loss=0.03545, over 4965.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03098, over 971599.04 frames.], batch size: 39, lr: 1.59e-04
2022-05-07 23:47:18,820 INFO [train.py:715] (2/8) Epoch 14, batch 4000, loss[loss=0.1513, simple_loss=0.2346, pruned_loss=0.03405, over 4966.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03111, over 972318.92 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:47:59,308 INFO [train.py:715] (2/8) Epoch 14, batch 4050, loss[loss=0.1386, simple_loss=0.2001, pruned_loss=0.03856, over 4862.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03156, over 971989.77 frames.], batch size: 32, lr: 1.59e-04
2022-05-07 23:48:40,124 INFO [train.py:715] (2/8) Epoch 14, batch 4100, loss[loss=0.137, simple_loss=0.2254, pruned_loss=0.02429, over 4984.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2104, pruned_loss=0.03164, over 972331.38 frames.], batch size: 24, lr: 1.59e-04
2022-05-07 23:49:21,557 INFO [train.py:715] (2/8) Epoch 14, batch 4150, loss[loss=0.1466, simple_loss=0.2184, pruned_loss=0.03741, over 4874.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03111, over 972016.96 frames.], batch size: 16, lr: 1.59e-04
2022-05-07 23:50:02,216 INFO [train.py:715] (2/8) Epoch 14, batch 4200, loss[loss=0.1271, simple_loss=0.1975, pruned_loss=0.0284, over 4843.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03103, over 972056.72 frames.], batch size: 30, lr: 1.59e-04
2022-05-07 23:50:43,291 INFO [train.py:715] (2/8) Epoch 14, batch 4250, loss[loss=0.1252, simple_loss=0.2003, pruned_loss=0.02501, over 4725.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03079, over 972382.01 frames.], batch size: 12, lr: 1.59e-04
2022-05-07 23:51:25,174 INFO [train.py:715] (2/8) Epoch 14, batch 4300, loss[loss=0.1427, simple_loss=0.2182, pruned_loss=0.03361, over 4933.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03125, over 971595.53 frames.], batch size: 39, lr: 1.59e-04
2022-05-07 23:52:06,417 INFO [train.py:715] (2/8) Epoch 14, batch 4350, loss[loss=0.1287, simple_loss=0.1987, pruned_loss=0.02933, over 4797.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03089, over 970523.68 frames.], batch size: 17, lr: 1.59e-04
2022-05-07 23:52:46,955 INFO [train.py:715] (2/8) Epoch 14, batch 4400, loss[loss=0.1359, simple_loss=0.2156, pruned_loss=0.02807, over 4693.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03076, over 970595.49 frames.], batch size: 15, lr: 1.59e-04
2022-05-07 23:53:27,637 INFO [train.py:715] (2/8) Epoch 14, batch 4450, loss[loss=0.1631, simple_loss=0.2469, pruned_loss=0.0396, over 4987.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2096, pruned_loss=0.03065, over 970988.40 frames.], batch size: 28, lr: 1.59e-04
2022-05-07 23:54:08,632 INFO [train.py:715] (2/8) Epoch 14, batch 4500, loss[loss=0.1088, simple_loss=0.1827, pruned_loss=0.01746, over 4814.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03079, over 970812.68 frames.], batch size: 21, lr: 1.59e-04
2022-05-07 23:54:48,680 INFO [train.py:715] (2/8) Epoch 14, batch 4550, loss[loss=0.1508, simple_loss=0.2142, pruned_loss=0.04372, over 4787.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03056, over 970744.57 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:55:27,632 INFO [train.py:715] (2/8) Epoch 14, batch 4600, loss[loss=0.1187, simple_loss=0.1939, pruned_loss=0.02174, over 4989.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03104, over 971149.27 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:56:08,483 INFO [train.py:715] (2/8) Epoch 14, batch 4650, loss[loss=0.1155, simple_loss=0.1981, pruned_loss=0.01649, over 4836.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03099, over 970848.78 frames.], batch size: 13, lr: 1.59e-04
2022-05-07 23:56:48,196 INFO [train.py:715] (2/8) Epoch 14, batch 4700, loss[loss=0.1434, simple_loss=0.2069, pruned_loss=0.03991, over 4976.00 frames.], tot_loss[loss=0.1356, simple_loss=0.209, pruned_loss=0.03113, over 971351.97 frames.], batch size: 14, lr: 1.59e-04
2022-05-07 23:57:26,882 INFO [train.py:715] (2/8) Epoch 14, batch 4750, loss[loss=0.1581, simple_loss=0.2415, pruned_loss=0.03731, over 4822.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03089, over 971176.84 frames.], batch size: 26, lr: 1.58e-04
2022-05-07 23:58:06,245 INFO [train.py:715] (2/8) Epoch 14, batch 4800, loss[loss=0.1512, simple_loss=0.2339, pruned_loss=0.03427, over 4977.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03111, over 972172.07 frames.], batch size: 15, lr: 1.58e-04
2022-05-07 23:58:46,079 INFO [train.py:715] (2/8) Epoch 14, batch 4850, loss[loss=0.1341, simple_loss=0.2032, pruned_loss=0.0325, over 4887.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03106, over 971695.91 frames.], batch size: 32, lr: 1.58e-04
2022-05-07 23:59:25,004 INFO [train.py:715] (2/8) Epoch 14, batch 4900, loss[loss=0.1629, simple_loss=0.2293, pruned_loss=0.04825, over 4931.00 frames.], tot_loss[loss=0.136, simple_loss=0.2099, pruned_loss=0.03109, over 971666.03 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:00:04,154 INFO [train.py:715] (2/8) Epoch 14, batch 4950, loss[loss=0.1213, simple_loss=0.1921, pruned_loss=0.02523, over 4812.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03087, over 972349.76 frames.], batch size: 26, lr: 1.58e-04
2022-05-08 00:00:44,230 INFO [train.py:715] (2/8) Epoch 14, batch 5000, loss[loss=0.126, simple_loss=0.2026, pruned_loss=0.02468, over 4887.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2102, pruned_loss=0.03121, over 972609.17 frames.], batch size: 22, lr: 1.58e-04
2022-05-08 00:01:23,509 INFO [train.py:715] (2/8) Epoch 14, batch 5050, loss[loss=0.1685, simple_loss=0.2251, pruned_loss=0.05598, over 4966.00 frames.], tot_loss[loss=0.1374, simple_loss=0.211, pruned_loss=0.0319, over 972156.18 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:02:02,197 INFO [train.py:715] (2/8) Epoch 14, batch 5100, loss[loss=0.1478, simple_loss=0.2127, pruned_loss=0.04142, over 4810.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2107, pruned_loss=0.03147, over 971691.83 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:02:41,795 INFO [train.py:715] (2/8) Epoch 14, batch 5150, loss[loss=0.1662, simple_loss=0.2377, pruned_loss=0.0473, over 4755.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03115, over 971270.17 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:03:21,427 INFO [train.py:715] (2/8) Epoch 14, batch 5200, loss[loss=0.1802, simple_loss=0.2462, pruned_loss=0.05709, over 4704.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03075, over 971793.89 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:03:59,951 INFO [train.py:715] (2/8) Epoch 14, batch 5250, loss[loss=0.1344, simple_loss=0.2009, pruned_loss=0.0339, over 4842.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03096, over 971540.24 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:04:38,446 INFO [train.py:715] (2/8) Epoch 14, batch 5300, loss[loss=0.1206, simple_loss=0.1903, pruned_loss=0.02545, over 4882.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2086, pruned_loss=0.03089, over 971263.08 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:05:17,626 INFO [train.py:715] (2/8) Epoch 14, batch 5350, loss[loss=0.1201, simple_loss=0.2008, pruned_loss=0.01965, over 4942.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03087, over 971974.77 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 00:05:56,199 INFO [train.py:715] (2/8) Epoch 14, batch 5400, loss[loss=0.1579, simple_loss=0.223, pruned_loss=0.0464, over 4928.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03085, over 971341.76 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:06:34,704 INFO [train.py:715] (2/8) Epoch 14, batch 5450, loss[loss=0.1438, simple_loss=0.2145, pruned_loss=0.03651, over 4886.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03076, over 971281.41 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:07:13,539 INFO [train.py:715] (2/8) Epoch 14, batch 5500, loss[loss=0.1373, simple_loss=0.2054, pruned_loss=0.03461, over 4987.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2094, pruned_loss=0.03114, over 971279.73 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 00:07:53,217 INFO [train.py:715] (2/8) Epoch 14, batch 5550, loss[loss=0.127, simple_loss=0.2007, pruned_loss=0.02671, over 4924.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03076, over 970324.53 frames.], batch size: 23, lr: 1.58e-04
2022-05-08 00:08:31,530 INFO [train.py:715] (2/8) Epoch 14, batch 5600, loss[loss=0.1447, simple_loss=0.2238, pruned_loss=0.03278, over 4904.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2095, pruned_loss=0.03043, over 971094.25 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:09:10,033 INFO [train.py:715] (2/8) Epoch 14, batch 5650, loss[loss=0.1176, simple_loss=0.2021, pruned_loss=0.01659, over 4797.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2098, pruned_loss=0.03028, over 971430.20 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:09:49,145 INFO [train.py:715] (2/8) Epoch 14, batch 5700, loss[loss=0.1253, simple_loss=0.1985, pruned_loss=0.02609, over 4742.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2099, pruned_loss=0.03045, over 970887.92 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 00:10:27,420 INFO [train.py:715] (2/8) Epoch 14, batch 5750, loss[loss=0.1507, simple_loss=0.2211, pruned_loss=0.04014, over 4856.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2101, pruned_loss=0.03039, over 971443.35 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 00:11:05,798 INFO [train.py:715] (2/8) Epoch 14, batch 5800, loss[loss=0.1371, simple_loss=0.2033, pruned_loss=0.03547, over 4774.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2096, pruned_loss=0.03049, over 971193.40 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:11:44,415 INFO [train.py:715] (2/8) Epoch 14, batch 5850, loss[loss=0.1283, simple_loss=0.2056, pruned_loss=0.02553, over 4814.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2097, pruned_loss=0.03027, over 972074.39 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 00:12:23,190 INFO [train.py:715] (2/8) Epoch 14, batch 5900, loss[loss=0.1086, simple_loss=0.1935, pruned_loss=0.01186, over 4812.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2083, pruned_loss=0.0295, over 971471.83 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 00:13:02,944 INFO [train.py:715] (2/8) Epoch 14, batch 5950, loss[loss=0.1663, simple_loss=0.2245, pruned_loss=0.05408, over 4937.00 frames.], tot_loss[loss=0.1355, simple_loss=0.21, pruned_loss=0.03055, over 972169.25 frames.], batch size: 35, lr: 1.58e-04
2022-05-08 00:13:42,636 INFO [train.py:715] (2/8) Epoch 14, batch 6000, loss[loss=0.1042, simple_loss=0.1762, pruned_loss=0.01607, over 4641.00 frames.], tot_loss[loss=0.135, simple_loss=0.2093, pruned_loss=0.03039, over 972907.29 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:13:42,637 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 00:13:52,503 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.105, simple_loss=0.1888, pruned_loss=0.01057, over 914524.00 frames.
2022-05-08 00:14:31,596 INFO [train.py:715] (2/8) Epoch 14, batch 6050, loss[loss=0.1694, simple_loss=0.2327, pruned_loss=0.05303, over 4742.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2094, pruned_loss=0.03047, over 972524.89 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:15:10,773 INFO [train.py:715] (2/8) Epoch 14, batch 6100, loss[loss=0.1484, simple_loss=0.2224, pruned_loss=0.03717, over 4954.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03053, over 972165.89 frames.], batch size: 24, lr: 1.58e-04
2022-05-08 00:15:50,806 INFO [train.py:715] (2/8) Epoch 14, batch 6150, loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02979, over 4824.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03088, over 972103.96 frames.], batch size: 26, lr: 1.58e-04
2022-05-08 00:16:30,398 INFO [train.py:715] (2/8) Epoch 14, batch 6200, loss[loss=0.1416, simple_loss=0.2023, pruned_loss=0.04043, over 4771.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03142, over 972437.56 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:17:10,266 INFO [train.py:715] (2/8) Epoch 14, batch 6250, loss[loss=0.1361, simple_loss=0.2049, pruned_loss=0.03368, over 4875.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03088, over 972824.77 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:17:49,641 INFO [train.py:715] (2/8) Epoch 14, batch 6300, loss[loss=0.1491, simple_loss=0.2184, pruned_loss=0.03986, over 4769.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.03065, over 972597.03 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:18:29,670 INFO [train.py:715] (2/8) Epoch 14, batch 6350, loss[loss=0.1314, simple_loss=0.2098, pruned_loss=0.02652, over 4945.00 frames.], tot_loss[loss=0.1343, simple_loss=0.208, pruned_loss=0.03034, over 972532.76 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:19:09,447 INFO [train.py:715] (2/8) Epoch 14, batch 6400, loss[loss=0.1565, simple_loss=0.2211, pruned_loss=0.04598, over 4955.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03035, over 972474.87 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:19:49,533 INFO [train.py:715] (2/8) Epoch 14, batch 6450, loss[loss=0.1353, simple_loss=0.1981, pruned_loss=0.03625, over 4843.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.0308, over 973151.98 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 00:20:29,521 INFO [train.py:715] (2/8) Epoch 14, batch 6500, loss[loss=0.1644, simple_loss=0.2398, pruned_loss=0.04457, over 4753.00 frames.], tot_loss[loss=0.1357, simple_loss=0.209, pruned_loss=0.03119, over 972701.39 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 00:21:09,180 INFO [train.py:715] (2/8) Epoch 14, batch 6550, loss[loss=0.1233, simple_loss=0.1864, pruned_loss=0.03012, over 4787.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2082, pruned_loss=0.0311, over 972944.51 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 00:21:49,050 INFO [train.py:715] (2/8) Epoch 14, batch 6600, loss[loss=0.133, simple_loss=0.2135, pruned_loss=0.02625, over 4808.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03094, over 972325.37 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:22:29,235 INFO [train.py:715] (2/8) Epoch 14, batch 6650, loss[loss=0.1223, simple_loss=0.1909, pruned_loss=0.02686, over 4877.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2092, pruned_loss=0.03112, over 971498.74 frames.], batch size: 20, lr: 1.58e-04
2022-05-08 00:23:08,970 INFO [train.py:715] (2/8) Epoch 14, batch 6700, loss[loss=0.1112, simple_loss=0.1771, pruned_loss=0.02267, over 4644.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03078, over 971126.62 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:23:48,850 INFO [train.py:715] (2/8) Epoch 14, batch 6750, loss[loss=0.1202, simple_loss=0.2007, pruned_loss=0.01987, over 4986.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03103, over 971083.47 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 00:24:28,845 INFO [train.py:715] (2/8) Epoch 14, batch 6800, loss[loss=0.1215, simple_loss=0.1955, pruned_loss=0.02378, over 4905.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.03136, over 971008.46 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:25:08,851 INFO [train.py:715] (2/8) Epoch 14, batch 6850, loss[loss=0.1586, simple_loss=0.2417, pruned_loss=0.03775, over 4897.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2113, pruned_loss=0.03203, over 970829.08 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:25:48,270 INFO [train.py:715] (2/8) Epoch 14, batch 6900, loss[loss=0.1573, simple_loss=0.2188, pruned_loss=0.04794, over 4754.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2114, pruned_loss=0.03193, over 971364.40 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:26:28,463 INFO [train.py:715] (2/8) Epoch 14, batch 6950, loss[loss=0.1312, simple_loss=0.2138, pruned_loss=0.02433, over 4790.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2115, pruned_loss=0.03214, over 970371.74 frames.], batch size: 24, lr: 1.58e-04
2022-05-08 00:27:08,572 INFO [train.py:715] (2/8) Epoch 14, batch 7000, loss[loss=0.1638, simple_loss=0.2268, pruned_loss=0.05035, over 4918.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03207, over 970510.57 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:27:48,556 INFO [train.py:715] (2/8) Epoch 14, batch 7050, loss[loss=0.1382, simple_loss=0.2254, pruned_loss=0.02553, over 4827.00 frames.], tot_loss[loss=0.1372, simple_loss=0.211, pruned_loss=0.03166, over 971451.52 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:28:27,885 INFO [train.py:715] (2/8) Epoch 14, batch 7100, loss[loss=0.1244, simple_loss=0.1969, pruned_loss=0.02595, over 4830.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2103, pruned_loss=0.03142, over 971904.93 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:29:07,966 INFO [train.py:715] (2/8) Epoch 14, batch 7150, loss[loss=0.1145, simple_loss=0.1962, pruned_loss=0.01635, over 4759.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2105, pruned_loss=0.03154, over 972256.48 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:29:48,176 INFO [train.py:715] (2/8) Epoch 14, batch 7200, loss[loss=0.1217, simple_loss=0.1931, pruned_loss=0.02514, over 4976.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2101, pruned_loss=0.03157, over 971683.61 frames.], batch size: 33, lr: 1.58e-04
2022-05-08 00:30:28,019 INFO [train.py:715] (2/8) Epoch 14, batch 7250, loss[loss=0.1326, simple_loss=0.2029, pruned_loss=0.03113, over 4850.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2097, pruned_loss=0.03153, over 971628.17 frames.], batch size: 20, lr: 1.58e-04
2022-05-08 00:31:08,145 INFO [train.py:715] (2/8) Epoch 14, batch 7300, loss[loss=0.1205, simple_loss=0.1836, pruned_loss=0.02869, over 4784.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03126, over 971672.86 frames.], batch size: 12, lr: 1.58e-04
2022-05-08 00:31:48,271 INFO [train.py:715] (2/8) Epoch 14, batch 7350, loss[loss=0.1307, simple_loss=0.204, pruned_loss=0.02872, over 4759.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03121, over 972109.46 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:32:28,615 INFO [train.py:715] (2/8) Epoch 14, batch 7400, loss[loss=0.1216, simple_loss=0.1908, pruned_loss=0.02616, over 4760.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2103, pruned_loss=0.03157, over 972011.53 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:33:08,065 INFO [train.py:715] (2/8) Epoch 14, batch 7450, loss[loss=0.1295, simple_loss=0.2165, pruned_loss=0.02125, over 4982.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2096, pruned_loss=0.03136, over 971790.05 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:33:47,757 INFO [train.py:715] (2/8) Epoch 14, batch 7500, loss[loss=0.1675, simple_loss=0.2299, pruned_loss=0.05253, over 4798.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03087, over 971791.74 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:34:27,413 INFO [train.py:715] (2/8) Epoch 14, batch 7550, loss[loss=0.1206, simple_loss=0.1956, pruned_loss=0.02283, over 4944.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03029, over 971352.25 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 00:35:06,412 INFO [train.py:715] (2/8) Epoch 14, batch 7600, loss[loss=0.1417, simple_loss=0.2235, pruned_loss=0.02994, over 4917.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02991, over 971023.18 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:35:46,366 INFO [train.py:715] (2/8) Epoch 14, batch 7650, loss[loss=0.129, simple_loss=0.2041, pruned_loss=0.02689, over 4827.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.0297, over 971332.43 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:36:25,269 INFO [train.py:715] (2/8) Epoch 14, batch 7700, loss[loss=0.12, simple_loss=0.191, pruned_loss=0.02447, over 4817.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02974, over 971161.25 frames.], batch size: 27, lr: 1.58e-04
2022-05-08 00:37:05,577 INFO [train.py:715] (2/8) Epoch 14, batch 7750, loss[loss=0.1198, simple_loss=0.2018, pruned_loss=0.01883, over 4986.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03017, over 971631.97 frames.], batch size: 26, lr: 1.58e-04
2022-05-08 00:37:44,379 INFO [train.py:715] (2/8) Epoch 14, batch 7800, loss[loss=0.1472, simple_loss=0.2168, pruned_loss=0.03876, over 4889.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03061, over 971045.79 frames.], batch size: 22, lr: 1.58e-04
2022-05-08 00:38:23,469 INFO [train.py:715] (2/8) Epoch 14, batch 7850, loss[loss=0.1325, simple_loss=0.2113, pruned_loss=0.02679, over 4790.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03068, over 971370.61 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:39:03,274 INFO [train.py:715] (2/8) Epoch 14, batch 7900, loss[loss=0.1397, simple_loss=0.2159, pruned_loss=0.03175, over 4763.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2101, pruned_loss=0.03141, over 971901.87 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:39:42,080 INFO [train.py:715] (2/8) Epoch 14, batch 7950, loss[loss=0.12, simple_loss=0.1957, pruned_loss=0.02213, over 4759.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03126, over 972052.23 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:40:21,690 INFO [train.py:715] (2/8) Epoch 14, batch 8000, loss[loss=0.1406, simple_loss=0.2214, pruned_loss=0.02988, over 4934.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03071, over 972519.02 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 00:41:00,527 INFO [train.py:715] (2/8) Epoch 14, batch 8050, loss[loss=0.107, simple_loss=0.1843, pruned_loss=0.01485, over 4810.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03065, over 972359.34 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 00:41:40,052 INFO [train.py:715] (2/8) Epoch 14, batch 8100, loss[loss=0.1626, simple_loss=0.2407, pruned_loss=0.04227, over 4912.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03052, over 973582.59 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:42:18,787 INFO [train.py:715] (2/8) Epoch 14, batch 8150, loss[loss=0.1167, simple_loss=0.1968, pruned_loss=0.01827, over 4957.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03094, over 972950.20 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:42:58,274 INFO [train.py:715] (2/8) Epoch 14, batch 8200, loss[loss=0.128, simple_loss=0.1973, pruned_loss=0.02937, over 4869.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03015, over 972507.10 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 00:43:37,713 INFO [train.py:715] (2/8) Epoch 14, batch 8250, loss[loss=0.1661, simple_loss=0.2446, pruned_loss=0.04381, over 4971.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03084, over 972394.98 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 00:44:17,184 INFO [train.py:715] (2/8) Epoch 14, batch 8300, loss[loss=0.1295, simple_loss=0.2004, pruned_loss=0.02926, over 4891.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2078, pruned_loss=0.03052, over 971978.24 frames.], batch size: 22, lr: 1.58e-04
2022-05-08 00:44:56,125 INFO [train.py:715] (2/8) Epoch 14, batch 8350, loss[loss=0.1443, simple_loss=0.222, pruned_loss=0.03329, over 4915.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.03064, over 972712.28 frames.], batch size: 23, lr: 1.58e-04
2022-05-08 00:45:35,325 INFO [train.py:715] (2/8) Epoch 14, batch 8400, loss[loss=0.1606, simple_loss=0.2266, pruned_loss=0.04726, over 4774.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03065, over 972402.70 frames.], batch size: 14, lr: 1.58e-04
2022-05-08 00:46:14,806 INFO [train.py:715] (2/8) Epoch 14, batch 8450, loss[loss=0.117, simple_loss=0.1783, pruned_loss=0.02789, over 4847.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.03066, over 972236.70 frames.], batch size: 12, lr: 1.58e-04
2022-05-08 00:46:53,357 INFO [train.py:715] (2/8) Epoch 14, batch 8500, loss[loss=0.1165, simple_loss=0.1886, pruned_loss=0.02215, over 4946.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03021, over 972200.58 frames.], batch size: 23, lr: 1.58e-04
2022-05-08 00:47:32,470 INFO [train.py:715] (2/8) Epoch 14, batch 8550, loss[loss=0.1522, simple_loss=0.2252, pruned_loss=0.03959, over 4813.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03126, over 972936.62 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 00:48:13,442 INFO [train.py:715] (2/8) Epoch 14, batch 8600, loss[loss=0.1782, simple_loss=0.2557, pruned_loss=0.05036, over 4763.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.0308, over 972101.52 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:48:52,732 INFO [train.py:715] (2/8) Epoch 14, batch 8650, loss[loss=0.1431, simple_loss=0.2175, pruned_loss=0.03435, over 4932.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03113, over 971759.68 frames.], batch size: 23, lr: 1.58e-04
2022-05-08 00:49:34,158 INFO [train.py:715] (2/8) Epoch 14, batch 8700, loss[loss=0.1189, simple_loss=0.1936, pruned_loss=0.02206, over 4820.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.0309, over 972510.69 frames.], batch size: 27, lr: 1.58e-04
2022-05-08 00:50:13,527 INFO [train.py:715] (2/8) Epoch 14, batch 8750, loss[loss=0.1048, simple_loss=0.18, pruned_loss=0.01474, over 4907.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.03045, over 972639.56 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:50:53,246 INFO [train.py:715] (2/8) Epoch 14, batch 8800, loss[loss=0.1358, simple_loss=0.2112, pruned_loss=0.0302, over 4790.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03083, over 973494.09 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:51:32,823 INFO [train.py:715] (2/8) Epoch 14, batch 8850, loss[loss=0.1358, simple_loss=0.2163, pruned_loss=0.02768, over 4936.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03083, over 973052.61 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 00:52:13,346 INFO [train.py:715] (2/8) Epoch 14, batch 8900, loss[loss=0.1264, simple_loss=0.1936, pruned_loss=0.02955, over 4753.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03062, over 972424.34 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 00:52:53,214 INFO [train.py:715] (2/8) Epoch 14, batch 8950, loss[loss=0.1459, simple_loss=0.2193, pruned_loss=0.03621, over 4779.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.03049, over 972538.27 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 00:53:33,011 INFO [train.py:715] (2/8) Epoch 14, batch 9000, loss[loss=0.1194, simple_loss=0.1986, pruned_loss=0.02007, over 4925.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02981, over 972789.98 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 00:53:33,012 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 00:53:47,940 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.1052, simple_loss=0.189, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 00:54:27,482 INFO [train.py:715] (2/8) Epoch 14, batch 9050, loss[loss=0.1269, simple_loss=0.203, pruned_loss=0.02541, over 4751.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02974, over 972669.72 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:55:07,804 INFO [train.py:715] (2/8) Epoch 14, batch 9100, loss[loss=0.1398, simple_loss=0.2247, pruned_loss=0.02743, over 4989.00 frames.], tot_loss[loss=0.134, simple_loss=0.2085, pruned_loss=0.02975, over 973128.84 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 00:55:47,312 INFO [train.py:715] (2/8) Epoch 14, batch 9150, loss[loss=0.1265, simple_loss=0.2017, pruned_loss=0.02565, over 4638.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03021, over 972060.68 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 00:56:27,173 INFO [train.py:715] (2/8) Epoch 14, batch 9200, loss[loss=0.1229, simple_loss=0.2046, pruned_loss=0.02059, over 4942.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02988, over 972055.59 frames.], batch size: 23, lr: 1.58e-04
2022-05-08 00:57:06,889 INFO [train.py:715] (2/8) Epoch 14, batch 9250, loss[loss=0.1189, simple_loss=0.1858, pruned_loss=0.02601, over 4758.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03017, over 971911.52 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:57:46,610 INFO [train.py:715] (2/8) Epoch 14, batch 9300, loss[loss=0.1145, simple_loss=0.1861, pruned_loss=0.02148, over 4786.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03043, over 972669.92 frames.], batch size: 17, lr: 1.58e-04
2022-05-08 00:58:26,526 INFO [train.py:715] (2/8) Epoch 14, batch 9350, loss[loss=0.1617, simple_loss=0.2326, pruned_loss=0.04546, over 4909.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03098, over 971375.06 frames.], batch size: 39, lr: 1.58e-04
2022-05-08 00:59:06,678 INFO [train.py:715] (2/8) Epoch 14, batch 9400, loss[loss=0.1071, simple_loss=0.186, pruned_loss=0.01411, over 4876.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03102, over 971700.79 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 00:59:46,297 INFO [train.py:715] (2/8) Epoch 14, batch 9450, loss[loss=0.1107, simple_loss=0.1887, pruned_loss=0.01629, over 4925.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03037, over 971892.96 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 01:00:26,056 INFO [train.py:715] (2/8) Epoch 14, batch 9500, loss[loss=0.1461, simple_loss=0.203, pruned_loss=0.04461, over 4743.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03035, over 971399.06 frames.], batch size: 12, lr: 1.58e-04
2022-05-08 01:01:05,842 INFO [train.py:715] (2/8) Epoch 14, batch 9550, loss[loss=0.1225, simple_loss=0.1916, pruned_loss=0.02673, over 4824.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03009, over 972545.93 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 01:01:45,999 INFO [train.py:715] (2/8) Epoch 14, batch 9600, loss[loss=0.1458, simple_loss=0.2043, pruned_loss=0.04365, over 4707.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03054, over 972621.73 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:02:25,431 INFO [train.py:715] (2/8) Epoch 14, batch 9650, loss[loss=0.1599, simple_loss=0.2313, pruned_loss=0.0442, over 4743.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2089, pruned_loss=0.03008, over 972509.83 frames.], batch size: 16, lr: 1.58e-04
2022-05-08 01:03:05,454 INFO [train.py:715] (2/8) Epoch 14, batch 9700, loss[loss=0.12, simple_loss=0.1922, pruned_loss=0.02391, over 4881.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03046, over 972694.97 frames.], batch size: 22, lr: 1.58e-04
2022-05-08 01:03:45,042 INFO [train.py:715] (2/8) Epoch 14, batch 9750, loss[loss=0.1174, simple_loss=0.1888, pruned_loss=0.02303, over 4918.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03046, over 973319.41 frames.], batch size: 23, lr: 1.58e-04
2022-05-08 01:04:25,346 INFO [train.py:715] (2/8) Epoch 14, batch 9800, loss[loss=0.1266, simple_loss=0.2033, pruned_loss=0.0249, over 4988.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03093, over 973616.43 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 01:05:04,567 INFO [train.py:715] (2/8) Epoch 14, batch 9850, loss[loss=0.1126, simple_loss=0.1828, pruned_loss=0.02119, over 4910.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03113, over 973562.11 frames.], batch size: 29, lr: 1.58e-04
2022-05-08 01:05:44,642 INFO [train.py:715] (2/8) Epoch 14, batch 9900, loss[loss=0.1412, simple_loss=0.2127, pruned_loss=0.03488, over 4817.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03076, over 973617.65 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 01:06:24,622 INFO [train.py:715] (2/8) Epoch 14, batch 9950, loss[loss=0.1014, simple_loss=0.1703, pruned_loss=0.01622, over 4778.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03083, over 973495.82 frames.], batch size: 12, lr: 1.58e-04
2022-05-08 01:07:03,946 INFO [train.py:715] (2/8) Epoch 14, batch 10000, loss[loss=0.1285, simple_loss=0.199, pruned_loss=0.02901, over 4882.00 frames.], tot_loss[loss=0.136, simple_loss=0.2099, pruned_loss=0.03104, over 973883.03 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 01:07:43,993 INFO [train.py:715] (2/8) Epoch 14, batch 10050, loss[loss=0.1187, simple_loss=0.1964, pruned_loss=0.02054, over 4852.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03037, over 972979.49 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 01:08:23,517 INFO [train.py:715] (2/8) Epoch 14, batch 10100, loss[loss=0.14, simple_loss=0.2125, pruned_loss=0.03374, over 4955.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2091, pruned_loss=0.0301, over 973770.27 frames.], batch size: 35, lr: 1.58e-04
2022-05-08 01:09:03,293 INFO [train.py:715] (2/8) Epoch 14, batch 10150, loss[loss=0.1164, simple_loss=0.1885, pruned_loss=0.02214, over 4901.00 frames.], tot_loss[loss=0.135, simple_loss=0.2092, pruned_loss=0.03044, over 973638.10 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 01:09:42,488 INFO [train.py:715] (2/8) Epoch 14, batch 10200, loss[loss=0.1389, simple_loss=0.2206, pruned_loss=0.02859, over 4988.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03059, over 971686.63 frames.], batch size: 28, lr: 1.58e-04
2022-05-08 01:10:22,735 INFO [train.py:715] (2/8) Epoch 14, batch 10250, loss[loss=0.115, simple_loss=0.1984, pruned_loss=0.01583, over 4976.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.03107, over 971670.99 frames.], batch size: 28, lr: 1.58e-04
2022-05-08 01:11:02,457 INFO [train.py:715] (2/8) Epoch 14, batch 10300, loss[loss=0.1094, simple_loss=0.1793, pruned_loss=0.01969, over 4854.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.03094, over 971419.21 frames.], batch size: 13, lr: 1.58e-04
2022-05-08 01:11:41,907 INFO [train.py:715] (2/8) Epoch 14, batch 10350, loss[loss=0.1817, simple_loss=0.2352, pruned_loss=0.06408, over 4861.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.0311, over 970839.94 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 01:12:22,109 INFO [train.py:715] (2/8) Epoch 14, batch 10400, loss[loss=0.1144, simple_loss=0.1949, pruned_loss=0.01694, over 4802.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03036, over 971730.45 frames.], batch size: 21, lr: 1.58e-04
2022-05-08 01:13:01,497 INFO [train.py:715] (2/8) Epoch 14, batch 10450, loss[loss=0.133, simple_loss=0.2076, pruned_loss=0.02916, over 4834.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03076, over 972471.04 frames.], batch size: 30, lr: 1.58e-04
2022-05-08 01:13:41,730 INFO [train.py:715] (2/8) Epoch 14, batch 10500, loss[loss=0.1128, simple_loss=0.1921, pruned_loss=0.01681, over 4826.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03042, over 972044.07 frames.], batch size: 26, lr: 1.58e-04
2022-05-08 01:14:21,001 INFO [train.py:715] (2/8) Epoch 14, batch 10550, loss[loss=0.1205, simple_loss=0.1926, pruned_loss=0.02423, over 4752.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.0306, over 972526.40 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 01:15:01,261 INFO [train.py:715] (2/8) Epoch 14, batch 10600, loss[loss=0.1439, simple_loss=0.2195, pruned_loss=0.03415, over 4808.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2099, pruned_loss=0.03084, over 971557.85 frames.], batch size: 25, lr: 1.58e-04
2022-05-08 01:15:40,590 INFO [train.py:715] (2/8) Epoch 14, batch 10650, loss[loss=0.1298, simple_loss=0.1911, pruned_loss=0.03423, over 4795.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03099, over 971141.77 frames.], batch size: 12, lr: 1.58e-04
2022-05-08 01:16:19,719 INFO [train.py:715] (2/8) Epoch 14, batch 10700, loss[loss=0.1821, simple_loss=0.2513, pruned_loss=0.05649, over 4932.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.031, over 970799.10 frames.], batch size: 18, lr: 1.58e-04
2022-05-08 01:16:58,897 INFO [train.py:715] (2/8) Epoch 14, batch 10750, loss[loss=0.1407, simple_loss=0.2099, pruned_loss=0.03574, over 4986.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03101, over 972216.71 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:17:38,326 INFO [train.py:715] (2/8) Epoch 14, batch 10800, loss[loss=0.1482, simple_loss=0.2138, pruned_loss=0.04132, over 4874.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03052, over 972524.39 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 01:18:17,863 INFO [train.py:715] (2/8) Epoch 14, batch 10850, loss[loss=0.1796, simple_loss=0.2361, pruned_loss=0.06158, over 4853.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03074, over 972932.20 frames.], batch size: 30, lr: 1.58e-04
2022-05-08 01:18:56,530 INFO [train.py:715] (2/8) Epoch 14, batch 10900, loss[loss=0.1417, simple_loss=0.2133, pruned_loss=0.0351, over 4983.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03093, over 973325.23 frames.], batch size: 15, lr: 1.58e-04
2022-05-08 01:19:36,725 INFO [train.py:715] (2/8) Epoch 14, batch 10950, loss[loss=0.114, simple_loss=0.1958, pruned_loss=0.01614, over 4756.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03039, over 972878.48 frames.], batch size: 19, lr: 1.58e-04
2022-05-08 01:20:17,492 INFO [train.py:715] (2/8) Epoch 14, batch 11000, loss[loss=0.1683, simple_loss=0.2243, pruned_loss=0.05613, over 4853.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03003, over 973380.87 frames.], batch size: 32, lr: 1.58e-04
2022-05-08 01:20:56,620 INFO [train.py:715] (2/8) Epoch 14, batch 11050, loss[loss=0.1312, simple_loss=0.2038, pruned_loss=0.02931, over 4817.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02996, over 973169.11 frames.], batch size: 25, lr: 1.57e-04
2022-05-08 01:21:37,679 INFO [train.py:715] (2/8) Epoch 14, batch 11100, loss[loss=0.1527, simple_loss=0.2254, pruned_loss=0.03997, over 4845.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2072, pruned_loss=0.03003, over 973481.02 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:22:18,236 INFO [train.py:715] (2/8) Epoch 14, batch 11150, loss[loss=0.1321, simple_loss=0.2009, pruned_loss=0.03165, over 4955.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2068, pruned_loss=0.03017, over 973764.16 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:22:58,451 INFO [train.py:715] (2/8) Epoch 14, batch 11200, loss[loss=0.1275, simple_loss=0.1998, pruned_loss=0.02761, over 4824.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03057, over 972925.21 frames.], batch size: 27, lr: 1.57e-04
2022-05-08 01:23:37,876 INFO [train.py:715] (2/8) Epoch 14, batch 11250, loss[loss=0.1637, simple_loss=0.2327, pruned_loss=0.04734, over 4794.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03087, over 973257.42 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 01:24:18,305 INFO [train.py:715] (2/8) Epoch 14, batch 11300, loss[loss=0.163, simple_loss=0.2296, pruned_loss=0.04822, over 4906.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03059, over 973909.34 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:24:58,564 INFO [train.py:715] (2/8) Epoch 14, batch 11350, loss[loss=0.1505, simple_loss=0.227, pruned_loss=0.03698, over 4773.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2108, pruned_loss=0.03083, over 974549.04 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 01:25:37,746 INFO [train.py:715] (2/8) Epoch 14, batch 11400, loss[loss=0.1249, simple_loss=0.1911, pruned_loss=0.02937, over 4795.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2105, pruned_loss=0.03093, over 973556.07 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:26:18,740 INFO [train.py:715] (2/8) Epoch 14, batch 11450, loss[loss=0.13, simple_loss=0.1989, pruned_loss=0.03058, over 4877.00 frames.], tot_loss[loss=0.135, simple_loss=0.2097, pruned_loss=0.03015, over 973215.03 frames.], batch size: 22, lr: 1.57e-04
2022-05-08 01:26:59,115 INFO [train.py:715] (2/8) Epoch 14, batch 11500, loss[loss=0.1281, simple_loss=0.1898, pruned_loss=0.0332, over 4855.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2094, pruned_loss=0.02999, over 973944.02 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:27:39,027 INFO [train.py:715] (2/8) Epoch 14, batch 11550, loss[loss=0.1964, simple_loss=0.2572, pruned_loss=0.06778, over 4961.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2096, pruned_loss=0.03038, over 974439.62 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 01:28:18,475 INFO [train.py:715] (2/8) Epoch 14, batch 11600, loss[loss=0.1316, simple_loss=0.1929, pruned_loss=0.03514, over 4987.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03067, over 973778.82 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 01:28:58,178 INFO [train.py:715] (2/8) Epoch 14, batch 11650, loss[loss=0.1383, simple_loss=0.2099, pruned_loss=0.03332, over 4849.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.0306, over 973452.30 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:29:37,888 INFO [train.py:715] (2/8) Epoch 14, batch 11700, loss[loss=0.1383, simple_loss=0.2165, pruned_loss=0.03005, over 4906.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03051, over 972913.80 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 01:30:17,153 INFO [train.py:715] (2/8) Epoch 14, batch 11750, loss[loss=0.1278, simple_loss=0.2024, pruned_loss=0.0266, over 4961.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03054, over 972042.72 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 01:30:56,855 INFO [train.py:715] (2/8) Epoch 14, batch 11800, loss[loss=0.1354, simple_loss=0.2043, pruned_loss=0.03322, over 4939.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03057, over 970907.12 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 01:31:35,982 INFO [train.py:715] (2/8) Epoch 14, batch 11850, loss[loss=0.1234, simple_loss=0.1873, pruned_loss=0.02976, over 4721.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.0303, over 970185.98 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 01:32:14,891 INFO [train.py:715] (2/8) Epoch 14, batch 11900, loss[loss=0.1289, simple_loss=0.2045, pruned_loss=0.02669, over 4766.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.03007, over 970759.13 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 01:32:54,216 INFO [train.py:715] (2/8) Epoch 14, batch 11950, loss[loss=0.1389, simple_loss=0.2165, pruned_loss=0.03061, over 4909.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03003, over 971327.11 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 01:33:33,586 INFO [train.py:715] (2/8) Epoch 14, batch 12000, loss[loss=0.1358, simple_loss=0.2112, pruned_loss=0.03022, over 4789.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.0306, over 971312.47 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:33:33,586 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 01:33:43,198 INFO [train.py:742] (2/8) 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] (2/8) Epoch 14, batch 12050, loss[loss=0.15, simple_loss=0.2179, pruned_loss=0.0411, over 4855.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03111, over 971372.81 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 01:35:01,860 INFO [train.py:715] (2/8) Epoch 14, batch 12100, loss[loss=0.1375, simple_loss=0.2141, pruned_loss=0.03045, over 4839.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03094, over 972424.72 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:35:41,279 INFO [train.py:715] (2/8) Epoch 14, batch 12150, loss[loss=0.126, simple_loss=0.193, pruned_loss=0.02951, over 4768.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03089, over 972334.97 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:36:20,618 INFO [train.py:715] (2/8) Epoch 14, batch 12200, loss[loss=0.1223, simple_loss=0.197, pruned_loss=0.02378, over 4884.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03024, over 971969.39 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 01:37:00,489 INFO [train.py:715] (2/8) Epoch 14, batch 12250, loss[loss=0.1569, simple_loss=0.2276, pruned_loss=0.04303, over 4979.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2099, pruned_loss=0.03083, over 972639.13 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 01:37:39,678 INFO [train.py:715] (2/8) Epoch 14, batch 12300, loss[loss=0.1182, simple_loss=0.1929, pruned_loss=0.02176, over 4852.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03068, over 972206.93 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 01:38:19,191 INFO [train.py:715] (2/8) Epoch 14, batch 12350, loss[loss=0.1058, simple_loss=0.1632, pruned_loss=0.02418, over 4982.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03085, over 972594.82 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 01:38:58,798 INFO [train.py:715] (2/8) Epoch 14, batch 12400, loss[loss=0.1492, simple_loss=0.2251, pruned_loss=0.03672, over 4990.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03028, over 972527.76 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 01:39:37,831 INFO [train.py:715] (2/8) Epoch 14, batch 12450, loss[loss=0.1278, simple_loss=0.1969, pruned_loss=0.02931, over 4775.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03029, over 972571.48 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 01:40:17,257 INFO [train.py:715] (2/8) Epoch 14, batch 12500, loss[loss=0.112, simple_loss=0.1815, pruned_loss=0.02125, over 4808.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03025, over 972681.78 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 01:40:57,007 INFO [train.py:715] (2/8) Epoch 14, batch 12550, loss[loss=0.1356, simple_loss=0.2073, pruned_loss=0.03192, over 4976.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02995, over 972712.39 frames.], batch size: 31, lr: 1.57e-04
2022-05-08 01:41:36,647 INFO [train.py:715] (2/8) Epoch 14, batch 12600, loss[loss=0.1236, simple_loss=0.2021, pruned_loss=0.02251, over 4873.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03005, over 972165.25 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 01:42:15,593 INFO [train.py:715] (2/8) Epoch 14, batch 12650, loss[loss=0.1114, simple_loss=0.1897, pruned_loss=0.01655, over 4686.00 frames.], tot_loss[loss=0.1347, simple_loss=0.209, pruned_loss=0.03022, over 971578.34 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 01:42:55,474 INFO [train.py:715] (2/8) Epoch 14, batch 12700, loss[loss=0.1379, simple_loss=0.2153, pruned_loss=0.03029, over 4988.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2093, pruned_loss=0.03042, over 972321.16 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:43:35,530 INFO [train.py:715] (2/8) Epoch 14, batch 12750, loss[loss=0.1264, simple_loss=0.1946, pruned_loss=0.02911, over 4957.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03031, over 972562.99 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 01:44:15,507 INFO [train.py:715] (2/8) Epoch 14, batch 12800, loss[loss=0.1502, simple_loss=0.2202, pruned_loss=0.04006, over 4937.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2096, pruned_loss=0.03058, over 972709.69 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 01:44:55,317 INFO [train.py:715] (2/8) Epoch 14, batch 12850, loss[loss=0.1396, simple_loss=0.202, pruned_loss=0.03858, over 4960.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2094, pruned_loss=0.03046, over 973399.31 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 01:45:35,523 INFO [train.py:715] (2/8) Epoch 14, batch 12900, loss[loss=0.1195, simple_loss=0.1894, pruned_loss=0.0248, over 4905.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.0301, over 972469.56 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 01:46:15,877 INFO [train.py:715] (2/8) Epoch 14, batch 12950, loss[loss=0.1518, simple_loss=0.2274, pruned_loss=0.03808, over 4774.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.03001, over 972251.18 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 01:46:55,838 INFO [train.py:715] (2/8) Epoch 14, batch 13000, loss[loss=0.1172, simple_loss=0.1949, pruned_loss=0.01971, over 4849.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03065, over 972101.85 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 01:47:36,080 INFO [train.py:715] (2/8) Epoch 14, batch 13050, loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02896, over 4970.00 frames.], tot_loss[loss=0.136, simple_loss=0.2103, pruned_loss=0.0309, over 972035.95 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 01:48:16,099 INFO [train.py:715] (2/8) Epoch 14, batch 13100, loss[loss=0.1336, simple_loss=0.2108, pruned_loss=0.0282, over 4980.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03089, over 971275.92 frames.], batch size: 24, lr: 1.57e-04
2022-05-08 01:48:56,282 INFO [train.py:715] (2/8) Epoch 14, batch 13150, loss[loss=0.1283, simple_loss=0.1968, pruned_loss=0.02995, over 4818.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2103, pruned_loss=0.03129, over 971525.09 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 01:49:36,379 INFO [train.py:715] (2/8) Epoch 14, batch 13200, loss[loss=0.1143, simple_loss=0.186, pruned_loss=0.02129, over 4743.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2106, pruned_loss=0.03144, over 972041.54 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 01:50:16,585 INFO [train.py:715] (2/8) Epoch 14, batch 13250, loss[loss=0.1224, simple_loss=0.1889, pruned_loss=0.02796, over 4748.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03139, over 971537.96 frames.], batch size: 16, lr: 1.57e-04
2022-05-08 01:50:56,841 INFO [train.py:715] (2/8) Epoch 14, batch 13300, loss[loss=0.1302, simple_loss=0.1995, pruned_loss=0.03046, over 4850.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03115, over 971662.00 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:51:36,426 INFO [train.py:715] (2/8) Epoch 14, batch 13350, loss[loss=0.159, simple_loss=0.224, pruned_loss=0.04699, over 4854.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03105, over 972319.03 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 01:52:15,921 INFO [train.py:715] (2/8) Epoch 14, batch 13400, loss[loss=0.1365, simple_loss=0.2212, pruned_loss=0.02587, over 4936.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03106, over 972561.82 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 01:52:55,516 INFO [train.py:715] (2/8) Epoch 14, batch 13450, loss[loss=0.157, simple_loss=0.2265, pruned_loss=0.04373, over 4867.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2102, pruned_loss=0.03109, over 971983.28 frames.], batch size: 32, lr: 1.57e-04
2022-05-08 01:53:35,077 INFO [train.py:715] (2/8) Epoch 14, batch 13500, loss[loss=0.131, simple_loss=0.2087, pruned_loss=0.02662, over 4788.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03146, over 971302.55 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:54:14,285 INFO [train.py:715] (2/8) Epoch 14, batch 13550, loss[loss=0.1112, simple_loss=0.1941, pruned_loss=0.01416, over 4819.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03077, over 971596.25 frames.], batch size: 27, lr: 1.57e-04
2022-05-08 01:54:53,677 INFO [train.py:715] (2/8) Epoch 14, batch 13600, loss[loss=0.1507, simple_loss=0.2229, pruned_loss=0.03929, over 4977.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.0305, over 971833.81 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 01:55:32,972 INFO [train.py:715] (2/8) Epoch 14, batch 13650, loss[loss=0.1204, simple_loss=0.1963, pruned_loss=0.02227, over 4791.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2078, pruned_loss=0.03024, over 971671.69 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 01:56:12,539 INFO [train.py:715] (2/8) Epoch 14, batch 13700, loss[loss=0.1355, simple_loss=0.2126, pruned_loss=0.02923, over 4868.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03017, over 972500.13 frames.], batch size: 16, lr: 1.57e-04
2022-05-08 01:56:51,587 INFO [train.py:715] (2/8) Epoch 14, batch 13750, loss[loss=0.149, simple_loss=0.2229, pruned_loss=0.03753, over 4762.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02975, over 972565.22 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 01:57:30,924 INFO [train.py:715] (2/8) Epoch 14, batch 13800, loss[loss=0.1282, simple_loss=0.2051, pruned_loss=0.0257, over 4987.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.03001, over 971617.19 frames.], batch size: 28, lr: 1.57e-04
2022-05-08 01:58:12,476 INFO [train.py:715] (2/8) Epoch 14, batch 13850, loss[loss=0.1348, simple_loss=0.2236, pruned_loss=0.02299, over 4905.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.03029, over 971721.83 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 01:58:51,821 INFO [train.py:715] (2/8) Epoch 14, batch 13900, loss[loss=0.1216, simple_loss=0.2005, pruned_loss=0.02131, over 4828.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03092, over 972512.76 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 01:59:31,448 INFO [train.py:715] (2/8) Epoch 14, batch 13950, loss[loss=0.132, simple_loss=0.1966, pruned_loss=0.03363, over 4973.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03061, over 972000.36 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 02:00:10,942 INFO [train.py:715] (2/8) Epoch 14, batch 14000, loss[loss=0.123, simple_loss=0.203, pruned_loss=0.02151, over 4925.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03072, over 971909.94 frames.], batch size: 29, lr: 1.57e-04
2022-05-08 02:00:50,382 INFO [train.py:715] (2/8) Epoch 14, batch 14050, loss[loss=0.1667, simple_loss=0.2439, pruned_loss=0.04477, over 4779.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03084, over 970963.61 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:01:30,048 INFO [train.py:715] (2/8) Epoch 14, batch 14100, loss[loss=0.1058, simple_loss=0.175, pruned_loss=0.01825, over 4797.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03095, over 972047.69 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:02:09,596 INFO [train.py:715] (2/8) Epoch 14, batch 14150, loss[loss=0.1312, simple_loss=0.2001, pruned_loss=0.03114, over 4971.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03104, over 972429.17 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:02:49,096 INFO [train.py:715] (2/8) Epoch 14, batch 14200, loss[loss=0.1198, simple_loss=0.2093, pruned_loss=0.01511, over 4867.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03097, over 972269.11 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 02:03:28,354 INFO [train.py:715] (2/8) Epoch 14, batch 14250, loss[loss=0.1473, simple_loss=0.2222, pruned_loss=0.03617, over 4812.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03094, over 972436.90 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:04:08,208 INFO [train.py:715] (2/8) Epoch 14, batch 14300, loss[loss=0.123, simple_loss=0.1981, pruned_loss=0.0239, over 4770.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03081, over 973315.11 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:04:47,399 INFO [train.py:715] (2/8) Epoch 14, batch 14350, loss[loss=0.1247, simple_loss=0.191, pruned_loss=0.02925, over 4845.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03064, over 973474.24 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 02:05:26,853 INFO [train.py:715] (2/8) Epoch 14, batch 14400, loss[loss=0.1275, simple_loss=0.2034, pruned_loss=0.02576, over 4811.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03037, over 972882.60 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:06:06,344 INFO [train.py:715] (2/8) Epoch 14, batch 14450, loss[loss=0.1549, simple_loss=0.2207, pruned_loss=0.04457, over 4788.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.03056, over 972441.98 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:06:45,901 INFO [train.py:715] (2/8) Epoch 14, batch 14500, loss[loss=0.1343, simple_loss=0.2153, pruned_loss=0.02664, over 4938.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03092, over 973397.51 frames.], batch size: 29, lr: 1.57e-04
2022-05-08 02:07:25,177 INFO [train.py:715] (2/8) Epoch 14, batch 14550, loss[loss=0.1372, simple_loss=0.1999, pruned_loss=0.03726, over 4854.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03072, over 973436.22 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 02:08:04,458 INFO [train.py:715] (2/8) Epoch 14, batch 14600, loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03049, over 4752.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03076, over 973224.83 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:08:44,678 INFO [train.py:715] (2/8) Epoch 14, batch 14650, loss[loss=0.1437, simple_loss=0.2204, pruned_loss=0.03352, over 4946.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.03102, over 972694.26 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 02:09:24,105 INFO [train.py:715] (2/8) Epoch 14, batch 14700, loss[loss=0.1532, simple_loss=0.225, pruned_loss=0.04065, over 4969.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03081, over 973174.96 frames.], batch size: 35, lr: 1.57e-04
2022-05-08 02:10:03,918 INFO [train.py:715] (2/8) Epoch 14, batch 14750, loss[loss=0.1289, simple_loss=0.2012, pruned_loss=0.02832, over 4972.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03106, over 972657.30 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:10:43,093 INFO [train.py:715] (2/8) Epoch 14, batch 14800, loss[loss=0.1473, simple_loss=0.2245, pruned_loss=0.03506, over 4809.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03123, over 972285.38 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:11:23,010 INFO [train.py:715] (2/8) Epoch 14, batch 14850, loss[loss=0.1396, simple_loss=0.2099, pruned_loss=0.03461, over 4783.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03129, over 973332.99 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:12:02,551 INFO [train.py:715] (2/8) Epoch 14, batch 14900, loss[loss=0.1267, simple_loss=0.2068, pruned_loss=0.02329, over 4760.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03095, over 973550.66 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:12:42,000 INFO [train.py:715] (2/8) Epoch 14, batch 14950, loss[loss=0.1286, simple_loss=0.2128, pruned_loss=0.0222, over 4957.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03063, over 972502.55 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:13:22,060 INFO [train.py:715] (2/8) Epoch 14, batch 15000, loss[loss=0.1408, simple_loss=0.218, pruned_loss=0.03181, over 4969.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03075, over 972727.28 frames.], batch size: 24, lr: 1.57e-04
2022-05-08 02:13:22,061 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 02:13:31,707 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.1052, simple_loss=0.1889, pruned_loss=0.01079, over 914524.00 frames.
2022-05-08 02:14:12,556 INFO [train.py:715] (2/8) Epoch 14, batch 15050, loss[loss=0.1314, simple_loss=0.2122, pruned_loss=0.02533, over 4903.00 frames.], tot_loss[loss=0.1355, simple_loss=0.21, pruned_loss=0.03048, over 973132.30 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:14:52,640 INFO [train.py:715] (2/8) Epoch 14, batch 15100, loss[loss=0.1674, simple_loss=0.2349, pruned_loss=0.04999, over 4790.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2101, pruned_loss=0.0306, over 973330.42 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 02:15:33,290 INFO [train.py:715] (2/8) Epoch 14, batch 15150, loss[loss=0.1651, simple_loss=0.2422, pruned_loss=0.04396, over 4826.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2101, pruned_loss=0.03056, over 972445.82 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 02:16:13,413 INFO [train.py:715] (2/8) Epoch 14, batch 15200, loss[loss=0.1536, simple_loss=0.2248, pruned_loss=0.04124, over 4843.00 frames.], tot_loss[loss=0.136, simple_loss=0.2103, pruned_loss=0.03083, over 972923.89 frames.], batch size: 30, lr: 1.57e-04
2022-05-08 02:16:54,056 INFO [train.py:715] (2/8) Epoch 14, batch 15250, loss[loss=0.1531, simple_loss=0.2241, pruned_loss=0.04111, over 4945.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2095, pruned_loss=0.03049, over 972120.64 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 02:17:33,928 INFO [train.py:715] (2/8) Epoch 14, batch 15300, loss[loss=0.116, simple_loss=0.195, pruned_loss=0.0185, over 4828.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2091, pruned_loss=0.03028, over 971514.78 frames.], batch size: 27, lr: 1.57e-04
2022-05-08 02:18:13,476 INFO [train.py:715] (2/8) Epoch 14, batch 15350, loss[loss=0.1274, simple_loss=0.199, pruned_loss=0.0279, over 4930.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03058, over 972295.76 frames.], batch size: 39, lr: 1.57e-04
2022-05-08 02:18:53,585 INFO [train.py:715] (2/8) Epoch 14, batch 15400, loss[loss=0.1305, simple_loss=0.2021, pruned_loss=0.02941, over 4778.00 frames.], tot_loss[loss=0.1351, simple_loss=0.209, pruned_loss=0.03067, over 971364.80 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 02:19:32,975 INFO [train.py:715] (2/8) Epoch 14, batch 15450, loss[loss=0.1395, simple_loss=0.2047, pruned_loss=0.03718, over 4649.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03046, over 970972.28 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:20:12,212 INFO [train.py:715] (2/8) Epoch 14, batch 15500, loss[loss=0.1244, simple_loss=0.1853, pruned_loss=0.03181, over 4816.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03046, over 971761.88 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:20:51,549 INFO [train.py:715] (2/8) Epoch 14, batch 15550, loss[loss=0.1339, simple_loss=0.2056, pruned_loss=0.03111, over 4952.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.0311, over 971407.36 frames.], batch size: 24, lr: 1.57e-04
2022-05-08 02:21:31,492 INFO [train.py:715] (2/8) Epoch 14, batch 15600, loss[loss=0.1333, simple_loss=0.2038, pruned_loss=0.03142, over 4777.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03081, over 972068.60 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:22:10,936 INFO [train.py:715] (2/8) Epoch 14, batch 15650, loss[loss=0.1362, simple_loss=0.2112, pruned_loss=0.03064, over 4696.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03068, over 971988.53 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:22:49,322 INFO [train.py:715] (2/8) Epoch 14, batch 15700, loss[loss=0.1383, simple_loss=0.217, pruned_loss=0.02974, over 4783.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.03063, over 971889.68 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 02:23:29,530 INFO [train.py:715] (2/8) Epoch 14, batch 15750, loss[loss=0.1179, simple_loss=0.1923, pruned_loss=0.02173, over 4925.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03034, over 971343.92 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 02:24:09,057 INFO [train.py:715] (2/8) Epoch 14, batch 15800, loss[loss=0.1383, simple_loss=0.2204, pruned_loss=0.02807, over 4919.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.0304, over 971900.70 frames.], batch size: 18, lr: 1.57e-04
2022-05-08 02:24:48,297 INFO [train.py:715] (2/8) Epoch 14, batch 15850, loss[loss=0.1177, simple_loss=0.199, pruned_loss=0.01826, over 4835.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03086, over 972005.97 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 02:25:27,576 INFO [train.py:715] (2/8) Epoch 14, batch 15900, loss[loss=0.1363, simple_loss=0.2137, pruned_loss=0.02941, over 4839.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.0309, over 971860.17 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:26:07,619 INFO [train.py:715] (2/8) Epoch 14, batch 15950, loss[loss=0.1546, simple_loss=0.2248, pruned_loss=0.04215, over 4957.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03027, over 971542.23 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:26:47,031 INFO [train.py:715] (2/8) Epoch 14, batch 16000, loss[loss=0.1202, simple_loss=0.1967, pruned_loss=0.02183, over 4758.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.0299, over 970990.86 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:27:25,749 INFO [train.py:715] (2/8) Epoch 14, batch 16050, loss[loss=0.168, simple_loss=0.2497, pruned_loss=0.04314, over 4865.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2095, pruned_loss=0.03033, over 970938.51 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 02:28:04,469 INFO [train.py:715] (2/8) Epoch 14, batch 16100, loss[loss=0.1268, simple_loss=0.2035, pruned_loss=0.02507, over 4984.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2092, pruned_loss=0.03046, over 970849.79 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:28:42,586 INFO [train.py:715] (2/8) Epoch 14, batch 16150, loss[loss=0.1535, simple_loss=0.229, pruned_loss=0.039, over 4788.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03056, over 970691.47 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:29:20,837 INFO [train.py:715] (2/8) Epoch 14, batch 16200, loss[loss=0.1304, simple_loss=0.2034, pruned_loss=0.0287, over 4798.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03009, over 970818.50 frames.], batch size: 17, lr: 1.57e-04
2022-05-08 02:29:59,440 INFO [train.py:715] (2/8) Epoch 14, batch 16250, loss[loss=0.1485, simple_loss=0.217, pruned_loss=0.04001, over 4803.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03077, over 970656.16 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:30:38,582 INFO [train.py:715] (2/8) Epoch 14, batch 16300, loss[loss=0.1423, simple_loss=0.2161, pruned_loss=0.03424, over 4935.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03074, over 970426.56 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 02:31:16,529 INFO [train.py:715] (2/8) Epoch 14, batch 16350, loss[loss=0.1502, simple_loss=0.2213, pruned_loss=0.03957, over 4800.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03088, over 969677.95 frames.], batch size: 12, lr: 1.57e-04
2022-05-08 02:31:55,704 INFO [train.py:715] (2/8) Epoch 14, batch 16400, loss[loss=0.1278, simple_loss=0.1989, pruned_loss=0.02832, over 4787.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03057, over 969756.97 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:32:35,414 INFO [train.py:715] (2/8) Epoch 14, batch 16450, loss[loss=0.1372, simple_loss=0.224, pruned_loss=0.02525, over 4815.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03014, over 969968.93 frames.], batch size: 25, lr: 1.57e-04
2022-05-08 02:33:14,852 INFO [train.py:715] (2/8) Epoch 14, batch 16500, loss[loss=0.1632, simple_loss=0.2492, pruned_loss=0.03864, over 4898.00 frames.], tot_loss[loss=0.1345, simple_loss=0.209, pruned_loss=0.03003, over 969689.95 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:33:53,745 INFO [train.py:715] (2/8) Epoch 14, batch 16550, loss[loss=0.1938, simple_loss=0.2539, pruned_loss=0.06686, over 4844.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2099, pruned_loss=0.03089, over 970348.14 frames.], batch size: 30, lr: 1.57e-04
2022-05-08 02:34:34,127 INFO [train.py:715] (2/8) Epoch 14, batch 16600, loss[loss=0.1092, simple_loss=0.1812, pruned_loss=0.01861, over 4771.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.03087, over 970849.22 frames.], batch size: 14, lr: 1.57e-04
2022-05-08 02:35:13,401 INFO [train.py:715] (2/8) Epoch 14, batch 16650, loss[loss=0.1409, simple_loss=0.2163, pruned_loss=0.03277, over 4833.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03065, over 971589.33 frames.], batch size: 26, lr: 1.57e-04
2022-05-08 02:35:55,021 INFO [train.py:715] (2/8) Epoch 14, batch 16700, loss[loss=0.1441, simple_loss=0.212, pruned_loss=0.03811, over 4839.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03099, over 971888.12 frames.], batch size: 30, lr: 1.57e-04
2022-05-08 02:36:34,902 INFO [train.py:715] (2/8) Epoch 14, batch 16750, loss[loss=0.1318, simple_loss=0.2077, pruned_loss=0.02794, over 4990.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2107, pruned_loss=0.03123, over 972232.56 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:37:15,257 INFO [train.py:715] (2/8) Epoch 14, batch 16800, loss[loss=0.1593, simple_loss=0.2351, pruned_loss=0.04172, over 4702.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2103, pruned_loss=0.0308, over 972573.11 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:37:54,765 INFO [train.py:715] (2/8) Epoch 14, batch 16850, loss[loss=0.1788, simple_loss=0.2485, pruned_loss=0.05456, over 4759.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2103, pruned_loss=0.03053, over 972362.36 frames.], batch size: 16, lr: 1.57e-04
2022-05-08 02:38:34,396 INFO [train.py:715] (2/8) Epoch 14, batch 16900, loss[loss=0.1234, simple_loss=0.2019, pruned_loss=0.02247, over 4796.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2105, pruned_loss=0.03113, over 972223.70 frames.], batch size: 24, lr: 1.57e-04
2022-05-08 02:39:15,386 INFO [train.py:715] (2/8) Epoch 14, batch 16950, loss[loss=0.1251, simple_loss=0.1938, pruned_loss=0.02821, over 4945.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2105, pruned_loss=0.03153, over 972848.45 frames.], batch size: 21, lr: 1.57e-04
2022-05-08 02:39:56,928 INFO [train.py:715] (2/8) Epoch 14, batch 17000, loss[loss=0.1459, simple_loss=0.2354, pruned_loss=0.02825, over 4866.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2097, pruned_loss=0.03142, over 972659.89 frames.], batch size: 20, lr: 1.57e-04
2022-05-08 02:40:37,819 INFO [train.py:715] (2/8) Epoch 14, batch 17050, loss[loss=0.1445, simple_loss=0.2165, pruned_loss=0.03626, over 4963.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2097, pruned_loss=0.0314, over 973049.43 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:41:18,920 INFO [train.py:715] (2/8) Epoch 14, batch 17100, loss[loss=0.1387, simple_loss=0.22, pruned_loss=0.02872, over 4907.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2104, pruned_loss=0.03139, over 972731.72 frames.], batch size: 19, lr: 1.57e-04
2022-05-08 02:42:01,002 INFO [train.py:715] (2/8) Epoch 14, batch 17150, loss[loss=0.1383, simple_loss=0.2043, pruned_loss=0.03612, over 4834.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03118, over 972599.04 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:42:41,739 INFO [train.py:715] (2/8) Epoch 14, batch 17200, loss[loss=0.1524, simple_loss=0.2349, pruned_loss=0.03491, over 4813.00 frames.], tot_loss[loss=0.1362, simple_loss=0.21, pruned_loss=0.03116, over 971878.28 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:43:22,717 INFO [train.py:715] (2/8) Epoch 14, batch 17250, loss[loss=0.1431, simple_loss=0.2337, pruned_loss=0.0263, over 4933.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03065, over 972249.27 frames.], batch size: 23, lr: 1.57e-04
2022-05-08 02:44:04,210 INFO [train.py:715] (2/8) Epoch 14, batch 17300, loss[loss=0.1566, simple_loss=0.2272, pruned_loss=0.04296, over 4838.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03066, over 972725.13 frames.], batch size: 15, lr: 1.57e-04
2022-05-08 02:44:45,871 INFO [train.py:715] (2/8) Epoch 14, batch 17350, loss[loss=0.1066, simple_loss=0.1749, pruned_loss=0.01911, over 4794.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.03064, over 973234.16 frames.], batch size: 13, lr: 1.57e-04
2022-05-08 02:45:26,238 INFO [train.py:715] (2/8) Epoch 14, batch 17400, loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03108, over 4920.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03077, over 972170.65 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 02:46:07,482 INFO [train.py:715] (2/8) Epoch 14, batch 17450, loss[loss=0.1465, simple_loss=0.2153, pruned_loss=0.03886, over 4875.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03081, over 972373.43 frames.], batch size: 38, lr: 1.56e-04
2022-05-08 02:46:49,056 INFO [train.py:715] (2/8) Epoch 14, batch 17500, loss[loss=0.1427, simple_loss=0.2104, pruned_loss=0.03744, over 4744.00 frames.], tot_loss[loss=0.136, simple_loss=0.2096, pruned_loss=0.0312, over 972179.97 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 02:47:29,791 INFO [train.py:715] (2/8) Epoch 14, batch 17550, loss[loss=0.1167, simple_loss=0.1895, pruned_loss=0.02195, over 4923.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2092, pruned_loss=0.03117, over 972074.63 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 02:48:10,319 INFO [train.py:715] (2/8) Epoch 14, batch 17600, loss[loss=0.1175, simple_loss=0.1795, pruned_loss=0.02768, over 4766.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2095, pruned_loss=0.03142, over 970942.48 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 02:48:52,025 INFO [train.py:715] (2/8) Epoch 14, batch 17650, loss[loss=0.1253, simple_loss=0.2066, pruned_loss=0.02193, over 4699.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03113, over 971077.70 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 02:49:33,154 INFO [train.py:715] (2/8) Epoch 14, batch 17700, loss[loss=0.1117, simple_loss=0.1904, pruned_loss=0.01655, over 4890.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03108, over 971119.83 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 02:50:13,641 INFO [train.py:715] (2/8) Epoch 14, batch 17750, loss[loss=0.1127, simple_loss=0.1825, pruned_loss=0.02143, over 4831.00 frames.], tot_loss[loss=0.1363, simple_loss=0.21, pruned_loss=0.03131, over 971349.07 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 02:50:55,011 INFO [train.py:715] (2/8) Epoch 14, batch 17800, loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03142, over 4931.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03177, over 972188.34 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 02:51:35,958 INFO [train.py:715] (2/8) Epoch 14, batch 17850, loss[loss=0.1618, simple_loss=0.2379, pruned_loss=0.04283, over 4918.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2106, pruned_loss=0.03206, over 972371.92 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 02:52:16,730 INFO [train.py:715] (2/8) Epoch 14, batch 17900, loss[loss=0.1404, simple_loss=0.2124, pruned_loss=0.03417, over 4885.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03125, over 973050.52 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 02:52:57,198 INFO [train.py:715] (2/8) Epoch 14, batch 17950, loss[loss=0.1618, simple_loss=0.2317, pruned_loss=0.04597, over 4793.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2091, pruned_loss=0.03135, over 973083.96 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 02:53:38,590 INFO [train.py:715] (2/8) Epoch 14, batch 18000, loss[loss=0.1354, simple_loss=0.2138, pruned_loss=0.02851, over 4885.00 frames.], tot_loss[loss=0.136, simple_loss=0.2095, pruned_loss=0.03126, over 972629.06 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 02:53:38,591 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 02:53:48,448 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.1052, simple_loss=0.1889, pruned_loss=0.01075, over 914524.00 frames.
2022-05-08 02:54:29,830 INFO [train.py:715] (2/8) Epoch 14, batch 18050, loss[loss=0.1622, simple_loss=0.2326, pruned_loss=0.04593, over 4842.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03121, over 972484.12 frames.], batch size: 30, lr: 1.56e-04
2022-05-08 02:55:10,975 INFO [train.py:715] (2/8) Epoch 14, batch 18100, loss[loss=0.1668, simple_loss=0.2468, pruned_loss=0.04339, over 4852.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2105, pruned_loss=0.03131, over 972987.84 frames.], batch size: 32, lr: 1.56e-04
2022-05-08 02:55:52,585 INFO [train.py:715] (2/8) Epoch 14, batch 18150, loss[loss=0.1344, simple_loss=0.2094, pruned_loss=0.02968, over 4971.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2116, pruned_loss=0.03174, over 972969.57 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 02:56:33,499 INFO [train.py:715] (2/8) Epoch 14, batch 18200, loss[loss=0.1327, simple_loss=0.2119, pruned_loss=0.02672, over 4992.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2113, pruned_loss=0.03149, over 972419.36 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 02:57:15,442 INFO [train.py:715] (2/8) Epoch 14, batch 18250, loss[loss=0.1632, simple_loss=0.2304, pruned_loss=0.04804, over 4969.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2104, pruned_loss=0.03144, over 972121.14 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 02:57:56,886 INFO [train.py:715] (2/8) Epoch 14, batch 18300, loss[loss=0.1481, simple_loss=0.2148, pruned_loss=0.04072, over 4852.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2101, pruned_loss=0.0311, over 972094.02 frames.], batch size: 32, lr: 1.56e-04
2022-05-08 02:58:36,494 INFO [train.py:715] (2/8) Epoch 14, batch 18350, loss[loss=0.1281, simple_loss=0.2098, pruned_loss=0.02325, over 4921.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03134, over 971568.38 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 02:59:17,357 INFO [train.py:715] (2/8) Epoch 14, batch 18400, loss[loss=0.1233, simple_loss=0.2007, pruned_loss=0.02288, over 4832.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03103, over 971761.94 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 02:59:57,989 INFO [train.py:715] (2/8) Epoch 14, batch 18450, loss[loss=0.1344, simple_loss=0.2144, pruned_loss=0.02722, over 4877.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03069, over 971014.47 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 03:00:38,223 INFO [train.py:715] (2/8) Epoch 14, batch 18500, loss[loss=0.1235, simple_loss=0.1923, pruned_loss=0.02736, over 4979.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.0308, over 971726.63 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:01:18,696 INFO [train.py:715] (2/8) Epoch 14, batch 18550, loss[loss=0.1232, simple_loss=0.201, pruned_loss=0.02264, over 4990.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03059, over 972192.88 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:01:59,556 INFO [train.py:715] (2/8) Epoch 14, batch 18600, loss[loss=0.1111, simple_loss=0.1901, pruned_loss=0.01602, over 4974.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02996, over 972891.97 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:02:39,862 INFO [train.py:715] (2/8) Epoch 14, batch 18650, loss[loss=0.1302, simple_loss=0.2074, pruned_loss=0.02651, over 4803.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.0299, over 972090.69 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 03:03:20,565 INFO [train.py:715] (2/8) Epoch 14, batch 18700, loss[loss=0.1354, simple_loss=0.2066, pruned_loss=0.03205, over 4874.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.0304, over 972875.42 frames.], batch size: 32, lr: 1.56e-04
2022-05-08 03:04:01,157 INFO [train.py:715] (2/8) Epoch 14, batch 18750, loss[loss=0.1399, simple_loss=0.1933, pruned_loss=0.04319, over 4960.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03048, over 972742.75 frames.], batch size: 35, lr: 1.56e-04
2022-05-08 03:04:41,123 INFO [train.py:715] (2/8) Epoch 14, batch 18800, loss[loss=0.1253, simple_loss=0.2041, pruned_loss=0.02326, over 4640.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03021, over 972862.60 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:05:21,090 INFO [train.py:715] (2/8) Epoch 14, batch 18850, loss[loss=0.1237, simple_loss=0.2074, pruned_loss=0.02001, over 4906.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.03032, over 973277.46 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:06:01,825 INFO [train.py:715] (2/8) Epoch 14, batch 18900, loss[loss=0.119, simple_loss=0.1848, pruned_loss=0.0266, over 4806.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03075, over 971895.00 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:06:42,898 INFO [train.py:715] (2/8) Epoch 14, batch 18950, loss[loss=0.1215, simple_loss=0.2082, pruned_loss=0.01742, over 4935.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03054, over 973057.89 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:07:23,141 INFO [train.py:715] (2/8) Epoch 14, batch 19000, loss[loss=0.1406, simple_loss=0.2265, pruned_loss=0.02733, over 4975.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.0305, over 972878.84 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:08:04,085 INFO [train.py:715] (2/8) Epoch 14, batch 19050, loss[loss=0.148, simple_loss=0.2163, pruned_loss=0.03979, over 4695.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03072, over 971814.45 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:08:45,082 INFO [train.py:715] (2/8) Epoch 14, batch 19100, loss[loss=0.1169, simple_loss=0.1914, pruned_loss=0.02116, over 4821.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2095, pruned_loss=0.03047, over 971531.02 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:09:25,464 INFO [train.py:715] (2/8) Epoch 14, batch 19150, loss[loss=0.1453, simple_loss=0.2134, pruned_loss=0.03862, over 4904.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.03081, over 972103.58 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:10:04,872 INFO [train.py:715] (2/8) Epoch 14, batch 19200, loss[loss=0.1235, simple_loss=0.1942, pruned_loss=0.02642, over 4826.00 frames.], tot_loss[loss=0.1347, simple_loss=0.209, pruned_loss=0.0302, over 972314.92 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:10:45,986 INFO [train.py:715] (2/8) Epoch 14, batch 19250, loss[loss=0.1483, simple_loss=0.2212, pruned_loss=0.03771, over 4713.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03064, over 972559.09 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:11:26,905 INFO [train.py:715] (2/8) Epoch 14, batch 19300, loss[loss=0.1258, simple_loss=0.1901, pruned_loss=0.03077, over 4975.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.0303, over 972205.65 frames.], batch size: 35, lr: 1.56e-04
2022-05-08 03:12:06,951 INFO [train.py:715] (2/8) Epoch 14, batch 19350, loss[loss=0.1535, simple_loss=0.2145, pruned_loss=0.04624, over 4722.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2092, pruned_loss=0.03053, over 971426.56 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 03:12:47,197 INFO [train.py:715] (2/8) Epoch 14, batch 19400, loss[loss=0.1268, simple_loss=0.2086, pruned_loss=0.02252, over 4872.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2091, pruned_loss=0.03001, over 971604.33 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 03:13:28,654 INFO [train.py:715] (2/8) Epoch 14, batch 19450, loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03053, over 4921.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2093, pruned_loss=0.03023, over 972203.59 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:14:08,961 INFO [train.py:715] (2/8) Epoch 14, batch 19500, loss[loss=0.1294, simple_loss=0.1996, pruned_loss=0.02958, over 4879.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03007, over 972025.25 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 03:14:49,607 INFO [train.py:715] (2/8) Epoch 14, batch 19550, loss[loss=0.1196, simple_loss=0.1933, pruned_loss=0.02302, over 4965.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03002, over 972235.95 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:15:30,069 INFO [train.py:715] (2/8) Epoch 14, batch 19600, loss[loss=0.1164, simple_loss=0.1816, pruned_loss=0.02566, over 4837.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02983, over 972009.19 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 03:16:10,999 INFO [train.py:715] (2/8) Epoch 14, batch 19650, loss[loss=0.1205, simple_loss=0.1937, pruned_loss=0.02362, over 4819.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02947, over 972367.30 frames.], batch size: 26, lr: 1.56e-04
2022-05-08 03:16:51,975 INFO [train.py:715] (2/8) Epoch 14, batch 19700, loss[loss=0.1399, simple_loss=0.217, pruned_loss=0.03137, over 4853.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02996, over 972295.15 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 03:17:32,731 INFO [train.py:715] (2/8) Epoch 14, batch 19750, loss[loss=0.1994, simple_loss=0.2642, pruned_loss=0.06733, over 4965.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03028, over 972535.21 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:18:13,651 INFO [train.py:715] (2/8) Epoch 14, batch 19800, loss[loss=0.1537, simple_loss=0.2232, pruned_loss=0.04214, over 4845.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03077, over 972602.08 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:18:54,280 INFO [train.py:715] (2/8) Epoch 14, batch 19850, loss[loss=0.1003, simple_loss=0.1724, pruned_loss=0.01409, over 4747.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03084, over 972205.52 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 03:19:35,280 INFO [train.py:715] (2/8) Epoch 14, batch 19900, loss[loss=0.1439, simple_loss=0.219, pruned_loss=0.03437, over 4770.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2095, pruned_loss=0.03103, over 972475.67 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:20:15,389 INFO [train.py:715] (2/8) Epoch 14, batch 19950, loss[loss=0.1549, simple_loss=0.2373, pruned_loss=0.03622, over 4982.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03158, over 972756.45 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:20:55,689 INFO [train.py:715] (2/8) Epoch 14, batch 20000, loss[loss=0.1316, simple_loss=0.1971, pruned_loss=0.03302, over 4701.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2103, pruned_loss=0.03121, over 973311.94 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:21:35,500 INFO [train.py:715] (2/8) Epoch 14, batch 20050, loss[loss=0.1421, simple_loss=0.2178, pruned_loss=0.03325, over 4919.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2103, pruned_loss=0.03128, over 972974.26 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:22:15,343 INFO [train.py:715] (2/8) Epoch 14, batch 20100, loss[loss=0.1042, simple_loss=0.1707, pruned_loss=0.01888, over 4818.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.0304, over 973717.08 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 03:22:55,784 INFO [train.py:715] (2/8) Epoch 14, batch 20150, loss[loss=0.1098, simple_loss=0.1809, pruned_loss=0.01932, over 4865.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03002, over 973622.55 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 03:23:35,883 INFO [train.py:715] (2/8) Epoch 14, batch 20200, loss[loss=0.1591, simple_loss=0.2307, pruned_loss=0.04374, over 4883.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.03005, over 973233.05 frames.], batch size: 32, lr: 1.56e-04
2022-05-08 03:24:16,356 INFO [train.py:715] (2/8) Epoch 14, batch 20250, loss[loss=0.1289, simple_loss=0.2058, pruned_loss=0.02601, over 4915.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2074, pruned_loss=0.03024, over 973916.66 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:24:56,501 INFO [train.py:715] (2/8) Epoch 14, batch 20300, loss[loss=0.1635, simple_loss=0.2361, pruned_loss=0.04548, over 4795.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03035, over 973800.98 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:25:37,272 INFO [train.py:715] (2/8) Epoch 14, batch 20350, loss[loss=0.1291, simple_loss=0.2106, pruned_loss=0.02384, over 4938.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03021, over 972870.99 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:26:17,609 INFO [train.py:715] (2/8) Epoch 14, batch 20400, loss[loss=0.1718, simple_loss=0.2351, pruned_loss=0.0543, over 4920.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.03005, over 973285.05 frames.], batch size: 39, lr: 1.56e-04
2022-05-08 03:26:58,057 INFO [train.py:715] (2/8) Epoch 14, batch 20450, loss[loss=0.1258, simple_loss=0.2056, pruned_loss=0.02307, over 4941.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2089, pruned_loss=0.03066, over 973407.23 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 03:27:39,214 INFO [train.py:715] (2/8) Epoch 14, batch 20500, loss[loss=0.1333, simple_loss=0.2132, pruned_loss=0.02675, over 4989.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03096, over 973668.05 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 03:28:19,567 INFO [train.py:715] (2/8) Epoch 14, batch 20550, loss[loss=0.1479, simple_loss=0.2277, pruned_loss=0.03408, over 4782.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03078, over 973961.10 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:29:00,471 INFO [train.py:715] (2/8) Epoch 14, batch 20600, loss[loss=0.1577, simple_loss=0.2096, pruned_loss=0.05286, over 4822.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2098, pruned_loss=0.03123, over 974588.34 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:29:41,266 INFO [train.py:715] (2/8) Epoch 14, batch 20650, loss[loss=0.1425, simple_loss=0.2184, pruned_loss=0.03331, over 4973.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2096, pruned_loss=0.0308, over 974457.51 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:30:22,941 INFO [train.py:715] (2/8) Epoch 14, batch 20700, loss[loss=0.1393, simple_loss=0.2246, pruned_loss=0.02697, over 4780.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03078, over 973458.49 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:31:03,258 INFO [train.py:715] (2/8) Epoch 14, batch 20750, loss[loss=0.139, simple_loss=0.2133, pruned_loss=0.03233, over 4937.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03046, over 973769.69 frames.], batch size: 23, lr: 1.56e-04
2022-05-08 03:31:43,458 INFO [train.py:715] (2/8) Epoch 14, batch 20800, loss[loss=0.1224, simple_loss=0.2, pruned_loss=0.02242, over 4918.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02977, over 973489.90 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:32:24,155 INFO [train.py:715] (2/8) Epoch 14, batch 20850, loss[loss=0.1648, simple_loss=0.2393, pruned_loss=0.04518, over 4833.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.0299, over 973246.55 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 03:33:04,692 INFO [train.py:715] (2/8) Epoch 14, batch 20900, loss[loss=0.1293, simple_loss=0.2117, pruned_loss=0.02343, over 4811.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2088, pruned_loss=0.02998, over 972943.74 frames.], batch size: 26, lr: 1.56e-04
2022-05-08 03:33:45,368 INFO [train.py:715] (2/8) Epoch 14, batch 20950, loss[loss=0.1254, simple_loss=0.21, pruned_loss=0.02039, over 4903.00 frames.], tot_loss[loss=0.1346, simple_loss=0.209, pruned_loss=0.03008, over 972180.50 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:34:25,915 INFO [train.py:715] (2/8) Epoch 14, batch 21000, loss[loss=0.1235, simple_loss=0.1893, pruned_loss=0.02886, over 4992.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02947, over 973183.68 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:34:25,916 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 03:34:37,000 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.1051, simple_loss=0.1889, pruned_loss=0.0107, over 914524.00 frames.
2022-05-08 03:35:17,899 INFO [train.py:715] (2/8) Epoch 14, batch 21050, loss[loss=0.1678, simple_loss=0.2448, pruned_loss=0.04538, over 4846.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02957, over 972791.93 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:35:58,607 INFO [train.py:715] (2/8) Epoch 14, batch 21100, loss[loss=0.1519, simple_loss=0.2335, pruned_loss=0.0351, over 4915.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2089, pruned_loss=0.03003, over 971754.47 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:36:39,419 INFO [train.py:715] (2/8) Epoch 14, batch 21150, loss[loss=0.1483, simple_loss=0.2323, pruned_loss=0.03219, over 4962.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2093, pruned_loss=0.03008, over 971896.29 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:37:18,916 INFO [train.py:715] (2/8) Epoch 14, batch 21200, loss[loss=0.1107, simple_loss=0.1852, pruned_loss=0.01811, over 4813.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2092, pruned_loss=0.03009, over 971319.68 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 03:37:59,344 INFO [train.py:715] (2/8) Epoch 14, batch 21250, loss[loss=0.1237, simple_loss=0.2023, pruned_loss=0.02256, over 4954.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03014, over 971677.33 frames.], batch size: 35, lr: 1.56e-04
2022-05-08 03:38:39,035 INFO [train.py:715] (2/8) Epoch 14, batch 21300, loss[loss=0.118, simple_loss=0.1955, pruned_loss=0.02024, over 4872.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2096, pruned_loss=0.03048, over 972181.79 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:39:17,957 INFO [train.py:715] (2/8) Epoch 14, batch 21350, loss[loss=0.1618, simple_loss=0.2512, pruned_loss=0.03625, over 4920.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2091, pruned_loss=0.03013, over 972007.89 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:39:58,399 INFO [train.py:715] (2/8) Epoch 14, batch 21400, loss[loss=0.1266, simple_loss=0.1952, pruned_loss=0.02898, over 4780.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03039, over 972499.76 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:40:38,647 INFO [train.py:715] (2/8) Epoch 14, batch 21450, loss[loss=0.1237, simple_loss=0.1939, pruned_loss=0.02677, over 4890.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.03109, over 972464.01 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 03:41:18,061 INFO [train.py:715] (2/8) Epoch 14, batch 21500, loss[loss=0.123, simple_loss=0.1993, pruned_loss=0.02335, over 4746.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03063, over 972176.75 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:41:57,081 INFO [train.py:715] (2/8) Epoch 14, batch 21550, loss[loss=0.1257, simple_loss=0.1921, pruned_loss=0.02966, over 4869.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2081, pruned_loss=0.03069, over 971941.21 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:42:37,074 INFO [train.py:715] (2/8) Epoch 14, batch 21600, loss[loss=0.1472, simple_loss=0.2262, pruned_loss=0.03413, over 4989.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2082, pruned_loss=0.03079, over 970887.69 frames.], batch size: 28, lr: 1.56e-04
2022-05-08 03:43:16,848 INFO [train.py:715] (2/8) Epoch 14, batch 21650, loss[loss=0.1183, simple_loss=0.1986, pruned_loss=0.01904, over 4928.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2083, pruned_loss=0.03074, over 971430.37 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 03:43:55,951 INFO [train.py:715] (2/8) Epoch 14, batch 21700, loss[loss=0.132, simple_loss=0.1986, pruned_loss=0.03269, over 4988.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03055, over 972203.92 frames.], batch size: 33, lr: 1.56e-04
2022-05-08 03:44:36,362 INFO [train.py:715] (2/8) Epoch 14, batch 21750, loss[loss=0.1475, simple_loss=0.2195, pruned_loss=0.03776, over 4902.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03076, over 973021.98 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:45:16,754 INFO [train.py:715] (2/8) Epoch 14, batch 21800, loss[loss=0.1214, simple_loss=0.2015, pruned_loss=0.02066, over 4822.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.0311, over 973622.74 frames.], batch size: 27, lr: 1.56e-04
2022-05-08 03:45:56,149 INFO [train.py:715] (2/8) Epoch 14, batch 21850, loss[loss=0.14, simple_loss=0.2137, pruned_loss=0.03317, over 4890.00 frames.], tot_loss[loss=0.1354, simple_loss=0.209, pruned_loss=0.03092, over 973796.87 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 03:46:35,754 INFO [train.py:715] (2/8) Epoch 14, batch 21900, loss[loss=0.1324, simple_loss=0.2005, pruned_loss=0.03213, over 4967.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03057, over 974124.28 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:47:16,026 INFO [train.py:715] (2/8) Epoch 14, batch 21950, loss[loss=0.1241, simple_loss=0.1949, pruned_loss=0.02666, over 4829.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03042, over 973565.02 frames.], batch size: 26, lr: 1.56e-04
2022-05-08 03:47:55,288 INFO [train.py:715] (2/8) Epoch 14, batch 22000, loss[loss=0.1525, simple_loss=0.2392, pruned_loss=0.03292, over 4917.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03051, over 974340.54 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:48:34,009 INFO [train.py:715] (2/8) Epoch 14, batch 22050, loss[loss=0.1355, simple_loss=0.2199, pruned_loss=0.02557, over 4822.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03016, over 974062.96 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 03:49:14,111 INFO [train.py:715] (2/8) Epoch 14, batch 22100, loss[loss=0.1313, simple_loss=0.2152, pruned_loss=0.02372, over 4962.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03019, over 973475.91 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:49:53,818 INFO [train.py:715] (2/8) Epoch 14, batch 22150, loss[loss=0.1386, simple_loss=0.2084, pruned_loss=0.03442, over 4989.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03015, over 973689.38 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:50:32,844 INFO [train.py:715] (2/8) Epoch 14, batch 22200, loss[loss=0.1214, simple_loss=0.1988, pruned_loss=0.02201, over 4943.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03029, over 973694.98 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:51:12,592 INFO [train.py:715] (2/8) Epoch 14, batch 22250, loss[loss=0.1221, simple_loss=0.1918, pruned_loss=0.02616, over 4935.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03052, over 973449.43 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:51:52,764 INFO [train.py:715] (2/8) Epoch 14, batch 22300, loss[loss=0.1295, simple_loss=0.2096, pruned_loss=0.02472, over 4783.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.0301, over 973161.40 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:52:32,253 INFO [train.py:715] (2/8) Epoch 14, batch 22350, loss[loss=0.1293, simple_loss=0.208, pruned_loss=0.02534, over 4822.00 frames.], tot_loss[loss=0.135, simple_loss=0.2095, pruned_loss=0.03023, over 973359.89 frames.], batch size: 13, lr: 1.56e-04
2022-05-08 03:53:11,404 INFO [train.py:715] (2/8) Epoch 14, batch 22400, loss[loss=0.1333, simple_loss=0.2011, pruned_loss=0.03278, over 4814.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2095, pruned_loss=0.0303, over 972657.32 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:53:51,751 INFO [train.py:715] (2/8) Epoch 14, batch 22450, loss[loss=0.1213, simple_loss=0.1946, pruned_loss=0.02398, over 4952.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2092, pruned_loss=0.03027, over 972456.31 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:54:31,161 INFO [train.py:715] (2/8) Epoch 14, batch 22500, loss[loss=0.1517, simple_loss=0.2286, pruned_loss=0.03738, over 4915.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03018, over 972590.32 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 03:55:10,457 INFO [train.py:715] (2/8) Epoch 14, batch 22550, loss[loss=0.1639, simple_loss=0.2314, pruned_loss=0.04825, over 4776.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03051, over 972481.55 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 03:55:50,813 INFO [train.py:715] (2/8) Epoch 14, batch 22600, loss[loss=0.149, simple_loss=0.2269, pruned_loss=0.03561, over 4852.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2096, pruned_loss=0.03057, over 972236.00 frames.], batch size: 20, lr: 1.56e-04
2022-05-08 03:56:31,696 INFO [train.py:715] (2/8) Epoch 14, batch 22650, loss[loss=0.135, simple_loss=0.2056, pruned_loss=0.03219, over 4872.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2096, pruned_loss=0.03058, over 972757.60 frames.], batch size: 16, lr: 1.56e-04
2022-05-08 03:57:11,533 INFO [train.py:715] (2/8) Epoch 14, batch 22700, loss[loss=0.1167, simple_loss=0.187, pruned_loss=0.02323, over 4961.00 frames.], tot_loss[loss=0.135, simple_loss=0.2095, pruned_loss=0.03028, over 973576.07 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:57:50,665 INFO [train.py:715] (2/8) Epoch 14, batch 22750, loss[loss=0.1281, simple_loss=0.1911, pruned_loss=0.03256, over 4985.00 frames.], tot_loss[loss=0.135, simple_loss=0.2096, pruned_loss=0.03018, over 973577.70 frames.], batch size: 14, lr: 1.56e-04
2022-05-08 03:58:32,001 INFO [train.py:715] (2/8) Epoch 14, batch 22800, loss[loss=0.1409, simple_loss=0.2283, pruned_loss=0.02675, over 4713.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2102, pruned_loss=0.0301, over 972591.03 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 03:59:12,917 INFO [train.py:715] (2/8) Epoch 14, batch 22850, loss[loss=0.1206, simple_loss=0.2056, pruned_loss=0.01782, over 4967.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2094, pruned_loss=0.02995, over 972464.91 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 03:59:53,208 INFO [train.py:715] (2/8) Epoch 14, batch 22900, loss[loss=0.1366, simple_loss=0.2025, pruned_loss=0.0354, over 4783.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2087, pruned_loss=0.02976, over 972388.33 frames.], batch size: 17, lr: 1.56e-04
2022-05-08 04:00:33,080 INFO [train.py:715] (2/8) Epoch 14, batch 22950, loss[loss=0.1625, simple_loss=0.2389, pruned_loss=0.04306, over 4875.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2091, pruned_loss=0.03022, over 972574.96 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 04:01:13,587 INFO [train.py:715] (2/8) Epoch 14, batch 23000, loss[loss=0.1634, simple_loss=0.2321, pruned_loss=0.04734, over 4955.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2101, pruned_loss=0.03073, over 973052.07 frames.], batch size: 39, lr: 1.56e-04
2022-05-08 04:01:53,101 INFO [train.py:715] (2/8) Epoch 14, batch 23050, loss[loss=0.1697, simple_loss=0.2385, pruned_loss=0.0505, over 4841.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03037, over 972859.09 frames.], batch size: 32, lr: 1.56e-04
2022-05-08 04:02:32,415 INFO [train.py:715] (2/8) Epoch 14, batch 23100, loss[loss=0.1234, simple_loss=0.1956, pruned_loss=0.02559, over 4860.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03038, over 973001.98 frames.], batch size: 12, lr: 1.56e-04
2022-05-08 04:03:13,066 INFO [train.py:715] (2/8) Epoch 14, batch 23150, loss[loss=0.1662, simple_loss=0.2267, pruned_loss=0.05284, over 4979.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03049, over 973009.79 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 04:03:54,340 INFO [train.py:715] (2/8) Epoch 14, batch 23200, loss[loss=0.1559, simple_loss=0.2281, pruned_loss=0.04183, over 4952.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03064, over 973246.69 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 04:04:33,072 INFO [train.py:715] (2/8) Epoch 14, batch 23250, loss[loss=0.1444, simple_loss=0.22, pruned_loss=0.03441, over 4817.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03047, over 972796.36 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 04:05:13,472 INFO [train.py:715] (2/8) Epoch 14, batch 23300, loss[loss=0.1143, simple_loss=0.1876, pruned_loss=0.02046, over 4795.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2091, pruned_loss=0.03005, over 972912.24 frames.], batch size: 24, lr: 1.56e-04
2022-05-08 04:05:54,161 INFO [train.py:715] (2/8) Epoch 14, batch 23350, loss[loss=0.1186, simple_loss=0.1924, pruned_loss=0.02241, over 4955.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02991, over 972608.29 frames.], batch size: 21, lr: 1.56e-04
2022-05-08 04:06:33,754 INFO [train.py:715] (2/8) Epoch 14, batch 23400, loss[loss=0.1303, simple_loss=0.2028, pruned_loss=0.02892, over 4988.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.0306, over 972757.99 frames.], batch size: 26, lr: 1.56e-04
2022-05-08 04:07:12,806 INFO [train.py:715] (2/8) Epoch 14, batch 23450, loss[loss=0.1335, simple_loss=0.2042, pruned_loss=0.03133, over 4833.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03095, over 973135.95 frames.], batch size: 15, lr: 1.56e-04
2022-05-08 04:07:53,402 INFO [train.py:715] (2/8) Epoch 14, batch 23500, loss[loss=0.135, simple_loss=0.2044, pruned_loss=0.03282, over 4821.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03076, over 972206.52 frames.], batch size: 26, lr: 1.56e-04
2022-05-08 04:08:34,058 INFO [train.py:715] (2/8) Epoch 14, batch 23550, loss[loss=0.1383, simple_loss=0.2091, pruned_loss=0.03369, over 4872.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03048, over 971646.17 frames.], batch size: 38, lr: 1.56e-04
2022-05-08 04:09:13,317 INFO [train.py:715] (2/8) Epoch 14, batch 23600, loss[loss=0.1386, simple_loss=0.2021, pruned_loss=0.03758, over 4880.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03039, over 971599.48 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 04:09:52,603 INFO [train.py:715] (2/8) Epoch 14, batch 23650, loss[loss=0.1332, simple_loss=0.2044, pruned_loss=0.03095, over 4815.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.03076, over 972148.40 frames.], batch size: 25, lr: 1.56e-04
2022-05-08 04:10:32,138 INFO [train.py:715] (2/8) Epoch 14, batch 23700, loss[loss=0.1159, simple_loss=0.1988, pruned_loss=0.01654, over 4889.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03043, over 971946.50 frames.], batch size: 22, lr: 1.56e-04
2022-05-08 04:11:11,202 INFO [train.py:715] (2/8) Epoch 14, batch 23750, loss[loss=0.1088, simple_loss=0.189, pruned_loss=0.01424, over 4766.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02988, over 972191.04 frames.], batch size: 19, lr: 1.56e-04
2022-05-08 04:11:50,484 INFO [train.py:715] (2/8) Epoch 14, batch 23800, loss[loss=0.1706, simple_loss=0.2363, pruned_loss=0.0524, over 4859.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.0298, over 973134.73 frames.], batch size: 32, lr: 1.56e-04
2022-05-08 04:12:30,656 INFO [train.py:715] (2/8) Epoch 14, batch 23850, loss[loss=0.1297, simple_loss=0.2036, pruned_loss=0.02788, over 4936.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02976, over 972539.95 frames.], batch size: 29, lr: 1.56e-04
2022-05-08 04:13:10,489 INFO [train.py:715] (2/8) Epoch 14, batch 23900, loss[loss=0.1395, simple_loss=0.2164, pruned_loss=0.03134, over 4789.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02977, over 972211.20 frames.], batch size: 18, lr: 1.56e-04
2022-05-08 04:13:49,738 INFO [train.py:715] (2/8) Epoch 14, batch 23950, loss[loss=0.1396, simple_loss=0.2232, pruned_loss=0.028, over 4849.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02945, over 972177.01 frames.], batch size: 30, lr: 1.55e-04
2022-05-08 04:14:30,061 INFO [train.py:715] (2/8) Epoch 14, batch 24000, loss[loss=0.1467, simple_loss=0.2228, pruned_loss=0.03532, over 4767.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03009, over 972279.72 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:14:30,062 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 04:14:41,438 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.1052, simple_loss=0.1889, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 04:15:21,385 INFO [train.py:715] (2/8) Epoch 14, batch 24050, loss[loss=0.1391, simple_loss=0.2182, pruned_loss=0.03001, over 4797.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03061, over 972119.20 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 04:16:02,438 INFO [train.py:715] (2/8) Epoch 14, batch 24100, loss[loss=0.1492, simple_loss=0.2289, pruned_loss=0.03478, over 4881.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03059, over 972800.57 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 04:16:41,514 INFO [train.py:715] (2/8) Epoch 14, batch 24150, loss[loss=0.1297, simple_loss=0.2058, pruned_loss=0.02678, over 4945.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.03052, over 972442.48 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 04:17:21,103 INFO [train.py:715] (2/8) Epoch 14, batch 24200, loss[loss=0.1123, simple_loss=0.1844, pruned_loss=0.02008, over 4977.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03053, over 972661.94 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 04:18:01,396 INFO [train.py:715] (2/8) Epoch 14, batch 24250, loss[loss=0.1575, simple_loss=0.2254, pruned_loss=0.04481, over 4928.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03046, over 973498.60 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 04:18:41,643 INFO [train.py:715] (2/8) Epoch 14, batch 24300, loss[loss=0.1317, simple_loss=0.1981, pruned_loss=0.03264, over 4775.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03041, over 973520.21 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 04:19:20,605 INFO [train.py:715] (2/8) Epoch 14, batch 24350, loss[loss=0.1167, simple_loss=0.1905, pruned_loss=0.02144, over 4896.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03039, over 973051.53 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:20:01,382 INFO [train.py:715] (2/8) Epoch 14, batch 24400, loss[loss=0.1047, simple_loss=0.1751, pruned_loss=0.0172, over 4836.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02983, over 972657.12 frames.], batch size: 30, lr: 1.55e-04
2022-05-08 04:20:43,022 INFO [train.py:715] (2/8) Epoch 14, batch 24450, loss[loss=0.1187, simple_loss=0.1951, pruned_loss=0.02114, over 4900.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02953, over 972397.24 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:21:22,337 INFO [train.py:715] (2/8) Epoch 14, batch 24500, loss[loss=0.1322, simple_loss=0.2092, pruned_loss=0.02757, over 4854.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2068, pruned_loss=0.02971, over 971443.99 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 04:22:02,601 INFO [train.py:715] (2/8) Epoch 14, batch 24550, loss[loss=0.1252, simple_loss=0.198, pruned_loss=0.02624, over 4954.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02933, over 971432.68 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 04:22:43,753 INFO [train.py:715] (2/8) Epoch 14, batch 24600, loss[loss=0.1538, simple_loss=0.2299, pruned_loss=0.03887, over 4813.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2062, pruned_loss=0.02957, over 971090.87 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 04:23:25,375 INFO [train.py:715] (2/8) Epoch 14, batch 24650, loss[loss=0.1219, simple_loss=0.1942, pruned_loss=0.02479, over 4877.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03004, over 970771.84 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 04:24:07,554 INFO [train.py:715] (2/8) Epoch 14, batch 24700, loss[loss=0.1226, simple_loss=0.1962, pruned_loss=0.02446, over 4974.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03063, over 971614.61 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:24:48,435 INFO [train.py:715] (2/8) Epoch 14, batch 24750, loss[loss=0.1149, simple_loss=0.1914, pruned_loss=0.01925, over 4877.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2093, pruned_loss=0.0311, over 972596.11 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 04:25:30,052 INFO [train.py:715] (2/8) Epoch 14, batch 24800, loss[loss=0.1148, simple_loss=0.1907, pruned_loss=0.01946, over 4955.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03076, over 972748.03 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 04:26:10,630 INFO [train.py:715] (2/8) Epoch 14, batch 24850, loss[loss=0.1233, simple_loss=0.1963, pruned_loss=0.02516, over 4710.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2104, pruned_loss=0.03119, over 972852.49 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:26:50,217 INFO [train.py:715] (2/8) Epoch 14, batch 24900, loss[loss=0.1577, simple_loss=0.2303, pruned_loss=0.04254, over 4881.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2102, pruned_loss=0.03068, over 972593.45 frames.], batch size: 32, lr: 1.55e-04
2022-05-08 04:27:31,156 INFO [train.py:715] (2/8) Epoch 14, batch 24950, loss[loss=0.1628, simple_loss=0.2354, pruned_loss=0.04503, over 4994.00 frames.], tot_loss[loss=0.1362, simple_loss=0.211, pruned_loss=0.03074, over 971947.65 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 04:28:12,052 INFO [train.py:715] (2/8) Epoch 14, batch 25000, loss[loss=0.1169, simple_loss=0.1954, pruned_loss=0.01925, over 4780.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2106, pruned_loss=0.03078, over 971711.13 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 04:28:51,315 INFO [train.py:715] (2/8) Epoch 14, batch 25050, loss[loss=0.1191, simple_loss=0.1936, pruned_loss=0.02227, over 4908.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2104, pruned_loss=0.03107, over 971247.36 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:29:32,178 INFO [train.py:715] (2/8) Epoch 14, batch 25100, loss[loss=0.1565, simple_loss=0.2341, pruned_loss=0.0395, over 4953.00 frames.], tot_loss[loss=0.136, simple_loss=0.2103, pruned_loss=0.03087, over 971614.70 frames.], batch size: 35, lr: 1.55e-04
2022-05-08 04:30:13,134 INFO [train.py:715] (2/8) Epoch 14, batch 25150, loss[loss=0.1333, simple_loss=0.2118, pruned_loss=0.0274, over 4929.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2105, pruned_loss=0.03123, over 972673.97 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 04:30:53,334 INFO [train.py:715] (2/8) Epoch 14, batch 25200, loss[loss=0.1558, simple_loss=0.2307, pruned_loss=0.04039, over 4687.00 frames.], tot_loss[loss=0.136, simple_loss=0.2103, pruned_loss=0.03087, over 971980.89 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:31:31,960 INFO [train.py:715] (2/8) Epoch 14, batch 25250, loss[loss=0.1095, simple_loss=0.1767, pruned_loss=0.02113, over 4878.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2095, pruned_loss=0.03075, over 971505.36 frames.], batch size: 32, lr: 1.55e-04
2022-05-08 04:32:12,608 INFO [train.py:715] (2/8) Epoch 14, batch 25300, loss[loss=0.1179, simple_loss=0.2016, pruned_loss=0.01711, over 4867.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03058, over 971620.06 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 04:32:53,033 INFO [train.py:715] (2/8) Epoch 14, batch 25350, loss[loss=0.1576, simple_loss=0.2213, pruned_loss=0.04694, over 4902.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03036, over 970982.46 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:33:31,588 INFO [train.py:715] (2/8) Epoch 14, batch 25400, loss[loss=0.152, simple_loss=0.2239, pruned_loss=0.04007, over 4848.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2101, pruned_loss=0.03086, over 970725.74 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 04:34:11,983 INFO [train.py:715] (2/8) Epoch 14, batch 25450, loss[loss=0.1581, simple_loss=0.2387, pruned_loss=0.03873, over 4794.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2096, pruned_loss=0.0304, over 971155.89 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 04:34:52,379 INFO [train.py:715] (2/8) Epoch 14, batch 25500, loss[loss=0.116, simple_loss=0.1969, pruned_loss=0.01754, over 4811.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.03021, over 972072.83 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 04:35:31,818 INFO [train.py:715] (2/8) Epoch 14, batch 25550, loss[loss=0.1768, simple_loss=0.2416, pruned_loss=0.05601, over 4868.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2101, pruned_loss=0.0311, over 972541.57 frames.], batch size: 30, lr: 1.55e-04
2022-05-08 04:36:10,560 INFO [train.py:715] (2/8) Epoch 14, batch 25600, loss[loss=0.1304, simple_loss=0.2073, pruned_loss=0.02676, over 4754.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2109, pruned_loss=0.03146, over 972215.66 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 04:36:50,633 INFO [train.py:715] (2/8) Epoch 14, batch 25650, loss[loss=0.1404, simple_loss=0.2135, pruned_loss=0.03363, over 4799.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03093, over 972595.48 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 04:37:30,747 INFO [train.py:715] (2/8) Epoch 14, batch 25700, loss[loss=0.1449, simple_loss=0.2176, pruned_loss=0.03604, over 4883.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03061, over 971881.27 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 04:38:09,214 INFO [train.py:715] (2/8) Epoch 14, batch 25750, loss[loss=0.1483, simple_loss=0.2243, pruned_loss=0.03616, over 4910.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2102, pruned_loss=0.03111, over 971894.98 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 04:38:48,531 INFO [train.py:715] (2/8) Epoch 14, batch 25800, loss[loss=0.1413, simple_loss=0.2165, pruned_loss=0.03299, over 4910.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2096, pruned_loss=0.03093, over 972406.48 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:39:28,745 INFO [train.py:715] (2/8) Epoch 14, batch 25850, loss[loss=0.1181, simple_loss=0.1924, pruned_loss=0.02194, over 4994.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.0308, over 972030.83 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 04:40:07,963 INFO [train.py:715] (2/8) Epoch 14, batch 25900, loss[loss=0.1159, simple_loss=0.1951, pruned_loss=0.01837, over 4813.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.03075, over 972965.31 frames.], batch size: 26, lr: 1.55e-04
2022-05-08 04:40:46,742 INFO [train.py:715] (2/8) Epoch 14, batch 25950, loss[loss=0.1437, simple_loss=0.223, pruned_loss=0.03215, over 4795.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03074, over 972124.41 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 04:41:26,884 INFO [train.py:715] (2/8) Epoch 14, batch 26000, loss[loss=0.1223, simple_loss=0.1878, pruned_loss=0.02835, over 4696.00 frames.], tot_loss[loss=0.1361, simple_loss=0.21, pruned_loss=0.03114, over 972072.54 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:42:06,870 INFO [train.py:715] (2/8) Epoch 14, batch 26050, loss[loss=0.1112, simple_loss=0.1833, pruned_loss=0.01951, over 4856.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03094, over 971217.75 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 04:42:44,779 INFO [train.py:715] (2/8) Epoch 14, batch 26100, loss[loss=0.1494, simple_loss=0.2168, pruned_loss=0.041, over 4853.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03097, over 971097.48 frames.], batch size: 30, lr: 1.55e-04
2022-05-08 04:43:24,715 INFO [train.py:715] (2/8) Epoch 14, batch 26150, loss[loss=0.1167, simple_loss=0.1966, pruned_loss=0.01843, over 4797.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03057, over 970935.54 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:44:05,200 INFO [train.py:715] (2/8) Epoch 14, batch 26200, loss[loss=0.1319, simple_loss=0.2126, pruned_loss=0.02556, over 4992.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03009, over 970930.59 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 04:44:44,005 INFO [train.py:715] (2/8) Epoch 14, batch 26250, loss[loss=0.1332, simple_loss=0.2023, pruned_loss=0.032, over 4748.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03059, over 971793.54 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:45:23,184 INFO [train.py:715] (2/8) Epoch 14, batch 26300, loss[loss=0.1141, simple_loss=0.1868, pruned_loss=0.02076, over 4801.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.03045, over 971027.05 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 04:46:03,661 INFO [train.py:715] (2/8) Epoch 14, batch 26350, loss[loss=0.1291, simple_loss=0.2059, pruned_loss=0.02611, over 4806.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.0307, over 970883.87 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 04:46:43,192 INFO [train.py:715] (2/8) Epoch 14, batch 26400, loss[loss=0.1308, simple_loss=0.2008, pruned_loss=0.03041, over 4835.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2095, pruned_loss=0.03116, over 971473.30 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:47:21,825 INFO [train.py:715] (2/8) Epoch 14, batch 26450, loss[loss=0.1389, simple_loss=0.2143, pruned_loss=0.03173, over 4988.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03072, over 971498.41 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:48:02,185 INFO [train.py:715] (2/8) Epoch 14, batch 26500, loss[loss=0.1081, simple_loss=0.1755, pruned_loss=0.0204, over 4771.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03028, over 971081.02 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:48:42,600 INFO [train.py:715] (2/8) Epoch 14, batch 26550, loss[loss=0.1279, simple_loss=0.2115, pruned_loss=0.02213, over 4881.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.0301, over 971379.17 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 04:49:21,895 INFO [train.py:715] (2/8) Epoch 14, batch 26600, loss[loss=0.1254, simple_loss=0.2049, pruned_loss=0.02289, over 4946.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02999, over 971342.65 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 04:50:00,872 INFO [train.py:715] (2/8) Epoch 14, batch 26650, loss[loss=0.1145, simple_loss=0.1838, pruned_loss=0.02256, over 4766.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03008, over 970709.04 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:50:41,180 INFO [train.py:715] (2/8) Epoch 14, batch 26700, loss[loss=0.1525, simple_loss=0.2286, pruned_loss=0.03826, over 4915.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2096, pruned_loss=0.03106, over 971326.74 frames.], batch size: 39, lr: 1.55e-04
2022-05-08 04:51:21,682 INFO [train.py:715] (2/8) Epoch 14, batch 26750, loss[loss=0.1185, simple_loss=0.1897, pruned_loss=0.02364, over 4776.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03087, over 971605.73 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 04:52:00,693 INFO [train.py:715] (2/8) Epoch 14, batch 26800, loss[loss=0.1226, simple_loss=0.1964, pruned_loss=0.02438, over 4810.00 frames.], tot_loss[loss=0.136, simple_loss=0.2097, pruned_loss=0.03113, over 972284.81 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 04:52:40,480 INFO [train.py:715] (2/8) Epoch 14, batch 26850, loss[loss=0.12, simple_loss=0.1913, pruned_loss=0.02435, over 4977.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2104, pruned_loss=0.03125, over 972712.88 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 04:53:20,916 INFO [train.py:715] (2/8) Epoch 14, batch 26900, loss[loss=0.1148, simple_loss=0.1841, pruned_loss=0.02277, over 4878.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2107, pruned_loss=0.03123, over 972094.20 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 04:54:00,760 INFO [train.py:715] (2/8) Epoch 14, batch 26950, loss[loss=0.1276, simple_loss=0.1998, pruned_loss=0.02776, over 4835.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2103, pruned_loss=0.03095, over 972685.83 frames.], batch size: 27, lr: 1.55e-04
2022-05-08 04:54:39,966 INFO [train.py:715] (2/8) Epoch 14, batch 27000, loss[loss=0.1377, simple_loss=0.2175, pruned_loss=0.02897, over 4792.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03098, over 972262.24 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 04:54:39,966 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 04:54:49,614 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.1049, simple_loss=0.1886, pruned_loss=0.01053, over 914524.00 frames.
2022-05-08 04:55:29,165 INFO [train.py:715] (2/8) Epoch 14, batch 27050, loss[loss=0.1163, simple_loss=0.1888, pruned_loss=0.02191, over 4880.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03103, over 971469.08 frames.], batch size: 32, lr: 1.55e-04
2022-05-08 04:56:09,817 INFO [train.py:715] (2/8) Epoch 14, batch 27100, loss[loss=0.1349, simple_loss=0.2166, pruned_loss=0.02663, over 4800.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2094, pruned_loss=0.0306, over 970994.85 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 04:56:50,343 INFO [train.py:715] (2/8) Epoch 14, batch 27150, loss[loss=0.1207, simple_loss=0.1987, pruned_loss=0.0213, over 4848.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03076, over 971721.60 frames.], batch size: 30, lr: 1.55e-04
2022-05-08 04:57:29,051 INFO [train.py:715] (2/8) Epoch 14, batch 27200, loss[loss=0.1317, simple_loss=0.21, pruned_loss=0.02667, over 4983.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2098, pruned_loss=0.03086, over 972782.10 frames.], batch size: 28, lr: 1.55e-04
2022-05-08 04:58:08,435 INFO [train.py:715] (2/8) Epoch 14, batch 27250, loss[loss=0.1621, simple_loss=0.2318, pruned_loss=0.04617, over 4744.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2094, pruned_loss=0.03046, over 972415.76 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 04:58:48,576 INFO [train.py:715] (2/8) Epoch 14, batch 27300, loss[loss=0.1488, simple_loss=0.2216, pruned_loss=0.03801, over 4830.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03036, over 971482.13 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 04:59:28,192 INFO [train.py:715] (2/8) Epoch 14, batch 27350, loss[loss=0.1387, simple_loss=0.2154, pruned_loss=0.03106, over 4774.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02986, over 970857.38 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:00:06,590 INFO [train.py:715] (2/8) Epoch 14, batch 27400, loss[loss=0.1128, simple_loss=0.193, pruned_loss=0.01625, over 4915.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2085, pruned_loss=0.02938, over 971667.71 frames.], batch size: 23, lr: 1.55e-04
2022-05-08 05:00:46,867 INFO [train.py:715] (2/8) Epoch 14, batch 27450, loss[loss=0.1081, simple_loss=0.1842, pruned_loss=0.01598, over 4912.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2083, pruned_loss=0.02953, over 971429.75 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:01:26,696 INFO [train.py:715] (2/8) Epoch 14, batch 27500, loss[loss=0.1419, simple_loss=0.2115, pruned_loss=0.03615, over 4911.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2085, pruned_loss=0.02971, over 971696.94 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:02:05,456 INFO [train.py:715] (2/8) Epoch 14, batch 27550, loss[loss=0.1946, simple_loss=0.2606, pruned_loss=0.06432, over 4880.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2101, pruned_loss=0.03061, over 970936.95 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 05:02:45,161 INFO [train.py:715] (2/8) Epoch 14, batch 27600, loss[loss=0.1428, simple_loss=0.2072, pruned_loss=0.03914, over 4693.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2102, pruned_loss=0.03081, over 970820.14 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:03:25,491 INFO [train.py:715] (2/8) Epoch 14, batch 27650, loss[loss=0.1485, simple_loss=0.2207, pruned_loss=0.03821, over 4982.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2104, pruned_loss=0.03092, over 971223.90 frames.], batch size: 35, lr: 1.55e-04
2022-05-08 05:04:04,755 INFO [train.py:715] (2/8) Epoch 14, batch 27700, loss[loss=0.1525, simple_loss=0.2161, pruned_loss=0.04447, over 4910.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.03093, over 971543.35 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 05:04:43,283 INFO [train.py:715] (2/8) Epoch 14, batch 27750, loss[loss=0.139, simple_loss=0.2173, pruned_loss=0.03034, over 4859.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2092, pruned_loss=0.03092, over 971159.10 frames.], batch size: 32, lr: 1.55e-04
2022-05-08 05:05:23,451 INFO [train.py:715] (2/8) Epoch 14, batch 27800, loss[loss=0.1452, simple_loss=0.2266, pruned_loss=0.03192, over 4975.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.03085, over 972205.59 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:06:03,188 INFO [train.py:715] (2/8) Epoch 14, batch 27850, loss[loss=0.1321, simple_loss=0.2045, pruned_loss=0.02989, over 4884.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03069, over 971261.63 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 05:06:41,703 INFO [train.py:715] (2/8) Epoch 14, batch 27900, loss[loss=0.1407, simple_loss=0.2117, pruned_loss=0.03484, over 4955.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03055, over 970947.64 frames.], batch size: 24, lr: 1.55e-04
2022-05-08 05:07:21,720 INFO [train.py:715] (2/8) Epoch 14, batch 27950, loss[loss=0.1099, simple_loss=0.1778, pruned_loss=0.02096, over 4973.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03054, over 971130.53 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 05:08:01,578 INFO [train.py:715] (2/8) Epoch 14, batch 28000, loss[loss=0.1423, simple_loss=0.2163, pruned_loss=0.03415, over 4891.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03096, over 971860.96 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:08:40,620 INFO [train.py:715] (2/8) Epoch 14, batch 28050, loss[loss=0.1302, simple_loss=0.204, pruned_loss=0.02821, over 4832.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.0301, over 972001.09 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:09:19,679 INFO [train.py:715] (2/8) Epoch 14, batch 28100, loss[loss=0.1312, simple_loss=0.2042, pruned_loss=0.02909, over 4951.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03006, over 971590.19 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 05:10:00,233 INFO [train.py:715] (2/8) Epoch 14, batch 28150, loss[loss=0.1394, simple_loss=0.211, pruned_loss=0.03385, over 4894.00 frames.], tot_loss[loss=0.1351, simple_loss=0.209, pruned_loss=0.03066, over 972400.06 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 05:10:39,942 INFO [train.py:715] (2/8) Epoch 14, batch 28200, loss[loss=0.1261, simple_loss=0.1873, pruned_loss=0.03244, over 4736.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03074, over 972293.65 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:11:17,982 INFO [train.py:715] (2/8) Epoch 14, batch 28250, loss[loss=0.1149, simple_loss=0.1865, pruned_loss=0.02161, over 4813.00 frames.], tot_loss[loss=0.136, simple_loss=0.21, pruned_loss=0.03101, over 972225.41 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 05:11:58,124 INFO [train.py:715] (2/8) Epoch 14, batch 28300, loss[loss=0.1831, simple_loss=0.2557, pruned_loss=0.05525, over 4919.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2096, pruned_loss=0.03076, over 972326.07 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:12:38,003 INFO [train.py:715] (2/8) Epoch 14, batch 28350, loss[loss=0.1067, simple_loss=0.1835, pruned_loss=0.015, over 4761.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03028, over 972205.60 frames.], batch size: 12, lr: 1.55e-04
2022-05-08 05:13:16,551 INFO [train.py:715] (2/8) Epoch 14, batch 28400, loss[loss=0.1703, simple_loss=0.2532, pruned_loss=0.04376, over 4705.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03007, over 971777.92 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:13:56,137 INFO [train.py:715] (2/8) Epoch 14, batch 28450, loss[loss=0.1329, simple_loss=0.2012, pruned_loss=0.03232, over 4940.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03023, over 972114.04 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:14:36,400 INFO [train.py:715] (2/8) Epoch 14, batch 28500, loss[loss=0.1223, simple_loss=0.2073, pruned_loss=0.01864, over 4938.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03011, over 971816.90 frames.], batch size: 29, lr: 1.55e-04
2022-05-08 05:15:15,662 INFO [train.py:715] (2/8) Epoch 14, batch 28550, loss[loss=0.159, simple_loss=0.2278, pruned_loss=0.04509, over 4830.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03088, over 972257.64 frames.], batch size: 30, lr: 1.55e-04
2022-05-08 05:15:54,181 INFO [train.py:715] (2/8) Epoch 14, batch 28600, loss[loss=0.1476, simple_loss=0.2156, pruned_loss=0.03985, over 4961.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2103, pruned_loss=0.03113, over 971469.32 frames.], batch size: 39, lr: 1.55e-04
2022-05-08 05:16:34,507 INFO [train.py:715] (2/8) Epoch 14, batch 28650, loss[loss=0.1338, simple_loss=0.2094, pruned_loss=0.0291, over 4878.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2105, pruned_loss=0.03134, over 970748.11 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:17:14,556 INFO [train.py:715] (2/8) Epoch 14, batch 28700, loss[loss=0.1157, simple_loss=0.1855, pruned_loss=0.02289, over 4773.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2099, pruned_loss=0.03113, over 971775.68 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 05:17:52,651 INFO [train.py:715] (2/8) Epoch 14, batch 28750, loss[loss=0.1273, simple_loss=0.2077, pruned_loss=0.02347, over 4988.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03039, over 971914.25 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 05:18:32,372 INFO [train.py:715] (2/8) Epoch 14, batch 28800, loss[loss=0.1327, simple_loss=0.2047, pruned_loss=0.03038, over 4834.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03033, over 972005.51 frames.], batch size: 13, lr: 1.55e-04
2022-05-08 05:19:12,481 INFO [train.py:715] (2/8) Epoch 14, batch 28850, loss[loss=0.1449, simple_loss=0.2251, pruned_loss=0.03239, over 4796.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2097, pruned_loss=0.03106, over 971990.43 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:19:52,378 INFO [train.py:715] (2/8) Epoch 14, batch 28900, loss[loss=0.1305, simple_loss=0.1963, pruned_loss=0.03237, over 4837.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03068, over 971419.95 frames.], batch size: 30, lr: 1.55e-04
2022-05-08 05:20:30,225 INFO [train.py:715] (2/8) Epoch 14, batch 28950, loss[loss=0.1836, simple_loss=0.2464, pruned_loss=0.0604, over 4970.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2099, pruned_loss=0.03052, over 971607.47 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 05:21:10,703 INFO [train.py:715] (2/8) Epoch 14, batch 29000, loss[loss=0.1296, simple_loss=0.1941, pruned_loss=0.03259, over 4893.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03038, over 971167.52 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:21:50,335 INFO [train.py:715] (2/8) Epoch 14, batch 29050, loss[loss=0.1435, simple_loss=0.2124, pruned_loss=0.03729, over 4873.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2096, pruned_loss=0.03068, over 971802.03 frames.], batch size: 16, lr: 1.55e-04
2022-05-08 05:22:29,103 INFO [train.py:715] (2/8) Epoch 14, batch 29100, loss[loss=0.1111, simple_loss=0.1903, pruned_loss=0.01598, over 4868.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2096, pruned_loss=0.03048, over 970793.28 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 05:23:08,496 INFO [train.py:715] (2/8) Epoch 14, batch 29150, loss[loss=0.1202, simple_loss=0.1949, pruned_loss=0.02275, over 4821.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2097, pruned_loss=0.03037, over 971316.11 frames.], batch size: 27, lr: 1.55e-04
2022-05-08 05:23:48,532 INFO [train.py:715] (2/8) Epoch 14, batch 29200, loss[loss=0.1537, simple_loss=0.2238, pruned_loss=0.04185, over 4866.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2097, pruned_loss=0.03053, over 972270.61 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 05:24:28,393 INFO [train.py:715] (2/8) Epoch 14, batch 29250, loss[loss=0.1328, simple_loss=0.2039, pruned_loss=0.03084, over 4933.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02991, over 972372.06 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:25:06,489 INFO [train.py:715] (2/8) Epoch 14, batch 29300, loss[loss=0.127, simple_loss=0.2001, pruned_loss=0.02694, over 4953.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03003, over 972605.34 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:25:46,610 INFO [train.py:715] (2/8) Epoch 14, batch 29350, loss[loss=0.1305, simple_loss=0.2076, pruned_loss=0.02664, over 4775.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03029, over 971268.44 frames.], batch size: 18, lr: 1.55e-04
2022-05-08 05:26:26,516 INFO [train.py:715] (2/8) Epoch 14, batch 29400, loss[loss=0.1103, simple_loss=0.1841, pruned_loss=0.01826, over 4902.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03005, over 972856.50 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 05:27:05,391 INFO [train.py:715] (2/8) Epoch 14, batch 29450, loss[loss=0.1199, simple_loss=0.1954, pruned_loss=0.02216, over 4983.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03015, over 972968.45 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 05:27:45,241 INFO [train.py:715] (2/8) Epoch 14, batch 29500, loss[loss=0.1301, simple_loss=0.2034, pruned_loss=0.02841, over 4781.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03002, over 973613.59 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 05:28:25,577 INFO [train.py:715] (2/8) Epoch 14, batch 29550, loss[loss=0.12, simple_loss=0.1976, pruned_loss=0.02124, over 4812.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.02998, over 973513.62 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 05:29:05,388 INFO [train.py:715] (2/8) Epoch 14, batch 29600, loss[loss=0.1469, simple_loss=0.2168, pruned_loss=0.03853, over 4810.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2079, pruned_loss=0.03048, over 973463.93 frames.], batch size: 14, lr: 1.55e-04
2022-05-08 05:29:44,399 INFO [train.py:715] (2/8) Epoch 14, batch 29650, loss[loss=0.143, simple_loss=0.2227, pruned_loss=0.03168, over 4906.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02991, over 972408.46 frames.], batch size: 19, lr: 1.55e-04
2022-05-08 05:30:25,220 INFO [train.py:715] (2/8) Epoch 14, batch 29700, loss[loss=0.1323, simple_loss=0.1987, pruned_loss=0.03297, over 4775.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.03001, over 972641.30 frames.], batch size: 17, lr: 1.55e-04
2022-05-08 05:31:06,276 INFO [train.py:715] (2/8) Epoch 14, batch 29750, loss[loss=0.1255, simple_loss=0.1969, pruned_loss=0.02705, over 4942.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03003, over 973016.63 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:31:45,877 INFO [train.py:715] (2/8) Epoch 14, batch 29800, loss[loss=0.1256, simple_loss=0.2011, pruned_loss=0.02504, over 4794.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03013, over 972633.71 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:32:26,701 INFO [train.py:715] (2/8) Epoch 14, batch 29850, loss[loss=0.1431, simple_loss=0.2112, pruned_loss=0.03756, over 4834.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03048, over 971988.64 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:33:06,687 INFO [train.py:715] (2/8) Epoch 14, batch 29900, loss[loss=0.121, simple_loss=0.1921, pruned_loss=0.02495, over 4849.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03083, over 972511.09 frames.], batch size: 32, lr: 1.55e-04
2022-05-08 05:33:46,334 INFO [train.py:715] (2/8) Epoch 14, batch 29950, loss[loss=0.1247, simple_loss=0.2083, pruned_loss=0.02058, over 4800.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03045, over 972676.84 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:34:25,103 INFO [train.py:715] (2/8) Epoch 14, batch 30000, loss[loss=0.1438, simple_loss=0.2274, pruned_loss=0.03011, over 4798.00 frames.], tot_loss[loss=0.135, simple_loss=0.2092, pruned_loss=0.0304, over 971693.41 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:34:25,104 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 05:34:42,242 INFO [train.py:742] (2/8) Epoch 14, validation: loss=0.1052, simple_loss=0.189, pruned_loss=0.01075, over 914524.00 frames.
2022-05-08 05:35:21,220 INFO [train.py:715] (2/8) Epoch 14, batch 30050, loss[loss=0.1153, simple_loss=0.1936, pruned_loss=0.01851, over 4905.00 frames.], tot_loss[loss=0.1347, simple_loss=0.209, pruned_loss=0.0302, over 973100.03 frames.], batch size: 22, lr: 1.55e-04
2022-05-08 05:36:01,188 INFO [train.py:715] (2/8) Epoch 14, batch 30100, loss[loss=0.16, simple_loss=0.231, pruned_loss=0.04445, over 4875.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03049, over 973130.56 frames.], batch size: 20, lr: 1.55e-04
2022-05-08 05:36:42,304 INFO [train.py:715] (2/8) Epoch 14, batch 30150, loss[loss=0.1249, simple_loss=0.1936, pruned_loss=0.02814, over 4695.00 frames.], tot_loss[loss=0.1351, simple_loss=0.209, pruned_loss=0.03058, over 973132.01 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:37:21,237 INFO [train.py:715] (2/8) Epoch 14, batch 30200, loss[loss=0.1485, simple_loss=0.2113, pruned_loss=0.04279, over 4804.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2098, pruned_loss=0.03131, over 972986.35 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:38:01,176 INFO [train.py:715] (2/8) Epoch 14, batch 30250, loss[loss=0.1283, simple_loss=0.2088, pruned_loss=0.02392, over 4938.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2102, pruned_loss=0.03106, over 973213.40 frames.], batch size: 21, lr: 1.55e-04
2022-05-08 05:38:41,875 INFO [train.py:715] (2/8) Epoch 14, batch 30300, loss[loss=0.1308, simple_loss=0.2038, pruned_loss=0.02892, over 4845.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2107, pruned_loss=0.03158, over 973893.81 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:39:21,374 INFO [train.py:715] (2/8) Epoch 14, batch 30350, loss[loss=0.1546, simple_loss=0.2203, pruned_loss=0.0445, over 4880.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2102, pruned_loss=0.03135, over 974189.99 frames.], batch size: 39, lr: 1.55e-04
2022-05-08 05:40:00,594 INFO [train.py:715] (2/8) Epoch 14, batch 30400, loss[loss=0.1362, simple_loss=0.2119, pruned_loss=0.03023, over 4814.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03045, over 973557.44 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:40:40,493 INFO [train.py:715] (2/8) Epoch 14, batch 30450, loss[loss=0.1421, simple_loss=0.2091, pruned_loss=0.03758, over 4813.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03092, over 973784.34 frames.], batch size: 15, lr: 1.55e-04
2022-05-08 05:41:20,817 INFO [train.py:715] (2/8) Epoch 14, batch 30500, loss[loss=0.1396, simple_loss=0.2118, pruned_loss=0.03369, over 4808.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03103, over 973632.78 frames.], batch size: 25, lr: 1.55e-04
2022-05-08 05:41:59,764 INFO [train.py:715] (2/8) Epoch 14, batch 30550, loss[loss=0.1208, simple_loss=0.2009, pruned_loss=0.02038, over 4883.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2096, pruned_loss=0.03133, over 973078.38 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 05:42:39,649 INFO [train.py:715] (2/8) Epoch 14, batch 30600, loss[loss=0.1414, simple_loss=0.2111, pruned_loss=0.03584, over 4750.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03101, over 972569.39 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 05:43:20,416 INFO [train.py:715] (2/8) Epoch 14, batch 30650, loss[loss=0.1389, simple_loss=0.2121, pruned_loss=0.03288, over 4973.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03094, over 972591.44 frames.], batch size: 24, lr: 1.54e-04
2022-05-08 05:43:59,988 INFO [train.py:715] (2/8) Epoch 14, batch 30700, loss[loss=0.1218, simple_loss=0.2029, pruned_loss=0.02033, over 4769.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03082, over 973038.88 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 05:44:39,752 INFO [train.py:715] (2/8) Epoch 14, batch 30750, loss[loss=0.117, simple_loss=0.1855, pruned_loss=0.02422, over 4825.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2088, pruned_loss=0.03078, over 972882.01 frames.], batch size: 12, lr: 1.54e-04
2022-05-08 05:45:19,650 INFO [train.py:715] (2/8) Epoch 14, batch 30800, loss[loss=0.1496, simple_loss=0.2118, pruned_loss=0.04371, over 4976.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03066, over 971833.16 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 05:46:00,445 INFO [train.py:715] (2/8) Epoch 14, batch 30850, loss[loss=0.121, simple_loss=0.2093, pruned_loss=0.01632, over 4890.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03055, over 972040.93 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 05:46:39,515 INFO [train.py:715] (2/8) Epoch 14, batch 30900, loss[loss=0.1306, simple_loss=0.2017, pruned_loss=0.02973, over 4961.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03016, over 971513.82 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 05:47:18,031 INFO [train.py:715] (2/8) Epoch 14, batch 30950, loss[loss=0.1535, simple_loss=0.2244, pruned_loss=0.04133, over 4899.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03054, over 972374.30 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 05:47:57,794 INFO [train.py:715] (2/8) Epoch 14, batch 31000, loss[loss=0.1325, simple_loss=0.2118, pruned_loss=0.02662, over 4797.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2098, pruned_loss=0.03063, over 971479.07 frames.], batch size: 25, lr: 1.54e-04
2022-05-08 05:48:37,477 INFO [train.py:715] (2/8) Epoch 14, batch 31050, loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02975, over 4944.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.03038, over 972229.54 frames.], batch size: 29, lr: 1.54e-04
2022-05-08 05:49:17,844 INFO [train.py:715] (2/8) Epoch 14, batch 31100, loss[loss=0.1207, simple_loss=0.194, pruned_loss=0.02369, over 4857.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03029, over 972355.71 frames.], batch size: 32, lr: 1.54e-04
2022-05-08 05:49:58,965 INFO [train.py:715] (2/8) Epoch 14, batch 31150, loss[loss=0.1069, simple_loss=0.1868, pruned_loss=0.01354, over 4822.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02998, over 972852.95 frames.], batch size: 27, lr: 1.54e-04
2022-05-08 05:50:40,120 INFO [train.py:715] (2/8) Epoch 14, batch 31200, loss[loss=0.115, simple_loss=0.188, pruned_loss=0.02102, over 4928.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02996, over 972697.43 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 05:51:19,911 INFO [train.py:715] (2/8) Epoch 14, batch 31250, loss[loss=0.15, simple_loss=0.2156, pruned_loss=0.04222, over 4809.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03081, over 973137.02 frames.], batch size: 13, lr: 1.54e-04
2022-05-08 05:52:00,304 INFO [train.py:715] (2/8) Epoch 14, batch 31300, loss[loss=0.1438, simple_loss=0.2077, pruned_loss=0.03997, over 4955.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2091, pruned_loss=0.03116, over 973101.06 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 05:52:41,149 INFO [train.py:715] (2/8) Epoch 14, batch 31350, loss[loss=0.1231, simple_loss=0.1928, pruned_loss=0.0267, over 4986.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2098, pruned_loss=0.03137, over 973845.00 frames.], batch size: 28, lr: 1.54e-04
2022-05-08 05:53:21,033 INFO [train.py:715] (2/8) Epoch 14, batch 31400, loss[loss=0.1193, simple_loss=0.19, pruned_loss=0.02431, over 4987.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03115, over 973441.94 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 05:54:00,705 INFO [train.py:715] (2/8) Epoch 14, batch 31450, loss[loss=0.1163, simple_loss=0.1871, pruned_loss=0.02269, over 4927.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2095, pruned_loss=0.03134, over 973170.04 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 05:54:40,743 INFO [train.py:715] (2/8) Epoch 14, batch 31500, loss[loss=0.1147, simple_loss=0.19, pruned_loss=0.01965, over 4939.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2087, pruned_loss=0.03093, over 973908.72 frames.], batch size: 21, lr: 1.54e-04
2022-05-08 05:55:21,329 INFO [train.py:715] (2/8) Epoch 14, batch 31550, loss[loss=0.1279, simple_loss=0.2086, pruned_loss=0.02357, over 4859.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03047, over 973837.87 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 05:56:01,192 INFO [train.py:715] (2/8) Epoch 14, batch 31600, loss[loss=0.1133, simple_loss=0.1841, pruned_loss=0.02128, over 4869.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02998, over 973575.76 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 05:56:40,685 INFO [train.py:715] (2/8) Epoch 14, batch 31650, loss[loss=0.1313, simple_loss=0.1978, pruned_loss=0.03241, over 4865.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03042, over 973785.01 frames.], batch size: 30, lr: 1.54e-04
2022-05-08 05:57:21,066 INFO [train.py:715] (2/8) Epoch 14, batch 31700, loss[loss=0.1196, simple_loss=0.201, pruned_loss=0.01909, over 4884.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02992, over 974226.12 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 05:58:00,665 INFO [train.py:715] (2/8) Epoch 14, batch 31750, loss[loss=0.1379, simple_loss=0.2002, pruned_loss=0.03779, over 4778.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03018, over 973690.43 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 05:58:40,566 INFO [train.py:715] (2/8) Epoch 14, batch 31800, loss[loss=0.1057, simple_loss=0.1759, pruned_loss=0.01779, over 4746.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03023, over 972433.97 frames.], batch size: 12, lr: 1.54e-04
2022-05-08 05:59:20,871 INFO [train.py:715] (2/8) Epoch 14, batch 31850, loss[loss=0.1481, simple_loss=0.2213, pruned_loss=0.03749, over 4791.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02993, over 972916.85 frames.], batch size: 24, lr: 1.54e-04
2022-05-08 06:00:01,587 INFO [train.py:715] (2/8) Epoch 14, batch 31900, loss[loss=0.1405, simple_loss=0.2015, pruned_loss=0.03981, over 4966.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03008, over 973181.65 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:00:40,982 INFO [train.py:715] (2/8) Epoch 14, batch 31950, loss[loss=0.1284, simple_loss=0.2, pruned_loss=0.02837, over 4983.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03028, over 972460.72 frames.], batch size: 39, lr: 1.54e-04
2022-05-08 06:01:20,558 INFO [train.py:715] (2/8) Epoch 14, batch 32000, loss[loss=0.1375, simple_loss=0.1979, pruned_loss=0.03853, over 4930.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03047, over 971898.77 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 06:02:01,140 INFO [train.py:715] (2/8) Epoch 14, batch 32050, loss[loss=0.1464, simple_loss=0.2096, pruned_loss=0.0416, over 4891.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03015, over 971832.69 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:02:40,616 INFO [train.py:715] (2/8) Epoch 14, batch 32100, loss[loss=0.1314, simple_loss=0.2041, pruned_loss=0.02939, over 4782.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02998, over 971393.52 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 06:03:20,378 INFO [train.py:715] (2/8) Epoch 14, batch 32150, loss[loss=0.1303, simple_loss=0.1966, pruned_loss=0.03204, over 4900.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2068, pruned_loss=0.02971, over 971036.80 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 06:04:00,804 INFO [train.py:715] (2/8) Epoch 14, batch 32200, loss[loss=0.1563, simple_loss=0.2267, pruned_loss=0.04295, over 4922.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02957, over 971068.52 frames.], batch size: 39, lr: 1.54e-04
2022-05-08 06:04:41,241 INFO [train.py:715] (2/8) Epoch 14, batch 32250, loss[loss=0.1462, simple_loss=0.2189, pruned_loss=0.03676, over 4795.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03001, over 970656.92 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:05:20,518 INFO [train.py:715] (2/8) Epoch 14, batch 32300, loss[loss=0.1628, simple_loss=0.2377, pruned_loss=0.04399, over 4943.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02976, over 970425.47 frames.], batch size: 21, lr: 1.54e-04
2022-05-08 06:06:00,144 INFO [train.py:715] (2/8) Epoch 14, batch 32350, loss[loss=0.1303, simple_loss=0.2047, pruned_loss=0.02791, over 4960.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02955, over 971583.09 frames.], batch size: 24, lr: 1.54e-04
2022-05-08 06:06:40,252 INFO [train.py:715] (2/8) Epoch 14, batch 32400, loss[loss=0.1123, simple_loss=0.182, pruned_loss=0.02127, over 4846.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2076, pruned_loss=0.0293, over 971279.53 frames.], batch size: 12, lr: 1.54e-04
2022-05-08 06:07:19,943 INFO [train.py:715] (2/8) Epoch 14, batch 32450, loss[loss=0.1574, simple_loss=0.2365, pruned_loss=0.03913, over 4992.00 frames.], tot_loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02968, over 971110.06 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:07:59,619 INFO [train.py:715] (2/8) Epoch 14, batch 32500, loss[loss=0.1305, simple_loss=0.1982, pruned_loss=0.03136, over 4972.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03004, over 971775.57 frames.], batch size: 35, lr: 1.54e-04
2022-05-08 06:08:39,980 INFO [train.py:715] (2/8) Epoch 14, batch 32550, loss[loss=0.1359, simple_loss=0.2037, pruned_loss=0.03407, over 4772.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2091, pruned_loss=0.03041, over 972117.66 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:09:20,726 INFO [train.py:715] (2/8) Epoch 14, batch 32600, loss[loss=0.1223, simple_loss=0.1937, pruned_loss=0.02542, over 4972.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02997, over 971837.78 frames.], batch size: 35, lr: 1.54e-04
2022-05-08 06:10:00,305 INFO [train.py:715] (2/8) Epoch 14, batch 32650, loss[loss=0.1398, simple_loss=0.2215, pruned_loss=0.02903, over 4914.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03054, over 971687.57 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:10:43,596 INFO [train.py:715] (2/8) Epoch 14, batch 32700, loss[loss=0.1275, simple_loss=0.1985, pruned_loss=0.02824, over 4766.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2099, pruned_loss=0.03098, over 972549.40 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 06:11:24,758 INFO [train.py:715] (2/8) Epoch 14, batch 32750, loss[loss=0.1283, simple_loss=0.207, pruned_loss=0.02482, over 4871.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2098, pruned_loss=0.03107, over 972351.87 frames.], batch size: 20, lr: 1.54e-04
2022-05-08 06:12:05,075 INFO [train.py:715] (2/8) Epoch 14, batch 32800, loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02891, over 4812.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03061, over 973443.95 frames.], batch size: 13, lr: 1.54e-04
2022-05-08 06:12:45,503 INFO [train.py:715] (2/8) Epoch 14, batch 32850, loss[loss=0.1363, simple_loss=0.2163, pruned_loss=0.02819, over 4773.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02989, over 973040.94 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:13:26,800 INFO [train.py:715] (2/8) Epoch 14, batch 32900, loss[loss=0.1434, simple_loss=0.2132, pruned_loss=0.03686, over 4784.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02966, over 972708.05 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:14:07,964 INFO [train.py:715] (2/8) Epoch 14, batch 32950, loss[loss=0.1496, simple_loss=0.2267, pruned_loss=0.0362, over 4861.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02989, over 971820.60 frames.], batch size: 32, lr: 1.54e-04
2022-05-08 06:14:47,661 INFO [train.py:715] (2/8) Epoch 14, batch 33000, loss[loss=0.1323, simple_loss=0.2148, pruned_loss=0.02487, over 4885.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.0302, over 971567.77 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 06:14:47,662 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 06:15:25,560 INFO [train.py:742] (2/8) 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] (2/8) Epoch 14, batch 33050, loss[loss=0.156, simple_loss=0.2209, pruned_loss=0.04549, over 4835.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03035, over 970610.44 frames.], batch size: 30, lr: 1.54e-04
2022-05-08 06:16:46,133 INFO [train.py:715] (2/8) Epoch 14, batch 33100, loss[loss=0.1253, simple_loss=0.209, pruned_loss=0.02077, over 4900.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03011, over 969986.28 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 06:17:27,356 INFO [train.py:715] (2/8) Epoch 14, batch 33150, loss[loss=0.155, simple_loss=0.2284, pruned_loss=0.04079, over 4746.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03018, over 970416.22 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 06:18:07,422 INFO [train.py:715] (2/8) Epoch 14, batch 33200, loss[loss=0.1383, simple_loss=0.2197, pruned_loss=0.02849, over 4917.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03043, over 971275.00 frames.], batch size: 29, lr: 1.54e-04
2022-05-08 06:18:47,771 INFO [train.py:715] (2/8) Epoch 14, batch 33250, loss[loss=0.1466, simple_loss=0.2212, pruned_loss=0.03598, over 4841.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03019, over 971223.48 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:19:28,519 INFO [train.py:715] (2/8) Epoch 14, batch 33300, loss[loss=0.1281, simple_loss=0.2067, pruned_loss=0.02472, over 4806.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03025, over 971673.71 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:20:09,718 INFO [train.py:715] (2/8) Epoch 14, batch 33350, loss[loss=0.1228, simple_loss=0.1974, pruned_loss=0.02414, over 4839.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03032, over 972718.69 frames.], batch size: 13, lr: 1.54e-04
2022-05-08 06:20:49,887 INFO [train.py:715] (2/8) Epoch 14, batch 33400, loss[loss=0.127, simple_loss=0.2062, pruned_loss=0.02391, over 4920.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03024, over 972289.29 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:21:30,268 INFO [train.py:715] (2/8) Epoch 14, batch 33450, loss[loss=0.1241, simple_loss=0.1906, pruned_loss=0.02877, over 4810.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03071, over 972393.39 frames.], batch size: 12, lr: 1.54e-04
2022-05-08 06:22:11,515 INFO [train.py:715] (2/8) Epoch 14, batch 33500, loss[loss=0.1015, simple_loss=0.1737, pruned_loss=0.01461, over 4814.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03041, over 972372.37 frames.], batch size: 27, lr: 1.54e-04
2022-05-08 06:22:51,808 INFO [train.py:715] (2/8) Epoch 14, batch 33550, loss[loss=0.1408, simple_loss=0.2239, pruned_loss=0.02884, over 4779.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03, over 972384.01 frames.], batch size: 18, lr: 1.54e-04
2022-05-08 06:23:32,994 INFO [train.py:715] (2/8) Epoch 14, batch 33600, loss[loss=0.1267, simple_loss=0.1973, pruned_loss=0.02804, over 4865.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02995, over 972603.57 frames.], batch size: 32, lr: 1.54e-04
2022-05-08 06:24:14,046 INFO [train.py:715] (2/8) Epoch 14, batch 33650, loss[loss=0.1181, simple_loss=0.1855, pruned_loss=0.02529, over 4848.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03001, over 973237.91 frames.], batch size: 20, lr: 1.54e-04
2022-05-08 06:24:54,965 INFO [train.py:715] (2/8) Epoch 14, batch 33700, loss[loss=0.1627, simple_loss=0.2409, pruned_loss=0.04227, over 4970.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03013, over 973052.62 frames.], batch size: 35, lr: 1.54e-04
2022-05-08 06:25:35,107 INFO [train.py:715] (2/8) Epoch 14, batch 33750, loss[loss=0.1628, simple_loss=0.2509, pruned_loss=0.0374, over 4888.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02968, over 973453.88 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:26:15,678 INFO [train.py:715] (2/8) Epoch 14, batch 33800, loss[loss=0.1436, simple_loss=0.2186, pruned_loss=0.0343, over 4772.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02997, over 973408.39 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:26:56,927 INFO [train.py:715] (2/8) Epoch 14, batch 33850, loss[loss=0.137, simple_loss=0.2162, pruned_loss=0.02895, over 4987.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03009, over 974083.50 frames.], batch size: 25, lr: 1.54e-04
2022-05-08 06:27:37,001 INFO [train.py:715] (2/8) Epoch 14, batch 33900, loss[loss=0.1382, simple_loss=0.2065, pruned_loss=0.03502, over 4887.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2087, pruned_loss=0.02993, over 974071.89 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 06:28:17,544 INFO [train.py:715] (2/8) Epoch 14, batch 33950, loss[loss=0.1363, simple_loss=0.2048, pruned_loss=0.0339, over 4639.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03051, over 973622.63 frames.], batch size: 13, lr: 1.54e-04
2022-05-08 06:28:58,265 INFO [train.py:715] (2/8) Epoch 14, batch 34000, loss[loss=0.1524, simple_loss=0.2222, pruned_loss=0.04132, over 4844.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.03073, over 973663.61 frames.], batch size: 13, lr: 1.54e-04
2022-05-08 06:29:39,243 INFO [train.py:715] (2/8) Epoch 14, batch 34050, loss[loss=0.1114, simple_loss=0.1923, pruned_loss=0.01527, over 4866.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2095, pruned_loss=0.03098, over 973183.87 frames.], batch size: 20, lr: 1.54e-04
2022-05-08 06:30:19,202 INFO [train.py:715] (2/8) Epoch 14, batch 34100, loss[loss=0.1551, simple_loss=0.2295, pruned_loss=0.04039, over 4843.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2097, pruned_loss=0.03089, over 972911.16 frames.], batch size: 15, lr: 1.54e-04
2022-05-08 06:30:59,693 INFO [train.py:715] (2/8) Epoch 14, batch 34150, loss[loss=0.1381, simple_loss=0.2048, pruned_loss=0.03574, over 4873.00 frames.], tot_loss[loss=0.1347, simple_loss=0.209, pruned_loss=0.03024, over 972549.55 frames.], batch size: 30, lr: 1.54e-04
2022-05-08 06:31:40,127 INFO [train.py:715] (2/8) Epoch 14, batch 34200, loss[loss=0.1369, simple_loss=0.2028, pruned_loss=0.03543, over 4874.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02996, over 972496.79 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 06:32:20,292 INFO [train.py:715] (2/8) Epoch 14, batch 34250, loss[loss=0.1321, simple_loss=0.2005, pruned_loss=0.03183, over 4827.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02975, over 972531.83 frames.], batch size: 26, lr: 1.54e-04
2022-05-08 06:33:00,829 INFO [train.py:715] (2/8) Epoch 14, batch 34300, loss[loss=0.1348, simple_loss=0.2127, pruned_loss=0.02844, over 4800.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02996, over 972866.99 frames.], batch size: 25, lr: 1.54e-04
2022-05-08 06:33:41,481 INFO [train.py:715] (2/8) Epoch 14, batch 34350, loss[loss=0.1296, simple_loss=0.2026, pruned_loss=0.02826, over 4922.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03005, over 973702.54 frames.], batch size: 17, lr: 1.54e-04
2022-05-08 06:34:22,181 INFO [train.py:715] (2/8) Epoch 14, batch 34400, loss[loss=0.1573, simple_loss=0.2286, pruned_loss=0.04297, over 4947.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03032, over 973686.33 frames.], batch size: 39, lr: 1.54e-04
2022-05-08 06:35:01,767 INFO [train.py:715] (2/8) Epoch 14, batch 34450, loss[loss=0.1223, simple_loss=0.1899, pruned_loss=0.0274, over 4897.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03061, over 973483.95 frames.], batch size: 22, lr: 1.54e-04
2022-05-08 06:35:42,630 INFO [train.py:715] (2/8) Epoch 14, batch 34500, loss[loss=0.1534, simple_loss=0.2192, pruned_loss=0.04376, over 4754.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2083, pruned_loss=0.03076, over 973383.47 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 06:36:23,318 INFO [train.py:715] (2/8) Epoch 14, batch 34550, loss[loss=0.1236, simple_loss=0.1968, pruned_loss=0.02525, over 4864.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03075, over 973401.74 frames.], batch size: 16, lr: 1.54e-04
2022-05-08 06:37:03,430 INFO [train.py:715] (2/8) Epoch 14, batch 34600, loss[loss=0.1097, simple_loss=0.1839, pruned_loss=0.01774, over 4937.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03082, over 973522.77 frames.], batch size: 29, lr: 1.54e-04
2022-05-08 06:37:43,644 INFO [train.py:715] (2/8) Epoch 14, batch 34650, loss[loss=0.1148, simple_loss=0.1957, pruned_loss=0.01702, over 4896.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03074, over 972759.77 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 06:38:24,396 INFO [train.py:715] (2/8) Epoch 14, batch 34700, loss[loss=0.1299, simple_loss=0.2053, pruned_loss=0.02725, over 4950.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03043, over 972421.37 frames.], batch size: 23, lr: 1.54e-04
2022-05-08 06:39:03,262 INFO [train.py:715] (2/8) Epoch 14, batch 34750, loss[loss=0.1358, simple_loss=0.2102, pruned_loss=0.03074, over 4759.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2092, pruned_loss=0.03102, over 971413.67 frames.], batch size: 19, lr: 1.54e-04
2022-05-08 06:39:40,047 INFO [train.py:715] (2/8) Epoch 14, batch 34800, loss[loss=0.1509, simple_loss=0.2252, pruned_loss=0.03833, over 4790.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2094, pruned_loss=0.03105, over 970868.53 frames.], batch size: 14, lr: 1.54e-04
2022-05-08 06:40:33,603 INFO [train.py:715] (2/8) Epoch 15, batch 0, loss[loss=0.1243, simple_loss=0.1951, pruned_loss=0.02681, over 4901.00 frames.], tot_loss[loss=0.1243, simple_loss=0.1951, pruned_loss=0.02681, over 4901.00 frames.], batch size: 19, lr: 1.49e-04
2022-05-08 06:41:12,921 INFO [train.py:715] (2/8) Epoch 15, batch 50, loss[loss=0.1386, simple_loss=0.2039, pruned_loss=0.03666, over 4835.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2046, pruned_loss=0.02963, over 219332.64 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:41:54,160 INFO [train.py:715] (2/8) Epoch 15, batch 100, loss[loss=0.1395, simple_loss=0.2132, pruned_loss=0.03288, over 4829.00 frames.], tot_loss[loss=0.1327, simple_loss=0.206, pruned_loss=0.02968, over 385947.97 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:42:35,660 INFO [train.py:715] (2/8) Epoch 15, batch 150, loss[loss=0.1428, simple_loss=0.2163, pruned_loss=0.03466, over 4959.00 frames.], tot_loss[loss=0.1319, simple_loss=0.205, pruned_loss=0.02941, over 515957.94 frames.], batch size: 24, lr: 1.49e-04
2022-05-08 06:43:15,915 INFO [train.py:715] (2/8) Epoch 15, batch 200, loss[loss=0.149, simple_loss=0.2255, pruned_loss=0.03628, over 4771.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2064, pruned_loss=0.02999, over 617847.30 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 06:43:56,378 INFO [train.py:715] (2/8) Epoch 15, batch 250, loss[loss=0.1276, simple_loss=0.1939, pruned_loss=0.03065, over 4975.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2063, pruned_loss=0.02962, over 696105.99 frames.], batch size: 14, lr: 1.49e-04
2022-05-08 06:44:37,760 INFO [train.py:715] (2/8) Epoch 15, batch 300, loss[loss=0.1483, simple_loss=0.2227, pruned_loss=0.03695, over 4915.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02993, over 757452.42 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 06:45:18,781 INFO [train.py:715] (2/8) Epoch 15, batch 350, loss[loss=0.1341, simple_loss=0.2153, pruned_loss=0.02646, over 4832.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02962, over 805873.22 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:45:58,465 INFO [train.py:715] (2/8) Epoch 15, batch 400, loss[loss=0.1423, simple_loss=0.2036, pruned_loss=0.04053, over 4898.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02916, over 843486.71 frames.], batch size: 19, lr: 1.49e-04
2022-05-08 06:46:39,360 INFO [train.py:715] (2/8) Epoch 15, batch 450, loss[loss=0.153, simple_loss=0.2305, pruned_loss=0.03776, over 4791.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02938, over 871505.96 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 06:47:20,092 INFO [train.py:715] (2/8) Epoch 15, batch 500, loss[loss=0.134, simple_loss=0.208, pruned_loss=0.03, over 4692.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.0298, over 892859.70 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:48:00,508 INFO [train.py:715] (2/8) Epoch 15, batch 550, loss[loss=0.1324, simple_loss=0.2106, pruned_loss=0.0271, over 4756.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02985, over 910365.47 frames.], batch size: 19, lr: 1.49e-04
2022-05-08 06:48:40,063 INFO [train.py:715] (2/8) Epoch 15, batch 600, loss[loss=0.1301, simple_loss=0.2085, pruned_loss=0.02584, over 4828.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03045, over 923852.94 frames.], batch size: 12, lr: 1.49e-04
2022-05-08 06:49:21,142 INFO [train.py:715] (2/8) Epoch 15, batch 650, loss[loss=0.1511, simple_loss=0.2216, pruned_loss=0.04032, over 4876.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2101, pruned_loss=0.03118, over 935215.73 frames.], batch size: 32, lr: 1.49e-04
2022-05-08 06:50:01,503 INFO [train.py:715] (2/8) Epoch 15, batch 700, loss[loss=0.1521, simple_loss=0.2266, pruned_loss=0.03886, over 4934.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03078, over 943512.47 frames.], batch size: 39, lr: 1.49e-04
2022-05-08 06:50:41,519 INFO [train.py:715] (2/8) Epoch 15, batch 750, loss[loss=0.142, simple_loss=0.2273, pruned_loss=0.0283, over 4835.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2106, pruned_loss=0.03124, over 949957.89 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:51:22,000 INFO [train.py:715] (2/8) Epoch 15, batch 800, loss[loss=0.1539, simple_loss=0.2294, pruned_loss=0.03918, over 4776.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2102, pruned_loss=0.03083, over 954929.76 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 06:52:02,798 INFO [train.py:715] (2/8) Epoch 15, batch 850, loss[loss=0.1215, simple_loss=0.2007, pruned_loss=0.0211, over 4944.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.0304, over 959193.87 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 06:52:43,875 INFO [train.py:715] (2/8) Epoch 15, batch 900, loss[loss=0.1486, simple_loss=0.2211, pruned_loss=0.03812, over 4900.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03035, over 961591.81 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 06:53:23,523 INFO [train.py:715] (2/8) Epoch 15, batch 950, loss[loss=0.1356, simple_loss=0.2153, pruned_loss=0.028, over 4930.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2085, pruned_loss=0.03056, over 964298.40 frames.], batch size: 23, lr: 1.49e-04
2022-05-08 06:54:04,064 INFO [train.py:715] (2/8) Epoch 15, batch 1000, loss[loss=0.1421, simple_loss=0.2186, pruned_loss=0.03282, over 4975.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2085, pruned_loss=0.03087, over 966671.54 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 06:54:44,290 INFO [train.py:715] (2/8) Epoch 15, batch 1050, loss[loss=0.1187, simple_loss=0.1966, pruned_loss=0.02043, over 4939.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2091, pruned_loss=0.03108, over 967776.91 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 06:55:23,571 INFO [train.py:715] (2/8) Epoch 15, batch 1100, loss[loss=0.161, simple_loss=0.2289, pruned_loss=0.04653, over 4958.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03074, over 968346.39 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 06:56:04,676 INFO [train.py:715] (2/8) Epoch 15, batch 1150, loss[loss=0.14, simple_loss=0.2139, pruned_loss=0.03307, over 4738.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2083, pruned_loss=0.03072, over 969644.04 frames.], batch size: 16, lr: 1.49e-04
2022-05-08 06:56:45,821 INFO [train.py:715] (2/8) Epoch 15, batch 1200, loss[loss=0.1252, simple_loss=0.2071, pruned_loss=0.02168, over 4794.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.0304, over 969750.56 frames.], batch size: 24, lr: 1.49e-04
2022-05-08 06:57:26,537 INFO [train.py:715] (2/8) Epoch 15, batch 1250, loss[loss=0.1125, simple_loss=0.1899, pruned_loss=0.01753, over 4941.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03064, over 970762.71 frames.], batch size: 29, lr: 1.49e-04
2022-05-08 06:58:05,999 INFO [train.py:715] (2/8) Epoch 15, batch 1300, loss[loss=0.1332, simple_loss=0.2034, pruned_loss=0.03155, over 4927.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03052, over 970835.41 frames.], batch size: 23, lr: 1.49e-04
2022-05-08 06:58:46,679 INFO [train.py:715] (2/8) Epoch 15, batch 1350, loss[loss=0.125, simple_loss=0.1947, pruned_loss=0.02761, over 4964.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.0303, over 971164.67 frames.], batch size: 24, lr: 1.49e-04
2022-05-08 06:59:27,340 INFO [train.py:715] (2/8) Epoch 15, batch 1400, loss[loss=0.1286, simple_loss=0.1991, pruned_loss=0.02907, over 4946.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2078, pruned_loss=0.03057, over 970492.84 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 07:00:07,533 INFO [train.py:715] (2/8) Epoch 15, batch 1450, loss[loss=0.1229, simple_loss=0.2012, pruned_loss=0.02234, over 4978.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2078, pruned_loss=0.03026, over 970740.12 frames.], batch size: 24, lr: 1.49e-04
2022-05-08 07:00:47,336 INFO [train.py:715] (2/8) Epoch 15, batch 1500, loss[loss=0.1377, simple_loss=0.2071, pruned_loss=0.03414, over 4737.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03043, over 970733.66 frames.], batch size: 16, lr: 1.49e-04
2022-05-08 07:01:28,502 INFO [train.py:715] (2/8) Epoch 15, batch 1550, loss[loss=0.1462, simple_loss=0.2295, pruned_loss=0.03146, over 4820.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.03055, over 970565.52 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 07:02:08,727 INFO [train.py:715] (2/8) Epoch 15, batch 1600, loss[loss=0.1325, simple_loss=0.2117, pruned_loss=0.02669, over 4797.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2078, pruned_loss=0.03044, over 971018.63 frames.], batch size: 14, lr: 1.49e-04
2022-05-08 07:02:47,766 INFO [train.py:715] (2/8) Epoch 15, batch 1650, loss[loss=0.1185, simple_loss=0.19, pruned_loss=0.02348, over 4939.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03005, over 970685.80 frames.], batch size: 21, lr: 1.49e-04
2022-05-08 07:03:28,297 INFO [train.py:715] (2/8) Epoch 15, batch 1700, loss[loss=0.1237, simple_loss=0.1932, pruned_loss=0.0271, over 4990.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.03, over 970372.05 frames.], batch size: 26, lr: 1.49e-04
2022-05-08 07:04:08,876 INFO [train.py:715] (2/8) Epoch 15, batch 1750, loss[loss=0.1153, simple_loss=0.1909, pruned_loss=0.01986, over 4948.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03059, over 970822.58 frames.], batch size: 24, lr: 1.49e-04
2022-05-08 07:04:48,958 INFO [train.py:715] (2/8) Epoch 15, batch 1800, loss[loss=0.1065, simple_loss=0.1756, pruned_loss=0.01868, over 4655.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03076, over 970348.49 frames.], batch size: 13, lr: 1.49e-04
2022-05-08 07:05:28,936 INFO [train.py:715] (2/8) Epoch 15, batch 1850, loss[loss=0.1405, simple_loss=0.2175, pruned_loss=0.03176, over 4873.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2093, pruned_loss=0.03117, over 970499.32 frames.], batch size: 16, lr: 1.49e-04
2022-05-08 07:06:09,783 INFO [train.py:715] (2/8) Epoch 15, batch 1900, loss[loss=0.1368, simple_loss=0.2113, pruned_loss=0.03115, over 4951.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2097, pruned_loss=0.03127, over 971608.15 frames.], batch size: 24, lr: 1.49e-04
2022-05-08 07:06:50,229 INFO [train.py:715] (2/8) Epoch 15, batch 1950, loss[loss=0.1471, simple_loss=0.2229, pruned_loss=0.03567, over 4988.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2088, pruned_loss=0.03124, over 970754.27 frames.], batch size: 28, lr: 1.49e-04
2022-05-08 07:07:29,402 INFO [train.py:715] (2/8) Epoch 15, batch 2000, loss[loss=0.1381, simple_loss=0.2121, pruned_loss=0.03199, over 4790.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2091, pruned_loss=0.0313, over 970766.85 frames.], batch size: 14, lr: 1.49e-04
2022-05-08 07:08:10,500 INFO [train.py:715] (2/8) Epoch 15, batch 2050, loss[loss=0.1272, simple_loss=0.1959, pruned_loss=0.02924, over 4840.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2081, pruned_loss=0.0308, over 970785.21 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 07:08:50,814 INFO [train.py:715] (2/8) Epoch 15, batch 2100, loss[loss=0.1156, simple_loss=0.1895, pruned_loss=0.02085, over 4870.00 frames.], tot_loss[loss=0.1347, simple_loss=0.208, pruned_loss=0.03073, over 970265.88 frames.], batch size: 20, lr: 1.49e-04
2022-05-08 07:09:30,719 INFO [train.py:715] (2/8) Epoch 15, batch 2150, loss[loss=0.1237, simple_loss=0.2096, pruned_loss=0.0189, over 4783.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2073, pruned_loss=0.03061, over 970553.26 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:10:10,972 INFO [train.py:715] (2/8) Epoch 15, batch 2200, loss[loss=0.1736, simple_loss=0.2564, pruned_loss=0.04543, over 4962.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2077, pruned_loss=0.0306, over 972038.72 frames.], batch size: 24, lr: 1.49e-04
2022-05-08 07:10:51,404 INFO [train.py:715] (2/8) Epoch 15, batch 2250, loss[loss=0.1207, simple_loss=0.1909, pruned_loss=0.0253, over 4753.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.03041, over 971783.10 frames.], batch size: 19, lr: 1.49e-04
2022-05-08 07:11:31,545 INFO [train.py:715] (2/8) Epoch 15, batch 2300, loss[loss=0.134, simple_loss=0.1985, pruned_loss=0.03478, over 4897.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2077, pruned_loss=0.03055, over 972016.12 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:12:11,045 INFO [train.py:715] (2/8) Epoch 15, batch 2350, loss[loss=0.1339, simple_loss=0.2067, pruned_loss=0.03052, over 4923.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2077, pruned_loss=0.03053, over 972001.26 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 07:12:51,319 INFO [train.py:715] (2/8) Epoch 15, batch 2400, loss[loss=0.1316, simple_loss=0.2013, pruned_loss=0.03094, over 4845.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2075, pruned_loss=0.03034, over 971204.85 frames.], batch size: 32, lr: 1.49e-04
2022-05-08 07:13:31,554 INFO [train.py:715] (2/8) Epoch 15, batch 2450, loss[loss=0.1198, simple_loss=0.1858, pruned_loss=0.0269, over 4791.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2077, pruned_loss=0.03069, over 970898.20 frames.], batch size: 12, lr: 1.49e-04
2022-05-08 07:14:11,483 INFO [train.py:715] (2/8) Epoch 15, batch 2500, loss[loss=0.1102, simple_loss=0.1844, pruned_loss=0.01802, over 4983.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2077, pruned_loss=0.03076, over 971754.81 frames.], batch size: 14, lr: 1.49e-04
2022-05-08 07:14:50,600 INFO [train.py:715] (2/8) Epoch 15, batch 2550, loss[loss=0.1367, simple_loss=0.2086, pruned_loss=0.03242, over 4989.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2084, pruned_loss=0.03108, over 972378.46 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 07:15:31,410 INFO [train.py:715] (2/8) Epoch 15, batch 2600, loss[loss=0.1523, simple_loss=0.221, pruned_loss=0.04174, over 4926.00 frames.], tot_loss[loss=0.135, simple_loss=0.2081, pruned_loss=0.031, over 972249.82 frames.], batch size: 18, lr: 1.49e-04
2022-05-08 07:16:12,097 INFO [train.py:715] (2/8) Epoch 15, batch 2650, loss[loss=0.134, simple_loss=0.2055, pruned_loss=0.03127, over 4978.00 frames.], tot_loss[loss=0.1347, simple_loss=0.208, pruned_loss=0.03075, over 972187.41 frames.], batch size: 35, lr: 1.49e-04
2022-05-08 07:16:51,587 INFO [train.py:715] (2/8) Epoch 15, batch 2700, loss[loss=0.1288, simple_loss=0.1903, pruned_loss=0.0336, over 4782.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.03074, over 972325.36 frames.], batch size: 13, lr: 1.49e-04
2022-05-08 07:17:33,123 INFO [train.py:715] (2/8) Epoch 15, batch 2750, loss[loss=0.1309, simple_loss=0.2142, pruned_loss=0.02382, over 4935.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2091, pruned_loss=0.03095, over 972703.34 frames.], batch size: 29, lr: 1.49e-04
2022-05-08 07:18:14,182 INFO [train.py:715] (2/8) Epoch 15, batch 2800, loss[loss=0.1165, simple_loss=0.1933, pruned_loss=0.01981, over 4930.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03061, over 972860.74 frames.], batch size: 29, lr: 1.49e-04
2022-05-08 07:18:54,879 INFO [train.py:715] (2/8) Epoch 15, batch 2850, loss[loss=0.1044, simple_loss=0.1761, pruned_loss=0.0163, over 4831.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03043, over 973025.65 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 07:19:34,214 INFO [train.py:715] (2/8) Epoch 15, batch 2900, loss[loss=0.1201, simple_loss=0.19, pruned_loss=0.02506, over 4840.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03032, over 973286.04 frames.], batch size: 32, lr: 1.49e-04
2022-05-08 07:20:14,828 INFO [train.py:715] (2/8) Epoch 15, batch 2950, loss[loss=0.1344, simple_loss=0.2016, pruned_loss=0.03358, over 4868.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.0302, over 972341.26 frames.], batch size: 32, lr: 1.49e-04
2022-05-08 07:20:55,610 INFO [train.py:715] (2/8) Epoch 15, batch 3000, loss[loss=0.1257, simple_loss=0.197, pruned_loss=0.02717, over 4792.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02983, over 971440.91 frames.], batch size: 24, lr: 1.49e-04
2022-05-08 07:20:55,611 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 07:21:13,096 INFO [train.py:742] (2/8) 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] (2/8) Epoch 15, batch 3050, loss[loss=0.2045, simple_loss=0.2607, pruned_loss=0.07415, over 4738.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03013, over 970883.94 frames.], batch size: 16, lr: 1.49e-04
2022-05-08 07:22:33,933 INFO [train.py:715] (2/8) Epoch 15, batch 3100, loss[loss=0.102, simple_loss=0.1851, pruned_loss=0.009458, over 4838.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.0302, over 971056.07 frames.], batch size: 15, lr: 1.49e-04
2022-05-08 07:23:14,650 INFO [train.py:715] (2/8) Epoch 15, batch 3150, loss[loss=0.1374, simple_loss=0.2187, pruned_loss=0.02808, over 4877.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03047, over 971799.16 frames.], batch size: 16, lr: 1.49e-04
2022-05-08 07:23:55,195 INFO [train.py:715] (2/8) Epoch 15, batch 3200, loss[loss=0.1478, simple_loss=0.2237, pruned_loss=0.03597, over 4983.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2089, pruned_loss=0.0303, over 971663.03 frames.], batch size: 33, lr: 1.49e-04
2022-05-08 07:24:35,381 INFO [train.py:715] (2/8) Epoch 15, batch 3250, loss[loss=0.1516, simple_loss=0.2143, pruned_loss=0.0445, over 4822.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2092, pruned_loss=0.03031, over 971061.72 frames.], batch size: 13, lr: 1.49e-04
2022-05-08 07:25:15,318 INFO [train.py:715] (2/8) Epoch 15, batch 3300, loss[loss=0.1242, simple_loss=0.1935, pruned_loss=0.02744, over 4750.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03032, over 971588.16 frames.], batch size: 16, lr: 1.49e-04
2022-05-08 07:25:56,108 INFO [train.py:715] (2/8) Epoch 15, batch 3350, loss[loss=0.1453, simple_loss=0.2103, pruned_loss=0.04018, over 4770.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2089, pruned_loss=0.03006, over 970798.21 frames.], batch size: 14, lr: 1.49e-04
2022-05-08 07:26:36,430 INFO [train.py:715] (2/8) Epoch 15, batch 3400, loss[loss=0.1187, simple_loss=0.1847, pruned_loss=0.02636, over 4829.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.0299, over 971260.43 frames.], batch size: 30, lr: 1.49e-04
2022-05-08 07:27:16,650 INFO [train.py:715] (2/8) Epoch 15, batch 3450, loss[loss=0.1358, simple_loss=0.2148, pruned_loss=0.02844, over 4770.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2087, pruned_loss=0.02998, over 971666.35 frames.], batch size: 14, lr: 1.49e-04
2022-05-08 07:27:56,921 INFO [train.py:715] (2/8) Epoch 15, batch 3500, loss[loss=0.1381, simple_loss=0.2144, pruned_loss=0.03093, over 4941.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03022, over 971659.60 frames.], batch size: 29, lr: 1.49e-04
2022-05-08 07:28:37,334 INFO [train.py:715] (2/8) Epoch 15, batch 3550, loss[loss=0.1484, simple_loss=0.2143, pruned_loss=0.04124, over 4885.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03015, over 971899.62 frames.], batch size: 32, lr: 1.49e-04
2022-05-08 07:29:17,840 INFO [train.py:715] (2/8) Epoch 15, batch 3600, loss[loss=0.1025, simple_loss=0.1839, pruned_loss=0.01051, over 4910.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.0302, over 972022.81 frames.], batch size: 17, lr: 1.49e-04
2022-05-08 07:29:57,637 INFO [train.py:715] (2/8) Epoch 15, batch 3650, loss[loss=0.1679, simple_loss=0.2412, pruned_loss=0.04733, over 4738.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03013, over 971967.27 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 07:30:38,278 INFO [train.py:715] (2/8) Epoch 15, batch 3700, loss[loss=0.1501, simple_loss=0.2279, pruned_loss=0.03617, over 4981.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03011, over 972070.97 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 07:31:19,136 INFO [train.py:715] (2/8) Epoch 15, batch 3750, loss[loss=0.1395, simple_loss=0.2188, pruned_loss=0.0301, over 4802.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03033, over 971359.51 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 07:31:58,790 INFO [train.py:715] (2/8) Epoch 15, batch 3800, loss[loss=0.1242, simple_loss=0.1957, pruned_loss=0.02638, over 4956.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03072, over 971973.17 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 07:32:38,809 INFO [train.py:715] (2/8) Epoch 15, batch 3850, loss[loss=0.1271, simple_loss=0.1961, pruned_loss=0.02903, over 4970.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03021, over 972183.31 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 07:33:19,078 INFO [train.py:715] (2/8) Epoch 15, batch 3900, loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02899, over 4872.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03036, over 972618.88 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 07:33:58,247 INFO [train.py:715] (2/8) Epoch 15, batch 3950, loss[loss=0.146, simple_loss=0.2314, pruned_loss=0.03025, over 4791.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03047, over 971945.40 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 07:34:37,989 INFO [train.py:715] (2/8) Epoch 15, batch 4000, loss[loss=0.1399, simple_loss=0.2183, pruned_loss=0.03076, over 4817.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2093, pruned_loss=0.03096, over 972269.97 frames.], batch size: 26, lr: 1.48e-04
2022-05-08 07:35:17,771 INFO [train.py:715] (2/8) Epoch 15, batch 4050, loss[loss=0.1656, simple_loss=0.2306, pruned_loss=0.05036, over 4892.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03099, over 972217.67 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 07:35:58,779 INFO [train.py:715] (2/8) Epoch 15, batch 4100, loss[loss=0.1227, simple_loss=0.2006, pruned_loss=0.02236, over 4981.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2093, pruned_loss=0.03085, over 972104.85 frames.], batch size: 28, lr: 1.48e-04
2022-05-08 07:36:37,611 INFO [train.py:715] (2/8) Epoch 15, batch 4150, loss[loss=0.1387, simple_loss=0.2117, pruned_loss=0.03281, over 4818.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02998, over 971964.46 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 07:37:17,773 INFO [train.py:715] (2/8) Epoch 15, batch 4200, loss[loss=0.1305, simple_loss=0.1982, pruned_loss=0.0314, over 4866.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2076, pruned_loss=0.03034, over 971308.58 frames.], batch size: 30, lr: 1.48e-04
2022-05-08 07:37:58,197 INFO [train.py:715] (2/8) Epoch 15, batch 4250, loss[loss=0.136, simple_loss=0.2111, pruned_loss=0.03043, over 4774.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03064, over 972551.36 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 07:38:38,202 INFO [train.py:715] (2/8) Epoch 15, batch 4300, loss[loss=0.1499, simple_loss=0.2347, pruned_loss=0.03258, over 4925.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03023, over 972388.24 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 07:39:18,219 INFO [train.py:715] (2/8) Epoch 15, batch 4350, loss[loss=0.1274, simple_loss=0.2078, pruned_loss=0.02347, over 4959.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02931, over 971744.84 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:39:58,272 INFO [train.py:715] (2/8) Epoch 15, batch 4400, loss[loss=0.139, simple_loss=0.2167, pruned_loss=0.03062, over 4978.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02957, over 971905.10 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 07:40:38,800 INFO [train.py:715] (2/8) Epoch 15, batch 4450, loss[loss=0.1334, simple_loss=0.2111, pruned_loss=0.02785, over 4900.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02979, over 972501.54 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 07:41:18,470 INFO [train.py:715] (2/8) Epoch 15, batch 4500, loss[loss=0.1145, simple_loss=0.1848, pruned_loss=0.02214, over 4814.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02994, over 972735.82 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:41:58,876 INFO [train.py:715] (2/8) Epoch 15, batch 4550, loss[loss=0.1088, simple_loss=0.1742, pruned_loss=0.02171, over 4981.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02989, over 973060.85 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 07:42:39,494 INFO [train.py:715] (2/8) Epoch 15, batch 4600, loss[loss=0.1839, simple_loss=0.2406, pruned_loss=0.06358, over 4756.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03031, over 972973.22 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 07:43:19,668 INFO [train.py:715] (2/8) Epoch 15, batch 4650, loss[loss=0.1273, simple_loss=0.1868, pruned_loss=0.03392, over 4785.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2092, pruned_loss=0.03064, over 972715.46 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 07:43:59,052 INFO [train.py:715] (2/8) Epoch 15, batch 4700, loss[loss=0.1198, simple_loss=0.1967, pruned_loss=0.02146, over 4804.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03077, over 971308.33 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 07:44:39,326 INFO [train.py:715] (2/8) Epoch 15, batch 4750, loss[loss=0.1524, simple_loss=0.2265, pruned_loss=0.03909, over 4826.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03063, over 971319.81 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 07:45:20,567 INFO [train.py:715] (2/8) Epoch 15, batch 4800, loss[loss=0.1614, simple_loss=0.2283, pruned_loss=0.04731, over 4971.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2092, pruned_loss=0.03068, over 972079.02 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 07:46:00,525 INFO [train.py:715] (2/8) Epoch 15, batch 4850, loss[loss=0.1307, simple_loss=0.198, pruned_loss=0.03168, over 4834.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03059, over 972144.23 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 07:46:41,240 INFO [train.py:715] (2/8) Epoch 15, batch 4900, loss[loss=0.1275, simple_loss=0.1968, pruned_loss=0.0291, over 4823.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03019, over 971496.47 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 07:47:21,680 INFO [train.py:715] (2/8) Epoch 15, batch 4950, loss[loss=0.1175, simple_loss=0.1981, pruned_loss=0.0185, over 4955.00 frames.], tot_loss[loss=0.135, simple_loss=0.2092, pruned_loss=0.03042, over 971317.59 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 07:48:02,263 INFO [train.py:715] (2/8) Epoch 15, batch 5000, loss[loss=0.1362, simple_loss=0.2138, pruned_loss=0.02928, over 4899.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2094, pruned_loss=0.0307, over 970630.47 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 07:48:41,753 INFO [train.py:715] (2/8) Epoch 15, batch 5050, loss[loss=0.1243, simple_loss=0.1937, pruned_loss=0.02739, over 4749.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2098, pruned_loss=0.03054, over 971136.53 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 07:49:21,839 INFO [train.py:715] (2/8) Epoch 15, batch 5100, loss[loss=0.1371, simple_loss=0.2041, pruned_loss=0.03509, over 4825.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03022, over 970131.08 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 07:50:02,144 INFO [train.py:715] (2/8) Epoch 15, batch 5150, loss[loss=0.1901, simple_loss=0.2491, pruned_loss=0.06553, over 4944.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2086, pruned_loss=0.03059, over 970706.83 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 07:50:42,064 INFO [train.py:715] (2/8) Epoch 15, batch 5200, loss[loss=0.1311, simple_loss=0.2149, pruned_loss=0.02366, over 4966.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.0303, over 971246.87 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 07:51:22,084 INFO [train.py:715] (2/8) Epoch 15, batch 5250, loss[loss=0.1241, simple_loss=0.1899, pruned_loss=0.02915, over 4836.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02982, over 971717.23 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 07:52:03,629 INFO [train.py:715] (2/8) Epoch 15, batch 5300, loss[loss=0.161, simple_loss=0.2386, pruned_loss=0.04167, over 4875.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02971, over 972043.68 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 07:52:45,864 INFO [train.py:715] (2/8) Epoch 15, batch 5350, loss[loss=0.1459, simple_loss=0.2167, pruned_loss=0.03761, over 4857.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02978, over 971092.23 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 07:53:26,856 INFO [train.py:715] (2/8) Epoch 15, batch 5400, loss[loss=0.1497, simple_loss=0.223, pruned_loss=0.03822, over 4763.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02987, over 971637.90 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 07:54:08,830 INFO [train.py:715] (2/8) Epoch 15, batch 5450, loss[loss=0.1185, simple_loss=0.1905, pruned_loss=0.02323, over 4924.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02984, over 971791.00 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 07:54:50,466 INFO [train.py:715] (2/8) Epoch 15, batch 5500, loss[loss=0.1282, simple_loss=0.2089, pruned_loss=0.02374, over 4918.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02993, over 972759.21 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 07:55:32,153 INFO [train.py:715] (2/8) Epoch 15, batch 5550, loss[loss=0.1245, simple_loss=0.1969, pruned_loss=0.02602, over 4898.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02973, over 972246.11 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 07:56:12,918 INFO [train.py:715] (2/8) Epoch 15, batch 5600, loss[loss=0.1471, simple_loss=0.2154, pruned_loss=0.03939, over 4835.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02996, over 972370.07 frames.], batch size: 30, lr: 1.48e-04
2022-05-08 07:56:54,779 INFO [train.py:715] (2/8) Epoch 15, batch 5650, loss[loss=0.107, simple_loss=0.1821, pruned_loss=0.01591, over 4932.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2075, pruned_loss=0.03013, over 973055.45 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 07:57:37,294 INFO [train.py:715] (2/8) Epoch 15, batch 5700, loss[loss=0.1273, simple_loss=0.1973, pruned_loss=0.02868, over 4748.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03022, over 973603.38 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 07:58:18,519 INFO [train.py:715] (2/8) Epoch 15, batch 5750, loss[loss=0.1455, simple_loss=0.2217, pruned_loss=0.03461, over 4755.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03097, over 973506.74 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 07:58:59,971 INFO [train.py:715] (2/8) Epoch 15, batch 5800, loss[loss=0.1247, simple_loss=0.2063, pruned_loss=0.02159, over 4980.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03095, over 973703.29 frames.], batch size: 28, lr: 1.48e-04
2022-05-08 07:59:41,228 INFO [train.py:715] (2/8) Epoch 15, batch 5850, loss[loss=0.135, simple_loss=0.1998, pruned_loss=0.03509, over 4766.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2065, pruned_loss=0.03016, over 974442.23 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:00:25,531 INFO [train.py:715] (2/8) Epoch 15, batch 5900, loss[loss=0.1366, simple_loss=0.2063, pruned_loss=0.03346, over 4898.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2069, pruned_loss=0.02996, over 974417.85 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:01:06,121 INFO [train.py:715] (2/8) Epoch 15, batch 5950, loss[loss=0.1393, simple_loss=0.2045, pruned_loss=0.03703, over 4709.00 frames.], tot_loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03026, over 973822.84 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:01:47,587 INFO [train.py:715] (2/8) Epoch 15, batch 6000, loss[loss=0.1306, simple_loss=0.2065, pruned_loss=0.02734, over 4810.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03018, over 973218.17 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 08:01:47,588 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 08:01:57,158 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.1051, simple_loss=0.1887, pruned_loss=0.01077, over 914524.00 frames.
2022-05-08 08:02:38,326 INFO [train.py:715] (2/8) Epoch 15, batch 6050, loss[loss=0.1216, simple_loss=0.2011, pruned_loss=0.02106, over 4810.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02956, over 973322.08 frames.], batch size: 26, lr: 1.48e-04
2022-05-08 08:03:20,378 INFO [train.py:715] (2/8) Epoch 15, batch 6100, loss[loss=0.1233, simple_loss=0.1991, pruned_loss=0.02374, over 4967.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02995, over 973573.66 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:04:00,129 INFO [train.py:715] (2/8) Epoch 15, batch 6150, loss[loss=0.1509, simple_loss=0.2332, pruned_loss=0.03424, over 4878.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02992, over 973240.40 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:04:41,009 INFO [train.py:715] (2/8) Epoch 15, batch 6200, loss[loss=0.1317, simple_loss=0.2143, pruned_loss=0.02453, over 4953.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02973, over 973612.98 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:05:20,632 INFO [train.py:715] (2/8) Epoch 15, batch 6250, loss[loss=0.1347, simple_loss=0.2067, pruned_loss=0.03131, over 4907.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02948, over 972805.48 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:06:01,394 INFO [train.py:715] (2/8) Epoch 15, batch 6300, loss[loss=0.1491, simple_loss=0.2308, pruned_loss=0.03371, over 4935.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02969, over 973097.03 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:06:41,233 INFO [train.py:715] (2/8) Epoch 15, batch 6350, loss[loss=0.136, simple_loss=0.2219, pruned_loss=0.02503, over 4925.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02943, over 973561.51 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 08:07:21,272 INFO [train.py:715] (2/8) Epoch 15, batch 6400, loss[loss=0.1352, simple_loss=0.2069, pruned_loss=0.03177, over 4916.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2081, pruned_loss=0.0292, over 973293.66 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:08:01,845 INFO [train.py:715] (2/8) Epoch 15, batch 6450, loss[loss=0.1223, simple_loss=0.1963, pruned_loss=0.02417, over 4783.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02921, over 972430.34 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:08:41,387 INFO [train.py:715] (2/8) Epoch 15, batch 6500, loss[loss=0.1349, simple_loss=0.2047, pruned_loss=0.03254, over 4928.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02943, over 972908.66 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:09:21,818 INFO [train.py:715] (2/8) Epoch 15, batch 6550, loss[loss=0.1468, simple_loss=0.2194, pruned_loss=0.03709, over 4797.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02991, over 973073.63 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 08:10:02,129 INFO [train.py:715] (2/8) Epoch 15, batch 6600, loss[loss=0.1393, simple_loss=0.2179, pruned_loss=0.03031, over 4796.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.02999, over 972052.42 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:10:42,818 INFO [train.py:715] (2/8) Epoch 15, batch 6650, loss[loss=0.1189, simple_loss=0.1962, pruned_loss=0.0208, over 4803.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02998, over 972403.55 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 08:11:22,255 INFO [train.py:715] (2/8) Epoch 15, batch 6700, loss[loss=0.1251, simple_loss=0.2054, pruned_loss=0.02239, over 4760.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02995, over 972066.75 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:12:02,758 INFO [train.py:715] (2/8) Epoch 15, batch 6750, loss[loss=0.1338, simple_loss=0.2169, pruned_loss=0.02539, over 4897.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.0297, over 972574.65 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 08:12:44,118 INFO [train.py:715] (2/8) Epoch 15, batch 6800, loss[loss=0.1168, simple_loss=0.1916, pruned_loss=0.02103, over 4796.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02991, over 972749.00 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:13:23,949 INFO [train.py:715] (2/8) Epoch 15, batch 6850, loss[loss=0.1211, simple_loss=0.2067, pruned_loss=0.01771, over 4818.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2081, pruned_loss=0.02948, over 972283.82 frames.], batch size: 27, lr: 1.48e-04
2022-05-08 08:14:03,539 INFO [train.py:715] (2/8) Epoch 15, batch 6900, loss[loss=0.1232, simple_loss=0.2053, pruned_loss=0.02052, over 4904.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02959, over 972711.70 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:14:44,367 INFO [train.py:715] (2/8) Epoch 15, batch 6950, loss[loss=0.1593, simple_loss=0.2343, pruned_loss=0.04214, over 4891.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03, over 972597.56 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 08:15:24,999 INFO [train.py:715] (2/8) Epoch 15, batch 7000, loss[loss=0.1445, simple_loss=0.2266, pruned_loss=0.03118, over 4763.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.03027, over 972850.82 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:16:03,970 INFO [train.py:715] (2/8) Epoch 15, batch 7050, loss[loss=0.1409, simple_loss=0.2222, pruned_loss=0.02977, over 4944.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03073, over 972240.71 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:16:44,704 INFO [train.py:715] (2/8) Epoch 15, batch 7100, loss[loss=0.1503, simple_loss=0.2174, pruned_loss=0.04155, over 4967.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03059, over 972423.53 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:17:25,231 INFO [train.py:715] (2/8) Epoch 15, batch 7150, loss[loss=0.1025, simple_loss=0.1788, pruned_loss=0.01308, over 4968.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03021, over 973078.10 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:18:05,136 INFO [train.py:715] (2/8) Epoch 15, batch 7200, loss[loss=0.1326, simple_loss=0.1968, pruned_loss=0.03426, over 4865.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02971, over 972974.27 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:18:44,340 INFO [train.py:715] (2/8) Epoch 15, batch 7250, loss[loss=0.1413, simple_loss=0.2138, pruned_loss=0.0344, over 4836.00 frames.], tot_loss[loss=0.1334, simple_loss=0.207, pruned_loss=0.02984, over 972383.62 frames.], batch size: 30, lr: 1.48e-04
2022-05-08 08:19:25,088 INFO [train.py:715] (2/8) Epoch 15, batch 7300, loss[loss=0.1248, simple_loss=0.1957, pruned_loss=0.02694, over 4771.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03029, over 973385.02 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:20:06,082 INFO [train.py:715] (2/8) Epoch 15, batch 7350, loss[loss=0.1608, simple_loss=0.238, pruned_loss=0.04186, over 4698.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2088, pruned_loss=0.03099, over 973039.40 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:20:45,516 INFO [train.py:715] (2/8) Epoch 15, batch 7400, loss[loss=0.1295, simple_loss=0.2004, pruned_loss=0.02935, over 4761.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03079, over 973536.12 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:21:25,986 INFO [train.py:715] (2/8) Epoch 15, batch 7450, loss[loss=0.1204, simple_loss=0.2033, pruned_loss=0.01877, over 4771.00 frames.], tot_loss[loss=0.135, simple_loss=0.2088, pruned_loss=0.03059, over 973625.56 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:22:06,375 INFO [train.py:715] (2/8) Epoch 15, batch 7500, loss[loss=0.15, simple_loss=0.2239, pruned_loss=0.03805, over 4842.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03039, over 973360.44 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:22:46,667 INFO [train.py:715] (2/8) Epoch 15, batch 7550, loss[loss=0.1291, simple_loss=0.2066, pruned_loss=0.02574, over 4752.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.0303, over 973648.47 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:23:25,909 INFO [train.py:715] (2/8) Epoch 15, batch 7600, loss[loss=0.125, simple_loss=0.2002, pruned_loss=0.02487, over 4698.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.0297, over 973261.45 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:24:05,908 INFO [train.py:715] (2/8) Epoch 15, batch 7650, loss[loss=0.1284, simple_loss=0.1937, pruned_loss=0.03158, over 4794.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02988, over 973137.25 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:24:45,956 INFO [train.py:715] (2/8) Epoch 15, batch 7700, loss[loss=0.1017, simple_loss=0.1791, pruned_loss=0.0121, over 4944.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2089, pruned_loss=0.0299, over 973457.38 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:25:24,892 INFO [train.py:715] (2/8) Epoch 15, batch 7750, loss[loss=0.1277, simple_loss=0.2141, pruned_loss=0.02067, over 4868.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2093, pruned_loss=0.02995, over 974161.42 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:26:04,531 INFO [train.py:715] (2/8) Epoch 15, batch 7800, loss[loss=0.1367, simple_loss=0.2078, pruned_loss=0.03282, over 4760.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02989, over 974500.28 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:26:43,769 INFO [train.py:715] (2/8) Epoch 15, batch 7850, loss[loss=0.1473, simple_loss=0.2137, pruned_loss=0.04049, over 4973.00 frames.], tot_loss[loss=0.135, simple_loss=0.2097, pruned_loss=0.0302, over 973694.42 frames.], batch size: 35, lr: 1.48e-04
2022-05-08 08:27:23,781 INFO [train.py:715] (2/8) Epoch 15, batch 7900, loss[loss=0.1578, simple_loss=0.2314, pruned_loss=0.04214, over 4945.00 frames.], tot_loss[loss=0.135, simple_loss=0.2097, pruned_loss=0.03011, over 973466.60 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:28:01,916 INFO [train.py:715] (2/8) Epoch 15, batch 7950, loss[loss=0.1342, simple_loss=0.1986, pruned_loss=0.03485, over 4930.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2091, pruned_loss=0.03015, over 972451.05 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:28:41,234 INFO [train.py:715] (2/8) Epoch 15, batch 8000, loss[loss=0.1112, simple_loss=0.186, pruned_loss=0.01818, over 4913.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2094, pruned_loss=0.02994, over 972467.94 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:29:20,803 INFO [train.py:715] (2/8) Epoch 15, batch 8050, loss[loss=0.1539, simple_loss=0.2319, pruned_loss=0.03792, over 4977.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2094, pruned_loss=0.03024, over 972298.19 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:29:59,772 INFO [train.py:715] (2/8) Epoch 15, batch 8100, loss[loss=0.1251, simple_loss=0.202, pruned_loss=0.02414, over 4880.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.02997, over 972117.08 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:30:38,763 INFO [train.py:715] (2/8) Epoch 15, batch 8150, loss[loss=0.1391, simple_loss=0.2064, pruned_loss=0.03585, over 4875.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03008, over 971723.33 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 08:31:18,841 INFO [train.py:715] (2/8) Epoch 15, batch 8200, loss[loss=0.1438, simple_loss=0.2072, pruned_loss=0.04021, over 4760.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03059, over 972221.34 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:31:57,572 INFO [train.py:715] (2/8) Epoch 15, batch 8250, loss[loss=0.1313, simple_loss=0.1992, pruned_loss=0.0317, over 4805.00 frames.], tot_loss[loss=0.136, simple_loss=0.2094, pruned_loss=0.0313, over 971721.24 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 08:32:36,563 INFO [train.py:715] (2/8) Epoch 15, batch 8300, loss[loss=0.1364, simple_loss=0.2182, pruned_loss=0.02723, over 4921.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2094, pruned_loss=0.03122, over 972794.72 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:33:15,789 INFO [train.py:715] (2/8) Epoch 15, batch 8350, loss[loss=0.1549, simple_loss=0.2238, pruned_loss=0.04299, over 4954.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2091, pruned_loss=0.03135, over 972538.65 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 08:33:55,985 INFO [train.py:715] (2/8) Epoch 15, batch 8400, loss[loss=0.1238, simple_loss=0.196, pruned_loss=0.02575, over 4918.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03067, over 972532.09 frames.], batch size: 23, lr: 1.48e-04
2022-05-08 08:34:35,523 INFO [train.py:715] (2/8) Epoch 15, batch 8450, loss[loss=0.1159, simple_loss=0.1872, pruned_loss=0.02228, over 4814.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2074, pruned_loss=0.03015, over 973203.78 frames.], batch size: 27, lr: 1.48e-04
2022-05-08 08:35:14,664 INFO [train.py:715] (2/8) Epoch 15, batch 8500, loss[loss=0.1506, simple_loss=0.217, pruned_loss=0.04217, over 4893.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02951, over 972791.47 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:35:54,837 INFO [train.py:715] (2/8) Epoch 15, batch 8550, loss[loss=0.1329, simple_loss=0.2135, pruned_loss=0.02611, over 4853.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02945, over 973231.86 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:36:33,510 INFO [train.py:715] (2/8) Epoch 15, batch 8600, loss[loss=0.1353, simple_loss=0.2056, pruned_loss=0.0325, over 4982.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02977, over 973206.45 frames.], batch size: 28, lr: 1.48e-04
2022-05-08 08:37:12,321 INFO [train.py:715] (2/8) Epoch 15, batch 8650, loss[loss=0.1262, simple_loss=0.2039, pruned_loss=0.02425, over 4955.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02993, over 972709.74 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 08:37:51,175 INFO [train.py:715] (2/8) Epoch 15, batch 8700, loss[loss=0.1354, simple_loss=0.2219, pruned_loss=0.02441, over 4810.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02967, over 972663.93 frames.], batch size: 25, lr: 1.48e-04
2022-05-08 08:38:30,425 INFO [train.py:715] (2/8) Epoch 15, batch 8750, loss[loss=0.1341, simple_loss=0.2038, pruned_loss=0.03222, over 4901.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03045, over 973476.93 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:39:08,911 INFO [train.py:715] (2/8) Epoch 15, batch 8800, loss[loss=0.1647, simple_loss=0.2351, pruned_loss=0.04713, over 4717.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03036, over 971834.16 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:39:47,403 INFO [train.py:715] (2/8) Epoch 15, batch 8850, loss[loss=0.1463, simple_loss=0.2216, pruned_loss=0.03547, over 4874.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02985, over 971887.98 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:40:26,825 INFO [train.py:715] (2/8) Epoch 15, batch 8900, loss[loss=0.1526, simple_loss=0.2328, pruned_loss=0.03625, over 4763.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02988, over 972063.87 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:41:06,361 INFO [train.py:715] (2/8) Epoch 15, batch 8950, loss[loss=0.1416, simple_loss=0.2111, pruned_loss=0.03607, over 4776.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03051, over 971587.95 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:41:45,490 INFO [train.py:715] (2/8) Epoch 15, batch 9000, loss[loss=0.133, simple_loss=0.2121, pruned_loss=0.027, over 4829.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03025, over 971805.70 frames.], batch size: 27, lr: 1.48e-04
2022-05-08 08:41:45,490 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 08:42:05,028 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.1051, simple_loss=0.1887, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 08:42:44,048 INFO [train.py:715] (2/8) Epoch 15, batch 9050, loss[loss=0.151, simple_loss=0.2189, pruned_loss=0.0415, over 4914.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03034, over 971984.88 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:43:23,567 INFO [train.py:715] (2/8) Epoch 15, batch 9100, loss[loss=0.126, simple_loss=0.1964, pruned_loss=0.02784, over 4821.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03038, over 971607.61 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:44:03,272 INFO [train.py:715] (2/8) Epoch 15, batch 9150, loss[loss=0.1228, simple_loss=0.1941, pruned_loss=0.02573, over 4964.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03083, over 973086.25 frames.], batch size: 24, lr: 1.48e-04
2022-05-08 08:44:42,057 INFO [train.py:715] (2/8) Epoch 15, batch 9200, loss[loss=0.16, simple_loss=0.2256, pruned_loss=0.04725, over 4894.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2091, pruned_loss=0.03073, over 972811.49 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:45:21,337 INFO [train.py:715] (2/8) Epoch 15, batch 9250, loss[loss=0.1218, simple_loss=0.2108, pruned_loss=0.01637, over 4889.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03055, over 973397.90 frames.], batch size: 16, lr: 1.48e-04
2022-05-08 08:46:01,216 INFO [train.py:715] (2/8) Epoch 15, batch 9300, loss[loss=0.1648, simple_loss=0.2288, pruned_loss=0.05042, over 4948.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2094, pruned_loss=0.03093, over 973431.95 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:46:41,140 INFO [train.py:715] (2/8) Epoch 15, batch 9350, loss[loss=0.1183, simple_loss=0.191, pruned_loss=0.02282, over 4934.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2082, pruned_loss=0.03054, over 973110.10 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:47:19,985 INFO [train.py:715] (2/8) Epoch 15, batch 9400, loss[loss=0.1533, simple_loss=0.2309, pruned_loss=0.03786, over 4877.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2075, pruned_loss=0.0302, over 974066.27 frames.], batch size: 22, lr: 1.48e-04
2022-05-08 08:47:59,299 INFO [train.py:715] (2/8) Epoch 15, batch 9450, loss[loss=0.1387, simple_loss=0.217, pruned_loss=0.03021, over 4986.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.03001, over 973212.76 frames.], batch size: 28, lr: 1.48e-04
2022-05-08 08:48:38,583 INFO [train.py:715] (2/8) Epoch 15, batch 9500, loss[loss=0.1245, simple_loss=0.194, pruned_loss=0.02745, over 4914.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2067, pruned_loss=0.02979, over 974031.86 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:49:16,967 INFO [train.py:715] (2/8) Epoch 15, batch 9550, loss[loss=0.1383, simple_loss=0.2119, pruned_loss=0.03237, over 4845.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2062, pruned_loss=0.0296, over 974044.91 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:49:56,306 INFO [train.py:715] (2/8) Epoch 15, batch 9600, loss[loss=0.1386, simple_loss=0.2118, pruned_loss=0.03273, over 4939.00 frames.], tot_loss[loss=0.133, simple_loss=0.2065, pruned_loss=0.02972, over 972119.10 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:50:35,891 INFO [train.py:715] (2/8) Epoch 15, batch 9650, loss[loss=0.1577, simple_loss=0.2405, pruned_loss=0.03742, over 4938.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2071, pruned_loss=0.03007, over 972377.75 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 08:51:15,438 INFO [train.py:715] (2/8) Epoch 15, batch 9700, loss[loss=0.1249, simple_loss=0.1953, pruned_loss=0.02729, over 4956.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2075, pruned_loss=0.03031, over 972154.15 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:51:53,983 INFO [train.py:715] (2/8) Epoch 15, batch 9750, loss[loss=0.1467, simple_loss=0.2355, pruned_loss=0.02897, over 4960.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03068, over 971663.75 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:52:33,226 INFO [train.py:715] (2/8) Epoch 15, batch 9800, loss[loss=0.1279, simple_loss=0.1989, pruned_loss=0.02842, over 4889.00 frames.], tot_loss[loss=0.135, simple_loss=0.2085, pruned_loss=0.03074, over 972128.24 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:53:12,403 INFO [train.py:715] (2/8) Epoch 15, batch 9850, loss[loss=0.1186, simple_loss=0.1884, pruned_loss=0.02435, over 4856.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.03081, over 971785.58 frames.], batch size: 20, lr: 1.48e-04
2022-05-08 08:53:50,989 INFO [train.py:715] (2/8) Epoch 15, batch 9900, loss[loss=0.1308, simple_loss=0.2219, pruned_loss=0.0198, over 4812.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2101, pruned_loss=0.03123, over 971048.27 frames.], batch size: 15, lr: 1.48e-04
2022-05-08 08:54:30,412 INFO [train.py:715] (2/8) Epoch 15, batch 9950, loss[loss=0.1285, simple_loss=0.2045, pruned_loss=0.02622, over 4819.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03113, over 971149.17 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 08:55:09,381 INFO [train.py:715] (2/8) Epoch 15, batch 10000, loss[loss=0.1147, simple_loss=0.1934, pruned_loss=0.01803, over 4847.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03038, over 971955.32 frames.], batch size: 13, lr: 1.48e-04
2022-05-08 08:55:48,595 INFO [train.py:715] (2/8) Epoch 15, batch 10050, loss[loss=0.1189, simple_loss=0.1821, pruned_loss=0.02787, over 4762.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02968, over 971529.88 frames.], batch size: 18, lr: 1.48e-04
2022-05-08 08:56:26,955 INFO [train.py:715] (2/8) Epoch 15, batch 10100, loss[loss=0.1274, simple_loss=0.2089, pruned_loss=0.02295, over 4875.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02948, over 971237.38 frames.], batch size: 38, lr: 1.48e-04
2022-05-08 08:57:05,756 INFO [train.py:715] (2/8) Epoch 15, batch 10150, loss[loss=0.1105, simple_loss=0.1828, pruned_loss=0.01912, over 4852.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.0298, over 971063.11 frames.], batch size: 32, lr: 1.48e-04
2022-05-08 08:57:45,595 INFO [train.py:715] (2/8) Epoch 15, batch 10200, loss[loss=0.1311, simple_loss=0.2006, pruned_loss=0.03079, over 4756.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2074, pruned_loss=0.0301, over 971585.06 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 08:58:23,919 INFO [train.py:715] (2/8) Epoch 15, batch 10250, loss[loss=0.1099, simple_loss=0.1819, pruned_loss=0.01897, over 4898.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03092, over 973025.34 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 08:59:03,216 INFO [train.py:715] (2/8) Epoch 15, batch 10300, loss[loss=0.1074, simple_loss=0.1692, pruned_loss=0.02282, over 4785.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03087, over 973445.23 frames.], batch size: 12, lr: 1.48e-04
2022-05-08 08:59:42,674 INFO [train.py:715] (2/8) Epoch 15, batch 10350, loss[loss=0.1574, simple_loss=0.2213, pruned_loss=0.04678, over 4920.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03057, over 973443.63 frames.], batch size: 29, lr: 1.48e-04
2022-05-08 09:00:21,802 INFO [train.py:715] (2/8) Epoch 15, batch 10400, loss[loss=0.1413, simple_loss=0.2164, pruned_loss=0.03307, over 4923.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03089, over 973370.12 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 09:00:59,816 INFO [train.py:715] (2/8) Epoch 15, batch 10450, loss[loss=0.1339, simple_loss=0.2073, pruned_loss=0.0303, over 4764.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03008, over 973307.58 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 09:01:38,798 INFO [train.py:715] (2/8) Epoch 15, batch 10500, loss[loss=0.1488, simple_loss=0.2129, pruned_loss=0.04237, over 4870.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03049, over 972700.31 frames.], batch size: 32, lr: 1.48e-04
2022-05-08 09:02:18,511 INFO [train.py:715] (2/8) Epoch 15, batch 10550, loss[loss=0.1211, simple_loss=0.1959, pruned_loss=0.02316, over 4747.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2093, pruned_loss=0.03046, over 972626.08 frames.], batch size: 19, lr: 1.48e-04
2022-05-08 09:02:56,675 INFO [train.py:715] (2/8) Epoch 15, batch 10600, loss[loss=0.1382, simple_loss=0.2198, pruned_loss=0.02826, over 4896.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.0301, over 972553.70 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 09:03:35,331 INFO [train.py:715] (2/8) Epoch 15, batch 10650, loss[loss=0.1167, simple_loss=0.1963, pruned_loss=0.01859, over 4961.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03012, over 972809.97 frames.], batch size: 14, lr: 1.48e-04
2022-05-08 09:04:14,410 INFO [train.py:715] (2/8) Epoch 15, batch 10700, loss[loss=0.1451, simple_loss=0.2187, pruned_loss=0.03574, over 4916.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03021, over 972550.15 frames.], batch size: 17, lr: 1.48e-04
2022-05-08 09:04:53,621 INFO [train.py:715] (2/8) Epoch 15, batch 10750, loss[loss=0.1149, simple_loss=0.1946, pruned_loss=0.01764, over 4946.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.03001, over 972611.61 frames.], batch size: 21, lr: 1.48e-04
2022-05-08 09:05:31,572 INFO [train.py:715] (2/8) Epoch 15, batch 10800, loss[loss=0.1366, simple_loss=0.209, pruned_loss=0.03212, over 4852.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02968, over 972814.55 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 09:06:11,099 INFO [train.py:715] (2/8) Epoch 15, batch 10850, loss[loss=0.1157, simple_loss=0.192, pruned_loss=0.01968, over 4851.00 frames.], tot_loss[loss=0.133, simple_loss=0.2068, pruned_loss=0.02965, over 972573.78 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 09:06:50,381 INFO [train.py:715] (2/8) Epoch 15, batch 10900, loss[loss=0.1257, simple_loss=0.2006, pruned_loss=0.02535, over 4799.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03002, over 972013.79 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:07:28,751 INFO [train.py:715] (2/8) Epoch 15, batch 10950, loss[loss=0.09969, simple_loss=0.169, pruned_loss=0.01517, over 4822.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02989, over 971797.63 frames.], batch size: 27, lr: 1.47e-04
2022-05-08 09:08:06,751 INFO [train.py:715] (2/8) Epoch 15, batch 11000, loss[loss=0.1285, simple_loss=0.1999, pruned_loss=0.02855, over 4836.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03003, over 971560.93 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 09:08:45,833 INFO [train.py:715] (2/8) Epoch 15, batch 11050, loss[loss=0.1534, simple_loss=0.2194, pruned_loss=0.04373, over 4983.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03023, over 971464.96 frames.], batch size: 28, lr: 1.47e-04
2022-05-08 09:09:25,325 INFO [train.py:715] (2/8) Epoch 15, batch 11100, loss[loss=0.123, simple_loss=0.1991, pruned_loss=0.0235, over 4882.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2077, pruned_loss=0.03035, over 971531.50 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:10:03,218 INFO [train.py:715] (2/8) Epoch 15, batch 11150, loss[loss=0.1422, simple_loss=0.2224, pruned_loss=0.03099, over 4881.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03022, over 971823.88 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 09:10:41,862 INFO [train.py:715] (2/8) Epoch 15, batch 11200, loss[loss=0.1251, simple_loss=0.1956, pruned_loss=0.02727, over 4932.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2075, pruned_loss=0.03043, over 971110.34 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:11:20,806 INFO [train.py:715] (2/8) Epoch 15, batch 11250, loss[loss=0.1597, simple_loss=0.2292, pruned_loss=0.04515, over 4717.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03027, over 971533.62 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:11:59,325 INFO [train.py:715] (2/8) Epoch 15, batch 11300, loss[loss=0.1444, simple_loss=0.2212, pruned_loss=0.03383, over 4836.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02975, over 971425.35 frames.], batch size: 30, lr: 1.47e-04
2022-05-08 09:12:37,831 INFO [train.py:715] (2/8) Epoch 15, batch 11350, loss[loss=0.118, simple_loss=0.1931, pruned_loss=0.02143, over 4788.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2067, pruned_loss=0.02992, over 971123.52 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:13:17,184 INFO [train.py:715] (2/8) Epoch 15, batch 11400, loss[loss=0.1376, simple_loss=0.2121, pruned_loss=0.03158, over 4807.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2065, pruned_loss=0.02965, over 971097.37 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:13:55,476 INFO [train.py:715] (2/8) Epoch 15, batch 11450, loss[loss=0.1429, simple_loss=0.2179, pruned_loss=0.03397, over 4943.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2068, pruned_loss=0.0298, over 971802.39 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 09:14:34,168 INFO [train.py:715] (2/8) Epoch 15, batch 11500, loss[loss=0.1377, simple_loss=0.2187, pruned_loss=0.02833, over 4932.00 frames.], tot_loss[loss=0.133, simple_loss=0.2064, pruned_loss=0.02975, over 971592.31 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 09:15:13,128 INFO [train.py:715] (2/8) Epoch 15, batch 11550, loss[loss=0.143, simple_loss=0.213, pruned_loss=0.03646, over 4950.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2062, pruned_loss=0.02982, over 972830.76 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 09:15:52,413 INFO [train.py:715] (2/8) Epoch 15, batch 11600, loss[loss=0.1483, simple_loss=0.2215, pruned_loss=0.03751, over 4979.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2072, pruned_loss=0.0303, over 971785.02 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:16:30,747 INFO [train.py:715] (2/8) Epoch 15, batch 11650, loss[loss=0.1221, simple_loss=0.1954, pruned_loss=0.02442, over 4789.00 frames.], tot_loss[loss=0.134, simple_loss=0.2073, pruned_loss=0.03036, over 972501.29 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:17:09,223 INFO [train.py:715] (2/8) Epoch 15, batch 11700, loss[loss=0.1214, simple_loss=0.2035, pruned_loss=0.01966, over 4806.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2084, pruned_loss=0.03087, over 972670.53 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 09:17:48,439 INFO [train.py:715] (2/8) Epoch 15, batch 11750, loss[loss=0.1539, simple_loss=0.2261, pruned_loss=0.04082, over 4910.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.0305, over 972599.95 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:18:27,440 INFO [train.py:715] (2/8) Epoch 15, batch 11800, loss[loss=0.127, simple_loss=0.197, pruned_loss=0.02855, over 4880.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03013, over 972731.40 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:19:05,508 INFO [train.py:715] (2/8) Epoch 15, batch 11850, loss[loss=0.1228, simple_loss=0.2013, pruned_loss=0.02211, over 4897.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02969, over 972219.16 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:19:45,034 INFO [train.py:715] (2/8) Epoch 15, batch 11900, loss[loss=0.1452, simple_loss=0.2172, pruned_loss=0.03663, over 4759.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02965, over 972745.30 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 09:20:25,114 INFO [train.py:715] (2/8) Epoch 15, batch 11950, loss[loss=0.1499, simple_loss=0.2069, pruned_loss=0.04646, over 4967.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02867, over 971922.74 frames.], batch size: 39, lr: 1.47e-04
2022-05-08 09:21:03,704 INFO [train.py:715] (2/8) Epoch 15, batch 12000, loss[loss=0.1339, simple_loss=0.21, pruned_loss=0.02888, over 4945.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02945, over 971800.92 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:21:03,704 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 09:21:20,395 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.105, simple_loss=0.1887, pruned_loss=0.01066, over 914524.00 frames.
2022-05-08 09:21:59,106 INFO [train.py:715] (2/8) Epoch 15, batch 12050, loss[loss=0.1267, simple_loss=0.1987, pruned_loss=0.02734, over 4787.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2072, pruned_loss=0.02992, over 971973.68 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:22:38,255 INFO [train.py:715] (2/8) Epoch 15, batch 12100, loss[loss=0.1243, simple_loss=0.2024, pruned_loss=0.02313, over 4750.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03065, over 972424.56 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 09:23:17,963 INFO [train.py:715] (2/8) Epoch 15, batch 12150, loss[loss=0.1603, simple_loss=0.2449, pruned_loss=0.03781, over 4804.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03046, over 971129.21 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 09:23:56,413 INFO [train.py:715] (2/8) Epoch 15, batch 12200, loss[loss=0.1612, simple_loss=0.2352, pruned_loss=0.04356, over 4789.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2081, pruned_loss=0.03068, over 972044.82 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:24:35,177 INFO [train.py:715] (2/8) Epoch 15, batch 12250, loss[loss=0.1521, simple_loss=0.2278, pruned_loss=0.03821, over 4945.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03086, over 972453.92 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:25:14,191 INFO [train.py:715] (2/8) Epoch 15, batch 12300, loss[loss=0.1236, simple_loss=0.2116, pruned_loss=0.01782, over 4761.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03055, over 972752.96 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 09:25:54,055 INFO [train.py:715] (2/8) Epoch 15, batch 12350, loss[loss=0.1378, simple_loss=0.2179, pruned_loss=0.02879, over 4973.00 frames.], tot_loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03019, over 972773.21 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:26:32,317 INFO [train.py:715] (2/8) Epoch 15, batch 12400, loss[loss=0.1156, simple_loss=0.1952, pruned_loss=0.01802, over 4983.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2074, pruned_loss=0.03012, over 972068.85 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:27:11,077 INFO [train.py:715] (2/8) Epoch 15, batch 12450, loss[loss=0.1445, simple_loss=0.2137, pruned_loss=0.03768, over 4773.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03007, over 971897.47 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:27:51,091 INFO [train.py:715] (2/8) Epoch 15, batch 12500, loss[loss=0.1359, simple_loss=0.2176, pruned_loss=0.02709, over 4775.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03006, over 972003.81 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 09:28:29,282 INFO [train.py:715] (2/8) Epoch 15, batch 12550, loss[loss=0.1002, simple_loss=0.1744, pruned_loss=0.01296, over 4862.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.03001, over 971528.37 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 09:29:08,342 INFO [train.py:715] (2/8) Epoch 15, batch 12600, loss[loss=0.1537, simple_loss=0.2324, pruned_loss=0.03754, over 4915.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2096, pruned_loss=0.03043, over 971706.56 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:29:46,868 INFO [train.py:715] (2/8) Epoch 15, batch 12650, loss[loss=0.1152, simple_loss=0.188, pruned_loss=0.02116, over 4744.00 frames.], tot_loss[loss=0.1357, simple_loss=0.2101, pruned_loss=0.03068, over 972184.30 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:30:26,460 INFO [train.py:715] (2/8) Epoch 15, batch 12700, loss[loss=0.1286, simple_loss=0.2032, pruned_loss=0.02703, over 4800.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2096, pruned_loss=0.03026, over 970834.91 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:31:04,794 INFO [train.py:715] (2/8) Epoch 15, batch 12750, loss[loss=0.1363, simple_loss=0.2094, pruned_loss=0.03161, over 4901.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03007, over 971512.98 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:31:43,628 INFO [train.py:715] (2/8) Epoch 15, batch 12800, loss[loss=0.1314, simple_loss=0.2015, pruned_loss=0.03065, over 4790.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02995, over 972025.79 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:32:23,162 INFO [train.py:715] (2/8) Epoch 15, batch 12850, loss[loss=0.1295, simple_loss=0.202, pruned_loss=0.02853, over 4972.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02988, over 972101.22 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:33:01,776 INFO [train.py:715] (2/8) Epoch 15, batch 12900, loss[loss=0.1321, simple_loss=0.1998, pruned_loss=0.03219, over 4807.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02998, over 972208.11 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:33:40,807 INFO [train.py:715] (2/8) Epoch 15, batch 12950, loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02937, over 4816.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.03003, over 971998.58 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:34:20,120 INFO [train.py:715] (2/8) Epoch 15, batch 13000, loss[loss=0.1185, simple_loss=0.1982, pruned_loss=0.01939, over 4860.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2088, pruned_loss=0.03026, over 972293.66 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 09:34:59,664 INFO [train.py:715] (2/8) Epoch 15, batch 13050, loss[loss=0.1246, simple_loss=0.1973, pruned_loss=0.02594, over 4765.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02992, over 971748.81 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:35:38,149 INFO [train.py:715] (2/8) Epoch 15, batch 13100, loss[loss=0.112, simple_loss=0.1883, pruned_loss=0.0178, over 4883.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02973, over 971139.55 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 09:36:17,621 INFO [train.py:715] (2/8) Epoch 15, batch 13150, loss[loss=0.1286, simple_loss=0.1937, pruned_loss=0.03177, over 4869.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.0295, over 970988.51 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 09:36:57,401 INFO [train.py:715] (2/8) Epoch 15, batch 13200, loss[loss=0.1228, simple_loss=0.1952, pruned_loss=0.02518, over 4969.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02965, over 971513.30 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:37:35,198 INFO [train.py:715] (2/8) Epoch 15, batch 13250, loss[loss=0.1648, simple_loss=0.2379, pruned_loss=0.04587, over 4827.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02989, over 971809.40 frames.], batch size: 27, lr: 1.47e-04
2022-05-08 09:38:14,325 INFO [train.py:715] (2/8) Epoch 15, batch 13300, loss[loss=0.1504, simple_loss=0.2091, pruned_loss=0.04589, over 4971.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02977, over 972625.80 frames.], batch size: 28, lr: 1.47e-04
2022-05-08 09:38:53,945 INFO [train.py:715] (2/8) Epoch 15, batch 13350, loss[loss=0.154, simple_loss=0.2132, pruned_loss=0.04746, over 4961.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03001, over 972370.78 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 09:39:34,556 INFO [train.py:715] (2/8) Epoch 15, batch 13400, loss[loss=0.1328, simple_loss=0.1961, pruned_loss=0.03476, over 4696.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03053, over 973265.02 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 09:40:13,178 INFO [train.py:715] (2/8) Epoch 15, batch 13450, loss[loss=0.1419, simple_loss=0.2192, pruned_loss=0.03233, over 4924.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.03005, over 972176.30 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:40:51,764 INFO [train.py:715] (2/8) Epoch 15, batch 13500, loss[loss=0.1206, simple_loss=0.1908, pruned_loss=0.02521, over 4817.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02948, over 973164.95 frames.], batch size: 27, lr: 1.47e-04
2022-05-08 09:41:31,299 INFO [train.py:715] (2/8) Epoch 15, batch 13550, loss[loss=0.1191, simple_loss=0.1912, pruned_loss=0.02348, over 4947.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02995, over 973508.08 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:42:09,578 INFO [train.py:715] (2/8) Epoch 15, batch 13600, loss[loss=0.15, simple_loss=0.219, pruned_loss=0.04053, over 4811.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03015, over 973817.92 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 09:42:48,559 INFO [train.py:715] (2/8) Epoch 15, batch 13650, loss[loss=0.1123, simple_loss=0.1874, pruned_loss=0.01863, over 4933.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03019, over 973583.00 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:43:27,803 INFO [train.py:715] (2/8) Epoch 15, batch 13700, loss[loss=0.1179, simple_loss=0.198, pruned_loss=0.01891, over 4817.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02993, over 973870.87 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 09:44:06,255 INFO [train.py:715] (2/8) Epoch 15, batch 13750, loss[loss=0.1613, simple_loss=0.2284, pruned_loss=0.04715, over 4988.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02998, over 974017.89 frames.], batch size: 28, lr: 1.47e-04
2022-05-08 09:44:44,978 INFO [train.py:715] (2/8) Epoch 15, batch 13800, loss[loss=0.1324, simple_loss=0.1977, pruned_loss=0.03357, over 4758.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02995, over 972924.19 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 09:45:23,193 INFO [train.py:715] (2/8) Epoch 15, batch 13850, loss[loss=0.1147, simple_loss=0.1945, pruned_loss=0.01738, over 4826.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02977, over 972900.23 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 09:46:05,197 INFO [train.py:715] (2/8) Epoch 15, batch 13900, loss[loss=0.1223, simple_loss=0.1991, pruned_loss=0.02278, over 4914.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03021, over 973179.62 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:46:43,308 INFO [train.py:715] (2/8) Epoch 15, batch 13950, loss[loss=0.1426, simple_loss=0.2217, pruned_loss=0.03172, over 4959.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03008, over 972606.32 frames.], batch size: 24, lr: 1.47e-04
2022-05-08 09:47:21,601 INFO [train.py:715] (2/8) Epoch 15, batch 14000, loss[loss=0.1337, simple_loss=0.2143, pruned_loss=0.02652, over 4864.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02987, over 973617.96 frames.], batch size: 30, lr: 1.47e-04
2022-05-08 09:48:00,879 INFO [train.py:715] (2/8) Epoch 15, batch 14050, loss[loss=0.1404, simple_loss=0.2044, pruned_loss=0.03819, over 4986.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02937, over 972564.69 frames.], batch size: 31, lr: 1.47e-04
2022-05-08 09:48:38,844 INFO [train.py:715] (2/8) Epoch 15, batch 14100, loss[loss=0.133, simple_loss=0.2079, pruned_loss=0.0291, over 4912.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02938, over 972958.91 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:49:17,894 INFO [train.py:715] (2/8) Epoch 15, batch 14150, loss[loss=0.1285, simple_loss=0.2027, pruned_loss=0.02711, over 4922.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.0298, over 973063.69 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:49:56,536 INFO [train.py:715] (2/8) Epoch 15, batch 14200, loss[loss=0.1371, simple_loss=0.2035, pruned_loss=0.03538, over 4970.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02978, over 972412.66 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 09:50:35,494 INFO [train.py:715] (2/8) Epoch 15, batch 14250, loss[loss=0.1208, simple_loss=0.2042, pruned_loss=0.0187, over 4830.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02953, over 972115.94 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 09:51:13,319 INFO [train.py:715] (2/8) Epoch 15, batch 14300, loss[loss=0.1277, simple_loss=0.2092, pruned_loss=0.02315, over 4929.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02961, over 972076.47 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:51:51,761 INFO [train.py:715] (2/8) Epoch 15, batch 14350, loss[loss=0.1662, simple_loss=0.2432, pruned_loss=0.04462, over 4817.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02947, over 971011.34 frames.], batch size: 27, lr: 1.47e-04
2022-05-08 09:52:30,861 INFO [train.py:715] (2/8) Epoch 15, batch 14400, loss[loss=0.1366, simple_loss=0.2143, pruned_loss=0.02942, over 4803.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02956, over 971281.71 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:53:08,604 INFO [train.py:715] (2/8) Epoch 15, batch 14450, loss[loss=0.1417, simple_loss=0.2161, pruned_loss=0.03366, over 4896.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02981, over 971540.56 frames.], batch size: 39, lr: 1.47e-04
2022-05-08 09:53:47,585 INFO [train.py:715] (2/8) Epoch 15, batch 14500, loss[loss=0.1147, simple_loss=0.1928, pruned_loss=0.01827, over 4923.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.03002, over 971392.30 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:54:25,849 INFO [train.py:715] (2/8) Epoch 15, batch 14550, loss[loss=0.1311, simple_loss=0.2075, pruned_loss=0.02735, over 4916.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02998, over 971277.03 frames.], batch size: 23, lr: 1.47e-04
2022-05-08 09:55:04,845 INFO [train.py:715] (2/8) Epoch 15, batch 14600, loss[loss=0.1172, simple_loss=0.1963, pruned_loss=0.01905, over 4834.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02951, over 971114.77 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:55:42,672 INFO [train.py:715] (2/8) Epoch 15, batch 14650, loss[loss=0.1692, simple_loss=0.2569, pruned_loss=0.04076, over 4936.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.02983, over 972202.09 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 09:56:20,650 INFO [train.py:715] (2/8) Epoch 15, batch 14700, loss[loss=0.09699, simple_loss=0.1712, pruned_loss=0.01141, over 4760.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02959, over 971793.04 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 09:56:59,717 INFO [train.py:715] (2/8) Epoch 15, batch 14750, loss[loss=0.1356, simple_loss=0.2167, pruned_loss=0.02726, over 4785.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2082, pruned_loss=0.02933, over 970824.39 frames.], batch size: 17, lr: 1.47e-04
2022-05-08 09:57:37,355 INFO [train.py:715] (2/8) Epoch 15, batch 14800, loss[loss=0.1096, simple_loss=0.1844, pruned_loss=0.01735, over 4989.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02916, over 970244.12 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 09:58:16,194 INFO [train.py:715] (2/8) Epoch 15, batch 14850, loss[loss=0.1163, simple_loss=0.1894, pruned_loss=0.02158, over 4972.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02907, over 970775.00 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 09:58:55,097 INFO [train.py:715] (2/8) Epoch 15, batch 14900, loss[loss=0.1359, simple_loss=0.2181, pruned_loss=0.02685, over 4853.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.0295, over 971112.94 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 09:59:33,267 INFO [train.py:715] (2/8) Epoch 15, batch 14950, loss[loss=0.1325, simple_loss=0.2102, pruned_loss=0.02745, over 4681.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02942, over 971341.69 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:00:11,563 INFO [train.py:715] (2/8) Epoch 15, batch 15000, loss[loss=0.1262, simple_loss=0.2105, pruned_loss=0.02095, over 4976.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2076, pruned_loss=0.02934, over 971146.55 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:00:11,564 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 10:00:26,344 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.1051, simple_loss=0.1887, pruned_loss=0.01077, over 914524.00 frames.
2022-05-08 10:01:05,813 INFO [train.py:715] (2/8) Epoch 15, batch 15050, loss[loss=0.1224, simple_loss=0.2071, pruned_loss=0.01884, over 4990.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02927, over 970931.84 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 10:01:43,978 INFO [train.py:715] (2/8) Epoch 15, batch 15100, loss[loss=0.1175, simple_loss=0.1843, pruned_loss=0.02529, over 4974.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02922, over 970988.42 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:02:23,330 INFO [train.py:715] (2/8) Epoch 15, batch 15150, loss[loss=0.1205, simple_loss=0.1979, pruned_loss=0.02152, over 4884.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2076, pruned_loss=0.0293, over 971173.89 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 10:03:01,053 INFO [train.py:715] (2/8) Epoch 15, batch 15200, loss[loss=0.122, simple_loss=0.1919, pruned_loss=0.02607, over 4837.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02953, over 971749.12 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:03:39,355 INFO [train.py:715] (2/8) Epoch 15, batch 15250, loss[loss=0.1181, simple_loss=0.1767, pruned_loss=0.02973, over 4767.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02978, over 971183.61 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:04:18,901 INFO [train.py:715] (2/8) Epoch 15, batch 15300, loss[loss=0.1244, simple_loss=0.2047, pruned_loss=0.02204, over 4893.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02993, over 971144.59 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 10:04:56,984 INFO [train.py:715] (2/8) Epoch 15, batch 15350, loss[loss=0.13, simple_loss=0.2149, pruned_loss=0.0226, over 4816.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02973, over 971585.23 frames.], batch size: 27, lr: 1.47e-04
2022-05-08 10:05:35,893 INFO [train.py:715] (2/8) Epoch 15, batch 15400, loss[loss=0.1388, simple_loss=0.2005, pruned_loss=0.0385, over 4877.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02997, over 972048.50 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:06:13,985 INFO [train.py:715] (2/8) Epoch 15, batch 15450, loss[loss=0.1452, simple_loss=0.225, pruned_loss=0.03268, over 4751.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03033, over 971650.56 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 10:06:52,892 INFO [train.py:715] (2/8) Epoch 15, batch 15500, loss[loss=0.1407, simple_loss=0.2084, pruned_loss=0.03648, over 4866.00 frames.], tot_loss[loss=0.1353, simple_loss=0.209, pruned_loss=0.03078, over 971121.55 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 10:07:31,440 INFO [train.py:715] (2/8) Epoch 15, batch 15550, loss[loss=0.1346, simple_loss=0.2023, pruned_loss=0.0334, over 4976.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03064, over 971755.35 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:08:10,327 INFO [train.py:715] (2/8) Epoch 15, batch 15600, loss[loss=0.1547, simple_loss=0.2242, pruned_loss=0.04264, over 4931.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03062, over 972099.16 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 10:08:49,134 INFO [train.py:715] (2/8) Epoch 15, batch 15650, loss[loss=0.1169, simple_loss=0.2048, pruned_loss=0.01451, over 4757.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03073, over 971391.23 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:09:27,216 INFO [train.py:715] (2/8) Epoch 15, batch 15700, loss[loss=0.1256, simple_loss=0.1968, pruned_loss=0.02722, over 4942.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2087, pruned_loss=0.03086, over 971690.00 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:10:05,788 INFO [train.py:715] (2/8) Epoch 15, batch 15750, loss[loss=0.1054, simple_loss=0.1742, pruned_loss=0.01824, over 4775.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.03051, over 972074.10 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:10:44,347 INFO [train.py:715] (2/8) Epoch 15, batch 15800, loss[loss=0.1279, simple_loss=0.2004, pruned_loss=0.02773, over 4828.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2077, pruned_loss=0.03047, over 972669.18 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:11:23,020 INFO [train.py:715] (2/8) Epoch 15, batch 15850, loss[loss=0.09953, simple_loss=0.1714, pruned_loss=0.01383, over 4955.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2084, pruned_loss=0.0306, over 972848.82 frames.], batch size: 24, lr: 1.47e-04
2022-05-08 10:12:01,146 INFO [train.py:715] (2/8) Epoch 15, batch 15900, loss[loss=0.1343, simple_loss=0.2134, pruned_loss=0.02761, over 4863.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2086, pruned_loss=0.03107, over 973278.47 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:12:39,299 INFO [train.py:715] (2/8) Epoch 15, batch 15950, loss[loss=0.1461, simple_loss=0.2261, pruned_loss=0.03304, over 4698.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.03055, over 973017.63 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:13:18,370 INFO [train.py:715] (2/8) Epoch 15, batch 16000, loss[loss=0.1105, simple_loss=0.1789, pruned_loss=0.02103, over 4706.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03039, over 973751.56 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:13:56,002 INFO [train.py:715] (2/8) Epoch 15, batch 16050, loss[loss=0.1536, simple_loss=0.2385, pruned_loss=0.03438, over 4814.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.0302, over 973417.88 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:14:34,593 INFO [train.py:715] (2/8) Epoch 15, batch 16100, loss[loss=0.181, simple_loss=0.2651, pruned_loss=0.04842, over 4965.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2089, pruned_loss=0.03025, over 973028.77 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:15:13,037 INFO [train.py:715] (2/8) Epoch 15, batch 16150, loss[loss=0.124, simple_loss=0.1975, pruned_loss=0.02524, over 4731.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03008, over 972372.30 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:15:51,555 INFO [train.py:715] (2/8) Epoch 15, batch 16200, loss[loss=0.131, simple_loss=0.2122, pruned_loss=0.02493, over 4762.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02994, over 972441.01 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:16:29,839 INFO [train.py:715] (2/8) Epoch 15, batch 16250, loss[loss=0.1296, simple_loss=0.2054, pruned_loss=0.02691, over 4897.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02968, over 972074.29 frames.], batch size: 22, lr: 1.47e-04
2022-05-08 10:17:08,216 INFO [train.py:715] (2/8) Epoch 15, batch 16300, loss[loss=0.1115, simple_loss=0.1854, pruned_loss=0.01883, over 4793.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02941, over 971567.29 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:17:46,752 INFO [train.py:715] (2/8) Epoch 15, batch 16350, loss[loss=0.1506, simple_loss=0.2207, pruned_loss=0.04025, over 4853.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02968, over 972510.38 frames.], batch size: 32, lr: 1.47e-04
2022-05-08 10:18:24,621 INFO [train.py:715] (2/8) Epoch 15, batch 16400, loss[loss=0.1171, simple_loss=0.1887, pruned_loss=0.02274, over 4957.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02936, over 972536.73 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:19:03,504 INFO [train.py:715] (2/8) Epoch 15, batch 16450, loss[loss=0.1496, simple_loss=0.2211, pruned_loss=0.03906, over 4974.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02944, over 972799.82 frames.], batch size: 31, lr: 1.47e-04
2022-05-08 10:19:41,759 INFO [train.py:715] (2/8) Epoch 15, batch 16500, loss[loss=0.134, simple_loss=0.2064, pruned_loss=0.03079, over 4866.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02948, over 972482.81 frames.], batch size: 34, lr: 1.47e-04
2022-05-08 10:20:20,130 INFO [train.py:715] (2/8) Epoch 15, batch 16550, loss[loss=0.1485, simple_loss=0.2216, pruned_loss=0.03767, over 4790.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02971, over 971994.42 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:20:58,277 INFO [train.py:715] (2/8) Epoch 15, batch 16600, loss[loss=0.1354, simple_loss=0.2116, pruned_loss=0.02964, over 4735.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02976, over 972008.54 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:21:37,051 INFO [train.py:715] (2/8) Epoch 15, batch 16650, loss[loss=0.1108, simple_loss=0.1847, pruned_loss=0.01848, over 4804.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02967, over 971454.24 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:22:16,818 INFO [train.py:715] (2/8) Epoch 15, batch 16700, loss[loss=0.1424, simple_loss=0.2087, pruned_loss=0.03802, over 4972.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02933, over 972063.45 frames.], batch size: 35, lr: 1.47e-04
2022-05-08 10:22:55,538 INFO [train.py:715] (2/8) Epoch 15, batch 16750, loss[loss=0.1213, simple_loss=0.2093, pruned_loss=0.01667, over 4956.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2079, pruned_loss=0.02937, over 972330.14 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:23:34,529 INFO [train.py:715] (2/8) Epoch 15, batch 16800, loss[loss=0.1123, simple_loss=0.1825, pruned_loss=0.021, over 4786.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02916, over 972066.21 frames.], batch size: 12, lr: 1.47e-04
2022-05-08 10:24:13,685 INFO [train.py:715] (2/8) Epoch 15, batch 16850, loss[loss=0.1502, simple_loss=0.2271, pruned_loss=0.0366, over 4766.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2084, pruned_loss=0.02963, over 972527.03 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:24:52,770 INFO [train.py:715] (2/8) Epoch 15, batch 16900, loss[loss=0.1463, simple_loss=0.2062, pruned_loss=0.04319, over 4990.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2092, pruned_loss=0.03008, over 971488.20 frames.], batch size: 33, lr: 1.47e-04
2022-05-08 10:25:31,721 INFO [train.py:715] (2/8) Epoch 15, batch 16950, loss[loss=0.131, simple_loss=0.2065, pruned_loss=0.02771, over 4962.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2089, pruned_loss=0.03002, over 971914.04 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 10:26:10,072 INFO [train.py:715] (2/8) Epoch 15, batch 17000, loss[loss=0.1362, simple_loss=0.2169, pruned_loss=0.02775, over 4913.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2091, pruned_loss=0.03028, over 971097.97 frames.], batch size: 39, lr: 1.47e-04
2022-05-08 10:26:49,334 INFO [train.py:715] (2/8) Epoch 15, batch 17050, loss[loss=0.1345, simple_loss=0.1961, pruned_loss=0.03648, over 4861.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.03019, over 971142.50 frames.], batch size: 13, lr: 1.47e-04
2022-05-08 10:27:27,262 INFO [train.py:715] (2/8) Epoch 15, batch 17100, loss[loss=0.1048, simple_loss=0.1828, pruned_loss=0.01346, over 4968.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.03009, over 972222.90 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:28:06,075 INFO [train.py:715] (2/8) Epoch 15, batch 17150, loss[loss=0.1282, simple_loss=0.1993, pruned_loss=0.02851, over 4794.00 frames.], tot_loss[loss=0.135, simple_loss=0.2094, pruned_loss=0.03033, over 972539.83 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:28:44,479 INFO [train.py:715] (2/8) Epoch 15, batch 17200, loss[loss=0.1289, simple_loss=0.2018, pruned_loss=0.02803, over 4808.00 frames.], tot_loss[loss=0.1346, simple_loss=0.209, pruned_loss=0.0301, over 972194.47 frames.], batch size: 25, lr: 1.47e-04
2022-05-08 10:29:23,148 INFO [train.py:715] (2/8) Epoch 15, batch 17250, loss[loss=0.138, simple_loss=0.2086, pruned_loss=0.03369, over 4769.00 frames.], tot_loss[loss=0.1344, simple_loss=0.209, pruned_loss=0.02988, over 971861.14 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 10:30:01,723 INFO [train.py:715] (2/8) Epoch 15, batch 17300, loss[loss=0.1533, simple_loss=0.225, pruned_loss=0.04079, over 4805.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2089, pruned_loss=0.02981, over 972012.02 frames.], batch size: 21, lr: 1.47e-04
2022-05-08 10:30:40,361 INFO [train.py:715] (2/8) Epoch 15, batch 17350, loss[loss=0.1466, simple_loss=0.2169, pruned_loss=0.03817, over 4696.00 frames.], tot_loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02974, over 972184.37 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:31:19,951 INFO [train.py:715] (2/8) Epoch 15, batch 17400, loss[loss=0.1085, simple_loss=0.1848, pruned_loss=0.01605, over 4910.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03004, over 971181.99 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 10:31:57,892 INFO [train.py:715] (2/8) Epoch 15, batch 17450, loss[loss=0.1395, simple_loss=0.2221, pruned_loss=0.02841, over 4918.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2079, pruned_loss=0.02941, over 970994.84 frames.], batch size: 19, lr: 1.47e-04
2022-05-08 10:32:36,871 INFO [train.py:715] (2/8) Epoch 15, batch 17500, loss[loss=0.146, simple_loss=0.2225, pruned_loss=0.03473, over 4863.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02944, over 970974.64 frames.], batch size: 20, lr: 1.47e-04
2022-05-08 10:33:15,844 INFO [train.py:715] (2/8) Epoch 15, batch 17550, loss[loss=0.1147, simple_loss=0.1897, pruned_loss=0.01978, over 4942.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02936, over 971377.13 frames.], batch size: 29, lr: 1.47e-04
2022-05-08 10:33:54,434 INFO [train.py:715] (2/8) Epoch 15, batch 17600, loss[loss=0.1263, simple_loss=0.2023, pruned_loss=0.02518, over 4822.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02983, over 971059.31 frames.], batch size: 26, lr: 1.47e-04
2022-05-08 10:34:32,813 INFO [train.py:715] (2/8) Epoch 15, batch 17650, loss[loss=0.1391, simple_loss=0.2037, pruned_loss=0.03728, over 4851.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02987, over 971435.68 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:35:11,434 INFO [train.py:715] (2/8) Epoch 15, batch 17700, loss[loss=0.1551, simple_loss=0.2452, pruned_loss=0.03251, over 4692.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03003, over 971560.71 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:35:50,324 INFO [train.py:715] (2/8) Epoch 15, batch 17750, loss[loss=0.1573, simple_loss=0.2294, pruned_loss=0.04258, over 4918.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.03021, over 972005.59 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 10:36:28,685 INFO [train.py:715] (2/8) Epoch 15, batch 17800, loss[loss=0.1075, simple_loss=0.1698, pruned_loss=0.02264, over 4774.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03003, over 972261.50 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:37:07,668 INFO [train.py:715] (2/8) Epoch 15, batch 17850, loss[loss=0.1403, simple_loss=0.2104, pruned_loss=0.03515, over 4988.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02985, over 972599.36 frames.], batch size: 14, lr: 1.47e-04
2022-05-08 10:37:46,657 INFO [train.py:715] (2/8) Epoch 15, batch 17900, loss[loss=0.1376, simple_loss=0.2088, pruned_loss=0.03324, over 4962.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02977, over 972922.56 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:38:25,489 INFO [train.py:715] (2/8) Epoch 15, batch 17950, loss[loss=0.1051, simple_loss=0.176, pruned_loss=0.01708, over 4828.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.03, over 972785.87 frames.], batch size: 15, lr: 1.47e-04
2022-05-08 10:39:03,819 INFO [train.py:715] (2/8) Epoch 15, batch 18000, loss[loss=0.1258, simple_loss=0.211, pruned_loss=0.02035, over 4787.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02958, over 972906.04 frames.], batch size: 18, lr: 1.47e-04
2022-05-08 10:39:03,820 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 10:39:13,329 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.1048, simple_loss=0.1885, pruned_loss=0.01059, over 914524.00 frames.
2022-05-08 10:39:51,810 INFO [train.py:715] (2/8) Epoch 15, batch 18050, loss[loss=0.121, simple_loss=0.1973, pruned_loss=0.02234, over 4749.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02967, over 973262.85 frames.], batch size: 16, lr: 1.47e-04
2022-05-08 10:40:30,477 INFO [train.py:715] (2/8) Epoch 15, batch 18100, loss[loss=0.1313, simple_loss=0.2087, pruned_loss=0.02698, over 4748.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2075, pruned_loss=0.03053, over 971882.22 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 10:41:09,231 INFO [train.py:715] (2/8) Epoch 15, batch 18150, loss[loss=0.1358, simple_loss=0.2016, pruned_loss=0.03499, over 4860.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2076, pruned_loss=0.03032, over 971894.22 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 10:41:47,120 INFO [train.py:715] (2/8) Epoch 15, batch 18200, loss[loss=0.1442, simple_loss=0.2216, pruned_loss=0.03341, over 4947.00 frames.], tot_loss[loss=0.1345, simple_loss=0.208, pruned_loss=0.03054, over 971131.86 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 10:42:25,781 INFO [train.py:715] (2/8) Epoch 15, batch 18250, loss[loss=0.1315, simple_loss=0.2098, pruned_loss=0.02664, over 4809.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2082, pruned_loss=0.03081, over 972169.09 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 10:43:04,443 INFO [train.py:715] (2/8) Epoch 15, batch 18300, loss[loss=0.1223, simple_loss=0.1986, pruned_loss=0.02302, over 4792.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03098, over 972007.29 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 10:43:42,534 INFO [train.py:715] (2/8) Epoch 15, batch 18350, loss[loss=0.1377, simple_loss=0.2121, pruned_loss=0.03166, over 4897.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2086, pruned_loss=0.03098, over 972110.31 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 10:44:21,120 INFO [train.py:715] (2/8) Epoch 15, batch 18400, loss[loss=0.1557, simple_loss=0.2301, pruned_loss=0.04058, over 4945.00 frames.], tot_loss[loss=0.1355, simple_loss=0.209, pruned_loss=0.03099, over 972328.69 frames.], batch size: 35, lr: 1.46e-04
2022-05-08 10:44:59,619 INFO [train.py:715] (2/8) Epoch 15, batch 18450, loss[loss=0.1095, simple_loss=0.1862, pruned_loss=0.01641, over 4942.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2094, pruned_loss=0.03081, over 971747.58 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 10:45:38,879 INFO [train.py:715] (2/8) Epoch 15, batch 18500, loss[loss=0.1353, simple_loss=0.2081, pruned_loss=0.03124, over 4908.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03026, over 972197.79 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 10:46:17,373 INFO [train.py:715] (2/8) Epoch 15, batch 18550, loss[loss=0.1253, simple_loss=0.1983, pruned_loss=0.0261, over 4773.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.03067, over 971943.48 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 10:46:55,963 INFO [train.py:715] (2/8) Epoch 15, batch 18600, loss[loss=0.1395, simple_loss=0.2158, pruned_loss=0.03164, over 4711.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03025, over 971753.61 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 10:47:34,881 INFO [train.py:715] (2/8) Epoch 15, batch 18650, loss[loss=0.1405, simple_loss=0.208, pruned_loss=0.03647, over 4778.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02972, over 971581.60 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 10:48:13,536 INFO [train.py:715] (2/8) Epoch 15, batch 18700, loss[loss=0.11, simple_loss=0.191, pruned_loss=0.01449, over 4939.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02981, over 972247.59 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 10:48:52,385 INFO [train.py:715] (2/8) Epoch 15, batch 18750, loss[loss=0.118, simple_loss=0.188, pruned_loss=0.02404, over 4800.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02919, over 972387.86 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 10:49:31,674 INFO [train.py:715] (2/8) Epoch 15, batch 18800, loss[loss=0.1582, simple_loss=0.2186, pruned_loss=0.04887, over 4872.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02964, over 972394.64 frames.], batch size: 38, lr: 1.46e-04
2022-05-08 10:50:10,917 INFO [train.py:715] (2/8) Epoch 15, batch 18850, loss[loss=0.1376, simple_loss=0.2196, pruned_loss=0.02784, over 4837.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02967, over 972216.90 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 10:50:49,337 INFO [train.py:715] (2/8) Epoch 15, batch 18900, loss[loss=0.1093, simple_loss=0.1834, pruned_loss=0.01762, over 4767.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02981, over 972332.27 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 10:51:28,550 INFO [train.py:715] (2/8) Epoch 15, batch 18950, loss[loss=0.1355, simple_loss=0.1965, pruned_loss=0.03729, over 4954.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03027, over 971632.56 frames.], batch size: 35, lr: 1.46e-04
2022-05-08 10:52:07,862 INFO [train.py:715] (2/8) Epoch 15, batch 19000, loss[loss=0.139, simple_loss=0.22, pruned_loss=0.02896, over 4828.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03027, over 971536.87 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 10:52:46,219 INFO [train.py:715] (2/8) Epoch 15, batch 19050, loss[loss=0.1217, simple_loss=0.2002, pruned_loss=0.02159, over 4786.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02992, over 970761.93 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 10:53:25,395 INFO [train.py:715] (2/8) Epoch 15, batch 19100, loss[loss=0.1598, simple_loss=0.2313, pruned_loss=0.04413, over 4965.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03012, over 971657.67 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 10:54:03,699 INFO [train.py:715] (2/8) Epoch 15, batch 19150, loss[loss=0.1157, simple_loss=0.1895, pruned_loss=0.02096, over 4966.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03011, over 971704.58 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 10:54:41,933 INFO [train.py:715] (2/8) Epoch 15, batch 19200, loss[loss=0.1348, simple_loss=0.205, pruned_loss=0.03228, over 4765.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2089, pruned_loss=0.03085, over 971560.14 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 10:55:19,948 INFO [train.py:715] (2/8) Epoch 15, batch 19250, loss[loss=0.1614, simple_loss=0.2178, pruned_loss=0.05254, over 4905.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2081, pruned_loss=0.03032, over 971294.84 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 10:55:58,063 INFO [train.py:715] (2/8) Epoch 15, batch 19300, loss[loss=0.1533, simple_loss=0.2341, pruned_loss=0.03626, over 4867.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03014, over 971009.77 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 10:56:36,950 INFO [train.py:715] (2/8) Epoch 15, batch 19350, loss[loss=0.1171, simple_loss=0.1831, pruned_loss=0.02559, over 4797.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03011, over 971201.24 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 10:57:14,728 INFO [train.py:715] (2/8) Epoch 15, batch 19400, loss[loss=0.1257, simple_loss=0.1992, pruned_loss=0.02616, over 4866.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03028, over 971500.15 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 10:57:53,614 INFO [train.py:715] (2/8) Epoch 15, batch 19450, loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.03017, over 4983.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.03002, over 972030.93 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 10:58:31,635 INFO [train.py:715] (2/8) Epoch 15, batch 19500, loss[loss=0.1316, simple_loss=0.1995, pruned_loss=0.03183, over 4854.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03021, over 972821.97 frames.], batch size: 32, lr: 1.46e-04
2022-05-08 10:59:09,770 INFO [train.py:715] (2/8) Epoch 15, batch 19550, loss[loss=0.1373, simple_loss=0.2206, pruned_loss=0.02699, over 4971.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03044, over 973017.47 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 10:59:48,218 INFO [train.py:715] (2/8) Epoch 15, batch 19600, loss[loss=0.1522, simple_loss=0.2222, pruned_loss=0.04109, over 4959.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03046, over 973560.15 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:00:26,254 INFO [train.py:715] (2/8) Epoch 15, batch 19650, loss[loss=0.1473, simple_loss=0.2211, pruned_loss=0.03675, over 4836.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2098, pruned_loss=0.03068, over 972841.79 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:01:05,291 INFO [train.py:715] (2/8) Epoch 15, batch 19700, loss[loss=0.1464, simple_loss=0.2187, pruned_loss=0.03704, over 4837.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03072, over 972250.97 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:01:42,980 INFO [train.py:715] (2/8) Epoch 15, batch 19750, loss[loss=0.1339, simple_loss=0.2064, pruned_loss=0.03068, over 4966.00 frames.], tot_loss[loss=0.1363, simple_loss=0.2104, pruned_loss=0.0311, over 971908.74 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 11:02:21,390 INFO [train.py:715] (2/8) Epoch 15, batch 19800, loss[loss=0.1295, simple_loss=0.2115, pruned_loss=0.02371, over 4906.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2097, pruned_loss=0.03078, over 972528.85 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:02:59,697 INFO [train.py:715] (2/8) Epoch 15, batch 19850, loss[loss=0.1311, simple_loss=0.2148, pruned_loss=0.02373, over 4782.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2097, pruned_loss=0.03063, over 971882.21 frames.], batch size: 23, lr: 1.46e-04
2022-05-08 11:03:37,787 INFO [train.py:715] (2/8) Epoch 15, batch 19900, loss[loss=0.1532, simple_loss=0.2153, pruned_loss=0.04561, over 4882.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03044, over 971995.69 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:04:16,956 INFO [train.py:715] (2/8) Epoch 15, batch 19950, loss[loss=0.1331, simple_loss=0.204, pruned_loss=0.03114, over 4958.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03018, over 972050.56 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:04:55,167 INFO [train.py:715] (2/8) Epoch 15, batch 20000, loss[loss=0.12, simple_loss=0.1933, pruned_loss=0.02332, over 4696.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02983, over 971533.01 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:05:33,565 INFO [train.py:715] (2/8) Epoch 15, batch 20050, loss[loss=0.1106, simple_loss=0.1816, pruned_loss=0.01983, over 4882.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2081, pruned_loss=0.02974, over 972572.23 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:06:11,838 INFO [train.py:715] (2/8) Epoch 15, batch 20100, loss[loss=0.124, simple_loss=0.1968, pruned_loss=0.02562, over 4954.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03008, over 973178.94 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:06:50,117 INFO [train.py:715] (2/8) Epoch 15, batch 20150, loss[loss=0.1481, simple_loss=0.2084, pruned_loss=0.04388, over 4964.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03045, over 972158.82 frames.], batch size: 35, lr: 1.46e-04
2022-05-08 11:07:28,125 INFO [train.py:715] (2/8) Epoch 15, batch 20200, loss[loss=0.1727, simple_loss=0.2419, pruned_loss=0.05176, over 4892.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.0304, over 972574.25 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:08:05,821 INFO [train.py:715] (2/8) Epoch 15, batch 20250, loss[loss=0.135, simple_loss=0.197, pruned_loss=0.03652, over 4841.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2093, pruned_loss=0.03053, over 972629.31 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:08:44,516 INFO [train.py:715] (2/8) Epoch 15, batch 20300, loss[loss=0.1248, simple_loss=0.2088, pruned_loss=0.02043, over 4986.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.03035, over 972786.75 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 11:09:22,704 INFO [train.py:715] (2/8) Epoch 15, batch 20350, loss[loss=0.1282, simple_loss=0.1987, pruned_loss=0.02883, over 4943.00 frames.], tot_loss[loss=0.1346, simple_loss=0.209, pruned_loss=0.03012, over 972776.45 frames.], batch size: 29, lr: 1.46e-04
2022-05-08 11:10:01,087 INFO [train.py:715] (2/8) Epoch 15, batch 20400, loss[loss=0.1385, simple_loss=0.2149, pruned_loss=0.03099, over 4899.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.02998, over 972472.80 frames.], batch size: 39, lr: 1.46e-04
2022-05-08 11:10:38,946 INFO [train.py:715] (2/8) Epoch 15, batch 20450, loss[loss=0.1313, simple_loss=0.2, pruned_loss=0.03136, over 4836.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03044, over 973090.09 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:11:17,697 INFO [train.py:715] (2/8) Epoch 15, batch 20500, loss[loss=0.1219, simple_loss=0.2039, pruned_loss=0.01996, over 4912.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03007, over 973844.90 frames.], batch size: 29, lr: 1.46e-04
2022-05-08 11:11:55,870 INFO [train.py:715] (2/8) Epoch 15, batch 20550, loss[loss=0.1171, simple_loss=0.1961, pruned_loss=0.01905, over 4973.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03035, over 974735.47 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:12:33,935 INFO [train.py:715] (2/8) Epoch 15, batch 20600, loss[loss=0.1374, simple_loss=0.2, pruned_loss=0.03746, over 4988.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.03034, over 974215.11 frames.], batch size: 31, lr: 1.46e-04
2022-05-08 11:13:12,995 INFO [train.py:715] (2/8) Epoch 15, batch 20650, loss[loss=0.1337, simple_loss=0.2097, pruned_loss=0.02886, over 4880.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2084, pruned_loss=0.0302, over 973300.03 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 11:13:51,755 INFO [train.py:715] (2/8) Epoch 15, batch 20700, loss[loss=0.1185, simple_loss=0.1902, pruned_loss=0.02337, over 4787.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03019, over 974074.46 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:14:31,101 INFO [train.py:715] (2/8) Epoch 15, batch 20750, loss[loss=0.1233, simple_loss=0.1959, pruned_loss=0.02538, over 4966.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03025, over 973728.81 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:15:09,402 INFO [train.py:715] (2/8) Epoch 15, batch 20800, loss[loss=0.1252, simple_loss=0.2014, pruned_loss=0.02452, over 4893.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.0301, over 973011.42 frames.], batch size: 32, lr: 1.46e-04
2022-05-08 11:15:48,778 INFO [train.py:715] (2/8) Epoch 15, batch 20850, loss[loss=0.1567, simple_loss=0.2181, pruned_loss=0.04762, over 4949.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03048, over 972682.15 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:16:28,006 INFO [train.py:715] (2/8) Epoch 15, batch 20900, loss[loss=0.1208, simple_loss=0.1963, pruned_loss=0.02267, over 4872.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.0306, over 972801.67 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 11:17:06,258 INFO [train.py:715] (2/8) Epoch 15, batch 20950, loss[loss=0.137, simple_loss=0.1965, pruned_loss=0.03869, over 4994.00 frames.], tot_loss[loss=0.1358, simple_loss=0.2096, pruned_loss=0.031, over 973516.84 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:17:45,544 INFO [train.py:715] (2/8) Epoch 15, batch 21000, loss[loss=0.1369, simple_loss=0.2058, pruned_loss=0.03396, over 4829.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03096, over 973659.87 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:17:45,544 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 11:17:56,038 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.1051, simple_loss=0.1887, pruned_loss=0.01075, over 914524.00 frames.
2022-05-08 11:18:35,305 INFO [train.py:715] (2/8) Epoch 15, batch 21050, loss[loss=0.1231, simple_loss=0.2007, pruned_loss=0.02277, over 4781.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2088, pruned_loss=0.03109, over 973174.77 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 11:19:14,785 INFO [train.py:715] (2/8) Epoch 15, batch 21100, loss[loss=0.1423, simple_loss=0.2155, pruned_loss=0.0345, over 4701.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2082, pruned_loss=0.03057, over 971393.83 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:19:53,802 INFO [train.py:715] (2/8) Epoch 15, batch 21150, loss[loss=0.1597, simple_loss=0.2288, pruned_loss=0.04531, over 4798.00 frames.], tot_loss[loss=0.135, simple_loss=0.2084, pruned_loss=0.0308, over 971000.73 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 11:20:32,268 INFO [train.py:715] (2/8) Epoch 15, batch 21200, loss[loss=0.1344, simple_loss=0.2042, pruned_loss=0.03229, over 4968.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2089, pruned_loss=0.03093, over 971915.30 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:21:11,105 INFO [train.py:715] (2/8) Epoch 15, batch 21250, loss[loss=0.1144, simple_loss=0.1896, pruned_loss=0.01959, over 4816.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03066, over 972396.88 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:21:49,144 INFO [train.py:715] (2/8) Epoch 15, batch 21300, loss[loss=0.1124, simple_loss=0.1766, pruned_loss=0.02416, over 4772.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2079, pruned_loss=0.03038, over 971927.16 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:22:26,789 INFO [train.py:715] (2/8) Epoch 15, batch 21350, loss[loss=0.1107, simple_loss=0.1722, pruned_loss=0.02457, over 4802.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2073, pruned_loss=0.03024, over 971729.74 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:23:05,097 INFO [train.py:715] (2/8) Epoch 15, batch 21400, loss[loss=0.1249, simple_loss=0.2086, pruned_loss=0.02058, over 4958.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2078, pruned_loss=0.03017, over 972503.82 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:23:43,352 INFO [train.py:715] (2/8) Epoch 15, batch 21450, loss[loss=0.1323, simple_loss=0.1993, pruned_loss=0.03263, over 4725.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03049, over 972769.98 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:24:21,358 INFO [train.py:715] (2/8) Epoch 15, batch 21500, loss[loss=0.1328, simple_loss=0.2035, pruned_loss=0.03104, over 4796.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.03039, over 971873.55 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:24:59,648 INFO [train.py:715] (2/8) Epoch 15, batch 21550, loss[loss=0.1204, simple_loss=0.1784, pruned_loss=0.03125, over 4794.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03069, over 972559.38 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:25:38,152 INFO [train.py:715] (2/8) Epoch 15, batch 21600, loss[loss=0.1117, simple_loss=0.1958, pruned_loss=0.01382, over 4780.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2088, pruned_loss=0.03115, over 972743.38 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 11:26:16,021 INFO [train.py:715] (2/8) Epoch 15, batch 21650, loss[loss=0.1733, simple_loss=0.2338, pruned_loss=0.05644, over 4852.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2084, pruned_loss=0.03108, over 973294.94 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 11:26:54,239 INFO [train.py:715] (2/8) Epoch 15, batch 21700, loss[loss=0.1414, simple_loss=0.2145, pruned_loss=0.03413, over 4862.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03074, over 972532.59 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 11:27:32,377 INFO [train.py:715] (2/8) Epoch 15, batch 21750, loss[loss=0.1459, simple_loss=0.2119, pruned_loss=0.03995, over 4921.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2087, pruned_loss=0.03106, over 971866.48 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 11:28:10,476 INFO [train.py:715] (2/8) Epoch 15, batch 21800, loss[loss=0.178, simple_loss=0.2452, pruned_loss=0.05537, over 4878.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2087, pruned_loss=0.03101, over 971159.23 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:28:48,402 INFO [train.py:715] (2/8) Epoch 15, batch 21850, loss[loss=0.1502, simple_loss=0.2254, pruned_loss=0.03752, over 4819.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2082, pruned_loss=0.03095, over 971779.33 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 11:29:29,584 INFO [train.py:715] (2/8) Epoch 15, batch 21900, loss[loss=0.1374, simple_loss=0.2186, pruned_loss=0.02808, over 4816.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2081, pruned_loss=0.0307, over 971666.42 frames.], batch size: 26, lr: 1.46e-04
2022-05-08 11:30:08,735 INFO [train.py:715] (2/8) Epoch 15, batch 21950, loss[loss=0.1296, simple_loss=0.2092, pruned_loss=0.02498, over 4917.00 frames.], tot_loss[loss=0.1343, simple_loss=0.208, pruned_loss=0.0303, over 972128.64 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:30:47,267 INFO [train.py:715] (2/8) Epoch 15, batch 22000, loss[loss=0.1507, simple_loss=0.2365, pruned_loss=0.03245, over 4975.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03003, over 972883.74 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 11:31:25,789 INFO [train.py:715] (2/8) Epoch 15, batch 22050, loss[loss=0.1182, simple_loss=0.1936, pruned_loss=0.0214, over 4884.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02998, over 973661.46 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 11:32:05,112 INFO [train.py:715] (2/8) Epoch 15, batch 22100, loss[loss=0.1317, simple_loss=0.2034, pruned_loss=0.02998, over 4899.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03006, over 973085.91 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:32:43,914 INFO [train.py:715] (2/8) Epoch 15, batch 22150, loss[loss=0.1252, simple_loss=0.1948, pruned_loss=0.0278, over 4823.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03041, over 972855.84 frames.], batch size: 27, lr: 1.46e-04
2022-05-08 11:33:22,285 INFO [train.py:715] (2/8) Epoch 15, batch 22200, loss[loss=0.1144, simple_loss=0.1847, pruned_loss=0.022, over 4978.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2091, pruned_loss=0.03007, over 972597.50 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:34:01,326 INFO [train.py:715] (2/8) Epoch 15, batch 22250, loss[loss=0.1437, simple_loss=0.2202, pruned_loss=0.03359, over 4836.00 frames.], tot_loss[loss=0.1349, simple_loss=0.209, pruned_loss=0.03036, over 972903.16 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:34:40,258 INFO [train.py:715] (2/8) Epoch 15, batch 22300, loss[loss=0.1465, simple_loss=0.2167, pruned_loss=0.03815, over 4979.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03028, over 972552.08 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 11:35:18,794 INFO [train.py:715] (2/8) Epoch 15, batch 22350, loss[loss=0.1503, simple_loss=0.2253, pruned_loss=0.03766, over 4815.00 frames.], tot_loss[loss=0.135, simple_loss=0.2092, pruned_loss=0.03037, over 972889.87 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:35:57,367 INFO [train.py:715] (2/8) Epoch 15, batch 22400, loss[loss=0.1477, simple_loss=0.22, pruned_loss=0.03769, over 4693.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02979, over 971814.65 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:36:36,629 INFO [train.py:715] (2/8) Epoch 15, batch 22450, loss[loss=0.1143, simple_loss=0.1933, pruned_loss=0.01766, over 4852.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.02924, over 971914.98 frames.], batch size: 32, lr: 1.46e-04
2022-05-08 11:37:15,518 INFO [train.py:715] (2/8) Epoch 15, batch 22500, loss[loss=0.1596, simple_loss=0.2259, pruned_loss=0.04664, over 4903.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02941, over 972431.19 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:37:54,243 INFO [train.py:715] (2/8) Epoch 15, batch 22550, loss[loss=0.141, simple_loss=0.2121, pruned_loss=0.03491, over 4875.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02962, over 972015.45 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:38:32,798 INFO [train.py:715] (2/8) Epoch 15, batch 22600, loss[loss=0.1824, simple_loss=0.2431, pruned_loss=0.0609, over 4966.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.0296, over 972019.88 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:39:11,719 INFO [train.py:715] (2/8) Epoch 15, batch 22650, loss[loss=0.1152, simple_loss=0.1897, pruned_loss=0.02041, over 4771.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03006, over 972324.44 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:39:50,374 INFO [train.py:715] (2/8) Epoch 15, batch 22700, loss[loss=0.1239, simple_loss=0.1914, pruned_loss=0.02815, over 4850.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2086, pruned_loss=0.03008, over 971992.81 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:40:29,113 INFO [train.py:715] (2/8) Epoch 15, batch 22750, loss[loss=0.155, simple_loss=0.2308, pruned_loss=0.03957, over 4855.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.03002, over 971030.39 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:41:08,455 INFO [train.py:715] (2/8) Epoch 15, batch 22800, loss[loss=0.1366, simple_loss=0.203, pruned_loss=0.03505, over 4973.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.03, over 971321.37 frames.], batch size: 35, lr: 1.46e-04
2022-05-08 11:41:47,279 INFO [train.py:715] (2/8) Epoch 15, batch 22850, loss[loss=0.1186, simple_loss=0.1951, pruned_loss=0.02105, over 4887.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02975, over 971343.60 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:42:26,023 INFO [train.py:715] (2/8) Epoch 15, batch 22900, loss[loss=0.1266, simple_loss=0.2074, pruned_loss=0.0229, over 4811.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02989, over 972436.70 frames.], batch size: 27, lr: 1.46e-04
2022-05-08 11:43:05,209 INFO [train.py:715] (2/8) Epoch 15, batch 22950, loss[loss=0.1529, simple_loss=0.2276, pruned_loss=0.0391, over 4861.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02986, over 972103.91 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:43:43,826 INFO [train.py:715] (2/8) Epoch 15, batch 23000, loss[loss=0.1309, simple_loss=0.2032, pruned_loss=0.02933, over 4884.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02966, over 972004.82 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 11:44:22,237 INFO [train.py:715] (2/8) Epoch 15, batch 23050, loss[loss=0.13, simple_loss=0.2051, pruned_loss=0.02751, over 4932.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03033, over 973075.60 frames.], batch size: 29, lr: 1.46e-04
2022-05-08 11:45:00,626 INFO [train.py:715] (2/8) Epoch 15, batch 23100, loss[loss=0.1215, simple_loss=0.1965, pruned_loss=0.02324, over 4815.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03042, over 972880.04 frames.], batch size: 27, lr: 1.46e-04
2022-05-08 11:45:39,478 INFO [train.py:715] (2/8) Epoch 15, batch 23150, loss[loss=0.1276, simple_loss=0.2119, pruned_loss=0.02172, over 4900.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03049, over 973215.38 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:46:17,453 INFO [train.py:715] (2/8) Epoch 15, batch 23200, loss[loss=0.1211, simple_loss=0.1933, pruned_loss=0.02449, over 4767.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02979, over 973634.70 frames.], batch size: 18, lr: 1.46e-04
2022-05-08 11:46:55,709 INFO [train.py:715] (2/8) Epoch 15, batch 23250, loss[loss=0.1198, simple_loss=0.2013, pruned_loss=0.01916, over 4936.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03026, over 973920.68 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 11:47:34,386 INFO [train.py:715] (2/8) Epoch 15, batch 23300, loss[loss=0.1127, simple_loss=0.1818, pruned_loss=0.02173, over 4935.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03038, over 973414.98 frames.], batch size: 29, lr: 1.46e-04
2022-05-08 11:48:12,428 INFO [train.py:715] (2/8) Epoch 15, batch 23350, loss[loss=0.1313, simple_loss=0.2047, pruned_loss=0.02892, over 4937.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.03004, over 973959.22 frames.], batch size: 29, lr: 1.46e-04
2022-05-08 11:48:50,739 INFO [train.py:715] (2/8) Epoch 15, batch 23400, loss[loss=0.1333, simple_loss=0.2109, pruned_loss=0.02785, over 4848.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02955, over 973622.42 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:49:28,562 INFO [train.py:715] (2/8) Epoch 15, batch 23450, loss[loss=0.1127, simple_loss=0.1833, pruned_loss=0.02104, over 4843.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02953, over 973241.01 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 11:50:07,086 INFO [train.py:715] (2/8) Epoch 15, batch 23500, loss[loss=0.127, simple_loss=0.2049, pruned_loss=0.02451, over 4827.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.0289, over 972559.75 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 11:50:44,859 INFO [train.py:715] (2/8) Epoch 15, batch 23550, loss[loss=0.1185, simple_loss=0.1875, pruned_loss=0.02472, over 4749.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02978, over 972465.91 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:51:22,854 INFO [train.py:715] (2/8) Epoch 15, batch 23600, loss[loss=0.1236, simple_loss=0.191, pruned_loss=0.0281, over 4819.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.0297, over 972532.37 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 11:52:01,278 INFO [train.py:715] (2/8) Epoch 15, batch 23650, loss[loss=0.1085, simple_loss=0.1777, pruned_loss=0.01962, over 4737.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02989, over 972021.23 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:52:39,167 INFO [train.py:715] (2/8) Epoch 15, batch 23700, loss[loss=0.1369, simple_loss=0.2138, pruned_loss=0.03, over 4820.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2085, pruned_loss=0.03014, over 971755.12 frames.], batch size: 27, lr: 1.46e-04
2022-05-08 11:53:17,232 INFO [train.py:715] (2/8) Epoch 15, batch 23750, loss[loss=0.1319, simple_loss=0.2106, pruned_loss=0.02663, over 4849.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03013, over 971881.03 frames.], batch size: 20, lr: 1.46e-04
2022-05-08 11:53:55,057 INFO [train.py:715] (2/8) Epoch 15, batch 23800, loss[loss=0.1379, simple_loss=0.2068, pruned_loss=0.03447, over 4944.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2084, pruned_loss=0.03036, over 972080.91 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 11:54:33,045 INFO [train.py:715] (2/8) Epoch 15, batch 23850, loss[loss=0.152, simple_loss=0.2275, pruned_loss=0.03827, over 4686.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2085, pruned_loss=0.03047, over 971990.13 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 11:55:11,362 INFO [train.py:715] (2/8) Epoch 15, batch 23900, loss[loss=0.2178, simple_loss=0.2634, pruned_loss=0.08608, over 4807.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2093, pruned_loss=0.03071, over 971914.07 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 11:55:48,902 INFO [train.py:715] (2/8) Epoch 15, batch 23950, loss[loss=0.1425, simple_loss=0.216, pruned_loss=0.0345, over 4795.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03033, over 971709.19 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 11:56:27,442 INFO [train.py:715] (2/8) Epoch 15, batch 24000, loss[loss=0.1272, simple_loss=0.2127, pruned_loss=0.02082, over 4790.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.03006, over 972338.77 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:56:27,443 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 11:56:37,034 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.105, simple_loss=0.1886, pruned_loss=0.01071, over 914524.00 frames.
2022-05-08 11:57:15,621 INFO [train.py:715] (2/8) Epoch 15, batch 24050, loss[loss=0.1696, simple_loss=0.24, pruned_loss=0.04961, over 4884.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02965, over 971531.85 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 11:57:54,186 INFO [train.py:715] (2/8) Epoch 15, batch 24100, loss[loss=0.1345, simple_loss=0.2001, pruned_loss=0.03448, over 4793.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02962, over 971844.30 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 11:58:32,190 INFO [train.py:715] (2/8) Epoch 15, batch 24150, loss[loss=0.1204, simple_loss=0.1948, pruned_loss=0.02298, over 4866.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02924, over 971938.76 frames.], batch size: 32, lr: 1.46e-04
2022-05-08 11:59:10,406 INFO [train.py:715] (2/8) Epoch 15, batch 24200, loss[loss=0.1134, simple_loss=0.1985, pruned_loss=0.01412, over 4898.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02946, over 972610.31 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 11:59:48,420 INFO [train.py:715] (2/8) Epoch 15, batch 24250, loss[loss=0.1261, simple_loss=0.2029, pruned_loss=0.02467, over 4801.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02946, over 972092.63 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 12:00:26,746 INFO [train.py:715] (2/8) Epoch 15, batch 24300, loss[loss=0.1197, simple_loss=0.1922, pruned_loss=0.02355, over 4904.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.0292, over 972403.39 frames.], batch size: 19, lr: 1.46e-04
2022-05-08 12:01:03,898 INFO [train.py:715] (2/8) Epoch 15, batch 24350, loss[loss=0.1064, simple_loss=0.1838, pruned_loss=0.01446, over 4811.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02941, over 972725.96 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 12:01:42,321 INFO [train.py:715] (2/8) Epoch 15, batch 24400, loss[loss=0.118, simple_loss=0.1936, pruned_loss=0.02117, over 4760.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02946, over 972178.84 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 12:02:20,844 INFO [train.py:715] (2/8) Epoch 15, batch 24450, loss[loss=0.1007, simple_loss=0.1707, pruned_loss=0.01535, over 4810.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02944, over 972136.10 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 12:02:58,825 INFO [train.py:715] (2/8) Epoch 15, batch 24500, loss[loss=0.1283, simple_loss=0.1963, pruned_loss=0.03014, over 4850.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.0293, over 972287.48 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 12:03:36,484 INFO [train.py:715] (2/8) Epoch 15, batch 24550, loss[loss=0.1495, simple_loss=0.2155, pruned_loss=0.04176, over 4859.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02954, over 971313.85 frames.], batch size: 30, lr: 1.46e-04
2022-05-08 12:04:14,726 INFO [train.py:715] (2/8) Epoch 15, batch 24600, loss[loss=0.1356, simple_loss=0.1876, pruned_loss=0.04174, over 4965.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02947, over 972205.99 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 12:04:53,494 INFO [train.py:715] (2/8) Epoch 15, batch 24650, loss[loss=0.1456, simple_loss=0.2168, pruned_loss=0.03722, over 4920.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02993, over 972286.63 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 12:05:31,175 INFO [train.py:715] (2/8) Epoch 15, batch 24700, loss[loss=0.1301, simple_loss=0.222, pruned_loss=0.0191, over 4812.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02959, over 972271.64 frames.], batch size: 27, lr: 1.46e-04
2022-05-08 12:06:09,581 INFO [train.py:715] (2/8) Epoch 15, batch 24750, loss[loss=0.1313, simple_loss=0.2179, pruned_loss=0.0224, over 4775.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02957, over 971679.17 frames.], batch size: 17, lr: 1.46e-04
2022-05-08 12:06:47,908 INFO [train.py:715] (2/8) Epoch 15, batch 24800, loss[loss=0.1176, simple_loss=0.1917, pruned_loss=0.0217, over 4824.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02943, over 972382.55 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 12:07:25,660 INFO [train.py:715] (2/8) Epoch 15, batch 24850, loss[loss=0.1312, simple_loss=0.1993, pruned_loss=0.03158, over 4985.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02977, over 972264.47 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 12:08:03,593 INFO [train.py:715] (2/8) Epoch 15, batch 24900, loss[loss=0.1291, simple_loss=0.1988, pruned_loss=0.02972, over 4887.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.0294, over 972490.24 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 12:08:41,840 INFO [train.py:715] (2/8) Epoch 15, batch 24950, loss[loss=0.1073, simple_loss=0.1875, pruned_loss=0.01349, over 4836.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02949, over 973148.68 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 12:09:20,947 INFO [train.py:715] (2/8) Epoch 15, batch 25000, loss[loss=0.1252, simple_loss=0.2109, pruned_loss=0.01973, over 4820.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.0296, over 972433.14 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 12:09:58,497 INFO [train.py:715] (2/8) Epoch 15, batch 25050, loss[loss=0.1344, simple_loss=0.2012, pruned_loss=0.0338, over 4811.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02981, over 972678.54 frames.], batch size: 12, lr: 1.46e-04
2022-05-08 12:10:36,536 INFO [train.py:715] (2/8) Epoch 15, batch 25100, loss[loss=0.1681, simple_loss=0.2451, pruned_loss=0.04555, over 4978.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2081, pruned_loss=0.0297, over 972546.24 frames.], batch size: 15, lr: 1.46e-04
2022-05-08 12:11:14,986 INFO [train.py:715] (2/8) Epoch 15, batch 25150, loss[loss=0.1245, simple_loss=0.2091, pruned_loss=0.01997, over 4813.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.0296, over 972127.47 frames.], batch size: 25, lr: 1.46e-04
2022-05-08 12:11:53,002 INFO [train.py:715] (2/8) Epoch 15, batch 25200, loss[loss=0.1409, simple_loss=0.2184, pruned_loss=0.03175, over 4966.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02962, over 972151.85 frames.], batch size: 24, lr: 1.46e-04
2022-05-08 12:12:30,794 INFO [train.py:715] (2/8) Epoch 15, batch 25250, loss[loss=0.1171, simple_loss=0.1885, pruned_loss=0.02286, over 4974.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02968, over 972660.45 frames.], batch size: 14, lr: 1.46e-04
2022-05-08 12:13:09,119 INFO [train.py:715] (2/8) Epoch 15, batch 25300, loss[loss=0.1363, simple_loss=0.2129, pruned_loss=0.0299, over 4836.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02947, over 972882.81 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 12:13:47,200 INFO [train.py:715] (2/8) Epoch 15, batch 25350, loss[loss=0.1121, simple_loss=0.1874, pruned_loss=0.01838, over 4826.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02964, over 972235.30 frames.], batch size: 13, lr: 1.46e-04
2022-05-08 12:14:24,744 INFO [train.py:715] (2/8) Epoch 15, batch 25400, loss[loss=0.1179, simple_loss=0.1962, pruned_loss=0.01974, over 4735.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2068, pruned_loss=0.02964, over 972258.14 frames.], batch size: 16, lr: 1.46e-04
2022-05-08 12:15:02,818 INFO [train.py:715] (2/8) Epoch 15, batch 25450, loss[loss=0.1507, simple_loss=0.2289, pruned_loss=0.03628, over 4888.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02975, over 972352.38 frames.], batch size: 22, lr: 1.46e-04
2022-05-08 12:15:41,206 INFO [train.py:715] (2/8) Epoch 15, batch 25500, loss[loss=0.131, simple_loss=0.202, pruned_loss=0.03003, over 4947.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.0296, over 971846.41 frames.], batch size: 21, lr: 1.46e-04
2022-05-08 12:16:18,763 INFO [train.py:715] (2/8) Epoch 15, batch 25550, loss[loss=0.1489, simple_loss=0.2267, pruned_loss=0.03553, over 4827.00 frames.], tot_loss[loss=0.1343, simple_loss=0.209, pruned_loss=0.02976, over 971412.84 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 12:16:56,915 INFO [train.py:715] (2/8) Epoch 15, batch 25600, loss[loss=0.135, simple_loss=0.2075, pruned_loss=0.03125, over 4799.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2085, pruned_loss=0.02958, over 971790.27 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:17:35,537 INFO [train.py:715] (2/8) Epoch 15, batch 25650, loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03247, over 4798.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02977, over 972089.02 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:18:13,814 INFO [train.py:715] (2/8) Epoch 15, batch 25700, loss[loss=0.1127, simple_loss=0.1924, pruned_loss=0.01648, over 4984.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03003, over 971834.63 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:18:51,202 INFO [train.py:715] (2/8) Epoch 15, batch 25750, loss[loss=0.1376, simple_loss=0.2097, pruned_loss=0.03274, over 4749.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.0303, over 971951.01 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 12:19:29,349 INFO [train.py:715] (2/8) Epoch 15, batch 25800, loss[loss=0.1526, simple_loss=0.2291, pruned_loss=0.038, over 4837.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03045, over 971879.87 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:20:07,976 INFO [train.py:715] (2/8) Epoch 15, batch 25850, loss[loss=0.1341, simple_loss=0.1912, pruned_loss=0.03847, over 4702.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.03044, over 971268.89 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:20:45,417 INFO [train.py:715] (2/8) Epoch 15, batch 25900, loss[loss=0.1508, simple_loss=0.2345, pruned_loss=0.03351, over 4801.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.03047, over 971350.45 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:21:24,014 INFO [train.py:715] (2/8) Epoch 15, batch 25950, loss[loss=0.1467, simple_loss=0.228, pruned_loss=0.0327, over 4745.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2078, pruned_loss=0.03051, over 971138.54 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 12:22:02,168 INFO [train.py:715] (2/8) Epoch 15, batch 26000, loss[loss=0.1687, simple_loss=0.2303, pruned_loss=0.05352, over 4784.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2072, pruned_loss=0.02997, over 970995.71 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:22:39,853 INFO [train.py:715] (2/8) Epoch 15, batch 26050, loss[loss=0.1383, simple_loss=0.209, pruned_loss=0.0338, over 4743.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03, over 970191.08 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 12:23:17,646 INFO [train.py:715] (2/8) Epoch 15, batch 26100, loss[loss=0.1671, simple_loss=0.233, pruned_loss=0.05065, over 4786.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02955, over 969654.69 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:23:56,077 INFO [train.py:715] (2/8) Epoch 15, batch 26150, loss[loss=0.147, simple_loss=0.2266, pruned_loss=0.03371, over 4765.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02958, over 970143.90 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:24:33,867 INFO [train.py:715] (2/8) Epoch 15, batch 26200, loss[loss=0.1269, simple_loss=0.1981, pruned_loss=0.02779, over 4692.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2066, pruned_loss=0.02977, over 969923.88 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:25:11,685 INFO [train.py:715] (2/8) Epoch 15, batch 26250, loss[loss=0.1195, simple_loss=0.1904, pruned_loss=0.0243, over 4905.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02952, over 969789.27 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 12:25:50,003 INFO [train.py:715] (2/8) Epoch 15, batch 26300, loss[loss=0.1375, simple_loss=0.2112, pruned_loss=0.03192, over 4761.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02937, over 970130.73 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:26:28,461 INFO [train.py:715] (2/8) Epoch 15, batch 26350, loss[loss=0.1229, simple_loss=0.2008, pruned_loss=0.02248, over 4951.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02918, over 970121.79 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:27:06,220 INFO [train.py:715] (2/8) Epoch 15, batch 26400, loss[loss=0.1555, simple_loss=0.2424, pruned_loss=0.03432, over 4795.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02944, over 970391.67 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:27:44,350 INFO [train.py:715] (2/8) Epoch 15, batch 26450, loss[loss=0.1226, simple_loss=0.194, pruned_loss=0.0256, over 4927.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02913, over 969793.65 frames.], batch size: 29, lr: 1.45e-04
2022-05-08 12:28:22,636 INFO [train.py:715] (2/8) Epoch 15, batch 26500, loss[loss=0.1227, simple_loss=0.1926, pruned_loss=0.02645, over 4810.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02956, over 969568.73 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 12:29:00,419 INFO [train.py:715] (2/8) Epoch 15, batch 26550, loss[loss=0.1174, simple_loss=0.1855, pruned_loss=0.02466, over 4777.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2079, pruned_loss=0.02939, over 969513.91 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:29:38,154 INFO [train.py:715] (2/8) Epoch 15, batch 26600, loss[loss=0.1399, simple_loss=0.2181, pruned_loss=0.03086, over 4891.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02915, over 969629.74 frames.], batch size: 22, lr: 1.45e-04
2022-05-08 12:30:16,186 INFO [train.py:715] (2/8) Epoch 15, batch 26650, loss[loss=0.1346, simple_loss=0.2041, pruned_loss=0.03252, over 4913.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02944, over 969139.46 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 12:30:54,318 INFO [train.py:715] (2/8) Epoch 15, batch 26700, loss[loss=0.1204, simple_loss=0.193, pruned_loss=0.02388, over 4823.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02953, over 970610.79 frames.], batch size: 26, lr: 1.45e-04
2022-05-08 12:31:31,944 INFO [train.py:715] (2/8) Epoch 15, batch 26750, loss[loss=0.1461, simple_loss=0.2223, pruned_loss=0.03495, over 4771.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02986, over 970599.84 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:32:10,363 INFO [train.py:715] (2/8) Epoch 15, batch 26800, loss[loss=0.1304, simple_loss=0.2023, pruned_loss=0.02921, over 4938.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02955, over 970806.27 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 12:32:48,681 INFO [train.py:715] (2/8) Epoch 15, batch 26850, loss[loss=0.1504, simple_loss=0.2203, pruned_loss=0.04027, over 4813.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.0296, over 970872.98 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 12:33:26,761 INFO [train.py:715] (2/8) Epoch 15, batch 26900, loss[loss=0.1439, simple_loss=0.2113, pruned_loss=0.03823, over 4766.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2079, pruned_loss=0.02937, over 970863.54 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 12:34:04,501 INFO [train.py:715] (2/8) Epoch 15, batch 26950, loss[loss=0.1742, simple_loss=0.2413, pruned_loss=0.05353, over 4778.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2085, pruned_loss=0.02962, over 970755.30 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:34:42,583 INFO [train.py:715] (2/8) Epoch 15, batch 27000, loss[loss=0.1593, simple_loss=0.22, pruned_loss=0.04929, over 4852.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2089, pruned_loss=0.03027, over 971577.02 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 12:34:42,584 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 12:34:52,203 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.1049, simple_loss=0.1884, pruned_loss=0.01064, over 914524.00 frames.
2022-05-08 12:35:31,311 INFO [train.py:715] (2/8) Epoch 15, batch 27050, loss[loss=0.1193, simple_loss=0.1945, pruned_loss=0.02203, over 4781.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02969, over 971683.53 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 12:36:10,019 INFO [train.py:715] (2/8) Epoch 15, batch 27100, loss[loss=0.1126, simple_loss=0.196, pruned_loss=0.0146, over 4762.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03007, over 971806.66 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:36:48,673 INFO [train.py:715] (2/8) Epoch 15, batch 27150, loss[loss=0.1502, simple_loss=0.2166, pruned_loss=0.04188, over 4984.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02993, over 972028.83 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:37:26,865 INFO [train.py:715] (2/8) Epoch 15, batch 27200, loss[loss=0.1302, simple_loss=0.2045, pruned_loss=0.02802, over 4872.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02963, over 972926.74 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 12:38:05,901 INFO [train.py:715] (2/8) Epoch 15, batch 27250, loss[loss=0.1261, simple_loss=0.2032, pruned_loss=0.02451, over 4975.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02974, over 972672.64 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 12:38:43,693 INFO [train.py:715] (2/8) Epoch 15, batch 27300, loss[loss=0.1522, simple_loss=0.2187, pruned_loss=0.04278, over 4860.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02983, over 972468.65 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 12:39:21,921 INFO [train.py:715] (2/8) Epoch 15, batch 27350, loss[loss=0.1277, simple_loss=0.2142, pruned_loss=0.02056, over 4833.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02963, over 972398.00 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 12:40:00,100 INFO [train.py:715] (2/8) Epoch 15, batch 27400, loss[loss=0.1389, simple_loss=0.2136, pruned_loss=0.03208, over 4902.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03049, over 973016.10 frames.], batch size: 22, lr: 1.45e-04
2022-05-08 12:40:38,395 INFO [train.py:715] (2/8) Epoch 15, batch 27450, loss[loss=0.1129, simple_loss=0.1939, pruned_loss=0.0159, over 4821.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.0301, over 973241.63 frames.], batch size: 27, lr: 1.45e-04
2022-05-08 12:41:16,660 INFO [train.py:715] (2/8) Epoch 15, batch 27500, loss[loss=0.1438, simple_loss=0.2285, pruned_loss=0.02956, over 4775.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03022, over 973055.30 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:41:54,850 INFO [train.py:715] (2/8) Epoch 15, batch 27550, loss[loss=0.1295, simple_loss=0.2124, pruned_loss=0.02336, over 4819.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2093, pruned_loss=0.03075, over 972917.22 frames.], batch size: 27, lr: 1.45e-04
2022-05-08 12:42:33,402 INFO [train.py:715] (2/8) Epoch 15, batch 27600, loss[loss=0.1295, simple_loss=0.2094, pruned_loss=0.02476, over 4827.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03043, over 972432.14 frames.], batch size: 27, lr: 1.45e-04
2022-05-08 12:43:10,760 INFO [train.py:715] (2/8) Epoch 15, batch 27650, loss[loss=0.1176, simple_loss=0.1945, pruned_loss=0.02039, over 4818.00 frames.], tot_loss[loss=0.1356, simple_loss=0.2095, pruned_loss=0.03079, over 972644.58 frames.], batch size: 27, lr: 1.45e-04
2022-05-08 12:43:49,458 INFO [train.py:715] (2/8) Epoch 15, batch 27700, loss[loss=0.1716, simple_loss=0.2391, pruned_loss=0.052, over 4836.00 frames.], tot_loss[loss=0.136, simple_loss=0.2098, pruned_loss=0.03114, over 972662.12 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 12:44:27,755 INFO [train.py:715] (2/8) Epoch 15, batch 27750, loss[loss=0.1088, simple_loss=0.1887, pruned_loss=0.01447, over 4962.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2086, pruned_loss=0.03031, over 973207.51 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 12:45:06,222 INFO [train.py:715] (2/8) Epoch 15, batch 27800, loss[loss=0.1257, simple_loss=0.2014, pruned_loss=0.02499, over 4896.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02966, over 973072.40 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 12:45:44,233 INFO [train.py:715] (2/8) Epoch 15, batch 27850, loss[loss=0.1299, simple_loss=0.2088, pruned_loss=0.02553, over 4980.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02963, over 972766.95 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 12:46:21,974 INFO [train.py:715] (2/8) Epoch 15, batch 27900, loss[loss=0.1198, simple_loss=0.2005, pruned_loss=0.01955, over 4982.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02981, over 971617.48 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:47:00,799 INFO [train.py:715] (2/8) Epoch 15, batch 27950, loss[loss=0.144, simple_loss=0.2284, pruned_loss=0.02984, over 4977.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03016, over 972032.07 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 12:47:38,667 INFO [train.py:715] (2/8) Epoch 15, batch 28000, loss[loss=0.1371, simple_loss=0.2058, pruned_loss=0.03421, over 4783.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03012, over 971706.25 frames.], batch size: 12, lr: 1.45e-04
2022-05-08 12:48:16,880 INFO [train.py:715] (2/8) Epoch 15, batch 28050, loss[loss=0.125, simple_loss=0.2037, pruned_loss=0.02318, over 4816.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03023, over 972513.18 frames.], batch size: 27, lr: 1.45e-04
2022-05-08 12:48:55,111 INFO [train.py:715] (2/8) Epoch 15, batch 28100, loss[loss=0.09158, simple_loss=0.1615, pruned_loss=0.01086, over 4902.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.0297, over 972233.71 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:49:33,362 INFO [train.py:715] (2/8) Epoch 15, batch 28150, loss[loss=0.1369, simple_loss=0.2229, pruned_loss=0.02547, over 4891.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03008, over 972109.91 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:50:11,124 INFO [train.py:715] (2/8) Epoch 15, batch 28200, loss[loss=0.1291, simple_loss=0.204, pruned_loss=0.02706, over 4809.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.0302, over 972355.07 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 12:50:49,025 INFO [train.py:715] (2/8) Epoch 15, batch 28250, loss[loss=0.1269, simple_loss=0.1942, pruned_loss=0.02975, over 4639.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03014, over 972455.37 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 12:51:28,180 INFO [train.py:715] (2/8) Epoch 15, batch 28300, loss[loss=0.1569, simple_loss=0.2385, pruned_loss=0.03761, over 4942.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2087, pruned_loss=0.03077, over 972292.00 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 12:52:05,676 INFO [train.py:715] (2/8) Epoch 15, batch 28350, loss[loss=0.1396, simple_loss=0.214, pruned_loss=0.03257, over 4987.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03059, over 972627.86 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 12:52:43,909 INFO [train.py:715] (2/8) Epoch 15, batch 28400, loss[loss=0.1109, simple_loss=0.1833, pruned_loss=0.01922, over 4684.00 frames.], tot_loss[loss=0.1354, simple_loss=0.2092, pruned_loss=0.03079, over 971685.62 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:53:22,224 INFO [train.py:715] (2/8) Epoch 15, batch 28450, loss[loss=0.1369, simple_loss=0.205, pruned_loss=0.03435, over 4909.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.0306, over 972409.10 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 12:54:00,368 INFO [train.py:715] (2/8) Epoch 15, batch 28500, loss[loss=0.1503, simple_loss=0.2335, pruned_loss=0.0336, over 4978.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.0303, over 972875.72 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 12:54:38,502 INFO [train.py:715] (2/8) Epoch 15, batch 28550, loss[loss=0.1375, simple_loss=0.206, pruned_loss=0.03454, over 4787.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03059, over 973169.04 frames.], batch size: 12, lr: 1.45e-04
2022-05-08 12:55:16,672 INFO [train.py:715] (2/8) Epoch 15, batch 28600, loss[loss=0.1286, simple_loss=0.2043, pruned_loss=0.02645, over 4855.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2084, pruned_loss=0.0301, over 973192.20 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 12:55:55,088 INFO [train.py:715] (2/8) Epoch 15, batch 28650, loss[loss=0.1441, simple_loss=0.2201, pruned_loss=0.03406, over 4795.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03036, over 973587.97 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 12:56:32,941 INFO [train.py:715] (2/8) Epoch 15, batch 28700, loss[loss=0.1179, simple_loss=0.185, pruned_loss=0.02543, over 4840.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02991, over 973746.45 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 12:57:11,383 INFO [train.py:715] (2/8) Epoch 15, batch 28750, loss[loss=0.129, simple_loss=0.2019, pruned_loss=0.0281, over 4913.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.0302, over 973594.00 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 12:57:50,112 INFO [train.py:715] (2/8) Epoch 15, batch 28800, loss[loss=0.1413, simple_loss=0.2109, pruned_loss=0.03587, over 4971.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03008, over 973421.14 frames.], batch size: 24, lr: 1.45e-04
2022-05-08 12:58:28,489 INFO [train.py:715] (2/8) Epoch 15, batch 28850, loss[loss=0.1529, simple_loss=0.2302, pruned_loss=0.03782, over 4903.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02989, over 973553.57 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 12:59:06,981 INFO [train.py:715] (2/8) Epoch 15, batch 28900, loss[loss=0.1289, simple_loss=0.2041, pruned_loss=0.02682, over 4949.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03039, over 973075.27 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 12:59:45,697 INFO [train.py:715] (2/8) Epoch 15, batch 28950, loss[loss=0.1464, simple_loss=0.229, pruned_loss=0.03189, over 4971.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03003, over 972825.77 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 13:00:24,869 INFO [train.py:715] (2/8) Epoch 15, batch 29000, loss[loss=0.1118, simple_loss=0.1836, pruned_loss=0.02003, over 4776.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02967, over 971961.81 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:01:03,444 INFO [train.py:715] (2/8) Epoch 15, batch 29050, loss[loss=0.121, simple_loss=0.1898, pruned_loss=0.02608, over 4832.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02983, over 972121.12 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 13:01:42,368 INFO [train.py:715] (2/8) Epoch 15, batch 29100, loss[loss=0.1349, simple_loss=0.1967, pruned_loss=0.03657, over 4978.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2068, pruned_loss=0.02983, over 972017.42 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:02:21,535 INFO [train.py:715] (2/8) Epoch 15, batch 29150, loss[loss=0.149, simple_loss=0.2256, pruned_loss=0.03623, over 4985.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2069, pruned_loss=0.02965, over 973232.82 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 13:03:00,545 INFO [train.py:715] (2/8) Epoch 15, batch 29200, loss[loss=0.1368, simple_loss=0.2164, pruned_loss=0.02861, over 4884.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2076, pruned_loss=0.03008, over 972876.66 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:03:38,957 INFO [train.py:715] (2/8) Epoch 15, batch 29250, loss[loss=0.1638, simple_loss=0.2388, pruned_loss=0.04444, over 4987.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2079, pruned_loss=0.03057, over 972232.64 frames.], batch size: 26, lr: 1.45e-04
2022-05-08 13:04:18,015 INFO [train.py:715] (2/8) Epoch 15, batch 29300, loss[loss=0.1375, simple_loss=0.2072, pruned_loss=0.03389, over 4871.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03024, over 971801.82 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 13:04:56,904 INFO [train.py:715] (2/8) Epoch 15, batch 29350, loss[loss=0.1258, simple_loss=0.1936, pruned_loss=0.02896, over 4867.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2072, pruned_loss=0.02994, over 971046.44 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 13:05:35,492 INFO [train.py:715] (2/8) Epoch 15, batch 29400, loss[loss=0.1516, simple_loss=0.2189, pruned_loss=0.04213, over 4948.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02988, over 970618.07 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:06:14,549 INFO [train.py:715] (2/8) Epoch 15, batch 29450, loss[loss=0.1524, simple_loss=0.2328, pruned_loss=0.03599, over 4750.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03005, over 970657.59 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:06:53,824 INFO [train.py:715] (2/8) Epoch 15, batch 29500, loss[loss=0.1089, simple_loss=0.1883, pruned_loss=0.01481, over 4938.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02957, over 971806.45 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:07:31,955 INFO [train.py:715] (2/8) Epoch 15, batch 29550, loss[loss=0.1557, simple_loss=0.2285, pruned_loss=0.04142, over 4832.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02984, over 971481.93 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:08:09,750 INFO [train.py:715] (2/8) Epoch 15, batch 29600, loss[loss=0.1522, simple_loss=0.2224, pruned_loss=0.04099, over 4983.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02956, over 971834.12 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:08:48,797 INFO [train.py:715] (2/8) Epoch 15, batch 29650, loss[loss=0.1243, simple_loss=0.1939, pruned_loss=0.0274, over 4760.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02952, over 972325.26 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:09:27,559 INFO [train.py:715] (2/8) Epoch 15, batch 29700, loss[loss=0.1255, simple_loss=0.193, pruned_loss=0.02895, over 4871.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2067, pruned_loss=0.02955, over 971818.62 frames.], batch size: 22, lr: 1.45e-04
2022-05-08 13:10:05,827 INFO [train.py:715] (2/8) Epoch 15, batch 29750, loss[loss=0.1388, simple_loss=0.2175, pruned_loss=0.03006, over 4982.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2073, pruned_loss=0.03003, over 971357.30 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 13:10:43,495 INFO [train.py:715] (2/8) Epoch 15, batch 29800, loss[loss=0.1373, simple_loss=0.211, pruned_loss=0.03181, over 4816.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02993, over 971805.63 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 13:11:22,786 INFO [train.py:715] (2/8) Epoch 15, batch 29850, loss[loss=0.1606, simple_loss=0.2344, pruned_loss=0.0434, over 4964.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02971, over 972512.07 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:12:04,496 INFO [train.py:715] (2/8) Epoch 15, batch 29900, loss[loss=0.1291, simple_loss=0.1972, pruned_loss=0.03051, over 4955.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02999, over 972276.54 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 13:12:43,057 INFO [train.py:715] (2/8) Epoch 15, batch 29950, loss[loss=0.126, simple_loss=0.2145, pruned_loss=0.01874, over 4696.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02994, over 971624.45 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:13:21,390 INFO [train.py:715] (2/8) Epoch 15, batch 30000, loss[loss=0.1424, simple_loss=0.2146, pruned_loss=0.03516, over 4991.00 frames.], tot_loss[loss=0.133, simple_loss=0.2063, pruned_loss=0.02985, over 971946.20 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 13:13:21,391 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 13:13:30,915 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.1049, simple_loss=0.1885, pruned_loss=0.01066, over 914524.00 frames.
2022-05-08 13:14:09,969 INFO [train.py:715] (2/8) Epoch 15, batch 30050, loss[loss=0.1195, simple_loss=0.1951, pruned_loss=0.02196, over 4704.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2059, pruned_loss=0.02928, over 972264.87 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:14:49,058 INFO [train.py:715] (2/8) Epoch 15, batch 30100, loss[loss=0.129, simple_loss=0.2107, pruned_loss=0.02361, over 4921.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2061, pruned_loss=0.02939, over 972645.36 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:15:28,216 INFO [train.py:715] (2/8) Epoch 15, batch 30150, loss[loss=0.1221, simple_loss=0.1873, pruned_loss=0.02845, over 4829.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2063, pruned_loss=0.0295, over 971929.45 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 13:16:07,079 INFO [train.py:715] (2/8) Epoch 15, batch 30200, loss[loss=0.129, simple_loss=0.2056, pruned_loss=0.02615, over 4847.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2071, pruned_loss=0.02988, over 972178.08 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:16:46,374 INFO [train.py:715] (2/8) Epoch 15, batch 30250, loss[loss=0.1143, simple_loss=0.1864, pruned_loss=0.02111, over 4800.00 frames.], tot_loss[loss=0.1335, simple_loss=0.207, pruned_loss=0.02998, over 971911.68 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 13:17:25,197 INFO [train.py:715] (2/8) Epoch 15, batch 30300, loss[loss=0.1196, simple_loss=0.1971, pruned_loss=0.02108, over 4783.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2085, pruned_loss=0.03065, over 972050.67 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:18:03,167 INFO [train.py:715] (2/8) Epoch 15, batch 30350, loss[loss=0.1416, simple_loss=0.2093, pruned_loss=0.03693, over 4780.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02997, over 971981.41 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:18:42,389 INFO [train.py:715] (2/8) Epoch 15, batch 30400, loss[loss=0.1358, simple_loss=0.217, pruned_loss=0.02728, over 4967.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.0292, over 971814.49 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 13:19:21,253 INFO [train.py:715] (2/8) Epoch 15, batch 30450, loss[loss=0.1202, simple_loss=0.1896, pruned_loss=0.02537, over 4978.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02961, over 972256.02 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:20:00,132 INFO [train.py:715] (2/8) Epoch 15, batch 30500, loss[loss=0.1182, simple_loss=0.1936, pruned_loss=0.02141, over 4976.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02988, over 972068.76 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 13:20:38,342 INFO [train.py:715] (2/8) Epoch 15, batch 30550, loss[loss=0.1039, simple_loss=0.1838, pruned_loss=0.01202, over 4989.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2073, pruned_loss=0.03011, over 972272.39 frames.], batch size: 26, lr: 1.45e-04
2022-05-08 13:21:17,388 INFO [train.py:715] (2/8) Epoch 15, batch 30600, loss[loss=0.1284, simple_loss=0.2101, pruned_loss=0.02333, over 4916.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02988, over 971656.41 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:21:56,193 INFO [train.py:715] (2/8) Epoch 15, batch 30650, loss[loss=0.1409, simple_loss=0.2183, pruned_loss=0.03177, over 4826.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2081, pruned_loss=0.02974, over 971745.05 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 13:22:34,336 INFO [train.py:715] (2/8) Epoch 15, batch 30700, loss[loss=0.1333, simple_loss=0.2157, pruned_loss=0.02541, over 4779.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.0295, over 972479.83 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:23:13,405 INFO [train.py:715] (2/8) Epoch 15, batch 30750, loss[loss=0.1325, simple_loss=0.2, pruned_loss=0.03248, over 4964.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02926, over 972768.93 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:23:52,074 INFO [train.py:715] (2/8) Epoch 15, batch 30800, loss[loss=0.1239, simple_loss=0.1956, pruned_loss=0.02608, over 4976.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02925, over 972822.27 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:24:30,181 INFO [train.py:715] (2/8) Epoch 15, batch 30850, loss[loss=0.137, simple_loss=0.2104, pruned_loss=0.03177, over 4873.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02912, over 972270.84 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:25:08,417 INFO [train.py:715] (2/8) Epoch 15, batch 30900, loss[loss=0.1618, simple_loss=0.2315, pruned_loss=0.04603, over 4807.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2077, pruned_loss=0.0291, over 972813.27 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 13:25:46,856 INFO [train.py:715] (2/8) Epoch 15, batch 30950, loss[loss=0.129, simple_loss=0.2047, pruned_loss=0.02663, over 4751.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.0295, over 972571.78 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 13:26:25,012 INFO [train.py:715] (2/8) Epoch 15, batch 31000, loss[loss=0.1934, simple_loss=0.2563, pruned_loss=0.06525, over 4818.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02988, over 972215.19 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:27:02,425 INFO [train.py:715] (2/8) Epoch 15, batch 31050, loss[loss=0.1301, simple_loss=0.2034, pruned_loss=0.0284, over 4897.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02972, over 972633.90 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:27:40,735 INFO [train.py:715] (2/8) Epoch 15, batch 31100, loss[loss=0.1382, simple_loss=0.2056, pruned_loss=0.03538, over 4887.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02961, over 972927.56 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 13:28:18,886 INFO [train.py:715] (2/8) Epoch 15, batch 31150, loss[loss=0.1453, simple_loss=0.2128, pruned_loss=0.03893, over 4842.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02977, over 973429.33 frames.], batch size: 30, lr: 1.45e-04
2022-05-08 13:28:57,279 INFO [train.py:715] (2/8) Epoch 15, batch 31200, loss[loss=0.1346, simple_loss=0.2081, pruned_loss=0.03053, over 4814.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02982, over 972412.35 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:29:34,876 INFO [train.py:715] (2/8) Epoch 15, batch 31250, loss[loss=0.1398, simple_loss=0.2089, pruned_loss=0.03537, over 4897.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02954, over 972928.93 frames.], batch size: 16, lr: 1.45e-04
2022-05-08 13:30:13,197 INFO [train.py:715] (2/8) Epoch 15, batch 31300, loss[loss=0.1308, simple_loss=0.1979, pruned_loss=0.03181, over 4975.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02956, over 972402.29 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:30:51,249 INFO [train.py:715] (2/8) Epoch 15, batch 31350, loss[loss=0.1438, simple_loss=0.2059, pruned_loss=0.04081, over 4819.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02919, over 972495.34 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:31:28,510 INFO [train.py:715] (2/8) Epoch 15, batch 31400, loss[loss=0.1423, simple_loss=0.2213, pruned_loss=0.03161, over 4840.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02913, over 972579.11 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:32:06,856 INFO [train.py:715] (2/8) Epoch 15, batch 31450, loss[loss=0.1177, simple_loss=0.1845, pruned_loss=0.02541, over 4955.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03021, over 971855.55 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 13:32:45,119 INFO [train.py:715] (2/8) Epoch 15, batch 31500, loss[loss=0.1689, simple_loss=0.2243, pruned_loss=0.0568, over 4860.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03007, over 971270.47 frames.], batch size: 32, lr: 1.45e-04
2022-05-08 13:33:23,446 INFO [train.py:715] (2/8) Epoch 15, batch 31550, loss[loss=0.1352, simple_loss=0.2009, pruned_loss=0.0347, over 4933.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03006, over 971417.70 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:34:01,212 INFO [train.py:715] (2/8) Epoch 15, batch 31600, loss[loss=0.1184, simple_loss=0.1979, pruned_loss=0.01942, over 4905.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02966, over 972070.99 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:34:39,668 INFO [train.py:715] (2/8) Epoch 15, batch 31650, loss[loss=0.1609, simple_loss=0.2264, pruned_loss=0.04771, over 4815.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02962, over 972177.42 frames.], batch size: 21, lr: 1.45e-04
2022-05-08 13:35:18,002 INFO [train.py:715] (2/8) Epoch 15, batch 31700, loss[loss=0.143, simple_loss=0.2242, pruned_loss=0.03087, over 4933.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02997, over 971828.25 frames.], batch size: 23, lr: 1.45e-04
2022-05-08 13:35:55,497 INFO [train.py:715] (2/8) Epoch 15, batch 31750, loss[loss=0.1432, simple_loss=0.2023, pruned_loss=0.04205, over 4838.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02951, over 972671.31 frames.], batch size: 13, lr: 1.45e-04
2022-05-08 13:36:34,378 INFO [train.py:715] (2/8) Epoch 15, batch 31800, loss[loss=0.1409, simple_loss=0.2175, pruned_loss=0.03217, over 4779.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02928, over 972650.34 frames.], batch size: 14, lr: 1.45e-04
2022-05-08 13:37:12,849 INFO [train.py:715] (2/8) Epoch 15, batch 31850, loss[loss=0.1552, simple_loss=0.2398, pruned_loss=0.03529, over 4858.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.0292, over 971981.09 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:37:52,389 INFO [train.py:715] (2/8) Epoch 15, batch 31900, loss[loss=0.1307, simple_loss=0.2095, pruned_loss=0.02599, over 4790.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.0297, over 972004.23 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:38:29,677 INFO [train.py:715] (2/8) Epoch 15, batch 31950, loss[loss=0.1417, simple_loss=0.2128, pruned_loss=0.0353, over 4865.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02984, over 972770.46 frames.], batch size: 38, lr: 1.45e-04
2022-05-08 13:39:08,343 INFO [train.py:715] (2/8) Epoch 15, batch 32000, loss[loss=0.132, simple_loss=0.2077, pruned_loss=0.02819, over 4772.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02971, over 972565.60 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:39:46,518 INFO [train.py:715] (2/8) Epoch 15, batch 32050, loss[loss=0.1342, simple_loss=0.2159, pruned_loss=0.02627, over 4928.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2061, pruned_loss=0.02946, over 971951.54 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 13:40:23,948 INFO [train.py:715] (2/8) Epoch 15, batch 32100, loss[loss=0.1242, simple_loss=0.2021, pruned_loss=0.02316, over 4924.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2066, pruned_loss=0.02981, over 971862.47 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:41:02,342 INFO [train.py:715] (2/8) Epoch 15, batch 32150, loss[loss=0.1498, simple_loss=0.2266, pruned_loss=0.03656, over 4907.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.02926, over 971304.44 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:41:40,505 INFO [train.py:715] (2/8) Epoch 15, batch 32200, loss[loss=0.1229, simple_loss=0.2062, pruned_loss=0.01981, over 4945.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.0294, over 971643.54 frames.], batch size: 29, lr: 1.45e-04
2022-05-08 13:42:19,024 INFO [train.py:715] (2/8) Epoch 15, batch 32250, loss[loss=0.2147, simple_loss=0.2827, pruned_loss=0.07331, over 4813.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02972, over 971041.28 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 13:42:56,891 INFO [train.py:715] (2/8) Epoch 15, batch 32300, loss[loss=0.1934, simple_loss=0.2638, pruned_loss=0.06145, over 4701.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03013, over 971630.15 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:43:35,757 INFO [train.py:715] (2/8) Epoch 15, batch 32350, loss[loss=0.1188, simple_loss=0.1964, pruned_loss=0.0206, over 4874.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02955, over 972254.04 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:44:14,155 INFO [train.py:715] (2/8) Epoch 15, batch 32400, loss[loss=0.1435, simple_loss=0.2123, pruned_loss=0.03734, over 4836.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02967, over 972557.15 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:44:51,907 INFO [train.py:715] (2/8) Epoch 15, batch 32450, loss[loss=0.1223, simple_loss=0.1848, pruned_loss=0.02991, over 4696.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.0296, over 972353.64 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:45:30,472 INFO [train.py:715] (2/8) Epoch 15, batch 32500, loss[loss=0.1943, simple_loss=0.2508, pruned_loss=0.06884, over 4931.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02968, over 972039.64 frames.], batch size: 18, lr: 1.45e-04
2022-05-08 13:46:08,936 INFO [train.py:715] (2/8) Epoch 15, batch 32550, loss[loss=0.1161, simple_loss=0.1993, pruned_loss=0.01639, over 4686.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02951, over 972438.47 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:46:47,816 INFO [train.py:715] (2/8) Epoch 15, batch 32600, loss[loss=0.1357, simple_loss=0.2181, pruned_loss=0.02667, over 4763.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02955, over 971710.81 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:47:26,415 INFO [train.py:715] (2/8) Epoch 15, batch 32650, loss[loss=0.1598, simple_loss=0.2388, pruned_loss=0.04039, over 4874.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02973, over 971783.12 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:48:05,088 INFO [train.py:715] (2/8) Epoch 15, batch 32700, loss[loss=0.1438, simple_loss=0.2145, pruned_loss=0.03656, over 4885.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03002, over 971634.80 frames.], batch size: 19, lr: 1.45e-04
2022-05-08 13:48:43,311 INFO [train.py:715] (2/8) Epoch 15, batch 32750, loss[loss=0.1291, simple_loss=0.2081, pruned_loss=0.02504, over 4861.00 frames.], tot_loss[loss=0.135, simple_loss=0.2091, pruned_loss=0.03049, over 971371.28 frames.], batch size: 20, lr: 1.45e-04
2022-05-08 13:49:21,521 INFO [train.py:715] (2/8) Epoch 15, batch 32800, loss[loss=0.1319, simple_loss=0.2004, pruned_loss=0.03175, over 4902.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03002, over 971478.74 frames.], batch size: 17, lr: 1.45e-04
2022-05-08 13:49:59,266 INFO [train.py:715] (2/8) Epoch 15, batch 32850, loss[loss=0.1195, simple_loss=0.1948, pruned_loss=0.02212, over 4987.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02955, over 971626.36 frames.], batch size: 28, lr: 1.45e-04
2022-05-08 13:50:37,502 INFO [train.py:715] (2/8) Epoch 15, batch 32900, loss[loss=0.1392, simple_loss=0.2112, pruned_loss=0.03362, over 4985.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.03014, over 972006.38 frames.], batch size: 33, lr: 1.45e-04
2022-05-08 13:51:16,096 INFO [train.py:715] (2/8) Epoch 15, batch 32950, loss[loss=0.1141, simple_loss=0.1876, pruned_loss=0.02032, over 4986.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2078, pruned_loss=0.02997, over 972760.42 frames.], batch size: 25, lr: 1.45e-04
2022-05-08 13:51:54,481 INFO [train.py:715] (2/8) Epoch 15, batch 33000, loss[loss=0.1269, simple_loss=0.2004, pruned_loss=0.02675, over 4975.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02989, over 973113.57 frames.], batch size: 35, lr: 1.45e-04
2022-05-08 13:51:54,482 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 13:52:03,986 INFO [train.py:742] (2/8) Epoch 15, validation: loss=0.1052, simple_loss=0.1886, pruned_loss=0.01088, over 914524.00 frames.
2022-05-08 13:52:42,029 INFO [train.py:715] (2/8) Epoch 15, batch 33050, loss[loss=0.15, simple_loss=0.2192, pruned_loss=0.04041, over 4924.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.0297, over 972904.71 frames.], batch size: 39, lr: 1.45e-04
2022-05-08 13:53:20,377 INFO [train.py:715] (2/8) Epoch 15, batch 33100, loss[loss=0.117, simple_loss=0.1941, pruned_loss=0.01994, over 4698.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.0292, over 972498.35 frames.], batch size: 15, lr: 1.45e-04
2022-05-08 13:53:58,098 INFO [train.py:715] (2/8) Epoch 15, batch 33150, loss[loss=0.1497, simple_loss=0.2281, pruned_loss=0.03567, over 4972.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02918, over 972494.06 frames.], batch size: 24, lr: 1.44e-04
2022-05-08 13:54:37,178 INFO [train.py:715] (2/8) Epoch 15, batch 33200, loss[loss=0.1501, simple_loss=0.2114, pruned_loss=0.04435, over 4759.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.0296, over 972161.56 frames.], batch size: 14, lr: 1.44e-04
2022-05-08 13:55:15,614 INFO [train.py:715] (2/8) Epoch 15, batch 33250, loss[loss=0.1936, simple_loss=0.2594, pruned_loss=0.06386, over 4942.00 frames.], tot_loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.02967, over 973001.15 frames.], batch size: 21, lr: 1.44e-04
2022-05-08 13:55:53,706 INFO [train.py:715] (2/8) Epoch 15, batch 33300, loss[loss=0.1095, simple_loss=0.1825, pruned_loss=0.01823, over 4987.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02994, over 972991.49 frames.], batch size: 14, lr: 1.44e-04
2022-05-08 13:56:31,676 INFO [train.py:715] (2/8) Epoch 15, batch 33350, loss[loss=0.2288, simple_loss=0.2813, pruned_loss=0.08813, over 4947.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02998, over 973525.10 frames.], batch size: 15, lr: 1.44e-04
2022-05-08 13:57:09,332 INFO [train.py:715] (2/8) Epoch 15, batch 33400, loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03292, over 4894.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02955, over 973387.93 frames.], batch size: 32, lr: 1.44e-04
2022-05-08 13:57:47,385 INFO [train.py:715] (2/8) Epoch 15, batch 33450, loss[loss=0.1143, simple_loss=0.1963, pruned_loss=0.01615, over 4924.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02963, over 973150.32 frames.], batch size: 29, lr: 1.44e-04
2022-05-08 13:58:25,101 INFO [train.py:715] (2/8) Epoch 15, batch 33500, loss[loss=0.1231, simple_loss=0.194, pruned_loss=0.02608, over 4698.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02991, over 972905.55 frames.], batch size: 15, lr: 1.44e-04
2022-05-08 13:59:02,923 INFO [train.py:715] (2/8) Epoch 15, batch 33550, loss[loss=0.1387, simple_loss=0.2081, pruned_loss=0.03465, over 4781.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03016, over 973046.54 frames.], batch size: 18, lr: 1.44e-04
2022-05-08 13:59:40,602 INFO [train.py:715] (2/8) Epoch 15, batch 33600, loss[loss=0.1337, simple_loss=0.2014, pruned_loss=0.03303, over 4776.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02994, over 972773.93 frames.], batch size: 18, lr: 1.44e-04
2022-05-08 14:00:18,652 INFO [train.py:715] (2/8) Epoch 15, batch 33650, loss[loss=0.14, simple_loss=0.2081, pruned_loss=0.0359, over 4823.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02961, over 972655.67 frames.], batch size: 12, lr: 1.44e-04
2022-05-08 14:00:56,119 INFO [train.py:715] (2/8) Epoch 15, batch 33700, loss[loss=0.1261, simple_loss=0.197, pruned_loss=0.02764, over 4965.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.02995, over 972266.59 frames.], batch size: 35, lr: 1.44e-04
2022-05-08 14:01:33,645 INFO [train.py:715] (2/8) Epoch 15, batch 33750, loss[loss=0.1158, simple_loss=0.1898, pruned_loss=0.02088, over 4793.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02974, over 972237.74 frames.], batch size: 21, lr: 1.44e-04
2022-05-08 14:02:11,483 INFO [train.py:715] (2/8) Epoch 15, batch 33800, loss[loss=0.123, simple_loss=0.1941, pruned_loss=0.02593, over 4859.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02993, over 972208.76 frames.], batch size: 20, lr: 1.44e-04
2022-05-08 14:02:48,674 INFO [train.py:715] (2/8) Epoch 15, batch 33850, loss[loss=0.1287, simple_loss=0.1925, pruned_loss=0.03248, over 4799.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02995, over 971883.68 frames.], batch size: 12, lr: 1.44e-04
2022-05-08 14:03:26,485 INFO [train.py:715] (2/8) Epoch 15, batch 33900, loss[loss=0.15, simple_loss=0.2225, pruned_loss=0.0388, over 4992.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02992, over 972132.27 frames.], batch size: 16, lr: 1.44e-04
2022-05-08 14:04:04,822 INFO [train.py:715] (2/8) Epoch 15, batch 33950, loss[loss=0.1312, simple_loss=0.2067, pruned_loss=0.02785, over 4823.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02944, over 972565.24 frames.], batch size: 27, lr: 1.44e-04
2022-05-08 14:04:42,873 INFO [train.py:715] (2/8) Epoch 15, batch 34000, loss[loss=0.1236, simple_loss=0.2044, pruned_loss=0.02138, over 4987.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02959, over 972903.12 frames.], batch size: 28, lr: 1.44e-04
2022-05-08 14:05:20,769 INFO [train.py:715] (2/8) Epoch 15, batch 34050, loss[loss=0.126, simple_loss=0.2103, pruned_loss=0.02087, over 4875.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02888, over 972153.98 frames.], batch size: 39, lr: 1.44e-04
2022-05-08 14:05:58,929 INFO [train.py:715] (2/8) Epoch 15, batch 34100, loss[loss=0.1077, simple_loss=0.1814, pruned_loss=0.017, over 4833.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02929, over 972643.43 frames.], batch size: 32, lr: 1.44e-04
2022-05-08 14:06:37,189 INFO [train.py:715] (2/8) Epoch 15, batch 34150, loss[loss=0.1169, simple_loss=0.1987, pruned_loss=0.01759, over 4949.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02889, over 972145.72 frames.], batch size: 29, lr: 1.44e-04
2022-05-08 14:07:14,888 INFO [train.py:715] (2/8) Epoch 15, batch 34200, loss[loss=0.1128, simple_loss=0.1906, pruned_loss=0.01745, over 4980.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02871, over 972024.89 frames.], batch size: 28, lr: 1.44e-04
2022-05-08 14:07:52,717 INFO [train.py:715] (2/8) Epoch 15, batch 34250, loss[loss=0.1486, simple_loss=0.2158, pruned_loss=0.0407, over 4982.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.0288, over 973177.54 frames.], batch size: 31, lr: 1.44e-04
2022-05-08 14:08:30,683 INFO [train.py:715] (2/8) Epoch 15, batch 34300, loss[loss=0.1249, simple_loss=0.1981, pruned_loss=0.02587, over 4980.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02863, over 973471.69 frames.], batch size: 26, lr: 1.44e-04
2022-05-08 14:09:08,610 INFO [train.py:715] (2/8) Epoch 15, batch 34350, loss[loss=0.122, simple_loss=0.2017, pruned_loss=0.02116, over 4988.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2055, pruned_loss=0.02841, over 972920.81 frames.], batch size: 25, lr: 1.44e-04
2022-05-08 14:09:45,971 INFO [train.py:715] (2/8) Epoch 15, batch 34400, loss[loss=0.124, simple_loss=0.1938, pruned_loss=0.02706, over 4883.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02845, over 973142.75 frames.], batch size: 16, lr: 1.44e-04
2022-05-08 14:10:24,172 INFO [train.py:715] (2/8) Epoch 15, batch 34450, loss[loss=0.1329, simple_loss=0.1984, pruned_loss=0.03367, over 4842.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02882, over 972724.77 frames.], batch size: 32, lr: 1.44e-04
2022-05-08 14:11:02,052 INFO [train.py:715] (2/8) Epoch 15, batch 34500, loss[loss=0.1167, simple_loss=0.1852, pruned_loss=0.0241, over 4901.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02884, over 973024.56 frames.], batch size: 22, lr: 1.44e-04
2022-05-08 14:11:39,388 INFO [train.py:715] (2/8) Epoch 15, batch 34550, loss[loss=0.1103, simple_loss=0.1876, pruned_loss=0.01654, over 4864.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02922, over 973129.18 frames.], batch size: 16, lr: 1.44e-04
2022-05-08 14:12:17,004 INFO [train.py:715] (2/8) Epoch 15, batch 34600, loss[loss=0.1442, simple_loss=0.2173, pruned_loss=0.03552, over 4945.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2079, pruned_loss=0.02939, over 972854.46 frames.], batch size: 21, lr: 1.44e-04
2022-05-08 14:12:54,929 INFO [train.py:715] (2/8) Epoch 15, batch 34650, loss[loss=0.1278, simple_loss=0.2062, pruned_loss=0.02475, over 4894.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02953, over 973268.55 frames.], batch size: 19, lr: 1.44e-04
2022-05-08 14:13:32,474 INFO [train.py:715] (2/8) Epoch 15, batch 34700, loss[loss=0.1438, simple_loss=0.2154, pruned_loss=0.03608, over 4955.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2084, pruned_loss=0.02962, over 972653.55 frames.], batch size: 39, lr: 1.44e-04
2022-05-08 14:14:09,603 INFO [train.py:715] (2/8) Epoch 15, batch 34750, loss[loss=0.1373, simple_loss=0.2001, pruned_loss=0.03726, over 4755.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03023, over 973282.03 frames.], batch size: 12, lr: 1.44e-04
2022-05-08 14:14:44,839 INFO [train.py:715] (2/8) Epoch 15, batch 34800, loss[loss=0.1542, simple_loss=0.2359, pruned_loss=0.03623, over 4919.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03004, over 973303.48 frames.], batch size: 18, lr: 1.44e-04
2022-05-08 14:15:33,455 INFO [train.py:715] (2/8) Epoch 16, batch 0, loss[loss=0.1413, simple_loss=0.2191, pruned_loss=0.03174, over 4763.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2191, pruned_loss=0.03174, over 4763.00 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:16:11,642 INFO [train.py:715] (2/8) Epoch 16, batch 50, loss[loss=0.1248, simple_loss=0.199, pruned_loss=0.02526, over 4819.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2051, pruned_loss=0.02994, over 219918.74 frames.], batch size: 26, lr: 1.40e-04
2022-05-08 14:16:50,215 INFO [train.py:715] (2/8) Epoch 16, batch 100, loss[loss=0.1266, simple_loss=0.1982, pruned_loss=0.02751, over 4783.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2067, pruned_loss=0.0297, over 388306.97 frames.], batch size: 17, lr: 1.40e-04
2022-05-08 14:17:27,947 INFO [train.py:715] (2/8) Epoch 16, batch 150, loss[loss=0.1166, simple_loss=0.196, pruned_loss=0.01861, over 4945.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02941, over 518921.81 frames.], batch size: 29, lr: 1.40e-04
2022-05-08 14:18:06,152 INFO [train.py:715] (2/8) Epoch 16, batch 200, loss[loss=0.1597, simple_loss=0.2267, pruned_loss=0.04636, over 4932.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2075, pruned_loss=0.02899, over 619654.97 frames.], batch size: 23, lr: 1.40e-04
2022-05-08 14:18:44,280 INFO [train.py:715] (2/8) Epoch 16, batch 250, loss[loss=0.1629, simple_loss=0.2153, pruned_loss=0.05521, over 4778.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2085, pruned_loss=0.02942, over 696966.76 frames.], batch size: 17, lr: 1.40e-04
2022-05-08 14:19:22,601 INFO [train.py:715] (2/8) Epoch 16, batch 300, loss[loss=0.1437, simple_loss=0.2163, pruned_loss=0.03554, over 4838.00 frames.], tot_loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02972, over 757916.03 frames.], batch size: 32, lr: 1.40e-04
2022-05-08 14:20:01,027 INFO [train.py:715] (2/8) Epoch 16, batch 350, loss[loss=0.1359, simple_loss=0.2046, pruned_loss=0.03362, over 4926.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02966, over 805359.40 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:20:38,708 INFO [train.py:715] (2/8) Epoch 16, batch 400, loss[loss=0.1495, simple_loss=0.2274, pruned_loss=0.03578, over 4792.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02989, over 843023.56 frames.], batch size: 17, lr: 1.40e-04
2022-05-08 14:21:17,414 INFO [train.py:715] (2/8) Epoch 16, batch 450, loss[loss=0.1432, simple_loss=0.2188, pruned_loss=0.03385, over 4934.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.0299, over 871699.45 frames.], batch size: 29, lr: 1.40e-04
2022-05-08 14:21:55,843 INFO [train.py:715] (2/8) Epoch 16, batch 500, loss[loss=0.1506, simple_loss=0.2206, pruned_loss=0.04035, over 4782.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02985, over 893424.67 frames.], batch size: 14, lr: 1.40e-04
2022-05-08 14:22:33,537 INFO [train.py:715] (2/8) Epoch 16, batch 550, loss[loss=0.1291, simple_loss=0.2026, pruned_loss=0.02778, over 4815.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02972, over 911531.73 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 14:23:12,214 INFO [train.py:715] (2/8) Epoch 16, batch 600, loss[loss=0.1191, simple_loss=0.1954, pruned_loss=0.02142, over 4952.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02988, over 925087.62 frames.], batch size: 29, lr: 1.40e-04
2022-05-08 14:23:50,877 INFO [train.py:715] (2/8) Epoch 16, batch 650, loss[loss=0.1267, simple_loss=0.1959, pruned_loss=0.0287, over 4969.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03006, over 934903.70 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:24:28,548 INFO [train.py:715] (2/8) Epoch 16, batch 700, loss[loss=0.1666, simple_loss=0.2409, pruned_loss=0.0462, over 4987.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02996, over 943518.55 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:25:06,447 INFO [train.py:715] (2/8) Epoch 16, batch 750, loss[loss=0.115, simple_loss=0.1823, pruned_loss=0.02388, over 4832.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.0304, over 949767.27 frames.], batch size: 13, lr: 1.40e-04
2022-05-08 14:25:45,234 INFO [train.py:715] (2/8) Epoch 16, batch 800, loss[loss=0.1526, simple_loss=0.2182, pruned_loss=0.04346, over 4946.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2089, pruned_loss=0.03041, over 955063.40 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:26:23,532 INFO [train.py:715] (2/8) Epoch 16, batch 850, loss[loss=0.1376, simple_loss=0.2092, pruned_loss=0.03302, over 4970.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02975, over 959151.01 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:27:01,576 INFO [train.py:715] (2/8) Epoch 16, batch 900, loss[loss=0.1183, simple_loss=0.1932, pruned_loss=0.02174, over 4873.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02972, over 961983.72 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:27:39,692 INFO [train.py:715] (2/8) Epoch 16, batch 950, loss[loss=0.1071, simple_loss=0.1766, pruned_loss=0.01883, over 4982.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02922, over 964403.83 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:28:18,128 INFO [train.py:715] (2/8) Epoch 16, batch 1000, loss[loss=0.1574, simple_loss=0.2349, pruned_loss=0.03992, over 4903.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02961, over 965845.09 frames.], batch size: 39, lr: 1.40e-04
2022-05-08 14:28:55,786 INFO [train.py:715] (2/8) Epoch 16, batch 1050, loss[loss=0.1526, simple_loss=0.2219, pruned_loss=0.04165, over 4940.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02993, over 967118.80 frames.], batch size: 35, lr: 1.40e-04
2022-05-08 14:29:33,187 INFO [train.py:715] (2/8) Epoch 16, batch 1100, loss[loss=0.1329, simple_loss=0.2039, pruned_loss=0.03102, over 4860.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02974, over 967910.68 frames.], batch size: 20, lr: 1.40e-04
2022-05-08 14:30:11,814 INFO [train.py:715] (2/8) Epoch 16, batch 1150, loss[loss=0.15, simple_loss=0.221, pruned_loss=0.03951, over 4924.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02964, over 969345.02 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:30:49,882 INFO [train.py:715] (2/8) Epoch 16, batch 1200, loss[loss=0.1203, simple_loss=0.1985, pruned_loss=0.02101, over 4963.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.0295, over 970397.67 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:31:27,246 INFO [train.py:715] (2/8) Epoch 16, batch 1250, loss[loss=0.1238, simple_loss=0.1976, pruned_loss=0.02499, over 4941.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2067, pruned_loss=0.02958, over 971520.52 frames.], batch size: 29, lr: 1.40e-04
2022-05-08 14:32:05,203 INFO [train.py:715] (2/8) Epoch 16, batch 1300, loss[loss=0.1236, simple_loss=0.1989, pruned_loss=0.02418, over 4796.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.0292, over 971594.20 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:32:43,364 INFO [train.py:715] (2/8) Epoch 16, batch 1350, loss[loss=0.1563, simple_loss=0.2385, pruned_loss=0.03698, over 4813.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02928, over 971510.95 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 14:33:21,097 INFO [train.py:715] (2/8) Epoch 16, batch 1400, loss[loss=0.1225, simple_loss=0.1987, pruned_loss=0.0232, over 4807.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02955, over 970917.73 frames.], batch size: 26, lr: 1.40e-04
2022-05-08 14:33:59,220 INFO [train.py:715] (2/8) Epoch 16, batch 1450, loss[loss=0.1133, simple_loss=0.1826, pruned_loss=0.02202, over 4835.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02962, over 970706.41 frames.], batch size: 13, lr: 1.40e-04
2022-05-08 14:34:37,202 INFO [train.py:715] (2/8) Epoch 16, batch 1500, loss[loss=0.1293, simple_loss=0.1983, pruned_loss=0.03018, over 4941.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02963, over 972070.68 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 14:35:14,921 INFO [train.py:715] (2/8) Epoch 16, batch 1550, loss[loss=0.1245, simple_loss=0.1961, pruned_loss=0.02647, over 4964.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02968, over 971967.32 frames.], batch size: 14, lr: 1.40e-04
2022-05-08 14:35:52,772 INFO [train.py:715] (2/8) Epoch 16, batch 1600, loss[loss=0.1289, simple_loss=0.2132, pruned_loss=0.02234, over 4974.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02936, over 971906.30 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:36:30,170 INFO [train.py:715] (2/8) Epoch 16, batch 1650, loss[loss=0.1093, simple_loss=0.1844, pruned_loss=0.01711, over 4888.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02985, over 971915.17 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:37:07,995 INFO [train.py:715] (2/8) Epoch 16, batch 1700, loss[loss=0.1399, simple_loss=0.2119, pruned_loss=0.03393, over 4959.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02995, over 971774.58 frames.], batch size: 35, lr: 1.40e-04
2022-05-08 14:37:46,154 INFO [train.py:715] (2/8) Epoch 16, batch 1750, loss[loss=0.1404, simple_loss=0.2101, pruned_loss=0.03535, over 4935.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02989, over 971896.20 frames.], batch size: 35, lr: 1.40e-04
2022-05-08 14:38:24,068 INFO [train.py:715] (2/8) Epoch 16, batch 1800, loss[loss=0.1496, simple_loss=0.2077, pruned_loss=0.04577, over 4693.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02929, over 971432.94 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:39:02,377 INFO [train.py:715] (2/8) Epoch 16, batch 1850, loss[loss=0.1338, simple_loss=0.2158, pruned_loss=0.02594, over 4887.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02995, over 971115.42 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:39:41,004 INFO [train.py:715] (2/8) Epoch 16, batch 1900, loss[loss=0.1315, simple_loss=0.2085, pruned_loss=0.02729, over 4842.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02962, over 971017.52 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 14:40:18,874 INFO [train.py:715] (2/8) Epoch 16, batch 1950, loss[loss=0.1236, simple_loss=0.1981, pruned_loss=0.02455, over 4858.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02982, over 971681.39 frames.], batch size: 20, lr: 1.40e-04
2022-05-08 14:40:57,048 INFO [train.py:715] (2/8) Epoch 16, batch 2000, loss[loss=0.137, simple_loss=0.2047, pruned_loss=0.03465, over 4772.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02947, over 971795.50 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:41:35,847 INFO [train.py:715] (2/8) Epoch 16, batch 2050, loss[loss=0.1646, simple_loss=0.2357, pruned_loss=0.04672, over 4818.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.02938, over 972549.96 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:42:14,574 INFO [train.py:715] (2/8) Epoch 16, batch 2100, loss[loss=0.09137, simple_loss=0.1643, pruned_loss=0.009199, over 4868.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2069, pruned_loss=0.02971, over 972566.01 frames.], batch size: 12, lr: 1.40e-04
2022-05-08 14:42:52,428 INFO [train.py:715] (2/8) Epoch 16, batch 2150, loss[loss=0.1079, simple_loss=0.1904, pruned_loss=0.01264, over 4933.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02937, over 972543.17 frames.], batch size: 23, lr: 1.40e-04
2022-05-08 14:43:31,558 INFO [train.py:715] (2/8) Epoch 16, batch 2200, loss[loss=0.144, simple_loss=0.2282, pruned_loss=0.02991, over 4778.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02918, over 973163.88 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:44:09,851 INFO [train.py:715] (2/8) Epoch 16, batch 2250, loss[loss=0.1468, simple_loss=0.2196, pruned_loss=0.03704, over 4725.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02942, over 973065.21 frames.], batch size: 15, lr: 1.40e-04
2022-05-08 14:44:47,485 INFO [train.py:715] (2/8) Epoch 16, batch 2300, loss[loss=0.1255, simple_loss=0.196, pruned_loss=0.02756, over 4763.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02947, over 973505.91 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 14:45:25,054 INFO [train.py:715] (2/8) Epoch 16, batch 2350, loss[loss=0.1308, simple_loss=0.2048, pruned_loss=0.02838, over 4930.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02963, over 973568.54 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:46:03,346 INFO [train.py:715] (2/8) Epoch 16, batch 2400, loss[loss=0.1109, simple_loss=0.1932, pruned_loss=0.0143, over 4795.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02986, over 974094.60 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:46:41,420 INFO [train.py:715] (2/8) Epoch 16, batch 2450, loss[loss=0.1191, simple_loss=0.1874, pruned_loss=0.02536, over 4899.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02922, over 974090.44 frames.], batch size: 17, lr: 1.40e-04
2022-05-08 14:47:18,881 INFO [train.py:715] (2/8) Epoch 16, batch 2500, loss[loss=0.1279, simple_loss=0.2074, pruned_loss=0.02425, over 4968.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02963, over 974845.27 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:47:57,274 INFO [train.py:715] (2/8) Epoch 16, batch 2550, loss[loss=0.1597, simple_loss=0.2397, pruned_loss=0.03987, over 4893.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03041, over 973757.64 frames.], batch size: 17, lr: 1.40e-04
2022-05-08 14:48:35,428 INFO [train.py:715] (2/8) Epoch 16, batch 2600, loss[loss=0.1503, simple_loss=0.2198, pruned_loss=0.04041, over 4841.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02984, over 973967.65 frames.], batch size: 32, lr: 1.40e-04
2022-05-08 14:49:13,162 INFO [train.py:715] (2/8) Epoch 16, batch 2650, loss[loss=0.1315, simple_loss=0.2146, pruned_loss=0.02422, over 4772.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02978, over 973553.92 frames.], batch size: 17, lr: 1.40e-04
2022-05-08 14:49:51,050 INFO [train.py:715] (2/8) Epoch 16, batch 2700, loss[loss=0.1287, simple_loss=0.2091, pruned_loss=0.02417, over 4913.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02984, over 973942.48 frames.], batch size: 23, lr: 1.40e-04
2022-05-08 14:50:29,676 INFO [train.py:715] (2/8) Epoch 16, batch 2750, loss[loss=0.1345, simple_loss=0.2154, pruned_loss=0.02683, over 4756.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2087, pruned_loss=0.03, over 974092.33 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:51:08,587 INFO [train.py:715] (2/8) Epoch 16, batch 2800, loss[loss=0.1278, simple_loss=0.2122, pruned_loss=0.02172, over 4988.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02986, over 973868.46 frames.], batch size: 27, lr: 1.40e-04
2022-05-08 14:51:46,948 INFO [train.py:715] (2/8) Epoch 16, batch 2850, loss[loss=0.1293, simple_loss=0.1966, pruned_loss=0.03094, over 4886.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.0296, over 973373.16 frames.], batch size: 16, lr: 1.40e-04
2022-05-08 14:52:24,998 INFO [train.py:715] (2/8) Epoch 16, batch 2900, loss[loss=0.1312, simple_loss=0.2103, pruned_loss=0.02603, over 4824.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02961, over 972871.41 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 14:53:03,774 INFO [train.py:715] (2/8) Epoch 16, batch 2950, loss[loss=0.1178, simple_loss=0.1938, pruned_loss=0.02091, over 4949.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02962, over 971878.15 frames.], batch size: 23, lr: 1.40e-04
2022-05-08 14:53:41,757 INFO [train.py:715] (2/8) Epoch 16, batch 3000, loss[loss=0.1415, simple_loss=0.2167, pruned_loss=0.03318, over 4807.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02983, over 971911.15 frames.], batch size: 21, lr: 1.40e-04
2022-05-08 14:53:41,758 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 14:53:51,190 INFO [train.py:742] (2/8) 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] (2/8) Epoch 16, batch 3050, loss[loss=0.1161, simple_loss=0.1887, pruned_loss=0.0217, over 4777.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03031, over 972067.76 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:55:09,458 INFO [train.py:715] (2/8) Epoch 16, batch 3100, loss[loss=0.1405, simple_loss=0.2091, pruned_loss=0.03596, over 4849.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2091, pruned_loss=0.03057, over 972095.92 frames.], batch size: 30, lr: 1.40e-04
2022-05-08 14:55:47,850 INFO [train.py:715] (2/8) Epoch 16, batch 3150, loss[loss=0.1295, simple_loss=0.2063, pruned_loss=0.02631, over 4779.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03008, over 971712.73 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:56:26,006 INFO [train.py:715] (2/8) Epoch 16, batch 3200, loss[loss=0.1195, simple_loss=0.1933, pruned_loss=0.02281, over 4931.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2084, pruned_loss=0.03026, over 972361.76 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:57:04,240 INFO [train.py:715] (2/8) Epoch 16, batch 3250, loss[loss=0.1199, simple_loss=0.1995, pruned_loss=0.0202, over 4916.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.02993, over 973278.21 frames.], batch size: 18, lr: 1.40e-04
2022-05-08 14:57:42,064 INFO [train.py:715] (2/8) Epoch 16, batch 3300, loss[loss=0.1656, simple_loss=0.2365, pruned_loss=0.04742, over 4930.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02988, over 972455.06 frames.], batch size: 23, lr: 1.40e-04
2022-05-08 14:58:20,053 INFO [train.py:715] (2/8) Epoch 16, batch 3350, loss[loss=0.1259, simple_loss=0.2012, pruned_loss=0.02536, over 4646.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02993, over 972270.02 frames.], batch size: 13, lr: 1.40e-04
2022-05-08 14:58:57,931 INFO [train.py:715] (2/8) Epoch 16, batch 3400, loss[loss=0.1204, simple_loss=0.1962, pruned_loss=0.0223, over 4865.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02983, over 972653.96 frames.], batch size: 32, lr: 1.40e-04
2022-05-08 14:59:35,865 INFO [train.py:715] (2/8) Epoch 16, batch 3450, loss[loss=0.1215, simple_loss=0.1998, pruned_loss=0.02158, over 4819.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02972, over 972449.73 frames.], batch size: 26, lr: 1.40e-04
2022-05-08 15:00:13,954 INFO [train.py:715] (2/8) Epoch 16, batch 3500, loss[loss=0.1331, simple_loss=0.216, pruned_loss=0.02511, over 4907.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02961, over 972953.31 frames.], batch size: 29, lr: 1.40e-04
2022-05-08 15:00:51,756 INFO [train.py:715] (2/8) Epoch 16, batch 3550, loss[loss=0.119, simple_loss=0.1987, pruned_loss=0.01966, over 4912.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02939, over 972770.32 frames.], batch size: 19, lr: 1.40e-04
2022-05-08 15:01:30,179 INFO [train.py:715] (2/8) Epoch 16, batch 3600, loss[loss=0.1457, simple_loss=0.2368, pruned_loss=0.02726, over 4957.00 frames.], tot_loss[loss=0.133, simple_loss=0.2077, pruned_loss=0.02919, over 972636.22 frames.], batch size: 24, lr: 1.40e-04
2022-05-08 15:02:07,898 INFO [train.py:715] (2/8) Epoch 16, batch 3650, loss[loss=0.111, simple_loss=0.1783, pruned_loss=0.02182, over 4969.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02927, over 971426.53 frames.], batch size: 14, lr: 1.40e-04
2022-05-08 15:02:46,543 INFO [train.py:715] (2/8) Epoch 16, batch 3700, loss[loss=0.1482, simple_loss=0.2189, pruned_loss=0.03872, over 4983.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02942, over 971314.79 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 15:03:25,028 INFO [train.py:715] (2/8) Epoch 16, batch 3750, loss[loss=0.1413, simple_loss=0.2095, pruned_loss=0.03656, over 4817.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2066, pruned_loss=0.02954, over 971223.72 frames.], batch size: 25, lr: 1.40e-04
2022-05-08 15:04:03,390 INFO [train.py:715] (2/8) Epoch 16, batch 3800, loss[loss=0.1078, simple_loss=0.1791, pruned_loss=0.01826, over 4695.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2062, pruned_loss=0.02946, over 971204.24 frames.], batch size: 12, lr: 1.40e-04
2022-05-08 15:04:42,256 INFO [train.py:715] (2/8) Epoch 16, batch 3850, loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.02999, over 4865.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02942, over 970802.44 frames.], batch size: 20, lr: 1.40e-04
2022-05-08 15:05:21,016 INFO [train.py:715] (2/8) Epoch 16, batch 3900, loss[loss=0.1266, simple_loss=0.2063, pruned_loss=0.02339, over 4924.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2064, pruned_loss=0.02956, over 971298.31 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 15:05:58,859 INFO [train.py:715] (2/8) Epoch 16, batch 3950, loss[loss=0.1223, simple_loss=0.1908, pruned_loss=0.02689, over 4838.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02968, over 972184.07 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 15:06:36,786 INFO [train.py:715] (2/8) Epoch 16, batch 4000, loss[loss=0.1437, simple_loss=0.2266, pruned_loss=0.03044, over 4910.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02969, over 972390.30 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:07:14,744 INFO [train.py:715] (2/8) Epoch 16, batch 4050, loss[loss=0.1453, simple_loss=0.2267, pruned_loss=0.03198, over 4776.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02982, over 972317.23 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:07:52,148 INFO [train.py:715] (2/8) Epoch 16, batch 4100, loss[loss=0.1135, simple_loss=0.1866, pruned_loss=0.02027, over 4811.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02971, over 972137.76 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 15:08:29,798 INFO [train.py:715] (2/8) Epoch 16, batch 4150, loss[loss=0.1292, simple_loss=0.2132, pruned_loss=0.02259, over 4829.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02975, over 971914.82 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 15:09:07,465 INFO [train.py:715] (2/8) Epoch 16, batch 4200, loss[loss=0.1221, simple_loss=0.2029, pruned_loss=0.02067, over 4863.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02964, over 971637.71 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 15:09:45,634 INFO [train.py:715] (2/8) Epoch 16, batch 4250, loss[loss=0.1261, simple_loss=0.1979, pruned_loss=0.02718, over 4969.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.0295, over 971815.08 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:10:23,341 INFO [train.py:715] (2/8) Epoch 16, batch 4300, loss[loss=0.1247, simple_loss=0.1912, pruned_loss=0.02913, over 4853.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02943, over 972220.83 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:11:01,195 INFO [train.py:715] (2/8) Epoch 16, batch 4350, loss[loss=0.1362, simple_loss=0.1954, pruned_loss=0.03855, over 4767.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03024, over 971867.22 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:11:39,306 INFO [train.py:715] (2/8) Epoch 16, batch 4400, loss[loss=0.1568, simple_loss=0.2361, pruned_loss=0.03879, over 4693.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03014, over 971800.88 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:12:17,134 INFO [train.py:715] (2/8) Epoch 16, batch 4450, loss[loss=0.1468, simple_loss=0.2218, pruned_loss=0.03585, over 4853.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03008, over 972524.81 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 15:12:54,751 INFO [train.py:715] (2/8) Epoch 16, batch 4500, loss[loss=0.1159, simple_loss=0.1912, pruned_loss=0.02027, over 4791.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2081, pruned_loss=0.02975, over 971901.32 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 15:13:32,861 INFO [train.py:715] (2/8) Epoch 16, batch 4550, loss[loss=0.167, simple_loss=0.2384, pruned_loss=0.04783, over 4687.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02963, over 972055.28 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:14:11,254 INFO [train.py:715] (2/8) Epoch 16, batch 4600, loss[loss=0.1179, simple_loss=0.1988, pruned_loss=0.01855, over 4821.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02948, over 971959.75 frames.], batch size: 27, lr: 1.39e-04
2022-05-08 15:14:49,231 INFO [train.py:715] (2/8) Epoch 16, batch 4650, loss[loss=0.1125, simple_loss=0.1935, pruned_loss=0.01576, over 4872.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02962, over 972236.42 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 15:15:27,630 INFO [train.py:715] (2/8) Epoch 16, batch 4700, loss[loss=0.1208, simple_loss=0.1997, pruned_loss=0.02097, over 4974.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02928, over 972009.03 frames.], batch size: 28, lr: 1.39e-04
2022-05-08 15:16:06,226 INFO [train.py:715] (2/8) Epoch 16, batch 4750, loss[loss=0.1279, simple_loss=0.196, pruned_loss=0.02983, over 4855.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02974, over 971873.10 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:16:44,831 INFO [train.py:715] (2/8) Epoch 16, batch 4800, loss[loss=0.1279, simple_loss=0.1978, pruned_loss=0.02899, over 4786.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02977, over 971248.98 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:17:23,108 INFO [train.py:715] (2/8) Epoch 16, batch 4850, loss[loss=0.1602, simple_loss=0.2468, pruned_loss=0.03684, over 4745.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02977, over 971024.69 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:18:01,809 INFO [train.py:715] (2/8) Epoch 16, batch 4900, loss[loss=0.1504, simple_loss=0.2173, pruned_loss=0.04176, over 4827.00 frames.], tot_loss[loss=0.1336, simple_loss=0.207, pruned_loss=0.03012, over 970967.87 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:18:40,682 INFO [train.py:715] (2/8) Epoch 16, batch 4950, loss[loss=0.1268, simple_loss=0.1963, pruned_loss=0.02859, over 4896.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02977, over 971217.92 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:19:18,925 INFO [train.py:715] (2/8) Epoch 16, batch 5000, loss[loss=0.1412, simple_loss=0.2185, pruned_loss=0.03196, over 4975.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.02933, over 971503.42 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 15:19:57,138 INFO [train.py:715] (2/8) Epoch 16, batch 5050, loss[loss=0.1433, simple_loss=0.2109, pruned_loss=0.0378, over 4819.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.0295, over 970980.78 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 15:20:35,446 INFO [train.py:715] (2/8) Epoch 16, batch 5100, loss[loss=0.13, simple_loss=0.2115, pruned_loss=0.02423, over 4921.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02933, over 971078.60 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 15:21:13,349 INFO [train.py:715] (2/8) Epoch 16, batch 5150, loss[loss=0.1715, simple_loss=0.2548, pruned_loss=0.0441, over 4772.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2064, pruned_loss=0.02959, over 971312.28 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:21:50,909 INFO [train.py:715] (2/8) Epoch 16, batch 5200, loss[loss=0.1264, simple_loss=0.1963, pruned_loss=0.0283, over 4920.00 frames.], tot_loss[loss=0.133, simple_loss=0.2063, pruned_loss=0.02979, over 971037.17 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 15:22:28,868 INFO [train.py:715] (2/8) Epoch 16, batch 5250, loss[loss=0.123, simple_loss=0.2031, pruned_loss=0.0214, over 4798.00 frames.], tot_loss[loss=0.1325, simple_loss=0.206, pruned_loss=0.02944, over 971236.89 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 15:23:07,105 INFO [train.py:715] (2/8) Epoch 16, batch 5300, loss[loss=0.1199, simple_loss=0.1923, pruned_loss=0.02374, over 4745.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2056, pruned_loss=0.02903, over 971528.58 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:23:45,228 INFO [train.py:715] (2/8) Epoch 16, batch 5350, loss[loss=0.1087, simple_loss=0.1754, pruned_loss=0.02098, over 4792.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2054, pruned_loss=0.02889, over 971986.46 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 15:24:23,038 INFO [train.py:715] (2/8) Epoch 16, batch 5400, loss[loss=0.1288, simple_loss=0.2075, pruned_loss=0.02504, over 4883.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2057, pruned_loss=0.02906, over 972324.53 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:25:00,889 INFO [train.py:715] (2/8) Epoch 16, batch 5450, loss[loss=0.1468, simple_loss=0.2073, pruned_loss=0.04312, over 4965.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02908, over 971906.06 frames.], batch size: 35, lr: 1.39e-04
2022-05-08 15:25:38,707 INFO [train.py:715] (2/8) Epoch 16, batch 5500, loss[loss=0.1221, simple_loss=0.206, pruned_loss=0.01915, over 4925.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.0293, over 972305.01 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 15:26:16,327 INFO [train.py:715] (2/8) Epoch 16, batch 5550, loss[loss=0.1055, simple_loss=0.1871, pruned_loss=0.0119, over 4820.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2066, pruned_loss=0.02979, over 972341.65 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 15:26:54,077 INFO [train.py:715] (2/8) Epoch 16, batch 5600, loss[loss=0.1087, simple_loss=0.1827, pruned_loss=0.01734, over 4803.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2067, pruned_loss=0.02969, over 972233.46 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 15:27:32,730 INFO [train.py:715] (2/8) Epoch 16, batch 5650, loss[loss=0.1327, simple_loss=0.1997, pruned_loss=0.03287, over 4911.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2057, pruned_loss=0.02885, over 971691.92 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 15:28:10,548 INFO [train.py:715] (2/8) Epoch 16, batch 5700, loss[loss=0.1136, simple_loss=0.19, pruned_loss=0.01862, over 4982.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02889, over 972168.92 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 15:28:48,370 INFO [train.py:715] (2/8) Epoch 16, batch 5750, loss[loss=0.1547, simple_loss=0.2316, pruned_loss=0.03892, over 4802.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.0292, over 972339.84 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 15:29:26,213 INFO [train.py:715] (2/8) Epoch 16, batch 5800, loss[loss=0.1349, simple_loss=0.2034, pruned_loss=0.03322, over 4858.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02973, over 972850.16 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 15:30:04,478 INFO [train.py:715] (2/8) Epoch 16, batch 5850, loss[loss=0.1454, simple_loss=0.2326, pruned_loss=0.02906, over 4777.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02941, over 972871.14 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:30:42,018 INFO [train.py:715] (2/8) Epoch 16, batch 5900, loss[loss=0.1257, simple_loss=0.2049, pruned_loss=0.0232, over 4777.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02931, over 972224.53 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:31:19,663 INFO [train.py:715] (2/8) Epoch 16, batch 5950, loss[loss=0.1185, simple_loss=0.1921, pruned_loss=0.02246, over 4750.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02925, over 972935.84 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:31:58,428 INFO [train.py:715] (2/8) Epoch 16, batch 6000, loss[loss=0.1172, simple_loss=0.1935, pruned_loss=0.02042, over 4973.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02953, over 973163.26 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 15:31:58,429 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 15:32:07,945 INFO [train.py:742] (2/8) 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] (2/8) Epoch 16, batch 6050, loss[loss=0.1381, simple_loss=0.212, pruned_loss=0.03208, over 4974.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02968, over 973085.49 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:33:25,022 INFO [train.py:715] (2/8) Epoch 16, batch 6100, loss[loss=0.1473, simple_loss=0.2148, pruned_loss=0.03991, over 4858.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02989, over 974403.17 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 15:34:02,794 INFO [train.py:715] (2/8) Epoch 16, batch 6150, loss[loss=0.09823, simple_loss=0.1726, pruned_loss=0.01194, over 4824.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03006, over 973963.03 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 15:34:40,934 INFO [train.py:715] (2/8) Epoch 16, batch 6200, loss[loss=0.1365, simple_loss=0.2137, pruned_loss=0.02966, over 4841.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02999, over 973830.38 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:35:19,471 INFO [train.py:715] (2/8) Epoch 16, batch 6250, loss[loss=0.1107, simple_loss=0.1907, pruned_loss=0.01536, over 4941.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02945, over 974679.11 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 15:35:57,115 INFO [train.py:715] (2/8) Epoch 16, batch 6300, loss[loss=0.1234, simple_loss=0.2003, pruned_loss=0.02323, over 4948.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02925, over 974443.06 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 15:36:34,886 INFO [train.py:715] (2/8) Epoch 16, batch 6350, loss[loss=0.1543, simple_loss=0.2337, pruned_loss=0.03751, over 4982.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02928, over 974104.81 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 15:37:13,402 INFO [train.py:715] (2/8) Epoch 16, batch 6400, loss[loss=0.1427, simple_loss=0.2197, pruned_loss=0.03283, over 4905.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02946, over 974391.21 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:37:51,670 INFO [train.py:715] (2/8) Epoch 16, batch 6450, loss[loss=0.1408, simple_loss=0.2188, pruned_loss=0.03141, over 4757.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02983, over 973385.15 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:38:29,441 INFO [train.py:715] (2/8) Epoch 16, batch 6500, loss[loss=0.119, simple_loss=0.1804, pruned_loss=0.02878, over 4792.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03014, over 973323.88 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 15:39:07,582 INFO [train.py:715] (2/8) Epoch 16, batch 6550, loss[loss=0.1393, simple_loss=0.2144, pruned_loss=0.03215, over 4817.00 frames.], tot_loss[loss=0.135, simple_loss=0.2087, pruned_loss=0.03068, over 973153.36 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 15:39:46,029 INFO [train.py:715] (2/8) Epoch 16, batch 6600, loss[loss=0.1467, simple_loss=0.2361, pruned_loss=0.0287, over 4831.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03046, over 972910.59 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:40:23,829 INFO [train.py:715] (2/8) Epoch 16, batch 6650, loss[loss=0.1436, simple_loss=0.212, pruned_loss=0.03763, over 4917.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2086, pruned_loss=0.03036, over 972886.89 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 15:41:01,686 INFO [train.py:715] (2/8) Epoch 16, batch 6700, loss[loss=0.1201, simple_loss=0.1907, pruned_loss=0.02474, over 4761.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02983, over 973563.99 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 15:41:39,712 INFO [train.py:715] (2/8) Epoch 16, batch 6750, loss[loss=0.1056, simple_loss=0.1791, pruned_loss=0.01603, over 4935.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2073, pruned_loss=0.03009, over 973075.98 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 15:42:17,831 INFO [train.py:715] (2/8) Epoch 16, batch 6800, loss[loss=0.1073, simple_loss=0.1855, pruned_loss=0.01455, over 4782.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.03, over 972828.40 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:42:54,813 INFO [train.py:715] (2/8) Epoch 16, batch 6850, loss[loss=0.1213, simple_loss=0.1918, pruned_loss=0.02543, over 4951.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02961, over 973212.35 frames.], batch size: 35, lr: 1.39e-04
2022-05-08 15:43:32,597 INFO [train.py:715] (2/8) Epoch 16, batch 6900, loss[loss=0.1354, simple_loss=0.2199, pruned_loss=0.02549, over 4838.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02944, over 973275.28 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:44:10,713 INFO [train.py:715] (2/8) Epoch 16, batch 6950, loss[loss=0.1486, simple_loss=0.2234, pruned_loss=0.03695, over 4853.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02942, over 974061.24 frames.], batch size: 20, lr: 1.39e-04
2022-05-08 15:44:48,419 INFO [train.py:715] (2/8) Epoch 16, batch 7000, loss[loss=0.1054, simple_loss=0.1896, pruned_loss=0.01059, over 4820.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02945, over 973070.81 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 15:45:26,357 INFO [train.py:715] (2/8) Epoch 16, batch 7050, loss[loss=0.1602, simple_loss=0.224, pruned_loss=0.04822, over 4790.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02957, over 972721.60 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:46:04,191 INFO [train.py:715] (2/8) Epoch 16, batch 7100, loss[loss=0.1432, simple_loss=0.2167, pruned_loss=0.03486, over 4703.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02931, over 972758.84 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:46:42,648 INFO [train.py:715] (2/8) Epoch 16, batch 7150, loss[loss=0.1727, simple_loss=0.2532, pruned_loss=0.04612, over 4927.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.0296, over 972409.63 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 15:47:19,961 INFO [train.py:715] (2/8) Epoch 16, batch 7200, loss[loss=0.1337, simple_loss=0.2037, pruned_loss=0.03187, over 4985.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02984, over 972141.56 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 15:47:57,929 INFO [train.py:715] (2/8) Epoch 16, batch 7250, loss[loss=0.1475, simple_loss=0.2268, pruned_loss=0.03407, over 4748.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2074, pruned_loss=0.03009, over 972421.21 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:48:36,997 INFO [train.py:715] (2/8) Epoch 16, batch 7300, loss[loss=0.1586, simple_loss=0.2301, pruned_loss=0.04359, over 4962.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2079, pruned_loss=0.03022, over 972420.44 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 15:49:15,791 INFO [train.py:715] (2/8) Epoch 16, batch 7350, loss[loss=0.1142, simple_loss=0.1941, pruned_loss=0.0171, over 4941.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.03005, over 971983.53 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 15:49:55,231 INFO [train.py:715] (2/8) Epoch 16, batch 7400, loss[loss=0.1273, simple_loss=0.2034, pruned_loss=0.02558, over 4925.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02988, over 972351.77 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 15:50:34,948 INFO [train.py:715] (2/8) Epoch 16, batch 7450, loss[loss=0.1184, simple_loss=0.1956, pruned_loss=0.02063, over 4930.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02977, over 972592.56 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 15:51:14,631 INFO [train.py:715] (2/8) Epoch 16, batch 7500, loss[loss=0.1454, simple_loss=0.2237, pruned_loss=0.03357, over 4763.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03007, over 973214.89 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:51:53,676 INFO [train.py:715] (2/8) Epoch 16, batch 7550, loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02888, over 4828.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03004, over 973424.55 frames.], batch size: 27, lr: 1.39e-04
2022-05-08 15:52:33,694 INFO [train.py:715] (2/8) Epoch 16, batch 7600, loss[loss=0.1416, simple_loss=0.2242, pruned_loss=0.02952, over 4970.00 frames.], tot_loss[loss=0.1342, simple_loss=0.208, pruned_loss=0.0302, over 973874.14 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:53:14,076 INFO [train.py:715] (2/8) Epoch 16, batch 7650, loss[loss=0.1287, simple_loss=0.2059, pruned_loss=0.02578, over 4736.00 frames.], tot_loss[loss=0.1334, simple_loss=0.207, pruned_loss=0.02988, over 972895.99 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 15:53:54,208 INFO [train.py:715] (2/8) Epoch 16, batch 7700, loss[loss=0.1671, simple_loss=0.2446, pruned_loss=0.04482, over 4915.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2074, pruned_loss=0.03009, over 973032.98 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:54:33,726 INFO [train.py:715] (2/8) Epoch 16, batch 7750, loss[loss=0.146, simple_loss=0.2261, pruned_loss=0.0329, over 4856.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2069, pruned_loss=0.02981, over 972361.77 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 15:55:13,915 INFO [train.py:715] (2/8) Epoch 16, batch 7800, loss[loss=0.1211, simple_loss=0.1953, pruned_loss=0.02342, over 4763.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02912, over 971847.24 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 15:55:54,761 INFO [train.py:715] (2/8) Epoch 16, batch 7850, loss[loss=0.1402, simple_loss=0.2124, pruned_loss=0.03394, over 4903.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02856, over 971208.50 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:56:34,168 INFO [train.py:715] (2/8) Epoch 16, batch 7900, loss[loss=0.1257, simple_loss=0.1914, pruned_loss=0.03, over 4775.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02882, over 971199.73 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 15:57:14,051 INFO [train.py:715] (2/8) Epoch 16, batch 7950, loss[loss=0.1247, simple_loss=0.1937, pruned_loss=0.02787, over 4758.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02963, over 972023.88 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 15:57:54,556 INFO [train.py:715] (2/8) Epoch 16, batch 8000, loss[loss=0.1313, simple_loss=0.1885, pruned_loss=0.03704, over 4825.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02974, over 972393.05 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 15:58:34,601 INFO [train.py:715] (2/8) Epoch 16, batch 8050, loss[loss=0.1204, simple_loss=0.1912, pruned_loss=0.02485, over 4844.00 frames.], tot_loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02982, over 972386.86 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 15:59:14,243 INFO [train.py:715] (2/8) Epoch 16, batch 8100, loss[loss=0.1294, simple_loss=0.2148, pruned_loss=0.02197, over 4842.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02931, over 972197.10 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 15:59:54,664 INFO [train.py:715] (2/8) Epoch 16, batch 8150, loss[loss=0.1521, simple_loss=0.2353, pruned_loss=0.03448, over 4930.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.0292, over 972013.04 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 16:00:35,741 INFO [train.py:715] (2/8) Epoch 16, batch 8200, loss[loss=0.1208, simple_loss=0.2057, pruned_loss=0.01795, over 4832.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02919, over 972694.85 frames.], batch size: 26, lr: 1.39e-04
2022-05-08 16:01:15,830 INFO [train.py:715] (2/8) Epoch 16, batch 8250, loss[loss=0.09803, simple_loss=0.1736, pruned_loss=0.01125, over 4883.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02914, over 973207.35 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 16:01:55,579 INFO [train.py:715] (2/8) Epoch 16, batch 8300, loss[loss=0.1414, simple_loss=0.2269, pruned_loss=0.02798, over 4875.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02945, over 972306.03 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 16:02:36,302 INFO [train.py:715] (2/8) Epoch 16, batch 8350, loss[loss=0.1413, simple_loss=0.2217, pruned_loss=0.03044, over 4950.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02986, over 973125.16 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:03:16,606 INFO [train.py:715] (2/8) Epoch 16, batch 8400, loss[loss=0.1585, simple_loss=0.2242, pruned_loss=0.04638, over 4915.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02995, over 973029.87 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 16:03:55,140 INFO [train.py:715] (2/8) Epoch 16, batch 8450, loss[loss=0.1306, simple_loss=0.2045, pruned_loss=0.02835, over 4883.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.0298, over 972752.52 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:04:34,541 INFO [train.py:715] (2/8) Epoch 16, batch 8500, loss[loss=0.1255, simple_loss=0.1986, pruned_loss=0.02615, over 4875.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02975, over 972213.62 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 16:05:13,260 INFO [train.py:715] (2/8) Epoch 16, batch 8550, loss[loss=0.117, simple_loss=0.197, pruned_loss=0.01855, over 4873.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02937, over 972112.67 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 16:05:51,577 INFO [train.py:715] (2/8) Epoch 16, batch 8600, loss[loss=0.1351, simple_loss=0.2104, pruned_loss=0.02992, over 4989.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02955, over 972168.63 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 16:06:29,596 INFO [train.py:715] (2/8) Epoch 16, batch 8650, loss[loss=0.1161, simple_loss=0.1849, pruned_loss=0.02371, over 4791.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02958, over 971374.67 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 16:07:08,667 INFO [train.py:715] (2/8) Epoch 16, batch 8700, loss[loss=0.1204, simple_loss=0.1958, pruned_loss=0.02247, over 4931.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.0297, over 972195.16 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 16:07:47,717 INFO [train.py:715] (2/8) Epoch 16, batch 8750, loss[loss=0.2071, simple_loss=0.2573, pruned_loss=0.07843, over 4887.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02925, over 972462.26 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 16:08:26,278 INFO [train.py:715] (2/8) Epoch 16, batch 8800, loss[loss=0.1255, simple_loss=0.2122, pruned_loss=0.01941, over 4958.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02942, over 971637.15 frames.], batch size: 35, lr: 1.39e-04
2022-05-08 16:09:04,968 INFO [train.py:715] (2/8) Epoch 16, batch 8850, loss[loss=0.1323, simple_loss=0.2039, pruned_loss=0.03031, over 4929.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02962, over 971928.95 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 16:09:44,465 INFO [train.py:715] (2/8) Epoch 16, batch 8900, loss[loss=0.1473, simple_loss=0.2289, pruned_loss=0.0328, over 4881.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02981, over 971596.78 frames.], batch size: 39, lr: 1.39e-04
2022-05-08 16:10:22,904 INFO [train.py:715] (2/8) Epoch 16, batch 8950, loss[loss=0.1458, simple_loss=0.2283, pruned_loss=0.03166, over 4752.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02958, over 971654.38 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:11:01,137 INFO [train.py:715] (2/8) Epoch 16, batch 9000, loss[loss=0.1448, simple_loss=0.2161, pruned_loss=0.03673, over 4896.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.0298, over 971259.74 frames.], batch size: 22, lr: 1.39e-04
2022-05-08 16:11:01,138 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 16:11:23,894 INFO [train.py:742] (2/8) 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] (2/8) Epoch 16, batch 9050, loss[loss=0.1255, simple_loss=0.1988, pruned_loss=0.02613, over 4905.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02933, over 970942.27 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 16:12:41,945 INFO [train.py:715] (2/8) Epoch 16, batch 9100, loss[loss=0.1186, simple_loss=0.1934, pruned_loss=0.02194, over 4765.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02945, over 971789.39 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 16:13:20,955 INFO [train.py:715] (2/8) Epoch 16, batch 9150, loss[loss=0.1136, simple_loss=0.1904, pruned_loss=0.01845, over 4760.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02929, over 971125.92 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:13:58,477 INFO [train.py:715] (2/8) Epoch 16, batch 9200, loss[loss=0.134, simple_loss=0.2115, pruned_loss=0.02826, over 4981.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.0295, over 971524.98 frames.], batch size: 28, lr: 1.39e-04
2022-05-08 16:14:37,129 INFO [train.py:715] (2/8) Epoch 16, batch 9250, loss[loss=0.1351, simple_loss=0.2172, pruned_loss=0.02651, over 4780.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02925, over 971324.86 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:15:16,083 INFO [train.py:715] (2/8) Epoch 16, batch 9300, loss[loss=0.149, simple_loss=0.2163, pruned_loss=0.04086, over 4837.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02956, over 971036.78 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:15:54,778 INFO [train.py:715] (2/8) Epoch 16, batch 9350, loss[loss=0.1379, simple_loss=0.2182, pruned_loss=0.02875, over 4781.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02957, over 971476.29 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 16:16:33,102 INFO [train.py:715] (2/8) Epoch 16, batch 9400, loss[loss=0.118, simple_loss=0.1882, pruned_loss=0.02391, over 4810.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02972, over 972100.50 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 16:17:11,620 INFO [train.py:715] (2/8) Epoch 16, batch 9450, loss[loss=0.149, simple_loss=0.2282, pruned_loss=0.03492, over 4927.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03019, over 971908.24 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 16:17:50,526 INFO [train.py:715] (2/8) Epoch 16, batch 9500, loss[loss=0.1606, simple_loss=0.2273, pruned_loss=0.047, over 4994.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03005, over 973383.20 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:18:28,784 INFO [train.py:715] (2/8) Epoch 16, batch 9550, loss[loss=0.1286, simple_loss=0.2029, pruned_loss=0.02718, over 4852.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.02995, over 973786.90 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 16:19:08,108 INFO [train.py:715] (2/8) Epoch 16, batch 9600, loss[loss=0.118, simple_loss=0.1852, pruned_loss=0.02538, over 4995.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2071, pruned_loss=0.02989, over 974212.54 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:19:47,957 INFO [train.py:715] (2/8) Epoch 16, batch 9650, loss[loss=0.1548, simple_loss=0.2143, pruned_loss=0.04762, over 4976.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2076, pruned_loss=0.03044, over 973727.86 frames.], batch size: 33, lr: 1.39e-04
2022-05-08 16:20:27,597 INFO [train.py:715] (2/8) Epoch 16, batch 9700, loss[loss=0.1297, simple_loss=0.205, pruned_loss=0.02718, over 4929.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2081, pruned_loss=0.0307, over 973484.99 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 16:21:08,016 INFO [train.py:715] (2/8) Epoch 16, batch 9750, loss[loss=0.1665, simple_loss=0.2415, pruned_loss=0.04574, over 4859.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2084, pruned_loss=0.03066, over 972957.33 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 16:21:49,081 INFO [train.py:715] (2/8) Epoch 16, batch 9800, loss[loss=0.1486, simple_loss=0.2162, pruned_loss=0.04051, over 4787.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.0306, over 972417.94 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:22:29,510 INFO [train.py:715] (2/8) Epoch 16, batch 9850, loss[loss=0.1705, simple_loss=0.2447, pruned_loss=0.04812, over 4914.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03027, over 972336.94 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 16:23:09,300 INFO [train.py:715] (2/8) Epoch 16, batch 9900, loss[loss=0.1456, simple_loss=0.2346, pruned_loss=0.02834, over 4953.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03022, over 972654.60 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:23:49,479 INFO [train.py:715] (2/8) Epoch 16, batch 9950, loss[loss=0.1283, simple_loss=0.202, pruned_loss=0.02729, over 4803.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02985, over 972344.02 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:24:30,458 INFO [train.py:715] (2/8) Epoch 16, batch 10000, loss[loss=0.1134, simple_loss=0.1998, pruned_loss=0.01345, over 4980.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02982, over 972611.37 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 16:25:09,383 INFO [train.py:715] (2/8) Epoch 16, batch 10050, loss[loss=0.1352, simple_loss=0.222, pruned_loss=0.02416, over 4769.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02981, over 973012.19 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:25:49,618 INFO [train.py:715] (2/8) Epoch 16, batch 10100, loss[loss=0.1392, simple_loss=0.209, pruned_loss=0.03474, over 4863.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02966, over 972877.01 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 16:26:30,394 INFO [train.py:715] (2/8) Epoch 16, batch 10150, loss[loss=0.1511, simple_loss=0.2214, pruned_loss=0.04037, over 4985.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02978, over 973470.20 frames.], batch size: 25, lr: 1.39e-04
2022-05-08 16:27:10,598 INFO [train.py:715] (2/8) Epoch 16, batch 10200, loss[loss=0.1768, simple_loss=0.2483, pruned_loss=0.05259, over 4701.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02928, over 972817.08 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:27:49,608 INFO [train.py:715] (2/8) Epoch 16, batch 10250, loss[loss=0.126, simple_loss=0.2003, pruned_loss=0.02586, over 4802.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02925, over 973838.00 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:28:29,499 INFO [train.py:715] (2/8) Epoch 16, batch 10300, loss[loss=0.1296, simple_loss=0.2113, pruned_loss=0.02392, over 4791.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2076, pruned_loss=0.02905, over 973548.52 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:29:09,153 INFO [train.py:715] (2/8) Epoch 16, batch 10350, loss[loss=0.149, simple_loss=0.2261, pruned_loss=0.03599, over 4831.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02948, over 973704.21 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:29:47,477 INFO [train.py:715] (2/8) Epoch 16, batch 10400, loss[loss=0.1509, simple_loss=0.2308, pruned_loss=0.03546, over 4839.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02919, over 972897.52 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:30:26,269 INFO [train.py:715] (2/8) Epoch 16, batch 10450, loss[loss=0.1071, simple_loss=0.1798, pruned_loss=0.01716, over 4754.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02951, over 972381.87 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:31:05,205 INFO [train.py:715] (2/8) Epoch 16, batch 10500, loss[loss=0.1062, simple_loss=0.1821, pruned_loss=0.01514, over 4931.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2084, pruned_loss=0.02974, over 972638.06 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 16:31:44,639 INFO [train.py:715] (2/8) Epoch 16, batch 10550, loss[loss=0.1269, simple_loss=0.189, pruned_loss=0.03239, over 4698.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02964, over 972615.23 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:32:22,612 INFO [train.py:715] (2/8) Epoch 16, batch 10600, loss[loss=0.1364, simple_loss=0.2033, pruned_loss=0.03478, over 4766.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02974, over 972408.67 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:33:01,319 INFO [train.py:715] (2/8) Epoch 16, batch 10650, loss[loss=0.1365, simple_loss=0.2186, pruned_loss=0.02721, over 4748.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02911, over 972260.35 frames.], batch size: 16, lr: 1.39e-04
2022-05-08 16:33:40,769 INFO [train.py:715] (2/8) Epoch 16, batch 10700, loss[loss=0.1218, simple_loss=0.1932, pruned_loss=0.02521, over 4958.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02903, over 972413.98 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:34:19,602 INFO [train.py:715] (2/8) Epoch 16, batch 10750, loss[loss=0.1327, simple_loss=0.215, pruned_loss=0.02514, over 4953.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02914, over 972195.54 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:34:58,501 INFO [train.py:715] (2/8) Epoch 16, batch 10800, loss[loss=0.1443, simple_loss=0.2117, pruned_loss=0.03843, over 4920.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02902, over 972409.42 frames.], batch size: 29, lr: 1.39e-04
2022-05-08 16:35:37,667 INFO [train.py:715] (2/8) Epoch 16, batch 10850, loss[loss=0.1359, simple_loss=0.2093, pruned_loss=0.03124, over 4859.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02895, over 972409.20 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 16:36:17,324 INFO [train.py:715] (2/8) Epoch 16, batch 10900, loss[loss=0.1096, simple_loss=0.1776, pruned_loss=0.02079, over 4767.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.0294, over 972815.25 frames.], batch size: 14, lr: 1.39e-04
2022-05-08 16:36:55,551 INFO [train.py:715] (2/8) Epoch 16, batch 10950, loss[loss=0.1339, simple_loss=0.2162, pruned_loss=0.02576, over 4905.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02921, over 971979.86 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:37:34,521 INFO [train.py:715] (2/8) Epoch 16, batch 11000, loss[loss=0.1644, simple_loss=0.2305, pruned_loss=0.04912, over 4833.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02922, over 972231.81 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 16:38:13,977 INFO [train.py:715] (2/8) Epoch 16, batch 11050, loss[loss=0.1402, simple_loss=0.2187, pruned_loss=0.03084, over 4770.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02925, over 971344.09 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:38:55,217 INFO [train.py:715] (2/8) Epoch 16, batch 11100, loss[loss=0.1301, simple_loss=0.1988, pruned_loss=0.03074, over 4847.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02923, over 972008.93 frames.], batch size: 32, lr: 1.39e-04
2022-05-08 16:39:33,650 INFO [train.py:715] (2/8) Epoch 16, batch 11150, loss[loss=0.1434, simple_loss=0.2194, pruned_loss=0.03372, over 4951.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02921, over 972092.83 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:40:12,897 INFO [train.py:715] (2/8) Epoch 16, batch 11200, loss[loss=0.1474, simple_loss=0.2216, pruned_loss=0.03657, over 4896.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02926, over 971662.84 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:40:51,684 INFO [train.py:715] (2/8) Epoch 16, batch 11250, loss[loss=0.133, simple_loss=0.2095, pruned_loss=0.02822, over 4906.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02901, over 971411.49 frames.], batch size: 19, lr: 1.39e-04
2022-05-08 16:41:29,875 INFO [train.py:715] (2/8) Epoch 16, batch 11300, loss[loss=0.1409, simple_loss=0.2284, pruned_loss=0.02666, over 4957.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02885, over 971622.69 frames.], batch size: 24, lr: 1.39e-04
2022-05-08 16:42:08,150 INFO [train.py:715] (2/8) Epoch 16, batch 11350, loss[loss=0.1308, simple_loss=0.2021, pruned_loss=0.02976, over 4691.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02896, over 972186.86 frames.], batch size: 15, lr: 1.39e-04
2022-05-08 16:42:47,130 INFO [train.py:715] (2/8) Epoch 16, batch 11400, loss[loss=0.137, simple_loss=0.2011, pruned_loss=0.03647, over 4928.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02885, over 971993.69 frames.], batch size: 35, lr: 1.39e-04
2022-05-08 16:43:25,160 INFO [train.py:715] (2/8) Epoch 16, batch 11450, loss[loss=0.1224, simple_loss=0.1957, pruned_loss=0.02457, over 4934.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02958, over 972583.61 frames.], batch size: 23, lr: 1.39e-04
2022-05-08 16:44:03,088 INFO [train.py:715] (2/8) Epoch 16, batch 11500, loss[loss=0.1187, simple_loss=0.1918, pruned_loss=0.02286, over 4842.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02889, over 972188.23 frames.], batch size: 13, lr: 1.39e-04
2022-05-08 16:44:41,781 INFO [train.py:715] (2/8) Epoch 16, batch 11550, loss[loss=0.1234, simple_loss=0.1976, pruned_loss=0.02458, over 4950.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02951, over 972357.95 frames.], batch size: 21, lr: 1.39e-04
2022-05-08 16:45:20,368 INFO [train.py:715] (2/8) Epoch 16, batch 11600, loss[loss=0.1485, simple_loss=0.2281, pruned_loss=0.03451, over 4782.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02917, over 972457.03 frames.], batch size: 17, lr: 1.39e-04
2022-05-08 16:45:57,962 INFO [train.py:715] (2/8) Epoch 16, batch 11650, loss[loss=0.1579, simple_loss=0.226, pruned_loss=0.04485, over 4771.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02931, over 972285.06 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:46:36,441 INFO [train.py:715] (2/8) Epoch 16, batch 11700, loss[loss=0.1784, simple_loss=0.2408, pruned_loss=0.058, over 4846.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02962, over 971978.56 frames.], batch size: 30, lr: 1.39e-04
2022-05-08 16:47:15,523 INFO [train.py:715] (2/8) Epoch 16, batch 11750, loss[loss=0.1126, simple_loss=0.1791, pruned_loss=0.023, over 4815.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02957, over 972345.37 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 16:47:53,674 INFO [train.py:715] (2/8) Epoch 16, batch 11800, loss[loss=0.1458, simple_loss=0.2251, pruned_loss=0.03325, over 4932.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02896, over 972767.56 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:48:31,494 INFO [train.py:715] (2/8) Epoch 16, batch 11850, loss[loss=0.1187, simple_loss=0.1937, pruned_loss=0.02181, over 4779.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02951, over 972747.58 frames.], batch size: 18, lr: 1.39e-04
2022-05-08 16:49:10,175 INFO [train.py:715] (2/8) Epoch 16, batch 11900, loss[loss=0.1449, simple_loss=0.2199, pruned_loss=0.03495, over 4854.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02968, over 972621.37 frames.], batch size: 38, lr: 1.39e-04
2022-05-08 16:49:48,595 INFO [train.py:715] (2/8) Epoch 16, batch 11950, loss[loss=0.1163, simple_loss=0.1915, pruned_loss=0.0206, over 4789.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02926, over 973227.95 frames.], batch size: 12, lr: 1.39e-04
2022-05-08 16:50:26,417 INFO [train.py:715] (2/8) Epoch 16, batch 12000, loss[loss=0.1583, simple_loss=0.2384, pruned_loss=0.03909, over 4801.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02908, over 972929.94 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 16:50:26,418 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 16:50:37,201 INFO [train.py:742] (2/8) Epoch 16, validation: loss=0.1049, simple_loss=0.1884, pruned_loss=0.01072, over 914524.00 frames.
2022-05-08 16:51:16,052 INFO [train.py:715] (2/8) Epoch 16, batch 12050, loss[loss=0.1455, simple_loss=0.2166, pruned_loss=0.03723, over 4872.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02898, over 973875.45 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 16:51:55,271 INFO [train.py:715] (2/8) Epoch 16, batch 12100, loss[loss=0.1491, simple_loss=0.2308, pruned_loss=0.03376, over 4693.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02944, over 973774.06 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 16:52:34,708 INFO [train.py:715] (2/8) Epoch 16, batch 12150, loss[loss=0.1324, simple_loss=0.2004, pruned_loss=0.03222, over 4977.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02993, over 973688.29 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 16:53:12,372 INFO [train.py:715] (2/8) Epoch 16, batch 12200, loss[loss=0.119, simple_loss=0.1964, pruned_loss=0.02084, over 4935.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02944, over 973851.62 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 16:53:50,653 INFO [train.py:715] (2/8) Epoch 16, batch 12250, loss[loss=0.1083, simple_loss=0.1804, pruned_loss=0.01811, over 4791.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02938, over 974194.14 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 16:54:29,690 INFO [train.py:715] (2/8) Epoch 16, batch 12300, loss[loss=0.1335, simple_loss=0.2107, pruned_loss=0.02818, over 4694.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.0291, over 973678.10 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 16:55:08,770 INFO [train.py:715] (2/8) Epoch 16, batch 12350, loss[loss=0.1882, simple_loss=0.2404, pruned_loss=0.06804, over 4935.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02923, over 973491.42 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 16:55:47,031 INFO [train.py:715] (2/8) Epoch 16, batch 12400, loss[loss=0.1123, simple_loss=0.1884, pruned_loss=0.01811, over 4807.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02909, over 973799.38 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 16:56:26,128 INFO [train.py:715] (2/8) Epoch 16, batch 12450, loss[loss=0.1458, simple_loss=0.2114, pruned_loss=0.04013, over 4863.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02934, over 974266.01 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 16:57:05,989 INFO [train.py:715] (2/8) Epoch 16, batch 12500, loss[loss=0.1437, simple_loss=0.2144, pruned_loss=0.03645, over 4966.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02983, over 974681.54 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 16:57:44,595 INFO [train.py:715] (2/8) Epoch 16, batch 12550, loss[loss=0.1062, simple_loss=0.1867, pruned_loss=0.01283, over 4772.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.0298, over 974403.68 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 16:58:23,184 INFO [train.py:715] (2/8) Epoch 16, batch 12600, loss[loss=0.1141, simple_loss=0.1949, pruned_loss=0.01665, over 4685.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.02981, over 974234.85 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 16:59:01,904 INFO [train.py:715] (2/8) Epoch 16, batch 12650, loss[loss=0.127, simple_loss=0.1999, pruned_loss=0.02709, over 4784.00 frames.], tot_loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02975, over 973113.16 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 16:59:40,535 INFO [train.py:715] (2/8) Epoch 16, batch 12700, loss[loss=0.1482, simple_loss=0.2203, pruned_loss=0.03807, over 4981.00 frames.], tot_loss[loss=0.1342, simple_loss=0.209, pruned_loss=0.0297, over 973859.46 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 17:00:18,085 INFO [train.py:715] (2/8) Epoch 16, batch 12750, loss[loss=0.1317, simple_loss=0.1998, pruned_loss=0.03177, over 4978.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2084, pruned_loss=0.02968, over 973842.62 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 17:00:57,706 INFO [train.py:715] (2/8) Epoch 16, batch 12800, loss[loss=0.1463, simple_loss=0.2186, pruned_loss=0.03698, over 4797.00 frames.], tot_loss[loss=0.1344, simple_loss=0.209, pruned_loss=0.02991, over 973300.19 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:01:36,684 INFO [train.py:715] (2/8) Epoch 16, batch 12850, loss[loss=0.1421, simple_loss=0.2158, pruned_loss=0.03416, over 4930.00 frames.], tot_loss[loss=0.134, simple_loss=0.2084, pruned_loss=0.02974, over 973070.04 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:02:15,045 INFO [train.py:715] (2/8) Epoch 16, batch 12900, loss[loss=0.1444, simple_loss=0.2155, pruned_loss=0.03666, over 4984.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02939, over 972995.12 frames.], batch size: 26, lr: 1.38e-04
2022-05-08 17:02:53,759 INFO [train.py:715] (2/8) Epoch 16, batch 12950, loss[loss=0.1439, simple_loss=0.2075, pruned_loss=0.04014, over 4874.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02932, over 973494.86 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:03:32,778 INFO [train.py:715] (2/8) Epoch 16, batch 13000, loss[loss=0.1616, simple_loss=0.2296, pruned_loss=0.04678, over 4882.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02909, over 972770.33 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:04:11,282 INFO [train.py:715] (2/8) Epoch 16, batch 13050, loss[loss=0.1225, simple_loss=0.1984, pruned_loss=0.02324, over 4826.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02879, over 972039.80 frames.], batch size: 26, lr: 1.38e-04
2022-05-08 17:04:49,802 INFO [train.py:715] (2/8) Epoch 16, batch 13100, loss[loss=0.138, simple_loss=0.2094, pruned_loss=0.03335, over 4969.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02846, over 972717.68 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 17:05:28,950 INFO [train.py:715] (2/8) Epoch 16, batch 13150, loss[loss=0.1329, simple_loss=0.2059, pruned_loss=0.02996, over 4832.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02898, over 971615.82 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:06:08,073 INFO [train.py:715] (2/8) Epoch 16, batch 13200, loss[loss=0.1103, simple_loss=0.19, pruned_loss=0.01536, over 4810.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02902, over 971917.56 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:06:46,154 INFO [train.py:715] (2/8) Epoch 16, batch 13250, loss[loss=0.1338, simple_loss=0.211, pruned_loss=0.02833, over 4961.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02913, over 971768.88 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:07:25,007 INFO [train.py:715] (2/8) Epoch 16, batch 13300, loss[loss=0.1352, simple_loss=0.2147, pruned_loss=0.0279, over 4883.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02962, over 971836.97 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:08:04,361 INFO [train.py:715] (2/8) Epoch 16, batch 13350, loss[loss=0.1493, simple_loss=0.2164, pruned_loss=0.04106, over 4782.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02944, over 972186.60 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:08:42,684 INFO [train.py:715] (2/8) Epoch 16, batch 13400, loss[loss=0.1569, simple_loss=0.2277, pruned_loss=0.04305, over 4967.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2087, pruned_loss=0.02979, over 972880.08 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:09:21,162 INFO [train.py:715] (2/8) Epoch 16, batch 13450, loss[loss=0.1393, simple_loss=0.2211, pruned_loss=0.02881, over 4888.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2085, pruned_loss=0.02936, over 972169.29 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:10:00,909 INFO [train.py:715] (2/8) Epoch 16, batch 13500, loss[loss=0.1323, simple_loss=0.2089, pruned_loss=0.0279, over 4927.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02948, over 972851.17 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 17:10:39,240 INFO [train.py:715] (2/8) Epoch 16, batch 13550, loss[loss=0.1061, simple_loss=0.1869, pruned_loss=0.01264, over 4787.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2084, pruned_loss=0.02948, over 971800.36 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:11:17,355 INFO [train.py:715] (2/8) Epoch 16, batch 13600, loss[loss=0.1181, simple_loss=0.1883, pruned_loss=0.02397, over 4977.00 frames.], tot_loss[loss=0.134, simple_loss=0.2088, pruned_loss=0.02965, over 971912.86 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:11:56,186 INFO [train.py:715] (2/8) Epoch 16, batch 13650, loss[loss=0.1814, simple_loss=0.2482, pruned_loss=0.05734, over 4769.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2088, pruned_loss=0.02977, over 971795.53 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:12:35,109 INFO [train.py:715] (2/8) Epoch 16, batch 13700, loss[loss=0.1296, simple_loss=0.197, pruned_loss=0.03115, over 4878.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.02959, over 971000.41 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:13:13,500 INFO [train.py:715] (2/8) Epoch 16, batch 13750, loss[loss=0.138, simple_loss=0.2209, pruned_loss=0.02753, over 4982.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02926, over 970651.47 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:13:52,007 INFO [train.py:715] (2/8) Epoch 16, batch 13800, loss[loss=0.1422, simple_loss=0.211, pruned_loss=0.03669, over 4958.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02901, over 971493.08 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:14:30,650 INFO [train.py:715] (2/8) Epoch 16, batch 13850, loss[loss=0.1301, simple_loss=0.2055, pruned_loss=0.02731, over 4773.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02884, over 970797.42 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:15:08,622 INFO [train.py:715] (2/8) Epoch 16, batch 13900, loss[loss=0.1492, simple_loss=0.2082, pruned_loss=0.04512, over 4865.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02899, over 972086.36 frames.], batch size: 34, lr: 1.38e-04
2022-05-08 17:15:46,307 INFO [train.py:715] (2/8) Epoch 16, batch 13950, loss[loss=0.1378, simple_loss=0.2106, pruned_loss=0.03246, over 4781.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02925, over 972356.93 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:16:24,661 INFO [train.py:715] (2/8) Epoch 16, batch 14000, loss[loss=0.125, simple_loss=0.197, pruned_loss=0.02655, over 4700.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.0295, over 972143.03 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:17:03,281 INFO [train.py:715] (2/8) Epoch 16, batch 14050, loss[loss=0.1531, simple_loss=0.2256, pruned_loss=0.04031, over 4777.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02991, over 972597.37 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:17:41,057 INFO [train.py:715] (2/8) Epoch 16, batch 14100, loss[loss=0.1624, simple_loss=0.2261, pruned_loss=0.04932, over 4836.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2087, pruned_loss=0.03038, over 972702.49 frames.], batch size: 30, lr: 1.38e-04
2022-05-08 17:18:18,776 INFO [train.py:715] (2/8) Epoch 16, batch 14150, loss[loss=0.1701, simple_loss=0.2549, pruned_loss=0.04271, over 4963.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03038, over 972803.48 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:18:57,316 INFO [train.py:715] (2/8) Epoch 16, batch 14200, loss[loss=0.1238, simple_loss=0.1997, pruned_loss=0.02393, over 4878.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02911, over 973278.11 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 17:19:36,007 INFO [train.py:715] (2/8) Epoch 16, batch 14250, loss[loss=0.1524, simple_loss=0.2193, pruned_loss=0.04273, over 4923.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02944, over 972511.29 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:20:14,628 INFO [train.py:715] (2/8) Epoch 16, batch 14300, loss[loss=0.1462, simple_loss=0.2152, pruned_loss=0.03864, over 4823.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02957, over 972402.05 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:20:53,330 INFO [train.py:715] (2/8) Epoch 16, batch 14350, loss[loss=0.1532, simple_loss=0.2158, pruned_loss=0.04531, over 4860.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02972, over 971456.71 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 17:21:32,523 INFO [train.py:715] (2/8) Epoch 16, batch 14400, loss[loss=0.1424, simple_loss=0.2182, pruned_loss=0.03336, over 4759.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02942, over 971588.46 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:22:10,259 INFO [train.py:715] (2/8) Epoch 16, batch 14450, loss[loss=0.1481, simple_loss=0.2084, pruned_loss=0.04394, over 4703.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02938, over 971727.22 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:22:49,092 INFO [train.py:715] (2/8) Epoch 16, batch 14500, loss[loss=0.1177, simple_loss=0.2006, pruned_loss=0.01743, over 4801.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02962, over 972091.03 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:23:28,026 INFO [train.py:715] (2/8) Epoch 16, batch 14550, loss[loss=0.1239, simple_loss=0.2051, pruned_loss=0.02138, over 4981.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02911, over 972583.59 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:24:06,692 INFO [train.py:715] (2/8) Epoch 16, batch 14600, loss[loss=0.1082, simple_loss=0.1805, pruned_loss=0.01789, over 4815.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02961, over 971725.76 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:24:44,963 INFO [train.py:715] (2/8) Epoch 16, batch 14650, loss[loss=0.142, simple_loss=0.2028, pruned_loss=0.04061, over 4774.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02963, over 971662.20 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:25:23,541 INFO [train.py:715] (2/8) Epoch 16, batch 14700, loss[loss=0.1485, simple_loss=0.2285, pruned_loss=0.03429, over 4772.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02975, over 971875.93 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:26:02,838 INFO [train.py:715] (2/8) Epoch 16, batch 14750, loss[loss=0.1314, simple_loss=0.2043, pruned_loss=0.02928, over 4902.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02992, over 972390.27 frames.], batch size: 19, lr: 1.38e-04
2022-05-08 17:26:40,632 INFO [train.py:715] (2/8) Epoch 16, batch 14800, loss[loss=0.1183, simple_loss=0.1936, pruned_loss=0.02151, over 4975.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.0301, over 972116.36 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 17:27:19,699 INFO [train.py:715] (2/8) Epoch 16, batch 14850, loss[loss=0.1259, simple_loss=0.205, pruned_loss=0.02341, over 4962.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02969, over 972124.48 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:27:58,606 INFO [train.py:715] (2/8) Epoch 16, batch 14900, loss[loss=0.1512, simple_loss=0.2199, pruned_loss=0.04128, over 4927.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02974, over 972450.46 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:28:37,058 INFO [train.py:715] (2/8) Epoch 16, batch 14950, loss[loss=0.1042, simple_loss=0.1691, pruned_loss=0.01963, over 4808.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02927, over 971237.43 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 17:29:16,115 INFO [train.py:715] (2/8) Epoch 16, batch 15000, loss[loss=0.1447, simple_loss=0.2207, pruned_loss=0.0343, over 4690.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02933, over 971162.31 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:29:16,116 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 17:29:25,726 INFO [train.py:742] (2/8) 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] (2/8) Epoch 16, batch 15050, loss[loss=0.1453, simple_loss=0.2211, pruned_loss=0.03475, over 4693.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2083, pruned_loss=0.03046, over 971191.59 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:30:42,064 INFO [train.py:715] (2/8) Epoch 16, batch 15100, loss[loss=0.1307, simple_loss=0.2001, pruned_loss=0.03069, over 4759.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03023, over 970689.39 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:31:20,869 INFO [train.py:715] (2/8) Epoch 16, batch 15150, loss[loss=0.1002, simple_loss=0.165, pruned_loss=0.01767, over 4791.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02987, over 971010.12 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 17:31:58,565 INFO [train.py:715] (2/8) Epoch 16, batch 15200, loss[loss=0.1324, simple_loss=0.2082, pruned_loss=0.02828, over 4922.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.02998, over 972035.43 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:32:36,117 INFO [train.py:715] (2/8) Epoch 16, batch 15250, loss[loss=0.1636, simple_loss=0.2337, pruned_loss=0.04673, over 4943.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2093, pruned_loss=0.03023, over 971334.90 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 17:33:14,310 INFO [train.py:715] (2/8) Epoch 16, batch 15300, loss[loss=0.1263, simple_loss=0.2063, pruned_loss=0.02309, over 4978.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.0297, over 971528.21 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:33:52,456 INFO [train.py:715] (2/8) Epoch 16, batch 15350, loss[loss=0.1499, simple_loss=0.2257, pruned_loss=0.03708, over 4915.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02973, over 971797.42 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:34:30,725 INFO [train.py:715] (2/8) Epoch 16, batch 15400, loss[loss=0.1287, simple_loss=0.2004, pruned_loss=0.02851, over 4917.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2089, pruned_loss=0.03014, over 971877.38 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 17:35:08,737 INFO [train.py:715] (2/8) Epoch 16, batch 15450, loss[loss=0.1346, simple_loss=0.2126, pruned_loss=0.02829, over 4973.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03008, over 972621.44 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 17:35:47,168 INFO [train.py:715] (2/8) Epoch 16, batch 15500, loss[loss=0.1234, simple_loss=0.2026, pruned_loss=0.02212, over 4868.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02992, over 972312.98 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:36:24,797 INFO [train.py:715] (2/8) Epoch 16, batch 15550, loss[loss=0.1433, simple_loss=0.2059, pruned_loss=0.0404, over 4798.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02983, over 971814.52 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:37:02,465 INFO [train.py:715] (2/8) Epoch 16, batch 15600, loss[loss=0.1062, simple_loss=0.1799, pruned_loss=0.01621, over 4838.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02961, over 971865.11 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 17:37:41,083 INFO [train.py:715] (2/8) Epoch 16, batch 15650, loss[loss=0.1868, simple_loss=0.2492, pruned_loss=0.06216, over 4856.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2071, pruned_loss=0.03002, over 970169.10 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:38:19,116 INFO [train.py:715] (2/8) Epoch 16, batch 15700, loss[loss=0.1073, simple_loss=0.1828, pruned_loss=0.01594, over 4849.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.02985, over 971332.54 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:38:56,851 INFO [train.py:715] (2/8) Epoch 16, batch 15750, loss[loss=0.1752, simple_loss=0.2559, pruned_loss=0.04728, over 4747.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2081, pruned_loss=0.03016, over 972482.65 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:39:34,740 INFO [train.py:715] (2/8) Epoch 16, batch 15800, loss[loss=0.1332, simple_loss=0.203, pruned_loss=0.03173, over 4987.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03028, over 972927.68 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:40:13,084 INFO [train.py:715] (2/8) Epoch 16, batch 15850, loss[loss=0.1328, simple_loss=0.2017, pruned_loss=0.03197, over 4991.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2087, pruned_loss=0.03048, over 973683.81 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:40:50,713 INFO [train.py:715] (2/8) Epoch 16, batch 15900, loss[loss=0.1476, simple_loss=0.2164, pruned_loss=0.03941, over 4856.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03029, over 973267.77 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 17:41:28,315 INFO [train.py:715] (2/8) Epoch 16, batch 15950, loss[loss=0.1473, simple_loss=0.2118, pruned_loss=0.04136, over 4874.00 frames.], tot_loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.0302, over 973261.27 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 17:42:06,731 INFO [train.py:715] (2/8) Epoch 16, batch 16000, loss[loss=0.1366, simple_loss=0.211, pruned_loss=0.03112, over 4934.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02953, over 971835.10 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:42:44,834 INFO [train.py:715] (2/8) Epoch 16, batch 16050, loss[loss=0.108, simple_loss=0.1825, pruned_loss=0.0167, over 4782.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02961, over 972612.80 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:43:22,459 INFO [train.py:715] (2/8) Epoch 16, batch 16100, loss[loss=0.1261, simple_loss=0.1935, pruned_loss=0.02936, over 4877.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02955, over 973754.60 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 17:43:59,972 INFO [train.py:715] (2/8) Epoch 16, batch 16150, loss[loss=0.1515, simple_loss=0.2173, pruned_loss=0.04282, over 4788.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02915, over 973171.14 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:44:38,353 INFO [train.py:715] (2/8) Epoch 16, batch 16200, loss[loss=0.1614, simple_loss=0.2437, pruned_loss=0.03951, over 4983.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02905, over 973127.62 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:45:15,918 INFO [train.py:715] (2/8) Epoch 16, batch 16250, loss[loss=0.1208, simple_loss=0.1956, pruned_loss=0.02302, over 4972.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02939, over 973314.93 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:45:53,549 INFO [train.py:715] (2/8) Epoch 16, batch 16300, loss[loss=0.1313, simple_loss=0.2016, pruned_loss=0.03046, over 4805.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02933, over 972883.03 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 17:46:31,875 INFO [train.py:715] (2/8) Epoch 16, batch 16350, loss[loss=0.1131, simple_loss=0.1869, pruned_loss=0.01962, over 4874.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.0292, over 971971.29 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 17:47:10,534 INFO [train.py:715] (2/8) Epoch 16, batch 16400, loss[loss=0.1307, simple_loss=0.2118, pruned_loss=0.02483, over 4765.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02935, over 971994.26 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:47:47,571 INFO [train.py:715] (2/8) Epoch 16, batch 16450, loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02845, over 4975.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02896, over 972000.41 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:48:25,521 INFO [train.py:715] (2/8) Epoch 16, batch 16500, loss[loss=0.1181, simple_loss=0.1901, pruned_loss=0.02309, over 4949.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02894, over 972859.30 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:49:04,092 INFO [train.py:715] (2/8) Epoch 16, batch 16550, loss[loss=0.1217, simple_loss=0.1983, pruned_loss=0.02258, over 4947.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02941, over 973024.14 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 17:49:41,515 INFO [train.py:715] (2/8) Epoch 16, batch 16600, loss[loss=0.1401, simple_loss=0.2107, pruned_loss=0.03473, over 4791.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02929, over 973382.89 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:50:19,531 INFO [train.py:715] (2/8) Epoch 16, batch 16650, loss[loss=0.1476, simple_loss=0.2206, pruned_loss=0.03734, over 4775.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02953, over 972657.56 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 17:50:57,794 INFO [train.py:715] (2/8) Epoch 16, batch 16700, loss[loss=0.1156, simple_loss=0.1973, pruned_loss=0.01694, over 4821.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2085, pruned_loss=0.02954, over 972087.15 frames.], batch size: 26, lr: 1.38e-04
2022-05-08 17:51:35,936 INFO [train.py:715] (2/8) Epoch 16, batch 16750, loss[loss=0.1221, simple_loss=0.2022, pruned_loss=0.02093, over 4687.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2083, pruned_loss=0.02943, over 971820.68 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:52:13,454 INFO [train.py:715] (2/8) Epoch 16, batch 16800, loss[loss=0.131, simple_loss=0.202, pruned_loss=0.03004, over 4654.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02987, over 971765.89 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:52:51,537 INFO [train.py:715] (2/8) Epoch 16, batch 16850, loss[loss=0.1456, simple_loss=0.2142, pruned_loss=0.03856, over 4919.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2085, pruned_loss=0.02982, over 972084.90 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 17:53:30,000 INFO [train.py:715] (2/8) Epoch 16, batch 16900, loss[loss=0.1492, simple_loss=0.2196, pruned_loss=0.03945, over 4813.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2088, pruned_loss=0.03003, over 971703.36 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 17:54:07,592 INFO [train.py:715] (2/8) Epoch 16, batch 16950, loss[loss=0.1672, simple_loss=0.2421, pruned_loss=0.04615, over 4829.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.02986, over 972989.50 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 17:54:45,477 INFO [train.py:715] (2/8) Epoch 16, batch 17000, loss[loss=0.1464, simple_loss=0.2138, pruned_loss=0.03952, over 4787.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.03008, over 972006.74 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 17:55:23,673 INFO [train.py:715] (2/8) Epoch 16, batch 17050, loss[loss=0.1413, simple_loss=0.2105, pruned_loss=0.03606, over 4699.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2085, pruned_loss=0.03038, over 972359.21 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:56:02,257 INFO [train.py:715] (2/8) Epoch 16, batch 17100, loss[loss=0.09974, simple_loss=0.1699, pruned_loss=0.01477, over 4952.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03006, over 972837.58 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 17:56:39,332 INFO [train.py:715] (2/8) Epoch 16, batch 17150, loss[loss=0.1334, simple_loss=0.2031, pruned_loss=0.03188, over 4825.00 frames.], tot_loss[loss=0.134, simple_loss=0.2076, pruned_loss=0.03018, over 973266.48 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 17:57:17,466 INFO [train.py:715] (2/8) Epoch 16, batch 17200, loss[loss=0.1128, simple_loss=0.19, pruned_loss=0.01782, over 4958.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2082, pruned_loss=0.0304, over 972815.07 frames.], batch size: 28, lr: 1.38e-04
2022-05-08 17:57:56,361 INFO [train.py:715] (2/8) Epoch 16, batch 17250, loss[loss=0.1275, simple_loss=0.2064, pruned_loss=0.02425, over 4967.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2083, pruned_loss=0.03038, over 972357.48 frames.], batch size: 28, lr: 1.38e-04
2022-05-08 17:58:33,737 INFO [train.py:715] (2/8) Epoch 16, batch 17300, loss[loss=0.1206, simple_loss=0.2014, pruned_loss=0.01991, over 4889.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02973, over 971808.49 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 17:59:11,266 INFO [train.py:715] (2/8) Epoch 16, batch 17350, loss[loss=0.1325, simple_loss=0.2076, pruned_loss=0.02868, over 4861.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02982, over 971988.40 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 17:59:49,076 INFO [train.py:715] (2/8) Epoch 16, batch 17400, loss[loss=0.1205, simple_loss=0.1945, pruned_loss=0.02321, over 4968.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02975, over 972489.42 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 18:00:27,743 INFO [train.py:715] (2/8) Epoch 16, batch 17450, loss[loss=0.1185, simple_loss=0.198, pruned_loss=0.01953, over 4897.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02975, over 973192.76 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 18:01:04,505 INFO [train.py:715] (2/8) Epoch 16, batch 17500, loss[loss=0.1619, simple_loss=0.2238, pruned_loss=0.05, over 4794.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02984, over 972974.26 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 18:01:42,654 INFO [train.py:715] (2/8) Epoch 16, batch 17550, loss[loss=0.1329, simple_loss=0.2051, pruned_loss=0.03034, over 4928.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03026, over 973682.12 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:02:21,340 INFO [train.py:715] (2/8) Epoch 16, batch 17600, loss[loss=0.118, simple_loss=0.1876, pruned_loss=0.0242, over 4915.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02984, over 974287.60 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:02:58,693 INFO [train.py:715] (2/8) Epoch 16, batch 17650, loss[loss=0.1128, simple_loss=0.1822, pruned_loss=0.02166, over 4783.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02943, over 972770.95 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 18:03:36,636 INFO [train.py:715] (2/8) Epoch 16, batch 17700, loss[loss=0.1283, simple_loss=0.1996, pruned_loss=0.0285, over 4806.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02932, over 972836.91 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 18:04:15,003 INFO [train.py:715] (2/8) Epoch 16, batch 17750, loss[loss=0.1436, simple_loss=0.2197, pruned_loss=0.03374, over 4973.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02962, over 973317.51 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 18:04:53,068 INFO [train.py:715] (2/8) Epoch 16, batch 17800, loss[loss=0.1486, simple_loss=0.2186, pruned_loss=0.03931, over 4805.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02988, over 973386.05 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:05:30,278 INFO [train.py:715] (2/8) Epoch 16, batch 17850, loss[loss=0.1176, simple_loss=0.1948, pruned_loss=0.02017, over 4979.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02976, over 973489.42 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 18:06:08,449 INFO [train.py:715] (2/8) Epoch 16, batch 17900, loss[loss=0.127, simple_loss=0.1965, pruned_loss=0.02877, over 4735.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02939, over 972595.95 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:06:46,890 INFO [train.py:715] (2/8) Epoch 16, batch 17950, loss[loss=0.1161, simple_loss=0.18, pruned_loss=0.02614, over 4991.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02884, over 972375.40 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 18:07:24,273 INFO [train.py:715] (2/8) Epoch 16, batch 18000, loss[loss=0.1154, simple_loss=0.1891, pruned_loss=0.02081, over 4982.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02948, over 972013.85 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 18:07:24,273 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 18:07:33,812 INFO [train.py:742] (2/8) Epoch 16, validation: loss=0.105, simple_loss=0.1884, pruned_loss=0.01082, over 914524.00 frames.
2022-05-08 18:08:11,768 INFO [train.py:715] (2/8) Epoch 16, batch 18050, loss[loss=0.105, simple_loss=0.1821, pruned_loss=0.01395, over 4916.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.02995, over 971440.78 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:08:50,179 INFO [train.py:715] (2/8) Epoch 16, batch 18100, loss[loss=0.158, simple_loss=0.2205, pruned_loss=0.04777, over 4960.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02994, over 972348.60 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 18:09:28,824 INFO [train.py:715] (2/8) Epoch 16, batch 18150, loss[loss=0.1291, simple_loss=0.207, pruned_loss=0.02559, over 4972.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02984, over 972172.58 frames.], batch size: 28, lr: 1.38e-04
2022-05-08 18:10:07,473 INFO [train.py:715] (2/8) Epoch 16, batch 18200, loss[loss=0.1528, simple_loss=0.2104, pruned_loss=0.04762, over 4829.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03059, over 972652.24 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 18:10:45,083 INFO [train.py:715] (2/8) Epoch 16, batch 18250, loss[loss=0.1266, simple_loss=0.2033, pruned_loss=0.02498, over 4808.00 frames.], tot_loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03065, over 971837.84 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:11:23,846 INFO [train.py:715] (2/8) Epoch 16, batch 18300, loss[loss=0.145, simple_loss=0.2188, pruned_loss=0.03559, over 4899.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03059, over 972227.22 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 18:12:02,948 INFO [train.py:715] (2/8) Epoch 16, batch 18350, loss[loss=0.1533, simple_loss=0.2352, pruned_loss=0.03566, over 4897.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03051, over 972594.29 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:12:40,719 INFO [train.py:715] (2/8) Epoch 16, batch 18400, loss[loss=0.1742, simple_loss=0.245, pruned_loss=0.05166, over 4954.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03037, over 972403.38 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 18:13:19,242 INFO [train.py:715] (2/8) Epoch 16, batch 18450, loss[loss=0.1264, simple_loss=0.1984, pruned_loss=0.02725, over 4937.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03004, over 972426.73 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:13:57,851 INFO [train.py:715] (2/8) Epoch 16, batch 18500, loss[loss=0.112, simple_loss=0.1892, pruned_loss=0.01742, over 4927.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02969, over 972048.43 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 18:14:36,371 INFO [train.py:715] (2/8) Epoch 16, batch 18550, loss[loss=0.1401, simple_loss=0.2152, pruned_loss=0.03247, over 4781.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02986, over 972495.43 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:15:13,856 INFO [train.py:715] (2/8) Epoch 16, batch 18600, loss[loss=0.1272, simple_loss=0.2073, pruned_loss=0.02354, over 4869.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02982, over 972875.15 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:15:52,138 INFO [train.py:715] (2/8) Epoch 16, batch 18650, loss[loss=0.1251, simple_loss=0.1969, pruned_loss=0.02667, over 4911.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2066, pruned_loss=0.02975, over 973441.60 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 18:16:30,640 INFO [train.py:715] (2/8) Epoch 16, batch 18700, loss[loss=0.1487, simple_loss=0.22, pruned_loss=0.03873, over 4809.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02958, over 972475.67 frames.], batch size: 27, lr: 1.38e-04
2022-05-08 18:17:08,139 INFO [train.py:715] (2/8) Epoch 16, batch 18750, loss[loss=0.1304, simple_loss=0.2042, pruned_loss=0.02837, over 4896.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2061, pruned_loss=0.0295, over 972107.69 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 18:17:45,512 INFO [train.py:715] (2/8) Epoch 16, batch 18800, loss[loss=0.1266, simple_loss=0.21, pruned_loss=0.02161, over 4757.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2059, pruned_loss=0.02922, over 971607.46 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:18:23,821 INFO [train.py:715] (2/8) Epoch 16, batch 18850, loss[loss=0.1423, simple_loss=0.2309, pruned_loss=0.02689, over 4983.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2056, pruned_loss=0.02925, over 971548.60 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 18:19:02,091 INFO [train.py:715] (2/8) Epoch 16, batch 18900, loss[loss=0.1206, simple_loss=0.1894, pruned_loss=0.02585, over 4750.00 frames.], tot_loss[loss=0.133, simple_loss=0.2065, pruned_loss=0.02974, over 971416.33 frames.], batch size: 12, lr: 1.38e-04
2022-05-08 18:19:39,522 INFO [train.py:715] (2/8) Epoch 16, batch 18950, loss[loss=0.1456, simple_loss=0.2261, pruned_loss=0.03251, over 4783.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02953, over 972626.61 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:20:17,360 INFO [train.py:715] (2/8) Epoch 16, batch 19000, loss[loss=0.1561, simple_loss=0.2302, pruned_loss=0.04102, over 4895.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02888, over 972054.92 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 18:20:55,964 INFO [train.py:715] (2/8) Epoch 16, batch 19050, loss[loss=0.1378, simple_loss=0.2139, pruned_loss=0.03079, over 4928.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02864, over 971793.94 frames.], batch size: 29, lr: 1.38e-04
2022-05-08 18:21:36,428 INFO [train.py:715] (2/8) Epoch 16, batch 19100, loss[loss=0.1199, simple_loss=0.2016, pruned_loss=0.01908, over 4887.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02839, over 972020.73 frames.], batch size: 22, lr: 1.38e-04
2022-05-08 18:22:14,089 INFO [train.py:715] (2/8) Epoch 16, batch 19150, loss[loss=0.1339, simple_loss=0.2084, pruned_loss=0.02972, over 4703.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02872, over 972269.02 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 18:22:52,370 INFO [train.py:715] (2/8) Epoch 16, batch 19200, loss[loss=0.1339, simple_loss=0.2142, pruned_loss=0.02682, over 4920.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02814, over 972637.74 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 18:23:31,021 INFO [train.py:715] (2/8) Epoch 16, batch 19250, loss[loss=0.1395, simple_loss=0.2186, pruned_loss=0.03019, over 4963.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02811, over 972183.38 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 18:24:08,554 INFO [train.py:715] (2/8) Epoch 16, batch 19300, loss[loss=0.1344, simple_loss=0.2059, pruned_loss=0.0314, over 4973.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02856, over 972205.13 frames.], batch size: 15, lr: 1.38e-04
2022-05-08 18:24:46,583 INFO [train.py:715] (2/8) Epoch 16, batch 19350, loss[loss=0.1821, simple_loss=0.2419, pruned_loss=0.06116, over 4947.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02904, over 972727.12 frames.], batch size: 39, lr: 1.38e-04
2022-05-08 18:25:25,226 INFO [train.py:715] (2/8) Epoch 16, batch 19400, loss[loss=0.1667, simple_loss=0.2361, pruned_loss=0.04861, over 4852.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.0293, over 973264.78 frames.], batch size: 20, lr: 1.38e-04
2022-05-08 18:26:03,260 INFO [train.py:715] (2/8) Epoch 16, batch 19450, loss[loss=0.1391, simple_loss=0.2104, pruned_loss=0.03387, over 4731.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02947, over 972782.31 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:26:40,798 INFO [train.py:715] (2/8) Epoch 16, batch 19500, loss[loss=0.1024, simple_loss=0.1781, pruned_loss=0.01338, over 4962.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02947, over 972546.20 frames.], batch size: 24, lr: 1.38e-04
2022-05-08 18:27:18,957 INFO [train.py:715] (2/8) Epoch 16, batch 19550, loss[loss=0.1269, simple_loss=0.2018, pruned_loss=0.02599, over 4925.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02971, over 973418.89 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:27:57,192 INFO [train.py:715] (2/8) Epoch 16, batch 19600, loss[loss=0.123, simple_loss=0.2075, pruned_loss=0.0192, over 4816.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02995, over 972556.98 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 18:28:34,600 INFO [train.py:715] (2/8) Epoch 16, batch 19650, loss[loss=0.1527, simple_loss=0.2248, pruned_loss=0.04024, over 4847.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2088, pruned_loss=0.03024, over 972263.75 frames.], batch size: 32, lr: 1.38e-04
2022-05-08 18:29:12,875 INFO [train.py:715] (2/8) Epoch 16, batch 19700, loss[loss=0.1198, simple_loss=0.1951, pruned_loss=0.02219, over 4813.00 frames.], tot_loss[loss=0.1345, simple_loss=0.209, pruned_loss=0.03001, over 973458.66 frames.], batch size: 27, lr: 1.38e-04
2022-05-08 18:29:51,093 INFO [train.py:715] (2/8) Epoch 16, batch 19750, loss[loss=0.1172, simple_loss=0.194, pruned_loss=0.0202, over 4930.00 frames.], tot_loss[loss=0.134, simple_loss=0.2085, pruned_loss=0.02976, over 973066.82 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 18:30:28,917 INFO [train.py:715] (2/8) Epoch 16, batch 19800, loss[loss=0.1425, simple_loss=0.2116, pruned_loss=0.03671, over 4787.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03013, over 973334.49 frames.], batch size: 14, lr: 1.38e-04
2022-05-08 18:31:06,637 INFO [train.py:715] (2/8) Epoch 16, batch 19850, loss[loss=0.1077, simple_loss=0.1816, pruned_loss=0.01695, over 4933.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03005, over 972870.39 frames.], batch size: 23, lr: 1.38e-04
2022-05-08 18:31:44,941 INFO [train.py:715] (2/8) Epoch 16, batch 19900, loss[loss=0.1732, simple_loss=0.2529, pruned_loss=0.04674, over 4951.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2087, pruned_loss=0.03, over 972259.43 frames.], batch size: 21, lr: 1.38e-04
2022-05-08 18:32:22,973 INFO [train.py:715] (2/8) Epoch 16, batch 19950, loss[loss=0.1402, simple_loss=0.2191, pruned_loss=0.03068, over 4849.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02983, over 972532.23 frames.], batch size: 20, lr: 1.38e-04
2022-05-08 18:33:00,614 INFO [train.py:715] (2/8) Epoch 16, batch 20000, loss[loss=0.1477, simple_loss=0.2152, pruned_loss=0.04006, over 4962.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02968, over 972470.81 frames.], batch size: 35, lr: 1.38e-04
2022-05-08 18:33:38,892 INFO [train.py:715] (2/8) Epoch 16, batch 20050, loss[loss=0.1119, simple_loss=0.183, pruned_loss=0.02035, over 4841.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2086, pruned_loss=0.02991, over 972963.93 frames.], batch size: 13, lr: 1.38e-04
2022-05-08 18:34:17,306 INFO [train.py:715] (2/8) Epoch 16, batch 20100, loss[loss=0.1456, simple_loss=0.2266, pruned_loss=0.0323, over 4902.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02952, over 972987.92 frames.], batch size: 17, lr: 1.38e-04
2022-05-08 18:34:54,671 INFO [train.py:715] (2/8) Epoch 16, batch 20150, loss[loss=0.1202, simple_loss=0.1943, pruned_loss=0.02309, over 4881.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02949, over 972592.86 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:35:32,576 INFO [train.py:715] (2/8) Epoch 16, batch 20200, loss[loss=0.128, simple_loss=0.192, pruned_loss=0.032, over 4787.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02981, over 973060.80 frames.], batch size: 18, lr: 1.38e-04
2022-05-08 18:36:10,896 INFO [train.py:715] (2/8) Epoch 16, batch 20250, loss[loss=0.1363, simple_loss=0.2132, pruned_loss=0.02968, over 4765.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2085, pruned_loss=0.02991, over 973387.32 frames.], batch size: 16, lr: 1.38e-04
2022-05-08 18:36:49,191 INFO [train.py:715] (2/8) Epoch 16, batch 20300, loss[loss=0.1176, simple_loss=0.1989, pruned_loss=0.01819, over 4806.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02939, over 973170.14 frames.], batch size: 25, lr: 1.38e-04
2022-05-08 18:37:27,018 INFO [train.py:715] (2/8) Epoch 16, batch 20350, loss[loss=0.1235, simple_loss=0.1913, pruned_loss=0.02785, over 4933.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.0297, over 973061.60 frames.], batch size: 23, lr: 1.37e-04
2022-05-08 18:38:05,175 INFO [train.py:715] (2/8) Epoch 16, batch 20400, loss[loss=0.1796, simple_loss=0.2573, pruned_loss=0.05097, over 4984.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02974, over 973345.79 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 18:38:43,167 INFO [train.py:715] (2/8) Epoch 16, batch 20450, loss[loss=0.1516, simple_loss=0.2292, pruned_loss=0.03704, over 4694.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02991, over 972088.16 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:39:21,071 INFO [train.py:715] (2/8) Epoch 16, batch 20500, loss[loss=0.1517, simple_loss=0.2221, pruned_loss=0.04066, over 4764.00 frames.], tot_loss[loss=0.134, simple_loss=0.2085, pruned_loss=0.02976, over 973830.80 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 18:39:58,717 INFO [train.py:715] (2/8) Epoch 16, batch 20550, loss[loss=0.1716, simple_loss=0.2302, pruned_loss=0.05647, over 4966.00 frames.], tot_loss[loss=0.1342, simple_loss=0.209, pruned_loss=0.0297, over 973304.15 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:40:37,508 INFO [train.py:715] (2/8) Epoch 16, batch 20600, loss[loss=0.1119, simple_loss=0.1877, pruned_loss=0.01811, over 4862.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2082, pruned_loss=0.02938, over 973342.43 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 18:41:15,474 INFO [train.py:715] (2/8) Epoch 16, batch 20650, loss[loss=0.1408, simple_loss=0.217, pruned_loss=0.03229, over 4907.00 frames.], tot_loss[loss=0.133, simple_loss=0.2077, pruned_loss=0.02912, over 971647.93 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 18:41:52,934 INFO [train.py:715] (2/8) Epoch 16, batch 20700, loss[loss=0.1138, simple_loss=0.1945, pruned_loss=0.01655, over 4922.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2073, pruned_loss=0.02875, over 971473.27 frames.], batch size: 29, lr: 1.37e-04
2022-05-08 18:42:31,440 INFO [train.py:715] (2/8) Epoch 16, batch 20750, loss[loss=0.1344, simple_loss=0.2113, pruned_loss=0.02878, over 4856.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02861, over 971028.91 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 18:43:09,458 INFO [train.py:715] (2/8) Epoch 16, batch 20800, loss[loss=0.1288, simple_loss=0.204, pruned_loss=0.02676, over 4795.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02852, over 971203.92 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 18:43:47,990 INFO [train.py:715] (2/8) Epoch 16, batch 20850, loss[loss=0.1172, simple_loss=0.1957, pruned_loss=0.01938, over 4828.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02853, over 972060.57 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 18:44:25,953 INFO [train.py:715] (2/8) Epoch 16, batch 20900, loss[loss=0.1195, simple_loss=0.1939, pruned_loss=0.02258, over 4986.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02829, over 972372.53 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 18:45:05,224 INFO [train.py:715] (2/8) Epoch 16, batch 20950, loss[loss=0.1345, simple_loss=0.222, pruned_loss=0.02353, over 4959.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02853, over 972005.91 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 18:45:43,432 INFO [train.py:715] (2/8) Epoch 16, batch 21000, loss[loss=0.1621, simple_loss=0.2431, pruned_loss=0.04056, over 4805.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02883, over 972230.53 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 18:45:43,432 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 18:45:53,028 INFO [train.py:742] (2/8) Epoch 16, validation: loss=0.1047, simple_loss=0.1882, pruned_loss=0.0106, over 914524.00 frames.
2022-05-08 18:46:31,912 INFO [train.py:715] (2/8) Epoch 16, batch 21050, loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02961, over 4925.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02883, over 972930.48 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 18:47:10,473 INFO [train.py:715] (2/8) Epoch 16, batch 21100, loss[loss=0.1279, simple_loss=0.2114, pruned_loss=0.02217, over 4692.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02918, over 972674.56 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:47:49,070 INFO [train.py:715] (2/8) Epoch 16, batch 21150, loss[loss=0.1062, simple_loss=0.1789, pruned_loss=0.01673, over 4726.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02897, over 972402.30 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 18:48:27,789 INFO [train.py:715] (2/8) Epoch 16, batch 21200, loss[loss=0.1084, simple_loss=0.188, pruned_loss=0.01437, over 4956.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02923, over 972440.88 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 18:49:06,847 INFO [train.py:715] (2/8) Epoch 16, batch 21250, loss[loss=0.1213, simple_loss=0.2017, pruned_loss=0.0204, over 4835.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02947, over 972248.43 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 18:49:44,919 INFO [train.py:715] (2/8) Epoch 16, batch 21300, loss[loss=0.1307, simple_loss=0.1935, pruned_loss=0.03398, over 4768.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02917, over 973075.45 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 18:50:23,522 INFO [train.py:715] (2/8) Epoch 16, batch 21350, loss[loss=0.1349, simple_loss=0.2087, pruned_loss=0.03054, over 4759.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02904, over 972894.41 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 18:51:01,536 INFO [train.py:715] (2/8) Epoch 16, batch 21400, loss[loss=0.1219, simple_loss=0.2037, pruned_loss=0.02007, over 4986.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02878, over 972860.29 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:51:39,054 INFO [train.py:715] (2/8) Epoch 16, batch 21450, loss[loss=0.1289, simple_loss=0.1958, pruned_loss=0.031, over 4914.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02923, over 973699.35 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 18:52:17,450 INFO [train.py:715] (2/8) Epoch 16, batch 21500, loss[loss=0.1099, simple_loss=0.1899, pruned_loss=0.01496, over 4802.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02928, over 972773.64 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 18:52:55,413 INFO [train.py:715] (2/8) Epoch 16, batch 21550, loss[loss=0.1317, simple_loss=0.1995, pruned_loss=0.0319, over 4635.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02963, over 971443.13 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 18:53:33,004 INFO [train.py:715] (2/8) Epoch 16, batch 21600, loss[loss=0.1286, simple_loss=0.2036, pruned_loss=0.02678, over 4789.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02949, over 971563.70 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 18:54:11,337 INFO [train.py:715] (2/8) Epoch 16, batch 21650, loss[loss=0.1265, simple_loss=0.1962, pruned_loss=0.0284, over 4810.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02951, over 971184.07 frames.], batch size: 27, lr: 1.37e-04
2022-05-08 18:54:49,121 INFO [train.py:715] (2/8) Epoch 16, batch 21700, loss[loss=0.1539, simple_loss=0.2318, pruned_loss=0.038, over 4915.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02965, over 971407.72 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 18:55:27,325 INFO [train.py:715] (2/8) Epoch 16, batch 21750, loss[loss=0.1356, simple_loss=0.2043, pruned_loss=0.03345, over 4701.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02941, over 970984.06 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:56:04,818 INFO [train.py:715] (2/8) Epoch 16, batch 21800, loss[loss=0.1182, simple_loss=0.1923, pruned_loss=0.02207, over 4702.00 frames.], tot_loss[loss=0.1334, simple_loss=0.207, pruned_loss=0.02992, over 971221.49 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:56:42,922 INFO [train.py:715] (2/8) Epoch 16, batch 21850, loss[loss=0.1473, simple_loss=0.2144, pruned_loss=0.04009, over 4846.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2079, pruned_loss=0.03019, over 971965.84 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:57:20,563 INFO [train.py:715] (2/8) Epoch 16, batch 21900, loss[loss=0.1193, simple_loss=0.199, pruned_loss=0.01983, over 4871.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02956, over 972170.46 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 18:57:57,978 INFO [train.py:715] (2/8) Epoch 16, batch 21950, loss[loss=0.1466, simple_loss=0.213, pruned_loss=0.04007, over 4969.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.0295, over 972518.45 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 18:58:36,387 INFO [train.py:715] (2/8) Epoch 16, batch 22000, loss[loss=0.1056, simple_loss=0.1754, pruned_loss=0.0179, over 4644.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02941, over 973024.88 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 18:59:13,999 INFO [train.py:715] (2/8) Epoch 16, batch 22050, loss[loss=0.1409, simple_loss=0.2245, pruned_loss=0.02859, over 4880.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.0297, over 972897.83 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 18:59:52,239 INFO [train.py:715] (2/8) Epoch 16, batch 22100, loss[loss=0.139, simple_loss=0.2203, pruned_loss=0.02892, over 4978.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02975, over 971904.12 frames.], batch size: 28, lr: 1.37e-04
2022-05-08 19:00:29,953 INFO [train.py:715] (2/8) Epoch 16, batch 22150, loss[loss=0.1424, simple_loss=0.2179, pruned_loss=0.0334, over 4759.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02954, over 972263.76 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:01:08,388 INFO [train.py:715] (2/8) Epoch 16, batch 22200, loss[loss=0.1291, simple_loss=0.2048, pruned_loss=0.02674, over 4975.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02927, over 972368.34 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 19:01:46,152 INFO [train.py:715] (2/8) Epoch 16, batch 22250, loss[loss=0.1307, simple_loss=0.2019, pruned_loss=0.0297, over 4788.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02946, over 971442.65 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:02:24,238 INFO [train.py:715] (2/8) Epoch 16, batch 22300, loss[loss=0.1193, simple_loss=0.1926, pruned_loss=0.02303, over 4986.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02913, over 972896.56 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 19:03:02,797 INFO [train.py:715] (2/8) Epoch 16, batch 22350, loss[loss=0.1317, simple_loss=0.2027, pruned_loss=0.03038, over 4830.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02961, over 972452.00 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:03:40,844 INFO [train.py:715] (2/8) Epoch 16, batch 22400, loss[loss=0.142, simple_loss=0.2049, pruned_loss=0.03955, over 4899.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02947, over 972002.02 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:04:19,201 INFO [train.py:715] (2/8) Epoch 16, batch 22450, loss[loss=0.1238, simple_loss=0.2014, pruned_loss=0.02312, over 4817.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02957, over 971457.94 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:04:57,329 INFO [train.py:715] (2/8) Epoch 16, batch 22500, loss[loss=0.1398, simple_loss=0.2042, pruned_loss=0.03773, over 4850.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02905, over 972378.40 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 19:05:35,514 INFO [train.py:715] (2/8) Epoch 16, batch 22550, loss[loss=0.1457, simple_loss=0.2147, pruned_loss=0.03834, over 4851.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02897, over 973073.27 frames.], batch size: 30, lr: 1.37e-04
2022-05-08 19:06:13,252 INFO [train.py:715] (2/8) Epoch 16, batch 22600, loss[loss=0.1158, simple_loss=0.1765, pruned_loss=0.0276, over 4758.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.0291, over 972598.82 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 19:06:50,941 INFO [train.py:715] (2/8) Epoch 16, batch 22650, loss[loss=0.1088, simple_loss=0.1729, pruned_loss=0.0223, over 4844.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2064, pruned_loss=0.02938, over 972582.03 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:07:29,634 INFO [train.py:715] (2/8) Epoch 16, batch 22700, loss[loss=0.1132, simple_loss=0.1829, pruned_loss=0.02169, over 4961.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02936, over 972767.47 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:08:07,678 INFO [train.py:715] (2/8) Epoch 16, batch 22750, loss[loss=0.1431, simple_loss=0.2237, pruned_loss=0.03124, over 4901.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02912, over 973476.72 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:08:45,789 INFO [train.py:715] (2/8) Epoch 16, batch 22800, loss[loss=0.1324, simple_loss=0.2155, pruned_loss=0.02469, over 4948.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02941, over 973681.93 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:09:23,699 INFO [train.py:715] (2/8) Epoch 16, batch 22850, loss[loss=0.1422, simple_loss=0.2126, pruned_loss=0.03592, over 4942.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2085, pruned_loss=0.03007, over 973825.60 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:10:01,846 INFO [train.py:715] (2/8) Epoch 16, batch 22900, loss[loss=0.1111, simple_loss=0.1987, pruned_loss=0.01173, over 4754.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02982, over 973828.12 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:10:39,883 INFO [train.py:715] (2/8) Epoch 16, batch 22950, loss[loss=0.1463, simple_loss=0.2089, pruned_loss=0.04188, over 4813.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02998, over 972524.13 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 19:11:17,829 INFO [train.py:715] (2/8) Epoch 16, batch 23000, loss[loss=0.1548, simple_loss=0.2181, pruned_loss=0.04575, over 4844.00 frames.], tot_loss[loss=0.135, simple_loss=0.2089, pruned_loss=0.03057, over 972094.74 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:11:56,367 INFO [train.py:715] (2/8) Epoch 16, batch 23050, loss[loss=0.1387, simple_loss=0.2237, pruned_loss=0.02688, over 4815.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.03064, over 972773.25 frames.], batch size: 27, lr: 1.37e-04
2022-05-08 19:12:34,516 INFO [train.py:715] (2/8) Epoch 16, batch 23100, loss[loss=0.1419, simple_loss=0.2173, pruned_loss=0.03322, over 4965.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2085, pruned_loss=0.03091, over 973171.65 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 19:13:12,451 INFO [train.py:715] (2/8) Epoch 16, batch 23150, loss[loss=0.1587, simple_loss=0.2433, pruned_loss=0.03711, over 4981.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2089, pruned_loss=0.03069, over 973725.85 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:13:50,196 INFO [train.py:715] (2/8) Epoch 16, batch 23200, loss[loss=0.1192, simple_loss=0.1911, pruned_loss=0.02366, over 4925.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2075, pruned_loss=0.02975, over 972738.44 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:14:28,510 INFO [train.py:715] (2/8) Epoch 16, batch 23250, loss[loss=0.1459, simple_loss=0.2285, pruned_loss=0.03158, over 4776.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02976, over 972627.01 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:15:06,176 INFO [train.py:715] (2/8) Epoch 16, batch 23300, loss[loss=0.1275, simple_loss=0.2005, pruned_loss=0.0273, over 4922.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02973, over 972143.65 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:15:44,247 INFO [train.py:715] (2/8) Epoch 16, batch 23350, loss[loss=0.1568, simple_loss=0.2308, pruned_loss=0.04142, over 4794.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02946, over 972606.50 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:16:21,895 INFO [train.py:715] (2/8) Epoch 16, batch 23400, loss[loss=0.1665, simple_loss=0.2367, pruned_loss=0.04816, over 4950.00 frames.], tot_loss[loss=0.1341, simple_loss=0.208, pruned_loss=0.0301, over 972499.62 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:16:59,784 INFO [train.py:715] (2/8) Epoch 16, batch 23450, loss[loss=0.1205, simple_loss=0.1874, pruned_loss=0.02678, over 4777.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02995, over 972711.50 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 19:17:37,690 INFO [train.py:715] (2/8) Epoch 16, batch 23500, loss[loss=0.1151, simple_loss=0.1873, pruned_loss=0.02146, over 4766.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.0294, over 972130.75 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:18:15,674 INFO [train.py:715] (2/8) Epoch 16, batch 23550, loss[loss=0.1272, simple_loss=0.2054, pruned_loss=0.02452, over 4766.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02973, over 971921.76 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:18:54,223 INFO [train.py:715] (2/8) Epoch 16, batch 23600, loss[loss=0.1286, simple_loss=0.1981, pruned_loss=0.02958, over 4961.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02941, over 972511.57 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:19:31,588 INFO [train.py:715] (2/8) Epoch 16, batch 23650, loss[loss=0.1293, simple_loss=0.19, pruned_loss=0.03427, over 4799.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02893, over 972594.00 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:20:09,501 INFO [train.py:715] (2/8) Epoch 16, batch 23700, loss[loss=0.117, simple_loss=0.1931, pruned_loss=0.0205, over 4956.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02865, over 972883.06 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:20:47,877 INFO [train.py:715] (2/8) Epoch 16, batch 23750, loss[loss=0.1161, simple_loss=0.1876, pruned_loss=0.02235, over 4874.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02922, over 974045.54 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:21:25,951 INFO [train.py:715] (2/8) Epoch 16, batch 23800, loss[loss=0.1866, simple_loss=0.2516, pruned_loss=0.06084, over 4838.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02933, over 973900.80 frames.], batch size: 30, lr: 1.37e-04
2022-05-08 19:22:04,206 INFO [train.py:715] (2/8) Epoch 16, batch 23850, loss[loss=0.1444, simple_loss=0.2134, pruned_loss=0.03772, over 4949.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02929, over 973190.75 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 19:22:42,141 INFO [train.py:715] (2/8) Epoch 16, batch 23900, loss[loss=0.1575, simple_loss=0.2379, pruned_loss=0.03852, over 4745.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02922, over 972774.06 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:23:20,419 INFO [train.py:715] (2/8) Epoch 16, batch 23950, loss[loss=0.1645, simple_loss=0.2402, pruned_loss=0.04439, over 4962.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02914, over 973428.18 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 19:23:57,817 INFO [train.py:715] (2/8) Epoch 16, batch 24000, loss[loss=0.1364, simple_loss=0.2025, pruned_loss=0.03512, over 4799.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.0292, over 973321.61 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:23:57,817 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 19:24:07,635 INFO [train.py:742] (2/8) Epoch 16, validation: loss=0.1049, simple_loss=0.1883, pruned_loss=0.01074, over 914524.00 frames.
2022-05-08 19:24:46,405 INFO [train.py:715] (2/8) Epoch 16, batch 24050, loss[loss=0.1334, simple_loss=0.2111, pruned_loss=0.02789, over 4741.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02919, over 972859.08 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:25:24,730 INFO [train.py:715] (2/8) Epoch 16, batch 24100, loss[loss=0.1384, simple_loss=0.2148, pruned_loss=0.03104, over 4793.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02917, over 972280.64 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:26:03,114 INFO [train.py:715] (2/8) Epoch 16, batch 24150, loss[loss=0.1144, simple_loss=0.1907, pruned_loss=0.01904, over 4766.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02854, over 972011.24 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:26:40,869 INFO [train.py:715] (2/8) Epoch 16, batch 24200, loss[loss=0.1385, simple_loss=0.2019, pruned_loss=0.0376, over 4837.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02866, over 972418.95 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:27:19,230 INFO [train.py:715] (2/8) Epoch 16, batch 24250, loss[loss=0.1231, simple_loss=0.199, pruned_loss=0.0236, over 4837.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02863, over 972314.89 frames.], batch size: 27, lr: 1.37e-04
2022-05-08 19:27:57,173 INFO [train.py:715] (2/8) Epoch 16, batch 24300, loss[loss=0.1515, simple_loss=0.2234, pruned_loss=0.03976, over 4963.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02905, over 972122.11 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:28:35,671 INFO [train.py:715] (2/8) Epoch 16, batch 24350, loss[loss=0.1345, simple_loss=0.2144, pruned_loss=0.02731, over 4836.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02946, over 972987.33 frames.], batch size: 30, lr: 1.37e-04
2022-05-08 19:29:13,224 INFO [train.py:715] (2/8) Epoch 16, batch 24400, loss[loss=0.1256, simple_loss=0.1896, pruned_loss=0.03078, over 4889.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.0297, over 972634.14 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:29:50,783 INFO [train.py:715] (2/8) Epoch 16, batch 24450, loss[loss=0.1827, simple_loss=0.2579, pruned_loss=0.05378, over 4914.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02955, over 972749.58 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:30:28,699 INFO [train.py:715] (2/8) Epoch 16, batch 24500, loss[loss=0.1462, simple_loss=0.2074, pruned_loss=0.04245, over 4824.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2063, pruned_loss=0.02921, over 972885.90 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 19:31:06,550 INFO [train.py:715] (2/8) Epoch 16, batch 24550, loss[loss=0.1457, simple_loss=0.2294, pruned_loss=0.03101, over 4697.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2067, pruned_loss=0.02949, over 972132.14 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:31:43,993 INFO [train.py:715] (2/8) Epoch 16, batch 24600, loss[loss=0.1371, simple_loss=0.2127, pruned_loss=0.03081, over 4891.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02949, over 971645.36 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:32:21,348 INFO [train.py:715] (2/8) Epoch 16, batch 24650, loss[loss=0.1398, simple_loss=0.2089, pruned_loss=0.03533, over 4767.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02948, over 972526.05 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:32:59,495 INFO [train.py:715] (2/8) Epoch 16, batch 24700, loss[loss=0.1374, simple_loss=0.2144, pruned_loss=0.0302, over 4940.00 frames.], tot_loss[loss=0.1336, simple_loss=0.207, pruned_loss=0.03006, over 972688.87 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:33:37,067 INFO [train.py:715] (2/8) Epoch 16, batch 24750, loss[loss=0.1477, simple_loss=0.2265, pruned_loss=0.03444, over 4818.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02986, over 971914.09 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 19:34:14,867 INFO [train.py:715] (2/8) Epoch 16, batch 24800, loss[loss=0.1457, simple_loss=0.2203, pruned_loss=0.03558, over 4981.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02962, over 972881.11 frames.], batch size: 31, lr: 1.37e-04
2022-05-08 19:34:52,612 INFO [train.py:715] (2/8) Epoch 16, batch 24850, loss[loss=0.1334, simple_loss=0.2085, pruned_loss=0.02918, over 4944.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02953, over 973103.23 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:35:30,368 INFO [train.py:715] (2/8) Epoch 16, batch 24900, loss[loss=0.1553, simple_loss=0.229, pruned_loss=0.04075, over 4900.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02968, over 973580.34 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:36:08,066 INFO [train.py:715] (2/8) Epoch 16, batch 24950, loss[loss=0.1258, simple_loss=0.2046, pruned_loss=0.02349, over 4774.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02949, over 973396.50 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 19:36:45,502 INFO [train.py:715] (2/8) Epoch 16, batch 25000, loss[loss=0.1472, simple_loss=0.2184, pruned_loss=0.03794, over 4974.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02933, over 973460.23 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 19:37:23,736 INFO [train.py:715] (2/8) Epoch 16, batch 25050, loss[loss=0.1486, simple_loss=0.2146, pruned_loss=0.04126, over 4891.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.0291, over 973927.46 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:38:02,501 INFO [train.py:715] (2/8) Epoch 16, batch 25100, loss[loss=0.1054, simple_loss=0.173, pruned_loss=0.01884, over 4771.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02949, over 972999.99 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 19:38:40,221 INFO [train.py:715] (2/8) Epoch 16, batch 25150, loss[loss=0.1255, simple_loss=0.1977, pruned_loss=0.02664, over 4865.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2058, pruned_loss=0.02903, over 972375.12 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 19:39:18,059 INFO [train.py:715] (2/8) Epoch 16, batch 25200, loss[loss=0.1318, simple_loss=0.2009, pruned_loss=0.03133, over 4820.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2056, pruned_loss=0.02893, over 972721.24 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:39:56,034 INFO [train.py:715] (2/8) Epoch 16, batch 25250, loss[loss=0.127, simple_loss=0.1986, pruned_loss=0.0277, over 4808.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2056, pruned_loss=0.02902, over 972390.76 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 19:40:33,645 INFO [train.py:715] (2/8) Epoch 16, batch 25300, loss[loss=0.1255, simple_loss=0.1969, pruned_loss=0.02708, over 4920.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02895, over 972881.91 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:41:10,910 INFO [train.py:715] (2/8) Epoch 16, batch 25350, loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02853, over 4860.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02924, over 971580.44 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 19:41:49,014 INFO [train.py:715] (2/8) Epoch 16, batch 25400, loss[loss=0.1512, simple_loss=0.2377, pruned_loss=0.03236, over 4773.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02926, over 971200.59 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:42:27,348 INFO [train.py:715] (2/8) Epoch 16, batch 25450, loss[loss=0.141, simple_loss=0.2235, pruned_loss=0.02919, over 4882.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2074, pruned_loss=0.02893, over 972145.77 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:43:04,843 INFO [train.py:715] (2/8) Epoch 16, batch 25500, loss[loss=0.1624, simple_loss=0.2388, pruned_loss=0.043, over 4968.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02866, over 972904.80 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:43:42,832 INFO [train.py:715] (2/8) Epoch 16, batch 25550, loss[loss=0.1289, simple_loss=0.2036, pruned_loss=0.02713, over 4986.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02901, over 973860.77 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 19:44:21,342 INFO [train.py:715] (2/8) Epoch 16, batch 25600, loss[loss=0.1249, simple_loss=0.1977, pruned_loss=0.02607, over 4965.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02937, over 973954.11 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:45:00,127 INFO [train.py:715] (2/8) Epoch 16, batch 25650, loss[loss=0.1276, simple_loss=0.2079, pruned_loss=0.02363, over 4959.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2079, pruned_loss=0.02936, over 973819.66 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:45:38,353 INFO [train.py:715] (2/8) Epoch 16, batch 25700, loss[loss=0.115, simple_loss=0.1864, pruned_loss=0.02182, over 4911.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02877, over 973719.32 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 19:46:16,990 INFO [train.py:715] (2/8) Epoch 16, batch 25750, loss[loss=0.1201, simple_loss=0.2059, pruned_loss=0.01722, over 4947.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02917, over 974215.63 frames.], batch size: 29, lr: 1.37e-04
2022-05-08 19:46:55,641 INFO [train.py:715] (2/8) Epoch 16, batch 25800, loss[loss=0.1088, simple_loss=0.1732, pruned_loss=0.02219, over 4881.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02903, over 973379.65 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 19:47:34,245 INFO [train.py:715] (2/8) Epoch 16, batch 25850, loss[loss=0.1317, simple_loss=0.2141, pruned_loss=0.02462, over 4907.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2071, pruned_loss=0.02875, over 973995.15 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 19:48:13,061 INFO [train.py:715] (2/8) Epoch 16, batch 25900, loss[loss=0.1345, simple_loss=0.1996, pruned_loss=0.03475, over 4831.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02889, over 973488.89 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:48:52,470 INFO [train.py:715] (2/8) Epoch 16, batch 25950, loss[loss=0.1664, simple_loss=0.2284, pruned_loss=0.05223, over 4860.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.0294, over 972823.01 frames.], batch size: 32, lr: 1.37e-04
2022-05-08 19:49:32,204 INFO [train.py:715] (2/8) Epoch 16, batch 26000, loss[loss=0.115, simple_loss=0.1888, pruned_loss=0.02058, over 4778.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02959, over 972007.13 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:50:11,556 INFO [train.py:715] (2/8) Epoch 16, batch 26050, loss[loss=0.1329, simple_loss=0.1973, pruned_loss=0.03424, over 4859.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02937, over 971157.97 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 19:50:50,796 INFO [train.py:715] (2/8) Epoch 16, batch 26100, loss[loss=0.1333, simple_loss=0.2049, pruned_loss=0.03081, over 4820.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02938, over 972076.10 frames.], batch size: 30, lr: 1.37e-04
2022-05-08 19:51:30,062 INFO [train.py:715] (2/8) Epoch 16, batch 26150, loss[loss=0.1188, simple_loss=0.1954, pruned_loss=0.02117, over 4928.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02899, over 971180.70 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:52:08,699 INFO [train.py:715] (2/8) Epoch 16, batch 26200, loss[loss=0.1253, simple_loss=0.1947, pruned_loss=0.02792, over 4975.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02871, over 971074.62 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 19:52:48,170 INFO [train.py:715] (2/8) Epoch 16, batch 26250, loss[loss=0.1355, simple_loss=0.2106, pruned_loss=0.0302, over 4771.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02891, over 971752.11 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:53:27,329 INFO [train.py:715] (2/8) Epoch 16, batch 26300, loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02925, over 4968.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02909, over 971975.14 frames.], batch size: 39, lr: 1.37e-04
2022-05-08 19:54:06,984 INFO [train.py:715] (2/8) Epoch 16, batch 26350, loss[loss=0.1093, simple_loss=0.1802, pruned_loss=0.01923, over 4946.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02924, over 972822.99 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:54:46,299 INFO [train.py:715] (2/8) Epoch 16, batch 26400, loss[loss=0.1227, simple_loss=0.2027, pruned_loss=0.02129, over 4979.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.0288, over 972441.35 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 19:55:26,182 INFO [train.py:715] (2/8) Epoch 16, batch 26450, loss[loss=0.127, simple_loss=0.1996, pruned_loss=0.02723, over 4833.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.0287, over 972533.97 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 19:56:05,125 INFO [train.py:715] (2/8) Epoch 16, batch 26500, loss[loss=0.119, simple_loss=0.1969, pruned_loss=0.02058, over 4968.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02836, over 973223.96 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 19:56:44,032 INFO [train.py:715] (2/8) Epoch 16, batch 26550, loss[loss=0.1399, simple_loss=0.2193, pruned_loss=0.03025, over 4968.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02821, over 972412.25 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 19:57:23,102 INFO [train.py:715] (2/8) Epoch 16, batch 26600, loss[loss=0.1354, simple_loss=0.2114, pruned_loss=0.02971, over 4807.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02856, over 973300.14 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:58:02,095 INFO [train.py:715] (2/8) Epoch 16, batch 26650, loss[loss=0.1723, simple_loss=0.2414, pruned_loss=0.05158, over 4967.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2055, pruned_loss=0.02862, over 973229.85 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 19:58:41,425 INFO [train.py:715] (2/8) Epoch 16, batch 26700, loss[loss=0.1137, simple_loss=0.1956, pruned_loss=0.01592, over 4884.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.02928, over 973328.63 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 19:59:20,663 INFO [train.py:715] (2/8) Epoch 16, batch 26750, loss[loss=0.128, simple_loss=0.2048, pruned_loss=0.02561, over 4795.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02943, over 972618.79 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 20:00:00,492 INFO [train.py:715] (2/8) Epoch 16, batch 26800, loss[loss=0.162, simple_loss=0.2457, pruned_loss=0.03913, over 4798.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02919, over 972332.47 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 20:00:39,343 INFO [train.py:715] (2/8) Epoch 16, batch 26850, loss[loss=0.1259, simple_loss=0.2014, pruned_loss=0.02524, over 4811.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.02929, over 972590.28 frames.], batch size: 27, lr: 1.37e-04
2022-05-08 20:01:18,825 INFO [train.py:715] (2/8) Epoch 16, batch 26900, loss[loss=0.1127, simple_loss=0.1913, pruned_loss=0.01705, over 4820.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2062, pruned_loss=0.02923, over 972510.29 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 20:01:58,324 INFO [train.py:715] (2/8) Epoch 16, batch 26950, loss[loss=0.18, simple_loss=0.2381, pruned_loss=0.06093, over 4903.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.0295, over 972674.07 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 20:02:37,506 INFO [train.py:715] (2/8) Epoch 16, batch 27000, loss[loss=0.14, simple_loss=0.2052, pruned_loss=0.03737, over 4791.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02981, over 972142.89 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 20:02:37,507 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 20:02:47,200 INFO [train.py:742] (2/8) Epoch 16, validation: loss=0.1048, simple_loss=0.1883, pruned_loss=0.01067, over 914524.00 frames.
2022-05-08 20:03:26,298 INFO [train.py:715] (2/8) Epoch 16, batch 27050, loss[loss=0.1326, simple_loss=0.1972, pruned_loss=0.03404, over 4803.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02979, over 972204.83 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 20:04:08,233 INFO [train.py:715] (2/8) Epoch 16, batch 27100, loss[loss=0.1411, simple_loss=0.217, pruned_loss=0.03254, over 4865.00 frames.], tot_loss[loss=0.133, simple_loss=0.2068, pruned_loss=0.0296, over 971939.92 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 20:04:47,172 INFO [train.py:715] (2/8) Epoch 16, batch 27150, loss[loss=0.1219, simple_loss=0.1918, pruned_loss=0.02598, over 4969.00 frames.], tot_loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02983, over 973126.70 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 20:05:26,602 INFO [train.py:715] (2/8) Epoch 16, batch 27200, loss[loss=0.1159, simple_loss=0.1834, pruned_loss=0.02416, over 4904.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2077, pruned_loss=0.03028, over 972634.46 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 20:06:05,791 INFO [train.py:715] (2/8) Epoch 16, batch 27250, loss[loss=0.1466, simple_loss=0.2354, pruned_loss=0.0289, over 4865.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02973, over 972939.07 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 20:06:45,181 INFO [train.py:715] (2/8) Epoch 16, batch 27300, loss[loss=0.1163, simple_loss=0.1845, pruned_loss=0.0241, over 4934.00 frames.], tot_loss[loss=0.134, simple_loss=0.2079, pruned_loss=0.03008, over 973344.38 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 20:07:24,244 INFO [train.py:715] (2/8) Epoch 16, batch 27350, loss[loss=0.1157, simple_loss=0.1792, pruned_loss=0.02613, over 4795.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02951, over 972781.47 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 20:08:03,612 INFO [train.py:715] (2/8) Epoch 16, batch 27400, loss[loss=0.1463, simple_loss=0.2273, pruned_loss=0.03263, over 4958.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02969, over 973542.08 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 20:08:42,901 INFO [train.py:715] (2/8) Epoch 16, batch 27450, loss[loss=0.115, simple_loss=0.1955, pruned_loss=0.01725, over 4829.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02944, over 973127.21 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 20:09:21,911 INFO [train.py:715] (2/8) Epoch 16, batch 27500, loss[loss=0.1564, simple_loss=0.2383, pruned_loss=0.03719, over 4869.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02931, over 973147.65 frames.], batch size: 16, lr: 1.37e-04
2022-05-08 20:10:01,272 INFO [train.py:715] (2/8) Epoch 16, batch 27550, loss[loss=0.1177, simple_loss=0.1874, pruned_loss=0.02399, over 4836.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02948, over 973331.68 frames.], batch size: 13, lr: 1.37e-04
2022-05-08 20:10:41,120 INFO [train.py:715] (2/8) Epoch 16, batch 27600, loss[loss=0.129, simple_loss=0.2102, pruned_loss=0.0239, over 4896.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02973, over 973362.88 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 20:11:20,147 INFO [train.py:715] (2/8) Epoch 16, batch 27650, loss[loss=0.1142, simple_loss=0.177, pruned_loss=0.02571, over 4828.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2073, pruned_loss=0.0299, over 972091.43 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 20:11:59,673 INFO [train.py:715] (2/8) Epoch 16, batch 27700, loss[loss=0.1555, simple_loss=0.2295, pruned_loss=0.04068, over 4833.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.03, over 971842.47 frames.], batch size: 26, lr: 1.37e-04
2022-05-08 20:12:38,976 INFO [train.py:715] (2/8) Epoch 16, batch 27750, loss[loss=0.1132, simple_loss=0.183, pruned_loss=0.02171, over 4969.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02975, over 972431.56 frames.], batch size: 24, lr: 1.37e-04
2022-05-08 20:13:18,197 INFO [train.py:715] (2/8) Epoch 16, batch 27800, loss[loss=0.1467, simple_loss=0.2316, pruned_loss=0.0309, over 4940.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.0292, over 972345.62 frames.], batch size: 23, lr: 1.37e-04
2022-05-08 20:13:57,555 INFO [train.py:715] (2/8) Epoch 16, batch 27850, loss[loss=0.1136, simple_loss=0.1867, pruned_loss=0.02025, over 4923.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02911, over 972927.97 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 20:14:36,984 INFO [train.py:715] (2/8) Epoch 16, batch 27900, loss[loss=0.1386, simple_loss=0.2161, pruned_loss=0.03051, over 4974.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02949, over 971851.89 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 20:15:16,664 INFO [train.py:715] (2/8) Epoch 16, batch 27950, loss[loss=0.1704, simple_loss=0.2439, pruned_loss=0.04843, over 4886.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02978, over 972161.36 frames.], batch size: 20, lr: 1.37e-04
2022-05-08 20:15:55,976 INFO [train.py:715] (2/8) Epoch 16, batch 28000, loss[loss=0.1275, simple_loss=0.1961, pruned_loss=0.02939, over 4935.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2081, pruned_loss=0.0304, over 972433.10 frames.], batch size: 35, lr: 1.37e-04
2022-05-08 20:16:35,540 INFO [train.py:715] (2/8) Epoch 16, batch 28050, loss[loss=0.1419, simple_loss=0.2117, pruned_loss=0.03605, over 4982.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2086, pruned_loss=0.0305, over 972810.84 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 20:17:15,206 INFO [train.py:715] (2/8) Epoch 16, batch 28100, loss[loss=0.1042, simple_loss=0.1858, pruned_loss=0.01132, over 4774.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03005, over 972092.05 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 20:17:54,190 INFO [train.py:715] (2/8) Epoch 16, batch 28150, loss[loss=0.1307, simple_loss=0.2176, pruned_loss=0.02193, over 4957.00 frames.], tot_loss[loss=0.1348, simple_loss=0.209, pruned_loss=0.03029, over 972755.55 frames.], batch size: 21, lr: 1.37e-04
2022-05-08 20:18:33,946 INFO [train.py:715] (2/8) Epoch 16, batch 28200, loss[loss=0.1774, simple_loss=0.2393, pruned_loss=0.05771, over 4795.00 frames.], tot_loss[loss=0.135, simple_loss=0.209, pruned_loss=0.03052, over 972386.00 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 20:19:13,276 INFO [train.py:715] (2/8) Epoch 16, batch 28250, loss[loss=0.1213, simple_loss=0.1834, pruned_loss=0.02958, over 4768.00 frames.], tot_loss[loss=0.1351, simple_loss=0.209, pruned_loss=0.03059, over 972785.52 frames.], batch size: 14, lr: 1.37e-04
2022-05-08 20:19:51,893 INFO [train.py:715] (2/8) Epoch 16, batch 28300, loss[loss=0.1134, simple_loss=0.1903, pruned_loss=0.01827, over 4754.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2086, pruned_loss=0.03023, over 972330.79 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 20:20:31,605 INFO [train.py:715] (2/8) Epoch 16, batch 28350, loss[loss=0.1412, simple_loss=0.2152, pruned_loss=0.03356, over 4885.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02987, over 972737.88 frames.], batch size: 19, lr: 1.37e-04
2022-05-08 20:21:11,562 INFO [train.py:715] (2/8) Epoch 16, batch 28400, loss[loss=0.1232, simple_loss=0.1925, pruned_loss=0.02694, over 4780.00 frames.], tot_loss[loss=0.134, simple_loss=0.2083, pruned_loss=0.02988, over 972648.04 frames.], batch size: 17, lr: 1.37e-04
2022-05-08 20:21:51,022 INFO [train.py:715] (2/8) Epoch 16, batch 28450, loss[loss=0.1186, simple_loss=0.1984, pruned_loss=0.01944, over 4980.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.0298, over 973000.35 frames.], batch size: 28, lr: 1.37e-04
2022-05-08 20:22:29,724 INFO [train.py:715] (2/8) Epoch 16, batch 28500, loss[loss=0.1549, simple_loss=0.2197, pruned_loss=0.04509, over 4873.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.03001, over 972710.77 frames.], batch size: 22, lr: 1.37e-04
2022-05-08 20:23:09,887 INFO [train.py:715] (2/8) Epoch 16, batch 28550, loss[loss=0.1137, simple_loss=0.1874, pruned_loss=0.01997, over 4720.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2075, pruned_loss=0.03029, over 972021.02 frames.], batch size: 12, lr: 1.37e-04
2022-05-08 20:23:49,357 INFO [train.py:715] (2/8) Epoch 16, batch 28600, loss[loss=0.1204, simple_loss=0.1886, pruned_loss=0.02612, over 4793.00 frames.], tot_loss[loss=0.1346, simple_loss=0.208, pruned_loss=0.03062, over 972764.96 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 20:24:28,946 INFO [train.py:715] (2/8) Epoch 16, batch 28650, loss[loss=0.1473, simple_loss=0.2162, pruned_loss=0.03923, over 4689.00 frames.], tot_loss[loss=0.1353, simple_loss=0.2088, pruned_loss=0.03085, over 972803.99 frames.], batch size: 15, lr: 1.37e-04
2022-05-08 20:25:08,099 INFO [train.py:715] (2/8) Epoch 16, batch 28700, loss[loss=0.1127, simple_loss=0.1979, pruned_loss=0.01375, over 4825.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2089, pruned_loss=0.03046, over 972535.66 frames.], batch size: 25, lr: 1.37e-04
2022-05-08 20:25:47,662 INFO [train.py:715] (2/8) Epoch 16, batch 28750, loss[loss=0.1056, simple_loss=0.1755, pruned_loss=0.01784, over 4908.00 frames.], tot_loss[loss=0.1348, simple_loss=0.2083, pruned_loss=0.03061, over 972604.88 frames.], batch size: 18, lr: 1.37e-04
2022-05-08 20:26:27,375 INFO [train.py:715] (2/8) Epoch 16, batch 28800, loss[loss=0.1238, simple_loss=0.2007, pruned_loss=0.02347, over 4689.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03044, over 971555.91 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:27:06,538 INFO [train.py:715] (2/8) Epoch 16, batch 28850, loss[loss=0.1357, simple_loss=0.1982, pruned_loss=0.03661, over 4980.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2084, pruned_loss=0.03048, over 971696.05 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 20:27:46,357 INFO [train.py:715] (2/8) Epoch 16, batch 28900, loss[loss=0.1148, simple_loss=0.1913, pruned_loss=0.01919, over 4781.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2082, pruned_loss=0.0301, over 971555.34 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 20:28:25,935 INFO [train.py:715] (2/8) Epoch 16, batch 28950, loss[loss=0.1439, simple_loss=0.2182, pruned_loss=0.03484, over 4945.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02963, over 970984.33 frames.], batch size: 29, lr: 1.36e-04
2022-05-08 20:29:05,872 INFO [train.py:715] (2/8) Epoch 16, batch 29000, loss[loss=0.1235, simple_loss=0.1991, pruned_loss=0.02399, over 4959.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02919, over 971719.16 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 20:29:45,341 INFO [train.py:715] (2/8) Epoch 16, batch 29050, loss[loss=0.1202, simple_loss=0.1981, pruned_loss=0.02112, over 4927.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02929, over 971194.77 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 20:30:25,180 INFO [train.py:715] (2/8) Epoch 16, batch 29100, loss[loss=0.1369, simple_loss=0.2218, pruned_loss=0.02604, over 4789.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2071, pruned_loss=0.02899, over 971359.20 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:31:06,235 INFO [train.py:715] (2/8) Epoch 16, batch 29150, loss[loss=0.1441, simple_loss=0.2272, pruned_loss=0.0305, over 4948.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02885, over 972111.38 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 20:31:46,274 INFO [train.py:715] (2/8) Epoch 16, batch 29200, loss[loss=0.1254, simple_loss=0.2024, pruned_loss=0.02417, over 4788.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02917, over 971578.95 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 20:32:27,440 INFO [train.py:715] (2/8) Epoch 16, batch 29250, loss[loss=0.123, simple_loss=0.2031, pruned_loss=0.02144, over 4800.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02916, over 972285.51 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 20:33:08,438 INFO [train.py:715] (2/8) Epoch 16, batch 29300, loss[loss=0.1198, simple_loss=0.1995, pruned_loss=0.02006, over 4936.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02916, over 972320.42 frames.], batch size: 29, lr: 1.36e-04
2022-05-08 20:33:49,834 INFO [train.py:715] (2/8) Epoch 16, batch 29350, loss[loss=0.1269, simple_loss=0.2088, pruned_loss=0.02252, over 4791.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.029, over 972846.20 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:34:30,963 INFO [train.py:715] (2/8) Epoch 16, batch 29400, loss[loss=0.1148, simple_loss=0.187, pruned_loss=0.02132, over 4794.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2078, pruned_loss=0.02902, over 972250.27 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 20:35:12,723 INFO [train.py:715] (2/8) Epoch 16, batch 29450, loss[loss=0.1152, simple_loss=0.1927, pruned_loss=0.01885, over 4909.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02901, over 971918.54 frames.], batch size: 29, lr: 1.36e-04
2022-05-08 20:35:54,213 INFO [train.py:715] (2/8) Epoch 16, batch 29500, loss[loss=0.1424, simple_loss=0.215, pruned_loss=0.0349, over 4806.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02901, over 971841.41 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 20:36:36,033 INFO [train.py:715] (2/8) Epoch 16, batch 29550, loss[loss=0.09659, simple_loss=0.1742, pruned_loss=0.009498, over 4810.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02907, over 971333.88 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 20:37:17,260 INFO [train.py:715] (2/8) Epoch 16, batch 29600, loss[loss=0.1213, simple_loss=0.194, pruned_loss=0.02433, over 4981.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02911, over 971345.44 frames.], batch size: 28, lr: 1.36e-04
2022-05-08 20:37:59,043 INFO [train.py:715] (2/8) Epoch 16, batch 29650, loss[loss=0.1231, simple_loss=0.1971, pruned_loss=0.02457, over 4909.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02918, over 972038.01 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:38:40,540 INFO [train.py:715] (2/8) Epoch 16, batch 29700, loss[loss=0.1294, simple_loss=0.1957, pruned_loss=0.03151, over 4863.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02954, over 971947.01 frames.], batch size: 20, lr: 1.36e-04
2022-05-08 20:39:21,784 INFO [train.py:715] (2/8) Epoch 16, batch 29750, loss[loss=0.1345, simple_loss=0.2063, pruned_loss=0.03133, over 4700.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02929, over 971352.46 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:40:02,889 INFO [train.py:715] (2/8) Epoch 16, batch 29800, loss[loss=0.1567, simple_loss=0.2249, pruned_loss=0.04425, over 4989.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02934, over 972203.62 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:40:44,769 INFO [train.py:715] (2/8) Epoch 16, batch 29850, loss[loss=0.1138, simple_loss=0.1851, pruned_loss=0.02128, over 4846.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02924, over 971741.06 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 20:41:26,314 INFO [train.py:715] (2/8) Epoch 16, batch 29900, loss[loss=0.1219, simple_loss=0.1975, pruned_loss=0.02312, over 4774.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02957, over 971100.51 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 20:42:07,624 INFO [train.py:715] (2/8) Epoch 16, batch 29950, loss[loss=0.131, simple_loss=0.1913, pruned_loss=0.03536, over 4860.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.0292, over 971418.00 frames.], batch size: 30, lr: 1.36e-04
2022-05-08 20:42:50,224 INFO [train.py:715] (2/8) Epoch 16, batch 30000, loss[loss=0.1542, simple_loss=0.2179, pruned_loss=0.0452, over 4984.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02942, over 971194.37 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 20:42:50,225 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 20:43:01,794 INFO [train.py:742] (2/8) Epoch 16, validation: loss=0.1047, simple_loss=0.1883, pruned_loss=0.01058, over 914524.00 frames.
2022-05-08 20:43:44,296 INFO [train.py:715] (2/8) Epoch 16, batch 30050, loss[loss=0.1404, simple_loss=0.21, pruned_loss=0.03539, over 4774.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02919, over 971682.99 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 20:44:26,141 INFO [train.py:715] (2/8) Epoch 16, batch 30100, loss[loss=0.1287, simple_loss=0.2059, pruned_loss=0.02575, over 4938.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2074, pruned_loss=0.02887, over 972461.96 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 20:45:06,925 INFO [train.py:715] (2/8) Epoch 16, batch 30150, loss[loss=0.1079, simple_loss=0.1842, pruned_loss=0.01581, over 4823.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02878, over 972581.01 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 20:45:48,616 INFO [train.py:715] (2/8) Epoch 16, batch 30200, loss[loss=0.1563, simple_loss=0.233, pruned_loss=0.03973, over 4689.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02906, over 972507.56 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:46:29,882 INFO [train.py:715] (2/8) Epoch 16, batch 30250, loss[loss=0.1649, simple_loss=0.228, pruned_loss=0.05089, over 4975.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02968, over 972155.08 frames.], batch size: 35, lr: 1.36e-04
2022-05-08 20:47:09,984 INFO [train.py:715] (2/8) Epoch 16, batch 30300, loss[loss=0.1476, simple_loss=0.2179, pruned_loss=0.03869, over 4775.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02964, over 972131.46 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 20:47:50,184 INFO [train.py:715] (2/8) Epoch 16, batch 30350, loss[loss=0.1305, simple_loss=0.1978, pruned_loss=0.03164, over 4850.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02968, over 971515.60 frames.], batch size: 20, lr: 1.36e-04
2022-05-08 20:48:30,610 INFO [train.py:715] (2/8) Epoch 16, batch 30400, loss[loss=0.13, simple_loss=0.1975, pruned_loss=0.03119, over 4874.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02939, over 971629.66 frames.], batch size: 22, lr: 1.36e-04
2022-05-08 20:49:10,251 INFO [train.py:715] (2/8) Epoch 16, batch 30450, loss[loss=0.1537, simple_loss=0.2369, pruned_loss=0.03523, over 4838.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02931, over 972349.78 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:49:49,410 INFO [train.py:715] (2/8) Epoch 16, batch 30500, loss[loss=0.1315, simple_loss=0.2017, pruned_loss=0.03069, over 4971.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02908, over 972606.09 frames.], batch size: 28, lr: 1.36e-04
2022-05-08 20:50:29,337 INFO [train.py:715] (2/8) Epoch 16, batch 30550, loss[loss=0.1284, simple_loss=0.2154, pruned_loss=0.02073, over 4978.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2083, pruned_loss=0.02957, over 971406.45 frames.], batch size: 28, lr: 1.36e-04
2022-05-08 20:51:09,799 INFO [train.py:715] (2/8) Epoch 16, batch 30600, loss[loss=0.157, simple_loss=0.2294, pruned_loss=0.04226, over 4778.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02928, over 970907.45 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 20:51:49,045 INFO [train.py:715] (2/8) Epoch 16, batch 30650, loss[loss=0.1357, simple_loss=0.2036, pruned_loss=0.03389, over 4957.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02903, over 971632.45 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 20:52:28,806 INFO [train.py:715] (2/8) Epoch 16, batch 30700, loss[loss=0.1242, simple_loss=0.2007, pruned_loss=0.02384, over 4927.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02888, over 971605.28 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 20:53:10,031 INFO [train.py:715] (2/8) Epoch 16, batch 30750, loss[loss=0.1302, simple_loss=0.2138, pruned_loss=0.02333, over 4946.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02856, over 972057.60 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 20:53:49,621 INFO [train.py:715] (2/8) Epoch 16, batch 30800, loss[loss=0.1436, simple_loss=0.2259, pruned_loss=0.03063, over 4879.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02877, over 971485.35 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 20:54:28,449 INFO [train.py:715] (2/8) Epoch 16, batch 30850, loss[loss=0.1512, simple_loss=0.2221, pruned_loss=0.04015, over 4695.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02925, over 971423.08 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 20:55:08,443 INFO [train.py:715] (2/8) Epoch 16, batch 30900, loss[loss=0.1883, simple_loss=0.2432, pruned_loss=0.06667, over 4778.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02939, over 973376.45 frames.], batch size: 12, lr: 1.36e-04
2022-05-08 20:55:47,874 INFO [train.py:715] (2/8) Epoch 16, batch 30950, loss[loss=0.1213, simple_loss=0.2011, pruned_loss=0.02072, over 4830.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02986, over 972750.99 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 20:56:26,903 INFO [train.py:715] (2/8) Epoch 16, batch 31000, loss[loss=0.1362, simple_loss=0.2053, pruned_loss=0.03353, over 4986.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02954, over 972605.67 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 20:57:06,084 INFO [train.py:715] (2/8) Epoch 16, batch 31050, loss[loss=0.1292, simple_loss=0.2051, pruned_loss=0.02661, over 4832.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02954, over 972703.90 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 20:57:45,839 INFO [train.py:715] (2/8) Epoch 16, batch 31100, loss[loss=0.1325, simple_loss=0.2046, pruned_loss=0.03018, over 4888.00 frames.], tot_loss[loss=0.134, simple_loss=0.2077, pruned_loss=0.03011, over 972225.95 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 20:58:25,690 INFO [train.py:715] (2/8) Epoch 16, batch 31150, loss[loss=0.1833, simple_loss=0.2621, pruned_loss=0.05225, over 4813.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2087, pruned_loss=0.03018, over 972622.91 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 20:59:04,395 INFO [train.py:715] (2/8) Epoch 16, batch 31200, loss[loss=0.1399, simple_loss=0.2205, pruned_loss=0.02968, over 4953.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2086, pruned_loss=0.02996, over 973024.53 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 20:59:44,071 INFO [train.py:715] (2/8) Epoch 16, batch 31250, loss[loss=0.1479, simple_loss=0.225, pruned_loss=0.03544, over 4967.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2082, pruned_loss=0.02945, over 972710.90 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 21:00:23,614 INFO [train.py:715] (2/8) Epoch 16, batch 31300, loss[loss=0.1188, simple_loss=0.1977, pruned_loss=0.02001, over 4806.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02885, over 972812.09 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:01:03,208 INFO [train.py:715] (2/8) Epoch 16, batch 31350, loss[loss=0.1079, simple_loss=0.185, pruned_loss=0.01537, over 4926.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02872, over 973318.03 frames.], batch size: 29, lr: 1.36e-04
2022-05-08 21:01:42,655 INFO [train.py:715] (2/8) Epoch 16, batch 31400, loss[loss=0.1083, simple_loss=0.1865, pruned_loss=0.01501, over 4961.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.0291, over 972933.48 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 21:02:22,720 INFO [train.py:715] (2/8) Epoch 16, batch 31450, loss[loss=0.1639, simple_loss=0.2361, pruned_loss=0.04588, over 4913.00 frames.], tot_loss[loss=0.1334, simple_loss=0.208, pruned_loss=0.02945, over 973362.13 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 21:03:01,699 INFO [train.py:715] (2/8) Epoch 16, batch 31500, loss[loss=0.1318, simple_loss=0.2076, pruned_loss=0.02795, over 4881.00 frames.], tot_loss[loss=0.134, simple_loss=0.2087, pruned_loss=0.02965, over 972338.10 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:03:40,541 INFO [train.py:715] (2/8) Epoch 16, batch 31550, loss[loss=0.1115, simple_loss=0.1895, pruned_loss=0.01671, over 4941.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2083, pruned_loss=0.02951, over 971850.04 frames.], batch size: 29, lr: 1.36e-04
2022-05-08 21:04:19,806 INFO [train.py:715] (2/8) Epoch 16, batch 31600, loss[loss=0.1332, simple_loss=0.214, pruned_loss=0.02622, over 4749.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.02964, over 972112.18 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:04:58,919 INFO [train.py:715] (2/8) Epoch 16, batch 31650, loss[loss=0.147, simple_loss=0.2289, pruned_loss=0.03256, over 4858.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2088, pruned_loss=0.02992, over 972594.55 frames.], batch size: 32, lr: 1.36e-04
2022-05-08 21:05:37,856 INFO [train.py:715] (2/8) Epoch 16, batch 31700, loss[loss=0.1683, simple_loss=0.234, pruned_loss=0.05129, over 4793.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2081, pruned_loss=0.02977, over 972266.97 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:06:17,107 INFO [train.py:715] (2/8) Epoch 16, batch 31750, loss[loss=0.1611, simple_loss=0.2341, pruned_loss=0.04403, over 4790.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02943, over 972199.54 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:06:56,957 INFO [train.py:715] (2/8) Epoch 16, batch 31800, loss[loss=0.1336, simple_loss=0.2245, pruned_loss=0.02141, over 4923.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2081, pruned_loss=0.02951, over 971705.90 frames.], batch size: 39, lr: 1.36e-04
2022-05-08 21:07:36,869 INFO [train.py:715] (2/8) Epoch 16, batch 31850, loss[loss=0.106, simple_loss=0.1794, pruned_loss=0.01635, over 4769.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02958, over 972292.67 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:08:15,870 INFO [train.py:715] (2/8) Epoch 16, batch 31900, loss[loss=0.1411, simple_loss=0.2123, pruned_loss=0.03492, over 4858.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2082, pruned_loss=0.02982, over 972279.60 frames.], batch size: 32, lr: 1.36e-04
2022-05-08 21:08:55,062 INFO [train.py:715] (2/8) Epoch 16, batch 31950, loss[loss=0.116, simple_loss=0.1877, pruned_loss=0.02209, over 4926.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02959, over 972044.27 frames.], batch size: 29, lr: 1.36e-04
2022-05-08 21:09:34,440 INFO [train.py:715] (2/8) Epoch 16, batch 32000, loss[loss=0.1107, simple_loss=0.1819, pruned_loss=0.01977, over 4780.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.0292, over 972246.33 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:10:13,548 INFO [train.py:715] (2/8) Epoch 16, batch 32050, loss[loss=0.16, simple_loss=0.2353, pruned_loss=0.04236, over 4942.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.02951, over 972124.30 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 21:10:53,099 INFO [train.py:715] (2/8) Epoch 16, batch 32100, loss[loss=0.1268, simple_loss=0.2077, pruned_loss=0.023, over 4939.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02913, over 973244.43 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 21:11:32,625 INFO [train.py:715] (2/8) Epoch 16, batch 32150, loss[loss=0.1519, simple_loss=0.2269, pruned_loss=0.03846, over 4784.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02969, over 971990.87 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 21:12:12,705 INFO [train.py:715] (2/8) Epoch 16, batch 32200, loss[loss=0.1471, simple_loss=0.2218, pruned_loss=0.0362, over 4957.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02946, over 972004.00 frames.], batch size: 39, lr: 1.36e-04
2022-05-08 21:12:51,831 INFO [train.py:715] (2/8) Epoch 16, batch 32250, loss[loss=0.1542, simple_loss=0.2249, pruned_loss=0.04177, over 4939.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.0295, over 971213.16 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 21:13:31,320 INFO [train.py:715] (2/8) Epoch 16, batch 32300, loss[loss=0.1344, simple_loss=0.2049, pruned_loss=0.03189, over 4959.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02918, over 971840.14 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:14:11,344 INFO [train.py:715] (2/8) Epoch 16, batch 32350, loss[loss=0.1209, simple_loss=0.1787, pruned_loss=0.03159, over 4741.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02892, over 971611.51 frames.], batch size: 12, lr: 1.36e-04
2022-05-08 21:14:50,975 INFO [train.py:715] (2/8) Epoch 16, batch 32400, loss[loss=0.1136, simple_loss=0.1885, pruned_loss=0.0194, over 4976.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02892, over 971599.23 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:15:30,000 INFO [train.py:715] (2/8) Epoch 16, batch 32450, loss[loss=0.126, simple_loss=0.2019, pruned_loss=0.02501, over 4841.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02925, over 972142.36 frames.], batch size: 20, lr: 1.36e-04
2022-05-08 21:16:10,034 INFO [train.py:715] (2/8) Epoch 16, batch 32500, loss[loss=0.1182, simple_loss=0.197, pruned_loss=0.01969, over 4945.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02947, over 972264.69 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 21:16:49,323 INFO [train.py:715] (2/8) Epoch 16, batch 32550, loss[loss=0.148, simple_loss=0.2079, pruned_loss=0.04409, over 4753.00 frames.], tot_loss[loss=0.1332, simple_loss=0.207, pruned_loss=0.02975, over 971957.00 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:17:28,288 INFO [train.py:715] (2/8) Epoch 16, batch 32600, loss[loss=0.1371, simple_loss=0.216, pruned_loss=0.02907, over 4778.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02969, over 971460.43 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:18:07,201 INFO [train.py:715] (2/8) Epoch 16, batch 32650, loss[loss=0.1595, simple_loss=0.2301, pruned_loss=0.04443, over 4922.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02962, over 971983.68 frames.], batch size: 39, lr: 1.36e-04
2022-05-08 21:18:46,415 INFO [train.py:715] (2/8) Epoch 16, batch 32700, loss[loss=0.1241, simple_loss=0.1965, pruned_loss=0.02581, over 4814.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02935, over 973292.90 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 21:19:25,737 INFO [train.py:715] (2/8) Epoch 16, batch 32750, loss[loss=0.1094, simple_loss=0.1891, pruned_loss=0.01483, over 4976.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02916, over 974040.41 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:20:05,462 INFO [train.py:715] (2/8) Epoch 16, batch 32800, loss[loss=0.138, simple_loss=0.2081, pruned_loss=0.03396, over 4874.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02922, over 973910.66 frames.], batch size: 22, lr: 1.36e-04
2022-05-08 21:20:44,827 INFO [train.py:715] (2/8) Epoch 16, batch 32850, loss[loss=0.1145, simple_loss=0.188, pruned_loss=0.02054, over 4783.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02908, over 973562.74 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:21:24,451 INFO [train.py:715] (2/8) Epoch 16, batch 32900, loss[loss=0.1358, simple_loss=0.2157, pruned_loss=0.02797, over 4815.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02908, over 973534.05 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:22:03,427 INFO [train.py:715] (2/8) Epoch 16, batch 32950, loss[loss=0.1257, simple_loss=0.2026, pruned_loss=0.02444, over 4773.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02917, over 972512.10 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:22:42,572 INFO [train.py:715] (2/8) Epoch 16, batch 33000, loss[loss=0.1313, simple_loss=0.2029, pruned_loss=0.02988, over 4987.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2079, pruned_loss=0.02954, over 972452.86 frames.], batch size: 28, lr: 1.36e-04
2022-05-08 21:22:42,573 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 21:22:55,770 INFO [train.py:742] (2/8) 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] (2/8) Epoch 16, batch 33050, loss[loss=0.1461, simple_loss=0.2248, pruned_loss=0.0337, over 4829.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.02978, over 972228.38 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:24:14,883 INFO [train.py:715] (2/8) Epoch 16, batch 33100, loss[loss=0.1422, simple_loss=0.2191, pruned_loss=0.0326, over 4930.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2088, pruned_loss=0.02997, over 971947.57 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 21:24:54,242 INFO [train.py:715] (2/8) Epoch 16, batch 33150, loss[loss=0.1468, simple_loss=0.2157, pruned_loss=0.03891, over 4845.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02985, over 971121.42 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:25:34,100 INFO [train.py:715] (2/8) Epoch 16, batch 33200, loss[loss=0.1237, simple_loss=0.1909, pruned_loss=0.02824, over 4767.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.0299, over 971929.81 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:26:13,850 INFO [train.py:715] (2/8) Epoch 16, batch 33250, loss[loss=0.1056, simple_loss=0.1773, pruned_loss=0.01691, over 4767.00 frames.], tot_loss[loss=0.134, simple_loss=0.2081, pruned_loss=0.02993, over 971661.19 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:26:53,438 INFO [train.py:715] (2/8) Epoch 16, batch 33300, loss[loss=0.1594, simple_loss=0.2312, pruned_loss=0.04385, over 4777.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02962, over 972059.07 frames.], batch size: 18, lr: 1.36e-04
2022-05-08 21:27:32,751 INFO [train.py:715] (2/8) Epoch 16, batch 33350, loss[loss=0.1365, simple_loss=0.2118, pruned_loss=0.03062, over 4685.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02928, over 971494.40 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:28:12,220 INFO [train.py:715] (2/8) Epoch 16, batch 33400, loss[loss=0.1432, simple_loss=0.2183, pruned_loss=0.03408, over 4864.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02908, over 970903.73 frames.], batch size: 20, lr: 1.36e-04
2022-05-08 21:28:51,454 INFO [train.py:715] (2/8) Epoch 16, batch 33450, loss[loss=0.1358, simple_loss=0.2134, pruned_loss=0.02913, over 4971.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02924, over 971561.73 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 21:29:30,508 INFO [train.py:715] (2/8) Epoch 16, batch 33500, loss[loss=0.1206, simple_loss=0.1923, pruned_loss=0.02444, over 4811.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02938, over 971935.23 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 21:30:09,460 INFO [train.py:715] (2/8) Epoch 16, batch 33550, loss[loss=0.1283, simple_loss=0.1963, pruned_loss=0.03012, over 4977.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02955, over 971979.23 frames.], batch size: 25, lr: 1.36e-04
2022-05-08 21:30:49,228 INFO [train.py:715] (2/8) Epoch 16, batch 33600, loss[loss=0.1206, simple_loss=0.1906, pruned_loss=0.02528, over 4831.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02942, over 972504.38 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 21:31:28,203 INFO [train.py:715] (2/8) Epoch 16, batch 33650, loss[loss=0.1348, simple_loss=0.2071, pruned_loss=0.03127, over 4762.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02951, over 972559.39 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 21:32:07,847 INFO [train.py:715] (2/8) Epoch 16, batch 33700, loss[loss=0.1435, simple_loss=0.2214, pruned_loss=0.03274, over 4876.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02951, over 972511.11 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:32:46,798 INFO [train.py:715] (2/8) Epoch 16, batch 33750, loss[loss=0.1128, simple_loss=0.1926, pruned_loss=0.01647, over 4826.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02953, over 971875.13 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 21:33:25,787 INFO [train.py:715] (2/8) Epoch 16, batch 33800, loss[loss=0.1525, simple_loss=0.2103, pruned_loss=0.04737, over 4973.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02956, over 972026.40 frames.], batch size: 35, lr: 1.36e-04
2022-05-08 21:34:05,042 INFO [train.py:715] (2/8) Epoch 16, batch 33850, loss[loss=0.14, simple_loss=0.2168, pruned_loss=0.03161, over 4817.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.0298, over 972046.88 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 21:34:44,269 INFO [train.py:715] (2/8) Epoch 16, batch 33900, loss[loss=0.1346, simple_loss=0.1997, pruned_loss=0.03475, over 4691.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.0292, over 971299.19 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:35:24,622 INFO [train.py:715] (2/8) Epoch 16, batch 33950, loss[loss=0.1368, simple_loss=0.2128, pruned_loss=0.03042, over 4758.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02952, over 971806.02 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:36:03,119 INFO [train.py:715] (2/8) Epoch 16, batch 34000, loss[loss=0.1197, simple_loss=0.192, pruned_loss=0.02371, over 4900.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02987, over 972126.21 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 21:36:43,156 INFO [train.py:715] (2/8) Epoch 16, batch 34050, loss[loss=0.1176, simple_loss=0.1939, pruned_loss=0.02063, over 4788.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02942, over 971859.61 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 21:37:22,552 INFO [train.py:715] (2/8) Epoch 16, batch 34100, loss[loss=0.1348, simple_loss=0.2095, pruned_loss=0.03003, over 4810.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02926, over 971502.35 frames.], batch size: 13, lr: 1.36e-04
2022-05-08 21:38:01,718 INFO [train.py:715] (2/8) Epoch 16, batch 34150, loss[loss=0.1124, simple_loss=0.1834, pruned_loss=0.02073, over 4761.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02935, over 971638.68 frames.], batch size: 12, lr: 1.36e-04
2022-05-08 21:38:41,101 INFO [train.py:715] (2/8) Epoch 16, batch 34200, loss[loss=0.1504, simple_loss=0.2241, pruned_loss=0.03841, over 4736.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02953, over 971267.64 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:39:20,448 INFO [train.py:715] (2/8) Epoch 16, batch 34250, loss[loss=0.1579, simple_loss=0.2355, pruned_loss=0.04011, over 4893.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02938, over 971383.69 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:40:00,519 INFO [train.py:715] (2/8) Epoch 16, batch 34300, loss[loss=0.1303, simple_loss=0.1934, pruned_loss=0.03363, over 4816.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02914, over 971880.43 frames.], batch size: 26, lr: 1.36e-04
2022-05-08 21:40:39,494 INFO [train.py:715] (2/8) Epoch 16, batch 34350, loss[loss=0.1441, simple_loss=0.2201, pruned_loss=0.03404, over 4735.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02956, over 972073.28 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:41:18,867 INFO [train.py:715] (2/8) Epoch 16, batch 34400, loss[loss=0.1412, simple_loss=0.2192, pruned_loss=0.03158, over 4974.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2069, pruned_loss=0.02991, over 971626.02 frames.], batch size: 15, lr: 1.36e-04
2022-05-08 21:41:58,450 INFO [train.py:715] (2/8) Epoch 16, batch 34450, loss[loss=0.1181, simple_loss=0.1925, pruned_loss=0.0219, over 4961.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2074, pruned_loss=0.0301, over 971663.78 frames.], batch size: 24, lr: 1.36e-04
2022-05-08 21:42:37,732 INFO [train.py:715] (2/8) Epoch 16, batch 34500, loss[loss=0.1324, simple_loss=0.2091, pruned_loss=0.02781, over 4808.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.0294, over 972232.49 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 21:43:17,126 INFO [train.py:715] (2/8) Epoch 16, batch 34550, loss[loss=0.1104, simple_loss=0.1742, pruned_loss=0.02328, over 4760.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02935, over 972323.70 frames.], batch size: 17, lr: 1.36e-04
2022-05-08 21:43:56,244 INFO [train.py:715] (2/8) Epoch 16, batch 34600, loss[loss=0.1261, simple_loss=0.2099, pruned_loss=0.02119, over 4761.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02907, over 971756.50 frames.], batch size: 19, lr: 1.36e-04
2022-05-08 21:44:36,200 INFO [train.py:715] (2/8) Epoch 16, batch 34650, loss[loss=0.1672, simple_loss=0.233, pruned_loss=0.05064, over 4883.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02901, over 971325.14 frames.], batch size: 16, lr: 1.36e-04
2022-05-08 21:45:15,692 INFO [train.py:715] (2/8) Epoch 16, batch 34700, loss[loss=0.1364, simple_loss=0.2167, pruned_loss=0.02803, over 4805.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02895, over 971258.95 frames.], batch size: 21, lr: 1.36e-04
2022-05-08 21:45:54,804 INFO [train.py:715] (2/8) Epoch 16, batch 34750, loss[loss=0.1096, simple_loss=0.1814, pruned_loss=0.01893, over 4788.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02908, over 971337.25 frames.], batch size: 14, lr: 1.36e-04
2022-05-08 21:46:32,020 INFO [train.py:715] (2/8) Epoch 16, batch 34800, loss[loss=0.1202, simple_loss=0.2033, pruned_loss=0.01861, over 4924.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02866, over 971678.13 frames.], batch size: 23, lr: 1.36e-04
2022-05-08 21:47:23,860 INFO [train.py:715] (2/8) Epoch 17, batch 0, loss[loss=0.1244, simple_loss=0.1854, pruned_loss=0.0317, over 4964.00 frames.], tot_loss[loss=0.1244, simple_loss=0.1854, pruned_loss=0.0317, over 4964.00 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 21:48:03,330 INFO [train.py:715] (2/8) Epoch 17, batch 50, loss[loss=0.1323, simple_loss=0.2039, pruned_loss=0.03034, over 4742.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2051, pruned_loss=0.02937, over 219256.69 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 21:48:44,397 INFO [train.py:715] (2/8) Epoch 17, batch 100, loss[loss=0.1164, simple_loss=0.1909, pruned_loss=0.02094, over 4983.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2061, pruned_loss=0.02938, over 386689.85 frames.], batch size: 35, lr: 1.32e-04
2022-05-08 21:49:25,322 INFO [train.py:715] (2/8) Epoch 17, batch 150, loss[loss=0.1154, simple_loss=0.1878, pruned_loss=0.02148, over 4809.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2062, pruned_loss=0.03002, over 517012.86 frames.], batch size: 13, lr: 1.32e-04
2022-05-08 21:50:06,411 INFO [train.py:715] (2/8) Epoch 17, batch 200, loss[loss=0.1531, simple_loss=0.2401, pruned_loss=0.03304, over 4864.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02978, over 617963.79 frames.], batch size: 20, lr: 1.32e-04
2022-05-08 21:50:49,389 INFO [train.py:715] (2/8) Epoch 17, batch 250, loss[loss=0.1321, simple_loss=0.2021, pruned_loss=0.031, over 4809.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02957, over 696933.88 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 21:51:30,987 INFO [train.py:715] (2/8) Epoch 17, batch 300, loss[loss=0.142, simple_loss=0.2093, pruned_loss=0.03729, over 4697.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02965, over 757568.55 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 21:52:11,851 INFO [train.py:715] (2/8) Epoch 17, batch 350, loss[loss=0.1206, simple_loss=0.1975, pruned_loss=0.02183, over 4977.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.0297, over 805467.85 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 21:52:52,793 INFO [train.py:715] (2/8) Epoch 17, batch 400, loss[loss=0.1468, simple_loss=0.2262, pruned_loss=0.03373, over 4739.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2082, pruned_loss=0.02951, over 842179.64 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 21:53:33,712 INFO [train.py:715] (2/8) Epoch 17, batch 450, loss[loss=0.1303, simple_loss=0.213, pruned_loss=0.02381, over 4776.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2077, pruned_loss=0.0291, over 871081.21 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 21:54:14,770 INFO [train.py:715] (2/8) Epoch 17, batch 500, loss[loss=0.155, simple_loss=0.2387, pruned_loss=0.03565, over 4985.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.02957, over 892970.52 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 21:54:56,770 INFO [train.py:715] (2/8) Epoch 17, batch 550, loss[loss=0.1262, simple_loss=0.1928, pruned_loss=0.02981, over 4647.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02962, over 910411.76 frames.], batch size: 13, lr: 1.32e-04
2022-05-08 21:55:37,897 INFO [train.py:715] (2/8) Epoch 17, batch 600, loss[loss=0.1483, simple_loss=0.225, pruned_loss=0.03579, over 4816.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02988, over 924966.67 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 21:56:20,088 INFO [train.py:715] (2/8) Epoch 17, batch 650, loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02932, over 4857.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02969, over 935841.25 frames.], batch size: 32, lr: 1.32e-04
2022-05-08 21:57:01,702 INFO [train.py:715] (2/8) Epoch 17, batch 700, loss[loss=0.1781, simple_loss=0.2503, pruned_loss=0.05292, over 4794.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03009, over 943620.63 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 21:57:42,594 INFO [train.py:715] (2/8) Epoch 17, batch 750, loss[loss=0.1201, simple_loss=0.1979, pruned_loss=0.02109, over 4919.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03006, over 949777.17 frames.], batch size: 29, lr: 1.32e-04
2022-05-08 21:58:23,378 INFO [train.py:715] (2/8) Epoch 17, batch 800, loss[loss=0.1111, simple_loss=0.1845, pruned_loss=0.01888, over 4922.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2065, pruned_loss=0.02966, over 954839.36 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 21:59:03,980 INFO [train.py:715] (2/8) Epoch 17, batch 850, loss[loss=0.1254, simple_loss=0.201, pruned_loss=0.02486, over 4891.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02964, over 959136.78 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 21:59:45,374 INFO [train.py:715] (2/8) Epoch 17, batch 900, loss[loss=0.1308, simple_loss=0.2117, pruned_loss=0.02498, over 4917.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02949, over 962126.56 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:00:26,260 INFO [train.py:715] (2/8) Epoch 17, batch 950, loss[loss=0.155, simple_loss=0.2147, pruned_loss=0.0476, over 4764.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02972, over 963430.57 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:01:07,638 INFO [train.py:715] (2/8) Epoch 17, batch 1000, loss[loss=0.1466, simple_loss=0.2184, pruned_loss=0.03734, over 4835.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.0294, over 965773.48 frames.], batch size: 26, lr: 1.32e-04
2022-05-08 22:01:48,892 INFO [train.py:715] (2/8) Epoch 17, batch 1050, loss[loss=0.1572, simple_loss=0.2303, pruned_loss=0.04205, over 4937.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2069, pruned_loss=0.03007, over 967781.47 frames.], batch size: 23, lr: 1.32e-04
2022-05-08 22:02:29,878 INFO [train.py:715] (2/8) Epoch 17, batch 1100, loss[loss=0.1312, simple_loss=0.2045, pruned_loss=0.02895, over 4912.00 frames.], tot_loss[loss=0.1344, simple_loss=0.208, pruned_loss=0.03039, over 968924.42 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:03:10,389 INFO [train.py:715] (2/8) Epoch 17, batch 1150, loss[loss=0.1433, simple_loss=0.2192, pruned_loss=0.03373, over 4741.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02999, over 970295.83 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:03:51,858 INFO [train.py:715] (2/8) Epoch 17, batch 1200, loss[loss=0.1435, simple_loss=0.2129, pruned_loss=0.03701, over 4890.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03007, over 970293.40 frames.], batch size: 32, lr: 1.32e-04
2022-05-08 22:04:32,827 INFO [train.py:715] (2/8) Epoch 17, batch 1250, loss[loss=0.1379, simple_loss=0.2175, pruned_loss=0.02911, over 4753.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03006, over 971140.70 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:05:13,881 INFO [train.py:715] (2/8) Epoch 17, batch 1300, loss[loss=0.1339, simple_loss=0.2002, pruned_loss=0.03383, over 4976.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2069, pruned_loss=0.02973, over 971289.47 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 22:05:55,240 INFO [train.py:715] (2/8) Epoch 17, batch 1350, loss[loss=0.1426, simple_loss=0.2124, pruned_loss=0.03643, over 4852.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02973, over 971697.38 frames.], batch size: 32, lr: 1.32e-04
2022-05-08 22:06:36,194 INFO [train.py:715] (2/8) Epoch 17, batch 1400, loss[loss=0.1696, simple_loss=0.2474, pruned_loss=0.04596, over 4932.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02931, over 971693.66 frames.], batch size: 29, lr: 1.32e-04
2022-05-08 22:07:16,797 INFO [train.py:715] (2/8) Epoch 17, batch 1450, loss[loss=0.1159, simple_loss=0.1937, pruned_loss=0.01906, over 4865.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02908, over 971874.11 frames.], batch size: 20, lr: 1.32e-04
2022-05-08 22:07:57,512 INFO [train.py:715] (2/8) Epoch 17, batch 1500, loss[loss=0.1152, simple_loss=0.1916, pruned_loss=0.01943, over 4841.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02971, over 972709.18 frames.], batch size: 30, lr: 1.32e-04
2022-05-08 22:08:39,007 INFO [train.py:715] (2/8) Epoch 17, batch 1550, loss[loss=0.1132, simple_loss=0.176, pruned_loss=0.02516, over 4832.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02954, over 971618.61 frames.], batch size: 12, lr: 1.32e-04
2022-05-08 22:09:20,350 INFO [train.py:715] (2/8) Epoch 17, batch 1600, loss[loss=0.1068, simple_loss=0.1805, pruned_loss=0.01658, over 4786.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02948, over 971424.83 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:10:01,026 INFO [train.py:715] (2/8) Epoch 17, batch 1650, loss[loss=0.1193, simple_loss=0.1988, pruned_loss=0.01988, over 4963.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02968, over 971541.12 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:10:42,362 INFO [train.py:715] (2/8) Epoch 17, batch 1700, loss[loss=0.1248, simple_loss=0.1883, pruned_loss=0.03071, over 4786.00 frames.], tot_loss[loss=0.1323, simple_loss=0.206, pruned_loss=0.02925, over 971225.33 frames.], batch size: 12, lr: 1.32e-04
2022-05-08 22:11:23,618 INFO [train.py:715] (2/8) Epoch 17, batch 1750, loss[loss=0.1373, simple_loss=0.2109, pruned_loss=0.03181, over 4917.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2062, pruned_loss=0.02944, over 971877.37 frames.], batch size: 29, lr: 1.32e-04
2022-05-08 22:12:04,539 INFO [train.py:715] (2/8) Epoch 17, batch 1800, loss[loss=0.1429, simple_loss=0.2211, pruned_loss=0.03239, over 4799.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2066, pruned_loss=0.02957, over 972365.34 frames.], batch size: 21, lr: 1.32e-04
2022-05-08 22:12:45,640 INFO [train.py:715] (2/8) Epoch 17, batch 1850, loss[loss=0.1397, simple_loss=0.2141, pruned_loss=0.03267, over 4776.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2067, pruned_loss=0.02946, over 972400.01 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 22:13:27,330 INFO [train.py:715] (2/8) Epoch 17, batch 1900, loss[loss=0.1563, simple_loss=0.2389, pruned_loss=0.03684, over 4879.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02901, over 972665.16 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 22:14:08,482 INFO [train.py:715] (2/8) Epoch 17, batch 1950, loss[loss=0.1031, simple_loss=0.1764, pruned_loss=0.01493, over 4809.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2054, pruned_loss=0.02873, over 973198.97 frames.], batch size: 12, lr: 1.32e-04
2022-05-08 22:14:49,361 INFO [train.py:715] (2/8) Epoch 17, batch 2000, loss[loss=0.134, simple_loss=0.2097, pruned_loss=0.02921, over 4768.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2054, pruned_loss=0.02873, over 972826.59 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:15:30,351 INFO [train.py:715] (2/8) Epoch 17, batch 2050, loss[loss=0.1316, simple_loss=0.2105, pruned_loss=0.02637, over 4868.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2055, pruned_loss=0.02881, over 972727.93 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:16:11,448 INFO [train.py:715] (2/8) Epoch 17, batch 2100, loss[loss=0.1377, simple_loss=0.2032, pruned_loss=0.03607, over 4854.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2054, pruned_loss=0.02879, over 973049.40 frames.], batch size: 30, lr: 1.32e-04
2022-05-08 22:16:52,788 INFO [train.py:715] (2/8) Epoch 17, batch 2150, loss[loss=0.1412, simple_loss=0.2157, pruned_loss=0.03337, over 4772.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02928, over 971802.08 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:17:34,181 INFO [train.py:715] (2/8) Epoch 17, batch 2200, loss[loss=0.1361, simple_loss=0.202, pruned_loss=0.0351, over 4886.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2067, pruned_loss=0.02973, over 971987.25 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 22:18:15,307 INFO [train.py:715] (2/8) Epoch 17, batch 2250, loss[loss=0.1598, simple_loss=0.2276, pruned_loss=0.04594, over 4851.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02977, over 972174.24 frames.], batch size: 30, lr: 1.32e-04
2022-05-08 22:18:56,046 INFO [train.py:715] (2/8) Epoch 17, batch 2300, loss[loss=0.1351, simple_loss=0.2135, pruned_loss=0.02832, over 4817.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02946, over 972157.82 frames.], batch size: 27, lr: 1.32e-04
2022-05-08 22:19:36,476 INFO [train.py:715] (2/8) Epoch 17, batch 2350, loss[loss=0.1312, simple_loss=0.2118, pruned_loss=0.02532, over 4920.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02934, over 973032.86 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:20:17,315 INFO [train.py:715] (2/8) Epoch 17, batch 2400, loss[loss=0.1526, simple_loss=0.2251, pruned_loss=0.04008, over 4895.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02957, over 972604.82 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 22:20:58,230 INFO [train.py:715] (2/8) Epoch 17, batch 2450, loss[loss=0.1282, simple_loss=0.2103, pruned_loss=0.02302, over 4774.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02933, over 972904.24 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 22:21:39,042 INFO [train.py:715] (2/8) Epoch 17, batch 2500, loss[loss=0.1491, simple_loss=0.2283, pruned_loss=0.03498, over 4980.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02951, over 973540.85 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:22:20,004 INFO [train.py:715] (2/8) Epoch 17, batch 2550, loss[loss=0.1334, simple_loss=0.2063, pruned_loss=0.03025, over 4791.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02983, over 974284.84 frames.], batch size: 14, lr: 1.32e-04
2022-05-08 22:23:00,956 INFO [train.py:715] (2/8) Epoch 17, batch 2600, loss[loss=0.1308, simple_loss=0.2121, pruned_loss=0.0248, over 4820.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02974, over 973885.57 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:23:42,200 INFO [train.py:715] (2/8) Epoch 17, batch 2650, loss[loss=0.1492, simple_loss=0.2269, pruned_loss=0.03574, over 4777.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2081, pruned_loss=0.02939, over 973289.70 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:24:22,844 INFO [train.py:715] (2/8) Epoch 17, batch 2700, loss[loss=0.1644, simple_loss=0.2325, pruned_loss=0.04814, over 4874.00 frames.], tot_loss[loss=0.134, simple_loss=0.2087, pruned_loss=0.02968, over 973259.37 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:25:04,063 INFO [train.py:715] (2/8) Epoch 17, batch 2750, loss[loss=0.1345, simple_loss=0.2041, pruned_loss=0.0324, over 4836.00 frames.], tot_loss[loss=0.1332, simple_loss=0.208, pruned_loss=0.02918, over 973122.34 frames.], batch size: 13, lr: 1.32e-04
2022-05-08 22:25:44,576 INFO [train.py:715] (2/8) Epoch 17, batch 2800, loss[loss=0.129, simple_loss=0.1987, pruned_loss=0.02968, over 4844.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2074, pruned_loss=0.02899, over 973475.72 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:26:25,557 INFO [train.py:715] (2/8) Epoch 17, batch 2850, loss[loss=0.1342, simple_loss=0.2038, pruned_loss=0.03228, over 4835.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02911, over 973122.40 frames.], batch size: 30, lr: 1.32e-04
2022-05-08 22:27:06,298 INFO [train.py:715] (2/8) Epoch 17, batch 2900, loss[loss=0.1573, simple_loss=0.2264, pruned_loss=0.04413, over 4846.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02909, over 972668.32 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:27:47,275 INFO [train.py:715] (2/8) Epoch 17, batch 2950, loss[loss=0.1333, simple_loss=0.2036, pruned_loss=0.03152, over 4869.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02922, over 972678.80 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:28:28,417 INFO [train.py:715] (2/8) Epoch 17, batch 3000, loss[loss=0.1162, simple_loss=0.1938, pruned_loss=0.01935, over 4805.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.0295, over 973153.53 frames.], batch size: 21, lr: 1.32e-04
2022-05-08 22:28:28,418 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 22:28:43,493 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1047, simple_loss=0.1882, pruned_loss=0.01063, over 914524.00 frames.
2022-05-08 22:29:24,683 INFO [train.py:715] (2/8) Epoch 17, batch 3050, loss[loss=0.1282, simple_loss=0.2035, pruned_loss=0.0265, over 4827.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02949, over 972616.18 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:30:05,289 INFO [train.py:715] (2/8) Epoch 17, batch 3100, loss[loss=0.1416, simple_loss=0.2105, pruned_loss=0.03636, over 4883.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02958, over 972319.49 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 22:30:46,406 INFO [train.py:715] (2/8) Epoch 17, batch 3150, loss[loss=0.157, simple_loss=0.2326, pruned_loss=0.04075, over 4985.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03003, over 972117.67 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:31:26,340 INFO [train.py:715] (2/8) Epoch 17, batch 3200, loss[loss=0.1453, simple_loss=0.225, pruned_loss=0.03282, over 4955.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2072, pruned_loss=0.03013, over 972220.94 frames.], batch size: 21, lr: 1.32e-04
2022-05-08 22:32:07,671 INFO [train.py:715] (2/8) Epoch 17, batch 3250, loss[loss=0.1546, simple_loss=0.2324, pruned_loss=0.03841, over 4812.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2071, pruned_loss=0.02991, over 973074.74 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:32:47,746 INFO [train.py:715] (2/8) Epoch 17, batch 3300, loss[loss=0.1266, simple_loss=0.2062, pruned_loss=0.02348, over 4749.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2069, pruned_loss=0.02989, over 973347.82 frames.], batch size: 16, lr: 1.32e-04
2022-05-08 22:33:28,432 INFO [train.py:715] (2/8) Epoch 17, batch 3350, loss[loss=0.1451, simple_loss=0.2212, pruned_loss=0.03453, over 4918.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2065, pruned_loss=0.02972, over 972803.63 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:34:09,184 INFO [train.py:715] (2/8) Epoch 17, batch 3400, loss[loss=0.13, simple_loss=0.192, pruned_loss=0.03403, over 4818.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2071, pruned_loss=0.0301, over 972609.36 frames.], batch size: 12, lr: 1.32e-04
2022-05-08 22:34:50,589 INFO [train.py:715] (2/8) Epoch 17, batch 3450, loss[loss=0.1407, simple_loss=0.2061, pruned_loss=0.03765, over 4916.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2069, pruned_loss=0.02998, over 972079.30 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:35:30,942 INFO [train.py:715] (2/8) Epoch 17, batch 3500, loss[loss=0.1055, simple_loss=0.1762, pruned_loss=0.01743, over 4755.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2071, pruned_loss=0.03007, over 971445.78 frames.], batch size: 12, lr: 1.32e-04
2022-05-08 22:36:11,162 INFO [train.py:715] (2/8) Epoch 17, batch 3550, loss[loss=0.1478, simple_loss=0.225, pruned_loss=0.03533, over 4696.00 frames.], tot_loss[loss=0.133, simple_loss=0.2065, pruned_loss=0.02975, over 970843.27 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:36:52,126 INFO [train.py:715] (2/8) Epoch 17, batch 3600, loss[loss=0.1332, simple_loss=0.2127, pruned_loss=0.02684, over 4694.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2064, pruned_loss=0.02938, over 971101.11 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:37:31,749 INFO [train.py:715] (2/8) Epoch 17, batch 3650, loss[loss=0.1128, simple_loss=0.1801, pruned_loss=0.0227, over 4885.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02915, over 971787.63 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:38:11,918 INFO [train.py:715] (2/8) Epoch 17, batch 3700, loss[loss=0.1231, simple_loss=0.1924, pruned_loss=0.02688, over 4700.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2062, pruned_loss=0.02966, over 971327.99 frames.], batch size: 15, lr: 1.32e-04
2022-05-08 22:38:52,857 INFO [train.py:715] (2/8) Epoch 17, batch 3750, loss[loss=0.1228, simple_loss=0.2111, pruned_loss=0.01726, over 4885.00 frames.], tot_loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02977, over 972039.30 frames.], batch size: 22, lr: 1.32e-04
2022-05-08 22:39:33,612 INFO [train.py:715] (2/8) Epoch 17, batch 3800, loss[loss=0.124, simple_loss=0.2058, pruned_loss=0.02108, over 4952.00 frames.], tot_loss[loss=0.132, simple_loss=0.2059, pruned_loss=0.02905, over 972623.89 frames.], batch size: 21, lr: 1.32e-04
2022-05-08 22:40:14,219 INFO [train.py:715] (2/8) Epoch 17, batch 3850, loss[loss=0.1321, simple_loss=0.2094, pruned_loss=0.02744, over 4843.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02923, over 973160.29 frames.], batch size: 30, lr: 1.32e-04
2022-05-08 22:40:54,271 INFO [train.py:715] (2/8) Epoch 17, batch 3900, loss[loss=0.1688, simple_loss=0.2284, pruned_loss=0.05456, over 4771.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02909, over 973055.34 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:41:35,753 INFO [train.py:715] (2/8) Epoch 17, batch 3950, loss[loss=0.1419, simple_loss=0.215, pruned_loss=0.03438, over 4952.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.0293, over 973311.78 frames.], batch size: 24, lr: 1.32e-04
2022-05-08 22:42:15,633 INFO [train.py:715] (2/8) Epoch 17, batch 4000, loss[loss=0.1749, simple_loss=0.2527, pruned_loss=0.0486, over 4947.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02948, over 973043.10 frames.], batch size: 24, lr: 1.32e-04
2022-05-08 22:42:56,130 INFO [train.py:715] (2/8) Epoch 17, batch 4050, loss[loss=0.1343, simple_loss=0.2075, pruned_loss=0.0305, over 4799.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02915, over 973260.72 frames.], batch size: 24, lr: 1.32e-04
2022-05-08 22:43:36,613 INFO [train.py:715] (2/8) Epoch 17, batch 4100, loss[loss=0.1546, simple_loss=0.2349, pruned_loss=0.03711, over 4814.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.0296, over 972093.82 frames.], batch size: 25, lr: 1.32e-04
2022-05-08 22:44:17,663 INFO [train.py:715] (2/8) Epoch 17, batch 4150, loss[loss=0.1065, simple_loss=0.1808, pruned_loss=0.01609, over 4786.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02927, over 971561.80 frames.], batch size: 17, lr: 1.32e-04
2022-05-08 22:44:56,902 INFO [train.py:715] (2/8) Epoch 17, batch 4200, loss[loss=0.1501, simple_loss=0.2277, pruned_loss=0.03632, over 4908.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02927, over 971087.86 frames.], batch size: 19, lr: 1.32e-04
2022-05-08 22:45:36,945 INFO [train.py:715] (2/8) Epoch 17, batch 4250, loss[loss=0.1332, simple_loss=0.2059, pruned_loss=0.03025, over 4775.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02986, over 971119.71 frames.], batch size: 18, lr: 1.32e-04
2022-05-08 22:46:18,112 INFO [train.py:715] (2/8) Epoch 17, batch 4300, loss[loss=0.1075, simple_loss=0.1786, pruned_loss=0.01821, over 4879.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.03006, over 971245.71 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 22:46:58,170 INFO [train.py:715] (2/8) Epoch 17, batch 4350, loss[loss=0.1376, simple_loss=0.2164, pruned_loss=0.02939, over 4947.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2083, pruned_loss=0.03055, over 971247.18 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 22:47:38,040 INFO [train.py:715] (2/8) Epoch 17, batch 4400, loss[loss=0.1192, simple_loss=0.1882, pruned_loss=0.02506, over 4925.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2078, pruned_loss=0.03041, over 971358.43 frames.], batch size: 29, lr: 1.31e-04
2022-05-08 22:48:18,912 INFO [train.py:715] (2/8) Epoch 17, batch 4450, loss[loss=0.1306, simple_loss=0.2129, pruned_loss=0.02417, over 4790.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2069, pruned_loss=0.02967, over 971941.11 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 22:48:59,899 INFO [train.py:715] (2/8) Epoch 17, batch 4500, loss[loss=0.1241, simple_loss=0.1913, pruned_loss=0.0284, over 4732.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2066, pruned_loss=0.02979, over 972761.28 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 22:49:39,762 INFO [train.py:715] (2/8) Epoch 17, batch 4550, loss[loss=0.1496, simple_loss=0.2252, pruned_loss=0.03701, over 4924.00 frames.], tot_loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.0297, over 972300.71 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 22:50:20,187 INFO [train.py:715] (2/8) Epoch 17, batch 4600, loss[loss=0.142, simple_loss=0.2013, pruned_loss=0.04139, over 4774.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2064, pruned_loss=0.02966, over 972349.17 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 22:51:01,207 INFO [train.py:715] (2/8) Epoch 17, batch 4650, loss[loss=0.1074, simple_loss=0.1754, pruned_loss=0.01973, over 4762.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.0295, over 972709.85 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 22:51:41,121 INFO [train.py:715] (2/8) Epoch 17, batch 4700, loss[loss=0.1328, simple_loss=0.2046, pruned_loss=0.03049, over 4780.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02937, over 972057.76 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 22:52:21,059 INFO [train.py:715] (2/8) Epoch 17, batch 4750, loss[loss=0.122, simple_loss=0.1932, pruned_loss=0.0254, over 4794.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02946, over 971529.06 frames.], batch size: 21, lr: 1.31e-04
2022-05-08 22:53:02,042 INFO [train.py:715] (2/8) Epoch 17, batch 4800, loss[loss=0.1244, simple_loss=0.2044, pruned_loss=0.0222, over 4790.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02927, over 971815.52 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 22:53:42,797 INFO [train.py:715] (2/8) Epoch 17, batch 4850, loss[loss=0.1418, simple_loss=0.2171, pruned_loss=0.03326, over 4888.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02886, over 971751.63 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 22:54:22,664 INFO [train.py:715] (2/8) Epoch 17, batch 4900, loss[loss=0.1305, simple_loss=0.1908, pruned_loss=0.03509, over 4897.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02906, over 972192.14 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 22:55:03,097 INFO [train.py:715] (2/8) Epoch 17, batch 4950, loss[loss=0.1687, simple_loss=0.2302, pruned_loss=0.05355, over 4887.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02915, over 972436.84 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 22:55:44,131 INFO [train.py:715] (2/8) Epoch 17, batch 5000, loss[loss=0.1226, simple_loss=0.1966, pruned_loss=0.02429, over 4852.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02916, over 971857.72 frames.], batch size: 32, lr: 1.31e-04
2022-05-08 22:56:24,631 INFO [train.py:715] (2/8) Epoch 17, batch 5050, loss[loss=0.1412, simple_loss=0.2245, pruned_loss=0.02893, over 4878.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02919, over 971693.31 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 22:57:04,158 INFO [train.py:715] (2/8) Epoch 17, batch 5100, loss[loss=0.1585, simple_loss=0.2297, pruned_loss=0.04371, over 4760.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02932, over 972194.20 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 22:57:44,980 INFO [train.py:715] (2/8) Epoch 17, batch 5150, loss[loss=0.1393, simple_loss=0.2085, pruned_loss=0.0351, over 4944.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02942, over 972295.74 frames.], batch size: 23, lr: 1.31e-04
2022-05-08 22:58:26,127 INFO [train.py:715] (2/8) Epoch 17, batch 5200, loss[loss=0.1364, simple_loss=0.209, pruned_loss=0.03185, over 4845.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02954, over 971665.37 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 22:59:05,337 INFO [train.py:715] (2/8) Epoch 17, batch 5250, loss[loss=0.1201, simple_loss=0.1948, pruned_loss=0.02271, over 4961.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02911, over 971692.55 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 22:59:44,886 INFO [train.py:715] (2/8) Epoch 17, batch 5300, loss[loss=0.182, simple_loss=0.2446, pruned_loss=0.05968, over 4968.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02929, over 972199.28 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:00:25,448 INFO [train.py:715] (2/8) Epoch 17, batch 5350, loss[loss=0.1113, simple_loss=0.1931, pruned_loss=0.01478, over 4881.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02911, over 972338.38 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 23:01:06,239 INFO [train.py:715] (2/8) Epoch 17, batch 5400, loss[loss=0.1419, simple_loss=0.2082, pruned_loss=0.03779, over 4762.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02944, over 972583.63 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:01:45,348 INFO [train.py:715] (2/8) Epoch 17, batch 5450, loss[loss=0.1199, simple_loss=0.1978, pruned_loss=0.02102, over 4828.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02947, over 972734.79 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 23:02:26,540 INFO [train.py:715] (2/8) Epoch 17, batch 5500, loss[loss=0.1293, simple_loss=0.197, pruned_loss=0.03075, over 4945.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2076, pruned_loss=0.03005, over 972535.94 frames.], batch size: 23, lr: 1.31e-04
2022-05-08 23:03:07,895 INFO [train.py:715] (2/8) Epoch 17, batch 5550, loss[loss=0.1403, simple_loss=0.212, pruned_loss=0.03434, over 4987.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02963, over 972902.48 frames.], batch size: 28, lr: 1.31e-04
2022-05-08 23:03:46,994 INFO [train.py:715] (2/8) Epoch 17, batch 5600, loss[loss=0.1842, simple_loss=0.2341, pruned_loss=0.06709, over 4841.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2059, pruned_loss=0.02955, over 973342.60 frames.], batch size: 32, lr: 1.31e-04
2022-05-08 23:04:27,246 INFO [train.py:715] (2/8) Epoch 17, batch 5650, loss[loss=0.1599, simple_loss=0.2257, pruned_loss=0.04712, over 4752.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2061, pruned_loss=0.02943, over 973483.05 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:05:08,283 INFO [train.py:715] (2/8) Epoch 17, batch 5700, loss[loss=0.1345, simple_loss=0.221, pruned_loss=0.024, over 4808.00 frames.], tot_loss[loss=0.133, simple_loss=0.2065, pruned_loss=0.02971, over 973429.15 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:05:48,475 INFO [train.py:715] (2/8) Epoch 17, batch 5750, loss[loss=0.1603, simple_loss=0.2184, pruned_loss=0.05113, over 4869.00 frames.], tot_loss[loss=0.133, simple_loss=0.2065, pruned_loss=0.0298, over 973272.76 frames.], batch size: 32, lr: 1.31e-04
2022-05-08 23:06:27,758 INFO [train.py:715] (2/8) Epoch 17, batch 5800, loss[loss=0.1326, simple_loss=0.2083, pruned_loss=0.02842, over 4754.00 frames.], tot_loss[loss=0.1338, simple_loss=0.207, pruned_loss=0.03028, over 973153.58 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:07:08,767 INFO [train.py:715] (2/8) Epoch 17, batch 5850, loss[loss=0.1136, simple_loss=0.1993, pruned_loss=0.01389, over 4821.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2067, pruned_loss=0.02994, over 972662.02 frames.], batch size: 27, lr: 1.31e-04
2022-05-08 23:07:49,078 INFO [train.py:715] (2/8) Epoch 17, batch 5900, loss[loss=0.1409, simple_loss=0.216, pruned_loss=0.03294, over 4904.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2067, pruned_loss=0.02969, over 972732.92 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:08:29,706 INFO [train.py:715] (2/8) Epoch 17, batch 5950, loss[loss=0.1287, simple_loss=0.2068, pruned_loss=0.02535, over 4793.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02992, over 972370.46 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 23:09:09,142 INFO [train.py:715] (2/8) Epoch 17, batch 6000, loss[loss=0.1422, simple_loss=0.2199, pruned_loss=0.03223, over 4987.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2071, pruned_loss=0.02982, over 973394.33 frames.], batch size: 28, lr: 1.31e-04
2022-05-08 23:09:09,143 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 23:09:23,454 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1047, simple_loss=0.1881, pruned_loss=0.01069, over 914524.00 frames.
2022-05-08 23:10:02,839 INFO [train.py:715] (2/8) Epoch 17, batch 6050, loss[loss=0.1115, simple_loss=0.1962, pruned_loss=0.01344, over 4925.00 frames.], tot_loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02974, over 973035.72 frames.], batch size: 29, lr: 1.31e-04
2022-05-08 23:10:43,307 INFO [train.py:715] (2/8) Epoch 17, batch 6100, loss[loss=0.1471, simple_loss=0.2194, pruned_loss=0.03738, over 4906.00 frames.], tot_loss[loss=0.1335, simple_loss=0.207, pruned_loss=0.02994, over 973232.02 frames.], batch size: 39, lr: 1.31e-04
2022-05-08 23:11:22,422 INFO [train.py:715] (2/8) Epoch 17, batch 6150, loss[loss=0.1248, simple_loss=0.2015, pruned_loss=0.02406, over 4881.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02955, over 973303.62 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:12:02,022 INFO [train.py:715] (2/8) Epoch 17, batch 6200, loss[loss=0.1449, simple_loss=0.2179, pruned_loss=0.03596, over 4758.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02963, over 972801.48 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:12:42,479 INFO [train.py:715] (2/8) Epoch 17, batch 6250, loss[loss=0.138, simple_loss=0.2052, pruned_loss=0.03544, over 4861.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02977, over 972890.93 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 23:13:22,262 INFO [train.py:715] (2/8) Epoch 17, batch 6300, loss[loss=0.129, simple_loss=0.2009, pruned_loss=0.02854, over 4795.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2064, pruned_loss=0.02948, over 972776.39 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 23:14:01,678 INFO [train.py:715] (2/8) Epoch 17, batch 6350, loss[loss=0.1143, simple_loss=0.2039, pruned_loss=0.01234, over 4763.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.0293, over 973076.72 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:14:41,496 INFO [train.py:715] (2/8) Epoch 17, batch 6400, loss[loss=0.1311, simple_loss=0.2078, pruned_loss=0.0272, over 4790.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02952, over 972919.36 frames.], batch size: 21, lr: 1.31e-04
2022-05-08 23:15:21,780 INFO [train.py:715] (2/8) Epoch 17, batch 6450, loss[loss=0.1124, simple_loss=0.1891, pruned_loss=0.01781, over 4800.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2059, pruned_loss=0.02952, over 973183.23 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 23:16:01,145 INFO [train.py:715] (2/8) Epoch 17, batch 6500, loss[loss=0.1248, simple_loss=0.1799, pruned_loss=0.03482, over 4774.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2055, pruned_loss=0.02896, over 973199.69 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 23:16:40,472 INFO [train.py:715] (2/8) Epoch 17, batch 6550, loss[loss=0.1192, simple_loss=0.1964, pruned_loss=0.02094, over 4891.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2059, pruned_loss=0.02943, over 973145.84 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:17:20,842 INFO [train.py:715] (2/8) Epoch 17, batch 6600, loss[loss=0.1288, simple_loss=0.2158, pruned_loss=0.02087, over 4895.00 frames.], tot_loss[loss=0.1322, simple_loss=0.206, pruned_loss=0.02919, over 973543.82 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:18:01,037 INFO [train.py:715] (2/8) Epoch 17, batch 6650, loss[loss=0.131, simple_loss=0.2016, pruned_loss=0.0302, over 4791.00 frames.], tot_loss[loss=0.1314, simple_loss=0.205, pruned_loss=0.02893, over 973849.81 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:18:40,483 INFO [train.py:715] (2/8) Epoch 17, batch 6700, loss[loss=0.1466, simple_loss=0.2223, pruned_loss=0.0354, over 4764.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.02896, over 974002.41 frames.], batch size: 18, lr: 1.31e-04
2022-05-08 23:19:20,729 INFO [train.py:715] (2/8) Epoch 17, batch 6750, loss[loss=0.1293, simple_loss=0.2014, pruned_loss=0.02857, over 4836.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02903, over 972642.27 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:20:00,492 INFO [train.py:715] (2/8) Epoch 17, batch 6800, loss[loss=0.1191, simple_loss=0.1826, pruned_loss=0.02778, over 4848.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02863, over 973257.10 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 23:20:41,161 INFO [train.py:715] (2/8) Epoch 17, batch 6850, loss[loss=0.1432, simple_loss=0.2193, pruned_loss=0.03353, over 4932.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02852, over 973388.45 frames.], batch size: 21, lr: 1.31e-04
2022-05-08 23:21:20,238 INFO [train.py:715] (2/8) Epoch 17, batch 6900, loss[loss=0.1416, simple_loss=0.2227, pruned_loss=0.03026, over 4797.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02946, over 973258.97 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:22:00,929 INFO [train.py:715] (2/8) Epoch 17, batch 6950, loss[loss=0.1234, simple_loss=0.195, pruned_loss=0.02587, over 4765.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02896, over 972162.50 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:22:40,662 INFO [train.py:715] (2/8) Epoch 17, batch 7000, loss[loss=0.1573, simple_loss=0.2214, pruned_loss=0.04658, over 4810.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02923, over 970982.91 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 23:23:20,236 INFO [train.py:715] (2/8) Epoch 17, batch 7050, loss[loss=0.1314, simple_loss=0.2084, pruned_loss=0.02718, over 4836.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02963, over 970527.48 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 23:24:00,504 INFO [train.py:715] (2/8) Epoch 17, batch 7100, loss[loss=0.1485, simple_loss=0.2203, pruned_loss=0.03832, over 4869.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02948, over 970420.37 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:24:40,018 INFO [train.py:715] (2/8) Epoch 17, batch 7150, loss[loss=0.1346, simple_loss=0.2061, pruned_loss=0.03158, over 4749.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02904, over 970546.68 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:25:19,626 INFO [train.py:715] (2/8) Epoch 17, batch 7200, loss[loss=0.1446, simple_loss=0.2166, pruned_loss=0.03627, over 4879.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02959, over 969989.72 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:25:58,583 INFO [train.py:715] (2/8) Epoch 17, batch 7250, loss[loss=0.1613, simple_loss=0.2378, pruned_loss=0.04236, over 4976.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02943, over 970740.35 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:26:39,070 INFO [train.py:715] (2/8) Epoch 17, batch 7300, loss[loss=0.1257, simple_loss=0.1913, pruned_loss=0.03009, over 4949.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2067, pruned_loss=0.02955, over 971738.45 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:27:18,027 INFO [train.py:715] (2/8) Epoch 17, batch 7350, loss[loss=0.1477, simple_loss=0.2165, pruned_loss=0.03944, over 4880.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02974, over 972163.26 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:27:56,383 INFO [train.py:715] (2/8) Epoch 17, batch 7400, loss[loss=0.1491, simple_loss=0.2236, pruned_loss=0.03724, over 4889.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02936, over 972633.15 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 23:28:36,428 INFO [train.py:715] (2/8) Epoch 17, batch 7450, loss[loss=0.1101, simple_loss=0.1818, pruned_loss=0.01917, over 4984.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.0293, over 972513.06 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:29:15,434 INFO [train.py:715] (2/8) Epoch 17, batch 7500, loss[loss=0.1498, simple_loss=0.2128, pruned_loss=0.04339, over 4688.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2062, pruned_loss=0.02936, over 972140.19 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:29:55,163 INFO [train.py:715] (2/8) Epoch 17, batch 7550, loss[loss=0.1139, simple_loss=0.1868, pruned_loss=0.02043, over 4682.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02893, over 971460.32 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:30:34,486 INFO [train.py:715] (2/8) Epoch 17, batch 7600, loss[loss=0.126, simple_loss=0.1977, pruned_loss=0.02717, over 4874.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.0288, over 971400.02 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 23:31:14,609 INFO [train.py:715] (2/8) Epoch 17, batch 7650, loss[loss=0.1625, simple_loss=0.2302, pruned_loss=0.04742, over 4832.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02915, over 971796.17 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 23:31:54,497 INFO [train.py:715] (2/8) Epoch 17, batch 7700, loss[loss=0.142, simple_loss=0.1979, pruned_loss=0.04304, over 4763.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02908, over 971406.96 frames.], batch size: 16, lr: 1.31e-04
2022-05-08 23:32:33,807 INFO [train.py:715] (2/8) Epoch 17, batch 7750, loss[loss=0.1639, simple_loss=0.258, pruned_loss=0.03487, over 4852.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.0287, over 971318.31 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:33:14,402 INFO [train.py:715] (2/8) Epoch 17, batch 7800, loss[loss=0.1238, simple_loss=0.2038, pruned_loss=0.02191, over 4944.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02909, over 970678.93 frames.], batch size: 23, lr: 1.31e-04
2022-05-08 23:33:54,602 INFO [train.py:715] (2/8) Epoch 17, batch 7850, loss[loss=0.1393, simple_loss=0.2085, pruned_loss=0.03504, over 4910.00 frames.], tot_loss[loss=0.1323, simple_loss=0.206, pruned_loss=0.02931, over 970809.77 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:34:34,847 INFO [train.py:715] (2/8) Epoch 17, batch 7900, loss[loss=0.1396, simple_loss=0.222, pruned_loss=0.02855, over 4984.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2056, pruned_loss=0.02899, over 971004.11 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:35:13,815 INFO [train.py:715] (2/8) Epoch 17, batch 7950, loss[loss=0.1724, simple_loss=0.2486, pruned_loss=0.04815, over 4923.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.0293, over 971196.75 frames.], batch size: 21, lr: 1.31e-04
2022-05-08 23:35:53,565 INFO [train.py:715] (2/8) Epoch 17, batch 8000, loss[loss=0.1453, simple_loss=0.2231, pruned_loss=0.03377, over 4937.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02916, over 970887.32 frames.], batch size: 39, lr: 1.31e-04
2022-05-08 23:36:33,459 INFO [train.py:715] (2/8) Epoch 17, batch 8050, loss[loss=0.1226, simple_loss=0.2009, pruned_loss=0.02214, over 4938.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02937, over 971793.43 frames.], batch size: 29, lr: 1.31e-04
2022-05-08 23:37:12,791 INFO [train.py:715] (2/8) Epoch 17, batch 8100, loss[loss=0.1518, simple_loss=0.2187, pruned_loss=0.04245, over 4965.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2072, pruned_loss=0.02994, over 972210.96 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:37:52,686 INFO [train.py:715] (2/8) Epoch 17, batch 8150, loss[loss=0.1422, simple_loss=0.2224, pruned_loss=0.03097, over 4646.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2075, pruned_loss=0.02995, over 972609.53 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 23:38:32,357 INFO [train.py:715] (2/8) Epoch 17, batch 8200, loss[loss=0.1408, simple_loss=0.218, pruned_loss=0.03177, over 4778.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02956, over 972458.94 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:39:14,690 INFO [train.py:715] (2/8) Epoch 17, batch 8250, loss[loss=0.1216, simple_loss=0.1948, pruned_loss=0.02419, over 4833.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2077, pruned_loss=0.02992, over 972994.90 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 23:39:53,899 INFO [train.py:715] (2/8) Epoch 17, batch 8300, loss[loss=0.1375, simple_loss=0.2215, pruned_loss=0.02679, over 4895.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.0296, over 972808.09 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 23:40:33,621 INFO [train.py:715] (2/8) Epoch 17, batch 8350, loss[loss=0.1651, simple_loss=0.253, pruned_loss=0.0386, over 4959.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02987, over 973180.31 frames.], batch size: 35, lr: 1.31e-04
2022-05-08 23:41:13,228 INFO [train.py:715] (2/8) Epoch 17, batch 8400, loss[loss=0.1229, simple_loss=0.1995, pruned_loss=0.02319, over 4827.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03027, over 972824.02 frames.], batch size: 26, lr: 1.31e-04
2022-05-08 23:41:52,758 INFO [train.py:715] (2/8) Epoch 17, batch 8450, loss[loss=0.1268, simple_loss=0.1986, pruned_loss=0.02749, over 4801.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02988, over 972912.89 frames.], batch size: 14, lr: 1.31e-04
2022-05-08 23:42:32,328 INFO [train.py:715] (2/8) Epoch 17, batch 8500, loss[loss=0.115, simple_loss=0.18, pruned_loss=0.02505, over 4744.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02926, over 972769.70 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 23:43:12,137 INFO [train.py:715] (2/8) Epoch 17, batch 8550, loss[loss=0.122, simple_loss=0.2028, pruned_loss=0.02065, over 4977.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02935, over 972788.85 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 23:43:52,000 INFO [train.py:715] (2/8) Epoch 17, batch 8600, loss[loss=0.1277, simple_loss=0.2016, pruned_loss=0.0269, over 4865.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02852, over 972597.10 frames.], batch size: 20, lr: 1.31e-04
2022-05-08 23:44:31,009 INFO [train.py:715] (2/8) Epoch 17, batch 8650, loss[loss=0.1229, simple_loss=0.1879, pruned_loss=0.02892, over 4909.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02879, over 973127.03 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:45:10,881 INFO [train.py:715] (2/8) Epoch 17, batch 8700, loss[loss=0.1124, simple_loss=0.1857, pruned_loss=0.01959, over 4814.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02919, over 972081.28 frames.], batch size: 25, lr: 1.31e-04
2022-05-08 23:45:50,293 INFO [train.py:715] (2/8) Epoch 17, batch 8750, loss[loss=0.1203, simple_loss=0.1873, pruned_loss=0.02665, over 4646.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02914, over 972229.15 frames.], batch size: 13, lr: 1.31e-04
2022-05-08 23:46:29,855 INFO [train.py:715] (2/8) Epoch 17, batch 8800, loss[loss=0.1436, simple_loss=0.2254, pruned_loss=0.03088, over 4709.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02979, over 973001.75 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:47:09,588 INFO [train.py:715] (2/8) Epoch 17, batch 8850, loss[loss=0.1254, simple_loss=0.2, pruned_loss=0.02536, over 4977.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.0297, over 973570.68 frames.], batch size: 28, lr: 1.31e-04
2022-05-08 23:47:48,798 INFO [train.py:715] (2/8) Epoch 17, batch 8900, loss[loss=0.1296, simple_loss=0.2082, pruned_loss=0.02557, over 4985.00 frames.], tot_loss[loss=0.133, simple_loss=0.2068, pruned_loss=0.02953, over 973530.98 frames.], batch size: 27, lr: 1.31e-04
2022-05-08 23:48:28,443 INFO [train.py:715] (2/8) Epoch 17, batch 8950, loss[loss=0.1418, simple_loss=0.2171, pruned_loss=0.03329, over 4832.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2072, pruned_loss=0.02997, over 973317.91 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:49:07,465 INFO [train.py:715] (2/8) Epoch 17, batch 9000, loss[loss=0.1132, simple_loss=0.1793, pruned_loss=0.02354, over 4793.00 frames.], tot_loss[loss=0.133, simple_loss=0.2066, pruned_loss=0.02969, over 972599.87 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 23:49:07,466 INFO [train.py:733] (2/8) Computing validation loss
2022-05-08 23:49:17,247 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1048, simple_loss=0.1882, pruned_loss=0.01072, over 914524.00 frames.
2022-05-08 23:49:56,411 INFO [train.py:715] (2/8) Epoch 17, batch 9050, loss[loss=0.1489, simple_loss=0.2227, pruned_loss=0.0376, over 4911.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2065, pruned_loss=0.02956, over 972777.92 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:50:36,247 INFO [train.py:715] (2/8) Epoch 17, batch 9100, loss[loss=0.1304, simple_loss=0.2047, pruned_loss=0.02804, over 4823.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02903, over 973141.52 frames.], batch size: 25, lr: 1.31e-04
2022-05-08 23:51:15,864 INFO [train.py:715] (2/8) Epoch 17, batch 9150, loss[loss=0.1415, simple_loss=0.2102, pruned_loss=0.03639, over 4767.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02944, over 973490.03 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:51:54,740 INFO [train.py:715] (2/8) Epoch 17, batch 9200, loss[loss=0.1467, simple_loss=0.2211, pruned_loss=0.03612, over 4704.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02963, over 973379.15 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:52:34,931 INFO [train.py:715] (2/8) Epoch 17, batch 9250, loss[loss=0.1317, simple_loss=0.2075, pruned_loss=0.02795, over 4846.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2074, pruned_loss=0.02976, over 972637.24 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 23:53:14,617 INFO [train.py:715] (2/8) Epoch 17, batch 9300, loss[loss=0.1241, simple_loss=0.2007, pruned_loss=0.02379, over 4776.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02945, over 971977.90 frames.], batch size: 17, lr: 1.31e-04
2022-05-08 23:53:53,953 INFO [train.py:715] (2/8) Epoch 17, batch 9350, loss[loss=0.1298, simple_loss=0.2058, pruned_loss=0.02692, over 4886.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02911, over 971863.49 frames.], batch size: 30, lr: 1.31e-04
2022-05-08 23:54:33,276 INFO [train.py:715] (2/8) Epoch 17, batch 9400, loss[loss=0.1545, simple_loss=0.2228, pruned_loss=0.04308, over 4797.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2064, pruned_loss=0.02966, over 971622.00 frames.], batch size: 24, lr: 1.31e-04
2022-05-08 23:55:13,700 INFO [train.py:715] (2/8) Epoch 17, batch 9450, loss[loss=0.1283, simple_loss=0.1956, pruned_loss=0.03053, over 4875.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2053, pruned_loss=0.02905, over 971675.45 frames.], batch size: 20, lr: 1.31e-04
2022-05-08 23:55:53,692 INFO [train.py:715] (2/8) Epoch 17, batch 9500, loss[loss=0.102, simple_loss=0.1669, pruned_loss=0.01856, over 4788.00 frames.], tot_loss[loss=0.1312, simple_loss=0.205, pruned_loss=0.02867, over 971911.69 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 23:56:32,928 INFO [train.py:715] (2/8) Epoch 17, batch 9550, loss[loss=0.1112, simple_loss=0.1729, pruned_loss=0.02477, over 4808.00 frames.], tot_loss[loss=0.132, simple_loss=0.2058, pruned_loss=0.02909, over 972397.59 frames.], batch size: 12, lr: 1.31e-04
2022-05-08 23:57:12,480 INFO [train.py:715] (2/8) Epoch 17, batch 9600, loss[loss=0.1335, simple_loss=0.2045, pruned_loss=0.03126, over 4888.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2056, pruned_loss=0.02956, over 972589.00 frames.], batch size: 22, lr: 1.31e-04
2022-05-08 23:57:52,761 INFO [train.py:715] (2/8) Epoch 17, batch 9650, loss[loss=0.1307, simple_loss=0.1995, pruned_loss=0.03095, over 4882.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2054, pruned_loss=0.02948, over 972643.89 frames.], batch size: 19, lr: 1.31e-04
2022-05-08 23:58:31,947 INFO [train.py:715] (2/8) Epoch 17, batch 9700, loss[loss=0.1328, simple_loss=0.2102, pruned_loss=0.02771, over 4865.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2044, pruned_loss=0.02893, over 972033.38 frames.], batch size: 20, lr: 1.31e-04
2022-05-08 23:59:11,716 INFO [train.py:715] (2/8) Epoch 17, batch 9750, loss[loss=0.1142, simple_loss=0.1826, pruned_loss=0.02295, over 4828.00 frames.], tot_loss[loss=0.1316, simple_loss=0.205, pruned_loss=0.02908, over 972700.06 frames.], batch size: 15, lr: 1.31e-04
2022-05-08 23:59:51,461 INFO [train.py:715] (2/8) Epoch 17, batch 9800, loss[loss=0.1475, simple_loss=0.2139, pruned_loss=0.04058, over 4861.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02969, over 972570.54 frames.], batch size: 13, lr: 1.31e-04
2022-05-09 00:00:31,045 INFO [train.py:715] (2/8) Epoch 17, batch 9850, loss[loss=0.1323, simple_loss=0.2096, pruned_loss=0.02752, over 4954.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.03003, over 972296.89 frames.], batch size: 24, lr: 1.31e-04
2022-05-09 00:01:10,446 INFO [train.py:715] (2/8) Epoch 17, batch 9900, loss[loss=0.1285, simple_loss=0.1955, pruned_loss=0.03076, over 4932.00 frames.], tot_loss[loss=0.1336, simple_loss=0.207, pruned_loss=0.03009, over 972446.66 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:01:49,850 INFO [train.py:715] (2/8) Epoch 17, batch 9950, loss[loss=0.1521, simple_loss=0.2288, pruned_loss=0.03765, over 4960.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02965, over 973259.89 frames.], batch size: 24, lr: 1.31e-04
2022-05-09 00:02:30,142 INFO [train.py:715] (2/8) Epoch 17, batch 10000, loss[loss=0.1713, simple_loss=0.2559, pruned_loss=0.0434, over 4992.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02989, over 973058.73 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:03:09,392 INFO [train.py:715] (2/8) Epoch 17, batch 10050, loss[loss=0.1344, simple_loss=0.212, pruned_loss=0.02838, over 4830.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2074, pruned_loss=0.03011, over 973245.33 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:03:48,278 INFO [train.py:715] (2/8) Epoch 17, batch 10100, loss[loss=0.144, simple_loss=0.2221, pruned_loss=0.03298, over 4816.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.0299, over 973920.53 frames.], batch size: 25, lr: 1.31e-04
2022-05-09 00:04:27,596 INFO [train.py:715] (2/8) Epoch 17, batch 10150, loss[loss=0.1385, simple_loss=0.214, pruned_loss=0.03153, over 4900.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02927, over 973900.24 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:05:06,925 INFO [train.py:715] (2/8) Epoch 17, batch 10200, loss[loss=0.129, simple_loss=0.2045, pruned_loss=0.02671, over 4823.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02887, over 973363.09 frames.], batch size: 30, lr: 1.31e-04
2022-05-09 00:05:44,878 INFO [train.py:715] (2/8) Epoch 17, batch 10250, loss[loss=0.1106, simple_loss=0.1843, pruned_loss=0.01847, over 4816.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02851, over 973233.84 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:06:24,647 INFO [train.py:715] (2/8) Epoch 17, batch 10300, loss[loss=0.1484, simple_loss=0.2183, pruned_loss=0.03927, over 4798.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2056, pruned_loss=0.02808, over 972770.83 frames.], batch size: 21, lr: 1.31e-04
2022-05-09 00:07:04,573 INFO [train.py:715] (2/8) Epoch 17, batch 10350, loss[loss=0.1245, simple_loss=0.2087, pruned_loss=0.02016, over 4815.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02852, over 972905.32 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:07:43,243 INFO [train.py:715] (2/8) Epoch 17, batch 10400, loss[loss=0.1444, simple_loss=0.206, pruned_loss=0.04142, over 4862.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02884, over 972187.98 frames.], batch size: 32, lr: 1.31e-04
2022-05-09 00:08:22,365 INFO [train.py:715] (2/8) Epoch 17, batch 10450, loss[loss=0.121, simple_loss=0.1989, pruned_loss=0.02154, over 4956.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02901, over 972036.66 frames.], batch size: 24, lr: 1.31e-04
2022-05-09 00:09:02,376 INFO [train.py:715] (2/8) Epoch 17, batch 10500, loss[loss=0.1525, simple_loss=0.2284, pruned_loss=0.03826, over 4933.00 frames.], tot_loss[loss=0.134, simple_loss=0.2084, pruned_loss=0.02974, over 971972.08 frames.], batch size: 23, lr: 1.31e-04
2022-05-09 00:09:41,414 INFO [train.py:715] (2/8) Epoch 17, batch 10550, loss[loss=0.146, simple_loss=0.2205, pruned_loss=0.03572, over 4867.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02936, over 971971.72 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:10:19,759 INFO [train.py:715] (2/8) Epoch 17, batch 10600, loss[loss=0.1183, simple_loss=0.2017, pruned_loss=0.01742, over 4786.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.0293, over 972344.30 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:10:59,071 INFO [train.py:715] (2/8) Epoch 17, batch 10650, loss[loss=0.1362, simple_loss=0.2124, pruned_loss=0.02995, over 4818.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02934, over 971963.40 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:11:38,577 INFO [train.py:715] (2/8) Epoch 17, batch 10700, loss[loss=0.1489, simple_loss=0.2278, pruned_loss=0.035, over 4882.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02941, over 971883.44 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:12:17,257 INFO [train.py:715] (2/8) Epoch 17, batch 10750, loss[loss=0.1129, simple_loss=0.187, pruned_loss=0.01944, over 4953.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02976, over 972965.13 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:12:56,255 INFO [train.py:715] (2/8) Epoch 17, batch 10800, loss[loss=0.1218, simple_loss=0.2071, pruned_loss=0.01829, over 4985.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.0295, over 973460.43 frames.], batch size: 28, lr: 1.31e-04
2022-05-09 00:13:36,023 INFO [train.py:715] (2/8) Epoch 17, batch 10850, loss[loss=0.1375, simple_loss=0.2142, pruned_loss=0.03045, over 4773.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02991, over 972695.27 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:14:15,586 INFO [train.py:715] (2/8) Epoch 17, batch 10900, loss[loss=0.1208, simple_loss=0.1956, pruned_loss=0.02304, over 4749.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02967, over 972376.69 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:14:53,760 INFO [train.py:715] (2/8) Epoch 17, batch 10950, loss[loss=0.1199, simple_loss=0.195, pruned_loss=0.02245, over 4828.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03003, over 972135.27 frames.], batch size: 13, lr: 1.31e-04
2022-05-09 00:15:33,874 INFO [train.py:715] (2/8) Epoch 17, batch 11000, loss[loss=0.127, simple_loss=0.1951, pruned_loss=0.02947, over 4705.00 frames.], tot_loss[loss=0.134, simple_loss=0.2082, pruned_loss=0.02986, over 971937.14 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:16:13,751 INFO [train.py:715] (2/8) Epoch 17, batch 11050, loss[loss=0.1264, simple_loss=0.1992, pruned_loss=0.02675, over 4821.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02887, over 972501.04 frames.], batch size: 13, lr: 1.31e-04
2022-05-09 00:16:52,429 INFO [train.py:715] (2/8) Epoch 17, batch 11100, loss[loss=0.1301, simple_loss=0.2055, pruned_loss=0.02735, over 4877.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02895, over 972552.33 frames.], batch size: 20, lr: 1.31e-04
2022-05-09 00:17:31,482 INFO [train.py:715] (2/8) Epoch 17, batch 11150, loss[loss=0.1058, simple_loss=0.1796, pruned_loss=0.01602, over 4963.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02905, over 971799.46 frames.], batch size: 29, lr: 1.31e-04
2022-05-09 00:18:11,487 INFO [train.py:715] (2/8) Epoch 17, batch 11200, loss[loss=0.1234, simple_loss=0.1957, pruned_loss=0.02557, over 4795.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.0292, over 971751.72 frames.], batch size: 17, lr: 1.31e-04
2022-05-09 00:18:51,597 INFO [train.py:715] (2/8) Epoch 17, batch 11250, loss[loss=0.1293, simple_loss=0.2138, pruned_loss=0.0224, over 4763.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02929, over 971691.55 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:19:29,833 INFO [train.py:715] (2/8) Epoch 17, batch 11300, loss[loss=0.121, simple_loss=0.1949, pruned_loss=0.02357, over 4768.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02909, over 972248.76 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:20:09,303 INFO [train.py:715] (2/8) Epoch 17, batch 11350, loss[loss=0.1281, simple_loss=0.198, pruned_loss=0.02916, over 4894.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02893, over 972504.20 frames.], batch size: 22, lr: 1.31e-04
2022-05-09 00:20:49,490 INFO [train.py:715] (2/8) Epoch 17, batch 11400, loss[loss=0.1128, simple_loss=0.1854, pruned_loss=0.02006, over 4678.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02886, over 972378.51 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:21:28,499 INFO [train.py:715] (2/8) Epoch 17, batch 11450, loss[loss=0.1269, simple_loss=0.209, pruned_loss=0.02244, over 4810.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02916, over 972597.96 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:22:07,512 INFO [train.py:715] (2/8) Epoch 17, batch 11500, loss[loss=0.125, simple_loss=0.2041, pruned_loss=0.02294, over 4930.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02918, over 972896.70 frames.], batch size: 29, lr: 1.31e-04
2022-05-09 00:22:47,222 INFO [train.py:715] (2/8) Epoch 17, batch 11550, loss[loss=0.1333, simple_loss=0.2134, pruned_loss=0.02659, over 4801.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02903, over 972637.62 frames.], batch size: 24, lr: 1.31e-04
2022-05-09 00:23:27,163 INFO [train.py:715] (2/8) Epoch 17, batch 11600, loss[loss=0.1303, simple_loss=0.2091, pruned_loss=0.02573, over 4818.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.0289, over 972858.91 frames.], batch size: 25, lr: 1.31e-04
2022-05-09 00:24:05,130 INFO [train.py:715] (2/8) Epoch 17, batch 11650, loss[loss=0.1437, simple_loss=0.2176, pruned_loss=0.03494, over 4862.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02887, over 973260.62 frames.], batch size: 20, lr: 1.31e-04
2022-05-09 00:24:44,956 INFO [train.py:715] (2/8) Epoch 17, batch 11700, loss[loss=0.1369, simple_loss=0.2133, pruned_loss=0.03022, over 4871.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02894, over 972818.99 frames.], batch size: 20, lr: 1.31e-04
2022-05-09 00:25:24,940 INFO [train.py:715] (2/8) Epoch 17, batch 11750, loss[loss=0.1421, simple_loss=0.2062, pruned_loss=0.03902, over 4968.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02887, over 973380.23 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:26:03,880 INFO [train.py:715] (2/8) Epoch 17, batch 11800, loss[loss=0.1226, simple_loss=0.2091, pruned_loss=0.01801, over 4745.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02887, over 972730.91 frames.], batch size: 16, lr: 1.31e-04
2022-05-09 00:26:42,876 INFO [train.py:715] (2/8) Epoch 17, batch 11850, loss[loss=0.1608, simple_loss=0.2218, pruned_loss=0.04991, over 4937.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02928, over 972069.93 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:27:22,149 INFO [train.py:715] (2/8) Epoch 17, batch 11900, loss[loss=0.1222, simple_loss=0.1998, pruned_loss=0.02229, over 4909.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02913, over 973009.40 frames.], batch size: 23, lr: 1.31e-04
2022-05-09 00:28:01,949 INFO [train.py:715] (2/8) Epoch 17, batch 11950, loss[loss=0.1268, simple_loss=0.2148, pruned_loss=0.01937, over 4853.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2078, pruned_loss=0.02899, over 972660.75 frames.], batch size: 20, lr: 1.31e-04
2022-05-09 00:28:40,965 INFO [train.py:715] (2/8) Epoch 17, batch 12000, loss[loss=0.1674, simple_loss=0.2428, pruned_loss=0.04606, over 4811.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2076, pruned_loss=0.02871, over 972294.61 frames.], batch size: 21, lr: 1.31e-04
2022-05-09 00:28:40,966 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 00:28:52,719 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1048, simple_loss=0.1882, pruned_loss=0.0107, over 914524.00 frames.
2022-05-09 00:29:31,826 INFO [train.py:715] (2/8) Epoch 17, batch 12050, loss[loss=0.1596, simple_loss=0.2252, pruned_loss=0.04698, over 4968.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2074, pruned_loss=0.0287, over 972374.52 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:30:10,919 INFO [train.py:715] (2/8) Epoch 17, batch 12100, loss[loss=0.1201, simple_loss=0.1909, pruned_loss=0.02468, over 4842.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2075, pruned_loss=0.02886, over 972389.06 frames.], batch size: 30, lr: 1.31e-04
2022-05-09 00:30:50,933 INFO [train.py:715] (2/8) Epoch 17, batch 12150, loss[loss=0.1162, simple_loss=0.1979, pruned_loss=0.0173, over 4793.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02919, over 973082.66 frames.], batch size: 21, lr: 1.31e-04
2022-05-09 00:31:29,659 INFO [train.py:715] (2/8) Epoch 17, batch 12200, loss[loss=0.1237, simple_loss=0.1934, pruned_loss=0.02704, over 4856.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.0291, over 972615.87 frames.], batch size: 30, lr: 1.31e-04
2022-05-09 00:32:08,195 INFO [train.py:715] (2/8) Epoch 17, batch 12250, loss[loss=0.1123, simple_loss=0.1921, pruned_loss=0.01624, over 4808.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02883, over 972443.56 frames.], batch size: 12, lr: 1.31e-04
2022-05-09 00:32:47,685 INFO [train.py:715] (2/8) Epoch 17, batch 12300, loss[loss=0.1376, simple_loss=0.2023, pruned_loss=0.03649, over 4976.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02866, over 972873.06 frames.], batch size: 31, lr: 1.31e-04
2022-05-09 00:33:26,862 INFO [train.py:715] (2/8) Epoch 17, batch 12350, loss[loss=0.1263, simple_loss=0.1964, pruned_loss=0.0281, over 4691.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02918, over 972741.40 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:34:05,566 INFO [train.py:715] (2/8) Epoch 17, batch 12400, loss[loss=0.151, simple_loss=0.2366, pruned_loss=0.03275, over 4902.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02914, over 972892.87 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:34:44,617 INFO [train.py:715] (2/8) Epoch 17, batch 12450, loss[loss=0.1543, simple_loss=0.225, pruned_loss=0.04179, over 4851.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02909, over 972239.43 frames.], batch size: 20, lr: 1.31e-04
2022-05-09 00:35:24,995 INFO [train.py:715] (2/8) Epoch 17, batch 12500, loss[loss=0.1552, simple_loss=0.2334, pruned_loss=0.03851, over 4912.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02874, over 973160.83 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:36:03,581 INFO [train.py:715] (2/8) Epoch 17, batch 12550, loss[loss=0.1397, simple_loss=0.2093, pruned_loss=0.03504, over 4791.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02879, over 972942.88 frames.], batch size: 14, lr: 1.31e-04
2022-05-09 00:36:42,957 INFO [train.py:715] (2/8) Epoch 17, batch 12600, loss[loss=0.1246, simple_loss=0.2068, pruned_loss=0.02118, over 4852.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2074, pruned_loss=0.02904, over 972233.36 frames.], batch size: 20, lr: 1.31e-04
2022-05-09 00:37:22,859 INFO [train.py:715] (2/8) Epoch 17, batch 12650, loss[loss=0.1229, simple_loss=0.1904, pruned_loss=0.02775, over 4813.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2076, pruned_loss=0.02886, over 972166.11 frames.], batch size: 12, lr: 1.31e-04
2022-05-09 00:38:02,853 INFO [train.py:715] (2/8) Epoch 17, batch 12700, loss[loss=0.1157, simple_loss=0.1894, pruned_loss=0.021, over 4893.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02939, over 972327.72 frames.], batch size: 22, lr: 1.31e-04
2022-05-09 00:38:42,180 INFO [train.py:715] (2/8) Epoch 17, batch 12750, loss[loss=0.1522, simple_loss=0.2386, pruned_loss=0.03288, over 4803.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02973, over 972249.77 frames.], batch size: 25, lr: 1.31e-04
2022-05-09 00:39:20,964 INFO [train.py:715] (2/8) Epoch 17, batch 12800, loss[loss=0.1271, simple_loss=0.2013, pruned_loss=0.02651, over 4919.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.02953, over 972475.88 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:40:00,596 INFO [train.py:715] (2/8) Epoch 17, batch 12850, loss[loss=0.1284, simple_loss=0.2112, pruned_loss=0.02283, over 4934.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2076, pruned_loss=0.02888, over 972446.99 frames.], batch size: 21, lr: 1.31e-04
2022-05-09 00:40:39,042 INFO [train.py:715] (2/8) Epoch 17, batch 12900, loss[loss=0.1378, simple_loss=0.2134, pruned_loss=0.0311, over 4908.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2072, pruned_loss=0.02873, over 972931.30 frames.], batch size: 18, lr: 1.31e-04
2022-05-09 00:41:18,427 INFO [train.py:715] (2/8) Epoch 17, batch 12950, loss[loss=0.1273, simple_loss=0.2167, pruned_loss=0.01895, over 4808.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02859, over 973207.50 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:41:57,022 INFO [train.py:715] (2/8) Epoch 17, batch 13000, loss[loss=0.1201, simple_loss=0.1861, pruned_loss=0.02704, over 4851.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02832, over 973172.27 frames.], batch size: 20, lr: 1.31e-04
2022-05-09 00:42:36,104 INFO [train.py:715] (2/8) Epoch 17, batch 13050, loss[loss=0.1071, simple_loss=0.1804, pruned_loss=0.0169, over 4962.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2066, pruned_loss=0.02835, over 973368.96 frames.], batch size: 24, lr: 1.31e-04
2022-05-09 00:43:15,227 INFO [train.py:715] (2/8) Epoch 17, batch 13100, loss[loss=0.116, simple_loss=0.1968, pruned_loss=0.01762, over 4976.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02897, over 972299.90 frames.], batch size: 24, lr: 1.31e-04
2022-05-09 00:43:54,023 INFO [train.py:715] (2/8) Epoch 17, batch 13150, loss[loss=0.1155, simple_loss=0.2076, pruned_loss=0.0117, over 4980.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2075, pruned_loss=0.02897, over 972105.07 frames.], batch size: 28, lr: 1.31e-04
2022-05-09 00:44:33,799 INFO [train.py:715] (2/8) Epoch 17, batch 13200, loss[loss=0.1429, simple_loss=0.2177, pruned_loss=0.03407, over 4979.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02868, over 972128.99 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:45:12,320 INFO [train.py:715] (2/8) Epoch 17, batch 13250, loss[loss=0.1118, simple_loss=0.1885, pruned_loss=0.01754, over 4936.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02906, over 972102.21 frames.], batch size: 29, lr: 1.31e-04
2022-05-09 00:45:51,624 INFO [train.py:715] (2/8) Epoch 17, batch 13300, loss[loss=0.1353, simple_loss=0.2098, pruned_loss=0.03041, over 4901.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02867, over 973288.68 frames.], batch size: 19, lr: 1.31e-04
2022-05-09 00:46:30,712 INFO [train.py:715] (2/8) Epoch 17, batch 13350, loss[loss=0.1347, simple_loss=0.2041, pruned_loss=0.03269, over 4811.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02883, over 973270.10 frames.], batch size: 26, lr: 1.31e-04
2022-05-09 00:47:09,940 INFO [train.py:715] (2/8) Epoch 17, batch 13400, loss[loss=0.1191, simple_loss=0.1934, pruned_loss=0.02241, over 4705.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02886, over 973786.19 frames.], batch size: 15, lr: 1.31e-04
2022-05-09 00:47:49,252 INFO [train.py:715] (2/8) Epoch 17, batch 13450, loss[loss=0.1323, simple_loss=0.2023, pruned_loss=0.03116, over 4951.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02888, over 973383.57 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 00:48:27,714 INFO [train.py:715] (2/8) Epoch 17, batch 13500, loss[loss=0.1555, simple_loss=0.2209, pruned_loss=0.04507, over 4746.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02893, over 972166.41 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 00:49:07,422 INFO [train.py:715] (2/8) Epoch 17, batch 13550, loss[loss=0.1201, simple_loss=0.2002, pruned_loss=0.01994, over 4800.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02876, over 971805.53 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 00:49:45,776 INFO [train.py:715] (2/8) Epoch 17, batch 13600, loss[loss=0.1045, simple_loss=0.1765, pruned_loss=0.01629, over 4790.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02905, over 971594.95 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 00:50:24,820 INFO [train.py:715] (2/8) Epoch 17, batch 13650, loss[loss=0.1378, simple_loss=0.2068, pruned_loss=0.03435, over 4874.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.0291, over 971518.36 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 00:51:04,639 INFO [train.py:715] (2/8) Epoch 17, batch 13700, loss[loss=0.1032, simple_loss=0.1737, pruned_loss=0.01635, over 4743.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02849, over 971253.63 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 00:51:43,961 INFO [train.py:715] (2/8) Epoch 17, batch 13750, loss[loss=0.1406, simple_loss=0.2206, pruned_loss=0.03035, over 4745.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.02814, over 971265.72 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 00:52:24,099 INFO [train.py:715] (2/8) Epoch 17, batch 13800, loss[loss=0.1383, simple_loss=0.2229, pruned_loss=0.02682, over 4749.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02869, over 971012.57 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 00:53:03,509 INFO [train.py:715] (2/8) Epoch 17, batch 13850, loss[loss=0.1089, simple_loss=0.1898, pruned_loss=0.01402, over 4826.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02884, over 972209.40 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 00:53:43,320 INFO [train.py:715] (2/8) Epoch 17, batch 13900, loss[loss=0.116, simple_loss=0.1857, pruned_loss=0.0231, over 4777.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02839, over 972163.10 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 00:54:22,827 INFO [train.py:715] (2/8) Epoch 17, batch 13950, loss[loss=0.1453, simple_loss=0.2139, pruned_loss=0.03833, over 4954.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02829, over 971820.62 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 00:55:02,840 INFO [train.py:715] (2/8) Epoch 17, batch 14000, loss[loss=0.1496, simple_loss=0.2054, pruned_loss=0.04694, over 4861.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02826, over 972717.15 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 00:55:42,012 INFO [train.py:715] (2/8) Epoch 17, batch 14050, loss[loss=0.1166, simple_loss=0.1886, pruned_loss=0.02227, over 4788.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2061, pruned_loss=0.02816, over 972433.76 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 00:56:21,074 INFO [train.py:715] (2/8) Epoch 17, batch 14100, loss[loss=0.12, simple_loss=0.1992, pruned_loss=0.02041, over 4707.00 frames.], tot_loss[loss=0.131, simple_loss=0.2057, pruned_loss=0.02816, over 972218.84 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 00:57:01,249 INFO [train.py:715] (2/8) Epoch 17, batch 14150, loss[loss=0.1502, simple_loss=0.213, pruned_loss=0.04368, over 4859.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02871, over 972502.64 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 00:57:40,316 INFO [train.py:715] (2/8) Epoch 17, batch 14200, loss[loss=0.1341, simple_loss=0.201, pruned_loss=0.03364, over 4881.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2065, pruned_loss=0.02848, over 972750.99 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 00:58:19,838 INFO [train.py:715] (2/8) Epoch 17, batch 14250, loss[loss=0.1263, simple_loss=0.2021, pruned_loss=0.02528, over 4749.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02878, over 972793.81 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 00:58:58,999 INFO [train.py:715] (2/8) Epoch 17, batch 14300, loss[loss=0.1193, simple_loss=0.2002, pruned_loss=0.01921, over 4823.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02868, over 972958.38 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 00:59:38,871 INFO [train.py:715] (2/8) Epoch 17, batch 14350, loss[loss=0.1353, simple_loss=0.2133, pruned_loss=0.02865, over 4849.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02893, over 973220.32 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:00:17,888 INFO [train.py:715] (2/8) Epoch 17, batch 14400, loss[loss=0.1274, simple_loss=0.2025, pruned_loss=0.02619, over 4636.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02898, over 972049.40 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:00:56,578 INFO [train.py:715] (2/8) Epoch 17, batch 14450, loss[loss=0.125, simple_loss=0.2002, pruned_loss=0.02496, over 4755.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02925, over 972099.50 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:01:36,312 INFO [train.py:715] (2/8) Epoch 17, batch 14500, loss[loss=0.1178, simple_loss=0.1886, pruned_loss=0.02352, over 4828.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02941, over 971992.90 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 01:02:15,674 INFO [train.py:715] (2/8) Epoch 17, batch 14550, loss[loss=0.134, simple_loss=0.2064, pruned_loss=0.03081, over 4981.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02935, over 972746.87 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:02:54,150 INFO [train.py:715] (2/8) Epoch 17, batch 14600, loss[loss=0.1327, simple_loss=0.211, pruned_loss=0.02719, over 4932.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02989, over 972748.07 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:03:33,788 INFO [train.py:715] (2/8) Epoch 17, batch 14650, loss[loss=0.1206, simple_loss=0.1966, pruned_loss=0.02227, over 4945.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02969, over 973213.97 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 01:04:13,438 INFO [train.py:715] (2/8) Epoch 17, batch 14700, loss[loss=0.1265, simple_loss=0.2007, pruned_loss=0.02617, over 4934.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02974, over 973209.39 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:04:52,653 INFO [train.py:715] (2/8) Epoch 17, batch 14750, loss[loss=0.1328, simple_loss=0.1988, pruned_loss=0.0334, over 4958.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02977, over 972935.52 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:05:31,535 INFO [train.py:715] (2/8) Epoch 17, batch 14800, loss[loss=0.1408, simple_loss=0.2112, pruned_loss=0.03518, over 4954.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02934, over 972392.90 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:06:11,617 INFO [train.py:715] (2/8) Epoch 17, batch 14850, loss[loss=0.1159, simple_loss=0.1995, pruned_loss=0.01621, over 4963.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02933, over 971533.93 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:06:50,382 INFO [train.py:715] (2/8) Epoch 17, batch 14900, loss[loss=0.1267, simple_loss=0.2016, pruned_loss=0.02586, over 4845.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02919, over 971628.08 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:07:29,329 INFO [train.py:715] (2/8) Epoch 17, batch 14950, loss[loss=0.1378, simple_loss=0.2126, pruned_loss=0.0315, over 4855.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02924, over 972287.59 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 01:08:09,032 INFO [train.py:715] (2/8) Epoch 17, batch 15000, loss[loss=0.1362, simple_loss=0.2111, pruned_loss=0.03064, over 4822.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02949, over 972658.64 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 01:08:09,033 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 01:08:19,083 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1046, simple_loss=0.1881, pruned_loss=0.01059, over 914524.00 frames.
2022-05-09 01:08:59,146 INFO [train.py:715] (2/8) Epoch 17, batch 15050, loss[loss=0.1185, simple_loss=0.1869, pruned_loss=0.02503, over 4834.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02915, over 972491.38 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:09:38,651 INFO [train.py:715] (2/8) Epoch 17, batch 15100, loss[loss=0.1803, simple_loss=0.2423, pruned_loss=0.05913, over 4921.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02929, over 972466.39 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:10:17,574 INFO [train.py:715] (2/8) Epoch 17, batch 15150, loss[loss=0.1481, simple_loss=0.222, pruned_loss=0.03714, over 4777.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.0291, over 971872.90 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 01:10:56,608 INFO [train.py:715] (2/8) Epoch 17, batch 15200, loss[loss=0.1543, simple_loss=0.2262, pruned_loss=0.0412, over 4820.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02919, over 972602.51 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:11:36,240 INFO [train.py:715] (2/8) Epoch 17, batch 15250, loss[loss=0.1274, simple_loss=0.1997, pruned_loss=0.02753, over 4889.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02961, over 972520.68 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:12:15,624 INFO [train.py:715] (2/8) Epoch 17, batch 15300, loss[loss=0.1188, simple_loss=0.1898, pruned_loss=0.02391, over 4975.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02909, over 972563.87 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:12:53,859 INFO [train.py:715] (2/8) Epoch 17, batch 15350, loss[loss=0.1304, simple_loss=0.2065, pruned_loss=0.02711, over 4959.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02903, over 972234.52 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 01:13:33,406 INFO [train.py:715] (2/8) Epoch 17, batch 15400, loss[loss=0.1267, simple_loss=0.2042, pruned_loss=0.02462, over 4954.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02885, over 971885.11 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 01:14:12,475 INFO [train.py:715] (2/8) Epoch 17, batch 15450, loss[loss=0.1311, simple_loss=0.2148, pruned_loss=0.02366, over 4981.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.0284, over 972622.38 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:14:51,826 INFO [train.py:715] (2/8) Epoch 17, batch 15500, loss[loss=0.1166, simple_loss=0.1888, pruned_loss=0.02216, over 4955.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02888, over 972405.43 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:15:30,648 INFO [train.py:715] (2/8) Epoch 17, batch 15550, loss[loss=0.1281, simple_loss=0.2094, pruned_loss=0.02343, over 4800.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02928, over 971716.66 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:16:10,371 INFO [train.py:715] (2/8) Epoch 17, batch 15600, loss[loss=0.1445, simple_loss=0.2122, pruned_loss=0.0384, over 4652.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02914, over 971306.53 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:16:49,790 INFO [train.py:715] (2/8) Epoch 17, batch 15650, loss[loss=0.1323, simple_loss=0.2031, pruned_loss=0.03078, over 4885.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02833, over 971432.80 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 01:17:27,917 INFO [train.py:715] (2/8) Epoch 17, batch 15700, loss[loss=0.1092, simple_loss=0.1808, pruned_loss=0.01878, over 4809.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02912, over 972374.28 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:18:07,728 INFO [train.py:715] (2/8) Epoch 17, batch 15750, loss[loss=0.1293, simple_loss=0.202, pruned_loss=0.02828, over 4982.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02942, over 972331.63 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:18:47,154 INFO [train.py:715] (2/8) Epoch 17, batch 15800, loss[loss=0.1168, simple_loss=0.1944, pruned_loss=0.01956, over 4750.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02884, over 972403.18 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:19:26,085 INFO [train.py:715] (2/8) Epoch 17, batch 15850, loss[loss=0.1217, simple_loss=0.1878, pruned_loss=0.02781, over 4987.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02935, over 972992.66 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 01:20:04,723 INFO [train.py:715] (2/8) Epoch 17, batch 15900, loss[loss=0.1597, simple_loss=0.2421, pruned_loss=0.03862, over 4929.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02948, over 974004.98 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 01:20:44,131 INFO [train.py:715] (2/8) Epoch 17, batch 15950, loss[loss=0.1012, simple_loss=0.1764, pruned_loss=0.01306, over 4929.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02942, over 973128.58 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:21:23,631 INFO [train.py:715] (2/8) Epoch 17, batch 16000, loss[loss=0.1082, simple_loss=0.1869, pruned_loss=0.01469, over 4937.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02961, over 973460.49 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 01:22:02,020 INFO [train.py:715] (2/8) Epoch 17, batch 16050, loss[loss=0.1345, simple_loss=0.2089, pruned_loss=0.03002, over 4915.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02962, over 973065.36 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:22:42,058 INFO [train.py:715] (2/8) Epoch 17, batch 16100, loss[loss=0.1213, simple_loss=0.1962, pruned_loss=0.02316, over 4735.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02905, over 972936.31 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:23:21,976 INFO [train.py:715] (2/8) Epoch 17, batch 16150, loss[loss=0.1149, simple_loss=0.1945, pruned_loss=0.01763, over 4641.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02928, over 972227.10 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:24:01,717 INFO [train.py:715] (2/8) Epoch 17, batch 16200, loss[loss=0.165, simple_loss=0.2446, pruned_loss=0.04275, over 4872.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02972, over 972407.23 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:24:43,133 INFO [train.py:715] (2/8) Epoch 17, batch 16250, loss[loss=0.1006, simple_loss=0.1801, pruned_loss=0.01056, over 4830.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02911, over 971294.27 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:25:23,146 INFO [train.py:715] (2/8) Epoch 17, batch 16300, loss[loss=0.1388, simple_loss=0.2214, pruned_loss=0.02807, over 4930.00 frames.], tot_loss[loss=0.1321, simple_loss=0.207, pruned_loss=0.02863, over 971219.47 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:26:02,234 INFO [train.py:715] (2/8) Epoch 17, batch 16350, loss[loss=0.1223, simple_loss=0.1912, pruned_loss=0.02671, over 4904.00 frames.], tot_loss[loss=0.133, simple_loss=0.2078, pruned_loss=0.02906, over 971114.91 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:26:40,879 INFO [train.py:715] (2/8) Epoch 17, batch 16400, loss[loss=0.1275, simple_loss=0.1938, pruned_loss=0.03059, over 4857.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.0288, over 970541.12 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 01:27:20,609 INFO [train.py:715] (2/8) Epoch 17, batch 16450, loss[loss=0.1221, simple_loss=0.2012, pruned_loss=0.02153, over 4874.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02866, over 971411.80 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:28:00,567 INFO [train.py:715] (2/8) Epoch 17, batch 16500, loss[loss=0.1139, simple_loss=0.1892, pruned_loss=0.01925, over 4842.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02877, over 971705.37 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:28:39,571 INFO [train.py:715] (2/8) Epoch 17, batch 16550, loss[loss=0.1364, simple_loss=0.2023, pruned_loss=0.03521, over 4801.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2081, pruned_loss=0.02917, over 971876.71 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:29:18,071 INFO [train.py:715] (2/8) Epoch 17, batch 16600, loss[loss=0.1359, simple_loss=0.2163, pruned_loss=0.02772, over 4880.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2081, pruned_loss=0.02913, over 972266.33 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 01:29:58,267 INFO [train.py:715] (2/8) Epoch 17, batch 16650, loss[loss=0.1372, simple_loss=0.1979, pruned_loss=0.0383, over 4907.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02906, over 972343.93 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:30:38,045 INFO [train.py:715] (2/8) Epoch 17, batch 16700, loss[loss=0.1434, simple_loss=0.2135, pruned_loss=0.03661, over 4786.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02896, over 973024.71 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 01:31:16,505 INFO [train.py:715] (2/8) Epoch 17, batch 16750, loss[loss=0.1283, simple_loss=0.2125, pruned_loss=0.02209, over 4931.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.0288, over 973316.94 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:31:56,313 INFO [train.py:715] (2/8) Epoch 17, batch 16800, loss[loss=0.1488, simple_loss=0.2264, pruned_loss=0.0356, over 4946.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02876, over 973715.11 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:32:35,737 INFO [train.py:715] (2/8) Epoch 17, batch 16850, loss[loss=0.1546, simple_loss=0.2132, pruned_loss=0.04798, over 4975.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02912, over 973530.72 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 01:33:15,657 INFO [train.py:715] (2/8) Epoch 17, batch 16900, loss[loss=0.1487, simple_loss=0.2104, pruned_loss=0.04348, over 4917.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02927, over 973912.75 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:33:53,874 INFO [train.py:715] (2/8) Epoch 17, batch 16950, loss[loss=0.134, simple_loss=0.2066, pruned_loss=0.03072, over 4946.00 frames.], tot_loss[loss=0.1333, simple_loss=0.207, pruned_loss=0.02976, over 974030.17 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:34:33,426 INFO [train.py:715] (2/8) Epoch 17, batch 17000, loss[loss=0.128, simple_loss=0.2022, pruned_loss=0.02688, over 4777.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2066, pruned_loss=0.02961, over 973386.48 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 01:35:12,910 INFO [train.py:715] (2/8) Epoch 17, batch 17050, loss[loss=0.1445, simple_loss=0.2113, pruned_loss=0.03885, over 4874.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02975, over 973269.72 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 01:35:51,177 INFO [train.py:715] (2/8) Epoch 17, batch 17100, loss[loss=0.1244, simple_loss=0.1947, pruned_loss=0.02705, over 4834.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02946, over 972891.82 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:36:30,680 INFO [train.py:715] (2/8) Epoch 17, batch 17150, loss[loss=0.1194, simple_loss=0.198, pruned_loss=0.02036, over 4930.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02945, over 972948.01 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:37:10,051 INFO [train.py:715] (2/8) Epoch 17, batch 17200, loss[loss=0.1361, simple_loss=0.2104, pruned_loss=0.03086, over 4976.00 frames.], tot_loss[loss=0.1321, simple_loss=0.206, pruned_loss=0.02911, over 973454.90 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:37:48,556 INFO [train.py:715] (2/8) Epoch 17, batch 17250, loss[loss=0.1406, simple_loss=0.2187, pruned_loss=0.03126, over 4852.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2058, pruned_loss=0.02931, over 973212.56 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 01:38:26,823 INFO [train.py:715] (2/8) Epoch 17, batch 17300, loss[loss=0.145, simple_loss=0.2248, pruned_loss=0.03261, over 4919.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02946, over 973373.33 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:39:06,137 INFO [train.py:715] (2/8) Epoch 17, batch 17350, loss[loss=0.1171, simple_loss=0.1888, pruned_loss=0.02274, over 4795.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02924, over 972715.02 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:39:45,335 INFO [train.py:715] (2/8) Epoch 17, batch 17400, loss[loss=0.103, simple_loss=0.1775, pruned_loss=0.01428, over 4875.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2073, pruned_loss=0.02997, over 971951.34 frames.], batch size: 22, lr: 1.30e-04
2022-05-09 01:40:23,324 INFO [train.py:715] (2/8) Epoch 17, batch 17450, loss[loss=0.1228, simple_loss=0.1998, pruned_loss=0.02288, over 4990.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02946, over 972007.47 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 01:41:03,011 INFO [train.py:715] (2/8) Epoch 17, batch 17500, loss[loss=0.1422, simple_loss=0.218, pruned_loss=0.03325, over 4881.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02904, over 971515.37 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 01:41:42,132 INFO [train.py:715] (2/8) Epoch 17, batch 17550, loss[loss=0.1292, simple_loss=0.2065, pruned_loss=0.02592, over 4826.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02938, over 972023.39 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 01:42:20,893 INFO [train.py:715] (2/8) Epoch 17, batch 17600, loss[loss=0.1543, simple_loss=0.2314, pruned_loss=0.03856, over 4982.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.0294, over 972385.05 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 01:42:59,401 INFO [train.py:715] (2/8) Epoch 17, batch 17650, loss[loss=0.1412, simple_loss=0.2157, pruned_loss=0.03336, over 4781.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.02954, over 973486.03 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:43:38,885 INFO [train.py:715] (2/8) Epoch 17, batch 17700, loss[loss=0.1506, simple_loss=0.2309, pruned_loss=0.03511, over 4913.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02939, over 973508.93 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:44:17,598 INFO [train.py:715] (2/8) Epoch 17, batch 17750, loss[loss=0.1271, simple_loss=0.199, pruned_loss=0.02759, over 4807.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02944, over 974180.90 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:44:56,094 INFO [train.py:715] (2/8) Epoch 17, batch 17800, loss[loss=0.1191, simple_loss=0.1908, pruned_loss=0.02375, over 4808.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.0298, over 974013.92 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 01:45:35,678 INFO [train.py:715] (2/8) Epoch 17, batch 17850, loss[loss=0.1406, simple_loss=0.2051, pruned_loss=0.03799, over 4990.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2074, pruned_loss=0.02989, over 974412.00 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:46:14,666 INFO [train.py:715] (2/8) Epoch 17, batch 17900, loss[loss=0.1466, simple_loss=0.2195, pruned_loss=0.03682, over 4957.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2077, pruned_loss=0.03007, over 974140.66 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 01:46:54,016 INFO [train.py:715] (2/8) Epoch 17, batch 17950, loss[loss=0.1087, simple_loss=0.1827, pruned_loss=0.01738, over 4829.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2076, pruned_loss=0.03037, over 973490.06 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 01:47:32,278 INFO [train.py:715] (2/8) Epoch 17, batch 18000, loss[loss=0.1388, simple_loss=0.2175, pruned_loss=0.03005, over 4885.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02945, over 972820.33 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 01:47:32,278 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 01:47:42,061 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1047, simple_loss=0.1881, pruned_loss=0.01066, over 914524.00 frames.
2022-05-09 01:48:20,782 INFO [train.py:715] (2/8) Epoch 17, batch 18050, loss[loss=0.1306, simple_loss=0.2078, pruned_loss=0.02669, over 4918.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2074, pruned_loss=0.02999, over 972966.29 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 01:49:00,410 INFO [train.py:715] (2/8) Epoch 17, batch 18100, loss[loss=0.1208, simple_loss=0.1928, pruned_loss=0.02437, over 4921.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02953, over 972906.74 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:49:39,797 INFO [train.py:715] (2/8) Epoch 17, batch 18150, loss[loss=0.1452, simple_loss=0.2128, pruned_loss=0.03882, over 4700.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02934, over 973132.56 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 01:50:17,779 INFO [train.py:715] (2/8) Epoch 17, batch 18200, loss[loss=0.1102, simple_loss=0.1849, pruned_loss=0.01771, over 4852.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.0291, over 973790.73 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:50:57,533 INFO [train.py:715] (2/8) Epoch 17, batch 18250, loss[loss=0.1199, simple_loss=0.1938, pruned_loss=0.02296, over 4778.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02944, over 973100.24 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 01:51:37,062 INFO [train.py:715] (2/8) Epoch 17, batch 18300, loss[loss=0.16, simple_loss=0.2381, pruned_loss=0.04093, over 4941.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02924, over 972692.19 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 01:52:15,576 INFO [train.py:715] (2/8) Epoch 17, batch 18350, loss[loss=0.1203, simple_loss=0.1972, pruned_loss=0.02164, over 4795.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2071, pruned_loss=0.02899, over 972399.56 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 01:52:55,001 INFO [train.py:715] (2/8) Epoch 17, batch 18400, loss[loss=0.1381, simple_loss=0.2135, pruned_loss=0.03135, over 4893.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02914, over 972413.35 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 01:53:33,898 INFO [train.py:715] (2/8) Epoch 17, batch 18450, loss[loss=0.1236, simple_loss=0.1972, pruned_loss=0.02504, over 4846.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02868, over 972083.79 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 01:54:13,089 INFO [train.py:715] (2/8) Epoch 17, batch 18500, loss[loss=0.1641, simple_loss=0.2437, pruned_loss=0.04225, over 4776.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02913, over 971933.28 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:54:51,418 INFO [train.py:715] (2/8) Epoch 17, batch 18550, loss[loss=0.1527, simple_loss=0.224, pruned_loss=0.04066, over 4935.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02969, over 971793.90 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 01:55:30,373 INFO [train.py:715] (2/8) Epoch 17, batch 18600, loss[loss=0.127, simple_loss=0.1927, pruned_loss=0.03063, over 4792.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02941, over 972025.35 frames.], batch size: 12, lr: 1.30e-04
2022-05-09 01:56:09,535 INFO [train.py:715] (2/8) Epoch 17, batch 18650, loss[loss=0.1189, simple_loss=0.1923, pruned_loss=0.02278, over 4956.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02914, over 971932.35 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 01:56:47,377 INFO [train.py:715] (2/8) Epoch 17, batch 18700, loss[loss=0.1364, simple_loss=0.2028, pruned_loss=0.03504, over 4804.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02884, over 971820.69 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 01:57:27,054 INFO [train.py:715] (2/8) Epoch 17, batch 18750, loss[loss=0.1143, simple_loss=0.19, pruned_loss=0.0193, over 4829.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02881, over 971451.48 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 01:58:06,643 INFO [train.py:715] (2/8) Epoch 17, batch 18800, loss[loss=0.1648, simple_loss=0.2508, pruned_loss=0.03936, over 4902.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02865, over 971800.94 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 01:58:45,353 INFO [train.py:715] (2/8) Epoch 17, batch 18850, loss[loss=0.1301, simple_loss=0.1959, pruned_loss=0.03215, over 4814.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02908, over 971991.44 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 01:59:23,459 INFO [train.py:715] (2/8) Epoch 17, batch 18900, loss[loss=0.1419, simple_loss=0.2191, pruned_loss=0.03231, over 4755.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02869, over 971851.59 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:00:02,554 INFO [train.py:715] (2/8) Epoch 17, batch 18950, loss[loss=0.1236, simple_loss=0.206, pruned_loss=0.02057, over 4836.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02841, over 972052.77 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 02:00:41,837 INFO [train.py:715] (2/8) Epoch 17, batch 19000, loss[loss=0.1175, simple_loss=0.1935, pruned_loss=0.02076, over 4877.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02876, over 972497.88 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 02:01:20,326 INFO [train.py:715] (2/8) Epoch 17, batch 19050, loss[loss=0.1237, simple_loss=0.2013, pruned_loss=0.02306, over 4974.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02918, over 970765.81 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 02:01:59,755 INFO [train.py:715] (2/8) Epoch 17, batch 19100, loss[loss=0.1357, simple_loss=0.2111, pruned_loss=0.0302, over 4769.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02935, over 971312.00 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:02:38,886 INFO [train.py:715] (2/8) Epoch 17, batch 19150, loss[loss=0.1163, simple_loss=0.1842, pruned_loss=0.02425, over 4834.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02889, over 971550.59 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 02:03:17,328 INFO [train.py:715] (2/8) Epoch 17, batch 19200, loss[loss=0.123, simple_loss=0.195, pruned_loss=0.02554, over 4933.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02862, over 971370.35 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 02:03:56,158 INFO [train.py:715] (2/8) Epoch 17, batch 19250, loss[loss=0.1146, simple_loss=0.1863, pruned_loss=0.02144, over 4865.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2058, pruned_loss=0.02894, over 971705.01 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 02:04:35,742 INFO [train.py:715] (2/8) Epoch 17, batch 19300, loss[loss=0.133, simple_loss=0.2058, pruned_loss=0.03007, over 4810.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2058, pruned_loss=0.02884, over 970978.78 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 02:05:15,464 INFO [train.py:715] (2/8) Epoch 17, batch 19350, loss[loss=0.1236, simple_loss=0.2057, pruned_loss=0.02079, over 4917.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2056, pruned_loss=0.02891, over 971323.10 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:05:54,627 INFO [train.py:715] (2/8) Epoch 17, batch 19400, loss[loss=0.1545, simple_loss=0.2336, pruned_loss=0.03768, over 4859.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2057, pruned_loss=0.02873, over 971718.88 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 02:06:34,194 INFO [train.py:715] (2/8) Epoch 17, batch 19450, loss[loss=0.1449, simple_loss=0.2172, pruned_loss=0.0363, over 4984.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02885, over 971968.94 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:07:13,776 INFO [train.py:715] (2/8) Epoch 17, batch 19500, loss[loss=0.1378, simple_loss=0.2146, pruned_loss=0.03048, over 4927.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02845, over 972425.77 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 02:07:53,344 INFO [train.py:715] (2/8) Epoch 17, batch 19550, loss[loss=0.1307, simple_loss=0.2013, pruned_loss=0.02999, over 4782.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.0288, over 972406.69 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:08:31,623 INFO [train.py:715] (2/8) Epoch 17, batch 19600, loss[loss=0.1518, simple_loss=0.2234, pruned_loss=0.04012, over 4764.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02847, over 972231.22 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:09:11,586 INFO [train.py:715] (2/8) Epoch 17, batch 19650, loss[loss=0.1136, simple_loss=0.193, pruned_loss=0.0171, over 4959.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02871, over 971337.16 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:09:51,476 INFO [train.py:715] (2/8) Epoch 17, batch 19700, loss[loss=0.1446, simple_loss=0.2164, pruned_loss=0.03642, over 4758.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02906, over 971200.46 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:10:30,061 INFO [train.py:715] (2/8) Epoch 17, batch 19750, loss[loss=0.1538, simple_loss=0.216, pruned_loss=0.04583, over 4837.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02898, over 971289.26 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 02:11:09,388 INFO [train.py:715] (2/8) Epoch 17, batch 19800, loss[loss=0.1488, simple_loss=0.2252, pruned_loss=0.03615, over 4820.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02964, over 971409.47 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 02:11:47,960 INFO [train.py:715] (2/8) Epoch 17, batch 19850, loss[loss=0.1394, simple_loss=0.2015, pruned_loss=0.03867, over 4965.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2086, pruned_loss=0.0298, over 972199.95 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 02:12:26,934 INFO [train.py:715] (2/8) Epoch 17, batch 19900, loss[loss=0.1394, simple_loss=0.2121, pruned_loss=0.03336, over 4960.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2087, pruned_loss=0.02946, over 971922.35 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 02:13:05,190 INFO [train.py:715] (2/8) Epoch 17, batch 19950, loss[loss=0.1236, simple_loss=0.1927, pruned_loss=0.02723, over 4925.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2093, pruned_loss=0.02996, over 972866.51 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:13:44,433 INFO [train.py:715] (2/8) Epoch 17, batch 20000, loss[loss=0.1204, simple_loss=0.1946, pruned_loss=0.02304, over 4937.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2083, pruned_loss=0.02942, over 973023.74 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 02:14:24,053 INFO [train.py:715] (2/8) Epoch 17, batch 20050, loss[loss=0.127, simple_loss=0.2015, pruned_loss=0.02628, over 4903.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2085, pruned_loss=0.02965, over 973511.10 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 02:15:03,200 INFO [train.py:715] (2/8) Epoch 17, batch 20100, loss[loss=0.1425, simple_loss=0.2162, pruned_loss=0.03435, over 4978.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2083, pruned_loss=0.02964, over 973079.97 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:15:42,010 INFO [train.py:715] (2/8) Epoch 17, batch 20150, loss[loss=0.1201, simple_loss=0.1981, pruned_loss=0.02109, over 4751.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02919, over 973437.03 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 02:16:22,311 INFO [train.py:715] (2/8) Epoch 17, batch 20200, loss[loss=0.1131, simple_loss=0.1903, pruned_loss=0.01801, over 4820.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.02885, over 972638.19 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 02:17:02,699 INFO [train.py:715] (2/8) Epoch 17, batch 20250, loss[loss=0.1221, simple_loss=0.2013, pruned_loss=0.02148, over 4879.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02877, over 972054.47 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 02:17:40,775 INFO [train.py:715] (2/8) Epoch 17, batch 20300, loss[loss=0.1151, simple_loss=0.1882, pruned_loss=0.02101, over 4915.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02891, over 972721.72 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 02:18:20,513 INFO [train.py:715] (2/8) Epoch 17, batch 20350, loss[loss=0.1626, simple_loss=0.2286, pruned_loss=0.04828, over 4700.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2072, pruned_loss=0.02875, over 972680.99 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:19:00,638 INFO [train.py:715] (2/8) Epoch 17, batch 20400, loss[loss=0.1353, simple_loss=0.2102, pruned_loss=0.0302, over 4809.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02885, over 973233.54 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 02:19:39,224 INFO [train.py:715] (2/8) Epoch 17, batch 20450, loss[loss=0.1649, simple_loss=0.2378, pruned_loss=0.04595, over 4909.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.0292, over 973638.24 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 02:20:17,940 INFO [train.py:715] (2/8) Epoch 17, batch 20500, loss[loss=0.12, simple_loss=0.2006, pruned_loss=0.01973, over 4830.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02918, over 973311.47 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 02:20:57,777 INFO [train.py:715] (2/8) Epoch 17, batch 20550, loss[loss=0.1502, simple_loss=0.2316, pruned_loss=0.03438, over 4784.00 frames.], tot_loss[loss=0.1331, simple_loss=0.208, pruned_loss=0.02912, over 974006.46 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:21:36,913 INFO [train.py:715] (2/8) Epoch 17, batch 20600, loss[loss=0.1286, simple_loss=0.204, pruned_loss=0.02665, over 4967.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2084, pruned_loss=0.02905, over 973718.83 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:22:15,101 INFO [train.py:715] (2/8) Epoch 17, batch 20650, loss[loss=0.1308, simple_loss=0.2123, pruned_loss=0.02468, over 4697.00 frames.], tot_loss[loss=0.133, simple_loss=0.2078, pruned_loss=0.02914, over 972374.21 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:22:54,075 INFO [train.py:715] (2/8) Epoch 17, batch 20700, loss[loss=0.1349, simple_loss=0.2123, pruned_loss=0.0287, over 4769.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02897, over 973319.95 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:23:33,734 INFO [train.py:715] (2/8) Epoch 17, batch 20750, loss[loss=0.1543, simple_loss=0.2349, pruned_loss=0.0368, over 4881.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02918, over 973085.39 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 02:24:12,679 INFO [train.py:715] (2/8) Epoch 17, batch 20800, loss[loss=0.1083, simple_loss=0.1889, pruned_loss=0.0139, over 4836.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.0289, over 972989.45 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 02:24:51,257 INFO [train.py:715] (2/8) Epoch 17, batch 20850, loss[loss=0.1408, simple_loss=0.2207, pruned_loss=0.03043, over 4983.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02888, over 972547.53 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:25:30,263 INFO [train.py:715] (2/8) Epoch 17, batch 20900, loss[loss=0.1324, simple_loss=0.2099, pruned_loss=0.02747, over 4968.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02928, over 973310.40 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:26:10,247 INFO [train.py:715] (2/8) Epoch 17, batch 20950, loss[loss=0.1304, simple_loss=0.203, pruned_loss=0.02888, over 4972.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02933, over 973935.07 frames.], batch size: 25, lr: 1.30e-04
2022-05-09 02:26:48,269 INFO [train.py:715] (2/8) Epoch 17, batch 21000, loss[loss=0.1227, simple_loss=0.2065, pruned_loss=0.01942, over 4968.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02891, over 973715.02 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:26:48,270 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 02:27:00,912 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1049, simple_loss=0.1882, pruned_loss=0.01077, over 914524.00 frames.
2022-05-09 02:27:38,932 INFO [train.py:715] (2/8) Epoch 17, batch 21050, loss[loss=0.1355, simple_loss=0.2081, pruned_loss=0.03148, over 4768.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2057, pruned_loss=0.0288, over 973194.93 frames.], batch size: 16, lr: 1.30e-04
2022-05-09 02:28:18,325 INFO [train.py:715] (2/8) Epoch 17, batch 21100, loss[loss=0.1322, simple_loss=0.2083, pruned_loss=0.02808, over 4699.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02907, over 973132.29 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:28:58,368 INFO [train.py:715] (2/8) Epoch 17, batch 21150, loss[loss=0.1648, simple_loss=0.2304, pruned_loss=0.04964, over 4949.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02919, over 972875.51 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 02:29:37,030 INFO [train.py:715] (2/8) Epoch 17, batch 21200, loss[loss=0.1226, simple_loss=0.1988, pruned_loss=0.02316, over 4815.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02915, over 973183.64 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 02:30:15,714 INFO [train.py:715] (2/8) Epoch 17, batch 21250, loss[loss=0.1552, simple_loss=0.2287, pruned_loss=0.04088, over 4773.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02869, over 972894.07 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:30:55,580 INFO [train.py:715] (2/8) Epoch 17, batch 21300, loss[loss=0.1436, simple_loss=0.2076, pruned_loss=0.03978, over 4983.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02847, over 973507.14 frames.], batch size: 31, lr: 1.30e-04
2022-05-09 02:31:35,364 INFO [train.py:715] (2/8) Epoch 17, batch 21350, loss[loss=0.1326, simple_loss=0.1975, pruned_loss=0.03386, over 4859.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02852, over 972812.01 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 02:32:13,593 INFO [train.py:715] (2/8) Epoch 17, batch 21400, loss[loss=0.1183, simple_loss=0.2025, pruned_loss=0.01709, over 4875.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02811, over 972251.07 frames.], batch size: 32, lr: 1.30e-04
2022-05-09 02:32:53,782 INFO [train.py:715] (2/8) Epoch 17, batch 21450, loss[loss=0.134, simple_loss=0.2207, pruned_loss=0.02365, over 4826.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2066, pruned_loss=0.02852, over 972107.19 frames.], batch size: 26, lr: 1.30e-04
2022-05-09 02:33:33,555 INFO [train.py:715] (2/8) Epoch 17, batch 21500, loss[loss=0.1429, simple_loss=0.2205, pruned_loss=0.03272, over 4780.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2073, pruned_loss=0.02889, over 972941.67 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 02:34:12,055 INFO [train.py:715] (2/8) Epoch 17, batch 21550, loss[loss=0.2045, simple_loss=0.2589, pruned_loss=0.07503, over 4763.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02925, over 971913.47 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:34:51,509 INFO [train.py:715] (2/8) Epoch 17, batch 21600, loss[loss=0.1008, simple_loss=0.1701, pruned_loss=0.01578, over 4793.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02938, over 971877.85 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:35:31,960 INFO [train.py:715] (2/8) Epoch 17, batch 21650, loss[loss=0.146, simple_loss=0.2185, pruned_loss=0.03679, over 4919.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02918, over 971852.45 frames.], batch size: 39, lr: 1.30e-04
2022-05-09 02:36:11,046 INFO [train.py:715] (2/8) Epoch 17, batch 21700, loss[loss=0.1336, simple_loss=0.2053, pruned_loss=0.03091, over 4866.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02933, over 972711.65 frames.], batch size: 20, lr: 1.30e-04
2022-05-09 02:36:49,702 INFO [train.py:715] (2/8) Epoch 17, batch 21750, loss[loss=0.1702, simple_loss=0.2357, pruned_loss=0.05231, over 4932.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02926, over 971903.77 frames.], batch size: 35, lr: 1.30e-04
2022-05-09 02:37:29,254 INFO [train.py:715] (2/8) Epoch 17, batch 21800, loss[loss=0.1261, simple_loss=0.2041, pruned_loss=0.024, over 4780.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.0293, over 971962.43 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:38:08,216 INFO [train.py:715] (2/8) Epoch 17, batch 21850, loss[loss=0.1422, simple_loss=0.2189, pruned_loss=0.03274, over 4825.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2077, pruned_loss=0.03037, over 971520.81 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 02:38:47,465 INFO [train.py:715] (2/8) Epoch 17, batch 21900, loss[loss=0.143, simple_loss=0.2158, pruned_loss=0.03513, over 4702.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03022, over 971609.07 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:39:25,953 INFO [train.py:715] (2/8) Epoch 17, batch 21950, loss[loss=0.1471, simple_loss=0.2139, pruned_loss=0.04017, over 4889.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2082, pruned_loss=0.03031, over 971582.31 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:40:05,671 INFO [train.py:715] (2/8) Epoch 17, batch 22000, loss[loss=0.1263, simple_loss=0.2052, pruned_loss=0.02377, over 4827.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02963, over 972126.19 frames.], batch size: 27, lr: 1.30e-04
2022-05-09 02:40:45,438 INFO [train.py:715] (2/8) Epoch 17, batch 22050, loss[loss=0.123, simple_loss=0.2026, pruned_loss=0.02166, over 4904.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.0294, over 972619.01 frames.], batch size: 17, lr: 1.30e-04
2022-05-09 02:41:23,864 INFO [train.py:715] (2/8) Epoch 17, batch 22100, loss[loss=0.111, simple_loss=0.1856, pruned_loss=0.01824, over 4910.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02928, over 973196.14 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 02:42:03,599 INFO [train.py:715] (2/8) Epoch 17, batch 22150, loss[loss=0.123, simple_loss=0.2034, pruned_loss=0.0213, over 4930.00 frames.], tot_loss[loss=0.133, simple_loss=0.2068, pruned_loss=0.0296, over 973195.12 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 02:42:43,498 INFO [train.py:715] (2/8) Epoch 17, batch 22200, loss[loss=0.1403, simple_loss=0.2239, pruned_loss=0.02837, over 4885.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02935, over 972894.23 frames.], batch size: 19, lr: 1.30e-04
2022-05-09 02:43:22,389 INFO [train.py:715] (2/8) Epoch 17, batch 22250, loss[loss=0.1784, simple_loss=0.2402, pruned_loss=0.05835, over 4838.00 frames.], tot_loss[loss=0.134, simple_loss=0.2078, pruned_loss=0.03013, over 972316.89 frames.], batch size: 30, lr: 1.30e-04
2022-05-09 02:44:01,344 INFO [train.py:715] (2/8) Epoch 17, batch 22300, loss[loss=0.1265, simple_loss=0.2079, pruned_loss=0.02249, over 4922.00 frames.], tot_loss[loss=0.1351, simple_loss=0.2088, pruned_loss=0.0307, over 972752.38 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 02:44:41,266 INFO [train.py:715] (2/8) Epoch 17, batch 22350, loss[loss=0.1465, simple_loss=0.2212, pruned_loss=0.0359, over 4940.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2083, pruned_loss=0.03013, over 972205.44 frames.], batch size: 21, lr: 1.30e-04
2022-05-09 02:45:20,839 INFO [train.py:715] (2/8) Epoch 17, batch 22400, loss[loss=0.133, simple_loss=0.2012, pruned_loss=0.0324, over 4835.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03005, over 972425.01 frames.], batch size: 13, lr: 1.30e-04
2022-05-09 02:45:59,650 INFO [train.py:715] (2/8) Epoch 17, batch 22450, loss[loss=0.1182, simple_loss=0.1938, pruned_loss=0.02127, over 4761.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02952, over 971540.60 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:46:38,623 INFO [train.py:715] (2/8) Epoch 17, batch 22500, loss[loss=0.1153, simple_loss=0.1882, pruned_loss=0.02117, over 4930.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2065, pruned_loss=0.02944, over 971515.51 frames.], batch size: 29, lr: 1.30e-04
2022-05-09 02:47:18,399 INFO [train.py:715] (2/8) Epoch 17, batch 22550, loss[loss=0.1226, simple_loss=0.1997, pruned_loss=0.02272, over 4928.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.0291, over 971892.01 frames.], batch size: 23, lr: 1.30e-04
2022-05-09 02:47:56,725 INFO [train.py:715] (2/8) Epoch 17, batch 22600, loss[loss=0.1347, simple_loss=0.2232, pruned_loss=0.0231, over 4835.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02908, over 971999.29 frames.], batch size: 15, lr: 1.30e-04
2022-05-09 02:48:36,267 INFO [train.py:715] (2/8) Epoch 17, batch 22650, loss[loss=0.1074, simple_loss=0.1772, pruned_loss=0.01885, over 4981.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02964, over 972416.31 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:49:15,734 INFO [train.py:715] (2/8) Epoch 17, batch 22700, loss[loss=0.1344, simple_loss=0.2173, pruned_loss=0.02574, over 4796.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02958, over 972508.61 frames.], batch size: 24, lr: 1.30e-04
2022-05-09 02:49:54,667 INFO [train.py:715] (2/8) Epoch 17, batch 22750, loss[loss=0.1135, simple_loss=0.1864, pruned_loss=0.02028, over 4909.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.0294, over 972540.20 frames.], batch size: 18, lr: 1.30e-04
2022-05-09 02:50:33,048 INFO [train.py:715] (2/8) Epoch 17, batch 22800, loss[loss=0.1474, simple_loss=0.223, pruned_loss=0.03585, over 4792.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02925, over 972219.24 frames.], batch size: 14, lr: 1.30e-04
2022-05-09 02:51:12,442 INFO [train.py:715] (2/8) Epoch 17, batch 22850, loss[loss=0.1396, simple_loss=0.211, pruned_loss=0.03414, over 4787.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02974, over 972456.80 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 02:51:52,336 INFO [train.py:715] (2/8) Epoch 17, batch 22900, loss[loss=0.1254, simple_loss=0.2119, pruned_loss=0.01946, over 4768.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2082, pruned_loss=0.03002, over 972989.22 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 02:52:30,189 INFO [train.py:715] (2/8) Epoch 17, batch 22950, loss[loss=0.1156, simple_loss=0.1947, pruned_loss=0.01826, over 4820.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02974, over 972859.66 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 02:53:10,085 INFO [train.py:715] (2/8) Epoch 17, batch 23000, loss[loss=0.12, simple_loss=0.1973, pruned_loss=0.0214, over 4867.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2084, pruned_loss=0.02993, over 972274.99 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 02:53:50,346 INFO [train.py:715] (2/8) Epoch 17, batch 23050, loss[loss=0.1492, simple_loss=0.2194, pruned_loss=0.03952, over 4956.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02925, over 971563.57 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 02:54:29,511 INFO [train.py:715] (2/8) Epoch 17, batch 23100, loss[loss=0.1567, simple_loss=0.2215, pruned_loss=0.04595, over 4694.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02947, over 972528.07 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 02:55:07,942 INFO [train.py:715] (2/8) Epoch 17, batch 23150, loss[loss=0.11, simple_loss=0.1875, pruned_loss=0.01629, over 4892.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2071, pruned_loss=0.02952, over 972640.40 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 02:55:47,706 INFO [train.py:715] (2/8) Epoch 17, batch 23200, loss[loss=0.157, simple_loss=0.2304, pruned_loss=0.04177, over 4976.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02941, over 972885.60 frames.], batch size: 39, lr: 1.29e-04
2022-05-09 02:56:27,705 INFO [train.py:715] (2/8) Epoch 17, batch 23250, loss[loss=0.1309, simple_loss=0.2124, pruned_loss=0.02473, over 4913.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02931, over 973105.73 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 02:57:05,642 INFO [train.py:715] (2/8) Epoch 17, batch 23300, loss[loss=0.1471, simple_loss=0.2247, pruned_loss=0.03473, over 4959.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02905, over 973165.80 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 02:57:44,992 INFO [train.py:715] (2/8) Epoch 17, batch 23350, loss[loss=0.1494, simple_loss=0.2198, pruned_loss=0.03946, over 4971.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.0291, over 973843.10 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 02:58:25,088 INFO [train.py:715] (2/8) Epoch 17, batch 23400, loss[loss=0.1283, simple_loss=0.2017, pruned_loss=0.02747, over 4850.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02862, over 973596.66 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 02:59:03,872 INFO [train.py:715] (2/8) Epoch 17, batch 23450, loss[loss=0.1196, simple_loss=0.1949, pruned_loss=0.02217, over 4899.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.0288, over 973722.15 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 02:59:42,967 INFO [train.py:715] (2/8) Epoch 17, batch 23500, loss[loss=0.128, simple_loss=0.2026, pruned_loss=0.02667, over 4877.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02896, over 973452.68 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:00:22,281 INFO [train.py:715] (2/8) Epoch 17, batch 23550, loss[loss=0.129, simple_loss=0.2071, pruned_loss=0.02547, over 4900.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02904, over 973252.37 frames.], batch size: 22, lr: 1.29e-04
2022-05-09 03:01:01,966 INFO [train.py:715] (2/8) Epoch 17, batch 23600, loss[loss=0.1267, simple_loss=0.2014, pruned_loss=0.026, over 4754.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02877, over 972943.14 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:01:40,305 INFO [train.py:715] (2/8) Epoch 17, batch 23650, loss[loss=0.1295, simple_loss=0.2047, pruned_loss=0.02718, over 4809.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02918, over 972698.40 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:02:19,921 INFO [train.py:715] (2/8) Epoch 17, batch 23700, loss[loss=0.1394, simple_loss=0.2091, pruned_loss=0.03481, over 4968.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2061, pruned_loss=0.0294, over 971973.34 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:02:59,508 INFO [train.py:715] (2/8) Epoch 17, batch 23750, loss[loss=0.1478, simple_loss=0.2265, pruned_loss=0.03455, over 4870.00 frames.], tot_loss[loss=0.1323, simple_loss=0.206, pruned_loss=0.0293, over 972161.22 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 03:03:38,388 INFO [train.py:715] (2/8) Epoch 17, batch 23800, loss[loss=0.1532, simple_loss=0.2191, pruned_loss=0.04364, over 4965.00 frames.], tot_loss[loss=0.133, simple_loss=0.2067, pruned_loss=0.02966, over 972147.09 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:04:16,663 INFO [train.py:715] (2/8) Epoch 17, batch 23850, loss[loss=0.1238, simple_loss=0.1997, pruned_loss=0.02389, over 4876.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2072, pruned_loss=0.02972, over 972504.94 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:04:56,708 INFO [train.py:715] (2/8) Epoch 17, batch 23900, loss[loss=0.1313, simple_loss=0.2147, pruned_loss=0.02397, over 4944.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2081, pruned_loss=0.03024, over 972955.84 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:05:35,871 INFO [train.py:715] (2/8) Epoch 17, batch 23950, loss[loss=0.1216, simple_loss=0.1992, pruned_loss=0.02199, over 4829.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02977, over 972174.88 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:06:14,216 INFO [train.py:715] (2/8) Epoch 17, batch 24000, loss[loss=0.1538, simple_loss=0.2205, pruned_loss=0.04358, over 4873.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2076, pruned_loss=0.02978, over 971599.12 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 03:06:14,217 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 03:06:24,068 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1047, simple_loss=0.1881, pruned_loss=0.01067, over 914524.00 frames.
2022-05-09 03:07:02,581 INFO [train.py:715] (2/8) Epoch 17, batch 24050, loss[loss=0.1279, simple_loss=0.2101, pruned_loss=0.02286, over 4764.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02948, over 971202.64 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:07:41,975 INFO [train.py:715] (2/8) Epoch 17, batch 24100, loss[loss=0.1581, simple_loss=0.2392, pruned_loss=0.03854, over 4796.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02999, over 971043.83 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:08:22,154 INFO [train.py:715] (2/8) Epoch 17, batch 24150, loss[loss=0.1318, simple_loss=0.2078, pruned_loss=0.0279, over 4897.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02971, over 971549.56 frames.], batch size: 22, lr: 1.29e-04
2022-05-09 03:09:00,901 INFO [train.py:715] (2/8) Epoch 17, batch 24200, loss[loss=0.1337, simple_loss=0.2055, pruned_loss=0.03097, over 4693.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02902, over 971613.34 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:09:42,458 INFO [train.py:715] (2/8) Epoch 17, batch 24250, loss[loss=0.1396, simple_loss=0.216, pruned_loss=0.0316, over 4695.00 frames.], tot_loss[loss=0.132, simple_loss=0.2059, pruned_loss=0.02902, over 971982.69 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:10:23,062 INFO [train.py:715] (2/8) Epoch 17, batch 24300, loss[loss=0.1585, simple_loss=0.2316, pruned_loss=0.04264, over 4783.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02907, over 971805.32 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 03:11:02,616 INFO [train.py:715] (2/8) Epoch 17, batch 24350, loss[loss=0.1204, simple_loss=0.2011, pruned_loss=0.01983, over 4649.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02927, over 972283.33 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:11:41,993 INFO [train.py:715] (2/8) Epoch 17, batch 24400, loss[loss=0.1363, simple_loss=0.2138, pruned_loss=0.02938, over 4844.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02915, over 972482.79 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 03:12:21,143 INFO [train.py:715] (2/8) Epoch 17, batch 24450, loss[loss=0.135, simple_loss=0.2169, pruned_loss=0.02651, over 4984.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.0294, over 972316.89 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 03:13:01,331 INFO [train.py:715] (2/8) Epoch 17, batch 24500, loss[loss=0.1357, simple_loss=0.1985, pruned_loss=0.03647, over 4972.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02901, over 972172.71 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:13:40,455 INFO [train.py:715] (2/8) Epoch 17, batch 24550, loss[loss=0.1437, simple_loss=0.2093, pruned_loss=0.03909, over 4728.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02935, over 972047.74 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 03:14:19,288 INFO [train.py:715] (2/8) Epoch 17, batch 24600, loss[loss=0.1118, simple_loss=0.1716, pruned_loss=0.02597, over 4769.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02934, over 971830.33 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:14:59,442 INFO [train.py:715] (2/8) Epoch 17, batch 24650, loss[loss=0.1359, simple_loss=0.2039, pruned_loss=0.03396, over 4879.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.0293, over 971462.91 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 03:15:39,739 INFO [train.py:715] (2/8) Epoch 17, batch 24700, loss[loss=0.1298, simple_loss=0.2069, pruned_loss=0.02637, over 4917.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02919, over 972289.00 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:16:18,263 INFO [train.py:715] (2/8) Epoch 17, batch 24750, loss[loss=0.131, simple_loss=0.1966, pruned_loss=0.03269, over 4992.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02928, over 973307.57 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:16:58,088 INFO [train.py:715] (2/8) Epoch 17, batch 24800, loss[loss=0.1034, simple_loss=0.1765, pruned_loss=0.0152, over 4649.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02946, over 973437.15 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:17:37,938 INFO [train.py:715] (2/8) Epoch 17, batch 24850, loss[loss=0.1422, simple_loss=0.2232, pruned_loss=0.03064, over 4816.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02921, over 973296.77 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:18:17,565 INFO [train.py:715] (2/8) Epoch 17, batch 24900, loss[loss=0.1023, simple_loss=0.18, pruned_loss=0.01236, over 4811.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2055, pruned_loss=0.02865, over 972286.22 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:18:56,117 INFO [train.py:715] (2/8) Epoch 17, batch 24950, loss[loss=0.1257, simple_loss=0.213, pruned_loss=0.01924, over 4787.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02841, over 972130.63 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:19:35,619 INFO [train.py:715] (2/8) Epoch 17, batch 25000, loss[loss=0.1273, simple_loss=0.2022, pruned_loss=0.02613, over 4801.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02853, over 971691.50 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 03:20:14,003 INFO [train.py:715] (2/8) Epoch 17, batch 25050, loss[loss=0.1288, simple_loss=0.2006, pruned_loss=0.02853, over 4819.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02872, over 972209.24 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:20:53,020 INFO [train.py:715] (2/8) Epoch 17, batch 25100, loss[loss=0.1159, simple_loss=0.1957, pruned_loss=0.01806, over 4895.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02853, over 972235.85 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 03:21:32,999 INFO [train.py:715] (2/8) Epoch 17, batch 25150, loss[loss=0.1349, simple_loss=0.2042, pruned_loss=0.03278, over 4731.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02896, over 972001.67 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 03:22:12,876 INFO [train.py:715] (2/8) Epoch 17, batch 25200, loss[loss=0.1448, simple_loss=0.2201, pruned_loss=0.03476, over 4856.00 frames.], tot_loss[loss=0.132, simple_loss=0.206, pruned_loss=0.02896, over 972381.16 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 03:22:51,915 INFO [train.py:715] (2/8) Epoch 17, batch 25250, loss[loss=0.1015, simple_loss=0.1777, pruned_loss=0.01268, over 4807.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02913, over 972341.23 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:23:31,035 INFO [train.py:715] (2/8) Epoch 17, batch 25300, loss[loss=0.1231, simple_loss=0.213, pruned_loss=0.01657, over 4788.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02912, over 971068.27 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:24:11,042 INFO [train.py:715] (2/8) Epoch 17, batch 25350, loss[loss=0.134, simple_loss=0.2207, pruned_loss=0.02365, over 4774.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02902, over 971735.67 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:24:49,785 INFO [train.py:715] (2/8) Epoch 17, batch 25400, loss[loss=0.1449, simple_loss=0.2287, pruned_loss=0.03055, over 4961.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02919, over 972083.01 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 03:25:28,959 INFO [train.py:715] (2/8) Epoch 17, batch 25450, loss[loss=0.133, simple_loss=0.2095, pruned_loss=0.02823, over 4985.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02913, over 972431.99 frames.], batch size: 28, lr: 1.29e-04
2022-05-09 03:26:08,065 INFO [train.py:715] (2/8) Epoch 17, batch 25500, loss[loss=0.1333, simple_loss=0.2097, pruned_loss=0.02847, over 4706.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02903, over 972328.37 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:26:47,862 INFO [train.py:715] (2/8) Epoch 17, batch 25550, loss[loss=0.1232, simple_loss=0.2129, pruned_loss=0.0168, over 4969.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02929, over 972376.37 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 03:27:26,919 INFO [train.py:715] (2/8) Epoch 17, batch 25600, loss[loss=0.1729, simple_loss=0.2353, pruned_loss=0.05523, over 4954.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02956, over 972672.53 frames.], batch size: 39, lr: 1.29e-04
2022-05-09 03:28:05,428 INFO [train.py:715] (2/8) Epoch 17, batch 25650, loss[loss=0.1101, simple_loss=0.1852, pruned_loss=0.01751, over 4817.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.0294, over 972521.92 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:28:45,199 INFO [train.py:715] (2/8) Epoch 17, batch 25700, loss[loss=0.1244, simple_loss=0.2057, pruned_loss=0.0216, over 4811.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02949, over 972885.54 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:29:24,288 INFO [train.py:715] (2/8) Epoch 17, batch 25750, loss[loss=0.1511, simple_loss=0.2325, pruned_loss=0.03482, over 4978.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02961, over 972290.78 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 03:30:03,682 INFO [train.py:715] (2/8) Epoch 17, batch 25800, loss[loss=0.1249, simple_loss=0.2, pruned_loss=0.02489, over 4989.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.0295, over 972883.94 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:30:43,162 INFO [train.py:715] (2/8) Epoch 17, batch 25850, loss[loss=0.1199, simple_loss=0.1981, pruned_loss=0.02091, over 4979.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2072, pruned_loss=0.02962, over 972512.37 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:31:22,528 INFO [train.py:715] (2/8) Epoch 17, batch 25900, loss[loss=0.1441, simple_loss=0.201, pruned_loss=0.04365, over 4829.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02933, over 973148.48 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:32:01,052 INFO [train.py:715] (2/8) Epoch 17, batch 25950, loss[loss=0.1079, simple_loss=0.1751, pruned_loss=0.0204, over 4991.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02901, over 973378.78 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:32:39,484 INFO [train.py:715] (2/8) Epoch 17, batch 26000, loss[loss=0.1462, simple_loss=0.2124, pruned_loss=0.04005, over 4702.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02931, over 972523.87 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:33:19,120 INFO [train.py:715] (2/8) Epoch 17, batch 26050, loss[loss=0.1595, simple_loss=0.2328, pruned_loss=0.04317, over 4848.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02918, over 971745.94 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 03:33:57,726 INFO [train.py:715] (2/8) Epoch 17, batch 26100, loss[loss=0.1345, simple_loss=0.1982, pruned_loss=0.03546, over 4778.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02891, over 972385.65 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 03:34:37,125 INFO [train.py:715] (2/8) Epoch 17, batch 26150, loss[loss=0.1261, simple_loss=0.2127, pruned_loss=0.01974, over 4883.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02929, over 972934.44 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:35:16,509 INFO [train.py:715] (2/8) Epoch 17, batch 26200, loss[loss=0.1395, simple_loss=0.2148, pruned_loss=0.03208, over 4932.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02907, over 973271.19 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:35:56,477 INFO [train.py:715] (2/8) Epoch 17, batch 26250, loss[loss=0.1494, simple_loss=0.2225, pruned_loss=0.03809, over 4769.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02864, over 972498.38 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:36:35,147 INFO [train.py:715] (2/8) Epoch 17, batch 26300, loss[loss=0.1201, simple_loss=0.1862, pruned_loss=0.02703, over 4739.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02906, over 972412.39 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:37:13,923 INFO [train.py:715] (2/8) Epoch 17, batch 26350, loss[loss=0.1325, simple_loss=0.2077, pruned_loss=0.02863, over 4918.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02881, over 972847.15 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 03:37:53,866 INFO [train.py:715] (2/8) Epoch 17, batch 26400, loss[loss=0.1182, simple_loss=0.1908, pruned_loss=0.02283, over 4916.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02928, over 972703.13 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:38:32,581 INFO [train.py:715] (2/8) Epoch 17, batch 26450, loss[loss=0.1422, simple_loss=0.2091, pruned_loss=0.03763, over 4986.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.0294, over 972417.69 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:39:11,788 INFO [train.py:715] (2/8) Epoch 17, batch 26500, loss[loss=0.1493, simple_loss=0.2294, pruned_loss=0.03461, over 4941.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02916, over 972123.21 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 03:39:51,012 INFO [train.py:715] (2/8) Epoch 17, batch 26550, loss[loss=0.1108, simple_loss=0.1878, pruned_loss=0.01688, over 4642.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02881, over 972351.77 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:40:29,942 INFO [train.py:715] (2/8) Epoch 17, batch 26600, loss[loss=0.1276, simple_loss=0.2065, pruned_loss=0.02436, over 4757.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.0291, over 972830.33 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:41:08,353 INFO [train.py:715] (2/8) Epoch 17, batch 26650, loss[loss=0.1544, simple_loss=0.2183, pruned_loss=0.04527, over 4815.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02885, over 972536.11 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:41:47,388 INFO [train.py:715] (2/8) Epoch 17, batch 26700, loss[loss=0.1445, simple_loss=0.2139, pruned_loss=0.03758, over 4879.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02904, over 972744.97 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 03:42:26,795 INFO [train.py:715] (2/8) Epoch 17, batch 26750, loss[loss=0.1707, simple_loss=0.2455, pruned_loss=0.04797, over 4915.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02945, over 973103.51 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 03:43:05,136 INFO [train.py:715] (2/8) Epoch 17, batch 26800, loss[loss=0.1281, simple_loss=0.2122, pruned_loss=0.02198, over 4813.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02902, over 972221.18 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 03:43:43,933 INFO [train.py:715] (2/8) Epoch 17, batch 26850, loss[loss=0.1224, simple_loss=0.1991, pruned_loss=0.02283, over 4825.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2058, pruned_loss=0.02889, over 972383.16 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:44:23,813 INFO [train.py:715] (2/8) Epoch 17, batch 26900, loss[loss=0.1269, simple_loss=0.2046, pruned_loss=0.02458, over 4745.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02843, over 972635.79 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:45:02,983 INFO [train.py:715] (2/8) Epoch 17, batch 26950, loss[loss=0.165, simple_loss=0.2488, pruned_loss=0.04056, over 4987.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02869, over 972613.61 frames.], batch size: 28, lr: 1.29e-04
2022-05-09 03:45:41,693 INFO [train.py:715] (2/8) Epoch 17, batch 27000, loss[loss=0.1372, simple_loss=0.2237, pruned_loss=0.02532, over 4916.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.0285, over 972408.96 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:45:41,694 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 03:45:51,479 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1047, simple_loss=0.188, pruned_loss=0.0107, over 914524.00 frames.
2022-05-09 03:46:30,449 INFO [train.py:715] (2/8) Epoch 17, batch 27050, loss[loss=0.1269, simple_loss=0.2061, pruned_loss=0.02387, over 4794.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02863, over 973041.54 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 03:47:09,965 INFO [train.py:715] (2/8) Epoch 17, batch 27100, loss[loss=0.1338, simple_loss=0.2038, pruned_loss=0.03188, over 4948.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2057, pruned_loss=0.02884, over 972949.57 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 03:47:49,460 INFO [train.py:715] (2/8) Epoch 17, batch 27150, loss[loss=0.1211, simple_loss=0.1954, pruned_loss=0.02334, over 4908.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.0288, over 973204.04 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 03:48:27,664 INFO [train.py:715] (2/8) Epoch 17, batch 27200, loss[loss=0.1267, simple_loss=0.1967, pruned_loss=0.02836, over 4653.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02865, over 972910.49 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:49:06,449 INFO [train.py:715] (2/8) Epoch 17, batch 27250, loss[loss=0.1278, simple_loss=0.206, pruned_loss=0.02485, over 4874.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2044, pruned_loss=0.02853, over 972949.61 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 03:49:46,077 INFO [train.py:715] (2/8) Epoch 17, batch 27300, loss[loss=0.1232, simple_loss=0.1883, pruned_loss=0.02911, over 4902.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2048, pruned_loss=0.02831, over 972951.32 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:50:25,157 INFO [train.py:715] (2/8) Epoch 17, batch 27350, loss[loss=0.1598, simple_loss=0.234, pruned_loss=0.04276, over 4907.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2053, pruned_loss=0.02875, over 972471.34 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 03:51:04,596 INFO [train.py:715] (2/8) Epoch 17, batch 27400, loss[loss=0.1715, simple_loss=0.2457, pruned_loss=0.04868, over 4738.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02889, over 973050.53 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 03:51:43,497 INFO [train.py:715] (2/8) Epoch 17, batch 27450, loss[loss=0.1305, simple_loss=0.1971, pruned_loss=0.03192, over 4761.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02902, over 973170.71 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:52:23,142 INFO [train.py:715] (2/8) Epoch 17, batch 27500, loss[loss=0.1298, simple_loss=0.1996, pruned_loss=0.02998, over 4989.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02875, over 973930.28 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 03:53:01,816 INFO [train.py:715] (2/8) Epoch 17, batch 27550, loss[loss=0.1149, simple_loss=0.186, pruned_loss=0.02191, over 4752.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02863, over 973477.06 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:53:40,308 INFO [train.py:715] (2/8) Epoch 17, batch 27600, loss[loss=0.1318, simple_loss=0.1947, pruned_loss=0.0344, over 4899.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02885, over 972783.93 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:54:19,262 INFO [train.py:715] (2/8) Epoch 17, batch 27650, loss[loss=0.1245, simple_loss=0.1906, pruned_loss=0.02922, over 4758.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02829, over 972597.14 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 03:54:57,853 INFO [train.py:715] (2/8) Epoch 17, batch 27700, loss[loss=0.1615, simple_loss=0.2442, pruned_loss=0.03938, over 4897.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2065, pruned_loss=0.02835, over 972511.46 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 03:55:37,178 INFO [train.py:715] (2/8) Epoch 17, batch 27750, loss[loss=0.1212, simple_loss=0.199, pruned_loss=0.02174, over 4862.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.0283, over 972111.74 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 03:56:16,913 INFO [train.py:715] (2/8) Epoch 17, batch 27800, loss[loss=0.1355, simple_loss=0.2066, pruned_loss=0.03219, over 4817.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02828, over 971050.10 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 03:56:57,485 INFO [train.py:715] (2/8) Epoch 17, batch 27850, loss[loss=0.1117, simple_loss=0.1824, pruned_loss=0.02046, over 4894.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02848, over 972423.44 frames.], batch size: 22, lr: 1.29e-04
2022-05-09 03:57:37,280 INFO [train.py:715] (2/8) Epoch 17, batch 27900, loss[loss=0.1509, simple_loss=0.2262, pruned_loss=0.03783, over 4838.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02859, over 972169.95 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 03:58:16,556 INFO [train.py:715] (2/8) Epoch 17, batch 27950, loss[loss=0.1258, simple_loss=0.1967, pruned_loss=0.02741, over 4823.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02904, over 972617.33 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 03:58:56,515 INFO [train.py:715] (2/8) Epoch 17, batch 28000, loss[loss=0.1172, simple_loss=0.1926, pruned_loss=0.02093, over 4748.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02851, over 972844.64 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 03:59:36,524 INFO [train.py:715] (2/8) Epoch 17, batch 28050, loss[loss=0.1225, simple_loss=0.1975, pruned_loss=0.02373, over 4939.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02843, over 972317.59 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:00:15,252 INFO [train.py:715] (2/8) Epoch 17, batch 28100, loss[loss=0.1412, simple_loss=0.2078, pruned_loss=0.03733, over 4913.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02885, over 972036.06 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:00:54,617 INFO [train.py:715] (2/8) Epoch 17, batch 28150, loss[loss=0.1585, simple_loss=0.2406, pruned_loss=0.03822, over 4905.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02894, over 972342.13 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:01:33,614 INFO [train.py:715] (2/8) Epoch 17, batch 28200, loss[loss=0.1105, simple_loss=0.1856, pruned_loss=0.01773, over 4950.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02867, over 973160.48 frames.], batch size: 29, lr: 1.29e-04
2022-05-09 04:02:12,001 INFO [train.py:715] (2/8) Epoch 17, batch 28250, loss[loss=0.1186, simple_loss=0.1848, pruned_loss=0.02619, over 4796.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02943, over 972319.63 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:02:50,450 INFO [train.py:715] (2/8) Epoch 17, batch 28300, loss[loss=0.1175, simple_loss=0.192, pruned_loss=0.02153, over 4753.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02944, over 972672.38 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:03:29,617 INFO [train.py:715] (2/8) Epoch 17, batch 28350, loss[loss=0.1308, simple_loss=0.1986, pruned_loss=0.03145, over 4803.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02929, over 972299.92 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:04:09,194 INFO [train.py:715] (2/8) Epoch 17, batch 28400, loss[loss=0.1558, simple_loss=0.221, pruned_loss=0.0453, over 4864.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2074, pruned_loss=0.02974, over 971769.55 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 04:04:48,214 INFO [train.py:715] (2/8) Epoch 17, batch 28450, loss[loss=0.1268, simple_loss=0.2001, pruned_loss=0.02679, over 4700.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02927, over 971458.96 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:05:26,443 INFO [train.py:715] (2/8) Epoch 17, batch 28500, loss[loss=0.1421, simple_loss=0.2163, pruned_loss=0.03394, over 4978.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2058, pruned_loss=0.02915, over 970447.91 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:06:06,463 INFO [train.py:715] (2/8) Epoch 17, batch 28550, loss[loss=0.1225, simple_loss=0.1951, pruned_loss=0.02494, over 4836.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02915, over 971736.91 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:06:45,105 INFO [train.py:715] (2/8) Epoch 17, batch 28600, loss[loss=0.1368, simple_loss=0.2117, pruned_loss=0.031, over 4817.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2056, pruned_loss=0.02911, over 971595.02 frames.], batch size: 27, lr: 1.29e-04
2022-05-09 04:07:23,875 INFO [train.py:715] (2/8) Epoch 17, batch 28650, loss[loss=0.1288, simple_loss=0.2005, pruned_loss=0.02851, over 4977.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2058, pruned_loss=0.0289, over 972581.57 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:08:02,258 INFO [train.py:715] (2/8) Epoch 17, batch 28700, loss[loss=0.1276, simple_loss=0.2017, pruned_loss=0.02677, over 4975.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.029, over 972479.39 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:08:41,577 INFO [train.py:715] (2/8) Epoch 17, batch 28750, loss[loss=0.1611, simple_loss=0.2341, pruned_loss=0.0441, over 4864.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02876, over 972922.97 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:09:20,210 INFO [train.py:715] (2/8) Epoch 17, batch 28800, loss[loss=0.1384, simple_loss=0.2096, pruned_loss=0.03364, over 4970.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02863, over 973197.38 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:09:58,906 INFO [train.py:715] (2/8) Epoch 17, batch 28850, loss[loss=0.1474, simple_loss=0.2168, pruned_loss=0.03899, over 4844.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02893, over 972902.56 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:10:37,995 INFO [train.py:715] (2/8) Epoch 17, batch 28900, loss[loss=0.127, simple_loss=0.1872, pruned_loss=0.03335, over 4647.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.0291, over 973348.94 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 04:11:16,523 INFO [train.py:715] (2/8) Epoch 17, batch 28950, loss[loss=0.1071, simple_loss=0.1869, pruned_loss=0.01361, over 4868.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.0294, over 972046.91 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:11:54,926 INFO [train.py:715] (2/8) Epoch 17, batch 29000, loss[loss=0.1428, simple_loss=0.2186, pruned_loss=0.0335, over 4903.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02923, over 971587.62 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:12:33,659 INFO [train.py:715] (2/8) Epoch 17, batch 29050, loss[loss=0.1291, simple_loss=0.2085, pruned_loss=0.02485, over 4941.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.02894, over 971662.86 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:13:13,011 INFO [train.py:715] (2/8) Epoch 17, batch 29100, loss[loss=0.1058, simple_loss=0.1643, pruned_loss=0.02366, over 4692.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02906, over 971291.53 frames.], batch size: 12, lr: 1.29e-04
2022-05-09 04:13:51,909 INFO [train.py:715] (2/8) Epoch 17, batch 29150, loss[loss=0.1185, simple_loss=0.19, pruned_loss=0.02354, over 4808.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02925, over 971616.51 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:14:30,016 INFO [train.py:715] (2/8) Epoch 17, batch 29200, loss[loss=0.1159, simple_loss=0.1972, pruned_loss=0.01735, over 4984.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02895, over 971802.35 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:15:09,521 INFO [train.py:715] (2/8) Epoch 17, batch 29250, loss[loss=0.1377, simple_loss=0.202, pruned_loss=0.03664, over 4970.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02905, over 972016.02 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 04:15:49,150 INFO [train.py:715] (2/8) Epoch 17, batch 29300, loss[loss=0.1397, simple_loss=0.2164, pruned_loss=0.03152, over 4876.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02929, over 972201.34 frames.], batch size: 39, lr: 1.29e-04
2022-05-09 04:16:27,571 INFO [train.py:715] (2/8) Epoch 17, batch 29350, loss[loss=0.1658, simple_loss=0.2336, pruned_loss=0.04904, over 4814.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2072, pruned_loss=0.02986, over 972317.38 frames.], batch size: 27, lr: 1.29e-04
2022-05-09 04:17:06,161 INFO [train.py:715] (2/8) Epoch 17, batch 29400, loss[loss=0.169, simple_loss=0.2329, pruned_loss=0.05254, over 4878.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.02972, over 972906.06 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 04:17:45,842 INFO [train.py:715] (2/8) Epoch 17, batch 29450, loss[loss=0.1194, simple_loss=0.1976, pruned_loss=0.02059, over 4966.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2067, pruned_loss=0.02954, over 973054.68 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:18:24,965 INFO [train.py:715] (2/8) Epoch 17, batch 29500, loss[loss=0.1327, simple_loss=0.1987, pruned_loss=0.03333, over 4893.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2071, pruned_loss=0.03, over 973666.76 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:19:03,885 INFO [train.py:715] (2/8) Epoch 17, batch 29550, loss[loss=0.1418, simple_loss=0.2127, pruned_loss=0.03549, over 4689.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2064, pruned_loss=0.02964, over 973358.67 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:19:43,166 INFO [train.py:715] (2/8) Epoch 17, batch 29600, loss[loss=0.1212, simple_loss=0.2015, pruned_loss=0.02047, over 4956.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2056, pruned_loss=0.02896, over 973827.18 frames.], batch size: 21, lr: 1.29e-04
2022-05-09 04:20:22,741 INFO [train.py:715] (2/8) Epoch 17, batch 29650, loss[loss=0.1392, simple_loss=0.2195, pruned_loss=0.0295, over 4772.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.02933, over 973463.15 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:21:01,516 INFO [train.py:715] (2/8) Epoch 17, batch 29700, loss[loss=0.1337, simple_loss=0.2159, pruned_loss=0.02581, over 4860.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2058, pruned_loss=0.02902, over 973465.11 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:21:40,466 INFO [train.py:715] (2/8) Epoch 17, batch 29750, loss[loss=0.1348, simple_loss=0.2061, pruned_loss=0.0318, over 4919.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.0291, over 972840.57 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:22:20,622 INFO [train.py:715] (2/8) Epoch 17, batch 29800, loss[loss=0.09832, simple_loss=0.1692, pruned_loss=0.0137, over 4844.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02887, over 972265.33 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 04:22:59,617 INFO [train.py:715] (2/8) Epoch 17, batch 29850, loss[loss=0.1267, simple_loss=0.2028, pruned_loss=0.02525, over 4931.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02867, over 971528.28 frames.], batch size: 29, lr: 1.29e-04
2022-05-09 04:23:38,913 INFO [train.py:715] (2/8) Epoch 17, batch 29900, loss[loss=0.1237, simple_loss=0.1999, pruned_loss=0.0238, over 4927.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02858, over 972271.62 frames.], batch size: 39, lr: 1.29e-04
2022-05-09 04:24:18,623 INFO [train.py:715] (2/8) Epoch 17, batch 29950, loss[loss=0.124, simple_loss=0.1998, pruned_loss=0.02411, over 4817.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02828, over 972340.33 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:24:58,027 INFO [train.py:715] (2/8) Epoch 17, batch 30000, loss[loss=0.1173, simple_loss=0.1958, pruned_loss=0.01941, over 4946.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.0287, over 973048.49 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:24:58,028 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 04:25:08,261 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1047, simple_loss=0.188, pruned_loss=0.01065, over 914524.00 frames.
2022-05-09 04:25:48,091 INFO [train.py:715] (2/8) Epoch 17, batch 30050, loss[loss=0.1418, simple_loss=0.207, pruned_loss=0.0383, over 4927.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02851, over 972535.11 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 04:26:27,727 INFO [train.py:715] (2/8) Epoch 17, batch 30100, loss[loss=0.1542, simple_loss=0.229, pruned_loss=0.03967, over 4855.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2065, pruned_loss=0.02845, over 972212.96 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:27:06,816 INFO [train.py:715] (2/8) Epoch 17, batch 30150, loss[loss=0.1445, simple_loss=0.2202, pruned_loss=0.03438, over 4916.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02907, over 971097.31 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:27:46,313 INFO [train.py:715] (2/8) Epoch 17, batch 30200, loss[loss=0.1295, simple_loss=0.2062, pruned_loss=0.02639, over 4982.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2074, pruned_loss=0.02922, over 972115.56 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:28:25,427 INFO [train.py:715] (2/8) Epoch 17, batch 30250, loss[loss=0.1315, simple_loss=0.2129, pruned_loss=0.02505, over 4830.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.0294, over 971522.82 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:29:04,422 INFO [train.py:715] (2/8) Epoch 17, batch 30300, loss[loss=0.1574, simple_loss=0.228, pruned_loss=0.04339, over 4843.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02884, over 971478.54 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:29:44,187 INFO [train.py:715] (2/8) Epoch 17, batch 30350, loss[loss=0.1398, simple_loss=0.2163, pruned_loss=0.03169, over 4903.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2068, pruned_loss=0.0282, over 971445.49 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:30:23,370 INFO [train.py:715] (2/8) Epoch 17, batch 30400, loss[loss=0.1777, simple_loss=0.2511, pruned_loss=0.05217, over 4869.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2069, pruned_loss=0.02827, over 971635.16 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:31:02,094 INFO [train.py:715] (2/8) Epoch 17, batch 30450, loss[loss=0.1206, simple_loss=0.2023, pruned_loss=0.01949, over 4858.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2053, pruned_loss=0.02765, over 971596.71 frames.], batch size: 20, lr: 1.29e-04
2022-05-09 04:31:41,827 INFO [train.py:715] (2/8) Epoch 17, batch 30500, loss[loss=0.1112, simple_loss=0.1799, pruned_loss=0.02129, over 4788.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.0279, over 971343.76 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:32:21,637 INFO [train.py:715] (2/8) Epoch 17, batch 30550, loss[loss=0.1371, simple_loss=0.2081, pruned_loss=0.03308, over 4968.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02846, over 971719.27 frames.], batch size: 35, lr: 1.29e-04
2022-05-09 04:33:01,422 INFO [train.py:715] (2/8) Epoch 17, batch 30600, loss[loss=0.1383, simple_loss=0.2234, pruned_loss=0.02662, over 4875.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02834, over 971689.26 frames.], batch size: 22, lr: 1.29e-04
2022-05-09 04:33:40,335 INFO [train.py:715] (2/8) Epoch 17, batch 30650, loss[loss=0.1452, simple_loss=0.2296, pruned_loss=0.03042, over 4965.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02863, over 972396.29 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:34:20,064 INFO [train.py:715] (2/8) Epoch 17, batch 30700, loss[loss=0.1291, simple_loss=0.2126, pruned_loss=0.02283, over 4752.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02861, over 971873.42 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:34:59,085 INFO [train.py:715] (2/8) Epoch 17, batch 30750, loss[loss=0.1492, simple_loss=0.2161, pruned_loss=0.04112, over 4842.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02904, over 972780.30 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 04:35:38,913 INFO [train.py:715] (2/8) Epoch 17, batch 30800, loss[loss=0.1384, simple_loss=0.2082, pruned_loss=0.0343, over 4864.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2065, pruned_loss=0.02845, over 972926.70 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:36:18,142 INFO [train.py:715] (2/8) Epoch 17, batch 30850, loss[loss=0.1461, simple_loss=0.2158, pruned_loss=0.03816, over 4986.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02908, over 972804.92 frames.], batch size: 15, lr: 1.29e-04
2022-05-09 04:36:58,360 INFO [train.py:715] (2/8) Epoch 17, batch 30900, loss[loss=0.1346, simple_loss=0.2068, pruned_loss=0.03122, over 4963.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02942, over 973369.32 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:37:38,030 INFO [train.py:715] (2/8) Epoch 17, batch 30950, loss[loss=0.1189, simple_loss=0.1908, pruned_loss=0.02347, over 4926.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02973, over 973823.69 frames.], batch size: 29, lr: 1.29e-04
2022-05-09 04:38:17,317 INFO [train.py:715] (2/8) Epoch 17, batch 31000, loss[loss=0.1465, simple_loss=0.2207, pruned_loss=0.03613, over 4886.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2083, pruned_loss=0.03009, over 972753.75 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:38:57,009 INFO [train.py:715] (2/8) Epoch 17, batch 31050, loss[loss=0.1217, simple_loss=0.2012, pruned_loss=0.02105, over 4932.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2083, pruned_loss=0.0299, over 972439.60 frames.], batch size: 29, lr: 1.29e-04
2022-05-09 04:39:36,074 INFO [train.py:715] (2/8) Epoch 17, batch 31100, loss[loss=0.1258, simple_loss=0.2055, pruned_loss=0.02305, over 4831.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2078, pruned_loss=0.02953, over 971441.60 frames.], batch size: 27, lr: 1.29e-04
2022-05-09 04:40:15,209 INFO [train.py:715] (2/8) Epoch 17, batch 31150, loss[loss=0.1475, simple_loss=0.2304, pruned_loss=0.0323, over 4789.00 frames.], tot_loss[loss=0.1336, simple_loss=0.208, pruned_loss=0.02953, over 971478.53 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:40:54,504 INFO [train.py:715] (2/8) Epoch 17, batch 31200, loss[loss=0.1488, simple_loss=0.2253, pruned_loss=0.03621, over 4922.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02965, over 970825.52 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:41:34,598 INFO [train.py:715] (2/8) Epoch 17, batch 31250, loss[loss=0.1768, simple_loss=0.2681, pruned_loss=0.04279, over 4840.00 frames.], tot_loss[loss=0.1334, simple_loss=0.208, pruned_loss=0.02944, over 970989.26 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 04:42:13,894 INFO [train.py:715] (2/8) Epoch 17, batch 31300, loss[loss=0.1368, simple_loss=0.2147, pruned_loss=0.02943, over 4901.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02943, over 970876.74 frames.], batch size: 17, lr: 1.29e-04
2022-05-09 04:42:53,284 INFO [train.py:715] (2/8) Epoch 17, batch 31350, loss[loss=0.1229, simple_loss=0.1874, pruned_loss=0.02924, over 4960.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02928, over 971066.29 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:43:32,643 INFO [train.py:715] (2/8) Epoch 17, batch 31400, loss[loss=0.1402, simple_loss=0.216, pruned_loss=0.03224, over 4890.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02938, over 970922.60 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:44:11,253 INFO [train.py:715] (2/8) Epoch 17, batch 31450, loss[loss=0.1298, simple_loss=0.2077, pruned_loss=0.026, over 4789.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02937, over 970517.70 frames.], batch size: 24, lr: 1.29e-04
2022-05-09 04:44:51,217 INFO [train.py:715] (2/8) Epoch 17, batch 31500, loss[loss=0.1463, simple_loss=0.2322, pruned_loss=0.0302, over 4755.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02914, over 970520.90 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:45:29,943 INFO [train.py:715] (2/8) Epoch 17, batch 31550, loss[loss=0.1074, simple_loss=0.1776, pruned_loss=0.0186, over 4985.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.02936, over 970899.91 frames.], batch size: 28, lr: 1.29e-04
2022-05-09 04:46:09,497 INFO [train.py:715] (2/8) Epoch 17, batch 31600, loss[loss=0.1189, simple_loss=0.1955, pruned_loss=0.02116, over 4941.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2078, pruned_loss=0.02969, over 971543.94 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:46:48,908 INFO [train.py:715] (2/8) Epoch 17, batch 31650, loss[loss=0.1467, simple_loss=0.224, pruned_loss=0.03475, over 4776.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02966, over 972598.94 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:47:28,185 INFO [train.py:715] (2/8) Epoch 17, batch 31700, loss[loss=0.1554, simple_loss=0.2339, pruned_loss=0.03848, over 4887.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2077, pruned_loss=0.02987, over 972203.70 frames.], batch size: 32, lr: 1.29e-04
2022-05-09 04:48:07,938 INFO [train.py:715] (2/8) Epoch 17, batch 31750, loss[loss=0.192, simple_loss=0.2757, pruned_loss=0.05413, over 4818.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.0302, over 971703.59 frames.], batch size: 26, lr: 1.29e-04
2022-05-09 04:48:47,178 INFO [train.py:715] (2/8) Epoch 17, batch 31800, loss[loss=0.09695, simple_loss=0.1656, pruned_loss=0.01414, over 4787.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02977, over 972396.55 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:49:27,380 INFO [train.py:715] (2/8) Epoch 17, batch 31850, loss[loss=0.1028, simple_loss=0.1763, pruned_loss=0.01471, over 4803.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02948, over 972381.78 frames.], batch size: 25, lr: 1.29e-04
2022-05-09 04:50:06,506 INFO [train.py:715] (2/8) Epoch 17, batch 31900, loss[loss=0.1154, simple_loss=0.1897, pruned_loss=0.02054, over 4908.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02936, over 972400.41 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:50:45,989 INFO [train.py:715] (2/8) Epoch 17, batch 31950, loss[loss=0.1212, simple_loss=0.1952, pruned_loss=0.02357, over 4927.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02966, over 972092.42 frames.], batch size: 23, lr: 1.29e-04
2022-05-09 04:51:25,762 INFO [train.py:715] (2/8) Epoch 17, batch 32000, loss[loss=0.1327, simple_loss=0.2032, pruned_loss=0.03105, over 4944.00 frames.], tot_loss[loss=0.1331, simple_loss=0.207, pruned_loss=0.0296, over 973498.34 frames.], batch size: 29, lr: 1.29e-04
2022-05-09 04:52:04,649 INFO [train.py:715] (2/8) Epoch 17, batch 32050, loss[loss=0.1119, simple_loss=0.1848, pruned_loss=0.01954, over 4893.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2055, pruned_loss=0.0288, over 973483.79 frames.], batch size: 19, lr: 1.29e-04
2022-05-09 04:52:44,368 INFO [train.py:715] (2/8) Epoch 17, batch 32100, loss[loss=0.1111, simple_loss=0.1847, pruned_loss=0.01874, over 4656.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2048, pruned_loss=0.02868, over 972907.27 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 04:53:23,409 INFO [train.py:715] (2/8) Epoch 17, batch 32150, loss[loss=0.1295, simple_loss=0.2057, pruned_loss=0.02668, over 4819.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2053, pruned_loss=0.02866, over 972752.68 frames.], batch size: 27, lr: 1.29e-04
2022-05-09 04:54:02,765 INFO [train.py:715] (2/8) Epoch 17, batch 32200, loss[loss=0.1501, simple_loss=0.229, pruned_loss=0.03563, over 4836.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2047, pruned_loss=0.02838, over 972954.26 frames.], batch size: 30, lr: 1.29e-04
2022-05-09 04:54:45,081 INFO [train.py:715] (2/8) Epoch 17, batch 32250, loss[loss=0.1297, simple_loss=0.1991, pruned_loss=0.03012, over 4881.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2047, pruned_loss=0.02802, over 973455.13 frames.], batch size: 16, lr: 1.29e-04
2022-05-09 04:55:24,423 INFO [train.py:715] (2/8) Epoch 17, batch 32300, loss[loss=0.1447, simple_loss=0.2133, pruned_loss=0.03801, over 4893.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02809, over 973067.15 frames.], batch size: 18, lr: 1.29e-04
2022-05-09 04:56:04,329 INFO [train.py:715] (2/8) Epoch 17, batch 32350, loss[loss=0.1244, simple_loss=0.2077, pruned_loss=0.02052, over 4769.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02841, over 973115.34 frames.], batch size: 14, lr: 1.29e-04
2022-05-09 04:56:43,383 INFO [train.py:715] (2/8) Epoch 17, batch 32400, loss[loss=0.1165, simple_loss=0.1876, pruned_loss=0.02265, over 4846.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2049, pruned_loss=0.02811, over 973443.12 frames.], batch size: 13, lr: 1.29e-04
2022-05-09 04:57:22,534 INFO [train.py:715] (2/8) Epoch 17, batch 32450, loss[loss=0.1518, simple_loss=0.2387, pruned_loss=0.03247, over 4903.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02845, over 972920.13 frames.], batch size: 19, lr: 1.28e-04
2022-05-09 04:58:02,556 INFO [train.py:715] (2/8) Epoch 17, batch 32500, loss[loss=0.1218, simple_loss=0.2093, pruned_loss=0.01715, over 4887.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02899, over 971866.96 frames.], batch size: 16, lr: 1.28e-04
2022-05-09 04:58:41,971 INFO [train.py:715] (2/8) Epoch 17, batch 32550, loss[loss=0.1568, simple_loss=0.223, pruned_loss=0.04525, over 4684.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02875, over 972009.80 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 04:59:21,577 INFO [train.py:715] (2/8) Epoch 17, batch 32600, loss[loss=0.1173, simple_loss=0.2, pruned_loss=0.01731, over 4974.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.0288, over 972155.13 frames.], batch size: 24, lr: 1.28e-04
2022-05-09 05:00:01,069 INFO [train.py:715] (2/8) Epoch 17, batch 32650, loss[loss=0.1314, simple_loss=0.2148, pruned_loss=0.02402, over 4781.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02891, over 972972.94 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:00:39,806 INFO [train.py:715] (2/8) Epoch 17, batch 32700, loss[loss=0.1187, simple_loss=0.1974, pruned_loss=0.01999, over 4977.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02925, over 973307.05 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:01:19,989 INFO [train.py:715] (2/8) Epoch 17, batch 32750, loss[loss=0.125, simple_loss=0.2057, pruned_loss=0.02218, over 4786.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02917, over 973968.09 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:01:59,337 INFO [train.py:715] (2/8) Epoch 17, batch 32800, loss[loss=0.1503, simple_loss=0.227, pruned_loss=0.03679, over 4695.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2074, pruned_loss=0.02895, over 972680.69 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:02:38,970 INFO [train.py:715] (2/8) Epoch 17, batch 32850, loss[loss=0.129, simple_loss=0.2107, pruned_loss=0.02367, over 4763.00 frames.], tot_loss[loss=0.1334, simple_loss=0.208, pruned_loss=0.02937, over 971887.44 frames.], batch size: 19, lr: 1.28e-04
2022-05-09 05:03:18,524 INFO [train.py:715] (2/8) Epoch 17, batch 32900, loss[loss=0.128, simple_loss=0.2063, pruned_loss=0.02482, over 4796.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2079, pruned_loss=0.02897, over 971890.35 frames.], batch size: 24, lr: 1.28e-04
2022-05-09 05:03:58,029 INFO [train.py:715] (2/8) Epoch 17, batch 32950, loss[loss=0.1188, simple_loss=0.1931, pruned_loss=0.02223, over 4934.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2072, pruned_loss=0.02864, over 972195.93 frames.], batch size: 23, lr: 1.28e-04
2022-05-09 05:04:36,961 INFO [train.py:715] (2/8) Epoch 17, batch 33000, loss[loss=0.1583, simple_loss=0.234, pruned_loss=0.04134, over 4939.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2068, pruned_loss=0.02853, over 972074.76 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:04:36,961 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 05:04:49,645 INFO [train.py:742] (2/8) Epoch 17, validation: loss=0.1049, simple_loss=0.1881, pruned_loss=0.0108, over 914524.00 frames.
2022-05-09 05:05:28,991 INFO [train.py:715] (2/8) Epoch 17, batch 33050, loss[loss=0.1565, simple_loss=0.2239, pruned_loss=0.04455, over 4858.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02932, over 971620.55 frames.], batch size: 32, lr: 1.28e-04
2022-05-09 05:06:08,147 INFO [train.py:715] (2/8) Epoch 17, batch 33100, loss[loss=0.1274, simple_loss=0.2051, pruned_loss=0.02483, over 4786.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02977, over 971457.32 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:06:47,450 INFO [train.py:715] (2/8) Epoch 17, batch 33150, loss[loss=0.1253, simple_loss=0.1991, pruned_loss=0.02579, over 4766.00 frames.], tot_loss[loss=0.1334, simple_loss=0.208, pruned_loss=0.02943, over 971868.50 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:07:27,186 INFO [train.py:715] (2/8) Epoch 17, batch 33200, loss[loss=0.1393, simple_loss=0.2135, pruned_loss=0.03258, over 4852.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2082, pruned_loss=0.02968, over 972874.53 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:08:06,797 INFO [train.py:715] (2/8) Epoch 17, batch 33250, loss[loss=0.1458, simple_loss=0.2191, pruned_loss=0.0362, over 4762.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2086, pruned_loss=0.02993, over 972419.34 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:08:46,106 INFO [train.py:715] (2/8) Epoch 17, batch 33300, loss[loss=0.1205, simple_loss=0.193, pruned_loss=0.02406, over 4964.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.0294, over 972815.39 frames.], batch size: 24, lr: 1.28e-04
2022-05-09 05:09:25,687 INFO [train.py:715] (2/8) Epoch 17, batch 33350, loss[loss=0.154, simple_loss=0.2184, pruned_loss=0.04473, over 4931.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02913, over 973699.26 frames.], batch size: 23, lr: 1.28e-04
2022-05-09 05:10:05,483 INFO [train.py:715] (2/8) Epoch 17, batch 33400, loss[loss=0.1073, simple_loss=0.1776, pruned_loss=0.01845, over 4644.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02857, over 973227.82 frames.], batch size: 13, lr: 1.28e-04
2022-05-09 05:10:44,826 INFO [train.py:715] (2/8) Epoch 17, batch 33450, loss[loss=0.1267, simple_loss=0.1955, pruned_loss=0.02895, over 4819.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02875, over 972817.08 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:11:24,374 INFO [train.py:715] (2/8) Epoch 17, batch 33500, loss[loss=0.1747, simple_loss=0.2501, pruned_loss=0.04963, over 4864.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02863, over 973006.91 frames.], batch size: 32, lr: 1.28e-04
2022-05-09 05:12:04,605 INFO [train.py:715] (2/8) Epoch 17, batch 33550, loss[loss=0.1261, simple_loss=0.2058, pruned_loss=0.02323, over 4691.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02838, over 972466.35 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:12:44,745 INFO [train.py:715] (2/8) Epoch 17, batch 33600, loss[loss=0.1247, simple_loss=0.2041, pruned_loss=0.02268, over 4788.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02842, over 971819.81 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:13:23,723 INFO [train.py:715] (2/8) Epoch 17, batch 33650, loss[loss=0.1608, simple_loss=0.2336, pruned_loss=0.04399, over 4687.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02869, over 970735.61 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:14:03,359 INFO [train.py:715] (2/8) Epoch 17, batch 33700, loss[loss=0.1329, simple_loss=0.2086, pruned_loss=0.02863, over 4830.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02818, over 971217.20 frames.], batch size: 13, lr: 1.28e-04
2022-05-09 05:14:42,578 INFO [train.py:715] (2/8) Epoch 17, batch 33750, loss[loss=0.142, simple_loss=0.2189, pruned_loss=0.03255, over 4876.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02833, over 971553.93 frames.], batch size: 22, lr: 1.28e-04
2022-05-09 05:15:21,399 INFO [train.py:715] (2/8) Epoch 17, batch 33800, loss[loss=0.1279, simple_loss=0.1995, pruned_loss=0.02813, over 4642.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02862, over 971358.77 frames.], batch size: 13, lr: 1.28e-04
2022-05-09 05:16:01,549 INFO [train.py:715] (2/8) Epoch 17, batch 33850, loss[loss=0.1319, simple_loss=0.2095, pruned_loss=0.02714, over 4783.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02844, over 971161.81 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:16:41,836 INFO [train.py:715] (2/8) Epoch 17, batch 33900, loss[loss=0.137, simple_loss=0.2102, pruned_loss=0.03186, over 4771.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2053, pruned_loss=0.02842, over 971571.59 frames.], batch size: 19, lr: 1.28e-04
2022-05-09 05:17:21,094 INFO [train.py:715] (2/8) Epoch 17, batch 33950, loss[loss=0.1318, simple_loss=0.2082, pruned_loss=0.02769, over 4899.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02819, over 972471.09 frames.], batch size: 17, lr: 1.28e-04
2022-05-09 05:18:00,093 INFO [train.py:715] (2/8) Epoch 17, batch 34000, loss[loss=0.1109, simple_loss=0.1953, pruned_loss=0.01329, over 4887.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2052, pruned_loss=0.02825, over 973184.44 frames.], batch size: 19, lr: 1.28e-04
2022-05-09 05:18:39,507 INFO [train.py:715] (2/8) Epoch 17, batch 34050, loss[loss=0.1258, simple_loss=0.1979, pruned_loss=0.02689, over 4857.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2052, pruned_loss=0.02827, over 972800.71 frames.], batch size: 34, lr: 1.28e-04
2022-05-09 05:19:19,506 INFO [train.py:715] (2/8) Epoch 17, batch 34100, loss[loss=0.1376, simple_loss=0.1986, pruned_loss=0.03828, over 4935.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02877, over 972060.78 frames.], batch size: 39, lr: 1.28e-04
2022-05-09 05:19:58,310 INFO [train.py:715] (2/8) Epoch 17, batch 34150, loss[loss=0.1162, simple_loss=0.1918, pruned_loss=0.02033, over 4802.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2071, pruned_loss=0.02878, over 971823.68 frames.], batch size: 25, lr: 1.28e-04
2022-05-09 05:20:37,451 INFO [train.py:715] (2/8) Epoch 17, batch 34200, loss[loss=0.1235, simple_loss=0.1973, pruned_loss=0.02488, over 4941.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02848, over 972636.65 frames.], batch size: 21, lr: 1.28e-04
2022-05-09 05:21:16,560 INFO [train.py:715] (2/8) Epoch 17, batch 34250, loss[loss=0.141, simple_loss=0.2085, pruned_loss=0.03677, over 4839.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2068, pruned_loss=0.02847, over 972754.82 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:21:55,282 INFO [train.py:715] (2/8) Epoch 17, batch 34300, loss[loss=0.1262, simple_loss=0.2043, pruned_loss=0.02401, over 4989.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02864, over 972864.17 frames.], batch size: 25, lr: 1.28e-04
2022-05-09 05:22:34,167 INFO [train.py:715] (2/8) Epoch 17, batch 34350, loss[loss=0.1256, simple_loss=0.202, pruned_loss=0.02458, over 4750.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02865, over 972556.21 frames.], batch size: 19, lr: 1.28e-04
2022-05-09 05:23:13,544 INFO [train.py:715] (2/8) Epoch 17, batch 34400, loss[loss=0.1026, simple_loss=0.1593, pruned_loss=0.023, over 4814.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2053, pruned_loss=0.02865, over 972413.97 frames.], batch size: 12, lr: 1.28e-04
2022-05-09 05:23:52,517 INFO [train.py:715] (2/8) Epoch 17, batch 34450, loss[loss=0.1595, simple_loss=0.2324, pruned_loss=0.04327, over 4903.00 frames.], tot_loss[loss=0.132, simple_loss=0.2059, pruned_loss=0.02908, over 971606.31 frames.], batch size: 39, lr: 1.28e-04
2022-05-09 05:24:30,969 INFO [train.py:715] (2/8) Epoch 17, batch 34500, loss[loss=0.1485, simple_loss=0.2169, pruned_loss=0.04011, over 4976.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.02935, over 971834.16 frames.], batch size: 15, lr: 1.28e-04
2022-05-09 05:25:09,847 INFO [train.py:715] (2/8) Epoch 17, batch 34550, loss[loss=0.1446, simple_loss=0.221, pruned_loss=0.03412, over 4784.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2062, pruned_loss=0.02918, over 972694.87 frames.], batch size: 17, lr: 1.28e-04
2022-05-09 05:25:48,995 INFO [train.py:715] (2/8) Epoch 17, batch 34600, loss[loss=0.1192, simple_loss=0.1981, pruned_loss=0.02016, over 4767.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02941, over 972072.91 frames.], batch size: 14, lr: 1.28e-04
2022-05-09 05:26:27,693 INFO [train.py:715] (2/8) Epoch 17, batch 34650, loss[loss=0.1511, simple_loss=0.2333, pruned_loss=0.03448, over 4893.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2068, pruned_loss=0.02947, over 972122.37 frames.], batch size: 19, lr: 1.28e-04
2022-05-09 05:27:06,962 INFO [train.py:715] (2/8) Epoch 17, batch 34700, loss[loss=0.1354, simple_loss=0.2127, pruned_loss=0.02909, over 4767.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02952, over 971398.97 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:27:45,508 INFO [train.py:715] (2/8) Epoch 17, batch 34750, loss[loss=0.1165, simple_loss=0.1873, pruned_loss=0.02287, over 4770.00 frames.], tot_loss[loss=0.132, simple_loss=0.2059, pruned_loss=0.02907, over 971278.83 frames.], batch size: 18, lr: 1.28e-04
2022-05-09 05:28:22,197 INFO [train.py:715] (2/8) Epoch 17, batch 34800, loss[loss=0.1292, simple_loss=0.2043, pruned_loss=0.02708, over 4930.00 frames.], tot_loss[loss=0.1314, simple_loss=0.205, pruned_loss=0.02886, over 970350.46 frames.], batch size: 23, lr: 1.28e-04
2022-05-09 05:29:12,361 INFO [train.py:715] (2/8) Epoch 18, batch 0, loss[loss=0.1384, simple_loss=0.2107, pruned_loss=0.03306, over 4747.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2107, pruned_loss=0.03306, over 4747.00 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:29:51,052 INFO [train.py:715] (2/8) Epoch 18, batch 50, loss[loss=0.1234, simple_loss=0.202, pruned_loss=0.02243, over 4798.00 frames.], tot_loss[loss=0.1282, simple_loss=0.2026, pruned_loss=0.02692, over 218817.78 frames.], batch size: 17, lr: 1.25e-04
2022-05-09 05:30:31,044 INFO [train.py:715] (2/8) Epoch 18, batch 100, loss[loss=0.1054, simple_loss=0.1861, pruned_loss=0.01236, over 4812.00 frames.], tot_loss[loss=0.1283, simple_loss=0.2026, pruned_loss=0.02697, over 385996.79 frames.], batch size: 24, lr: 1.25e-04
2022-05-09 05:31:10,962 INFO [train.py:715] (2/8) Epoch 18, batch 150, loss[loss=0.1524, simple_loss=0.2145, pruned_loss=0.04512, over 4867.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2043, pruned_loss=0.02819, over 516444.18 frames.], batch size: 32, lr: 1.25e-04
2022-05-09 05:31:50,263 INFO [train.py:715] (2/8) Epoch 18, batch 200, loss[loss=0.1379, simple_loss=0.1988, pruned_loss=0.03853, over 4798.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02926, over 617748.48 frames.], batch size: 13, lr: 1.25e-04
2022-05-09 05:32:29,110 INFO [train.py:715] (2/8) Epoch 18, batch 250, loss[loss=0.115, simple_loss=0.1987, pruned_loss=0.01563, over 4988.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02957, over 696946.49 frames.], batch size: 24, lr: 1.25e-04
2022-05-09 05:33:08,568 INFO [train.py:715] (2/8) Epoch 18, batch 300, loss[loss=0.1366, simple_loss=0.2126, pruned_loss=0.03034, over 4829.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.02996, over 757407.01 frames.], batch size: 27, lr: 1.25e-04
2022-05-09 05:33:48,412 INFO [train.py:715] (2/8) Epoch 18, batch 350, loss[loss=0.2018, simple_loss=0.2895, pruned_loss=0.0571, over 4947.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03007, over 804817.67 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 05:34:27,356 INFO [train.py:715] (2/8) Epoch 18, batch 400, loss[loss=0.1724, simple_loss=0.2442, pruned_loss=0.05024, over 4956.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2079, pruned_loss=0.02991, over 842591.22 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:35:07,145 INFO [train.py:715] (2/8) Epoch 18, batch 450, loss[loss=0.142, simple_loss=0.2135, pruned_loss=0.0352, over 4774.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02949, over 871936.42 frames.], batch size: 14, lr: 1.25e-04
2022-05-09 05:35:47,331 INFO [train.py:715] (2/8) Epoch 18, batch 500, loss[loss=0.1526, simple_loss=0.2411, pruned_loss=0.0321, over 4876.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02927, over 893863.80 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:36:27,097 INFO [train.py:715] (2/8) Epoch 18, batch 550, loss[loss=0.1542, simple_loss=0.2233, pruned_loss=0.04256, over 4908.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02931, over 911070.12 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:37:06,106 INFO [train.py:715] (2/8) Epoch 18, batch 600, loss[loss=0.1428, simple_loss=0.2068, pruned_loss=0.03941, over 4974.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2088, pruned_loss=0.03013, over 924847.61 frames.], batch size: 39, lr: 1.25e-04
2022-05-09 05:37:45,640 INFO [train.py:715] (2/8) Epoch 18, batch 650, loss[loss=0.122, simple_loss=0.21, pruned_loss=0.01702, over 4827.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2081, pruned_loss=0.02964, over 935480.23 frames.], batch size: 26, lr: 1.25e-04
2022-05-09 05:38:25,479 INFO [train.py:715] (2/8) Epoch 18, batch 700, loss[loss=0.1374, simple_loss=0.2057, pruned_loss=0.03454, over 4855.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.0289, over 943998.62 frames.], batch size: 30, lr: 1.25e-04
2022-05-09 05:39:04,428 INFO [train.py:715] (2/8) Epoch 18, batch 750, loss[loss=0.1373, simple_loss=0.207, pruned_loss=0.03384, over 4796.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02943, over 950506.46 frames.], batch size: 24, lr: 1.25e-04
2022-05-09 05:39:43,256 INFO [train.py:715] (2/8) Epoch 18, batch 800, loss[loss=0.1619, simple_loss=0.2398, pruned_loss=0.04193, over 4921.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2078, pruned_loss=0.02926, over 955776.16 frames.], batch size: 18, lr: 1.25e-04
2022-05-09 05:40:22,750 INFO [train.py:715] (2/8) Epoch 18, batch 850, loss[loss=0.1195, simple_loss=0.1984, pruned_loss=0.02033, over 4825.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02922, over 960358.20 frames.], batch size: 26, lr: 1.25e-04
2022-05-09 05:41:02,301 INFO [train.py:715] (2/8) Epoch 18, batch 900, loss[loss=0.1218, simple_loss=0.1923, pruned_loss=0.02567, over 4899.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02938, over 962352.14 frames.], batch size: 17, lr: 1.25e-04
2022-05-09 05:41:41,279 INFO [train.py:715] (2/8) Epoch 18, batch 950, loss[loss=0.1246, simple_loss=0.1951, pruned_loss=0.02708, over 4843.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02946, over 963736.00 frames.], batch size: 32, lr: 1.25e-04
2022-05-09 05:42:20,888 INFO [train.py:715] (2/8) Epoch 18, batch 1000, loss[loss=0.146, simple_loss=0.2238, pruned_loss=0.03407, over 4795.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02965, over 965822.43 frames.], batch size: 24, lr: 1.25e-04
2022-05-09 05:43:00,533 INFO [train.py:715] (2/8) Epoch 18, batch 1050, loss[loss=0.1207, simple_loss=0.2062, pruned_loss=0.01757, over 4926.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02939, over 966757.02 frames.], batch size: 23, lr: 1.25e-04
2022-05-09 05:43:39,937 INFO [train.py:715] (2/8) Epoch 18, batch 1100, loss[loss=0.1219, simple_loss=0.194, pruned_loss=0.02488, over 4827.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02954, over 968459.13 frames.], batch size: 30, lr: 1.25e-04
2022-05-09 05:44:18,725 INFO [train.py:715] (2/8) Epoch 18, batch 1150, loss[loss=0.1128, simple_loss=0.1977, pruned_loss=0.01394, over 4944.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02909, over 969355.11 frames.], batch size: 23, lr: 1.25e-04
2022-05-09 05:44:58,553 INFO [train.py:715] (2/8) Epoch 18, batch 1200, loss[loss=0.1046, simple_loss=0.1774, pruned_loss=0.01589, over 4782.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02913, over 969714.10 frames.], batch size: 17, lr: 1.25e-04
2022-05-09 05:45:38,524 INFO [train.py:715] (2/8) Epoch 18, batch 1250, loss[loss=0.1069, simple_loss=0.187, pruned_loss=0.01337, over 4895.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02893, over 970474.86 frames.], batch size: 22, lr: 1.25e-04
2022-05-09 05:46:17,551 INFO [train.py:715] (2/8) Epoch 18, batch 1300, loss[loss=0.1126, simple_loss=0.1863, pruned_loss=0.01947, over 4775.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02885, over 969845.55 frames.], batch size: 14, lr: 1.25e-04
2022-05-09 05:46:56,375 INFO [train.py:715] (2/8) Epoch 18, batch 1350, loss[loss=0.1563, simple_loss=0.2295, pruned_loss=0.0415, over 4796.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2054, pruned_loss=0.02881, over 970216.53 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 05:47:35,783 INFO [train.py:715] (2/8) Epoch 18, batch 1400, loss[loss=0.1167, simple_loss=0.1946, pruned_loss=0.01937, over 4820.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2047, pruned_loss=0.02848, over 970799.29 frames.], batch size: 27, lr: 1.25e-04
2022-05-09 05:48:15,008 INFO [train.py:715] (2/8) Epoch 18, batch 1450, loss[loss=0.191, simple_loss=0.2541, pruned_loss=0.06393, over 4774.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2052, pruned_loss=0.02891, over 970492.75 frames.], batch size: 19, lr: 1.25e-04
2022-05-09 05:48:53,403 INFO [train.py:715] (2/8) Epoch 18, batch 1500, loss[loss=0.1156, simple_loss=0.1845, pruned_loss=0.02337, over 4782.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02882, over 971526.23 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 05:49:32,908 INFO [train.py:715] (2/8) Epoch 18, batch 1550, loss[loss=0.1246, simple_loss=0.198, pruned_loss=0.02556, over 4840.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02876, over 972465.51 frames.], batch size: 13, lr: 1.25e-04
2022-05-09 05:50:12,323 INFO [train.py:715] (2/8) Epoch 18, batch 1600, loss[loss=0.1377, simple_loss=0.2097, pruned_loss=0.03289, over 4700.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02898, over 973142.87 frames.], batch size: 15, lr: 1.25e-04
2022-05-09 05:50:51,522 INFO [train.py:715] (2/8) Epoch 18, batch 1650, loss[loss=0.1275, simple_loss=0.1961, pruned_loss=0.02941, over 4765.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02905, over 972460.66 frames.], batch size: 18, lr: 1.25e-04
2022-05-09 05:51:30,470 INFO [train.py:715] (2/8) Epoch 18, batch 1700, loss[loss=0.1322, simple_loss=0.2085, pruned_loss=0.02796, over 4893.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02869, over 973185.76 frames.], batch size: 22, lr: 1.25e-04
2022-05-09 05:52:09,887 INFO [train.py:715] (2/8) Epoch 18, batch 1750, loss[loss=0.1177, simple_loss=0.1903, pruned_loss=0.02255, over 4786.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02875, over 973162.31 frames.], batch size: 12, lr: 1.25e-04
2022-05-09 05:52:49,187 INFO [train.py:715] (2/8) Epoch 18, batch 1800, loss[loss=0.1617, simple_loss=0.2299, pruned_loss=0.04671, over 4757.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.02932, over 973515.38 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:53:27,453 INFO [train.py:715] (2/8) Epoch 18, batch 1850, loss[loss=0.1258, simple_loss=0.2011, pruned_loss=0.02525, over 4949.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02913, over 972175.58 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 05:54:06,243 INFO [train.py:715] (2/8) Epoch 18, batch 1900, loss[loss=0.138, simple_loss=0.2052, pruned_loss=0.03536, over 4914.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.02965, over 972384.82 frames.], batch size: 23, lr: 1.25e-04
2022-05-09 05:54:45,622 INFO [train.py:715] (2/8) Epoch 18, batch 1950, loss[loss=0.1253, simple_loss=0.2003, pruned_loss=0.02514, over 4820.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02933, over 972144.74 frames.], batch size: 25, lr: 1.25e-04
2022-05-09 05:55:24,354 INFO [train.py:715] (2/8) Epoch 18, batch 2000, loss[loss=0.1187, simple_loss=0.2006, pruned_loss=0.01835, over 4804.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02902, over 972568.80 frames.], batch size: 24, lr: 1.25e-04
2022-05-09 05:56:02,842 INFO [train.py:715] (2/8) Epoch 18, batch 2050, loss[loss=0.1359, simple_loss=0.2058, pruned_loss=0.033, over 4926.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02875, over 972345.56 frames.], batch size: 18, lr: 1.25e-04
2022-05-09 05:56:42,080 INFO [train.py:715] (2/8) Epoch 18, batch 2100, loss[loss=0.1413, simple_loss=0.2167, pruned_loss=0.0329, over 4905.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02878, over 972021.72 frames.], batch size: 17, lr: 1.25e-04
2022-05-09 05:57:21,527 INFO [train.py:715] (2/8) Epoch 18, batch 2150, loss[loss=0.1214, simple_loss=0.1886, pruned_loss=0.02717, over 4774.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02894, over 971878.07 frames.], batch size: 18, lr: 1.25e-04
2022-05-09 05:57:59,832 INFO [train.py:715] (2/8) Epoch 18, batch 2200, loss[loss=0.1513, simple_loss=0.2203, pruned_loss=0.04112, over 4756.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02908, over 971066.07 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:58:39,482 INFO [train.py:715] (2/8) Epoch 18, batch 2250, loss[loss=0.1248, simple_loss=0.1988, pruned_loss=0.0254, over 4874.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02892, over 971284.44 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:59:18,830 INFO [train.py:715] (2/8) Epoch 18, batch 2300, loss[loss=0.137, simple_loss=0.2209, pruned_loss=0.0266, over 4877.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02855, over 971386.94 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 05:59:57,624 INFO [train.py:715] (2/8) Epoch 18, batch 2350, loss[loss=0.1358, simple_loss=0.2126, pruned_loss=0.02957, over 4761.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02857, over 971442.37 frames.], batch size: 16, lr: 1.25e-04
2022-05-09 06:00:36,235 INFO [train.py:715] (2/8) Epoch 18, batch 2400, loss[loss=0.1351, simple_loss=0.1971, pruned_loss=0.03658, over 4785.00 frames.], tot_loss[loss=0.131, simple_loss=0.205, pruned_loss=0.02848, over 971646.98 frames.], batch size: 14, lr: 1.25e-04
2022-05-09 06:01:15,695 INFO [train.py:715] (2/8) Epoch 18, batch 2450, loss[loss=0.1333, simple_loss=0.205, pruned_loss=0.03083, over 4770.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02868, over 971674.13 frames.], batch size: 12, lr: 1.25e-04
2022-05-09 06:01:55,087 INFO [train.py:715] (2/8) Epoch 18, batch 2500, loss[loss=0.1411, simple_loss=0.222, pruned_loss=0.0301, over 4799.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02837, over 971587.11 frames.], batch size: 24, lr: 1.25e-04
2022-05-09 06:02:33,097 INFO [train.py:715] (2/8) Epoch 18, batch 2550, loss[loss=0.1195, simple_loss=0.1959, pruned_loss=0.02157, over 4919.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02861, over 972057.30 frames.], batch size: 29, lr: 1.25e-04
2022-05-09 06:03:11,866 INFO [train.py:715] (2/8) Epoch 18, batch 2600, loss[loss=0.1241, simple_loss=0.2069, pruned_loss=0.02066, over 4798.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02847, over 972353.96 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 06:03:51,792 INFO [train.py:715] (2/8) Epoch 18, batch 2650, loss[loss=0.1338, simple_loss=0.1963, pruned_loss=0.03564, over 4711.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02835, over 972627.49 frames.], batch size: 15, lr: 1.25e-04
2022-05-09 06:04:30,526 INFO [train.py:715] (2/8) Epoch 18, batch 2700, loss[loss=0.1594, simple_loss=0.2309, pruned_loss=0.04397, over 4945.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02865, over 972639.20 frames.], batch size: 24, lr: 1.25e-04
2022-05-09 06:05:08,890 INFO [train.py:715] (2/8) Epoch 18, batch 2750, loss[loss=0.1512, simple_loss=0.2193, pruned_loss=0.04157, over 4993.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02828, over 972655.66 frames.], batch size: 14, lr: 1.25e-04
2022-05-09 06:05:47,977 INFO [train.py:715] (2/8) Epoch 18, batch 2800, loss[loss=0.1432, simple_loss=0.2245, pruned_loss=0.03097, over 4759.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02867, over 973319.02 frames.], batch size: 19, lr: 1.25e-04
2022-05-09 06:06:27,520 INFO [train.py:715] (2/8) Epoch 18, batch 2850, loss[loss=0.1219, simple_loss=0.2049, pruned_loss=0.01938, over 4805.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02854, over 972530.86 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 06:07:06,091 INFO [train.py:715] (2/8) Epoch 18, batch 2900, loss[loss=0.126, simple_loss=0.2163, pruned_loss=0.01784, over 4804.00 frames.], tot_loss[loss=0.132, simple_loss=0.207, pruned_loss=0.02849, over 972275.93 frames.], batch size: 21, lr: 1.25e-04
2022-05-09 06:07:44,917 INFO [train.py:715] (2/8) Epoch 18, batch 2950, loss[loss=0.139, simple_loss=0.2164, pruned_loss=0.03078, over 4830.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.02934, over 970694.20 frames.], batch size: 26, lr: 1.25e-04
2022-05-09 06:08:24,283 INFO [train.py:715] (2/8) Epoch 18, batch 3000, loss[loss=0.1087, simple_loss=0.1741, pruned_loss=0.02166, over 4846.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02906, over 970765.70 frames.], batch size: 30, lr: 1.25e-04
2022-05-09 06:08:24,283 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 06:08:34,097 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1047, simple_loss=0.1881, pruned_loss=0.01065, over 914524.00 frames.
2022-05-09 06:09:14,108 INFO [train.py:715] (2/8) Epoch 18, batch 3050, loss[loss=0.1541, simple_loss=0.2271, pruned_loss=0.04056, over 4907.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2076, pruned_loss=0.02963, over 970328.90 frames.], batch size: 19, lr: 1.25e-04
2022-05-09 06:09:52,622 INFO [train.py:715] (2/8) Epoch 18, batch 3100, loss[loss=0.1351, simple_loss=0.2075, pruned_loss=0.03134, over 4848.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02902, over 970716.18 frames.], batch size: 20, lr: 1.25e-04
2022-05-09 06:10:31,510 INFO [train.py:715] (2/8) Epoch 18, batch 3150, loss[loss=0.1391, simple_loss=0.2173, pruned_loss=0.03048, over 4902.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02865, over 971239.54 frames.], batch size: 19, lr: 1.25e-04
2022-05-09 06:11:10,546 INFO [train.py:715] (2/8) Epoch 18, batch 3200, loss[loss=0.151, simple_loss=0.2179, pruned_loss=0.04208, over 4968.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02832, over 971855.23 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:11:50,029 INFO [train.py:715] (2/8) Epoch 18, batch 3250, loss[loss=0.1432, simple_loss=0.2168, pruned_loss=0.03481, over 4839.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02837, over 971624.91 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 06:12:28,195 INFO [train.py:715] (2/8) Epoch 18, batch 3300, loss[loss=0.1324, simple_loss=0.2135, pruned_loss=0.02565, over 4748.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02863, over 972663.82 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 06:13:07,649 INFO [train.py:715] (2/8) Epoch 18, batch 3350, loss[loss=0.1415, simple_loss=0.2124, pruned_loss=0.03528, over 4809.00 frames.], tot_loss[loss=0.133, simple_loss=0.2076, pruned_loss=0.02922, over 972256.76 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:13:47,790 INFO [train.py:715] (2/8) Epoch 18, batch 3400, loss[loss=0.1204, simple_loss=0.1899, pruned_loss=0.02547, over 4853.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.0285, over 972170.91 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 06:14:26,392 INFO [train.py:715] (2/8) Epoch 18, batch 3450, loss[loss=0.103, simple_loss=0.1708, pruned_loss=0.01757, over 4784.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02838, over 972562.12 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:15:05,251 INFO [train.py:715] (2/8) Epoch 18, batch 3500, loss[loss=0.1474, simple_loss=0.2132, pruned_loss=0.04082, over 4855.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2063, pruned_loss=0.02825, over 972837.43 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 06:15:45,338 INFO [train.py:715] (2/8) Epoch 18, batch 3550, loss[loss=0.1295, simple_loss=0.2131, pruned_loss=0.02295, over 4778.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02842, over 972578.24 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:16:24,511 INFO [train.py:715] (2/8) Epoch 18, batch 3600, loss[loss=0.1854, simple_loss=0.266, pruned_loss=0.05237, over 4954.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02828, over 973394.03 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 06:17:03,260 INFO [train.py:715] (2/8) Epoch 18, batch 3650, loss[loss=0.1323, simple_loss=0.2041, pruned_loss=0.03022, over 4782.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02863, over 973221.12 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:17:42,732 INFO [train.py:715] (2/8) Epoch 18, batch 3700, loss[loss=0.1193, simple_loss=0.1868, pruned_loss=0.02591, over 4790.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02897, over 973154.08 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:18:22,002 INFO [train.py:715] (2/8) Epoch 18, batch 3750, loss[loss=0.1408, simple_loss=0.222, pruned_loss=0.02977, over 4749.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02856, over 972816.56 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:18:59,956 INFO [train.py:715] (2/8) Epoch 18, batch 3800, loss[loss=0.1138, simple_loss=0.193, pruned_loss=0.01735, over 4983.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02813, over 972739.23 frames.], batch size: 31, lr: 1.24e-04
2022-05-09 06:19:39,328 INFO [train.py:715] (2/8) Epoch 18, batch 3850, loss[loss=0.1229, simple_loss=0.1945, pruned_loss=0.02566, over 4772.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02858, over 971644.33 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:20:19,344 INFO [train.py:715] (2/8) Epoch 18, batch 3900, loss[loss=0.1235, simple_loss=0.1856, pruned_loss=0.03075, over 4645.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02903, over 971984.94 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 06:20:57,825 INFO [train.py:715] (2/8) Epoch 18, batch 3950, loss[loss=0.1528, simple_loss=0.2381, pruned_loss=0.03375, over 4683.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.0291, over 971125.56 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:21:37,238 INFO [train.py:715] (2/8) Epoch 18, batch 4000, loss[loss=0.1078, simple_loss=0.1854, pruned_loss=0.01507, over 4946.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02929, over 971320.26 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:22:16,738 INFO [train.py:715] (2/8) Epoch 18, batch 4050, loss[loss=0.1419, simple_loss=0.2214, pruned_loss=0.03114, over 4807.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02889, over 970595.05 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:22:56,014 INFO [train.py:715] (2/8) Epoch 18, batch 4100, loss[loss=0.1376, simple_loss=0.2086, pruned_loss=0.0333, over 4780.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2073, pruned_loss=0.02889, over 970362.28 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:23:34,761 INFO [train.py:715] (2/8) Epoch 18, batch 4150, loss[loss=0.1264, simple_loss=0.1967, pruned_loss=0.02808, over 4832.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02849, over 970653.84 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 06:24:14,199 INFO [train.py:715] (2/8) Epoch 18, batch 4200, loss[loss=0.1537, simple_loss=0.2202, pruned_loss=0.0436, over 4828.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02839, over 971419.18 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:24:53,583 INFO [train.py:715] (2/8) Epoch 18, batch 4250, loss[loss=0.1372, simple_loss=0.2047, pruned_loss=0.03484, over 4750.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2062, pruned_loss=0.0282, over 971909.04 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 06:25:32,492 INFO [train.py:715] (2/8) Epoch 18, batch 4300, loss[loss=0.1179, simple_loss=0.196, pruned_loss=0.01987, over 4855.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02823, over 971985.76 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 06:26:12,617 INFO [train.py:715] (2/8) Epoch 18, batch 4350, loss[loss=0.145, simple_loss=0.2134, pruned_loss=0.03826, over 4907.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.0282, over 972302.03 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 06:26:52,061 INFO [train.py:715] (2/8) Epoch 18, batch 4400, loss[loss=0.1276, simple_loss=0.2019, pruned_loss=0.02666, over 4815.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02862, over 972638.38 frames.], batch size: 27, lr: 1.24e-04
2022-05-09 06:27:31,550 INFO [train.py:715] (2/8) Epoch 18, batch 4450, loss[loss=0.1155, simple_loss=0.1895, pruned_loss=0.02074, over 4895.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02835, over 972294.32 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:28:09,902 INFO [train.py:715] (2/8) Epoch 18, batch 4500, loss[loss=0.1341, simple_loss=0.2021, pruned_loss=0.03303, over 4859.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02848, over 972103.40 frames.], batch size: 34, lr: 1.24e-04
2022-05-09 06:28:49,167 INFO [train.py:715] (2/8) Epoch 18, batch 4550, loss[loss=0.1527, simple_loss=0.2297, pruned_loss=0.03783, over 4768.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02923, over 971150.81 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:29:29,017 INFO [train.py:715] (2/8) Epoch 18, batch 4600, loss[loss=0.1276, simple_loss=0.1942, pruned_loss=0.03053, over 4910.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02896, over 970923.12 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 06:30:07,896 INFO [train.py:715] (2/8) Epoch 18, batch 4650, loss[loss=0.1415, simple_loss=0.1998, pruned_loss=0.04155, over 4888.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02921, over 971346.86 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 06:30:47,010 INFO [train.py:715] (2/8) Epoch 18, batch 4700, loss[loss=0.1261, simple_loss=0.2011, pruned_loss=0.02551, over 4812.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02935, over 971102.09 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:31:26,066 INFO [train.py:715] (2/8) Epoch 18, batch 4750, loss[loss=0.1545, simple_loss=0.2322, pruned_loss=0.03834, over 4758.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02977, over 970492.04 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 06:32:06,197 INFO [train.py:715] (2/8) Epoch 18, batch 4800, loss[loss=0.1362, simple_loss=0.2172, pruned_loss=0.02765, over 4971.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2071, pruned_loss=0.02964, over 970833.18 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:32:44,916 INFO [train.py:715] (2/8) Epoch 18, batch 4850, loss[loss=0.1691, simple_loss=0.2491, pruned_loss=0.04452, over 4898.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02928, over 972219.77 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:33:24,373 INFO [train.py:715] (2/8) Epoch 18, batch 4900, loss[loss=0.133, simple_loss=0.2049, pruned_loss=0.03057, over 4803.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02884, over 972711.14 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:34:04,568 INFO [train.py:715] (2/8) Epoch 18, batch 4950, loss[loss=0.1504, simple_loss=0.2211, pruned_loss=0.03991, over 4774.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02895, over 973109.12 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:34:43,674 INFO [train.py:715] (2/8) Epoch 18, batch 5000, loss[loss=0.1396, simple_loss=0.2079, pruned_loss=0.03569, over 4894.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02855, over 973193.00 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:35:22,356 INFO [train.py:715] (2/8) Epoch 18, batch 5050, loss[loss=0.09448, simple_loss=0.1669, pruned_loss=0.01102, over 4836.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02888, over 973385.56 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 06:36:01,525 INFO [train.py:715] (2/8) Epoch 18, batch 5100, loss[loss=0.1245, simple_loss=0.1907, pruned_loss=0.02921, over 4957.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02893, over 973283.60 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:36:41,090 INFO [train.py:715] (2/8) Epoch 18, batch 5150, loss[loss=0.1485, simple_loss=0.2279, pruned_loss=0.03453, over 4933.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02901, over 972894.64 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 06:37:19,649 INFO [train.py:715] (2/8) Epoch 18, batch 5200, loss[loss=0.145, simple_loss=0.2185, pruned_loss=0.03573, over 4958.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2055, pruned_loss=0.02868, over 972943.61 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:37:59,035 INFO [train.py:715] (2/8) Epoch 18, batch 5250, loss[loss=0.1609, simple_loss=0.2219, pruned_loss=0.05, over 4936.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02874, over 972822.93 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 06:38:38,954 INFO [train.py:715] (2/8) Epoch 18, batch 5300, loss[loss=0.1413, simple_loss=0.2114, pruned_loss=0.03562, over 4862.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.0286, over 972807.33 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 06:39:18,981 INFO [train.py:715] (2/8) Epoch 18, batch 5350, loss[loss=0.1464, simple_loss=0.219, pruned_loss=0.03695, over 4982.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02836, over 972302.20 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:39:57,063 INFO [train.py:715] (2/8) Epoch 18, batch 5400, loss[loss=0.1499, simple_loss=0.228, pruned_loss=0.03588, over 4960.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02815, over 972142.45 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 06:40:38,720 INFO [train.py:715] (2/8) Epoch 18, batch 5450, loss[loss=0.1229, simple_loss=0.1885, pruned_loss=0.02859, over 4979.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02848, over 972418.76 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 06:41:19,101 INFO [train.py:715] (2/8) Epoch 18, batch 5500, loss[loss=0.1311, simple_loss=0.2077, pruned_loss=0.0273, over 4762.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.02857, over 972855.21 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:41:58,082 INFO [train.py:715] (2/8) Epoch 18, batch 5550, loss[loss=0.1352, simple_loss=0.2138, pruned_loss=0.02827, over 4897.00 frames.], tot_loss[loss=0.133, simple_loss=0.2078, pruned_loss=0.02915, over 972975.51 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 06:42:36,884 INFO [train.py:715] (2/8) Epoch 18, batch 5600, loss[loss=0.1199, simple_loss=0.1879, pruned_loss=0.02594, over 4830.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.0291, over 973230.83 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:43:15,923 INFO [train.py:715] (2/8) Epoch 18, batch 5650, loss[loss=0.1155, simple_loss=0.1774, pruned_loss=0.02676, over 4816.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02901, over 973235.07 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 06:43:55,546 INFO [train.py:715] (2/8) Epoch 18, batch 5700, loss[loss=0.1499, simple_loss=0.2277, pruned_loss=0.03604, over 4792.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02898, over 973862.23 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:44:33,666 INFO [train.py:715] (2/8) Epoch 18, batch 5750, loss[loss=0.1333, simple_loss=0.2091, pruned_loss=0.02875, over 4824.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02903, over 973851.91 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:45:12,598 INFO [train.py:715] (2/8) Epoch 18, batch 5800, loss[loss=0.1525, simple_loss=0.226, pruned_loss=0.03954, over 4765.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02939, over 973575.50 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:45:52,344 INFO [train.py:715] (2/8) Epoch 18, batch 5850, loss[loss=0.09826, simple_loss=0.1587, pruned_loss=0.01893, over 4844.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.02892, over 973495.15 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 06:46:31,447 INFO [train.py:715] (2/8) Epoch 18, batch 5900, loss[loss=0.1374, simple_loss=0.2062, pruned_loss=0.03431, over 4929.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02958, over 973428.66 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 06:47:10,143 INFO [train.py:715] (2/8) Epoch 18, batch 5950, loss[loss=0.127, simple_loss=0.1989, pruned_loss=0.02757, over 4855.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2077, pruned_loss=0.02964, over 973336.26 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 06:47:49,554 INFO [train.py:715] (2/8) Epoch 18, batch 6000, loss[loss=0.1405, simple_loss=0.2262, pruned_loss=0.0274, over 4811.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02952, over 973142.65 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 06:47:49,554 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 06:47:59,475 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1047, simple_loss=0.188, pruned_loss=0.01075, over 914524.00 frames.
2022-05-09 06:48:39,116 INFO [train.py:715] (2/8) Epoch 18, batch 6050, loss[loss=0.1225, simple_loss=0.1992, pruned_loss=0.02296, over 4984.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02953, over 972722.53 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 06:49:18,285 INFO [train.py:715] (2/8) Epoch 18, batch 6100, loss[loss=0.1603, simple_loss=0.2328, pruned_loss=0.04388, over 4844.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02915, over 972990.77 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 06:49:56,630 INFO [train.py:715] (2/8) Epoch 18, batch 6150, loss[loss=0.1559, simple_loss=0.2335, pruned_loss=0.03916, over 4772.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.0289, over 972770.18 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:50:35,919 INFO [train.py:715] (2/8) Epoch 18, batch 6200, loss[loss=0.1314, simple_loss=0.209, pruned_loss=0.02686, over 4886.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02908, over 972089.02 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 06:51:15,502 INFO [train.py:715] (2/8) Epoch 18, batch 6250, loss[loss=0.1556, simple_loss=0.2129, pruned_loss=0.04918, over 4783.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02918, over 973267.29 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 06:51:54,532 INFO [train.py:715] (2/8) Epoch 18, batch 6300, loss[loss=0.13, simple_loss=0.2097, pruned_loss=0.02516, over 4933.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02915, over 973294.01 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 06:52:33,702 INFO [train.py:715] (2/8) Epoch 18, batch 6350, loss[loss=0.1494, simple_loss=0.2279, pruned_loss=0.03539, over 4972.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02895, over 972477.08 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:53:12,892 INFO [train.py:715] (2/8) Epoch 18, batch 6400, loss[loss=0.1562, simple_loss=0.2287, pruned_loss=0.04181, over 4823.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02879, over 972898.46 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:53:52,077 INFO [train.py:715] (2/8) Epoch 18, batch 6450, loss[loss=0.1404, simple_loss=0.224, pruned_loss=0.02836, over 4903.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02929, over 972597.29 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 06:54:30,357 INFO [train.py:715] (2/8) Epoch 18, batch 6500, loss[loss=0.1162, simple_loss=0.1869, pruned_loss=0.02278, over 4844.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02879, over 972865.97 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 06:55:08,643 INFO [train.py:715] (2/8) Epoch 18, batch 6550, loss[loss=0.1145, simple_loss=0.1841, pruned_loss=0.02245, over 4693.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02864, over 972546.70 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:55:48,101 INFO [train.py:715] (2/8) Epoch 18, batch 6600, loss[loss=0.1307, simple_loss=0.205, pruned_loss=0.02818, over 4865.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02849, over 973385.32 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 06:56:27,456 INFO [train.py:715] (2/8) Epoch 18, batch 6650, loss[loss=0.145, simple_loss=0.2266, pruned_loss=0.03168, over 4959.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02857, over 973105.70 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:57:05,503 INFO [train.py:715] (2/8) Epoch 18, batch 6700, loss[loss=0.1602, simple_loss=0.2349, pruned_loss=0.0427, over 4769.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02912, over 972388.60 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 06:57:44,486 INFO [train.py:715] (2/8) Epoch 18, batch 6750, loss[loss=0.1487, simple_loss=0.2185, pruned_loss=0.03944, over 4717.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02928, over 973067.50 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 06:58:23,844 INFO [train.py:715] (2/8) Epoch 18, batch 6800, loss[loss=0.1361, simple_loss=0.2148, pruned_loss=0.02876, over 4880.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.02966, over 972864.51 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 06:59:02,551 INFO [train.py:715] (2/8) Epoch 18, batch 6850, loss[loss=0.1535, simple_loss=0.2179, pruned_loss=0.04451, over 4982.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02947, over 971674.35 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 06:59:40,722 INFO [train.py:715] (2/8) Epoch 18, batch 6900, loss[loss=0.1666, simple_loss=0.2417, pruned_loss=0.0457, over 4892.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02921, over 971712.96 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 07:00:20,317 INFO [train.py:715] (2/8) Epoch 18, batch 6950, loss[loss=0.1269, simple_loss=0.2079, pruned_loss=0.02293, over 4888.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02892, over 972102.10 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 07:00:59,047 INFO [train.py:715] (2/8) Epoch 18, batch 7000, loss[loss=0.1424, simple_loss=0.2041, pruned_loss=0.04038, over 4976.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02928, over 972267.72 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:01:37,420 INFO [train.py:715] (2/8) Epoch 18, batch 7050, loss[loss=0.1016, simple_loss=0.1722, pruned_loss=0.01548, over 4963.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2061, pruned_loss=0.02923, over 972782.03 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:02:16,624 INFO [train.py:715] (2/8) Epoch 18, batch 7100, loss[loss=0.1352, simple_loss=0.204, pruned_loss=0.0332, over 4842.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2058, pruned_loss=0.02878, over 971634.99 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:02:56,206 INFO [train.py:715] (2/8) Epoch 18, batch 7150, loss[loss=0.1493, simple_loss=0.2319, pruned_loss=0.03336, over 4695.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02873, over 971504.12 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:03:34,828 INFO [train.py:715] (2/8) Epoch 18, batch 7200, loss[loss=0.117, simple_loss=0.1941, pruned_loss=0.01996, over 4817.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.0288, over 971484.95 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 07:04:13,067 INFO [train.py:715] (2/8) Epoch 18, batch 7250, loss[loss=0.1193, simple_loss=0.1935, pruned_loss=0.02255, over 4910.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.0287, over 971302.73 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:04:52,165 INFO [train.py:715] (2/8) Epoch 18, batch 7300, loss[loss=0.1059, simple_loss=0.1799, pruned_loss=0.01596, over 4897.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02851, over 971518.50 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 07:05:31,289 INFO [train.py:715] (2/8) Epoch 18, batch 7350, loss[loss=0.1338, simple_loss=0.2106, pruned_loss=0.02852, over 4853.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02862, over 972122.09 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:06:09,359 INFO [train.py:715] (2/8) Epoch 18, batch 7400, loss[loss=0.1847, simple_loss=0.2615, pruned_loss=0.05392, over 4884.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02871, over 972996.61 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 07:06:48,514 INFO [train.py:715] (2/8) Epoch 18, batch 7450, loss[loss=0.1412, simple_loss=0.2304, pruned_loss=0.02598, over 4833.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02854, over 972667.50 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:07:27,763 INFO [train.py:715] (2/8) Epoch 18, batch 7500, loss[loss=0.1078, simple_loss=0.1837, pruned_loss=0.01596, over 4915.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.0288, over 972997.02 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 07:08:05,375 INFO [train.py:715] (2/8) Epoch 18, batch 7550, loss[loss=0.1045, simple_loss=0.1781, pruned_loss=0.01542, over 4795.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02866, over 973143.20 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 07:08:43,907 INFO [train.py:715] (2/8) Epoch 18, batch 7600, loss[loss=0.1301, simple_loss=0.201, pruned_loss=0.02957, over 4917.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02934, over 972531.76 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 07:09:23,640 INFO [train.py:715] (2/8) Epoch 18, batch 7650, loss[loss=0.1363, simple_loss=0.2147, pruned_loss=0.02892, over 4985.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.0294, over 971975.58 frames.], batch size: 31, lr: 1.24e-04
2022-05-09 07:10:02,903 INFO [train.py:715] (2/8) Epoch 18, batch 7700, loss[loss=0.1093, simple_loss=0.1775, pruned_loss=0.0205, over 4826.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02921, over 971831.75 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 07:10:41,612 INFO [train.py:715] (2/8) Epoch 18, batch 7750, loss[loss=0.1147, simple_loss=0.1866, pruned_loss=0.02143, over 4767.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02949, over 971947.89 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:11:21,215 INFO [train.py:715] (2/8) Epoch 18, batch 7800, loss[loss=0.1216, simple_loss=0.1944, pruned_loss=0.02437, over 4816.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02943, over 972937.59 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 07:12:01,108 INFO [train.py:715] (2/8) Epoch 18, batch 7850, loss[loss=0.1358, simple_loss=0.2153, pruned_loss=0.02812, over 4915.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02918, over 972930.28 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 07:12:40,497 INFO [train.py:715] (2/8) Epoch 18, batch 7900, loss[loss=0.108, simple_loss=0.1806, pruned_loss=0.01768, over 4893.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02933, over 973436.19 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 07:13:19,697 INFO [train.py:715] (2/8) Epoch 18, batch 7950, loss[loss=0.1105, simple_loss=0.1883, pruned_loss=0.01633, over 4990.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02907, over 973719.22 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:13:59,119 INFO [train.py:715] (2/8) Epoch 18, batch 8000, loss[loss=0.1373, simple_loss=0.2041, pruned_loss=0.03529, over 4988.00 frames.], tot_loss[loss=0.133, simple_loss=0.2072, pruned_loss=0.0294, over 973643.97 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:14:38,129 INFO [train.py:715] (2/8) Epoch 18, batch 8050, loss[loss=0.1712, simple_loss=0.2461, pruned_loss=0.0482, over 4773.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02917, over 972883.28 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:15:16,607 INFO [train.py:715] (2/8) Epoch 18, batch 8100, loss[loss=0.1174, simple_loss=0.1924, pruned_loss=0.02121, over 4936.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02926, over 972526.42 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 07:15:55,249 INFO [train.py:715] (2/8) Epoch 18, batch 8150, loss[loss=0.1359, simple_loss=0.2084, pruned_loss=0.03167, over 4931.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02934, over 972925.43 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:16:34,309 INFO [train.py:715] (2/8) Epoch 18, batch 8200, loss[loss=0.1789, simple_loss=0.2564, pruned_loss=0.05075, over 4703.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02927, over 972953.56 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:17:12,928 INFO [train.py:715] (2/8) Epoch 18, batch 8250, loss[loss=0.1302, simple_loss=0.2055, pruned_loss=0.02746, over 4983.00 frames.], tot_loss[loss=0.133, simple_loss=0.2078, pruned_loss=0.02906, over 973702.85 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:17:51,223 INFO [train.py:715] (2/8) Epoch 18, batch 8300, loss[loss=0.108, simple_loss=0.1842, pruned_loss=0.01593, over 4812.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02929, over 973017.75 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 07:18:31,282 INFO [train.py:715] (2/8) Epoch 18, batch 8350, loss[loss=0.1358, simple_loss=0.2039, pruned_loss=0.03383, over 4841.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2073, pruned_loss=0.0296, over 972378.04 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 07:19:10,480 INFO [train.py:715] (2/8) Epoch 18, batch 8400, loss[loss=0.1159, simple_loss=0.186, pruned_loss=0.02289, over 4696.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02937, over 972074.74 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:19:48,920 INFO [train.py:715] (2/8) Epoch 18, batch 8450, loss[loss=0.1297, simple_loss=0.2199, pruned_loss=0.01977, over 4961.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02899, over 972733.62 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:20:28,156 INFO [train.py:715] (2/8) Epoch 18, batch 8500, loss[loss=0.1297, simple_loss=0.2034, pruned_loss=0.02799, over 4777.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02883, over 972833.41 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:21:07,335 INFO [train.py:715] (2/8) Epoch 18, batch 8550, loss[loss=0.1373, simple_loss=0.2093, pruned_loss=0.03262, over 4836.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02847, over 973573.96 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:21:46,039 INFO [train.py:715] (2/8) Epoch 18, batch 8600, loss[loss=0.1254, simple_loss=0.2047, pruned_loss=0.02308, over 4905.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2052, pruned_loss=0.02866, over 972515.24 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 07:22:24,246 INFO [train.py:715] (2/8) Epoch 18, batch 8650, loss[loss=0.1523, simple_loss=0.2257, pruned_loss=0.03944, over 4761.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2053, pruned_loss=0.02861, over 972317.26 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:23:03,809 INFO [train.py:715] (2/8) Epoch 18, batch 8700, loss[loss=0.121, simple_loss=0.1872, pruned_loss=0.02735, over 4789.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2043, pruned_loss=0.02821, over 972703.14 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:23:43,636 INFO [train.py:715] (2/8) Epoch 18, batch 8750, loss[loss=0.1165, simple_loss=0.1905, pruned_loss=0.02122, over 4690.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2052, pruned_loss=0.02862, over 972486.95 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:24:23,137 INFO [train.py:715] (2/8) Epoch 18, batch 8800, loss[loss=0.1264, simple_loss=0.2108, pruned_loss=0.021, over 4867.00 frames.], tot_loss[loss=0.131, simple_loss=0.205, pruned_loss=0.02853, over 973055.24 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 07:25:01,506 INFO [train.py:715] (2/8) Epoch 18, batch 8850, loss[loss=0.1725, simple_loss=0.2404, pruned_loss=0.05226, over 4694.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02867, over 971536.85 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:25:41,128 INFO [train.py:715] (2/8) Epoch 18, batch 8900, loss[loss=0.1274, simple_loss=0.1935, pruned_loss=0.03071, over 4752.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02852, over 971522.05 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 07:26:19,641 INFO [train.py:715] (2/8) Epoch 18, batch 8950, loss[loss=0.1347, simple_loss=0.2167, pruned_loss=0.02631, over 4765.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.0284, over 972038.83 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:26:58,105 INFO [train.py:715] (2/8) Epoch 18, batch 9000, loss[loss=0.1349, simple_loss=0.2035, pruned_loss=0.03322, over 4761.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02847, over 972314.99 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 07:26:58,106 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 07:27:08,040 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1045, simple_loss=0.1879, pruned_loss=0.01057, over 914524.00 frames.
2022-05-09 07:27:46,935 INFO [train.py:715] (2/8) Epoch 18, batch 9050, loss[loss=0.1157, simple_loss=0.1956, pruned_loss=0.01787, over 4793.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02851, over 971682.51 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:28:26,538 INFO [train.py:715] (2/8) Epoch 18, batch 9100, loss[loss=0.1422, simple_loss=0.2114, pruned_loss=0.03645, over 4690.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02908, over 972028.11 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:29:05,675 INFO [train.py:715] (2/8) Epoch 18, batch 9150, loss[loss=0.1408, simple_loss=0.2073, pruned_loss=0.0371, over 4969.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2066, pruned_loss=0.02931, over 972229.93 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 07:29:43,362 INFO [train.py:715] (2/8) Epoch 18, batch 9200, loss[loss=0.1439, simple_loss=0.2101, pruned_loss=0.03879, over 4865.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02919, over 972738.48 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:30:22,561 INFO [train.py:715] (2/8) Epoch 18, batch 9250, loss[loss=0.1095, simple_loss=0.1815, pruned_loss=0.0188, over 4758.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02907, over 972258.71 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:31:01,723 INFO [train.py:715] (2/8) Epoch 18, batch 9300, loss[loss=0.1396, simple_loss=0.2087, pruned_loss=0.03522, over 4866.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02931, over 973013.89 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:31:39,925 INFO [train.py:715] (2/8) Epoch 18, batch 9350, loss[loss=0.1259, simple_loss=0.2032, pruned_loss=0.02427, over 4775.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02914, over 972758.84 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:32:18,513 INFO [train.py:715] (2/8) Epoch 18, batch 9400, loss[loss=0.1518, simple_loss=0.2302, pruned_loss=0.03668, over 4774.00 frames.], tot_loss[loss=0.1338, simple_loss=0.2078, pruned_loss=0.02987, over 972763.81 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:32:58,077 INFO [train.py:715] (2/8) Epoch 18, batch 9450, loss[loss=0.1611, simple_loss=0.2343, pruned_loss=0.04399, over 4850.00 frames.], tot_loss[loss=0.1349, simple_loss=0.2088, pruned_loss=0.03051, over 972852.88 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:33:36,484 INFO [train.py:715] (2/8) Epoch 18, batch 9500, loss[loss=0.1414, simple_loss=0.2202, pruned_loss=0.03131, over 4986.00 frames.], tot_loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03062, over 973672.64 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 07:34:14,738 INFO [train.py:715] (2/8) Epoch 18, batch 9550, loss[loss=0.1478, simple_loss=0.2314, pruned_loss=0.03211, over 4758.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2085, pruned_loss=0.03024, over 974490.49 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:34:53,872 INFO [train.py:715] (2/8) Epoch 18, batch 9600, loss[loss=0.1476, simple_loss=0.2296, pruned_loss=0.03276, over 4768.00 frames.], tot_loss[loss=0.134, simple_loss=0.208, pruned_loss=0.03002, over 972865.86 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:35:33,429 INFO [train.py:715] (2/8) Epoch 18, batch 9650, loss[loss=0.1517, simple_loss=0.2175, pruned_loss=0.04294, over 4846.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2079, pruned_loss=0.02981, over 972861.91 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 07:36:12,259 INFO [train.py:715] (2/8) Epoch 18, batch 9700, loss[loss=0.0963, simple_loss=0.169, pruned_loss=0.0118, over 4848.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2074, pruned_loss=0.02958, over 973272.78 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 07:36:50,944 INFO [train.py:715] (2/8) Epoch 18, batch 9750, loss[loss=0.1304, simple_loss=0.2064, pruned_loss=0.02724, over 4833.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02942, over 973053.73 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 07:37:31,047 INFO [train.py:715] (2/8) Epoch 18, batch 9800, loss[loss=0.1134, simple_loss=0.1843, pruned_loss=0.02129, over 4835.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02935, over 973106.10 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:38:09,636 INFO [train.py:715] (2/8) Epoch 18, batch 9850, loss[loss=0.1081, simple_loss=0.1867, pruned_loss=0.01481, over 4812.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02893, over 972680.56 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 07:38:47,995 INFO [train.py:715] (2/8) Epoch 18, batch 9900, loss[loss=0.1145, simple_loss=0.1939, pruned_loss=0.01754, over 4979.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02848, over 972453.91 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:39:27,319 INFO [train.py:715] (2/8) Epoch 18, batch 9950, loss[loss=0.1326, simple_loss=0.2028, pruned_loss=0.03124, over 4923.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02845, over 972611.68 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 07:40:06,406 INFO [train.py:715] (2/8) Epoch 18, batch 10000, loss[loss=0.1314, simple_loss=0.1956, pruned_loss=0.03365, over 4929.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02838, over 972903.23 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:40:45,257 INFO [train.py:715] (2/8) Epoch 18, batch 10050, loss[loss=0.1228, simple_loss=0.2028, pruned_loss=0.02136, over 4871.00 frames.], tot_loss[loss=0.131, simple_loss=0.206, pruned_loss=0.02798, over 973507.37 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:41:23,501 INFO [train.py:715] (2/8) Epoch 18, batch 10100, loss[loss=0.1102, simple_loss=0.1887, pruned_loss=0.01585, over 4690.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02834, over 973744.89 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:42:02,512 INFO [train.py:715] (2/8) Epoch 18, batch 10150, loss[loss=0.1055, simple_loss=0.1771, pruned_loss=0.017, over 4951.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02884, over 974239.14 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:42:41,665 INFO [train.py:715] (2/8) Epoch 18, batch 10200, loss[loss=0.1242, simple_loss=0.2013, pruned_loss=0.02357, over 4805.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02862, over 974536.42 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 07:43:20,197 INFO [train.py:715] (2/8) Epoch 18, batch 10250, loss[loss=0.1512, simple_loss=0.2256, pruned_loss=0.03846, over 4787.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02842, over 973676.82 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:43:59,314 INFO [train.py:715] (2/8) Epoch 18, batch 10300, loss[loss=0.1239, simple_loss=0.2071, pruned_loss=0.02032, over 4987.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.0284, over 974096.10 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 07:44:39,643 INFO [train.py:715] (2/8) Epoch 18, batch 10350, loss[loss=0.1551, simple_loss=0.2283, pruned_loss=0.041, over 4793.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02874, over 973744.00 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:45:18,121 INFO [train.py:715] (2/8) Epoch 18, batch 10400, loss[loss=0.126, simple_loss=0.1984, pruned_loss=0.02676, over 4750.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02903, over 973812.26 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 07:45:56,570 INFO [train.py:715] (2/8) Epoch 18, batch 10450, loss[loss=0.13, simple_loss=0.1981, pruned_loss=0.03095, over 4866.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02889, over 972994.25 frames.], batch size: 20, lr: 1.24e-04
2022-05-09 07:46:36,309 INFO [train.py:715] (2/8) Epoch 18, batch 10500, loss[loss=0.138, simple_loss=0.2095, pruned_loss=0.03324, over 4950.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02893, over 973196.44 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 07:47:15,165 INFO [train.py:715] (2/8) Epoch 18, batch 10550, loss[loss=0.1559, simple_loss=0.2234, pruned_loss=0.04425, over 4867.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.0294, over 972834.56 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:47:53,899 INFO [train.py:715] (2/8) Epoch 18, batch 10600, loss[loss=0.1552, simple_loss=0.228, pruned_loss=0.04121, over 4900.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02938, over 972818.79 frames.], batch size: 22, lr: 1.24e-04
2022-05-09 07:48:33,504 INFO [train.py:715] (2/8) Epoch 18, batch 10650, loss[loss=0.1355, simple_loss=0.1968, pruned_loss=0.03714, over 4788.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02911, over 972709.21 frames.], batch size: 14, lr: 1.24e-04
2022-05-09 07:49:13,192 INFO [train.py:715] (2/8) Epoch 18, batch 10700, loss[loss=0.1567, simple_loss=0.2193, pruned_loss=0.04702, over 4975.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02878, over 972174.48 frames.], batch size: 31, lr: 1.24e-04
2022-05-09 07:49:52,111 INFO [train.py:715] (2/8) Epoch 18, batch 10750, loss[loss=0.1165, simple_loss=0.1949, pruned_loss=0.01905, over 4816.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02863, over 972601.18 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 07:50:31,124 INFO [train.py:715] (2/8) Epoch 18, batch 10800, loss[loss=0.1544, simple_loss=0.2309, pruned_loss=0.0389, over 4905.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02837, over 972726.56 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 07:51:10,556 INFO [train.py:715] (2/8) Epoch 18, batch 10850, loss[loss=0.1559, simple_loss=0.2125, pruned_loss=0.04961, over 4702.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02892, over 972046.30 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 07:51:49,056 INFO [train.py:715] (2/8) Epoch 18, batch 10900, loss[loss=0.1687, simple_loss=0.2363, pruned_loss=0.05057, over 4744.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02906, over 971911.80 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:52:27,644 INFO [train.py:715] (2/8) Epoch 18, batch 10950, loss[loss=0.1258, simple_loss=0.2043, pruned_loss=0.02365, over 4944.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2057, pruned_loss=0.02886, over 972439.66 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 07:53:07,689 INFO [train.py:715] (2/8) Epoch 18, batch 11000, loss[loss=0.1205, simple_loss=0.1975, pruned_loss=0.02174, over 4909.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02851, over 972850.39 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 07:53:46,747 INFO [train.py:715] (2/8) Epoch 18, batch 11050, loss[loss=0.1373, simple_loss=0.2127, pruned_loss=0.03094, over 4980.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02889, over 972883.92 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:54:26,303 INFO [train.py:715] (2/8) Epoch 18, batch 11100, loss[loss=0.1409, simple_loss=0.2251, pruned_loss=0.02834, over 4749.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2051, pruned_loss=0.02862, over 972890.06 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:55:05,205 INFO [train.py:715] (2/8) Epoch 18, batch 11150, loss[loss=0.1488, simple_loss=0.2188, pruned_loss=0.03943, over 4741.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2056, pruned_loss=0.02906, over 972818.77 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 07:55:44,749 INFO [train.py:715] (2/8) Epoch 18, batch 11200, loss[loss=0.1108, simple_loss=0.1839, pruned_loss=0.01887, over 4943.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2057, pruned_loss=0.02947, over 972657.45 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 07:56:23,196 INFO [train.py:715] (2/8) Epoch 18, batch 11250, loss[loss=0.1324, simple_loss=0.2016, pruned_loss=0.03154, over 4753.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.02893, over 972356.43 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 07:57:01,931 INFO [train.py:715] (2/8) Epoch 18, batch 11300, loss[loss=0.1236, simple_loss=0.1967, pruned_loss=0.02527, over 4885.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02912, over 972758.87 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 07:57:41,019 INFO [train.py:715] (2/8) Epoch 18, batch 11350, loss[loss=0.1086, simple_loss=0.1776, pruned_loss=0.01983, over 4930.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02888, over 973035.48 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 07:58:20,186 INFO [train.py:715] (2/8) Epoch 18, batch 11400, loss[loss=0.1136, simple_loss=0.1801, pruned_loss=0.02357, over 4967.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2053, pruned_loss=0.0288, over 972253.09 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 07:58:59,553 INFO [train.py:715] (2/8) Epoch 18, batch 11450, loss[loss=0.117, simple_loss=0.1982, pruned_loss=0.01793, over 4824.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2049, pruned_loss=0.02865, over 971912.00 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 07:59:38,060 INFO [train.py:715] (2/8) Epoch 18, batch 11500, loss[loss=0.1591, simple_loss=0.2418, pruned_loss=0.03826, over 4932.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2057, pruned_loss=0.02899, over 971476.28 frames.], batch size: 29, lr: 1.24e-04
2022-05-09 08:00:17,724 INFO [train.py:715] (2/8) Epoch 18, batch 11550, loss[loss=0.1206, simple_loss=0.1825, pruned_loss=0.02938, over 4984.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02859, over 971960.10 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 08:00:57,127 INFO [train.py:715] (2/8) Epoch 18, batch 11600, loss[loss=0.1398, simple_loss=0.2236, pruned_loss=0.028, over 4986.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02842, over 971348.13 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 08:01:35,953 INFO [train.py:715] (2/8) Epoch 18, batch 11650, loss[loss=0.1002, simple_loss=0.1733, pruned_loss=0.01353, over 4807.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02808, over 971143.27 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 08:02:15,661 INFO [train.py:715] (2/8) Epoch 18, batch 11700, loss[loss=0.1345, simple_loss=0.2095, pruned_loss=0.02975, over 4860.00 frames.], tot_loss[loss=0.131, simple_loss=0.2059, pruned_loss=0.02807, over 971179.58 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 08:02:54,939 INFO [train.py:715] (2/8) Epoch 18, batch 11750, loss[loss=0.1145, simple_loss=0.1897, pruned_loss=0.01959, over 4796.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2062, pruned_loss=0.02818, over 972128.00 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 08:03:34,981 INFO [train.py:715] (2/8) Epoch 18, batch 11800, loss[loss=0.1386, simple_loss=0.2134, pruned_loss=0.03188, over 4937.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2063, pruned_loss=0.02815, over 972511.31 frames.], batch size: 21, lr: 1.24e-04
2022-05-09 08:04:13,542 INFO [train.py:715] (2/8) Epoch 18, batch 11850, loss[loss=0.1489, simple_loss=0.2229, pruned_loss=0.03744, over 4790.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2062, pruned_loss=0.02825, over 972716.35 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 08:04:53,376 INFO [train.py:715] (2/8) Epoch 18, batch 11900, loss[loss=0.1592, simple_loss=0.2144, pruned_loss=0.05199, over 4882.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2065, pruned_loss=0.02807, over 972275.42 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 08:05:32,231 INFO [train.py:715] (2/8) Epoch 18, batch 11950, loss[loss=0.1258, simple_loss=0.2035, pruned_loss=0.024, over 4904.00 frames.], tot_loss[loss=0.1317, simple_loss=0.207, pruned_loss=0.02824, over 972319.14 frames.], batch size: 17, lr: 1.24e-04
2022-05-09 08:06:10,824 INFO [train.py:715] (2/8) Epoch 18, batch 12000, loss[loss=0.1246, simple_loss=0.1992, pruned_loss=0.02502, over 4804.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2067, pruned_loss=0.02832, over 971316.46 frames.], batch size: 25, lr: 1.24e-04
2022-05-09 08:06:10,824 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 08:06:20,737 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1046, simple_loss=0.188, pruned_loss=0.01063, over 914524.00 frames.
2022-05-09 08:07:00,013 INFO [train.py:715] (2/8) Epoch 18, batch 12050, loss[loss=0.1267, simple_loss=0.2053, pruned_loss=0.02403, over 4933.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.0283, over 971502.18 frames.], batch size: 39, lr: 1.24e-04
2022-05-09 08:07:39,524 INFO [train.py:715] (2/8) Epoch 18, batch 12100, loss[loss=0.1235, simple_loss=0.194, pruned_loss=0.02648, over 4823.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02873, over 971763.55 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:08:19,052 INFO [train.py:715] (2/8) Epoch 18, batch 12150, loss[loss=0.1285, simple_loss=0.2022, pruned_loss=0.02744, over 4954.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02898, over 971667.87 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 08:08:59,342 INFO [train.py:715] (2/8) Epoch 18, batch 12200, loss[loss=0.1457, simple_loss=0.2176, pruned_loss=0.0369, over 4867.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02953, over 972688.31 frames.], batch size: 30, lr: 1.24e-04
2022-05-09 08:09:38,284 INFO [train.py:715] (2/8) Epoch 18, batch 12250, loss[loss=0.1294, simple_loss=0.2043, pruned_loss=0.02722, over 4881.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02927, over 972972.17 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 08:10:18,807 INFO [train.py:715] (2/8) Epoch 18, batch 12300, loss[loss=0.1354, simple_loss=0.2061, pruned_loss=0.03239, over 4940.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2079, pruned_loss=0.02946, over 971986.61 frames.], batch size: 35, lr: 1.24e-04
2022-05-09 08:10:58,226 INFO [train.py:715] (2/8) Epoch 18, batch 12350, loss[loss=0.1298, simple_loss=0.2177, pruned_loss=0.02093, over 4968.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02937, over 972481.10 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 08:11:37,140 INFO [train.py:715] (2/8) Epoch 18, batch 12400, loss[loss=0.1396, simple_loss=0.215, pruned_loss=0.03214, over 4813.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.0289, over 972316.99 frames.], batch size: 26, lr: 1.24e-04
2022-05-09 08:12:16,682 INFO [train.py:715] (2/8) Epoch 18, batch 12450, loss[loss=0.1387, simple_loss=0.2086, pruned_loss=0.0344, over 4844.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02907, over 973480.89 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 08:12:55,938 INFO [train.py:715] (2/8) Epoch 18, batch 12500, loss[loss=0.1054, simple_loss=0.1881, pruned_loss=0.0113, over 4816.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02922, over 973052.37 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 08:13:36,313 INFO [train.py:715] (2/8) Epoch 18, batch 12550, loss[loss=0.1479, simple_loss=0.2115, pruned_loss=0.04217, over 4977.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2061, pruned_loss=0.02921, over 973878.16 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 08:14:14,824 INFO [train.py:715] (2/8) Epoch 18, batch 12600, loss[loss=0.1255, simple_loss=0.1948, pruned_loss=0.02812, over 4837.00 frames.], tot_loss[loss=0.132, simple_loss=0.2058, pruned_loss=0.02907, over 974053.18 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 08:14:54,514 INFO [train.py:715] (2/8) Epoch 18, batch 12650, loss[loss=0.1108, simple_loss=0.191, pruned_loss=0.01523, over 4817.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2057, pruned_loss=0.02881, over 974113.13 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:15:33,311 INFO [train.py:715] (2/8) Epoch 18, batch 12700, loss[loss=0.1099, simple_loss=0.1853, pruned_loss=0.01726, over 4938.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02886, over 973555.65 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 08:16:12,933 INFO [train.py:715] (2/8) Epoch 18, batch 12750, loss[loss=0.1226, simple_loss=0.1988, pruned_loss=0.0232, over 4988.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2058, pruned_loss=0.02901, over 973012.73 frames.], batch size: 28, lr: 1.24e-04
2022-05-09 08:16:52,482 INFO [train.py:715] (2/8) Epoch 18, batch 12800, loss[loss=0.1107, simple_loss=0.1768, pruned_loss=0.02227, over 4863.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2055, pruned_loss=0.02882, over 972318.25 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 08:17:31,838 INFO [train.py:715] (2/8) Epoch 18, batch 12850, loss[loss=0.1083, simple_loss=0.1764, pruned_loss=0.02004, over 4877.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2055, pruned_loss=0.02888, over 972813.87 frames.], batch size: 12, lr: 1.24e-04
2022-05-09 08:18:11,707 INFO [train.py:715] (2/8) Epoch 18, batch 12900, loss[loss=0.1409, simple_loss=0.2247, pruned_loss=0.02857, over 4955.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.02892, over 972585.41 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 08:18:50,196 INFO [train.py:715] (2/8) Epoch 18, batch 12950, loss[loss=0.1585, simple_loss=0.25, pruned_loss=0.03345, over 4920.00 frames.], tot_loss[loss=0.1322, simple_loss=0.206, pruned_loss=0.02917, over 973152.67 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 08:19:30,197 INFO [train.py:715] (2/8) Epoch 18, batch 13000, loss[loss=0.1225, simple_loss=0.2006, pruned_loss=0.02217, over 4907.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2063, pruned_loss=0.02942, over 973086.59 frames.], batch size: 19, lr: 1.24e-04
2022-05-09 08:20:09,527 INFO [train.py:715] (2/8) Epoch 18, batch 13050, loss[loss=0.1278, simple_loss=0.2001, pruned_loss=0.02779, over 4788.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2059, pruned_loss=0.02919, over 974012.76 frames.], batch size: 24, lr: 1.24e-04
2022-05-09 08:20:48,612 INFO [train.py:715] (2/8) Epoch 18, batch 13100, loss[loss=0.1398, simple_loss=0.2261, pruned_loss=0.02671, over 4934.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2062, pruned_loss=0.02934, over 973319.52 frames.], batch size: 23, lr: 1.24e-04
2022-05-09 08:21:28,137 INFO [train.py:715] (2/8) Epoch 18, batch 13150, loss[loss=0.1108, simple_loss=0.1849, pruned_loss=0.01837, over 4649.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2061, pruned_loss=0.02915, over 973160.59 frames.], batch size: 13, lr: 1.24e-04
2022-05-09 08:22:07,409 INFO [train.py:715] (2/8) Epoch 18, batch 13200, loss[loss=0.1404, simple_loss=0.2182, pruned_loss=0.03135, over 4873.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2062, pruned_loss=0.02939, over 973178.75 frames.], batch size: 32, lr: 1.24e-04
2022-05-09 08:22:47,221 INFO [train.py:715] (2/8) Epoch 18, batch 13250, loss[loss=0.1452, simple_loss=0.2177, pruned_loss=0.03635, over 4829.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02913, over 973225.27 frames.], batch size: 15, lr: 1.24e-04
2022-05-09 08:23:25,811 INFO [train.py:715] (2/8) Epoch 18, batch 13300, loss[loss=0.1599, simple_loss=0.2425, pruned_loss=0.03864, over 4890.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02917, over 973487.12 frames.], batch size: 38, lr: 1.24e-04
2022-05-09 08:24:05,552 INFO [train.py:715] (2/8) Epoch 18, batch 13350, loss[loss=0.1377, simple_loss=0.2059, pruned_loss=0.03473, over 4736.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2071, pruned_loss=0.02938, over 973251.77 frames.], batch size: 16, lr: 1.24e-04
2022-05-09 08:24:44,554 INFO [train.py:715] (2/8) Epoch 18, batch 13400, loss[loss=0.1488, simple_loss=0.2162, pruned_loss=0.0407, over 4776.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02919, over 972524.35 frames.], batch size: 18, lr: 1.24e-04
2022-05-09 08:25:25,445 INFO [train.py:715] (2/8) Epoch 18, batch 13450, loss[loss=0.1224, simple_loss=0.193, pruned_loss=0.02584, over 4778.00 frames.], tot_loss[loss=0.1328, simple_loss=0.207, pruned_loss=0.02929, over 972629.43 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:26:05,139 INFO [train.py:715] (2/8) Epoch 18, batch 13500, loss[loss=0.1331, simple_loss=0.2143, pruned_loss=0.02592, over 4877.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02929, over 972951.42 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:26:44,101 INFO [train.py:715] (2/8) Epoch 18, batch 13550, loss[loss=0.1251, simple_loss=0.1976, pruned_loss=0.02633, over 4849.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02934, over 972747.49 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 08:27:23,344 INFO [train.py:715] (2/8) Epoch 18, batch 13600, loss[loss=0.131, simple_loss=0.2002, pruned_loss=0.03089, over 4973.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02888, over 972322.32 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 08:28:02,157 INFO [train.py:715] (2/8) Epoch 18, batch 13650, loss[loss=0.1106, simple_loss=0.1861, pruned_loss=0.01751, over 4936.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02896, over 972408.24 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 08:28:41,563 INFO [train.py:715] (2/8) Epoch 18, batch 13700, loss[loss=0.1188, simple_loss=0.2004, pruned_loss=0.01861, over 4945.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.0286, over 971473.27 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 08:29:20,646 INFO [train.py:715] (2/8) Epoch 18, batch 13750, loss[loss=0.1391, simple_loss=0.203, pruned_loss=0.03759, over 4770.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2058, pruned_loss=0.02884, over 972558.02 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:29:59,733 INFO [train.py:715] (2/8) Epoch 18, batch 13800, loss[loss=0.1381, simple_loss=0.2093, pruned_loss=0.0334, over 4932.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02881, over 972144.98 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 08:30:39,486 INFO [train.py:715] (2/8) Epoch 18, batch 13850, loss[loss=0.1224, simple_loss=0.2132, pruned_loss=0.01579, over 4824.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2061, pruned_loss=0.02885, over 972408.54 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 08:31:18,291 INFO [train.py:715] (2/8) Epoch 18, batch 13900, loss[loss=0.1096, simple_loss=0.1801, pruned_loss=0.01962, over 4887.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.029, over 972511.02 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 08:31:57,757 INFO [train.py:715] (2/8) Epoch 18, batch 13950, loss[loss=0.1031, simple_loss=0.1755, pruned_loss=0.01531, over 4967.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02861, over 973271.56 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 08:32:37,349 INFO [train.py:715] (2/8) Epoch 18, batch 14000, loss[loss=0.1502, simple_loss=0.2336, pruned_loss=0.03341, over 4774.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02871, over 973713.31 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 08:33:17,110 INFO [train.py:715] (2/8) Epoch 18, batch 14050, loss[loss=0.1367, simple_loss=0.2056, pruned_loss=0.03387, over 4971.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02865, over 972377.68 frames.], batch size: 39, lr: 1.23e-04
2022-05-09 08:33:56,295 INFO [train.py:715] (2/8) Epoch 18, batch 14100, loss[loss=0.1252, simple_loss=0.1975, pruned_loss=0.02642, over 4937.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2077, pruned_loss=0.02856, over 972451.92 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 08:34:35,396 INFO [train.py:715] (2/8) Epoch 18, batch 14150, loss[loss=0.1399, simple_loss=0.2174, pruned_loss=0.0312, over 4978.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2078, pruned_loss=0.02876, over 973582.18 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 08:35:14,782 INFO [train.py:715] (2/8) Epoch 18, batch 14200, loss[loss=0.1297, simple_loss=0.2144, pruned_loss=0.02248, over 4977.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02856, over 972876.36 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 08:35:54,059 INFO [train.py:715] (2/8) Epoch 18, batch 14250, loss[loss=0.1271, simple_loss=0.1891, pruned_loss=0.03258, over 4831.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02853, over 972824.40 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 08:36:34,010 INFO [train.py:715] (2/8) Epoch 18, batch 14300, loss[loss=0.1582, simple_loss=0.2313, pruned_loss=0.04252, over 4963.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02884, over 971936.35 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 08:37:13,313 INFO [train.py:715] (2/8) Epoch 18, batch 14350, loss[loss=0.1243, simple_loss=0.1997, pruned_loss=0.02444, over 4987.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02914, over 972274.13 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 08:37:52,859 INFO [train.py:715] (2/8) Epoch 18, batch 14400, loss[loss=0.1237, simple_loss=0.1974, pruned_loss=0.02498, over 4984.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2078, pruned_loss=0.02975, over 971591.82 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 08:38:32,503 INFO [train.py:715] (2/8) Epoch 18, batch 14450, loss[loss=0.1375, simple_loss=0.2249, pruned_loss=0.02503, over 4791.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02909, over 971901.74 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:39:11,250 INFO [train.py:715] (2/8) Epoch 18, batch 14500, loss[loss=0.129, simple_loss=0.2048, pruned_loss=0.02661, over 4885.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02922, over 971007.16 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 08:39:50,390 INFO [train.py:715] (2/8) Epoch 18, batch 14550, loss[loss=0.145, simple_loss=0.2149, pruned_loss=0.03756, over 4856.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.0294, over 971220.99 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 08:40:29,526 INFO [train.py:715] (2/8) Epoch 18, batch 14600, loss[loss=0.1458, simple_loss=0.2107, pruned_loss=0.04044, over 4747.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2083, pruned_loss=0.02974, over 971742.86 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 08:41:09,225 INFO [train.py:715] (2/8) Epoch 18, batch 14650, loss[loss=0.1288, simple_loss=0.2095, pruned_loss=0.024, over 4942.00 frames.], tot_loss[loss=0.1342, simple_loss=0.2084, pruned_loss=0.03005, over 973079.71 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 08:41:48,697 INFO [train.py:715] (2/8) Epoch 18, batch 14700, loss[loss=0.1332, simple_loss=0.2127, pruned_loss=0.02689, over 4816.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2079, pruned_loss=0.0296, over 971152.67 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 08:42:28,035 INFO [train.py:715] (2/8) Epoch 18, batch 14750, loss[loss=0.1198, simple_loss=0.1981, pruned_loss=0.02072, over 4844.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02945, over 971481.43 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 08:43:07,466 INFO [train.py:715] (2/8) Epoch 18, batch 14800, loss[loss=0.1323, simple_loss=0.2047, pruned_loss=0.02991, over 4790.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02947, over 971255.87 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 08:43:46,222 INFO [train.py:715] (2/8) Epoch 18, batch 14850, loss[loss=0.1238, simple_loss=0.1972, pruned_loss=0.02524, over 4748.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02929, over 970266.81 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 08:44:25,880 INFO [train.py:715] (2/8) Epoch 18, batch 14900, loss[loss=0.1437, simple_loss=0.2038, pruned_loss=0.04182, over 4915.00 frames.], tot_loss[loss=0.1339, simple_loss=0.208, pruned_loss=0.02985, over 970451.13 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 08:45:05,551 INFO [train.py:715] (2/8) Epoch 18, batch 14950, loss[loss=0.1351, simple_loss=0.2062, pruned_loss=0.03197, over 4974.00 frames.], tot_loss[loss=0.1338, simple_loss=0.208, pruned_loss=0.02983, over 971154.70 frames.], batch size: 31, lr: 1.23e-04
2022-05-09 08:45:44,835 INFO [train.py:715] (2/8) Epoch 18, batch 15000, loss[loss=0.1171, simple_loss=0.1943, pruned_loss=0.0199, over 4791.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02945, over 971611.05 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 08:45:44,835 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 08:45:54,766 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1048, simple_loss=0.1881, pruned_loss=0.01071, over 914524.00 frames.
2022-05-09 08:46:34,352 INFO [train.py:715] (2/8) Epoch 18, batch 15050, loss[loss=0.1314, simple_loss=0.2001, pruned_loss=0.03138, over 4893.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2082, pruned_loss=0.03018, over 970959.42 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 08:47:13,527 INFO [train.py:715] (2/8) Epoch 18, batch 15100, loss[loss=0.1368, simple_loss=0.2108, pruned_loss=0.03142, over 4992.00 frames.], tot_loss[loss=0.1341, simple_loss=0.2081, pruned_loss=0.03002, over 970951.62 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 08:47:53,261 INFO [train.py:715] (2/8) Epoch 18, batch 15150, loss[loss=0.1367, simple_loss=0.2123, pruned_loss=0.03051, over 4893.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02948, over 970823.28 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:48:32,389 INFO [train.py:715] (2/8) Epoch 18, batch 15200, loss[loss=0.1338, simple_loss=0.2075, pruned_loss=0.03002, over 4922.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02946, over 971321.09 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 08:49:11,931 INFO [train.py:715] (2/8) Epoch 18, batch 15250, loss[loss=0.1786, simple_loss=0.2503, pruned_loss=0.05349, over 4768.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02987, over 971788.09 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 08:49:51,793 INFO [train.py:715] (2/8) Epoch 18, batch 15300, loss[loss=0.12, simple_loss=0.2052, pruned_loss=0.01747, over 4977.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02935, over 971364.72 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 08:50:31,165 INFO [train.py:715] (2/8) Epoch 18, batch 15350, loss[loss=0.1073, simple_loss=0.1869, pruned_loss=0.0138, over 4863.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02878, over 971841.44 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 08:51:10,085 INFO [train.py:715] (2/8) Epoch 18, batch 15400, loss[loss=0.115, simple_loss=0.1928, pruned_loss=0.01863, over 4791.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02942, over 972154.09 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 08:51:49,373 INFO [train.py:715] (2/8) Epoch 18, batch 15450, loss[loss=0.1458, simple_loss=0.2088, pruned_loss=0.04137, over 4851.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2072, pruned_loss=0.02951, over 971007.61 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 08:52:29,004 INFO [train.py:715] (2/8) Epoch 18, batch 15500, loss[loss=0.1252, simple_loss=0.194, pruned_loss=0.02823, over 4990.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02953, over 971251.95 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 08:53:08,161 INFO [train.py:715] (2/8) Epoch 18, batch 15550, loss[loss=0.1146, simple_loss=0.191, pruned_loss=0.01905, over 4945.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02895, over 971526.44 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 08:53:47,894 INFO [train.py:715] (2/8) Epoch 18, batch 15600, loss[loss=0.1325, simple_loss=0.2031, pruned_loss=0.03096, over 4911.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02899, over 972447.80 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 08:54:28,032 INFO [train.py:715] (2/8) Epoch 18, batch 15650, loss[loss=0.1115, simple_loss=0.1854, pruned_loss=0.01875, over 4916.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02969, over 972741.77 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 08:55:07,636 INFO [train.py:715] (2/8) Epoch 18, batch 15700, loss[loss=0.1562, simple_loss=0.2306, pruned_loss=0.04094, over 4916.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.0293, over 972318.60 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 08:55:46,519 INFO [train.py:715] (2/8) Epoch 18, batch 15750, loss[loss=0.13, simple_loss=0.2007, pruned_loss=0.02965, over 4862.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02959, over 971882.62 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 08:56:25,960 INFO [train.py:715] (2/8) Epoch 18, batch 15800, loss[loss=0.1104, simple_loss=0.1877, pruned_loss=0.01653, over 4876.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02912, over 971854.07 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 08:57:05,870 INFO [train.py:715] (2/8) Epoch 18, batch 15850, loss[loss=0.1229, simple_loss=0.2116, pruned_loss=0.01711, over 4818.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2078, pruned_loss=0.02941, over 972723.04 frames.], batch size: 26, lr: 1.23e-04
2022-05-09 08:57:45,103 INFO [train.py:715] (2/8) Epoch 18, batch 15900, loss[loss=0.1578, simple_loss=0.2411, pruned_loss=0.03727, over 4789.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2082, pruned_loss=0.02964, over 971596.15 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 08:58:24,418 INFO [train.py:715] (2/8) Epoch 18, batch 15950, loss[loss=0.1403, simple_loss=0.2183, pruned_loss=0.0312, over 4898.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2078, pruned_loss=0.02956, over 971695.91 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:59:04,895 INFO [train.py:715] (2/8) Epoch 18, batch 16000, loss[loss=0.181, simple_loss=0.2378, pruned_loss=0.06213, over 4906.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02941, over 971375.96 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 08:59:45,385 INFO [train.py:715] (2/8) Epoch 18, batch 16050, loss[loss=0.153, simple_loss=0.2201, pruned_loss=0.04296, over 4805.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2075, pruned_loss=0.02947, over 971283.74 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:00:24,419 INFO [train.py:715] (2/8) Epoch 18, batch 16100, loss[loss=0.1281, simple_loss=0.2009, pruned_loss=0.0277, over 4804.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2076, pruned_loss=0.02894, over 970914.24 frames.], batch size: 26, lr: 1.23e-04
2022-05-09 09:01:03,601 INFO [train.py:715] (2/8) Epoch 18, batch 16150, loss[loss=0.1145, simple_loss=0.1913, pruned_loss=0.0189, over 4823.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2071, pruned_loss=0.02867, over 971076.67 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:01:43,698 INFO [train.py:715] (2/8) Epoch 18, batch 16200, loss[loss=0.1154, simple_loss=0.1985, pruned_loss=0.01614, over 4915.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02918, over 971148.85 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 09:02:22,641 INFO [train.py:715] (2/8) Epoch 18, batch 16250, loss[loss=0.1162, simple_loss=0.196, pruned_loss=0.01819, over 4814.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2081, pruned_loss=0.02945, over 971802.21 frames.], batch size: 26, lr: 1.23e-04
2022-05-09 09:03:01,671 INFO [train.py:715] (2/8) Epoch 18, batch 16300, loss[loss=0.141, simple_loss=0.2179, pruned_loss=0.03205, over 4810.00 frames.], tot_loss[loss=0.133, simple_loss=0.2074, pruned_loss=0.02934, over 972119.99 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:03:41,211 INFO [train.py:715] (2/8) Epoch 18, batch 16350, loss[loss=0.1638, simple_loss=0.2321, pruned_loss=0.04781, over 4947.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.0296, over 973158.23 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:04:20,349 INFO [train.py:715] (2/8) Epoch 18, batch 16400, loss[loss=0.1297, simple_loss=0.2046, pruned_loss=0.02737, over 4911.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02907, over 972816.89 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 09:04:59,287 INFO [train.py:715] (2/8) Epoch 18, batch 16450, loss[loss=0.1186, simple_loss=0.2004, pruned_loss=0.01837, over 4985.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2076, pruned_loss=0.02941, over 972695.76 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 09:05:38,806 INFO [train.py:715] (2/8) Epoch 18, batch 16500, loss[loss=0.1382, simple_loss=0.2154, pruned_loss=0.03047, over 4903.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02926, over 972658.48 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:06:18,651 INFO [train.py:715] (2/8) Epoch 18, batch 16550, loss[loss=0.113, simple_loss=0.1878, pruned_loss=0.01908, over 4779.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02938, over 972215.57 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:06:57,075 INFO [train.py:715] (2/8) Epoch 18, batch 16600, loss[loss=0.1475, simple_loss=0.2204, pruned_loss=0.03732, over 4989.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02943, over 972613.05 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 09:07:36,516 INFO [train.py:715] (2/8) Epoch 18, batch 16650, loss[loss=0.1394, simple_loss=0.2159, pruned_loss=0.03147, over 4822.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2067, pruned_loss=0.02979, over 972099.47 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:08:15,860 INFO [train.py:715] (2/8) Epoch 18, batch 16700, loss[loss=0.1401, simple_loss=0.2216, pruned_loss=0.02927, over 4980.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02907, over 973234.46 frames.], batch size: 39, lr: 1.23e-04
2022-05-09 09:08:55,199 INFO [train.py:715] (2/8) Epoch 18, batch 16750, loss[loss=0.1306, simple_loss=0.2031, pruned_loss=0.02899, over 4952.00 frames.], tot_loss[loss=0.133, simple_loss=0.207, pruned_loss=0.02952, over 973412.59 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:09:34,649 INFO [train.py:715] (2/8) Epoch 18, batch 16800, loss[loss=0.1666, simple_loss=0.2405, pruned_loss=0.04633, over 4936.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02905, over 973133.56 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:10:13,855 INFO [train.py:715] (2/8) Epoch 18, batch 16850, loss[loss=0.1173, simple_loss=0.1877, pruned_loss=0.02341, over 4827.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02907, over 973743.56 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 09:10:53,313 INFO [train.py:715] (2/8) Epoch 18, batch 16900, loss[loss=0.1569, simple_loss=0.2362, pruned_loss=0.0388, over 4976.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2073, pruned_loss=0.02951, over 974467.22 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:11:32,156 INFO [train.py:715] (2/8) Epoch 18, batch 16950, loss[loss=0.1439, simple_loss=0.2168, pruned_loss=0.03551, over 4722.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.0291, over 973955.30 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 09:12:11,612 INFO [train.py:715] (2/8) Epoch 18, batch 17000, loss[loss=0.1062, simple_loss=0.171, pruned_loss=0.0207, over 4816.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02881, over 973117.58 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:12:51,063 INFO [train.py:715] (2/8) Epoch 18, batch 17050, loss[loss=0.129, simple_loss=0.1991, pruned_loss=0.02941, over 4979.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02888, over 973292.59 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 09:13:30,536 INFO [train.py:715] (2/8) Epoch 18, batch 17100, loss[loss=0.1692, simple_loss=0.2409, pruned_loss=0.04881, over 4868.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02911, over 972293.39 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:14:10,115 INFO [train.py:715] (2/8) Epoch 18, batch 17150, loss[loss=0.1412, simple_loss=0.2052, pruned_loss=0.03861, over 4748.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02889, over 972748.80 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:14:49,242 INFO [train.py:715] (2/8) Epoch 18, batch 17200, loss[loss=0.1132, simple_loss=0.1771, pruned_loss=0.02465, over 4747.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02878, over 972260.40 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:15:28,966 INFO [train.py:715] (2/8) Epoch 18, batch 17250, loss[loss=0.1425, simple_loss=0.2074, pruned_loss=0.03879, over 4843.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02868, over 972946.71 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 09:16:08,223 INFO [train.py:715] (2/8) Epoch 18, batch 17300, loss[loss=0.1249, simple_loss=0.1966, pruned_loss=0.02659, over 4886.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.0288, over 973482.62 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:16:48,155 INFO [train.py:715] (2/8) Epoch 18, batch 17350, loss[loss=0.1343, simple_loss=0.2068, pruned_loss=0.03092, over 4770.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02914, over 972871.59 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:17:27,219 INFO [train.py:715] (2/8) Epoch 18, batch 17400, loss[loss=0.1498, simple_loss=0.2237, pruned_loss=0.03792, over 4944.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02922, over 972810.62 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:18:07,007 INFO [train.py:715] (2/8) Epoch 18, batch 17450, loss[loss=0.1324, simple_loss=0.2056, pruned_loss=0.02963, over 4885.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02935, over 972170.86 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 09:18:46,085 INFO [train.py:715] (2/8) Epoch 18, batch 17500, loss[loss=0.1346, simple_loss=0.2114, pruned_loss=0.02897, over 4974.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.0287, over 972836.04 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:19:24,715 INFO [train.py:715] (2/8) Epoch 18, batch 17550, loss[loss=0.1164, simple_loss=0.1894, pruned_loss=0.02172, over 4947.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02861, over 972372.28 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:20:04,281 INFO [train.py:715] (2/8) Epoch 18, batch 17600, loss[loss=0.1417, simple_loss=0.2108, pruned_loss=0.03631, over 4709.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02871, over 971600.63 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:20:43,554 INFO [train.py:715] (2/8) Epoch 18, batch 17650, loss[loss=0.1317, simple_loss=0.1987, pruned_loss=0.03234, over 4900.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.0285, over 972041.81 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:21:22,849 INFO [train.py:715] (2/8) Epoch 18, batch 17700, loss[loss=0.1367, simple_loss=0.2009, pruned_loss=0.03625, over 4793.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02887, over 971780.37 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:22:01,951 INFO [train.py:715] (2/8) Epoch 18, batch 17750, loss[loss=0.138, simple_loss=0.2097, pruned_loss=0.03316, over 4984.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02864, over 972316.33 frames.], batch size: 31, lr: 1.23e-04
2022-05-09 09:22:41,556 INFO [train.py:715] (2/8) Epoch 18, batch 17800, loss[loss=0.1223, simple_loss=0.1968, pruned_loss=0.02389, over 4827.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02845, over 972328.65 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:23:20,835 INFO [train.py:715] (2/8) Epoch 18, batch 17850, loss[loss=0.1158, simple_loss=0.1728, pruned_loss=0.02938, over 4853.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02831, over 972940.50 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:23:59,346 INFO [train.py:715] (2/8) Epoch 18, batch 17900, loss[loss=0.1227, simple_loss=0.1932, pruned_loss=0.02608, over 4963.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2055, pruned_loss=0.02863, over 972405.48 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 09:24:39,459 INFO [train.py:715] (2/8) Epoch 18, batch 17950, loss[loss=0.1155, simple_loss=0.1874, pruned_loss=0.02178, over 4848.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02856, over 972454.93 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 09:25:18,519 INFO [train.py:715] (2/8) Epoch 18, batch 18000, loss[loss=0.1382, simple_loss=0.2185, pruned_loss=0.029, over 4828.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2051, pruned_loss=0.02836, over 972634.22 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:25:18,520 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 09:25:28,382 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1046, simple_loss=0.1878, pruned_loss=0.01063, over 914524.00 frames.
2022-05-09 09:26:07,770 INFO [train.py:715] (2/8) Epoch 18, batch 18050, loss[loss=0.1163, simple_loss=0.195, pruned_loss=0.0188, over 4699.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2057, pruned_loss=0.0287, over 972335.91 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:26:47,169 INFO [train.py:715] (2/8) Epoch 18, batch 18100, loss[loss=0.1257, simple_loss=0.2043, pruned_loss=0.02354, over 4960.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02862, over 972336.01 frames.], batch size: 28, lr: 1.23e-04
2022-05-09 09:27:26,274 INFO [train.py:715] (2/8) Epoch 18, batch 18150, loss[loss=0.1273, simple_loss=0.2139, pruned_loss=0.02031, over 4977.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02812, over 971961.91 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 09:28:06,063 INFO [train.py:715] (2/8) Epoch 18, batch 18200, loss[loss=0.13, simple_loss=0.2026, pruned_loss=0.02865, over 4973.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02812, over 973028.62 frames.], batch size: 39, lr: 1.23e-04
2022-05-09 09:28:45,774 INFO [train.py:715] (2/8) Epoch 18, batch 18250, loss[loss=0.1221, simple_loss=0.1999, pruned_loss=0.02214, over 4908.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2047, pruned_loss=0.0277, over 972431.39 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:29:24,156 INFO [train.py:715] (2/8) Epoch 18, batch 18300, loss[loss=0.1374, simple_loss=0.2032, pruned_loss=0.03578, over 4977.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2054, pruned_loss=0.02758, over 971736.91 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:30:03,828 INFO [train.py:715] (2/8) Epoch 18, batch 18350, loss[loss=0.1575, simple_loss=0.2393, pruned_loss=0.03781, over 4927.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2052, pruned_loss=0.02772, over 972369.19 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 09:30:43,382 INFO [train.py:715] (2/8) Epoch 18, batch 18400, loss[loss=0.1273, simple_loss=0.2086, pruned_loss=0.02299, over 4764.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02834, over 972175.38 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:31:22,382 INFO [train.py:715] (2/8) Epoch 18, batch 18450, loss[loss=0.1194, simple_loss=0.2061, pruned_loss=0.01634, over 4755.00 frames.], tot_loss[loss=0.1308, simple_loss=0.206, pruned_loss=0.02776, over 971624.63 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 09:32:01,513 INFO [train.py:715] (2/8) Epoch 18, batch 18500, loss[loss=0.1197, simple_loss=0.1918, pruned_loss=0.02377, over 4926.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2062, pruned_loss=0.02812, over 972116.26 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:32:40,867 INFO [train.py:715] (2/8) Epoch 18, batch 18550, loss[loss=0.1603, simple_loss=0.229, pruned_loss=0.04581, over 4951.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2061, pruned_loss=0.02802, over 973070.44 frames.], batch size: 39, lr: 1.23e-04
2022-05-09 09:33:20,072 INFO [train.py:715] (2/8) Epoch 18, batch 18600, loss[loss=0.1518, simple_loss=0.2314, pruned_loss=0.03609, over 4781.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02866, over 972590.94 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 09:33:58,716 INFO [train.py:715] (2/8) Epoch 18, batch 18650, loss[loss=0.1153, simple_loss=0.2056, pruned_loss=0.01254, over 4924.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02872, over 972313.10 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 09:34:38,209 INFO [train.py:715] (2/8) Epoch 18, batch 18700, loss[loss=0.1707, simple_loss=0.2411, pruned_loss=0.05018, over 4945.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02887, over 971509.15 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:35:17,419 INFO [train.py:715] (2/8) Epoch 18, batch 18750, loss[loss=0.1169, simple_loss=0.1887, pruned_loss=0.02258, over 4744.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02943, over 971125.93 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:35:56,634 INFO [train.py:715] (2/8) Epoch 18, batch 18800, loss[loss=0.1741, simple_loss=0.2352, pruned_loss=0.05649, over 4904.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02923, over 971543.91 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:36:35,999 INFO [train.py:715] (2/8) Epoch 18, batch 18850, loss[loss=0.135, simple_loss=0.2095, pruned_loss=0.03028, over 4979.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02913, over 970856.31 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 09:37:15,848 INFO [train.py:715] (2/8) Epoch 18, batch 18900, loss[loss=0.1457, simple_loss=0.2209, pruned_loss=0.0353, over 4925.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2069, pruned_loss=0.02923, over 972040.67 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:37:54,910 INFO [train.py:715] (2/8) Epoch 18, batch 18950, loss[loss=0.125, simple_loss=0.1931, pruned_loss=0.02844, over 4986.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02904, over 972494.05 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:38:33,361 INFO [train.py:715] (2/8) Epoch 18, batch 19000, loss[loss=0.1325, simple_loss=0.209, pruned_loss=0.02804, over 4876.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02933, over 971706.60 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 09:39:12,867 INFO [train.py:715] (2/8) Epoch 18, batch 19050, loss[loss=0.1155, simple_loss=0.2009, pruned_loss=0.01508, over 4924.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2066, pruned_loss=0.02917, over 972298.23 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:39:51,873 INFO [train.py:715] (2/8) Epoch 18, batch 19100, loss[loss=0.1366, simple_loss=0.2087, pruned_loss=0.03227, over 4899.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02897, over 972429.41 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:40:31,189 INFO [train.py:715] (2/8) Epoch 18, batch 19150, loss[loss=0.1137, simple_loss=0.1868, pruned_loss=0.02027, over 4909.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02892, over 972809.01 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 09:41:11,059 INFO [train.py:715] (2/8) Epoch 18, batch 19200, loss[loss=0.1306, simple_loss=0.2067, pruned_loss=0.02723, over 4648.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02902, over 972657.10 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 09:41:50,578 INFO [train.py:715] (2/8) Epoch 18, batch 19250, loss[loss=0.1407, simple_loss=0.2174, pruned_loss=0.03202, over 4909.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02915, over 971755.71 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:42:29,654 INFO [train.py:715] (2/8) Epoch 18, batch 19300, loss[loss=0.1173, simple_loss=0.1863, pruned_loss=0.02413, over 4759.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02901, over 971827.43 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:43:08,117 INFO [train.py:715] (2/8) Epoch 18, batch 19350, loss[loss=0.109, simple_loss=0.1827, pruned_loss=0.01764, over 4802.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02924, over 971506.40 frames.], batch size: 12, lr: 1.23e-04
2022-05-09 09:43:47,529 INFO [train.py:715] (2/8) Epoch 18, batch 19400, loss[loss=0.1395, simple_loss=0.2243, pruned_loss=0.02735, over 4969.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02899, over 971388.47 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:44:26,737 INFO [train.py:715] (2/8) Epoch 18, batch 19450, loss[loss=0.1102, simple_loss=0.1913, pruned_loss=0.01455, over 4872.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02841, over 971644.11 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 09:45:05,482 INFO [train.py:715] (2/8) Epoch 18, batch 19500, loss[loss=0.1225, simple_loss=0.2025, pruned_loss=0.02124, over 4980.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02819, over 972030.93 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:45:44,654 INFO [train.py:715] (2/8) Epoch 18, batch 19550, loss[loss=0.1272, simple_loss=0.2019, pruned_loss=0.02627, over 4831.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02842, over 971849.66 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 09:46:24,060 INFO [train.py:715] (2/8) Epoch 18, batch 19600, loss[loss=0.1325, simple_loss=0.2007, pruned_loss=0.03218, over 4792.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02879, over 972332.03 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:47:02,888 INFO [train.py:715] (2/8) Epoch 18, batch 19650, loss[loss=0.1684, simple_loss=0.2569, pruned_loss=0.03993, over 4975.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02908, over 972150.19 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:47:41,711 INFO [train.py:715] (2/8) Epoch 18, batch 19700, loss[loss=0.1436, simple_loss=0.2194, pruned_loss=0.03391, over 4845.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2078, pruned_loss=0.02931, over 972008.44 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 09:48:21,732 INFO [train.py:715] (2/8) Epoch 18, batch 19750, loss[loss=0.1344, simple_loss=0.2005, pruned_loss=0.03412, over 4892.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02875, over 971757.77 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 09:49:01,596 INFO [train.py:715] (2/8) Epoch 18, batch 19800, loss[loss=0.1541, simple_loss=0.2289, pruned_loss=0.03965, over 4951.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02863, over 971733.59 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 09:49:40,678 INFO [train.py:715] (2/8) Epoch 18, batch 19850, loss[loss=0.1374, simple_loss=0.2124, pruned_loss=0.0312, over 4954.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02891, over 971039.47 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 09:50:20,121 INFO [train.py:715] (2/8) Epoch 18, batch 19900, loss[loss=0.1213, simple_loss=0.2044, pruned_loss=0.01907, over 4933.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02864, over 970722.01 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 09:50:59,799 INFO [train.py:715] (2/8) Epoch 18, batch 19950, loss[loss=0.1255, simple_loss=0.203, pruned_loss=0.02395, over 4804.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02846, over 971152.93 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:51:39,047 INFO [train.py:715] (2/8) Epoch 18, batch 20000, loss[loss=0.1227, simple_loss=0.1961, pruned_loss=0.02467, over 4919.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.02808, over 972303.20 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 09:52:18,803 INFO [train.py:715] (2/8) Epoch 18, batch 20050, loss[loss=0.1466, simple_loss=0.2194, pruned_loss=0.03684, over 4850.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2048, pruned_loss=0.02825, over 971737.09 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:52:59,024 INFO [train.py:715] (2/8) Epoch 18, batch 20100, loss[loss=0.12, simple_loss=0.1991, pruned_loss=0.02038, over 4835.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02789, over 971889.15 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 09:53:39,142 INFO [train.py:715] (2/8) Epoch 18, batch 20150, loss[loss=0.1386, simple_loss=0.2114, pruned_loss=0.03287, over 4859.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2045, pruned_loss=0.0278, over 972517.65 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 09:54:18,209 INFO [train.py:715] (2/8) Epoch 18, batch 20200, loss[loss=0.153, simple_loss=0.2206, pruned_loss=0.04268, over 4978.00 frames.], tot_loss[loss=0.1307, simple_loss=0.205, pruned_loss=0.02817, over 972220.85 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 09:54:57,197 INFO [train.py:715] (2/8) Epoch 18, batch 20250, loss[loss=0.129, simple_loss=0.203, pruned_loss=0.02748, over 4808.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02846, over 972170.91 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 09:55:36,877 INFO [train.py:715] (2/8) Epoch 18, batch 20300, loss[loss=0.161, simple_loss=0.2203, pruned_loss=0.05082, over 4844.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02819, over 972396.58 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 09:56:16,003 INFO [train.py:715] (2/8) Epoch 18, batch 20350, loss[loss=0.1173, simple_loss=0.1926, pruned_loss=0.02104, over 4847.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02808, over 972112.86 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 09:56:55,260 INFO [train.py:715] (2/8) Epoch 18, batch 20400, loss[loss=0.1422, simple_loss=0.2174, pruned_loss=0.03354, over 4881.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02882, over 971527.97 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 09:57:34,099 INFO [train.py:715] (2/8) Epoch 18, batch 20450, loss[loss=0.1205, simple_loss=0.1935, pruned_loss=0.02374, over 4901.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02864, over 972465.38 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 09:58:14,215 INFO [train.py:715] (2/8) Epoch 18, batch 20500, loss[loss=0.132, simple_loss=0.2144, pruned_loss=0.02479, over 4874.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02841, over 972253.30 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 09:58:52,926 INFO [train.py:715] (2/8) Epoch 18, batch 20550, loss[loss=0.1443, simple_loss=0.2175, pruned_loss=0.03558, over 4965.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.0286, over 971200.05 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 09:59:31,854 INFO [train.py:715] (2/8) Epoch 18, batch 20600, loss[loss=0.1393, simple_loss=0.2134, pruned_loss=0.03258, over 4977.00 frames.], tot_loss[loss=0.131, simple_loss=0.2054, pruned_loss=0.02832, over 971612.10 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:00:10,871 INFO [train.py:715] (2/8) Epoch 18, batch 20650, loss[loss=0.1315, simple_loss=0.193, pruned_loss=0.03505, over 4887.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2055, pruned_loss=0.02792, over 972310.32 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:00:50,439 INFO [train.py:715] (2/8) Epoch 18, batch 20700, loss[loss=0.1399, simple_loss=0.2089, pruned_loss=0.03551, over 4780.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2052, pruned_loss=0.02801, over 971759.25 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 10:01:28,859 INFO [train.py:715] (2/8) Epoch 18, batch 20750, loss[loss=0.1169, simple_loss=0.1803, pruned_loss=0.02674, over 4787.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02868, over 971537.60 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:02:08,334 INFO [train.py:715] (2/8) Epoch 18, batch 20800, loss[loss=0.1558, simple_loss=0.2298, pruned_loss=0.04092, over 4962.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.02819, over 970634.41 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:02:47,783 INFO [train.py:715] (2/8) Epoch 18, batch 20850, loss[loss=0.1171, simple_loss=0.1972, pruned_loss=0.01851, over 4793.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02851, over 971443.08 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 10:03:26,624 INFO [train.py:715] (2/8) Epoch 18, batch 20900, loss[loss=0.1291, simple_loss=0.1989, pruned_loss=0.02966, over 4849.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02882, over 972170.08 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 10:04:05,319 INFO [train.py:715] (2/8) Epoch 18, batch 20950, loss[loss=0.127, simple_loss=0.2002, pruned_loss=0.0269, over 4966.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.0285, over 972533.81 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:04:44,841 INFO [train.py:715] (2/8) Epoch 18, batch 21000, loss[loss=0.1136, simple_loss=0.1927, pruned_loss=0.0173, over 4981.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02871, over 972947.01 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:04:44,842 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 10:04:54,817 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1046, simple_loss=0.1879, pruned_loss=0.01059, over 914524.00 frames.
2022-05-09 10:05:34,567 INFO [train.py:715] (2/8) Epoch 18, batch 21050, loss[loss=0.1487, simple_loss=0.2207, pruned_loss=0.0384, over 4878.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02882, over 973453.31 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 10:06:14,355 INFO [train.py:715] (2/8) Epoch 18, batch 21100, loss[loss=0.127, simple_loss=0.2047, pruned_loss=0.02466, over 4990.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2065, pruned_loss=0.02921, over 973822.44 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:06:53,517 INFO [train.py:715] (2/8) Epoch 18, batch 21150, loss[loss=0.143, simple_loss=0.2209, pruned_loss=0.03259, over 4900.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02924, over 973224.56 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:07:33,007 INFO [train.py:715] (2/8) Epoch 18, batch 21200, loss[loss=0.118, simple_loss=0.1926, pruned_loss=0.0217, over 4922.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02869, over 972865.33 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 10:08:12,711 INFO [train.py:715] (2/8) Epoch 18, batch 21250, loss[loss=0.1287, simple_loss=0.2045, pruned_loss=0.02643, over 4985.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02886, over 972721.51 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:08:51,641 INFO [train.py:715] (2/8) Epoch 18, batch 21300, loss[loss=0.1448, simple_loss=0.2264, pruned_loss=0.03158, over 4961.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02931, over 973005.76 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 10:09:30,196 INFO [train.py:715] (2/8) Epoch 18, batch 21350, loss[loss=0.1202, simple_loss=0.1996, pruned_loss=0.0204, over 4822.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02875, over 973532.56 frames.], batch size: 26, lr: 1.23e-04
2022-05-09 10:10:09,587 INFO [train.py:715] (2/8) Epoch 18, batch 21400, loss[loss=0.1272, simple_loss=0.1954, pruned_loss=0.02951, over 4971.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02907, over 972726.28 frames.], batch size: 35, lr: 1.23e-04
2022-05-09 10:10:51,785 INFO [train.py:715] (2/8) Epoch 18, batch 21450, loss[loss=0.1293, simple_loss=0.2066, pruned_loss=0.02597, over 4881.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02875, over 972521.70 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 10:11:30,946 INFO [train.py:715] (2/8) Epoch 18, batch 21500, loss[loss=0.1662, simple_loss=0.2315, pruned_loss=0.05051, over 4643.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02844, over 972475.97 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 10:12:09,695 INFO [train.py:715] (2/8) Epoch 18, batch 21550, loss[loss=0.1148, simple_loss=0.1849, pruned_loss=0.02235, over 4751.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02894, over 972860.10 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:12:49,109 INFO [train.py:715] (2/8) Epoch 18, batch 21600, loss[loss=0.13, simple_loss=0.2085, pruned_loss=0.02575, over 4993.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02925, over 973127.43 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 10:13:28,302 INFO [train.py:715] (2/8) Epoch 18, batch 21650, loss[loss=0.1525, simple_loss=0.2167, pruned_loss=0.0441, over 4905.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02925, over 973408.73 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 10:14:06,696 INFO [train.py:715] (2/8) Epoch 18, batch 21700, loss[loss=0.1341, simple_loss=0.2099, pruned_loss=0.02917, over 4987.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02927, over 973996.99 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:14:45,681 INFO [train.py:715] (2/8) Epoch 18, batch 21750, loss[loss=0.1338, simple_loss=0.2169, pruned_loss=0.02533, over 4978.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02919, over 973928.87 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:15:24,827 INFO [train.py:715] (2/8) Epoch 18, batch 21800, loss[loss=0.1415, simple_loss=0.2294, pruned_loss=0.02676, over 4968.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2078, pruned_loss=0.02921, over 973916.31 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 10:16:04,132 INFO [train.py:715] (2/8) Epoch 18, batch 21850, loss[loss=0.1305, simple_loss=0.211, pruned_loss=0.02501, over 4942.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2079, pruned_loss=0.02922, over 973649.82 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 10:16:43,562 INFO [train.py:715] (2/8) Epoch 18, batch 21900, loss[loss=0.1329, simple_loss=0.2097, pruned_loss=0.028, over 4795.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2076, pruned_loss=0.02899, over 972565.87 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 10:17:23,084 INFO [train.py:715] (2/8) Epoch 18, batch 21950, loss[loss=0.1291, simple_loss=0.2138, pruned_loss=0.02221, over 4748.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2074, pruned_loss=0.02872, over 971437.86 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:18:02,137 INFO [train.py:715] (2/8) Epoch 18, batch 22000, loss[loss=0.1443, simple_loss=0.2178, pruned_loss=0.03541, over 4960.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2073, pruned_loss=0.02861, over 971579.19 frames.], batch size: 39, lr: 1.23e-04
2022-05-09 10:18:41,240 INFO [train.py:715] (2/8) Epoch 18, batch 22050, loss[loss=0.1355, simple_loss=0.216, pruned_loss=0.02748, over 4951.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2072, pruned_loss=0.0283, over 971261.35 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 10:19:20,735 INFO [train.py:715] (2/8) Epoch 18, batch 22100, loss[loss=0.1083, simple_loss=0.1833, pruned_loss=0.01668, over 4829.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2071, pruned_loss=0.02872, over 971716.42 frames.], batch size: 13, lr: 1.23e-04
2022-05-09 10:19:59,607 INFO [train.py:715] (2/8) Epoch 18, batch 22150, loss[loss=0.12, simple_loss=0.1954, pruned_loss=0.02234, over 4921.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02876, over 972297.61 frames.], batch size: 29, lr: 1.23e-04
2022-05-09 10:20:39,097 INFO [train.py:715] (2/8) Epoch 18, batch 22200, loss[loss=0.1141, simple_loss=0.183, pruned_loss=0.02259, over 4990.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02878, over 972578.66 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:21:17,772 INFO [train.py:715] (2/8) Epoch 18, batch 22250, loss[loss=0.156, simple_loss=0.2233, pruned_loss=0.04436, over 4781.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02931, over 973335.36 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:21:57,020 INFO [train.py:715] (2/8) Epoch 18, batch 22300, loss[loss=0.1038, simple_loss=0.1765, pruned_loss=0.01556, over 4870.00 frames.], tot_loss[loss=0.133, simple_loss=0.2071, pruned_loss=0.02941, over 973476.39 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 10:22:35,718 INFO [train.py:715] (2/8) Epoch 18, batch 22350, loss[loss=0.1308, simple_loss=0.1994, pruned_loss=0.03111, over 4925.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2075, pruned_loss=0.02957, over 973266.39 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 10:23:14,499 INFO [train.py:715] (2/8) Epoch 18, batch 22400, loss[loss=0.1197, simple_loss=0.1945, pruned_loss=0.02246, over 4948.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02932, over 973095.93 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:23:53,399 INFO [train.py:715] (2/8) Epoch 18, batch 22450, loss[loss=0.1476, simple_loss=0.2105, pruned_loss=0.04233, over 4963.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2069, pruned_loss=0.02947, over 972554.25 frames.], batch size: 24, lr: 1.23e-04
2022-05-09 10:24:32,485 INFO [train.py:715] (2/8) Epoch 18, batch 22500, loss[loss=0.0977, simple_loss=0.1645, pruned_loss=0.01546, over 4932.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02923, over 973003.49 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:25:11,265 INFO [train.py:715] (2/8) Epoch 18, batch 22550, loss[loss=0.1338, simple_loss=0.2122, pruned_loss=0.02768, over 4768.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2062, pruned_loss=0.02949, over 972225.32 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:25:50,061 INFO [train.py:715] (2/8) Epoch 18, batch 22600, loss[loss=0.1239, simple_loss=0.2014, pruned_loss=0.0232, over 4753.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2061, pruned_loss=0.02928, over 972554.64 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:26:29,082 INFO [train.py:715] (2/8) Epoch 18, batch 22650, loss[loss=0.1278, simple_loss=0.1973, pruned_loss=0.02913, over 4811.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2058, pruned_loss=0.02917, over 972310.78 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:27:07,865 INFO [train.py:715] (2/8) Epoch 18, batch 22700, loss[loss=0.124, simple_loss=0.1976, pruned_loss=0.0252, over 4788.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2055, pruned_loss=0.02883, over 972764.84 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:27:46,838 INFO [train.py:715] (2/8) Epoch 18, batch 22750, loss[loss=0.1367, simple_loss=0.2091, pruned_loss=0.03213, over 4891.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02863, over 973027.47 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 10:28:26,216 INFO [train.py:715] (2/8) Epoch 18, batch 22800, loss[loss=0.1317, simple_loss=0.215, pruned_loss=0.02422, over 4762.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02907, over 973881.46 frames.], batch size: 16, lr: 1.23e-04
2022-05-09 10:29:04,927 INFO [train.py:715] (2/8) Epoch 18, batch 22850, loss[loss=0.118, simple_loss=0.1895, pruned_loss=0.02327, over 4913.00 frames.], tot_loss[loss=0.1327, simple_loss=0.207, pruned_loss=0.02923, over 973777.72 frames.], batch size: 23, lr: 1.23e-04
2022-05-09 10:29:43,878 INFO [train.py:715] (2/8) Epoch 18, batch 22900, loss[loss=0.142, simple_loss=0.2243, pruned_loss=0.02978, over 4892.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.0291, over 973658.58 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 10:30:22,781 INFO [train.py:715] (2/8) Epoch 18, batch 22950, loss[loss=0.1376, simple_loss=0.2148, pruned_loss=0.03024, over 4810.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02894, over 973292.74 frames.], batch size: 25, lr: 1.23e-04
2022-05-09 10:31:02,201 INFO [train.py:715] (2/8) Epoch 18, batch 23000, loss[loss=0.1455, simple_loss=0.2163, pruned_loss=0.03737, over 4816.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2055, pruned_loss=0.02888, over 972559.73 frames.], batch size: 27, lr: 1.23e-04
2022-05-09 10:31:40,967 INFO [train.py:715] (2/8) Epoch 18, batch 23050, loss[loss=0.1427, simple_loss=0.2165, pruned_loss=0.03446, over 4873.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2064, pruned_loss=0.02927, over 972771.98 frames.], batch size: 22, lr: 1.23e-04
2022-05-09 10:32:20,094 INFO [train.py:715] (2/8) Epoch 18, batch 23100, loss[loss=0.1358, simple_loss=0.1993, pruned_loss=0.03613, over 4854.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02854, over 972742.31 frames.], batch size: 30, lr: 1.23e-04
2022-05-09 10:32:59,657 INFO [train.py:715] (2/8) Epoch 18, batch 23150, loss[loss=0.1196, simple_loss=0.2024, pruned_loss=0.01842, over 4896.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02815, over 973091.72 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:33:38,765 INFO [train.py:715] (2/8) Epoch 18, batch 23200, loss[loss=0.1284, simple_loss=0.198, pruned_loss=0.02939, over 4971.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2052, pruned_loss=0.02825, over 973260.78 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:34:17,630 INFO [train.py:715] (2/8) Epoch 18, batch 23250, loss[loss=0.1467, simple_loss=0.2179, pruned_loss=0.03776, over 4892.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02808, over 973085.96 frames.], batch size: 38, lr: 1.23e-04
2022-05-09 10:34:56,943 INFO [train.py:715] (2/8) Epoch 18, batch 23300, loss[loss=0.1328, simple_loss=0.1988, pruned_loss=0.03341, over 4897.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02787, over 973302.99 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:35:36,585 INFO [train.py:715] (2/8) Epoch 18, batch 23350, loss[loss=0.1546, simple_loss=0.2264, pruned_loss=0.04142, over 4850.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02863, over 973705.55 frames.], batch size: 32, lr: 1.23e-04
2022-05-09 10:36:15,549 INFO [train.py:715] (2/8) Epoch 18, batch 23400, loss[loss=0.1273, simple_loss=0.2076, pruned_loss=0.02349, over 4777.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02827, over 973608.00 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:36:54,044 INFO [train.py:715] (2/8) Epoch 18, batch 23450, loss[loss=0.1342, simple_loss=0.2027, pruned_loss=0.03286, over 4765.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2049, pruned_loss=0.02811, over 973237.01 frames.], batch size: 19, lr: 1.23e-04
2022-05-09 10:37:33,555 INFO [train.py:715] (2/8) Epoch 18, batch 23500, loss[loss=0.1588, simple_loss=0.2332, pruned_loss=0.04217, over 4844.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02853, over 972766.19 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:38:12,432 INFO [train.py:715] (2/8) Epoch 18, batch 23550, loss[loss=0.1618, simple_loss=0.232, pruned_loss=0.04583, over 4915.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02873, over 973525.91 frames.], batch size: 18, lr: 1.23e-04
2022-05-09 10:38:51,086 INFO [train.py:715] (2/8) Epoch 18, batch 23600, loss[loss=0.1304, simple_loss=0.2094, pruned_loss=0.02573, over 4847.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02869, over 973773.84 frames.], batch size: 20, lr: 1.23e-04
2022-05-09 10:39:30,022 INFO [train.py:715] (2/8) Epoch 18, batch 23650, loss[loss=0.1419, simple_loss=0.217, pruned_loss=0.0334, over 4912.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02854, over 973579.15 frames.], batch size: 17, lr: 1.23e-04
2022-05-09 10:40:08,660 INFO [train.py:715] (2/8) Epoch 18, batch 23700, loss[loss=0.1368, simple_loss=0.2192, pruned_loss=0.02716, over 4977.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02849, over 972947.94 frames.], batch size: 15, lr: 1.23e-04
2022-05-09 10:40:47,472 INFO [train.py:715] (2/8) Epoch 18, batch 23750, loss[loss=0.1322, simple_loss=0.2027, pruned_loss=0.03081, over 4797.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02877, over 972483.71 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 10:41:26,884 INFO [train.py:715] (2/8) Epoch 18, batch 23800, loss[loss=0.1184, simple_loss=0.1977, pruned_loss=0.01957, over 4803.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2052, pruned_loss=0.0285, over 972296.52 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 10:42:06,549 INFO [train.py:715] (2/8) Epoch 18, batch 23850, loss[loss=0.1128, simple_loss=0.1838, pruned_loss=0.02089, over 4786.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2049, pruned_loss=0.02826, over 972712.00 frames.], batch size: 14, lr: 1.23e-04
2022-05-09 10:42:45,368 INFO [train.py:715] (2/8) Epoch 18, batch 23900, loss[loss=0.1369, simple_loss=0.2156, pruned_loss=0.02915, over 4950.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2048, pruned_loss=0.02815, over 972451.66 frames.], batch size: 21, lr: 1.23e-04
2022-05-09 10:43:24,102 INFO [train.py:715] (2/8) Epoch 18, batch 23950, loss[loss=0.1359, simple_loss=0.2142, pruned_loss=0.02877, over 4884.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02817, over 972685.63 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 10:44:03,429 INFO [train.py:715] (2/8) Epoch 18, batch 24000, loss[loss=0.1354, simple_loss=0.2077, pruned_loss=0.03153, over 4710.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02834, over 972970.89 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 10:44:03,429 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 10:44:13,350 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1045, simple_loss=0.1878, pruned_loss=0.01057, over 914524.00 frames.
2022-05-09 10:44:52,999 INFO [train.py:715] (2/8) Epoch 18, batch 24050, loss[loss=0.114, simple_loss=0.1838, pruned_loss=0.0221, over 4695.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02851, over 972479.30 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 10:45:31,813 INFO [train.py:715] (2/8) Epoch 18, batch 24100, loss[loss=0.1267, simple_loss=0.2033, pruned_loss=0.02501, over 4921.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2052, pruned_loss=0.02845, over 972713.42 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 10:46:10,744 INFO [train.py:715] (2/8) Epoch 18, batch 24150, loss[loss=0.1241, simple_loss=0.2001, pruned_loss=0.02401, over 4930.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02881, over 973169.65 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 10:46:50,171 INFO [train.py:715] (2/8) Epoch 18, batch 24200, loss[loss=0.1134, simple_loss=0.1864, pruned_loss=0.02026, over 4895.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02833, over 973571.24 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 10:47:29,227 INFO [train.py:715] (2/8) Epoch 18, batch 24250, loss[loss=0.1224, simple_loss=0.2001, pruned_loss=0.0223, over 4750.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02844, over 973926.79 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 10:48:08,107 INFO [train.py:715] (2/8) Epoch 18, batch 24300, loss[loss=0.1341, simple_loss=0.2224, pruned_loss=0.02293, over 4934.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.0285, over 973652.74 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 10:48:46,585 INFO [train.py:715] (2/8) Epoch 18, batch 24350, loss[loss=0.09696, simple_loss=0.1719, pruned_loss=0.011, over 4989.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2071, pruned_loss=0.02802, over 973556.60 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 10:49:25,641 INFO [train.py:715] (2/8) Epoch 18, batch 24400, loss[loss=0.1146, simple_loss=0.1906, pruned_loss=0.01935, over 4992.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2071, pruned_loss=0.02814, over 973611.42 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 10:50:04,248 INFO [train.py:715] (2/8) Epoch 18, batch 24450, loss[loss=0.1267, simple_loss=0.2013, pruned_loss=0.02608, over 4776.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2065, pruned_loss=0.02823, over 972976.42 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 10:50:42,848 INFO [train.py:715] (2/8) Epoch 18, batch 24500, loss[loss=0.121, simple_loss=0.195, pruned_loss=0.0235, over 4767.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02831, over 973165.03 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 10:51:22,301 INFO [train.py:715] (2/8) Epoch 18, batch 24550, loss[loss=0.09528, simple_loss=0.1661, pruned_loss=0.01221, over 4733.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02849, over 972928.06 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 10:52:01,510 INFO [train.py:715] (2/8) Epoch 18, batch 24600, loss[loss=0.1425, simple_loss=0.2242, pruned_loss=0.03046, over 4914.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02854, over 972909.88 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 10:52:40,239 INFO [train.py:715] (2/8) Epoch 18, batch 24650, loss[loss=0.1191, simple_loss=0.1962, pruned_loss=0.02101, over 4974.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02886, over 972575.28 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 10:53:18,843 INFO [train.py:715] (2/8) Epoch 18, batch 24700, loss[loss=0.1575, simple_loss=0.224, pruned_loss=0.0455, over 4845.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02848, over 972365.57 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 10:53:58,062 INFO [train.py:715] (2/8) Epoch 18, batch 24750, loss[loss=0.1278, simple_loss=0.2072, pruned_loss=0.02422, over 4876.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02804, over 971200.33 frames.], batch size: 38, lr: 1.22e-04
2022-05-09 10:54:37,028 INFO [train.py:715] (2/8) Epoch 18, batch 24800, loss[loss=0.1236, simple_loss=0.1993, pruned_loss=0.024, over 4922.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02804, over 971635.40 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 10:55:16,443 INFO [train.py:715] (2/8) Epoch 18, batch 24850, loss[loss=0.1185, simple_loss=0.1976, pruned_loss=0.01972, over 4775.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02831, over 972924.22 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 10:55:55,500 INFO [train.py:715] (2/8) Epoch 18, batch 24900, loss[loss=0.1305, simple_loss=0.2119, pruned_loss=0.0246, over 4919.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02853, over 973137.68 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 10:56:35,064 INFO [train.py:715] (2/8) Epoch 18, batch 24950, loss[loss=0.1465, simple_loss=0.2254, pruned_loss=0.03382, over 4980.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02858, over 973435.23 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 10:57:14,185 INFO [train.py:715] (2/8) Epoch 18, batch 25000, loss[loss=0.1148, simple_loss=0.1964, pruned_loss=0.01661, over 4967.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02889, over 973305.04 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 10:57:52,843 INFO [train.py:715] (2/8) Epoch 18, batch 25050, loss[loss=0.1256, simple_loss=0.2132, pruned_loss=0.01901, over 4806.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02874, over 973293.85 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 10:58:32,136 INFO [train.py:715] (2/8) Epoch 18, batch 25100, loss[loss=0.1291, simple_loss=0.2072, pruned_loss=0.0255, over 4734.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02872, over 972250.77 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 10:59:11,694 INFO [train.py:715] (2/8) Epoch 18, batch 25150, loss[loss=0.1452, simple_loss=0.2168, pruned_loss=0.03678, over 4877.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02839, over 972938.67 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 10:59:50,260 INFO [train.py:715] (2/8) Epoch 18, batch 25200, loss[loss=0.1379, simple_loss=0.2032, pruned_loss=0.03636, over 4906.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02842, over 972304.68 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:00:29,817 INFO [train.py:715] (2/8) Epoch 18, batch 25250, loss[loss=0.1537, simple_loss=0.2293, pruned_loss=0.03905, over 4809.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02863, over 971600.03 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 11:01:09,550 INFO [train.py:715] (2/8) Epoch 18, batch 25300, loss[loss=0.1226, simple_loss=0.2028, pruned_loss=0.02116, over 4939.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02866, over 972133.67 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:01:48,682 INFO [train.py:715] (2/8) Epoch 18, batch 25350, loss[loss=0.1482, simple_loss=0.2212, pruned_loss=0.03764, over 4869.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2053, pruned_loss=0.02892, over 971725.09 frames.], batch size: 38, lr: 1.22e-04
2022-05-09 11:02:27,383 INFO [train.py:715] (2/8) Epoch 18, batch 25400, loss[loss=0.1421, simple_loss=0.2164, pruned_loss=0.03387, over 4899.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.0289, over 972154.69 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:03:06,967 INFO [train.py:715] (2/8) Epoch 18, batch 25450, loss[loss=0.1148, simple_loss=0.1797, pruned_loss=0.02495, over 4971.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02883, over 972751.41 frames.], batch size: 31, lr: 1.22e-04
2022-05-09 11:03:45,938 INFO [train.py:715] (2/8) Epoch 18, batch 25500, loss[loss=0.1294, simple_loss=0.2068, pruned_loss=0.02598, over 4700.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02909, over 972420.10 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:04:24,925 INFO [train.py:715] (2/8) Epoch 18, batch 25550, loss[loss=0.1177, simple_loss=0.1898, pruned_loss=0.02277, over 4917.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02908, over 972188.38 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:05:04,555 INFO [train.py:715] (2/8) Epoch 18, batch 25600, loss[loss=0.1288, simple_loss=0.1967, pruned_loss=0.03045, over 4944.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02917, over 971587.41 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 11:05:44,107 INFO [train.py:715] (2/8) Epoch 18, batch 25650, loss[loss=0.1211, simple_loss=0.1901, pruned_loss=0.02601, over 4975.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2078, pruned_loss=0.02931, over 973117.27 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 11:06:23,311 INFO [train.py:715] (2/8) Epoch 18, batch 25700, loss[loss=0.1451, simple_loss=0.2168, pruned_loss=0.0367, over 4961.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02909, over 973258.25 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:07:02,568 INFO [train.py:715] (2/8) Epoch 18, batch 25750, loss[loss=0.1421, simple_loss=0.2324, pruned_loss=0.02592, over 4877.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02895, over 973352.40 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:07:41,975 INFO [train.py:715] (2/8) Epoch 18, batch 25800, loss[loss=0.1389, simple_loss=0.2248, pruned_loss=0.02656, over 4783.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02838, over 972640.80 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 11:08:20,795 INFO [train.py:715] (2/8) Epoch 18, batch 25850, loss[loss=0.1341, simple_loss=0.2085, pruned_loss=0.02985, over 4928.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.02847, over 972242.40 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:08:59,116 INFO [train.py:715] (2/8) Epoch 18, batch 25900, loss[loss=0.1183, simple_loss=0.1875, pruned_loss=0.02458, over 4976.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02842, over 972518.61 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:09:38,436 INFO [train.py:715] (2/8) Epoch 18, batch 25950, loss[loss=0.132, simple_loss=0.2082, pruned_loss=0.02794, over 4956.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02881, over 973304.19 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:10:17,516 INFO [train.py:715] (2/8) Epoch 18, batch 26000, loss[loss=0.1618, simple_loss=0.2428, pruned_loss=0.04045, over 4989.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02886, over 972338.73 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 11:10:56,986 INFO [train.py:715] (2/8) Epoch 18, batch 26050, loss[loss=0.1341, simple_loss=0.2078, pruned_loss=0.03018, over 4779.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02868, over 971882.41 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:11:36,120 INFO [train.py:715] (2/8) Epoch 18, batch 26100, loss[loss=0.122, simple_loss=0.198, pruned_loss=0.02298, over 4818.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02898, over 971557.53 frames.], batch size: 27, lr: 1.22e-04
2022-05-09 11:12:15,695 INFO [train.py:715] (2/8) Epoch 18, batch 26150, loss[loss=0.1251, simple_loss=0.1952, pruned_loss=0.02747, over 4846.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2057, pruned_loss=0.02921, over 971303.55 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 11:12:54,905 INFO [train.py:715] (2/8) Epoch 18, batch 26200, loss[loss=0.1362, simple_loss=0.2167, pruned_loss=0.02781, over 4762.00 frames.], tot_loss[loss=0.1311, simple_loss=0.205, pruned_loss=0.02856, over 971335.62 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:13:33,239 INFO [train.py:715] (2/8) Epoch 18, batch 26250, loss[loss=0.1643, simple_loss=0.2385, pruned_loss=0.04508, over 4655.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2062, pruned_loss=0.02894, over 971314.88 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:14:12,860 INFO [train.py:715] (2/8) Epoch 18, batch 26300, loss[loss=0.1028, simple_loss=0.1831, pruned_loss=0.01129, over 4931.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.0287, over 971005.27 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 11:14:51,550 INFO [train.py:715] (2/8) Epoch 18, batch 26350, loss[loss=0.1341, simple_loss=0.2096, pruned_loss=0.02926, over 4748.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02898, over 971372.52 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:15:30,592 INFO [train.py:715] (2/8) Epoch 18, batch 26400, loss[loss=0.1355, simple_loss=0.2118, pruned_loss=0.02967, over 4778.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02904, over 971301.86 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:16:09,487 INFO [train.py:715] (2/8) Epoch 18, batch 26450, loss[loss=0.1155, simple_loss=0.1927, pruned_loss=0.01911, over 4827.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02947, over 971141.34 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 11:16:49,044 INFO [train.py:715] (2/8) Epoch 18, batch 26500, loss[loss=0.1377, simple_loss=0.211, pruned_loss=0.03224, over 4908.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02941, over 971002.71 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:17:28,074 INFO [train.py:715] (2/8) Epoch 18, batch 26550, loss[loss=0.164, simple_loss=0.2494, pruned_loss=0.03927, over 4958.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02914, over 970974.38 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:18:06,864 INFO [train.py:715] (2/8) Epoch 18, batch 26600, loss[loss=0.1362, simple_loss=0.1999, pruned_loss=0.03625, over 4800.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02892, over 971037.56 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 11:18:46,128 INFO [train.py:715] (2/8) Epoch 18, batch 26650, loss[loss=0.1221, simple_loss=0.2008, pruned_loss=0.02173, over 4880.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02882, over 970876.69 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 11:19:25,269 INFO [train.py:715] (2/8) Epoch 18, batch 26700, loss[loss=0.1646, simple_loss=0.2257, pruned_loss=0.05181, over 4834.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02876, over 972364.15 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 11:20:05,261 INFO [train.py:715] (2/8) Epoch 18, batch 26750, loss[loss=0.1728, simple_loss=0.2424, pruned_loss=0.05163, over 4919.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02886, over 971891.55 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:20:43,661 INFO [train.py:715] (2/8) Epoch 18, batch 26800, loss[loss=0.1487, simple_loss=0.2158, pruned_loss=0.04076, over 4949.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02883, over 971497.40 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 11:21:23,706 INFO [train.py:715] (2/8) Epoch 18, batch 26850, loss[loss=0.1433, simple_loss=0.2199, pruned_loss=0.03333, over 4941.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02919, over 971611.45 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:22:03,382 INFO [train.py:715] (2/8) Epoch 18, batch 26900, loss[loss=0.1283, simple_loss=0.2047, pruned_loss=0.02596, over 4750.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02864, over 972227.49 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:22:41,420 INFO [train.py:715] (2/8) Epoch 18, batch 26950, loss[loss=0.1106, simple_loss=0.1803, pruned_loss=0.02042, over 4706.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.0287, over 972419.30 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:23:20,805 INFO [train.py:715] (2/8) Epoch 18, batch 27000, loss[loss=0.1228, simple_loss=0.1991, pruned_loss=0.0233, over 4783.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02865, over 973616.49 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:23:20,805 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 11:23:30,797 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1044, simple_loss=0.1877, pruned_loss=0.01055, over 914524.00 frames.
2022-05-09 11:24:11,110 INFO [train.py:715] (2/8) Epoch 18, batch 27050, loss[loss=0.1138, simple_loss=0.1948, pruned_loss=0.01633, over 4989.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02835, over 972963.23 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 11:24:50,013 INFO [train.py:715] (2/8) Epoch 18, batch 27100, loss[loss=0.1402, simple_loss=0.2136, pruned_loss=0.0334, over 4703.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02865, over 972256.92 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:25:29,322 INFO [train.py:715] (2/8) Epoch 18, batch 27150, loss[loss=0.1306, simple_loss=0.1954, pruned_loss=0.03288, over 4981.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02875, over 972067.00 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:26:08,687 INFO [train.py:715] (2/8) Epoch 18, batch 27200, loss[loss=0.1206, simple_loss=0.1975, pruned_loss=0.02187, over 4808.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02831, over 971988.11 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:26:47,916 INFO [train.py:715] (2/8) Epoch 18, batch 27250, loss[loss=0.1517, simple_loss=0.2185, pruned_loss=0.04247, over 4900.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02903, over 971459.39 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:27:26,983 INFO [train.py:715] (2/8) Epoch 18, batch 27300, loss[loss=0.1309, simple_loss=0.2052, pruned_loss=0.0283, over 4972.00 frames.], tot_loss[loss=0.132, simple_loss=0.2062, pruned_loss=0.02892, over 971968.50 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 11:28:05,826 INFO [train.py:715] (2/8) Epoch 18, batch 27350, loss[loss=0.1273, simple_loss=0.1998, pruned_loss=0.02736, over 4903.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2051, pruned_loss=0.02837, over 971637.33 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:28:46,003 INFO [train.py:715] (2/8) Epoch 18, batch 27400, loss[loss=0.1382, simple_loss=0.2105, pruned_loss=0.03293, over 4911.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02857, over 972606.24 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 11:29:25,406 INFO [train.py:715] (2/8) Epoch 18, batch 27450, loss[loss=0.1181, simple_loss=0.1975, pruned_loss=0.01929, over 4797.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2054, pruned_loss=0.02881, over 972880.53 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 11:30:04,447 INFO [train.py:715] (2/8) Epoch 18, batch 27500, loss[loss=0.1468, simple_loss=0.2161, pruned_loss=0.03878, over 4977.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02864, over 973839.18 frames.], batch size: 33, lr: 1.22e-04
2022-05-09 11:30:44,164 INFO [train.py:715] (2/8) Epoch 18, batch 27550, loss[loss=0.1152, simple_loss=0.1928, pruned_loss=0.0188, over 4802.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02893, over 973839.75 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 11:31:23,283 INFO [train.py:715] (2/8) Epoch 18, batch 27600, loss[loss=0.1155, simple_loss=0.1916, pruned_loss=0.0197, over 4796.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2054, pruned_loss=0.02864, over 973886.64 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:32:01,946 INFO [train.py:715] (2/8) Epoch 18, batch 27650, loss[loss=0.1314, simple_loss=0.2018, pruned_loss=0.03045, over 4855.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02834, over 974140.73 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 11:32:40,855 INFO [train.py:715] (2/8) Epoch 18, batch 27700, loss[loss=0.1148, simple_loss=0.1906, pruned_loss=0.01951, over 4962.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2065, pruned_loss=0.02814, over 974241.91 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:33:20,161 INFO [train.py:715] (2/8) Epoch 18, batch 27750, loss[loss=0.1211, simple_loss=0.2001, pruned_loss=0.02104, over 4958.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2059, pruned_loss=0.02784, over 974002.58 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:33:59,639 INFO [train.py:715] (2/8) Epoch 18, batch 27800, loss[loss=0.1289, simple_loss=0.2079, pruned_loss=0.02499, over 4971.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2056, pruned_loss=0.02763, over 973615.55 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:34:38,870 INFO [train.py:715] (2/8) Epoch 18, batch 27850, loss[loss=0.127, simple_loss=0.208, pruned_loss=0.02298, over 4869.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2051, pruned_loss=0.02795, over 973006.78 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 11:35:18,480 INFO [train.py:715] (2/8) Epoch 18, batch 27900, loss[loss=0.1247, simple_loss=0.1984, pruned_loss=0.0255, over 4747.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02837, over 972290.39 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:35:57,742 INFO [train.py:715] (2/8) Epoch 18, batch 27950, loss[loss=0.1272, simple_loss=0.2049, pruned_loss=0.0248, over 4820.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2047, pruned_loss=0.0281, over 972301.42 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:36:37,002 INFO [train.py:715] (2/8) Epoch 18, batch 28000, loss[loss=0.1598, simple_loss=0.2305, pruned_loss=0.0446, over 4861.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2054, pruned_loss=0.02872, over 972453.01 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 11:37:16,535 INFO [train.py:715] (2/8) Epoch 18, batch 28050, loss[loss=0.1469, simple_loss=0.2198, pruned_loss=0.03696, over 4917.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02879, over 972595.89 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:37:56,320 INFO [train.py:715] (2/8) Epoch 18, batch 28100, loss[loss=0.1353, simple_loss=0.2153, pruned_loss=0.02762, over 4849.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02904, over 972947.54 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 11:38:35,509 INFO [train.py:715] (2/8) Epoch 18, batch 28150, loss[loss=0.1218, simple_loss=0.1992, pruned_loss=0.02214, over 4931.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02871, over 972829.62 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 11:39:13,847 INFO [train.py:715] (2/8) Epoch 18, batch 28200, loss[loss=0.1364, simple_loss=0.2155, pruned_loss=0.0287, over 4936.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02871, over 973292.08 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 11:39:53,469 INFO [train.py:715] (2/8) Epoch 18, batch 28250, loss[loss=0.1323, simple_loss=0.223, pruned_loss=0.02083, over 4747.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02857, over 973714.73 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:40:32,294 INFO [train.py:715] (2/8) Epoch 18, batch 28300, loss[loss=0.1218, simple_loss=0.1963, pruned_loss=0.02367, over 4852.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02881, over 974208.27 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:41:11,198 INFO [train.py:715] (2/8) Epoch 18, batch 28350, loss[loss=0.1559, simple_loss=0.2287, pruned_loss=0.04156, over 4779.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.02879, over 973803.01 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:41:50,496 INFO [train.py:715] (2/8) Epoch 18, batch 28400, loss[loss=0.1306, simple_loss=0.2039, pruned_loss=0.02861, over 4828.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02895, over 973376.30 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:42:29,772 INFO [train.py:715] (2/8) Epoch 18, batch 28450, loss[loss=0.1127, simple_loss=0.191, pruned_loss=0.01717, over 4798.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02919, over 972471.85 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 11:43:08,847 INFO [train.py:715] (2/8) Epoch 18, batch 28500, loss[loss=0.1306, simple_loss=0.2102, pruned_loss=0.02545, over 4889.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02908, over 972279.93 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 11:43:47,954 INFO [train.py:715] (2/8) Epoch 18, batch 28550, loss[loss=0.1429, simple_loss=0.2092, pruned_loss=0.03832, over 4865.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02865, over 972816.80 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:44:27,960 INFO [train.py:715] (2/8) Epoch 18, batch 28600, loss[loss=0.1288, simple_loss=0.2014, pruned_loss=0.02808, over 4792.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02854, over 972578.86 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:45:06,654 INFO [train.py:715] (2/8) Epoch 18, batch 28650, loss[loss=0.1369, simple_loss=0.2239, pruned_loss=0.02499, over 4780.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2062, pruned_loss=0.02812, over 972100.11 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:45:45,609 INFO [train.py:715] (2/8) Epoch 18, batch 28700, loss[loss=0.1045, simple_loss=0.1759, pruned_loss=0.0166, over 4852.00 frames.], tot_loss[loss=0.131, simple_loss=0.2057, pruned_loss=0.02816, over 972115.73 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 11:46:25,177 INFO [train.py:715] (2/8) Epoch 18, batch 28750, loss[loss=0.1559, simple_loss=0.2242, pruned_loss=0.04382, over 4975.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2055, pruned_loss=0.02809, over 971880.13 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 11:47:04,223 INFO [train.py:715] (2/8) Epoch 18, batch 28800, loss[loss=0.137, simple_loss=0.207, pruned_loss=0.03346, over 4957.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02846, over 972434.28 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 11:47:43,076 INFO [train.py:715] (2/8) Epoch 18, batch 28850, loss[loss=0.132, simple_loss=0.1983, pruned_loss=0.03286, over 4777.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02838, over 972217.32 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:48:21,623 INFO [train.py:715] (2/8) Epoch 18, batch 28900, loss[loss=0.1465, simple_loss=0.2143, pruned_loss=0.03934, over 4748.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02852, over 971705.25 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:49:01,748 INFO [train.py:715] (2/8) Epoch 18, batch 28950, loss[loss=0.1328, simple_loss=0.21, pruned_loss=0.02784, over 4798.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2056, pruned_loss=0.0281, over 971899.32 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:49:40,559 INFO [train.py:715] (2/8) Epoch 18, batch 29000, loss[loss=0.1428, simple_loss=0.2167, pruned_loss=0.03446, over 4771.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02853, over 972716.59 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:50:19,733 INFO [train.py:715] (2/8) Epoch 18, batch 29050, loss[loss=0.1319, simple_loss=0.2116, pruned_loss=0.02605, over 4981.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2068, pruned_loss=0.02866, over 971205.97 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 11:50:59,123 INFO [train.py:715] (2/8) Epoch 18, batch 29100, loss[loss=0.1534, simple_loss=0.218, pruned_loss=0.04443, over 4984.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02873, over 971170.34 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 11:51:38,415 INFO [train.py:715] (2/8) Epoch 18, batch 29150, loss[loss=0.1659, simple_loss=0.2263, pruned_loss=0.05274, over 4931.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02862, over 972650.57 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 11:52:17,131 INFO [train.py:715] (2/8) Epoch 18, batch 29200, loss[loss=0.1279, simple_loss=0.2059, pruned_loss=0.02496, over 4893.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02853, over 972234.62 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:52:55,645 INFO [train.py:715] (2/8) Epoch 18, batch 29250, loss[loss=0.09924, simple_loss=0.1727, pruned_loss=0.01291, over 4747.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2053, pruned_loss=0.02842, over 972032.46 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 11:53:35,212 INFO [train.py:715] (2/8) Epoch 18, batch 29300, loss[loss=0.1474, simple_loss=0.2191, pruned_loss=0.03786, over 4759.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02841, over 972913.68 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 11:54:13,913 INFO [train.py:715] (2/8) Epoch 18, batch 29350, loss[loss=0.1387, simple_loss=0.2189, pruned_loss=0.02919, over 4930.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2053, pruned_loss=0.02814, over 973158.92 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:54:52,615 INFO [train.py:715] (2/8) Epoch 18, batch 29400, loss[loss=0.141, simple_loss=0.2203, pruned_loss=0.03082, over 4780.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2048, pruned_loss=0.02802, over 973512.90 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 11:55:33,953 INFO [train.py:715] (2/8) Epoch 18, batch 29450, loss[loss=0.17, simple_loss=0.2393, pruned_loss=0.05031, over 4898.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02844, over 973907.03 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 11:56:12,983 INFO [train.py:715] (2/8) Epoch 18, batch 29500, loss[loss=0.132, simple_loss=0.206, pruned_loss=0.029, over 4956.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.0288, over 972810.10 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 11:56:52,073 INFO [train.py:715] (2/8) Epoch 18, batch 29550, loss[loss=0.1371, simple_loss=0.2167, pruned_loss=0.02876, over 4979.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02906, over 973066.50 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 11:57:30,048 INFO [train.py:715] (2/8) Epoch 18, batch 29600, loss[loss=0.164, simple_loss=0.2365, pruned_loss=0.04574, over 4756.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2075, pruned_loss=0.02918, over 972993.60 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 11:58:09,260 INFO [train.py:715] (2/8) Epoch 18, batch 29650, loss[loss=0.1273, simple_loss=0.1968, pruned_loss=0.02892, over 4978.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02922, over 973446.06 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 11:58:48,237 INFO [train.py:715] (2/8) Epoch 18, batch 29700, loss[loss=0.131, simple_loss=0.206, pruned_loss=0.02803, over 4975.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2061, pruned_loss=0.02905, over 973738.03 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 11:59:26,592 INFO [train.py:715] (2/8) Epoch 18, batch 29750, loss[loss=0.1495, simple_loss=0.2253, pruned_loss=0.03685, over 4843.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2069, pruned_loss=0.02935, over 974319.90 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 12:00:05,914 INFO [train.py:715] (2/8) Epoch 18, batch 29800, loss[loss=0.1446, simple_loss=0.2191, pruned_loss=0.03508, over 4787.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02983, over 974047.66 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:00:45,627 INFO [train.py:715] (2/8) Epoch 18, batch 29850, loss[loss=0.1258, simple_loss=0.2002, pruned_loss=0.02567, over 4752.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2077, pruned_loss=0.02975, over 974547.03 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:01:24,703 INFO [train.py:715] (2/8) Epoch 18, batch 29900, loss[loss=0.1359, simple_loss=0.2208, pruned_loss=0.02545, over 4786.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2076, pruned_loss=0.02926, over 974159.45 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:02:03,293 INFO [train.py:715] (2/8) Epoch 18, batch 29950, loss[loss=0.1415, simple_loss=0.2178, pruned_loss=0.03259, over 4949.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2079, pruned_loss=0.02998, over 973920.62 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:02:43,064 INFO [train.py:715] (2/8) Epoch 18, batch 30000, loss[loss=0.1254, simple_loss=0.2032, pruned_loss=0.02386, over 4990.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02983, over 973074.99 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 12:02:43,065 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 12:02:52,968 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1047, simple_loss=0.188, pruned_loss=0.01071, over 914524.00 frames.
2022-05-09 12:03:33,196 INFO [train.py:715] (2/8) Epoch 18, batch 30050, loss[loss=0.1279, simple_loss=0.2055, pruned_loss=0.02513, over 4960.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2087, pruned_loss=0.03022, over 973460.74 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:04:12,321 INFO [train.py:715] (2/8) Epoch 18, batch 30100, loss[loss=0.1491, simple_loss=0.2141, pruned_loss=0.04208, over 4978.00 frames.], tot_loss[loss=0.1347, simple_loss=0.2089, pruned_loss=0.03029, over 972787.02 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:04:50,506 INFO [train.py:715] (2/8) Epoch 18, batch 30150, loss[loss=0.1318, simple_loss=0.2112, pruned_loss=0.02618, over 4793.00 frames.], tot_loss[loss=0.1337, simple_loss=0.208, pruned_loss=0.02966, over 971825.23 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:05:29,936 INFO [train.py:715] (2/8) Epoch 18, batch 30200, loss[loss=0.1147, simple_loss=0.1931, pruned_loss=0.0181, over 4905.00 frames.], tot_loss[loss=0.1335, simple_loss=0.208, pruned_loss=0.02951, over 973031.13 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:06:09,183 INFO [train.py:715] (2/8) Epoch 18, batch 30250, loss[loss=0.1305, simple_loss=0.2019, pruned_loss=0.02949, over 4864.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2072, pruned_loss=0.02898, over 973182.49 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 12:06:48,875 INFO [train.py:715] (2/8) Epoch 18, batch 30300, loss[loss=0.1337, simple_loss=0.2023, pruned_loss=0.03249, over 4990.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2072, pruned_loss=0.02892, over 973559.73 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 12:07:27,512 INFO [train.py:715] (2/8) Epoch 18, batch 30350, loss[loss=0.1255, simple_loss=0.2032, pruned_loss=0.02385, over 4803.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02888, over 973886.79 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:08:07,404 INFO [train.py:715] (2/8) Epoch 18, batch 30400, loss[loss=0.1237, simple_loss=0.2008, pruned_loss=0.02326, over 4742.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02911, over 973551.34 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:08:46,434 INFO [train.py:715] (2/8) Epoch 18, batch 30450, loss[loss=0.1255, simple_loss=0.2065, pruned_loss=0.02227, over 4971.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.0285, over 974053.17 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:09:24,922 INFO [train.py:715] (2/8) Epoch 18, batch 30500, loss[loss=0.1342, simple_loss=0.2059, pruned_loss=0.03126, over 4814.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2067, pruned_loss=0.02842, over 973459.24 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 12:10:04,132 INFO [train.py:715] (2/8) Epoch 18, batch 30550, loss[loss=0.1409, simple_loss=0.2252, pruned_loss=0.02828, over 4814.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02832, over 973243.43 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:10:42,818 INFO [train.py:715] (2/8) Epoch 18, batch 30600, loss[loss=0.1342, simple_loss=0.2139, pruned_loss=0.02727, over 4762.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.02797, over 973535.92 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:11:21,486 INFO [train.py:715] (2/8) Epoch 18, batch 30650, loss[loss=0.1423, simple_loss=0.2237, pruned_loss=0.03046, over 4875.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2063, pruned_loss=0.02806, over 973371.92 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:12:00,155 INFO [train.py:715] (2/8) Epoch 18, batch 30700, loss[loss=0.1428, simple_loss=0.2131, pruned_loss=0.03623, over 4879.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.0283, over 973160.61 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:12:39,282 INFO [train.py:715] (2/8) Epoch 18, batch 30750, loss[loss=0.116, simple_loss=0.1904, pruned_loss=0.02077, over 4935.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02806, over 972838.58 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 12:13:18,039 INFO [train.py:715] (2/8) Epoch 18, batch 30800, loss[loss=0.1511, simple_loss=0.2164, pruned_loss=0.04291, over 4812.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02849, over 972981.02 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:13:56,475 INFO [train.py:715] (2/8) Epoch 18, batch 30850, loss[loss=0.1232, simple_loss=0.1957, pruned_loss=0.02535, over 4939.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2066, pruned_loss=0.02839, over 972385.95 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:14:35,513 INFO [train.py:715] (2/8) Epoch 18, batch 30900, loss[loss=0.1473, simple_loss=0.2152, pruned_loss=0.0397, over 4733.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02819, over 972345.67 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:15:14,123 INFO [train.py:715] (2/8) Epoch 18, batch 30950, loss[loss=0.1302, simple_loss=0.2171, pruned_loss=0.02166, over 4811.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2062, pruned_loss=0.02825, over 972298.61 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:15:52,436 INFO [train.py:715] (2/8) Epoch 18, batch 31000, loss[loss=0.1366, simple_loss=0.2026, pruned_loss=0.03532, over 4843.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2066, pruned_loss=0.02839, over 973515.33 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 12:16:31,404 INFO [train.py:715] (2/8) Epoch 18, batch 31050, loss[loss=0.1552, simple_loss=0.2273, pruned_loss=0.04151, over 4906.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2066, pruned_loss=0.02854, over 973772.93 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:17:10,963 INFO [train.py:715] (2/8) Epoch 18, batch 31100, loss[loss=0.1317, simple_loss=0.2144, pruned_loss=0.02455, over 4813.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02873, over 973453.25 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:17:49,895 INFO [train.py:715] (2/8) Epoch 18, batch 31150, loss[loss=0.1219, simple_loss=0.1999, pruned_loss=0.02199, over 4952.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02885, over 972641.86 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 12:18:28,838 INFO [train.py:715] (2/8) Epoch 18, batch 31200, loss[loss=0.1574, simple_loss=0.2391, pruned_loss=0.03787, over 4988.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02843, over 972279.82 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 12:19:08,218 INFO [train.py:715] (2/8) Epoch 18, batch 31250, loss[loss=0.1022, simple_loss=0.181, pruned_loss=0.01174, over 4891.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02841, over 972327.73 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:19:47,257 INFO [train.py:715] (2/8) Epoch 18, batch 31300, loss[loss=0.1391, simple_loss=0.2031, pruned_loss=0.03756, over 4825.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2063, pruned_loss=0.02897, over 972185.15 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 12:20:25,880 INFO [train.py:715] (2/8) Epoch 18, batch 31350, loss[loss=0.1271, simple_loss=0.1969, pruned_loss=0.02867, over 4831.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02903, over 971550.40 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 12:21:05,051 INFO [train.py:715] (2/8) Epoch 18, batch 31400, loss[loss=0.1546, simple_loss=0.2211, pruned_loss=0.04402, over 4911.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02937, over 972143.58 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 12:21:44,554 INFO [train.py:715] (2/8) Epoch 18, batch 31450, loss[loss=0.137, simple_loss=0.2165, pruned_loss=0.02877, over 4799.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2076, pruned_loss=0.02972, over 971494.63 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:22:23,390 INFO [train.py:715] (2/8) Epoch 18, batch 31500, loss[loss=0.129, simple_loss=0.201, pruned_loss=0.02848, over 4888.00 frames.], tot_loss[loss=0.1329, simple_loss=0.207, pruned_loss=0.02945, over 972722.70 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 12:23:01,622 INFO [train.py:715] (2/8) Epoch 18, batch 31550, loss[loss=0.1429, simple_loss=0.2171, pruned_loss=0.03439, over 4926.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2068, pruned_loss=0.02895, over 973008.48 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 12:23:41,440 INFO [train.py:715] (2/8) Epoch 18, batch 31600, loss[loss=0.124, simple_loss=0.1955, pruned_loss=0.02626, over 4782.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02925, over 972670.04 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:24:20,709 INFO [train.py:715] (2/8) Epoch 18, batch 31650, loss[loss=0.1196, simple_loss=0.1937, pruned_loss=0.02275, over 4949.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.02933, over 972264.76 frames.], batch size: 35, lr: 1.22e-04
2022-05-09 12:24:59,690 INFO [train.py:715] (2/8) Epoch 18, batch 31700, loss[loss=0.1315, simple_loss=0.2111, pruned_loss=0.02601, over 4877.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02923, over 972071.78 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:25:38,826 INFO [train.py:715] (2/8) Epoch 18, batch 31750, loss[loss=0.1256, simple_loss=0.1947, pruned_loss=0.02824, over 4811.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02887, over 971659.63 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 12:26:18,651 INFO [train.py:715] (2/8) Epoch 18, batch 31800, loss[loss=0.1193, simple_loss=0.1895, pruned_loss=0.0246, over 4924.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02868, over 971228.13 frames.], batch size: 29, lr: 1.22e-04
2022-05-09 12:26:58,017 INFO [train.py:715] (2/8) Epoch 18, batch 31850, loss[loss=0.1376, simple_loss=0.2081, pruned_loss=0.03358, over 4855.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02872, over 970901.78 frames.], batch size: 20, lr: 1.22e-04
2022-05-09 12:27:36,976 INFO [train.py:715] (2/8) Epoch 18, batch 31900, loss[loss=0.1477, simple_loss=0.2214, pruned_loss=0.03694, over 4883.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2074, pruned_loss=0.02911, over 971563.63 frames.], batch size: 39, lr: 1.22e-04
2022-05-09 12:28:16,149 INFO [train.py:715] (2/8) Epoch 18, batch 31950, loss[loss=0.09496, simple_loss=0.1664, pruned_loss=0.01177, over 4965.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2077, pruned_loss=0.0293, over 971307.08 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 12:28:54,461 INFO [train.py:715] (2/8) Epoch 18, batch 32000, loss[loss=0.1352, simple_loss=0.209, pruned_loss=0.03073, over 4836.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02902, over 971877.42 frames.], batch size: 30, lr: 1.22e-04
2022-05-09 12:29:32,616 INFO [train.py:715] (2/8) Epoch 18, batch 32050, loss[loss=0.119, simple_loss=0.1882, pruned_loss=0.02489, over 4814.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02885, over 971649.69 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:30:11,877 INFO [train.py:715] (2/8) Epoch 18, batch 32100, loss[loss=0.08699, simple_loss=0.1523, pruned_loss=0.01082, over 4792.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02906, over 971752.31 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:30:51,369 INFO [train.py:715] (2/8) Epoch 18, batch 32150, loss[loss=0.1213, simple_loss=0.2019, pruned_loss=0.02034, over 4886.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02894, over 970830.21 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:31:30,534 INFO [train.py:715] (2/8) Epoch 18, batch 32200, loss[loss=0.137, simple_loss=0.2097, pruned_loss=0.03215, over 4879.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2067, pruned_loss=0.02911, over 970998.43 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:32:08,907 INFO [train.py:715] (2/8) Epoch 18, batch 32250, loss[loss=0.1336, simple_loss=0.2099, pruned_loss=0.02861, over 4809.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.0291, over 971223.80 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:32:48,156 INFO [train.py:715] (2/8) Epoch 18, batch 32300, loss[loss=0.1359, simple_loss=0.2105, pruned_loss=0.03065, over 4755.00 frames.], tot_loss[loss=0.133, simple_loss=0.2076, pruned_loss=0.02922, over 971225.84 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:33:26,711 INFO [train.py:715] (2/8) Epoch 18, batch 32350, loss[loss=0.1168, simple_loss=0.1915, pruned_loss=0.02108, over 4927.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.02916, over 971142.18 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 12:34:05,363 INFO [train.py:715] (2/8) Epoch 18, batch 32400, loss[loss=0.1054, simple_loss=0.1815, pruned_loss=0.01471, over 4846.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02947, over 971535.80 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 12:34:44,785 INFO [train.py:715] (2/8) Epoch 18, batch 32450, loss[loss=0.1173, simple_loss=0.1914, pruned_loss=0.02159, over 4978.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2072, pruned_loss=0.02927, over 972408.53 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 12:35:23,650 INFO [train.py:715] (2/8) Epoch 18, batch 32500, loss[loss=0.1203, simple_loss=0.1859, pruned_loss=0.02733, over 4666.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02904, over 971554.28 frames.], batch size: 14, lr: 1.22e-04
2022-05-09 12:36:02,871 INFO [train.py:715] (2/8) Epoch 18, batch 32550, loss[loss=0.1278, simple_loss=0.2094, pruned_loss=0.02306, over 4864.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02866, over 970803.55 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:36:42,029 INFO [train.py:715] (2/8) Epoch 18, batch 32600, loss[loss=0.1205, simple_loss=0.1927, pruned_loss=0.02421, over 4874.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02891, over 971281.27 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:37:21,456 INFO [train.py:715] (2/8) Epoch 18, batch 32650, loss[loss=0.1568, simple_loss=0.2199, pruned_loss=0.04689, over 4767.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02878, over 970962.88 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:37:59,897 INFO [train.py:715] (2/8) Epoch 18, batch 32700, loss[loss=0.1195, simple_loss=0.1959, pruned_loss=0.0215, over 4977.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02878, over 970763.67 frames.], batch size: 28, lr: 1.22e-04
2022-05-09 12:38:38,641 INFO [train.py:715] (2/8) Epoch 18, batch 32750, loss[loss=0.1039, simple_loss=0.1778, pruned_loss=0.01497, over 4742.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02882, over 971935.08 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:39:17,961 INFO [train.py:715] (2/8) Epoch 18, batch 32800, loss[loss=0.1451, simple_loss=0.2281, pruned_loss=0.03104, over 4815.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02853, over 971444.84 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:39:57,152 INFO [train.py:715] (2/8) Epoch 18, batch 32850, loss[loss=0.1568, simple_loss=0.2348, pruned_loss=0.03938, over 4929.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02902, over 971236.72 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:40:35,664 INFO [train.py:715] (2/8) Epoch 18, batch 32900, loss[loss=0.1566, simple_loss=0.2359, pruned_loss=0.03862, over 4936.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02919, over 971416.48 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 12:41:14,762 INFO [train.py:715] (2/8) Epoch 18, batch 32950, loss[loss=0.1251, simple_loss=0.205, pruned_loss=0.02256, over 4750.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02892, over 971866.64 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:41:53,958 INFO [train.py:715] (2/8) Epoch 18, batch 33000, loss[loss=0.1184, simple_loss=0.1946, pruned_loss=0.02108, over 4759.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02882, over 971699.59 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:41:53,958 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 12:42:03,826 INFO [train.py:742] (2/8) Epoch 18, validation: loss=0.1046, simple_loss=0.1878, pruned_loss=0.01068, over 914524.00 frames.
2022-05-09 12:42:43,656 INFO [train.py:715] (2/8) Epoch 18, batch 33050, loss[loss=0.1202, simple_loss=0.1945, pruned_loss=0.02292, over 4828.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02846, over 970503.10 frames.], batch size: 26, lr: 1.22e-04
2022-05-09 12:43:22,621 INFO [train.py:715] (2/8) Epoch 18, batch 33100, loss[loss=0.1571, simple_loss=0.2238, pruned_loss=0.04521, over 4853.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02865, over 971259.08 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 12:44:02,124 INFO [train.py:715] (2/8) Epoch 18, batch 33150, loss[loss=0.1222, simple_loss=0.2083, pruned_loss=0.01809, over 4967.00 frames.], tot_loss[loss=0.1326, simple_loss=0.207, pruned_loss=0.02909, over 972486.80 frames.], batch size: 24, lr: 1.22e-04
2022-05-09 12:44:41,963 INFO [train.py:715] (2/8) Epoch 18, batch 33200, loss[loss=0.1103, simple_loss=0.1746, pruned_loss=0.02299, over 4755.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02869, over 971872.22 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:45:20,901 INFO [train.py:715] (2/8) Epoch 18, batch 33250, loss[loss=0.1308, simple_loss=0.2088, pruned_loss=0.02635, over 4976.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.0289, over 971635.66 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:45:59,529 INFO [train.py:715] (2/8) Epoch 18, batch 33300, loss[loss=0.1321, simple_loss=0.2137, pruned_loss=0.02528, over 4765.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02875, over 972038.29 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:46:38,969 INFO [train.py:715] (2/8) Epoch 18, batch 33350, loss[loss=0.1068, simple_loss=0.1813, pruned_loss=0.01617, over 4939.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02832, over 972505.32 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 12:47:18,356 INFO [train.py:715] (2/8) Epoch 18, batch 33400, loss[loss=0.121, simple_loss=0.2009, pruned_loss=0.02052, over 4878.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.0284, over 972736.24 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:47:57,080 INFO [train.py:715] (2/8) Epoch 18, batch 33450, loss[loss=0.1576, simple_loss=0.2179, pruned_loss=0.04863, over 4978.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2055, pruned_loss=0.02863, over 973143.09 frames.], batch size: 31, lr: 1.22e-04
2022-05-09 12:48:36,024 INFO [train.py:715] (2/8) Epoch 18, batch 33500, loss[loss=0.1444, simple_loss=0.2232, pruned_loss=0.03276, over 4906.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02855, over 972986.42 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 12:49:15,396 INFO [train.py:715] (2/8) Epoch 18, batch 33550, loss[loss=0.1685, simple_loss=0.2408, pruned_loss=0.04806, over 4744.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2059, pruned_loss=0.02862, over 973640.88 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:49:54,441 INFO [train.py:715] (2/8) Epoch 18, batch 33600, loss[loss=0.1363, simple_loss=0.2173, pruned_loss=0.02767, over 4749.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02856, over 972923.64 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:50:32,505 INFO [train.py:715] (2/8) Epoch 18, batch 33650, loss[loss=0.1137, simple_loss=0.1823, pruned_loss=0.02252, over 4841.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02822, over 973928.70 frames.], batch size: 13, lr: 1.22e-04
2022-05-09 12:51:11,948 INFO [train.py:715] (2/8) Epoch 18, batch 33700, loss[loss=0.1159, simple_loss=0.1928, pruned_loss=0.01954, over 4763.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2048, pruned_loss=0.02822, over 972701.80 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 12:51:51,115 INFO [train.py:715] (2/8) Epoch 18, batch 33750, loss[loss=0.1097, simple_loss=0.1877, pruned_loss=0.01581, over 4713.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2045, pruned_loss=0.02792, over 972957.28 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 12:52:30,432 INFO [train.py:715] (2/8) Epoch 18, batch 33800, loss[loss=0.1158, simple_loss=0.1941, pruned_loss=0.01872, over 4888.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2055, pruned_loss=0.02885, over 973671.94 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 12:53:09,718 INFO [train.py:715] (2/8) Epoch 18, batch 33850, loss[loss=0.1316, simple_loss=0.2096, pruned_loss=0.02676, over 4982.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2052, pruned_loss=0.02851, over 973759.97 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:53:49,536 INFO [train.py:715] (2/8) Epoch 18, batch 33900, loss[loss=0.09891, simple_loss=0.1724, pruned_loss=0.0127, over 4772.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2042, pruned_loss=0.02806, over 972214.75 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:54:28,738 INFO [train.py:715] (2/8) Epoch 18, batch 33950, loss[loss=0.1266, simple_loss=0.2008, pruned_loss=0.02624, over 4903.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2044, pruned_loss=0.02805, over 972110.90 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:55:07,059 INFO [train.py:715] (2/8) Epoch 18, batch 34000, loss[loss=0.1283, simple_loss=0.2024, pruned_loss=0.02714, over 4825.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2045, pruned_loss=0.02823, over 972089.54 frames.], batch size: 15, lr: 1.22e-04
2022-05-09 12:55:46,477 INFO [train.py:715] (2/8) Epoch 18, batch 34050, loss[loss=0.1076, simple_loss=0.18, pruned_loss=0.01763, over 4888.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2048, pruned_loss=0.02828, over 972431.74 frames.], batch size: 22, lr: 1.22e-04
2022-05-09 12:56:25,891 INFO [train.py:715] (2/8) Epoch 18, batch 34100, loss[loss=0.1329, simple_loss=0.2169, pruned_loss=0.0244, over 4987.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2045, pruned_loss=0.02801, over 972739.09 frames.], batch size: 25, lr: 1.22e-04
2022-05-09 12:57:05,037 INFO [train.py:715] (2/8) Epoch 18, batch 34150, loss[loss=0.1191, simple_loss=0.2015, pruned_loss=0.01832, over 4762.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2046, pruned_loss=0.02761, over 971917.72 frames.], batch size: 12, lr: 1.22e-04
2022-05-09 12:57:44,078 INFO [train.py:715] (2/8) Epoch 18, batch 34200, loss[loss=0.1115, simple_loss=0.1856, pruned_loss=0.01872, over 4945.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2048, pruned_loss=0.02774, over 972294.76 frames.], batch size: 23, lr: 1.22e-04
2022-05-09 12:58:23,226 INFO [train.py:715] (2/8) Epoch 18, batch 34250, loss[loss=0.1184, simple_loss=0.1957, pruned_loss=0.02053, over 4784.00 frames.], tot_loss[loss=0.1303, simple_loss=0.205, pruned_loss=0.02777, over 973413.70 frames.], batch size: 17, lr: 1.22e-04
2022-05-09 12:59:02,029 INFO [train.py:715] (2/8) Epoch 18, batch 34300, loss[loss=0.1523, simple_loss=0.232, pruned_loss=0.03629, over 4772.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2053, pruned_loss=0.02799, over 973241.58 frames.], batch size: 18, lr: 1.22e-04
2022-05-09 12:59:40,336 INFO [train.py:715] (2/8) Epoch 18, batch 34350, loss[loss=0.1269, simple_loss=0.1927, pruned_loss=0.03055, over 4902.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02812, over 973429.18 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 13:00:19,866 INFO [train.py:715] (2/8) Epoch 18, batch 34400, loss[loss=0.128, simple_loss=0.2001, pruned_loss=0.02799, over 4819.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.0282, over 972942.35 frames.], batch size: 27, lr: 1.22e-04
2022-05-09 13:00:59,463 INFO [train.py:715] (2/8) Epoch 18, batch 34450, loss[loss=0.1218, simple_loss=0.2095, pruned_loss=0.01706, over 4790.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02833, over 972134.40 frames.], batch size: 21, lr: 1.22e-04
2022-05-09 13:01:39,391 INFO [train.py:715] (2/8) Epoch 18, batch 34500, loss[loss=0.1319, simple_loss=0.2028, pruned_loss=0.03051, over 4852.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02841, over 971946.77 frames.], batch size: 32, lr: 1.22e-04
2022-05-09 13:02:18,915 INFO [train.py:715] (2/8) Epoch 18, batch 34550, loss[loss=0.1375, simple_loss=0.2058, pruned_loss=0.03454, over 4742.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02883, over 972275.08 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 13:02:58,572 INFO [train.py:715] (2/8) Epoch 18, batch 34600, loss[loss=0.1398, simple_loss=0.2208, pruned_loss=0.02934, over 4757.00 frames.], tot_loss[loss=0.1334, simple_loss=0.2075, pruned_loss=0.02965, over 971897.57 frames.], batch size: 16, lr: 1.22e-04
2022-05-09 13:03:37,761 INFO [train.py:715] (2/8) Epoch 18, batch 34650, loss[loss=0.1309, simple_loss=0.2011, pruned_loss=0.03038, over 4766.00 frames.], tot_loss[loss=0.133, simple_loss=0.2069, pruned_loss=0.02951, over 971263.21 frames.], batch size: 19, lr: 1.22e-04
2022-05-09 13:04:17,392 INFO [train.py:715] (2/8) Epoch 18, batch 34700, loss[loss=0.1269, simple_loss=0.1999, pruned_loss=0.02696, over 4855.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2071, pruned_loss=0.0297, over 972308.82 frames.], batch size: 30, lr: 1.21e-04
2022-05-09 13:04:56,522 INFO [train.py:715] (2/8) Epoch 18, batch 34750, loss[loss=0.1225, simple_loss=0.2008, pruned_loss=0.02208, over 4745.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02937, over 972621.91 frames.], batch size: 19, lr: 1.21e-04
2022-05-09 13:05:34,144 INFO [train.py:715] (2/8) Epoch 18, batch 34800, loss[loss=0.1106, simple_loss=0.1718, pruned_loss=0.02473, over 4768.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2061, pruned_loss=0.02929, over 972254.69 frames.], batch size: 12, lr: 1.21e-04
2022-05-09 13:06:24,925 INFO [train.py:715] (2/8) Epoch 19, batch 0, loss[loss=0.111, simple_loss=0.1769, pruned_loss=0.02254, over 4964.00 frames.], tot_loss[loss=0.111, simple_loss=0.1769, pruned_loss=0.02254, over 4964.00 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:07:03,498 INFO [train.py:715] (2/8) Epoch 19, batch 50, loss[loss=0.1213, simple_loss=0.1963, pruned_loss=0.02316, over 4704.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2085, pruned_loss=0.02927, over 219853.40 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:07:43,524 INFO [train.py:715] (2/8) Epoch 19, batch 100, loss[loss=0.1195, simple_loss=0.1952, pruned_loss=0.02186, over 4970.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02906, over 386442.96 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:08:23,942 INFO [train.py:715] (2/8) Epoch 19, batch 150, loss[loss=0.1273, simple_loss=0.2048, pruned_loss=0.02493, over 4808.00 frames.], tot_loss[loss=0.133, simple_loss=0.2075, pruned_loss=0.02929, over 516073.86 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:09:04,145 INFO [train.py:715] (2/8) Epoch 19, batch 200, loss[loss=0.1448, simple_loss=0.2225, pruned_loss=0.03359, over 4913.00 frames.], tot_loss[loss=0.133, simple_loss=0.2073, pruned_loss=0.02932, over 616959.79 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 13:09:44,075 INFO [train.py:715] (2/8) Epoch 19, batch 250, loss[loss=0.1163, simple_loss=0.1954, pruned_loss=0.01855, over 4798.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.0285, over 695702.81 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:10:24,213 INFO [train.py:715] (2/8) Epoch 19, batch 300, loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02851, over 4786.00 frames.], tot_loss[loss=0.132, simple_loss=0.2064, pruned_loss=0.02878, over 757463.23 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:11:04,702 INFO [train.py:715] (2/8) Epoch 19, batch 350, loss[loss=0.1657, simple_loss=0.228, pruned_loss=0.05169, over 4754.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02876, over 804410.41 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:11:43,715 INFO [train.py:715] (2/8) Epoch 19, batch 400, loss[loss=0.1186, simple_loss=0.2031, pruned_loss=0.01701, over 4920.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02906, over 841596.63 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 13:12:24,043 INFO [train.py:715] (2/8) Epoch 19, batch 450, loss[loss=0.1145, simple_loss=0.1995, pruned_loss=0.01472, over 4836.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2071, pruned_loss=0.02876, over 871341.34 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:13:04,618 INFO [train.py:715] (2/8) Epoch 19, batch 500, loss[loss=0.1489, simple_loss=0.2432, pruned_loss=0.02732, over 4821.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02869, over 893626.82 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:13:44,286 INFO [train.py:715] (2/8) Epoch 19, batch 550, loss[loss=0.1424, simple_loss=0.2188, pruned_loss=0.03296, over 4927.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2074, pruned_loss=0.02902, over 911011.87 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:14:24,232 INFO [train.py:715] (2/8) Epoch 19, batch 600, loss[loss=0.1363, simple_loss=0.2065, pruned_loss=0.03299, over 4986.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2074, pruned_loss=0.02882, over 924896.42 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 13:15:04,549 INFO [train.py:715] (2/8) Epoch 19, batch 650, loss[loss=0.1214, simple_loss=0.1931, pruned_loss=0.02482, over 4772.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2071, pruned_loss=0.02867, over 935558.18 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:15:44,872 INFO [train.py:715] (2/8) Epoch 19, batch 700, loss[loss=0.1357, simple_loss=0.2148, pruned_loss=0.02831, over 4811.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2069, pruned_loss=0.02876, over 943110.78 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:16:24,137 INFO [train.py:715] (2/8) Epoch 19, batch 750, loss[loss=0.1097, simple_loss=0.1823, pruned_loss=0.01855, over 4767.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2071, pruned_loss=0.02875, over 948970.59 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 13:17:03,940 INFO [train.py:715] (2/8) Epoch 19, batch 800, loss[loss=0.135, simple_loss=0.2189, pruned_loss=0.02556, over 4987.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02838, over 954391.22 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:17:44,201 INFO [train.py:715] (2/8) Epoch 19, batch 850, loss[loss=0.1371, simple_loss=0.2122, pruned_loss=0.03098, over 4964.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2059, pruned_loss=0.02816, over 959275.80 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:18:24,376 INFO [train.py:715] (2/8) Epoch 19, batch 900, loss[loss=0.1361, simple_loss=0.2153, pruned_loss=0.0284, over 4798.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02829, over 961913.69 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:19:03,891 INFO [train.py:715] (2/8) Epoch 19, batch 950, loss[loss=0.1298, simple_loss=0.1997, pruned_loss=0.02995, over 4850.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.0282, over 964358.85 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 13:19:43,250 INFO [train.py:715] (2/8) Epoch 19, batch 1000, loss[loss=0.1392, simple_loss=0.2171, pruned_loss=0.03069, over 4809.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2045, pruned_loss=0.02813, over 965476.70 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 13:20:23,195 INFO [train.py:715] (2/8) Epoch 19, batch 1050, loss[loss=0.163, simple_loss=0.2268, pruned_loss=0.04962, over 4911.00 frames.], tot_loss[loss=0.1307, simple_loss=0.205, pruned_loss=0.02816, over 966995.60 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:21:02,190 INFO [train.py:715] (2/8) Epoch 19, batch 1100, loss[loss=0.1353, simple_loss=0.2081, pruned_loss=0.03123, over 4912.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.02801, over 968561.63 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:21:42,017 INFO [train.py:715] (2/8) Epoch 19, batch 1150, loss[loss=0.1657, simple_loss=0.2273, pruned_loss=0.05206, over 4941.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2043, pruned_loss=0.02754, over 969721.50 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:22:21,961 INFO [train.py:715] (2/8) Epoch 19, batch 1200, loss[loss=0.111, simple_loss=0.1847, pruned_loss=0.01869, over 4810.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2051, pruned_loss=0.02762, over 970872.50 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:23:01,715 INFO [train.py:715] (2/8) Epoch 19, batch 1250, loss[loss=0.1145, simple_loss=0.1758, pruned_loss=0.02655, over 4778.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2055, pruned_loss=0.02808, over 971396.52 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 13:23:41,057 INFO [train.py:715] (2/8) Epoch 19, batch 1300, loss[loss=0.1215, simple_loss=0.195, pruned_loss=0.02395, over 4786.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2049, pruned_loss=0.0278, over 971699.84 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:24:20,593 INFO [train.py:715] (2/8) Epoch 19, batch 1350, loss[loss=0.1214, simple_loss=0.1896, pruned_loss=0.02666, over 4744.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2046, pruned_loss=0.02765, over 971581.41 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:25:00,618 INFO [train.py:715] (2/8) Epoch 19, batch 1400, loss[loss=0.1618, simple_loss=0.2177, pruned_loss=0.05299, over 4838.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02809, over 971705.11 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:25:39,916 INFO [train.py:715] (2/8) Epoch 19, batch 1450, loss[loss=0.1546, simple_loss=0.2331, pruned_loss=0.03806, over 4732.00 frames.], tot_loss[loss=0.131, simple_loss=0.2054, pruned_loss=0.0283, over 971987.98 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:26:20,232 INFO [train.py:715] (2/8) Epoch 19, batch 1500, loss[loss=0.1228, simple_loss=0.1897, pruned_loss=0.02796, over 4912.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2054, pruned_loss=0.02857, over 972280.53 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:27:00,285 INFO [train.py:715] (2/8) Epoch 19, batch 1550, loss[loss=0.1312, simple_loss=0.2076, pruned_loss=0.02738, over 4926.00 frames.], tot_loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02834, over 972579.36 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:27:40,366 INFO [train.py:715] (2/8) Epoch 19, batch 1600, loss[loss=0.1183, simple_loss=0.1941, pruned_loss=0.02125, over 4794.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2049, pruned_loss=0.02828, over 972527.31 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:28:19,707 INFO [train.py:715] (2/8) Epoch 19, batch 1650, loss[loss=0.1331, simple_loss=0.2036, pruned_loss=0.0313, over 4838.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2057, pruned_loss=0.02878, over 972925.78 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:28:59,073 INFO [train.py:715] (2/8) Epoch 19, batch 1700, loss[loss=0.1112, simple_loss=0.1938, pruned_loss=0.01434, over 4837.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02857, over 973353.64 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:29:39,059 INFO [train.py:715] (2/8) Epoch 19, batch 1750, loss[loss=0.1238, simple_loss=0.2015, pruned_loss=0.02303, over 4905.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02879, over 973692.57 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:30:18,169 INFO [train.py:715] (2/8) Epoch 19, batch 1800, loss[loss=0.1122, simple_loss=0.1909, pruned_loss=0.01674, over 4826.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.0285, over 974167.94 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 13:30:57,612 INFO [train.py:715] (2/8) Epoch 19, batch 1850, loss[loss=0.1223, simple_loss=0.1976, pruned_loss=0.02344, over 4771.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02833, over 974014.87 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:31:36,857 INFO [train.py:715] (2/8) Epoch 19, batch 1900, loss[loss=0.1268, simple_loss=0.2033, pruned_loss=0.0251, over 4763.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02802, over 973011.22 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:32:16,780 INFO [train.py:715] (2/8) Epoch 19, batch 1950, loss[loss=0.1932, simple_loss=0.2466, pruned_loss=0.06992, over 4971.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2042, pruned_loss=0.02776, over 973542.16 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:32:55,077 INFO [train.py:715] (2/8) Epoch 19, batch 2000, loss[loss=0.1059, simple_loss=0.1718, pruned_loss=0.02004, over 4808.00 frames.], tot_loss[loss=0.1296, simple_loss=0.204, pruned_loss=0.0276, over 973821.58 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 13:33:34,213 INFO [train.py:715] (2/8) Epoch 19, batch 2050, loss[loss=0.144, simple_loss=0.2066, pruned_loss=0.04063, over 4833.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02791, over 973214.50 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 13:34:13,318 INFO [train.py:715] (2/8) Epoch 19, batch 2100, loss[loss=0.1701, simple_loss=0.2411, pruned_loss=0.04959, over 4740.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2049, pruned_loss=0.02827, over 972743.24 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:34:52,133 INFO [train.py:715] (2/8) Epoch 19, batch 2150, loss[loss=0.1426, simple_loss=0.2179, pruned_loss=0.03366, over 4804.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.0282, over 971746.25 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:35:31,127 INFO [train.py:715] (2/8) Epoch 19, batch 2200, loss[loss=0.1406, simple_loss=0.2203, pruned_loss=0.03048, over 4690.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.0286, over 971985.30 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:36:09,828 INFO [train.py:715] (2/8) Epoch 19, batch 2250, loss[loss=0.1509, simple_loss=0.2266, pruned_loss=0.0376, over 4801.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02874, over 971891.74 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:36:49,417 INFO [train.py:715] (2/8) Epoch 19, batch 2300, loss[loss=0.1192, simple_loss=0.1845, pruned_loss=0.02699, over 4983.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2058, pruned_loss=0.02853, over 972269.84 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 13:37:28,008 INFO [train.py:715] (2/8) Epoch 19, batch 2350, loss[loss=0.1273, simple_loss=0.2092, pruned_loss=0.02266, over 4691.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02806, over 972140.75 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:38:07,166 INFO [train.py:715] (2/8) Epoch 19, batch 2400, loss[loss=0.1327, simple_loss=0.208, pruned_loss=0.02871, over 4687.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02859, over 971599.13 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:38:46,614 INFO [train.py:715] (2/8) Epoch 19, batch 2450, loss[loss=0.1333, simple_loss=0.2113, pruned_loss=0.02763, over 4974.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02904, over 970946.36 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 13:39:25,449 INFO [train.py:715] (2/8) Epoch 19, batch 2500, loss[loss=0.1165, simple_loss=0.1925, pruned_loss=0.02021, over 4949.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02912, over 970990.25 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:40:04,469 INFO [train.py:715] (2/8) Epoch 19, batch 2550, loss[loss=0.1142, simple_loss=0.1886, pruned_loss=0.01992, over 4986.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2066, pruned_loss=0.02938, over 971387.99 frames.], batch size: 31, lr: 1.18e-04
2022-05-09 13:40:44,011 INFO [train.py:715] (2/8) Epoch 19, batch 2600, loss[loss=0.1155, simple_loss=0.1921, pruned_loss=0.01947, over 4956.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2061, pruned_loss=0.02931, over 971010.95 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 13:41:26,471 INFO [train.py:715] (2/8) Epoch 19, batch 2650, loss[loss=0.1413, simple_loss=0.2303, pruned_loss=0.02619, over 4972.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02914, over 972210.68 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 13:42:05,377 INFO [train.py:715] (2/8) Epoch 19, batch 2700, loss[loss=0.1619, simple_loss=0.231, pruned_loss=0.04647, over 4890.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02884, over 971599.57 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:42:44,053 INFO [train.py:715] (2/8) Epoch 19, batch 2750, loss[loss=0.1312, simple_loss=0.2078, pruned_loss=0.02733, over 4885.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.0289, over 972101.81 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 13:43:23,788 INFO [train.py:715] (2/8) Epoch 19, batch 2800, loss[loss=0.1104, simple_loss=0.184, pruned_loss=0.01838, over 4646.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02857, over 972121.66 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:44:03,073 INFO [train.py:715] (2/8) Epoch 19, batch 2850, loss[loss=0.1544, simple_loss=0.2262, pruned_loss=0.04134, over 4821.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02871, over 972935.04 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 13:44:42,006 INFO [train.py:715] (2/8) Epoch 19, batch 2900, loss[loss=0.1394, simple_loss=0.2145, pruned_loss=0.03215, over 4751.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2056, pruned_loss=0.02894, over 972121.80 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:45:20,759 INFO [train.py:715] (2/8) Epoch 19, batch 2950, loss[loss=0.1216, simple_loss=0.192, pruned_loss=0.02557, over 4744.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2047, pruned_loss=0.02828, over 971644.47 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:46:00,074 INFO [train.py:715] (2/8) Epoch 19, batch 3000, loss[loss=0.1546, simple_loss=0.2236, pruned_loss=0.04281, over 4836.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2047, pruned_loss=0.02799, over 972453.62 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 13:46:00,074 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 13:46:10,051 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1045, simple_loss=0.1877, pruned_loss=0.01062, over 914524.00 frames.
2022-05-09 13:46:50,342 INFO [train.py:715] (2/8) Epoch 19, batch 3050, loss[loss=0.1273, simple_loss=0.203, pruned_loss=0.02583, over 4762.00 frames.], tot_loss[loss=0.131, simple_loss=0.2054, pruned_loss=0.02824, over 973131.02 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 13:47:29,701 INFO [train.py:715] (2/8) Epoch 19, batch 3100, loss[loss=0.1201, simple_loss=0.1987, pruned_loss=0.0208, over 4968.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02807, over 973603.62 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 13:48:08,833 INFO [train.py:715] (2/8) Epoch 19, batch 3150, loss[loss=0.1131, simple_loss=0.1959, pruned_loss=0.01516, over 4958.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2068, pruned_loss=0.02838, over 973132.79 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 13:48:48,670 INFO [train.py:715] (2/8) Epoch 19, batch 3200, loss[loss=0.1435, simple_loss=0.2141, pruned_loss=0.03649, over 4950.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2066, pruned_loss=0.02835, over 972653.28 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 13:49:27,694 INFO [train.py:715] (2/8) Epoch 19, batch 3250, loss[loss=0.1242, simple_loss=0.2068, pruned_loss=0.02081, over 4939.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02796, over 972099.16 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:50:07,132 INFO [train.py:715] (2/8) Epoch 19, batch 3300, loss[loss=0.122, simple_loss=0.1985, pruned_loss=0.02281, over 4964.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02834, over 972576.68 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 13:50:46,363 INFO [train.py:715] (2/8) Epoch 19, batch 3350, loss[loss=0.1604, simple_loss=0.2377, pruned_loss=0.04152, over 4752.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.02803, over 972869.15 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:51:26,507 INFO [train.py:715] (2/8) Epoch 19, batch 3400, loss[loss=0.1472, simple_loss=0.206, pruned_loss=0.04422, over 4862.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02805, over 973042.40 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 13:52:05,356 INFO [train.py:715] (2/8) Epoch 19, batch 3450, loss[loss=0.1223, simple_loss=0.1969, pruned_loss=0.02388, over 4746.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02865, over 972603.14 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 13:52:44,615 INFO [train.py:715] (2/8) Epoch 19, batch 3500, loss[loss=0.133, simple_loss=0.2098, pruned_loss=0.02809, over 4746.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02908, over 972480.33 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 13:53:23,734 INFO [train.py:715] (2/8) Epoch 19, batch 3550, loss[loss=0.1241, simple_loss=0.2102, pruned_loss=0.01902, over 4819.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02848, over 972234.76 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:54:02,621 INFO [train.py:715] (2/8) Epoch 19, batch 3600, loss[loss=0.1243, simple_loss=0.1962, pruned_loss=0.02623, over 4782.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2052, pruned_loss=0.02792, over 971637.98 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:54:42,250 INFO [train.py:715] (2/8) Epoch 19, batch 3650, loss[loss=0.1096, simple_loss=0.1836, pruned_loss=0.01781, over 4939.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2055, pruned_loss=0.02779, over 971877.47 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:55:21,396 INFO [train.py:715] (2/8) Epoch 19, batch 3700, loss[loss=0.1345, simple_loss=0.2138, pruned_loss=0.02757, over 4816.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2054, pruned_loss=0.02777, over 972192.88 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:56:01,853 INFO [train.py:715] (2/8) Epoch 19, batch 3750, loss[loss=0.123, simple_loss=0.2011, pruned_loss=0.02248, over 4799.00 frames.], tot_loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.02786, over 971715.34 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 13:56:40,835 INFO [train.py:715] (2/8) Epoch 19, batch 3800, loss[loss=0.1565, simple_loss=0.2187, pruned_loss=0.04715, over 4646.00 frames.], tot_loss[loss=0.13, simple_loss=0.2048, pruned_loss=0.02759, over 971311.70 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 13:57:19,814 INFO [train.py:715] (2/8) Epoch 19, batch 3850, loss[loss=0.1468, simple_loss=0.2162, pruned_loss=0.03871, over 4777.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02808, over 971861.93 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 13:57:59,509 INFO [train.py:715] (2/8) Epoch 19, batch 3900, loss[loss=0.1239, simple_loss=0.2011, pruned_loss=0.02339, over 4813.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02851, over 971100.71 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:58:38,561 INFO [train.py:715] (2/8) Epoch 19, batch 3950, loss[loss=0.1444, simple_loss=0.227, pruned_loss=0.03092, over 4828.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02845, over 971530.35 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 13:59:17,192 INFO [train.py:715] (2/8) Epoch 19, batch 4000, loss[loss=0.1179, simple_loss=0.1926, pruned_loss=0.02159, over 4780.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2053, pruned_loss=0.02791, over 971640.91 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 13:59:56,646 INFO [train.py:715] (2/8) Epoch 19, batch 4050, loss[loss=0.1285, simple_loss=0.2147, pruned_loss=0.02109, over 4910.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2058, pruned_loss=0.02777, over 972412.80 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 14:00:36,779 INFO [train.py:715] (2/8) Epoch 19, batch 4100, loss[loss=0.1483, simple_loss=0.2153, pruned_loss=0.04063, over 4918.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2055, pruned_loss=0.02797, over 972216.18 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:01:15,966 INFO [train.py:715] (2/8) Epoch 19, batch 4150, loss[loss=0.1121, simple_loss=0.1879, pruned_loss=0.0182, over 4797.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2053, pruned_loss=0.02793, over 971464.86 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 14:01:54,756 INFO [train.py:715] (2/8) Epoch 19, batch 4200, loss[loss=0.1131, simple_loss=0.1806, pruned_loss=0.02282, over 4932.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2047, pruned_loss=0.02754, over 971503.29 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:02:33,999 INFO [train.py:715] (2/8) Epoch 19, batch 4250, loss[loss=0.1177, simple_loss=0.1827, pruned_loss=0.02639, over 4890.00 frames.], tot_loss[loss=0.131, simple_loss=0.2056, pruned_loss=0.02825, over 972451.29 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:03:13,062 INFO [train.py:715] (2/8) Epoch 19, batch 4300, loss[loss=0.1353, simple_loss=0.2134, pruned_loss=0.02864, over 4806.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2055, pruned_loss=0.02799, over 972441.80 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 14:03:52,550 INFO [train.py:715] (2/8) Epoch 19, batch 4350, loss[loss=0.1295, simple_loss=0.2053, pruned_loss=0.02684, over 4826.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2049, pruned_loss=0.02789, over 972808.01 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:04:31,621 INFO [train.py:715] (2/8) Epoch 19, batch 4400, loss[loss=0.1572, simple_loss=0.2217, pruned_loss=0.0463, over 4787.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02813, over 973431.54 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:05:11,664 INFO [train.py:715] (2/8) Epoch 19, batch 4450, loss[loss=0.1273, simple_loss=0.205, pruned_loss=0.02479, over 4695.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2046, pruned_loss=0.02784, over 973035.03 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:05:50,511 INFO [train.py:715] (2/8) Epoch 19, batch 4500, loss[loss=0.1236, simple_loss=0.2025, pruned_loss=0.02236, over 4927.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.0279, over 973020.54 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 14:06:29,203 INFO [train.py:715] (2/8) Epoch 19, batch 4550, loss[loss=0.1301, simple_loss=0.1988, pruned_loss=0.03071, over 4810.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2053, pruned_loss=0.02798, over 973134.03 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 14:07:08,895 INFO [train.py:715] (2/8) Epoch 19, batch 4600, loss[loss=0.1299, simple_loss=0.2031, pruned_loss=0.02835, over 4781.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02814, over 972393.55 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:07:48,139 INFO [train.py:715] (2/8) Epoch 19, batch 4650, loss[loss=0.1283, simple_loss=0.2064, pruned_loss=0.02514, over 4940.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02843, over 972271.21 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 14:08:27,121 INFO [train.py:715] (2/8) Epoch 19, batch 4700, loss[loss=0.1121, simple_loss=0.1824, pruned_loss=0.02094, over 4962.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02894, over 972307.31 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 14:09:06,336 INFO [train.py:715] (2/8) Epoch 19, batch 4750, loss[loss=0.1515, simple_loss=0.2229, pruned_loss=0.04006, over 4828.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2054, pruned_loss=0.02874, over 972194.68 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:09:46,298 INFO [train.py:715] (2/8) Epoch 19, batch 4800, loss[loss=0.1327, simple_loss=0.2014, pruned_loss=0.03203, over 4925.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02796, over 973033.13 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 14:10:25,681 INFO [train.py:715] (2/8) Epoch 19, batch 4850, loss[loss=0.1263, simple_loss=0.1979, pruned_loss=0.02729, over 4902.00 frames.], tot_loss[loss=0.13, simple_loss=0.2043, pruned_loss=0.02784, over 973447.26 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:11:04,336 INFO [train.py:715] (2/8) Epoch 19, batch 4900, loss[loss=0.1377, simple_loss=0.2112, pruned_loss=0.03207, over 4981.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02825, over 973714.20 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 14:11:44,092 INFO [train.py:715] (2/8) Epoch 19, batch 4950, loss[loss=0.1372, simple_loss=0.209, pruned_loss=0.03268, over 4946.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02852, over 973552.11 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:12:23,740 INFO [train.py:715] (2/8) Epoch 19, batch 5000, loss[loss=0.1325, simple_loss=0.2165, pruned_loss=0.02423, over 4819.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.0288, over 972818.79 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 14:13:02,751 INFO [train.py:715] (2/8) Epoch 19, batch 5050, loss[loss=0.1571, simple_loss=0.2227, pruned_loss=0.04575, over 4688.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02875, over 971833.89 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:13:41,119 INFO [train.py:715] (2/8) Epoch 19, batch 5100, loss[loss=0.1242, simple_loss=0.2048, pruned_loss=0.02183, over 4983.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02874, over 972939.45 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 14:14:21,161 INFO [train.py:715] (2/8) Epoch 19, batch 5150, loss[loss=0.1298, simple_loss=0.2103, pruned_loss=0.02464, over 4768.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02883, over 973205.54 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:15:00,191 INFO [train.py:715] (2/8) Epoch 19, batch 5200, loss[loss=0.1265, simple_loss=0.2139, pruned_loss=0.01952, over 4897.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02868, over 973578.77 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:15:38,853 INFO [train.py:715] (2/8) Epoch 19, batch 5250, loss[loss=0.1234, simple_loss=0.204, pruned_loss=0.02143, over 4908.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02848, over 973521.69 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:16:18,551 INFO [train.py:715] (2/8) Epoch 19, batch 5300, loss[loss=0.1519, simple_loss=0.2307, pruned_loss=0.03657, over 4807.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.0281, over 973539.23 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:16:58,486 INFO [train.py:715] (2/8) Epoch 19, batch 5350, loss[loss=0.1363, simple_loss=0.2089, pruned_loss=0.03188, over 4971.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02811, over 972958.32 frames.], batch size: 35, lr: 1.18e-04
2022-05-09 14:17:38,565 INFO [train.py:715] (2/8) Epoch 19, batch 5400, loss[loss=0.1451, simple_loss=0.2119, pruned_loss=0.03915, over 4848.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2053, pruned_loss=0.02845, over 972882.94 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 14:18:17,842 INFO [train.py:715] (2/8) Epoch 19, batch 5450, loss[loss=0.1452, simple_loss=0.2309, pruned_loss=0.02976, over 4968.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02869, over 972572.81 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 14:18:58,018 INFO [train.py:715] (2/8) Epoch 19, batch 5500, loss[loss=0.1377, simple_loss=0.2104, pruned_loss=0.03246, over 4970.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.0287, over 972724.89 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 14:19:37,204 INFO [train.py:715] (2/8) Epoch 19, batch 5550, loss[loss=0.1193, simple_loss=0.195, pruned_loss=0.02186, over 4762.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02892, over 972882.62 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:20:16,794 INFO [train.py:715] (2/8) Epoch 19, batch 5600, loss[loss=0.1357, simple_loss=0.2151, pruned_loss=0.0282, over 4807.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02876, over 972439.63 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:20:56,101 INFO [train.py:715] (2/8) Epoch 19, batch 5650, loss[loss=0.1169, simple_loss=0.2015, pruned_loss=0.01619, over 4859.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02912, over 973462.54 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 14:21:35,826 INFO [train.py:715] (2/8) Epoch 19, batch 5700, loss[loss=0.1408, simple_loss=0.2082, pruned_loss=0.03667, over 4981.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2074, pruned_loss=0.02881, over 973165.79 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 14:22:15,332 INFO [train.py:715] (2/8) Epoch 19, batch 5750, loss[loss=0.1067, simple_loss=0.1866, pruned_loss=0.01334, over 4872.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2066, pruned_loss=0.02844, over 973556.10 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 14:22:53,919 INFO [train.py:715] (2/8) Epoch 19, batch 5800, loss[loss=0.1495, simple_loss=0.2253, pruned_loss=0.03684, over 4823.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2074, pruned_loss=0.02889, over 973578.67 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:23:33,192 INFO [train.py:715] (2/8) Epoch 19, batch 5850, loss[loss=0.1702, simple_loss=0.2478, pruned_loss=0.0463, over 4872.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2076, pruned_loss=0.02901, over 973439.02 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 14:24:11,658 INFO [train.py:715] (2/8) Epoch 19, batch 5900, loss[loss=0.1416, simple_loss=0.2083, pruned_loss=0.03747, over 4705.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2067, pruned_loss=0.02899, over 972723.80 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:24:51,083 INFO [train.py:715] (2/8) Epoch 19, batch 5950, loss[loss=0.1206, simple_loss=0.1887, pruned_loss=0.02624, over 4984.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02906, over 972444.71 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 14:25:30,284 INFO [train.py:715] (2/8) Epoch 19, batch 6000, loss[loss=0.1296, simple_loss=0.2159, pruned_loss=0.02165, over 4936.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02868, over 973277.40 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 14:25:30,285 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 14:25:40,196 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1046, simple_loss=0.1878, pruned_loss=0.01067, over 914524.00 frames.
2022-05-09 14:26:19,488 INFO [train.py:715] (2/8) Epoch 19, batch 6050, loss[loss=0.1253, simple_loss=0.2105, pruned_loss=0.02002, over 4897.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.0287, over 972676.73 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:26:58,343 INFO [train.py:715] (2/8) Epoch 19, batch 6100, loss[loss=0.12, simple_loss=0.1949, pruned_loss=0.02257, over 4808.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2056, pruned_loss=0.02829, over 972112.98 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:27:37,412 INFO [train.py:715] (2/8) Epoch 19, batch 6150, loss[loss=0.1006, simple_loss=0.1755, pruned_loss=0.01281, over 4816.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02877, over 972561.17 frames.], batch size: 27, lr: 1.18e-04
2022-05-09 14:28:15,611 INFO [train.py:715] (2/8) Epoch 19, batch 6200, loss[loss=0.09878, simple_loss=0.1752, pruned_loss=0.01117, over 4821.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02873, over 972719.39 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:28:55,855 INFO [train.py:715] (2/8) Epoch 19, batch 6250, loss[loss=0.1223, simple_loss=0.1924, pruned_loss=0.02611, over 4821.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2063, pruned_loss=0.02913, over 973052.41 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:29:35,037 INFO [train.py:715] (2/8) Epoch 19, batch 6300, loss[loss=0.1324, simple_loss=0.2074, pruned_loss=0.02872, over 4882.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02888, over 973208.24 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 14:30:14,721 INFO [train.py:715] (2/8) Epoch 19, batch 6350, loss[loss=0.1302, simple_loss=0.1995, pruned_loss=0.03042, over 4640.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02868, over 972832.34 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 14:30:54,200 INFO [train.py:715] (2/8) Epoch 19, batch 6400, loss[loss=0.1387, simple_loss=0.2167, pruned_loss=0.03035, over 4823.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02909, over 971772.51 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:31:33,476 INFO [train.py:715] (2/8) Epoch 19, batch 6450, loss[loss=0.1077, simple_loss=0.1777, pruned_loss=0.01886, over 4865.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2074, pruned_loss=0.02953, over 972038.54 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 14:32:12,984 INFO [train.py:715] (2/8) Epoch 19, batch 6500, loss[loss=0.107, simple_loss=0.1804, pruned_loss=0.01686, over 4693.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2074, pruned_loss=0.02939, over 971825.34 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:32:51,558 INFO [train.py:715] (2/8) Epoch 19, batch 6550, loss[loss=0.1172, simple_loss=0.1883, pruned_loss=0.02307, over 4810.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.0284, over 972000.87 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:33:31,045 INFO [train.py:715] (2/8) Epoch 19, batch 6600, loss[loss=0.137, simple_loss=0.2052, pruned_loss=0.03438, over 4982.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.02883, over 971968.37 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 14:34:10,214 INFO [train.py:715] (2/8) Epoch 19, batch 6650, loss[loss=0.1315, simple_loss=0.2116, pruned_loss=0.02565, over 4764.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.02911, over 971605.95 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:34:48,937 INFO [train.py:715] (2/8) Epoch 19, batch 6700, loss[loss=0.1121, simple_loss=0.1985, pruned_loss=0.01283, over 4687.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02908, over 972141.16 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:35:28,067 INFO [train.py:715] (2/8) Epoch 19, batch 6750, loss[loss=0.1213, simple_loss=0.1987, pruned_loss=0.02192, over 4992.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02883, over 972606.95 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 14:36:07,536 INFO [train.py:715] (2/8) Epoch 19, batch 6800, loss[loss=0.1345, simple_loss=0.1951, pruned_loss=0.03696, over 4854.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02848, over 972089.09 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 14:36:46,932 INFO [train.py:715] (2/8) Epoch 19, batch 6850, loss[loss=0.1265, simple_loss=0.2018, pruned_loss=0.02562, over 4965.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.0284, over 971931.48 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:37:25,089 INFO [train.py:715] (2/8) Epoch 19, batch 6900, loss[loss=0.1057, simple_loss=0.1795, pruned_loss=0.01593, over 4863.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02841, over 972048.55 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 14:38:04,132 INFO [train.py:715] (2/8) Epoch 19, batch 6950, loss[loss=0.1174, simple_loss=0.1939, pruned_loss=0.02048, over 4789.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.02881, over 971811.37 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:38:43,600 INFO [train.py:715] (2/8) Epoch 19, batch 7000, loss[loss=0.1518, simple_loss=0.2208, pruned_loss=0.04138, over 4811.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2071, pruned_loss=0.02884, over 971828.21 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 14:39:22,853 INFO [train.py:715] (2/8) Epoch 19, batch 7050, loss[loss=0.1326, simple_loss=0.1969, pruned_loss=0.0341, over 4915.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.0284, over 972587.56 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:40:02,436 INFO [train.py:715] (2/8) Epoch 19, batch 7100, loss[loss=0.1233, simple_loss=0.2008, pruned_loss=0.02292, over 4875.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.028, over 973440.54 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 14:40:42,073 INFO [train.py:715] (2/8) Epoch 19, batch 7150, loss[loss=0.121, simple_loss=0.1992, pruned_loss=0.02143, over 4933.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2059, pruned_loss=0.02796, over 972979.87 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:41:20,982 INFO [train.py:715] (2/8) Epoch 19, batch 7200, loss[loss=0.1591, simple_loss=0.2347, pruned_loss=0.04177, over 4957.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2056, pruned_loss=0.02787, over 973185.44 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 14:41:59,733 INFO [train.py:715] (2/8) Epoch 19, batch 7250, loss[loss=0.1098, simple_loss=0.1828, pruned_loss=0.01838, over 4920.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02821, over 973166.90 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 14:42:39,095 INFO [train.py:715] (2/8) Epoch 19, batch 7300, loss[loss=0.144, simple_loss=0.2192, pruned_loss=0.03437, over 4935.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02837, over 973547.00 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 14:43:18,263 INFO [train.py:715] (2/8) Epoch 19, batch 7350, loss[loss=0.1393, simple_loss=0.2217, pruned_loss=0.02847, over 4982.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02823, over 973164.17 frames.], batch size: 28, lr: 1.18e-04
2022-05-09 14:43:57,164 INFO [train.py:715] (2/8) Epoch 19, batch 7400, loss[loss=0.1158, simple_loss=0.189, pruned_loss=0.0213, over 4786.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2059, pruned_loss=0.02797, over 973235.49 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:44:37,624 INFO [train.py:715] (2/8) Epoch 19, batch 7450, loss[loss=0.1149, simple_loss=0.1928, pruned_loss=0.01848, over 4958.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2057, pruned_loss=0.02772, over 973361.67 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 14:45:17,479 INFO [train.py:715] (2/8) Epoch 19, batch 7500, loss[loss=0.1091, simple_loss=0.1915, pruned_loss=0.0134, over 4922.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2059, pruned_loss=0.02786, over 973894.11 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 14:45:56,702 INFO [train.py:715] (2/8) Epoch 19, batch 7550, loss[loss=0.1486, simple_loss=0.2111, pruned_loss=0.04303, over 4850.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02833, over 974027.95 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 14:46:36,070 INFO [train.py:715] (2/8) Epoch 19, batch 7600, loss[loss=0.1161, simple_loss=0.1921, pruned_loss=0.02008, over 4962.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2065, pruned_loss=0.02826, over 973734.77 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:47:16,851 INFO [train.py:715] (2/8) Epoch 19, batch 7650, loss[loss=0.1424, simple_loss=0.2207, pruned_loss=0.0321, over 4882.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2068, pruned_loss=0.02819, over 972878.71 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 14:47:56,151 INFO [train.py:715] (2/8) Epoch 19, batch 7700, loss[loss=0.1132, simple_loss=0.1893, pruned_loss=0.01857, over 4810.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2068, pruned_loss=0.02843, over 973269.88 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 14:48:34,962 INFO [train.py:715] (2/8) Epoch 19, batch 7750, loss[loss=0.1346, simple_loss=0.2072, pruned_loss=0.03101, over 4852.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.0283, over 973290.78 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:49:14,658 INFO [train.py:715] (2/8) Epoch 19, batch 7800, loss[loss=0.1528, simple_loss=0.2143, pruned_loss=0.04564, over 4690.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.0285, over 973082.55 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:49:54,094 INFO [train.py:715] (2/8) Epoch 19, batch 7850, loss[loss=0.1161, simple_loss=0.1986, pruned_loss=0.01679, over 4963.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2052, pruned_loss=0.02767, over 972756.84 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 14:50:33,361 INFO [train.py:715] (2/8) Epoch 19, batch 7900, loss[loss=0.1294, simple_loss=0.2021, pruned_loss=0.02833, over 4850.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2059, pruned_loss=0.02819, over 973131.55 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 14:51:11,761 INFO [train.py:715] (2/8) Epoch 19, batch 7950, loss[loss=0.118, simple_loss=0.1935, pruned_loss=0.02127, over 4907.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2065, pruned_loss=0.02839, over 973080.51 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:51:51,106 INFO [train.py:715] (2/8) Epoch 19, batch 8000, loss[loss=0.1199, simple_loss=0.1846, pruned_loss=0.02761, over 4825.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2062, pruned_loss=0.02827, over 972658.91 frames.], batch size: 30, lr: 1.18e-04
2022-05-09 14:52:30,294 INFO [train.py:715] (2/8) Epoch 19, batch 8050, loss[loss=0.1442, simple_loss=0.2197, pruned_loss=0.03441, over 4779.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02816, over 972136.96 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 14:53:08,821 INFO [train.py:715] (2/8) Epoch 19, batch 8100, loss[loss=0.1175, simple_loss=0.1936, pruned_loss=0.02065, over 4844.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02848, over 972075.82 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 14:53:48,277 INFO [train.py:715] (2/8) Epoch 19, batch 8150, loss[loss=0.1379, simple_loss=0.2055, pruned_loss=0.0351, over 4770.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02856, over 971537.08 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 14:54:27,922 INFO [train.py:715] (2/8) Epoch 19, batch 8200, loss[loss=0.1266, simple_loss=0.2054, pruned_loss=0.02392, over 4895.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2073, pruned_loss=0.02923, over 971835.18 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 14:55:06,906 INFO [train.py:715] (2/8) Epoch 19, batch 8250, loss[loss=0.1245, simple_loss=0.1912, pruned_loss=0.02886, over 4868.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2065, pruned_loss=0.02895, over 972078.91 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 14:55:45,535 INFO [train.py:715] (2/8) Epoch 19, batch 8300, loss[loss=0.1144, simple_loss=0.1783, pruned_loss=0.02522, over 4770.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2062, pruned_loss=0.0291, over 972069.49 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:56:25,222 INFO [train.py:715] (2/8) Epoch 19, batch 8350, loss[loss=0.1094, simple_loss=0.1853, pruned_loss=0.01674, over 4810.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2063, pruned_loss=0.02908, over 972764.04 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:57:04,452 INFO [train.py:715] (2/8) Epoch 19, batch 8400, loss[loss=0.1303, simple_loss=0.2059, pruned_loss=0.02732, over 4943.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.0291, over 972037.72 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 14:57:43,454 INFO [train.py:715] (2/8) Epoch 19, batch 8450, loss[loss=0.1124, simple_loss=0.1773, pruned_loss=0.02382, over 4792.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2065, pruned_loss=0.02888, over 971985.25 frames.], batch size: 12, lr: 1.18e-04
2022-05-09 14:58:23,232 INFO [train.py:715] (2/8) Epoch 19, batch 8500, loss[loss=0.1278, simple_loss=0.2135, pruned_loss=0.021, over 4797.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02863, over 972389.26 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 14:59:01,925 INFO [train.py:715] (2/8) Epoch 19, batch 8550, loss[loss=0.1526, simple_loss=0.2215, pruned_loss=0.04188, over 4768.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02871, over 971535.14 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 14:59:41,017 INFO [train.py:715] (2/8) Epoch 19, batch 8600, loss[loss=0.1614, simple_loss=0.2212, pruned_loss=0.05083, over 4848.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02845, over 971783.70 frames.], batch size: 34, lr: 1.18e-04
2022-05-09 15:00:20,522 INFO [train.py:715] (2/8) Epoch 19, batch 8650, loss[loss=0.1321, simple_loss=0.2033, pruned_loss=0.03043, over 4951.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2049, pruned_loss=0.02829, over 971960.83 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 15:01:00,040 INFO [train.py:715] (2/8) Epoch 19, batch 8700, loss[loss=0.1359, simple_loss=0.2045, pruned_loss=0.03362, over 4960.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02856, over 972190.92 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 15:01:39,199 INFO [train.py:715] (2/8) Epoch 19, batch 8750, loss[loss=0.1582, simple_loss=0.2288, pruned_loss=0.04381, over 4653.00 frames.], tot_loss[loss=0.1308, simple_loss=0.205, pruned_loss=0.02834, over 971572.67 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 15:02:17,957 INFO [train.py:715] (2/8) Epoch 19, batch 8800, loss[loss=0.1384, simple_loss=0.2209, pruned_loss=0.02796, over 4806.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2056, pruned_loss=0.02884, over 971317.28 frames.], batch size: 21, lr: 1.18e-04
2022-05-09 15:02:57,609 INFO [train.py:715] (2/8) Epoch 19, batch 8850, loss[loss=0.1324, simple_loss=0.2087, pruned_loss=0.02808, over 4807.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.0282, over 971967.75 frames.], batch size: 25, lr: 1.18e-04
2022-05-09 15:03:36,664 INFO [train.py:715] (2/8) Epoch 19, batch 8900, loss[loss=0.128, simple_loss=0.1986, pruned_loss=0.02876, over 4848.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02831, over 971041.58 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 15:04:16,007 INFO [train.py:715] (2/8) Epoch 19, batch 8950, loss[loss=0.1107, simple_loss=0.1764, pruned_loss=0.02243, over 4868.00 frames.], tot_loss[loss=0.1308, simple_loss=0.205, pruned_loss=0.02829, over 970981.10 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 15:04:54,904 INFO [train.py:715] (2/8) Epoch 19, batch 9000, loss[loss=0.1426, simple_loss=0.2147, pruned_loss=0.03523, over 4914.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2059, pruned_loss=0.02873, over 971021.62 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 15:04:54,905 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 15:05:04,819 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1047, simple_loss=0.1879, pruned_loss=0.01072, over 914524.00 frames.
2022-05-09 15:05:44,269 INFO [train.py:715] (2/8) Epoch 19, batch 9050, loss[loss=0.1131, simple_loss=0.1857, pruned_loss=0.0202, over 4982.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2054, pruned_loss=0.02842, over 971088.98 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 15:06:23,988 INFO [train.py:715] (2/8) Epoch 19, batch 9100, loss[loss=0.1135, simple_loss=0.1934, pruned_loss=0.01679, over 4891.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02828, over 971559.56 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 15:07:03,251 INFO [train.py:715] (2/8) Epoch 19, batch 9150, loss[loss=0.1316, simple_loss=0.2046, pruned_loss=0.02928, over 4885.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02852, over 972506.18 frames.], batch size: 17, lr: 1.18e-04
2022-05-09 15:07:42,032 INFO [train.py:715] (2/8) Epoch 19, batch 9200, loss[loss=0.1079, simple_loss=0.177, pruned_loss=0.01946, over 4906.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02869, over 972425.89 frames.], batch size: 29, lr: 1.18e-04
2022-05-09 15:08:21,755 INFO [train.py:715] (2/8) Epoch 19, batch 9250, loss[loss=0.1356, simple_loss=0.209, pruned_loss=0.03112, over 4911.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02845, over 973257.58 frames.], batch size: 18, lr: 1.18e-04
2022-05-09 15:09:00,951 INFO [train.py:715] (2/8) Epoch 19, batch 9300, loss[loss=0.1535, simple_loss=0.2296, pruned_loss=0.03871, over 4968.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02862, over 973388.42 frames.], batch size: 39, lr: 1.18e-04
2022-05-09 15:09:39,865 INFO [train.py:715] (2/8) Epoch 19, batch 9350, loss[loss=0.1234, simple_loss=0.1956, pruned_loss=0.02559, over 4645.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.0284, over 972874.69 frames.], batch size: 13, lr: 1.18e-04
2022-05-09 15:10:19,971 INFO [train.py:715] (2/8) Epoch 19, batch 9400, loss[loss=0.1171, simple_loss=0.1972, pruned_loss=0.01848, over 4880.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02875, over 973127.76 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 15:11:00,062 INFO [train.py:715] (2/8) Epoch 19, batch 9450, loss[loss=0.1428, simple_loss=0.2225, pruned_loss=0.03154, over 4862.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2062, pruned_loss=0.02839, over 973114.88 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 15:11:38,884 INFO [train.py:715] (2/8) Epoch 19, batch 9500, loss[loss=0.14, simple_loss=0.21, pruned_loss=0.03499, over 4750.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02829, over 972265.11 frames.], batch size: 16, lr: 1.18e-04
2022-05-09 15:12:18,098 INFO [train.py:715] (2/8) Epoch 19, batch 9550, loss[loss=0.134, simple_loss=0.2192, pruned_loss=0.02435, over 4887.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02853, over 971952.59 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 15:12:57,475 INFO [train.py:715] (2/8) Epoch 19, batch 9600, loss[loss=0.161, simple_loss=0.2361, pruned_loss=0.043, over 4850.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2065, pruned_loss=0.0286, over 972103.47 frames.], batch size: 20, lr: 1.18e-04
2022-05-09 15:13:36,652 INFO [train.py:715] (2/8) Epoch 19, batch 9650, loss[loss=0.1151, simple_loss=0.1951, pruned_loss=0.01752, over 4896.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02879, over 972300.49 frames.], batch size: 22, lr: 1.18e-04
2022-05-09 15:14:14,975 INFO [train.py:715] (2/8) Epoch 19, batch 9700, loss[loss=0.1173, simple_loss=0.1896, pruned_loss=0.0225, over 4956.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02854, over 972064.33 frames.], batch size: 15, lr: 1.18e-04
2022-05-09 15:14:54,706 INFO [train.py:715] (2/8) Epoch 19, batch 9750, loss[loss=0.1546, simple_loss=0.2226, pruned_loss=0.04335, over 4870.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02867, over 972184.38 frames.], batch size: 32, lr: 1.18e-04
2022-05-09 15:15:34,784 INFO [train.py:715] (2/8) Epoch 19, batch 9800, loss[loss=0.1208, simple_loss=0.1916, pruned_loss=0.02503, over 4798.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02855, over 971089.19 frames.], batch size: 24, lr: 1.18e-04
2022-05-09 15:16:14,507 INFO [train.py:715] (2/8) Epoch 19, batch 9850, loss[loss=0.1341, simple_loss=0.2203, pruned_loss=0.02395, over 4929.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02901, over 972155.36 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 15:16:53,381 INFO [train.py:715] (2/8) Epoch 19, batch 9900, loss[loss=0.1511, simple_loss=0.238, pruned_loss=0.0321, over 4907.00 frames.], tot_loss[loss=0.1332, simple_loss=0.2078, pruned_loss=0.02929, over 973093.38 frames.], batch size: 19, lr: 1.18e-04
2022-05-09 15:17:33,334 INFO [train.py:715] (2/8) Epoch 19, batch 9950, loss[loss=0.143, simple_loss=0.2148, pruned_loss=0.03558, over 4835.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2074, pruned_loss=0.02884, over 973121.24 frames.], batch size: 26, lr: 1.18e-04
2022-05-09 15:18:12,862 INFO [train.py:715] (2/8) Epoch 19, batch 10000, loss[loss=0.1408, simple_loss=0.211, pruned_loss=0.03534, over 4951.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2075, pruned_loss=0.02877, over 973086.43 frames.], batch size: 23, lr: 1.18e-04
2022-05-09 15:18:51,543 INFO [train.py:715] (2/8) Epoch 19, batch 10050, loss[loss=0.1053, simple_loss=0.1724, pruned_loss=0.01907, over 4951.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2075, pruned_loss=0.02937, over 972454.88 frames.], batch size: 14, lr: 1.18e-04
2022-05-09 15:19:31,283 INFO [train.py:715] (2/8) Epoch 19, batch 10100, loss[loss=0.104, simple_loss=0.1733, pruned_loss=0.01733, over 4828.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.02943, over 973053.40 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 15:20:10,773 INFO [train.py:715] (2/8) Epoch 19, batch 10150, loss[loss=0.1232, simple_loss=0.2062, pruned_loss=0.02008, over 4800.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2057, pruned_loss=0.02895, over 972209.55 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 15:20:49,760 INFO [train.py:715] (2/8) Epoch 19, batch 10200, loss[loss=0.1695, simple_loss=0.2396, pruned_loss=0.04966, over 4786.00 frames.], tot_loss[loss=0.131, simple_loss=0.205, pruned_loss=0.02852, over 972645.48 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 15:21:29,155 INFO [train.py:715] (2/8) Epoch 19, batch 10250, loss[loss=0.1196, simple_loss=0.1888, pruned_loss=0.0252, over 4971.00 frames.], tot_loss[loss=0.1305, simple_loss=0.205, pruned_loss=0.02803, over 973187.39 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 15:22:09,286 INFO [train.py:715] (2/8) Epoch 19, batch 10300, loss[loss=0.1215, simple_loss=0.2053, pruned_loss=0.01884, over 4787.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02853, over 972562.98 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 15:22:48,846 INFO [train.py:715] (2/8) Epoch 19, batch 10350, loss[loss=0.1243, simple_loss=0.2014, pruned_loss=0.02363, over 4842.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02836, over 972459.02 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:23:27,535 INFO [train.py:715] (2/8) Epoch 19, batch 10400, loss[loss=0.1369, simple_loss=0.2001, pruned_loss=0.0369, over 4859.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02819, over 972376.07 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 15:24:07,296 INFO [train.py:715] (2/8) Epoch 19, batch 10450, loss[loss=0.1142, simple_loss=0.1955, pruned_loss=0.01642, over 4782.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02793, over 972237.44 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:24:47,013 INFO [train.py:715] (2/8) Epoch 19, batch 10500, loss[loss=0.1267, simple_loss=0.2034, pruned_loss=0.02501, over 4757.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2045, pruned_loss=0.02807, over 972027.97 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:25:25,932 INFO [train.py:715] (2/8) Epoch 19, batch 10550, loss[loss=0.1215, simple_loss=0.2028, pruned_loss=0.02012, over 4920.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02817, over 972877.98 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 15:26:04,898 INFO [train.py:715] (2/8) Epoch 19, batch 10600, loss[loss=0.1252, simple_loss=0.1879, pruned_loss=0.03126, over 4742.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2052, pruned_loss=0.02829, over 971921.30 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:26:47,172 INFO [train.py:715] (2/8) Epoch 19, batch 10650, loss[loss=0.138, simple_loss=0.2118, pruned_loss=0.03213, over 4879.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.02815, over 971590.44 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:27:26,343 INFO [train.py:715] (2/8) Epoch 19, batch 10700, loss[loss=0.1377, simple_loss=0.2022, pruned_loss=0.03661, over 4784.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.0286, over 971263.51 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:28:05,708 INFO [train.py:715] (2/8) Epoch 19, batch 10750, loss[loss=0.1407, simple_loss=0.2179, pruned_loss=0.03172, over 4923.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02844, over 970946.01 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 15:28:45,287 INFO [train.py:715] (2/8) Epoch 19, batch 10800, loss[loss=0.1319, simple_loss=0.2117, pruned_loss=0.02599, over 4986.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2051, pruned_loss=0.02814, over 972015.27 frames.], batch size: 28, lr: 1.17e-04
2022-05-09 15:29:25,026 INFO [train.py:715] (2/8) Epoch 19, batch 10850, loss[loss=0.1161, simple_loss=0.1959, pruned_loss=0.01817, over 4879.00 frames.], tot_loss[loss=0.13, simple_loss=0.2047, pruned_loss=0.02767, over 972457.85 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:30:03,636 INFO [train.py:715] (2/8) Epoch 19, batch 10900, loss[loss=0.1283, simple_loss=0.2036, pruned_loss=0.02649, over 4758.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2051, pruned_loss=0.02764, over 971950.72 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:30:42,652 INFO [train.py:715] (2/8) Epoch 19, batch 10950, loss[loss=0.1266, simple_loss=0.1993, pruned_loss=0.02692, over 4983.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2052, pruned_loss=0.02755, over 972138.21 frames.], batch size: 28, lr: 1.17e-04
2022-05-09 15:31:22,355 INFO [train.py:715] (2/8) Epoch 19, batch 11000, loss[loss=0.1378, simple_loss=0.2115, pruned_loss=0.03208, over 4923.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2056, pruned_loss=0.0278, over 972209.65 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:32:02,211 INFO [train.py:715] (2/8) Epoch 19, batch 11050, loss[loss=0.1237, simple_loss=0.207, pruned_loss=0.02019, over 4838.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2053, pruned_loss=0.02767, over 972382.62 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 15:32:40,682 INFO [train.py:715] (2/8) Epoch 19, batch 11100, loss[loss=0.1078, simple_loss=0.185, pruned_loss=0.01525, over 4941.00 frames.], tot_loss[loss=0.1302, simple_loss=0.205, pruned_loss=0.02771, over 972357.19 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 15:33:20,042 INFO [train.py:715] (2/8) Epoch 19, batch 11150, loss[loss=0.1453, simple_loss=0.2227, pruned_loss=0.0339, over 4694.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02852, over 972323.45 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:33:59,455 INFO [train.py:715] (2/8) Epoch 19, batch 11200, loss[loss=0.1143, simple_loss=0.1821, pruned_loss=0.02327, over 4760.00 frames.], tot_loss[loss=0.1309, simple_loss=0.205, pruned_loss=0.02841, over 972428.22 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:34:38,838 INFO [train.py:715] (2/8) Epoch 19, batch 11250, loss[loss=0.1346, simple_loss=0.2099, pruned_loss=0.02963, over 4759.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02844, over 972217.30 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:35:18,203 INFO [train.py:715] (2/8) Epoch 19, batch 11300, loss[loss=0.1471, simple_loss=0.219, pruned_loss=0.03762, over 4812.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02818, over 972546.60 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 15:35:56,992 INFO [train.py:715] (2/8) Epoch 19, batch 11350, loss[loss=0.1133, simple_loss=0.1947, pruned_loss=0.01599, over 4780.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2058, pruned_loss=0.02866, over 972496.21 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 15:36:36,602 INFO [train.py:715] (2/8) Epoch 19, batch 11400, loss[loss=0.1457, simple_loss=0.2135, pruned_loss=0.03899, over 4860.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2057, pruned_loss=0.02909, over 972092.55 frames.], batch size: 38, lr: 1.17e-04
2022-05-09 15:37:16,209 INFO [train.py:715] (2/8) Epoch 19, batch 11450, loss[loss=0.1275, simple_loss=0.2016, pruned_loss=0.02674, over 4851.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2058, pruned_loss=0.02886, over 973205.34 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 15:37:56,152 INFO [train.py:715] (2/8) Epoch 19, batch 11500, loss[loss=0.1163, simple_loss=0.1938, pruned_loss=0.01936, over 4780.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02833, over 973256.95 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 15:38:35,584 INFO [train.py:715] (2/8) Epoch 19, batch 11550, loss[loss=0.09808, simple_loss=0.178, pruned_loss=0.009057, over 4811.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02831, over 972505.91 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 15:39:14,553 INFO [train.py:715] (2/8) Epoch 19, batch 11600, loss[loss=0.1105, simple_loss=0.194, pruned_loss=0.01348, over 4943.00 frames.], tot_loss[loss=0.1305, simple_loss=0.205, pruned_loss=0.028, over 972768.75 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 15:39:54,373 INFO [train.py:715] (2/8) Epoch 19, batch 11650, loss[loss=0.108, simple_loss=0.1862, pruned_loss=0.01495, over 4787.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2054, pruned_loss=0.02817, over 972843.11 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 15:40:33,463 INFO [train.py:715] (2/8) Epoch 19, batch 11700, loss[loss=0.1349, simple_loss=0.2076, pruned_loss=0.03112, over 4945.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.028, over 972476.09 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 15:41:13,007 INFO [train.py:715] (2/8) Epoch 19, batch 11750, loss[loss=0.1158, simple_loss=0.1896, pruned_loss=0.02096, over 4759.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.02815, over 971380.56 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:41:52,544 INFO [train.py:715] (2/8) Epoch 19, batch 11800, loss[loss=0.1163, simple_loss=0.1943, pruned_loss=0.01917, over 4821.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02814, over 971309.34 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 15:42:32,204 INFO [train.py:715] (2/8) Epoch 19, batch 11850, loss[loss=0.1479, simple_loss=0.2222, pruned_loss=0.03682, over 4736.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02822, over 972509.27 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 15:43:11,796 INFO [train.py:715] (2/8) Epoch 19, batch 11900, loss[loss=0.1223, simple_loss=0.1986, pruned_loss=0.023, over 4861.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02826, over 972882.47 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 15:43:51,307 INFO [train.py:715] (2/8) Epoch 19, batch 11950, loss[loss=0.1147, simple_loss=0.1866, pruned_loss=0.02143, over 4949.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02822, over 972432.75 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 15:44:30,454 INFO [train.py:715] (2/8) Epoch 19, batch 12000, loss[loss=0.1638, simple_loss=0.2406, pruned_loss=0.04356, over 4751.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02803, over 971182.24 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:44:30,454 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 15:44:40,311 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1044, simple_loss=0.1877, pruned_loss=0.01054, over 914524.00 frames.
2022-05-09 15:45:20,291 INFO [train.py:715] (2/8) Epoch 19, batch 12050, loss[loss=0.1237, simple_loss=0.2058, pruned_loss=0.02083, over 4929.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02831, over 970832.73 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 15:46:00,177 INFO [train.py:715] (2/8) Epoch 19, batch 12100, loss[loss=0.1242, simple_loss=0.1986, pruned_loss=0.02492, over 4828.00 frames.], tot_loss[loss=0.1324, simple_loss=0.207, pruned_loss=0.0289, over 971676.27 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 15:46:39,304 INFO [train.py:715] (2/8) Epoch 19, batch 12150, loss[loss=0.1335, simple_loss=0.218, pruned_loss=0.02453, over 4831.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02944, over 971695.44 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 15:47:18,770 INFO [train.py:715] (2/8) Epoch 19, batch 12200, loss[loss=0.1429, simple_loss=0.2153, pruned_loss=0.03531, over 4880.00 frames.], tot_loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02903, over 972534.41 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 15:47:58,235 INFO [train.py:715] (2/8) Epoch 19, batch 12250, loss[loss=0.1307, simple_loss=0.212, pruned_loss=0.02464, over 4876.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02905, over 972228.16 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 15:48:37,874 INFO [train.py:715] (2/8) Epoch 19, batch 12300, loss[loss=0.1246, simple_loss=0.2036, pruned_loss=0.02279, over 4842.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2066, pruned_loss=0.02877, over 972499.66 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:49:17,552 INFO [train.py:715] (2/8) Epoch 19, batch 12350, loss[loss=0.1402, simple_loss=0.2191, pruned_loss=0.03065, over 4692.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02828, over 972398.11 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:49:56,112 INFO [train.py:715] (2/8) Epoch 19, batch 12400, loss[loss=0.1156, simple_loss=0.194, pruned_loss=0.01859, over 4794.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2065, pruned_loss=0.02842, over 972307.57 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 15:50:35,580 INFO [train.py:715] (2/8) Epoch 19, batch 12450, loss[loss=0.115, simple_loss=0.1948, pruned_loss=0.01754, over 4766.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2063, pruned_loss=0.02823, over 971211.13 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 15:51:14,298 INFO [train.py:715] (2/8) Epoch 19, batch 12500, loss[loss=0.1261, simple_loss=0.2022, pruned_loss=0.025, over 4800.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2067, pruned_loss=0.02846, over 970818.79 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 15:51:53,639 INFO [train.py:715] (2/8) Epoch 19, batch 12550, loss[loss=0.1432, simple_loss=0.2272, pruned_loss=0.02963, over 4950.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2066, pruned_loss=0.02847, over 971134.79 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 15:52:33,123 INFO [train.py:715] (2/8) Epoch 19, batch 12600, loss[loss=0.1394, simple_loss=0.2199, pruned_loss=0.02944, over 4876.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02797, over 970741.93 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 15:53:12,608 INFO [train.py:715] (2/8) Epoch 19, batch 12650, loss[loss=0.1358, simple_loss=0.2215, pruned_loss=0.02501, over 4968.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2055, pruned_loss=0.02789, over 970560.50 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 15:53:51,569 INFO [train.py:715] (2/8) Epoch 19, batch 12700, loss[loss=0.1168, simple_loss=0.1884, pruned_loss=0.02255, over 4796.00 frames.], tot_loss[loss=0.131, simple_loss=0.2059, pruned_loss=0.02806, over 971249.89 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:54:30,763 INFO [train.py:715] (2/8) Epoch 19, batch 12750, loss[loss=0.1365, simple_loss=0.2076, pruned_loss=0.0327, over 4866.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2068, pruned_loss=0.02851, over 972080.82 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 15:55:10,391 INFO [train.py:715] (2/8) Epoch 19, batch 12800, loss[loss=0.1364, simple_loss=0.1951, pruned_loss=0.0388, over 4863.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02847, over 972272.53 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 15:55:49,792 INFO [train.py:715] (2/8) Epoch 19, batch 12850, loss[loss=0.106, simple_loss=0.1814, pruned_loss=0.01527, over 4796.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.0282, over 972958.50 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 15:56:28,730 INFO [train.py:715] (2/8) Epoch 19, batch 12900, loss[loss=0.1584, simple_loss=0.2347, pruned_loss=0.04109, over 4961.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02879, over 973658.44 frames.], batch size: 39, lr: 1.17e-04
2022-05-09 15:57:08,292 INFO [train.py:715] (2/8) Epoch 19, batch 12950, loss[loss=0.1312, simple_loss=0.2118, pruned_loss=0.02531, over 4874.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2068, pruned_loss=0.02888, over 973331.87 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 15:57:47,534 INFO [train.py:715] (2/8) Epoch 19, batch 13000, loss[loss=0.1254, simple_loss=0.2001, pruned_loss=0.02535, over 4784.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02822, over 972528.87 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 15:58:26,679 INFO [train.py:715] (2/8) Epoch 19, batch 13050, loss[loss=0.1351, simple_loss=0.2193, pruned_loss=0.02543, over 4961.00 frames.], tot_loss[loss=0.1306, simple_loss=0.205, pruned_loss=0.02805, over 973258.61 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 15:59:05,573 INFO [train.py:715] (2/8) Epoch 19, batch 13100, loss[loss=0.1218, simple_loss=0.1823, pruned_loss=0.03061, over 4959.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.02815, over 973321.86 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 15:59:44,834 INFO [train.py:715] (2/8) Epoch 19, batch 13150, loss[loss=0.1308, simple_loss=0.2127, pruned_loss=0.0245, over 4816.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.02858, over 973808.67 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 16:00:24,450 INFO [train.py:715] (2/8) Epoch 19, batch 13200, loss[loss=0.1472, simple_loss=0.2227, pruned_loss=0.03581, over 4818.00 frames.], tot_loss[loss=0.131, simple_loss=0.2053, pruned_loss=0.02841, over 973207.61 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:01:03,678 INFO [train.py:715] (2/8) Epoch 19, batch 13250, loss[loss=0.1234, simple_loss=0.2148, pruned_loss=0.01597, over 4703.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.02851, over 972865.98 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:01:42,979 INFO [train.py:715] (2/8) Epoch 19, batch 13300, loss[loss=0.1211, simple_loss=0.2003, pruned_loss=0.0209, over 4961.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02846, over 973153.21 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:02:22,613 INFO [train.py:715] (2/8) Epoch 19, batch 13350, loss[loss=0.1601, simple_loss=0.2318, pruned_loss=0.04423, over 4946.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.02871, over 973577.16 frames.], batch size: 39, lr: 1.17e-04
2022-05-09 16:03:01,279 INFO [train.py:715] (2/8) Epoch 19, batch 13400, loss[loss=0.1089, simple_loss=0.1914, pruned_loss=0.01314, over 4885.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2063, pruned_loss=0.0286, over 973160.51 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 16:03:40,771 INFO [train.py:715] (2/8) Epoch 19, batch 13450, loss[loss=0.1391, simple_loss=0.2111, pruned_loss=0.03355, over 4767.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02841, over 973432.03 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:04:20,050 INFO [train.py:715] (2/8) Epoch 19, batch 13500, loss[loss=0.115, simple_loss=0.188, pruned_loss=0.02096, over 4984.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02847, over 973349.22 frames.], batch size: 28, lr: 1.17e-04
2022-05-09 16:04:59,472 INFO [train.py:715] (2/8) Epoch 19, batch 13550, loss[loss=0.1395, simple_loss=0.2177, pruned_loss=0.03061, over 4827.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02859, over 973241.70 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:05:38,405 INFO [train.py:715] (2/8) Epoch 19, batch 13600, loss[loss=0.1048, simple_loss=0.1808, pruned_loss=0.01441, over 4977.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02811, over 972591.22 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:06:17,586 INFO [train.py:715] (2/8) Epoch 19, batch 13650, loss[loss=0.1389, simple_loss=0.2148, pruned_loss=0.03152, over 4764.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2056, pruned_loss=0.02787, over 972672.85 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:06:57,013 INFO [train.py:715] (2/8) Epoch 19, batch 13700, loss[loss=0.1529, simple_loss=0.2294, pruned_loss=0.03818, over 4890.00 frames.], tot_loss[loss=0.131, simple_loss=0.206, pruned_loss=0.02799, over 972957.29 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:07:35,733 INFO [train.py:715] (2/8) Epoch 19, batch 13750, loss[loss=0.1467, simple_loss=0.2256, pruned_loss=0.03387, over 4792.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02835, over 972486.14 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:08:15,018 INFO [train.py:715] (2/8) Epoch 19, batch 13800, loss[loss=0.129, simple_loss=0.1965, pruned_loss=0.03071, over 4770.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02842, over 972123.16 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:08:55,083 INFO [train.py:715] (2/8) Epoch 19, batch 13850, loss[loss=0.1242, simple_loss=0.2014, pruned_loss=0.02348, over 4795.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2065, pruned_loss=0.0285, over 972147.87 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:09:34,682 INFO [train.py:715] (2/8) Epoch 19, batch 13900, loss[loss=0.1436, simple_loss=0.2087, pruned_loss=0.03924, over 4789.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02853, over 971511.39 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:10:14,444 INFO [train.py:715] (2/8) Epoch 19, batch 13950, loss[loss=0.1273, simple_loss=0.2086, pruned_loss=0.02298, over 4954.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02802, over 971528.06 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 16:10:53,214 INFO [train.py:715] (2/8) Epoch 19, batch 14000, loss[loss=0.1171, simple_loss=0.1998, pruned_loss=0.01725, over 4810.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2055, pruned_loss=0.028, over 971712.67 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:11:32,643 INFO [train.py:715] (2/8) Epoch 19, batch 14050, loss[loss=0.173, simple_loss=0.2405, pruned_loss=0.05269, over 4832.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02803, over 972570.45 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 16:12:11,843 INFO [train.py:715] (2/8) Epoch 19, batch 14100, loss[loss=0.1418, simple_loss=0.2118, pruned_loss=0.03592, over 4693.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02852, over 972315.76 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:12:51,003 INFO [train.py:715] (2/8) Epoch 19, batch 14150, loss[loss=0.1313, simple_loss=0.2038, pruned_loss=0.02943, over 4958.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02849, over 972833.93 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:13:30,124 INFO [train.py:715] (2/8) Epoch 19, batch 14200, loss[loss=0.1332, simple_loss=0.214, pruned_loss=0.02623, over 4832.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2069, pruned_loss=0.02883, over 972555.87 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:14:08,936 INFO [train.py:715] (2/8) Epoch 19, batch 14250, loss[loss=0.1655, simple_loss=0.2432, pruned_loss=0.04389, over 4947.00 frames.], tot_loss[loss=0.1335, simple_loss=0.2082, pruned_loss=0.02947, over 972300.32 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 16:14:48,114 INFO [train.py:715] (2/8) Epoch 19, batch 14300, loss[loss=0.15, simple_loss=0.2245, pruned_loss=0.03774, over 4791.00 frames.], tot_loss[loss=0.1337, simple_loss=0.2084, pruned_loss=0.02951, over 972071.51 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:15:27,233 INFO [train.py:715] (2/8) Epoch 19, batch 14350, loss[loss=0.1135, simple_loss=0.1822, pruned_loss=0.02235, over 4976.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2073, pruned_loss=0.02911, over 972103.81 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 16:16:06,786 INFO [train.py:715] (2/8) Epoch 19, batch 14400, loss[loss=0.1203, simple_loss=0.2014, pruned_loss=0.01962, over 4888.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2076, pruned_loss=0.02953, over 972056.30 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:16:45,670 INFO [train.py:715] (2/8) Epoch 19, batch 14450, loss[loss=0.1602, simple_loss=0.2247, pruned_loss=0.04779, over 4780.00 frames.], tot_loss[loss=0.1339, simple_loss=0.2081, pruned_loss=0.02988, over 971864.86 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:17:24,669 INFO [train.py:715] (2/8) Epoch 19, batch 14500, loss[loss=0.1162, simple_loss=0.1938, pruned_loss=0.01928, over 4815.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2076, pruned_loss=0.029, over 971898.12 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:18:03,481 INFO [train.py:715] (2/8) Epoch 19, batch 14550, loss[loss=0.1305, simple_loss=0.2026, pruned_loss=0.02918, over 4690.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2066, pruned_loss=0.02858, over 971735.21 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:18:43,155 INFO [train.py:715] (2/8) Epoch 19, batch 14600, loss[loss=0.1106, simple_loss=0.1881, pruned_loss=0.01654, over 4969.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2072, pruned_loss=0.02916, over 971769.35 frames.], batch size: 28, lr: 1.17e-04
2022-05-09 16:19:22,223 INFO [train.py:715] (2/8) Epoch 19, batch 14650, loss[loss=0.1413, simple_loss=0.2154, pruned_loss=0.03363, over 4954.00 frames.], tot_loss[loss=0.1323, simple_loss=0.207, pruned_loss=0.02881, over 972007.53 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 16:20:01,158 INFO [train.py:715] (2/8) Epoch 19, batch 14700, loss[loss=0.1243, simple_loss=0.1986, pruned_loss=0.02504, over 4849.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02845, over 971815.86 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:20:40,520 INFO [train.py:715] (2/8) Epoch 19, batch 14750, loss[loss=0.1368, simple_loss=0.222, pruned_loss=0.02576, over 4799.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02861, over 971954.19 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:21:19,782 INFO [train.py:715] (2/8) Epoch 19, batch 14800, loss[loss=0.1228, simple_loss=0.2015, pruned_loss=0.02204, over 4901.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02862, over 972464.53 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:21:58,086 INFO [train.py:715] (2/8) Epoch 19, batch 14850, loss[loss=0.1298, simple_loss=0.2092, pruned_loss=0.02522, over 4885.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2057, pruned_loss=0.02838, over 971559.21 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 16:22:37,374 INFO [train.py:715] (2/8) Epoch 19, batch 14900, loss[loss=0.1437, simple_loss=0.217, pruned_loss=0.03523, over 4828.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2064, pruned_loss=0.02826, over 972297.27 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:23:16,321 INFO [train.py:715] (2/8) Epoch 19, batch 14950, loss[loss=0.1007, simple_loss=0.1748, pruned_loss=0.01332, over 4784.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02808, over 972023.35 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:23:55,095 INFO [train.py:715] (2/8) Epoch 19, batch 15000, loss[loss=0.1572, simple_loss=0.2268, pruned_loss=0.04377, over 4989.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02857, over 972439.81 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:23:55,096 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 16:24:07,488 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1045, simple_loss=0.1877, pruned_loss=0.01064, over 914524.00 frames.
2022-05-09 16:24:46,719 INFO [train.py:715] (2/8) Epoch 19, batch 15050, loss[loss=0.1361, simple_loss=0.2078, pruned_loss=0.03221, over 4801.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02894, over 971704.71 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:25:26,173 INFO [train.py:715] (2/8) Epoch 19, batch 15100, loss[loss=0.1376, simple_loss=0.2138, pruned_loss=0.03065, over 4979.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2061, pruned_loss=0.02866, over 971665.46 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 16:26:05,808 INFO [train.py:715] (2/8) Epoch 19, batch 15150, loss[loss=0.1606, simple_loss=0.2329, pruned_loss=0.04417, over 4882.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.0285, over 971707.32 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 16:26:45,268 INFO [train.py:715] (2/8) Epoch 19, batch 15200, loss[loss=0.1332, simple_loss=0.2177, pruned_loss=0.02433, over 4890.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2055, pruned_loss=0.0285, over 972968.55 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:27:24,248 INFO [train.py:715] (2/8) Epoch 19, batch 15250, loss[loss=0.1205, simple_loss=0.1886, pruned_loss=0.02619, over 4841.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02841, over 973114.51 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:28:04,173 INFO [train.py:715] (2/8) Epoch 19, batch 15300, loss[loss=0.1163, simple_loss=0.1987, pruned_loss=0.01691, over 4984.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02877, over 974073.22 frames.], batch size: 28, lr: 1.17e-04
2022-05-09 16:28:43,741 INFO [train.py:715] (2/8) Epoch 19, batch 15350, loss[loss=0.1403, simple_loss=0.2102, pruned_loss=0.03515, over 4945.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02811, over 974461.12 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:29:23,528 INFO [train.py:715] (2/8) Epoch 19, batch 15400, loss[loss=0.1574, simple_loss=0.2356, pruned_loss=0.0396, over 4758.00 frames.], tot_loss[loss=0.1313, simple_loss=0.206, pruned_loss=0.02834, over 973671.92 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:30:03,013 INFO [train.py:715] (2/8) Epoch 19, batch 15450, loss[loss=0.1281, simple_loss=0.208, pruned_loss=0.02411, over 4886.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2052, pruned_loss=0.02796, over 973285.79 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:30:42,451 INFO [train.py:715] (2/8) Epoch 19, batch 15500, loss[loss=0.114, simple_loss=0.1913, pruned_loss=0.01838, over 4912.00 frames.], tot_loss[loss=0.131, simple_loss=0.2061, pruned_loss=0.02795, over 972854.57 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 16:31:21,338 INFO [train.py:715] (2/8) Epoch 19, batch 15550, loss[loss=0.1236, simple_loss=0.1978, pruned_loss=0.02477, over 4933.00 frames.], tot_loss[loss=0.1319, simple_loss=0.207, pruned_loss=0.02838, over 972559.44 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 16:32:00,424 INFO [train.py:715] (2/8) Epoch 19, batch 15600, loss[loss=0.1173, simple_loss=0.1885, pruned_loss=0.02301, over 4837.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02815, over 972645.39 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 16:32:40,092 INFO [train.py:715] (2/8) Epoch 19, batch 15650, loss[loss=0.1305, simple_loss=0.1997, pruned_loss=0.03064, over 4837.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02798, over 972910.28 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:33:19,043 INFO [train.py:715] (2/8) Epoch 19, batch 15700, loss[loss=0.1329, simple_loss=0.2091, pruned_loss=0.02831, over 4981.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2054, pruned_loss=0.028, over 973052.92 frames.], batch size: 28, lr: 1.17e-04
2022-05-09 16:33:59,137 INFO [train.py:715] (2/8) Epoch 19, batch 15750, loss[loss=0.1466, simple_loss=0.2159, pruned_loss=0.03869, over 4836.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2045, pruned_loss=0.02761, over 972615.62 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 16:34:38,396 INFO [train.py:715] (2/8) Epoch 19, batch 15800, loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02971, over 4956.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02837, over 973250.17 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:35:17,529 INFO [train.py:715] (2/8) Epoch 19, batch 15850, loss[loss=0.1319, simple_loss=0.2041, pruned_loss=0.02984, over 4985.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02821, over 972898.48 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:35:56,469 INFO [train.py:715] (2/8) Epoch 19, batch 15900, loss[loss=0.1635, simple_loss=0.2565, pruned_loss=0.03523, over 4898.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.0283, over 972706.68 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:36:35,616 INFO [train.py:715] (2/8) Epoch 19, batch 15950, loss[loss=0.09552, simple_loss=0.1699, pruned_loss=0.01056, over 4809.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2053, pruned_loss=0.02781, over 972293.24 frames.], batch size: 27, lr: 1.17e-04
2022-05-09 16:37:15,277 INFO [train.py:715] (2/8) Epoch 19, batch 16000, loss[loss=0.1324, simple_loss=0.2044, pruned_loss=0.03016, over 4780.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2058, pruned_loss=0.02783, over 972547.73 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 16:37:53,934 INFO [train.py:715] (2/8) Epoch 19, batch 16050, loss[loss=0.1147, simple_loss=0.1897, pruned_loss=0.01981, over 4946.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2062, pruned_loss=0.02804, over 972667.84 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:38:33,244 INFO [train.py:715] (2/8) Epoch 19, batch 16100, loss[loss=0.1502, simple_loss=0.2326, pruned_loss=0.03391, over 4875.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02825, over 972194.30 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:39:12,544 INFO [train.py:715] (2/8) Epoch 19, batch 16150, loss[loss=0.125, simple_loss=0.2125, pruned_loss=0.01874, over 4884.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2078, pruned_loss=0.02873, over 972336.24 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:39:51,597 INFO [train.py:715] (2/8) Epoch 19, batch 16200, loss[loss=0.1318, simple_loss=0.2031, pruned_loss=0.03028, over 4953.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2075, pruned_loss=0.02859, over 971667.97 frames.], batch size: 39, lr: 1.17e-04
2022-05-09 16:40:29,890 INFO [train.py:715] (2/8) Epoch 19, batch 16250, loss[loss=0.1417, simple_loss=0.2127, pruned_loss=0.03534, over 4686.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2073, pruned_loss=0.02882, over 971529.40 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:41:08,935 INFO [train.py:715] (2/8) Epoch 19, batch 16300, loss[loss=0.1535, simple_loss=0.2362, pruned_loss=0.03535, over 4873.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2072, pruned_loss=0.02878, over 970660.85 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 16:41:48,391 INFO [train.py:715] (2/8) Epoch 19, batch 16350, loss[loss=0.1291, simple_loss=0.2035, pruned_loss=0.02734, over 4949.00 frames.], tot_loss[loss=0.133, simple_loss=0.2083, pruned_loss=0.02882, over 969969.64 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:42:26,936 INFO [train.py:715] (2/8) Epoch 19, batch 16400, loss[loss=0.1282, simple_loss=0.2078, pruned_loss=0.02432, over 4874.00 frames.], tot_loss[loss=0.133, simple_loss=0.2084, pruned_loss=0.02877, over 970283.14 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 16:43:05,776 INFO [train.py:715] (2/8) Epoch 19, batch 16450, loss[loss=0.1236, simple_loss=0.1983, pruned_loss=0.02446, over 4804.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2075, pruned_loss=0.02869, over 969690.82 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:43:44,324 INFO [train.py:715] (2/8) Epoch 19, batch 16500, loss[loss=0.1322, simple_loss=0.2075, pruned_loss=0.02847, over 4864.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02837, over 970687.47 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 16:44:23,743 INFO [train.py:715] (2/8) Epoch 19, batch 16550, loss[loss=0.1278, simple_loss=0.2067, pruned_loss=0.02448, over 4838.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2067, pruned_loss=0.02879, over 971091.47 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 16:45:02,745 INFO [train.py:715] (2/8) Epoch 19, batch 16600, loss[loss=0.1488, simple_loss=0.2282, pruned_loss=0.03474, over 4970.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2067, pruned_loss=0.02902, over 971405.10 frames.], batch size: 28, lr: 1.17e-04
2022-05-09 16:45:41,759 INFO [train.py:715] (2/8) Epoch 19, batch 16650, loss[loss=0.1015, simple_loss=0.1762, pruned_loss=0.01342, over 4815.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02854, over 971665.94 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:46:21,732 INFO [train.py:715] (2/8) Epoch 19, batch 16700, loss[loss=0.1144, simple_loss=0.1887, pruned_loss=0.02008, over 4827.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.0285, over 971782.20 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:47:00,842 INFO [train.py:715] (2/8) Epoch 19, batch 16750, loss[loss=0.1065, simple_loss=0.1878, pruned_loss=0.01265, over 4848.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02821, over 972513.22 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 16:47:40,567 INFO [train.py:715] (2/8) Epoch 19, batch 16800, loss[loss=0.1434, simple_loss=0.2101, pruned_loss=0.03836, over 4815.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2047, pruned_loss=0.02751, over 972040.05 frames.], batch size: 27, lr: 1.17e-04
2022-05-09 16:48:19,961 INFO [train.py:715] (2/8) Epoch 19, batch 16850, loss[loss=0.1205, simple_loss=0.1849, pruned_loss=0.02811, over 4804.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2056, pruned_loss=0.02795, over 971793.92 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:48:59,493 INFO [train.py:715] (2/8) Epoch 19, batch 16900, loss[loss=0.1909, simple_loss=0.2406, pruned_loss=0.07057, over 4878.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02876, over 973066.80 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:49:38,113 INFO [train.py:715] (2/8) Epoch 19, batch 16950, loss[loss=0.1199, simple_loss=0.1943, pruned_loss=0.0227, over 4789.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02857, over 973222.81 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:50:17,673 INFO [train.py:715] (2/8) Epoch 19, batch 17000, loss[loss=0.201, simple_loss=0.2541, pruned_loss=0.07396, over 4747.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02901, over 972687.11 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:50:57,095 INFO [train.py:715] (2/8) Epoch 19, batch 17050, loss[loss=0.1299, simple_loss=0.2015, pruned_loss=0.02911, over 4774.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2064, pruned_loss=0.02918, over 972251.95 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:51:36,156 INFO [train.py:715] (2/8) Epoch 19, batch 17100, loss[loss=0.1352, simple_loss=0.2091, pruned_loss=0.03068, over 4850.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2052, pruned_loss=0.02812, over 972745.41 frames.], batch size: 30, lr: 1.17e-04
2022-05-09 16:52:15,339 INFO [train.py:715] (2/8) Epoch 19, batch 17150, loss[loss=0.1502, simple_loss=0.2163, pruned_loss=0.04199, over 4728.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02819, over 972501.38 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 16:52:54,352 INFO [train.py:715] (2/8) Epoch 19, batch 17200, loss[loss=0.144, simple_loss=0.2252, pruned_loss=0.03136, over 4930.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2049, pruned_loss=0.02787, over 972401.29 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:53:33,090 INFO [train.py:715] (2/8) Epoch 19, batch 17250, loss[loss=0.1567, simple_loss=0.2241, pruned_loss=0.04468, over 4923.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2049, pruned_loss=0.02772, over 972224.05 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 16:54:12,078 INFO [train.py:715] (2/8) Epoch 19, batch 17300, loss[loss=0.1351, simple_loss=0.2097, pruned_loss=0.0303, over 4969.00 frames.], tot_loss[loss=0.13, simple_loss=0.2048, pruned_loss=0.02755, over 972486.88 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 16:54:51,731 INFO [train.py:715] (2/8) Epoch 19, batch 17350, loss[loss=0.1472, simple_loss=0.2148, pruned_loss=0.0398, over 4891.00 frames.], tot_loss[loss=0.1296, simple_loss=0.2046, pruned_loss=0.02732, over 971250.98 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 16:55:31,273 INFO [train.py:715] (2/8) Epoch 19, batch 17400, loss[loss=0.1436, simple_loss=0.2157, pruned_loss=0.03574, over 4897.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2056, pruned_loss=0.02768, over 971211.66 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 16:56:10,522 INFO [train.py:715] (2/8) Epoch 19, batch 17450, loss[loss=0.1169, simple_loss=0.1956, pruned_loss=0.01912, over 4934.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2053, pruned_loss=0.02768, over 971647.45 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 16:56:49,874 INFO [train.py:715] (2/8) Epoch 19, batch 17500, loss[loss=0.1375, simple_loss=0.2119, pruned_loss=0.03158, over 4987.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2056, pruned_loss=0.02759, over 972494.64 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 16:57:29,143 INFO [train.py:715] (2/8) Epoch 19, batch 17550, loss[loss=0.1394, simple_loss=0.2211, pruned_loss=0.02887, over 4806.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2055, pruned_loss=0.02776, over 972960.24 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:58:08,754 INFO [train.py:715] (2/8) Epoch 19, batch 17600, loss[loss=0.1356, simple_loss=0.2123, pruned_loss=0.02944, over 4949.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.02789, over 973163.42 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 16:58:47,928 INFO [train.py:715] (2/8) Epoch 19, batch 17650, loss[loss=0.1162, simple_loss=0.1842, pruned_loss=0.0241, over 4907.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02829, over 972827.57 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 16:59:27,081 INFO [train.py:715] (2/8) Epoch 19, batch 17700, loss[loss=0.1197, simple_loss=0.1908, pruned_loss=0.0243, over 4921.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02821, over 972745.45 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:00:06,647 INFO [train.py:715] (2/8) Epoch 19, batch 17750, loss[loss=0.1452, simple_loss=0.2184, pruned_loss=0.03606, over 4910.00 frames.], tot_loss[loss=0.1321, simple_loss=0.207, pruned_loss=0.02863, over 972839.01 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 17:00:45,238 INFO [train.py:715] (2/8) Epoch 19, batch 17800, loss[loss=0.1504, simple_loss=0.2184, pruned_loss=0.04119, over 4794.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.0287, over 972462.34 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 17:01:24,011 INFO [train.py:715] (2/8) Epoch 19, batch 17850, loss[loss=0.1108, simple_loss=0.1854, pruned_loss=0.01813, over 4829.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02836, over 972591.14 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 17:02:03,485 INFO [train.py:715] (2/8) Epoch 19, batch 17900, loss[loss=0.1299, simple_loss=0.1948, pruned_loss=0.03252, over 4688.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2056, pruned_loss=0.02859, over 971332.35 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:02:41,974 INFO [train.py:715] (2/8) Epoch 19, batch 17950, loss[loss=0.1468, simple_loss=0.2097, pruned_loss=0.04192, over 4975.00 frames.], tot_loss[loss=0.1321, simple_loss=0.206, pruned_loss=0.02915, over 971715.50 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 17:03:21,256 INFO [train.py:715] (2/8) Epoch 19, batch 18000, loss[loss=0.1331, simple_loss=0.2178, pruned_loss=0.02421, over 4793.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2066, pruned_loss=0.02901, over 972093.51 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:03:21,257 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 17:03:31,129 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1046, simple_loss=0.1877, pruned_loss=0.01074, over 914524.00 frames.
2022-05-09 17:04:10,640 INFO [train.py:715] (2/8) Epoch 19, batch 18050, loss[loss=0.1195, simple_loss=0.1978, pruned_loss=0.02059, over 4796.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02944, over 970850.57 frames.], batch size: 24, lr: 1.17e-04
2022-05-09 17:04:50,209 INFO [train.py:715] (2/8) Epoch 19, batch 18100, loss[loss=0.1418, simple_loss=0.2229, pruned_loss=0.0304, over 4981.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2066, pruned_loss=0.02909, over 970906.11 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:05:30,069 INFO [train.py:715] (2/8) Epoch 19, batch 18150, loss[loss=0.1405, simple_loss=0.2192, pruned_loss=0.03084, over 4794.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02888, over 970044.46 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 17:06:09,190 INFO [train.py:715] (2/8) Epoch 19, batch 18200, loss[loss=0.1466, simple_loss=0.2319, pruned_loss=0.0306, over 4940.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02885, over 970716.09 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:06:48,115 INFO [train.py:715] (2/8) Epoch 19, batch 18250, loss[loss=0.149, simple_loss=0.2255, pruned_loss=0.03631, over 4744.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02895, over 971542.01 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:07:28,076 INFO [train.py:715] (2/8) Epoch 19, batch 18300, loss[loss=0.1419, simple_loss=0.2175, pruned_loss=0.03314, over 4912.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2055, pruned_loss=0.02863, over 971546.82 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:08:07,532 INFO [train.py:715] (2/8) Epoch 19, batch 18350, loss[loss=0.1194, simple_loss=0.2044, pruned_loss=0.0172, over 4792.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.0293, over 970865.28 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:08:47,419 INFO [train.py:715] (2/8) Epoch 19, batch 18400, loss[loss=0.1142, simple_loss=0.1943, pruned_loss=0.01708, over 4933.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.02911, over 971346.02 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:09:26,677 INFO [train.py:715] (2/8) Epoch 19, batch 18450, loss[loss=0.154, simple_loss=0.2214, pruned_loss=0.0433, over 4769.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02889, over 971673.16 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:10:06,112 INFO [train.py:715] (2/8) Epoch 19, batch 18500, loss[loss=0.1206, simple_loss=0.1987, pruned_loss=0.02129, over 4792.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.0287, over 971042.30 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:10:45,348 INFO [train.py:715] (2/8) Epoch 19, batch 18550, loss[loss=0.1575, simple_loss=0.2265, pruned_loss=0.04425, over 4987.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.0291, over 970938.81 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:11:24,400 INFO [train.py:715] (2/8) Epoch 19, batch 18600, loss[loss=0.1154, simple_loss=0.1999, pruned_loss=0.01539, over 4815.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02846, over 971743.58 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:12:06,310 INFO [train.py:715] (2/8) Epoch 19, batch 18650, loss[loss=0.1086, simple_loss=0.1724, pruned_loss=0.02245, over 4761.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02851, over 971450.01 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 17:12:45,150 INFO [train.py:715] (2/8) Epoch 19, batch 18700, loss[loss=0.1189, simple_loss=0.1965, pruned_loss=0.02058, over 4908.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2061, pruned_loss=0.02856, over 972284.30 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:13:24,440 INFO [train.py:715] (2/8) Epoch 19, batch 18750, loss[loss=0.1162, simple_loss=0.1864, pruned_loss=0.02299, over 4818.00 frames.], tot_loss[loss=0.132, simple_loss=0.2063, pruned_loss=0.02884, over 971286.55 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 17:14:04,383 INFO [train.py:715] (2/8) Epoch 19, batch 18800, loss[loss=0.1395, simple_loss=0.2119, pruned_loss=0.03351, over 4778.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02875, over 971377.63 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:14:44,266 INFO [train.py:715] (2/8) Epoch 19, batch 18850, loss[loss=0.1153, simple_loss=0.1854, pruned_loss=0.02253, over 4991.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02849, over 972372.11 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 17:15:23,449 INFO [train.py:715] (2/8) Epoch 19, batch 18900, loss[loss=0.1281, simple_loss=0.203, pruned_loss=0.0266, over 4836.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02874, over 972458.33 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 17:16:02,833 INFO [train.py:715] (2/8) Epoch 19, batch 18950, loss[loss=0.1365, simple_loss=0.2149, pruned_loss=0.02901, over 4966.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02839, over 973437.94 frames.], batch size: 40, lr: 1.17e-04
2022-05-09 17:16:42,871 INFO [train.py:715] (2/8) Epoch 19, batch 19000, loss[loss=0.1492, simple_loss=0.2305, pruned_loss=0.03394, over 4759.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02866, over 972845.35 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:17:22,369 INFO [train.py:715] (2/8) Epoch 19, batch 19050, loss[loss=0.1552, simple_loss=0.2189, pruned_loss=0.04574, over 4794.00 frames.], tot_loss[loss=0.1318, simple_loss=0.206, pruned_loss=0.0288, over 972798.74 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 17:18:01,435 INFO [train.py:715] (2/8) Epoch 19, batch 19100, loss[loss=0.1163, simple_loss=0.191, pruned_loss=0.02083, over 4946.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2056, pruned_loss=0.02843, over 972930.60 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:18:41,050 INFO [train.py:715] (2/8) Epoch 19, batch 19150, loss[loss=0.1225, simple_loss=0.2006, pruned_loss=0.02219, over 4776.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02875, over 972590.07 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:19:20,397 INFO [train.py:715] (2/8) Epoch 19, batch 19200, loss[loss=0.1234, simple_loss=0.2024, pruned_loss=0.02218, over 4767.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02804, over 971995.64 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:19:59,879 INFO [train.py:715] (2/8) Epoch 19, batch 19250, loss[loss=0.1526, simple_loss=0.2346, pruned_loss=0.03533, over 4749.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2051, pruned_loss=0.02788, over 971575.05 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:20:39,161 INFO [train.py:715] (2/8) Epoch 19, batch 19300, loss[loss=0.1196, simple_loss=0.1914, pruned_loss=0.02394, over 4992.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02829, over 972118.47 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:21:19,533 INFO [train.py:715] (2/8) Epoch 19, batch 19350, loss[loss=0.1291, simple_loss=0.2077, pruned_loss=0.02525, over 4856.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02848, over 972859.64 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 17:21:58,947 INFO [train.py:715] (2/8) Epoch 19, batch 19400, loss[loss=0.1481, simple_loss=0.2275, pruned_loss=0.03436, over 4775.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02866, over 972094.77 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:22:38,653 INFO [train.py:715] (2/8) Epoch 19, batch 19450, loss[loss=0.1402, simple_loss=0.2183, pruned_loss=0.03099, over 4777.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02848, over 971918.68 frames.], batch size: 14, lr: 1.17e-04
2022-05-09 17:23:18,396 INFO [train.py:715] (2/8) Epoch 19, batch 19500, loss[loss=0.1185, simple_loss=0.2006, pruned_loss=0.01825, over 4784.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.0284, over 971600.82 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:23:57,786 INFO [train.py:715] (2/8) Epoch 19, batch 19550, loss[loss=0.09082, simple_loss=0.1653, pruned_loss=0.008167, over 4756.00 frames.], tot_loss[loss=0.131, simple_loss=0.2052, pruned_loss=0.02842, over 971861.05 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 17:24:36,967 INFO [train.py:715] (2/8) Epoch 19, batch 19600, loss[loss=0.11, simple_loss=0.1895, pruned_loss=0.01522, over 4775.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02825, over 971922.60 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 17:25:17,643 INFO [train.py:715] (2/8) Epoch 19, batch 19650, loss[loss=0.1111, simple_loss=0.1931, pruned_loss=0.01453, over 4916.00 frames.], tot_loss[loss=0.1308, simple_loss=0.205, pruned_loss=0.02825, over 971761.89 frames.], batch size: 23, lr: 1.17e-04
2022-05-09 17:25:56,983 INFO [train.py:715] (2/8) Epoch 19, batch 19700, loss[loss=0.1052, simple_loss=0.1856, pruned_loss=0.01238, over 4958.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2048, pruned_loss=0.02807, over 971264.17 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:26:35,806 INFO [train.py:715] (2/8) Epoch 19, batch 19750, loss[loss=0.1269, simple_loss=0.2012, pruned_loss=0.02629, over 4891.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02805, over 972084.91 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 17:27:16,061 INFO [train.py:715] (2/8) Epoch 19, batch 19800, loss[loss=0.1217, simple_loss=0.2018, pruned_loss=0.02082, over 4864.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02818, over 973326.11 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 17:27:55,917 INFO [train.py:715] (2/8) Epoch 19, batch 19850, loss[loss=0.1191, simple_loss=0.1948, pruned_loss=0.02171, over 4844.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.0282, over 972386.73 frames.], batch size: 27, lr: 1.17e-04
2022-05-09 17:28:35,200 INFO [train.py:715] (2/8) Epoch 19, batch 19900, loss[loss=0.1136, simple_loss=0.197, pruned_loss=0.01505, over 4805.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02798, over 971965.06 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:29:13,881 INFO [train.py:715] (2/8) Epoch 19, batch 19950, loss[loss=0.1342, simple_loss=0.2049, pruned_loss=0.03175, over 4936.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2056, pruned_loss=0.02769, over 971755.17 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:29:53,614 INFO [train.py:715] (2/8) Epoch 19, batch 20000, loss[loss=0.1527, simple_loss=0.2266, pruned_loss=0.0394, over 4875.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02829, over 972264.12 frames.], batch size: 20, lr: 1.17e-04
2022-05-09 17:30:33,006 INFO [train.py:715] (2/8) Epoch 19, batch 20050, loss[loss=0.1378, simple_loss=0.2131, pruned_loss=0.03121, over 4743.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2067, pruned_loss=0.02887, over 972284.54 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:31:12,659 INFO [train.py:715] (2/8) Epoch 19, batch 20100, loss[loss=0.1498, simple_loss=0.2246, pruned_loss=0.03749, over 4771.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2072, pruned_loss=0.02911, over 972727.02 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:31:52,173 INFO [train.py:715] (2/8) Epoch 19, batch 20150, loss[loss=0.09836, simple_loss=0.164, pruned_loss=0.01635, over 4760.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02836, over 972442.56 frames.], batch size: 12, lr: 1.17e-04
2022-05-09 17:32:31,839 INFO [train.py:715] (2/8) Epoch 19, batch 20200, loss[loss=0.1229, simple_loss=0.1921, pruned_loss=0.02685, over 4804.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02842, over 972109.09 frames.], batch size: 25, lr: 1.17e-04
2022-05-09 17:33:11,346 INFO [train.py:715] (2/8) Epoch 19, batch 20250, loss[loss=0.1358, simple_loss=0.2071, pruned_loss=0.03221, over 4959.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02824, over 972365.77 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 17:33:50,681 INFO [train.py:715] (2/8) Epoch 19, batch 20300, loss[loss=0.1474, simple_loss=0.2267, pruned_loss=0.03407, over 4731.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02829, over 971491.91 frames.], batch size: 16, lr: 1.17e-04
2022-05-09 17:34:30,221 INFO [train.py:715] (2/8) Epoch 19, batch 20350, loss[loss=0.1217, simple_loss=0.2102, pruned_loss=0.01658, over 4979.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02845, over 972184.14 frames.], batch size: 39, lr: 1.17e-04
2022-05-09 17:35:09,434 INFO [train.py:715] (2/8) Epoch 19, batch 20400, loss[loss=0.1286, simple_loss=0.2085, pruned_loss=0.02434, over 4769.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2053, pruned_loss=0.02825, over 972209.07 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:35:48,299 INFO [train.py:715] (2/8) Epoch 19, batch 20450, loss[loss=0.1557, simple_loss=0.2384, pruned_loss=0.03653, over 4869.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02815, over 972409.58 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 17:36:28,041 INFO [train.py:715] (2/8) Epoch 19, batch 20500, loss[loss=0.1303, simple_loss=0.2039, pruned_loss=0.02829, over 4963.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02813, over 972599.16 frames.], batch size: 35, lr: 1.17e-04
2022-05-09 17:37:07,744 INFO [train.py:715] (2/8) Epoch 19, batch 20550, loss[loss=0.1212, simple_loss=0.1943, pruned_loss=0.02405, over 4829.00 frames.], tot_loss[loss=0.132, simple_loss=0.2068, pruned_loss=0.02859, over 971792.49 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 17:37:46,563 INFO [train.py:715] (2/8) Epoch 19, batch 20600, loss[loss=0.1303, simple_loss=0.2021, pruned_loss=0.02929, over 4959.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2071, pruned_loss=0.02871, over 972110.67 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:38:26,013 INFO [train.py:715] (2/8) Epoch 19, batch 20650, loss[loss=0.1245, simple_loss=0.198, pruned_loss=0.0255, over 4976.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2069, pruned_loss=0.02865, over 972319.18 frames.], batch size: 33, lr: 1.17e-04
2022-05-09 17:39:05,347 INFO [train.py:715] (2/8) Epoch 19, batch 20700, loss[loss=0.146, simple_loss=0.2178, pruned_loss=0.03709, over 4903.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2073, pruned_loss=0.02874, over 973012.42 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:39:44,824 INFO [train.py:715] (2/8) Epoch 19, batch 20750, loss[loss=0.1624, simple_loss=0.2381, pruned_loss=0.04335, over 4791.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2074, pruned_loss=0.02898, over 972982.15 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:40:23,541 INFO [train.py:715] (2/8) Epoch 19, batch 20800, loss[loss=0.12, simple_loss=0.1946, pruned_loss=0.02271, over 4969.00 frames.], tot_loss[loss=0.1322, simple_loss=0.207, pruned_loss=0.02873, over 971832.55 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:41:02,813 INFO [train.py:715] (2/8) Epoch 19, batch 20850, loss[loss=0.1273, simple_loss=0.2075, pruned_loss=0.02358, over 4901.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02842, over 972415.54 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:41:42,483 INFO [train.py:715] (2/8) Epoch 19, batch 20900, loss[loss=0.141, simple_loss=0.2156, pruned_loss=0.03324, over 4831.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2063, pruned_loss=0.02812, over 972455.05 frames.], batch size: 26, lr: 1.17e-04
2022-05-09 17:42:21,287 INFO [train.py:715] (2/8) Epoch 19, batch 20950, loss[loss=0.1146, simple_loss=0.1887, pruned_loss=0.02023, over 4847.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02792, over 971359.63 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 17:43:01,040 INFO [train.py:715] (2/8) Epoch 19, batch 21000, loss[loss=0.1327, simple_loss=0.1982, pruned_loss=0.03361, over 4867.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2043, pruned_loss=0.02751, over 971362.34 frames.], batch size: 32, lr: 1.17e-04
2022-05-09 17:43:01,040 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 17:43:11,505 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1045, simple_loss=0.1878, pruned_loss=0.01062, over 914524.00 frames.
2022-05-09 17:43:51,341 INFO [train.py:715] (2/8) Epoch 19, batch 21050, loss[loss=0.1387, simple_loss=0.2102, pruned_loss=0.03361, over 4968.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.02792, over 971635.06 frames.], batch size: 15, lr: 1.17e-04
2022-05-09 17:44:31,301 INFO [train.py:715] (2/8) Epoch 19, batch 21100, loss[loss=0.1101, simple_loss=0.1866, pruned_loss=0.01683, over 4903.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2049, pruned_loss=0.02762, over 972534.83 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:45:10,118 INFO [train.py:715] (2/8) Epoch 19, batch 21150, loss[loss=0.1304, simple_loss=0.2086, pruned_loss=0.0261, over 4892.00 frames.], tot_loss[loss=0.1305, simple_loss=0.205, pruned_loss=0.02802, over 972268.35 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 17:45:49,702 INFO [train.py:715] (2/8) Epoch 19, batch 21200, loss[loss=0.1233, simple_loss=0.1916, pruned_loss=0.0275, over 4845.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02827, over 971956.35 frames.], batch size: 13, lr: 1.17e-04
2022-05-09 17:46:28,949 INFO [train.py:715] (2/8) Epoch 19, batch 21250, loss[loss=0.1658, simple_loss=0.2383, pruned_loss=0.04668, over 4914.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02858, over 972268.29 frames.], batch size: 17, lr: 1.17e-04
2022-05-09 17:47:08,006 INFO [train.py:715] (2/8) Epoch 19, batch 21300, loss[loss=0.09975, simple_loss=0.1833, pruned_loss=0.008119, over 4761.00 frames.], tot_loss[loss=0.131, simple_loss=0.2056, pruned_loss=0.02823, over 972427.68 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:47:46,812 INFO [train.py:715] (2/8) Epoch 19, batch 21350, loss[loss=0.1086, simple_loss=0.182, pruned_loss=0.01767, over 4892.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02821, over 972857.77 frames.], batch size: 19, lr: 1.17e-04
2022-05-09 17:48:26,340 INFO [train.py:715] (2/8) Epoch 19, batch 21400, loss[loss=0.1518, simple_loss=0.2327, pruned_loss=0.03543, over 4906.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2064, pruned_loss=0.0282, over 973259.37 frames.], batch size: 22, lr: 1.17e-04
2022-05-09 17:49:05,855 INFO [train.py:715] (2/8) Epoch 19, batch 21450, loss[loss=0.1322, simple_loss=0.2072, pruned_loss=0.02861, over 4919.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2064, pruned_loss=0.02798, over 974032.64 frames.], batch size: 29, lr: 1.17e-04
2022-05-09 17:49:44,653 INFO [train.py:715] (2/8) Epoch 19, batch 21500, loss[loss=0.1374, simple_loss=0.2124, pruned_loss=0.0312, over 4934.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2068, pruned_loss=0.0285, over 974024.34 frames.], batch size: 21, lr: 1.17e-04
2022-05-09 17:50:24,359 INFO [train.py:715] (2/8) Epoch 19, batch 21550, loss[loss=0.1253, simple_loss=0.2029, pruned_loss=0.02388, over 4930.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2065, pruned_loss=0.0283, over 974033.90 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:51:04,081 INFO [train.py:715] (2/8) Epoch 19, batch 21600, loss[loss=0.1331, simple_loss=0.2113, pruned_loss=0.02744, over 4911.00 frames.], tot_loss[loss=0.1308, simple_loss=0.206, pruned_loss=0.02786, over 974012.59 frames.], batch size: 18, lr: 1.17e-04
2022-05-09 17:51:43,845 INFO [train.py:715] (2/8) Epoch 19, batch 21650, loss[loss=0.1286, simple_loss=0.2079, pruned_loss=0.02467, over 4803.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2068, pruned_loss=0.02842, over 973660.12 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 17:52:22,736 INFO [train.py:715] (2/8) Epoch 19, batch 21700, loss[loss=0.1265, simple_loss=0.2002, pruned_loss=0.02639, over 4982.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02834, over 973939.87 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 17:53:02,152 INFO [train.py:715] (2/8) Epoch 19, batch 21750, loss[loss=0.1107, simple_loss=0.1923, pruned_loss=0.01458, over 4919.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.02831, over 973752.48 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 17:53:43,112 INFO [train.py:715] (2/8) Epoch 19, batch 21800, loss[loss=0.1382, simple_loss=0.2157, pruned_loss=0.03037, over 4893.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02805, over 973810.91 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 17:54:22,937 INFO [train.py:715] (2/8) Epoch 19, batch 21850, loss[loss=0.1232, simple_loss=0.1865, pruned_loss=0.02999, over 4825.00 frames.], tot_loss[loss=0.13, simple_loss=0.2045, pruned_loss=0.02775, over 973954.30 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 17:55:03,316 INFO [train.py:715] (2/8) Epoch 19, batch 21900, loss[loss=0.1453, simple_loss=0.2089, pruned_loss=0.04088, over 4945.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2047, pruned_loss=0.02798, over 973475.24 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 17:55:43,308 INFO [train.py:715] (2/8) Epoch 19, batch 21950, loss[loss=0.1149, simple_loss=0.1895, pruned_loss=0.02019, over 4817.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2046, pruned_loss=0.02806, over 972863.46 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 17:56:22,528 INFO [train.py:715] (2/8) Epoch 19, batch 22000, loss[loss=0.1, simple_loss=0.1712, pruned_loss=0.0144, over 4801.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02795, over 972917.16 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 17:57:01,819 INFO [train.py:715] (2/8) Epoch 19, batch 22050, loss[loss=0.127, simple_loss=0.2081, pruned_loss=0.02288, over 4918.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02817, over 973751.28 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 17:57:41,390 INFO [train.py:715] (2/8) Epoch 19, batch 22100, loss[loss=0.1381, simple_loss=0.2077, pruned_loss=0.03421, over 4839.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02838, over 973927.53 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 17:58:21,390 INFO [train.py:715] (2/8) Epoch 19, batch 22150, loss[loss=0.1418, simple_loss=0.2092, pruned_loss=0.03717, over 4864.00 frames.], tot_loss[loss=0.131, simple_loss=0.2054, pruned_loss=0.02825, over 972996.85 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 17:59:00,690 INFO [train.py:715] (2/8) Epoch 19, batch 22200, loss[loss=0.1341, simple_loss=0.21, pruned_loss=0.02906, over 4851.00 frames.], tot_loss[loss=0.131, simple_loss=0.2054, pruned_loss=0.02825, over 973261.88 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 17:59:40,598 INFO [train.py:715] (2/8) Epoch 19, batch 22250, loss[loss=0.1076, simple_loss=0.1916, pruned_loss=0.0118, over 4961.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2062, pruned_loss=0.02833, over 972815.60 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 18:00:20,345 INFO [train.py:715] (2/8) Epoch 19, batch 22300, loss[loss=0.1231, simple_loss=0.1936, pruned_loss=0.02628, over 4971.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02838, over 972509.20 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 18:00:59,304 INFO [train.py:715] (2/8) Epoch 19, batch 22350, loss[loss=0.1297, simple_loss=0.2001, pruned_loss=0.02969, over 4890.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.02863, over 972609.49 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:01:38,320 INFO [train.py:715] (2/8) Epoch 19, batch 22400, loss[loss=0.133, simple_loss=0.2021, pruned_loss=0.03195, over 4828.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02859, over 972510.17 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:02:17,607 INFO [train.py:715] (2/8) Epoch 19, batch 22450, loss[loss=0.1161, simple_loss=0.2026, pruned_loss=0.01475, over 4899.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2057, pruned_loss=0.02842, over 972431.66 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:02:57,545 INFO [train.py:715] (2/8) Epoch 19, batch 22500, loss[loss=0.1376, simple_loss=0.2111, pruned_loss=0.03203, over 4795.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2056, pruned_loss=0.02813, over 971943.02 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 18:03:36,398 INFO [train.py:715] (2/8) Epoch 19, batch 22550, loss[loss=0.1319, simple_loss=0.2059, pruned_loss=0.02895, over 4939.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2054, pruned_loss=0.02787, over 971634.12 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 18:04:16,060 INFO [train.py:715] (2/8) Epoch 19, batch 22600, loss[loss=0.1266, simple_loss=0.2086, pruned_loss=0.02229, over 4949.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02811, over 972121.98 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:04:55,709 INFO [train.py:715] (2/8) Epoch 19, batch 22650, loss[loss=0.1307, simple_loss=0.2045, pruned_loss=0.0284, over 4866.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2063, pruned_loss=0.02821, over 972447.36 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 18:05:34,681 INFO [train.py:715] (2/8) Epoch 19, batch 22700, loss[loss=0.1375, simple_loss=0.2118, pruned_loss=0.03154, over 4869.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02807, over 972710.18 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:06:13,668 INFO [train.py:715] (2/8) Epoch 19, batch 22750, loss[loss=0.1444, simple_loss=0.213, pruned_loss=0.03792, over 4819.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2056, pruned_loss=0.02788, over 972653.61 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:06:53,392 INFO [train.py:715] (2/8) Epoch 19, batch 22800, loss[loss=0.1224, simple_loss=0.1905, pruned_loss=0.02712, over 4982.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2052, pruned_loss=0.02767, over 972254.46 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 18:07:33,730 INFO [train.py:715] (2/8) Epoch 19, batch 22850, loss[loss=0.1441, simple_loss=0.2306, pruned_loss=0.02879, over 4699.00 frames.], tot_loss[loss=0.131, simple_loss=0.2058, pruned_loss=0.02813, over 972272.26 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:08:11,749 INFO [train.py:715] (2/8) Epoch 19, batch 22900, loss[loss=0.1063, simple_loss=0.1787, pruned_loss=0.01695, over 4977.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2051, pruned_loss=0.02775, over 972131.57 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:08:51,146 INFO [train.py:715] (2/8) Epoch 19, batch 22950, loss[loss=0.1227, simple_loss=0.1952, pruned_loss=0.0251, over 4836.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2052, pruned_loss=0.02775, over 972155.02 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 18:09:31,744 INFO [train.py:715] (2/8) Epoch 19, batch 23000, loss[loss=0.1454, simple_loss=0.2184, pruned_loss=0.03621, over 4949.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02804, over 971968.12 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 18:10:12,239 INFO [train.py:715] (2/8) Epoch 19, batch 23050, loss[loss=0.108, simple_loss=0.1761, pruned_loss=0.01999, over 4863.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2056, pruned_loss=0.0285, over 971596.62 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 18:10:52,468 INFO [train.py:715] (2/8) Epoch 19, batch 23100, loss[loss=0.1586, simple_loss=0.2373, pruned_loss=0.03991, over 4922.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.02821, over 972106.23 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:11:33,194 INFO [train.py:715] (2/8) Epoch 19, batch 23150, loss[loss=0.1373, simple_loss=0.2082, pruned_loss=0.03319, over 4939.00 frames.], tot_loss[loss=0.132, simple_loss=0.2061, pruned_loss=0.02894, over 972386.38 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:12:14,188 INFO [train.py:715] (2/8) Epoch 19, batch 23200, loss[loss=0.1062, simple_loss=0.1815, pruned_loss=0.0155, over 4782.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2061, pruned_loss=0.02869, over 972702.20 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:12:53,604 INFO [train.py:715] (2/8) Epoch 19, batch 23250, loss[loss=0.1146, simple_loss=0.1842, pruned_loss=0.02247, over 4977.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02838, over 973004.35 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 18:13:34,360 INFO [train.py:715] (2/8) Epoch 19, batch 23300, loss[loss=0.106, simple_loss=0.1875, pruned_loss=0.01229, over 4979.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2071, pruned_loss=0.02881, over 973814.94 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 18:14:16,102 INFO [train.py:715] (2/8) Epoch 19, batch 23350, loss[loss=0.1512, simple_loss=0.2323, pruned_loss=0.035, over 4993.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2073, pruned_loss=0.02904, over 973180.59 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:14:56,711 INFO [train.py:715] (2/8) Epoch 19, batch 23400, loss[loss=0.1222, simple_loss=0.1906, pruned_loss=0.02688, over 4890.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2073, pruned_loss=0.0294, over 973232.33 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 18:15:37,870 INFO [train.py:715] (2/8) Epoch 19, batch 23450, loss[loss=0.1384, simple_loss=0.216, pruned_loss=0.03044, over 4901.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2068, pruned_loss=0.02927, over 973535.35 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:16:19,138 INFO [train.py:715] (2/8) Epoch 19, batch 23500, loss[loss=0.1279, simple_loss=0.1922, pruned_loss=0.03175, over 4866.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02865, over 973724.05 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:17:00,516 INFO [train.py:715] (2/8) Epoch 19, batch 23550, loss[loss=0.1175, simple_loss=0.1993, pruned_loss=0.01781, over 4904.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02875, over 973604.23 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:17:41,323 INFO [train.py:715] (2/8) Epoch 19, batch 23600, loss[loss=0.1414, simple_loss=0.215, pruned_loss=0.0339, over 4822.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2059, pruned_loss=0.0284, over 973611.82 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:18:22,138 INFO [train.py:715] (2/8) Epoch 19, batch 23650, loss[loss=0.1241, simple_loss=0.1996, pruned_loss=0.02431, over 4820.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2066, pruned_loss=0.02838, over 973846.18 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 18:19:04,120 INFO [train.py:715] (2/8) Epoch 19, batch 23700, loss[loss=0.1413, simple_loss=0.2205, pruned_loss=0.03106, over 4822.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02822, over 974279.84 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:19:44,516 INFO [train.py:715] (2/8) Epoch 19, batch 23750, loss[loss=0.1297, simple_loss=0.212, pruned_loss=0.02368, over 4878.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02813, over 974127.23 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 18:20:24,723 INFO [train.py:715] (2/8) Epoch 19, batch 23800, loss[loss=0.1368, simple_loss=0.2133, pruned_loss=0.03014, over 4885.00 frames.], tot_loss[loss=0.1307, simple_loss=0.205, pruned_loss=0.02816, over 973944.04 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:21:05,131 INFO [train.py:715] (2/8) Epoch 19, batch 23850, loss[loss=0.1325, simple_loss=0.2174, pruned_loss=0.02381, over 4766.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2051, pruned_loss=0.02821, over 972801.16 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:21:45,586 INFO [train.py:715] (2/8) Epoch 19, batch 23900, loss[loss=0.1462, simple_loss=0.2138, pruned_loss=0.03926, over 4963.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2046, pruned_loss=0.02761, over 972821.62 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 18:22:24,882 INFO [train.py:715] (2/8) Epoch 19, batch 23950, loss[loss=0.1306, simple_loss=0.2114, pruned_loss=0.02491, over 4811.00 frames.], tot_loss[loss=0.13, simple_loss=0.2052, pruned_loss=0.02743, over 973360.07 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:23:05,246 INFO [train.py:715] (2/8) Epoch 19, batch 24000, loss[loss=0.1175, simple_loss=0.2012, pruned_loss=0.01691, over 4796.00 frames.], tot_loss[loss=0.13, simple_loss=0.2052, pruned_loss=0.02737, over 973051.31 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:23:05,247 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 18:23:15,158 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1046, simple_loss=0.1878, pruned_loss=0.01073, over 914524.00 frames.
2022-05-09 18:23:55,499 INFO [train.py:715] (2/8) Epoch 19, batch 24050, loss[loss=0.1257, simple_loss=0.2043, pruned_loss=0.02356, over 4836.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2053, pruned_loss=0.02754, over 972199.02 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 18:24:36,270 INFO [train.py:715] (2/8) Epoch 19, batch 24100, loss[loss=0.129, simple_loss=0.1981, pruned_loss=0.03001, over 4848.00 frames.], tot_loss[loss=0.1296, simple_loss=0.2049, pruned_loss=0.02709, over 973245.26 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 18:25:16,111 INFO [train.py:715] (2/8) Epoch 19, batch 24150, loss[loss=0.1405, simple_loss=0.2129, pruned_loss=0.03403, over 4965.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2052, pruned_loss=0.02727, over 972761.79 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 18:25:56,269 INFO [train.py:715] (2/8) Epoch 19, batch 24200, loss[loss=0.1627, simple_loss=0.2245, pruned_loss=0.05047, over 4842.00 frames.], tot_loss[loss=0.1311, simple_loss=0.206, pruned_loss=0.02808, over 972812.57 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:26:36,613 INFO [train.py:715] (2/8) Epoch 19, batch 24250, loss[loss=0.1447, simple_loss=0.2244, pruned_loss=0.0325, over 4896.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2056, pruned_loss=0.02789, over 972338.37 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 18:27:17,345 INFO [train.py:715] (2/8) Epoch 19, batch 24300, loss[loss=0.1188, simple_loss=0.1945, pruned_loss=0.02156, over 4782.00 frames.], tot_loss[loss=0.1309, simple_loss=0.206, pruned_loss=0.02785, over 972962.21 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:27:56,396 INFO [train.py:715] (2/8) Epoch 19, batch 24350, loss[loss=0.142, simple_loss=0.22, pruned_loss=0.03194, over 4915.00 frames.], tot_loss[loss=0.1309, simple_loss=0.206, pruned_loss=0.02786, over 973468.81 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 18:28:36,036 INFO [train.py:715] (2/8) Epoch 19, batch 24400, loss[loss=0.1382, simple_loss=0.2209, pruned_loss=0.02773, over 4779.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2063, pruned_loss=0.02802, over 973047.77 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:29:16,439 INFO [train.py:715] (2/8) Epoch 19, batch 24450, loss[loss=0.1338, simple_loss=0.2058, pruned_loss=0.03093, over 4920.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2063, pruned_loss=0.02817, over 973523.42 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 18:29:55,853 INFO [train.py:715] (2/8) Epoch 19, batch 24500, loss[loss=0.113, simple_loss=0.1782, pruned_loss=0.02393, over 4699.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02793, over 973442.00 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:30:34,373 INFO [train.py:715] (2/8) Epoch 19, batch 24550, loss[loss=0.14, simple_loss=0.2218, pruned_loss=0.02909, over 4779.00 frames.], tot_loss[loss=0.131, simple_loss=0.2057, pruned_loss=0.0281, over 973037.19 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:31:13,266 INFO [train.py:715] (2/8) Epoch 19, batch 24600, loss[loss=0.1172, simple_loss=0.1897, pruned_loss=0.02228, over 4860.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2064, pruned_loss=0.02842, over 972863.39 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 18:31:52,760 INFO [train.py:715] (2/8) Epoch 19, batch 24650, loss[loss=0.1367, simple_loss=0.2035, pruned_loss=0.03491, over 4962.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02877, over 972159.76 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 18:32:31,487 INFO [train.py:715] (2/8) Epoch 19, batch 24700, loss[loss=0.1541, simple_loss=0.2236, pruned_loss=0.04231, over 4872.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2068, pruned_loss=0.0291, over 972344.97 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:33:10,029 INFO [train.py:715] (2/8) Epoch 19, batch 24750, loss[loss=0.1325, simple_loss=0.207, pruned_loss=0.02903, over 4839.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02928, over 972622.72 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:33:50,382 INFO [train.py:715] (2/8) Epoch 19, batch 24800, loss[loss=0.1223, simple_loss=0.1917, pruned_loss=0.02649, over 4655.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02912, over 972351.40 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:34:30,019 INFO [train.py:715] (2/8) Epoch 19, batch 24850, loss[loss=0.1237, simple_loss=0.1996, pruned_loss=0.02388, over 4853.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2067, pruned_loss=0.02924, over 972526.77 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 18:35:09,100 INFO [train.py:715] (2/8) Epoch 19, batch 24900, loss[loss=0.1853, simple_loss=0.2536, pruned_loss=0.05848, over 4893.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02937, over 973173.98 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:35:48,554 INFO [train.py:715] (2/8) Epoch 19, batch 24950, loss[loss=0.1284, simple_loss=0.1972, pruned_loss=0.0298, over 4864.00 frames.], tot_loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02945, over 973557.94 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 18:36:28,357 INFO [train.py:715] (2/8) Epoch 19, batch 25000, loss[loss=0.1463, simple_loss=0.2304, pruned_loss=0.03111, over 4880.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2077, pruned_loss=0.02949, over 973488.96 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 18:37:07,181 INFO [train.py:715] (2/8) Epoch 19, batch 25050, loss[loss=0.1226, simple_loss=0.2065, pruned_loss=0.01937, over 4810.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2068, pruned_loss=0.0288, over 973335.40 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:37:46,486 INFO [train.py:715] (2/8) Epoch 19, batch 25100, loss[loss=0.1358, simple_loss=0.2117, pruned_loss=0.02998, over 4839.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2076, pruned_loss=0.02909, over 973620.26 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 18:38:26,082 INFO [train.py:715] (2/8) Epoch 19, batch 25150, loss[loss=0.1223, simple_loss=0.207, pruned_loss=0.01882, over 4813.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2071, pruned_loss=0.0291, over 973328.54 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:39:05,718 INFO [train.py:715] (2/8) Epoch 19, batch 25200, loss[loss=0.1183, simple_loss=0.1938, pruned_loss=0.02142, over 4818.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2065, pruned_loss=0.02909, over 973114.07 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:39:44,330 INFO [train.py:715] (2/8) Epoch 19, batch 25250, loss[loss=0.1286, simple_loss=0.208, pruned_loss=0.02458, over 4753.00 frames.], tot_loss[loss=0.1321, simple_loss=0.2064, pruned_loss=0.02892, over 972612.24 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 18:40:23,574 INFO [train.py:715] (2/8) Epoch 19, batch 25300, loss[loss=0.1216, simple_loss=0.2029, pruned_loss=0.02015, over 4772.00 frames.], tot_loss[loss=0.1319, simple_loss=0.206, pruned_loss=0.02883, over 971905.40 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:41:03,219 INFO [train.py:715] (2/8) Epoch 19, batch 25350, loss[loss=0.134, simple_loss=0.2097, pruned_loss=0.02917, over 4936.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2063, pruned_loss=0.0288, over 971460.15 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 18:41:42,431 INFO [train.py:715] (2/8) Epoch 19, batch 25400, loss[loss=0.1289, simple_loss=0.1999, pruned_loss=0.02896, over 4779.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02839, over 971428.20 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:42:21,493 INFO [train.py:715] (2/8) Epoch 19, batch 25450, loss[loss=0.132, simple_loss=0.2012, pruned_loss=0.03141, over 4961.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02873, over 971470.71 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:43:00,715 INFO [train.py:715] (2/8) Epoch 19, batch 25500, loss[loss=0.1265, simple_loss=0.199, pruned_loss=0.02697, over 4820.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02824, over 971749.55 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 18:43:39,823 INFO [train.py:715] (2/8) Epoch 19, batch 25550, loss[loss=0.1027, simple_loss=0.1848, pruned_loss=0.0103, over 4971.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2061, pruned_loss=0.02813, over 972476.59 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 18:44:18,035 INFO [train.py:715] (2/8) Epoch 19, batch 25600, loss[loss=0.224, simple_loss=0.2937, pruned_loss=0.07715, over 4779.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02835, over 972379.93 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:44:56,957 INFO [train.py:715] (2/8) Epoch 19, batch 25650, loss[loss=0.1516, simple_loss=0.2208, pruned_loss=0.04122, over 4757.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2057, pruned_loss=0.02852, over 972564.57 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:45:36,002 INFO [train.py:715] (2/8) Epoch 19, batch 25700, loss[loss=0.1397, simple_loss=0.2033, pruned_loss=0.03811, over 4983.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.0282, over 973034.87 frames.], batch size: 31, lr: 1.16e-04
2022-05-09 18:46:14,565 INFO [train.py:715] (2/8) Epoch 19, batch 25750, loss[loss=0.1548, simple_loss=0.2177, pruned_loss=0.04592, over 4933.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02861, over 972691.41 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:46:53,572 INFO [train.py:715] (2/8) Epoch 19, batch 25800, loss[loss=0.1349, simple_loss=0.2081, pruned_loss=0.0308, over 4957.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02856, over 972369.13 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 18:47:32,969 INFO [train.py:715] (2/8) Epoch 19, batch 25850, loss[loss=0.121, simple_loss=0.197, pruned_loss=0.02253, over 4767.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2064, pruned_loss=0.0286, over 972696.04 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:48:12,302 INFO [train.py:715] (2/8) Epoch 19, batch 25900, loss[loss=0.1375, simple_loss=0.2121, pruned_loss=0.03145, over 4920.00 frames.], tot_loss[loss=0.1314, simple_loss=0.206, pruned_loss=0.02837, over 972912.01 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:48:50,876 INFO [train.py:715] (2/8) Epoch 19, batch 25950, loss[loss=0.1848, simple_loss=0.2748, pruned_loss=0.04737, over 4916.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02826, over 973840.45 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:49:30,501 INFO [train.py:715] (2/8) Epoch 19, batch 26000, loss[loss=0.1517, simple_loss=0.2375, pruned_loss=0.03297, over 4910.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2063, pruned_loss=0.02817, over 973256.15 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 18:50:10,474 INFO [train.py:715] (2/8) Epoch 19, batch 26050, loss[loss=0.1354, simple_loss=0.2091, pruned_loss=0.03092, over 4823.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02851, over 972696.43 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:50:49,159 INFO [train.py:715] (2/8) Epoch 19, batch 26100, loss[loss=0.1213, simple_loss=0.194, pruned_loss=0.02436, over 4782.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02842, over 971839.19 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 18:51:28,555 INFO [train.py:715] (2/8) Epoch 19, batch 26150, loss[loss=0.1511, simple_loss=0.2182, pruned_loss=0.04198, over 4880.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02815, over 971846.03 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:52:07,557 INFO [train.py:715] (2/8) Epoch 19, batch 26200, loss[loss=0.1223, simple_loss=0.2082, pruned_loss=0.01821, over 4789.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2046, pruned_loss=0.02748, over 971429.38 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 18:52:47,074 INFO [train.py:715] (2/8) Epoch 19, batch 26250, loss[loss=0.09006, simple_loss=0.1618, pruned_loss=0.009149, over 4789.00 frames.], tot_loss[loss=0.1291, simple_loss=0.2042, pruned_loss=0.02706, over 971176.04 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 18:53:25,455 INFO [train.py:715] (2/8) Epoch 19, batch 26300, loss[loss=0.1256, simple_loss=0.1953, pruned_loss=0.02792, over 4738.00 frames.], tot_loss[loss=0.1295, simple_loss=0.2043, pruned_loss=0.02729, over 970933.02 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 18:54:04,837 INFO [train.py:715] (2/8) Epoch 19, batch 26350, loss[loss=0.124, simple_loss=0.1966, pruned_loss=0.02573, over 4784.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2046, pruned_loss=0.02754, over 970489.63 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 18:54:44,060 INFO [train.py:715] (2/8) Epoch 19, batch 26400, loss[loss=0.1423, simple_loss=0.2235, pruned_loss=0.03059, over 4939.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2054, pruned_loss=0.02784, over 970752.25 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:55:23,160 INFO [train.py:715] (2/8) Epoch 19, batch 26450, loss[loss=0.1373, simple_loss=0.2127, pruned_loss=0.03093, over 4814.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.0282, over 971750.79 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 18:56:02,218 INFO [train.py:715] (2/8) Epoch 19, batch 26500, loss[loss=0.1373, simple_loss=0.2087, pruned_loss=0.03302, over 4937.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2046, pruned_loss=0.02758, over 972930.31 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 18:56:40,885 INFO [train.py:715] (2/8) Epoch 19, batch 26550, loss[loss=0.139, simple_loss=0.208, pruned_loss=0.035, over 4916.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2053, pruned_loss=0.02765, over 972946.72 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 18:57:21,642 INFO [train.py:715] (2/8) Epoch 19, batch 26600, loss[loss=0.1357, simple_loss=0.2125, pruned_loss=0.02939, over 4887.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02835, over 972406.80 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 18:58:02,784 INFO [train.py:715] (2/8) Epoch 19, batch 26650, loss[loss=0.1351, simple_loss=0.215, pruned_loss=0.02761, over 4692.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.028, over 971267.53 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 18:58:41,698 INFO [train.py:715] (2/8) Epoch 19, batch 26700, loss[loss=0.1246, simple_loss=0.1992, pruned_loss=0.02499, over 4846.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2061, pruned_loss=0.02807, over 971570.10 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 18:59:21,014 INFO [train.py:715] (2/8) Epoch 19, batch 26750, loss[loss=0.147, simple_loss=0.2295, pruned_loss=0.03223, over 4796.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02819, over 971643.26 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:00:00,978 INFO [train.py:715] (2/8) Epoch 19, batch 26800, loss[loss=0.1177, simple_loss=0.1864, pruned_loss=0.02446, over 4832.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2061, pruned_loss=0.02802, over 972132.53 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:00:41,175 INFO [train.py:715] (2/8) Epoch 19, batch 26850, loss[loss=0.133, simple_loss=0.2065, pruned_loss=0.0297, over 4897.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2066, pruned_loss=0.02847, over 972120.61 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 19:01:20,367 INFO [train.py:715] (2/8) Epoch 19, batch 26900, loss[loss=0.1312, simple_loss=0.2025, pruned_loss=0.02999, over 4987.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2069, pruned_loss=0.02912, over 972344.18 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 19:02:00,249 INFO [train.py:715] (2/8) Epoch 19, batch 26950, loss[loss=0.1448, simple_loss=0.2212, pruned_loss=0.03423, over 4835.00 frames.], tot_loss[loss=0.132, simple_loss=0.2067, pruned_loss=0.02869, over 972156.06 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 19:02:39,731 INFO [train.py:715] (2/8) Epoch 19, batch 27000, loss[loss=0.1475, simple_loss=0.2171, pruned_loss=0.03899, over 4841.00 frames.], tot_loss[loss=0.132, simple_loss=0.2069, pruned_loss=0.02858, over 972460.38 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:02:39,731 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 19:02:49,599 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1047, simple_loss=0.1878, pruned_loss=0.0108, over 914524.00 frames.
2022-05-09 19:03:29,485 INFO [train.py:715] (2/8) Epoch 19, batch 27050, loss[loss=0.1181, simple_loss=0.2025, pruned_loss=0.01688, over 4811.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2077, pruned_loss=0.02924, over 972199.96 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 19:04:09,792 INFO [train.py:715] (2/8) Epoch 19, batch 27100, loss[loss=0.1164, simple_loss=0.1953, pruned_loss=0.01874, over 4853.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2071, pruned_loss=0.02877, over 972075.93 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:04:50,656 INFO [train.py:715] (2/8) Epoch 19, batch 27150, loss[loss=0.1186, simple_loss=0.1887, pruned_loss=0.02429, over 4793.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2067, pruned_loss=0.02857, over 972270.66 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:05:30,586 INFO [train.py:715] (2/8) Epoch 19, batch 27200, loss[loss=0.142, simple_loss=0.2232, pruned_loss=0.03043, over 4888.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2071, pruned_loss=0.02892, over 972767.49 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 19:06:11,124 INFO [train.py:715] (2/8) Epoch 19, batch 27250, loss[loss=0.1414, simple_loss=0.2048, pruned_loss=0.03898, over 4906.00 frames.], tot_loss[loss=0.1326, simple_loss=0.2071, pruned_loss=0.02907, over 973216.09 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:06:52,921 INFO [train.py:715] (2/8) Epoch 19, batch 27300, loss[loss=0.1224, simple_loss=0.1951, pruned_loss=0.02486, over 4983.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2068, pruned_loss=0.02931, over 973246.25 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:07:33,654 INFO [train.py:715] (2/8) Epoch 19, batch 27350, loss[loss=0.1449, simple_loss=0.212, pruned_loss=0.03895, over 4830.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2065, pruned_loss=0.02924, over 973709.84 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 19:08:14,904 INFO [train.py:715] (2/8) Epoch 19, batch 27400, loss[loss=0.1198, simple_loss=0.1886, pruned_loss=0.02551, over 4772.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2058, pruned_loss=0.02858, over 974019.77 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:08:54,850 INFO [train.py:715] (2/8) Epoch 19, batch 27450, loss[loss=0.1382, simple_loss=0.2162, pruned_loss=0.03005, over 4782.00 frames.], tot_loss[loss=0.131, simple_loss=0.2051, pruned_loss=0.0285, over 973545.77 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:09:36,477 INFO [train.py:715] (2/8) Epoch 19, batch 27500, loss[loss=0.1183, simple_loss=0.2006, pruned_loss=0.01804, over 4867.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2041, pruned_loss=0.028, over 973740.21 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:10:17,083 INFO [train.py:715] (2/8) Epoch 19, batch 27550, loss[loss=0.119, simple_loss=0.1917, pruned_loss=0.02312, over 4907.00 frames.], tot_loss[loss=0.1292, simple_loss=0.2037, pruned_loss=0.02734, over 974031.95 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:10:57,701 INFO [train.py:715] (2/8) Epoch 19, batch 27600, loss[loss=0.1192, simple_loss=0.1929, pruned_loss=0.02274, over 4751.00 frames.], tot_loss[loss=0.13, simple_loss=0.205, pruned_loss=0.02753, over 973297.96 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:11:38,775 INFO [train.py:715] (2/8) Epoch 19, batch 27650, loss[loss=0.12, simple_loss=0.1989, pruned_loss=0.02058, over 4790.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2051, pruned_loss=0.02762, over 973440.20 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:12:19,400 INFO [train.py:715] (2/8) Epoch 19, batch 27700, loss[loss=0.132, simple_loss=0.2145, pruned_loss=0.02475, over 4862.00 frames.], tot_loss[loss=0.1302, simple_loss=0.205, pruned_loss=0.0277, over 973463.86 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 19:13:00,033 INFO [train.py:715] (2/8) Epoch 19, batch 27750, loss[loss=0.1208, simple_loss=0.2035, pruned_loss=0.01905, over 4807.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2058, pruned_loss=0.02799, over 973244.63 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:13:40,111 INFO [train.py:715] (2/8) Epoch 19, batch 27800, loss[loss=0.122, simple_loss=0.1965, pruned_loss=0.02374, over 4989.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2055, pruned_loss=0.02806, over 972604.11 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 19:14:21,131 INFO [train.py:715] (2/8) Epoch 19, batch 27850, loss[loss=0.1421, simple_loss=0.2131, pruned_loss=0.03555, over 4891.00 frames.], tot_loss[loss=0.13, simple_loss=0.2047, pruned_loss=0.0276, over 972837.88 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:15:01,159 INFO [train.py:715] (2/8) Epoch 19, batch 27900, loss[loss=0.1399, simple_loss=0.216, pruned_loss=0.03192, over 4752.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2052, pruned_loss=0.02759, over 973372.54 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:15:41,280 INFO [train.py:715] (2/8) Epoch 19, batch 27950, loss[loss=0.09417, simple_loss=0.1626, pruned_loss=0.01286, over 4810.00 frames.], tot_loss[loss=0.13, simple_loss=0.2046, pruned_loss=0.02768, over 972859.69 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 19:16:21,257 INFO [train.py:715] (2/8) Epoch 19, batch 28000, loss[loss=0.1287, simple_loss=0.196, pruned_loss=0.03066, over 4813.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2048, pruned_loss=0.02798, over 972457.89 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:17:02,088 INFO [train.py:715] (2/8) Epoch 19, batch 28050, loss[loss=0.111, simple_loss=0.1869, pruned_loss=0.01751, over 4783.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2048, pruned_loss=0.02822, over 973341.36 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:17:42,528 INFO [train.py:715] (2/8) Epoch 19, batch 28100, loss[loss=0.1255, simple_loss=0.2138, pruned_loss=0.01857, over 4791.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02812, over 974390.35 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:18:22,473 INFO [train.py:715] (2/8) Epoch 19, batch 28150, loss[loss=0.1101, simple_loss=0.1857, pruned_loss=0.01725, over 4864.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2045, pruned_loss=0.02814, over 974341.30 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:19:02,917 INFO [train.py:715] (2/8) Epoch 19, batch 28200, loss[loss=0.09228, simple_loss=0.1666, pruned_loss=0.008961, over 4842.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2041, pruned_loss=0.02801, over 974562.71 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 19:19:42,610 INFO [train.py:715] (2/8) Epoch 19, batch 28250, loss[loss=0.1634, simple_loss=0.231, pruned_loss=0.04795, over 4835.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2048, pruned_loss=0.02816, over 974339.43 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:20:22,517 INFO [train.py:715] (2/8) Epoch 19, batch 28300, loss[loss=0.1201, simple_loss=0.1933, pruned_loss=0.02349, over 4991.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2052, pruned_loss=0.0285, over 973245.75 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:21:02,188 INFO [train.py:715] (2/8) Epoch 19, batch 28350, loss[loss=0.1486, simple_loss=0.2305, pruned_loss=0.03332, over 4734.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02852, over 973072.90 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:21:42,214 INFO [train.py:715] (2/8) Epoch 19, batch 28400, loss[loss=0.1436, simple_loss=0.2233, pruned_loss=0.03193, over 4780.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02795, over 973088.48 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:22:22,333 INFO [train.py:715] (2/8) Epoch 19, batch 28450, loss[loss=0.1171, simple_loss=0.1927, pruned_loss=0.0207, over 4972.00 frames.], tot_loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.02786, over 973275.06 frames.], batch size: 28, lr: 1.16e-04
2022-05-09 19:23:02,150 INFO [train.py:715] (2/8) Epoch 19, batch 28500, loss[loss=0.1366, simple_loss=0.2138, pruned_loss=0.02972, over 4973.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2049, pruned_loss=0.02796, over 972934.78 frames.], batch size: 39, lr: 1.16e-04
2022-05-09 19:23:42,842 INFO [train.py:715] (2/8) Epoch 19, batch 28550, loss[loss=0.1406, simple_loss=0.2184, pruned_loss=0.03143, over 4922.00 frames.], tot_loss[loss=0.1305, simple_loss=0.205, pruned_loss=0.02802, over 973132.08 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:24:22,313 INFO [train.py:715] (2/8) Epoch 19, batch 28600, loss[loss=0.1202, simple_loss=0.1968, pruned_loss=0.0218, over 4936.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2048, pruned_loss=0.02795, over 972455.04 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 19:25:02,349 INFO [train.py:715] (2/8) Epoch 19, batch 28650, loss[loss=0.1244, simple_loss=0.1916, pruned_loss=0.02866, over 4686.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2052, pruned_loss=0.02848, over 972594.36 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:25:43,121 INFO [train.py:715] (2/8) Epoch 19, batch 28700, loss[loss=0.1177, simple_loss=0.1903, pruned_loss=0.02257, over 4793.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2054, pruned_loss=0.02852, over 973280.36 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:26:22,654 INFO [train.py:715] (2/8) Epoch 19, batch 28750, loss[loss=0.1159, simple_loss=0.1893, pruned_loss=0.02121, over 4892.00 frames.], tot_loss[loss=0.1317, simple_loss=0.206, pruned_loss=0.02873, over 973470.05 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:27:02,564 INFO [train.py:715] (2/8) Epoch 19, batch 28800, loss[loss=0.1297, simple_loss=0.2013, pruned_loss=0.02905, over 4790.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2056, pruned_loss=0.02873, over 973274.04 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:27:41,941 INFO [train.py:715] (2/8) Epoch 19, batch 28850, loss[loss=0.1122, simple_loss=0.1849, pruned_loss=0.0197, over 4815.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02874, over 973565.94 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 19:28:21,327 INFO [train.py:715] (2/8) Epoch 19, batch 28900, loss[loss=0.1243, simple_loss=0.2034, pruned_loss=0.02266, over 4771.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2055, pruned_loss=0.02833, over 973187.66 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:28:59,436 INFO [train.py:715] (2/8) Epoch 19, batch 28950, loss[loss=0.1417, simple_loss=0.2241, pruned_loss=0.02964, over 4906.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02863, over 973151.63 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:29:38,325 INFO [train.py:715] (2/8) Epoch 19, batch 29000, loss[loss=0.121, simple_loss=0.1957, pruned_loss=0.02313, over 4800.00 frames.], tot_loss[loss=0.131, simple_loss=0.2054, pruned_loss=0.02832, over 972390.73 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:30:17,559 INFO [train.py:715] (2/8) Epoch 19, batch 29050, loss[loss=0.1316, simple_loss=0.2123, pruned_loss=0.02545, over 4765.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2055, pruned_loss=0.02793, over 971792.17 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:30:56,440 INFO [train.py:715] (2/8) Epoch 19, batch 29100, loss[loss=0.1674, simple_loss=0.2465, pruned_loss=0.04413, over 4690.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2051, pruned_loss=0.02781, over 972913.94 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:31:35,383 INFO [train.py:715] (2/8) Epoch 19, batch 29150, loss[loss=0.1392, simple_loss=0.21, pruned_loss=0.03417, over 4985.00 frames.], tot_loss[loss=0.13, simple_loss=0.2044, pruned_loss=0.02777, over 972611.33 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 19:32:14,170 INFO [train.py:715] (2/8) Epoch 19, batch 29200, loss[loss=0.09924, simple_loss=0.1751, pruned_loss=0.0117, over 4794.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2048, pruned_loss=0.02798, over 972271.18 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:32:53,530 INFO [train.py:715] (2/8) Epoch 19, batch 29250, loss[loss=0.1328, simple_loss=0.2068, pruned_loss=0.02943, over 4716.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02813, over 971679.82 frames.], batch size: 12, lr: 1.16e-04
2022-05-09 19:33:32,155 INFO [train.py:715] (2/8) Epoch 19, batch 29300, loss[loss=0.1452, simple_loss=0.2183, pruned_loss=0.03604, over 4847.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02846, over 971484.31 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:34:11,676 INFO [train.py:715] (2/8) Epoch 19, batch 29350, loss[loss=0.1489, simple_loss=0.2103, pruned_loss=0.04378, over 4847.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2061, pruned_loss=0.02851, over 971112.75 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 19:34:50,602 INFO [train.py:715] (2/8) Epoch 19, batch 29400, loss[loss=0.1325, simple_loss=0.2029, pruned_loss=0.03099, over 4986.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02825, over 972320.02 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 19:35:29,746 INFO [train.py:715] (2/8) Epoch 19, batch 29450, loss[loss=0.1109, simple_loss=0.1898, pruned_loss=0.016, over 4925.00 frames.], tot_loss[loss=0.131, simple_loss=0.2059, pruned_loss=0.02799, over 972917.82 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 19:36:09,169 INFO [train.py:715] (2/8) Epoch 19, batch 29500, loss[loss=0.1064, simple_loss=0.1869, pruned_loss=0.01297, over 4890.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02826, over 972743.62 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 19:36:48,558 INFO [train.py:715] (2/8) Epoch 19, batch 29550, loss[loss=0.1383, simple_loss=0.2207, pruned_loss=0.02797, over 4877.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2064, pruned_loss=0.02829, over 972701.49 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 19:37:28,176 INFO [train.py:715] (2/8) Epoch 19, batch 29600, loss[loss=0.1324, simple_loss=0.2112, pruned_loss=0.02677, over 4960.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2063, pruned_loss=0.02848, over 972510.90 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 19:38:07,308 INFO [train.py:715] (2/8) Epoch 19, batch 29650, loss[loss=0.1162, simple_loss=0.193, pruned_loss=0.01973, over 4920.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2061, pruned_loss=0.02823, over 972358.04 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 19:38:47,452 INFO [train.py:715] (2/8) Epoch 19, batch 29700, loss[loss=0.1291, simple_loss=0.2184, pruned_loss=0.01989, over 4835.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2055, pruned_loss=0.02802, over 971706.18 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:39:26,745 INFO [train.py:715] (2/8) Epoch 19, batch 29750, loss[loss=0.1311, simple_loss=0.2069, pruned_loss=0.02766, over 4903.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02835, over 972696.25 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 19:40:06,091 INFO [train.py:715] (2/8) Epoch 19, batch 29800, loss[loss=0.1678, simple_loss=0.2258, pruned_loss=0.0549, over 4823.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2059, pruned_loss=0.02849, over 972763.89 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:40:45,393 INFO [train.py:715] (2/8) Epoch 19, batch 29850, loss[loss=0.1075, simple_loss=0.1841, pruned_loss=0.0154, over 4961.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2055, pruned_loss=0.02845, over 972300.84 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:41:24,811 INFO [train.py:715] (2/8) Epoch 19, batch 29900, loss[loss=0.1267, simple_loss=0.195, pruned_loss=0.02915, over 4915.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2047, pruned_loss=0.02803, over 972325.84 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:42:04,772 INFO [train.py:715] (2/8) Epoch 19, batch 29950, loss[loss=0.1272, simple_loss=0.203, pruned_loss=0.02568, over 4829.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2047, pruned_loss=0.02806, over 971961.92 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:42:43,616 INFO [train.py:715] (2/8) Epoch 19, batch 30000, loss[loss=0.1264, simple_loss=0.2032, pruned_loss=0.02485, over 4785.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2047, pruned_loss=0.02801, over 972369.20 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:42:43,617 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 19:42:53,508 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1045, simple_loss=0.1877, pruned_loss=0.01067, over 914524.00 frames.
2022-05-09 19:43:32,626 INFO [train.py:715] (2/8) Epoch 19, batch 30050, loss[loss=0.1259, simple_loss=0.202, pruned_loss=0.02491, over 4811.00 frames.], tot_loss[loss=0.13, simple_loss=0.2044, pruned_loss=0.02783, over 972100.71 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:44:12,189 INFO [train.py:715] (2/8) Epoch 19, batch 30100, loss[loss=0.1353, simple_loss=0.2073, pruned_loss=0.03169, over 4982.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2058, pruned_loss=0.02793, over 972956.35 frames.], batch size: 28, lr: 1.16e-04
2022-05-09 19:44:51,311 INFO [train.py:715] (2/8) Epoch 19, batch 30150, loss[loss=0.1537, simple_loss=0.2285, pruned_loss=0.03943, over 4764.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2053, pruned_loss=0.02807, over 972691.78 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 19:45:31,083 INFO [train.py:715] (2/8) Epoch 19, batch 30200, loss[loss=0.1223, simple_loss=0.1902, pruned_loss=0.0272, over 4851.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2057, pruned_loss=0.02821, over 973204.10 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 19:46:09,573 INFO [train.py:715] (2/8) Epoch 19, batch 30250, loss[loss=0.161, simple_loss=0.2379, pruned_loss=0.04209, over 4831.00 frames.], tot_loss[loss=0.1304, simple_loss=0.205, pruned_loss=0.02793, over 972805.44 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:46:48,894 INFO [train.py:715] (2/8) Epoch 19, batch 30300, loss[loss=0.1359, simple_loss=0.2133, pruned_loss=0.02929, over 4834.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02803, over 973451.04 frames.], batch size: 30, lr: 1.16e-04
2022-05-09 19:47:28,482 INFO [train.py:715] (2/8) Epoch 19, batch 30350, loss[loss=0.1462, simple_loss=0.2117, pruned_loss=0.04038, over 4957.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2049, pruned_loss=0.02772, over 973364.14 frames.], batch size: 31, lr: 1.16e-04
2022-05-09 19:48:08,088 INFO [train.py:715] (2/8) Epoch 19, batch 30400, loss[loss=0.1248, simple_loss=0.1966, pruned_loss=0.02656, over 4947.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2056, pruned_loss=0.02797, over 973112.51 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:48:47,856 INFO [train.py:715] (2/8) Epoch 19, batch 30450, loss[loss=0.1366, simple_loss=0.2155, pruned_loss=0.02886, over 4875.00 frames.], tot_loss[loss=0.1299, simple_loss=0.205, pruned_loss=0.02739, over 972873.40 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:49:26,663 INFO [train.py:715] (2/8) Epoch 19, batch 30500, loss[loss=0.1308, simple_loss=0.2071, pruned_loss=0.02723, over 4991.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2056, pruned_loss=0.02779, over 972950.02 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:50:06,591 INFO [train.py:715] (2/8) Epoch 19, batch 30550, loss[loss=0.1401, simple_loss=0.2211, pruned_loss=0.02949, over 4918.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02847, over 972647.68 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 19:50:45,746 INFO [train.py:715] (2/8) Epoch 19, batch 30600, loss[loss=0.142, simple_loss=0.2146, pruned_loss=0.03469, over 4848.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2063, pruned_loss=0.02857, over 972841.38 frames.], batch size: 34, lr: 1.16e-04
2022-05-09 19:51:25,828 INFO [train.py:715] (2/8) Epoch 19, batch 30650, loss[loss=0.1265, simple_loss=0.1909, pruned_loss=0.03105, over 4819.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02838, over 973244.77 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 19:52:05,609 INFO [train.py:715] (2/8) Epoch 19, batch 30700, loss[loss=0.1443, simple_loss=0.2147, pruned_loss=0.03697, over 4733.00 frames.], tot_loss[loss=0.1316, simple_loss=0.206, pruned_loss=0.02857, over 973207.55 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:52:45,188 INFO [train.py:715] (2/8) Epoch 19, batch 30750, loss[loss=0.1588, simple_loss=0.2391, pruned_loss=0.0392, over 4877.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02822, over 972862.88 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:53:25,709 INFO [train.py:715] (2/8) Epoch 19, batch 30800, loss[loss=0.1332, simple_loss=0.2084, pruned_loss=0.02896, over 4902.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2051, pruned_loss=0.02804, over 972160.15 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:54:05,654 INFO [train.py:715] (2/8) Epoch 19, batch 30850, loss[loss=0.1303, simple_loss=0.202, pruned_loss=0.0293, over 4986.00 frames.], tot_loss[loss=0.1315, simple_loss=0.206, pruned_loss=0.02853, over 972675.39 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 19:54:46,449 INFO [train.py:715] (2/8) Epoch 19, batch 30900, loss[loss=0.1443, simple_loss=0.2154, pruned_loss=0.03661, over 4870.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02867, over 972629.18 frames.], batch size: 22, lr: 1.16e-04
2022-05-09 19:55:26,510 INFO [train.py:715] (2/8) Epoch 19, batch 30950, loss[loss=0.1328, simple_loss=0.2075, pruned_loss=0.02906, over 4845.00 frames.], tot_loss[loss=0.132, simple_loss=0.2065, pruned_loss=0.02875, over 972668.76 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 19:56:07,135 INFO [train.py:715] (2/8) Epoch 19, batch 31000, loss[loss=0.1285, simple_loss=0.2097, pruned_loss=0.02368, over 4968.00 frames.], tot_loss[loss=0.1324, simple_loss=0.2069, pruned_loss=0.02888, over 973567.19 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 19:56:47,796 INFO [train.py:715] (2/8) Epoch 19, batch 31050, loss[loss=0.1172, simple_loss=0.1948, pruned_loss=0.01979, over 4987.00 frames.], tot_loss[loss=0.1316, simple_loss=0.2062, pruned_loss=0.02849, over 973876.32 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 19:57:28,117 INFO [train.py:715] (2/8) Epoch 19, batch 31100, loss[loss=0.1218, simple_loss=0.2026, pruned_loss=0.02049, over 4820.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2062, pruned_loss=0.02884, over 973735.91 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 19:58:08,818 INFO [train.py:715] (2/8) Epoch 19, batch 31150, loss[loss=0.145, simple_loss=0.2258, pruned_loss=0.03212, over 4898.00 frames.], tot_loss[loss=0.1325, simple_loss=0.2069, pruned_loss=0.02904, over 972107.65 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 19:58:49,190 INFO [train.py:715] (2/8) Epoch 19, batch 31200, loss[loss=0.108, simple_loss=0.1769, pruned_loss=0.0195, over 4805.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02854, over 971825.14 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 19:59:30,187 INFO [train.py:715] (2/8) Epoch 19, batch 31250, loss[loss=0.1126, simple_loss=0.1993, pruned_loss=0.01294, over 4920.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2062, pruned_loss=0.02855, over 972652.21 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:00:09,923 INFO [train.py:715] (2/8) Epoch 19, batch 31300, loss[loss=0.1328, simple_loss=0.2071, pruned_loss=0.02927, over 4770.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2052, pruned_loss=0.02768, over 972666.40 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 20:00:50,566 INFO [train.py:715] (2/8) Epoch 19, batch 31350, loss[loss=0.1528, simple_loss=0.226, pruned_loss=0.03977, over 4809.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2053, pruned_loss=0.02756, over 972568.82 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 20:01:31,179 INFO [train.py:715] (2/8) Epoch 19, batch 31400, loss[loss=0.104, simple_loss=0.1823, pruned_loss=0.01288, over 4789.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2054, pruned_loss=0.0278, over 972274.75 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 20:02:11,464 INFO [train.py:715] (2/8) Epoch 19, batch 31450, loss[loss=0.1208, simple_loss=0.1984, pruned_loss=0.02158, over 4987.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2046, pruned_loss=0.02764, over 972551.67 frames.], batch size: 27, lr: 1.16e-04
2022-05-09 20:02:52,729 INFO [train.py:715] (2/8) Epoch 19, batch 31500, loss[loss=0.1193, simple_loss=0.1919, pruned_loss=0.02333, over 4916.00 frames.], tot_loss[loss=0.1293, simple_loss=0.2041, pruned_loss=0.02723, over 972633.94 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 20:03:32,862 INFO [train.py:715] (2/8) Epoch 19, batch 31550, loss[loss=0.1129, simple_loss=0.1894, pruned_loss=0.01816, over 4969.00 frames.], tot_loss[loss=0.1285, simple_loss=0.203, pruned_loss=0.02698, over 971398.64 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 20:04:13,426 INFO [train.py:715] (2/8) Epoch 19, batch 31600, loss[loss=0.1129, simple_loss=0.1825, pruned_loss=0.0216, over 4854.00 frames.], tot_loss[loss=0.1291, simple_loss=0.204, pruned_loss=0.02704, over 971321.49 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 20:04:53,561 INFO [train.py:715] (2/8) Epoch 19, batch 31650, loss[loss=0.1346, simple_loss=0.2041, pruned_loss=0.03251, over 4959.00 frames.], tot_loss[loss=0.1291, simple_loss=0.204, pruned_loss=0.02709, over 972236.95 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 20:05:33,936 INFO [train.py:715] (2/8) Epoch 19, batch 31700, loss[loss=0.1282, simple_loss=0.2094, pruned_loss=0.02356, over 4827.00 frames.], tot_loss[loss=0.1296, simple_loss=0.2043, pruned_loss=0.02742, over 971986.29 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 20:06:14,418 INFO [train.py:715] (2/8) Epoch 19, batch 31750, loss[loss=0.1422, simple_loss=0.213, pruned_loss=0.03576, over 4800.00 frames.], tot_loss[loss=0.1297, simple_loss=0.2046, pruned_loss=0.02738, over 972849.08 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 20:06:54,582 INFO [train.py:715] (2/8) Epoch 19, batch 31800, loss[loss=0.1564, simple_loss=0.2326, pruned_loss=0.04014, over 4952.00 frames.], tot_loss[loss=0.1302, simple_loss=0.205, pruned_loss=0.02769, over 973211.61 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:07:35,675 INFO [train.py:715] (2/8) Epoch 19, batch 31850, loss[loss=0.1436, simple_loss=0.2103, pruned_loss=0.03849, over 4810.00 frames.], tot_loss[loss=0.1303, simple_loss=0.205, pruned_loss=0.02776, over 972936.78 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:08:15,981 INFO [train.py:715] (2/8) Epoch 19, batch 31900, loss[loss=0.1344, simple_loss=0.2112, pruned_loss=0.02877, over 4748.00 frames.], tot_loss[loss=0.1296, simple_loss=0.2044, pruned_loss=0.02742, over 973201.51 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 20:08:56,573 INFO [train.py:715] (2/8) Epoch 19, batch 31950, loss[loss=0.1317, simple_loss=0.1943, pruned_loss=0.03457, over 4930.00 frames.], tot_loss[loss=0.1302, simple_loss=0.205, pruned_loss=0.02775, over 972811.87 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 20:09:36,642 INFO [train.py:715] (2/8) Epoch 19, batch 32000, loss[loss=0.1432, simple_loss=0.2224, pruned_loss=0.03202, over 4878.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2048, pruned_loss=0.02779, over 972978.63 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 20:10:16,974 INFO [train.py:715] (2/8) Epoch 19, batch 32050, loss[loss=0.1315, simple_loss=0.2048, pruned_loss=0.02915, over 4919.00 frames.], tot_loss[loss=0.1299, simple_loss=0.2043, pruned_loss=0.02773, over 973031.97 frames.], batch size: 18, lr: 1.16e-04
2022-05-09 20:10:57,315 INFO [train.py:715] (2/8) Epoch 19, batch 32100, loss[loss=0.1141, simple_loss=0.1826, pruned_loss=0.02284, over 4965.00 frames.], tot_loss[loss=0.1296, simple_loss=0.2039, pruned_loss=0.02763, over 972084.49 frames.], batch size: 14, lr: 1.16e-04
2022-05-09 20:11:37,116 INFO [train.py:715] (2/8) Epoch 19, batch 32150, loss[loss=0.1175, simple_loss=0.1869, pruned_loss=0.02405, over 4833.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2044, pruned_loss=0.02765, over 972332.69 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 20:12:18,362 INFO [train.py:715] (2/8) Epoch 19, batch 32200, loss[loss=0.1153, simple_loss=0.1925, pruned_loss=0.01902, over 4808.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2047, pruned_loss=0.02781, over 973466.76 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:12:58,103 INFO [train.py:715] (2/8) Epoch 19, batch 32250, loss[loss=0.1223, simple_loss=0.1938, pruned_loss=0.02543, over 4846.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2052, pruned_loss=0.02781, over 973045.25 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 20:13:38,495 INFO [train.py:715] (2/8) Epoch 19, batch 32300, loss[loss=0.1503, simple_loss=0.2103, pruned_loss=0.04515, over 4966.00 frames.], tot_loss[loss=0.1298, simple_loss=0.2047, pruned_loss=0.02743, over 972746.56 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:14:19,665 INFO [train.py:715] (2/8) Epoch 19, batch 32350, loss[loss=0.1127, simple_loss=0.1773, pruned_loss=0.02407, over 4854.00 frames.], tot_loss[loss=0.1295, simple_loss=0.2045, pruned_loss=0.02728, over 972582.16 frames.], batch size: 13, lr: 1.16e-04
2022-05-09 20:15:00,202 INFO [train.py:715] (2/8) Epoch 19, batch 32400, loss[loss=0.1295, simple_loss=0.1902, pruned_loss=0.0344, over 4957.00 frames.], tot_loss[loss=0.1294, simple_loss=0.2044, pruned_loss=0.02726, over 973391.66 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:15:40,802 INFO [train.py:715] (2/8) Epoch 19, batch 32450, loss[loss=0.1551, simple_loss=0.239, pruned_loss=0.03561, over 4817.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2052, pruned_loss=0.02777, over 972971.81 frames.], batch size: 25, lr: 1.16e-04
2022-05-09 20:16:20,818 INFO [train.py:715] (2/8) Epoch 19, batch 32500, loss[loss=0.1332, simple_loss=0.2163, pruned_loss=0.02504, over 4971.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2062, pruned_loss=0.02809, over 973123.48 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:17:01,573 INFO [train.py:715] (2/8) Epoch 19, batch 32550, loss[loss=0.1185, simple_loss=0.1933, pruned_loss=0.02187, over 4898.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2059, pruned_loss=0.02816, over 972951.24 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 20:17:41,588 INFO [train.py:715] (2/8) Epoch 19, batch 32600, loss[loss=0.1396, simple_loss=0.2146, pruned_loss=0.0323, over 4957.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2055, pruned_loss=0.02816, over 972853.47 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:18:21,666 INFO [train.py:715] (2/8) Epoch 19, batch 32650, loss[loss=0.1098, simple_loss=0.1807, pruned_loss=0.01949, over 4784.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2059, pruned_loss=0.02834, over 971755.37 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 20:19:02,309 INFO [train.py:715] (2/8) Epoch 19, batch 32700, loss[loss=0.1454, simple_loss=0.2187, pruned_loss=0.03605, over 4961.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2061, pruned_loss=0.02839, over 971437.80 frames.], batch size: 24, lr: 1.16e-04
2022-05-09 20:19:42,130 INFO [train.py:715] (2/8) Epoch 19, batch 32750, loss[loss=0.1223, simple_loss=0.2059, pruned_loss=0.01942, over 4764.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2057, pruned_loss=0.02803, over 970904.82 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 20:20:21,842 INFO [train.py:715] (2/8) Epoch 19, batch 32800, loss[loss=0.1101, simple_loss=0.1917, pruned_loss=0.01419, over 4928.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2056, pruned_loss=0.02786, over 972345.55 frames.], batch size: 23, lr: 1.16e-04
2022-05-09 20:21:00,654 INFO [train.py:715] (2/8) Epoch 19, batch 32850, loss[loss=0.1294, simple_loss=0.2039, pruned_loss=0.02745, over 4978.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2058, pruned_loss=0.0283, over 972470.19 frames.], batch size: 35, lr: 1.16e-04
2022-05-09 20:21:39,682 INFO [train.py:715] (2/8) Epoch 19, batch 32900, loss[loss=0.1339, simple_loss=0.2213, pruned_loss=0.02329, over 4940.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2052, pruned_loss=0.02798, over 972095.86 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:22:18,348 INFO [train.py:715] (2/8) Epoch 19, batch 32950, loss[loss=0.1229, simple_loss=0.1998, pruned_loss=0.02297, over 4875.00 frames.], tot_loss[loss=0.1317, simple_loss=0.2064, pruned_loss=0.02854, over 970824.24 frames.], batch size: 16, lr: 1.16e-04
2022-05-09 20:22:57,678 INFO [train.py:715] (2/8) Epoch 19, batch 33000, loss[loss=0.1329, simple_loss=0.2044, pruned_loss=0.03072, over 4899.00 frames.], tot_loss[loss=0.132, simple_loss=0.2066, pruned_loss=0.02867, over 970687.67 frames.], batch size: 19, lr: 1.16e-04
2022-05-09 20:22:57,678 INFO [train.py:733] (2/8) Computing validation loss
2022-05-09 20:23:07,491 INFO [train.py:742] (2/8) Epoch 19, validation: loss=0.1048, simple_loss=0.1878, pruned_loss=0.01088, over 914524.00 frames.
2022-05-09 20:23:46,772 INFO [train.py:715] (2/8) Epoch 19, batch 33050, loss[loss=0.1219, simple_loss=0.2024, pruned_loss=0.02068, over 4819.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2062, pruned_loss=0.02868, over 970769.76 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:24:26,212 INFO [train.py:715] (2/8) Epoch 19, batch 33100, loss[loss=0.1119, simple_loss=0.1772, pruned_loss=0.0233, over 4810.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2046, pruned_loss=0.02828, over 970689.84 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:25:05,032 INFO [train.py:715] (2/8) Epoch 19, batch 33150, loss[loss=0.1067, simple_loss=0.182, pruned_loss=0.01565, over 4807.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2059, pruned_loss=0.02886, over 971361.18 frames.], batch size: 26, lr: 1.16e-04
2022-05-09 20:25:44,208 INFO [train.py:715] (2/8) Epoch 19, batch 33200, loss[loss=0.1325, simple_loss=0.2111, pruned_loss=0.02694, over 4791.00 frames.], tot_loss[loss=0.1327, simple_loss=0.2067, pruned_loss=0.0294, over 971682.37 frames.], batch size: 17, lr: 1.16e-04
2022-05-09 20:26:23,767 INFO [train.py:715] (2/8) Epoch 19, batch 33250, loss[loss=0.1165, simple_loss=0.1986, pruned_loss=0.01721, over 4927.00 frames.], tot_loss[loss=0.1323, simple_loss=0.2064, pruned_loss=0.02908, over 972145.60 frames.], batch size: 29, lr: 1.16e-04
2022-05-09 20:27:03,192 INFO [train.py:715] (2/8) Epoch 19, batch 33300, loss[loss=0.1825, simple_loss=0.2356, pruned_loss=0.06467, over 4863.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2066, pruned_loss=0.02887, over 972818.68 frames.], batch size: 32, lr: 1.16e-04
2022-05-09 20:27:42,909 INFO [train.py:715] (2/8) Epoch 19, batch 33350, loss[loss=0.1151, simple_loss=0.1834, pruned_loss=0.02338, over 4827.00 frames.], tot_loss[loss=0.1322, simple_loss=0.2064, pruned_loss=0.02895, over 972608.88 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:28:22,074 INFO [train.py:715] (2/8) Epoch 19, batch 33400, loss[loss=0.1184, simple_loss=0.1974, pruned_loss=0.01968, over 4866.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2055, pruned_loss=0.02873, over 973037.63 frames.], batch size: 20, lr: 1.16e-04
2022-05-09 20:29:01,063 INFO [train.py:715] (2/8) Epoch 19, batch 33450, loss[loss=0.1379, simple_loss=0.2195, pruned_loss=0.02815, over 4802.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2055, pruned_loss=0.0285, over 973262.02 frames.], batch size: 21, lr: 1.16e-04
2022-05-09 20:29:40,022 INFO [train.py:715] (2/8) Epoch 19, batch 33500, loss[loss=0.1145, simple_loss=0.1856, pruned_loss=0.02167, over 4696.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2048, pruned_loss=0.02798, over 972351.92 frames.], batch size: 15, lr: 1.16e-04
2022-05-09 20:30:18,907 INFO [train.py:715] (2/8) Epoch 19, batch 33550, loss[loss=0.1107, simple_loss=0.182, pruned_loss=0.01968, over 4807.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02809, over 972212.40 frames.], batch size: 25, lr: 1.15e-04
2022-05-09 20:30:58,237 INFO [train.py:715] (2/8) Epoch 19, batch 33600, loss[loss=0.1525, simple_loss=0.2133, pruned_loss=0.04588, over 4779.00 frames.], tot_loss[loss=0.131, simple_loss=0.205, pruned_loss=0.02854, over 972633.12 frames.], batch size: 17, lr: 1.15e-04
2022-05-09 20:31:37,216 INFO [train.py:715] (2/8) Epoch 19, batch 33650, loss[loss=0.1482, simple_loss=0.2243, pruned_loss=0.036, over 4990.00 frames.], tot_loss[loss=0.1314, simple_loss=0.2053, pruned_loss=0.0287, over 972627.59 frames.], batch size: 15, lr: 1.15e-04
2022-05-09 20:32:16,619 INFO [train.py:715] (2/8) Epoch 19, batch 33700, loss[loss=0.1203, simple_loss=0.1858, pruned_loss=0.02745, over 4991.00 frames.], tot_loss[loss=0.131, simple_loss=0.2051, pruned_loss=0.02842, over 972571.94 frames.], batch size: 14, lr: 1.15e-04
2022-05-09 20:32:55,331 INFO [train.py:715] (2/8) Epoch 19, batch 33750, loss[loss=0.1344, simple_loss=0.1983, pruned_loss=0.03522, over 4788.00 frames.], tot_loss[loss=0.1315, simple_loss=0.2057, pruned_loss=0.02866, over 973435.81 frames.], batch size: 12, lr: 1.15e-04
2022-05-09 20:33:34,125 INFO [train.py:715] (2/8) Epoch 19, batch 33800, loss[loss=0.1357, simple_loss=0.2041, pruned_loss=0.03364, over 4937.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2052, pruned_loss=0.02853, over 974078.93 frames.], batch size: 29, lr: 1.15e-04
2022-05-09 20:34:12,736 INFO [train.py:715] (2/8) Epoch 19, batch 33850, loss[loss=0.1228, simple_loss=0.2011, pruned_loss=0.02224, over 4945.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2054, pruned_loss=0.02825, over 972943.47 frames.], batch size: 21, lr: 1.15e-04
2022-05-09 20:34:51,528 INFO [train.py:715] (2/8) Epoch 19, batch 33900, loss[loss=0.1239, simple_loss=0.2056, pruned_loss=0.02108, over 4981.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2059, pruned_loss=0.02824, over 972777.98 frames.], batch size: 24, lr: 1.15e-04
2022-05-09 20:35:31,249 INFO [train.py:715] (2/8) Epoch 19, batch 33950, loss[loss=0.1277, simple_loss=0.2069, pruned_loss=0.02423, over 4819.00 frames.], tot_loss[loss=0.1319, simple_loss=0.2065, pruned_loss=0.02866, over 972122.47 frames.], batch size: 26, lr: 1.15e-04
2022-05-09 20:36:10,894 INFO [train.py:715] (2/8) Epoch 19, batch 34000, loss[loss=0.1252, simple_loss=0.2082, pruned_loss=0.02113, over 4867.00 frames.], tot_loss[loss=0.1313, simple_loss=0.2058, pruned_loss=0.02837, over 971536.93 frames.], batch size: 20, lr: 1.15e-04
2022-05-09 20:36:50,180 INFO [train.py:715] (2/8) Epoch 19, batch 34050, loss[loss=0.1007, simple_loss=0.1782, pruned_loss=0.01159, over 4949.00 frames.], tot_loss[loss=0.1308, simple_loss=0.2052, pruned_loss=0.02822, over 971693.05 frames.], batch size: 21, lr: 1.15e-04
2022-05-09 20:37:28,971 INFO [train.py:715] (2/8) Epoch 19, batch 34100, loss[loss=0.1485, simple_loss=0.2232, pruned_loss=0.03692, over 4695.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2044, pruned_loss=0.02808, over 971605.08 frames.], batch size: 15, lr: 1.15e-04
2022-05-09 20:38:08,483 INFO [train.py:715] (2/8) Epoch 19, batch 34150, loss[loss=0.1561, simple_loss=0.2223, pruned_loss=0.04496, over 4972.00 frames.], tot_loss[loss=0.1306, simple_loss=0.2049, pruned_loss=0.02816, over 971283.08 frames.], batch size: 35, lr: 1.15e-04
2022-05-09 20:38:48,060 INFO [train.py:715] (2/8) Epoch 19, batch 34200, loss[loss=0.1111, simple_loss=0.1786, pruned_loss=0.02177, over 4968.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2046, pruned_loss=0.02811, over 971489.67 frames.], batch size: 14, lr: 1.15e-04
2022-05-09 20:39:27,602 INFO [train.py:715] (2/8) Epoch 19, batch 34250, loss[loss=0.1133, simple_loss=0.188, pruned_loss=0.0193, over 4826.00 frames.], tot_loss[loss=0.1302, simple_loss=0.2045, pruned_loss=0.02797, over 971479.34 frames.], batch size: 15, lr: 1.15e-04
2022-05-09 20:40:06,950 INFO [train.py:715] (2/8) Epoch 19, batch 34300, loss[loss=0.1365, simple_loss=0.2142, pruned_loss=0.02945, over 4883.00 frames.], tot_loss[loss=0.1303, simple_loss=0.2048, pruned_loss=0.02793, over 971614.05 frames.], batch size: 16, lr: 1.15e-04
2022-05-09 20:40:46,126 INFO [train.py:715] (2/8) Epoch 19, batch 34350, loss[loss=0.1386, simple_loss=0.2198, pruned_loss=0.02877, over 4976.00 frames.], tot_loss[loss=0.1301, simple_loss=0.2048, pruned_loss=0.02769, over 971978.06 frames.], batch size: 15, lr: 1.15e-04
2022-05-09 20:41:25,882 INFO [train.py:715] (2/8) Epoch 19, batch 34400, loss[loss=0.1169, simple_loss=0.1919, pruned_loss=0.02093, over 4838.00 frames.], tot_loss[loss=0.131, simple_loss=0.206, pruned_loss=0.02803, over 972294.35 frames.], batch size: 32, lr: 1.15e-04
2022-05-09 20:42:05,039 INFO [train.py:715] (2/8) Epoch 19, batch 34450, loss[loss=0.1365, simple_loss=0.2056, pruned_loss=0.03367, over 4751.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2066, pruned_loss=0.02845, over 972681.57 frames.], batch size: 16, lr: 1.15e-04
2022-05-09 20:42:44,562 INFO [train.py:715] (2/8) Epoch 19, batch 34500, loss[loss=0.1275, simple_loss=0.1975, pruned_loss=0.02875, over 4981.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02818, over 971125.77 frames.], batch size: 28, lr: 1.15e-04
2022-05-09 20:43:24,265 INFO [train.py:715] (2/8) Epoch 19, batch 34550, loss[loss=0.1364, simple_loss=0.214, pruned_loss=0.02939, over 4895.00 frames.], tot_loss[loss=0.1312, simple_loss=0.206, pruned_loss=0.02819, over 970954.34 frames.], batch size: 17, lr: 1.15e-04
2022-05-09 20:44:03,128 INFO [train.py:715] (2/8) Epoch 19, batch 34600, loss[loss=0.1111, simple_loss=0.1864, pruned_loss=0.01788, over 4812.00 frames.], tot_loss[loss=0.1312, simple_loss=0.2063, pruned_loss=0.02806, over 970965.58 frames.], batch size: 25, lr: 1.15e-04
2022-05-09 20:44:45,165 INFO [train.py:715] (2/8) Epoch 19, batch 34650, loss[loss=0.1109, simple_loss=0.1935, pruned_loss=0.01413, over 4774.00 frames.], tot_loss[loss=0.1309, simple_loss=0.2059, pruned_loss=0.02794, over 971434.94 frames.], batch size: 18, lr: 1.15e-04
2022-05-09 20:45:24,629 INFO [train.py:715] (2/8) Epoch 19, batch 34700, loss[loss=0.1255, simple_loss=0.1977, pruned_loss=0.02667, over 4824.00 frames.], tot_loss[loss=0.1311, simple_loss=0.2058, pruned_loss=0.02813, over 971379.03 frames.], batch size: 26, lr: 1.15e-04
2022-05-09 20:46:02,677 INFO [train.py:715] (2/8) Epoch 19, batch 34750, loss[loss=0.1446, simple_loss=0.2149, pruned_loss=0.03712, over 4943.00 frames.], tot_loss[loss=0.1305, simple_loss=0.2053, pruned_loss=0.02783, over 971314.19 frames.], batch size: 21, lr: 1.15e-04
2022-05-09 20:46:39,959 INFO [train.py:715] (2/8) Epoch 19, batch 34800, loss[loss=0.1359, simple_loss=0.2062, pruned_loss=0.03276, over 4780.00 frames.], tot_loss[loss=0.1304, simple_loss=0.2048, pruned_loss=0.02794, over 971658.97 frames.], batch size: 12, lr: 1.15e-04
2022-05-09 20:46:47,940 INFO [train.py:915] (2/8) Done!